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Single-cell dissection of shoulder-derived mesenchymal stem cells reveals source-specific progenitor heterogeneity and a SIX2-driven chondrogenic program.
BACKGROUND: Mesenchymal stem cells (MSCs) within the shoulder joint serve as a critical progenitor pool for regenerating the fibrocartilaginous enthesis during rotator cuff repair. However, the chondrogenic potential of MSCs across distinct rotator cuff sites, along with the underlying molecular mechanisms, remains to be elucidated. METHODS: We performed a comparative analysis of human subacromial bursa-derived MSCs (sMSCs) and rotator cuff enthesis-derived MSCs (rMSCs). By integrating functional chondrogenic assays with longitudinal bulk and single-cell RNA sequencing, we reconstructed source-specific differentiation trajectories and identified key regulatory drivers. RESULTS: rMSCs exhibited superior chondrogenic potency compared to sMSCs. Transcriptional profiling identified a region-specific pro-chondrogenic module in rMSCs driven by the transcription factor SIX2. Single-cell atlas construction revealed that tissue origin dictates lineage fate: rMSCs were enriched for a high-potency SIX2+ progenitor population that differentiated into metabolic-active S100A2+ chondrocytes. Conversely, sMSCs were dominated by DPP4+ progenitors that preferentially bifurcated into an aberrant CXCL8+ inflammatory trajectory. Mechanistically, we demonstrated that SIX2 acted as a competency factor, coordinating early proliferative expansion with late-stage matrix assembly and, crucially, active suppression of inflammatory signaling. Accordingly, lentiviral overexpression of SIX2 in sMSCs was sufficient to rescue their chondrogenic defects and rewire their trajectory toward a regenerative phenotype. CONCLUSIONS: Our findings define the transcriptional hierarchy of shoulder-resident progenitors, identifying SIX2 as a master regulator that couples chondrogenesis with immune evasion. This establishes a molecular framework for precision cell sourcing and rational design of lineage-specific therapies.
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Identification of Hub Genes Associated With Clear Cell Renal Cell Carcinoma by Integrated Bioinformatics Analysis.
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a common genitourinary cancer type with a high mortality rate. Due to a diverse range of biochemical alterations and a high level of tumor heterogeneity, it is crucial to select highly validated prognostic biomarkers to be able to identify subtypes of ccRCC early and apply precision medicine approaches. METHODS: Transcriptome data of ccRCC and clinical traits of patients were obtained from the GSE126964 dataset of Gene Expression Omnibus and The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database. Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening were applied to detect common differentially co-expressed genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, survival analysis, prognostic model establishment, and gene set enrichment analysis were also performed. Immunohistochemical analysis results of the expression levels of prognostic genes were obtained from The Human Protein Atlas. Single-gene RNA sequencing data were obtained from the GSE131685 and GSE171306 datasets. RESULTS: In the present study, a total of 2,492 DEGs identified between ccRCC and healthy controls were filtered, revealing 1,300 upregulated genes and 1,192 downregulated genes. Using WGCNA, the turquoise module was identified to be closely associated with ccRCC. Hub genes were identified using the maximal clique centrality algorithm. After having intersected the hub genes and the DEGs in GSE126964 and TCGA-KIRC dataset, and after performing univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses, ALDOB, EFHD1, and ESRRG were identified as significant prognostic factors in patients diagnosed with ccRCC. Single-gene RNA sequencing analysis revealed the expression profile of ALDOB, EFHD1, and ESRRG in different cell types of ccRCC. CONCLUSIONS: The present results demonstrated that ALDOB, EFHD1, and ESRRG may act as potential targets for medical therapy and could serve as diagnostic biomarkers for ccRCC.
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Comprehensive Pan-Cancer Analysis of the Prognostic Role of KLF Transcription Factor 2 (KLF2) in Human Tumors.
BACKGROUND: KLF2 is a transcription factor expressed early in mammalian development that plays a role in many processes of development and disease. Recently, increasing studies revealed that KLF2 plays a key role in the occurrence and progression of cancer. PURPOSE: The aim of this study was to explore the role of KLF2 in various tumor types using the Cancer Genome Atlas dataset. METHODS: Here, we set out to explore the role of KLF2 in 33 tumor types using TCGA (The Cancer Genome Atlas), GEO (Gene Expression Omnibus) dataset, Human Protein Atlas (HPA), UALCAN database, CancerSEA, GSCALite and several bioinformatic tools. Furthermore, we also performed immunohistochemistry and qPCR to further validate the role of KLF2 in multiple cancers and its correlation with prognosis. RESULTS: We found that KLF2 was underexpressed in most tumors and generally predicted poor OS in tumor patients. We found that amplification of KLF2 may be a risk factor for patients with OV (Ovarian serous cystadenocarcinoma). We also analyzed the abundance of checkpoints and markers of specific immune subsets including CD8+ T lymphocytes (T cells), CD4+ T cells, macrophages, and endothelial cells that significantly correlated with the expression level of KLF2 in pan-carcinoma tissues. In some cancers, KLF2 expression levels are positively correlated with gene promoter DNA methylation and drug sensitivity. In addition, we found that KLF2 is involved in single-cell level cell invasion in some cancers. In addition, KLF2 is co-expressed with several intracellular signal transduction genes involved in immune system processes. Immunohistochemistry and qPCR confirmed the low expression of KLF2 in STAD (stomach adenocarcinoma) and renal cancer. CONCLUSION: Our pan-cancer analysis provides a comprehensive overview of the oncogenic roles of KLF2 in multiple human cancers and can be regarded as a potential prognostic marker and a novel target for cancer immunotherapy.
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The role of single cell transcriptomics for efficacy and toxicity profiling of chimeric antigen receptor (CAR) T cell therapies.
CAR T cells are genetically modified T cells that target specific epitopes. CAR T cell therapy has proven effective in difficult-to-treat B cell cancers and is now expanding into hematology and solid tumors. To date, approved CAR therapies target only two specific epitopes on cancer cells. Identifying more suitable targets is challenged by the lack of truly cancer-specific structures and the potential for on-target off-tumor toxicity. We analyzed gene expression of potential targets in single-cell data from cancer and healthy tissues. Because safety and efficacy can ultimately only be defined clinically, we selected approved and investigational targets for which clinical trail data are available. We generated atlases using >300,000 cells from 48 patients with follicular lymphoma, multiple myeloma, and B-cell acute lymphoblastic leukemia, and integrated over 3 million cells from 35 healthy tissues, harmonizing datasets from over 300 donors. To contextualize findings, we compared target expression patterns with outcome data from clinical trials, linking target profiles to efficacy and toxicity, and ranked 15 investigational targets based on their similarity to approved ones. Target expression did not significantly correlate with reported clinical toxicities in patients undergoing therapy. This may be attributed to the intricate interplay of patient-specific variables, the limited amount of metadata, and the complexity underlying toxicity. Nevertheless, our study serves as a resource for retrospective and prospective target evaluation to improve the safety and efficacy of CAR therapies.
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Molecular Characteristics and Distribution of Adult Human Corneal Immune Cell Types.
BACKGROUND: The limbus is located at a 2-mm-wide area between the bulbar conjunctiva and the cornea and has been suggested to be the niche of corneal epithelial stem cells and immune cells. Like the skin and intestines, the cornea is also an important mucosal surface, and immune cells on the cornea play critical roles in immune surveillance to ensure barrier surface homeostasis and protection from various environmental damage and infections. Single-cell RNA sequencing (scRNA-seq) analysis of protein tyrosine phosphatase receptor type C positive (PTPRC+) hematopoietic cells from the corneal limbus could provide a single cell atlas of all the immune cell subsets. METHODS: We performed single-cell RNA sequencing to generate transcriptomic profile for 804 sort-purified hematopoietic cells from the corneal limbus of three healthy donors. RESULTS: Our analysis identified a primary transcriptomic pattern for multiple immune cell subtypes, including naive T cells, antiviral effector CD8+ T cells, and innate immune cells such as IDO1+ mature regulatory dendritic cells (mregDCs), macrophages, monocytes, and basophils in the human corneal limbus. CONCLUSION: Overall, single-cell transcriptomic analysis of limbal immune cells suggested the possible contribution of these cells on the adaptive and innate immune response of the human cornea.
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Distinct single-nucleus RNA-seq changes among non-neuronal cells in ADNC, LATE-NC, and mixed pathologies.
INTRODUCTION: Limbic-predominant age-related transactive response DNA binding protein 43 kDa encephalopathy neuropathological change (LATE-NC) frequently co-occurs with Alzheimer's disease neuropathologic change (ADNC), complicating classification. Cell type-specific molecular features distinguishing LATE-NC-dominant, ADNC-dominant, and mixed LATE-NC and ADNC pathology remain incompletely characterized. METHODS: We analyzed single-nucleus RNA sequencing data from the Seattle Alzheimer's Disease Brain Cell Atlas consortium, focusing on non-neuronal cells in the middle temporal gyrus (MTG). Donors were stratified into LATE-NC dominant, ADNC dominant, and mixed LATE-NC and ADNC. Differential expression and gene set enrichment analyses were performed across glial and vascular cell supertypes. RESULTS: Distinct transcriptional patterns differentiated pathologies. Astrocytes, microglia, and oligodendrocytes exhibited differential expression between LATE-NC- and ADNC-dominant cases. Representative genes and pathways highlighted these distinctions, with LATE-NC dominant showing enrichment in signaling and RNA regulation, ADNC dominant in mitochondrial dysfunction, and mixed pathology in translational dysregulation and lipid remodeling, suggesting a distinct biological state. DISCUSSION: Findings highlight biological heterogeneity and non-neuronal molecular features relevant to disease classification and future research. CLINICAL TRIAL REGISTRATION INFORMATION: Not applicable. This study is a secondary analysis of publicly available Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) data.
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Single-Nucleus Transcriptomic Atlas Reveals Disease-Associated Cellular States in Human Glomerular Diseases.
INTRODUCTION: Glomerular diseases are a major cause of chronic kidney disease and kidney failure worldwide, yet the cellular mechanisms underlying their heterogeneity remain incompletely understood. Single-nucleus RNA sequencing (snRNA-seq) enables high-resolution characterization of transcriptional alterations across diverse kidney cell populations. METHODS: We performed snRNA-seq on snap-frozen kidney biopsy tissues obtained from treatment-naïve patients with minimal change disease (MCD), membranous nephropathy (MN), IgA nephropathy (IgAN), and diabetic kidney disease (DKD), together with control kidney samples. In total, 333,938 nuclei from 37 individuals were analyzed to define disease-associated transcriptional programs across kidney cell types. RESULTS: Upregulated transcripts formed disease-specific programs that were shared across multiple cell types, whereas downregulated transcripts were largely cell type-specific and conserved across diseases. We identified disease-associated cellular states, including a glomerular epithelial population exhibiting a hybrid transcriptional profile shared between podocytes and parietal epithelial cells (PECs); a podocyte subpopulation with endocytosis-related gene expression, correlated with proteinuria severity and treatment response; and expansion of periostin (POSTN)-expressing myofibroblasts, inversely associated with estimated glomerular filtration rate (eGFR) and spatially colocalized with podocyte injury. Each cellular state was independently recovered in external single-cell cohorts. CONCLUSION: Single-nucleus transcriptomic profiling of human kidney biopsy tissues reveals disease-specific and shared cellular programs across major glomerular diseases. This resource provides insight into the cellular mechanisms underlying glomerular injury and identifies disease-associated cell states that may contribute to disease progression.
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Migrasome-related long non-coding RNAs orchestrate immune microenvironment and serve as a novel prognostic model in clear cell renal cell carcinoma.
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) represents the most common histological subtype of kidney cancer and constitutes a major global health burden. Despite increasing recognition of the roles of migrasome-related long non-coding RNAs (MRLs) in tumor biology, their prognostic relevance in ccRCC remains largely undefined. Therefore, this study aimed to systematically identify MRLs associated with ccRCC prognosis and construct a robust prognostic model to improve risk stratification and guide potential therapeutic strategies. METHODS: To identify MRLs, we initially performed a correlation analysis integrating transcriptomic profiles with clinical parameters of ccRCC patients from The Cancer Genome Atlas (TCGA). Leveraging the expression matrix of MRLs, we subsequently employed the least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic signature. A comprehensive assessment was carried out to determine the model's predictive robustness. At single-cell resolution, we delineated the cellular composition of ccRCC, capturing transcriptional heterogeneity across distinct cell populations. Ultimately, long non-coding RNAs (lncRNAs) derived from the prognostic model were experimentally validated in clinical specimens through reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and the landscape of migrasome-related genes (MRGs) was further defined using single-cell RNA sequencing (scRNA-seq) data. RESULTS: A prognostic signature comprising five MRLs-EMX2OS, AC106897.1, AC087645.2, AC121338.2, and C5orf66-was established to stratify patient risk. The derived risk score was subsequently validated as an independent predictor of overall survival (OS) in ccRCC patients. A nomogram integrating this score exhibited strong predictive capability. Immune landscape analysis uncovered marked differences in functional immune features between high- and low-risk cohorts defined by the lncRNA-based model. Notably, the high-risk group displayed enrichment of immune-related processes, whereas the low-risk group demonstrated enhanced predicted responsiveness to a spectrum of therapeutic compounds. scRNA-seq identified 17 distinct cellular subpopulations and highlighted the involvement of tumor cell-intrinsic vascular endothelial growth factor (VEGF) signaling in modulating migrasome-associated molecular programs. CONCLUSIONS: This study emphasizes the prognostic utility of a signature comprising five MRLs for ccRCC, offering valuable insights for clinical risk stratification and therapeutic decision-making. Additionally, modulation of tumor cell migration and migrasome function via the VEGF signaling pathway offers a mechanistic basis for targeted intervention.
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KMT2D deficiency leads to cellular developmental disorders and enhancer dysregulation in neural-crest-containing brain organoids.
KMT2D, a H3K4me1 methyltransferase primarily regulating enhancers, is a leading cause of KABUKI syndrome. This multisystem disorder leads to craniofacial and cognitive abnormalities, possibly through neural crest and neuronal lineages. However, the impacted cell-of-origin and molecular mechanism of KMT2D during the development of KABUKI disease remains unknown. Here we have optimized a brain organoid model to investigate neural crest and neuronal differentiation. To pinpoint KMT2D's enhancer target, we developed a genome-wide cis-regulatory element explorer (GREE) based on single-cell multiomic integration. Single cell RNA-seq revealed that KMT2D-knockout (KO) and patient-derived organoids exhibited neural crest deformities and GABAergic overproduction. Mechanistically, GREE identified that KMT2D targets a roof-plate-like niche cell and activates the niche cell-specific WNT3A enhancer, providing the microenvironment for neural crest and neuronal development. Interestingly, KMT2D-mutated mice displayed decreased WNT3A expression in the diencephalon roof plate, indicating impaired niche cell function. Deleting the WNT3A enhancer in the organoids presented phenotypic similarities to KMT2D-depletion, emphasizing the WNT3A enhancer as the predominant target of KMT2D. Conversely, reactivating WNT signaling in KMT2D-KO rescued the lineage defects by restoring the microenvironment. Overall, our discovery of KMT2D's primary target provides insights for reconciling complex phenotypes of KABUKI syndrome and establishes a new paradigm for dissecting the mechanisms of genetic disorders from genotype to phenotype.
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The cell type composition of the adult mouse brain revealed by single cell and spatial genomics.
The function of the mammalian brain relies upon the specification and spatial positioning of diversely specialized cell types. Yet, the molecular identities of the cell types, and their positions within individual anatomical structures, remain incompletely known. To construct a comprehensive atlas of cell types in each brain structure, we paired high-throughput single-nucleus RNA-seq with Slide-seq-a recently developed spatial transcriptomics method with near-cellular resolution-across the entire mouse brain. Integration of these datasets revealed the cell type composition of each neuroanatomical structure. Cell type diversity was found to be remarkably high in the midbrain, hindbrain, and hypothalamus, with most clusters requiring a combination of at least three discrete gene expression markers to uniquely define them. Using these data, we developed a framework for genetically accessing each cell type, comprehensively characterized neuropeptide and neurotransmitter signaling, elucidated region-specific specializations in activity-regulated gene expression, and ascertained the heritability enrichment of neurological and psychiatric phenotypes. These data, available as an online resource (BrainCellData.org) should find diverse applications across neuroscience, including the construction of new genetic tools, and the prioritization of specific cell types and circuits in the study of brain diseases.