The emerging diversity of single cell RNAseq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies. Here, real biological differences are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms. We show that Harmony requires dramatically fewer computational resources. It is the only currently available algorithm that makes the integration of ~10 6 cells feasible on a personal computer. We apply Harmony to PBMCs from datasets with large experimental differences, 5 studies of pancreatic islet cells, mouse embryogenesis datasets, and cross-modality spatial integration. Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

The rapidly emerging diversity of single cell RNAseq datasets allows us to characterize the transcriptional behav-1 ior of cell types across a wide variety of biological and clinical conditions. With this comprehensive breadth comes a major 2 analytical challenge. The same cell type across tissues, from different donors, or in different disease states, may appear 3 to express different genes. A joint analysis of multiple datasets requires the integration of cells across diverse conditions. 4 This is particularly challenging when datasets are assayed with different technologies in which real biological differences 5 are interspersed with technical differences. We present Harmony, an algorithm that projects cells into a shared embedding 6 in which cells group by cell type rather than dataset-specific conditions. Unlike available single-cell integration methods, 7 Harmony can simultaneously account for multiple experimental and biological factors. We develop objective metrics to 8 evaluate the quality of data integration. In four separate analyses, we demonstrate the superior performance of Harmony to 9 four single-cell-specific integration algorithms. Moreover, we show that Harmony requires dramatically fewer computational 10 resources. It is the only available algorithm that makes the integration of ∼ 10 6 cells feasible on a personal computer. We 11 demonstrate that Harmony identifies both broad populations and fine-grained subpopulations of PBMCs from datasets with 12 large experimental differences. In a meta-analysis of 14,746 cells from 5 studies of human pancreatic islet cells, Harmony 13 accounts for variation among technologies and donors to successfully align several rare subpopulations. In the resulting in-14 tegrated embedding, we identify a previously unidentified population of potentially dysfunctional alpha islet cells, enriched 15 for genes active in the Endoplasmic Reticulum (ER) stress response. The abundance of these alpha cells correlates across 16 donors with the proportion of dysfunctional beta cells also enriched in ER stress response genes. Harmony is a fast and 17 flexible general purpose integration algorithm that enables the identification of shared fine-grained subpopulations across a 18 variety of experimental and biological conditions. 19Recent technological advances 1 have enabled unbiased single cell transcriptional profiling of thousands of cells in a 20 single experiment. Projects such as the Human Cell Atlas 2 (HCA) and Accelerating Medicines Partnership 3, 4 exemplify 21 the growing body of reference datasets of primary human tissues. While individual experiments contribute incrementally 22 to our understanding of cell types, a comprehensive catalogue of healthy and diseased cells will require the integration of 23 multiple datasets across donors, studies, and technological platforms. Moreover, in translational research, joint analyses 24 across tissues and clinical conditions will be essential to identify disease expanded populations. However, meaningful 25 biological variatio...

This study reveals a potential role for MAIT cells in patients with AS and is the first linking IL-7 to the elevated IL-17 profile in patients through the AS-associated risk gene IL7R.

ObjectiveTo identify an immunologic basis for the male sex bias in ankylosing spondylitis (AS).MethodsCohorts of male and female patients with AS and age‐ and sex‐matched healthy control subjects were selected, and the levels of serum cytokines (interferon‐γ [IFNγ], tumor necrosis factor α, interleukin‐17A [IL‐17A], and IL‐6) were examined by enzyme‐linked immunosorbent assay, the frequencies of Th1 and Th17 cells were assessed by flow cytometry, and whole blood gene expression was analyzed using both microarray and NanoString approaches.ResultsThe frequency of IL‐17A and Th17 cells, both of which are key factors in the inflammatory Th17 axis, was elevated in male patients with AS but not in female patients with AS. In contrast, AS‐associated alterations in the Th1 axis, such as the frequency of IFNγ and Th1 cells in serum, were independent of a patient's sex. Results of microarray analysis supported an altered Th17 axis in male patients, with a specific increase in IL17RA. In addition, male and female patients with AS displayed shared gene expression patterns, while male patients with AS had additional alterations in gene expression that were not seen in female patients with AS. The differential sex‐related immune profiles were independent of HLA–B27 status, clinical disease activity (as measured by the Bath Ankylosing Spondylitis Disease Activity Index), or treatment (with nonsteroidal antiinflammatory drugs or biologic agents), implicating intrinsic sexual dimorphism in AS.ConclusionThe results of this study demonstrate distinct sexual dimorphism in the activation status of the immune system in patients with AS, particularly in the Th17 axis. This dimorphism could underlie sex‐related differences in the clinical features of AS and could provide a rationale for sex‐specific treatment of AS.

The transcription factor AhR modulates immunity at multiple levels. Here we report phagocytes exposed to apoptotic cells exhibited rapid activation of AhR, which drove production of interleukin 10. Activation of AhR was dependent on interactions between apoptotic-cell DNA and the pattern-recognition receptor TLR9 that was required for prevention of immune responses to DNA and histones in vivo. Moreover, disease progression in murine systemic lupus erythematosus (SLE) correlated with strength of the AhR signal, and disease course could be altered by modulation of AhR activity. Deletion of AhR in the myeloid lineage caused systemic autoimmunity in mice and an increased AhR transcriptional signature correlated with disease in patients with SLE. Thus, AhR activity induced by apoptotic cell phagocytes maintains peripheral tolerance.

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Genetic studies have revealed that autoimmune susceptibility variants are over-represented in memory CD4 + T cell regulatory elements 1 – 3 . Understanding how genetic variation affects gene expression in different T cell physiological states is essential for deciphering genetic mechanisms of autoimmunity 4 , 5 . Here we characterized the dynamics of genetic regulatory effects at eight time points during memory CD4 + T cell activation with high depth RNA-seq in healthy individuals. We discovered widespread dynamic allele-specific expression across the genome, where the balance of alleles changes over time. These genes were four-fold enriched within autoimmune loci. We found pervasive dynamic regulatory effects within six HLA genes. HLA-DQB1 alleles had one of three distinct transcriptional regulatory programs. Using CRISPR/Cas9 genomic editing we demonstrated that a promoter variant is causal for T cell-specific control of HLA-DQB1 expression. Our study shows that genetic variation in cis regulatory elements affects gene expression in a lymphocyte activation status-dependent manner contributing to the inter-individual complexity of immune responses.

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