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AnnSQL: A Python SQL-based package for fast large-scale single-cell genomics analysis using minimal computational resources.
UNLABELLED: As single-cell genomics technologies continue to accelerate biological discovery, software tools that use elegant syntax and minimal computational resources to analyze atlas-scale datasets are increasingly needed. Here we introduce AnnSQL, a Python package that constructs an AnnData-inspired database using the in-process DuckDb engine, enabling orders-of-magnitude performance enhancements for parsing single-cell genomics datasets with the ease of SQL. We highlight AnnSQL functionality and demonstrate transformative runtime improvements by comparing AnnData or AnnSQL operations on a 4.4 million cell single-nucleus RNA-seq dataset: AnnSQL-based operations were executed in minutes on a laptop for which equivalent AnnData operations largely failed (or were ∼700x slower) on a high-performance computing cluster. AnnSQL lowers computational barriers for large-scale single-cell/nucleus RNA-seq analysis on a personal computer, while demonstrating a promising computational infrastructure extendable for complete single-cell workflows across various genome-wide measurements. AVAILABILITY AND IMPLEMENTATION: AnnSQL is a pip installable package that can be found at https://github.com/ArpiarSaundersLab/annsql along with documentation at https://docs.annsql.com .
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A unified single-cell atlas of HNSCC: Toward characterizing HPV- and sex-associated TME variability.
Head and neck squamous cell carcinoma (HNSCC) is highly heterogeneous, with variations driven by human papillomavirus (HPV) status and sex. However, existing single-cell RNA sequencing (scRNA-seq) studies are often limited in sample size and lack standardized methodologies, limiting cross-study comparisons. To address this, we integrated scRNA-seq data from 78 patients (274,911 cells) across multiple studies, creating a unified HNSCC atlas that harmonizes annotations and enables robust tumor microenvironment (TME) analyses. Using STACAS for semi-supervised integration and automated annotation tools such as Ikarus and scGate, we improved tumor and immune cell classification. Leveraging our atlas, we identified HPV-specific shifts in immune and stromal composition, with HPV+ tumors enriched in adaptive immune cells and HPV- tumors showing more stromal and myeloid populations. Preliminary sex-stratified analyses suggested distinct microenvironmental patterns, warranting further investigation. This publicly available atlas provides a comprehensive framework for reproducibly studying HNSCC biology, improving patient stratification, and may help inform personalized therapies.
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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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scRNA-seq assessment of the human lung, spleen, and esophagus tissue stability after cold preservation.
BACKGROUND: The Human Cell Atlas is a large international collaborative effort to map all cell types of the human body. Single-cell RNA sequencing can generate high-quality data for the delivery of such an atlas. However, delays between fresh sample collection and processing may lead to poor data and difficulties in experimental design. RESULTS: This study assesses the effect of cold storage on fresh healthy spleen, esophagus, and lung from ≥ 5 donors over 72 h. We collect 240,000 high-quality single-cell transcriptomes with detailed cell type annotations and whole genome sequences of donors, enabling future eQTL studies. Our data provide a valuable resource for the study of these 3 organs and will allow cross-organ comparison of cell types. We see little effect of cold ischemic time on cell yield, total number of reads per cell, and other quality control metrics in any of the tissues within the first 24 h. However, we observe a decrease in the proportions of lung T cells at 72 h, higher percentage of mitochondrial reads, and increased contamination by background ambient RNA reads in the 72-h samples in the spleen, which is cell type specific. CONCLUSIONS: In conclusion, we present robust protocols for tissue preservation for up to 24 h prior to scRNA-seq analysis. This greatly facilitates the logistics of sample collection for Human Cell Atlas or clinical studies since it increases the time frames for sample processing.
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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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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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Molecular heterogeneity of C. elegans glia across sexes.
A comprehensive description of nervous system function, and sex dimorphism within, is incomplete without clear assessment of the diversity of its component cell types, neurons and glia. C. elegans has an invariant nervous system with the first mapped connectome of a multicellular organism and single-cell atlas of component neurons. Here we present single nuclear RNA-seq evaluation of glia across the entire adult C. elegans nervous system, including both sexes. Machine learning models enabled us to identify both sex-shared and sex-specific glia and glial subclasses. We have identified and validated molecular markers in silico and in vivo for these molecular subcategories. Comparative analytics also reveals previously unappreciated molecular heterogeneity in anatomically identical glia between and within sexes, indicating consequent functional heterogeneity. Furthermore, our datasets reveal that while adult C. elegans glia express neuropeptide genes, they lack the canonical unc-31/CAPS-dependent dense core vesicle release machinery. Thus, glia employ alternate neuromodulator processing mechanisms. Overall, this molecular atlas, available at www.wormglia.org, reveals rich insights into heterogeneity and sex dimorphism in glia across the entire nervous system of an adult animal.
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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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scClassify: sample size estimation and multiscale classification of cells using single and multiple reference.
Automated cell type identification is a key computational challenge in single-cell RNA-sequencing (scRNA-seq) data. To capitalise on the large collection of well-annotated scRNA-seq datasets, we developed scClassify, a multiscale classification framework based on ensemble learning and cell type hierarchies constructed from single or multiple annotated datasets as references. scClassify enables the estimation of sample size required for accurate classification of cell types in a cell type hierarchy and allows joint classification of cells when multiple references are available. We show that scClassify consistently performs better than other supervised cell type classification methods across 114 pairs of reference and testing data, representing a diverse combination of sizes, technologies and levels of complexity, and further demonstrate the unique components of scClassify through simulations and compendia of experimental datasets. Finally, we demonstrate the scalability of scClassify on large single-cell atlases and highlight a novel application of identifying subpopulations of cells from the Tabula Muris data that were unidentified in the original publication. Together, scClassify represents state-of-the-art methodology in automated cell type identification from scRNA-seq data.
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Cellular resolution anatomical and molecular atlases for prenatal human brains.
Increasing interest in studies of prenatal human brain development, particularly using new single-cell genomics and anatomical technologies to create cell atlases, creates a strong need for accurate and detailed anatomical reference atlases. In this study, we present two cellular-resolution digital anatomical atlases for prenatal human brain at postconceptional weeks (PCW) 15 and 21. Both atlases were annotated on sequential Nissl-stained sections covering brain-wide structures on the basis of combined analysis of cytoarchitecture, acetylcholinesterase staining, and an extensive marker gene expression dataset. This high information content dataset allowed reliable and accurate demarcation of developing cortical and subcortical structures and their subdivisions. Furthermore, using the anatomical atlases as a guide, spatial expression of 37 and 5 genes from the brains, respectively, at PCW 15 and 21 was annotated, illustrating reliable marker genes for many developing brain structures. Finally, the present study uncovered several novel developmental features, such as the lack of an outer subventricular zone in the hippocampal formation and entorhinal cortex, and the apparent extension of both cortical (excitatory) and subcortical (inhibitory) progenitors into the prenatal olfactory bulb. These comprehensive atlases provide useful tools for visualization, segmentation, targeting, imaging, and interpretation of brain structures of prenatal human brain, and for guiding and interpreting the next generation of cell census and connectome studies.