Literature DB >> 24191009

Application of user-guided automated cytometric data analysis to large-scale immunoprofiling of invariant natural killer T cells.

Xinli Hu1, Hyun Kim, Patrick J Brennan, Buhm Han, Clare M Baecher-Allan, Philip L De Jager, Michael B Brenner, Soumya Raychaudhuri.   

Abstract

Defining and characterizing pathologies of the immune system requires precise and accurate quantification of abundances and functions of cellular subsets via cytometric studies. At this time, data analysis relies on manual gating, which is a major source of variability in large-scale studies. We devised an automated, user-guided method, X-Cyt, which specializes in rapidly and robustly identifying targeted populations of interest in large data sets. We first applied X-Cyt to quantify CD4(+) effector and central memory T cells in 236 samples, demonstrating high concordance with manual analysis (r = 0.91 and 0.95, respectively) and superior performance to other available methods. We then quantified the rare mucosal associated invariant T cell population in 35 samples, achieving manual concordance of 0.98. Finally we characterized the population dynamics of invariant natural killer T (iNKT) cells, a particularly rare peripheral lymphocyte, in 110 individuals by assaying 19 markers. We demonstrated that although iNKT cell numbers and marker expression are highly variable in the population, iNKT abundance correlates with sex and age, and the expression of phenotypic and functional markers correlates closely with CD4 expression.

Entities:  

Keywords:  automated analysis; flow cytometry

Mesh:

Substances:

Year:  2013        PMID: 24191009      PMCID: PMC3839720          DOI: 10.1073/pnas.1318322110

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  27 in total

Review 1.  Turned on by danger: activation of CD1d-restricted invariant natural killer T cells.

Authors:  Victoria Lawson
Journal:  Immunology       Date:  2012-09       Impact factor: 7.397

2.  Rapid cell population identification in flow cytometry data.

Authors:  Nima Aghaeepour; Radina Nikolic; Holger H Hoos; Ryan R Brinkman
Journal:  Cytometry A       Date:  2011-01       Impact factor: 4.355

Review 3.  Toward an understanding of NKT cell biology: progress and paradoxes.

Authors:  Mitchell Kronenberg
Journal:  Annu Rev Immunol       Date:  2005       Impact factor: 28.527

4.  Automated high-dimensional flow cytometric data analysis.

Authors:  Saumyadipta Pyne; Xinli Hu; Kui Wang; Elizabeth Rossin; Tsung-I Lin; Lisa M Maier; Clare Baecher-Allan; Geoffrey J McLachlan; Pablo Tamayo; David A Hafler; Philip L De Jager; Jill P Mesirov
Journal:  Proc Natl Acad Sci U S A       Date:  2009-05-14       Impact factor: 11.205

5.  Data reduction for spectral clustering to analyze high throughput flow cytometry data.

Authors:  Habil Zare; Parisa Shooshtari; Arvind Gupta; Ryan R Brinkman
Journal:  BMC Bioinformatics       Date:  2010-07-28       Impact factor: 3.169

6.  A comprehensive ex vivo functional analysis of human NKT cells reveals production of MIP1-α and MIP1-β, a lack of IL-17, and a Th1-bias in males.

Authors:  Jennifer E Snyder-Cappione; Camilla Tincati; Ijeoma G Eccles-James; Amedeo J Cappione; Lishomwa C Ndhlovu; Laura L Koth; Douglas F Nixon
Journal:  PLoS One       Date:  2010-11-03       Impact factor: 3.240

Review 7.  CD1: antigen presentation and T cell function.

Authors:  Manfred Brigl; Michael B Brenner
Journal:  Annu Rev Immunol       Date:  2004       Impact factor: 28.527

8.  Unraveling the autoimmune translational research process layer by layer.

Authors:  Richard S Blumberg; Bonnie Dittel; David Hafler; Matthias von Herrath; Frank O Nestle
Journal:  Nat Med       Date:  2012-01-06       Impact factor: 53.440

9.  Functionally distinct subsets of CD1d-restricted natural killer T cells revealed by CD1d tetramer staining.

Authors:  Jenny E Gumperz; Sachiko Miyake; Takashi Yamamura; Michael B Brenner
Journal:  J Exp Med       Date:  2002-03-04       Impact factor: 14.307

10.  Critical assessment of automated flow cytometry data analysis techniques.

Authors:  Nima Aghaeepour; Greg Finak; Holger Hoos; Tim R Mosmann; Ryan Brinkman; Raphael Gottardo; Richard H Scheuermann
Journal:  Nat Methods       Date:  2013-02-10       Impact factor: 28.547

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  6 in total

1.  flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification.

Authors:  Mehrnoush Malek; Mohammad Jafar Taghiyar; Lauren Chong; Greg Finak; Raphael Gottardo; Ryan R Brinkman
Journal:  Bioinformatics       Date:  2014-10-16       Impact factor: 6.937

Review 2.  Immune cell profiling to guide therapeutic decisions in rheumatic diseases.

Authors:  Joerg Ermann; Deepak A Rao; Nikola C Teslovich; Michael B Brenner; Soumya Raychaudhuri
Journal:  Nat Rev Rheumatol       Date:  2015-06-02       Impact factor: 20.543

3.  BayesFlow: latent modeling of flow cytometry cell populations.

Authors:  Kerstin Johnsson; Jonas Wallin; Magnus Fontes
Journal:  BMC Bioinformatics       Date:  2016-01-12       Impact factor: 3.169

Review 4.  Leveraging blood and tissue CD4+ T cell heterogeneity at the single cell level to identify mechanisms of disease in rheumatoid arthritis.

Authors:  Chamith Y Fonseka; Deepak A Rao; Soumya Raychaudhuri
Journal:  Curr Opin Immunol       Date:  2017-09-06       Impact factor: 7.486

5.  Regulation of gene expression in autoimmune disease loci and the genetic basis of proliferation in CD4+ effector memory T cells.

Authors:  Xinli Hu; Hyun Kim; Towfique Raj; Patrick J Brennan; Gosia Trynka; Nikola Teslovich; Kamil Slowikowski; Wei-Min Chen; Suna Onengut; Clare Baecher-Allan; Philip L De Jager; Stephen S Rich; Barbara E Stranger; Michael B Brenner; Soumya Raychaudhuri
Journal:  PLoS Genet       Date:  2014-06-26       Impact factor: 5.917

6.  OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis.

Authors:  Greg Finak; Jacob Frelinger; Wenxin Jiang; Evan W Newell; John Ramey; Mark M Davis; Spyros A Kalams; Stephen C De Rosa; Raphael Gottardo
Journal:  PLoS Comput Biol       Date:  2014-08-28       Impact factor: 4.475

  6 in total

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