Flow and mass cytometry techniques have enabled simultaneous single-cell analysis of millions of cells with heterogeneity in multiple parameters. In Science, Pe'er and Nolan and their colleagues describe a 'computational toolbox' that can provide robust statistical analysis of such high-dimensional data sets. They have developed a visualization tool, DREVI, and a versatile metric, DREMI, that are able to quantify the strength of directional relationships in signaling cascades in heterogeneous cell populations. They illustrate their computational methods by comparing dynamic changes in the phosphorylation status of the invariant signaling protein CD3z, adaptor SLP-76, kinase Erk and ribosomal protein S6 that occur after engagement of the T cell antigen receptor. Because multiple cell types, such as naive T cells and effector memory T cells, can be analyzed simultaneously in the same sample, changes in the activity of signaling pathways can be observed. Such tools can thus provide new understanding about how even subtle changes can lead to profound differences in cellular responses.
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Dempsey, L. Computational toolbox. Nat Immunol 15, 1103 (2014). https://doi.org/10.1038/ni.3039
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DOI: https://doi.org/10.1038/ni.3039