Naveen Jain

How does a cell learn? In our new perspective in @Nature we propose a model where the properties of the AP-1 family of transcription factors – stress-induced feedback, regulatory combinatorics & cellular memory – encode a mechanism for cellular learning. nature.com/articles/s4158…

We’re unwrapping a new method today @MSKCancerCenter! Led by @SydneyBlattman, @NabihMaslah, @AustinAVarela, Ronan Chaligne and @dana_peer, plus colleagues at @10xGenomics, we’re delighted to share Genotyping In Fixed Transcriptomes (GIFT): biorxiv.org/content/10.648… 1/n

Our Human Multiomic Development Atlas paper is out in Nature today! A heart-felt "thank you" to all co-authors for their tireless work on this complex yet exciting project! Congrats all! nature.com/articles/s4158…

🤔How do you design a promoter for stable titration of expression? 💡Try programmable promoter editing with DIAL! 📰Now published at @NatureBiotech 🧵 [1/n] 🔗 below

Review @NatureCancer @BizarMd @elhamazizi @Columbia Single-cell and spatial profiling in cancer biology and clinical oncology nature.com/articles/s4301…

ft. J. Li, @PavithranRavind, @ryanboe0, @uffdaALLberries (@CAMBUpenn), @O_Farrell_Aoife, @giannatbusch, M. Dunagin, Y. Goyal (@pennbioeng), Z. Niu, S. Woo (@PennSAS), @sarma_lab, M. Herlyn (@TheWistar) & @arjunrajlab (@PennGenetics) @naturecomms nature.com/articles/s4146…

New preprint with Garnett lab 🚀: from descriptive → causal single-cell atlases in CRC.We build the largest CRC atlas (>300 pts, 1.5M cells) using continual learning, and link cell states to causal drivers via Tahoe-100M, validated in organoids! biorxiv.org/content/10.648…

Pre-existing cell states predict resistance to multiple treatments dlvr.it/TRn0Zw

Single-cell technologies now let us profile entire transcriptomes in individual cells. But how do we make sense of this complexity in a biologically meaningful way? Many methods summarise cells into a single embedding, but this often comes at the cost of interpretability, especially when multiple gene programs are active at once. We developed Tripso, a self-supervised transformer model that represents cells through multiple gene program-specific embeddings, while also uncovering new programs directly from the data. Instead of collapsing biology into a single vector, Tripso decomposes cell state into multiple representations, each reflecting a different gene program. We explored this across multiple systems. In human hematopoiesis, spanning development to aging, Tripso identified distinct age-associated program activity, including stronger JAK-STAT signalling in early life and dynamic IKZF1-related changes during B cell maturation. By comparing in vitro culture conditions with in vivo hematopoietic stem cell states, Tripso suggested that targeting the SEC61 translocon could enhance stem cell maintenance ex vivo, a prediction that we subsequently validated experimentally. In parallel, we identified a previously uncharacterised tissue-resident memory T-cell program associated with atopic dermatitis and mapped it to distinct spatial immune niches Together, these results show how modelling cells through gene programs can lead to interpretable and experimentally testable insights. More broadly, this work points toward a more interpretable and biologically grounded models of cell state. As single-cell datasets continue to grow, we hope approaches like Tripso will help bridge the gap between data-driven representations and biological insight. This work wouldn’t have been possible without the contributions of an amazing team. Thank you to co-first authors @mariemoullet, @Tomo_Isobe, @AmirhVahidi, @CarloLeonardi7, and everyone from @roserventotormo's Lab, @HaniffaLab, Nicola Wilson and @BertieGottgens's Lab, bringing together expertise across @SCICambridge, @OpenTargets, @sangerinstitute and @Cambridge_Uni. @mariemoullet is one of the very best PhD students I have ever supervised. She is truly a force of nature, exceptionally resourceful, deeply innovative, and one of the most impressive scientists I have worked with. I am immensely proud of her and all that she has accomplished. As she begins her internship at @genentech , I have no doubt she will do amazing work there and continue to make her mark. paper:biorxiv.org/content/10.648… code: github.com/Lotfollahi-lab…

❓A one-time, durable, and even reversible way to prevent thrombosis? In this preprint, led by @lrbzldz, we show that epigenome editing of 🩸 #StemCells can durably reprogram platelet function: biorxiv.org/content/10.648…

Advancing precision health discovery in a genetically diverse health system @CellCellPress @GeschwindLab @HaasRoni cell.com/cell/fulltext/…

What if a small molecule could activate a transcription factor program in one cell type and destroy the same pathway in another? In our new preprint, we describe one such story on bifunctional molecules that toggle between transactivation and repression.

Duck gliding through cherry blossom water

The most jarring part of “Project Hail Mary” wasn’t the sentient rock organism, astrophage rocket propulsion, or xenon-eating alien bacteria, but was when Ryan Gosling ran a centrifuge with an unbalanced arrangement of microfuge tubes. Honestly a jump scare for me!

MSK-IMPACT is truly the gift that keeps on giving! Cancer type-specific variation in patterns of driver alterations across 50,000 tumors (!!) cell.com/cancer-cell/fu… "One-third of all drivers arose in non-canonical contexts" Congratulations @MFBerger1 @DSolit & team.

Excited to share my postdoc work with @JD_Buenrostro now out in @Nature! "Epigenetic memory of colitis promotes tumour growth" nature.com/articles/s4158… We wanted to understand how transient inflammation can create a long-lived increase in cancer risk, even after full recovery 🧵

ft. @SamReffsin, Margaret Dunagin, @yael_heyman, @arjunrajlab (@pennbioeng), Jesse Miller, Kasirajan Ayyanathan, Sara Cherry (@PennPathLabMed), @uffdaALLberries (@CAMBUPenn) & @UpennHTSC (@BB_UPenn) @PennGenetics @PennMicro @CellPressNews sciencedirect.com/science/articl…

In 1923 Warburg published a paper showing that cancer cells generated energy by a metabolism that differed from normal cells. Fast forward to 2026. A group of @StanfordMed scientists genetically modify immune cells to 'sense' the changes in metabolism of cancer cells and home in on the tumor cells, killing them. Imagine programming 'blood hound' immune cells to kill cancers, not based on 20th century surface proteins, but on their broken way of making energy, ATP. This is the kind of exciting immunology research that belongs in a 21st century NIAID. The paper: doi.org/10.1038/s41590…

So excited to see this paper from @jess_ljx out in final form! One of the most beautiful papers from our lab: shows that a single cell can learn by forming memories to adapt to new challenges.

🚨🧬🩸 Delighted to have this @NatureRevGenet review w/ @michaelpoeschla published today - Genetic Influences on #hematopoiesis: rdcu.be/e9vlD