Analysis of Neat Biofluids Obtained During Cardiac Surgery Using Nanoparticle Tracking Analysis: Methodological Considerations - Shearn, Andrew I. U.; Aday, Sezin; Ben-Aicha, Soumaya; Carnell-Morris, Pauline; Siupa, Agnieszka; Angelini, Gianni D.; Clayton, Aled; Boulanger, Chantal; Punjabi, Prakash; Emanueli, Costanza; Biglino, Giovanni (Frontiers in Cell and Developmental Biology, 2020)
Small extracellular vesicles (sEVs) are those nanovesicles 30-150nm in size with a role in cell signalling and potential as biomarkers of disease. Nanoparticle Tracking Analysis (NTA) techniques are commonly used to measure sEV concentration in biofluids. However, this quantification technique can be susceptible to sample handing and machine settings. Moreover, some classes of lipoproteins are of similar sizes and could therefore confound sEV quantification, particularly in blood-derived preparations, such serum and plasma. Here we have provided methodological information on NTA measurements and systematically investigated potential factors that could interfere with the reliability and repeatability of results obtained when looking at neat biofluids (i.e. human serum and pericardial fluid) obtained from patients undergoing cardiac surgery and from healthy controls. Data suggest that variables that can affect vesicle quantification include the level of contamination from lipoproteins, number of sample freeze/thaw cycles, sample filtration, using saline-based diluents, video length and keeping the number of particles per frame within defined limits. Those parameters that are of less concern include focus, the “Maximum Jump” setting and the number of videos recorded. However, if these settings are clearly inappropriate the results obtained will be spurious. Similarly, good experimental practice suggests that multiple videos should be recorded. In conclusion, NTA is a perfectible, but still commonly used system for sEVs analyses. Provided users handle their samples with a highly robust and consistent protocol, and accurately report these aspects, they can obtain data that could potentially translate into new clinical biomarkers for diagnosis and monitoring of cardiovascular disease.