Analysis of Targeted Proteomics data across different datasets with reference characteristics

Abstract

Background: An early diagnosis or the detection of signals, markers, and patterns that indicate harmful events, before the disease can develop is crucial in many diseases. For infectious diseases the characterization of the inflammatory response can assist in making a rapid clinical assessment of early infection diagnosis For chronic diseases the characterization of disease-associated parameters can assist in representing a disease as an individual specific health continuum. The objective of this study is to develop an approach for the analyses of high-dimensional multi-batch proteomics datasets and to develop a pipeline to analyze multi-dimensional data keeping the control dataset as the reference for parameter range estimations. This to discover the relationships across and within these datatypes, and to identify relevant patterns. Methods: In a pilot project (I AM Frontier), Cross-omics data (+1000 proteins, +250 metabolites) were provided by VITO health. In an attempt to test the central hypotheses of precision medicine In I AM Frontier (IAF), VITO collected blood, urine, stool, activity measurements and anthropometric information from a small cohort of 30 healthy but “at risk” (45-60 years old) individuals, on a longitudinal basis for 13 months. External datasets were included to complement the reference data with robust disease endpoints and to validate our findings with a sepsis dataset consisting of patients with infections of different aetiology. For the targeted proteomics samples were analyzed using 92-plex proteomics panels (including an inflammation panel) based on a proximity extension assay (PEA) with oligonucleotide-labelled antibody probe pairs (OLINK, Uppsala Sweden). Unsupervised differential expression analysis using hierarchical clustering t, k-means clustering and PCA were performed in R. Supervised differential expression analysis using Welch’s t test and elastic net regression analyses where performed to confirm unsupervised analyses. A complete normalization and bridging workflow for multi-batch proteomics experiments across cohorts was applied. Results: We establish a workflow to use IAF data as a reference dataset to analyze targeted proteomics datasets. Inverse normalized rank based transformation of the data followed by cosine similarity calculations of the OLINK pooled plasma samples showed to be a robust method for comparing this type of cross-omics multi batch data across cohorts. A 92-plex proteomics dataset with 406 sepsis patients unsupervised, hierarchical clustering revealed that inflammatory response is more strongly related to disease severity than to aetiology or site of infection. A subgroup of influenza showed to result in clearly distinct inflammatory protein profiles compared to other infections causing sepsis. Conclusions: We built a cross-study integration workflow for targeted proteomics (OLINK) utilizing a uniquely available timeseries dataset from IAF as the reference for determining individual variations per parameter. The proposed workflow is validated in an independent sepsis dataset. Several differentially expressed inflammatory proteins were identified that could be used as biomarkers for sepsis. A promising methodology and data availability is in place to analyze disease profiles of additional (chronic) diseases across cohorts in a search for biomolecular markers. The ranges of the proteins established by our workflow can be of value for outcome prediction, patient monitoring, and directing further diagnostics.