Abstract
Extracellular vesicles (EVs) are lipid vesicles secreted by all cells into the extracellular space and act as nanosized biological messengers among cells. They carry a specific molecular cargo, composed of lipids, proteins, nucleic acids, and carbohydrates, which reflects the state of their parent cells. Due to their remarkable structural and compositional heterogeneity, characterizing EVs, particularly from a biochemical perspective, presents complex challenges. In this context, mid-infrared (IR) spectroscopy is emerging as a valuable tool, providing researchers with a comprehensive and label-free spectral fingerprint of EVs in terms of their specific molecular content. This review aims to provide an up-to-date critical overview of the major advancements in mid-IR spectroscopy of extracellular vesicles, encompassing both fundamental and applied research achievements. We also systematically emphasize the new possibilities offered by the integration of emerging cutting-edge IR technologies, such as tip-enhanced and surface-enhanced spectroscopy approaches, along with the growing use of machine learning for data analysis and spectral interpretation. Additionally, to assist researchers in navigating this intricate subject, our manuscript includes a wide and detailed collection of the spectral peaks that have been assigned to EV molecular constituents up to now in the literature.
| Lingua originale | Inglese |
|---|---|
| pagine (da-a) | N/A-N/A |
| Rivista | Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy |
| Volume | 305 |
| Numero di pubblicazione | N/A |
| DOI | |
| Stato di pubblicazione | Pubblicato - 2024 |
OSS delle Nazioni Unite
Questo processo contribuisce al raggiungimento dei seguenti obiettivi di sviluppo sostenibile
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SDG 3 Salute e benessere
All Science Journal Classification (ASJC) codes
- Chimica Analitica
- Fisica Atomica e Molecolare, Ottica
- Strumentazione
- Spettroscopia
Keywords
- Biomarkers
- Extracellular Vesicles
- FTIR
- Label-Free Spectroscopy
- Liquid Biopsy
- Machine Learning
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