Publications

Regulatory T cells (Tregs), characterized by FOXP3 expression, are essential for maintaining immune homeostasis by controlling inflammation. However, in autoimmune diseases such as rheumatoid arthritis (RA), impaired Treg function contributes to immune dysregulation and disease pathology. While most studies of human Tregs have focused on blood, here we analyzed Tregs in synovial tissues from RA patients using single cell RNA sequencing (scRNAseq). We identified two predominant Treg states, CD25 hi CXCR6 pos Tregs with strong suppressive function, and CD25 lo AREG pos Tregs, a dysfunctional state exclusively enriched in synovial tissues but not in blood. Computational and in vitro analyses revealed that cortisol induced AREG expression, suppressed glycolysis, and impaired the suppressive function of CD25 lo AREG pos Tregs. In turn, AREG promoted an IL-33 + inflammatory phenotype in synovial fibroblasts. Importantly, we found that TNFR2 engagement can prevent or reverse this dysfunctional Treg state. In contrast to CD25 lo AREG pos Tregs, CD25 hi CXCR6 pos Tregs were highly suppressive, showed coordinated abundance with macrophages in synovial tissue, and functionally interacted with membrane-bound TNFα expressed by macrophages, which promoted their functional suppressive state. These two Treg subsets were similarly found in the synovial tissue in Juvenile Idiopathic Arthritis (JIA), another inflammatory arthritic disorder, indicating conserved mechanisms across arthritic diseases. Together, our findings define distinct pathways driving divergent functional and dysfunctional Treg states in inflamed tissues and point to interventions that may prevent or reverse the development of the dysfunctional state.

Aging of the blood system impacts systemic health and can be traced to hematopoietic stem cells (HSCs). Despite multiple reports on human HSC aging, a unified map detailing their molecular age-related changes is lacking. We developed a consensus map of gene expression in HSCs by integrating seven single-cell datasets. This map revealed previously unappreciated heterogeneity within the HSC population. It also links inflammatory pathway activation (TNF/NFκB, AP-1) and quiescence within a single gene expression program. This program dominates an inflammatory HSC subpopulation that increases with age, highlighting a potential target for further experimental studies and anti-aging interventions.

T cells recognize antigens and induce specialized gene expression programs (GEPs), enabling functions like proliferation, cytotoxicity and cytokine production. Traditionally, different T cell classes are thought to exhibit mutually exclusive responses, including TH1, TH2 and TH17 programs. However, single-cell RNA sequencing has revealed a continuum of T cell states without clearly distinct subsets, necessitating new analytical frameworks. Here, we introduce T-CellAnnoTator (TCAT), a pipeline that improves T cell characterization by simultaneously quantifying predefined GEPs capturing activation states and cellular subsets. Analyzing 1,700,000 T cells from 700 individuals spanning 38 tissues and five disease contexts, we identify 46 reproducible GEPs reflecting core T cell functions including proliferation, cytotoxicity, exhaustion and effector states. We experimentally demonstrate new activation programs and apply TCAT to characterize activation GEPs that predict immune checkpoint inhibitor response across multiple tumor types. Our software package starCAT generalizes this framework, enabling reproducible annotation in other cell types and tissues.

Monocytes and macrophages in patients with lupus nephritis exhibit altered behavior compared with healthy kidneys. How to optimally use mouse models to develop treatments targeting these cells is poorly understood. This study compared intrarenal myeloid cells in four mouse models and 155 lupus nephritis patients using single-cell profiling, spatial transcriptomics, and functional studies. Across mouse models, monocyte and macrophage subsets consistently expanded or contracted in disease. A subset of murine classical monocytes expanded in disease; these cells expressed Cd9, Spp1, Ctsd, Cd63, Apoe, and Trem2, genes associated with tissue injury in other organs that play roles in inflammation, lipid metabolism, and tissue repair. Resident macrophages expressed similar genes in clinical disease. In humans, we identified analogous disease-associated monocytes and macrophages that were associated with kidney histological subtypes and disease progression, sharing gene expression and localizing to similar kidney microenvironments as in mice. This cross-species analysis supports the use of mouse functional studies for understanding human lupus nephritis.

T cell receptors (TCRs) orchestrate adaptive immunity, yet the complex, repetitive architecture of the TCR loci has impeded systematic characterization of human genetic variation in the genes encoding the TCR. Using public long-read sequencing data from 2,668 donors, we build a near-complete map of common alleles in TCR V, D, and J genes, revealing amino acid variation at almost every position within V genes. We discover pervasive evidence of natural selection on TCR genes, including balancing selection on a TRAJ gene recognizing an immunodominant influenza epitope and positive selection on a TRAV gene. We find TCR allelic polymorphism alters core functional properties of T cells, including thymic fate commitment, phenotypes in diseased tissues, and cell-surface receptor abundance. Collectively, our findings position inherited variation in TCR genes as a key axis of immunological diversity that may shape interindividual differences in immune responses.

Genetic studies have identified thousands of individual disease-associated non-coding alleles, but the identification of the causal alleles and their functions remains a critical bottleneck1. CRISPR-Cas editing has enabled targeted modification of DNA to introduce and test disease alleles. However, the combination of inefficient editing, heterogeneous editing outcomes in individual cells and nonspecific transcriptional changes caused by editing and culturing conditions limits the ability to detect the functional consequences of disease alleles2,3. To overcome these challenges, we present a multi-omic single-cell sequencing approach that directly identifies genomic DNA edits, assays the transcriptome and measures cell-surface protein expression. We apply this approach to investigate the effects of gene disruption, deletions in regulatory regions, non-coding single-nucleotide polymorphism alleles and multiplexed editing. We identify the effects of individual single-nucleotide polymorphisms, including the state-specific effects of an IL2RA autoimmune variant in primary human T cells. Multimodal functional genomic single-cell assays, including DNA sequencing, enable the identification of causal variation in primary human cells and bridge a crucial gap in our understanding of complex human diseases.

Lupus nephritis (LN) is a frequent manifestation of systemic lupus erythematosus, and fewer than half of patients achieve complete renal response with standard immunosuppressants. Identifying non-invasive, blood-based immune alterations associated with renal injury could aid therapeutic decisions. Here, we used mass cytometry immunophenotyping of peripheral blood mononuclear cells in 145 patients with biopsy-proven LN and 40 healthy controls to evaluate the heterogeneity of immune activation and identify correlates of renal parameters. Unbiased analysis identified three immunologically distinct groups of patients that were associated with different patterns of histopathology, renal cell infiltrates, urine proteomic profiles, and treatment response at one year. Patients with enriched circulating granzyme B+ T cells showed more active disease and increased numbers of activated CD8 T cells in the kidney, yet they had the highest likelihood of treatment response. A second group characterized by a high type I interferon signature had a lower likelihood of response to therapy, while a third group appeared immunologically inactive but with chronic renal injuries. The major immunologic axes of variation could be distilled down to five simple cytometric parameters that recapitulate several clinical associations, highlighting the potential for blood immunoprofiling to translate to clinically useful non-invasive metrics to assess immune-mediated disease in LN.

Differentiation of induced pluripotent stem cells (iPSCs) into specialized cell types is essential for uncovering cell-type specific molecular mechanisms and interrogating cellular function. Transcription factor screens have enabled efficient production of a few cell types; however, engineering cell types that require complex transcription factor combinations remains challenging. Here, we report an iterative, high-throughput single-cell transcription factor screening method that enables the identification of transcription factor combinations for specialized cell differentiation, which we validated by differentiating human microglia-like cells. We found that the expression of six transcription factors, SPI1, CEBPA, FLI1, MEF2C, CEBPB, and IRF8, is sufficient to differentiate human iPSC into cells with transcriptional and functional similarity to primary human microglia within 4 days. Through this screening method, we also describe a novel computational method allowing the exploration of single-cell RNA sequencing data derived from transcription factor perturbation assays to construct causal gene regulatory networks for future cell fate engineering.

Autoimmune rheumatic diseases are a heterogeneous group of conditions, including rheumatoid arthritis (RA) and systemic lupus erythematosus. With the increasing availability of large single-cell datasets, novel disease-associated cell types continue to be identified and characterized at multiple omics layers, for example, 'T peripheral helper' (TPH) (CXCR5- PD-1hi) cells in RA and systemic lupus erythematosus and MerTK+ myeloid cells in RA. Despite efforts to define disease-relevant cell atlases, the very definition of a 'cell type' or 'lineage' has proven controversial as higher resolution assays emerge. This Review explores the cell types and states involved in disease pathogenesis, with a focus on the shifting perspectives on immune and stromal cell taxonomy. These understandings of cell identity are closely related to the computational methods adopted for analysis, with implications for the interpretation of single-cell data. Understanding the underlying cellular architecture of disease is also crucial for therapeutic research as ambiguity hinders translation to the clinical setting. We discuss the implications of different frameworks for cell identity for disease treatment and the discovery of predictive biomarkers for stratified medicine - an unmet clinical need for autoimmune rheumatic diseases.

BACKGROUND: Chromatin accessibility, measured via single-nucleus Assay for Transposase-Accessible Chromatin with sequencing (snATAC-seq), can reveal the underpinnings of transcriptional regulation across heterogeneous cell states. As the number and scale of snATAC-seq datasets increases, we need robust computational pipelines to integrate samples within a dataset and datasets across studies. These integration pipelines should correct cell-state-obfuscating technical effects while conserving underlying biological cell states, as has been shown for single-cell RNA-seq (scRNA-seq) pipelines. However, scRNA-seq integration methods have performed inconsistently on snATAC-seq datasets, potentially due to sparsity and genomic feature differences.

RESULTS: Using single-nucleus multimodal datasets profiling ATAC and RNA simultaneously, we can measure snATAC-seq integration method performance by comparison to independently integrated snRNA-seq gold standard embeddings and annotations. Here, we benchmark 58 pipelines, incorporating 7 integration methods plus 1 embedding correction method with 5 feature sets. Using our command-line tool, we assessed 5 multimodal datasets at 3 different resolutions using 2 novel metrics to determine the best practices for multi-sample snATAC-seq integration. ATAC features outperformed Gene Activity Score (GAS) features, and embedding correction with Harmony was generally useful. SnapATAC2, PeakVI, and ArchR's iterative Latent Semantic Indexing (LSI) performed well.

CONCLUSIONS: We recommend SnapATAC2 + Harmony with pre-defined ENCODE candidate cis -regulatory element (cCRE) features as a first-pass pipeline given its metric performance, generalizability of features, and method resource-efficiency. This and other high-performing pipelines will guide future comprehensive gene regulation maps.