Cell-DRL Reconstructs Unseen Cellular Paths in Health and Disease

Cell-DRL Reconstructs Unseen Cellular Paths in Health and Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Cell-DRL Reconstructs Unseen Cellular Paths in Health and Disease Abdelrahman Mahmoud, Andy Chan, Sabine Rebs, Katrin Streckfuss-Bomeke, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3461523/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Diseases can be conceived as deviations from healthy cell state manifolds. Numerous methods leverage the power of single-cell RNA sequencing for reconstructing cell state trajectories. Such approaches generally rely on sufficient sampling of cell states covering the entire trajectory. Since patients typically undergo treatment only after symptoms have already manifested, clinical samples covering intermediate disease states are generally unavailable, limiting our ability to understand the underlying intermediate paths of disease evolution. Reconstruction of missing states on disease trajectories is a major open challenge. To overcome the limitations of current approaches, we developed Cell-DRL, a deep reinforcement learning agent capable of geometric reasoning on biological manifolds, generating actions in gene expression space, and learning stochastic policies to reconstruct trajectories connecting two distant anchoring cellular states. We validated Cell-DRL on ground truths scenarios by hiding intermediate states and demonstrated the capacity to reconstruct multipotent hematopoietic stem cell states from distinct lineage-specific progenitors. We showcased the power of Cell-DRL to recover unseen cellular states in healthy as well as disease scenarios at the individual patient level. Finally, Cell-DRL predicted a novel human cardiac fibroblast-to-cardiomyocyte trans-differentiation path, which we validated in vitro. We expect that Cell-DRL will be crucial to gain valuable mechanistic insights into the development and progression of diseases at high temporal resolution. Biological sciences/Computational biology and bioinformatics/Machine learning Biological sciences/Computational biology and bioinformatics/Computational platforms and environments Biological sciences/Systems biology Full Text Additional Declarations Yes there is potential Competing Interest. DG serves on the scientific advisory board of Gordian Biotechnology. Supplementary Files Mahmoudetalsupplementrevisionclean19082025.pdf Extended Data Figures Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3461523","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":503747520,"identity":"07bf40fd-94ce-4d7f-a1a8-0c3fa1a37b7c","order_by":0,"name":"Abdelrahman Mahmoud","email":"","orcid":"","institution":"Broad Institute of MIT and Harvard, Cambridge, MA, USA","correspondingAuthor":false,"prefix":"","firstName":"Abdelrahman","middleName":"","lastName":"Mahmoud","suffix":""},{"id":503747521,"identity":"9ae006af-0546-43a7-b9eb-3378ea8b85a1","order_by":1,"name":"Andy 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Numerous methods leverage the power of single-cell RNA sequencing for reconstructing cell state trajectories. Such approaches generally rely on sufficient sampling of cell states covering the entire trajectory. Since patients typically undergo treatment only after symptoms have already manifested, clinical samples covering intermediate disease states are generally unavailable, limiting our ability to understand the underlying intermediate paths of disease evolution. Reconstruction of missing states on disease trajectories is a major open challenge. To overcome the limitations of current approaches, we developed Cell-DRL, a deep reinforcement learning agent capable of geometric reasoning on biological manifolds, generating actions in gene expression space, and learning stochastic policies to reconstruct trajectories connecting two distant anchoring cellular states. We validated Cell-DRL on ground truths scenarios by hiding intermediate states and demonstrated the capacity to reconstruct multipotent hematopoietic stem cell states from distinct lineage-specific progenitors. We showcased the power of Cell-DRL to recover unseen cellular states in healthy as well as disease scenarios at the individual patient level. Finally, Cell-DRL predicted a novel human cardiac fibroblast-to-cardiomyocyte trans-differentiation path, which we validated in vitro. 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