Matching phosphorylation response patterns of antigen-receptor-stimulated T cells via flow cytometry

BackgroundWhen flow cytometric data on mixtures of cell populations are collected from samples under different experimental conditions, computational methods are needed (a) to classify the samples into similar groups, and (b) to characterize the changes within the corresponding populations due to the different conditions. Manual inspection has been used in the past to study such changes, but high-dimensional experiments necessitate developing new computational approaches to this problem. A robust solution to this problem is to construct distinct templates to summarize all samples from a class, and then to compare these templates to study the changes across classes or conditions.ResultsWe designed a hierarchical algorithm, flowMatch, to first match the corresponding clusters across samples for producing robust meta-clusters, and to then construct a high-dimensional template as a collection of meta-clusters for each class of samples. We applied the algorithm on flow cytometry data obtained from human blood cells before and after stimulation with anti-CD3 monoclonal antibody, which is reported to change phosphorylation responses of memory and naive T cells. The flowMatch algorithm is able to construct representative templates from the samples before and after stimulation, and to match corresponding meta-clusters across templates. The templates of the pre-stimulation and post-stimulation data corresponding to memory and naive T cell populations clearly show, at the level of the meta-clusters, the overall phosphorylation shift due to the stimulation.ConclusionsWe concisely represent each class of samples by a template consisting of a collection of meta-clusters (representative abstract populations). Using flowMatch, the meta-clusters across samples can be matched to assess overall differences among the samples of various phenotypes or time-points.

[1]  Jianhong Wu,et al.  Data clustering - theory, algorithms, and applications , 2007 .

[2]  Cliburn Chan,et al.  Statistical mixture modeling for cell subtype identification in flow cytometry , 2008, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[3]  Linda S. Wicker,et al.  Allelic variant in CTLA4 alters T cell phosphorylation patterns , 2007, Proceedings of the National Academy of Sciences.

[4]  S. Hussain,et al.  Effector CD4 T cells are biochemically distinct from the memory subset: evidence for long-term persistence of effectors in vivo. , 1999, Journal of immunology.

[5]  Huaiyu Zhu.  On Information and Sufficiency , 1997 .

[6]  Ariful Azad,et al.  Identifying Rare Cell Populations in Comparative Flow Cytometry , 2010, WABI.

[7]  Marion G. Macey.  Comprar Flow Cytometry · Principles and Applications | Macey, Marion G. | 9781588296917 | Springer , 2007 .

[8]  C. Antoniak.  Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems , 1974 .

[9]  M. Roederer,et al.  Data analysis in flow cytometry: The future just started , 2010, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[10]  J. Mesirov,et al.  Automated high-dimensional flow cytometric data analysis , 2009, Proceedings of the National Academy of Sciences.

[11]  P. Beverley,et al.  In vitro responses of human CD45R0brightRA− and CD45R0−RAbright T cell subsets and their relationship to memory and naive T cells , 1997, European journal of immunology.

[12]  Radford M. Neal.  Markov Chain Sampling Methods for Dirichlet Process Mixture Models , 2000 .

[13]  F. Traganos.  Flow cytometry: principles and applications. I. , 1984, Cancer investigation.

[14]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[15]  Kim Bottomly,et al.  Differential T cell receptor‐mediated signaling in naive and memory CD4 T cells , 1997, European journal of immunology.

[16]  S. Hussain,et al.  Heterogeneity of the Memory CD4 T Cell Response: Persisting Effectors and Resting Memory T Cells1 , 2001, The Journal of Immunology.