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Background Dilated cardiomyopathy (DCM) is one of the most common causes of heart failure. Infiltration and alterations in non-cardiomyocytes of the human heart involve crucially in the occurrence of DCM and associated immunotherapeutic approaches. Methods We constructed a single-cell transcriptional atlas of DCM and normal patients. Then, the xCell algorithm, EPIC algorithm, MCP counter algorithm, and CIBERSORT method were applied to identify DCM-related cell types with a high degree of precision and specificity using RNA-seq datasets. We further analyzed the heterogeneity among cell types, performed trajectory analysis, examined transcription factor regulatory networks, investigated metabolic heterogeneity, and conducted intercellular communication analysis. Finally, we used bulk RNA-seq data to confirm the roles of M2-like2 subpopulations and GAS6 in DCM. Results We integrated and analyzed Single-cell sequencing (scRNA-seq) data from 7 DCM samples and 3 normal heart tissue samples, totaling 70,958 single-cell data points. Based on gene-specific expression and prior marker genes, we identified 9 distinct subtypes, including fibroblasts, endothelial cells, myeloid cells, pericytes, T/NK cells, smooth muscle cells, neuronal cells, B cells, and cardiomyocytes. Using machine learning methods to quantify bulk RNA-seq data, we found significant differences in fibroblasts, T cells, and macrophages between DCM and normal samples. Further analysis revealed high heterogeneity in tissue preference, gene expression, functional enrichment, immunodynamics, transcriptional regulatory factors, metabolic changes, and communication patterns in fibroblasts and myeloid cells. Among fibroblast subpopulations, proliferative F3 cells were implicated in the fibroblast transition process in DCM, while myofibroblast F6 cells promoted the fibroblast transition to a late cell state in DCM. Additionally, two subpopulations of M2 macrophages, M2-like1 and M2-like2, were identified with distinct features. The M2-like2 cell subpopulation, which was enriched in glycolysis and fatty acid metabolism, involved in inflammation inhibition and fibrosis promotion. Cell‒cell communication analysis indicated the GAS6-MERTK axis might exhibit interaction between M2 macrophage and M2-like1 macrophage. Furthermore, deconvolution analysis for bulk RNA-seq data revealed a significant increase in M2-like2 subpopulations in DCM, suggesting a more important role for this cell population in DCM. Conclusions We revealed the heterogeneity of non-cardiomyocytes in DCM and identified subpopulations of myofibroblast and macrophages engaged in DCM, which suggested a potential significance of non-cardiomyocytes in treatment of DCM.

Osteoarthritis (OA) stands as the most prevalent joint disorder. Mitochondrial dysfunction has been linked to the pathogenesis of OA. The main goal of this study is to uncover the pivotal role of mitochondria in the mechanisms driving OA development. We acquired seven bulk RNA-seq datasets from the Gene Expression Omnibus (GEO) database and examined the expression levels of differentially expressed genes related to mitochondria in OA. We utilized single-sample gene set enrichment analysis (ssGSEA), gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA) analyses to explore the functional mechanisms associated with these genes. Seven machine learning algorithms were utilized to identify hub mitochondria-related genes and develop a predictive model. Further analyses included pathway enrichment, immune infiltration, gene-disease relationships, and mRNA-miRNA network construction based on these hub mitochondria-related genes. genome-wide association studies (GWAS) analysis was performed using the Gene Atlas database. GSEA, gene set variation analysis (GSVA), protein pathway analysis, and WGCNA were employed to investigate relevant pathways in subtypes. The Harmonizome database was employed to analyze the expression of hub mitochondria-related genes across various human tissues. Single-cell data analysis was conducted to examine patterns of gene expression distribution and pseudo-temporal changes. Additionally, The real-time polymerase chain reaction (RT-PCR) was used to validate the expression of these hub mitochondria-related genes. In OA, the mitochondria-related pathway was significantly activated. Nine hub mitochondria-related genes (SIRT4, DNAJC15, NFS1, FKBP8, SLC25A37, CARS2, MTHFD2, ETFDH, and PDK4) were identified. They constructed predictive models with good ability to predict OA. These genes are primarily associated with macrophages. Unsupervised consensus clustering identified two mitochondria-associated isoforms that are primarily associated with metabolism. Single-cell analysis showed that they were all expressed in single cells and varied with cell differentiation. RT-PCR showed that they were all significantly expressed in OA. SIRT4, DNAJC15, NFS1, FKBP8, SLC25A37, CARS2, MTHFD2, ETFDH, and PDK4 are potential mitochondrial target genes for studying OA. The classification of mitochondria-associated isoforms could help to personalize treatment for OA patients.

Since the current melanoma clinicopathological staging system remains restricted to predicting survival outcomes, establishing precise prognostic targets is needed. Here, we used gene expression signature (GES) classification and Cox regression analyses to biologically characterize melanoma cells at the single-cell level and construct a prognosis-related gene signature for melanoma. By analyzing publicly available scRNA-seq data, we identified six distinct GESs (named: “Anti-apoptosis”, “Immune cell interactions”, “Melanogenesis”, “Ribosomal biogenesis”, “Extracellular structure organization”, and “Epithelial-Mesenchymal Transition (EMT)”). We verified these GESs in the bulk RNA-seq data of patients with skin cutaneous melanoma (SKCM) from The Cancer Genome Atlas (TCGA). Four GESs (“Immune cell interactions”, “Melanogenesis”, “Ribosomal biogenesis”, and “Extracellular structure organization”) were significantly correlated with prognosis (p = 1.08 × 10−5, p = 0.042, p = 0.001, and p = 0.031, respectively). We identified a prognostic signature of melanoma composed of 45 genes (MPS_45). MPS_45 was validated in TCGA-SKCM (HR = 1.82, p = 9.08 × 10−6) and three other melanoma datasets (GSE65904: HR = 1.73, p = 0.006; GSE19234: HR = 3.83, p = 0.002; and GSE53118: HR = 1.85, p = 0.037). MPS_45 was independently associated with survival (p = 0.002) and was proved to have a high potential for predicting prognosis in melanoma patients.

Integrin‐based focal adhesion is one of the major mechanosensory in osteocytes. The aim of this study was to mine the hub genes associated with focal adhesion and investigate their roles in osteoporosis based on the data of single‐cell RNA sequencing and RNA‐sequencing. Two hub genes (FAM129A and RNF24) with the same expression trend and AUC values greater than 0.7 in both GSE56815 and GSE56116 cohorts were uncovered. The nomogram was created to predict the risk of OP based on two hub genes. Subsequently, the competing endogenous RNA network was established based on two hub genes, 14 microRNAs and five long noncoding RNAs. Meanwhile, transcription factors‐hub gene network was established based on two hub genes and 14 TFs. Finally, 73 drugs were predicted, of which there were 13 drugs targeting FAM129A and 66 drugs targeting RNF24. In both mouse and human blood samples, FAM129A expression was decreased in granulocytes and RNF24 expression was increased in monocytes. In the mouse experiment, FAM129A and anti‐RNF24 were found to partially alleviate the progression of osteoporosis. In conclusion, two hub genes related to focal adhesion were identified by combined scRNA‐seq and RNA‐seq analyses, which might supply a new insight for the treatment and evaluation of OP.

Plasma cells serve a crucial role in the human immune system and are important in tumor progression. However, the specific role of plasma cell immune-related genes (PCIGs) in tumor progression remains unclear. Therefore, the present study aimed to establish a risk assessment model for patients with lung adenocarcinoma (LUAD) based on PCIGs. The data used in the present study were obtained from The Cancer Genome Atlas and the Gene Expression Omnibus databases. After identifying nine PCIGs, a risk assessment model was constructed and a nomogram was developed for predicting patient prognosis. To explore the molecular mechanism and clinical significance, gene set enrichment analysis (GSEA), tumor mutational burden (TMB) analysis, tumor microenvironment (TME) analysis and drug sensitivity prediction were performed. Furthermore, the accuracy of the model was validated using reverse transcription-quantitative PCR (RT-qPCR). The present study constructed a risk assessment model consisting of nine PCIGs. Kaplan-Meier survival curves indicated a worse prognosis in the high-risk subgroup (risk score ≥0.982) compared with that in the low-risk subgroup. The nomogram exhibited predictive value for survival prediction (area under the curve=0.727). GSEA enrichment analysis revealed enrichment of the focal adhesion and extracellular matrix-receptor interaction pathways in the high-risk group. Moreover, the high-risk group exhibited a higher TMB, as demonstrated by the TME analysis showing lower ESTIMATE scores. Drug sensitivity prediction facilitated potential drug selection. Subsequently, differential gene expression was validated in multiple LUAD cell lines using RT-qPCR. In conclusion, the risk assessment model based on nine PCIGs may be used to predict the prognosis and drug selection in patients with LUAD.

Background The highly heterogeneous nature of hepatocellular carcinoma (HCC) results in different responses and prognoses to the same treatment in patients with similar clinical stages. Aims Thus, it is imperative to investigate the association between HCC tumor heterogeneity and treatment response and prognosis. Methods and Results At first, we downloaded scRNA‐seq, bulk RNA‐seq, and clinical data from TCGA and GEO databases. We conducted quality control, normalization using SCTransform, dimensionality reduction using PCA, batch effect removal using Harmony, dimensionality reduction using UMAP, and cell annotation‐based marker genes on the scRNA‐seq data. We recognized tumor cells, identified tumor‐related genes (TRGs), and performed cell communication analysis. Next, we developed a prognostic model using univariable Cox, LASSO, and multivariate Cox analyses. The signature was evaluated using survival analysis, ROC curves, C‐index, and nomogram. Last, we studied the predictability of the signature in terms of prognosis and immunotherapeutic response for HCC, assessed a variety of drugs for clinical treatment, and used the qRT‐PCR analysis to validate the mRNA expression levels of prognostic TRGs. Conclusion To conclude, this study expounded upon the influence of tumor cell heterogeneity on the prediction of treatment outcomes and prognosis in HCC. This, in turn, enhances the predictive ability of the TNM staging system and furnishes novel perspectives on the prognostic assessment and therapy of HCC.

Background RNA-sequencing technology provides an effective tool for understanding miRNA regulation in complex human diseases, including cancers. A large number of computational methods have been developed to make use of bulk and single-cell RNA-sequencing data to identify miRNA regulations at the resolution of multiple samples (i.e. group of cells or tissues). However, due to the heterogeneity of individual samples, there is a strong need to infer miRNA regulation specific to individual samples to uncover miRNA regulation at the single-sample resolution level. Results Here, we develop a framework, Scan, for scanning sample - specific miRNA regulation. Since a single network inference method or strategy cannot perform well for all types of new data, Scan incorporates 27 network inference methods and two strategies to infer tissue-specific or cell-specific miRNA regulation from bulk or single-cell RNA-sequencing data. Results on bulk and single-cell RNA-sequencing data demonstrate the effectiveness of Scan in inferring sample-specific miRNA regulation. Moreover, we have found that incorporating the prior information of miRNA targets can generally improve the accuracy of miRNA target prediction. In addition, Scan can contribute to construct cell/tissue correlation networks and recover aggregate miRNA regulatory networks. Finally, the comparison results have shown that the performance of network inference methods is likely to be data-specific, and selecting optimal network inference methods is required for more accurate prediction of miRNA targets. Conclusions Scan provides a useful method to help infer sample-specific miRNA regulation for new data, benchmark new network inference methods and deepen the understanding of miRNA regulation at the resolution of individual samples.

Abstract Alveolar macrophages (AMs) are functionally important innate cells involved in lung homeostasis and immunity and whose diversity in health and disease is a subject of intense investigations. Yet, it remains unclear to what extent conditions like smoking or chronic obstructive pulmonary disease (COPD) trigger changes in the AM compartment. Here, we aimed to explore heterogeneity of human AMs isolated from healthy nonsmokers, smokers without COPD, and smokers with COPD by analyzing BAL fluid cells by flow cytometry and bulk and single-cell RNA sequencing. We found that subpopulations of BAL fluid CD206+ macrophages could be distinguished based on their degree of autofluorescence in each subject analyzed. CD206+ autofluorescenthigh AMs were identified as classical, self-proliferative AM, whereas autofluorescentlow AMs were expressing both monocyte and classical AM-related genes, supportive of a monocytic origin. Of note, monocyte-derived autofluorescentlow AMs exhibited a functionally distinct immunoregulatory profile, including the ability to secrete the immunosuppressive cytokine IL-10. Interestingly, single-cell RNA-sequencing analyses showed that transcriptionally distinct clusters of classical and monocyte-derived AM were uniquely enriched in smokers with and without COPD as compared with healthy nonsmokers. Of note, such smoking-associated clusters exhibited gene signatures enriched in detoxification, oxidative stress, and proinflammatory responses. Our study independently confirms previous reports supporting that monocyte-derived macrophages coexist with classical AM in the airways of healthy subjects and patients with COPD and identifies smoking-associated changes in the AM compartment that may favor COPD initiation or progression.

Background Relative cell type fraction estimates in bulk RNA-sequencing data are important to control for cell composition differences across heterogenous tissue samples. While there exist algorithms to estimate the cell type proportions in tissues, a major challenge is the algorithms can show reduced performance if using tissues that have varying cell sizes, such as in brain tissue. In this way, without adjusting for differences in cell sizes, computational algorithms estimate the relative fraction of RNA attributable to each cell type, rather than the relative fraction of cell types, leading to potentially biased estimates in cellular composition. Furthermore, these tools were built on different frameworks with non-uniform input data formats while addressing different types of systematic errors or unwanted bias. Results We present lute , a software tool to accurately deconvolute cell types with varying sizes. Our package lute wraps existing deconvolution algorithms in a flexible and extensible framework to enable easy benchmarking and comparison of existing deconvolution algorithms. Using simulated and real datasets, we demonstrate how lute adjusts for differences in cell sizes to improve the accuracy of cell composition. Conclusions Our software ( https://bioconductor.org/packages/lute ) can be used to enhance and improve existing deconvolution algorithms and can be used broadly for any type of tissue containing cell types with varying cell sizes.

Background The pathogenesis of vitiligo remains unclear. The genes encoding vitiligo-related RNA-binding proteins (RBPs) and their underlying pathogenic mechanism have not been determined. Results Single-cell transcriptome sequencing (scRNA-seq) data from the CNCB database was obtained to identify distinct cell types and subpopulations and the relative proportion changes in vitiligo and healthy samples. We identified 14 different cell types and 28 cell subpopulations. The proportion of each cell subpopulation significantly differed between the patients with vitiligo and healthy groups. Using RBP genes for unsupervised clustering, we obtained the specific RBP genes of different cell types in vitiligo and healthy groups. The RBP gene expression was highly heterogeneous; there were significant differences in some cell types, such as keratinocytes, Langerhans, and melanocytes, while there were no significant differences in other cells, such as T cells and fibroblasts, in the two groups. The melanocyte-specific RBP genes were enriched in the apoptosis and immune-related pathways in the patients with vitiligo. Combined with the bulk RNA-seq data of melanocytes, key RBP genes related to melanocytes were identified, including eight upregulated RBP genes ( CDKN2A, HLA-A, RPL12, RPL29, RPL31, RPS19, RPS21 , and RPS28 ) and one downregulated RBP gene ( SLC3A2 ). Cell experiments were conducted to explore the role of the key RBP gene SLC3A2 in vitiligo. Cell experiments confirmed that melanocyte proliferation decreased, whereas apoptosis increased, after SLC3A2 knockdown. SLC3A2 knockdown in melanocytes also decreased the SOD activity and melanin content; increased the Fe 2+ , ROS, and MDA content; significantly increased the expression levels of TYR and COX2; and decreased the expression levels of glutathione and GPX4. Conclusion We identified the RBP genes of different cell subsets in patients with vitiligo and confirmed that downregulating SLC3A2 can promote ferroptosis in melanocytes. These findings provide new insights into the pathogenesis of vitiligo.