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Synthetic plasma pool cohort correction for affinity-based proteomics datasets allows multiple study comparison

Dries Heylen, Murih Pusparum, Jurgis Kuliesius, Jim Wilson, Young-Chan Park, Jacek Jamiołkowski, Valentino D'Onofrio (UGent) , Dirk Valkenborg, Jan Aerts, Gökhan Ertaylan, et al.
(2024) BRIEFINGS IN BIOINFORMATICS. 26(1).
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Abstract
Proteomics stands as the crucial link between genomics and human diseases. Quantitative proteomics provides detailed insights into protein levels, enabling differentiation between distinct phenotypes. OLINK, a biotechnology company from Uppsala, Sweden, offers a targeted, affinity-based protein measurement method called Target 96, which has become prominent in the field of proteomics. The SCALLOP consortium, for instance, contains data from over 70.000 individuals across 45 independent cohort studies, all sampled by OLINK. However, when independent cohorts want to collaborate and quantitatively compare their target 96 protein values, it is currently advised to include 'identical biological bridging' samples in each sampling run to perform a reference sample normalization, correcting technical variations across measurements. Such a 'biological bridging sample' approach requires each of the involved cohorts to resend their biological bridging samples to OLINK to run them all together, which is logistically challenging, costly and time-consuming. Hence alternatives are searched and an evaluation of the current state of the art exposes the need for a more robust method that allows all OLINK Target 96 studies to compare proteomics data accurately and cost-efficiently. To meet these goals we developed the Synthetic Plasma Pool Cohort Correction, the 'SPOC correction' approach, based on the use of an OLINK-composed synthetic plasma sample. The method can easily be implemented in a federated data-sharing context which is illustrated on a sepsis use case.
Keywords
proteomics, biomarkers, normalization, protein quantification

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MLA
Heylen, Dries, et al. “Synthetic Plasma Pool Cohort Correction for Affinity-Based Proteomics Datasets Allows Multiple Study Comparison.” BRIEFINGS IN BIOINFORMATICS, vol. 26, no. 1, 2024, doi:10.1093/bib/bbae657.
APA
Heylen, D., Pusparum, M., Kuliesius, J., Wilson, J., Park, Y.-C., Jamiołkowski, J., … Hooyberghs, J. (2024). Synthetic plasma pool cohort correction for affinity-based proteomics datasets allows multiple study comparison. BRIEFINGS IN BIOINFORMATICS, 26(1). https://doi.org/10.1093/bib/bbae657
Chicago author-date
Heylen, Dries, Murih Pusparum, Jurgis Kuliesius, Jim Wilson, Young-Chan Park, Jacek Jamiołkowski, Valentino D’Onofrio, et al. 2024. “Synthetic Plasma Pool Cohort Correction for Affinity-Based Proteomics Datasets Allows Multiple Study Comparison.” BRIEFINGS IN BIOINFORMATICS 26 (1). https://doi.org/10.1093/bib/bbae657.
Chicago author-date (all authors)
Heylen, Dries, Murih Pusparum, Jurgis Kuliesius, Jim Wilson, Young-Chan Park, Jacek Jamiołkowski, Valentino D’Onofrio, Dirk Valkenborg, Jan Aerts, Gökhan Ertaylan, and Jef Hooyberghs. 2024. “Synthetic Plasma Pool Cohort Correction for Affinity-Based Proteomics Datasets Allows Multiple Study Comparison.” BRIEFINGS IN BIOINFORMATICS 26 (1). doi:10.1093/bib/bbae657.
Vancouver
1.
Heylen D, Pusparum M, Kuliesius J, Wilson J, Park Y-C, Jamiołkowski J, et al. Synthetic plasma pool cohort correction for affinity-based proteomics datasets allows multiple study comparison. BRIEFINGS IN BIOINFORMATICS. 2024;26(1).
IEEE
[1]
D. Heylen et al., “Synthetic plasma pool cohort correction for affinity-based proteomics datasets allows multiple study comparison,” BRIEFINGS IN BIOINFORMATICS, vol. 26, no. 1, 2024.
@article{01JFEXGZP5MDSYN12TC485ZXHW,
  abstract     = {{Proteomics stands as the crucial link between genomics and human diseases. Quantitative proteomics provides detailed insights into protein levels, enabling differentiation between distinct phenotypes. OLINK, a biotechnology company from Uppsala, Sweden, offers a targeted, affinity-based protein measurement method called Target 96, which has become prominent in the field of proteomics. The SCALLOP consortium, for instance, contains data from over 70.000 individuals across 45 independent cohort studies, all sampled by OLINK. However, when independent cohorts want to collaborate and quantitatively compare their target 96 protein values, it is currently advised to include 'identical biological bridging' samples in each sampling run to perform a reference sample normalization, correcting technical variations across measurements. Such a 'biological bridging sample' approach requires each of the involved cohorts to resend their biological bridging samples to OLINK to run them all together, which is logistically challenging, costly and time-consuming. Hence alternatives are searched and an evaluation of the current state of the art exposes the need for a more robust method that allows all OLINK Target 96 studies to compare proteomics data accurately and cost-efficiently. To meet these goals we developed the Synthetic Plasma Pool Cohort Correction, the 'SPOC correction' approach, based on the use of an OLINK-composed synthetic plasma sample. The method can easily be implemented in a federated data-sharing context which is illustrated on a sepsis use case.}},
  articleno    = {{bbae657}},
  author       = {{Heylen, Dries and Pusparum, Murih and Kuliesius, Jurgis and Wilson, Jim and Park, Young-Chan and Jamiołkowski, Jacek and D'Onofrio, Valentino and Valkenborg, Dirk and Aerts, Jan and Ertaylan, Gökhan and Hooyberghs, Jef}},
  issn         = {{1467-5463}},
  journal      = {{BRIEFINGS IN BIOINFORMATICS}},
  keywords     = {{proteomics,biomarkers,normalization,protein quantification}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{9}},
  title        = {{Synthetic plasma pool cohort correction for affinity-based proteomics datasets allows multiple study comparison}},
  url          = {{http://doi.org/10.1093/bib/bbae657}},
  volume       = {{26}},
  year         = {{2024}},
}
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