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Senescent fibroblasts drive CD8+ T cell dysfunction in colorectal cancer via CD36-mediated lipid transfer and peroxidation.
BACKGROUND: Functional exhaustion of tumor-infiltrating CD8+ T cells represents a hallmark of colorectal cancer (CRC) immunosuppression, though its mechanistic drivers remain elusive. Given the established correlation between CRC progression and stromal senescence characterized by pathological lipid accumulation and impaired immunity, we investigated whether and how senescent fibroblasts actively regulate CD8+ T cell dysfunction. METHODS: Single-cell RNA sequencing (scRNA-seq) analysis was conducted to unveil the diverse fibroblast populations and the significant lipid metabolism changes between senescent fibroblasts and non-senescent fibroblasts in human CRC specimens and adjacent normal mucosa. Machine-learning identified senescent fibroblasts with a distinct gene signature. Cell-cell communication analysis was used to evaluate the interactions between senescent fibroblasts and CD8+ T cells in colorectal cancer. Co-culture experiments were conducted among senescent fibroblasts, CD8+ T cells and patient-derived organoids of CRC (CRC-PDOs), with the results evaluated with high-content imaging and propidium iodide/Hoechst 33,342 staining. Flow cytometry, ELISA and lipid pulse-chase with BODIPY FL C16 were performed to detect the alterations of CD8+ T cell cytotoxic function and metabolic status. AOM/DSS-induced CRC mouse model was used to conduct in vivo validation to evaluate whether senolytics could suppress CRC progression. Patients from the Cancer Genome Atlas colorectal cancer cohort were stratified into CD36-high and CD36-low groups by median expression, and drug sensitivity for GDSC2 compounds was predicted computationally using the oncoPredict R package. RESULTS: ScRNA-seq demonstrated the specific cell population presence and divergence of senescent fibroblasts between neoplastic and histologically normal adjacent cell clusters in CRC. Random Forest was employed for cell senescence classification. Feature importance analysis identified five genes as key contributors to the model’s decision process. Cell-cell communication analysis revealed enhanced interactions between senescent fibroblasts and CD8+ T cells in CRC. Co-culture of senescent fibroblasts significantly impaired the cytotoxic functions of CD8+ T cells on CRC-PDOs, which was reflected by the declined proportions of granzyme B (GZMB) + and interferon gamma (IFNγ) + CD8+ T cells and enhanced viability of CRC-PDOs. Mechanistically, the co-culture with senescent fibroblasts promoted the lipid shuttling into CD8+ T cells to induce lipid peroxidation and downstream impairment of cytotoxicity. Furthermore, the inhibition of CD36, the specific scavenger receptor for lipid uptake of CD8+ T cells, effectively suppressed lipid transfer and peroxidation thereby preserving the effector functions of CD8+ T cells and ultimately promoting tumor apoptosis. Complementarily, in vivo senolytic treatment significantly suppressed CRC progression in AOM-DSS CRC mouse models. Top 12 therapeutic agents were identified significantly enhanced predicted efficacy in CD36-high tumors. CONCLUSIONS: Our study identified a substantial population of senescent fibroblasts in human CRC through single cell transcriptomics, machine-learning and clinical biopsies. These senescent fibroblasts impair CD8+ T cell-mediated killing of CRC-PDOs via CD36-dependent lipid transfer, suggesting senolytic targeting of stromal cells as a promising immunotherapeutic strategy for CRC.
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Uncovering the Key Circuit FOSL2/FOS/EGR3/EGR1, Contributing to the Hyperexcitability of Excitatory Neurons in the Epileptic Temporal Cortex and Hippocampus.
Epilepsy is mainly characterized by spontaneous seizures caused by hyperactive neural circuits. To delineate the cell-type-specific mechanisms underlying neuronal hyperexcitability, we resolve the hyperexcitability of excitatory neurons across epileptic human brain trans-foci at single-cell resolution to identify the key drivers and potential diagnostic signatures. We constructed a comprehensive atlas encompassing 240,000 cells derived from the temporal cortex and hippocampus, detecting trans-regional cellular and molecular diversity. We further delineated dynamic trajectories, gene expression patterns, and functional reorganization across cell types. Using the LASSO and random forest algorithms, we prioritized the core genes and developed a logistic regression-based diagnostic model. Despite transregional cellular landscape conservation, major cell types varied in abundance. Detailed analysis delineated various excitatory neuron subtypes' dynamic trajectories, intricate expression, and functional reorganization, with pronounced dysfunction in the posterior hippocampal and temporal cortex networks, indicating hyperactive pro-epileptic effects. Excitatory neurons exhibit an intrinsic ability to autonomously organize themselves into distinct, highly active modules, characterized by a high activation state during epileptogenesis, as illustrated by ten epilepsy-associated functions. Transcription circuits FOSL2/FOS/EGR3/EGR1 promote neuronal hyperexcitability. Integrating epilepsy bulk RNA-seq data, we identified 24 overlapping genes between differential genes and circuit targets. The LASSO and random forest algorithms prioritized three core genes (IL1B, SOCS6, and COL4A1). A logistic regression model based on these three genes showed variable performance, with an apparent AUC of 1.000 in the discovery cohort (GSE256068) and AUCs of 0.974 and 0.722 in and two validation cohorts, indicating the need for further validation. Our study establishes the FOSL2/FOS/EGR3/EGR1 circuit as a master regulator of pathological neuronal hyperactivity across epileptic foci, linking transcriptional activation to network dysfunction. Identifying overactive factors may represent a candidate molecular pathway for future therapeutic exploration against hyperexcitability.
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ScType enables fast and accurate cell type identification from spatial transcriptomics data.
SUMMARY: The limited resolution of spatial transcriptomics (ST) assays in the past has led to the development of cell type annotation methods that separate the convolved signal based on available external atlas data. In light of the rapidly increasing resolution of the ST assay technologies, we made available and investigated the performance of a deconvolution-free marker-based cell annotation method called scType. In contrast to existing methods, the spatial application of scType does not require computationally strenuous deconvolution, nor large single-cell reference atlases. We show that scType enables ultra-fast and accurate identification of abundant cell types from ST data, especially when a large enough panel of genes is detected. Examples of such assays are Visium and Slide-seq, which currently offer the best trade-off between high resolution and number of genes detected by the assay for cell type annotation. AVAILABILITY AND IMPLEMENTATION: scType source R and python codes for spatial data are openly available in GitHub (https://github.com/kris-nader/sp-type or https://github.com/kris-nader/sc-type-py). Step-by-step tutorials for R and python spatial data analysis can be found in https://github.com/kris-nader/sp-type and https://github.com/kris-nader/sc-type-py/blob/main/spatial_tutorial.md, respectively.
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A lamprey neural cell type atlas illuminates the origins of the vertebrate brain.
The vertebrate brain emerged more than ~500 million years ago in common evolutionary ancestors. To systematically trace its cellular and molecular origins, we established a spatially resolved cell type atlas of the entire brain of the sea lamprey-a jawless species whose phylogenetic position affords the reconstruction of ancestral vertebrate traits-based on extensive single-cell RNA-seq and in situ sequencing data. Comparisons of this atlas to neural data from the mouse and other jawed vertebrates unveiled various shared features that enabled the reconstruction of cell types, tissue structures and gene expression programs of the ancestral vertebrate brain. However, our analyses also revealed key tissues and cell types that arose later in evolution. For example, the ancestral brain was probably devoid of cerebellar cell types and oligodendrocytes (myelinating cells); our data suggest that the latter emerged from astrocyte-like evolutionary precursors in the jawed vertebrate lineage. Altogether, our work illuminates the cellular and molecular architecture of the ancestral vertebrate brain and provides a foundation for exploring its diversification during evolution.
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Generating and Using Transcriptomically Based Retinal Cell Atlases.
It has been known for over a century that the basic organization of the retina is conserved across vertebrates. It has been equally clear that retinal cells can be classified into numerous types, but only recently have methods been devised to explore this diversity in unbiased, scalable, and comprehensive ways. Advances in high-throughput single-cell RNA sequencing (scRNA-seq) have played a pivotal role in this effort. In this article, we outline the experimental and computational components of scRNA-seq and review studies that have used them to generate retinal atlases of cell types in several vertebrate species. These atlases have enabled studies of retinal development, responses of retinal cells to injury, expression patterns of genes implicated in retinal disease, and the evolution of cell types. Recently, the inquiry has expanded to include the entire eye and visual centers in the brain. These studies have enhanced our understanding of retinal function and dysfunction and provided tools and insights for exploring neural diversity throughout the brain.
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Cell type-specific analysis by single-cell profiling identifies a stable mammalian tRNA-mRNA interface and increased translation efficiency in neurons.
The correlation between codon and anticodon pools influences the efficiency of translation, but whether differences exist in these pools across individual cells is unknown. We determined that codon usage and amino acid demand are highly stable across different cell types using available mouse and human single-cell RNA-sequencing atlases. After showing the robustness of ATAC-sequencing measurements for the analysis of tRNA gene usage, we quantified anticodon usage and amino acid supply in both mouse and human single-cell ATAC-seq atlases. We found that tRNA gene usage is overall coordinated across cell types, except in neurons, which clustered separately from other cell types. Integration of these data sets revealed a strong and statistically significant correlation between amino acid supply and demand across almost all cell types. Neurons have an enhanced translation efficiency over other cell types, driven by an increased supply of tRNAAla (AGC) anticodons. This results in faster decoding of the Ala-GCC codon, as determined by cell type-specific ribosome profiling, suggesting that the reduction of tRNAAla (AGC) anticodon pools may be implicated in neurological pathologies. This study, the first such examination of codon usage, anticodon usage, and translation efficiency resolved at the cell-type level with single-cell information, identifies a conserved landscape of translation elongation across mammalian cellular diversity and evolution.
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DIANA-miRPath v4.0: expanding target-based miRNA functional analysis in cell-type and tissue contexts.
DIANA-miRPath is an online miRNA analysis platform harnessing predicted or experimentally supported miRNA interactions towards the exploration of combined miRNA effects. In its latest version (v4.0, http://www.microrna.gr/miRPathv4), DIANA-miRPath breaks new ground by introducing the capacity to tailor its target-based miRNA functional analysis engine to specific biological and/or experimental contexts. Via a redesigned modular interface with rich interaction, annotation and parameterization options, users can now perform enrichment analysis on Gene Ontology (GO) terms, KEGG and REACTOME pathways, sets from Molecular Signatures Database (MSigDB) and PFAM. Included miRNA interaction sets are derived from state-of-the-art resources of experimentally supported (DIANA-TarBase v8.0, miRTarBase and microCLIP cell-type-specific interactions) or from in silico miRNA-target interactions (updated DIANA-microT-CDS and TargetScan predictions). Bulk and single-cell expression datasets from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx) and adult/fetal single-cell atlases are integrated and can be used to assess the expression of enriched term components across a wide range of states. A discrete module enabling enrichment analyses using CRISPR knock-out screen datasets enables the detection of selected miRNAs with potentially crucial roles within conditions under study. Notably, the option to upload custom interaction, term, expression and screen sets further expands the versatility of miRPath webserver.
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Molecular signals associated with intraperitoneal chemotherapy resistance in gastric cancer with peritoneal metastasis through PIPS GC trial integrated translational research.
Purpose Recent studies have investigated intraperitoneal paclitaxel (IP PTX) combined with systemic chemotherapy for the treatment of gastric cancer peritoneal metastases (GCPM). This clinical trial integrated translational study was conducted to identify biomarkers that can predict patient responses to IP PTX plus systemic systemic S-1 plus oxaliplatin (SOX) chemotherapy. Materials and methods Patients from the PIPS-GC phase Ib/II clinical trial, enrolled at the Korea University Guro Hospital, were included in analyses. Whole exome and transcriptome sequencing were performed on formalin-fixed, paraffin-embedded gastric tumor, normal gastric, and peritoneal tumor tissues. A metastatic tumor-specific gene set was analyzed using The Cancer Genome Atlas (TCGA) gastric cancer data and publicly available single-cell RNA sequencing (scRNA-seq) datasets. Spatial transcriptomic analysis of primary and peritoneal tumors from a non-responder was performed to validate candidate biomarkers. Results Nine patients with gastric cancer were enrolled; six in the response group and three in the non-response group. Candidate genes for predicting IP PTX plus systemic SOX chemotherapy response were identified and compared with TCGA-GC and scRNA-seq datasets. Spatial transcriptomics revealed higher expression of thrombospondin type 1 domain-containing protein 4 (THSD4) in peritoneal tumors, which was associated with chemotherapy resistance via midkine and epithelial-mesenchymal transition pathways. Conclusion This PIPS-GC clinical trial identified THSD4 as a potential biomarker for predicting IP PTX plus systemic SOX response in gastric cancer peritoneal metastasis. Further research is required to elucidate the mechanism of action by which THSD4 affects GCPM and validate its clinical utility as a predictive biomarker for IP PTX response.
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Multi-omics identification of an antibody dependent cellular phagocytosis related signature for hepatocellular carcinoma.
BACKGROUND: The reduced phagocytic activity of macrophages in the tumor microenvironment (TME) and the presence of anti-phagocytic factors in cancer cells hinder the effectiveness of immune checkpoint inhibitors (ICIs) in clinical settings. Consequently, our objective was to create a risk assessment model using antibody-dependent cellular phagocytosis (ADCP) regulators to serve as prognostic indicators and immunotherapy markers for individuals with hepatocellular carcinoma (HCC). METHODS: This research incorporated The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) group, the GSE14520 group, the International Cancer Genome Consortium (ICGC) group, and the single-cell dataset GSE166635. Machine learning was used to discover ADCP genes closely linked to prognosis, helping to determine the molecular subtypes of ADCP in HCC samples. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression algorithms were used to develop the ADCP-related signature (ADCPRS). This signature was evaluated using both training and external validation datasets. Furthermore, a nomogram that integrates ADCPRS was created to function as a quantitative instrument for predicting outcomes in clinical settings. A comprehensive multi-omics investigation was performed, integrating genomic, single-cell transcriptomic, and bulk transcriptomic analyses, with the aim of gaining a more profound understanding of the prognostic signature. We assessed how different risk categories respond to immunotherapy and identified drugs tailored to target these specific groups for customized treatment. RESULTS: We identified two ADCP subgroups by analyzing 91 genes linked to HCC prognosis. The two ADCP subgroups showed distinct outcomes, pathway activation, immune checkpoint gene expression levels, and immune cell infiltration within the TME. We developed a unified ADCPRS that excels in forecasting clinicopathological characteristics, patient outcomes, and TME stromal activity in HCC. The nomogram incorporating ADCPRS offered a numerical tool for use in medical practice. Notably, the high-risk group presented inflamed TME with an improved response to ICI. In addition, the high-ADCPRS group presented a high sensitivity to the derivative compounds provided by Cancer Therapeutics Response Portal (CTRP) and Profiling Relative Inhibition Simultaneously in Mixtures (PRISM). CONCLUSIONS: Our research developed a signature linked to ADCP, which shows promise as a key tool for predicting outcomes, implementing prevention, and enabling personalized treatment in HCC.
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Spatial single-cell atlas reveals regional variations in healthy and diseased human lung.
Integration of scRNA-seq data from millions of cells revealed a high diversity of cell types in the healthy and diseased human lung. In a large and complex organ, constantly exposed to external agents, it is crucial to understand the influence of lung tissue topography or external factors on gene expression variability within cell types. Here, we apply three spatial transcriptomics approaches, to: (i) localize the majority of lung cell types, including rare epithelial cells within the tissue topography, (ii) describe consistent anatomical and regional gene expression variability within and across cell types, and (iii) reveal distinct cellular neighborhoods in specific anatomical regions and examine gene expression variations in them. We thus provide a spatially resolved tissue reference atlas in three representative regions of the healthy human lung. We further demonstrate its utility by defining previously unknown imbalances of epithelial cell type compositions in chronic obstructive pulmonary disease lungs. Our topographic atlas enables a precise description of characteristic regional cellular responses upon experimental perturbations or during disease progression.