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...

To define the cell populations that drive joint inflammation in rheumatoid arthritis (RA), we applied single-cell RNA sequencing (scRNA-seq), mass cytometry, bulk RNA-seq and flow cytometry to T cells, B cells, monocytes and fibroblasts from 51 samples of synovial tissue from patients with RA or osteoarthritis. Utilizing an integrated strategy based on canonical correlation analysis of 5,265 scRNA-seq profiles, we identified 18 unique cell populations. Combining mass cytometry and transcriptomics together revealed cell states expanded in RA synovia: THY1(CD90) + HLA-DRA hi sublining fibroblasts, IL1B + pro-inflammatory monocytes, ITGAX + TBX21 + autoimmune-associated B cells and PDCD1 + T peripheral helper (Tph) and T follicular helper (Tfh). We defined distinct subsets of CD8 + T cells characterized by a GZMK + , GZMB + and GNLY + phenotype. We mapped inflammatory mediators to their source cell populations; for example, we attributed IL6 expression to THY1 + HLA-DRA hi fibroblasts, and IL1B production to pro-inflammatory monocytes. These populations are potentially key mediators of RA pathogenesis.

The molecular pathways involved in the differentiation of hematopoietic progenitors are unknown. Here we report that chemokine-mediated interactions of megakaryocyte progenitors with sinusoidal bone marrow endothelial cells (BMECs) promote thrombopoietin (TPO)-independent platelet production. Megakaryocyte-active cytokines, including interleukin-6 (IL-6) and IL-11, did not induce platelet production in thrombocytopenic, TPO-deficient (Thpo(-/-)) or TPO receptor-deficient (Mpl(-/-)) mice. In contrast, megakaryocyte-active chemokines, including stromal-derived factor-1 (SDF-1) and fibroblast growth factor-4 (FGF-4), restored thrombopoiesis in Thpo(-/-) and Mpl(-/-) mice. FGF-4 and SDF-1 enhanced vascular cell adhesion molecule-1 (VCAM-1)- and very late antigen-4 (VLA-4)-mediated localization of CXCR4(+) megakaryocyte progenitors to the vascular niche, promoting survival, maturation and platelet release. Disruption of the vascular niche or interference with megakaryocyte motility inhibited thrombopoiesis under physiological conditions and after myelosuppression. SDF-1 and FGF-4 diminished thrombocytopenia after myelosuppression. These data suggest that TPO supports progenitor cell expansion, whereas chemokine-mediated interaction of progenitors with the bone marrow vascular niche allows the progenitors to relocate to a microenvironment that is permissive and instructive for megakaryocyte maturation and thrombopoiesis. Progenitor-active chemokines offer a new strategy to restore hematopoiesis in a clinical setting.

Lupus nephritis is a potentially fatal autoimmune disease for which the current treatment is ineffective and often toxic. To develop mechanistic hypotheses of disease, we analyzed kidney samples from patients with lupus nephritis and from healthy control subjects using single-cell RNA sequencing. Our analysis revealed 21 subsets of leukocytes active in disease, including multiple populations of myeloid cells, T cells, natural killer cells and B cells that demonstrated both pro-inflammatory responses and inflammation-resolving responses. We found evidence of local activation of B cells correlated with an age-associated B-cell signature and evidence of progressive stages of monocyte differentiation within the kidney. A clear interferon response was observed in most cells. Two chemokine receptors, CXCR4 and CX3CR1 , were broadly expressed, implying a potentially central role in cell trafficking. Gene expression of immune cells in urine and kidney was highly correlated, which would suggest that urine might serve as a surrogate for kidney biopsies.

Macrophages tailor their function according to the signals found in tissue microenvironments, assuming a wide spectrum of phenotypes. A detailed understanding of macrophage phenotypes in human tissues is limited. Using single-cell RNA sequencing, we defined distinct macrophage subsets in the joints of patients with the autoimmune disease rheumatoid arthritis (RA), which affects ~1% of the population. The subset we refer to as HBEGF+ inflammatory macrophages is enriched in RA tissues and is shaped by resident fibroblasts and the cytokine tumor necrosis factor (TNF). These macrophages promoted fibroblast invasiveness in an epidermal growth factor receptor–dependent manner, indicating that intercellular cross-talk in this inflamed setting reshapes both cell types and contributes to fibroblast-mediated joint destruction. In an ex vivo synovial tissue assay, most medications used to treat RA patients targeted HBEGF+ inflammatory macrophages; however, in some cases, medication redirected them into a state that is not expected to resolve inflammation. These data highlight how advances in our understanding of chronically inflamed human tissues and the effects of medications therein can be achieved by studies on local macrophage phenotypes and intercellular interactions.

BACKGROUND-Rheumatoid arthritis, like many inflammatory diseases, is characterized by episodes of quiescence and exacerbation (flares). The molecular events leading to flares are unknown.METHODS-We established a clinical and technical protocol for repeated home collection of blood in patients with rheumatoid arthritis to allow for longitudinal RNA sequencing (RNA-seq). Specimens were obtained from 364 time points during eight flares over a period of 4 years in our index patient, as well as from 235 time points during flares in three additional patients. We identified transcripts that were differentially expressed before flares and compared these with data from synovial single-cell RNA-seq. Flow cytometry and sorted-blood-cell RNA-seq in additional patients were used to validate the findings. RESULTS-Consistent changes were observed in blood transcriptional profiles 1 to 2 weeks before a rheumatoid arthritis flare. B-cell activation was followed by expansion of circulating CD45−CD31−PDPN+ preinflammatory mesenchymal, or PRIME, cells in the blood from patients with rheumatoid arthritis; these cells shared features of inflammatory synovial fibroblasts. Levels of circulating PRIME cells decreased during flares in all 4 patients, and flow cytometry and sorted-cell RNA-seq confirmed the presence of PRIME cells in 19 additional patients with rheumatoid arthritis. CONCLUSIONS-Longitudinal genomic analysis of rheumatoid arthritis flares revealed PRIME cells in the blood during the period before a flare and suggested a model in which these cells

Trace elements play important roles in human health, but little is known about their functions in humoral immunity. Here, we show an important role for iron in inducing cyclin E and B cell proliferation. We find that iron-deficient individuals exhibit a significantly reduced antibody response to the measles vaccine when compared to iron-normal controls. Mice with iron deficiency also exhibit attenuated T-dependent or T-independent antigen-specific antibody responses. We show that iron is essential for B cell proliferation; both iron deficiency and α-ketoglutarate inhibition could suppress cyclin E1 induction and S phase entry of B cells upon activation. Finally, we demonstrate that three demethylases, KDM2B, KDM3B and KDM4C, are responsible for histone 3 lysine 9 (H3K9) demethylation at the cyclin E1 promoter, cyclin E1 induction and B cell proliferation. Thus, our data reveal a crucial role of H3K9 demethylation in B cell proliferation, and the importance of iron in humoral immunity.