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#scrnaseq — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #scrnaseq, aggregated by home.social.

  1. The main #ECCB2026 conference may be over, but today lots of great people are meeting to discuss single-cell standards and collaborations:
    Single-cell and spatial data FAIRification, community standards and training
    organised by @fbastian @SIB & @elixir_europe
    eccb2026.org/communities-day#single-cell-and-spatial-data-fairification
    #scRNAseq #SingleCell #FAIRdata #bioinformatics #OpenData #GenevaLovesData

  2. Pipeline release! nf-core/scnanoseq v1.3.0 - nf-core/scnanoseq v1.3.0 - Steel Elephant!
    Single-cell/nuclei pipeline for data derived from Oxford Nanopore and 10X Genomics
    Please see the changelog: github.com/nf-core/scnanoseq/r

    #10xgenomics #longreadsequencing #nanopore #rnaseq #rnaseq #scrnaseq #singlecell #nfcore #openscience #nextflow #bioinformatics

  3. Join us for a 2-part workshop on Mastering Reproducible Enrichment Analysis! 📊

    Presented by Anusuiya Bora and myself, with a focus on reproducibility and best practices.

    📅 When: 12 and 13 May 2026
    🕑 Time: 2:00 PM – 4:00 PM (AEST)
    📍 Where: Online
    💰 Cost: FREE for academic sector (places are limited!)

    🔗Registration form link: lnkd.in/gQcHggGF

    #Bioinformatics #RNAseq #scRNAseq #Genomics #ReproducibleResearch #OpenScience #RStats

  4. Aligning #scRNAseq datasets along a shared temporal axis across studies, species & systems is hard. This study uses meta-analytic models to develop a #transcriptomic measure of #neurodevelopmental timing that is applicable to different organisms & tissue types @PLOSBiology plos.io/4ch0XiX

  5. Our new pre‑print is out!

    scReady – an automated and accessible pipeline for single‑cell RNA‑Seq preprocessing: Empowering novice bioinformaticians

    wellcomeopenresearch.org/artic

    @haessar.bsky.social @fionan-a.bsky.social @yiyicheng

    #scRNAseq #bioinformatics

  6. #NeuralStemCells (NSCs) & ependymal cells (ECs) are derived from #RadialGlialCells. This study uses #scRNAseq to characterize cell fate trajectories in the developing #VentricularZone, identifying TFEB as a regulator of the NSC/EPC balance @PLOSBiology plos.io/3HbrsJG

  7. Yuyao Song presents ScGOclust to compare #singlecell #scRNAseq between distant species, such as fly and mammal: gene level comparisons don’t work because there has been too much divergence. 💡 Instead of genes, use GO terms has features to compare cells. #ismbeccb2025
    doi.org/10.1093/bioinformatics

  8. Single cell RNA-sequencing (#scRNAseq) is an essential method to learn about cells in health and disease. Here we have studied "multiplets", an important source of error of scRNAseq. We find that multiplets are astonishingly frequent and hard to eliminate.
    doi.org/10.1101/2025.06.09.658

  9. How do #brain cells change over #evolution? @bentonlab compare #scRNAseq from ecologically distinct #drosophilid species to identify changes in composition & gene expression of different cell types, revealing higher divergence in #glia than #neurons @PLOSBiology plos.io/4js7Rms

  10. How does transcriptional patterning regulate #SalivaryGland #morphogenesis? Annabel May & @katjaroeper use #scRNAseq of early morphogenesis of the #Drosophila salivary gland placode to reveal regulation by induction & exclusion of regulatory factors @PLOSBiology plos.io/4cUw827

  11. Our new preprint is now out!

    Dynamic transcriptional heterogeneity in pituitary corticotrophs

    biorxiv.org/content/10.1101/20

    We analysed publicly available single-cell RNA sequencing data of pituitary gland tissue and looked at corticotrophs, cells that are central to mediate stress responses.

    We identified several transcriptional states in these cells that are related to how they respond to stress. Cells are able to transition between these states and this might be helpful for them to respond to stress coming at unpredictable times.

    We also highlight issues related to using scRNAseq to look at functional subpopulations of cells.

    #scrnaseq #stress #physiology #cellbiology #bioinformatics #corticotrophs #pituitary

  12. mascarade package implements a procedure to automatically generate 2D masks for clusters on single-cell dimensional reduction plots like t-SNE or UMAP github.com/alserglab/mascarade #rstats #scRNAseq

  13. chatomics! How to Fine-Tune the Best Clustering Resolution for #scRNAseq Data 🎯 🧵

  14. Insbesondere bei der Interpretation von #RNA-Sequenzanalysen einzelner Zellen (#scRNAseq) sind Techniken zur Dimensionen-Reduktion wie #UMAP der neuste Schrei. Doch bilden UMAP-Plots tatsächlich die Realität ab? Es gibt Zweifel … Zur Methoden-Kontroverse: laborjournal.de/rubric/hinterg

  15. paraCell: A novel software tool for the interactive analysis and visualization of host-parasite single cell RNA-Seq data (without knowing programming) biorxiv.org/content/10.1101/20 . Interested in the datasets? Here: cellatlas.mvls.gla.ac.uk

    #scRNAseq #parasites

  16. "By using time-resolved analyses of scRNA-seq data, we determined the potential transitional trajectories of tumor cells and identified the metastasis-initiating subpopulations"

    link.springer.com/article/10.1

    Reading right now. The identification of cells that initiate #metastasis are of interest, although n=2 paired primary and #BoneMarrow samples may be a bit limited.

    #scRNAseq #tumour #Neuroblastoma #pseudotime

  17. " Instead, #Hepatoblastoma #tumors contain a large suppressive #myeloid compartment which poses opportunities for targeting. Specifically, therapeutic antibodies
    targeting the #tumor antigen #GPC3 to induce ADCP by #macrophages, in combination with myeloid
    #ImmuneCheckpointBlockade (#CD47/SIRPa, or VISTA) could be a feasible strategy to explore in future studies"

    Krijgsman et al. from Vercoulen lab in the #UMCUtrecht, on #bioRXiv:
    biorxiv.org/content/10.1101/20

    #scRNAseq #spatial #CyTOF #immunology