U.S. patent application number 17/066961 was filed with the patent office on 2021-05-06 for fibroblast growth patterns for diagnosis of alzheimer's disease.
The applicant listed for this patent is WEST VIRGINIA UNIVERSITY. Invention is credited to Daniel L. ALKON, Florin Valentin CHIRILA, Tapan Kumar KHAN.
Application Number | 20210132045 17/066961 |
Document ID | / |
Family ID | 1000005329036 |
Filed Date | 2021-05-06 |
United States Patent
Application |
20210132045 |
Kind Code |
A1 |
CHIRILA; Florin Valentin ;
et al. |
May 6, 2021 |
FIBROBLAST GROWTH PATTERNS FOR DIAGNOSIS OF ALZHEIMER'S DISEASE
Abstract
Methods of diagnosing Alzheimer's disease are provided. At least
five methods of diagnostic measurements are presented: Method 1:
Integrated score; Method 2: Average aggregate area per number of
aggregates; Method 3: Cell migration analysis; Method 4: Fractal
analysis; Method 5: Lacunarity Analysis. In certain embodiments, a
sample of a subject's skin provides a network of fibroblasts that
is imaged and a fractal dimension of the image is calculated. The
fractal dimension can be compared to an aged-matched control
(non-Alzheimer's) database to determine if the subject has
Alzheimer's disease. The network of fibroblasts may be cultured in
a matrix, for example in a protein mixture.
Inventors: |
CHIRILA; Florin Valentin;
(Morgantown, WV) ; KHAN; Tapan Kumar; (Morgantown,
WV) ; ALKON; Daniel L.; (Bethesda, MD) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
WEST VIRGINIA UNIVERSITY |
Morgantown |
WV |
US |
|
|
Family ID: |
1000005329036 |
Appl. No.: |
17/066961 |
Filed: |
October 9, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14176233 |
Feb 10, 2014 |
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17066961 |
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12896862 |
Oct 2, 2010 |
8658134 |
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14176233 |
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61248368 |
Oct 2, 2009 |
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61344045 |
May 13, 2010 |
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61362518 |
Jul 8, 2010 |
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61365545 |
Jul 19, 2010 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 33/5091 20130101;
G01N 2800/2821 20130101; G01N 33/6896 20130101 |
International
Class: |
G01N 33/50 20060101
G01N033/50; G01N 33/68 20060101 G01N033/68 |
Claims
1.-94. (canceled)
95. A composition of matter comprising (i) 700 .mu.l of BD Matrigel
Matrix Growth Factor Reduced having on top of it (ii) a suspension
of human skin fibroblasts.
96. The composition of claim 95, wherein the Matrigel Matrix Growth
Factor Reduced is homogeneous.
97. The composition of claim 95, wherein the Matrigel Matrix Growth
Factor Reduced is at a temperature of 37.degree. C.
98. The composition of claim 95, wherein the suspension of human
skin fibroblasts is at a density having a lower threshold of 45
cells/mm.sup.3 and a higher threshold of 62 cells/mm.sup.3.
99. The composition of claim 95, wherein the suspension of human
skin fibroblasts is at a density of 50 cells/mm.sup.3.
100. The composition of claim 95, wherein the composition of matter
is present in a well within a 12-well culture plate.
101. A composition of matter comprising (i) 700 .mu.l of BD
Matrigel Matrix Growth Factor Reduced having on top of it (ii) a
suspension of human skin fibroblasts, wherein (i) the Matrigel
Matrix Growth Factor Reduced is homogeneous, (ii) the Matrigel
Matrix Growth Factor Reduced is at a temperature of 37.degree. C.,
(iii) the suspension of human skin fibroblasts is at a density of
50 cells/mm.sup.3, and (iv) the composition of matter is present in
a well within a 12-well culture plate.
102. A method for making a composition of matter comprising (i) 700
.mu.l of BD Matrigel Matrix Growth Factor Reduced having thereupon
(ii) a suspension of human skin fibroblasts, which method comprises
the step of placing a suspension of human skin fibroblasts on top
of 700 .mu.l of BD Matrigel Matrix Growth Factor Reduced.
103. The method of claim 102, wherein the Matrigel Matrix Growth
Factor Reduced is homogeneous.
104. The method of claim 102, wherein the Matrigel Matrix Growth
Factor Reduced is at a temperature of 37.degree. C.
105. The method of claim 102, wherein the suspension of human skin
fibroblasts, once atop the Matrigel Matrix Growth Factor Reduced,
is adjusted to a density having a lower threshold of 45
cells/mm.sup.3 and a higher threshold of 62 cells/mm.sup.3.
106. The method of claim 102, wherein the suspension of human skin
fibroblasts, once atop the Matrigel Matrix Growth Factor Reduced,
is adjusted to a density of 50 cells/mm.sup.3.
107. The method of claim 102, wherein the composition of matter is
present in a well within a 12-well culture plate.
108. A method for making a composition of matter comprising (i) 700
.mu.l of BD Matrigel Matrix Growth Factor Reduced having thereupon
(ii) a suspension of human skin fibroblasts, which method comprises
the step of placing a suspension of human skin fibroblasts on top
of 700 .mu.l of BD Matrigel Matrix Growth Factor Reduced, wherein
(i) the Matrigel Matrix Growth Factor Reduced is homogeneous, (ii)
the Matrigel Matrix Growth Factor Reduced is at a temperature of
37.degree. C., (iii) the suspension of human skin fibroblasts, once
atop the Matrigel Matrix Growth Factor Reduced, is adjusted to a
density of 50 cells/mm.sup.3, and (iv) the composition of matter is
present in a well within a 12-well culture plate.
Description
[0001] The instant application is a continuation of U.S. Ser. No.
14/176,233, filed on Feb. 10, 2014, which is a divisional of U.S.
Ser. No. 12/896,862, filed on Oct. 2, 2010, now U.S. Pat. No.
8,658,134, issued on Feb. 25, 2014, which claims the benefit of
priority to U.S. Provisional Application No. 61/248,368, filed Oct.
2, 2009, and U.S. Provisional Application No. 61/344,045, filed May
13, 2010, and U.S. Provisional Application No. 61/362,518, filed
Jul. 8, 2010, and U.S. Provisional Application No. 61/365,545,
filed Jul. 19, 2010, the disclosures of which are hereby
incorporated herein in their entireties.
FIELD OF THE INVENTION
[0002] The present invention relates to methods to diagnose
Alzheimer's disease using fibroblast growth patterns as a
biomarker.
BACKGROUND OF THE INVENTION
[0003] Alzheimer's disease (AD) is a neurodegenerative disorder
characterized by the progressive decline of memory and cognitive
functions. It is estimated that over five million Americans are
living with this progressive and fatal disease. Alzheimer's
destroys brain cells, causing memory loss and problems with
thinking and behavior that decrease quality of life. AD has no
known cure, but treatments for symptoms can improve the quality of
life of the millions of people, and their families, suffering from
AD. An early diagnosis of AD gives the patient time to make choices
that maximize quality of life, reduces anxiety about unknown
problems, gives more time to plan for the future, and provides a
better chance of benefiting from treatment.
[0004] There exists a need for highly sensitive and highly specific
tests to diagnose Alzheimer's Disease. The present inventors have
identified, for the first time, unique Alzheimer's Disease-specific
biomarkers useful for the diagnosis of Alzheimer's Disease in a
highly sensitive and highly specific manner compared to previously
known diagnostic tests. Specifically, the inventors have identified
fibroblast growth patterns as biomarkers for the diagnosis of
Alzheimer's Disease. Thus, the unique Alzheimer's Disease-specific
biomarkers disclosed herein serve as the basis for diagnostic
methods having a high degree of sensitivity and specificity for the
detection and diagnosis of Alzheimer's Disease. The unique
Alzheimer's Disease-specific biomarkers of the present invention
may also be useful as a model of brain networks and for screening
methods to identify compounds which may be used as therapeutic
agents in the treatment and prevention of Alzheimer's Disease.
SUMMARY OF THE INVENTION
[0005] The instant invention, in certain preferred embodiments, is
directed to methods of diagnosing Alzheimer's Disease using assays
directed to five separate methodologies, referred to herein as (1)
the integrated score methods; (2) the average aggregate area per
number of aggregates methods; (3) the cell migration analysis
methods; (4) the fractal analysis methods; and (5) the lacunarity
analysis methods.
[0006] In certain embodiments, the invention is directed to methods
of diagnosing Alzheimer's Disease in a human subject comprising the
steps of (a) obtaining one or more cells from a human subject; (b)
culturing said one or more cells for a time period; (c) determining
the average area of cell aggregates and dividing said average area
by the number of aggregates to obtain the area per number of
aggregates; (d) comparing the determination of step (c) with the
area per number of aggregates determined using non-Alzheimer's
Disease cells; and (e) diagnosing the presence or absence of
Alzheimer's Disease based on the comparison in step (d).
[0007] The method is positive for Alzheimer's Disease if the area
per number of aggregates determined in step (c) is greater than the
area per number of aggregates determined in step (d). In certain
preferred embodiments, the difference is statistically
significant.
[0008] In preferred embodiments, the diagnosis is confirmed using
one or more additional diagnostic methods. The method one or more
additional diagnostic methods are selected from the group
consisting of methods comprising determining an integrated score,
methods comprising calculating area per number of aggregates,
methods comprising cell migration analysis, methods comprising
fractal analysis and methods comprising lacunarity analysis.
[0009] In preferred embodiments, the methods disclosed herein use
cells that are fibroblasts although other cells such as blood cells
or neural cells may be used.
[0010] In certain embodiments, the known non-Alzheimer's Disease
cells are AC cells.
[0011] In certain embodiments, the cells are cultured in a protein
mixture. The protein mixture may comprise an extracellular matrix
preparation comprising laminin, collagen, heparin sulfate
proteoglycans, entactin/nidogen, and/or combinations thereof. The
protein mixture may further comprise growth factor. The
extracellular matrix protein may be extracted from a tumor. In
certain embodiments, the tumor is the EHS mouse sarcoma.
[0012] In certain embodiments, the invention is directed to methods
comprising (a) obtaining one or more cells from a human subject;
(b) culturing said one or more cells for a time period; (c)
obtaining an image of said cells at the conclusion of said time
period; (d) determining a fractal dimension associated with a
network of cells on said image; (e) comparing the determination of
step (d) with an independently determined fractal dimension
associated with known non-Alzheimer's disease cells.
[0013] In certain embodiments, if the fractal dimension calculated
in step (d) is statistically significantly lower than the fractal
dimension associated with known non-Alzheimer's Disease cells, the
comparison is indicative of Alzheimer's Disease.
[0014] In preferred embodiments, the AD is confirmed using one or
more additional diagnostic methods. The method one or more
additional diagnostic methods are selected from the group
consisting of methods comprising determining an integrated score,
methods comprising calculating area per number of aggregates,
methods comprising cell migration analysis, methods comprising
fractal analysis and methods comprising lacunarity analysis.
[0015] In certain embodiments, the fractal dimension is calculated
using a box counting procedure. In certain embodiments, the box
counting procedure comprises an edge detection procedure.
[0016] In certain embodiments, the subject is aged-matched with a
control subject providing known non-Alzheimer's disease cells. In
certain embodiments, the cell culture period is about 24 hours or
about 36 hours or about 48 hours.
[0017] In certain embodiments, the cells are cultured in a protein
mixture. The protein mixture may comprise an extracellular matrix
preparation comprising laminin, collagen, heparin sulfate
proteoglycans, entactin/nidogen, and/or combinations thereof. The
protein mixture may further comprise growth factor. The
extracellular matrix protein may be extracted from a tumor. In
certain embodiments, the tumor is the EHS mouse sarcoma.
[0018] In certain embodiments, the invention is directed to methods
comprising: (a) determining a fractal dimension of an image of a
network of fibroblasts from a human subject, (b) determining a
fractal dimension of an image of a network of fibroblasts from
kno\.vn non-Alzheimer's disease cells; (c) comparing the
determinations of steps (a) and (b).
[0019] In certain embodiments, if the fractal dimension determined
in step (a) is statistically significantly lower than the fractal
dimension determined in step (b), the diagnosis is indicative of
Alzheimer's Disease.
[0020] In certain embodiments, said subject is aged-matched with a
control subject providing said known non-Alzheimer's Disease
cells.
[0021] In certain embodiments, the invention is directed to methods
of diagnosing Alzheimer's disease in a human subject, comprising:
(a) calculating a fractal dimension of an image of a network of
fibroblasts from said subject; (b) comparing the calculation of
step (a) with an independently determined fractal dimension
associated with known non-Alzheimer's disease cells; wherein if the
fractal dimension calculated in step (a) is statistically
significantly lower than the fractal dimension associated with
known non-Alzheimer's disease cells, the diagnosis is positive for
Alzheimer's Disease in said subject.
[0022] In certain embodiments, said subject is aged-matched with a
control subject providing said known non-Alzheimer's disease
cells.
[0023] In certain embodiments, the invention is directed to methods
of diagnosing Alzheimer's disease in a human subject, the method
comprising: (a) using a surgical blade to obtain a sample of said
subject's peripheral skin fibroblasts; (b) using an incubator to
incubate said sample for a time period; (c) using an imager to take
an image of said sample at the conclusion of said time period; (d)
using a computer to calculate a fractal dimension associated with a
network of fibroblasts on said image; (e) comparing the calculation
of step (d) with an independently determined fractal dimension
associated with known non-Alzheimer's disease cells, wherein if the
fractal dimension calculated in step (d) is statistically
significantly lower than the fractal dimension associated with
known non-Alzheimer's disease cells, the diagnosis is positive for
Alzheimer's Disease in said subject.
[0024] In certain embodiments, the cells are cultured in a protein
mixture. The protein mixture may comprise an extracellular matrix
preparation comprising laminin, collagen, heparin sulfate
proteoglycans, entactin/nidogen, and/or combinations thereof. The
protein mixture may further comprise growth factor. The
extracellular matrix protein may be extracted from a tumor. In
certain embodiments, the rumor is the EHS mouse sarcoma.
[0025] In certain embodiments, the invention is directed to methods
of diagnosing Alzheimer's Disease in a human subject, the methods
comprising: (a) using a surgical blade to obtain a sample of said
subject's peripheral skin fibroblasts; (b) using an incubator to
incubate said sample for a time period; (c) using an imager to take
an image of said sample at the conclusion of said time period; (d)
using a computer to calculate a fractal dimension associated with a
network of fibroblasts on said image; (e) using a computer to input
the fractal dimension of step (d) into a database having fractal
dimension data generated from non-Alzheimer's disease cells
obtained from control subjects of various ages; (f) using a
computer to diagnose said subject by comparing the calculated
fractal dimension of step (d) with the data of said database.
[0026] In certain embodiments, the sample is incubated in a
gelatinous protein mixture.
[0027] In certain embodiments, the cells are cultured or incubated
in a gelatinous protein mixture. The protein mixture may comprise
an extracellular matrix preparation comprising laminin, collagen,
heparin sulfate proteoglycans, entactin/nidogen, and/or
combinations thereof. The protein mixture may further comprise
growth factor. The extracellular matrix protein may be extracted
from a tumor. In certain embodiments, the tumor is the EHS mouse
sarcoma.
[0028] In certain embodiments, the invention is directed to a
computer readable medium having a database of fractal dimension
data generated from non-Alzheimer's disease cells obtained from
control subjects of various ages, said medium containing
instructions to: (a) calculate a fractal dimension of an image; (b)
compare said fractal dimension with said database of fractal
dimension data; and (c) output a diagnosis based on the comparison
of step (b).
[0029] In certain embodiments, the invention is directed to methods
comprising: (a) culturing a skin cell from a human subject for a
time period; (b) measuring cell morphology characteristics
associated with a network of fibroblasts of said cell; (c)
performing a calculation related to said cell morphology
characteristics; and (d) comparing the calculation of step (c) with
an independently determined parameter associated with known
non-Alzheimer's disease cells.
[0030] In certain embodiments, the cell morphology characteristics
are selected from the group consisting of: number of fibroblast
clumps (or aggregates), size of fibroblast clumps (or aggregates),
growth of fibroblast clumps (or aggregates), and combinations
thereof.
[0031] In certain embodiments, the cell morphology characteristics
are the presence or absence of big clumps (or aggregates), the
presence or absence of cells attached to the clumps (or
aggregates), the presence or absence of big clumps (or aggregates)
growing, the number of clumps (or aggregates), the presence or
absence of remnant edges from a previously formed network of said
clumps (or aggregates), the number of cells migrating, the presence
or absence of cells being near percolation.
[0032] In certain embodiments, the calculation of step (c)
comprises assigning a discrete value for each of said cell
morphology characteristics and summing said values.
[0033] In certain embodiments, the summation is used to diagnose AD
or the absence of AD.
[0034] In certain embodiments, the cells are cultured in a protein
mixture. The protein mixture may comprise an extracellular matrix
preparation comprising laminin, collagen, heparin sulfate
proteoglycans, entactin/nidogen, and/or combinations thereof. The
protein mixture may further comprise growth factor. The
extracellular matrix protein may be extracted from a tumor. In
certain embodiments, the tumor is the EHS mouse sarcoma.
[0035] In certain embodiments, the invention is directed to methods
of diagnosing Alzheimer's Disease in a subject comprising the steps
of: (a) obtaining one or more cells from said subject and growing
said one or more cells in a tissue culture medium; (b) measuring
the fractal dimension of said one or more cells over a time period;
(c) plotting said fractal dimension as a function of time to obtain
a fractal dimension curve: (d) comparing said fractal dimension
curve to fractal dimension curves obtained from non-Alzheimer's
Disease cells and non-Alzheimer's Disease Dementia (non-ADD) cells;
and (e) diagnosing the presence or absence of Alzheimer's Disease
in said subject.
[0036] In certain embodiments, the diagnosis is positive for
Alzheimer's Disease in said subject if said fractal dimension curve
measured from a cell or cells obtained from said subject is
statistically significantly different from said fractal dimension
curves obtained from said non-Alzheimer's Disease cells and said
non-ADD cells.
[0037] In certain embodiments, said cell or cells obtained from
said subject is a fibroblast cell.
[0038] In preferred embodiments, the diagnosis is confirmed using
one or more additional diagnostic methods. The method one or more
additional diagnostic methods are selected from the group
consisting of methods comprising determining an integrated score,
methods comprising calculating area per number of aggregates,
methods comprising cell migration analysis, methods comprising
fractal analysis and methods comprising lacunarity analysis.
[0039] In certain embodiments, the invention is directed to methods
of diagnosing Alzheimer's Disease in a subject comprising the steps
of (a) obtaining one or more cells from said subject and growing
said one or more cells in a tissue culture medium; (b) determining
an integrated score based on one or more characteristics of said
cultured cells; (c) comparing said integrated score to an
integrated score determined for non-Alzheimer's Disease cells; (d)
diagnosing the presence or absence of Alzheimer's Disease in said
subject.
[0040] In certain embodiments, said characteristics used to
calculate said integrated score are selected from the group
consisting of aggregate size, attachment of cells to aggregates,
evidence of aggregate growth, number of aggregates, edges within
networks, evidence of cell migration and closeness to percolation
limit (or cell density).
[0041] In preferred embodiments, the diagnosis is confirmed using
one or more additional diagnostic methods. The method one or more
additional diagnostic methods are selected from the group
consisting of methods comprising determining an integrated score,
methods comprising calculating area per number of aggregates,
methods comprising cell migration analysis, methods comprising
fractal analysis and methods comprising lacunarity analysis.
[0042] In certain embodiments, the invention is directed to methods
of diagnosing Alzheimer's Disease in a subject comprising the steps
of (a) obtaining one or more cells from said subject and growing
said one or more cells in a tissue culture medium; (b) determining
the number of migrating cells; (c) comparing the number of
migrating cells to the number of migrating cells for
non-Alzheimer's Disease cells; (d) diagnosing the presence or
absence of Alzheimer's Disease in said subject.
[0043] In certain embodiments, the diagnosis is positive for AD if
the number of migrating cells obtained from said subject is
statistically significantly smaller than the number of migrating
non-Alzheimer's Disease cells.
[0044] In certain embodiments, said cells are fibroblasts.
[0045] In preferred embodiments, the diagnosis is confirmed using
one or more additional diagnostic methods. The method one or more
additional diagnostic methods are selected from the group
consisting of methods comprising determining an integrated score,
methods comprising calculating area per number of aggregates,
methods comprising cell migration analysis, methods comprising
fractal analysis and methods comprising lacunarity analysis.
[0046] In certain embodiments, the invention is directed to methods
of diagnosing Alzheimer's Disease in a subject comprising the steps
of (a) obtaining one or more cells from said subject and growing
said one or more cells in a tissue culture medium; (b) determining
the lacunarity of said cells; (c) comparing the lacunarity of said
cells to the lacunarity of non-Alzheimer's Disease cells; (d)
diagnosing the presence or absence of Alzheimer's Disease in said
subject.
[0047] In certain embodiments, the diagnosis is positive for AD if
the lacunarity of the cells taken from said subject is
statistically significantly higher than the lacunarity of the
non-Alzheimer's Disease cells.
[0048] In preferred embodiments, the diagnosis is confirmed using
one or more additional diagnostic methods. The method one or more
additional diagnostic methods are selected from the group
consisting of methods comprising determining an integrated score,
methods comprising calculating area per number of aggregates,
methods comprising cell migration analysis, methods comprising
fractal analysis and methods comprising lacunarity analysis.
[0049] In certain embodiments, said cells are fibroblasts.
[0050] In certain embodiments, the invention is directed to methods
of screening fora lead compound useful for the development of one
or more drug candidates for the treatment or prevention of
Alzheimer's disease comprising the steps of (a) growing one or more
AD cells in a cell culture medium; (b) contacting said AD cells
with a compound; (c) determining whether one or more
characteristics of said AD cells is altered to resemble the
characteristics of non-Alzheimer's Disease cells that have not been
contacted with said compound.
[0051] In certain embodiments said cells are fibroblasts.
[0052] In certain embodiments, said characteristic is fractal
dimension or an integrated score or an average aggregate area per
number of aggregates, or cell migration, or lacunarity.
[0053] In certain embodiments, the invention is directed to methods
of determining Alzheimer's Disease duration in a subject comprising
(a) obtaining one or more cells from said subject; (b) measuring
cell migration characteristics or average area per number of
aggregates for known AD cell lines; (c) preparing standard curves
using the data obtained in step (b); measuring migration
characteristics or average area per number of aggregates for the
cells obtained in step (a) and (d) determining AD disease duration
in said subject.
[0054] In certain embodiments, said cells are fibroblasts.
[0055] In certain embodiments, subjects identified as having AD for
10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 years or less are identified as
having increased responsiveness to treatment of AD.
[0056] In certain embodiments, the invention is directed to methods
of distinguishing between the presence of Alzheimer's Disease (AD)
and non-Alzheimer's Disease Dementia (non-ADD) in a subject
comprising: (a) obtaining one or more cells from a subject (b)
measuring the fractal dimension of said one or more cells over a
time period; (c) plotting said fractal dimension as a function of
time to obtain a fractal dimension curve; (d) comparing said
fractal dimension curve to fractal dimension curves obtained from
known non-Alzheimer's Disease cells, known non-Alzheimer's Disease
Dementia (non-ADD) cells and known AD cells; and (e) distinguishing
between AD and non-ADD in said subject.
[0057] In certain embodiments, said cells are fibroblasts.
[0058] In certain embodiments, the invention is directed to methods
of distinguishing between the presence of Alzheimer's Disease and
non-Alzheimer's Disease Dementia in a subject comprising: (a)
obtaining one or more cells from a subject (b) obtaining one or
more cells from said subject and growing said one or more cells in
a tissue culture medium; (c) determining the number of migrating
cells; (d) comparing the number of migrating cells to the number of
migrating cells for known non-Alzheimer's Disease cells, known AD
cells and known non-ADD cells; (e) distinguishing between AD and
non-ADD in said subject.
[0059] In certain embodiments, said cells are fibroblasts.
[0060] In one embodiment, the invention provides a method of
diagnosing Alzheimer's disease in a human subject, the method
comprising: (a) calculating a fractal dimension of an image of a
network of fibroblasts from said subject; (b) comparing the
calculations of step (a) with an independently determined fractal
dimension associated with known non-Alzheimer's disease cells;
wherein if the fractal dimension calculated in step (a) is
statistically significantly lower than the fractal dimension
associated with known non-Alzheimer's disease cells, the diagnosis
is positive, and the diagnosis is negative otherwise; and (c)
diagnosing.
BRIEF DESCRIPTION OF THE DRAWINGS
[0061] FIG. 1: Initial Preparation of Fibroblasts
[0062] FIG. 2: Integrated Score Protocol
[0063] FIG. 3: Fractal Analysis Protocol
[0064] FIG. 4: Integrated Score Method. Total scores representing
the sum of eight characteristics for skin cell fibroblasts (AC=age
matched controls; AD=Alzheimer's Disease: Non-ADD=Non-Alzheimer's
such as Parkinson's disease (PD) and Huntington's disease (HD)
dementia at 48 hours after plating.
[0065] FIGS. 5A and 5B: Examples of aggregates for Alzheimer's
disease fibroblasts (FIG. 5A) and normal controls (FIG. 5B). The
area was measured in .mu.m.sup.2 by fitting an ellipse across the
aggregates and the aggregates were counted manually on the
10.times. images. Ellipses were fitted across each aggregate so
that the edges of aggregates are inside the ellipse. The same
procedure was used uniformly across all the images.
[0066] FIG. 6: Fibroblasts at 48 hours. The average area per number
of aggregates for 31 cell lines: age matched controls
(N.sub.AC=10), Alzheimer's disease (N.sub.AD=12), and
Non-Alzheimer's dementia (N.sub.Non-ADD=9) such as Parkinson's
disease (PD) and Huntington's disease (HD). The error bars
represent the standard error of the mean.
[0067] FIG. 7: Repeatability of the results. The average area per
number of aggregates for four repeated cell lines. Experiments were
at least one month apart for the same cell lines. Initial number of
cells was within 10%.
[0068] FIGS. SA and SB: Examples of freely migrating cells marked
with red dots. Left picture (FIG. 8A) Alzheimer's disease (AD) and
right picture (FIG. 8B) non-Alzheimer's dementia (Non-ADD;
Huntington's disease) fibroblasts at 48 hours after plating.
[0069] FIG. 9: Migration rate versus number of migrating cells.
Green squares-Alzheimer's disease (N.sub.AD=10), blue
triangles-Non-Alzheimer's dementia (N.sub.Non-ADD=7), and red
circles-age matched controls (N.sub.AC=9). Blue lines are
separating thresholds.
[0070] FIG. 10: Migration rate times the number of migrating cells.
Green squares--Alzheimer's disease (N.sub.AD=10), blue
triangles-Non-Alzheimer's dementia (N.sub.Non-ADD=7), and red
circles-age matched controls (N.sub.AC=9).
[0071] FIGS. 11A and 11B: Fractal analysis. FIG. 11A: Examples of
fractal curves and linear it of the recovery region. FIG. 11B:
Population data showing the slope versus intercept for fractal
curves (N=31; N.sub.AC=10; N.sub.AD=12; N.sub.Non-ADD=9).
[0072] FIGS. 12A and 12B: Lacunarity analysis. FIG. 12A: Examples
of lacunarityl curves. FIG. 12B: Population data showing the
average lacunarity (N=8; N.sub.AC=1; N.sub.AD=4;
N.sub.Non-ADD=3).
[0073] FIG. 13: Proportionality relation between matrigel thickness
and volume in 12 well plates. When the matrigel volume is in the
range 400 to 800 .mu.l the thickness of the matrigel layer is in
the range of 1.04 to 2.08 mm.
[0074] FIGS. 14A and 14B: Sensitivity of fractal dimension, and
lacunarity, on the initial volume of matrigel. Fractal dimension
(FIG. 14A), and lacunarity (FIG. 14B) have a qualitatively
different trend for small volumes of matrigel 400 .mu.l (red) and
500 .mu.l (green) when compared with larger volumes of matrigel 600
.mu.l (blue), 700 .mu.l (pink), and 800 .mu.l (turquoise). For
large volumes(>600 .mu.l) the more matrigel is added the larger
the effect on fractal dimension (FIG. 14A), and lacunarity (FIG.
14B). For reference, in all of the previous experiments we used 700
.mu.l of matrigel.
[0075] FIGS. 15A and 15B: Sensitivity of AD aggregates at 48 hours
on the initial volume of matrigel. FIG. 15A: Area per number of
aggregates at 48 h and 79 h versus the initial volume of matrigel.
FIG. 15B: Rate of change for area/number as a function of initial
volume of matrigel. The graphs illustrate (1) the importance of
using 700 .mu.l of Matrigel where the curves show a peak and the
effect is maximum. (2) The increase of the Alzheimer's aggregates
in size and reduction in number in this time window 40-80 h. This
is illustrated in panel A by showing the aggregate area per number
at two different time points 48 h and 79 h. The green curve is
above the red curve indicating a growth in area and/or reduction in
number. Experimentally both are observed. Panel B shows a rate of
change in this measure Area/# between 48 h and 79 h. In other
words, take the curves from panel A, subtract them and divide by
the time interval. The AD fibroblasts cells are unable to migrate
away from the aggregates after 40 h. Therefore, the aggregates grow
bigger for AD cells in this time window. For the control cases, AC,
this is not observed and cells are able to migrate away from the
aggregates.
[0076] FIG. 16: Depicts age matched control (AC) fibroblasts
networks after 24 hours of incubation.
[0077] FIG. 17: Depicts Alzheimer's Disease (AD) patient
fibroblasts networks after 24 hours of incubation.
[0078] FIG. 18: Depicts the fractal dimensions of AC and AD
subjects versus time.
[0079] FIG. 19: Depicts the fractal dimension of AD versus AC
networks after 24 hours of incubation.
[0080] FIGS. 20A and 20B: FIG. 20A: AD fibroblast cell lines. Graph
showing a linear increase of the average aggregate area per number
of aggregates with disease duration. In other words, a direct
correlation exists between disease duration and the (average
aggregate area)/(number of aggregates). The number next to each
square is the number of cell lines tested. FIG. 20B: AD fibroblast
cell lines. Linear correlation between disease duration and number
of migrating cells. The number next to each square is the number of
cell lines tested. Using these correlations, it is possible to
identify patients that are in the early, middle or late stages of
Alzheimer's Disease. Patients in the earlier stages of the disease
have an increased responsiveness to treatment. Knowledge of how
long a patient has had Alzheimer's Disease helps guide the
therapeutic goals and strategies employed in a treatment regime on
a patient-by-patient basis.
DETAILED DESCRIPTION
[0081] Abbreviations: AC: age matched controls; AD: Alzheimer's
Disease; AvC: Average number of cells; DC: density of cells; DMEM:
Dulbecco's Modified Eagle Medium; EtOH: Ethanol; FBS: Fetal Bovine
Serum; Non-ADD: non Alzheimer's dementias; RIM: Room
Temperature.
[0082] As used herein, "lacunarity" refers to a measure of how a
fractal fills space. It is used to further classify fractals and
textures which, while they may share the same fractal dimension,
appear very visually different. Dense fractals have a low
lacunarity. As the coarseness of the fractal increases, so does the
lacunarity; intuitively from lacuna meaning "gap" ( . . . more
gaps=higher lacunarity). Lacunarity is typically represented by the
symbol L.
L .function. ( r ) = m = 1 r 2 .times. m 2 .times. P .function. ( m
, r ) - ( m = 1 r 2 .times. mP .function. ( m , r ) ) 2 ( m = 1 r 2
.times. mP .function. ( m , r ) ) 2 ##EQU00001##
[0083] The present invention in certain embodiments, is related to
methods to diagnose Alzheimer's disease (AD) using peripheral skin
fibroblasts. In various embodiments of the invention, quantitative,
qualitative, and/or semi-quantitative aspects of the fibroblasts
are used to determine the presence or absence of AD.
[0084] In one embodiment, the method involves the quantification of
the complexity of the human skin fibroblast networks with fractal
dimensions measurements. In another embodiment, the method involves
calculating a total score based on the sum of characteristics of
skin cell fibroblasts. In another embodiment, the method involves
calculating the area per number of clumps of skin cell fibroblasts.
The methods allow for early screening of AD patients from non-AD
dementia, and from age-matched control (AC) cases.
[0085] A method to diagnose Alzheimer's disease (AD) using
peripheral skin fibroblasts is described. This method quantifies
the complexity of human skin fibroblasts patterns of growth with
measures of network formation, aggregation, communication, dynamic
mobility on a specialized substrate (Matrigel), and fibroblast
aggregates morphology.
[0086] Matrigel matrix is extracted from mouse sarcoma, rich in
extracellular matrix (ECM) proteins. It consists of laminin,
followed by collagen TV, heparan sulfate proteoglycans, and
entactin 1. At 37.degree. C., matrigel polymerizes to produce
biologically active matrix material resembling the mammalian
cellular basement membrane. BD Matrigel Matrix Growth Factor
Reduced (GFR) is found to be particularly well suited for
applications requiring a more highly defined basement membrane
preparation of the gel substrate.
[0087] Five methods of diagnostic measurements are presented:
[0088] 1. Method 1: Integrated score
[0089] 2. Method 2: Average aggregate area per number of
aggregates
[0090] 3. Method 3: Cell migration analysis
[0091] 4. Method 4 Fractal analysis
[0092] 5. Method 5: Lacunarity Analysis
[0093] Additional measures of fibroblasts growth patterns may be
developed to diagnostically distinguish between Alzheimer's disease
(AD), non-Alzheimer's dementia (non-ADD) and age matched controls
(AC) cells taken from biopsy. Diagnostic efficacy may be improved
by adding extracellular matrix modifying agents.
Method 1--Integrated Score
[0094] In this study skin fibroblasts within 1 to 2 hours in
culture connect to form measurable networks on matrigel. This
condition provides a physiologically relevant environment for
studying cell morphology, cellular biochemical functions, cell
motility or invasions, and gene expression. After one day these
networks degenerate and edges retract to leave behind measurable
aggregates.
[0095] Eight parameters are used to separate AD fibroblasts from
age matched controls (AC) and to non-Alzheimer's dementia (Non-ADD)
at 48 hours after plating on matrigel:
[0096] 1. Existence of large aggregates.
[0097] 2. Attachment of cells to the aggregates.
[0098] 3. Evidence of aggregates growing.
[0099] 4. Small number of aggregates (<10 on a 10.times.
image).
[0100] 5. Large number of aggregates (>10 on a 10.times.
image).
[0101] 6. Measurable edges within networks.
[0102] 7. Evidence of cell migrations.
[0103] 8. Closeness to percolation limit (cells form continuous
streams).
[0104] From these 8 parameters a quantitative score is introduced
as follows:
[0105] 1. The first tour parameters above are specific to
Alzheimer's disease (AD) and score with "-1" for each if present
and with "0" if absent.
[0106] 2. The last four parameters are specific to non-AD and AC,
and score with "+1" if present and with "0" if absent.
[0107] 3. A total score is calculated as the sum of all eight
values. If the total score is positive or zero the cells are AC or
Non-ADD. If the total score is negative the cells are AD.
[0108] The total score representing the sum of eight
characteristics of skin cell fibroblasts at 48 hours after plating
is represented in the FIG. 4.
Method 2--Area Per Number of Aggregates
[0109] Two of the eight parameters are expressed in the measure
area per number of aggregates, which is considerably higher for AD
than for AC, and non-ADD (Diagnostic accuracy 96%, N=31
(n.sub.AD=12, n.sub.AC=10, and n.sub.Non-ADD=9) p<0.000001 for
AD vs AC, and p<0.00001 for AD vs non-ADD).
[0110] The AD cells show big, isolated aggregates, and little or no
migrations (FIG. 5A). The normal controls and non-ADD fibroblasts
show numerous smaller clumps and high level of migration between
the aggregates (Figure SB).
Method 3--Cell Migration
[0111] Unlike the Integrated Score Method, the Cell Migration
Method is able to distinguish between AD, AC and non-ADD cells. See
FIGS. 9 and 10.
[0112] Freely migrating cells are counted at 48 hours, N.sub.t, and
approximately 7 hours later, N2, and the migration rate is
calculated as R=(N2-N.sub.t)/L\T, where L\T is the time interval
between counts. A freely migrating cell is a cell which is not
attached to the aggregates, as depicted by the red dots in FIG.
8.
[0113] The population data (FIGS. 9, 10) shows that Alzheimer's
disease fibroblasts (AD-green squares) and non-Alzheimer's dementia
fibroblasts (Non-ADD--blue triangles) have a significantly smaller
number of migrating cells and rate of migration when compared with
age matched control fibroblasts (AC red circles). Alzheimer's
disease fibroblasts (green squares) show the smallest number of
migrating cells and the lowest migration rate while age matched
controls (red circles) show the highest number of migrating cells
and the highest migration rate. Interestingly non-ADD cells
separate (with one exception) from AD and AC (FIGS. 5 and 6).
[0114] From the point of view of migration Non-Alzheimer's dementia
fibroblasts separate well from Alzheimer's disease fibroblast.
Method 4--Fractal Analysis
[0115] Unlike the Integrated Score Method, the Fractal Analysis
Method is able to distinguish between AD, AC and non-ADD cells
(p<0.01). See FIG. 11B.
[0116] The fractal analysis method utilizes the complexity of the
networks as measured by fractal dimension. Cells, preferably
fibroblasts, taken from patients suffering from Alzheimer's Disease
have a statistically significant lower fractal dimension than AC
cells when grown in tissue culture. The complexity of the networks
measured by this physical parameter is also markedly different for
fibroblasts taken from AD when compared to AC and non-ADD
fibroblasts. After network degeneration (.about.48 h), cells
migrate and within a few days reach confluence. This recovery is
captured by a linear increase in fractal dimension (FIG. 11A). The
slope versus the intercept of each curve that tracks fractal
dimension as a function of time is markedly different in the three
groups AC, AD, and Non-ADD (96% accuracy, n=3 1 (N.sub.AD=12,
N.sub.AC=10, N.sub.non-ADD=9); p<0.0001 for AD vs AC, and
p<0.00001 for AD vs non-ADD). Unlike the first method the second
one distinguishes between AC and non-ADD (p<0.01) (FIG.
11B).
Method 5--Lacunarity Analysis
[0117] The lacunarity analysis method quantifies the gaps of the
fibroblast patterns and is a complementary measure of complexity
used as a second level of discrimination. The average lacunarity of
the fibroblasts is also higher for fibroblasts taken from AD when
compared to AC and non-ADD fibroblasts. Typically, the lacunarity
increases and peaks when the network degeneration is maximal i.e.
when only isolated aggregates are visible (FIG. 12A). The lacunary
drops as the network regeneration starts.
[0118] These measures of the dynamics of complexity, offer a new
opportunity to diagnose AD patients with a minimally invasive
procedure. The simplicity and low cost of the method are a useful
screen for AD patients. Human skin fibroblast networks like the
neural networks in the AD brain show a reduction in complexity as
measured by fractal dimension. Human skin fibroblast networks
provide a model of brain networks useful for accurate AD diagnosis
and drug screening.
Impaired Vertical Migration of Alzheimer's Disease Fibroblasts.
[0119] The same number of fibroblast cells (50 cells/mm) was plated
on increasing volumes of matrigel, from 400 .mu.l to 800 .mu.l with
an increment of 100 .mu.l, on 12 well plates for an AD cell line.
The increase in the matrigel volume, V, produces a proportional
increase of the thickness of the matrigel layer, h, according to
the relation: V=(nr2) h, where r=l 1.05 mm (FIG. 13).
[0120] The vertical cell migration from the top surface to the
bottom surface becomes more difficult with the increase in the
thickness of the matrigel layer. This difficulty in migration is
quantified here by the fractal dimension, lacunarity and number
(FIG. 14).
[0121] After approximately 24 hours the networks degenerate and
aggregates are left behind. Here we show the dependence of the area
per number on the initial volume of matrigel (FIG. 12). For small
volumes of matrigel, 400, and 500 .mu.l, there are no aggregates
while for larger volumes, >500 .mu.l, the area of aggregates
divided by their number is a curve which peaks at 700 .mu.l. For a
very limited number of the Alzheimer's disease cases the area
divided by number of aggregates is near the threshold (see FIG. 6)
and a measure of aggregates at a later time will help to better
separate these cases. After 79 h these aggregates increase in size
and their number decreases so that the ratio area/number increases
even further (green curve in FIG. 15A). For both 48 and 79 hours
the effect is optimum for an initial volume of 700 .mu.l. The rate
of change for the area/number, FIG. 3B, is also a curve with a
peak, enforcing the idea that at optimum initial volume of matrigel
is 700 .mu.l.
[0122] In the experiments presented above we used 1.5 ml Dulbecco's
Modified Eagle Medium (DMEM) with 10% fetal bovine serum (FBS) and
1% penicillin/streptomycin (PS). Serum starvation will have a
further major perturbation of the measures presented.
Fractal Dimension Methods
[0123] In one embodiment, the fractal dimension is calculated using
a standard box counting procedure after the raw images, which may
be digital images, are filtered through an edge detection procedure
which uses, for example the difference of two Gaussians. AD can be
diagnosed based on the quantitative image analysis of cultured
human skin fibroblasts. In one embodiment, samples are taken
through punch-biopsy. In general, a surgical blade can be used. The
population data show that AC cases have a significantly higher
fractal dimension than that of AD cases. A reduced complexity of
human skin fibroblast networks AD cases provides distinctions from
AC and non-AD dementia cases.
[0124] Other image processing routines can be used with the
invention instead of box counting or line detection,
[0125] The simplicity and low cost of the method is helpful for
screening AD patients before resorting to other elaborate and
costly techniques. Human skin fibroblast networks, like the neural
networks in AD brain, show a reduction in complexity as measured by
fractal dimension. In one embodiment, human skin fibroblasts
networks may be a model of brain networks that may be useful for
new drug screening.
[0126] FIG. 16 depicts age matched control (AC) fibroblasts
networks after 24 hours of incubation. In one embodiment, a digital
image of the network is taken. FIG. 17 depicts Alzheimer's Disease
(AD) patient fibroblasts networks after 24 hours of incubation. In
one embodiment, a digital image of the network is taken. FIG. 18
depicts the fractal dimensions of AC and AD subjects versus time.
The dynamics of cellular network measured by fractal dimension for
the two cell lines shows a higher fractal dimension for AC than for
AD. A significant separation is noticeable after approximately a
few hours of incubation. FIG. 19 depicts a scatter plot the fractal
dimension of AD versus AC networks after 24 hours of
incubation.
[0127] A fractal is generally a rough or fragmented geometric shape
that can be split into parts, each of which is (at least
approximately) a reduced-size copy of the whole, a property called
"self-similarity." The object (fractal) need not exhibit exactly
the same structure at all scales, but the same "type" of structures
must appear on all scales. Human skin fibroblast networks are an
example of naturally-occurring fractals.
[0128] Consider a line. If the line is subdivided in half, it takes
two of these halves to recreate the original line. If the line is
subdivided into four pieces, it takes four of them to cover the
line. Generally, given a line segment of length "s," the number of
segments that will cover the original line is given by
N(s)=(1/s).sup.t.
[0129] Consider a square. If the square is subdivided into smaller
squares, each with one half the side length then it takes four
(2.sup.2=4) of these smaller squares to form the original square.
If the square is subdivided into smaller squares each with one
quarter of the side length then it takes sixteen (2.sup.4=16) of
them to form the original square. As above we can write an
expression for the number of pieces we need of size "s" to cover
the original square, it is N(s)=(1/s)2. Fora cube, the result is
N(s)=(1/s)3.
[0130] The exponents 1, 2, and 3 in the above examples are the
dimensions of the line, square, and cube respectively. This can be
generalized to N(s)=(1/s).degree. here Dis the dimension, an
integer as above, but it need not be. If we take logarithms of both
sides, we have log(N(s))=D log(1/s), in order words we can estimate
the dimension by plotting log(N(s)) against log(1/s) the slope (D)
of which is the dimension. If the slope is a non-integer, then the
object is a fractal, and the dimension is a fractional (fractal)
dimension.
[0131] Complexity is the study of how living and nonliving things
organize themselves into patterns and interact as systems.
Complexity is extremely multidisciplinary and involves scientists
in a vast assortment of fields from biology to physics. Complexity
of human skin fibroblast networks can be quantified by computing
their fractal dimensions.
[0132] In one embodiment, edge detection is used in the present
invention. Edge detection is a term used in the field of image
processing, particularly in the areas of feature detection and
feature extraction, to refer to algorithms which aim at identifying
points in a digital image at which, for example, the image
brightness changes sharply or has other discontinuities.
[0133] It can be shown that under rather general assumptions for an
image formation model, discontinuities in image brightness are
likely to correspond to one or more of discontinuities in depth,
discontinuities in surface orientation, changes in material
properties and variations in scene illumination.
[0134] In the ideal case, the result of applying an edge detector
to an image may lead to a set of connected curves that indicate the
boundaries of objects, the boundaries of surface markings as well
curves that correspond to discontinuities in surface orientation.
Thus, applying an edge detector to an image may significantly
reduce the amount of data to be processed and may therefore filter
out information that may be regarded as less relevant, while
preserving the important structural properties of an image. If the
edge detection step is successful, the subsequent task of
interpreting the information content in the original image may
therefore be substantially simplified.
[0135] There are many methods for edge detection, but most of them
can be grouped into two categories, search-based and zero-crossing
based. The search-based methods detect edges by first computing a
measure of edge strength, usually a first-order derivative
expression such as the gradient magnitude, and then searching for
local directional maxima of the gradient magnitude using a computed
estimate of the local orientation of the edge, usually the gradient
direction. The zero-crossing based methods search for zero
crossings in a second-order derivative expression computed from the
image in order to find edges, usually the zero-crossings of the
Laplacian or the zero-crossings of a nonlinear differential
expression. As a pre-processing step to edge detection, a smoothing
stage, for example Gaussian smoothing, may be applied. In other
embodiments noise filtering algorithms may be employed.
[0136] The edge detection methods that have been published mainly
differ in the types of smoothing filters that are applied and the
way the measures of edge strength are computed. As many edge
detection methods rely on the computation of image gradients, they
also differ in the types of filters used for computing gradient
estimates in the x- and y-directions.
[0137] In one embodiment, the method uses a box counting procedure.
The image is covered with boxes, for example by a computer. The
goal is to find how the number of boxes needed to cover the image
changes with the size of the boxes. If the object is I-dimensional,
such as a line, we expect N(s)=(Vs).sup.1, as described above. And
so on for higher dimensions. Such a procedure can be implemented on
a computer using the digital images of the samples.
[0138] In one embodiment a database can be made of many different
non-Alzheimer's control (AC) subjects of various ages. The database
can be made such that the human subject being tested can be
evaluated versus age-matched AC data.
[0139] In one embodiment, the complexity of the fibroblast networks
1s quantified by measurement of fractal dimension and lacunarity
curves. The complexity of the networks measured by these physical
parameters also markedly differs for fibroblasts taken from AD when
compared to AC and non-ADD fibroblasts. After network degeneration,
by way of example after approximately 48 hours, cells migrate and
within a few days reach confluence. In one embodiment, this
recovery is captured by a linear increase in fractal dimension. The
slope versus the intercept of each curve that tracks fractal
dimension as a function of time is markedly different in the three
groups AC, AD, and non-ADD (100% accuracy, n=26 (AD=10, AC=10,
non-ADD=6); p<0.0001 for AD vs AC, and p<0.00001 for AD vs
non-ADD). This method shows distinguishable differences between AC
and non-ADD (p<0.01).
Methods Utilizing Cell Morphology Characteristics
[0140] Within a short time after being cultured, for example within
an hour, measurable networks form. In one embodiment, culturing
takes place in a gelatinous protein mixture which provides a viable
environment for studying cell morphology. After a time, for example
after about one day, these networks degenerate and edges retract to
leave behind measurable "clumps" or aggregates.
[0141] As with any of the methods of the invention, the image may
be prepared by obtaining a cell or a sample and culturing or
incubating the cell or sample for a period of time. In one
embodiment, the period of time is about 48 hours or any one-hour
increment subdivision thereof. During the period of time, the cell
or sample fibroblast network changes. An image is then taken.
Quantitative, qualitative, and semi-quantitative information can be
gathered from the image.
[0142] (4) In one embodiment, certain characteristics of the image
can be assigned values. For example, by inspecting the image, the
following non-exhaustive, and non-limiting characteristics can be
ascertained and optionally assigned values: (1) Are there big
clumps? (2) Are the cells attached to the clumps? (3) Are the big
clumps growing? (4) Are there just a few clumps? For example, less
than or equal to five on a 10.times. image? (5) Are there multiple
clumps (for example, greater than five on a 10.times. image)? (6)
Are there remnant edges from a network previously formed (for
example, in matrigel)? (7) Are there many cells migrating? (8) Are
the cells near percolation (i.e., cells which form a substantially
continuous stream from left right or up down of the image)?
[0143] In some embodiments, only a partial listing of these
characteristics may be considered. Two of the eight parameters are
expressed in the ratio of a measured area per number of aggregates.
This ratio is considerably higher for AD than for age matched
controls (AC), and non-Alzheimer's degeneration (non-ADD)
(Diagnostic accuracy 96%, N=30 (AD=12, AC=10, and non-ADD=8)
p<0.000001 for AD vs AC, and p<0.00001 for AD vs non-ADD).
Any or even all of these characteristics can be ascertained
manually or via image processing methods as is known in the
relevant arts.
[0144] In one embodiment, the "characteristics," for example the
eight characteristics (or a subset thereof or an augmented set of
characteristics) are assigned values. The values can be assigned
according to correlation studies, for example according to being
correlated with AD cells or being correlated with AC or non-ADD
cells. In one embodiment, the characteristics (1) through (4)
mentioned above are correlated with AD fibroblasts, and are then
assigned a value of, for example -1 if present or O if absent. The
actual values are given by way of example only, as other values can
also be assigned. In one embodiment, characteristics (5) through
(8) mentioned above are correlated with AC and non-ADD fibroblasts.
Parkinson's Disease (PD) and Huntington's Disease (HD) are
non-limiting examples of non-ADD cells. Characteristics (5) through
(8) are assigned a value of +1 if present or O if absent. In one
embodiment, the assigned values can be summed for each clump. The
summed values can then be plotted, as is shown in FIG. 4.
[0145] In another embodiment, the values of the characteristics can
be assigned intermediate values according to the "strength" of the
characteristic being measured. For example, the characteristic (1)
"are there big clumps" ? can be assigned any intermediate value
between -1 (for extremely large clumps) through zero (for extremely
small clumps). For example, a value of -0.9 can be assigned for
relatively "large" clumps, a value of -0.8 assigned to slightly
smaller (yet still "large") clumps, and so on. A graduated scale
for any of the above-mentioned characteristics (or others) can be
formulated through routine experimentation. In one embodiment, the
method can be fully automated using image processing techniques,
and moreover all (or perhaps only some) of the characteristics can
be quantified on a fully graduated, i.e., digital, basis.
[0146] As illustrated in FIG. 4, AD cells, such as those shown in
FIG. SA characteristically display big, isolated clumps with little
to no migration compared to AC cells and non-ADD cells.
Consequently, the AD cells typically have values summing to
relatively low numbers, typically negative numbers, in this scheme.
The normal controls and non-ADD fibroblasts, such as those shown in
Figure SB show numerous smaller clumps and high level of migration
between the clumps. Consequently, the AC and non-ADD cells
typically have values summing to relatively high numbers, typically
positive numbers, in this scheme. The above method provides yet
another way for diagnosing AD.
Methods Utilizing Area
[0147] In another embodiment, the area of clumps is calculated. For
example, the area of the clumps shown in Figure SB (AC cells) is
calculated. This can be done by any suitable method, for example
but not limited to, by fitting an ellipse across the clump. The
clumps can then be com1ted on the images. The counting as well as
area calculation can either be done manually or can be automated,
for example by image processing techniques known in the relevant
arts. The numbers shown on Figure SB represent the area of the
clumps in square microns, .mu..sup.2 Similarly, the area of AD
cells, such as those shown in Figure SA, can be calculated. By way
of example, the area shown on Figure SA is 12,670 .mu.2, a much
larger area than associated with the areas of the AC cells depicted
in FIG. 5B. The area per number of clumps can be plotted as is
depicted in FIG. 7.
[0148] FIG. 7 is a logarithmic plot of the area per number of
clumps as calculated by the above method. Of note, the area per
number of clumps for AD cells is significantly higher than the area
per number of clumps for either the AC or the non-ADD cells. The
above method provides yet another way for diagnosing AD.
[0149] In other embodiments of the invention, any of the above
methods can be combined. For example, the fractal dimension can be
calculated, and/or the characteristics can be assigned and summed,
and/or the area per number of clumps can be calculated. In one
embodiment, a positive diagnosis for AD is made only when two or
more of the above methods independently would indicate a positive
diagnosis. In other embodiments, a positive diagnosis for AD is
made only when all methods (for example three different methods,
specifically for example, the fractal dimension, summation of
characteristics, and area methods) would independently indicate a
positive diagnosis. In other embodiments, false positives and
negatives can be avoided or minimized by adjusting the definition
of "statistically significant," for example by setting a diagnosis
threshold at a certain multiple of population standard deviations
for any of the above-mentioned variables.
[0150] In any embodiments of the invention, a cell may be cultured
or incubated in a protein mixture. In one embodiment, the protein
mixture is a gelatinous protein mixture. A non-limiting exemplary
gelatinous protein mixture is Matrigel.TM.. Matrigel.TM. is the
trade name for a gelatinous protein mixture secreted by
Engelbreth-Holm-Swarm (EHS) mouse sarcoma cells and marketed by BD
Biosciences. This mixture resembles the complex extracellular
environment found in many tissues and is used by cell biologists as
a substrate for cell culture. BD Bioscience maintains a website at
http://www.hdbiosiences.ca.
[0151] In one embodiment, a cell is cultured or incubated in a
basement membrane preparation. In one embodiment, this preparation
is solubilized. In one embodiment, a basement membrane preparation
is extracted from a tumor. In one embodiment, the tumor is the
Engelbreth-Holm-Swarm (EIS) mouse sarcoma, a tumor rich in
extracellular matrix proteins. Its major component is laminin,
followed by collagen IV, heparan sulfate proteoglycans,
entactin/nidogen. In certain embodiments, this preparation contains
TGF-beta, epidermal growth factor, insulin-like growth factor,
fibroblast growth factor, tissue plasminogen activator, and/or
other growth factors which may or may not occur naturally in the
EHS tumor.
[0152] In one embodiment, a cell is cultured or incubated in a
preparation comprising extracellular matrix proteins. In one
embodiment, the preparation comprises laminin, collagen, heparin
sulfate proteoglycans, entactin/nidogen, and/or combinations
thereof. In one embodiment, the preparation is extracted from a
tumor. In one embodiment, the tumor is the Engelbreth-Holm-Swarm
(EHS mouse sarcoma. In one embodiment, the preparation further
comprises growth factor. In one embodiment, the preparation further
comprises TGF-beta, epidermal growth factor, insulin-like growth
factor, fibroblast growth factor, tissue plasminogen activator,
and/or combinations thereof, and/or other growth factors. In one
embodiment, the TGF-beta, epidermal growth factor, insulin-like
growth factor, fibroblast growth factor, tissue plasminogen
activator, and/or other growth factors occur naturally in a tumor.
In one embodiment, the growth factors occur naturally in the EHS
mouse sarcoma.
[0153] In one embodiment, the preparation comprises an
extracellular matrix protein preparation which is effective for the
attachment and differentiation of both normal and transformed
anchorage dependent epithelioid and other cell types. These include
neurons, hepatocytes, Sertoli cells, chick lens, and vascular
endothelial cells. In one embodiment, the extracellular matrix
protein preparation may influence gene expression in adult rat
hepatocytes as well as three-dimensional culture in mouse and human
mammary epithelial cells. In one embodiment, this is the basis for
several types of tumor cell invasion assays, will support in vivo
peripheral nerve regeneration, and provides the substrate necessary
for the study of angiogenesis both in vivo and in vivo. In one
embodiment, an extracellular matrix protein also supports in tit
propagation of human tumors in immunosuppressed mice.
[0154] In one embodiment, a volume of chilled extracellular matrix
protein is dispensed onto tissue culture labware. As used herein,
"chilled" refers to a temperature less than room temperature,
preferably less than about 15.degree. C., more preferably less than
about 10.degree. C., more preferably less than about 5.degree. C.,
most preferably about 4.degree. C. When incubated at an elevated
temperature, the extracellular matrix proteins self-assemble
producing a thin film that covers the surface of the labware. As
used herein, "elevated" refers to a temperature above room
temperature, preferably above about 20.degree. C., more preferably
above about 25.degree. C., more preferably above about 30.degree.
C., more preferably above about 35.degree. C., and most preferably
about 37.degree. C., which is approximately average human body
temperature.
[0155] Cells cultured on extracellular matrix protein demonstrate
complex cellular behavior that is otherwise difficult to observe
under laboratory conditions. For example, endothelial cells create
intricate spider web-like networks on extracellular matrix protein
coated surfaces but not on plastic surfaces. Such networks are
highly suggestive of the microvascular capillary systems that
suffuse living tissues with blood. Hence, the process by which
endothelial cells construct such networks is of great interest to
biological researchers and extracellular matrix proteins allow them
to observe this.
[0156] In some embodiments, it may be preferable to use greater
volumes of extracellular matrix proteins to produce thick
three-dimensional gels. The utility of thick gels is that they
induce cells to migrate from the surface to the interior of the
gel. In some embodiments, this migratory behavior is studied by
researchers as a model of tumor cell metastasis.
[0157] The ability of extracellular matrix proteins to stimulate
complex cell behavior is a consequence of their heterogeneous
composition. In some embodiments, the chief components of
extracellular matrix proteins are structural proteins such as
laminin and collagen which present cultured cells with the adhesive
peptide sequences that they would encounter in their natural
environment. Some embodiments also employ growth factors that
promote differentiation and proliferation of many cell types.
Extracellular matrix proteins may also contain numerous other
proteins in small amounts.
[0158] Measures of the dynamics of fibroblast network complexity,
as disclosed herein, offer a new opportunity to diagnose AD
patients with a minimally invasive procedure. Human skin fibroblast
networks, like the neural networks in the AD brain, show a
reduction in complexity as measured by fractal dimension compared
to AC and non-ADD cells. Human skin fibroblast networks provide a
model of brain networks useful for accurate AD diagnosis and drug
screening.
[0159] All books, articles, or patents references herein are
incorporated by reference to the extent not inconsistent with the
present disclosure. The present invention will now be described by
way of examples, which are meant to illustrate, but not limit, the
scope of the invention.
Example 1: Coating the 12 Well Plates with BD Matrigel Matrix
Growth Factor Reduced
[0160] Equipment and Materials: Class II A/B 3 biological safety
cabinet (Forma Scientific). CO2 water-jacket incubator (Forma
Scientific). Inverted microscope. Pasteur pipettes. Serological
pipettes. Pipette aids (Omega Cat. No. P5017). BD Matrigel Matrix
Growth Factor Reduced (BD Biosciences, Cat. No. 354230), (Aliquot
800 .mu.l and store at -20.degree. C.). Sterile 12 well culture
plates (Coming Inc., Cat. No. 3512)
[0161] Procedure: Thaw BD Matrigel Matrix Growth Factor Reduced at
4.degree. C. on ice 30 min. before use, and use pre-cooled
pipettes, tips, and 12 well culture plates. Make sure Matrigel is
liquid and has no solid aggregates.
[0162] Thick Gel Method: Using cooled pipettes, mix the BD Matrigel
Matrix Growth Factor Reduced to homogeneity. Keep 12 well culture
plates on ice 30 min. prior to use and during the adding of BD
Matrigel Matrix Growth Factor Reduced, 700 .mu.L per well. Verify
the homogeneity of the gel on the surface of the cell culture
plates under the inverted microscope, and avoid bubbles. Place 12
well plates at 37.degree. C. for 30 minutes. Add the cell
suspension on top of BD Matrigel Matrix Growth Factor Reduced. The
density of cells is adjusted to 50 cells/mm (See below).
Example 2: Preparing of Human Skin Fibroblast for Plating
[0163] Equipment and Materials: Class II A/B 3 biological safety
cabinet (Forma Scientific). CO2 water-jacket incubator (Forma
Scientific). Inverted microscope. T-E (Trypsin-EDT A solution
1.times.) (stored at .about.20 C). M-2 (Medium-2) DMEM with 10%
FBS, and 1% PS. Pasteur pipettes. Serological pipettes. Pipette
aids (Omega Cat. No. P5017). Culture flask, vent cap, 25 cm.sup.2.
15 ml and 50 ml sterile plastic tube. 500 ml Bottle Top Filter.
Water bath Centrifuge.
[0164] Procedure: Thaw and warm T-E and M-2 medium at 37.degree. C.
in the water bath.
[0165] Flask cultures containing tissue fragments: Remove and
discard culture medium from flask by suction. To eliminate serum
residue that could inactivate trypsin, add 2 ml T-E and suck out
immediately. Add 2 ml of T-E to flask and incubate at 37.degree. C.
for 3-5 minutes. Time of detachment of cells from the surface of
culture flasks is not the same for all patients and needs to be
adjusted for each case in the range 3-5 minutes. Observe the cells
under microscope: if rounded, they are detached. If most are not
rounded, leave the suspension in the incubator for another minute
or two until they appear rounded. Add 5 ml of M-2 medium to inhibit
trypsin activity. Gently triturate by pipetting to detach cells
from the bottom of the flask, but be careful not to touch, or
detach, the tissue fragments. Transfer the cells suspension (by
pipette) to a 15 ml sterile plastic tube, centrifuge it at 1000 RPM
(speed 3) for 5 minutes, discard the supernatant, and suspend the
cells in 3 ml M-2 medium. Gently triturate by pipetting to detach
cells from the bottom of the 15 ml sterile plastic tube.
Example 3: Counting of Human Skin Fibroblasts
[0166] Equipment and Materials: Class II A/B 3 biological safety
cabinet (Forma Scientific). Inverted microscope. Cell counting
chamber-Ley Double (VWR scientific, Cat. No. 15170-208). Pasteur
pipettes. Serological pipettes. Pipette aids (Omega Cat. No.
P5017). Sterile 12 well culture plates (Coming Inc., Cat. No.
3512)
[0167] Procedure: Add 0.25 ml of cell suspension into the cell
counting chamber and put the cover glass on the top.
[0168] Cell counting chamber-Levy Double containing fibroblast
cells: Let the cell stabilize and then count the number of cells in
the nine big squares under the inverted microscope. The average
number of cells (AvC), multiplied by 10, gives the density of cells
(DC) expressed in number of cells/mm.sup.3. Dilute the cell
suspension with M-2 medium to reach the density of 50
cells/mm.sup.3 in a total volume of 1.5 ml. Use (1.5
ml).times.(50)/AvC for the cell suspension and complete the rest up
to 1.5 ml with M-2 medium. Three wells are used for each cell line.
Therefore, multiply the numbers in step 3 by 3. Prepare the 4.5 ml
mixture in a 15 ml sterile plastic tube and gently homogenize using
a 10 ml pipettes. Add 1.5 ml in each of the three wells. Label the
three well plates with patient code, date, and passage number.
Spray outside of 12 well culture plates with 70% EtOH and place in
the incubator.
Example 4: Verifying the Initial Cell Density of Human Skin
Fibroblast Through Image Analysis
[0169] Equipment and Materials: Inverted Microscope. Image J.
[0170] Procedure: After 10 minutes in the incubator take the 12
well culture plate out and put it under the inverted
microscope.
[0171] Cell counting using image analysis: Under the 4.times.
objective align the center of the well with the center of the
viewing field. Change the objective to 10.times. and take one image
in the center plus other four by moving one field of view to the
left/right, and up/down. Load the image under ImageJ and go to
Process/Noise/Despeckle. Then go to
Process/Noise/Binary/MakeBinary. Despeckle the binary image 2-3
times then use Analyze/Analyze Particles for counting the cells.
The result is under Summary/Count. Be aware that automatic counting
of cells overestimates the manual counting by .about.12%. The lower
threshold for initial cell density is 45 cell/mm.sup.3, which
corresponds to a cell number of 190 cells under 10.times., and to a
fractal dimension of 1.4. The higher threshold for initial cell
density is 62 cell/mm.sup.3, which corresponds to a cell number of
650 cells under 10.times., and to a fractal dimension of 1.62. Any
well that has an average number of cells outside the two thresholds
is discarded.
Example 5: Method 1; Scores
[0172] Equipment and Materials: Inverted microscope (Westover
Digital AMID Model 2000). Micron 2.0.0 Westover Scientific 2008.
Image J
[0173] Procedure: Pictures taken at 48 hours and after to measure
total score based on 8 criteria. Three criteria (1, 4, and 5 see
below) are represented quantitatively by the average area per
number of aggregates.
[0174] Parameters used for screening: [0175] 1. Existence of large
aggregates. [0176] 2. Attachment of cells to the aggregates. [0177]
3. Evidence of aggregates growing. [0178] 4. Small number of
aggregates (<10 on a 10.times. image). [0179] 5. Large number of
aggregates (>10 on a 10.times. image). [0180] 6. Measurable
edges within networks. [0181] 7. Evidence of cell migration. [0182]
8. Closeness to percolation limit (cells form continuous
streams).
[0183] Total score: (1) First four parameters are specific to
Alzheimer's disease (AD) and score with "-1" if present and with
"0" if absent. (2) The last four parameters are specific to age
matched controls (AC) and to non Alzheimer's dementias (Non-ADD),
and score with "+1" if present and with "0" if absent. (3)
Calculate the total score as the sum of all eight values. If total
score is positive or zero the cells is AC or Non-ADD. If total
score is negative the cells are AD.
[0184] Average area per average number of aggregates: (1) Import
images into Micron 2.0.0 and under Measurement/Ellipse Area measure
the aggregates one by one. Fit an ellipse on aggregate and area is
provided automatically by the software. (2) Collect the areas in a
spreadsheet and extract the number of aggregates automatically with
the function COUNT. (3) Calculate the average area and the average
number of aggregates for each image as well as the ratio of the
two. (4) Average the area per # aggregates for all five images for
each well. (5) Average the area per # aggregates for all three
wells for the same cell line. (6) If area per # aggregates is
smaller than 1000 the cell lines are AC or Non-ADD and if it is
bigger than 1000 then the cells are AD.
Example 6: Method 2; Fractal Analysis
[0185] Equipment and Materials: Inverted microscope (Westover
Digital AMID Model 2000). Image J. Plug in FracLac.
[0186] Procedure: Pictures taken after 48 hours and calculate
fractal dimension.
[0187] Parameters used for screening: (1) Existence of large
aggregates.
Example 7: Preparation of Mediums
[0188] Preparation of mediums: DMEM (high glucose), Cat. No.
10313-039, Invitrogen Gibco (Store in 4.degree. C. refrigerator);
FBS, Cat. No. 10082-147, Invitrogen Gibco (Aliquot 50 ml and store
at -20.degree. C.); PS (Penicillin and streptomycin solution) Cat.
No. 15140-122, Invitrogen (Aliquot 5 ml and store at -20.degree.
C.). M-1 (Medium-1) DMEM with 45% and 1% PS. M-2 (Medium-2) DMEM
with 10% and 1% PS. Filter, label and store in 4.degree. C.
refrigerator, up to 1 month).
* * * * *
References