Herbert B Schiller

Director, Research Unit for Precision Regenerative Medicine @HelmholtzMunich;

Professor @LMU_Muenchen

[Herbert B Schiller @SchillerLab]

https://t.co/pFmPsthm35

[Jacob Kimmel @jacobkimmel]

on day 1 @newlimit, we imagined it would take 10+ years to invent real medicines.

our recent results have accelerated the timeline to next year. we've raised a Series C led by @foundersfund alongside @ThriveCapital, @Greenoaks, and many others to bring therapies to the clinic.

medicines for aging are among the most valuable possible technologies. we are grateful to our partners for the opportunity to pursue this mission.

[Brian Roemmele @BrianRoemmele]

Hermes Agent on @NVIDIARTXSpark superchip and integrated with the new OpenShell runtime.

This is powerful and will be the substrate we have been waiting for at The Zero-Human Company.

[Romain Lopez @_romain_lopez_]

🚀 We are introducing PerturbPair (with @TakaKud0) — a platform that combines parallel Perturb-seq and optical pooled screening (OPS/PerturbView) in primary cells to systematically map at massive scale how genetic perturbations reshape cellular states across modalities.
With wonderful collaborators @TakaKud0, @AnaMeireles, @AntRios, @jchuetter, @MinOta, @ORozenblattRosen, @LeviAGarraway, @KGeiger, @avtarsingh, @jkpritch, and Aviv Regev.

Paper link: https://t.co/fnSUymW95s

[Rahul Satija @satijalab]

Our 10th Single Cell Genomics Day is next Friday (6/12)!

Thanks to amazing speakers Aviv Regev @xinjin @anshulkundaje @junyue_cao and many more! Talks are live-streamed on YouTube and are free (no registration required) at https://t.co/G5Pq3EwyHF

[Naftali Kaminski @KaminskiMed]

snRNASeq of Early IPF lungs reveals emergence of aberrant basaloid cells & alveolar intermediate epithelial states & marked loss of AT2 and AT1 cells in early IPF.
These changes associate with subsequent disease progression 🙀
#EarlyIPFisIPF
https://t.co/yyX28bn7D2

[Naftali Kaminski @KaminskiMed]

The first snRNASeq & spatial transcriptomic analysis of bronchoscopic cryobiopsies from patients with Early IPF & preserved pulmonary functions is out on @biorxivpreprint and message is Early IPF is IPF‼️
https://t.co/yyX28bn7D2

[ZhuangLab @ZhuangLab]

What is the global structure of cell-state space—and how do perturbations drive transitions within it?

Excited to share our new preprint (https://t.co/ZTlAY4eaJf), a work in collaboration with @JswLab.

[Prof. Akiko Iwasaki @VirusesImmunity]

Excited to share our study by @keylas3 et al. on pathological autoantibodies in people with Long COVID. We asked whether IgG in patients with Long COVID bind to human tissues/antigens and cause pathologies when transferred into mice. With @PutrinoLab

https://t.co/tcowCufWyf

[Matthias Mann Lab @labs_mann]

1/5
Cell identity is written in the proteome, not in the DNA, and not always in the RNA. Out on bioRxiv today: The first cell type-resolved, MS-based proteomic atlas of the human body.
https://t.co/5RJ0nVoQ81

[Norn Group @NornGroup]

The trajectory of AI for bio is becoming clear for those with the eyes to see:

Biology is moving toward a combination of large foundational datasets and closed-loop systems where AI generates hypotheses, designs experiments, and self-improves based on the results.

@ARPA_H's Intelligent Generator of Research (IGoR) program is a sign of that. It aims to combine AI with experimental biology to enable verification at scale.

But how? https://t.co/iKMs7FlI0w

[Florian S @airesearch12]

Just for clarity:

This has the potential to fundamentally disrupt both the current AI compute market and the frontier LLM training race.

Why?

Because Parallax by Chutes is starting to demonstrate something that was widely considered impractical until now:
large-scale decentralized training for frontier style MoE models.

If this continues to scale (we have reasons to believe it will), the implications are huge:

- frontier training across massively distributed hardware
- significantly more cost efficient to train
- has the potential to free up huge load of the severely constrained data center capacity, that is currently tied up for centralized training (estimates say 20-50% of dc compute is locked for training)
- heterogeneous training fleets, e.g. high-end NVIDIA B300 GPUs alongside consumer hardware like MacBooks
- the possibility for ordinary people to contribute compute, participate in training frontier AI, and earn crypto rewards with it, basically: models from the people for the people.

Yes, this is basically SETI@Home for frontier model training. Democratizing the benefits of AI. AKA one of the core visions of Bittensor as I understood it. /cc @const_reborn , please correct me if wrong.

And the most insane part actually comes AFTER training:
The same architecture choices also appear to massively improve inference efficiency. Think multiple times better revenue/cost ratio for inference providers. Oh, and btw, Chutes is an inference provider.

Most people still underestimate how big this could become.
Stay tuned.
Or no, even better: Please share. We need to get the message out there.
Except if you are an investor or want to train a huge model, in which case: please reach out via DM.

$TAO

[rohan ganesh @certifiedrohan]

With a nod to @leopoldasch, we're releasing Biological Awareness - our musings on the next decade at the frontiers of AI and life sciences.

Six ideas we explore:
→ Task-specific experimental tokens are the new gold in biology
→ Bio AI data factories will pioneer new business models
→ Smoothing out the jagged frontier for pharma enterprises is where fortunes will be made
→ Clinical trial intelligence will be financialized before it is operationalized
→ The bioweapons threat is real, accelerating, and underpriced
→ The BIOSECURE Act will fundamentally reshape supply chains

2026 marks ten years of Obvious investing at this intersection. A lot has happened. Even more is coming.

Big thanks to: @kyosu, @syntenyAI, @GabriCorso, @inductivebio, @mithrl_ai , @instancebio , @strangemonad, @exnx and many others for their help!

[Fabian Theis @fabian_theis]

Excited to be at the 90th Cold Spring Harbor Symposium on AI in Biology 🧬🤖

Amazing speakers & discussions amongst others on how AI moves from analyzing biology to actively helping shape next experiments. 🚀

Will talk about closing the loop in single-cell perturbation bio. 🧫

[Norn Group @NornGroup]

The principles for AI-enabling longevity data are:

1⃣ Maximize answers per unit time by starting the gating experiments now and avoiding sequential dependencies, since much biology can’t be sped up.

2⃣ Data has to span the layer where we want AI to provide answers (physiological or organismal for longevity) to be task-shaped.

3⃣ Richer data is better, since models can find structure we wouldn’t have known to look for.

4⃣ When we want treatments, the data needs to contain or link to outcomes generated by interventions or by time, to enable causal inference.

And we will get the most progress if we focus on how to bridge slow and fast, to feed AIs the right data ASAP.

Read the full piece here: https://t.co/A070O4BDNp

[David Lagares, PhD, MBA @david_lagares]

Our latest paper is out @ScienceTM! Natural killer cell immunotherapy reverses lung fibrosis by eliminating senescent fibroblasts | Science Translational Medicine https://t.co/YPwY048I3I #Immunotherapy #Fibrosis #Aging #Senescence #NKcell

[Fabian Theis @fabian_theis]

Excited to share our RegVelo paper in Cell
https://t.co/ZAnQphaXsg

We unify RNA velocity + GRNs into one model → better OOD prediction of perturbations (e.g. gene KOs), with examples incl. neural crest KO predictions 🔬

Big thanks to W Wang, Z Hu & T Sauka-Spengler 🙏

[Jonas Schupp @jonas_schupp]

TWEETORIAL | We mapped PPFE at single-cell resolution — and found an unexpected blueprint for lung fibrosis. A thread on our new paper in @ScienceAdvances https://t.co/5B0D2Num8B

Data live at https://t.co/VHDwQlCikP

[Norn Group @NornGroup]

In 2005, Irina and Michael Conboy at Stanford asked whether young blood could rejuvenate old tissue.

Years later, this fueled a wave of longevity-pilled “young blood” headlines, startups, and even Bryan Johnson’s experiments using his son’s plasma.

But what does the science really show, and where did the young blood story go off the rails?

[Micha Breakstone @MichaBreakstone]

“Where does biology actually compute?”

I had the privilege of unpacking this question today on a fantastic panel with 2 of the sharpest minds in the space: @iamjohnnyyu of @tahoe_ai & @glebkuz of @ManifoldBio, masterfully moderated by @FabioZB_I of Polyphron. 🧵

[ilyas sahin, MD @ilyassahinMD]

AI in pathology may be entering a new phase.

A new @NatureMedicine Research Briefing * highlights SPARK, an “agentic AI” system that did more than detect patterns on pathology slides, it autonomously generated biological hypotheses from pathology and spatial omics data.

The system linked tumor morphology with prognosis, metastasis, MSI, PD-L1 status, and even possible tumor evolution patterns across multiple cancer types.

Still early and entirely retrospective, but the idea is fascinating: AI not only helping diagnose cancer, but potentially helping researchers discover new biology.