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Scarf enables a highly memory-efficient analysis of large-scale single-cell genomics data.
As the scale of single-cell genomics experiments grows into the millions, the computational requirements to process this data are beyond the reach of many. Herein we present Scarf, a modularly designed Python package that seamlessly interoperates with other single-cell toolkits and allows for memory-efficient single-cell analysis of millions of cells on a laptop or low-cost devices like single-board computers. We demonstrate Scarf's memory and compute-time efficiency by applying it to the largest existing single-cell RNA-Seq and ATAC-Seq datasets. Scarf wraps memory-efficient implementations of a graph-based t-stochastic neighbour embedding and hierarchical clustering algorithm. Moreover, Scarf performs accurate reference-anchored mapping of datasets while maintaining memory efficiency. By implementing a subsampling algorithm, Scarf additionally has the capacity to generate representative sampling of cells from a given dataset wherein rare cell populations and lineage differentiation trajectories are conserved. Together, Scarf provides a framework wherein any researcher can perform advanced processing, subsampling, reanalysis, and integration of atlas-scale datasets on standard laptop computers. Scarf is available on Github: https://github.com/parashardhapola/scarf .
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Neurotransmitter receptor-related gene signature as potential prognostic and therapeutic biomarkers in colorectal cancer.
Background: Colorectal cancer is one of the most common malignant tumors worldwide. A various of neurotransmitter receptors have been found to be expressed in tumor cells, and the activation of these receptors may promote tumor growth and metastasis. This study aimed to construct a novel neurotransmitter receptor-related genes signature to predict the survival, immune microenvironment, and treatment response of colorectal cancer patients. Methods: RNA-seq and clinical data of colorectal cancer from The Cancer Genome Atlas database and Gene Expression Omnibus were downloaded. Neurotransmitter receptor-related gene were collected from publicly available data sources. The Weighted Gene Coexpression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Random Forest (RF) algorithms were employed to construct the Neurotransmitter receptor-related gene prognostic signature. Further analyses, functional enrichment, CIBERSORTx, The Tumor Immune Single Cell Center (TISCH), survival analysis, and CellMiner, were performed to analyze immune status and treatment responses. Quantitative real-time polymerase chain reaction (qRT-PCR) assays were carried out to confirm the expression levels of prognostic genes. Results: By combining machine learning algorithm and WGCNA, we identified CHRNA3, GABRD, GRIK3, and GRIK5 as Neurotransmitter receptor-related prognostic genes signature. Functional enrichment analyses showed that these genes were enriched with cellular metabolic-related pathways, such as organic acid, inorganic acid, and lipid metabolism. CIBERSORTx and Single cell analysis showed that the high expression of genes were positively correlated with immunosuppressive cells infiltration, and the genes were mainly expressed in cancer-associated fibroblasts and endothelial cells. A nomogram was further built to predict overall survival (OS). The expression of CHRNA3, GABRD, GRIK3, and GRIK5 in cancer cells significantly impacted their response to chemotherapy. Conclusion: A neurotransmitter receptor-related prognostic gene signature was developed and validated in the current study, giving novel sights of neurotransmitter in predicting the prognostic and improving the treatment of CRC.
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TRIM27 is an adverse prognostic biomarker and associated with immune and molecular profiles in right-sided colon cancer.
Right-sided colon cancer (RCC), as an independent tumor entity, shows a poor prognosis. It is imperative to detect immune microenvironment-related genes for predicting RCC patient prognosis and study their function in RCC. Tripartite motif-containing 27 (TRIM27) was identified as a risk signature from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets by using weighted gene co-expression network analysis, differentially expressed analysis, and univariate Cox analysis. It predicted a poorer overall survival and increased lymph node metastasis, which were then validated in our 48 clinical samples. Using immunohistochemistry, TRIM27 was found to be highly expressed in both cancer cells and surrounding immunocytes, and its expression in tumor or immune cells both predicted a poorer prognosis. Thereafter, the functional mechanism, immune and molecular characteristics of TRIM27 were investigated using gene set enrichment analysis (GSEA), ESTIMATE, CIBERSORT, and gene set variation analysis (GSVA) at the single-cell, somatic mutation, and RNA-seq level. Patients with highly expressed TRIM27 presented lower CD4+ T cell infiltration and activation of the mTORC1/glycolysis pathway. In addition, patients with highly expressed TRIM27 were characterized by hypermetabolism, higher tumor purity, more BRAF mutation, and more chromosomal instability. Collectively, TRIM27 is an important immune-related prognostic biomarker in patients with RCC. It may function via activating the mTORC1/glycolysis pathway and suppressing CD4+ T cells. These results indicated that TRIM27 could be a promising therapeutic target in RCC.
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Single-cell atlases: shared and tissue-specific cell types across human organs.
The development of single-cell and spatial transcriptomics methods was instrumental in the conception of the Human Cell Atlas initiative, which aims to generate an integrated map of all cells across the human body. These technology advances are bringing increasing depth and resolution to maps of human organs and tissues, as well as our understanding of individual human cell types. Commonalities as well as tissue-specific features of primary and supportive cell types across human organs are beginning to emerge from these human tissue maps. In this Review, we highlight key biological insights obtained from cross-tissue studies into epithelial, fibroblast, vascular and immune cells based on single-cell gene expression data in humans and contrast it with mechanisms reported in mice.
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Single-cell RNA-seq reveals heterogeneity in hiPSC-derived muscle progenitors and E2F family as a key regulator of proliferation.
Human pluripotent stem cell-derived muscle progenitor cells (hiPSC-MuPCs) resemble fetal-stage muscle progenitor cells and possess in vivo regeneration capacity. However, the heterogeneity of hiPSC-MuPCs is unknown, which could impact the regenerative potential of these cells. Here, we established an hiPSC-MuPC atlas by performing single-cell RNA sequencing of hiPSC-MuPC cultures. Bioinformatic analysis revealed four cell clusters for hiPSC-MuPCs: myocytes, committed, cycling, and noncycling progenitors Using FGFR4 as a marker for noncycling progenitors and cycling cells and CD36 as a marker for committed and myocyte cells, we found that FGFR4+ cells possess a higher regenerative capacity than CD36+ cells. We also identified the family of E2F transcription factors are key regulators of hiPSC-MuPC proliferation. Our study provides insights on the purification of hiPSC-MuPCs with higher regenerative potential and increases the understanding of the transcriptional regulation of hiPSC-MuPCs.
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Spatial compartmentalization of melanoma cell states reveals CAF-associated tumor cells with enhanced proliferative capacity.
BACKGROUND: Cancer-associated fibroblasts (CAFs) are key components of the melanoma tumor microenvironment and have been implicated in immune evasion and therapy resistance. However, the spatial relationships between CAFs, extracellular matrix (ECM) remodeling, and immune exclusion remain incompletely characterized. METHODS: We integrated single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics from primary melanoma samples (892 regions of interest) to investigate the landscape of CAF-immune interactions. CAF scores were calculated based on five fibroblast-associated genes. Differential expression analysis, Spearman correlation, and cell state subclustering were performed to characterize the spatial organization of the tumor microenvironment. Findings were examined using TCGA bulk RNA-seq data and drug sensitivity was assessed using the GDSC database. RESULTS: CAF-high regions exhibited significantly lower immune scores compared to CAF-low regions (ρ = -0.442, p < 0.001), with enrichment of ECM-related genes (COL1A1, COL1A2, DCN, FBLN1) and downregulation of immune genes (PTPRC, CD4, CD8A). Spatial co-expression of FN1 and its integrin receptor subunit ITGB1 was observed (ρ = 0.100, p = 0.003), supporting a potential role for ECM-receptor signaling in immune exclusion. Six distinct melanoma cell states were identified, with CAF-associated tumor states (State 4) exhibiting significantly higher proliferative capacity than immune-associated states (State 5) (p = 0.007). The CAF signature correlated with cisplatin sensitivity (ρ = -0.627, p < 0.001) and trametinib resistance (ρ = 0.354, p = 0.009). CONCLUSIONS: This study provides a spatial atlas of CAF-immune interactions in melanoma. CAF abundance is negatively correlated with immune infiltration at the spatial level, with ECM remodeling and FN1-ITGB1 signaling potentially mediating this immune exclusion. The CAF signature showed correlations with drug sensitivity in cell line models, warranting further investigation in CAF-containing systems.
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Integrated transcriptomic analysis and machine learning identify immunogenic cell death genes as prognostic markers and therapeutic targets in non-small cell lung cancer.
BACKGROUND: Immunogenic cell death (ICD) is a regulated cell death that activates antitumor immunity, yet its prognostic role in non-small cell lung cancer (NSCLC) remains unclear. This study aimed to develop and validate a robust ICD-related gene (ICDRG) signature for predicting survival and characterizing the tumor immune microenvironment in NSCLC. METHODS: In this retrospective prognostic model development and validation study, transcriptomic and clinical data from 598 NSCLC patients in The Cancer Genome Atlas (TCGA) cohort were used for model training. External validation was performed using three independent Gene Expression Omnibus (GEO) cohorts [GSE11969, GSE68465 and GSE81089, total n=441 from GSE11969/GSE68465, with GSE81089 providing an additional RNA sequencing (RNA-seq)-based validation set]. Based on 34 literature-curated ICD genes, we identified prognostic candidates through single-cell sequencing analysis and weighted gene co-expression network analysis (WGCNA). An 8-gene prognostic signature based on the ICD-related risk score (ICDRS) was constructed using least absolute shrinkage and selection operator (LASSO)-Cox regression and validated with a machine learning (ML) ensemble framework (10 algorithms). Model performance was assessed using Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC) curves, and multivariate Cox regression. Associations between the ICDRS and immune infiltration or drug sensitivity were further evaluated. RESULTS: The ICDRS model stratified patients into high- and low-risk groups with significantly different overall survival (OS) in both the training and validation cohorts (all log-rank P<0.05). The model demonstrated robust predictive accuracy for 1-, 3-, and 5-year survival [area under the curve (AUC) >0.70]. Multivariate analysis confirmed the ICDRS as an independent prognostic factor [hazard ratio (HR) >2.0, P<0.001]. Notably, consistent prognostic performance was observed across microarray-based (GSE11969/GSE68465) and RNA-seq-based (GSE81089) platforms, underscoring the signature's robustness to technical variations. Furthermore, the high-risk group was characterized by an immunosuppressive microenvironment and higher predicted resistance to common chemotherapeutic agents. CONCLUSIONS: We developed and validated an 8-gene ICD-related signature that serves as an independent prognostic biomarker for NSCLC. This model provides insights into the immunogenic landscape of tumors and offers a potential tool for personalizing immunotherapy strategies.
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Yang-Xue-An-Tai Decoction Treats URSA by regulating the differentiation of proliferating decidual stromal cells through multiple targets.
ETHNOPHARMACOLOGICAL RELEVANCE: Unexplained recurrent spontaneous abortion (URSA) accounts for approximately 50% of all recurrent spontaneous abortion cases, severely endangering the physical and mental health of women of childbearing age. Yang-Xue-An-Tai Decoction (YXAT), a traditional Chinese medicine (TCM) in-hospital preparation, has been clinically effective and safe for URSA treatment for over 30 years, but its underlying mechanism remains unclear. AIM OF THE STUDY: The purpose of this study was to explore the pharmacological mechanism of YXAT in the treatment of URSA, focusing on clarifying its multi-target mechanism in regulating the differentiation of decidual stromal cell (DSC) subtypes. MATERIALS AND METHODS: The classic Clark mouse model of URSA was established to verify YXAT's improvement effect on abortion. Single-cell RNA sequencing (scRNA-seq) was employed to construct the uterine single-cell atlas of model and YXAT-treated mice at gestational day 7.5 (GD7.5). YXAT's regulation on decidualization was verified by detecting decidualization markers; decidual stromal cell subtypes were analyzed, and its regulatory effect on subtype differentiation was confirmed via pseudotime analysis and intercellular communication. Moreover, YXAT's role in regulating abnormal decidual stromal cell differentiation in URSA was validated in animal and clinical samples. RESULTS: YXAT significantly improved decidualization and improved pregnancy outcomes in URSA mice. Subcluster analysis of DSCs revealed that proliferating DSCs (proDSCs)-a central hub for DSC differentiation-were abnormally downregulated in URSA mice, and this reduction was restored by YXAT, with a significantly better effect than that of the positive drug dydrogesterone. Integrated analysis identified Lgals1 as a key overlapping gene regulated by YXAT and linked to proDSC differentiation. We propose that Lgals1 acts as a candidate regulator, and the recovery of Lgals1 expression coincides with the restoration of proDSC differentiation following YXAT treatment. Additionally, URSA mice exhibited enhanced NK cell-proDSC communication with excessive inflammatory activation (e.g., Spp1-Cd44, Gzma-Pard3) and immune suppression-related ligand-receptor pairs (e.g., Spp1-(Itgav + Itgb5), Lamb2-Dag1), which were attenuated by YXAT. The abnormal downregulation of proDSCs was further verified in human URSA samples, highlighting its clinical relevance. CONCLUSIONS: Disordered proDSC differentiation correlates with defective decidualization and adverse pregnancy phenotypes in URSA mice. Multiple favorable cellular and molecular changes are observed alongside YXAT intervention, including recovered proDSC abundance, restored expression of the differentiation-associated gene Lgals1, and normalized abnormal NK cell-proDSC intercellular signaling. These correlative observations offer new insights into the clinical potential of YXAT and advance our understanding of TCM interventions against URSA.
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Single-cell transcriptomic analysis reveals metabolic reprogramming and tumor microenvironment remodeling in aldosterone-producing adenoma.
Aldosterone-producing adenoma (APA) is a significant cause of primary aldosteronism, however, its cellular heterogeneity remains unclear. We performed single-cell RNA sequencing on adrenal tissues from three APA patients and three controls. We identified CAPS+ zona glomerulosa (ZG)-like cells in APA with upregulated lipogenesis, oxidative phosphorylation, and mTOR signaling, indicating metabolic reprogramming. Pseudotime analysis revealed disrupted differentiation and aberrant CYP11B1, CYP11B2, and SULT2A1 expression. Oncogenic Myc, Wnt, and G2/M pathways were activated in ZR-like clusters. APA showed immune infiltration (B cells, CD8+ T cells, M1 macrophages), angiogenic activation (VWF+ endothelial cells), and fibroblast-driven stromal remodeling via Hedgehog signaling. Our study provides a comprehensive single-cell atlas of APA, uncovering key tumorigenic mechanisms and identifying potential biomarkers (e.g., CAPS, VWF, UPK3B) and therapeutic targets, including mTOR and Hedgehog/Wnt pathways. These findings advance the genomic understanding of adrenal tumors and support precision medicine development.
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Targeting CLIC6 with theaflavin enhances radiotherapy sensitivity in ER+/HER2- breast cancer.
BACKGROUND: Radiotherapy is a cornerstone of breast cancer treatment; however, estrogen receptor-positive (ER+)/human epidermal growth factor receptor 2-negative (HER2-) tumors frequently exhibit intrinsic radioresistance. Understanding the molecular mechanisms underlying this resistance is crucial for improving therapeutic outcomes. This study aims to identify key molecular determinants of intrinsic radioresistance in ER+/HER2- breast cancer and to explore potential radiosensitization strategies targeting these determinants. METHODS: We integrated single-cell RNA sequencing (scRNA-seq) data from ER+/HER2- breast tumors before and after radiotherapy with transcriptomic profiles from The Cancer Genome Atlas (TCGA). A high-dimensional weighted gene co-expression network analysis (hdWGCNA) was employed to identify gene modules associated with clinical traits. Functional assays were conducted in ER+ breast cancer cells with chloride intracellular channel 6 (CLIC6) overexpression or knockdown. A molecular docking analysis revealed that theaflavin specifically interacts with CLIC6. Further, clonogenic assays and Cell Counting Kit-8 (CCK-8) proliferation assays were performed to validate the biological effects of theaflavin and CLIC6 following radiotherapy. RESULTS: The hdWGCNA identified a luminal A-like gene module correlated with progesterone receptor (PR) positivity and low Ki67 expression, within which CLIC6 emerged as a key hub gene. Elevated CLIC6 expression enhanced radioresistance, whereas CLIC6 knockdown increased radiosensitivity. Molecular docking confirmed that theaflavin binds to CLIC6 with high affinity (binding energy -8.8 kcal/mol). Theaflavin treatment significantly enhanced radiosensitivity in wild-type and CLIC6-overexpressing cells but had no effect in CLIC6-knockdown or ER-negative (ER-) cells. CONCLUSIONS: This study identified CLIC6 as a novel gene associated with radioresistance in ER+/HER2- breast cancer and showed that theaflavin functions as a natural radiosensitizer in a CLIC6-dependent manner, consistent with its high-affinity interaction with CLIC6. Targeting CLIC6 may provide a promising strategy to enhance radiotherapy efficacy in this common breast cancer subtype.