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Analysis of candidate genes for cleft lip ± cleft palate using murine single-cell expression data.
Introduction: Cleft lip ± cleft palate (CL/P) is one of the most common birth defects. Although research has identified multiple genetic risk loci for different types of CL/P (i.e., syndromic or non-syndromic forms), determining the respective causal genes and understanding the relevant functional networks remain challenging. The recent introduction of single-cell RNA sequencing (scRNA-seq) has provided novel opportunities to study gene expression patterns at cellular resolution. The aims of our study were to: (i) aggregate available scRNA-seq data from embryonic mice and provide this as a resource for the craniofacial community; and (ii) demonstrate the value of these data in terms of the investigation of the gene expression patterns of CL/P candidate genes. Methods and Results: First, two published scRNA-seq data sets from embryonic mice were re-processed, i.e., data representing the murine time period of craniofacial development: (i) facial data from embryonic day (E) E11.5; and (ii) whole embryo data from E9.5-E13.5 from the Mouse Organogenesis Cell Atlas (MOCA). Marker gene expression analyses demonstrated that at E11.5, the facial data were a high-resolution representation of the MOCA data. Using CL/P candidate gene lists, distinct groups of genes with specific expression patterns were identified. Among others we identified that a co-expression network including Irf6, Grhl3 and Tfap2a in the periderm, while it was limited to Irf6 and Tfap2a in palatal epithelia, cells of the ectodermal surface, and basal cells at the fusion zone. The analyses also demonstrated that additional CL/P candidate genes (e.g., Tpm1, Arid3b, Ctnnd1, and Wnt3) were exclusively expressed in Irf6+ facial epithelial cells (i.e., as opposed to Irf6- epithelial cells). The MOCA data set was finally used to investigate differences in expression profiles for candidate genes underlying different types of CL/P. These analyses showed that syndromic CL/P genes (syCL/P) were expressed in significantly more cell types than non-syndromic CL/P candidate genes (nsCL/P). Discussion: The present study illustrates how scRNA-seq data can empower research on craniofacial development and disease.
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GLA is associated with ESCC progression and chemotherapy response via DNA damage repair-related pathways.
BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a highly lethal malignancy frequently complicated by chemoresistance. α-Galactosidase A (GLA), a lysosomal hydrolase associated with poor prognosis in multiple cancers, has not been investigated in ESCC. METHODS: Two independent ESCC datasets (GSE161533, GSE38129) were used to screen commonly upregulated genes, which were ranked by diagnostic performance and further validated in an integrated single-cell RNA-seq atlas and an independent dataset (GSE20347), identifying GLA. GLA expression was validated by immunohistochemistry, qRT-PCR, and Western blot. Its biological function was assessed by siRNA-mediated knockdown combined with CCK-8, EdU, colony formation, Transwell, and wound healing assays. KEGG, GO, and GSEA were performed to explore underlying mechanisms. The effect of GLA knockdown on chemosensitivity, and the combination of the GLA pharmacological chaperone Migalastat with gemcitabine or cisplatin, were evaluated by CCK-8 assays in two ESCC cell lines. RESULTS: GLA was significantly upregulated in ESCC, showed strong diagnostic performance across datasets, and was specifically enriched in malignant epithelial cells at the single-cell level. Multiple validation approaches confirmed GLA overexpression in ESCC cell lines and patient tissues. GLA knockdown markedly suppressed proliferation, colony formation, and migration. Enrichment analyses suggested that high GLA expression was associated with DNA damage repair, cell cycle, and DNA replication-related transcriptional programs, as well as with gemcitabine/cisplatin resistance-related genes. GLA knockdown increased the sensitivity of ESCC cells to gemcitabine and cisplatin, and Migalastat combined with either agent enhanced cytotoxicity in two ESCC cell lines, with combination index analysis indicating synergy. CONCLUSION: GLA is a novel, upregulated gene in ESCC with diagnostic relevance and an oncogenic role, and is associated with DNA damage repair-related transcriptional programs and chemotherapy response. Combining Migalastat with chemotherapy may represent a candidate strategy that warrants further mechanistic and in vivo investigation.
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Integrative analysis of programmed cell death pathways reveals prognostic biomarkers and immune infiltration signatures in coronary artery disease.
BACKGROUND: Coronary artery disease (CAD) is a major global health burden characterized by complex pathophysiological mechanisms. Programmed cell death (PCD) pathways, including apoptosis, autophagy, necroptosis, and pyroptosis, have been implicated in the development and progression of CAD. Recent research has demonstrated that these forms of PCD interact through highly regulated and interconnected molecular networks, ultimately shaping disease progression in the cardiovascular system. However, the prognostic significance of these PCD mechanisms in CAD remains unclear. This study aims to comprehensively analyze the involvement of various PCD pathways in CAD and to identify prognostic biomarkers by integrating gene expression data and machine learning approaches. METHODS: Gene expression analysis from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets and the single-sample Gene Set Enrichment Analysis (ssGSEA) assessed PCD pathway activity. Machine learning identified core PCD genes and developed the PCDscore. Immune cell infiltration and pan-cancer analysis were conducted, along with single-cell RNA sequencing (scRNA-seq). RESULTS: Differential gene expression in CAD samples and varied PCD pathway activities were observed. Core genes (GZMB, CXCR4, SFN, ATP6V0A4, and GSDMA) were identified, with the PCDscore effectively stratifying CAD patients. Immune cell differences and correlations with key genes were noted. Pan-cancer analysis and single-cell data provided further insights. CONCLUSIONS: The study highlights the importance of PCD pathways in CAD and the prognostic value of the PCDscore, offering a comprehensive tool for personalized treatment strategies.
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NovoSpaRc: flexible spatial reconstruction of single-cell gene expression with optimal transport.
Single-cell RNA-sequencing (scRNA-seq) technologies have revolutionized modern biomedical sciences. A fundamental challenge is to incorporate spatial information to study tissue organization and spatial gene expression patterns. Here, we describe a detailed protocol for using novoSpaRc, a computational framework that probabilistically assigns cells to tissue locations. At the core of this framework lies a structural correspondence hypothesis, that cells in physical proximity share similar gene expression profiles. Given scRNA-seq data, novoSpaRc spatially reconstructs tissues based on this hypothesis, and optionally, by including a reference atlas of marker genes to improve reconstruction. We describe the novoSpaRc algorithm, and its implementation in an open-source Python package ( https://pypi.org/project/novosparc ). NovoSpaRc maps a scRNA-seq dataset of 10,000 cells onto 1,000 locations in <5 min. We describe results obtained using novoSpaRc to reconstruct the mouse organ of Corti de novo based on the structural correspondence assumption and human osteosarcoma cultured cells based on marker gene information, and provide a step-by-step guide to Drosophila embryo reconstruction in the Procedure to demonstrate how these two strategies can be combined.
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A Comprehensive Analysis of Novel Variations Associated with Bile Duct Cancer: Insights into Expression, Methylation, and 3D Protein Structure.
Cholangiocarcinoma is a rare but highly lethal cancer of the biliary epithelium, marked by heterogeneous molecular subtypes, unclear etiology, and poor five-year survival, highlighting the need for new diagnostic and therapeutic strategies; therefore, this study integrates genomic, transcriptomic, single-cell, methylomic, and molecular-dynamics data to pinpoint pathogenic variants. We performed an integrative multi-omics analysis of publicly available datasets. Somatic variants from 23 tumor samples in The Cancer Genome Atlas were annotated with 11 pathogenicity tools (AUC ≥ 0.86 across EVE, REVEL, SIFT, AlphaMissense, DEOGEN2 were the most stringent). Differential gene expression was assessed in matched bulk RNA-seq (tumor vs. non-tumor) using DESeq2 with Benjamini-Hochberg FDR correction. A single-cell RNA-seq dataset comprising 23,782 cells from an intrahepatic cholangiocarcinoma was clustered with marker genes identified by Wilcoxon rank-sum tests. Illumina 450 K methylation arrays (52 tumors, 12 normal livers) were analyzed with limma and DMRcate to detect differentially methylated probes and regions. AlphaFold3 models of wild-type and MAP2K1R49C were subjected to 50 ns all-atom molecular-dynamics simulations in GROMACS; conformational shifts were quantified by RMSD/RMSF and stability tested with FoldX5. Twenty-four tumor-specific missense variants were detected. The four highest-confidence pathogenic substitutions (EVE, REVEL, SIFT, AlphaMissense, DEOGEN2) occurred in TUBB3, FLNC, ABCA1, and MAP2K1. Bulk RNA-seq confirmed significant dysregulation of these genes and enrichment of extracellular-matrix organization, cytoskeletal remodeling, MAPK signaling, and cholesterol-efflux pathways. Single-cell analysis resolved 23 transcriptionally distinct clusters; proliferative malignant cholangiocytes selectively over-expressed ABCA1 and MAP2K1, indicating tumor-cell specificity. Methylome profiling identified 148,928 DMPs and 7040 DMRs; promoter hypomethylation of TUBB3 and ABCA1 correlated with their transcriptional activation. Substituting Arg-49 with Cys in MAP2K1 dismantles the Arg-centred hydrogen-bond/salt-bridge cluster, reduces hydrophobic packing, and, corroborated by 50 ns MD (Welch's t = -58.06, p = 3.17 × 10-230) and FoldX5 (ΔΔG = +2.3 kcal mol-1), significantly destabilises the protein, manifesting as higher backbone RMSD and increased local flexibility relative to wild type. This multi-omics, public data-driven synthesis delineates a coherent network of genomic, epigenomic, transcriptomic, and structural vulnerabilities, offering a rational framework for therapeutic targeting of cholangiocarcinoma. This study reveals novel bile duct-associated variations that expand our understanding of cholangiocarcinoma pathogenesis and provide potential targets for precision medicine approaches.
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Identification of novel tumor microenvironment-associated genes in gastric cancer based on single-cell RNA-sequencing datasets.
Tumor microenvironment and heterogeneity play vital roles in the development and progression of gastric cancer (GC). In the past decade, a considerable amount of single-cell RNA-sequencing (scRNA-seq) studies have been published in the fields of oncology and immunology, which improve our knowledge of the GC immune microenvironment. However, much uncertainty still exists about the relationship between the macroscopic and microscopic data in transcriptomics. In the current study, we made full use of scRNA-seq data from the Gene Expression Omnibus database (GSE134520) to identify 25 cell subsets, including 11 microenvironment-related cell types. The MIF signaling pathway network was obtained upon analysis of receptor-ligand pairs and cell-cell interactions. By comparing the gene expression in a wide variety of cells between intestinal metaplasia and early gastric cancer, we identified 64 differentially expressed genes annotated as immune response and cellular communication. Subsequently, we screened these genes for prognostic clinical value based on the patients' follow-up data from The Cancer Genome Atlas. TMPRSS15, VIM, APOA1, and RNASE1 were then selected for the construction of LASSO risk scores, and a nomogram model incorporating another five clinical risk factors was successfully created. The effectiveness of least absolute shrinkage and selection operator risk scores was validated using gene set enrichment analysis and levels of immune cell infiltration. These findings will drive the development of prognostic evaluations affected by the immune tumor microenvironment in GC.
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Integrated analysis of single-cell, spatial and bulk RNA-sequencing identifies a cell-death signature for predicting the outcomes of head and neck cancer.
BACKGROUND: Cell death plays an essential role in carcinogenesis, but its function in the recurrence and postoperative prognosis of head and neck cancer (HNC), which ranks as the 7th most common malignancy globally, remains unclear. METHODS: Data from five main subtypes of HNC related single-cell RNA sequencing (scRNA-seq) were recruited to establish a single-cell atlas, and the distribution of cell death models (CDMs) across different tissues as well as cell subtypes were analyzed. Bulk RNA-seq from the Cancer Genome Atlas Program (TCGA) dataset was subjected to a machine learning-based integrative procedure for constructing a consensus cell death-related signature risk score (CDRscore) model and validated by external data. The biofunctions including different expression analysis, immune cell infiltration, genomic mutations, enrichment analysis as well as cellchat analysis were compared between the high- and low- risk score groups categorized by this CDRscore model. Finally, samples from laryngeal squamous cell cancer (LSCC) were conducted by spatial transcriptomics (ST) to further validate the results of CDRscore model. RESULTS: T cells from HNC patients manifested the highest levels of cell death while HPV infection attenuates malignant cell death based on single-cell atlas. CDMs are positively correlated with the tumor-cell stemness, immune-related score and T cells are infiltrated. A CDRscore model was established based on the transcription of ten cell death prognostic genes (MRPL10, DDX19A, NDFIP1, PCMT1, HPRT1, SLC2A3, EFNB2, HK1, BTG3 and MAP2K7). It functions as an independent prognostic factor for overall survival in HNC and displays stable and powerful performance validated by GSE41613 and GSE65858 datasets. Patients in high CDRscore manifested worse overall survival, more active of epithelial mesenchymal transition, TGF-β-related pathways and hypoxia, higher transcription of T cell exhausted markers, and stronger TP53 mutation. ST from LSCC showed that spots with high-risk scores were colocalized with TGF-β and the proliferating malignant cells, additionally, the risk scores have a negative correlation with TCR signaling but positive association with LAG3 transcription. CONCLUSION: The CDRscore model could be utilized as a powerful prognostic indicator for HNC.
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Integrated analysis of miRNAs and mRNAs in thousands of single cells.
The simultaneous sequencing of multiple types of biomolecules can facilitate understanding various forms of regulation occurring in cells. Cosequencing of miRNA and mRNA at single-cell resolution is challenging, and to date, only a few such studies (examining a quite limited number of cells) have been reported. Here, we developed a parallel single-cell small RNA and mRNA coprofiling method (PSCSR-seq V2) that enables miRNA and mRNA coexpression analysis in many cells. The PSCSR-seq V2 method is highly sensitive for miRNA analysis, and it also provides rich mRNA information about the examined cells at the same time. We employed PSCSR-seq V2 to profile miRNA and mRNA in 2310 cultured cells, and detected an average of 181 miRNA species and 7354 mRNA species per cell. An integrated analysis of miRNA and mRNA profiles linked miRNA functions with the negative regulation of tumor suppressor and reprogramming of cellular metabolism. We coprofiled miRNA and mRNA in 9403 lung cells and generated a coexpression atlas for known cell populations in mouse lungs, and detected conserved expression patterns of miRNAs among lineage-related cells. Based on this information, we identified informative age-associated miRNAs in mouse and human lung cells including miR-29, which can be understood as a conserved marker for immunosenescence. PSCSR-seq V2 offers unique functionality to users conducting functional studies of miRNAs in clinical and basic biological research.
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Single-cell RNA sequencing uncovers dynamic roadmap during chicken spermatogenesis.
BACKGROUND: Spermatogenesis is a pivotal biological process for the precise transmission of paternal genetic information, governed by a highly complex and dynamically regulated testicular microenvironment. Although mammalian research has characterized germ cell development at the single-cell level, differences in reproductive strategies limit the relevance of these findings to avian species. RESULTS: We employed single-cell RNA sequencing (scRNA-seq) to analyze the testes of the "Yufen 1" H line roosters at five distinct developmental stages: birth, rapid testicular development, sexual maturity, physical maturity, and senescence. By constructing a single-cell transcriptomic atlas, we identified ten somatic cell subtypes and four germ cell subtypes, thereby elucidating the dynamic changes in gene expression during spermatogenesis. Notably, our findings indicate that meiosis initiates relatively early in chickens, with the formation of the blood-testis barrier being closely associated with pachytene spermatocytes. Additionally, the testicular microenvironment undergoes age-related adaptive changes. Furthermore, we observed that support cells at 20 and 80 weeks of age exhibit similar transcriptional profiles, while macrophages and T cells play a pivotal role in the formation of the testicular cords and vascular networks during the early developmental stages. CONCLUSION: This study offers a comprehensive atlas of testicular development in chickens, elucidating the sequential cell fate transitions from spermatogonial stem cells to mature sperm, alongside the dynamic and intricate developmental trajectories of somatic cells within the testicular microenvironment. These findings present novel insights into avian testicular development and establish a theoretical foundation for future research in reproductive biology and breeding strategies.
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Scoring gene importance by interpreting single-cell foundation models.
Determining a gene's functional importance within a cellular context has long been a challenge, as absolute expression level is an unreliable indicator. Here we introduce SIGnature, a framework for scoring gene importance using attributions derived from single-cell RNA-sequencing (scRNA-seq) foundation models. Attribution scores reduce technical noise, emphasize regulatory genes and facilitate cross-dataset comparison-a core challenge for scRNA-seq analyses. We developed the SIGnature package as a tool for generating and querying attributions, enabling rapid gene set searches across large scRNA-seq atlases. We demonstrate its utility using the MS1 monocyte signature, a poorly understood gene program activated in severe COVID-19 and sepsis. Searching 400 studies identified associations between the MS1 signature and multiple hyperinflammatory conditions, including Kawasaki disease. Experimental validation confirmed that serum from persons with Kawasaki disease induces the MS1 phenotype. These findings highlight that SIGnature can uncover shared mechanisms across conditions, demonstrating its power for large-scale signature scoring and cross-disease analysis.