Non-invasive real-time investigation of colorectal cells tight junctions by Raman microspectroscopy analysis combined with machine learning algorithms for organ-on-chip applications
Experimental results demonstrate that this approach, combined with supervised Machine Learning, can investigate dynamic biomolecular changes in real-time with high spatial resolution. This represents a promising non-invasive alternative technique for characterizing cells and biological barriers in organoids and OoC platforms, with potential applications in cytology diagnostics, tumor monitoring, and drug efficacy analysis.
Authors
- Laura Blasi (ORCID: https://orcid.org/0000-0002-9702-0272)
- Luca Francioso (ORCID: https://orcid.org/0000-0002-4500-0421)
- Elisa Sciurti (ORCID: https://orcid.org/0000-0003-1914-0153)
- Pietro Siciliano (ORCID: https://orcid.org/0000-0002-1312-4593)
- Denise Bellisario (ORCID: https://orcid.org/0000-0001-5588-8738)
- Vincent Esposito (ORCID: https://orcid.org/0000-0001-6153-133X)
- Flavio Casino
- A. Calogiuri
Institutions
- Institute for Microelectronics and Microsystems (IT)
Publication Details
- Journal
- Frontiers in Bioengineering and Biotechnology
- Published
- 2024-11-11
- DOI
- https://doi.org/10.3389/fbioe.2024.1458404
- Citations
- 7
- Primary Topic
- Spectroscopy Techniques in Biomedical and Chemical Research
- Type
- article
- Field-Weighted Citation Impact
- 4.64
Funders
- Consiglio Nazionale delle Ricerche