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Phenotype Classification of Intact Cells by NMR Spectroscopy through Machine Learning Approaches
Carlo Mengucci1, Claudia Dell'Amico2,3, Simona Del Giudice4
1Department of Agri-Food Science and Technology, University of Bologna, Piazza Goidanich 60, Cesena 47521, Italy.
Journal of the American Chemical Society
|April 22, 2026
Summary
Machine learning applied to NMR spectra classifies live cells, even with signal overlap. This technique can identify cell types and may aid in diagnosing central nervous system conditions noninvasively.
Area of Science:
- Biophysics
- Metabolomics
- Machine Learning
Background:
- Nuclear Magnetic Resonance (NMR) spectroscopy analyzes biological samples via metabolic fingerprinting.
- Current NMR methods struggle with live cells and in vivo imaging due to signal overlap from sample inhomogeneity.
- High-resolution NMR is effective for biofluids and cell extracts but limited for complex live systems.
Purpose of the Study:
- To investigate the use of machine learning for classifying live cell types using NMR spectroscopy.
- To overcome signal overlap limitations in NMR spectra of intact cells.
- To assess the potential for in vivo applications in diagnostics.
Main Methods:
- Applied machine learning algorithms to poorly resolved 1D NMR spectra of live, intact cells.
- Recorded NMR spectra at high magnetic fields.
- Trained classifiers on specific cell types (neural progenitor cells, neurons, astrocytes) and mixed cultures.
Main Results:
- Successfully classified distinct physiopathologically relevant cell types in vitro using machine learning on NMR data.
- Demonstrated classification of mixed cell type samples.
- Showed that classifiers trained at high fields can discriminate cells analyzed at lower fields, relevant to MRI.
Conclusions:
- Machine learning enables classification of live cell types from challenging NMR spectra.
- This approach holds promise for future Magnetic Resonance Spectroscopic Imaging (MRSI) data analysis.
- Potential for noninvasive diagnostics of central nervous system lesions, reducing biopsy needs.
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