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Integrated MicroRNA-mRNA Analysis Reveals miR-204 Inhibits Cell Proliferation in Gastric Cancer by Targeting CKS1B, CXCL1 and GPRC5A.
Gastric cancer (GC) is the second most frequent cause of cancer-related deaths worldwide. MicroRNAs are single-stranded RNA molecules of 21-23 nucleotides that regulate target gene expression through specific base-pairing interactions between miRNA and untranslated regions of targeted mRNAs. In this study, we generated a multistep approach for the integrated analysis of miRNA and mRNA expression. First, both miRNA and mRNA expression profiling datasets in gastric cancer from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) identified 79 and 1042 differentially expressed miRNAs and mRNAs, respectively, in gastric cancer. Second, inverse correlations between miRNA and mRNA expression levels identified 3206 miRNA-mRNA pairs combined with 79 dysregulated miRNAs and their 774 target mRNAs predicted by three prediction tools, miRanda, PITA, and RNAhybrid. Additionally, miR-204, which was found to be down-regulated in gastric cancer, was ectopically over-expressed in the AGS gastric cancer cell line and all down-regulated targets were identified by RNA sequencing (RNA-seq) analysis. Over-expression of miR-204 reduced the gastric cancer cell proliferation and suppressed the expression of three targets which were validated by qRT-PCR and luciferase assays. For the first time, we identified that CKS1B, CXCL1, and GPRC5A are putative targets of miR-204 and elucidated that miR-204 acted as potential tumor suppressor and, therefore, are useful as a promising therapeutic target for gastric cancer.
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The Human Protein Atlas and Antibody-Based Tissue Profiling in Clinical Proteomics.
Immunohistochemistry (IHC) is a standard method for spatial proteomics and allows for exploration of protein expression at single-cell resolution within the intact tissue environment. Stringent procedures and proper antibody validation strategies are however needed to ensure reliability of results. Application-specific strategies have been proposed by the scientific community to ensure high quality despite variations in sample preparation between different antibody-based methods. Here, the entire workflow utilized within the Human Protein Atlas, from sample preparation to annotation of the IHC staining patterns is described in detail, with important notes on various factors that can affect the outcome of IHC. Methods include tissue microarray (TMA) production, tissue sectioning, IHC, annotation, and validation. Also, building on previously suggested validation strategies, IHC-specific orthogonal and independent validation methods are outlined.
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Nova-ST: Nano-patterned ultra-dense platform for spatial transcriptomics.
Spatial transcriptomics workflows using barcoded capture arrays are commonly used for resolving gene expression in tissues. However, existing techniques are either limited by capture array density or are cost prohibitive for large-scale atlasing. We present Nova-ST, a dense nano-patterned spatial transcriptomics technique derived from randomly barcoded Illumina sequencing flow cells. Nova-ST enables customized, low-cost, flexible, and high-resolution spatial profiling of large tissue sections. Benchmarking on mouse brain sections demonstrates significantly higher sensitivity compared to existing methods at a reduced cost.
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A multi-center cross-platform single-cell multimodal atlas of the mouse cerebral cortex.
As the central functional hub of the central nervous system, the cerebral cortex has long been a major frontier in neuroscience research. With the increasing maturity and widespread application of single-cell RNA sequencing technologies, numerous studies have been conducted to systematically decipher the complexity and diversity of cortical cellular composition. However, substantial variations in sequencing platforms, cohort sizes, and sequencing depth have impeded deeper investigation into the functions of cortical cells and their underlying molecular mechanisms. Here, we present a multicenter, multi-platform atlas of cortical cells based on nine mouse datasets, with platform-specific integration and analysis performed independently for 10x Genomics and Drop-seq single-cell and single-nucleus RNA-seq data. Following rigorous quality control, we systematically analyzed 173,081 high-quality cells, providing a comprehensive characterization of cellular composition, intercellular communication networks, chromatin accessibility, and functional properties. To further validate the reliability and generalizability of the integrated atlas, we performed integrative analyses with cross-species datasets and systematically characterized conserved and adaptive divergence patterns of distinct cortical cell types across primates. Our study provides a benchmark multimodal dataset of the mouse cerebral cortex and establishes a standardized framework for integrative analyses across technologies and species.
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The construction of a testis transcriptional cell atlas from embryo to adult reveals various somatic cells and their molecular roles.
BACKGROUND: The testis is a complex organ that undergoes extensive developmental changes from the embryonic stage to adulthood. The development of germ cells, which give rise to spermatozoa, is tightly regulated by the surrounding somatic cells. METHODS: To better understand the dynamics of these changes, we constructed a transcriptional cell atlas of the testis, integrating single-cell RNA sequencing data from over 26,000 cells across five developmental stages: fetal germ cells, infants, childhood, peri-puberty, and adults. We employed various analytical techniques, including clustering, cell type assignments, identification of differentially expressed genes, pseudotime analysis, weighted gene co-expression network analysis, and evaluation of paracrine cell-cell communication, to comprehensively analyze this transcriptional cell atlas of the testis. RESULTS: Our analysis revealed remarkable heterogeneity in both somatic and germ cell populations, with the highest diversity observed in Sertoli and Myoid somatic cells, as well as in spermatogonia, spermatocyte, and spermatid germ cells. We also identified key somatic cell genes, including RPL39, RPL10, RPL13A, FTH1, RPS2, and RPL18A, which were highly influential in the weighted gene co-expression network of the testis transcriptional cell atlas and have been previously implicated in male infertility. Additionally, our analysis of paracrine cell-cell communication supported specific ligand-receptor interactions involved in neuroactive, cAMP, and estrogen signaling pathways, which support the crucial role of somatic cells in regulating germ cell development. CONCLUSIONS: Overall, our transcriptional atlas provides a comprehensive view of the cell-to-cell heterogeneity in the testis and identifies key somatic cell genes and pathways that play a central role in male fertility across developmental stages.
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Temporal and spatial stability of the EM/PM molecular subtypes in adult diffuse glioma.
Detailed characterizations of genomic alterations have not identified subtype-specific vulnerabilities in adult gliomas. Mapping gliomas into developmental programs may uncover new vulnerabilities that are not strictly related to genomic alterations. After identifying conserved gene modules co-expressed with EGFR or PDGFRA (EM or PM), we recently proposed an EM/PM classification scheme for adult gliomas in a histological subtype- and grade-independent manner. By using cohorts of bulk samples, paired primary and recurrent samples, multi-region samples from the same glioma, single-cell RNA-seq samples, and clinical samples, we here demonstrate the temporal and spatial stability of the EM and PM subtypes. The EM and PM subtypes, which progress in a subtype-specific mode, are robustly maintained in paired longitudinal samples. Elevated activities of cell proliferation, genomic instability and microenvironment, rather than subtype switching, mark recurrent gliomas. Within individual gliomas, the EM/PM subtype was preserved across regions and single cells. Malignant cells in the EM and PM gliomas were correlated to neural stem cell and oligodendrocyte progenitor cell compartment, respectively. Thus, while genetic makeup may change during progression and/or within different tumor areas, adult gliomas evolve within a neurodevelopmental framework of the EM and PM molecular subtypes. The dysregulated developmental pathways embedded in these molecular subtypes may contain subtype-specific vulnerabilities.
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The Kidney Precision Medicine Project and Single-Cell Biology of the Injured Proximal Tubule.
Single-cell RNA sequencing (scRNA-seq) has led to major advances in our understanding of proximal tubule subtypes in health and disease. The proximal tubule serves essential functions in overall homeostasis, but pathologic or physiological perturbations can affect its transcriptomic signature and corresponding tasks. These alterations in proximal tubular cells are often described within a scRNA-seq atlas as cell states, which are pathophysiological subclassifications based on molecular and morphologic changes in a cell's response to that injury compared with its native state. This review describes the major cell states defined in the Kidney Precision Medicine Project's scRNA-seq atlas. It then identifies the overlap between the Kidney Precision Medicine Project and other seminal works that may use different nomenclature or cluster proximal tubule cells at different resolutions to define cell state subtypes. The goal is for the reader to understand the key transcriptomic markers of important cellular injury and regeneration processes across this highly dynamic and evolving field.
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Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis.
Genome-wide association studies (GWAS) have revealed risk alleles for ulcerative colitis (UC). To understand their cell type specificities and pathways of action, we generate an atlas of 366,650 cells from the colon mucosa of 18 UC patients and 12 healthy individuals, revealing 51 epithelial, stromal, and immune cell subsets, including BEST4+ enterocytes, microfold-like cells, and IL13RA2+IL11+ inflammatory fibroblasts, which we associate with resistance to anti-TNF treatment. Inflammatory fibroblasts, inflammatory monocytes, microfold-like cells, and T cells that co-express CD8 and IL-17 expand with disease, forming intercellular interaction hubs. Many UC risk genes are cell type specific and co-regulated within relatively few gene modules, suggesting convergence onto limited sets of cell types and pathways. Using this observation, we nominate and infer functions for specific risk genes across GWAS loci. Our work provides a framework for interrogating complex human diseases and mapping risk variants to cell types and pathways.
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Single-cell differential expression analysis between conditions within nested settings.
Differential expression analysis provides insights into fundamental biological processes and with the advent of single-cell transcriptomics, gene expression can now be studied at the level of individual cells. Many analyses treat cells as samples and assume statistical independence. As cells are pseudoreplicates, this assumption does not hold, leading to reduced robustness, reproducibility, and an inflated type 1 error rate. In this study, we investigate various methods for differential expression analysis on single-cell data, conduct extensive benchmarking, and give recommendations for method choice. The tested methods include DESeq2, MAST, DREAM, scVI, the permutation test, distinct, and the t-test. We additionally adapt hierarchical bootstrapping to differential expression analysis on single-cell data and include it in our benchmark. We found that differential expression analysis methods designed specifically for single-cell data do not offer performance advantages over conventional pseudobulk methods such as DESeq2 when applied to individual datasets. In addition, they mostly require significantly longer run times. For atlas-level analysis, permutation-based methods excel in performance but show poor runtime, suggesting to use DREAM as a compromise between quality and runtime. Overall, our study offers the community a valuable benchmark of methods across diverse scenarios and offers guidelines on method selection.
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EpiScanpy: integrated single-cell epigenomic analysis.
EpiScanpy is a toolkit for the analysis of single-cell epigenomic data, namely single-cell DNA methylation and single-cell ATAC-seq data. To address the modality specific challenges from epigenomics data, epiScanpy quantifies the epigenome using multiple feature space constructions and builds a nearest neighbour graph using epigenomic distance between cells. EpiScanpy makes the many existing scRNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities, including methods for common clustering, dimension reduction, cell type identification and trajectory learning techniques, as well as an atlas integration tool for scATAC-seq datasets. The toolkit also features numerous useful downstream functions, such as differential methylation and differential openness calling, mapping epigenomic features of interest to their nearest gene, or constructing gene activity matrices using chromatin openness. We successfully benchmark epiScanpy against other scATAC-seq analysis tools and show its outperformance at discriminating cell types.