An optimised method for proteomic analysis of plasma extracellular vesicles (EVs), developed by researchers at the Mayo Clinic (Rochester, MN, USA), is now documented in Cell Reports Methods.<super-script>1<super-script> The clinically relevant method utilises qEV isolation (Izon’s size-exclusion chromatography-based qEV columns and the Izon Automatic Fraction Collector) and high-resolution mass spectrometry to yield deeper proteomic coverage, higher precision, and higher reproducibility, when compared with more commonly used approaches.
Proteins are an abundant cargo found in EVs and are of major interest for their diagnostic and prognostic potential. Therefore, there is a need to purify EVs in a way that is compatible with downstream molecular analysis. However, common isolation techniques such as differential ultracentrifugation (UC) and commercial kit-based methods are not compatible with downstream EV protein analysis and suffer from poor reproducibility.<super-script>2,3<super-script> For instance, proteomic analysis of EVs is confounded by the co-isolation of plasma protein contaminants<super-script>4,5<super-script> and the low EV yield that results from successive rounds of centrifugation.<super-script>6<super-script> Therefore, the authors optimised a workflow for precise, in-depth analysis of EV proteins and benchmarked the analytical performance in relation to other EV isolation approaches. They also demonstrated the capability of SEC-MS to detect physiological changes in the circulating EV proteome, using exercise as a proof-of-concept stimulus.
The suitability of different isolation techniques for proteomic profiling was compared using platelet-poor plasma and several different measures, including: number of detected exosome proteins (as defined by the ExoCarta database<super-script>7<super-script>) and the number of detected canonical EV marker proteins (CD9, CD81, CD63, PDCD6IP, ADAM10, SDCB1, FLOT1, ANXA5, OIT3). By these measures, SEC-MS outperformed the three other methods compared: ExoEasy, UC, and ExoQuick. For instance, more than 400 ExoCarta proteins were identified using qEV isolation, compared to approximately 250 for UC. Also, SEC identified eight of the nine canonical EV proteins, compared to ExoEasy (three detected), UC (three detected), and ExoQuick (none detected).
The label-free, SEC-MS assay was optimised and characterised by assessing elution profiles of samples processed with qEV isolation. Contaminating proteins could be distinguished from EV markers by comparing their elution patterns; the apex of profiles related to fibrinogen, apolipoprotein B and other high-molecular weight plasma proteins were right-shifted.
This ‘peak-filtering strategy’ was extended to an assessment of the ECT100, i.e., the most commonly observed proteins in exosomes. A striking majority (79/84) of the detected ECT100 proteins peaked across three fractions – and those proteins that peaked outside of those fractions displayed properties suggesting they might be of plasma origin, rather than EV-derived.
Linearity, precision and reproducibility were demonstrated in several ways. For example:
Compared to analysing individual collection volumes (referred to as ‘fractions’ in the paper), pooling EV-containing purified collection volumes produced a similar number of proteins. Importantly, however, pooling the collection volumes improved the CV of protein intensities, highlighting the predictable and reproducible manner of EV elution that occurs in qEV columns.
To identify proteins that might be unique to EV-producing tissues, Vanderboom and colleagues completed a series of cell culture-based experiments. Primary human myotubes, primary human adipocytes, Huh7 hepatocytes, leukocytes, and isolated human platelets were all cultured separately, and their EV protein cargo was profiled using MS following qEV isolation. While many identified proteins were common across all cell types, many were unique to each cell culture, including 18 proteins from the platelet culture, 310 from leukocyte EVs, 350 from myotube EVs, 180 from hepatocyte EVs, and 121 from adipocyte EVs. This work was extended to develop a resource for tracking the origin of circulating EVs; SEC-MS data regarding putative cell-specific EV proteins was merged with data from the Human Protein Atlas, to see how each protein set aligned.
SEC-MS analyses of EV preparations from platelet-poor plasma were compared with those of standard plasma:
The results provide evidence for ex vivo release of EVs from platelets and leukocytes and demonstrate the importance of taking appropriate measures to limit this – as spelled out by the International Society of Extracellular Vesicles (ISEV) and the International Society on Thrombosis and Haemostasis (ISTH).
The final study involved implementing a physiological intervention to determine whether SEC-MS would be sufficiently sensitive to detect EV proteome changes. Specifically, EV protein abundance was compared before and after a single bout of high-intensity aerobic exercise (cycling: four bouts of four minutes at 90% of VO2 peak, with three minutes of pedaling with no load between bouts) or resistance exercise (one-legged extensions, three sets of 10 repetitions at 70% of one repetition-maximum, with one-minute rests between sets). Acute exercise was selected as a proof-of-concept stimulus, given previous reports of exercise-induced secretion of EV-contained proteins<super-script>8,9<super-script>.
SEC-MS revealed a significant increase of circulating EV protein abundance immediately after aerobic exercise, which returned to pre-training levels by three hours post-exercise – as confirmed via nano flow cytometry measurements of ADAM10-positive particles. Particle count was also assessed using nanoparticle tracking analysis (NTA), however NTA did not detect a post-exercise increase in particle count. The low resolution of NTA reported here is in agreement with several recent studies comparing analytical techniques for the measurement of biological<super-script>10<super-script> and synthetic particles<super-script>11<super-script> where the resolution of NTA was far lower than that of nano flow cytometry and tunable resistive pulse sensing.
More than 300 proteins were significantly increased after high-intensity aerobic exercise – a substantially greater response in comparison with the resistance exercise, whereas only seven EV proteins were upregulated following exercise. This part of the study was included to confirm whether SEC-MS would be sensitive enough to detect changes to the EV proteome following a major physiological event – rather than to compare findings with previous reports. Regardless, the group made several interesting EV-related observations that highlight potential roles for EV proteins in regulating the metabolic and immune responses to exercise. By overlaying cell culture data from their previous experiment (discussed above), and by harnessing functional enrichment analysis and Ingenuity Pathway Analysis, the likely functional roles and tissue sources of upregulated EV proteins were identified. Upregulated proteins include those involved in angiogenesis, wound healing, neutrophil action, glycolysis, and signalling via interleukin-8 and integrin. By overlaying cell-culture-based EV profiling experience, the group identified candidate tissue sources of EV proteins released immediately after exercise, including myotubes, hepatocytes, adipocytes, leukocytes, and platelets.
This study demonstrates the many advantages of harnessing qEV isolation within a SEC-MS workflow for EV proteomics analysis. SEC-MS provides excellent coverage of proteins from circulating EVs, enabling detection of a greater number of proteins compared to MS combined with UC or commercial kit-based isolation methods. As qEV isolation is highly reliable and reproducible, plasma-based and EV-derived proteins can be distinguished by comparing the position of the intensity apex.
Linearity and precision were demonstrated across a physiological range, using serial dilutions of platelet-rich plasma and exosome-depleted bovine serum. Furthermore, the approach of overlaying data from cell culture-based EV experiments with proteomics data from plasma enabled insights into the likely sources of EV proteins. Together, SEC-MS was shown to be a powerful and sensitive tool for studying the composition and function of plasma EV proteins in biological systems and enabled an evaluation of acute exercise and its effect on the EV proteome cargo. Following the high performance of the SEC-MS method described, the authors describe this work as an important advance in the quest to determine the tissues of origin of circulating EVs.