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Cell Differentiation Trajectory in Liver Cirrhosis Predicts Hepatocellular Carcinoma Prognosis and Reveals Potential Biomarkers for Progression of Liver Cirrhosis to Hepatocellular Carcinoma.
Most hepatocellular carcinoma (HCC) patients occur on a background of liver cirrhosis, the molecular mechanisms of liver cirrhosis and its progression to HCC remain to be fully elucidated. Single cell differentiation trajectory analysis has been used in cell classification and tumor molecular typing, which correlated with disease progression and patient prognosis. Here we use cell differentiation trajectory analysis to investigate the relevance of liver cirrhosis and HCC. Single-cell RNA sequencing (scRNA-seq) data of liver cirrhosis and bulk RNA-seq and clinical data of HCC were downloaded from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) for analysis. HCC samples were divided into three subtypes, based on differentiation-related genes (DRGs) of liver cirrhosis, each with a different expression profile and overall survival (OS). A two- DRGs (CD34 and RAMP3) based prognostic risk scoring (RS) signature was established which could differentiate OS between high-risk and low-risk groups. And expression levels of CD34 and RAMP3 were predominantly high in endothelial cells. By integrating the RS and clinicopathological features, a nomogram was constructed and can accurately predicted the 1-year, 3-years, and 5-years OS. In conclusion, cell differentiation trajectory of liver cirrhosis can predict the prognosis of HCC, and provides new perspectives on the mechanisms of progression of liver cirrhosis to HCC.
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Association of the CXCL-ACKR1 Signaling Axis with the Angiogenic Microenvironment in Endometrial Cancer: A Single-Cell Transcriptomic Analysis.
PURPOSE: To investigate the role of the CXCL signaling pathway in angiogenesis of endometrial cancer and evaluate its potential as a therapeutic target. PATIENTS AND METHODS: Single-cell RNA sequencing (scRNA-seq) data from 18 endometrial cancer and normal control tissues were integrated with The Cancer Genome Atlas (TCGA) database. Data were analyzed through clustering and cell type annotation to explore the tumor microenvironment, followed by cell-cell interaction analysis to assess the activity of the CXCL signaling pathway. We then quantified CXCL pathway activity using CellChat-derived ligand-receptor communication metrics (communication probability/interaction strength) and evaluated supporting gene expression differences for key ligands and receptor across tumor versus control samples. RESULTS: ScRNA-seq revealed a complex tumor microenvironment composed of epithelial, stromal, immune, and endothelial cells. The CXCL signaling pathway was significantly activated in endometrial cancer. Dominant signaling axes such as CXCL8-ACKR1, CXCL2-ACKR1, and CXCL3-ACKR1 were identified. CXCL8, CXCL2, and CXCL3 were highly expressed in tumor tissues and were predicted to interact with ACKR1 expressed on endothelial cells, suggesting a potential role of the CXCL-ACKR1 axis in regulating tumor-associated angiogenic signaling. CONCLUSION: This study support an association between the CXCL-ACKR1 axis and endothelial-targeted signaling patterns consistent with angiogenesis in endometrial cancer. ACKR1 may serve as a promising anti-angiogenic therapeutic target, offering new insights into precision treatment strategies for endometrial cancer.
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Single-cell transcriptomics and time-series metabolite profiling reveal the spatiotemporal regulation of flavonoid biosynthesis genes and phytohormone homeostasis by PAP1 in Arabidopsis.
BACKGROUND: Understanding the spatiotemporal regulation of specialized metabolism in plants is critical for advancing both basic plant biology and biotechnological applications. PRODUCTION OF ANTHOCYANIN PIGMENT 1 (PAP1) is a well-known transcription factor that plays a key regulatory role in the biosynthesis pathway of plant flavonoids. Similar to other secondary metabolites, flavonoid biosynthesis displays cell heterogeneity. However, the cell-specific regulation network of the flavonoid biosynthetic pathway remains unclear. RESULTS: In this study, we utilized single-cell RNA sequencing (scRNA-seq) and time-series metabolite profiling to investigate the regulation of flavonoid biosynthesis and phytohormone homeostasis in Arabidopsis thaliana by PAP1. By comparing single-cell transcriptomes of the pap1-D mutant and wild-type plant leaves, we constructed a cell-type-specific atlas of gene expression and high-resolution dynamics of metabolites across developmental stages. Our findings reveal that PAP1 overexpression induces distinct spatiotemporal regulation of phenylpropanoid pathway genes in different cell types and widespread upregulation of glycosylation processes. Metabolomic profiling validated these transcriptional patterns and showed significant changes of metabolites in phenylalanine metabolic processes as pap1-D leaf matures. Additionally, PAP1 overexpression leads to significant changes in phytohormone levels, particularly jasmonate and salicylate, indicating complex crosstalk between flavonoid biosynthesis and hormone homeostasis. CONCLUSIONS: This integrated multi-omics approach provides unprecedented insights into the cell-specific regulatory networks controlling specialized metabolism and establishes a valuable framework for optimizing metabolic engineering strategies to enhance the production of bioactive plant compounds.
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Single-Cell RNA Sequencing Reveals the Cellular Landscape of Longissimus Dorsi in a Newborn Suhuai Pig.
The introduction of single-cell RNA sequencing (scRNA-seq) technology has spurred additional advancements in analyzing the cellular composition of tissues. The longissimus dorsi (LD) in pigs serves as the primary skeletal muscle for studying meat quality in the pig industry. However, the single-cell profile of porcine LD is still in its infancy stage. In this study, we profiled the transcriptomes of 16,018 cells in the LD of a newborn Suhuai pig at single-cell resolution. Subsequently, we constructed a cellular atlas of the LD, identifying 11 distinct cell populations, including endothelial cells (24.39%), myotubes (18.82%), fibro-adipogenic progenitors (FAPs, 18.11%), satellite cells (16.74%), myoblasts (3.99%), myocytes (5.74%), Schwann cells (3.81%), smooth muscle cells (3.22%), dendritic cells (2.99%), pericytes (1.86%), and neutrophils (0.33%). CellChat was employed to deduce the cell-cell interactions by evaluating the gene expression of receptor-ligand pairs across different cell types. The results show that FAPs and pericytes are the primary signal contributors in LD. In addition, we delineated the developmental trajectory of myogenic cells and examined alterations in the expression of various marker genes and molecular events throughout various stages of differentiation. Moreover, we found that FAPs can be divided into three subclusters (NR2F2-FAPs, LPL-FAPs, and TNMD-FAPs) according to their biological functions, suggesting that the FAPs could be associated with the differentiation of tendon cell. Taken together, we constructed the cellular atlas and cell communication network in LD of a newborn Suhuai pig, and analyzed the developmental trajectory of myogenic cells and the heterogeneity of FAPs subpopulation cells. This enhances our comprehension of the molecular features involved in skeletal muscle development and the meat quality control in pigs.
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Single-cell insights: pioneering an integrated atlas of chromatin accessibility and transcriptomic landscapes in diabetic cardiomyopathy.
BACKGROUND: Diabetic cardiomyopathy (DCM) poses a growing health threat, elevating heart failure risk in diabetic individuals. Understanding DCM is crucial, with fibroblasts and endothelial cells playing pivotal roles in driving myocardial fibrosis and contributing to cardiac dysfunction. Advances in Multimodal single-cell profiling, such as scRNA-seq and scATAC-seq, provide deeper insights into DCM's unique cell states and molecular landscape for targeted therapeutic interventions. METHODS: Single-cell RNA and ATAC data from 10x Multiome libraries were processed using Cell Ranger ARC v2.0.1. Gene expression and ATAC data underwent Seurat and Signac filtration. Differential gene expression and accessible chromatin regions were identified. Transcription factor activity was estimated with chromVAR, and Cis-coaccessibility networks were calculated using Cicero. Coaccessibility connections were compared to the GeneHancer database. Gene Ontology analysis, biological process scoring, cell-cell communication analysis, and gene-motif correlation was performed to reveal intricate molecular changes. Immunofluorescent staining utilized various antibodies on paraffin-embedded tissues to verify the findings. RESULTS: This study integrated scRNA-seq and scATAC-seq data obtained from hearts of WT and DCM mice, elucidating molecular changes at the single-cell level throughout the diabetic cardiomyopathy progression. Robust and accurate clustering analysis of the integrated data revealed altered cell proportions, showcasing decreased endothelial cells and macrophages, coupled with increased fibroblasts and myocardial cells in the DCM group, indicating enhanced fibrosis and endothelial damage. Chromatin accessibility analysis unveiled unique patterns in cell types, with heightened transcriptional activity in myocardial cells. Subpopulation analysis highlighted distinct changes in cardiomyocytes and fibroblasts, emphasizing pathways related to fatty acid metabolism and cardiac contraction. Fibroblast-centered communication analysis identified interactions with endothelial cells, implicating VEGF receptors. Endothelial cell subpopulations exhibited altered gene expressions, emphasizing contraction and growth-related pathways. Candidate regulators, including Tcf21, Arnt, Stat5a, and Stat5b, were identified, suggesting their pivotal roles in DCM development. Immunofluorescence staining validated marker genes of cell subpopulations, confirming PDK4, PPARγ and Tpm1 as markers for metabolic pattern-altered cardiomyocytes, activated fibroblasts and endothelial cells with compromised proliferation. CONCLUSION: Our integrated scRNA-seq and scATAC-seq analysis unveils intricate cell states and molecular alterations in diabetic cardiomyopathy. Identified cell type-specific changes, transcription factors, and marker genes offer valuable insights. The study sheds light on potential therapeutic targets for DCM.
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Single-cell and high-resolution spatial profiling of podocytopathies reveals core mechanisms of podocyte injury.
Podocytes (PODs) play a critical role in maintaining glomerular filtration function, and their injury is a key driver of progressive podocytopathies (PCPs). Despite their clinical importance, the molecular and spatial mechanisms underlying POD injury remain poorly understood. We generated a single-nucleus RNA sequencing (snRNA-seq) and high-resolution spatial transcriptomic (ST) dataset from kidney tissues of patients with five major PCP subtypes. Our analysis revealed distinct molecular signatures among PCP subtypes, injury-related cytoskeletal alterations, and POD-centered microenvironmental features associated with clinical outcomes. Through integrative analysis, we found PDE4DIP as a previously unrecognized regulator of the POD cytoskeleton. Functional experiments through in vitro and in vivo gene editing demonstrated that PDE4DIP maintains cytoskeletal integrity by scaffolding with AKAP9 and activating RAS-ERK and AKT signaling. Its expression correlated with renal function and prognosis. This study provides the comprehensive snRNA-seq and high-resolution ST atlas of PCPs, offering a valuable resource for understanding POD injury and identifying potential therapeutic targets.
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Inhibitory CD161 receptor identified in glioma-infiltrating T cells by single-cell analysis.
T cells are critical effectors of cancer immunotherapies, but little is known about their gene expression programs in diffuse gliomas. Here, we leverage single-cell RNA sequencing (RNA-seq) to chart the gene expression and clonal landscape of tumor-infiltrating T cells across 31 patients with isocitrate dehydrogenase (IDH) wild-type glioblastoma and IDH mutant glioma. We identify potential effectors of anti-tumor immunity in subsets of T cells that co-express cytotoxic programs and several natural killer (NK) cell genes. Analysis of clonally expanded tumor-infiltrating T cells further identifies the NK gene KLRB1 (encoding CD161) as a candidate inhibitory receptor. Accordingly, genetic inactivation of KLRB1 or antibody-mediated CD161 blockade enhances T cell-mediated killing of glioma cells in vitro and their anti-tumor function in vivo. KLRB1 and its associated transcriptional program are also expressed by substantial T cell populations in other human cancers. Our work provides an atlas of T cells in gliomas and highlights CD161 and other NK cell receptors as immunotherapy targets.
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Integrated single-cell and bulk transcriptomic analysis identifies a novel senescent fibroblast subtype associated with poor prognosis in acral melanoma.
BACKGROUND: Acral melanoma (AM) exhibits significant intratumoral heterogeneity, but its tumor microenvironment (TME) and immune regulation remain unclear. This study aims to dissect TME heterogeneity and establish a prognostic model based on key cell subpopulations. METHODS: We collected AM single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA). Unsupervised clustering, CellChat, and Scissor analysis were performed to characterize cellular heterogeneity, cell-cell communication, and prognosis-related cell subpopulations. Kaplan-Meier analysis was used to assess the prognostic value of key genes, which were further validated by multiplex immunohistochemistry (mIHC). RESULTS: In AM, Mel_C2, C7, and C9 with high SEMA6A and KIT expression were strongly linked to poor prognosis. We further identified a senescent fibroblast subpopulation (sCAF_CDKN2A) characterized by high fibroblast senescence signature (FSS) scores. Integrating Scissor analysis of fibroblast subtypes with bulk prognostic data, we identified COL3A1, VCAN, and KIT as prognosis-associated genes upregulated in poor-outcome-related fibroblast subsets. Cell-cell communication analysis revealed that sCAF_CDKN2A engages in an immunosuppressive network, interacting with regulatory T cells (Tregs) via MIF signaling and receiving signals from exhausted CD8+ T cells through PPIA-BSG interactions. Using transcription factor expression patterns from these fibroblast subtypes, we constructed a prognostic model that effectively stratified patients into distinct risk groups with significant differences in overall survival (OS). mIHC confirmed significantly higher protein levels of SEMA6A and COL3A1 in tumor tissues compared to matched normal tissues. CONCLUSIONS: We established a novel prognostic model for AM and identified sCAF_CDKN2A as an immunosuppressive senescent fibroblast subpopulation driving poor prognosis.
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Single-cell RNA profiling of oligodendroglial lineage cells derived from iPSCs carrying Parkinson's disease-relevant LRRK2-G2019S mutation.
Recent findings indicate oligodendrocytes (ODCs) and their progenitors are vulnerable in Parkinson's disease (PD) pathogenesis. Notably, oligodendrocyte precursor cells (OPCs) exhibit high endogenous expression of the PD-relevant gene LRRK2. We differentiated iPSCs carrying the PD-relevant LRRK2 p.G2019S mutation into oligodendroglial lineages and performed single-cell RNA sequencing. Cell-type composition analysis revealed an increased proportion of OPCs, proliferating OPCs, and ciliated ependymal cells in LRRK2 lines, all of which are characterized by LRRK2 expression. Differential expression analysis revealed transcriptomic changes in several pathways, including downregulation of genes related to myelin assembly in ODCs, semaphorin-plexin pathway in OPCs, and cilium movement in proliferating OPCs. Pseudotemporal trajectory analysis revealed that SHH had significantly altered expression along the pseudotime, accompanied by higher expression levels in LRRK2 lines. We provide a single-cell transcriptomic atlas of iPSC-derived oligodendroglial lineage cells carrying the PD-relevant LRRK2 p.G2019S mutation and control lines.
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Decoding the Spermatogenesis Program: New Insights from Transcriptomic Analyses.
Spermatogenesis is a complex differentiation process coordinated spatiotemporally across and along seminiferous tubules. Cellular heterogeneity has made it challenging to obtain stage-specific molecular profiles of germ and somatic cells using bulk transcriptomic analyses. This has limited our ability to understand regulation of spermatogenesis and to integrate knowledge from model organisms to humans. The recent advancement of single-cell RNA-sequencing (scRNA-seq) technologies provides insights into the cell type diversity and molecular signatures in the testis. Fine-grained cell atlases of the testis contain both known and novel cell types and define the functional states along the germ cell developmental trajectory in many species. These atlases provide a reference system for integrated interspecies comparisons to discover mechanistic parallels and to enable future studies. Despite recent advances, we currently lack high-resolution data to probe germ cell-somatic cell interactions in the tissue environment, but the use of highly multiplexed spatial analysis technologies has begun to resolve this problem. Taken together, recent single-cell studies provide an improvedunderstanding of gametogenesis to examine underlying causes of infertility and enable the development of new therapeutic interventions.