The transcriptional landscape of αβ T cell differentiation

Journal name:
Nature Immunology
Year published:
Published online
Corrected online


The differentiation of αβT cells from thymic precursors is a complex process essential for adaptive immunity. Here we exploited the breadth of expression data sets from the Immunological Genome Project to analyze how the differentiation of thymic precursors gives rise to mature T cell transcriptomes. We found that early T cell commitment was driven by unexpectedly gradual changes. In contrast, transit through the CD4+CD8+ stage involved a global shutdown of housekeeping genes that is rare among cells of the immune system and correlated tightly with expression of the transcription factor c-Myc. Selection driven by major histocompatibility complex (MHC) molecules promoted a large-scale transcriptional reactivation. We identified distinct signatures that marked cells destined for positive selection versus apoptotic deletion. Differences in the expression of unexpectedly few genes accompanied commitment to the CD4+ or CD8+ lineage, a similarity that carried through to peripheral T cells and their activation, demonstrated by mass cytometry phosphoproteomics. The transcripts newly identified as encoding candidate mediators of key transitions help define the 'known unknowns' of thymocyte differentiation.

At a glance


  1. 'Birds-eye' view of transcriptome changes during the course of T cell differentiation.
    Figure 1: 'Birds-eye' view of transcriptome changes during the course of T cell differentiation.

    (a) Expression of Tcra, Rag1 and Kit in various bone marrow and thymocyte populations (horizontal axis; abbreviations, Supplementary Table 1), presented as the maximum-normalized mean. (bd) Hierarchical clustering (b), principal-component analysis (c) and heat map of Euclidian distances between various populations (d; top and right margins), calculated with the 15% of probes with the greatest difference in expression among these subsets and with expression values >120 for at least one of the subsets. Expression values were log2-transformed and row-standardized before the analysis. (e) Probes upregulated (red) or downregulated (blue) by twofold or more at various transitions (arrows, horizontal axis) during T cell differentiation. Filled symbols, total probes; open symbols, probes not part of the proliferation signature.

  2. Dynamics of gene expression during early T cell differentiation.
    Figure 2: Dynamics of gene expression during early T cell differentiation.

    K-means clusters of genes with different expression profiles during early T cell differentiation. (a) Expression of genes (log2-transformed mean-centered; black lines) and cluster centroids (gray line) for each cluster (named for a characteristic gene or group of genes in the cluster; complete list of genes in each, Supplementary Table 2). (b) Heat maps of the expression of genes (right margin) in the PU.1 (left), TCF-1–LEF-1 (top right) and CD4-CD8 (bottom right) clusters. (c) Heat maps of the expression of genes encoding transcriptional regulators (right margin) from various clusters (left margin). (d) Heat map of the expression of genes encoding diacylglycerol kinases (right margin) whose expression changed by threefold or more during early T cell differentiation.

  3. Transcriptional footprint of [beta]-selection.
    Figure 3: Transcriptional footprint of β-selection.

    (a) Change in expression (horizontal axis) versus P value (t-test; vertical axis) for all probes (black), those corresponding to the proliferation signature (blue) and components of Notch signaling pathway (red) in thymocyte stage DN3b versus (vs) thymocyte stage DN3a, presented as a volcano plot. Numbers in corners indicate proliferation-associated genes (blue)/total genes (black) with a 'fold change' of <0.5 (bottom left) or >2 (bottom right). (b) Comparison of changes in gene expression induced by pre-TCR signaling in DN thymocytes (DN3b versus DN3a; horizontal axis) and those induced by αβTCR signaling in DP thymocytes (top; CD69+ DP thymocytes (DP69+) versus small DP thymocytes (DPsm); vertical axis) or in peripheral T cells (bottom; OT-I T cells 24 h after infection with Listeria-OVA (OT-I T8Eff.24hr.LisOva) versus naive OT-I cells (OT-I T8Nve); vertical axis). Genes that belong to the proliferation signature have been removed for clarity. Blue lines mark a 'fold change' of 2. (c) Expression of Notch1 and various Notch target genes (key) in various thymocyte subsets (horizontal axis), presented as maximum-normalized mean. (d) Expression of c-Myc in various cell subsets (key), detected by flow cytometry (left), and expression (maximum-normalized) of Myc mRNA and mRNA encoding negative regulators of c-Myc, as well as c-Myc protein (key), in various thymocyte populations (right). Ctrl, control staining. Data are representative of three independent experiments. (e) Proposed model of the interplay among the functions of the pre-TCR, Notch1, IL-7R and c-Myc in T cell physiology during early T cell differentiation.

  4. Transcriptional shutdown in small DP thymocytes.
    Figure 4: Transcriptional shutdown in small DP thymocytes.

    (a) Heat maps of normalized expression values for probes belonging to the gene-ontology categories of ribosome and translation (GO:0005840 and GO:0006412; top), mitochondrion (GO:0005739; middle) and cell cycle (GO:0007049; bottom) in bone marrow progenitor, B cell, myeloid and T cell subsets. Probes are sorted by increasing value in small DP cells (DPsm). (b) Scatter plot of proliferation and translation indexes, calculated by averaging of row maximum-normalized expression values for each probe in the proliferation signature (horizontal axis) or translation and ribosome categories (vertical) for populations in a. (c) Total RNA abundance (top) in various thymocyte subsets (key), quantified by staining with pyronin Y, and mean fluorescence intensity (MFI) of pyronin Y (bottom) in 2N cells of various thymocyte subsets (horizontal axis). (d) Incorporation of eU by thymocytes (subset, top right corner) incubated for 2 h in the presence of eU, followed by flow cytometry, to assess transcriptional activity (left), and mean fluorescence intensity of eU incorporation by various thymocyte subsets (horizontal axis). Data are representative of two independent experiments (c,d; error bars (d), s.d.).

  5. Reactivation of housekeeping activities after positive selection.
    Figure 5: Reactivation of housekeeping activities after positive selection.

    (a) Change in gene expression (horizontal axis) and P values (t-test; vertical axis) in CD69+ DP thymocytes (DP69+) versus small DP thymocytes (DPsm), presented as a volcano plot. Colors indicate signature genes of TCR-stimulated DP thymocytes (derived from a published transcriptome analysis of BDC2.5 DP thymocytes30) that were induced (violet) or repressed (cyan), with a 'fold change' threshold of 2, at 3 h or 7 h relative to their expression untreated cells after in vitro stimulation with mimotope-pulsed splenic APCs; orange indicates immediate-early-response genes (derived from transcriptome analysis of a cell line stimulated with platelet-derived growth factor85). Numbers in top corners indicate the number of signature genes of the upregulated (violet) and downregulated (cyan) activation signatures in the induced (right) or repressed (left) quadrants. (b) Gene probes remaining after filtering-out of genes whose expression changed after TCR stimulation among DP signature genes (shown in a); colors indicate upregulated (violet) and downregulated (cyan) transcriptional regulators (among 1,680 known or putative transcriptional regulators, Supplementary Table 4). (c) Expression of MHC class I (MHCI) on various thymocyte subsets (horizontal axis) from Nlrc5-sufficient mice (Nlrc5+/+) and Nlrc5-deficient mice (Nlrc5−/−), detected by flow cytometry with anti–H2-Kb or anti–H2-Db (key) and presented relative to expression in small DP thymocytes. (d) Expression (maximum-normalized mean) of eight metabolism-related transcripts regulated at the transition from small DP thymocyte to CD69+ DP thymocyte (left), whose products control key steps of the glycolytic and tricarboxylic acid (TCA) cycle pathways (right). Glu, glucose; P, phosphate; G, glycerate; Pyr, pyruvate; PDH, pyruvate dehydrogenase; HK, hexokinase. (e) Expression (maximum-normalized mean) of 20 genes encoding ribosomes and translation-related proteins regulated at the transition from small DP thymocyte to CD69+ DP thymocyte. Genes encoding structural ribosomal proteins: Rpl3, Rpl37, Rpl38, Rpl39, Rplp1, Rps12, Rps20, Rps25, Rps28, Rps29, Rrp15 and Rpsa.

  6. Transcriptional and functional 'footprints' of clonal deletion in CD69+ DP thymocytes.
    Figure 6: Transcriptional and functional 'footprints' of clonal deletion in CD69+ DP thymocytes.

    (a) Change in expression (horizontal axis) and P values (t-test; vertical axis) in CD69+ DP thymocytes (DP69+) and small DP thymocytes (DPsm), presented as a volcano plot; numbers in corners indicate total genes with a 'fold change' of >2 (bottom right) or <0.5 (bottom left) in CD69+DP thymocytes relative to their expression in small DP; red indicates genes reproducibly associated with negative selection in TCR-transgenic models. (b) Comparison of gene-expression changes induced in CD69+ DP thymocytes (DP69+; horizontal axis) and intermediate thymocytes (CD4+CD8int; vertical axis) versus small DP thymocytes (DPsm). Colors indicate genes induced over twofold in CD69+ DP thymocytes with greater (violet), similar (gray) or lower (red) upregulation in intermediate thymocytes (blue lines as in Fig. 3b). (c) Expression of CD4 and CD8 on CD69MHCI (top) or CD69hiMHCI (bottom) thymocytes (gating strategy, Supplementary Fig. 7). Numbers adjacent to outlined areas indicate percent cells in eachgate. (d) Cell-surface expression of PD-1, IL-7R, CD5 and TCRβ and intracellular expression of Helios (IKZF2), NR4A1 and Bcl-2 by various cell subpopulations (key). (e) Intracellular expression of the activated, cleaved form of caspase-3 (Act casp3) and CD69 on subpopulations of cells (defined in c,d) from wild-type mice (WT) and mice doubly deficient in MHC class I and MHC class II (MHC-KO). (f) Proportion of cells with activated caspase-3 among various thymocyte subpopulations (key) from mice doubly deficient in MHC class I and MHC class II (MHC-KO) or Bim (Bim-KO) and their wild-type counterparts. Data are representative of three independent experiments (c,d) or one experiment (e).

  7. Acquisition of CD4+ and CD8+ transcriptional identities during thymocyte differentiation.
    Figure 7: Acquisition of CD4+ and CD8+ transcriptional identities during thymocyte differentiation.

    (a) Comparison of gene-expression changes induced in mature CD4SP thymocytes (4SP24; horizontal axis) and mature CD8SP thymocytes (8SP24; vertical axis) versus small DP (DPsm). Colors indicate genes induced with similar (gray) or 'preferential' upregulation during CD4SP (red) or CD8SP (blue) differentiation (threshold as in Fig. 5a); numbers in bottom right corner indicate total genes per group (blue lines as in Fig. 3b). (b) Comparison of the gene-expression changes in a, filtered for a list of 1,680 known or putative transcriptional regulators (Supplementary Table 4).

  8. Definition of CD4+ and CD8+ transcriptional identities.
    Figure 8: Definition of CD4+ and CD8+ transcriptional identities.

    (a) Comparison of gene-expression changes induced in lymph node CD4+ T cells (horizontal axis) and CD8+ T cells (vertical axis) relative to that in their thymic parental populations (mature CD4SP thymocytes (4SP24) and mature CD8SP (8SP24), respectively). Orange indicates genes upregulated by over twofold in peripheral CD4+ or CD8+ T cells relative to their thymic counterparts; green indicates regulatory T cell (Treg cell)–related signature genes; blue indicates proliferation (prolif) signature (blue lines as in Fig. 3b). (b) Comparison of gene expression in lymph node CD4+ T cells (horizontal axis) and CD8+ T cells (vertical axis); colors indicate genes 'preferentially' expressed in CD4+ (red) or CD8+ (blue) T cells; numbers in top left and bottom right corners indicate total corresponding gene probes; open circles indicate genes encoding transcriptional regulators. (c) Heat maps of the phosphorylation (p-) of various signaling molecules (left margin) in lymph node CD8+ T cells (left) and CD4+ T cells (right), detected simultaneously by mass cytometry at multiple time points after crosslinking with anti-CD3 and anti-CD28. Far left (0), unstimulated cells; far right (PMA), cells stimulated with the phorbol ester PMA (positive control). (d) Comparison of gene-expression changes induced in lymph node CD4+ T cells (horizontal axis) and CD8+ T cells (vertical axis) stimulated for 1 h, 5 h or 24 h (above plots) with anti-CD3 and anti-CD28, relative to that in the corresponding unstimulated cell populations. Blue lines as in a; colors indicate genes with similar (gray) or 'preferential' regulation in the CD4+ T cell (red) or CD8+ T cell (blue) lineage (threshold as in Fig. 5a; numbers in corners as in b). Data are representative of four independent experiments (c).

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Change history

Corrected online 13 May 2013
In the version of this article initially published online, the eighth and ninth author names were incorrect. Those should be Matthew H. Spitzer and Garry P. Nolan. The error has been corrected for the print, PDF and HTML versions of this article.


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Author information

  1. These authors contributed equally to this work.

    • Michael Mingueneau,
    • Taras Kreslavsky,
    • Daniel Gray &
    • Tracy Heng


  1. Division of Immunology, Department of Microbiology and Immunobiology, Harvard Medical School, Boston, Massachusetts, USA.

    • Michael Mingueneau,
    • Richard Cruse,
    • Jeffrey Ericson,
    • Diane Mathis,
    • Christophe Benoist,
    • Tracy Heng,
    • Katherine Rothamel &
    • Adriana Ortiz-Lopez
  2. Dana-Farber Cancer Institute, Boston, Massachusetts, USA.

    • Taras Kreslavsky,
    • Koichi Kobayashi &
    • Harald von Boehmer
  3. Department of Medical Biology, University of Melbourne, Parkville, Australia.

    • Daniel Gray
  4. Molecular Genetics of Cancer Division and Immunology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.

    • Daniel Gray
  5. Department of Anatomy and Developmental Biology, Monash University, Clayton, Victoria, Australia.

    • Tracy Heng
  6. Department of Microbiology and Immunology, Stanford University School of Medicine, Palo Alto, California, USA.

    • Sean Bendall,
    • Matthew H Spitzer &
    • Garry P Nolan
  7. Department of Microbial and Molecular Pathogenesis, Texas A&M Health Science Center, College Station, Texas, USA.

    • Koichi Kobayashi
  8. A full list of members and affiliations appears at the end of the paper.

    • the Immunological Genome Consortium
  9. Division of Biological Sciences, University of California San Diego, La Jolla, California, USA.

    • Adam J Best,
    • Jamie Knell &
    • Ananda Goldrath
  10. Computer Science Department, Stanford University, Stanford, California, USA.

    • Vladimir Jojic &
    • Daphne Koller
  11. Broad Institute and Department of Biology, MIT, Cambridge, Massachusetts, USA.

    • Tal Shay &
    • Aviv Regev
  12. Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, Massachusetts, USA.

    • Nadia Cohen,
    • Patrick Brennan &
    • Michael Brenner
  13. Joslin Diabetes Center, Boston, Massachusetts, USA.

    • Francis Kim,
    • Tata Nageswara Rao &
    • Amy Wagers
  14. Department of Microbiology & Immunology, University of California San Francisco, San Francisco, California, USA.

    • Natalie A Bezman,
    • Joseph C Sun,
    • Gundula Min-Oo,
    • Charlie C Kim &
    • Lewis L Lanier
  15. Icahn Medical Institute, Mount Sinai Hospital, New York, New York, USA.

    • Jennifer Miller,
    • Brian Brown,
    • Miriam Merad,
    • Emmanuel L Gautier,
    • Claudia Jakubzick &
    • Gwendalyn J Randolph
  16. Department of Pathology & Immunology, Washington University, St. Louis, Missouri, USA.

    • Emmanuel L Gautier &
    • Gwendalyn J Randolph
  17. Department of Medicine, Boston University, Boston, Massachusetts, USA.

    • Paul Monach
  18. Skirball Institute of Biomolecular Medicine, New York University School of Medicine, New York, New York, USA.

    • David A Blair &
    • Michael L Dustin
  19. Fox Chase Cancer Center, Philadelphia, Pennsylvania, USA.

    • Susan A Shinton &
    • Richard R Hardy
  20. Computer Science Department, Brown University, Providence, Rhode Island, USA.

    • David Laidlaw
  21. Department of Biomedical Engineering, Howard Hughes Medical Institute, Boston University, Boston, Massachusetts, USA.

    • Jim Collins
  22. Program in Molecular Medicine, Children's Hospital, Boston, Massachusetts, USA.

    • Roi Gazit &
    • Derrick J Rossi
  23. Department of Pathology, University of Massachusetts Medical School, Worcester, Massachusetts, USA.

    • Nidhi Malhotra,
    • Katelyn Sylvia &
    • Joonsoo Kang
  24. Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, USA.

    • Taras Kreslavsky,
    • Anne Fletcher,
    • Kutlu Elpek,
    • Angelique Bellemare-Pelletier,
    • Deepali Malhotra &
    • Shannon Turley


  1. the Immunological Genome Consortium

    • Adam J Best,
    • Jamie Knell,
    • Ananda Goldrath,
    • Vladimir Jojic,
    • Daphne Koller,
    • Tal Shay,
    • Aviv Regev,
    • Nadia Cohen,
    • Patrick Brennan,
    • Michael Brenner,
    • Francis Kim,
    • Tata Nageswara Rao,
    • Amy Wagers,
    • Tracy Heng,
    • Jeffrey Ericson,
    • Katherine Rothamel,
    • Adriana Ortiz-Lopez,
    • Diane Mathis,
    • Christophe Benoist,
    • Natalie A Bezman,
    • Joseph C Sun,
    • Gundula Min-Oo,
    • Charlie C Kim,
    • Lewis L Lanier,
    • Jennifer Miller,
    • Brian Brown,
    • Miriam Merad,
    • Emmanuel L Gautier,
    • Claudia Jakubzick,
    • Gwendalyn J Randolph,
    • Paul Monach,
    • David A Blair,
    • Michael L Dustin,
    • Susan A Shinton,
    • Richard R Hardy,
    • David Laidlaw,
    • Jim Collins,
    • Roi Gazit,
    • Derrick J Rossi,
    • Nidhi Malhotra,
    • Katelyn Sylvia,
    • Joonsoo Kang,
    • Taras Kreslavsky,
    • Anne Fletcher,
    • Kutlu Elpek,
    • Angelique Bellemare-Pelletier,
    • Deepali Malhotra &
    • Shannon Turley


M.M., T.K., D.G. and T.H. designed, did and analyzed some of the experiments and wrote the manuscript; R.C. and J.E. helped with microarray data analysis; S.B. and M.H.S. designed, did and analyzed some of the experiments; G.P.N. and K.K. and H.v.B. contributed resources and assisted with data analysis; and D.M. and C.B. directed the study, analyzed and interpreted results and wrote the manuscript.

Competing financial interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to:

Author details

Supplementary information

PDF files

  1. Supplementary Text and Figures (2 MB)

    Supplementary Figures 1–7

Excel files

  1. Supplementary Table 1 (12 KB)

    Supplementary Table 1

  2. Supplementary Table 2 (614 KB)

    Supplementary Table 2

  3. Supplementary Table 3 (270 KB)

    Supplementary Table 3

  4. Supplementary Table 4 (365 KB)

    Supplementary Table 4

Additional data