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Single-Cell Profiles of Retinal Ganglion Cells Differing in Resilience to Injury Reveal Neuroprotective Genes.
Neuronal types in the central nervous system differ dramatically in their resilience to injury or other insults. Here we studied the selective resilience of mouse retinal ganglion cells (RGCs) following optic nerve crush (ONC), which severs their axons and leads to death of ∼80% of RGCs within 2 weeks. To identify expression programs associated with differential resilience, we first used single-cell RNA-seq (scRNA-seq) to generate a comprehensive molecular atlas of 46 RGC types in adult retina. We then tracked their survival after ONC; characterized transcriptomic, physiological, and morphological changes that preceded degeneration; and identified genes selectively expressed by each type. Finally, using loss- and gain-of-function assays in vivo, we showed that manipulating some of these genes improved neuronal survival and axon regeneration following ONC. This study provides a systematic framework for parsing type-specific responses to injury and demonstrates that differential gene expression can be used to reveal molecular targets for intervention.
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Deterministic genetic barcoding for multiplexed behavioral and single-cell transcriptomic studies.
Advances in single-cell sequencing technologies have provided novel insights into the dynamics of gene expression and cellular heterogeneity within tissues and have enabled the construction of transcriptomic cell atlases. However, linking anatomical information to transcriptomic data and positively identifying the cell types that correspond to gene expression clusters in single-cell sequencing data sets remains a challenge. We describe a straightforward genetic barcoding approach that takes advantage of the powerful genetic tools in Drosophila to allow in vivo tagging of defined cell populations. This method, called Targeted Genetically-Encoded Multiplexing (TaG-EM), involves inserting a DNA barcode just upstream of the polyadenylation site in a Gal4-inducible UAS-GFP construct so that the barcode sequence can be read out during single-cell sequencing, labeling a cell population of interest. By creating many such independently barcoded fly strains, TaG-EM enables positive identification of cell types in cell atlas projects, identification of multiplet droplets, and barcoding of experimental timepoints, conditions, and replicates. Furthermore, we demonstrate that TaG-EM barcodes can be read out using next-generation sequencing to facilitate population-scale behavioral measurements. Thus, TaG-EM has the potential to enable large-scale behavioral screens in addition to improving the ability to multiplex and reliably annotate single-cell transcriptomic experiments.
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Unbiased Single-Cell Sequencing of Hematopoietic and Immune Cells from Aplastic Anemia Reveals the Contributors of Hematopoiesis Failure and Dysfunctional Immune Regulation.
Aplastic anemia (AA) is a bone marrow (BM) failure syndrome mediated by hyperactivated T-cells with heterogeneous pathogenic factors. The onset of BM failure cannot be accurately determined in humans; therefore, exact pathogenesis remains unclear. In this study, a cellular atlas and microenvironment interactions is established using unbiased single-cell RNA-seq, along with multi-omics analyses (mass cytometry, cytokine profiling, and oxidized fatty acid metabolomics). A new KIR+ CD8+ regulatory T cells (Treg) subset is identified in patients with AA that engages in immune homeostasis. Conventional CD4+ T-cells differentiate into highly differentiated T helper cells with type 2 cytokines (IL-4, IL-6, and IL-13), GM-SCF, and IL-1β. Immunosuppressive homeostasis is impaired by enhanced apoptosis of activated Treg cells. Pathological Vδ1 cells dominated the main fraction of γδ T-cells. The B/plasma, erythroid, and myeloid lineages also exhibit substantial pathological features. Interactions between TNFSF12-TNFRSF12A, TNF-TNFRSF1A, and granzyme-gasdermin are associated with the cell death of hematopoietic stem/progenitor (HSPCs), Treg, and early erythroid cells. Ferroptosis, a major driver of HSPCs destruction, is identified in patients with AA. Furthermore, a case of twins with AA is reported to enhance the persuasiveness of the analysis. These results collectively constitute the cellular atlas and microenvironment interactions in patients with AA and provide novel insights into the development of new therapeutic opportunities.
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Spatiotemporal and single-cell atlases to dissect regional specific cell types of the developing ovary.
Characterization of cellular heterogeneity and molecular diversity greatly enhances our understanding of the ovary differentiation process. However, regional specification remains largely unexplored during ovary development. Here, we combine spatial transcriptomics and single-cell RNA sequencing to build a spatiotemporal developmental atlas of ovaries from the fetal stage through adulthood. We construct the developmental trajectory of female germ cells and identify key genes essential for primordial follicle assembly. Specifically, we characterize the regional heterogeneity of granulosa cell subtypes contributing to the folliculogenesis, and analyze the molecular distinctions between healthy and atretic follicles. Notably, we discover that Onecut2-positive luteal cells exhibit a specific spatial distribution. Furthermore, lineage tracing reveals that these Onecut2-positive luteal cells originate from Foxl2-positive granulosa cells and Cyp17a1-positive theca cells. Collectively, this study provides a comprehensive delineation of ovary development and regional specificity of ovarian cell types.
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Utilizing a novel mitochondrial-related gene signature for predicting the prognosis and immunological impact in bladder cancer.
BACKGROUND: Mounting evidence highlights the critical role of mitochondrial dysfunction, driven by mitochondrial-related genes (MTRGs), in the development, progression, and therapeutic response of cancer. However, a comprehensive analysis linking specific Mitochondria-Related Gene Signature (MTRGS) to Bladder Cancer (BLCA) prognosis and immunotherapy efficacy remains largely unexplored. Therefore, this study aims to investigate the role of MTRGs in BLCA, construct and validate a novel MTRGs-based prognostic signature, and explore its potential for guiding personalized treatment strategies. MATERIALS AND METHODS: Leveraging transcriptomic and clinical data from The Cancer Genome Atlas (TCGA-BLCA) cohort, we constructed a mitochondrial-related risk score model using LASSO, univariate and multivariate Cox regression analyses. This model was subsequently validated in an independent Gene Expression Omnibus (GEO) dataset. We then employed integrated bioinformatics approaches (implemented in R with online databases) to characterize features of the tumor microenvironment (TME), immune cell infiltration, Gene Set Enrichment Analysis (GSEA), tumor mutational burden (TMB), and drug sensitivity across different risk groups. Additionally, using data from public databases, we further verified our findings through single-cell RNA sequencing (scRNA-seq) analyses. RESULTS: Using 104 mitochondria-related differentially expressed genes (MTR-DEGs), unsupervised non-negative matrix factorization (NMF) clustering stratified BLCA patients into three molecular subtypes (Clusters 1-3). Survival analysis revealed that patients in Cluster 3 had significantly longer overall survival than those in Clusters 1 and 2. Our mitochondrial-related risk model incorporating six core genes (MAP1B, PYCR1, HSD3B1, KLK6, AKR1B15, and TAT) - exhibited robust prognostic capability (3-years AUC = 0.695 in TCGA-BLCA, 0.798 in GEO-GSE32894, 0.703 in GSE13507). The risk model revealed distinct immune infiltration patterns between high- and low-risk groups. Furthermore, Tumor Immune Dysfunction and Exclusion (TIDE) and immunophenotype score (IPS) analyses demonstrated that integrating risk scores with stromal/immune signatures significantly enhanced immunotherapy benefit prediction across BLCA risk-subgroups. Crucially, the model demonstrated predictive power for therapy response: low-risk patients showed potential benefit from immune checkpoint inhibitors, while high-risk patients exhibited heightened sensitivity to specific chemotherapy agents or targeted therapies (e.g., Tozasertib, Gemcitabine) and may require intensified regimens. CONCLUSION: This validated mitochondrial risk model delivers a clinically actionable biomarker for BLCA prognosis stratification and guides personalized therapeutic selection, enabling precision treatment intensification.
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PRMT5/WDR77 Enhances the Proliferation of Squamous Cell Carcinoma via the ΔNp63α-p21 Axis.
Protein arginine methyltransferase 5 (PRMT5) is a critical oncogenic factor in various cancers, and its inhibition has shown promise in suppressing tumor growth. However, the role of PRMT5 in squamous cell carcinoma (SCC) remains largely unexplored. In this study, we analyzed SCC patient data from The Cancer Genome Atlas (TCGA) and the Cancer Dependency Map (DepMap) to investigate the relationship between PRMT5 and SCC proliferation. We employed competition-based cell proliferation assays, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assays, flow cytometry, and in vivo mouse modeling to examine the regulatory roles of PRMT5 and its binding partner WDR77 (WD repeat domain 77). We identified downstream targets, including the p63 isoform ΔNp63α and the cyclin-dependent kinase inhibitor p21, through single-cell RNA-seq, RT-qPCR, and Western blot analyses. Our findings demonstrate that upregulation of PRMT5 and WDR77 correlates with the poor survival of head and neck squamous cell carcinoma (HNSCC) patients. PRMT5/WDR77 regulates the HNSCC-specific transcriptome and facilitates SCC proliferation by promoting cell cycle progression. The PRMT5 and WDR77 stabilize the ΔNp63α Protein, which in turn, inhibits p21. Moreover, depletion of PRMT5 and WDR77 repress SCC in vivo. This study reveals for the first time that PRMT5 and WDR77 synergize to promote SCC proliferation via the ΔNp63α-p21 axis, highlighting a novel therapeutic target for SCC.
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A succinylation-based classifier predicts chemotherapy response in prostate cancer and reveals KAT2A as a therapeutic target.
BACKGROUND: Chemotherapy resistance remains a critical hurdle in advanced prostate cancer (PCa). Succinylation, an essential post-translational modification linking cellular metabolism with epigenetic regulation, has been implicated in tumour progression; however, its contribution to PCa chemoresistance remains poorly defined. OBJECTIVE: This study aimed to evaluate the prognostic significance of succinylation in PCa, develop a succinylation-based biomarker, and elucidate the mechanisms driving chemotherapy resistance. METHODS: We generated a succinylation score (SS) by applying single-sample gene set enrichment analysis (ssGSEA) to transcriptomic profiles from the TCGA-PRAD cohort. Its relationships with survival, the tumour microenvironment (TME), and treatment susceptibility were examined using CIBERSORT, GSEA, TIDE, oncoPredict, and single-cell RNA sequencing (scRNA-seq). Findings were functionally validated in patient-derived organoids, PCa cell lines, and xenograft models through genetic manipulation, chemosensitivity assays, and mechanistic studies. RESULTS: High SS correlated with favourable prognosis, lower Gleason scores, absent lymph node metastasis, and an immune-active TME enriched in CD8+ precursor exhausted T cells. High-SS tumours showed enhanced sensitivity to docetaxel and cisplatin. scRNA‑seq identified KAT2A as a key driver in low‑SS malignant clusters with chemoresistance features. KAT2A was elevated in chemoresistant tissues, cell lines, and organoids. KAT2A knockout sensitised cells to chemotherapy, while ectopic expression promoted resistance in vitro and in vivo. Mechanistically, KAT2A-mediated succinylation of PIK3R2 at K477 and K564 inhibited its ubiquitination and proteasomal degradation, stabilising PIK3R2 to drive chemoresistance. The KAT2A inhibitor Butyrolactone 3 synergised with standard chemotherapy to suppress tumour growth. CONCLUSION: The succinylation score serves as a robust prognostic biomarker integrating metabolic and immunological features in PCa. The KAT2A-PIK3R2 succinylation pathway represents a newly defined driver of chemoresistance and points to MB-3-based combination therapy as a potential strategy for resistant advanced disease. KEY POINTS: A transcriptome-based succinylation score (SS) stratifies prostate cancer by prognosis, immune contexture, and chemotherapy sensitivity. KAT2A is enriched in low-SS, chemoresistant tumours and promotes resistance by succinylating PIK3R2. KAT2A-mediated succinylation competitively inhibits PIK3R2 ubiquitination, stabilising PIK3R2 and driving chemoresistance. The KAT2A inhibitor MB-3 synergises with chemotherapy to suppress tumour growth in both chemoresistant and chemosensitive models.
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Hemocyte Nuclei Isolation from Adult Drosophila melanogaster for snRNA-seq.
In adult Drosophila, most of the hemocytes are macrophage-like cells (so called plasmatocytes), which serve various functions in organ homeostasis and immune defense. Ontogeny and functions are largely conserved between vertebrate and invertebrate macrophages. Hence, Drosophila offers a powerful genetic toolbox to study macrophage function and genetically modulate these cells. Technological advances in high-throughput sequencing approaches allowed to give an in-depth characterization of vertebrate macrophage populations and their heterogenous composition within different organs as well as changes in disease. Embryonic and larval hemocytes in Drosophila have been recently analyzed in single-cell RNA-sequencing (scRNA-seq) approaches during infection and steady state. These analyses revealed anatomical and functional Drosophila hemocyte subtypes dedicated to specific tasks. Only recently, the Fly Cell Atlas provided a whole transcriptomic single-cell atlas via single-nuclei RNA-sequencing (snRNA-seq) of adult Drosophila including many different tissues and cell types where hemocytes were also included. Yet, a specific protocol to isolate nuclei from adult hemocytes for snRNA-seq and study these cells in different experimental conditions was not available. In this chapter, we give a detailed protocol to purify hemocyte nuclei from adult Drosophila, which can be used in subsequent analyses such as snRNA-seq.
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AI-driven body composition atlas reveals its association with NSCLC immunotherapy outcome and molecular background: a multicenter study.
Although previous studies have linked body composition to immunotherapy efficacy, comprehensive multidimensional analyses with biological explanations remain lacking. This study integrated eight independent cohorts comprising 2,132 non-small cell lung cancer (NSCLC) patients, including five immune checkpoint inhibitor prognostic cohorts (n = 1,919), two bulk RNA-seq cohorts (n = 190), and one prospective single-cell RNA-seq cohort (n = 23). Using deep learning algorithms, we automatically extracted 92 body composition parameters from computed tomography images. The AI-based segmentation system demonstrated high consistency with manual measurements (intraclass correlation coefficient >0.87) with significantly improved efficiency. In male patients, higher intermuscular fat volume (IMFV) and 14 other indicators were independent predictors of overall survival; in female patients, T12 subcutaneous fat density and 6 other indicators showed potential associations with survival. Male patients with high IMFV exhibited significant upregulation of interferon-related pathways in CD8 + T cells and NK cells, along with lower exhaustion scores, while female patients with high T12 subcutaneous fat density showed macrophage polarization toward the M1 phenotype. This study underscores the importance of multidimensional body composition in NSCLC patient management, demonstrating that specific parameters are not only closely related to survival outcomes but also exhibit unique gender differences and location variations, providing new insights for optimizing immunotherapy strategies.
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PolyASite v3.0: a multi-species atlas of polyadenylation sites inferred from single-cell RNA-sequencing data.
The broadly used 10X Genomics technology for single-cell RNA sequencing (scRNA-seq) captures RNA 3' ends. Thus, some reads contain part of the non-templated polyadenosine tails, providing direct evidence for the sites of 3' end cleavage and polyadenylation on the respective RNAs. Taking advantage of this property, we recently developed the SCINPAS workflow to infer polyadenylation sites (PASs) from scRNA-seq data. Here, we used this workflow to construct version 3.0 (v3.0, https://polyasite.unibas.ch/) of the PolyASite Atlas from a big compendium of publicly available human, mouse and worm scRNA-seq datasets obtained from healthy tissues. As the resolution of scRNA-seq was too low for robust detection of cell-level differences in PAS usage, we aggregated samples based on their tissue-of-origin to construct tissue-level catalogs of PASs. These provide qualitatively new information about PAS usage, in comparison to the previous PAS catalogs that were based on bulk 3' end sequencing experiments primarily in cell lines. In the new version, we document stringency levels associated with each PAS so that users can balance sensitivity and specificity in their analysis. We also upgraded the integration with the UCSC Genome Browser and developed track hubs conveniently displaying pooled and tissue-specific expression of PASs.