Standardized Multi-Color Flow Cytometry and Computational Biomarker Discovery.

Multi-color flow cytometry has become a valuable and highly informative tool for diagnosis and therapeutic monitoring of patients with immune deficiencies or inflammatory disorders. However, the method complexity and error-prone conventional manual data analysis often result in a high variability between different analysts and research laboratories. Here, we provide strategies and guidelines aiming at a more standardized multi-color flow cytometric staining and unsupervised data analysis for whole blood patient samples.

[1]  J. Curtis.  Hypertension in the renal transplant patient , 1988 .

[2]  Adrian E. Raftery,et al.  Model-based clustering and data transformations for gene expression data , 2001, Bioinform..

[3]  M. Roederer,et al.  Probability binning comparison: a metric for quantitating univariate distribution differences. , 2001, Cytometry.

[4]  D. Allison,et al.  Towards sound epistemological foundations of statistical methods for high-dimensional biology , 2004, Nature Genetics.

[5]  M. Altfeld,et al.  Standardization of cytokine flow cytometry assays , 2005, BMC Immunology.

[6]  Holden T Maecker,et al.  Flow cytometry controls, instrument setup, and the determination of positivity , 2006, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[7]  Vincent J Carey,et al.  Bioconductor: an open source framework for bioinformatics and computational biology. , 2006, Methods in enzymology.

[8]  Leonore A Herzenberg,et al.  Interpreting flow cytometry data: a guide for the perplexed , 2006, Nature Immunology.

[9]  Maura Gasparetto,et al.  Data quality assessment of ungated flow cytometry data in high throughput experiments , 2007, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[10]  Robert Gentleman,et al.  Using flowViz to visualize flow cytometry data , 2008, Bioinform..

[11]  M. Goto.  Inflammaging (inflammation + aging): A driving force for human aging based on an evolutionarily antagonistic pleiotropy theory? , 2008, Bioscience trends.

[12]  Raphael Gottardo,et al.  flowClust: a Bioconductor package for automated gating of flow cytometry data , 2009, BMC Bioinformatics.

[13]  Arvind Gupta,et al.  Data reduction for spectral clustering to analyze high throughput flow cytometry data , 2010, BMC Bioinformatics.

[14]  Greg Finak,et al.  Merging Mixture Components for Cell Population Identification in Flow Cytometry , 2009, Adv. Bioinformatics.

[15]  Wade T. Rogers,et al.  FlowFP: A Bioconductor Package for Fingerprinting Flow Cytometric Data , 2009, Adv. Bioinformatics.

[16]  Robert Gentleman,et al.  flowCore: a Bioconductor package for high throughput flow cytometry , 2009, BMC Bioinformatics.

[17]  P. Busse,et al.  Innate and adaptive immunosenescence. , 2010, Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology.

[18]  H. Maecker,et al.  A Model for Harmonizing Flow Cytometry in Clinical Trials , 2022 .

[19]  Nikesh Kotecha,et al.  Web‐Based Analysis and Publication of Flow Cytometry Experiments , 2010, Current protocols in cytometry.

[20]  Ryan R Brinkman,et al.  Per‐channel basis normalization methods for flow cytometry data , 2009, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[21]  Ryan R Brinkman,et al.  Rapid cell population identification in flow cytometry data , 2011, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[22]  Sean C. Bendall,et al.  Extracting a Cellular Hierarchy from High-dimensional Cytometry Data with SPADE , 2011, Nature Biotechnology.

[23]  Ariful Azad,et al.  Matching phosphorylation response patterns of antigen-receptor-stimulated T cells via flow cytometry , 2012, BMC Bioinformatics.

[24]  R. Nussenblatt,et al.  Standardizing immunophenotyping for the Human Immunology Project , 2012, Nature Reviews Immunology.

[25]  N. Aghaeepour,et al.  The phenotypic distribution and functional profile of tuberculin‐specific CD4 T‐cells characterizes different stages of TB infection , 2012, Cytometry. Part B, Clinical cytometry.

[26]  Alireza Hadj Khodabakhshi,et al.  B cells with high side scatter parameter by flow cytometry correlate with inferior survival in diffuse large B-cell lymphoma. , 2012, American journal of clinical pathology.

[27]  H. Hoos,et al.  RchyOptimyx: Cellular hierarchy optimization for flow cytometry , 2012, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[28]  Greg Finak,et al.  QUAliFiER: An automated pipeline for quality assessment of gated flow cytometry data , 2012, BMC Bioinformatics.

[29]  Pratip K. Chattopadhyay,et al.  Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays , 2012, Bioinform..

[30]  Tomáš Sieger,et al.  Detection and monitoring of normal and leukemic cell populations with hierarchical clustering of flow cytometry data , 2012, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[31]  N. Jenny.  Inflammation in aging: cause, effect, or both? , 2012, Discovery medicine.

[32]  S. Sealfon,et al.  flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding , 2012, Bioinform..

[33]  N. Aghaeepour,et al.  Automated analysis of multidimensional flow cytometry data improves diagnostic accuracy between mantle cell lymphoma and small lymphocytic lymphoma. , 2012, American journal of clinical pathology.

[34]  Surajit Ray,et al.  A Computational Framework to Emulate the Human Perspective in Flow Cytometric Data Analysis , 2012, PloS one.

[35]  P. Qiu.  Inferring Phenotypic Properties from Single-Cell Characteristics , 2012, PloS one.

[36]  Cliburn Chan,et al.  Hierarchical Modeling for Rare Event Detection and Cell Subset Alignment across Flow Cytometry Samples , 2013, PLoS Comput. Biol..

[37]  Gholamreza Haffari,et al.  Scoring relevancy of features based on combinatorial analysis of Lasso with application to lymphoma diagnosis , 2013, BMC Genomics.

[38]  Nolwenn Le Meur,et al.  Computational methods for evaluation of cell-based data assessment--Bioconductor. , 2013 .

[39]  C. Meisel,et al.  Standardization of whole blood immune phenotype monitoring for clinical trials: panels and methods from the ONE study , 2013, Transplantation research.

[40]  Greg Finak,et al.  Critical assessment of automated flow cytometry data analysis techniques , 2013, Nature Methods.

[41]  Jill P. Mesirov,et al.  GenePattern flow cytometry suite , 2013, Source Code for Biology and Medicine.

[42]  Sean C. Bendall,et al.  viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia , 2013, Nature Biotechnology.

[43]  Greg Finak,et al.  OpenCyto: An Open Source Infrastructure for Scalable, Robust, Reproducible, and Automated, End-to-End Flow Cytometry Data Analysis , 2014, PLoS Comput. Biol..

[44]  C. Castelo-Branco,et al.  The immune system and aging: a review , 2014, Gynecological endocrinology : the official journal of the International Society of Gynecological Endocrinology.

[45]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[46]  A. Larbi,et al.  From “truly naïve” to “exhausted senescent” T cells: When markers predict functionality , 2014, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[47]  Holger H. Hoos,et al.  Enhanced flowType/RchyOptimyx: a Bioconductor pipeline for discovery in high-dimensional cytometry data , 2014, Bioinform..

[48]  Murat Dundar,et al.  A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects , 2014, BMC Bioinformatics.

[49]  C. Franceschi,et al.  Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. , 2014, The journals of gerontology. Series A, Biological sciences and medical sciences.

[50]  N. Aghaeepour,et al.  Computational analysis optimizes the flow cytometric evaluation for lymphoma , 2013, Cytometry. Part B, Clinical cytometry.

[51]  Jonathan A. Rebhahn,et al.  SWIFT—Scalable Clustering for Automated Identification of Rare Cell Populations in Large, High-Dimensional Flow Cytometry Datasets, Part 2: Biological Evaluation , 2014, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[52]  Greg Finak,et al.  flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification , 2015, Bioinform..