Azenta to provide proteomics profiling for Finnish personalised medicine project
The FinnGen project, a large-scale Finnish research initiative in personalised medicine, has announced a collaboration with Azenta, Inc. to provide proteomics profiling for up to 20,000 individuals. This partnership aims to integrate proteomic insights with existing genomic data, with the aim advancing our understanding of complex diseases and paving the way for more targeted treatments.
Background of the FinnGen project
The FinnGen project is a public-private partnership that has collected genomic and health data from 500,000 Finnish biobank participants. This ambitious endeavour seeks to generate medically and therapeutically relevant insights whilst creating a valuable resource for future studies.
Professor Aarno Palotie, FinnGen Scientific Director from the Institute for Molecular Medicine Finland (FIMM), University of Helsinki, explained the significance of incorporating proteomics into the study: “FinnGen’s adoption of proteomics marks a pivotal advancement in our quest to decode the complexities of human health and disease. By integrating the dynamic insights of proteomics with our extensive genomic data, we can gain a deeper understanding of disease mechanisms, identify new biomarkers for early detection, and uncover novel therapeutic targets.”
Azenta’s role in the collaboration
Azenta will employ Olink’s advanced proteomics technology, which utilises the proprietary Proximity Extension Assay, to provide high-quality protein-level measurements across major biological pathways. This comprehensive approach is expected to yield valuable data that can be integrated with the existing genomic information.
Dr Ginger Zhou, Senior Vice President and General Manager of GENEWIZ Multiomics & Synthesis Solutions at Azenta Life Sciences, commented on the potential impact of their involvement: “Multiomics technologies from Azenta enable researchers to efficiently generate detailed proteomics data from study participants carrying medically and clinically significant genetic variants, opening up a world of possibilities for understanding disease mechanisms and developing targeted treatments.”
Implications for personalised medicine
The integration of proteomic data with the extensive genomic and health information already collected by FinnGen represents a significant step forward in the field of personalised medicine. By analysing both genetic and protein-level data, researchers may be able to:
1. Identify new biomarkers for early disease detection
2. Gain deeper insights into disease mechanisms
3. Discover novel therapeutic targets
4. Develop more targeted and effective treatments
This multifaceted approach could potentially lead to more precise diagnoses and tailored treatment strategies, ultimately improving patient outcomes.
Challenges and considerations
Whilst the addition of proteomics data to the FinnGen project holds great promise, it is important to consider the challenges that may arise. These could include:
1. Data integration complexity: Combining large-scale genomic and proteomic datasets presents significant computational and analytical challenges.
2. Interpretation of results: The interplay between genetic variants and protein expression is complex, requiring sophisticated analysis to draw meaningful conclusions.
3. Translating findings into clinical practice: Bridging the gap between research insights and practical applications in healthcare settings may require substantial time and resources.
Future prospects
The collaboration between Azenta and the FinnGen project represents a significant step towards a more comprehensive understanding of human biology and disease. As one of the first personalised medicine projects of this scale to incorporate both genomic and proteomic data, FinnGen may serve as a model for future large-scale biomedical research initiatives.
The insights gained from this project could potentially inform drug discovery efforts, improve risk prediction models, and contribute to the development of more targeted therapeutic interventions. Moreover, the public-private collaborative nature of FinnGen may provide valuable lessons for future partnerships between academia, healthcare institutions, and industry.
