Nicholas Ho

We asked a basic unanswered question in tissue biology: Can the many molecular exchanges between neighboring cells be organized into reusable communication programs - much like genes into pathways? We introduce SpiderNet, which learns cell-cell meta-interactions (MIs): directed sender->receiver programs linking sender regulators, ligand-receptor bridges, and receiver responses, while mapping where each program is active in tissue. Across diverse contexts, MIs recur, form multicellular relays, predict neighboring-cell responses to perturbation, and connect local signaling to tissue states and phenotypes. We hope MIs provide an intermediate language for spatial omics, moving from catalogs of local contacts toward interpretable, comparable, and eventually perturbable programs of multicellular regulation. 📚 Preprint: biorxiv.org/content/10.64898… 🚀 Led by the amazing Junjie Tang @JJTang_1995 in the lab.

I spoke with @Nature, alongside several other researchers, about why many AI researchers still choose academia despite the growing pull of industry. Academia's unusual freedom to choose which questions are worth pursuing is powerful. But I do not think academia and industry need to be opposites. As AI reshapes science, we need models that let them co-evolve, combining academia's long-term freedom and openness with industry's resources, scale, and ability to rapidly translate ideas into impact. nature.com/articles/d41586-0…

20 years ago today, on April 7, 2006, I submitted the only paper I published during my PhD to @genomeresearch. By today's standards, the work feels too simple, and every figure looks plain. Science has moved fast, and I am working on completely different problems now. That era is gone. genome.cshlp.org/content/16/…

very comprehensive Review on single-cell foundation models from @JiayuanDing, @Xiaojie_Qiu, @TheodorisLab, and colleagues. After going over it, I would encourage the authors to make it more critical rather than primarily an inventory of models. First, I think Fig 1 needs some rethinking. The definition of scFM has become too loose. For example, the actual model unit of GET is celltype pseudobulk aggregated from scATAC-seq, hence not single cell model. Also thanks for highlighting our TissueNarrator, but we certainly do not consider it a bona fide scFM - it is a specialized spatial transcriptomics model built by leverging an existing foundation LLM (Qwen). TissueNarrator is very interesting and we re having lots of fun w/ it but it's not a scFM. Anyway - Fig 1 seems to mix FMs, specialized models built on FMs, and task-specific models into one big family tree. More broadly, I think the more interesting question for a scFM review in 2026 is not how many scFMs now exist or how their architectures differ, but what has the scFM paradigm actually bought us? Since the first wave around scBERT/Geneformer/scGPT, the number of models, parameters, and pretraining cells has exploded, but as a field we are not convinced that capabilities have advanced proportionally. - Where has large scale pretraining enabled something that a strong specialized model could not do ? - Where do scFMs consistently beat strong task specific or even simple baselines? - Where is the broad evidence for true cross-context generalization on biologically interesting tasks? The Review mentions many of these challenges indeed, but I think they should be much closer to the central theme of the article rather than appearing mainly in a later Challenges section :-) I might almost suggest to flip the review around and instead ask: - what have we actually learned after several years and many scFMs? - What claims have survived independent benchmarking? - What has scaled and what has saturated? - What capabilities are genuinely attributable to pretraining and where do we still need much more evidnece? - What important biological problems remain essentially unsolved by scFMs ? Given the current rate of model proliferation, there may be another 50 scFMs by this time next year and this inventory / figure will become outdated very quickly. To me, a more useful and timely "state of the field" critique would be much more valuable and long-lasting than another "model zoo" review .