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SummaryGene expression programs are regulated by enhancers which act in a context-specific manner, and can reside at great distances from their target genes. Extensive three-dimensional (3D) genome reorganization occurs in senescence, but how enhancer interactomes are reconfigured during this process is just beginning to be understood. Here we generated high-resolution contact maps of active enhancers and their target genes, assessed chromatin accessibility, and established one-dimensional maps of various histone modifications and transcription factors to comprehensively understand the regulation of enhancer configuration during senescence. Hyper-connected enhancer communities/cliques formed around genes that are highly expressed and within essential gene pathways in each cell state. In addition, motif analysis indicates the involvement of specific transcription factors in hyper-connected regulatory elements in each condition; importantly, MafK, a bZIP family transcription factor, was upregulated in senescence, and reduced expression of MafK ameliorated the senescence phenotypes. Because the accumulation of senescent cells is a key feature of aging, we further investigated enhancer connectomes in the liver of young and aged mice. Hyper-connected enhancer communities were identified during aging, which regulate essential genes that maintain cell differentiation and homeostasis. These findings reveal that hyper-connected enhancer communities correlate with high gene expression in senescence and aging and provide potential hotspots for therapeutic intervention in aging and age-associated diseases.

Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To address these unmet needs, we developed AnnoSpat (Annotator and Spatial Pattern Finder) that uses neural network and point process algorithms to automatically identify cell types and quantify cell-cell proximity relationships. Our study of data from IMC and CODEX show the superior performance of AnnoSpat in rapid and accurate annotation of cell types compared to alternative approaches. Moreover, the application of AnnoSpat to type 1 diabetic, non-diabetic autoantibody-positive, and non-diabetic organ donor cohorts recapitulated known islet pathobiology and showed differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8+T cells infiltration in islets during type 1 diabetes progression.

Multi-enhancer hubs are spatial clusters of enhancers which have been recently characterized across numerous developmental programs. Yet, the functional relevance of these three-dimensional (3D) structures is poorly understood. Here we show that the multiplicity of enhancers interacting with the transcription factorEts1is essential to control the precise expression level of this gene in response to cellular cues, and the failure to do so can lead to allergic diseases. Focusing on T cells as a model, we identified a highly connected multi-enhancer hub at theEts1locus, comprising a noncoding regulatory element that is a hotspot for sequence variation associated with allergic diseases. We deleted this hotspot and found that the multi-enhancer connectivity is dispensable for T cell development but required for CD4+T helper (Th1) differentiation in response to changes in the cytokine milieu. Mice lacking this hotspot are thus protected from Th1-mediated colitis but demonstrate an overt allergic response to house dust mites, a T cell-mediated response which is dampened by Th1 cells. Mechanistically, the multi-enhancer hub controls the expression level ofEts1that is dispensable for the active enhancer landscape but required for the Th1-specific genome topology through recruitment of CTCF. Together, we establish a paradigm for the functional and mechanistic relevance of multi-enhancer hubs controlling cellular competence to respond specifically to an inductive cue.

SummaryAlthough the molecular rules governing genome organization are being quickly elucidated, relatively few proteins regulating this process have been identified. To address this gap, we developed a fully automated imaging pipeline, called HiDRO (high-throughput DNA or RNA labeling with optimized Oligopaints), that permits quantitative measurement of chromatin interactions across a large number of samples. Using HiDRO, we screened the human druggable genome and identified >300 factors that regulate chromatin folding during interphase, including 43 validated hits that either increase or decrease interactions between topological associating domains (TADs). We discovered that genetic or chemical inhibition of the ubiquitous kinase GSK3A enhances long-range interactions by dysregulating cohesin-mediated chromatin looping. Collectively, these results highlight a noncanonical role for GSK3A signaling in nuclear architecture and underscore the broader utility of HiDRO-based screening to identify novel mechanisms that drive the spatial organization of the genome.

SummaryMultiple islet autoantibodies (AAb) predict type 1 diabetes (T1D) and hyperglycemia within 10 years. By contrast, T1D develops in just ∼15% of single AAb+ (generally against glutamic acid decarboxylase, GADA+) individuals; hence the single GADA+ state may represent an early stage of T1D amenable to interventions. Here, we functionally, histologically, and molecularly phenotype human islets from non-diabetic, GADA+ and T1D donors. Similar to the few remaining beta cells in T1D islets, GADA+ donor islets demonstrated a preserved insulin secretory response. By contrast, alpha cell glucagon secretion was dysregulated in both T1D and GADA+ islets with impaired glucose suppression of glucagon secretion. Single cell RNA sequencing (scRNASeq) of GADA+ alpha cells revealed distinct abnormalities in glycolysis and oxidative phosphorylation pathways and a marked downregulation of PKIB, providing a molecular basis for the loss of glucose suppression and the increased effect of IBMX observed in GADA+ donor islets. The striking observation of a distinct early defect in alpha cell function that precedes beta cell loss in T1D suggests that not only overt disease, but also the progression to T1D itself, is bihormonal in nature.

Architectural stripes tend to form at genomic regions harboring genes with salient roles in cell identity and function. Therefore, the accurate identification and quantification of these features is essential for the understanding of lineage-specific gene regulation. Here, we present Stripenn, an algorithm rooted in computer vision to systematically detect and quantitate architectural stripes from chromatin conformation measurements of various technologies. We demonstrate that Stripenn outperforms existing methods, highlight its biological applications in the context of B and T lymphocytes, and examine the role of sequence variation on architectural stripes by studying the conservation of these features in inbred strains of mice. In summary, Stripenn is a computational method which borrows concepts from widely used image processing techniques for demarcation and quantification of architectural stripes.

Type 1 diabetes (T1D) is an autoimmune disease of only partially defined etiology in which immune cells destroy insulin-producing beta cells. Using single-cell transcriptomics and an advanced analytical strategy to assess pancreatic islets of T1D, autoantibody-positive, and non-diabetic organ donors, we identified both canonical cell types and rare insulin-expressing cells with a hybrid mixture of endocrine and exocrine gene signatures within all donors. We further found elevated expression of MHC Class II pathway genes in exocrine ductal cells of T1D donors, which we confirmed through CyTOF, in situ imaging mass cytometry, and immunofluorescence analysis. Taken together, our multimodal analyses identify novel cell types and processes that may contribute to T1D immunopathogenesis and provide new cellular and molecular insights into human pancreas function.

Isolated reports of new-onset diabetes in individuals with COVID-19 have led to the hypothesis that SARS-CoV-2 is directly cytotoxic to pancreatic islet β cells. This would require binding and entry of SARS-CoV-2 into β cells via co-expression of its canonical cell entry factors, angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2); however, their expression in human pancreas has not been clearly defined. We analyzed six transcriptional datasets of primary human islet cells and found that ACE2 and TMPRSS2 were not co-expressed in single β cells. In pancreatic sections, ACE2 and TMPRSS2 protein was not detected in β cells from donors with and without diabetes. Instead, ACE2 protein was expressed in islet and exocrine tissue microvasculature and in a subset of pancreatic ducts, whereas TMPRSS2 protein was restricted to ductal cells. These findings reduce the likelihood that SARS-CoV-2 directly infects β cells in vivo through ACE2 and TMPRSS2., Graphical Abstract, Highlights • ACE2 and TMPRSS2 mRNA are expressed in the human pancreas • ACE2 protein is expressed in some islet and exocrine capillaries and pericytes • ACE2 and TMPRSS2 proteins are co-expressed in some pancreatic ducts • ACE2 and TMPRSS2 protein is not detected in β cells of the human pancreas, Coate et al. examined expression of canonical SARS-CoV-2 entry proteins ACE2 and TMPRSS2 in the human pancreas and report ACE2 expression in the microvasculature, including islet pericytes, whereas both ACE2 and TMPRSS2 are expressed in some ducts. Conversely, neither protein is detected in β cells, arguing against direct β cell viral infection in vivo.

Lymphocyte migration is essential for human adaptive immune surveillance. However, our current understanding of this process is rudimentary, because most human studies to date have been restricted to immunological analyses of blood and various tissues. To address this issue, we used an integrated approach to characterize tissue-emigrant immune cells in thoracic duct lymph (TDL). In humans and non-human primates, lymphocytes were by far the most abundant immune lineage population in efferent lymph, and a vast majority of these lymphocytes were T cells. Cytolytic CD8+ T cell subsets were clonotypically discrete and selectively confined to the intravascular circulation, persisting for months after inhibition of S1P-dependent tissue egress by FTY-720. In contrast, non-cytolytic CD8+ T cell subsets with stem-like epigenetic and transcriptional signatures predominated in tissues and TDL. Collectively, these data provide an atlas of the migratory immune system and define the nature of tissue-emigrant CD8+ T cells that recirculate via TDL.

Transcriptional programs contribute to phenotypic and functional cell states. While elucidation of cell state heterogeneity and its role in biology and pathobiology has been advanced by studying single cell level measurements, the underlying assumptions of current analytical methods limit the identification and exploration of cell clades. Unlike other methods, which produce a single uni-layer partition of cells ignoring echelons of cell states, we presentTooManyCells, a software consisting of a suite of graph-based tools for efficient, global, and unbiased identification and visualization of cell clades while maintaining and presenting the relationship between cell states.TooManyCellsprovides a set of tools based on a matrix-free efficient divisive hierarchical spectral clustering algorithm wholly different from the prevalent Louvain-based methods.BirchBeer, the visualization component ofTooManyCells, introduces a new approach for single cell analysis that is built on a concept intentionally orthogonal to the widely used dimensionality reduction methods. Together, this suite of tools provide a paradigm shift in the analysis and interpretation of single cell data by enabling simultaneous comparisons of cell states at context-and application-dependent scales. A byproduct of this shift is the immediate detection and visualization of rare populations that outperforms previous algorithms as demonstrated by applying these tools to existing single cell RNA-seq data sets from various mouse organs.

CD4(+) T cells are critical for the elimination of an immense array of microbial pathogens. Although there are aspects of helper T-cell differentiation that can be modeled as a classic cell-fate commitment, CD4(+) T cells also maintain considerable flexibility in their transcriptional program. Here, we present an overview of chromatin biology during cellular reprogramming and, within this context, envision how the scope of cellular reprogramming may be expanded to further our understanding of the controversy surrounding CD4(+) T lymphocyte plasticity or determinism.