Native characterization and QC profiling of human amniotic mesenchymal stromal cell vesicular fractions for secretome-based therapy.

Human amniotic mesenchymal stromal cells (hAMSCs) have unique immunomodulatory properties making them attractive candidates for regenerative applications in inflammatory diseases. Most of their beneficial properties are mediated through their secretome. The bioactive factors concurring to its therapeutic activity are still unknown. Evidence suggests synergy between the two main components of the secretome, soluble factors and vesicular fractions, pivotal in shifting inflammation and promoting self-healing. Biological variability and the absence of quality control (QC) protocols hinder secretome-based therapy translation to clinical applications. Moreover, vesicular secretome contains a multitude of particles with varying size, cargos and functions whose complexity hinders full characterization and comprehension. This study achieved a significant advancement in secretome characterization by utilizing native, FFF-based separation and characterizing extracellular vesicles derived from hAMSCs. This was accomplished by obtaining dimensionally homogeneous fractions then characterized based on their protein content, potentially enabling the identification of subpopulations with diverse functionalities. This method proved to be successful as an independent technique for secretome profiling, with the potential to contribute to the standardization of a qualitative method. Additionally, it served as a preparative separation tool, streamlining populations before ELISA and LC-MS characterization. This approach facilitated the categorization of distinctive and recurring proteins, along with the identification of clusters associated with vesicle activity and functions. However, the presence of proteins unique to each fraction obtained through the FFF separation tool presents a challenge for further analysis of the protein content within these cargoes.

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Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.

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Funding The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper: Ornella Parolini reports financial support was provided by PRIN 2017 program of Italian Ministry of Research and University (MIUR, Grant No. 2017RSAFK7). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.