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ChatMDV: reducing technical barriers in bioinformatics analysis using large language models.
BACKGROUND: The rapid advancement in single-cell, spatial omics, imaging, and genomic technologies requires robust analytical and visualisation platforms capable of managing complex biological data. Tools such as Multi-Dimensional Viewer (MDV) offer comprehensive interfaces for data exploration but often require advanced computational expertise and manual configuration to generate visualisation outputs, limiting accessibility for many users. RESULTS: We present ChatMDV, a natural language interface integrated with MDV that enables users to generate high-quality, interactive visualisations and analyses through natural language commands. ChatMDV employs a retrieval-augmented generation pipeline in combination with large language models to translate user queries into executable, reproducible Python code and interactive output. This conversational layer facilitates both exploratory and targeted analyses in diverse biological domains. We demonstrate ChatMDV's capabilities using 3 datasets of increasing complexity: the Peripheral Blood Mononuclear Cells 3K single-cell RNA-sequencing (scRNA-seq) dataset, the lung cancer atlas scRNA-seq dataset included in the Human Cell Atlas, and the longitudinal TAURUS study scRNA-seq dataset. Across all use cases, ChatMDV produced high-quality, reproducible visualisations from simple natural language queries, achieving a high semantic success rate between 79% and 97% when visualising the datasets. CONCLUSIONS: By bridging the gap between natural language processing and bioinformatics visualisation, ChatMDV reduces technical barriers, enhances reproducibility, and supports more inclusive scientific inquiry. Its modular design and adherence to Findability, Accessibility, Interoperability, and Reuse (FAIR) principles make it a scalable and adaptable framework for accelerating biological data analysis.
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A single-cell transcriptome atlas profiles early organogenesis in human embryos.
The early window of human embryogenesis is largely a black box for developmental biologists. Here we probed the cellular diversity of 4-6 week human embryos when essentially all organs are just laid out. On the basis of over 180,000 single-cell transcriptomes, we generated a comprehensive atlas of 313 clusters in 18 developmental systems, which were annotated with a collection of ontology and markers from 157 publications. Together with spatial transcriptome on embryonic sections, we characterized the molecule and spatial architecture of previously unappreciated cell types. Combined with data from other vertebrates, the rich information shed light on spatial patterning of axes, systemic temporal regulation of developmental progression and potential human-specific regulation. Our study provides a compendium of early progenitor cells of human organs, which can serve as the root of lineage analysis in organogenesis.
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Unveiling Novel Double-Negative Prostate Cancer Subtypes Through Single-Cell RNA Sequencing Analysis.
Recent advancements in single-cell RNA sequencing (scRNAseq) have facilitated the discovery of previously unrecognized subtypes within prostate cancer (PCa), offering new insights into disease heterogeneity and progression. In this study, we integrated scRNAseq data from multiple studies, comprising both publicly available cohorts and data generated by our research team, and established the HuPSA (Human Prostate Single cell Atlas) and the MoPSA (Mouse Prostate Single cell Atlas) datasets. Through comprehensive analysis, we identified two novel double-negative PCa populations: KRT7 cells characterized by elevated KRT7 expression, and progenitor-like cells marked by SOX2 and FOXA2 expression, distinct from NEPCa, and displaying stem/progenitor features. Furthermore, HuPSA-based deconvolution allowed for the re-classification of human PCa specimens, validating the presence of these novel subtypes. Leveraging these findings, we developed a user-friendly web application, "HuPSA-MoPSA" (https://pcatools.shinyapps.io/HuPSA-MoPSA/), for visualizing gene expression across all newly-established datasets. Our study provides comprehensive tools for PCa research and uncovers novel cancer subtypes that can inform clinical diagnosis and treatment strategies.
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Single-Nucleus RNA Sequencing Reveals Cellular Heterogeneity and Trajectories of Lineage Differentiation during Chicken Skeletal Muscle Development.
Understanding chicken skeletal muscle development is crucial for meat production, yet its cellular and molecular mechanisms are not fully understood. To address this, we built a single-nucleus RNA sequencing atlas of chicken skeletal muscle across key developmental stages (E4-P30) using 111,328 nuclei. We identified 22 cell populations, including nine myogenic ones, revealing extensive heterogeneity and transcriptional diversity. Myonuclei were classified into three fiber-type-associated subpopulations: FGF13+ type IIA fast-twitch, MYH1F+ type IIB fast-twitch, and MYH7B+ type I slow-twitch. Pseudotime analysis reconstructed the developmental trajectory from myogenic progenitors to mature myofibers and highlighted RBM24 as a pivotal regulator. Functional validation confirmed that RBM24 promotes myoblast differentiation while inhibiting proliferation. We further identified putative fiber-type-specific regulators (e.g., TBX15 for IIA, TNNT3 for IIB, and FGFRL1 for I). This study delineates myogenic lineage development and identifies key regulatory genes, offering valuable insights into chicken skeletal muscle biology.
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An atlas of gene regulatory elements in adult mouse cerebrum.
The mammalian cerebrum performs high-level sensory perception, motor control and cognitive functions through highly specialized cortical and subcortical structures1. Recent surveys of mouse and human brains with single-cell transcriptomics2-6 and high-throughput imaging technologies7,8 have uncovered hundreds of neural cell types distributed in different brain regions, but the transcriptional regulatory programs that are responsible for the unique identity and function of each cell type remain unknown. Here we probe the accessible chromatin in more than 800,000 individual nuclei from 45 regions that span the adult mouse isocortex, olfactory bulb, hippocampus and cerebral nuclei, and use the resulting data to map the state of 491,818 candidate cis-regulatory DNA elements in 160 distinct cell types. We find high specificity of spatial distribution for not only excitatory neurons, but also most classes of inhibitory neurons and a subset of glial cell types. We characterize the gene regulatory sequences associated with the regional specificity within these cell types. We further link a considerable fraction of the cis-regulatory elements to putative target genes expressed in diverse cerebral cell types and predict transcriptional regulators that are involved in a broad spectrum of molecular and cellular pathways in different neuronal and glial cell populations. Our results provide a foundation for comprehensive analysis of gene regulatory programs of the mammalian brain and assist in the interpretation of noncoding risk variants associated with various neurological diseases and traits in humans.
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Single-Cell Landscape of Mouse Islet Allograft and Syngeneic Graft.
Islet transplantation to treat the late stage of type 1 diabetic patient (T1DM) has recently made inspiring success in clinical trials. However, most patients experience a decline in islet graft function in one to three years due to immune rejection. Although the mechanisms of immune cells, including macrophages, dendritic cells (DCs), neutrophils, natural killer cells (NKs), B cells, and T cells, that mediate immune rejection have been investigated, the overall characteristics of immune infiltrates in islet allografts and syngeneic grafts remain unclear. Single-cell RNA sequencing (scRNA-seq) has provided us with new opportunities to study the complexity of the immune microenvironment in islet transplants. In the present study, we used scRNA-seq to comprehensively analyze the immune heterogeneity in the mouse model of islet transplantation. Our data revealed T lymphocytes and myeloid cells as the main immune components of grafts 7 days post-islet transplantation, especially in allografts. Moreover, our results indicated that allogeneic islet cells were transformed into antigen-presenting cell-like cells with highly expressed MHC class I molecules and genes involved in MHC class I-mediated antigen presentation. This transformation may dramatically facilitate the interaction with cytotoxic CD8+ T cells and promote the destruction of islet allografts. Our study provides insight into the transcriptomics and diverse microenvironment of islet grafts and their impacts on immune rejection.
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SMaSH: a scalable, general marker gene identification framework for single-cell RNA-sequencing.
BACKGROUND: Single-cell RNA-sequencing is revolutionising the study of cellular and tissue-wide heterogeneity in a large number of biological scenarios, from highly tissue-specific studies of disease to human-wide cell atlases. A central task in single-cell RNA-sequencing analysis design is the calculation of cell type-specific genes in order to study the differential impact of different replicates (e.g. tumour vs. non-tumour environment) on the regulation of those genes and their associated networks. The crucial task is the efficient and reliable calculation of such cell type-specific 'marker' genes. These optimise the ability of the experiment to isolate highly-specific cell phenotypes of interest to the analyser. However, while methods exist that can calculate marker genes from single-cell RNA-sequencing, no such method places emphasise on specific cell phenotypes for downstream study in e.g. differential gene expression or other experimental protocols (spatial transcriptomics protocols for example). Here we present SMaSH, a general computational framework for extracting key marker genes from single-cell RNA-sequencing data which reliably characterise highly-specific and niche populations of cells in numerous different biological data-sets. RESULTS: SMaSH extracts robust and biologically well-motivated marker genes, which characterise a given single-cell RNA-sequencing data-set better than existing computational approaches for general marker gene calculation. We demonstrate the utility of SMaSH through its substantial performance improvement over several existing methods in the field. Furthermore, we evaluate the SMaSH markers on spatial transcriptomics data, demonstrating they identify highly localised compartments of the mouse cortex. CONCLUSION: SMaSH is a new methodology for calculating robust markers genes from large single-cell RNA-sequencing data-sets, and has implications for e.g. effective gene identification for probe design in downstream analyses spatial transcriptomics experiments. SMaSH has been fully-integrated with the ScanPy framework and provides a valuable bioinformatics tool for cell type characterisation and validation in every-growing data-sets spanning over 50 different cell types across hundreds of thousands of cells.
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A single-cell resolved genotype-phenotype map using genome-wide genetic and environmental perturbations.
Heterogeneity is inherent to living organisms and it determines cell fate and phenotypic variability. Despite its ubiquity, the underlying molecular mechanisms and the genetic basis linking genotype to-phenotype heterogeneity remain a central challenge. Here we construct a yeast knockout library with a clone and genotype RNA barcoding structure suitable for genome-scale analyses to generate a high-resolution single-cell yeast transcriptome atlas of 3500 mutants under control and stress conditions. We find that transcriptional heterogeneity reflects the coordinated expression of specific gene programs, generating a continuous of cell states that can be responsive to external insults. Cell state plasticity can be genetically modulated with mutants that act as state attractors and disruption of state homeostasis results in decreased adaptive fitness. Leveraging on intra-genetic variability, we establish that regulators of transcriptional heterogeneity are functionally diverse and influenced by the environment. Our multimodal perturbation-based single-cell Genotype-to-Transcriptome Atlas in yeast provides insights into organism-level responses.
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Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas.
Although multiple pancreatic islet single-cell RNA-sequencing (scRNA-seq) datasets have been generated, a consensus on pancreatic cell states in development, homeostasis and diabetes as well as the value of preclinical animal models is missing. Here, we present an scRNA-seq cross-condition mouse islet atlas (MIA), a curated resource for interactive exploration and computational querying. We integrate over 300,000 cells from nine scRNA-seq datasets consisting of 56 samples, varying in age, sex and diabetes models, including an autoimmune type 1 diabetes model (NOD), a glucotoxicity/lipotoxicity type 2 diabetes model (db/db) and a chemical streptozotocin β-cell ablation model. The β-cell landscape of MIA reveals new cell states during disease progression and cross-publication differences between previously suggested marker genes. We show that β-cells in the streptozotocin model transcriptionally correlate with those in human type 2 diabetes and mouse db/db models, but are less similar to human type 1 diabetes and mouse NOD β-cells. We also report pathways that are shared between β-cells in immature, aged and diabetes models. MIA enables a comprehensive analysis of β-cell responses to different stressors, providing a roadmap for the understanding of β-cell plasticity, compensation and demise.
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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/.