Genetics professor interested in RNA dynamics and mito-nuclear balance @Harvard; physics PhD @Stanford; visiting scientist @OpenAI
Cambridge, MA
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Come join us! In addition to being a core member of the amazing Mol Bio department at MGH, the recruit will be a part of my department at Harvard Medical School.
Our MGH Dept of Molecular Biology is hiring. It’s an exceptional *basic* science dept with a longstanding track record for curiosity driven science, great colleagues / infrastructure and generous support.
Assistant Professor nature.com/naturecareers/job…
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Agree. That's why it's important to focus on imminent cyber threats and make sure whatever legislative window we get goes toward that, not far-off theoreticals.
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I am quite optimistic and AI-pilled, no worries! But curing all diseases in 5 years is a bit of a stretch for any amount of optimism (based on reality). Mostly because it will involve testing, in a lab... I'm more willing to say 10 years is possible. Just not 5 :)
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Her timeline differs from where the conversation has been. Eventually, virology will take less time, because of AI, but this will be in >10yrs.
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This is why I'm not worried about AI biosecurity threats 👇
(I've been groaning out a less elegant version of these points to anyone who asks me what I think of AI biosecurity -- thanks @DavidRBellamy for saying it so clearly)
I must be among an extremely small group of people (n=1?) that have both 1) trained a frontier LLM and 2) designed and synthesized custom viruses in a lab with my own two hands.
And I think that the takes on AI killing us all by creating dangerous viruses is total bogus.
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Thanks, Alex! Perturbation studies are a natural use case. Separating changes in RNA synthesis from changes in decay can reveal what a transcriptional regulator (or a drug targeting it) is actually doing.
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Related, but not the same. RNA velocity generally infers dynamics from spliced/unspliced RNA. AIR-seq experimentally marks newly synthesized RNA, separating new, old and total RNA and enabling kinetic measurements. The two approaches could be very complementary.
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Congratulations to first authors @lisan_hansen and Mary Couvillion! This was a COVID-era project dreamed up by @ErikMcshane and me.
And huge thanks to @TreutleinLab, especially @NadyaAzbukina, for the single-cell collaboration!
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AIR-seq "blows" past the chemical-conversion and enrichment bottlenecks that have kept RNA dynamics specialized. It makes dynamics a routine layer of bulk and single-cell transcriptomics. So we can ask not just how much RNA is there, but how it got there. biorxiv.org/content/10.64898…
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The same idea works for standard single-cell workflows. Add an NHC pulse before a 10x experiment, then capture and sequence the library as usual. The mismatch information lets us recover new, old, and total-RNA layers for each cell without redesigning the capture chemistry. (5/6)
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AIR-seq is therefore close to regular RNA-seq: pulse with NHC, extract RNA, and make a standard library with no enrichment or post-labeling chemistry. From the same reads, we can measure RNA abundance and distinguish newly synthesized from pre-existing RNA. (4/6)
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AIR-seq uses NHC, the active form of the COVID-19 antiviral molnupiravir, which mutagenizes viral RNA. We predicted that NHC would also mutagenize newly synthesized Pol II transcripts, creating an intrinsic record readable by sequencing. (3/6)
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Why aren't RNA dynamics routinely measured? Current approaches label new RNA, then enrich it or chemically convert the label after RNA extraction. Those steps add work, damage RNA, and are especially difficult to combine with single-cell workflows. (2/6)
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RNA-seq tells us how much RNA is present in the cell. But to understand gene regulation, we need to easily measure the synthesis and decay rates driving this abundance. We introduce AIR-seq: analog intrinsic recoding sequencing. (1/6) biorxiv.org/content/10.64898…
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I’m spending three months at @OpenAI on a mini-sabbatical and just finished month one with the Rosalind team.
I've loved watching this new version come together from up close. I’m learning a ton and I’m excited for people to try it. Thanks to @joyjiao12 and team for hosting me!
We’re bringing new capabilities to GPT-Rosalind, a model series purpose-built for life sciences research at enterprise scale.
It brings GPT-5.5’s agentic coding and tool use together with stronger intelligence for drug discovery, analysis, design, and experimental workflows.
openai.com/index/introducing…
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11/ Together, our results suggest that SRSF1 coordinates splicing, transcription, and 3′-end processing to shape mRNA isoform identity.
Thanks to @anafiszbein and team for their help!
Read the preprint here: biorxiv.org/content/10.64898…
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10/ These effects also appear relevant beyond cell lines.
In breast cancer tumors, lower SRSF1 levels are associated with more distal alternative last exon usage, consistent with what we observe after SRSF1 depletion.
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