Stability and reproducibility of proteomic profiles in epidemiological studies: comparing the Olink and SOMAscan platforms

Abstract

Limited data exist on the performance of high‐throughput proteomics profiling in epidemiological settings, including the impact of specimen collection and within‐person variability over time. Thus, the Olink (972 proteins) and SOMAscan7Kv4.1 (7322 proteoforms of 6596 proteins) assays were utilized to measure protein concentrations in archived plasma samples from the Nurses’ Health Studies and Health Professionals Follow‐Up Study. Spearman's correlation coefficients (r) and intraclass correlation coefficients (ICCs) were used to assess agreement between (1) 42 triplicate samples processed immediately, 24‐h or 48‐h after blood collection from 14 participants; and (2) 80 plasma samples from 40 participants collected 1‐year apart. When comparing samples processed immediately, 24‐h, and 48‐h later, 55% of assays had an ICC/r ≥ 0.75 and 87% had an ICC/r ≥ 0.40 in Olink compared to 44% with an ICC/r ≥ 0.75 and 72% with an ICC/r ≥ 0.40 in SOMAscan7K. For both platforms, >90% of the assays were stable (ICC/r ≥ 0.40) in samples collected 1‐year apart. Among 817 proteins measured with both platforms, Spearman's correlations were high (r > 0.75) for 14.7% and poor (r < 0.40) for 44.8% of proteins. High‐throughput proteomics profiling demonstrated reproducibility in archived plasma samples and stability after delayed processing in epidemiological studies, yet correlations between proteins measured with the Olink and SOMAscan7K platforms were highly variable.

Document Details

Document Type
Pub Defense Publication
Publication Date
May 31, 2022
Source ID
10.1002/pmic.202100170

Entities

People

  • A Heather Eliassen
  • Andrew T Chan
  • Danielle E Haslam
  • Frank B. Hu
  • Jun Li
  • Kathryn L. Terry
  • Liming Liang
  • Meir J. Stampfer
  • Naoko Sasamoto
  • Oana A Zeleznik
  • Robert E. Gerszten
  • Samia Mora
  • Shilpa N. Bhupathiraju
  • Simon T. Dillon
  • Towia A Libermann
  • Xuehong Zhang
  • Xuesong Gu
  • Yin Cao
  • Zsu‐zsu Chen

Organizations

  • Cancer Research UK
  • Harvard Medical School
  • Harvard University
  • J. Willard and Alice S. Marriott Foundation
  • National Institutes of Health
  • United States Department of Defense
  • Washington University in St. Louis

Tags

Fields of Study

  • Biology

Readers

  • Molecular Genetics
  • Oncology and Biomarker-Based Cancer Detection.
  • Regression Analysis.

Technology Areas

  • Biotechnology