| Title: |
Advances in Extracellular Vesicle Research Over the Past Decade: Source and Isolation Method are Connected with Cargo and Function |
| Authors: |
Poupardin, Rodolphe; Wolf, Martin; Maeding, Nicole; Paniushkina, Liliia; Geissler, Sven; Bergese, Paolo; Witwer, Kenneth W.; Schallmoser, Katharina; Fuhrmann, Gregor; Strunk, Dirk |
| Contributors: |
Poupardin Rodolphe; Wolf Martin; Maeding Nicole; Paniushkina Liliia; Geissler Sven; Bergese Paolo; Witwer Kenneth W.; Schallmoser Katharina; Fuhrmann Gregor; Strunk Dirk |
| Publication Year: |
2024 |
| Collection: |
Università degli Studi di Brescia: OPENBS - Open Archive UniBS |
| Subject Terms: |
extracellular vesicles (EVs); isolation method; machine‐learning; meta‐analysi; nanoparticle; regenerative medicine; stem cells |
| Description: |
The evolution of extracellular vesicle (EV) research has introduced nanotechnology into biomedical cell communication science while recognizing what is formerly considered cell "dust" as constituting an entirely new universe of cell signaling particles. To display the global EV research landscape, a systematic review of 20 364 original research articles selected from all 40 684 EV-related records identified in PubMed 2013-2022 is performed. Machine-learning is used to categorize the high-dimensional data and further dissected significant associations between EV source, isolation method, cargo, and function. Unexpected correlations between these four categories indicate prevalent experimental strategies based on cargo connectivity with function of interest being associated with certain EV sources or isolation strategies. Conceptually relevant association of size-based EV isolation with protein cargo and uptake function will guide strategic conclusions enhancing future EV research and product development. Based on this study, an open-source database is built to facilitate further analysis with conventional or AI tools to identify additional causative associations of interest.A total of 20 364 original extracellular vesicle (EV) research articles for the decade 2013-2022 are analyzed for the presence or absence of 36 selected parameters in the four categories EV source, isolation, cargo, and function. The results are displayed in machine-learning-based 2D landscapes and further dissected by correlation analysis to identify conceptually relevant associations and draw strategic conclusions. image |
| Document Type: |
article in journal/newspaper |
| File Description: |
ELETTRONICO |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/pmid/38270559; info:eu-repo/semantics/altIdentifier/wos/WOS:001204649000001; journal:ADVANCED HEALTHCARE MATERIALS; https://hdl.handle.net/11379/600665 |
| DOI: |
10.1002/adhm.202303941 |
| Availability: |
https://hdl.handle.net/11379/600665; https://doi.org/10.1002/adhm.202303941; https://onlinelibrary.wiley.com/doi/10.1002/adhm.202303941 |
| Rights: |
info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.9424CC87 |
| Database: |
BASE |