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Single cell spatial transcriptomic profiling of childhood-onset lupus nephritis reveals complex interactions between kidney stroma and infiltrating immune cells.
Children with systemic lupus erythematosus (SLE) are at increased risk of developing kidney disease, termed childhood-onset lupus nephritis (cLN). Single cell transcriptomics of dissociated kidney tissue has advanced our understanding of LN pathogenesis, but loss of spatial resolution prevents interrogation of in situ cellular interactions. Using a technical advance in spatial transcriptomics, we generated a spatially resolved, single cell resolution atlas of kidney tissue (>400,000 cells) from eight cLN patients and two controls. Annotated cells were assigned to 35 reference cell types, including major kidney subsets and infiltrating immune cells. Analysis of spatial distribution demonstrated that individual immune lineages localize to specific regions in cLN kidneys, including myeloid cells trafficking to inflamed glomeruli and B cells clustering within tubulointerstitial immune hotspots. Notably, gene expression varied as a function of tissue location, demonstrating how incorporation of spatial data can provide new insights into the immunopathogenesis of SLE. Alterations in immune phenotypes were accompanied by parallel changes in gene expression by resident kidney stromal cells. However, there was little correlation between histologic scoring of cLN disease activity and glomerular cell transcriptional signatures at the level of individual glomeruli. Finally, we identified modules of spatially-correlated gene expression with predicted roles in induction of inflammation and the development of tubulointerstitial fibrosis. In summary, single cell spatial transcriptomics allows unprecedented insights into the molecular heterogeneity of cLN, paving the way towards more targeted and personalized treatment approaches.
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Integrated bioinformatics analysis and experimental validation identified CDCA families as prognostic biomarkers and sensitive indicators for rapamycin treatment of glioma.
The cell division cycle associated (CDCA) genes regulate the cell cycle; however, their relationship with prognosis in glioma has been poorly reported in the literature. The Cancer Genome Atlas (TCGA) was utilized to probe the CDCA family in relation to the adverse clinical features of glioma. Glioma single-cell atlas reveals specific expression of CDCA3, 4, 5, 8 in malignant cells and CDCA7 in neural progenitor cells (NPC)-like malignant cells. Glioma data from TCGA, the China Glioma Genome Atlas Project (CGGA) and the gene expression omnibus (GEO) database all demonstrated that CDCA2, 3, 4, 5, 7 and 8 are prognostic markers for glioma. Further analysis identified CDCA2, 5 and 8 as independent prognostic factors for glioma. Lasso regression-based risk models for CDCA families demonstrated that high-risk patients were characterized by high tumor mutational burden (TMB), low levels of microsatellite instability (MSI), and low tumor immune dysfunction and rejection (TIDE) scores. These pointed to immunotherapy for glioma as a potentially viable treatment option Further CDCA clustering suggested that the high CDCA subtype exhibited a high macrophage phenotype and was associated with a higher antigen presentation capacity and high levels of immune escape. In addition, hsa-mir-15b-5p was predicted to be common regulator of CDCA3 and CDCA4, which was validated in U87 and U251 cells. Importantly, we found that CDCAs may indicate response to drug treatment, especially rapamycin, in glioma. In summary, our results suggest that CDCAs have potential applications in clinical diagnosis and as drug sensitivity markers in glioma.
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Construction and validation of immune-related LncRNAs classifier to predict prognosis and immunotherapy response in laryngeal squamous cell carcinoma.
BACKGROUND: Rapid advances in transcriptomic profiles have resulted in recognizing IRLs (immune-related long noncoding RNAs), as modulators of the expression of genes related to immune cells that mediate immune inhibition as well as immune stimulatory, indicating LncRNAs play fundamental roles in immune modulation. Hence, we establish an IRL classifier to precisely predict prognosis and immunotherapeutic efficiency in laryngeal squamous cell carcinoma (LSCC). METHODS: LSCC RNA-seq (RNA sequencing) datasets, somatic mutation data, and corresponding clinicopathologic information were acquired from TCGA (the Cancer Genome Atlas) and Gene Expression Omnibus (GEO) databases. Spearman correlation analysis identified LncRNAs associated with immune-related genes (IRG). Based on Lasso penalized regression and random forest (RF), we constructed an IRL classifier associated with prognosis. GEO database was utilized to validate the IRL classifier. The predictive precision and clinical application of the IRL classifier were assessed and compared to clinicopathologic features. The immune cell infiltration of LSCC was calculated via CIBERSORTx tools and ssGSEA (single-sample gene set enrichment analysis). Then, we systematically correlated the IRL classifier with immunological characteristics from multiple perspectives, such as immune-related cells infiltrating, tumor microenvironment (TME) scoring, microsatellite instability (MSI), tumor mutation burden (TMB), and chemokines. Finally, the TIDE (tumor immune dysfunction and exclusion) algorithm was used to predict response to immunotherapy. RESULTS: Based on machine learning approach, three prognosis-related IRLs (BARX1-DT, KLHL7-DT, and LINC02154) were selected to build an IRL classifier. The IRL classifier could availably classify patients into the low-risk and high-risk groups based on the different endpoints, including recurrence-free survival (RFS) and overall survival (OS). In terms of predictive ability and clinical utility, the IRL classifier was superior to other clinical characteristics. Encouragingly, similar results were observed in the GEO databases. Immune infiltration analysis displayed immune cells that are significantly richer in low-risk group, CD8 T cells and activated NK cells via CIBERSORTx algorithm as well as activated CD8 T cell via ssGSEA. Additionally, compared with the high-risk group, immune score, CD8 T effector was higher in the low-risk group, yet stromal score, score of p53 signaling pathway and TGFher in the Tx algorithm, was lower in the low-risk group. Corresponding results were confirmed in GEO dataset. Finally, TIDE analysis uncovered that the IRL classifier may be effectually predict the clinical response of immunotherapy in LSCC. CONCLUSION: Based on BARX1-DT, KLHL7-DT, and LINC02154, the IRL classifier was established, which can be used to predict the prognosis, immune infiltration status, and immunotherapy response in LSCC patients and might facilitate personalized counseling for immunotherapy.
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Integrative Multi-Omics and Functional Characterization Reveal MCM4 as a Key Oncogenic Regulator in Hepatocellular Carcinoma.
BACKGROUND: Hepatocellular carcinoma (HCC) is a highly lethal malignancy with limited therapeutic options and poor prognosis. Identifying robust prognostic biomarkers and therapeutic targets is essential for improving patient outcomes. Minichromosome maintenance complex component 4 (MCM4), a DNA replication licensing factor, has been associated various malignancies, yet its involvement in HCC remains underexplored. METHODS: We performed integrative bioinformatics analyses on three public HCC datasets (GSE14520, GSE56545, and GSE84402) to identify consistently dysregulated genes. Functional enrichment analyses were conducted using GO, KEGG, and Reactome databases. PPI networks were constructed via STRING. The expression and prognostic value of MCM4 were evaluated using GEPIA, Human Protein Atlas, and KM-Plotter. Single-cell and spatial transcriptomics from the HCCDB were analyzed to explore MCM4 localization. Functional roles of MCM4 were validated in vitro using siRNA-mediated knockdown and plasmid-based overexpression in HepG2 and Huh7 cells. RESULTS: MCM4 was identified as a consistently upregulated gene in HCC and was associated with poor overall, disease-free, recurrence-free, and disease-specific survival. Single-cell and spatial transcriptomic analyses revealed MCM4 enrichment in proliferative tumor regions. Functional assays demonstrated that MCM4 promotes HCC cell growth, motility, invasiveness, and enhances EMT and stemness. Conversely, MCM4 knockdown attenuated these malignant phenotypes. CONCLUSION: Our study establishes MCM4 as a key regulator of HCC progression and a potential prognostic biomarker. These findings suggest that MCM4 may serve as a potential target and underscore integrative and spatial transcriptomic approaches in cancer biomarker discovery.
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A single-nucleus chromatin accessibility atlas reveals epigenetic dynamics in aging mouse skeletal muscle.
Skeletal muscle undergoes a progressive decline in mass and function with aging, a condition that in its extreme form is known as sarcopenia. This is driven by complex cellular and molecular alterations, such as shifts in myonucleus composition, increased fibrosis, and fat or immune cell infiltration. Despite extensive research, effective therapeutic interventions for sarcopenia remain limited. Recent advances in single-cell omics technologies have begun to unravel the cellular and molecular heterogeneity of mouse and human skeletal muscle across the lifespan, identifying age-enriched cell states and dynamic transcriptional changes. However, epigenetic regulation during skeletal muscle aging is less well characterized. To help address this gap, we performed single-nucleus Assay for Transposase-Accessible Chromatin using sequencing (snATAC-seq) on skeletal muscle from young adult and aged male mice, generating chromatin accessibility profiles from over 43,000 nuclei. Among other findings, our analyses reveal an age-enriched pro-atrophy subpopulation of type IIb myonuclei marked by increased chromatin accessibility at the Ampd3 locus. Furthermore, we delineate the epigenetic mechanisms underlying the transition of healthy type IIb myonuclei into Ampd3+ myonuclei, revealing key chromatin remodeling events that drive this phenotypic shift. Moreover, by integrating with an existing single-nucleus RNA sequencing dataset of the same anatomical origin, we identified thousands of cell-type-specific cis-regulatory elements related to aging programs. Within these elements, we observed a broad depletion of binding motifs for transcription factors with roles in cellular identity and muscle regeneration, concomitant with the gain of stress-responsive transcription factors. Our work helps understand the epigenetic events underlying mammalian skeletal muscle aging.
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Single-cell RNA sequencing reveals the CRTAC1+ population actively contributes to the pathogenesis of spinal ligament degeneration by SPP1+ macrophage.
Degenerative spinal stenosis is a chronic disease that affects the spinal ligaments and associated bones, resulting in back pain and disorders of the limbs among the elderly population. There are few preventive strategies for such ligament degeneration. We here aimed to establish a comprehensive transcriptomic atlas of ligament tissues to identify high-priority targets for pharmaceutical treatment of ligament degeneration. Here, single-cell RNA sequencing was performed on six degenerative ligaments and three traumatic ligaments to understand tissue heterogeneity. After stringent quality control, high-quality data were obtained from 32,014 cells. Distinct cell clusters comprising stromal and immune cells were identified in ligament tissues. Among them, we noted that collagen degradation associated with CTHRC1+ fibroblast-like cells and calcification linked to CRTAC1+ chondrocyte-like cells were key features of ligament degeneration. SCENIC analysis and further experiments identified ATF3 as a key transcription factor regulating the pathogenesis of CRTAC1+ chondrocyte-like cells. Typically, immune cells infiltrate localized organs, causing tissue damage. In our study, myeloid cells were found to be inflammatory-activated, and SPP1+ macrophages were notably enriched in degenerative ligaments. Further exploration via CellChat analysis demonstrated a robust interaction between SPP1+ macrophages and CRTAC1+ chondrocyte-like cells. Activated by SPP1, ATF3 propels the CRTAC1/MGP/CLU axis, fostering ligament calcification. Our unique resource provides novel insights into possible mechanisms underlying ligament degeneration, the target cell types, and molecules that are expected to mitigate degenerative spinal ligament. We also highlight the role of immune regulation in ligament degeneration and calcification, enhancing our understanding of this disease.
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Bioinformatics Analysis and Experimental Validation of Lactylation Related Genes in Lung Adenocarcinoma.
PURPOSE: Lactylation, a novel post-translational modification, is dysregulated in various tumors and influences lung cancer progression. However, its role in lung adenocarcinoma (LUAD) remains unclear. Based on multi-omics analysis results, this study investigated lactylation levels in LUAD tissues and explored the dual research positioning of lactylation as a prognostic marker and therapeutic target. METHODS: Lactylation levels in LUAD tissue microarrays were assessed using immunohistochemistry and immunofluorescence. Western blot analysis validated these findings. Differential expression analysis of lactylation-related genes was conducted using The Cancer Genome Atlas (TCGA, n=365), based on |log2 fold-change (FC)|≥2. KEGG pathway analysis identified key biological pathways, and COX regression analysis pinpointed prognostic genes. Single-cell RNA sequencing data from the GEO database validated these genes, with mitochondrial gene threshold <20%. RESULTS: Lactylation levels were significantly elevated in LUAD tissues compared to adjacent non-cancerous tissues, as shown by immunohistochemistry and confirmed by Western blot analysis. Differential analysis identified 17 lactylation-related genes enriched in pathways such as AMPK signaling and cellular senescence. COX regression analysis identified five risk genes: KIF2C, MKI67, HMGA1, PFKP, and CCNA2. Validation with single-cell RNA sequencing data revealed high expression levels of these genes in LUAD tissues and the LUAD cell line H1299. Functional validation revealed that the 5 genes panel significantly regulates global lactylation modification in vitro. CONCLUSION: LUAD tissues exhibit elevated lactylation levels, suggesting their potential as prognostic biomarkers. The identified genes-KIF2C, MKI67, HMGA1, PFKP, and CCNA2-are highly expressed in cancerous tissues and correlate with LUAD prognosis. These findings highlight their value as tumor biomarkers and therapeutic targets, offering new opportunities for targeted LUAD treatments.
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Systematic identification of clinically relevant miRNAs for potential miRNA-based therapy in lung adenocarcinoma.
Lung adenocarcinoma (LUAD), the most common histological type of non-small cell lung cancer, is one of the most malignant and deadly diseases. Current treatments for advanced LUAD patients are far from ideal and require further improvements. Here, we utilized a systematic integrative analysis of LUAD microRNA sequencing (miRNA-seq) and RNA-seq data from The Cancer Genome Atlas (TCGA) to identify clinically relevant tumor suppressor miRNAs. Three miRNA candidates (miR-195-5p, miR-101-3p, and miR-338-5p) were identified based on their differential expressions, survival significance levels, correlations with targets, and an additive effect on survival among them. We further evaluated mimics of the three miRNAs to determine their therapeutic potential in inhibiting cancer progression. The results showed not only that each of the miRNA mimics alone but also the three miRNA mimics in combination were efficient at inhibiting tumor growth and progression with equal final concentrations, meaning that the three miRNA mimics in combination were more effective than the single miRNA mimics. Moreover, the combined miRNA mimics provided significant therapeutic effects in terms of reduced tumor volume and metastasis nodules in lung tumor animal models. Hence, our findings show the potential of using the three miRNAs in combination to treat LUAD patients with poor survival outcomes.
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q-Diffusion leverages the full dimensionality of gene coexpression in single-cell transcriptomics.
Unlocking the full dimensionality of single-cell RNA sequencing data (scRNAseq) is the next frontier to a richer, fuller understanding of cell biology. We introduce q-diffusion, a framework for capturing the coexpression structure of an entire library of genes, improving on state-of-the-art analysis tools. The method is demonstrated via three case studies. In the first, q-diffusion helps gain statistical significance for differential effects on patient outcomes when analyzing the CALGB/SWOG 80405 randomized phase III clinical trial, suggesting precision guidance for the treatment of metastatic colorectal cancer. Secondly, q-diffusion is benchmarked against existing scRNAseq classification methods using an in vitro PBMC dataset, in which the proposed method discriminates IFN-γ stimulation more accurately. The same case study demonstrates improvements in unsupervised cell clustering with the recent Tabula Sapiens human atlas. Finally, a local distributional segmentation approach for spatial scRNAseq, driven by q-diffusion, yields interpretable structures of human cortical tissue.
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Identification of key ferroptosis genes and subtypes in kidney renal clear cell carcinoma.
Tumour immunity is highly important for the occurrence and development of tumours, and many cancers are resistant to ferroptosis. This study aims to explore the relationship between ferroptosis-related genes (FRGs) and the immunological characteristics of kidney renal clear cell carcinoma (KIRC). We obtained RNA-seq profiles and clinical data of KIRC patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and identified CD44 and GLRX5 as the key FRGs involved in KIRC immune infiltration through Spearman's correlation analysis. Based on the expression of CD44 and GLRX5, the consensus clustering algorithm was used to classify the TCGA-KIRC samples into two clusters. A nomogram was constructed to evaluate the prognosis of KIRC patients. ESTIMATE, CIBERSORT, and single-sample gene set enrichment analysis (ssGSEA) were performed to evaluate immune infiltration between the two clusters. A weighted gene co-expression network analysis (WGCNA) was used to identify the most relevant genes to the clusters and immunity. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. The external dataset GSE53757 was used to validate the immunological features between the two clusters. Cluster 2 patients had more active immune infiltration and might be more sensitive to immunotherapy; Cluster 2 patients also had a worse prognosis and might be at a more advanced stage of KIRC. We identified key ferroptosis-related genes and subgroups involved in the immune infiltration of KIRC, which is highly important for exploring the molecular mechanisms and treatments of KIRC.