A curated index of articles, updates, events, and published work across single-cell research.
Discover how multi-omics integration is reshaping drug discovery by uncovering disease mechanisms, prioritizing drug targets, and connecting genomics, epigenomics, transcriptomics, proteomics, and metabolomics into a usable biological model.
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An updated guide to the most useful public single-cell RNA-seq databases in 2026, including archives, atlas portals, and domain-specific resources for data discovery and reuse.
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Deep learning and agentic AI analysis of single-cell RNAseq data.
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Veronique Brault uses single-cell RNA-seq to study how DYRK1A gene dosage affects striatal development in Down syndrome and intellectual disability mouse models.
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Ram Krishna Thakur, Göran Karlsson explore how single-cell omics reveals cellular heterogeneity driving clinical outcomes in CML.
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CyteType uses a five-agent AI framework for accurate cell type annotation in scRNA-seq data. Outperforms reference-based methods by 300%+ in benchmarking.
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Explore why LLMs alone fail at cell annotation and how CyteType fixed it.
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Nygen Analytics wins 1M SEK Vinnova grant to develop AI-powered predictive models for cancer immunotherapy with VLP Therapeutics, advancing personalized treatment selection.
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Inside the Institute of Bioorganic Chemistry Polish Academy of Sciences, a state-of-the-art laboratory charts an ambitious course for single-cell analysis in Poland.
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Hands-on single-cell RNA-Seq and multi-omics analysis course at IBCH Poznan.
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Accelerated single-cell data analysis course with LMU Klinikum.
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Hands-on single-cell data analysis course at University of St. Andrews.
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Hands-on single-cell data analysis course at King's College London.
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CyteType is multi-agentic annotation system designed for single-cell RNA-seq cluster annotation. Designed to deploy three specialized AI agents to provide accurate cell type identification, literature validation, and pathway-level reasoning beyond traditional marker-based methods and beyond. Built for researchers seeking precise, evidence-backed single-cell data analysis with comprehensive biological context.
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An year-old facility is quietly revolutionizing how core services support cutting-edge research. Through a partnership built on scientific curiosity and shared problem-solving,
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Accelerated single-cell data analysis course with Max-Planck-Institut.
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Discover how Porto's i3S bioinformatics core facility is democratizing single-cell analysis for researchers across cancer, neurobiology, and infectious disease studies.
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Hands-on single-cell RNA-Seq and multi-omics analysis course at i3S Porto.
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Meet Aleksandra Wcislo, the sole operator of BBSRC's Single Cell Sequencing Platform at St Andrews. Discover how one scientist manages everything from bat genomes to placental cells, proving that excellence in genomics doesn't require a large team.
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Accelerated single-cell data analysis course with IGBMC.
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Discover how integrating spatial transcriptomics with scRNA-seq data enhances biological insights by mapping gene expression to tissue architecture. Learn key integration methods and real-world applications.
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Discover how spatial transcriptomics and single-cell RNA-seq complement each other to drive next-generation biological insights. Learn about emerging platforms, multi-omics integration, and how Nygen Analytics empowers researchers to leverage both technologies.
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Accelerated single-cell data analysis course with UCM Madrid.
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In sunny Alicante, two specialists have built an impressive Omics Core Facility that rivals larger institutions. We sat down with Antonio and José to understand their science, approach to experiment design and collaborative methods drives breakthrough research with a lean, efficient operation.
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Nygen Analytics, the only single-cell–focused omics data-analytics platform to pair ISO 27001 certification and SOC 2 compliance with full GDPR alignment
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Discover when single-cell RNA sequencing is sufficient for your research needs. This guide explains ideal use cases, analysis techniques, and practical implementations for leveraging scRNA-seq data effectively.
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Learn how to identify marker genes in single-cell RNA-seq data without coding using Nygen's intuitive platform. This step-by-step guide covers data upload, quality control, clustering, marker detection, and dynamic analysis with detailed screenshots and expert tips.
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Hands-on single-cell data analysis course at UMH Alicante.
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Uncover the essentials of marker gene identification in single-cell RNA sequencing. This article covers no-code solutions, methodologies, and case studies across various fields.
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Accelerated single-cell data analysis course with Medical University of Graz.
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Learn the essentials of designing robust single-cell RNA-seq experiments with our practical guide for wet-lab scientists. Covers sample preparation, controls, sequencing parameters, and analysis approaches—including how no-code platforms eliminate computational barriers.
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Explore how ASI transforms drug discovery by harnessing domain-specific models and advanced computing tools for faster, smarter biomedical innovation.
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A practical guide to scRNA-seq cluster annotation in 2026, covering marker gene inspection, reference atlas mapping, supervised classification tools, and strategies for resolving ambiguous or disease-associated populations.
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Uncover the top single-cell RNA sequencing (scRNA-seq) and multi-omics data analytics tools of 2025. This comprehensive guide reviews 8 platforms to facilitate data analysis and reveal cellular insights.
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Sara Palo et al. reveal how chromatin accessibility patterns shape lineage plasticity during blood cell development.
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Momoko Ishikawa et al. show IL-12-expressing alphavirus particles reprogram tumor immune cells to induce anti-tumor responses.
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Pavan Prabhala et al. map the transcriptional landscape of human airway epithelium, identifying HLF as a key regulator.
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Gautam Ahuja et al. demonstrate how multi-agent AI systems enable evidence-based cell annotation in scRNA-seq data.
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Discover how building cell atlases parallels Google Maps - transforming scattered cellular data into integrated, navigable maps for drug discovery.
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A practical guide to batch effect correction and normalization in scRNA-seq data, covering when integration is justified, leading tools including Harmony, Seurat, BBKNN, and scVI, evaluation strategies, and how to avoid overcorrection.
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Explore key challenges and advanced strategies in scRNA-seq data analysis for both new and experienced researchers.
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Discover single-cell transcriptomics, a transformative technique for analyzing gene expression at the cellular level in biology and medicine.
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Discover solutions to bridge bioinformatics skill gaps in single-cell research, enabling easier scRNA-seq analysis for wet-lab scientists.
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Daniel Zucha et al. map glial cell responses over time in a mouse brain ischemia model using spatial transcriptomics.
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Sijie Li et al. introduce EpiCarousel for efficient metacell identification in large-scale chromatin accessibility datasets.
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Radek Sindelka et al. characterize cells that initiate regeneration during Xenopus tail regrowth.
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Liang Wang et al. present CTEC, an ensemble clustering method for robust scRNA-seq data analysis.
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Rebecca Warfvinge et al. link single-cell multi-omics heterogeneity in CML to variations in therapy response.
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Ivan Imaz-Rosshandler et al. predict and validate cell differentiation trajectories during early mammalian organogenesis.
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Mikael N. E. Sommarin et al. define developmental-specific populations in human fetal blood formation using multi-omics.
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Sagnik Yarlagadda and Todd D. Giorgio provide a practical guide to scRNA-seq analysis using web-based platforms.
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Parashar Dhapola et al. introduce Scarf for memory-efficient single-cell sequencing analysis, published in Nature Communications.
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