Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data.

In a mouse model of menstruation stromal cells respond to breakdown of the tissue by changing their identity to become epithelial cells that are incorporated into the luminal epithelium which is rapidly 'healed' without scarring.

In a mouse model of menstruation stromal cells respond to breakdown of the tissue by changing their identity to become epithelial cells that are incorporated into the luminal epithelium which is rapidly 'healed' without scarring.

Decision letter: Single-cell RNA sequencing of the Strongylocentrotus purpuratus larva reveals the blueprint of major cell types and nervous system of a non-chordate deuterostome

Dental niche cells directly contribute to tooth reconstitution and morphogenesis.

BackgroundLow back pain is a leading cause of disability worldwide and is frequently attributed to intervertebral disc (IVD) degeneration. Though the contributions of the adjacent cartilage endplates (CEP) to IVD degeneration are well documented, the phenotype and functions of the resident CEP cells are critically understudied. To better characterize CEP cell phenotype and possible mechanisms of CEP degeneration, bulk and single-cell RNA sequencing of non-degenerated and degenerated CEP cells were performed.MethodsHuman lumbar CEP cells from degenerated (Thompson grade ≥ 4) and non-degenerated (Thompson grade ≤ 2) discs were expanded for bulk (N=4 non-degenerated, N=4 degenerated) and single-cell (N=1 non-degenerated, N=1 degenerated) RNA sequencing. Genes identified from bulk RNA sequencing were categorized by function and their expression in non-degenerated and degenerated CEP cells were compared. A PubMed literature review was also performed to determine which genes were previously identified and studied in the CEP, IVD, and other cartilaginous tissues. For single-cell RNA sequencing, different cell clusters were resolved using unsupervised clustering and functional annotation. Differential gene expression analysis and Gene Ontology, respectively, were used to compare gene expression and functional enrichment between cell clusters, as well as between non-degenerated and degenerated CEP samples.ResultsBulk RNA sequencing revealed 38 genes were significantly upregulated and 15 genes were significantly downregulated in degenerated CEP cells relative to non-degenerated cells (|fold change| ≥ 1.5). Of these, only 2 genes were previously studied in CEP cells, and 31 were previously studied in the IVD and other cartilaginous tissues. Single-cell RNA sequencing revealed 11 unique cell clusters, including multiple chondrocyte and progenitor subpopulations with distinct gene expression and functional profiles. Analysis of genes in the bulk RNA sequencing dataset showed that progenitor cell clusters from both samples were enriched in “non-degenerated” genes but not “degenerated” genes. For both bulk- and single-cell analyses, gene expression and pathway enrichment analyses highlighted several pathways that may regulate CEP degeneration, including transcriptional regulation, translational regulation, intracellular transport, and mitochondrial dysfunction.ConclusionsThis thorough analysis using RNA sequencing methods highlighted numerous differences between non-degenerated and degenerated CEP cells, the phenotypic heterogeneity of CEP cells, and several pathways of interest that may be relevant in CEP degeneration.

Systematic single-cell pathway analysis to characterize early Tcell activation.

A single-cell analysis of the Arabidopsis vegetative shoot apex.

The Molecular Anatomy of Mouse Skin during Hair Growth and Rest.

Multi-omic profiling of tumor-infiltrating T cells provides new insights into the differences in the effectiveness of CDK4 inhibitor between human papillomavirus (HPV)-positive and HPV-negative head and neck squamous cell carcinoma patients.

Multi-omic profiling of tumor-infiltrating T cells provides new insights into the differences in the effectiveness of CDK4 inhibitor between human papillomavirus (HPV)-positive and HPV-negative head and neck squamous cell carcinoma patients.

While tau and aging have highly overlapping differential gene expression signatures, they diverge in the affected cell types, with aging having a wide-ranging impact and tau-triggered changes instead polarized to excitatory neurons and glia.

Single-cell transcriptomics provides insights into hypertrophic cardiomyopathy.

Single Cell Transcriptome Analysis Reveals Disease-Defining T Cell Subsets in the Tumor Microenvironment of Classic Hodgkin Lymphoma

Single-cell RNA sequencing (scRNA-seq) allows for an unbiased assessment of cellular phenotypes by enabling the extraction of transcriptomic data. An important question in downstream analysis is how to evaluate biological similarities and differences between samples in high dimensional space. This becomes especially complex when there is cellular heterogeneity within the samples. Here, we present scCompare, a computational pipeline for comparison of scRNA-seq data sets. Phenotypic identities from a known data set are transferred onto another data set using correlation-based mapping to average transcriptomic signatures from each cluster of cells' annotated phenotype. Statistically derived lower cutoffs for phenotype inclusivity allow for cells to be unmapped if they are distinct from the known phenotypes, facilitating potential novel cell type detection. In a comparison of our tool using scRNA-seq data sets from human peripheral blood mononuclear cells (PBMCs), we show that scCompare outperforms single-cell variational inference (scVI) in higher precision and sensitivity for most of the cell types. scCompare was used on a cardiomyocyte data set where it confirmed the discovery of a distinct cluster of cells that differed between the 2 protocols for differentiation. Further use of scCompare on cell atlas data sets revealed insights into the cellular heterogeneity underpinning biological diversity between samples. In addition, we used a cell atlas to better understand the effect of key parameters used in the scCompare pipeline. We envision that scCompare will be of value to the research community when comparing large scRNA-seq data sets.

Endometriosis is a common chronic neuroinflammatory disease with a poorly understood pathogenesis. Molecular changes and specific immune cell infiltration in the eutopic endometrium are critical to disease progression. This study aims to explore immune mechanisms and molecular differences in the proliferative eutopic endometrium of endometriosis by integrating bulk RNA-seq and single-cell RNA sequencing (scRNA-seq) data, and to develop diagnostic and predictive models for the disease. Gene expression profiles from the proliferative endometrium of endometriosis patients and healthy controls were obtained from the Gene Expression Omnibus. Single-cell RNA-seq data were processed using R packages, and cell clusters' contributions to endometriosis were calculated. Differentially expressed genes (DEGs) from bulk RNA-seq were intersected with significant mesenchymal cell genes from scRNA-seq, and a predictive model was constructed using LASSO analysis. Key gene mechanisms were explored through Gene Set Enrichment and Variation Analyses. miRNA networks and transcriptional regulation analyses were conducted, and potential drugs were predicted using the Connectivity Map database. RT-qPCR validated key gene expression. Mesenchymal cells in the proliferative eutopic endometrium were identified as major contributors to endometriosis pathogenesis. LASSO regression identified eight key genes: SYNE2, TXN, NUPR1, CTSK, GSN, MGP, IER2, and CXCL12. The predictive model based on these genes achieved AUC values of 1.00 and 0.8125 in training and validation cohorts. Immune infiltration analysis showed increased CD8+ T cells and monocytes in the eutopic endometrium of endometriosis patients. Drug target prediction indicated that drugs like Retinol, Orantinib, Piperacillin, and NECA were negatively correlated with the expression profiles of endometriosis. RT-qPCR validated gene expression in patients aligned with bioinformatics analysis. Significant transcriptomic changes and altered immune cell infiltration in the proliferative eutopic endometrium potentially contribute to endometriosis pathogenesis. Our predictive model based on the key genes demonstrates high diagnostic accuracy, offering insights for diagnosis and potential treatment strategies.