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The emerging diversity of single-cell RNA-seq 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, because biological and technical differences are interspersed. We present Harmony (https://github.com/immunogenomics/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 while requiring fewer computational resources. Harmony enables the integration of ~106 cells on a personal computer. We apply Harmony to peripheral blood mononuclear cells from datasets with large experimental differences, five studies of pancreatic islet cells, mouse embryogenesis datasets and the integration of scRNA-seq with spatial transcriptomics data.Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.

Recent advances in single-cell technologies and integration algorithms make it possible to construct comprehensive reference atlases encompassing many donors, studies, disease states, and sequencing platforms. Much like mapping sequencing reads to a reference genome, it is essential to be able to map query cells onto complex, multimillion-cell reference atlases to rapidly identify relevant cell states and phenotypes. We present Symphony ( https://github.com/immunogenomics/symphony ), an algorithm for building large-scale, integrated reference atlases in a convenient, portable format that enables efficient query mapping within seconds. Symphony localizes query cells within a stable low-dimensional reference embedding, facilitating reproducible downstream transfer of reference-defined annotations to the query. We demonstrate the power of Symphony in multiple real-world datasets, including (1) mapping a multi-donor, multi-species query to predict pancreatic cell types, (2) localizing query cells along a developmental trajectory of fetal liver hematopoiesis, and (3) inferring surface protein expression with a multimodal CITE-seq atlas of memory T cells. The number of single-cell RNA-seq datasets generated is increasing rapidly, making methods that map cell types to well-curated references increasingly important. Here, the authors propose an accurate method for mapping single cells onto a reference atlas in seconds.

Rheumatoid arthritis is a prototypical autoimmune disease that causes joint inflammation and destruction 1 . There is currently no cure for rheumatoid arthritis, and the effectiveness of treatments varies across patients, suggesting an undefined pathogenic diversity 1 , 2 . Here, to deconstruct the cell states and pathways that characterize this pathogenic heterogeneity, we profiled the full spectrum of cells in inflamed synovium from patients with rheumatoid arthritis. We used multi-modal single-cell RNA-sequencing and surface protein data coupled with histology of synovial tissue from 79 donors to build single-cell atlas of rheumatoid arthritis synovial tissue that includes more than 314,000 cells. We stratified tissues into six groups, referred to as cell-type abundance phenotypes (CTAPs), each characterized by selectively enriched cell states. These CTAPs demonstrate the diversity of synovial inflammation in rheumatoid arthritis, ranging from samples enriched for T and B cells to those largely lacking lymphocytes. Disease-relevant cell states, cytokines, risk genes, histology and serology metrics are associated with particular CTAPs. CTAPs are dynamic and can predict treatment response, highlighting the clinical utility of classifying rheumatoid arthritis synovial phenotypes. This comprehensive atlas and molecular, tissue-based stratification of rheumatoid arthritis synovial tissue reveal new insights into rheumatoid arthritis pathology and heterogeneity that could inform novel targeted treatments. Single-cell transcriptomic and proteomic data from synovial tissue from individuals with rheumatoid arthritis classify patients into groups based on abundance of cell states that can provide insights into pathology and predict individual treatment responses.

Spatial transcriptomics facilitates gene expression analysis of cells in their spatial anatomical context. Batch effects hinder visualization of gene spatial patterns across samples. We present the Crescendo algorithm to correct for batch effects at the gene expression level and enable accurate visualization of gene expression patterns across multiple samples. We show Crescendo’s utility and scalability across three datasets ranging from 170,000 to 7 million single cells across spatial and single-cell RNA sequencing technologies. By correcting for batch effects, Crescendo enhances spatial transcriptomics analyses to detect gene colocalization and ligand-receptor interactions and enables cross-technology information transfer.

The human leukocyte antigen (HLA) locus plays a critical role in complex traits spanning autoimmune and infectious diseases, transplantation and cancer. While coding variation in HLA genes has been extensively documented, regulatory genetic variation modulating HLA expression levels has not been comprehensively investigated. Here we mapped expression quantitative trait loci (eQTLs) for classical HLA genes across 1,073 individuals and 1,131,414 single cells from three tissues. To mitigate technical confounding, we developed scHLApers, a pipeline to accurately quantify single-cell HLA expression using personalized reference genomes. We identified cell-type-specific cis- eQTLs for every classical HLA gene. Modeling eQTLs at single-cell resolution revealed that many eQTL effects are dynamic across cell states even within a cell type. HLA-DQ genes exhibit particularly cell-state-dependent effects within myeloid, B and T cells. For example, a T cell HLA-DQA1 eQTL ( rs3104371 ) is strongest in cytotoxic cells. Dynamic HLA regulation may underlie important interindividual variability in immune responses. scHLApers is an analysis pipeline that quantifies single-cell expression of HLA genes using a personalized genomic reference. Mapping of HLA expression quantitative trait loci at single-cell resolution identifies dynamic effects across cell states.

ObjectivesIn lupus nephritis the pathological diagnosis from tissue retrieved during kidney biopsy drives treatment and management. Despite recent approval of new drugs, complete remission rates remain well under aspirational levels, necessitating identification of new therapeutic targets by greater dissection of the pathways to tissue inflammation and injury. This study assessed the safety of kidney biopsies in patients with SLE enrolled in the Accelerating Medicines Partnership, a consortium formed to molecularly deconstruct nephritis.Methods475 patients with SLE across 15 clinical sites in the USA consented to obtain tissue for research purposes during a clinically indicated kidney biopsy. Adverse events (AEs) were documented for 30 days following the procedure and were determined to be related or unrelated by all site investigators. Serious AEs were defined according to the National Institutes of Health reporting guidelines.Results34 patients (7.2%) experienced a procedure-related AE: 30 with haematoma, 2 with jets, 1 with pain and 1 with an arteriovenous fistula. Eighteen (3.8%) experienced a serious AE requiring hospitalisation; four patients (0.8%) required a blood transfusion related to the kidney biopsy. At one site where the number of cores retrieved during the biopsy was recorded, the mean was 3.4 for those who experienced a related AE (n=9) and 3.07 for those who did not experience any AE (n=140). All related AEs resolved.ConclusionsProcurement of research tissue should be considered feasible, accompanied by a complication risk likely no greater than that incurred for standard clinical purposes. In the quest for targeted treatments personalised based on molecular findings, enhanced diagnostics beyond histology will likely be required.

Granzymes are a family of serine proteases that are mainly expressed by CD8 + T cells, natural killer cells and innate-like lymphocytes 1 . Although their primary function is thought to be the induction of cell death in virally infected cells and tumours, accumulating evidence indicates that some granzymes can elicit inflammation by acting on extracellular substrates 1 . We previously found that most tissue CD8 + T cells in rheumatoid arthritis synovium, and in inflamed organs for some other diseases, express granzyme K (GZMK) 2 , a tryptase-like protease with poorly defined function. Here, we show that GZMK can activate the complement cascade by cleaving the C2 and C4 proteins. The nascent C4b and C2b fragments form a C3 convertase that cleaves C3, enabling the assembly of a C5 convertase that cleaves C5. The resulting convertases generate all the effector molecules of the complement cascade: the anaphylatoxins C3a and C5a, the opsonins C4b and C3b, and the membrane attack complex. In rheumatoid arthritis synovium, GZMK is enriched in regions with abundant complement activation, and fibroblasts are the main producers of complement proteins that serve as substrates for GZMK-mediated complement activation. Furthermore, Gzmk -deficient mice are significantly protected from inflammatory disease, exhibiting reduced arthritis and dermatitis, with concomitant decreases in complement activation. Our findings describe the discovery of a previously unidentified mechanism of complement activation that is driven entirely by lymphocyte-derived GZMK. Given the widespread abundance of GZMK -expressing T cells in tissues in chronic inflammatory diseases, GZMK-mediated complement activation is likely to be an important contributor to tissue inflammation in multiple disease contexts. A study finds that a protease called granzyme K can activate the entire complement cascade, explaining how it can drive destructive inflammation in inflammatory diseases such as rheumatoid arthritis.

Systemic lupus erythematosus (SLE) is prototypical autoimmune disease driven by pathological T cell–B cell interactions 1 , 2 . Expansion of T follicular helper (T FH ) and T peripheral helper (T PH ) cells, two T cell populations that provide help to B cells, is a prominent feature of SLE 3 , 4 . Human T FH and T PH cells characteristically produce high levels of the B cell chemoattractant CXCL13 (refs.  5 , 6 ), yet regulation of T cell CXCL13 production and the relationship between CXCL13 + T cells and other T cell states remains unclear. Here, we identify an imbalance in CD4 + T cell phenotypes in patients with SLE, with expansion of PD-1 + /ICOS + CXCL13 + T cells and reduction of CD96 hi IL-22 + T cells. Using CRISPR screens, we identify the aryl hydrocarbon receptor (AHR) as a potent negative regulator of CXCL13 production by human CD4 + T cells. Transcriptomic, epigenetic and functional studies demonstrate that AHR coordinates with AP-1 family member JUN to prevent CXCL13 + T PH /T FH cell differentiation and promote an IL-22 + phenotype. Type I interferon, a pathogenic driver of SLE 7 , opposes AHR and JUN to promote T cell production of CXCL13. These results place CXCL13 + T PH /T FH cells on a polarization axis opposite from T helper 22 (T H 22) cells and reveal AHR, JUN and interferon as key regulators of these divergent T cell states. Insufficient AHR activation has been suggested in SLE, and augmenting AHR activation therapeutically may prevent CXCL13 + T PH /T FH differentiation and the subsequent recruitment of B cells and formation of lymphoid aggregates in inflamed tissues.

Recent advances in single-cell technologies have enabled the characterization of heterogeneous cell types in human diseases by measuring various features of individual cells, such as their transcriptomic, proteomic, and epigenomic profiles in the context of their spatial location in tissue. Due to the expensive cost and the high-dimensionality, sparsity, and noisiness of single-cell data investigators who wish to use single-cell technologies face key challenges in designing single-cell studies, performing integrative analysis of cells from multiple samples, and gleaning biological understanding from these data. In this dissertation, I present the development and application of novel computational methods and analysis frameworks that help address these challenges.First, I introduce scPOST, an algorithm for simulating large-scale, multi-sample single-cell RNA-sequencing datasets. scPOST enables investigators to simulate their future single-cell studies with different parameters, such as the number of cells, number of cells per sample, and number of batches. This allows investigators to determine the optimal design parameters for their study.Next, I introduce the development and application of two algorithms, Harmony and Crescendo, which are batch correction algorithms designed to help remove the batch effects that are prominent in single-cell data. I show that these algorithms feature superior performance in removing batch effects and are fast and scalable to large single-cell datasets that contain hundreds of thousands or even millions of cells. Finally, I showcase the application of these methods to analyzing a large 82-sample cohort of rheumatoid arthritis (RA) patients containing 314,000 cells. After performing batch correction with Harmony and a prospective power analysis with scPOST, I introduce a novel framework called cell-type abundance phenotypes (CTAPs) for classifying samples based on the abundance of cell types present in the sample. I then discuss how we used the CTAP framework to characterize the diversity of synovial inflammation in RA, identify disease-relevant cell states and transcriptomic signatures for different phenotypes of RA, and predict disease response.Overall, this work features a collection of computational methods that investigators can use to design their studies and analyze their single-cell data. These approaches are broadly applicable to many single-cell technologies and different diseases and will help investigators gain a greater understanding of how cells contribute to the pathology of a disease.

Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer–gene maps from disease-relevant tissues. Building enhancer–gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer–gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer–gene maps, essential for defining noncoding variant function. SCENT is a nonparametric method that models association between chromatin accessibility and gene expression in single-cell multimodal datasets, enabling construction of cell-type-specific enhancer–gene maps to aid mapping of candidate causal variants and genes for common diseases.