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Single-cell transcriptomics reveals tumor microenvironment changes and prognostic gene signatures in hepatocellular carcinoma.
BACKGROUND: Hepatocellular Carcinoma (HCC) is the most common type of primary liver cancer, accounting for the majority of liver cancer cases. Hepatocellular Carcinoma not only exhibits high heterogeneity but also possesses an immune-suppressive tumor microenvironment that promotes tumor evasion, posing substantial difficulties for efficient therapy. Our aim is to utilize single-cell RNA transcriptome data to investigate the dynamic changes in the tumor microenvironment during the malignant progression of HCC, the communication among immune cells, and the marker genes associated with patient prognosis. METHODS: We constructed expression matrices from open single-cell RNA transcriptome data (GSE149614) of HCC patients (representing stages I-IV), establishing single-cell RNA transcriptional atlases for different stages of HCC progression. For each stage, we conducted cell subgroup analysis to identify cell types at each stage. Horizontally, we explored the dynamic changes of the same cell type across different stages, performing trajectory analysis and prognosis analysis. Vertically, we investigated pairwise comparisons of different stages of HCC progression, probing the dynamic alterations in tumor microenvironment immune cell signaling pathways. Finally, potential drugs for the treatment of HCC were predicted based on relevant genes. FINDINGS: As the HCC advances towards increased malignancy, there is a shift in the predominant composition of the tumor microenvironment, with a decline in the dominance of hepatic cells. Tumor-infiltrating immune cells migrate and accumulate within the tumor microenvironment, where T cells and myeloid cells display distinct patterns of change. Genes associated with cancer-associated fibroblasts (CAFs) and T cells are correlated with adverse patient outcomes. In the late stages of HCC, the tumor microenvironment is infiltrated by more myeloid-derived suppressor cells (MDSCs), and a prognostic model constructed based on genes related to myeloid cells can predict patient outcomes. Additionally, in the analysis of transcription factors, YY1 and MYC are found to be highly expressed. Cell communication analysis among tumor-infiltrating immune cells reveals significant differences in the main signaling pathways at different stages of HCC progression. Finally, drug sensitivity analysis based on key genes identifies Acetalax, Allopurinol, and Amonafide as potential candidates for HCC treatment.
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Single-cell transcriptomic analysis of neuroepithelial cells and other cell types of the gills of zebrafish (Danio rerio) exposed to hypoxia.
The fish gill is a multifunctional organ involved in numerous physiological processes, such as gas exchange and sensing of hypoxia by respiratory chemoreceptors, called neuroepithelial cells (NECs). Many studies have focused on zebrafish (Danio rerio) to investigate the structure, function and development of the gills, yet the transcriptomic profile of most gill cells remains obscure. We present the results of a comprehensive transcriptomic analysis of the gills of zebrafish using single-cell RNA sequencing (scRNA-seq). Gill cells from ETvmat2:EGFP zebrafish were individually labelled before scRNA-seq library construction using 10× Genomics Chromium technology. 12,819 cells were sequenced with an average depth of over 27,000 reads per cell. We identified a median of 485 genes per cell and 16 cell clusters, including NECs, neurons, pavement cells, endothelial cells and mitochondrion-rich cells. The identity of NECs was confirmed by expression of slc18a2, encoding the vesicular monoamine transporter, Vmat2. Highly differentially-expressed genes in NECs included tph1a, encoding tryptophan hydroxylase, sv2 (synaptic vesicle protein), and proteins implicated in O2 sensing (ndufa4l2a, cox8al and epas1a). In addition, NECs and neurons expressed genes encoding transmembrane receptors for serotonergic, cholinergic or dopaminergic neurotransmission. Differential expression analysis showed a clear shift in the transcriptome of NECs following 14 days of acclimation to hypoxia. NECs in the hypoxia group showed high expression of genes involved in cell cycle control and proliferation. The present article provides a complete cell atlas for the zebrafish gill and serves as a platform for future studies investigating the molecular biology and physiology of this organ.
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Differential cellular communication inference framework for large-scale single-cell RNA-sequencing data.
Single-cell transcriptomics data have been widely used to characterize biological systems, particularly in studying cell-cell communication, which plays a significant role in many biological processes. Despite the availability of various computational tools for inferring cellular communication, quantifying variations across different experimental conditions at both intercellular and intracellular levels remains challenging. Moreover, available methods are in general limited in terms of flexibility in analyzing different experimental designs and the ability to visualize results in an easily interpretable way. Here, we present a generalizable computational framework designed to infer and support differential cellular communication analysis across two experimental conditions from large-scale single-cell transcriptomics data. The scSeqCommDiff tool employs a statistical and network-based computational approach for characterizing altered cellular cross-talk in a fast and memory-efficient way. The framework is complemented with CClens, a user-friendly Shiny app to facilitate interactive analysis of inferred cell-cell communication. Validation through spatial transcriptomics data, comparison with other tools, and application to large-scale datasets (including a cell atlas) confirms the reliability, scalability, and efficiency of the framework. Moreover, the application to a single-nucleus transcriptomics dataset shows the validity and ability of the proposed workflow to support and unravel alterations in cell-cell interactions among patients with amyotrophic lateral sclerosis and healthy subjects.
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[Identification of prognostic genes in prostate cancer by single-cell sequencing combined with Mendelian randomization].
OBJECTIVE: To identify the key genes involved in the development and progression of prostate cancer (PCa) and those associated with the prognosis of the malignancy. METHODS: We obtained the single-cell sequencing data on 4 cases of PCa from the GSE156632 database. Using R language and the Seurat package, we performed cell clustering and annotation, selected the subpopulations of epithelial cells for differential analysis after quality control and cell type identification, and conducted enrichment analysis of the identified differential genes using the Hiplot website. Then we downloaded the single nucleotide polymorphism (SNP) loci corresponding to the expression quantitative trait loci (eQTL) of these genes from the UK Biobank (UKB) database, and the clinical data and corresponding gene expression data on PCa patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), followed by univariate COX regression analysis of the impact of the genes on the prognosis of the patients after Mendelian randomization. RESULTS: A total of 1 566 genes were identified and subjected to enrichment analysis, which indicated that the differential genes might be enriched in the Ras, apoptosis and oxidative phosphorylation signaling pathways. Subsequent Mendelian randomization revealed 74 potential causal genes among the 1 566 genes, and univariate COX regression analysis of the 74 genes identified 4 possibly related genes FAM3B, JUNB, TMEM59, and KRT5. Comparison of the results of Mendelian randomization and univariate COX regression showed that KRT5 might be the most important gene influencing PCa. CONCLUSION: FAM3B, JUNB, TMEM59 and KRT5 may play a role in the progression of PCa, and KRT5 may potentially serve as a prognostic predictor and therapeutic target for the malignancy.
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Comparative cellular analysis of motor cortex in human, marmoset and mouse.
The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.
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Single-nucleus RNA sequencing reveals cell type-specific responses to heat stress in bovine mammary gland.
BACKGROUND: Heat stress (HS) poses a major challenge to the dairy industry by reducing milk production, yet its cell type-specific effects in the bovine mammary gland remain incompletely defined. In this study, we recorded production traits and collected mammary biopsies from cows under thermoneutral (TN), HS, and pair-fed (PF) conditions. RESULTS: Clinical measurements confirmed HS-induced physiological alterations. Compared with TN cows, HS cows exhibited reduced dry matter intake (DMI), milk yield, and yields of fat, protein, and lactose, along with increased water intake and milk urea nitrogen. The use of PF controls indicated that decreased DMI accounted for 45% of the milk-yield reduction, whereas direct HS effects accounted for the remaining reduction. We applied single-nucleus RNA-seq (snRNA-seq) on mammary biopsies to generate cell-resolved HS responses. We identified 14 distinct cell clusters, including epithelial, immune, and stromal populations. Under the TN condition, casein genes (e.g., CSN1S1, CSN2) were broadly expressed across luminal cells but were attenuated under HS, whereas luminal alveolar cells showed relative upregulation. Heat shock protein genes were strongly induced by HS, primarily in epithelial clusters. Gene-set enrichment analyses revealed increased ribosomal activities across HS-responsive clusters and enrichment of protein folding and metabolic pathways in luminal alveolar cells, suggesting elevated proteostasis demands under stress. Pseudotime analysis positioned luminal cells along a progenitor-to-secretory trajectory under TN, accompanied by increased casein gene expression, whereas under HS, mature luminal cells shifted toward a homeostasis regulatory state. Cell-cell communication analysis demonstrated HS-induced remodeling of interepithelial signaling, including altered ERBB4-mediated signaling from luminal hormone-sensing to alveolar lineages. Finally, transcription factor activity profiling highlighted cell type-specific HS-activated regulators and their downstream target genes. CONCLUSIONS: Together, this cell type-resolved atlas delineates how HS alters bovine mammary epithelial function, developmental state, and intercellular crosstalk. These findings point to proteostasis pressure, disrupted signaling pathways, and rewired regulatory networks as mechanistic contributors to reduced lactational performance under HS, offering insights for improving heat resilience in dairy cattle.
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Estrogen signaling in PDGFRα+ cells positively regulates cortical bone metabolism via IGFBP5 in female mice.
The prevalence of both osteoporosis and sarcopenia increases with age and about 60% of elderly sarcopenia patients also develop osteoporosis. However, the co-occurrence of osteoporosis and sarcopenia remains unclear. We performed single-cell 5' RNA-seq on human skeletal muscle tissues and investigated the enrichment of heritability for musculoskeletal traits in cell type specific cis-regulatory regions. We found the fibroblast-specific cis-regulatory regions are highly enriched in the heritability of bone mineral density (BMD). Using genome-wide association study, we identified estrogen receptor α (ESR1) as a common transcription factor that correlated with both BMD and lean mass. We hypothesized that deficiency of estrogen signaling in fibroblast may attenuate musculoskeletal homeostasis. Therefore, we generated mice lacking Esr1 in PDGFRα (a fibroblast marker) + cells (Esr1ΔPα ). Although muscle mass and grip strength were not different between groups, distal femoral BMD and cortical thickness were significantly lower in Esr1ΔPα compared to control. Bone histomorphometry showed that cortical bone in Esr1ΔPα exhibited a high turnover bone phenotype. Bulk RNA-seq using PDGFRα+ cells revealed that insulin-like growth factor-binding protein 5 (Igfbp5) expression was significantly higher in Esr1ΔPα compared to control. Furthermore, serum IGFBP5 level was significantly higher in Esr1ΔPα . IGFBP5 treatment in vitro significantly suppressed osteoblast differentiation and facilitated osteoclast differentiation. These results suggest that estrogen signaling in PDGFRα+ cells suppresses Igfbp5 expression, then maintains bone mass, indicating that estrogen signaling in PDGFRα+ cells plays a significant role in bone metabolism.
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Single-cell RNA sequencing reveals a B cell-related immunosuppressive landscape and a potential suppressor in hepatocellular carcinoma.
BACKGROUND: The sophisticated tumour microenvironment is responsible for the malignant progression and poor prognosis of hepatocellular carcinoma (HCC). Discovering new therapeutic targets is desired for the preferable treatment of HCC patients. METHODS: To characterize the HCC microenvironment, the single-cell transcriptomes of HCC tissues and corresponding noncancerous tissues were analysed. Differentially expressed genes (DEGs), enriched pathways and subgroups were analysed in B cells. Moreover, heterogeneity between malignant and normal hepatocytes was further investigated, which revealed potential biomarkers for HCC progression. The candidate biomarkers were further explored in datasets from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Serum amyloid A2 (SAA2) was detected and further validated in HCC tissues by immunohistochemistry (IHC) and western blot analysis. The biological roles of SAA2 were further investigated in HCC cells. RESULTS: The number of B cells in HCC tissues was significantly lower than that in noncancerous tissues, which may result in an immunosuppressive status of the HCC microenvironment. Differentially expressed genes (DEGs) and functional enrichment analysis revealed that B cells might participate in the immunosuppression of HCC by regulating lipid metabolism. Analysis on B cell subgroups demonstrated Naïve B cells were significantly reduced in HCC tissues compared with noncancerous tissues, which indicated that Naïve B cells might be pivotal in the B cell-related immunosuppressive landscape in HCC. Further analysis of hepatocytes revealed highly expressed genes in normal hepatocytes derived from noncancerous liver tissues, which were validated in datasets from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Interestingly, serum amyloid A2 (SAA2) was highly expressed in normal liver tissues compared with HCC tissues. The results were validated in clinical HCC samples by immunohistochemistry (IHC) and western blot assays. Moreover, investigations in HCC cells revealed that SAA2 acted as a tumour suppressor in HCC progression. CONCLUSIONS: Taken together, the present findings elucidated the B cell-related immunosuppressive landscape in HCC and identified SAA2 as a novel suppressor in HCC, providing a better understanding of the HCC landscape and suggesting a promising therapeutic target for HCC patients.
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Single-cell transcriptomic analysis of the human vascular atlas provides new insights into vasorelaxation redundancy and heterogeneity.
BACKGROUND: Endothelial cells (ECs) induce vascular smooth muscle cells (VSMCs) relaxation via nitric oxide (NO), prostacyclin (PGI₂) and hyperpolarizing factors. Recent whole-genomic, single-cell transcriptomic analysis of human vascular cells has revealed angiotypic heterogeneity. However, it remains unknown whether vasorelaxant mediators reiterate this pattern. WORKING HYPOTHESIS: The expression of "sentinel" gene transcripts provides a first insight into angiotypic and organotypic heterogeneity of vasorelaxation. METHODS: The expression of NO- and PGI2-generating enzymes and potassium channels was evaluated by analyzing single-cell RNA-sequencing data derived from the Human Vascular Cell Atlas. The data were transformed into a Seurat object (ShinyCell) and processed using the RStudio software. The results were visualized through uniform manifold approximation (UMAP) and projection representations of single-cell profiles. RESULTS: NO synthase (NOS3) expression differed across EC subpopulations, with the highest enrichment in spleen littoral ECs, followed by venous, arterial, and capillary ECs. PGI2 synthase (PTGIS) demonstrated the highest frequency in arterial and venous ECs and VSMCs. At the same time, it was low in capillary, littoral, and lymphatic ECs and pericytes. The PGI2 receptor gene (PTGIR) was expressed in vascular mural cells. A marked angiotypic heterogeneity was noted regarding potassium channels. Overall, the gene transcripts mentioned above were rarely co-expressed. Comparing two cohorts aged 20 to 49 and 50 to 80 revealed NOS3 expression was less frequent in venous and littoral ECs of older individuals. In contrast, arterial and capillary ECs were modestly affected by age. PTGIS frequency was elevated with aging in VSMCs and, to a lesser extent, in venous and arterial ECs. The KCNMA1 gene, which encodes the big potassium channel alpha subunit 1, was almost doubled in the VSMCs from the older group. Finally, organotypic differences were identified in vascular cells derived from the coronary arteries, brain, and uterus. CONCLUSION: This initial report indicates a striking heterogeneity in the expression of genes encoding vasorelaxant pathways in human vascular cells, with age exerting angiotypic influences. Reiteration on a larger number of cells and genes, as well as validation of data using post-transcriptional methods, is warranted to firmly confirm the hypothesis about redundant heterogeneity of vasorelaxant mechanisms.
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Analysis of cell fate from single-cell gene expression profiles in C. elegans.
The C. elegans cell lineage provides a unique opportunity to look at how cell lineage affects patterns of gene expression. We developed an automatic cell lineage analyzer that converts high-resolution images of worms into a data table showing fluorescence expression with single-cell resolution. We generated expression profiles of 93 genes in 363 specific cells from L1 stage larvae and found that cells with identical fates can be formed by different gene regulatory pathways. Molecular signatures identified repeating cell fate modules within the cell lineage and enabled the generation of a molecular differentiation map that reveals points in the cell lineage when developmental fates of daughter cells begin to diverge. These results demonstrate insights that become possible using computational approaches to analyze quantitative expression from many genes in parallel using a digital gene expression atlas.