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: cell.com/cell/fulltext/S0092… 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: doi.org/10.1242/dev.201492

Experienced researchers are less likely to produce ‘disruptive’ science than are those just starting their careers, finds an analysis of the scientific papers published by 12.5 million researchers over 60 years. go.nature.com/4ux0FKV

Super excited to introduce QUICHE (Quantitative InterCellular Niche Enrichment) - a statistical method that can be used to discover local cellular niches differentially enriched in spatial regions, longitudinal samples, or clinical patient groups :) (1/10) biorxiv.org/content/10.1101/…

This paper from Harvard and MIT quietly answers the most important AI question nobody benchmarks properly: Can LLMs actually discover science, or are they just good at talking about it? The paper is called “Evaluating Large Language Models in Scientific Discovery”, and instead of asking models trivia questions, it tests something much harder: Can models form hypotheses, design experiments, interpret results, and update beliefs like real scientists? Here’s what the authors did differently 👇 • They evaluate LLMs across the full discovery loop hypothesis → experiment → observation → revision • Tasks span biology, chemistry, and physics, not toy puzzles • Models must work with incomplete data, noisy results, and false leads • Success is measured by scientific progress, not fluency or confidence What they found is sobering. LLMs are decent at suggesting hypotheses, but brittle at everything that follows. ✓ They overfit to surface patterns ✓ They struggle to abandon bad hypotheses even when evidence contradicts them ✓ They confuse correlation for causation ✓ They hallucinate explanations when experiments fail ✓ They optimize for plausibility, not truth Most striking result: `High benchmark scores do not correlate with scientific discovery ability.` Some top models that dominate standard reasoning tests completely fail when forced to run iterative experiments and update theories. Why this matters: Real science is not one-shot reasoning. It’s feedback, failure, revision, and restraint. LLMs today: • Talk like scientists • Write like scientists • But don’t think like scientists yet The paper’s core takeaway: Scientific intelligence is not language intelligence. It requires memory, hypothesis tracking, causal reasoning, and the ability to say “I was wrong.” Until models can reliably do that, claims about “AI scientists” are mostly premature. This paper doesn’t hype AI. It defines the gap we still need to close. And that’s exactly why it’s important.

Celebrating Christina Curtis and her well-deserved Paul Marks Prize! Some tumors are born bad. Christina uses math to find them early, predict their paths, and outsmart them. A win for cancer biology and for MSK to cheer.

Registration has officially opened for the 4th annual Spatial Biology Summit! Join us in person or virtually this September for an incredible lineup of speakers angelolab.com/spatial-biolog… @b_iologist @CandaceCLiu @MikeAngeloLab @Bendall_Lab @mina_pichavant

Excited to share our latest publication on expanding the multiplexing capability of MIBI-TOF , this can be extended to any technique implementing metal tags for mass spectrometry imaging. @MikeAngeloLab @Bendall_Lab @favaropb @felixjhartmann pubs.acs.org/doi/10.1021/acs…

🧵 (1/8) Today, @BenOberlton and I are excited to share the preprint of our work on: A comprehensive multi-omic study of human gliomas, spanning diagnosis, treatment, and recurrence. We address the longstanding challenge of developing effective therapies for gliomas, which have shown limited response to immunotherapy and targeted treatments compared to other malignancies. #GliomaResearch #NeuroOncology #CancerImmunotherapy biorxiv.org/content/10.1101/…

I am deeply honored to be named by the Stanford Medicine Alumni Association this year for the “Arthur Kornberg and Paul Berg Lifetime Achievement Award in Biomedical Sciences.” As a Stanford graduate student in the 1980s, I took a remarkable class in plasmid and phage replication from Dr. Kornberg. I marveled at his incisive manner of teaching complex concepts in science. I learned fundamentals of retroviral gene transfer from postdoctoral fellows and technical staff in Dr. Berg’s laboratory. I was in constant awe of Dr. Berg’s statesmanship on the world stage as a spokesman for science. Arthur Kornberg, M.D. and Paul Berg, Ph.D. were luminaries long before I was a graduate student. Professor Kornberg won the Nobel in Physiology or Medicine for isolating DNA polymerase and demonstrating how it could replicate DNA in a test tube. Professor Berg won the Nobel in Chemistry by showing the first inter-species DNA hybrid of bacterial and eukaryotic viral DNA and replicating it in bacteria. Professor Berg was instrumental in pointing out the dilemmas around recombinant DNA at the foundational Asilomar Conference in 1975, a critical meeting focusing on the ethical uses of rec-DNA technology. Dr. Kornberg’s and Dr. Berg’s efforts deeply touched biomedical sciences and helped patients worldwide. They symbolized science's dual role in fundamental discovery and service to humanity.

Kicking off the last day of #SBS24, the Science Grandfather himself @GarryPNolan took us on a journey from Merkel cell carcinoma to tissue schematics to LLMs (check out Cellformatica!). No UAPs but maybe next time...

Please RT. I am currently hiring graduate students and postdoc fellows. Details can be found here: postdocs.stanford.edu/prospe… My lab is dedicated to developing and integrating imaging, AI, and spatial omics technologies to uncover mechanisms driving disease initiation, progression, and therapeutic response, with the ultimate goal of advancing next-generation theragnostics to cure diseases. We are excited to be equipped with cutting-edge spatial omics technologies, including Xenium spatial transcriptomics @10xGenomics and Phenocycler-fusion @AkoyaBio .

Preprint alert! A spatial atlas of human gastro-intestinal acute GVHD reveals epithelial and immune dynamics underlying disease pathophysiology biorxiv.org/cgi/content/shor…. We examined how immune cells are organized in the gut and what happens to them in GVHD. TL;DR Timing is key!

Our human multipotent lymphoid progenitor paper is finally out! nature.com/articles/s41467-0… After revision, we added more in vitro validations of the lympho-myeloid potential of this HSPC population. Lots of thanks to @Bendall_Lab & @WJGreenleaf