Single-cell Transcriptomic Datasets Research Articles

M5C related-regulator-mediated methylation modification patterns and prognostic significance in breast cancer

5-Methylcytosine (m5C) is closely associated with cancer. However, the role of m5C in breast cancer(BC)remains unclear. This study combined single-cell RNA sequencing (scRNA-Seq) and transcriptomics datasets to screen m5C regulators associated with BC progression and analyze their clinical values. Firstly, This study elucidates the mechanisms of the m5C landscape and the specific roles of m5C regulators in BC patients. we found that the dysregulation of m5C regulators with m5Cscore play the essential role of the carcinogenesis and progression in epithelial cells and myeloid cells of BC at single cell level. External validation was conducted using an independent scRNA-Seq datasets. Then, three distinct m5C modification patterns were identified by transcriptomics datasets. Based on the m5C differentially expressed regulators, the m5Cscore was constructed, and used to divide patients with BC into high and low m5Cscore groups. Patients with a high m5Cscore had more abundant immune cell infiltration, stronger antitumor immunity, and better prognoses. Finally, Quantitative real-time (PCR) and immunohistochemistry were used for the in vitro experimental validation, which had extensive prognostic value. In this study, we aimed to assess the expression of m5C regulators involved in BC and investigate their correlation with the tumor microenvironment, clinicopathological characteristics, and prognosis of BC. The m5C regulators could be used to effectively assess the cell specific regulation prognosis of patients with BC and develop more effective immunotherapy strategies.

Single cell analysis identified a basal cell transition state associated with the development and progression of bladder cancer

BackgroundBladder cancer (BC) is a prevalent malignancy characterized by significant cellular heterogeneity. While single-cell multi-omics studies have provided valuable insights, much of the existing data remains underexplored, limiting our understanding of BC’s molecular mechanisms. Uncovering the pathogenesis of BC and finding new treatment methods are urgent problems to be solved. This study aims to address this gap by re-analyzing available single-cell datasets to uncover novel insights into BC.MethodsIn this study, we retrieved three single-cell transcriptome datasets by searching the Gene Expression Omnibus (GEO) database, focusing on single-cell sequencing of normal mouse bladder within the past 5 years. Through quality control and batch effect elimination, we obtained a total of 24,930 cells including epithelial, stromal, and immune cells. Subgroup analysis, pseudotemporal analysis, cell–cell communication, and transcription factor analysis were conducted specifically on epithelial cells to identify a transitional state during basal cell differentiation. We further compared the expression profiles of key transcription factors in cancer and normal tissues. In addition, we also performed immunohistochemical staining and survival analysis for key transcription factors.ResultsSubgroup analysis revealed multiple subtypes of epithelial cells, including basal, umbrella, and intermediate cells. Through pseudotemporal analysis, we discovered the developmental trajectory from basal cells to umbrella cells and further found that Basal_I is a transitional state for basal cell differentiation. Cell-to-cell communication analyses highlighted the pivotal role of Basal_I in cell–cell interactions, and key ligand-receptor pairs associated with cancer progression were also identified. Furthermore, elevated expression levels of key transcription factors in Basal_I were found to be closely associated with the stage and prognosis of BC. Immunohistochemical staining results further confirmed the upregulated expression of these transcription factors in BC.ConclusionsCollectively, we found a transitional state of basal cells in normal bladder epithelial cells in mice, which may be related to the occurrence and development of BC, providing important clues for further understanding of the pathogenesis of BC. Our study provided possible molecular mechanisms or target for the research and treatment of BC.

Five-gene prognostic model based on autophagy-dependent cell death for predicting prognosis in lung adenocarcinoma

Non-small cell lung adenocarcinoma (LUAD) is the predominant form of lung cancer originating from lung epithelial cells, making it the most prevalent pathological type. Currently, reliable indicators for predicting treatment efficacy and disease prognosis are lacking. Despite extensive validation of autophagy-dependent cell death (ADCD) in solid tumor studies and its correlation with immunotherapy effectiveness and cancer prognosis, systematic research on ADCD-related genes in LUAD is limited. We utilized AddModuleScore, ssGSEA, and WGCNA to identify genes associated with ADCD across single-cell and bulk transcriptome datasets. The TCGA dataset, comprising 598 cases, was randomly divided into training and validation sets to develop an ADCD-related LUAD prediction model. Internal validation was performed using the TCGA validation set. For external validation, datasets GSE13213 (119 LUAD samples), GSE26939 (115 LUAD samples), GSE29016 (39 LUAD samples), and GSE30219 (86 LUAD samples) were employed. We evaluated the model’s accuracy and effectiveness in predicting prognostic risk. Additionally, CIBERSORT, ESTIMATE, and ssGSEA techniques were used to explore immunological characteristics, drug response, and gene expression in LUAD. Real-time RT-PCR was conducted to assess variations in mRNA expression levels of the gene XCR1 between cancerous and normal tissues in 10 lung cancer patients. We identified 249 genes associated with autophagy-dependent cell death (ADCD) at both single-cell and bulk transcriptome levels. Univariate COX regression analysis revealed that 18 genes were significantly associated with overall survival (OS). Using LASSO-Cox analysis, we developed an ADCD signature based on five genes (BIRC3, TAP1, SLAMF1, XCR1, and HLA-DMB) and created the ADCD-related risk scoring system (ADCDRS). Validation of this model demonstrated its ability to predict disease prognosis and its correlation with clinical characteristics, immune cell infiltration, and the tumor microenvironment. To enhance clinical applicability, we integrated an ADCDRS nomogram. Furthermore, we identified potential drugs targeting specific risk subgroups. We successfully identified a model based on five ADCD genes to predict disease prognosis and treatment efficacy in LUAD, as well as to assess the tumor immune microenvironment. An efficient and practical ADCDRS nomogram was designed.

Single-cell and bulk transcriptomic datasets enable the development of prognostic models based on dynamic changes in the tumor immune microenvironment in patients with hepatocellular carcinoma and portal vein tumor thrombus.

Hepatocellular carcinoma (HCC) patients exhibiting portal vein tumor thrombosis (PVTT) face a high risk of rapid malignant progression and poor outcomes, with this issue being compounded by a lack of effective treatment options. The integration of bulk RNA-sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) datasets focused on samples from HCC patients with PVTT has the potential to yield unprecedented insight into the dynamic changes in the tumor microenvironment (TME) and associated immunological characteristics in these patients, providing an invaluable tool for the reliable prediction of disease progression and treatment responses. scRNA-seq data from both primary tumor (PT) and PVTT cells were downloaded from the Gene Expression Omnibus (GEO) database, while the International Cancer Genome Consortium (ICGC) and Cancer Genome Atlas (TCGA) databases were used to access bulk RNA-seq datasets. scRNA-seq, clustering, GSVA enrichment, mutational profiling, and predictive immunotherapeutic treatment analyses were conducted using these data with the goal of systematically assessing the heterogeneity of PT and PVTT cells and establishing a model capable of predicting immunotherapeutic and prognostic outcomes in patients with HCC. These analyses revealed that PVTT cells exhibited patterns of tumor proliferation, stromal activation, and low levels of immune cell infiltration, presenting with immune desert and immune rejection-like phenotypes. PT cells, in contrast, were found to exhibit a pattern of immunoinflammatory activity. Core PVTT-associated genes were clustered into three patterns consistent with the tumor immune rejection and immune desert phenotypes. An established clustering model was capable of predicting tumor inflammatory stage, subtype, TME stromal activity, and patient outcomes. PVTT signature genes were further used to establish a risk model, with the risk scores derived from this model providing a tool to evaluate patient clinicopathological features including clinical stage, tumor differentiation, histological subtype, microsatellite instability status, and tumor mutational burden. These risk scores were also able to serve as an independent predictor of patient survival outcomes, responses to adjuvant chemotherapy, and responses to immunotherapy. In vitro experiments were used to partially validate the biological prediction results. These results offer new insight into the biological and immunological landscape of PVTT in HCC patients, By utilizing individual patient risk scores, providing an opportunity to guide more effective immunotherapeutic interventional efforts.

SCG2 Mediates HNSCC Progression With CCL2/TGFβ1high M2 Macrophage Infiltration.

This study aims to unravel the mechanisms underlying M2 macrophage polarization in head and neck squamous cell carcinoma (HNSCC), and identify potential therapeutic targets. We conducted an integrated bioinformatic analysis using HNSCC bulk transcriptomes from TCGA and GEO databases to pinpoint critical factors influencing M2 macrophage polarization and tumor prognosis. The significance of these genes was validated in function analysis, single-cell transcriptome datasets, and invitro experiments. Their mechanisms in modulating M2 macrophage polarization were further explored by gene knockdown, cell coculture, and other assays for quantification. We identified a novel prognostic signature of five genes associated with M2 macrophage infiltration, in which SCG2 emerged as a pivotal factor in M2 macrophage polarization in HNSCC. High expression of SCG2 in tumor patients correlated with poorer prognoses, and knocking down SCG2 reduced the proliferation and migration of HNSCC cells, disrupting M2 macrophage polarization. Furthermore, interference of SCG2 resulted in a significant decrease in the secretion of pro-tumor cytokines such as CCL2 and TGFβ1. Our findings provide deeper insights into the pathogenesis of HNSCC and offer promising therapeutic targets for HNSCC, especially SCG2, to inhibit M2 macrophage polarization and modulate cytokine secretion.

Decreased miR-486-5p is involved in LPS-induced HTR-8/SVneo cell dysfunction by promoting Smad2 expression.

Placenta-associated pathologies, including early pregnancy loss (EPL) and preeclampsia (PE), share a common phenomenon of insufficient extravillous trophoblasts (EVTs) invasion. It was previously observed that down-regulated miR-486-5p expression inhibited the invasion of EVTs, and a decreased peripheral miR-486-5p was associated with EPL. However, the exact roles of miR-486-5p played in pathogenesis of EPL, as well as the molecular pathway underlying roles of miR-486-5p in EVTs invasion, remains poorly understood. In this study, a decreased miR-486-5p expression in uterine embryo implantation site at gestation day (GD) 8.5, and an increased expression of Smad2, a target of miR-486-5p, were observed in the lipopolysaccharide (LPS)-induced EPL mouse model. The invasion and viability of immortalized human EVTs line, HTR-8/SVneo, were inhibited by LPS, accompanied with a reduced miR-486-5p expression. LPS showed a promoting effect on the Smad2 expression, of which could be attenuated by miR-486-5p mimics. And the down-regulated Smad2 could effectively restore the impaired invasion and viability of HTR-8/SVneo cells caused by LPS or miR-486-5p inhibitor. Furthermore, LPS could promote the TNFα production in HTR-8/SVneo cells, whereas both of siSmad and miR-486-5p mimics could reverse such an effect. By analyzing the human decidua single-cell RNA sequencing and transcriptome datasets derived from the Gene Expression Omnibus, it was found that, compared to control early pregnant women, the Smad2 expression was significantly increased in recurrent miscarriages (RM) patients. Collectively, these data suggested that, decreased miR-486-5p expression might lead to EPL at least partially by inhibiting invasion and/or promoting TNFα production of EVTs via targeting Smad2.

Multiscale mapping of transcriptomic signatures for cardiotoxic drugs

Drug-induced gene expression profiles can identify potential mechanisms of toxicity. We focus on obtaining signatures for cardiotoxicity of FDA-approved tyrosine kinase inhibitors (TKIs) in human induced-pluripotent-stem-cell-derived cardiomyocytes, using bulk transcriptomic profiles. We use singular value decomposition to identify drug-selective patterns across cell lines obtained from multiple healthy human subjects. Cellular pathways affected by cardiotoxic TKIs include energy metabolism, contractile, and extracellular matrix dynamics. Projecting these pathways to published single cell expression profiles indicates that TKI responses can be evoked in both cardiomyocytes and fibroblasts. Integration of transcriptomic outlier analysis with whole genomic sequencing of our six cell lines enables us to correctly reidentify a genomic variant causally linked to anthracycline-induced cardiotoxicity and predict genomic variants potentially associated with TKI-induced cardiotoxicity. We conclude that mRNA expression profiles when integrated with publicly available genomic, pathway, and single cell transcriptomic datasets, provide multiscale signatures for cardiotoxicity that could be used for drug development and patient stratification.

A single-cell transcriptomic dataset of pluripotent stem cell-derived astrocytes via NFIB/SOX9 overexpression

Astrocytes, the predominant glial cells in the central nervous system, play essential roles in maintaining brain function. Reprogramming induced pluripotent stem cells (iPSCs) to become astrocytes through overexpression of the transcription factors, NFIB and SOX9, is a rapid and efficient approach for studying human neurological diseases and identifying therapeutic targets. However, the precise differentiation path and molecular signatures of induced astrocytes remain incompletely understood. Accordingly, we performed single-cell RNA sequencing analysis on 64,736 cells to establish a comprehensive atlas of NFIB/SOX9-directed astrocyte differentiation from human iPSCs. Our dataset provides detailed information about the path of astrocyte differentiation, highlighting the stepwise molecular changes that occur throughout the differentiation process. This dataset serves as a valuable reference for dissecting uncharacterized transcriptomic features of NFIB/SOX9-induced astrocytes and investigating lineage progression during astrocyte differentiation. Moreover, these findings pave the way for future studies on neurological diseases using the NFIB/SOX9-induced astrocyte model.

Dissection of intratumor microbiome–host interactions at single-cell level in lung cancer

The intratumor microbiome, one of the hallmarks of cancer, plays a crucial role in cancer progression through interaction with the host. However, the underlying mechanisms remain poorly understood. Herein, we investigated the heterogeneity of host–microbiome interactions at the single-cell level by integrating six single-cell transcriptomic lung cancer datasets using single-cell analysis of host–microbiome interactions (SAHMI). Our findings indicate that primary tumor tissues display a high proportion of fungi-associated cells, whereas metastatic brain tissues predominantly feature bacteria-associated cells. A distinct distribution of fungal and bacterial taxa across various cell types was observed. Notably, the presence of specific bacteria significantly influences the transcriptome of resident host cells, including T cells and macrophages, by modulating pathways related to ribosomal RNA (rRNA) processing, cellular responses to stress and stimuli, and the metabolism of RNA and proteins. Finally, specific cell-associated bacteria are significantly correlated with clinical features, such as lung cancer stages and smoking frequency. These single-cell insights into microbiome–host interactions facilitate our understanding of lung cancer development and progression, offering potential micro-ecological and diagnostic insights.

Synaptic connectome of a neurosecretory network in the Drosophila brain.

Hormones mediate inter-organ signaling which is crucial in orchestrating diverse behaviors and physiological processes including sleep and activity, feeding, growth, metabolism and reproduction. The pars intercerebralis and pars lateralis in insects represent major hubs which contain neurosecretory cells (NSC) that produce various hormones. To obtain insight into how hormonal signaling is regulated, we have characterized the synaptic connectome of NSC in the adult Drosophila brain. Identification of neurons providing inputs to multiple NSC subtypes implicates diuretic hormone 44-expressing NSC as a major coordinator of physiology and behavior. Surprisingly, despite most NSC having dendrites in the subesophageal zone (primary taste processing center), gustatory inputs to NSC are largely indirect. We also deciphered pathways via which diverse olfactory inputs are relayed to NSC. Further, our analyses revealed substantial inputs from descending neurons to NSC, suggesting that descending neurons regulate both endocrine and motor output to synchronize physiological changes with appropriate behaviors. In contrast to NSC inputs, synaptic output from NSC is sparse and mostly mediated by corazonin NSC. Therefore, we additionally determine putative paracrine interconnectivity between NSC subtypes and hormonal pathways from NSC to peripheral tissues by analyzing single-cell transcriptomic datasets. Our comprehensive characterization of the Drosophila neurosecretory network connectome provides a platform to understand complex hormonal networks and how they orchestrate animal behaviors and physiology.

Comprehensive pan-cancer analysis reveals EPHB2 is a novel predictive biomarker for prognosis and immunotherapy response

PurposeRecent studies have increasingly linked Ephrin receptor B2 (EPHB2) to cancer progression. However, comprehensive investigations into the immunological roles and prognostic significance of EPHB2 across various cancers remain lacking.MethodsWe employed various databases and bioinformatics tools to investigate the impact of EPHB2 on prognosis, immune infiltration, genome instability, and response to immunotherapy. Validation of the correlation between EPHB2 expression and M2 macrophages included analyses using bulk and single-cell transcriptomic datasets, spatial transcriptomics, and multi-fluorescence staining. Moreover, we performed cMap web tool to screen for EPHB2-targeted compounds and assessed their potential through molecular docking and dynamics simulations. Additionally, in vitro validation using lung adenocarcinoma (LUAD) cell lines was conducted to confirm the bioinformatics predictions about EPHB2.ResultsEPHB2 dysregulation was observed across multiple cancer types, where it demonstrated significant diagnostic and prognostic value. Gene Set Enrichment Analysis (GSEA) indicated that EPHB2 is involved in enhancing cellular proliferation, invasiveness of cancer cells, and modulation of the anti-cancer immune response. Furthermore, it is emerged as a pan-cancer marker for M2 macrophage infiltration, supported by integrated analyses of transcriptomics and multiple fluorescence staining. In LUAD cells, knockdown of EPHB2 expression led to a decrease in both cell proliferation and migratory activity.ConclusionEPHB2 expression may serve as a pivotal indicator of M2 macrophage infiltration, offering vital insights into tumor dynamics and progression across various cancers, including lung adenocarcinoma, highlighting its significant prognostic and therapeutic potential for further exploration.

An integrated single-cell reference atlas of the human endometrium

The complex and dynamic cellular composition of the human endometrium remains poorly understood. Previous endometrial single-cell atlases profiled few donors and lacked consensus in defining cell types. We introduce the Human Endometrial Cell Atlas (HECA), a high-resolution single-cell reference atlas (313,527 cells) combining published and new endometrial single-cell transcriptomics datasets of 63 women with and without endometriosis. HECA assigns consensus and identifies previously unreported cell types, mapped in situ using spatial transcriptomics and validated using a new independent single-nuclei dataset (312,246 nuclei, 63 donors). In the functionalis, we identify intricate stromal–epithelial cell coordination via transforming growth factor beta (TGFβ) signaling. In the basalis, we define signaling between fibroblasts and an epithelial population expressing progenitor markers. Integration of HECA with large-scale endometriosis genome-wide association study data pinpoints decidualized stromal cells and macrophages as most likely dysregulated in endometriosis. The HECA is a valuable resource for studying endometrial physiology and disorders, and for guiding microphysiological in vitro systems development.

UBAP2 contributes to radioresistance by enhancing homologous recombination through SLC27A5 ubiquitination in hepatocellular carcinoma

Radiotherapy stands as an effective method in the clinical treatment of hepatocellular carcinoma (HCC) patients. However, both primary and acquired radioresistance limit its clinical application in HCC. Therefore, investigating the mechanism of radioresistance may provide other options for treating HCC. Based on single-cell RNA sequencing (scRNA-seq) and HCC transcriptome datasets, 227 feature genes with prognostic value were selected to establish the tSNE score. The tSNE score emerged as an independent prognostic factor for HCC and correlated with cell proliferation and radioresistance-related biological functions. UBAP2 was identified as the most relevant gene with the tSNE score, consistently elevated in human HCC samples, and positively associated with patient prognosis. Functionally, UBAP2 knockdown impeded HCC development and reduced radiation resistance in vitro and in vivo. The ectopic expression of SLC27A5 reversed the effects of UBAP2. Mechanically, we uncovered that UBAP2, through the ubiquitin-proteasome system, decreased the homologous recombination-related gene RAD51, not the non-homologous end-joining (NHEJ)-related gene CTIP, by degrading the antioncogene SLC27A5, thereby generating radioresistance in HCC. The findings recapitulated that UBAP2 promoted HCC progression and radioresistance via SLC27A5 stability mediated by the ubiquitin-proteasome pathway. It was also suggested that targeting the UBAP2/SLC27A5 axis could be a valuable radiosensitization strategy in HCC.

G0 arrest gene patterns to predict the prognosis and drug sensitivity of patients with lung adenocarcinoma.

G0 arrest (G0A) is widely recognized as a crucial factor contributing to tumor relapse. The role of genes related to G0A in lung adenocarcinoma (LUAD) was unclear. This study aimed to develop a gene signature based on for LUAD patients and investigate its relationship with prognosis, tumor immune microenvironment, and therapeutic response in LUAD. We use the TCGA-LUAD database as the discovery cohort, focusing specifically on genes associated with the G0A pathway. We used various statistical methods, including Cox and lasso regression, to develop the model. We validated the model using bulk transcriptome and single-cell transcriptome datasets (GSE50081, GSE72094, GSE127465, GSE131907 and EMTAB6149). We used GSEA enrichment and the CIBERSORT algorithm to gain insight into the annotation of the signaling pathway and the characterization of the tumor microenvironment. We evaluated the response to immunotherapy, chemotherapy, and targeted therapy in these patients. The expression of six genes was validated in cell lines by quantitative real-time PCR (qRT-PCR). Our study successfully established a six-gene signature (CHCHD4, DUT, LARP1, PTTG1IP, RBM14, and WBP11) that demonstrated significant predictive power for overall survival in patients with LUAD. It demonstrated independent prognostic value in LUAD. To enhance clinical applicability, we developed a nomogram based on this gene signature, which showed high reliability in predicting patient outcomes. Furthermore, we observed a significant association between G0A-related risk and tumor microenvironment as well as drug susceptibility, highlighting the potential of the gene signature to guide personalized treatment strategies. The expression of six genes were significantly upregulated in the LUAD cell lines. This signature holds the potential to contribute to improved prognostic prediction and new personalized therapies specifically for LUAD patients.

Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells

BackgroundCells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and conditions. How the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear.ResultsWe analyze existing bulk and single-cell transcriptomic datasets to uncover the prevalence of transcriptional adaptation in mammalian systems across diverse contexts and cell types. We perform regulon gene expression analyses of transcription factor target sets in both bulk and pooled single-cell genetic perturbation datasets. Our results reveal greater robustness in expression of regulons of transcription factors exhibiting transcriptional adaptation compared to those of transcription factors that do not. Stochastic mathematical modeling of minimal compensatory gene networks qualitatively recapitulates several aspects of transcriptional adaptation, including paralog upregulation and robustness to mutation. Combined with machine learning analysis of network features of interest, our framework offers potential explanations for which regulatory steps are most important for transcriptional adaptation.ConclusionsOur integrative approach identifies several putative hits—genes demonstrating possible transcriptional adaptation—to follow-up on experimentally and provides a formal quantitative framework to test and refine models of transcriptional adaptation.

AKNNO: single-cell and spatial transcriptomics clustering with an optimized adaptive k-nearest neighbor graph

Typical clustering methods for single-cell and spatial transcriptomics struggle to identify rare cell types, while approaches tailored to detect rare cell types gain this ability at the cost of poorer performance for grouping abundant ones. Here, we develop aKNNO to simultaneously identify abundant and rare cell types based on an adaptive k-nearest neighbor graph with optimization. Benchmarking on 38 simulated and 20 single-cell and spatial transcriptomics datasets demonstrates that aKNNO identifies both abundant and rare cell types more accurately than general and specialized methods. Using only gene expression aKNNO maps abundant and rare cells more precisely compared to integrative approaches.

Muscle cell-derived Ccl8 is a negative regulator of skeletal muscle regeneration.

Skeletal muscles undergo robust regeneration upon injury, and infiltrating immune cells play a major role in not only clearing damaged tissues but also regulating the myogenic process through secreted cytokines. Chemokine C-C motif ligand 8 (Ccl8), along with Ccl2 and Ccl7, has been reported to mediate inflammatory responses to suppress muscle regeneration. Ccl8 is also expressed by muscle cells, but a role of the muscle cell-derived Ccl8 in myogenesis has not been reported. In this study, we found that knockdown of Ccl8, but not Ccl2 or Ccl7, led to increased differentiation of C2C12 myoblasts. Analysis of existing single-cell transcriptomic datasets revealed that both immune cells and muscle stem cells (MuSCs) in regenerating muscles express Ccl8, with the expression by MuSCs at a much lower level, and that the temporal patterns of Ccl8 expression were different in MuSCs and macrophages. To probe a function of muscle cell-derived Ccl8 invivo, we utilized a mouse system in which Cas9 was expressed in Pax7+ myogenic progenitor cells (MPCs) and Ccl8 gene editing was induced by AAV9-delivered sgRNA. Depletion of Ccl8 in Pax7+ MPCs resulted in accelerated muscle regeneration after barium chloride-induced injury in both young and middle-aged mice, and intramuscular administration of a recombinant Ccl8 reversed the phenotype. Accelerated regeneration was also observed when Ccl8 was depleted in Myf5+ or MyoD+ MPCs by similar approaches. Our results suggest that muscle cell-derived Ccl8 plays a unique role in regulating the initiation of myogenic differentiation during injury-induced muscle regeneration.

Identification of tumor-specific T cell signature predicting cancer immunotherapy response in bladder cancer by multi-omics analysis and experimental verification

BackgroundNumerous gene signatures predicting the prognosis of bladder cancer have been identified. However, a tumor-specific T cell signature related to immunotherapy response in bladder cancer remains under investigation.MethodsSingle-cell RNA and TCR sequencing from the Gene expression omnibus (GEO) database were used to identify tumor-specific T cell-related genes in bladder cancer. Subsequently, we constructed a tumor-specific T cell signature (TstcSig) and validated its clinical relevance for predicting immunotherapy response in multiple immunotherapy cohorts. Further analyses explored the immune characteristics of TstcSig in bladder cancer patients from other cohorts in the TCGA and GEO databases. Western blot (WB), multicolor immunofluorescence (MIF), qRT-PCR and flow cytometry assays were performed to validate the results of bioinformatics analysis.ResultsThe established TstcSig, based on five tumor-specific T cell-related genes, could predict outcomes in a bladder cancer immunotherapy cohort. This was verified using two additional immunotherapy cohorts and showed better predictive performance compared to 109 published T cell signatures. TstcSig was strongly correlated with immune characteristics such as immune checkpoint gene expression, tumor mutation burden, and T cell infiltration, as validated by single-cell and spatial transcriptomics datasets. Notably, the positive correlation between TstcSig and T cell infiltration was confirmed in the TCGA cohort. Furthermore, pan-cancer analysis demonstrated the heterogeneity of the prognostic value of TstcSig. Tumor-specific T cells highly expressed CD27, IFNG, GZMB and CXCL13 and secreted more effector cytokines for tumor cell killing, as validated experimentally.ConclusionWe developed a five-gene signature (including VAMP5, TIGIT, LCK, CD27 and CACYBP) based on tumor-specific T cell-related genes to predict the immunotherapy response in bladder cancer patients.

Cathepsin D is essential for the degradomic shift of macrophages required to resolve liver fibrosis

Background and objectivesFibrosis contributes to 45% of deaths in industrialized nations and is characterized by an abnormal accumulation of extracellular matrix (ECM). There are no specific anti-fibrotic treatments for liver fibrosis, and previous unsuccessful attempts at drug development have focused on preventing ECM deposition. Because liver fibrosis is largely acknowledged to be reversible, regulating fibrosis resolution could offer novel therapeutical options. However, little is known about the mechanisms controlling ECM remodeling during resolution. Changes in proteolytic activity are essential for ECM homeostasis and macrophages are an important source of proteases. Herein, in this study we evaluate the role of macrophage-derived cathepsin D (CtsD) during liver fibrosis. MethodsCtsD expression and associated pathways were characterized in single-cell RNA sequencing and transcriptomic datasets in human cirrhosis. Liver fibrosis progression, reversion and functional characterization were assessed in novel myeloid-CtsD and hepatocyte-CtsD knock-out mice. ResultsAnalysis of single-cell RNA sequencing datasets demonstrated CtsD was expressed in macrophages and hepatocytes in human cirrhosis. Liver fibrosis progression, reversion and functional characterization were assessed in novel myeloid-CtsD (CtsDΔMyel) and hepatocyte-CtsD knock-out mice. CtsD deletion in macrophages, but not in hepatocytes, resulted in enhanced liver fibrosis. Both inflammatory and matrisome proteomic signatures were enriched in fibrotic CtsDΔMyel livers. Besides, CtsDΔMyel liver macrophages displayed functional, phenotypical and secretomic changes, which resulted in a degradomic phenotypical shift, responsible for the defective proteolytic processing of collagen I in vitro and impaired collagen remodeling during fibrosis resolution in vivo. Finally, CtsD-expressing mononuclear phagocytes of cirrhotic human livers were enriched in lysosomal and ECM degradative signaling pathways. ConclusionsOur work describes for the first-time CtsD-driven lysosomal activity as a central hub for restorative macrophage function during fibrosis resolution and opens new avenues to explore their degradome landscape to inform drug development.

Axon guidance cue SEMA3A promotes the aggressive phenotype of basal-like PDAC

ObjectiveThe dysregulation of the axon guidance pathway is common in pancreatic ductal adenocarcinoma (PDAC), yet our understanding of its biological relevance is limited. Here, we investigated the functional role of...