Thermo Fisher Scientific completed an Olink Explore HT proteomic analysis of approximately 5,500 research samples from the Michael J. Fox Foundation’s (MJFF) landmark study, the Parkinson’s Precision Medicine Initiative (PPMI). The resulting data are now in PPMI’s data repository, where they are available to the global research community.

The project creates a large-scale proteomics resource designed to help researchers study Parkinson’s disease at the protein level and investigate biological signals associated with disease progression, patient heterogeneity, and potential biomarker development. By applying Olink technology to deeply characterized PPMI samples, Thermo Fisher officials said the company is expanding access to MJFF’s dataset that may help advance precision medicine approaches to Parkinson’s research.

Deep proteomic insights

An estimated 10 million people worldwide are living with Parkinson’s disease, and that number is expected to rise as populations age. Although the disease is still diagnosed and monitored largely through clinical symptoms, research increasingly shows Parkinson’s disease involves multiple biological pathways and distinct patient subtypes. That complexity has made it difficult to develop biomarkers that reliably identify patient groups, track disease progression, and support more targeted therapeutic development.

As the field shifts toward biology-driven precision medicine, large-scale proteomic datasets are helping researchers better understand disease biology and generate hypotheses that can be tested across cohorts and methods.

Olink, part of Thermo Fisher, was designed to enable high-throughput, affinity-based proteomic analysis using Proximity Extension Assay (PEA) technology. Olink’s PEA-based platform is a scalable solution for protein biomarker analysis, supporting applications from discovery through translation and population-scale proteogenomics, according to Thermo.

Proteomics can help researchers identify patient subtypes, characterize disease progression, uncover biological pathways linked to inflammation, lysosomal function, and neuronal stress, and discover candidate biomarkers for future validation. Combining proteomic insights with longitudinal clinical, genetic, imaging, and other molecular data can further enrich understanding of health and disease.

Defining Parkinson’s by its underlying biology

PPMI, sponsored by the Michael J. Fox Foundation, launched in 2010 and was recently renamed the Parkinson’s Precision Medicine Initiative to reflect the field’s shift toward defining Parkinson’s by its underlying biology rather than clinical symptoms alone. PPMI’s open-access data are available to researchers worldwide and have been downloaded more than 50 million times.

“Parkinson’s disease is incredibly complex, and understanding the biological changes that drive its onset and progression requires looking across many layers of biology,” explained Samantha Hutten, PhD, principal biomarker scientist, translational research, MJFF. “This is what the Foundation’s global PPMI study was built to do. By expanding proteomic analyses within the study, including through partners like Olink, we’re creating new opportunities to identify biomarkers and uncover pathways that may lead to earlier diagnosis, better disease monitoring, and more targeted therapeutic approaches for people living with Parkinson’s disease.”

The analysis was completed as proteomics and multi-omics approaches gain broader use in neurodegenerative disease research. These methods can reveal dynamic biological changes that may not be captured through genomics or clinical assessment alone. Biobanks and large population studies are also adopting proteomic analysis to build richer molecular datasets that support biomarker discovery and precision medicine.

“Discovery is only the beginning in Parkinson’s research,” said Yan Zhang, PhD, president of proteomic sciences at Thermo Fisher. “The next challenge is determining which molecular signals are reproducible, clinically meaningful, and useful for advancing Parkinson’s precision medicine. Making these data available to the research community helps put that validation work into motion.”

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