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

A lymphocyte chemoaffinity axis for lung, non-intestinal mucosae and CNS

Tissue-selective chemoattractants direct lymphocytes to epithelial surfaces to establish local immune environments, regulate immune responses to food antigens and commensal organisms, and protect from pathogens. Homeostatic chemoattractants for small intestines, colon and skin are known1,2, but chemotropic mechanisms selective for respiratory tract and other non-intestinal mucosal tissues remain poorly understood. Here we leveraged diverse omics datasets to identify GPR25 as a lymphocyte receptor for CXCL17, a chemoattractant cytokine whose expression by epithelial cells of airways, upper gastrointestinal and squamous mucosae unifies the non-intestinal mucosal tissues and distinguishes them from intestinal mucosae. Single-cell transcriptomic analyses show that GPR25 is induced on innate lymphocytes before emigration to the periphery, and is imprinted in secondary lymphoid tissues on activated B and T cells responding to immune challenge. GPR25 characterizes B and T tissue resident memory cells and regulatory T lymphocytes in non-intestinal mucosal tissues and lungs in humans and mediates lymphocyte homing to barrier epithelia of the airways, oral cavity, stomach, and biliary and genitourinary tracts in mouse models. GPR25 is also expressed by T cells in cerebrospinal fluid and CXCL17 by neurons, suggesting a role in central nervous system (CNS) immune regulation. We reveal widespread imprinting of GPR25 on regulatory T cells, suggesting a mechanistic link to population genetics evidence that GPR25 is protective in autoimmunity3,4. Our results define a GPR25-CXCL17 chemoaffinity axis with the potential to integrate immunity and tolerance at non-intestinal mucosae and the CNS.

A multi-omic atlas of human embryonic skeletal development

Human embryonic bone and joint formation is determined by coordinated differentiation of progenitors in the nascent skeleton. The cell states, epigenetic processes and key regulatory factors that underlie lineage commitment of these cells remain elusive. Here we applied paired transcriptional and epigenetic profiling of approximately 336,000 nucleus droplets and spatial transcriptomics to establish a multi-omic atlas of human embryonic joint and cranium development between 5 and 11 weeks after conception. Using combined modelling of transcriptional and epigenetic data, we characterized regionally distinct limb and cranial osteoprogenitor trajectories across the embryonic skeleton and further described regulatory networks that govern intramembranous and endochondral ossification. Spatial localization of cell clusters in our in situ sequencing data using a new tool, ISS-Patcher, revealed mechanisms of progenitor zonation during bone and joint formation. Through trajectory analysis, we predicted potential non-canonical cellular origins for human chondrocytes from Schwann cells. We also introduce SNP2Cell, a tool to link cell-type-specific regulatory networks to polygenic traits such as osteoarthritis. Using osteolineage trajectories characterized here, we simulated in silico perturbations of genes that cause monogenic craniosynostosis and implicate potential cell states and disease mechanisms. This work forms a detailed and dynamic regulatory atlas of bone and cartilage maturation and advances our fundamental understanding of cell-fate determination in human skeletal development.

A cost-effective protocol for single-cell RNA sequencing of human skin

Introduction

Single-cell RNA sequencing (scRNAseq) and flow cytometry studies in skin are methodologically complex and costly, limiting their accessibility to researchers worldwide. Ideally, RNA and protein-based analyses should be performed on the same lesion to obtain more comprehensive data. However, current protocols generally focus on either scRNAseq or flow cytometry of healthy skin.

Methods

We present a novel label-free sample multiplexing strategy, building on the souporcell algorithm, which enables scRNAseq analysis of paired blood and skin samples. Additionally, we provide detailed instructions for simultaneous flow cytometry analysis from the same sample, with necessary adaptations for both healthy and inflamed skin specimens.

Results

This tissue multiplexing strategy mitigates technical batch effects and reduces costs by 2-4 times compared to existing protocols. We also demonstrate the effects of varying enzymatic incubation durations (1, 3, and 16 hours, with and without enzyme P) on flow cytometry outcomes. Comprehensive explanations of bioinformatic demultiplexing steps and a detailed step-by-step protocol of the entire experimental procedure are included.

Discussion

The protocol outlined in this article will make scRNAseq and flow cytometry analysis of skin samples more accessible to researchers, especially those new to these techniques.

Cell2Sentence: Teaching Large Language Models the Language of Biology

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into "cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the finetuning of language models for diverse tasks in biology, including cell generation, complex celltype annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S fine-tuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications.

The pyruvate-GPR31 axis promotes transepithelial dendrite formation in human intestinal dendritic cells

The intestinal lumen is rich in gut microbial metabolites that serve as signaling molecules for gut immune cells. G-protein-coupled receptors (GPCRs) sense metabolites and can act as key mediators that translate gut luminal signals into host immune responses. However, the impacts of gut microbe-GPCR interactions on human physiology have not been fully elucidated. Here, we show that GPR31, which is activated by the gut bacterial metabolite pyruvate, is specifically expressed on type 1 conventional dendritic cells (cDC1s) in the lamina propria of the human intestine. Using human induced pluripotent stem cell-derived cDC1s and a monolayer human gut organoid coculture system, we show that cDC1s extend their dendrites toward pyruvate on the luminal side, forming transepithelial dendrites (TED). Accordingly, GPR31 activation via pyruvate enhances the fundamental function of cDC1 by allowing efficient uptake of gut luminal antigens, such as dietary compounds and bacterial particles through TED formation. Our results highlight the role of GPCRs in tuning the human gut immune system according to local metabolic cues.

Deciphering STAT3's negative regulation of LHPP in ESCC progression through single-cell transcriptomics analysis

BACKGROUND

Esophageal Squamous Cell Carcinoma (ESCC) remains a predominant health concern in the world, characterized by high prevalence and mortality rates. Advances in single-cell transcriptomics have revolutionized cancer research by enabling a precise dissection of cellular and molecular diversity within tumors.

OBJECTIVE

This study aims to elucidate the cellular dynamics and molecular mechanisms in ESCC, focusing on the transcriptional influence of STAT3 (Signal Transducer and Activator of Transcription 3) and its interaction with LHPP, thereby uncovering potential therapeutic targets.

METHODS

Single-cell RNA sequencing was employed to analyze 44,206 cells from tumor and adjacent normal tissues of ESCC patients, identifying distinct cell types and their transcriptional shifts. We conducted differential gene expression analysis to assess changes within the tumor microenvironment (TME). Validation of key regulatory interactions was performed using qPCR in a cohort of 21 ESCC patients and further substantiated through experimental assays in ESCC cell lines.

RESULTS

The study revealed critical alterations in cell composition and gene expression across identified cell populations, with a notable shift towards pro-tumorigenic states. A significant regulatory influence of STAT3 on LHPP was discovered, establishing a novel aspect of ESCC pathogenesis. Elevated levels of STAT3 and suppressed LHPP expression were validated in clinical samples. Functional assays confirmed that STAT3 directly represses LHPP at the promoter level, and disruption of this interaction by promoter mutations diminished STAT3's repressive effect.

CONCLUSION

This investigation underscores the central role of STAT3 as a regulator in ESCC, directly impacting LHPP expression and suggesting a regulatory loop crucial for tumor behavior. The insights gained from our comprehensive cellular and molecular analysis offer a deeper understanding of the dynamics within the ESCC microenvironment. These findings pave the way for targeted therapeutic interventions focusing on the STAT3-LHPP axis, providing a strategic approach to improve ESCC management and prognosis.

Biologically informed machine learning modeling of immune cells to reveal physiological and pathological aging process

The immune system undergoes progressive functional remodeling from neonatal stages to old age. Therefore, understanding how aging shapes immune cell function is vital for precise treatment of patients at different life stages. Here, we constructed the first transcriptomic atlas of immune cells encompassing human lifespan, ranging from newborns to supercentenarians, and comprehensively examined gene expression signatures involving cell signaling, metabolism, differentiation, and functions in all cell types to investigate immune aging changes. By comparing immune cell composition among different age groups, HLA highly expressing NK cells and CD83 positive B cells were identified with high percentages exclusively in the teenager (Tg) group, whereas unknown_T cells were exclusively enriched in the supercentenarian (Sc) group. Notably, we found that the biological age (BA) of pediatric COVID-19 patients with multisystem inflammatory syndrome accelerated aging according to their chronological age (CA). Besides, we proved that inflammatory shift- myeloid abundance and signature correlate with the progression of complications in Kawasaki disease (KD). The shift- myeloid signature was also found to be associated with KD treatment resistance, and effective therapies improve treatment outcomes by reducing this signaling. Finally, based on those age-related immune cell compositions, we developed a novel BA prediction model PHARE ( https://xiazlab.org/phare/ ), which can apply to both scRNA-seq and bulk RNA-seq data. Using this model, we found patients with coronary artery disease (CAD) also exhibit accelerated aging compared to healthy individuals. Overall, our study revealed changes in immune cell proportions and function associated with aging, both in health and disease, and provided a novel tool for successfully capturing features that accelerate or delay aging.

IL-1β-induced epithelial cell and fibroblast transdifferentiation promotes neutrophil recruitment in chronic rhinosinusitis with nasal polyps

Neutrophilic inflammation contributes to multiple chronic inflammatory airway diseases, including asthma and chronic rhinosinusitis with nasal polyps (CRSwNP), and is associated with an unfavorable prognosis. Here, using single-cell RNA sequencing (scRNA-seq) to profile human nasal mucosa obtained from the inferior turbinates, middle turbinates, and nasal polyps of CRSwNP patients, we identify two IL-1 signaling-induced cell subsets-LY6D+ club cells and IDO1+ fibroblasts-that promote neutrophil recruitment by respectively releasing S100A8/A9 and CXCL1/2/3/5/6/8 into inflammatory regions. IL-1β, a pro-inflammatory cytokine involved in IL-1 signaling, induces the transdifferentiation of LY6D+ club cells and IDO1+ fibroblasts from primary epithelial cells and fibroblasts, respectively. In an LPS-induced neutrophilic CRSwNP mouse model, blocking IL-1β activity with a receptor antagonist significantly reduces the numbers of LY6D+ club cells and IDO1+ fibroblasts and mitigates nasal inflammation. This study implicates the function of two cell subsets in neutrophil recruitment and demonstrates an IL-1-based intervention for mitigating neutrophilic inflammation in CRSwNP.

Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations

Intratumor heterogeneity reduces treatment efficacy and complicates our understanding of tumor progression and there is a pressing need to understand the functions of heterogeneous tumor cell subpopulations within a tumor, yet systems to study these processes in vitro are limited. Single-cell RNA sequencing (scRNA-seq) has revealed that some cancer cell lines include distinct subpopulations. Here, we present clusterCleaver, a computational package that uses metrics of statistical distance to identify candidate surface markers maximally unique to transcriptomic subpopulations in scRNA-seq which may be used for FACS isolation. With clusterCleaver, ESAM and BST2/tetherin were experimentally validated as surface markers which identify and separate major transcriptomic subpopulations within MDA-MB-231 and MDA-MB-436 cells, respectively. clusterCleaver is a computationally efficient and experimentally validated workflow for identification of surface markers for tracking and isolating transcriptomically distinct subpopulations within cell lines. This tool paves the way for studies on coexisting cancer cell subpopulations in well-defined in vitro systems.

TGF-β-mediated crosstalk between TIGIT+ Tregs and CD226+CD8+ T cells in the progression and remission of type 1 diabetes

Type 1 diabetes (T1D) is a chronic autoimmune condition characterized by hyperglycemia resulting from the destruction of insulin-producing β-cells that is traditionally deemed irreversible, but partial remission (PR) with temporary reversal of hyperglycemia is sometimes observed. Here we use single-cell RNA sequencing to delineate the immune cell landscape across patients in different T1D stages. Together with cohort validation and functional assays, we observe dynamic changes in TIGIT+CCR7- Tregs and CD226+CCR7-CD8+ cytotoxic T cells during the peri-remission phase. Machine learning algorithms further identify TIGIT+CCR7- Tregs and CD226+CD8+ T cells as biomarkers for β-cell function decline in a predictive model, while cell communication analysis and in vitro assays suggest that TIGIT+CCR7- Tregs may inhibit CD226+CCR7-CD8+ T cells via TGF-β signaling. Lastly, in both cyclophosphamide-induced and streptozotocin (STZ)-induced mouse diabetes models, CD226 inhibition postpones insulitis onset and reduces hyperglycemia severity. Our results thus identify two interrelated immune cell subsets that may serve as biomarkers for monitoring disease progression and targets for therapeutic intervention of T1D.

Cross-tissue human fibroblast atlas reveals myofibroblast subtypes with distinct roles in immune modulation

Fibroblasts, known for their functional diversity, play crucial roles in inflammation and cancer. In this study, we conduct comprehensive single-cell RNA sequencing analyses on fibroblast cells from 517 human samples, spanning 11 tissue types and diverse pathological states. We identify distinct fibroblast subpopulations with universal and tissue-specific characteristics. Pathological conditions lead to significant shifts in fibroblast compositions, including the expansion of immune-modulating fibroblasts during inflammation and tissue-remodeling myofibroblasts in cancer. Within the myofibroblast category, we identify four transcriptionally distinct subpopulations originating from different developmental origins, with LRRC15+ myofibroblasts displaying terminally differentiated features. Both LRRC15+ and MMP1+ myofibroblasts demonstrate pro-tumor potential that contribute to the immune-excluded and immune-suppressive tumor microenvironments (TMEs), whereas PI16+ fibroblasts show potential anti-tumor functions in adjacent non-cancerous regions. Fibroblast-subtype compositions define patient subtypes with distinct clinical outcomes. This study advances our understanding of fibroblast biology and suggests potential therapeutic strategies for targeting specific fibroblast subsets in cancer treatment.

Integrated electrophysiological and genomic profiles of single cells reveal spiking tumor cells in human glioma

Prior studies have described the complex interplay that exists between glioma cells and neurons; however, the electrophysiological properties endogenous to glioma cells remain obscure. To address this, we employed Patch-sequencing (Patch-seq) on human glioma specimens and found that one-third of patched cells in IDH mutant (IDHmut) tumors demonstrate properties of both neurons and glia. To define these hybrid cells (HCs), which fire single, short action potentials, and discern if they are of tumoral origin, we developed the single cell rule association mining (SCRAM) computational tool to annotate each cell individually. SCRAM revealed that HCs possess select features of GABAergic neurons and oligodendrocyte precursor cells, and include both tumor and non-tumor cells. These studies characterize the combined electrophysiological and molecular properties of human glioma cells and describe a cell type in human glioma with unique electrophysiological and transcriptomic properties that may also exist in the non-tumor brain.

Rediscovering the human thymus through cutting-edge technologies

Recent technological advances have transformed our understanding of the human thymus. Innovations such as high-resolution imaging, single-cell omics, and organoid cultures, including thymic epithelial cell (TEC) differentiation and culture, and improvements in biomaterials, have further elucidated the thymus architecture, cellular dynamics, and molecular mechanisms underlying T cell development, and have unraveled previously unrecognized levels of stromal cell heterogeneity. These advancements offer unprecedented insights into thymic biology and hold promise for the development of novel therapeutic strategies for immune-related disorders.

C-ziptf: stable tensor factorization for zero-inflated multi-dimensional genomics data

In the past two decades, genomics has advanced significantly, with single-cell RNA-sequencing (scRNA-seq) marking a pivotal milestone. ScRNA-seq provides unparalleled insights into cellular diversity and has spurred diverse studies across multiple conditions and samples, resulting in an influx of complex multidimensional genomics data. This highlights the need for robust methodologies capable of handling the complexity and multidimensionality of such genomics data. Furthermore, single-cell data grapples with sparsity due to issues like low capture efficiency and dropout effects. Tensor factorizations (TF) have emerged as powerful tools to unravel the complex patterns from multi-dimensional genomics data. Classic TF methods, based on maximum likelihood estimation, struggle with zero-inflated count data, while the inherent stochasticity in TFs further complicates result interpretation and reproducibility. Our paper introduces Zero Inflated Poisson Tensor Factorization (ZIPTF), a novel method for high-dimensional zero-inflated count data factorization. We also present Consensus-ZIPTF (C-ZIPTF), merging ZIPTF with a consensus-based approach to address stochasticity. We evaluate our proposed methods on synthetic zero-inflated count data, simulated scRNA-seq data, and real multi-sample multi-condition scRNA-seq datasets. ZIPTF consistently outperforms baseline matrix and tensor factorization methods, displaying enhanced reconstruction accuracy for zero-inflated data. When dealing with high probabilities of excess zeros, ZIPTF achieves up to 2.4 × better accuracy. Moreover, C-ZIPTF notably enhances the factorization's consistency. When tested on synthetic and real scRNA-seq data, ZIPTF and C-ZIPTF consistently uncover known and biologically meaningful gene expression programs. Access our data and code at: https://github.com/klarman-cell-observatory/scBTF and https://github.com/klarman-cell-observatory/scbtf_experiments .

Single-cell landscape of bronchoalveolar immune cells in patients with immune checkpoint inhibitor-related pneumonitis

The pathophysiology of immune checkpoint inhibitor-related pneumonitis remains incompletely understood. We conducted single-cell and T-cell receptor transcriptomic sequencing on the bronchoalveolar lavage fluid from five patients with grade ≥2 immune checkpoint inhibitor-related pneumonitis. Our analyses revealed a prominent enrichment of T cells in the bronchoalveolar lavage fluid of patients with immune checkpoint inhibitor-related pneumonitis. Within the CD4 + T cell subset, Tfh-like T cells were highly enriched and exhibited signatures associated with inflammation and clonal expansion. Regulatory T cells were also enriched and displayed enhanced inhibitory functions. Within the CD8 + T-cell subset, effector memory/tissue-resident memory T cells with an elevated cytotoxic phenotype were highly infiltrated. Among myeloid cells, alveolar macrophages were depleted, while pro-inflammatory intermediate monocytes were elevated. Dendritic cells demonstrated enhanced antigen presentation capabilities. Cytokines CXCR4, CXCL13, TNF-α, IFN-α, IFN-γ, and TWEAK were elevated. Through a comprehensive single-cell analysis, we depicted the landscape of immune checkpoint inhibitor-related pneumonitis.

Single cell RNA-sequencing identifies the effect of Normothermic ex vivo liver perfusion on liver-resident T cells

BACKGROUND

Normothermic ex vivo liver perfusion (NEVLP) is an exciting strategy to preserve livers prior to transplant, however, the effects of NEVLP on the phenotype of tissue-resident immune cells is largely unknown. The presence of tissue-resident memory T cells (TRM) in the liver may protect against acute rejection and decrease allograft dysfunction. Therefore, we investigated the effects of NEVLP on liver TRMs and assessed the ability of anti-inflammatory cytokines to reduce TRM activation during NEVLP.

METHODS

Rat livers underwent NEVLP with or without the addition of IL-10 and TGF-β. Naïve and cold storage livers served as controls. Following preservation, TRM T cell gene expression profiles were assessed through single cell RNA sequencing (scRNA-seq). Differential gene expression analysis was performed with Wilcoxon rank sum test to identify differentially expressed genes (DEGs) associated with a specific treatment group. Using the online Database for Annotation, Visualization and Integrated Discovery (DAVID), gene set enrichment was then conducted with Fisher's exact test on DEGs to highlight differentially regulated pathways and functional terms associated with treatment groups.

RESULTS

Through scRNA-seq analysis, an atlas of liver-resident memory T cell subsets was created for all livers. TRM T cells could be identified in all livers, and through scRNA-seq, DEG was identified with Wilcoxon rank sum test at FDR < 0.05. Based on the gene set enrichment analysis of DEGs using Fisher's exact test, NEVLP is associated with downregulation of multiple gene enrichment pathways associated with surface proteins. Furthermore, NEVLP with anti-inflammatory cytokines was associated with down regulation of 52 genes in TRM T cells when compared to NEVLP alone (FDR <0.05), most of which are pro-inflammatory.

CONCLUSION

This is the first study to create an atlas of liver TRM T cells in the rat liver undergoing NEVLP and demonstrate the effects of NEVLP on liver TRM T cells at the single cell gene expression level.

Identification of immunosenescence of unconventional T cells in hepatocellular carcinoma

Accumulation of senescent cells is a recognized feature in hepatocellular carcinoma (HCC), but their specific types and prognostic implications remain under investigation. This study aimed to delineate senescent cell types and their senescent patterns in HCC using publicly available bulk and single-cell mRNA sequencing data. Through gene expression and gene set enrichment analysis, we identified distinct senescent patterns within HCC samples. Notably, unconventional T cells, specifically natural killer T cells and γδT cells, were found to be the predominant senescent cell types. These cells exhibited enriched pathways related to DNA damage, senescence and the negative regulation of lymphocyte activation. Furthermore, we observed upregulation of the mTOR signaling pathway, which correlated positively with the expression of senescence-associated genes. This suggests a potential regulatory role for mTOR in the senescence of HCC. Strikingly, patients with elevated expression of senescence markers, including p16INK4A, p21, and GLB1, demonstrated significantly reduced overall survival rates. Our findings indicate that immunosenescence in unconventional T cells may play a role in HCC progression. The potential therapeutic implications of targeting the mTOR pathway or eliminating senescent unconventional T cells warrant further exploration to improve HCC patient outcomes.

A self-training interpretable cell type annotation framework using specific marker gene

MOTIVATION

Recent advances in sequencing technology provide opportunities to study biological processes at a higher resolution. Cell type annotation is an important step in scRNA-seq analysis, which often relies on established marker genes. However, most of the previous methods divide the identification of cell types into two stages, clustering and assignment, whose performances are susceptible to the clustering algorithm, and the marker information cannot effectively guide the clustering process. Furthermore, their linear heuristic-based cell assignment process is often insufficient to capture potential dependencies between cells and types.

RESULTS

Here, we present Interpretable Cell Type Annotation based on self-training (sICTA), a marker-based cell type annotation method that combines the self-training strategy with pseudo-labeling and the nonlinear association capturing capability of Transformer. In addition, we incorporate biological priori knowledge of genes and pathways into the classifier through an attention mechanism to enhance the transparency of the model. A benchmark analysis on 11 publicly available single-cell datasets demonstrates the superiority of sICTA compared to state-of-the-art methods. The robustness of our method is further validated by evaluating the prediction accuracy of the model on different cell types for each single-cell data. Moreover, ablation studies show that self-training and the ability to capture potential dependencies between cells and cell types, both of which are mutually reinforcing, work together to improve model performance. Finally, we apply sICTA to the pancreatic dataset, exemplifying the interpretable attention matrix captured by sICTA.

AVAILABILITY AND IMPLEMENTATION

The source code of sICTA is available in public at https://github.com/nbnbhwyy/sICTA. The processed datasets can be found at https://drive.google.com/drive/folders/1jbqSxacL_IDIZ4uPjq220C9Kv024m9eL. The final version of the model will be permanently available at https://doi.org/10.5281/zenodo.13474010.

Immunocytes do not mediate food intake and the causal relationship with allergic rhinitis: a comprehensive Mendelian randomization

Background

Observational studies indicate a correlation between food intake and allergic rhinitis. The potential interplay between the immune system and allergic rhinitis might contribute causally to both food intake and allergic rhinitis, providing promising therapeutic avenues. However, elucidating the causal relationship and immune-mediated mechanisms between food intake and allergic rhinitis remains a pending task.

Methods

We utilized a two-sample Mendelian randomization (MR) methodology to explore the causal relationship between food intake and allergic rhinitis. Furthermore, we investigated the potential causal relationship of immune cell signals with allergic rhinitis, as well as the potential causal relationship between food intake and immune cell signals. Moreover, employing both two-step Mendelian randomization and multivariable Mendelian randomization, we delved into the mediating role of immune cell signals in the causal relationship between food intake and allergic rhinitis. Leveraging publicly accessible genetic datasets, our analysis encompassed 903 traits, comprising 171 food intake features, 731 immune cell features, and one trait related to allergic rhinitis.

Result

We found causal relationships between seven types of food intake and allergic rhinitis, as well as between 30 immune cell phenotypes and allergic rhinitis. Furthermore, our two-step Mendelian randomization analysis and multivariable Mendelian randomization analysis indicate that immune cells do not mediate the causal relationship between food intake and allergic rhinitis.

Conclusion

To the best of our knowledge, we are the first to incorporate a large-scale dataset integrating immune cell features, food intake features, and allergic rhinitis into Mendelian randomization analysis. Our research findings indicate that there are causal relationships between six types of food intake and allergic rhinitis, as well as between 30 immune cell phenotypes and allergic rhinitis. Additionally, immune cells do not mediate these relationships.

Clinical, mechanistic, and therapeutic landscape of cutaneous fibrosis

When dysregulated, skin fibrosis can lead to a multitude of pathologies. We provide a framework for understanding the wide clinical spectrum, mechanisms, and management of cutaneous fibrosis encompassing a variety of matrix disorders, fibrohistiocytic neoplasms, injury-induced scarring, and autoimmune scleroses. Underlying such entities are common mechanistic pathways that leverage morphogenic signaling, immune activation, and mechanotransduction to modulate fibroblast function. In light of the limited array of available treatments for cutaneous fibrosis, scientific insights have opened new therapeutic and investigative avenues for conditions that still lack effective interventions.

A novel method for characterizing cell-cell interactions at single-cell resolution reveals unique signatures in blood T cell-monocyte complexes during infection

Communication between immune cells through direct contact is a critical feature of immune responses. Here, we developed a novel high-throughput method to study the transcriptome and adaptive immune receptor repertoire of single cells forming complexes without needing bioinformatic deconvolution. We found that T cells and monocytes forming complexes in blood during active tuberculosis (TB) and dengue hold unique transcriptomic signatures indicative of TCR/MCH-II immune synapses. Additionally, T cells in complexes showed enrichment for effector phenotypes, imaging and transcriptomic features of active TCR signaling, and increased immune activity at diagnosis compared to after anti-TB therapy. We also found evidence for bidirectional RNA exchange between T cells and monocytes, since complexes were markedly enriched for "dual-expressing" cells (i.e., co-expressing T cell and monocyte genes). Thus, studying immune cell complexes at a single-cell resolution offers novel perspectives on immune synaptic interactions occurring in blood during infection.

Robust self-supervised learning strategy to tackle the inherent sparsity in single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for elucidating cellular heterogeneity and tissue function in various biological contexts. However, the sparsity in scRNA-seq data limits the accuracy of cell type annotation and transcriptomic analysis due to information loss. To address this limitation, we present scRobust, a robust self-supervised learning strategy to tackle the inherent sparsity of scRNA-seq data. Built upon the Transformer architecture, scRobust employs a novel self-supervised learning strategy comprising contrastive learning and gene expression prediction tasks. We demonstrated the effectiveness of scRobust using nine benchmarks, additional dropout scenarios, and combined datasets. scRobust outperformed recent methods in cell-type annotation tasks and generated cell embeddings that capture multi-faceted clustering information (e.g. cell types and HbA1c levels). In addition, cell embeddings of scRobust were useful for detecting specific marker genes related to drug tolerance stages. Furthermore, when we applied scRobust to scATAC-seq data, high-quality cell embedding vectors were generated. These results demonstrate the representational power of scRobust.

Protocol for the characterization of the pancreatic tumor microenvironment using organoid-derived mouse models and single-nuclei RNA sequencing

Single-nuclei RNA sequencing (snRNA-seq) allows for obtaining gene expression profiles from frozen or hard-to-dissociate tissues at the single-nuclei level. Here, we describe a protocol to obtain snRNA-seq data of pancreatic tumors from orthotopically grafted organoid-derived mouse models. We provide details on the establishment of these mouse models, the isolation of single nuclei from pancreatic tumors, and the analysis of the snRNA-seq datasets. For complete details on the use and execution of this protocol, please refer to Mucciolo et al.1.

Single-cell RNA sequencing analysis of peripheral blood mononuclear cells in PD-1-induced renal toxicity in patients with lung cancer

BACKGROUND

Although the patient survival rate for many malignancies has been improved with immune checkpoint inhibitors (ICIs), some patients experience various immune-related adverse events (irAEs). IrAEs impact several organ systems, including the kidney. With anti-programmed cell death protein 1 (PD-1) therapy (pembrolizumab), kidney-related adverse events occur relatively rarely compared with other irAEs. However, the occurrence of AKI usually leads to anti-PD-1 therapy interruption or discontinuation. Therefore, there is an urgent need to clarify the mechanisms of renal irAEs (R-irAEs) to facilitate early management. This study aimed to analyse the characteristics of peripheral blood mononuclear cells (PBMCs) in R-irAEs.

METHODS

PBMCs were collected from three patients who developed R-irAEs after anti-PD-1 therapy and three patients who did not. The PBMCs were subjected to scRNA-seq to identify cell clusters and differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses were performed to investigate the most active biological processes in immune cells.

RESULTS

Fifteen cell clusters were identified across the two groups. FOS, RPS26, and JUN were the top three upregulated genes in CD4+ T cells. The DEGs in CD4+ T cells were enriched in Th17 differentiation, Th1 and Th2 cell differentiation, NF-kappa B, Nod-like receptor, TNF, IL-17, apoptosis, and NK cell-mediated cytotoxicity signaling pathways. RPS26, TRBV25-1, and JUN were the top three upregulated genes in CD8+ T cells. The DEGs in CD8+ T cells were enriched in Th17 cell differentiation, antigen processing and presentation, natural killer cell-mediated cytotoxicity, the intestinal immune network for IgA production, the T-cell receptor signalling pathway, Th1 and Th2 cell differentiation, the phagosome, and cell adhesion molecules.

CONCLUSIONS

In conclusion, R-irAEs are associated with immune cell dysfunction. DEGs and their enriched pathways identified in CD4+ T cells and CD8+ T cells play important roles in the development of renal irAEs related to anti-PD-1 therapy. These findings offer fresh perspectives on the pathogenesis of renal damage caused by anti-PD-1 therapy.

A primer on single-cell RNA-seq analysis using dendritic cells as a case study

Recent advances in single-cell (sc) transcriptomics have revolutionized our understanding of dendritic cells (DCs), pivotal players of the immune system. ScRNA-sequencing (scRNA-seq) has unraveled a previously unrecognized complexity and heterogeneity of DC subsets, shedding light on their ontogeny and specialized roles. However, navigating the rapid technological progress and computational methods can be daunting for researchers unfamiliar with the field. This review aims to provide immunologists with a comprehensive introduction to sc transcriptomic analysis, offering insights into recent developments in DC biology. Addressing common analytical queries, we guide readers through popular tools and methodologies, supplemented with references to benchmarks and tutorials for in-depth understanding. By examining findings from pioneering studies, we illustrate how computational techniques have expanded our knowledge of DC biology. Through this synthesis, we aim to equip researchers with the necessary tools and knowledge to navigate and leverage scRNA-seq for unraveling the intricacies of DC biology and advancing immunological research.

DeepKINET: a deep generative model for estimating single-cell RNA splicing and degradation rates

Messenger RNA splicing and degradation are critical for gene expression regulation, the abnormality of which leads to diseases. Previous methods for estimating kinetic rates have limitations, assuming uniform rates across cells. DeepKINET is a deep generative model that estimates splicing and degradation rates at single-cell resolution from scRNA-seq data. DeepKINET outperforms existing methods on simulated and metabolic labeling datasets. Applied to forebrain and breast cancer data, it identifies RNA-binding proteins responsible for kinetic rate diversity. DeepKINET also analyzes the effects of splicing factor mutations on target genes in erythroid lineage cells. DeepKINET effectively reveals cellular heterogeneity in post-transcriptional regulation.

Answering open questions in biology using spatial genomics and structured methods

Genomics methods have uncovered patterns in a range of biological systems, but obscure important aspects of cell behavior: the shapes, relative locations, movement, and interactions of cells in space. Spatial technologies that collect genomic or epigenomic data while preserving spatial information have begun to overcome these limitations. These new data promise a deeper understanding of the factors that affect cellular behavior, and in particular the ability to directly test existing theories about cell state and variation in the context of morphology, location, motility, and signaling that could not be tested before. Rapid advancements in resolution, ease-of-use, and scale of spatial genomics technologies to address these questions also require an updated toolkit of statistical methods with which to interrogate these data. We present a framework to respond to this new avenue of research: four open biological questions that can now be answered using spatial genomics data paired with methods for analysis. We outline spatial data modalities for each open question that may yield specific insights, discuss how conflicting theories may be tested by comparing the data to conceptual models of biological behavior, and highlight statistical and machine learning-based tools that may prove particularly helpful to recover biological understanding.

Neuroimmune Crossroads: The Interplay of the Enteric Nervous System and Intestinal Macrophages in Gut Homeostasis and Disease

A defining unique characteristic of the gut immune system is its ability to respond effectively to foreign pathogens while mitigating unnecessary inflammation. Intestinal macrophages serve as the cornerstone of this balancing act, acting uniquely as both the sword and shield in the gut microenvironment. The GI tract is densely innervated by the enteric nervous system (ENS), the intrinsic nervous system of the gut. Recent advances in sequencing technology have increasingly suggested neuroimmune crosstalk as a critical component for homeostasis both within the gut and in other tissues. Here, we systematically review the ENS-macrophage axis. We focus on the pertinent molecules produced by the ENS, spotlight the mechanistic contributions of intestinal macrophages to gut homeostasis and inflammation, and discuss both existing and potential strategies that intestinal macrophages use to integrate signals from the ENS. This review aims to elucidate the complex molecular basis governing ENS-macrophage signaling, highlighting their cooperative roles in sustaining intestinal health and immune equilibrium.

scDiffusion: conditional generation of high-quality single-cell data using diffusion model

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) data are important for studying the laws of life at single-cell level. However, it is still challenging to obtain enough high-quality scRNA-seq data. To mitigate the limited availability of data, generative models have been proposed to computationally generate synthetic scRNA-seq data. Nevertheless, the data generated with current models are not very realistic yet, especially when we need to generate data with controlled conditions. In the meantime, diffusion models have shown their power in generating data with high fidelity, providing a new opportunity for scRNA-seq generation.

RESULTS

In this study, we developed scDiffusion, a generative model combining the diffusion model and foundation model to generate high-quality scRNA-seq data with controlled conditions. We designed multiple classifiers to guide the diffusion process simultaneously, enabling scDiffusion to generate data under multiple condition combinations. We also proposed a new control strategy called Gradient Interpolation. This strategy allows the model to generate continuous trajectories of cell development from a given cell state. Experiments showed that scDiffusion could generate single-cell gene expression data closely resembling real scRNA-seq data. Also, scDiffusion can conditionally produce data on specific cell types including rare cell types. Furthermore, we could use the multiple-condition generation of scDiffusion to generate cell type that was out of the training data. Leveraging the Gradient Interpolation strategy, we generated a continuous developmental trajectory of mouse embryonic cells. These experiments demonstrate that scDiffusion is a powerful tool for augmenting the real scRNA-seq data and can provide insights into cell fate research.

AVAILABILITY AND IMPLEMENTATION

scDiffusion is openly available at the GitHub repository https://github.com/EperLuo/scDiffusion or Zenodo https://zenodo.org/doi/10.5281/zenodo.13268742.

Bin2cell reconstructs cells from high resolution Visium HD data

SUMMARY

Visium HD by 10X Genomics is the first commercially available platform capable of capturing full scale transcriptomic data paired with a reference morphology image from archived FFPE blocks at sub-cellular resolution. However, aggregation of capture regions to single cells poses challenges. Bin2cell reconstructs cells from the highest resolution data (2 μm bins) by leveraging morphology image segmentation and gene expression information. It is compatible with established Python single cell and spatial transcriptomics software, and operates efficiently in a matter of minutes without requiring a GPU. We demonstrate improvements in downstream analysis when using the reconstructed cells over default 8 μm bins on mouse brain and human colorectal cancer data.

AVAILABILITY AND IMPLEMENTATION

Bin2cell is available at https://github.com/Teichlab/bin2cell, along with documentation and usage examples, and can be installed from pip. Probe design functionality is available at https://github.com/Teichlab/gene2probe.

Inferring Characteristics of the Tumor Immune Microenvironment of Patients with HNSCC from Single-Cell Transcriptomics of Peripheral Blood

In this study, we explore the possibility of inferring characteristics of the tumor immune microenvironment from the blood. Specifically, we investigate two datasets of patients with head and neck squamous cell carcinoma with matched single-cell RNA sequencing (scRNA-seq) from peripheral blood mononuclear cells (PBMCs) and tumor tissues. Our analysis shows that the immune cell fractions and gene expression profiles of various immune cells within the tumor microenvironment can be inferred from the matched PBMC scRNA-seq data. We find that the established exhausted T-cell signature can be predicted from the blood and serve as a valuable prognostic blood biomarker of immunotherapy response. Additionally, our study reveals that the inferred ratio between tumor memory B- and regulatory T-cell fractions is predictive of immunotherapy response and is superior to the well-established cytolytic and exhausted T-cell signatures. These results highlight the promising potential of PBMC scRNA-seq in cancer immunotherapy and warrant, and will hopefully facilitate, further investigations on a larger scale. The code for predicting tumor immune microenvironment from PBMC scRNA-seq, TIMEP, is provided, offering other researchers the opportunity to investigate its prospective applications in various other indications.

SIGNIFICANCE

Our work offers a new and promising paradigm in liquid biopsies to unlock the power of blood single-cell transcriptomics in cancer immunotherapy.

Revealing gene function with statistical inference at single-cell resolution

Single-cell and spatial molecular profiling assays have shown large gains in sensitivity, resolution and throughput. Applying these technologies to specimens from human and model organisms promises to comprehensively catalogue cell types, reveal their lineage origins in development and discern their contributions to disease pathogenesis. Moreover, rapidly dropping costs have made well-controlled perturbation experiments and cohort studies widely accessible, illuminating mechanisms that give rise to phenotypes at the scale of the cell, the tissue and the whole organism. Interpreting the coming flood of single-cell data, much of which will be spatially resolved, will place a tremendous burden on existing computational pipelines. However, statistical concepts, models, tools and algorithms can be repurposed to solve problems now arising in genetic and molecular biology studies of development and disease. Here, I review how the questions that recent technological innovations promise to answer can be addressed by the major classes of statistical tools.

Epidemiologic and genetic associations between primary biliary cholangitis and extrahepatic rheumatic diseases

Patients with primary biliary cholangitis (PBC) commonly experience extrahepatic rheumatic diseases. However, the epidemiologic and genetic associations as well as causal relationship between PBC and these extrahepatic conditions remain undetermined. In this study, we first conducted systematic review and meta-analyses by analyzing 73 studies comprising 334,963 participants across 17 countries and found strong phenotypic associations between PBC and rheumatic diseases. Next, we utilized large-scale genome-wide association study summary data to define the shared genetic architecture between PBC and rheumatic diseases, including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), systemic sclerosis (SSc) and Sjögren's syndrome (SS). We observed significant genetic correlations between PBC and each of the four rheumatic diseases. Pleiotropy and heritability enrichment analysis suggested the involvement of humoral immunity and interferon-associated processes for the comorbidity. Of note, we identified four variants shared between PBC and RA (rs80200208), SLE (rs9843053), and SSc (rs27524, rs3873182) using cross-trait meta-analysis. Additionally, several pleotropic loci for PBC and rheumatic diseases were found to share causal variants with gut microbes possessing immunoregulatory functions. Finally, Mendelian randomization revealed consistent evidence for a causal effect of PBC on RA, SLE, SSc, and SS, but no or inconsistent evidence for a causal effect of extrahepatic rheumatic diseases on PBC. Our study reveals a profound genetic overlap and causal relationships between PBC and extrahepatic rheumatic diseases, thus providing insights into shared biological mechanisms and novel therapeutic interventions.

Tissue-specific features of innate lymphoid cells in antiviral defense

Innate lymphocytes (ILCs) rapidly respond to and protect against invading pathogens and cancer. ILCs include natural killer (NK) cells, ILC1s, ILC2s, ILC3s, and lymphoid tissue inducer (LTi) cells and include type I, type II, and type III immune cells. While NK cells have been well recognized for their role in antiviral immunity, other ILC subtypes are emerging as players in antiviral defense. Each ILC subset has specialized functions that uniquely impact the antiviral immunity and health of the host depending on the tissue microenvironment. This review focuses on the specialized functions of each ILC subtype and their roles in antiviral immune responses across tissues. Several viruses within infection-prone tissues will be highlighted to provide an overview of the extent of the ILC immunity within tissues and emphasize common versus virus-specific responses.

Repression of SMAD3 by STAT3 and c-Ski induces conventional dendritic cell differentiation

A pleiotropic immunoregulatory cytokine, TGF-β, signals via the receptor-regulated SMADs: SMAD2 and SMAD3, which are constitutively expressed in normal cells. Here, we show that selective repression of SMAD3 induces cDC differentiation from the CD115+ common DC progenitor (CDP). SMAD3 was expressed in haematopoietic cells including the macrophage DC progenitor. However, SMAD3 was specifically down-regulated in CD115+ CDPs, SiglecH- pre-DCs, and cDCs, whereas SMAD2 remained constitutive. SMAD3-deficient mice showed a significant increase in cDCs, SiglecH- pre-DCs, and CD115+ CDPs compared with the littermate control. SMAD3 repressed the mRNA expression of FLT3 and the cDC-related genes: IRF4 and ID2. We found that one of the SMAD transcriptional corepressors, c-SKI, cooperated with phosphorylated STAT3 at Y705 and S727 to repress the transcription of SMAD3 to induce cDC differentiation. These data indicate that STAT3 and c-Ski induce cDC differentiation by repressing SMAD3: the repressor of the cDC-related genes during the developmental stage between the macrophage DC progenitor and CD115+ CDP.

Monocyte Single-Cell Multimodal Profiling in Cardiovascular Disease Risk States

BACKGROUND

Monocytes are a critical innate immune system cell type that serves homeostatic and immunoregulatory functions. They have been identified historically by the cell surface expression of CD14 and CD16. However, recent single-cell studies have revealed that they are much more heterogeneous than previously realized.

METHODS

We utilized cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and single-cell RNA sequencing to describe the comprehensive transcriptional and phenotypic landscape of 437 126 monocytes.

RESULTS

This high-dimensional multimodal approach identified vast phenotypic diversity and functionally distinct subsets, including IFN-responsive, MHCIIhi (major histocompatibility complex class II), monocyte-platelet aggregates, as well as nonclassical, and several subpopulations of classical monocytes. Using flow cytometry, we validated the existence of MHCII+CD275+ MHCIIhi, CD42b+ monocyte-platelet aggregates, CD16+CD99- nonclassical monocytes, and CD99+ classical monocytes. Each subpopulation exhibited unique characteristics, developmental trajectories, transcriptional regulation, and tissue distribution. In addition, alterations associated with cardiovascular disease risk factors, including race, smoking, and hyperlipidemia were identified. Moreover, the effect of hyperlipidemia was recapitulated in mouse models of elevated cholesterol.

CONCLUSIONS

This integrative and cross-species comparative analysis provides a new perspective on the comparison of alterations in monocytes in pathological conditions and offers insights into monocyte-driven mechanisms in cardiovascular disease and the potential for monocyte subpopulation targeted therapies.

Pan-cancer single-cell dissection reveals phenotypically distinct B cell subtypes

Characterizing the compositional and phenotypic characteristics of tumor-infiltrating B cells (TIBs) is important for advancing our understanding of their role in cancer development. Here, we establish a comprehensive resource of human B cells by integrating single-cell RNA sequencing data of B cells from 649 patients across 19 major cancer types. We demonstrate substantial heterogeneity in their total abundance and subtype composition and observe immunoglobulin G (IgG)-skewness of antibody-secreting cell isotypes. Moreover, we identify stress-response memory B cells and tumor-associated atypical B cells (TAABs), two tumor-enriched subpopulations with prognostic potential, shared in a pan-cancer manner. In particular, TAABs, characterized by a high clonal expansion level and proliferative capacity as well as by close interactions with activated CD4 T cells in tumors, are predictive of immunotherapy response. Our integrative resource depicts distinct clinically relevant TIB subsets, laying a foundation for further exploration of functional commonality and diversity of B cells in cancer.

scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis

The rapid rise in the availability and scale of scRNA-seq data needs scalable methods for integrative analysis. Though many methods for data integration have been developed, few focus on understanding the heterogeneous effects of biological conditions across different cell populations in integrative analysis. Our proposed scalable approach, scParser, models the heterogeneous effects from biological conditions, which unveils the key mechanisms by which gene expression contributes to phenotypes. Notably, the extended scParser pinpoints biological processes in cell subpopulations that contribute to disease pathogenesis. scParser achieves favorable performance in cell clustering compared to state-of-the-art methods and has a broad and diverse applicability.

Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage

The Human BioMolecular Atlas Program (HuBMAP) aims to construct a reference 3D structural, cellular, and molecular atlas of the healthy adult human body. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) serves experimental datasets and supports data processing, search, filtering, and visualization. The Human Reference Atlas (HRA) Portal (https://humanatlas.io) provides open access to atlas data, code, procedures, and instructional materials. Experts from more than 20 consortia are collaborating to construct the HRA's Common Coordinate Framework (CCF), knowledge graphs, and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes, and biomarkers) and to use the HRA to understand changes that occur at each of these levels with aging, disease, and other perturbations. The 6th release of the HRA v2.0 covers 36 organs with 4,499 unique anatomical structures, 1,195 cell types, and 2,089 biomarkers (e.g., genes, proteins, lipids) linked to ontologies and 2D/3D reference objects. New experimental data can be mapped into the HRA using (1) three cell type annotation tools (e.g., Azimuth) or (2) validated antibody panels (OMAPs), or (3) by registering tissue data spatially. This paper describes the HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interface (APIs), flexible hybrid cloud infrastructure, and previews atlas usage applications.

Single-cell chromatin accessibility and transposable element landscapes reveal shared features of tissue-residing immune cells

Tissue adaptation is required for regulatory T (Treg) cell function within organs. Whether this program shares aspects with other tissue-localized immune populations is unclear. Here, we analyzed single-cell chromatin accessibility data, including the transposable element (TE) landscape of CD45+ immune cells from colon, skin, adipose tissue, and spleen. We identified features of organ-specific tissue adaptation across different immune cells. Focusing on tissue Treg cells, we found conservation of the Treg tissue adaptation program in other tissue-localized immune cells, such as amphiregulin-producing T helper (Th)17 cells. Accessible TEs can act as regulatory elements, but their contribution to tissue adaptation is not understood. TE landscape analysis revealed an enrichment of specific transcription factor binding motifs in TE regions within accessible chromatin peaks. TEs, specifically from the LTR family, were located in enhancer regions and associated with tissue adaptation. These findings broaden our understanding of immune tissue residency and provide an important step toward organ-specific immune interventions.

Dysfunctional tumor-infiltrating Vδ1 + T lymphocytes in microsatellite-stable colorectal cancer

Although γδ T cells are known to participate in immune dysregulation in solid tumors, their relevance to human microsatellite-stable (MSS) colorectal cancer (CRC) is still undefined. Here, using integrated gene expression analysis and T cell receptor sequencing, we characterized γδ T cells in MSS CRC, with a focus on Vδ1 + T cells. We identified Vδ1+ T cells with shared motifs in the third complementarity-determining region of the δ-chain, reflective of antigen recognition. Changes in gene and protein expression levels suggested a dysfunctional effector state of Vδ1+ T cells in MSS CRC, distinct from Vδ1+ T cells in microsatellite-instable (MSI). Interaction analysis highlighted an immunosuppressive role of fibroblasts in the dysregulation of Vδ1+ T cells in MSS CRC via the TIGIT-NECTIN2 axis. Blocking this pathway with a TIGIT antibody partially restored cytotoxicity of the dysfunctional Vδ1 phenotype. These results define an operative pathway in γδ T cells in MSS CRC.

Building a learnable universal coordinate system for single-cell atlas with a joint-VAE model

A universal coordinate system that can ensemble the huge number of cells and capture their heterogeneities is of vital importance for constructing large-scale cell atlases as references for molecular and cellular studies. Studies have shown that cells exhibit multifaceted heterogeneities in their transcriptomic features at multiple resolutions. This nature of complexity makes it hard to design a fixed coordinate system through a combination of known features. It is desirable to build a learnable universal coordinate model that can capture major heterogeneities and serve as a controlled generative model for data augmentation. We developed UniCoord, a specially-tuned joint-VAE model to represent single-cell transcriptomic data in a lower-dimensional latent space with high interpretability. Each latent dimension can represent either discrete or continuous feature, and either supervised by prior knowledge or unsupervised. The latent dimensions can be easily reconfigured to generate pseudo transcriptomic profiles with desired properties. UniCoord can also be used as a pre-trained model to analyze new data with unseen cell types and thus can serve as a feasible framework for cell annotation and comparison. UniCoord provides a prototype for a learnable universal coordinate framework to enable better analysis and generation of cells with highly orchestrated functions and heterogeneities.

DAB2 + macrophages support FAP + fibroblasts in shaping tumor barrier and inducing poor clinical outcomes in liver cancer

Background: Cancer-associated fibroblasts (CAFs) are the key components of the immune barrier in liver cancer. Therefore, gaining a deeper understanding of the heterogeneity and intercellular communication of CAFs holds utmost importance in boosting immunotherapy effectiveness and improving clinical outcomes. Methods: A comprehensive analysis by combing single-cell, bulk, and spatial transcriptome profiling with multiplexed immunofluorescence was conducted to unravel the complexities of CAFs in liver cancer. Results: Through an integrated approach involving 235 liver cancer scRNA-seq samples encompassing over 1.2 million cells, we found that CAFs were particularly increased in hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). FAP + fibroblasts were identified as the dominant subtype of CAFs, and which were mainly involved in extracellular matrix organization and angiogenesis. These CAFs were enriched in the tumor boundary of HCC, but diffusely scattered within ICC. The DAB2 + and SPP1 + tumor-associated macrophages (TAMs) reinforce the function of FAP + CAFs through signals such as TGF-β, PDGF, and ADM. Notably, the interaction between DAB2 + TAMs and FAP + CAFs promoted the formation of immune barrier and correlated with poorer patient survival, non-response to immunotherapy in HCC. High FAP and DAB2 immunohistochemical scores predicted shorter survival and higher serum AFP concentration in a local clinical cohort of 90 HCC patients. Furthermore, this communication pattern might be applicable to other solid malignancies as well. Conclusions: The interaction between DAB2 + TAMs and FAP + CAFs appears crucial in shaping the immune barrier. Strategies aimed at disrupting this communication or inhibiting the functions of FAP + CAFs could potentially enhance immunotherapy effectiveness and improve clinical outcomes.

Multimodal single-cell analyses reveal mechanisms of perianal fistula in diverse patients with Crohn's disease

BACKGROUND

Crohn's disease complicated by perianal fistulae is more prevalent and severe in patients of African ancestry.

METHODS

We profiled single cells from diverse patients with Crohn's disease with perianal fistula from colorectal mucosa and fistulous tracts. Immunofluorescence was performed to validate predicted cell-cell interactions. Unstimulated monocytes were chronically cultured in diverse cohorts. A subset was analyzed by single-nucleus RNA + ATAC sequencing.

FINDINGS

Fistulous tract cells from complete proctectomies demonstrated enrichment of myeloid cells compared to paired rectal tissues. Ligand-receptor analysis highlights myeloid-stromal cross-talk and cellular senescence, with cellular co-localization validated by immunofluorescence. Chitinase-3 like-protein-1 (CHI3L1) is a top upregulated gene in stromal cells from fistulae expressing both destructive and fibrotic gene signatures. Monocyte cultures from patients of African ancestry and controls demonstrated differences in CHI3L1 and oncostatin M (OSM) expression upon differentiation compared to individuals of European ancestry. Activating protein-1 footprints are present in ATAC-seq peaks in stress response genes, including CHI3L1 and OSM; genome-wide chromatin accessibility including JUN footprints was observed, consistent with reported mechanisms of inflammatory memory. Regulon analyses confirm known cell-specific transcription factor regulation and implicate novel ones in fibroblast subsets. All pseudo-bulked clusters demonstrate enrichment of genetic loci, establishing multicellular contributions. In the most significant African American Crohn's genetic locus, upstream of prostaglandin E receptor 4, lymphoid-predominant ATAC-seq peaks were observed, with predicted RORC footprints.

CONCLUSIONS

Population differences in myeloid-stromal cross-talk implicate fibrotic and destructive fibroblasts, senescence, epigenetic memory, and cell-specific enhancers in perianal fistula pathogenesis. The transcriptomic and epigenetic data provided here may guide optimization of promising mesenchymal stem cell therapies for perianal fistula.

FUNDING

This work was supported by grants U01DK062422, U24DK062429, and R01DK123758.

Transcriptomic Signatures in Lung Allografts and Their Therapeutic Implications

Ex vivo lung perfusion (EVLP) is a well-established method of lung preservation in clinical transplantation. Transcriptomic analyses of cells and tissues uncover gene expression patterns which reveal granular molecular pathways and cellular programs under various conditions. Coupling EVLP and transcriptomics may provide insights into lung allograft physiology at a molecular level with the potential to develop targeted therapies to enhance or repair the donor lung. This review examines the current landscape of transcriptional analysis of lung allografts in the context of state-of-the-art therapeutics that have been developed to optimize lung allograft function.

Microsatellite instability at U2AF-binding polypyrimidic tract sites perturbs alternative splicing during colorectal cancer initiation

BACKGROUND

Microsatellite instability (MSI) due to mismatch repair deficiency (dMMR) is common in colorectal cancer (CRC). These cancers are associated with somatic coding events, but the noncoding pathophysiological impact of this genomic instability is yet poorly understood. Here, we perform an analysis of coding and noncoding MSI events at the different steps of colorectal tumorigenesis using whole exome sequencing and search for associated splicing events via RNA sequencing at the bulk-tumor and single-cell levels.

RESULTS

Our results demonstrate that MSI leads to hundreds of noncoding DNA mutations, notably at polypyrimidine U2AF RNA-binding sites which are endowed with cis-activity in splicing, while higher frequency of exon skipping events are observed in the mRNAs of MSI compared to non-MSI CRC. At the DNA level, these noncoding MSI mutations occur very early prior to cell transformation in the dMMR colonic crypt, accounting for only a fraction of the exon skipping in MSI CRC. At the RNA level, the aberrant exon skipping signature is likely to impair colonic cell differentiation in MSI CRC affecting the expression of alternative exons encoding protein isoforms governing cell fate, while also targeting constitutive exons, making dMMR cells immunogenic in early stage before the onset of coding mutations. This signature is characterized by its similarity to the oncogenic U2AF1-S34F splicing mutation observed in several other non-MSI cancer.

CONCLUSIONS

Overall, these findings provide evidence that a very early RNA splicing signature partly driven by MSI impairs cell differentiation and promotes MSI CRC initiation, far before coding mutations which accumulate later during MSI tumorigenesis.

Deciphering breast cancer prognosis: a novel machine learning-driven model for vascular mimicry signature prediction

Background

In the ongoing battle against breast cancer, a leading cause of cancer-related mortality among women globally, the urgent need for innovative prognostic markers and therapeutic targets is undeniable. This study pioneers an advanced methodology by integrating machine learning techniques to unveil a vascular mimicry signature, offering predictive insights into breast cancer outcomes. Vascular mimicry refers to the phenomenon where cancer cells mimic blood vessel formation absent of endothelial cells, a trait associated with heightened tumor aggression and diminished response to conventional treatments.

Methods

The study's comprehensive analysis spanned data from over 6,000 breast cancer patients across 12 distinct datasets, incorporating both proprietary clinical data and single-cell data from 7 patients, accounting for a total of 43,095 cells. By employing an integrative strategy that utilized 10 machine learning algorithms across 108 unique combinations, the research scrutinized 100 existing breast cancer signatures. Empirical validation was sought through immunohistochemistry assays, alongside explorations into potential immunotherapeutic and chemotherapeutic avenues.

Results

The investigation successfully identified six genes related to vascular mimicry from multi-center cohorts, laying the groundwork for a novel predictive model. This model outstripped the prognostic accuracy of traditional clinical and molecular indicators in forecasting recurrence and mortality risks. High-risk individuals identified by our model faced worse outcomes. Further validation through IHC assays in 30 patients underscored the model's extensive applicability. Notably, the model unveiled varying therapeutic responses; low-risk patients might achieve greater benefits from immunotherapy, whereas high-risk patients demonstrated a particular sensitivity to certain chemotherapies, such as ispinesib.

Conclusions

This model marks a significant step forward in the precise evaluation of breast cancer prognosis and therapeutic responses across different patient groups. It heralds the possibility of refining patient outcomes through tailored treatment strategies, accentuating the potential of machine learning in revolutionizing cancer prognosis and management.

Single-cell RNA sequencing reveals that an imbalance in monocyte subsets rather than changes in gene expression patterns is a feature of postmenopausal osteoporosis

The role of monocytes in postmenopausal osteoporosis is widely recognized; however, the mechanisms underlying monocyte reprogramming remain unknown. In this study, single-cell RNA sequencing (scRNA-seq) was conducted on CD14+ bone marrow monocytes obtained from 3 postmenopausal women with normal BMD and 3 women with postmenopausal osteoporosis (PMOP). Monocle2 was used to classify the monocytes into 7 distinct clusters. The proportion of cluster 1 significantly decreased in PMOP patients, while the proportion of cluster 7 increased. Further analysis via GSEA, transcription factor activity analysis, and sc-metabolic analysis revealed significant differences between clusters 1 and 7. Cluster 7 exhibited upregulated pathways associated with inflammation, immunity, and osteoclast differentiation, whereas cluster 1 demonstrated the opposite results. Monocle2, TSCAN, VECTOR, and scVelo data indicated that cluster 1 represented the initial subset and that cluster 7 represents one of the terminal subsets. BayesPrism and ssGSEA were employed to analyze the bulk transcriptome data obtained from the GEO database. The observed alterations in the proportions of 1 and 7 were validated and found to have diagnostic significance. CD16 serves as the marker gene for cluster 7, thus leading to an increased proportion of CD16+ monocytes in women with PMOP. Flow cytometry was used to assess the consistency of outcomes with those of the bioinformatic analysis. Subsequently, an additional scRNA-seq analysis was conducted on bone marrow mononuclear cells obtained from 3 patients with PMOP and 3 postmenopausal women with normal BMD. The differential proportions of cluster 1 and cluster 7 were once again confirmed, with the pathological effect of cluster 7 may attribute to cell-cell communication. The scRNA-seq findings suggest that an imbalance in monocyte subsets is a characteristic feature of PMOP. These findings elucidate the limitations of utilizing bulk transcriptome data for detecting alterations in monocytes, which may influence novel research inquiries.

scTab: Scaling cross-tissue single-cell annotation models

Identifying cellular identities is a key use case in single-cell transcriptomics. While machine learning has been leveraged to automate cell annotation predictions for some time, there has been little progress in scaling neural networks to large data sets and in constructing models that generalize well across diverse tissues. Here, we propose scTab, an automated cell type prediction model specific to tabular data, and train it using a novel data augmentation scheme across a large corpus of single-cell RNA-seq observations (22.2 million cells). In this context, we show that cross-tissue annotation requires nonlinear models and that the performance of scTab scales both in terms of training dataset size and model size. Additionally, we show that the proposed data augmentation schema improves model generalization. In summary, we introduce a de novo cell type prediction model for single-cell RNA-seq data that can be trained across a large-scale collection of curated datasets and demonstrate the benefits of using deep learning methods in this paradigm.