In multi-cellular organisms, biological function emerges when heterogeneous cell types form complex organs. Nevertheless dissection of tissues into mixtures of cellular subpopulations is currently challenging. We introduce an automated massively parallel single-cell RNA sequencing approach for analyzing in vivo transcriptional states in thousands of single cells. Combined with unsupervised classification algorithms, this facilitates ab initio cell type characterization of splenic tissues. Modeling single-cell transcriptional states in dendritic cells and additional hematopoietic cell types uncovers rich cell-type heterogeneity and gene-modules activity in steady-state and after pathogen activation. Cellular diversity is thereby approached through inference of variable and dynamic pathway activity rather than a fixed pre-programmed cell-type hierarchy. These data demonstrate single-cell RNA-Seq as an effective tool for comprehensive cellular decomposition of complex tissues.
Within the bone marrow, stem cells differentiate and give rise to diverse blood cell types and functions. Currently, hematopoietic progenitors are defined using surface markers combined with functional assays that are not directly linked with in vivo differentiation potential or gene regulatory mechanisms. Here, we comprehensively map myeloid progenitor subpopulations by transcriptional sorting of single cells from the bone marrow. We describe multiple progenitor subgroups, showing unexpected transcriptional priming toward seven differentiation fates but no progenitors with a mixed state. Transcriptional differentiation is correlated with combinations of known and previously undefined transcription factors, suggesting that the process is tightly regulated. Histone maps and knockout assays are consistent with early transcriptional priming, while traditional transplantation experiments suggest that in vivo priming may still allow for plasticity given strong perturbations. These data establish a reference model and general framework for studying hematopoiesis at single-cell resolution.
Innate lymphoid cells (ILCs) represent innate versions of T helper and cytotoxic T cells that differentiate from committed ILC precursors (ILCPs). How ILCPs give rise to mature tissue-resident ILCs remains unclear. Here, we identify circulating and tissue ILCPs in humans that fail to express the transcription factors and cytokine outputs of mature ILCs but have these signature loci in an epigenetically poised configuration. Human ILCPs robustly generate all ILC subsets in vitro and in vivo. While human ILCPs express low levels of retinoic acid receptor (RAR)-related orphan receptor C (RORC) transcripts, these cells are found in RORC-deficient patients and retain potential for EOMES natural killer (NK) cells, interferon gamma-positive (IFN-γ) ILC1s, interleukin (IL)-13 ILC2s, and for IL-22, but not for IL-17A ILC3s. Our results support a model of tissue ILC differentiation ("ILC-poiesis"), whereby diverse ILC subsets are generated in situ from systemically distributed ILCPs in response to local environmental signals.
Innate lymphoid cells (ILCs) are critical modulators of mucosal immunity, inflammation, and tissue homeostasis, but their full spectrum of cellular states and regulatory landscapes remains elusive. Here, we combine genome-wide RNA-seq, ChIP-seq, and ATAC-seq to compare the transcriptional and epigenetic identity of small intestinal ILCs, identifying thousands of distinct gene profiles and regulatory elements. Single-cell RNA-seq and flow and mass cytometry analyses reveal compartmentalization of cytokine expression and metabolic activity within the three classical ILC subtypes and highlight transcriptional states beyond the current canonical classification. In addition, using antibiotic intervention and germ-free mice, we characterize the effect of the microbiome on the ILC regulatory landscape and determine the response of ILCs to microbial colonization at the single-cell level. Together, our work characterizes the spectrum of transcriptional identities of small intestinal ILCs and describes how ILCs differentially integrate signals from the microbial microenvironment to generate phenotypic and functional plasticity.
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