New Developments in Plasma Proteomics

angellodeco – stock. adobe. com

Advances in plasma proteomics are expanding the understanding of disease mechanisms and have the potential to transform clinical practice by enabling early disease detection and personalized treatment.

High-resolution mass spectrometry (MS) has greatly improved the sensitivity and accuracy of using plasma samples for proteomics analysis by enabling the identification and quantification of low-abundance proteins and post-translational modifications (PTMs). Over the past two decades, new methodologies combined with technological advancements have helped the field of plasma proteomics evolve as an extremely powerful tool for protein scientists, biologists, and clinical researchers (1). Research has shown the capabilities of a rapid and highly reproducible proteomic workflow to deliver a systemic-view proteomic portrait of a person’s health state from a single drop of blood (2).

Yet performing protein analysis directly from plasma poses some analytical challenges. Protein expression is not only influenced by an individual's genetic background, but also by various factors such as time, location, and physiological responses to external stimuli such as stress, disease, aging, and physical activity. Additionally, a single protein or gene expression product can exist in multiple proteoforms due to alternative splicing, point mutations, PTMs, and endogenous proteolysis, each with distinct biological activities (3,4).

The use of orthogonal approaches in plasma proteomics can help address this complexity, leading to more accurate and comprehensive data that can advance our understanding of disease mechanisms and aid in the development of new treatments. Technological advances open new possibilities for improvements in early disease detection and the development of personalized treatments.

Using prm-PASEF for advanced proteomics analysis

Parallel accumulation serial fragmentation (PASEF) is a technique used in MS-based proteomics for peptide sequencing and identification. A recently developed parallel reaction monitoring-PASEF (prm-PASEF) approach allows the addition of ion mobility as a new dimension to liquid chromatography–mass spectrometry (LC–MS) proteomics and increases proteome coverage at a reduced analysis time. The combination of trapped ion mobility spectrometry (TIMS) with a rapid time-of-flight (TOF) mass spectrometer improves the sensitivity for proteomics, allowing detection of the amounts found in single cells.

As a result, prm-PASEF can take advantage of a dual ion mobility trap, enabling highly multiplexed targeted acquisition and thus requiring a shorter chromatographic separation without sacrificing the number of targeted peptides (5). Additionally, the TIMS design allows researchers to reproducibly measure the collisional cross section (CCS) values for all detected ions, to further increase the system’s selectivity, enabling more reliable relative quantitation information from complex samples and short gradient analyses (Figure 1) (6).

ALL FIGURES ARE COURTESY OF THE AUTHORS. Figure 1. Large-scale peptide collisional cross section (CCS) measurement with trapped ion mobility spectrometry (TIMS) and parallel accumulation serial fragmentation (PASEF).

4D proteomics

Four-dimensional (4D) proteomics seeks to analyze proteins by combining the traditional three dimensions of proteomics (protein identity, abundance, and PTMs) with a fourth dimension, which is the localization of proteins within their cellular and tissue contexts. By analyzing proteins in all four dimensions, 4D proteomics aims to provide a more comprehensive and detailed understanding of protein function and regulation. This can be particularly valuable in the development of personalized medicine by helping to identify disease-specific biomarkers and potential targets for drug development.

Using a TIMS mass spectrometer with PASEF and high-throughput LC separation, research has shown 4D proteomics using a match between runs (MBR) approach can extract intensity information for all peptides to be quantified using narrow m/z and retention time windows for biomarker discovery in large sample cohorts of human blood plasma. 4D-MBR adds an extra CCS filter to the first m/z and retention time ones, allowing for much greater specificity (Figure 2) (7).

Figure 2. Four-dimensional match between runs (4D-MBR) applied on high-throughput plasma proteomics.

diaPASEF

Unlike traditional PASEF, which uses a data-dependent acquisition (DDA) approach where precursor ions are selected based on their intensity, dia-PASEF uses a data-independent acquisition (DIA) approach. In DIA, all precursor ions are fragmented, allowing for the detection and quantification of a larger number of peptides and proteins in a sample. Dia-PASEF combines DIA with the high-resolution ion mobility separation provided by TIMS, enabling the rapid and sensitive identification and quantification of peptides in complex mixtures (Figure 3). The technique has the potential to provide higher coverage and accuracy in the identification and quantification of peptides compared to traditional DIA methods.

Figure 3. Overview of data-independent acquisition-parallel accumulation serial fragmentation (dia-PASEF) workflow. A) Peptides eluting from the chromatographic column are ionized and enter the mass spectrometer through a glass capillary. B) In the dual trapped ion mobility spectrometry (TIMS) analyzer, the first TIMS section traps and stores ion packets, and the second resolves them by ion mobility. C) Ion mobility separated ions are released sequentially from the second TIMS analyzer as a function of decreasing electrical field strength.D) and E) For dia-PASEF. couple DIA isolation windows to the precise ion mobility elution of the corresponding ions. Within a single TIMS separation, multiple precursor windows are set.

Dia-PASEF has been used in a variety of proteomics applications, including the identification and quantification of proteins in clinical samples, the analysis of PTMs, and the characterization of protein-protein interactions. Research has shown dia-PASEF delivers extremely reproducible identification and quantification information over a concentration of five orders of magnitude making the approach perfectly suited for the challenges of quantitative proteomics (8).

Conclusion

Early detection of diseases is a significant challenge in human health, and there is a pressing need for more precise biomarkers, improved patient stratification, and methods for predicting treatment response. Advancements in unbiased and comprehensive 4D plasma proteomics offer unique advantages to provide solutions to these challenges through exceptional sensitivity and selectivity. Together, TIMS and PASEF allow for high-throughput analysis of complex protein samples, resulting in improved sensitivity, dynamic range, and throughput compared to traditional proteomics methods. These technological developments have the potential to provide new insights into disease mechanisms and potential therapeutic targets.

References

1. Geyer, P. E.; Holdt, L. M.; Teupser, D.; Mann, M. Revisiting Biomarker Discovery by Plasma Proteomics. Mol. Syst. Biol. 2017, 13 (9), 942. DOI: 10.15252/msb.20156297
2, Geyer, P. E.; Kulak, N. A.; Pichler, G.; et al. Plasma Proteome Profiling to Assess Human Health and Disease. Cell Syst. 2016, 2 (3), 185–195. DOI: 10.1016/j.cels.2016.02.015
3. Ignjatovic, V.; Geyer, P. E.; Palaniappan, K. K.; et al. Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data. J. Proteome Res. 2019, 18 (12), 4085–4097. DOI: 10.1021/acs.jproteome.9b00503
4. Gegner, H. M.; Naake, T.; Dugourd, A.; et al. Pre-Analytical Processing of Plasma and Serum Samples for Combined Proteome and Metabolome Analysis. Front. Mol. Biosci. 2022, 9, 961448. DOI: 10.3389/fmolb.2022.961448
5. Brzhozovskiy, A.; Kononikhin, A.; Bugrova, A. E.; et al. The Parallel Reaction Monitoring-Parallel Accumulation–Serial Fragmentation (prm-PASEF) Approach for Multiplexed Absolute Quantitation of Proteins in Human Plasma. Anal. Chem. 2022, 94, 4, 2016–2022. DOI: 10.1021/acs.analchem.1c03782
6. Meier, F.; Köhler, N. D.; Brunner, A. D.; et al. Deep Learning the Collisional Cross Sections if the Peptide Universe from a Million Experimental Values. Nat. Commun. 2021, 12, 1185. DOI: 10.1038/s41467-021-21352-8
7. Kosinski, T.; Heilig, R.; Bensaddek, D.; et al. Plasma Proteomics Goes High Throughput—timsTOF Pro with PASEF and 4D Feature Alignment to Quantify 500 Plasma Proteins in 11.5 Min. Bruker Daltonics (March 2019).
8. Kaspar-Schoenefeld, S.; Marx, K.; Gandhi, T.; et al. diaPASEF: Label-Free Quantification of Highly Complex Proteomes. Bruker Daltonics (September 2019).

About the authors

Shourjo Ghose is proteomics business development manager—North America, and Andreas Schmidt is application development scientist; both at Bruker Daltonics.