Robust and efficient annotation of cell states through gene signature scoring.

Cell type identification plays a vital role in single-cell RNA sequencing (scRNA-seq) data analysis. Although many deep embedded methods to cluster scRNA-seq data have been proposed, they still fail in elucidating the intrinsic properties of cells and genes. Here, we present a novel end-to-end deep graph clustering model for single-cell transcriptomics data based on unsupervised Gene-Cell Collective representation learning and Optimal Transport (scGCOT) which integrates both cell and gene correlations. Specifically, scGCOT learns the latent embedding of cells and genes simultaneously and reconstructs the cell graph, the gene graph, and the gene expression count matrix. A zero-inflated negative binomial (ZINB) model is estimated via the reconstructed count matrix to capture the essential properties of scRNA-seq data. By leveraging the optimal transport-based joint representation alignment, scGCOT learns the clustering process and the latent representations through a mutually supervised self optimization strategy. Extensive experiments with 14 competing methods on 15 real scRNA-seq datasets demonstrate the competitive edges of scGCOT.

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.

Unsupervised clustering is a fundamental step of single-cell RNA-sequencing (scRNA-seq) data analysis. This issue has inspired several clustering methods to classify cells in scRNA-seq data. However, accurate prediction of the cell clusters remains a substantial challenge. In this study, we propose a new algorithm for scRNA-seq data clustering based on Sparse Optimization and low-rank matrix factorization (scSO). We applied our scSO algorithm to analyze multiple benchmark datasets and showed that the cluster number predicted by scSO was close to the number of reference cell types and that most cells were correctly classified. Our scSO algorithm is available at https://github.com/QuKunLab/scSO. Overall, this study demonstrates a potent cell clustering approach that can help researchers distinguish cell types in single- scRNA-seq data.

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.

BackgroundHepatocellular carcinoma (HCC) remains a leading cause of cancer-related deaths globally. The Like-Smith (LSM) family members are involved in RNA metabolism and tumor progression, but their role in HCC remains unclear. This study aims to construct a novel signature based on LSM family members-related genes and explore its clinical value in HCC.MethodsMolecular patterns related to LSM family members were identified through clustering analysis. Differential expression analysis was used to identify genes with potential prognostic significance. Multivariate Cox regression analysis was performed to construct a signature with The Cancer Genome Atlas (TCGA) cohort. The International Cancer Genome Consortium (ICGC) cohort served as external validation. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were used to evaluate the predictive ability. Enrichment analysis, immune infiltration assessment, and single-cell RNA sequencing (scRNA-seq) data analysis were conducted to explore the underlying mechanisms.ResultsTwo genes—paired-like homeodomain 2 (PITX2) and chromogranin A (CHGA)—were ultimately identified as a novel signature for HCC. Based on the risk score derived from the signature, samples were divided into high- and low-risk groups. Results indicated that the high-risk group had significantly poorer overall survival in both TCGA and ICGC cohorts. The ROC curves demonstrated that the signature exhibits stable predictive accuracy. Enrichment analysis showed that the high-risk group was associated with tumor-related pathways. Differences in immune infiltration were observed between high- and low-risk groups. scRNA-seq analysis indicated that PITX2 and CHGA were highly expressed in hepatocytes.ConclusionsThe novel two-gene signature comprising PITX2 and CHGA effectively predicts survival outcomes in HCC patients and is closely associated with tumor metabolism and immune regulation. This signature may serve as a valuable tool for prognostic evaluation and guiding personalized treatment strategies for HCC patients.

Analysis of single-cell RNA sequencing (scRNA-seq) data of immune cells from the tumor microenvironment (TME) may identify tumor progression biomarkers. This study was designed to investigate the prognostic value of differentially expressed genes (DEGs) in intrahepatic cholangiocarcinoma (ICC) using scRNA-seq. We downloaded the scRNA-seq data of 33,991 cell samples, including 17,090 ICC cell samples and 16,901 ICC adjacent tissue cell samples regarded as normal cells. scRNA-seq data were processed and classified into 20 clusters. The immune cell clusters were extracted and processed again in the same way, and each type of immune cells was divided into several subclusters. In total, 337 marker genes of macrophages and 427 marker genes of B cells were identified by comparing ICC subclusters with normal subclusters. Finally, 659 DEGs were obtained by merging B cell and macrophage marker genes. ICC sample clinical information and gene expression data were downloaded. A nine-prognosis-related-gene (PRG) signature was established by analyzing the correlation between DEGs and overall survival in ICC. The robustness and validity of the signature were verified. Functional enrichment analysis revealed that the nine PRGs were mainly involved in tumor immune mechanisms. In conclusion, we established a PRG signature based on scRNA-seq data from immune cells of patients with ICC. This PRG signature not only reflects the TME immune status but also provides new biomarkers for ICC prognosis.

Apigenin inhibits recurrent bladder cancer progression by targeting VEGF-β.

We present GranatumX, a next-generation software environment for single-cell RNA sequencing (scRNA-seq) data analysis. GranatumX is inspired by the interactive webtool Granatum. GranatumX enables biologists to access the latest scRNA-seq bioinformatics methods in a web-based graphical environment. It also offers software developers the opportunity to rapidly promote their own tools with others in customizable pipelines. The architecture of GranatumX allows for easy inclusion of plugin modules, named Gboxes, which wrap around bioinformatics tools written in various programming languages and on various platforms. GranatumX can be run on the cloud or private servers and generate reproducible results. It is a community-engaging, flexible, and evolving software ecosystem for scRNA-seq analysis, connecting developers with bench scientists. GranatumX is freely accessible at http://garmiregroup.org/granatumx/app.

Single-cell transcriptome analysis has made outstanding contributions to the identification of new cell lineages and the study of cancer immune microenvironment. Yet, the characterization of human liver type 1 innate lymphoid cells (ILC1s) and their dynamic changes in the tumor microenvironment have not been thoroughly studied at this detailed level. Here, we performed an integrated analysis of mouse and human liver immune cells to identify human liver ILC1s based on identified mouse liver ILC1s and to verify its functional similarity. Additionally, our findings highlighted the different expression patterns of the transcription factor EOMES in human versus mouse liver ILC1s, suggesting its reduced regulatory significance in human liver nature killer (NK) cells and ILC1s compared to murine models. A unique subset of intermediate innate lymphoid cells (intILCs) was identified, exhibiting traits of both human liver NK cells and ILC1s. Single-cell RNA sequencing (scRNA-seq) data analysis and TCGA dataset were utilized to characterize the distinct alterations in the genes and functions of NK cells, ILC1s, and intILCs in human hepatocellular carcinoma (HCC). It was found that the dynamic changes of liver ILC1s and intILCs, along with some of their subpopulations, may be key factors in tumor progression. This study provided new insights into the identification of ILC1s in human liver and the immunologic changes and mechanism of innate lymphoid cells (ILCs) in the tumor microenvironment, and these findings may be applicable to improving the diagnosis and treatment of hepatocellular carcinoma.

In the analysis of single-cell RNA-sequencing (scRNA-seq) data, how to effectively and accurately identify cell clusters from a large number of cell mixtures is still a challenge. Low-rank representation (LRR) method has achieved excellent results in subspace clustering. But in previous studies, most LRR-based methods usually choose the original data matrix as the dictionary. In addition, the methods based on LRR usually use spectral clustering algorithm to complete cell clustering. Therefore, there is a matching problem between the spectral clustering method and the affinity matrix, which is difficult to ensure the optimal effect of clustering. Considering the above two points, we propose the DLNLRR method to better identify the cell type. First, DLNLRR can update the dictionary during the optimization process instead of using the predefined fixed dictionary, so it can realize dictionary learning and LRR learning at the same time. Second, DLNLRR can realize subspace clustering without relying on spectral clustering algorithm, that is, we can perform clustering directly based on the low-rank matrix. Finally, we carry out a large number of experiments on real single-cell datasets and experimental results show that DLNLRR is superior to other scRNA-seq data analysis algorithms in cell type identification.

BackgroundTumor-associated macrophages (TAMs) play pivotal roles in innate immunity and contribute to the advancement of lung cancer. We aimed to identify novel TAM-related biomarkers and significance of macrophage infiltration in lung adenocarcinoma (LUAD) through an integrative analysis of single-cell RNA-sequencing (scRNA-seq) data. To describe the cell atlas and construct a novel prognostic signature in LUAD.MethodsThe gene signature linked to TAMs was identified utilizing Scanpy from the scRNA-seq dataset GSE131907. Subsequent analysis involved evaluating the expression levels of these genes, their potential molecular mechanisms, and prognostic significance in LUAD using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We also constructed a risk score models through LASSO Cox regression for these genes. The underlying mechanism was further elucidated through the application of GSEA, ESTIMATE, TIDE, and other bioinformatic algorithms.ResultsSingle-cell atlas was described by analyze 29 scRNA-seq samples from 19 LUAD patients. The TAMs-related gene signature (TGS) was identified as an independent prognostic factor by LASSO Cox regression analysis using differential expression genes (DEGs) derived from pro- and anti-inflammatory macrophage cells. Risk score model including nine TAMs-related genes (FOSL1, ZNF697, ADM, UBE2S, TICAM1, S100P, BIRC3, TLE1, and DEFB1) were obtained for prognosis construction. Moreover, the risk model underwent additional validation in four external GEO cohorts: GSE31210, GSE72094, GSE26939, and GSE30219. Interestingly, TGS-high tumors revealed enrichments in TGF-β signaling and hypoxia pathways, which shown low immune infiltration and immunosuppression by ESTIMATE and TIDE algorithm. The TGS-high risk group exhibited lower richness and diversity in the T-cell receptor (TCR) repertoire.ConclusionThis study introduces a novel TGS score developed through LASSO Cox regression analysis, utilizing DEGs in pro- and anti-inflammatory macrophage cells. High TGS tumors exhibited enrichment in TGF-β signaling and hypoxia pathways, suggesting their potential utility in predicting prognosis and immune responses in patients with LUAD. These results offer promising implications for the development of therapeutic strategies for LUAD.

Lung cancer exhibits the world's highest mortality rate among malignant cancers worldwide, thereby presenting a significant global challenge in terms of reducing patient mortality. In the field of oncology, targeted immunotherapy has emerged as a novel therapeutic approach for lung cancer. This study aims to explore potential targets for immunotherapy in lung adenocarcinoma (LUAD) through the analysis of Ferroptosis Index (FPI) and Single Cell RNA-Sequencing (scRNA-seq) data. The findings of this research can potentially offer valuable insights for improving LUAD immunotherapy strategies and informing clinical decision-making. Firstly, the relationship between survival and ferroptosis in LUAD patients was analyzed by FPI. Subsequently, the association between ferroptosis and infiltration and regulation of immune cells was explored by immune infiltration analysis and correlation statistics. Lastly, the relationship between major infiltrating immune cell populations and related pathways and prognosis of LUAD patients was analyzed by GSEA and GSVA. To screen out core genes regulating infiltration of immune cell populations, scRNA-seq data of cancer and para-cancerous tissues of LUAD patients were downloaded, followed by cell clustering analysis, cell identification of core subpopulations, pseudotime analysis, single-cell GSVA and pathway enrichment analysis, and identification and functional analysis of core regulatory genes. Moreover, the expression levels of core functional genes in LUAD tissue microarray were detected by immunohistochemistry, and its relationship with the prognosis of LUAD patients was verified. Finally, we used lentivirus with WDFY4 to transfect LUAD A549 cells. CCK-8, flow cytometry apoptosis detection, Scratch wound healing assay, Transwell migration assay, Xenograft nude mice model, immunohistochemical analysis and other experimental methods were used to explore the biological effects of WDFY4 on LUAD in vitro and in vivo. Survival analysis of FPI values in LUAD patients revealed a positive correlation between smaller FPI values and longer overall survival. Immuno-infiltration analysis and its correlation with FPI values revealed that B cells were most strongly associated with ferroptosis. Ferroptosis of cancer cells could promote infiltration and activation of B cell populations, and LUAD patients with more infiltration of B cell populations had longer long-term survival. scRNA-seq data analysis indicated that the B cell population is one of the major cell populations infiltrated by immune cells in LUAD. During the later phases of B cell differentiation in LUAD, there was a decrease in the expression levels of ACAP1, LINC00926, TLR10, MS4A1, WDFY4, and TRIM22 genes, whereas the expression levels of TMEM59, TP53INP1, and METTL7A genes were elevated. The protein-protein interaction (PPI) network analysis indicated that WDFY4 plays a crucial role in regulating B cell differentiation in LUAD. Immunohistochemical analysis of LUAD tissue microarray revealed a significant downregulation of WDFY4 expression, which was closely related to the occurrence sites of LUAD. Moreover, LUAD patients with a low WDFY4 expression exhibited a poorer prognosis. Additionally, experimental findings demonstrated that the overexpression of WDFY4 could inhibit the proliferation and metastasis of A549 cells while promoting apoptosis. It was also confirmed that WDFY4 could inhibit cancer growth in vivo. The results indicate that promoting infiltration and activation of B cell populations could improve the long-term survival of LUAD patients, thereby offering a potential novel immunotherapeutic approach for LUAD. Besides, the promotion of cancer cell ferroptosis and upregulation of WDFY4 expression have been shown to induce the infiltration and activation of B cell populations. Furthermore, the overexpression of WDFY4 can significantly inhibit the growth of lung adenocarcinoma in vitro and in vivo, highlighting its potential as a target for immunotherapy in 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.

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.

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