IMAGE Lab @LabPhan@qoto.org

Although B cells are implicated in multiple sclerosis (MS) pathophysiology, a predictive or diagnostic autoantibody remains elusive. Here, the Department of Defense Serum Repository (DoDSR), a cohort of over 10 million individuals, was used to generate whole-proteome autoantibody profiles of hundreds of patients with MS (PwMS) years before and subsequently after MS onset. This analysis defines a unique cluster of PwMS that share an autoantibody signature against a common motif that has similarity with many human pathogens. These patients exhibit antibody reactivity years before developing MS symptoms and have higher levels of serum neurofilament light (sNfL) compared to other PwMS. Furthermore, this profile is preserved over time, providing molecular evidence for an immunologically active prodromal period years before clinical onset. This autoantibody reactivity was validated in samples from a separate incident MS cohort in both cerebrospinal fluid (CSF) and serum, where it is highly specific for patients eventually diagnosed with MS. This signature is a starting point for further immunological characterization of this MS patient subset and may be clinically useful as an antigen-specific biomarker for high-risk patients with clinically- or radiologically-isolated neuroinflammatory syndromes. ### Competing Interest Statement MRW receives unrelated research grant funding from Roche/Genentech and Novartis, and has received speaking honoraria from Genentech, Takeda, WebMD and Novartis. ### Funding Statement This work was supported by the Valhalla Foundation (SLH, MRW, JRO, BAC), the Westridge Foundation (MRW, HCvB, AJG), NINDS R35NS122073 (SLH, MRW and RD), National Multiple Sclerosis Society RFA-2104-37504 (MRW, MTW, JH, BAC, CRZ), NMSS RFA-2104-3747 (JRO), Bruce Sachs, the UCSF Dean's Office Medical Student Research Program (GMS), Chan Zuckerberg Biohub (JLD, SAM), John A. Watson Scholar Program, UCSF (CMB), Hanna H. Gray Fellowship, Howard Hughes Medical Institute (CMB), NIH NIH R01AI158861 (JAH and KJW) and the University of California President's Postdoctoral Fellowship Program (CMB). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB of University of California, San Francisco gave ethical approval for this work (14-15278) IRB of Veterans Affairs Medical Center gave ethical approval for this work (IRB-1624644-4) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available online at [https://github.com/UCSF-Wilson-Lab/MS\_DoD\_and\_ORIGINS\_study_data][1] [1]: https://github.com/UCSF-Wilson-Lab/MS_DoD_and_ORIGINS_study_data

People living with rare and serious disorders should have access to genetic testing to improve medical care early...

“Some perspective for scientists who may be asked to review computational methods: Computational methods are indeed innovated by taking existing mathematical and algorithmic atoms and putting them together in a novel way. Second, small changes in the steps can have a large…”

“Single-cell genomics meets human genetics https://t.co/BHPDqhJsO5 #Review by @AnnaSECuomo, @aparnanathan, @soumya_boston, @dgmacarthur and @drjosephpowell #singlecellgenetics”

“. @shivemshah just published two 1st author papers @NatureMaterials & @ACSCentSci. Priceless expression from behind-the-scene conversations abt where to publish his PhD work! Will present both work @SFBiomaterials this week. #1 https://t.co/2GY98vUfMe #2 https://t.co/zyl7t4pVtT”

While single-cell technologies have allowed scientists to characterize cell states that emerge during cancer progression through temporal sampling, connecting these samples over time and inferring gene-gene relationships that promote cancer plasticity remains a challenge. To address these challenges, we developed TrajectoryNet, a neural ordinary differential equation network that learns continuous dynamics via interpolation of population flows between sampled timepoints. By running causality analysis on the output of TrajectoryNet, we compute rich and complex gene-gene networks that drive pathogenic trajectories forward. Applying this pipeline to scRNAseq data generated from in vitro models of breast cancer, we identify and validate a refined CD44 hi EPCAM+CAV1 + marker profile that improves the identification and isolation of cancer stem cells (CSCs) from bulk cell populations. Studying the cell plasticity trajectories emerging from this population, we identify comprehensive temporal regulatory networks that drive cell fate decisions between an epithelial-to-mesenchymal (EMT) trajectory, and a mesenchymal-to-epithelial (MET) trajectory. Through these studies, we identify and validate estrogen related receptor alpha as a critical mediator of CSC plasticity. We further apply TrajectoryNet to an in vivo xenograft model and demonstrate it’s ability to elucidate trajectories governing primary tumor metastasis to the lung, identifying a dominant EMT trajectory that includes elements of our newly-defined temporal EMT regulatory network. Demonstrated here in cancer, the TrajectoryNet pipeline is a transformative approach to uncovering temporal molecular programs operating in dynamic cell systems from static single-cell data. ### Competing Interest Statement The authors have declared no competing interest.