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Continuous lifelong learning for modeling of gene regulation from single cell multiome data by leveraging atlas-scale external data.
Accurate context-specific Gene Regulatory Networks (GRNs) inference from genomics data is a crucial task in computational biology. However, existing methods face limitations, such as reliance on gene expression data alone, lower resolution from bulk data, and data scarcity for specific cellular systems. Despite recent technological advancements, including single-cell sequencing and the integration of ATAC-seq and RNA-seq data, learning such complex mechanisms from limited independent data points still presents a daunting challenge, impeding GRN inference accuracy. To overcome this challenge, we present LINGER (LIfelong neural Network for GEne Regulation), a novel deep learning-based method to infer GRNs from single-cell multiome data with paired gene expression and chromatin accessibility data from the same cell. LINGER incorporates both 1) atlas-scale external bulk data across diverse cellular contexts and 2) the knowledge of transcription factor (TF) motif matching to cis-regulatory elements as a manifold regularization to address the challenge of limited data and extensive parameter space in GRN inference. Our results demonstrate that LINGER achieves 2-3 fold higher accuracy over existing methods. LINGER reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Additionally, following the GRN inference from a reference sc-multiome data, LINGER allows for the estimation of TF activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies. Overall, LINGER provides a comprehensive tool for robust gene regulation inference from genomics data, empowering deeper insights into cellular mechanisms.
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Cell atlas of the immune microenvironment in gastrointestinal cancers: Dendritic cells and beyond.
Gastrointestinal (GI) cancers occur in the alimentary tract and accessory organs. They exert a global burden with high morbidity and mortality. Inside the tumor microenvironment, dendritic cells (DCs) are the most efficient antigen-presenting cells and are necessary for adaptive immune responses such as T and B-cell maturation. However, the subsets of DCs revealed before were mostly based on flow cytometry and bulk sequencing. With the development of single-cell RNA sequencing (scRNA-seq), the tumor and microenvironment heterogeneity of GI cancer has been illustrated. In this review, we summarize the classification and development trajectory of dendritic cells at the single-cell level in GI cancer. Additionally, we focused on the interaction of DCs with T cells and their effect on the response to immunotherapy. Specifically, we focused on the newly identified tumor-infiltrating dendritic cells and discuss their potential function in antitumor immunity.
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Spatial Cell Atlas of Lateral Septum Reveals Changes Underlying Anxiety and Fear Learning Deficits in Mice with Abnormal Immunity.
The lateral septum (LS) regulates affective-cognitive processes and is dysregulated in human psychiatric disorders. Its functional complexity stems from heterogeneous connectivities and neuronal subtype compositions across anatomical subregions. Comprehensive understanding of LS function has been hindered by the lack of a three-dimensional (3D) neuronal atlas. Moreover, the association between peripheral immune disturbances and psychiatric disorders underscores the necessity to investigate potential 3D LS neuronal heterogeneities across distinct immunological states. This study employed single-nucleus RNA sequencing to analyze LS differences in C57J, BALB/c, and nude mice with distinct immune contexts (i.e., immunocompetent, enhanced Th2 reactivity, and T-cell deficiency, respectively), and identified global alterations in non-neuronal cells alongside with neuron-specific changes. Through combining publicly available multiplexed error-robust fluorescence in situ hybridization (MERFISH) data with neural circuit tracing, this study further constructed a 3D neuronal atlas of the entire LS with their projection connectivities. Notably, an Esr1-positive neuronal subpopulation distributed in the ventral LS exhibited potential responsiveness to changes in peripheral immunity and may participate in anxiety regulation. Furthermore, the dorsal LS demonstrated heterogeneity in fear memory regulation associated with T cell homeostasis. These findings underscore the critical role of immune-neural crosstalk in emotional regulation while shedding light on potential therapeutic targets for emotion-related disorders linked to T-cell homeostatic disturbances.
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SinCMat: A single-cell-based method for predicting functional maturation transcription factors.
A major goal of regenerative medicine is to generate tissue-specific mature and functional cells. However, current cell engineering protocols are still unable to systematically produce fully mature functional cells. While existing computational approaches aim at predicting transcription factors (TFs) for cell differentiation/reprogramming, no method currently exists that specifically considers functional cell maturation processes. To address this challenge, here, we develop SinCMat, a single-cell RNA sequencing (RNA-seq)-based computational method for predicting cell maturation TFs. Based on a model of cell maturation, SinCMat identifies pairs of identity TFs and signal-dependent TFs that co-target genes driving functional maturation. A large-scale application of SinCMat to the Mouse Cell Atlas and Tabula Sapiens accurately recapitulates known maturation TFs and predicts novel candidates. We expect SinCMat to be an important resource, complementary to preexisting computational methods, for studies aiming at producing functionally mature cells.
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MSCsDB: a database of single-cell transcriptomic profiles and in-depth comprehensive analyses of human mesenchymal stem cells.
Mesenchymal stem cells (MSCs) possess multipotent properties that make them promising candidates for immunomodulation and regenerative medicine. However, MSC heterogeneity poses challenges to their research reproducibility and clinical application. The emergence of single-cell RNA sequencing (scRNA-seq) technology has enabled a thorough examination of MSC heterogeneity, underscoring the necessity for a specialized platform to systematically analyze the published datasets derived from MSC scRNA-seq experiments. However, large-scale integration and in-depth exploration of MSC scRNA-seq datasets to comprehensively depict their developmental patterns, relationships, and knowledge are still lacking. Here, we present MSCsDB ( http://mscsdb.jflab.ac.cn:18088/index/ ), an interactive database that has been constructed using high-quality scRNA-seq datasets from all published sources on MSCs. MSCsDB provides a one-stop interactive query for regulon activities, gene ontology enrichment, signature gene visualization and transcription factor regulon analysis. Additionally, the dedicated module within MSCsDB was developed to facilitate the evaluation of MSC quality, thereby promoting the standardization of MSC subtype usage. Notably, MSCsDB enables users to analyze their MSCs scRNA-seq data directly, yielding visually appealing outputs of exceptional quality that can be conveniently downloaded via email. Furthermore, MSCsDB integrates the current comprehensive MSC atlas taxonomy, which includes 470,000 cells and 5 tissues from 26 subjects, as publicly available references. These references provide molecular characterization and phenotypic prediction for annotating MSC subsets. In summary, MSCsDB serves as a user-friendly and contemporary data repository for human MSCs, offering a dedicated platform that enables users to effectively conduct comprehensive analyses on their individual MSCs scRNA-seq data.
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Exploring myometrial microenvironment changes at the single-cell level from nonpregnant to term pregnant states.
The microenvironment and cell populations within the myometrium play crucial roles in maintaining uterine structural integrity and protecting the fetus during pregnancy. However, the specific changes occurring at the single-cell level in the human myometrium between nonpregnant (NP) and term pregnant (TP) states remain unexplored. In this study, we used single-cell RNA sequencing (scRNA-Seq) and spatial transcriptomics (ST) to construct a transcriptomic atlas of individual cells in the myometrium of NP and TP women. Integrated analysis of scRNA-Seq and ST data revealed spatially distinct transcriptional characteristics and examined cell-to-cell communication patterns based on ligand-receptor interactions. We identified and categorized 87,845 high-quality individual cells into 12 populations from scRNA-Seq data of 12 human myometrium tissues. Our findings demonstrated alterations in the proportions of five subpopulations of smooth muscle cells in TP. Moreover, an increase in monocytic cells, particularly M2 macrophages, was observed in TP myometrium samples, suggesting their involvement in the anti-inflammatory response. This study provides unprecedented single-cell resolution of the NP and TP myometrium, offering new insights into myometrial remodeling during pregnancy.NEW & NOTEWORTHY Using single-cell RNA sequencing and spatial transcriptomics, the myometrium was examined at the single-cell level during pregnancy. We identified spatially distinct cell populations and observed alterations in smooth muscle cells and increased M2 macrophages in term pregnant women. These findings offer unprecedented insights into myometrial remodeling and the anti-inflammatory response during pregnancy. The study advances our understanding of pregnancy-related myometrial changes.
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Single-cell RNA sequencing revealed subclonal heterogeneity and gene signatures of gemcitabine sensitivity in pancreatic cancer.
Introduction: Resistance to gemcitabine is common and critically limits its therapeutic efficacy in pancreatic ductal adenocarcinoma (PDAC). Methods: We constructed 17 patient-derived xenograft (PDX) models from PDAC patient samples and identified the most notable responder to gemcitabine by screening the PDX sets in vivo. To analyze tumor evolution and microenvironmental changes pre- and post-chemotherapy, single-cell RNA sequencing (scRNA-seq) was performed. Results: ScRNA-seq revealed that gemcitabine promoted the expansion of subclones associated with drug resistance and recruited macrophages related to tumor progression and metastasis. We further investigated the particular drug-resistant subclone and established a gemcitabine sensitivity gene panel (GSGP) (SLC46A1, PCSK1N, KRT7, CAV2, and LDHA), dividing PDAC patients into two groups to predict the overall survival (OS) in The Cancer Genome Atlas (TCGA) training dataset. The signature was successfully validated in three independent datasets. We also found that 5-GSGP predicted the sensitivity to gemcitabine in PDAC patients in the TCGA training dataset who were treated with gemcitabine. Discussion and conclusion: Our study provides new insight into the natural selection of tumor cell subclones and remodeling of tumor microenvironment (TME) cells induced by gemcitabine. We revealed a specific drug resistance subclone, and based on the characteristics of this subclone, we constructed a GSGP that can robustly predict gemcitabine sensitivity and prognosis in pancreatic cancer, which provides a theoretical basis for individualized clinical treatment.
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Fly Cell Atlas: A single-nucleus transcriptomic atlas of the adult fruit fly.
For more than 100 years, the fruit fly Drosophila melanogaster has been one of the most studied model organisms. Here, we present a single-cell atlas of the adult fly, Tabula Drosophilae, that includes 580,000 nuclei from 15 individually dissected sexed tissues as well as the entire head and body, annotated to >250 distinct cell types. We provide an in-depth analysis of cell type-related gene signatures and transcription factor markers, as well as sexual dimorphism, across the whole animal. Analysis of common cell types between tissues, such as blood and muscle cells, reveals rare cell types and tissue-specific subtypes. This atlas provides a valuable resource for the Drosophila community and serves as a reference to study genetic perturbations and disease models at single-cell resolution.
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A Temporal Single-Cell Multi-Omics Atlas of Murine Pancreatic Islet Remodeling During Hyperglycaemia Progression.
Pancreatic islets undergo coordinated cellular remodeling during obesity-induced insulin resistance (IR). However, the associated molecular changes across endocrine and non-endocrine compartments remain largely unexplored. Here, using longitudinal single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) on islets from C57BL/6 mice subjected to high-fat diet (HFD) feeding for 8, 16, and 24 weeks, along with age-matched controls on regular chow, we mapped dynamic changes in islet cell composition and transcriptional states. Beta cells demonstrated pronounced stress-induced reprogramming, with the emergence of proliferative and dysfunctional subsets. Alpha and delta cell fractions declined under HFD, despite increased polyhormonal biosynthesis, suggesting functional rather than numerical adaptation. Immune profiling showed robust expansion of proinflammatory M1 macrophages and upregulation of NF-κB and chemotaxis pathways, particularly at 16 weeks. Notably, cell-cell communication analyses revealed diet-specific disruption in signaling networks. Under HFD conditions, intercellular communication among beta cells, macrophages, and delta cells was markedly altered, leading to the disruption of key signaling pathways such as the gastric inhibitory polypeptide receptor (GIPR) and major histocompatibility complex-I (MHC-I). Notably, C-C motif chemokine ligand 27A ( Ccl27a ) expression and chromatin accessibility were significantly altered in a distinct subpopulation of beta cells under HFD condition, indicative of a niche-specific regulatory mechanism. Integration with human islet datasets from obese and type 2 diabetes (T2D) donors confirmed conserved shifts in beta cell identity and immune activation. This study presents a comprehensive high-resolution atlas of islet remodeling under metabolic stress, identifying key communication nodes and transcriptional programs pertinent to T2D pathogenesis.
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TemporalVAE: atlas-assisted temporal mapping of time-series single-cell transcriptomes during embryogenesis.
International efforts have yielded extensive single-cell time-series atlas datasets, such as those on mouse embryogenesis, providing a reference for mapping disease models across biomedical research. However, effectively using such data for temporal analysis of individual datasets is challenging due to the intricate nature of cell states and the tight coupling between time stamps and experimental batches. Here we introduce TemporalVAE, a deep generative model in a dual-objective setting that infers the biological time of each cell from a compressed latent space, even in a zero-shot setting. With a mouse development atlas, we demonstrated its scalability with millions of cells, accuracy in atlas-based cell staging across platforms and interpretability by identifying temporally sensitive genes with in silico perturbation. TemporalVAE effectively stages cells during human peri-implantation under both in vivo and in vitro conditions, and supports cross-primate comparisons among human, cynomolgus and marmoset embryos, highlighting its potential for broad biomedical applications.