Extracellular Vesicles in Regenerative Processes Associated with Muscle Injury Recovery of Professional Athletes Undergoing Sub Maximal Strength Rehabilitation.
Giulia CatittiMaria Concetta CufaroDomenico De BellisIlaria CicaliniSimone VespaFederico TonelliGiulia MisciaLorenzo SecondiPasquale SimeoneVincenzo De LaurenziDamiana PieragostinoPiero Del BoccioPaola LanutiPublished in: International journal of molecular sciences (2022)
Platelet-rich plasma (PRP) has great potential in regenerative medicine. In addition to the well-known regenerative potential of secreted growth factors, extracellular vesicles (EVs) are emerging as potential key players in the regulation of tissue repair. However, little is known about their therapeutic potential as regenerative agents. In this study, we have identified and subtyped circulating EVs (platelet-, endothelial-, and leukocyte-derived EVs) in the peripheral blood of athletes recovering from recent muscular injuries and undergoing a submaximal strength rehabilitation program. We found a significant increase in circulating platelet-derived EVs at the end of the rehabilitation program. Moreover, EVs from PRP samples were isolated by fluorescence-activated cell sorting and analyzed by label-free proteomics. The proteomic analysis of PRP-EVs revealed that 32% of the identified proteins were associated to "defense and immunity", and altogether these proteins were involved in vesicle-mediated transport (GO: 0016192; FDR = 3.132 × 10 -19 ), as well as in wound healing (GO: 0042060; FDR = 4.252 × 10 -13 ) and in the events regulating such a process (GO: 0061041; FDR = 2.812 × 10 -12 ). Altogether, these data suggest that platelet-derived EVs may significantly contribute to the regeneration potential of PRP preparations.
Keyphrases
- platelet rich plasma
- stem cells
- label free
- cell therapy
- peripheral blood
- mesenchymal stem cells
- wound healing
- single cell
- human health
- mass spectrometry
- resistance training
- tissue engineering
- climate change
- electronic health record
- heart rate
- blood pressure
- single molecule
- body composition
- artificial intelligence
- deep learning
- high intensity
- big data
- quantum dots