Advancements to measure circulating proteins have opened up new possibilities for exploring the blood on a large scale [1]. This progress is set to revolutionize our ability to probe human health at a molecular level and unlock the potential of precision medicine. While proteomics data already offers valuable insights into human health, it is essential to acknowledge that each sample analyzed is unique and merely represents the investigated phenotype [2].
In this presentation, I will discuss our experiences and insights gained from using advanced affinity proteomics approaches in different projects, where we studied circulating proteins to characterize human diseases, health perturbations, disease risk, and the molecular consequences of medication use [3,4]. The overall aim is to better understand the dynamic architecture of the circulating proteome by learning from different phenotypes.
Irrespective of the chosen method, it is crucial to address potential pre-analytical biases, understand influential clinical traits, and assume disease-unspecific effects. We have, for example, characterized person-specific proteomes in longitudinal analyses [5,6] and applied data-driven analysis strategies to reveal subtypes in human diseases. To ensure reliability on an experimental level, adopting validation schemes, incorporating diverse data types, and employing complementary methods are important considerations when moving forward with proteomics data [7].
More recently, our focus has shifted towards exploring the feasibility of remote self-sampling from the general population outside traditional hospital settings [8]. Conducting in-depth proteome profiling with affinity-based assays that only require minimal sample volumes from microsampled blood offers exceptional opportunities to monitor health and disease states [9]. However, novel sample types and sampling procedures also introduce new challenges in disentangling the dynamic of the circulating proteome's architecture.