🧬 Could a single metric decode how genes are regulated across cells?
🔗 Regulation Ratio: A Singular Multi-Omic Measurement of Gene Regulatory Mechanisms. Computational and Structural Biotechnology Journal (CSBJ). DOI: https://doi.org/10.34133/csbj.0044
📚 CSBJ - A Science Partner Journal: https://spj.science.org/journal/csbj
#GeneRegulation #Genomics #MultiOmics #SystemsBiology #Bioinformatics #ComputationalBiology #MolecularBiology #RNA #GeneExpression #Epigenetics #Transcriptomics
🧬 What if disease isn’t written in DNA, but in how RNA is edited and spliced?
🔗 Long-Read Sequencing Reveals RNA Splicing Complexity in Human Diseases. Computational and Structural Biotechnology Journal (CSBJ). DOI: https://doi.org/10.34133/csbj.0052
📚 CSBJ - A Science Partner Journal: https://spj.science.org/journal/csbj
#RNASequencing #Genomics #Transcriptomics #PrecisionMedicine #MolecularBiology #Bioinformatics #NextGenSequencing #RNA #DNA
"Cellular morphology emerges from polygenic, distributed transcriptional variation", Paylakhi et al. 2026
https://www.biorxiv.org/content/10.64898/2026.03.12.711281v1
Height and most disease risk are known polygenic traits: characteristics governed by multiple genes at different loci instead of a select few. Though we are beginning to understand how genetic variation impacts cell morphology, whether such an analogous polygenic architecture operates at the cellular level, where morphology integrates cytoskeletal organization, organelle positioning, and metabolic state, has yet to be systematically tested. Here, we demonstrate that cellular morphology behaves as a polygenic trait by integrating multimodal modeling, perturbation profiling, and population scale genetic variation. A shared latent-space autoencoder trained on four large perturbation datasets predicts morphology from gene expression and generalizes without retraining to matched RNA-seq and Cell Painting profiles from 100 genetically diverse iPSC donors. The model predicted 17 morphological features (R > 0.6, permutation FDR q < 0.05), enriched for spatial organelle distribution and cytoskeletal architecture. Predictive performance does not arise from dominant gene-phenotype relationships: individual genes contribute modestly, and marginal gene-morphology correlations are uniformly weak, revealing a distributed regulatory architecture. Despite this polygenicity, CRISPR perturbation data from the JUMP consortium validates specific model-prioritized genes, such as the cytoskeletal regulator TIAM1, membrane trafficking factor RAB31, and mitochondrial-associated membrane transporter ABCC5, as molecular anchors whose disruption produces feature-specific morphological shifts. Transcriptome-wide association analyses identify correlational variant-gene-morphology chains linking cis-regulatory variation through mitochondrial metabolism (PDHX) and iron transport (SLC11A2) to cellular architecture. These results establish cellular morphology as a polygenic systems phenotype, extending the omnigenic framework to the cellular level and providing a biological basis for interpreting cross-modal prediction in functional genomics. ### Competing Interest Statement The authors have declared no competing interest. AnalytiXIN Fellowship in Life Sciences
Is spatial transcriptomics data preprocessing giving you a headache? You're not alone! 🤯
Join our webinar designed for new researchers to simplify the complex world of image-based spatial omics. We'll walk through the practical workflow step-by-step, from initial cell segmentation to cleaning up your data.
Get the foundational skills you need. 📅 Sign up here: https://t1p.de/lqbg1
#Transcriptomics #SpatialTranscriptomics #Omics #DataScience #ResearchWebinar #GHGA #ELIXIR #deNBI
Research Associate
University of Ottawa
See the full job description on jobRxiv: https://jobrxiv.org/job/university-of-ottawa-27778-research-associate-2/
#cellbiology #molecularbiology #transcriptomics #ScienceJobs #hiring #research
https://jobrxiv.org/job/university-of-ottawa-27778-research-associate-2/?fsp_sid=4260
Our review article got published in 'Biotechnology Advances':
"Decoding bioprocesses with transcriptomics: current status and future potential"
https://doi.org/10.1016/j.biotechadv.2025.108736
The Galaxy single-cell and spatial omics community (SPOC) is thrilled to share the latest updates on tools, datasets, and collaborative initiatives driving open and reproducible single-cell and spatial omics research in 2025.
https://galaxyproject.org/news/2025-10-14-spoc-cellgenomics/
@galaxyfreiburg
#UseGalaxy #GalaxyProject #EOSC #SingleCell #SpatialOmics #Omics #Transcriptomics #Bioinformatics #Genomics #ComputationalBiology #EuroScienceGateway #OpenScience #ReproducibleResearch #GalaxyCommunity #SPOC (1/2)
The Galaxy single-cell and spatial omics community (SPOC) is thrilled to share the latest updates on tools, datasets, and collaborative initiatives driving open and reproducible single-cell and spatial omics research in 2025.
https://galaxyproject.org/news/2025-10-14-spoc-cellgenomics/
@galaxyproject
#UseGalaxy #GalaxyProject #EOSC #UniFreiburg #SingleCell #SpatialOmics #Omics #Transcriptomics #Bioinformatics #Genomics #ComputationalBiology #EuroScienceGateway #OpenScience #ReproducibleResearch (1/2)
CSBJ
Albert Cardona
galaxyproject
de.NBI & ELIXIR Germany
GHGA
jobRxiv
Boas Pucker
Galaxy-Team der Uni Freiburg