Bendall Lab

Prof. Nikolai Slavov Verified account @slavov_n · May 11

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/…

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.20…

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nature Verified account @Nature · May 7

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

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Alex Veremeyenko Verified account @alex_verem · Dec 18, 2025

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.

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dana_peer @dana_peer · Dec 5, 2025

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.

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