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Gene set optimization for single cell transcriptomics.
Although single cell RNA-sequencing (scRNA-seq) provides unprecedented insights into the biology of complex tissues, analyzing such data on a gene-by-gene basis is challenging due to the large number of tested hypotheses and consequent low statistical power and difficult interpretation. These issues are magnified by the increased noise, significant sparsity and multi-modal distributions characteristic of single cell data. One promising approach for addressing these challenges is gene set testing, or pathway analysis. Unfortunately, statistical and biological differences between single cell and bulk transcriptomic data make it challenging to use existing gene set collections, which were developed for bulk tissue analysis, on scRNA-seq data. In this paper, we describe a procedure for customizing gene set collections originally created for bulk tissue analysis to reflect the structure of gene activity within specific cell types. Our approach leverages information about mean gene expression in the 81 human cell types profiled via scRNA-seq by the Human Protein Atlas (HPA) Single Cell Type Atlas. This HPA information is used to compute cell type-specific gene and gene set weights that can be used to filter or weight gene set collections. As demonstrated through the analysis of immune cell scRNA-seq data using gene sets from the Molecular Signatures Database (MSigDB), accounting for cell type-specificity can significantly improve gene set testing power and interpretability.
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Single-cell RNA sequencing technologies and applications: A brief overview.
Single-cell RNA sequencing (scRNA-seq) technology has become the state-of-the-art approach for unravelling the heterogeneity and complexity of RNA transcripts within individual cells, as well as revealing the composition of different cell types and functions within highly organized tissues/organs/organisms. Since its first discovery in 2009, studies based on scRNA-seq provide massive information across different fields making exciting new discoveries in better understanding the composition and interaction of cells within humans, model animals and plants. In this review, we provide a concise overview about the scRNA-seq technology, experimental and computational procedures for transforming the biological and molecular processes into computational and statistical data. We also provide an explanation of the key technological steps in implementing the technology. We highlight a few examples on how scRNA-seq can provide unique information for better understanding health and diseases. One important application of the scRNA-seq technology is to build a better and high-resolution catalogue of cells in all living organism, commonly known as atlas, which is key resource to better understand and provide a solution in treating diseases. While great promises have been demonstrated with the technology in all areas, we further highlight a few remaining challenges to be overcome and its great potentials in transforming current protocols in disease diagnosis and treatment.
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A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors.
Single-cell genomics is essential to chart tumor ecosystems. Although single-cell RNA-Seq (scRNA-Seq) profiles RNA from cells dissociated from fresh tumors, single-nucleus RNA-Seq (snRNA-Seq) is needed to profile frozen or hard-to-dissociate tumors. Each requires customization to different tissue and tumor types, posing a barrier to adoption. Here, we have developed a systematic toolbox for profiling fresh and frozen clinical tumor samples using scRNA-Seq and snRNA-Seq, respectively. We analyzed 216,490 cells and nuclei from 40 samples across 23 specimens spanning eight tumor types of varying tissue and sample characteristics. We evaluated protocols by cell and nucleus quality, recovery rate and cellular composition. scRNA-Seq and snRNA-Seq from matched samples recovered the same cell types, but at different proportions. Our work provides guidance for studies in a broad range of tumors, including criteria for testing and selecting methods from the toolbox for other tumors, thus paving the way for charting tumor atlases.
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The Establishment and Experimental Verification of an lncRNA-Derived CD8+ T Cell Infiltration ceRNA Network in Colorectal Cancer.
BACKGROUND: Long noncoding RNAs (LncRNA) lead a vital role in colorectal cancer (CRC) development. The infiltrating CD8+ T cell is the main target of immunotherapy. Our study aimed to figure out the potential mechanism of lncRNAs regulating the function of CD8+ T cells in CRC. METHODS: We collected bulk RNA-seq, miRNA-seq, and single-cell RNA-seq (scRNA-seq) data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The cibersort algorithm and correlation analysis were used to estimate the abundance of CD8+ T cells and screened out the most relevant lncRNAs. We used scRNA-seq data to identify the main cell lncRNA expressed. Furthermore, one competing endogenous RNA (ceRNA) network focusing on the potential mechanism of lncRNA-derived CD8+ T cell infiltration was constructed. We established a co-culture system to assess the immunosuppressive function of the lncRNA. And we evaluated the effects of the lncRNA on CD8+ T cell cytotoxicity by flow cytometry, qPCR, and clone formation assay. RESULTS: Three CD8+ T cell infiltration-related lncRNAs were identified, and LINC00657 was expressed mainly in tumor cells, negatively associated with CD8+ T cell infiltration. Hsa-miRNA-1224-3p and hsa-miRNA-338-5p and SCD, ETS2, UBE2H, and YY1 were identified to construct the ceRNA network. Immunosuppression-related tumor marker CD155 was proved to be positively correlated with LINC00657 and mRNAs in the ceRNA network. In addition, we proved that LINC00657 could impair the cytotoxicity of CD8+ T cells, and its expression was positively associated with CD155 in vitro. CONCLUSIONS: We successfully constructed an lncRNA-derived CD8+ T cell infiltration ceRNA network in CRC. LINC00657 may play a leading role in the CRC immune escape and could be a novel immunotherapy target.
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Integrative multi-omics analysis reveals an 11-gene malignant-myeloid interaction signature and identifies TPST1 as a potential regulator of immunosuppressive microenvironment in glioma.
BACKGROUND: Glioblastoma (GBM) is a highly aggressive brain tumor with a profoundly immunosuppressive tumor microenvironment (TME). Myeloid cells, especially tumor-associated macrophages, play a key role in immune evasion, yet regulators within tumor cells that shape myeloid-mediated immunosuppression remain poorly characterized. METHODS: We integrated five single-cell RNA-seq datasets from glioma tissues, seven bulk RNA-seq cohorts with survival data, and seven immunotherapy transcriptomic datasets. Myeloid functional gene sets (microglia, border-associated macrophages, dendritic cells) were established via reference mapping. Weighted gene co-expression network analysis (WGCNA) identified a myeloid-associated module, whose overlap with malignant cell markers defined an 11-gene Malignant–Myeloid Interaction Signature (MMIS). Prognostic and immunotherapy response values were evaluated through meta-analysis. Immune infiltration, immunosuppression markers, and pathway activities were assessed using deconvolution algorithms and correlation analyses. RESULTS: The 11-gene MMIS (CLU, MAP1B, IGFBP7, NNMT, EMP1, EFEMP1, PAM, TPST1, MT2A, CHI3L1, ACTN1) was strongly correlated with myeloid function and poor prognosis. A risk score based on MMIS was constructed, which outperformed standard clinicopathological factors (including Age, IDH status, 1p/19q codeletion, and MGMT promoter methylation). The signature correlated positively with macrophage infiltration, immunosuppressive markers (e.g., CD163, TGFB1, IL10), and T cell exhaustion signatures (e.g., PDCD1, CTLA4, BATF). Among these, TPST1 was associated with immunotherapy resistance and was upregulated in high-grade glioma. TPST1-high tumor cells exhibited proliferative enrichment and potential interaction with myeloid cells via PTN-NCL and EREG/AREG-EGFR signaling pathways. CONCLUSIONS: We identified and validated an 11-gene signature that reflects malignant–myeloid crosstalk and predicts prognosis and immunotherapy response in GBM. These findings reveal novel mechanisms through which glioma cells modulate the immunosuppressive microenvironment and highlight TPST1 as a potential therapeutic target associated with T-cell exclusion and poor immunotherapy response mechanisms, though further validation in glioma-specific cohorts is required.
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TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment.
Cancer immunotherapy targeting co-inhibitory pathways by checkpoint blockade shows remarkable efficacy in a variety of cancer types. However, only a minority of patients respond to treatment due to the stochastic heterogeneity of tumor microenvironment (TME). Recent advances in single-cell RNA-seq technologies enabled comprehensive characterization of the immune system heterogeneity in tumors but posed computational challenges on integrating and utilizing the massive published datasets to inform immunotherapy. Here, we present Tumor Immune Single Cell Hub (TISCH, http://tisch.comp-genomics.org), a large-scale curated database that integrates single-cell transcriptomic profiles of nearly 2 million cells from 76 high-quality tumor datasets across 27 cancer types. All the data were uniformly processed with a standardized workflow, including quality control, batch effect removal, clustering, cell-type annotation, malignant cell classification, differential expression analysis and functional enrichment analysis. TISCH provides interactive gene expression visualization across multiple datasets at the single-cell level or cluster level, allowing systematic comparison between different cell-types, patients, tissue origins, treatment and response groups, and even different cancer-types. In summary, TISCH provides a user-friendly interface for systematically visualizing, searching and downloading gene expression atlas in the TME from multiple cancer types, enabling fast, flexible and comprehensive exploration of the TME.
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Marine Invertebrates One Cell at A Time: Insights from Single-Cell Analysis.
Over the past decade, single-cell RNA-sequencing (scRNA-seq) has made it possible to study the cellular diversity of a broad range of organisms. Technological advances in single-cell isolation and sequencing have expanded rapidly, allowing the transcriptomic profile of individual cells to be captured. As a result, there has been an explosion of cell type atlases created for many different marine invertebrate species from across the tree of life. Our focus in this review is to synthesize current literature on marine invertebrate scRNA-seq. Specifically, we provide perspectives on key insights from scRNA-seq studies, including descriptive studies of cell type composition, how cells respond in dynamic processes such as development and regeneration, and the evolution of new cell types. Despite these tremendous advances, there also lie several challenges ahead. We discuss the important considerations that are essential when making comparisons between experiments, or between datasets from different species. Finally, we address the future of single-cell analyses in marine invertebrates, including combining scRNA-seq data with other 'omics methods to get a fuller understanding of cellular complexities. The full diversity of cell types across marine invertebrates remains unknown and understanding this diversity and evolution will provide rich areas for future study.
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Genomic Exploration of Nonalcoholic Fatty Liver Disease: Insights From Gene Expression and Variation in Morbidly Obese Individuals.
Nonalcoholic fatty liver disease (NAFLD) is a common liver condition resulting from metabolic syndrome characterized by fat accumulation in the liver. It is often associated with obesity and diabetes, contributing to hepatic steatosis in liver cells. The prevalence of NAFLD is increasing globally, with 32% of the adult population affected. Genetic modifiers, such as single nucleotide polymorphisms, can increase susceptibility to the disease. Gene expression analysis and genetic variation can help identify disease-causing pathways and reveal biomarkers involved in NAFLD. This study employed integrative bioinformatics analysis, including bulk RNA-seq and single-cell RNA-seq, to explore differentially expressed genes and their genetic variants in NAFLD vs. control and NAFLD vs. cirrhosis, highlighting genes influencing NAFLD progression. Moreover, this study identified AKR1D1, LIPC, UGT2B17, DGAT2, and SERPINE1 implicated in metabolic, immune, and lipid functions while being overexpressed in both hepatocyte cells among obese patients identified and validated through Liver Cell Atlas, highlighting their pivotal role in the pathogenesis of the disease in obese patients through perturbed hepatocytes. Furthermore, novel pathogenic variants of AKR1D1, LIPC, and SERPINE1, associated with congenital bile acid synthesis defects, abnormal circulating lipid concentrations, and plasminogen activator inhibitor type 1 deficiency conditions, were identified. Conclusively, this integrative multiomics study highlights the novel pathogenic variants of AKR1D1, LIPC, and SERPINE1 in metabolic, immune, and lipid pathways that are highly expressed among hepatocytes in obese patients while possibly carrying pathogenic mutations that may be associated with NAFLD, emphasizing their potential as novel targets for therapeutic strategies and biomarker development in early diagnosis and treatment before the onset of cirrhosis or hepatocellular carcinoma.
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Construction of Bone Metastasis-Specific Regulation Network Based on Prognostic Stemness-Related Signatures in Breast Invasive Carcinoma.
BACKGROUND: Bone is the most common metastatic site of Breast invasive carcinoma (BRCA). In this study, the bone metastasis-specific regulation network of BRCA was constructed based on prognostic stemness-related signatures (PSRSs), their upstream transcription factors (TFs) and downstream pathways. METHODS: Clinical information and RNA-seq data of 1,080 primary BRCA samples (1,048 samples without bone metastasis and 32 samples with bone metastasis) were downloaded from The Cancer Genome Atlas (TCGA). The edgeR method was performed to identify differential expressed genes (DEGs). Next, mRNA stemness index (mRNAsi) was calculated by one-class logistic regression (OCLR). To analyze DEGs by classification, similar genes were integrated into the same module by weighted gene co-expression network analysis (WGCNA). Then, univariate and multivariate Cox proportional hazard regression were applied to find the PSRSs. Furthermore, PSRSs, 318 TFs obtained from Cistrome database and 50 hallmark pathways quantified by GSVA were integrated into co-expression analysis. Significant co-expression patterns were used to construct the bone metastasis-specific regulation network. Finally, spatial single-cell RNA-seq and chromatin immunoprecipitation sequence (ChIP-seq) data and multi-omics databases were applied to validate the key scientific hypothesis in the regulation network. Additionally, Connectivity Map (CMap) was utilized to select the potential inhibitors of bone metastasis-specific regulation network in BRCA. RESULTS: Based on edgeR and WGCNA method, 43 PSRSs were identified. In the bone metastasis-specific regulation network, MAF positively regulated CD248 (R = 0.435, P < 0.001), and hallmark apical junction was the potential pathway of CD248 (R = 0.353, P < 0.001). This regulatory pattern was supported by spatial single-cell RNA sequence, ChIP-seq data and multi-omics online databases. Additionally, alexidine was identified as the possible inhibitor for bone metastasis of BRCA by CMap analysis. CONCLUSION: PSRSs played important roles in bone metastasis of BRCA, and the prognostic model based on PSRSs showed good performance. Especially, we proposed that CD248 was the most significant PSRS, which was positively regulated by MAF, influenced bone metastasis via apical junction pathway. And this axis might be inhibited by alexidine, which providing a potential treatment strategy for bone metastasis of BRCA.
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Single-cell RNA-sequencing of Nicotiana attenuata corolla cells reveals the biosynthetic pathway of a floral scent.
High-throughput single-cell RNA sequencing (scRNA-Seq) identifies distinct cell populations based on cell-to-cell heterogeneity in gene expression. By examining the distribution of the density of gene expression profiles, we can observe the metabolic features of each cell population. Here, we employ the scRNA-Seq technique to reveal the entire biosynthetic pathway of a flower volatile. The corolla of the wild tobacco Nicotiana attenuata emits a bouquet of scents that are composed mainly of benzylacetone (BA). Protoplasts from the N. attenuata corolla limbs and throat cups were isolated at three different time points, and the transcript levels of > 16 000 genes were analyzed in 3756 single cells. We performed unsupervised clustering analysis to determine which cell clusters were involved in BA biosynthesis. The biosynthetic pathway of BA was uncovered by analyzing gene co-expression in scRNA-Seq datasets and by silencing candidate genes in the corolla. In conclusion, the high-resolution spatiotemporal atlas of gene expression provided by scRNA-Seq reveals the molecular features underlying cell-type-specific metabolism in a plant.