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Establishment and validation of a novel CD8+ T cell-associated prognostic signature for predicting clinical outcomes and immunotherapy response in hepatocellular carcinoma via integrating single-cell RNA-seq and bulk RNA-seq.
CD8+ T lymphocytes are critical in the immune response against neoplasms, yet the prognostic relevance of CD8+ T cell-associated genes in hepatocellular carcinoma (HCC) is not fully understood. We sourced single-cell RNA-sequencing (scRNA-seq) and bulk RNA-seq data for HCC from the GSE98638 dataset and The Cancer Genome Atlas (TCGA) repository. We utilized Weighted Gene Correlation Network Analysis (WGCNA) to identify CD8+ T cell-related genes. A clinical prognostic model for risk stratification was then constructed via Cox-Lasso regression analysis. The Immunophenotypic Score (IPS) was utilized to evaluate the potential of immunotherapeutic interventions in the categorized cohorts. Validation of the expression of CD8+ T cell-associated risk genes was performed using quantitative reverse transcription PCR (qRT-PCR). Integrating scRNA-seq with RNA-seq data, we identified five CD8+ T cell-related signature genes: IKBKE, ATP1B3, MSC, ADA, and BATF. Notably, HCC patients in the high-risk group had markedly decreased overall survival. Elevated infiltration levels of CD8+ T cells, B cells, and macrophages were observed in the high-risk group. Moreover, there was a positive correlation between the risk score and immune checkpoints (ICPs), including PDCD1, CD274, and CTLA4. Patients within the high-risk group subject to PD1 and CTLA4 blockade exhibited higher IPS levels. Additionally, the expression of the five risk genes was upregulated in HCC cell lines and tissues compared to normal cells and tissues. Our findings establish a prognostic signature based on CD8+ T cells, offering a potent predictive model for clinical outcomes and responsiveness to immunotherapy in HCC patients.
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Spatio-temporal landscape of mouse epididymal cells and specific mitochondria-rich segments defined by large-scale single-cell RNA-seq.
Spermatozoa acquire their fertilizing ability and forward motility during epididymal transit, suggesting the importance of the epididymis. Although the cell atlas of the epididymis was reported recently, the heterogeneity of the cells and the gene expression profile in the epididymal tube are still largely unknown. Considering single-cell RNA sequencing results, we thoroughly studied the cell composition, spatio-temporal differences in differentially expressed genes (DEGs) in epididymal segments and mitochondria throughout the epididymis with sufficient cell numbers. In total, 40,623 cells were detected and further clustered into 8 identified cell populations. Focused analyses revealed the subpopulations of principal cells, basal cells, clear/narrow cells, and halo/T cells. Notably, two subtypes of principal cells, the Prc7 and Prc8 subpopulations were enriched as stereocilia-like cells according to GO analysis. Further analysis demonstrated the spatially specific pattern of the DEGs in each cell cluster. Unexpectedly, the abundance of mitochondria and mitochondrial transcription (MT) was found to be higher in the corpus and cauda epididymis than in the caput epididymis by scRNA-seq, immunostaining, and qPCR validation. In addition, the spatio-temporal profile of the DEGs from the P42 and P56 epididymis, including transiting spermatozoa, was depicted. Overall, our study presented the single-cell transcriptome atlas of the mouse epididymis and revealed the novel distribution pattern of mitochondria and key genes that may be linked to sperm functionalities in the first wave and subsequent wave of sperm, providing a roadmap to be emulated in efforts to achieve sperm maturation regulation in the epididymis.
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Single-Cell Trajectory Inference Guided Enhancement of Thyroid Maturation In Vitro Using TGF-Beta Inhibition.
The thyroid gland regulates metabolism and growth via secretion of thyroid hormones by thyroid follicular cells (TFCs). Loss of TFCs, by cellular dysfunction, autoimmune destruction or surgical resection, underlies hypothyroidism. Recovery of thyroid hormone levels by transplantation of mature TFCs derived from stem cells in vitro holds great therapeutic promise. However, the utilization of in vitro derived tissue for regenerative medicine is restricted by the efficiency of differentiation protocols to generate mature organoids. Here, to improve the differentiation efficiency for thyroid organoids, we utilized single-cell RNA-Seq to chart the molecular steps undertaken by individual cells during the in vitro transformation of mouse embryonic stem cells to TFCs. Our single-cell atlas of mouse organoid systematically and comprehensively identifies, for the first time, the cell types generated during production of thyroid organoids. Using pseudotime analysis, we identify TGF-beta as a negative regulator of thyroid maturation in vitro. Using pharmacological inhibition of TGF-beta pathway, we improve the level of thyroid maturation, in particular the induction of Nis expression. This in turn, leads to an enhancement of iodide organification in vitro, suggesting functional improvement of the thyroid organoid. Our study highlights the potential of single-cell molecular characterization in understanding and improving thyroid maturation and paves the way for identification of therapeutic targets against thyroid disorders.
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A harmonized atlas of mouse spinal cord cell types and their spatial organization.
Single-cell RNA sequencing data can unveil the molecular diversity of cell types. Cell type atlases of the mouse spinal cord have been published in recent years but have not been integrated together. Here, we generate an atlas of spinal cell types based on single-cell transcriptomic data, unifying the available datasets into a common reference framework. We report a hierarchical structure of postnatal cell type relationships, with location providing the highest level of organization, then neurotransmitter status, family, and finally, dozens of refined populations. We validate a combinatorial marker code for each neuronal cell type and map their spatial distributions in the adult spinal cord. We also show complex lineage relationships among postnatal cell types. Additionally, we develop an open-source cell type classifier, SeqSeek, to facilitate the standardization of cell type identification. This work provides an integrated view of spinal cell types, their gene expression signatures, and their molecular organization.
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Multi-omics profiling of the choroid plexus reveals developmental cellular remodeling, early immune gene activation, and epithelial cell heterogeneity.
Brain development and function are influenced by the choroid plexus, whose dynamic cellular and transcriptional changes shape immune cell trafficking and cerebrospinal fluid composition. Here, we performed a comprehensive multi-omics analysis-including bulk and single-cell transcriptomics and epigenomics-of the lateral ventricle choroid plexus across postnatal and adult stages in mice and rats. We identified changes in the choroid plexus cellular composition and gene expression profiles, including early and progressive activation of several immune related genes in main cell types from early postnatal to young adult stage. Epithelial cells exhibited developmental remodeling over time, characterized by distinct gene expression programs, locus-specific changes in chromatin accessibility, and cellular diversity. In addition, we uncovered an epithelial cell population characterized by a distinct transcriptional signature. Our study offers a comprehensive atlas of transcriptional activity and chromatin accessibility in choroid plexus cells, providing a valuable resource to guide future research.
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Multi-organ single-cell RNA sequencing in mice reveals early hyperglycemia responses that converge on fibroblast dysregulation.
Diabetes causes a range of complications that can affect multiple organs. Hyperglycemia is an important driver of diabetes-associated complications, mediated by biological processes such as dysfunction of endothelial cells, fibrosis, and alterations in leukocyte number and function. Here, we dissected the transcriptional response of key cell types to hyperglycemia across multiple tissues using single-cell RNA sequencing (scRNA-seq) and identified conserved, as well as organ-specific, changes associated with diabetes complications. By studying an early time point of diabetes, we focus on biological processes involved in the initiation of the disease, before the later organ-specific manifestations had supervened. We used a mouse model of type 1 diabetes and performed scRNA-seq on cells isolated from the heart, kidney, liver, and spleen of streptozotocin-treated and control male mice after 8 weeks and assessed differences in cell abundance, gene expression, pathway activation, and cell signaling across organs and within organs. In response to hyperglycemia, endothelial cells, macrophages, and monocytes displayed organ-specific transcriptional responses, whereas fibroblasts showed similar responses across organs, exhibiting altered metabolic gene expression and increased myeloid-like fibroblasts. Furthermore, we found evidence of endothelial dysfunction in the kidney, and of endothelial-to-mesenchymal transition in streptozotocin-treated mouse organs. In summary, our study represents the first single-cell and multi-organ analysis of early dysfunction in type 1 diabetes-associated hyperglycemia, and our large-scale dataset (comprising 67 611 cells) will serve as a starting point, reference atlas, and resource for further investigating the events leading to early diabetic disease.
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Single-cell RNA sequencing revealed the liver heterogeneity between egg-laying duck and ceased-laying duck.
BACKGROUND: In the late phase of production, ducks untimely cease laying, leading to a lower feed conversion. Liver plays a vital role in the synthesis and transport of yolk materials during egg formation in birds. However, the molecular mechanism of liver in ceased-laying duck is far from clear, higher resolution and deeper analysis is needed. Sing-cell RNA-sequencing of 10 × Genomics platform can help to map the liver single cell gene expression atlas of Shaoxing duck and provide new insights into the liver between egg-laying and ceased-laying ducks. RESULTS: About 20,000 single cells were profiled and 22 clusters were identified. All the clusters were identified as 6 cell types. The dominant cell type is hepatocyte, accounted for about 60% of all the cells. Of note, the heterogeneity of cells between egg-laying duck and ceased-laying duck mainly occurred in hepatocytes. Cells of cluster 3 and 12 were the unique hepatocyte states of egg-laying ducks, while cells of cluster 0 and 15 were the unique hepatocyte states of ceased-laying ducks. The expression mode of yolk precursor transporters, lipid metabolizing enzymes and fibrinogens were different in hepatocytes between egg-laying duck and ceased-laying duck. APOV1, VTG2, VTG1, APOB, RBP, VTDB and SCD might be activated in egg-laying ducks, while APOA1, APOA4, APOC3, FGB and FGG might be activated in ceased-laying ducks. CONCLUSIONS: Our study further proofs that APOV1 and APOB play key roles in egg production, rather than APOA1 and APOA4. It is also the first to detect a correlation between the higher expression of APOC3, FGB, FGG and ceased-laying in duck.
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Integrated single-cell and bulk RNA sequencing analysis identifies a cancer associated fibroblast-related signature for predicting prognosis and therapeutic responses in colorectal cancer.
BACKGROUND: Cancer-associated fibroblasts (CAFs) contribute notably to colorectal cancer (CRC) tumorigenesis, stiffness, angiogenesis, immunosuppression and metastasis, and could serve as a promising therapeutic target. Our purpose was to construct CAF-related prognostic signature for CRC. METHODS: We performed bioinformatics analysis on single-cell transcriptome data derived from Gene Expression Omnibus (GEO) and identified 208 differentially expressed cell markers from fibroblasts cluster. Bulk gene expression data of CRC was obtained from The Cancer Genome Atlas (TCGA) and GEO databases. Univariate Cox regression and least absolute shrinkage operator (LASSO) analyses were performed on TCGA training cohort (n = 308) for model construction, and was validated in TCGA validation (n = 133), TCGA total (n = 441), GSE39582 (n = 470) and GSE17536 (n = 177) datasets. Microenvironment Cell Populations-counter (MCP-counter) and Estimate the Proportion of Immune and Cancer cells (EPIC) methods were applied to evaluated CAFs infiltrations from bulk gene expression data. Real-time polymerase chain reaction (qPCR) was performed in tissue microarrays containing 80 colon cancer samples to further validate the prognostic value of the CAF model. pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to predict chemosensitivity and immunotherapy response. Human Protein Atlas (HPA) databases and immunohistochemistry were used to evaluate the protein expressions. RESULTS: A nine-gene prognostic CAF-related signature was established in training cohort. Kaplan-Meier survival analyses revealed patients with higher CAF risk scores were correlated with adverse prognosis in each cohort. MCP-counter and EPIC results consistently revealed CAFs infiltrations were significantly higher in high CAF risk group. Patients with higher CAF risk scores were more prone to not respond to immunotherapy, but were more sensitive to several conventional chemotherapeutics, suggesting a potential strategy of combining chemotherapy with anti-CAF therapy to improve the efficacy of current T-cell based immunotherapies. Univariate and multivariate Cox regression analyses verified the CAF model was as an independent prognostic indicator in predicting overall survival, and a CAF-based nomogram was then built for clinical utility in predicting prognosis of CRC. CONCLUSION: To conclude, the CAF-related signature could serve as a robust prognostic indicator in CRC, which provides novel genomics evidence for anti-CAF immunotherapeutic strategies.
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A single-cell RNA-seq analysis of Brachyury-expressing cell clusters suggests a morphogenesis-associated signal center of oral ectoderm in sea urchin embryos.
Brachyury is a T-box family transcription factor and plays pivotal roles in morphogenesis. In sea urchin embryos, Brachyury is expressed in the invaginating endoderm, and in the oral ectoderm of the invaginating mouth opening. The oral ectoderm is hypothesized to serve as a signaling center for oral (ventral)-aboral (dorsal) axis formation and to function as a ventral organizer. Our previous results of a single-cell RNA-seq (scRNA-seq) atlas of early Strongylocentrotus purpuratus embryos categorized the constituent cells into 22 clusters, in which the endoderm consists of three clusters and the oral ectoderm four clusters (Foster et al., 2020). Here we examined which clusters of cells expressed Brachyury in relation to the morphogenesis and the identity of the ventral organizer. Our results showed that cells of all three endoderm clusters expressed Brachyury in blastulae. Based on expression profiles of genes involved in the gene regulatory networks (GRNs) of sea urchin embryos, the three clusters are distinguishable, two likely derived from the Veg2 tier and one from the Veg1 tier. On the other hand, of the four oral-ectoderm clusters, cells of two clusters expressed Brachyury at the gastrula stage and genes that are responsible for the ventral organizer at the late blastula stage, but the other two clusters did not. At a single-cell level, most cells of the two oral-ectoderm clusters expressed organizer-related genes, nearly a half of which coincidently expressed Brachyury. This suggests that the ventral organizer contains Brachyury-positive cells which invaginate to form the stomodeum. This scRNA-seq study therefore highlights significant roles of Brachyury-expressing cells in body-plan formation of early sea urchin embryos, though cellular and molecular mechanisms for how Brachyury functions in these processes remain to be elucidated in future studies.
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Cellular heterogeneity of the developing worker honey bee (Apis mellifera) pupa: a single cell transcriptomics analysis.
It is estimated that animals pollinate 87.5% of flowering plants worldwide and that managed honey bees (Apis mellifera) account for 30-50% of this ecosystem service to agriculture. In addition to their important role as pollinators, honey bees are well-established insect models for studying learning and memory, behavior, caste differentiation, epigenetic mechanisms, olfactory biology, sex determination, and eusociality. Despite their importance to agriculture, knowledge of honey bee biology lags behind many other livestock species. In this study, we have used scRNA-Seq to map cell types to different developmental stages of the worker honey bee (prepupa at day 11 and pupa at day 15) and sought to determine their gene expression signatures. To identify cell-type populations, we examined the cell-to-cell network based on the similarity of the single-cells transcriptomic profiles. Grouping similar cells together we identified 63 different cell clusters of which 17 clusters were identifiable at both stages. To determine genes associated with specific cell populations or with a particular biological process involved in honey bee development, we used gene coexpression analysis. We combined this analysis with literature mining, the honey bee protein atlas, and gene ontology analysis to determine cell cluster identity. Of the cell clusters identified, 17 were related to the nervous system and sensory organs, 7 to the fat body, 19 to the cuticle, 5 to muscle, 4 to compound eye, 2 to midgut, 2 to hemocytes, and 1 to malpighian tubule/pericardial nephrocyte. To our knowledge, this is the first whole single-cell atlas of honey bees at any stage of development and demonstrates the potential for further work to investigate their biology at the cellular level.