U.S. patent application number 10/082036 was filed with the patent office on 2002-08-29 for method of characterizing potential therapeutics by determining cell-cell interactions.
This patent application is currently assigned to Cytokinetics, Inc., a Delaware Corporation. Invention is credited to Elias, Kathleen A..
Application Number | 20020119441 10/082036 |
Document ID | / |
Family ID | 24981887 |
Filed Date | 2002-08-29 |
United States Patent
Application |
20020119441 |
Kind Code |
A1 |
Elias, Kathleen A. |
August 29, 2002 |
Method of characterizing potential therapeutics by determining
cell-cell interactions
Abstract
A method quantitatively analyzes images of two different cell
types that interact in producing and maintaining a disease state or
other biological condition. The two separate cell types are exposed
to an agent or stimulus suspected of influencing the biological
condition (e.g., the agent might be a potential therapeutic for
treating a cancer). The two different cell types are co-cultured or
otherwise allowed to interact with one another before and during
exposure to the agent. The images of the cells show how the agent
affects the cells' phenotypes, including their viability, migration
patterns, etc. The method generates a quantitative phenotype for
each cell type by quantitatively analyzing the cell images via an
automatic procedure. The quantitative phenotypes typically take the
form of a group of scalar or vector descriptors that together
provide a "fingerprint." The descriptors may be size values,
positions, morphological values, intensity distributions, etc.
Inventors: |
Elias, Kathleen A.; (San
Francisco, CA) |
Correspondence
Address: |
BEYER WEAVER & THOMAS LLP
P.O. BOX 778
BERKELEY
CA
94704-0778
US
|
Assignee: |
Cytokinetics, Inc., a Delaware
Corporation
|
Family ID: |
24981887 |
Appl. No.: |
10/082036 |
Filed: |
February 20, 2002 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10082036 |
Feb 20, 2002 |
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09741721 |
Dec 18, 2000 |
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Current U.S.
Class: |
435/4 ; 382/128;
435/7.23; 702/19 |
Current CPC
Class: |
G01N 33/5091 20130101;
G01N 2015/1488 20130101; G01N 2015/149 20130101; G01N 33/5011
20130101; G01N 2015/1497 20130101; G01N 2015/1006 20130101; G01N
2015/1477 20130101; G01N 2015/1486 20130101; G16B 20/00 20190201;
G01N 15/1475 20130101 |
Class at
Publication: |
435/4 ; 435/7.23;
702/19; 382/128 |
International
Class: |
C12Q 001/00; G01N
033/574; G06K 009/00; G06F 019/00 |
Claims
What is claimed is:
1. A method of evaluating the effect of interactions between
distinct cell types, the method comprising: (a) providing a first
cell culture of a first cell type and a second cell culture of a
second cell type in a microenvironment in which the cells of the
first and second cell cultures share a common medium, and wherein
the first and second cell types interact in the common medium; (b)
imaging the first and second cell types after exposure to an agent
or stimulus; and (c) quantitatively evaluating one or more images
obtained in (b) to identify any effects of the agent on
quantitative representations of the phenotypes of the cells in the
first and second cell cultures, which effects are mediated by
interactions between the first and second cell cultures.
2. The method of claim 1, wherein quantitatively evaluating one or
more images and extracellular matrix deposition of the cells of at
least one of the first and second cell cultures.
3. The method of claim 1, wherein the agent is a chemical compound
or a biological material
4. The method of claim 1, wherein the agent is a drug
candidate.
5. The method of claim 1, wherein the agent is electromagnetic
radiation, particle radiation, a non-ambient temperature, a
non-ambient pressure, acoustic energy, a mechanical force, an
electrical field, a magnetic field, and combinations thereof.
6. The method of claim 1, wherein the biological condition is a
disease.
7. The method of claim 6, wherein the biological condition is a
cancer, Type I diabetes, Type II diabetes, a neurodegenerative
disease, a cardiovascular disease, vascular disease or an
auto-immune disease.
8. The method of claim 1, wherein the biological condition is
normal unperturbed functioning of an organ or tissue and the agent
causes one or more of the cell types to become abnormal.
9. The method of claim 1, wherein the biological condition is a
cancer, wherein the first cell type is a cancerous epithelial cell
type and the second cell type is a mesenchymal cell type, and
wherein the first and second cell types are from the same tissue or
organ.
10. The method of claim 1, wherein the biological condition is a
cancer, wherein the first cell type is a cancerous epithelial cell
type and the second cell type is an endothelial cell type, and
wherein the first and second cell types are from the same tissue or
organ.
11. The method of claim 1, wherein the biological condition is a
cancer and wherein the first cell type is a cancerous cell type and
the second cell type is an immune system cell type.
12. The method of claim 1, wherein the biological condition is an
auto-immune disease, and wherein the first cell type is an immune
system cell type and the second cell type is a different cell type
that is attacked by cells of the first cell type in the auto-immune
disease.
13. The method of claim 1, wherein the biological condition is a
neuro-degenerative disease, and wherein the first cell type is a
neuron cell type and the second cell type is a neuroglial cell
type.
14. The method of claim 13, wherein the biological condition is
Parkinson's disease, and wherein the first cell type is a neuron
cell type and the second cell type is an astrocyte cell type,
oligodendricyte cell type, immune system cell type, or a vascular
cell type.
15. The method of claim 13, wherein the biological condition is
Alzheimer's disease, and wherein the first cell type is a
cholinergic neuron cell type and the second cell type is a
neuroglial cell type.
16. The method of claim 1, wherein the biological condition is Type
II diabetes, and wherein the first cell type is a muscle cell type
and the second cell type is an adipocyte cell type, an immune cell
type, or a vascular cell type.
17. The method of claim 1, wherein the biological condition is
cardiac disease, and wherein the first cell type is a cardiac
myocyte and the second cell type is a stem cell, primary cell,
fibroblast or endothelial cell of cardiac origin.
18. The method of claim 1, wherein identifying any effects of the
agents comprises determining a cell killing potency of the
agent.
19. The method of claim 18, wherein the biological condition is a
cancer, wherein the first cell type is a cancerous epithelial cell
type and the second cell type is a mesenchymal cell type, wherein
the first and second cell types are from the same tissue or organ,
and wherein the agent is predicted to be effective against cancer
when the one or more images show that it has an EC50 for the
cancerous epithelial cells that is substantially greater than the
EC50 for the mesenchymal cells.
20. The method of claim 1, wherein the common medium is a cell
growth medium or a cell support medium.
21. The method of claim 1, further comprising: prior to imaging,
allowing the cells of the first and second cell types to grow in
the common growth medium.
22. The method of claim 1, wherein the microenvironment comprises:
a first compartment in which the first cell culture is grown, and a
second compartment in which the second cell culture is grown, and
wherein the common medium contacts the first and second
compartments and the first and second cell cultures.
23. The method of claim 22, wherein the first compartment is a base
compartment holding the first cell culture at first level, wherein
the second compartment is provided as an insert to the base
compartment, and wherein the second compartment holds the second
cell culture at a second level, that is above the first level.
24. A method of evaluating the effect of an agent on a biological
condition, the method comprising: (a) providing a first cell
culture of cells of a first cell type and a second cell culture of
cells of a second cell type in a microenvironment in which the
cells of first and second cell cultures share a common medium,
wherein the first and second cell types interact as part of the
biological condition; (b) exposing the cells of the
microenvironment to the agent; (c) imaging the first and second
cell types after exposure to the agent; and (d) quantitatively
evaluating one or more images obtained in (c) to determine how the
agent affects quantitative representations of phenotypes of the
cells, thereby predicting the effect of the agent in treating the
biological condition.
25. The method of claim 24, wherein quantitatively evaluating one
or more images identifies at least one of a change in migration
pattern, growth rate, endocytosis, cell shape, and extracellular
matrix deposition of the cells of at least one of the first and
second cell cultures.
26. The method of claim 24, wherein the agent is a drug
candidate.
27. The method of claim 24, wherein the biological condition is a
disease.
28. The method of claim 24, wherein the biological condition is
normal unperturbed functioning of an organ or tissue and the agent
causes one or more of the cell types to become abnormal.
29. The method of claim 24, wherein the common medium is a cell
growth medium or a cell support medium.
30. The method of claim 24, wherein the microenvironment comprises:
a first compartment in which the first cell culture is grown, and a
second compartment in which the second cell culture is grown, and
wherein the common medium contacts the first and second
compartments and the first and second cell cultures.
31. A method of evaluating an agent's effect on a biological
condition, the method comprising: (a) exposing cells of a first
cell type and cells of a second cell type to the agent, wherein the
first and second cell types interact in producing the biological
condition; (b) imaging cells of the first and second cell types
after exposure to the agent; (c) quantitatively evaluating images
obtained in (b) to identify any effects of the agent on
quantitative representations of phenotypes of the cells of the
first and second cell types; and (d) based upon any effects
identified at (c), predicting the agent's effect on the biological
condition.
32. The method of claim 31, wherein quantitatively evaluating one
or more images obtained in (b) to identify any effects of the agent
comprises identifying changes in at least one of the viability, the
function, and the morphology of the cells of at least one of the
first and second cell types.
33. The method of claim 31, wherein quantitatively evaluating one
or more images identifies at least one of a change in migration
pattern, growth rate, endocytosis, cell shape, and extracellular
matrix deposition of the cells of at least one of the first and
second cell cultures.
34. The method of claim 31, wherein the agent is a chemical
compound or a biological material
35. The method of claim 31, wherein the agent is electromagnetic
radiation, particle radiation, a non-ambient temperature, a
non-ambient pressure, acoustic energy, a mechanical force, an
electrical field, a magnetic field, and combinations thereof.
36. The method of claim 31, wherein the biological condition is a
disease.
37. The method of claim 36, wherein the biological condition is a
cancer, Type I diabetes, Type II diabetes, a neurodegenerative
disease, a cardiovascular disease, or an auto-immune disease.
38. The method of claim 31, wherein the biological condition is
normal unperturbed functioning of an organ or tissue and the agent
causes one or more of the cell types to become abnormal.
39. The method of claim 31, wherein the biological condition is a
cancer, wherein the first cell type is a cancerous epithelial cell
type and the second cell type is a mesenchymal cell type, and
wherein the first and second cell types are from the same tissue or
organ.
40. The method of claim 31, wherein the biological condition is a
cancer, wherein the first cell type is a cancerous epithelial cell
type and the second cell type is an endothelial cell type, and
wherein the first and second cell types are from the same tissue or
organ.
41. The method of claim 31, wherein the biological condition is a
cancer and wherein the first cell type is a cancerous cell type and
the second cell type is an immune system cell type.
42. The method of claim 31, wherein the biological condition is an
auto-immune disease, and wherein the first cell type is an immune
system cell type and the second cell type is a different cell type
that is attacked by cells of the first cell type in the auto-immune
disease.
43. The method of claim 31, wherein the biological condition is a
neuro-degenerative disease, and wherein the first cell type is a
neuron cell type and the second cell type is a neuroglial cell
type.
44. The method of claim 31, wherein the biological condition is
Type II diabetes, and wherein the first cell type is a muscle cell
type and the second cell type is an adipocyte cell type, an immune
cell type, or a vascular cell type.
45. The method of claim 31, wherein the biological condition is
cardiac disease, and wherein the first cell type is a cardiac
myocyte and the second cell type is a stem cell, primary cell,
fibroblast or endothelial cell of cardiac origin.
Description
CROSS-REFERENCE TO RELATED PATENT APPLICATIONS
[0001] This application is related to PCT patent application number
PCT/US00/13154, filed May 12, 2000 in the name of Sabry et al. It
is also related to U.S. Patent Application No. 09/______,______
(Attorney Docket No. CYTOP005) filed on Dec. 4, 2000 in the name of
Vaisberg and Coleman. Both applications are incorporated herein by
reference for all purposes.
FIELD OF THE INVENTION
[0002] The systems and methods described herein provide for image
capturing of living, dead, or fixed cells or cell fractions used to
identify information about substances used on the cells or
information about the cells themselves. Accordingly, the present
invention can enable researchers and scientists to identify
promising candidates in the search for new and better treatments
and medicines, for example, in drug discovery and development. The
principles enumerated herein may, with equal facility, be applied
to other applications, including but not limited to use in
environmental applications such as determining chemical toxicities
and other non-pharmaceutical toxicology uses.
BACKGROUND OF THE INVENTION
[0003] Purified substances having a desirable combination
bio-active properties are rare and often difficult to identify.
Recent advances in traditional organic chemistry and the
development of rapid combinatorial chemistry techniques have
increased the number of compounds that researchers can test for a
specific biological activity (e.g., binding to a target).
Unfortunately, the vast majority of "hits" generated by such
techniques do not possess the right combination of properties to
qualify as therapeutic compounds. When these substances are
subjected to low throughput cellular and animal tests to establish
their therapeutic usefulness, they are typically found to fail in
some regard. Unfortunately, such tests are time consuming and
costly, thus limiting the number of substances that can be tested.
In a like regard, the few hits that do possess the right
combination of properties avoid recognition until after the
throughput tests are conducted. With better early evaluation
techniques, such promising candidates could be identified earlier
in the development process and put on a fast track to the
marketplace.
[0004] There have been some attempts to use image acquisition
techniques to screen a large number of substances based upon
biological cell information. One such attempt is described in
International Application No. WO 98/38490 in the names of Dunlay,
et al. Dunlay et al. generally describes a conventional image
acquisition system. This conventional system collects and saves
cellular images based on certain criteria that are predefined.
Unfortunately, this system is has only a limited ability to predict
a therapeutic usefulness of particular compounds or other
agents.
[0005] One difficulty in predicting the clinical effectiveness of
any agent is determining what concurrent effects it produces in
normal cells, diseased versions of the normal cells, and other
related cells. Diseases such as cancer often involve the
interaction of various cell types such as cancerous epithelial
cells and their stromal cells. During development of a potential
new therapeutic, most research at the early stages focuses
separately on the diseased cells or normal cells. To the extent
that both cell types interact in producing or maintaining a disease
state, there is no systematically rigorous technique for evaluating
how a potential therapeutic affects their interaction.
[0006] What is needed therefore is a technique for quickly and
quantitatively evaluating the affect of a potential therapeutic on
a combination of various cell types that interact to produce or
maintain a biological condition (e.g., cancer).
SUMMARY OF THE INVENTION
[0007] This need may be addressed by quantitatively analyzing
images of two different cell types that interact in producing
and/or maintaining a disease state or other biological condition.
The two separate cell types are exposed to an agent or stimulus
suspected of influencing the biological condition (e.g., the agent
might be a potential therapeutic for treating a cancer). Typically,
though not necessarily, the two different cell types are
co-cultured or otherwise allowed to interact with one another
before and during exposure to the agent. The images of the cells
show how the agent separately affects each of the cell types.
Specifically, the images show how the phenotype of each type
changes (or does not change) upon exposure to the agent. In the
context of this invention, the concept of a phenotype encompasses
visual indicators showing viability, migration patterns, growth
rates, extracellular matrix depositions, etc. The method generates
quantitative phenotypes of the cells of cell types by
quantitatively analyzing the cell images, usually via an automatic
procedure. The quantitative phenotypes typically take the form of a
group of scalar or vector descriptors that together provide a
"fingerprint." The descriptors may be size values, positions,
morphological values, intensity distributions, etc.
[0008] One aspect of the invention provides a method of evaluating
the effect of interactions between distinct cell types. The method
may be characterized by the following sequence: (a) providing a
first cell culture of a first cell type and a second cell culture
of a second cell type in a microenvironment; (b) imaging the first
and second cell types after exposure to the agent; and (c)
quantitatively evaluating one or more images obtained in (b) to
identify any effects of the agent. To this end, the method employs
quantitative representations of the phenotypes of the cells in the
first and second cell cultures. This may show how the effects of
the agent are mediated by interactions between the first and second
cell cultures. The microenvironment mentioned above is typically a
contained environment in which the cells of the first and second
cell cultures share a common medium, thereby allowing the first and
second cell types to interact in the common medium. In alternative
embodiments, the cells of the distinct cell types are separately
cultured and imaged. During the process, one of the cell types may
be exposed to factors produced the other.
[0009] Frequently, a method of this invention includes separate
operations of exposing the cells of the microenvironment to the
agent and then imaging the first and second cell types. Thereafter,
in determining how the agent affects quantitative representations
of phenotypes of the cells, the system may be able to predict the
effect of the agent in treating the biological condition of
interest. The quantitative representation typically includes two or
more scalar values or vectors that characterize morphological
and/or compositional features of a cell.
[0010] The agents or stimuli considered for use with methods of
this invention include a wide variety of perturbing influences,
and, in some cases where the two cell types interact in
particularly interesting ways, may even constitute the absence of a
perturbation. Examples of agents contemplated for use with this
invention will be discussed below. In many important applications,
the agent is a biological material or chemical compound such as a
drug candidate.
[0011] Many different biological conditions may be analyzed with
the methods of this invention. Diseases are an important class of
biological conditions. Specific examples of biological conditions
that may be analyzed using this invention include cancers, Type I
diabetes, Type II diabetes, neurodegenerative diseases,
cardiovascular diseases, vascular disease, auto-immune diseases. In
certain cases, the biological condition is normal unperturbed
functioning of an organ or tissue and the agent causes one or more
of the cell types to become abnormal.
[0012] The first and second cell types used with this invention are
chosen to shed light on a particular biological condition. As
mentioned, many of these cell types interact with one another to
produce and/or maintain the biological condition. For example,
where the biological condition is a cancer, the first cell type may
be a cancerous epithelial cell type and the second cell type may be
a mesenchymal (stromal) cell type, with both cell types taken from
the same tissue or organ.
[0013] In one embodiment, the method involves applying the agent to
both cancerous epithelial cells and either endothelial or stromal
cells from the same tissue or organ. Then both cell types are
imaged and the resulting images are evaluated to identify changes
in at least one of the viability and the morphology of epithelial
and endothelial or stromal cells. The changes of interest result
from exposure to said agent. Finally, based upon any changes
identified, predicting the agent's effect on the cancer.
[0014] Aspects of this invention also specify criteria for
determining whether a particular agent will be "effective" in
treating a particular biological condition. For example, when the
biological condition is cancer, a potential therapeutic agent will
be predicted to be effective when the images show that it has an
EC50 for the cancerous epithelial cells that is substantially
higher than the EC50 for the mesenchymal cells. Various other
effects (beyond EC50) may be considered. These include changes in
migration, extracellular matrix deposition, endocytosis, and cell
shape.
[0015] Another aspect of the invention pertains to computer program
products including a machine-readable media on which is stored
program instructions for implementing a portion of or an entire
method as described above. Any of the methods of this invention may
be represented, in whole or in part, as program instructions that
can be provided on such computer readable media.
[0016] These and other features and advantages of the present
invention will be described below in more detail with reference to
the associated drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0017] FIG. 1 is a process flow diagram depicting at a high level
the important steps in an end-to-end process of this invention.
[0018] FIG. 2 is a process flow diagram showing a procedure
generating and using a quantitative phenotype.
[0019] FIG. 3A is a cross-sectional view of a simple plate in which
two separate cell lines are growing and interacting.
[0020] FIG. 3B is a top view of an assay plate having some wells
which hold cells of only single cell types and another well that
holds cells from two or more different and interacting cell
types.
[0021] FIG. 4 is a cross-sectional view of an "insert" type
microenvironment for co-culturing two different cell lines but
preventing them from contacting one another.
[0022] FIG. 5A is a cross-sectional view of three-dimensional
microenvironment having two separated cell lines at the beginning
of an experiment.
[0023] FIG. 5B is a cross-sectional view of the microenvironment of
FIG. 5A, after some time has elapsed and the initially separated
cell lines have moved in a third-dimension, toward each other and
away from their initial positions.
[0024] FIG. 6 is a block diagram of a computer system that may be
used to implement various aspects of this invention such as the
various image analysis algorithms of this invention.
[0025] FIG. 7 is a simplified diagram of a complete system for
evaluating a biological condition according to an embodiment of the
present invention.
DETAILED DESCRIPTION OF THE INVENTION
[0026] Overview
[0027] Generally, this invention relates to image analysis
processes (methods) and apparatus allowing image analysis. But the
image analysis is provided the context of a higher level process
that involves developing experiments and research strategies for
understanding certain biological conditions and developing agents
for effectively alter these conditions. FIG. 1 presents a
high-level process flow chart setting forth a sequence that might
typically be followed in accordance with this invention.
[0028] As shown in FIG. 1, a research process 101 begins at 103
where two or more cell types are caused to interact with one
another. Typically, these are cell types that are known to interact
(or suspected to interact) in producing and/or maintaining the
biological condition of interest. For example, cancerous epithelial
cells and endothelial cells interact in some manner to facilitate
vascularization (a biological condition) of tumors. Co-culturing
the two or more cell types will allow them to interact. Other types
of interaction conditions are possible.
[0029] After the cells have been allowed to interact for some
period of time, they will be imaged (105). Typically, the imaging
will involve capturing digital images of cells in a well or other
culture medium. It may also involve some level of image processing,
but for this example the sophisticated image processing will occur
later at 111. After the cells have interacted for a period of time
and have been imaged, they are treated with an agent (107).
Numerous treatments are possible and will be discussed below.
Following treatment with the agent, the cells are again imaged at
109. Depending upon the type of treatment, the cells may be imaged
multiple times to understand how their phenotypes change over time.
In some cases, the amount or level of the agent will be increased
or decreased over time. In such cases, the process may capture
separate images after each change in exposure to the agent.
[0030] With multiple images now available, the process generates a
"quantitative phenotype" for each cell or each population of cells.
For example, an image analysis system may generate one quantitative
phenotype for the cell type A prior to exposure to the agent,
another quantitative phenotype for cell type A after exposure to a
first concentration of the agent, and yet another quantitative
phenotype for cell type A after exposure to a second concentration
of the agent. The system may generate a similar sequence of
quantitative phenotypes for cell type B at the various stages of
treatment. The concept of a "quantitative phenotype" will be set
forth below. For now recognize that the quantitative features of
the phenotype are chosen to capture expected interesting features
the cells, such as dispersed Golgi, bias toward a particular cell
cycle phase, migration, cord formation, and extracellular matrix
deposition. Typically, the phenotype is developed across a
population of cells that have been subject to a particular set of
conditions. This addresses the wide variability in phenotype for
any given cell type when exposed to particular sets of
conditions.
[0031] After the relevant quantitative phenotypes have been
generated they are evaluated and compared to draw conclusions about
the effect of the agent on the biological condition. See 113. Agent
induced changes to the phenotype can be quantified and used to
identify significant changes in the biological condition (e.g.,
arrested mitosis, increased protein synthesis, reduced migration,
etc.).
[0032] Definitions
[0033] Some of terms used herein are not commonly used in the art.
Other terms have multiple meanings in the art. Therefore, the
following definitions are provided as an aid to understanding the
description that follows. The invention as set forth in the claims
should not necessarily be limited by these definitions.
[0034] The term "biological condition" refers to a particular state
of an organism, an organ, a tissue, individual cells, subcellular
organelles, cellular pathways and the like. Examples of such states
include a disease state, a normal unperturbed state, a quiescent
state, an active state, a particular state within the cell division
cycle, and the like. Specific examples of biological conditions
include various types of cancer, infection by various pathogens,
obesity, neuro-degenerative diseases, diabetes, cardiovascular
disease, vascular disease and the like. Of particular interest in
the context of this invention are those biological conditions that
are believed to require the participation of two or more cell
types. Such cell types interact to produce or maintain the
condition.
[0035] The term "agent" refers to something that may influence a
biological condition. Often the term will be synonymous with
"stimulus" or "stimuli" or "manipulation." Agents may be materials,
radiation (including all manner of electromagnetic and particle
radiation), forces (including mechanical, electrical, magnetic, and
nuclear), fields, and the like. Examples of materials that may be
used as agents include organic and inorganic chemical compounds,
biological materials such as nucleic acids, carbohydrates, proteins
and peptides, lipids, various infectious agents, mixtures of the
foregoing, and the like. Other specific examples of agents include
non-ambient temperature, non-ambient pressure, acoustic energy,
electromagnetic radiation of all frequencies, the lack of a
particular material (e.g., the lack of oxygen as in ischemia),
etc.
[0036] The term "phenotype" generally refers to the total
appearance of an organism or cell from an organism. In the context
of this invention, cellular phenotypes and their representations in
processing systems (e.g., computers) are particularly interesting.
A given cell's phenotype is a function of its genetic constitution
and environment. Often a particular phenotype can be correlated or
associated with a particular biological condition. Typically, cells
undergoing a change in biological conditions will undergo a
corresponding change in phenotype.
[0037] Thus, cellular phenotypic data and characterizations may be
exploited to deduce mechanisms of action and other features of
cellular responses to various stimuli. Such data and
characterizations represent a quantitative cellular phenotype. Such
quantitative phenotype may comprise multiple cellular attributes
that can be collectively stored and/or indexed, numerically or
otherwise. The attributes are typically quantified in the context
of specific cellular markers. Measured attributes useful for
characterizing an associated phenotype include geometric parameters
(e.g., size, shape, and/or location of the organelle) and
composition (e.g., concentration of particular biomolecules within
the organelle). Other attributes include changes in a migration
pattern, a growth rate, cord formation, an extracellular matrix
deposition, and even cell count.
[0038] The phenotype may be characterized by exposing various cell
lines to an agent of interest at various levels (e.g., doses of
radiation or concentrations of a compound). In each example within
this range, the attributes of interest are measured. Ultimately,
certain phenotypic features (combinations of attribute values) are
associated with the agent of interest. The combination of these
features provides a particular quantitative phenotype. This
combination is also sometimes referred to as a phenotypic
fingerprint or just "fingerprint."
[0039] Particularly interesting quantitative phenotypes may be
employed as "target phenotypes." These phenotypes are understood to
represent a particular transition or state of a cell. For example,
a target phenotype might represent a cancer cell that has a pathway
relevant to the cancer state blocked or limited. In another
example, a target phenotype represents a budding yeast cell that
has had its microtubules depolymerized. In assessing affects of
particular agents on cells, one can use target phenotypes to
specifically characterize the effect of that agent in terms of
mechanism of action, potency, and the like. A mathematical
"distance" between a quantitative target phenotype and a phenotype
under consideration, both before and after exposure to the agent,
will indicate how the agent is acting. In some cases, however,
simple changes in a quantitative phenotype--independent of
comparison to any "target phenotype"--may indicate important
events.
[0040] The term "cell type" refers to a phenotypically distinct
cell. Typically, in the methods of this invention, a first cell
type and a second, different, cell type are chosen for study
because they interact to produce or affect a biological condition.
Often they interact synergistically with respect to the condition.
The two cell types are distinguished from one another by, at least,
their phenotype. In some instances, they may also have different
genotypes. Commonly, two cell types having the same genotype will
be studied in accordance with this invention. And often both cell
types will be taken from the same organ or tissue. Examples of
distinct cell types include various immune system cells, various
epithelial cells, stromal cells such as fibroblasts from various
organs, various neurons, etc. Stromal cells are supporting cells
that are mesenchymal in origin. Examples of stromal cells include
fibroblasts of the connective tissue, blood elements such as
macrophages, and nervous system supporting cells such as
oligodendricytes and Schwann cells.
[0041] The term "microenvironment" refers to the local environment
in which two or more cell types exist. In such environment, the two
or more cell types may interact with and/or influence one another.
This may come about by contact between the cell types, exposure to
substances that the individual cell types consume or produce, etc.
In addition, two or more cell types exposed to the same
microenvironment may be similarly exposed to a common agent
introduced to the microenvironment. In one sense, a
microenvironment mimics the environment that the cells experience
in vivo. In the context of this invention, a microenvironment will
often refer to an in vitro environment such as a well, compartment,
or insert in which two or more cell types can reside in a
co-culture or other medium allowing them to interact with and/or
influence one another.
[0042] Application to Exemplary Biological Conditions
[0043] 1. Cancer
[0044] Various cell types may participate in producing and
maintaining a cancer. Thus, it is likely that some effective
therapeutic agents will simultaneously modify the functioning of
the various cell types participating in the cancerous state.
Traditionally, drug discovery efforts have focused on a single cell
type--those cells that have become cancerous. However, certain
aspects of the disease may bring different cell types into play.
For example, vascularization of a tumor involves endothelial cells
and possibly supporting (stromal) cells, in addition to the
cancerous cells. Also, cancerous epithelial cells may induce, in
some manner, endothelial cells to form blood vessels throughout a
tumor. Metastasis, immune response to cancer, and other mechanisms
associated with cancer, each bring into play multiple different
cell types.
[0045] Increasing evidence points to certain necessary interactions
between cancerous and non-cancerous cells to sustain tumor growth
and/or metastasis. Tumors and their stroma have been demonstrated
to interact to alter paracrine growth factor production, migration
patterns, growth rates and extracellular matrix depositions. For
example, cancerous epithelial cells may by some mechanism induce
local non-cancerous mesenchymal cells to produce large quantities
of paracrine growth factor, thereby stimulating tumor growth. By
providing both cancerous epithelial cells and associated
mesenchymal cells in the same microenvironment, one can test the
effects of various agents on a system that exploits the known
mesenchymal-epithelial interactions. This allows in vitro
prediction of maximum clinical efficacy. The assumption here is
that, in vivo, site-specific functional interactions between
mesenchymal and epithelial cancer cells modulate the behavior of
the tumors. Therefore, by demonstrating even subtle effects on the
mesenchymal cells with a decent effect on the cancerous epithelial
cells, a better therapeutic is identified.
[0046] For example, a given agent could directly affect cancerous
epithelial cells (by killing them for example) and directly or
indirectly affect non-cancerous non-epithelial cells to increase
extracellular matrix deposition or by altering paracrine growth
factor interactions. These affects are reflected in changes in the
quantitative representation of the phenotype; e.g., changes in the
size, shape, and/or composition of cellular organelles and
cytoskeleton. In addition, the affects can be manifested by cell
count, migration, and/or invasion (which can also be
characteristics of the cells' quantitative phenotypes).
[0047] In a worst case (aggressive cancer remains unchecked), the
cancerous epithelial cells (e.g. prostate tumor cells) continue to
grow and invade the support matrix of the microenvironment in which
they are grown. But perhaps a drug (agent) has a strong killing
effect on prostate tumor cells and also causes a decrease in
protein synthesis in the prostate fibroblasts (suggesting a
decrease in possible growth factors). Such potential drug would
likely have a greater efficacy than a drug that elicited a response
in only one cell type. By using this invention to quantitatively
phenotype both types of cells, as they are affected by the agent,
one can more accurately predict preclinical efficacy by determining
and correlating changes to a specific drug on both the cancer cells
(epithelium) and on its supporting cells (e.g., fibroblasts).
[0048] In a specific preferred approach, a very potent GI50 on
epithelial cells with a very potent GI50 on the mesenchyme would be
too toxic, while a modest GI50 on the epithelial cells and no
affect on the mesenchyme would be too ineffective. A potent or
modest epithelial GI50 teamed with a modest affect on mesenchymal
cells would be optimal.
[0049] The cell killing potency of an agent can be quantified by
various techniques using this invention. For example, a collection
of cells can be imaged immediately before exposure to the agent and
then again one or more times later. An image analysis algorithm
counts the living cells in each image. If the cell count does not
reach an expected number after exposure to the agent the cell
killing potency of the agent can be assessed. More specifically,
the cell count provides the EC50 and GI50 of the agent. Important
information may also be derived from the cell division cycle stage
at which the growth was arrested.
[0050] The cell killing potency of the agent can also be determined
by looking for characteristic phenotypes. For example, apoptosis
can be detected by a fragmented nucleus. The nucleus can be
visualized by staining the cell with a DNA stain such as DAPI or
Hoechst 33341 available from Molecular Probes, Inc. of Eugene,
Oreg. Necrosis may be detected by cell membrane disruption. The
cell membrane can be visualized with a suitable stain such as FM
1-43 available from Molecular Probes, Inc. of Eugene, Oreg.
[0051] Protein synthesis can be monitored by various techniques.
For example, the quantity of certain organelles associated with
protein synthesis (e.g., endoplasmic reticulum and vacuoles) can be
measured. An increase in the quantity of such organelles indicates
an increase in protein synthesis. Similarly, a decrease in the
quantity of such organelles indicates a decrease in protein
synthesis. Protein synthesis can also be monitored by indicia of
metabolism, such as the amount of mitochondia in a cell. Each of
these measures serves as at least one component of a quantitative
phenotype.
[0052] As mentioned, at least two cell types interact during
vascularization of tumors. Thus, this invention can be employed to
model angiogenesis and correlate drug responses of these models.
Obviously, endothelial cell lines would be used in these models. In
such examples, maximum clinical efficacy would be defined when a
proposed therapeutic resulted has a potent GI50 on cancerous
epithelial cells, a select and potent GI50 on endothelial cells (or
other assays of angiogenesis), and a modest effect on the
mesenchyme.
[0053] In one approach, a series of experiments is conducted. A set
of control experiments is conducted with cancerous epithelial cells
alone, HUVEC (human umbilical venus endothelial cells) alone, and
stromal cells alone. Normally, the epithelial and stromal cells
will not change significantly over time. The endothelial cells on
Matrigel are expected to form cords as the precursors to blood
vessels. Exposure to an agent of interest may cause changes in the
expected behavior of each these cell types (as measured by
analyzing images of the cells). More interestingly, epithelial
cells and endothelial cells can be placed in the same
microenvironment, where they interact with each other. Possibly,
the epithelial cells will stimulate the endothelial cells to form
cords faster, induce increased branching of the cords or cause
degradation of the matrix. Also, the epithelial cells may form
tight cords around the epithelial cells. Ideally, a potential
therapeutic will limit the growth of the epithelial cells and the
cord formation of the endothelial cells.
[0054] In another model, the cancerous epithelial cells, the
endothelial cells, and the supporting fibroblasts are all provided
in the same microenvironment. In the worst case, the islands of
epithelial cells proliferate and invade a supporting matrix. In
addition, the endothelial cells form tight cords around the
epithelial cells. In the best case, the agent under consideration
has a strong killing effect on the cancerous epithelial and the
endothelial cells, but only a moderate effect on the fibroblasts.
For example, the GI50 values of a promising therapeutic candidate
may be 100 nM for the epithelial cells, 100 nM for the endothelial
cells, and 800 nM for the fibroblasts. In a separate assay, the
agent could be tested against a different type of vascular cell
such as aortic endothelial cells. In this example, the promising
candidate might have a GI50 of 1 mM or greater for the aortic
cells. In each assay/model, results are assessed from an image
analysis to generate quantitative phenotypes. Cord formation can be
monitored by identifying endothelial cells in an image and then
determining if they align themselves in elongated groups. Such
arrangements can be discerned using conventional image analysis
techniques.
[0055] Metastasis is another mechanism that may be investigated
using the present invention. Recent evidence suggests that some
cancer cells induce their stromal cells to express substances,
which attack basal membranes that might normally confine the cancer
cells. For example, collagenase-3 (MMP-13) has recently been
identified as a member of a gene family that is expressed in breast
carcinomas and in articular cartilage from arthritic patients.
These investigations have found that collagenase-3 is expressed by
stromal cells immediately adjacent to epithelial tumor cells but
not by the tumor cells themselves. Further, it is not expressed by
normal breast glandular epithelium or the associated normal stromal
cells. Co-culture experiments using human fibroblasts and the MCF-7
breast cancer cells revealed that conditioned medium from breast
cancer cells stimulated fibroblastic expression of collagenase-3
mRNA. See Uria et al., "Regulation of Collagenase-3 Expression in
Human Breast Carcinomas is Mediated by Stromal-Epithelial Cell
Interactions," Cancer Research, 1997; 57 (21): 4882-8. This result
suggests that transcription of collagenase-3 in stromal cells is
activated by diffusible factors released from epithelial breast
cancer cells. Accordingly, collagenase-3 may be a molecular factor
important in the stromal reaction to invasive breast cancer and, by
concerted action, may be essential for tumor growth and
progression.
[0056] In the context of this invention, a microenvironment
harboring both human breast fibroblasts and MCF-7 breast cancer
cells can be subjected to a potential therapeutic agent. By
monitoring how such agent changes the quantitative cellular
phenotype of both the human fibroblasts and the MCF-7 cells, one
can predict a likely therapeutic outcome. For example, as described
above, one can use the phenotype to indicate cellular movement,
EC50s, and protein synthesis. Such studies may become even more
valuable when coupled with an assay for the relevant factor:
collagenase-3 or collagenase-3 mRNA in this example.
[0057] Another relevant interaction that can be modeled by the
present invention is the interaction between cancer cells and
immune cells. By co-culturing cancer cells and relevant immune
cells such as macrophages, one can see how a particular agent
affects the phenotypes of both cell types. Further, valuable
information could be obtained by co-culturing normal cells,
cancerous variants of the normal cells, and macrophages. An agent
that selectively stimulates anti-cellular activity of macrophages
against cancer cells, but not against normal cells, while all three
cell types are co-cultured may be a valuable therapeutic.
[0058] Preferably, in most of the above-described investigations of
cancer, each of the various cell lines used in this study would
derive from the same site of origin; for example, prostatic
epithelial cells and prostatic fibroblasts. Further, the agents
being evaluated should be administered to a cell culture or other
system that accurately reflects the tissue microenvironment. Very
often, co-cultures may be suitable in this regard.
[0059] 2. Neural Degenerative Disease
[0060] Selective cellular degeneration occurs in different cell
populations in the central (CNS) and peripheral nervous system
(PNS), causing different progressive, crippling and eventually
fatal neurodegenerative diseases. Degeneration of the dopaminergic
neurons in the substantia nigra causes Parkinson's disease;
degeneration of the cholinergic neurons in the basal forebrain is
associated with Alzheimer's disease; and degeneration of the
cholinergic motor neurons in the brain stem and spinal cord causes
amyotrophic lateral sclerosis (ALS; Lou Gehrig's disease). In these
diseases, there is increasing evidence that points to interactions
between the supporting cells of the CNS or PNS and the neurons
themselves. These supporting cells (neuroglia) in the CNS include
astrocytes, oligodendroglia, microglia and ependymal cells and in
the PNS, Schwann and satellite cells. The analysis of this
interaction between the neuroglia and neurons and the cell specific
changes that occur after addition of possible therapeutics, in
accordance with this invention, results in a better assessment of
the therapeutics.
[0061] The importance of growth factors in neural development is
well established and includes NGF, NT-3, NT4/5, IGF-1 and estrogen.
Growth, differentiation and survival of glia and glia progenitors
are influenced by PDGF, bFGF, IGF-1 and 2, NT-3, CNTF, retinoic
acid, IL-6, and LIF. Most of these factors are supplied from
neurons or other neuroglia. Growth factors can act on one or
multiple cell types. For example, PDGF is a potent regulator of
oligodendrocyte progenitor migration and proliferation, while IGF-1
acts both on neurons and myelin-forming cells to promote
myelination. In vivo the balance between proliferation and
differentiation appears to be controlled by different sets of
growth factors locally synthesized in the CNS.
[0062] The relationship between axons and glia is reciprocal and
complex. Astrocytes can block axon nerve fibers by contact. In
vitro however, axons will grow on astrocyte monolayers although not
in 3-dimentional cultures. Myelinating Schwann cells, which are
activated and have an extensive extracellular matrix, are
permissive for axonal growth both in the mammalian PNS and CNS. The
ability of Schwann cells to influence regeneration derives in part
from their ability to produce trophic factors including NGF, BDNF
and CNTF, but also their expression of cell adhesion molecules
known to promote neurite growth. In the CNS, altered levels of
proteins from neuroglia which induce extracellular matrix
degradation (MTI-MMP and MMP-2) have been implicated in Alzheimer's
disease and multiple sclerosis.
[0063] In Parkinson's disease, a loss of 60% of substantia nigra
cells results in the manifestations of clinical symptoms including
bradykinesia and tremors. Current therapies are directed at
replacing the deficient neurotransmitter, dopamine, or maintaining
its presence by blocking its metabolism. Glial cells may also
participate in the pathophysiology of this disease. Glial cells can
produce trophic factors that may stimulate neural survival or
produce toxic compounds that may be involved in neural
degeneration. By co-incubation of the different cell types, and
administering potential therapeutics, the cell specific change in
the context of the complex interplay between the cell types can be
assessed with the technology of this invention.
[0064] In the context of this invention, a microenvironment
harboring both human brain supporting cells neuroglia and
dopaminergic neural cells can be subjected to a potential
therapeutic agent would be utilized for drugs for possible use in
Parkinson's disease. The source of the dopaminergic cells may
include, but is not limited to, neural stem cells, primary cells
from the basal ganglia, limbic system, substantia nigra,
hypothalamus, the medulla cortex or other cells lines of neural or
adrenal origin (such as PC12). By monitoring how such agent changes
the phenotype of both, neuroglia and dopaminergic neural cells one
can better predict the likely therapeutic outcome. For example,
certain drugs may elicit a growth factor from the neuroglia or
neural cells that would act in a paracrine fashion on the other
cell type to maintain a specific architecture or a healthier
state.
[0065] Some examples of the functional biology to be assessed
morphologically would include: general cell health of both cell
types would be determined by cytoskeletal characterization
(microtubule, microfilament, actin) and changes in mitochondrial
number and intracellular placement of the organelles. Determination
of changes in cell shape by assessment of neurite outgrowth and
neuroglia extensions, and hypertrophy of each cell type.
Determining tight junctional complexes between similar and
different cell types would be assessed by antibodies to N-catenin
and N-cadherin. The frequency and shape of processes and
interconnections between cell types such as astrocyte foot process
and oligodendrocyte membranous sheets would be determined. All of
these morphological changes can be identified by conventional image
analysis technology of the type described below.
[0066] For Parkinson's disease, the ideal therapeutic would
maintain the healthiest cells types (demonstrated by cytoskeletal
characterization and organelle placement) with extensive
arborization of the neural cells while maintaining a healthy, but
non-reactive neuroglia (as demonstrated by few extensions; no
increase in the intracellular proteins GFAP or CD45).
[0067] Alzheimer's disease is characterized by progressive loss of
memory and often cognitive functions due to degeneration of
cholinergic neurons in the basal forebrain. The disease accounts
for the vast majority of cases of senile dementia. In addition to
the loss of cholinergic nerve terminals in the cerebral cortex and
hippo campus, there is a severe loss of cholinergic neurons in the
nucleus basalis of Meynert and related nuclei that contain the cell
bodies of cholinergic neurons which project to the hippo campus,
the amygdala, and all the neocortex. The cause of the degeneration
of the cholinergic neurons in Alzheimer's is unknown. However,
cytopathological hallmarks include alterations in both nerve fibers
and reactive glial cells.
[0068] In the context of this invention, a microenvironment
harboring both neuroglia and neural cells can be subjected to a
potential therapeutic agent would be utilized for Alzheimer's
disease. The interaction between the divergent cell types can give
a better assessment of the likely therapeutic outcome. The source
of the basal forebrain cells may include, but is not limited to,
neural stem cells, primary cells from the basal forebrain,
hippocampus, neocortical regions, or cells lines of neural origin.
By monitoring how such agents change the phenotype of both,
neuroglia and cholnergic neural cells one can better predict the
likely therapeutic outcome. As in the Parkinson's example, certain
drugs may elicit a growth factor or toxic factor from the neuroglia
or neural cells that would act in a paracrine fashion on the other
cell type to maintain or compromise its architecture or health
status.
[0069] It is important to assess the changes in both cell types
simultaneously after administration of potential drugs. To be
assessed morphologically would include cell shape that would
include arborization of the neural cell body (neurite outgrowth)
and neuroglia body, and hypertrophy of each cell type. The
formation of tight junctions between the different cell types
including N-catenin and N-cadherins, and processes such as
astrocyte foot process and oligodendrocyte membrane sheets. The
health of both cell types would be determined by cytoskeletal
characterization (microtubule, microfilament, actin) and changes in
mitochondrial number and intracellular organelle placement.
[0070] For Alzheimer's disease, the ideal therapeutic would
maintain the healthiest neural cells types with arborization and
maintain a healthy but non-reactive neuroglia (few extensions; no
increase in GFAP or CD45) with particular emphasis on maintaining a
non-reactive astrocyte population.
[0071] Amyotrophic lateral sclerosis (ALS; Lou Gehrig's disease) is
a progressive disease characterized by gradual degeneration of the
motor neurons in the spinal cord and brain stem. The progressive
neural loss eventually results in a severe muscular weakness and
wasting, increasing difficulty in breathing and swallowing, with
many sufferers become victims of pneumonia.
[0072] In the context of this invention, a microenvironment
harboring both human peripheral supporting cells (Schwann and
satellite cells) and neural CNS or PNS can be subjected to a
potential therapeutic agent would be utilized for cell maintenance.
By monitoring how such agent changes the phenotype of both,
supporting cells and peripheral neural cells, one can better define
a successful therapeutic outcome. The source of the motor neuron
cell may include, but is not limited to, neural stem cells, primary
cells from the spinal cord, or motor cells lines of neural origin.
By monitoring how such agents change the phenotype of both
neuroglia and motor neurons cells one can better predict the likely
therapeutic outcome. Certain drugs may elicit a growth factor or
toxic factor from the neuroglia or neural cells that would act in a
paracrine fashion on the other cell type to maintain or compromise
its architecture or a healthier state.
[0073] Similar to the Parkinson's example, co-culture of these
cells would result in a combination of effects with the neural
neuroglia interplay. The ideal effect that would define a useful
therapeutic would include that the motor neurons should maintain a
healthy phenotype as demonstrated by proper organelle placement and
number, cytoskeletal maintenance and numerous arboreal extensions.
Proper organelle placement and number, cytoskeletal maintenance of
cell shape, and a population responsive to known growth factors
would complete the ideal phenotype for the Schwann cells.
[0074] 3. Cardiac Disease
[0075] Heart failure in one of the leading causes of death in the
United States. Although myocardial infarction may result in
substantial loss of functional myocardium and lead to acute cardiac
decompensation, it is now well recognized that the subsequent
changes in the noninfarcted myocardium play an important role in
the longer term. This process, which involves changes in myocytes
(hypertrophy, dysfunction) and in the extracellular matrix (ECM),
has been termed remodeling.
[0076] In vitro hypoxia induces apoptosis in cardiac myocytes but
does not cause apoptosis in cardiac fibroblasts. At the molecular
level, an increase in ECM proteins is observed in the chronically
ischemic heart that are produced predominately by cardiac
fibroblasts that surround the cardiac myocytes. Overproduction of
the ECM eventually results in adverse effects on the contractility
of the myocardium. A wide variety of growth factors can regulate
cell proliferation and ECM synthesis. One of these growth factors
is connective tissue growth factor (CTGF). Significant upregulation
of CTGF is detected in human heart samples derived from patients
diagnosed with cardiac ischemia and in animal models of myocardial
infarction. CTGF expression is regulated by TGF-b in both cardiac
fibroblasts and cardiac myocytes.
[0077] There are significant increases in mRNA expression of
several proinflammatory cytokines and growth factors by cardiac
nonmyocyte cells isolated from the noninfarcted myocardium. It is
likely that these cytokines and growth factors produced by the
nonmyocytes play an important role in postinfarct myocytes
hypertrophy and contractile protein expression. This highlights the
importance of examining cell specific changes within the context of
cell to cell interactions. Alterations in nonmyocytes may be
involved in the mechanism by drugs such as angiotensin-converting
enzyme inhibitors favorably impact on the postinfarction remodeling
process.
[0078] In the context of this invention, a microenvironment
harboring both human nonmyocyte cells and cardiac myocytes can be
subjected to a potential therapeutic agent and the cell specific
responses within the context of cell to cell interaction can be
evaluated. By monitoring how such agent changes the phenotype of
both, one can better define a successful therapeutic. The source of
the nonmyocytes cell may include, but is not limited to, stem
cells, primary cells, fibroblasts and endothelial cells lines of
cardiac origin. The source of myocytes may include, but is not
limited to primary fetal, neonatal and adult human and animal
ventricular myocytes, stem cells, and atrial and ventricular
cardiac cell lines and cell lines derived from other sources
including skeletal and smooth muscle.
[0079] In one example, certain drugs may protect the cardiac cells
from hypoxia but induce cytokines release from myocytes and
nonmyocytes that would act in a paracrine and autocrine fashion on
the nonmyocytes cells to initiate fibrosis and subsequent
pathology. This would not be an ideal therapeutic. The ideal cell
phenotypes induced by a possible therapeutic would maintain healthy
responsive cardiac myocytes as demonstrated by cytoskeletal
characterization, organelle placement and intracellular calcium
levels, but with no proliferation of the nonmyocyte cells, and
maintaining a low and constant amount of the protein synthesis
machinery (ER and Golgi) within the cell.
[0080] 4. Other Biological Conditions
[0081] The above examples are by no means the only conditions that
may be analyzed via the methods of this invention. Another example
is auto-immune disease, which may be investigated using one cell
type that is an immune system cell and a second cell type that is
attacked by cells of the first cell type in the auto-immune
disease. In another example, the biological condition is Type II
diabetes. This condition may be investigated with a first cell type
that is a muscle cell and a second cell type is an adipocyte cell
type, an immune cell type, or a vascular cell type.
[0082] Markers and Descriptors for the Quantitative Phenotype
[0083] Regarding quantitative phenotypes, a compound or other agent
is analyzed in terms of its effect on the two or more interacting
cell lines. More specifically, the compound is linked to a
particular phenotype for each of these cell lines. Two or more
values or measures of cellular attributes characterize each
phenotype. These attributes are quantified in the context of
specific cellular markers, as described below.
[0084] The phenotype may be characterized by administering a
compound of interest in various concentrations to the two or more
interacting cell lines. In each example within this matrix, the
attributes of interest are measured. Ultimately, certain phenotypic
features (combinations of attribute values) are associated with the
compound of interest. These features provide a template for the
phenotype. Particular quantitative phenotypes can be characterized
by comparison to known quantitative phenotypes. These other
quantitative phenotypes appear only when a particular condition
occurs; e.g., a compound acts according to an associated mechanism
of action that results in a phenotypic signature.
[0085] The known phenotypes may also be generated by genetic
alteration via a genetic or epigenetic process that affects the
expression level or activity of a particular protein. In the
context of drug discovery, a gene encoding for a particular target
can be genetically knocked out, under expressed, over expressed,
expressed in a non-native state, etc. More generally, any cellular
stimulus (e.g., radiation level and type, gravity level, magnetic
field, acoustic perturbations, etc.) can be used to generate the
cell line of interest. Importantly, this stimulus affects the
phenotype and can be correlated therewith.
[0086] This may be accomplished via standard procedures involving
genomic modification, translation or transcription apparatus
modification (e.g., use of antisense nucleic acids), blocking
target activity (using antibodies to a receptor site for example),
and the like. These processes will generally affect the phenotype
in some quantifiable way. Importantly, they clearly and
unambiguously define a cellular phenotype associated with altering
the activity of the target protein.
[0087] Analyzing biological conditions based upon phenotype can
take many paths. Most will involve some mathematical basis. For
example, the phenotype defined at can be represented as a
fingerprint or vector comprised of multiple scalar values of
cellular attributes (as described above). The phenotype
representation can then be compared against known phenotypes
characterized by the same format (e.g., they are all characterized
as vectors having the same attribute set, but with different values
of the attributes). The comparison may be as simple as an Euclidean
distance or more sophisticated as a neural network or multivariate
statistical correlation.
[0088] The known compounds and associated phenotypes may be stored
as database records or other data structures that can be queried or
otherwise accessed as part of the identification procedure. The
compounds may also be associated with other relevant data such as
clinical toxicity, cellular toxicity, hypersensitivity, mechanism
of action, etc. (when available).
[0089] FIG. 2 illustrates a representative block flow diagram of
simplified process steps of a method for developing quantitative
phenotypes resulting from the effects of an agent on one or more
portions of one or more cells. This diagram is merely an
illustration and should not limit the scope of the claims herein.
One of ordinary skill in the art would recognize other variations,
modifications, and alternatives. In operation 200, the two
interacting cell types are provided. At this point, the cells can
be live, dead, or fixed cells, or cell fractions. The cells also
can be in one of many cell cycle stages, including G0, G1, S, G2 or
M phase, M phase including the following cell cycle stages:
interphase, prophase, prometaphase, metaphase, anaphase, and
telophase.
[0090] Cell markers tracked in presently preferable embodiments can
include proteins, protein modifications, genetically manipulated
proteins, exogenous proteins, enzymatic activities, nucleic acids,
lipids, carbohydrates, organic and inorganic ion concentrations,
sub-cellular structures, organelles, plasma membrane, adhesion
complex, ion channels, ion pumps, integral membrane proteins, cell
surface receptors, G-protein coupled receptors, tyrosine kinase
receptors, nuclear membrane receptors, ECM binding complexes,
endocytotic machinery, exocytotic machinery, lysosomes,
peroxisomes, vacuoles, mitochondria, Golgi apparatus, cytoskeletal
filament network, endoplasmic reticulum, nuclear membrane,
proteosome apparatus, chromatin, nucleolus, cytoplasm, cytoplasmic
signaling apparatus, microbe specializations and plant
specializations.
[0091] The following table illustrates some markers and cell
components that may be used in embodiments of the present
invention. Other markers can be used in various embodiments without
departing from the scope of the invention.
1 Cell component Marker Disease State Plasma membrane Carbocyanine
dyes Apoptosis-Cancer (including overall cell Phosphatidylserine
Apoptosis-Neural shape) Various lipids degenerative Ds
Glycoproteins Adhesion complexes Cadherins Thrombosis Integrins
Metastasis Occludin Wound healing Gap junction Inflammatory Ds ERM
proteins Dermatologic Ds CAMs Catenins Desmosomes Ion Channels and
Pumps Na/K Atpase Cystic fibrosis Calcium channels Depression
Serotonin reuptake pump Congestive Heart Failure CFTR Epilepsy
SERCA G coupled receptors .beta. adrenergic receptor Hypertension
Angiotensin receptor Heart Failure Angina Tyrosine kinase receptors
PDGF receptor Cancer FGF receptor Wound healing IGF receptor
Angiogenesis Cerebrovascular Ds ECM binding complexes Dystroglycan
Muscular Dystrophy Syndecan Endocytotic machinery Clathrin
Alzheimer's Ds Adaptor proteins COPs Presenilins Dynamin Exocytotic
machinery SNAREs Epilepsy Vesicles Tetanus Systemic Inflammation
Allergic Reactions Lysosomes Acid phosphatase Viral diseases
Transferrin Lysotracker Red Peroxisomes/Vacuoles Neural
degenerative Ds Mitochondria Caspases Apoptosis Apoptosis inducing
factor Neural degenerative Ds F1 ATPase Mitochondrial Cytopathies
Fluorescein Inflammatory Ds Cyclo-oxygenase Metabolic Ds
Mitotracker Red Mitotracker Green Golgi Apparatus Lens Culinaris
DiOC6 carbocyanine dye COPs Antibodies specific for Golgi
Cytoskeletal Filament Microtubules Cancer Networks Actin Neural
degenerative Ds Intermediate Filaments Inflammatory Ds Kinesin,
dynein, myosin Cardiovascular Ds Microtubule associated Skin Ds
proteins Actin binding proteins Rac/Rho Keratins GFAP Von
Wiltbrand's factor Endoplasmic Reticulum SNARE Neural degenerative
Ds PDI Ribosomes Nuclear Membrane Lamins Cancer Nuclear Pore
Complex Proteosome Apparatus Ubiquityl transferases Cancer
Chromatin DNA Cancer Histone proteins Aging Histone deacetylases
Telomerases Nucleolus Phase markers Cytoplasm Intermediary
Metabolic Cancer Enzymes BRCA1 Cytoplasmic Signaling Calcium
Cardiovascular Ds Apparatus Camp Migraine PKC Apoptosis pH Cancer
Microbe Specializations Flagella Infectious Ds Cilia Cell Wall
components: Chitin synthase Plant specializations Choloroplast Crop
Protection Cell Wall components
[0092] At 202, each of the two or more interacting cell lines is
manipulated. Such manipulations represent exposure to agents or
stimuli as described above. In general, relevant manipulations can
comprise exposure to one or any combination of chemical,
biological, mechanical, thermal, electromagnetic, gravitational,
nuclear, or temporal factors, for example. For example,
manipulations could include exposure to chemical compounds,
including compounds of known biological activity such as
therapeutics or drugs, or also compounds of unknown biological
activity. Or exposure to biologics that may or may not be used as
drugs such as hormones, growth factors, antibodies, or
extracellular matrix components. Or exposure to biologics such as
infective materials such as viruses that may be naturally occurring
viruses or viruses engineered to express exogenous genes at various
levels. Bioengineered viruses are one example of manipulations via
gene transfer. Other means of gene transfer are well known in the
art and include but are not limited to electroporation, calcium
phosphate precipitation, and lipid-based transfection.
Manipulations could also include delivery of antisense
polynucleotides by similar means as gene transfection. Other
genetic manipulations include gene knock-outs or gene mutations.
Manipulations also could include cell fusion. Physical
manipulations could include exposing cells to shear stress under
different rates of fluid flow, exposure of cells to different
temperatures, exposure of cells to vacuum or positive pressure, or
exposure of cells to sonication. Manipulations could also include
applying centrifugal force. Manipulations could also include
changes in gravitational force, including sub-gravitation.
Manipulations could include application of a constant or pulsed
electrical current. Manipulations could also include irradiation.
Manipulations could also include photobleaching, which in some
embodiments may include prior addition of a substance that would
specifically mark areas to be photobleached by subsequent light
exposure. In addition, these types of manipulations may be varied
as to time of exposure, or cells could be subjected to multiple
manipulations in various combinations and orders of addition. Of
course, the type of manipulation used depends upon the
application.
[0093] At 204, one or more descriptors of a state in the portions
of the cells in the presence of the manipulation can be determined
using the images collected on the imaging system. Descriptors can
comprise scalar or vector values, representing quantities such as
geometric parameters (e.g., size, shape, and/or location of the
organelle) and composition (e.g., concentration of particular
biomolecules within the organelle, as represented by intensity and
gray level, for example).
[0094] Other types of descriptors include, but are not limited to,
one or any combination of characteristics such as a cell count, an
area, a perimeter, a length, a breadth, a fiber length, a fiber
breadth, a shape factor, a elliptical form factor, an inner radius,
an outer radius, a mean radius, an equivalent radius, an equivalent
sphere volume, an equivalent prolate volume, an equivalent oblate
volume, an equivalent sphere surface area, an average intensity, a
total intensity, an optical density, a radial dispersion, and a
texture difference. These descriptors can be average or standard
deviation values, or frequency statistics from the descriptors
collected across a population of cells. These descriptors can be
further reduced using other methods such as principal component
analysis and the like. In some embodiments, the descriptors include
features from different cell portions or cell types. That is, a
first feature can be from a nuclei and a second feature is from
another cell structure such as Golgi apparatus, mitochondria,
spacing between cell structures or cells themselves, as well as
many others.
[0095] The quantitative phenotypes employed in this invention may
include any descriptor that represents some aspect of the
appearance of a cell. Examples of some descriptors that have been
found suitable are included in the following table. Other
descriptors can also be used without departing from the scope of
the invention.
2 Name of Parameter Explanation/Comments Count Number of objects
Area Perimeter Length X axis Width Y axis Shape Factor Measure of
roundness of an object Height Z axis Radius Distribution of
Brightness Radius of Dispersion Measure of how dispersed the marker
is from its centroid Centroid location x-y position of center of
mass Number of holes in closed objects Derivatives of this
measurement might include, for example, Euler number (= number of
objects - number of holes) Elliptical Fourier Analysis (EFA)
Multiple frequencies that describe the shape of a closed object
Wavelet Analysis As in EFA, but using wavelet transform Interobject
Orientation Polar Coordinate analysis of relative location
Distribution Interobject Distances Including statistical
characteristics Spectral Output Measures the wavelength spectrum of
the reporter dye. Includes FRET Optical density Absorbance of light
Phase density Phase shifting of light Reflection interference
Measure of the distance of the cell membrane from the surface of
the substrate 1, 2 and 3 dimensional Fourier Spatial frequency
analysis of non closed objects Analysis 1, 2 and 3 dimensional
Wavelet Spatial frequency analysis of non closed objects Analysis
Eccentricity The eccentricity of the ellipse that has the same
second moments as the region. A measure of object elongation. Long
axis/Short Axis Length Another measure of object elongation. Convex
perimeter Perimeter of the smallest convex polygon surrounding an
object Convex area Area of the smallest convex polygon surrounding
an object Solidity Ratio of polygon bounding box area to object
area. Extent proportion of pixels in the bounding box that are also
in the region Granularity Pattern matching Significance of
similarity to reference pattern Volume measurements As above, but
adding a z axis
[0096] At 205, a database of cell information can be provided to
allow characterization of the quantitative phenotypes at issue.
Next, at 206, a plurality of quantitative phenotypes can be
searched from a database of cell information in order to locate
interesting stored phenotypes based upon one of the phenotypes
generated by the manipulation. Then, at 208, effects of the
manipulation are predicted based upon the properties of the located
phenotypes. Properties can comprise toxicity, specificity against a
subset of tumors, mechanisms of chemical activity, mechanisms of
biological activity, structure, adverse biological effects,
biological pathways, clinical effects, cellular availability,
pharmacological availability, pharmacodynamic properties, clinical
uses and indications, pharmacological properties, such as
absorption, excretion, distribution, metabolism and the like.
[0097] In a particular embodiment, operation 206 comprises
determining matching descriptors in the database corresponding to a
prior administration of the manipulation to the descriptors of the
present administration of the manipulation. In a particular
embodiment according to the present invention, combinations of
measurements of scalar values can provide predictive information. A
database can be provided having one or more "cellular fingerprints"
comprised of descriptors of cell-substance interactions of drugs
having known mechanisms of action with cells. Such descriptors can
be analyzed, classified, and compared using a plurality of
techniques, such as statistical classification and clustering,
heuristic classification techniques, a technique of creating
"phylogenetic trees" based on various distance measures between
descriptors from various drugs. In this embodiment, numeric values
for the descriptors can be used by comparison techniques. A
phylogenetic tree can be created that illustrates a statistical
significance of the similarity between descriptors for the drugs in
the database. Because the drugs used to build the initial database
are of known mechanism, it can be determined whether a particular
scalar value in a descriptor is statistically predictive. Finally,
a compound descriptor with no known mechanism of action can be
queried against the database and be statistically compared and
classified among the drugs in the database that the compound most
resembles.
[0098] In a particular embodiment, relationships between measured
morphological properties of images and physiological conditions can
be determined. Relationships can include, for example, treatment of
different cell lines with chemical compounds, or comparing cells
from a patient with control cells, and the like. In a presently
preferred embodiment, comparisons can be performed on acquired
image features. Some embodiments can comprise statistical and
neural network--based approaches to perform comparisons of various
features. The foregoing is provided as merely an example, and is
not intended to limit the scope of the present invention. Other
techniques can be included for different types of data.
[0099] In some embodiments, classification, clustering and other
types of predictive data analysis can be performed on features
extracted from cell images. In a presently preferable embodiment,
statistical procedures for comparisons, classification and
clustering are performed on data obtained from imaging cells.
Embodiments can perform such analysis based upon factors such as
numerical value, statistical properties, relationships with other
values, and the like.
[0100] Markers can be from any of a large variety of normal and
transformed cell lines from sources such as for example, human
beings, fungi, or other species. The markers can be chosen to cover
many areas of cell biology, such as, for example markers comprising
the cytoskeleton of a cell. The cytoskeleton is one of a plurality
of components that determine a cell's architecture, or
"cytoarchitecture". A cytoarchitecture comprises structures that
can mediate most cellular processes, such as cell growth and
division, for example. Because the cytoskeleton is a dynamic
structure, it provides a constant indication of the processes
occurring within the cell. The cytoarchitecture of a cell can be
quantified to produce a one or more scalar values corresponding to
many possible cellular markers, such as cytoskeleton, organelles,
signaling molecules, adhesion molecules and the like. Such
quantification can be performed in the presence and absence of
drugs, peptides, proteins, anti-sense oligonucleotides, antibodies,
genetic alterations and the like. Scalar values obtained from such
quantification can provide information about the shape and
metabolic state of the cell.
[0101] In a presently preferred embodiment, scalar values can
comprise morphometric, frequency, multi-dimensional parameters and
the like, extracted from one or more fluorescence images taken from
a number of cellular markers from a population of cells. Two or
more such scalar values extracted from a plurality of cell lines
and markers grown in the same condition together comprise a unique
"fingerprint" or quantitative phenotype that can be incorporated
into a database. Such cellular phenotypes will change in the
presence of drugs, peptides, proteins, antisense oligonucleotides,
antibodies or genetic alterations. Such changes can be sufficiently
unique to permit a correlation to be drawn between similar
phenotypes. Such correlations can predict similar properties or
characteristics with regard to mechanism of action, toxicity,
animal model effectiveness, clinical trial effectiveness, patient
responses and the like. In a presently preferred embodiment, a
database can be built from a plurality of such phenotypes from
different cell lines, cellular markers, and compounds having known
mechanisms of action (or structure, or gene response, or
toxicity).
[0102] The present invention also employs database to facilitate
quantitative phenotype comparisons. Once a set of
features/descriptors has been extracted, the feature set may be
used to populate a database. Accordingly, the database includes
many sets of features, where each set corresponds to a different
manipulation for a selected cell. A database can be provided having
one or more quantitative phenotypes of cell substance interactions
for drugs having known mechanisms of action with cells. Such
phenotypes can be compared against those generated during
multi-cell type experiments of this invention using a variety of
algorithms. Such algorithms can comprise techniques for statistical
classification, statistical clustering, distance based clustering,
linear and non-linear regression analysis, self-organizing
networks, rule-based classification, etc. One may also employ a
technique of creating "phylogenetic trees" of a statistical
similarity between the fingerprints from various drugs.
[0103] In some cases the extracted features may be viewed as simple
features, from which composite features can be generated. Such
composite features are sometimes more convenient to store and/or
visualize than a collection of simple features. More than one
simple feature can be combined in a variety of different ways to
form these composite features. In particular, the composite feature
can be any function or combination of a simple feature and other
composite features. The function can be algebraic, logical,
sinusoidal, logarithmic, linear, hyperbolic, statistical, and the
like. Alternatively, more than one simple feature can be combined
in a functional manner (e.g., arithmetic, algebraic). As merely an
example, the composite feature equals a sum or product of feature 1
and feature 2, where these features correspond to the same
manipulation.
[0104] Microenvironments
[0105] As mentioned, it is generally preferable to have the first
and second cell lines interact prior to, during, and after
administering a test compound or other agent. Various cell support
designs allow this. In a simple design, cells from two different
cell lines are simply plated together on a flat surface of a
support vessel such as a well of an plate. FIGS. 3A and 3B depict
this scenario. As shown in FIG. 3A, a cell culture device 301
includes a rigid support structure 303 such as a polymeric
cup-shaped (or generally concave) structure. This structure may be
fabricated from any structurally suitable and inert material such
as polycarbonate, polypropylene, polyvinyl chloride, etc. At the
base of structure 303 a layer of cell growth support matrix such as
a feeder layer or "Matrigel" matrix (Becton-Dickinson, Franklin
Lakes, N.J.). The additives necessary to support cell growth are
well known to those of skill in the art. On the support matrix,
cells 307 of a first cell type and cells 309 of a second cell type
are growing and interacting. As indicated above, the interaction
may include contact interactions and exposure to factors produced
by the other cell type(s). The support matrix 305 may be chosen to
facilitate such interaction.
[0106] One parameter to consider in controlling the
microenvironment is the relative percentages of the different cell
types. Preferably, the percentages are chosen to reflect the in
vivo environment of the cells. For example, an investigation of
Parkinson's disease might employ a microenvironment in which the
ratio of neurons to neuroglial cells matches that of the substantia
nigra. Another factor to consider is the degree to which the cells
are dispersed or localized in the microenvironment. If the cells of
the cell types are tightly coupled during in vivo, then the
microenvironment should allow such coupling. If the cells are
widely separated by extracellular matrix or the like, then the
microenvironment should also allow such separation. Another factor
of relevance is the relative timing of the introduction of the two
or more cell types. In some designs, the cells of the first cell
type are added prior to the second cell type. This may represent
the actual stages of a biological condition such as tumor
growth.
[0107] FIG. 3B depicts a plate having multiple wells, some of which
are control wells. As shown in the figure, a plate section 315
includes three wells: well 317, control well 319, and control well
321. Each well has a different group of cells. Control well 319
contains only cells of a first cell type. Control well 321 contains
only cells of a second cell type. But well 317 includes cells from
both cell types. Images taken from all three wells can be compared
to determine the effect of the interaction between the cell types.
For example if the cells of the first cell type growing well 317
have a significantly different phenotype than the cells growing in
control well 319, then it can be deduced that the interaction
between the two cell types is significant. A potential therapeutic
that addresses this interaction is likely be superior to one that
does not.
[0108] FIG. 4 depicts another microenvironment of this invention.
This microenvironment maintains respective cell lines in discrete
locations (flat planes) but allows them to interact through soluble
factors and other species. In this figure, a cell culture device
401 includes an outer rigid support 403 and an insert (support)
405. As shown, insert 405 rests cleanly within the interior of
support 403. The bases of supports 403 and 405 rest in close
proximity to one another--e.g., within 1-5 millimeters. Each of
supports 403 and 405 support a different cell line. In this
example, support 403 supports a first cell line 411 and support 405
supports a second cell line 413. A three-dimensional layer of
support media 409 (e.g., Matrigel) contacts both cell lines 411 and
413. This allows factors produced by the individual cell lines to
diffuse to the other cell lines and influence their growth. Note
that the base of insert 405 includes a filter 407 or other porous
support that is rigid and fine enough to prevent cell movement, but
porous enough to allow easy ingress and egress of factors. In one
embodiment, a simple insert-type cell culture device is the
"Transwell" available from Corning Life Sciences, Acton, Mass.
[0109] Another microenvironment initially provides cells of the
first and second cell types at separate locations, but allows them
to move in three dimensions over the coarse of time. In some cases,
this will demonstrate cell migration (relevant to metastasis for
example), cell motility, and/or dendrite growth (of relevance in
studying neurological conditions). FIGS. 5A and 5B depict a
microenvironment 501 in which cells are initially separated, but
allowed to move over time.
[0110] As shown in FIG. 5A, cells of a first cell line 503
initially reside as a flat layer at the base of a rigid container
505. A layer of three-dimensional support/growth medium 507 (e.g.,
Matrigel) fills the lower portion of rigid container 505.
Initially, as shown in FIG. 5A, cells of the second cell type 509
reside as a flat layer on top of support medium 507. Thus, the
cells of the two cell lines are separated as two dimensional layers
but allowed to interact via soluble factors, etc.
[0111] Turning now to FIG. 5B, at some later time, the cells have
been growing and moving. As shown, in this hypothetical example,
some cells of first cell line 503 have grown dendrites 513 that
reach upward toward the cells of the second cell line 509. In
addition, some cells of the second cell line 509 have begun to
migrate downward, through the support medium 507, toward the cells
of the first cell line 503. Dendrites and migration patterns may be
identified by image analysis in the vertical direction. This
information can be used as separate descriptors to be incorporated
in the quantitative phenotypes of the respective cell lines.
[0112] Software/Hardware
[0113] Generally, embodiments of the present invention employ
various processes involving data stored in or transferred through
one or more computer systems. Embodiments of the present invention
also relate to an apparatus for performing these operations. This
apparatus may be specially designed and constructed for the
required purposes, or it may be a general-purpose computer
selectively activated or configured by a computer program,
programmed logic, and/or data structure stored in the computer. The
processes presented herein are not inherently related to any
particular computer or other apparatus. In particular, various
general-purpose machines may be used with programs written in
accordance with the teachings herein, or it may be more convenient
to construct a more specialized apparatus to perform the required
method steps. A particular structure for a variety of these
machines will appear from the description given below.
[0114] In addition, embodiments of the present invention relate to
computer readable media or computer program products that include
program instructions and/or data (including data structures) for
performing various computer-implemented operations. Examples of
computer-readable media include, but are not limited to, magnetic
media such as hard disks, floppy disks, and magnetic tape; optical
media such as CD-ROM disks; magneto-optical media; semiconductor
memory devices, and hardware devices that are specially configured
to store and perform program instructions, such as read-only memory
devices (ROM) and random access memory (RAM). The data and program
instructions of this invention may also be embodied on a carrier
wave or other transport medium. Examples of program instructions
include both machine code, such as produced by a compiler, and
files containing higher level code that may be executed by the
computer using an interpreter.
[0115] FIG. 6 illustrates a typical computer system that, when
appropriately configured or designed, can serve as an experimental
control and/or image analysis apparatus of this invention. The
computer system 600 includes any number of processors 602 (also
referred to as central processing units, or CPUs) that are coupled
to storage devices including primary storage 606 (typically a
random access memory, or RAM), primary storage 604 (typically a
read only memory, or ROM). CPU 602 may be of various types
including microcontrollers and microprocessors such as programmable
devices (e.g., CPLDs and FPGAs) and unprogrammable devices such as
gate array ASICs or general-purpose microprocessors. As is well
known in the art, primary storage 604 acts to transfer data and
instructions uni-directionally to the CPU and primary storage 606
is used typically to transfer data and instructions in a
bi-directional manner. Both of these primary storage devices may
include any suitable computer-readable media such as those
described above. A mass storage device 608 is also coupled
bi-directionally to CPU 602 and provides additional data storage
capacity and may include any of the computer-readable media
described above. Mass storage device 608 may be used to store
programs, data and the like and is typically a secondary storage
medium such as a hard disk. It will be appreciated that the
information retained within the mass storage device 608, may, in
appropriate cases, be incorporated in standard fashion as part of
primary storage 606 as virtual memory. A specific mass storage
device such as a CD-ROM 614 may also pass data uni-directionally to
the CPU.
[0116] CPU 602 is also coupled to an interface 610 that connects to
one or more input/output devices such as such as video monitors,
track balls, mice, keyboards, microphones, touch-sensitive
displays, transducer card readers, magnetic or paper tape readers,
tablets, styluses, voice or handwriting recognizers, or other
well-known input devices such as, of course, other computers.
Finally, CPU 602 optionally may be coupled to an external device
such as a database or a computer or telecommunications network
using an external connection as shown generally at 612. With such a
connection, it is contemplated that the CPU might receive
information from the network, or might output information to the
network in the course of performing the method steps described
herein.
[0117] In one embodiment, the computer system 600 is directly
coupled to (or forms part of) an image acquisition system such as
an optical imaging system that captures images of cells. Digital
images from the image generating system are provided via interface
612 for image analysis by system 600. Alternatively, the images
processed by system 600 are provided from an image storage source
such as a database or other repository of cell images. Again, the
images are provided via interface 612. Once in the image analysis
apparatus 600, a memory device such as primary storage 606 or mass
storage 608 buffers or stores, at least temporarily, digital images
of the cell. Typically, the cell images will show locations where
certain cell markers exist within the cells. In these images, local
values of an image parameter (e.g., radiation intensity) associated
with a cell marker correspond to amounts or levels of the marker at
the locations within the cell shown on the image. With this data,
the image analysis apparatus 600 can perform various image analysis
operations such as extracting relevant parameters from the cell
images, generating the quantitative phenotypes from the relevant
parameters, comparing quantitative phenotypes with standards and
with quantitative phenotypes from other cell types employed in the
studies, and storing the phenotypic information in a database. To
this end, the processor may perform various operations on the
stored digital image.
[0118] FIG. 7 is a simplified diagram of a complete system 710 for
evaluating a biological condition according to an embodiment of the
present invention. This diagram is merely an example and should not
limit the scope of the claims herein. The system 710 includes a
variety of elements including a computing device 713, which is
coupled to an image processor 715 and is coupled to a database 721.
The image processor receives information from an image capturing
device 717, which image processor and image capturing device are
collectively referred to as the imaging system herein. Suitable
imaging systems are discussed in PCT PCT/US00/13154, filed May 12,
2000 in the name of Sabry et al., previously incorporated herein by
reference. The image-capturing device obtains information from a
plate 719, which includes a plurality of sites for cells. These
cells can be biological cells that are living, fixed, dead, cell
fractions, cells in a tissue, and the like. The computing device
retrieves the information, which has been digitized, from the image
processing device and stores such information into the database. A
user interface device 711, which can be a personal computer, a work
station, a network computer, a personal digital assistant, or the
like, is coupled to the computing device.
[0119] Although the above has generally described the present
invention according to specific processes and apparatus, the
present invention has a much broader range of applicability. In
particular, the present invention is not limited to a particular
kind of data about a particular cell, but can be applied to
virtually any cellular data where an understanding about the
workings of the cell is desired. Thus, in some embodiments, the
techniques of the present invention could provide information about
many different types or groups of cells, substances, and genetic
processes of all kinds. Of course, one of ordinary skill in the art
would recognize other variations, modifications, and
alternatives.
* * * * *