Gilberto Duran-Bishop

BSc in Genomic Sciences, @lcgunam.
PhD in biochemistry, UdeM, IRCM

[Martin Pera @martinperaJAX]

Poising and connectivity of emergent human developmental enhancers in the transition from naive to primed pluripotency: Cell Reports https://t.co/jYOvIV2IGL

[nature @Nature]

Bad experiences with supervisors are common among early-career researchers and one of the leading reasons they leave academia

https://t.co/n8g2sVTCq8

[Martin Pera @martinperaJAX]

Garge, R.K., Lynch, V., Fields, R. et al. The proteomic landscape and temporal dynamics of human and mouse gastruloid development. Nat Cell Biol 28, 1015–1030 (2026). https://t.co/W9jjSNrMwQ

[Prof. Nikolai Slavov @slavov_n]

Single-cell proteomics illuminated new mechanisms of mammalian development.

We found that spatially polarized protein distributions and intracellular protein gradients emerge during the earliest stages of mammalian embryogenesis and help bias subsequent cell fate decisions. Critically, these developmental mechanisms are not reflected in mRNA abundance: the key biology resides in the spatial organization, abundance, and asymmetric localization of proteins within and between cells.

The results show that early developmental patterning is associated with polarized localization of specific proteins and coordinated proteomic asymmetries across blastomeres, linking protein organization directly to lineage specification. These findings support a model in which cell fate in the mammalian embryo is not determined solely by stochastic transcriptional programs, but is strongly shaped by inherited and dynamically regulated protein states that establish developmental competence before overt differentiation: https://t.co/lvpZ2TOaIC

Our results depended critically on single-cell proteomics analysis, on direct measurement of the molecular effectors that execute developmental decisions — capturing gradients, localization, stoichiometry, and post-transcriptional regulation. The future of developmental biology will depend increasingly on quantitative single-cell protein measurements capable of resolving the molecular architecture of cell fate determination: https://t.co/92S1z9WEBp

[Fabian Theis @fabian_theis]

Excited to share our RegVelo paper in Cell
https://t.co/ZAnQphaXsg

We unify RNA velocity + GRNs into one model → better OOD prediction of perturbations (e.g. gene KOs), with examples incl. neural crest KO predictions 🔬

Big thanks to W Wang, Z Hu & T Sauka-Spengler 🙏

[Molecular Cell @MolecularCell]

Online Now: Alu repeat-containing RNAs spatially organize actively transcribed genomic regions around nuclear speckles https://t.co/8hqfNqTKY5

[@BioAI_Neuro @BioAI_NeuralNet]

Establishing whole-body cellomics | Nature Methods https://t.co/Yoyvwg13wM

[Martin Pera @martinperaJAX]

Sun, S., Zheng, Y., Kim, Y.S. et al. A transgene-free, human peri-gastrulation embryo model presents trilaminar embryonic disc-, amnion- and yolk sac-like structures. Nat Cell Biol (2026). https://t.co/YTKYLo16OP

[Nature Methods @naturemethods]

Predictive virtual embryo models integrate single-cell and spatial data with AI, offering a tool for modeling mammalian embryogenesis across scales, as discussed in this Comment.

https://t.co/QYUSHqhWWc

[William J. Greenleaf @WJGreenleaf]

Our Human Multiomic Development Atlas paper is out in Nature today! A heart-felt "thank you" to all co-authors for their tireless work on this complex yet exciting project! Congrats all! https://t.co/iUiZz00KOt

[Nature Methods @naturemethods]

stVCR models and reconstructs single-cell dynamics of cell differentiation, proliferation and migration from time-series spatial transcriptome data.

https://t.co/S3bDTEnJcA

[Micah G. Allen @micahgallen]

A recent study in Nature Communications reveals over 250 metabolic enzymes located directly on chromatin, a discovery that vastly expands the previously known count of only about 20.
https://t.co/8ucPDDVX01

[Alfonso Martinez Arias @AMartinezArias]

Sheep and pig #gastruloids @IftachN: https://t.co/yJ4KkMpPhE
The accumulating knowledge of the relationship between #SCBEMs and #Embryos, allow us to learn about species similarities and differences in gastrulation and body plan elaboration.

[Martin Pera @martinperaJAX]

Tam, T.T.K.K., Xu, S., Li, Y. et al. Amniogenesis in embryos and stem cell models. Nat Cell Biol (2026). https://t.co/1MMlDDFZhw

[Jorge Bravo Abad @bravo_abad]

AlphaGenome: Decoding the dark matter of the genome with a unified deep learning model

More than 98% of human genetic variation lies outside protein-coding regions. These "non-coding" variants can disrupt gene regulation in remarkably diverse ways: altering chromatin accessibility, shifting 3D genome architecture, modifying splicing, or changing expression levels—often in tissue-specific patterns. Yet existing computational models face a fundamental trade-off: either they capture long-range regulatory interactions (like distant enhancers) but blur fine-scale features, or they achieve nucleotide resolution but miss distal context. And most specialize in a single modality, leaving users to stitch together predictions from many separate tools.

Žiga Avsec and coauthors at Google DeepMind present AlphaGenome, a model that sidesteps these trade-offs. It takes 1 megabase of DNA as input and predicts ~6,000 genome tracks—spanning gene expression, splicing (sites, usage, and junctions), chromatin accessibility, histone modifications, transcription factor binding, and 3D contact maps—at up to single-base-pair resolution.

The architecture combines a U-Net backbone with transformer blocks: convolutions capture local motifs essential for splice sites and TF footprints, while transformers model long-range dependencies like enhancer–promoter interactions. Training uses a two-stage approach—pretraining on experimental data followed by distillation from an ensemble of teachers using mutationally perturbed sequences—yielding a single model that scores variants across all modalities in one pass.

The results are striking: AlphaGenome achieves state-of-the-art performance on 25 of 26 variant effect prediction benchmarks, including a 25% improvement in predicting eQTL direction over the previous best model. It outperforms specialized models on their own tasks—beating SpliceAI-class methods on 6 of 7 splicing benchmarks and ChromBPNet on accessibility QTLs. Critically, the multimodal outputs enable mechanistic interpretation: for oncogenic mutations near the TAL1 gene in T-cell leukemia, AlphaGenome simultaneously predicts neo-enhancer formation (increased H3K27ac), chromatin opening, and elevated gene expression—recapitulating experimentally validated mechanisms.

This points toward a future where interpreting non-coding variation no longer requires assembling a patchwork of specialized models. A unified framework that jointly predicts molecular consequences across modalities could accelerate rare disease diagnostics, guide therapeutic oligonucleotide design, and help prioritize variants in GWAS loci—moving us closer to truly reading the regulatory code written in DNA.

Paper: https://t.co/3WzrnGNUSw

[Cell @CellCellPress]

In the latest issue! Fertilization triggers early proteomic symmetry breaking in mammalian embryos https://t.co/ulR39RWvXl

[Magdalena Zernicka-Goetz @ZernickaGoetz]

We are delighted to share our latest paper in Cell (Cell Press) reporting the earliest known symmetry breaking in mammalian development — at the zygote stage. We show fertilization triggers reproducible proteomic asymmetries that already bias the embryo’s very first decisions.

[Martin Pera @martinperaJAX]

A 3D in vitro model for studying human implantation and implantation failure: Cell https://t.co/PG6u4m7gFR

[eLife - the journal @eLife]

Cells that polarise early during the 8-cell stage of development in mouse embryos are more likely to become part of the placenta.
https://t.co/TOGUu27LoX

[Gilberto Duran-Bishop @duran_bishop]

I'm excited to share the new pre-print from my lab and my first paper of my PhD! If you want to know about molecular mechanisms in toripotent-like cells this is the perfect read for you!

[biorxiv_devbio @biorxiv_devbio]

Induction of cellular totipotency by NACC1-driven feed-forward regulation. https://t.co/SKn41dOvUJ #biorxiv_devbio