Standardized multi-color flow cytometry and computational biomarker discovery

Methods in Molecular Biology

Volumn 1371, Issue , 2015, Pages 225-238

  Schlickeiser, Stephan ^a   Streitz, Mathias ^a   Sawitzki, Birgit ^a  

^a NONE

Author keywords

Data analysis; Flow cytometry; Immune monitoring; Standardization

Indexed keywords

BIOLOGICAL MARKER;

B LYMPHOCYTE; BIOINFORMATICS; BLOOD SAMPLING; BLOOD STAIN; COMPUTER PROGRAM; CONTROLLED STUDY; DATA ANALYSIS; FLOW CYTOMETRY; HUMAN; HUMAN CELL; LEUKOCYTE; MULTI COLOR FLOW CYTOMETRY; PRACTICE GUIDELINE; STANDARDIZATION; BIOLOGY; CYTOLOGY; IMMUNE SYSTEM; IMMUNOLOGY; IMMUNOPHENOTYPING; METABOLISM; PHYSIOLOGY; PROCEDURES; STANDARDS;

BIOMARKERS; COMPUTATIONAL BIOLOGY; FLOW CYTOMETRY; HUMANS; IMMUNE SYSTEM; IMMUNOPHENOTYPING; LEUKOCYTES;

EID: 84946562563     PISSN: 10643745     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-4939-3139-2_15     Document Type: Chapter

References (52)

1. Streitz M, Miloud T, Kapinsky M, Reed MR, Magari R, Geissler EK, Hutchinson JA, Vogt K, Schlickeiser S, Kverneland AH, Meisel C, Volk HD, Sawitzki B (2013) Standardization of whole blood immune phenotype monitoring for clinical trials: panels and methods from the ONE study. Transplant Res 2:17. doi: 10.1186/2047-1440-1182-1117
2. Agarwal S, Busse PJ (2010) Innate and adaptive immunosenescence. Ann Allergy Asthma Immunol 104:183–190, quiz 190–182
3. Castelo-Branco C, Soveral I (2014) The immune system and aging: a review. Gynecol Endocrinol 30:16–22, doi: 10.3109/0951359 0.09512013.09852531. Epub 09512013 Nov 09513512
4. Franceschi C, Campisi J (2014) Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. J Gerontol A Biol Sci Med Sci 69:S4–S9. doi: 10.1093/gerona/glu1057
5. Goto M (2008) Inflammaging (inflammation + aging): a driving force for human aging based on an evolutionarily antagonistic pleiotropy theory? Biosci Trends 2:218–230
6. Jenny NS (2012) Inflammation in aging: cause, effect, or both? Discov Med 13:451–460
7. Larbi A, Fulop T (2014) From “truly naive” to “exhausted senescent” T cells: when markers predict functionality. Cytometry A 85:25–35, doi: 10.1002/cyto.a.22351. Epub 22013 Sep 22330
8. Herzenberg LA, Tung J, Moore WA, Herzenberg LA, Parks DR (2006) Interpreting flow cytometry data: a guide for the perplexed. Nat Immunol 7:681–685
9. Maecker HT, McCoy JP Jr, Consortium FHI, Amos M, Elliott J, Gaigalas A, Wang L, Aranda R, Banchereau J, Boshoff C, Braun J, Korin Y, Reed E, Cho J, Hafler D, Davis M, Fathman CG, Robinson W, Denny T, Weinhold K, Desai B, Diamond B, Gregersen P, Di Meglio P, Nestle FO, Peakman M, Villanova F, Ferbas J, Field E, Kantor A, Kawabata T, Komocsar W, Lotze M, Nepom J, Ochs H, O’Lone R, Phippard D, Plevy S, Rich S, Roederer M, Rotrosen D, Yeh JH (2010) A model for harmonizing flow cytometry in clinical trials. Nat Immunol 11:975–978
10. Maecker HT, McCoy JP, Nussenblatt R (2012) Standardizing immunophenotyping for the Human Immunology Project. Nat Rev Immunol 12:191–200, doi: 110.1038/nri3158
11. Maecker HT, Trotter J (2006) Flow cytometry controls, instrument setup, and the determination of positivity. Cytometry A 69:1037–1042
12. R_Core_Team_(2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/
13. Le Meur N (2013) Computational methods for evaluation of cell-based data assessment – bioconductor. Curr Opin Biotechnol 24:105–111
14. Reimers M, Carey VJ (2006) Bioconductor: an open source framework for bioinformatics and computational biology. Methods Enzymol 411:119–134
15. Maecker HT, Rinfret A, D’Souza P, Darden J, Roig E, Landry C, Hayes P, Birungi J, Anzala O, Garcia M, Harari A, Frank I, Baydo R, Baker M, Holbrook J, Ottinger J, Lamoreaux L, Epling CL, Sinclair E, Suni MA, Punt K, Calarota S, El-Bahi S, Alter G, Maila H, Kuta E, Cox J, Gray C, Altfeld M, Nougarede N, Boyer J, Tussey L, Tobery T, Bredt B, Roederer M, Koup R, Maino VC, Weinhold K, Pantaleo G, Gilmour J, Horton H, Sekaly RP (2005) Standardization of cytokine flow cytometry assays. BMC Immunol 6:13
16. Streitz M, Miloud T, Kapinsky M, Reed MR, Magari R, Geissler EK, Hutchinson JA, Vogt K, Schlickeiser S, Kverneland AH, Meisel C, Volk HD, Sawitzki B (2013) Standardization of whole blood immune phenotype monitoring for clinical trials: panels and methods from the ONE study. Transplant Res 2:17
17. Aghaeepour N, Finak G, Flaow CAPC, Consortium FHI, Hoos H, Mosmann TR, Brinkman R, Gottardo R, Scheuermann RH (2013) Critical assessment of automated flow cytometry data analysis techniques. Nat Methods 10:228–238
18. Spidlen J, Barsky A, Breuer K, Carr P, Nazaire MD, Hill BA, Qian Y, Liefeld T, Reich M, Mesirov JP, Wilkinson P, Scheuermann RH, Sekaly RP, Brinkman RR (2013) GenePattern flow cytometry suite. Source Code Biol Med 8:14
19. Kotecha N, Krutzik PO, Irish JM (2010) Webbased analysis and publication of flow cytometry experiments. Curr Protoc Cytom Chapter 10: Unit10 17
20. Amirel AD, Davis KL, Tadmor MD, Simonds EF, Levine JH, Bendall SC, Shenfeld DK, Krishnaswamy S, Nolan GP, Pe’er D (2013) VISNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol 31:545–552
21. Aghaeepour N, Chattopadhyay PK, Ganesan A, O’Neill K, Zare H, Jalali A, Hoos HH, Roederer M, Brinkman RR (2012) Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays. Bioinformatics 28:1009–1016
22. Bashashati A, Johnson NA, Khodabakhshi AH, Whiteside MD, Zare H, Scott DW, Lo K, Gottardo R, Brinkman FS, Connors JM, Slack GW, Gascoyne RD, Weng AP, Brinkman RR (2012) B cells with high side scatter parameter by flow cytometry correlate with inferior survival in diffuse large B-cell lymphoma. Am J Clin Pathol 137:805–814
23. Zare H, Bashashati A, Kridel R, Aghaeepour N, Haffari G, Connors JM, Gascoyne RD, Gupta A, Brinkman RR, Weng AP (2012) Automated analysis of multidimensional flow cytometry data improves diagnostic accuracy between mantle cell lymphoma and small lymphocytic lymphoma. Am J Clin Pathol 137: 75–85
24. Qiu P (2012) Inferring phenotypic properties from single-cell characteristics. PLoS One 7:e37038
25. Craig FE, Brinkman RR, Ten Eyck S, Aghaeepour N (2014) Computational analysis optimizes the flow cytometric evaluation for lymphoma. Cytometry B Clin Cytom 86:18–24
26. Fiser K, Sieger T, Schumich A, Wood B, Irving J, Mejstrikova E, Dworzak MN (2012) Detection and monitoring of normal and leukemic cell populations with hierarchical clustering of flow cytometry data. Cytometry A 81:25–34
27. Zare H, Shooshtari P, Gupta A, Brinkman RR (2010) Data reduction for spectral clustering to analyze high throughput flow cytometry data. BMC Bioinformatics 11:403
28. Dundar M, Akova F, Yerebakan HZ, Rajwa B (2014) A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects. BMC Bioinformatics 15:314
29. Cron A, Gouttefangeas C, Frelinger J, Lin L, Singh SK, Britten CM, Welters MJ, van der Burg SH, West M, Chan C (2013) Hierarchical modeling for rare event detection and cell subset alignment across flow cytometry samples. PLoS Comput Biol 9:e1003130
30. Mosmann TR, Naim I, Rebhahn J, Datta S, Cavenaugh JS, Weaver JM, Sharma G (2014) SWIFT-scalable clustering for automated identification of rare cell populations in large, high- dimensional flow cytometry datasets, part 2: biological evaluation. Cytometry A 85:422–433
31. Qiu P, Simonds EF, Bendall SC, Gibbs KD Jr, Bruggner RV, Linderman MD, Sachs K, Nolan GP, Plevritis SK (2011) Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol 29:886–891
32. Hahne F, Khodabakhshi AH, Bashashati A, Wong CJ, Gascoyne RD, Weng AP, Seyfert-Margolis V, Bourcier K, Asare A, Lumley T, Gentleman R, Brinkman RR (2010) Perchannel basis normalization methods for flow cytometry data. Cytometry A 77:121–131
33. Aghaeepour N, Jalali A, O’Neill K, Chattopadhyay PK, Roederer M, Hoos HH, Brinkman RR (2012) RchyOptimyx: cellular hierarchy optimization for flow cytometry. Cytometry A 81:1022–1030
34. Streitz M, Fuhrmann S, Thomas D, Cheek E, Nomura L, Maecker H, Martus P, Aghaeepour N, Brinkman RR, Volk HD, Kern F (2012) The phenotypic distribution and functional profile of tuberculin-specific CD4 T-cells characterizes different stages of TB infection. Cytometry B Clin Cytom 82:360–368
35. Zare H, Haffari G, Gupta A, Brinkman RR (2013) Scoring relevancy of features based on combinatorial analysis of Lasso with application to lymphoma diagnosis. BMC Genomics 14(Suppl 1):S14
36. Mehta T, Tanik M, Allison DB (2004) Towards sound epistemological foundations of statistical methods for high-dimensional biology. Nat Genet 36:943–947
37. Hahne F, LeMeur N, Brinkman RR, Ellis B, Haaland P, Sarkar D, Spidlen J, Strain E, Gentleman R (2009) flow Core: a bioconductor package for high throughput flow cytometry. BMC Bioinformatics 10:106
38. Sarkar D, Le Meur N, Gentleman R (2008) Using flow Viz to visualize flow cytometry data. Bioinformatics 24:878–879
39. Le Meur N, Rossini A, Gasparetto M, Smith C, Brinkman RR, Gentleman R (2007) Data quality assessment of ungated flow cytometry data in high throughput experiments. Cytometry A 71:393–403
40. Finak G, Jiang W, Pardo J, Asare A, Gottardo R (2012) QUAliFiER: an automated pipeline for quality assessment of gated flow cytometry data. BMC Bioinformatics 13:252
41. Roederer M, Treister A, Moore W, Herzenberg LA (2001) Probability binning comparison: a metric for quantitating univariate distribution differences. Cytometry 45:37–46
42. Rogers WT, Holyst HA. Flow FP: A Bioconductor Package for Fingerprinting Flow Cytometric Data. Adv Bioinformatics. 2009:193947. doi: 10.1155/2009/193947 . Epub 2009 Sep 24. PubMed PMID: 19956416; PubMed Central PMCID: PMC2777013.
43. Malek M, Taghiyar MJ, Chong L, Finak G, Gottardo R, Brinkman RR. Flow Density: reproducing manual gating of flow cytometry data by automated density-based cell population identification. Bioinformatics. 2015 Feb 15;31(4):606–7. doi: 10.1093/bioinformatics/ btu677 . Epub 2014 Oct 16. PubMed PMID: 25378466; PubMed Central PMCID: PMC4325545.
44. Aghaeepour N, Nikolic R, Hoos HH, Brinkman RR (2011) Rapid cell population identification in flow cytometry data. Cytometry A 79:6–13
45. Ray S, Pyne S (2012) A computational framework to emulate the human perspective in flow cytometric data analysis. PLoS One 7:e35693
46. Yeung KY, Fraley C, Murua A, Raftery AE, Ruzzo WL (2001) Model-based clustering and data transformations for gene expression data. Bioinformatics 17:977–987
47. Lo K, Hahne F, Brinkman RR, Gottardo R (2009) Flow Clust: a bioconductor package for automated gating of flow cytometry data. BMC Bioinformatics 10:145
48. Finak G, Bashashati A, Brinkman R, Gottardo R. Merging mixture components for cell population identification in flow cytometry. Adv Bioinformatics. 2009:247646. doi: 10.1155/2009/247646 . Epub 2009 Nov 12. PubMed PMID: 20049161; PubMed Central PMCID: PMC2798116.
49. Ge Y, Sealfon SC (2012) Flow Peaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding. Bioinformatics 28:2052–2058
50. Azad A, Pyne S, Pothen A (2012) Matching phosphorylation response patterns of antigenreceptor- stimulated T cells via flow cytometry. BMC Bioinformatics 13(Suppl 2):S10
51. Finak G, Frelinger J, Jiang W, Newell EW, Ramey J, Davis MM, Kalams SA, De Rosa SC, Gottardo R (2014) OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis. PLoS Comput Biol 10: e1003806
52. O’Neill K, Jalali A, Aghaeepour N, Hoos H, Brinkman RR (2014) Enhanced flow Type/ RchyOptimyx: a bioconductor pipeline for discovery in high-dimensional cytometry data. Bioinformatics 30:1329–1330