MoMa Awardees Tackle Meta-Analysis of CRN Omics Data
Aligning Science Across Parkinson’s (ASAP) is a global research initiative that brings together cross-sector and interdisciplinary expertise to create collaborative and transparent research processes to accelerate discovery in Parkinson’s disease (PD) research. As part of this effort, a central approach is expanding access to datasets and tools that allow researchers at all career levels to build on past discoveries.
Guided by our open science approach, ASAP launched the Multi-Omics Meta-Analysis (MoMa) funding opportunity to support Collaborative Research Network (CRN) researchers interested in conducting meta-analysis of omics data housed within our publicly accessible database, the CRN Cloud.
Since we launched the CRN in 2020, teams have generated large omics datasets using a diverse set of assays and techniques, including bulk RNAseq, single-nucleotide RNAseq, single-cell RNAseq, ATACseq, and spatial transcriptomics datasets derived from both human and nonhuman models. The CRN Cloud was established in 2024 as a central platform for storing, analyzing, and sharing CRN omics data with researchers around the world, with the goal of streamlining collaboration and supporting reproducible science. With harmonized omics datasets stored in a central location, bypassing the need for local hardware maintenance, the CRN Cloud presents an ideal environment to ascertain patterns across multiple types of data. This approach aims to elucidate novel mechanistic insights behind Parkinson’s disease manifestation and progression that may only be uncovered through a combination of data types.
The Multi-Omics Meta-Analysis Working Group emerged from the CRN to enhance the interpretation of omics data by applying advanced analytical techniques and tools. In 2025, the working group reviewed meta-analysis proposals from CRN researchers that focused on enhancing CRN Cloud data literacy, supporting data re-use, and bridging the gap from genetics to therapeutics. Nine of these projects were selected for funding. Major themes associated with these projects include the exploration of genetic risk, the development of new analytical approaches, and the identification of novel therapeutic targets. Learn more about the exciting work being conducted below!
Team Hardy
Postdoctoral Researcher, UK Dementia Research Institute
Project Title:
Differential Transcript Use Analyses Using Single-Nucleus RNA-Sequencing Data Across PD and Control Samples
Aim:
To provide essential insights into cell-type-specific transcriptional alterations in Parkinson’s disease and potentially identify novel therapeutic targets, such as antisense oligonucleotides, by extending a novel differential 3’ untranslated region (UTR) usage analysis on single-nucleus RNA-sequencing (snRNA-seq) data from the CRN Cloud.
Team Hardy
Postdoctoral Research Fellow, University College London
Project Title:
Multimodal Machine Learning Models for Predicting Lewy Body Pathology Using Post-Mortem Brain Data
Aim:
To use explainable multimodal machine learning (IML) to accurately predict and explain Lewy Body Disease (LBD) pathology and nominate new molecular signatures using post-mortem brain (CRN Cloud) and genetic data (GP2).
Team Scherzer
Research Associate, Yale University
Project Title:
From Omics to Action: A Multimodal Pipeline for Druggable Target Discovery and Real-World Validation in PD
Aim:
To integrate large-scale multi-omics data with an AI-driven druggability framework to identify and prioritize actionable therapeutic targets for Parkinson’s disease, and to enable systematic target discovery, drug repurposing, and real-world validation by combining molecular evidence with real-world patient data.
Team Scherzer
Computational Group Leader, Yale University
Project Title:
Cell Type meta-EQTL Study in Human Brains
Aim:
To map single-cell eQTLs in four additional brain regions using CRN Cloud data. The improved meta brain cell-eQTLs will detect additional PD risk genes and will interpret PD GWAS functions across brain cell types and brain regions.
Team Voet
Professor of Neurology and Neurogenetics, University College London
Project Title:
Detection of Somatic Mutations (Single Nucleotide Variants) Using snRNAseq from Parkinson’s Disease and Control Brain
Aim:
To analyze single-nucleus RNA sequencing (snRNAseq) data from Parkinson’s disease and control brains to detect and compare the frequency and type of somatic single nucleotide variants (SNVs) using the SComatic algorithm.
Team Wood
Research Associate | University College London
Project Title:
Computational Pipeline to Generate Cell Type Specific 5’ UTR Analyses
Aim:
To develop a computational pipeline to generate region-specific 5’ UTRomes from long-read sequencing data and compare them against 3’ UTR analyses to guide optimal sequencing strategies.
Team Chen
Research Associate, Massachusetts General Hospital
Project Title:
Differential Expression of Aging-Related Genes and Co-Expression with SNCA and MAPT in Parkinson’s Disease Patients and Healthy Controls
Aim:
To determine whether aging-related genes are repressed in PD patients and how they interact with alpha-synuclein and tau by analyzing bulk and single-cell RNA sequencing data.
Team Wood
Senior Clinical Research Fellow, University College London
Project Title:
Exploring the Role of Adenosine-to-Inosine RNA Editing in Parkinson’s Disease
Aim:
To develop an RNA editing pipeline that includes CRN Cloud datasets, to generate the world’s largest analysis of RNA editing in PD, aiming to establish A-to-I RNA editing as a mechanistic link between immune activation and neurodegeneration, which will inform future therapeutic strategies.
Team Hafler
Research Associate, Yale University
Project Title:
Integrative Single-Cell and Enhancer Profiling of CSF Immune Cells in Parkinson’s Disease
Aim:
To combine single-cell RNA sequencing data from cerebrospinal fluid (CSF) immune cells to identify immune system changes unique to Parkinson’s disease (PD), and to employ a new computational approach, ReapTEC, to map key gene-regulating elements (enhancers) in these immune cells, connecting genetic risk factors to disease progression to help find new therapeutic targets.
We look forward to sharing all the future discoveries of the MoMa grantees with the research community!
