Asst Professor @USC with interest in the brain, BBB, stem cells, AI + what can go wrong after stroke & AD. Alum @ETH_en. Fixing science @ResearchHub

Los Angeles, USA
Joined June 2019
Ruslan Rust retweeted
The hard part about AI in bio is that by the time you can test the predictions from model X, you already have better predictions from model X+2 or X+3.
god these two charts really hurt my soul 64 hours to dry lab design an multi-binding antivenom and 39 days for results and 4-7 years to release
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Ruslan Rust retweeted
Cool paper from Gladyshev lab - mice, rats, macaques and humans age with a shared transcriptomic signature. Shared enough to predict time to death. They pooled 11,000 transcriptomes from over 25 tissues across those four species. Genes tied to senescence, inflammation and apoptosis went up with age in all of them. Two hits, CDKN1A and LGALS3, also tracked mortality and multimorbidity at the protein level in UK Biobank. The resulting clocks predicted time to death in people about as well as second-generation epigenetic clocks, and in rodents they picked up known lifespan-extending interventions. What I find most interesting is the module clocks. You get separate readouts for separate parts of the transcriptome. Chronic disease mostly aged the inflammatory module. Caloric restriction and Klotho deficiency acted on mitochondrial and metabolic modules. A single "biological age" number can't tell you which part of aging an intervention touched. I think this matters most for animal research. If the molecular readout is conserved, a mouse study and a human trial can report aging on the same scale. That's the translation bridge we've mostly been missing. It would be very cool to link this with the automated Intrinsic Capacity panel we're building with ARPA.
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Ruslan Rust retweeted
We need to do experiments to figure out exactly how these work. But there are way too many candidates! So we created an explorable website (compass.arcinstitute.org/) and MCP (compass.arcinstitute.org/age…) for the research community to mine the COMPASS clusters and make their own discoveries!
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Ruslan Rust retweeted
One 45 minute bike ride changed 3,007 proteins across blood, saliva, and urine. In blood alone, 40% of the measured proteome shifted. A new Nature Metabolism atlas maps where those changes come from and which ones may matter for disease. Researchers at the University of Copenhagen, led by Nigel Kurgan, Bente Klarlund Pedersen, and Atul Deshmukh, tackled a basic question. We know exercise protects against diabetes, dementia, and heart disease. Meeting the 150 minutes per week guideline is linked to roughly 33% lower mortality. But how do organs coordinate that response? The answer matters for a practical reason. It may help settle whether the best workout is a specific format or simply consistent movement. The leading idea is exerkines (molecules released during exercise that carry signals between organs). Most past studies only looked at blood, with limited coverage and few timepoints. This one sampled plasma, saliva, and urine. The discovery cohort was 25 healthy men, average age 29, who cycled for 45 minutes at about 75% of heart rate reserve. The team collected blood, saliva, and urine before exercise, then again at 0, 0.5, 1, 3, and 24 hours afterward (urine was only collected before, right after, and at 24 hours). Across the three fluids, they measured 10,128 proteins. To see whether the results held up, they ran a second study in 24 inactive adults, average age 55, 15 of them women. Each person completed steady aerobic exercise, high intensity interval training (HIIT), and resistance training in random order, with blood drawn before and after each session. Right after exercise, 3,007 of the 10,128 proteins (30%) had changed. Blood showed the biggest shift: 2,156 proteins, or 40% of the plasma proteome (the full set of proteins in blood plasma), moved, and most had returned to baseline by 3 hours. Saliva told a different story. 601 proteins (31%) changed, and they stayed altered through the 24 hour mark. Urine was the quietest. 272 proteins (10%) shifted briefly and had fully resolved by 24 hours. Then came the replication test. In the older, mixed-sex cohort, 1,212 proteins (56% of the discovery set) responded again. About 74% moved in the same direction immediately after exercise, but that agreement dropped to 38% by 24 hours. Exercise type mattered less than you might expect. Aerobic, HIIT, and resistance sessions mostly changed how much a protein moved, not which direction it went. Only 2 proteins showed a true mode-by-time interaction (a pattern that depends on both the type of workout and when you measure it). For anyone choosing between steady cardio, intervals, and lifting, that is the practical takeaway. The core molecular response to a single session doesn't appear to depend on which one you pick. The dose you repeat matters more than the format. Put the two timing results together and a pattern seems to emerge. The first hours after a session look largely universal across age, sex, and workout type. By 24 hours, the responses diverge and become individual. That raises the obvious question of what the different fluids are doing, and why they follow such different clocks. The answer starts with the fluids themselves. First, each fluid has its own kinetics (the speed at which proteins rise and clear). Plasma acts like a highway with fast on-ramps and fast exits. Proteins spike, then get cleared. Saliva reflects local gland activity plus signals from the liver and immune cells, which is why it lingers. Urine mostly captures filtered leftovers from tissue turnover and the kidneys. That difference in timing sets up the next question: if the fluids run on separate clocks, are they still connected? Second, the fluids are talking to each other. The team measured how proteins in one fluid track with proteins in another across participants. Cross-fluid correlations rose about 1.7-fold immediately after exercise. Early hubs involved vesicle trafficking (small packages cells use to ship proteins) and immune activation. By 24 hours, the networks leaned toward growth factors and tissue repair. Third, the extracellular matrix (ECM, the scaffold of collagen and related proteins that holds tissues together) stayed central at every timepoint. The authors propose that matrix remodeling helps route exerkines to where they need to go. That is a hypothesis, not something they tested directly. Then they asked which proteins are likely to matter for disease. They used cis-pQTL colocalization, a genetic method that checks whether the same DNA variant drives both a protein's blood level and a disease risk. Proteins that responded consistently across age, sex, and exercise type were enriched for links to cardiometabolic disease. Mode-specific proteins mostly linked to physiological traits like body composition and kidney function. Of the secreted proteins with a genetic signal, 69 are targets of FDA-approved drugs. Some of the strongest signals showed up in saliva. 52 of 145 proteins in one sustained cluster were linked to type 2 diabetes risk. Why this matters: the proteins that move after one session are not random noise. They sit in the same biology that drives metabolic disease, and many are already druggable. That is a plausible route by which regular training lowers risk. This study doesn't prove it does. Finally, the team tested whether any of these proteins actually do something. A protein that rises after exercise may matter, or it may just be along for the ride. They used fruit flies (Drosophila), where muscle genes are easy to edit. In each experiment, they turned down one gene only in muscle, trained the flies for four days, and measured endurance. They tested nine proteins that humans release during exercise. Some were needed for the training benefit. With less PDGF, TNF, or FGF in muscle, flies lost their endurance gains, while untrained endurance didn't change. Others acted like a brake. With less myostatin (a protein that limits muscle growth), baseline endurance went up and training added nothing on top. With less GDF15 or BMP4, endurance rose in trained and untrained flies. Turning down INHBB even changed night sleep and fat storage, which suggests these signals reach beyond muscle. So at least some of these proteins help regulate adaptation. These are fly results, so they show the idea is plausible, not that the same thing happens in people. The limitations are real. There was no resting control group, so circadian rhythm, fasting, and fluid loss can't be fully separated from exercise. The authors controlled posture, fasting, and hydration, and many known exerkines reproduced. But it is still a design gap. The discovery group was 25 young men. Replication covered plasma only, so the saliva and urine findings haven't been replicated. Urine was sampled at just three timepoints. Most new exerkines were measured on one platform and need confirmation. The genetic colocalization is exploratory and doesn't prove causality. And flies are not mammals. The authors say a rodent model is the next step. This also isn't a training prescription. Nobody measured whether these proteins predict who gets healthier. Clinical use, such as biomarkers for exercise dose or drugs that mimic exercise, is likely years away. What it does give us is a map. The conserved response to a single session overlaps with the biology of diabetes, heart disease, and dementia. It also appears across ages, sexes, and workout types, which fits the idea that consistent movement matters more than the perfect format. No single drug is likely to recreate what exercise does across this many organs. The benefits of exercise aren't one signal from one tissue. They're a coordinated conversation across the whole body, and a single workout starts it.
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Ruslan Rust retweeted
CellDiffusion: Enhancing scRNA-seq data integration with accurate inference of cell-cell association science.org/doi/10.1126/scia…
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Ruslan Rust retweeted
Delighted to share a landmark preprint from our lab, using genome language models and AI agents for biological discovery and genome mining! The stretches of DNA between protein-coding genes are often called the genome’s “dark matter,” but they are far from empty. They encode RNA molecules that sense metabolites, regulate genes, and control some of molecular biology's most important tools (such as programmable genome editors e.g. CRISPR/bridge) Much of this biology is unexplored. One reason is that RNAs can retain similar structures and functions even as their DNA sequences change dramatically, making their relationships difficult to recognize with conventional computational tools
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At @OpenAI we are hiring a Life Sciences Partnerships Lead to shape and execute OpenAI’s global life sciences partnership strategy. You will lead high-impact research, product, and platform partnerships across the life sciences ecosystem. Learn more and apply at openai.com/careers/business-…
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Ruslan Rust retweeted
Just published @ScienceMagazine Higher medical accuracy at lower cost A 3-agent AI for sequential diagnosis nearly doubled accuracy compared with physicians for >300 @NEJM CPC cases and >300 ER cases AND substantially lowered cost, also exceeded performance of many AI models, physicians plus AI @HarshaNori @erichorvitz @Dominic1King @mustafasuleyman
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Ruslan Rust retweeted
I *wish* the bottleneck in drug discovery was new ideas.
Rosalind for new drug discovery ideas:
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🧪 @UWproteindesign + 💚 NVIDIA = 👶 RFD4-Proteina Proud parents ✨ Gifted kid
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Ruslan Rust retweeted
Two VIPerturb-seq updates: 1. Updated preprint w/@CalebLareau where we enrich for VIPs using RNA markers before sequencing: biorxiv.org/content/10.64898… 2. Delighted to share that @10xGenomics is releasing a commercial VIPerturb-seq product! Check it out at pages.10xgenomics.com/WEB-20…
Excited to share VIPerturb-seq! New tech from my lab which aims to improve the cost, data quality, and efficiency of single-cell CRISPR screens so that they are accessible to any lab - even at genome-wide scale Preprint and 🧵 (1/): biorxiv.org/content/10.64898…
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Ruslan Rust retweeted
What distinguishes successful from failed reprogramming to iPSC? In this new preprint, part of our @IGVFConsortium efforts, we used single cell methylation, Hi-C, RNA and ATAC, and spatial RNA to answer this question biorxiv.org/content/10.64898…
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so cool!
Introducing TrackedGait™ Insights: TrackedBio's gait analysis application, now on iOS and Android. Record a short walk, get your gait score and the breakdown behind it: posture, balance and more. iOS: apps.apple.com/us/app/tracke… Android: play.google.com/store/apps/d…
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Innovations like AlphaFold, Evo2, ESM, Hippocratic, and OpenEvidence have transformed the way drugs are developed and disease is diagnosed. AI is now used in some step to aid in the development of almost every drug coming to market. 81% of US physicians now integrate AI into clinical workflows. We are even seeing this scale to individual therapeutics, curing cancers that were never before possible, such as when GitLab founder Sid Sijbrandij recently generated 25 terabytes of single-cell sequence data to achieve measurable remission for recurrent osteosarcoma (a rare, aggressive type of malignant bone cancer). For the next YC Paper Club, we invite the authors of these works to discuss the exciting future of AI for Biology. Please join us in Mountain View on 11/4! RSVP below: events.ycombinator.com/yc-pa…
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We used GPT Sol 6.1 to draft 10 preregistered proposals to test this hypothesis in the wet lab. 1. Funders: pool funds on the RFP 2. Scientists: upvote, comment, or review to strengthen the experimental design 3. Wet labs: comment if you want to run one researchhub.com/rfp/33104/te…
GPT-Rosalind surfaced a new lead for drug discovery: could restoring cholesterol transport protect neurons from degeneration? DCTN1 is a gene linked to Perry syndrome, a rare neurodegenerative disorder. In mouse-brain data, disrupting it increased expression of genes involved in making cholesterol. GPT-Rosalind suggested a possible explanation: cholesterol may not be reaching where neurons need it. That gives scientists a specific mechanism to test and, if confirmed, a direction for developing new treatments for neurodegenerative disorders. With @perturbai_tx and @scrippsresearch, a team of thousands of GPT-Rosalind agents worked in parallel, connecting gene-expression results from millions of mouse brain cells with scientific literature in just 32 minutes. We’re now expanding this collaboration to identify new drug targets. openai.com/index/scaling-ai-…
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"the journal editor in question gave the clinician submitting the manuscript a quote: 'An initial payment of 2 million yuan (US$299,000), with more potentially required later.'"😅
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Astrocyte‐Associated ABCA1/APOE Responses Accompany Hippocampal Lipid Remodeling and Ectopic Neuronal Accumulation During Epileptogenesis - Chen - Advanced Science - Wiley Online Library advanced.onlinelibrary.wiley…
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Borrowing an idea from one research discipline to solve a thorny problem in another is an age-old scientific trick. Charles Darwin’s theory of natural selection, for example, famously inspired algorithms in computer science that “evolve” solutions to complex problems. But such clever leaps are fleetingly rare. For #AI, however, hopping between fields can be a breeze. Researchers highlighted that ability with the release of 36 preprints produced with a single suite of AI tools called BootLoops. The so-called harness can be used with any large language model, such as Anthropic’s Claude or OpenAI’s ChatGPT. And in just 3 months, researchers used Claude outfitted with BootLoops to cross-pollinate at a massive scale, spotting—and precisely solving—problems in seemingly unrelated fields that share the same mathematical structures. The resulting papers range widely, from figuring out the age of sunspots, to analyzing government health policies, to attempting to decipher a mysterious medieval manuscript. Learn more: scim.ag/3TUUyDJ
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