Anchored-fusion enables targeted fusion search in bulk and single-cell RNA sequencing data

Chronic kidney disease (CKD) emerges as a substantial contributor to various cardiovascular disorders, including ischemic cardiomyopathy (ICM). However, the underlying molecular mechanisms linking CKD and ICM remain elusive. Our study aims to unravel these connections by integrating publicly available bulk and single-cell RNA sequencing (scRNA-seq) data. Expression profiles from two ICM datasets obtained from heart tissue and one CKD with Peripheral Blood Mononuclear Cell (CKD-PBMC) dataset were collected. We initiated by identifying shared differentially expressed genes (DEGs) between ICM and CKD. Subsequent functional enrichment analysis shed light on the mechanisms connecting CKD to ICM. Machine learning algorithms enabled the identification of 13 candidate genes, including AGRN, COL16A1, COL1A2, FAP, FRZB, GPX3, ITIH5, NFASC, PTN, SLC38A1, STARD7, THBS2, and VPS35. Their expression patterns in ICM were investigated via scRNA-seq data analysis. Notably, most of them were enriched in fibroblasts. COL16A1, COL1A2, PTN, and FAP were enriched in scar-formation fibroblasts, while GPX3 and THBS2 showed enrichment in angiogenesis fibroblasts. A Gaussian naïve Bayes model was developed for diagnosing CKD-related ICM, bolstered by SHapley Additive exPlanations interpretability and validated internally and externally. In conclusion, our investigation unveils the extracellular matrix's role in CKD and ICM interplay, identifies 13 candidate genes, and showcases their expression patterns in ICM. We also constructed a diagnostic model using 13 gene features and presented an innovative approach for managing CKD-related ICM through serum-based diagnostic strategies.

A Survey on Methods for Predicting Polyadenylation Sites from DNA Sequences, Bulk RNA-seq, and Single-cell RNA-seq

Hypertrophic scar (HS) is a common skin disorder characterized by excessive extracellular matrix (ECM) deposition. However, it is still unclear how the cellular composition, cell-cell communications, and crucial transcriptionally regulatory network were changed in HS. In the present study, we found that FB-1, which was identified a major type of fibroblast and had the characteristics of myofibroblast, was significantly expanded in HS by integrative analysis of the single-cell and bulk RNA sequencing (RNA-seq) data. Moreover, the proportion of KC-2, which might be a differentiated type of keratinocyte (KC), was reduced in HS. To decipher the intercellular signaling, we conducted the cell-cell communication analysis between the cell types, and found the autocrine signaling of HB-1 through COL1A1/2-CD44 and CD99-CD99 and the intercellular contacts between FB-1/FB-5 and KC-2 through COL1A1/COL1A2/COL6A1/COL6A2-SDC4. Almost all the ligands and receptors involved in the autocrine signaling of HB-1 were upregulated in HS by both scRNA-seq and bulk RNA-seq data. In contrast, the receptor of KC-2, SDC4, which could bind to multiple ligands, was downregulated in HS, suggesting that the reduced proportion of KC-2 and apoptotic phenotype of KC-2 might be associated with the downregulation of SDC4. Furthermore, we also investigated the transcriptionally regulatory network involved in HS formation. The integrative analysis of the scRNA-seq and bulk RNA-seq data identified CREB3L1 and TWIST2 as the critical TFs involved in the myofibroblast of HS. In summary, the integrative analysis of the single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data greatly improved our understanding of the biological characteristics during the HS formation.

Background Cancer stem cells (CSCs) play a crucial role in the progression of lung adenocarcinoma (LUAD).This study aimed to explore the gene signatures of tumor stem cells in LUAD through Single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing (RNA-seq) data, and establish a tumor stem cell marker signature(TSCMS)prognostic risk model. Methods The LUAD scRNA-seq data and bulk RNA-seq data from the GEO and TCGA databases were collected. CytoTRACE software was used to quantify the stemness score of tumor-derived epithelial cell clusters. Gene Set Variation Analysis (GSVA) was performed to identify potential biological functions in different clusters. The TSCMS prognostic risk model was constructed using Lasso-Cox regression analysis, and its prognostic value was assessed through Kaplan-Meier, Cox regression, and receiver-operating characteristic (ROC) curve analyses. The Cibersortx algorithm was used to evaluate immune infiltration, and drug response prediction was conducted using the pRRophetic package. Functional investigations of TAF10 in LUAD cells were performed using bioinformatics analysis, qRT-PCR, Western blot, Immunohistochemistry, cell proliferation and clone formation assay. Results Seven distinct cell clusters were identified by CytoTRACE (Epi C1 to C7), with Epi C1 demonstrating the highest stemness potential. The TSCMS prognostic risk model incorporated 49 tumor stemness-related genes, and high-risk patients exhibited reduced immune scores, lower ESTIMATE scores, and increased tumor purity. Furthermore, significant differences in immune landscapes and chemotherapy sensitivity were observed between high and low risk groups. TAF10 was found to be positively correlated with the RNA expression-based stemness score (RNAss) in various tumors, including LUAD. And we demonstrated that TAF10 was over-expressed in LUAD cell lines and tumor tissues of clinical patients, and high TAF10 expression was correlated with poor prognosis in LUAD patients. Silencing TAF10 inhibited LUAD cell proliferation and clone formation. Conclusions Our investigation highlights the prognostic utility of the TSCMS model for evaluating the clinical outcomes of LUAD patients, uncovering critical insights into immune cell infiltration and therapeutic response, and positions TAF10 as a novel therapeutic target for LUAD.

Background: Cancer stem cells (CSCs) play a key role in tumor initiation, progression, and resistance to conventional therapies. These cells possess the ability to self-renew and differentiate, contributing to tumor heterogeneity and recurrence. Preclinical data indicate that targeting cancer stem cell-specific pathways could lead to more effective treatments and prevent relapse, thereby improving outcomes. However, there is limited clinical data to support this, in part due to the challenges of measuring CSCs in the clinical setting. Single-cell RNA sequencing (scRNAseq) data in combination with stemness gene expression signatures were investigated as a novel approach to detect and assess changes in CSC numbers in response to treatment. Methods: Single-cell RNAseq datasets from two clinical trials were interrogated – OPPORTUNE, a window-of-opportunity trial evaluating anastrozole vs anastrozole plus pictilisib (PI3K inhibitor) in 75 patients with ER+ HER2- early breast cancer, and FELINE, a neoadjuvant trial which compared letrozole plus placebo with letrozole plus ribociclib (CDK4/6 inhibitor) in 120 patients with ER+ HER2- early breast cancer. The primary endpoint for OPPORTUNE was the inhibition of tumor cell proliferation as measured by Ki67. The primary endpoint for FELINE was the rate of preoperative endocrine prognostic index (PEPI) score 0 after neoadjuvant endocrine therapy. Thirty-four patients from the FELINE study had available scRNAseq data for analysis; this number was 62 patients for the OPPORTUNE study. Stemness gene expression signatures with published evidence of selectivity for breast CSCs were identified from the literature and their utility in detecting CSCs compared in the datasets. Subsequently, changes in CSC fraction with treatment was assessed. Results: Eight different stemness gene signatures were identified from the published literature. Among these, four (Kim_myc, benporath_es2, Bhattacharya_hESC, and Shats_consensus) exhibited selective expression in a minority of tumor cells, with the Shats consensus signature demonstrating the highest specificity to tumor cells over non-malignant cells. Utilizing a Gaussian mixture model, we estimated that approximately 4.5% of cells within a tumor are likely stem cells. In the OPPORTUNE study, higher stemness scores were observed in luminal B compared to luminal A tumors, but there was no association with PIK3CA mutation status. Changes to Ki67 were inversely associated with the CSC fraction – tumors with higher stemness scores were less likely to achieve a complete cell cycle arrest versus endocrine-sensitive tumors. Similarly, in the FELINE trial, non-responders in the letrozole arm showed a trend towards elevated stemness scores. Conclusion: The use of stemness gene expression signatures in scRNAseq data is a feasible method to assess changes in putative CSC fraction with treatment in breast cancer. Increased stemness scores were associated with more aggressive subtypes and resistance to treatment. Citation Format: Peter Hall,Alejandro Chibly, Peter Schmid, Sarah E Pinder, Arnie Purushotham, Alastair Thompson, Steven Gendreau. Evaluation of breast cancer stem cell gene expression signatures in single-cell RNA sequencing (scRNAseq) data from the OPPORTUNE and FELINE trials, and the association with treatment resistance [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr PS13-01.

Cell type prediction is one of the most challenging goals in single-cell RNA sequencing (scRNA-seq) data. Existing methods use unsupervised learning to identify signature genes in each cluster, followed by a literature survey to look up those genes for assigning cell types. However, finding potential marker genes in each cluster is cumbersome, which impedes the systematic analysis of single-cell RNA sequencing data. To address this challenge, we proposed a framework based on regularized multi-task learning (RMTL) that enables us to simultaneously learn the subpopulation associated with a particular cell type. Learning the structure of subpopulations is treated as a separate task in the multi-task learner. Regularization is used to modulate the multi-task model (e.g., W1, W2, … Wt) jointly, according to the specific prior. For validating our model, we trained it with reference data constructed from a single-cell RNA sequencing experiment and applied it to a query dataset. We also predicted completely independent data (the query dataset) from the reference data which are used for training. We have checked the efficacy of the proposed method by comparing it with other state-of-the-art techniques well known for cell type detection. Results revealed that the proposed method performed accurately in detecting the cell type in scRNA-seq data and thus can be utilized as a useful tool in the scRNA-seq pipeline.

Hypertrophic cardiomyopathy (HCM) is a common inherited heart disease characterized by left ventricular hypertrophy and excessive myocardial contraction. Endothelial cells (ECs) play a key role in the pathogenesis of HCM, and their secreted growth factors regulate the growth and function of cardiomyocytes. However, the specific molecular mechanisms and biomarkers associated with endothelial dysfunction in HCM remain unclear. Single-cell RNA sequencing (scRNA-seq) data were downloaded from the Gene Expression Omnibus (GEO) database (GSE255296), and bulk RNA sequencing data were obtained from GSE249925 and GSE180313. The scRNA-seq data were processed using the Seurat package for cell type annotation and batch effect removal. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed, and pathway activity was evaluated using the AUCell algorithm. SCENIC was applied to construct transcription factor-specific regulatory networks. Differential gene expression analysis was conducted using the limma package, and key genes were determined by intersecting differentially expressed genes (DEGs) with EC-specific highly expressed genes. The diagnostic efficacy of these key genes was assessed via receiver operating characteristic (ROC) curve analysis. The mRNA expression of genes in HUVECs treated with or without angiotensin II (Ang II) was assessed using qPCR. Cell viability was determined using the CCK-8 assay, and cell invasive ability was evaluated through Transwell assays to investigate the regulatory effect of TMTC1 overexpression on Ang II-induced injury in HUVECs. Seven major cell types were identified, with cardiomyocytes and ECs being the main cell populations in HCM. KEGG enrichment analysis revealed significant activation of pathways such as the transforming growth factor-beta (TGF-β) signaling pathway in ECs. High-activity transcription factors NFIB, MECOM, and FLI1 were identified in ECs, whose target genes were enriched in processes related to cell morphogenesis. Three key genes-TMTC1, RPS6KA2, and F8-were selected based on differential expression and EC specificity. These genes were lowly expressed in HCM and exhibited high diagnostic efficacy, with area under the curve (AUC) values > 0.7. Ultimately, the CCK-8 assay results indicated thatAng II inhibited the viability of HUVECs, and this inhibitory effect could be reversed by the overexpression of TMTC1 (oe-TMTC1). Furthermore, Transwell assays indicated that Ang II impaired cellular migration, and this impairment could be restored by oe-TMTC1. The identified key genes and pathways provide new targets for therapeutic intervention and contribute to a deeper understanding of the pathogenesis of HCM.

This chapter will review the statistical methods used in RNA sequencing data analysis, including bulk RNA sequencing and single-cell RNA sequencing. RNA sequencing data analysis has been widely used in biomedical and biological research to identify genes associated with certain conditions or diseases. Many statistical methods have been proposed to analyze bulk and single-cell RNA sequencing data. Several studies have compared the performance of different statistical methods for RNA sequencing data analysis through simulation studies and real data evaluations. This chapter will summarize the statistical methods and the evaluation results for comparing different statistical analysis methods used for RNA sequencing data analysis. It will cover the statistical models, model assumptions, and challenges encountered in the RNA sequencing data analysis. It is hoped that this chapter will help researchers learn more about the statistical perspective of the RNA sequencing data analysis and enable them to choose appropriate statistical analysis methods for their own RNA sequencing data analysis.

The incidence and mortality rates of lung cancer are exceptionally high. Many patients are diagnosed with early stage lung cancer but experience rapid recurrence post-surgery. Many research studies have shown that the unfavorable prognosis of patients may be associated with micro-metastasis in the lymph nodes. Our research aimed to develop a nomogram to predict the prognosis of lung adenocarcinoma (LUAD). Single-cell RNA sequencing (scRNA-seq) data were analyzed to identify 11 cell clusters. Patterns of incoming and outgoing signals were identified across the entire cell population. A weighted gene co-expression network analysis (WGCNA) was conducted to uncover critical genes in LUAD. The intersecting marker genes were used to construct the prognostic model. scRNA-seq data were analyzed to identify 19 cell clusters. We identified 3,464 marker genes from the scRNA-seq dataset, 1,994 differentially expressed genes from the bulk RNA sequencing (RNA-seq) dataset, and 1,863 genes associated with a key module identified by the WGCNA. After performing the intersection, univariate Cox, and least absolute shrinkage and selection operator analyses, a prognostic model was established based on the expression levels of 13 signature genes. Subsequent functional experiments confirmed the role of selected regulated genes. Through the integration of scRNA-seq data and bulk RNA-seq data, we developed an innovative model to predict the prognosis of patients. The risk score was found to be a significant independent predictor and clinical-pathological features of LUAD.

BackgroundGastric cancer (GC) is still imposing a severe threat to human health. An increasing number of studies have found that neural activity plays an important role in the tumor microenvironment. However, the clinical implications of nerve-related genes (NRGs) remain largely unexplored.MethodsMatched GC and adjacent normal tissue from 8 patients were collected for single-cell RNA sequencing (scRNA-seq) analysis. Gene expression and patient information were downloaded from TCGA and GEO databases. A total of 441 NRGs were collected from the KEGG database, and LASSO regression analysis was used to construct the nerve-related risk score (NRRS). The survival, immune microenvironment, and mutation analyses were then carried out. Finally, scRNA-seq analysis was used to analyze the distribution of NRGs in GC patients.ResultsWe enrolled 441 NRGs and analyzed the association between NRGs expression and overall survival (OS). Finally, 8 NRGs highly associated with OS were identified to construct the NRRS model. Patients with low NRRS had significantly longer OS compared with high NRRS patients. We then analyzed the distribution of gene mutation landscape, enrichment annotation and immune infiltration in different NRRS subtypes. It was found that high NRRS patients displayed a significantly higher infiltration abundance of immune cell subtypes and immune checkpoint molecules. In addition, scRNA-seq was used to analyze the distribution of NRGs in 8 GC patients. We obtained 55, 052 cells in the scRNA-seq data, and the NRRS signature was significantly higher in GC tissues. EPHB3 and LPAR2 were highly expressed in epithelial cells, while NRP1, GNAI1, and SEMA6A were highly expressed in endothelial cells.ConclusionsTaken together, NRRS could serve as a stable and powerful model for survival prediction, and can help to identify GC patients who may benefit from chemotherapy and immunotherapy.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12885-025-15202-9.

Transcriptomic analysis across species is increasingly used to reveal conserved gene regulations which implicate crucial regulators. Cross-species analysis of single-cell RNA sequencing (scRNA-seq) data provides new opportunities to identify the cellular and molecular conservations, especially for cell types and cell type-specific gene regulations. However, few methods have been developed to analyze cross-species scRNA-seq data to uncover both molecular and cellular conservations. Here, we built a tool called CACIMAR, which can perform cross-species analysis of cell identities, markers, regulations, and interactions using scRNA-seq profiles. Based on the weighted sum models of the conserved features, we developed different conservation scores to measure the conservation of cell types, regulatory networks, and intercellular interactions. Using publicly available scRNA-seq data on retinal regeneration in mice, zebrafish, and chick, we demonstrated four main functions of CACIMAR. First, CACIMAR allows to identify conserved cell types even in evolutionarily distant species. Second, the tool facilitates the identification of evolutionarily conserved or species-specific marker genes. Third, CACIMAR enables the identification of conserved intracellular regulations, including cell type-specific regulatory subnetworks and regulators. Lastly, CACIMAR provides a unique feature for identifying conserved intercellular interactions. Overall, CACIMAR facilitates the identification of evolutionarily conserved cell types, marker genes, intracellular regulations, and intercellular interactions, providing insights into the cellular and molecular mechanisms of species evolution.

Osteoporosis is a common disease, especially among the elderly. This study aimed to comprehensively examine the roles of immune microenvironment in osteoporosis pathogenesis. Expression profiles of GSE35959, GSE7158, and GSE13850 datasets were used to analyze differential expression and identify hub genes related to immune features. Based on the single-cell RNA sequencing (scRNA-seq) data of an osteoporosis patient, different cell types were classified and the relation between immune environment and osteoporosis was explored. Twelve hub genes significantly associated with immune features were selected and 11 subgroups were defined using scRNA-seq data. The expression of two hub genes (CDKN1A and TEFM) was greatly altered during the transformation from mesenchymal stem cells (MSCs) to osteoblasts. Chemokines and chemokine receptors were differentially enriched in different cell types. CXCL12 was high-expressed in MSCs. This study emphasized that immune microenvironment played a critical role in the pathogenesis of osteoporosis. Chemokines and chemokine receptors can modify cell development and affect the interactions among different cell types, leading to unbalanced bone remodeling.

Immunotherapy represents a pivotal therapeutic modality in endometrial cancer (EC). Nevertheless, the efficacy of this treatment is limited to a subset of patients. The present investigation endeavors to amalgamate multi-omics data in order to elucidate the determinants impacting individual immune responsiveness and enhance the optimization of immunotherapy for EC. To differentiate EC patients into non-response (NR) and response (R), multi-omics data from publicly available databases were employed in conjunction with the TIDE computational framework. The validity of these findings was further confirmed through the utilization of the EaSIeR and ImmunoPhenoScore algorithms. The study employed functional enrichment and gene set variant analysis to discern noteworthy disparities in biological pathways across various groups. Moreover, three deconvolution algorithms (ESTIMATE, TIMER, and EPIC) were employed to quantify the tumor microenvironment (TME). Somatic mutation and copy number variant (CNV) analyses were also conducted to identify genomic alterations that impact immunotherapy. Integrated bulk and single-cell RNA sequencing (scRNA-seq) data were employed to identify cell populations linked to efficacy and deduce cell-cell interactions. The immunotherapy response rates were found to be greater in elderly EC patients aged 65 years and above. The NR group of patients displayed notable enrichment in cellular differentiation, angiogenesis, and tumor proliferation characteristics, as evidenced by higher tumor purity and lower expression of immune checkpoints. Conversely, the R group exhibited a stronger correlation with immunity, as indicated by pathway enrichment and composition of TME. Patients in the NR group demonstrated higher frequencies of somatic mutations, with a 2- to 6-fold disparity between the groups in genes such as RPRD1B and CTNNB1. Patients in the R group exhibited elevated mutation scores and higher mutation frequencies at the same mutation loci compared to those in the NR group. Moreover, the incidence of mutations was more prevalent among patients in the R group. In independent cohorts, the Scissor algorithm suggests that macrophages may exert a substantial impact on immunotherapy response in patients with EC. Subsequent analysis unveiled an enrichment of M2-like tumor-associated macrophages (TAMs) within the TME of patients in the NR group. These macrophages facilitate angiogenesis and cell proliferation through intercellular communication with subpopulations such as endothelial and epithelial cells. TME of patients in the R group exhibited an enrichment of M1-like TAMs, which primarily engaged with immune cells via diverse immune-activating factors. Furthermore, immunohistochemistry and flow cytometry demonstrated that responders to immunotherapy had significantly increased infiltration of M1-like TAMs. M1-like TAMs were shown to inhibit proliferation and migration of Ishikawa cells in co-culture assays. This research offers a comprehensive insight into the multi-omics level factors influencing the immunotherapy response of EC patients, emphasizing the influence of genomic variants and TAMs on said response. This contributes to an enhanced comprehension of the biological mechanisms underlying EC immunotherapy response and aids in the optimization of EC immunotherapy.

Low back pain (LBP) is a prevalent health problem worldwide that affects over 80% of adults during their lifetime. Intervertebral disc degeneration (IDD) is a well-recognized leading cause of LBP. IDD is classified into five grades according to the Pfirrmann classification system. The purpose of this study was to identify potential biomarkers in different IDD grades through an integrated analysis of proteome sequencing (PRO-seq), bulk RNA sequencing (bRNA-seq) and single-cell RNA sequencing (scRNA-seq) data. Eight cases of grade I-IV IDD were obtained. Grades I and II were considered non-degenerative discs (relatively normal), whereas grades III and IV were considered degenerative discs. PRO-seq analysis was performed to identify differentially expressed proteins (DEPs) in various IDD grades. Variation analysis was performed on bRNA-seq data to differentiate expressed genes (DEGs) in normal and degenerated discs. In addition, scRNA-seq was performed to validate DEGs in degenerated and non-degenerated nucleus pulposus (NP). Machine learning (ML) algorithms were used to screen hub genes. The receiver operating characteristic (ROC) curve was used to validate the efficiency of the screened hub genes to predict IDD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to analyze function enrichment and signaling pathways. Protein-protein interaction (PPI) network was used to prioritize disease-related proteins. SERPINA1, ORM2, FGG and COL1A1 were identified through PRO-seq as the hub proteins involved in regulating IDD. ML algorithms selected ten hub genes, including IBSP, COL6A2, MMP2, SERPINA1, ACAN, FBLN7, LAMB2, TTLL7, COL9A3, and THBS4 in bRNA-seq. Since serine protease inhibitor clade A member 1 (SERPINA1) was the only common gene, its accuracy in degenerated and non-degenerated NP cells was validated using scRNA-seq. Then, the rat degeneration model of caudal vertebra was established. The expression of SERPINA1 and ORM2 was detected using immunohistochemical staining of human and rat intervertebral discs. The results showed that SERPINA1 was poorly expressed in the degenerative group. We further explored the potential function of SERPINA1 by Gene Set Enrichment Analysis (GSEA) and cell-cell communication. Therefore, SERPINA1 can be used as a biomarker to regulate or predict the progress of disc degeneration.

Differential gene expression analysis is one of the significant efforts in single cell RNA sequencing (scRNAseq) analysis to discover the specific changes in expression levels of individual cell types. Since scRNAseq exhibits multimodality, large amounts of zero counts, and sparsity, it is different from the traditional bulk RNA sequencing (RNAseq) data. The new challenges of scRNAseq data promote the development of new methods for identifying differentially expressed (DE) genes. In this study, we proposed a new method, SigEMD, that combines a logistic regression model and a nonparametric method based on Earth Mover's Distance, to precisely and efficiently identify DE genes in scRNAseq data. The regression model is used to reduce the impact of large amounts of zero counts, and the nonparametric method is used to improve the sensitivity of detecting DE genes from multimodal scRNAseq data. By additionally employing gene interaction network information to adjust the final states of DE genes, we further reduce the false positives of calling DE genes. We used simulated data and real data to evaluate the detection accuracy of the proposed method and to compare its performance with those of other differential expression analysis methods. Results indicate that the proposed method has an overall powerful performance in terms of precision in detection, sensitivity, and specificity.