Overview of clinical flow cytometry data analysis: Recent advances and future challenges

Trends in Biotechnology

Volumn 31, Issue 7, 2013, Pages 415-425

  Pedreira, Carlos E ^a   Costa, Elaine S ^a   Lecrevisse, Quentin ^b   van Dongen, Jacques J M ^c   Orfao, Alberto ^b  

^a FEDERAL UNIVERSITY OF RIO DE JANEIRO   (Brazil)

^b CSIC   (Spain)

^c ERASMUS MEDICAL CENTER   (Netherlands)

Author keywords

Automation; Data analysis; Data visualization; Immunophenotype; Multiparameter flow cytometry

Indexed keywords

BIOMEDICAL RESEARCH; CLINICAL DIAGNOSTICS; DATA ANALYSIS TOOL; FUTURE CHALLENGES; IMMUNOPHENOTYPES; MULTI-PARAMETER FLOW CYTOMETRY; SINGLE CELLS; TECHNOLOGICAL ADVANCES;

AUTOMATION; DATA REDUCTION; FLOW CYTOMETRY; VISUALIZATION;

DATA VISUALIZATION;

AUTOMATION; CELL POPULATION; CLINICAL LABORATORY; COMPUTER; DATA ANALYSIS; DATA PROCESSING; DATA SYNTHESIS; FLOW CYTOMETRY; FLUORESCENCE; INFORMATION PROCESSING; LASER; LIGHT SCATTERING; MATHEMATICAL ANALYSIS; MEDICAL RESEARCH; PRIORITY JOURNAL; REVIEW;

BIOMEDICAL RESEARCH; CLINICAL LABORATORY TECHNIQUES; FLOW CYTOMETRY; HUMANS; STATISTICS AS TOPIC;

EID: 84879265763     PISSN: 01677799     EISSN: 18793096     Source Type: Journal    
DOI: 10.1016/j.tibtech.2013.04.008     Document Type: Review

References (78)

1. Flow Cytometry Principles for Clinical Laboratory Practice: Quality Assurance for Quantitative Immunophenotyping 1995, Wiley-Liss. M.A. Owens, M.R. Loken (Eds.).
2. Freer G., Rindi L. Intracellular cytokine detection by fluorescence-activated flow cytometry: basic principles and recent advances. Methods 2013, 10.1016/j.ymeth.2013.03.035.
3. Martini J., et al. Time encoded multicolor fluorescence detection in a microfluidic flow cytometer. Lab Chip 2012, 12:5057-5062.
4. Robinson J.P., et al. Computational analysis of high-throughput flow cytometry data. Expert Opin. Drug Discov. 2012, 7:679-693.
5. Orfao A., et al. Useful information provided by the flow cytometric immunophenotyping of hematological malignancies: current status and future directions. Clin. Chem. 1999, 45:1708-1717.
6. Stewart J.W. The use of electronic blood-cell counters in routine haematology. Br. J. Haematol. 1967, 13:11-18.
7. Hulett H.R., et al. Cell sorting: automated separation of mammalian cells as a function of intracellular fluorescence. Science 1969, 166:747-749.
8. Bonner W.A., et al. Fluorescence activated cell sorting. Rev. Sci. Instrum. 1972, 43:404-409.
9. Hulett H.R., et al. Development and application of a rapid cell sorter. Clin. Chem. 1973, 19:813-816.
10. Stöhr M., Futterman G. Visualization of multidimensional spectra in flow cytometry. J. Histochem. Cytochem. 1979, 27:560-563.
11. Murphy R.F., Chused T.M. A proposal for a flow cytometric data file standard. Cytometry 1984, 5:553-555.
12. Stewart S.S., Price G.B. Realtime acquisition, storage, and display of correlated three-parameter flow cytometric data. Cytometry 1986, 7:82-88.
13. Data file standard for flow cytometry. Cytometry 1990, 11:323-332. Data File Standards Committee of the Society for Analytical Cytology.
14. Naivar M.A., et al. Open, reconfigurable cytometric acquisition system: ORCAS. Cytometry A 2007, 71:915-924.
15. Zilmer N.A., et al. Flow cytometric analysis using digital signal processing. Cytometry 1995, 20:102-117.
16. Seamer L.C., et al. Proposed new data file standard for flow cytometry, version FCS 3.0. Cytometry 1997, 28:118-122.
17. Hale L.P. Histologic and molecular assessment of human thymus. Ann. Diagn. Pathol. 2004, 8:50-60.
18. + bleb acquisition by closely associated IL-17-committed innate-like lymphocytes. PLoS ONE 2012, 7:e38258.
19. Arnoulet C., et al. Four- and five-color flow cytometry analysis of leukocyte differentiation pathways in normal bone marrow: a reference document based on a systematic approach by the GTLLF and GEIL. Cytometry B: Clin. Cytom. 2010, 78:4-10.
20. Szczepański T., et al. Flow-cytometric immunophenotyping of normal and malignant lymphocytes. Clin. Chem. Lab. Med. 2006, 44:775-796.
21. + plasma cells. Haematologica 2010, 95:1016-1020.
22. Perez-Andres M., et al. Human peripheral blood B-cell compartments: a crossroad in B-cell traffic. Cytometry B: Clin. Cytom. 2010, 78(Suppl. 1):S47-S60.
23. Costa E.S., et al. A new automated flow cytometry data analysis approach for the diagnostic screening of neoplastic B-cell disorders in peripheral blood samples with absolute lymphocytosis. Leukemia 2006, 20:1221-1230.
24. Festin R., et al. Multicolor flow cytometric analysis of the CD45 antigen provides improved lymphoid cell discrimination in bone marrow and tissue biopsies. J. Immunol. Methods 1994, 177:215-224.
25. Vanderlaan M., et al. Improved high-affinity monoclonal antibody to iododeoxyuridine. Cytometry 1986, 7:499-507.
26. Allard W.J., et al. Antigen binding properties of highly purified bispecific antibodies. Mol. Immunol. 1992, 29:1219-1227.
27. Phung Y., et al. High-affinity monoclonal antibodies to cell surface tumor antigen glypican-3 generated through a combination of peptide immunization and flow cytometry screening. MAbs 2012, 4:592-599.
28. Perfetto S.P., et al. Seventeen-colour flow cytometry: unravelling the immune system. Nat. Rev. Immunol. 2004, 4:648-655.
29. Sewell W.A., Smith S.A. Polychromatic flow cytometry in the clinical laboratory. Pathology 2011, 43:580-591.
30. Hoffmann J., et al. High-throughput 13-parameter immunophenotyping identifies shifts in the circulating T-cell compartment following reperfusion in patients with acute myocardial infarction. PLoS ONE 2012, 7:e47155.
31. Gondois-Rey F., et al. Multiparametric cytometry for exploration of complex cellular dynamics. Cytometry A 2012, 81:332-342.
32. Nolan J.P., Condello D. Spectral flow cytometry. Curr. Protoc. Cytom. 2013, 27:1-13. Chapter 1: Unit.
33. Matutes E., et al. Clinical and laboratory features of 78 cases of T-prolymphocytic leukemia. Blood 1991, 78:3269-3274.
34. Böttcher S., et al. Flow cytometric MRD detection in selected mature B-cell malignancies. Methods Mol. Biol. 2013, 971:149-174.
35. Denys B., et al. Improved flow cytometric detection of minimal residual disease in childhood acute lymphoblastic leukemia. Leukemia 2013, 27:635-641.
36. -/+ dim chronic natural killer cell large granular lymphocytosis. Am. J. Pathol. 2004, 165:1117-1127.
37. Pedreira C.E., et al. Generation of flow cytometric data files with a potentially infinite number of dimensions. Cytometry A 2008, 73A:834-846.
38. van Dongen J.J., et al. EuroFlow antibody panels for standardized n-dimensional flow cytometric immunophenotyping of normal, reactive and malignant leukocytes. Leukemia 2012, 26:1908-1975.
39. Kalina T., et al. EuroFlow standardization of flow cytometer instrument settings and immunophenotyping protocols. Leukemia 2012, 26:1986-2010.
40. Rawstron A.C., et al. Chronic lymphocytic leukaemia (CLL) and CLL-type monoclonal B-cell lymphocytosis (MBL) show differential expression of molecules involved in lymphoid tissue homing. Cytometry B: Clin. Cytom. 2010, 78(Suppl. 1):S42-S46.
41. van Dongen J.J., et al. EuroFlow: resetting leukemia and lymphoma immunophenotyping. Basis for companion diagnostics and personalized medicine. Leukemia 2012, 26:1899-1907.
42. Fernandez C., et al. Newly diagnosed adult AML and MPAL patients frequently show clonal residual hematopoiesis. Leukemia 2013, 10.1038/leu.2013.109.
43. Rubnitz J.E., et al. Minimal residual disease-directed therapy for childhood acute myeloid leukaemia: results of the AML02 multicentre trial. Lancet Oncol. 2010, 11:543-552.
44. Wood B.L., et al. 2006 Bethesda International Consensus recommendations on the immunophenotypic analysis of hematolymphoid neoplasia by flow cytometry: optimal reagents and reporting for the flow cytometric diagnosis of hematopoietic neoplasia. Cytometry B: Clin. Cytom. 2007, 72(Suppl. 1):S14-S22.
45. Qiu P., et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol. 2011, 29:886-891.
46. Larsen M., et al. Multiparameter grouping delineates heterogeneous populations of human IL-17 and/or IL-22 T-cell producers that share antigen specificities with other T-cell subsets. Eur. J. Immunol. 2011, 41:2596-2605.
47. Duque R.E. Minimal disease detection of B-cell lymphoproliferative disorders by flow cytometry: multidimensional cluster analysis. Hematology 2012, 17(Suppl. 1):S63-S65.
48. Sekiguchi D.R., et al. Circulating lymphocyte subsets in normal adults are variable and can be clustered into subgroups. Cytometry B: Clin. Cytom. 2011, 80:291-299.
49. Pedreira C.E. Automating flow cytometry. Cytometry A 2012, 81:110-111.
50. Stuchlý J., et al. An automated analysis of highly complex flow cytometry-based proteomic data. Cytometry A 2012, 81:120-129.
51. Lugli E., et al. Subject classification obtained by cluster analysis and principal component analysis applied to flow cytometric data. Cytometry A 2007, 71:334-344.
52. Petrausch U., et al. Polychromatic flow cytometry: a rapid method for the reduction and analysis of complex multiparameter data. Cytometry A 2006, 69:1162-1173.
53. Pedreira C.E., et al. A multidimensional classification approach for the automated analysis of flow cytometry data. IEEE Trans. Biomed. Eng. 2008, 55:1155-1162.
54. Pattern Classification 2001, John Wiley and Sons, pp. 20, 36, 174, 517. R.O. Duda (Ed.).
55. Costa E.S., et al. Automated pattern-guided principal component analysis versus expert-based immunophenotypic classification of hematological malignancies: a step forward in the standardization of clinical immunophenotyping. Leukemia 2010, 24:1927-1933.
56. Boddy L., et al. Pattern recognition in flow cytometry. Cytometry 2001, 44:195-209.
57. Roederer M., et al. SPICE: exploration and analysis of post-cytometric complex multivariate datasets. Cytometry A 2011, 79A:167-174.
58. van Putten W.L.J., et al. Three-marker phenotypic analysis of lymphocytes based on two-color immunofluorescence using a multinomial model for flow cytometric counts and maximum likelihood estimation. Cytometry 1993, 14:179-187.
59. Cover T.M., Hart P.E. Nearest neighbor pattern classification. IEEE Trans. Inform. Theory 1967, 13:21-27.
60. Aghaeepour N., et al. Critical assessment of automated flow cytometry data analysis techniques. Nat. Methods 2013, 10:228-238.
61. Pedreira C.E. Learning vector quantization with training data selection. IEEE Trans. Pattern Anal. Mach. Intell. 2006, 28:157-162.
62. Pedreira C.E., et al. A probabilistic approach for the evaluation of minimal residual disease by multiparameter flow cytometry in leukemic B-cell chronic lymphoproliferative disorders. Cytometry A 2008, 73A:1141-1150.
63. Pyne S., et al. Automated high-dimensional flow cytometric data analysis. Proc. Natl. Acad. Sci. U.S.A. 2009, 106:8519-8524.
64. Bashashati A., Brinkman R.C. A survey of flow cytometry data analysis methods. Adv. Bioinform. 2009, 2009:1-19.
65. Coustan-Smith E., et al. New markers for minimal residual disease detection in acute lymphoblastic leukemia. Blood 2011, 117:6267-6276.
66. Campana D. Minimal residual disease monitoring in childhood acute lymphoblastic leukemia. Curr. Opin. Hematol. 2012, 19:313-318.
67. Schrappe M. Minimal residual disease: optimal methods, timing, and clinical relevance for an individual patient. Hematology 2012, 2012:137-142.
68. Hristozova T., et al. A simple multicolor flow cytometry protocol for detection and molecular characterization of circulating tumor cells in epithelial cancers. Cytometry A 2012, 81:489-495.
69. Serrano M.J., et al. Circulating tumour cells in peripheral blood: potential impact on breast cancer outcome. Clin. Transl. Oncol. 2011, 13:204-208.
70. Matsusaka S., et al. Circulating endothelial progenitors and CXCR4-positive circulating endothelial cells are predictive markers for bevacizumab. Cancer 2011, 117:4026-4032.
71. Ferreira-Facio C.S., et al. Contribution of multiparameter flow cytometry immunophenotyping to the diagnostic screening and classification of pediatric cancer. PLoS ONE 2013, 8:e55534.
72. Modern Multidimensional Scaling: Theory and Applications 2005, Springer-Verlag. I. Borg, P. Groenen (Eds.).
73. Rawstron A.C., et al. Report of the European Myeloma Network on multiparametric flow cytometry in multiple myeloma and related disorders. Haematologica 2008, 93:431-438.
74. Watson D.A., et al. Spectral measurements of large particles by flow cytometry. Cytometry A 2009, 75:460-464.
75. Shapiro H.M., et al. A flow cytometer designed for fluorescence calibration. Cytometry 1998, 33:280-287.
76. Murthi S., et al. Performance analysis of a dual-buffer architecture for digital flow cytometry. Cytometry A 2005, 66:109-118.
77. Lo K., et al. Automated gating of flow cytometry data via robust model-based clustering. Cytometry A 2008, 73A:321-332.
78. Tanner S.D., et al. An introduction to mass cytometry: fundamentals and applications. Cancer Immunol. Immunother. 2013, 62:955-965.