Keisuke Ishihara

@ishiharaksk.bsky.social

Assistant Professor, University of Pittsburgh. cell biology, biological physics, synthetic morphogenesis of brain and cardiac organoids www.ishiharalab.bio

So excited to build this with @mweber.bsky.social and @hattaca.bsky.social ! Together we’re combining geometric AI, multi-species genomics, and human cardiac organoids to ask why some animals regenerate their hearts and we can’t. Grateful to #Googleorg for the support. #AIforScience

Computational & Systems Biology Dept. at Pitt @csbpitt.bsky.social · 2w ago

We're thrilled to share that the lab of Assistant Professor Keisuke Ishihara has been selected as one of 17 recipients of the Google.org Impact Challenge: AI for Science! Learn more about the Impact Challenge and the Ishihara lab's work: tinyurl.com/IshiharaImpact #Googleorg #AIforScience

Keisuke Ishihara

Are you a biologist working with microscopy images? A computer scientist wanting to work with microscopy data? Their boss??? None of these, but curious how AI has transformed microscopy image analysis? 🔬🤖 This one's for you: 📄 doi.org/10.1146/annu... More in the thread! 🧵

From Pixels to Discovery: Microscopy Image Analysis in the Age of Artificial Intelligence

Modern microscopy generates data volumes that far exceed human analytical capacity. Over the past decade, artificial intelligence (AI) has transformed microscopy image analysis into an engine for quan...

doi.org

We’re happy to see our pykarambola software paper out in @bioinfoadv.bsky.social ! Minkowski tensor computation is now immediately accessible to Python-based research community. Stay tuned for a story on how we leverage this for 3D bioimage analysis 🔬

Bioinformatics Advances@bioinfoadv.bsky.social · last mo.

📐 New in Bioinformatics Advances: "pykarambola: Minkowski tensor morphometry of 3D structures"  Read it here: https://doi.org/10.1093/bioadv/vbag273 Authors include: @ishiharaksk.bsky.social

New paper 🧵 What if the "landscape" that guides cell differentiation isn't fixed, but is sculpted, in real time, by the very cells moving through it? We call this a 'sandscape' : grains of sand that carve dunes as they move, while the dunes' shape simultaneously steers where the grains go next.

Our latest: Minimal essential requirements for neural tube self-organisation How does a single cell give rise to a tissue with the right cell types in the right proportions? We deconstruct and rebuild a self-organising tissue from first principles A thread🧵 www.biorxiv.org/content/10.6...

Minimal essential requirements for neural tube self-organisation

The reliable generation of diverse cell types in precise proportions is essential for the formation of functional tissues during embryonic development. Three-dimensional organoid models derived from p...

biorxiv.org

Out now in Science! Our study challenges long-standing assumptions about transcription factor specificity in eukaryotes. Novel single-molecule measurements of TF behavior in living cells reveal an independence of locus-specific binding from DNA sequence recognition.🧵 www.science.org/doi/10.1126/...

Unstructured transcription factor interactions enable emergent specificity

How intrinsically disordered regions (IDRs) shape chromatin binding and nuclear organization of transcription factors (TFs) remains unclear. We used proximity-assisted photoactivation (PAPA), a single...

science.org

To probe gene-scale chromatin physics, we built 96-mer (20 kb) arrays with defined histone marks. Combining single-molecule tracking, AFM imaging, and developing in vitro Hi-C, we saw how specific modifications dictate chromatin structure and dynamics. www.science.org/doi/10.1126/...

Gene-scale in vitro reconstitution reveals histone acetylation directly controls chromatin architecture

Reconstituting 20-kb chromatin shows that tuning acetylation alone reshapes its folding, dynamics, and contact domain formation.

science.org

✨New preprint!✨ We built dLEM - a differentiable Loop Extrusion Model that bridges biophysics and machine learning for 3D genome folding. dLEM makes loop extrusion learnable and interpretable—predicting how genomes fold and how they respond to perturbations. 🧵

bioRxiv Genomics@biorxiv-genomic.bsky.social · last yr.

Mechanistic Genome Folding at Scale through the Differentiable Loop Extrusion Model https://www.biorxiv.org/content/10.1101/2025.10.17.682904v1