Sox9 regulates alternative splicing and pancreatic beta cell function.

Despite significant research, mechanisms underlying the failure of islet beta cells that result in type 2 diabetes (T2D) are still under investigation. Here, we report that Sox9, a transcriptional regulator of pancreas development, also functions in mature beta cells. Our results show that Sox9-depleted rodent beta cells have defective insulin secretion, and aging animals develop glucose intolerance, mimicking the progressive degeneration observed in T2D. Using genome editing in human stem cells, we show that beta cells lacking SOX9 have stunted first-phase insulin secretion. In human and rodent cells, loss of Sox9 disrupts alternative splicing and triggers accumulation of non-functional isoforms of genes with key roles in beta cell function. Sox9 depletion reduces expression of protein-coding splice variants of the serine-rich splicing factor arginine SRSF5, a major splicing enhancer that regulates alternative splicing. Our data highlight the role of SOX9 as a regulator of alternative splicing in mature beta cell function.

Ikaros is a principal regulator of Aire+ mTEC homeostasis, thymic mimetic cell diversity, and central tolerance.

Mutations in the gene encoding the zinc-finger transcription factor Ikaros (IKZF1) are found in patients with immunodeficiency, leukemia, and autoimmunity. Although Ikaros has a well-established function in modulating gene expression programs important for hematopoietic development, its role in other cell types is less well defined. Here, we uncover functions for Ikaros in thymic epithelial lineage development in mice and show that Ikzf1 expression in medullary thymic epithelial cells (mTECs) is required for both autoimmune regulator-positive (Aire+) mTEC development and tissue-specific antigen (TSA) gene expression. Accordingly, TEC-specific deletion of Ikzf1 in mice results in a profound decrease in Aire+ mTECs, a global loss of TSA gene expression, and the development of autoimmunity. Moreover, Ikaros shapes thymic mimetic cell diversity, and its deletion results in a marked expansion of thymic tuft cells and muscle-like mTECs and a loss of other Aire-dependent mimetic populations. Single-cell analysis reveals that Ikaros modulates core transcriptional programs in TECs that correlate with the observed cellular changes. Our findings highlight a previously undescribed role for Ikaros in regulating epithelial lineage development and function and suggest that failed thymic central tolerance could contribute to the autoimmunity seen in humans with IKZF1 mutations.

The genetic underpinnings of variable penetrance and expressivity of pathogenic mutations in cardiometabolic traits.

Over three percent of people carry a dominant pathogenic mutation, yet only a fraction of carriers develop disease (incomplete penetrance), and phenotypes from mutations in the same gene range from mild to severe (variable expressivity). Here, we investigate underlying mechanisms for this heterogeneity: variable variant effect sizes, carrier polygenic backgrounds, and modulation of carrier effect by genetic background (epistasis). We leveraged exomes and clinical phenotypes from the UK Biobank and the Mt. Sinai Bio Me Biobank to identify carriers of pathogenic variants affecting cardiometabolic traits. We employed recently developed methods to study these cohorts, observing strong statistical support and clinical translational potential for all three mechanisms of variable penetrance and expressivity. For example, scores from our recent model of variant pathogenicity were tightly correlated with phenotype amongst clinical variant carriers, they predicted effects of variants of unknown significance, and they distinguished gain- from loss-of-function variants. We also found that polygenic scores predicted phenotypes amongst pathogenic carriers and that epistatic effects can exceed main carrier effects by an order of magnitude.

Genome-wide prediction of disease variant effects with a deep protein language model.

Predicting the effects of coding variants is a major challenge. While recent deep-learning models have improved variant effect prediction accuracy, they cannot analyze all coding variants due to dependency on close homologs or software limitations. Here we developed a workflow using ESM1b, a 650-million-parameter protein language model, to predict all ~450 million possible missense variant effects in the human genome, and made all predictions available on a web portal. ESM1b outperformed existing methods in classifying ~150,000 ClinVar/HGMD missense variants as pathogenic or benign and predicting measurements across 28 deep mutational scan datasets. We further annotated ~2 million variants as damaging only in specific protein isoforms, demonstrating the importance of considering all isoforms when predicting variant effects. Our approach also generalizes to more complex coding variants such as in-frame indels and stop-gains. Together, these results establish protein language models as an effective, accurate and general approach to predicting variant effects.

Alpha cell dysfunction in type 1 diabetes is independent of a senescence program.

Type 1 Diabetes (T1D) is caused by insulin deficiency, due to progressive autoimmune destruction of pancreatic ß cells. Glucagon-secreting α cells become dysfunctional in T1D and contribute to pathophysiology, however, the mechanisms involved are unclear. While the majority of ß cells are destroyed in T1D, some ß cells escape this fate and become senescent but whether α cell dysfunction involves a senescence program has not been explored. Here we addressed the question of whether α cells become senescent during the natural history of T1D in the non-obese diabetic (NOD) mouse model and humans. NOD mice had several distinct subpopulations of α cells, but none were defined by markers of senescence at the transcriptional or protein level. Similarly, α cells of human T1D donors did not express senescence markers. Despite the lack of senescence in α cells in vivo, using a human islet culture model, we observed that DNA damage-induced senescence led to alterations in islet glucagon secretion, which could be rescued by inhibiting the senescence-associated secretory phenotype (SASP). Together our results suggest that α cell dysfunction in T1D is not due to activation of a senescence program, however, senescent ß cell accumulation in the islet microenvironment may have a negative effect on α cell function.

Single-cell multiomics defines tolerogenic extrathymic Aire-expressing populations with unique homology to thymic epithelium.

The autoimmune regulator (Aire), a well-defined transcriptional regulator in the thymus, is also found in extrathymic Aire-expressing cells (eTACs) in the secondary lymphoid organs. eTACs are hematopoietic antigen-presenting cells and inducers of immune tolerance, but their precise identity has remained unclear. Here, we use single-cell multiomics, transgenic murine models, and functional approaches to define eTACs at the transcriptional, genomic, and proteomic level. We find that eTACs consist of two similar cell types: CCR7+ Aire-expressing migratory dendritic cells (AmDCs) and an Airehi population coexpressing Aire and retinoic acid receptor­related orphan receptor γt (RORγt) that we term Janus cells (JCs). Both JCs and AmDCs have the highest transcriptional and genomic homology to CCR7+ migratory dendritic cells. eTACs, particularly JCs, have highly accessible chromatin and broad gene expression, including a range of tissue-specific antigens, as well as remarkable homology to medullary thymic epithelium and RANK-dependent Aire expression. Transgenic self-antigen expression by eTACs is sufficient to induce negative selection and prevent autoimmune diabetes. This transcriptional, genomic, and functional symmetry between eTACs (both JCs and AmDCs) and medullary thymic epithelium­the other principal Aire-expressing population and a key regulator of central tolerance­identifies a core program that may influence self-representation and tolerance across the spectrum of immune development.

A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex.

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1-3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas-containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities-is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis.

Extrathymic Aire-expressing cells support maternal-fetal tolerance.

Healthy pregnancy requires tolerance to fetal alloantigens as well as syngeneic embryonic and placental antigens. Given the importance of the autoimmune regulator (Aire) gene in self-tolerance, we investigated the role of Aire-expressing cells in maternal-fetal tolerance. We report that maternal ablation of Aire-expressing (Aire +) cells during early mouse pregnancy caused intrauterine growth restriction (IUGR) in both allogeneic and syngeneic pregnancies. This phenotype is immune mediated, as IUGR was rescued in Rag1-deficient mice, and involved a memory response, demonstrated by recurrence of severe IUGR in second pregnancies. Single-cell RNA sequencing demonstrated that Aire + cell depletion in pregnancy results in expansion of activated T cells, particularly T follicular helper cells. Unexpectedly, selective ablation of either Aire-expressing medullary thymic epithelial cells or extrathymic Aire-expressing cells (eTACs) mapped the IUGR phenotype exclusively to eTACs. Thus, we report a previously undescribed mechanism for the maintenance of maternal-fetal immune homeostasis and demonstrate that eTACs protect the conceptus from immune-mediated IUGR.

Single-cell transcriptional profiling of human thymic stroma uncovers novel cellular heterogeneity in the thymic medulla.

The thymus' key function in the immune system is to provide the necessary environment for the development of diverse and self-tolerant T lymphocytes. While recent evidence suggests that the thymic stroma is comprised of more functionally distinct subpopulations than previously appreciated, the extent of this cellular heterogeneity in the human thymus is not well understood. Here we use single-cell RNA sequencing to comprehensively profile the human thymic stroma across multiple stages of life. Mesenchyme, pericytes and endothelial cells are identified as potential key regulators of thymic epithelial cell differentiation and thymocyte migration. In-depth analyses of epithelial cells reveal the presence of ionocytes as a medullary population, while the expression of tissue-specific antigens is mapped to different subsets of epithelial cells. This work thus provides important insight on how the diversity of thymic cells is established, and how this heterogeneity contributes to the induction of immune tolerance in humans.

Determining sequencing depth in a single-cell RNA-seq experiment.

An underlying question for virtually all single-cell RNA sequencing experiments is how to allocate the limited sequencing budget: deep sequencing of a few cells or shallow sequencing of many cells? Here we present a mathematical framework which reveals that, for estimating many important gene properties, the optimal allocation is to sequence at a depth of around one read per cell per gene. Interestingly, the corresponding optimal estimator is not the widely-used plug-in estimator, but one developed via empirical Bayes.

The barcode, UMI, set format and BUStools.

SUMMARY: We introduce the Barcode-UMI-Set format (BUS) for representing pseudoalignments of reads from single-cell RNA-seq experiments. The format can be used with all single-cell RNA-seq technologies, and we show that BUS files can be efficiently generated. BUStools is a suite of tools for working with BUS files and facilitates rapid quantification and analysis of single-cell RNA-seq data. The BUS format therefore makes possible the development of modular, technology-specific and robust workflows for single-cell RNA-seq analysis. AVAILABILITY AND IMPLEMENTATION: http://BUStools.github.io/ and http://pachterlab.github.io/kallisto/singlecell.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Targeted Elimination of Senescent Beta Cells Prevents Type 1 Diabetes.

Type 1 diabetes (T1D) is an organ-specific autoimmune disease characterized by hyperglycemia due to progressive loss of pancreatic beta cells. Immune-mediated beta cell destruction drives the disease, but whether beta cells actively participate in the pathogenesis remains unclear. Here, we show that during the natural history of T1D in humans and the non-obese diabetic (NOD) mouse model, a subset of beta cells acquires a senescence-associated secretory phenotype (SASP). Senescent beta cells upregulated pro-survival mediator Bcl-2, and treatment of NOD mice with Bcl-2 inhibitors selectively eliminated these cells without altering the abundance of the immune cell types involved in the disease. Significantly, elimination of senescent beta cells halted immune-mediated beta cell destruction and was sufficient to prevent diabetes. Our findings demonstrate that beta cell senescence is a significant component of the pathogenesis of T1D and indicate that clearance of senescent beta cells could be a new therapeutic approach for T1D.

A discriminative learning approach to differential expression analysis for single-cell RNA-seq.

Single-cell RNA-seq makes it possible to characterize the transcriptomes of cell types across different conditions and to identify their transcriptional signatures via differential analysis. Our method detects changes in transcript dynamics and in overall gene abundance in large numbers of cells to determine differential expression. When applied to transcript compatibility counts obtained via pseudoalignment, our approach provides a quantification-free analysis of 3' single-cell RNA-seq that can identify previously undetectable marker genes.

Deterministic column subset selection for single-cell RNA-Seq.

Analysis of single-cell RNA sequencing (scRNA-Seq) data often involves filtering out uninteresting or poorly measured genes and dimensionality reduction to reduce noise and simplify data visualization. However, techniques such as principal components analysis (PCA) fail to preserve non-negativity and sparsity structures present in the original matrices, and the coordinates of projected cells are not easily interpretable. Commonly used thresholding methods to filter genes avoid those pitfalls, but ignore collinearity and covariance in the original matrix. We show that a deterministic column subset selection (DCSS) method possesses many of the favorable properties of common thresholding methods and PCA, while avoiding pitfalls from both. We derive new spectral bounds for DCSS. We apply DCSS to two measures of gene expression from two scRNA-Seq experiments with different clustering workflows, and compare to three thresholding methods. In each case study, the clusters based on the small subset of the complete gene expression profile selected by DCSS are similar to clusters produced from the full set. The resulting clusters are informative for cell type.

Fumarates target the metabolic-epigenetic interplay of brain-homing T cells in multiple sclerosis.

Cell-permeable formulations of metabolites, such as fumaric acid esters, have been used as highly effective immunomodulators in patients with multiple sclerosis and yet their mechanism of action remains elusive. Since fumaric acid esters are metabolites, and cell metabolism is highly intertwined with the epigenetic regulation of gene expression, we investigated whether this metabolic-epigenetic interplay could be leveraged for therapeutic purposes. To this end we recruited 47 treatment-naïve and 35 fumaric acid ester-treated patients with multiple sclerosis, as well as 16 glatiramer acetate-treated patients as a non-metabolite treatment control. Here we identify a significant immunomodulatory effect of fumaric acid esters on the expression of the brain-homing chemokine receptor CCR6 in CD4 and CD8 T cells of patients with multiple sclerosis, which include T helper-17 and T cytotoxic-17 cells. We report differences in DNA methylation of CD4 T cells isolated from untreated and treated patients with multiple sclerosis, using the Illumina EPIC 850K BeadChip. We first demonstrate that Krebs cycle intermediates, such as fumaric acid esters, have a significantly higher impact on epigenome-wide DNA methylation changes in CD4 T cells compared to amino-acid polymers such as glatiramer acetate. We then define a fumaric acid ester treatment-specific hypermethylation effect on microRNA MIR-21, which is critical for the differentiation of T helper-17 cells. This hypermethylation effect was attributed to the subpopulation of T helper-17 cells using a decomposition analysis and was further validated in an independent prospective cohort of seven patients before and after treatment with fumaric acid esters. In vitro treatment of CD4 and CD8 T cells with fumaric acid esters supported a direct and dose-dependent effect on DNA methylation at the MIR-21 promoter. Finally, the upregulation of miR-21 transcripts and CCR6 expression was inhibited if CD4 or CD8 T cells stimulated under T helper-17 or T cytotoxic-17 polarizing conditions were treated with fumaric acid esters in vitro. These data collectively define a direct link between fumaric acid ester treatment and hypermethylation of the MIR-21 locus in both CD4 and CD8 T cells and suggest that the immunomodulatory effect of fumaric acid esters in multiple sclerosis is at least in part due to the epigenetic regulation of the brain-homing CCR6+ CD4 and CD8 T cells.

Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts.

Current approaches to single-cell transcriptomic analysis are computationally intensive and require assay-specific modeling, which limits their scope and generality. We propose a novel method that compares and clusters cells based on their transcript-compatibility read counts rather than on the transcript or gene quantifications used in standard analysis pipelines. In the reanalysis of two landmark yet disparate single-cell RNA-seq datasets, we show that our method is up to two orders of magnitude faster than previous approaches, provides accurate and in some cases improved results, and is directly applicable to data from a wide variety of assays.