Single-cell Data Research Articles

Integrated analysis of single-cell and bulk RNA sequencing to construct a CD8 + T cell-related immune gene signature in pancreatic cancer

Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant tumor that responds poorly to immunotherapy. The pivotal influence of CD8 + T-cell infiltration on immunotherapy has been documented in various solid tumors. However, the specific contribution of CD8 + T cell-associated immune genes (TIGs) to the tumor immune microenvironment remains unclear. Methods: We obtained CD8 + T cell-related immune genes from the INNATE and IMMPORT databases. Univariate analysis and lasso regression analysis were utilized to screen hub TIGs and develop a prognostic signature, the TIGs score. This score was used to evaluate prognosis, immunocyte infiltration, cancer-associated signaling pathways, and the potential responsiveness of immunotherapy. The transcriptomic data and single-cell data from the GSE183795 and GE212966 datasets are employed to validate the reliability of the findings. Results: Our findings suggest that patients with low TIGs scores have stronger immune effector functions and immune checkpoint activation, resulting in a more favorable response to PD-L1 inhibitors. TIGs scores were significantly correlated with various molecular characteristics and clinical outcomes, such as tumor mutation burden, multiple tumor-associated pathways, and chemotherapeutic drug sensitivity. PSME2 was identified as a potential prognostic biomarker for predicting survival in patients with PDAC. Conclusions: This study elucidates the intricate regulatory mechanisms of TIGs within the tumor immune microenvironment of pancreatic cancer. Our findings strongly suggest that the TIGs score is a robust prognostic marker for prognosis and immunotherapy responsiveness. Additionally, targeting PSME2 may offer a novel avenue for enhancing the effectiveness of immunotherapy in pancreatic cancer.

Unraveling the Heterogeneity of CD8+ T-Cell Subsets in Liver Cirrhosis: Implications for Disease Progression.

: Liver cirrhosis involves chronic inflammation and progressive fibrosis. Among various immune cells, CD8+ T cells are considered a major contributor to hepatic inflammation and fibrosis. However, the exact molecular pathways governing CD8+ T-cell-mediated effects in cirrhosis remain unclear. : This study analyzed transcriptomic and single-cell sequencing data to elucidate CD8+ T-cell heterogeneity and implications in cirrhosis. : Weighted gene co-expression analysis of bulk RNA-seq data revealed an association between cirrhosis severity and activated T-cell markers like HLA and chemokine genes. Furthermore, single-cell profiling uncovered eight CD8+ T-cell subtypes, notably, effector memory (Tem) and exhausted (Tex) T cells. Tex cells, defined by PDCD1, LAG3, and CXCL13 expression, were increased in cirrhosis, while Tem cells were decreased. Lineage tracing and differential analysis highlighted CXCL13+ Tex cells as a terminal, exhausted subtype of cells with roles in PD-1 signaling, glycolysis, and T-cell regulation. CXCL13+ Tex cells displayed T-cell exhaustion markers like PDCD1, HAVCR2, TIGIT, and TNFRSF9. Functional analysis implicated potential roles of these cells in immunosuppression. Finally, a CXCL13+ Tex-cell gene signature was found that correlated with cirrhosis severity and poorer prognosis of liver cancer. : In summary, this comprehensive study defines specialized CD8+ T-cell subpopulations in cirrhosis, with CXCL13+ Tex cells displaying an exhausted phenotype associated with immune dysregulation and advanced disease. Key genes and pathways regulating these cells present potential therapeutic targets.

Single-cell and bulk RNA-sequencing reveal SPP1 and CXCL12 as cell-to-cell communication markers to predict prognosis in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) generally presents as an immunosuppressive microenvironment. The characteristics of cell-to-cell communication in the LUAD microenvironment has been unclear. In this study, the LUAD bulk RNA-seq data and single-cell RNA-seq data were retrieved from public dataset. Differential expression genes (DEGs) between LUAD tumor and adjacent non-tumor tissues were calculated by limma algorithm, and then detected by PPI, KEGG, and GO analysis. Cell-cell interactions were explored using the single-cell RNA-seq data. Finally, the first 15 CytoHubba genes were used to establish related pathways and these pathways were used to characterize the immune-related ligands and their receptors in LUAD. Our analyses showed that monocytes or macrophages interact with tissue stem cells and NK cells via SPP1 signaling pathway and tissue stem cells interact with T and B cells via CXCL signaling pathway in different states. Hub genes of SPP1 participated in SPP1 signaling pathway, which was negatively correlated with CD4+ T cell and CD8+ T cell. The expression of SPP1 in LUAD tumor tissues was negatively correlated with the prognosis. While CXCL12 participated in CXCL signaling pathway, which was positively correlated with CD4+ T cell and CD8+ T cell. The role of CXCL12 in LUAD tumor tissues exhibits an opposite effect to that of SPP1. This study reveals that tumor-associated monocytes or macrophages may affect tumor progression. Moreover, the SPP1 and CXCL12 may be the critic genes of cell-to-cell communication in LUAD, and targeting these pathways may provide a new molecular mechanism for the treatment of LUAD.

Deciphering programmed cell death mechanisms in osteosarcoma for prognostic modeling.

Osteosarcoma (OS), known for its high recurrence and metastasis rates, poses a significant challenge in oncology. Our research investigates the role of programmed cell death (PCD) genes in OS and develops a prognostic model using advanced bioinformatics. We analyzed single-cell sequencing data from the Gene Expression Omnibus (GEO) database to identify subpopulations, distinguish malignant from non-malignant cells, assess cell cycle phases, and map PCD gene distribution. Additionally, we applied consistency clustering to bulk sequencing data from GEO and TARGET (Therapeutically Applicable Research to Generate Effective Treatments) databases, facilitating survival analysis across clusters with the Kaplan-Meier method. We calculated PCD scores for each cluster using the Single-sample Gene Set Enrichment Analysis (ssGSEA), which enabled a detailed examination of PCD-related gene expression and pathway scores. Our study also explored drug sensitivity differences and conducted comprehensive immune cell infiltration analyses using various algorithms. We identified differentially expressed genes, leading to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses that provided insights into relevant biological processes and pathways. The prognostic model, based on five pivotal genes (BAMBI, TMCC2, NOX4, DKK1, and CBS), was developed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and validated in the TARGET-OS and GSE16091 datasets, showing significant predictive accuracy. This research enhances our understanding of PCD in OS and supports the development of effective treatments.

Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data.

Existing methods for gene regulatory network (GRN) inference rely on gene expression data alone or on lower resolution bulk data. Despite the recent integration of chromatin accessibility and RNA sequencing data, learning complex mechanisms from limited independent data points still presents a daunting challenge. Here we present LINGER (Lifelong neural network for gene regulation), a machine-learning method to infer GRNs from single-cell paired gene expression and chromatin accessibility data. LINGER incorporates atlas-scale external bulk data across diverse cellular contexts and prior knowledge of transcription factor motifs as a manifold regularization. LINGER achieves a fourfold to sevenfold relative increase in accuracy over existing methods and reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Following the GRN inference from reference single-cell multiome data, LINGER enables the estimation of transcription factor activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies.

Integrative analysis of single-cell and bulk transcriptome data reveal the significant role of macrophages in lupus nephritis

ObjectiveWe attempted to identify abnormal immune cell components and signaling pathways in lupus nephritis (LN) and to identify potential therapeutic targets.MethodsDifferentially expressed genes (DEGs) between LN and normal kidney tissues were identified from bulk transcriptome data, and functional annotation was performed. The phenotypic changes in macrophages and aberrant intercellular signaling communications within immune cells were imputed from LN scRNA-seq data using trajectory analysis and verified using immunofluorescence staining. Finally, lentivirus-mediated overexpression of LGALS9, the gene encoding Galectin 9, in THP-1 cells was used to study the functional effect of this gene on monocytic cells.ResultsFrom bulk transcriptome data, a significant activation of interferon (IFN) signaling was observed, and its intensity showed a significantly positive correlation with the abundance of infiltrating macrophages in LN. Analysis of scRNA-seq data revealed 17 immune cell clusters, with macrophages showing the highest enrichment of intercellular signal communication in LN. Trajectory analysis revealed macrophages in LN undergo a phenotypic change from inflammatory patrolling macrophages to phagocytic and then to antigen-presenting macrophages, and secrete various pro-inflammatory factors and complement components. LGALS9 was found significantly upregulated in macrophages in LN, which was confirmed by the immunofluorescence assay. Gene functional study showed that LGALS9 overexpression in THP-1 cells significantly elicited pro-inflammatory activation, releasing multiple immune cell chemoattractants.ConclusionOur results present an important pathophysiological role for macrophages in LN, and our preliminary results demonstrate significant pro-inflammatory effects of LGALS9 gene in LN macrophages.

Split Pool Ligation-based Single-cell Transcriptome sequencing (SPLiT-seq) data processing pipeline comparison

BackgroundSingle-cell sequencing techniques are revolutionizing every field of biology by providing the ability to measure the abundance of biological molecules at a single-cell resolution. Although single-cell sequencing approaches have been developed for several molecular modalities, single-cell transcriptome sequencing is the most prevalent and widely applied technique. SPLiT-seq (split-pool ligation-based transcriptome sequencing) is one of these single-cell transcriptome techniques that applies a unique combinatorial-barcoding approach by splitting and pooling cells into multi-well plates containing barcodes. This unique approach required the development of dedicated computational tools to preprocess the data and extract the count matrices. Here we compare eight bioinformatic pipelines (alevin-fry splitp, LR-splitpipe, SCSit, splitpipe, splitpipeline, SPLiTseq-demultiplex, STARsolo and zUMI) that have been developed to process SPLiT-seq data. We provide an overview of the tools, their computational performance, functionality and impact on downstream processing of the single-cell data, which vary greatly depending on the tool used.ResultsWe show that STARsolo, splitpipe and alevin-fry splitp can all handle large amount of data within reasonable time. In contrast, the other five pipelines are slow when handling large datasets. When using smaller dataset, cell barcode results are similar with the exception of SPLiTseq-demultiplex and splitpipeline. LR-splitpipe that is originally designed for processing long-read sequencing data is the slowest of all pipelines. Alevin-fry produced different down-stream results that are difficult to interpret. STARsolo functions nearly identical to splitpipe and produce results that are highly similar to each other. However, STARsolo lacks the function to collapse random hexamer reads for which some additional coding is required.ConclusionOur comprehensive comparative analysis aids users in selecting the most suitable analysis tool for efficient SPLiT-seq data processing, while also detailing the specific prerequisites for each of these pipelines. From the available pipelines, we recommend splitpipe or STARSolo for SPLiT-seq data analysis.

ScDAC: deep adaptive clustering of single-cell transcriptomic data with coupled autoencoder and dirichlet process mixture model.

Clustering analysis for single-cell RNA sequencing (scRNA-seq) data is an important step in revealing cellular heterogeneity. Many clustering methods have been proposed to discover heterogenous cell types from scRNA-seq data. However, adaptive clustering with accurate cluster number reflecting intrinsic biology nature from large-scale scRNA-seq data remains quite challenging. Here we propose a single-cell Deep Adaptive Clustering (scDAC) model by coupling the Autoencoder (AE) and the Dirichlet Process Mixture Model (DPMM). By jointly optimizing the model parameters of AE and DPMM, scDAC achieves adaptive clustering with accurate cluster numbers on scRNA-seq data. We verify the performance of scDAC on five subsampled datasets with different numbers of cell types and compare it with fifteen widely-used clustering methods across nine scRNA-seq datasets. Our results demonstrate that scDAC can adaptively find accurate numbers of cell types or subtypes and outperforms other methods. Moreover, the performance of scDAC is robust to hyperparameter changes. The scDAC is implemented in Python. The source code is available at https://github.com/labomics/scDAC. Supplementary data are available at Bioinformatics online.

Esm-1 mediates transcriptional polarization associated with diabetic kidney disease.

Esm-1, endothelial cell-specific molecule-1, is a susceptibility gene for diabetic kidney disease (DKD) and is a secreted proteoglycan, with notable expression in kidney, which attenuates inflammation and albuminuria. However, little is known about Esm1 expression in mature tissues in the presence or absence of diabetes. We utilized publicly available single-cell RNA sequencing data to characterize Esm1 expression in 27,786 renal endothelial cells (RECs) obtained from four human and three mouse databases. We validated our findings using bulk transcriptome data from 20 healthy subjects and 41 patients with DKD and using RNAscope. In both mice and humans, Esm1 is expressed in a subset of all RECs types and represents a minority of glomerular RECs. In patients, Esm1(+) cells exhibit conserved enrichment for blood vessel development genes. With diabetes, these cells are fewer in number and shift expression towards chemotaxis pathways. Esm1 correlates with a majority of genes within these pathways, delineating a glomerular transcriptional polarization reflected by the magnitude of Esm1 deficiency. Diabetes correlates with lower Esm1 expression and with changes in the functional characterization of Esm1(+) cells. Esm1 thus appears as a marker for glomerular transcriptional polarization in DKD.

Expression characteristics and potential function of non-coding RNA in mouse cortical cells

Non-coding RNAs (ncRNAs) play essential regulatory functions in various physiological and pathological processes in the brain. To systematically characterize the ncRNA profile in cortical cells, we downloaded single-cell SMART-Seq v4 data of mouse cerebral cortex. Our results revealed that the ncRNAs alone are sufficient to define the identity of most cortical cell types. We identified 1,600 ncRNAs that exhibited cell type specificity, even yielding to distinguish microglia from perivascular macrophages with ncRNA. Moreover, we characterized cortical layer and region specific ncRNAs, in line with the results by spatial transcriptome (ST) data. By constructing a co-expression network of ncRNAs and protein-coding genes, we predicted the function of ncRNAs. By integrating with genome-wide association studies data, we established associations between cell type-specific ncRNAs and traits related to neurological disorders. Collectively, our study identified differentially expressed ncRNAs at multiple levels and provided the valuable resource to explore the functions and dysfunctions of ncRNAs in cortical cells.

Single-Cell DNA Sequencing Reveals an Evolutionary Pattern of CHIP in Transplant Eligible Multiple Myeloma Patients

Clonal hematopoiesis of indeterminate potential (CHIP) refers to the phenomenon where a hematopoietic stem cell acquires fitness-increasing mutation(s), resulting in its clonal expansion. CHIP is frequently observed in multiple myeloma (MM) patients, and it is associated with a worse outcome. High-throughput amplicon-based single-cell DNA sequencing was performed on circulating CD34+ cells collected from twelve MM patients before autologous stem cell transplantation (ASCT). Moreover, in four MM patients, longitudinal samples either before or post-ASCT were collected. Single-cell sequencing and data analysis were assessed using the MissionBio Tapestri® platform, with a targeted panel of 20 leukemia-associated genes. We detected CHIP pathogenic mutations in 6/12 patients (50%) at the time of transplant. The most frequently mutated genes were TET2, EZH2, KIT, DNMT3A, and ASXL1. In two patients, we observed co-occurring mutations involving an epigenetic modifier (i.e., DNMT3A) and/or a gene involved in splicing machinery (i.e., SF3B1) and/or a tyrosine kinase receptor (i.e., KIT) in the same clone. Longitudinal analysis of paired samples revealed a positive selection of mutant high-fitness clones over time, regardless of their affinity with a major or minor sub-clone. Copy number analysis of the panel of all genes did not show any numerical alterations present in stem cell compartment. Moreover, we observed a tendency of CHIP-positive patients to achieve a suboptimal response to therapy compared to those without. A sub-clone dynamic of high-fitness mutations over time was confirmed.

Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles.

Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining noncoding variant function.

PMF-GRN: a variational inference approach to single-cell gene regulatory network inference using probabilistic matrix factorization.

Inferring gene regulatory networks (GRNs) from single-cell data is challenging due to heuristic limitations. Existing methods also lack estimates of uncertainty. Here we present Probabilistic Matrix Factorization for Gene Regulatory Network Inference (PMF-GRN). Using single-cell expression data, PMF-GRN infers latent factors capturing transcription factor activity and regulatory relationships. Using variational inference allows hyperparameter search for principled model selection and direct comparison to other generative models. We extensively test and benchmark our method using real single-cell datasets and synthetic data. We show that PMF-GRN infers GRNs more accurately than current state-of-the-art single-cell GRN inference methods, offering well-calibrated uncertainty estimates.

Transfer learning reveals cancer-associated fibroblasts are associated with epithelial-mesenchymal transition and inflammation in cancer cells in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by an immunosuppressive tumor microenvironment enriched with cancer associated fibroblasts (CAFs). This study utilized a convergence approach to identify tumor cell and CAF interactions through the integration of single-cell data from human tumors with human organoid co-culture experiments. Analysis of a comprehensive atlas of PDAC single-cell RNA sequencing (scRNA-seq) data indicated that CAF density is associated with increased inflammation and epithelial-mesenchymal transition (EMT) in epithelial cells. Transfer learning using transcriptional data from patient-derived organoid and CAF co-cultures provided in silico validation of CAF induction of inflammatory and EMT epithelial cell states. Further experimental validation in co-cultures demonstrated integrin beta 1 (ITGB1) and vascular endothelial factor A (VEGF-A) interactions with neuropilin-1 (NRP1) mediating CAF-epithelial cell crosstalk. Together, this study introduces transfer learning from human single-cell data to organoid co-culture analyses for experimental validation of discoveries of cell-cell crosstalk and identifies fibroblast-mediated regulation of EMT and inflammation.

Single-cell RNA sequencing and ATAC sequencing identify novel biomarkers for bicuspid aortic valve-associated thoracic aortic aneurysm

IntroductionBicuspid aortic valve (BAV) is the most prevalent congenital cardiovascular defect and known to cause thoracic aortic aneurysms (TAAs). To improve our understanding of BAV pathogenesis, we characterized the cellular composition of BAV tissues and identified molecular changes in each cell population.MethodsTissue samples from two patients with BAV and two heart transplant donors were analyzed using single-cell RNA sequencing, assay for transposase-accessible chromatin using sequencing, and weighted gene coexpression network analysis for differential gene analysis. TAA-related changes were evaluated by comparing the proportion of each cell type and gene expression profiles between TAA and control tissues. Further, by combining our single-cell RNA sequencing data with publicly available data from genome-wide association studies, we determined critical genes for BAV.ResultsWe found 20 cell subpopulations in TAA tissues, including multiple subtypes of smooth muscle cells, fibroblasts, macrophages, and T lymphocytes. This result suggested that these cells play multiple functional roles in BAV development. Several differentially expressed genes, including CD9, FHL1y, HSP90AA1, GAS6, PALLD, and ACTA2, were identified.DiscussionWe believe that this comprehensive assessment of the cellular composition of TAA tissues and the insights into altered gene expression patterns can facilitate identification of novel diagnostic biomarkers and therapeutic targets for BAV-associated TAA.

Drug repositioning for immunotherapy in breast cancer using single-cell analysis.

Immunomodulatory peptides, while exhibiting potential antimicrobial, antifungal, and/or antiviral properties, can play a role in stimulating or suppressing the immune system, especially in pathological conditions like breast cancer (BC). Thus, deregulation of these peptides may serve as an immunotherapeutic strategy to enhance the immune response. In this meta-analysis, we utilized single-cell RNA sequencing data and known therapeutic peptides to investigate the deregulation of these peptides in malignant versus normal human breast epithelial cells. We corroborated our findings at the chromatin level using ATAC-seq. Additionally, we assessed the protein levels in various BC cell lines. Moreover, our in-house drug repositioning approach was employed to identify potential drugs that could positively impact the relapse-free survival of BC patients. Considering significantly deregulated therapeutic peptides and their role in BC pathology, our approach aims to downregulate B2M and SLPI, while upregulating PIGR, DEFB1, LTF, CLU, S100A7, and SCGB2A1 in BC epithelial cells through our drug repositioning pipeline. Leveraging the LINCS L1000 database, we propose BRD-A06641369 for B2M downregulation and ST-4070043 and BRD-K97926541 for SLPI downregulation without negatively affecting the MHC complex as a significantly correlated pathway with these two genes. Furthermore, we have compiled a comprehensive list of drugs for the upregulation of other selected immunomodulatory peptides. Employing an immunotherapeutic approach by integrating our drug repositioning pipeline with single-cell analysis, we proposed potential drugs and drug targets to fortify the immune system against BC.

Single-cell RNA sequencing reveals aberrant sphingolipid metabolism in non-small cell lung cancer impacts tumor-associated macrophages and stimulates angiogenesis via macrophage inhibitory factor signaling.

Sphingolipids not only serve as structural components for maintaining cell membrane fluidity but also function as bioactive molecules involved in cell signaling and the regulation of various biological processes. Their pivotal role in cancer cell development, encompassing cancer cell proliferation, migration, angiogenesis, and metastasis, has been a focal point for decades. However, the contribution of sphingolipids to the complexity of tumor microenvironment promoting cancer progression has been rarely investigated. Through the integration of publicly available bulk RNA-seq and single-cell RNA-seq data, we conducted a comprehensive analysis to compare the transcriptomic features between tumors and adjacent normal tissues, thus elucidating the intricacies of the tumor microenvironment (TME). Disparities in sphingolipid metabolism (SLM)-associated genes were observed between normal and cancerous tissues, with the TME characterized by the enrichment of sphingolipid signaling in macrophages. Cellular interaction analysis revealed robust communication between macrophages and cancer cells exhibiting low SLM, identifying the crucial ligand-receptor pair, macrophage inhibitory factor (MIF)-CD74. Pseudo-time analysis unveiled the involvement of SLM in modulating macrophage polarization towards either M1 or M2 phenotypes. Categorizing macrophages into six subclusters based on gene expression patterns and function, the SPP1+ cluster, RGS1+ cluster, and CXCL10+ cluster were likely implicated in sphingolipid-induced M2 macrophage polarization. Additionally, the CXCL10+, AGER+, and FABP4+ clusters were likely to be involved in angiogenesis through their interaction with endothelial cells. Based on multiple scRNA-seq datasets, we propose that a MIF-targeted strategy could potentially impede the polarization from M1 to M2 and impair tumor angiogenesis in low-SLM non-small cell lung cancer (NSCLC), demonstrating its potent antitumor efficacy.

Immune cell-related prognostic risk model and tumor immune environment modulation in esophageal carcinoma based on single-cell and bulk RNA sequencing.

Immune cells play a pivotal role in the tumor microenvironment, exerting significant influence on tumor progression and patient outcomes, but the current biomarkers are insufficient to fully capture the complex and diverse tumor immune microenvironment and the impact of immunotherapy. The advent of single-cell sequencing allows us to explore the tumor microenvironment at an unprecedented resolution, enabling the identification and characterization of distinct subsets of immune cells, thereby paving the way for the development of prognostic models using immune cells. Leveraging single-cell data, our study deeply investigated the intricacies of immune microenvironment heterogeneity in esophageal carcinoma. We elucidated the composition, functionality, evolution, and intercellular communication patterns of immune cells, culminating in the construction of an independent prognostic model at the single-cell level. Furthermore, we conducted a comprehensive analysis of disparities in immune infiltration and immune checkpoint expression between patients categorized into high- and low-risk groups, which may impact patient prognosis. In summary, our study harnessed multiomics data to delineate the immune profile of esophageal carcinoma patients, provide a method for leveraging molecular signatures of immune cells to identify potential biomarkers, while concurrently providing evidence for the potential benefits of immunotherapy.

Identification of key TE associated with myocarditis based on RNA and single-cell sequencing data mining

Cardiomyopathy is a severe cardiac condition characterized by complex immune regulatory mechanisms. While the role of immune genes is recognized, the specifics of their regulation in cardiomyopathy are not fully understood. Recent studies highlight the significance of transposable elements (TEs) in various diseases, particularly their potential to modulate immune responses. This paper utilizes publicly available databases to explore the role of TEs in myocarditis: RNA Seq data and single-cell sequencing data were analyzed, with a focus on the mouse model of experimental autoimmune myocarditis (EAM). The RNA-Seq analysis revealed substantial upregulation of a range of immune genes in cardiac tissue. Further investigation using single-cell sequencing of cardiac immune cells identified specific expression of certain transposable elements (TEs) across different types of immune cells in the heart. Additionally, there was an overall increase in the expression of the ERVB7-1. LTR-MM transposon across various cells in the EAM model, suggesting a widespread impact of this transposon on the immune response in this disease context. The findings of this study highlight the intricate interplay between transposable elements and the immune system in cardiomyopathy, providing new insights into the molecular mechanisms underlying this condition. The discovery of specific TEs expression in cardiac immune cells and the overall increase in ERVB7-1. LTR-MM expression across the EAM model underscore the potential of these elements in modulating immune responses and contribute to our understanding of cardiomyopathy's pathogenesis. These observations open avenues for further research into the role of TEs in cardiac disases and may lead to novel therapeutic strategies.

Intestinal Stromal Cells in the Turmoil of Inflammation and Defective Connective Tissue Remodeling in Inflammatory Bowel Disease.

In steady state, intestinal subepithelial myofibroblasts form a thin layer below the basement membrane. Unlike the rest of the stromal cells in the lamina propria, they express tensile proteins, guide epithelial regeneration, and sense luminal microbiota. Upon inflammation in inflammatory bowel disease (IBD), they express activation markers, accept trophic signaling by infiltrating neutrophils and macrophages, and are activated by cytokines from helper T cells to produce a narrow spectrum of cytokines and a wider spectrum of chemokines, attract cells of innate and adaptive immunity, orchestrate inflammatory responses, and qualitatively and quantitatively modify the extracellular matrix. Thus, beyond being structural tissue components, they assume active roles in the pathogenesis of complicated IBD. Discrimination between myofibroblasts and fibroblasts may be an oversimplification in light of single-cell sequencing data unveiling the complexity of multiple phenotypes of stromal cells with distinct roles and plasticity. Spatial transcriptomics revealed distinct phenotypes by histologic localization and, more intriguingly, the assembly of mucosal neighborhoods that support spatially distinct functions. Current IBD treatments target inflammation but fail in fibrostenotic or fistulizing disease. Baseline and recent findings on stromal cells, molecules, and pathways involved in disrupted extracellular matrix homeostasis are reviewed to provide relevant pharmacologic targets.