U.S. patent application number 17/231533 was filed with the patent office on 2021-08-19 for micro-array devices for capturing cells in blood and methods of their use.
The applicant listed for this patent is University of Louisville Research Foundation, Inc., Worcester Polytechnic Institute. Invention is credited to Farhad Khosravi, Balaji Panchapakesan, Shesh N. Rai.
Application Number | 20210255186 17/231533 |
Document ID | / |
Family ID | 1000005539286 |
Filed Date | 2021-08-19 |
United States Patent
Application |
20210255186 |
Kind Code |
A1 |
Panchapakesan; Balaji ; et
al. |
August 19, 2021 |
Micro-Array Devices for Capturing Cells in Blood and Methods of
Their Use
Abstract
The present disclosure provides micro-array devices for
capturing cells in blood and methods of their use. In some aspects,
a method for counting cells in a blood sample is provided, the
method comprising applying a blood sample onto a CNT device;
allowing cells in the blood sample to differentially settle on the
CNT device, and identifying and counting cells of preselected type
in the blood sample.
Inventors: |
Panchapakesan; Balaji;
(Worcester, MA) ; Khosravi; Farhad; (Worcester,
MA) ; Rai; Shesh N.; (Louisville, KY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Worcester Polytechnic Institute
University of Louisville Research Foundation, Inc. |
Worcester
Louisville |
MA
KY |
US
US |
|
|
Family ID: |
1000005539286 |
Appl. No.: |
17/231533 |
Filed: |
April 15, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15718692 |
Sep 28, 2017 |
11002737 |
|
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17231533 |
|
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62401394 |
Sep 29, 2016 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 33/5748 20130101;
G01N 2015/1006 20130101; G01N 15/1031 20130101; G01N 27/125
20130101; G01N 33/574 20130101; G01N 33/57492 20130101; G01N
2333/71 20130101; G01N 2015/0065 20130101; G01N 15/1056 20130101;
G01N 27/127 20130101; G01N 2333/705 20130101; G01N 33/57415
20130101; G01N 2015/1062 20130101 |
International
Class: |
G01N 33/574 20060101
G01N033/574; G01N 15/10 20060101 G01N015/10; G01N 27/12 20060101
G01N027/12 |
Goverment Interests
STATEMENT OF GOVERNMENT SUPPORT
[0002] This invention was made with Government Support under Grant
Numbers 1463869, 1410678 and 1463987 awarded by the National
Science Foundation and Grant Numbers R15CA156322 and 7R15CA156322
awarded by the National Cancer Institute. The Government has
certain rights in the invention.
Claims
1) A method for specific detection of cellular targets in the blood
sample comprising: applying a blood sample onto a CNT device;
receiving an electrical signal from the CNT device, the signal
being indicative of interactions between cellular targets in the
blood sample and the CNT device; and applying a statistical method
to distinguish between specific interactions and non-specific
interactions.
2) The method of claim 1, wherein the cellular targets are breast
cancer cells.
3) The method of claim 1, wherein the CNT device comprises: a
substrate; a thin film of carbon nanotubes (CNTs) disposed on the
substrate; the CNTS being functionalized with one or more
antibodies capable to bind to a cellular target to be captured; and
a plurality of conductive contacts disposed on the substrate and
electrically coupled to the thin film, wherein the plurality of
conductive contacts are configured detect a capture of the cellular
targets by the one or more antibodies.
4) The method of claim 3, wherein the film is formed from a single
layer of carbon nanotubes.
5) The method of claim 3, wherein the one or more antibodies
include with EpCAM and anti-Her2 conjugated to the film with
1-Pyrenebutanoic Acid Succinimidyl Ester (PASE).
6) The method of claim 3, wherein the film has a density may be
between 3 and 5 nanotubes per micrometer.
7) The method of claim 3, wherein the film is functionalized with
1-Pyrenebutanoic Acid Succinimidyl Ester (PASE) to conjugate one or
more antibodies to the film.
8) The method of claim 1, wherein the blood sample comprises the
cellular targets comprising cells of pre-selected type and the
blood further comprises cells of non-pre-selected type, and the
specific interactions are between the cells of pre-selected type
and the CNT device and the non-specific interactions are between
the cells of non-pre-selected type and the CNT device.
9) The method of claim 8 further comprising upon the identification
of the specific interactions, counting the captured cells of the
pre-selected type in the blood sample.
10) A method for specific detection of cellular targets in a blood
sample comprising: applying a blood sample onto a CNT device;
receiving an electrical signal from the CNT device, the signal
being indicative of interactions between the blood sample and the
CNT device; assigning the electrical signal into a zone of a zone
classification scheme, wherein the zone is indicative of
interactions between the blood sample and the CNT device; and
distinguishing between specific interactions and non-specific
interactions based on the assigned zone.
11) The method of claim 10 wherein the cellular targets are breast
cancer cells.
12) The method of claim 10 wherein the CNT device comprises: a
substrate; a thin film of carbon nanotubes (CNTs) disposed on the
substrate, the CNTS being functionalized with one or more
antibodies capable to bind to a cellular target to be captured; and
a plurality of conductive contacts disposed on the substrate and
electrically coupled to the thin film, wherein the plurality of
conductive contacts are configured detect a capture of the cellular
targets by the one or more antibodies.
13) The method of claim 12 wherein the film is formed from a single
layer of carbon nanotubes.
14) The method of claim 12 wherein the one or more antibodies
include with EpCAM and anti-Her2 conjugated to the film with
1-Pyrenebutanoic Acid Succinimidyl Ester (PASE).
15) A method for rapid and label-free capturing of cellular targets
in a blood sample comprising: applying a blood sample onto a carbon
nanotubes (CNT) device; allowing cellular targets in the blood
sample to adsorb onto the CNT device; and identifying and counting
the cellular targets absorbed onto the CNT device.
16) The method of claim 15, wherein the CNT device comprises: a
substrate; a thin film of carbon nanotubes (CNTs) disposed on the
substrate, the CNTS being functionalized with one or more
antibodies capable to bind to a cellular target to be captured; and
a plurality of conductive contacts disposed on the substrate and
electrically coupled to the thin film, wherein the plurality of
conductive contacts are configured detect a capture of the cellular
targets by the one or more antibodies.
17) The method of claim 16, wherein the film is formed from a
single layer of carbon nanotubes.
18) The method of claim 16, wherein the one or more antibodies
include with EpCAM and anti-Her2 conjugated to the film with
1-Pyrenebutanoic Acid Succinimidyl Ester (PASE).
19) The method of claim 15, wherein the cellular targets are breast
cancer cells.
Description
CROSS REFERENCE TO RELATED APPLICATION
[0001] This application is a divisional application of U.S.
application Ser. No. 15/718,692, filed Sep. 28, 2017, which claims
priority to and the benefit of U.S. Provisional Patent Application
No. 62/401,394, filed on Sep. 28, 2016, the entirety of each of
which is incorporated herein by reference.
BACKGROUND
[0003] Circulating tumor cells (CTC) detection in blood can help
study various cancers. The field of isolation and study of
circulating tumor cells has been growing with many types of devices
reported in the recent past based on immunomagnetic methods,
microfluidic chips, laser scanning cytometry, high-throughput
optical-imaging systems fiber optic array scanning technology and
nano-Velcro arrays. While each method has advantages, there also
many disadvantages associated with each method. However, there is
still a need for devices and methods for isolation of CTCs from
blood.
SUMMARY
[0004] The present disclosure provides micro-array devices for
capturing cells in blood and methods of their use.
[0005] In some aspects, a method for counting cells in a blood
sample is provided, the method comprising applying a blood sample
onto a CNT device; allowing cells in the blood sample to
differentially settle on the CNT device, and identifying and
counting cells of preselected type in the blood sample.
[0006] In some aspects, there is provided a method for rapid and
label-free capturing of cellular targets in a blood sample
comprising applying a blood sample onto a carbon nanotubes (CNT)
device; allowing cellular targets in the blood sample to adsorb
onto the CNT device; and identifying and counting the cellular
targets absorbed onto the CNT device. In some embodiments, the
cellular targets are breast cancer cells.
[0007] In some aspects, a method for specific detection of cellular
targets in the blood sample is provide, the method comprising
applying a blood sample onto a CNT device; receiving an electrical
signal from the CNT device, the signal being indicative of
interactions between cellular targets in the blood sample and the
CNT device; and applying a statistical method to distinguish
between specific interactions and non-specific interactions.
[0008] In some aspects, a method for specific detection of cellular
targets in a blood sample is provided, the method comprising
applying a blood sample onto a CNT device; receiving an electrical
signal from the CNT device, the signal being indicative of
interactions between the blood sample and the CNT device; assigning
the electrical signal into a zone of a zone classification scheme,
wherein the zone is indicative of interactions between the blood
sample and the CNT device; and distinguishing between specific
interactions and non-specific interactions based on the assigned
zone.
[0009] In some aspects, a method for counting cells in a blood
sample is provided, the method comprising applying a blood sample
onto a CNT device; allowing cells in the blood sample to
differentially settle on the CNT device, and identifying and
counting cells of preselected type in the blood sample.
[0010] In some embodiments, the CNT device comprises a substrate; a
thin film of carbon nanotubes (CNTs) disposed on the substrate, the
CNTS being functionalized with one or more antibodies capable to
bind to a cellular target to be captured; and a plurality of
conductive contacts disposed on the substrate and electrically
coupled to the thin film, wherein the plurality of conductive
contacts are configured detect a capture of the cellular targets by
the one or more antibodies.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] The present disclosure is further described in the detailed
description which follows, in reference to the noted plurality of
drawings by way of non-limiting examples of exemplary embodiments,
in which like reference numerals represent similar parts throughout
the several views of the drawings, and wherein:
[0012] FIG. 1 presents a diagnostic chip according to some
embodiments of the present disclosure.
[0013] FIG. 2A presents a top view of a carbon nanotube device
according to some embodiments of the present disclosure.
[0014] FIG. 2B and FIG. 2C present a side view of a carbon nanotube
device according to some embodiments of the present disclosure.
[0015] FIGS. 3A-3H presents a biosensor array micro-fabrication
process.
[0016] FIGS. 4A-FIG. 4D present data showing interactions between
antibodies and cells in a blood sample. FIG. 4A presents a specific
interaction with an Anti-Her2 functionalized device with 1000 SKBR3
cells spiked in blood. FIG. 4B shows a non-specific interaction
with an Anti-Her2 functionalized device with 1000 MCF10A cells
spiked in blood. FIG. 4C shows another Non-Specific Interaction
with Anti-Her2 functionalized device with plain blood adsorption.
FIG. 4D shows the zone classification scheme of the electrical
signals.
[0017] FIG. 5A and FIG. 5B present a merger of device array data
for 1000 cells/5 .mu.L and 100 cells/5 .mu.l variation using the
zone classification scheme.
[0018] FIG. 6A presents a heat map, a summary of the relationship
between electrical signatures and the cellular-proteomic features,
namely overexpression of Her2.
[0019] FIG. 6B presents the Her2 clustering data, suggesting all
SKBR3 cells in blood overexpressing Her2 are clustered/partitioned
together.
[0020] FIG. 7A, FIG. 7B, and FIG. 7C demonstrate cell capture.
[0021] FIG. 8A and FIG. 8B present cell differentiation using
Confocal Microscopy with SKBR3 cells captured on chips from 3
devices showing positive staining for DAPI, cytokeratin (CK-19) and
negative for CD45 and two images of leukocytes captured showing
positive for DAPI and CD45 while negative for cytokeratin. In both
FIG. 8A and FIG. 8B, Column 1 shows a series of optical images;
Column 2 from the left shows DAPI nuclear stain; Column 3 shows a
CK19 stain for cancer cell; Column 4 shows CD45 for leukocytes and
Column 5 shows a merged image of nuclear and cytoplasmic
stains.
[0022] FIG. 9A and FIG. 9B present transistor properties versus
film mass. FIG. 9A shows mobility and on-off ratio of a back-gated
CNT device in one plot. As CNT film mass increases on-off ratio
decreases and mobility increases FIG. 9B shows the on-off ratio for
a top-liquid-gated CNT device, gate is applied by Ag/AgCl reference
electrode through electrolyte solution (1.times.PBS).
[0023] FIG. 10A-FIG. 10J present the device array data for 1000
cells/5 .mu.L variation.
[0024] FIG. 11A-FIG. 11J present the device array data for 100
cells/5 .mu.L variation.
[0025] FIG. 12A and FIG. 12B show a normalized live response of six
CNT devices, three functionalized with streptavidin (FIG. 12A) and
three not functionalized bare CNT devices (FIG. 12B).
[0026] FIG. 13A, FIG. 13B, and FIG. 13C present DTW-based
classification. FIG. 13A shows the cross-validation estimated AUC
for discriminating a high concentration of SKBR-3 cells given
anti-HER-2 antibody. FIG. 13B presents the cross-validation
estimated AUC for discriminating a low concentration of SKBR-3
cells given anti-HER-2 antibody. FIG. 13C presents the
cross-validation estimated AUC for discriminating antibodies,
anti-HER-2 and IgG functionalized devices, in 1000 SKBR-3 cell
concentration.
[0027] FIG. 14A-FIG. 141 present cell capture and enumeration.
[0028] FIG. 15A and FIG. 15B present cancer cell capture and
enumeration with optical microscopic images of 1000 SKBR3 spiked
blood sample droplets on top of the device.
[0029] FIG. 16A and FIG. 16B present cancer cell capture with the
optical images of part of the devices imaged that were adsorbed
with 100 SKBR3 cells spiked blood sample at 20.times.
magnification.
[0030] FIG. 17A, FIG. 17B, and FIG. 17C present optical microscope
images showing the cells cultured at .about.80% confluency. FIG.
17D shows an optical microscope image of the detached SKBR-3
cells.
[0031] FIG. 18 shows the Raman spectrum of semiconducting carbon
nanotubes showing a large G band, small D band and 2D band. The
IG/ID .about.30 was observed in these nanotubes.
[0032] FIG. 19A, FIG. 19B, and FIG. 19C present the electrical
characteristics of nanotube devices. FIG. 19A shows a SEM image of
the actual device fabricated from 4 .mu.g, 2 .mu.g, and 1 .mu.g of
carbon nanotube film and their corresponding high magnification;
FIG. 19B shows the film resistance before and after annealing at
250 C; FIG. 19C presents a histogram suggesting a high degree of
control in device resistance over 58 devices.
[0033] FIG. 20A, FIG. 20B, FIG. 20C, and FIG. 20D present an
understanding of a semiconducting nanotube network. FIG. 20A
presents a real time response of device to concentrations of NH4+,
300 nM to 1.3 mM, and Hg2+, 30 pM to 13 .mu.M, ions; FIG. 20B shows
a voltage sweep before and after exposure to NH4+ and Hg2+ ions;
FIG. 20C shows the concentration sensitivity of the voltage sweep
for NH4+ and Hg2+ ions; FIG. 20D presents a normalized signal
conductance versus a concentration of Hg2+ ions, suggesting
Langmuir-adsorption isotherm. The inset shows the percentage
sensitivity versus concentration for Hg2+. Similar results were
seen for NH4+ ions suggesting same sensing mechanism.
[0034] FIG. 21A, FIG. 21B, FIG. 21C, and FIG. 21D present the
functionalization of antibodies. FIG. 21A presents a schematic of
the PASE functionalization protocol; FIG. 21B shows a SEM image of
positive control suggesting a high degree of antibody
functionalization; FIG. 21C shows a SEM image of negative control
suggesting no functionalization; FIG. 21D presents a comparison of
different functionalization protocols including PASE-antibody,
streptavidin-biotin and amine-polymer-NP conjugation.
[0035] FIG. 22A, FIG. 22B, FIG. 23A, and FIG. 23B present testing
in cell cultures. FIG. 22A shows a normalized device conductance of
an anti-EpCAM functionalized device with SKBR3 cancer cells; FIG.
22B shows a normalized device conductance of anti-EpCAM
functionalized device with MCF7 cancer cells; FIG. 23A shows a
normalized device conductance of anti-EpCAM functionalized device
with MCF10 A normal cells; FIG. 23B shows a representative graph of
normalized device conductance of anti-IgG functionalized device
with SKBR3, MCF7 and MCF10A cells.
[0036] FIG. 24A-24B present a merge of sensor arrays with
statistical classifiers in a training set, showing conductance
versus time from arrays of sensors.
[0037] FIG. 25 presents a heat map showing a summary of the
relationship between electrical signatures and the
cellular-proteomic features namely overexpression of EpCAM in
spiked buffy coats versus buffy coats.
[0038] FIG. 26A and FIG. 26B present cell capture using nanotube
devices based on optical microscopy. FIG. 26A presents the optical
microscopy of spiked cells in buffy coats using nanotube devices;
FIG. 26B presents optical microscopy of plain buffy coats.
[0039] FIG. 27 presents cell capture using nanotube devices using a
confocal microscope, showing representative confocal images from 6
devices imaged (4 shown) of captured cells in spiked buffy coats
ranging from 1-17 cells per 5 .mu.l sample.
[0040] FIG. 28A presents the Amine-Au nanoparticle
functionalization results.
[0041] FIG. 28B presents Streptavidin-Biotin conjugation results on
carbon nanotube devices.
[0042] FIG. 29 presents a representative graph of the testing
protocol.
[0043] FIG. 30A-FIG. 30D present two qualitatively similar spiked
signals.
[0044] FIG. 31A-FIG. 31D present two qualitatively different
signals from different groups of samples.
[0045] FIG. 32 presents the cross-validation results where, for
each choice of k, 10,000 bootstrapped datasets were used to measure
the misclassification rate.
[0046] While the above-identified drawings set forth presently
disclosed embodiments, other embodiments are also contemplated, as
noted in the discussion. This disclosure presents illustrative
embodiments by way of representation and not limitation. Numerous
other modifications and embodiments can be devised by those skilled
in the art which fall within the scope and spirit of the principles
of the presently disclosed embodiments.
DETAILED DESCRIPTION
[0047] In some embodiments, the present disclosure provides devices
and methods for static capturing, detection, isolation and counting
of cellular targets from blood. In some embodiments, this can be
achieved through fractionation of blood into small droplets in a
micro-array format. In some embodiments, such cellular targets can
comprise Circulating tumor cells (CTCs), such as breast cancer
cells. In particular, the present disclosure provides devices and
methods for rapid and label-free capture of breast cancer cells
spiked in blood using nanotube-antibody micro-arrays. It should be
noted, however, that while this disclosure describes the present
devices and methods in connection with CTCs and breast cancer
cells, the present devices and methods can be adapted to capture
other types of cells from blood or other bodily or exogenous
fluids.
[0048] In some embodiments, 76-element single wall carbon nanotube
arrays may be manufactured using photo-lithography, metal
deposition, and etching techniques. Anti-epithelial cell adhesion
molecule (anti-EpCAM), Anti-human epithelial growth factor receptor
2 (anti-Her2) and non-specific IgG antibodies can be functionalized
to the surface of the nanotube devices, for example, using
1-pyrene-butanoic acid succinimidyl ester. Following device
functionalization, blood spiked with SKBR3, MCF7 and MCF10A cells
(100/1000 cells per 5 .mu.l per device, 170 elements totalling 0.85
ml of whole blood) can be adsorbed on to the nanotube device
arrays, and electrical signatures may be recorded from each device
to screen the samples for differences in interaction (specific or
non-specific) between samples and devices. A zone classification
scheme can be used to enable the classification of all 170 elements
in a single map.
[0049] The present disclosure further provides a kernel-based
statistical classifier for the `liquid biopsy` to create a
predictive model based on dynamic time warping series (DTW) to
classify device electrical signals that correspond to plain blood
(control) or SKBR3 spiked blood (case) on anti-Her2 functionalized
devices with .about.90% sensitivity, and 90% specificity in capture
of 1000 SKBR3 breast cancer cells in blood using anti-Her2
functionalized devices. Screened devices that gave positive
electrical signatures can be confirmed using optical/confocal
microscopy to hold spiked cancer cells. Confocal microscopic
analysis of devices that are classified to hold spiked blood based
on their electrical signatures can confirm the presence of cancer
cells through staining for DAPI (nuclei), cytokeratin (cancer
cells) and CD45 (hematologic cells) with single cell sensitivity.
The devices of the present disclosure may enable a 55-100% cancer
cell capture yield. In some embodiments, such yield may depend on
the active device area for blood adsorption with mean of 62%
(.about.12,500 captured off 20,000 spiked cells in 0.1 ml
blood).
[0050] The present disclosure provides the static isolation and
enumeration methods that may offer one or more of the following
advantages: 1) micro-array format enabling a large volume of blood
to be fractionated into smaller portions that may enable better
capture sensitivity and enable verifiable and reproducible results
as well as the ability to scale the number of sensors; 2) the
nanotube-micro-arrays include both detection and capture technology
unlike microfluidics which only captures; 3) a wide variety of
antibodies can be functionalized on the same nanotube micro-array
that can capture CTCs overexpressing different receptors; 4)
classification of the detected electrical signals using
kernel-based DTW classifiers with high sensitivity and specificity;
5) transfer of cells is not required to do further microscopic
analysis thereby minimizing loss of CTCs; 6) the design is tunable
to capturing 1 to 80,000 individual cells/22,500 cell clusters per
device by changing the ratio of the area of the active region of
the pad to the droplet area; 7) the entire assembly can be
automated into a compact handheld device or a laboratory based
instrument similar to the immunomagnetic methods of enrichment and
capture; 8) the captured cells are viable, enabling downstream
molecular analysis such as qPCR.
[0051] In some embodiments, the spiked cancer cells in blood can be
captured using carbon nanotube micro-arrays.
[0052] FIG. 1A presents a diagnostic chip 10 of one or more carbon
nanotube (CNT) devices 12 for detection and capturing cancer cells.
In some embodiments, the chip 10 can include a 76-element array of
carbon nanotube devices. In this manner, the device is able to
capture a large number of cells. In some embodiments, the number of
elements may be increased or decreased depending on the volume of
blood to be processed by the chip 10. The blood can be applied
using a pipette. In some embodiments, a saline solution may be
applied to the device and then the blood may be applied into the
saline solution.
[0053] FIG. 2A presents a top view of a CNT device 12 of the
present disclosure, showing carbon nanotube film 20 and a plurality
of conductive contacts or electrodes 23.
[0054] FIG. 2B and FIG. 2C present a side view of a CNT device 12
of the present disclosure. The CNT device 12 includes a carbon
nanotubes (CNT) film 20 disposed on a substrate 18 with a layer of
a dielectric material 16 disposed between the nanotubes film and
the substrate. The CNT devices 12 of the present disclosure may be
used to both detect and capture cancer cells in blood.
[0055] The CNT film 20 can act as a surface for detecting surface
receptors or markers in cellular targets in bodily sources, e.g.,
cancer cells in blood. The CNT film 20 can be functionalized with
one or more agents 14 selected for capturing the desired target. In
some embodiments, the nanotubes may be functionalized with
anti-epithelial-cell-adhesion-molecule (EpCAM), anti-human
epithelial growth factor receptor 2 (anti-Her2) and nonspecific
immunoglobin (IgG) antibodies, PSMA, EGFR, her2-neu, her1, her3,
G-protein coupled receptors and combinations thereof. In some
embodiments, the surface of the CNT film 12 may be functionalized
with EpCAM and anti-Her2, by themselves or in combination with
other capture agents. In some embodiments, a surfactant may be
deposited on non-functionalized nanotubes to eliminate or at least
reduce non-specific absorption of cancer cells onto the CNT film.
In some embodiments, Tween-20 or sodium dodecyl benzene sulfonate
may be used.
[0056] The CNT device 12 further includes the plurality of
conductive contacts 23 disposed on the substrate and electrically
coupled to the CNT film. When blood 22 mixed with cancer cells 24
is brought into contact with the CNT film 12 that has been
functionalized with monoclonal antibodies, the conductivity of the
thin film changes, and typically does so in a manner that is
directly related to the number of cancer cells in blood. For
example, FIG. 2C shows a cooperative binding of anti-EPCAM
antibodies to their corresponding receptors in cells on top of
nanotube biosensors creating increase in electrical conductance. By
applying a known voltage across the CNT film through conductive
contacts 23, the presence of cancer cells can be determined from
the current sensed through the CNT film, with the current
decreasing a function of the number of cells in the blood. Various
embodiments of carbon nanotube devices useful in the currently
disclosed methods are described, for example, U.S. Ser. No.
13/045,135, which is incorporated herein by reference in its
entirety. In some embodiments, the CNT devices may include a
passivation layer 27 (such as SU-8) to expose only the active
nanotube elements. In some embodiments, the contacts may be formed
from an alloy of Nickel (Ni) and Gold (Au).
[0057] In some embodiments, the CNT film 20 is formed from a single
layer of semiconducting nanotubes. In some embodiments, single wall
carbon nanotubes may be employed. In some embodiments, the
nanotubes are highly purified having a purity of higher than 90%,
or, in some embodiments, higher than 95% or higher than 99.5%. In
some embodiments, the density of the nanotubes may be between 1 and
5 nanotubes per micrometer. In some embodiments, the density may be
between 3 and 5 nanotubes per micrometer. In some embodiments, the
density may be 5 nanotubes per micrometer. The density may be
controlled through a filtration process using a known concentration
of nanotubes in the starting material. In some embodiments, the
nanotubes are deposited in a single layer.
[0058] In some embodiments, the nanotube-CTC-chip can use static
isolation technique for the capture of CTCs. As noted above, the
CNT film 20 may be functionalized with one or more antibodies to
capture the cancer cells in blood. In some embodiments, the film is
functionalized with 1-Pyrenebutanoic Acid Succinimidyl Ester (PASE)
to conjugate one or more antibodies to the film. In some
embodiments, the CNT film 20 may be chemically functionalized using
sidewall PASE functionalization. The side wall functionalization
enables access to the .pi. orbitals of the carbon nanotubes. The
strong .pi.-.pi. interactions between the pyrene fragment of the
PASE molecule and the nanotube surface can creates a method for
stable functionalization of carbon nanotubes. The pyrene rings of
the PASE adsorb on to the sidewalls of the single wall carbon
nanotube (SWNT) through .pi. stacking and produce a stable
nanotube-PASE composite. The succinimidyl ester on the other end of
the PASE provides the attachment site for the antibodies. In some
embodiments, the antibodies may be selected from one or more of
anti-EpCAM, anti-Her2 and IgG. In some embodiments, the antibodies
may include one or more of the following: EGFR, EPCAM, Her2, PSMA,
and Vimentin.
[0059] In some embodiments, the CNT film may also be functionalize
with metal nanoparticles, such as gold nanoparticles.
[0060] In operation, a blood sample including cancer cells can be
introduced onto the diagnostic chip 10. Spiked breast cancer cells
can be captured by the CNT film without pre-labeling, pre-fixation,
or any other processing steps. Blood can simply be adsorbed and
electrical sensing and DTW classification can enable detection and
stratification. Classified devices can then be analyzed using
optical/confocal microscopy on chip. The devices that give a rise
in electrical signal can be classified to hold cancer cells. This
is the detection. Once several devices are classified, these can
then be taken for optical/confocal microscopy to see the presence
of cancer cells. For confocal microscopy, the cells can be stained
for DAPI, cytokeratin Ck-19 and CD45 to show they are indeed cancer
cells.
[0061] In some embodiments, the present disclosure provides a
method for rapid and label-free capturing of breast cancer cells in
a blood sample comprising applying a blood sample onto a CNT device
12, allowing breast cancer cells in the blood sample to adsorb onto
the CNT device 12, and identifying and counting breast cancer cells
absorbed onto the CNT device.
[0062] In some embodiments, the present disclosure provides a
method for specific detection of cancer cells in the blood sample.
In some embodiments, such method may comprise applying a blood
sample onto a CNT device 12, receiving an electrical signal from
the CNT device, the signal being indicative of interactions between
the blood sample and the CNT device, and applying a statistical
method to distinguish between specific interactions and
non-specific interactions. In some embodiments, a predictive model
based on statistical methods, such as for example, dynamic time
warping series, is provided to classify device electrical signals
that correspond to specific interactions versus non-specific
interactions. In some embodiments, a rise in electrical signal from
the buffer level may indicate specific interactions. Non-specific
interaction may be indicated by no change or decrease in electrical
signal.
[0063] In the CNT devices of the present disclosure three different
electrical signals can be identified: 1) the characteristic signals
are classified as specific interactions that give rise to an
increase in signal conductance followed by saturation at higher
level of conductance; 2) non-specific interactions are
characterized by a decrease in electrical signal or 3) no change in
conductance, or at the same level as buffer. A statistical
technique, such as a kernel-based classifier employing dynamic time
warping (DTW), can be used to distinguish between specific and
non-specific interaction of cancer cells which is probably better
than traditional Euclidean distance metric.
[0064] Biomolecular reactions are driven thermodynamically by the
reduction in free energy (G) of the system. For specific
interactions, the reduction in free energy is higher than
non-specific interactions. A spectrum of energy domains can be used
to transduce the change in the free energy of the specific
interactions into mechanics, electricity, thermal or magnetism. The
measurement of specific versus non-specific binding events in cells
as electrical spikes in a fast manner and the ability to stratify
them rapidly in blood using dynamic time warping (DTW) enables both
detection and capture on chip.
[0065] In general,
.DELTA.Gspecific<<<.DELTA.Gnon-specific, with negative
.DELTA.G is favorable. Since the reduction in free energy is
universal for specific and non-specific pairs, this can be used for
detection of specific versus non-specific interactions in cells. In
some embodiments, extracellular overexpressed receptors, namely
EpCAM and Her2, in breast cancer cells can interact with the
anti-EpCAM and anti-Her2 antibodies on the surface the CNT film.
The cooperative specific interaction of thousands of extracellular
receptors with specific antibodies on nanotube surface creates
spikes in the normalized electrical conductance versus time.
Capturing cells based on both EpCAM and Her2 can optimize CTC
capture efficiency for breast cancer, as EpCAM expression in CTCs
may be transient and dependent upon the local micro-environment,
whereas the use of anti-EpCAM antibodies to target the EpCAM
receptor for cell capture is an example of a specific
interaction.
[0066] Non-specific samples, such as plain blood, can also create
such spikes in the electrical conductance versus time data, with
much lower slopes. Such spikes in the signals can carry meaningful
information about the sample condition/interaction that could then
be analyzed using microscopy of captured CTCs.
[0067] In some embodiments, a statistical technique can be used to
classify the signatures that represented specific versus
non-specific interactions. In some embodiments, a kernel-based
classifier employing dynamic time warping (DTW) can be employed. In
some embodiments, these can be classified with .about.90%
sensitivity and .about.90% specificity in classifying devices
specifically based on Her2 signatures for spiked SKBR3 (breast
adenocarcinoma) cells in blood. In some embodiments, because an
electrical signal due to specific interaction can be distinguished
from an electrical signal due non-specific interactions, cancer
cells can be identified in the blood sample based on their effect
on CNT electrical properties. In some embodiments, the capture of
cells can be based on static isolation in a micro-array format
followed by microscopy on chip.
[0068] In some embodiments, the present disclosure provides a
method for distinguishing between specific and non-specific
interactions by creating a zone classification scheme for
electrical signals due to the interactions of the blood sample with
the CNT devices. For example, specific interaction can indicate an
increase in the CNT device conductance whereas non-specific
interaction can result in a decrease or no change in CNT device
conductance. Specific interactions, such as, for example,
interactions between cancer cells and the CNT film, and
non-specific interactions, such as interactions between blood cells
and the CNT film, can be visually classified into these three
groups: 1) "Zone 1" or an increase in the device electrical signal;
2) "Zone 2" or no change and 3) "Zone 3", decrease in device
electrical signal. Using the zone classification, all data can be
viewed in a single map. In some embodiments, when 10 devices are
tested using 1 antibody and if 3 devices give rise to increase in
signal, then microscopy can be conducted on all 10 samples to count
the presence of cancer cells. However, the number of positive
results may be more than 3 or fewer than 3.
[0069] In some embodiments, the testing platform can be set up on
Signatone probe station. An Agilent 4156C semiconductor parameter
analyzer equipped with a custom LabVIEW interface can be used for
monitoring the sensors and data collection. A 100 mV DC bias can be
applied to source electrodes and 0 V VG can be applied using a
Ag/AgCl reference electrode via the sample droplet. The
source-drain current, ISD, can be recorded for the duration of the
test. The accuracy of the semiconductor parameter analyzer can be
set to 1 fA. The entire probe station assembly can be placed on an
optical table that is vibration isolated using air on all four
legs. A metal box can cover the entire assembly to avoid
electromagnetic interference. The probes can be connected to the
parameter analyzer using a triaxial cable that is EM shielded.
Throughout the testing the devices can be maintained inside a
humidified chamber to prevent evaporation of the sample droplet.
The testing protocol can start with a hydrated device topped by a
20 .mu.l droplet of 1.times.PBS, which can be placed immediately
after functionalization. The bias can be applied, and the sensor
can be monitored for the initial 4 min, then 5 .mu.l droplets of
the sample solution, plain or spiked blood, can be pipetted
directly into the standing 20 .mu.l 1.times.PBS droplets. Devices
can be monitored for 360 seconds after addition of the sample
solution. The total duration of one test can be 10 minutes long. To
compare results among devices, ISD data can be normalized to obtain
the G/G0 values for conductance. The sensor element can also be
imaged on an optical microscope to confirm the presence of cancer
cells. The spiked donor blood samples can consist of 100 or 1000
MCF7/SKBR3/MCF10A cells per 5 .mu.l per device for these
experiments. The surface of the CNT device can interact with over
20 000 cells at one time, therefore fully capable of capturing 1000
or 100 spiked cells. Using electrical station with parameter
analyzers may increase precision of the tests. It should be noted
that other test methods and set up protocols may be used.
[0070] In some embodiments, the present disclosure provides a
method for collecting and counting cells in a blood sample
comprising applying a blood sample onto a CNT device 12, allowing
the blood sample to settle, and identifying and counting cells of
preselected type in the blood sample.
[0071] As shown in FIG. 2B, when a blood sample is applied onto the
CNT device 12, various blood cell types in the sample settle
differentially due to the forces of gravity and surface texture and
charge, resulting in cells coming in contact and interacting with
the carbon nanotube surface. Due to the hydrophobicity of nanotube
film, the blood droplet may be localized on the device creating a
layer of plasma and white blood cells on the top layer and red
blood cells and cancer cells in the bottom. Once the various cells
in the blood sample differentially settled, they can be identified
and counted using, for example, microscopy. As necessary, the top
level of plasma and white blood cells can be removed to count the
cancer cells. In some embodiments, the size of the droplet and the
size of the active area of the device and the hydrophobicity of the
CNT film can be varied to determine the cell capture efficiency. In
some embodiments, the size of droplet may range between 3 mm to 5
mm. In some embodiments, the contact angle of the nanotubes is
between 105-140 degrees. The ratio between the areas can enable
tunable design of devices to capture a specific number of cells
both in large and small volumes. For example, by changing the ratio
of the pad area to droplet area, the capture of cells can be tuned.
It is the droplet area to the device area which matters. In some
embodiments, the ratio may be between 0.5 to 1.0, which may result
in between 50% to 100% capture. In some embodiments, capture yield
can be controlled by changing the area of the pad to the area of
the droplet. In some embodiments, the hydrophobicity of the film
may be further enhanced by including one or more fluoro carbon
polymers with the film. In some embodiments, one or more carboxylic
acids can be applied on the film to change hydrophobicity of the
film.
[0072] CNTs device 12 of the present disclosure can be prepared by
a variety of manufacturing methods. FIG. 3A-FIG. 3H presents a
non-limiting example of one such fabrication process, but other
processes known in the art can also be used. FIG. 3A shows a 4''
silicon wafer. FIG. 3B shows dry-thermal oxidation. FIG. 3C shows
the CNT film transfer. FIG. 3D presents the first photolithography
step followed by patterning CNT film, and Reactive-ion etching.
FIG. 3E shows the second photolithography for patterning
electrodes. FIG. 3F shows the sputtering Ni/Au and lift-off. FIG.
3G shows how the SU-8 photoresist is spun on top of wafer. FIG. 3H
shows how the third photolithography step is performed to open
window through SU-8 layer and expose the sensor surface. In some
embodiments, 76 element array of 3 mm.times.3 mm devices is
provided, where one can capture 1000 cells per device. Different
number of arrays and devices of different size can be provided. In
some embodiments, a highly specific PASE functionalization protocol
is used that enables antibodies to attach specifically to the
nanotubes. In some embodiments, Tween-20 is used after antibody
functionalization to cover the uncoated nanotubes to avoid
non-specific adsorption, so the present devices are highly
specific. In some embodiments, 5-20 micro-litters of blood can be
used per device. The total volume tested can be 0.85 ml. In some
embodiment, all nanotubes are semiconducting nanotubes. In some
embodiments, only a single layer of nanotubes is created on the
surface. In some embodiments, the nanotubes are highly purified
semiconducting nanotubes. This is suitable for PASE-antibody
functionalization as PASE interacts with semiconducting nanotubes
better than metallic nanotubes, making the devices highly specific.
In some embodiments, optical microscopy can be used to identify
cancer cells settle to the bottom and white blood cells settle at
the top. The device architecture and hydrophobic nature of
nanotubes enables differential settling there by resulting in
cancer cells going to the bottom versus white blood cells settling
on top, which can facilitate the capture of cells. In some
embodiments, the electrical signals can be classified using a zone
classification scheme where the electrical signals increase, stay
at the buffer level or decrease. Using the zone classification, all
data can be viewed in a single map. This will be useful in clinic.
When 10 devices are tested using 1 antibody and if 3 devices give
rise to increase in signal, then microscopy can be conducted on all
10 samples to count the presence of cancer cells. In some
embodiments, in some methods, the present devices due to the large
area of the device can enable capture of up to 80,000 cancer cells
from blood. In some embodiments, DTW classification can be used to
cluster electrical signals from samples overexpressing Her2 in
cells in blood. In some embodiments, the present devices and
methods enable identification of cancer cells from leukocytes in
the blood using confocal microscopy. In some embodiments, the
devices were completely shielded electromagnetically while testing.
In some embodiments, cell capture yield per device and also over 20
devices can be identified. The yield is 55-100% per device, with
mean of 62% over 20 devices. In some embodiments, cells separation
can be achieved based on her2, which signals aggressive breast
cancer can be removed from the array. In some embodiment, the
methods include use of DTW and designed heatmaps that show the
signals and sample condition.
[0073] The devices and methods of the present disclosure are
described in the following Examples, which are set forth to aid in
the understanding of the disclosure, and should not be construed to
limit in any way the scope of the disclosure as defined in the
claims which follow thereafter. The following examples are put
forth so as to provide those of ordinary skill in the art with a
complete disclosure and description of how to make and use the
embodiments of the present disclosure, and are not intended to
limit the scope of what the inventors regard as their invention nor
are they intended to represent that the experiments below are all
or the only experiments performed. Efforts have been made to ensure
accuracy with respect to numbers used (e.g. amounts, temperature,
etc.) but some experimental errors and deviations should be
accounted for.
EXAMPLES
Example 1: Capture of Spiked Breast Cancer Cells
[0074] CNT-Network Formation
[0075] Iso-semiconducting single wall carbon nanotubes were
purchased from Nanointegris LLC. The manufacturer specified
diameter was in the range of 1.2 nm to 1.7 nm and length was in the
range of 100 nm to 4 Nanotubes were suspended in surfactant
solution at 1 mg/100 ml as received. 600 .mu.L of the stock
solution was then mixed with 85 ml of DI water and 15 ml of 1% w/v
sodium dodecyl sulfate (Sigma-Aldrich, Cat. No. 436143), for a
final concentration of 6 .mu.g/100 ml.
[0076] Vacuum Filtration
[0077] The 100 ml solution was vacuum filtered over a cellulose
membrane, 0.05 .mu.m pore size (Millipore, No. VMWP09025). The
vacuum filtration method self-regulates the deposition rate of
nanotubes on the membrane to produce an evenly distributed
conductive network. The CNT film network was then pressed onto a
dry oxidized (300 nm thickness) 4'' silicon wafer for 30 min. Next
the wafer was transferred to an acetone vapor bath that dissolved
the overlaying filter membrane.
[0078] Clean Room Processing
[0079] Patterning of the nanotube film and electrode and insulating
layer fabrication were done by photolithography in the cleanroom.
The S1813 photoresist was used to mask the nanotube film areas
needed for the sensor elements. Exposed nanotubes were etched away
in a March reactive ion etcher for 90 s at 200 W power and 10%
O.sub.2. The S1813 photoresist was also used to mask the electrode
pattern. Electrodes, 15 nm Ni and a 90 nm Au layers, were deposited
by sputtering in a Leskar PVD 75 system, 300 W DC power. The
lift-off process was conducted in an acetone bath to remove the
excess Ni/Au layers. Lastly, the sensors were covered with
SU8-2005, a 5 .mu.m thick photopolymer layer. A window over each of
the nanotube sensor elements was developed, but the electrodes
remained insulated beneath the SU8.
[0080] Device Functionalization
[0081] Finished carbon nanotube sensors were functionalized with
anti-EpCAM, anti-Her2 and IgG by a pyrene linker molecule. The
pyrene rings of 1-pyrenebutanoic acid, succinimidyl ester adsorb
onto carbon nanotube sidewalls by .pi.-stacking. The ester on the
other end of the molecule provided an attachment point for
antibodies. PASE (AnaSpec, Cat. No. 81238) was dissolved in
methanol at 1 mM. Devices were incubated in the PASE solution for 2
h at room temperature, and then rinsed with methanol and dried
using a nitrogen air gun. Devices were then incubated in Anti-EpCAM
(EMD Bioscience, Cat. No. OP187), anti-HER-2 (Cell Signaling tech.,
Cat. No 2242S), or IgG (EMD Millipore, Cat. No. 411550), 20 .mu.g
ml.sup.-1 in 1.times.PBS, for 2 h at room temperature. After
incubation, devices were rinsed in 1.times.PBS three times. Tween20
was used to block unfunctionalized nanotube sidewalls to minimize
non-specific interactions. Devices were incubated with 0.5% Tween20
for 2 h at room temperature. After incubation, devices were rinsed
with 1.times.PBS, then incubated in 20 .mu.l droplets of
1.times.PBS overnight in a humid chamber at 4.degree. C. before
testing.
[0082] Cell Culture and Preparation
[0083] The breast adenocarcinoma cell lines MCF7 and SKBR3 (ATCC,
Cat. No. HTB-22; HTB-30), were cultured under conditions as
recommended by ATCC. MCF10A (ATCC, Cat. No. CRL-10317) is a
non-tumorigenic cell line that is EpCAM negative, while MCF7 and
SKBR3 are EpCAM positive cell lines. SKBR3 is a Her2 positive cell
line. Cells were grown for 3-4 days to reach .about.80% confluence
(FIG. 15A and FIG. 15B). Cells were then detached from the culture
flask using Accutase enzyme solution (Sigma, Cat. No. A6964),
centrifuged and suspended in 1.times.PBS buffer solution and taken
for counting using a hemocytometer as presented in FIG. 17A-FIG.
17D. Finally cells for each cell line were prepared at fixed
concentrations of 800,000 cells/mL and 80,000 cells/mL in
1.times.PBS solution. Next the cell samples were diluted 1:3 in
blood for a final spiked cell concentration of 1000 cells/5 .mu.L
and 100 cells/5 .mu.L. At this stage spiked blood samples were
stored at 4.degree. C. for same day testing. As test samples were
injected onto the device at fixed volumes of 5 .mu.l the total
number of the spiked cells injected to each device was fixed at
1000 or 100 accordingly.
[0084] FIG. 17A, FIG. 17B, and FIG. 17C present optical microscope
images showing the cells cultured at .about.80% confluency. FIG.
17D shows an optical microscope image of the detached SKBR-3 cells.
Cells are detached at this point to be counted and prepared for
testing.
[0085] Blood Sample Preparation
[0086] Blood was donated by a single donor and was the subject of
all blood draws. Blood samples used are all traced back to the same
donor as the only source and sole blood donor. This allowed for a
control and identified blood source with the least degree of
variation in each batch of blood samples. The following protocol
was developed to collect and handle blood samples for the purposes
of the experiments presented here. As the blood sample volume
defined for each set of testing are set at 5 the total blood volume
needed for each day of testing did not exceed 100 .mu.L. Therefore,
the blood draw protocol was generated around the volumes needed to
minimize biohazardous waste generation and to maintain the health
of the blood donor as the blood draws had to be executed frequently
throughout the 21 days of testing. As a result, capillary sampling
protocol provided by World Health Organization (WHO), "WHO
guidelines on drawing blood: best practices in phlebotomy", was
adopted to generate a protocol to collect blood samples from a
finger tip of the subject. PBS or CTC spiked PBS were diluted 1:3
into collected blood, as described above. This dilution protocol
prevented the blood from clotting without the addition of any
additional chemicals and allowed for determining an exact
concentration of spiked CTCs levels in each sample.
[0087] Confocal Microscopy
[0088] After experimental data had been collected, the devices were
saved and taken for staining and confocal imaging. The devices were
first rinsed with PBS to remove excess cells and fragments and then
incubated with 4% paraformaldehyde (Santa Cruz Biotechnology Inc.,
Cat. No. sc-281692). After initial preparation devices were stained
with DAPI (Molecular Probes, Cat. No. D1306), anti-cytokeratin
(CK19) (Santa Cruz Biotechnology Inc., Cat. No. sc-33119) and
anti-CD45 (Santa Cruz Biotechnology Inc., Cat. No. sc-1187)
according to the standard confocal staining protocol. A coverslip
was placed on top of each device and sealed before imaging.
Confocal laser scanning microscopy images were obtained on a Nikon
Eclipse T. with coverslip corrected objective focused at
600.times..
[0089] Device Testing
[0090] The testing platform was set up on Signatone probe station.
An Agilent 4156C Semiconductor Parameter Analyzer equipped with a
custom LabVIEW interface was used for monitoring the sensors and
data collection. A 100 mV bias was applied to source electrodes and
0 V V.sub.G was applied using a Ag/AgCl reference electrode via the
sample droplet, The source-drain current, I.sub.SD, was recorded
for the duration of the test. The accuracy of the semiconductor
parameter analyzer is 1 fA. The entire probe station assembly was
placed on an optical table that was vibration isolated using air on
all four legs. A metal box covered the entire assembly to avoid
electromagnetic interference. The probes were connected to the
parameter analyzer using a triaxial cable that is EM shielded.
Throughout the testing the devices were maintained inside a
humidified chamber to prevent evaporation of the sample droplet.
The testing protocol started with a hydrated device topped by a 20
.mu.l droplet of 1.times.PBS, which was placed immediately after
functionalization. The bias was applied, and the sensor was
monitored for the initial 4 min, then 5 .mu.L droplets of the
sample solution, plain or spiked blood, were pipetted directly into
the standing 20 .mu.L 1.times.PBS droplets. Devices were monitored
for 360 seconds after addition of the sample solution. The total
duration of one test was 10 minutes long. To compare results among
devices, I.sub.SD data were normalized to obtain the G/G0 values
for conductance. The sensor element was also imaged on an optical
microscope to confirm the presence of cancer cells. The spiked
donor blood samples consisted of 100 or 1000 MCF7/SKBR3/MCF10A
cells per 5 .mu.l per device for these experiments. The surface of
the CNT device is capable of interacting with over 20,000 cells at
one time, therefore fully capable of capturing 1,000 or 100 spiked
cells.
[0091] Statistical Classifier
[0092] Statistical classification was done using DTW package in R.
The sensitivity, specificity, and misclassification rate were then
computed, considering spiked blood to be a positive test and normal
blood to be a negative test. Sensitivity is defined as TP/P, where
TP denotes the number of positive test outcomes, and P denotes the
number of true positives. Specificity is defined as TN/N, where
denotes the number of negative test outcomes, and N denotes the
number of true negatives.
[0093] Arrays
[0094] The arrays can be fabricated using lithography, reactive ion
etching and post-processing. With a 3 mm.times.3 mm device size and
at the rate of 5-20 .mu.l per device, the 76-element arrays can
process anywhere from .about.0.3 ml to 4.8 ml of blood, large
enough to get meaningful information about the sample condition. A
total of .about.0.85 ml of blood and 170 elements, each analyzing 5
.mu.l per device, was used in order to get a variety of information
from this array using different antibodies. A drop of 5-30 .mu.l
was expected to contain 1-9,000 epithelial cells. It has also been
reported that 1 g of tumor tissue (109 cells) sheds about
3-4.times.106 tumor cells into the blood stream per day and thus
presents clinical value in their enumeration. The spike
concentration of cells was chosen to be relevant to the upper limit
of clinical samples, and as a proof of concept of this static
isolation approach. The surface of the CNT device is capable of
capturing over 80,000 individual cells/22,500 cell clusters at one
time, therefore fully capable of capturing 100-1,000 spiked cells.
Similarly, arrays can be automated to handle more than 100 elements
at the same time to enable results within minutes.
[0095] The chip consisting of the 76-element array of CNT micro
devices was specifically designed for the need to process large
volumes of blood. Each device in the 76-element array was 3
mm.times.3 mm and can hold about 20 .mu.l of blood. The 76-element
device included a simple two-terminal design at their core with a
CNT ultra-thin film network connecting the source and drain
electrodes. In this design, the sensing channel consists of a CNT
network connecting the two electrodes, 3000 .mu.m in length and
3000 .mu.m in width. As a result, the sensing area of each device
is 9.times.10.sup.6 .mu.m2, allowing for .about.80,000 cells,
assuming an average 10-12 .mu.m diameter, to be captured on the
device.
[0096] The 76-element array of nanotube devices used for the
testing in blood were fabricated using a 6 .mu.g CNT film. Past
devices on 60-element arrays were fabricated using 4 .mu.g film
with an active area of 100 .mu.m.times.80 .mu.m which gave a
uniform distribution of the CNT network. A higher relative film
concentration was selected for the 76-element array devices with
respect to the device size, 3 mm.times.3 mm, to maintain a uniform,
continuous, and conductive CNT network. The nanotube elements were
highly purified semiconducting CNT (Iso-semiconducting nanotubes,
Nanolntegris LLC). The CNT devices had an average resistance of 0.2
M.OMEGA. after annealing, and average mobility was calculated for
these thin film devices as .about.4.95 cm.sup.2/V-s, with a bandgap
of 0.26-0.5 eV. The on-off ratio was determined for these devices
using both back-gating configuration, I.sub.on/I.sub.off=11.2 (FIG.
9A and FIG. 9B), and also with electrolyte liquid-gating
configuration I.sub.on/I.sub.off=134 (FIG. 9A and FIG. 9B). The
on-off ratio decreases with increasing CNT mass and the mobility
increases. The results show that there is an inherent trade-off
between the on-off ratio and mobilities with increasing CNT mass,
which suggest high quality of nanotubes and thin film transistor
characteristics, in line with previous reports.
[0097] Chemical Functionalization
[0098] Carbon nanotubes that are highly compatible with
functionalization chemistry are used to manufacture these devices.
The nanotube device elements are chemically functionalized using
sidewall PASE functionalization. The side wall functionalization
enables access to the .pi. orbitals of the carbon nanotubes. The
strong .pi.-.pi. interactions between the pyrene fragment of the
PASE molecule and the nanotube surface creates a method for stable
functionalization of carbon nanotubes. The pyrene rings of the PASE
adsorb on to the sidewalls of the single wall carbon nanotube
(SWNT) through .pi. stacking and produce a stable nanotube-PASE
composite. The succinimidyl ester on the other end of the PASE
provides the attachment site for the antibodies. Pyrene interacts
strongly with the surface of carbon nanotubes of different
chiralities, but the interaction with zigzag nanotubes
(semiconducting) is stronger than with armchair (metallic) ones of
the same diameter. The same functionalization method for antibody
functionalization and testing in blood is used. Overall, the PASE
enables sidewall functionalization, the ester provides attachment
to the antibodies and enables stability over many weeks.
Functionalized devices can be kept at 4.degree. C. for 1-2 weeks
and still maintain the integrity of the functionalization
process.
[0099] Design of Experiments
[0100] There are two main variables within the design of experiment
in the array, sample type (plain blood and blood spiked with MCF7
(mammary gland adenocarcinoma), MCF10A (normal human mammary
cells), or SKBR3 (mammary gland adenocarcinoma) cells and device
type (IgG, anti-HER-2, or anti-EpCAM antibody functionalized
device). In addition, two cell concentrations were defined for each
cell spiked sample type, 100 or 1000 cells per 5 .mu.l.
TABLE-US-00001 TABLE 1 Design of Experiments Sample Type SKBR-3
MCF-7 MCF-10A Spiked Spiked Spiked Plain Blood Blood Blood Blood
CNT IgG 10 20 20 20 Device (negative (negative ) (negative
(negative Functionali- control) control control) control) zation
HER-2 10 20 20 (negative (positive (negative control) control)
control) EpCAM 10 20 20 (negative (positive (negative control)
control) control)
[0101] Table 1 presents the design of experiments and the number of
replicates. Over all there were 10 replicates for each combination,
10 replicates for 100 cells and 10 replicates for 1000 cells for
each type of cancer cell spiking. Three cell lines of SKBR-3,
positive control (over expressing HER-2), MCF-7, positive control
(overexpressing EpCAM), and MCF-10A, negative control normal
epithelial cell line (not overexpressing HER-2 or EpCAM), were
cultured and prepared for these experiments. In addition to these
three spiked cell types, plain non-spiked blood was also tested as
the fourth sample type, a negative control, with each device
type.
[0102] CNT devices were divided into three groups; the first batch
was functionalized with anti-HER-2 antibody, the second batch was
functionalized with anti-EpCAM antibody, and the third batch was
functionalized with non-specific IgG antibody via PASE linker
molecule. Tween-20 nonionic detergent was adsorbed after
functionalization to minimize non-specific adsorption to the
non-functionalized CNT surface. In these experiments, there are 17
unique combinations within sample types, device functionalization,
and cell concentration, with 170 technical array replicates, each
holding 5 .mu.l drops, resulting in 0.85 ml of blood processed in
the array. Four combinations were designed as positive cases with
specific interactions expected (SKBR-3 spiked blood vs. HER-2
functionalized CNT device and MCF-7 spiked blood vs. EpCAM
functionalized CNT device, both at 100 and 1000 cell
concentrations), with 40 overall replicates, shown in Table 1.
Thirteen other combinations were designed as negative controls (not
spiked) or positive controls with non-specific interactions
expected (SKBR-3 spiked blood vs IgG).
[0103] Electrical Detection and Zone Classification Scheme
[0104] FIG. 4A and FIG. 4B present the electrical sensing from
specific and non-specific interactions in blood. Three types of
signals were identified. SKBR3 spiked blood that was adsorbed on
anti-Her2 antibody device produced a characteristic device
signature with increase in signal conductance (FIG. 4A). Similarly,
MCF7 cells spiked in blood and adsorbed on anti-EpCAM nanotube
surface produced a rise in signal conductance (shown in FIG. 5A and
FIG. 5B). Similarly, MCF10A cells spiked in blood produced a
characteristic device signature that was either no change or led to
a decrease in electrical signal (FIG. 4B). Finally, plain blood
adsorption on the anti-Her2 functionalized device produced a
decrease in the device electrical signal or stayed at the buffer
level suggesting no change (FIG. 4C). The specific and non-specific
signals were visually classified into these three groups: 1) "Zone
1" or an increase in the device electrical signal; 2) "Zone 2" or
no change and 3) "Zone 3", decrease in device electrical signal.
FIG. 4D shows the zone classification scheme of the electrical
signals.
[0105] FIG. 5A and FIG. 5B present electrical signatures of 1000
cells/5 .mu.L cell and 100 cells/5 .mu.L cell concentration
variation (all 170 elements classified by device type and sample
type), collected during spiked blood experiments and classified
according to the proposed scheme presented in FIG. 4C. The entire
data with all 170 data sets, 17 combinations, 4 positive controls
and 13 negative controls, were defined in these experiments as they
are presented in FIG. 5A and FIG. 5B and can be represented in one
map that can enable fast analysis by a technician. For example, the
signal rise is indicated by blocks that are in the increasing
direction. For no change the blocks are at the X-axis level and for
decrease, the blocks go in the negative direction. The data
visualized in this manner presents an easy way to analyze the
sample condition and is suitable for the clinic.
[0106] The entire data can be divided into different experiments
with both specific and non-specific interactions. FIG. 5A is a data
series based on 1000 cells spiked in blood; FIG. 5B is a data
series based on 100 cells spiked in blood. In both FIG. 5A and FIG.
5B, each panel correlates with one combination of sample type and
device type with respect to design of experiment, 10 replicates
each. Each row correlates to one type of device functionalization
such as anti-HER-2, anti-EpCAM, and IgG. The top row represents the
type of cells spiked namely SkBr3, MCF7, MCF10A. From left to
right, the panels show: SKBR-3, MCF-7, MCF-1-A, and plain blood
samples. The control buffer signal is shown as the x-axis. The
symbol (+) represents signal increase, (0) represents no change and
(-) represents signal decrease.
[0107] The actual data is presented in FIG. 10A-FIG. 10J and FIG.
11A-FIG. 11J for the 170 datasets.
[0108] FIG. 10A-FIG. 10J present the device array data for 1000
cells/5 .mu.L variation. Each figure correlates with one
combination of sample type and device type with respect to design
of experiment presented in Table 1, 10 replicates each. FIG. 10A,
FIG. 10B, and FIG. 10C correlate to HER-2 device functionalization,
FIG. 10D, FIG. 10E, and FIG. 10F correlate to EpCAM device
functionalization, and FIG. 10G, FIG. 10H, FIG. 10I, and FIG. 10J
correlate to IgG device functionalization. Plain blood samples are
shown in FIG. 10C, FIG. 10F, and FIG. 10J. MCF-10A are shown in
FIG. 10B. FIG. 10E and FIG. 10 I. MCF-7 are shown in FIG. 10D and
FIG. 10H. SKBR-3 are shown in FIGS. 10A and 10G. The control buffer
signal is shown in black circular marks in each panel as point of
reference.
[0109] FIG. 11A-FIG. 11J present the device array data for 100
cells/5 .mu.L variation. Each figure correlates with one
combination of sample and device type with respect to design of
experiment presented in Table 1, 10 replicates each. FIG. 11A, FIG.
11B, and FIG. 11C correlate to HER-2 device functionalization, FIG.
11D, FIG. 11E, and FIG. 11F correlate to EpCAM device
functionalization, and FIG. 11G, FIG. 11H, FIG. 11I, and FIG. 11J
correlate to IgG device functionalization. Plain blood samples are
shown in FIG. 11C, FIG. 11F, and FIG. 11J. MCF-10A are shown in
FIG. 11B, FIG. 11E and FIG. 11 I. MCF-7 are shown in FIG. 11D and
FIG. 11H. SKBR-3 are shown in FIGS. 11A and 11G. The control buffer
signal is shown in black circular marks in each panel for point of
reference.
[0110] Again, three types of signals were observed with respect to
the buffer control: signal that increased, decreased, or did not
significantly change with respect to the buffer control. The
majority of signals in all 4 positive controls were located in
"Zone 1" showing an increase in device conductance, with respect to
the buffer control. On the other hand the majority of the signals,
11 out of the 13, in negative controls landed in "Zone 3", showing
a decrease in their signal, device conductance, after addition of
the sample droplet with respect to the buffer control.
TABLE-US-00002 TABLE 2 Electrical Signal Classification Results
Experiment Cell Device Sample # Concentration Type Type Zone 1 Zone
2 Zone 3 Total 1 1000 HER-2 SKBR-3 6 1 3 10 2 1000 HER-2 MCF-10A 3
2 5 10 3 -- HER-2 Plain 1 3 6 10 4 1000 EpCAM MCF-7 5 2 3 10 5 1000
EpCAM MCF-10A 3 1 6 10 6 1000 EpCAM Plain 3 2 5 10 7 1000 IgG
SKBR-3 0 0 10 10 8 1000 IgG MCF-7 5 2 3 10 9 1000 IgG MCF-10A 3 1 6
10 10 -- IgG Plain 2 0 8 10 11 100 HER-2 SKBR-3 6 0 4 10 12 100
HER-2 MCF-10A 3 0 7 10 13 100 EpCAM MCF-7 6 1 3 10 14 100 EpCAM
MCF-10A 3 2 5 10 15 100 IgG SKBR-3 3 5 2 10 16 100 IgG MCF-7 1 2 7
10 17 100 IgG MCF-10A 3 0 7 10
[0111] Table 2 summarizes the zone classification results for all
the data presented here. Overall, specific interaction indicated an
increase in the device conductance whereas non-specific interaction
resulted in a decrease or no change in device conductance. These
results show spiked cancer cells can be discriminated in blood
based on their effect on CNT electrical properties. In all the
experiments the detection specificity for anti-Her2 devices were
better than anti-EpCAM devices in these blood experiments.
[0112] Protein experiments were also conducted to ascertain the
hypothesis using a streptavidin-biotin model system for signal
rise. FIG. 12A and FIG. 12B present the normalized live response of
six CNT devices, three functionalized with streptavidin (FIG. 12A)
and three non-functionalized bare CNT devices (FIG. 12B). 5 .mu.L
of biotin solution, 1 ng/mL in 1.times.PBS, was added to each
device at 60 seconds and the electrical current of the device was
recorded for the remaining 240 seconds. The functionalized devices
current increased after biotin introduction suggesting negatively
charged biotin molecules interacting with streptavidin is
equivalent to applying a negative gate voltage, thus increasing the
nanotube-complex device conductance while, the bare devices show no
significant change. Schematics in the right hand side of the
figures illustrate the device functionalization state and its
interaction with biotin molecule. This indicates that the primary
effect of the nanotube-streptavidin-biotin binding is a
charge-transfer reaction.
[0113] In general membrane potential of cancer cells are different
from normal cells and recently they have been suggested as a
valuable clinical biomarker for tumor detection.
Electrophysiological analyses in many cancer cell types have
revealed a depolarized Vm that favors cell proliferation and new
data suggest level of Vm has functional roles in cancer cell
migration. Vm changes because of alterations in the conductance of
one or more types of ions. The Goldman-Hodgkin-Katz equation shows
the Vm dependency on both the intracellular and extracellular
concentrations of major ions (Na+/K+). Thus interaction of specific
antibodies to the extracellular receptors affects Vm. MCF7, MCF10A
and SKBR3 cells have all negative resting potential. For example,
the MCF7 potential was reported to vary from -58.6 mV to -2.7 mV
with the cell cycle. Extracellular EpCAM receptors on MCF7 cells
interacting with anti-EpCAM antibodies on nanotube surface can thus
lead to an increase in conductance of the nanotube-complex similar
to negatively charged proteins. These specific interactions are
characteristically different compared to non-specific
interactions.
[0114] Table 2 summarizes the zone classification quantitatively.
All the specific interactions had maximum of 6/10 signals in "Zone
1", whereas non-specific interactions had 9/10 signals in
combination of "Zone 2" and "Zone 3". This suggest that this
classification scheme may be highly useful in clinic, where one can
define a threshold of at least 5/10 devices in "Zone 1" as a
"diagnostic gray zone" which may indicate the presence of cancer
cells in blood on the device. While Table 2 presents the
quantifiable data, the blocks can also be added in each zone in
FIG. 5A and FIG. 5B to come to the same conclusion. One can then
further analyze the devices for cell capture using optical and
confocal microscopy if the threshold exceeds 5/10 devices. It
should be noted that some devices for specific interactions do not
increase in signal.
[0115] Dynamic Time Warping Classification
[0116] Dynamic Time Warping (DTW) is a dynamic programming
algorithm that seeks to find an optimal global or local alignment
of two series in the time domain to minimize the total distance
between the series with respect to a traditional distance metric
such as Euclidean distance metric. The DTW-distance can then be
used as dissimilarity metric for developing kernel-based
classifiers. A k-nearest neighbours kernel-based statistical
classifier was developed using pairwise DTW-distances between
samples. A kernel-based learning method was chosen as a parametric
model for CTC evaluation. In order to test whether the HER-2
functionalized devices could discriminate between SKBR3 spiked
blood samples and controls (plain blood and MCF10A spiked blood), a
classifier was constructed and the area under the receiver operator
curve (AUC) was estimated by 10-fold cross-validation. Algorithm
parameters and series normalization method were also determined by
cross-validation. The series normalization methods evaluated were:
mean-variance normalization of the entire series, slope correction
followed by normalization, and slope correction followed by scaling
by the value at a fixed time-point. Mean-variance normalization was
defined as
y i = y i - y _ i s y i ##EQU00001##
where y.sub.i represents the mean of the ith replicate series and
s.sub.y.sub.i represents the standard deviation. Slope correction
was conducted by fitting a linear model to the first 75 series
sampling points to estimate the machine drift of the devices. In
addition to evaluating slope correction followed by mean-variance
normalization, slope correction followed by scaling the series by
dividing by series value at a standardized time-point (50) was
evaluated. Prior to normalization or scaling, the series were
truncated to have length 150, with the length symmetric about the
time-point of droplet deposition. Both high (1,000/5 .mu.L) and low
(100/5 .mu.L) concentrations of spiked cells were evaluated,
separately. A total of 30 replicates (10 replicates with 1,000
SKBR3 cells/5 .mu.L spiked, 10 replicates with no cells added, and
10 replicates with 1,000 MCF-10A cells/5 .mu.L spiked) were used in
the construction of the classifier. To ensure balance between the
SKBR-3 positive condition and negative condition, a random sample
of 5 replicates with no spiked cells and 5 replicates with 1,000
MCF-10A cells/5 .mu.L spiked was combined as SKBR3 negative.
Whether the series observed with anti-HER2 antibody functionalized
devices and SKBR3 spiked blood could be discriminated from IgG
functionalized devices was then evaluated to determine if the
anti-HER-2 antibody and SKBR3 spiked blood resulted in a specific
as opposed to non-specific antibody-antigen interaction.
[0117] Cross validation (CV)-estimated AUC for the DTW distance
based k-nn classifiers for the high concentration of SKBR3 cells
with anti-HER-2 functionalized devices are shown in FIG. 13A. The
highest AUC for this condition was observed for the 1-nearest
neighbor classifier with slope corrected normalized series.
TABLE-US-00003 TABLE 3 Dynamic Time Warping: CV-Estimated Confusion
Matrix for 1000 SKBR-3 Cells in Blood Predicted Class Negatives
(Not Spiked/ Positives True Class MCF-10A) (SkBr3 Spiked) Negatives
(Not Spiked/ 9 1 MCF-10A) Positive (SkBr3 Spiked) 1 9
[0118] Class prediction using CV-estimation confusion matrix for
high concentration SKBR-3 cells is presented in Table 3. AUC for
discriminating a low concentration condition is shown in FIG. 13B.
AUC for the discrimination tests between antibodies (anti-HER2
versus IgG) with SKBR3 cells spiked in blood is shown in FIG.
13C.
[0119] Devices functionalized with anti-HER2 antibody were able to
discriminate between blood spiked with a high concentration of
SKBR-3 cells (1,000/5 .mu.L) and control blood (spiked with MCF-10A
cells, or not spiked, plain blood). 10-fold cross-validation
estimated AUC for the 1-nearest neighbor DTW-distance based
classifier was 0.90 after slope correction and normalization. Table
3 presents the confusion matrix. One false negative and one false
positive out of 10 positives and 10 negatives were observed in the
cross-validation procedure, showing a cross-validation estimated
sensitivity of .about.90% and specificity of .about.90%. Of the
replicates identified as true positives, 6/9 series finished
higher, "Zone 1", after the rebound that followed droplet injection
than the initial period, indicating a favorable .DELTA.G; of the
replicates identified as true negatives, 7/9 series finished lower,
"zone 3" after the rebound that followed droplet injection than the
initial period.
[0120] Devices functionalized with HER2 antibody were unable to
significantly discriminate (>50%) between blood spiked with a
low concentration of SKBR-3 cells (100 cells/5 .mu.L) and control
blood using a kernel-based classifier. The signals can be
analytically differentiated as presented in Table 2.
[0121] Good discrimination was achieved based on DTW from the
cancer cell concentration of 1000/5 .mu.L or 200,000/mL, which is
at the high concentration end of the real patient range of
50-300,000/mL.
[0122] Fixing sample phenotype (blood spiked with 1000 SKBR3 cells)
allowed for discrimination of antibody functionalization,
anti-HER-2 vs. IgG functionalized device. 10-fold cross-validation
estimated AUC for the 1-nearest neighbor DTW-distance based
classifier was 0.70 after slope correction and normalization (FIG.
13C). Based on the results from fixing the sample phenotype and
separately device phenotype, the significant difference seen in
electrical signal with positive controls is a result of HER2
receptor, on the cell membrane, interaction with the anti-HER2
antibody on the surface of the nanotube device.
[0123] FIG. 6A presents the heatmap of the between signal DTW
distances for the signals used in the classifier employed for
discriminating the high concentration of SKBR3 cells. In the
margins of this figure a dendrogram of the complete-linkage
agglomerative hierarchical clustering of the same is shown. Within
the clusters determined by the complete-linkage agglomerative
hierarchical clustering there is evidence of sample type confusion
however this confusion was reduced when considering 1-nearest
neighbor kernels for classification. The kernel-based DTW
classifier partitioned the SKBR3 spiked blood and controls (MCF7
spiked blood and non-spiked blood) suggesting specific interactions
are quite unique in their electrical signatures compared to
non-specific interactions and establishes a relationship between
electrical conductance data with biological and possibly proteomic
features (presence or absence of cancer cells in blood versus
presence or absence of Her2). FIG. 6B presents the Her2 clustering
data. The classifier is thus able to naturally partition the SKBR3
cells in blood data overexpressing Her2. This type of clustering is
useful for the clinic to stratify devices based on Her2/other
receptor data.
[0124] Optical Microscopy and Enumeration of CTCs
[0125] In a static blood sample/droplet the cells inside the blood
start to settle immediately due to the forces of gravity, resulting
in cells coming in contact and interacting with the base substrate
surface. One can tune the surface interactions for efficient
capture of CTCs. The initial observations on the optical microscope
of cancer cell spiked blood sample droplets on top of the devices
showed that the spiked cancer cells as part of their settling
process get buried under RBCs and on top of the CNT network
established on silicon substrate, getting sandwiched in
between.
[0126] FIG. 7A shows optical microscopic images of spiked blood
sample droplets on top of the device. Three devices of spiked
cancer cell blood samples are shown. Spiked cancer cells are
observed in the blood and marked with an arrow, one can clearly
observe the cancer cells buried under the RBCs and on top of the
CNT film. High concentration of RBCs is apparent on top of the
marked cancer cells in "Zone 1". When the objective is focuses on
"Zone 2" WBC's are seen in the spiked samples. FIG. 7B shows a
plain blood sample. There are no cancer cells or WBCs observed in
plain blood samples when the microscope is focuses in "Zone 1"
plane. On the other hand when image is focused in the plane of
"Zone 2" WBCs are apparent in the image (right panel), floating
above the RBCs. FIG. 7C presents a schematic illustration of the
mechanism of differential settling of blood sample on device
observations under the microscope, showing the approximate
arrangement of RBCs, WBCs, and spiked cancer cells and the
classification of "Zone 1" and "Zone 2" accordingly.
[0127] Three examples of spiked cancer cell blood samples are
presented in FIG. 7A at two different magnifications. Spiked cancer
cells are observed in the blood and marked with an arrow, with the
cancer cells buried under the RBCs and on top of the substrate.
Cells that were imaged in "Zone 2" in the spiked blood samples are
also shown. The size, shape and the number of cells seen in "Zone
2" is consistent with WBCs. A high concentration of RBCs is
apparent on top of the marked cancer cells in "Zone 1". In FIG. 7B,
optical microscopy of plain blood sample is presented at the same
two magnifications. There are no spiked cancer cells seen as the
microscope is focused in "Zone 1" plane. On the other hand, when
the objective is focused on the "Zone 2" plane, WBCs are apparent
and come into focus in the image (right panel), floating above the
RBCs. A model of differential settling is presented in FIG. 7C in
direct identification of spiked cancer cells and distinguishing
from leukocytes using nanotube devices in an optical microscopy
setting. The results of the settling of cancer cells to the bottom
is also consistent with the rise in electrical signal which could
come from the interaction of the cell surface receptors with the
antibodies on the nanotube surface. Further, enhancement of
enrichment can also be enabled through the presence of an electric
field to separate the larger cancer cells displacing a volume in
blood thereby changing the electric field.
[0128] As a result of the observations with regards to blood cell
settling as illustrated in FIG. 7C, "Zone 1" was imaged and the
captured spiked cancer cells further analyzed using optical
microscopy. A number of devices with 1000 spiked cells and 100
spiked cell samples were selected and their respective optical
microscope images were taken. 20 devices imaged at 20.times.
magnification, and 15 devices, imaged at 5.times. magnification,
were selected for further processing and analysis.
[0129] Spiked Breast Cancer Cell Enumeration in Blood
[0130] FIG. 14A-FIG. 141 present cell capture and enumeration. FIG.
14A and FIG. 14D present optical microscope image of devices with
1000 SKBR3 cells spiked blood sample droplet top at 20.times. and
5.times. magnification. The insert is the histogram of the number
of devices versus number of cells counted in the active area (CNT)
of the device. FIG. 14B and FIG. 14E show binary conversion of the
optical microscope images, highlighting the spiked cells in black
and separating them from the background. FIG. 14C and FIG. 14F
present the ImageJ analyses of binary images, based on diameter and
shape, showing cell count map of each original optical image. FIG.
14G and FIG. 141 demonstrate surface area comparison of the square
shaped device vs. circular blood droplet covering the device. CNT
device is only covered by .about.62% of the total droplet area
which gave rise to the electrical signal and optical microscopy was
only done on this active area for these demonstrations.
[0131] FIG. 14A and FIG. 14D present an example set of 20.times.
and 5.times. optical images taken for image analysis. ImageJ
software was used to process each image. The histograms in each
image present the number of cells counted in each device. Original
optical images were converted to binary images and processed using
NIH ImageJ software to distinguish spiked cells, shown in black
circles, from the RBCs in the background by using the color
threshold function as presented in FIG. 14B and FIG. 14E. Next,
ImageJ software's particle analysis function was used to count the
number of cells and cell diameter for each set of images knowing
the scale bar for each image (FIG. 14C and FIG. 14F).
TABLE-US-00004 TABLE 4 Cell Count Data per Device Based on 20
.times. Images for 1000 Cell Count Spiked in Blood Number of Device
Number of Average Cells per Number Cells Cells/mm.sup.2 diameter
(.mu.m) Device 1 33 78 10 706 2 31 74 11 663 3 24 57 10 514 4 24 57
15 514 5 15 36 14 321 6 33 78 14 706 7 32 76 11 685 8 22 52 13 471
9 16 38 13 342 10 32 76 13 685 11 36 86 12 771 12 24 57 14 514 13
39 93 15 835 14 30 71 16 642 15 30 71 14 642 16 25 59 15 535 17 35
83 15 749 18 49 117 14 1049 19 37 88 14 792 20 16 38 14 342 Average
29 69 13 624 Std. Dev. 8 20 2 178
[0132] All the data from these analyses, including number of cells
per image, cells per unit of area (mm2), diameter of cells, and
number of cells per device calculated based on cell counts and
total device area, 9 mm2, are presented in the histograms in FIG.
14A-FIG. 141 and in Table 4.
[0133] The imaging was done on only .about.62% active area of the
device or the nanotube surface. In these experiments, blood did
spread outside the active area of the device in some of the samples
and CTCs in those areas were not counted, nor did they contribute
to the electrical signal. These devices were initially designed for
sensing experiments. An SU8 layer was used to isolate all the
electrical layers except the active nanotube layer using an extra
photo-lithography step. Even with only .about.62% active area of
device imaged and counted, this resulted in anywhere between
342-1049 cells captured, using optical microscopy on the active
area of the nanotube device, resulting in a capture yield
(normalized to 62%) of 55% to 100%. Even without normalization, the
capture yield still represents 34.2% to 100%. The slightly greater
than 1000 cells counted is a result of small variations in cells
counted in spiking experiments using a hemocytometer. This error is
common and has been reported in other CTC reports. The two
different magnifications also give the same average number of cells
counted per device, suggesting these are spiked cancer cells.
[0134] FIG. 15A and FIG. 15B present cancer cell capture and
enumeration with optical microscopic images of 1000 SKBR3 spiked
blood sample droplets on top of the device. 15 devices of spiked
SKBR3 cells in blood is shown at 20.times. magnification. FIG. 15A
shows the device imaged on the Zone 1 plane. 15 out of the 20
images analyzed to generate the data presented in Table 4 are shown
here. FIG. 15B shows a plain blood sample. There are no cancer
cells or WBCs observed in plain blood samples when the microscope
is focused in "Zone 1" plane (first column). On the other hand when
image is focused in the plane of "Zone 2" WBCs are apparent in the
image (right panel). Scale bars on the first row correlate to all
images in each column. Scale bars indicated in top row is the same
for that column.
[0135] FIG. 15A and FIG. 15B show the reproducibility of this
technique from device to device. 15 such devices are presented that
were adsorbed with 1000 SKBR3 cells spiked in blood. "Zone 1" and
"Zone 2" images are clearly distinguishable. Zone 1 shows the
presence of spiked cancer cells in all the devices. Similarly Zone
1 imaging of plain blood, no spiked cells are seen. The WBCs are
also seen in the plain blood in "Zone 2" (FIG. 15B). The images
illustrate that blood does settle in all the devices in the same
way and that this effect is reproducible. Table 4 presents the
quantitative data from these processed images at 20.times. and
5.times. magnification respectively. The tables suggest hundreds of
cells are captured in each of the devices. In Table 4, at 20.times.
magnification anywhere from 342-1049 cells were captured in the
active area suggesting a normalized capture yield of 55-100% as
mentioned before. The cell count data in Table 4 indicate higher
resolution and more accurate counts is possible in a small window
area at the 20.times. magnification. The cellular diameters are
also more representative.
TABLE-US-00005 TABLE 5 Cell Count Data per Device Based on 5
.times. Images for 1000 Cell Count Spiked in Blood Number of Device
Number of Average Cells per Number Cells Cells/mm.sup.2 diameter
(.mu.m) Device 1 339 61 10 552 2 441 80 12 719 3 308 56 18 501 4
340 62 10 554 5 550 100 10 896 6 376 68 13 613 7 344 62 12 561 8
496 90 12 808 9 435 79 12 708 10 300 54 15 489 11 371 67 12 604 12
335 61 11 546 13 327 59 10 533 14 392 71 11 639 15 378 69 10 617
Average 382 69 12 623 Std. Dev. 68 12 2 111
[0136] Table 5 presents the 5.times. magnified cell count data. The
cell count data in this table indicate one can get a more uniform
distribution of the cell counts at the smaller magnification. Here,
anywhere from 489-808 number of cells were captured, for a capture
yield of 48.9% to 80.8%. Since the active area of 62% was only
imaged, this would represent a normalized yield of 78.8% to 100%.
The counts from both magnifications confirm the imaged cells are
spiked breast cancer cells. Exceeding 100% is as a result of
variation in spiking done manually using a hemocytometer as
mentioned before. Similarly, FIG. 16A and FIG. 16B present
representative images of the cell capture data for 100 SKBR3 spiked
cancer cells in blood. All the regions are seen, namely CTC in
"Zone 1" and WBC in "Zone 2". The spiked cancer cells were also 10
times sparser than the 1000 SKBR3 spiked blood images.
[0137] FIG. 16A and FIG. 16B present cancer cell capture with the
optical images of part of the devices imaged that were adsorbed
with 100 SKBR3 cells spiked blood sample at 20.times.
magnification. 6 such devices are shown in FIG. 16A imaged in Zone
1 plane; Histogram of the number of cells counted in each device is
present in bottom panel. 100% capture in 100 SKBR3 cells spiked in
blood was observed. FIG. 16B shows plain blood images.
TABLE-US-00006 TABLE 6 Cell Count Data per Device Based on 20
.times. Images for 100 Cell Count Spiked in Blood Number of Device
Number of Average Cells per Number Cells Cells/mm.sup.2 diameter
(.mu.m) Device 1 5 12 17 107 2 5 12 12 107 3 2 5 11 43 4 4 10 8 86
5 2 5 19 43 6 3 7 9 64 7 2 5 8 43 8 2 5 16 43 Average 3 7 13 67
Std. Dev. 1 3 4 27
[0138] Table 6 presents the cell count data and the diameters of
the captured cells. Anywhere between 43 cells to 107 cells were
imaged and counted on the active area of the device, suggesting a
normalized capture yield of 69% to 100%. While the capture yield of
100% is seen, these are on an individual device level. Summing all
the cell counts for the 20 devices presented in Table 4 suggest
capture of 12,478 cells out of 20,000 cell spiked in 0.1 ml of
blood or 62.39% capture yield, similar to the first CTC chip.
Finally, the results in Table 4, Table 5, and Table 6 show that
100% yield was achieved in both 1000 and 100 cell spiked blood at
different magnifications, suggesting that the imaged cells are
indeed spiked cells.
[0139] Confocal Microscopy
[0140] To confirm the captured cells were indeed spiked breast
cancer cells, four anti-HER2 functionalized devices with adsorbed
SKBR3 spiked blood that gave positive electrical signatures were
further analyzed using confocal microscopy. The samples were washed
three times to assess binding of cells and stained for cytokeratin,
CD45 and DAPI. FIG. 8A demonstrates captured SKBR3 cells on the
device stained for cytokeratin (CK-19 positive for epithelial cells
and negative for hematological cells) and CD45 (negative for
epithelial cells and positive for hematological cells). CTCs
captured on the device were identified by staining with
4,6-diamidino-2-phenylindole (DAPI) for DNA content, and using
rhodamine-conjugated anti-cytokeratin antibodies for epithelial
cells, and fluorescein-conjugated anti-CD45 antibodies for
hematologic cells. Cells captured by an anti-Her2 functionalized
device that showed positive staining for CK-19 were identified as
cancer cells, whereas CD45-positive cells were identified as
leukocytes, as presented in FIG. 8B. The morphologic
characteristics exhibited by the captured cells were consistent
with malignant cells, including large cellular size in the merged
image. Single cells could be identified in the merged image,
suggesting that the nanotube-CTC chip is a viable technique for
identification of CTCs from leukocytes using cytokeratin and CD45
antibodies.
[0141] Cells can also be removed from the active area of the device
that gave positive electrical signatures for confocal microscopy
and qPCR. Another way is to stratify all the devices that gave
positive electrical signatures and remove all the cells from those
devices and do confocal analysis in one step for enumeration. The
ability to do staining and identify cancer cells from leukocytes
shows the viability of this technology for CTC capture. Most of the
leukocytes were washed away, with only one or two remaining to be
imaged. These results also suggest that the leukocytes were not
bound to the nanotube substrate, in line with the model proposed on
differential settling of blood.
Example 2: Capture of Breast Cancer Cells Spiked in Buffy Coats
[0142] Rapid and label-free capture of breast cancer cells spiked
in buffy coats using nanotube-antibody micro-arrays is described.
Single wall carbon nanotube arrays were manufactured using
photo-lithography, metal deposition, and etching techniques.
Anti-EpCAM antibodies were functionalized to the surface of the
nanotube devices using 1-pyrene-butanoic acid succinimidyl ester
(PASE) functionalization method. Following functionalization, plain
buffy coat and MCF7 cell spiked buffy coats were adsorbed on to the
nanotube device and electrical signatures were recorded for
differences in interaction between samples. A statistical
classifier for the "liquid biopsy" was developed to create a
predictive model based on Dynamic Time Warping (DTW) to classify
device electrical signals that corresponded to plain (control) or
spiked buffy coats (case). In training test, the device electrical
signals originating from buffy versus spiked buffy samples were
classified with .about.100% sensitivity, .about.91% specificity and
.about.96% accuracy. In the blinded test, the signals were
classified with .about.91% sensitivity, .about.82% specificity and
.about.86% accuracy. A heatmap was generated to visually capture
the relationship between electrical signatures and the sample
condition. Confocal microscopic analysis of devices that were
classified as spiked buffy coats based on their electrical
signatures confirmed the presence of cancer cells, their attachment
to the device and overexpression of EpCAM receptors. The cell
numbers were counted to be .about.1-17 cells per 5 .mu.l per device
suggesting single cell sensitivity in spiked buffy coats that is
scalable to higher volumes using the micro-arrays.
[0143] The present disclosure provides devices and method for
capture of breast cancer cells spiked in buffy coats using carbon
nanotube micro-arrays functionalized with anti-EpCAM antibodies and
their stratification based on their electrical signatures using
classifier based on Dynamic Time Warping (DTW). Epithelial Cell
Adhesion Molecule (EpCAM), is a mesenchymal marker that is
overexpressed in all epithelial cancer cells. EpCAM is
overexpressed in carcinomas and also is upregulated in metastases
thereby making it a highly valuable diagnostic marker. A
multiplexed micro-array of nanotube sensors functionalized with
anti-EpCAM antibodies enables measurement of electrical signatures
of specific and non-specific interactions as characteristic spikes
in conductance versus time data. These signatures are then analyzed
using Dynamic Time Warping (DTW) technique to develop heatmap with
integrated dendrogram to enable classification of the electrical
signatures and relate it to their sample condition (MCF7 spiked
buffy or plain buffy), for the ease of reference, refers to cases
and controls. The experiments demonstrated "spikes" in electrical
device signatures in cell cultures and cancer cells spiked in buffy
coats with natural partitioning between pain buffy coats and spiked
buffy coats using nanotube-antibody arrays. To predict
classification between cases and controls, the training set data
indicated .about.100% sensitivity, .about.90% specificity, and
.about.96% accuracy in classifying devices that corresponded to
un-spiked and spiked buffy coats based on their electrical
signatures. A blinded test to classify case and control samples
revealed .about.91% sensitivity, .about.90% specificity, and
.about.86% accuracy. Staining of captured cells on the device based
on electrical signatures using confocal microscopy revealed the
overexpression of EpCAM with each device capable of capturing
anywhere from 1-17 cells per device suggesting single cell
sensitivity. In some embodiments, the combination of multiplexed
micro-arrays, sensitive nanotube elements and statistical data
mining can enable devices for isolation of cells based on their
mesenchymal biomarker profiles both in fine needle aspirates and
for large volume samples in isolation and analysis of circulating
tumor cells.
[0144] Results
[0145] FIG. 2B presents the schematic of the sensing technique.
Biomolecular reactions are driven thermodynamically by the
reduction in free energy of the system. For specific interactions,
the reduction in free energy should be higher than non-specific
interactions as presented in equation 1. Otherwise, non-specific
interactions would prevail resulting in loss of cellular
specificity and directionality in function. One can use a spectrum
of energy domains to transduce the change in the free energy of the
specific interactions into mechanics, electricity, thermal or
magnetism.
.DELTA.Gspecific>>>.DELTA.Gnon-specific (1)
[0146] Since the reduction in free energy is universal for specific
and non-specific pairs, this may also be true for detection of
specific versus non-specific interactions in cells. Extracellular
overexpressed receptors namely EpCAM interacts with their
corresponding anti-EPCAM antibodies on the nanotube surface. The
cooperative specific interaction of thousands of extracellular
receptors with specific antibodies on nanotube surface creates
spikes in the normalized electrical conductance versus time.
Non-specific samples such as plain buffy coats also create such
spikes in the electrical conductance versus time data with
differences in their slopes. Whether such spikes in the signals
could carry meaningful information about the sample
condition/interaction that could be indicator of disease status is
explored.
[0147] Fabrication of Nanotube Sensor Arrays:
[0148] FIG. 2A presents the wafer scale image of the 60 element
array of nanotube network sensors. The sensor arrays were developed
using a combination of vacuum filtration of carbon nanotube network
film onto oxide coated wafers followed by multiple
photo-lithography and reactive ion etching as presented in
schematic (FIG. 3A-FIG. 3H). Vacuum filtration is one of the most
preferred methods of making macroscopic and transparent networks of
randomly oriented/highly aligned carbon nanotubes thin films and
transistor devices. The film can be formed by using stock solutions
of known concentrations. Vacuum filtration of SWNT suspension
creates a concentration gradient due to the fluid velocity across
the membrane. With appropriate bulk solution concentration and
fluid velocity, one can form either isotropic or highly oriented
nanotube films. While vacuum filtration has been used, in general,
to make randomly oriented bucky papers, the isotropic-nematic
transition of semiconducting nanotube films at ultra-low
concentrations can be used. These semiconducting nanotube films are
finding applications as thin-film transistors with high mobility
and ON/OFF ratio's, nanotube liquid crystal elastomer based
light-driven actuators and as chemical and biological sensors as
presented here. Films were very low concentrations (1-4 .mu.g) and
most of the films were isotropic with the 2-4 .mu.g samples
partially transitioning into the nematic domains and also mostly
single layers laying on the substrate as shown in the SEM image of
FIG. 2A.
[0149] FIG. 18 presents the Raman spectroscopic micrograph of the
nanotube network suggesting a very large G band (1590 cm-1), small
D band (1340 cm-1) and large 2D band (2673 cm-1). This suggests
very low amorphous carbon content. Further, the IG/ID ratio was
measured for several networks measured IG/ID .about.30 suggesting a
low density of defects and suitable for high-quality sensing
applications. The Iso-semiconducting nanotubes had iodine as
specified by the manufacturer (5% by mass using neutron activation
analysis) in the form of iodixanol. This was observed in the shift
in radial RBM mode .omega.r=174 cm-1 suggesting iodine doped
nanotubes due to poly iodide ions or I3- and I5-in the RBM mode at
the same excitation wavelength. The iodine also decreased the
tangential mode to 1590 cm-1 in these samples as compared to
pristine nanotube samples (1593 cm-1) in line with past
reports.
[0150] Following fabrication, the devices were investigated for
their electrical resistance change with annealing. FIG. 19A
presents the SEM image of the devices made from for different
nanotube film concentrations. Films of three different
concentrations were made, and devices were fabricated and tested
for their electrical properties before and after annealing. It is
observed that as the concentration increased so did the network
density. At the film concentration of .about.1 .mu.g, the nanotube
contacts were not established at some points, and this is reflected
in their high electrical resistance. For samples with higher
concentrations, 4 .mu.g, uniform network density was observed. 4
.mu.g films were used with more directional nanotubes with
optimized electrical properties. The film electrical resistance
before and after annealing was measured as presented in FIG. 19B.
Annealing at 250 C decreased the resistance of the devices. It was
observed that the decrease in electrical resistance of the device
with annealing was directly related to the concentration of the
nanotube films. Devices made with 1 .mu.g films registered a larger
decrease in electrical resistance of .about.95%, compared to
devices with larger film concentrations (.about.57% and .about.47%
decrease in resistance for 2 .mu.g and 4 .mu.g respectively). The
electrical resistivity (.rho.) was measured to be in the range of
50 k.OMEGA.-m for the low concentration 1 .mu.g film to 180
.OMEGA.-m for the devices made from 4 .mu.g film (R.about.106-108
.OMEGA., 1=100 .mu.m and W=80 .mu.m). The large decrease in
electrical resistance suggests healing and refining the nanotube
network structure to enable stable electrical pathways associated
with the device. FIG. 19C presents the distribution of nanotube
device resistance versus number of counts for 58 devices suggesting
narrow distribution of network resistance in the 106-107.OMEGA.
range and quite repeatable and suitable for further sensing
applications. A linear sheet conductance with nanotube film mass
suggests samples above the percolation threshold.
TABLE-US-00007 TABLE 7 Film Resistance Summary CNT Film
Concentration 4 .mu.g 2 .mu.g 1 .mu.g Sheet Resistance Before
Annealing 4.16E+06 3.30+07 5.82E+08 (ohm/sq.) Sheet Resistance
After Annealing 2.25E+06 1.41E+07 2.40E+07 (ohm/sq.) CNT Film
Resistance After Annealing 2.81E+06 1.76E+07 3.00E+07 (ohms) % Drop
In Film Resistance After 45.92% 57.38% 95.88% Annealing
[0151] Table 7 presents the summary of the 4 point probe (1 mm
probe spacing) measurements for the sheet resistance before and
after annealing and final device resistance. In some embodiments,
the devices were optimized for 4 .mu.g nanotube films to provide
stable network connectivity and stability were used for all the
studies henceforth presented.
[0152] Understanding Semiconducting Nanotube Network for Chemical
Sensing:
[0153] Series of experiments were conducted in Hg2+ and NH4+ ions
to understand the change in electrical characteristics of the bare
nanotube devices. HgCl2 in DI water was prepared to create Hg2+
ions with concentrations ranging from 30 pM to 13 .mu.M. Similarly,
NH4OH in DI water was prepared with concentrations ranging from 300
nM to 1.5 mM. FIG. 20A presents the real time monitoring of both
NH4+ and Hg2+ ions with each spike is a measure of increasing
concentration from 300 nm to 1.3 mM for NH4+ ions and 30 pM to 13
.mu.M for Hg2+ ions respectively. It was found that both Hg2+ and
NH4+ ions increased the conductance of the nanotube network as the
concentration of ions increased. It is seen that for some
intermediate concentrations, the amplitude of the spike decrease
suggesting saturation of the sensor but they are also seen to
recover as the amplitudes increase with time at higher
concentrations. This is clearly observed in Hg2+ ion sensing. In
past Hg2+ has been shown to increase the conductance of nanotube
device, while NH4+ ions decreased the conductance of the device.
The increase in conductance with concentration for both Hg2+ and
NH4+ ion are as a result of doping. Typical electron-acceptor
dopants such as I2, and Br2 are expected to transfer electrons from
the carbon .pi. states in the tubes to the dopant molecules,
creating an increase in hole carriers in the SWNTs. This can
increase the conductivity due to charging when positively charged
molecules such as NH4+ and Hg2+ interact with the nanotube thereby
increasing the current of the p-type nanotube. The shift in the RBM
and G mode of the Raman spectrum presented (FIG. 18) also agrees
with the presence of iodine as dopant in these nanotubes. An
interesting result is that the doped nanotube devices did take the
level of detection of both Hg2+ and NH4+ ions to .about.30 pM and
.about.300 nM respectively, which is significantly higher in
sensitivity compared to past reports on pristine nanotubes where
the limits of detection has been shown to be .about.10 nM for Hg2+.
Voltage sweeps as presented in FIG. 20B shows a shift in threshold
voltage towards more negative Vg suggesting p-type behavior. The
real time Hg2+ and NH4+ chemical sensing therefore correlates well
with the voltage sweeps. FIG. 20C presents the change in Ids vs Vg
for different concentrations of Hg2+ and NH4+ ions. As the
concentration increases, so does the current suggesting an increase
in hole carrier density for both types of ions. A .DELTA.G/G0 vs
log [A] is presented in FIG. 20D suggesting a Langmuir-adsorption
isotherm with a linear response centered around [A]=1/K. The higher
(.DELTA.G/G0) for the doped nanotubes (y-axis) presented here in
the Langmuir adsorption isotherm compared to past networks of mixed
metallic and semiconducting nanotube also suggests the increased
sensitivity of the iodine doped semiconducting nanotube sensors and
lower limits of detection. The mechanism of sensing ions for both
NH4+ and Hg2+ is therefore due to an increase in carrier density of
iodine doped nanotubes. The same increase in conductance for the
same type of charge also suggests similar mechanism and predictable
sensor response of the doped nanotubes sensor devices.
[0154] Antibody Functionalization:
[0155] The prepared SWNT sensors were functionalized with
anti-EpCAM antibodies through a pyrene linker molecule. The pyrene
rings of the 1-pyrenebutanoic acid, succinimidyl ester (PASE)
adsorb on to the sidewalls of the SWNT through .pi. stacking and
produce a stable nanotube-PASE composite. The ester on the other
end of the PASE provides the attachment to the antibodies as
presented in FIG. 21A schematic. Antibody conjugated gold
nanoparticles (15 nm) were targeted to the PASE functionalized
nanotubes and imaged in a scanning electron microscope (SEM) to
assess their binding to nanotube. The antibody conjugated
nanoparticles were observed to be arranged on the nanotube side
wall as presented in FIG. 21B. A negative control experiment was
conducted by targeting antibody conjugated gold nanoparticles to
bare nanotube surface without PASE functionalization to the
nanotube surface. The results in FIG. 21C conclusively suggest that
antibody functionalized nanoparticles attached to the nanotube side
wall by binding to PASE while no functionalization accrued between
bare nanotubes and antibody conjugated nanoparticles without the
presence of the PASE linker molecule. Overall the PASE conjugation
provided a stable platform for all sensing in cell cultures and
buffy coats.
[0156] Two other functionalization methods were also investigated,
namely: (a) streptavidin-biotin conjugation chemistry and (b)
Au-amine-polymer-conjugation chemistry (FIG. 28A and FIG. 28B) and
compared with antibody-PASE functionalization (FIG. 21D). It was
found that the antibody-PASE functionalization may be preferred
with .about.76 particles per .mu.m2, followed by
streptavidin-biotin conjugation chemistry of .about.71 particles
per .mu.m2, followed by Au-amine-polymer functionalization of
.about.21 particles per .mu.m2. Non-specific controls yielded about
.about.1-6 particles per .mu.m2. Standard deviation of the particle
count by each method was plotted suggesting high degree of control
and reproducibility of the functionalization and attachment of
antibody to the PASE molecule. The devices had channel length of
100 .mu.m and width of 80 .mu.m. For a 4 .mu.g network distributed
uniformly and assuming 1 antibody site per particle, this would
translate into 608,000 sites for antibody binding to PASE for the
entire device. Similarly, for the streptavidin-biotin chemistry,
this would translate into 568,000 binding sites and for the
amine-polymer-Au nanoparticle functionalization protocol, this
would translate into 168,000 binding sites. For non-specific
controls this would translate into 8000-48,000 binding sites.
Typically, a cancer cell surface has 250,000 receptors
overexpressed all over the surface compared to normal cells which
has less than 10,000. With 608,000 sites, this is a ratio 2.4
antibody available per receptor for interaction.
[0157] Testing in Cell Cultures:
[0158] In these experiments several cell lines namely normal breast
cell line (MCF10A), breast cancer cell lines (SKBR3, MCF7) were
used. Anti-EpCAM antibodies were used as specific controls while
anti-IgG antibody was used as non-specific controls. Both SKBR3 and
MCF7 overexpress EpCAM while MCF10A normal cells do not. A
representative micrograph of the testing protocol is presented in
FIG. 29. First, a drop of 5-10 .mu.l PBS is added to the sensor
followed by a wait period of several hundred seconds, followed by a
second PBS drop, followed by a wait period of several hundred
seconds, followed by the addition of a spiked buffy or plain buffy
coat. This ensures the sample is behaving the same way over a time
period before addition of the actual sample.
[0159] Two differential signals were generated between positive and
negative controls. The positive controls such as targeting EpCAM on
SKBR3 (FIG. 22A) and MCF7 (FIG. 22B) produced characteristic spikes
in the electrical signatures with positive slopes that stabilized
after few seconds. It was also noted that the stabilized
conductance levels even after 60 seconds were higher than initial
value before the sample addition suggesting an irreversible change
in the electrical signature or forward reaction kinetics for the
formation of the antibody-receptor complex. Similarly, the negative
controls namely targeting EpCAM in MCF10A (FIG. 23A) and targeting
IgG in all the cells (FIG. 23B) and even PBS produced similar
spikes with negative slopes and stabilized after several seconds.
It was also noted that the final conductance after cell addition
was higher than the initial conductance value and stabilized at
that value. In determining the statistical significance, the
average slope of .about.0.5 seconds following the inflection point
was calculated for each signal. Both data sets were determined to
be random and of equal variance. A final p value of 0.0011<0.05
was calculated showing a statistically significant difference
between all positive and negative controls as presented in Table
8.
TABLE-US-00008 TABLE 8 Statistical Comparison Between Specific and
Control Signals in Cell Cultures Specific Non-specific G4: Igg- Gl:
EpCAM- G2: EpCAM- G3: EpCAM- (SKBR3, MCF7, SKBR3 MCF7 MCF10A
MCF10A) Calculated 0.0249 0.0498 -0.0471 -0.1761 Slopes After
Sample 0.1205 0.0296 -0.0279 -0.0432 Droplet 0.0414 0.0943 -0.0258
-0.1197 Addition Average 0.0623 0.0579 -0.0336 -0.1130 Std. 0.0417
0.0270 0.0095 0.0544 Deviation P Value G1 vs. G2: 0.9072 G1 vs. G3
: 0.0340 G1 vs. G4: 0.0225 G2 vs. G3 : 0.0107 G2 vs. G4 : 0.0164 G3
vs. G4 : 0.1120 Final P Specific vs. 0.0011 Value Non-specific
[0160] It should be noted that the positive controls consisted of
two different cell lines MCF7 and SKBR3 targeted for EpCAM
mentioned as G1 and G2 respectively in Table 8. Similarly the
negative control consisted of EpCAM-MCF10A, and IgG-(MCF7, MCF10A,
SKBR3) mentioned as G3 and G4. The p values in the Table 8 suggest
that there is a significant statistical difference between specific
and non-specific electrical signatures. It is also observed that
within the same group, the p values do not reflect a significant
difference suggesting natural partitioning of specific and
non-specific signals. This suggests that the nanotube sensor is
capable of differentiating between two different cell populations
which are similar except for their surface markers. The similar
results over wide ranging cell lines and antibodies suggest
specific interactions gave rise to characteristic spikes in
electrical signatures due to their cooperative binding of
antibodies to their receptors.
[0161] Testing in Buffy Coats and Development of a Statistical
Classifier for Liquid Biopsy:
[0162] ANOVA balanced design was used to reduce any systematic
variability. Factors in ANOVA design includes medium (Bare, Igg
EpCAM) and three types of samples (PBS: control 1, Buffy: control 2
and Spiked Buffy: case) with fixed number of replicates. Dynamic
Time Warping (DTW) was used for classification.
[0163] To demonstrate the ability of the devices to discriminate
between buffy and spiked buffy signals, a training-test approach
was used to building a k-nearest neighbor classifier using DTW. To
evaluate if devices could differentiate between MCF7 positive
samples and MCF7 negative samples, both were tested utilizing the
devices. The negative samples consisted of a buffy coat sample from
the biorepository without the presence of breast cancer cells. The
positive samples consisted of the same buffy coat sample that was
spiked with MCF7 breast cancer cells (10,000/.mu.l) as a proof of
concept. The drain current from the nanotube devices was recorded
continuously throughout each experiment. A representative figure of
the testing protocol is presented in FIG. 29. The resulting data
consisted of time series with characteristic "spikes" occurring
after the application of the sample as shown in FIG. 24A-24B for
the training set.
[0164] Construction of the Classifier:
[0165] A set of training signals from the devices was used for
selecting the tuning parameters for the classifier. An independent
set of mixed signals was then classified using the classifier.
Sensitivity, specificity, and the misclassification rate of the
classifier on the test set were then used to evaluate
performance.
[0166] To describe the construction of the classifier, the
following notation can be used. A device signal is represented as a
time series, y={y.sub.t, t.di-elect cons.T} where T is a finite set
that indexes time. To indicate that the signal is the ith replicate
from the jth group (Buffy vs. Spiked buffy) the notation y.sub.i,j
was used. Before a classifier could be constructed, the raw signals
were processed so that the signals had comparable a sampling rate,
length, and intensity. The signals y.sub.i,j were first averaged so
that each signal had the same sampling rate of one sample per
second. The signal was then truncated by removing series values
between the first value and five seconds after signal most extreme
peak-resulting in a new t.sub.0 for each signal. Experimental
variation in the timing of the replicate drops contacting the
device resulted in non-uniform length time indices. For length
standardization, the signals were then truncated at 253 seconds.
The final processing step was mean and variance standardization so
that each signal had mean 0 and variance 1.
[0167] To compare signals and develop a classifier, a measure of
signal similarity is needed. The technique employed to develop a
similarity measure in this analysis is known as Dynamic Time
Warping (DTW).
[0168] DTW is a method of aligning two-time series so that a
traditional distance metric (such as the Euclidean metric) can be
used as a measure of similarity. To measure the similarity between
two signals y.sub.i and y.sub.2, two matricies .DELTA..di-elect
cons..sup.m.times.n and .GAMMA..di-elect
cons..sup.(m+1).times.(n+1) were computed. The first matrix .DELTA.
has entries representing the pairwise distances between points in
the series, that is:
.delta.(t,t')=d(y.sub.1,t,y.sub.2,t)
[0169] where d is a distance metric. For this analysis the
Euclidean distance metric was used. Once this distance matrix was
defined, the elements of .GAMMA. were determined using a recurrence
relation that weights steps through a cumulative distance matrix.
Two formulations for this matrix were considered using the
following recursions:
.gamma..sub.1(i,j)=min{.delta.(i,j)+.gamma..sub.1(i-1,j),.delta.(i,j)+.g-
amma..sub.1(i-1,j-1),.delta.(i,j)+.gamma..sub.1(i,j-1)},
.gamma..sub.2(i,j)=min{.delta.(i,j)+.gamma..sub.2(i-1,j),2.times..delta.-
(i,j)+.gamma..sub.2(i-1,j-1),.delta.(i,j)+.gamma..sub.2(i,j-1)}.
[0170] Once the cumulative distance matrix F has been defined, a
warping curve was sought that aligns the time indices for each time
series:
.PHI.(k)=(.PHI..sub.s(k),.PHI..sub.s'(k)),k.di-elect cons.{1, . . .
,K},.PHI.(k).di-elect cons.{1, . . . ,m}.times.{1, . . . ,n}.
[0171] This warping curve was chosen such that once y.sub.1 and
y.sub.2 are aligned, the cumulative distance between the series is
minimized. To find such a curve, the lowest scoring path through
the cumulative distance matrix F was found subject to the following
constraints:
.PHI.(1)=(1,1)
.PHI.(T)=(m,n)
(.PHI.(k)-.PHI.(k-1)).di-elect
cons.{(1,1),(1,0),(0,1)}.A-inverted.k>1.
[0172] Constraint (1) and (2) ensure that the beginning and the end
of the signals y.sub.i and y.sub.2 are aligned. Constraint (3)
ensures uniform length of step sizes and that the warping curve is
monotonic increasing. An illustration of the alignment process for
a pair of qualitatively similar and qualitatively different signals
are presented (FIG. 30A-FIG. 30D and FIG. 31A-FIG. 31D)
respectively. Once a warping path has been defined, the Dynamic
Time Warping distance between two signals (not a formal distance
metric) is given by:
d.sub.DTW(y.sub.1,y.sub.2)=.SIGMA..sub.k=1.sup.T.DELTA.(.PHI.(k)).
[0173] For each step pattern, a dissimilarity matrix using DTW
distance as the dissimilarity measure was constructed for the
signals using the DTW package in R.
[0174] A training-test approach was used to develop a k nearest
neighbor (k-nn) classifier from the DTW distance dissimilarity
matrices. The training set consisted of 10 Buffy signals and 28
Spiked Buffy signals. 10-fold cross-validation was employed using
10,000 bootstrapped samples from the training set to determine
which step pattern (symmetric 1 vs symmetric 2) and a number of
nearest neighbors yielded the lowest misclassification rate (FIG.
32). The misclassification rate is defined as:
Err=.SIGMA..sub.i=1.sup.N1(Y.sub.i.noteq.)
[0175] where Y.sub.i denotes the true class of the signal y.sub.i
and denotes the k-nn classifier predicted class the for
y.sub.i.
[0176] Once the optimal step pattern and classifier design had been
selected, the classifier was evaluated on the 22 signals of the
test set. The study personnel who conducted the classification on
this test set were blinded to the true class (Buffy vs Spiked
Buffy) of the signals.
[0177] Training Set Classification
[0178] A training validation test set approach was used to
construct DTW distance based probabilistic modeling of surrounding
observations (such as .kappa.th-nearest neighbors). The training
data consisted of plain PBS (FIG. 23A as a reference, 10 buffy coat
samples and 17 spiked buffy coat samples as presented in FIG. 23B
and FIG. 23C. A .kappa.-fold cross-validation parameter selection
was conducted using 10-fold cross validation on 10,000 bootstrapped
samples from the training set of 27 signals for which the class
(buffy vs. spiked buffy) was known. The tuning parameters selected
were those that minimized the mean and variance of the
misclassification rate. The test set misclassification rate,
classifier sensitivity, and classifier specificity were then used
as criteria to measure the success of the devices in discriminating
between positive and negative samples.
TABLE-US-00009 TABLE 9 Training Classification of Biosensor Signals
Based on Dynamic Time Warping (DTW) Condition Training Condition
Condition Positive Negative Test Test True Positive = False
Positive = 1 PPV = 94% Outcome Outcome 17 (Type I Error) Positive
Test False True Negative = 9 NPV = 100% Outcome Negative = 0
Negative (Type II Error) Sensitivity = Specificity = Accuracy =
100% 90% 96.3%
[0179] The statistical table for training set is presented in Table
9. A positive predictor value (PPV)=94% and negative predictor
value (NPV)=100% with accuracy=96.3% was obtained for training set.
One data was misclassified and was false positive or type I error.
No false negatives were observed in training set.
[0180] Blinded Test Classification
[0181] FIG. 24A and FIG. 24B present the electrical signals for the
blind test. Based on the training set, the tuning parameters were
selected, 22 test signals (of class unknown to the personnel
constructing the classifier) were classified using the training
signals as reference signals.
TABLE-US-00010 TABLE 10 Blinded Set Classification of Biosensor
Signals Based on Dynamic Time Warping (DTW) Blind Test Condition
Positive Condition Negative Test True Positive = 10 False Positive
= 2 PPV = 83% Outcome (Type I Error) Positive Test False Negative =
1 True Negative = 9 NPV = 90% Outcome Negative (Type II Error)
Sensitivity = 91% Specificity = 90% Accuracy = 86%
[0182] Table 10 presents the corresponding classification table for
the blind test. A PPV=83%, NPV=90% and accuracy=86% was observed. A
misclassification rate of .about.14% was observed in these blind
testing. 3 samples over 22 samples were misclassified. Two samples
that were misclassified were false positive or type I error and one
sample that was misclassified was false negative or type II error.
This suggests the classifier is capable of differentiating
electrical signals between samples that were plain buffy coat or
buffy coat with cancer cells which is the first accomplishment for
any nanotube biosensor device.
[0183] Heatmap
[0184] FIG. 25 is the heat map of the between signal DTW distances
for the signals used in the k-fold cross-validation tuning
parameter selection and as reference signals for the classifier.
10-fold cross-validation on the bootstrap samples resulted in a
final DTW distance based on .kappa.-nn classifier utilizing a
symmetric 2 step pattern and 3 nearest neighbors. In the margins of
this figure a dendrogram of the complete-linkage agglomerative
hierarchical clustering of the same is shown. This demonstrates
that on the training data, DTW distance as a dissimilarity measure,
naturally partitions the sample data into two distinct clusters
(with one misclassification) according to sample class. The
statistical classifier naturally partitions the buffy versus spiked
buffy coats suggesting specific interactions are quite unique in
their electrical signatures compared to non-specific interactions
and establishes a relationship between electrical conductance data
with biological and possibly proteomic features (presence or
absence of cancer cells in buffy coats versus presence or absence
of mesenchymal marker EpCAM).
[0185] Cell Capture with Single Cell Sensitivity and Confocal
Microscopy:
[0186] The devices that gave positive signatures were then further
processed to assess the ability to capture spiked MCF7 cells from
buffy coats. FIG. 26A and FIG. 26B are the images from 22 processed
samples from the blind test with both buffy and spiked buffy coats.
Each image was taken of the device immediately after taking the
electrical signature measurement.
TABLE-US-00011 TABLE 11 Captured Cells on Each Device for Both Case
and Controls Using Optical Microscope Spiked Device No. (+) (-)
Number of Cells 1 25 2 22 3 3 4 14 5 6 6 9 7 6 8 19 9 4 10 19 11 X
0 12 X 0 13 X 1 14 X 0 15 X 0 16 X 1 17 X 0 18 X 0 19 X 2 20 X
2
[0187] For easier presentation, the spiked buffy coats are
presented in FIG. 26A, the plain buffy coats are presented in FIG.
26B and Table 11 consisting of number of cells captured on the
device in FIG. 26A and FIG. 26B. The cells could be counted after
imaging in a Nikon Eclipse optical microscope in the buffy coat
using imaging software. The images show the ability to capture 1 to
20 cells per device and their positive/negative electrical
signatures assessed from the classifier. Buffy coat was mainly
proteins and denatured hematologic cells, so the MCF7 cells were
identified which were quite distinct as these were spiked cells.
The single cells captured in the plain buffy coat are believed to
be one or two hematologic or white blood cells that were bound to
the device.
[0188] Six devices were removed and also imaged using confocal
microscopy as presented in FIG. 27 (only 4 shown). The samples were
washed three times to assess binding and stained for DAPI and
EpCAM. A cover slip was placed on the device and imaged using
confocal microscope. The images from devices as presented suggest
single cell sensitivity as well as positive for EpCAM
overexpression. The results suggest that the samples were bound to
the nanotube device through cooperative binding of the receptors to
the antibodies. Again, anywhere .about.1-17 cells were observed in
line with optical microscopy observations.
[0189] Methods:
[0190] CNT-Network Formation:
[0191] The first step in the process was assembling the nanotube
network. A 9.about.9% weight, IsoNanotubes-Semiconducting single
wall carbon nanotube mixture was purchased from Nanolntegris LLC.
The diameter of the nanotubes was between 1.2-1.7 nm diameter and
300 nm to 5 .mu.m in length. Nanotubes were suspended in surfactant
solution at one mg/100 mL when received. 100, 150, 200, and 400
.mu.L of the stock solution. 1, 1.5, 2, and 4 .mu.g of CNT, were
separately aspirated and then diluted in 85 mL of DI water and 15
mL of 1% w/v sodium dodecyl Sulfate (Sigma-Aldrich, Cat. No.
436143), for a final concentration of 1, 1.5, 2, and 4 .mu.g/100
mL.
[0192] Vacuum Filtration
[0193] The 100 mL solutions each were vacuum filtered over a
cellulose membrane, 0.05 .mu.m pore size (Millipore, No.
VMWP09025). Four CNT film networks were generated at four different
concentrations. The vacuum filtration method self-regulates the
deposition rate of nanotubes on the membrane to produce an evenly
distributed conductive network. The CNT film network was then
pressed onto a dry oxidized (300 nm thickness) 4'' silicon wafer
for 30 minutes. Next the wafer was transferred to an acetone vapor
bath that dissolved the overlaying filter membrane.
[0194] Clean Room Processing
[0195] Patterning of the nanotube film and electrode and insulating
layer fabrication were done by photolithography in the cleanroom.
The S1813 photoresist was used to mask the nanotube film areas
needed for the sensor elements. Exposed nanotubes were etched away
in a March Reactive Ion Etcher (RIE) for 90 s at 200 W power and
10% O.sub.2. The S1813 photoresist was also used to mask the
electrode pattern. Electrodes, 15 nm Ni and a 90 nm Au layers, were
deposited by sputtering in a Leskar PVD 75 system, 300 W DC power.
Lift-off process was conducted in an acetone bath to remove the
excess Ni/Au layers. Lastly, the sensors were covered with
SU8-2005, a 5 .mu.m thick photopolymer layer. A window over each of
the nanotube sensor elements was developed, but the electrodes
remain insulated beneath the SU8.
[0196] Raman Spectroscopy
[0197] Raman analysis was done at an excitation wavelength of
.about.532 nm using a XploRA Raman spectrometer (Horiba
Scientific). The laser beam is focused onto the surface of the CNT
film on top of a Si wafer substrate through a 50.times. objective
lens. This measurement was repeated six times at different
locations of the sample. The RBM (172 cm-1), Si (518 cm-1), D (1340
cm-1), G (1590 cm-1), and 2D (2673 cm-1) peaks were identified
throughout the sample.
[0198] Device Functionalization
[0199] Finished carbon nanotube sensors were functionalized with
anti-EpCAM by a pyrene linker molecule. The pyrene rings of
1-Pyrenebutanoic acid, succinimidyl ester (PASE AnaSpec #81238)
adsorb onto carbon nanotube sidewalls by .pi.-stacking. The ester
on the other end of the molecule provided an attachment point for
antibodies. PASE (AnaSpec, Cat. No. 81238) were dissolved in
methanol at one mM. Devices were incubated in the PASE solution for
2 hours at room temperature, and then rinsed with methanol and then
DI water. Devices were then incubated in Anti-EpCAM (EMD
Bioscience, Cat. No. OP187) or anti-IgG (EMD Millipore, Cat. No.
411550), 20 .mu.g/mL in 1.times. Phosphate-buffered saline (PBS),
for 2 hours at room temperature. After incubation, devices were
rinsed in DI water 3.times.. Tween20 was used to block
unfunctionalized nanotube sidewalls to minimize non-specific
interactions. Devices were incubated with 0.5% Tween20 for 2 hours
at room temperature. After incubation, devices were washed with
water, then incubated in 5 .mu.l droplets of PBS overnight in a
humid chamber at 4.degree. C. before testing.
[0200] Hg2+ and NH4+ Ion Experimentation
[0201] HgCl2 (Sigma-Aldrich, Cat. No. 215465) in DI water was
prepared to create Hg2+ ions with concentrations ranging from 30
.mu.M to 13 .mu.M. Similarly, NH4OH (Sigma-Aldrich, Cat. No.
338818) in DI water was prepared with concentrations ranging from
300 nM to 1.5 mM. Array of un-functionalized bare devices were
prepared with initial 5 .mu.L droplet of DI water suspended on top.
100 mV source drain bias and 0V gate voltage applied by reference
Ag/AgCl electrode. Device current was monitored as ion
concentrations were increased on the devices every 180 second. In
addition voltage-sweep readings were taken after addition of each
ion concentration.
[0202] Cell Culture and Preparation
[0203] The breast adenocarcinoma cell line, MCF7, MCF10 A and SKBR3
(ATCC, Cat. No. HTB-22; CRL-10317; HTB-30), was cultured under
conditions as recommended by the supplier. MCF10 A is
non-tumorigenic cells that are EpCAM negative and MCF7 and SKBR3
are EpCAM positive cell lines. Cells were grown for 3-4 days to
reach .about.80% confluence. The cells were then detached using
Accutase solution (Sigma, Cat. No. A6964), centrifuged and
resuspended in 1.times.PBS at 20,000 cells/.mu.L. This solution was
used to spike the healthy buffy coat sample (James Graham Brown
Cancer Center, Study No. C020-01) at 1:1 ratio for a final MCF7
concentration of 10,000 cells/.mu.L.
[0204] Confocal Microscopy:
[0205] After experimental data had been collected, the devices were
saved and taken for staining and confocal imaging. The devices were
first rinsed with PBS to remove excess cells and buffy coat
fragments and then incubated with 4% paraformaldehyde (Santa Cruz
Biotechnology Inc., Cat. No. sc-281692). After initial preparation
device were stained with anti-EpCAM (EMD Bioscience, Cat. No.
OP187) primary antibody, anti-mouse IgG-TR (Santa Cruz
Biotechnology Inc., Cat. No. sc-2781) Texas Red conjugated
secondary antibody, and DAPI (Molecular Probes, Cat. No. D1306)
according to the standard confocal staining protocol. A coverslip
was placed on top of each device and sealed before imaging.
Confocal laser scanning microscopy images were obtained on a Nikon
Eclipse T. with coverslip corrected objective focused at
600.times..
[0206] Gold Nanoparticle Functionalization
[0207] Similar to device functionalization process, devices were
prepared and functionalized. Instead of using anti-EpCAM, an
antibody conjugated gold NPs (Nanopartz, Cat. No. C11-15-TX-50)
were used to functionalize with the PASE linker molecule. The NPs
were diluted 1:250 in 1.times.PBS and incubated on top of the
device for 2 hours. Finally, devices were rinsed with water
3.times. and samples were taken to scanning electron microscopy
(SEM) for imaging.
[0208] Device Testing
[0209] The testing platform was set up on Signatone probe station.
Agilent 4156C Semiconductor Parameter Analyzer equipped with a
custom LabVIEW interface was used for monitoring the sensors and
data collection. 100 mV bias was applied and the source-drain
current, ISD, was recorded for the duration of the test. The
accuracy of the semiconductor parameter analyzer is 1 fA. The
entire probe station assembly is placed on an optical table that is
vibration isolated using air on all four legs. A metal box covers
the entire assembly to avoid electromagnetic interference. The
probes are connected to the parameter analyzer using a triaxial
cable that is EM shielded. Throughout the testing the devices were
maintained inside a humidified chamber to prevent evaporation of
the sample droplet. The testing protocol started with a hydrated
device, and a 5 .mu.L droplet of 1.times.PBS, which was placed
immediately after functionalization and left overnight. The bias
was applied, and the sensor was monitored for the initial 3
minutes, then 5 .mu.L droplets of the sample solution, 1.times.PBS
or buffy coat suspension, was pipetted directly into the standing 5
.mu.L droplets. Devices were monitored for 360 seconds after each
addition of a new sample solution. The total duration of one test
varied from 540 to 1980 seconds. To compare results among devices,
ISD data were normalized to obtain the G/G0 values for conductance.
The sensor element was also imaged on an optical microscope to
confirm the presence of cancer cells. The spiked buffy coat samples
consisted of .about.10,000 MCF7 cells/.mu.L for these
demonstrations. Such concentration was chosen to cover consistently
the active sensor element surface with 0-30 cells at the sensor
window for assessing cell capture per device.
[0210] Statistical Classifier:
[0211] Statistical classification was done using DTW package in R.
The sensitivity, specificity, and misclassification rate were then
computed, considering Spiked Buffy to be a positive test and Buffy
to be a negative test. Sensitivity is defined as TP/P, where TP
denotes the number of positive test outcomes, and P denotes the
number of true positives. Specificity is defined as TN/N, where TN
denotes the number of negative test outcomes, and N denotes the
number of true negatives.
[0212] All patents, patent applications, and published references
cited herein are hereby incorporated by reference in their
entirety. It should be emphasized that the above-described
embodiments of the present disclosure are merely possible examples
of implementations, merely set forth for a clear understanding of
the principles of the disclosure. Many variations and modifications
may be made to the above-described embodiment(s) without departing
substantially from the spirit and principles of the disclosure. It
can be appreciated that several of the above-disclosed and other
features and functions, or alternatives thereof, may be desirably
combined into many other different systems or applications. All
such modifications and variations are intended to be included
herein within the scope of this disclosure, as fall within the
scope of the appended claims.
* * * * *