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

Summary

The protocol describes Raman spectral analysis of ssDNA-wrapped SWCNT nanosensor, enabling the improved selective measurement of serotonin against dopamine with the assistance of a machine learning model.

Abstract

Serotonin (5-hydroxytryptamine, 5-HT) plays critical roles in neuromodulation, yet current detection methods struggle to provide real-time sensing of 5-HT with high sensitivity and selectivity. We previously developed a near-infrared serotonin nanosensor (nIRHT), which consists of ssDNA-wrapped single-walled carbon nanotube that sensitively detects 5-HT. However, nIRHT’s 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 (ΔRaman) to isolate analyte-specific spectral changes and trained three machine learning models for classification. The random forest model with ΔRaman 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 µM 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 single-walled carbon nanotube (SWCNT)-based molecular sensing.

Introduction

Serotonin (5-hydroxytryptamine, 5-HT) is a critical neurotransmitter regulating mood, cognition, sleep, and appetite, with dysfunction implicated in depression, anxiety, and other neuropsychiatric disorders¹. Real-time monitoring of serotonin dynamics in biological systems remains challenging due to the millisecond timescale of synaptic transmission and the complex chemical environment of neural tissue². Despite significant advances in neurotransmitter detection technologies, achieving both high sensitivity and molecular selectivity for serotonin continues to pose substantial analytical challenges.

Current serotonin detection methods span from traditional analytical techniques to emerging nanosensor platforms. High-performance liquid chromatography with electrochemical detection (HPLC-ED) remains the gold standard, achieving detection limits of 0.056 nM with excellent reproducibility³. However, HPLC requires extensive sample preparation and 15-30 min analysis times, precluding real-time monitoring⁴. Fast-scan cyclic voltammetry (FSCV) enables subsecond temporal resolution at carbon fiber microelectrodes but suffers from electrode fouling and limited selectivity between structurally similar neurotransmitters^5,6. Recent developments in genetically encoded fluorescent sensors, including iSeroSnFR⁷ and GRAB-5HT⁸, achieve remarkable sensitivity and dynamic range but require genetic modification of target tissues, thereby limiting clinical translation.

Single-walled carbon nanotube (SWCNT) optical sensors have emerged as promising platforms for label-free neurotransmitter detection⁹. DNA-functionalized SWCNTs exhibit near-infrared (nIR) fluorescence modulation upon analyte binding, enabling detection in the tissue-transparent window (900-1400 nm)^10,11. Landry's group developed the nIRHT sensor through systematic evolution of ligands by exponential enrichment (SELEC), screening ~10¹⁰ unique ssDNA sequences to identify serotonin-responsive constructs¹². This sensor achieves micromolar sensitivity with 200% fluorescence enhancement upon serotonin binding, operating independently of dopamine or serotonin oxidation. To rule out oxidative interference, our previous study confirmed the stability of these neurotransmitter solutions; UV-Vis and NMR analyses revealed no oxidative changes or new peak formations compared to pure 5HT¹². Furthermore, the short incubation time and neutral pH conditions used in the system are designed to prevent significant quinone formation. Similarly, the Strano group pioneered corona phase molecular recognition (CoPhMoRe), demonstrating that (GT)₁₅-wrapped SWCNTs detect dopamine at 11 nM concentrations¹³. Our group recently advanced this field through machine learning-guided optimization, achieving 2.5-fold sensitivity improvements using high-throughput screening of systematically mutated sequences¹⁴.

Despite these advances, a fundamental limitation persists in the insufficient selectivity between serotonin and dopamine. Both neurotransmitters contain aromatic rings and ethylamine chains, differing by only a single hydroxyl group¹⁵. Current fluorescence-based SWCNT sensors typically achieve selectivity ratios below 5:1, inadequate for distinguishing co-released neurotransmitters in synaptic environments¹⁶. This selectivity challenge is particularly critical given that serotonin and dopamine often colocalize in brain regions governing reward and motivation¹⁷.

Raman spectroscopy offers complementary chemical information through vibrational mode analysis of the SWCNT-analyte complex¹⁸. The G-band (~1580 cm⁻¹) of SWCNTs splits into G⁺ and G⁻ components, with frequencies sensitive to molecular adsorption and electronic doping¹⁹. A previous study demonstrated that non-covalent surfactants on SWCNT induce distinct Raman spectral changes in the G-band region²⁰.

In this work, we demonstrate machine learning-assisted Raman spectral analysis of the nIRHT sensor to achieve enhanced selectivity between serotonin and dopamine. We systematically characterized concentration-dependent Raman responses from 0.01 to 100 µM, revealing distinct spectral fingerprints for each neurotransmitter. Three machine learning models—convolutional neural network (CNN), support vector machine (SVM), and random forest (RF) were trained on spectral datasets to classify analyte identity. Critically, we introduce differential Raman processing, which removes baseline signals to isolate analyte-specific features. The optimized RF model with DR processing achieves 95.8% classification accuracy, representing a significant advance in molecular discrimination. This multimodal approach combining nIR fluorescence sensitivity with Raman chemical specificity provides a pathway toward real-time, selective serotonin monitoring in complex biological environments.

Protocol

1. ssDNA-functionalized SWCNT nIRHT nanosensor fabrication

1. Combine 1 mg of single-walled carbon nanotubes (SWCNTs), 100 µL of 1 mM E6#9 ssDNA, and 900 µL of 1x phosphate-buffered saline (PBS) in a 1.5 mL tube.
   CAUTION: Dry SWCNT powder is a potential inhalation hazard. Handle the powder inside a certified chemical fume hood and wear appropriate personal protective equipment, including a mask and gloves.
2. Disperse the mixture using a bath sonicator for 5 min. Sonicate the mixture using a tip sonicator equipped with a 3 mm probe at 50% amplitude for 30 min in an ice bath.
3. Centrifuge the sonicated solution at 21,500 x g for 1 h to precipitate non-dispersed SWCNTs. Carefully collect 850 µL of the supernatant and transfer it to a new tube.
4. Measure the absorbance of the collected SWCNT suspension at a wavelength of 632 nm. Calculate the concentration using the SWCNT extinction coefficient of 0.036 (mg/L)⁻¹ cm⁻¹. Store the final nIRHT sensor solution at 4 °C until further use.

2. Raman spectra measurement

1. Load 90 µL of the 10 mg/L nIRHT dispersion into a 96-well plate. Measure the Raman spectrum of the sensor in a PBS buffer using a 532 nm laser excitation source. Set this recorded spectrum as the baseline.
   CAUTION: It is crucial to strictly maintain the laser power at 10 mW and wavelength at 532nm when investigating live biological samples. Higher laser powers can induce severe phototoxicity and compromise cell viability. The 532 nm laser at 10 mW utilized in this protocol has been well-established as safe and non-destructive for live-cell analysis, preserving sensitive organelles²¹ and the electrophysiological activity of primary neurons²² while ensuring robust Raman signal acquisition.
2. Spike each 90 µL sensor solution (10mg/L) with 10 µL of the analyte solution to reach final concentrations of 0.01, 0.1, 1, 5, 10, 50, and 100 µM. Wait for 5 min after the analyte addition to allow for sufficient incubation.
3. Measure the Raman spectra of the incubated samples. Prepare samples in triplicate for each concentration and repeat steps 2.1 to 2.2 to ensure reproducibility.

3. Machine learning-based data analysis

1. Construct the dataset from a total of 208 Raman spectra, comprising 77 dopamine (DA) responses, 76 serotonin (5HT) responses, and 47 control (PBS) group responses.
2. Perform background subtraction on the G-band region of the spectral data. Normalize the background-subtracted data based on the G+ band peak.
3. Apply a median filter to reduce noise in the normalized spectra. Isolate spectral changes (ΔRaman) by subtracting the normalized control spectrum from each analyte-treated spectrum.
4. Develop a Convolutional Neural Network (CNN), a Random Forest (RF), and a Support Vector Machine (SVM) model for the ternary classification of 5HT, DA, or the control.
5. Split the entire dataset into training, validation, and test sets using a 6:2:2 ratio.
6. Configure the CNN architecture with three 1D convolution layers utilizing a ReLU activation function and a max pooling size of 2, followed by two fully connected layers.
7. Configure the SVM using a Radial Basis Function (RBF) kernel for non-linear classification.
8. Configure the RF model using default hyperparameters.
9. Train the constructed models using 5-fold cross-validation. Calculate the average performance metrics across all 5 folds to ensure statistical robustness.
10. Select the model that yields the highest accuracy on the validation data for the final analysis.
11. Evaluate the selected model on the test set to verify its generalization capability.
12. Determine the indirect limit of detection (LOD) by sequentially excluding data from the training set that falls below a concentration threshold of 0.1 µM.
13. Test the trained model on the entire concentration range, including concentrations below the criteria, to establish the final LOD.

Results

The E6#9 ssDNA-functionalized SWCNT nanosensor (nIRHT), previously developed through SELEC methodology, demonstrates reversible nIR fluorescence enhancement upon 5HT binding, enabling real-time 5HT imaging in vitro and ex vivo¹². The E6#9 sequence (5'-CCCCCCAGCACCAGACAGCACACTCCCCCC-3') wraps around SWCNTs to create binding sites for 5HT. However, this sensor lacks specificity: both 5-HT and DA result in comparable intensity increase (Figure 1B), limi...

Discussion

Raman spectroscopic analysis provided a significant enhancement in the specificity of the nIRHT sensor, a capability not achievable with its conventional fluorescence response. We observed that DA induced a more pronounced suppression of the G⁻ band (1570 cm^-1) compared to 5HT. These differential signatures likely arise from varying molecular orientations and binding geometries on the SWCNT surface. Specifically, DA's catechol moiety likely forms stronger π-π interactions with the SWCNT sur...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
HiPCo raw	Nanointegris
iDus1.7 InGaAs	ANDOR	DU490A-1,9
Ramantouch	Nano Photon
Vibra Cell 130	SONICS