Cross-tissue immune cell analysis reveals tissue-specific features in humans

A single-nucleus and spatial transcriptomic atlas of the COVID-19 liver reveals topological, functional, and regenerative organ disruption in patients

BACKGROUND

The molecular underpinnings of organ dysfunction in severe COVID-19 and its potential long-term sequelae are under intense investigation. To shed light on these in the context of liver function, we perform single-nucleus RNA-seq and spatial transcriptomic profiling of livers from 17 COVID-19 decedents.

RESULTS

We identify hepatocytes positive for SARS-CoV-2 RNA with an expression phenotype resembling infected lung epithelial cells, and a central role in a pro-fibrotic TGFβ signaling cell-cell communications network. Integrated analysis and comparisons with healthy controls reveal extensive changes in the cellular composition and expression states in COVID-19 liver, providing the underpinning of hepatocellular injury, ductular reaction, pathologic vascular expansion, and fibrogenesis characteristic of COVID-19 cholangiopathy. We also observe Kupffer cell proliferation and erythrocyte progenitors for the first time in a human liver single-cell atlas. Despite the absence of a clinical acute liver injury phenotype, endothelial cell composition is dramatically impacted in COVID-19, concomitantly with extensive alterations and profibrogenic activation of reactive cholangiocytes and mesenchymal cells.

CONCLUSIONS

Our atlas provides novel insights into liver physiology and pathology in COVID-19 and forms a foundational resource for its investigation and understanding.

Single-cell spatial transcriptomics unravels cell states and ecosystems associated with clinical response to immunotherapy

BACKGROUND

The tumor microenvironment (TME) is a complex and dynamic ecosystem that is known to influence responses to immunotherapy. We leveraged single-cell spatial transcriptomics to systematically dissect the intricate complexity of the TME, in particular the cellular heterogeneity and spatial interactions. Their collective impact on immunotherapy efficacy was studied in the context of a homogeneous group of patients with vulvar high-grade squamous intraepithelial lesions (vHSIL) treated with an immunotherapeutic tumor-specific peptide vaccine.

METHODS

We performed single-cell spatial transcriptomics on 20 pretreatment vHSIL lesions, stratified by clinical response to immunotherapeutic vaccination into complete responders (CR), partial responders (PR) and non-responders (NR). Using a 1,000-gene panel, we mapped over 274,000 single cells in situ, identifying 18 cell clusters and 99 distinct non-epithelial cell states. Findings were validated against public single-cell transcriptomic data sets to assess their broader relevance across tumor types.

RESULTS

Profound heterogeneity within the TME was detected across the response groups. CR lesions exhibited a higher ratio of immune-supportive to immune-suppressive cells-a pattern mirrored in other solid tumors following neoadjuvant checkpoint blockade. Key immune populations enriched in CRs included CD4+CD161+ effector T cells and chemotactic CD4+ and CD8+ T cells. Conversely, PRs were characterized by increased proportions of T helper 2 cells and CCL18-expressing macrophages, which are associated with the recruitment of type 2 T cells and regulatory T cells. NRs displayed preferential infiltration with immunosuppressive fibroblasts. Distinct spatial immune ecosystems further defined response groups. Although a number of immune cells were detected in all patients, type 1 effector cells dominated interactions in CRs, type 2 cells were prominently interacting in PRs, while NRs lacked organized immune cell interactions.

CONCLUSIONS

This study underscores the dual importance of both cellular composition and spatial organization in steering clinical response to immunotherapy.

Single-cell RNA-seq data have prevalent blood contamination but can be rescued by Originator, a computational tool separating single-cell RNA-seq by genetic and contextual information

Single-cell RNA sequencing (scRNA-seq) data from complex human tissues have prevalent blood cell contamination during the sample preparation process. They may also comprise cells of different genetic makeups. We propose a new computational framework, Originator, which deciphers single cells by genetic origin and separates immune cells of blood contamination from those of expected tissue-resident cells. We demonstrate the accuracy of Originator at separating immune cells from the blood and tissue as well as cells of different genetic origins, using a variety of artificially mixed and real datasets, including pancreatic cancer and placentas as examples.

The polyamine-regulating enzyme SSAT1 impairs tissue regulatory T cell function in chronic cutaneous inflammation

Regulatory T (Treg) cells are a critical immune component guarding against excessive inflammation. Treg cell dysfunction can lead to chronic inflammatory diseases with current therapies aimed at inhibiting effector T cells rather than rescuing Treg cell function. We utilized single-cell RNAsequencing data from patients with chronic inflammation to identify SAT1, the gene encoding spermidine/spermine N1-acetyltransferase (SSAT), as a driver of skin-resident Treg cell dysfunction. CRISPRa-driven SAT1 expression in human skin-derived Treg cells impaired their suppressive function and induced a pro-inflammatory phenotype. During cutaneous type-17 inflammation, keratinocyte 4-1BBL induces SAT1 on Treg cells. In a mouse model of psoriasis, pharmacological inhibition of SSAT rescued Treg cell number and function. Together, these data show that SAT1 expression has severe functional consequences on Treg cells and suggest a therapeutic target to treat chronic inflammatory disease.

A serum- and feeder-free system to generate CD4 and regulatory T cells from human iPSCs

iPSCs can serve as a renewable source of a consistent edited cell product, overcoming limitations of primary cells. While feeder-free generation of clinical grade iPSC-derived CD8 T cells has been achieved, differentiation of iPSC-derived CD4sp and regulatory T cells requires mouse stromal cells in an artificial thymic organoid. Here we report a serum- and feeder-free differentiation process suitable for large-scale production. Using an optimized concentration of PMA/Ionomycin, we generated iPSC-CD4sp T cells at high efficiency and converted them to Tregs using TGFβ and ATRA. Using genetic engineering, we demonstrated high, non-viral, targeted integration of an HLA-A2 CAR in iPSCs. iPSC-Tregs ± HLA-A2-targeted CAR phenotypically, transcriptionally and functionally resemble primary Tregs and suppress T-cell proliferation in vitro. Our work is the first to demonstrate an iPSC-based platform amenable to manufacturing CD4 T cells to complement iPSC-CD8 oncology products and functional iPSC-Tregs to deliver Treg cell therapies at scale.

Aging activates escape of the silent X chromosome in the female mouse hippocampus

Women live longer than men and exhibit less cognitive aging. The X chromosome contributes to sex differences, as females harbor an inactive X (Xi) and active X (Xa), in contrast to males with only an Xa. Thus, reactivation of silent Xi genes may contribute to sex differences. We use allele-specific, single-nucleus RNA sequencing to show that aging remodels transcription of the Xi and Xa across hippocampal cell types. Aging preferentially changed gene expression on the X's relative to autosomes. Select genes on the Xi underwent activation, with new escape across cells including in the dentate gyrus, critical to learning and memory. Expression of the Xi escapee Plp1, a myelin component, was increased in the aging hippocampus of female mice and parahippocampus of women. AAV-mediated Plp1 elevation in the dentate gyrus of aging male and female mice improved cognition. Understanding how the Xi may confer female advantage could lead to novel targets that counter brain aging and disease in both sexes.

ENACT: End-to-End Analysis of Visium High Definition (HD) Data

MOTIVATION

Spatial transcriptomics (ST) enables the study of gene expression within its spatial context in histopathology samples. To date, a limiting factor has been the resolution of sequencing based ST products. The introduction of the Visium High Definition (HD) technology opens the door to cell resolution ST studies. However, challenges remain in the ability to accurately map transcripts to cells and in assigning cell types based on the transcript data.

RESULTS

We developed ENACT, a self-contained pipeline that integrates advanced cell segmentation with Visium HD transcriptomics data to infer cell types across whole tissue sections. Our pipeline incorporates novel bin-to-cell assignment methods, enhancing the accuracy of single-cell transcript estimates. Validated on diverse synthetic and real datasets, our approach is both scalable to samples with hundreds of thousands of cells and effective, offering a robust solution for spatially resolved transcriptomics analysis.

AVAILABILITY AND IMPLEMENTATION

ENACT source code is available at https://github.com/Sanofi-Public/enact-pipeline. Experimental data are available at https://zenodo.org/records/14748859.

Long-term B cell memory emerges at uniform relative rates in the human immune response

B cells generate pathogen-specific antibodies and play an essential role in providing adaptive protection against infection. Antibody genes are modified in evolutionary processes acting on the B cell populations within an individual. These populations proliferate, differentiate, and migrate to long-term niches in the body. However, the dynamics of these processes in the human immune system are primarily inferred from mouse studies. We addressed this gap by sequencing the antibody repertoire and transcriptomes from single B cells in four immune-rich tissues from six individuals. We find that B cells descended from the same pre-B cell ("lineages") often colocalize within the same tissue, with the bone marrow harboring the largest excess of lineages without representation in other tissues. Within lineages, cells with different levels of somatic hypermutation are uniformly distributed among tissues and functional states. This suggests that the relative probabilities of localization and differentiation outcomes change negligibly during affinity maturation, and quantitatively agrees with a simple dynamical model of B cell differentiation. While lineages strongly colocalize, we find individual B cells nevertheless appear to make independent differentiation decisions. Proliferative antibody-secreting cells, however, deviate from these global patterns. These cells are often clonally expanded, their clones appear universally distributed among all sampled organs, and form lineages with an excess of cells of the same type. Collectively, our findings show the limits of peripheral blood monitoring of the immune repertoire, and provide a probabilistic model of the dynamics of antibody memory formation in humans.

Deep proteome profiling of metabolic dysfunction-associated steatotic liver disease

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) affects roughly 1 in 3 adults and is a leading cause of liver transplants and liver related mortality. A deeper understanding of disease pathogenesis is essential to assist in developing blood-based biomarkers.

METHODS

Here, we use data-independent acquisition mass spectrometry to assess disease-state associated protein profiles in human liver, blood plasma, and white adipose tissue (WAT).

RESULTS

In liver, we find that MASLD is associated with an increased abundance of proteins involved in immune response and extracellular matrix (ECM) and a decrease in proteins involved in metabolism. Cell type deconvolution of the proteome indicates liver endothelial and hepatic stellate cells are the main source of ECM rearrangements, and hepatocytes are the major contributor to the changes in liver metabolism. In the blood, profiles of several MASLD-associated proteins correlate with expression in WAT rather than liver and so could serve as suitable liver disease predictors in a multi-protein panel marker. Moreover, our proteomics-based logistic regression models perform better than existing methods for predicting MASLD and liver fibrosis from human blood samples.

CONCLUSIONS

Our comprehensive proteomic analysis deepens the understanding of liver function and MASLD pathology by elucidating key cellular mechanisms and multi-organ interactions, and demonstrates the robustness of a proteomics-based biomarker panel to enhance diagnosis of MASLD and significant fibrosis.

Circadian immune system in solid organ transplantation: a review article

The innate and adaptive immune systems are intricately regulated by the circadian clock machinery. Recent clinical investigations have shed light on the influence of timing in organ procurement and transplantation on graft survival. In this review, we explore various mechanisms of immunological functions associated with the steps involved in organ transplantation, spanning from surgical harvesting to reperfusion and linking to the circadian rhythm. A deeper understanding of these processes has the potential to extend the principles of chrono-immunotherapy to the realm of organ transplantation, with the aim of enhancing graft durability and improving patient outcomes. This review concludes with some perspectives on future directions in this exciting and still evolving field of research.

Androgen Receptor Inhibition Increases MHC Class I Expression and Improves Immune Response in Prostate Cancer

SIGNIFICANCE

Immunotherapy options for immune cold tumors, like prostate cancer, are limited. We show that AR downregulates MHCI expression/antigen presentation and that AR inhibition improves T-cell responses and tumor control. This suggests that treatments combining AR inhibitors and checkpoint blockade may improve tumor immune surveillance and antitumor immunity in patients.

Profiling immune cell tissue niches in the spatial -omics era

Immune responses require complex, spatially coordinated interactions between immune cells and their tissue environment. For decades, we have imaged tissue sections to visualize a limited number of immune-related macromolecules in situ, functioning as surrogates for cell types or processes of interest. However, this inevitably provides a limited snapshot of the tissue's immune landscape. Recent developments in high-throughput spatial -omics technologies, particularly spatial transcriptomics, and its application to human samples has facilitated a more comprehensive understanding of tissue immunity by mapping fine-grained immune cell states to their precise tissue location while providing contextual information about their immediate cellular and tissue environment. These data provide opportunities to investigate mechanisms underlying the spatial distribution of immune cells and its functional implications, including the identification of immune niches, although the criteria used to define this term have been inconsistent. Here, we review recent technological and analytic advances in multiparameter spatial profiling, focusing on how these methods have generated new insights in translational immunology. We propose a 3-step framework for the definition and characterization of immune niches, which is powerfully facilitated by new spatial profiling methodologies. Finally, we summarize current approaches to analyze adaptive immune repertoires and lymphocyte clonal expansion in a spatially resolved manner.

Tissue-resident memory CD8 T cell diversity is spatiotemporally imprinted

Tissue-resident memory CD8 T (TRM) cells provide protection from infection at barrier sites. In the small intestine, TRM cells are found in at least two distinct subpopulations: one with higher expression of effector molecules and another with greater memory potential1. However, the origins of this diversity remain unknown. Here we proposed that distinct tissue niches drive the phenotypic heterogeneity of TRM cells. To test this, we leveraged spatial transcriptomics of human samples, a mouse model of acute systemic viral infection and a newly established strategy for pooled optically encoded gene perturbations to profile the locations, interactions and transcriptomes of pathogen-specific TRM cell differentiation at single-transcript resolution. We developed computational approaches to capture cellular locations along three anatomical axes of the small intestine and to visualize the spatiotemporal distribution of cell types and gene expression. Our study reveals that the regionalized signalling of the intestinal architecture supports two distinct TRM cell states: differentiated TRM cells and progenitor-like TRM cells, located in the upper villus and lower villus, respectively. This diversity is mediated by distinct ligand-receptor activities, cytokine gradients and specialized cellular contacts. Blocking TGFβ or CXCL9 and CXCL10 sensing by antigen-specific CD8 T cells revealed a model consistent with anatomically delineated, early fate specification. Ultimately, our framework for the study of tissue immune networks reveals that T cell location and functional state are fundamentally intertwined.

InfoScan: A New Transcript Identification Tool Based on scRNA-Seq and Its Application in Glioblastoma

InfoScan is a novel bioinformatics tool designed for the comprehensive analysis of full-length single-cell RNA sequencing (scRNA-seq) data. It enables the identification of unannotated transcripts and rare cell populations, providing a powerful platform for transcriptome characterization. In this study, InfoScan was applied to glioblastoma multiforme (GBM), identifying a rare "neoplastic-stemness" subpopulation exhibiting cancer stem cell-like features. Functional analyses suggested that tumor-associated macrophages (TAMs) secrete SPP1, which binds to CD44 on neoplastic-stemness cells, activating the PI3K/AKT pathway and driving lncRNA transcription to promote metastasis. Integration of TCGA and CGGA datasets further supported these findings, highlighting key mutations associated with the neoplastic-stemness subpopulation. Drug sensitivity assays indicated that neoplastic-stemness cells might be sensitive to omipalisib, a PI3K inhibitor, pointing to a potential therapeutic target. InfoScan offers a robust framework for exploring complex transcriptomic landscapes and characterizing rare cell populations, providing valuable insights into GBM biology and advancing precision cancer therapy.

TcEVdb: a database for T-cell-derived small extracellular vesicles from single-cell transcriptomes

T-Cell-derived extracellular vesicles (TcEVs) play key roles in immune regulation and tumor microenvironment modulation. However, the heterogeneity of TcEV remains poorly understood due to technical limitations of EV analysis and the lack of comprehensive data. To address this, we constructed TcEVdb, a comprehensive database that explores the expression and cluster of TcEV by the SEVtras method from T-cell single-cell RNA sequencing data. TcEVdb contains 277 265 EV droplets from 51 T-cell types across 221 samples from 21 projects, covering 9 tissue sources and 23 disease conditions. The database provides two main functional modules. The Browse module enables users to investigate EV secretion activity indices across samples, visualize TcEV clusters, analyze differentially expressed genes (DEGs) and pathway enrichment in TcEV subpopulations, and compare TcEV transcriptomes with their cellular origins. The Search module allows users to query specific genes across all datasets and visualize their expression distribution. Furthermore, our analysis of TcEV in diffuse large B-cell lymphoma revealed increased EV secretion in CD4+ T exhausted cells compared to healthy controls. Subsequent analyses identified distinct droplet clusters with differential expression genes, including clusters enriched for genes associated with cell motility and mitochondrial function. Overall, TcEVdb serves as a comprehensive resource for exploring the transcriptome of TcEV, which will contribute to advancements in EV-based diagnostics and therapeutics across a wide range of diseases. Database URL: https://guolab.wchscu.cn/TcEVdb.

Immune Imbalance in Primary Membranous Nephropathy at Single-cell Resolution

BACKGROUND

Primary membranous nephropathy (pMN) often progresses to end-stage renal disease (ESRD) in the absence of immunosuppressive therapy. The immunological mechanisms driving pMN progression remain insufficiently understood.

METHODS

We developed a single-cell transcriptomic profile of peripheral blood mononuclear cells (PBMCs) from 11 newly-diagnosed pMN patients and 5 healthy donors. Through correlation analysis, we identified potential biomarkers for disease stratification and poor prognosis.

RESULTS

Expression levels of several proinflammatory factors were significantly increased in patients compared to healthy donors, such as interleukins (IL1B, IL8, and IL15) and interferon G (IFNG). Multiple pattern recognition receptors involved in proinflammatory signaling were also upregulated in patients, including NOD-like receptors (NLRs) (NLRP1, NLRP3, and NLRC5), RNA helicases (DDX58, IFIH1, DHX9, and DHX36), cGAS (cyclic GMP-AMP synthase) and IFI16 (interferon gamma inducible protein 16). Additionally, human leukocyte antigen molecules HLA-DQA1 and HLA-DRB1 enriched in memory B cells were upregulated in patients. More importantly, we found that the genes for antiviral defense response were significantly elevated in high-risk patients relative to the low-risk group. More than twenty genes were negatively correlated with estimated glomerular filtration rate (eGFR), such as BST2 (bone marrow stromal cell antigen 2) and SLC35F1 (solute carrier family 35 member F1). Their predicted values were confirmed in a larger population with nephrotic syndrome or other chronic kidney diseases from a public database. Furthermore, we developed a series of scoring systems for distinguishing high-risk patients from low- and moderate-risk individuals.

CONCLUSIONS

Our study provides insight into the immunological mechanism of pMN and identifies numerous biomarkers and signaling pathways as potential therapeutic targets for managing the progression of high-risk pMN.

Single-Cell Transcriptomic Profile of Innate Cell Populations in Mesenteric Lymph Nodes of Inflammatory Bowel Disease Patients

BACKGROUND AND AIMS

Innate immune cells, including dendritic cells (DCs), monocytes (Mono), macrophages (Mac), natural killer (NK), and innate lymphoid cells (ILC), contribute to chronic inflammation in lymphoid tissues. Here, we characterized the innate immune cell landscape in inflamed mesenteric lymph nodes (MLNs) of patients with inflammatory bowel diseases (IBD) at the single-cell level.

METHODS

Surgically resected colonic MLNs were obtained from patients with Crohn's disease (CD; n = 3), ulcerative colitis (UC; n = 3), non-inflamed UC (n = 1), and non-IBD (n = 2). CD45+CD3-CD19- non-T/non-B cells were FACS-sorted to capture rare innate immune cells. Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) was performed on the BD Rhapsody platform alongside multiparameter flow cytometry staining.

RESULTS

CITE-seq analysis unveiled the molecular signature of 11 Mono/Mac/DC (MMDC) and 7 NK/ILC enriched clusters in human MLNs. DC clusters included 3 newly characterized DC clusters such as CD1c/CD163/VCAN/CD64-expressing DC3; AXL-expressing DCs; and a CD103+ DC subset, expressing LTB, S100B, and IL22RA2 (encoding IL22BP). Mono/Mac clusters comprised inflammatory monocytes, which accumulated in IBD compared to non-IBD MLNs. Among NK/ILC clusters, we identified a cytotoxic ILC subset (IL7R, KLRD1, GNLY), previously not reported in MLNs, reminiscent of cytotoxic ILC1-like cells found in inflamed gut mucosa.

CONCLUSION

CITE-seq and flow-cytometry analyses of colonic MLNs from patients with active IBD reveal the molecular signature and cell distribution of previously uncharacterized DC and ILC subpopulations in human MLNs. These findings expand our understanding of immune responses during chronic inflammation in IBD.

Comprehensive single-cell analysis of triple-negative breast cancer based on cDC1 immune-related genes: prognostic model construction and immunotherapy potential

BACKGROUND

Various components of the immunological milieu surrounding tumors have become a key focus in cancer immunotherapy research. There are currently no reliable biomarkers for triple-negative breast cancer (TNBC), leading to limited clinical benefits. However, some studies have indicated that patients with TNBC may achieve better outcomes after immunotherapy. Therefore, this study aimed to identify molecular features potentially associated with conventional type 1 dendritic cell (cDC1) immunity to provide new insights into TNBC prognostication and immunotherapy decision-making.

METHODS

Single-cell ribonucleic acid sequencing data from the Gene Expression Omnibus database were analyzed to determine which genes are differentially expressed genes (DEGs) in cDC1s. We then cross-referenced cDC1-related DEGs with gene sets linked to immunity from the ImmPort and InnateDB databases to screen for the genes linked to the immune response and cDC1s. We used univariate Cox and least absolute shrinkage and selection operator regression analyses to construct a risk assessment model based on four genes in patients with TNBC obtained from the Cancer Genome Atlas, which was validated in a testing group. This model was also used to assess immunotherapy responses among the IMvigor210 cohort. We subsequently utilized single sample Gene Set Enrichment Analysis, CIBERSORT, and ESTIMATE to analyze the immunological characteristics of the feature genes and their correlation with drug response.

RESULTS

We identified 93 DEGs related to the immune response and cDC1s, of which four (IDO1, HLA-DOB, CTSD, and IL3RA) were substantially linked to the overall survival rate of TNBC patients. The risk assessment model based on these genes stratified patients into high- and low-risk groups. Low-risk patients exhibited enriched ''hot tumor'' phenotypes, including higher infiltration of memory-activated CD4 + T cells, CD8 + T cells, gamma delta T cells, and M1 macrophages, as well as elevated immune checkpoint expression and tumor mutational burden, suggesting potential responsiveness to immunotherapy. Conversely, high-risk patients displayed "cold tumor" characteristics, with higher infiltration of M0 and M2 macrophages and lower immune scores, which may be poorer in response to immunotherapy. However, experimental validation and larger clinical studies are necessary to confirm these findings and explore the underlying mechanisms of the identified genes.

CONCLUSION

This study developed a robust risk assessment model using four genes that effectively forecast the outcome of patients with TNBC and have the potential to guide immunotherapy. This model provided new theoretical insights for knowing the TNBC immune microenvironment and developing personalized treatment strategies.

Construction, Deployment, and Usage of the Human Reference Atlas Knowledge Graph for Linked Open Data

The Human Reference Atlas (HRA) for the healthy, adult body is being developed by a team of international, interdisciplinary experts across 20+ consortia. It provides standard terminologies and data structures for describing specimens, biological structures, and spatial positions of experimental datasets and ontology-linked reference anatomical structures (AS), cell types (CT), and biomarkers (B). We introduce the HRA Knowledge Graph (KG) as central data resource for HRA v2.2, supporting cross-scale, biological queries to Resource Description Framework graphs using SPARQL. In February 2025, the HRA KG covered 71 organs with 5,800 AS, 2,268 CT, 2,531 B; it had 10,064,033 nodes, 171,250,177 edges, and a size of 125.84 GB. The HRA KG comprises 13 types of Digital Objects (DOs) using the Common Coordinate Framework Ontology to standardize core concepts and relationships across DOs. We (1) provide data and code for HRA KG construction; (2) detail HRA KG deployment by Linked Open Data principles; and (3) illustrate HRA KG usage via application programming interfaces, user interfaces, data products. A companion website is at https://cns-iu.github.io/hra-kg-supporting-information.

Contrasting cytotoxic and regulatory T cell responses underlying distinct clinical outcomes to anti-PD-1 plus lenvatinib therapy in cancer

Combination of anti-PD-1 with lenvatinib showed clinical efficacy in multiple cancers, yet the underlying immunological mechanisms are unclear. Here, we compared T cells in hepatocellular carcinoma (HCC) patients before and after combination treatment using single-cell transcriptomics and T cell receptor (scTCR) clonotype analyses. We found that tumor-infiltrating GZMK+ CD8+ effector/effector memory T (Teff/Tem) cells, showing a favorable response to combination therapy, comprise progenitor exhausted T (Tpex) cells and also unappreciated circulating Tem (cTem) cells enriched with hepatitis B virus (HBV) specificity. Further integrated analyses revealed that cTem cells are specifically associated with responsiveness to the combination therapy, whereas Tpex cells contribute to responses in both combination therapy and anti-PD-1 monotherapy. Notably, an underexplored KIR+ CD8+ T cell subset in the tumor and FOXP3+ CD4+ regulatory T cells are specifically enriched in non-responders after the combination therapy. Our study thus elucidated T cell subsets associated with clinical benefits and resistance in cancer immunotherapy.

Genetic and Plasma Proteomic Approaches to Identify Therapeutic Targets for Graves' Disease and Graves' Ophthalmopathy

Background

The blood proteome is a major source of biomarkers and therapeutic targets. We aimed to identify the causal proteins and potential targets for Graves' disease (GD) and Graves' ophthalmopathy (GO) via systematic genetic analyses.

Methods

Genome-wide association studies (GWASs) on the UK Biobank- Pharma Proteomics Project (UKB-PPP) collected 2923 Olink proteins from 54,219 participants. We conducted a proteome-wide Mendelian randomization (MR) study with cis-pQTLs to identify candidate proteins for GD and GO risk. Colocalization analysis and the Heidi test were used to examine whether the identified proteins and diseases shared the same variant. More proteins with potential causal associations were identified in Summary-data-based MR (SMR) analyses using trans-pQTLs. Then, downstream analyses were performed to detect protein interactions, gene function, cell type-specific expression and druggable information.

Results

This study genetically predicted levels of 62 plasma proteins were associated with GD risk. Four proteins (CD40, TINAGL1, GMPR and CXCL10) were prioritized with the evidence of sharing the same variants with GD. Specifically, some proteins had potential associations with GD with trans-pQTLs mapping in CD40. The four prioritized protein-coding genes were mainly enriched in the regulation of apoptotic and death processes. In addition, GMPR was associated with both GO and GD in a consistent direction. BTN1A1 and FCRL1 were prioritized as the causal proteins for GO onset and were not associated with GD.

Conclusion

By synthesizing proteomic and genetic data, we identified several protein biomarkers for GD, with one linked to both GD and GO and two other protein biomarkers specific to GO onset, which provides valuable insights into the etiology and potential therapeutic targets for the two diseases.

Distinct immune cell infiltration patterns in pancreatic ductal adenocarcinoma (PDAC) exhibit divergent immune cell selection and immunosuppressive mechanisms

Pancreatic ductal adenocarcinoma has a dismal prognosis. A comprehensive analysis of single-cell multi-omic data from matched tumour-infiltrated CD45+ cells and peripheral blood in 12 patients, and two published datasets, reveals a complex immune infiltrate. Patients have either a myeloid-enriched or adaptive-enriched tumour microenvironment. Adaptive immune cell-enriched is intrinsically linked with highly distinct B and T cell clonal selection, diversification, and differentiation. Using TCR data, we see the largest clonal expansions in CD8 effector memory, senescent cells, and highly activated regulatory T cells which are induced within the tumour from naïve cells. We identify pathways that potentially lead to a suppressive microenvironment, including investigational targets TIGIT/PVR and SIRPA/CD47. Analysis of patients from the APACT clinical trial shows that myeloid enrichment had a shorter overall survival compared to those with adaptive cell enrichment. Strategies for rationale therapeutic development in this disease include boosting of B cell responses, targeting immunosuppressive macrophages, and specific Treg cell depletion approaches.

Identifying Reproducible Transcription Regulator Coexpression Patterns with Single Cell Transcriptomics

The proliferation of single cell transcriptomics has potentiated our ability to unveil patterns that reflect dynamic cellular processes such as the regulation of gene transcription. In this study, we leverage a broad collection of single cell RNA-seq data to identify the gene partners whose expression is most coordinated with each human and mouse transcription regulator (TR). We assembled 120 human and 103 mouse scRNA-seq datasets from the literature (>28 million cells), constructing a single cell coexpression network for each. We aimed to understand the consistency of TR coexpression profiles across a broad sampling of biological contexts, rather than examine the preservation of context-specific signals. Our workflow therefore explicitly prioritizes the patterns that are most reproducible across cell types. Towards this goal, we characterize the similarity of each TR's coexpression within and across species. We create single cell coexpression rankings for each TR, demonstrating that this aggregated information recovers literature curated targets on par with ChIP-seq data. We then combine the coexpression and ChIP-seq information to identify candidate regulatory interactions supported across methods and species. Finally, we highlight interactions for the important neural TR ASCL1 to demonstrate how our compiled information can be adopted for community use.

Cross-tissue multi-omics analyses reveal the gut microbiota's absence impacts organ morphology, immune homeostasis, bile acid and lipid metabolism

The gut microbiota influences host immunity and metabolism, and changes in its composition and function have been implicated in several non-communicable diseases. Here, comparing germ-free (GF) and specific pathogen-free (SPF) mice using spatial transcriptomics, single-cell RNA sequencing, and targeted bile acid metabolomics across multiple organs, we systematically assessed how the gut microbiota's absence affected organ morphology, immune homeostasis, bile acid, and lipid metabolism. Through integrated analysis, we detect marked aberration in B, myeloid, and T/natural killer cells, altered mucosal zonation and nutrient uptake, and significant shifts in bile acid profiles in feces, liver, and circulation, with the alternate synthesis pathway predominant in GF mice and pronounced changes in bile acid enterohepatic circulation. Particularly, autophagy-driven lipid droplet breakdown in ileum epithelium and the liver's zinc finger and BTB domain-containing protein (ZBTB20)-Lipoprotein lipase (LPL) (ZBTB20-LPL) axis are key to plasma lipid homeostasis in GF mice. Our results unveil the complexity of microbiota-host interactions in the crosstalk between commensal gut bacteria and the host.

FCGR2C Q13 and FCGR3A V176 alleles jointly associate with worse natural killer cell-mediated antibody-dependent cellular cytotoxicity and microvascular inflammation in kidney allograft antibody-mediated rejection

Natural killer (NK) cell-mediated antibody-dependent cellular cytotoxicity (ADCC) is a major mechanism of humoral allograft injury. FCGR3A V176/F176 polymorphism influences ADCC activity. Additionally, NK cell FcγRIIc expression, dictated by the Q13/STP13 polymorphism, was never investigated in kidney transplantation. To assess the clinical relevance of FCGR2C Q13/STP13 polymorphism in conjunction with FCGR3A V176/F176 polymorphism, 242 kidney transplant recipients were genotyped. NK cell Fc gamma receptor (FcγR) expression and ADCC activity were assessed. RNA sequencing was performed on kidney allograft biopsies to explore the presence of infiltrating FcγR+ NK cells. The FCGR2C Q13 allele was enriched in antibody-mediated rejection patients. FcγRIIc Q13+ NK cells had higher ADCC activity than FcγRIIc Q13- NK cells. In combination with the high-affinity FCGR3A V176 allele, Q13+V176+ NK cells were the most functionally potent. Q13+ was associated with worse microvascular inflammation and a higher risk of allograft loss. Among V176- patients, previously described in the literature as lower-risk patients, Q13+V176- showed a lower graft survival than Q13-V176- patients. In antibody-mediated rejection biopsies, FCGR2C transcripts were enriched and associated with ADCC-related transcripts. Our results suggest that FCGR2C Q13 in addition to FCGR3A V176 is a significant risk allele that may enhance NK cell-mediated ADCC and contribute to allograft injury and poor survival.

High order expression dependencies finely resolve cryptic states and subtypes in single cell data

Single cells are typically typed by clustering into discrete locations in reduced dimensional transcriptome space. Here we introduce Stator, a data-driven method that identifies cell (sub)types and states without relying on cells' local proximity in transcriptome space. Stator labels the same single cell multiply, not just by type and subtype, but also by state such as activation, maturity or cell cycle sub-phase, through deriving higher-order gene expression dependencies from a sparse gene-by-cell expression matrix. Stator's finer resolution is clear from analyses of mouse embryonic brain, and human healthy or diseased liver. Rather than only coarse-scale labels of cell type, Stator further resolves cell types into subtypes, and these subtypes into stages of maturity and/or cell cycle phases, and yet further into portions of these phases. Among cryptically homogeneous embryonic cells, for example, Stator finds 34 distinct radial glia states whose gene expression forecasts their future GABAergic or glutamatergic neuronal fate. Further, Stator's fine resolution of liver cancer states reveals expression programmes that predict patient survival. We provide Stator as a Nextflow pipeline and Shiny App.

Type 2 conventional dendritic cell functional heterogeneity: ontogenically committed or environmentally plastic?

Conventional dendritic cells (cDCs) are sentinels of the mammalian immune system that sense a wide range of danger and homeostatic signals to induce appropriately targeted T cell immune responses. Traditionally classified into two main subsets, cDC1 and cDC2, recent research shows that cDC2s exhibit significant heterogeneity and can be further subdivided. Studies in mice and humans show that, beyond their ontogeny, cDC2s acquire dynamic and tissue-specific characteristics that are influenced by local environmental signals, which impact on their functions during homeostasis, inflammation, and infection. The novel concept is proposed that tissue-derived signals and tissue plasticity can override preestablished developmental programming, thereby redefining developmental trajectories and cDC2 functionality. Ultimately, understanding cDC2 heterogeneity and plasticity has important implications for modulating T cell immunity in health and disease.

Integrative mapping of human CD8+ T cells in inflammation and cancer

CD8+ T cells exhibit remarkable phenotypic diversity in inflammation and cancer. However, a comprehensive understanding of their clonal landscape and dynamics remains elusive. Here we introduce scAtlasVAE, a deep-learning-based model for the integration of large-scale single-cell RNA sequencing data and cross-atlas comparisons. scAtlasVAE has enabled us to construct an extensive human CD8+ T cell atlas, comprising 1,151,678 cells from 961 samples across 68 studies and 42 disease conditions, with paired T cell receptor information. Through incorporating information in T cell receptor clonal expansion and sharing, we have successfully established connections between distinct cell subtypes and shed light on their phenotypic and functional transitions. Notably, our approach characterizes three distinct exhausted T cell subtypes and reveals diverse transcriptome and clonal sharing patterns in autoimmune and immune-related adverse event inflammation. Furthermore, scAtlasVAE facilitates the automatic annotation of CD8+ T cell subtypes in query single-cell RNA sequencing datasets, enabling unbiased and scalable analyses. In conclusion, our work presents a comprehensive single-cell reference and computational framework for CD8+ T cell research.

A cell atlas foundation model for scalable search of similar human cells

Single-cell RNA sequencing has profiled hundreds of millions of human cells across organs, diseases, development and perturbations to date. Mining these growing atlases could reveal cell-disease associations, identify cell states in unexpected tissue contexts and relate in vivo biology to in vitro models. These require a common measure of cell similarity across the body and an efficient way to search. Here we develop SCimilarity, a metric-learning framework to learn a unified and interpretable representation that enables rapid queries of tens of millions of cell profiles from diverse studies for cells that are transcriptionally similar to an input cell profile or state. We use SCimilarity to query a 23.4-million-cell atlas of 412 single-cell RNA-sequencing studies for macrophage and fibroblast profiles from interstitial lung disease1 and reveal similar cell profiles across other fibrotic diseases and tissues. The top scoring in vitro hit for the macrophage query was a 3D hydrogel system2, which we experimentally demonstrated reproduces this cell state. SCimilarity serves as a foundation model for single-cell profiles that enables researchers to query for similar cellular states across the human body, providing a powerful tool for generating biological insights from the Human Cell Atlas.

HCNetlas: A reference database of human cell type-specific gene networks to aid disease genetic analyses

Cell type-specific actions of disease genes add a significant layer of complexity to the genetic architecture underlying diseases, obscuring our understanding of disease mechanisms. Single-cell omics have revealed the functional roles of genes at the cellular level, identifying cell types critical for disease progression. Often, a gene impact on disease through its altered network within specific cell types, rather than mere changes in expression levels. To explore the cell type-specific roles of disease genes, we developed HCNetlas (human cell network atlas), a resource cataloging cell type-specific gene networks (CGNs) for various healthy tissue cells. We also devised 3 network analysis methods to investigate cell type-specific functions of disease genes. These methods involve comparing HCNetlas CGNs with those derived from disease-affected tissue samples. These methods find that systemic lupus erythematosus genes predominantly function in myeloid cells, and Alzheimer's disease genes mainly play roles in inhibitory and excitatory neurons. Additionally, they suggest that many lung cancer-related genes may exert their roles in immune cells. These findings suggest that HCNetlas has the potential to link disease-associated genes to cell types of action, facilitating development of cell type-resolved diagnostics and therapeutic strategies for complex human diseases.

Organ-specific microenvironments drive divergent T cell evolution in acute graft-versus-host disease

Tissue-specific T cell immune responses play a critical role in maintaining organ health but can also drive immune pathology during both autoimmunity and alloimmunity. The mechanisms controlling intratissue T cell programming remain unclear. Here, we leveraged a nonhuman primate model of acute graft-versus-host disease (aGVHD) after allogeneic hematopoietic stem cell transplantation to probe the biological underpinnings of tissue-specific alloimmune disease using a comprehensive systems immunology approach including multiparameter flow cytometry, population-based transcriptional profiling, and multiplexed single-cell RNA sequencing and TCR sequencing. Transcriptional profiling revealed substantial biological differences between T cells infiltrating the lung and liver during aGVHD. These included enrichment for transcriptional pathways controlling extracellular matrix remodeling and chemotaxis in the lung and enrichment for transcriptional pathways linked to nucleic acid metabolism and proliferation in the liver. Single-cell RNA sequencing and TCR sequencing substantiated divergent organ-specific transcriptional programing of tissue-infiltrating T cells, which was linked to clonal expansion, with expanded clones progressively enriched for C-X3-C motif chemokine receptor 1 (CX3CR1)-expressing CD8 effector T cells in the lung and eomesodermin (EOMES)-expressing CD8 effector-memory T cells in the liver. This divergent evolution of T cells was maintained even for T cells sharing the same TCRs, indicating its independence from antigen specificity. Together, these results provide insights into the role that tissue microenvironment-derived signals play in local T cell transcriptional programming during alloimmune-mediated clonal expansion and suggest potential opportunities to develop tissue-specific therapeutics to curtail pathogenic immunity after transplant.

The T cell receptor sequence influences the likelihood of T cell memory formation

The amino acid sequence of the T cell receptor (TCR) varies between T cells of an individual's immune system. Particular TCR residues nearly guarantee mucosal-associated invariant T (MAIT) and natural killer T (NKT) cell transcriptional fates. To define how the TCR sequence affects T cell fates, we analyze the paired αβTCR sequence and transcriptome of 961,531 single cells. We find that hydrophobic complementarity-determining region (CDR)3 residues promote regulatory T cell fates in both the CD8 and CD4 lineages. Most strikingly, we find a set of TCR sequence features that promote the T cell transition from naive to memory. We quantify the extent of these features through our TCR scoring function "TCR-mem." Using TCR transduction experiments, we demonstrate that increased TCR-mem promotes T cell activation, even among T cells that recognize the same antigen. Our results reveal a common set of TCR sequence features that enable T cell activation and immunological memory.

Anatomical, subset, and HIV-dependent expression of viral sensors and restriction factors

We developed viral sensor and restriction factor-cytometry by time of flight (VISOR-CyTOF), which profiles 19 viral sensors and restriction factors (VISORs) simultaneously in single cells, and applied it to 41 postmortem tissues from people with HIV. Mucosal myeloid cells are well equipped with SAMHD1 and sensors of viral capsid and DNA while CD4+ T cells are not. In lymph node CD4+ Tfh, VISOR expression patterns reflect those favoring integration but blocking HIV gene expression, thus favoring viral latency. We also identify small subsets of bone marrow-, lung-, and gut-associated CD4+ T and myeloid cells expressing high levels of restriction factors targeting most stages of the HIV replication cycle. In vitro, HIV preferentially fuses to CD4+ T cells with a permissive VISOR profile, but early induction of select VISORs by T1IFN prevents productive HIV infection. Our findings document the diverse patterns of VISOR profiles across tissues and cellular subsets and define their association with susceptibility to HIV.

Multimodal hierarchical classification of CITE-seq data delineates immune cell states across lineages and tissues

Single-cell RNA sequencing (scRNA-seq) is invaluable for profiling cellular heterogeneity and transcriptional states, but transcriptomic profiles do not always delineate subsets defined by surface proteins. Cellular indexing of transcriptomes and epitopes (CITE-seq) enables simultaneous profiling of single-cell transcriptomes and surface proteomes; however, accurate cell-type annotation requires a classifier that integrates multimodal data. Here, we describe multimodal classifier hierarchy (MMoCHi), a marker-based approach for accurate cell-type classification across multiple single-cell modalities that does not rely on reference atlases. We benchmark MMoCHi using sorted T lymphocyte subsets and annotate a cross-tissue human immune cell dataset. MMoCHi outperforms leading transcriptome-based classifiers and multimodal unsupervised clustering in its ability to identify immune cell subsets that are not readily resolved and to reveal subset markers. MMoCHi is designed for adaptability and can integrate annotation of cell types and developmental states across diverse lineages, samples, or modalities.

Coordinated immune networks in leukemia bone marrow microenvironments distinguish response to cellular therapy

Understanding how intratumoral immune populations coordinate antitumor responses after therapy can guide treatment prioritization. We systematically analyzed an established immunotherapy, donor lymphocyte infusion (DLI), by assessing 348,905 single-cell transcriptomes from 74 longitudinal bone marrow samples of 25 patients with relapsed leukemia; a subset was evaluated by both protein- and transcriptome-based spatial analysis. In acute myeloid leukemia (AML) DLI responders, we identified clonally expanded ZNF683+ CD8+ cytotoxic T lymphocytes with in vitro specificity for patient-matched AML. These cells originated primarily from the DLI product and appeared to coordinate antitumor immune responses through interaction with diverse immune cell types within the marrow microenvironment. Nonresponders lacked this cross-talk and had cytotoxic T lymphocytes with elevated TIGIT expression. Our study identifies recipient bone marrow microenvironment differences as a determinant of an effective antileukemia response and opens opportunities to modulate cellular therapy.

Consensus representation of multiple cell-cell graphs from gene signaling pathways for cell type annotation

BACKGROUND

Recent advancements in single-cell RNA sequencing have greatly expanded our knowledge of the heterogeneous nature of tissues. However, robust and accurate cell type annotation continues to be a major challenge, hindered by issues such as marker specificity, batch effects, and a lack of comprehensive spatial and interaction data. Traditional annotation methods often fail to adequately address the complexity of cellular interactions and gene regulatory networks.

RESULTS

We proposed scMCGraph, a comprehensive computational framework that integrates gene expression with pathway activity to accurately annotate cell types within diverse scRNA-seq datasets. Initially, our model constructs multiple pathway-specific views using various pathway databases, which reflect both gene expression and pathway activities. These pathway-specific views are then integrated into a consensus graph. The consensus graph is subsequently utilized to reconstruct the multiple pathway views. Our model demonstrated exceptional robustness and accuracy across various analyses, including cross-platform, cross-time, cross-sample, and clinical dataset evaluations.

CONCLUSIONS

scMCGraph represents a significant advance in cell type annotation. The experiments have demonstrated that introducing pathway information significantly improves the learning of cell-cell graphs, with their resulting consensus graph enhancing the predictive performance of cell type prediction. Different pathway databases provide complementary data, and an increase in the number of pathways can also boost model performance. Extensive testing shows that in various cross-dataset application scenarios, scMCGraph consistently exhibits both accuracy and robustness.

Single-cell analysis of cerebrospinal fluid reveals common features of neuroinflammation

Neuroinflammation is often characterized by immune cell infiltrates in the cerebrospinal fluid (CSF). Here, we apply single-cell RNA sequencing to explore the functional characteristics of these cells in patients with various inflammatory, infectious, and non-inflammatory neurological disorders. We show that CSF is distinct from the peripheral blood in terms of both cellular composition and gene expression. We report that the cellular and transcriptional landscape of CSF is altered in neuroinflammation but is strikingly similar across different neuroinflammatory disorders. We find clonal expansion of CSF lymphocytes in all disorders but most pronounced in inflammatory diseases, and we functionally characterize the transcriptional features of these cells. Finally, we explore the genetic control of gene expression in CSF lymphocytes. Our results highlight the common features of immune cells in the CSF compartment across diverse neurological diseases and may help to identify new targets for drug development or repurposing in multiple sclerosis (MS).

The Single-Cell Landscape of Peripheral and Tumor-infiltrating Immune Cells in HPV- HNSCC

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. HPV-negative HNSCC, which arises in the upper airway mucosa, is particularly aggressive, with nearly half of patients succumbing to the disease within five years and limited response to immune checkpoint inhibitors compared to other cancers. There is a need to further explore the complex immune landscape in HPV-negative HNSCC to identify potential therapeutic targets. Here, we integrated two single-cell RNA sequencing datasets from 29 samples and nearly 300,000 immune cells to investigate immune cell dynamics across tumor progression and lymph node metastasis. Notable shifts toward adaptative immune cell populations were observed in the 14 distinct HNSCC-associated peripheral blood mononuclear (PBMCs) and 21 tumor-infiltrating immune cells (TICs) considering disease stages. All PBMCs and TICs revealed unique molecular signatures correlating with lymph node involvement; however, broadly, TICs increased ligand expression among effector cytokines, growth factors, and interferon-related genes. Pathway analysis comparing PBMCs and TICs further confirmed active cell signaling among Monocyte-Macrophage, Dendritic cell, Natural Killer (NK), and T cell populations. Receptor-ligand analysis revealed significant communication patterns shifts among TICs, between CD8+ T cells and NK cells, showing heightened immunosuppressive signaling that correlated with disease progression. In locally invasive HPV-negative HNSCC samples, highly multiplexed immunofluorescence assays highlighted peri-tumoral clustering of exhausted CD8+ T and NK cells, alongside their exclusion from intra-tumoral niches. These findings emphasize cytotoxic immune cells as valuable biomarkers and therapeutic targets, shedding light on the mechanisms by which the HNSCC sustainably evades immune responses.

Ribosome profiling and single-cell RNA sequencing identify the unfolded protein response as a key regulator of pigeon lactation

Pigeons and certain other avian species produce a milk-like secretion in their crop sacs to nourish offspring, yet the detailed processes involved are not fully elucidated. This study investigated the crop sacs of 225-day-old unpaired non-lactating male pigeons (MN) and males initiating lactation on the first day after incubation (ML). Using RNA sequencing, ribosome profiling, and single-cell transcriptome sequencing (scRNA-seq), we identified a significant up-regulation of genes associated with ribosome assembly and protein synthesis in ML compared to MN. Results from scRNA-seq analysis identified 12 distinct cell types and 22 clusters, with secretory epithelial cells (SECs) exhibiting marked expression of plasma cell markers, including IGLL1 and MZB1. RNA fluorescence in situ hybridization (RNA FISH) and IgY quantification confirmed the critical role of SECs in producing endogenous IgY during lactation. We propose that fibroblast-derived BAFF signals activate SECs, mimicking B cell transformation and enhancing protein production through the unfolded protein response (UPR). These findings shed light on the cellular dynamics of pigeon milk production and contribute to a broader understanding of avian biology.

SwarmMAP: Swarm Learning for Decentralized Cell Type Annotation in Single Cell Sequencing Data

Rapid technological advancements have made it possible to generate single-cell data at a large scale. Several laboratories around the world can now generate single-cell transcriptomic data from different tissues. Unsupervised clustering, followed by annotation of the cell type of the identified clusters, is a crucial step in single-cell analyses. However, there is no consensus on the marker genes to use for annotation, and cell-type annotation is currently mostly done by manual inspection of marker genes, which is irreproducible, and poorly scalable. Additionally, patient-privacy is also a critical issue with human datasets. There is a critical need to standardize and automate cell-type annotation across datasets in a privacy-preserving manner. Here, we developed SwarmMAP that uses Swarm Learning to train machine learning models for cell-type classification based on single-cell sequencing data in a decentralized way. SwarmMAP does not require any exchange of raw data between data centers. SwarmMAP has a F1-score of 0.93, 0.98, and 0.88 for cell type classification in human heart, lung, and breast datasets, respectively. Swarm Learning-based models yield an average performance of 0.907 which is on par with the performance achieved by models trained on centralized data (p-val=0.937, Mann-Whitney U Test). We also find that increasing the number of datasets increases cell-type prediction accuracy and enables handling higher cell-type diversity. Together, these findings demonstrate that Swarm Learning is a viable approach to automate cell-type annotation. SwarmMAP is available at https://github.com/hayatlab/SwarmMAP.

Infiltrating plasma cells maintain glioblastoma stem cells through IgG-Tumor binding

Glioblastoma is a highly aggressive primary brain tumor with glioblastoma stem cells (GSCs) enforcing the intra-tumoral hierarchy. Plasma cells (PCs) are critical effectors of the B-lineage immune system, but their roles in glioblastoma remain largely unexplored. Here, we leverage single-cell RNA and B cell receptor sequencing of tumor-infiltrating B-lineage cells and reveal that PCs are aberrantly enriched in the glioblastoma-infiltrating B-lineage population, experience low level of somatic hypermutation, and are associated with poor prognosis. PCs secrete immunoglobulin G (IgG), which stimulates GSC proliferation via the IgG-FcγRIIA-AKT-mTOR axis. Disruption of IgG-FcγRIIA paracrine communication inhibits GSC proliferation and self-renewal. Glioblastoma-infiltrating PCs are recruited to GSC niches via CCL2-CCR2 chemokine program. GSCs further derive pro-proliferative signals from broadly utilized monoclonal antibody-based immune checkpoint inhibitors via FcγRIIA signaling. Our data generate an atlas of B-lineage cells in glioblastoma with a framework for combinatorial targeting of both tumor cell-intrinsic and microenvironmental dependencies.

A comprehensive analysis framework for evaluating commercial single-cell RNA sequencing technologies

This study examined nine prominent commercially available single-cell RNA sequencing (scRNA-seq) kits across four technology groups. Each kit was characterized using peripheral blood mononuclear cells (PBMCs) from a single donor, which enabled consistent assessment of factors such as analytical performance, protocol duration and cost. The Chromium Fixed RNA Profiling kit from 10× Genomics, with its probe-based RNA detection method, demonstrated the best overall performance. The Rhapsody WTA kit from Becton Dickinson exhibited a balance between performance and cost. Importantly, we introduce the read utilization metric, which differentiates scRNA-seq kits based on the efficiency of converting sequencing reads into usable counts. Thus, read utilization is an important feature that substantially impacts sensitivity and cost. With data from 169, 262 cells, our work provides a comprehensive comparison of commercial scRNA-seq technologies to facilitate the effective implementation of single-cell studies.

Single-cell analysis of the multiple myeloma microenvironment after γ-secretase inhibition and CAR T-cell therapy

ABSTRACT

Chimeric antigen receptor (CAR) T cells and bispecific antibodies targeting B-cell maturation antigen (BCMA) have significantly advanced the treatment of relapsed and refractory multiple myeloma. Resistance to BCMA-targeting therapies, nonetheless, remains a significant challenge. BCMA shedding by γ-secretase is a known resistance mechanism, and preclinical studies suggest that inhibition may improve anti-BCMA therapy. Leveraging a phase 1 clinical trial of the γ-secretase inhibitor (GSI), crenigacestat, with anti-BCMA CAR T cells (FCARH143), we used single-nuclei RNA sequencing and assay for transposase-accessible chromatin sequencing to characterize the effects of GSI on the tumor microenvironment. The most significant impacts of GSI involved effects on monocytes, which are known to promote tumor growth. In addition to observing a reduction in the frequency of nonclassical monocytes, we also detected significant changes in gene expression, chromatin accessibility, and inferred cell-cell interactions after exposure to GSI. Although many genes with altered expression are associated with γ-secretase-dependent signaling, such as Notch, other pathways were affected, indicating GSI has far-reaching effects. Finally, we detected monoallelic deletion of the BCMA locus in some patients with prior exposure to anti-BCMA therapy, which significantly correlated with reduced progression-free survival (PFS; median PFS, 57 vs 861 days). GSIs are being explored in combination with the full spectrum of BCMA-targeting agents, and our results reveal widespread effects of GSI on both tumor and immune cell populations, providing insight into mechanisms for enhancing BCMA-directed therapies.

Functional genomics implicates natural killer cells in the pathogenesis of ankylosing spondylitis

Multiple lines of evidence indicate that ankylosing spondylitis (AS) is a lymphocyte-driven disease. However, which lymphocyte populations are critical in AS pathogenesis is not known. In this study, we aimed to identify the key cell types mediating the genetic risk in AS using an unbiased functional genomics approach. We integrated genome-wide association study (GWAS) data with epigenomic and transcriptomic datasets of human immune cells. To quantify enrichment of cell type-specific open chromatin or gene expression in AS risk loci, we used three published methods-LDSC-SEG, SNPsea, and scDRS-that have successfully identified relevant cell types in other diseases. Natural killer (NK) cell-specific open chromatin regions are significantly enriched in heritability for AS, compared to other immune cell types such as T cells, B cells, and monocytes. This finding was consistent between two AS GWAS. Using RNA sequencing data, we validated that genes in AS risk loci are enriched in NK cell-specific gene expression. Using the human Space-Time Gut Cell Atlas, we also found significant upregulation of AS-associated genes predominantly in NK cells. We performed co-localization analyses between GWAS risk loci and genetic variants associated with gene expression (eQTL) to find putative target genes. This revealed four AS risk loci affecting regulation of candidate target genes in NK cells: two known loci, ERAP1 and TNFRSF1A, and two understudied loci, ENTR1 (SDCCAG3) and B3GNT2. Our findings suggest that NK cells may play a crucial role in AS development and highlight four putative target genes for functional follow-up in NK cells.

Transient EZH2 Suppression by Tazemetostat during In Vitro Expansion Maintains T-Cell Stemness and Improves Adoptive T-Cell Therapy

The histone methyltransferase enhancer of zeste homolog 2 (EZH2) plays important roles in T-cell differentiation, proliferation, and function. Previous studies have demonstrated that genetic deletion of EZH2 in CD8+ or total T cells impairs their antiviral and antitumor activities, cytokine production, and ability to expand upon rechallenge. Contrary to the detrimental role of deleting T cell-intrinsic EZH2, in this study, we demonstrated that transient inhibition of EZH2 in T cells prior to the phenotypic onset of exhaustion with a clinically approved inhibitor, tazemetostat (Taz), delayed their dysfunctional progression and preserved T-cell stemness and polyfunctionality but had no negative impact on cell proliferation. Taz-induced T-cell epigenetic reprogramming increased the expression of the self-renewal T-cell transcription factor TCF1 by reducing H3K27 methylation at its promoter preferentially in rapidly dividing T cells. In a murine melanoma model, T cells depleted of EZH2 induced poor tumor control, whereas adoptively transferred T cells pretreated with Taz exhibited superior antitumor immunity, especially when used in combination with anti-PD-1 blockade. Collectively, these data highlight the potential of transient epigenetic reprogramming by EZH2 inhibition to enhance adoptive T-cell immunotherapy.

CSI-GEP: A GPU-based unsupervised machine learning approach for recovering gene expression programs in atlas-scale single-cell RNA-seq data

Exploratory analysis of single-cell RNA sequencing (scRNA-seq) typically relies on hard clustering over two-dimensional projections like uniform manifold approximation and projection (UMAP). However, such methods can severely distort the data and have many arbitrary parameter choices. Methods that can model scRNA-seq data as non-discrete "gene expression programs" (GEPs) can better preserve the data's structure, but currently, they are often not scalable, not consistent across repeated runs, and lack an established method for choosing key parameters. Here, we developed a GPU-based unsupervised learning approach, "consensus and scalable inference of gene expression programs" (CSI-GEP). We show that CSI-GEP can recover ground truth GEPs in real and simulated atlas-scale scRNA-seq datasets, significantly outperforming cutting-edge methods, including GPT-based neural networks. We applied CSI-GEP to a whole mouse brain atlas of 2.2 million cells, disentangling endothelial cell types missed by other methods, and to an integrated scRNA-seq atlas of human tumors and cell lines, discovering mesenchymal-like GEPs unique to cancer cells growing in culture.

scImmOmics: a manually curated resource of single-cell multi-omics immune data

Single-cell sequencing technology has enabled the discovery and characterization of subpopulations of immune cells with unique functions, which is critical for revealing immune responses under healthy or disease conditions. Efforts have been made to collect and curate single-cell RNA sequencing (scRNA-seq) data, yet an immune-specific single-cell multi-omics atlas with harmonized metadata is still lacking. Here, we present scImmOmics (https://bio.liclab.net/scImmOmics/home), a manually curated single-cell multi-omics immune database constructed based on high-quality immune cells with known immune cell labels. Currently, scImmOmics documents >2.9 million cell-type labeled immune cells derived from seven single-cell sequencing technologies, involving 131 immune cell types, 47 tissues and 4 species. To ensure data consistency, we standardized the nomenclature of immune cell types and presented them in a hierarchical tree structure to clearly describe the lineage relationships within the immune system. scImmOmics also provides comprehensive immune regulatory information, including T-cell/B-cell receptor sequencing clonotype information, cell-specific regulatory information (e.g. gene/chromatin accessibility/protein/transcription factor states within known cell types, cell-to-cell communication and co-expression networks) and immune cell responses to cytokines. Collectively, scImmOmics is a comprehensive and valuable platform for unraveling the heterogeneity and diversity of immune cells and elucidating the specific regulatory mechanisms at the single-cell level.

Immunosenescence Inventory-a multi-omics database for immune aging research

The immune system is intricately interconnected with all other bodily systems. As individuals age, the immune system undergoes changes known as immunosenescence, increasing susceptibility to disease, and contributing significantly to the morbidity and mortality observed in older populations. Immunosenescence drives systemic aging and therefore represents a key therapeutic target to extend healthy aging. In recent years, the extensive application of omics technologies has broadened our understanding of aging and immunity, necessitating a comprehensive database to encapsulate these advancements and deepen our insights into immune aging in the era of artificial intelligence. The Immunosenescence Inventory is a pioneering database designed to provide a multidimensional and integrative view of the aging immune system. By leveraging cutting-edge omics technologies and analytical tools, Immunosenescence Inventory offers a comprehensive resource for researchers to explore the intricate relationship between immunosenescence and age-related health outcomes. Furthermore, the database, which aids in the creation of diagnostic tools for immune aging conditions, is now publicly available at https://ngdc.cncb.ac.cn/iaa/home.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.