U.S. patent application number 15/844879 was filed with the patent office on 2018-04-19 for cell analysis using dynamic biophysical methods.
The applicant listed for this patent is Bar-Ilan University. Invention is credited to Irena BRONSHTEIN BERGER, Yuval GARINI, Eldad KEPTEN.
Application Number | 20180106781 15/844879 |
Document ID | / |
Family ID | 52781685 |
Filed Date | 2018-04-19 |
United States Patent
Application |
20180106781 |
Kind Code |
A1 |
GARINI; Yuval ; et
al. |
April 19, 2018 |
CELL ANALYSIS USING DYNAMIC BIOPHYSICAL METHODS
Abstract
A method for identifying the expression or activity of a nuclear
protein in a cell, the method comprising: analyzing multiple images
of one or more labeled genetic entities; determining from said
analysis, a motile property of said one or more labeled genetic
entities; and identifying an expression or activity of a nuclear
protein of said cell associated with said motile property.
Inventors: |
GARINI; Yuval; (D.N. Misgav,
IL) ; BRONSHTEIN BERGER; Irena; (Jerusalem, IL)
; KEPTEN; Eldad; (D.N. Misgav, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Bar-Ilan University |
Ramat Gan |
|
IL |
|
|
Family ID: |
52781685 |
Appl. No.: |
15/844879 |
Filed: |
December 18, 2017 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
15044207 |
Feb 16, 2016 |
9885705 |
|
|
15844879 |
|
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 15/1429 20130101;
G01N 15/1427 20130101; G01N 2333/66 20130101; G01N 21/6408
20130101; G01N 33/5029 20130101; G01N 2015/1006 20130101; G01N
2015/1488 20130101; G01N 2333/78 20130101; G01N 21/6458 20130101;
G01N 15/1475 20130101; G01N 2333/4712 20130101 |
International
Class: |
G01N 33/50 20060101
G01N033/50; G01N 15/14 20060101 G01N015/14; G01N 21/64 20060101
G01N021/64 |
Foreign Application Data
Date |
Code |
Application Number |
Feb 16, 2015 |
GB |
1502579.4 |
Claims
1. A method for identifying the expression or activity of a protein
in a cell, the method comprising using at least one hardware
processor for: analyzing multiple images of one or more labeled
genetic entities; determining from said analysis, a motile property
of said one or more labeled genetic entities; and identifying an
expression or activity of a protein of said cell associated with
said motile property.
2. The method of claim 1, wherein said genetic entities are
selected from the group consisting of: telomeres, centromeres, a
specific gene locus, nuclear bodies, a nucleolus, and one or more
proteins bound thereto.
3. The method of claim 1, wherein said protein is different than
said genetic entity.
4. The method of claim 1, wherein said protein is selected from the
group consisting of Lamin A, Lamin B, Lamin C, Lap2.alpha.,
Lap2-beta, BAF, actin and emerin.
5. The method of claim 1, wherein analyzing said multiple images
further comprises eliminating a rotational and/or translational
movement attributed to a nucleus of said cell.
6. The method of claim 1, wherein said motile property comprises a
diffusion characteristic of said labeled genetic entities, wherein
determining said diffusion characteristic comprises calculating a
space function with respect to time, wherein said space function is
derived from multiple path trajectories calculated from said
multiple images of said one or more labeled genetic entities, and
is selected from any of: a) a mean square distance (MSD) function
and b) a mean square volume (MSV) function.
7. The method of claim 6, wherein said identifying comprises
applying to said space function a rule that associates any of: a) a
linear property of said space function with a normal diffusion
characteristic for said labeled genetic entities and a depletion of
a concentration of said protein, b) a logarithmic property of said
space function with an anomalous sub diffusion characteristic for
said labeled genetic entities and a normal concentration of said
protein, c) an exponentially increasing property of said space
function with an anomalous subdiffusion or superdiffusion
characteristic for said protein, and d) a linear property of said
space function for a first time period and a logarithmic property
of said space function for a second time period with a normal
diffusion characteristic for said labeled genetic entities within a
restricted volume of said cell.
8. A system for determining a cell characteristic, the system
comprising: a microscope configured to magnify one or more labeled
genetic entities; a camera configured to capture multiple images of
said one or more labeled genetic entities, wherein said camera is
coupled to said microscope; and a processor configured to analyze
said multiple images; determine from said analysis, a motile
property of said one or more labeled genetic entities; and identify
an expression or activity of a nuclear protein of said cell
associated with said motile property.
9. The system of claim 8, wherein said processor is further
configured to analyze said captured images by eliminating a
rotational and/or translational movement attributed to a nucleus of
said cell.
10. The system of claim 8, wherein said processor is configured to
determine said diffusion characteristic by calculating a space
function with respect to time.
11. The system of claim 8, wherein said genetic entities are
selected from the group consisting of: telomeres, centromeres, a
specific gene locus, nuclear bodies, a nucleolus, and one or more
proteins bound thereto.
12. The system of claim 8, wherein said protein is different than
said genetic entity, and wherein said protein is selected from the
group consisting of Lamin A, Lamin B, Lamin C, Lap2.alpha.,
Lap2-beta, BAF, actin and emerin.
13. The system of claim 8, wherein said motile property comprises a
diffusion characteristic of said labeled genetic entities.
14. The system of claim 8, wherein said space function is derived
from multiple path trajectories calculated from said multiple
images of said one or more labeled genetic entities.
15. The system of claim 8, wherein said space function is selected
from a mean square distance (MSD) function and/or a mean square
volume (MSV) function.
16. The system of claim 8, wherein said identifying comprises
applying to said space function a rule that associates any of: a) a
linear property of said space function with a normal diffusion
characteristic for said labeled genetic entities and a depletion of
a concentration of said protein, b) a logarithmic property of said
space function with an anomalous sub diffusion characteristic for
said labeled genetic entities and a normal concentration of said
protein, c) an exponentially increasing property of said space
function with an anomalous subdiffusion or superdiffusion
characteristic for said protein, and d) a linear property of said
space function for a first time period and a logarithmic property
of said space function for a second time period with a normal
diffusion characteristic for said labeled genetic entities within a
restricted volume of said cell.
Description
RELATED APPLICATION/S
[0001] This application is a divisional of U.S. application Ser.
No. 15/044,207 filed on Feb. 16, 2016, which claims the benefit of
priority of GB Patent Application No. 1502579.4 filed Feb. 16,
2015. The content of both applications is incorporated herein by
reference in its entirety.
FIELD OF THE INVENTION
[0002] The invention relates to the field of cell analysis.
BACKGROUND
[0003] It is common to divide dynamic processes in live cells to
one of three categories: diffusion of free or partly free proteins
and inter-cellular entities; binding and unbinding of proteins to
different inter-cellular entities; and structural changes to all or
part of the cell. In general, each one of these categories
describes a process that occurs in a different time scale, such as
described in Wachsmuth et al., Genome organization: Balancing
stability and plasticity. Biochimica et Biophysica Acta, 2008.
1783: p. 2061-2079.
[0004] Unbound molecules, such as a freely diffusing protein in a
cell may travel a distance of micrometer in a fraction of a
millisecond, whereas binding and unbinding of proteins may occur
over a time range of between a millisecond to several seconds, and
changes to cell structure typically happen over a time range of
minutes to hours. Below we describe few of the most relevant
methods for measuring the above-described processes in live cells.
These methods, with very few exceptions, require labeling the
studied proteins or structures with fluorescent dyes. Such methods
are well developed through inserted fluorescent molecules, or
through transfection of the cells with the DNA transcripts of
fluorescent proteins that expresses the fluorescent proteins in the
live cells.
[0005] Fluorescence correlation spectroscopy (FCS) is one common
method for measuring motion of molecules while they bounce in and
out of a small, defined region in a cell, as described in Ries, J.
and P. Schwille, Fluorescence correlation spectroscopy. Bioassays,
2012. 34: p. 361-368. An entity, such as a molecule, may be labeled
with a fluorescent dye and measured via fluorescence microscopy,
where the size of an illuminated spot may be bound by the optics of
the microscope used to measure it, and which may be limited by the
point spread function of the microscope optics. The intensity of
the illuminated spot may fluctuate and an autocorrelation function
of the intensity may be calculated. The typical time that a
particle remains within the small spot under measurement may
correspond to the typical time that the autocorrelation function
reduces to zero. This method may be applicable for measuring freely
diffusing entities in the cell, where the diffusion coefficients
are usually in the range of 1-100.times.10.sup.-4 .mu.m.sup.2/sec.
Although FCS may also be used for measuring binding processes, it
may become complex and the extracted data may depend on the model
that is being used for the analysis.
[0006] Another relevant method for measuring entity motion within a
cell is fluorescence recovery after photobleaching (FRAP). In this
method, both the diffusion and the binding properties of a selected
protein may be measured. This method typically requires labeling
the protein with a fluorescent dye. A sample of one or more cells
may be initially measured. A high-intensity laser may then mark, or
`burn` the fluorescing molecules within a defined region in a
technique also known as `bleaching`. The cellular sample may be
repeatedly measured. If the sample in the bleached area recovers,
one may extrapolate the typical time it takes the molecules to be
replaced within the bleached area.
[0007] Continuous photobleaching (CP) is third method for measuring
the motion of inter-cellular entities, and is typically simpler
than FRAP.
[0008] The foregoing examples of the related art and limitations
related therewith are intended to be illustrative and not
exclusive. Other limitations of the related art will become
apparent to those of skill in the art upon a reading of the
specification and a study of the figures.
SUMMARY
[0009] The following embodiments and aspects thereof are described
and illustrated in conjunction with systems, tools and methods
which are meant to be exemplary and illustrative, not limiting in
scope.
[0010] There is provided, in accordance with an embodiment, a
method for identifying the expression or activity of a protein in a
cell, the method comprising using at least one hardware processor
for: analyzing multiple images of one or more labeled genetic
entities; determining from said analysis, a motile property of said
one or more labeled genetic entities; and identifying an expression
or activity of a protein of said cell associated with said motile
property.
[0011] There is further provided, in accordance with an embodiment,
a computer program product for determining a cell characteristic,
the computer program product comprising a non-transitory
computer-readable storage medium having program code embodied
therewith, the program code executable by at least one hardware
processor to: analyze multiple images of one or more labeled
genetic entities; determine from said analysis, a motile property
of said one or more labeled genetic entities; and identify an
expression or activity of a nuclear protein of said cell associated
with said motile property.
[0012] There is provided, in accordance with an embodiment, a
system for determining a cell characteristic, the system
comprising: a microscope configured to magnify one or more labeled
genetic entities; a camera configured to capture multiple images of
said one or more labeled genetic entities, wherein said camera is
coupled to said microscope; and a processor configured to analyze
said multiple images; determine from said analysis, a motile
property of said one or more labeled genetic entities; and identify
an expression or activity of a nuclear protein of said cell
associated with said motile property.
[0013] There is further provided, in accordance with an embodiment,
a system for determining a cell characteristic, the system
comprising: a camera configured to capture multiple images of one
or more labeled genetic entities; and a processor configured to
analyze said multiple images; determine from said analysis, a
motile property of said one or more labeled genetic entities; and
identify an expression or activity of a protein of said cell
associated with said motile property.
[0014] There is provided, in accordance with an embodiment, an
apparatus for determining a cell characteristic, the apparatus
comprising: a processor configured to analyze multiple images of
one or more labeled intra-nuclear genetic entities; determine from
said analysis, a motile property of said one or more labeled
intra-nuclear genetic entities; and identify an expression or
activity of a nuclear protein of said cell associated with said
motile property.
[0015] In some embodiments, said camera comprises a charged coupled
device or a confocal microscope. In some embodiments, said
processor is further configured to analyze said captured images by
eliminating a rotational and/or translational movement attributed
to a nucleus of said cell. In some embodiments, said images are
captured via a camera comprising a charged coupled device or a
confocal microscope. In some embodiments, said processor is further
configured to identify by applying a rule to said space
function.
[0016] In some embodiments, said genetic entities are selected from
the group consisting of: telomeres, centromeres, a specific gene
locus, nuclear bodies, a nucleolus, and one or more proteins bound
thereto.
[0017] In some embodiments, said labeling comprises fluorescently
labeling. In some embodiments, said labeled genetic entities are
fluorescently labeled.
[0018] In some embodiments, said protein comprises a structural
nuclear protein. In some embodiments, said protein is different
than said genetic entity. In some embodiments, said protein is
selected from the group consisting of Lamin A, Lamin B, Lamin C,
Lap2.alpha., Lap2-beta, BAF, actin and emerin.
[0019] In some embodiments, analyzing said captured images further
comprises eliminating a rotational and/or translational movement
attributed to a nucleus of said cell.
[0020] In some embodiments, said images are captured via a charged
coupled device or via using confocal microscopy.
[0021] In some embodiments, said motile property comprises a
diffusion characteristic of said labeled genetic entities.
[0022] In some embodiments, determining said diffusion
characteristic comprises calculating a space function with respect
to time. In some embodiments, said space function is derived from
multiple path trajectories calculated from said multiple images of
said one or more labeled genetic entities. In some embodiments,
said space function is a mean square distance (MSD) function. In
some embodiments, the space function is a mean square volume (MSV)
function.
[0023] In some embodiments, said space function represents a space
spanned by said labeled genetic entities. In some embodiments, said
space function represents a space scanned by said labeled genetic
entities.
[0024] In some embodiments, said space function comprises a
two-dimensional area function. In some embodiments, said space
function comprises a three-dimensional volume function.
[0025] In some embodiments, said identifying comprises applying a
rule to said space function. In some embodiments, said rule
associates a linear property of said space function with a normal
diffusion characteristic for said labeled genetic entities and a
depletion of a concentration of said protein. In some embodiments,
said rule associates a logarithmic property of said space function
with an anomalous sub diffusion characteristic for said labeled
genetic entities and a normal concentration of said protein. In
some embodiments, said rule comprises associating an exponentially
increasing property of said space function with an anomalous
subdiffusion or superdiffusion characteristic for said protein. In
some embodiments, said rule comprises associating a linear property
of said space function for a first time period and a logarithmic
property of said space function for a second time period with a
normal diffusion characteristic for said labeled genetic entities
within a restricted volume of said cell.
[0026] There is provided, in accordance with an embodiment, a
method for determining the ratio of bound to unbound molecules in a
cell, comprising: obtaining, from a selected area of a cell, data
comprising time-resolved emitted light intensity measurements and
time-resolved fluorescent lifetime measurements of fluorescently
labeled bound molecules and fluorescently labeled unbound
molecules; determining from the obtained data 1) a continuous
photobleaching (CP) curve and 2) fluorescent lifetime histograms
for each of the time-resolved emitted light intensity measurements
and fluorescent lifetime measurements; and calculating a ratio of
the fluorescently labeled bound molecules to the fluorescently
labeled unbound molecules in the selected regions using the CP
curve and the fluorescent lifetime histograms.
[0027] In some embodiments, the method further comprises
fluorescently labeling the molecules; selecting the area;
illuminating the selected area with light corresponding to the
molecules' absorption spectrum; detecting the light intensity
emitted from the illuminated area; and measuring the fluorescent
lifetimes of the bound molecules to the unbound molecules.
[0028] In some embodiments, the method further comprises
synchronizing the illumination and detection steps.
[0029] In some embodiments, synchronizing comprises applying any of
a gating technique, time correlated single photon counting
technique, and phase modulation technique.
[0030] In some embodiments, calculating further comprises
correlating the CP curve with the fluorescent lifetime histograms
over time.
[0031] In some embodiments, illuminating comprises emitting a laser
pulse having a duration ranging between 1 and 1000 picoseconds.
[0032] In some embodiments, the molecules are fluorescently labeled
using fluorescing molecules selected from the group consisting of
molecular probes, fluorescent proteins, quantum dots, metallic
particles, and a dye measurable via bright field microscopy.
[0033] In some embodiments, the measuring step is performed using
any of: a fluorescent microscope, a transmission microscope, a
dark-field microscopy apparatus, a confocal microscope, a total
internal reflection microscopy apparatus, a super resolution
microscopy apparatus, and a fluorescence life-time microscopy
apparatus.
[0034] In some embodiments, the measuring step comprises
determining any of the fluorescent light intensity and an emitted
photon count per a predetermined time unit.
[0035] In some embodiments, the method further comprises repeating
performing said obtaining, determining and calculating steps for a
time window ranging from one millisecond to one hundred
seconds.
[0036] In some embodiments, measuring the light intensity comprises
optically filtering the spectral range of the excitation spectral
band from the emission spectral band of the emitted light.
[0037] In some embodiments, the method further comprises rendering
the calculated ratio of bound molecules to unbound molecules on a
user interface.
[0038] There is provided, in accordance with an embodiment, a
system for determining the ratio of bound to unbound molecules in a
cell, comprising: a processor configured to: obtain, from a
selected area of a cell, data comprising time-resolved emitted
light intensity measurements and time-resolved fluorescent lifetime
measurements of fluorescently labeled bound molecules and
fluorescently labeled unbound molecules; determining from the
obtained data 1) a continuous photobleaching (CP) curve and 2)
fluorescent lifetime histograms for each of the time-resolved
emitted light intensity measurements and fluorescent lifetime
measurements; and calculate a ratio of the fluorescently labeled
bound molecules to the fluorescently labeled unbound molecules in
the selected area using the CP curve and the fluorescent lifetime
histograms.
[0039] In some embodiments, the processor is further configured to
correlate the CP curve with the fluorescent lifetime histograms
over time.
[0040] In some embodiments, the light source comprises a pulsed
picosecond diode laser configured to emit a pulse having a duration
ranging between 1 and 1000 picoseconds.
[0041] In some embodiments, the molecules are fluorescently labeled
using fluorescing molecules selected from the group consisting of
molecular probes, fluorescent proteins, quantum dots, metallic
particles, and a dye measurable via bright field microscopy.
[0042] In some embodiments, the system further comprises: a light
source configured to illuminate the selected region with light
corresponding to the molecules' absorption spectrum; and a
microscope configured to detect the light intensity emitted from
the illuminated molecule.
[0043] In some embodiments, the microscope comprises any of an
avalanche photodiode, a single photon avalanche detector, and a
hybrid detector.
[0044] In some embodiments, the microscope comprises any of: a
fluorescent microscope, a transmission microscope, a dark-field
microscopy apparatus, a confocal microscope, a total internal
reflection microscopy apparatus, a super resolution microscopy
apparatus, and a fluorescence life-time microscopy apparatus.
[0045] In some embodiments, the system further comprises an
electronic counter configured to synchronize the illumination by
the light source with the measuring by the microscope, thereby
measuring the fluorescent lifetimes of the bound molecules to the
unbound molecules.
[0046] In some embodiments, synchronizing comprises applying any of
a gating technique, time correlated single photon counting
technique, and phase modulation technique.
[0047] In some embodiments, the electronic synchronizer is
configured to measure a time duration ranging from 0.5 picoseconds
to multiple seconds.
[0048] In some embodiments, the microscope is configured to measure
any of the fluorescent light intensity and an emitted photon count
per a predetermined time unit.
[0049] In some embodiments, the system further comprises an optical
filter configured to filter the spectral range of the excitation
spectral band from the emission spectral band of the emitted
light.
[0050] In some embodiments, the system further comprises a light
separator configured to separate the polarization of the excitation
light from the polarization of the fluorescent light.
[0051] In some embodiments, the system further comprises a user
interface configured to render the calculated ratio of bound
molecules to unbound molecules.
[0052] There is provided, in accordance with an embodiment, a
computer program product for determining a cell characteristic, the
computer program product comprising a non-transitory
computer-readable storage medium having program code embodied
therewith, the program code executable by at least one hardware
processor to: obtain, from a selected area of a cell, data
comprising time-resolved emitted light intensity measurements and
time-resolved fluorescent lifetime measurements of time-resolved
fluorescent lifetime measurements of fluorescently bound molecules
and time-resolved fluorescent lifetime measurements of
fluorescently unbound molecules; determine from the obtained data
1) a continuous photobleaching (CP) curve and 2) fluorescent
lifetime histograms for each of the time-resolved emitted light
intensity measurements and fluorescent lifetime measurements;
calculate a ratio of the time-resolved fluorescent lifetime
measurements of fluorescently bound molecules to the time-resolved
fluorescent lifetime measurements of fluorescently unbound
molecules in the selected area using the CP curve and the
fluorescent lifetime histograms.
[0053] In some embodiments, calculating comprises correlating the
CP curve with the fluorescent lifetime histograms over time.
[0054] In some embodiments, illuminating comprises emitting a laser
pulse having a duration ranging between 1 and 1000 picoseconds.
[0055] In some embodiments, the molecules are fluorescently labeled
using fluorescing molecules selected from the group consisting of
molecular probes, fluorescent proteins, quantum dots, metallic
particles, and a dye measurable via bright field microscopy.
[0056] In some embodiments, the measuring step is performed using
any of: a fluorescent microscope, a transmission microscope, a
dark-field microscopy apparatus, a confocal microscope, a total
internal reflection microscopy apparatus, a super resolution
microscopy apparatus, and a fluorescence life-time microscopy
apparatus.
[0057] In some embodiments, the time-resolved emitted light
intensity measurements comprise an emitted photon count per a
predetermined time unit.
[0058] In some embodiments, the program code is further executable
to repeat performing said obtaining, determining and calculating
steps for a time window ranging from one millisecond to one hundred
seconds.
[0059] In some embodiments, the program code is further executable
to render the ratio of bound molecules to unbound molecules on a
user interface.
[0060] In addition to the exemplary aspects and embodiments
described above, further aspects and embodiments will become
apparent by reference to the figures and by study of the following
detailed description.
BRIEF DESCRIPTION OF THE FIGURES
[0061] Exemplary embodiments are illustrated in referenced figures.
Dimensions of components and features shown in the figures are
generally chosen for convenience and clarity of presentation and
are not necessarily shown to scale. The figures are listed
below.
[0062] FIG. 1A shows a schematic diagram of a biological entity
having a bound subpopulation (indicated by the inner dashed circle)
and a freely diffusing subpopulation;
[0063] FIG. 1B shows a schematic diagram of a biological entity
having free fluorescent molecules which may be bound to freely
diffusing proteins within the biological entity;
[0064] FIG. 1C shows measurements of fluorescing intensity with
respect to time for a freely diffusing protein subject to
continuous photobleaching (CP);
[0065] FIG. 1D shows a schematic diagram of multiple bound
fluorescent molecules which may be bound to one or more proteins
that are bound within a biological entity;
[0066] FIG. 1E shows the fluorescing intensity of a bound molecule
exponentially decaying with respect to time when subject to CP;
[0067] FIG. 1F shows measurements of fluorescence intensity levels
with respect to time for labeled particles as measured in small
regions in a cell nucleus, in accordance with an embodiment of the
invention;
[0068] FIG. 1G shows a typical lifetime histogram for a fluorescent
molecule;
[0069] FIG. 1H shows a lifetime histogram measured for a population
of fluorescent molecules having two different lifetimes fitted
against two different exponential curves;
[0070] FIGS. 1I-1K show histogram distributions of average
lifetimes for a population of fluorescent molecules including two
subpopulations, each having a different lifetime, as measured over
a 1.4 nanoseconds (ns) time-window and a 4 ns time-window, where
FIG. 1I shows a distribution for a population whose ratio of short
lifetime sub-population to long lifetime sub-population is
approximately 80%, FIG. 1J shows a distribution for a population
whose ratio of short lifetime sub-population to long lifetime
sub-population ranges from approximately 20%-40%, and FIG. 1K shows
a distribution for a population whose ratio of short lifetime
sub-population to long lifetime sub-population is approximately
5%-10%;
[0071] FIG. 2A shows a three-dimensional time-path of a moving
particle, determined in accordance with an embodiment of the
invention;
[0072] FIG. 2B shows a two-dimensional path of a moving particle,
determined in accordance with an embodiment of the invention;
[0073] FIG. 3 illustrates various mean square displacement (MSD)
curves, <r.sup.2> as a function of time;
[0074] FIG. 4 illustrates exemplary experimental results of a
method for calculating the volume that is spanned by diffusing
particles within the nucleus of a cell within a given
timeframe;
[0075] FIG. 5 illustrates exemplary results of a method for
capturing images of particles labeled with green fluorescent
proteins (GFP) within the nucleus of a live cell;
[0076] FIGS. 6A-6B, taken together, illustrate normal diffusion and
sub-diffusion MSD patterns, in accordance with an embodiment
described in two different axes system;
[0077] FIGS. 7A-7B, taken together, illustrate experimental results
of calculated MSD values for wild type cells and cells with
depleted lamin A, in accordance with an embodiment of the
invention;
[0078] FIGS. 8A-8C, taken together, show experimental results of an
area scanned by 350 randomly selected telomeres during 15 minutes
as calculated using a convex hull algorithm, in accordance with an
embodiment of the invention. Data is shown for two cell types;
[0079] FIG. 9 illustrates an experimentally measured range of
distribution for Lmna.sup.+/+ cells, Lmna.sup.-/- cells and
Lap2.alpha..sup.-/- cells, in accordance with an embodiment of the
invention;
[0080] FIG. 10A illustrates a system for identifying a
characteristic of a cell by analyzing multiple images of one or
more labeled genetic entities, in accordance with an embodiment of
the invention; and
[0081] FIG. 10B illustrates a simplified conceptual illustration of
a system for determining the bound to unbound ratio of proteins
within a cell.
DETAILED DESCRIPTION
[0082] A system and method is disclosed herein to identify an
expression or activity of a protein in a cell by analyzing multiple
images of labeled genetic entities over time using the techniques
disclosed herein, and determining a motile property, such as
diffusion characteristic of the genetic entities.
[0083] In general, dynamic processes in live cells may be
categorized in several ways: a) as diffusion of free or partly free
proteins and entities, b) as binding and unbinding of proteins to
different subcellular entities, and c) as structural changes to
part or all of the cell, where each process type occurs over a
different time scale. For example, a freely diffusing protein in a
cell may travel over a distance of a micrometer in a fraction of a
millisecond, proteins may bind and unbind over a time duration
ranging from several milliseconds to a few seconds, and cellular
structural changes transpire over a time duration ranging from
several minutes to hours (a cell typically divides in about 20
hours). Due to the large range of time durations, different
measuring techniques may be used for these categories.
[0084] In one embodiment, a method is disclosed to identify the
type of dynamic process by imaging specific labeled proteins in a
live cell and analyzing the images. The fluorescent lifetime of
fluorescently labeled proteins expressed in cells are measured, and
the variations in intensity are tracked over time. Since the
fluorescent lifetime is dependent on the state of the protein, such
as if it is bound or free, the combination of the intensity as a
function of time with the lifetime information may allow
determining the ratio of bound to free proteins in the cell.
[0085] In another embodiment, the diffusion characteristic may be
determined by calculating a function of space that is covered by
the labeled genetic entities over time, and that may be derived
from multiple path trajectories determined from the multiple
images. The space function may comprise a two-dimensional area
function, or alternatively, a three-dimensional volume
function.
[0086] One or more rules may be applied to the space function to
associate a property, such as the shape or derivative of the space
function, with a diffusion characteristic corresponding to an
expression of the protein that may characterize the cell. For
example, a linear property of the function may be associated with a
normal diffusion for the labeled entities, a logarithmic property
of the function may be associated with an anomalous subdiffusion
for the labeled entities, and an exponential property of the
function may be associated with an anomalous subdiffusion or
superdiffusion for the labeled entities. Additionally, a linear
property of the function for a first time period and a logarithmic
property of the function for a second time period may be associated
with a normal diffusion characteristic for the labeled entities
within a restricted volume of the cell. A more detailed discussion
associating these diffusion types with the cell characteristic is
given below.
[0087] Without limiting the present invention to any particular
theory, an analysis of the diffusion properties of intra-nuclear
genetic entities may determine the presence or depletion of one or
more nuclear proteins that may indicate a genetic abnormality, or
phenotype of a disease, such as cancer, de novo syndrome, and/or an
inherited disease such as muscular dystrophy.
[0088] As used herein, the term "genetic entities" refers to a
chromosomal region that can be specifically labeled, such as
telomeres, centromeres, a specific gene locus, nuclear bodies such
as a promyelocytic leukemia protein (PML), the nucleolus, and/or
any proteins or nucleic acid molecule bound thereto. These genetic
entities may be labeled using any suitable means, such as via a
fluorescent probe including any of molecular probes, fluorescent
proteins, quantum dots, metallic particles, or a dye which, in some
embodiments, can be measured with a light or bright field
microscope.
[0089] In one embodiment, the determined diffusion characteristics
of labeled genetic entities may be applied to determine the
expression, activity, depletion and/or concentration levels of any
of the nuclear or structural nuclear proteins, including but not
limited to: Lamin A, Lamin B, Lamin C, lamina-associated
polypeptide-2 (Lap2).alpha., Lap2-beta, barrier-to-autointegration
factor (BAF), actin, or emerin.
[0090] In some embodiments, said nuclear proteins are inner nuclear
membrane (INM) proteins such as Lap1, Lap2, lamin B receptor (LBR),
emerin, and LEM domain-containing protein 3 (LEMD3; also known as
MAN1).
[0091] In one embodiment, the invention provides methods for
identifying the expression, activity, depletion and/or
concentration levels of a lamin protein such as lamin A. In some
embodiments, the methods of the inventions are useful for
diagnosing laminopathy or nuclear envelopathies in a subject in
need thereof, or whether said subject is at risk at developing
laminopathy or nuclear envelopathies. Laminopathies and other
nuclear envelopathies are known in the art and include Atypical
Werner syndrome, Barraquer-Simons syndrome, Buschke-Ollendorff
syndrome, Cardiomyopathy, Charcot-Marie-Tooth disease,
Emery-Dreifuss muscular dystrophy, Familial partial lipodystrophy
of the Dunnigan type (FPLD), Greenberg dysplasia,
Hutchinson-Gilford progeria syndrome (HGPS), Leukodystrophy,
Limb-girdle muscular dystrophy type 1B (LGMD1B), Lipoatrophy with
diabetes, Mandibuloacral dysplasia with type A lipodystrophy
(MADA), Mandibuloacral dysplasia with type B lipodystrophy (MADB),
Pelger-Huet anomaly (PHA), Pelizaeus-Merzbacher disease and tight
skin contracture syndrome, among others.
[0092] In another embodiment, the labeled genetic entity that is
analyzed to determine a motile property therefrom, is different
than the nuclear protein for which the expression or activity is
identified thereto.
[0093] The term "fluorescent marker" as used herein, refers to a
substance or a site thereof that is detectable by fluorescence
within a detectable range. These markers include proteins or
peptides that can be detected by fluorescence within or compounds
that show fluorescence within a specific wavelength range. Examples
of proteins used as fluorescence markers including but not limited
to green fluorescent protein (GFP), modified green fluorescent
protein (mGFP), enhanced green fluorescent protein (EGFP), red
fluorescent protein (RFP), modified red fluorescent protein (mRFP),
enhanced red fluorescent protein (ERFP), blue fluorescent protein
(BFP), enhanced blue fluorescent protein (EBFP), yellow fluorescent
protein (YFP), enhanced yellow fluorescent protein (EYFP), cyan
fluorescent protein (CFP) and enhanced cyan fluorescentprotein
(ECFP). FITC (Fluorescein isothiocyanate), TRTTC
(tetramethyl-rhodamine isothiocyanate), Cy3 (Cyanine 3), Cy5
(Cyanine 5) or rhodamine may be also used as a fluorescence or UV
indicator. Additional fluorescence or UV markers that can be used
in the methods of the invention are well under the skill of an
ordinary artisan. Other markers may include quantum dots of
different sizes, normally made of elements from the 2-6 columns of
the periodic table (e.g. CdTe. ZnTe) or nanometer-size metal
particles that can also fluoresce.
[0094] The genetic entities (also denoted herein "particles") may
be labeled by applying a fluorescent marker via a transfection
procedure. For example, telomeres in the cell's nucleus may be
labeled by transfecting the cell with DNA that expresses GFP-TRF2,
a protein that naturally caps the telomeres, resulting in a bright
and stable signal. Thus labeled, the dynamics of the marked
particles within the cell may be observed and measured over a
defined area and/or timeframe. The particle dynamics may be
affected by one or more characters of the cell, such as the
structure of the chromatin complex, which in turn may be affected
by the expression of a protein within the cell. Thus, an analysis
of the dynamics for one type of subcellular particle may be applied
to determine the presence, concentration or effect of an indicative
particle, such as a protein.
[0095] The particle dynamics may be tracked using any suitable
apparatus, such by capturing images of the particles using a
fluorescent microscope with an array detector such as charged
coupled device (CCD) or complementary metal oxide semiconductor
(CMOS) camera, or a confocal microscope with suitable illumination
laser light sources and single-point detectors such as
photomultiplier (PMT), avalanche photodiode (APD), hybrid detector,
or any other suitable detector that can measure very low light
intensity.
[0096] To measure time-intervals of 10.sup.-1-10.sup.2 seconds,
two-dimensional confocal microscopy at a frame-rate of 2 Hz may be
applied with low photobleaching. For longer durations where nucleic
motion may be significant, three-dimensional (3D) confocal
microscopy may be used to capture 3D images at predefined
intervals, such as every 1-100 seconds. The 3D information is
important, because during a long measurement, the studied cell may
move, and the 3D information can be used for correcting its motion.
In both techniques, the measurement precision of the telomere spots
may be estimated at approximately 5-200 nm. This may be extracted
from the measurement of genomic regions in formaldehyde-fixed cells
under similar measurement conditions. For faster image acquisition,
a similar microscope may be used with a sensitive cooled electron
multiplying CCD (EM-CCD), such as the Andor, DU-885 that can
provide images at a frame rate of 1-400 images at each second.
[0097] With the CCD, a single focal plane may be selected in the
cell. Typically, 10-20 telomeres may be observed on one plane, and
the measurement may be captured at an image rate of 85 images per
second over a duration of 20 seconds, during which the cell's
movement may be insignificant, and thus the telomeres remain within
the focal plane. Typically, about 50-70 telomeres may be observed
when using the confocal microscope in the whole nucleus volume. The
measurements may be performed using a confocal microscope or any
confocal setup that allows illuminating a single point or several
individual points in a selected area.
[0098] The captured images may be analyzed by a computerized
hardware processor to determine the respective paths and diffusion
characteristics of the labeled entities and the resulting cell
characteristic. In one embodiment, the processor may be configured
with the image capturing apparatus. Alternatively, the processor
may obtain the image data from the image capturing apparatus using
wired or wireless means.
[0099] The processor may compute entities such as: the area or
volume spanned by the tracked particles in a given period, or, the
mean square distance (MSD) as a function or time. The MSD function
may be applied to determine one or more diffusion characteristics
in accordance with one or more rules. For example, the MSD function
may be compared to a set of predefined functions associated with
different diffusion characteristics to determine if the particle
diffusion type within the cell is normal or anomalous.
[0100] For example, Lamin A is a component of the nuclear lamina
that contributes to peripheral heterochromatin association and to
nuclear integrity, and may also be found in the nucleoplasm.
Deficiency of lamin A may affect nucleus plasticity and chromatin
dynamics leading to increased genome mobility, and may cause
particle diffusion to change from slow anomalous diffusion,
associated with healthy cells, to fast and normal diffusion,
associated with lamin A depleted cells.
[0101] Thus, the volume or area traversed by a tracked particle in
a lamin A-depleted region may be greater than the volume traversed
in a region with normal levels of lamin A. By analyzing the paths
travelled by tracked particles within the nucleus, levels of
indicative proteins, such as lamin A, may be determined.
[0102] An analysis of the path, area, or volume traversed by a
particle over time may be repeated for many particles, leading to a
statistical analysis that may be used to determine a cell's genetic
type.
[0103] In an embodiment, a continuous photo-bleaching method (CP)
may be applied to a selected area within a cell to determine a cell
characteristic, such as a motile property of a labeled protein of
interest. Bleaching may affect the observed intensity level of a
particle as a function of the particle's exposure to the bleaching
compound: the longer the particle is exposed to the bleaching
compound, the lower the observed intensity level may be for the
particle. Thus, the observed intensity level of a slow-moving
particle, such as a bound particle, within the bleached area may
diminish substantially over an observed duration, due to its
relatively long exposure to the bleaching compound. Conversely, the
observed intensity level of a fast-moving particle, such as a
freely diffusing or unbound particle, within the bleached area may
diminish only marginally or not at all due to its short exposure to
the bleaching compound. Since the nature of the observed entity
determines the rate of decay of its intensity level, this property
may be applied to determine the ratio of bound to unbound proteins
within the cell.
[0104] Over a given time duration, the bound proteins may exhibit a
high rate of bleaching due to their continued exposure to the
bleaching compound, and the unbound proteins may exhibit a low rate
of bleaching due to their fleeting exposure to the bleaching
compound. A curve representing the measured intensity of the
bleached area as a function of time may exhibit an initial
exponential decay corresponding to the bleaching of any bound
particles present in the area of observation, followed by a
leveling off to either a constant intensity level or slow linearly
decaying intensity, corresponding to the unbound particles present
in the area of observation. An analysis of the decay of the
intensity levels of the observed proteins may yield a ratio of
bound to unbound proteins within the cell, and which may signify a
characteristic of the cell such as may be used to diagnose an
abnormality.
[0105] For example, referring to FIG. 1A, a conceptual illustration
of a cell 100 is shown having bound entities 102 and unbound
entities 104. FIG. 1B shows a conceptual illustration of a cell 106
with unbound entities diffusing through a bleached measurement area
108. FIG. 1C shows the measured intensity of the unbound entities
within area 108 with respect to times. Due to the relatively fast
motion of the unbound entities particles through area 108, their
intensity undergoes relatively little bleaching, as indicated by
the slight decay in the corresponding intensity curve of FIG.
1C.
[0106] In contrast, referring to FIGS. 1D-1E, a conceptual
illustration of a cell 110 is shown having bound entities within a
bleached measurement area 112. Since the bound entities remain in
area 112, their intensity undergoes significant bleaching, as
indicated by the marked decay of the corresponding intensity curve
of FIG. 1E.
[0107] Therefore, when both bound and unbound entities are present,
the corresponding intensity curve may indicate an initial
exponential decay attributable to the bound entities, and a
subsequent constant or slow decay attributable to the unbound
entities. The ratio of the bound to unbound entities may be
determined by analyzing the shape of the intensity curve. If the
binding-unbinding time-scale .tau..sub.B of the bound entities is
large relative to the diffusion time .tau..sub.D of the free
entities through the measured spot, such as by an order of
magnitude or more, and thus .tau..sub.D<<.tau..sub.B, the
analysis may be straightforward.
[0108] Measurement may be obtained using a confocal inverted
microscope, such as an Olympus, IX81 microscope and FV-1000
confocal, combined with a sensitive detection system such as a
provided by a Picoquant Microtime 200 (MT200) system may be used in
accordance with any of the methods disclosed herein. The MT200
system may use a 20 MHz 470 nanometer (nm) pulsed picosecond diode
laser, such as a LDH-P-C-470B, PicoQuant. The light may be coupled
to the FV-1000 via an optical fiber and focused onto a small
confocal volume through a 60.times. water immersion objective lens
with NA=1.2 (UPIanSApo, Olympus). The emitted light may be
collected through the objective, filtered from the excitation light
through a dichroic mirror (405/488 nm), and transmitted through a
confocal pinhole (D=120 .mu.m) and detected with a single photon
avalanche detector (SPAD-170 .mu.m Perkin Elmer SPCM-AQRH 13)
through a 520/35 nm Band-pass filter (FF01-520/35-25, Semrock
Rochester N.Y., USA). This setup may be used to obtain any of the
measurements described herein. Additionally or alternatively, any
of fluorescent microscopy, transmission microscopy, dark-field
microscopy, confocal microscopy, total internal reflection
fluorescence microscopy, super resolution microscopy including any
of stimulated emission depletion (STED), photoactivated
localization microscopy (PALM), stochastic optical reconstruction
microscopy (STORM), or structured illumination microscopy (SIM),
and fluorescence life-time microscopy may be used.
[0109] The laser intensity may be initially calibrated to
.about.2.7 .mu.W at the back aperture of the objective. A
measurement of the cell may be performed with the confocal
microscope. Then, a specific point may be chosen in the nuclear
interior and a "point measurement" may be captured with the FV1000
confocal imaging set up for approximately 60 seconds. This
measurement may be obtained by using the MT200 laser for excitation
and SPAD detector. Additional measurements may be similarly
obtained from additional cells.
[0110] The intensity of a fluorescent signal detected in a CP
experiment may be proportional to the ratio of un-bleached
fluorescent molecules that are still present in the optical volume.
The CP data may be analyzed using any suitable method, such as via
a Matlab program. The data may be smoothed, such as within a 0.1
seconds window, in an initial step. The starting point t=0 when the
laser is turned on, may correspond to the maximum intensity of the
time trace (FIG. 1). The observed intensity trace may be fit to a
model, such as I(t)=ae.sup.-bt+ct+d. The ratio of unbound proteins
to the total volume of proteins may be calculated as the ratio
between the extrapolated value for an initial linear intensity
value d, corresponding to the intensity of the unbound protein
volume at t=0, and the measured initial intensity value I(0),
measured at t=0, and corresponding to the initial intensity value
of the total volume of proteins comprising both the bound and
unbound proteins.
[0111] Reference is made to FIG. 1F, which shows CP measurements of
intensity levels for labeled particles, in accordance with an
embodiment of the invention. FIG. 1F illustrates measurements that
were obtained for free green fluorescent protein (GFP) and for
lamin A protein in a normal live cell. Curve 102 corresponds to a
freely diffusing GFP in the nucleus, whereas curve 104 corresponds
to fluorescently labeled Lamin A particles, and may be divided into
two portions: a decaying portion for 0<t<10 seconds, and a
linear portion for t>10 seconds. The linear portion of the curve
may correspond to freely-diffusing particles while the decaying
portion of the curve may correspond to bound particles. The
proportion of free particles to the bound particles may be found by
determining the ratio of the intensity of the linear fit curve (at
the crossing point at t=0) and the total intensity measured at t=0.
The intensity curves may be analyzed as described above. In this
case, it was deduced that approximately 40% of the lamin A proteins
were bound and approximately 60% were free.
[0112] Alternatively, if the difference between .tau..sub.D and
.tau..sub.B is not substantial, such as if they are within a factor
of 2, 5 or even 10, the CP experiment may be performed while
monitoring the fluorescent lifetime of the molecules used for the
experiment.
[0113] A fluorescent molecule is excitable via illumination with
light having a spectral range corresponding to the molecule's
absorption spectrum. On illumination, an electron in the molecule
is excited to a higher energy state where it remains until it falls
back to the original state and emits a photon at a wavelength
corresponding to the emission spectral band of the molecule. A
typical lifetime of the excited state ranges from approximately
1-10 nanoseconds, and may exceed or fall below this range.
[0114] Reference is now made to FIG. 1G, which illustrates a
lifetime histogram for a fluorescent molecule. Typically, a
fluorescent sample is illuminated with a very short laser pulse,
such as having a pulse duration in the range of 1-1000 picoseconds,
and the resultant fluorescence emission may be measured at
different time intervals following the pulse. The emitted photons
may be collected and counted over each time interval, and plotted
as a histogram which is fitted against an exponential curve,
(t)=I.sub.0exp [-t/.tau..sub.F] where .tau..sub.F is the
fluorescence life-time and I.sub.0 is the maximal intensity that is
emitted over a very short time interval.
[0115] Techniques for measuring fluorescent lifetimes using a
microscope include gating, time correlated single photon counting
(TCSPC), and phase modulation. Gating and TCSPC typically use a
pulsed laser with a repetition rate that can be as high as 100
Megahertz (MHz) and provide the fluorescent light intensity, and an
emitted photon count, respectively, over time. The gating technique
typically uses a high time-resolution electronic counter to
synchronize a detector with the illumination pulse. The electronic
counter may be synchronized with an electronic clock that can
measure time differences ranging from as few as 0.5 ps to several
seconds. The electronic counter begins counting time from moment
the sample is illuminated with the laser pulse. After a variable,
predetermined time interval, such as 100 picoseconds (ps), the
detector is signaled to begin counting photons emitted by the
illuminated sample for a preset time window, such as 10 ps. This
process may be repeated for varying time intervals from when the
laser pulse is emitted. Fluorescent photon emission may thus be
counted starting from as soon as 100 ps to several nanoseconds (ns)
after the laser pulse illuminated the sample, allowing the
exponential decay curve of FIG. 1G to be plotted. TCSPC provides
similar information by measuring the time duration between emitting
the laser pulse that illuminates the fluorescent sample, and
detecting the first fluorescently emitted photon by the detector.
This procedure may be repeated multiple times to collect sufficient
data and create the lifetime histogram of FIG. 1G.
[0116] Phase modulation operates somewhat differently. The
illumination laser is modulated so that its intensity has a
modulating cosine-like function, and the sample is illuminating
accordingly. A detector that modulates its response function with a
similar cosine function to synchronize the detection with the
illumination is used to detect the light emitted by the illuminated
sample. The detector is controllably synchronized to detect the
fluorescent signal at varying phase delays between the laser
intensity and detector response. The resultant intensity curve as a
function of phase delay may be used to derive the fluorescent
lifetime.
[0117] Any of the above-mentioned techniques may be used to collect
fluorescent lifetime measurements from 1 to as many as 100 million
measurements per second.
[0118] Referring to FIG. 10B, a simplified conceptual illustration
of a system for determining the bound to unbound ratio of proteins
within a cell is shown. It may be noted that FIG. 10B is not shown
to scale. A spot 1008 on a sample 1010 is illuminated using a light
source 1012, such as a pulsed diode laser source, at a wavelength
corresponding to the emission spectral band of sample 1008, causing
the illuminated spot on sample 1008 to fluoresce and emit a photon.
The emitted light may be measured using a microscope 1002.
Microscope 1002 may include an objective 1014 configured to collect
the emitted light, and a mirror 1016 configured to filter the
collected light. The filtered light may then be transmitted through
a confocal pinhole camera 1000 and detected using a detector 1018.
An electronic counter 1020 synchronizes the emission by light
source 1012 with the detection by detector 1018 as described above.
The intensity and fluorescent lifetime information gathered by
detector 1018 and and/or counter 1020 is transmitted to a processor
1004 to determine the fluorescent light intensity and emitted
photon count per predetermined time unit, and use this data to
compute the ratio of bound to unbound particles, and which will be
described in greater detail below. The calculated ratio may be
rendered on a user interface 1006. The optical apparatus of FIG.
10B may additionally or alternatively include any of: one or more
optical filters for separating the spectral range of the excitation
spectral band from the emission spectral band of the fluorescent
molecule; a beam splitter for separating the polarization of the
excitation light and the polarization of the fluorescent light.
Microscope 1002 may comprise any of: a fluorescent microscope, a
transmission microscope, a dark-field microscopy apparatus, a
confocal microscope, a total internal reflection microscopy
apparatus, a super resolution microscopy apparatus, and a
fluorescence life-time microscopy apparatus.
[0119] In one embodiment, the CP measurements may be combined with
the time resolved measurements of the fluorescence signal in a
method referred to as time resolved intensity photobleaching, or
TRIP. By analyzing the combined data the ratio of the bound to
unbound particles may be determined with higher accuracy. This may
be due to the effect that the state of a molecule, i.e. bound or
unbound, has on its fluorescence. For example, the fluorescent
lifetime, its polarization and its polarization anisotropy lifetime
may vary depending on whether the particle is bound or unbound.
Various optical techniques may be used to measuring the fluorescent
lifetimes and distinguish bound from free molecules.
[0120] Referring back to FIG. 1F, when a time-resolved system
measures the CP intensity curve, the fluorescent lifetime of each
of the molecules that takes part in the process is measured. If
there is a difference in the fluorescent lifetime of the bound
molecules with respect to the lifetime of the free molecules, then
the lifetime that is measured for each of the bound and free
populations should be different. Moreover, referring to FIG. 1H,
the measured lifetime histogram of such a hybrid measurement can be
analyzed by fitting against two different exponential curves, 118
and 120, and finding the ratio of intensities that each population
is contributing.
[0121] Alternatively, the time-axis of the CP curve of FIG. 1F may
be sectioned to smaller time-windows, such as 1 millisecond (ms)
time windows. For each of these time-windows, the average
fluorescent lifetime may be calculated. These average values may be
accrued over a longer period to yield sufficient data to plot the
histograms shown in 1I-K. For example, average values for 1 ms time
windows determined over a period of 2 s will yield 2000 values,
resulting in a histogram shown in FIGS. 1I-K comprising the
addition of two Gaussians, each corresponding to the bound and free
populations, respectively. This process may be repeated for time
windows ranging from as little as one millisecond to one hundred
seconds, or more.
[0122] This procedure may be repeated over different time intervals
along curve 116 of FIG. 1F, indicated as A, B, C. It may be noted
that FIG. 1F is not shown to scale. For example, assuming that 80%
of the molecules in the measured region are bound and 20% are free,
at the beginning of the measurement, from the start of illuminating
the sample (FIG. 1F point A), the distribution of the average
fluorescent lifetimes may correspond to FIG. 1I, where Gaussian 122
represents the distribution of the lifetimes for the bound
molecules, and Gaussian 124 represents the distribution of the
lifetimes for the free molecules. As time passes, the fraction of
the bound molecules that contributes to the diffusion will
decrease. FIG. 1J shows an average fluorescent lifetime
distribution for the next time period corresponding to point B,
following point A of FIG. 1F, where Gaussian 122 corresponding to
the bound molecules indicates substantially lower intensity levels
associated with rapid decay, and Gaussian 124 corresponding to the
free molecules indicates stable or slowly decaying intensity
levels. After an additional time period the fraction of the bound
molecules contributing to the diffusion decreases even more, in
accordance with the exponential decay shown in FIG. 1F. FIG. 1K
shows an average fluorescent lifetime at point C following point B
of FIG. 1F, after illuminating the sample, where Gaussian 122
indicates very low intensity levels corresponding to the rapidly
decaying bound molecules, and Gaussian 124 remains substantially
stable, corresponding to the slowly decaying free molecules. It may
be noted the time axes of FIGS. 1J-1K relate to each time interval,
A, B, C.
[0123] Accordingly, the method may include the following steps:
[0124] 1. Fluorescently labeling the selected protein by
transfection. For example, the protein lamin A may be labeled using
a green fluorescent protein by transfecting a live cell with a DNA
transcript that encodes these two proteins together. [0125] 2.
Selecting one or more areas of the cell for performing the
measurement and illuminating the selected area with light
corresponding to the protein's absorption spectrum. [0126] 3.
Detecting the intensity of the fluorescent light emitted from the
illuminated area with respect to time, and measuring, using a
fluorescent microscope, the fluorescent lifetimes of the bound
molecules to the unbound molecules. [0127] 4. Providing the data
comprising the time-resolved emitted light intensity measurements
and the time-resolved fluorescent lifetime measurements to a
processor. This step can be repeated for the one or more selected
areas over the course of the experiment. [0128] 5. Determining the
CP curve and the lifetime histograms of the time resolved intensity
and the fluorescent lifetime measurements using the techniques
described hereinabove. The fluorescent lifetime distributions may
be calculated for time windows ranging from 1 ms to 100 seconds
over the course of the experiment. [0129] 6. Calculating the ratio
of bound molecules to free molecules in the measured regions, such
as by correlating the data from the intensity curve as a function
of time (FIG. 1F) with the lifetime histogram data collected over
different time-windows, as shown in FIGS. 1I-K. Any combination of
the intensity curve information and lifetime histogram data may be
used to determine the ratio of free to bound molecules. [0130] 7.
The calculated ratio of bound molecules to free molecules may be
rendered on a user interface.
[0131] The analysis may be performed in accordance with any of:
linear polarization of the excitation light, linear polarization of
the emitted light, circular polarization of the excitation light,
and circular polarization of the emitted light. The polarization of
the excitation and the emission light may be parallel to each
other, perpendicular to each other, same-oriented circular
polarizations, such as both being clockwise or counter-clockwise,
or circular polarization where one is clockwise and the other is
counter-clockwise.
[0132] Reference is now made to FIGS. 2A-2B which, taken together,
illustrate exemplary experimental results of applying a single
particle tracking (SPT) method to measure a labeled particle's
trajectory within a genomic site.
[0133] Multiple such path trajectories {right arrow over (r)}(t)
may be derived for any number of particles (FIG. 2B) and analyzed
to calculate a mean square displacement with respect to time (FIG.
3). The MSD measures the average distance that is travelled by the
particles at a given time t. By repeating the calculation for
different times, the MSD curve is found. The MSD may be applied to
characterize the type of diffusion of labeled subcellular particles
to determine a cell characteristic, such as the presence of an
indicative protein.
[0134] Optical microscopy with high magnification may be used for
capturing SPT data of particles labeled with a fluorescent dye
within a live cell. A charged coupled device (CCD) camera may be
used to capture two dimensional images, and confocal microscopy may
be used to capture three dimensional images, such as described in
Bronshtein Berger, I., E. Kepten, and Y. Garini, Single particle
tracking for studying the dynamic properties of genomic regions in
live cells, in Imaging gene expression Methods and Protocols, Y.
Shav-Tal, Editor. 2013, Springer.
[0135] FIG. 2A shows the three-dimensional time-path of a moving
particle determined from data captured using the confocal
microscope, and FIG. 2B shows a similar two-dimensional path that
is analyzed by using the CCD and the shading of the path indicates
time along the path. This time-path may be analyzed to identify one
or more dynamic, motile properties of the measured particle.
[0136] For a 2-dimensional path, the x- and y-coordinates of a
tracked particle may be determined by applying an image analysis
algorithm, such as by fitting a two dimensional (2D) Gaussian
function to the fluorescence intensity profile captured in an image
of the particle. If the fluorescent spot is bright enough and has a
high signal to noise ratio, the center position may be determined
to within .about.10 nm precision, such as described in Yildiz, A.,
et al., Myosin V walks hand-over-hand: single fluorophore imaging
with 1.5-nm localization, Science, 2003. 300: p. 2061-2066. It may
be noted that precision in this case should not be confused with
the spatial resolution limit, which is in the order of 200 nm due
to optical diffraction limitations.
[0137] Alternatively, for a three dimensional (3D) path, the 3D
position of a particle at each point in time may be determined by
applying an appropriate 3D imaging method, such as via confocal
microscopy, and the trajectory of a moving particle, expressed as
r(t)=[x(t), y(t), z(t)], may be used for further analysis. It is
also possible to extract the three dimensional path and use only
two dimensional (2D) projection of the 3D path on the plane by
taking for example only r(t)=[x(t), y(t)].
[0138] In an embodiment, a quantitative SPT analysis of one or more
time-lapse image sequences may be performed using an image analysis
software package, such as the Imaris (Bitplane) or Matlab, to
determine one or more coordinates of labeled genomic loci. The
images may be segmented to find the center of gravity for each
identified spot, and may be repeated for multiple time-lapse images
to determine a time-path for each telomere. The telomere
fluorescence spots may typically be easily discernible, yielding
well defined time-paths, and any missing points along the time-path
of a telomere may be ignored from the analysis.
[0139] The SPT analysis may include the following steps:
[0140] i) The time-lapsed images may be analyzed in order to
extract the position of the labeled particles (e.g. telomere) in
the images,
[0141] ii) The time-path coordinates for of the labeled particles
may be used for calculating the MSD,
[0142] iii) The MSDs that are calculated for multiple particles may
be used for calculating an average MSD, and
[0143] iv) The average MSD may be characterized from the equation
type.
[0144] A more detailed description of the above-listed steps is now
given.
[0145] i) In the first step, the position-coordinates of the
particles in each image of the measured sequence of images may be
extracted, and may be expressed as r(t)=[x(t), y(t), z(t)], where r
may represent the position described in 3D coordinates, or
alternatively, in 2D coordinates. An intensity threshold value may
be selected for the images. Pixels that have an intensity value
that is lower than the threshold value may be ignored, and the
pixels having an intensity value above the threshold may be used
for the analysis, such as by organizing those pixels into clusters,
or `spots`. The center of each spot may be identified using any
suitable method. For example, the center may correspond to a center
of mass, and values for (x.sub.c, y.sub.c, z.sub.c) may be
calculated using the following equation:
x C = j I j x j j I j ##EQU00001##
where the sum includes all the pixels j that belong to one spot,
and where I.sub.j is the intensity value for pixel j. Values for
y.sub.c and z.sub.c may be similarly determined.
[0146] In another embodiment, the intensity function of each spot
may be fitted to a 3D Gaussian function describing the snot
intensity, as follows:
I ( x , y , z ) = C exp [ - ( x j - x C ) 2 2 .sigma. X - ( y j - y
C ) 2 2 .sigma. Y - ( z j - z C ) 2 2 .sigma. Z ] ##EQU00002##
where any suitable optimization algorithm may be used for finding
(x.sub.c, y.sub.c, z.sub.c). Alternatively, any of the above
methods may be applied in 2D. Such a procedure can be performed for
example by the Imaris image analysis software package (BitPlane
Company) or ImageJ or by writing the code in Matlab.
[0147] ii) The above procedure may be repeated for multiple spots
identified in the captured image. For each image, the coordinates
of the same spot may be listed over time as: r.sub.i(t.sub.1),
r.sub.i(t.sub.2), . . . r.sub.i(t.sub.n) where i is the index of
each particle, and t.sub.1 . . . t.sub.n are the times at which the
n images where measured. In this manner, the positions of the spots
over time may be described as a sequence of 3D or 2D
coordinate-sets over time. This time-based sequence may be used for
subsequent analysis.
[0148] In addition, the time coordinates for each telomere may be
used to eliminate rotational and/or translational movement
attributed to the nucleus. Correction for nucleus drift and
rotation may be applied in accordance with the distribution of the
telomeres that are measured for each image. The center of gravity
of the telomeres may be calculated for every recorded point in
time, and a movement of each specific telomere in an image may be
calculated with respect to the motion of the center of gravity. A
corrective factor may be added to eliminate the effect of the
motion of the nucleus on the telomeres. Correction due to rotation
of the nucleus may be performed by calculating the rotation of the
nucleus around the whole image center of gravity and applying a
suitable counter-rotation.
[0149] (iii) Multiple path trajectories derived from the SPT data
thus measured may be applied to calculate the space spanned by the
labeled particles as a function of time to determine their
diffusion characteristics. Alternatively, the multiple path
trajectories derived from the captured images by be used to
calculate a space scanned by the labeled particles. These multiple
path trajectories may be applied to calculate a mean square
distance (MSD) function which may be used to analyze and determine
the diffusion characteristics of the labeled particles. Since the
average distance of a particle from the origin does not change with
time, the MSD, <r.sup.2(t)>, of a particle may be used to
determine the quadratic length of a particle's excursions from the
origin.
[0150] Normal subcellular particle diffusion, such as Brownian
motion, may exhibit a linear increase with respect to time, and may
be described as: r.sup.2(t)=2dDt, with d being the trajectory
dimension, and D being the diffusion constant. Conversely, if a
particle is slowed down by the surrounding environment, an
anomalous subdiffusion may be observed, and may be described by
r.sup.2 (t)=At.sup..alpha. where A is a constant and is a positive
number, .alpha.<1.
[0151] The MSD may be calculated as an average distance travelled
over time t by an ensemble of N particles, and may be described
by:
r 2 ( t ) = 1 N i = 1 N [ r i ( t ) - r i ( 0 ) ] 2 ( Eq . 1 )
##EQU00003##
[0152] In one embodiment, a time averaged MSD may be calculated
over a path for a single particle over time T, by calculating the
average square distance for all steps with time difference t, where
a bar indicates averaging over time:
r 2 ( t ) _ = 1 T - t .tau. = 0 T - t [ r ( .tau. + t ) - r ( .tau.
) ] 2 ( Eq . 2 ) ##EQU00004##
[0153] This time averaged MSD may be analyzed to identify one or
more properties of the labeled particles, such as their diffusion
characteristics, to determine a property of the cell, as
follows:
[0154] In simple Brownian motion, both the ensemble averaged MSD
and the time averaged MSD measurements may follow a linear
dependence with time t:
r.sup.2(t)=2nDt (Eq. 3)
[0155] where n represents the spatial dimension, and where D
represents the diffusion coefficient. For example, for a 2D
measurement n=2. A linear MSD function, such as described by
equation 3, may be correspond to a linear diffusion process and may
be designated as `normal diffusion`.
[0156] In cases where the MSD function is not linear with respect
to time t, the diffusion may be designated `anomalous`. When MSD
grows as a power of time, it may be described as follows:
r.sup.2(t)=At.sup..alpha. (Eq. 4)
[0157] When 0<.alpha.<1, the particles move slower than
normal diffusing particles and have a higher probability of
interaction with nearby targets in a process known as
`subdiffusion`. This may indicate that the particle is diffusing in
a non-homogeneous space. Conversely, when 1<.alpha., the
particles may move faster than for normal diffusion, in a process
known as `super-diffusion`.
[0158] Thus, analysis of anomalous diffusion of particles may be
applied to determine a characteristic of a cell by quantifying the
crowdedness of the cytoplasm at the molecular scale or to identify
molecular interactions, such as described in Bronstein, I., et al.,
Transient anomalous diffusion of telomeres in the nucleus of
mammalian cells, Physical Review Letters, 2009. 103: p. 018102.
Anomalous diffusion pattern may correspond to a healthy cell,
whereas normal diffusion pattern may correspond to a deficiency in
a protein that is critical to the cell's function. In this manner,
the motile property manifested as a diffusion characteristic may
identify an expression or activity of a nuclear protein in the
cell.
[0159] Reference is now made to FIG. 3 which illustrates various
mean square displacement curves (MSD), <r.sup.2> as a
function of time. The shape of the MSD curve, such as expressed by
the derivative, or slope of the curve with respect to time, may be
applied to determine a diffusion characteristic of the nuclear
protein. Curve 302 with a linear shape may correspond to a normal
diffusion pattern, curve 304 with an exponentially increasing shape
may correspond to an anomalous super diffusion pattern, curve 306
with a logarithmic shape, may correspond to an anomalous
sub-diffusion pattern, and curve 308, beginning with a linear shape
that levels off after a given time duration may correspond to a
normal diffusion pattern within a restricted volume of space. These
curve shapes may comprise a set of rules that may be applied to
identify the diffusion characteristic for a given particle. A more
detailed explanation of how these rules may be applied is given
below with respect to FIGS. 6A-6B.
[0160] An MSD graph may be derived using the method disclosed
herein for one or more labeled particles, and may be compared to
such a set of predefined curves that are each associated with a
diffusion characteristic. The diffusion pattern associated with the
best fitting curve may be identified with the labeled particles,
and compared to a predefined shape associated with a diffusion
type, such as the shapes illustrated in FIG. 3, to identify the
diffusion type of the labeled particles, thereby determining a
characteristic of the cell, such as the presence or lack of an
indicative protein.
[0161] In another embodiment, a convex hull function may be applied
to determine the area, or volume that is scanned, or spanned by the
particle, or telomere, for the duration of the experiment. A convex
hull function is known to one skilled in the art as well as from
Franco Preparata & S. J. Hong, "Convex Hulls of Finite Sets of
Points in Two and Three Dimensions", Communications of the ACM 20,
87-93 (1977). Given the coordinates of the particle over a
timeframe, such as obtained above in step (ii), a flexible surface
such as a `plastic bag` may be spread to cover the coordinates, and
the amount of `plastic bag` required to cover the coordinates, such
the area or volume of the plastic bag, may provide a measurement of
the scanned area or volume, accordingly. This area or volume as a
function of time may provide a measure of the dynamics of the
particle, and may be compared across different cells or treatment
types, and analyzed as described above. Alternatively, a predefined
threshold may be compared to this measurement to distinguish
between normal and anomalous cells.
[0162] Reference is now made to FIG. 4, which illustrates exemplary
experimental results of a method for calculating the volume scanned
by diffusing particles within the nucleus of a cell over a given
timeframe. The volume spanned by each diffusing marked particle may
be calculated, in either two or three dimensions, over the
predefined time period, and the average volume spanned by all the
marked particles may be computed. In the example of FIG. 4, the
diffusion volume was calculated in three dimensions over a time
period of 20 minutes, and a convex hull algorithm was applied to
determine the spanned space. This average spanned space value may
be applied to determine another dynamic property of the dynamics of
the marked particles. For example, a small average space may
indicate `slow` diffusion associated with bound particles, whereas
a large average space may indicate `fast` diffusion associated with
unbound particles. It may be noted that in this context, the terms
`slow` and `fast` are to be understood as some of multiple
qualitative descriptive parameters of the particles dynamics, and
that the described volume is related to the diffusion in a rather
complex manner.
[0163] In an embodiment, time averaged MSD may be calculated for a
genome locus as the average squared displacement realized between
any two time points and separated by a time interval .tau., where
.tau.=n.delta.t, .delta.t is the time interval between any two
measurements, and n is an integer. The average displacement may be
computed over the measured time duration, and may be expressed
as:
r 2 ( t ) = 1 N - n m = 1 N - n [ r -> ( ( m - 1 ) .delta. t +
.tau. ) - r -> ( ( m - 1 ) .delta. t ) ] 2 ( Eq . 5 )
##EQU00005##
[0164] Here, {right arrow over (r)} represent a two or three
dimensional position vector of the particle at each time point and
N represent the total number of time points measured (total number
of images), and the sum runs over m. In an embodiment, analysis of
the measured data may be performed only on the planar X-Y plain
motion since the Z axis (optical axis) may have higher measurement
errors, and can be neglected.
[0165] Reference is now made to FIG. 5, which illustrates exemplary
results of a method for capturing images of particles labeled with
green fluorescent proteins (GFP) within the nucleus of a live cell.
It may be noted that other fluorescent proteins may be used to mark
the particles. Thus, it may be possible to label many different
proteins and other such structures in a living cell. In an
embodiment, a transfection method, as described in Day, R. N. and
M. W. Davidson, The fluorescent protein palette: tools for cellular
imaging, Chemical Society Reviews, 2009. 38: p. 2887-2921, may be
applied to label subcellular particles. The measurement may be
obtained at an image rate of 100 Hz (every 10 milliseconds), or at
a longer time-gap (lower rate) and the sample may be measured for a
total long or short times. In one embodiment, if a high rate is
used, such as 100 Hz, data may be gathered over a timeframe ranging
from about 10-30 seconds, to collect approximately 100 images or
more. If a slower rate is used, such as by measuring an image every
20 seconds, the measurement may continue for an hour where 180
images will be collected. It is also possible to repeat the
measurement few times, once at a high frame rate and then again at
a lower frame rate. By combining the data, it is possible to
calculate values such as the MSD for a broader time gap.
[0166] For example, telomeres or centromeres in the nucleus of
eukaryotic cells may be labeled. Mouse embryonic fibroblasts
lacking lamin A/C (Lmna.sup.-/-) and their wild type (Lmna.sup.+/+)
MEFs may be maintained in a medium such as Dulbecco's low glucose
modified Eagles medium containing 10% of bovine serum, 1% of
penicillin and streptomycin antibiotics. Both cell types may be
transfected with GFP-TRF2 plasmid, to allow observation of the
telomeres with fluorescence microscopy.
[0167] (iv) Having obtained MSD, or alternatively, MSV values with
respect to time using any of the methods described above, the MSD
or MSV may be fit to one or more predefined curves, such as
described in Equation 6 below, to determine a characteristic curve,
or equation type and characterized according to the equation with
the best fit.
[0168] Reference is now made to FIGS. 6A-6B which illustrate normal
diffusion and sub-diffusion MSD patterns, in accordance with an
embodiment. Referring to FIG. 6A, MSD values are shown plotted with
respect to time. MSD curve 602a, shown with a solid line, increases
at a constant rate corresponding to normal diffusion behavior, and
for a diffusion in a plane, may be represented by
<r.sup.2(.tau.)>=4D.tau., where D is the diffusion
coefficient. Conversely, curve 604a, shown with a dashed line,
decays exponentially with time corresponding to sub-diffusion
behavior.
[0169] Referring to FIG. 6B, the same MSD values are shown plotted
with respect to time on a log-log scale, but here the plotted MSD
is divided by time. Here, normal diffusion translates to a
horizontal line with a zero slope, shown as curve 602b with a solid
line, anomalous sub-diffusion translates to a negative-slope curve,
shown as curve 604b with a dashed line, and anomalous
super-diffusion may have a positive slope (not shown). The slope of
each curve can be used to extract the anomalous coefficient .alpha.
as follows:
< r 2 >= D .alpha. .tau. .alpha. ( Eq . 6 ) < r 2 >
.tau. = D .alpha. .tau. .alpha. - 1 log < r 2 > .tau. = (
.alpha. - 1 ) log .tau. + log D .alpha. ##EQU00006##
[0170] Accordingly, different values for .alpha. may characterize
the diffusion type, and the underlying cell characteristic. For
example, .alpha.=0 may correspond to normal diffusion, .alpha.<0
may correspond to anomalous sub-diffusion, and .alpha.>0 may
correspond to anomalous super-diffusion.
[0171] In an embodiment, it may be possible to improve the accuracy
of the MSD curves applying the method described in Kepten, E., I.
Bronshtein, and Y. Garini, Improved Estimation of Anomalous
Diffusion Exponents in Single-Particle Tracking Experiments.
Physical Review E, 2013. 87: p. 052713.
[0172] In another embodiment, data gathered from the continuous
photo-bleaching (CP) technique may be similarly analyzed either
alone or in combination with any of the methods described above,
such as STP, MSD, and/or MSV, to identify a motile property of a
labeled protein, and determine a cell characteristic associated
with that motile property.
Experimental Results
[0173] Reference is now made to FIGS. 7A-7B which, taken together,
illustrate experimental results of calculated MSD values for wild
type cells and cells with depleted lamin A, using the system and
methods described herein.
[0174] Referring to FIG. 7A, curve 702 shows the MSDs calculated
for telomeres in mouse embryonic fibroblasts labeled with GFP-TRF2
for Lmna+/+ cells, with respect to time. Upon analyzing curve 702,
the diffusion was determined to be anomalous, with a value for
.alpha.=0.43.+-.0.15. Slow anomalous sub-diffusion can thus be
regarded as a typical diffusion of genomic sites in the nucleus,
exhibiting slow and localized motion. In contrast, curve 704 shows
a normal diffusion of telomeres for Lmna-/- MEFs for .tau.>7
with .alpha.=1.+-.0.2. Referring to FIG. 7B, a transition of the
diffusion pattern from anomalous to normal may be demonstrated in
the histograms of individual telomere exponents .alpha., where
values corresponding to individual telomeres in Lmna+/+ are shown
as white bars, and values corresponding to to Lmna-/- cells, shown
as black bars.
[0175] Referring back to FIG. 7A, curve 706 shows an expression of
transfected GFP-pre-lamin A in Lmna-/- MEFs restored the anomalous
nature of telomere diffusion, with .alpha.=0.6.+-.0.1.
[0176] Reference is now made to FIG. 8A which shows experimental
results of an area scanned by 350 randomly selected telomeres
during 15 minutes as calculated using a convex hull algorithm. FIG.
8A illustrates measured results for 15 Lmna+/+ cells. A similar
figure (not shown) could show results for 15 Lmna-/- cells, where
scanned areas are shaded according to the same logarithmic scale.
In this experiment, Lamin A depletion led to faster genome dynamics
as well as a larger scanned nuclear areas, is clearly demonstrated
by the plotted telomere and centromere movement areas. FIG. 8B
shows average scanned volumes by telomeres over 15 min in Lmna+/+,
Lmna-/- cells and in these same cells transfected with
GFP-pre-lamin A. Without lamin A, the volume of telomere motion
greatly increases, thus these plots may be used to determine
presence or concentration of lamin A, the lack of which may be
related to different diseases that are collectively named
laminopathis.
[0177] Reference is now made to FIG. 9 which illustrates an
experimentally measured range of distribution of dynamic parameters
for Lmna.sup.+/+ cells, Lmna.sup.-/- cells and Lap2.alpha..sup.-/-
cells, in accordance with an embodiment of the invention. Each
cell-type may be related to a different regime of the dynamic
plot.
[0178] The value of the diffusion exponent, .alpha., as given in
Equation 4, may provide information regarding the diffusion type.
As describe above, values of .alpha.<1 may be related to normal
cells, while values of .alpha.=1 may be related to cells with a
protein deficiency. In addition, the volume scanned by each
particle, and the average of many particles, also reflects the
dynamics that is affected by certain proteins. Therefore, by
plotting a dynamic diagram of both parameters, important
information is observed and can be used for identifying the
structural status of cells in different expression levels and
diseases. Such a plot is shown in FIG. 9. Different areas in this
plot are related to different cell types.
[0179] Reference is now made to FIG. 10A which illustrates a system
for determining a cell characteristic from one or more images of
labeled genetic entities in accordance with the methods and
apparatus described above.
[0180] An image capturing apparatus, such as a camera 1000 coupled
with a microscope 1002 comprising any of the CCD or confocal
microscope apparatus described herein, may capture multiple images
of multiple labeled genetic entities. The images may be captured
using any of the following techniques: fluorescent microscopy,
transmission microscopy, dark-field microscopy, confocal
microscopy, total internal reflection fluorescence microscopy,
phase microscopy, polarization microscopy, super resolution
microscopy (STED, PALM, STORM, structured illumination microscopy),
fluorescence life-time microscopy, or application of fluorescent
proteins. Images of living cells may be obtained by placing the
cells in an incubator, such as a Tokai incubator, that is
maintained at 37 degrees C. with a 5% CO2 level, and placed on an
inverted fluorescence microscope, such as Olympus IX-81. The
microscope may be coupled to a confocal setup, such as an FV-1000
confocal setup (Olympus). Imaging may be performed with a UPLSAPO
60.times. objective, with NA=1.35.
[0181] The captured images may be received at a processor 1004 via
a wired or wireless communications system and analyzed in
accordance with the methods described herein to determining a cell
characteristic. Processor 1004 may perform any of the calculations
described hereinabove and render the results on a display 1006.
[0182] For example, processor 1004 may calculate an MSD function
with respect to time by analyzing multiple such images of labeled
genetic entities. The analysis may include: calculating the MSD at
different time-ranges; fitting the MSD to a power-law function over
at least one time-range to extract a power parameter; calculating a
range, area or volume scanned by the labeled entities over the
given time-range; calculating and extracting a coefficient
preceding the power-law function; calculating one or more
statistics of the parameters calculated above to determine any of:
a mean, median, mode, standard deviation, range, percentile,
skewness, kurtosis, moments; and render a histogram of the
calculated statistics on display 1006.
[0183] One or more rules, such as any of the rules described above,
may be applied to the MSD function to identify the diffusion type
and determine the cell characteristic. The rules may include: a
shape of the MSD curve, the type of labeled genetic entities
captured in the images, a derivative of the MSD curve, the curve
shape along the time duration of the MSD curve, the power parameter
corresponding to the power-law function fitted to the MSD curve, to
name a few.
[0184] Additionally, processor 1004 may compute the ratios of free
to bound molecules using the techniques described above with
respect to CP.
[0185] Numerous modifications and alternative embodiments of the
present invention will be apparent to those skilled in the art in
view of the foregoing description. Accordingly, this description is
to be construed as illustrative only and is for the purpose of
teaching those skilled in the art the best mode for carrying out
the present invention. Details of the structure may vary
substantially without departing from the spirit of the present
invention, and exclusive use of all modifications that come within
the scope of the appended claims is reserved. Within this
specification embodiments have been described in a way which
enables a clear and concise specification to be written, but it is
intended and will be appreciated that embodiments may be variously
combined or separated without parting from the invention. It is
intended that the present invention be limited only to the extent
required by the appended claims and the applicable rules of
law.
[0186] It is to be understood that the following claims are to
cover all generic and specific features of the invention described
herein, and all statements of the scope of the invention which, as
a matter of language, might be said to fall there between.
[0187] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0188] The computer readable storage medium can be a
non-transitory, tangible device that can retain and store
instructions for use by an instruction execution device. The
computer readable storage medium may be, for example, but is not
limited to, an electronic storage device, a magnetic storage
device, an optical storage device, an electromagnetic storage
device, a semiconductor storage device, or any suitable combination
of the foregoing. A non-exhaustive list of more specific examples
of the computer readable storage medium includes the following: a
portable computer diskette, a hard disk, a random access memory
(RAM), a read-only memory (ROM), an erasable programmable read-only
memory (EPROM or Flash memory), a static random access memory
(SRAM), a portable compact disc read-only memory (CD-ROM), a
digital versatile disk (DVD), a memory stick, a floppy disk, a
mechanically encoded device such as punch-cards or raised
structures in a groove having instructions recorded thereon, and
any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0189] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0190] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Java, Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0191] Aspects of the present invention may be described herein
with reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0192] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0193] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0194] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
[0195] The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the described embodiments. The terminology used
herein was chosen to best explain the principles of the
embodiments, the practical application or technical improvement
over technologies found in the marketplace, or to enable others of
ordinary skill in the art to understand the embodiments disclosed
herein.
* * * * *