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Functional characterization of the 12p12.1 renal cancer-susceptibility locus implicates BHLHE41.
Genome-wide association studies have identified multiple renal cell carcinoma (RCC) susceptibility loci. Here, we use regional imputation and bioinformatics analysis of the 12p12.1 locus to identify the single-nucleotide polymorphism (SNP) rs7132434 as a potential functional variant. Luciferase assays demonstrate allele-specific regulatory activity and, together with data from electromobility shift assays, suggest allele-specific differences at rs7132434 for AP-1 transcription factor binding. In an analysis of The Cancer Genome Atlas data, SNPs highly correlated with rs7132434 show allele-specific differences in BHLHE41 expression (trend P value=6.3 × 10(-7)). Cells overexpressing BHLHE41 produce larger mouse xenograft tumours, while RNA-seq analysis reveals that constitutively increased BHLHE41 induces expression of IL-11. We conclude that the RCC risk allele at 12p12.1 maps to rs7132434, a functional variant in an enhancer that upregulates BHLHE41 expression which, in turn, induces IL-11, a member of the IL-6 cytokine family.
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Multi-modal refinement of the human heart atlas during the first gestational trimester.
Forty first-trimester human hearts were studied to lay groundwork for further studies of the mechanisms underlying congenital heart defects. We first sampled 49,227 cardiac nuclei from three fetuses at 8.6, 9.0, and 10.7 post-conceptional weeks (pcw) for single-nucleus RNA sequencing, enabling the distinction of six classes comprising 21 cell types. Improved resolution led to the identification of previously unappreciated cardiomyocyte populations and minority autonomic and lymphatic endothelial transcriptomes, among others. After integration with 5-7 pcw heart single-cell RNA-sequencing data, we identified a human cardiomyofibroblast progenitor preceding the diversification of cardiomyocyte and stromal lineages. Spatial transcriptomic analysis (six Visium sections from two additional hearts) was aided by deconvolution, and key spatial markers validated on sectioned and whole hearts in two- and three-dimensional space and over time. Altogether, anatomical-positional features, including innervation, conduction and subdomains of the atrioventricular septum, translate latent molecular identity into specialized cardiac functions. This atlas adds unprecedented spatial and temporal resolution to the characterization of human-specific aspects of early heart formation.
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Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data.
Although an essential step, cell functional annotation often proves particularly challenging from single-cell transcriptional data. Several methods have been developed to accomplish this task. However, in most cases, these rely on techniques initially developed for bulk RNA sequencing or simply make use of marker genes identified from cell clustering followed by supervised annotation. To overcome these limitations and automatize the process, we have developed two novel methods, the single-cell gene set enrichment analysis (scGSEA) and the single-cell mapper (scMAP). scGSEA combines latent data representations and gene set enrichment scores to detect coordinated gene activity at single-cell resolution. scMAP uses transfer learning techniques to re-purpose and contextualize new cells into a reference cell atlas. Using both simulated and real datasets, we show that scGSEA effectively recapitulates recurrent patterns of pathways' activity shared by cells from different experimental conditions. At the same time, we show that scMAP can reliably map and contextualize new single-cell profiles on a breast cancer atlas we recently released. Both tools are provided in an effective and straightforward workflow providing a framework to determine cell function and significantly improve annotation and interpretation of scRNA-seq data.
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A multi-omics atlas of the human retina at single-cell resolution.
Cell classes in the human retina are highly heterogeneous with their abundance varying by several orders of magnitude. Here, we generated and integrated a multi-omics single-cell atlas of the adult human retina, including more than 250,000 nuclei for single-nuclei RNA-seq and 137,000 nuclei for single-nuclei ATAC-seq. Cross-species comparison of the retina atlas among human, monkey, mice, and chicken revealed relatively conserved and non-conserved types. Interestingly, the overall cell heterogeneity in primate retina decreases compared with that of rodent and chicken retina. Through integrative analysis, we identified 35,000 distal cis-element-gene pairs, constructed transcription factor (TF)-target regulons for more than 200 TFs, and partitioned the TFs into distinct co-active modules. We also revealed the heterogeneity of the cis-element-gene relationships in different cell types, even from the same class. Taken together, we present a comprehensive single-cell multi-omics atlas of the human retina as a resource that enables systematic molecular characterization at individual cell-type resolution.
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Single-cell and spatial omics in plants: from cellular atlases to regulatory mechanisms.
Single-cell RNA sequencing (scRNA-seq) has transformed transcriptomic studies by enabling gene expression profiling at the resolution of individual cells within and across a broad range of tissue types, revealing cellular heterogeneity that is obscured in bulk tissue transcriptomes. Over the past decade, improvements in microfluidics and library preparation have drastically increased throughput, allowing tens of thousands of cells to be assayed in a single experiment. Although initially developed in animal systems, scRNA-seq has rapidly emerged as a powerful and widely adopted approach in plant biology. Beyond transcriptomics, the integration of single-cell data with chromatin accessibility, proteomics, metabolomics, and spatial omics is enabling a system-level understanding of plant gene regulation and cellular organization. Network-based analytical frameworks further support the reconstruction of gene regulatory networks and the interpretation of complex single-cell data. In this review, we summarize the current technological landscape of plant single-cell studies, discuss key experimental and analytical challenges, and review emerging strategies for validating single-cell discoveries. We also discuss future directions in applying single-cell technologies to woody perennials plants and bioenergy-relevant crops, emphasizing their potential to accelerate the discovery of cell type-specific regulatory mechanisms underlying growth, stress resilience, and biomass production.
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Multi-omics integration and machine learning-driven construction of an immunogenic cell death prognostic model for colon cancer and functional validation of FCGR2A.
BACKGROUND: Immunogenic cell death (ICD) influences tumor immune microenvironment remodeling and immunotherapy response. However, the prognostic value of ICD-related genes in colon cancer has not been systematically clarified. This study aimed to develop an ICD-based prognostic model and explore its association with the immune microenvironment and treatment sensitivity. METHODS: Transcriptomic and clinical data of colon adenocarcinoma (COAD) patients were obtained from TCGA and GTEx, with GSE17538 and GSE38832 used as external validation cohorts. Single-cell RNA-seq data from the Colon Cancer Atlas were analyzed to characterize ICD-associated T-cell states. Differentially expressed genes between high and low ICD-score T cells were identified using ssGSEA, followed by WGCNA to select immune-related modules. One hundred seventeen machine-learning model combinations were evaluated to construct the optimal prognostic signature. Immune infiltration was assessed using CIBERSORT, ssGSEA, and ESTIMATE. GSEA explored pathway differences, while drug sensitivity was predicted using pRRophetic. The top-weighted gene was validated through in vitro assays. RESULTS: Seven major cell types were identified within the tumor microenvironment. T cells with high and low ICD scores exhibited distinct functional and spatial patterns. WGCNA identified a key module highly correlated with ICD scores, and 51 genes were screened. The Random Survival Forest model yielded a 15-gene ICD-related signature with strong prognostic performance (C-index 0.968 in TCGA; 0.767 and 0.855 in validation cohorts). High-risk patients consistently showed poorer survival (p < 0.001). A combined nomogram demonstrated stable predictive accuracy. High-risk patients displayed increased M2 macrophages and Tregs, whereas low-risk patients exhibited higher activated CD4+ T cells and plasma cells. EMT and angiogenesis pathways were enriched in the high-risk group, while metabolic pathways predominated in the low-risk group. High-risk patients were more sensitive to drugs such as Dasatinib. FCGR2A overexpression promoted proliferation, migration, and invasion in vitro. CONCLUSION: The 15-gene ICD-based model effectively predicts COAD prognosis, reflects immune microenvironment heterogeneity, and offers insights for individualized treatment planning.
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CellFishing.jl: an ultrafast and scalable cell search method for single-cell RNA sequencing.
Recent technical improvements in single-cell RNA sequencing (scRNA-seq) have enabled massively parallel profiling of transcriptomes, thereby promoting large-scale studies encompassing a wide range of cell types of multicellular organisms. With this background, we propose CellFishing.jl, a new method for searching atlas-scale datasets for similar cells and detecting noteworthy genes of query cells with high accuracy and throughput. Using multiple scRNA-seq datasets, we validate that our method demonstrates comparable accuracy to and is markedly faster than the state-of-the-art software. Moreover, CellFishing.jl is scalable to more than one million cells, and the throughput of the search is approximately 1600 cells per second.
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P3H1 promotes malignant progression of esophageal squamous cell carcinoma (ESCC) and modulates the immune microenvironment.
BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is the predominant subtype of esophageal cancer (EC) in China and carries high lethality, in part because early disease is typically asymptomatic and thus diagnosed at advanced stages. To address the need for new biomarkers and targets, we investigated prolyl 3‑hydroxylase 1 (P3H1), a collagen‑modifying enzyme with emerging roles in cancer, aiming to clarify its contribution to ESCC progression and its potential as a prognostic biomarker and therapeutic target. METHODS: We integrated clinical and transcriptional profiles of ESCC from public datasets [The Cancer Genome Atlas (TCGA); Gene Expression Omnibus (GEO) cohorts GSE53623 and GSE53624] and single‑cell RNA‑seq data (GSE154763) to evaluate P3H1 expression, clinicopathologic associations, survival, and diagnostic performance. In vitro, we selected the high‑expressing ESCC line KYSE‑180 and performed lentiviral shRNA-mediated knockdown with validation by reverse transcription quantitative polymerase chain reaction (PCR) and Western blot. Functional assays [Cell Counting Kit-8 (CCK-8) proliferation, colony formation, wound‑healing migration, and Transwell migration/invasion] were used to assess malignant phenotypes. Immune relevance was explored with immunedeconv [Tumor IMmune Estimation Resource (TIMER), xCell, Microenvironment Cell Populations (MCP)‑counter, Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT), Estimating the Proportions of Immune and Cancer cells (EPIC)] and correlations with immune‑checkpoint genes (e.g., HAVCR2, SIGLEC15). Single‑cell analyses profiled cell‑type-specific P3H1 expression. Statistical analyses included Kaplan-Meier survival and receiver operating characteristic (ROC)/Youden index determinations. Human tissue immunohistochemistry (IHC) provided orthogonal validation. RESULTS: P3H1 was significantly overexpressed in ESCC versus normal tissues across TCGA and GEO, confirmed by IHC of tumor/peritumoral pairs and by elevated mRNA/protein in ESCC cell lines relative to HET‑1A. Higher P3H1 is associated with larger tumor size, lymph‑node metastasis, advanced tumor-node-metastasis (TNM) stage, and poorer survival. Diagnostic performance for ESCC was high. In KYSE‑180 cells, shRNA knockdown of P3H1 reduced proliferation (CCK-8, colony formation) and impaired migration/invasion (wound healing, Transwell). Immune deconvolution linked P3H1 to multiple immune‑cell compartments-particularly T‑cell signatures-and showed positive correlations with checkpoint genes including HAVCR2 and SIGLEC15. Single‑cell analyses indicated enriched P3H1 expression in plasmacytoid dendritic cells (pDCs), suggesting a role in shaping an immunoregulatory milieu that may favor tumor progression. CONCLUSIONS: Our study demonstrates that P3H1 is markedly upregulated in ESCC and contributes to tumor proliferation, migration, and invasion. In addition, P3H1 shows strong associations with immune infiltration and checkpoint pathways, suggesting a role in shaping the tumor immune microenvironment. These findings support P3H1 as a potential diagnostic biomarker and therapeutic target in ESCC.
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A single-cell transcriptome atlas for zebrafish development.
The ability to define cell types and how they change during organogenesis is central to our understanding of animal development and human disease. Despite the crucial nature of this knowledge, we have yet to fully characterize all distinct cell types and the gene expression differences that generate cell types during development. To address this knowledge gap, we produced an atlas using single-cell RNA-sequencing methods to investigate gene expression from the pharyngula to early larval stages in developing zebrafish. Our single-cell transcriptome atlas encompasses transcriptional profiles from 44,102 cells across four days of development using duplicate experiments that confirmed high reproducibility. We annotated 220 identified clusters and highlighted several strategies for interrogating changes in gene expression associated with the development of zebrafish embryos at single-cell resolution. Furthermore, we highlight the power of this analysis to assign new cell-type or developmental stage-specific expression information to many genes, including those that are currently known only by sequence and/or that lack expression information altogether. The resulting atlas is a resource for biologists to generate hypotheses for functional analysis, which we hope integrates with existing efforts to define the diversity of cell-types during zebrafish organogenesis, and to examine the transcriptional profiles that produce each cell type over developmental time.
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Optimized protocol for mouse footpad immune cell isolation for single-cell RNA sequencing and flow cytometry.
Single-cell RNA sequencing (scRNA-seq) requires the preparation of a highly viable single-cell suspension to get reliable sequencing results. Here, we present a protocol for isolating mouse footpad leukocytes while maintaining high viability. We describe steps for footpad collection, enzymatic tissue dissociation, leukocyte isolation and purification, and cell fixation and preservation. We then detail combinatorial barcoding, library preparation, scRNA-seq, and data analysis. Cells can be used to generate a complete molecular atlas at the single cell level.