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A cell atlas of adult muscle precursors uncovers early events in fibre-type divergence in Drosophila.
In Drosophila, the wing disc-associated muscle precursor cells give rise to the fibrillar indirect flight muscles (IFM) and the tubular direct flight muscles (DFM). To understand early transcriptional events underlying this muscle diversification, we performed single-cell RNA-sequencing experiments and built a cell atlas of myoblasts associated with third instar larval wing disc. Our analysis identified distinct transcriptional signatures for IFM and DFM myoblasts that underlie the molecular basis of their divergence. The atlas further revealed various states of differentiation of myoblasts, thus illustrating previously unappreciated spatial and temporal heterogeneity among them. We identified and validated novel markers for both IFM and DFM myoblasts at various states of differentiation by immunofluorescence and genetic cell-tracing experiments. Finally, we performed a systematic genetic screen using a panel of markers from the reference cell atlas as an entry point and found a novel gene, Amalgam which is functionally important in muscle development. Our work provides a framework for leveraging scRNA-seq for gene discovery and details a strategy that can be applied to other scRNA-seq datasets.
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BMP4 drives primed to naïve transition through PGC-like state.
Multiple pluripotent states have been described in mouse and human stem cells. Here, we apply single-cell RNA-seq to a newly established BMP4 induced mouse primed to naïve transition (BiPNT) system and show that the reset is not a direct reversal of cell fate but goes through a primordial germ cell-like cells (PGCLCs) state. We first show that epiblast stem cells bifurcate into c-Kit+ naïve and c-Kit- trophoblast-like cells, among which, the naïve branch undergoes further transition through a PGCLCs intermediate capable of spermatogenesis in vivo. Mechanistically, we show that DOT1L inhibition permits the transition from primed pluripotency to PGCLCs in part by facilitating the loss of H3K79me2 from Gata3/6. In addition, Prdm1/Blimp1 is required for PGCLCs and naïve cells, while Gata2 inhibits PGC-like state by promoting trophoblast-like fate. Our work not only reveals an alternative route for primed to naïve transition, but also gains insight into germ cell development.
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A single-cell transcriptomic atlas of peripheral blood immune cells spanning progressive canine leishmaniosis.
Dogs play a major role in sustaining the transmission of Leishmania infantum to people, making prevention and treatment of canine leishmaniosis (CanL) public health priorities. However, immune mechanisms underlying progression from subclinical stages to terminal disease in dogs remain ill-defined. To address this gap, we generated a single-cell RNA sequencing map of peripheral immune cells from uninfected and naturally infected dogs across well-defined stages of L. infantum infection. Disease progression was marked by a shift from a lymphoid-to a myeloid-dominated immune landscape. CD4+ T cells transition from naive to effector states, with TH1 cells showing progressive exhaustion signatures paralleling disease progression, while CD8+ T cells exhibited TPEX-like phenotypes with differentiation toward effector and proliferative programs during severe L. infantum infection. Monocytes showed inflammatory remodeling across clinical stages. The LeishDog Atlas provides a framework for understanding immune dysregulation in CanL and a resource for comparative and translational studies of its immunopathology.
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scTWAS Atlas: an integrative knowledgebase of single-cell transcriptome-wide association studies.
Single-cell transcriptome-wide association studies (scTWAS) is a new method for conducting TWAS analysis at the cellular level to identify gene-trait associations with higher precision. This approach helps overcome the challenge of interpreting cell-type heterogeneity in traditional TWAS results. As the field of scTWAS rapidly advances, there is a growing need for additional database platforms to integrate this wealth of data and knowledge effectively. To address this gap, we present scTWAS Atlas (https://ngdc.cncb.ac.cn/sctwas/), a comprehensive database of scTWAS information integrating literature curation and data analysis. The current version of scTWAS Atlas amasses 2,765,211 associations encompassing 34 traits, 30 cell types, 9 cell conditions and 16,470 genes. The database features visualization tools, including an interactive knowledge graph that integrates single-cell expression quantitative trait loci (sc-eQTL) and scTWAS associations to build a multi-omics level regulatory network at the cellular level. Additionally, scTWAS Atlas facilitates cross-cell-type analysis, highlighting cell-type-specific and shared TWAS genes. The database is designed with user-friendly interfaces and allows for easy browsing, searching, and downloading of relevant information. Overall, scTWAS Atlas is instrumental in exploring the genetic regulatory mechanisms at the cellular level and shedding light on the role of various cell types in biological processes, offering novel insights for human health research.
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Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data.
Existing methods for gene regulatory network (GRN) inference rely on gene expression data alone or on lower resolution bulk data. Despite the recent integration of chromatin accessibility and RNA sequencing data, learning complex mechanisms from limited independent data points still presents a daunting challenge. Here we present LINGER (Lifelong neural network for gene regulation), a machine-learning method to infer GRNs from single-cell paired gene expression and chromatin accessibility data. LINGER incorporates atlas-scale external bulk data across diverse cellular contexts and prior knowledge of transcription factor motifs as a manifold regularization. LINGER achieves a fourfold to sevenfold relative increase in accuracy over existing methods and reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Following the GRN inference from reference single-cell multiome data, LINGER enables the estimation of transcription factor 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.
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Gene-regulatory programs that specify age-related differences during thymocyte development.
T cell development is fundamental to immune system establishment, but how this development changes with age remains poorly understood. Here, we construct a transcriptional and chromatin accessibility atlas of T cell developmental programs in neonatal and adult mice, revealing the ontogeny of divergent gene-regulatory programs and their link to age-related differences. Specifically, we identify a gene module that diverges with age from the earliest stages of genesis and includes programs that govern the effector response and cell cycle. Moreover, we reveal that neonates possess more accessible chromatin during early thymocyte development, likely establishing poised gene expression programs that manifest later in thymocyte development. Finally, we leverage this atlas, employing a CRISPR-based perturbation approach coupled with single-cell RNA sequencing readout, to uncover a conserved transcriptional regulator, Zbtb20, that contributes to age-dependent differences in T cell development. In summary, our study defines gene-regulatory programs that regulate age-specific differences in T cell development.
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Single-Cell Analysis of Regional Differences in Adult V-SVZ Neural Stem Cell Lineages.
The ventricular-subventricular zone (V-SVZ) harbors adult neural stem cells. V-SVZ neural stem cells exhibit features of astrocytes, have a regional identity, and depending on their location in the lateral or septal wall of the lateral ventricle, generate different types of neuronal and glial progeny. We performed large-scale single-cell RNA sequencing to provide a molecular atlas of cells from the lateral and septal adult V-SVZ of male and female mice. This revealed regional and sex differences among adult V-SVZ cells. We uncovered lineage potency bias at the single-cell level among lateral and septal wall astrocytes toward neurogenesis and oligodendrogenesis, respectively. Finally, we identified transcription factor co-expression modules marking key temporal steps in neurogenic and oligodendrocyte lineage progression. Our data suggest functionally important spatial diversity in neurogenesis and oligodendrogenesis in the adult brain and reveal molecular correlates of adult NSC dormancy and lineage specialization.
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SCMO: a deep learning model integrating the single-cell resolution TME ecosystem and multi-omics for survival prediction in CRC patients.
BACKGROUND: Colorectal cancer (CRC) remains a leading cause of global cancer mortality, highlighting the need for precise survival prediction to guide clinical decisions. Although tissue-level multi-omics is widely utilized for survival prediction, its limited resolution cannot capture tumor heterogeneity. Single-cell RNA sequencing (scRNA-seq) enables dissection of the tumor microenvironment (TME) at cellular resolution, supporting personalized prognostic assessment. METHODS: We collected 213 CRC scRNA-seq samples and established a CRC-specific TME atlas comprising 339,060 cells. Using this atlas as a reference, we deconvolved bulk RNA-seq data from TCGA-CRC cohort with the EcoTyper algorithm to reconstruct TME features. Clinical, genomic, and transcriptomic data were obtained from the Xena platform; microbial data were sourced from the BIC database. We integrated TME and multi-omics features through a self-normalizing neural network to construct a deep learning model (single-cell resolution TME ecosystem with multi-omics data [SCMO]) for survival prediction. To enhance interpretability, we utilized the Integrated Gradients algorithm and spatial transcriptomic data to analyze multi-omics and TME features. We performed anticancer drug screening with tumor necrosis factor receptor-associated protein 1 (TRAP1), a critical feature according to the Integrated Gradients algorithm, as a potential target. RESULTS: We identified 13 survival-related TME features from the CRC-specific atlas: 12 cell states and one multi-cellular ecosystem. SCMO, which combined TME and multi-omics features, improved survival prediction and outperformed existing methods, achieving a concordance index of 0.762. The SCMO demonstrated robust performance for long-term predictions, achieving areas under the curve (AUCs) of 0.752, 0.772, and 0.869 for 1-, 3-, and 5-year predictions in the training set, with corresponding test set AUCs of 0.639, 0.756, and 0.772. TME features from the SCMO model revealed that ecosystem density increased with CRC malignancy. Multi-omics features included TRAP1 as a potential drug target. Drug screening identified saikosaponin A as a novel TRAP1 inhibitor, and its anticancer activity was validated in vitro. We developed SCMO-Lite, a simplified model incorporating 12 high-attribution-weight multi-omics features, which demonstrated robust risk stratification. CONCLUSIONS: SCMO combines analytical precision with biological interpretability, offering novel insights for oncology survival prediction.
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A Vascular-Extracellular Matrix Molecular Program Identifies High-Risk Diffuse Glioma Across Independent Multi-Omics.
BACKGROUND: Gliomas are characterized by a high degree of molecular heterogeneity, which impairs the reproducibility of predictive biomarkers derived from bulk-based molecular profiling due to immune/stromal contamination of tumors and the high prevalence of the IDH mutation signature. METHODS: In this study, we used MOFA+ to derive intrinsic molecular signatures from transcriptional, methylation, and genomic profiles of a cohort of 667 diffuse gliomas in the Cancer Genome Atlas database. Thereafter, factor scores were derived for two separate Chinese Glioma Genome Atlas batches (Batch 1, n = 325; Batch 2, n = 693) without any retraining on the model. The prognostic independence of identified molecular signatures was assessed using multivariable Cox regression adjusted for IDH mutation status and tumor purity; purity-residualized survival analyses; IDH-stratified Cox regression in each cohort; validation by concordance index against established molecular signatures; and survival extreme profiling. To characterize the biological significance of factor signatures, we projected gene set signatures corresponding to each factor signature onto a single-cell RNA-seq dataset of GBM (GSE131928). RESULTS: MOFA+ identified 12 latent factors, of which a vascular-extracellular matrix (ECM) remodeling axis (Factor 1) explained the highest multi-omics variance (24.9%) and was the strongest independent prognostic factor. In multivariable Cox regression adjusting for IDH status and tumor purity, Factor 1 remained independently prognostic (HR = 1.67, 95% CI 1.27-2.20, p = 0.0002); in a fully-adjusted model additionally including age, WHO grade, MGMT methylation, and 1p/19q codeletion (plus radiotherapy and chemotherapy status in the CGGA cohorts), Factor 1 remained prognostic in both CGGA cohorts (CGGA1: HR = 1.50, p = 3.8 × 10-5; CGGA2: HR = 1.18, p = 0.003) but lost significance in TCGA (HR = 1.04, p = 0.83), consistent with the cohort-dependent magnitude reported in the IDH-stratified and meta-regression analyses below. Purity-residualized survival analysis showed negligible attenuation of the Factor 1 signal (raw HR = 3.57 vs. residualized HR = 3.72; concordance 96.5%). Within IDH-wildtype gliomas, Factor 1 was significant in both external validation cohorts (CGGA1: HR = 1.64, FDR = 4.6 × 10-6; CGGA2: HR = 1.20, FDR = 0.02), though the TCGA IDH-wildtype subgroup showed a trend that did not survive FDR correction (FDR = 0.060). All validation was performed without model retraining. Within IDH-mutant gliomas, Factor 1 was strongly prognostic in both CGGA cohorts but was not significant in TCGA (HR = 1.17, FDR = 0.33). These findings should therefore be interpreted as consistent in directionality across cohorts but not uniformly replicated at the FDR-adjusted significance threshold in the TCGA discovery dataset. Concordance index benchmarking on a matched subset (n = 503) showed Factor 1 achieved discrimination comparable to the Mesenchymal signature (C = 0.797 vs. 0.801; ΔC = -0.004) while outperforming four other established classifiers. Factor 1 consistently separated patients with extreme survival phenotypes (OS < 6 vs. >15 months) across all three cohorts (all log-rank p < 0.001). Projection onto a single-cell GBM atlas (GSE131928), supported by inferCNV-based malignant-cell classification, localized the Vascular-ECM program to malignant cells and the Immune-ECM axis to myeloid compartments. CONCLUSIONS: The Vascular-ECM axis is a consistent, prognostic program robust to purity adjustment for diffuse gliomas that remains relevant across IDH-defined subgroups in three independent datasets comprising 1685 patients. The Vascular-ECM axis is a reproducible, purity-robust prognostic program in diffuse glioma, with directionally consistent adverse effects across TCGA, CGGA Batch 1, and CGGA Batch 2 (pooled n = 1685). Given the strong co-loading of endothelial, ECM, and myeloid genes observed in the single-cell projection, Factor 1 is best interpreted as a vascular/ECM-associated tumor-microenvironment ecosystem program rather than a malignant-cell-autonomous signature. Its FDR-adjusted significance within IDH-stratified subgroups is cohort-dependent and robust in both CGGA cohorts but attenuated in the TCGA IDH-wildtype (FDR = 0.060) and TCGA IDH-mutant (FDR = 0.33) strata. The pooled signal should therefore be interpreted as evidence of a generalizable biological program rather than a uniformly replicated subgroup-specific biomarker. It is possible to calculate factor scores based on RNA sequencing alone using fixed loadings (Z = XWᵀ), which may have implications for future translational applications. All findings are correlative; a causal role for the Vascular-ECM program in glioma progression, invasion, or therapy resistance remains to be established through functional perturbation experiments.
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Processing and cryopreservation of human ureter tissues for single-cell and spatial transcriptomics assays.
Characterizing the cellular heterogeneity of human ureter tissues using single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics provides a detailed atlas of cell types, signaling networks, and potential cell-cell cross talk underlying developmental and regenerative pathways. We describe an optimized protocol for generating, cryopreserving, and thawing single-cell suspensions from ureter tissues isolated post-cystectomy for scRNA-seq. In addition, we describe an optimized protocol for cryopreserving human ureter tissues for 10x Genomics Visium spatial gene expression platform. For complete details on the use and execution of this protocol, please refer to Fink et al. (2022).1.