Machine Learning-Assisted High-Throughput SERS Classification of Cell Secretomes.

Autor: Plou J; CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, 20014, Spain.; Biomedical Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Donostia-San Sebastián, 20014, Spain., Valera PS; CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, 20014, Spain.; Biomedical Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Donostia-San Sebastián, 20014, Spain.; CIC bioGUNE, Basque Research and Technology Alliance (BRTA), Derio, 48160, Spain.; Department of Applied Chemistry, University of the Basque Country, Donostia, 20018, Spain., García I; CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, 20014, Spain.; Biomedical Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Donostia-San Sebastián, 20014, Spain., Vila-Liarte D; CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, 20014, Spain.; Biomedical Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Donostia-San Sebastián, 20014, Spain., Renero-Lecuna C; CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, 20014, Spain.; Biomedical Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Donostia-San Sebastián, 20014, Spain., Ruiz-Cabello J; CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, 20014, Spain.; IKERBASQUE, Basque Foundation for Science, Bilbao, 48009, Spain.; Biomedical Research Networking Center in Respiratory Diseases (CIBERES), Madrid, 28029, Spain.; Universidad Complutense de Madrid, Madrid, 28040, Spain., Carracedo A; CIC bioGUNE, Basque Research and Technology Alliance (BRTA), Derio, 48160, Spain.; IKERBASQUE, Basque Foundation for Science, Bilbao, 48009, Spain.; Biomedical Research Networking Center in Cancer (CIBERONC), Derio, 48160, Spain.; Translational Prostate Cancer Research Lab, CIC bioGUNE-Basurto, Biocruces Bizkaia Health Research Institute, Derio, 48160, Spain., Liz-Marzán LM; CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, 20014, Spain.; Biomedical Research Networking Center in Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Donostia-San Sebastián, 20014, Spain.; IKERBASQUE, Basque Foundation for Science, Bilbao, 48009, Spain.; Cinbio, Universidade de Vigo, Vigo, 36310, Spain.
Jazyk: angličtina
Zdroj: Small (Weinheim an der Bergstrasse, Germany) [Small] 2023 Dec; Vol. 19 (51), pp. e2207658. Date of Electronic Publication: 2023 Apr 12.
DOI: 10.1002/smll.202207658
Abstrakt: During the response to different stress conditions, damaged cells react in multiple ways, including the release of a diverse cocktail of metabolites. Moreover, secretomes from dying cells can contribute to the effectiveness of anticancer therapies and can be exploited as predictive biomarkers. The nature of the stress and the resulting intracellular responses are key determinants of the secretome composition, but monitoring such processes remains technically arduous. Hence, there is growing interest in developing tools for noninvasive secretome screening. In this regard, it has been previously shown that the relative concentrations of relevant metabolites can be traced by surface-enhanced Raman scattering (SERS), thereby allowing label-free biofluid interrogation. However, conventional SERS approaches are insufficient to tackle the requirements imposed by high-throughput modalities, namely fast data acquisition and automatized analysis. Therefore, machine learning methods were implemented to identify cell secretome variations while extracting standard features for cell death classification. To this end, ad hoc microfluidic chips were devised, to readily conduct SERS measurements through a prototype relying on capillary pumps made of filter paper, which eventually would function as the SERS substrates. The developed strategy may pave the way toward a faster implementation of SERS into cell secretome classification, which can be extended even to laboratories lacking highly specialized facilities.
(© 2023 The Authors. Small published by Wiley-VCH GmbH.)
Databáze: MEDLINE
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