Large-scale comparison of immunoassay- and aptamer-based plasma proteomics through genetics and disease

Abstract

High-throughput proteomics platforms measuring thousands of proteins in blood combined with genomic information have the power to bridge the gap between the genome and diseases and in that capture some of the environmental contributions to their risk and pathogenesis. Although such methods have already demonstrated their utility, the validation of their actual protein targets is lacking. Here we present a large-scale analysis of levels of proteins in plasma and protein quantitative trait loci (pQTLs) detected using the Olink Explore 1536 (1,459 immunoassays) in 47,151 European participants from the UK Biobank with 57.7 million imputed sequence variants. We compared the results with those of a large-scale SomaScan v4 study (35,559 participants and 4,907 aptamer-based assays) in order to assess and compare the qualities of these two platforms. The correlation between levels of proteins targeted by the two platforms is modest (median Spearman correlation 0.46). The vast majority of assays on the Olink Explore platform had cis pQTLs, evidence that they correctly target their intended proteins (84%), while the assays on the SomaScan v4 platform were half as likely to have cis pQTLs (38%). We also highlight novel pQTLs discovered using the Olink Explore platform, not captured by SomaScan v4, and describe their colocalization with disease-associated sequence variants as well as associations between protein levels and diseases. Our results further underscore the value of proteomics data and highlight the major differences in quality between the two most commonly used high-throughput proteomics platforms.

Scalable search of massively pooled nucleic acid samples enabled by a molecular database query language

“…Should the integration of automation and microfluidic technologies, along with the advent of alternative encapsulation chemistries beyond silica, streamline the encapsulation and barcoding processes, we foresee the inclusion of additional biomarkers such as proteins and metabolites in our storage and query system, forming a general-purpose biosample database. Moreover, with the ongoing transformation of immunoassays (37) and spatial tissue data (38) into DNA molecules, we anticipate that the encapsulation and barcoding approach that we have demonstrated here can be used to store and query a comprehensive range of genomic, transcriptomic, and proteomic data. Finally, the prospect of encoding digital data, such as health records, into DNA, envisages a future where vast biological information could be efficiently stored, marking a significant leap forward in the compact and versatile storage of biological information in the palm of the hand.…”