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bioRxiv · 10.1101/2025.10.02.679925

Machine Learning-assisted Raman Spectral Analysis of Serotonin-responsive ssDNA-SWCNT Nanosensor for Improved Selectivity against Dopamine

Jeong, S.
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Abstract

Serotonin (5-hydroxytryptamine, 5-HT) plays critical roles in neuromodulation, yet current detection methods struggle for real-time sensing of 5HT with high sensitivity and selectivity. We previously developed nIRHT (near-infrared serotonin nanosensor), which consists of ssDNA-wrapped single walled carbon nanotube that sensitively detects 5-HT. However, nIRHTs fluorescence response cannot discriminate between 5HT and dopamine (DA), limiting its practical applications. In this study, Raman spectroscopy combined with machine learning overcomes this selectivity challenge. G-band spectral features revealed distinct signatures for 5HT versus DA binding to nIRHT, with DA causing greater G- band suppression. We employed differential Raman ({Delta}Raman) to isolate analyte-specific spectral changes and trained three machine learning models for classification. The random forest model with {Delta}Raman achieved optimal performance with 95.8% accuracy, significantly outperforming models using raw Raman spectra. This approach showed improved specificity, with negligible responses to acetylcholine, GABA, and glutamate, and achieved a detection limit of 0.1 M suitable for physiological applications. This Raman based approach transforms the non-selective nIRHT fluorescence sensor into a platform capable of robust neurotransmitter discrimination, overcoming selectivity issues in SWCNT-based molecular sensing.

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Jeong, S.. 2025-10-04. Machine Learning-assisted Raman Spectral Analysis of Serotonin-responsive ssDNA-SWCNT Nanosensor for Improved Selectivity against Dopamine. https://doi.org/10.1101/2025.10.02.679925

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