Nitrilotriacetic acid functionalized gold nanopillars enable stochastic detection and deep learning analysis of prolines and hydroxyprolines by surface enhanced Raman spectroscopy

Abstract

Proline hydroxylation is crucial for monitoring diseases related to collagen metabolism, analyzing metabolic pathways, and evaluating therapeutic or nutritional outcomes. However, the small differences in the hydroxyl group between prolines (Pro) and hydroxyprolines (Hyp) are challenging for reliable label-free discrimination by surface-enhanced Raman scattering (SERS) based on silver and gold nanoparticles. Adsorption of Pro and Hyp on metal colloids took 72 and 48 hours, respectively, which led to occupation of the colloid surface by Hyp and thus overwhelming SERS signals of Hyp against those of Pro. Here, we developed an stochastic SERS method to detect the prolines and hydroxyprolines within 30 minutes by functionalizing gold nanopillars with nitrilotriacetic acid (NTA) and nickel ions (Ni2+) to form the NTA-Ni structure for reversible and transient binding of the Pro/Hyp. By analyzing the SERS time series of the NTA-Ni-Pro/Hyp using the event occurrence frequency, we extracted their SERS feature for study of binding kinetics and quantification with the detection limits down to 0.20 nM for Pro and 0.23 nM for Hyp in mixture, respectively. To overcome the signal fluctuation, we developed a one-dimensional convolutional neural network model to identify NTA-Ni-Pro and NTA -Ni-Hyp with high accuracies of 86.9% and 89.6%, respectively. Our study demonstrated a new SERS strategy of hydroxylation detection by combining stochastic sensing and deep learning analysis. The excellent practicality of our method is promising for binding and analyzing post translational modifications in biofluids for biomedical complex quantitative analysis and early diagnosis of diseases.