Identification of extracellular vesicle (EV) subpopulations remains an open challenge. To date, the common strategy is based on searching and probing set of molecular components and physical properties intended to be univocally characteristics of the target subpopulation. Pitfalls include the risk to opt for an unsuitable marker set–which may either not represent the subpopulation or also cover other unintended subpopulations–and the need to use different characterization techniques and equipment. This approach focused on specific markers may result inadequate to routinely deal with EV subpopulations that have an intrinsic high level of heterogeneity. In this paper, we show that Fourier-transform Infrared (FT-IR) spectroscopy can provide a collective fingerprint of EV subpopulations in one single experiment. FT-IR measurements were performed on large (LEVs, ~600 nm), medium (MEVs, ~200 nm) and small (SEVs ~60 nm) EVs enriched from two different cell lines medium: murine prostate cancer (TRAMP-C2) and skin melanoma (B16). Spectral regions between 3100–2800 cm−1 and 1880–900 cm−1, corresponding to functional groups mainly ascribed to lipid and protein contributions, were acquired and processed by Principal Component Analysis (PCA). LEVs, MEVs and SEVs were separately grouped for both the considered cell lines. Moreover, subpopulations of the same size but from different sources were assigned (with different degrees of accuracy) to two different groups. These findings demonstrate that FT-IR has the potential to quickly fingerprint EV subpopulations as a whole, suggesting an appealing complement/alternative for their characterization and grading, extendable to healthy and pathological EVs and fully artificial nanovesicles.
CITATION STYLE
Paolini, L., Federici, S., Consoli, G., Arceri, D., Radeghieri, A., Alessandri, I., & Bergese, P. (2020). Fourier-transform Infrared (FT-IR) spectroscopy fingerprints subpopulations of extracellular vesicles of different sizes and cellular origin. Journal of Extracellular Vesicles, 9(1). https://doi.org/10.1080/20013078.2020.1741174
Mendeley helps you to discover research relevant for your work.