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

The role of single cell transcriptomics for efficacy and toxicity profiling of chimeric antigen receptor (CAR) T cell therapies

CAR T cells are genetically modified T cells that target specific epitopes. CAR T cell therapy has proven effective in difficult-to-treat B cell cancers and is now expanding into hematology and solid tumors. To date, approved CAR therapies target only two specific epitopes on cancer cells. Identifying more suitable targets is challenged by the lack of truly cancer-specific structures and the potential for on-target off-tumor toxicity. We analyzed gene expression of potential targets in single-cell data from cancer and healthy tissues. Because safety and efficacy can ultimately only be defined clinically, we selected approved and investigational targets for which clinical trail data are available. We generated atlases using >300,000 cells from 48 patients with follicular lymphoma, multiple myeloma, and B-cell acute lymphoblastic leukemia, and integrated over 3 million cells from 35 healthy tissues, harmonizing datasets from over 300 donors. To contextualize findings, we compared target expression patterns with outcome data from clinical trials, linking target profiles to efficacy and toxicity, and ranked 15 investigational targets based on their similarity to approved ones. Target expression did not significantly correlate with reported clinical toxicities in patients undergoing therapy. This may be attributed to the intricate interplay of patient-specific variables, the limited amount of metadata, and the complexity underlying toxicity. Nevertheless, our study serves as a resource for retrospective and prospective target evaluation to improve the safety and efficacy of CAR therapies.

Immunopipe: a comprehensive and flexible scRNA-seq and scTCR-seq data analysis pipeline

Single-cell sequencing technologies provide us with information at the level of individual cells. Combining single-cell RNA-seq and single-cell TCR-seq profiling enables the exploration of cell heterogeneity and T-cell receptor repertoires simultaneously. Integrating both types of data can play a crucial role in enhancing our understanding of T-cell-mediated immunity and, in turn, facilitate the advancement of immunotherapy. Here, we present immunopipe, a comprehensive and flexible pipeline to perform integrated analysis of scRNA-seq and scTCR-seq data. In addition to the command line tool, we provide a user-friendly web interface for pipeline configuration and execution monitoring, benefiting researchers without extensive programming experience. With its comprehensive functionality and ease of use, immunopipe empowers researchers to uncover valuable insights from scRNA-seq and scTCR-seq data, ultimately advancing the understanding of immune responses and immunotherapy development.

Discovering governing equations of biological systems through representation learning and sparse model discovery

Understanding the governing rules of complex biological systems remains a significant challenge due to the nonlinear, high-dimensional nature of biological data. In this study, we present CLERA, a novel end-to-end computational framework designed to uncover parsimonious dynamical models and identify active gene programs from single-cell RNA sequencing data. By integrating a supervised autoencoder architecture with Sparse Identification of Nonlinear Dynamics, CLERA leverages prior knowledge to simultaneously extract related low-dimensional representation and uncover the underlying dynamical systems that drive the processes. Through the analysis of both synthetic and biological data, CLERA demonstrates robust performance in reconstructing gene expression dynamics, identifying key regulatory genes, and capturing temporal patterns across distinct cell types. CLERA's ability to generate dynamic interaction networks, combined with network rewiring using Personalized PageRank to highlight central genes and active gene programs, offers new insights into the complex regulatory mechanisms underlying cellular processes.

An automatic annotation tool and reference database for T cell subtypes and states at single-cell resolution

T cells have various subtypes and states with different functions. However, a reference list and automated annotation tool for T cell subtypes and states are lacking, which is critical for analyzing and comparing T cells under various conditions. We constructed the largest human T cell reference, containing 1,348,268 T cells from 35 conditions and 16 tissues. We classified T cells into 33 subtypes and further stratified them into 68 categories according to subtype and state. Based on this reference, we developed a tool named STCAT to automatically annotate T cells from scRNA-seq data by hierarchical models and marker correction. The accuracy of STCAT was 28% higher than that of existing tools validated on six independent datasets, including cancer and healthy samples. Using STCAT, we consistently discovered that CD4+ Th17 cells were enriched in late-stage lung cancer patients in multiple datasets, whereas MAIT cells were prevalent in milder-stage COVID-19 patients. We also confirmed a decrease in Treg cytotoxicity in post-treatment ovarian cancer. Systematic landscape analyses of CD4+ and CD8+ T cell references revealed that CD4+ Treg cells were enriched in tumor samples and that CD8+ naive-related cells were abundant in healthy individuals. Finally, we deposited all the T cell references and annotations into a TCellAtlas (https://guolab.wchscu.cn/TCellAtlas) database, which allows users to browse T cell expression profiles and analyze customized scRNA-seq data by STCAT. In conclusion, comprehensive human T cell subtypes and states reference, automated annotation tool, and database will greatly facilitate research on T cell immunity and tumor immunology.

A single-cell atlas reveals immune heterogeneity in anti-PD-1-treated non-small cell lung cancer

Anti-PD-(L)1 treatment is standard for non-small cell lung cancer (NSCLC), but patients show variable responses to the same regimen. The tumor immune microenvironment (TIME) is associated with immunotherapy response, yet the heterogeneous underlying therapeutic outcomes remain underexplored. We applied single-cell RNA and TCR sequencing (scRNA/TCR-seq) to analyze surgical tumor samples from 234 NSCLC patients post-neoadjuvant chemo-immunotherapy. Analyses revealed five distinct TIME subtypes with varying major pathological response (MPR) rates. MPR patients had elevated levels of FGFBP2+ NK/NK-like T cells, memory B cells, or effector T cells, while non-MPR patients showed higher CCR8+ Tregs. T cell clonal expansion analyses unveiled heterogeneity in non-MPR patients, marked by varying expansions of Tex-relevant cells and CCR8+ Tregs. Precursor exhausted T cells (Texp cells) correlated with recurrence-free survival, identifying a patient subgroup with reduced recurrence risk despite lack of MPR. Our study dissects TIME heterogeneity in response to chemoimmunotherapy, offering insights for NSCLC management.

Durable response to CAR T is associated with elevated activation and clonotypic expansion of the cytotoxic native T cell repertoire

While Chimeric Antigen Receptor (CAR) T cell therapy may result in durable remissions in recurrent large B cell lymphoma, persistence is limited and the mechanisms underlying long-term response are not fully elucidated. Using longitudinal single-cell immunoprofiling, here we compare the immune landscape in durable remission versus early relapse patients following CD19 CAR T cell infusion in the NCT02348216 (ZUMA-1) trial. Four weeks post-infusion, both cohorts demonstrate low circulating CAR T cells. We observe that long-term remission is associated with elevated native cytotoxic and proinflammatory effector cells, and post-infusion clonotypic expansion of effector memory T cells. Conversely, early relapse is associated with impaired NK cell cytotoxicity and elevated immunoregulatory cells, potentially dampening native T cell activation. Thus, we suggest that durable remission to CAR T is associated with a distinct T cell signature and pattern of clonotypic expansion within the native T cell compartment post-therapy, consistent with their contribution to the maintenance of response.

Type 2 cytokines act on enteric sensory neurons to regulate neuropeptide-driven host defense

Enteric nervous system (ENS)-derived neuropeptides modulate immune cell function, yet our understanding of how inflammatory cues directly influence enteric neuron responses during infection is considerably lacking. Here, we characterized a primary enteric sensory neuron (PSN) subset producing the neuropeptides neuromedin U (NMU) and calcitonin gene-related peptide β (CGRPβ) and coexpressing receptors for the type 2 cytokines interleukin-4 (IL-4) and IL-13. Type 2 cytokines amplified NMU and CGRPβ expression in PSNs, in vitro and in vivo, which was abrogated by PSN-specific Il13ra1 deletion. Deletion of Il13ra1 in PSNs impaired host defense to the gastrointestinal helminth Heligmosomoides polygyrus and blunted muscularis immune responses. Co-administration of NMU23 and CGRPβ rescued helminth clearance deficits and restored anti-helminth immunity, highlighting the essential bi-directional neuro-immune crosstalk regulating intestinal type 2 inflammation.

CellWalker2: Multi-omic discovery using hierarchical cell type relationships

Tissues are composed of cells with a wide range of similarities to each other, yet existing methods for single-cell genomics treat cell types as discrete labels. To address this gap, we developed CellWalker2, a graph diffusion-based model for the annotation and mapping of multi-modal data. With our open-source software package, hierarchically related cell types can be probabilistically matched across contexts and used to annotate cells, genomic regions, or gene sets. Additional features include estimating statistical significance and enabling gene expression and chromatin accessibility to be jointly modeled. Through simulation studies, we show that CellWalker2 performs better than existing methods in cell-type annotation and mapping. We then use multi-omics data from the brain and immune system to demonstrate CellWalker2's ability to assign high-resolution cell-type labels to regulatory elements and TFs and to quantify both conserved and divergent cell-type relationships between species.

Low T cell diversity associates with poor outcome in bladder cancer: A comprehensive longitudinal analysis of the T cell receptor repertoire

T cells are crucial effector cells in the endogenous defense against cancer, yet the clinical impact of their quantity, diversity, and dynamics remains underexplored. Here, we investigate the clinical relevance of the T cell receptor (TCR) repertoire in patients with bladder cancer. In advanced-stage disease, low pre-treatment peripheral TCR diversity is associated with worse overall survival (p = 0.024), particularly when coupled with low circulating T cell fractions (p = 0.00049). These low-diversity repertoires are dominated by hyper-expanded clones that persist throughout treatment. Further longitudinal analysis reveals reductions in TCR diversity after treatment, indicating adverse effects on the immune system. In early-stage disease, immunotherapy increases TCR diversity in patients with good outcomes. Furthermore, single-cell sequencing identifies most hyper-expanded clones as cytotoxic T cells, while non-expanded clones are predominantly naive T cells. Overall, this highlights TCR diversity as a promising biomarker, offering opportunities for tailored oncological treatments to enhance clinical outcomes.

YTHDF2: a key RNA reader and antitumor target

N6-methyladenosine (m6A) is a key mRNA modification influencing mRNA stability and translation. YTHDF2, a major m6A 'reader', was initially recognized for promoting mRNA decay but is now also known to enhance translation by binding to methylated mRNAs. YTHDF2 maintains the function of immune suppressive cells, including tumor-associated macrophages (TAMs), myeloid-derived suppressor cells (MDSCs), and regulatory T cells (Tregs), while also supporting cytotoxic immune cells, including natural killer (NK) and CD8+ T cells. Additionally, YTHDF2 acts as a tumor-intrinsic regulator orchestrating tumor immune evasion. Its multifaceted roles in tumor immunity make YTHDF2 a promising yet challenging therapeutic target. This review explores the complex roles and mechanisms of YTHDF2 in cancers, immune regulation, and tumor immune evasion and highlights emerging therapeutic strategies that target YTHDF2.

Novel marker genes and small molecule drugs for radiotherapy resistance in cervical cancer identified based on single-cell multi-omics analysis

Radiotherapy is the cornerstone of treatment for cervical cancer, yet the variability of patient response demands a deeper understanding of the molecular determinants of radioresistance. In this study, we investigated the molecular and cellular mechanisms of radioresistance in cervical cancer through a comprehensive multi-omics and machine learning approach. We downloaded and processed transcriptome sequencing, methylation and single-cell sequencing data from the TCGA and GEO databases. Differential gene and methylation analyses were performed to identify radioresistance-related markers. Single-cell data were processed using Seurat and annotated using CellTypist. Prognostic models were constructed and validated through downscaling, cell scoring, trajectory analysis and machine learning. Additionally, immune infiltration and drug sensitivity analyses were conducted. The differential analysis identified 845 up-regulated and 460 down-regulated genes associated with radioresistance. The methylation analysis identified 3042 down-regulated and 158 up-regulated gene loci. Single-cell sequencing revealed 43,475 cells and 13 cell types, with aneuploid cells predominantly present in epithelial cells. Cell scoring highlighted dispersed immune cells, with monocytes, ILCs, and T cells being the most relevant to radiotherapy resistance. The machine learning approach constructed a robust prognostic model using Cox regression and validated it on multiple datasets. The prognostic model demonstrated good predictive ability in assessing radiotherapy efficacy and immune infiltration. Drug screening identified several potential therapeutic candidates with high sensitivity for high-risk patients. This study provides a comprehensive multi-omics analysis and machine learning framework for identifying and validating molecular markers and prognostic models associated with radioresistance in cervical cancer, providing insights for personalized treatment strategies.

PERK+ Macrophages Drive Immunotherapy Resistance in Lymph Node Metastases of Oral Squamous Cell Carcinoma

PURPOSE

Neoadjuvant anti-PD-1 immunotherapy combined with chemotherapy has shown promising pathologic responses in various cancers, including oral squamous cell carcinoma (OSCC). However, the pathologic response of lymph node (LN) metastases remains poorly understood. This study aims to systematically evaluate the pathologic response rates (pRR) of LN metastases in patients with OSCC and identify potential targets to improve therapeutic outcomes.

PATIENTS AND METHODS

We assessed the pRRs of LN metastases and matched primary tumors (PT) in patients with OSCC enrolled in a randomized, two-arm, phase II clinical trial (NCT04649476). Single-cell and spatial transcriptomics and multiplex IHC were performed to analyze the tumor microenvironment and identify potential therapeutic targets in LN metastases. A neoadjuvant orthotopic OSCC mouse model was established to evaluate the therapeutic potential of these targets.

RESULTS

We observed significant heterogeneity in pathologic regression of LN metastases, with lower pRRs compared with PTs. pRRs in LN metastases were correlated with overall and disease-free survival in patients with OSCC. We identified an abundance of macrophages in LN metastases exhibiting an unfolded protein response and activated protein kinase RNA-like endoplasmic reticulum kinase (PERK) signaling. These macrophages contributed to an extracellular matrix-enriched microenvironment through interactions with fibroblasts, which hindered T cell-mediated cytotoxicity. Pharmacologic inhibition of the PERK pathway significantly enhanced anti-PD-1 therapy in LN metastases and PTs in the mouse model.

CONCLUSIONS

Our study confirms that the pathologic response of LN metastases in patients with OSCC undergoing neoadjuvant immunotherapy or immunochemotherapy is inferior to that of PTs. It suggests that targeting the PERK pathway in macrophages could be a potential strategy to enhance treatment outcomes.

Enhanced interpretation of immune cell phenotype and function through a rhesus macaque single-cell atlas

Single-cell RNA sequencing (scRNA-seq) allows cell classification using genome-wide transcriptional state; however, high-dimensional transcriptomic profiles, and the unsupervised analyses employed to interpret them, provide a systematically different view of biology than well-established functional/lineage definitions of immunocytes. Understanding these differences and limits is essential for accurate interpretation of these rich data. We present the Rhesus Immune Reference Atlas (RIRA), the first immune-focused macaque single-cell multi-tissue atlas. We contrasted transcriptional profiles against immune lineages, using surface protein and marker genes as ground truth. While the pattern of clustering can align with cell type, this is not always true. Especially within T and natural killer (NK) cells, many functionally distinct subsets lack defining markers, and strong shared expression programs, such as cytotoxicity, result in systematic intermingling by unsupervised clustering. We identified gene programs with high discriminatory/diagnostic value, including multi-gene signatures that model T/NK cell maturation. Directly measuring these diagnostic programs complements unsupervised analyses.

Acute exposure to high-fat diet impairs ILC3 functions and gut homeostasis

Prolonged exposure to a high-fat diet (HFD) exacerbates intestinal disease pathology, yet the early events preceding the development of gut inflammation remain poorly understood. Here, we show that within 48 h, HFD impairs intestinal group 3 innate lymphoid cells (ILC3s) and their capacity to produce interleukin-22 (IL-22), critical for maintaining gut homeostasis. This loss of function was associated with rapid dysbiosis, increased gut permeability, and reduced production of antimicrobial peptides, mucus, and tight-junction proteins. While saturated fatty acids metabolized through oxidation impaired ILC3 function, unsaturated fatty acids sustained IL-22 secretion by ILC3s through the formation of lipid droplets using diacylglycerol O-acyltransferase (DGAT) enzymes. Upon inflammation, saturated fatty acids impaired IL-22 production by ILC3s and increased the susceptibility of the gut to injury. Our findings reveal the differential acute impact of saturated and unsaturated fatty acids on gut homeostasis through distinct metabolic pathways in ILC3s.

Regulatory T cell depletion promotes myeloid cell activation and glioblastoma response to anti-PD1 and tumor-targeting antibodies

Glioblastoma is invariably lethal and responds poorly to immune checkpoint blockade. Here, we examined the impact of regulatory T (Treg) cell depletion on glioblastoma progression and immunotherapy responsiveness. In human glioblastoma, elevated Treg cell signatures correlated with poorer survival outcomes, with these cells expressing high levels of CD25. In Nf1-/-Pten-/-EGFRvIII+ glioblastoma-bearing mice, a single dose of non-interleukin-2 (IL-2) blocking (NIB) anti-CD25 (anti-CD25NIB) antibody depleted Treg cells and promoted CD8+ T cell clonal expansion and partial tumor control, further enhanced by programmed cell death-1 (PD1)-blockade. Treg cell depletion induced interferon-γ (IFN-γ)-dependent tumor microenvironment remodeling, increasing Fcγ receptor (FcγR) expression on intratumoral myeloid cells and enhancing phagocytosis. Combination of anti-CD25NIB with anti-EGFRvIII tumor-targeting antibodies resulted in complete tumor control. Anti-human CD25NIB treatment of glioblastoma patient-derived tumor fragments effectively depleted Treg cells and activated CD8+ T cells. These findings underscore the therapeutic relevance of Treg targeting in glioblastoma and unveil potent combination strategies for anti-CD25NIB based on innate cell activation.

CIEC: Cross-tissue Immune Cell Type Enrichment and Expression Map Visualization for Cancer

Single-cell transcriptome sequencing technology has been applied to decode the cell types and functional states of immune cells, revealing their tissue-specific gene expression patterns and functions in cancer immunity. Comprehensive assessments of immune cells within and across tissues will provide us with a deeper understanding of the tumor immune system in general. Here, we present Cross-tissue Immune cell type or state Enrichment analysis of gene lists for Cancer (CIEC), the first web-based application that integrates database and enrichment analysis to estimate the cross-tissue immune cell types or states. CIEC version 1.0 consists of 480 samples covering primary tumor, adjacent normal tissue, lymph node, metastasis tissue, and peripheral blood from 323 cancer patients. By applying integrative analysis, we constructed an immune cell type/state map for each context, and adopted our previously developed Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology Based Annotation System (KOBAS) algorithm to estimate the enrichment for context-specific immune cell types/states. In addition, CIEC also provides an easy-to-use online interface for users to comprehensively analyze the immune cell characteristics mapped across multiple tissues, including expression map, correlation, similar gene detection, signature score, and expression comparison. We believe that CIEC will be a valuable resource for exploring the intrinsic characteristics of immune cells in cancer patients and for potentially guiding novel cancer-immune biomarker development and immunotherapy strategies. CIEC is freely accessible at http://ciec.gene.ac/.

Efferocytosis in inflammatory bone disorders

Efferocytosis, the clearance of apoptotic cells (ACs) by phagocytes, is crucial for bone homeostasis and immune balance. This tightly regulated process depends on molecular markers such as phosphatidylserine on ACs and MERTK on phagocytes. In the bone microenvironment, multiple cell types participate in efferocytosis, including osteal macrophages, mesenchymal stem cells, osteoblasts, and osteoclasts, directly influencing bone remodeling and immune responses. Impaired efferocytosis disrupts bone turnover, exacerbates inflammation, and contributes to inflammatory bone diseases. Despite its recognized importance, the precise mechanisms regulating efferocytosis in osteoimmunology remain underexplored, including specific signaling pathways, cell-specific interactions, and therapeutic applications. Recent advances highlight the therapeutic potential of targeting efferocytosis using modalities and biomaterial-based strategies. This review systematically examines the role of efferocytosis in osteoimmunology, discusses key challenges in its therapeutic translation, and explores emerging strategies to optimize efferocytosis-based interventions for inflammatory bone disorders.

A highly resolved integrated transcriptomic atlas of human breast cancers

In this study, we developed an integrated single cell transcriptomic (scRNAseq) atlas of human breast cancer (BC), the largest resource of its kind, totaling > 600,000 cells across 138 patients. Rigorous integration and annotation of publicly available scRNAseq data enabled a highly resolved characterization of epithelial, immune, and stromal heterogeneity within the tumor microenvironment (TME). Within the immune compartment we were able to characterize heterogeneity of CD4, CD8 T cells and macrophage subpopulations. Within the stromal compartment, subpopulations of endothelial cells (ECs) and cancer associated fibroblasts (CAFs) were resolved. Within the cancer epithelial compartment, we characterized the functional heterogeneity of cells across the axes of stemness, epithelial-mesenchymal plasticity, and canonical cancer pathways. Across all subpopulations observed in the TME, we performed a multi-resolution survival analysis to identify epithelial cell states and immune cell types which conferred a survival advantage in both The Cancer Genome Atlas (TCGA) and METABRIC. We also identified robust associations between TME composition and clinical phenotypes such as tumor subtype and grade that were not discernible when the analysis was limited to individual datasets, highlighting the need for atlas-based analyses. This atlas represents a valuable resource for further high-resolution analyses of TME heterogeneity within BC.

Lymph nodes link sex-biased immune aging to compromised antigen recognition

A diverse naive CD8 T cell repertoire is essential to provide broad protection against infection and cancer. Aging diminishes naive T cells, reducing potential diversity and leading to lymph node contraction. Here, we revealed that this decline occurs earlier in males, resulting in significant sex differences in immunity during middle age. Earlier in life, naive CD8 T cells in males become virtual memory cells prone to premature senescence. Due to androgen-driven thymic atrophy in males, naive CD8 T cells are insufficiently replenished. Therapeutic thymus rejuvenation via testosterone ablation restored naive CD8 T cells in lymph nodes of middle-aged male mice, leading to enhanced tumor recognition. These findings show the crucial role of sex and age on lymph node T cell repertoires and suggest potential strategies to restore immune function in males during aging.

A unified analysis of atlas single-cell data

Recent efforts to generate atlas-scale single-cell data provide opportunities for joint analysis across tissues and modalities. Existing methods use cells as the reference unit, hindering downstream gene-based analysis and removing genuine biological variation. Here we present GIANT, an integration method designed for atlas-scale gene analysis across cell types and tissues. GIANT converts data sets into gene graphs and recursively embeds genes without additional alignment. Applying GIANT to two recent atlas data sets yields unified gene-embedding spaces across human tissues and data modalities. Further evaluations demonstrate GIANT's usefulness in discovering diverse gene functions and underlying gene regulation in cells from different tissues.

scRCA: A Siamese network-based pipeline for annotating cell types using noisy single-cell RNA-seq reference data

Accurate cell type annotation is fundamentally critical for single-cell sequencing (scRNA-seq) data analysis to provide insightful knowledge of tissue-specific cell heterogeneity and cell state transition tracking. Cell type annotation is usually conducted by comparative analysis with known data (i.e., reference) - which contains a presumably accurate representation of cell types. However, this assumption is often problematic, as factors such as human errors in wet-lab experiments and methodological limitations can introduce annotation errors in the reference dataset. As current pipelines for single-cell transcriptomic analysis do not adequately consider this challenge, there is a major demand for constructing a computational pipeline that achieves high-quality cell type annotation using reference datasets containing inherent errors (referred to as "noise" in this study). Here, we built a Siamese network-based pipeline, termed scRCA, to accurately annotate cell types based on noisy reference data. To help users evaluate the reliability of scRCA annotations, an interpreter was also developed to explore the factors underlying the model's predictions. Our experiments demonstrate that, across 14 datasets, scRCA outperformed other widely adopted reference-based methods for cell type annotation. Using an independent dataset of four multiple myeloma patients, we further illustrated that scRCA can distinguish cancerous cells based on gene expression levels and identify genes closely associated with multiple myeloma through scRCA's interpretable module, providing significant information for subsequent clinical treatments. With these advancements, we anticipate that scRCA will serve as a practical reference-based approach for accurate annotating cell type annotation.

Towards a consensus atlas of human and mouse adipose tissue at single-cell resolution

Adipose tissue (AT) is a complex connective tissue with a high relative proportion of adipocytes, which are specialized cells with the ability to store lipids in large droplets. AT is found in multiple discrete depots throughout the body, where it serves as the primary repository for excess calories. In addition, AT has an important role in functions as diverse as insulation, immunity and regulation of metabolic homeostasis. The Human Cell Atlas Adipose Bionetwork was established to support the generation of single-cell atlases of human AT as well as the development of unified approaches and consensus for cell annotation. Here, we provide a first roadmap from this bionetwork, including our suggested cell annotations for humans and mice, with the aim of describing the state of the field and providing guidelines for the production, analysis, interpretation and presentation of AT single-cell data.

STAT3-orchestrated gene expression signatures and tumor microenvironment in esophageal squamous cell carcinoma uncovered by single-cell sequencing

BACKGROUND

The progression of Esophageal Squamous Cell Carcinoma (ESCC) can be dissected with greater precision using multi-omics and single-cell RNA sequencing (scRNA-seq) compared to traditional methodologies. These advanced approaches enable a comprehensive understanding of cellular heterogeneity and molecular dynamics, offering higher resolution insights into cancer development. Moreover, analyzing transcription factor regulatory networks provides innovative avenues for identifying cancer biomarkers and therapeutic targets, driving new perspectives in cancer research.

OBJECTIVE

To explore the molecular mechanisms and cellular dynamics of ESCC.

METHODS

Utilizing bulk-RNA-seq and single-cell transcriptomics, our study identify major cell types, transcriptomic gene and function changes during ESCC progression. Validation experiments in clinical sample tissues and ESCC cell lines to confirm core regulation factor in ESCC.

RESULTS

We identified six major cell types in the ESCC scRNA-seq dataset and revealed profound shifts in cellular composition and transcriptional profiles. Notably, STAT3 was found to be a core regulator in ESCC and negatively regulated LHPP expression at promoter sites. Elevated STAT3 and reduced LHPP expression were consistently observed in patient samples, highlighting their inverse relationship in ESCC pathogenesis.

CONCLUSION

This study integrates bulk-seq and scRNA-seq data to reveal the pivotal role of STAT3 in ESCC. STAT3 negatively regulates LHPP expression, driving tumor progression. These findings underscore the therapeutic potential of targeting STAT3 in ESCC.

KEY WORDS

ESCC, single-cell transcriptomics, ESCC microenvironment, STAT3.

Single-cell landscape of peripheral immune cells in MASLD/MASH

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) progresses to metabolic dysfunction-associated steatohepatitis (MASH) and is a major cause of liver cirrhosis. Although liver inflammation is the hallmark feature of MASH versus MASLD, the involvement of the peripheral immune cell compartments in disease progression is poorly understood, and single-cell profiles of peripheral immune cells in MASLD/MASH are not known.

METHODS

Patients with MASLD/MASH and healthy volunteers have been prospectively enrolled in a cross-sectional study. Patients have been histologically stratified and further characterized by liver bulk RNA sequencing (RNA-Seq). Peripheral immune cells from patients and control blood samples have been comprehensively profiled using bulk and single RNA-Seq.

RESULTS

Twenty-two patients with fibrosis stage less than F3 have been histologically stratified into patients with low, medium, and high disease activity scores (NAFLD activity score [NAS]). In contrast to fibrosis, the NAS group correlated with noninvasive imaging readouts and blood biomarkers of liver damage and inflammation (ALT, AST). The prevalence of type 2 diabetes and obesity increased with the NAS stage. Bulk RNA-seq profiling of patient liver biopsies revealed gene signatures that were positively and negatively associated with NAS. Known marker genes for liver fibrosis where upregulated on RNA level. Blood bulk RNA-seq showed only moderate differences in patients versus healthy controls. In contrast, single-cell analysis of white blood cells revealed multiple alterations of immune (sub-)populations, including an increased abundance of immature B cells and myeloid suppressor cells in patients with MASLD/MASH as compared to healthy controls.

CONCLUSIONS

The study gives new insights into the pathophysiology of MASLD/MASH already manifesting relatively early in peripheral immune cell compartments. This opens new avenues for the development of new biomarker diagnostics and disease therapies.

N-terminomics and proteomics analysis of Calpain-2 reveal key proteolytic processing of metabolic and cell adhesion proteins

Aberrant levels of the cysteine protease Calpain-2 have been linked to neurodegeneration, inflammation, and cancer, yet our understanding of this protease and its substrates remains limited. Systematic studies to identify Calpain-2 substrates have been largely confined to peptide libraries or in vitro studies, which fail to represent physiological cellular conditions and physiologically relevant substrates. To identify existing and novel Calpain-2 substrates, we used a genetic approach to knockout Calpain-2 in the THP-1 human monocyte-like cells, followed by proteomic and N-terminomic/TAILS mass spectrometry approaches to identify Calpain-2 substrates. We identified 51 substrates that may be cleaved directly by Calpain-2 or indirectly by downstream proteases. The direct cleavage of selected substrates by Calpain-2 was confirmed using in vitro assays. Finally, metabolomics analysis identified a role for Calpain-2 in the regulation of pyrimidine and glutathione metabolism. Our unbiased and quantitative mass spectrometry analytical pipeline provides new evidence on the physiological functions of Calpain-2 and its newly identified substrates in THP-1 cells.

scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery

Single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) produces vast amounts of individual cell profiling data. Its analysis presents a significant challenge in accurately annotating cell types and their associated biomarkers. Different pipelines based on deep neural network (DNN) methods have been employed to tackle these issues. These pipelines have arisen as a promising resource and can extract meaningful and concise features from noisy, diverse, and high-dimensional data to enhance annotations and subsequent analysis. Existing tools require high computational resources to execute large sample datasets. We have developed a cutting-edge platform known as scaLR (Single-cell analysis using low resource) that efficiently processes data into feature subsets, samples in batches to reduce the required memory for processing large datasets, and running DNN models in multiple central processing units. scaLR is equipped with data processing, feature extraction, training, evaluation, and downstream analysis. Its novel feature extraction algorithm first trains the model on a feature subset and stores the importance of the features for all the features in that subset. At the end of the training of all subsets, the top-K features are selected based on their importance. The final model is trained on top-K features; its performance evaluation and associated downstream analysis provide significant biomarkers for different cell types and diseases/traits. Our findings indicate that scaLR offers comparable prediction accuracy and requires less model training time and computational resources than existing Python-based pipelines. We present scaLR, a Python-based platform, engineered to utilize minimal computational resources while maintaining comparable execution times and analysis costs to existing frameworks.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

FPCAM: A Weighted Dictionary-Driven Model for Single-Cell Annotation in Pulmonary Fibrosis

The groundbreaking development of scRNA-seq has significantly improved cellular resolution. However, accurate cell-type annotation remains a major challenge. Existing annotation tools are often limited by their reliance on reference datasets, the heterogeneity of marker genes, and subjective biases introduced through manual intervention, all of which impact annotation accuracy and reliability. To address these limitations, we developed FPCAM, a fully automated pulmonary fibrosis cell-type annotation model. Built on the R Shiny platform, FPCAM utilizes a matrix of up-regulated marker genes and a manually curated gene-cell association dictionary specific to pulmonary fibrosis. It achieves accurate and efficient cell-type annotation through similarity matrix construction and optimized matching algorithms. To evaluate its performance, we compared FPCAM with state-of-the-art annotation models, including SCSA, SingleR, and SciBet. The results showed that FPCAM and SCSA both achieved an accuracy of 89.7%, outperforming SingleR and SciBet. Furthermore, FPCAM demonstrated high accuracy in annotating the external validation dataset GSE135893, successfully identifying multiple cell subtypes. In summary, FPCAM provides an efficient, flexible, and accurate solution for cell-type identification and serves as a powerful tool for scRNA-seq research in pulmonary fibrosis and other related diseases.

Zero-shot evaluation reveals limitations of single-cell foundation models

Foundation models such as scGPT and Geneformer have not been rigorously evaluated in a setting where they are used without any further training (i.e., zero-shot). Understanding the performance of models in zero-shot settings is critical to applications that exclude the ability to fine-tune, such as discovery settings where labels are unknown. Our evaluation of the zero-shot performance of Geneformer and scGPT suggests that, in some cases, these models may face reliability challenges and could be outperformed by simpler methods. Our findings underscore the importance of zero-shot evaluations in development and deployment of foundation models in single-cell research.

Hypoxia-induced Wnt5a-secreting fibroblasts promote colon cancer progression

Wnt5a, a representative Wnt ligand that activates the β-catenin-independent pathway, has been shown to promote tumorigenesis. However, it is unclear where Wnt5a is produced and how it affects colon cancer aggressiveness. In this study, we demonstrate that Wnt5a is expressed in fibroblasts near the luminal side of the tumor, and its depletion suppresses mouse colon cancer formation. To characterize the specific fibroblast subtype, a meta-analysis of human and mouse colon fibroblast single-cell RNA-seq data is performed. The results show that Wnt5a is expressed in hypoxia-induced inflammatory fibroblast (InfFib), accompanied by the activation of HIF2. Moreover, Wnt5a maintains InfFib through the suppression of angiogenesis mediated by soluble VEGF receptor1 (Flt1) secretion from endothelial cells, thereby inducing further hypoxia. InfFib also produces epiregulin, which promotes colon cancer growth. Here, we show that Wnt5a acts on endothelial cells, inducing a hypoxic environment that maintains InfFib, thereby contributing to colon cancer progression through InfFib.

scPRINT: pre-training on 50 million cells allows robust gene network predictions

A cell is governed by the interaction of myriads of macromolecules. Inferring such a network of interactions has remained an elusive milestone in cellular biology. Building on recent advances in large foundation models and their ability to learn without supervision, we present scPRINT, a large cell model for the inference of gene networks pre-trained on more than 50 million cells from the cellxgene database. Using innovative pretraining tasks and model architecture, scPRINT pushes large transformer models towards more interpretability and usability when uncovering the complex biology of the cell. Based on our atlas-level benchmarks, scPRINT demonstrates superior performance in gene network inference to the state of the art, as well as competitive zero-shot abilities in denoising, batch effect correction, and cell label prediction. On an atlas of benign prostatic hyperplasia, scPRINT highlights the profound connections between ion exchange, senescence, and chronic inflammation.

Screening colorectal cancer associated autoantigens through multi-omics analysis and diagnostic performance evaluation of corresponding autoantibodies

BACKGROUND

This study aims to screen, validate novel biomarkers and develop a user-friendly online tool for the detection of colorectal cancer (CRC).

METHODS

Multi-omics approach, comprising proteomic analysis and single-cell transcriptomic analysis, was utilized to discover candidate tumor-associated antigens (TAAs). The presence of tumor-associated autoantibodies (TAAbs) in serum was subsequently assessed using enzyme-linked immunosorbent assays (ELISA) in 300 CRC patients and 300 healthy controls. Ten machine learning algorithms were utilized to develop diagnostic models, with the optimal one selected and integrated into an R Shiny-based GUI to enhance usability and accessibility.

RESULTS

We identified twelve potential TAAs: HMGA1, NPM1, EIF1AX, CKS1B, HSP90AB1, ACTG1, S100A11, maspin, ANXA3, eEF2, P4HB, and HKDC1. ELISA results showed that five TAAbs including anti-CKS1B, anti-S100A11, anti-maspin, anti-ANXA3, and anti-eEF2 were potential diagnostic biomarkers during the diagnostic evaluation phase (all P < 0.05). The Random Forest model yielded an AUC of 0.82 (95% CI: 0.78-0.88) on the training set and 0.75 (95% CI: 0.68-0.82) on the test set, demonstrating the robustness of the results. Web-based implementations of CRC diagnostic tools are publicly accessible via weblink https://qzan.shinyapps.io/CRCPred/ .

CONCLUSIONS

A five biomarker panel can server as complementary biomarker to CEA and CA19-9 in CRC detection.

Deciphering immune predictors of immunotherapy response: A multiomics approach at the pan-cancer level

Immune checkpoint blockade (ICB) therapy has transformed cancer treatment, yet many patients fail to respond. Employing single-cell multiomics, we unveil T cell dynamics influencing ICB response across 480 pan-cancer and 27 normal tissue samples. We identify four immunotherapy response-associated T cells (IRATs) linked to responsiveness or resistance and analyze their pseudotemporal patterns, regulatory mechanisms, and T cell receptor clonal expansion profiles specific to each response. Notably, transforming growth factor β1 (TGF-β1)+ CD4+ and Temra CD8+ T cells negatively correlate with therapy response, in stark contrast to the positive response associated with CXCL13+ CD4+ and CD8+ T cells. Validation with a cohort of 23 colorectal cancer (CRC) samples confirms the significant impact of TGF-β1+ CD4+ and CXCL13+ CD4+ and CD8+ T cells on ICB efficacy. Our study highlights the effectiveness of single-cell multiomics in pinpointing immune markers predictive of immunotherapy outcomes, providing an important resource for crafting targeted immunotherapies for successful ICB treatment across cancers.

Whole genome sequencing identifies novel candidate genetic variants in canine stomatocytosis

Stomatocytosis is a rare spectrum of red blood cell (RBC) disorders. In humans, stomatocytosis is typically caused by genetic changes in specific ion exchange and transport genes. Stomatocytosis has been identified in dogs, however the underlying genetic causes are unknown. Recently, stomatocytosis was reported in a Beagle and Australian Cattle Dog for the first time. Here, whole-genome sequencing (WGS) of these dogs was undertaken to identify candidate genetic variants driving or impacting stomatocytosis. Cases were compared to WGS of 119 controls of several breeds and > 1,000 dogs from public and private datasets. Candidate genes were identified, including genes linked to stomatocytosis in humans: SPTB and KCNN4. Notably, each case carried a different homozygous intronic SNP in SPTB only 24 bases apart (Beagle - chr8:39,194,923; ACD - chr8:39,194,947; CanFam3.1), which were not homozygous in other dogs. Variants with predicted deleterious impact in additional ion transport-related genes were also identified: SLC8A3, DYSF, SLC12A8, INPP5E, SLC1A1, and a novel SLC41A3 genetic change carried by the Australian Cattle Dog. Human and mouse scRNAseq and proteomics data indicate that these candidate genes are expressed in RBCs or their immature precursors. Taken together, these genetic data obtained from spontaneous stomatocytosis in a non-human species provide novel insights and candidate genes for evaluation of rare red cell disorders in humans.

T Cells Promote Distinct Transcriptional Programs of Cutaneous Inflammatory Disease in Keratinocytes and Dermal Fibroblasts

T cells and structural cells coordinate appropriate inflammatory responses and restoration of barrier integrity following insult. Dysfunctional T cells precipitate skin pathology occurring alongside altered structural cell frequencies and transcriptional states, but to what extent different T cells promote disease-associated changes remains unclear. We show that functionally diverse circulating and skin-resident CD4+CLA+ T-cell populations promote distinct transcriptional outcomes in human keratinocytes and fibroblasts associated with inflamed or healthy tissue. We identify T helper 17 cell-induced genes in keratinocytes that are enriched in psoriasis patient skin and normalized by anti-IL-17 therapy. We also describe a CD103+ skin-resident T-cell-induced transcriptional module enriched in healthy controls that is diminished during psoriasis and scleroderma and show that CD103+ T-cell frequencies are altered during disease. Interrogating clinical data using immune-dependent transcriptional signatures defines the T-cell subsets and genes distinguishing inflamed from healthy skin and allows investigation of heterogeneous patient responses to biologic therapy.

Soluble CTLA-4 regulates immune homeostasis and promotes resolution of inflammation by suppressing type 1 but allowing type 2 immunity

Cytotoxic T-lymphocyte-associated antigen -4 (CTLA-4) is a co-inhibitory receptor that restricts T cell activation. CTLA-4 exists as membrane (mCTLA-4) and soluble (sCTLA-4) forms, but the key producers, kinetics, and functions of sCTLA-4 are unclear. Here, we investigated the roles of sCTLA-4 in immune regulation under non-inflammatory and inflammatory conditions. Effector regulatory T (Treg) cells were the most active sCTLA-4 producers in basal and inflammatory states, with distinct kinetics upon T cell receptor (TCR) stimulation. We generated mice specifically deficient in sCTLA-4 production, which exhibited spontaneous activation of type 1 immune cells and heightened autoantibody/immunoglobulin E (IgE) production. Conversely, mCTLA-4-deficient mice developed severe type 2-skewed autoimmunity. sCTLA-4 blockade of CD80/86 on antigen-presenting cells inhibited T helper (Th)1, but not Th2, differentiation in vitro. In vivo, Treg-produced sCTLA-4, suppressed Th1-mediated experimental colitis, and enhanced wound healing but hampered tumor immunity. Thus, sCTLA-4 is essential for immune homeostasis and controlling type 1 immunity while allowing type 2 immunity to facilitate resolution in inflammatory conditions.

MIDAA: deep archetypal analysis for interpretable multi-omic data integration based on biological principles

High-throughput multi-omic molecular profiling allows the probing of biological systems at unprecedented resolution. However, integrating and interpreting high-dimensional, sparse, and noisy multimodal datasets remains challenging. Deriving new biological insights with current methods is difficult because they are not rooted in biological principles but prioritise tasks like dimensionality reduction. Here, we introduce a framework that combines archetypal analysis, an approach grounded in biological principles, with deep learning. Using archetypes based on evolutionary trade-offs and Pareto optimality, MIDAA finds extreme data points that define the geometry of the latent space, preserving the complexity of biological interactions while retaining an interpretable output. We demonstrate that these extreme points represent cellular programmes reflecting the underlying biology. Moreover, we show that, compared to alternative methods, MIDAA can identify parsimonious, interpretable, and biologically relevant patterns from real and simulated multi-omics.

Cross one single body 49 tissues single-cell transcriptome reveals detailed macrophage heterogeneity during pig pregnancy

Introduction

Pregnancy involves complex physiological adaptations across maternal organs and the immune system to support fetal development. Macrophages play a dual role during pregnancy: defending against pathogens and supporting tissue adaptation. However, comprehensive and in-depth studies of cross-tissue transcriptional heterogeneity of macrophages during healthy pregnancy at the single-cell level remain elusive.

Methods

We performed single-cell RNA sequencing (scRNA-seq) to profile macrophages from a healthy pregnant pig across 49 tissues. Immunofluorescence was performed to verify the specific expression of transcription factors.

Results

In this study, we generated a macrophage atlas containing 114,881 macrophages from 49 tissues/organs within one single healthy pregnant pig, identified 33 subtypes, and revealed extensive tissue-specific diversity. We observed significant heterogeneity of macrophage subtypes across five different anatomical sites of adipose tissue. Notably, the Mφ MARCO+ subtype, primarily derived from mesenteric adipose tissue, showed higher activity in pattern recognition receptor signaling pathways compared to subtypes in other tissues, including different fat depots. Cross-tissue analysis revealed distinct expression patterns of transcription factors, cytokines, and cell surface receptors, including the transcription factor PLSCR1, specifically expressed in lung macrophages and verified by immunofluorescence. Cross-species analysis unveiled conservation and heterogeneity among macrophages in pigs, humans, and mice.

Conclusion

We constructed a multiple-tissue single-cell transcriptome atlas of macrophages in one single healthy pregnant pig, revealing their molecular differences and commonalities across tissues and species. Our study provides a valuable resource for understanding macrophage diversity and tissue-specific macrophage adaptations during pregnancy in pigs.

Single-cell immune aging clocks reveal inter-individual heterogeneity during infection and vaccination

Aging affects human immune system functionality, increasing susceptibility to immune-mediated diseases. While gene expression programs accurately reflect immune function, their relationship with biological immune aging and health status remains unclear. Here we developed robust, cell-type-specific aging clocks (sc-ImmuAging) for the myeloid and lymphoid immune cell populations in circulation within peripheral blood mononuclear cells, using single-cell RNA-sequencing data from 1,081 healthy individuals aged from 18 to 97 years. Application of sc-ImmuAging to transcriptome data of patients with COVID-19 revealed notable age acceleration in monocytes, which decreased during recovery. Furthermore, inter-individual variations in immune aging induced by vaccination were identified, with individuals exhibiting elevated baseline interferon response genes showing age rejuvenation in CD8+ T cells after BCG vaccination. sc-ImmuAging provides a powerful tool for decoding immune aging dynamics, offering insights into age-related immune alterations and potential interventions to promote healthy aging.

The integrated stress response pathway controls cytokine production in tissue-resident memory CD4+ T cells

Tissue-resident memory T (TRM) cells are a specialized T cell population that reside in tissues and provide a rapid protective response upon activation. Here, we showed that human and mouse CD4+ TRM cells existed in a poised state and stored messenger RNAs encoding proinflammatory cytokines without protein production. At steady state, cytokine mRNA translation in TRM cells was suppressed by the integrated stress response (ISR) pathway. Upon activation, the central ISR regulator, eIF2α, was dephosphorylated and stored cytokine mRNA was translated for immediate cytokine production. Genetic or pharmacological activation of the ISR-eIF2α pathway reduced cytokine production and ameliorated autoimmune kidney disease in mice. Consistent with these results, the ISR pathway in CD4+ TRM cells was downregulated in patients with immune-mediated diseases of the kidney and the intestine compared to healthy controls. Our results indicated that stored cytokine mRNA and translational regulation in CD4+ TRM cells facilitate rapid cytokine production during local immune response.

Application of bioinformatic tools in cell type classification for single-cell RNA-seq data

The advancements in single-cell RNA sequencing (scRNAseq) technology have significantly transformed genomics research, enabling the handling of thousands of cells in each experiment. As of now, 32,068 research studies have been cataloged in the Pubmed database. The primary aim of scRNAseq investigations is to identify cell types, understand the antitumor immune response, and identify new and uncommon cell types. Traditional techniques for identifying cell types include microscopy, histology, and pathological characteristics. However, the complexity of instruments and the need for precise experimental design make it difficult to fully capture the overall heterogeneity. Unsupervised clustering and supervised classification methods have been used to solve this task. Supervised cell type classification methods have gained popularity as large-scale, high-quality, well-annotated and more robust results compared to clustering methods. A recent study showed that support vector machine (SVM) gives a high-quality classification performance in different scenarios. In this article, we compare and evaluate the performance of four different kernels (sigmoid, linear, radial, polynomial) of SVM. The results of the experiments on three standard scRNA-seq datasets indicate that SVM with linear and SVM with sigmoid kernel classify the cells more accurately (approx. 99 %) where SVM linear kernel method has remarkably fast computation time and we also evaluate the results using some single cell specific evaluation matrices F-1 score, MCC, AUC value. Additionally, it sheds light on the potential use of kernels of SVM to give underlying information of single-cell RNA-Seq data more effectively.

Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas construction and usage

The Human BioMolecular Atlas Program (HuBMAP) aims to construct a 3D Human Reference Atlas (HRA) of the healthy adult body. Experts from 20+ consortia collaborate to develop a 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 characterize changes that occur with aging, disease and other perturbations. HRA v.2.0 covers 4,499 unique anatomical structures, 1,195 cell types and 2,089 biomarkers (such as genes, proteins and lipids) from 33 ASCT+B tables and 65 3D Reference Objects linked to ontologies. New experimental data can be mapped into the HRA using (1) cell type annotation tools (for example, Azimuth), (2) validated antibody panels or (3) by registering tissue data spatially. This paper describes HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interfaces, flexible hybrid cloud infrastructure and previews atlas usage applications.

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.

scHeteroNet: A Heterophily-Aware Graph Neural Network for Accurate Cell Type Annotation and Novel Cell Detection

Single-cell RNA sequencing (scRNA-seq) has unveiled extensive cellular heterogeneity, yet precise cell type annotation and the identification of novel cell populations remain significant challenges. scHeteroNet, a novel graph neural network framework specifically designed to leverage heterophily in scRNA-seq data, is presented. Unlike traditional methods that assume homophily, scHeteroNet captures complex cell-cell interactions by integrating information from both immediate and extended cellular neighborhoods, resulting in highly accurate cell representations. Additionally, scHeteroNet incorporates an innovative novelty propagation mechanism that robustly detects previously uncharacterized cell types. Comprehensive evaluations across diverse scRNA-seq datasets demonstrate that scHeteroNet consistently outperforms state-of-the-art approaches in both cell type classification and novel cell detection. This heterophily-aware approach enhances the ability to uncover cellular diversity, providing deeper insights into complex biological systems and advancing the field of single-cell analysis.

Cellular blueprint of healthy and diseased human epiglottis and subglottis-a study of the Canadian Airways Research (CARE) group

BACKGROUND

The larynx consists of the supraglottis, glottis, and subglottis and each differ in tissue composition, lymphatic drainage, ability to counter infections, and response to injuries. However, the cellular mechanisms driving laryngeal homoeostasis remain largely unexplored. As a result, understanding disease pathogenesis within the larynx including idiopathic subglottic stenosis (iSGS) and intubation-related traumatic stenosis has been challenging. Here, we sought to characterise the cellular processes governing laryngeal health and disease.

METHODS

As part of the prospective Canadian Airways Research (CARE) iSGS study, we characterised 122,004 high-quality transcriptomes using single nucleus RNA-sequencing to profile 11 human epiglottis and 17 human subglottis biopsies across three different conditions: control, iSGS, and intubation-related traumatic stenosis to define cell populations and pathways associated with disease. We validated our results using cohort-level bulk transcriptomics using 114 human epiglottis and 121 human subglottis.

FINDINGS

We defined the single-cell taxonomy of the human subglottis and epiglottis using single-nucleus sequencing in both healthy and disease states. Mechanistically, we discovered the presence of unique epithelial and fibroblast progenitor subsets within the control subglottis but not within the anatomically adjacent epiglottis. The uncontrolled proliferation of these cellular subsets exhibited skewed sex hormone signalling and orchestrated a fibro-inflammatory cascade. We leveraged cohort-level bulk transcriptomics to define hallmarks of iSGS associated with disease covariates and introduced the first biomarker associated with recurrent relapse. Longitudinal sampling demonstrated that the subglottic microenvironment in patients with iSGS is changing dynamically with and without therapeutic intervention.

INTERPRETATION

Together, our data refines our understanding of laryngeal biology, nominates candidate compounds for iSGS treatment, and serves as a transformative platform for future clinical investigations to further precision laryngology.

FUNDING

This study was funded by a grant from the American Laryngology Association (#1082), an Academic Medical Organisation of Southwestern Ontario innovation fund grant (INN21-016), grant support from the Departments of Otolaryngology-Head and Neck Surgery at University of Toronto, Canada and Western University, Canada. ACN was supported by the Wolfe Surgical Research Professorship in the Biology of Head and Neck Cancers Fund. PYFZ was supported by a Vanier Canada Graduate Scholarship and PSI Foundation fellowship.

scPANDA: PAN-Blood Data Annotator with a 10-Million Single-Cell Atlas

OBJECTIVES

Recent advancements in single-cell RNA sequencing (scRNA-seq) have revolutionized the study of cellular heterogeneity, particularly within the hematological system. However, accurately annotating cell types remains challenging due to the complexity of immune cells. To address this challenge, we develop a PAN-blood single-cell Data Annotator (scPANDA), which leverages a comprehensive 10-million-cell atlas to provide precise cell type annotation.

METHODS

The atlas, constructed from data collected in 16 studies, incorporated rigorous quality control, preprocessing, and integration steps to ensure a high-quality reference for annotation. scPANDA utilizes a three-layer inference approach, progressively refining cell types from broad compartments to specific clusters. Iterative clustering and harmonization processes were employed to maintain cell type purity throughout the analysis. Furthermore, the performance of scPANDA was evaluated in three external datasets.

RESULTS

The atlas was structured hierarchically, consisting of 16 compartments, 54 classes, 4,‍460 low-level clusters (pd_cc_cl_tfs), and 611 high-level clusters (pmid_cts). Robust performance of the tool was demonstrated in annotating diverse immune scRNA-seq datasets, analyzing immune-tumor coexisting clusters in renal cell carcinoma, and identifying conserved cell clusters across species.

CONCLUSIONS

scPANDA exemplifies effective reference mapping with a large-scale atlas, enhancing the accuracy and reliability of blood cell type identification.

uHAF: a unified hierarchical annotation framework for cell type standardization and harmonization

SUMMARY

In single-cell transcriptomics, inconsistent cell type annotations due to varied naming conventions and hierarchical granularity impede data integration, machine learning applications, and meaningful evaluations. To address this challenge, we developed the unified Hierarchical Annotation Framework (uHAF), which includes organ-specific hierarchical cell type trees (uHAF-T) and a mapping tool (uHAF-Agent) based on large language models. uHAF-T provides standardized hierarchical references for 38 organs, allowing for consistent label unification and analysis at different levels of granularity. uHAF-Agent leverages GPT-4 to accurately map diverse and informal cell type labels onto uHAF-T nodes, streamlining the harmonization process. By simplifying label unification, uHAF enhances data integration, supports machine learning applications, and enables biologically meaningful evaluations of annotation methods. Our framework serves as an essential resource for standardizing cell type annotations and fostering collaborative refinement in the single-cell research community.

AVAILABILITY AND IMPLEMENTATION

uHAF is publicly available at: https://uhaf.unifiedcellatlas.org and https://github.com/SuperBianC/uhaf.

An aging bone marrow exacerbates lung fibrosis by fueling profibrotic macrophage persistence

Pulmonary fibrosis is an incurable disease that manifests with advanced age. Yet, how hematopoietic aging influences immune responses and fibrosis progression remains unclear. Using heterochronic bone marrow transplant mouse models, we found that an aged bone marrow exacerbates lung fibrosis irrespective of lung tissue age. Upon lung injury, there was an increased accumulation of monocyte-derived alveolar macrophages (Mo-AMs) driven by cell-intrinsic hematopoietic aging. These Mo-AMs exhibited an enhanced profibrotic profile and stalled maturation into a homeostatic, tissue-resident phenotype. This delay was shaped by cell-extrinsic environmental signals such as reduced pulmonary interleukin-10 (IL-10), perpetuating a profibrotic macrophage state. We identified regulatory T cells (Tregs) as critical providers of IL-10 upon lung injury that promote Mo-AM maturation and attenuate fibrosis progression. Our study highlights the impact of an aging bone marrow on lung immune regulation and identifies Treg-mediated IL-10 signaling as a promising target to mitigate fibrosis and promote tissue repair.

Cell Function Graphics: TOGGLE delineates fate and function within individual cell types via single-cell transcriptomics

Functional RNA plays a crucial role in regulating cellular processes throughout the life cycle of a cell. Identifying functional changes at each stage, from inception to development to maturation, functional execution, and eventual death or pathological transformation, often requires systematic comparisons of functional expression across cell populations. However, because cells of the same type often exhibit similar gene expression patterns regardless of function or fate, it is challenging to distinguish the stages of cellular fate or functional states within the same cell type, which also limits our understanding of cellular memory. Cells of the same type that share structural and gene expression similarities but originate from different regions and perform slightly distinct functions often retain unique epigenetic memory signatures. Although RNA serves as a key executor of fundamental cellular functions, its high expression similarity among cells of the same type limits its ability to distinguish functional heterogeneity. To overcome this challenge, we developed TOGGLE, utilizing higher-resolution analytical methods to uncover functional diversity at the cellular level. Then we based on TOGGLE developed an innovative Graph Diffusion Functional Map, which can significantly reduce noise, thereby more clearly displaying the functional grouping of RNA and enabling the capture of more subtle functional differences in high-dimensional data. Ultimately, this method effectively removes the influence of baseline functions from classification criteria and identifies key trajectories of cell fate determination.

Deep immunophenotyping reveals circulating activated lymphocytes in individuals at risk for rheumatoid arthritis

Rheumatoid arthritis (RA) is a systemic autoimmune disease currently with no universally highly effective prevention strategies. Identifying pathogenic immune phenotypes in at-risk populations prior to clinical onset is crucial to establishing effective prevention strategies. Here, we applied multimodal single-cell technologies (mass cytometry and CITE-Seq) to characterize the immunophenotypes in blood from at-risk individuals (ARIs) identified through the presence of serum antibodies against citrullinated protein antigens (ACPAs) and/or first-degree relative (FDR) status, as compared with patients with established RA and people in a healthy control group. We identified significant cell expansions in ARIs compared with controls, including CCR2+CD4+ T cells, T peripheral helper (Tph) cells, type 1 T helper cells, and CXCR5+CD8+ T cells. We also found that CD15+ classical monocytes were specifically expanded in ACPA-negative FDRs, and an activated PAX5lo naive B cell population was expanded in ACPA-positive FDRs. Further, we uncovered the molecular phenotype of the CCR2+CD4+ T cells, expressing high levels of Th17- and Th22-related signature transcripts including CCR6, IL23R, KLRB1, CD96, and IL22. Our integrated study provides a promising approach to identify targets to improve prevention strategy development for RA.