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A single-cell transposable element atlas of human cell identity.
Single cell RNA sequencing (scRNA-seq) is revolutionizing the study of complex biological systems. However, most sequencing studies overlook the contribution of transposable element (TE) expression to the transcriptome. In both scRNA-seq and bulk tissue RNA sequencing (RNA-seq), quantification of TE expression is challenging due to repetitive sequence content and poorly characterized TE gene models. Here, we developed a tool and analysis pipeline for Single cell Transposable Element Locus Level Analysis of scRNA Sequencing (Stellarscope) that reassigns multi-mapped reads to specific genomic loci using an expectation-maximization algorithm. Using Stellarscope, we built an atlas of TE expression in human PBMCs. We found that locus-specific TEs delineate cell types and define new cell subsets not identified by standard mRNA expression profiles. Altogether, this study provides comprehensive insights into the influence of transposable elements in human biology.
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Single-Cell Atlas of the Drosophila Leg Disc Identifies a Long Non-Coding RNA in Late Development.
The Drosophila imaginal disc has been an excellent model for the study of developmental gene regulation. In particular, long non-coding RNAs (lncRNAs) have gained widespread attention in recent years due to their important role in gene regulation. Their specific spatiotemporal expressions further support their role in developmental processes and diseases. In this study, we explored the role of a novel lncRNA in Drosophila leg development by dissecting and dissociating w1118 third-instar larval third leg (L3) discs into single cells and single nuclei, and performing single-cell RNA-sequencing (scRNA-seq) and single-cell assays for transposase-accessible chromatin (scATAC-seq). Single-cell transcriptomics analysis of the L3 discs across three developmental timepoints revealed different cell types and identified lncRNA:CR33938 as a distal specific gene with high expression in late development. This was further validated by fluorescence in-situ hybridization (FISH). The scATAC-seq results reproduced the single-cell transcriptomics landscape and elucidated the distal cell functions at different timepoints. Furthermore, overexpression of lncRNA:CR33938 in the S2 cell line increased the expression of leg development genes, further elucidating its potential role in development.
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deCS: A Tool for Systematic Cell Type Annotations of Single-cell RNA Sequencing Data among Human Tissues.
Single-cell RNA sequencing (scRNA-seq) is revolutionizing the study of complex and dynamic cellular mechanisms. However, cell type annotation remains a main challenge as it largely relies on a priori knowledge and manual curation, which is cumbersome and subjective. The increasing number of scRNA-seq datasets, as well as numerous published genetic studies, has motivated us to build a comprehensive human cell type reference atlas.Here, we present decoding Cell type Specificity (deCS), an automatic cell type annotation method augmented by a comprehensive collection of human cell type expression profiles and marker genes. We used deCS to annotate scRNA-seq data from various tissue types and systematically evaluated the annotation accuracy under different conditions, including reference panels, sequencing depth, and feature selection strategies. Our results demonstrate that expanding the references is critical for improving annotation accuracy. Compared to many existing state-of-the-art annotation tools, deCS significantly reduced computation time and increased accuracy. deCS can be integrated into the standard scRNA-seq analytical pipeline to enhance cell type annotation. Finally, we demonstrated the broad utility of deCS to identify trait-cell type associations in 51 human complex traits, providing deep insights into the cellular mechanisms underlying disease pathogenesis. All documents for deCS, including source code, user manual, demo data, and tutorials, are freely available at https://github.com/bsml320/deCS.
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Correlation of LLT-1 and NLRC4 inflammasome and its effect on glioblastoma prognosis.
PURPOSE: LLT-1 is a well-known ligand for the natural killer (NK) cell inhibitory receptor NKRP1A. Here, we examined NLRC4 inflammasome components and LLT-1 expression in glioblastoma (GBM) tissues to elucidate potential associations and interactions between these factors. METHODS: GBM tissues were collected for RNA sequencing (RNA-seq) and Immunofluorescent experiments. Colocalization of LLT-1 and other proteins was assessed by immunofluorescence. Computational analyses utilized RNA-seq data from 296 to 52 patients from the Chinese Glioma Genome Atlas and CHA medical records, respectively. These data were subjected to survival, non-negative matrix factorization clustering, Gene Ontology enrichment, and protein-protein interaction analyses. Receptor-ligand interactions between tumor and immune cells were confirmed by single-cell RNA-seq analysis. RESULTS: In GBM tissues, LLT-1 was predominantly colocalized with glial fibrillary acidic protein (GFAP)-expressing astrocytes, but not with microglial markers like Iba-1. Additionally, LLT-1 and activated NLRC4 inflammasomes were mainly co-expressed in intratumoral astrocytes, suggesting an association between LLT-1, NLRC4, and glioma malignancy. High LLT-1 expression correlates with poor prognosis, particularly in the mesenchymal subtype, and is associated with TNF and NOD-like receptor signaling pathway enrichment, indicating a potential role in tumor inflammation and progression. At the single-cell level, mesenchymal-like malignant cells showed high NF, NLR, and IL-1 signaling pathway enrichment compared to other malignant cell types. CONCLUSION: We revealed an association between NLRC4 inflammasome activity and LLT-1 expression, suggesting a novel regulatory pathway involving TNF, inflammasomes, and IL-1, potentially offering new NK-cell-mediated anti-glioma approaches.
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CKS2 modulates cell-cycle progression of tongue squamous cell carcinoma cells partly via modulating the cellular distribution of DUTPase.
BACKGROUND: CKS2 (CDC28 Protein Kinase Regulatory Subunit 2) is a gene that encodes CKS2 protein that has been characterized as a binding partner of the catalytic subunit of the cyclin-dependent kinases. However, its expression profile and regulatory effects in tongue squamous cell carcinoma has not yet been explored. METHODS: Bioinformatic analysis was conducted using bulk-seq data from The Cancer Genome Atlas and single-cell RNA-seq data from GSE103322. SCC9 and CAL27 cells were used as in vitro cell models for cellular and molecular studies. RESULTS: CKS2 expression was significantly upregulated in tongue squamous cell carcinoma tissues (N = 128) compared with adjacent normal tissues (N = 13). Its upregulation was associated with significantly shorter disease-specific survival and progression-free survival. Cellular status estimation in tumor cells indicated that CKS2 expression was moderately and positively correlated with cell-cycle progression. CKS2 inhibition in SCC9 and CAL27 cells resulted in decreased proliferation, weakened colony formation capability, and cell-cycle arrest at the G2/M phase. Immunofluorescence staining and co-Immunoprecipitation (co-IP) assay confirmed co-localization and interaction between CKS2 and DUTPase. CKS2 knockdown did not alter DUTPase expression but reduced its nuclear distribution. Both CKS2 and DUT expression were moderately correlated with their gene-level copy number. CONCLUSION: CKS2 expression is associated with unfavorable survival of patients with tongue squamous cell carcinoma. Inhibiting its expression could reduce tongue squamous cell carcinoma cell growth and induce G2/M arrest. CKS2 may interact with DUTPase and regulate its nuclear localization. Gene-level copy amplification might be an important mechanism of upregulated CKS2 and DUT in the tumor.
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FOXI1 expression in chromophobe renal cell carcinoma and renal oncocytoma: a study of The Cancer Genome Atlas transcriptome-based outlier mining and immunohistochemistry.
FOXI1 is a forkhead family transcription factor that plays a key role in differentiation and functional maintenance for the renal intercalated cell (IC). The diagnostic utility of FOXI1 is rarely studied thus far. Comparative analyses of FOXI1 mRNA expression in normal kidney tissue and different renal neoplasms including chromophobe renal cell carcinoma (chRCC), renal oncocytoma (RO), and other renal cell carcinomas were conducted using transcriptomic data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus, and single-cell RNA-seq datasets, in combination with integrative analyses using mutant data, karyotype data, and digital slides for cases with anomalous FOXI1 expression in TCGA. Formalin-fixed, paraffin-embedded whole-tissue slides of varied primary renal neoplasms (n = 367) were subjected to FOXI1 staining for validating FOXI1 transcription levels. We confirmed that FOXI1 was significantly upregulated at mRNA levels in ICs, chRCCs, and ROs compared with other renal tubule cell and renal cell carcinoma subtypes. Furthermore, most of the cases with FOXI1 expression outliers were misclassified in the TCGA kidney cancer project. An underlying novel entity with frequent mutations involved in the mTOR pathway was also found. FOXI1 immunoreactivity was consistently noted in ICs of the distal nephron. FOXI1 staining was positive in 85 of 93 chRCCs and 13 of 18 ROs, respectively. FOXI1 staining was not seen in renal neoplasms (n = 254) derived from non-ICs. In conclusion, FOXI1 expression in normal kidney tissue is restricted to ICs. This cell type-specific expression is retained during neoplastic transformation from ICs to chRCCs or ROs. FOXI1 is thereby a potential biomarker of IC-related tumors.
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Reference-based cell type matching of in situ image-based spatial transcriptomics data on primary visual cortex of mouse brain.
With the advent of multiplex fluorescence in situ hybridization (FISH) and in situ RNA sequencing technologies, spatial transcriptomics analysis is advancing rapidly, providing spatial location and gene expression information about cells in tissue sections at single cell resolution. Cell type classification of these spatially-resolved cells can be inferred by matching the spatial transcriptomics data to reference atlases derived from single cell RNA-sequencing (scRNA-seq) in which cell types are defined by differences in their gene expression profiles. However, robust cell type matching of the spatially-resolved cells to reference scRNA-seq atlases is challenging due to the intrinsic differences in resolution between the spatial and scRNA-seq data. In this study, we systematically evaluated six computational algorithms for cell type matching across four image-based spatial transcriptomics experimental protocols (MERFISH, smFISH, BaristaSeq, and ExSeq) conducted on the same mouse primary visual cortex (VISp) brain region. We find that many cells are assigned as the same type by multiple cell type matching algorithms and are present in spatial patterns previously reported from scRNA-seq studies in VISp. Furthermore, by combining the results of individual matching strategies into consensus cell type assignments, we see even greater alignment with biological expectations. We present two ensemble meta-analysis strategies used in this study and share the consensus cell type matching results in the Cytosplore Viewer ( https://viewer.cytosplore.org ) for interactive visualization and data exploration. The consensus matching can also guide spatial data analysis using SSAM, allowing segmentation-free cell type assignment.
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A systematic overview of single-cell transcriptomics databases, their use cases, and limitations.
Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of transcriptomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Such platforms can help bridge the gap between computational and wet lab scientists through user-friendly web-based interfaces needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field.
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A Systematic Overview of Single-Cell Transcriptomics Databases, their Use cases, and Limitations.
Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of genomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Furthermore, we propose that bridging the gap between computational and wet lab scientists through user-friendly web-based platforms is needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field.
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Interpretable single-cell factor decomposition using sciRED.
Single-cell RNA sequencing (scRNA-seq) maps gene expression heterogeneity within a tissue. However, identifying biological signals in this data is challenging due to confounding technical factors, sparsity, and high dimensionality. Data factorization methods address this by separating and identifying signals in the data, such as gene expression programs, but the resulting factors must be manually interpreted. We developed Single-Cell Interpretable REsidual Decomposition (sciRED) to improve the interpretation of scRNA-seq factor analysis. sciRED removes known confounding effects, uses rotations to improve factor interpretability, maps factors to known covariates, identifies unexplained factors that may capture hidden biological phenomena and determines the genes and biological processes represented by the resulting factors. We apply sciRED to multiple scRNA-seq datasets and identify sex-specific variation in a kidney map, discern strong and weak immune stimulation signals in a PBMC dataset, reduce ambient RNA contamination in a rat liver atlas to help identify strain variation, and reveal rare cell type signatures and anatomical zonation gene programs in a healthy human liver map. These demonstrate that sciRED is useful in characterizing diverse biological signals within scRNA-seq data.