📣 We are hiring! 📣 Since our NIH R01 on functionally engineered pericytes for Alzheimer's disease got funded, I am now looking for new people to join the lab at different levels (e.g., research tech, grad student, postdoc). A little bit of background: Pericytes keep the blood-brain barrier intact, and are lost early in Alzheimer's disease. We will engineer human iPSC-derived pericytes and test whether they can replace the lost cells and restore vascular and behavioral function. Great to have experience in (some of those) iPSC differentiation, cell engineering, AD mouse models, BBB biology or single-cell omics. But I highly value independent, creative and self-motivated scientists who are also great team players! We are located at the health science campus at USC in Los Angeles. If this sounds like you, email me at rrust@usc.edu with a short message and your CV. This is a really cool project and I truly believe it has the potential to generate the next-generation of cell therapies for Alzheimer's 🙂 As a little motivator I am attaching a human iPSC-pericyte homing to mouse vessels! ❗ Please share with anyone who might be a good fit❗ #Alzheimers #Pericytes #BloodBrainBarrier #CellTherapy #HiringNow

The atlas of gene expression in the prefrontal cortex was created using samples from almost 1,500 donors from infants to centenarians go.nature.com/47jdzT6

Single-cell atlas of transcriptomic vulnerability across brain disorders idp.nature.com/authorize?res…

Single-nucleus atlas of cell-type specific genetic regulation in the human brain idp.nature.com/authorize?res…

I just funded: Predicting Circulating Tumor Cell Genomic Clonality from Immunofluorescent Nuclear Images in Liquid Biopsy on ResearchHub! researchhub.com/proposal/328…

A human iPSC-based perfusable and membrane-free neurovascular unit-on-chip connecting brain organoids to the blood–brain barrier pubs.rsc.org/lc/article/doi/…

Blood vessels in the brain deteriorate in Alzheimer’s, especially in APOE4 carriers. But what drives this damage? Our new study, out now in @CellPressNews , uncovers a mechanism linking APOE4 to vascular fibrosis and amyloid accumulation in the brain. 🧠🧬

We’ve set up a molecular biology lab at Anthropic and we’re announcing our first discovery! Claude discovered a new CRISPR-like enzyme. 950 agents spent 21 hours searching through a database of DNA sequences until one of the agents found something striking: “[The DNA next to the RT] is spectacular: I can see by eye a tandem repeat array … that's a CRISPR-like … repeat array?!”. After analysis and testing in our lab, we found that the sequence is a previously uncharacterized enzyme system. We don’t know what it does yet, but it has features reminiscent of CRISPR. Our lab looks like a typical molecular biology lab. Our research only involves the lower-levels of biosafety risk level; we don’t handle pathogens that can infect humans, and all the lab work is performed by human scientists. We’re sharing these early findings with the community to show how Claude can be used to accelerate fundamental research in biology.

Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student. The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out. It’s easy to dismiss this as a one-off or curiosity, but we’ve repeatedly seen a pattern where AI performance in new intellectual domains goes from weak to superhuman in a matter of a few years. In 2023 models struggled to do math at the level of an average high-school student. In 2024 they started to do well on math competitions for the best high-schoolers in the country, in 2025 they started to solve minor open problems, in early 2026 more significant open problems, and in late 2026 they are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend. The main difference between biology and mathematics, of course, is that math can be done purely theoretically, while biology requires experimentation. Some have used this to draw the conclusion that AI’s utility in biology will be limited. We think this is wrong. As we’ve demonstrated today, humans can collaborate with AI to perform the experiments, validate key results in a few weeks and, if necessary, work with the AI to iterate on what they find. Eventually it may even be possible for Claude itself to safely perform the experiments by autonomously controlling lab equipment, with appropriate safeguards in place, but we aren’t doing that today (our lab is also a BSL1/BSL2 facility that doesn't handle materials dangerous to humans). More broadly, biomedical advancement has many stages — from fundamental biology discoveries, to translational research, to drug discovery, clinical trials, and finally the actual delivery of medicines and health care to patients. We are also interested in these later stages, but even simply accelerating the first stage of fundamental biological discoveries has the potential to speed up and broaden the entire pipeline. Improving our understanding of biology and sharpening biologists’ tools can drive forward all of the later stages, for example by identifying new drug targets, finding new therapeutic modalities, allowing for more precise measurement, and speeding up the experimental loop which itself further accelerates our understanding of biology. This will not in itself speed up clinical trial times, but if it succeeds it could greatly increase the number of promising candidates that go into the pipeline — an increase in throughput even though latency remains. In Machines of Loving Grace, I wrote about AI’s potential to “cure most diseases in 5-10 years” — a goal that sounds impossible, but one I believe is just barely possible if AI is applied to every stage of the pipeline. The first step is showing that AI can first help with, and then drive, biological discoveries. Claude’s discovery is the latest in a line of related prior work that goes back decades, beginning with systems like CRISPR, and continuing with discoveries like the bridge recombinase and VIPR in the past few years. Recently, there has been heightened interest in systems based on reverse transcriptase (RT) enzymes, the enzyme underlying the system Claude identified. And most recently, a Stanford team working independently described a novel RT system with an associated non-coding array that is in some ways similar to the one Claude found, though they are distinct systems that evolved independently from each other. I believe that we’re at the very beginning of finding such systems and developing them into powerful tools for biotechnology. I’m proud of the resources Anthropic has invested in accelerating the public benefits of AI through the life sciences, and we’re aiming both to grow our life sciences team and to work with other scientists to extend this approach to a broad range of problems. If you have a proposal for a research collaboration or are interested in joining our life sciences team, please reach out.

Landmark map of human brain’s gene activity holds clues to Alzheimer’s disease and more nature.com/articles/d41586-0… @Nature

Associate Professor at @USCGero, @BBParis1984, is raising $10K on ResearchHub to test promising anti-aging drug candidates directly in human ovarian cells. The project is already 32% funded. Help get it across the finish line. Fund the research. 🧵

I'm hosting a dinner next month in Boston for scientists and engineers interested in safely increasing human intelligence. I'm interested in both treating cognitive impairment and helping healthy people improve their cognitive abilities. Could we develop treatments that help people learn faster, reason better, and solve harder problems? I'm especially interested in approaches that could work in adults. There's a lot we don't know, including what's possible and what would be safe. I'd like to bring together people across neuroscience, genetics, drug development, and computational biology/ML to discuss promising research and what it would take to turn it into useful treatments. I'm also interested in backing the right founding team if one comes together. If you're working in this area or have relevant expertise, please apply at the link below. Happy to cover travel if needed. Or if you can't make it to Boston, let us know - I may host one virtually for those who can't attend in person.

Targeting glial PD-1/PD-L1 restores microglial homeostasis and attenuates neuronal hyperactivity in an Alzheimer’s disease model science.org/doi/abs/10.1126/… (There is also a preprint available if you do not have access: biorxiv.org/content/10.1101/…)

The evolving landscape of drug targets nature.com/articles/s41573-0… rdcu.be/22o1FPpNJ3Ga In the past 25 years, advances in areas such as genomics and the diversification of therapeutic modalities have expanded the drug target landscape, which now includes ~700 targets mapped here

What if you could explore an entire brain at nearly the scale of individual cells? Researchers including UCLA neuroanatomist Hong-Wei Dong created a 3D mouse brain atlas with 1-μm resolution, mapping 916 brain structures in remarkable detail. The atlas could help researchers more precisely locate cells and connections throughout the brain. More 👇 pubmed.ncbi.nlm.nih.gov/4060…. #UCLA #Neuroscience #BrainMapping #Neuroanatomy

Agree with all of this (obviously!). But we clearly live in parallel universes. Some of us are already thinking way past papers, while probably half of the leading senior scientists still tell me they might try this "preprint" thing next year for the first time 😅

If it's isn't obvious already, it is the end of papers as a measure of productivity, expertise or accomplishment. The odds of publishing in "glam" conferences & journals also very quickly approaches random & the time for gate keeping review increasing exponentially. 1/

The longevity gene APOE2 enhances pericyte function and reduces lipid droplets academic.oup.com/brain/artic…

Science – debatably the #1 journal in the world – published a paper on a "virtual biotech" AI system this week but the authors never even test if it works well! all the evidence for its performance is 3 anecdotes the paper has no benchmark scores, no baselines, no replicates

We harness large-scale brain data, artificial intelligence, and imaging technology to reveal patterns that deepen our understanding of the human brain. 🧠 ini.usc.edu

Co‐Delivery of Sustained Release Chondroitinase ABC‐37 With Human iPSC‐Derived Neural Progenitors Promotes Transplant Survival and Functional Recovery in a Rodent Model of Stroke - Letko Khait - Advanced Science advanced.onlinelibrary.wiley…