The Circulating Proteome─Technological Developments, Current Challenges, and Future Trends

Abstract

Recent improvements in proteomics technologies have fundamentally altered our capacities to characterize human biology. There is an ever-growing interest in using these novel methods for studying the circulating proteome, as blood offers an accessible window into human health. However, every methodological innovation and analytical progress calls for reassessing our existing approaches and routines to ensure that the new data will add value to the greater biomedical research community and avoid previous errors. As representatives of HUPO's Human Plasma Proteome Project (HPPP), we present our 2024 survey of the current progress in our community, including the latest build of the Human Plasma Proteome PeptideAtlas that now comprises 4608 proteins detected in 113 data sets. We then discuss the updates of established proteomics methods, emerging technologies, and investigations of proteoforms, protein networks, extracellualr vesicles, circulating antibodies and microsamples. Finally, we provide a prospective view of using the current and emerging proteomics tools in studies of circulating proteins.

Keywords: PTM; PeptideAtlas; affinity; biomarker discovery; blood; extracellular vesicle; mass spectrometry; microsampling; plasma; serum.

Figure 1

Protein abundance and confidence of detection by affinity assays. The boxplots show the MS-based abundance levels from the 2023 Human Plasma PeptideAtlas build and proteins classified by Eldjarn et al. into confidence tiers (categories) when comparing data from Olink and SomaLogic assays. Proteins with higher support from cross-platform evidence (tier 1 and 2) have a higher abundance than proteins of lower confidence (tier 3) or those that were not detected by these affinity assays but were part of the 2023 Human Plasma PeptideAtlas build (Not found).

Figure 2

Number of data set submissions to ProteomeXchange over time. The submissions include only MS-based proteomics data sets. The increase in 2021 is explained by the release of the Blood Proteoform Atlas data sets.

Figure 3

Trends from the 2023-04 build of the Human Plasma PeptideAtlas. (A) Number of identified spectra over time as more data sets are added. The axis is truncated to omit data sets with very high spectrum counts. A median-filter trend indicates an increase in the typical number of identified spectra per data set from 50 000 15 years ago to half a million spectra today. (B) The number of distinct canonical proteins identified over time as more data sets are added to the PeptideAtlas build. The typical number of canonical proteins detected has risen from 100 to 200 in early data sets to 500 proteins today in DDA. (C) The number of cumulative peptides in the Plasma PeptideAtlas continues to increase. The top axis provides the approximate years when data sets were added to the Plasma PeptideAtlas. (D) The number of proteins added per data set has slowed substantially, increasing by only 200 proteins despite the recent addition of nearly 50 million PSMs.

Figure 4

Comparison of protein abundances in plasma/serum samples versus EV samples. (A) Protein abundance estimated from log₁₀ PSM counts for proteins seen in both PeptideAtlas builds. Most proteins correlate between plasma/serum quite well. Still, a noticeable population of proteins has a much higher abundance in plasma/serum than in EVs, while the opposite is not observed. (B) Overlap in protein identifications (blue) between plasma/serum build (red) versus EV build (orange), displayed as a function of estimated abundance (log₁₀ PSM counts). 50% of proteins are seen in both builds, with ∼25% unique to each build.