Phenotype Classification of Intact Cells by NMR Spectroscopy through Machine Learning Approaches
. We demonstrate the successful classification of neural progenitor cells, neurons, and astrocytes, as well as the classification of mixed cell type samples, and show that a classifier trained on high-field NMR spectra can discriminate cells analyzed at lower fields, approaching those of current MRI instruments. In the future, this approach could be further developed for MRSI data analysis applications, potentially offering a noninvasive diagnostic tool for lesions of the central nervous system and reducing the need for biopsies.
Authors
- Claudia Dell’Amico (ORCID: https://orcid.org/0000-0002-8234-3423)
- Luca Massacesi (ORCID: https://orcid.org/0000-0001-5083-372X)
- Alice Mariottini (ORCID: https://orcid.org/0000-0002-1869-7067)
- S. Giudice
- Marco Onorati (ORCID: https://orcid.org/0000-0001-8517-930X)
- Lucia Banci (ORCID: https://orcid.org/0000-0003-0562-5774)
- Carlo Mengucci (ORCID: https://orcid.org/0000-0003-0602-3003)
- Enrico Luchinat (ORCID: https://orcid.org/0000-0003-4183-4311)
- Letizia Barbieri (ORCID: https://orcid.org/0000-0002-7457-490X)
Institutions
- University of Pisa (IT)
- Azienda Ospedaliero-Universitaria Careggi (IT)
- Interuniversity Consortium for Magnetic Resonance (IT)
- University of Florence (IT)
- University of Bologna (IT)
Publication Details
- Journal
- Journal of the American Chemical Society
- Published
- 2026-04-22
- DOI
- https://doi.org/10.1021/jacs.6c01100
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Commission
- Ministero dell'Università e della Ricerca