Phenotype Classification of Intact Cells by NMR Spectroscopy through Machine Learning Approaches.
Overview
- Department of Agri-Food Science and Technology, University of Bologna, Piazza Goidanich 60, Cesena 47521, Italy
- Department of Biology, University of Pisa, via Luca Ghini 13, Pisa 56126, Italy
- Department of Clinical and Experimental Medicine, University of Pisa, via Savi 10, Pisa 56126, Italy
- Magnetic Resonance Center − CERM, University of Florence, via Luigi Sacconi 6, Sesto Fiorentino 50019, Italy
- Interuniversity Consortium for Magnetic Resonance of Metalloproteins − CIRMMP, via Luigi Sacconi 6, Sesto Fiorentino 50019, Italy
- Department of Neurosciences, Psychology, Drug Research and Child Health, University of Florence, viale Pieraccini 6, Florence 50139, Italy
- Department of Emergency Neurology, Careggi University Hospital, largo Piero Palagi, 1, Florence 50139, Italy
- Department of Chemistry “Ugo Schiff”, University of Florence, via della Lastruccia 3, Sesto Fiorentino 50019, Italy
Abstract
NMR spectroscopy is a powerful, noninvasive tool to analyze complex biological samples. In vitro, high-resolution, 1D NMR spectra of biofluids and cell extracts make it possible to classify biological samples based on their metabolic fingerprint. However, such analysis is currently not possible with live cells or tissues, or by spectroscopic imaging in vivo, due to the line broadening arising from the intrinsic inhomogeneity of such samples, causing severe signal overlap. Here, we show that machine learning approaches applied to poorly resolved NMR spectra of live, intact cells recorded at high fields allow for the classification of different physiopathologically relevant cell types cultured in vitro. 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.
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
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Data
Datasets cited
- zenodo:15858419, at Zenodo; found in DataCite
- zenodo:15858420, at Zenodo; found in “Data Availability Statement”
Data Availability Statement
All data are available in the main text or the Supporting Information (https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 29 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 9 MeSH terms, 2 funders, 50 references.
Cite
This paper
Mengucci, C., Dell'Amico, C., Del Giudice, S., Barbieri, L., Mariottini, A., Onorati, M., Massacesi, L., Luchinat, E., & Banci, L. (2026). Phenotype Classification of Intact Cells by NMR Spectroscopy through Machine Learning Approaches. Journal of the American Chemical Society, 148(17), 17920-17930. https://
BibTeX
@article{mengucci2026phe
author = {Mengucci, Carlo and Dell'Amico, Claudia and Del Giudice, Simona and Barbieri, Letizia and Mariottini, Alice and Onorati, Marco and Massacesi, Luca and Luchinat, Enrico and Banci, Lucia},
title = {{Phenotype Classification of Intact Cells by NMR Spectroscopy through Machine Learning Approaches}},
journal = {Journal of the American Chemical Society},
year = {2026},
month = apr,
volume = {148},
number = {17},
pages = {17920--17930},
publisher = {American Chemical Society},
issn = {0002-7863},
doi = {10.1021/
url = {https://
pmid = {42017787},
pmcid = {PMC13154173}
}
RIS
TY - JOUR
AU - Mengucci, Carlo
AU - Dell'Amico, Claudia
AU - Del Giudice, Simona
AU - Barbieri, Letizia
AU - Mariottini, Alice
AU - Onorati, Marco
AU - Massacesi, Luca
AU - Luchinat, Enrico
AU - Banci, Lucia
TI - Phenotype Classification of Intact Cells by NMR Spectroscopy through Machine Learning Approaches
T2 - Journal of the American Chemical Society
J2 - J Am Chem Soc
PY - 2026
DA - 2026/
VL - 148
IS - 17
SP - 17920
EP - 17930
SN - 0002-7863
PB - American Chemical Society
DO - 10.1021/
UR - https://
LA - en
ER -
CSL-JSON
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