Mo Lotfollahi
@mo_lotfollahi • 12,977 subscribers
ML for biology and drug discovery | Faculty @sangerinstitute @Cambridge_uni
Videos
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 Marie, Tomoya Isobe, Amirhosein Vahidi, Carlo Leonardi, and everyone from roser's Lab, Haniffa Lab, Nicola Wilson and Bertie Gottgens's Lab, bringing together expertise across Cambridge Stem Cell Institute, Open Targets, Wellcome Sanger Institute and Cambridge University. Marie 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: code:
Mo Lotfollahi22,373 views • 2 months ago
Excited to share our new work. Over the past decade, single-cell genomics has transformed our ability to map cellular systems. But a major question remains: Can we predict how perturbations reshape cellular trajectories over time? In 2018, we first showed that it is possible to predict cellular responses to perturbations — ranging from disease signals to chemical treatments — even in unseen contexts. In 2022, we introduced CPA (MSB 2022; NeurIPS 2022), extending this idea to predict responses to unseen chemical and genetic perturbations, including their combinations. Since then, the field of perturbation modeling has grown enormously. The community has pushed the space forward with many creative ideas and powerful models. It’s exciting to see how fast things are moving — even though many fundamental challenges remain. One of the biggest is that cells are not static. They move through trajectories during development, immune responses, and disease. Yet most current models still predict perturbation effects within a single state, rather than how early perturbations propagate across future states and reshape downstream outcomes. To address this, we developed PerturbGen, a trajectory-aware generative AI model that predicts how genetic perturbations reshape downstream cellular states. Huge credit to the people who made this work possible. Thanks to co-first authors Kevin Ly, Adib Miraki, Tomoya Isobe, AmirHoss3in Vahidi, Delshad Vaghari & Anthony Rostron. Special recognition to Kevin Ly and Adib Miraki for driving this work over the finish line. Grateful for our outstanding collaborators from Haniffa Lab, Bertie Gottgens lab Gosia Trynka and many others — a true cross-institute effort across Cambridge Stem Cell Institute, Open Targets ,Wellcome Sanger Institute and Cambridge University.🎉 PerturbGen learns transcriptional dynamics across cellular trajectories. By introducing perturbations at an early source state, it can simulate how these effects propagate into future states along differentiation trajectories. Scaling this across genes enables the creation of dynamic in silico perturbation atlases — maps of how perturbations reshape biological trajectories over time. We explored this idea across three biological questions. First, in a human in vivo LPS immune challenge, PerturbGen predicted that perturbing a transient IL1B signal dampens downstream inflammatory programs in myeloid cells, with pathway changes reversing signatures observed in an independent IL-1β stimulation experiment. Second, in human hematopoiesis, PerturbGen predicted transcriptional responses to CRISPR transcription factor knockouts and enabled construction of perturbation atlases revealing lineage- and age-specific regulatory programs. These programs could also be linked to human genetics and blood diseases, including recapitulation of signatures associated with ETV6-related thrombocytopenia. Finally, we asked whether perturbation modeling could help improve complex tissue models. We built a dynamic perturbation atlas of human skin organoids to identify perturbations that could guideorganoid cells towardhuman fetal skin states. PerturbGen prioritized activation of Wnt signaling via GSK3β inhibition. Experimental validation confirmed the prediction: treatment with CHIR99021 induced stromal gene programs and shifted organoid fibroblasts toward transcriptional states observed in fetal skin stroma. Together, these results show how trajectory-aware perturbation modeling can connect gene perturbations to developmental programs, human genetics, disease mechanisms, and experimental interventions. More broadly, we think these point toward a future where single-cell atlases become predictive systems. As atlases expand across tissues, developmental windows, and modalities, models like PerturbGen could enable dynamic, virtual perturbation atlases— allowing us to simulate interventions, generate hypotheses, and design experiments before stepping into the lab. Preprint Code Excited to see how the community builds on this work.
Mo Lotfollahi16,947 views • 3 months ago
Excited to share our new work on building a multimodal atlas of human skin in health and inflammatory disease — a project I’m especially proud of, bringing together AI, high-throughput genomics, and clinical science to accelerate discovery. Over the past decade, single-cell genomics has transformed how we map cells in human tissues. But a major challenge remains: can we systematically decode how cells organize into functional niches in situ — including those invisible to standard histopathology? To address this, we integrated large-scale scRNA-seq, spatial transcriptomics, histopathology, and AI-driven modeling frameworks to build an in situ atlas of human skin across health and disease. Led by Lloyd Steele, an MD/PhD student working between Haniffa Lab and my lab at Wellcome Sanger Institute and Cambridge University . Another amazing collaboration with Muzz Haniffa, the mastermind behind the work as part of Human Cell Atlas. A key part of this study is that we didn’t build everything from scratch — we leveraged and combined AI methods that actually work! and showed how they can be used together to extract biological insight at scale. We used: • scArches to build and map into a reference scRNA-seq atlas of human skin: • NicheCompass to identify and characterize spatial niches: • MINT-Flow to extract microenvironment-induced cell states and gene programs: Together, these enabled an end-to-end workflow from atlas construction to spatial mapping, niche discovery, and cell state decoding. At scale, we integrated ~5 million cells and 100+ spatial sections, enabling a systematic view of tissue organization. Using this framework, we identified 26 niches in skin, including known histopathologic structures as well as hidden disease-associated niches not visible on H&E. Among the most striking findings were a resident memory T cell-rich sebaceous gland niche and a plasma cell-rich sweat gland niche, suggesting that appendageal structures act as active immunological microenvironments and may contribute to inflammatory memory and disease persistence. Importantly, this atlas is not just descriptive — it is usable. It can support mapping of new datasets, resolve finer cell types and niches, extract microenvironment-driven programs, and enable predictive analyses at scale. More broadly, this work shows what becomes possible when AI, spatial genomics, and atlas-scale data are integrated end-to-end: not just mapping tissues, but systematically decoding them. This was a massive collaboration, and I’m very grateful to the amazing scientists April Foster, Kenny Roberts, and Chloe Admane. Lloyd is an amazing scientist, and I’m especially excited for the community to see more of his work soon — stay tuned. The data and pre-trained models will be released soon. Preprint:
Mo Lotfollahi11,723 views • 2 months ago
1/n: 🔬We introduce NicheCompass (NC) a graph learning model to build spatial atlases across millions of cells, quantitatively characterize cell niches, infer cellular communication, and map new data into these atlases. NC can be used with various spatial technologies, ranging
Mo Lotfollahi61,747 views • 2 years ago
1/10 image-based screening advanced drug discovery but scaling to massive perturbation space is hard! Given a cell image, we asked if we could predict the morphological effect induced by a perturbation! Led by Alessandro Palma & w Fabian Theis we propose IMPA
Mo Lotfollahi40,650 views • 2 years ago
No more content to load