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Perturb-Seqr: Signature Search Engine Integrating Connectivity Mapping Resources.
High-throughput transcriptomics can be used to develop Connectivity Mapping resources for identifying potential reversers and mimickers of disease signatures for drug and target discovery. Recently, several new Connectivity Mapping datasets have emerged, but no efforts were made so far to evaluate and integrate them. Motivated by this opportunity, we have developed Perturb-Seqr, a comprehensive Connectivity Mapping search engine that integrates nine small molecule transcriptomics Connectivity Maps and seven single gene perturbation followed by expression Connectivity Maps. Specifically, Perturb-Seqr contains processed data from LINCS (L1000), Tahoe-100M, Novartis Institute for Biomedical Research (DRUG-seq), the original CMap (cDNA microarrays), Ginkgo Bioworks (DRUG-seq), SciPlex (scRNA-seq), DeepCover MoA (PISA proteomics), Perturb Atlas (RNA-seq), Replogle et al. (Perturb-seq), CREEDS (cDNA microarrays), CM4AI (Perturb-seq), and RummaGEO (RNA-seq). To serve these Connectivity Maps in one unifying search engine, we converted the expression signatures from these resources into 450,271 gene sets of up- and down-regulated genes measuring the effects of 9,856 unique drug and 17,643 unique gene perturbations, targeting 917 cell lines and cell types. We demonstrate how Perturb-Seqr, and the gene sets it serves, can be used to identify targets for drugs and drugs for targets, uncover new pathways, and identify potential side effects for preclinical compounds. Overall, Perturb-Seqr is a comprehensive up-to-date Connectivity Mapping search engine and database, enabling the discovery of new drugs and targets. Perturb-Seqr is accessible from: https://perturbseqr.maayanlab.cloud/ .
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Single Cell Transcriptomic Atlas Reveals Cellular Heterogeneity and Molecular Mechanisms in Pediatric Airway Hyperresponsiveness Epithelium.
BACKGROUND: Airway hyperresponsiveness (AHR) is a clinically important feature of chronic pediatric inflammatory airway disease, but its cellular architecture remains incompletely resolved. Because bulk transcriptomic approaches can mask signals from rare or state-specific cell populations, single-cell RNA sequencing (scRNA-seq) offers a useful framework for examining epithelial and immune cell diversity in hyperresponsive airways. METHODS: Publicly available scRNA-seq data (GEO: GSM8722221) from bronchial brushing specimens of pediatric AHR patients (10x Genomics Chromium platform) were processed with Cell Ranger, quality-filtered, and analyzed using PCA (15 PCs), UMAP/tSNE embedding, and Leiden clustering (resolution = 0.8). Cell types were annotated by canonical markers and validated via Seurat label transfer. Pseudotime trajectories were inferred with Monocle (DDRTree) and validated by scVelo, Slingshot, and PAGA. Macrophage subpopulations were resolved at resolution = 1.2 and profiled by hdWGCNA. GO and KEGG enrichment used clusterProfiler (Benjamini-Hochberg; adjusted p <0.05). Network pharmacology integrated TCM BTMAN 2.0, Swiss Target Prediction, SEA, and PPI topology to identify targets and herbal compounds from Xiao Qing Long Tang. RESULTS: Quality control supported the analytical suitability of the dataset (mitochondrial fraction vs. nCount_RNA r = -0.31; inter-dataset r = 0.96). Actb, Gapdh, Tuba1b, Hmga2, Vim, Col1a1, Zeb1, Snai2, Twist1, and Mmp9 were among the top variable genes, and the first 15 PCs were retained after JackStraw testing. UMAP and tSNE resolved eight airway mucosal cell populations with concordant separation. Cell-type-specific expression of TSLP, IL33, CCL26, S100A8, POSTN, CLCA1, FCGR3B, TNFRSF8, FOXJ1, and SCGB1A1 was consistent with known inflammatory, remodeling, and mucociliary programs. High-resolution macrophage analysis identified 23 putative subpopulations, which were interpreted cautiously as computationally defined states. Enrichment analysis highlighted cytoplasmic translation, ribosome biogenesis, oxidative phosphorylation, and related mitochondrial signatures. Network pharmacology identified VEGFA, PTGS2, and ESR1 among prioritized targets, yielding 83 shared candidates between disease-associated and compound-associated target sets. CONCLUSIONS: This study provides a pediatric AHR single-cell atlas and an integrated, hypothesis-generating view of epithelial-immune heterogeneity, macrophage state diversity, and candidate therapeutic networks. The findings should be interpreted as computational evidence that requires validation in healthy pediatric controls, independent cohorts, and functional experiments.
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Single cell RNA sequencing and its promise in reconstructing plant vascular cell lineages.
In the last decade, recent advances in single-cell RNA sequencing coupled with computational algorithms have opened new avenues to study the cell type composition of tissues and organs as well as to infer cell developmental trajectories. These technologies have been used to resolve and map atlases of tissues and organs in many animal species as well as to further order cell developmental trajectories. Despite these advances in animals, many of the current plant cell type expression profiles confound multiple developmental stages preventing an accurate monitoring of cell lineage. In this review, we propose how the application of single-cell sequencing will improve our molecular understanding of cell type differentiation. Using root vascular cells as a model, we highlight the potential of single cell transcriptomics as well as its limitations to monitor the progression of vascular maturation. By comparing cell morphology, functionality and gene expression, we aim to provide a new perspective of plant cell type differentiation.
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Deciphering the impact of aging on splenic endothelial cell heterogeneity and immunosenescence through single-cell RNA sequencing analysis.
BACKGROUND: Aging is associated with significant structural and functional changes in the spleen, leading to immunosenescence, yet the detailed effects on splenic vascular endothelial cells (ECs) and their immunomodulatory roles are not fully understood. In this study, a single-cell RNA (scRNA) atlas of EC transcriptomes from young and aged mouse spleens was constructed to reveal age-related molecular changes, including increased inflammation and reduced vascular development and also the potential interaction between splenic endothelial cells and immune cells. RESULTS: Ten clusters of splenic endothelial cells were identified. DEGs analysis across different EC clusters revealed the molecular changes with aging, showing the increase in the overall inflammatory microenvironment and the loss in vascular development function of aged ECs. Notably, four EC clusters with immunological functions were identified, suggesting an Endothelial-to-Immune-like Cell Transition (EndICLT) potentially driven by aging. Pseudotime analysis of the Immunology4 cluster further indicated a possible aging-induced transitional state, potentially initiated by Ctss gene activation. Finally, the effects of aging on cell signaling communication between different EC clusters and immune cells were analyzed. CONCLUSIONS: This comprehensive atlas elucidates the complex interplay between ECs and immune cells in the aging spleen, offering new insights into endothelial heterogeneity, reprogramming, and the mechanisms of immunosenescence.
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scPlant: A versatile framework for single-cell transcriptomic data analysis in plants.
Single-cell transcriptomics has been fully embraced in plant biological research and is revolutionizing our understanding of plant growth, development, and responses to external stimuli. However, single-cell transcriptomic data analysis in plants is not trivial, given that there is currently no end-to-end solution and that integration of various bioinformatics tools involves a large number of required dependencies. Here, we present scPlant, a versatile framework for exploring plant single-cell atlases with minimum input data provided by users. The scPlant pipeline is implemented with numerous functions for diverse analytical tasks, ranging from basic data processing to advanced demands such as cell-type annotation and deconvolution, trajectory inference, cross-species data integration, and cell-type-specific gene regulatory network construction. In addition, a variety of visualization tools are bundled in a built-in Shiny application, enabling exploration of single-cell transcriptomic data on the fly.
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A single-cell atlas of murine reproductive tissues during preterm labor.
Preterm birth, the leading cause of perinatal morbidity and mortality worldwide, frequently results from the syndrome of preterm labor. The best-established causal link to preterm labor is intra-amniotic infection, which involves premature activation of the parturition cascade in the reproductive tissues. Herein, we utilize single-cell RNA sequencing (scRNA-seq) to generate a single-cell atlas of the murine uterus, decidua, and cervix in a model of infection-induced preterm labor. We show that preterm labor affects the transcriptomic profiles of specific immune and non-immune cell subsets. Shared and tissue-specific gene expression signatures are identified among affected cells. Determination of intercellular communications implicates specific cell types in preterm labor-associated signaling pathways across tissues. In silico comparison of murine and human uterine cell-cell interactions reveals conserved signaling pathways implicated in labor. Thus, our scRNA-seq data provide insights into the preterm labor-driven cellular landscape and communications in reproductive tissues.
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Single-Cell and Single-Nuclei transcriptomics profiling reveals dynamic cellular features in tumor-related adipose microenvironment of breast cancer patients with high BMI.
OBJECTIVES: High body mass index (BMI), encompassing overweight and obesity, is a well-established risk factor for developing breast cancer (BC). The underlying mechanisms linking elevated BMI to increased BC risk involve metabolic reprogramming and chronic inflammatory microenvironments regulated by cellular networks within breast white adipose tissue (WAT). However, the complicated landscape and specific cell chat leading to BC-related adipose microenvironment remained unclear. MATERIALS AND METHODS: We unveiled a comprehensive cell atlas by employing single-cell (N = 27) and single-nuclei (N = 6) transcriptomics to address dynamic changes of immune and stromal cell components within WAT in high BMI population. Bulk RNA-seq data sets were used for validation. RESULTS: Characteristics of adipose-infiltrating tissue-resident macrophages (PVMs), APOD+γδ T cells, and mature FKBP5+ adipocytes in breast cancer women with high BMI were revealed, in terms of transcriptional genes, metabolism features, developmental trajectories and gene set enrichment analysis (GSEA). PVMs upregulated c-Maf combined with its co-activator CREB1 to increase TCA cycles. APOD+γδ T cells were found to elevate intracellular lipid metabolism, leading to poor clinical prognosis. Mature FKBP5+adipocytes served as an advanced adipogenesis mediator to promote tumor aggressiveness. In-depth analysis of cell-cell interactions uncovered a remodeling trend towards metabolic dysfunction and chronic inflammation in WAT with weight gain via EGF, CXCL, and CCL signalings. CONCLUSION: These results provided a novel understanding of detailed and unbiased cellular landscape of WAT in breast cancer with high BMI from single-cell atlas perspective, uncovering interplays between breast adipose-infiltrating immune cells and stromal cells that promote progression of BC under high BMI conditions.
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[Ethical and clinical aspects of genetic analysis in neurodegenerative diseases].
OBJECTIVE: Too conduct a comprehensive analytical review of the limitations of traditional genetic methodologies compared with contemporary high-resolution technologies, such as single-cell and single-nucleus RNA sequencing (sc/snRNA-seq). It will explore in detail the mechanisms underlying the incomplete penetrance of pivotal risk genes, namely LRRK2 and GBA1, and systematically organize data on pathological alterations in specific target cell types, including microglia, astrocytes, oligodendrocytes, and selectively vulnerable neurons. MATERIAL AND METHODS: The analysis presented in this paper is grounded in a review of contemporary transcriptomic, genomic, and clinical research. Emphasis is placed on integrating data from genome-wide association studies (GWAS) with single-cell expression atlases, as well as findings from functional studies conducted in model organisms and in cell cultures derived from mutation carriers. RESULTS: The findings indicate that conventional sequencing techniques are inadequate for detecting cell-specific dysfunctions, particularly within rare cell populations. Technologies such as scRNA-seq have demonstrated that the LRRK2 and GBA1 genes exhibit significant expression levels in glial cells, where they are involved in critical processes including immune response modulation, lysosomal function, and cytoskeletal organization. Mutations in LRRK2 reprogram pro-inflammatory microglia and alter astrocyte morphology through hyperphosphorylation of ERM proteins. In contrast, GBA1 deficiency initiates a cascade of lysosomal stress that impacts not only neurons but also astrocytes and oligodendrocytes. The phenomenon of incomplete penetrance is examined, revealing penetrance rates ranging from 10% to 85% that vary by ethnic background and polygenic factors, thereby posing significant challenges for medical and genetic counseling. CONCLUSIONS: Neurodegeneration is a systemic process characterized by disruption of glial cell homeostasis, which acts as a pathogenic driver. The advancement of single-cell analytical techniques paves the way for the identification of new therapeutic targets to correct the dysfunction of specific cell populations. The phenomenon of incomplete penetrance necessitates a reevaluation of the strategies used to predict and apply polygenic risk assessments in clinical practice.
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Homologous recombination deficiency serves as a prognostic biomarker in clear cell renal cell carcinoma.
Kidney renal clear cell carcinoma (KIRC) is a frequent malignant tumor characterized by a high degree of heterogeneity and genetic instability. DNA double-strand breaks generated by homologous recombination deficit (HRD) are a well-known contributor to genomic instability, which can encourage tumor development. It is not known, however, whether the molecular characteristics linked with HRD have a predictive role in KIRC. The discovery cohort comprised 501 KIRC patients from The Cancer Genome Atlas database. Genome and transcriptome data of HRD patients were used for comprehensive analysis. Single cell RNA sequencing (scRNA-seq) was used to verify the test results of bulk RNA-seq. In the present study, patients with a high HRD score had a worse prognosis compared with those with a low HRD score. The DNA damage response signaling pathways and immune-related signaling pathways were notably enriched in the HRD-positive subgroup. Further comprehensive analysis of the tumor microenvironment (TME) revealed that the signal of exhausted CD8+ T cells was enriched in the HRD-positive subgroup. Finally, scRNA-seq analyses confirmed that the immune-related signaling pathways were upregulated in HRD-positive patients. In conclusion, the present study not only demonstrated that a high HRD score is a valid prognostic biomarker in KIRC patients, but also revealed the TME in HRD-positive tumors.
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BCMA: An integrative and versatile database for multi-scale and multi-omics molecular atlas of breast cancer.
Breast cancer (BC) is one of the most common cancer types among women worldwide. Understanding the complex molecular and cellular characteristics of BC is crucial for advancing precision treatment. To enable more reliable and reproducible biological discoveries, it is critical to collect molecular data from diverse BC cohorts and establish an integrative, versatile analysis platform. Here, we present BCMA (Breast Cancer Molecular Atlas, http://lifeome.net/database/bcma/), a multi-scale, multi-omics BC database that encompasses 6 bulk multi-omics datasets and 9 single-cell transcriptomics datasets, collectively covering 5424 cases and 236,363 cells. The BCMA systemically characterizes the molecular features of BC, including gene mutations, copy number alterations, RNA expression, miRNA expression, DNA methylation, as well as clinical phenotypes and cell heterogeneity. Meanwhile, a user-friendly interface for gene-centered search is provided, achieving the clinical information statistics, genomic events analysis, differential multi-omics feature identification, functional enrichment analysis, survival analysis, co-expression analysis, as well as single-cell gene expression profiling and cell type annotation. This platform holds great potential to enhance the understanding of molecular characteristics underlying BC and to facilitate the identification of disease-associated biomarkers.