Blood and cell analysis using an imaging flow cytometer Number:7,522,758 from the United States Patent and Trademark Office (PTO) owispatent

Title: Blood and cell analysis using an imaging flow cytometer

Abstract: Multimodal/multispectral images of a population of cells are simultaneously collected. Photometric and/or morphometric features identifiable in the images are used to separate the population of cells into a plurality of subpopulations. Where the population of cells includes diseased cells and healthy cells, the images can be separated into a healthy subpopulation, and a diseased subpopulation. Where the population of cells does not include diseased cells, one or more ratios of different cell types in patients not having a disease condition can be compared to the corresponding ratios in patients having the disease condition, enabling the disease condition to be detected. For example, blood cells can be separated into different types based on their images, and an increase in the number of lymphocytes, a phenomenon associated with chronic lymphocytic leukemia, can readily be detected.

Patent Number: 7,522,758 Issued on 04/21/2009 to Ortyn,   et al.

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Inventors:	Ortyn; William E. (Bainbridge Island, WA), Basiji; David A. (Seattle, WA), Morrissey; Philip (Bellevue, WA), George; Thaddeus (Seattle, WA), Hall; Brian (Seattle, WA), Zimmerman; Cathleen (Bainbridge Island, WA), Perry; David (Woodinville, WA)
Assignee:	Amnis Corporation (Seattle, WA)
Appl. No.:	11/344,941
Filed:	February 1, 2006

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Related U.S. Patent Documents

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	Application Number	Filing Date	Patent Number	Issue Date
	11123610	May., 2005	7450229
	10628662	Dec., 2005	6975400
	09976257	Aug., 2003	6608682
	09820434	Oct., 2002	6473176
	09538604	Apr., 2001	6211955
	09490478	Jun., 2001	6249341
	60649373	Feb., 2005
	60567911	May., 2004
	60240125	Oct., 2000
	60117203	Jan., 1999

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Current U.S. Class:	382/133
Current International Class:	G06K 9/00 (20060101)
Field of Search:	382/128,133,134 356/39

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References Cited [Referenced By]

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U.S. Patent Documents

3922069	November 1975	Kishikawa et al.
4635293	January 1987	Watanabe
4662742	May 1987	Chupp
4677680	June 1987	Harima et al.
4770992	September 1988	Van den Engh et al.
4777525	October 1988	Preston, Jr.
4786165	November 1988	Yamamoto et al.
5096807	March 1992	Leaback
5141609	August 1992	Sweedler et al.
5153916	October 1992	Inagaki et al.
5159397	October 1992	Kosaka et al.
5159398	October 1992	Maekawa et al.
5159642	October 1992	Kosaka
5247339	September 1993	Ogino
5247340	September 1993	Ogino
5257182	October 1993	Luck et al.
5272354	December 1993	Kosaka
5351311	September 1994	Rogers et al.
5422712	June 1995	Ogino
5444527	August 1995	Kosaka
5471294	November 1995	Ogino
5548349	August 1996	Mizuguchi et al.
5548395	August 1996	Kosaka
5568315	October 1996	Shuman
5596401	January 1997	Kusuzawa
5621460	April 1997	Hatlestad et al.
5633503	May 1997	Kosaka
5644388	July 1997	Maekawa et al.
5674743	October 1997	Ulmer
5695934	December 1997	Brenner
5733721	March 1998	Hemstreet, III et al.
5754291	May 1998	Kain
5760899	June 1998	Eismann
RE35868	August 1998	Kosaka
5831723	November 1998	Kubota et al.
5848123	December 1998	Strommer
5855753	January 1999	Trau et al.
5900942	May 1999	Spiering
5929986	July 1999	Slater et al.
5959953	September 1999	Alon
6007994	December 1999	Ward et al.
6007996	December 1999	McNamara et al.
6014468	January 2000	McCarthy et al.
6066459	May 2000	Garini et al.
6116739	September 2000	Ishihara et al.
6156465	December 2000	Cao et al.
6210973	April 2001	Pettit
6211955	April 2001	Basiji et al.
6249341	June 2001	Basiji et al.
6256096	July 2001	Johnson
6259807	July 2001	Ravkin
6330081	December 2001	Scholten
6330361	December 2001	Mitchell et al.
6381363	April 2002	Murching et al.
6473176	October 2002	Basiji et al.
6507391	January 2003	Riley et al.
6522781	February 2003	Norikane et al.
6532061	March 2003	Ortyn et al.
6549664	April 2003	Daiber et al.
6563583	May 2003	Ortyn et al.
6580504	June 2003	Ortyn et al.
6583865	June 2003	Basiji et al.
6608680	August 2003	Basiji et al.
6608682	August 2003	Ortyn et al.
6618140	September 2003	Frost et al.
6620591	September 2003	Dunlay et al.
6671044	December 2003	Ortyn et al.
6707551	March 2004	Ortyn et al.
6727066	April 2004	Kaser
6763149	July 2004	Riley et al.
6778263	August 2004	Ortyn et al.
6875973	April 2005	Ortyn et al.
6906792	June 2005	Ortyn et al.
6934408	August 2005	Frost et al.
6947128	September 2005	Basiji et al.
6947136	September 2005	Ortyn et al.
6975400	December 2005	Ortyn et al.
7006710	February 2006	Riley et al.
7057732	June 2006	Jorgenson et al.
7079708	July 2006	Riley et al.
7087877	August 2006	Ortyn et al.
7190832	March 2007	Frost et al.
7221457	May 2007	Jorgenson et al.
2001/0006416	July 2001	Johnson
2002/0126275	September 2002	Johnson

Foreign Patent Documents

	WO 98/53093		Nov., 1998		WO
	WO 98/53300		Nov., 1998		WO
	WO 99/24458		May., 1999		WO
	WO 00/14545		Mar., 2000		WO
	WO 00/42412		Jul., 2000		WO
	WO 01/46675		Jun., 2001		WO

Other References

Hecht, Eugene. "Optics 4.sup.th ed." 2002. Addison-Wesley Longman, Inc., XP-002465391. ISBN: 0/8053-8566-5, p. 66. cited by other . Kubota, Fumio et al. 1995. "Flow Cytometer and Imaging Device Used in Combination." Cytometry: 21:129-132. cited by other . Kubota, F. 2003. "Analysis of red cell and platelet morphology using an imaging-combined flow cytometer." Clin. Lab. Haem.: 25:71-76. cited by other . Ong, Sim Heng. 1985. Development of a System for Imaging and Classifying Biological Cells in a Flow Cytometer. Doctor of Philosophy Thesis. University of Sydney, School of Electrical Engineering. (Aug.). cited by other . Ong, S.H. et al. 1987. "Development of an Image Flow Cytometer." Analytical and Quantitative Cytology and Histology. XIVth International Conference on Medical and Biological Engineering and the VIIth International Conference on Medical Physics, Finland. (Aug.): 375-382. cited by other . Ong, S.H. and P.M. Nickolls. 1991. "Optical Design in a Flow System For Imaging Cells." Sciences in Medicine: 14:2:74-80. cited by other . Ong, S.H. and P.M. Nickolls. 1994. "Analysis of MTF Degradation in the Imaging of Cells in a Flow System." International Journal of Imaging Systems & Technology: 5:243-250. cited by other . Satoh, Kaneo et al. 2002. "Small Aggregates of Platelets Can Be Detected Sensitively by a Flow Cytometer Equipped With an Imaging Device: Mechanisms of Epinephrine-Induced Aggregation and Antiplatelet Effects of Beraprost." Cytometry: 48:194-201. cited by other . Wang, Fu-sheng and Fumio Kubota. 2002. "A Novel Apoptosis Research Method With Imaging-Combined Flow Cytometer and HITC OR IR-125 Staining." Cytometry: 50:267-274. cited by other . Wietzorrek, Joachim et al. 1999. "A New Multiparameter Flow Cytometer: Optical and Electrical Cell Analysis in Combination With Video Microscopy in Flow." Cytometry: 35:291-301. cited by other . George, Thaddeus, David A. Basiji, Brian E. Hall, David H. Lynch, William E. Ortyn, David J. Perry, Michael J. Seo, Cathleen A. Zimmerman, and Philip J. Morrissey. "Distinguishing Modes of Cell Death Using the ImageStream.RTM. Multispectral Imaging Flow Cytometer" Cytometry Part A 59A:237-245 (2004). cited by other.

Primary Examiner: Johns; Andrew W
Attorney, Agent or Firm: Anderson; Ronald M.

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Government Interests

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GOVERNMENT RIGHTS

This invention was funded at least in part with a grant (No. R43 CA 94590-01) from the National Cancer Institute, and the U.S. government may have certain rights in this invention.

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Parent Case Text

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RELATED APPLICATIONS

This application is based on a prior copending provisional application, Ser. No. 60/649,373, filed on Feb. 1, 2005, the benefit of the filing date of which is hereby claimed under 35 U.S.C. .sctn. 119(e). This application is also a continuation application based on a prior copending conventional application, Ser. No. 11/123,610, filed on May 4, 2005, now U.S. Pat. No. 7,450,229, which itself is based on a prior copending provisional application, Ser. No. 60/567,911, filed on May 4, 2004, and which is also a continuation-in-part of prior copending patent application Ser. No. 10/628,662, filed on Jul. 28, 2003, which issued as U.S. Pat. No. 6,975,400 on Dec. 13, 2005, which itself is a continuation-in-part application of prior copending patent application Ser. No. 09/976,257, filed on Oct. 12, 2001, which issued as U.S. Pat. No. 6,608,682 on Aug. 19, 2003, which itself is a continuation-in-part application of prior copending patent application Ser. No. 09/820,434, filed on Mar. 29, 2001, which issued as U.S. Pat. No. 6,473,176 on Oct. 29, 2002, which itself is a continuation-in-part application of prior copending patent application Ser. No. 09/538,604, filed on Mar. 29, 2000, which issued as U.S. Pat. No. 6,211,955 on Apr. 3, 2001, which itself is a continuation-in-part application of prior copending application patent application Ser. No. 09/490,478, filed on Jan. 24, 2000, which issued as U.S. Pat. No. 6,249,341 on Jun. 19, 2001, which itself is based on prior copending provisional patent application Ser. No. 60/117,203, filed on Jan. 25, 1999, the benefit of the filing dates of which is hereby claimed under 35 U.S.C. .sctn.120 and 35 U.S.C. .sctn.119(e). patent application Ser. No. 09/976,257, noted above, is also based on prior copending provisional application Ser. No. 60/240,125, filed on Oct. 12, 2000, the benefit of the filing date of which is hereby claimed under 35 U.S.C. .sctn. 119(e).

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Claims

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The invention in which an exclusive right is claimed is defined by the following:

1. A method for detecting a disease condition from images collected from a population of cells, comprising the steps of: (a) spectrally dispersing light from the population of cells into a plurality of light beams having different spectral content, for imaging the population of cells to collect image data, such that a plurality of images for each cell are simultaneously separately formed and collected, each of the plurality of images being formed from a different one of the plurality of light beams, the plurality of images comprising at least one of the following two types of images: (i)multispectral images; and (ii)multimodal images; and (b) analyzing the image data collected to detect the disease condition in a subpopulation of the cells.

2. The method of claim 1, further comprising the steps of imaging a diseased population of cells known to exhibit the disease condition to obtain image data indicative of the disease condition, to identify at least one marker indicative of the disease condition.

3. The method of claim 1, wherein the disease condition is chronic lymphocytic leukemia.

4. The method of claim 3, wherein the step of analyzing the image data comprises the step of determining if the population of cells includes a larger proportion of lymphocytes than would normally be present in a healthy population of cells.

5. The method of claim 1, further comprising the steps of: (a) after a disease condition has been detected in the population of cells, treating the disease condition in a patient from which the population of cells was obtained; (b) subsequently collecting a new population of cells from the patient after the patient has been treated for the disease condition; (c) imaging the new population of cells to collect image data; and (d) analyzing the new image data to evaluate an effectiveness of the treatment.

6. The method of claim 1, wherein the step of imaging the population of cells to obtain the image data comprises the step of imaging the population of cells while there is relative movement between the population of cells and an imaging system used to image the population of cells.

7. The method of claim 1, such that simultaneously collecting the plurality of images occurs during an image formation process, rather than by post image acquisition processing performed after acquiring a composite image.

8. The method of claim 1, wherein the plurality of images are simultaneously collected by implementing the steps of: (a) focusing the plurality of light beams to produce respective images, each respective image corresponding to a different one of the plurality of light beams; and (b) simultaneously detecting each respective image.

9. The method of claim 8, wherein the step of simultaneously detecting each respective image comprises the step of providing a time delay integration (TDI) detector disposed to receive the respective images, the steps of dispersing the light and providing the TDI detector being implemented such that the dispersed light is dispersed across the detector in a direction that is substantially orthogonal to a direction of a motion of the respective images across the TDI detector, enabling a plurality of different images of the cell to be acquired simultaneously.

10. A method for detecting a disease condition from images collected from a population of cells, comprising the steps of: (a) spectrally dispersing light from the population of cells into a plurality of light beams having different spectral content, for imaging the population of cells to collect image data, such that the image data includes a plurality of images of each cell that are simultaneously separately formed and collected, each of the plurality of images being formed from a different one of the plurality of light beams; and (b) analyzing the image data collected to detect an indication of the disease condition.

11. The method of claim 10, further comprising the steps of imaging a diseased population of cells known to exhibit the disease condition to obtain image data indicative of the disease condition.

12. The method of claim 11, wherein the step of imaging the diseased population of cells to obtain the image data indicative of the disease condition comprises the step of identifying at least one marker indicative of the disease condition.

13. The method of claim 12, wherein the step of identifying at least one marker indicative of the disease condition comprises the step of identifying at least one marker comprising a photometric parameter.

14. The method of claim 12, wherein the step of identifying at least one marker indicative of the disease condition comprises the step of identifying at least one marker comprising a morphometric parameter.

15. The method of claim 12, wherein the step of identifying at least one marker indicative of the disease condition comprises the step of identifying a cell type present in the diseased population of cells but not present in a healthy population of cells.

16. The method of claim 12, wherein the step of identifying at least one marker indicative of the disease condition comprises the step of identifying a ratio of different cell types that is indicative of the disease condition.

17. The method of claim 12, wherein the step of analyzing the image data collected to determine if an indication of the disease condition is detected comprises the step of determining if at least one marker indicative of the disease condition is present in the image data.

18. The method of claim 10, wherein the step of imaging the population of cells to obtain the image data comprises the step of imaging the population of cells while there is relative movement between the population of cells and an imaging system used to image the population of cells.

19. The method of claim 10, wherein the disease condition is chronic lymphocytic leukemia.

20. The method of claim 19, wherein the step of analyzing the image data comprises the step of determining if the population of cells includes a larger proportion of lymphocytes than would be present in a healthy population of cells.

21. The method of claim 10, further comprising the step of adding a reagent to the population of cells before imaging the population of cells, wherein the reagent comprises at least one reagent selected from the group consisting of: (a) a label that facilitates identification of one of more cellular features; (b) a label that facilitates identification of a diseased cell; (c) a label that facilitates identification of an abnormal cell; and (d) a reactive substrate having a unique optical signal, the reactive substrate being configured to selectively bind to a free bio-molecule indicative of the disease condition, such that detection of the unique optical signal in the image data indicates that the disease condition is present.

22. The method of claim 10, further comprising the steps of: (a) after a disease condition has been detected in the population of cells, treating the disease condition in a patient from which the population of cells was obtained; (b) subsequently collecting a new population of cells from the patient after the patient has been treated for the disease condition; (c) imaging the new population of cells to collect image data; and (d) analyzing the new image data to evaluate an effectiveness of the treatment.

23. The method of claim 10, wherein the step of imaging the population of cells to obtain the image data comprises the step of illuminating the population of cells using a light source that produces ultraviolet light.

24. The method of claim 10, wherein the step of imaging the population of cells to collect the image data comprises the step of simultaneously collecting at least two types of images selected from the group consisting of the following types of images: a brightfield image, a darkfield image, and a fluorescent image.

25. The method of claim 10, such that simultaneously collecting the plurality of images occurs during an image formation process, rather than by post image acquisition processing performed after acquiring a composite image.

26. The method of claim 10, wherein the plurality of images are simultaneously collected by implementing the steps of: (a) focusing the plurality of light beams to produce respective images, each respective image corresponding to a different one of the plurality of light beams; and (b) simultaneously detecting each respective image.

27. The method of claim 26 wherein the step of simultaneously detecting each respective image comprises the step of providing a time delay integration (TDI) detector disposed to receive the respective images, the steps of dispersing the light and providing the TDI detector being implemented such that the dispersed light is dispersed across the detector in a direction that is substantially orthogonal to a direction of a motion of the respective images across the TDI detector, enabling a plurality of different images of the cell to be acquired simultaneously.

28. A method for detecting a disease condition from images collected from a population of cells, comprising the steps of: a spectrally dispersing light from a diseased population of cells into a plurality of light beams having different spectral content, for imaging the diseased population of cells known to exhibit the disease condition to obtain diseased image data indicative of the disease condition, to identify a marker indicative of the disease condition, such that the diseased image data includes a plurality of images of individual cells that are simultaneously collected, each of the plurality of images being formed from a different one of the plurality of light beams, the simultaneous collection of the plurality of images occurring during an image formation process, rather than by post image acquisition processing performed after acquiring a composite image; (b) imaging the population of cells to collect image data such that the image data includes a plurality of images of individual cells that are simultaneously collected; and (c) analyzing the image data collected for the population of cells, to determine if the marker indicative of the disease condition is detected, indicating that the disease condition is present in the population of cells.

29. A method for evaluating treatment of a disease condition from images collected from a population of cells, where at least one marker indicative of the disease condition can be detected from image data corresponding to a population of cells in which the disease condition is present, where such image data include a plurality of images of individual cells that are simultaneously collected, the method comprising the steps of: (a) spectrally dispersing light from a first population of cells into a first plurality of light beams having different spectral content, for imaging the first population of cells from a patient expressing the disease condition to obtain pre-treatment image data, the pre-treatment image data including a plurality of images of individual cells that are simultaneously collected, each of the plurality of images for the first population being formed from a different one of the first plurality of light beams, the simultaneous collection of the plurality of images occurring during an image formation process, rather than by post image acquisition processing performed after acquiring a composite image; (b) treating the disease condition expressed by the patient; (c) spectrally dispersing light from a second population of cells into a second plurality of light beams having different spectral content, for imaging the second population of cells from the patient to obtain post-treatment image data, the post-treatment image data including a plurality of images of individual cells that are simultaneously collected, each of the plurality of images for the second population being formed from a different one of the second plurality of light beams; and (d) comparing the pre-treatment and post-treatment image data, to quantify a reduction in the disease condition due to treating the patient.

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Description

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BACKGROUND

Cellular hematopathologies have been traditionally identified and studied by a variety of slide based techniques that include morphological analysis of May-Grunwald/Giemsa or Wright/Giemsa stained blood films and cytoenzymology. Additionally, other techniques, such as cell population analysis by flow cytometry, and molecular methods, such as polymerase chain reaction (PCR) or in situ hybridization to determine gene expression, gene mutations, chromosomal translocations and duplications, have added to the understanding of these pathologies.

Although progress has been made using such techniques in advancing diagnostic capabilities, understanding the mechanisms and the progression of disease, as well as evaluating new therapeutics, such technologies each offer challenges with regard to standardization and robustness, and to a large degree, they have not yet evolved to become routine laboratory tests.

The conventional hematology clinical laboratory includes technologies to rapidly and automatically analyze large numbers of samples of peripheral blood, with minimal human intervention. Companies such as Abbott Laboratories (Abbott Park, Ill.), Beckman Coulter Inc. (Fullerton, Calif.), and TOA Corporation (Kobe, Japan) continue to advance these technologies with regard to throughput levels, the degree of accuracy of the analysis, as well as moderately increasing the information content gathered in each sample run. However, in regard to any sample suggestive of a cellular hematopathology, i.e., falling outside the accepted degree of variance for any particular parameter, traditional slide based methodologies are largely used to determine the probable cause of the abnormality.

Diagnostic criteria in hematology are based on the morphological identification of abnormalities in cell numbers, size, shape and staining patterns. Although these have been supplemented over the past decades with cell population analysis, by staining with monoclonal antibodies to various cell surface determinants and acquiring data via flow cytometry, the most important element in the diagnostic evaluation is the visual inspection of the peripheral blood film, bone marrow and lymph node biopsy using a microscope, which enables a subjective categorization of putative abnormalities.

The manual evaluation of tissue and blood films from patients is tedious, time consuming, and subject to significant intra-laboratory and intra-observer variability. This process suffers from many sources of variability and error, including staining variability (which adversely affects longitudinal analysis), bias of the evaluator, and suboptimal sample preparation (blood films with increased "smudge" cells and atypical lymphocytes). The manual classification of a few hundred cells by morphological appearance results in poor statistical power and low confidence in evaluating observed changes over time, or as a result of treatment.

Chronic lymphocytic leukemia (CLL) is a type of cancer in which the bone marrow produces an excess of lymphocytes (a type of white blood cell) due to a malignant transformation event (e.g., chromosomal translocation). CLL is the most frequent type of leukemia in the Western world. Normally, stem cells (immature cells) develop into mature blood cells by a process of ordered differentiation, which occurs in the bone marrow. There are three types of mature blood cells: (1) red blood cells that carry oxygen to all tissues of the body; (2) white blood cells that fight infection; and, (3) platelets that help prevent bleeding by forming blood clots. Normally, the numbers and types of these blood cells are tightly regulated. In CLL, there is a chronic pathological overproduction of a type of white blood cell called lymphocytes. There are three types of lymphocytes: (1) B lymphocytes that make antibodies to help fight infection; (2) T lymphocytes that help B lymphocytes make antibodies to fight infection; and, (3) killer cells that attack cancer cells and viruses. CLL is a disease involving an increase in B lymphocyte cell numbers in the peripheral blood, usually reflective of a clonal expansion of a malignantly transformed CD5+B lymphocyte cell.

Currently, established chemotherapeutic treatments are used to treat this condition, but a number of newer therapeutics, involving monoclonal antibodies to cell surface antigens expressed on CLL cells (e.g., Rituximab), have been developed. Recent data from the National Cancer Data Base indicate that the 5-year survival for this disease condition is about 48%, with only 23% of patients surviving the disease condition after 10 years. Recently, a number of prognostic factors have been identified that allow stratification of the patient population into two subpopulations with distinct clinical outcomes. Factors that tend to correlate with decreased survival are: ZAP7O expression (a tyrosine kinase required for T lymphocyte cell signaling), increased CD38 expression, un-mutated Ig Vh genes, and chromosomal abnormalities. However, routine assessment of these factors has not evolved to a standard clinical practice, due to technical challenges with data standardization and interpretation.

Morphological evaluation remains the "gold standard" in the assessment of hematopathologies, and patients with CLL present with morphological heterogeneity. Attempts to correlate a particular morphological profile with clinical prognosis have been made, but to date, no association has been widely accepted, and the morphologic sub-classification of CLL and its correlation with clinical prognosis remains to be explored.

It would therefore be desirable to provide a method and apparatus suitable for automatically analyzing blood, including peripheral blood leukocytes, and cellular components such as bone marrow and lymph nodes (whose cells are readily amenable to being processed in suspension), to facilitate researching blood related diseases and abnormalities. It would be particularly desirable to provide a method and apparatus for rapidly collecting imagery from blood and other bodily fluids (and cellular compartments), and to provide software tools for analyzing such imagery to identify cellular abnormalities or cellular distribution abnormalities associated with a disease condition.

SUMMARY

Aspects of the concepts disclosed herein relate to the collection of multispectral images from a population of cells, and the analysis of the collected images to measure at least one characteristic of the population, using photometric and/or morphometric markers identifiable in the collection of images, where the marker is associated with a disease condition. The term marker is intended to refer to an optical or spatial characteristic of a cell (or a group of cells) that is determined using one or more images of that cell (or that group of cells). In an exemplary application, the cells are obtained from bodily fluids and cellular compartments, and in a particularly preferred implementation, from blood, most preferably where the cellular compartments are bone marrow and lymph nodes. In a further particularly preferred implementation, both photometric and morphometric markers are used in the analysis. In a particularly preferred, but not limiting implementation, the plurality of images for each individual object are collected simultaneously.

Exemplary steps that can be used to analyze biological cells in accord with an aspect of the concepts disclosed herein includes collecting image data from a population of cells, and identifying one or more subpopulations of cells from the image data. In one implementation, a subpopulation corresponding to cells exhibiting abnormalities associated with a disease condition is identified. Such subpopulations can be identified based on empirical evidence indicating that one or more photometric and/or morphometric features are typically associated with the cellular abnormality associated with disease condition. For example, photometric and/or morphometric data from the collected images are analyzed. Such data can relate to one or more features of the cells. The term feature is intended to refer to a particular structure, region, characteristic, property, or portion of cell that can be readily discerned from one or more images of the cell. The photometric and/or morphometric data from the collected images are analyzed to enable at least one characteristic of a selected feature to be measured. Characteristics that have been empirically associated with the cellular abnormalities present during a particular disease condition can be detected in the data to determine whether a particular disease condition is present in the population of cells originally imaged.

In yet another implementation, a disease condition may be detected even when the cells themselves do not exhibit any abnormalities that can be identified by photometric and/or morphometric parameters. In such an implementation, a sample will include a plurality of different subpopulations, each of which is identified by its normal characteristic morphometric and photometric markers. Where a disease condition is not present, the ratio of the subpopulations relative to one another will vary within a determinable range across different patients. Where a malignant disease condition is present, the disease condition can alter the ratio of subpopulations, such that a change in the ratio beyond a normal range can indicate the presence of a disease condition. For example, CLL (the disease condition discussed in the Background above) alters the ratio of lymphocytes in blood. While the lymphocytes themselves may not exhibit any abnormalities, an increase in the number of lymphocytes beyond a normal range is indicative of the disease condition, which may be a consequence of a normal response to an infection, or a malignant transformation event.

Consider a population of blood cells from healthy patient. The ratio of lymphocytes to other types of blood cells can be determined by analyzing image data of the entire population of blood cells to classify the images according to blood cell type. When this same process is applied to a population of blood cells from a patient with CLL, the ratio of lymphocytes to other types of blood cells will be significantly different than the ratio identified in a patient not afflicted with CLL. Thus, a disease condition can be detected by analyzing a population of cells to identify subpopulations present in the population, and by determining changes in the ratios of the subpopulations that suggest the presence of a disease condition.

In yet another implementation, a disease condition may be detected by the presence of an uncharacteristic cell type. In such an implementation, a sample will include a plurality of different subpopulations, each of which is identified by its characteristic morphometric and photometric markers. Where a disease condition is not present, only the expected subpopulations will be evident within the sample, though they vary within a determinable range across different patients. Where a disease condition is present, an entirely atypical cell type may be evident in the sample. For example, metastatic cancer of the breast may be evidenced by the presence of distinctive epithelial cells at some level in the blood. Thus, a disease condition can be detected by analyzing a population of cells to identify subpopulations present in the population, and determining the prevalence of atypical subpopulations that suggest the presence of a disease condition. The disease condition may be further refined by analyzing the morphometric and photometric markers of the atypical cell population to determine if it includes characteristic subpopulations. For example, the presence of a large fraction of rapidly dividing cells, as evidenced by a marker defining a high nuclear to cellular size ratio, may characterize a cancer as aggressive.

In still another implementation, a disease condition may be detected by the analysis not only of the cell subpopulations and their relative abundance, but also by an analysis of free (not cell-associated) bio-molecules within the cell sample. In such an implementation, a reagent may be added to the cell sample, the reagent comprising reactive substrates, each of which indicates the abundance of a particular bio-molecule. Each reactive substrate (e.g. a microsphere) includes a unique optical signature that both identifies the species of bio-molecule to which it preferentially binds, as well as indicating the abundance of that bio-molecule in the sample. By analyzing the imagery of a co-mingled sample of reactive substrates and cells, the former may be distinguished from the latter, and both a molecular and cellular analysis can be performed on the sample in a multiplexed fashion.

Image data for the population and subpopulation(s) can be manipulated using several different techniques. An exemplary technique is referred to as gating, a manipulation of data relating to photometric or morphometric imaging. A further exemplary technique is backgating, which involves further defining a subset of the gated data. While not strictly required, signal processing is preferably performed on the collected image data to reduce crosstalk and enhance spatial resolution, particularly for image data collected using simultaneous multi-channel imaging.

In a particularly preferred implementation, image data from a population of cells exhibiting a disease condition are collected. One or more photometric or morphometric markers associated with the disease condition are identified. As noted above, such a marker may be indicative of a measurable difference of some parameter between a healthy cell and a diseased cell. Such photometric or morphometric markers used to distinguish healthy cells from diseased cells are generally associated with specific features. The identified marker can represent data present in image data collected from diseased cells, but not likely to be present in image data collected from healthy cells. The identified marker can also represent data present in image data collected from cells exhibiting the disease condition, and also likely to be present in image data collected from healthy cells, yet present to a different degree that is quantifiable and identifiable. It should also be recognized that the marker can represent a measurable change in subpopulations associated with a disease condition, as opposed to subpopulations associated with the absence of the disease condition. Using the example provided above, an increase in the number of lymphocytes in blood relative to other blood cell types is indicative of the CLL disease condition.

Once one or more identifying markers have been empirically established, a population of cells can be imaged and analyzed to determine whether the identifying marker(s) is/are present in the sample population, and to determine whether the disease condition is present. In a particularly preferred, yet not limiting implementation, the disease condition is chronic lymphocytic leukemia, and the marker relates to an increase in the size or shape of the lymphocytic cellular subpopulation.

To facilitate analysis, at least one aspect of the concepts disclosed herein is directed to labeling either diseased cells or healthy cells, and imaging a mixed population of healthy and diseased cells together, such that the identifying markers are determined from a mixed population of cells. The labels enable a subpopulation of labeled cells to be extracted from the imaged data collected from the mixed population sample. The labeling thus facilitates separating the aggregate image data into images corresponding to diseased cells and images corresponding to healthy cells, which enables the photometric and/or morphometric markers corresponding to the disease condition to be more readily identified.

Yet another aspect of the techniques disclosed herein relates to monitoring the treatment of a patient exhibiting a disease condition. Baseline data are collected by imaging a population of cells from the patient before treatment. Preferably, the population of cells is obtained from a bodily fluid, such as blood. During the course of treatment, additional data are obtained by imaging additional populations of cells collected from the patient during and after various stages of the treatment process. Such data will provide a quantitative indication of the improved condition of the patient suffering from the disease condition, as indicated by either the amount of cells expressing the disease condition versus normal cells, or by a change in a ratio of the subpopulations present in the population. Significantly, such quantification is not feasible with standard microscopy and/or conventional flow cytometry.

In a preferred implementation of the techniques disclosed herein, the imagery collected from a population of biological cells includes collection of multimodal images. That is, the images collected will include at least two of the following types of images: one or more images corresponding to light emitted from the cell, one or more images corresponding to light transmitted by the cell, and one or more images corresponding to light scattered by the cell. Such multimode imaging can encompass any of the following types of images or combinations: (1) one or more fluorescent images and at least one bright field image; (2) one or more fluorescent images and at least one dark field image; (3) one or more fluorescent images, a bright field image, and a dark field image; and (4) a bright field image. Simultaneous collection of a plurality of different fluorescent images (separated by spectrum) can also be beneficial, as well as simultaneous collection of a plurality of different bright field images (using transmitted light with two different spectral filters). Preferably, the multimode images are collected simultaneously.

This Summary has been provided to introduce a few concepts in a simplified form that are further described in detail below in the Description. However, this Summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

DRAWINGS

Various aspects and attendant advantages of one or more exemplary embodiments and modifications thereto will become more readily appreciated as the same becomes better understood by reference to the following detailed description, when taken in conjunction with the accompanying drawings, wherein:

FIG. 1A is a schematic diagram of an exemplary flow imaging system that can be used to simultaneously collect a plurality of images from an object in flow;

FIG. 1B is a plan view of exemplary flow imaging system that employs a spectral dispesion component commprising a plurality of stacked dichroic fillters employed to spectrally separate the light to simultaneously collect a plurality of images from an object in flow;

FIG.1C illustrates an exemplary setb of images projected onto the TDI detector when using the spectral dispersing filter system of the FIG 1B;

FIG. 2 is a pictorial representation of an image recorded by the flow imaging system of FIG. 1;

FIG. 3 is a flow chart of the overall method steps implemented in one aspect of the concepts disclosed herein;

FIG. 4 is an exemplary graphical user interface used to implement the method steps of FIG. 3;

FIG. 5 is an exemplary graphical user interface used to implement the method steps of FIG. 3 as applied to the analysis of human peripheral blood;

FIG. 6 includes images of normal (i.e., healthy) mammary epithelial cells;

FIG. 7 includes images of mammary carcinoma (i.e., diseased) cells, illustrating how quantificatio