Interpretable machine learning classifiers implicate GPC6 in Parkinson's disease from single-nuclei midbrain transcriptomes

Abstract Parkinson’s disease (PD) is a progressive and devastating neurodegenerative disease. An incomplete understanding of its genetic architecture remains a major barrier to the clinical translation of targeted therapeutics, necessitating novel approaches to uncover elusive genetic determinants. Single-cell and single-nuclear RNA sequencing (scnRNAseq) can help bridge this gap by profiling individual cells for disease-associated differential gene expression and nominating genes for targeted genomic analyses. Here, we introduce a machine learning framework to identify molecular features that characterize post-mortem brain cells from PD patients. We train classifiers to distinguish between PD and healthy cells, then decode the models to unravel the ‘reasons’ behind the classifications, revealing key genes expression signatures that characterize cells from the parkinsonian brain. Application of this framework to three publicly available snRNAseq datasets characterizing the post-mortem midbrain identified cell-type-specific gene sets that accurately classify PD cells across all datasets, demonstrating our approach’s capacity to identify robust molecular markers of disease. Targeted genomic analyses of the key genes characterizing PD cells revealed a previously undescribed association between PD and rare variants in GPC6, a member of the heparan sulfate proteoglycan family, which have been implicated in the intracellular accumulation of α-synuclein preformed fibrils. We replicate this association in three separate case-control cohorts. Our method promises to enhance understanding of the genetic architecture in complex diseases like PD, representing a critical step toward targeted therapeutics. Our publicly available framework is readily applicable across diseases. Competing Interest Statement The authors have declared no competing interest. Funding Statement This work was supported by the Michael J. Fox Foundation [MJFF-021629 to EAF, SMKF, and RAT] and a Fonds d'Acceleration des Collaborations en Sante (FACS) grant from CQDM/MEI [to EAF]. 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: The study only used openly available human data. SnRNAseq data prepared by Kamath et al. is available from the Single Cell Portal (https://singlecell.broadinstitute.org/single_cell/study/SCP1768). SnRNAseq data prepared by Wang et al. is available from the GEO with accession code GSE184950. SnRNAseq data prepared by Smajic et al. is available from the GEO with accession code GSE157783. ScRNAseq data chracterizing the iPSC-derrived DaNeurons prepared by Bressan et al. are available from the PPMI database (www.ppmi-info.org/access-dataspecimens/download-data), RRID:SCR 006431. The genomics data from the PD Genome Project and IPDGC Exome Sequencing Project are available from the Parkinson's Disease Variant Browser (https://pdgenetics.shinyapps.io/VariantBrowser/). 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 Footnotes ↵* Shared co-senior authorship michael.fiorini{at}mail.mcgill.ca; jialun.li{at}mail.mcgill.ca; ted.fon{at}mcgill.ca;