Cardiovascular disease (CVD) is the leading cause of death worldwide. To this end, human cardiac organoids (hCOs) have been developed for improved organotypic CVD modeling over conventional in vivo animal models. Utilizing human cells, hCOs hold great promise to bridge key gaps in CVD research pertaining to human-specific conditions. hCOs are multicellular 3D models which resemble heart structure and function. Varying hCOs fabrication techniques leads to functional and phenotypic differences. To investigate heterogeneity across hCO platforms, we performed a transcriptomic analysis utilizing bulk RNA-sequencing from four previously published unique hCO studies. We further compared selected hCOs to 2D and 3D hiPSC-derived cardiomyocytes (hiPSC-CMs), as well as fetal and adult human myocardium bulk RNA-sequencing samples. Upon investigation utilizing Principal Component Analysis, K-means clustering analysis of key genes, and further downstream analyses such as Gene Set Enrichment (GSEA), Gene Set Variation (GSVA), and GO term enrichment, we found that hCO fabrication method influences maturity and cellular heterogeneity across models. Thus, we propose that adjustment of fabrication method will result in an hCO with a defined maturity and transcriptomic profile to facilitate its specified applications, in turn maximizing its modeling potential.

UNASSIGNED: Fibroblasts play an important role in the development of idiopathic pulmonary fibrosis (IPF).
UNASSIGNED: We employed single-cell RNA-sequencing data obtained from the Gene Expression Omnibus database to perform cell clustering and annotation analyses. We then performed secondary clustering of fibroblasts and conducted functional enrichment and cell trajectory analyses of the two newly defined fibroblast subtypes. Bulk RNA-sequencing data were used to perform consensus clustering and weighted gene co-expression network analysis. We constructed a fibroblast-related prognostic model using least absolute shrinkage, selection operator regression, and Cox regression analysis. The prognostic model was validated using a validation dataset. Immune infiltration and functional enrichment analyses were conducted for patients in the high- and low-risk IPF groups.
UNASSIGNED: We characterized two fibroblast subtypes that are active in IPF (F3+ and ROBO2+). Using fibroblast-related genes, we identified five genes (CXCL14, TM4SF1, CYTL1, SOD3, and MMP10) for the prognostic model. The area under the curve values of our prognostic model were 0.852, 0.859, and 0.844 at one, two, and three years in the training set, and 0.837, 0.758, and 0.821 at one, two, and three years in the validation set, respectively.
UNASSIGNED: This study annotates and characterizes different subtypes of fibroblasts in IPF.

UNASSIGNED: Macrophages play a crucial role in the progression of AF, closely linked to atrial inflammation and myocardial fibrosis. However, the functions and molecular mechanisms of different phenotypic macrophages in AF are not well understood. This study aims to analyze the infiltration characteristics of atrial immune cells in AF patients and further explore the role and molecular expression patterns of M2 macrophage-related genes in AF.
UNASSIGNED: This study integrates single-cell and large-scale sequencing data to analyze immune cell infiltration and molecular characterization of the LAA in patients with AF, using SR as a control group. CIBERSORT assesses immune cell types in LAA tissues; WGCNA identifies signature genes; cell clustering analyzes cell types and subpopulations; cell communication explores macrophage interactions; hdWGCNA identifies M2 macrophage gene modules in AF. AF biomarkers are identified using LASSO and Random Forest, validated with ROC curves and RT-qPCR. Potential molecular mechanisms are inferred through TF-miRNA-mRNA networks and single-gene enrichment analyses.
UNASSIGNED: Myeloid cell subsets varied considerably between the AF and SR groups, with a significant increase in M2 macrophages in the AF group. Signals of inflammation and matrix remodeling were observed in AF. M2 macrophage-related genes IGF1, PDK4, RAB13, and TMEM176B were identified as AF biomarkers, with RAB13 and TMEM176B being novel markers. A TF-miRNA-mRNA network was constructed using target genes, which are enriched in the PPAR signaling pathway and fatty acid metabolism.
UNASSIGNED: Over infiltration of M2 macrophages may be an important factor in the progression of AF. The M2 macrophage-related genes IGF1, RAB13, TMEM176B and PDK4 may regulate the progression of AF through the PPAR signaling pathway and fatty acid metabolism.

Triple-negative breast cancer (TNBC) is a particularly aggressive mammary neoplasia with a high fatality rate, mainly because of the development of resistance to administered chemotherapy, the standard treatment for this disease. In this study, we employ both bulk RNA-sequencing and single-cell RNA-sequencing (scRNA-seq) to investigate the transcriptional landscape of TNBC cells cultured in two-dimensional monolayers or three-dimensional spheroids, before and after developing resistance to the chemotherapeutic agents paclitaxel and doxorubicin. Our findings reveal significant transcriptional heterogeneity within the TNBC cell populations, with the scRNA-seq identifying rare subsets of cells that express resistance-associated genes not detected by the bulk RNA-seq. Furthermore, we observe a partial shift towards a highly mesenchymal phenotype in chemoresistant cells, suggesting the epithelial-to-mesenchymal transition (EMT) as a prevalent mechanism of resistance in subgroups of these cells. These insights highlight potential therapeutic targets, such as the PDGF signaling pathway mediating EMT, which could be exploited in this setting. Our study underscores the importance of single-cell approaches in understanding tumor heterogeneity and developing more effective, personalized treatment strategies to overcome chemoresistance in TNBC.

Background: The role of endothelial cells in tumor progression is considerable, yet the effect of endothelial cell immune-related genes (EIRGs) is still unclear. This research aimed to scrutinize the prognostic value of EIRGs in lung adenocarcinoma (LUAD) and provide further insights into the abovementioned uncertainties. Methods: After single-cell RNA sequencing (scRNA-seq) samples were obtained from the Gene Expression Omnibus (GEO) database, they were integrated with bulk RNA sequencing data from The Cancer Genome Atlas (TCGA). Prognostic markers were determined and a prognostic model was developed. From this model, a nomogram was constructed. We analyzed the biological mechanism of the EIRGs in LUAD, including functional enrichment, tumor mutational burden (TMB), tumor microenvironment (TME) analyses and drug sensitivity. We validated the signature by validating the external cohort GSE31210 and RT-qPCR. Results: After analyzing the model constructed from eight EIRGs, we observed that high-risk group (HG) LUAD patients (a risk score exceeding 4.65) exhibited unfavorable outcomes according to Kaplan‒Meier survival curves. This outcome was confirmed by GSE31210. The nomogram based on the model demonstrated significant predictive value. HG was influenced primarily by steroid hormone biosynthesis and ECM receptor interactions. The TMB in HGs was greater than that in the LG. Analysis of drug sensitivity revealed the direction for individualized treatment for both risk cohorts. Variations in the expression of EIRGs have been confirmed via RT-qPCR in several LUAD cell lines. Conclusions: The prognostic model and nomogram above are valuable for determining the survival rate and treatment options for LUAD patients.

Relative cell type fraction estimates in bulk RNA-sequencing data are important to control for cell composition differences across heterogenous tissue samples. Current computational tools estimate relative RNA abundances rather than cell type proportions in tissues with varying cell sizes, leading to biased estimates. We present lute, a computational tool to accurately deconvolute cell types with varying sizes. Our software wraps existing deconvolution algorithms in a standardized framework. Using simulated and real datasets, we demonstrate how lute adjusts for differences in cell sizes to improve the accuracy of cell composition. Software is available from https://bioconductor.org/packages/lute.

Despite advancements in cancer immunotherapy, solid tumors remain formidable challenges. In glioma, profound inter- and intra-tumoral heterogeneity of antigen landscape hampers therapeutic development. Therefore, it is critical to consider alternative sources to expand the repertoire of targetable (neo-)antigens and improve therapeutic outcomes. Accumulating evidence suggests that tumor-specific alternative splicing (AS) could be an untapped reservoir of antigens. In this study, we investigated tumor-specific AS events in glioma, focusing on those predicted to generate major histocompatibility complex (MHC)-presentation-independent, cell-surface antigens that could be targeted by antibodies and chimeric antigen receptor-T cells. We systematically analyzed bulk RNA-sequencing datasets comparing 429 tumor samples (from The Cancer Genome Atlas) and 9166 normal tissue samples (from the Genotype-Tissue Expression project), and identified 13 AS events in 7 genes predicted to be expressed in more than 10% of the patients, including PTPRZ1 and BCAN, which were corroborated by an external RNA-sequencing dataset. Subsequently, we validated our predictions and elucidated the complexity of the isoforms using full-length transcript amplicon sequencing on patient-derived glioblastoma cells. However, analyses of the RNA-sequencing datasets of spatially mapped and longitudinally collected clinical tumor samples unveiled remarkable spatiotemporal heterogeneity of the candidate AS events. Furthermore, proteomics analysis did not reveal any peptide spectra matching the putative antigens. Our investigation illustrated the diverse characteristics of the tumor-specific AS events and the challenges of antigen exploration due to their notable spatiotemporal heterogeneity and elusive nature at the protein levels. Redirecting future efforts toward intracellular, MHC-presented antigens could offer a more viable avenue.

Moyamoya disease (MMD) remains a chronic progressive cerebrovascular disease with unknown etiology. A growing number of reports describe the development of MMD relevant to infection or autoimmune diseases. Identifying biomarkers of MMD is to understand the pathogenesis and development of novel targeted therapy and may be the key to improving the patient\'s outcome. Here, we analyzed gene expression from two GEO databases. To identify the MMD biomarkers, the weighted gene co-expression network analysis (WGCNA) and the differential expression analyses were conducted to identify 266 key genes. The KEGG and GO analyses were then performed to construct the protein interaction (PPI) network. The three machine-learning algorithms of support vector machine-recursive feature elimination (SVM-RFE), random forest and least absolute shrinkage and selection operator (LASSO) were used to analyze the key genes and take intersection to construct MMD diagnosis based on the four core genes found (ACAN, FREM1, TOP2A and UCHL1), with highly accurate AUCs of 0.805, 0.903, 0.815, 0.826. Gene enrichment analysis illustrated that the MMD samples revealed quite a few differences in pathways like one carbon pool by folate, aminoacyl-tRNA biosynthesis, fat digestion and absorption and fructose and mannose metabolism. In addition, the immune infiltration profile demonstrated that ACAN expression was associated with mast cells resting, FREM1 expression was associated with T cells CD4 naive, TOP2A expression was associated with B cells memory, UCHL1 expression was associated with mast cells activated. Ultimately, the four key genes were verified by qPCR. Taken together, our study analyzed the diagnostic biomarkers and immune infiltration characteristics of MMD, which may shed light on the potential intervention targets of moyamoya disease patients.

The highly heterogeneous nature of hepatocellular carcinoma (HCC) results in different responses and prognoses to the same treatment in patients with similar clinical stages.
Thus, it is imperative to investigate the association between HCC tumor heterogeneity and treatment response and prognosis.
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.
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.

UNASSIGNED: The airway epithelium plays a central role in the pathogenesis of chronic respiratory diseases such as asthma and chronic rhinosinusitis with nasal polyps (CRSwNP), but the mechanisms by which airway epithelial cells (EpCs) maintain inflammation are poorly understood.
UNASSIGNED: We hypothesized that transcriptomic assessment of sorted airway EpCs across the spectrum of differentiation would allow us to define mechanisms by which EpCs perpetuate airway inflammation.
UNASSIGNED: Ethmoid sinus EpCs from adult patients with CRS were sorted into 3 subsets, bulk RNA sequenced, and analyzed for differentially expressed genes and pathways. Single cell RNA-seq (scRNA-seq) datasets from eosinophilic and non-eosinophilic CRSwNP and bulk RNA-seq of EpCs from mild/moderate and severe asthma were assessed. Immunofluorescent staining and ex vivo functional analysis of sinus EpCs were used to validate our findings.
UNASSIGNED: Analysis within and across purified EpC subsets revealed an enrichment in glycolytic programming in CRSwNP vs CRSsNP. Correlation analysis identified mammalian target of rapamycin complex 1 (mTORC1) as a potential regulator of the glycolytic program and identified EpC expression of cytokines and wound healing genes as potential sequelae. mTORC1 activity was upregulated in CRSwNP, and ex vivo inhibition demonstrated that mTOR is critical for EpC generation of CXCL8, IL-33, and CXCL2. Across patient samples, the degree of glycolytic activity was associated with T2 inflammation in CRSwNP, and with both T2 and non-T2 inflammation in severe asthma.
UNASSIGNED: Together, these findings highlight a metabolic axis required to support epithelial generation of cytokines critical to both chronic T2 and non-T2 inflammation in CRSwNP and asthma.