Single-cell RNA-seq reveals cell type-specific molecular and genetic associations to lupus

Single-cell analysis in rheumatic and allergic diseases: insights for clinical practice

Since the advent of single-cell RNA sequencing (scRNA-seq) methodology, single-cell analysis has become a powerful tool for exploration of cellular networks and dysregulated immune responses in disease pathogenesis. Advanced bioinformatics tools have enabled the combined analysis of scRNA-seq data and information on various cell properties, such as cell surface molecular profiles, chromatin accessibility and spatial information, leading to a deeper understanding of pathology. This Review provides an overview of the achievements in single-cell analysis applied to clinical samples of rheumatic and allergic diseases, including rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, allergic airway diseases and atopic dermatitis, with an expanded scope beyond peripheral blood cells to include local diseased tissues. Despite the valuable insights that single-cell analysis has provided into disease pathogenesis, challenges remain in translating single-cell findings into clinical practice and developing personalized treatment strategies. Beyond understanding the atlas of cellular diversity, we discuss the application of data obtained in each study to clinical practice, with a focus on identifying biomarkers and therapeutic targets.

PELI2 is a negative regulator of STING signaling that is dynamically repressed during viral infection

The innate immune cGAS-STING pathway is activated by cytosolic double-stranded DNA (dsDNA), a ubiquitous danger signal, to produce interferon, a potent anti-viral and anti-cancer cytokine. However, STING activation must be tightly controlled because aberrant interferon production leads to debilitating interferonopathies. Here, we discover PELI2 as a crucial negative regulator of STING. Mechanistically, PELI2 inhibits the transcription factor IRF3 by binding to phosphorylated Thr354 and Thr356 on the C-terminal tail of STING, leading to ubiquitination and inhibition of the kinase TBK1. PELI2 sets a threshold for STING activation that tolerates low levels of cytosolic dsDNA, such as that caused by silenced TREX1, RNASEH2B, BRCA1, or SETX. When this threshold is reached, such as during viral infection, STING-induced interferon production temporarily downregulates PELI2, creating a positive feedback loop allowing a robust immune response. Lupus patients have insufficient PELI2 levels and high basal interferon production, suggesting that PELI2 dysregulation may drive the onset of lupus and other interferonopathies.

A robust model for cell type-specific interindividual variation in single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) has been widely used to characterize cell types based on their average gene expression profiles. However, most studies do not consider cell type-specific variation across donors. Modelling this cell type-specific inter-individual variation could help elucidate cell type-specific biology and inform genes and cell types underlying complex traits. We therefore develop a new model to detect and quantify cell type-specific variation across individuals called CTMM (Cell Type-specific linear Mixed Model). We use extensive simulations to show that CTMM is powerful and unbiased in realistic settings. We also derive calibrated tests for cell type-specific interindividual variation, which is challenging given the modest sample sizes in scRNA-seq. We apply CTMM to scRNA-seq data from human induced pluripotent stem cells to characterize the transcriptomic variation across donors as cells differentiate into endoderm. We find that almost 100% of transcriptome-wide variability between donors is differentiation stage-specific. CTMM also identifies individual genes with statistically significant stage-specific variability across samples, including 85 genes that do not have significant stage-specific mean expression. Finally, we extend CTMM to partition interindividual covariance between stages, which recapitulates the overall differentiation trajectory. Overall, CTMM is a powerful tool to illuminate cell type-specific biology in scRNA-seq.

Fine-mapping causal tissues and genes at disease-associated loci

Heritable diseases often manifest in a highly tissue-specific manner, with different disease loci mediated by genes in distinct tissues or cell types. We propose Tissue-Gene Fine-Mapping (TGFM), a fine-mapping method that infers the posterior probability (PIP) for each gene-tissue pair to mediate a disease locus by analyzing GWAS summary statistics (and in-sample LD) and leveraging eQTL data from diverse tissues to build cis-predicted expression models; TGFM also assigns PIPs to causal variants that are not mediated by gene expression in assayed genes and tissues. TGFM accounts for both co-regulation across genes and tissues and LD between SNPs (generalizing existing fine-mapping methods), and incorporates genome-wide estimates of each tissue's contribution to disease as tissue-level priors. TGFM was well-calibrated and moderately well-powered in simulations; unlike previous methods, TGFM was able to attain correct calibration by modeling uncertainty in cis-predicted expression models. We applied TGFM to 45 UK Biobank diseases/traits (average N = 316K) using eQTL data from 38 GTEx tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease/trait, of which 11% were gene-tissue pairs. Implicated gene-tissue pairs were concentrated in known disease-critical tissues, and causal genes were strongly enriched in disease-relevant gene sets. Causal gene-tissue pairs identified by TGFM recapitulated known biology (e.g., TPO -thyroid for Hypothyroidism), but also included biologically plausible novel findings (e.g., SLC20A2 -artery aorta for Diastolic blood pressure). Further application of TGFM to single-cell eQTL data from 9 cell types in peripheral blood mononuclear cells (PBMC), analyzed jointly with GTEx tissues, identified 30 additional causal gene-PBMC cell type pairs at PIP > 0.5-primarily for autoimmune disease and blood cell traits, including the biologically plausible example of CD52 in classical monocyte cells for Monocyte count. In conclusion, TGFM is a robust and powerful method for fine-mapping causal tissues and genes at disease-associated loci.

Disease activity drives divergent epigenetic and transcriptomic reprogramming of monocyte subpopulations in systemic lupus erythematosus

OBJECTIVES

Systemic lupus erythematosus (SLE) is characterised by systemic inflammation involving various immune cell types. Monocytes, pivotal in promoting and regulating inflammation in SLE, differentiate from classic monocytes into intermediate and non-classic monocytes, assuming diverse roles and changing their proportions in inflammation. In this study, we investigated the epigenetic and transcriptomic profiles of these and novel monocyte subsets in SLE in relation to activity and progression.

METHODS

We obtained the DNA methylomes and transcriptomes of classic, intermediate, non-classic monocytes in patients with SLE (at first and follow-up visits) and healthy donors. We integrated these data with single-cell transcriptomics of SLE and healthy donors and interrogated their relationships with activity and progression.

RESULTS

In addition to shared DNA methylation and transcriptomic alterations associated with a strong interferon signature, we identified monocyte subset-specific alterations, especially in DNA methylation, which reflect an impact of SLE on monocyte differentiation. SLE classic monocytes exhibited a proinflammatory profile and were primed for macrophage differentiation. SLE non-classic monocytes displayed a T cell differentiation-related phenotype, with Th17-regulating features. Changes in monocyte proportions, DNA methylation and expression occurred in relation to disease activity and involved the STAT pathway. Integration of bulk with single-cell RNA sequencing datasets revealed disease activity-dependent expansion of SLE-specific monocyte subsets, further supported the interferon signature for classic monocytes, and associated intermediate and non-classic populations with exacerbated complement activation.

CONCLUSIONS

Disease activity in SLE drives a subversion of the epigenome and transcriptome programme in monocyte differentiation, impacting the function of different subsets and allowing to generate predictive methods for activity and progression.

Machine learning integrative approaches to advance computational immunology

The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.

Single-cell sequencing reveals distinct immune cell features in cutaneous lesions of pemphigus vulgaris and bullous pemphigoid

Bullous pemphigoid (BP) and pemphigus vulgaris (PV) are two common subtypes of autoimmune bullous disease (AIBD). The key role of circulating autoreactive immune cells contributing to skin damage of AIBD has been widely recognized. Nevertheless, the immune characteristics in cutaneous lesions remain unclear. Here, we performed single-cell RNA sequencing (scRNA-seq) and single-cell VDJ sequencing (scRNA-seq) to generate transcriptional profiles for cells and T/B cell clonetype in skin lesions of BP and PV. We found that the proportions of NK&T, macrophages/ dendritic cells, B cells, and mast cells increased in BP and PV lesions. Then, BP and PV cells constituted over 75% of all myeloid cell subtypes, CD4+ T cell subtypes and CD8+ T cell subtypes. Strikingly, CD8+ Trm was identified to be expanded in PV, and located in the intermediate state of the pseudotime trajectory from CD8+ Tm to CD8+ Tem. Interestingly, CD8+ Tem and CD4+ Treg highly expressed exhaustion-related genes, especially in BP lesions. Moreover, the enhanced cell communication between stromal cells and immune cells like B cells and macrophages/ dendritic cells was also identified in BP and PV lesions. Finally, clone expansion was observed in T cells of BP and PV compared with HC, while CD8+ Trm represented the highest ratio of hyperexpanded TCR clones among all T cell subtypes. Our study generally depicts a large and comprehensive single-cell landscape of cutaneous lesions and highlights immune cell features in BP and PV. This offers potential research targets for further investigation.

Granzyme serine proteases in inflammation and rheumatic diseases

Granzymes (granule-secreted enzymes) are a family of serine proteases that have been viewed as redundant cytotoxic enzymes since their discovery more than 30 years ago. Predominantly produced by cytotoxic lymphocytes and natural killer cells, granzymes are delivered into the cytoplasm of target cells through immunological synapses in cooperation with the pore-forming protein perforin. After internalization, granzymes can initiate cell death through the cleavage of intracellular substrates. However, evidence now also demonstrates the existence of non-cytotoxic, pro-inflammatory, intracellular and extracellular functions that are granzyme specific. Under pathological conditions, granzymes can be produced and secreted extracellularly by immune cells as well as by non-immune cells. Depending on the granzyme, accumulation in the extracellular milieu might contribute to inflammation, tissue injury, impaired wound healing, barrier dysfunction, osteoclastogenesis and/or autoantigen generation.

Multiomic analyses uncover immunological signatures in acute and chronic coronary syndromes

Acute and chronic coronary syndromes (ACS and CCS) are leading causes of mortality. Inflammation is considered a key pathogenic driver of these diseases, but the underlying immune states and their clinical implications remain poorly understood. Multiomic factor analysis (MOFA) allows unsupervised data exploration across multiple data types, identifying major axes of variation and associating these with underlying molecular processes. We hypothesized that applying MOFA to multiomic data obtained from blood might uncover hidden sources of variance and provide pathophysiological insights linked to clinical needs. Here we compile a longitudinal multiomic dataset of the systemic immune landscape in both ACS and CCS (n = 62 patients in total, n = 15 women and n = 47 men) and validate this in an external cohort (n = 55 patients in total, n = 11 women and n = 44 men). MOFA reveals multicellular immune signatures characterized by distinct monocyte, natural killer and T cell substates and immune-communication pathways that explain a large proportion of inter-patient variance. We also identify specific factors that reflect disease state or associate with treatment outcome in ACS as measured using left ventricular ejection fraction. Hence, this study provides proof-of-concept evidence for the ability of MOFA to uncover multicellular immune programs in cardiovascular disease, opening new directions for mechanistic, biomarker and therapeutic studies.

Systematic decoding of cis gene regulation defines context-dependent control of the multi-gene costimulatory receptor locus in human T cells

Cis-regulatory elements (CREs) interact with trans regulators to orchestrate gene expression, but how transcriptional regulation is coordinated in multi-gene loci has not been experimentally defined. We sought to characterize the CREs controlling dynamic expression of the adjacent costimulatory genes CD28, CTLA4 and ICOS, encoding regulators of T cell-mediated immunity. Tiling CRISPR interference (CRISPRi) screens in primary human T cells, both conventional and regulatory subsets, uncovered gene-, cell subset- and stimulation-specific CREs. Integration with CRISPR knockout screens and assay for transposase-accessible chromatin with sequencing (ATAC-seq) profiling identified trans regulators influencing chromatin states at specific CRISPRi-responsive elements to control costimulatory gene expression. We then discovered a critical CCCTC-binding factor (CTCF) boundary that reinforces CRE interaction with CTLA4 while also preventing promiscuous activation of CD28. By systematically mapping CREs and associated trans regulators directly in primary human T cell subsets, this work overcomes longstanding experimental limitations to decode context-dependent gene regulatory programs in a complex, multi-gene locus critical to immune homeostasis.

Cell-type specific inference from bulk RNA-sequencing data by integrating single cell reference profiles via EPIC-unmix

Cell type specific (CTS) analysis is essential to reveal biological insights obscured in bulk tissue data. However, single-cell (sc) or single-nuclei (sn) resolution data are still cost-prohibitive for large-scale samples. Thus, computational methods to perform deconvolution from bulk tissue data are highly valuable. We here present EPIC-unmix, a novel two-step empirical Bayesian method integrating reference sc/sn RNA-seq data and bulk RNA-seq data from target samples to enhance the accuracy of CTS inference. We demonstrate through comprehensive simulations across three tissues that EPIC-unmix achieved 4.6% - 109.8% higher accuracy compared to alternative methods. By applying EPIC-unmix to human bulk brain RNA-seq data from the ROSMAP and MSBB cohorts, we identified multiple genes differentially expressed between Alzheimer's disease (AD) cases versus controls in a CTS manner, including 57.4% novel genes not identified using similar sample size sc/snRNA-seq data, indicating the power of our in-silico approach. Among the 6-69% overlapping, 83%-100% are in consistent direction with those from sc/snRNA-seq data, supporting the reliability of our findings. EPIC-unmix inferred CTS expression profiles similarly empowers CTS eQTL analysis. Among the novel eQTLs, we highlight a microglia eQTL for AD risk gene AP3B2, obscured in bulk and missed by sc/snRNA-seq based eQTL analysis. The variant resides in a microglia-specific cCRE, forming chromatin loop with AP3B2 promoter region in microglia. Taken together, we believe EPIC-unmix will be a valuable tool to enable more powerful CTS analysis.

Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges

Single-cell RNA sequencing (scRNA-Seq) is a recent technology that allows for the measurement of the expression of all genes in each individual cell contained in a sample. Information at the single-cell level has been shown to be extremely useful in many areas. However, performing single-cell experiments is expensive. Although cellular deconvolution cannot provide the same comprehensive information as single-cell experiments, it can extract cell-type information from bulk RNA data, and therefore it allows researchers to conduct studies at cell-type resolution from existing bulk datasets. For these reasons, a great effort has been made to develop such methods for cellular deconvolution. The large number of methods available, the requirement of coding skills, inadequate documentation, and lack of performance assessment all make it extremely difficult for life scientists to choose a suitable method for their experiment. This paper aims to fill this gap by providing a comprehensive review of 53 deconvolution methods regarding their methodology, applications, performance, and outstanding challenges. More importantly, the article presents a benchmarking of all these 53 methods using 283 cell types from 30 tissues of 63 individuals. We also provide an R package named DeconBenchmark that allows readers to execute and benchmark the reviewed methods (https://github.com/tinnlab/DeconBenchmark).

Uncoupling interferons and the interferon signature explains clinical and transcriptional subsets in SLE

Systemic lupus erythematosus (SLE) displays a hallmark interferon (IFN) signature. Yet, clinical trials targeting type I IFN (IFN-I) have shown variable efficacy, and blocking IFN-II failed to treat SLE. Here, we show that IFN type levels in SLE vary significantly across clinical and transcriptional endotypes. Whereas skin involvement correlated with IFN-I alone, systemic features like nephritis associated with co-elevation of IFN-I, IFN-II, and IFN-III, indicating additive IFN effects in severe SLE. Notably, while high IFN-II/-III levels without IFN-I had a limited effect on disease activity, IFN-II was linked to IFN-I-independent transcriptional profiles (e.g., OXPHOS and CD8+GZMH+ cells), and IFN-III enhanced IFN-induced gene expression when co-elevated with IFN-I. Moreover, dysregulated IFNs do not explain the IFN signature in 64% of patients or clinical manifestations including cytopenia, serositis, and anti-phospholipid syndrome, implying IFN-independent endotypes in SLE. This study sheds light on mechanisms underlying SLE heterogeneity and the variable response to IFN-targeted therapies in clinical trials.

Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes

Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants. Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants. EXPRESSO substantially improves existing methods. We apply EXPRESSO to analyze multi-ancestry GWAS datasets for 14 autoimmune diseases. EXPRESSO uniquely identifies 958 novel gene x trait associations, which is 26% more than the second-best method. Among them, 492 are unique to cell type level analysis and missed by TWAS using whole blood. We also develop a cell type aware drug repurposing pipeline, which leverages EXPRESSO results to identify drug compounds that can reverse disease gene expressions in relevant cell types. Our results point to multiple drugs with therapeutic potentials, including metformin for type 1 diabetes, and vitamin K for ulcerative colitis.

Increased Thyroid DPP4 Expression Is Associated With Inflammatory Process in Patients With Hashimoto Thyroiditis

CONTEXT

Dipeptidyl peptidase-4 (DPP4) is originally described as a surface protein in lymphocytes. Lymphocyte infiltration and subsequent destruction of thyroid tissue have been considered as the central pathological mechanism in Hashimoto thyroiditis (HT).

OBJECTIVE

The present study aimed to investigate DPP4 expression in peripheral blood and thyroid tissue in HT patients, and explore the role of DPP4 in the pathophysiological process of HT.

METHODS

This case-control study recruited 40 drug-naive HT patients and 81 control individuals. Peripheral blood and thyroid specimens were collected for assessing the expression and activity of DPP4. Moreover, single-cell RNA sequencing (scRNA-seq) analysis of 6 "para-tumor tissues" samples from scRNA-seq data set GSE184362 and in vitro cell experiments were also conducted.

RESULTS

The HT patients had similar DPP4 serum concentration and activity as the controls. However, the expression and activity of DPP4 was significantly increased in the thyroid of the HT group than in the control group. The scRNA-seq analysis showed that DPP4 expression was significantly increased in the HT group, and mainly expressed in T cells. Further in vitro studies showed that inhibition of lymphocyte DPP4 activity with sitagliptin downregulated the production of inflammatory factors in co-cultured thyroid cells.

CONCLUSION

DPP4 expression was significantly increased in the thyroid of the HT group compared with the control group, and was mainly localized in the lymphocytes. Inhibition of lymphocyte DPP4 activity reduced the production of inflammatory factors in co-cultured thyroid cells. Therefore, inhibition of DPP4 may have a beneficial effect by alleviating inflammatory reactions in HT patients.

Efficient and accurate mixed model association tool for single-cell eQTL analysis

Understanding the genetic basis of gene expression can help us understand the molecular underpinnings of human traits and disease. Expression quantitative trait locus (eQTL) mapping can help in studying this relationship but have been shown to be very cell-type specific, motivating the use of single-cell RNA sequencing and single-cell eQTLs to obtain a more granular view of genetic regulation. Current methods for single-cell eQTL mapping either rely on the "pseudobulk" approach and traditional pipelines for bulk transcriptomics or do not scale well to large datasets. Here, we propose SAIGE-QTL, a robust and scalable tool that can directly map eQTLs using single-cell profiles without needing aggregation at the pseudobulk level. Additionally, SAIGE-QTL allows for testing the effects of less frequent/rare genetic variation through set-based tests, which is traditionally excluded from eQTL mapping studies. We evaluate the performance of SAIGE-QTL on both real and simulated data and demonstrate the improved power for eQTL mapping over existing pipelines.

Unraveling transcriptomic signatures and dysregulated pathways in systemic lupus erythematosus across disease states

OBJECTIVES

This study aims to elucidate the transcriptomic signatures and dysregulated pathways in patients with Systemic Lupus Erythematosus (SLE), with a particular focus on those persisting during disease remission.

METHODS

We conducted bulk RNA-sequencing of peripheral blood mononuclear cells (PBMCs) from a well-defined cohort comprising 26 remission patients meeting the Low Lupus Disease Activity State (LLDAS) criteria, 76 patients experiencing disease flares, and 15 healthy controls. To elucidate immune signature changes associated with varying disease states, we performed extensive analyses, including the identification of differentially expressed genes and pathways, as well as the construction of protein-protein interaction networks.

RESULTS

Several transcriptomic features recovered during remission compared to the active disease state, including down-regulation of plasma and cell cycle signatures, as well as up-regulation of lymphocytes. However, specific innate immune response signatures, such as the interferon (IFN) signature, and gene modules involved in chromatin structure modification, persisted across different disease states. Drug repurposing analysis revealed certain drug classes that can target these persistent signatures, potentially preventing disease relapse.

CONCLUSION

Our comprehensive transcriptomic study revealed gene expression signatures for SLE in both active and remission states. The discovery of gene expression modules persisting in the remission stage may shed light on the underlying mechanisms of vulnerability to relapse in these patients, providing valuable insights for their treatment.

Deficiency of CBL and CBLB ubiquitin ligases leads to hyper T follicular helper cell responses and lupus by reducing BCL6 degradation

Recent evidence reveals hyper T follicular helper (Tfh) cell responses in systemic lupus erythematosus (SLE); however, molecular mechanisms responsible for hyper Tfh cell responses and whether they cause SLE are unclear. We found that SLE patients downregulated both ubiquitin ligases, casitas B-lineage lymphoma (CBL) and CBLB (CBLs), in CD4+ T cells. T cell-specific CBLs-deficient mice developed hyper Tfh cell responses and SLE, whereas blockade of Tfh cell development in the mutant mice was sufficient to prevent SLE. ICOS was upregulated in SLE Tfh cells, whose signaling increased BCL6 by attenuating BCL6 degradation via chaperone-mediated autophagy (CMA). Conversely, CBLs restrained BCL6 expression by ubiquitinating ICOS. Blockade of BCL6 degradation was sufficient to enhance Tfh cell responses. Thus, the compromised expression of CBLs is a prevalent risk trait shared by SLE patients and causative to hyper Tfh cell responses and SLE. The ICOS-CBLs axis may be a target to treat SLE.

From genetic associations to genes: methods, applications, and challenges

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

Kinetics of IFNγ-Induced Cytokines and Development of Immune-Related Adverse Events in Patients Receiving PD-(L)1 Inhibitors

Immune checkpoint inhibitors (ICI) have the potential to trigger unpredictable immune-related adverse events (irAEs), which can be severe. The underlying mechanisms of these events are not fully understood. As PD-L1 is upregulated by IFN, the heightened immune activation resulting from PD-1/PD-L1 inhibition may enhance the IFN response, triggering the expression of IFN-inducible genes and contributing to irAE development and its severity. In this study, we investigated the interplay between irAEs and the expression of IFN-inducible chemokines and cytokines in 134 consecutive patients with solid tumours treated with PD-(L)1 inhibitors as monotherapy or in combination with chemotherapy or other immunotherapy agents. We compared the plasma levels of IFN-associated cytokines (CXCL9/10/11, IL-18, IL-10, IL-6 and TGFβ) at various time points (at baseline, at the onset of irAE and previous to irAE onset) in three patient groups categorized by irAE development and severity: patients with serious irAEs, mild irAEs and without irAEs after PD-(L)1 inhibitors. No differences were observed between groups at baseline. However, patients with serious irAEs exhibited significant increases in CXCL9/10/11, IL-18 and IL-10 levels at the onset of the irAE compared to baseline. A network analysis and correlation patterns highlighted a robust relationship among these chemokines and cytokines at serious-irAE onset. Combining all of the analysed proteins in a cluster analysis, we identified a subgroup of patients with a higher incidence of serious irAEs affecting different organs or systems. Finally, an ROC analysis and a decision tree model proposed IL-18 levels ≥ 807 pg/mL and TGFβ levels ≤ 114 pg/mL as predictors for serious irAEs in 90% of cases. In conclusion, our study elucidates the dynamic changes in cytokine profiles associated with serious irAE development during treatment with PD-(L)1 inhibitors. The study's findings offer valuable insights into the intricate IFN-induced immune responses associated with irAEs and propose potential predictive markers for their severity.

Single-nucleotide variant calling in single-cell sequencing data with Monopogen

Single-cell omics technologies enable molecular characterization of diverse cell types and states, but how the resulting transcriptional and epigenetic profiles depend on the cell's genetic background remains understudied. We describe Monopogen, a computational tool to detect single-nucleotide variants (SNVs) from single-cell sequencing data. Monopogen leverages linkage disequilibrium from external reference panels to identify germline SNVs and detects putative somatic SNVs using allele cosegregating patterns at the cell population level. It can identify 100 K to 3 M germline SNVs achieving a genotyping accuracy of 95%, together with hundreds of putative somatic SNVs. Monopogen-derived genotypes enable global and local ancestry inference and identification of admixed samples. It identifies variants associated with cardiomyocyte metabolic levels and epigenomic programs. It also improves putative somatic SNV detection that enables clonal lineage tracing in primary human clonal hematopoiesis. Monopogen brings together population genetics, cell lineage tracing and single-cell omics to uncover genetic determinants of cellular processes.

A multi-organ map of the human immune system across age, sex and ethnicity

Understanding tissue biology's heterogeneity is crucial for advancing precision medicine. Despite the centrality of the immune system in tissue homeostasis, a detailed and comprehensive map of immune cell distribution and interactions across human tissues and demographics remains elusive. To fill this gap, we harmonised data from 12,981 single-cell RNA sequencing samples and curated 29 million cells from 45 anatomical sites to create a comprehensive compositional and transcriptional healthy map of the healthy immune system. We used this resource and a novel multilevel modelling approach to track immune ageing and test differences across sex and ethnicity. We uncovered conserved and tissue-specific immune-ageing programs, resolved sex-dependent differential ageing and identified ethnic diversity in clinically critical immune checkpoints. This study provides a quantitative baseline of the immune system, facilitating advances in precision medicine. By sharing our immune map, we hope to catalyse further breakthroughs in cancer, infectious disease, immunology and precision medicine.

hadge: a comprehensive pipeline for donor deconvolution in single-cell studies

Single-cell multiplexing techniques (cell hashing and genetic multiplexing) combine multiple samples, optimizing sample processing and reducing costs. Cell hashing conjugates antibody-tags or chemical-oligonucleotides to cell membranes, while genetic multiplexing allows to mix genetically diverse samples and relies on aggregation of RNA reads at known genomic coordinates. We develop hadge (hashing deconvolution combined with genotype information), a Nextflow pipeline that combines 12 methods to perform both hashing- and genotype-based deconvolution. We propose a joint deconvolution strategy combining best-performing methods and demonstrate how this approach leads to the recovery of previously discarded cells in a nuclei hashing of fresh-frozen brain tissue.

Skin in the game: a review of single-cell and spatial transcriptomics in dermatological research

Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) are two emerging research technologies that uniquely characterize gene expression microenvironments on a cellular or subcellular level. The skin, a clinically accessible tissue composed of diverse, essential cell populations, serves as an ideal target for these high-resolution investigative approaches. Using these tools, researchers are assembling a compendium of data and discoveries in healthy skin as well as a range of dermatologic pathophysiologies, including atopic dermatitis, psoriasis, and cutaneous malignancies. The ongoing advancement of single-cell approaches, coupled with anticipated decreases in cost with increased adoption, will reshape dermatologic research, profoundly influencing disease characterization, prognosis, and ultimately clinical practice.

B-cell immune dysregulation with low soluble CD22 levels in refractory seronegative myasthenia gravis

Myasthenia gravis (MG), primarily caused by acetylcholine receptor (AChR) autoantibodies, is a chronic autoimmune disorder causing severe muscle weakness and fatigability. In particular, seronegative MG constitutes 10%-15% of MG cases and presents diagnostic challenges especially in early-onset female patients who often show severe disease and resistance to immunosuppressive therapy. Furthermore, the immunopathology of seronegative MG remains unclear. Thus, in this study, we aimed to elucidate the pathogenic mechanism of seronegative MG using scRNA-seq analysis and plasma proteome analysis; in particular, we investigated the relationship between immune dysregulation status and disease severity in refractory seronegative MG. Employing single-cell RNA-sequencing and plasma proteome analyses, we analyzed peripheral blood samples from 30 women divided into three groups: 10 healthy controls, 10 early-onset AChR-positive MG, and 10 refractory early-onset seronegative MG patients, both before and after intravenous immunoglobulin treatment. The disease severity was evaluated using the MG-Activities of Daily Living (ADL), MG composite (MGC), and revised 15-item MG-Quality of Life (QOL) scales. We observed numerical abnormalities in multiple immune cells, particularly B cells, in patients with refractory seronegative MG, correlating with disease activity. Notably, severe MG cases had fewer regulatory T cells without functional abnormalities. Memory B cells were found to be enriched in peripheral blood cells compared with naïve B cells. Moreover, plasma proteome analysis indicated significantly lower plasma protein levels of soluble CD22, expressed in the lineage of B-cell maturation (including mature B cells and memory B cells), in refractory seronegative MG patients than in healthy donors or patients with AChR-positive MG. Soluble CD22 levels were correlated with disease severity, B-cell frequency, and RNA expression levels of CD22. In summary, this study elucidates the immunopathology of refractory seronegative MG, highlighting immune disorders centered on B cells and diminished soluble CD22 levels. These insights pave the way for novel MG treatment strategies focused on B-cell biology.

Shared genetic architecture between autoimmune disorders and B-cell acute lymphoblastic leukemia: insights from large-scale genome-wide cross-trait analysis

BACKGROUND

To study the shared genetic structure between autoimmune diseases and B-cell acute lymphoblastic leukemia (B-ALL) and identify the shared risk loci and genes and genetic mechanisms involved.

METHODS

Based on large-scale genome-wide association study (GWAS) summary-level data sets, we observed genetic overlaps between autoimmune diseases and B-ALL, and cross-trait pleiotropic analysis was performed to detect shared pleiotropic loci and genes. A series of functional annotation and tissue-specific analysis were performed to determine the influence of pleiotropic genes. The heritability enrichment analysis was used to detect crucial immune cells and tissues. Finally, bidirectional Mendelian randomization (MR) methods were utilized to investigate the casual associations.

RESULTS

Our research highlighted shared genetic mechanisms between seven autoimmune disorders and B-ALL. A total of 73 pleiotropic loci were identified at the genome-wide significance level (P < 5 × 10-8), 16 of which had strong evidence of colocalization. We demonstrated that several loci have been previously reported (e.g., 17q21) and discovered some novel loci (e.g., 10p12, 5p13). Further gene-level identified 194 unique pleiotropic genes, for example IKZF1, GATA3, IKZF3, GSDMB, and ORMDL3. Pathway analysis determined the key role of cellular response to cytokine stimulus, B cell activation, and JAK-STAT signaling pathways. SNP-level and gene-level tissue enrichment suggested that crucial role pleiotropic mechanisms involved in the spleen, whole blood, and EBV-transformed lymphocytes. Also, hyprcoloc and stratified LD score regression analyses revealed that B cells at different developmental stages may be involved in mechanisms shared between two different diseases. Finally, two-sample MR analysis determined causal effects of asthma and rheumatoid arthritis on B-ALL.

CONCLUSIONS

Our research proved shared genetic architecture between autoimmune disorders and B-ALL and shed light on the potential mechanism that might involve in.

Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq

We present a non-parametric statistical method called TDEseq that takes full advantage of smoothing splines basis functions to account for the dependence of multiple time points in scRNA-seq studies, and uses hierarchical structure linear additive mixed models to model the correlated cells within an individual. As a result, TDEseq demonstrates powerful performance in identifying four potential temporal expression patterns within a specific cell type. Extensive simulation studies and the analysis of four published scRNA-seq datasets show that TDEseq can produce well-calibrated p-values and up to 20% power gain over the existing methods for detecting temporal gene expression patterns.

High-throughput identification of functional regulatory SNPs in systemic lupus erythematosus

Genome-wide association studies implicate multiple loci in risk for systemic lupus erythematosus (SLE), but few contain exonic variants, rendering systematic identification of non-coding variants essential to decoding SLE genetics. We utilized SNP-seq and bioinformatic enrichment to interrogate 2180 single-nucleotide polymorphisms (SNPs) from 87 SLE risk loci for potential binding of transcription factors and related proteins from B cells. 52 SNPs that passed initial screening were tested by electrophoretic mobility shift and luciferase reporter assays. To validate the approach, we studied rs2297550 in detail, finding that the risk allele enhanced binding to the transcription factor Ikaros (IKZF1), thereby modulating expression of IKBKE. Correspondingly, primary cells from genotyped healthy donors bearing the risk allele expressed higher levels of the interferon / NF-κB regulator IKKϵ. Together, these findings define a set of likely functional non-coding lupus risk variants and identify a new regulatory pathway involving rs2297550, Ikaros, and IKKϵ implicated by human genetics in risk for SLE.

Single-cell profiling reveals kidney CD163+ dendritic cell participation in human lupus nephritis

OBJECTIVES

The current work aimed to provide a comprehensive single-cell landscape of lupus nephritis (LN) kidneys, including immune and non-immune cells, identify disease-associated cell populations and unravel their participation within the kidney microenvironment.

METHODS

Single-cell RNA and T cell receptor sequencing were performed on renal biopsy tissues from 40 patients with LN and 6 healthy donors as controls. Matched peripheral blood samples from seven LN patients were also sequenced. Multiplex immunohistochemical analysis was performed on an independent cohort of 60 patients and validated using flow cytometric characterisation of human kidney tissues and in vitro assays.

RESULTS

We uncovered a notable enrichment of CD163+ dendritic cells (DC3s) in LN kidneys, which exhibited a positive correlation with the severity of LN. In contrast to their counterparts in blood, DC3s in LN kidney displayed activated and highly proinflammatory phenotype. DC3s showed strong interactions with CD4+ T cells, contributing to intrarenal T cell clonal expansion, activation of CD4+ effector T cell and polarisation towards Th1/Th17. Injured proximal tubular epithelial cells (iPTECs) may orchestrate DC3 activation, adhesion and recruitment within the LN kidneys. In cultures, blood DC3s treated with iPTECs acquired distinct capabilities to polarise Th1/Th17 cells. Remarkably, the enumeration of kidney DC3s might be a potential biomarker for induction treatment response in LN patients.

CONCLUSION

The intricate interplay involving DC3s, T cells and tubular epithelial cells within kidneys may substantially contribute to LN pathogenesis. The enumeration of renal DC3 holds potential as a valuable stratification feature for guiding LN patient treatment decisions in clinical practice.

A novel subpopulation of monocytes with a strong interferon signature indicated by SIGLEC-1 is present in patients with in recent-onset type 1 diabetes

AIMS/HYPOTHESIS

Type 1 diabetes is a T cell-mediated autoimmune disease characterised by pancreatic beta cell destruction. In this study, we explored the pathogenic immune responses in initiation of type 1 diabetes and new immunological targets for type 1 diabetes prevention and treatment.

METHODS

We obtained peripheral blood samples from four individuals with newly diagnosed latent autoimmune diabetes in adults (LADA) and from four healthy control participants. Single-cell RNA-sequencing (scRNA-seq) was performed on peripheral blood mononuclear cells to uncover transcriptomic profiles of early LADA. Validation was performed through flow cytometry in a cohort comprising 54 LADA, 17 adult-onset type 2 diabetes, and 26 healthy adults, matched using propensity score matching (PSM) based on age and sex. A similar PSM method matched 15 paediatric type 1 diabetes patients with 15 healthy children. Further flow cytometry analysis was performed in both peripheral blood and pancreatic tissues of non-obese diabetic (NOD) mice. Additionally, cell adoptive transfer and clearance assays were performed in NOD mice to explore the role of this monocyte subset in islet inflammation and onset of type 1 diabetes.

RESULTS

The scRNA-seq data showed that upregulated genes in peripheral T cells and monocytes from early-onset LADA patients were primarily enriched in the IFN signalling pathway. A new cluster of classical monocytes (cluster 4) was identified, and the proportion of this cluster was significantly increased in individuals with LADA compared with healthy control individuals (11.93% vs 5.93%, p=0.017) and that exhibited a strong IFN signature marked by SIGLEC-1 (encoding sialoadhesin). These SIGLEC-1+ monocytes expressed high levels of genes encoding C-C chemokine receptors 1 or 2, as well as genes for chemoattractants for T cells and natural killer cells. They also showed relatively low levels of genes for co-stimulatory and HLA molecules. Flow cytometry analysis verified the elevated levels of SIGLEC-1+ monocytes in the peripheral blood of participants with LADA and paediatric type 1 diabetes compared with healthy control participants and those with type 2 diabetes. Interestingly, the proportion of SIGLEC-1+ monocytes positively correlated with disease activity and negatively with disease duration in the LADA patients. In NOD mice, the proportion of SIGLEC-1+ monocytes in the peripheral blood was highest at the age of 6 weeks (16.88%), while the peak occurred at 12 weeks in pancreatic tissues (23.65%). Adoptive transfer experiments revealed a significant acceleration in diabetes onset in the SIGLEC-1+ group compared with the SIGLEC-1- or saline control group.

CONCLUSIONS/INTERPRETATION

Our study identified a novel group of SIGLEC-1+ monocytes that may serve as an important indicator for early diagnosis, activity assessment and monitoring of therapeutic efficacy in type 1 diabetes, and may also be a novel target for preventing and treating type 1 diabetes.

DATA AVAILABILITY

RNA-seq data have been deposited in the GSA human database ( https://ngdc.cncb.ac.cn/gsa-human/ ) under accession number HRA003649.

Vascular damage in systemic lupus erythematosus

Vascular disease is a major cause of morbidity and mortality in patients with systemic autoimmune diseases, particularly systemic lupus erythematosus (SLE). Although comorbid cardiovascular risk factors are frequently present in patients with SLE, they do not explain the high burden of premature vascular disease. Profound innate and adaptive immune dysregulation seems to be the primary driver of accelerated vascular damage in SLE. In particular, evidence suggests that dysregulation of type 1 interferon (IFN-I) and aberrant neutrophils have key roles in the pathogenesis of vascular damage. IFN-I promotes endothelial dysfunction directly via effects on endothelial cells and indirectly via priming of immune cells that contribute to vascular damage. SLE neutrophils are vasculopathic in part because of their increased ability to form immunostimulatory neutrophil extracellular traps. Despite improvements in clinical care, cardiovascular disease remains the leading cause of mortality among patients with SLE, and treatments that improve vascular outcomes are urgently needed. Improved understanding of the mechanisms of vascular injury in inflammatory conditions such as SLE could also have implications for common cardiovascular diseases, such as atherosclerosis and hypertension, and may ultimately lead to personalized therapeutic approaches to the prevention and treatment of this potentially fatal complication.

Exploring COVID-19 causal genes through disease-specific Cis-eQTLs

Genome-wide association study (GWAS) analysis has exposed that genetic factors play important roles in COVID-19. Whereas a deeper understanding of the underlying mechanism of COVID-19 was hindered by the lack of expression of quantitative trait loci (eQTL) data specific for disease. To this end, we identified COVID-19-specific cis-eQTLs by integrating nucleotide sequence variations and RNA-Seq data from COVID-19 samples. These identified eQTLs have different regulatory effect on genes between patients and controls, indicating that SARS-CoV-2 infection may cause alterations in the human body's internal environment. Individuals with the TT genotype in the rs1128320 region seemed more susceptible to SARS-CoV-2 infection and developed into severe COVID-19 due to the abnormal expression of IFITM1. We subsequently discovered potential causal genes, of the result, a total of 48 genes from six tissues were identified. siRNA-mediated depletion assays in SARS-CoV-2 infection proved that 14 causal genes were directly associated with SARS-CoV-2 infection. These results enriched existing research on COVID-19 causal genes and provided a new sight in the mechanism exploration for COVID-19.

Integrative systems biology reveals NKG2A-biased immune responses correlate with protection in infectious disease, autoimmune disease, and cancer

Infection, autoimmunity, and cancer are principal human health challenges of the 21st century. Often regarded as distinct ends of the immunological spectrum, recent studies hint at potential overlap between these diseases. For example, inflammation can be pathogenic in infection and autoimmunity. T resident memory (TRM) cells can be beneficial in infection and cancer. However, these findings are limited by size and scope; exact immunological factors shared across diseases remain elusive. Here, we integrate large-scale deeply clinically and biologically phenotyped human cohorts of 526 patients with infection, 162 with lupus, and 11,180 with cancer. We identify an NKG2A+ immune bias as associative with protection against disease severity, mortality, and autoimmune/post-acute chronic disease. We reveal that NKG2A+ CD8+ T cells correlate with reduced inflammation and increased humoral immunity and that they resemble TRM cells. Our results suggest NKG2A+ biases as a cross-disease factor of protection, supporting suggestions of immunological overlap between infection, autoimmunity, and cancer.

eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings

BACKGROUND

Single-cell RNA-sequencing (scRNA) datasets are becoming increasingly popular in clinical and cohort studies, but there is a lack of methods to investigate differentially expressed (DE) genes among such datasets with numerous individuals. While numerous methods exist to find DE genes for scRNA data from limited individuals, differential-expression testing for large cohorts of case and control individuals using scRNA data poses unique challenges due to substantial effects of human variation, i.e., individual-level confounding covariates that are difficult to account for in the presence of sparsely-observed genes.

RESULTS

We develop the eSVD-DE, a matrix factorization that pools information across genes and removes confounding covariate effects, followed by a novel two-sample test in mean expression between case and control individuals. In general, differential testing after dimension reduction yields an inflation of Type-1 errors. However, we overcome this by testing for differences between the case and control individuals' posterior mean distributions via a hierarchical model. In previously published datasets of various biological systems, eSVD-DE has more accuracy and power compared to other DE methods typically repurposed for analyzing cohort-wide differential expression.

CONCLUSIONS

eSVD-DE proposes a novel and powerful way to test for DE genes among cohorts after performing a dimension reduction. Accurate identification of differential expression on the individual level, instead of the cell level, is important for linking scRNA-seq studies to our understanding of the human population.

Low CD4 + T cell count is related to specific anti-nuclear antibodies, IFNα protein positivity and disease activity in systemic lupus erythematosus pregnancy

BACKGROUND

Lymphopenia, autoantibodies and activation of the type I interferon (IFN) system are common features in systemic lupus erythematosus (SLE). We speculate whether lymphocyte subset counts are affected by pregnancy and if they relate to autoantibody profiles and/or IFNα protein in SLE pregnancy.

METHODS

Repeated blood samples were collected during pregnancy from 80 women with SLE and 51 healthy controls (HC). Late postpartum samples were obtained from 19 of the women with SLE. Counts of CD4 + and CD8 + T cells, B cells and NK cells were measured by flow cytometry. Positivity for anti-nuclear antibodies (ANA) fine specificities (double-stranded DNA [dsDNA], Smith [Sm], ribonucleoprotein [RNP], chromatin, Sjögren's syndrome antigen A [SSA] and B [SSB]) and anti-phospholipid antibodies (cardiolipin [CL] and β2 glycoprotein I [β2GPI]) was assessed with multiplexed bead assay. IFNα protein concentration was quantified with Single molecule array (Simoa) immune assay. Clinical data were retrieved from medical records.

RESULTS

Women with SLE had lower counts of all lymphocyte subsets compared to HC throughout pregnancy, but counts did not differ during pregnancy compared to postpartum. Principal component analysis revealed that low lymphocyte subset counts differentially related to autoantibody profiles, cluster one (anti-dsDNA/anti-Sm/anti-RNP/anti-Sm/RNP/anti-chromatin), cluster two (anti-SSA/anti-SSB) and cluster three (anti-CL/anti-β2GPI), IFNα protein levels and disease activity. CD4 + T cell counts were lower in women positive to all ANA fine specificities in cluster one compared to those who were negative, and B cell numbers were lower in women positive for anti-dsDNA and anti-Sm compared to negative women. Moreover, CD4 + T cell and B cell counts were lower in women with moderate/high compared to no/low disease activity, and CD4 + T cell count was lower in IFNα protein positive relative to negative women. Finally, CD4 + T cell count was unrelated to treatment.

CONCLUSION

Lymphocyte subset counts are lower in SLE compared to healthy pregnancies, which seems to be a feature of the disease per se and not affected by pregnancy. Our results also indicate that low lymphocyte subset counts relate differentially to autoantibody profiles, IFNα protein levels and disease activity, which could be due to divergent disease pathways.

SingleQ: a comprehensive database of single-cell expression quantitative trait loci (sc-eQTLs) cross human tissues

Mapping of expression quantitative trait loci (eQTLs) and other molecular QTLs can help characterize the modes of action of disease-associated genetic variants. However, current eQTL databases present data from bulk RNA-seq approaches, which cannot shed light on the cell type- and environment-specific regulation of disease-associated genetic variants. Here, we introduce our Single-cell eQTL Interactive Database which collects single-cell eQTL (sc-eQTL) datasets and provides online visualization of sc-eQTLs across different cell types in a user-friendly manner. Although sc-eQTL mapping is still in its early stage, our database curates the most comprehensive summary statistics of sc-eQTLs published to date. sc-eQTL studies have revolutionized our understanding of gene regulation in specific cellular contexts, and we anticipate that our database will further accelerate the research of functional genomics. Database URL: http://www.sqraolab.com/scqtl.

eSVD-DE: Cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings

Background

Single-cell RNA-sequencing (scRNA) datasets are becoming increasingly popular in clinical and cohort studies, but there is a lack of methods to investigate differentially expressed (DE) genes among such datasets with numerous individuals. While numerous methods exist to find DE genes for scRNA data from limited individuals, differential-expression testing for large cohorts of case and control individuals using scRNA data poses unique challenges due to substantial effects of human variation, i.e., individual-level confounding covariates that are difficult to account for in the presence of sparsely-observed genes.

Results

We develop the eSVD-DE, a matrix factorization that pools information across genes and removes confounding covariate effects, followed by a novel two-sample test in mean expression between case and control individuals. In general, differential testing after dimension reduction yields an inflation of Type-1 errors. However, we overcome this by testing for differences between the case and control individuals' posterior mean distributions via a hierarchical model. In previously published datasets of various biological systems, eSVD-DE has more accuracy and power compared to other DE methods typically repurposed for analyzing cohort-wide differential expression.

Conclusions

eSVD-DE proposes a novel and powerful way to test for DE genes among cohorts after performing a dimension reduction. Accurate identification of differential expression on the individual level, instead of the cell level, is important for linking scRNA-seq studies to our understanding of the human population.

Functional genomics in inborn errors of immunity

Inborn errors of immunity (IEI) comprise a diverse spectrum of 485 disorders as recognized by the International Union of Immunological Societies Committee on Inborn Error of Immunity in 2022. While IEI are monogenic by definition, they illuminate various pathways involved in the pathogenesis of polygenic immune dysregulation as in autoimmune or autoinflammatory syndromes, or in more common infectious diseases that may not have a significant genetic basis. Rapid improvement in genomic technologies has been the main driver of the accelerated rate of discovery of IEI and has led to the development of innovative treatment strategies. In this review, we will explore various facets of IEI, delving into the distinctions between PIDD and PIRD. We will examine how Mendelian inheritance patterns contribute to these disorders and discuss advancements in functional genomics that aid in characterizing new IEI. Additionally, we will explore how emerging genomic tools help to characterize new IEI as well as how they are paving the way for innovative treatment approaches for managing and potentially curing these complex immune conditions.

UNC93B1 variants underlie TLR7-dependent autoimmunity

UNC93B1 is critical for trafficking and function of nucleic acid-sensing Toll-like receptors (TLRs) TLR3, TLR7, TLR8, and TLR9, which are essential for antiviral immunity. Overactive TLR7 signaling induced by recognition of self-nucleic acids has been implicated in systemic lupus erythematosus (SLE). Here, we report UNC93B1 variants (E92G and R336L) in four patients with early-onset SLE. Patient cells or mouse macrophages carrying the UNC93B1 variants produced high amounts of TNF-α and IL-6 and upon stimulation with TLR7/TLR8 agonist, but not with TLR3 or TLR9 agonists. E92G causes UNC93B1 protein instability and reduced interaction with TLR7, leading to selective TLR7 hyperactivation with constitutive type I IFN signaling. Thus, UNC93B1 regulates TLR subtype-specific mechanisms of ligand recognition. Our findings establish a pivotal role for UNC93B1 in TLR7-dependent autoimmunity and highlight the therapeutic potential of targeting TLR7 in SLE.

Single-cell transcriptome landscape of circulating CD4+ T cell populations in autoimmune diseases

CD4+ T cells are key mediators of various autoimmune diseases; however, their role in disease progression remains unclear due to cellular heterogeneity. Here, we evaluated CD4+ T cell subpopulations using decomposition-based transcriptome characterization and canonical clustering strategies. This approach identified 12 independent gene programs governing whole CD4+ T cell heterogeneity, which can explain the ambiguity of canonical clustering. In addition, we performed a meta-analysis using public single-cell datasets of over 1.8 million peripheral CD4+ T cells from 953 individuals by projecting cells onto the reference and cataloging cell frequency and qualitative alterations of the populations in 20 diseases. The analyses revealed that the 12 transcriptional programs were useful in characterizing each autoimmune disease and predicting its clinical status. Moreover, genetic variants associated with autoimmune diseases showed disease-specific enrichment within the 12 gene programs. The results collectively provide a landscape of single-cell transcriptomes of CD4+ T cell subpopulations involved in autoimmune disease.

The genetic basis of autoimmunity seen through the lens of T cell functional traits

Autoimmune disease heritability is enriched in T cell-specific regulatory regions of the genome. Modern-day T cell datasets now enable association studies between single nucleotide polymorphisms (SNPs) and a myriad of molecular phenotypes, including chromatin accessibility, gene expression, transcriptional programs, T cell antigen receptor (TCR) amino acid usage, and cell state abundances. Such studies have identified hundreds of quantitative trait loci (QTLs) in T cells that colocalize with genetic risk for autoimmune disease. The key challenge facing immunologists today lies in synthesizing these results toward a unified understanding of the autoimmune T cell: which genes, cell states, and antigens drive tissue destruction?

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.

Transcriptome-Wide Association Studies (TWAS): Methodologies, Applications, and Challenges

Transcriptome-wide association study (TWAS) methodologies aim to identify genetic effects on phenotypes through the mediation of gene transcription. In TWAS, in silico models of gene expression are trained as functions of genetic variants and then applied to genome-wide association study (GWAS) data. This post-GWAS analysis identifies gene-trait associations with high interpretability, enabling follow-up functional genomics studies and the development of genetics-anchored resources. We provide an overview of commonly used TWAS approaches, their advantages and limitations, and some widely used applications. © 2024 Wiley Periodicals LLC.

SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models

Genetic regulation of gene expression is a complex process, with genetic effects known to vary across cellular contexts such as cell types and environmental conditions. We developed SURGE, a method for unsupervised discovery of context-specific expression quantitative trait loci (eQTLs) from single-cell transcriptomic data. This allows discovery of the contexts or cell types modulating genetic regulation without prior knowledge. Applied to peripheral blood single-cell eQTL data, SURGE contexts capture continuous representations of distinct cell types and groupings of biologically related cell types. We demonstrate the disease-relevance of SURGE context-specific eQTLs using colocalization analysis and stratified LD-score regression.

A cis-regulatory element regulates ERAP2 expression through autoimmune disease risk SNPs

Single-nucleotide polymorphisms (SNPs) near the ERAP2 gene are associated with various autoimmune conditions, as well as protection against lethal infections. Due to high linkage disequilibrium, numerous trait-associated SNPs are correlated with ERAP2 expression; however, their functional mechanisms remain unidentified. We show by reciprocal allelic replacement that ERAP2 expression is directly controlled by the splice region variant rs2248374. However, disease-associated variants in the downstream LNPEP gene promoter are independently associated with ERAP2 expression. Allele-specific conformation capture assays revealed long-range chromatin contacts between the gene promoters of LNPEP and ERAP2 and showed that interactions were stronger in patients carrying the alleles that increase susceptibility to autoimmune diseases. Replacing the SNPs in the LNPEP promoter by reference sequences lowered ERAP2 expression. These findings show that multiple SNPs act in concert to regulate ERAP2 expression and that disease-associated variants can convert a gene promoter region into a potent enhancer of a distal gene.

PharmGWAS: a GWAS-based knowledgebase for drug repurposing

Leveraging genetics insights to promote drug repurposing has become a promising and active strategy in pharmacology. Indeed, among the 50 drugs approved by FDA in 2021, two-thirds have genetically supported evidence. In this regard, the increasing amount of widely available genome-wide association studies (GWAS) datasets have provided substantial opportunities for drug repurposing based on genetics discoveries. Here, we developed PharmGWAS, a comprehensive knowledgebase designed to identify candidate drugs through the integration of GWAS data. PharmGWAS focuses on novel connections between diseases and small-molecule compounds derived using a reverse relationship between the genetically-regulated expression signature and the drug-induced signature. Specifically, we collected and processed 1929 GWAS datasets across a diverse spectrum of diseases and 724 485 perturbation signatures pertaining to a substantial 33609 molecular compounds. To obtain reliable and robust predictions for the reverse connections, we implemented six distinct connectivity methods. In the current version, PharmGWAS deposits a total of 740 227 genetically-informed disease-drug pairs derived from drug-perturbation signatures, presenting a valuable and comprehensive catalog. Further equipped with its user-friendly web design, PharmGWAS is expected to greatly aid the discovery of novel drugs, the exploration of drug combination therapies and the identification of drug resistance or side effects. PharmGWAS is available at https://ngdc.cncb.ac.cn/pharmgwas.

A comprehensive exploration of the heterogeneity of immune cells in Han and Zang systemic lupus erythematosus patients via single-cell RNA sequencing

Systemic Lupus Erythematosus (SLE) is an autoimmune sickness with unclear pathogenesis. The goal of this research was to reveal the heterogeneity of immune cells in SLE patients of Han and Zang nationality by single-cell RNA sequencing (scRNA-seq) and bioinformatics profiling.

METHODS

A total of 94,102 peripheral blood mononuclear cells (PBMCs) from six volunteers with SLE (3 Zang, 3 Han) and six healthy controls were first conducted through scRNA-seq analysis. The immune cell subsets in the pathogenesis of SLE were analyzed as well. Real-time quantitative PCR (RT-qPCR) was applied to confirm the results of sc-RNA seq analysis.

RESULTS

For the Tibetan samples, the ratios of Naïve CD4 RPS4Y1 cells, Naïve CD4 cells, Memory BC CD24 and Memory BC differed significantly between the SLE and control samples, while that of CD8 CTL MAL cells was significantly different between the two groups in Han nationality samples. Variable differentiation states of CD8 CTL MAL cells, CD8 CTL GZMK cells, and Naïve CD4 cells were detected through pseudotime analysis. Moreover, T-cell receptor (TCR) abundance was notably higher in Tibetan SLE specimens than that in controls, while B-cell receptor (BCR) abundance in Tibetan and Han samples was higher than in control groups.

CONCLUSIONS

In summary, the immune cellular heterogeneity of SLE patients both Han and Zang nationality was explored based on various bioinformatics approaches, providing new perspectives for immunological characteristics of SLE among different ethnic groups.

Differential detection workflows for multi-sample single-cell RNA-seq data

In single-cell transcriptomics, differential gene expression (DE) analyses typically focus on testing differences in the average expression of genes between cell types or conditions of interest. Single-cell transcriptomics, however, also has the promise to prioritise genes for which the expression differ in other aspects of the distribution. Here we develop a workflow for assessing differential detection (DD), which tests for differences in the average fraction of samples or cells in which a gene is detected. After benchmarking eight different DD data analysis strategies, we provide a unified workflow for jointly assessing DE and DD. Using simulations and two case studies, we show that DE and DD analysis provide complementary information, both in terms of the individual genes they report and in the functional interpretation of those genes.

STACCato: Supervised Tensor Analysis tool for studying Cell-cell Communication using scRNA-seq data across multiple samples and conditions

Research on cell-cell communication (CCC) is crucial for understanding biology and diseases. Many existing CCC inference tools neglect potential confounders, such as batch and demographic variables, when analyzing multi-sample, multi-condition scRNA-seq datasets. To address this significant gap, we introduce STACCato, a Supervised Tensor Analysis tool for studying Cell-cell Communication, that identifies CCC events and estimates the effects of biological conditions (e.g., disease status, tissue types) on such events, while adjusting for potential confounders. Application of STACCato to both simulated data and real scRNA-seq data of lupus and autism studies demonstrate that incorporating sample-level variables into CCC inference consistently provides more accurate estimations of disease effects and cell type activity patterns than existing methods that ignore sample-level variables. A computational tool implementing the STACCato framework is available on GitHub.