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Dissection of cellular and molecular mechanisms of aristolochic acid-induced hepatotoxicity via single-cell transcriptomics.
BACKGROUND: Aristolochic acids (AAs), a class of carcinogenic and mutagenic natural products from Aristolochia and Asarum plants, are well-known to be responsible for inducing nephrotoxicity and urothelial carcinoma. Recently, accumulating evidence suggests that exposure to AAs could also induce hepatotoxicity and even hepatocellular carcinoma, though the mechanisms are poorly defined. METHODS: Here, we aimed to dissect the underlying cellular and molecular mechanisms of aristolochic acid I (AAI)-induced hepatotoxicity by using advanced single-cell RNA sequencing (scRNA-seq) and proteomics techniques. We established the first single-cell atlas of mouse livers in response to AAI. RESULTS: In hepatocytes, our results indicated that AAI activated NF-κB and STAT3 signaling pathways, which may contribute to the inflammatory response and apoptosis. In liver sinusoidal endothelial cells (LSECs), AAI activated multiple oxidative stress and inflammatory associated signaling pathways and induced apoptosis. Importantly, AAI induced infiltration of cytotoxic T cells and activation of proinflammatory macrophage and neutrophil cells in the liver to produce inflammatory cytokines to aggravate inflammation. CONCLUSIONS: Collectively, our study provides novel knowledge of AAs-induced molecular characteristics of hepatotoxicity at a single-cell level and suggests future treatment options for AAs associated hepatotoxicity.
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Single-cyst transcriptome analysis of Drosophila male germline stem cell lineage.
The Drosophila male germline stem cell (GSC) lineage provides a great model to understand stem cell maintenance, proliferation, differentiation and dedifferentiation. Here, we use the Drosophila GSC lineage to systematically analyze the transcriptome of discrete but continuously differentiating germline cysts. We first isolated single cysts at each recognizable stage from wild-type testes, which were subsequently applied for RNA-seq analyses. Our data delineate a high-resolution transcriptome atlas in the entire male GSC lineage: the most dramatic switch occurs from early to late spermatocyte, followed by the change from the mitotic spermatogonia to early meiotic spermatocyte. By contrast, the transit-amplifying spermatogonia cysts display similar transcriptomes, suggesting common molecular features among these stages, which may underlie their similar behavior during both differentiation and dedifferentiation processes. Finally, distinct differentiating germ cell cyst samples do not exhibit obvious dosage compensation of X-chromosomal genes, even considering the paucity of X-chromosomal gene expression during meiosis, which is different from somatic cells. Together, our single cyst-resolution, genome-wide transcriptional profile analyses provide an unprecedented resource to understand many questions in both germ cell biology and stem cell biology fields.
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scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics.
Single-cell RNA sequencing has achieved massive success in biological research fields. Discovering novel cell types from single-cell transcriptomics has been demonstrated to be essential in the field of biomedicine, yet is time-consuming and needs prior knowledge. With the unprecedented boom in cell atlases, auto-annotation tools have become more prevalent due to their speed, accuracy and user-friendly features. However, existing tools have mostly focused on general cell-type annotation and have not adequately addressed the challenge of discovering novel rare cell types. In this work, we introduce scNovel, a powerful deep learning-based neural network that specifically focuses on novel rare cell discovery. By testing our model on diverse datasets with different scales, protocols and degrees of imbalance, we demonstrate that scNovel significantly outperforms previous state-of-the-art novel cell detection models, reaching the most AUROC performance(the only one method whose averaged AUROC results are above 94%, up to 16.26% more comparing to the second-best method). We validate scNovel's performance on a million-scale dataset to illustrate the scalability of scNovel further. Applying scNovel on a clinical COVID-19 dataset, three potential novel subtypes of Macrophages are identified, where the COVID-related differential genes are also detected to have consistent expression patterns through deeper analysis. We believe that our proposed pipeline will be an important tool for high-throughput clinical data in a wide range of applications.
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A single-cell atlas of the peripheral immune response to severe COVID-19.
There is an urgent need to better understand the pathophysiology of Coronavirus disease 2019 (COVID-19), the global pandemic caused by SARS-CoV-2. Here, we apply single-cell RNA sequencing (scRNA-seq) to peripheral blood mononuclear cells (PBMCs) of 7 patients hospitalized with confirmed COVID-19 and 6 healthy controls. We identify substantial reconfiguration of peripheral immune cell phenotype in COVID-19, including a heterogeneous interferon-stimulated gene (ISG) signature, HLA class II downregulation, and a novel B cell-derived granulocyte population appearing in patients with acute respiratory failure requiring mechanical ventilation. Importantly, peripheral monocytes and lymphocytes do not express substantial amounts of pro-inflammatory cytokines, suggesting that circulating leukocytes do not significantly contribute to the potential COVID-19 cytokine storm. Collectively, we provide the most thorough cell atlas to date of the peripheral immune response to severe COVID-19.
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Single-cell profiling reveals IFI44L as a central regulator of immune dysregulation in high HIV viral load.
Human Immunodeficiency Virus (HIV) infection impairs the immune system, diminishing its ability to defend against pathogens and increasing susceptibility to infections and diseases. To gain a comprehensive understanding of the heterogeneity, functions, and regulatory mechanisms of immune cells affected by different HIV viral loads, we investigated the biological impacts of HIV infection on the human body. We reanalyzed a publicly available peripheral blood mononuclear cell (PBMC) single-cell RNA sequencing dataset (GSE157829) comprising three people living with HIV with high viral load (HL-HIV), three people living with HIV with low viral load (LL-HIV), and one healthy control donor. We constructed single-cell immune atlases that represent different viral load profiles. We also analyzed the dynamic changes in specific immune cell subpopulations to gain insights into their signaling pathways, developmental trajectories, and key transcriptional regulators. At the single-cell level, non-classical monocyte subpopulations, specifically ncMono_LYN_TCF7L2 and ncMono_IL32, functioned as immunosurveillance elements but also activated pro-inflammatory pathways, leading to immune activation. B_CD83 subpopulation was critical in B cell development, facilitating B cell differentiation and tolerance. Naive.T_TNFAIP3 subpopulation exhibited anti-inflammatory effects and protected against aberrant apoptosis. Central memory T (TCM)_TCF7_CCR7 subpopulation played a crucial role in regulating the expansion of HIV-specific CD8+ T cells and maintaining the immune response. Additionally, we observed a significant enrichment of IFI44L-positive subpopulations across different cell types, particularly in HL-HIV samples. Our findings demonstrate that HIV infection increases the complexity of immune cell subpopulations. Notably, IFI44L has emerged as a potential disease hub gene in HL-HIV samples, highlighting its potential as a novel biomarker for HIV.
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One section, two worlds: single-cell integration of MALDI-MSI and spatial transcriptomics on the same single tissue section.
Understanding tissue complexity requires spatially resolved multi-omics data at single-cell resolution. Here, we present a workflow integrating high-resolution matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) with Xenium spatial transcriptomics (SPT) on a single tissue section. This strategy ensures pixel-scale spatial correspondence between metabolic and transcriptomic features, avoiding misalignment issues of serial sections, where even minor offsets result in sampling different cells. We investigated MALDI-MSI compatibility with downstream SPT revealing that the number of transcripts per cell decreased by ~ 30% after MSI, whilst cell recovery and cell-type assignments are preserved. Validated using mouse brain and demonstrated using human glioblastoma tissues, we achieved pixel-scale modality co-registration, enabling per-cell MALDI spectra extraction aligned with gene expression. Integrated clustering revealed enhanced cell-type resolution and identified metabolic heterogeneity within transcriptionally defined populations. This facilitates precise correlations of a cell's function and its biochemical state, providing a holistic view of cellular function, heterogeneity, and interaction in health and disease. Our workflow provides a scalable path to multi-omic atlases, advancing both data integration and translational research.
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Comprehensive ceRNA Profiling Uncovers Clinically Relevant Hub lncRNAs in Glioblastoma.
Long non-coding RNAs (lncRNAs) can function as competing endogenous RNAs (ceRNAs) that rewire post-transcriptional regulation in glioblastoma (GBM). Previous GBM studies have focused on either single lncRNA ceRNA axis in isolation or used in silico predictions with small patient cohorts (< 200). In this study, we integrated RNA-seq data from 372 TCGA-GBM tumors, 5 matched adjacent TCGA-normal brain and 2 931 GTEx-normal brain (n = 3 308) samples to build an experimentally informed ceRNA atlas. Limma-voom differential analysis, intersection with 2 experimentally supported interaction databases (ENCORI and miRTarBase) distilled 517 high-confidence lncRNA-miRNA-mRNA triplets. Twelve hub lncRNAs coordinated 3 downregulated miRNAs and 262 target mRNAs enriched for cell-cycle, p53 signaling and homologous recombination pathways. Two co-expressed hubs, CYTOR and MIR4435-2HG, were significantly over-expressed in GBM tumors in comparison with normal brain tissue and independently predicted poor overall survival (log-rank P < .01). Their shared 25 targets include oncogenic YBX1, MDM4 and TGFBR1 mRNAs, underscoring the redundant regulation of oncogenic pathways, suggesting the need to explore combination lncRNA inhibition strategies. This population-scale analysis prioritizes CYTOR and MIR4435-2HG for functional interrogation and offers a framework for exploring biomarkers and RNA-targeted strategies in GBM.
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A single-cell atlas of the peripheral immune response in patients with influenza A virus infection.
Influenza A virus (IAV) remains a pressing global health concern, yet our understanding of the specific nature and functional roles of certain circulating cell subsets in relation to this viral infection remains unclear. We performed single-cell RNA sequencing (scRNA-seq) on single-cell whole-blood (scWB) isolated from various populations using the Singleron Matrix platform. Our investigation showed a significant upregulation of the IFN-stimulated gene, IFN-α-inducible protein 27 (IFI27), in patients affected by IAV infection and further found that the heightened expression of IFI27 was primarily concentrated in specific immune cell populations, including monocytes and conventional dendritic cells (cDCs). Notably, we identified a specific subset of neutrophils, neutrophil_ISG15, which implicates interferon (IFN) signaling in IAV infection. Our findings provide a comprehensive understanding of the cellular subtypes and molecular characteristics of scWB across different populations with IAV infection.
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Unveiling novel double-negative prostate cancer subtypes through single-cell RNA sequencing analysis.
Recent advancements in single-cell RNA sequencing (scRNAseq) have facilitated the discovery of previously unrecognized subtypes within prostate cancer (PCa), offering new insights into cancer heterogeneity and progression. In this study, we integrated scRNAseq data from multiple studies, comprising publicly available cohorts and data generated by our research team, and established the Human Prostate Single cell Atlas (HuPSA) and Mouse Prostate Single cell Atlas (MoPSA) datasets. Through comprehensive analysis, we identified two novel double-negative PCa populations: KRT7 cells characterized by elevated KRT7 expression and progenitor-like cells marked by SOX2 and FOXA2 expression, distinct from NEPCa, and displaying stem/progenitor features. Furthermore, HuPSA-based deconvolution re-classified human PCa specimens, validating the presence of these novel subtypes. We then developed a user-friendly web application, "HuPSA-MoPSA" ( https://pcatools.shinyapps.io/HuPSA-MoPSA/ ), for visualizing gene expression across all newly established datasets. Our study provides comprehensive tools for PCa research and uncovers novel cancer subtypes that can inform clinical diagnosis and treatment strategies.
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Single-cell transcriptomics reveals conserved cell identities and fibrogenic phenotypes in zebrafish and human liver.
The mechanisms underlying liver fibrosis are multifaceted and remain elusive with no approved antifibrotic treatments available. The adult zebrafish has been an underutilized tool to study liver fibrosis. We aimed to characterize the single-cell transcriptome of the adult zebrafish liver to determine its utility as a model for studying liver fibrosis. We used single-cell RNA sequencing (scRNA-seq) of adult zebrafish liver to study the molecular and cellular dynamics at a single-cell level. We performed a comparative analysis to scRNA-seq of human liver with a focus on hepatic stellate cells (HSCs), the driver cells in liver fibrosis. scRNA-seq reveals transcriptionally unique populations of hepatic cell types that comprise the zebrafish liver. Joint clustering with human liver scRNA-seq data demonstrates high conservation of transcriptional profiles and human marker genes in zebrafish. Human and zebrafish HSCs show conservation of transcriptional profiles, and we uncover collectin subfamily member 11 (colec11) as a novel, conserved marker for zebrafish HSCs. To demonstrate the power of scRNA-seq to study liver fibrosis using zebrafish, we performed scRNA-seq on our zebrafish model of a pediatric liver disease with mutation in mannose phosphate isomerase (MPI) and characteristic early liver fibrosis. We found fibrosis signaling pathways and upstream regulators conserved across MPI-depleted zebrafish and human HSCs. CellPhoneDB analysis of zebrafish transcriptome identified neuropilin 1 as a potential driver of liver fibrosis. Conclusion: This study establishes the first scRNA-seq atlas of the adult zebrafish liver, highlights the high degree of similarity to human liver, and strengthens its value as a model to study liver fibrosis.