U.S. patent application number 15/034746 was filed with the patent office on 2016-10-06 for methods for profiliing and quantitating cell-free rna.
The applicant listed for this patent is The Board of Trustees of the Leland Stanford Junior University. Invention is credited to Hei-Mun Christina FAN, Lian Chye Winston KOH, Wenying PAN, Stephen R. QUAKE.
Application Number | 20160289762 15/034746 |
Document ID | / |
Family ID | 72145550 |
Filed Date | 2016-10-06 |
United States Patent
Application |
20160289762 |
Kind Code |
A1 |
KOH; Lian Chye Winston ; et
al. |
October 6, 2016 |
METHODS FOR PROFILIING AND QUANTITATING CELL-FREE RNA
Abstract
The invention generally relates to methods for assessing a
neurological disorder by characterizing circulating nucleic acids
in a blood sample. According to certain embodiments, methods for
assessing a neurological disorder include obtaining RNA present in
a blood sample of a patient suspected of having a neurological
disorder, determining a level of RNA present in the sample that is
specific to brain tissue, comparing the sample level of RNA to a
reference level of RNA specific to brain tissue, determining
whether a difference exists between the sample level and the
reference level, and indicating a neurological disorder if a
difference is determined.
Inventors: |
KOH; Lian Chye Winston;
(Stanford, CA) ; QUAKE; Stephen R.; (Stanford,
CA) ; FAN; Hei-Mun Christina; (Fremont, CA) ;
PAN; Wenying; (Stanford, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
The Board of Trustees of the Leland Stanford Junior
University |
Palo Alto |
CA |
US |
|
|
Family ID: |
72145550 |
Appl. No.: |
15/034746 |
Filed: |
November 6, 2014 |
PCT Filed: |
November 6, 2014 |
PCT NO: |
PCT/US2014/064355 |
371 Date: |
May 5, 2016 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
61900927 |
Nov 6, 2013 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
C12Q 1/6883 20130101;
C12Q 2600/118 20130101; C12Q 2600/112 20130101; C12Q 2600/158
20130101 |
International
Class: |
C12Q 1/68 20060101
C12Q001/68 |
Claims
1. A method for characterizing a neurological disorder of a
patient, the method comprising: obtaining RNA from a blood sample
of a patient suspected of having a neurological disorder;
converting the RNA obtained in the sample into cDNA; determining a
level of the sample cDNA that corresponds to RNA originating from
brain tissue; comparing the level of the sample cDNA to a reference
level of circulating RNA originating from brain tissue; and
indicating a neurological disorder based upon a
statistically-significant deviation between the level of sample
cDNA and the reference level.
2. The method of claim 1, further comprising the step of
determining a stage of the indicated neurological disorder.
3. The method of claim 2, wherein the stage is selected from the
group consisting of no cognitive impairment, mild cognitive
impairment, moderate cognitive impairment, and severe cognitive
impairment.
4. The method of claim 1, wherein the neurological disorder is
Alzheimer's disease.
5. The method of claim 1, wherein the level of the sample cDNA and
the reference level correspond to an amount of circulating RNA
released from brain tissue selected from the group consisting of
spinal cord, pituitary, hypothalamus, thalamus, corpus callosum,
cerebrum, cerebral cortex, and combinations thereof.
6. The method of claim 1, further comprising the step of monitoring
progression of the neurological disorder by repeating the steps of
obtaining through comparing.
7. The method of claim 1, wherein the reference level comprises a
level of cDNA corresponding to a patient population without
cognitive impairment.
8. The method of claim 1, wherein the reference level comprises a
level of cDNA corresponding to a patient population diagnosed with
a neurological disorder.
9. The method of claim 1, wherein the blood sample is plasma or
serum.
10. The method of claim 1, wherein the determining step is
performed via a sequencing technique, a microarray technique, or
both.
11. A method for characterizing a neurological disorder of a
patient, the method comprising: obtaining RNA from a blood sample
of a patient suspected of having a neurological disorder,
converting the RNA obtained in the sample into cDNA; determining a
level of the sample cDNA that corresponds to RNA originating from
brain tissue; comparing the level of the sample cDNA to a set of
variables correlated with a neurological disorder, wherein the
variables comprise reference levels of cDNA that correspond to
circulating RNA originating from brain tissue and to one or more
stages of the neurological disorder; and indicating a stage of a
neurological disorder of the patient based upon a statistically
significant deviation between the level of the sample cDNA and the
set of variables correlated with a neurological disorder.
12. The method of claim 11, wherein the reference levels of cDNA
further correspond to patient populations of certain ages.
13. The method of claim 11, wherein the level of the sample cDNA
and reference levels of cDNA correspond to an amount of circulating
RNA released from brain tissue that is selected from the group
consisting of pituitary, hypothalamus, thalamus, corpus callosum,
cerebrum, cerebral cortex, and combinations thereof.
14. The method of claim 11, further comprising monitoring
progression of the neurological disorder by repeating the detecting
step through the indicating step at a future time.
15. The method of claim 11, wherein the stages are selected from
the group consisting of no cognitive impairment, mild cognitive
impairment, moderate cognitive impairment, and severe cognitive
impairment.
16. The method of claim 11, wherein the neurological disorder is
Alzheimer's disease.
17. The method of claim 11, wherein the blood sample is plasma or
serum.
18. The method of claim 11, wherein the determining step is
performed via a sequencing technique, a microarray technique, or
both.
19. A method of characterizing a neurological disorder, comprising
the steps of obtaining RNA from a blood sample of a patient
suspected of having a neurological disorder; determining a level of
RNA present in the sample that is specific to brain tissue;
comparing the sample level of RNA to a reference level of RNA
specific to brain tissue; determining whether a difference exists
between the sample level and the reference level; and indicating a
neurological disorder if a difference is determined.
20. The method of claim 19, further comprising the step of
determining a stage of the indicated neurological disorder.
21. The method of claim 19, wherein the stage is selected from the
group consisting of no cognitive impairment, mild cognitive
impairment, moderate cognitive impairment, and severe cognitive
impairment.
22. The method of claim 19, wherein the neurological disorder is
Alzheimer's disease.
23. The method of claim 19, wherein the level of sample RNA and the
reference level of RNA correspond to an amount of circulating RNA
released from brain tissue selected from the group consisting of
pituitary, hypothalamus, thalamus, corpus callosum, cerebrum,
cerebral cortex, and combinations thereof.
24. The method of claim 19, further comprising the step of
monitoring progression of the neurological disorder by repeating
the steps of obtaining through comparing at a future time.
25. The method of claim 19, wherein the reference level of RNA
corresponds to a patient population diagnosed with a neurological
disorder.
26. The method of claim 19, wherein the blood sample is plasma or
serum.
27. The method of claim 19, wherein the determining step is
performed via a sequencing technique, a microarray technique, or
both.
28. A method for identifying one or more biomarkers associated with
a neurological disorder, the method comprising obtaining RNA
present in a blood sample of a patient suspected of having a
neurological disorder; converting the RNA in the sample into cDNA;
determining levels of the sample cDNA that corresponds to RNA
originating from brain tissue; comparing the levels of the sample
cDNA to one or more reference levels that correspond to circulating
RNA originating from brain tissue; and identifying as a biomarker
for a neurological disorder a level of sample cDNA that is
statistically different from a reference level.
Description
RELATED APPLICATIONS
[0001] This application claims the benefit of and priority to U.S.
Provisional No. 61/900,927, filed Nov. 6, 2013, and is a
continuation-in-part of U.S. Non-Provisional Ser. No. 13/752,131,
filed Jan. 28, 2013, which claims the benefit of and priority to
U.S. Provisional No. 61/591,642, filed on Jan. 27, 2012. The
entirety of each foregoing application is incorporated herein by
reference.
TECHNICAL FIELD
[0002] The present invention relates to assessing neurological
disorders based on nucleic acid specific to brain tissue.
BACKGROUND
[0003] Dementia is a catchall term used to characterize cognitive
declines that interfere with one's ability to perform everyday
activities. Signs of dementia include declines in the following
mental functions: memory, communication and language, ability to
focus and pay attention, reasoning, judgment, motor skills, and
visual perception. While there are several neurological disorders
that cause dementia, Alzheimer's disease is the most common,
accounting for 60 to 80 percent of all dementia cases.
[0004] Alzheimer's disease is a progressive disease that gradually
destroys memory and mental functions in patients. Symptoms manifest
initially as a decline in memory followed by deterioration of other
cognitive functions as well as by abnormal behavior. Individuals
with Alzheimer's disease usually begin to show dementia symptoms
later in life (e.g., 65 years or older), but a small percentage of
individuals in their 40s and 50s experience early onset Alzheimer's
disease. Alzheimer's disease is associated with the damage and
degeneration of neurons in several regions of the brain. The
neuropathic characteristics of Alzheimer's disease include the
presence of plaques and tangles, synaptic loss, and selective
neuronal cell death. Plaques are abnormal levels of protein
fragments called beta-amyloid that accumulate between nerve cells.
Tangles are twisted fibers of a protein known as tau that
accumulate within nerve cells.
[0005] While the above-described neuropathic characteristics are
hallmarks of the disease, the exact cause of Alzheimer's disease is
unknown and there are no specific tests that confirm whether an
individual has Alzheimer's disease. For diagnosis of Alzheimer's,
clinicians assess a combination of clinical criteria, which may
include a neurological exam, mental status tests, and brain
imaging. Efforts are being made to determine the genetic causes in
order to help definitively diagnose Alzheimer's disease. However,
only a handful of genetic markers associated with Alzheimer's have
been characterized to date, and diagnostic tests for those markers
require invasive brain biopsies.
SUMMARY
[0006] The present invention provides methods for assessing
neurological conditions using circulating nucleic acid (such as DNA
or RNA) that is specific to brain tissue. In particular
embodiments, the invention involves a comparative analysis of
levels of circulating nucleic acid in a patient that are specific
to brain tissue with reference levels of circulating nucleic acid
that are specific to brain tissue. The present invention recognizes
that abnormal deviations in circulating nucleic acid result from
tissue-specific nucleic acid being released into the blood in large
amounts as tissue begins to fail and degrade. By focusing on genes
the expression of which is highly specific to brain tissue, methods
of the invention allow one to characterize the extent of brain
degradation based on statistically-significant levels of
circulating brain-specific transcripts; and use that
characterization to diagnose and determine the stage of the
neurological disease. Accordingly, methods of the invention allow
one to characterize neurological disorders without focusing on
small subset of known biomarkers, but rather focusing on the extent
to which nucleic acid is released into blood from brain tissue
affected by disease. Methods of the invention are particularly
useful in diagnosing and determining the stage of Alzheimer's
disease.
[0007] In particular embodiments, methods of the invention include
obtaining RNA from a blood sample of a patient suspected of having
a neurological disorder, and determining a level of the sample RNA
that originated from brain tissue. In certain embodiments, the RNA
is converted to cDNA. The level of the sample RNA specific to brain
tissue is then compared to a reference level of RNA that is
specific to brain tissue. The reference level may be derived from a
subject or patient population having a neurological disorder or
from a normal/control subject or patient population. Depending on
the reference level chosen, similarities or variances between the
level of sample RNA and the reference level of RNA are indicative
of the neurological disorder, the type of neurological disorder
and/or the stage of the neurological disorder. In certain
embodiments, only similarities or variances of statistical
significance are indicative of the neurological disorder. Whether a
variance is significant depends upon the chosen reference
population.
[0008] Additional aspects of the invention involve assessing a
neurological disorder using a set of predictive variables
correlated with a neurological disorder. In such aspects, methods
of the invention involve detecting RNA present in a biological
sample obtained from a patient suspected of having a neurological
disorder. In certain embodiments, the RNA is converted to cDNA.
Sample levels of one or more RNA transcripts that are specific to
brain tissue are determined, and the sample levels of RNA
transcripts specific to brain tissue are compared to a set of
predictive variables correlated with a neurological disorder. The
predictive variables may include reference levels of RNA
transcripts that are specific to brain tissue and correspond to one
or more stages of the neurological disorders. In certain
embodiments, the predictive variables may include brain-specific
reference levels of transcripts that correlate to other factors
such as age, sex, environmental exposure, familial history of
dementia, dementia symptoms. The stage of a neurological disorder
of the patient may be indicated based on variances or similarities
between the level of sample RNA and the predictive variables.
[0009] RNA obtained from the blood sample may be converted into
synthetic cDNA. In such instances, the sample levels of cDNA that
correspond to RNA originating from brain tissue may be compared to
reference levels of RNA or references levels of cDNA that
correspond to RNA originating from brain tissue. For example,
methods of the invention may include the steps of detecting
circulating RNA in a sample obtained from a patient suspected of
having a neurological disorder and converting the circulating RNA
from the sample into cDNA. The next steps involve determining
levels of the sample cDNA that correspond to RNA originating from
brain tissue, and comparing the determined levels of the cDNA to a
reference level of cDNA. The reference level of cDNA may also
correspond to RNA originating from brain tissue. The neurological
condition of the patient may then be indicated based similarities
or differences between the patient cDNA levels and the reference
cDNA levels.
[0010] Methods of the invention are also useful to identify one or
more biomarkers associated with a neurological disorder. In such
aspects, brain-specific transcripts of an individual or patient
population suspected of having or actually having a neurological
disorder (e.g. exhibiting impaired cognitive functions) are
compared to a reference (e.g. brain-specific transcripts of a
healthy, normal population). The brain-specific transcripts of the
individual or patient population that are differentially expressed
as compared to the reference may then be identified as biomarkers
of the neurological disorder. In certain embodiments, only
differentially expressed brain-specific transcripts that are
statistically significant are identified as biomarkers. Methods of
determining statistical significance are known in the art.
[0011] The reference level of RNA or cDNA specific to brain tissue
may pertain to a patient population having a particular condition
or pertain to a normal/control patient population. In one
embodiment, the reference level of RNA or cDNA specific to brain
tissue may be levels of RNA or cDNA specific to brain tissue in a
normal patient population. In another embodiment, the reference
level of RNA or cDNA may be the level of RNA or cDNA specific to
brain tissue in a patient population having a certain neurological
disorder. The certain neurological disorder may be mild cognitive
impairment or moderate-to-severe cognitive impairment. The various
levels of cognitive impairment may be indicative of a stage of
Alzheimer's disease. In further embodiments, the reference level of
RNA or cDNA may be the level of RNA or cDNA specific to brain
tissue having a certain neurological disorder at a certain age.
Other embodiments may include reference levels that correspond to a
variety of predictive variables, including type of neurological
disorder, stage of neurological disorder, age, sex, environmental
exposure, familial history of dementia, dementia symptoms.
[0012] Methods of the invention involve assaying biological samples
for circulating nucleic acid (RNA or DNA). Suitable biological
samples may include blood, blood fractions, plasma, saliva, sputum,
urine, semen, transvaginal fluid, and cerebrospinal fluid.
Preferably, the sample is a blood sample. The blood sample may be
plasma or serum.
[0013] The present invention also provides methods for profiling
the origin of the cell-free RNA to assess the health of an organ or
tissue. Deviations in normal cell-free transcriptomes are caused
when organ/tissue-specific transcripts are released in to the blood
in large amounts as those organs/tissue begin to fail or are
attacked by the immune system or pathogens. As a result
inflammation process can occur as part of body's complex biological
response to these harmful stimuli. The invention, according to
certain aspects, utilizes tissue-specific RNA transcripts of
healthy individuals to deduce the relative optimal contributions of
different tissues in the normal cell-free transcriptome, with each
tissue-specific RNA transcript of the sample being indicative of
the apotopic rate of that tissue. The normal cell-free
transcriptome serves as a baseline or reference level to assess
tissue health of other individuals. The invention includes a
comparative measurement of the cell-free transcriptome of a sample
to the normal cell free transcriptome to assess the sample levels
of tissue-specific transcripts circulating in plasma and to assess
the health of tissues contributing to the cell-free
transcriptome.
[0014] In addition to cell-free transcriptomes reference levels of
normal patient populations, methods of the invention also utilize
reference levels for cell-free transcriptomes specific to other
patient populations. Using methods of the invention one can
determine the relative contribution of tissue-specific transcripts
to the cell-free transcriptome of maternal subjects, fetus
subjects, and/or subjects having a condition or disease.
[0015] By analyzing the health of tissue based on tissue-specific
transcripts, methods of the invention advantageously allow one to
assess the health of a tissue without relying on disease-related
protein biomarkers. In certain aspects, methods of the invention
assess the health of a tissue by comparing a sample level of RNA in
a biological sample to a reference level of RNA specific to a
tissue, determining whether a difference exists between the sample
level and the reference level, and characterizing the tissue as
abnormal if a difference is detected. For example, if a patient's
RNA expression levels for a specific tissue differs from the RNA
expression levels for the specific tissue in the normal cell-free
transcriptome, this indicates that patient's tissue is not
functioning properly.
[0016] In certain aspects, methods of the invention involve
assessing health of a tissue by characterizing the tissue as
abnormal if a specified level of RNA is present in the blood. The
method may further include detecting a level of RNA in a blood
sample, comparing the sample level of RNA to a reference level of
RNA specific to a tissue, determining whether a difference exists
between the sample level and the reference level, and
characterizing the tissue as abnormal if the sample level and the
reference level are the same.
[0017] The present invention also provides methods for
comprehensively profiling fetal specific cell-free RNA in maternal
plasma and deconvoluting the cell-free transcriptome of fetal
origin with relative proportion to different fetal tissue types.
Methods of the invention involve the use of next-generation
sequencing technology and/or microarrays to characterize the
cell-free RNA transcripts that are present in maternal plasma at
different stages of pregnancy. Quantification of these transcripts
allows one to deduce changes of these genes across different
trimesters, and hence provides a way of quantification of temporal
changes in transcripts.
[0018] Methods of the invention allow diagnosis and identification
of the potential for complications during or after pregnancy.
Methods also allow the identification of pregnancy-associated
transcripts which, in turn, elucidates maternal and fetal
developmental programs. Methods of the invention are useful for
preterm diagnosis as well as elucidation of transcript profiles
associated with fetal developmental pathways generally. Thus,
methods of the invention are useful to characterize fetal
development and are not limited to characterization only of disease
states or complications associated with pregnancy. Exemplary
embodiments of the methods are described in the detailed
description, claims, and figures provided below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0019] FIG. 1 depicts a listing of the top detected female
pregnancy associated differentially expressed transcripts.
[0020] FIG. 2 shows plots of the two main principal components for
cell free RNA transcript levels obtained in Example 1.
[0021] FIG. 3A depicts a heatmap of the top 100 cell free
transcript levels exhibiting different temporal levels in preterm
and normal pregnancy using microarrays. The heat map of FIG. 3A is
split across FIG. 3A-1 and FIG. 3A-2, as indicated by the graphical
figure outline.
[0022] FIG. 3B depicts heatmap of the top 100 cell free transcript
levels exhibiting different temporal levels in preterm and normal
pregnancy using RNA-Seq. The heat map of FIG. 3B is split across
FIG. 3B-1 and FIG. 3B-2, as indicated by the graphical figure
outline.
[0023] FIG. 4 depicts a ranking of the top 20 transcripts
differentially expressed between pre-term and normal pregnancy.
[0024] FIG. 5 depicts results of a Gene Ontology analysis on the
top 20 common RNA transcripts of FIG. 4, showing those transcripts
enriched for proteins that are attached (integrated or loosely
bound) to the plasma membrane or on the membranes of the
platelets.
[0025] FIG. 6 depicts that the gene expression profile for PVALB
across the different trimesters shows the premature births
[highlighted in blue] has higher levels of cell free RNA
transcripts found as compared to normal pregnancy.
[0026] FIG. 7 outlines exemplary process steps for determining the
relative tissue contributions to a cell-free transcriptome of a
sample. FIG. 7 is split across FIGS. 7A and 7B, as indicated by the
graphical figure outline.
[0027] FIG. 8 depicts the panel of selected fetal tissue-specific
transcripts generated in Example 2. FIG. 8 is split across FIGS. 8A
and 8B, as indicated by the graphical figure outline.
[0028] FIGS. 9A and 9B depict the raw data of parallel
quantification of the fetal tissue-specific transcripts showing
changes across maternal time-points (first trimester, second
trimester, third trimester, and post partum) using the actual cell
free RNA as well as the cDNA library of the same cell free RNA.
[0029] FIG. 10 illustrates relative expression of placental genes
across maternal time points (first trimester, second trimester,
third trimester, and post partum). FIG. 10 is split across FIGS.
10A and 10B, as indicated by the graphical figure outline. In FIG.
10, relative expression fold changes of each trimester as compared
to post-partum for the panel of placental genes. Plotted are the
results for two subjects done at two different concentrations each,
each point represent one subject sampled at a particular trimester,
and the cell free RNA went through the described protocol at two
concentration levels. FIG. 10B depicts the same results segmented
across the two subjects labeled as P53 & P54.
[0030] FIG. 11 illustrates relative expression of fetal brain genes
across maternal time points (first trimester, second trimester,
third trimester, and post partum). FIG. 11 is split across FIGS.
11A and 11B, as indicated by the graphical figure outline. In FIG.
11A, relative expression folds changes of each trimester as
compared to post-partum for the panel of Fetal Brain genes. Plotted
are the results for two subjects done at two different
concentrations each, each point represent one subject sampled at a
particular trimester, and the cell free RNA went through the
described protocol at two concentration levels. FIG. 11B depicts
the same results segmented across the two subjects labeled as P53
& P54.
[0031] FIG. 12 illustrates relative expression of fetal liver genes
across maternal time points (first trimester, second trimester,
third trimester, and post partum). FIG. 12 is split across FIGS.
12A and 12B, as indicated by the graphical figure outline. In FIG.
12A, relative expression fold changes of each trimester as compared
to post-partum for the panel of Fetal Liver genes. Plotted are the
results for two subjects done at two different concentrations each,
each point represent one subject sampled at a particular trimester,
and the cell free RNA went through the described protocol at two
concentration levels. FIG. 12B depicts the same results segmented
across the two subjects labeled as P53 & P54.
[0032] FIG. 13 illustrates the relative composition of different
organs contribution towards a plasma adult cell free
transcriptome.
[0033] FIG. 14 illustrates a decomposition of decomposition of
organ contribution towards a plasma adult cell free transcriptome
using RNA-seq data.
[0034] FIG. 15 shows a heat map of the tissue specific transcripts
of Table 2 of Example 3, being detectable in the cell free RNA.
[0035] FIG. 16 depicts a flow-diagram of a method of the invention
according to certain embodiments.
[0036] FIG. 17 illustrates identifying brain-specific cell-free RNA
transcripts that differ between Alzheimer's subjects and control
subjects.
[0037] FIG. 18 illustrates an experimental design comparing
microarray, RNA-seq and quantitative PCR for a customized
bioinformatics pipeline. In the experiment, 11 pregnant women and 4
non-pregnant control subjects were recruited. For all the pregnant
patients, blood was drawn at 1st, 2nd, 3rd trimester and
postpartum. The cell-free plasma RNA were then extracted, amplified
and characterized by Affymetrix microarray, Illumina sequencer and
quantitative PCR.
[0038] FIG. 19 illustrates a heat map of temporal varying genes
obtained from microarray analysis. Unsupervised clustering was
performed on genes across different time points. Cluster of genes
belongs to the CGB family of genes which are known to be expressed
at high levels during the first trimester exhibited corresponding
high levels of RNA in the first trimester.
[0039] FIG. 20 illustrates another heat map of temporal varying
genes obtained from microarray analysis. Unsupervised clustering
was performed on genes across different time points. Cluster of
genes belongs to the CGB family of genes which are known to be
expressed at high levels during the first trimester exhibited
corresponding high levels of RNA in the first trimester.
[0040] FIG. 21 illustrates a list of genes identified with fetal
SNPs using the experimental design of FIG. 18. List of identified
Gene Transcripts with identified fetal SNPs and the captured
temporal dynamics. The barplot reflects the relative contribution
of fetal SNPs as reflected in the sequencing data. The red color
bar reflects the extent of the relative Fetal SNP contribution.
[0041] FIG. 22 identifies placental specific transcripts measured
by qPCR in the experimental design of FIG. 18. As shown in FIG. 22,
the time course of placental specific genes is measured by qPCR.
Plot showing the Delta Ct value with respect to the housekeeping
gene ACTB across the different trimesters of pregnancy including
after birth. General trends show elevated levels during the
trimesters with a decline to low levels after the baby is born.
[0042] FIG. 23 identifies fetal brain specific transcripts measured
byq. As shown in FIG. 23, the time course of fetal brain specific
genes is measured by qPCR. Plot showing the Delta Ct value with
respect to the housekeeping gene ACTB across the different
trimesters of pregnancy including after birth. General trends show
elevated levels during the trimesters with a decline to low levels
after the baby is born.
[0043] FIG. 24 identifies fetal liver specific transcripts measured
by qPCR. As shown in FIG. 24, the time course of fetal liver
specific genes is measured by qPCR. Plot showing the Delta Ct value
with respect to the housekeeping gene ACTB across the different
trimesters of pregnancy including after birth. General trends show
elevated levels during the trimesters with a decline to low levels
after the baby is born.
[0044] FIG. 25 illustrates tissue composition of the adult cell
free transcriptome in typical adult plasma as a summation of RNAs
from different tissue types.
[0045] FIG. 26 illustrates decomposition of Cell-free RNA
transcriptome of normal adult into their respective tissues types
using microarray data and quadratic programming.
[0046] FIG. 27 depicts a Principle Component Analysis (PCA) space
reflecting the unsupervised clustering of the patients using the
gene expression data from the 48 genes assay.
[0047] FIG. 28 depicts the measured APP levels in patients. The
left panel shows the levels of APP transcripts across different age
groups in the study. The right panel shows the different levels of
the APP transcripts of the combined population of patients.
[0048] FIG. 29 depicts the measured MOBP levels in patients. The
left panel shows the levels of the MOBP transcripts across
different age groups in the study. The right panel shows the
different levels of the MOBP transcripts of the combined population
of patients.
[0049] FIG. 30 depicts classification results using combined
Z-scores.
DETAILED DESCRIPTION
[0050] Methods and materials described herein apply a combination
of next-generation sequencing and microarray techniques for
detecting, quantitating and characterizing RNA present in a
biological sample. In certain embodiments, the biological sample
contains a mixture of genetic material from different genomic
sources, i.e. pregnant female and a fetus.
[0051] Unlike other methods of digital analysis in which the
nucleic acid in the sample is isolated to a nominal single target
molecule in a small reaction volume, methods of the present
invention are conducted without diluting or distributing the
genetic material in the sample. Methods of the invention allow for
simultaneous screening of multiple transcriptomes, and provide
informative sequence information for each transcript at the
single-nucleotide level, thus providing the capability for
non-invasive, high throughput screening for a broad spectrum of
diseases or conditions in a subject from a limited amount of
biological sample.
[0052] In one particular embodiment, methods of the invention
involve analysis of mixed fetal and maternal RNA in the maternal
blood to identify differentially expressed transcripts throughout
different stages of pregnancy that may be indicative of a preterm
or pathological pregnancy. Differential detection of transcripts is
achieved, in part, by isolating and amplifying plasma RNA from the
maternal blood throughout the different stages of pregnancy, and
quantitating and characterizing the isolated transcripts via
microarray and RNA-Seq.
[0053] Methods and materials specific for analyzing a biological
sample containing RNA (including non-maternal, maternal,
maternal-fetus mixed) as described herein, are merely one example
of how methods of the invention can be applied and are not intended
to limit the invention. Methods of the invention are also useful to
screen for the differential expression of target genes related to
cancer diagnosis, progression and/or prognosis using cell-free RNA
in blood, stool, sputum, urine, transvaginal fluid, breast nipple
aspirate, cerebrospinal fluid, etc.
[0054] In certain embodiments, methods of the invention generally
include the following steps: obtaining a biological sample
containing genetic material from different genomic sources,
isolating total RNA from the biological sample containing
biological sample containing a mixture of genetic material from
different genomic sources, preparing amplified cDNA from total RNA,
sequencing amplified cDNA, and digital counting and analysis, and
profiling the amplified cDNA.
[0055] Methods of the invention also involve assessing the health
of a tissue contributing to the cell-free transcriptome. In certain
embodiments, the invention involves assessing the cell-free
transcriptome of a biological sample to determine tissue-specific
contributions of individual tissues to the cell-free transcriptome.
According to certain aspects, the invention assesses the health of
a tissue by detecting a sample level of RNA in a biological sample,
comparing the sample level of RNA to a reference level of RNA
specific to the tissue, and characterizing the tissue as abnormal
if a difference is detected. This method is applicable to
characterize the health of a tissue in non-maternal subjects,
pregnant subjects, and live fetuses. FIG. 16 depicts a flow-diagram
of this method according to certain embodiments.
[0056] In certain aspects, methods of the invention employ a
deconvolution of a reference cell-free RNA transcriptome to
determine a reference level for a tissue. Preferably, the reference
cell-free RNA transcriptome is a normal, healthy transcriptome, and
the reference level of a tissue is a relative level of RNA specific
to the tissue present in the blood of healthy, normal individuals.
Methods of the invention assume that apoptotic cells from different
tissue types release their RNA into plasma of a subject. Each of
these tissues expresses a specific number of genes unique to the
tissue type, and the cell-free RNA transcriptome of a subject is a
summation of the different tissue types. Each tissue may express
one or more numbers of genes. In certain embodiments, the reference
level is a level associated with one of the genes expressed by a
certain tissue. In other embodiments, the reference level is a
level associated with a plurality of genes expressed by a certain
tissue. It should be noted that a reference level or threshold
amount for a tissue-specific transcript present in circulating RNA
may be zero or a positive number.
[0057] For healthy, normal subjects, the relative contributions of
circulating RNA from different tissue types are relatively stable,
and each tissue-specific RNA transcript of the cell-free RNA
transcriptome for normal subjects can serve as a reference level
for that tissue. Applying methods of the invention, a tissue is
characterized as unhealthy or abnormal if a sample includes a level
of RNA that differs from a reference level of RNA specific to the
tissue. The tissue of the sample may be characterized as unhealthy
if the actual level of RNA is statistically different from the
reference level. Statistical significance can be determined by any
method known in the art. These measurements can be used to screen
for organ health, as diagnostic tool, and as a tool to measure
response to pharmaceuticals or in clinical trials to monitor
health.
[0058] If a difference is detected between the sample level of RNA
and the reference level of RNA, such difference suggests that the
associated tissue is not functioning properly. The change in
circulating RNA may be the precursor to organ failure or indicate
that the tissue is being attacked by the immune system or
pathogens. If a tissue is identified as abnormal, the next step(s),
according to certain embodiments, may include more extensive
testing of the tissue (e.g. invasive biopsy of the tissue),
prescribing course of treatment specific to the tissue, and/or
routine monitoring of the tissue.
[0059] Methods of the invention can be used to infer organ health
non-invasively. This non-invasive testing can be used to screen for
appendicitis, incipient diabetes and pathological conditions
induced by diabetes such as nephropathy, neuropathy, retinopathy
etc. In addition, the invention can be used to determine the
presence of graft versus host disease in organ transplants,
particularly in bone marrow transplant recipients whose new immune
system is attacking the skin, GI tract or liver. The invention can
also be used to monitor the health of solid organ transplant
recipients such as heart, lung and kidney. The methods of the
invention can assess likelihood of prematurity, preeclampsia and
anomalies in pregnancy and fetal development. In addition, methods
of the invention could be used to identify and monitor neurological
disorders (e.g. multiple sclerosis and Alzheimer's disease) that
involve cell specific death (e.g. of neurons or due to
demyelination) or that involve the generation of plaques or protein
aggregation.
[0060] A cell-free transcriptome for purposes of determining a
reference level for tissue-specific transcripts can be the
cell-free transcriptome of one or more normal subjects, maternal
subjects, subjects having a certain conditions and diseases, or
fetus subjects. In the case of certain conditions, the reference
level of a tissue is a level of RNA specific to the tissue present
in blood of one or more subjects having a certain disease or
condition. In such aspect, the method includes detecting a level of
RNA in a blood, comparing the sample level of RNA to a reference
level of RNA specific to a tissue, determining whether a difference
exists between the sample level and the reference level, and
characterizing the as abnormal if the sample level and the
reference level are the same.
[0061] A deconvolution of a cell-free transcriptome is used to
determine the relative contribution of each tissue type towards the
cell-free RNA transcriptome. The following steps are employed to
determine the relative RNA contributions of certain tissues in a
sample. First, a panel of tissue-specific transcripts is
identified. Second, total RNA in plasma from a sample is determined
using methods known in the art. Third, the total RNA is assessed
against the panel of tissue-specific transcripts, and the total RNA
is considered a summation these different tissue-specific
transcripts. Quadratic programming can be used as a constrained
optimization method to deduce the relative optimal contributions of
different organs/tissues towards the cell-free transcriptome of the
sample.
[0062] One or more databases of genetic information can be used to
identify a panel of tissue-specific transcripts. Accordingly,
aspects of the invention provide systems and methods for the use
and development of a database. Particularly, methods of the
invention utilize databases containing existing data generated
across tissue types to identify the tissue-specific genes.
Databases utilized for identification of tissue-specific genes
include the Human 133A/GNF1H Gene Atlas and RNA-Seq Atlas, although
any other database or literature can be used. In order to identify
tissue-specific transcripts from one or more databases, certain
embodiments employ a template-matching algorithm to the databases.
Template matching algorithms used to filter data are known in the
art, see e.g., Pavlidis P, Noble W S (2001) Analysis of strain and
regional variation in gene expression in mouse brain. Genome Biol
2:research0042.1-0042.15.
[0063] In certain embodiments, quadratic programming is used as a
constrained optimization method to deduce relative optimal
contributions of different organs/tissues towards the cell-free
transcriptome in a sample. Quadratic programming is known in the
art and described in detail in Goldfarb and A. Idnani (1982). Dual
and Primal-Dual Methods for Solving Strictly Convex Quadratic
Programs. In J. P. Hennart (ed.), Numerical Analysis,
Springer-Verlag, Berlin, pages 226-239, and D. Goldfarb and A.
Idnani (1983). A numerically stable dual method for solving
strictly convex quadratic programs. Mathematical Programming, 27,
1-33.
[0064] FIG. 7 outlines exemplary process steps for determining the
relative tissue contributions to a cell-free transcriptome of a
sample. Using information provided by one or more tissue-specific
databases, a panel of tissue-specific genes is generated with a
template-matching function. A quality control function can be
applied to filter the results. A blood sample is then analyzed to
determine the relative contribution of each tissue-specific
transcript to the total RNA of the sample. Cell-free RNA is
extracted from the sample, and the cell-free RNA extractions are
processed using one or more quantification techniques (e.g.
standard mircoarrays and RNA-sequence protocols). The obtained gene
expression values for the sample are then normalized. This involves
rescaling of all gene expression values to the housekeeping genes.
Next, the sample's total RNA is assessed against the panel of
tissue-specific genes using quadratic programming in order to
determine the tissue-specific relative contributions to the
sample's cell-free transcriptome. The following constraints are
employed to obtain the estimated relative contributions during the
quadratic programming analysis: a) the RNA contributions of
different tissues are greater than or equal to zero, and b) the sum
of all contributions to the cell-free transcriptome equals one.
[0065] Method of the invention for determining the relative
contributions for each tissue can be used to determine the
reference level for the tissue. That is, a certain population of
subjects (e.g., maternal, normal, cancerous, Alzheimer's (and
various stages thereof)) can be subject to the deconvolution
process outlined in FIG. 7 to obtain reference levels of
tissue-specific gene expression for that patient population. When
relative tissue contributions are considered individually,
quantification of each of these tissue-specific transcripts can be
used as a measure for the reference apoptotic rate of that
particular tissue for that particular population. For example,
blood from one or more healthy, normal individuals can be analyzed
to determine the relative RNA contribution of tissues to the
cell-free RNA transcriptome for healthy, normal individuals. Each
relative RNA contribution of tissue that makes up the normal RNA
transcriptome is a reference level for that tissue.
[0066] According to certain embodiments, an unknown sample of blood
can be subject to process outlined in FIG. 7 to determine the
relative tissue contributions to the cell-free RNA transcriptome of
that sample. The relative tissue contributions of the sample are
then compared to one or more reference levels of the relative
contributions to a reference cell-free RNA transcriptome. If a
specific tissue shows a contribution to the cell-free RNA
transcriptome in the sample that is greater or less than the
contribution of the specific tissue in a reference cell-free RNA
transcriptome, then the tissue exhibiting differential contribution
may be characterized accordingly. If the reference cell-free
transcriptome represents a healthy population, a tissue exhibiting
a differential RNA contribution in a sample cell-free transcriptome
can be classified as unhealthy.
[0067] The biological sample can be blood, saliva, sputum, urine,
semen, transvaginal fluid, cerebrospinal fluid, sweat, breast milk,
breast fluid (e.g., breast nipple aspirate), stool, a cell or a
tissue biopsy. In certain embodiments, the samples of the same
biological sample are obtained at multiple different time points in
order to analyze differential transcript levels in the biological
sample over time. For example, maternal plasma may be analyzed in
each trimester. In some embodiments, the biological sample is drawn
blood and circulating nucleic acids, such as cell-free RNA. The
cell-free RNA may be from different genomic sources is found in the
blood or plasma, rather than in cells.
[0068] In a particular embodiment, the drawn blood is maternal
blood. In order to obtain a sufficient amount of nucleic acids for
testing, it is preferred that approximately 10-50 mL of blood be
drawn. However, less blood may be drawn for a genetic screen in
which less statistical significance is required, or in which the
RNA sample is enriched for fetal RNA.
[0069] Methods of the invention involve isolating total RNA from a
biological sample. Total RNA can be isolated from the biological
sample using any methods known in the art. In certain embodiments,
total RNA is extracted from plasma. Plasma RNA extraction is
described in Enders et al., "The Concentration of Circulating
Corticotropin-releasing Hormone mRNA in Maternal Plasma Is
Increased in Preeclampsia," Clinical Chemistry 49: 727-731, 2003.
As described there, plasma harvested after centrifugation steps is
mixed Trizol LS reagent (Invitrogen) and chloroform. The mixture is
centrifuged, and the aqueous layer transferred to new tubes.
Ethanol is added to the aqueous layer. The mixture is then applied
to an RNeasy mini column (Qiagen) and processed according to the
manufacturer's recommendations.
[0070] In the embodiments where the biological sample is maternal
blood, the maternal blood may optionally be processed to enrich the
fetal RNA concentration in the total RNA. For example, after
extraction, the RNA can be separated by gel electrophoresis and the
gel fraction containing circulatory RNA with a size of
corresponding to fetal RNA (e.g., <300 bp) is carefully excised.
The RNA is extracted from this gel slice and eluted using methods
known in the art.
[0071] Alternatively, fetal specific RNA may be concentrated by
known methods, including centrifugation and various enzyme
inhibitors. The RNA is bound to a selective membrane (e.g., silica)
to separate it from contaminants. The RNA is preferably enriched
for fragments circulating in the plasma, which are less than less
300 bp. This size selection is done on an RNA size separation
medium, such as an electrophoretic gel or chromatography
material.
[0072] Flow cytometry techniques can also be used to enrich for
fetal cells in maternal blood (Herzenberg et al., PNAS 76:
1453-1455 (1979); Bianchi et al., PNAS 87: 3279-3283 (1990); Bruch
et al., Prenatal Diagnosis 11: 787-798 (1991)). U.S. Pat. No.
5,432,054 also describes a technique for separation of fetal
nucleated red blood cells, using a tube having a wide top and a
narrow, capillary bottom made of polyethylene. Centrifugation using
a variable speed program results in a stacking of red blood cells
in the capillary based on the density of the molecules. The density
fraction containing low-density red blood cells, including fetal
red blood cells, is recovered and then differentially hemolyzed to
preferentially destroy maternal red blood cells. A density gradient
in a hypertonic medium is used to separate red blood cells, now
enriched in the fetal red blood cells from lymphocytes and ruptured
maternal cells. The use of a hypertonic solution shrinks the red
blood cells, which increases their density, and facilitates
purification from the more dense lymphocytes. After the fetal cells
have been isolated, fetal RNA can be purified using standard
techniques in the art.
[0073] Further, an agent that stabilizes cell membranes may be
added to the maternal blood to reduce maternal cell lysis including
but not limited to aldehydes, urea formaldehyde, phenol
formaldehyde, DMAE (dimethylaminoethanol), cholesterol, cholesterol
derivatives, high concentrations of magnesium, vitamin E, and
vitamin E derivatives, calcium, calcium gluconate, taurine, niacin,
hydroxylamine derivatives, bimoclomol, sucrose, astaxanthin,
glucose, amitriptyline, isomer A hopane tetral phenylacetate,
isomer B hopane tetral phenylacetate, citicoline, inositol, vitamin
B, vitamin B complex, cholesterol hemisuccinate, sorbitol, calcium,
coenzyme Q, ubiquinone, vitamin K, vitamin K complex, menaquinone,
zonegran, zinc, ginkgo biloba extract, diphenylhydantoin,
perftoran, polyvinylpyrrolidone, phosphatidylserine, tegretol,
PABA, disodium cromglycate, nedocromil sodium, phenyloin, zinc
citrate, mexitil, dilantin, sodium hyaluronate, or polaxamer
188.
[0074] An example of a protocol for using this agent is as follows:
The blood is stored at 4.degree. C. until processing. The tubes are
spun at 1000 rpm for ten minutes in a centrifuge with braking power
set at zero. The tubes are spun a second time at 1000 rpm for ten
minutes. The supernatant (the plasma) of each sample is transferred
to a new tube and spun at 3000 rpm for ten minutes with the brake
set at zero. The supernatant is transferred to a new tube and
stored at -80.degree. C. Approximately two milliliters of the
"buffy coat," which contains maternal cells, is placed into a
separate tube and stored at -80.degree. C.
[0075] Methods of the invention also involve preparing amplified
cDNA from total RNA. cDNA is prepared and indiscriminately
amplified without diluting the isolated RNA sample or distributing
the mixture of genetic material in the isolated RNA into discrete
reaction samples. Preferably, amplification is initiated at the 3'
end as well as randomly throughout the whole transcriptome in the
sample to allow for amplification of both mRNA and
non-polyadenylated transcripts. The double-stranded cDNA
amplification products are thus optimized for the generation of
sequencing libraries for Next Generation Sequencing platforms.
Suitable kits for amplifying cDNA in accordance with the methods of
the invention include, for example, the Ovation.RTM. RNA-Seq
System.
[0076] Methods of the invention also involve sequencing the
amplified cDNA. While any known sequencing method can be used to
sequence the amplified cDNA mixture, single molecule sequencing
methods are preferred. Preferably, the amplified cDNA is sequenced
by whole transcriptome shotgun sequencing (also referred to herein
as ("RNA-Seq"). Whole transcriptome shotgun sequencing (RNA-Seq)
can be accomplished using a variety of next-generation sequencing
platforms such as the Illumina Genome Analyzer platform, ABI Solid
Sequencing platform, or Life Science's 454 Sequencing platform.
[0077] Methods of the invention further involve subjecting the cDNA
to digital counting and analysis. The number of amplified sequences
for each transcript in the amplified sample can be quantitated via
sequence reads (one read per amplified strand). Unlike previous
methods of digital analysis, sequencing allows for the detection
and quantitation at the single nucleotide level for each transcript
present in a biological sample containing a genetic material from
different genomic sources and therefore multiple
transcriptomes.
[0078] After digital counting, the ratios of the various amplified
transcripts can compared to determine relative amounts of
differential transcript in the biological sample. Where multiple
biological samples are obtained at different time-points, the
differential transcript levels can be characterized over the course
of time.
[0079] Differential transcript levels within the biological sample
can also be analyzed using via microarray techniques. The amplified
cDNA can be used to probe a microarray containing gene transcripts
associated with one or conditions or diseases, such as any prenatal
condition, or any type of cancer, inflammatory, or autoimmune
disease.
[0080] It will be understood that methods and any flow diagrams
disclosed herein can be implemented by computer program
instructions. These program instructions may be provided to a
computer processor, such that the instructions, which execute on
the processor, create means for implementing the actions specified
in the flowchart blocks or described in methods for assessing
tissue disclosed herein. The computer program instructions may be
executed by a processor to cause a series of operational steps to
be performed by the processor to produce a computer implemented
process. The computer program instructions may also cause at least
some of the operational steps to be performed in parallel.
Moreover, some of the steps may also be performed across more than
one processor, such as might arise in a multi-processor computer
system. In addition, one or more processes may also be performed
concurrently with other processes or even in a different sequence
than illustrated without departing from the scope or spirit of the
invention.
[0081] The computer program instructions can be, stored on any
suitable computer-readable medium including, but not limited to,
RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM,
digital versatile disks (DVD) or other optical storage, magnetic
cassettes, magnetic tape, magnetic disk storage or other magnetic
storage devices, or any other medium which can be used to store the
desired information and which can be accessed by a computing
device.
[0082] In certain aspects, methods of the invention can be used to
determine cell-free RNA transcripts specific to the certain tissue,
and use those transcripts to diagnose disorders and diseases
associated with that tissue. In certain embodiments, methods of the
invention can be used to determine cell-free RNA transcripts
specific to the brain, and use those transcripts to diagnose
neurological disorders (such as Alzheimer's disease). For example,
methods of profiling cell-free RNA described herein can be used to
differentiate subjects with neurological disorders from normal
subjects because cell-free RNA transcripts associated with certain
neurological disorders present at statistically-significant
different levels than the same cell-free RNA transcripts in normal
healthy populations. As a result, one is able to utilize levels of
those RNA transcripts for clear and simple diagnostic tests.
[0083] In accordance with certain embodiments, cell-free RNA
transcripts that source from brain tissue can be further examined
as potential biomarkers for neurological disorders. In certain
embodiments, once a brain-specific cell-free RNA transcript is
determined, levels of the brain-specific cell-free RNA transcripts
in normal patients are compared to patients with certain
neurological disorders. In instances where the levels of brain
specific cell-free RNA transcript consistently exhibit a
statistically significant difference between subjects with a
certain neurological disorder and normal subjects, then that
brain-specific cell-free RNA transcript can be used as a biomarker
for that neurological disorder. For example, the inventors have
found that measurements of PSD3 and APP cell-free RNA transcript
levels in plasma for Alzheimer disorder patients are statistically
different from the levels of PSD3 and APP cell-free RNA in normal
subjects.
[0084] According to certain aspects, a neurological disorder is
indicated in a patient based on a comparison of the patient's
circulating nucleic acid that is specific to brain tissue and
circulating nucleic acid of a reference or multiple references that
is specific to brain tissue. In particular, the circulating nucleic
acid is RNA, but may also be DNA. In certain embodiments, levels of
brain-specific circulating RNA present in a reference population
are used as thresholds that are indicative with a condition. The
condition may be a normal healthy condition or may be a diseased
condition (e.g. neurological disorder, Alzheimer's disease
generally or particular stage of Alzheimer's disease). When the
threshold is indicative of a diseased condition, the patient's
transcript levels that are underexpressed or overexpressed in
comparison to the threshold may indicate that the patient does not
have the disease. When the threshold is indicative of normal
condition, the patient's transcript levels that are underexpressed
or overexpressed in comparison to the threshold may indicate that
the patient has the disease.
[0085] Reference RNA levels (e.g. levels of circulating RNA) may be
obtained by statistically analyzing the brain-specific transcript
levels of a defined patient population. The reference levels may
pertain to a healthy patient population or a patient population
with a particular neurological disorder. In further examples, the
references levels may be tailored to a more specific patient
population. For example, a reference level may correlate to a
patient population of a certain age and/or correspond to a patient
population exhibiting symptoms associated with a particular stage
of a neurological disorder. Other factors for tailoring the patient
population for reference levels may include sex, familial history,
environmental exposure, and/or phenotypic traits.
[0086] Brain-specific genes or transcripts may be determined by
deconvolving the cell-free transcriptome as described above and
outlined in FIG. 7. Brain-specific genes or transcripts may also be
determined by directly analyzing brain tissue. In addition, Tables
1 and 2, as listed in Example 4 below, provide genes whose
expression profiles are unique to certain tissue types.
Particularly, Tables 1 and 2 list brain-specific genes
corresponding with hypothalamus as well as genes corresponding with
the whole brain (e.g. most brain tissue), prefrontal cortex,
thalamus, etc. In certain embodiments, brain-specific genes or
transcripts include APP, PSD3, MOBP, MAG, SLC2A1, TCF7L2, CDH22,
CNTF, and PAQR6.
[0087] The brain-specific transcripts used in methods of the
invention may correspond to cell-free transcripts released from
certain types of brain tissue. The types of brain tissue include
the pituitary, hypothalamus, thalamus, corpus callosum, cerebrum,
cerebral cortex, and combinations thereof. In particular
embodiments, the brain-specific transcripts correspond with the
hypothalamus. The hypothalamus is bounded by specialized brain
regions that lack an effective blood/brain barrier, and thus
transcripts released from the hypothalamus are likely to be
introduced into blood or plasma.
[0088] FIG. 19 illustrates the difference in levels of PSD3 and APP
cell-free RNA between subjects with Alzheimer's and normal
subjects. Measurements of PSD3 and APP cell free RNA transcripts
levels in plasma shows that the levels of these two transcripts are
elevated in AD patients and can be used to cleanly group the AD
patients from the normal patients. Shown in the figure are only two
potential transcripts showing significant diagnostic potential.
High throughput microfluidics chip allow for simultaneous
measurements of other brain specific transcripts which can improve
the classification process.
[0089] In particular aspects, brain-specific transcripts are used
to characterize and diagnose neurological disorders. The
neurological disorder characterized may include degenerative
neurological disorders, such as Alzheimer's disease, Parkinson's
disease, Huntington's disease, and some types of multiple
sclerosis. The most common neurological disorder is Alzheimer's
disease. In some instances, the neurological disorder is classified
by the extent of cognitive impairment, which may include no
impairment, mild impairment, moderate impairment, and severe
impairment.
[0090] Alzheimer's disease is characterized into stages based on
the cognitive symptoms that occur as the disease progresses. Stage
1 involves no impairment (normal function). The person does not
experience any memory problems or signs of dementia. Stage 2
involves a very mild decline in cognitive functions. During Stage
2, a person may experience mild memory loss, but cognitive
impairment is not likely noticeable by friends, family, and
treating physicians. Stage 3 involves a mild cognitive decline, in
which friends, family, and treating physicians may notice
difficulties in the individual's memory and ability to perform
tasks. For example, trouble identifying certain words, noticeable
difficulty in performing tasks in social or work settings,
forgetting just-read materials. Stage 4 involves moderate cognitive
decline, which is noticeable and causes a significant impairment on
the individual's daily life. In Stage 4, the individual will have
trouble performing everyday complex tasks, such as managing
financings and planning social gatherings, will have trouble
remembering their own personal history, and becomes moody or
withdrawn. Stage 5 involves moderately severe cognitive decline, in
which gaps in memory and thinking are noticeable and the individual
will begin to need help with certain activities. In Stage 5,
individuals will be confused about the day, will have trouble with
recalling particular details (such as phone number and street
address), but will be able to remember significant details about
themselves and their loved ones. Stage 6 involves severe cognitive
decline, as the individual's memory continues to worsen.
Individuals in Stage 6 will likely need extensive help with daily
activities because they lose awareness of their surroundings and
while they often remember certain tasks, they forget how to
complete them or make mistakes (e.g. wearing pajamas during the
day, forgetting to rinse after shampooing, wearing shoes on wrong
side of the foot). Stage 7 involves very severe cognitive decline
and is the final stage of Alzheimer's disease. In Stage 7,
individuals lose their ability to respond to the environment,
remember others, carry on a conversation, and control movement.
Individuals need help with daily care, eating, dressing, using the
bathroom, and have abnormal reflexes and tense muscles. Individuals
may still be verbal, but will not make sense or relate to the
present.
[0091] In certain embodiments, methods for assessing a neurological
disorder involve a comparison of one or more brain-specific
transcripts of an individual to a set of predictive variables
correlated with the neurological disorder. The set of predictive
variables may include a variety of reference levels that are brain
specific. For instance, the set of predictive variables may include
brain-specific transcript levels of a plurality of references. For
example, one reference level may correspond to a normal patient
population and another reference level may correspond to a patient
population with the neurological disorder. In further examples, the
references may correspond to more specific patient populations. For
example, each reference level may correlate to a patient population
of a certain age and/or correspond to a patient population
exhibiting symptoms associated with a particular stage of a
neurological disorder. Other factors for tailoring the patient
population for reference levels may include sex, familial history,
environmental exposure, and/or phenotypic traits.
[0092] Statistical analyses can be used to determine brain-specific
reference levels of certain patient populations (such as those
discussed above). Statistical analyses for identifying trends in
patient populations and comparing patient populations are known in
the art. Suitable statistical analyses include, but are not limited
to, clustering analysis, principle component analysis,
non-parametric statistical analyses (e.g. Wilcoxon tests), etc.
[0093] In addition, statistical analyses may be used to
statistically significant deviations between the individual's
circulating nucleic specific to brain tissue and that of a
reference. When the reference is based on a diseased population,
statistically significant deviations of the individual's
brain-specific circulating RNA to those of the diseased population
are indicative of no neurological disorder. When the reference is
based on a normal population, statistically significant deviations
of the individual's brain-specific circulating RNA to those of the
normal population are indicative of a neurological disorder.
Methods of determining statistical significance are known in the
art. P-values and odds ratio can be used for statistical inference.
Logistic regression models are common statistical classification
models. In addition, Chi-Square tests and T-test may also be used
to determine statistical significance.
[0094] Methods of the invention can also be used to identify one or
more biomarkers associated with a neurological disorder. In such
aspects, brain-specific transcripts of an individual or patient
population suspected of having or actually having a neurological
disorder (e.g. exhibiting impaired cognitive functions) are
compared to reference brain-specific transcript (e.g. a healthy,
normal control). The brain-specific transcripts of the individual
or patient population that are differentially expressed as compared
to the reference may then be identified as biomarkers of the
neurological disorder. In certain embodiments, only differentially
expressed brain-specific transcripts that are statistically
significant are identified as biomarkers.
[0095] In certain embodiments, methods of the invention provide
recommend a course of treatment based on the clinical indications
determined by comparing of the patient's circulating brain-specific
RNA and the reference. Depending on the diagnosis, the course of
treatment may include medicinal therapy, behavioral therapy, sleep
therapy, and combinations thereof. The course of treatment and
diagnosis may be provided in a read-out or a report.
EXAMPLES
Example 1
Profiling Maternal Plasma Cell-Free RNA by RNA Sequencing-A
Comprehensive Approach
Overview
[0096] The plasma RNA profiles of 5 pregnant women were collected
during the first trimester, second trimester, post-partum, as well
as those of 2 anon-pregnant female donors and 2 male donors using
both microarray and RNA-Seq.
[0097] Among these pregnancies, there were 2 pregnancies with
clinical complications such as premature birth and one pregnancy
with bi-lobed placenta. Comparison of these pregnancies against
normal cases reveals genes that exhibit significantly different
gene expression pattern across different temporal stages of
pregnancy. Application of such technique to samples associated with
complicated pregnancies may help identify transcripts that can be
used as molecular markers that are predictive of these
pathologies.
Study Design and Methods:
Subjects
[0098] Samples were collected from 5 pregnant women were during the
first trimester, second trimester, third trimester, and
post-partum. As a control, blood plasma samples were also collected
from 2 non-pregnant female donors and 2 male donors.
Blood Collection and Processing
[0099] Blood samples were collected in EDTA tube and centrifuged at
1600 g for 10 min at 4.degree. C. Supernatant were placed in 1 ml
aliquots in a 1.5 ml microcentrifuge tube which were then
centrifuged at 16000 g for 10 min at 4.degree. C. to remove
residual cells. Supernatants were then stored in 1.5 ml
microcentrifuge tubes at -80.degree. C. until use.
RNA Extraction and Amplification
[0100] The cell-free maternal plasma RNAs was extracted by Trizol
LS reagent. The extracted and purified total RNA was converted to
cDNA and amplified using the RNA-Seq Ovation Kit (NuGen). (The
above steps were the same for both Microarray and RNA-Seq sample
preparation).
[0101] The cDNA was fragmented using DNase I and labeled with
Biotin, following by hybridization to Affymetrix GeneChip ST 1.0
microarrays. The Illumina sequencing platform and standard Illumina
library preparation protocols were used for sequencing.
Data Analysis:
Correlation Between Microarray and RNA-Seq
[0102] The RMA algorithm was applied to process the raw microarray
data for background correction and normalization. RPKM values of
the sequenced transcripts were obtained using the CASAVA 1.7
pipeline for RNA-seq. The RPKM in the RNA-Seq and the probe
intensities in the microarray were converted to log 2 scale. For
the RNA-Seq data, to avoid taking the log of 0, the gene
expressions with RPKM of 0 were set to 0.01 prior to taking logs.
Correlation coefficients between these two platforms ranges were
then calculated.
Differential Expression of RNA Transcripts Levels Using RNA-Seq
[0103] Differential gene expression analysis was performed using
edgeR, a set of library functions which are specifically written to
analyze digital gene expression data. Gene Ontology was then
performed using DAVID to identify for significantly enriched GO
terms.
Principle Component Analysis & Identification of Significant
Time Varying Genes
[0104] Principle component analysis was carried out using a custom
script in R. To identify time varying genes, the time course
library of functions in R were used to implement empirical Bayes
methods for assessing differential expression in experiments
involving time course which in our case are the different
trimesters and post-partum for each individual patients.
Results and Discussion
[0105] RNA-Seq Reveals that Pregnancy-Associated Transcripts are
Detected at Significantly Different Levels Between Pregnant and Non
Pregnant Subjects.
[0106] A comparison of the transcripts level derived using RNA-Seq
and Gene Ontology Analysis between pregnant and non-pregnant
subjects revealed that transcripts exhibiting differential
transcript levels are significantly associated with female
pregnancy, suggesting that RNA-Seq are enabling observation of real
differences between these two class of transcriptome due to
pregnancy. The top rank significantly expressed gene is PLAC4 which
has also been known as a target in previous studies for developing
RNA based test for trisomy 21. A listing of the top detected female
pregnancy associated differentially expressed transcripts is shown
in FIG. 1.
[0107] Principle Component Analysis (PCA) on Plasma Cell Free RNA
Transcripts Levels in Maternal Plasma Distinguishes Between
Pre-Mature and Normal Pregnancy
[0108] Using the plasma cell free transcript level profiles as
inputs for Principle Component Analysis, the profile from each
patient at different time points clustered into different
pathological clusters suggesting that cell free plasma RNA
transcript profile in maternal plasma may be used to distinguish
between pre-term and non-preterm pregnancy.
[0109] Plasma Cell free RNA levels were quantified using both
microarray and RNA-Seq. Transcripts expression levels profile from
microarray and RNA-Seq from each patient are correlated with a
Pearson correlation of approximately 0.7. Plots of the two main
principal components for cell free RNA transcript levels is shown
in FIG. 2.
[0110] Identification of Cell Free RNA Transcripts in Maternal
Plasma Exhibiting Significantly Different Time Varying Trends
Between Pre-Term and Normal Pregnancy Across all Three Trimesters
and Post Partum
[0111] A heatmap of the top 100 cell free transcript levels
exhibiting different temporal levels in preterm and normal
pregnancy using microarrays is shown in FIG. 3A. A heatmap of the
top 100 cell free transcript levels exhibiting different temporal
levels in preterm and normal pregnancy using RNA-Seq is shown in
FIG. 3B.
[0112] Common Cell Free RNA Transcripts Identified by Microarray
and RNA-Seq which Exhibit Significantly Different Time Varying
Trends Between Pre-Term and Normal Pregnancy Across all Three
Trimesters and Post-Partum
[0113] A ranking of the top 20 transcripts differentially expressed
between pre-term and normal pregnancy is shown in FIG. 4. These top
20 common RNA transcripts were analyzed using Gene Ontology and
were shown to be enriched for proteins that are attached
(integrated or loosely bound) to the plasma membrane or on the
membranes of the platelets (see FIG. 5).
Gene Expression Profiles for PVALB
[0114] The protein encoded by PVALB gene is a high affinity calcium
ion-binding protein that is structurally and functionally similar
to calmodulin and troponin C. The encoded protein is thought to be
involved in muscle relaxation. As shown in FIG. 6, the gene
expression profile for PVALB across the different trimesters shows
the premature births [highlighted in blue] has higher levels of
cell free RNA transcripts found as compared to normal
pregnancy.
Conclusion:
[0115] Results from quantification and characterization of maternal
plasma cell-free RNA using RNA-Seq strongly suggest that pregnancy
associated transcripts can be detected.
[0116] Furthermore, both RNA-Seq and microarray methods can detect
considerable gene transcripts whose level showed differential time
trends that has a high probability of being associated with
premature births.
[0117] The methods described herein can be modified to investigate
pregnancies of different pathological situations and can also be
modified to investigate temporal changes at more frequent time
points.
Example 2
Quantification of Tissue-Specific Cell-Free RNA Exhibiting Temporal
Variation During Pregnancy
Overview
[0118] Cell-free fetal DNA found in maternal plasma has been
exploited extensively for non-invasive diagnostics. In contrast,
cell-free fetal RNA which has been shown to be similarly detected
in maternal circulation has yet been applied widely as a form of
diagnostics. Both fetal cell-free RNA and DNA face similar
challenges in distinguishing the fetal from maternal component
because in both cases the maternal component dominates. To detect
cell-free RNA of fetal origin, focus can be placed on genes that
are highly expressed only during fetal development, which are
subsequently inferred to be of fetal in origin and easily
distinguished from background maternal RNA. Such a perspective is
collaborated by studies that has established that cell-free fetal
RNA derived from genes that are highly expressed in the placenta
are detectable in maternal plasma during pregnancy.
[0119] A significant characteristic that set RNA apart from DNA can
be attributed to RNA transcripts dynamic nature which is well
reflected during fetal development. Life begins as a series of
well-orchestrated events that starts with fertilization to form a
single-cell zygote and ends with a multi-cellular organism with
diverse tissue types. During pregnancy, majority of fetal tissues
undergoes extensive remodeling and contain functionally diverse
cell types. This underlying diversity can be generated as a result
of differential gene expression from the same nuclear repertoire;
where the quantity of RNA transcripts dictate that different cell
types make different amount of proteins, despite their genomes
being identical. The human genome comprises approximately 30,000
genes. Only a small set of genes are being transcribed to RNA
within a particular differentiated cell type. These tissue specific
RNA transcripts have been identified through many studies and
databases involving developing fetuses of classical animal models.
Combining known literature available with high throughput data
generated from samples via sequencing, the entire collection of RNA
transcripts contained within maternal plasma can be
characterized.
[0120] Fetal organ formation during pregnancy depends on successive
programs of gene expression. Temporal regulation of RNA quantity is
necessary to generate this progression of cell differentiation
events that accompany fetal organ genesis. To unravel similar
temporal dynamics for cell free RNA, the expression profile of
maternal plasma cell free RNA, especially the selected fetal tissue
specific panel of genes, as a function across all three trimesters
during pregnancy and post-partum were analyzed. Leveraging high
throughput qPCR and sequencing technologies capability for
simultaneous quantification of cell free fetal tissue specific RNA
transcripts, a system level view of the spectrum of RNA transcripts
with fetal origins in maternal plasma was obtained. In addition,
maternal plasma was analyzed to deconvolute the heterogeneous cell
free transcriptome of fetal origin a relative proportion of the
different fetal tissue types. This approach incorporated physical
constraints regarding the fetal contributions in maternal plasma,
specifically the fraction of contribution of each fetal tissues
were required to be non-negative and sum to one during all three
trimesters of the pregnancy. These constraints on the data set
enabled the results to be interpreted as relative proportions from
different fetal organs. That is, a panel of previously selected
fetal tissue-specific RNA transcripts exhibiting temporal variation
can be used as a foundation for applying quadratic programing in
order to determine the relative tissue-specific RNA contribution in
one or more samples.
[0121] When considered individually, quantification of each of
these fetal tissue specific transcripts within the maternal plasma
can be used as a measure for the apoptotic rate of that particular
fetal tissue during pregnancy. Normal fetal organ development is
tightly regulated by cell division and apoptotic cell death.
Developing tissues compete to survive and proliferate, and organ
size is the result of a balance between cell proliferation and
death. Due to the close association between aberrant cell death and
developmental diseases, therapeutic modulation of apoptosis has
become an area of intense research, but with this comes the demand
for monitoring the apoptosis rate of specific. Quantification of
fetal cell-free RNA transcripts provide such prognostic value,
especially in premature births where the incidence of apoptosis in
various organs of these preterm infants has been have been shown to
contribute to neurodevelopmental deficits and cerebral palsy of
preterm infants.
[0122] Sample Collection and Study Design
[0123] Selection of Fetal Tissue Specific Transcript Panel
[0124] To detect the presence of these fetal tissue-specific
transcripts, a list of known fetal tissue specific genes was
prepared from known literature and databases. The specificity for
fetal tissues was validated by cross referencing between two main
databases: TISGeD (Xiao, S.-J., Zhang, C. & Ji, Z.-L. TiSGeD: a
Database for Tissue-Specific Genes. Bioinformatics (Oxford,
England) 26, 1273-1275 (2010)) and BioGPS (Wu, C. et al. BioGPS: an
extensible and customizable portal for querying and organizing gene
annotation resources. Genome biology 10, R130 (2009); Su, A. I. et
al. A gene atlas of the mouse and human protein-encoding
transcriptomes. Proceedings of the National Academy of Sciences of
the United States of America 101, 6062-7 (2004)). Most of these
selected transcripts are associated with known fetal developmental
processes. This list of genes was overlapped with RNA sequencing
and microarray data to generate the panel of selected fetal
tissue-specic transcripts shown in FIG. 8.
[0125] Subjects
[0126] Samples of maternal blood were collected from normal
pregnant women during the first trimester, second trimester, third
trimester, and post-partum. For positive controls, fetal tissue
specific RNA from the various fetal tissue types were bought from
Agilent. Negative controls for the experiments were performed with
the entire process with water, as well as with samples that did not
undergoes the reverse transcription process.
[0127] Blood Collection and Processing
[0128] At each time-point, 7 to 15 mL of peripheral blood was drawn
from each subject. Blood was centrifuged at 1600 g for 10 mins and
transferred to microcentrifuge tubes for further centrifugation at
16000 g for 10 mins to remove residual cells. The above steps were
carried out within 24 hours of the blood draw. Resulting plasma is
stored at -80 Celsius for subsequent RNA extractions.
[0129] RNA Extraction
[0130] Cell free RNA extractions were carried using Trizol followed
by Qiagen's RNeasy Mini Kit. To ensure that there are no
contaminating DNA, DNase digestion is performed after RNA elution
using RNase free DNase from Qiagen. Resulting cell free RNA from
the pregnant subjects was then processed using standard microarrays
and Illumina RNA-seq protocols. These steps generate the sequencing
library that we used to generate RNA-seq data as well as the
microarray expression data. The remaining cell free RNA are then
used for parallel qPCR.
[0131] Parallel qPCR of Selected Transcripts
[0132] Accurate quantification of these fetal tissue specific
transcripts was carried out using the Fluidigm BioMark system (See
e.g. Spurgeon, S. L., Jones, R. C. & Ramakrishnan, R. High
throughput gene expression measurement with real time PCR in a
microfluidic dynamic array. PloS one 3, e1662 (2008)). This system
allows for simultaneous query of a panel of fetal tissue specific
transcripts. Two parallel forms of inquiry were conducted using
different starting source of material. One was using the cDNA
library from the Illumina sequencing protocol and the other uses
the eluted RNA directly. Both sources of material were amplified
with evagreen primers targeting the genes of interest. Both
sources, RNA and cDNA, were preamplified. cDNA is preamplifed using
evagreen PCR supermix and primers. RNA source is preamplified using
the CellsDirect One-Step qRT-PCR kit from Invitrogen. Modifications
were made to the default One-Step qRT-PCR protocol to accomodate a
longer incubation time for reverse transcription. 19 cycles of
preamplfication were conducted for both sources and the collected
PCR products were cleaned up using Exonuclease I Treatment. To
increase the dynamic range and the ability to quantify the
efficiency of the later qPCR steps, serial dilutions were performed
on the PCR products from 5 fold, 10 fold and 10 fold dilutions.
Each of the collected maternal plasma from individual pregnant
women across the time points went through the same procedures and
was loaded onto 48.times.48 Dynamic Arrary Chips from Fluidigm to
perform the qPCR. For positive control, fetal tissue specific RNA
from the various fetal tissue types were bought from Agilent. Each
of these RNA from fetal tissues went through the same
preamplification and clean-up steps. A pool sample with equal
proportions of different fetal tissues was created as well for
later analysis to deconvolute the relative contribution of each
tissue type in the pooled samples. All collected data from the
Fluidigm BioMark system were pre-processed using Fluidigm Real Time
PCR Analysis software to obtain the respective Ct values for each
of the transcript across all samples. Negative controls of the
experiments were performed with the entire process with water, as
well as with samples that did not undergoes the reverse
transcription process.
[0133] Data Analysis:
[0134] Fetal tissue specific RNA transcripts clear from the
maternal peripheral bloodstream within a short period after birth.
That is, the post-partum cell-free RNA transcriptome of maternal
blood lacks fetal tissue specific RNA transcripts. As a result, it
is expected that the quantity of these fetal tissue-specific
transcripts to be higher before than after birth. The data of
interest were the relative quantitative changes of the tissue
specific transcripts across all three trimesters of pregnancy as
compared to this baseline level after the baby is born. As
described the methods, the fetal tissue-specific transcripts were
quantified in parallel both using the actual cell-free RNA as well
as the cDNA library of the same cell-free RNA. An example of the
raw data obtained is shown in FIGS. 9A and 9B. The qPCR system gave
a better quality readout using the cell-free RNA as the initial
source. Focusing on the qPCR results from the direct cell-free RNA
source, the analysis was conducted by comparing the fold changes
level of each of these fetal tissue specific transcripts across all
three trimesters using the post-partum level as the baseline for
comparison. The Delta-Delta Ct method was employed (Schmittgen, T.
D. & Livak, K. J. Analyzing real-time PCR data by the
comparative CT method. Nature Protocols 3, 1101-1108 (2008)). Each
of the transcript expression level was compared to the housekeeping
genes to get the delta Ct value. Subsequently, to compare each
trimesters to after birth, the delta-delta Ct method was applied
using the post-partum data as the baseline.
[0135] Results and Discussion:
[0136] As shown in FIGS. 10, 11, and 12, the tissue-specific
transcripts are generally found to be at a higher level during the
trimesters as compared to after-birth. In particular, the
tissue-specific panel of placental, fetal brain and fetal liver
specific transcripts showed the same bias, where these transcripts
are typically found to exist at higher levels during pregnancy then
compared to after birth. Between the different trimesters, a
general trend showed that the quantity of these transcripts
increase with the progression into pregnancy.
[0137] Biological Significance of Quantified Fetal Tissue-Specific
RNA:
[0138] Most of the transcripts in the panel were involved in fetal
organ development and many are also found within the amniotic
fluid. Once such example is ZNF238. This transcript is specific to
fetal brain tissue and is known to be vital for cerebral cortex
expansion during embryogenesis when neuronal layers are formed.
Loss of ZNF238 in the central nervous system leads to severe
disruption of neurogenesis, resulting in a striking postnatal
small-brain phenotype. Using methods of the invention, one can
determine whether ZNF238 is presenting in healthy, normal levels
according to the stage of development.
[0139] Known defects due to the loss of ZNF238 include a striking
postnatal small-brain phenotype: microcephaly, agenesis of the
corpus callosum and cerebellar hypoplasia. Microcephaly can
sometimes be diagnosed before birth by prenatal ultrasound. In many
cases, however, it might not be evident by ultrasound until the
third trimester. Typically, diagnosis is not made until birth or
later in infancy upon finding that the baby's head circumference is
much smaller than normal. Microcephaly is a life-long condition and
currently untreatable. A child born with microcephaly will require
frequent examinations and diagnostic testing by a doctor to monitor
the development of the head as he or she grows. Early detection of
ZNf238 differential expression using methods of the invention
provides for prenatal diagnosis and may hold prognostic value for
drug treatments and dosing during course of treatment.
[0140] Beyond ZNF238, many of the characterized transcripts may
hold diagnostic value in developmental diseases involving
apoptosis, i.e., diseases caused by removal of unnecessary neurons
during neural development. Seeing that apoptosis of neurons is
essential during development, one could extrapolate that similar
apoptosis might be activated in neurodegenerative diseases such as
Alzheimer's disease, Huntington's disease, and amyotrophic lateral
sclerosis. In such a scenario, the methodology described herein
will allow for close monitoring for disease progression and
possibly an ideal dosage according to the progression.
[0141] Deducing Relative Contributions of Different Fetal Tissue
Types:
[0142] Differential rate of apoptosis of specific tissues may
directly correlate with certain developmental diseases. That is,
certain developmental diseases may increase the levels of a
particular specific RNA transcripts being observed in the maternal
transcriptome. Knowledge of the relative contribution from various
tissue types will allow for observations of these types of changes
during the progression of these diseases. The quantified panel of
fetal tissue specific transcripts during pregnancy can be
considered as a summation of the contributions from the various
fetal tissues (See FIG. 25).
[0143] Expressing,
Y i = j .pi. i x ij + ##EQU00001##
where Y is the observed transcript quantity in maternal plasma for
gene i, X is the known transcript quantity for gene i in known
fetal tissue j and .epsilon. the normally distributed error.
Additional physical constraints includes: [0144] 1. Summation of
all fraction contributing to the observed quantification is 1,
given by the condition: .SIGMA..pi..sub.i=1 [0145] 2. All the
contribution from each tissue type has to greater than or equal
zero. There is no physical meaning to having a negative
contribution. This is given by .pi..sub.i.gtoreq.0, since .pi. is
defined as the fractional contribution of each tissue types.
[0146] Consequently to obtain the optimal fractional contribution
of each tissue type, the least-square error is minimized. The above
equations are then solved using quadratic programming in R to
obtain the optimal relative contributions of the tissue types
towards the maternal cell free RNA transcripts. In the workflow,
the quantity of RNA transcripts are given relative to the
housekeeping genes in terms of Ct values obtained from qPCR.
Therefore, the Ct value can be considered as a proxy of the
measured transcript quantity. An increase in Ct value of one is
similar to a two-fold change in transcript quantity, i.e. 2 raised
to the power of 1. The process beings with normalizing all of the
data in CT relative to the housekeeping gene, and is followed by
quadratic programming.
[0147] As a proof of concept for the above scheme, different fetal
tissue types (Brain, Placenta, Liver, Thymus, Lung) were mixed in
equal proportions to generate a pool sample. Each fetal tissue
types (Brain, Placenta, Liver, Thymus, Lung) along with the pooled
sample were quantified using the same Fluidigm Biomark System to
obtain the Ct values from qPCR for each fetal tissue specific
transcript across all tissues and the pooled sample. These values
were used to perform the same deconvolution. The resulting fetal
fraction of each of the fetal tissue organs (Brain, Placenta,
Liver, Thymus, Lung) was 0.109, 0.206, 0.236, 0.202 & 0.245
respectively.
[0148] Conclusion:
[0149] In summary, the panel of fetal specific cell free
transcripts provides valuable biological information across
different fetal tissues at once. Most particularly, the method can
deduce the different relative proportions of fetal tissue-specific
transcripts to total RNA, and, when considered individually, each
transcript can be indicative of the apoptotic rate of the fetal
tissue. Such measurements have numerous potential applications for
developmental and fetal medicine. Most human fetal development
studies have relied mainly on postnatal tissue specimens or aborted
fetuses. Methods described herein provide quick and rapid assay of
the rate of fetal tissue/organ growth or death on live fetuses with
minimal risk to the pregnant mother and fetus. Similar methods may
be employed to monitor major adult organ tissue systems that
exhibit specific cell free RNA transcripts in the plasma.
Example 3
Additional Study for Quantification of Tissue-Specific Cell-Free
RNA Exhibiting Temporal Variation During Pregnancy
[0150] High-throughput methods of microarray and next-generation
sequencing were used to characterize the landscape of cell-free RNA
transcriptome of healthy adults and of pregnant women across all
three trimesters of pregnancy and post-partum. The results confirm
the study presented in Example 2, by showing that it is possible to
monitor the gene expression status of many tissues and the temporal
expression of certain genes can be measured across the stages of
human development. The study also investigated the role of
cell-free RNA in adult's suffering from neurodegenerative disorder
Alzheimer's and observed a marked increase of neuron-specific
transcripts in the blood of affected individuals. Thus, this study
shows that the same principles of observing tissue-specific RNA to
assess development can also be applied to assess the deterioration
of brain tissue associated with neurological disorders.
[0151] Overview
[0152] An additional study following the guidance of Example 2 was
conducted to illustrate the temporal variation among
tissue-specific cell-free RNA across trimesters. FIG. 18 outlines
the experimental design for this study, which examined cell-free
plasma samples of 15 subjects, of which 11 were pregnant and 4 were
not pregnant (2 males; 2 females). The blood samples were taken
over several time-points: 1st, 2nd, and 3rd Trimester and
Post-Partum. The cell-free plama RNA were then extracted,
amplified, and characterized by Affymetrix microarray, IIlumina
Sequencer, and quantitative PCR. For each plasma sample, .about.20
million sequencing reads were generated, .about.80% of which could
be mapped against the human reference genome (hg19). As the plasma
RNA is of low concentration and vulnerable to degradation,
contamination from the plasma DNA is a concern. To assess the
quality of the sequencing library, the number of reads assigned to
different regions was counted: 34% mapped to exons, 18% mapped to
introns, and 24% mapped to ribosomal RNA and tRNA. Therefore,
dominant portion of the reads originated from RNA transcripts
rather than DNA contamination. To validate the RNA-seq
measurements, all of the plasma samples were also analyzed with
gene expression microarrays.
[0153] Apoptotic cells from different tissue types release their
RNA into the cell-free RNA component in plasma. Each of these
tissues expresses a number of genes unique to their tissue type,
and the observed cell-free RNA transcriptomes can be considered as
a summation of contributions from these different tissue types.
Using expression data of different tissue types available in public
databases, the cell-free RNA transcriptome from our four
nonpregnant subjects were deconvoluted using quadratic programming
to reveal the relative contributions of different tissue types
(FIG. 26). These contributions identified different tissue types
which are consistent among different control subjects. Whole blood,
as expected, is the major contributor (.about.40%) toward the
cell-free RNA transcriptome. Other major contributing tissue types
include the bone marrow and lymph nodes. One also sees consistent
contributions from smooth muscle, epithelial cells, thymus, and
hypothalamus.
[0154] Results and Discussion
[0155] Within the cohort, about 100 genes were analyzed whose RNA
transcripts contained paternal SNPs that were distinct from the
maternal inheritance to explicitly demonstrate that the fetus
contributes a substantial amount of RNA to the mother's blood (See
FIG. 21). To accurately quantify and verify the relative fetal
contribution, the following were genotyped: a mother and her fetus
and inferred paternal genotype. The weighted average fraction of
fetal-originated cell-free RNA was quantified using paternal SNPs.
Cell-free RNA fetal fraction depends on gene expression and varies
greatly across different genes. In general, the fetal fraction of
cell-free RNA increases as the pregnancy progress and decreases
after delivery. The weighted average fetal fraction started at 0.4%
in the first trimester, increased to 3.4% in the second trimester,
and peaked at 15.4% in the third trimester. Although fetal RNA
should be cleared after delivery, there was still 0.3% of fetal RNA
as calculated, which can be attributed to background noise arising
from misalignment and sequencing errors.
[0156] In addition to monitoring fetal tissue-specific mRNA,
noncoding transcripts present in the cell-free compartment across
pregnancy were identified. These noncoding transcripts include long
noncoding RNAs (lncRNAs), as well as circular RNAs (circRNA).
Additional PCR assays were designed to specifically amplify and
validate the presence of these circRNA in plasma. circRNAs have
recently been shown to be widely expressed in human cells and have
greater stability than their linear counterparts, potentially
making them reliable biomarkers for capturing transient events.
Several of the circRNA species appear to be specifically expressed
during different trimesters of pregnancy. The identification of
these cell-free noncoding RNAs during pregnancy improve our ability
to monitor the health of the mother and fetus.
[0157] There is a general increase in the number of genes detected
across the different trimesters followed by a steep drop after the
pregnancy. Such an increase in the number of genes detected
suggests that unique transcripts are expressed specifically during
particular time intervals in the developing fetus. FIGS. 18 and 19
show the heatmap of genes whose level changed over time during
pregnancy, as detected by microarray. ANOVA was applied to identify
genes that varied in expression in a statistically significant
manner across different trimesters. An additional condition
filtering for transcripts that were expressed at low levels in both
the postpartum plasma of pregnant subjects and in nonpregnant
controls. Using these conditions, 39 genes from RNA-seq and 34
genes from microarray were identified, of which there were 17 genes
in common. Gene Ontology (GO) performed on the identified genes
using Database for Annotation, Visualization and Integrated
Discovery (DAVID) revealed that the identified gene list is
enriched for the following GO terms: female pregnancy
(Bonferroni-corrected P=5.5.times.10.sup.-5), extracellular region
(corrected P=6.6.times.10.sup.-3), and hormone activity (corrected
P=6.3.times.10.sup.-9). These RNA transcripts show a general trend
of having low expression postpartum and the highest expression
during the third trimester. Most of these transcripts are
specifically expressed in the placenta, and their levels reach a
maximum in the later stages of pregnancy.
[0158] Other nonplacental transcripts that share similar temporal
trends. Two such significant transcripts were RAB6B and MARCH2,
which are known to be expressed specifically in CD71+ erythrocytes.
Erythrocytes enriched for CD71+ have been shown to contain fetal
hemoglobin and are interpreted to be of fetal origin. The presence
of transcripts with known specificity to different fetal tissue
types reflects the fact that the cell-free transcriptome during the
period of pregnancy can be considered as a summation of
transcriptomes from various different fetal tissues on top of a
maternal background.
[0159] This analysis detected the presence of numerous transcripts
that are specifically expressed in several other fetal tissues,
although the available sequencing depth resulted in limited
concordance between samples. To verify the presence of these and
other potential fetal tissue-specific transcripts, a panel of fetal
tissue-specific transcripts was devised for detailed quantification
using the more sensitive method of quantitative PCR (qPCR). Three
main sources were focused on, which are of interest to fetal
neurodevelopment and metabolism: placenta, fetal brain, and fetal
liver. In FIGS. 22-24, the levels of these groups of fetal
tissue-specific transcripts at different trimesters were
systematically compared to the level seen in maternal serum after
delivery. To illustrate the temporal trends, housekeeping genes as
the baseline were used as a baseline, and .DELTA.Ct analysis was
applied to find the level of relative expression these fetal
tissue-specific transcripts with respect to the housekeeping genes.
Many of these tissue-specific transcripts expressed at
substantially higher levels during the pregnancy compared with
postpartum. There was a general trend of an increase in the
quantity of these transcripts across advancing gestation.
[0160] The placental qPCR assay focused on genes that are known to
be highly expressed in the placenta, many of which encode for
proteins that have been shown to be present in the maternal blood.
The serum levels of these proteins are known to be involved in
pregnancy complications such as preeclampsia and premature births.
Examples in our panel includes ADAM12, which encodes for
disintegrin, and metalloproteinase domain-containing protein 12.
These proteinases are highly expressed in human placenta and are
present at high concentrations in maternal serum as early as the
first trimester. ADAM12 serum concentrations are known to be
significantly reduced in pregnancies complicated by fetal trisomy
18 and trisomy 21 and may therefore be of potential use in
conjunction with cell-free DNA for the detection of chromosomal
abnormalities. Similarly, placental alkaline phosphatase, encoded
by the ALPP gene, is a tissue-specific isoform expressed
increasingly throughout pregnancy until term in the placenta. It is
anchored to the plasma membrane of the syncytiotrophoblast and to a
lesser extent of cytotrophoblastic cells. This enzyme is also
released into maternal serum, and variations of its concentration
are related with several clinical disorders such as preterm
delivery. Another gene in the panel, BACE2, encoded the .beta. site
APP-cleaving enzyme, which generates amyloid-.beta. protein by
endoproteolytic processing. Brain deposition of amyloid-.beta.
protein is a frequent complication of Down syndrome patients, and
BACE-2 is known to be overexpressed in Down syndrome.
[0161] Other transcripts in our placental assay are known to be
transcribed at high levels in the placenta, and levels of these
mRNAs are important for normal placental function and development
in pregnancy. TAC3 is mainly expressed in the placenta and is
significantly elevated in preeclamptic human placentas at term.
Similarly, PLAC1 is essential for normal placental development.
PLAC1 deficiency results in a hyperplastic placenta, characterized
by an enlarged and dysmorphic junctional zone. An increase in
cell-free mRNA of PLAC1 has been suggested to be correlated with
the occurrence of preeclampsia.
[0162] On the fetal liver tissue-specific panel, one of the
characterized transcripts is AFP. AFP encodes for
.alpha.-fetoprotein and is transcribed mainly in the fetal liver.
AFP is the most abundant plasma protein found in the human fetus.
Clinically, AFP protein levels are measured in pregnant women in
either maternal blood or amniotic fluid and serve as a screening
marker for fetal aneuploidy, as well as neural tube and abdominal
wall defects. Other fetal liver-specific transcripts that were
characterized are highly involved in metabolism. An example is
fetal liver-specific monooxygenase CYP3A7, which catalyzes many
reactions involved in synthesis of cholesterol and steroids and is
responsible for the metabolism of more than 50% of all clinical
pharmaceuticals. In drug-treated diabetic pregnancies in which
glucose levels in the woman are uncontrolled, neural tube and
cardiac defects in the early developing brain, spine, and heart
depend on functional GLUT2 carriers, whose transcripts are well
characterized in the panel. Mutations in this gene results in
Fanconi-Bickel syndrome, a congenital defect of facilitative
glucose transport. Monitoring of fetal liver-specific transcripts
during the drug regime may enable analysis of the fetuses` response
to drug therapy that the mother is undergoing.
Example 4
Deconvolution of Adult Cell-Free Transcriptome
Overview
[0163] The plasma RNA profiles of 4 healthy, normal adults were
analyzed. Based on the gene expression profile of different tissue
types, the methods described quantify the relative contributions of
each tissue type towards the cell-free RNA component in a donor's
plasma. For quantification, apoptotic cells from different tissue
types are assumed to release their RNA into the plasma. Each of
these tissues expressed a specific number of genes unique to the
tissue type, and the observed cell-free RNA transcriptome is a
summation of these different tissue types.
Study Design and Methods:
[0164] To determine the contribution of tissue-specific transcripts
to the cell-free adult transriptome, a list of known
tissue-specific genes was prepared from known literature and
databases. Two database sources were utilized: Human U133A/GNF1H
Gene Atlas and RNA-Seq Atlas. Using the raw data from these two
database, tissue-specific genes were identified by the following
method. A template-matching process was applied to data obtained
from the two databases for the purpose of identifying
tissue-specific gene. The list of tissue specific genes identified
by the method is provided in Table 1 below. The specificity and
sensitivity of the panel is constrained by the number of tissue
samples in the database. For example, the Human U133A/GNF1H Gene
Atlas dataset includes 84 different tissue samples, and a panel's
specificity from that database is constrained by the 84 sample
sets. Similarly, for the RNA-seq atlas, there are 11 different
tissue samples and specificity is limited to distinguishing between
these 11 tissues. After obtaining a list of tissue-specific
transcripts from the two databases, the specificity of these
transcripts was verified with literature as well as the TisGED
database.
[0165] The adult cell-free transcriptome can be considered as a
summation of the tissue-specific transcripts obtained from the two
databases. To quantitatively deduce the relative proportions of the
different tissues in an adult cell-free transcriptome, quadratic
programming is performed as a constrained optimization method to
deduce the relative optimal contributions of different
organs/tissues towards the cell free-transcriptome. The specificity
and accuracy of this process is dependent on the table of genes
(Table 2 below) and the extent by which that they are detectable in
RNA-seq and microarray.
[0166] Subjects: Plasma samples were collected from 4 healthy,
normal adults.
Initial Results:
[0167] Deconvolution of our adult cell-free RNA transcriptome from
microarray using the above methods revealed the relative
contributions of the different tissue and organs are tabulated in
FIG. 13.
[0168] FIG. 13 shows that the normal cell free transcriptome for
adults is consistent across all 4 subjects. The relative
contributions between the 4 subjects do not differ greatly,
suggesting that the relative contributions from different tissue
types are relatively stable between normal adults. Out of the 84
tissue types available, the deduced optimal major contributing
tissues are from whole blood and bone marrow.
[0169] An interesting tissue type contributing to circulating RNA
is the hypothalamus. The hypothalamus is bounded by specialized
brain regions that lack an effective blood-brain barrier; the
capillary endothelium at these sites is fenestrated to allow free
passage of even large proteins and other molecules which in our
case we believed that RNA transcripts from apoptotic cells in that
region could be released into the plasma cell free RNA
component.
[0170] The same methods were performed on the subjects using
RNA-seq. The results described herein are limited due to the amount
of tissue-specific RNA-Seq data available. However, it is
understood that tissue-specific data is expanding with the
increasing rate of sequencing of various tissue rates, and future
analysis will be able to leverage those datasets. For RNA-seq data
(as compared to microarray), whole blood nor the bone marrow
samples are not available. The cell free transcriptome can only be
decomposed to the available 11 different tissue types of RNA-seq
data. Of which, only relative contributions from the hypothalamus
and spleen were observed, as shown in FIG. 14.
[0171] A list of 84 tissue-specific genes (as provided in Table 2)
was further selected for verification with qPCR. The Fluidigm
BioMark Platform was used to perform the qPCR on RNA derived from
the following tissues: Brain, Cerebellum, Heart, Kidney, Liver and
Skin. Similar qPCR workflow was applied to the cell free RNA
component as well. The delta Ct values by comparing with the
housekeeping genes: ACTB was plotted in the heatmap format in FIG.
15, which shows that these tissue specific transcripts are
detectable in the cell free RNA.
Tables for Example 4
[0172] The following table lists the tissue-specific genes for
Example 4 that was obtained using raw data from the Human
U133A/GNF1H Gene Atlas and RNA-Seq Atlas databases.
TABLE-US-00001 TABLE 1 List of Tissue-Specific Genes Determined by
Deconvolution of Adult Transcriptome Gene Tissue A4GALT Uterus
Corpus A4GNT Superior Cervical Ganglion AADAC small intestine AASS
Ovary ABCA12 Tonsil ABCA4 retina ABCB4 CD19 Bcells neg. sel. ABCB6
CD71 Early Erythroid ABCB7 CD71 Early Erythroid ABCC2 Pancreatic
Islet ABCC3 Adrenal Cortex ABCC9 Dorsal Root Ganglion ABCF3 Adrenal
gland ABCG1 Lung ABCG2 CD71 Early Erythroid ABHD4 Adipocyte ABHD5
Whole Blood ABHD6 pineal night ABHD8 Whole Brain ABO Heart ABT1
X721 B lymphoblasts ABTB2 Placenta ACAA1 Liver ACACB Adipocyte
ACAD8 Kidney ACADL Thyroid ACADS Liver ACADSB Fetal liver ACAN
Trachea ACBD4 Liver ACCN3 Prefrontal Cortex ACE2 Testis Germ Cell
ACHE CD71 Early Erythroid ACLY Adipocyte ACOT1 Adipocyte ACOX2
Liver ACP2 Liver ACP5 Lung ACP6 CD34 ACPP Prostate ACR Testis
Intersitial ACRV1 Testis Intersitial ACSBG2 Testis Intersitial
ACSF2 Kidney ACSL4 Fetal liver ACSL5 small intestine ACSL6 CD71
Early Erythroid ACSM3 Leukemia chronic Myelogenous K562 ACSM5 Liver
ACSS3 Adipocyte ACTA1 Skeletal Muscle ACTC1 Heart ACTG1 CD71 Early
Erythroid ACTL7A Testis Intersitial ACTL7B Testis Intersitial ACTN3
Skeletal Muscle ACTR8 Superior Cervical Ganglion ADA Leukemia
lymphoblastic MOLT 4 ADAM12 Placenta ADAM17 CD33 Myeloid ADAM2
Testis Intersitial ADAM21 Appendix ADAM23 Thalamus ADAM28 CD19
Bcells neg. sel. ADAM30 Testis Germ Cell ADAM5P Testis Intersitial
ADAM7 Testis Leydig Cell ADAMTS12 Atrioventricular Node ADAMTS20
Appendix ADAMTS3 CD105 Endothelial ADAMTS8 Lung ADAMTS9 Dorsal Root
Ganglion ADAMTSL2 Ciliary Ganglion ADAMTSL3 retina ADAMTSL4
Atrioventricular Node ADARB2 Skeletal Muscle ADAT1 CD71 Early
Erythroid ADCK4 Ciliary Ganglion ADCY1 Fetal brain ADCY9 Lung
ADCYAP1 Pancreatic Islet ADH7 Tongue ADIPOR1 Bone marrow ADM2
Pituitary ADORA3 Olfactory Bulb ADRA1D Skeletal Muscle ADRA2A Lymph
node ADRA2B Superior Cervical Ganglion ADRB1 pineal night AFF3
Trigeminal Ganglion AFF4 Testis Intersitial AGPAT2 Adipocyte AGPAT3
CD33 Myeloid AGPAT4 CD71 Early Erythroid AGPS Testis Intersitial
AGR2 Trachea AGRN Colorectal adenocarcinoma AGRP Superior Cervical
Ganglion AGXT Liver AIFM1 X721 B lymphoblasts AIM2 CD19 Bcells neg.
sel. AJAP1 BDCA4 Dentritic Cells AKAP10 CD33 Myeloid AKAP3 Testis
Intersitial AKAP6 Medulla Oblongata AKAP7 Fetal brain AKAP8L CD71
Early Erythroid AKR1C4 Liver AKR7A3 Liver AKT2 Thyroid ALAD CD71
Early Erythroid ALDH3D2 Tongue ALDH6A1 Kidney ALDH7A1 Ovary ALDOA
Skeletal Muscle ALG12 CD4 T cells ALG13 CD19 Bcells neg. sel. ALG3
Liver ALOX12 Whole Blood ALOX12B Tonsil ALOX15B Prostate ALPI small
intestine ALPK3 Skeletal Muscle ALPL Whole Blood ALPP Placenta
ALPPL2 Placenta ALX1 Superior Cervical Ganglion ALX4 Superior
Cervical Ganglion AMBN pineal day AMDHD2 BDCA4 Dentritic Cells
AMELY Subthalamic Nucleus AMHR2 Heart AMPD1 Skeletal Muscle AMPD2
pineal night AMPD3 CD71 Early Erythroid ANAPC1 X721 B lymphoblasts
ANG Liver ANGEL2 CD8 T cells ANGPT1 CD35 ANGPT2 Ciliary Ganglion
ANGPTL2 Uterus Corpus ANGPTL3 Fetal liver ANK1 CD71 Early Erythroid
ANKFY1 CD8 T cells ANKH Cerebellum Peduncles ANKLE2 Testis ANKRD1
Skeletal Muscle ANKRD2 Skeletal Muscle ANKRD34C Thalamus ANKRD5
Skeletal Muscle ANKRD53 Skeletal Muscle ANKRD57 Bronchial
Epithelial Cells ANKS1B Superior Cervical Ganglion ANTXR1 Uterus
Corpus ANXA13 small intestine ANXA2P1 Bronchial Epithelial Cells
ANXA2P3 Bronchial Epithelial Cells AOC2 retina AP1G1 Testis Germ
Cell AP1M2 Kidney AP3S1 Heart APBA1 Dorsal Root Ganglion APBB1IP
Whole Blood APBB2 Superior Cervical Ganglion APC Fetal brain APEX2
Colorectal adenocarcinoma APIP Trachea APOA1 Liver APOA4 small
intestine APOB48R Whole Blood APOBEC1 small intestine APOBEC2
Skeletal Muscle APOBEC3B Colorectal adenocarcinoma APOC4 Liver APOF
Liver APOL5 Bone marrow APOOL Superior Cervical Ganglion AQP2
Kidney AQP5 Testis Intersitial AQP7 Adipocyte AR Liver ARCN1
Trigeminal Ganglion ARFGAP1 Lymphoma burkitts Raji ARG1 Fetal liver
ARHGAP11A Trigeminal Ganglion ARHGAP19 Olfactory Bulb ARHGAP22 CD36
ARHGAP28 Testis Intersitial ARHGAP6 Prostate ARHGEF1 CD4 T cells
ARHGEF5 Pancreas ARHGEF7 Thymus ARID3A Placenta ARID3B X721 B
lymphoblasts ARL15 Uterus Corpus ARMC4 Superior Cervical Ganglion
ARMC8 CD71 Early Erythroid ARMCX5 small intestine ARR3 retina ARSA
Liver ARSB Superior Cervical Ganglion ARSE Liver ARSF Globus
Pallidus ART1 Cardiac Myocytes ART3 Testis ART4 CD71 Early
Erythroid ASB1 Trigeminal Ganglion ASB7 Globus Pallidus ASB8
Superior Cervical Ganglion ASCC2 CD71 Early Erythroid ASCL2
Superior Cervical Ganglion ASCL3 Superior Cervical Ganglion ASF1A
CD71 Early Erythroid ASIP BDCA4 Dentritic Cells ASL Liver ASPN
Uterus ASPSCR1 Colorectal adenocarcinoma ASTE1 CD8 T cells ASTN2
pineal day ATF5 Liver ATG4A CD71 Early Erythroid ATG7 CD14
Monocytes ATN1 Prefrontal Cortex ATOH1 Superior Cervical Ganglion
ATP10A CD56 NK Cells ATP10D Placenta ATP11A Superior Cervical
Ganglion ATP12A Trachea ATP13A3 Smooth Muscle ATP1B3 Adrenal Cortex
ATP2C2 Colon ATP4A Adrenal gland ATP4B Parietal Lobe ATP5G1 Heart
ATP5G3 Heart ATP5J2 Superior Cervical Ganglion ATP6V0A2 CD37
ATP6V1B1 Kidney ATP7A CD71 Early Erythroid ATRIP CD14 Monocytes
ATXN3L Superior Cervical Ganglion ATXN7L1 Skeletal Muscle
AURKC Testis Seminiferous Tubule AVEN Bronchial Epithelial Cells
AVIL Dorsal Root Ganglion AVP Hypothalamus AXIN1 CD56 NK Cells AXL
Cardiac Myocytes AZI1 CD71 Early Erythroid B3GALNT1 Amygdala
B3GALT5 CD105 Endothelial B3GNT2 CD71 Early Erythroid B3GNT3
Placenta B3GNTL1 CD38 BAAT Liver BACH2 Lymphoma burkitts Daudi BAD
Whole Brain BAG2 Uterus BAG4 Superior Cervical Ganglion BAI1
Cingulate Cortex BAIAP2 Liver BAIAP2L2 Superior Cervical Ganglion
BAMBI Colorectal adenocarcinoma BANK1 CD19 Bcells neg. sel. BARD1
X721 B lymphoblasts BARX1 Atrioventricular Node BATF3 X721 B
lymphoblasts BBOX1 Kidney BBS4 pineal day BCAM Thyroid BCAR3
Placenta BCAS3 X721 B lymphoblasts BCKDK Liver BCL10 Colon BCL2L1
CD71 Early Erythroid BCL2L10 Trigeminal Ganglion BCL2L13 pineal day
BCL2L14 Testis BCL3 Whole Blood BDH1 Liver BDKRB1 Smooth Muscle
BDKRB2 Smooth Muscle BDNF Smooth Muscle BECN1 Ciliary Ganglion
BEST1 retina BET1L Superior Cervical Ganglion BHLHB9 pineal night
BIRC3 CD19 Bcells neg. sel. BLK CD19 Bcells neg. sel. BLVRA CD105
Endothelial BMP1 Placenta BMP2K CD71 Early Erythroid BMP3 Temporal
Lobe BMP5 Trigeminal Ganglion BMP8A Fetal Thyroid BMP8B Superior
Cervical Ganglion BMPR1B Skeletal Muscle BNC1 Bronchial Epithelial
Cells BNC2 Uterus BNIP3L CD71 Early Erythroid BOK Thalamus BPHL
Kidney BPI Bone marrow BPY2 Adrenal gland BRAF Superior Cervical
Ganglion BRAP Testis Intersitial BRE Adrenal gland BRS3 Skeletal
Muscle BRSK2 Cerebellum Peduncles BSDC1 CD71 Early Erythroid BTBD2
Prefrontal Cortex BTD Superior Cervical Ganglion BTN2A3 Appendix
BTN3A1 CD8 T cells BTRC CD71 Early Erythroid BUB1 X721 B
lymphoblasts BYSL Leukemia chronic Myelogenous K563 C10orf118
Testis Leydig Cell C10orf119 CD33 Myeloid C10orf28 Superior
Cervical Ganglion C10orf57 Ciliary Ganglion C10orf72 Adrenal Cortex
C10orf76 CD19 Bcells neg. sel. C10orf81 Dorsal Root Ganglion
C10orf84 Superior Cervical Ganglion C10orf88 Testis Seminiferous
Tubule C10orf95 Superior Cervical Ganglion C11orf41 Fetal brain
C11orf48 Adipocyte C11orf57 Appendix C11orf67 Skeletal Muscle
C11orf71 Thyroid C11orf80 Leukemia lymphoblastic MOLT 5 C12orf4
CD71 Early Erythroid C12orf43 Whole Brain C12orf47 CD8 T cells
C12orf49 CD56 NK Cells C13orf23 Placenta C13orf27 Testis Leydig
Cell C13orf34 CD71 Early Erythroid C14orf106 CD33 Myeloid C14orf118
Superior Cervical Ganglion C14orf138 CD19 Bcells neg. sel.
C14orf162 Cerebellum C14orf169 Testis C14orf56 Superior Cervical
Ganglion C15orf2 Cerebellum C15orf29 Fetal brain C15orf39 Whole
Blood C15orf44 Testis C15orf5 Superior Cervical Ganglion C16orf3
Dorsal Root Ganglion C16orf53 pineal day C16orf59 CD71 Early
Erythroid C16orf68 Testis C16orf71 Testis Seminiferous Tubule
C17orf42 X721 B lymphoblasts C17orf53 Dorsal Root Ganglion C17orf59
Dorsal Root Ganglion C17orf68 CD8 T cells C17orf73 Cardiac Myocytes
C17orf80 Testis Germ Cell C17orf81 Testis Intersitial C17orf85
BDCA4 Dentritic Cells C17orf88 Superior Cervical Ganglion C19orf29
Leukemia chronic Myelogenous K564 C19orf61 Leukemia lymphoblastic
MOLT 6 C1GALT1C1 Superior Cervical Ganglion C1orf103 Leukemia
chronic Myelogenous K565 C1orf105 Testis Intersitial C1orf106 small
intestine C1orf114 Testis Intersitial C1orf135 Testis C1orf14
Testis Leydig Cell C1orf156 CD19 Bcells neg. sel. C1orf175 Testis
Intersitial C1orf222 Testis C1orf25 CD71 Early Erythroid C1orf27
pineal night C1orf35 CD71 Early Erythroid C1orf50 Testis C1orf66
Leukemia chronic Myelogenous K566 C1orf68 Liver C1orf89
Atrioventricular Node C1orf9 CD71 Early Erythroid C1QTNF1 Smooth
Muscle C1QTNF3 Spinal Cord C2 Liver C20orf191 Superior Cervical
Ganglion C20orf29 Superior Cervical Ganglion C21orf45 CD105
Endothelial C21orf7 Whole Blood C21orf91 Testis Intersitial
C22orf24 Superior Cervical Ganglion C22orf26 Ciliary Ganglion
C22orf30 Trigeminal Ganglion C22orf31 Uterus Corpus C2CD2 Adrenal
Cortex C2orf18 Cerebellum C2orf34 pineal day C2orf42 Testis C2orf43
X721 B lymphoblasts C2orf54 Trigeminal Ganglion C3AR1 CD14
Monocytes C3orf37 Lymphoma burkitts Daudi C3orf64 pineal day
C4orf19 Placenta C4orf23 Superior Cervical Ganglion C4orf6 Superior
Cervical Ganglion C5 Fetal liver C5AR1 Whole Blood C5orf23 CD39
C5orf28 Thyroid C5orf4 CD71 Early Erythroid C5orf42 Superior
Cervical Ganglion C6orf103 Testis Intersitial C6orf105 Colon
C6orf108 Lymphoma burkitts Raji C6orf124 Fetal brain C6orf162
Pituitary C6orf208 Superior Cervical Ganglion C6orf25 Superior
Cervical Ganglion C6orf27 Superior Cervical Ganglion C6orf35
Appendix C6orf54 Skeletal Muscle C6orf64 Testis C7orf10 Bronchial
Epithelial Cells C7orf25 Superior Cervical Ganglion C7orf58
Leukemia chronic Myelogenous K567 C8G Liver C8orf17 Superior
Cervical Ganglion C8orf41 Leukemia lymphoblastic MOLT 7 C9 Liver
C9orf116 Testis C9orf27 Trigeminal Ganglion C9orf3 Uterus C9orf38
Superior Cervical Ganglion C9orf40 CD71 Early Erythroid C9orf46
Bronchial Epithelial Cells C9orf68 Skeletal Muscle C9orf86 CD71
Early Erythroid C9orf9 Testis Intersitial CA1 CD71 Early Erythroid
CA12 Kidney CA3 Thyroid CA4 Lung CA5A Liver CA5B Superior Cervical
Ganglion CA6 Salivary gland CA7 Atrioventricular Node CA9 Skin
CAB39L Prostate CABP5 retina CABYR Testis Intersitial CACNA1B
Superior Cervical Ganglion CACNA1D Pancreas CACNA1E Superior
Cervical Ganglion CACNA1F pineal day CACNA1G Cerebellum CACNA1H
Adrenal Cortex CACNA1I Prefrontal Cortex CACNA1S Skeletal Muscle
CACNA2D1 Superior Cervical Ganglion CACNA2D3 CD14 Monocytes CACNB1
Skeletal Muscle CACNG2 Cerebellum Peduncles CACNG4 Skeletal Muscle
CADM4 Prostate CADPS2 Cerebellum Peduncles CALCA Dorsal Root
Ganglion CALCRL Fetal lung CALML5 Skin CAMK1G Whole Brain CAMK4
Testis Intersitial CAMTA2 pineal night CAND2 Heart CANT1 Prostate
CAPN5 Colon CAPN6 Placenta CAPN7 Superior Cervical Ganglion CARD14
CD71 Early Erythroid CASP10 CD4 T cells CASP2 Leukemia
lymphoblastic MOLT 8 CASP9 Adrenal Cortex
CASQ2 Heart CASR Kidney CASS4 Cingulate Cortex CATSPERB Superior
Cervical Ganglion CAV3 Superior Cervical Ganglion CBFA2T3 BDCA4
Dentritic Cells CBL Testis Germ Cell CBLC Bronchial Epithelial
Cells CBX2 Trachea CCBP2 Superior Cervical Ganglion CCDC132
Trigeminal Ganglion CCDC19 Testis Intersitial CCDC21 CD71 Early
Erythroid CCDC25 CD33 Myeloid CCDC28B Lymphoma burkitts Raji CCDC33
Superior Cervical Ganglion CCDC41 CD40 CCDC46 Testis Intersitial
CCDC51 Leukemia promyelocytic HL60 CCDC6 Colon CCDC64 CD8 T cells
CCDC68 Fetal lung CCDC76 CD8 T cells CCDC81 Superior Cervical
Ganglion CCDC87 Testis CCDC88A BDCA4 Dentritic Cells CCDC88C CD56
NK Cells CCDC99 Leukemia lymphoblastic MOLT 9 CCHCR1 Testis CCIN
Testis Intersitial CCKAR Uterus Corpus CCL11 Smooth Muscle CCL13
small intestine CCL18 Thymus CCL2 Smooth Muscle CCL21 Lymph node
CCL22 X721 B lymphoblasts CCL24 Uterus Corpus CCL27 Skin CCL3 CD33
Myeloid CCL4 CD56 NK Cells CCL7 Smooth Muscle CCND1 Colorectal
adenocarcinoma CCNF CD71 Early Erythroid CCNJ Ciliary Ganglion
CCNJL Atrioventricular Node CCNL2 CD4 T cells CCNO Testis CCR10
X721 B lymphoblasts CCR3 Whole Blood CCR5 CD8 T cells CCR6 CD19
Bcells neg. sel. CCRL2 CD71 Early Erythroid CCRN4L Appendix CCS
CD71 Early Erythroid CCT4 Superior Cervical Ganglion CD160 CD56 NK
Cells CD180 CD19 Bcells neg. sel. CD1C Thymus CD207 Appendix CD209
Lymph node CD22 Lymphoma burkitts Raji CD226 Superior Cervical
Ganglion CD244 CD56 NK Cells CD248 Adipocyte CD320 Heart CD3EAP
Dorsal Root Ganglion CD3G Thymus CD4 BDCA4 Dentritic Cells CD40
Lymphoma burkitts Raji CD40LG CD41 CD5L CD105 Endothelial CD79B
Lymphoma burkitts Raji CD80 X721 B lymphoblasts CD81 CD71 Early
Erythroid CDC14A Testis CDC25C Testis Intersitial CDC27 CD71 Early
Erythroid CDC34 CD71 Early Erythroid CDC42EP2 Smooth Muscle CDC6
Colorectal adenocarcinoma CDC73 Colon CDCA4 CD71 Early Erythroid
CDCP1 Bronchial Epithelial Cells CDH13 Uterus CDH15 Cerebellum
CDH18 Subthalamic Nucleus CDH20 Superior Cervical Ganglion CDH22
Cerebellum Peduncles CDH3 Bronchial Epithelial Cells CDH4 Amygdala
CDH5 Placenta CDH6 Trigeminal Ganglion CDH7 Skeletal Muscle CDK5R2
Whole Brain CDK6 CD42 CDK8 Colorectal adenocarcinoma CDKL2 Superior
Cervical Ganglion CDKL3 Superior Cervical Ganglion CDKL5 Superior
Cervical Ganglion CDKN2D CD71 Early Erythroid CDON Tonsil CDR1
Cerebellum CDS1 small intestine CDSN Skin CDX4 Superior Cervical
Ganglion CDYL CD71 Early Erythroid CEACAM21 Bone marrow CEACAM3
Whole Blood CEACAM5 Colon CEACAM7 Colon CEACAM8 Bone marrow CEBPA
Liver CEBPE Bone marrow CELSR3 Fetal brain CEMP1 Skeletal Muscle
CENPE CD71 Early Erythroid CENPI Appendix CENPQ Trigeminal Ganglion
CENPT CD71 Early Erythroid CEP170 Fetal brain CEP55 X721 B
lymphoblasts CEP63 Whole Blood CEP76 CD71 Early Erythroid CER1
Superior Cervical Ganglion CES1 Liver CES2 Liver CES3 Colon CETN1
Testis CFHR4 Liver CFHR5 Liver CFI Fetal liver CGB Placenta CGRRF1
Testis Intersitial CHAD Trachea CHAF1A Leukemia lymphoblastic MOLT
10 CHAF1B Leukemia lymphoblastic MOLT 11 CHAT Uterus Corpus CHD3
Fetal brain CHD8 Trigeminal Ganglion CHI3L1 Uterus Corpus CHIA Lung
CHIT1 Lymph node CHKA Testis Intersitial CHML Superior Cervical
Ganglion CHMP1B Superior Cervical Ganglion CHMP6 Heart CHODL Testis
Germ Cell CHPF Colorectal adenocarcinoma CHRM2 Skeletal Muscle
CHRM3 Prefrontal Cortex CHRM4 Superior Cervical Ganglion CHRM5
Skeletal Muscle CHRNA2 Heart CHRNA4 Skeletal Muscle CHRNA5 Appendix
CHRNA6 Temporal Lobe CHRNA9 Appendix CHRNB3 Superior Cervical
Ganglion CHST10 Whole Brain CHST12 CD56 NK Cells CHST3 Testis Germ
Cell CHST4 Uterus Corpus CHST7 Ovary CHSY1 Placenta CIB2 BDCA4
Dentritic Cells CIDEA Ciliary Ganglion CIDEB Liver CIDEC Adipocyte
CISH Leukemia chronic Myelogenous K568 CKAP2 CD71 Early Erythroid
CKM Skeletal Muscle CLCA4 Colon CLCF1 Uterus Corpus CLCN1 Skeletal
Muscle CLCN2 Olfactory Bulb CLCN5 Appendix CLCN6 Whole Brain CLCNKA
Kidney CLCNKB Kidney CLDN10 Kidney CLDN11 Heart CLDN15 small
intestine CLDN4 Colorectal adenocarcinoma CLDN7 Colon CLDN8
Salivary gland CLEC11A CD43 CLEC16A Lymphoma burkitts Raji CLEC4M
Lymph node CLEC5A CD33 Myeloid CLGN Testis Intersitial CLIC2 CD71
Early Erythroid CLIC5 Skeletal Muscle CLMN Testis Intersitial CLN3
Placenta CLN5 Thyroid CLN6 pineal day CLPB Testis Intersitial
CLTCL1 Testis CLUL1 retina CMA1 Adrenal Cortex CMAH Uterus CMAS
CD71 Early Erythroid CMKLR1 BDCA4 Dentritic Cells CNGA1 Uterus
Corpus CNIH3 Amygdala CNNM1 Prefrontal Cortex CNNM4 pineal day CNR1
Fetal brain CNR2 Uterus Corpus CNTFR Cardiac Myocytes CNTLN
Trigeminal Ganglion CNTN2 Thalamus COBLL1 Placenta COG7 Prostate
COL11A1 Adipocyte COL13A1 Cardiac Myocytes COL14A1 Uterus COL17A1
Bronchial Epithelial Cells COL19A1 Trigeminal Ganglion COL7A1 Skin
COL8A2 retina COL9A1 pineal night COL9A2 retina COLEC10 Appendix
COLEC11 Liver COMP Adipocyte COMT Liver COQ4 Thyroid COQ6 Testis
CORIN Superior Cervical Ganglion CORO1B CD14 Monocytes CORO2A
Bronchial Epithelial Cells COX6B1 Superior Cervical Ganglion CP
Fetal liver CPA3 CD44 CPM Adipocyte CPN2 Liver CPNE6 Amygdala CPNE7
Leukemia chronic Myelogenous K569 CPOX Fetal liver CPT1A X721 B
lymphoblasts CPZ Placenta CR1 Whole Blood CREBZF CD8 T cells
CRH Placenta CRHR1 Cerebellum Peduncles CRIM1 Placenta CRISP2
Testis Intersitial CRLF1 Adipocyte CRLF2 Skeletal Muscle CRTAC1
Lung CRTAP Adipocyte CRY2 pineal night CRYAA Kidney CRYBA2
Pancreatic Islet CRYBA4 Superior Cervical Ganglion CRYBB1 Superior
Cervical Ganglion CRYBB2 retina CRYBB3 Superior Cervical Ganglion
CSAD Fetal brain CSAG2 Leukemia chronic Myelogenous K570 CSDC2
Heart CSF2 Colorectal adenocarcinoma CSF2RA BDCA4 Dentritic Cells
CSF3 Smooth Muscle CSF3R Whole Blood CSN3 Salivary gland CSNK1G3
CD19 Bcells neg. sel. CSPG4 Trigeminal Ganglion CST2 Salivary gland
CST4 Salivary gland CST5 Salivary gland CST7 CD56 NK Cells CSTF2T
CD105 Endothelial CTAG2 X721 B lymphoblasts CTBS Whole Blood CTDSPL
Colorectal adenocarcinoma CTF1 Superior Cervical Ganglion CTLA4
Superior Cervical Ganglion CTNNA3 Testis Intersitial CTPS2 Ciliary
Ganglion CTSD Lung CTSG Bone marrow CTSK Uterus Corpus CTTNBP2NL
CD8 T cells CUBN Kidney CUEDC1 BDCA4 Dentritic Cells CUL1 Testis
Intersitial CUL7 Smooth Muscle CXCL1 Smooth Muscle CXCL3 Smooth
Muscle CXCL5 Smooth Muscle CXCL6 Smooth Muscle CXCR3 BDCA4
Dentritic Cells CXCR5 CD19 Bcells neg. sel. CXorf1 pineal day
CXorf40A Adrenal Cortex CXorf56 Superior Cervical Ganglion CXorf57
Hypothalamus CYB561 Prostate CYLC1 Testis Seminiferous Tubule CYLD
CD4 T cells CYorf15B CD4 T cells CYP19A1 Placenta CYP1A1 Lung
CYP1A2 Liver CYP20A1 BDCA4 Dentritic Cells CYP26A1 Fetal brain
CYP27A1 Liver CYP27B1 Bronchial Epithelial Cells CYP2A6 Liver
CYP2A7 Liver CYP2B7P1 Superior Cervical Ganglion CYP2C19
Atrioventricular Node CYP2C8 Liver CYP2C9 Liver CYP2D6 Liver CYP2E1
Liver CYP2F1 Superior Cervical Ganglion CYP2W1 Skin CYP3A43 Liver
CYP3A5 small intestine CYP3A7 Fetal liver CYP4F11 Liver CYP4F2
Liver CYP4F8 Prostate CYP7B1 Ciliary Ganglion DACT1 Fetal brain
DAGLA Amygdala DAO Kidney DAPK2 Atrioventricular Node DAZ1 Testis
Leydig Cell DAZL Testis DBI CD71 Early Erythroid DBNDD1 Trigeminal
Ganglion DBP Thyroid DCBLD2 Trigeminal Ganglion DCC Testis
Seminiferous Tubule DCHS2 Cerebellum DCI Liver DCLRE1A X721 B
lymphoblasts DCP1A CD4 T cells DCT retina DCUN1D1 CD71 Early
Erythroid DCUN1D2 Heart DCX Fetal brain DDX10 Leukemia
promyelocytic HL61 DDX17 Heart DDX23 Thymus DDX25 Testis Leydig
Cell DDX28 CD14 Monocytes DDX31 Superior Cervical Ganglion DDX43
Testis Seminiferous Tubule DDX5 Liver DDX51 BDCA4 Dentritic Cells
DDX52 Colorectal adenocarcinoma DECR2 Liver DEFA4 Bone marrow DEFA5
small intestine DEFA6 small intestine DEFB126 Testis Germ Cell
DEGS1 Skin DENND1A X721 B lymphoblasts DENND2A Atrioventricular
Node DENND3 CD33 Myeloid DENND4A pineal night DEPDC5 Lymphoma
burkitts Raji DES Skeletal Muscle DGAT1 small intestine DGCR14
Testis Intersitial DGCR6L Trigeminal Ganglion DGCR8 Leukemia
chronic Myelogenous K571 DGKA CD4 T cells DGKB Caudate nucleus DGKE
Superior Cervical Ganglion DGKG Cerebellum DGKQ Superior Cervical
Ganglion DHDDS pineal day DHODH Liver DHRS1 Liver DHRS12 Liver
DHRS2 Colorectal adenocarcinoma DHRS9 Trachea DHTKD1 Liver DHX29
CD71 Early Erythroid DHX35 Leukemia lymphoblastic MOLT 12 DHX38
CD56 NK Cells DHX57 Testis Seminiferous Tubule DIAPH2 Testis Germ
Cell DIDO1 CD8 T cells DIO2 Thyroid DIO3 Cerebellum Peduncles
DKFZP434L187 Atrioventricular Node DKK2 Ciliary Ganglion DKK4
Pancreas DLAT Adipocyte DLEU2 CD71 Early Erythroid DLG3 Fetal brain
DLK2 Testis Leydig Cell DLL3 Fetal brain DLX2 Fetal brain DLX4
Placenta DLX5 Placenta DMC1 Superior Cervical Ganglion DMD
Olfactory Bulb DMPK Heart DMWD Atrioventricular Node DNA2 X721 B
lymphoblasts DNAH17 Testis DNAH2 Atrioventricular Node DNAH9
Cardiac Myocytes DNAI1 Testis DNAI2 Testis DNAJC1 CD56 NK Cells
DNAJC9 CD71 Early Erythroid DNAL4 Testis DNALI1 Testis Intersitial
DNASE1L1 CD14 Monocytes DNASE1L2 Tonsil DNASE1L3 BDCA4 Dentritic
Cells DNASE2B Salivary gland DND1 Testis DNM2 BDCA4 Dentritic Cells
DNMT3A Superior Cervical Ganglion DNMT3B Leukemia chronic
Myelogenous K572 DNMT3L Liver DOC2B Adrenal gland DOCK5 Superior
Cervical Ganglion DOCK6 Lung DOK2 CD14 Monocytes DOK3 Superior
Cervical Ganglion DOK4 Fetal brain DOK5 Fetal brain DOLK Testis
DOPEY2 Skeletal Muscle DOT1L Superior Cervical Ganglion DPAGT1 X721
B lymphoblasts DPEP3 Testis DPF3 Cerebellum DPH2 Skeletal Muscle
DPM2 CD71 Early Erythroid DPP4 Smooth Muscle DPPA4 CD45 DPT
Adipocyte DPY19L2P2 Leukemia lymphoblastic MOLT 13 DRD2 Caudate
nucleus DSC1 Skin DSG1 Skin DTL CD105 Endothelial DTX2 Skeletal
Muscle DTYMK CD105 Endothelial DUSP10 X721 B lymphoblasts DUSP26
Skeletal Muscle DUSP4 Placenta DUSP7 Bronchial Epithelial Cells
DVL3 Placenta DYNC2H1 Pituitary DYRK2 CD8 T cells DYRK4 Testis
Intersitial DYSF Whole Blood E2F1 CD71 Early Erythroid E2F2 CD71
Early Erythroid E2F4 CD71 Early Erythroid E2F5 Lymphoma burkitts
Daudi E2F8 CD71 Early Erythroid E4F1 CD4 T cells EAF2 CD19 Bcells
neg. sel. EBI3 Placenta ECHDC1 Adipocyte ECHS1 Liver ECM1 Tongue
ECSIT Heart EDA Trigeminal Ganglion EDA2R Superior Cervical
Ganglion EDC3 Testis EDIL3 Occipital Lobe EDN2 Superior Cervical
Ganglion EDN3 retina EDNRA Uterus EFCAB1 Superior Cervical Ganglion
EFHC1 Testis Intersitial EFHC2 Appendix EFNA4 Prostate EFNB1
Colorectal adenocarcinoma EFNB3 Fetal brain EGF Kidney EGFR
Placenta EGLN1 Whole Blood EIF1AY CD71 Early Erythroid
EIF2AK1 CD71 Early Erythroid EIF2B4 Testis EIF2C2 CD71 Early
Erythroid EIF2C3 Pituitary EIF3K Superior Cervical Ganglion EIF4G2
Liver EIF5A2 Ciliary Ganglion ELF3 Colon ELL2 Pancreatic Islet
ELMO3 CD71 Early Erythroid ELOVL6 Adipocyte ELSPBP1 Testis Leydig
Cell ELTD1 Smooth Muscle EMID1 Fetal brain EMILIN2 Superior
Cervical Ganglion EML1 Fetal brain EMR3 Whole Blood EMX2 Uterus EN1
Adipocyte ENDOG Liver ENO3 Skeletal Muscle ENOX1 Fetal brain ENPP1
Thyroid ENTPD1 X721 B lymphoblasts ENTPD2 Superior Cervical
Ganglion ENTPD3 Caudate nucleus ENTPD4 Smooth Muscle ENTPD7 Bone
marrow EPB41 CD71 Early Erythroid EPB41L4A Trigeminal Ganglion
EPHA1 Liver EPHA3 Fetal brain EPHA5 Fetal brain EPN2 CD71 Early
Erythroid EPN3 Thalamus EPS15L1 Appendix EPS8L1 Placenta EPS8L3
Pancreas EPX Bone marrow EPYC Placenta ERCC1 Heart ERCC4 Superior
Cervical Ganglion ERCC6 Ovary ERCC8 Uterus Corpus EREG CD46 ERF
Ciliary Ganglion ERG CD47 ERICH1 Superior Cervical Ganglion ERLIN2
Thyroid ERMAP CD71 Early Erythroid ERMP1 CD56 NK Cells ERN1 Liver
ERO1LB Pancreatic Islet ESM1 CD105 Endothelial ESR1 Uterus ETFB
Liver ETNK1 Colon ETNK2 Liver ETV3 Superior Cervical Ganglion ETV4
Colorectal adenocarcinoma EVPL Tongue EXOSC1 Trigeminal Ganglion
EXOSC2 X721 B lymphoblasts EXOSC4 Testis EXOSC5 X721 B lymphoblasts
EXPH5 Placenta EXT2 Smooth Muscle EXTL3 Subthalamic Nucleus EYA3
Cardiac Myocytes EYA4 Skin F10 Liver F11 Pancreas F12 Liver F13B
Fetal liver F2R Cardiac Myocytes F2RL1 Colon FAAH pineal night
FABP6 small intestine FABP7 Fetal brain FADS1 Adipocyte FAH Liver
FAIM Colorectal adenocarcinoma FAM105A BDCA4 Dentritic Cells
FAM106A Atrioventricular Node FAM108B1 Whole Brain FAM110B
Trigeminal Ganglion FAM118A CD33 Myeloid FAM119B Uterus Corpus
FAM120C Ovary FAM125B Spinal Cord FAM127B Thyroid FAM135A Appendix
FAM149A pineal day FAM48A Testis Intersitial FAM50B Whole Brain
FAM55D Colon FAM5C Amygdala FAM63A Whole Blood FAM86A Pituitary
FAM86B1 Skeletal Muscle FAM86C Leukemia promyelocytic HL62 FANCE
Lymphoma burkitts Daudi FANCG Leukemia lymphoblastic MOLT 14 FARP2
Testis FARS2 Heart FAS Whole Blood FASLG CD56 NK Cells FASTK Heart
FASTKD2 X721 B lymphoblasts FAT4 Fetal brain FBLN2 Adipocyte FBN2
Placenta FBP1 Liver FBP2 Skeletal Muscle FBXL12 Thymus FBXL15 Whole
Brain FBXL4 CD71 Early Erythroid FBXL6 Pancreas FBXL8 X721 B
lymphoblasts FBXO17 Leukemia chronic Myelogenous K573 FBXO38 CD8 T
cells FBXO4 Trigeminal Ganglion FBXO46 X721 B lymphoblasts FCGR2A
Whole Blood FCGR2B Placenta FCHO1 Lymphoma burkitts Raji FCN2 Liver
FCRL2 CD19 Bcells neg. sel. FECH CD71 Early Erythroid FEM1B Testis
Intersitial FEM1C Cerebellum FER1L4 Trigeminal Ganglion FETUB Liver
FEZF2 Amygdala FFAR2 Whole Blood FFAR3 Temporal Lobe FGD1 Fetal
brain FGD2 CD33 Myeloid FGF12 Occipital Lobe FGF14 Cerebellum FGF17
Cingulate Cortex FGF2 Smooth Muscle FGF22 Ovary FGF23 Superior
Cervical Ganglion FGF3 Colorectal adenocarcinoma FGF4 Olfactory
Bulb FGF5 Superior Cervical Ganglion FGF8 Superior Cervical
Ganglion FGF9 Cerebellum Peduncles FGFR1OP Testis Intersitial FGFR4
Liver FGL1 Fetal liver FGL2 CD14 Monocytes FHIT CD4 T cells FHL3
Skeletal Muscle FHL5 Testis Intersitial FILIP1L Uterus FKBP10
Smooth Muscle FKBP14 Smooth Muscle FKBP6 Testis FKBPL CD105
Endothelial FKRP Superior Cervical Ganglion FLG Skin FLJ20712
Temporal Lobe FLNC Skeletal Muscle FLOT2 Whole Blood FLT1 Superior
Cervical Ganglion FLT4 Placenta FMO2 Lung FMO3 Liver FMO6P Appendix
FN3K Superior Cervical Ganglion FNBP1L Fetal brain FNDC8 Testis
Intersitial FOLH1 Prostate FOSL1 Colorectal adenocarcinoma FOXA1
Prostate FOXA2 Pancreatic Islet FOXB1 Superior Cervical Ganglion
FOXC1 Salivary gland FOXC2 Superior Cervical Ganglion FOXD3
Superior Cervical Ganglion FOXD4 Globus Pallidus FOXE1 Thyroid
FOXE3 Superior Cervical Ganglion FOXK2 Adrenal Cortex FOXL1 Liver
FOXN1 Superior Cervical Ganglion FOXN2 Appendix FOXP3 Adrenal
Cortex FPGS Ovary FPGT pineal day FPR2 Whole Blood FPR3 Superior
Cervical Ganglion FRAT1 Whole Blood FRAT2 Whole Blood FRK Superior
Cervical Ganglion FRMD8 Superior Cervical Ganglion FRS2 Pituitary
FRS3 Testis FRZB retina FSHB Pituitary FSHR Superior Cervical
Ganglion FST Bronchial Epithelial Cells FSTL3 Placenta FSTL4
Appendix FTCD Liver FTSJ1 Bronchial Epithelial Cells FXC1 Superior
Cervical Ganglion FXN CD105 Endothelial FXYD2 Kidney FYCO1 Tongue
FZD4 Adipocyte FZD5 Colon FZD7 Cerebellum FZD8 Superior Cervical
Ganglion FZD9 Appendix FZR1 CD71 Early Erythroid G6PC Liver G6PC2
Superior Cervical Ganglion GAB1 Superior Cervical Ganglion GABRA4
Caudate nucleus GABRA5 Amygdala GABRB2 Skin GABRE Placenta GABRG3
Subthalamic Nucleus GABRP Tonsil GABRQ Skeletal Muscle GAD2 Caudate
nucleus GADD45G Placenta GADD45GIP1 Heart GAL3ST1 Spinal Cord GALK1
Liver GAL2 Leukemia chronic Myelogenous K574 GALNS CD33 Myeloid
GALNT12 Colon GALNT14 Kidney GALNT4 CD71 Early Erythroid GALNT6
CD71 Early Erythroid GALNT8 Trigeminal Ganglion GALR2 Superior
Cervical Ganglion GALT Liver GAMT Liver GAPDHS Testis Intersitial
GAPVD1 CD71 Early Erythroid GARNL3 Appendix GAST Cerebellum
GATA4 Heart GATAD1 Leukemia chronic Myelogenous K575 GATC Superior
Cervical Ganglion GBA Placenta GBX1 Bone marrow GCAT Liver GCDH
Liver GCGR Liver GCHFR Liver GCKR Liver GCLC CD71 Early Erythroid
GCLM CD71 Early Erythroid GCM1 Placenta GCM2 Skeletal Muscle GCNT1
CD19 Bcells neg. sel. GCNT2 CD71 Early Erythroid GDAP1L1 Fetal
brain GDF11 retina GDF15 Placenta GDF2 Subthalamic Nucleus GDF5
Fetal liver GDF9 Testis Leydig Cell GDPD3 Colon GEM Uterus Corpus
GEMIN4 Testis Intersitial GEMIN8 Skeletal Muscle GFOD2 Superior
Cervical Ganglion GFRA3 Liver GFRA4 Pons GGTLC1 Lung GH2 Placenta
GHRHR Pituitary GHSR Superior Cervical Ganglion GIF Superior
Cervical Ganglion GIMAP4 Whole Blood GINS4 X721 B lymphoblasts GIP
small intestine GIPC2 small intestine GJA3 Superior Cervical
Ganglion GJA4 Lung GJA5 Superior Cervical Ganglion GJA8 Skeletal
Muscle GJB1 Liver GJB3 Bronchial Epithelial Cells GJB5 Bronchial
Epithelial Cells GJC1 Superior Cervical Ganglion GJC2 Spinal Cord
GK Whole Blood GK2 Testis Intersitial GK3P Testis Germ Cell GKN1
small intestine GLE1 Testis Intersitial GLI1 Atrioventricular Node
GLMN Skeletal Muscle GLP2R Superior Cervical Ganglion GLRA1
Superior Cervical Ganglion GLRA2 Uterus Corpus GLS2 Liver GLT8D2
Smooth Muscle GLTP Tonsil GLTPD1 Heart GMDS Colon GMEB1 CD56 NK
Cells GML Trigeminal Ganglion GNA13 BDCA4 Dentritic Cells GNA14
Superior Cervical Ganglion GNAT1 retina GNAZ Fetal brain GNB1L
Leukemia chronic Myelogenous K576 GNG4 Superior Cervical Ganglion
GNLY CD56 NK Cells GNRHR Pituitary GOLT1B Smooth Muscle GON4L
Leukemia chronic Myelogenous K577 GP5 Trigeminal Ganglion GP6
Superior Cervical Ganglion GP9 Whole Blood GPATCH1 CD8 T cells
GPATCH2 Testis Seminiferous Tubule GPATCH3 CD14 Monocytes GPATCH4
Atrioventricular Node GPATCH8 CD56 NK Cells GPC4 Pituitary GPC5
pineal day GPD1 Adipocyte GPI CD71 Early Erythroid GPKOW CD71 Early
Erythroid GPR124 retina GPR137 Testis GPR143 retina GPR153 Fetal
brain GPR157 Globus Pallidus GPR161 Uterus GPR17 Whole Brain
GPR172B Placenta GPR176 Smooth Muscle GPR18 CD19 Bcells neg. sel.
GPR182 Superior Cervical Ganglion GPR20 Trigeminal Ganglion GPR21
Globus Pallidus GPR31 Superior Cervical Ganglion GPR32 Superior
Cervical Ganglion GPR35 Pancreas GPR37L1 Amygdala GPR39 Superior
Cervical Ganglion GPR4 Lung GPR44 Thymus GPR50 Superior Cervical
Ganglion GPR52 Superior Cervical Ganglion GPR6 Caudate nucleus
GPR64 Testis Leydig Cell GPR65 CD56 NK Cells GPR68 Skeletal Muscle
GPR87 Bronchial Epithelial Cells GPR98 Medulla Oblongata GPRIN2
Superior Cervical Ganglion GPT Liver GPX5 Testis Leydig Cell
GRAMD1C Appendix GRB7 Liver GREM1 Smooth Muscle GRID2 Superior
Cervical Ganglion GRIK3 Superior Cervical Ganglion GRIK4 Olfactory
Bulb GRIN2A Subthalamic Nucleus GRIN2B Skeletal Muscle GRIN2C
Thyroid GRIN2D Superior Cervical Ganglion GRIP1 Superior Cervical
Ganglion GRIP2 CD48 GRK1 Superior Cervical Ganglion GRK4 Testis
GRM1 Cerebellum GRM2 Heart GRM4 Cerebellum Peduncles GRRP1 Globus
Pallidus GRTP1 Superior Cervical Ganglion GSR X721 B lymphoblasts
GSTCD Atrioventricular Node GSTM1 Liver GSTM2 Liver GSTM4 small
intestine GSTT2 Whole Brain GSTTP1 Testis Intersitial GSTZ1 Liver
GTF2IRD1 Colorectal adenocarcinoma GTF3C5 Heart GTPBP1 CD71 Early
Erythroid GUCY1A2 Superior Cervical Ganglion GUCY1B2 Superior
Cervical Ganglion GUCY2C Colon GUCY2D BDCA4 Dentritic Cells GUF1
Superior Cervical Ganglion GULP1 Placenta GYG2 Adipocyte GYPE CD71
Early Erythroid GYS1 Heart GZMK CD8 T cells H2AFB1 Testis HAAO
Liver HAL Fetal liver HAMP Liver HAO1 Liver HAO2 Kidney HAPLN1
Cardiac Myocytes HAPLN2 Spinal Cord HAS2 Skeletal Muscle HBE1
Leukemia chronic Myelogenous K578 HBQ1 CD71 Early Erythroid HBS1L
CD71 Early Erythroid HBXIP Kidney HCCS CD71 Early Erythroid HCFC2
Testis Intersitial HCG4 Superior Cervical Ganglion HCG9 Liver HCN4
Testis Leydig Cell HCRT Hypothalamus HCRTR1 Bone marrow HCRTR2
Atrioventricular Node HDAC11 Testis HDGF CD71 Early Erythroid
HEATR6 Atrioventricular Node HECTD3 CD71 Early Erythroid HECW1
Atrioventricular Node HEPH Leukemia chronic Myelogenous K579 HEXIM1
CD71 Early Erythroid HEY2 retina HGC6.3 Skeletal Muscle HGF Smooth
Muscle HGFAC Liver HHAT BDCA4 Dentritic Cells HHIPL2 Testis
Intersitial HHLA1 Adrenal gland HHLA3 Liver HIC1 Superior Cervical
Ganglion HIC2 Leukemia chronic Myelogenous K580 HIF3A Superior
Cervical Ganglion HIGD1B Lung HIP1R CD19 Bcells neg. sel. HIPK3
CD33 Myeloid HIST1H1E Leukemia chronic Myelogenous K581 HIST1H1T
Dorsal Root Ganglion HIST1H2AB CD19 Bcells neg. sel. HIST1H2BC
Leukemia chronic Myelogenous K582 HIST1H2BG CD8 T cells HIST1H2BJ
Ciliary Ganglion HIST1H2BM Superior Cervical Ganglion HIST1H2BN
small intestine HIST1H3F Uterus Corpus HIST1H3I Cardiac Myocytes
HIST1H3J Atrioventricular Node HIST1H4A CD71 Early Erythroid
HIST1H4E Superior Cervical Ganglion HIST1H4G Skeletal Muscle
HIST3H2A Leukemia chronic Myelogenous K583 HIVEP2 Fetal brain HKDC1
pineal night HLA-DOB CD19 Bcells neg. sel. HLCS Thyroid HMBS CD71
Early Erythroid HMGA2 Bronchial Epithelial Cells HMGB3 Placenta
HMGCL Liver HMGCS2 Liver HMHB1 Skeletal Muscle HNF4G Ovary
HNRNPA2B1 Liver HOOK1 Testis Intersitial HOOK2 Thyroid HOXA1
Leukemia chronic Myelogenous K584 HOXA10 Uterus HOXA3 Superior
Cervical Ganglion HOXA6 Kidney HOXA7 Adrenal Cortex HOXA9
Colorectal adenocarcinoma HOXB1 Cingulate Cortex HOXB13 Prostate
HOXB5 Colorectal adenocarcinoma HOXB6 Colorectal adenocarcinoma
HOXB7 Colorectal adenocarcinoma HOXB8 Superior Cervical
Ganglion
HOXC11 Superior Cervical Ganglion HOXC5 Liver HOXC8 Skeletal Muscle
HOXD1 Trigeminal Ganglion HOXD10 Uterus HOXD11 Appendix HOXD12
Skeletal Muscle HOXD3 Uterus HOXD4 Uterus HOXD9 Uterus HP Liver
HPGD Placenta HPN Liver HPR Liver HPS1 CD71 Early Erythroid HPS4
CD105 Endothelial HR pineal day HRC Heart HRG Liver HRK CD19 Bcells
neg. sel. HS1BP3 CD14 Monocytes HS3ST1 Ovary HS3ST3B1 Heart HS6ST1
Superior Cervical Ganglion HSD11B1 Liver HSD17B1 Placenta HSD17B2
Placenta HSD17B6 Liver HSD17B8 Liver HSD3B1 Placenta HSF1 Heart
HSFX1 Cardiac Myocytes HSP90AA1 Heart HSPA1L Testis Intersitial
HSPA4L Testis Intersitial HSPA6 Whole Blood HSPB2 Heart HSPB3 Heart
HSPC159 Superior Cervical Ganglion HTN1 Salivary gland HTR1A Liver
HTR1B Heart HTR1D Skeletal Muscle HTR1E pineal night HTR1F Appendix
HTR2A Prefrontal Cortex HTR2C Caudate nucleus HTR3A Dorsal Root
Ganglion HTR3B Skin HTR5A Skeletal Muscle HTR7 Cardiac Myocytes
HTRA2 CD71 Early Erythroid HUS1 Superior Cervical Ganglion HYAL2
Lung HYAL4 Superior Cervical Ganglion ICAM4 CD71 Early Erythroid
ICAM5 Amygdala ICOSLG Skeletal Muscle IDE Testis Germ Cell IDH3G
Heart IER3IP1 Smooth Muscle IFI44 CD33 Myeloid IFIT1 Whole Blood
IFIT2 Whole Blood IFIT5 Whole Blood IFNA21 Testis Seminiferous
Tubule IFNA4 Dorsal Root Ganglion IFNA5 Superior Cervical Ganglion
IFNA6 Superior Cervical Ganglion IFNAR1 Superior Cervical Ganglion
IFNG CD56 NK Cells IFNW1 Ovary IFT140 Thyroid IFT52 CD71 Early
Erythroid IFT81 Testis Leydig Cell IGF1R Prostate IGF2AS
Subthalamic Nucleus IGFALS Liver IGLL1 CD49 IGLV6-57 Lymph node IHH
Heart IKZF3 CD8 T cells IKZF5 CD8 T cells IL10 Atrioventricular
Node IL11 Smooth Muscle IL11RA CD4 T cells IL12A Uterus Corpus
IL12RB2 CD56 NK Cells IL13 Testis Intersitial IL13RA2 Testis
Intersitial IL15 pineal night IL17B Olfactory Bulb IL17RA CD33
Myeloid IL17RB Kidney IL18RAP CD56 NK Cells IL19 Trachea IL1B
Smooth Muscle IL1F6 Superior Cervical Ganglion IL1F7 Skeletal
Muscle IL1F9 Superior Cervical Ganglion IL1RAPL1 Prefrontal Cortex
IL1RAPL2 Superior Cervical Ganglion IL1RL1 Placenta IL2 Heart
IL20RA Ciliary Ganglion IL21 Superior Cervical Ganglion IL22
Superior Cervical Ganglion IL24 Smooth Muscle IL25 Pons IL2RA
Superior Cervical Ganglion IL2RB CD56 NK Cells IL3RA BDCA4
Dentritic Cells IL4 Atrioventricular Node IL4R CD19 Bcells neg.
sel. IL5 Atrioventricular Node IL5RA Ciliary Ganglion IL9 Leukemia
promyelocytic HL63 IL9R Testis Intersitial ILVBL Heart IMPG1 retina
INCENP Leukemia lymphoblastic MOLT 15 INE1 Atrioventricular Node
ING1 CD19 Bcells neg. sel. INHA Testis Germ Cell INHBA Placenta
INHBE Liver INPP5B X721 B lymphoblasts INSIG2 X721 B lymphoblasts
INSL4 Placenta INSL6 Superior Cervical Ganglion INSRR Superior
Cervical Ganglion INTS12 BDCA4 Dentritic Cells INTS5 Liver IPO8 CD4
T cells IQCB1 Lymphoma burkitts Daudi IRF2 Whole Blood IRF6
Bronchial Epithelial Cells IRS4 Skeletal Muscle IRX4 Skin IRX5 Lung
ISCA1 CD71 Early Erythroid ISL1 Pancreatic Islet ISOC2 Liver ISYNA1
Testis Germ Cell ITCH Testis Intersitial ITFG2 CD4 T cells ITGA2
Bronchial Epithelial Cells ITGA3 Bronchial Epithelial Cells ITGA9
Testis Seminiferous Tubule ITGB1BP3 Heart ITGB5 Colorectal
adenocarcinoma ITGB6 Bronchial Epithelial Cells ITGB8 Appendix
ITGBL1 Adipocyte ITIH4 Liver ITIH5 Placenta ITM2B X721 B
lymphoblasts ITPKA Whole Brain ITSN1 CD71 Early Erythroid IVL
Tongue JAKMIP2 Prefrontal Cortex JMJD5 Liver JPH2 Superior Cervical
Ganglion KAL1 Spinal Cord KAZALD1 Skeletal Muscle KCNA1 Superior
Cervical Ganglion KCNA10 Skeletal Muscle KCNA2 Skeletal Muscle
KCNA3 Dorsal Root Ganglion KCNA4 Superior Cervical Ganglion KCNAB1
Caudate nucleus KCNAB3 Subthalamic Nucleus KCNB2 Trigeminal
Ganglion KCNC3 Lymphoma burkitts Daudi KCND1 Thyroid KCND2
Cerebellum Peduncles KCNE1 Pancreas KCNE1L Superior Cervical
Ganglion KCNE4 Uterus Corpus KCNG1 CD19 Bcells neg. sel. KCNG2
Superior Cervical Ganglion KCNH1 Appendix KCNH2 CD105 Endothelial
KCNH4 Superior Cervical Ganglion KCNJ1 Kidney KCNJ10 Occipital Lobe
KCNJ13 Superior Cervical Ganglion KCNJ14 Appendix KCNJ2 Whole Blood
KCNJ3 Superior Cervical Ganglion KCNJ6 Cingulate Cortex KCNJ9
Cerebellum KCNK10 BDCA4 Dentritic Cells KCNK12 Olfactory Bulb KCNK2
Atrioventricular Node KCNK7 Superior Cervical Ganglion KCNMA1
Uterus KCNMB3 Testis Intersitial KCNN2 Adrenal gland KCNN4 CD71
Early Erythroid KCNS3 Lung KCNV2 retina KCTD14 Adrenal gland KCTD15
Kidney KCTD17 pineal day KCTD20 CD71 Early Erythroid KCTD5 BDCA4
Dentritic Cells KCTD7 pineal night KDELC1 Cardiac Myocytes KDELR3
Smooth Muscle KDSR Olfactory Bulb KIAA0040 CD19 Bcells neg. sel.
KIAA0087 Trigeminal Ganglion KIAA0090 Placenta KIAA0100 BDCA4
Dentritic Cells KIAA0141 Superior Cervical Ganglion KIAA0196 CD14
Monocytes KIAA0319 Fetal brain KIAA0556 pineal day KIAA0586 Testis
Intersitial KIAA1024 Adrenal Cortex KIAA1199 Smooth Muscle KIAA1310
Uterus Corpus KIAA1324 Prostate KIAA1539 CD71 Early Erythroid
KIAA1609 Bronchial Epithelial Cells KIAA1751 Superior Cervical
Ganglion KIF17 Cingulate Cortex KIF18A X721 B lymphoblasts KIF18B
Leukemia lymphoblastic MOLT 16 KIF21B Fetal brain KIF22 CD71 Early
Erythroid KIF25 Superior Cervical Ganglion KIF26B Ciliary Ganglion
KIF5A Whole Brain KIFC1 CD71 Early Erythroid KIR2DL2 CD56 NK Cells
KIR2DL3 CD56 NK Cells KIR2DL4 CD56 NK Cells KIR2DS4 CD56 NK Cells
KIR3DL1 CD56 NK Cells KIR3DL2 CD56 NK Cells KIRREL Superior
Cervical Ganglion KISS1 Placenta KL Kidney KLF12 CD8 T cells KLF15
Liver KLF3 CD71 Early Erythroid
KLF8 Spinal Cord KLHDC4 CD56 NK Cells KLHL11 Temporal Lobe KLHL12
Testis Intersitial KLHL18 CD105 Endothelial KLHL21 Heart KLHL25
Atrioventricular Node KLHL26 Whole Brain KLHL29 Uterus Corpus KLHL3
Cerebellum KLHL4 Fetal brain KLK10 Tongue KLK12 Tongue KLK13 Tongue
KLK14 Atrioventricular Node KLK15 Pancreas KLK2 Prostate KLK3
Prostate KLK5 Testis Intersitial KLK7 Pancreas KLK8 Tongue KLRC3
CD56 NK Cells KLRF1 CD56 NK Cells KLRK1 CD8 T cells KNTC1 Leukemia
lymphoblastic MOLT 17 KPNA4 X721 B lymphoblasts KPTN Cerebellum
KRT1 Skin KRT10 Skin KRT12 Liver KRT17 Tongue KRT2 Skin KRT23
Colorectal adenocarcinoma KRT3 Superior Cervical Ganglion KRT33A
Superior Cervical Ganglion KRT34 Skin KRT36 Superior Cervical
Ganglion KRT38 Atrioventricular Node KRT6B Tongue KRT84 Superior
Cervical Ganglion KRT86 Placenta KRT9 Superior Cervical Ganglion
KRTAP1-1 Superior Cervical Ganglion KRTAP1-3 Ciliary Ganglion
KRTAP4-7 Superior Cervical Ganglion KRTAP5-9 Superior Cervical
Ganglion L1TD1 Dorsal Root Ganglion L2HGDH Superior Cervical
Ganglion LACTB2 small intestine LAD1 Bronchial Epithelial Cells
LAIR1 BDCA4 Dentritic Cells LAIR2 CD56 NK Cells LALBA Ovary LAMA2
Adipocyte LAMA3 Bronchial Epithelial Cells LAMA4 Smooth Muscle
LAMA5 Colorectal adenocarcinoma LAMB3 Bronchial Epithelial Cells
LAMC2 Bronchial Epithelial Cells LANCL2 Testis LAT CD4 T cells LAX1
CD4 T cells LCAT Liver LCMT2 CD105 Endothelial LCT Trigeminal
Ganglion LDB1 CD105 Endothelial LDB3 Skeletal Muscle LDHAL6B Testis
LDHB Liver LDLR Adrenal Cortex LECT1 CD105 Endothelial LEF1 Thymus
LEFTY1 Colon LEFTY2 Uterus Corpus LENEP Salivary gland LEP Placenta
LETM1 Thymus LFNG Liver LGALS13 Placenta LGALS14 Placenta LGR4
Colon LHB Pituitary LHCGR Superior Cervical Ganglion LHX2 Fetal
brain LHX5 Superior Cervical Ganglion LHX6 Fetal brain LIG3
Leukemia lymphoblastic MOLT 18 LILRB4 BDCA4 Dentritic Cells LILRB5
Skeletal Muscle LIM2 CD56 NK Cells LIMS2 Uterus LIPF small
intestine LIPG Thyroid LIPT1 CD8 T cells LMCD1 Skeletal Muscle LMF1
Liver LMO1 retina LMTK2 Superior Cervical Ganglion LMX1B Superior
Cervical Ganglion LOC1720 Superior Cervical Ganglion LOC388796
Lymphoma burkitts Raji LOC390561 Uterus Corpus LOC390940 Superior
Cervical Ganglion LOC399904 Temporal Lobe LOC441204 Appendix
LOC442421 Superior Cervical Ganglion LOC51145 Appendix LOC93432
Ovary LOH3CR2A Appendix LOR Skin LPAL2 Uterus Corpus LPAR3 Testis
Germ Cell LPIN2 CD71 Early Erythroid LRAT Pons LRCH3 CD8 T cells
LRDD Pancreas LRFN3 Superior Cervical Ganglion LRFN4 Fetal brain
LRIT1 Superior Cervical Ganglion LRP1B Amygdala LRP2 Thyroid LRP5L
Superior Cervical Ganglion LRRC16A Testis Germ Cell LRRC17 Smooth
Muscle LRRC2 Thyroid LRRC20 Skeletal Muscle LRRC3 Skeletal Muscle
LRRC31 Colon LRRC32 Lung LRRC36 Testis Intersitial LRRC37A4
Cerebellum LRRK1 Lymphoma burkitts Daudi LST1 Whole Blood LST-3TM12
Fetal liver LTB4R CD33 Myeloid LTB4R2 Temporal Lobe LTBP4 Thyroid
LTC4S Lung LTK BDCA4 Dentritic Cells LUC7L Whole Blood LY6D Tongue
LY6E Lung LY6G5C CD71 Early Erythroid LY6G6D Pancreas LY6G6E Ovary
LY6H Amygdala LY96 Whole Blood LYL1 CD71 Early Erythroid LYPD1
Smooth Muscle LYST Whole Blood LYVE1 Fetal lung LYZL6 Testis
Intersitial LZTFL1 Leukemia lymphoblastic MOLT 19 LZTS1 Skeletal
Muscle MACROD1 Heart MAF small intestine MAFF Placenta MAFK
Superior Cervical Ganglion MAGEA1 X721 B lymphoblasts MAGEA2
Leukemia chronic Myelogenous K585 MAGEA5 X721 B lymphoblasts MAGEA8
Placenta MAGEB1 Testis Germ Cell MAGEC1 Leukemia chronic
Myelogenous K586 MAGEC2 Skeletal Muscle MAGED4 Fetal brain MAGEL2
Hypothalamus MAGI1 Globus Pallidus MAGIX Superior Cervical Ganglion
MAGOHB CD105 Endothelial MALL small intestine MAML3 Ovary MAMLD1
Testis Germ Cell MAN1A2 Placenta MAN1C1 Placenta MAN2C1 CD8 T cells
MAP2K3 CD71 Early Erythroid MAP2K5 Globus Pallidus MAP2K7
Atrioventricular Node MAP3K12 Cerebellum MAP3K14 CD19 Bcells neg.
sel. MAP3K6 Lung MAP4K2 X721 B lymphoblasts MAPK4 Skeletal Muscle
MAPK7 CD56 NK Cells MAPKAP1 X721 B lymphoblasts MAPKAPK3 Heart
MARK2 Globus Pallidus MARK3 CD71 Early Erythroid MAS1 Appendix
MASP1 Heart MASP2 Liver MAST1 Fetal brain MATK CD56 NK Cells MATN1
Trachea MATN4 Lymphoma burkitts Raji MBNL3 CD71 Early Erythroid
MBTPS1 pineal night MBTPS2 Dorsal Root Ganglion MC2R Adrenal Cortex
MC3R Superior Cervical Ganglion MC4R Superior Cervical Ganglion
MCCC2 X721 B lymphoblasts MCF2 pineal day MCM10 CD105 Endothelial
MCM9 CD19 Bcells neg. sel. MCOLN3 Adrenal Cortex MCPH1 Thymus MCTP1
Caudate nucleus MCTP2 Whole Blood ME1 Adipocyte MECR Heart MED1
Thymus MED15 CD8 T cells MED22 CD19 Bcells neg. sel. MED31
Cerebellum MED7 Testis Intersitial MEGF6 Lung MEGF8 Skeletal Muscle
MEOX2 Fetal lung MEP1B small intestine MET Bronchial Epithelial
Cells METTL4 CD8 T cells METTL8 CD19 Bcells neg. sel. MEX3D
Subthalamic Nucleus MFAP5 Adipocyte MFI2 Uterus Corpus MFN1
Lymphoma burkitts Raji MFSD7 Ovary MGA CD8 T cells MGAT4A CD8 T
cells MGAT5 Temporal Lobe MGC29506 Thymus MGC4294 Superior Cervical
Ganglion MGC5590 Cardiac Myocytes MGMT Liver MGST3 Lymphoma
burkitts Daudi MIA2 Superior Cervical Ganglion MIA3 BDCA4 Dentritic
Cells MICALL2 Colorectal adenocarcinoma MIER2 Lung MIPEP Kidney
MITF Uterus MKS1 Superior Cervical Ganglion MLANA retina MLF1
Testis Intersitial
MLH3 Whole Blood MLL2 Liver MLLT1 Superior Cervical Ganglion MLLT10
Dorsal Root Ganglion MLLT3 CD8 T cells MLN Liver MLNR Superior
Cervical Ganglion MMACHC Liver MME Adipocyte MMP10 Uterus Corpus
MMP11 Placenta MMP12 Tonsil MMP15 Thyroid MMP24 Cerebellum
Peduncles MMP26 Skeletal Muscle MMP28 Lung MMP3 Smooth Muscle MMP8
Bone marrow MMP9 Bone marrow MN1 Fetal brain MNDA Whole Blood
MOBKL3 Adrenal Cortex MOCOS Adrenal gland MOCS3 Atrioventricular
Node MOGAT2 Liver MON1B Prostate MORC4 Placenta MORF4L2 Heart MORN1
Cingulate Cortex MOS Superior Cervical Ganglion MOSC2 Kidney MOSPD2
CD33 Myeloid MPL Skeletal Muscle MPP3 Cerebellum MPP5 Placenta MPP6
Testis Germ Cell MPPED1 Fetal brain MPPED2 Thyroid MPZL1 Smooth
Muscle MPZL2 Colorectal adenocarcinoma MRAS Heart MREG pineal day
MRPL17 X721 B lymphoblasts MRPL46 X721 B lymphoblasts MRPS18A Heart
MRPS18C Atrioventricular Node MRS2 X721 B lymphoblasts MRTO4
Leukemia promyelocytic HL64 MS4A12 Colon MS4A2 Ciliary Ganglion
MS4A4A Placenta MS4A5 Testis Intersitial MSC X721 B lymphoblasts
MSH4 Uterus Corpus MSLN Lung MSRA Kidney MST1 Liver MST1R
Colorectal adenocarcinoma MSX1 Colorectal adenocarcinoma MT4
Lymphoma burkitts Raji MTERFD1 CD105 Endothelial MTERFD2 CD8 T
cells MTF1 CD33 Myeloid MTHFSD Testis MTMR10 CD71 Early Erythroid
MTMR12 CD71 Early Erythroid MTMR3 CD71 Early Erythroid MTMR4
Placenta MTMR7 Superior Cervical Ganglion MTMR8 Skeletal Muscle
MTNR1A Superior Cervical Ganglion MTNR1B Superior Cervical Ganglion
MTTP small intestine MUC1 Lung MUC13 Pancreas MUC16 Trachea MUC2
Colon MUC5B Trachea MUM1 Testis MUSK Skeletal Muscle MUTYH Leukemia
lymphoblastic MOLT 20 MVD Adipocyte MXD1 Whole Blood MYBPC1
Skeletal Muscle MYBPC3 Heart MYBPH Superior Cervical Ganglion MYCN
Fetal brain MYCT1 Trigeminal Ganglion MYF5 Superior Cervical
Ganglion MYF6 Skeletal Muscle MYH1 Skeletal Muscle MYH13 Skeletal
Muscle MYH15 Appendix MYH7B Superior Cervical Ganglion MYL7 Heart
MYNN Trigeminal Ganglion MYO16 Fetal brain MYO1A small intestine
MYO1B Bronchial Epithelial Cells MYO5A Superior Cervical Ganglion
MYO5C Salivary gland MYO7B Liver MYOC retina MYST2 Testis MYT1
pineal night N4BP1 Whole Blood N6AMT1 Trigeminal Ganglion NAALAD2
Pituitary NAALADL1 Liver NAB2 Cerebellum NAPG Superior Cervical
Ganglion NARF CD71 Early Erythroid NAT1 Colon NAT2 Colon NAT8
Kidney NAT8B Kidney NAV2 Fetal brain NAV3 Fetal brain NBEA Fetal
brain NBEAL2 Lymphoma burkitts Raji NCAM2 Superior Cervical
Ganglion NCAPG2 CD71 Early Erythroid NCBP1 X721 B lymphoblasts NCLN
BDCA4 Dentritic Cells NCOA2 Whole Blood NCR1 CD56 NK Cells NCR2
Lymphoma burkitts Raji NCR3 CD56 NK Cells NDP Amygdala NDUFA4L2
Pancreas NDUFB2 Heart NDUFB7 Heart NECAB2 Caudate nucleus NEIL3
Leukemia lymphoblastic MOLT 21 NEK11 Uterus Corpus NEK3 Pancreas
NEK4 Testis Germ Cell NELF Colorectal adenocarcinoma NELL1 Whole
Brain NES Olfactory Bulb NETO2 Fetal brain NEU3 Atrioventricular
Node NEUROD6 Fetal brain NEUROG3 Superior Cervical Ganglion NFATC1
CD19 Bcells neg. sel. NFATC3 Thymus NFE2 CD71 Early Erythroid
NFE2L3 Colorectal adenocarcinoma NFKB2 Lymphoma burkitts Raji
NFKBIB Testis NFKBIL2 Atrioventricular Node NFX1 BDCA4 Dentritic
Cells NFYA Cardiac Myocytes NGB CD71 Early Erythroid NGF Ciliary
Ganglion NGFR Colorectal adenocarcinoma NHLH2 Hypothalamus NINJ1
Whole Blood NIPSNAP3B Superior Cervical Ganglion NKAIN1 Fetal brain
NKX2-2 Spinal Cord NKX2-5 Heart NKX2-8 Superior Cervical Ganglion
NKX3-2 Colon NKX6-1 Skeletal Muscle NLE1 Lymphoma burkitts Raji
NMBR Superior Cervical Ganglion NMD3 Bronchial Epithelial Cells
NME5 Testis Intersitial NMU Leukemia chronic Myelogenous K587 NMUR1
CD56 NK Cells NOC2L Lymphoma burkitts Raji NOC3L X721 B
lymphoblasts NOC4L Testis NOL10 Superior Cervical Ganglion NOL3
Heart NOS1 Uterus Corpus NOS3 Placenta NOTCH1 Leukemia
lymphoblastic MOLT 22 NOX1 Colon NOX3 CD105 Endothelial NOX4 Kidney
NPAS2 Smooth Muscle NPAT CD8 T cells NPC1L1 Fetal liver NPFFR1
Subthalamic Nucleus NPHP4 CD50 NPHS2 Kidney NPM3 Bronchial
Epithelial Cells NPPA Heart NPPB Heart NPPC Superior Cervical
Ganglion NPTXR Skeletal Muscle NPY Prostate NPY1R Fetal brain NPY2R
Superior Cervical Ganglion NQO2 Kidney NR0B2 Liver NR1D1 pineal day
NR1H2 Lung NR1H4 Fetal liver NR1I3 Liver NR2C1 Superior Cervical
Ganglion NR2C2 Testis Leydig Cell NR2E1 Amygdala NR2E3 retina NR4A1
Adrenal Cortex NR4A2 Adrenal Cortex NR4A3 Adrenal Cortex NR5A1
Globus Pallidus NR6A1 Testis NRAP Heart NRAS BDCA4 Dentritic Cells
NRBF2 Whole Blood NRG2 Superior Cervical Ganglion NRIP2 Olfactory
Bulb NRL retina NRP2 Skeletal Muscle NRTN Superior Cervical
Ganglion NRXN3 Cerebellum Peduncles NSUN3 CD71 Early Erythroid
NSUN6 CD4 T cells NT5DC3 Fetal brain NT5M CD71 Early Erythroid
NTAN1 CD71 Early Erythroid NTHL1 Liver NTN1 Superior Cervical
Ganglion NTNG1 Uterus Corpus NTSR1 Colorectal adenocarcinoma NUDT1
CD71 Early Erythroid NUDT15 Colorectal adenocarcinoma NUDT18 CD19
Bcells neg. sel. NUDT4 CD71 Early Erythroid NUDT6 Leukemia
lymphoblastic MOLT 23 NUDT7 Superior Cervical Ganglion NUFIP1 CD105
Endothelial NUMB Whole Blood NUP155 Testis Intersitial NUPL1 Fetal
brain NUPL2 Colorectal adenocarcinoma NXPH3 Cerebellum OAS1 CD14
Monocytes OAS2 Lymphoma burkitts Daudi OAS3 CD33 Myeloid OASL Whole
Blood OAZ3 Testis Intersitial
OBFC2A Uterus Corpus OBSCN Temporal Lobe OCEL1 CD14 Monocytes OCLM
Superior Cervical Ganglion OCLN Skeletal Muscle ODF1 Testis
Intersitial ODZ4 Fetal brain OGFRL1 Whole Blood OLAH Placenta OLFM4
small intestine OLFML3 Adipocyte OLR1 Placenta OMD Superior
Cervical Ganglion OMP Superior Cervical Ganglion ONECUT1 Liver OPA3
Colorectal adenocarcinoma OPLAH Heart OPN1LW retina OPN1SW Superior
Cervical Ganglion OPRD1 Thalamus OPRL1 Lymphoma burkitts Raji
OR10C1 Superior Cervical Ganglion OR10H1 Trigeminal Ganglion OR10H3
Pons OR10J1 Superior Cervical Ganglion OR11A1 Superior Cervical
Ganglion OR1A1 Superior Cervical Ganglion OR2B2 Superior Cervical
Ganglion OR2B6 Superior Cervical Ganglion OR2C1 Superior Cervical
Ganglion OR2H1 Skeletal Muscle OR2J3 Superior Cervical Ganglion
OR2S2 Uterus Corpus OR2W1 Superior Cervical Ganglion OR3A2 Superior
Cervical Ganglion OR52A1 Testis Seminiferous Tubule OR5I1 Lymphoma
burkitts Raji OR6A2 Superior Cervical Ganglion OR7A5 Appendix OR7C1
Testis Seminiferous Tubule OR7E19P Superior Cervical Ganglion ORAI2
CD19 Bcells neg. sel. ORM1 Liver OSBP2 CD71 Early Erythroid OSBPL10
CD19 Bcells neg. sel. OSBPL3 Colorectal adenocarcinoma OSBPL7
Tonsil OSGEPL1 CD4 T cells OSM CD71 Early Erythroid OSR2 Uterus
OTUD3 Prefrontal Cortex OTUD7B Heart OXCT2 Testis Intersitial OXSM
X721 B lymphoblasts OXT Hypothalamus P2RX2 Superior Cervical
Ganglion P2RX3 CD71 Early Erythroid P2RX6 Skeletal Muscle P2RY10
CD19 Bcells neg. sel. P2RY2 Bronchial Epithelial Cells P2RY4
Superior Cervical Ganglion PADI3 Pons PAEP Uterus PAFAH2 Thymus
PAGE1 X721 B lymphoblasts PAK1IP1 Prostate PAK7 Fetal brain PALB2
X721 B lymphoblasts PALMD Fetal liver PANK4 Lymphoma burkitts Raji
PANX1 Bronchial Epithelial Cells PAPOLG Fetal brain PAPPA2 Placenta
PAQR3 Testis Germ Cell PARD3 Bronchial Epithelial Cells PARG
Superior Cervical Ganglion PARN X721 B lymphoblasts PARP11 Appendix
PARP16 Atrioventricular Node PARP3 X721 B lymphoblasts PART1
Prostate PAWR Uterus PAX1 Thymus PAX2 Kidney PAX4 Superior Cervical
Ganglion PAX7 Atrioventricular Node PCCA Colon PCDH1 Placenta
PCDH11X Fetal brain PCDH17 Testis Intersitial PCDH7 Prefrontal
Cortex PCDHB1 Superior Cervical Ganglion PCDHB11 Uterus Corpus
PCDHB13 Pancreatic Islet PCDH63 Testis PCDHB6 Superior Cervical
Ganglion PCK2 Liver PCNP Liver PCNT Skeletal Muscle PCNX CD8 T
cells PCNXL2 Prefrontal Cortex PCOLCE Liver PCOLCE2 Adipocyte PCSK1
Pancreatic Islet PCYOX1 Adipocyte PCYT1A Testis PDC retina PDCD1
Pons PDCD1LG2 Superior Cervical Ganglion PDE10A Caudate nucleus
PDE1B Caudate nucleus PDE1C pineal night PDE3B CD8 T cells PDE6A
retina PDE6G retina PDE7B Trigeminal Ganglion PDE9A Prostate PDGFRL
Fetal Thyroid PDHA2 Testis Intersitial PDIA2 Pancreas PDK3 X721 B
lymphoblasts PDLIM3 Skeletal Muscle PDLIM4 Colorectal
adenocarcinoma PDPN Placenta PDPR Superior Cervical Ganglion PDSS1
Leukemia lymphoblastic MOLT 24 PDX1 Heart PDXP CD14 Monocytes PDZD3
Superior Cervical Ganglion PDZK1IP1 Kidney PDZRN4 Atrioventricular
Node PECR Liver PEPD Kidney PER3 retina PET112L Heart PEX11A
Prostate PEX13 Testis Intersitial PEX19 Adipocyte PEX3 X721 B
lymphoblasts PEX5L Superior Cervical Ganglion PF4 Whole Blood PF4V1
Whole Blood PFKFB1 Liver PFKFB2 Pancreatic Islet PFKFB3 Skeletal
Muscle PGA3 small intestine PGAM1 CD71 Early Erythroid PGAP1
Adrenal Cortex PGGT1B Ciliary Ganglion PGK2 Testis Intersitial
PGLYRP4 Superior Cervical Ganglion PGM3 Smooth Muscle PGPEP1 Kidney
PGR Uterus PHACTR4 X721 B lymphoblasts PHC1 Testis Germ Cell PHEX
BDCA4 Dentritic Cells PHF7 Testis Intersitial PHKG1 Superior
Cervical Ganglion PHKG2 Testis PHLDA2 Placenta PHOX2A Uterus Corpus
PI15 Testis Leydig Cell PI3 Tonsil PI4K2A CD71 Early Erythroid
PIAS2 Testis Intersitial PIAS3 pineal day PIAS4 Whole Brain PIBF1
Testis Intersitial PICK1 Cerebellum Peduncles PIGB X721 B
lymphoblasts PIGL Colorectal adenocarcinoma PIGR Trachea PIGV
Testis PIGZ Pancreas PIK3C2B Thymus PIK3CA CD8 T cells PIK3R2 Fetal
brain PIK3R5 CD56 NK Cells PIP5K1B CD71 Early Erythroid PIPOX Liver
PIR Bronchial Epithelial Cells PITPNM3 Superior Cervical Ganglion
PITX1 Tongue PITX2 retina PITX3 Adrenal gland PKD2 Uterus PKDREJ
CD14 Monocytes PKLR Liver PKMYT1 CD71 Early Erythroid PKP2 Colon
PLA1A X721 B lymphoblasts PLA2G12A CD105 Endothelial PLA2G2E
Superior Cervical Ganglion PLA2G2F Trigeminal Ganglion PLA2G3
Skeletal Muscle PLA2G4A Smooth Muscle PLA2G7 CD14 Monocytes PLAA
X721 B lymphoblasts PLAC1 Placenta PLAC4 Placenta PLAG1 Trigeminal
Ganglion PLAGL2 Testis PLCB2 CD14 Monocytes PLCB3 small intestine
PLCB4 Thalamus PLCXD1 X721 B lymphoblasts PLD1 X721 B lymphoblasts
PLEK2 Bronchial Epithelial Cells PLEKHA2 Superior Cervical Ganglion
PLEKHA6 Placenta PLEKHA8 CD56 NK Cells PLEKHF2 CD19 Bcells neg.
sel. PLEKHH3 Superior Cervical Ganglion PLK1 X721 B lymphoblasts
PLK3 CD33 Myeloid PLK4 CD71 Early Erythroid PLN Uterus PLOD2 Smooth
Muscle PLS1 Colon PLSCR2 Testis Intersitial PLUNC Trachea PLXNA1
Fetal brain PLXNC1 Whole Blood PMCH Hypothalamus PMCHL1
Hypothalamus PMEPA1 Prostate PNMT Adrenal Cortex PNPLA2 Adipocyte
PNPLA3 Atrioventricular Node PNPLA4 Bronchial Epithelial Cells
POF1B Skin POFUT2 Smooth Muscle POLE2 Leukemia lymphoblastic MOLT
25 POLL CD71 Early Erythroid POLM CD19 Bcells neg. sel. POLQ
Lymphoma burkitts Daudi POLR1C Leukemia promyelocytic HL65 POLR2D
Testis POLR2J Trigeminal Ganglion POLR3B X721 B lymphoblasts POLR3C
CD71 Early Erythroid POLR3D X721 B lymphoblasts POLR3G Leukemia
promyelocytic HL66 POLRMT Testis POM121L2 Superior Cervical
Ganglion POMC Pituitary POMGNT1 Heart POMT1 Testis
POMZP3 Testis Germ Cell PON3 Liver POP1 Dorsal Root Ganglion POPDC2
Heart POSTN Cardiac Myocytes POU2F3 Trigeminal Ganglion POU3F3
Superior Cervical Ganglion POU3F4 Ciliary Ganglion POU4F2 Superior
Cervical Ganglion POU5F1 Pituitary POU5F1P3 Uterus Corpus POU5F1P4
Ciliary Ganglion PP14571 Placenta PPA1 Heart PPARD Placenta PPARG
Adipocyte PPARGC1A Salivary gland PPAT X721 B lymphoblasts PPBPL2
Superior Cervical Ganglion PPCDC X721 B lymphoblasts PPEF2 retina
PPFIA2 pineal day PPFIBP1 Colorectal adenocarcinoma PPIL2 Leukemia
chronic Myelogenous K588 PPIL6 Liver PPM1D CD51 PPM1H Cerebellum
PPOX CD71 Early Erythroid PPP1R12B Uterus PPP1R13B Thyroid PPP1R3D
Whole Blood PPP2R2D Whole Brain PPP3R1 Whole Blood PPP5C X721 B
lymphoblasts PPRC1 CD105 Endothelial PPT2 Olfactory Bulb PPY
Pancreatic Islet PPY2 Superior Cervical Ganglion PQLC2 Skeletal
Muscle PRAME Leukemia chronic Myelogenous K589 PRDM1 Superior
Cervical Ganglion PRDM11 CD52 PRDM12 Cardiac Myocytes PRDM13
Superior Cervical Ganglion PRDM16 Superior Cervical Ganglion PRDM5
Skeletal Muscle PRDM8 Superior Cervical Ganglion PREP X721 B
lymphoblasts PRF1 CD56 NK Cells PRG3 Bone marrow PRICKLE3 X721 B
lymphoblasts PRKAA1 Testis Intersitial PRKAB1 CD71 Early Erythroid
PRKAB2 Dorsal Root Ganglion PRKCG Superior Cervical Ganglion PRKCH
CD56 NK Cells PRKRIP1 Colorectal adenocarcinoma PRKY CD4 T cells
PRL Pituitary PRLH Trigeminal Ganglion PRM2 Testis Leydig Cell
PRMT3 Leukemia promyelocytic HL67 PRMT7 BDCA4 Dentritic Cells PRND
Testis Germ Cell PRO1768 Trigeminal Ganglion PRO2012 Appendix PROC
Liver PROCR Placenta PROL1 Salivary gland PROP1 Trigeminal Ganglion
PROZ Superior Cervical Ganglion PRPS2 Ovary PRR3 Leukemia
lymphoblastic MOLT 26 PRR5 CD71 Early Erythroid PRR7 X721 B
lymphoblasts PRRC1 BDCA4 Dentritic Cells PRRG1 Spinal Cord PRRG2
Parietal Lobe PRRG3 Salivary gland PRRX1 Adipocyte PRSS12 Superior
Cervical Ganglion PRSS16 Thymus PRSS21 Testis PRSS8 Placenta PSCA
Prostate PSD Subthalamic Nucleus PSG1 Placenta PSG11 Placenta PSG2
Placenta PSG3 Placenta PSG4 Placenta PSG5 Placenta PSG6 Placenta
PSG7 Placenta PSG9 Placenta PSKH1 Testis PSMB4 Superior Cervical
Ganglion PSMD5 Leukemia chronic Myelogenous K590 PSPH Lymphoma
burkitts Raji PSPN Trigeminal Ganglion PSTPIP2 Bone marrow PTCH2
Fetal brain PTDSS2 Lymphoma burkitts Raji PTER Kidney PTGDR CD56 NK
Cells PTGER2 CD56 NK Cells PTGES2 X721 B lymphoblasts PTGES3
Superior Cervical Ganglion PTGFR Uterus PTGIR CD14 Monocytes PTGS1
Smooth Muscle PTGS2 Smooth Muscle PTH2R Superior Cervical Ganglion
PTHLH Bronchial Epithelial Cells PTK7 BDCA4 Dentritic Cells PTPLA
CD53 PTPN1 CD19 Bcells neg. sel. PTPN21 Testis PTPN3 Thalamus PTPN9
Appendix PTPRG Adipocyte PTPRH Pancreas PTPRS BDCA4 Dentritic Cells
PURG Skeletal Muscle PUS3 Skeletal Muscle PUS7L Superior Cervical
Ganglion PVALB Cerebellum PVRL3 Placenta PXDN Smooth Muscle PXMP2
Liver PXMP4 Lung PYGM Skeletal Muscle PYGO1 Skeletal Muscle PYHIN1
Superior Cervical Ganglion PYY Colon PZP Skin QPRT Liver QRSL1 CD19
Bcells neg. sel. QTRT1 Thyroid RAB11B Thyroid RAB11FIP3 Kidney
RAB17 Liver RAB23 Uterus RAB25 Tongue RAB30 Liver RAB33A Whole
Brain RAB38 Bronchial Epithelial Cells RAB3D Atrioventricular Node
RAB40A Dorsal Root Ganglion RAB40C Superior Cervical Ganglion RAB4B
BDCA4 Dentritic Cells RABL2A Fetal brain RAC3 Whole Brain RAD51L1
Superior Cervical Ganglion RAD52 Lymphoma burkitts Raji RAD9A CD105
Endothelial RAG1 Thymus RALGPS1 Fetal brain RAMP1 Uterus RAMP2 Lung
RAMP3 Lung RANBP10 CD71 Early Erythroid RANBP17 Colorectal
adenocarcinoma RAP2C Uterus RAPGEF1 Uterus Corpus RAPGEF4 Amygdala
RAPGEFL1 Whole Brain RAPSN Skeletal Muscle RARA Whole Blood RARB
Superior Cervical Ganglion RARS2 Uterus Corpus RASA1 Placenta RASA2
CD8 T cells RASA3 CD56 NK Cells RASAL1 Lymphoma burkitts Raji
RASGRF1 Cerebellum RASGRP3 CD19 Bcells neg. sel. RASSF7 Pancreas
RASSF8 Testis Intersitial RASSF9 Appendix RAVER2 Ciliary Ganglion
RAX Cerebellum Peduncles RBBP5 CD14 Monocytes RBM19 Superior
Cervical Ganglion RBM4B Fetal brain RBM7 Whole Blood RBMY1A1 Testis
RBP4 Liver RBPJL Pancreas REX1 CD71 Early Erythroid RC3H2 BDCA4
Dentritic Cells RCAN3 Prostate RCBTB2 Leukemia lymphoblastic MOLT
27 RCN3 Smooth Muscle RDH11 Prostate RDH16 Liver RDH8 retina RECQL4
CD105 Endothelial RECQL5 Skeletal Muscle RELB Lymphoma burkitts
Raji REN Ovary RENBP Kidney RERGL Uterus RETSAT Adipocyte REV3L
Uterus REXO4 CD19 Bcells neg. sel. RFC1 Leukemia lymphoblastic MOLT
28 RFC2 X721 B lymphoblasts RFNG Liver RFPL3 Superior Cervical
Ganglion RFWD3 CD105 Endothelial RFX1 Superior Cervical Ganglion
RFX3 Trigeminal Ganglion RFXAP Pituitary RGN Adrenal gland RGPD5
Testis Intersitial RGR retina RGS14 Caudate nucleus RGS17
Pancreatic Islet RGS3 Heart RGS6 pineal night RGS9 Caudate nucleus
RHAG CD71 Early Erythroid RHBDF1 Olfactory Bulb RHBDL1 Lymphoma
burkitts Raji RHBG Atrioventricular Node RHCE CD71 Early Erythroid
RHD CD71 Early Erythroid RHO retina RHOBTB1 Placenta RHOBTB2 Lung
RHOD Bronchial Epithelial Cells RIBC2 Testis Intersitial RIC3
Cingulate Cortex RIC8B Caudate nucleus RIN3 CD14 Monocytes RINT1
Superior Cervical Ganglion RIOK2 Smooth Muscle RIT1 Whole Blood
RIT2 Fetal brain RLBP1 retina RLN1 Prostate RLN2 Superior Cervical
Ganglion RMI1 X721 B lymphoblasts RMND1 Trigeminal Ganglion
RMND5A CD71 Early Erythroid RMND5B Testis RNASE3 Bone marrow
RNASEH2B Leukemia lymphoblastic MOLT 29 RNASEL Whole Blood RNF10
CD71 Early Erythroid RNF121 Subthalamic Nucleus RNF123 CD71 Early
Erythroid RNF125 CD8 T cells RNF14 CD71 Early Erythroid RNF141
Testis Intersitial RNF17 Testis Intersitial RNF170 Thyroid RNF185
Superior Cervical Ganglion RNF19A CD71 Early Erythroid RNF32 Testis
Intersitial RNF40 CD71 Early Erythroid RNFT1 Testis Leydig Cell
RNMTL1 Testis ROBO1 Fetal brain ROPN1 Testis Intersitial ROR1
Adipocyte RORB Superior Cervical Ganglion RORC Liver RP2 Whole
Blood RPA4 Superior Cervical Ganglion RPAIN Lymphoma burkitts Daudi
RPE Leukemia promyelocytic HL68 RPE65 retina RPGRIP1 Testis
Intersitial RPGRIP1L Superior Cervical Ganglion RPH3AL Pancreatic
Islet RPL10L Testis RPL3L Skeletal Muscle RPP38 Testis Germ Cell
RPRM Fetal brain RPS6KA4 Pons RPS6KA6 Appendix RPS6KB1 CD4 T cells
RPS6KC1 Testis Intersitial RRAD Skeletal Muscle RRAGB Superior
Cervical Ganglion RRH retina RRN3 CD56 NK Cells RRP12 CD33 Myeloid
RRP9 X721 B lymphoblasts R51 retina RSAD2 CD71 Early Erythroid RSF1
Uterus RTDR1 Testis RTN2 Skeletal Muscle RUNX1T1 Fetal brain RUNX2
Pons RWDD2A Testis Germ Cell RXFP3 Superior Cervical Ganglion RYR2
Prefrontal Cortex S100A12 Bone marrow S100A2 Bronchial Epithelial
Cells S100A3 Colorectal adenocarcinoma S100A5 Liver S100G Uterus
Corpus S1PR5 CD56 NK Cells SAA1 Salivary gland SAA3P Skin SAA4
Liver SAC3D1 Testis SAG retina SAMHD1 CD33 Myeloid SAMSN1 Leukemia
chronic Myelogenous K591 SAR1B small intestine SARDH Liver SATB2
Fetal brain SBNO1 Appendix SCAMP3 Atrioventricular Node SCAND2
Superior Cervical Ganglion SCAPER Fetal brain SCARA3 Uterus Corpus
SCGB1D2 Skin SCGB2A2 Skin SCGN Pancreatic Islet SCIN Trigeminal
Ganglion SCLY Liver SCN3A Fetal brain SCN4A Skeletal Muscle SCN5A
Heart SCN8A Superior Cervical Ganglion SCNN1B Lung SCNN1D Superior
Cervical Ganglion SCO2 CD33 Myeloid SCRIB Heart SCRT1 Superior
Cervical Ganglion SCT BDCA4 Dentritic Cells SCUBE3 Superior
Cervical Ganglion SCYL2 BDCA4 Dentritic Cells SCYL3 BDCA4 Dentritic
Cells SDCCAG3 Lymphoma burkitts Raji SDF2 Whole Blood SDPR Fetal
lung SDS Liver SEC14L3 Trigeminal Ganglion SEC14L4 CD71 Early
Erythroid SEC22B Placenta SECTM1 Whole Blood SEL1L Pancreas SELE
retina SELP Whole Blood SEMA3A Appendix SEMA3B Placenta SEMA3D
Trigeminal Ganglion SEMA4G Fetal liver SEMA5A Olfactory Bulb SEMA7A
Superior Cervical Ganglion SEMG1 Prostate SEMG2 Prostate SENP2
Testis Intersitial SEPHS1 Leukemia lymphoblastic MOLT 30 SERPINA10
Liver SERPINA7 Fetal liver SERPINB13 Tongue SERPINB3 Trachea
SERPINB4 Superior Cervical Ganglion SERPINB8 CD33 Myeloid SERPINE1
Cardiac Myocytes SERPINF2 Liver SETD4 Testis SETD8 CD71 Early
Erythroid SETMAR Atrioventricular Node SF3A3 Leukemia chronic
Myelogenous K592 SFMBT1 Testis Germ Cell SFRP5 retina SFTPA2 Lung
SFTPD Lung SGCA Heart SGCB Olfactory Bulb SGPL1 Colorectal
adenocarcinoma SGPP1 Placenta SGTA Heart SH2D1A Leukemia
lymphoblastic MOLT 31 SH2D3C Thymus SH3BGR Skeletal Muscle SH3TC1
Thymus SH3TC2 Placenta SHANK1 CD56 NK Cells SHC2 Pancreatic Islet
SHC3 Prefrontal Cortex SHH Superior Cervical Ganglion SHOX2
Thalamus SHQ1 Leukemia lymphoblastic MOLT 32 SHROOM2 pineal night
SI small intestine SIAH1 Placenta SIAH2 CD71 Early Erythroid
SIGLEC1 Lymph node SIGLEC5 Superior Cervical Ganglion SIGLEC6
Placenta SILV retina SIM1 Superior Cervical Ganglion SIM2 Skeletal
Muscle SIRPB1 Whole Blood SIRT1 CD19 Bcells neg. sel. SIRT4
Superior Cervical Ganglion SIRT5 Heart SIRT7 CD33 Myeloid SIX1
Pituitary SIX2 Pituitary SIX3 retina SIX5 Superior Cervical
Ganglion SKAP1 CD8 T cells SLAMF1 X721 B lymphoblasts SLC10A1 Liver
SLC10A2 small intestine SLC12A1 Kidney SLC12A2 Trachea SLC12A6
Testis Intersitial SLC12A9 CD14 Monocytes SLC13A2 Kidney SLC13A3
Kidney SLC13A4 pineal night SLC14A1 CD71 Early Erythroid SLC15A1
Superior Cervical Ganglion SLC16A10 Superior Cervical Ganglion
SLC16A4 Placenta SLC16A8 retina SLC17A1 Superior Cervical Ganglion
SLC17A3 Kidney SLC17A4 Superior Cervical Ganglion SLC17A5 Placenta
SLC18A1 Skeletal Muscle SLC18A2 Uterus SLC19A2 Adrenal Cortex
SLC19A3 Placenta SLC1A5 Colorectal adenocarcinoma SLC1A6 Cerebellum
SLC1A7 Trigeminal Ganglion SLC20A2 Thyroid SLC22A1 Liver SLC22A13
Superior Cervical Ganglion SLC22A18AS Lymphoma burkitts Raji
SLC22A2 Kidney SLC22A3 Prostate SLC22A4 CD71 Early Erythroid
SLC22A6 Kidney SLC22A7 Liver SLC22A8 Kidney SLC24A1 retina SLC24A2
Ciliary Ganglion SLC24A6 Adrenal gland SLC25A10 Liver SLC25A11
Heart SLC25A17 X721 B lymphoblasts SLC25A21 Leukemia chronic
Myelogenous K593 SLC25A28 BDCA4 Dentritic Cells SLC25A31 Testis
SLC25A37 Bone marrow SLC25A38 CD71 Early Erythroid SLC25A4 Skeletal
Muscle SLC25A42 Superior Cervical Ganglion SLC26A2 Colon SLC26A3
Colon SLC26A4 Thyroid SLC26A6 Leukemia lymphoblastic MOLT 33
SLC27A2 Kidney SLC27A5 Liver SLC27A6 Olfactory Bulb SLC28A3 Pons
SLC29A1 CD71 Early Erythroid SLC2A11 pineal day SLC2A14 Colorectal
adenocarcinoma SLC2A2 Fetal liver SLC2A6 CD14 Monocytes SLC30A10
Fetal liver SLC31A1 CD105 Endothelial SLC33A1 BDCA4 Dentritic Cells
SLC34A1 Kidney SLC35A3 Colon SLC35C1 Colorectal adenocarcinoma
SLC35E3 Prostate SLC37A1 X721 B lymphoblasts SLC37A4 Liver SLC38A3
Liver SLC38A4 Fetal liver SLC38A6 CD105 Endothelial SLC38A7
Prefrontal Cortex
SLC39A7 Prostate SLC3A1 Kidney SLC41A3 Testis SLC45A2 retina
SLC47A1 Adrenal Cortex SLC4A1 CD71 Early Erythroid SLC4A3 Heart
SLC5A1 small intestine SLC5A2 Kidney SLC5A4 Superior Cervical
Ganglion SLC5A5 Thyroid SLC5A6 Placenta SLC6A11 Skeletal Muscle
SLC6A12 Kidney SLC6A14 Fetal lung SLC6A15 Bronchial Epithelial
Cells SLC6A20 Trigeminal Ganglion SLC6A4 pineal night SLC6A7
Superior Cervical Ganglion SLC6A9 CD71 Early Erythroid SLC9A1
Placenta SLC9A3 Superior Cervical Ganglion SLC9A5 Prefrontal Cortex
SLC9A8 CD33 Myeloid SLCO2B1 Liver SLCO4C1 Ciliary Ganglion SLCO5A1
X721 B lymphoblasts SLFN12 CD33 Myeloid SLIT1 Leukemia
lymphoblastic MOLT 34 SLIT3 Adipocyte SLITRK3 Subthalamic Nucleus
SLMO1 Superior Cervical Ganglion SLURP1 Tongue SMC2 Leukemia
lymphoblastic MOLT 35 SMCHD1 Whole Blood SMCP Testis Intersitial
SMG6 Appendix SMR3A Salivary gland SMR3B Salivary gland SMURF1
Testis SMYD3 Leukemia chronic Myelogenous K594 SMYD5 Pancreas
SNAPC1 Testis Intersitial SNAPC4 Testis SNCAIP Uterus Corpus SNIP1
Globus Pallidus SNX1 Fetal Thyroid SNX16 Trigeminal Ganglion SNX19
Superior Cervical Ganglion SNX2 CD19 Bcells neg. sel. SNX24 Spinal
Cord SOAT1 Adrenal gland SOAT2 Fetal liver SOCS1 Lymphoma burkitts
Raji SOCS2 Leukemia chronic Myelogenous K595 SOCS6 Colon SOD3
Thyroid SOHLH2 X721 B lymphoblasts SOS1 Adipocyte SOSTDC1 retina
SOX1 Superior Cervical Ganglion SOX11 Fetal brain SOX12 Fetal brain
SOX18 Superior Cervical Ganglion SOX5 Testis Intersitial SP140 CD19
Bcells neg. sel. SPA17 Testis Intersitial SPAG1 Appendix SPAG11B
Testis Leydig Cell SPAG6 Testis SPANXB1 Testis Seminiferous Tubule
SPAST Fetal brain SPATA2 Testis SPATA5L1 Leukemia promyelocytic
HL69 SPATA6 Testis Intersitial SPC25 Leukemia chronic Myelogenous
K596 SPCS3 BDCA4 Dentritic Cells SPDEF Prostate SPEG Uterus SPIB
Lymphoma burkitts Raji SPINT3 Testis Germ Cell SPO11 Trigeminal
Ganglion SPPL2B CD54 SPR Liver SPRED2 Thymus SRD5A1 Fetal brain
SRD5A2 Liver SREBF1 Adrenal Cortex SRF CD71 Early Erythroid SRR
Superior Cervical Ganglion SSH3 Bronchial Epithelial Cells SSR3
Prostate SSSCA1 CD105 Endothelial SST Pancreatic Islet SSTR1
Atrioventricular Node SSTR4 Ciliary Ganglion SSTR5 Subthalamic
Nucleus SSX2 Superior Cervical Ganglion SSX5 Liver ST3GAL1 CD8 T
cells ST6GALNAC4 CD71 Early Erythroid ST7 X721 B lymphoblasts ST7L
Ovary ST8SIA2 Superior Cervical Ganglion ST8SIA4 Whole Blood
ST8SIA5 Adrenal gland STAB2 Lymph node STAC Ciliary Ganglion
STAG3L4 Appendix STAM2 Testis Intersitial STARD13 X721 B
lymphoblasts STARD5 Uterus Corpus STAT2 BDCA4 Dentritic Cells
STAT5A Leukemia lymphoblastic MOLT 36 STBD1 Pancreatic Islet STC1
Smooth Muscle STEAP1 Prostate STEAP3 CD71 Early Erythroid STIL
Trigeminal Ganglion STK11 CD71 Early Erythroid STK16 X721 B
lymphoblasts STMN3 Amygdala STON1 Uterus STRN Ciliary Ganglion
STRN3 Uterus STS Placenta STX17 Superior Cervical Ganglion STX2 CD8
T cells STX3 Whole Blood STX6 Whole Blood STYK1 Trigeminal Ganglion
SUCLG1 Kidney SULT1A3 Ciliary Ganglion SULT2A1 Adrenal gland
SULT2B1 Tongue SUOX Liver SUPT3H Testis Seminiferous Tubule SUPV3L1
Leukemia promyelocytic HL70 SURF2 Testis Germ Cell SUV39H1 CD71
Early Erythroid SVEP1 Placenta SYCP1 Testis Intersitial SYCP2
Testis Leydig Cell SYDE1 Placenta SYF2 Skeletal Muscle SYN3
Skeletal Muscle SYNGR4 Testis SYNPO2L Heart SYP pineal night SYT12
Trigeminal Ganglion T X721 B lymphoblasts TAAR3 Superior Cervical
Ganglion TAAR5 Superior Cervical Ganglion TAC1 Caudate nucleus TAC3
Placenta TACR3 Pancreas TAF4 Leukemia lymphoblastic MOLT 37 TAF5L
CD71 Early Erythroid TAF7L Testis Germ Cell TAL1 CD71 Early
Erythroid TANC2 Superior Cervical Ganglion TAP2 CD56 NK Cells
TARBP1 CD55 TAS2R1 Globus Pallidus TAS2R14 Superior Cervical
Ganglion TAS2R7 Superior Cervical Ganglion TAS2R9 Subthalamic
Nucleus TASP1 Superior Cervical Ganglion TAT Liver TBC1D12 Spinal
Cord TBC1D13 Kidney TBC1D16 Adipocyte TBC1D22A CD19 Bcells neg.
sel. TBC1D22B CD71 Early Erythroid TBC1D29 Dorsal Root Ganglion
TBC1D8B Pituitary TBCA Superior Cervical Ganglion TBCD Leukemia
lymphoblastic MOLT 38 TBCE CD56 TBL1Y Superior Cervical Ganglion
TBL2 Testis TBP Testis Intersitial TBRG4 Lymphoma burkitts Raji
TBX10 Skeletal Muscle TBX19 Pituitary TBX21 CD56 NK Cells TBX3
Adrenal gland TBX4 Temporal Lobe TBX5 Superior Cervical Ganglion
TCHH Placenta TCL1B Atrioventricular Node TCL6 Cardiac Myocytes
TCN2 Kidney TCP11 Testis Intersitial TDP1 Testis Intersitial TEAD3
Placenta TEAD4 Colorectal adenocarcinoma TEC Liver TECTA Superior
Cervical Ganglion TESK2 CD19 Bcells neg. sel. TEX13B Skeletal
Muscle TEX14 Testis Seminiferous Tubule TEX15 Testis Seminiferous
Tubule TEX28 Testis TFAP2A Placenta TFAP2B Skeletal Muscle TFAP2C
Placenta TFB1M Leukemia promyelocytic HL71 TFB2M Leukemia chronic
Myelogenous K597 TFCP2L1 Salivary gland TFDP1 CD71 Early Erythroid
TFDP3 Superior Cervical Ganglion TFEC CD33 Myeloid TFF3 Pancreas
TFR2 Liver TGDS Pancreas TGFB1I1 Uterus TGM2 Placenta TGM3 Tongue
TGM4 Prostate TGM5 Liver TGS1 CD105 Endothelial THADA CD4 T cells
THAP10 Whole Brain THAP3 Lymphoma burkitts Raji THBS3 Testis THG1L
CD105 Endothelial THNSL2 Liver THRB Superior Cervical Ganglion
THSD1 Pancreas THSD4 Superior Cervical Ganglion THSD7A Placenta
THUMPD2 Leukemia lymphoblastic MOLT 39 TIMM22 Whole Brain TIMM50
Skin TIMM8B Heart TIMP2 Placenta TLE3 Whole Blood TLE6 CD71 Early
Erythroid TLL1 Superior Cervical Ganglion TLL2 Heart
TLR3 Testis Intersitial TLR7 BDCA4 Dentritic Cells TLX3 Cardiac
Myocytes TM4SF20 small intestine TM4SF5 Liver TM7SF2 Adrenal gland
TMCC1 Pancreas TMCC2 CD71 Early Erythroid TMCO3 Smooth Muscle
TMEM104 Skin TMEM11 CD71 Early Erythroid TMEM110 Liver TMEM121 CD14
Monocytes TMEM135 Adipocyte TMEM140 Whole Blood TMEM149 BDCA4
Dentritic Cells TMEM159 Heart TMEM186 X721 B lymphoblasts TMEM187
Lung TMEM19 Superior Cervical Ganglion TMEM2 Placenta TMEM209
Superior Cervical Ganglion TMEM39A Pituitary TMEM45A Skin TMEM48
X721 B lymphoblasts TMEM53 Liver TMEM57 CD71 Early Erythroid TMEM62
Cingulate Cortex TMEM63A CD4 T cells TMEM70 Skeletal Muscle TMLHE
Superior Cervical Ganglion TMPRSS2 Prostate TMPRSS3 small intestine
TMPRSS5 Olfactory Bulb TMPRSS6 Liver TNFAIP6 Smooth Muscle
TNFRSF10C Whole Blood TNFRSF10D Cardiac Myocytes TNFRSF11A Appendix
TNFRSF11B Thyroid TNFRSF14 Lymphoma burkitts Raji TNFRSF25 CD4 T
cells TNFRSF4 Lymph node TNFRSF8 X721 B lymphoblasts TNFRSF9
Ciliary Ganglion THFSF11 Lymph node TNFSF14 X721 B lymphoblasts
THFSF8 CD4 T cells TNFSF9 Leukemia promyelocytic HL72 TNIP2
Lymphoma burkitts Raji TNN pineal night TNNI1 Skeletal Muscle TNNI3
Heart TNNI3K Superior Cervical Ganglion TNNT1 Skeletal Muscle TNNT2
Heart TNP1 Testis Intersitial TNP2 Testis Intersitial TNR Skeletal
Muscle TNS4 Colorectal adenocarcinoma TNXA Adrenal Cortex TNXB
Adrenal Cortex TOM1L1 Bronchial Epithelial Cells TOMM22 X721 B
lymphoblasts TOP3B Leukemia chronic Myelogenous K598 TOX3 Colon
TOX4 Superior Cervical Ganglion TP53BP1 pineal night TP73 Skeletal
Muscle TPPP3 Placenta TPSAB1 Lung TRABD BDCA4 Dentritic Cells TRADD
CD4 T cells TRAF1 X721 B lymphoblasts TRAF2 Lymphoma burkitts Raji
TRAF3IP2 Bronchial Epithelial Cells TRAF6 Leukemia chronic
Myelogenous K599 TRAK1 CD19 Bcells neg. sel. TRAK2 CD71 Early
Erythroid TRDMT1 Superior Cervical Ganglion TRDN Tongue TREH Kidney
TREML2 Placenta TRH Hypothalamus TRIM10 CD71 Early Erythroid TRIM13
Testis Intersitial TRIM15 Pancreas TRIM17 Ciliary Ganglion TRIM21
Whole Blood TRIM23 Amygdala TRIM25 Placenta TRIM29 Tongue TRIM31
Skeletal Muscle TRIM32 Cerebellum TRIM36 Amygdala TRIM46 CD71 Early
Erythroid TRIM68 CD56 NK Cells TRIO Fetal brain TRIP10 Skeletal
Muscle TRIP11 Testis Intersitial TRMT12 CD105 Endothelial TRMU CD8
T cells TRPA1 Superior Cervical Ganglion TRPC5 Superior Cervical
Ganglion TRPM1 retina TRPM2 BDCA4 Dentritic Cells TRPM8 Skeletal
Muscle TRPV4 Superior Cervical Ganglion TRRAP Leukemia
lymphoblastic MOLT 40 TSGA10 Testis Intersitial TSHB Pituitary TSKS
Testis Intersitial TSPAN1 Trachea TSPAN15 Olfactory Bulb TSPAN32
CD8 T cells TSPAN5 CD71 Early Erythroid TSPAN9 Heart TSSC4 Heart
TSTA3 CD105 Endothelial TTC15 Testis Intersitial TTC22 Superior
Cervical Ganglion TTC23 Lymphoma burkitts Raji TTC27 Leukemia
chronic Myelogenous K600 TTC28 Fetal brain TTC9 Fetal brain TTLL12
CD105 Endothelial TTLL4 Testis TTLL5 Testis Intersitial TTPA
Atrioventricular Node TTTY9A Superior Cervical Ganglion TUBA4B
Lymphoma burkitts Raji TUBA8 Superior Cervical Ganglion TUBAL3
small intestine TUBB4Q Skeletal Muscle TUBD1 Superior Cervical
Ganglion TUFM Superior Cervical Ganglion TUFT1 Skin TWSG1 Smooth
Muscle TYR retina TYRP1 retina U2AF1 Superior Cervical Ganglion
UAP1L1 X721 B lymphoblasts UBA1 Superior Cervical Ganglion UBE2D1
Whole Blood UBE2D4 Liver UBFD1 CD105 Endothelial UBQLN3 Testis
Intersitial UCN pineal night UCP1 Fetal Thyroid UFC1 Trigeminal
Ganglion UGT2A1 Atrioventricular Node UGT2B15 Liver UGT2B17
Appendix ULBP1 Cerebellum ULBP2 Bronchial Epithelial Cells UMOD
Kidney UNC119 Lymphoma burkitts Raji UNC5C Superior Cervical
Ganglion UNC93A Fetal liver UNC93B1 BDCA4 Dentritic Cells UPB1
Liver UPF1 Prostate UPK1A Prostate UPK1B Trachea UPK3A Prostate
UPK3B Lung UPP1 Bronchial Epithelial Cells UQCC Lymphoma burkitts
Raji UQCRC1 Heart UQCRFS1 Superior Cervical Ganglion URM1 Heart
UROD CD71 Early Erythroid USH2A pineal day USP10 Whole Blood USP12
CD71 Early Erythroid USP13 Skeletal Muscle USP18 X721 B
lymphoblasts USP19 Trigeminal Ganglion USP2 Testis Germ Cell USP27X
Superior Cervical Ganglion USP29 Superior Cervical Ganglion USP32
Testis Intersitial USP6NL Atrioventricular Node UTRN Testis
Intersitial UTS2 CD56 NK Cells UTY Ciliary Ganglion UVRAG CD19
Bcells neg. sel. VAC14 Skeletal Muscle VARS X721 B lymphoblasts
VASH1 pineal night VASH2 Fetal brain VASP Whole Blood VAV2 CD19
Bcells neg. sel. VAV3 Placenta VAX2 Superior Cervical Ganglion
VCPIP1 CD33 Myeloid VENTX CD33 Myeloid VGF Pancreatic Islet VGLL1
Placenta VGLL3 Placenta VILL Colon VIPR1 Lung VLDLR Pancreatic
Islet VNN2 Whole Blood VNN3 CD33 Myeloid VPRBP Testis Intersitial
VPREB1 CD57 VPS13B CD8 T cells VPS33B Testis VPS45 pineal day VPS53
Skin VSIG4 Lung VSX1 Superior Cervical Ganglion VTCN1 Trachea WARS2
X721 B lymphoblasts WASL Colon WDR18 X721 B lymphoblasts WDR25 Lung
WDR43 Lymphoma burkitts Daudi WDR55 CD4 T cells WDR5B Superior
Cervical Ganglion WDR60 Testis Intersitial WDR67 CD56 NK Cells
WDR70 BDCA4 Dentritic Cells WDR78 Testis Seminiferous Tubule WDR8
Lymphoma burkitts Raji WDR91 X721 B lymphoblasts WHSC1L1 Ovary
WHSC2 Lymphoma burkitts Raji WIPI1 CD71 Early Erythroid WISP1
Uterus Corpus WISP3 Superior Cervical Ganglion WNT11 Uterus Corpus
WNT2B retina WNT3 Superior Cervical Ganglion WNT4 Pancreatic Islet
WNT5A Colorectal adenocarcinoma WNT5B Prostate WNT6 Colorectal
adenocarcinoma WNT7A Bronchial Epithelial Cells WNT7B Skeletal
Muscle WNT8B Skin WRNIP1 Trigeminal Ganglion WT1 Uterus WWC3 CD19
Bcells neg. sel. XCL1 CD56 NK Cells XK CD71 Early Erythroid
XPNPEP2 Kidney XPO4 pineal day XPO6 Whole Blood XPO7 CD71 Early
Erythroid XRCC3 Colorectal adenocarcinoma YAF2 Skeletal Muscle YBX2
Testis YIF1A Liver YIPF6 CD71 Early Erythroid YWHAQ Skeletal Muscle
YY2 Uterus Corpus ZAK Dorsal Root Ganglion ZAP70 CD56 NK Cells
ZBED4 Dorsal Root Ganglion ZBTB10 Superior Cervical Ganglion ZBTB17
Lymphoma burkitts Raji ZBTB24 Skin ZBTB3 Superior Cervical Ganglion
ZBTB33 Superior Cervical Ganglion ZBTB40 CD4 T cells ZBTB43 CD33
Myeloid ZBTB5 CD19 Bcells neg. sel. ZBTB6 Superior Cervical
Ganglion ZBTB7B Ovary ZC3H12A Smooth Muscle ZC3H14 Testis
Intersitial ZCCHC2 Salivary gland ZCWPW1 Testis Germ Cell ZDHHC13
X721 B lymphoblasts ZDHHC14 Lymphoma burkitts Raji ZDHHC18 Whole
Blood ZDHHC3 Testis Intersitial ZER1 CD71 Early Erythroid ZFHX4
Smooth Muscle ZFP2 Superior Cervical Ganglion ZFP30 Ciliary
Ganglion ZFPM2 Cerebellum ZFR2 Trigeminal Ganglion ZFYVE9 Cingulate
Cortex ZG16 Colon ZGPAT Liver ZIC3 Cerebellum ZKSCAN1 Pancreas
ZKSCAN5 CD19 Bcells neg. sel. ZMAT5 Liver ZMYM1 Superior Cervical
Ganglion ZMYND10 Testis ZNF124 Uterus Corpus ZNF132 Skin ZNF133
CD58 ZNF135 CD59 ZNF136 CD8 T cells ZNF14 Trigeminal Ganglion
ZNF140 Superior Cervical Ganglion ZNF157 Trigeminal Ganglion ZNF167
Appendix ZNF175 Leukemia chronic Myelogenous K601 ZNF177 Testis
Seminiferous Tubule ZNF185 Tongue ZNF193 Ovary ZNF200 Whole Blood
ZNF208 Liver ZNF214 Superior Cervical Ganglion ZNF215 Dorsal Root
Ganglion ZNF223 Ciliary Ganglion ZNF224 CD8 T cells ZNF226 pineal
night ZNF23 CD71 Early Erythroid ZNF235 Superior Cervical Ganglion
ZNF239 Testis Seminiferous Tubule ZNF250 Skin ZNF253 Superior
Cervical Ganglion ZNF259 Testis ZNF264 CD4 T cells ZNF267 Whole
Blood ZNF273 Skin ZNF274 CD19 Bcells neg. sel. ZNF280B Testis
Intersitial ZNF286A Superior Cervical Ganglion ZNF304 Superior
Cervical Ganglion ZNF318 X721 B lymphoblasts ZNF323 Superior
Cervical Ganglion ZNF324 Thymus ZNF331 Adrenal Cortex ZNF34 Fetal
Thyroid ZNF343 Ciliary Ganglion ZNF345 Superior Cervical Ganglion
ZNF362 Atrioventricular Node ZNF385D Superior Cervical Ganglion
ZNF391 Testis Intersitial ZNF415 Testis Intersitial ZNF430 CD8 T
cells ZNF434 Globus Pallidus ZNF443 Trigeminal Ganglion ZNF446
Superior Cervical Ganglion ZNF45 CD60 ZNF451 CD71 Early Erythroid
ZNF460 Trigeminal Ganglion ZNF467 Whole Blood ZNF468 CD56 NK Cells
ZNF471 Skeletal Muscle ZNF484 Atrioventricular Node ZNF507 Fetal
liver ZNF510 Appendix ZNF516 Uterus ZNF550 Temporal Lobe ZNF556
Ciliary Ganglion ZNF557 Ciliary Ganglion ZNF587 Superior Cervical
Ganglion ZNF589 Superior Cervical Ganglion ZNF606 Fetal brain
ZNF672 CD71 Early Erythroid ZNF696 Trigeminal Ganglion ZNF7
Skeletal Muscle ZNF711 Testis Germ Cell ZNF717 Appendix ZNF74
Dorsal Root Ganglion ZNF770 Skeletal Muscle ZNF771 Atrioventricular
Node ZNF780A Superior Cervical Ganglion ZNF79 Leukemia
lymphoblastic MOLT 41 ZNF8 Superior Cervical Ganglion ZNF80
Trigeminal Ganglion ZNF804A Lymphoma burkitts Daudi ZNF821 Testis
Intersitial ZNHIT2 Testis ZP2 Cerebellum ZPBP Testis Intersitial
ZSCAN16 CD19 Bcells neg. sel. ZSCAN2 Skeletal Muscle ZSWIM1 Ciliary
Ganglion ZW10 Superior Cervical Ganglion ZXDB Ciliary Ganglion ZZZ3
CD61
[0173] The following table (Table 2) lists panel of 94
tissue-specific genes in Example 4 that were verified with
qPCR.
TABLE-US-00002 TABLE 2 Panel of 94 tissue-specific genes in Example
4 that were verified with qPCR. Gene Tissue PMCH Amygdala HAPLN1
Bronchial epithelial cells PRDM12 Cardiac myocytes ARPP-21 Caudate
nucleus GPR88 Caudate nucleus PDE10A Caudate nucleus CBLN1
Cerebellum CDH22 Cerebellum DGKG Cerebellum CDR1 Cerebellum FAT2
Cerebellum GABRA6 Cerebellum KCNJ12 Cerebellum KIAA0802 Cerebellum
NEUROD1 Cerebellum NRXN3 Cerebellum PPFIA4 Cerebellum ZIC1
Cerebellum SAA4 Cervix SERPINC1 Cervix CALML4 Colon DSC2 Colon
ACTC1 Heart NKX2-5 Heart CASQ2 Heart CKMT2 Heart HRC Heart HSPB3
Heart HSPB7 Heart ITGB1BP3 Heart MYL3 Heart MYL7 Heart MYOZ2 Heart
NPPB Heart CSRP3 Heart MYBPC3 Heart PGAM2 Heart TNNI3 Heart SLC4A3
Heart TNNT2 Heart SYNPO2L Heart AVP Liver ACTB Housekeeping GAPDH
Housekeeping MAB21L2 Housekeeping HCRT Hypothalamus OXT
Hypothalamus BBOX1 Kidney AQP2 Kidney KCNJ1 Kidney FMO1 Kidney NAT8
Kidney XPNPEP2 Kidney PDZK1IP1 Kidney PTH1R Kidney SLC12A1 Kidney
SLC13A3 Kidney SLC22A6 Kidney SLC22A8 Kidney SLC7A9 Kidney UMOD
Kidney SLC17A3 Kidney AKR1C4 Liver C8G Liver APOF Liver AQP9 Liver
CYP2A6 Liver CYP1A2 Liver CYP2C8 Liver CYP2D6 Liver CYP2E1 Liver
ITIH4 Liver HRG Liver FTCD Liver IGFALS Liver RDH16 Liver SDS Liver
SLC22A1 Liver TBX3 Liver SLC27A5 Liver KCNK12 Olfactory bulb MPZ
Olfactory bulb C21ORF7 Whole blood FFAR2 Whole blood FCGR3A Whole
blood EMR2 Whole blood FAM5B Whole blood FCGR3B Whole blood FPR2
Whole blood MLH3 Whole blood PF4 Whole blood PF4V1 Whole blood PPBP
Whole blood TLR1 Whole blood TNFRSF10C Whole blood ZDHHC18 Whole
blood
Example 5
Using Tissue-Specific Cell-Free RNA to Assess Alzheimer's
[0174] The analysis of fetal brain-specific transcripts, in
Examples 2 and 3, leads to the assessment of brain-specific
transcripts for neurological disorder. Particularly, the qPCR brain
panel detected fetal brain-specific transcripts in maternal blood,
whereas the whole transcriptome deconvolution analysis in our
nonpregnant adult samples, in Examples 2 and 3, revealed that the
hypothalamus is a significant contributor to the whole cell-free
transcriptome. Since the hypothalamus is bounded by specialized
brain regions that lack an effective blood-brain barrier, cell-free
DNA in the blood was examined in the current study to measure
neuronal death. qPCR was used to measure the expression levels of
selected brain transcripts in the plasma of both Alzheimer's
patients and age-matched normal controls. These measurements were
made for a cohort of 16 patients: 6 diagnosed as Alzheimer's and 10
normal subjects. FIG. 17 depicts the measurements of PSD3 and APP
cell-free RNA transcript levels in plasma. As provided in FIG. 17,
the levels of PSD3 and APP cell-free RNA transcripts are elevated
in Alzheimer's (AD) patients as compared to normal patients and can
be used to characterize the different patient populations.
[0175] The APP transcript encodes for the precursor molecule whose
proteolysis generates .beta. amyloid, which is the primary
component of amyloid plaques found in the brain of Alzheimer's
disease patients. Preliminary measurements of the plasma APP
transcript corroborate the known biology behind progression of
Alzheimer's disease and showed a significant increase in patients
with Alzheimer's disease compared with normal subjects, suggesting
that plasma APP mRNA levels may be a good marker for diagnosing
Alzheimer's disease. Similarly, the gene PSD3, which is highly
expressed in the nervous system and localized to the postsynaptic
density based on sequence similarities, shows an increase in the
plasma of patients with Alzheimer's disease. By plotting the
.DELTA.Ct values of APP against PSD3, AD patients were clustered
away from the normal patients. In light of the cluster variants,
cell-free RNA may serve as a blood-based diagnostic test for
Alzheimer's disease and other neurodegenerative disorders.
Example 6
Assessing Neurological Disorders with Brain-Specific
Transcripts
Overview
[0176] This study expands upon Example 5 and was designed to
determine brain-specific tissue transcripts that correlate with the
various stages of Alzheimer's disease. The study examined a cohort
of patients from different centers that have previously collected
Alzheimer's patents and age controlled references. There were a
total of 254 plasma samples available from the different centers.
Cell free RNA was extracted from each of the samples. The extracted
cell free RNA from each of these samples were then assayed using
high throughput qPCR on the Biomark Fluidigm system. Each of the
samples was assayed using a panel of 48 genes of which 43 genes are
known to be brain specific. The resulting measurements from each of
the samples were put through a very stringent quality control
process. The first step includes measuring the distribution of
housekeeping genes: ACTB and GAPDH. By observing the levels of
housekeeping genes across the sample from different batches,
batches with significantly lower levels of housekeeping genes were
removed from downstream analysis. The next step in quality control
is by the number of failed gene assays in each of the patient
sample. Sample where 8 or more assays failed to amplify are
removed. This results in 125 good quality samples:
[0177] I. 27 Alzheimers Patients (AD)
[0178] II. 52 Mild Cognitive Impairment Patients (MCI)
[0179] III. 46 Normal patients.
[0180] IV.
[0181] Analysis and Results
[0182] An unsupervised method of Principle Component Analysis (PCA)
was applied to the qPCR gene expression of the 43 brain-specific
transcripts in order to differentiate between Alzheimer's and
Normal patients. FIG. 27 illustrates the PCA space reflecting the
unsupervised clustering of the patients using the gene expression
data from the 48-gene assay. As shown in FIG. 27 two different
populations are formed which correspond to the neurological disease
state of the patients.
[0183] Additionally, a Wilcox non-parametric statistical test was
performed between Alzheimer's and normal patients for each of the
brain specific transcripts. The resulting p-values were bonferroni
corrected for multiple testing. Brain specific transcripts whose
p-values that are significant at the 0.05 levels were cataloged as
transcripts that high distinguishing power between alzheimer's and
normal patients. Amongst all the assayed brain specific
transcripts, two of them are elevated in Alzheimer patients: APP
and PSD3. Another 7 transcripts were below normal levels at a
significant level: MOBP; MAG; SLC2A1; TCF7L2; CDH22; CNTF and
PAQR6. FIG. 28 shows the boxplot of the different levels of APP
transcripts across the different patient groups and the corrected
P-value indicating the significance of the transcripts in
distinguishing Alzheimer's. FIG. 29 illustrates the alternate
trends where the levels of the measure brain transcript MOBP were
lower in the Alzheimer population as compared to the normal
population. MOBP is a myelin-associated oligodendrocyte
protein-coding gene which is known to play a role in compacting or
stabilizing the myelin sheath.
[0184] Methods of Normalization for Comparison Across Sample
Batches
[0185] Considerable heterogeneity may be present between different
batches of samples collected. A normalization scheme may be
deployed to allow for valid comparison across samples from
different batches, and such scheme was deployed in the present
study. For each gene assay within each batch, the delta ct values
of each sample was used to generate a z-score by using the mean and
standard deviation inferred from the population of normal samples
within the batch. This z-score is then used to as the normalized
expression value for downstream analysis, as discussed below.
[0186] Classification Results Using Combined Z-Scores (See FIG.
30)
[0187] To incorporate the different measurements across the brain
specific genes into a single distinct measure for classification of
the patients, the method of combined z-scores was employed. The
combined z-scores measure the deviation of the brain specific
transcripts from the mean expected value of the normal controls and
combine these deviations into a single measure for distinguishing
Alzheimer's. To analyze the utility of such a measure in
distinguishing Alzheimer's, a receiver-operator analysis was
performed and achieved an area under curve (AUC) of 0.79 (See FIG.
30).
INCORPORATION BY REFERENCE
[0188] References and citations to other documents, such as
patents, patent applications, patent publications, journals, books,
papers, web contents, have been made throughout this disclosure.
All such documents are hereby incorporated herein by reference in
their entirety for all purposes.
EQUIVALENTS
[0189] The invention may be embodied in other specific forms
without departing from the spirit or essential characteristics
thereof. The foregoing embodiments are therefore to be considered
in all respects illustrative rather than limiting on the invention
described herein. Scope of the invention is thus indicated by the
appended claims rather than by the foregoing description, and all
changes which come within the meaning and range of equivalency of
the claims are therefore intended to be embraced therein.
* * * * *