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Characterization of Alternative Splicing (AS) Events during Chicken (Gallus gallus) Male Germ-Line Stem Cell Differentiation with Single-Cell RNA-seq.
Alternative splicing (AS) is a ubiquitous, co-transcriptional, and post-transcriptional regulation mechanism during certain developmental processes, such as germ cell differentiation. A thorough understanding of germ cell differentiation will help us to open new avenues for avian reproduction, stem cell biology, and advances in medicines for human consumption. Here, based on single-cell RNA-seq, we characterized genome-wide AS events in manifold chicken male germ cells: embryonic stem cells (ESCs), gonad primordial germ cells (gPGCs), and spermatogonia stem cells (SSCs). A total of 38,494 AS events from 15,338 genes were detected in ESCs, with a total of 48,955 events from 14,783 genes and 49,900 events from 15,089 genes observed in gPGCs and SSCs, respectively. Moreover, this distribution of AS events suggests the diverse splicing feature of ESCs, gPGCs, and SSCs. Finally, several crucial stage-specific genes, such as NANOG, POU5F3, LIN28B, BMP4, STRA8, and LHX9, were identified in AS events that were transmitted in ESCs, gPGCs, and SSCs. The gene expression results of the RNA-seq data were validated by qRT-PCR. In summary, we provided a comprehensive atlas of the genome-wide scale of the AS event landscape in male chicken germ-line cells and presented its distribution for the first time. This research may someday improve treatment options for men suffering from male infertility.
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Construction and validation of prognosis and treatment outcome models based on plasma membrane tension characteristics in bladder cancer.
BACKGROUND: Plasma membrane tension-related genes (MTRGs) are known to play a crucial role in tumor progression by influencing cell migration and adhesion. However, their specific mechanisms in bladder cancer (BLCA) remain unclear. METHODS: Transcriptomic, clinical and mutation data from BLCA patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Clusters associated with MTRGs were identified by consensus unsupervised cluster analysis. The genes of different clusters were analyzed by GO and KEGG gene enrichment analysis. Differentially expressed genes (DEGs) were screened from different clusters. Consensus cluster analysis of prognostic DEGs was performed to identify gene subtypes. Patients were then randomly divided into training and validation groups, and MTRG scores were constructed by logistic minimum absolute contraction and selection operator (LASSO) and Cox regression analysis. We assessed changes in clinical outcomes and immune-related factors between different patient groups. The single-cell RNA sequencing (scRNA-seq) dataset for BLCA was collected and analyzed from the Tumor Immune Single-cell Hub (TISCH) database. Biological functions were investigated using a series of experiments including quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), wound healing, transwell, etc. RESULTS: Our MTRG score is based on eight genes (HTRA1, GOLT1A, DCBLD2, UGT1A1, FOSL1, DSC2, IGFBP3 and TAC3). Higher scores were characterized by lower cancer stem cell (CSC) indices, as well as higher tumor microenvironment (TME) stromal and immune scores, suggesting that high scores were associated with poorer prognosis. In addition, some drugs such as cisplatin, paclitaxel, doxorubicin, and docetaxel exhibited lower IC50 values in the high MTRG score group. Functional experiments have demonstrated that downregulation of DCBLD2 affects tumor cell migration, but not proliferation. CONCLUSIONS: Our study sheds light on the prognostic significance of MTRGs within the TME and their correlation with immune infiltration patterns, ultimately impacting patient survival in BLCA. Notably, our findings highlight DCBLD2 as a promising candidate for targeted therapeutic interventions in the clinical management of BLCA.
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Generation of novel lipid metabolism-based signatures to predict prognosis and immunotherapy response for colorectal adenocarcinoma.
Lipid metabolism reprogramming involves in epithelial-mesenchymal transition (EMT), cancer stemness and immune checkpoints (ICs), which influence the metastasis of cancer. This study aimed to generate lipid metabolism-based signatures to predict prognosis, immunotherapy and chemotherapy response for colorectal adenocarcinoma (COAD). Transcriptome data and clinical information of COAD patients were collected from the cancer genome atlas (TCGA) database. The expression of EMT-, stem cell-, and IC-related genes were assessed between COAD and control samples. Modules and genes correlated EMT, ICs and stemness signatures were identified through weighted gene co-expression network analysis (WGCNA). Prognostic signatures were generated and then the distribution of risk genes was evaluated using single-cell RNA sequencing (scRNA-seq) data from GSE132465 dataset. COAD patients exhibited increased EMT score and stemness along with decreased ICs. Next, 12 hub genes (PIK3CG, ALOX5AP, PIK3R5, TNFAIP8L2, DPEP2, PIK3CD, PIK3R6, GGT5, ELOVL4, PTGIS, CYP7B1 and PRKD1) were found within green and yellow modules correlated with EMT, stemness and ICs. Lipid metabolism-based prognostic signatures were generated based on PIK3CG, GGT5 and PTGIS. Patients with high-risk group had poor prognosis, elevated ESTIMATEScore and StromalScore, 100% mutation rate and higher TIDE score. Samples in low-risk group had more immunogenicity on ICIs. Notably, PIK3CG was expressed in B cells, while GGT5 and PTGIS were expressed in stromal cells. This study generates lipid metabolism-based signatures correlated with EMT, stemness and ICs for predicting prognosis of COAD, and provides potential therapeutic targets for immunotherapy in COAD.
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Single-cell RNA sequencing reveals the altered innate immunity in immune checkpoint inhibitor-related myocarditis.
Myocarditis has emerged as a rare but lethal immune checkpoint inhibitor (ICI)-associated toxicity. However, the exact mechanism and the specific therapeutic targets remain underexplored. In this study, we aim to characterise the transcriptomic profiles based on single-cell RNA sequencing from ICI-related myocarditis. Peripheral blood mononuclear cell (PBMC) samples were collected from four groups for single-cell RNA sequencing: (1) patients with newly diagnosed lung squamous cell carcinoma before treatment (Control Group); (2) patients with lung squamous cell carcinoma with PD-1 inhibitor therapy who did not develop myocarditis (PD-1 Group); (3) patients during fulminant ICI-related myocarditis onset (Myocarditis Group); and (4) Patients with fulminant ICI-related myocarditis during disease remission (Recovery Group). Subcluster determination, functional analysis, single-cell trajectory and cell-cell interaction analysis were performed after scRNA-seq. Bulk-RNA sequencing was performed for further validation. Our results revealed the diversity of cellular populations in ICI-related myocarditis, marked by their distinct transcriptional profiles and biological functions. Monocytes, NKs as well as B cells contribute to the regulation of innate immunity and inflammation in ICI-related myocarditis. With integrated analysis of scRNA-seq and bulk sequencing, we identified S100A protein family as a potential serum marker for ICI-related myocarditis. Our study has created a cell atlas of PBMC during ICI-related myocarditis, which would shed light on the pathophysiological mechanism and potential therapeutic targets of ICI-related myocarditis in continuous exploration.
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Single-cell transcriptomic landscape of human blood cells.
High throughput single-cell RNA-seq has been successfully implemented to dissect the cellular and molecular features underlying hematopoiesis. However, an elaborate and comprehensive transcriptome reference of the whole blood system is lacking. Here, we profiled the transcriptomes of 7551 human blood cells representing 32 immunophenotypic cell types, including hematopoietic stem cells, progenitors and mature blood cells derived from 21 healthy donors. With high sequencing depth and coverage, we constructed a single-cell transcriptional atlas of blood cells (ABC) on the basis of both protein-coding genes and long noncoding RNAs (lncRNAs), and showed a high consistence between them. Notably, putative lncRNAs and transcription factors regulating hematopoietic cell differentiation were identified. While common transcription factor regulatory networks were activated in neutrophils and monocytes, lymphoid cells dramatically changed their regulatory networks during differentiation. Furthermore, we showed a subset of nucleated erythrocytes actively expressing immune signals, suggesting the existence of erythroid precursors with immune functions. Finally, a web portal offering transcriptome browsing and blood cell type prediction has been established. Thus, our work provides a transcriptional map of human blood cells at single-cell resolution, thereby offering a comprehensive reference for the exploration of physiological and pathological hematopoiesis.
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Single-cell Atlas Reveals Myelopoietic Bias and Immunosuppressive Reprogramming in Metastatic Bone Marrow Across Cancers.
Bone metastasis can remodel the bone marrow microenvironment, yet how metastatic tumors reshape hematopoiesis and immunity across marrow regions remains poorly defined. Here, we profile a cross-cancer single-cell atlas with 126,986 cells by integrating bone marrow samples (tumor, involved, and distal sites) from liver, prostate, and kidney cancer bone metastases, as well as benign controls, resolving 7 major lineages and 66 subpopulations. We identified coordinated immune suppression and niche remodeling that innate cells rose in peri-tumoral marrow but dropped in tumor sites, while adaptive T/B cells progressively depleted and stromal/epithelial compartments expanded. We further revealed a shift in myelopoiesis toward immunosuppressive monocyte/macrophage states with GMP-level lineage bias, alongside impaired erythropoiesis and B lymphopoiesis driven by myeloid-like reprogramming of precursors. Tumor sites were enriched with exhausted/stressed T cells, linked to inhibitory progenitor-T cell interactions (e.g., LGALS9-HAVCR2 and CLEC2-KLRB1). Non-hematopoietic support signals (CXCL12-CXCR4, ICAM1-SPN) were attenuated, and tumor programs included a marrow-specific metaprogram with IGFBP3/NAMPT signaling predicted to further destabilize the HSC niche. Overall, our study offers an integrated framework for decoding the metastatic marrow ecosystem by jointly targeting hematopoietic distortion, stromal collapse, and immune dysfunction.
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Single-cell RNA sequencing of mouse brain and lung vascular and vessel-associated cell types.
Vascular diseases are major causes of death, yet our understanding of the cellular constituents of blood vessels, including how differences in their gene expression profiles create diversity in vascular structure and function, is limited. In this paper, we describe a single-cell RNA sequencing (scRNA-seq) dataset that defines vascular and vessel-associated cell types and subtypes in mouse brain and lung. The dataset contains 3,436 single cell transcriptomes from mouse brain, which formed 15 distinct clusters corresponding to cell (sub)types, and another 1,504 single cell transcriptomes from mouse lung, which formed 17 cell clusters. In order to allow user-friendly access to our data, we constructed a searchable database (http://betsholtzlab.org/VascularSingleCells/database.html). Our dataset constitutes a comprehensive molecular atlas of vascular and vessel-associated cell types in the mouse brain and lung, and as such provides a strong foundation for future studies of vascular development and diseases.
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Integrative analysis and experimental validation identify the role of CD44 and Nucleolin in regulating gliogenesis following spinal cord injury.
Spinal cord injury (SCI) triggers a complex cascade of cellular and molecular responses, yet the complex cellular communication remains incompletely understood. This study explored how intercellular communication contributes to the activation of microglia and astrocytes after SCI. Here, we integrated four datasets using single-cell RNA sequencing (scRNA-seq) or single-nucleus RNA sequencing (snRNA-seq) and constructed a comprehensive cellular atlas of the injured spinal cord. Transcriptomic changes in microglia and astrocytes were analyzed. We identified CD44 as a key receptor in SPP1-mediated microglial activation, which represented a subpopulation involved in inflammatory response in microglia. We defined a gliogenesis subpopulation of astrocytes that emerged at 3 dpi, which became the predominant cell type in the injured spinal cord. These astrocytes highly expressed the Nucleolin (Ncl) gene and interacted via the Pleiotrophin (Ptn) signaling pathway, which is associated with astrocyte proliferation. To validate these findings, we utilized a crush injury model. Flow cytometry of isolated microglia and astrocytes confirmed the upregulation of CD44 in microglia and NCL in astrocytes in response to SCI. In vivo results confirmed that the CD44 positive microglia accumulated and PLA results further confirmed the combination of SPP1 with CD44. In parallel, the upregulated expression of NCL in astrocytes facilitated their proliferation, underscoring the role of the NCL receptor in gliogenesis after SCI. In vitro validation demonstrated that exogenous SPP1 upregulates CD44 expression by promoting the phosphorylation of p65 and activating the NF-κB pathways in BV2 microglia, and that high expression of IL-6 indicates the activation of inflammation. PTN may enhance NCL expression and thus facilitates astrocyte proliferation. Collectively, our study identified key receptors that regulated inflammation responses and gliogenesis. Targeting the CD44 and NCL receptors may provide promising therapeutic strategies to modulate inflammation and promote tissue repair after SCI.
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Confounding factors in assessing the enriched expression of somatic mutant alleles in bulk tumor samples.
Allele-specific expression (ASE) of somatic mutations can be caused by cis-activation of the mutant allele or silencing of the wild-type allele and has been investigated by examining the enrichment of mutant allele in RNA relative to DNA. Here we show that this mutation-based approach can be confounded by gene expression differences in tumor and normal cells that coexist in most bulk tumor samples. We model mutant allele expression by incorporating tumor/normal expression difference, mutant allele dosage, tumor purity, and nonsense-mediated decay (NMD) efficiency, projecting that such enrichments can occur without ASE. This confounding effect is exacerbated with low tumor purity and is dependent on mutant allele dosage for NMD-triggering mutations. The model predictions are validated by a pancancer bulk tumor analysis with somatic insertions/deletions (indels) from 9101 The Cancer Genome Atlas (TCGA) samples. A single-cell analysis in five cutaneous squamous cell carcinomas demonstrates the robustness of this model to intratumor heterogeneity. As a byproduct of this confounding effect, we evaluate whether the inverse relationship between mutant allele enrichment in RNA and tumor purity could be leveraged to complement DNA-based somatic mutation detection in low purity samples. Indeed, our de novo somatic indel calling from TCGA RNA-seq increases the TCGA driver indel repertoire by ∼14%, especially in samples with purity less than 0.4, including actionable EGFR indels in lung adenocarcinoma and FLT3 in acute myeloid leukemia. Our study not only reveals confounders in somatic mutant ASE analysis but also demonstrates their utility in RNA-based mutation calling.
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CENPA acts as a prognostic factor that relates to immune infiltrates in gliomas.
BACKGROUND: Glioma is the most common primary tumor of the central nervous system (CNS). Centromere protein A (CENPA) plays an essential role in ensuring that mitosis proceeds normally. The effect of CENPA on glioma is rarely reported. However, the current study aims to explore whether aberrant CENPA expression promotes glioma progression and the potential mechanisms involved. METHODS: The GEPIA website, The Cancer Genome Atlas, and the Gene Expression Omnibus (GEO) were used to assess the expression of CENPA in glioma. The results were validated by real-time quantitative polymerase chain reaction and immunohistochemical staining of clinical samples. The relationship between the expression and prognostic value of the CENPA gene in glioma was investigated by Kaplan-Meier (KM) survival analysis with RNA-seq and clinical profiles downloaded from the Chinese Glioma Genome Atlas (CGGA) and UCSC Xena. The association between CENPA and clinical characteristics was also evaluated. Cell Counting Kit-8 (CCK8) assay, wound healing assay using two glioma cell lines, gene set enrichment analysis (GSEA), KEGG and gene ontology (GO) enrichment analysis, immune infiltration analysis, temozolomide (TMZ) sensitivity analysis, and single-cell sequence analysis were performed to explore the underlying mechanisms of high CENPA expression and its effect on glioma development. Finally, we performed a Cox analysis based on the expression of CENPA to predict patient prognosis. RESULTS: CENPA was significantly upregulated in glioma tissue samples and correlated with patient prognosis. Moreover, the downregulation of CENPA inhibited the migration and proliferation of glioma cells. In addition, the expression level of CENPA was significantly correlated with the grade, primary-recurrent-secondary (PRS) type, IDH mutation status, and 1p19q codeletion status. Furthermore, CENPA could serve as an independent prognostic factor for glioma that mainly interferes with the normal progression of mitosis and regulates the tumor immune microenvironment favoring glioma development. CONCLUSION: CENPA may act as a prognostic factor in patients with glioma and provide a novel target for the treatment of gliomas.