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Single Nucleus RNA Sequencing of Remnant Kidney Biopsies and Urine Cell RNA Sequencing Reveal Cell Specific Markers of Covid-19 Acute Kidney Injury.
UNLABELLED: Acute kidney injury (AKI) in COVID-19 patients is associated with high mortality and morbidity. Critically ill COVID-19 patients are at twice the risk of in-hospital mortality compared to non-COVID AKI patients. We know little about the cell-specific mechanism in the kidney that contributes to worse clinical outcomes in these patients. New generation single cell technologies have the potential to provide insights into physiological states and molecular mechanisms in COVID-AKI. One of the key limitations is that these patients are severely ill posing significant risks in procuring additional biopsy tissue. We recently generated single nucleus RNA-sequencing data using COVID-AKI patient biopsy tissue as part of the human kidney atlas. Here we describe this approach in detail and report deeper comparative analysis of snRNAseq of 4 COVID-AKI, 4 reference, and 6 non-COVID-AKI biopsies. We also generated and analyzed urine transcriptomics data to find overlapping COVID-AKI-enriched genes and their corresponding cell types in the kidney from snRNA-seq data. We identified all major and minor cell types and states by using by using less than a few cubic millimeters of leftover tissue after pathological workup in our approach. Differential expression analysis of COVID-AKI biopsies showed pathways enriched in viral response, WNT signaling, kidney development, and cytokines in several nephron epithelial cells. COVID-AKI profiles showed a much higher proportion of altered TAL cells than non-COVID AKI and the reference samples. In addition to kidney injury and fibrosis markers indicating robust remodeling we found that, 17 genes overlap between urine cell COVID-AKI transcriptome and the snRNA-seq data from COVID-AKI biopsies. A key feature was that several of the distal nephron and collecting system cell types express these markers. Some of these markers have been previously observed in COVID-19 studies suggesting a common mechanism of injury and potentially the kidney as one of the sources of soluble factors with a potential role in disease progression. TRANSLATIONAL STATEMENT: The manuscript describes innovation, application and discovery that impact clinical care in kidney disease. First, the approach to maximize use of remnant frozen clinical biopsies to inform on clinically relevant molecular features can augment existing pathological workflow for any frozen tissue without much change in the protocol. Second, this approach is transformational in medical crises such as pandemics where mechanistic insights are needed to evaluate organ injury, targets for drug therapy and diagnostic and prognostic markers. Third, the cell type specific and soluble markers identified and validated can be used for diagnoses or prognoses in AKI due to different etiologies and in multiorgan injury.
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Immune features revealed by single-cell RNA and single-cell TCR/BCR sequencing in patients with rheumatoid arthritis receiving COVID-19 booster vaccination.
Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, have profoundly affected human health. Booster COVID-19 vaccines have demonstrated significant efficacy in reducing infection and severe cases. However, the effects of booster COVID-19 vaccines on key immune cell subsets and their responses in rheumatoid arthritis (RA) are not well understood. By using single-cell RNA sequencing (scRNA-seq) combined with scTCR/BCR-seq analysis, a total of 8 major and 27 minor cell clusters were identified from paired peripheral blood mononuclear cells (PBMCs) which were collected 1 week before and 4 weeks after booster vaccination in stable RA patients. Booster vaccination only had limited impact on the composition and proportions of PBMCs cell clusters. CD8+ cytotoxic T cells (CD8+T_CTL) showed a trend toward an increase after vaccination, while naive B cells and conventional dendritic cells (cDCs) showed a trend toward a decrease. Transcriptomic changes were observed after booster vaccination, primarily involving T/B cell receptor signaling pathways, phagosome, antigen processing and presenting, and viral myocarditis pathways. Interferon (IFN) and pro-inflammatory response gene sets were slightly upregulated across most major cell subpopulations in COVID-19 booster-vaccinated RA individuals. Plasma neutralizing antibody titers significantly increased after booster COVID-19 vaccination (p = 0.037). Single-cell TCR/BCR analysis revealed increased B cell clone expansion and repertoire diversity postvaccination, with no consistent alterations in T cells. Several clonotypes of BCRs and TCRs were identified to be significantly over-represented after vaccination, such as IGHV3-15 and TRBV28. Our study provided a comprehensive single-cell atlas of the peripheral immune response and TCR/BCR immune repertoire profiles to inactivated SARS-CoV-2 booster vaccination in RA patients, which helps us to understand vaccine-induced immune responses better.
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Single-Cell Sequencing of Hepatocellular Carcinoma Reveals Cell Interactions and Cell Heterogeneity in the Microenvironment.
BACKGROUND: Hepatocellular carcinoma (HCC) is the main histological subtype of liver cancer, which has the characteristics of poor prognosis and high fatality rate. Single-cell sequencing can provide quantitative and unbiased characterization of cell heterogeneity by analyzing the molecular profile of the whole genome of thousands of single cells. Thus, the purpose of this study was to identify novel prognostic markers for HCC based on single-cell sequencing data. METHODS: Single-cell sequencing of 21 HCC samples and 256 normal liver tissue samples in the GSE124395 dataset was collected from the Gene Expression Omnibus (GEO) database. The quality-controlled cells were grouped by unsupervised cluster analysis and identified the marker genes of each cell cluster. Hereafter, these cell clusters were annotated by singleR and CellMarker according to the expression patterns of the marker genes. Pseudotime analysis was performed to construct the trajectory of cell evolution and to define hub genes in the evolution process. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore the potential regulatory mechanism of hub genes in HCC. Next, the differential expression of hub genes and the correlation of the expression of these genes with patients' survival and diagnosis were investigated in The Cancer Genome Atlas (TCGA) database. RESULTS: A total of 9 clusters corresponding to 9 cell types, including NKT cells, hepatocytes, endothelial cells, Kupffer cells, EPCAM+ cells, cancer cells, plasma cells (B cells), immature B cells, and myofibroblasts were identified. We screened 63 key genes related to cell differentiation through trajectory analysis, which were enriched in the process of coagulation. Ultimately, we identified 10 survival-related hub genes in the TCGA database, namely ALDOB, APOC3, APOH, CYP2E1, CYP3A4, GC, HRG, LINC01554, PDK4, and TXN. CONCLUSION: In conclusion, ALDOB, APOC3, APOH, CYP2E1, CYP3A4, GC, HRG, LINC01554, PDK4, and TXN may serve as hub genes in the diagnosis and prognosis for HCC.
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SpatialQuery: scalable discovery and molecular characterization of multicellular motifs from spatial omics data.
Spatially resolved single-cell technologies enable profiling of cells in situ, yet computational approaches that jointly discover multicellular spatial patterns and characterize their molecular programs remain limited. Here we introduce SpatialQuery, a framework that can both identify cellular motifs, i.e. recurrent multicellular co-localization patterns, and perform molecular analyses focused on the motifs. It uncovers genes modulated by spatial contexts through differential expression analysis, and detects coordinated expression changes through covariation analysis. SpatialQuery can identify functional tissue units, and goes beyond pairwise analyses to characterize multicellular interactions. Applications to both spatial transcriptomics and proteomics data uncover cross-germ-layer signaling in gut tube patterning, disease-specific fibrotic and immunosuppressive niches in kidney and colon, and regional determinants of motif-associated transcriptional programs in a mouse brain atlas. SpatialQuery is available as a Python package, and we demonstrate how its light computational footprint enables integration into web-based cell atlas portals for interactive visualization and exploration.
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Altered immune cell in human severe acute pancreatitis revealed by single-cell RNA sequencing.
BACKGROUND: Severe acute pancreatitis (SAP) is characterized by inflammation, with inflammatory immune cells playing a pivotal role in disease progression. This study aims to understand variations in specific immune cell subtypes in SAP, uncover their mechanisms of action, and identify potential biological markers for predicting Acute Pancreatitis (AP) severity. METHODS: We collected peripheral blood from 7 untreated SAP patients and employed single-cell RNA sequencing for the first time to construct a transcriptome atlas of peripheral blood mononuclear cells (PBMCs) in SAP. Integrating SAP transcriptomic data with 6 healthy controls from the GEO database facilitated the analysis of immune cell roles in SAP. We obtained comprehensive transcriptomic datasets from AP samples in the GEO database and identified potential biomarkers associated with AP severity using the "Scissor" tool in single-cell transcriptomic data. RESULTS: This study presents the inaugural construction of a peripheral blood single-cell atlas for SAP patients, identifying 20 cell subtypes. Notably, there was a significant decrease in effector T cell subsets and a noteworthy increase in monocytes compared to healthy controls. Moreover, we identified a novel monocyte subpopulation expressing high levels of PPBP and PF4 which was significantly elevated in SAP. The proportion of monocyte subpopulations with high CCL3 expression was also markedly increased compared to healthy controls, as verified by flow cytometry. Additionally, cell communication analysis revealed insights into immune and inflammation-related signaling pathways in SAP patient monocytes. Finally, our findings suggest that the subpopulation with high CCL3 expression, along with upregulated pro-inflammatory genes such as S100A12, IL1B, and CCL3, holds promise as biomarkers for predicting AP severity. CONCLUSION: This study reveals monocytes' crucial role in SAP initiation and progression, characterized by distinct pro-inflammatory features intricately linked to AP severity. A monocyte subpopulation with elevated PPBP and CCL3 levels emerges as a potential biomarker and therapeutic target.
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Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID.
Because of the stochasticity associated with high-throughput single-cell sequencing, current methods for exploring cell-type diversity rely on clustering-based computational approaches in which heterogeneity is characterized at cell subpopulation rather than at full single-cell resolution. Here we present Cell-ID, a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell sequencing data. We applied Cell-ID to data from multiple human and mouse samples, including blood cells, pancreatic islets and airway, intestinal and olfactory epithelium, as well as to comprehensive mouse cell atlas datasets. We demonstrate that Cell-ID signatures are reproducible across different donors, tissues of origin, species and single-cell omics technologies, and can be used for automatic cell-type annotation and cell matching across datasets. Cell-ID improves biological interpretation at individual cell level, enabling discovery of previously uncharacterized rare cell types or cell states. Cell-ID is distributed as an open-source R software package.
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Pan-cancer analysis implicates novel insights of cholesterol biosynthesis into immunotherapy response prediction and survival prognostication.
Cancer therapy has been transformed by immune checkpoint inhibitors (ICIs); however, their limited response rates and significant heterogeneity remain major challenges, necessitating continued focus. Targeting cholesterol biosynthesis (CB) in tumor cells has emerged as a promising strategy to enhance anti-tumor immune responses, representing a novel therapeutic approach. To elucidate the relationship between CB and ICI responsiveness, we analyzed publicly accessible single-cell RNA sequencing (scRNA-seq) data from patients treated with ICIs. Our analysis integrated a comprehensive pan-cancer dataset comprising 40 scRNA-seq datasets, 30 Cancer Genome Atlas (TCGA) pan-cancer cohorts, and 9 ICI transcriptomics cohorts. We formulated and validated a novel CB-associated signature (CB.SIG). The therapeutic potential of CB. SIG genes were further explored using 17 CRISPR datasets to identify potential immunotherapy targets. In two scRNA-seq immunotherapy cohorts, the CB score significantly correlated with resistance to immunotherapy. Across the pan-cancer dataset, CB. SIG exhibited a significant negative correlation with the imbalance of anti-tumor immunity and immune cell infiltration, while positively correlated with tumor-promoting pathways. Using CB. SIG, machine learning models predicted immunotherapy response and patient prognosis with the AUC values of 0.73 on both validation and test sets. Notably, CB. SIG outperformed previously reported biomarkers across multiple cancer types. Through CRISPR screening, TNFRSF10B was identified as a pan-cancer biomarker predictive of immunotherapy responsiveness and survival, a finding further supported by spatial transcriptomics and immunohistochemistry datasets. Functional assays indicated that silencing TNFRSF10B reduced proliferation, migration, and invasion while increasing apoptosis in non-small cell lung cancer and breast cancer cells. Overall, CB. SIG performed better in predicting ICI responses than alternative signatures and offers a potent tool for identifying patients suitable for immunotherapy.
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The prognostic, immunological and single-cell features of m6A molecules in cervical cancer.
Cervical cancer (CC) is a growing health concern, emphasizing the need for reliable biomarkers in treatment selection and prognosis assessment. We analyzed gene expression profiles and clinicopathological data from The Cancer Genome Atlas (TCGA) for CC. Using Consensus Cluster Plus, we applied machine learning to cluster the CC cohort. Differential analysis was performed using the edge R package, while weighted correlation network analysis (WGCNA) was conducted using the WGCNA package. Single-sample gene set enrichment analysis (ssGSEA) evaluated immune cell abundance and computed the m6Ascore. Western blot and Q-PCR validated the m6A score in CC. Common copy number variation alterations were observed in the 23 m6A-related genes in CC, and their mutation frequency was summarized in a waterfall chart. Patients were grouped into two clusters, m6AclusterA and m6AclusterB. Improved clinical outcomes were observed in m6AclusterA, while m6AclusterB exhibited higher infiltration of 14 immune cell types. WGCNA analysis generated seven integrated modules, enriched in several biological processes. Prognostic differential genes were used to generate two gene clusters (gene Cluster I and gene Cluster II). Using ssGSEA, the m6Ascore was calculated for each patient. Lower m6Ascore correlated with better clinical outcomes, lower gene mutation frequency, and wild-type status. We investigated the sensitivity of high and low m6Ascore to immunotherapy, visualized through violin and UMAP diagrams showcasing crosstalk among single-cell clusters. The key gene PFKFB4 showed higher expression in CC cell lines and tumor tissues compared to normal cells and tissue. Our study elucidates the role of m6A molecules in predicting prognosis, biological features, and appropriate treatment for CC patients.
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Regulation of DNA methylation during the testicular development of Shaziling pigs.
DNA methylation is one of the key epigenetic regulatory mechanisms in development and spermatogenesis. However, the dynamic regulatory mechanisms of genome-wide DNA methylation during testicular development remain largely unknown. Herein, we generated a single-base resolution DNA methylome and transcriptome atlas of precocious porcine testicular tissues across three developmental stages (1, 75, and 150 days old). The results showed that the dynamic methylation patterns were directly related to the expression of the DNMT3A gene. Conjoint analysis revealed a negative regulatory pattern between promoter methylation and the positive regulation of 3'-untranslated region (3'UTR) methylation. Mechanistically, the decrease in promoter methylation affected the upregulation of meiosis-related genes, such as HORMAD1, SPO11, and SYCE1. Demethylation in the 3'UTR induced the downregulation of the INHBA gene and knockdown of INHBA inhibited cell proliferation by reducing the synthesis of activin A. These findings contribute to exploring the regulatory mechanisms of DNA methylation in testicular development.
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A pluripotent stem cell atlas of multilineage differentiation.
Human pluripotent stem cells offer a scalable platform to study genetic and signalling mechanisms governing cell lineage decisions during differentiation. Genome-wide and single-cell transcriptomics technologies likewise offer high-throughput analysis of heterogeneous cell differentiation states. While in vivo development has been extensively characterised using these technologies, there remains a need for comprehensive single-cell transcriptomic profiling of stem cell differentiation from pluripotency. Understanding gene expression changes governing differentiation in vitro is key to developing high fidelity differentiation protocols and understanding fundamental mechanisms of development. We generated a single-cell RNA sequencing time course to study the role of developmental signalling pathways on multilineage diversification from pluripotency in vitro. The combined dataset of over 60,000 cells spans cell types from a time course of differentiation across all germ layers, ranging from gastrulation cell states to progenitor and committed cell types. These data provide a diverse benchmarking reference point to compare against in vivo development and advance understanding of signalling regulation of differentiation, providing insights into protocol development, drug screening, and regenerative medicine applications.