U.S. patent application number 13/699506 was filed with the patent office on 2013-10-03 for holographic fluctuation microscopy apparatus and method for determining mobility of particle and/or cell dispersions.
This patent application is currently assigned to ARRYX, INC.. The applicant listed for this patent is Osman Akcakir. Invention is credited to Osman Akcakir.
Application Number | 20130260396 13/699506 |
Document ID | / |
Family ID | 45004244 |
Filed Date | 2013-10-03 |
United States Patent
Application |
20130260396 |
Kind Code |
A1 |
Akcakir; Osman |
October 3, 2013 |
HOLOGRAPHIC FLUCTUATION MICROSCOPY APPARATUS AND METHOD FOR
DETERMINING MOBILITY OF PARTICLE AND/OR CELL DISPERSIONS
Abstract
The present invention relates to an instrument and a measurement
apparatus and methodology that yields a measurement and test
methodology that characterizes a population of cells/particles or
detects a sub-population of cells/particles based on their detected
mobility in a quick and efficient manner.
Inventors: |
Akcakir; Osman; (Plainville,
MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Akcakir; Osman |
Plainville |
MA |
US |
|
|
Assignee: |
ARRYX, INC.
Chicago
IL
|
Family ID: |
45004244 |
Appl. No.: |
13/699506 |
Filed: |
May 25, 2011 |
PCT Filed: |
May 25, 2011 |
PCT NO: |
PCT/US11/00929 |
371 Date: |
June 20, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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61348072 |
May 25, 2010 |
|
|
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61347946 |
May 25, 2010 |
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Current U.S.
Class: |
435/7.25 ;
435/287.2; 435/34; 702/21 |
Current CPC
Class: |
G01N 33/54313 20130101;
G03H 2001/005 20130101; G16B 99/00 20190201; G03H 1/0866 20130101;
C40B 30/04 20130101; G01N 21/453 20130101; G03H 2001/221 20130101;
G03H 1/0443 20130101; G01N 15/1463 20130101; G01N 2015/1497
20130101; G01N 33/5029 20130101; G01N 33/80 20130101; G01N 33/53
20130101; G01N 2015/0216 20130101; G01N 15/0211 20130101 |
Class at
Publication: |
435/7.25 ;
702/21; 435/34; 435/287.2 |
International
Class: |
G06F 19/10 20060101
G06F019/10 |
Claims
1. A method of determining interactions between a plurality of
particles and a surface of a sample holder, comprising: applying a
physical force to at least one of a sample of particles on the
sample holder, or to the surface of the sample holder, using a
physical force application means; illuminating said particles using
an illumination source of an imaging apparatus having a microscope
with a field-of-view; measuring a response of said particles to
said physical force application means by acquiring a sequence of
images of said particles in said field-of-view using said imaging
apparatus, said acquisition of said images being synchronized with
said physical force application means; statistically analyzing,
using a processor of a computer system, said images of said
particles captured by said imaging apparatus; wherein said images
include a first component that is diffracted by said particles, and
a second component that is undiffracted by said particles, and said
two components interfere in an imaging plane, yielding an
interference pattern produced by said processor, that represents
particle by particle intensity fluctuation values; and wherein said
particles that are able to move through one of said physical force
application means or diffusion, display high intensity
fluctuations, and those that are bound to the surface of the sample
holder, display low intensity fluctuations, yielding a nature of
the interactions on the surface of the sample holder.
2. A method of performing holographic optical focusing on a
plurality of particles in a sample chamber, comprising:
illuminating a sample of particles in a transparent sample chamber
using a coherent light source of an imaging apparatus; acquiring
images of said particles using a focusing camera; displaying images
of an out-of-focus diffraction pattern of said particles on a
display; performing numerical focusing of an imaged hologram of one
of said images using a processor of a computer system, to determine
a focal plane of said particles; wherein said numerical focusing
includes a propagation of said out-of-focus image to different
distances which allows a focus measure to be determined numerically
by said processor; associating said focus measure with each
numerically propagated image, using said processor, such that an
extremum in said focus measure with each numerically propagated
image can be found; and allowing said computer system to perform a
single stage movement of said sample chamber to position said
sample in a required focal position.
3. A method of determining interactions between a plurality of
particles and a surface of a sample holder, comprising:
illuminating a sample of particles disposed on a transparent bottom
surface of a fluidic flow device, using an illuminating source of
an imaging apparatus having a microscope with a field-of-view;
measuring a movement of said particles at thermal equilibrium by
acquiring a stack of images of said particles in said field-of-view
using said imaging apparatus; statistically analyzing, using a
processor of a computer system, said images of said particles
captured by said imaging apparatus; wherein said statistical
analysis includes determining each pixel position through said
stack of images, to determine each pixel's standard deviation and
its average pixel value; generating a fluctuation image of each
said pixel, using said processor; wherein said fluctuation image is
a representation of a normalized standard deviation distribution
for each said pixel, for said stack of images; processing said
fluctuation image, using said processor, to generate a distribution
of average normalized standard deviations over each of said
particles; wherein relatively larger fluctuations in signal
intensity indicate said particles are moving, and relatively
smaller fluctuations in signal intensity indicate said particles
are immobilized by surface interactions; and yielding information
about a mobility of said sample and the interaction of said
particles on the surface of the sample holder.
4. A method of determining interactions between a plurality of
particles and a surface of a sample holder, comprising:
illuminating a sample of particles disposed on a transparent bottom
surface of a fluidic flow device, using an illuminating source of
an imaging apparatus having a microscope with a field-of-view;
measuring a movement of said particles at thermal equilibrium by
acquiring a stack of images of said particles in said field-of-view
using said imaging apparatus; statistically analyzing, using a
processor of a computer system, said images of said particles
captured by said imaging apparatus; wherein said statistical
analysis includes determining each pixel position through said
stack of images, to determine each pixel's standard deviation and
its average pixel value; generating a fluctuation image of each
said pixel, using said processor; wherein said fluctuation image is
a representation of a normalized standard deviation distribution
for each said pixel, for said stack of images; processing said
fluctuation image, using said processor, to generate a distribution
of average normalized standard deviations over each of said
particles; wherein relatively larger fluctuations in signal
intensity indicate said particles are moving, and relatively
smaller fluctuations or a lack of fluctuations in signal intensity
indicate said particles are immobilized by surface interactions;
and thereby yielding information about a mobility of said sample
and the interaction of said particles on the surface of the sample
holder.
5. A method of selective detection of different types of particles
on a surface of a sample holder, comprising: introducing a sample
of particles in a solution onto an antibody coated surface of the
sample holder, said particles being coated with either one or
another type of antigen; wherein particles coated with one type of
antigen are specifically bound to immobilized specific antibodies
coated on the sample holder, thereby restricting a motion of said
particles; wherein particles coated with another type of antigen
are not specifically bound to the sample holder, and said particles
freely diffuse in said solution on the surface of the sample
holder; illuminating said sample of particles disposed on the
sample holder, using an illuminating source of an imaging apparatus
having a microscope with a field-of-view; measuring a movement of
said particles at thermal equilibrium by acquiring a stack of
images of said particles in said field-of-view using said imaging
apparatus; statistically analyzing, using a processor of a computer
system, said images of said particles captured by said imaging
apparatus; wherein said statistical analysis includes determining
each pixel position through said stack of images, to determine each
pixel's standard deviation and its average pixel value; generating
a fluctuation image of each said pixel, using said processor;
wherein said fluctuation image is a representation of a normalized
standard deviation distribution for each said pixel, for said stack
of images; processing said fluctuation image, using said processor,
to generate a distribution of average normalized standard
deviations over each of said particles; and wherein said
specifically bound particles in said field-of-view exhibit
relatively lower magnitude and relatively narrower distributions in
a normalized standard deviation measure with relatively lower
average value, and freely diffusing particles exhibit a relatively
higher magnitude and relatively wider distribution with a
substantially relatively higher average value of said normalized
standard deviation distribution; thereby determining said one or
another type of antigens on said particles or said specific
antibodies on the sample holder.
6. A method of detection of particle shape as a diagnostic
procedure, comprising: illuminating a sample of particles disposed
on a sample holder, using an illuminating source of an imaging
apparatus having a microscope with a field-of-view; measuring a
shape of said particles at thermal equilibrium by acquiring a
sequence of images of said particles in said field-of-view using
said imaging apparatus; statistically analyzing, using a processor
of a computer system, said images of said particles captured by
said imaging apparatus; wherein said statistical analysis includes
determining a spatial distribution of intensity over said particles
over said sequence of images; generating a fluctuation image using
each said pixel, using said processor; wherein said fluctuation
image is a representation of a normalized standard deviation
distribution for each said pixel, for said sequence of images;
processing said fluctuation image, using said processor, to
generate a distribution of average normalized standard deviations
over each of said particles; wherein said particles that change
their shape more readily due to one of thermal fluctuations or
externally applied forces demonstrate relatively higher spatial
intensity fluctuations compared to said particles that are
relatively more rigid; and wherein such normalized standard
deviation distributions of said statistical analysis of said
spatial intensity of said particles over said sequence of images
may be used as a diagnostic of at least one of particle
flexibility, elasticity/visco-elasticity, health or disease, age,
solution conditions, or binding state.
7. A method of determining an interaction between particles in a
sample holder, comprising: (1) illuminating a sample of particles
disposed on a sample holder, using an illuminating source of an
imaging apparatus having a microscope with a field-of-view; (2)
measuring a shape of said particles at thermal equilibrium, using a
processor of a computer system, by acquiring a sequence of images
of said particles in said field-of-view using said imaging
apparatus; (3) detecting each of said particles in each of said
images using said processor; (4) establishing that a pair of said
particles that are adjacent to one another in each of said images,
may be bound together and may demonstrate correlated motion, using
said processor; (5) extracting an image of each of said adjacent
pair of particles, using said processor, creating two sub-images;
(6) multiplying said two sub-images together, using said processor,
to yield a product sub-image; (7) repeating steps (3)-(6) for all
pairs of adjacent particles in all of said images, resulting in a
sequence of product sub-images corresponding to each of said pair
of adjacent particles; (8) calculating, using said processor, a
pixel-wise standard deviation of said sequence of product
sub-images; (9) generating an average standard-deviation for one of
said pair of adjacent particles by calculating, using said
processor, an average value of said pixel-wise standard-deviation
divided by an average value for each pixel over a whole product
sub-image; wherein adjacent pairs of particles that are bound
together have correlated motions which increase a corresponding
normalized standard deviation of product sub-image values, than
those adjacent pairs of particles that are not bound together, to
yield said normalized standard deviation of product sub-image
values that are relatively lower with relatively narrower
distribution, than unbound particles which exhibit uncorrelated
motion, wherein said normalized standard deviation of product
sub-image values are relatively higher with relatively broader
distribution, such that bound pairs of particles are distinguished
from unbound pairs of particles.
8. An apparatus for measuring, testing and characterizing a
population or sub-population of particles based on their detected
mobility, comprising: an imaging forming apparatus, including: a
coherent light source which emits a light beam; and a collimator
which collimates said light beam from said coherent light source; a
transparent sample holder of a microscope on which a sample is
disposed and which is illuminated by said collimated light beam,
said sample which comprises a dispersion of particles; and means
for measuring a mobility of said particles on said sample holder,
in order to infer a presence or absence of interactions of said
particles with said sample holder.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present invention claims priority from U.S. Provisional
Patent Application No. 61/347,956, filed May 25, 2010, and U.S.
Provisional Patent Application No. 61/348,072, filed May 25, 2010,
the contents of both of which are herein incorporated by reference
in their entirety.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The present invention relates to an instrument and a
measurement apparatus and methodology that yields a measurement and
test method that characterizes a population of cells/particles
and/or their interactions with a chemically modified (or
unmodified) surface, and/or detects a sub-population of
cells/particles based on their detected mobility in a quick and
efficient manner.
[0004] 2. Description of the Related Art
[0005] Traditional cell/particle tracking methods may utilize
standard microscopy techniques such as brightfield, darkfield, and
fluorescence to detect particles moving in two or three dimensions.
However, at lower magnifications, there are a large number of
cells/beads present in a field of view, which causes traditional
frame-by-frame particle detection and tracking approaches to be
slower than required. Since it is critical to minimize testing time
for high-throughput diagnostic applications, for example, a new
microscopy method and apparatus which can measure particle mobility
more quickly, is desired.
SUMMARY OF THE INVENTION
[0006] The present invention relates to an instrument and a
measurement apparatus and methodology that yields a measurement and
test methodology that characterizes a population of cells/particles
or detects a sub-population of cells/particles based on their
detected mobility in a quick and efficient manner.
[0007] In one embodiment consistent with the present invention, an
apparatus for measuring mobility of particles includes a coherent
light source (e.g. laser, superluminescent diode); a collimator
which collimates the light source (which may be fiber coupled); a
transparent sample holder which holds a sample of particles or
cells, the particles/cells which are illuminated by the laser beam;
a transparent coverslip disposed on the sample holder, the
coverslip which is treated with a predetermined linker molecule
which attaches to an appropriate antibody/antigen on a surface of
the coverslip to allow specific binding of the particles/cells; an
objective lens which receives the laser beam from the transparent
sample holder; and a monitor which receives the laser beam from the
objective lens, the monitor which images a magnified pattern of the
particles/cells, illuminated in transmission.
[0008] In one embodiment, an apparatus for measuring, testing and
characterizing a population or sub-population of particles based on
their detected mobility, includes: an imaging forming apparatus,
including: a coherent light source which emits a light beam; and a
collimator which collimates said light beam from said coherent
light source; a transparent sample holder of a microscope on which
a sample is disposed and which is illuminated by said collimated
light beam, said sample which comprises a dispersion of particles;
and means for measuring a mobility of said particles on said sample
holder, in order to infer a presence or absence of interactions of
said particles with said sample holder.
[0009] In a similar embodiment, a transparent, semi-transparent, or
partially mirrored sample and sample chamber may be measured in
reflection mode, involving the laser illumination to illuminate the
sample from the collection side, and image formation occurring from
the light reflected from the sample that is subsequently imaged
onto the monitor.
[0010] In yet another embodiment consistent with the present
invention, the transparent, semi-transparent, or partially mirrored
sample holder is an automated fluidic device or microtiter plate
device with data acquisition and analysis capability.
[0011] The mobility of the cells/particles may be a function of
particular properties that affect the interaction between the
cells/particles and the substrate (e.g., specific binding of
surface antigens to surface bound antigens, hydrophobic,
electrostatic etc.). The type and magnitude of the
cell/particle-surface interaction will affect the extent of the
thermally generated motion of cells/microscopic particles at
thermal equilibrium. It will also affect the response of the
cell/particle to applied physical forces. Cells/particles with
negligible interactions with the surface undergo Brownian-type
motion, while interactions that are sufficiently strong will tend
to limit the range of motion observed on the surface (e.g.,
hindered Brownian motion). Similarly, cells/particles with weak
interactions with the surface will have a greater response to
applied physical forces, though in the presence of stronger surface
interactions the response will be attenuated.
[0012] The mobility of the cells/particle dispersion is also a
function of collective properties such as their effective
viscosity, visco-elasticity etc., in the medium measured at thermal
equilibrium. Cells/particles in a more viscous medium for example,
will also have their thermally (or physically) driven range of
motion attenuated compared to identical cells/particles in a less
viscous medium.
[0013] The present invention characterizes dispersions of
cells/particles based on their detected diffusional properties
(e.g., effective diffusion coefficient(s), effective viscosity)
measured at thermal equilibrium or in the presence of applied
perturbing forces.
[0014] As an alternative to traditional cell/particle tracking
methods, the holographic fluctuation microscopy apparatus and
method (technique) described herein is readily applicable to
low-magnification measurements (allowing increased throughput) and
has less stringent focusing requirements. An additional advantage
of this apparatus and technique is that due to the possibility of
imaging the sample with coherent illumination, significantly
out-of-focus images of the samples' diffraction patterns may be
imaged. The diffraction patterns may then be mathematically
transformed and numerically propagated to calculate an extremum for
a focus measure (see W. Li et al., J. Opt. Soc. Am. A, Vol. 24, No.
10, 3054-62, 2007, for example). The numerically propagated
distance is related to the distance from focus, which is determined
by the use of a calibration curve that relates the location of the
numerically calculated focus measure extremum to the actual
distance from focus. This feature allows focusing of the sample in
a quick and automatic fashion, to the desired imaging plane,
obviating the need for additional auto-focusing apparatuses, and
time consuming mechanical focus scans.
[0015] A method of performing holographic optical focusing on a
plurality of particles in a sample chamber, includes: illuminating
a sample of particles in a transparent sample chamber using a
coherent light source of an imaging apparatus; acquiring images of
said particles using a focusing camera; displaying images of an
out-of-focus diffraction pattern of said particles on a display;
performing numerical focusing of an imaged hologram of one of said
images using a processor of a computer system, to determine a focal
plane of said particles; wherein said numerical focusing includes a
propagation of said out-of-focus image to different distances which
allows a focus measure to be determined numerically by said
processor; associating said focus measure with each numerically
propagated image, using said processor, such that an extremum in
said focus measure with each numerically propagated image can be
found; and allowing said computer system to perform a single stage
movement of said sample chamber to position said sample in a
required focal position.
[0016] In one embodiment consistent with the present invention, a
method of measuring a particle's affinity to the surface through a
measurement of its mobility includes applying a physical force to a
plurality of particles disposed on a surface using a physical force
application means; and measuring a response of the plurality of
particles to the physical force; wherein the measuring step
includes one of impulse response measurement or frequency response
measurement.
[0017] In one embodiment consistent with the present invention, a
method of measuring the mobility of particles includes the
application of a physical force to a plurality of particles using a
physical force application means; measuring a response of the
particles to the physical force; wherein the measuring step
includes acquiring a sequence of holographic images of the
particles in a field of view; and performing a statistical analysis
of the sequence of the holographic images synchronized with the
application of the physical force to the plurality of particles, to
yield a measurement of the mobility of the particles.
[0018] In one embodiment, the particles are disposed on a surface,
and the physical force application means includes a translation
stage that may move the sample, wherein a translation stage
movement of sufficient acceleration causes additional particle
motion with respect to the surface, and wherein the movement is one
of abrupt (e.g. step-like, impulse response measurement) or
continuous motion (e.g. frequency response measurement).
[0019] In one embodiment, the physical force application means
includes optically generated forces using optical forcing means.
Alternatively, the physical force application means includes
external means, the external means including one of an ultrasonic
means, acoustic means, physical probe contact with beads/cells,
physical motion of the stage, or an automated fluidic flow device
which provides a fluidic flow.
[0020] In one embodiment, a method of determining interactions
between a plurality of particles and a surface of a sample holder,
includes: applying a physical force to at least one of a sample of
particles on the sample holder, or to the surface of the sample
holder, using a physical force application means; illuminating said
particles using an illumination source of an imaging apparatus
having a microscope with a field-of-view; measuring a response of
said particles to said physical force application means by
acquiring a sequence of holographic images of said particles in
said field-of-view using said imaging apparatus, said acquisition
of said images being synchronized with said physical force
application means; statistically analyzing, using a processor of a
computer system, said holographic images of said particles captured
by said imaging apparatus; wherein said images include a first
component that is diffracted by said particles, and a second
component that is undiffracted by said particles, and said two
components interfere in an imaging plane, yielding an interference
pattern produced by said processor, that represents particle by
particle intensity fluctuation values; and wherein said particles
that are able to move through one of said physical force
application means or diffusion, display high intensity
fluctuations, and those that are bound to the surface of the sample
holder, display low intensity fluctuations, yielding a nature of
the interactions on the surface of the sample holder.
[0021] In one embodiment, the sequence of images of the sample of
particles are captured using a holographic microscope apparatus;
wherein each of the images exhibits an interference pattern
representing the sample of particles, the interference pattern
including a diffracted component and an undiffracted component; and
adjusting an amount of defocus of the images to improve a
signal-to-noise ratio of the interference pattern. The
signal-to-noise ratio is improved when the interference pattern has
a higher contrast. The sequence of images is then processed,
yielding a statistical image(s) yielding the particle dynamic
information. The statistical image(s), which reflect intensity
fluctuations of the pixels in the sequence of images of the
particles that respond to the physical force, may then be masked
with a masking image comprised of particle neighborhoods. The
particle neighborhood mask image may be generated by applying
standard image processing techniques (e.g. background correction,
edge detection, image filters) to the image(s). One frame in the
sequence of acquired images is sufficient to determine the
neighborhoods of the particles, since the change in the particle
positions over the time duration of the image sequence is
significantly less than the average interparticle distance. This
means that particles may be unambiguously identified with the
particle neighborhoods generated using only one image from the
sequence. Upon multiplying the statistical image(s) with the
particle neighborhood mask, a list of statistical quantities
associated with each particle may be generated. These quantities
may be averaged over each particle to generate a statistical
measurement of particle mobility or particle movement that in
aggregate, yield a histogram distribution of such quantities for
the field of view. Such distributions may be generated with
particles from partial fields of view as well as multiple fields of
view as well.
[0022] In one embodiment, the statistical image is generated by
calculating the pixel-wise standard deviation of the pixel
intensities, divided by the pixel-wise average value of the pixel
intensities, yielding a statistical image of the normalized
standard deviation of pixel intensities for the sequence of images.
The statistical image may be masked with an image of particle
neighborhoods in order to generate normalized standard deviation
values averaged over each particle. The average normalized standard
deviation values so generated for each particle may be plotted in a
histogram to generate a distribution of values. Particles with high
normalized standard deviation are particles that demonstrate high
mobility or large movement relative to particles with low
normalized standard deviation values. A threshold value of
normalized standard deviation may be selected to distinguish the
minimally moving fraction of particles (i.e., low normalized
standard deviation values) from the particles that demonstrate more
freedom of movement (i.e., higher normalized standard deviation).
Other threshold values of normalized standard deviation may be
applied to select other fractions with intermediate normalized
standard deviation values.
[0023] In one embodiment, assays involving the measurement of the
fraction of particles bound may be performed by measuring the
fraction of particles with a value of the normalized standard
deviation that is less than the threshold value for bound
particles. For a statistically significant fraction of particles to
be bound, the background signal level must be characterized. The
background signal may be characterized by measuring a similar
dispersion of particles that nominally should not be bound under
the experimental conditions. The fraction of such unbound particles
that are found to be bound (i.e., the fraction of such particles
that have a normalized standard deviation value less than the
threshold value) under such conditions is the background signal.
Knowing this fraction, as well as the number of particles tested in
an experiment, allows the application of the binomial probability
distribution to determine the statistical probability of generating
a given experimental bound fraction measurement from a background
source. A minimum threshold of fraction of particles bound may be
determined, based on the desired statistical significance of the
results, in order to decide whether a positive reaction between the
particle dispersion and the treated surface has occurred.
[0024] In one embodiment, a calibration curve may be generated
using either controlled particle movement calibration experiments,
or simulations of controlled particle movements that generate a
relationship between the particles' physical motion (e.g., root
mean squared distance travelled) and its normalized standard
deviation values.
[0025] In one embodiment, a statistical image(s) is generated by
some combination of pixel statistical measures including average
pixel value, pixel standard deviation, pixel variance, higher order
pixel fluctuations, pixel temporal correlation functions, pixel
spatial correlation functions, pixel spatio-temporal correlation
functions, background pixel value, background pixel standard
deviation, background pixel variance, higher order background pixel
fluctuations, background pixel temporal correlation functions,
background pixel spatial correlation functions, background pixel
spatio-temporal correlation functions. The pixel statistical
measures may be generated pixel-wise over the image sequence and
then averaged over the particle neighborhood. The pixel statistical
measures may be calculated over the particle neighborhood, and then
calculated over the corresponding neighborhoods in the other images
in the sequence. The particle neighborhood mask may be generated
using one frame in the sequence. Multiple frames from the sequence
may be used to generate particle neighborhood masks. Pixel
statistical measures may be calculated over subsets of the image
sequence. Pixel statistical measures may be calculated over
successive subsets of the image sequence generating time varying
statistical measurements per particle. Time varying statistical
measurements per particle may be associated with time varying
experimental conditions (e.g., physical movement, vibration,
solution conditions, flow conditions, other environmental effects).
Spatial and temporal correlations of particle-based statistical
measurements may be performed. Time varying spatial and temporal
correlations of particle-based statistical measurements may be
calculated (as opposed to pixel-based statistical measurements).
Thresholds in the statistical particle measurements may be chosen
to select fractions with desired surface affinity, interaction
characteristics that have commercial, diagnostic relevance.
Thresholds in selected fractions may be chosen to indicate the
minimum fractional level necessary to be measured before a positive
result is indicated, based on the desired statistical
significance.
[0026] In one embodiment, particle positions are tracked over time,
and statistical measures of particle positions may be generated
(e.g., mean squared displacement, net displacement etc.) and
distributions of these quantities plotted for multiple particles.
Thresholds in particle position measures and particle statistical
quantities based on particle positions may be applied to determine
fractions with target particle-surface affinities. Statistical
measures of particle movement may be based on mean squared particle
displacement, mean particle displacement, net particle
displacement, higher-order particle position statistics, or a
combination of any or all of these quantities. Similar measures of
particle movement may be measured for control purposes (e.g.,
background correction).
[0027] In one embodiment, a method of determining interactions
between a plurality of particles and a surface of a sample holder,
includes: illuminating a sample of particles disposed on a
transparent bottom surface of a fluidic flow device, using an
illuminating source of an imaging apparatus having a microscope
with a field-of-view; measuring a movement of said particles at
thermal equilibrium by acquiring a stack of holographic images of
said particles in said field-of-view using said imaging apparatus;
statistically analyzing, using a processor of a computer system,
said holographic images of said particles captured by said imaging
apparatus; wherein said statistical analysis includes determining
each pixel position through said stack of holographic images, to
determine each pixel's standard deviation and its average pixel
value; generating a holographic fluctuation image of each said
pixel, using said processor; wherein said holographic fluctuation
image is a representation of a normalized standard deviation
distribution for each said pixel, for said stack of images;
processing said holographic fluctuation image, using said
processor, to generate a distribution of average normalized
standard deviations over each of said particles; wherein relatively
larger fluctuations in signal intensity indicate said particles are
moving, and relatively smaller fluctuations or a lack of
fluctuations in signal intensity indicate said particles are not
present; and thereby yielding information about a mobility of said
sample and the interaction of said particles on the surface of the
sample holder.
[0028] In one embodiment, fluctuations of motion of the particles
are analyzed at thermal equilibrium, and statistical analysis
includes capturing the sequence of images of said sample of
particles using a holographic microscope apparatus; analyzing the
sequence of images to determine a standard deviation and an average
value of each pixel; generating a holographic fluctuation image
whereby each pixel value is equal to a value of the standard
deviation of the pixel over time, divided by the average value of
the pixel; and processing the holographic fluctuation image to
generate a distribution of average normalized standard deviations
(NSD) over each of the particles.
[0029] In one embodiment, the motion of unbound or partially bound
particles is more confined at a lower temperature than at a higher
temperature. In one example, plots of normalized standard deviation
distributions of 4.8 .mu.m silica beads diffusing on a plane glass
coverslip taken at three different temperatures, shows that the
average value of the distribution increases with temperature,
reflecting the increased mean squared displacement of the beads as
a function of temperature.
[0030] For free diffusion, the mean squared displacement is
linearly proportional to the temperature:
.DELTA.x.sup.2=4Dt, D=k.sub.BT/(6.pi..eta.r),
[0031] where D is the diffusion coefficient (or effective diffusion
coefficient), t is the time interval between particle position
measurements, k.sub.B is Boltzmann's constant, T is the temperature
in Kelvin, .eta. is the viscosity (or effective viscosity) and r is
the radius of the spherical diffuser. A comparison between the
measured and simulated bead motion values confirms the choice of
the normalized standard deviation as an excellent measure of bead
mobility. The formula for free diffusion noted above, may also be
applied to beads/cells that demonstrate hindered diffusion,
generating effective diffusion coefficient estimates.
[0032] In one embodiment, the statistical analysis includes
capturing the sequence of images of the particles (i.e., red blood
cells) using a holographic microscope apparatus; analyzing the
sequence of images to determine a standard deviation and an average
value of each pixel; generating a holographic fluctuation image
whereby each pixel value is equal to a value of the standard
deviation of the pixel over time, divided by the average value of
the pixel; and processing the holographic fluctuation image of
normalized standard deviation values to generate a distribution of
average normalized standard deviations (NSD) over each of the
particles.
[0033] In one embodiment, a method of determining interactions
between a plurality of particles and a surface of a sample holder,
includes: illuminating a sample of particles disposed on a
transparent bottom surface of a fluidic flow device, using an
illuminating source of an imaging apparatus having a microscope
with a field-of-view; measuring a movement of said particles at
thermal equilibrium by acquiring a stack of holographic images of
said particles in said field-of-view using said imaging apparatus;
statistically analyzing, using a processor of a computer system,
said holographic images of said particles captured by said imaging
apparatus; wherein said statistical analysis includes determining
each pixel position through said stack of holographic images, to
determine each pixel's standard deviation and its average pixel
value; generating a holographic fluctuation image of each said
pixel, using said processor; wherein said holographic fluctuation
image is a representation of a normalized standard deviation
distribution for each said pixel, for said stack of images;
processing said holographic fluctuation image, using said
processor, to generate a distribution of average normalized
standard deviations over each of said particles; wherein relatively
larger fluctuations in signal intensity indicate said particles are
moving, and relatively smaller fluctuations or a lack of
fluctuations in signal intensity indicate said particles are not
present; and thereby yielding information about a mobility of said
sample and the interaction of said particles on the surface of the
sample holder.
[0034] In one embodiment a method of determining an interaction
between particles in a sample holder, includes: (1) illuminating a
sample of particles disposed on a sample holder, using an
illuminating source of an imaging apparatus having a microscope
with a field-of-view; (2) measuring a shape of said particles at
thermal equilibrium, using a processor of a computer system, by
acquiring a sequence of holographic images of said particles in
said field-of-view using said imaging apparatus; (3) detecting each
of said particles in each of said holographic images using said
processor; (4) establishing that a pair of said particles that are
adjacent to one another in each of said holographic images, may be
bound together and may demonstrate correlated motion, using said
processor; (5) extracting an image of each of said adjacent pair of
particles, using said processor, creating two sub-images; (6)
multiplying said two sub-images together, using said processor, to
yield a product sub-image; (7) repeating steps (3)-(6) for all
pairs of adjacent particles in all of said holographic images,
resulting in a sequence of product sub-images corresponding to each
of said pair of adjacent particles; (8) calculating, using said
processor, a pixel-wise standard deviation of said sequence of
product sub-images; (9) generating an average standard-deviation
for one of said pair of adjacent particles by calculating, using
said processor, an average value of said pixel-wise
standard-deviation divided by an average value for each pixel over
a whole product sub-image; wherein adjacent pairs of particles that
are bound together have correlated motions which increase a
corresponding normalized standard deviation of product sub-image
values, than those adjacent pairs of particles that are not bound
together, to yield said normalized standard deviation of product
sub-image values that are relatively lower with relatively narrower
distribution, than unbound particles which exhibit uncorrelated
motion, wherein said normalized standard deviation of product
sub-image values are relatively higher with relatively broader
distribution, such that bound pairs of particles are distinguished
from unbound pairs of particles.
[0035] In one embodiment consistent with the present invention, the
statistical analysis includes: capturing the sequence of images of
the sample of particles as holographic images, using a holographic
microscope apparatus; detecting each of the particles in each of
the holographic images; determining pairs of adjacent particles in
each of the holographic images; analyzing each of the pairs of
particles by extracting an image of each particle in each of the
pairs, creating two sub-images; multiplying (or adding) each of the
two sub-images together to yield a product (summed) sub-image;
repeating the analysis step for all of the pairs of the adjacent
particles; repeating the analysis step for all of the holographic
images in the sequence, resulting in a sequence of product (summed)
sub-images corresponding to each unique pair of adjacent particles;
computing a pixel-wise standard deviation and pixel-wise average
arrays of the sequence of the product (summed) sub-images for each
of the pairs; calculating an average value of the pixel-wise
standard deviation divided by an average value for each pixel over
the product (summed) sub-images, to yield an average normalized
standard deviation over each of the particles.
[0036] In one embodiment consistent with the present invention, the
particles/cells are disposed on a surface treated with an antibody,
such that the antibody will selectively bind the particles/cells
coated with a predetermined antigen. Further, motion of the
particles/cells is restricted based on binding of the
particles/cells with the antibody on the surface. The
particles/cells which do not react with the antibody are unbound
and diffuse freely (relatively freely) on the surface.
[0037] In one embodiment consistent with the present invention, the
particles are red blood cells, and the red blood cells are bound to
antibodies on the surface that are specific to antigens on the red
blood cells' surface.
[0038] In one embodiment consistent with the present invention, the
particles are cells which may be expressing surface antigens (e.g.,
surface receptors), and the cells that are bound to specific
antibodies and/or receptor ligands affixed to the surface, have
their mobility reduced. Thus by measuring the mobility (e.g.,
normalized standard deviation), the presence, absence and/or degree
of antigen coverage on a cell surface may be determined.
[0039] In one embodiment consistent with the present invention, the
particles are cells/beads with surface antigens that may be in
competition with freely diffusing antigens for surface binding
sites. The presence of such diffusing species may affect the
mobility of the bound cells/beads in a concentration-dependent way.
This type of measurement may be used to determine the presence,
absence and/or concentration of freely diffusing species that are
in competition with the bead/cell bound species for surface binding
sites.
[0040] In one embodiment, a method of selective detection of
different types of particles on a surface of a sample holder,
includes: introducing a sample of particles in a solution onto an
antibody coated surface of the sample holder, said particles being
coated with either one or another type of antigen; wherein
particles coated with one type of antigen are specifically bound to
immobilized specific antibodies coated on the sample holder,
thereby restricting a motion of said particles; wherein particles
coated with another type of antigen are not specifically bound to
the sample holder, and said particles freely diffuse in said
solution on the surface of the sample holder; illuminating said
sample of particles disposed on the sample holder, using an
illuminating source of an imaging apparatus having a microscope
with a field-of-view; measuring a movement of said particles at
thermal equilibrium by acquiring a stack of holographic images of
said particles in said field-of-view using said imaging apparatus;
statistically analyzing, using a processor of a computer system,
said holographic images of said particles captured by said imaging
apparatus; wherein said statistical analysis includes determining
each pixel position through said stack of holographic images, to
determine each pixel's standard deviation and its average pixel
value; generating a holographic fluctuation image of each said
pixel, using said processor; wherein said holographic fluctuation
image is a representation of a normalized standard deviation
distribution for each said pixel, for said stack of images;
processing said holographic fluctuation image, using said
processor, to generate a distribution of average normalized
standard deviations over each of said particles; and wherein said
specifically bound particles in said field-of-view exhibit
relatively lower magnitude and relatively narrower distributions in
a normalized standard deviation measure with relatively lower
average value, and freely diffusing particles exhibit a relatively
higher magnitude and relatively wider distribution with a
substantially relatively higher average value of said normalized
standard deviation distribution; thereby determining said one or
another type of antigens on said particles or said specific
antibodies on the sample holder.
[0041] In one embodiment consistent with the present invention, the
particles are cells/beads with surface antigens which may be bound
to a diffusing moiety present in the solution. Furthermore this
moiety may simultaneously be able to bind to an appropriately
treated surface (solid-phase). In this manner, the presence or
quantity of the moiety in solution may be measured by measuring the
mobility of appropriately coated particles on appropriately treated
surfaces (solid-phase). The presence of such diffusing species may
affect the mobility of the bound cells/beads in a
concentration-dependent way. This type of measurement may be used
to determine the presence, absence and/or concentration of the
freely diffusing target moiety which acts as a capture agent for
the particles.
[0042] In one embodiment, the blood cells not bound to antibodies
on the surface demonstrate a broad distribution of the normalized
standard distributions, with a high average value of the normalized
standard deviation, and blood cells which are bound to antibodies
on the surface demonstrate narrow distribution and low average
value of the normalized standard distributions.
[0043] In one embodiment, intermediate levels of binding are
detectable, as indicated by an increased fraction of blood cells
with intermediate normalized standard deviations.
[0044] In one embodiment, particles with heterogeneous binding
properties, detectable by measurement of heterogeneous effective
diffusional properties are detectable by an analysis of the width
and shape of the distribution, and fitting experimentally measured
distributions to heterogeneous diffusion models, yielding the
estimates of the population's distribution of diffusional
properties which may reflect the distribution of particle-surface
interactions and affinities.
[0045] In one embodiment, correlated fluctuations occur when there
are in-phase motions of the pairs of the particles that are bound
together. Further, adjacent pairs of the particles that are bound
together will have correlated fluctuations which will increase a
value of a corresponding normalized standard deviation of the
product (summed) sub-image, in comparison to those that are not
bound together and which have relatively lower normalized standard
deviations due to uncorrelated fluctuations.
[0046] In one embodiment, a method of detection of particle shape
as a diagnostic procedure, includes: illuminating a sample of
particles disposed on a sample holder, using an illuminating source
of an imaging apparatus having a microscope with a field-of-view;
measuring a shape of said particles at thermal equilibrium by
acquiring a sequence of holographic images of said particles in
said field-of-view using said imaging apparatus; statistically
analyzing, using a processor of a computer system, said holographic
images of said particles captured by said imaging apparatus;
wherein said statistical analysis includes determining a spatial
intensity of said particles over said sequence of holographic
images; generating a holographic fluctuation image of each said
pixel, using said processor; wherein said holographic fluctuation
image is a representation of a normalized standard deviation
distribution for each said pixel, for said sequence of images;
processing said holographic fluctuation image, using said
processor, to generate a distribution of average normalized
standard deviations over each of said particles; wherein said
particles that change their shape more readily due to one of
thermal fluctuations or externally applied forces demonstrate
relatively higher spatial intensity fluctuations compared to said
particles that are relatively more rigid; and wherein such
normalized standard deviation distributions of said statistical
analysis of said spatial intensity of said particles over said
sequence of holographic images may be used as a diagnostic of at
least one of particle flexibility, elasticity/visco-elasticity,
health or disease, age, solution conditions, or binding state.
[0047] Thus has been outlined, some features consistent with the
present invention in order that the detailed description thereof
that follows may be better understood, and in order that the
present contribution to the art may be better appreciated. There
are, of course, additional features consistent with the present
invention that will be described below and which will follow the
subject matter of the features appended hereto.
[0048] In this respect, before explaining at least one embodiment
consistent with the present invention in detail, it is to be
understood that the invention is not limited in its application to
the details of construction and to the arrangements of the
components set forth in the following description or illustrated in
the drawings. Methods and apparatuses consistent with the present
invention are capable of other embodiments and of being practiced
and carried out in various ways. Also, it is to be understood that
the phraseology and terminology employed herein, as well as the
abstract included below, are for the purpose of description and
should not be regarded as limiting.
[0049] As such, those skilled in the art will appreciate that the
conception upon which this disclosure is based may readily be
utilized as a basis for the designing of other structures, methods
and systems for carrying out the several purposes of the present
invention. It is important, therefore, that the features be
regarded as including such equivalent constructions insofar as they
do not depart from the spirit and scope of the methods and
apparatuses consistent with the present invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0050] FIG. 1A is a schematic diagram, showing an in-line
holographic microscopy apparatus designed to measure the mobility
of cells/particles in order to infer surface interactions and/or
collective diffusion/visco-elastic properties of the cell/particle
dispersion.
[0051] FIG. 1B is a cross-sectional view of a sample holder having
a transparent surface thereon, on which is provided at least one
cell/particle.
[0052] FIG. 1C is cross-sectional view of a transparent sample
holder having a glass slide, spacer and treated coverslip, forming
a sample chamber containing a dispersion of particles. The
particles may be imaged using the illumination source, microscope
objective, and imaging optics and acquisition hardware of FIG.
1A.
[0053] FIG. 1D is a cross-sectional view of a sample holder having
a reflective/partially reflective surface on lowermost surface (1)
or on uppermost surface (2) of a sample holder, on which is
provided at least one cell/particle, allowing measurements to be
performed in a reflection mode as well. The sample chamber includes
a dispersion of particles which settles to a treated coverslip
surface.
[0054] FIG. 2 illustrates how the hologram of FIG. 1B shifts due to
diffusion and may cause large fluctuations in pixel intensity.
[0055] FIG. 3A shows a cross-section of a sample chamber with a
sample fluid moving therethrough.
[0056] FIG. 3B shows a top-down view of differentially treated
surface areas (A, B, C and D) of a coverslip which each have a
heterogeneous mixture of particles, where each type of particle is
distinguishable (by fluorescent label or holographic image
characteristics for example).
[0057] FIG. 4, screenshot A shows a holographic image of 2 .mu.m
silica beads, and screenshot B shows a fluctuation image generated
from a stack of holographic images (60 images).
[0058] FIG. 5A shows an analysis of a stack of holographic images,
yielding the holographic fluctuation image S(i, j) which is
constructed by dividing the pixel-wise standard deviation by the
pixel-wise average, for each pixel, over the stack of images.
[0059] FIG. 5B shows how per-particle fluctuation measurements are
calculated using the fluctuation image (i.e., normalized standard
deviation (NSD) image) that is masked with a particle neighborhood
masking image generated from one image in the sequence of
holograms. The normalized standard deviation values over each
particle neighborhood are averaged, and then plotted as a
histogram.
[0060] FIG. 6, screenshot A, shows a holographic fluctuation image
of 2 .mu.m silica beads in deionized water at 5.degree. C.
(constructed from a 60 frame stack), and screenshot B shows a
holographic fluctuation image of 2 .mu.m silica beads in deionized
water at 47.degree. C. (constructed from a 60 frame stack). The
light areas, which represent the high intensity fluctuation areas
due to particle movement, are more confined at the lower
temperature.
[0061] FIG. 7 shows three histograms of normalized standard
deviations of 4.8 .mu.m diameter silica beads in water measured at
6.8.degree. C. (lower histogram), 28.degree. C. (middle histogram),
and 46.3.degree. C. (top histogram). The main peaks in the
histograms correspond to the freely diffusing beads, while the
minor peak at lower values of NSD (5-10%) represent the much
smaller fraction of beads that are stuck or partially stuck to the
surface.
[0062] FIG. 8 is a plot of the mean and standard deviation of NSD
values for freely diffusing 4.8 .mu.m beads measured at various
temperatures (open squares).
[0063] FIG. 9, slide (1), shows each type of particle is placed on
an antibody coated surface (i.e., direct immunoassay). The particle
coated with A antigen is specifically bound to the immobilized
specific antibodies (A antibodies) coated on the coverslip surface,
thereby restricting the motion of the particle.
[0064] FIG. 9, slide (2), shows the differing behavior of each type
of particle, where the B-antigen coated particle which is not
specifically bound, is allowed to freely diffuse in the solution,
on the surface, while the A type particle motion is severely
restricted due to specific binding with the surface antibodies.
[0065] FIG. 10A shows plots of normalized standard deviation
histograms of two different samples or RBCs (red blood cells)
measured on an anti-A coated surface. Red blood cells with A
antigen on the surface (i.e., A type) specifically bind the anti-A
coated surface, while blood cells without the A antigen (i.e., A
negative) do not bind, and are thus, free to diffuse.
[0066] FIG. 10B shows plots where the specifically bound cells that
are positive for antigen A (type A, black histogram) have a
similarly narrow distribution with similarly low average value,
compared to the A positive histogram of FIG. 10A.
[0067] FIG. 11, slide (1), shows each type of particle is placed on
an antigen coated surface that is in the presence of a
complementary antibody in solution (i.e., indirect immunoassay).
The A antibody in solution is able to bind to the A antigen coated
surface, as well as simultaneously bind to a particle coated with A
antigen.
[0068] FIG. 11, slide (2), shows the differing behavior of each
type of particle, where the B-antigen coated particle which is not
specifically bound, is allowed to freely diffuse in the solution,
on the surface, while the A type particle motion is severely
restricted due to specific binding with the antibody that is itself
specifically bound to the surface.
[0069] FIGS. 12A and 12B show a series of NSD histograms for two
samples of red blood cells taken at different times. FIG. 12A
includes type A red blood cells in the presence of a high
concentration of anti-A antibody (100 nM) dispersed in synthetic
plasma onto a surface with type B antigens on a coverslip surface.
The cells are able to diffuse, as evidenced by the high average
values of the NSD observable. FIG. 12B shows a time series of
histograms under similar conditions to those in FIG. 12A, except
that the surface had type A antigens on it (unlike the type B
antigen surface measurements of FIG. 12A). Comparison of the
histograms of FIGS. 12A and 12B allows us to choose a threshold
value for NSD to determine whether a cell is bound or not.
[0070] FIGS. 13A and 13B are similar to the conditions measured in
FIGS. 12A and 12B except that a much lower concentration of
antibody, 1 nM anti-A, was used in the synthetic plasma. In FIG.
13A, the A type cells on the B type surface are mostly unbound when
first measured at 19 minutes after the sample was introduced onto
the surface. In FIG. 13B the A type cells in synthetic plasma
containing 1 nM anti-A were measured after being introduced onto a
surface with A type antigens.
[0071] FIG. 14 shows the cell adhesion kinetics for the same sample
studied in FIGS. 13A and 13B.
[0072] FIG. 15, slide A, shows an image of a 4.8 .mu.m diameter
silica bead, where a sequence of 40 frames of this diffusing bead
(acquired at 5 frames per second) was analyzed using a particle
tracking algorithm which yielded a sequence of shifts in the
measured centroid position, .DELTA.x and .DELTA.y, as plotted in
FIG. 15, slide B.
[0073] FIG. 16, slide A, shows the image of the NSD calculated from
the same measured sequence of 40 frames that was used in the
particle tracking analysis shown in FIG. 15, slide B, which yielded
a NSD value, averaged over the bead area, of 23.6% NSD. The
averaged NSD value over the bead area in each member of the
ensemble is plotted in FIG. 16, slide B, showing excellent
agreement with the experimentally measured NSD.
[0074] FIG. 17 shows a calibration curve relating the mean of the
measured bead NSD distribution to root mean squared
displacement.
[0075] FIG. 18 shows the calibration curve plotted in FIG. 17, as
well as two other calibration curves generated from two other bead
images (4.8 .mu.m diameter silica).
[0076] FIG. 19 shows the beads from FIG. 18, along with a quantity
termed the contrast correction factor. This factor is determined by
calculating the standard deviation of the pixel intensities in the
neighborhood of each bead (i.e., within rectangle surrounding each
bead), divided by the mean value of the pixel intensities in the
same region (i.e., normalized spatial standard deviation).
[0077] FIG. 20, plot A, is an NSD histogram of B type red blood
cells in synthetic plasma which includes anti-B, dispersed on a
surface with A antigens. FIG. 20, plot B, is a scatter plot that
relates the NSD value of each red blood cell measured in FIG. 20
plot A, to its normalized spatial standard deviation.
[0078] FIG. 21, plot A, shows an NSD histogram of B-type red blood
cells in synthetic plasma which includes anti-B, dispersed on a
surface with B antigens. The anti-B antibodies attach to the B
antigens on the red blood cells, as well as the B antigens on the
surface, thereby immobilizing the cells. As a consequence the
intensity fluctuations generated are significantly reduced compared
to the cells on the A antigen patch (FIG. 20, plot A), as is shown
by the lower NSD measurements in FIG. 21, plot A. FIG. 21, plot B,
is a scatter plot that relates the NSD value of each the red blood
measured in FIG. 21, plot A, to its normalized spatial standard
deviation.
[0079] FIG. 22, plots A and B, show corrected and uncorrected
histograms of unbound (FIG. 20, plot A) and bound (FIG. 21, plot A)
red blood cell NSD histograms, where corrections were applied by
using the linear fits (FIG. 20, plot B and FIG. 21, plot B) to
remove the NSDs dependence on the normalized spatial standard
deviation.
[0080] FIG. 23 is a plot of the cumulative probability distribution
of the histograms shown in FIG. 22, plot A, and FIG. 22, plot B
(i.e., a plot of the fraction of cells that have an NSD less than
or equal to a particular NSD value).
[0081] FIG. 24 shows an NSD histogram of 4.8 .mu.m diameter beads
in water (top histogram) as well as a simulated NSD histogram using
a Gaussian distribution that yields a square-root mean squared
displacement of 0.131 .mu.m (bottom histogram).
[0082] FIG. 25 shows an NSD histogram of 4.8 .mu.m diameter beads
in water with 0.4% saline (top histogram) as well as a simulated
NSD histogram using a Gaussian distribution that yields a
square-root mean squared displacement of 0.0064 .mu.m (bottom
histogram).
[0083] FIG. 26, plots A, B, and C, show NSD histograms of type A
red blood cells in synthetic plasma containing 5 nM anti-A
dispersed on a surface with B antigen.
[0084] FIG. 27, plots A and B, are plots of the normalized spatial
standard deviation values for unbound and bound red bloods
respectively measured from one image.
[0085] FIG. 28, plots A and B, plot the average spatial normalized
standard deviation for 100 cells in the unbound population of FIG.
20, plot A, and FIG. 24, plot A (FIG. 28, plot A), as well as that
for 100 cells in the bound population of FIG. 20, plot B, and FIG.
24, plot B (FIG. 28, plot B) over all 40 frames in the sequence. In
both of the plots the error bars plot the standard deviation of
each cell's spatial normalized deviation over the 40 frame
sequence.
[0086] FIG. 29 plots the standard deviation of the spatial
normalized standard deviation measured for each cell, over the 40
frames in the sequence for both the bound cells as well as the
unbound cells.
[0087] FIG. 30 shows a calibration curve for using holographic
focusing. The calibration sample containing particles is displaced
known distances from the focal position, and an image is acquired
at each distance. Each image is then numerically propagated and the
position of its focal measure extremum is plotted as a function of
sample distance from focus.
[0088] FIG. 31 shows the final position of the sample that
initially started in a range of initial positions between +1 mm and
-1 mm away from focus. The in-focus position was determined
visually and has an error estimated as .+-.1 .mu.m. The accuracy of
the holographic focusing is comparable to the error in the focal
position estimation.
[0089] FIGS. 32A and 32B show a correlation figure for pairs of
particles/cell where sub-images are used to distinguish bound
particle/cell pairs from unbound particle/cell pairs.
[0090] FIG. 33A shows bead intensity fluctuations (NSD, normalized
standard deviation values) of two different samples of bead
dispersions (4.8 .mu.m silica) measured without the application of
any external forces (i.e., a thermal equilibrium measurement),
where one sample was bound to the surface (dense cross-hatched
histogram) and the other was unbound to the surface (histogram with
sparse diagonal line pattern). The beads were made to bind to the
surface of a glass coverslip by diluting them in a 0.9% saline
solution, while the beads were made to freely to diffuse on the
coverslip by dilution in deionized water. The unbound beads are
distinct from the bound ones under these experimental conditions
(i.e., minimal overlap of distributions). FIG. 33B shows the same
samples measured after the application of stage movements. Each
image in the stack of analyzed histograms was taken after a
piezo-electric stage moved the sample 25 .mu.m back and forth along
one axis. This physical perturbation on the system did not affect
the mobility of the bound population of beads (dense cross-hatched
histogram) in comparison to the measurements taken without force
application, as measured by the NSD measurements. However the
unbound beads displayed higher NSD values than their non-forced
counterparts in FIG. 33A as a result of the increased bead mobility
generated by the stage motions. Physical force application can thus
better resolve bound and unbound populations as evidenced by the
greater separation between the bound and the unbound bead
histograms in FIG. 33B.
DESCRIPTION OF THE INVENTION
[0091] The present invention relates to an instrument and
measurement apparatus and methodology used for measurements and
testing, that characterize a population of cells/particles or which
can detect a sub-population of cells/particles, based on their
detected mobility, in a quick and efficient manner.
[0092] Apparatus
[0093] In one exemplary embodiment, FIG. 1A shows a schematic
diagram of an in-line holographic microscope apparatus 10 designed
as the means to measure the mobility of particles (i.e., cells) in
order to infer surface interactions (i.e., presence or absence of
specific surface--particle interactions), and/or collective
diffusion/visco-elastic properties of the particle dispersion
(e.g., effective viscosity).
[0094] In the exemplary embodiment of FIGS. 1A and 1C, the
apparatus 10 used for carrying out the present invention includes a
coherent light source (e.g., laser, superluminescent diode) 100
that is collimated by a collimator 101, which may be coupled to an
optical-fiber 103, and whose laser beam 104 illuminates a
transparent sample holder 105 (i.e., microscope slide) of a
microscope 106.
[0095] In one embodiment, the coherent light source 100 is a laser
which has a short coherence length (<400 .mu.m), and operates at
660 nm. Other wavelengths and types of illumination sources,
including non-laser sources (e.g., superluminescent diodes) and
conventional light sources (e.g. LED, incandescent, arc lamp) may
be used as well. Holographic optical trapping apparatuses are well
known in the art, as disclosed in U.S. Pat. No. 7,161,140, to Grier
et al., for example, which is herein incorporated by reference in
its entirety.
[0096] In one embodiment, the sample 107 disposed on the sample
holder 105, includes a dispersion of particles 108 (i.e., cells)
disposed on a treated or untreated transparent surface 109 (i.e.,
coverslip 109A). The sample 107 of cells/particles 108 may be
introduced into the sample holder 105 manually or through an
automated fluidic device 116 (discussed later), the structure and
operation of which is well known in the art.
[0097] The particles 108 may settle on the surface 109 to test
particle-surface interaction. In one embodiment, the particles 108
may settle on the surface 109 due to gravitational forces. In
another embodiment, the particles 108 may settle on the surface 109
due to centrifugal forces applied to a sample chamber 118 (see FIG.
3A) using a centrifuge (117, for example). Further, in another
embodiment, the particles 108 may settle on the surface 109 due to
other forces applied to the particles 108 (discussed further
herein).
[0098] The particles 108 used may have a variety of physical and
chemical attributes, and may be of different types, based on size,
shape, and materials, yielding distinguishable images on the image
formation apparatus. For example, a particle 108 may be a regularly
shaped bead with some symmetry (e.g., spherical, prolate spheroid,
oblate spheroid), or an irregularly shaped bead. A particle 108 may
be made of one type of material or of multiple types of materials.
A particle 108 may be solid, porous, or have a hollow core. A
particle 108 may be fully or partially coated with other
material(s). A particle 108 may be metallic or partly metallic. A
particle 108 may be non-metallic or partly non-metallic. A
particle's 108 surface may be treated to apply a texture. A
particle 108 may be a silica bead with linker molecules on the
surface, or a silica bead with biomolecules or synthetic molecules
attached to the surface. A particle 108 may also be a silica bead
with biomolecules or synthetic molecules attached to the linker
molecules on the surface. A particle 108 may be a bead coated with
or otherwise embedded with a fluorescent or luminescent label
molecule(s) covalently or non-covalently attached to it or
integrated with it, which may also distinguish particle type based
on fluorescent or luminescent emission spectrum. A particle 108 may
be a bead coated or embedded with a combination of different
fluorescent or luminescent label molecule(s) covalently or
non-covalently attached to it or integrated with it, which may also
distinguish particle type based on fluorescent or luminescent
emission spectrum. A particle 108 may be a bead with a
nanoparticle(s), or magnetic nanoparticle(s), or fluorescent
nanoparticle(s), covalently or non-covalently attached to it or
integrated with it.
[0099] In further example, a particle 108 may be a biological cell.
A particle 108 may be a genetically engineered biological cell or a
descendant of a genetically engineered biological cell. A particle
108 may be a cell that is treated with biomolecules and/or
synthetic molecules. A particle 108 may be a cell that is treated
with linker molecules. A particle 108 may be such a cell that is
treated with biomolecules and/or synthetic molecules that attach to
the linker molecules. A particle 108 may be a cell with a
fluorescent or luminescent label molecule(s) covalently or
non-covalently attached to it or integrated with it. A particle 108
may be a cell with a combination of different fluorescent or
luminescent label molecule(s) covalently or non-covalently attached
to it or integrated with it. A particle 108 may be a cell with a
nanoparticle(s) covalently or non-covalently attached to it or
integrated with it. A particle 108 may be a cell with a magnetic
nanoparticle(s) covalently or non-covalently attached to it or
integrated with it. A particle 108 may be a cell with a fluorescent
nanoparticle(s) covalently or non-covalently attached to it or
integrated with it. A particle 108 may be a cell that naturally
expresses or is genetically altered to express fluorescent
protein(s).
[0100] In one embodiment, the sample 107 of particles 108 may
further be modified by introducing reagents or non-reactive
solutions onto the sample holder 105--before, during or after the
measurements. For example, as shown in FIG. 1B, for the measurement
of the surface interactions of the cells/particles 108, the
transparent surface 109 (e.g., coverslip 109A) of the sample holder
105 onto which the cells/particles 108 settle, may be provided with
special treatment.
[0101] For example, the surface 109 (including coverslip 109A) may
be provided in a variety of ways. In one embodiment, the surface
109 may be flat and transparent. Further, a surface 109 may be flat
and partially transparent. Still further, a surface 109 may be a
flat and fully or partially reflective. A surface 109 may also be a
textured flat surface. A surface 109 may be treated with
biomolecules or synthetic molecules. A surface 109 may be treated
with linker molecules. A surface 109 may be treated with
biomolecules or synthetic molecules linked to the linker molecules.
A surface 109 may be differentially treated with a variety of
molecules. A surface 109 may be differentially treated with a
variety of linker molecules. A surface 109 may be treated with a
mixture of molecules. A surface 109 may be part of a microfluidic
device. A surface 109 may be part of a microtiter plate. A surface
109 may be part of a transparent or partially transparent sample
chamber 118 (see FIGS. 1D and 3A). A surface 109 may be part of a
reflective or partially reflective sample chamber 118 (i.e.,
reflective or partially reflective surface 120 of FIG. 1D). A
surface 109 may be sufficiently microscopically smooth to allow
imaging of particles 108.
[0102] In one example, FIG. 3B shows a top-down view of
differentially treated surface areas (A, B, C and D) of a coverslip
109A on a sample holder 105 which each have a heterogeneous mixture
of particles 108, where each type of particle 108 is
distinguishable (by fluorescent label or holographic image
characteristics, for example). Note that each surface 109, 109A
yields a differing interaction (i.e., different types of particles
108 are able to diffuse in each area) with respect to the mobility
measure of the heterogeneous population of particles 108. FIG. 3B
demonstrates how many different types of interactions may be probed
simultaneously (i.e., multiplexed measurements) by using a
distinguishable heterogeneity of particles 108 with or without
differentially treated surface 109, 109A areas, in accordance with
the present invention.
[0103] In one embodiment, a heterogeneous population of particles
108 may be measured simultaneously, with each type of particle
testing for different quantities or regimes in similar quantities
(e.g., multiplexed measurements) (see FIG. 3B). All or some types
of particles 108 may be distinguished based on a label (e.g.,
fluorescent, nanoparticle), particle image (due to different
absorption, scattering, fluorescence, luminescence characteristics,
fluorescence or luminescence emission profiles, fluorescent or
luminescent decay lifetime), and/or particle position (assuming
controlled deposition of particle types). All or some types of
particles 108 may be distinguished by a multimodal collection of
data of each particle 108. All or some types of particles 108 may
be distinguished based on their holograms generated by a coherent
light source. All or some types of particles 108 may be
distinguished based on their focused and/or defocused holograms
generated by a coherent light source 100. All or some types of
particles 108 may be distinguished based on their holograms
collected at more than one focal plane. A homogenous population of
particles 108 may be measured simultaneously.
[0104] In one embodiment, the sample 107 includes particles 108
coated with biomolecule A, surface- (i.e., solid-phase) coated with
biomolecule B in the presence of solution containing (or not
containing) biomolecule A, as well as containing (or not
containing) other types of biomolecules (e.g., biomolecule C, D, E,
etc.). The sample measurement of this method yields information of
biomolecular interactions between the particles 108 and the surface
109 in the presence of the solution for research, industrial and/or
clinical purposes by an analysis of particle mobility in response
to controlled or thermal forces.
[0105] In one embodiment consistent with the present invention, the
particles 108 are cells/beads 108 with surface antigens which may
be bound to a diffusing moiety present in the solution. Furthermore
this moiety may simultaneously be able to bind to an appropriately
treated surface (solid-phase). In this manner, the presence or
quantity of the moiety in solution may be measured by measuring the
mobility of appropriately coated particles 108 on appropriately
treated surfaces 109 (solid-phase). The presence of such diffusing
species may affect the mobility of the bound cells/beads 108 in a
concentration-dependent way. This type of measurement may be used
to determine the presence, absence and/or concentration of the
freely diffusing target moiety which acts as a capture agent for
the particles.
[0106] In an exemplary embodiment, if particular antigens are
probed on the cell/particle surface 108a (see FIGS. 1B and 1C), an
appropriate surface treatment for the transparent surface 109 may
include chemically modifying the surface 109 with a suitable linker
molecule to be able to attach the appropriate antibody to the
surface 109, which allows specific binding of the cell/particle 108
presenting the target antigen with the surface antibody on the
transparent surface 109. Cells/particles 108 that do not have the
target antigen on their surface 108a do not specifically bind to
the surface of the transparent surface 109, in this case. In the
case of measuring cell/particle 108 diffusional properties (i.e.,
effective diffusion coefficient, effective viscosity or
visco-elasticity properties), for example (discussed later), an
inert surface 109 would be used.
[0107] The cells/particles 108 may be imaged by a microscope
objective lens 110 (see FIG. 1A) of a microscope apparatus, and a
tube lens 111 which allows the magnified pattern of the
cell/particles 108 to be imaged on a CCD or CMOS camera 112, which
is connected to a computer 113 for image processing etc. Different
areas of the sample 107 may be imaged by translating the sample 107
with a translation stage 114 (e.g., motorized microscope
translation stage), which is well-known in the art. The sample
holder 105 may also allow all or parts of the measured areas to be
temperature controlled using a temperature controlling apparatus
therein, allowing for example incubations to occur at optimal
temperatures for biomolecular interactions to take place.
Furthermore, the focal plane that is imaged by the microscope 106
may be adjusted by using the focus control 115 (which may be motor
driven as well).
[0108] In one embodiment, when the particles 108 are coated or
otherwise have embedded fluorescent, or luminescent molecules or
nanoparticles, which are distinguished by particle type based on
fluorescent or luminescent emission spectrum, the apparatus 10
shall be equipped with a fluorescent, luminescent excitation source
100, appropriate filters (not shown) and dichroic elements (not
shown) as well as color detection capabilities (e.g., color camera
113 and/or emission filter selections). The introduction of
different types of particles 108 with each type being uniquely
coated may allow multiplexing the measurements (i.e., measurement
of multiple types of interactions simultaneously).
[0109] Unlike in many traditional fluorometric multiplexed
measurements, a separate washing step is not needed, since it is
not the presence of a given particle 108 that marks a positive
binding interaction, it is the mobility measurement of the particle
that indicates positive binding.
[0110] In another embodiment consistent with the present invention,
a transparent, semi-transparent, or partially mirrored sample 107
and sample chamber 118 with reflective coating 120 (see FIG. 1D),
may be measured in reflection mode, involving the laser
illumination (or alternative illumination source) to illuminate the
sample 107 from the collection side, and image formation occurring
from the light reflected from the sample 107 that is subsequently
imaged onto the computer 113 monitor.
[0111] In yet another embodiment consistent with the present
invention, the transparent sample holder 105 is an automated
fluidic device 116 or microtiter plate device with data acquisition
and analysis capability (see FIG. 3A), as described herein. The
particles 108 are flowed into the sample chamber 118 of the
microfluidic device 116, or introduced into the microtiter plates
by a robotic microtiter apparatus which introduces a number of
samples 107 (particles 108 and solution) into different wells. Each
well may have a unique surface chemistry, indexed by position,
allowing multiple tests to be performed, such as independent
binding assays, ultimately allowing a single sample 107 (or a
plurality of samples 107) to be tested with a plurality of surfaces
109.
[0112] The transparent surface 109 on which the particles 108
settle may be made of treated plastic or glass. The microtiter
plate may or may not have a customized configuration, including
optically transparent caps, sample delivery zones, sample viewing
zones etc. The instrumentation outlined herein may be integrated
with a robotic microtiter plate handling machine for automated (and
parallelizable) fluid delivery from sample containers to each well,
sample mixing and incubation capabilities, as well as parallelized
microtiter plate measurement capabilities, which may be
programmable and automated. Thus, an assay may be designed to
perform multiple tests on one or a number of samples 107, in a
parallel fashion. In addition, multiple microtiter plates may be
measured in a parallel fashion with a suitably parallelized optical
train and detection set-up. A robotic apparatus may feed microtiter
plates into the detection area for measurement in an automatic
fashion, allowing stacks of microtiter plates to be measured
without user intervention.
[0113] Measurements in a given sample chamber 105, microfluidic
sample chamber 105 or microtiter plate well, may be repeated after
the addition of solutions, particles or mixtures, or the exchange
of solutions, particles or mixtures, and/or incubations at
different temperatures.
[0114] Titration measurements may be performed in a given sample
chamber 105, microfluidic sample chamber(s) 105 or microtiter plate
well(s) by introducing additional analytes into the chamber/well
solution(s).
[0115] Further, kinetic experiments may be performed in a given
sample chamber 105, microfluidic sample chamber(s) 105 or
microtiter plate well(s) by following the time-course of the
particle mobility measurements (e.g. NSD) after the introduction of
additional analytes into the chamber/well solution(s) (or not).
[0116] The disclosed assay methods may be implemented as a computer
program product for use with a computer system. Such
implementations may include a series of computer instructions fixed
either on a tangible medium, such as a computer readable medium
(e.g., a diskette, CD-ROM, ROM, or fixed disk) or transmittable to
a computer system, via a modem or other interface device, such as a
communications adapter connected to a network over a medium. The
medium may be either a tangible medium (e.g., optical or analog
communications lines) or a medium implemented with wireless
techniques (e.g., microwave, infrared or other transmission
techniques). The series of computer instructions embodies all or
part of the functionality previously described herein with respect
to the system. Those skilled in the art should appreciate that such
computer instructions can be written in a number of programming
languages for use with many computer architectures or operating
systems.
[0117] It is expected that such a computer program product may be
distributed as a removable medium with accompanying printed or
electronic documentation (e.g., shrink wrapped software), preloaded
with a computer system (e.g., on system ROM or fixed disk), or
distributed from a server or electronic bulletin board over the
network (e.g., the Internet or World Wide Web). Of course, some
embodiments of the invention may be implemented as a combination of
both software (e.g., a computer program product) and hardware.
Still other embodiments of the invention are implemented as
entirely hardware, or entirely software (e.g., a computer program
product).
[0118] Binding Techniques
[0119] In one embodiment, the plurality of particles 108, such as
cells 108 expressing receptors, are disposed on a surface 109
(i.e., solid-phase), treated with an analyte such as a receptor
ligand (i.e., solid-phase) such that said ligands will selectively
detect some fraction of particles 108 coated with receptors
complementary to the ligands.
[0120] The particles 108 compete with the target receptor in the
sample solution for binding sites on the solid-phase, for the
purposes of determining receptor concentration in the sample
solution. The solution containing the target receptor may be
preincubated on the solid-phase surface 109 before the introduction
of the coated particles 108.
[0121] In one embodiment, a plurality of particles 108 are cells
108 expressing proteins on their surface, and are disposed on a
surface 109 (i.e., solid-phase) which is treated with a moiety such
as a protein binding receptor or an antibody such that said surface
proteins will selectively be bound by said solid-phase receptors or
antibodies. In other words, the particles 108 are coated with
complementary antigen, which then competes with antigen in the
sample solution for binding sites on protein binding
receptor/antibodies immobilized on solid-phase. As noted above, the
sample solution with target antigen may be preincubated in a sample
chamber 105, microfluidic sample chamber 105 or microtiter well,
containing the solid-phase, before introduction of the coated
particles 108, which then bind to unoccupied antibody sites on
solid-phase, and are distinguishable from unbound particles 108
based on their mobility. With calibration, the method may yield
target antigen concentration in the sample solution.
[0122] The coverage density of antigens/antibodies/analytes/surface
proteins on particles 108 (or expression levels of surface proteins
in cells) may be estimated based on the degree of restricted motion
observed on a known solid-phase, and with known solution
conditions.
[0123] Solid-phase consists of immobilized capture antibody
specific to a particular epitope(s) of an analyte of interest, and
particles 108 are coated with a secondary antibody (i.e., like a
"label antibody") specific to epitopes on different regions of the
same analyte such that the solid-phase capture antibody and
particle bound "label antibody" may be simultaneously bound to the
analyte (i.e., sandwich assay). Particles 108 that bind to the
surface 109 due to such an interaction may be used as a measurement
of the analyte concentration of the unknown sample 107. In
addition, the kinetics of fractional particle binding (i.e.,
fraction of particles bound) may be used as a method to measure
analyte concentration as well as analyte biochemical properties
(e.g., rate constants, equilibrium constants etc.). Such a
measurement does not require a "label antibody" wash step, since
the bound "label antibodies" (i.e., antibody coated particles) may
be distinguished from the unbound particles 108 by their diffusive
behavior (i.e., bound particles show significantly diminished
mobility), or response to physical forces (i.e., bound particles
show significantly diminished response to physical force). Unlike
in traditional immunometric assays, "label antibodies" do not
necessarily indicate a positive binding interaction. Their presence
is necessary but not sufficient. The positive binding interaction
is finally determined by the particle's mobility measurement.
[0124] In one embodiment, the plurality of particles 108 are red
blood cells 108, and are bound to the antibody on the surface
109.
[0125] In one embodiment, the target antibodies are taken from a
blood sample, and testing is done against an array of uniquely
treated surfaces to determine an antibody profile. Specifically,
the target antibodies are taken from a blood sample for the
purposes of detecting viral infection. Proteins that occur on the
surface of a given virus may be immobilized on the surface (i.e.,
solid-phase) thereby being able to capture the specific antibody to
that virus. In addition, particles coated with antibodies
complementary to another region of the virus antibody are present
in the test, such that in the presence of the target virus
antibody, immobilization of particles may occur, signaling the
presence of the antibody in the blood sample. Such measurements are
performed in order to diagnose infection, or quantify target
antibody concentration, with suitable controls.
[0126] The magnitude of particle binding, and the kinetics of
binding strength, as measured by decreased particle mobility with
increased particle binding, may be used as a method to measure
analyte concentration and biochemical properties (e.g., rate
constants, equilibrium constants etc.).
[0127] Holographic Focusing
[0128] The measurement of multiple fields of view, sample chambers
118 (see FIG. 3A), microfluidic chambers 118 and/or microtiter well
plate chambers may require multiple refocusing events. To increase
the throughput of such a measurement, focusing time should be kept
to a minimum.
[0129] Traditional focusing techniques require multiple focus
shifts, and a comparison of the images acquired at these different
focal positions. By applying a focus measure to each image, a
determination may be made as to whether the focus shifts are
approaching the true focal position or not. The fact that the
sample 107 is physically moved multiple times to determine the
focus makes such techniques time consuming.
[0130] However, in contrast to traditional techniques, the use of a
coherent source 100 to image the particles 108 allows numerical
processing of an imaged hologram to determine the focal plane of
the particles 108 in a quick fashion. Illuminating the sample 107
of particles 108 in a transparent sample chamber 118, for example,
with a coherent source 100, allows imaging of particles' 108
diffraction pattern even when significantly out of focus. A
numerical solution to focusing allows quick focusing over a
long-range of out-of-focus distances. Numerical focusing involves
the propagation of the out-of-focus image to different distances
which allows the focus to be determined numerically. By associating
a focus measure with each numerically propagated image, an extremum
in the focus measure may be found, allowing a single stage movement
to position the sample in the required focal position. This is in
contrast to the traditional focusing methods which require
comparatively slow physical scans of a sample through different
focal distances to determine the focal position according to some
focus measure.
[0131] Thus, the acquired out-of-focus diffraction pattern may be
numerically propagated over a range of distances to determine the
distance that maximizes the focus measure. Once this distance is
numerically determined, a single stage movement may be performed to
position the sample 107 to the required focal position.
[0132] A dedicated focusing camera 217 (see FIG. 1A) may be used in
acquiring images upon which the numerical propagation calculations
are performed. In one embodiment, this camera may be placed at an
imaging plane different to that of the camera 112 used for mobility
measurement by means of a beam splitter 218. Thus, focusing
calculations are performed from images collected from this separate
focusing camera 217 which is positioned in a plane that is
out-of-focus compared to the measurement camera 112.
[0133] It is also possible to use a single camera 112 for both
focusing as well as mobility measurement. An image for focusing
purposes may be collected after controlled defocusing of the sample
107.
[0134] FIG. 30 illustrates a calibration curve generated from
numerically propagating images form a calibration sample 107 that
was displaced from 1 mm above the true focus position to 1 mm below
focus. The images were collected on a dedicated focusing camera
that was displaced 1 mm away from the focus, in object space. The
objective 110 used had a low-magnification and low-numerical
aperture objective. Calibration curves may also be generated with
different objectives 110 and CCD positions as well. The x-axis of
the calibration curve shows the actual distance the calibration
sample 107 was moved, with respect to the focal position, and the
y-axis value indicates the location of the numerically propagated
peak of the focal measure. For samples 107 that are defocused
significantly far from the focal position, more than one numerical
focus iteration may be required to attain the desired focusing
accuracy.
[0135] FIG. 31 illustrates the focusing performance using the
calibration curve of FIG. 30 when three focusing iterations are
performed for a range of initial starting positions ranging from +1
mm to -1 mm. The true focal position was determined visually and
has an error of approximately .+-.1 .mu.m. The values of the final
position with respect to the focal-plane are accurate to within the
estimated error of the visually determined focal position
measurement.
[0136] Methods and Apparatuses of Performing the Invention There
are a number of methods of performing the present invention with
the above-described apparatus--which are described herein
below.
[0137] Physical Force Application Method
[0138] In one embodiment consistent with the present invention, a
physical force is applied to the cells/particles 108 and/or surface
108a thereof, using physical force application means 10 (see FIGS.
1A and 1B), and the response of the cell/particle 108 motions can
be measured (i.e., impulse response measurement, frequency response
measurement) using the computer 113. The physical force application
means 10 may include an abrupt or continuously periodic movement of
the translation stage 114, or optically generated forces using
optical forcing means 100, 101 etc., or by the application of other
external means such as an ultrasonic means 117 which uses an
ultrasonic emitter 117 which applies ultrasonic waves to the sample
107, acoustic means (i.e. acoustic wave application), or a physical
probe contact, or by fluid flow from an automated fluidic flow
(microfluidic) device 116, for example, all of which are well-known
to one of ordinary skill in the art.
[0139] In the physical force application method stated above, the
measurement methods of the present invention involve acquiring
sequences of holographic images of cells/particles 108 in a field
of view (e.g., as viewed through the microscope 106) (see FIGS. 1A,
1B, and 2).
[0140] In the case of a measurement done in concert with the
application of an external force (i.e., physical force application
method), a statistical analysis of a sequence of holographic
microscope images synchronized with the physical force application,
yields the nature of the interactions on the surface 108a or the
diffusional/visco-elastic characteristics of the cell/particle 108
dispersion.
[0141] Specifically, in the analysis by the computer 113 of the
images of the cell/particles 108 captured by the imaging device
112, the images include a component that is diffracted by the
cells/particles 108, as well as an undiffracted component. These
two components interfere in the imaging plane, yielding an
interference pattern that represents the sample 107. The use of
coherent illumination that is spatially coherent allows the sample
107 to be imaged even when it is significantly out of focus. By
adjusting the amount of defocus, the interference pattern may be
adjusted in order to improve the signal-to-noise ratio of the
measurements.
[0142] In the exemplary measurements discussed below, the focal
distance was adjusted so that the interference pattern had a trough
of low intensity in the center (see FIG. 2) with a bright intensity
ring surrounding it. Additional concentric dark and bright rings
with a lower modulation depth extend away from the center. A high
signal-to-noise ratio may be achieved by forming an interference
pattern with a low central intensity and a high surrounding ring of
intensity.
[0143] In this embodiment, beads or cells (i.e. particles) 108 that
do not move, mean that the fluctuation of their hologram pixel
counts is dominated by the effects from environmental vibrations,
photon statistics and detector noise which is a relatively small in
magnitude. Particles or cells 108 that do move (e.g., by physical
force or diffusion) will have their interference patterns shift as
the particles or cells 108 move. When a cell or particle 108 moves
in one frame with respect to a previous frame, a pixel with a low
count in the central trough in the first frame may be exposed to
the adjacent bright ring in the succeeding frame. This large
fluctuation of pixel intensity from low count value to high count
value, if repeated, will yield an average pixel fluctuation value
which is high (see graph in FIG. 2). Normalizing such fluctuations
(i.e., dividing the standard deviation of the pixel values by their
average values, pixel by pixel), weighs fluctuations in intensity
that occur at the center of the particle/cell 108 hologram the
highest, since the average values are the lowest in this
region.
[0144] In summary, this embodiment yields particle by particle
intensity fluctuation values, whereby particles 108 that are able
to move through physical force application or diffusion, display
high intensity fluctuations, and those that are bound to the
surface 109 display low intensity fluctuations. This intensity
fluctuation measurement is the measurable that is used to determine
particle binding, in particle binding based assays.
[0145] Unlike in many other types of binding assays, washing of the
surface 109 to remove unbound particles 108 is not required since
binding is determined based on the mobility measurement, and
unbound particles 108 are distinct from bound ones in this
regard.
[0146] Specifically, FIG. 2 illustrates how the hologram shifts due
to diffusion and may cause large fluctuations in pixel intensity.
For pixels that are in the central, low intensity portion of the
hologram, shifts in the hologram position cause large fluctuations
when the surrounding bright fringe of the hologram is detected.
Pixel k in the diagram detects the central minima of the hologram
when the hologram is in position x.sub.1. A shift of the hologram
to position x.sub.2 causes pixel k to see the peak intensity of the
surrounding ring. A return of the hologram to the original position
x.sub.1 causes pixel k to return to a low count rate again. The
overall effect is that the central region of minimal count values
picks up large pixel intensity fluctuations by virtue of hologram
movements causing the surrounding bright fringe to periodically
contribute high pixel intensities.
[0147] As stated above, the coherent laser source 100 has a short
coherence length (<400 .mu.m), and operates at 660 nm. The short
coherence length prevents interference fringes from being formed,
which would be due to the coherent superposition of optical
reflections with the transmitted beam. As stated above, other
wavelengths and types of illumination sources, including non-laser
sources (e.g., superluminescent diodes) and conventional light
sources (e.g. LED, incandescent, arc lamp). Any source 100 that
allows the particles 108 to be imaged with sufficient contrast is
usable.
[0148] In this embodiment, the CCD 112 is used to capture the
magnified images of the sample 107. The exposure time of the CCD
112 (or the pulse duration of a pulsed laser 100, in the case of
using a pulsed laser as a strobe source) should be significantly
shorter than the diffusional time constant. This ensures that the
cells/particles 108 do not significantly diffuse during the
exposure time, thereby blurring the image.
[0149] For impulse-response measurements, appropriate impulse
generating apparatuses 117 may be attached to the microscope 106 in
order to apply forces to the cells/particles 108 that are
synchronized with image acquisition in order to measure their
responses.
[0150] FIG. 33A shows bead intensity fluctuations (NSD, normalized
standard deviation values) of two different samples of bead (4.8
.mu.m silica) dispersions measured without the application of any
external forces (i.e., thermal equilibrium measurement), where one
sample was bound to the surface (dense cross-hatched histogram) and
the other was unbound to the surface (histogram with sparse
diagonal line pattern). The beads were made to bind to the surface
of a glass coverslip by diluting them in a 0.9% saline solution,
while the beads were made to freely to diffuse on the coverslip by
dilution in deionized water. The unbound beads are distinct from
the bound ones under these experimental conditions (i.e., minimal
overlap of distributions).
[0151] FIG. 33B shows the same samples measured after the
application of stage movements. Each image in the stack of analyzed
histograms was taken after a piezo-electric stage moved the sample
25 .mu.m back and forth along one axis. This physical perturbation
on the system did not affect the mobility of the bound population
of beads (dense cross-hatched histogram) in comparison to the
measurements taken without force application, as measured by the
NSD measurements. However the unbound beads displayed higher NSD
values than their non-forced counterparts in FIG. 33A as a result
of the increased bead mobility generated by the stage motions.
Physical force application can thus better resolve bound and
unbound populations as evidenced by the greater separation between
the bound and the unbound bead histograms in FIG. 33B. Although the
resolution between bound and unbound bead populations was excellent
even without force application (i.e., thermal equilibrium
measurement FIG. 33A), there may be samples and circumstances which
warrant the greater resolving capability of bead or cell mobility
measurements under the influence of physical force.
[0152] All aspects of the testing, from robotic sample container
manipulation, to pump or micropipette delivery of samples and their
temperature control, to focusing and sampling of different regions
of the sample 107, to synchronized force application, image
acquisition and data analysis may be automated and
computer-controlled (i.e., by computer 113), yielding measurements
and results without human intervention.
[0153] Thermal Equilibrium Measurement Method
[0154] In another exemplary embodiment, analysis of the statistical
properties of the sequence of holographic images in the passive
probe method, yields a measurement that is related to the extent of
the cell/particle 108 motion generated at thermal equilibrium in
the sample 107. In this embodiment, a passive probe method is
utilized, where the sample 107 is measured by analyzing the
fluctuations in microscopic cell/particle 108 motions at thermal
equilibrium (e.g., equilibrium fluctuation measurement), using a
modified apparatus of FIG. 1A. In lieu of a separate, externally
applied physical probe technique, the passive probe method,
conducted at thermal equilibrium, uses the thermal energy of the
fluid molecules surrounding the beads/cells 108, causing the free
beads/cells 108 to undergo Brownian motion. The extent of this
Brownian motion may then be measured with the techniques described
herein to draw conclusions about the sample.
[0155] In the passive probe method, as in the physical force
application method stated above, the measurement methods of this
embodiment involve acquiring sequences of holographic images of
cells/particles 108 in a field of view (e.g., as viewed through a
microscope 106) (see FIG. 2).
[0156] Specifically, in the thermal equilibrium method, a
microfluidic (or microtiter plate) based apparatus 116 is used,
where a flow of particles/cells 108 enters into a sample chamber
118, where a sufficiently dilute dispersion of the particles/cells
108, which has settled to the bottom, optically transparent imaging
surface 119 of the sample chamber 118, is illuminated with the
laser light source 100. The microfluidic (or microtiter plate)
apparatus 116 may allow for multiple solutions, and dispersions to
be mixed, incubated and measured in appropriate chambers 118. The
microfluidic (micropipettiter plate) apparatus 116 will also
contain necessary pumping (pipetting) capabilities, temperature
control capabilities, and possibly centrifugation capabilities,
known to one of ordinary skill in the art.
[0157] FIG. 4, screen shot A, shows a holographic image of 2 .mu.m
silica beads. A sequence of such holographic images are acquired by
the imaging device 112 and statistically analyzed by the computer
113.
[0158] FIG. 5A illustrates how each pixel position through the
stack is analyzed to determine its standard deviation as well as
its average pixel value. A holographic fluctuation image is then
generated by the computer 113, whereby each pixel value is given by
the value of the pixel's standard deviation over time, divided by
the pixel's average value.
[0159] In one embodiment, a statistical image(s) is generated by
some combination of pixel statistical measures including average
pixel value, pixel standard deviation, pixel variance, higher order
pixel fluctuations, pixel temporal correlation functions, pixel
spatial correlation functions, pixel spatio-temporal correlation
functions, background pixel value, background pixel standard
deviation, background pixel variance, higher order background pixel
fluctuations, background pixel temporal correlation functions,
background pixel spatial correlation functions, and background
pixel spatio-temporal correlation functions. The pixel statistical
measures may be generated pixel-wise over the image sequence and
then averaged over the particle neighborhood. The pixel statistical
measures may be calculated over the particle neighborhood, and then
calculated over the corresponding neighborhoods in the other images
in the sequence. The particle neighborhood mask may be generated
using one frame in the sequence. Multiple frames from the sequence
may be used to generate particle neighborhood masks. Pixel
statistical measures may be calculated over subsets of the image
sequence. Pixel statistical measures may be calculated over
successive subsets of the image sequence generating time varying
statistical measurements per particle. Time varying statistical
measurements per particle may be associated with time varying
experimental conditions (e.g., physical movement, vibration,
solution conditions, flow conditions, other environmental effects).
Spatial and temporal correlations of particle-based statistical
measurements may be performed. Time varying spatial and temporal
correlations of particle-based statistical measurements may be
calculated (as opposed to pixel-based statistical measurements).
Thresholds in the statistical particle measurements may be chosen
to select fractions with desired surface affinity, interaction
characteristics that have commercial, diagnostic relevance.
Thresholds in selected fractions may be chosen to indicate minimum
fractional level necessary to be measured before positive result is
indicated, based on desired statistical significance.
[0160] In one embodiment, particle positions are tracked over time,
and statistical measurements of particle positions may be generated
(e.g., mean squared displacement, net displacement, etc.) and
distributions of these quantities plotted for multiple particles.
Thresholds in particle position measurements and particle
statistical quantities based on particle positions may be applied
to determine fractions with target particle-surface affinities.
Statistical measures of particle movement may be based on mean
squared particle displacement, mean particle displacement, net
particle displacement, higher-order particle position statistics,
or a combination of any or all of these quantities. Similar
measures of particle movement may be measured for control purposes
(e.g., background correction).
[0161] An example of a holographic fluctuation image generated from
a sequence or stack of holographic images (i.e., 60 images), as
discussed above with respect to FIG. 5A, is shown in FIG. 4, screen
shot B. The light areas in the image correspond to areas in the
sample plane where there are large fluctuations in signal intensity
(corresponding to where particles/cells 108 are moving), while the
dark background demonstrates that the fluctuations in regions where
there are no particles/cells 108 present, are much lower.
[0162] Accordingly, FIG. 5A shows an analysis of a stack of
holographic images, yielding the holographic fluctuation image S(i,
j) which is constructed by dividing the pixel-wise standard
deviation by the pixel-wise average, for each pixel, over the stack
of images.
[0163] More specifically, the fluctuation image, S(i,j), is a
representation of the normalized standard deviation distribution
(i.e., standard deviation divided by the mean, pixel by pixel) for
the stack of images. This image is then processed to generate a
distribution of average normalized standard deviations over each
cell/particle 108. It is this distribution, or histogram of
normalized standard deviations of cells/particles (NSD) 108 which
yields the desired information about the sample 107 mobility and
hence the particle-surface interactions.
[0164] In another embodiment, which utilizes the apparatus of FIG.
1A as shown, 2 .mu.m silica beads 108 are dispersed in water,
diffusing on the coverslip surface 109, and holographic fluctuation
images (i.e., images of normalized standard deviations over the
sequences of images) are shown in FIG. 6, screenshot A and B. Data
was taken on a temperature-controlled microscope stage 114.
[0165] Specifically, FIG. 6, screenshot A, is a holographic
fluctuation image (i.e. normalized standard deviation image) of the
sample at 5.degree. C. (constructed from a 60 frame stack), and
FIG. 6, screenshot B, shows the sample's 107 holographic
fluctuation image of 2 .mu.m silica beads in deionized water when
equilibrated to 47.degree. C. (constructed from a 60 frame stack).
Each holographic fluctuation image is generated from 60 images
acquired over approximately 11 seconds.
[0166] It can be seen from the screenshots that the regions of high
intensity at the lower temperature (FIG. 6A) are more confined
compared to the high intensity regions at the higher temperature
(FIG. 6B). This indicates that the particle motion generating the
high intensity fluctuations is more confined at the lower
temperature. This is expected from diffusion theory since the
mean-squared displacement of a particle is decreased as the
temperature decreases.
[0167] FIG. 7 shows three histograms of normalized standard
deviations of 4.8 .mu.m diameter silica beads in water on a glass
coverslip (40 frames acquired at 5 frames per second), measured at
temperatures of 6.8.degree. C. (lower histogram), 28.degree. C.
(middle histogram), and 46.3.degree. C. (top histogram). The
distribution measured at the lowest temperature (6.8.degree. C.)
also has the lowest values of the normalized standard deviation
(NSD). The main peaks in the histograms correspond to the freely
diffusing beads 108, while the minor peak at lower values of NSD
(5-10%) represent the much smaller fraction of beads 108 that are
stuck or partially stuck to the surface.
[0168] The main peak shifts to higher values of NSD as the
temperature is increased. The beads at higher temperatures undergo
higher-amplitude thermally induced motion, causing greater pixel
intensity fluctuations which are detected as higher bead NSD
values.
[0169] Specifically, for free diffusion, the mean squared
displacement is linearly proportional to the temperature:
.DELTA.x.sup.2=4Dt, D=k.sub.BT/(6.pi..eta.r),
[0170] where D is the diffusion coefficient (or effective diffusion
coefficient), t is the time interval between particle position
measurements, k.sub.B is Boltzmann's constant, T is the temperature
in Kelvin, .eta. is the viscosity (or effective viscosity) and r is
the radius of the spherical diffuser. The viscosity of water
decreases as the temperature increases, causing an additional
temperature dependence that increases the mean squared displacement
as the temperature increases. FIG. 8 is a plot of the mean value
and standard deviation of the normalized standard deviation
distributions of the 4.8 .mu.m silica beads freely diffusing on a
glass coverslip at different temperatures (open squares). The upper
x-axis reflects the temperature the measurements were taken at,
while the lower x-axis shows the theoretically expected square-root
of the mean squared displacement, given the bead diameter,
temperature (which was placed on a temperature controlled sample
stage), temperature dependent viscosity of water, surface-viscosity
correction (Faxen's Law) and time interval. The measured NSD value
is plotted against the temperature (upper axis) as well as the
corresponding square-rooted mean squared displacement (lower axis)
calculated based on the diffusion formula (see above) and a surface
viscosity correction factor (Faxen's Law).
[0171] The second plot in FIG. 8 (black squares), shows the mean
and standard deviation of simulated bead motion and generated by
displacing sub-images of beads by step-sizes that are governed by a
Gaussian distribution whose mean is zero and that has various
different widths (corresponding to various square-rooted mean
squared displacements). Forty such displaced sub-images are
generated, simulating a single bead's image stack. An ensemble of
250 such simulations was performed for a range of step-sizes (i.e.,
square-root mean square displacements). The identical algorithm for
calculating the beads normalized intensity fluctuations (normalized
standard deviation (NSD)) that was applied to the experimental data
(which was 40 frames in duration) was then applied to the simulated
data, generating a distribution of simulated NSD values at each
step size, the mean and standard deviation of which are
plotted.
[0172] The comparison between the measured and simulated values is
excellent, confirming the choice of the normalized standard
deviation as an excellent measure of quantifiably measuring the
changes in the mean-squared displacements of the beads as a
function of temperature, and as an alternative to applying particle
tracking algorithms. As noted above, the slightly larger error bars
and lower average value of the normalized standard deviation values
for the measured beads may be explained by the small fraction of
bound beads in the largely unbound population (note small peak
between 5-10% NSD in histograms of FIG. 7).
[0173] There are a number of advantages to using the normalized
standard deviation as a measure for quantifying particle mobility.
First, only one image, representing a spatial map of the pixel-wise
statistics through the stack, is required to reflect the dynamics
of the collection of particles 108. This statistical image related
to pixel fluctuations may also be generated in real-time, as the
computational load required to compute it is relatively low.
Furthermore, calculating this statistical image does not require
any assumptions concerning cell/particle shape.
[0174] Second, particle positions may be established using only one
frame from the sequence. To assign fluctuation observables to each
particle 108, one additional image from the sequence is required in
order to determine the location of each particle 108. The particle
neighborhoods are then masked with the statistical pixel
fluctuation image to generate average fluctuations per particle for
all the particles in the field of view (see FIG. 5B). Since the
particles diffuse/move only short distances compared to the average
inter-particle distances, one frame is sufficient to establish
particle neighborhoods for generating normalized standard deviation
distributions from the statistical pixel fluctuation image. This
approach allows the computational requirements for object
recognition normally applicable to each image in the sequence to be
significantly relaxed.
[0175] Third, the normalized standard deviation measurement method
readily scales to lower magnification allowing more particles to be
measured per field of view. This is especially useful when particle
binding probability is expected to be low, since it allows more
particles to be measured at one time, improving the binding
detection statistics of the measurement.
[0176] Using a coherent source 100 to illuminate the sample 107 of
particles 108 provides additional advantages for sample 107
measurement. While diffusing objects with low contrast under
conventional illumination, may present challenges to robust
detection and position tracking due to the greater effect of
background image noise on the measurement, using a coherent source
100 to image, higher-contrast images may be generated by tuning the
amount of defocus. In addition, with a coherent source 100, the
focal plane position may be determined numerically (e.g.
holographic focusing), obviating the time consuming mechanical
focal scan methods traditionally used. This is possible since
de-focused samples 107 illuminated by a coherent source 100
generate diffraction patterns which may then be numerically
propagated over a range of distances to find the required focus
position.
[0177] Surface Binding Detection Measurements
[0178] In another embodiment consistent with the present invention,
an application of the above instrumentation and data analysis
technique is the detection of particles 108 binding to a surface
109, as illustrated in FIG. 9. The apparatus of FIG. 1A is used in
this exemplary embodiment as well.
[0179] For example, FIG. 9 shows a diagram of components of a
sample 107 on a treated surface 109, of coated beads 108 with
either one type of antigen (A-.smallcircle.) or another type of
antigen (B-.quadrature.), with the goal of the assay being the
selective detection of each type of bead based on its interaction
with an antibody coated surface.
[0180] FIG. 9, slide 1, shows each type of particle 108 is placed
on an antibody coated surface 109. The particle 108 coated with A
antigen is specifically bound to the immobilized specific
antibodies (A antibodies) coated on the coverslip surface 109,
thereby restricting the motion of the particle 108. FIG. 9, slide
2, shows the differing behavior of each type of particle 108, where
the B-antigen coated particle 108 which is not specifically bound,
is allowed to freely diffuse in the solution, on the surface 109,
while the A type particle motion is severely restricted due to
specific binding with the surface antibodies.
[0181] A similar type of result may be achieved with cells 108. The
following discusses experiments conducted on red blood cells, to
distinguish their surface antigens based on interactions with
specific antibodies coated on a coverslip surface 109, using an
apparatus [0182] as described in FIGS. 1A, 1B, and 3.
[0183] For example, red blood cells may be characterized by their
ABO blood group. A person may be tested as a Type A, meaning that
they have red blood cells (RBCs) with type A antigen on their
surface; or as a Type B with type B antigens on their surface; or
as a type O having neither antigen present; or as a type AB and
have both antigens present on the surface.
[0184] In addition to the A and B antigens, red blood cells are
also tested for the presence or absence of RhD antigen, with the
presence of the antigen denoted by a "+" sign or "positive", and
the absence of the antigen indicated by a "-" sign or "negative".
Tests were performed by imaging the interaction of the patient's
red blood cells on three separate regions on the surface 109 using
the apparatus of FIG. 1A. The surface 109 included a chemically
treated coverslip 109 allowing specific antibodies to be linked to
the surface 109. The surface 109 included three regions, one region
which had anti-A antibodies linked to the surface 109, one with
anti-B linked, and one with anti-RhD linked.
[0185] The patient's blood was diluted and then introduced into the
sample chamber 118 whose bottom surface 119 was the treated
coverslip 109 glass with immobilized antibody patches. Red blood
cells settled to the bottom surface 119 and interacted with the
particular antibodies present on that patch. Cells 108 with the
specific antigen on the surface 119 corresponding to the antibodies
present on the underlying patch were specifically bound to the
surface 119. Cells 108 without specific antigens corresponding to
the underlying immobilized antibody were free to diffuse on the
surface 119. This type of testing (i.e., typing of the antigens on
the red blood cell membrane surface) is similar to a forward typing
of the blood sample (as opposed to reverse typing, which is a
complementary typing technique that measures the presence of
antibodies specific to surface antigens, in the blood plasma).
[0186] The holographic fluctuation method outlined above is used to
determine if, a) the red blood cells are specifically stuck to a
particular patch, indicating a positive reaction to the antibodies
present on that patch, or b) they are freely diffusing, indicating
a negative reaction, and therefore a lack of specific interaction.
This determination is made based on the measured histograms of
cellular normalized standard deviations of their holographic pixel
fluctuations. Specifically bound cells 108 in the field of view are
reflected by narrow distributions in the normalized standard
deviation measure with relatively low average value. Free cells
demonstrate a wider distribution with a significantly higher
average value of NSD (normalized standard deviation).
[0187] FIG. 10A shows plots of normalized standard deviation
histograms of two different samples 107 measured on an anti-A
coated surface. Red blood cells from a donor that is positive for A
antigen on a surface with immobilized anti-A antibody, is indicated
in black (A type), while RBCs from another donor that is negative
for A antigen is indicated by a hatched pattern (B type
donor--i.e., A antigen is not present on cells), on a similarly
anti-A coated surface. The type A cells (black) are specifically
stuck to the surface, drastically restricting their range of motion
as reflected by the low values of the cellular normalized standard
deviation. The type B cells (hatched pattern) on the other hand do
not bind to the surface, as evidenced by large fluctuations in the
cellular pixel values, and the high normalized standard deviation
values.
[0188] The type B blood cells are not specifically bound to the
anti-A surface as demonstrated by the large magnitude and broad
distribution of the normalized standard deviations values. The type
A blood is specifically bound, as is reflected by the narrow
distribution and low magnitude of the normalized standard
distribution values.
[0189] In addition to clearly differentiating between bound and
unbound cells, intermediate levels of binding may also be
detectable. FIG. 10B shows plots of normalized standard deviation
histograms of two different samples 107 measured on an anti-A
coated surface, measured over one hour after measurements in FIG.
10A.
[0190] The type A distribution (A positive) in FIG. 10B looks
similar to that of FIG. 10A, however the type B distribution (A
negative) in FIG. 10B features a second peak, indicated by an
increased fraction of cells with intermediate normalized standard
deviation values, indicating a partial or intermediate state of
binding to the surface, possibly due to a time-dependant
non-specific cell-surface interaction.
[0191] In FIG. 10B, the Type B donor cells (hatched pattern) are
not specifically bound (i.e., negative for antigen A) and have a
broad histogram, reflective of diffusive behavior. However a
subpopulation with a peak at .about.7% NSD (Normalized Standard
Deviation) is apparent after the passage of one hour. These cells
may represent a partially bound fraction due to non-specific
interactions with the surface that become more dominant with time.
Although this subpopulation demonstrates an increased
immobilization compared to the other unbound cells, the
subpopulation is still distinct from the specifically bound
population (i.e., population positive for antigen A), allowing the
technique to differentiate specific binding from non-specific
binding.
[0192] It is apparent that cells with "intermediate" levels of
binding are still distinguishable from the specifically bound
cells, recommending that this technique can resolve sub-populations
of cells interacting differently with the substrate.
[0193] Similar data may be collected with the donor cells allowed
to interact with patches coated with anti-B as well as anti-RhD
immobilized to the surface. In this way each donor's red blood
cells may be probed for the presence of A, B, and/or RhD antigens,
yielding a forward blood typing test result.
[0194] FIG. 11, slide 1, shows two types of particles, a type A
particle coated with type A antigen, as well as a type B particle
coated with a type B antigen. The surface in this configuration is
coated with antigen A. Binding to the surface is possible for type
A particles in the presence of A-antibody (e.g. IgM) which may
simultaneously bind to both the A antigen coated particle and the A
antigen coated surface, thereby immobilizing the particle. The test
involves mixing the patient's blood plasma (containing the
antibodies), and incubating it with particles with known surface
antigens, on surfaces with known antigen type. The goal of the test
is to determine which type of antibody is present in the plasma, by
measuring the behavior of each type of control particle on each
type of surface.
[0195] The type of antibody present in the solution may be
determined by measuring the mobility of particles on surfaces which
are coated with appropriate antigens. Conversely with known
antibodies, the type of particles may be determined by measuring
their mobility on surfaces coated with the appropriate antigen(s).
The A antibody in solution is able to bind to the A antigen coated
surface, as well as simultaneously bind to a particle coated with A
antigen. FIG. 11, slide 2, shows the differing behavior of each
type of particle, where the B-antigen coated particle which is not
specifically bound, is allowed to freely diffuse in the solution,
on the surface, while the A type particle motion is severely
restricted due to specific binding with the antibody that is itself
specifically bound to the surface. Thus, FIG. 11, slide 2 shows how
the unbound type B particle may be distinguished from the bound
type A particle by measuring particle mobility.
[0196] FIG. 12A shows a series of NSD histograms for a sample of
red blood cells taken at different times. The sample consists of
type A red blood cells in the presence of a high concentration of
anti-A IgM antibody (100 nM) dispersed in synthetic plasma onto a
surface with type B antigens on a glass coverslip surface. This
measurement configuration is similar to the reverse blood typing
method wherein the plasma of the subject is tested for the presence
or absence of naturally occurring antibodies by being able to
detect the extent of binding between blood cells of known type on
surfaces of known type. The presence of binding in such a scenario
indicates the presence of antibodies that are able to
simultaneously bind the cells to the surface, thereby immobilizing
them. Essentially, forward typing involves detecting the presence
of particular antigens on the red blood cell surface, while reverse
typing detects the presence of particular antibodies in the blood
plasma. Assays may be designed to perform reverse blood typing
results based on the detected presence or absence of blood group
antibodies which would be reflected in the degree of fluctuations
in the bead/cell hologram sequence.
[0197] The bottom histogram in FIG. 12A, was measured 13 minutes
after type A cells dispersed in synthetic plasma with 100 nM anti-A
was introduced onto a surface with B antigens. Each histogram was
generated by an analysis (cellular NSD calculation) of a sequence
of 40 frames acquired at a rate of 5 frames per second. The cells
are able to diffuse, as evidenced by the high average values of the
NSD observable. The cells show similar diffusive behavior at later
times as well, as may be seen by the similarity of the histograms
measured at the 21, 29, 37 and 45 minute time marks.
[0198] FIG. 12B shows a time series of histograms under similar
conditions to those in FIG. 12A, except that the surface had type A
antigens on it (unlike the type B antigen surface measurements of
FIG. 12A). This high concentration of anti-A (100 nM) was chosen to
ensure that A cells were bound to the A type surface (as was
visually confirmed). The first measured histogram (bottom
histogram), taken 14 minutes after the cells were introduced onto
the surface, show that the cells are immobilized (low average NSD
and narrow width), as expected, given the high concentration of
antibody in the synthetic plasma. At later times the cells remain
bound as well, as seen by the similar NSD histograms at the 22, 30,
38 and 46 minute marks. Comparison of the histograms of FIGS. 12A
and 12B allows us to choose a threshold value for NSD to determine
whether a cell is bound or not. A threshold value of 7% NSD under
these conditions is appropriate, with a majority of the cells in
the unbound population of FIG. 12A being above this threshold, and
the majority of cells in the bound population of FIG. 12B being
below this threshold.
[0199] FIGS. 13A and 13B are similar to the conditions measured in
FIGS. 12A and 12B except that a much lower concentration of
antibody was used, only 1 nM of anti-A in the synthetic plasma was
present. In FIG. 13A, the A type cells in synthetic plasma
containing 1 nM anti-A dispersed on the B type surface are mostly
unbound when first measured at 19 minutes after the sample was
introduced onto the surface. Subsequent measurements at the 25, 31,
37, 43, 49, and 55 minute mark show similar behavior. In FIG. 13B
the A type cells in synthetic plasma containing 1 nM anti-A were
measured after being introduced onto a surface with A type
antigens. After 18 minutes, a slightly higher fraction of cells
with low NSD values can be detected over the control set of cells
(i.e. unbound cells) measured in FIG. 13A. With time, this low NSD
fraction increases in magnitude, as can be seen by the progressive
leftward shift in the histograms. The low 1 nM concentration of
anti-A decreases the rate at which bonds between the cells and the
surface are made, in comparison to the much faster binding
interaction between the cells and the surface when 100 nM anti-A
was used (FIG. 12B), where practically all cells were found to be
bound within 14 minutes.
[0200] In another embodiment, a succession of binding assays may be
performed on given samples, yielding particle adhesion kinetics
curves. Such assays may yield information on not only the degree of
binding at a given time, but also information on the rate of change
of binding, allowing binding kinetics to be modeled. FIG. 14 shows
the cell adhesion kinetics for the same sample studied in FIGS. 13A
and 13B. At each time point (sample prepared at t=0) the fraction
of cells with an NSD value less than the threshold value 7% NSD was
calculated for each surface, to estimate the fraction of cells
bound on each surface. For the A cells on the B surface, negligible
binding is detectable (i.e., .about.0.5%), although it does rise
slowly over time (i.e., there exists time-dependent non-specific
cell-surface interactions which increases the bound fraction over
time). The A cells on the A surface on the other hand show
significantly higher binding at the first measured time point, and
the bound fraction increases significantly after that over the
course of about 35 minutes (i.e., from .about.1% to .about.5%).
[0201] By measuring the mobility in a cell by cell fashion, the
fraction of cells that are bound may be measured. In contrast to
many bulk measurements where a significant fraction of the cells
must react to yield a signal, this technique, due to its
single-cell sensitivity is able to measure positive reactions when
only a fraction of the cells are bound.
[0202] Diffusion Modeling (Calibration) and Simulation Results
[0203] A connection exists between the particle's normalized
standard deviation measurement and its underlying particle position
measurements. Particle motion was simulated in order to model
experimentally measured NSD distributions.
[0204] FIG. 15, slide A, shows an image of a 4.8 .mu.m diameter
silica bead. A sequence of 40 frames of this diffusing bead
(acquired at 5 frames per second) was analyzed using a particle
tracking algorithm which yielded a sequence of shifts in the
measured centroid position, .DELTA.x and .DELTA.y, plotted in FIG.
15, plot B. The standard deviation in the x and y directions was
measured to be 0.17 .mu.m (comparable to the theoretically expected
value of 0.16 .mu.m calculated from diffusion theory for an
equivalently sized bead in water, and including the
surface-viscosity correction factor from Faxen's law).
[0205] The identical sequence of frames analyzed by particle
tracking in FIG. 15 may be reanalyzed by an analysis of the pixel
fluctuation using the normalized standard deviation method.
[0206] FIG. 16, slide A, shows the image of the NSD calculated from
the same measured sequence of 40 frames that was used in the
particle tracking analysis shown in FIG. 15B, which yielded a NSD
value, averaged over the bead area, of 23.6% NSD.
[0207] Simulations using the image of the bead from one frame in
the sequence were performed by generating a sequence of 40
displaced bead images, with the displacements governed by a
Gaussian distribution whose width was experimentally measured using
the particle tracking result from FIG. 15, plot B. An ensemble of
250 such simulated bead sequences was generated, along with 250
calculated NSD images (i.e., one NSD image for each sequence). The
averaged NSD value over the bead area for each member of the
ensemble is plotted in FIG. 16, plot B, with a mean value of 23.7%
NSD and a standard deviation of 2.1% NSD, showing excellent
agreement with the experimentally measured NSD (see FIG. 16, slide
A).
[0208] A series of simulations may be performed to relate the NSD
results to particle motions with varying step-sizes (i.e., Gaussian
distributions with varying widths).
[0209] Such a calibration curve relating the mean of the measured
bead NSD distribution to the root mean squared displacement for a
series of simulations is shown in FIG. 17 (filled squares). The
curve was generated by simulating frame to frame bead shifts with
Gaussian distributions that have a range of widths, corresponding
to bead motion with a range of mean-squared displacements. Each
point in the curve was generated with an ensemble of 250 simulated
bead sequences (40 frames each), generating an NSD distribution
whose mean (filled squares) and standard deviation (open squares)
are plotted. Note that the curve is linear for root mean squared
displacements (labeled as "std dev" in the FIG. 17) less than 0.1
.mu.m. For larger displacements, however (0.3 .mu.m-0.4 .mu.m), the
NSD values level off and start to decrease in magnitude. This
effect is due to the bead going beyond the borders of its
neighborhood over the course of 40 frames, thus, causing the
fluctuations that are registered in the neighborhood to decrease as
well.
[0210] FIG. 18 shows the calibration curve plotted in FIG. 17
(filled squares), as well as two other calibration curves generated
from two other images of 4.8 .mu.m diameter silica beads (circles
and triangles). Although each image is displaced using an identical
range of distributions, the curves are significantly different,
indicating the difference in intensity distribution of the bead
images is the source of the difference. The open symbols indicate
the experimentally measured mean NSD values for a 40 frame sequence
of each bead. The experimental results track the simulated
ones.
[0211] FIG. 19 shows the beads from FIG. 18, along with a quantity
termed the contrast correction factor. This factor is determined by
calculating the standard deviation of the pixel intensities in the
neighborhood of each bead (i.e., within the rectangle surrounding
each bead), divided by the mean value of the pixel intensities in
the same region (i.e., normalized spatial standard deviation).
Higher contrast images tend to have higher contrast correction
factors. A diffusing bead with a higher contrast correction factor
also generates greater pixel fluctuations than a similarly
diffusing bead with a lower contrast correction factor, causing a
comparatively higher NSD value to be measured. To correct for this
image artifact each bead's curve was scaled with respect to the
contrast correction factor of bead 1. The rescaling causes the
three curves in FIG. 18 to collapse to the curve of bead 1,
approximately. The open symbols indicate the rescaled
experimentally measured mean NSD values, similarly rescaled. These
show a tighter distribution as well. Since the NSD parameter is
intended to reflect the dynamics of the beads/cells and not their
contrast, factoring out the contrast dependent contribution to
pixel fluctuation magnitude (i.e., contrast correction factor)
improves the utility of normalized standard deviation (NSD) as a
dynamical observable.
[0212] Contrast correction may also be applied to a population of
cells dispersed on treated surfaces. FIG. 20, plot A, is an NSD
histogram of B type red blood cells in synthetic plasma which
includes anti-B, dispersed on a surface with A antigens. Since the
anti-B antibodies which attach to the B cells do not bind to the A
antigens on the surface, they are free to diffuse. FIG. 20, plot B,
is a scatter plot that relates the NSD value of each red blood cell
measured in FIG. 20, plot A, to its normalized spatial standard
deviation (i.e., contrast correction factor). The positive
correlation, shown by the fit, indicates that low NSD values may be
generated by low normalized spatial standard deviation,
irrespective of the dynamics. Since the dynamics of cells/beads is
basically independent of the normalized spatial standard deviation,
the dynamical observable (NSD) is corrected against dependence on
the normalized spatial standard deviation using the linear fit to
generate contrast corrected data.
[0213] FIG. 21, plot A, shows an NSD histogram of B-type red blood
cells in synthetic plasma which includes anti-B, dispersed on a
surface with B antigens. The anti-B antibodies attach to the B
antigens on the red blood cells, as well as the B antigens on the
surface, thereby immobilizing the cells. As a consequence the
intensity fluctuations generated are significantly reduced compared
to the cells on the A antigen patch (FIG. 20, plot A), as is shown
by the lower NSD measurements in FIG. 21, plot A. FIG. 21, plot B
is a scatter plot that relates the NSD value of each red blood cell
measured in FIG. 21, plot A, to its normalized spatial standard
deviation. As in FIG. 20, plot B above, there is a positive
correlation. Using the linear fit, the NSD values are corrected
against their spurious dependence on normalized spatial standard
deviation values.
[0214] FIG. 22, plots A and B, show corrected and uncorrected
histograms of unbound (FIG. 20, plot A) and bound (FIG. 21, plot A)
red blood cell NSD histograms, where corrections were applied by
using the linear fits (FIG. 20, plot B, and FIG. 21, plot B) to
remove the NSDs dependence on the normalized spatial standard
deviation. The correction does not significantly alter the overall
shapes of the histograms. The most significant effect this has is
on attenuating the trailing tail of the unbound NSD distribution.
Thresholds may be applied to NSD values in order to determine
whether cells/beads are "bound" (i.e., "bound" fraction=fraction of
cells in NSD histogram<threshold; "unbound" fraction=fraction of
cells in NSD histogram>threshold). The attenuation of the
trailing edge for the corrected NSD distribution implies that there
will be a smaller fraction of cells that are "bound" when threshold
values in the tail region are used, thereby decreasing the so
called "non-specifically bound" fraction of the unbound population.
This feature of the correction is equivalent to a lowering of the
"non-specific background" of the test. Another option for
minimizing artifacts due to contrast contributions to the NSD is to
set up minimum and/or maximum thresholds on allowable spatial
standard deviations, and only analyzing those particles within the
threshold(s).
[0215] FIG. 23 is a plot of the cumulative probability distribution
of the histograms shown in FIG. 22, plot A, and FIG. 22, plot B
(i.e., a plot of the fraction of cells that have an NSD less than
or equal to that particular NSD value). The corrected "unbound"
histogram has a significantly smaller fraction of cells with NSD
values less than or equal to a threshold value of 9 (0.93%),
compared to the uncorrected fraction of 1.7%. This has the effect
of lowering the background value against which red blood cell
binding quantities may be compared.
[0216] Heterogeneous Diffusion Dynamics Detection and
Characterization
[0217] Uniformly sized beads diffusing on a uniform surface should
display diffusion expressible by a single diffusion coefficient.
FIG. 24 shows an NSD histogram of 4.8 .mu.m diameter beads in water
on a glass coverslip (top histogram) as well as a simulated NSD
histogram using a Gaussian distribution that yields a square-root
mean squared displacement of 0.131 .mu.m (bottom histogram). The
simulated histogram compares well with the free beads in the
measured histogram (i.e., NSD>7%). The small fraction of beads
that are measured to be stuck (due to non-specific binding) are not
included in the comparison (i.e., NSD<7%).
[0218] By increasing the salt concentration to 0.4% saline, the
beads may be made to stick to the surface. FIG. 25 shows an NSD
histogram of 4.8 .mu.m diameter beads in water with 0.4% saline
(top histogram) as well as a simulated NSD histogram using a
Gaussian distribution that yields a square-root mean squared
displacement of 0.0064 .mu.m (bottom histogram). The simulated
histogram compares well to the measured histogram for these beads
which show much greater restriction in their motion. That is, beads
that appear to be bound are in fact undergoing diffusion-like
motion with much smaller amplitude, and may be modeled as such.
Thus, bound beads may be characterized by their degree of binding,
such that beads with smaller root-mean squared displacements (and
consequently smaller NSD values) are more highly bound.
[0219] FIG. 26, plots A, B, and C, show an NSD histogram of type A
red blood cells in synthetic plasma containing 5 nM anti-A
dispersed on a surface with B antigen (FIG. 26, plot A, top
histogram, 2653 cells measured). The anti-A antibody cannot make
bonds bridging the cells to the surface, allowing the cells to
diffuse. A simulation of RBC diffusion with a single characteristic
root mean-squared displacement was found to model the average NSD
value, when a root mean-squared displacement of 0.069 .mu.m was
used for the width of the Gaussian step-size distribution. The
simulation was performed 2650 times to get a distribution of NSD
values (FIG. 26, plot B, middle histogram). Note that the measured
histogram is significantly broader than the simulated histogram,
indicating that a single root-mean displacement does not
characterize the dynamics of the RBCs on the surface. FIG. 26, plot
C, shows a simulated NSD distribution (2650 trials) using a
Gaussian distribution of root mean-squared displacement values
(0.069.+-.0.02 .mu.m), modeling the measured cellular diffusion as
a heterogeneous distribution of cell diffusivities. Modeling the
diffusivities as a distribution of freely diffusing spheres that
yield an identical distribution of root mean-squared displacements,
yields a Gaussian distribution of radii that vary by more than a
factor of three (within one standard deviation of the mean radius),
implying significant heterogeneity in the diffusion dynamics. Since
the size distribution of the cells is much more narrow, this result
indicates heterogeneous interactions of the cells with the surface,
due to surface and/or cellular heterogeneities that cause
heterogeneities in the interaction potentials of the cells on the
surface (i.e., diffusion is not free, and it is not uniform). Thus,
the invention allows the detection of heterogeneous as well as
homogeneous diffusion dynamics, which may be used as a diagnostic
of the distribution of cellular properties in the presence of known
surface conditions. In particular, such measurements and analysis
may yield information of the heterogeneity of cell (particle)
properties (e.g., surface binding density, affinity etc.) for
research or diagnostic purposes, in the presence of known surfaces.
For example, a population of cells may express a range of surface
proteins with a range of expression levels. Determining the
fraction of cells expressing a given density of surface proteins
may be of diagnostic interest. Cells expressing a higher surface
protein concentration will be more strongly bound to a
complementary surface than cells with a lower surface protein
concentration, as reflected in their mobility observable (e.g.
cellular normalized standard deviation value). Furthermore, having
detected the target fraction of cells, they may be isolated and/or
tested with other surfaces and under other conditions for
generating information of additional diagnostic value. Such cells
may also be isolated for further manipulation (e.g., genetic) as
well as culturing purposes. Culturing of selected cells may be for
the purposes of expressing and purifying therapeutically useful
cellular components (e.g. proteins).
[0220] Complementarily, such measurements and analysis may yield
information of the heterogeneity of surface properties (e.g., for
quality control, inspection etc.) when measured in the presence of
known particles. For example, by using calibration particles with a
given surface density of antigens on test surfaces, a homogeneous
mobility response from the population indicates a homogenous
surface, while a heterogeneous mobility response indicates a
surface with some degree of heterogeneity (e.g. non-uniform surface
density of surface linked moieties).
[0221] Cellular Spatial Statistics and Cell Shape Change
Dynamics
[0222] Information about the state of binding may also be found in
the spatial normalized standard deviation of the red blood cells.
FIG. 27, plot A and plot B, are plots of the normalized spatial
standard deviation values for the unbound and bound red blood
cells, measured in FIG. 20, plot A, and FIG. 21, plot A,
respectively. The bound population of red blood cells has a
slightly lower mean value, though there is significant overlap with
the unbound population's normalized spatial standard deviation
values. Cells that are bound to the surface are slightly flattened
compared to the unbound cells, which feature a biconcave geometry
typical of red blood cells. This slight flattening in turn
decreases the contrast of the cell by a small amount. Although
there is an average difference in the populations, the
distributions of the bound and unbound spatial normalized standard
deviation values overlap significantly. The normalized spatial
standard deviation of each particular cell does not vary
significantly from frame to frame over the duration of the
measurement (see FIG. 28, plot A and FIG. 28, plot B), justifying
our calibration method outlined above.
[0223] FIG. 28, plot A, and FIG. 28, plot B, plot the average
spatial normalized standard deviation for 100 cells in the unbound
population of FIG. 20, plot A, and FIG. 24, plot A (FIG. 28, plot
A), as well as that for 100 cells in the bound population of FIG.
20, plot B, and FIG. 24, plot B (FIG. 28, plot B) over each of the
40 frames in the sequences. In both of the plots the error bars
indicate the standard deviation of each cell's spatial normalized
deviation over the 40 frame sequence. The variation of a cell over
frames is insignificant compared to the variation over cells.
[0224] Information about the cellular shape change dynamics may
also be extracted and utilized by an analysis of the cellular
spatial intensity statistics over a sequence of images. Cells that
are "floppier" (i.e., change their shape more readily due to either
thermal fluctuations or externally applied forces) should
demonstrate higher spatial intensity fluctuations compared to cells
that are relatively more rigid. Note that the intensity
fluctuations described in this section (spatial intensity) are
calculated in each cell's frame of reference so that center of mass
cell motion (i.e., the cellular mobility measured using the
previously described normalized standard deviation measurement)
does not interfere with the calculation of the cellular spatial
dynamics.
[0225] FIG. 29 plots the standard deviation of the spatial
normalized standard deviation measured for each cell, over the 40
frames in the sequence for both the bound cells as well as the
unbound cells. Even though the magnitude of the frame to frame
changes in the normalized spatial standard deviations are small,
there turn out to be significant differences between the variation
in normalized spatial standard deviation of bound and unbound red
blood cells. The bound cells have a broader distribution and have
significantly more low-amplitude varying cells, compared to the
unbound cells, indicating that a higher fraction of bound cells
have low amplitude spatial intensity fluctuations, compared to
unbound cells. Nineteen percent (19%) of the bound cells in FIG. 29
have a standard deviation of the normalized spatial standard
deviation (measured over 40 frames taken at 5 frames per second)
less than 0.005, while among the unbound population only 0.3% of
the cells have a value less than 0.005. Unlike the analyses done
previously, cell neighborhoods are determined in each frame of the
40 frame sequence (as opposed to determining the cell neighborhood
in only 1 frame from the sequence) to correct for center of mass
motion contributions to fluctuations in the spatial standard
deviation. Such distributions of the spatial statistics of cells
over a sequence of frames may be used as a diagnostic of membrane
flexibility, cytoplasmic elasticity/visco-elasticity, cell
health/disease, cell age, solution conditions, binding state, etc.,
all of which may influence the dynamics of cellular shape changes
which are reflected in the dynamics of the cellular spatial
statistics over time and in the distributions of such quantities.
Frame exposure times must be short enough to freeze the image of
the fluctuating cell, if the frame exposure time is too long then
cellular motions will be averaged over during the exposure,
decreasing the sensitivity of the technique.
[0226] Correlated Motion Detection
[0227] The above analytical technique is sufficient for cases which
do not require the detection of correlation of bead motions.
However, assays may be based on detecting bead-bead binding, as
opposed to bead-surface binding. Positive binding events (i.e.,
binding of two or more beads) require a detectable difference
between correlated bead motion and uncorrelated bead motion. Beads
that are bound to each other will have their movements correlated
in time, while beads that are unbound, though neighboring each
other, will not demonstrate correlated bead motion.
[0228] Accordingly, in another embodiment consistent with the
present invention, one method of distinguishing neighboring
beads/cells that are bound to each other as opposed to just resting
next to each other is as follows.
[0229] In this method, for each neighboring pair of
cells/particles, the correlated fluctuations are calculated by
multiplying (or alternatively, summing) the neighborhood of each
pair of cells/particles sub-images frame by frame. This yields a
"correlation neighborhood", which can be quantified by calculating
its average normalized standard deviation.
[0230] Fluctuations in the correlation neighborhood increase in
amplitude when there are in-phase motions of pairs of beads that
are bound together.
[0231] Just as the mobility of independent beads/cells may be
quantified by comparing the extent of the average fluctuations in
each bead/cell neighborhood, the correlated mobility of each
adjacent bead/cell pair may be quantified by comparing the extent
of the average fluctuations for each correlation neighborhood.
[0232] The technique for detecting correlated motion involves, in
step 200, detecting each bead/cell 18 in each holographic image
120. Beads/cells 18 that are adjacent in the image are candidates
for being bound together and thus demonstrating correlated motion.
Upon establishing the beads/cells 18 that have adjacent neighbors
in step 201, a list of bead/cell pairs may be made in step 202.
[0233] Each adjacent bead/cell 18 pair 121 is analyzed by
extracting the image of each bead/cell 18 in the pair 121, creating
two sub-images, in step 203. The two sub-images are then multiplied
together in step 204, yielding a product sub-image. This procedure
is repeated for all pairs of adjacent cells/beads 18 in step 205,
and then for all frames 122 in the sequence as well in step 206,
resulting in a sequence of product sub-images corresponding to each
adjacent bead/cell 18 pair 121 (see FIG. 32A). Corresponding to
each such sequence for each bead/cell 18 pair 121, the pixel-wise
standard deviation of the product sub-image sequence is computed in
step 207, which finally yields an average standard-deviation
generated by calculating the average value of the pixel-wise
standard-deviation divided by the average value for each pixel over
the whole product sub-image, in step 208 (see FIG. 32B, the
correlation figure).
[0234] The average normalized standard-deviation (NSDP, normalized
standard deviation of product sub-images) is a single number
attributed to a single adjacent cell/bead 18 pair in the sequence
of images 120, 122. Adjacent pairs 121 of cells/beads 18 that are
bound together will have correlated motions which will tend to
increase the corresponding NSDP value, than those that are not
bound together, which may thus diffuse independently to yield NSDP
values that are lower, due to uncorrelated motion. On this basis,
bound cell/bead 18 pairs 121 may be distinguished from unbound
cell/bead 18 pairs 121.
[0235] Thus, a statistical analysis is used to determine particle
binding, which includes processing a holographic fluctuation image
to generate an average normalized standard deviation for each
particle, which is a measure of particles mobility on given surface
which may be a diagnostic of particle properties, solution
properties, surface properties or some combination of such
properties, for research, industrial, and/or clinical purposes, and
which may include an analysis of each particle's spatial statistics
over a sequence of frames, which may be a measure of shape change
dynamics, and diagnostic of particle properties, solution
properties, surface properties or some combination of such
properties, for research, industrial, and/or clinical purposes.
[0236] Particles not bound to antibodies on said surface
demonstrate large magnitude and broad distribution of said
normalized standard deviation distributions, and particles which
are bound to antibodies on the surface demonstrate narrow
distribution and low magnitude of normalized standard deviation
distributions.
[0237] In one embodiment, the particles are red blood cells, but
may also be cells other than red blood cells. Intermediate levels
of particle binding are detectable, as indicated by a fraction of
particles with intermediate values of normalized standard
deviations.
[0238] Distributions of normalized standard deviations are analyzed
for diffusional heterogeneity to characterize particle properties,
solution properties, surface properties or some combination of such
properties, for research, industrial, and/or clinical purposes.
[0239] It should be emphasized that the above-described embodiments
of the invention are merely possible examples of implementations
set forth for a clear understanding of the principles of the
invention. Variations and modifications may be made to the
above-described embodiments of the invention without departing from
the spirit and principles of the invention. All such modifications
and variations are intended to be included herein within the scope
of the invention and protected by the following features.
* * * * *