On data-driven affinity proteomics analysis

QC 2026-05-19

Abstract

Proteins are diverse biological macromolecules that play essential roles in many biological functions. The study of proteins has led to important biological and medical discoveries. With the advancement of technology, an increasing number of proteins can be measured in a single experiment. Today, from just a drop of blood, we can measure hundreds or thousands of proteins in the study of proteomes. This field of study is called proteomics and allows us to survey the vast array of proteins in our bodies to discover biological relationships that can improve our understanding of health and disease.

As the number of proteins that we can measure increases, so does the burden of analysing the increasingly large data sets. Data analysis pipelines must be crafted with care at every step, from data preprocessing to analysis, visualisation, and presentation. This thesis presents studies that showcase how one may go about interacting with large affinity proteomics data sets. 

To study the proteome we must be able to reliably measure proteins. Antibodies are used heavily in affinity proteomics for their excellent sensitivity. Their selectivity, however, must be validated thoroughly to ensure that we are measuring the correct protein. In study A, we validate antibodies targeting a clinically important family of proteins. The results have been published in an interactive web application open for anyone to browse.

The type of biological sample we use influences what proteins are present and what research questions we can answer. Blood is a practical sample type for its minimal invasiveness and its ability to provide a systemic view of an individual’s health. Dried blood spots (DBS) can be used as an alternative to venous blood draws that may also be performed without medical expertise, allowing remote self-sampling. In study B we perform a population study during the COVID-19 pandemic using DBS and demonstrate the feasibility of the sampling method for population proteomics. Study C expands on the previous study, establishing a general pipeline for immune phenotype exploration. These studies demonstrate the utility of the sampling and data analysis methods for profiling immune phenotypes through serology and proteomics. In study D we explore the biological differences between DBS and blood plasma in a large proteome survey using multiple proteomics technologies. Together, these studies provide insights into the dried blood proteome and how such data may be processed and analysed.

While the full breadth of proteomics and the analysis of such data cannot be captured in this one thesis, the work presented herein provides important contributions toward the reproducible analysis of large-scale affinity proteomics data.

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