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scTIM: seeking cell-type-indicative marker from single cell RNA-seq data by consensus optimization.
MOTIVATION: Single cell RNA-seq data offers us new resource and resolution to study cell type identity and its conversion. However, data analyses are challenging in dealing with noise, sparsity and poor annotation at single cell resolution. Detecting cell-type-indicative markers is promising to help denoising, clustering and cell type annotation. RESULTS: We developed a new method, scTIM, to reveal cell-type-indicative markers. scTIM is based on a multi-objective optimization framework to simultaneously maximize gene specificity by considering gene-cell relationship, maximize gene's ability to reconstruct cell-cell relationship and minimize gene redundancy by considering gene-gene relationship. Furthermore, consensus optimization is introduced for robust solution. Experimental results on three diverse single cell RNA-seq datasets show scTIM's advantages in identifying cell types (clustering), annotating cell types and reconstructing cell development trajectory. Applying scTIM to the large-scale mouse cell atlas data identifies critical markers for 15 tissues as 'mouse cell marker atlas', which allows us to investigate identities of different tissues and subtle cell types within a tissue. scTIM will serve as a useful method for single cell RNA-seq data mining. AVAILABILITY AND IMPLEMENTATION: scTIM is freely available at https://github.com/Frank-Orwell/scTIM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays.
Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.
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Integration of eQTL and a Single-Cell Atlas in the Human Eye Identifies Causal Genes for Age-Related Macular Degeneration.
Age-related macular degeneration (AMD) is a leading cause of vision loss. To better understand disease pathogenesis and identify causal genes in GWAS loci for AMD risk, we present a comprehensive database of human retina and retinal pigment epithelium (RPE). Our database comprises macular and non-macular RNA sequencing (RNA-seq) profiles from 129 donors, a genome-wide expression quantitative trait loci (eQTL) dataset that includes macula-specific retina and RPE/choroid, and single-nucleus RNA-seq (NucSeq) from human retina and RPE with subtype resolution from more than 100,000 cells. Using NucSeq, we find enriched expression of AMD candidate genes in RPE cells. We identify 15 putative causal genes for AMD on the basis of co-localization of genetic association signals for AMD risk and eye eQTL, including the genes TSPAN10 and TRPM1. These results demonstrate the value of our human eye database for elucidating genetic pathways and potential therapeutic targets for ocular diseases.
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EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data.
Cell type heterogeneity presents a challenge to the interpretation of epigenome data, compounded by the difficulty in generating reliable single-cell DNA methylomes for large numbers of cells and samples. We present EPISCORE, a computational algorithm that performs virtual microdissection of bulk tissue DNA methylation data at single cell-type resolution for any solid tissue. EPISCORE applies a probabilistic epigenetic model of gene regulation to a single-cell RNA-seq tissue atlas to generate a tissue-specific DNA methylation reference matrix, allowing quantification of cell-type proportions and cell-type-specific differential methylation signals in bulk tissue data. We validate EPISCORE in multiple epigenome studies and tissue types.
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Integrated single-cell transcriptomic analyses identify a novel lineage plasticity-related cancer cell type involved in prostate cancer progression.
BACKGROUND: Cancer cell plasticity is the ability of neoplastic cells to alter their identity and acquire new biological properties under microenvironmental pressures. In prostate cancer (PCa), lineage plasticity often results in therapy resistance and trans-differentiation to neuroendocrine (NE) lineage. However, identifying the cancer cells harboring lineage plasticity-related status remains challenging. METHODS: Based on 13 multi-center human PCa bulk transcriptomic cohorts (samples = 3314) and 9 bulk transcriptomic datasets derived from PCa experimental models, we established an integrated lineage plasticity-related gene signature, termed LPSig. Leveraging this gene signature, AUCell enrichment analysis was applied to identify the cell population with high lineage plasticity from a comprehensive single-cell RNA-sequencing (scRNA-seq) meta-atlas assembled by us, which consisted of 10 public human PCa scRNA-seq datasets (samples = 93, cells = 222,529). Moreover, additional scRNA-seq dataset of human PCa, multiplex immunohistochemistry staining for human PCa tissues, in vitro and in vivo functional experiments, as well as qPCR and Western blot analyses were employed to validate our findings. FINDINGS: We found that LPSig could finely capture the dynamics of tumor lineage plasticity throughout the progression of PCa, accurately estimating the status of lineage plasticity. Based on LPSig, we identified a previously undefined minority population of lineage plasticity-related PCa cells (LPCs) from the human PCa scRNA-seq meta-atlas assembled by this study. Furthermore, in-depth dissection revealed pivotal roles of LPCs in trans-differentiation, tumor recurrence, and poor patient survival during PCa progression. Furthermore, we identified HMMR as a representative cell surface marker for LPCs, which was validated using additional scRNA-seq datasets and multiplexed immunohistochemistry. Moreover, HMMR was transcriptionally inhibited by androgen receptor (AR), and was required for the aggressive adenocarcinoma features and NE phenotype. INTERPRETATION: Our study uncovers a novel population of lineage plasticity-related cells with low AR activity, stemness-like traits, and elevated HMMR expression, that may facilitate poor prognosis in PCa. FUNDING: This work was supported by National Key R&D Program of China (2022YFA0807000), National Natural Science Foundation of China (82160584), Advanced Prostate Cancer Diagnosis and Treatment Technology Innovation Team of Kunming Medical University (CXTD202216), and Reserve Talents of Young and Middle-aged Academic Leaders in Yunnan Province (202105AC160013).
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Single-Nucleus RNA-Seq Reveals Aerobic Exercise-Induced Remodeling of Spinal Cord Aging in Mice.
Spinal cord aging is a critical physiological process that compromises central nervous system (CNS) homeostasis and plasticity. Exercise, as a systemic intervention with broad health benefits, has been shown to delay neurodegeneration and preserve tissue function; however, its impact on dynamic cellular lineage evolution and intercellular communication within the aging spinal cord remains poorly characterized. In this study, we employed single-nucleus RNA sequencing (snRNA-seq) to construct a high-resolution cellular atlas of the mouse spinal cord under young, aged, and aerobic exercise-intervened conditions. By integrating unsupervised clustering, cell proportion analysis, pseudotime trajectory reconstruction, gene regulatory network (GRN) inference, and intercellular communication mapping, we systematically characterized transcriptional and cellular alterations associated with aging and their modulation through exercise. Aging induced pronounced shifts in cell-type composition and subpopulation structures, which were partially reversed by exercise intervention. Pseudotime analyses of oligodendrocytes, astrocytes, and microglia revealed that exercise remodeled their differentiation trajectories and restored functional states associated with myelin formation, metabolic homeostasis, and inflammation control. GRN analysis identified several key regulators whose centrality and expression were disrupted during aging but reestablished after exercise, suggesting a recovery of transcriptional network organization. Furthermore, intercellular communication analysis revealed reduced signaling strength and connectivity during aging, particularly within gap junction pathways, which were partially restored by exercise, indicating improved cellular coordination. Together, these findings provide a comprehensive single-cell landscape of the aging spinal cord and demonstrate that exercise reprograms cellular lineages and regulatory networks, offering mechanistic insights into how it mitigates CNS aging and preserves neural function.
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Single-Cell Transcriptomics: A High-Resolution Avenue for Plant Functional Genomics.
Plant function is the result of the concerted action of single cells in different tissues. Advances in RNA-seq technologies and tissue processing allow us now to capture transcriptional changes at single-cell resolution. The incredible potential of single-cell RNA-seq lies in the novel ability to study and exploit regulatory processes in complex tissues based on the behaviour of single cells. Importantly, the independence from reporter lines allows the analysis of any given tissue in any plant. While there are challenges associated with the handling and analysis of complex datasets, the opportunities are unique to generate knowledge of tissue functions in unprecedented detail and to facilitate the application of such information by mapping cellular functions and interactions in a plant cell atlas.
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Construction of transcriptome atlas of white yak hair follicle during anagen and catagen using single-cell RNA sequencing.
BACKGROUND: As the direct organ of villus, hair follicles have obvious seasonal cycles. The hair follicle cycle is orchestrated by multiple cell types that together direct cell renewal and differentiation. But the regulation property of hair follicle cells from anagen to catagen in yak is still unknown. RESULTS: In this study, single-cell RNA sequencing was performed on 24,124 single cells of the scapular skin from white yak. Based on tSNE cluster analysis, the cell types of IFE-DC, epidermal cell lines, fibroblasts, keratinocytes, IRS, DS, INFU, and other cells in yak hair follicles during anagen and catagen were successfully identified, and the gene expression profiles were described. The GO enrichment analysis indicated the different cells characteristic genes to be mainly enriched in the epidermal development, epithelial cell differentiation and wound healing pathways. The pseudotime trajectory analysis described the differentiation trajectory of the epidermal lineage and dermal lineage of the hair follicle during anagen and catagen. Moreover, the dynamic changes of the genes like LHX2, KRT25, and KRT71 were found to be highly expressed in HS and IRS, but not in the IFE-DC, INFU, and keratinocyte during differentiation. CONCLUSIONS: Our results analyzed the time-varying process of gene expression in the dermal cell lineage and epidermal cell lineage of hair follicles during anagen and catagen during fate differentiation was expounded at the single cell level, revealing the law of fate specialization of different types of cells. In addition, based on the enrichment analysis, the transcriptional regulatory factors involved in the different cell fates were also revealed. These results will help to enhance our understanding of yak hair follicle cycle and promote the development and utilization of yak villus.
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The single-cell atlas of colorectal cancer reveals cellular heterogeneity and key molecular features of the tumor immune microenvironment.
BACKGROUND: Colorectal cancer (CRC) is a highly heterogeneous malignancy, with its tumor immune microenvironment (TIME) playing a crucial role in tumor progression, immune evasion, and treatment response. Despite insights from single-cell technologies, challenges such as small samples and inconsistent annotations have limited a comprehensive understanding. Thus, a large-scale, high-resolution single-cell atlas is needed to fully explore the heterogeneity of CRC TIME and guide personalized treatment. METHODS: We analyzed single-cell transcriptomic data from 100 CRC samples (371,223 cells). Immune, stromal, and epithelial cells in the TIME were clustered, annotated, and functionally analyzed. Transcription factor networks were inferred using SCENIC, differentiation states assessed with CytoTRACE, and cell-cell communication analyzed via CellPhoneDB. TIME subtypes were constructed from bulk RNA-seq data, followed by survival analysis. SCRN1 was further validated through in vitro experiments. RESULTS: This study presents a comprehensive single-cell CRC atlas, revealing significant heterogeneity in T cells, B cells, myeloid cells, fibroblasts, endothelial cells, and epithelial cells within the TIME. We defined two immune ecological subtypes: subtype 1 (enriched in metabolic and motility pathways with poor prognosis) and subtype 2 (enriched in immune response pathways, with better prognosis and greater immunotherapy potential). High SCRN1 expression in malignant epithelial cells correlated with poor prognosis and metastasis. In vitro experiments confirmed the role of SCRN1 in promoting CRC cell proliferation and migration. CONCLUSIONS: This study provides a comprehensive characterization of the CRC TIME, links TIME subtypes to prognosis, and identifies SCRN1 as a potential therapeutic target, offering insights for CRC precision subtyping and immunotherapy strategies.
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Integrative multi-omics increase resolution of the sea urchin posterior gut gene regulatory network at single-cell level.
Drafting gene regulatory networks (GRNs) requires embryological knowledge pertaining to the cell type families, information on the regulatory genes, causal data from gene knockdown experiments and validations of the identified interactions by cis-regulatory analysis. We use multi-omics involving next-generation sequencing to obtain the necessary information for drafting the Strongylocentrotus purpuratus (Sp) posterior gut GRN. Here, we present an update to the GRN using: (1) a single-cell RNA-sequencing-derived cell atlas highlighting the 2 day-post-fertilization (dpf) sea urchin gastrula cell type families, as well as the genes expressed at the single-cell level; (2) a set of putative cis-regulatory modules and transcription factor-binding sites obtained from chromatin accessibility ATAC-seq data; and (3) interactions directionality obtained from differential bulk RNA sequencing following knockdown of the transcription factor Sp-Pdx1, a key regulator of gut patterning in sea urchins. Combining these datasets, we draft the GRN for the hindgut Sp-Pdx1-positive cells in the 2 dpf gastrula embryo. Overall, our data suggest the complex connectivity of the posterior gut GRN and increase the resolution of gene regulatory cascades operating within it.