U.S. patent application number 14/240870 was filed with the patent office on 2014-08-07 for characterization of motion-related error in a stream of moving micro-entities.
This patent application is currently assigned to Sony Corporation of America. The applicant listed for this patent is Gary Durack, Stephen D. Fleischer, Jeremy Hatcher, David Roberts, Michael Zordan. Invention is credited to Gary Durack, Stephen D. Fleischer, Jeremy Hatcher, David Roberts, Michael Zordan.
Application Number | 20140220621 14/240870 |
Document ID | / |
Family ID | 47746889 |
Filed Date | 2014-08-07 |
United States Patent
Application |
20140220621 |
Kind Code |
A1 |
Durack; Gary ; et
al. |
August 7, 2014 |
CHARACTERIZATION OF MOTION-RELATED ERROR IN A STREAM OF MOVING
MICRO-ENTITIES
Abstract
Apparatus and methods for detecting and characterizing
motion-related error of moving micro-entities are described.
Motion-related error may occur in streams of moving micro-entities,
and may represent a deviation in and expected arrival time or an
uncertainty in position of a micro-entity. Motion-related error of
micro-entities is observed in a flow cytometer, e.g., as pulse
jitter, and is found to have a functional dependence on a parameter
related to a system clock. The motion-related error may be
characterized by correlating measurements of micro-entities moving
within a fluid stream.
Inventors: |
Durack; Gary; (Urbana,
IL) ; Fleischer; Stephen D.; (Honolulu, HI) ;
Hatcher; Jeremy; (Urbana, IL) ; Roberts; David;
(Evans, GA) ; Zordan; Michael; (Champaign,
IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Durack; Gary
Fleischer; Stephen D.
Hatcher; Jeremy
Roberts; David
Zordan; Michael |
Urbana
Honolulu
Urbana
Evans
Champaign |
IL
HI
IL
GA
IL |
US
US
US
US
US |
|
|
Assignee: |
Sony Corporation of America
New York
NY
Sony Corporation
Tokyo
|
Family ID: |
47746889 |
Appl. No.: |
14/240870 |
Filed: |
August 24, 2012 |
PCT Filed: |
August 24, 2012 |
PCT NO: |
PCT/US12/52202 |
371 Date: |
February 25, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61527340 |
Aug 25, 2011 |
|
|
|
Current U.S.
Class: |
435/34 ;
435/288.7; 702/150 |
Current CPC
Class: |
G01N 15/1459 20130101;
G01N 15/1429 20130101; G01N 15/1404 20130101; G01N 2015/1438
20130101; G01N 2015/1415 20130101; G01N 15/1427 20130101; G01N
2015/1477 20130101; G01N 2015/1075 20130101; G01N 2015/1006
20130101; G01N 2015/149 20130101 |
Class at
Publication: |
435/34 ; 702/150;
435/288.7 |
International
Class: |
G01N 15/14 20060101
G01N015/14 |
Claims
1. A system for moving micro-entities between a first location and
one or more additional locations, the system comprising: detection
apparatus to detect the presence of a micro-entity at least at the
first location; and one or more processors configured to: receive a
first signal, for each of a plurality of micro-entities, indicating
a first arrival time at the first location; measure, for each of
the plurality of micro-entities, a respective delay time, wherein
the respective delay time is measured between the first arrival
time and a reference signal provided by the system; receive
additional signals, for each of the plurality of micro-entities
from each of the one or more additional locations, indicating one
or more respective additional arrival times at each of the one or
more additional locations; measure, for each of the plurality of
micro-entities, a respective deviation for each respective
additional arrival time from a respective expected arrival time at
each of the one or more additional locations; and store the
measured respective delay times in association with the measured
respective deviations.
2. The system of claim 1, wherein the system is a microfluidic
system and the micro-entities are moved from the first location to
the one or more additional locations in a microfluidic stream.
3. The system of claim 1, wherein the system is a flow cytometer
and the micro-entities are moved from the first location to the one
or more additional locations in a fluid stream.
4. The system of claim 3, further comprising a drop clock that is
used to time the formation of droplets in the flow cytometer and
wherein the reference signal is derived from the drop clock.
5. The system of claim 1, further comprising an energy coupler that
is used to couple energy to a stream that conveys the
micro-entities between the first location and the one or more
additional locations, wherein the reference signal comprises a
feature of a periodic signal provided to the energy coupler.
6. The system of claim 1, further comprising: one or more optical
sources configured to probe the micro-entities at the first
location and at one or more of the additional location or
locations; one or more optical detectors configured to receive
optical signals from the probed micro-entities; and electronics
configured to provide electrical signals to the one or more
processors representative of the received optical signals.
7. A method for characterizing motion-related error associated with
micro-entities that move in a system between a first location and a
second location, the method comprising: detecting with first
detection apparatus, for each of one or more micro-entities, a
first arrival time at the first location; measuring, by one or more
processors, a respective delay time for each of the one or more
micro-entities, wherein each respective delay time is measured
between the first arrival time and a reference signal provided by
the system; detecting with second detection apparatus, for each of
the one or more micro-entities, a respective second arrival time at
the second location; measuring, by one or more processors, a
respective deviation for each second arrival time, wherein the
respective deviation is measured with respect to a respective
expected second arrival time; and storing the measured respective
delay time or times in association with the measured respective
deviation or deviations in each second arrival time.
8. The method of claim 7, further comprising repeating the acts of
detecting a respective second arrival time, measuring a respective
deviation, and storing for one or more additional locations to
which the micro-entities move.
9. The method of claim 7, wherein the system is a microfluidic
system and the micro-entities are moved from the first location to
the second location in a microfluidic stream.
10. The method of claim 7, wherein the system is a flow cytometer
and the micro-entities are moved from the first location to the
second location in a fluid stream.
11. The method of claim 10, wherein the reference signal is derived
from a drop clock that is used to time the formation of droplets in
the flow cytometer.
12. The method of claim 7, wherein the reference signal comprises a
feature of a periodic signal that is used to couple energy to a
stream that conveys the micro-entities between the first location
and the second location.
13. The method of claim 7, wherein the first and second locations
are located along a stream of a flow cytometer.
14. The method of claim 13, wherein the first and second locations
are spaced apart by more than approximately 1 millimeter.
15. The method of claim 7, wherein the micro-entities comprise any
of the following items: cells, molecules, proteins, peptides,
microparticles, microstructures, and calibration beads.
16. The method of claim 7, further comprising: periodically
coupling energy to a stream that conveys the micro-entities; and
using a feature of a signal that drives the periodic coupling of
energy as the reference signal.
17. The method of claim 7, wherein the method is executed in real
time during a procedure in which the micro-entities are being
analyzed.
18. A manufactured data-storage device embodying machine-readable
instructions that, when executed by one or more processors of a
system in which micro-entities move from a first location to one or
more additional locations, adapt the one or more processors to:
receive a first signal, for each of a plurality of micro-entities,
indicating a first arrival time at the first location; measure, for
each of the plurality of micro-entities, a respective delay time,
wherein the respective delay time is measured between the first
arrival time and a reference signal provided by the system; receive
additional signals, for each of the plurality of micro-entities
from each of the one or more additional locations, indicating one
or more respective additional arrival times at each of the one or
more additional locations; measure, for each of the plurality of
micro-entities, a respective deviation for each respective
additional arrival time from a respective expected arrival time at
each of the one or more additional locations; and store the
measured respective delay times in association with the measured
respective deviations.
19. The manufactured data-storage device of claim 18, further
comprising instructions that adapt the one or more processors to
record, in a look-up table, first values representative of the
respective delay times in association with second values
representative of the respective deviations.
20. The manufactured data-storage device of claim 19, wherein the
second values are binned averages of the measured respective
deviations.
21. The manufactured data-storage device of claim 18, further
comprising instructions that adapt the one or more processors to
fit a function to the respective delay times and respective
deviations.
22. The manufactured data-storage device of claim 21, wherein the
function is a sinusoidal function having an argument comprising a
measured respective delay value.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority under 35 U.S.C. 119(e) to
U.S. provisional application No. 61/527,340 titled, "System and
Method for Correction of Pulse Jitter in Flow Cytometers" and filed
on Aug. 25, 2011, which is incorporated by reference in its
entirety.
FIELD
[0002] Methods and apparatus described relate to characterizing
motion-related error in a stream of moving micro-entities.
Evaluation of higher-order motion characteristics may be used to
improve measurements of and/or operations on moving micro-entities.
Areas of applications include flow cytometry, microfluidics, and
nanofabrication.
BACKGROUND
[0003] The areas of analysis, development, and fabrication of
micro-entities have become widespread in various technologies in
recent years. Micro-entities, as used herein, may include cells,
microorganisms, micro- or nano-particles, droplets, molecules,
proteins, peptides, calibration particles, and microfabricated
structures such as microelectromechanical (MEM) structures,
microelectronic chips, and microsensors. A micro-entity may include
any entity (man-made or naturally occurring) that has a maximum
dimension less than about 1 millimeter (mm). In some research,
medical, or fabrication applications, micro-entities may be moved
in a stream in large numbers. The stream may be part of a system
that controls the movement and the metering of micro-entities into
the stream. One example application is the characterization and
sorting of cells in a flow cytometer. Another example may be
movement, observation, and sorting of micro-entities in
microfluidic channels.
[0004] In some applications, it may be desirable to know the
locations precisely of a selected micro-entity within the moving
stream at one or more selected times, or in other embodiments, to
know the times precisely when a selected micro-entity will arrive
at one or more locations. For example and referring to FIG. 1, a
micro-entity 125 may move in a stream 120 of micro-entities. A
stream 120 of micro-entities may comprise a fluid stream (e.g., a
gas, liquid, or particulate stream) or flow path capable of
conveying the micro-entities. The micro-entities may be in
suspension in the stream. In some cases, a stream 120 of
micro-entities may comprise a dense collection of the
micro-entities that moves in a stream-like manner. A stream 120 of
micro-entities may be conveyed by any combination of mechanical,
electrical, and magnetic means.
[0005] A micro-entity 125 may be detected at a first location
P.sub.1 at a first time t.sub.1, and it may be desirable to know at
what time t.sub.2 the micro-entity 125 will arrive at a
pre-selected second location P.sub.2 or a plurality of other
locations along the stream. Conversely, the micro-entity 125 may be
detected at the first location P.sub.1 at a first time t.sub.1, and
it may be desirable to know at what location P.sub.3, or locations,
the micro-entity 125 will be after a pre-selected elapsed time
t.sub.3, or times. In the former case, predicting the arrival time
t.sub.2 can be useful in determining when a measurement or an
operation (e.g., a sorting operation, an imaging operation, a
charging operation, a transforming operation, an illumination of
the entity, a signal detection etc.) may be performed on the
micro-entity at the second location P.sub.2. In the latter case,
predicting the location P.sub.3 of the micro-entity 125 may be
useful for precisely tracking the movement and/or evolution of the
entity in the stream, e.g., acquiring and overlaying multiple
images of the micro-entity as it moves along the stream.
[0006] One method for predicting the arrival time t.sub.2 or
location P.sub.3 of a micro-entity in a moving stream is to
calculate an expected value of t.sub.2 or P.sub.3 based upon an
average stream velocity v.sub.avg. The average stream velocity may
be measured or determined in any suitable way. If v.sub.avg is
found or known, then either t.sub.2 or P.sub.3 may be determined
through the relation d=v.sub.avg.times.t, where d is the distance
traveled by the micro-entity 125 in an elapsed time t. An
alternative method for predicting an arrival time t.sub.2 at a
predetermined location P.sub.2 is to add an average transit time or
delay time .DELTA.t.sub.avg to the value of t.sub.1 after observing
the entity at P.sub.1. The time .DELTA.t.sub.avg may be measured or
determined in any suitable way. In some cases, these methods of
predicting arrival times or locations of moving micro-entities may
be sufficient.
[0007] Flow cytometers are examples of systems which utilize
streams to transport micro-entities for purposes of characterizing
and sorting the micro-entities, such as biological cells. Flow
cytometers are used widely for rapidly analyzing heterogeneous cell
suspensions to identify constituent sub-populations. Examples of
the many applications where flow cytometry cell sorting is finding
use include isolation of rare populations of immune system cells
for AIDS research, isolation of genetically atypical cells for
cancer research, isolation of specific chromosomes for genetic
studies, and isolation of various species of microorganisms for
environmental studies. For example, fluorescently labeled
monoclonal antibodies are often used as "markers" to identify
immune cells such as T lymphocytes and B lymphocytes, clinical
laboratories use this technology to count the number of "CD4
positive" T lymphocytes in HIV infected patients, and they also use
this technology to identify cells associated with a variety of
leukemia and lymphoma cancers.
[0008] Recently, two areas of interest are moving cell sorting
towards clinical, patient-care applications, rather than strictly
research applications. First is the move away from chemical
pharmaceutical development to the development of
biopharmaceuticals. For example, many new cancer therapies utilize
biological material. These include a class of antibody-based cancer
therapeutics. Cell sorters can play an important role in the
identification, development, purification and, ultimately,
production of these products.
[0009] Related to this is a move toward the use of cell replacement
therapy for patient care. Much of the current interest in stem
cells revolves around a new area of medicine often referred to as
regenerative therapy or regenerative medicine. These therapies may
often require that relatively rare cells be isolated from patient
tissue. For example, adult stem cells may be isolated from bone
marrow and ultimately used as part of a re-infusion back into the
patient from whom they were removed. Flow cytometry and cell
sorting are important tissue processing tools that may enable
delivery of such therapies.
SUMMARY
[0010] Apparatus and methods for characterizing motion-related
error of micro-entities moving in a stream are described.
Motion-related error of moving micro-entities may include any type
and form of error that contributes to a deviation from an expected
arrival time or expected location of a micro-entity at a second
location or second time given the detection of the same
micro-entity at a first location or first time. Motion-related
errors of moving micro-entities may comprise real physical movement
and/or apparent movement of the micro-entities. It will be
appreciated that motion-related errors can pertain to multiple
arrival times and locations at various points along a stream. The
expected arrival time(s) or expected location(s) may be based on a
fundamental assumption of motion within the stream, e.g., constant
velocity and/or constant acceleration. In some instances,
characterization of motion-related errors may result in a more
accurate model of the micro-entity motion that accounts for various
types of motion-related error. Characterization of motion-related
errors of micro-entities can be useful for diagnosing and improving
the performance of apparatus designed to measure and/or operate on
micro-entities moving in streams.
[0011] In one example, techniques and apparatus are developed to
characterize motion-related error for a flow cytometer. Deviations
in interrogation-point arrival times of micro-entities moving in a
flow cytometer stream are found to be stably and functionally
dependent upon a system parameter. A model of the functional
dependence of the deviations can be used to reduce measurement
errors and improve operation (e.g., cell throughput and/or sort
purity) of the flow cytometer.
[0012] According to some embodiments, a system for characterizing
motion-related error associated with micro-entities that move in
the system between a first location and one or more additional
locations comprises detection apparatus, configured to detect the
presence of a micro-entity at least at the first location, and one
or more processors. The one or more processors may be configured to
receive a first signal that indicates a first arrival time at the
first location for each of a plurality of micro-entities. The
processors may further be configured to measure, for each of the
plurality of micro-entities, a respective delay time, wherein the
respective delay time is measured between the first arrival time
and a reference signal provided by the system. According to some
embodiments, the processors may further be configured to receive
additional signals, for each of the plurality of micro-entities
from each of the one or more additional locations, indicating one
or more respective additional arrival times at each of the one or
more additional locations, and to measure, for each of the
plurality of micro-entities, a respective deviation for each
respective additional arrival time from a respective expected
arrival time at each of the one or more additional locations. In
various embodiments, the one or more processors may be adapted to
store the measured respective delay times in association with the
measured respective deviations.
[0013] In stating that measurements are made for each of a
plurality of micro-entities, it will be appreciated that the
measurements may not be made for each and every micro-entity in the
stream. The plurality of micro-entities may be a subset of the
total number of micro-entities, and may refer to a portion of the
micro-entities for which valid measurements are obtained.
[0014] In some embodiments, a system for characterizing
motion-related error associated with moving micro-entities
comprises a source of periodic energy configured to couple the
periodic energy to the stream of moving micro-entities. A feature
of a driving signal for the periodic energy may be used as the
reference signal by which delays are measured in connection with
the first arrival times. The system may further include lasers or
sources of focused radiation that are used to optically probe the
moving micro-entities at selected interrogation points along the
stream. The system may further include optical detectors configured
to detect radiation from the optically-probed micro-entities.
[0015] In some implementations, a system for characterizing
motion-related error associated with micro-entities that move in
the system between a first location and one or more additional
locations comprises detection apparatus, configured to detect the
presence of a micro-entity at least at the first location, and one
or more processors. The one or more processors may be configured to
receive a first signal that indicates a first arrival time at the
first location for each of a plurality of micro-entities. The one
or more processors may further be configured to determine a value
based upon the received first signal with respect to a parameter of
the system. The value may be a component of an error model that
characterizes motion-related error for the micro-entity. The one or
more processors may be further configured to receive additional
signals, for each of the plurality of micro-entities from each of
the one or more additional locations, indicating one or more
respective additional arrival times at each of the one or more
additional locations. Further, the one or more processors may be
configured to measure, for each of the plurality of micro-entities,
a respective deviation for each respective additional arrival time
from a respective expected arrival time at each of the one or more
additional locations, and to store the determined values in
association with the measured respective deviations.
[0016] Associated methods for characterizing motion-related errors
of micro-entities moving in a stream are also contemplated. For
example, a method for characterizing motion-related errors may
comprise detecting, with detection apparatus for each of a
plurality of micro-entities, a first arrival time at the first
location. The method may further include measuring, by at least one
processor for each of the plurality of micro-entities, a respective
delay time, wherein the respective delay time is measured between
the first arrival time and a reference signal provided by the
system. The method may further comprise detecting, for each of the
plurality of micro-entities, a respective one or more second
arrival times at the one or more second locations, and measuring,
for each of a plurality of micro-entities, a respective variation
in the one or more second arrival times, wherein the respective
variation is measured with respect to an expected one or more
second arrival times. The method may further include storing the
measured respective delay times in association with the measured
respective variations in the one or more second arrival times.
[0017] Also contemplated as being within the scope of the invention
is tangible, manufactured computer-readable medium and/or storage
devices that include machine-readable instructions that, when
executed by at least one processor, adapt the at least one
processor to execute one or more acts of methods for characterizing
motion-related errors of micro-entities moving in streams. The
computer-readable media and/or storage devices may be used to adapt
instruments that are designed to measure and/or operate on
micro-entities that move in streams.
[0018] The foregoing and other aspects, embodiments, and features
of the present teachings can be more fully understood from the
following description in conjunction with the accompanying
drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The skilled artisan will understand that the figures,
described herein, are for illustration purposes only. It is to be
understood that in some instances various aspects of the invention
may be shown exaggerated or enlarged to facilitate an understanding
of the invention. In the drawings, like reference characters
generally refer to like features, functionally similar and/or
structurally similar elements throughout the various figures. The
drawings are not necessarily to scale, emphasis instead being
placed upon illustrating the principles of the teachings. The
drawings are not intended to limit the scope of the present
teachings in any way.
[0020] FIG. 1 depicts a stream of moving micro-entities;
[0021] FIG. 2 schematically depicts selected components of a flow
cytometer, according to one embodiment;
[0022] FIG. 3A schematically depicts selected components of a flow
cytometer for purposes of explaining calibration of droplet
dynamics;
[0023] FIGS. 3B-3G depict graphs of signals that may be used to
indicate the presence of a micro-entity at a respective
interrogation point or to characterize a micro-entity; according to
some embodiments;
[0024] FIGS. 4A-4F are plots depicting various system signals and
data that may be used or observed in a flow cytometer, according to
some embodiments;
[0025] FIG. 5 is one example of pulse jitter observed in a flow
cytometer;
[0026] FIG. 6A depicts a delay of a trigger signal from a
micro-entity detected at a first location as measured with respect
to a reference signal in a flow cytometer, according to one
embodiment;
[0027] FIG. 6B depicts a corresponding variation in arrival time at
a second location for the micro-entity observed in FIG. 6A;
[0028] FIGS. 7A-7C are examples of jitter plots measured for a flow
cytometer;
[0029] FIG. 8A shows background jitter measured in a flow cytometer
when the sample fluid conduit was not centrally aligned within the
nozzle, according to one embodiment;
[0030] FIG. 8B shows background jitter in the same system when the
sample fluid conduit was centrally aligned;
[0031] FIG. 9 depicts one embodiment of a method for characterizing
motion-related error;
[0032] FIGS. 10A-10D are jitter plots recorded for a flow cytometer
under different experimental conditions;
[0033] FIGS. 11A-11C are jitter plots recorded for three
interrogation points along a stream in a flow cytometer, according
to one embodiment;
[0034] FIG. 12 shows jitter plots recorded for three interrogation
points along a stream in a flow cytometer and for different
experimental conditions;
[0035] FIG. 13 shows a sinusoidal fit (black line) to a recorded
jitter plot, according to one embodiment.
[0036] The features and advantages of the present invention will
become more apparent from the detailed description set forth below
when taken in conjunction with the drawings.
DETAILED DESCRIPTION
[0037] As described above, in some applications involving moving
micro-entities, it may be desirable to predict or know an arrival
time of a micro-entity at a pre-selected location, or conversely to
predict or know a location of the micro-entity at a selected time.
For low-accuracy and/or low-speed systems, predictions based on
fundamental assumptions (e.g., average velocity v.sub.avg of the
entities within the stream, or average transit times
.DELTA.t.sub.avg, constant velocity, constant acceleration) may be
sufficient for obtaining reasonably accurate predictions of a
micro-entity arrival time or location for purposes of measurements
or operations on the micro-entity. However, for high-accuracy
and/or high velocity systems, higher-order motion characteristics
of the micro-entities within the stream may lead to unacceptably
large uncertainties in arrival times or locations of the entities
within the stream. Large uncertainties can limit the ability to
operate on the moving micro-entities and/or collect data
representative of the moving micro-entities. As a result, large
uncertainties in time or location can impose limits on the speed,
accuracy, and performance of the system.
[0038] According to some embodiments, motion-related errors of
micro-entities that move in a system between a first location and
at least a second location can be characterized and compensated. In
some systems, motion-related error may exhibit a functional and/or
stable dependence on a value for a moving micro-entity that is
measured with respect to a parameter of the system. Measurements of
the moving micro-entities may be made to characterize the
motion-related errors and reveal a dependence of the error on the
system parameter. From such characterization measurements, a model
of motion-related error may be constructed and used to predict
and/or compensate motion-related errors of micro-entities moving in
the system.
[0039] FIG. 2 schematically depicts certain elements of a flow
cytometer 200. A flow cytometer is one example of a system that
includes a stream of moving micro-entities 225 (typically cells
moving in single file). Although embodiments of the invention will
be described primarily in connection with a flow cytometer,
equivalent techniques and apparatus may be used in other systems or
instruments that utilize streams of moving micro-entities. For
example, embodiments of the invention may be implemented in
microfluidic systems and micro- or nano-fabrication systems in
which micro- or nano-entities are fabricated in fluidic streams.
The various systems may be adapted to perform one or more
operations on the moving micro-entities. Types of operations in
these systems may include any combination of the following
operations: measuring a characteristic of the micro-entity,
imaging, illuminating to detect fluorescence, sorting, transforming
(e.g., introducing a chemical or electrical charge, illuminating to
cross-link a polymer, illuminating to transform a component of the
micro-entity), size analysis, and weight analysis.
I. OVERVIEW OF A FLOW CYTOMETER SYSTEM
[0040] Flow-cytometry-based cell sorting was first introduced to
the research community more than 30 years ago. It is a technology
that has been widely applied in many areas of life science
research, serving as a highly useful tool for those working in
fields such as genetics, immunology, molecular biology and
environmental science. Unlike bulk cell separation techniques such
as immuno-panning or magnetic column separation,
flow-cytometry-based cell sorting instruments measure, classify and
then sort individual micro-entities serially at rates of several
thousand cells per second and higher. This rapid "one-by-one"
processing of single cells has made flow cytometry a valuable tool
for extracting highly pure sub-populations of cells from otherwise
heterogeneous cell suspensions.
[0041] Cells targeted for sorting are usually labeled in some
manner with a fluorescent material. The fluorescent probes bound to
a cell fluoresce as the cell passes through a tightly focused, high
intensity, light beam (typically a laser beam). A computer records
emission intensities for each cell. These data are then used to
classify each cell for specific sorting operations.
Flow-cytometry-based cell sorting has been successfully applied to
many cell types, cell constituents and microorganisms, as well as
many types of inorganic particles of comparable size.
[0042] There are two basic types of cell sorters in current use.
They are the "droplet cell sorter" and the "fluid switching cell
sorter." The droplet cell sorter utilizes micro-droplets as
containers to transport or convey selected cells to a collection
vessel. The micro-droplets are formed by coupling ultrasonic energy
to a jetting stream. Droplets containing selected cells are then
electrostatically steered to a desired location to sort the
selected cells from a larger population. This can be a very
efficient process, allowing as many as 90,000 cells per second to
be sorted from a single stream.
[0043] The second type of flow cytometry-based cell sorter is the
fluid switching cell sorter. Some fluid switching cell sorters
utilize a piezoelectric device to drive a mechanical system which
diverts a segment of the flowing sample stream into a collection
vessel. Compared to droplet cell sorters, fluid switching cell
sorters can have a lower maximum cell sorting rate due to the cycle
time of the mechanical system used to divert the sample stream.
This cycle time, the time between initial sample diversion and when
stable non-sorted flow is restored, is typically significantly
greater than the period of a droplet generator on a droplet cell
sorter. This longer cycle time limits some fluid switching cell
sorters to processing rates of several hundred cells per second.
For the same reason, the stream segment switched by a fluid cell
sorter is usually at least ten times the volume of a single
micro-drop from a droplet generator. This results in a
correspondingly lower concentration of cells in the fluid switching
sorter's collection vessel as compared to a droplet sorter's
collection vessel.
[0044] Recently, microfluidic sorters have been developed that
utilize MEMS actuators or electric fields to divert micro-entities
as needed. These sorters may sort within a completely enclosed
chip, such as a microfluidic chip. The use of microfluidics can
enable many parallel sorts to occur from a same input channel and
enhance the net throughput in microfluidic sorters.
[0045] With reference to FIG. 2, a flow cytometer 200 may comprise
a nozzle 202 from which a fluidic stream 220 issues. The nozzle 202
may include a first chamber 205 that contains a sheath fluid. The
nozzle may also include a conduit 203 that conveys a sample fluid
to a core of the stream 220. Micro-entities 225 may be in
suspension in the sample fluid. The sheath fluid and sample fluid
may flow toward a nozzle orifice 208 and exit from the nozzle 202
in the stream 220. The sheath fluid may form a sheath around the
periphery of the stream.
[0046] The nozzle 202 may further include a transducer 212
configured to couple energy into at least the sheath fluid. The
transducer 212 may be an acoustic transducer that couples acoustic
energy into the sheath fluid on a regular periodic basis. The
coupling of periodic energy into the sheath and/or sample fluids
can regulate, with high uniformity, the formation of droplets 228
in the stream 220 issuing from the nozzle 202. Perturbations in the
stream 220 result from acoustic energy that is coupled to the fluid
inside the nozzle (e.g., from a periodic vibration of a
piezoelectric transducer in the nozzle). Droplets 228 form, due to
surface tension as predicted by Rayleigh, at the acoustic
frequency. Within an acceptable range of parameters (frequency,
sheath pressure, transducer drive amplitude, etc.) droplets break
free at a uniform distance from the nozzle. In some embodiments, a
cell sorter may implement a feedback system that keeps the
break-off position 226 at an approximately constant distance from
the nozzle orifice 208. Satellite droplets 227 may also form along
with the droplets 228.
[0047] As depicted in FIG. 2, the jet exiting the nozzle of a
droplet cell sorter carries the micro-entities 225 for several
millimeters down the stream 220 until they are encased in one of
the micro-droplets 228 that synchronously break away from the
stream. The micro-entities may move at approximately a constant
velocity along the stream, in some embodiments. In some droplet
cell sorters, the time of flight (t.sub.f) from a measurement point
(e.g., the first interrogation point at beam B1) to where the cell
passes through the break-off point 226, where the stream breaks
into a free droplet, is constant to within about 1% of the droplet
generation period.
[0048] The formation of droplets 228 may be affected by a
drop-drive frequency f.sub.d and amplitude A.sub.d of a signal
applied to transducer 212. In some embodiments, the drop-drive
frequency f.sub.d may be adjusted by a drop clock 265, which in
turn may be controlled by a processor 250 of the system. The
drop-drive amplitude A.sub.d may be adjusted by a driver 260
configured to provide a drive signal to transducer 212. In some
embodiments, the driver 260 may receive an amplitude control signal
from processor 250. According to some embodiments, increasing the
drop-drive amplitude A.sub.d moves the droplet break-off point 226
toward the nozzle. Changing the drive frequency f.sub.d changes the
frequency at which droplets are produced. In some embodiments, a
drive frequency may be selected to have any value between about 10
kHz and about 250 kHz.
[0049] The processor 250 may be any type and form of data
processing device, e.g., a microprocessor, microcontroller, a
computer connected to the system, or a field-programmable gate
array (FPGA). There may be more than one processor in the system in
some embodiments. In some cases, there may be a combination of
processor types, e.g., a microprocessor and one or more FPGAs.
[0050] The system 200 may also include apparatus for placing a
charge on each droplet 228. Charging of droplets may be used for
sorting operations of the individual droplets, and thereby sorting
of the micro-entities contained in the droplet. A charge source 270
may be controlled by the processor 250 to apply a selected electric
potential value for a selected amount of time to a conductive probe
207 that is in electrical contact with the sheath fluid and/or
sample fluid. For example, as a last attached droplet 228-0 is
forming in the stream 220, a first electric potential may be
applied to conductive probe 207. Since the fluids are conductive,
the surface of the fluid charges and the last attached droplet
acquires an electric charge. When the last attached droplet breaks
from the stream to form a droplet, e.g., moves to the position of
the preceding droplet 228-1, the newly formed droplet retains a
charge that is dependent on the electric potential applied to the
fluid and stream at the time the last attached droplet 228-0 broke
free of the stream 220. In some cases, the charge on the newly
formed droplet is proportional to the square root of the electric
potential applied to the fluid. After the last attached droplet
breaks free, a second electric potential may be applied to the
conductive probe 207, so that the next droplet that forms as the
last attached droplet will acquire a charge different from, or same
as, its predecessor. In some instances, no charge (e.g., a ground
potential or neutral charge) may be applied to a droplet.
[0051] The system 200 may also include apparatus for sorting the
droplets, and thereby sort any micro-entities that are conveyed by
the droplets. The sorting apparatus may include electrostatic
deflection plates 290 to which an electric potential is applied so
as to establish an electric field between the plates. As a charged
droplet traverses the electric field, the droplet may be deflected
laterally and collected in a sample container (not shown). Droplets
that are given a neutral charge may travel between the deflection
plates 290 without being deflected by the plates. In this or a
similar manner, micro-entities may be sorted.
[0052] In various embodiments, the system 200 may include
components configured for detection operations, e.g., to detect one
or more signals that may be used to characterize the moving
micro-entities. The one or more signals may be used in making a
decision about a subsequent operation, e.g., a sorting operation,
performed on a stream segment containing the micro-entity. For the
embodiment depicted in FIG. 2, the detection operations may include
illuminating the micro-entities with optical radiation from one or
more radiation sources S1, S2, . . . S5. The radiation sources may
emit beams of radiation B1, B2, . . . B5 at different wavelengths.
In some embodiments, one or more sources may emit radiation at a
same wavelength. The radiation sources S1, S2, . . . S5 may be
laser sources in some embodiments, high-intensity arc-lamp sources
in some embodiments, high-intensity light-emitting diodes in some
embodiments, or any suitable radiation source. The beams of
radiation may be separated and arranged to intersect the stream 220
near the nozzle 202. In some embodiments, the beams may be focused
onto the stream 220. The beams of radiation B1, B2, . . . B5 may
interact with the micro-entities to generate scattered radiation or
excite fluorescent radiation. The scattered or fluorescent
radiation may be detected, e.g., with a detector D1, which may
generate an electrical signal representative of the detected
radiation. One or more signals representative of fluorescent
radiation may be detected, e.g., with one or more detectors D2, D3,
. . . D5. Fluorescent radiation and/or scattered radiation from the
micro-entities may be collected with an optical element 215 and
directed to the detectors.
[0053] In some flow cytometers and other systems, e.g.,
microfluidic systems, other types of particle detection and sensing
may be employed. In some embodiments, direct electrical sensing or
Coulter-type measurements may be made of the micro-entities moving
along the stream. In some cases, Coulter volume measurements may be
made to characterize the micro-entities. In some implementations,
other types of sensing may be employed, e.g., magnetic,
magneto-optic, electro-optic, thermal. Signals or measurements made
using any of these detection and sensing techniques may be used to
characterize and/or compensate for motion-related errors of the
micro-entities. For example, a signal or measurement obtained using
one or more of these detection and sensing techniques may be used
to develop a model of motion-related error for the micro-entities,
and subsequent signals or measurements may be used to determine
correction amounts that compensate for the motion-related
error.
[0054] A droplet cell sorter may be configured to operate as a
sense-in-air (SIA) instrument. In such a system, at least one
excitation laser beam is focused onto the stream 220 comprising a
flowing sheath stream and an inner core stream conveying
micro-entities (aligned in single file). The stream is positioned
such that this intersection occurs at the focus of one or more
light collection systems. The point of common foci is often
referred to as the interrogation point. There may be multiple laser
systems and multiple interrogation points spatially separated along
the stream 220, as depicted in FIG. 2. Each of these interrogation
points may have one or more separate laser beams and signal
detection apparatus. The interrogation points may be limited
spatially to an interrogation region 222 near the nozzle 202.
Spatial filtering may be used to reduce an observation region (also
referred to as a field of view (FOV)) associated with a single
interrogation point and to mitigate cross-detection of nearby
interrogation points. A FOV for a single interrogation point may be
on the order of 50 microns. Therefore, careful and precise
alignment of the stream, laser focus, and optical system's focus
may be required. In some implementations, the observation region
associated with a single interrogation point may be limited to a
few tens of microns. As a micro-entity travels down the stream, it
passes sequentially through each interrogation point. Radiation
from the micro-entity may be measured and recorded from each
interrogation point. These measurements may be correlated prior to
separation from the stream of the segment containing the
micro-entity. The charged drop may then travel through air to the
electrostatic plates 290 where they will be deflected for sorting
purposes.
[0055] Though five probing beams and five detectors are shown in
FIG. 2, fewer or more probing beams and fewer or more detectors may
be used in some systems. In some implementations, there may be more
than one detector for a probing beam, and optical components may
divide and direct optical emission to more than one detector. In
some cases, optical and/or spatial filtering may be used in the
detection optics to separate fluorescent signals and/or block
radiation from the radiation sources. One example of a flow
cytometer that utilizes five optical sources to probe the
micro-entities is the sy3200.TM. flow cytometer (available from
iCyt Mission Technology of Champaign, Ill.).
[0056] Time-varying signals from the detectors D1, D2, D3, . . . D5
may be collected, e.g., digitally sampled with data acquisition
hardware 230 that may be clocked by an analog-to-digital converter
clock 245, and stored in memory 240 for subsequent analysis by
processor 250. In some embodiments, the data may be provided
directly to processor 250 for analysis. In some implementations,
the data is stored temporarily in memory 240, e.g., in a buffer or
a suitable random access device, and then read out by the processor
250 for analysis.
[0057] As may be appreciated from the description of the flow
cytometer above, the data signals from the detectors may be pulses
representative of the micro-entities 225 that traverse the beams of
radiation B1, B2, . . . B5. Each pulse may have a peak height,
width, and area. The pulse heights, widths, and areas may be
evaluated by processor 250 and used to characterize each
micro-entity 225. The evaluated characteristics may then be used to
make a decision about a subsequent operation on each micro-entity
225 or droplet that contains one or more micro-entities.
[0058] Although the droplets 228 may be formed at periodic
intervals, the micro-entities 225 may be dispersed in the stream
220 at random intervals. For example, the micro-entities may be in
a dilute suspension in the sample fluid, which is fed to the nozzle
202. The micro-entities may then arrive at random times at the
nozzle's orifice 208. As a result and as depicted in FIG. 2, some
droplets may not contain a micro-entity and some may contain more
than one micro-entity, e.g., doublets, triplets, or more.
[0059] Proper operation of a cell sorter requires that measurement
data recorded from each interrogation point be correctly correlated
for each micro-entity that generated the data. To correctly
characterize a selected micro-entity in a stream 220, it is
necessary to know what signals from the detectors D1, D2, D3, . . .
D5 correspond to the selected micro-entity. To correctly perform an
operation (e.g., sorting) on the selected micro-entity when encased
in a droplet, it is necessary to further know in which droplet 228
that micro-entity will be encased. For example, it is necessary to
properly assign each detected signal to a same micro-entity (and
droplet that will contain the micro-entity) from which the detected
signals were produced, so that an accurate characterization of that
micro-entity can be determined. Additionally and for sorting
purposes in the flow cytometer example, it is necessary to know
when the micro-entity will arrive at the last attached droplet
228-0 in order to apply a correct electrical potential to the
conductive probe 207 at the correct time, so as to impart a desired
charge to the droplet 228-0 that contains the micro-entity. When
properly charged, the last attached droplet will subsequently
detach as a free droplet and be sorted according to its charge.
[0060] As an aid in understanding aspects of the invention as
implemented in a flow cytometer, details of data handling, timing
of operations, and calibration methodologies for a sort-in-air flow
cytometer will now be described. However, the described techniques
and apparatus are applicable to systems other than flow cytometers
in which micro-entities are moved from at least a first location to
one or more second locations and in which operations may be
performed on the moving micro-entities.
II. CALIBRATION
[0061] Data handling and timing of system operations for a system
in which micro-entities move from a first location to one or more
additional locations may be established in calibration trials using
calibration particles. As one example, a droplet cell sorter may be
calibrated so that the data acquisition system can associate data
collected for each cell or micro-entity with the specific drop that
will carry it (and possibly other micro-entities) away from the
stream and into a collection vessel. Calibration trials or runs may
be executed at a manufacturing facility to initially set up the
system and/or they may be run on a routine basis, e.g., prior to
each experiment, to verify operation of the system. The calibration
may be accomplished through empirical observation using highly
fluorescent micro-entities. The sort outcome may be observed by
sorting drops on to a microscope slide for manual viewing or by
using an automated detection system to record emission from
droplets. According to some embodiments, the micro-entities used
for calibration purposes may comprise calibration beads, e.g.,
fluorophore-tagged polystyrene micro-beads. In some embodiments,
fluorescent cells may be used for calibration purposes. The
calibration beads may be diluted in the sample fluid to an extent
that there may be two or fewer beads in the stream 220 issuing from
the nozzle 202 at any given instant in time, as depicted in FIG. 3A
for example.
[0062] As a micro-entity 225 or calibration bead travels along the
stream 220, it crosses the interrogation beams B1, B2, . . . B5
that are present in the stream, and is later encapsulated by a
droplet 228-1 that has broken free from the stream. For a
multi-interrogation point system, it is necessary to correlate
multiple measurements (e.g., signals as depicted in FIGS. 3B-3F)
made for a single micro-entity as it passes sequentially through
each interrogation point. The correlated data are used to make sort
decisions for each micro-entity, and affect the sorting of droplets
that contain the micro-entities.
[0063] The correct identification of signals from multiple
interrogation points as being associated with a single micro-entity
and droplet may be determined in calibrations runs. By running
calibration micro-entities through the system, the data acquisition
system may be configured such that a passing of a single
micro-entity through a selected "trigger" interrogation point
(e.g., a first interrogation point having a beam B1) triggers a
timing sequence that "opens" or selects narrow data acquisition
windows (e.g., selects segments of data streams from each of the
interrogation points) precisely as the micro-entity is expected to
arrive at each of the other interrogation points. To improve system
throughput (important for a cell sorter), these timing windows
should be kept as short in duration as possible.
[0064] In modern digital data acquisition systems, it is common to
buffer the analog-to-digital converter (ADC) samples for
measurement data from all interrogation points. In some cases, the
data is buffered long enough to allow triggering to occur from
downstream interrogation points, rather than relying only on
triggering from a first interrogation point along the stream.
Stated alternatively, when data is buffered, a trigger
interrogation point may be a point at which a trigger signal is
generated after one or more signals for a micro-entity have been
generated at other interrogation points. Consequently, a selected
"trigger" interrogation point may be any one of the interrogation
points in the system.
[0065] According to some embodiments, the beam crossing times and
arrival at the last attached drop may be estimated from a
calculation based on the crossing event of the first beam B1, if
one were to know the velocity of the stream and the distances to
each interrogation point in the stream and the distance to the
pinch-off point 226. However, in some systems the distances may
vary from day-to-day or run-to-run operation, and there may not be
a convenient way to measure the distances or fluid velocity
directly.
[0066] In some embodiments, effective transit times to the beam
crossings and pinch-off point may be determined by calibration
runs. As an example, a fluorescent calibration bead crossing the
first beam B1 may generate a fluorescent signal at time t.sub.0 as
depicted in FIG. 3B. In some embodiments, this signal may be used
as a "trigger" signal that identifies to the system the arrival of
a new micro-entity to be analyzed and subsequently processed (e.g.,
sorted). In other embodiments and as noted above, another
interrogation point may be used to provide a trigger signal rather
than the first interrogation point. Along with and corresponding to
the trigger signal, a series of signals or events occurring at
approximate times t.sub.1, t.sub.2, t.sub.3, t.sub.4, t.sub.c may
be generated by the same micro-entity responsible for the trigger
signal, as depicted in FIGS. 3C-3G.
[0067] By running a number of calibration beads through the system,
the average time offsets or transit times dt.sub.1, dt.sub.2,
dt.sub.3, dt.sub.4, dt.sub.c that occur between signals and/or
events can be determined. When these times are known, it is then
possible to determine which signals are to be associated with a
micro-entity that initiated a trigger event. For example, the
correct signals appear in the other detector data streams at times
dt.sub.1, dt.sub.2, dt.sub.3, dt.sub.4 spaced from the trigger
event at time t.sub.0. It is further possible to determine, after
processing the signals, what charge value to apply to a droplet
that will contain that micro-entity. In some embodiments, signals
from other micro-entities in or near the segment of the stream
carrying a micro-entity of interest may be processed to determine
what charge will be applied to a droplet formed from the stream
segment. When the time offsets are known, the system may be run
with useful samples that are to be analyzed and processed instead
of the calibration beads.
[0068] In some embodiments, additional signals, e.g., pulses at
times t.sub.1, t.sub.2, t.sub.3, t.sub.4, may or may not be
generated as the same micro-entity crosses the other interrogation
beams B2, B3, B4, B5. A signal may not be produced at an
interrogation point, e.g., as depicted in FIG. 3E, if a fluorescent
micro-entity is not responsive to an excitation radiation at the
interrogation point. A signal may be generated as the micro-entity
arrives at the last attached drop 228-0 at time t.sub.c. According
to some embodiments, the micro-entity may be imaged as it arrives
at the last attached drop 228-0 and at the time of charging of the
last attached drop via stroboscopic excitation.
[0069] Since the signals from interrogation points may be digitally
sampled and recorded, a signal from an interrogation point may be
represented by a set of digital samples within a digital data
stream, e.g., a data stream from an analog-to-digital converter.
Accordingly, the calibration procedure may identify what data
stream segment, or observation window, in a data stream from
detection apparatus for an interrogation point corresponds to a
micro-entity that initiated a trigger event upon crossing a trigger
interrogation point. The calibration procedure may configure the
data acquisition system to identify timing or locations of data
segments in the data streams from each interrogation point that are
associated with a same micro-entity. In some embodiments, time
stamps associated with the generated data from each detector may be
used to aid in identifying pulses that belong to each
micro-entity.
[0070] As may be appreciated from the above discussion, there are
two problems to be solved by the system 200 upon the occurrence of
a trigger pulse. The first problem pertains to identifying what
signals from the other detector data streams are generated by the
same micro-entity that generated the trigger pulse. As noted above,
the correct signals or data stream segments in other detector data
streams can be identified from the time offsets dt.sub.1, dt.sub.2,
dt.sub.3, dt.sub.4 derived from the calibration runs, or using
other suitable internal correlations. When the signals or data
stream segments are correctly identified, the micro-entity may be
characterized properly for a subsequent operation such as
sorting.
[0071] The second problem pertains to droplet membership or
determining what droplet the micro-entity will end up in, so that a
correct charge may be applied to that droplet. The second problem
is somewhat particular to a droplet cell sorter, but may be
pertinent to any system that forms regularly-spaced capsules that
convey micro-entities. For example, the second problem may pertain
to a microfluidic system in which alternating types of immiscible
fluids are introduced into a channel, or in which bubbles of air
are periodically introduced into a channel to form separated
capsules of fluid. In such systems, the formation of droplets or
capsules may be on a periodic basis, and the arrival of
micro-entities into the stream may be on a random basis.
[0072] In further detail with regard to the formation of droplets
and determining droplet membership, a drop clock signal 401
depicted in FIG. 4A may be used to generate a periodic transducer
signal 403 (FIG. 4B) that is applied to the transducer 212 in the
flow cytometer nozzle 202. As described above, the transducer 212
imparts acoustic energy to fluid in the nozzle and stream 220.
Additionally, charging pulses (depicted in FIG. 4C) may be applied
to the conductive probe 207, so as to charge the last attached drop
228-0. The transducer signal may be synchronized or phase-locked to
the drop clock signal. The amplitude of the transducer signal may
be adjusted to move the break-off point 226 to a selected location
or distance from the nozzle 202. The charging pulses may be phase
locked to the drop clock and may or may not be in phase with the
drop clock and/or transducer signal. According to some embodiments,
there is no variable phase adjustment that is used to adjust a
phase between the charging pulses and transducer signal and/or drop
clock in an experimental or calibration run of the system 200.
[0073] According to some embodiments, the drive amplitude to the
transducer is adjusted to produce clean sort streams when different
sort charge pulses are applied to the last attached drop. With the
transducer signal amplitude set to produce clean sort streams and
the break-off point 226 maintained at an approximately fixed
distance from the nozzle 202, fluorescent calibration beads or
micro-entities may be run through the system to determine droplet
membership. In some calibration runs, calibration entities can be
fed through the system at a low rate, so that there will be no
confusion of data signals. By detecting the calibration entities in
the pinch-off region, a determination can be made as to which
signals from the interrogation points for a particular micro-entity
correspond to a droplet formation and/or droplet charging time at
the pinch-off region. In some implementations, this determination
may amount to finding a value for dt.sub.c. Once the determination
is made, membership of a micro-entity's signals to a particular
droplet can be assigned.
[0074] With droplet membership determined, sorting decisions for
one or more micro-entities within a droplet can be made. Sorting
decisions can be made based upon correlations of signals for the
one or more micro-entities that will be within the droplet. The
physical act of sorting can be implemented by assigning a droplet
charge value to the droplet. For example a droplet charge value 412
may be assigned to the droplet. In some embodiments, charge values
applied to the droplets may be quasi-discrete. For example, the
charge values may be separated by first amounts corresponding to
the discrete sort streams, although a final charge amount may be
adjusted to account for higher-order effects, e.g., capacitive
coupling to adjacent droplets.
[0075] In some embodiments, the average time offsets dt.sub.1,
dt.sub.2, dt.sub.3, dt.sub.4, dt.sub.c that are found in
calibration runs may be computed in terms of system clock cycles,
e.g., in terms of drop clock cycles (FIG. 4A) or analog-to-digital
(ADC) clock cycles (FIG. 4D) or a combination of both clock cycles.
A high resolution of the offsets may be obtained by using a system
clock that operates at a higher frequency than other clocks. In a
flow cytometer, an ADC clock may run at 10's or 100's of MHz,
whereas a drop clock may run at frequencies several orders of
magnitudes slower than these values. A delay value computed in
terms of ADC clock cycles will provide a higher resolution. In some
implementations, long delays may be calculated in terms of slower
clock cycles, e.g., drop clock cycles. In some cases, long delay
may be calculated in terms of a mix of slower and faster clock
cycles. For example, if the period of the drop clock is T.sub.d and
the period of the ADC clock is T.sub.ADC, then a delay may be
calculated as
dt=NT.sub.d+aT.sub.d (1)
where N is an integer and a represents a fraction: 0<a<1. The
value a may be computed as
a=MT.sub.ADC/T.sub.d (2)
where M is an integer. Combining EQS. 1-2 yields
dt=NT.sub.d+MT.sub.ADC.
III. DATA ACQUISITION
[0076] As noted above, the time offsets or delays dt.sub.1,
dt.sub.2, dt.sub.3, dt.sub.4 can be used to select the appropriate
times for observation timing windows 310 or data stream segments in
which a signal from the micro-entity is expected to appear for each
interrogation point and for a same micro-entity that has initiated
a trigger event (e.g., crossing of trigger interrogation point at
B1). Data appearing within the observation windows would be
ascribed to that micro-entity and processed to make a decision
about an operation (e.g., a sorting operation) on that
micro-entity. As noted above, since data from the interrogation
points may be buffered, any interrogation point may serve as a
trigger point and the observations windows for a single
micro-entity may occur before and after a trigger signal.
[0077] In some embodiments, the observation windows 310 identify
time segments in data streams from the detectors D1, D2, . . . D5
at which a signal is to be attributed to a particular micro-entity.
For example, each of the detectors D1, D2, . . . D5 in the system
may output a stream of data comprising samples of signals produced
by micro-entities crossing a respective interrogation beam B1, B2,
. . . B5. The samples may be acquired at a high data rate according
to an analog-to-digital converter clock, e.g. as depicted in FIGS.
4D-4F. A micro-entity crossing a selected beam that will be used as
a trigger (beam B1 in this example), will generate a trigger signal
(e.g., a pulse) that can be detected and used to identify an
arrival time of that micro-entity at the trigger interrogation
point. In the following illustrative example, a feature of the
trigger signal used to mark arrival time or a reference time
t.sub.01 is taken to be a peak of the trigger pulse, but other
values may be used in other embodiments, e.g., a rising edge
threshold level selected by a system user, a threshold value of an
integral of the pulse. The reference time t.sub.01 of the trigger
event may be associated with a clock cycle, as depicted in FIG.
4D.
[0078] With the reference time t.sub.01 established for the
micro-entity and a delay (e.g., delay dt.sub.2) known from a
calibration run, it can be determined, in terms of clock cycles or
data samples, where to look in a data stream for a subsequent event
(e.g., crossing of beam B3) for the same micro-entity that moves
through the system. For example and with reference to FIGS. 4D-4F,
a first micro-entity may cross a trigger beam (taken as beam B1 in
this example), and generate a trigger pulse 410 at time t.sub.01.
The trigger pulse 410 may be detected in a stream of data from
detector D1. The stream of data may include subsequent trigger
pulses 412, 414 for other micro-entities. By knowing the delay time
dt.sub.2, a subsequent signal generated by the first micro-entity
as it crosses beam B3 can be found in a data stream from detector
D3. The subsequent signal would appear in an observation window 310
that straddles an expected arrival time t.sub.21 of the
micro-entity at beam B3. The observation window would be offset
from the trigger event t.sub.01 by a number of clock cycles (e.g.,
ADC clock cycles in this example) that correspond to the delay time
dt.sub.2. In this observation window 310, a pulse 422 appears to
arrive late, as depicted in FIG. 4F. Other pulses corresponding to
other micro-entities also appear in the trace of FIG. 4F.
[0079] Data received from the detectors D1, D2, . . . D5 may be
stored in temporary data storage, e.g., a FIFO buffer. In some
embodiments, the data may be stored in long-term data storage.
Since the data is stored, there is greater flexibility in handling
and processing the data because it need not be processed in real
time. For example, with data storage, it is possible to look
backwards and forward in time from a selected event. According to
some embodiments, the aspects of determining locations of
observation windows according to clock cycles can be transformed to
determining locations of data signals according to memory address
or memory location (e.g., provided the data is stored sequentially
in address space or stored in association with an observation
time). Delay values in time, e.g., dt.sub.2, can be transformed to
offsets in address space.
[0080] In such implementations, any of the beam-crossing events in
the stream 220 may be used as a reference trigger event for a
particular micro-entity. For example, a signal detected for a
micro-entity crossing a second beam B2 in the stream may be used as
a trigger event. Corresponding data for other beam crossings for
the same micro-entity may be found by looking forward and backward
in address space by address offsets corresponding to the
appropriate delay times.
[0081] Determining the timing or locations of the data acquisition
observation windows 310 assures that signals, which may include the
absence of a signal, are recorded correctly for each micro-entity
that traverses the system. As depicted in FIG. 3E some
micro-entities may not generate a signal at an interrogation point,
e.g., provide no measurable signal over a background level. For
example, some micro-entities in a sample may be labeled with a
fluorescent tag that is sensitive to some interrogation beams and
not sensitive to other interrogation beams. The absence of a
recognizable signal above a background noise level can be important
in classifying or characterizing the micro-entity. Therefore, a
correct determination of an observation window 310 can aid in
confirming the absence of a signal for a micro-entity at that
interrogation point or beam crossing.
IV. MOTION-RELATED ERROR
[0082] In systems having streams with moving micro-entities, such
as a flow cytometer, the micro-entities 225 may arrive at the
interrogation points along the stream with deviations from expected
arrival times. For example, even though the flow rate of the stream
220 may be substantially constant and the time offsets dt.sub.1,
dt.sub.2, dt.sub.3, dt.sub.4 determined carefully through
calibration, there can still exist uncertainties in the arrival
times of a micro-entity at a second interrogation point and each
interrogation point or location along the stream 220 with respect
to a trigger event initiated by the entity. The uncertainty in
arrival times, or positions of the micro-entity, may be due to one
or more types of motion-related errors.
[0083] Motion-related error of moving micro-entities may include
any type and form of error that contributes to a deviation from an
expected arrival time (or times) or expected location (or
locations) of a micro-entity at a second location or second time
(or additional locations or additional times) given the detection
of the same micro-entity at a first location or first time.
Motion-related errors of moving micro-entities may be due to real
and/or apparent causes. Real motion-related error may comprise
physical movement of a micro-entity within the stream from an
expected location, e.g., energy coupled to the stream may perturb
or dither the position of the micro-entity within the stream as it
moves along the stream, even though the stream may flow without
turbulence in some embodiments. Apparent motion-related error may
comprise an observational effect that suggests movement of the
micro-entity when there is no corresponding physical motion.
Examples of apparent motion-related error may include, without
being limited to, electronic detection effects (e.g., variable
electronic delays, digitization errors) and optical effects (e.g.,
variable lensing effects due to an undulating fluidic stream).
[0084] In the field of flow cytometry, the motion-related errors
associated with uncertainty in arrival times of micro-entities at
interrogation points may be referred to as "pulse jitter." Pulse
jitter provides one example of motion-related error for moving
micro-entities in commercial systems, and can be visible when
viewing signal pulses from the micro-entities on an oscilloscope.
Pulse jitter can be due to real and/or apparent causes. An instance
of pulse jitter is depicted by the late arrival of pulse 422, with
respect to an expected peak arrival time t.sub.21, as depicted in
FIG. 4F. The inventors have found that pulse jitter can be observed
without application of periodic energy to the stream 220 via
transducer 212, and becomes markedly more pronounced with the
application of periodic energy to the stream. Pulse jitter is
observed to some degree on all multi-laser, SIA cell sorters
currently in commercial use.
[0085] An example of pulse jitter is depicted in FIG. 5. In FIG. 5,
multiple pulses from multiple micro-entities observed at a same
downstream location and triggered from nominally a same condition
at an upstream location have been overlaid on the graph. The
variations in pulse position on the plot reflects variations in
arrival times at the downstream location and represents
motion-related error in the system. The variations in arrival time
can be greater than several pulse lengths in some systems. The
variations in pulse position on the plot alternatively reflects an
uncertainty in knowing the position of a micro-entity at a selected
time.
[0086] In FIGS. 3C-3G, the observation windows 310, represented by
vertical dashed lines, are sized to reflect the amount of pulse
jitter at each interrogation beam location. As explained above, the
observation windows are times at which data may be collected and
ascribed to a same micro-entity that initiates a trigger pulse. The
observation windows increase in size with distance from the nozzle,
which reflects the observation that pulse jitter increases with
distance from the nozzle. Greater amounts of pulse jitter require
larger spans of time for the observation windows 310.
[0087] In various embodiments, the observation windows 310 are
sized so that the majority of pulses generated by micro-entities at
the corresponding interrogation point are captured and are
discernible within the observation window 310. In some embodiments,
the observation windows 310 are sized so that a desired fraction of
pulses generated by micro-entities at the corresponding
interrogation point are captured within the observation window 310.
A desired fraction may be between about 0.95 and about 1, between
about 0.90 and about 0.95, between about 0.8 and about 0.9, between
about 0.7 and about 0.8, or between about 0.6 and about 0.7 in
various embodiments. In some implementations, if pulses arrive at a
location with a distribution of times characterized by a standard
deviation a, then the observation window 310 may be sized to be a
first value that is proportional to an average width of the pulses,
w.sub.avg, plus a second value that is proportional to the standard
deviation. For example, the width of the observation window, W, may
be expressed by the following relation: W=cw.sub.avg+f.sigma. where
c is a real number greater than 0, and f is a real number greater
than 0. If one of the terms is significantly greater than the
other, an embodiment may have the window width determined by
cw.sub.avg or f.sigma..
[0088] As may be appreciated, motion-related erro (e.g., pulse
jitter in a flow cytometer) can impose limits on system operation
and throughput. For example, in order to correctly ascribe data
from interrogation points to a correct micro-entity, each
observation window would preferably not significantly overlap with
a preceding window or a successive observation window in a data
stream. In some instances, a small amount of overlap may be
acceptable. In some implementations, there may be no overlap of
observation windows, and there may be a buffer, or dead time,
between each observation window. Maintaining a small overlap or
preventing overlap of observation windows may place limits on the
micro-entity throughput rate and/or the size of the interrogation
region 222. For example, micro-entities in the stream 220 must be
spaced far enough apart such that the observations may be correctly
deconvolved and assigned to the micro-entity of interest at a
desired probability or accuracy. In some implementations, the
windows for successive micro-entities minimally overlap or do not
overlap. Alternatively, or in addition, interrogation points must
be constrained to a region near the nozzle orifice 208 where the
amount of pulse jitter is within an acceptable limit.
[0089] Pulse jitter is found to be smallest when measuring pulses
from interrogation points closest to the exit orifice 208, and
corresponding data acquisition windows can have the shortest
duration for interrogation points near the orifice while
maintaining a desired level of measurement accuracy. The inventors
have observed that the jitter increases appreciably with distance
from the nozzle 202. This increase in jitter can impose limits on
the size of the interrogation region 222. For example, the
interrogation region may be restricted in size to where the jitter,
for a given throughput, does not cause significant overlap of
observation windows for a given throughput. In some systems, pulse
jitter can restrict the total interrogation region to a small
region of the stream (about 1 mm or less) close to the nozzle
orifice 208. A limit on the size of the interrogation region can
limit the number of interrogation beams that may be used in the
system, and a limit on the number of interrogation beams constrains
the analysis of the micro-entities.
[0090] Further, the inventors have found that jitter increases with
the amplitude of the drive signal applied to the transducer 212.
This increase in jitter may place a limit on how hard the
transducer can be driven. Driving the transducer 212 with higher
amplitudes may be desirable in some applications, since this
generally leads to better uniformity and stability of droplet
formation. However, an associated increase in pulse jitter may
prevent driving the transducer with higher amplitudes at high
throughput rates.
V. CHARACTERIZING MOTION-RELATED ERROR
[0091] A series of experiments were carried out to characterize
motion-related error in a droplet cell-sorter flow cytometer, and
to derive a model for the motion-related error. Since droplet
generation was substantially periodic and sinusoidal (e.g., due to
the form of the drive signal applied to the transducer 212), it was
postulated that motion-related error for the flow cytometer may be
correlated to the periodic perturbations applied to the stream 220,
and that a model incorporating a periodic function may be
representative of motion-related error in the system. Accordingly,
it was postulated that pulse jitter J in the flow cytometer at any
location along the stream 220 might exhibit an approximately
sinusoidal correlation to a time offset .DELTA.t.sub.dc or phase
.phi..sub.dc measured from a reference or feature point in a drop
clock cycle (e.g., a leading edge) to a reference point on a
detected "trigger" pulse (e.g., a pulse peak or selected threshold
value on a leading or trailing edge of the pulse).
[0092] For purposes of understanding and without being bound to any
particular theory, this correlation may be expressed mathematically
as follows:
J.about.A sin(.DELTA.t.sub.dcF+.theta.) (3)
where A is an amplitude that is to be determined, F represents the
droplet generation frequency or frequency of the drop clock, and
.theta. represents a phase that is to be determined. According to
some embodiments, A and .theta. may be determined experimentally
for a system in a calibration run. In other embodiments, A and
.theta. may be determined on the fly as data is being accumulated
by the system during the analysis and processing of useful samples.
As will be apparent to those skilled in the art, other models
(e.g., simple or higher-order Bessel functions, higher-order
trigonometric functions, Gaussian functions, superposition of same
or different functions) may be used to represent motion-related
error in systems having moving micro-entities. It was also
recognized that there could be additional components of
motion-related error in the flow cytometer, such as errors
introduced in the optical detection apparatus due to a moving and
undulating stream and errors introduced in the electronics of the
data acquisition system used to collect data from the interrogation
points. Some of the additional components of error may or may not
be represented by the same model. Some may require different
models.
[0093] According to one embodiment, the jitter J for a flow
cytometer can be measured on a system having a stream of moving
micro-entities as a time offset from an expected arrival time in an
observation window to an actual arrival time. The expected arrival
time (e.g., times t.sub.1, t.sub.2, t.sub.3, t.sub.4 with reference
to FIGS. 3C-3F) at any location in the stream 220 and observation
window 310 may be established through a calibration procedure as
described above. The observation window for the selected location
may be set up to straddle the expected arrival time. The actual
arrival time may be measured as a time at which a selected
reference point on the pulse is detected. The choices of where an
expected arrival time occurs within an observation window (e.g.,
beginning, middle, end) and what is used as a reference point on a
pulse (e.g., leading edge threshold, peak, trailing edge threshold,
integral threshold) may be dependent upon the particular
implementation, and is less important for the development of a
model for motion-related error, as long as the selected arrival
time and reference point are used consistently.
[0094] For purposes of understanding aspects of the invention as
applied to a flow cytometer, FIGS. 6A-6B graphically depict jitter
J and the time offset .DELTA.t.sub.dc from the drop clock. FIG. 6A
may represent a signal received from a trigger interrogation point,
and FIG. 6B may represent a signal received from another
interrogation point along the stream 220. Both values can be
recorded in terms of clock cycles and converted to actual times by
multiplying the clock cycles by the clock period, in some
embodiments. For example, the time offset .DELTA.t.sub.dc and
jitter J may be measured in terms of ADC clock cycles, drop clock
cycles, or a combination thereof, as described above in connection
with EQS. 1-2. In this example, the time offset .DELTA.t.sub.dc is
measured from a rising edge of a drop clock to a peak of a detected
trigger pulse 610. The jitter J is measured from an expected
arrival time t.sub.a at the center of an observation window 310 to
a peak of a second detected pulse 620. In some embodiments, the
trigger pulse 610 may be generated by a micro-entity when it
crosses a first beam B3, and the second detected pulse 620 may be a
pulse detected from another interrogation point in the stream 220.
In some embodiments, other reference points on the pulse, drop
clock, and observation window may be used. If the postulated
relation between pulse jitter and droplet generation were correct,
then the observed jitter J for a plurality of micro-entities would
be related to a measured time offset .DELTA.t.sub.dc according to
the constructed error model, e.g. the model of EQ. 3.
[0095] FIG. 7A shows a result of measurements of pulse jitter
amplitude J as a function of time offset .DELTA.t.sub.dc or phase
.phi..sub.dc as recorded for a plurality of micro-entities in a
flow cytometer. Plots of jitter data as depicted in FIG. 7A may be
referred to as "jitter plots." The measurements were carried out on
the sy3200.TM. system described above using calibration beads. The
nozzle orifice was 70 microns in diameter and the pressure on the
fluid was 50 psi. The droplet generation frequency was 84.9 kHz,
and the jitter measurement was made using the interrogation beam
farthest from the nozzle (about 2 mm from the nozzle). The
resulting jitter plot clearly shows an approximately sinusoidal
correlation between the jitter amplitude J and offset in trigger
pulse timing .DELTA.t.sub.dc.
[0096] FIG. 7B shows results of jitter measurements under the same
conditions as in FIG. 7A, but wherein the measurements were made
using the interrogation beam second from farthest from the nozzle
212. The jitter plot shows a change in jitter amplitude (decreasing
nearer the nozzle) but no change in phase .theta. of the jitter
plot. For the plots in FIGS. 7A and 7B, a phase delay associated
with the delay along the stream between the two interrogation
points has been subtracted. The results indicate that the
motion-related error, whether due to real or apparent causes,
travels with the micro-entity along the stream at a same phase and
increasing in amplitude.
[0097] A change of phase .theta. of the jitter plot is observed in
FIG. 7C. For this trial, the droplet frequency was changed to 77.9
kHz, and all other conditions were the same. The change in
frequency resulted in a change of the jitter plot phase. A change
in jitter plot phase is also observed when the position of the
nozzle 212 is changed with respect to the location of the
interrogation beam that is used as the trigger. No change in jitter
plot phase, at any interrogation point, is observed when the
amplitude of the transducer drive signal is altered.
[0098] A change in the amplitude of a jitter plot is observed when
the amplitude of the transducer drive signal is altered. Also, as
noted above, the amplitude of a jitter plot varies in relation to
the distance of the interrogation beam from the nozzle. (Compare
FIGS. 7A and 7B.) The amplitudes of jitter plots measured at beams
farther from the nozzle are larger than amplitudes of jitter plots
measured at beams closer to the nozzle.
[0099] The jitter plot results shown in FIGS. 7A-7C indicate that
there is a predictive or deterministic component of motion-related
error for micro-entities that may be corrected or compensated for
in the flow cytometer system, or any system that is similarly
configured. For example, if the time offset .DELTA.t.sub.d, from a
feature point on the drop clock signal is measured for a trigger
pulse, then a correction amount for adjusting the arrival time of a
micro-entity at another location in the stream 220 can be computed
from EQ. 3 and/or from the data of the jitter plots. The computed
correction amount may be used, for example, to adjust the timing of
observation windows 310 for each interrogation point for a
micro-entity as it traverses the system. Such an adjustment may be
used to re-center each observation timing window about an expected
occurrence of each pulse. In terms of data streams emanating from
the interrogation points, a computed correction amount may be used
to adjust positions of the data segments within the data streams or
memory addresses that store data that correspond to a signal for a
same micro-entity. Because there is a predictive component of the
pulse jitter, the predictive component may be compensated for in
the system's data acquisition apparatus. The motion-related error
itself may not be physically corrected. It will be appreciated that
compensation for motion-related error may be carried out in other
systems for which there is a predictable or deterministic component
of motion-related error that can be modeled, e.g., represented by a
mathematical model such as EQ. 3.
[0100] Further improvements may be made to the system. For example,
as the dominant source of motion-related error is corrected for in
a system, higher-order motion errors may become observable. The
higher-order errors may in turn be characterized, modeled, and
compensated for following processes described herein.
[0101] Using the techniques described above, the inventors have
also discovered that there exists a second component of
motion-related error in a flow cytometer. This second component of
motion-related error is referred to as "background jitter." By
making measurements to characterize the error, it was found that
background jitter was independent of the time offset
.DELTA.t.sub.dc and the system's droplet generator. Background
jitter was observed even when the system's droplet generator 260
was turned off, i.e., no droplets 228 were formed in the stream.
FIG. 8A shows one example of background jitter measured in a flow
cytometer with droplet generation turned off. For the measurement,
a 70-micron-diameter nozzle orifice was used and the sheath
pressure was 50 psi.
[0102] Although the background jitter did not have a predictive
quality based upon the time offset .DELTA.t.sub.dc as described
above, the measurements to characterize the jitter revealed that
the amplitude of background jitter showed a dependency on a
centering of the sample fluid conduit 203 within the nozzle 202.
The data of FIG. 8A was obtained when the sample fluid conduit was
not centered within the nozzle. A minimum of background jitter was
observed when the conduit 203 was approximately centered within the
nozzle 202, as recorded in FIG. 8B. Background jitter was also
observed to vary when the laser beam at the interrogation point was
scanned across (side-to-side) the stream 220. For FIGS. 8A and 8B,
the jitter was measured at the interrogation point farthest from
the nozzle 202 using the same procedure that was used to measure
pulse jitter with the droplet generator turned on. Because the
background jitter showed a dependence on conduit position within
the flow cytometer nozzle, measurement and characterization of
jitter in the system, e.g., with the droplet generator turned off,
can provide information about the mechanical alignment of the
system that is useful for improving the performance of the system.
In some flow cytometer or microfluidic systems, background jitter
may be dependent upon other mechanical features that may affect the
flow dynamics of the stream 220, e.g., location of conductive probe
207, location on transducer 212, location of a device inserted into
a microfluidic channel.
[0103] In the above instance, background jitter was observed to
have a functional dependence on a mechanical alignment parameter of
the system. According to some embodiments, a system may be
configured to provide feedback to a system operator relating a
level of background jitter. The feedback could be a visual display
that depicts an amount of background jitter and shows a reduction
or increase in approximately real time responsive to mechanical
alignment of a system component.
[0104] As may be appreciated from the foregoing discussions,
measurement and characterization of motion-related errors for
micro-entities in moving streams can be used to diagnose and
improve system performance. Techniques for compensating for
motion-related error are described in a patent application filed
concurrently by a common inventive entity and titled, "Compensation
of Motion-Related Error in a Stream of Moving Micro-Entities" which
is incorporated herein by reference in its entirety.
[0105] One embodiment of a method 1100 for characterizing
motion-related error in a stream of moving micro-entities is
depicted in the flow chart of FIG. 9. The steps shown in FIG. 9 may
not all be included in various embodiments of methods for
characterizing motion-related error. Some embodiments may only
include a subset of the steps shown. Some embodiments may include
additional steps not shown in the drawing, but described herein.
Though the steps in FIG. 9 relate to characterizing pulse jitter in
a flow cytometer, it will be appreciated that the method
encompasses equivalent embodiments for characterizing
motion-related error in other systems such as microfluidic and
microfabrication systems.
[0106] According to one embodiment, a method 1100 for
characterizing motion-related error may comprise starting 1105 a
system in which micro-entities move from a first location to a
second location or one or more additional locations. The system may
be a flow cytometer, a microfluidic system, or any system
configured to measure and/or operate on moving micro-entities.
According to some embodiments, the system may be one in which
microstructures are fabricated in fluidic streams, e.g., as in the
systems described in U.S. patent application publication No.
2010/0172898 to Doyle et al. The method may include an act of
initializing 1110 the system. The act of initializing may comprise
verifying and/or setting operating parameters of the system, e.g.,
setting a velocity of the moving micro-entities. In a flow
cytometer, the act of initializing 1110 may comprise performing
calibrations for signal correlation and droplet membership as
described above.
[0107] The method 1100 may further include measuring 1120
motion-related error, such as pulse jitter data. The measured
motion-related error may be representative of deviations or
variations in actual arrival times from expected arrival times. An
expected arrival time of a micro-entity at a selected location may
be based upon a separately detected event (e.g., a trigger event)
that is caused by the micro-entity at a trigger location. The
deviations in actual arrival times may be evaluated with respect to
expected arrival times based on other considerations, e.g., average
velocity of the micro-entities or average transit times between a
"trigger" location and the measurement location. In addition to
measuring the deviations of actual arrival times, the act of
measuring 1120 motion-related error may include measuring an aspect
of the trigger event (e.g., measuring an occurrence of a trigger
event with respect to a system metric, such as a system reference
signal or system clock, a pulse profile, a distance, or
wavelength). In a droplet cell-sorting flow cytometer, an aspect of
the trigger event may be its occurrence in time with respect to a
reference point (e.g., rising edge) of a drop clock that is used to
time the formation of droplets. In other embodiments, timing of a
trigger event may be measured with respect to a timing of coupling
energy into a stream in which the micro-entities are conveyed. In
some embodiments, a system metric may include a sensed value, e.g.,
a temperature of the stream, a pressure of the fluid, an
instantaneous velocity of the stream, an external acoustic level,
an acceleration value measured for a system component.
[0108] Continuing with the example of the flow cytometer, measuring
1120 motion-related error may comprise measuring the following
values for each pulse: drop clock phase (or time offset from a
reference point on the drop clock) when the pulse arrives at a
selected trigger channel, and time offset between the expected and
actual pulse arrival times for a selected interrogation point or
detection channel associated with the interrogation point. The
collected data may be stored for processing by a next stage in the
process 1100.
[0109] There may be several possible embodiments by which the pulse
arrival time is measured at a selected trigger channel. One method
is to measure the peak location of each pulse. Another embodiment
is to measure the point at which the rising edge of a pulse crosses
a selected threshold value. Yet another embodiment comprises
measuring the point at which an integral of the pulse, i.e. the
area under the pulse, crosses a selected threshold value.
[0110] In some embodiments, motion-related error data (such as
jitter-plot data) may be acquired in error calibration and
characterization runs. The error calibration and characterization
runs may be executed after drop-delay calibration runs in some
implementations. In other embodiments, motion-related error data
may be acquired on the fly during an experiment in which
micro-entities are being analyzed. When motion-related error data
is collected on the fly, error characterization and compensation
may be a dynamic process in which an error model is updated
periodically based on observational data. Accordingly, error
characterization and compensation may change over time to reflect
any changes in collected error data. In some embodiments, the use
of Bayesian priors may be useful to construct an initial error
model and to support the updating of the error model given further
observations of motion-related error in the system.
[0111] In some embodiments, characterization of motion-related
error for any interrogation point in the stream 220 involves
determining 1130 a relation between motion-related error E at a
selected interrogation point and a measurable system quantity upon
which the error may depend. Once the error data has been measured,
the data may be processed to determine 1130 a relation between
motion-related error E and a measurable system variable, e.g., an
offset (.DELTA.t.sub.dc or .phi..sub.dc) of a trigger event with
respect to a system reference signal, as described above for a flow
cytometer. In some embodiments, the relation between E and
.DELTA.t.sub.dc or .phi..sub.dc may be determined empirically
through error calibration and characterization measurements, e.g.,
measurements like those described above in connection with FIGS.
7A-7C. Calibration beads or fluorescent cells may be run through
the system to obtain jitter plots, like those of FIGS. 7A-7C, for
each interrogation point. In some implementations, the relation may
be modeled by a function, e.g., identifying a function to fit the
measured data.
[0112] A relation between motion-related error E and a measurable
system variable may be determined 1130 for each point of interest
in the stream of moving micro-entities. A point of interest may be
a location in the stream at which a measurement is made of the
micro-entity or an operation is performed on the micro-entity. In
some embodiments, more than one measurement may be made at a point
or location of interest. For example, in a flow cytometer there may
be several detection channels for a single interrogation point.
Because each detection channel in a system may exhibit different
errors (e.g., different amounts of pulse jitter) in some cases, a
relation between motion-related error E and a measurable system
variable may be determined for each channel.
[0113] The method 1100 may include an act of determining 1140
whether a relation between motion-related error E and a measurable
system variable (e.g., .DELTA.t.sub.dc) has been evaluated for each
point of interest, e.g., each interrogation point in the stream and
each detection channel associated with each interrogation point. In
some cases, there may be more than one detection channel per
interrogation point, and the method 1100 may include an act of
determining 1140 whether a relation between motion-related error E
and a measurable system variable has been evaluated for each
detection channel. If a relation between motion-related error E and
a measurable system variable has not been evaluated for each point
of interest in the stream and/or detection channel, the method flow
may return to a step of measuring 1120 motion-related error data
for a next point of interest in the stream and/or detection
channel. For an embodiment in which data for one or more points of
interest are interpolated, the method flow may return to a step of
determining 1130 a relation between motion-related error E and a
measurable system variable, as indicated by the dashed arrow in
FIG. 9. The error compensation values may be determined by
interpolation techniques between measured end points. If a relation
between motion-related error E and a measurable system variable has
been evaluated for each point of interest in the stream and/or
detection channel, the method flow may proceed to an act of
calculating 1150 correction values that may be used to characterize
and compensate the motion-related error.
[0114] According to some embodiments, calculating 1150 correction
values comprises performing a curve fit to the measured error data.
The measured error data may or may not be plotted on one or more
graphs. One or more functions, e.g., a periodic function as in EQ.
3, may be fit to each plot or its corresponding data, and the
resulting fitted functions may be used at each interrogation point
to compensate for motion-related error in subsequent experimental
runs. By determining parameters of the fitted function (e.g.,
amplitude, phase, delay, coefficients) characterization and
compensation values can be computed from an analytical function for
every pulse received.
[0115] Alternatively, motion-related error data for each
interrogation point may be compiled and stored into look-up tables
(LUTs), which may be used for each interrogation point to
compensate for pulse jitter in subsequent experimental runs. In
some embodiments, calculating 1150 correction values may comprise
generating a look-up table of values, where each element of the LUT
corresponds to a trigger phase or delay bin, and the value of each
element is determined from the motion-related error data for the
phase or delay bin. Possible methods for determining the value of
each element in the LUT include, but are not limited to, computing
the mean, median, and mode of the motion-related error data for
that bin. An optional addition to this stage is to track the
residual error and drive it to zero-mean. In some embodiments, this
can be accomplished by recalculating the nominal offset so that the
values in the LUT are zero-mean. This is accomplished by computing
the mean of the measurement data, subtracting it from the data, and
adding it to the nominal offset. In still other embodiments, a
fraction of the nominal offset, e.g. 1/2 of said offset, is added
to achieve a convergence to zero mean.
[0116] In some embodiments, the calculated LUT values may be
limited in number where each LUT value may span a range of
motion-related error conditions. Continuing with the above example
of the flow cytometer, calculated LUT values may be limited to
discrete trigger time or phase offset bins, e.g., 36 bins each
spanning about 10 degrees of trigger phase offset values as
depicted in FIG. 13. Therefore, any measured trigger offset phase
falling within a 10-degree bin would receive a same averaged value.
As may be appreciated, trigger offset may be expressed in terms of
time in some embodiments, e.g., time delay with respect to a
reference point on the drop clock signal (.DELTA.t.sub.dc), or may
be expressed in terms of phase .phi..sub.dc, e.g., phase of the
drop clock cycle.
[0117] In some embodiments, interpolation techniques may be used
when compiling LUTs. For example, measurements may be made to
determine LUT values for 10-degree bin increments as described
above. Interpolated LUT values may then be computed by processor
250 based upon linear or higher-order interpolation. The
interpolation may be executed in a field-programmable gate array in
some embodiments. The measured and interpolated LUT values could
then be stored in a LUT data store for subsequent use.
[0118] In some embodiments, the act of calculating 1150 correction
values may be executed on the fly during normal system operation,
e.g., during a cell-sorting run, or in other embodiments it may be
executed prior to normal operation, e.g., during a calibration run.
As one example, a measured trigger offset may be used to compute a
correction value from a functional fit on the fly. In the case of
the flow cytometer described above, the act of calculating 1150
correction values may comprise calculating offset times that may be
used to shift the time observation windows 310 for acquiring pulse
data from each interrogation point. The calculated time offsets may
be based on the motion-related error measurement data and may be
such that the calculated offset time compensates for an average
amount of motion-related error measured at a selected location.
Calculated correction values may be used to compensate for a
measured error and then discarded, or may be stored in a look-up
table and accessed subsequently according to a measured trigger
offset to compensate for motion-related error.
[0119] In some implementations, the acts of measuring 1120
motion-related error data and determining 1130 a relation between
motion-related error E and a measurable system variable may be done
in real time, e.g., during a normal sorting run in which
micro-entities are analyzed. Motion-related error data acquired in
real time may be processed and used to calculate error compensation
values as the system is running. In some embodiments, the
calculated values may be used subsequently to compensate
motion-related error. In such implementations, separate
calibrations runs for measuring and characterizing motion-related
error may not be necessary, or any motion-related error
compensation data previously calculated may be updated in real time
during a normal run. In some embodiments, the use of Bayesian
statistics can provide a useful framework for real-time updating of
an error model for the system.
[0120] In some embodiments, the acts of measuring 1120
motion-related error data, determining 1130 a relation between
error E and a measurable system variable, and calculating 1150
correction values may be executed for each point or location of
interest and/or each detection channel. The measured data may be
processed separately. For example, data from each measurement may
be fit to a functional form, or may be compiled into a look-up
table specific to the location and/or channel corresponding to the
measured data.
[0121] As noted above, any one of the interrogation points in a
stream may be used as a trigger interrogation point when data from
the interrogation points are buffered. Accordingly, a system may be
configured to determine correction values based upon one trigger
interrogation point (e.g., the first interrogation point
encountered along the stream from the flow cytometer nozzle), and
subsequently change operation to use another interrogation point
(e.g., the second interrogation point) as the trigger interrogation
point. In some embodiments, it may not be necessary to execute a
re-calibration procedure on the system to determine correction
values for the new trigger interrogation point. Instead, the stored
correction values, functional fit, or LUT may be transformed
mathematically to reflect the change in trigger interrogation
point. According to one embodiment for a flow cytometer, if the
correction values are implemented as LUTs indexed by a phase or
time offset that is determined when a particle crosses the trigger
point, and the trigger interrogation point is changed from the
first interrogation point along the stream to the second point
along the stream, then the following mathematical transformation
may be applied: first, the original LUT values for the second
interrogation point are subtracted from each of the interrogation
point LUTs on a per-bin basis. For the second interrogation point,
this subtracts the original LUT values from themselves, which
correctly leaves zero values in the LUT for the new interrogation
point. Second, each of the new LUTs for other interrogation points
must be circularly shifted by a number of bins equivalent to a
phase difference between the first and second interrogation points
that has been determined for the current drop clock frequency and
stream velocity.
[0122] Since it is possible for multiple data acquisition channels
to be used for a selected interrogation point and for all of these
channels to have substantially equivalent motion-related error
characteristics, it may be beneficial to reconcile the pulse jitter
measurement data across these common channels. One embodiment
comprises selecting one of the common channels at an interrogation
point (e.g., a channel with a best signal-to-noise ratio for the
detected micro-entities) to use for determining motion-related
error values. According to another embodiment, all motion-related
error data measured for all channels at an interrogation point may
be combined before calculating the correction values. Yet another
method comprises computing a metric for each channel that indicates
how well the correction values fit the measurement data, and select
the correction values from the channel with the best metric. By way
of example, the metric may use a mean-squared error (MSE) or
root-mean-squared error (RMSE) algorithm, or may compute a total
residual error.
[0123] In some implementations, determining 1130 a relation between
motion-related error E and a measurable system variable and/or
calculating 1150 correction values may comprise measuring
motion-related error data at a first interrogation point and at a
second interrogation point. There may be one or more interrogation
points between the first and second interrogation points. According
to some embodiments, data for the one or more points between the
first and second points may be interpolated from the data measured
for the first and second interrogation points. The interpolation
may follow a linear model, non-linear model, or a selected
functional form. The data for each point, whether measured of
interpolated, may be fit to a functional form, or may be compiled
into a look-up table specific to each interrogation point.
[0124] In terms of system hardware and/or software and referring to
FIG. 2, motion-related error characterization and compensation may
be implemented with a combination of one or more processors 250,
data acquisition device 230 and memory 240. According to one
embodiment, motion-related error characterization and compensation
may be implemented by adjusting the timing of data acquisition
windows opened for the detection of pulses by data acquisition
device 230 after detection and processing of a trigger event.
Processor 250 may identify a trigger event, e.g., a pulse from
detector D1, and compare a reference point (e.g., a leading edge
threshold value) on the trigger pulse with a reference point (e.g.,
a rising edge) on the drop clock. Based on a time difference
between these two events, processor 250 may adjust the timings of
observation windows opened by the data acquisition device 230 to
receive pulse data from other interrogation point detectors D2, D3,
D4, D5. The observation windows may be advanced in time or
delayed.
[0125] According to another embodiment, all data received from the
interrogation point detectors may be temporarily stored to a memory
240, e.g., a FIFO or ring buffer memory. Processor 250 may be
configured to select data segments from each detector data stream
based upon the timing of a trigger event with respect to the drop
clock, e.g., based upon a measured value for time offset
.DELTA.t.sub.dc or drop clock phase .phi..sub.dc.
[0126] As noted above for a flow cytometer, there are aspects of
the jitter plots that depend upon certain system conditions. The
amplitude of a jitter plot may depend upon an amplitude or amount
of energy coupled from the droplet transducer 212 into the stream
220 and upon the distance of the interrogation point from the
nozzle or transducer. The phase of a jitter plot may depend upon a
frequency of the drop drive signal and also upon a distance of the
trigger interrogation point from the nozzle. Therefore, if the
amplitude of the drop-drive signal, frequency of the drop-drive
signal, and/or distance of the interrogation point from the nozzle
change, then the system may need to be recalibrated to obtain an
updated model for the motion-related error. In a system configured
for on-the-fly error data collection, characterization, and
compensation, characterization and compensation values may be
continuously updated so that the system tracks changes in amplitude
of the drop-drive signal, frequency of the drop-drive signal,
and/or distance of the interrogation point from the nozzle so that
separate error re-calibration runs may not be needed.
[0127] Characterization and compensation of motion-related error
has several beneficial aspects for a system having a stream of
moving micro-entities. Characterization of motion-related error can
permit the error to be modeled and reduced by employing
compensation techniques. Compensation of motion-related error in
data analysis can reduce the width of the observation windows 310
at any interrogation point. Reduction of observation windows can
permit detection, measurement, and sorting of micro-entities with
closer spacing in the stream and thus allow higher throughput for
the system.
[0128] One benefit associated with characterizing and compensating
motion-related error (e.g., pulse jitter) in a flow cytometer
relates to the detection apparatus for detecting the
micro-entities. Because pulse jitter can be compensated in a flow
cytometer, larger amounts of pulse jitter can be tolerated in the
system. Accordingly, the interrogation points can be spaced farther
apart in the stream. In some embodiments, the interrogation region
can extend over 2 millimeters. The increased spacing of
interrogation points reduces the cross-talk of signals from each
interrogation point, thereby improving the signal-to-noise ratio
for each interrogation point.
[0129] Characterization and compensation of motion-related error
can also improve the accuracy in determination of drop membership
for each micro-entity, since deviations of the position of a
micro-entity from a central location within a segment of the stream
220 that will form a droplet are better determined. Narrow
observation windows can allow finer resolution for determining the
position of a micro-entity within a segment of the stream that will
form into a droplet. Knowing the position of a micro-entity within
a segment with finer resolution improves determination of drop
membership. A more accurate determination of drop membership can
improve sort quality, e.g., sort purity.
[0130] Sort purity may also be improved by better resolution of
doublets or multiplets. Some stream segments that will form
droplets may carry more than one micro-entity. Narrower observation
windows may allow doublets or multiplet to be more clearly
resolved, e.g., indicating the presence of more than one
micro-entity within a droplet whereas a previous system may have
only indicated one entity. Narrower observation windows may also
allow better determination of which drop a micro-entity will end up
in if the micro-entity is near a stream-segment boundary between
two droplets to be formed.
[0131] As noted above, motion-related error characterization and
compensation may allow larger interrogation regions 222. For
example, flow cytometers which could not tolerate large amounts of
pulse jitter that occur at greater distances from the nozzle (e.g.,
greater than about 1 mm, greater than about 1.5 mm) may use jitter
characterization and compensation techniques to mitigate
undesirable effects of pulse jitter. Larger observation regions can
permit additional interrogation beams to be used for more complex
analysis of the micro-entities.
VI. MODULATION OF STREAMS OF MOVING MICRO-ENTITIES
[0132] In the flow cytometer embodiments described above, acoustic
energy is coupled to a stream that conveys micro-entities for
purposes of forming droplets so that the micro-entities may be
contained within droplets that may be sorted according to an
applied charge. The acoustic energy is coupled to the stream 220
according to a regular periodic cycle, but the micro-entities
arrive in the stream 220 at random times. The application of
periodic acoustic energy to the stream assists in the control of
the individual micro-entities that arrive randomly in the stream,
e.g., assists in identifying and sorting each micro-entity.
Although the coupling of acoustic energy introduces an amount of
motion-related error to the system, in the form of pulse jitter for
the flow cytometer, the error has a deterministic nature, as shown
in FIGS. 7A-7C, for example, which allows for compensation of the
error to enable more accurate analyses of the micro-entities and
higher system throughput.
[0133] There may exist other areas of technology in which
application of acoustic energy to a stream of moving micro-entities
may be beneficial for micro-entity analysis and control. For
example, in microfluidic systems that may be used for analyses or
manufacture of micro-entities, periodic acoustic energy may be
coupled to channels through which the micro-entities move. Although
micro-entities may arrive randomly in a channel, the periodic
acoustic energy and error characterization and compensation
techniques described above may be used to better determine the
location of individual micro-entities within the channel, or a time
at a selected location at which to perform an operation on a
micro-entity. Knowing a location of a micro-entity within a channel
or an arrival time of the micro-entity at a selected location more
accurately may improve analysis and or sorting operations of the
microfluidic system. Acoustic energy may be coupled into a channel
through integrated acoustic transducers located on a microfluidic
chip, in some embodiments.
[0134] In some embodiments, acoustic energy coupled to a stream of
moving micro-entities may improve imaging of the micro-entities.
For example, a system may be configured to image micro-entities
flowing in a stream using a time delay integration (TDI)
multi-pixel imaging device. One example of TDI imaging is described
in U.S. Pat. No. 6,608,680 to D. Basiji et al, which is
incorporated herein by reference. Knowing more accurately
successive locations of a micro-entity moving in a stream allows
better alignment of successive images formed of the moving
micro-entity. Better alignment of successive images can be used to
reduce image noise and improve image resolution.
VII. EXAMPLES
[0135] By way of instruction, and without being bound to any
particular implementation, examples of detecting an compensating
pulse jitter in a flow cytometer are described in the following
Examples.
Example 1
[0136] In this example, a droplet cell sorter was configured with a
light collection optic referred to as Reflection Collection Optic-5
(RCO-5) that enables probing of and light collection from five
interrogation points along the stream 220. (RCO-5 is available from
iCyt Mission Technology, 2100 South Oak Street, Champaign, Ill.
61820, USA). The RCO-5 permits various separation distances among
the interrogation points. In this example, the vertical distance
(the length of the path a cell would travel from a top
interrogation point (#1) to the bottom interrogation point (#5) is
2 mm. The RCO-5 was tested with droplet cell sorter nozzles having
orifice diameters of 70, 100, and 126 microns and various stream
velocities from 10 m/s to 40 m/s. Pulse jitter was measured by
utilizing a data acquisition system (product BW-5) from an iCyt
Reflection System (available from iCyt Mission Technology, 2100
South Oak Street, Champaign, Ill. 61820, USA). This data
acquisition system digitally samples the analog signal produced by
the cell sorter's detectors (Hamamatsu photomultiplier tubes (PMTs)
with 45 MHz bandwidth) at 105 MHZ. Pulse jitter was assessed by
measuring the .DELTA.t between the crossing of the leading edge of
the pulse at 10% peak height for a signal produced at interrogation
point #2 and a signal from the same micro-entity at interrogation
point #5. All five interrogation points were within about 2 mm of
the nozzle orifice 208. Droplets did not separate from the stream
until about 6 mm from the nozzle orifice.
[0137] When the droplet generator was not active (i.e., the stream
passed into a waste vessel without separating into droplets), the
jitter was observed to be less than 100 ns. When the droplet
generator was engaged, various amounts of jitter were recorded for
each operating point (combination of sheath pressure, droplet
frequency, droplet transducer drive intensity). The highest value
of pulse jitter observed was a total variation of 3 microseconds
(or approximately three times the average width of the pulses).
[0138] The following observations were also made: (1) without
droplet generation (i.e., just a flowing stream) the .DELTA.t for
any two spots is almost constant; (2) when droplet generation is
turned on significant variations in .DELTA.t are observed between
the first and last (farthest separated) interrogation points, and
may in some cases be as much as 4 pulse lengths; (3) the amount or
severity of pulse jitter increases with increase in droplet
transducer drive amplitude; and (4) the amount or severity of pulse
jitter increases as the droplet break-off point moves closer to the
exit of the nozzle.
Example 2
[0139] A prototype bench-top cell sorter was used for this example.
This instrument was configured with a Bittware data acquisition
system, a 100-micron-diameter nozzle, fluid pressure of 24 psi, 40
KHz droplet generation, and multiple interrogation lasers emitting
at the following wavelengths: 488 nm, 640 nm, 405 nm, and 561
nm.
[0140] Test software based upon the test software of the iCyt
sy3200.TM. cell sorter (available from iCyt Mission Technology,
2100 South Oak Street, Champaign, Ill. 61820, USA) was developed
that would provide two additional data features (as compared to the
released version of the instrument software). These two additional
data features were: [0141] x--the time offset, at 105 MHz
resolution, from the leading edge of the data acquisition window to
a selected threshold value on the leading edge of a pulse for each
detector pulse. [0142] y--the time offset, at 105 MHz resolution,
from the last drop clock leading edge to the leading edge of the
pulse width threshold crossing for each detector pulse.
[0143] For this experiment the cell sorter was first calibrated to
have a sort phase setting of zero. This was verified using the
instrument automated sort calibration system and confirmed with
sorts of 360 micro-entities onto microscope slides which were
manually counted (typical prior art manual calibration method). The
sort calibration phase setting was then adjusted during the
experiment by adjusting the nozzle height based on observation of
reference points for fractional droplet wavelengths as established
previously with a droplet imaging system.
[0144] With this phase=0 calibration, it was expected that
parameter y above would essentially be the relative positional
offset of the cell within the stream segment that would become a
free drop (i.e., a future drop). This is because the zero phase
calibration aligns the timing of the drop clock with droplet
formation. As a result, the sort delay is exactly an integer number
of droplet periods, with no fraction of a period added.
[0145] Parameter x above is essentially a direct measure of jitter
J. If this offset is constant, then there is no jitter. The window
timing was set up so that the trigger was generated from
interrogation point #2. Acquisition windows for interrogation
points #3-#5 were set with the droplet generator off (essentially
no pulse jitter). Timing and window width were adjusted so that the
non-jitter pulses were centered in the window with approximately
.+-.3 microseconds before and after the pulse. When the droplet
generator was turned on each acquisition window was observed to
ensure that the maximum pulse jitter was captured inside the window
boundaries.
[0146] It was hypothesized that there would be strong correlation
between the measured parameters x and y. This correlation was
hypothesized to be approximately
x.about.A sin(yF+.phi.+.PHI.) (4)
where A represents an amplitude, F is the droplet generation
frequency, and .phi. represents a phase offset to be determined,
and .PHI. represents the sort phase for the flow cytometer.
[0147] The data measured for x and y at interrogation point #5 for
various sort phases are shown in FIGS. 10A-10D. The relationship
appears periodic and approximately sinusoidal. The scatter plots of
the measured pulse jitter vs. the time offset or phase of drop
clock are referred to as "jitter plots."
[0148] FIGS. 11A-11C illustrates that jitter is observed at each of
the interrogation points #3-#5. In some systems, points near the
nozzle, such as interrogation point #3 are considered to be "jitter
free." However, the result in FIGS. 11A-11C shows that jitter is
present at all interrogation points. It is also observed that while
the amplitude of the jitter plot increases with the distance from
the nozzle, the relative phase of the jitter plot remains the same
for each interrogation point.
Example 3
[0149] Additional experiments were conducted on the flow cytometer
system to determine how the jitter plot compared among the
interrogation points for a given operating point, and how the
jitter plot changed for various operating points. For these
experiments, data acquisition was triggered from interrogation
point #2 and data were collected for interrogation points
#3-#5.
[0150] Representative data for various operating conditions are
shown in the array of jitter plots in FIG. 12. In the vertical
direction, jitter plots for interrogation points #3-#5, with data
for interrogation point #3 at the top of each column and data for
interrogation point #5 at the bottom of each column. The first two
columns from the left are at a drop clock frequency F=54.2 kHz. The
next two columns are at F=60.2 kHz. Actual sort delay phase
settings (set using the sort calibration feature of the instrument)
are indicated at the top of each column. Sort calibration in all
cases was verified with an actual micro-entity sort. The jitter
plot relative phase remains the same for each interrogation point
along the stream. The jitter plot relative phase varies with
changes in sort delay phase, and with droplet frequency. A 70
micron nozzle orifice was used for the measurements, and the fluid
pressure was 30 psi.
[0151] It is observed that for each operating point, the jitter
plot for each interrogation point along the stream has the same
relative phase. The amplitude or maximum jitter variation (A in EQ.
3 above) increases at more distant interrogation points (e.g.,
increases as the micro-entity moves farther from the nozzle and
closer to the droplet pinch-off point). As the intensity of the
droplet generator transducer drive is increased (moving the droplet
pinch-off point closer to the nozzle), A increases but the phase of
the jitter plot remains the same. It was also found from the
experimental measurements that the jitter plot is highly
reproducible for a given system operating point. At a selected
operating point and sort phase calibration, the jitter phase plot
exhibits a reproducible phase.
[0152] Data that was collected in the laboratory experiments were
subjected to analyses to determine how well a sine function could
be fit to the data. Results of these analyses appear in FIG. 13. In
FIG. 13, a jitter plot is shown for measurements obtained from an
interrogation point farthest from the nozzle (interrogation point
#5). The recorded data represent measured jitter (in nanoseconds)
as a function of trigger pulse offset from a reference point on the
drop clock signal, expressed as phase. Shown in the plot are both a
least-squares sinusoidal curve fit to the data (black line) and bin
averages from a look-up table computation (white circles). In this
example, the LUT values were calculated by taking an average value
of the jitter data within discrete phase-angle bins. The
phase-angle bins each spanned about 10 degrees. Though both the
fitted function and LUT values represent the measured data well,
the LUT values may more accurately characterize the jitter. For
example, the LUT values reflect higher-order effects in the jitter
data that is more clearly visible near the maximum and minimum
values of the jitter plot.
VIII. CONCLUSION
[0153] All literature and similar material cited in this
application, including, but not limited to, patents, patent
applications, articles, books, treatises, and web pages, regardless
of the format of such literature and similar materials, are
expressly incorporated by reference in their entirety. In the event
that one or more of the incorporated literature and similar
materials differs from or contradicts this application, including
but not limited to defined terms, term usage, described techniques,
or the like, this application controls.
[0154] The section headings used herein are for organizational
purposes only and are not to be construed as limiting the subject
matter described in any way.
[0155] While the present teachings have been described in
conjunction with various embodiments and examples, it is not
intended that the present teachings be limited to such embodiments
or examples. On the contrary, the present teachings encompass
various alternatives, modifications, and equivalents, as will be
appreciated by those of skill in the art.
[0156] While various inventive embodiments have been described and
illustrated herein, those of ordinary skill in the art will readily
envision a variety of other means and/or structures for performing
the function and/or obtaining the results and/or one or more of the
advantages described herein, and each of such variations and/or
modifications is deemed to be within the scope of the inventive
embodiments described herein. More generally, those skilled in the
art will readily appreciate that all parameters, dimensions,
materials, and configurations described herein are meant to be
exemplary and that the actual parameters, dimensions, materials,
and/or configurations will depend upon the specific application or
applications for which the inventive teachings is/are used. Those
skilled in the art will recognize, or be able to ascertain using no
more than routine experimentation, many equivalents to the specific
inventive embodiments described herein. It is, therefore, to be
understood that the foregoing embodiments are presented by way of
example only and that, within the scope of the appended claims and
equivalents thereto, inventive embodiments may be practiced
otherwise than as specifically described and claimed. Inventive
embodiments of the present disclosure are directed to each
individual feature, system, article, material, kit, system upgrade,
and/or method described herein. In addition, any combination of two
or more such features, systems, articles, materials, kits, and/or
methods, if such features, systems, articles, materials, kits,
system upgrade, and/or methods are not mutually inconsistent, is
included within the inventive scope of the present disclosure.
[0157] The above-described embodiments of the invention can be
implemented in any of numerous ways. For example, some embodiments
may be implemented using hardware, software or a combination
thereof. When any aspect of an embodiment is implemented at least
in part in software, the software code can be executed on any
suitable processor or collection of processors, whether provided in
a single computer or distributed among multiple computers.
[0158] In this respect, various aspects of the invention, e.g.,
analysis of detected pulses, calibration, and compensation of pulse
jitter, may be embodied and/or implemented at least in part as a
computer readable storage medium (or multiple computer readable
storage media) (e.g., a computer memory, one or more floppy discs,
compact discs, optical discs, magnetic tapes, flash memories,
circuit configurations in Field Programmable Gate Arrays (FPGAs) or
other semiconductor devices, or other tangible computer storage
medium or non-transitory medium) encoded with one or more programs
that, when executed on one or more computers or other processor(s),
perform methods that implement the various embodiments of the
technology discussed above. The computer readable medium or media
can be transportable, such that the program or programs stored
thereon can be loaded onto one or more different computers or other
processors to implement various aspects of the present technology
as discussed above. As one example, in some embodiments processing
of data and system operation may be implemented entirely, or at
least in part, in FPGAs.
[0159] The terms "program" or "software" are used herein in a
generic sense to refer to any type of computer code or set of
computer-executable instructions that can be employed to program a
computer or other processor to implement various aspects of the
present technology as discussed above. Additionally, it should be
appreciated that according to one aspect of this embodiment, one or
more computer programs that when executed perform methods of the
present technology need not reside on a single computer or
processor, but may be distributed in a modular fashion amongst a
number of different computers or processors to implement various
aspects of the present technology.
[0160] Computer-executable instructions may be in many forms, such
as program modules, executed by one or more computers or other
devices. Generally, program modules include routines, programs,
objects, components, data structures, etc. that perform particular
tasks or implement particular abstract data types. Typically the
functionality of the program modules may be combined or distributed
as desired in various embodiments.
[0161] Also, the technology described herein may be embodied as a
method, of which at least one example has been provided. The acts
performed as part of the method may be ordered in any suitable way.
Accordingly, embodiments may be constructed in which acts are
performed in an order different than illustrated, which may include
performing some acts simultaneously, even though shown as
sequential acts in illustrative embodiments.
[0162] All definitions, as defined and used herein, should be
understood to control over dictionary definitions, definitions in
documents incorporated by reference, and/or ordinary meanings of
the defined terms.
[0163] The indefinite articles "a" and "an," as used herein in the
specification and in the claims, unless clearly indicated to the
contrary, should be understood to mean "at least one."
[0164] The phrase "and/or," as used herein in the specification and
in the claims, should be understood to mean "either or both" of the
elements so conjoined, i.e., elements that are conjunctively
present in some cases and disjunctively present in other cases.
Multiple elements listed with "and/or" should be construed in the
same fashion, i.e., "one or more" of the elements so conjoined.
Other elements may optionally be present other than the elements
specifically identified by the "and/or" clause, whether related or
unrelated to those elements specifically identified. Thus, as a
non-limiting example, a reference to "A and/or B", when used in
conjunction with open-ended language such as "comprising" can
refer, in one embodiment, to A only (optionally including elements
other than B); in another embodiment, to B only (optionally
including elements other than A); in yet another embodiment, to
both A and B (optionally including other elements); etc.
[0165] As used herein in the specification and in the claims, "or"
should be understood to have the same meaning as "and/or" as
defined above. For example, when separating items in a list, "or"
or "and/or" shall be interpreted as being inclusive, i.e., the
inclusion of at least one, but also including more than one, of a
number or list of elements, and, optionally, additional unlisted
items. Only terms clearly indicated to the contrary, such as "only
one of" or "exactly one of," or, when used in the claims,
"consisting of," will refer to the inclusion of exactly one element
of a number or list of elements. In general, the term "or" as used
herein shall only be interpreted as indicating exclusive
alternatives (i.e. "one or the other but not both") when preceded
by terms of exclusivity, such as "either," "one of," "only one of,"
or "exactly one of." "Consisting essentially of," when used in the
claims, shall have its ordinary meaning as used in the field of
patent law.
[0166] As used herein in the specification and in the claims, the
phrase "at least one," in reference to a list of one or more
elements, should be understood to mean at least one element
selected from any one or more of the elements in the list of
elements, but not necessarily including at least one of each and
every element specifically listed within the list of elements and
not excluding any combinations of elements in the list of elements.
This definition also allows that elements may optionally be present
other than the elements specifically identified within the list of
elements to which the phrase "at least one" refers, whether related
or unrelated to those elements specifically identified. Thus, as a
non-limiting example, "at least one of A and B" (or, equivalently,
"at least one of A or B," or, equivalently "at least one of A
and/or B") can refer, in one embodiment, to at least one,
optionally including more than one, A, with no B present (and
optionally including elements other than B); in another embodiment,
to at least one, optionally including more than one, B, with no A
present (and optionally including elements other than A); in yet
another embodiment, to at least one, optionally including more than
one, A, and at least one, optionally including more than one, B
(and optionally including other elements); etc.
[0167] In the claims, as well as in the specification above, all
transitional phrases such as "comprising," "including," "carrying,"
"having," "containing," "involving," "holding," "composed of," and
the like are to be understood to be open-ended, i.e., to mean
including but not limited to. Only the transitional phrases
"consisting of" and "consisting essentially of" shall be closed or
semi-closed transitional phrases, respectively, as set forth in the
United States Patent Office Manual of Patent Examining Procedures,
Section 2111.03.
[0168] The claims should not be read as limited to the described
order or elements unless stated to that effect. It should be
understood that various changes in form and detail may be made by
one of ordinary skill in the art without departing from the spirit
and scope of the appended claims. All embodiments that come within
the spirit and scope of the following claims and equivalents
thereto are claimed.
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