Meta-analysis of transcriptomic variation in T-cell populations reveals both variable and consistent signatures of gene expression and splicing

  1. Kristen W. Lynch1,2,3,5
  1. 1Cell and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
  2. 2Immunology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
  3. 3Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
  4. 4Department of Computer Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
  5. 5Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
  1. Corresponding authors: klync{at}pennmedicine.upenn.edu, yosephb{at}seas.upenn.edu

Abstract

Human CD4+ T cells are often subdivided into distinct subtypes, including Th1, Th2, Th17, and Treg cells, that are thought to carry out distinct functions in the body. Typically, these T-cell subpopulations are defined by the expression of distinct gene repertoires; however, there is variability between studies regarding the methods used for isolation and the markers used to define each T-cell subtype. Therefore, how reliably studies can be compared to one another remains an open question. Moreover, previous analysis of gene expression in CD4+ T-cell subsets has largely focused on gene expression rather than alternative splicing. Here we take a meta-analysis approach, comparing eleven independent RNA-seq studies of human Th1, Th2, Th17, and/or Treg cells to determine the consistency in gene expression and splicing within each subtype across studies. We find that known master-regulators are consistently enriched in the appropriate subtype; however, cytokines and other genes often used as markers are more variable. Importantly, we also identify previously unknown transcriptomic markers that appear to consistently differentiate between subsets, including a few Treg-specific splicing patterns. Together this work highlights the heterogeneity in gene expression between samples designated as the same subtype, but also suggests additional markers that can be used to define functional groupings.

Keywords

  • Received April 15, 2020.
  • Accepted June 12, 2020.

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