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HemaScope: A Tool for Analyzing Single-cell and Spatial Transcriptomics Data of Hematopoietic Cells.
Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) techniques hold great value in evaluating the heterogeneity and spatial characteristics of hematopoietic cells within tissues. These two techniques are highly complementary, with scRNA-seq offering single-cell resolution and ST retaining spatial information. However, there is an urgent demand for well-organized and user-friendly toolkits capable of handling single-cell and spatial information. Here, we present HemaScope, a specialized bioinformatics toolkit featuring modular designs to analyze scRNA-seq and ST data generated from hematopoietic cells. It enables users to perform quality control, basic analysis, cell atlas construction, cellular heterogeneity exploration, and dynamical examination on scRNA-seq data. Also, it can perform spatial analysis and microenvironment analysis on ST data. Meanwhile, HemaScope takes into consideration hematopoietic cell-specific features, including lineage affiliation evaluation, cell cycle prediction, and marker gene collection. To enhance the user experience, we have deployed the toolkit in user-friendly forms: HemaScopeR (an R package), HemaScopeCloud (a web server), HemaScopeDocker (a Docker image), and HemaScopeShiny (a graphical interface). In case studies, we employed it to construct a cell atlas of human bone marrow, analyze age-related changes, and identify acute myeloid leukemia cells in mice. Moreover, we characterized the microenvironments in angioimmunoblastic T cell lymphoma and primary central nervous system lymphoma, elucidating tumor boundaries. HemaScope is freely available at https://zhenyiwangthu.github.io/HemaScope_Tutorial/.
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Ploidy inference from single-cell data: application to human and mouse cell atlases.
Ploidy is relevant to numerous biological phenomena, including development, metabolism, and tissue regeneration. Single-cell RNA-seq and other omics studies are revolutionizing our understanding of biology, yet they have largely overlooked ploidy. This is likely due to the additional assay step required for ploidy measurement. Here, we developed a statistical method to infer ploidy from single-cell ATAC-seq data, addressing this gap. When applied to data from human and mouse cell atlases, our method enabled systematic detection of polyploidy across diverse cell types. This method allows for the integration of ploidy analysis into single-cell studies. Additionally, this method can be adapted to detect the proliferating stage in the cell cycle and copy number variations in cancer cells. The software is implemented as the scPloidy package of the R software and is freely available from CRAN.
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Single-Cell RNA Sequencing of Rabbit Sclera at Different Developmental Stages: Unveiling Scleral Cells Atlas and the Heterogeneity of Fibroblasts.
PURPOSE: This study aims to construct a single-cell transcriptomic atlas of the developing rabbit sclera to elucidate fibroblast heterogeneity, differentiation trajectories, matrisome expression patterns, and intercellular communication, while revealing conserved molecular features of scleral cells through cross-species analysis. METHODS: Single-cell RNA sequencing (scRNA-seq) was performed on scleral tissues from New Zealand rabbits at embryonic day 25 (E25) and postnatal days 7 (P7), 21 (P21), and 180 (P180). Libraries were prepared using the DNBelab C Series Kit and sequenced on the BGISEQ-2000 platform. Sequencing reads were aligned to the OryCun2.0 genome using STAR, and unique molecular identifier (UMI) count matrices were generated with PISA. Data preprocessing was conducted using Seurat. Fibroblast lineage differentiation was analyzed via VIA, intercellular communication via CellChat, matrisome expression patterns via AUCell, and cross-species analyses via CACIMAR and hdWGCNA. RESULTS: We identified 7 major cell types and 15 subpopulations, with fibroblasts dominating the cellular landscape. Distinct fibroblast subtypes exhibited varied expression profiles and functions: KERAlow SPARCL1⁺ fibroblasts showed stem/progenitor-like features, while KERAhigh myocilin (MYOC)⁺ fibroblasts displayed senescence-associated phenotypes. Matrisome analysis revealed dynamic alterations in collagen and extracellular matrix (ECM)-related genes, and intercellular communication analysis highlighted complex signaling networks, particularly the MDK/PTN pathway. Cross-species comparisons demonstrated high conservation of fibroblasts between rabbit and human sclera, identifying four conserved co-expression modules. CONCLUSIONS: This study presents the first single-cell atlas of rabbit scleral development, unveiling fibroblast heterogeneity, ECM remodeling mechanisms, and cross-species conserved features. These findings enhance our understanding of scleral biology and provide valuable insights for future research on ocular development and associated diseases, including myopia.
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Single-cell RNA sequencing identifies cancer-associated fibroblast marker genes for determining cancer subtypes in oral squamous cell carcinoma and predicting patient prognosis.
BACKGROUND: Oral squamous cell carcinoma (OSCC) is an extremely aggressive cancer of which its progression and prognosis are strongly affected by cancer-associated fibroblast (CAF) and immunosuppression procedure. This study aimed to identify CAF marker genes through single-cell RNA sequencing (scRNA-seq) data and clarify the roles of CAF-related genes in tumor typing and disease prognosis prediction for OSCC patients. METHODS: Gene expression data of OSCC patients were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. The differential expression of CAF genes was identified using the limma R package. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO) Cox analysis, and multivariate Cox analysis were performed to construct a CAF-related prognostic signature. The accuracy of prediction performance was assessed by the time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier analysis, principal component analysis (PCA), decision curve analysis, calibration curve, and concordance index. Differences of tumor mutational burden, tumor-infiltrating immune cells, and response to immunotherapy between the high- or low-risk groups were investigated. RESULTS: Our results showed that the CAF-high subtype exhibited poorer survival outcomes than the CAF-low subtype. The CAF-high subtype exhibited higher immune infiltration, stromal components, and immune escape potential than the CAF-low subtype. The CAF-high subtype OSCC patients demonstrated reduced sensitivity to immunotherapy than the CAF-low subtype. CONCLUSIONS: Our prognostic model could predict overall survival (OS) and the immunotherapy response of OSCC patients. These findings highlight the importance of targeting CAF in OSCC treatment and provide valuable insights into the tumor microenvironment (TME) heterogeneity and immune responses.
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Multi-omics analyses reveal DjTcf4 critical for proper timing of differentiation in planarian regeneration.
The blastema is key to forming complete tissues in regenerating Dugesia japonica (D. japonica). However, the dynamic changes in cellular compositions and transcription landscapes in blastema during regeneration are understudied. Here, through genome reannotation, 3D spatial transcriptome construction, single-cell RNA sequencing (scRNA-seq), and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) analyses of changes in gene expression and chromatin structures, we delineate key transcription factors regulating the developmental trajectories of major cell clusters in the regenerating head. Importantly, we find that the T cell factor 4 (DjTcf4)-positive cells highly accumulate at wound areas, and its gene network is critical for the proper timing of development during regeneration in multiple progenitor cells. Depletion of DjTcf4 and its target genes leads to singular eye and/or dull tail phenotypes and delays regeneration. Taken together, we build multi-omics atlases in D. japonica and reveal the noncanonical function of the DjTcf4 network in developmental pattern formation, laying a foundation for studies of regeneration in D. japonica.
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Single-cell RNA sequencing reveals oocyte-granulosa crosstalk and regulatory networks driving chicken primordial follicle assembly.
Primordial follicle assembly is crucial for ovarian development and reproductive capacity in chickens, yet the regulatory mechanisms remain unclear. Using single-cell RNA sequencing, we characterized germ cell-granulosa cell interactions during key ovarian developmental stages. Here, we present a single-cell transcriptomic atlas of chicken germ cells and granulosa cells at single-cell resolution. Our analysis revealed significant cellular heterogeneity among both germ cells and granulosa cells during primordial follicle assembly. Germ cells were categorized into six subtypes: mitotic S, mitotic G2/M, pre-meiosis, meiosis, oocyte, and dying oocyte by using UMAP analysis. Results revealed that germ cell fate determination is governed by stage-specific genes (KIF11, C14ORF39, LHX8) and transcription factors (MCM5, SMC1B, GDF9, SQSTM1), ensuring meiotic fidelity and high-quality primordial follicle formation. Pseudotime trajectory analysis demonstrated that oocytes undergo autophagy and apoptosis under bidirectional regulation by metabolic and stress-responsive pathways. Four granulosa cell subtypes (PreGC1-4) were identified, with roles in: steroid synthesis (CYP17A1, STAR), extracellular matrix formation (COL1A1, COL3A1), proliferation regulation (PCNA, MCM3), and cellular communication (NTN1). CellChat analysis enhanced crosstalk via WNT, ACTIVIN, BMP pathways, safeguarding structural and functional follicle integrity. Notably, the WNT4/β-catenin pathway acts as a central regulator during the late stage of assembly by activating granulosa cell function. It drives proliferation via CDK2 upregulation and guides differentiation through the transcription factor WT1, thereby promoting follicular formation. This study provides a molecular framework for chicken primordial follicle assembly, identifying key genes/pathways that lay a foundation for optimizing poultry reproductive efficiency.
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[Deciphering the fate of granulation tissue in the human inflammatory alveolar microenvironment using single-cell RNA sequencing].
Objective: To investigate the cellular composition and heterogeneity of granulation tissue in human alveolar sockets and construct single-cell transcriptomic maps to elucidate the potential outcomes of natural resolution in the inflammatory microenvironment. Methods: Granulation tissues from 12 alveolar sockets undergoing tooth extraction due to periodontitis and scheduled for delayed site preservation or autologous tooth transplantation were collected in the Department of Oral and Maxillofacial Surgery, School of Stomatology,The Fourth Military Medical University from September 2022 to August 2023. This study used HE staining and single-cell RNA sequencing (scRNA-seq) to observe the cellular composition and morphological changes of different types of granulation tissues. This approach enabled us to identify cellular sequence variations in the inflammatory dental alveolar granulation tissue within specific microenvironments, construct single-cell atlases for different types of human dental alveolar granulation tissues, and explore the spatiotemporal patterns of cell type distribution and key gene changes during the resolution process of inflammatory granulation tissue. Results: HE staining revealed extensive infiltration of inflammatory cells in the dental alveolar inflammatory granulation tissue. After allowing the inflammatory granulation tissue to naturally resolve for three weeks, its histological morphology was essentially consistent with that of reparative granulation tissue. ScRNA-seq captured a total of 20 448 cells from granulation tissues, and the gene expression quantification analysis revealed total gene counts of 33 835 for inflammatory granulation tissue, 36 058 for naturally resolved granulation tissue, and 34 719 for reparative granulation tissue. At the single-cell level, granulation tissue was annotated into ten cell subgroups, including vascular endothelial cells, mesenchymal stem cells, and fibroblasts. Differences were observed in the proportion of cell compositions between inflammatory and naturally resolved granulation tissues. Pseudo-temporal analysis illustrated two main outcomes in tissue resolution and healing, involving immune responses and angiogenesis. Among these, genes associated with inflammation regulation and immune response, such as Igbp5, Zfp36, and Hspa1b, as well as genes involved in extracellular matrix secretion and the formation of vessels and fibers such as Timp3, Postn, and Rgs5, showed significant differences in expression between the two types of granulation tissues. Conclusions: Inflammatory granulation tissue exhibits heterogeneity in cell composition, gene expression, and biological functions compared to naturally resolved granulation tissue. When the inflammatory granulation tissue in the alveolar socket is left undisturbed to undergo natural resolution, it displays a cellular composition similar to that of reparative granulation tissue at both the histological and single-cell levels. Moreover, it modulates the inflammatory response and the healing process through immune reactions and tissue remodeling.
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Integration of single-nuclei transcriptome and bulk RNA-seq to unravel the role of AhWRKY70 in regulating stem cell development in Arachis hypogaea L.
Peanut stem is a vital organ to provide mechanical support and energy for aerial tissue development. However, the transcriptional regulatory mechanisms underlying stem development at a single-cell resolution remain unclear. Herein, single-nuclei isolation coupled with fluorescent-activated cell sorting was employed to construct a cell atlas of peanut seedling stems using microdroplets-based single-nuclei RNA-sequencing. This approach yielded 29 308 cells with 53 349 expressed genes underlying the identification of five cell types characterized by known marker genes. Additionally, 2053 differentially expressed genes (DEGs) were identified across different cell types. Furthermore, 3306 core-DEGs involved in cell development trajectories were used to construct a transcription factor (TF) interaction network, providing insights into specific biological pathways and transcriptional regulation dynamics underlying cell-type differentiation. Additionally, 1446 DEGs associated with different cell-cycle profile were identified, revealing that peanut stem elongation and cell expansion are closely linked to auxin-responsive pathway. This was supported by the examination of endogenous phytohormones and the identification of 10 hormone-responsive DEGs. Moreover, AhWRKY70 was localized in the nucleus and is highly enriched in stem cortex and xylem cells and exhibits a tissue-specific expression pattern that regulates stem growth. Overexpression of AhWRKY70 in Arabidopsis led to accelerated stem growth by modulating the phytohormone signalling pathway, influencing the expression of sixteen auxin and ethylene-responsive genes as demonstrated by transcriptome sequencing. In conclusion, the single-cell atlas provides a foundational dataset for understanding gene expression heterogeneity in peanut seedling stems. The elucidation of AhWRKY70 function expands our understanding of the roles of WRKY family members in peanut.
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Integrated single-cell and bulk RNA-seq analysis reveals prognostic stemness genes in leiomyosarcoma.
INTRODUCTION: Leiomyosarcoma (LMS) is a rare and aggressive soft tissue sarcoma with limited therapeutic options and poor prognosis. Identifying reliable prognostic markers and therapeutic targets is critical for improving personalized treatment strategies. METHODS: We integrated single-cell RNA sequencing (scRNA-seq) data of LMS from the Gene Expression Omnibus (GEO) with bulk RNA-seq and clinical data from The Cancer Genome Atlas (TCGA). Malignant cells were identified using machine learning approaches, and their stemness index was calculated. Cells were stratified into high and low stemness index groups, and differential gene expression analysis was performed. Prognostic markers were identified through a sequential pipeline: univariate Cox regression to screen candidate genes, Lasso regression for feature selection, and multivariate Cox regression for model construction and survival analysis. RESULTS: Cells with a high stemness index exhibited a more complex tumor immune microenvironment (TIME) and enhanced intercellular interactions compared to those with a low stemness index. Differential expression analysis identified genes distinguishing high versus low stemness cells. Through the regression pipeline, six prognostic markers were identified: BOP1, CTBP1, DSE, PMSD10, SRPK1, and HACD4. These markers were significantly associated with tumor cell proliferation and patient survival outcomes. DISCUSSION: Our findings suggest that stemness-related heterogeneity in LMS shapes the tumor immune microenvironment and contributes to disease progression. The six identified prognostic markers not only provide insights into the molecular mechanisms of LMS but also represent potential therapeutic targets for personalized treatment.
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Mapping cell diversity in human sporadic cerebral cavernous malformations.
BACKGROUND: Cerebral cavernous malformation (CCM) is a low-flow, bleeding-prone vascular disease that can cause cerebral hemorrhage, seizure and neurological deficits. Its inheritance mode includes sporadic or autosomal dominant inheritance with incomplete penetrance, namely sporadic CCM (SCCM) and familial CCM. SCCM is featured by single lesion and single affection in a family. Among CCM patients especially SCCM, the pathogenesis of the corresponding phenotypes and pathological features or candidate genes have not been fully elucidated yet. METHODS: Here, we performed in-depth single-cell RNA sequencing (scRNA-Seq) and bulk assay for transposase-accessible chromatin sequencing (ATAC-Seq) in SCCM and control patients. Further validation was conducted for the gene of interest using qPCR and RNA in situ hybridization (RNA FISH) techniques to provide further atlas and evidence for SCCM generative process. RESULTS: We identified six cell types in the SCCM and control vessels and found that the expression of NEK1, RNPC3, FBRSL1, IQGAP2, MCUB, AP3B1, ESCO1, MYO9B and PVT1 were up-regulated in SCCM tissues. Among the six cell types, we found that compared with control conditions, PVT1 showed a rising peak which followed the pseudo-time axis in endothelial cell clusters of SCCM samples, while showed an increasing trend in smooth muscle cell clusters of SCCM samples. Further experiments indicated that, compared with the control vessels, PVT1 exhibited significantly elevated expression in SCCM samples. CONCLUSION: In SCCM conditions, We found that in the process of development from control to lesion conditions, PVT1 showed a rising peak in endothelial cells and showed an increasing trend in smooth muscle cells at the same time. Overall, there was a significantly elevated expression of NEK1, RNPC3, FBRSL1, IQGAP2, MCUB, AP3B1, ESCO1, MYO9B and PVT1 in SCCM specimens compared to control samples.