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

Javier Plou1,2, Pablo S Valera1,2,3,4, Isabel García1,2

  • 1CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, 20014, Spain.

Small (Weinheim an Der Bergstrasse, Germany)
|April 12, 2023
PubMed
Summary

Researchers developed a novel method using surface-enhanced Raman scattering (SERS) and machine learning for noninvasive cell secretome analysis. This approach enables rapid classification of cell death, aiding in biomarker discovery for cancer therapies.

Keywords:
artificial Intelligencebiosensorscell devicesdrug screeningmetabolic profiles

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Area of Science:

  • Biotechnology
  • Analytical Chemistry
  • Cell Biology

Background:

  • Cellular stress triggers metabolite release, forming the secretome.
  • Secretomes from dying cells are crucial for anticancer therapy and can serve as predictive biomarkers.
  • Monitoring secretome composition is challenging but vital for understanding cellular responses.

Purpose of the Study:

  • To develop a noninvasive method for high-throughput cell secretome screening.
  • To enable rapid and automated analysis of cell secretome variations.
  • To facilitate cell death classification using SERS and machine learning.

Main Methods:

  • Utilized surface-enhanced Raman scattering (SERS) for label-free biofluid interrogation.
  • Developed ad hoc microfluidic chips with filter paper-based capillary pumps as SERS substrates.
  • Implemented machine learning algorithms for analyzing SERS data and classifying cell death.

Main Results:

  • Demonstrated the feasibility of tracing metabolite concentrations using SERS.
  • Achieved identification of cell secretome variations and cell death classification.
  • The prototype microfluidic system facilitated rapid SERS measurements.

Conclusions:

  • The developed strategy offers a faster implementation of SERS for cell secretome classification.
  • This approach can be extended to laboratories with limited specialized facilities.
  • Enables noninvasive screening and biomarker discovery for improved cancer therapies.