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.

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.

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.

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.

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.

Type 1 Diabetes (T1D) is caused by insulin deficiency, due to progressive autoimmune destruction of pancreatic β cells. Glucagon-secreting α cells become dysfunctional in T1D and contribute to pathophysiology, however, the mechanisms involved are unclear. While the majority of β cells are destroyed in T1D, some β 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 β cell accumulation in the islet microenvironment may have a negative effect on α cell function.

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.

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.

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.