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In silico screening using bulk and single-cell RNA-seq data identifies RIMS2 as a prognostic marker in basal-like breast cancer: A retrospective study.
Single-cell RNA-seq has become a powerful tool to understand tumor cell heterogenicity. This study tried to screen prognosis-related genes in basal-like breast tumors and evaluate their correlations with cellular states at the single-cell level.Bulk RNA-seq data of basal-like tumor cases from The Cancer Genome Atlas-Breast Cancer (TCGA-BRCA) and single-cell RNA-seq from GSE75688 were retrospectively reviewed. Kaplan-Meier survival curves, univariate and multivariate analysis based on Cox regression model were conducted for survival analysis. Gene set enrichment analysis (GSEA) and single-cell cellular functional state analysis were performed.Twenty thousand five hundred thirty genes with bulk RNA-seq data in TCGA were subjected to screening. Preliminary screening identified 10 candidate progression-related genes, including CDH19, AQP5, SDR16C5, NCAN, TTYH1, XAGE2, RIMS2, GZMB, LY6D, and FAM3B. By checking their profiles using single-cell RNA-seq data, only CDH19, SDR16C5, TTYH1, and RIMS2 had expression in primary triple-negative breast cancer (TNBC) cells. Prognostic analysis only confirmed that RIMS2 expression was an independent prognostic indicator of favorable progression free survival (PFS) (HR: 0.78, 95%: 0.64-0.95, P = .015). GSEA analysis showed that low RIMS2 group expression had genes significantly enriched in DNA Repair, and MYC Targets V2. Among the 89 basal-like cells, RIMS2 expression was negatively correlated with DNA repair and epithelial-to-mesenchymal transition (EMT).RIMS2 expression was negatively associated with DNA repair capability of basal-like breast tumor cells and might serve as an independent indicator of favorable PFS.
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The cell rejuvenation atlas: leveraging network biology to identify master regulators of rejuvenation strategies.
Current rejuvenation strategies, which range from calorie restriction to in vivo partial reprogramming, only improve a few specific cellular processes. In addition, the molecular mechanisms underlying these approaches are largely unknown, which hinders the design of more holistic cellular rejuvenation strategies. To address this issue, we developed SINGULAR (Single-cell RNA-seq Investigation of Rejuvenation Agents and Longevity), a cell rejuvenation atlas that provides a unified system biology analysis of diverse rejuvenation strategies across multiple organs at single-cell resolution. In particular, we leverage network biology approaches to characterize and compare the effects of each strategy at the level of intracellular signaling, cell-cell communication, and transcriptional regulation. As a result, we identified master regulators orchestrating the rejuvenation response and propose that targeting a combination of them leads to a more holistic improvement of age-dysregulated cellular processes. Thus, the interactive database accompanying SINGULAR is expected to facilitate the future design of synthetic rejuvenation interventions.
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A scalable approach to topic modelling in single-cell data by approximate pseudobulk projection.
Probabilistic topic modelling has become essential in many types of single-cell data analysis. Based on probabilistic topic assignments in each cell, we identify the latent representation of cellular states. A dictionary matrix, consisting of topic-specific gene frequency vectors, provides interpretable bases to be compared with known cell type-specific marker genes and other pathway annotations. However, fitting a topic model on a large number of cells would require heavy computational resources-specialized computing units, computing time and memory. Here, we present a scalable approximation method customized for single-cell RNA-seq data analysis, termed ASAP, short for Annotating a Single-cell data matrix by Approximate Pseudobulk estimation. Our approach is more accurate than existing methods but requires orders of magnitude less computing time, leaving much lower memory consumption. We also show that our approach is widely applicable for atlas-scale data analysis; our method seamlessly integrates single-cell and bulk data in joint analysis, not requiring additional preprocessing or feature selection steps.
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Characterization of stem cell landscape and identification of stemness-related gene ENY2 in colorectal cancer by intergrated transcriptomic analysis.
BACKGROUND: Cancer stem cells (CSCs) drive colorectal cancer (CRC) progression, metastasis, and therapy resistance, but their heterogeneity limits targeted treatment efficacy. Clarifying the stemness landscape and underlying mechanisms is crucial for developing effective CRC therapies. METHODS: By integrating 10 ×single-cell data from GSE201348 and GSE161277, we constructed a single-cell atlas of nine primary CRC samples. CSCs were identified via scRNA-seq analyses. Using integrative bioinformatics tools, we identified ENY2 as a stemness-related marker and explored its potential role in CRC. We further compared genetic variants, immune infiltration, and drug sensitivity between high and low ENY2 expression groups. RESULTS: Stem cell clusters (M0 and M7) in CRC were identified based on copy number variation, pseudotime trajectory, and CytoTRACE analyses. By integrating marker gene profiles, DEGs from GSE33113, CytoTRACE-based CSC genes, and prognostic genes from GSE17536, we identified two key stemness-related markers: ENY2 and PKM. ENY2 was prioritized for further analysis due to its limited investigation in CRC. Bioinformatic analyses revealed that ENY2 was significantly upregulated and associated with poor prognosis. Enrichment analyses indicated its involvement in MYC and E2F targets, G2M checkpoint, and EMT pathways. Drug sensitivity prediction suggested that high ENY2 expression may confer responsiveness to 5-fluorouracil, capecitabine, oxaliplatin, and 24 other potential agents. CONCLUSIONS: ENY2 may act as a key CSC-associated biomarker that promotes CRC tumorigenesis and metastasis via the EMT pathway, which enhance our understanding of CRC pathogenesis and highlight ENY2 as a potential target for diagnosis and therapy.
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Interpretation of T cell states from single-cell transcriptomics data using reference atlases.
Single-cell RNA sequencing (scRNA-seq) has revealed an unprecedented degree of immune cell diversity. However, consistent definition of cell subtypes and cell states across studies and diseases remains a major challenge. Here we generate reference T cell atlases for cancer and viral infection by multi-study integration, and develop ProjecTILs, an algorithm for reference atlas projection. In contrast to other methods, ProjecTILs allows not only accurate embedding of new scRNA-seq data into a reference without altering its structure, but also characterizing previously unknown cell states that "deviate" from the reference. ProjecTILs accurately predicts the effects of cell perturbations and identifies gene programs that are altered in different conditions and tissues. A meta-analysis of tumor-infiltrating T cells from several cohorts reveals a strong conservation of T cell subtypes between human and mouse, providing a consistent basis to describe T cell heterogeneity across studies, diseases, and species.
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Role of alternative polyadenylation dynamics in acute myeloid leukaemia at single-cell resolution.
Alternative polyadenylation (APA) has been discovered to play regulatory roles in the development of many cancer cells through preferential addition of a poly(A) tail at specific sites of pre-mRNA. A recent study found that APA was involved in the mediation of acute myeloid leukaemia (AML). However, unlike gene expression heterogeneity, little attention has been directed toward variations in single-cell APA for different cell types during AML development. Here, we used single-cell RNA-seq data of a massive population of 16,843 bone marrow mononuclear cells (BMMCs) from healthy and AML patient samples to investigate dynamic APA usage in different cell types. Abnormalities of APA dynamics in the BMMCs from AML patient samples were uncovered compared to the stable APA dynamics in samples from healthy individuals, as well as lower APA diversity between eight cell types in AML patients. Genes with APA dynamics specific to the AML samples were significantly enriched in cellular signal transduction pathways that contribute to AML development. Moreover, many leukaemic cell marker genes such as NF-κB, GATA2 and IAP-Family genes exhibited APA dynamics that specifically affected abnormal proliferation and differentiation of leukemic BMMCs. Additionally, mature erythroid cells displayed greater APA dynamics and global 3' UTR shortening compared with other cell types. Our results revealed extensive involvement of APA regulation in leukemia development and erythropoiesis at the single-cell level, providing a high-resolution atlas to navigate cellular mRNA processing landscapes of differentiated cells in AML.
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A single-cell atlas depicting the cellular and molecular features in human anterior cruciate ligamental degeneration: A single cell combined spatial transcriptomics study.
BACKGROUND: To systematically identify cell types in the human ligament, investigate how ligamental cell identities, functions, and interactions participated in the process of ligamental degeneration, and explore the changes of ligamental microenvironment homeostasis in the disease progression. METHODS: Using single-cell RNA sequencing and spatial RNA sequencing of approximately 49,356 cells, we created a comprehensive cell atlas of healthy and degenerated human anterior cruciate ligaments. We explored the variations of the cell subtypes' spatial distributions and the different processes involved in the disease progression, linked them with the ligamental degeneration process using computational analysis, and verified findings with immunohistochemical and immunofluorescent staining. RESULTS: We identified new fibroblast subgroups that contributed to the disease, mapped out their spatial distribution in the tissue and revealed two dynamic trajectories in the process of the degenerative process. We compared the cellular interactions between different tissue states and identified important signaling pathways that may contribute to the disease. CONCLUSIONS: This cell atlas provides the molecular foundation for investigating how ligamental cell identities, biochemical functions, and interactions contributed to the ligamental degeneration process. The discoveries revealed the pathogenesis of ligamental degeneration at the single-cell and spatial level, which is characterized by extracellular matrix remodeling. Our results provide new insights into the control of ligamental degeneration and potential clues to developing novel diagnostic and therapeutic strategies. FUNDING: This study was funded by the National Natural Science Foundation of China (81972123, 82172508, 82372490) and 1.3.5 Project for Disciplines of Excellence of West China Hospital Sichuan University (ZYJC21030, ZY2017301).
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Dynamic changes in cellular atlases and communication patterns within yak ovaries across diverse reproductive states unveiled by single-cell analysis.
Yaks (Bos grunniens) exhibit exceptional adaptation to the challenging high-altitude environment of the Qinghai-Tibetan plateau, making them the sole bovine species capable of thriving in such exreme conditions. Investigating the cellular and molecular characteristics of yak ovaries across different reproductive states is crucial for gaining insight into their ovarian functions. Herein, the cellular atlases of yak ovaries in different reproductive states were depicted by single-cell RNA-sequencing (scRNA-seq). The cellular atlases of the ovaries were established by identifying specific gene expression patterns of various cell types, including granulosa cells, theca cells, stromal cells, smooth muscle cells, endothelial cells, glial cell, macrophages, natural killer cells, and proliferating cells. The cellular compositions of the ovaries vary among different reproductive states. Furthermore, the granulosa cells comprise six cell subtypes, while theca cells consist of eight cell subtypes. The granulosa cells and theca cells exhibit distinct biological functions throughout different reproductive states. The two cell types were aligned along their respective pseudotime trajectories. Moreover, a cell-to-cell communication network was constructed among distinct cell types within the ovary, spanning the three reproductive states. Notably, during the estrus period, the granulosa cells demonstrated more prominent interactions with other cell types compared to the remaining reproductive states.
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Identification of a Recurrence Gene Signature for Ovarian Cancer Prognosis by Integrating Single-Cell RNA Sequencing and Bulk Expression Datasets.
Ovarian cancer is one of the most common gynecological malignancies in women, with a poor prognosis and high mortality. With the expansion of single-cell RNA sequencing technologies, the inner biological mechanism involved in tumor recurrence should be explored at the single-cell level, and novel prognostic signatures derived from recurrence events were urgently identified. In this study, we identified recurrence-related genes for ovarian cancer by integrating two Gene Expression Omnibus datasets, including an ovarian cancer single-cell RNA sequencing dataset (GSE146026) and a bulk expression dataset (GSE44104). Based on these recurrence genes, we further utilized the merged expression dataset containing a total of 524 ovarian cancer samples to identify prognostic signatures and constructed a 13-gene risk model, named RMGS (recurrence marker gene signature). Based on the RMGS score, the samples were stratified into high-risk and low-risk groups, and these two groups displayed significant survival difference in two independent validation cohorts including The Cancer Genome Atlas (TCGA). Also, the RMGS score remained significantly independent in multivariate analysis after adjusting for clinical factors, including the tumor grade and stage. Furthermore, there existed close associations between the RMGS score and immune characterizations, including checkpoint inhibition, EMT signature, and T-cell infiltration. Finally, the associations between RMGS scores and molecular subtypes revealed that samples with mesenchymal subtypes displayed higher RMGS scores. In the meanwhile, the genomics characterization from these two risk groups was also identified. In conclusion, the recurrence-related RMGS model we identified could provide a new understanding of ovarian cancer prognosis at the single-cell level and offer a reference for therapy decisions for patient treatment.
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Evaluating stably expressed genes in single cells.
BACKGROUND: Single-cell RNA-seq (scRNA-seq) profiling has revealed remarkable variation in transcription, suggesting that expression of many genes at the single-cell level is intrinsically stochastic and noisy. Yet, on the cell population level, a subset of genes traditionally referred to as housekeeping genes (HKGs) are found to be stably expressed in different cell and tissue types. It is therefore critical to question whether stably expressed genes (SEGs) can be identified on the single-cell level, and if so, how can their expression stability be assessed? We have previously proposed a computational framework for ranking expression stability of genes in single cells for scRNA-seq data normalization and integration. In this study, we perform detailed evaluation and characterization of SEGs derived from this framework. RESULTS: Here, we show that gene expression stability indices derived from the early human and mouse development scRNA-seq datasets and the "Mouse Atlas" dataset are reproducible and conserved across species. We demonstrate that SEGs identified from single cells based on their stability indices are considerably more stable than HKGs defined previously from cell populations across diverse biological systems. Our analyses indicate that SEGs are inherently more stable at the single-cell level and their characteristics reminiscent of HKGs, suggesting their potential role in sustaining essential functions in individual cells. CONCLUSIONS: SEGs identified in this study have immediate utility both for understanding variation and stability of single-cell transcriptomes and for practical applications such as scRNA-seq data normalization. Our framework for calculating gene stability index, "scSEGIndex," is incorporated into the scMerge Bioconductor R package (https://sydneybiox.github.io/scMerge/reference/scSEGIndex.html) and can be used for identifying genes with stable expression in scRNA-seq datasets.