The Immunological Genome Project combines immunology and computational biology laboratories in an effort to establish a complete 'road map' of gene-expression and regulatory networks in all immune cells.

γδ T cells function in the early phase of immune responses. Although innate γδ T cells have primarily been studied as one homogenous population, they can be functionally classified into effector subsets based on the production of signature cytokines, analogous to adaptive T helper subsets. Unlike adaptive T cells, however, γδ T effector function correlates with genomically encoded TCR chains, suggesting that clonal TCR selection is not the primary determinant of γδ effector differentiation. A high resolution transcriptome analysis of all emergent γδ thymocyte subsets segregated based on TCRγ/δ chain usage indicates the existence of three separate subtypes of γδ effectors in the thymus. The immature γδ subsets are distinguished by unique transcription factor modules that program effector function.

SUMMARY How innate lymphoid cells (ILCs) in the thymus and gut become specialized effectors is unclear. The prototypic innate-like γδ T cells (Tγδ17) are a major source of interleukin-17 (IL-17). We demonstrate that Tγδ17 cells are programmed by a gene regulatory network consisting of a quartet of High Mobility Group box (HMG) transcription factors, SOX4, SOX13, TCF1 and LEF1, and not by conventional TCR signaling. SOX4 and SOX13 directly regulated the two requisite Tγδ17 cell-specific genes, Rorc and Blk, whereas TCF1 and LEF1 countered the SOX proteins and induced genes of alternate effector subsets. The T cell lineage specification factor TCF1 was also indispensable for the generation of IL-22 producing gut NKp46+ ILCs and restrained cytokine production by Lymphoid Tissue inducer-like effectors. These results indicate that similar gene network architecture programs innate sources of IL-17, independent of anatomical origins.

The differentiation of hematopoietic stem cells into immune cells has been extensively studied in mammals, but the transcriptional circuitry controlling it is still only partially understood. Here, the Immunological Genome Project gene expression profiles across mouse immune lineages allowed us to systematically analyze these circuits. Using a computational algorithm called Ontogenet, we uncovered differentiation-stage specific regulators of mouse hematopoiesis, identifying many known hematopoietic regulators, and 175 new candidate regulators, their target genes, and the cell types in which they act. Among the novel regulators, we highlight the role of ETV5 in γδT cells differntiation. Since the transcriptional program of human and mouse cells is highly conserved1, it is likely that many lessons learned from the mouse model apply to humans.

Summary Lineage-committed αβ and γδ T cells are thought to originate from common intrathymic multipotent progenitors following instructive T cell receptor (TCR) signals. A subset of lymph node and mucosal Vγ2+ γδ T cells is progγδTCR in development of these cells remains controversial. Here we generated reporter mice for the rammed intrathymically to produce IL-17 (Tγδ17 cells), however the role of the Tγδ17 lineage-defining transcription factor SOX13 and identified fetal-origin, intrathymic Sox13+ progenitors. In organ culture developmental assays, Tγδ17 cells derived primarily from Sox13+ progenitors, and not from other known lymphoid progenitors. Single cell transcriptome assays of the progenitors found in TCR-deficient mice demonstrated that Tγδ17 lineage programming was independent of γδTCR. Instead, generation of the lineage committed progenitors and Tγδ17 cells was controlled by TCF1 and SOX13. Thus, T lymphocyte lineage fate can be prewired cell-intrinsically and is not necessarily specified by clonal antigen receptor signals.

CD4 + effector lymphocytes (Teff) are traditionally classified by the cytokines they produce. To determine the states that Teff actually adopt in frontline tissues in vivo , we applied single-cell transcriptome and chromatin analysis on colonic Teff cells, in germ-free or conventional mice, or after challenge with a range of phenotypically biasing microbes. Subsets were marked by expression of interferon-signature or myeloid-specific transcripts, but transcriptome or chromatin structure could not resolve discrete clusters fitting classic T H subsets. At baseline or at different times of infection, transcripts encoding cytokines or proteins commonly used as T H markers distributed in a polarized continuum, which was also functionally validated. Clones derived from single progenitors gave rise to both IFN-γ and IL17-producing cells. Most transcriptional variance was tied to the infecting agent, independent of the cytokines produced, and chromatin variance primarily reflected activity of AP1 and IRF transcription factor families, not the canonical subset master regulators T-bet, GATA3, RORγ.

To determine the breadth and underpinning of changes in immunocyte gene expression due to genetic variation in mice we performed, as part of the Immunological Genome Project, gene expression profiling for CD4+ T cells and neutrophils purified from 39 inbred strains of the Mouse Phenome Database. Considering both cell types, a large number of transcripts showed significant variation across the inbred strains, 22% of the transcriptome varying by two-fold or more. These included 119 loci with apparently complete loss-of-function, where the corresponding transcript was not expressed in some of the strains, representing a useful resource of “natural knockouts”. We identified 1,222 cis- expression quantitative trait loci (cis-eQTL) that control some of this variation. Most (60%) cis-eQTLs were shared between T cells and neutrophils, but a significant portion uniquely impacted one of the cell types, suggesting cell-type specific regulatory mechanisms. Using a conditional regression algorithm we predicted regulatory interactions between transcription factors and potential targets, and demonstrated that these predictions overlap with regulatory interactions inferred from transcriptional changes during immunocyte differentiation. Finally, comparison of these and parallel data from CD4+ T cells of healthy humans demonstrated intriguing similarities in variability of a gene's expression: the most variable genes tended to be the same in both species, and there was an overlap in genes subject to strong cis-acting genetic variants. We speculate that this “conservation of variation” reflects a differential constraint on intra-species variation in expression levels of different genes, either through lower pressure for some genes, or by favoring variability for others.

Alternative splicing (AS) allows increased diversity and orthogonal regulation of the transcriptional products of mammalian genomes. To assess the distribution and variation of alternative splicing across cell lineages of the immune system, we comprehensively analyzed RNA sequencing and microarray data generated by the Immunological Genome Project Consortium. AS is pervasive: 60% of genes showed frequent AS isoforms in T or B lymphocytes, with 7,599 previously unreported isoforms. Distinct cell specificity was observed, with differential exon skipping in 5% of genes otherwise coexpressed in both B and T cells. The distribution of isoforms was mostly all or none, suggesting on/off switching as a frequent mode of AS regulation in lymphocytes. From the identification of differential exon use in the microarray data, clustering of exon inclusion/exclusion patterns across all Immunological Genome Project cell types showed that ∼70% of AS exons are distributed along a common pattern linked to lineage differentiation and cell cycling. Other AS events distinguished myeloid from lymphoid cells or affected only a small set of exons without clear lineage specificity (e.g., Ptprc). Computational analysis predicted specific associations between AS exons and splicing regulators, which were verified by detection of the hnRPLL/ Ptprc connection.A lternative splicing (AS), the process of selectively including or removing exons to create a variety of transcripts from the same pre-mRNA, plays an important role in amplifying the diversity and flexibility of genome-encoded molecules (1, 2). AS can result in different protein isoforms or generate mRNAs of identical coding sequence but varying in their stability, localization, susceptibility to translational control, or microRNA regulation. AS is frequent and ubiquitous, affecting 55-95% of multiexon genes in mammals in different estimates (3-6). It is involved in a wide range of biological phenomena, ranging from sex determination to apoptosis or tumor formation. It also allows evolutionary tinkering with transcript structure and gradual transitions in gene function (7).Splicing events are overrepresented in genes involved in signaling and transcriptional regulation (receptors, signaling transduction, and transcription factors) and immune and nervous system processes. It has been hypothesized that alternative splicing is particularly valuable in complex systems, where information is processed differently at different times (immune response) or fine-tuning of signal integration is important (5). In the immune system, the first instance of AS recognized was the now textbook case of differential processing of primary Ig transcripts generating either a membrane receptor in naïve B cells or a secreted protein after antigen-induced differentiation (8). Other notable examples are the splicing of transcripts encoding adhesion molecules such as PECAM1 or CD44, which modulate cell-stroma interactions, or the extracellular domain of the coinhibitory molecule CTLA4 (9). A particularly well-studie...