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SCIG: Machine learning uncovers cell identity genes in single cells by genetic sequence codes.
Deciphering cell identity genes is pivotal to understanding cell differentiation, development, and cell identity dysregulation involving diseases. Here, we introduce SCIG, a machine-learning method to uncover cell identity genes in single cells. In alignment with recent reports that cell identity genes (CIGs) are regulated with unique epigenetic signatures, we found CIGs exhibit distinctive genetic sequence signatures, e.g. unique enrichment patterns of cis-regulatory elements. Using these genetic sequence signatures, along with gene expression information from single-cell RNA-seq data, SCIG uncovers the identity genes of a cell without a need for comparison to other cells. CIG score defined by SCIG surpassed expression value in network analysis to reveal the master transcription factors (TFs) regulating cell identity. Applying SCIG to the human endothelial cell atlas revealed that the tissue microenvironment is a critical supplement to master TFs for cell identity refinement. SCIG is publicly available at https://doi.org/10.5281/zenodo.14726426 , offering a valuable tool for advancing cell differentiation, development, and regenerative medicine research.
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Updated skin transcriptomic atlas depicted by reciprocal contribution of single-nucleus RNA sequencing and single-cell RNA sequencing.
BACKGROUND: Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of skin biology, but its utility is restricted by the requirement of fresh samples, inadequate dissociation-induced cell loss or death, and activation during tissue digestion. Single-nucleus RNA sequencing (snRNA-seq) can use frozen, hard-to-dissociate materials, which might be a promising method to circumvent the limitations of scRNA-seq for the skin tissue. OBJECTIVE: To profile skin cells using snRNA-seq in parallel with scRNA-seq. METHODS: We performed snRNA-seq in parallel with scRNA-seq for the bisected skin sample of one person and integrated previously published scRNA-seq data for analysis. We comparatively analyzed the differences in cell proportions and gene expression between the two methods. The differentiation trajectories of keratinocytes and fibroblasts were analyzed by Slingshot analysis. RESULTS: snRNA-seq was less susceptible to contamination from mitochondrial and ribosomal RNA, and exhibited a greater capacity to detect transcription factors. snRNA-seq identified more spatially and functionally relevant keratinocyte clusters that constitute cell trajectories with expected differentiation dynamics. Novel markers, e.g., LYPD3, EMP2, and CSTB, were revealed for different differentiation stages of keratinocytes, and NFIB and GRHL1 were identified as transcription factors involving in the proliferation and functional differentiation of keratinocytes. Fibroblasts were found in a state of activation in scRNA-seq. And scRNA-seq detected a greater number of immune cells. CONCLUSIONS: We generated an updated atlas of the skin transcriptome based on the reciprocal contribution of scRNA-seq and snRNA-seq.
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The ALDH Family Contributes to Immunocyte Infiltration, Proliferation and Epithelial-Mesenchymal Transformation in Glioma.
Gliomas are malignant tumors that originate from the central nervous system. The aldehyde dehydrogenase family has been documented to affect cancer progression; however, its role in gliomas remains largely unexplored. Bulk RNA-seq analysis and single-cell RNA-Seq analysis were performed to explore the role of the aldehyde dehydrogenases family in gliomas. Training cohort contained The Cancer Genome Atlas data, while data from Chinese Glioma Genome Atlas and Gene Expression Omnibus were set as validation cohorts. Our scoring system based on the aldehyde dehydrogenases family suggested that high-scoring samples were associated with worse survival outcomes. The enrichment score of pathways were calculated by AUCell to substantiate the biofunction prediction results that the aldehyde dehydrogenases family affected glioma progression by modulating tumor cell proliferation, migration, and immune landscape. Tumor immune landscape was mapped from high-scoring samples. Moreover, ALDH3B1 and ALDH16A1, two main contributors of the scoring system, could affect glioblastoma cell proliferation and migration by inducing cell-cycle arrest and the epithelial-mesenchymal transition. Taken together, the aldehyde dehydrogenases family could play a significant role in the tumor immune landscape and could be used to predict patient prognosis. ALDH3B1 and ALDH16A1 could influence tumor cell proliferation and migration.
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Refining the identity of mesenchymal cell types associated with murine periosteal and endosteal bone.
Single-cell RNA-seq has led to novel designations for mesenchymal cells associated with bone as well as multiple designations for what appear to be the same cell type. The main goals of this study were to increase the amount of single-cell RNA sequence data for osteoblasts and osteocytes, to compare cells from the periosteum to those inside bone, and to clarify the major categories of cell types associated with murine bone. We created an atlas of murine bone-associated cells by harmonizing published datasets with in-house data from cells targeted by Osx1-Cre and Dmp1-Cre driver strains. Cells from periosteal bone were analyzed separately from those isolated from the endosteum and trabecular bone. Over 100,000 mesenchymal cells were mapped to reveal 11 major clusters designated fibro-1, fibro-2, chondrocytes, articular chondrocytes, tenocytes, adipo-Cxcl12 abundant reticular (CAR), osteo-CAR, preosteoblasts, osteoblasts, osteocytes, and osteo-X, the latter defined in part by periostin expression. Osteo-X, osteo-CAR, and preosteoblasts were closely associated with osteoblasts at the trabecular bone surface. Wnt16 was expressed in multiple cell types from the periosteum but not in cells from endocortical or cancellous bone. Fibro-2 cells, which express markers of stem cells, localized to the periosteum but not trabecular bone in adult mice. Suppressing bone remodeling eliminated osteoblasts and altered gene expression in preosteoblasts but did not change the abundance or location of osteo-X or osteo-CAR cells. These results provide a framework for identifying bone cell types in murine single-cell RNA-seq datasets and suggest that osteoblast progenitors reside near the surface of remodeling bone.
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Single-cell atlases reveal leaf cell-type-specific regulation of metal transporters in the hyperaccumulator Sedum alfredii under cadmium stress.
Hyperaccumulation in plants is a complex and dynamic biological process. Sedum alfredii, the most studied Cd hyperaccumulator, can accumulate up to 9000 mg kg-1 Cd in its leaves without suffering toxicity. Although several studies have reported the molecular mechanisms of Cd hyperaccumulation, our understanding of the cell-type-specific transcriptional regulation induced by Cd remains limited. In this study, the first full-length transcriptome of S. alfredii was generated using the PacBio Iso-Seq technology. A total of 18,718,513 subreads (39.90 Gb) were obtained, with an average length of 2133 bp. The single-cell RNA sequencing was employed on leaves of S. alfredii grown under Cd stress. A total of 12,616 high-quality single cells were derived from the control and Cd-treatment samples of S. alfredii leaves. Based on cell heterogeneity and the expression profiles of previously reported marker genes, seven cell types with 12 transcriptionally distinct cell clusters were identified, thereby constructing the first single-cell atlas for S. alfredii leaves. Metal transporters such as CAX5, COPT5, ZIP5, YSL7, and MTP1 were up-regulated in different cell types of S. alfredii leaves under Cd stress. The distinctive gene expression patterns of metal transporters indicate special gene regulatory networks underlying Cd tolerance and hyperaccumulation in S. alfredii. Collectively, our findings are the first observation of the cellular and molecular responses of S. alfredii leaves under Cd stress and lay the cornerstone for future hyperaccumulator scRNA-seq investigations.
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Multi-omics integration identifies ribosome biogenesis-active macrophage subpopulation and its key gene GNL2 in driving liver hepatocellular carcinoma progression and mechanisms.
BACKGROUND: Liver hepatocellular carcinoma (LIHC) is a common malignancy, yet the core genes driving its progression and potential therapeutic targets remain insufficiently explored. Ribosome biogenesis (RB) is a critical biological process linked to various cancers; however, its systematic role in LIHC remains unclear. METHODS: This study integrated LIHC single-cell RNA-Seq, bulk RNA-Seq, and spatial transcriptomic data with ribosome biogenesis-related gene sets to construct a single-cell atlas of LIHC. Weighted Gene Co-expression Network Analysis (WGCNA) was employed to characterize myeloid cell subsets. Furthermore, an LIHC prognostic risk model based on RB-related genes was developed using 117 machine-learning algorithm combinations. Key findings were subsequently corroborated through experimental validation and clinical sample analysis. RESULTS: We identified a distinct macrophage subpopulation with high ribosome biogenesis activity, termed ribosome biogenesis-active macrophages (RAMs). These cells exhibited strong communication with inflammatory macrophages, potentially mediated by MIF-related receptor-ligand interactions. We further constructed an 8-gene prognostic model (PA2G4, GNL2, PWP1, DDX49, NOC4L, GDI2, CST7, and RCL1), which showed good predictive performance. Drug sensitivity analysis suggested that the high-risk group may be more responsive to several agents, including docetaxel. Among these genes, GNL2 was selected for further investigation. Elevated GNL2 expression was associated with increased stemness features in myeloid cells. Molecular docking analysis identified several candidate compounds with potential binding affinity to GNL2. Functionally, GNL2 knockdown in macrophages reduced TGF-β and TNF-α expression and was associated with decreased proliferation, migration, and invasion of LIHC cells. CONCLUSION: We identified a highly active ribosome biogenesis-macrophage subpopulation (RAM), and constructed a robust risk model to aid in the diagnosis, prognosis, and treatment of LIHC. GNL2 is associated with increased expression of TGF-β and TNF-α and may contribute to LIHC progression.
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A cell atlas of the chick retina based on single-cell transcriptomics.
Retinal structure and function have been studied in many vertebrate orders, but molecular characterization has been largely confined to mammals. We used single-cell RNA sequencing (scRNA-seq) to generate a cell atlas of the chick retina. We identified 136 cell types plus 14 positional or developmental intermediates distributed among the six classes conserved across vertebrates - photoreceptor, horizontal, bipolar, amacrine, retinal ganglion, and glial cells. To assess morphology of molecularly defined types, we adapted a method for CRISPR-based integration of reporters into selectively expressed genes. For Müller glia, we found that transcriptionally distinct cells were regionally localized along the anterior-posterior, dorsal-ventral, and central-peripheral retinal axes. We also identified immature photoreceptor, horizontal cell, and oligodendrocyte types that persist into late embryonic stages. Finally, we analyzed relationships among chick, mouse, and primate retinal cell classes and types. Our results provide a foundation for anatomical, physiological, evolutionary, and developmental studies of the avian visual system.
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Cancer-prone Phenotypes and Gene Expression Heterogeneity at Single-cell Resolution in Cigarette-smoking Lungs.
UNLABELLED: Single-cell RNA sequencing (scRNA-seq) technologies have been broadly utilized to reveal molecular mechanisms of respiratory pathology and physiology at single-cell resolution. Here, we established single-cell meta-analysis (scMeta-analysis) by integrating data from eight public datasets, including 104 lung scRNA-seq samples with clinicopathologic information and designated a cigarette-smoking lung atlas. The atlas revealed early carcinogenesis events and defined the alterations of single-cell transcriptomics, cell population, and fundamental properties of biological pathways induced by smoking. In addition, we developed two novel scMeta-analysis methods: VARIED (Visualized Algorithms of Relationships In Expressional Diversity) and AGED (Aging-related Gene Expressional Differences). VARIED analysis revealed expressional diversity associated with smoking carcinogenesis. AGED analysis revealed differences in gene expression related to both aging and smoking status. The scMeta-analysis paves the way to utilize publicly-available scRNA-seq data and provide new insights into the effects of smoking and into cellular diversity in human lungs, at single-cell resolution. SIGNIFICANCE: The atlas revealed early carcinogenesis events and defined the alterations of single-cell transcriptomics, cell population, and fundamental properties of biological pathways induced by smoking.
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Analysis of microarray and single-cell RNA-seq identifies gene co-expression, cell-cell communication, and tumor environment associated with metabolite interconversion enzyme in prostate cancer.
BACKGROUND: Prostate cancer (PCa) is the second most common malignant neoplasm in males and is the fifth leading cause of cancer-related mortality. Due to the use of prostate-specific antigen (PSA) screening and improved biopsy techniques, persons identified with early-stage prostate cancer often have a positive prognosis after comprehensive treatment. Nonetheless, prostate cancer is a latent illness that may present as an asymptomatic tumor in individuals aged 20-30. The overall survival (OS) of men with advanced PCa is significantly diminished. Consequently, there is an immediate want for innovative, accurate biomarkers to detect early prostate cancer. METHODS: This research analyzed the interaction network of differentially expressed genes (DEGs) related to metabolite interconversion enzymes in PCa by gene expression microarray data, single-cell RNA sequencing, oncogenes, and tumor suppressor genes (TSGs) utilizing bioinformatics techniques. This kind of analysis has not been documented in prior studies. RESULTS: We then used a dataset acquired by the Cancer Genome Atlas (TCGA) to confirm our findings. Genes including CYP3A5, PDE8B, AOX1, BNIPL, FADS2, RRM2, ALDH3B2, and GSTM2 may be significant in the diagnosis and treatment of PCa. CONCLUSION: Our objective was to provide new perspectives on the molecular properties and pathways of DEGs in PCa and to uncover potential biomarkers that play a crucial role in the genesis and progression of PCa.
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A single-cell atlas of the healthy breast tissues reveals clinically relevant clusters of breast epithelial cells.
Single-cell RNA sequencing (scRNA-seq) is an evolving technology used to elucidate the cellular architecture of adult organs. Previous scRNA-seq on breast tissue utilized reduction mammoplasty samples, which are often histologically abnormal. We report a rapid tissue collection/processing protocol to perform scRNA-seq of breast biopsies of healthy women and identify 23 breast epithelial cell clusters. Putative cell-of-origin signatures derived from these clusters are applied to analyze transcriptomes of ~3,000 breast cancers. Gene signatures derived from mature luminal cell clusters are enriched in ~68% of breast cancers, whereas a signature from a luminal progenitor cluster is enriched in ~20% of breast cancers. Overexpression of luminal progenitor cluster-derived signatures in HER2+, but not in other subtypes, is associated with unfavorable outcome. We identify TBX3 and PDK4 as genes co-expressed with estrogen receptor (ER) in the normal breasts, and their expression analyses in >550 breast cancers enable prognostically relevant subclassification of ER+ breast cancers.