Artificial Intelligence‐Assisted Label‐Free Spectroscopic Quantification of Global DNA Cytosine Methylation in a Miniature Plasmonic Pickering Emulsion
Yangcenzi Xie, Mingyang Chen, Xinyu Liu, Xiaoming Su, Ming Li
Abstract
Abstract Epigenetic DNA methylations are early and frequently observed events in a diversity of diseases such as cancer. Despite the considerable clinical values for cancer liquid biopsy, quantitative analysis of DNA methylations remains a major challenge due to the lack of rapid, sensitive detection techniques. Here, an artificial intelligence‐assisted label‐free surface‐enhanced Raman spectroscopy (SERS) (iMeSERS) biosensor is reported for simultaneous quantification of C 5 ‐methylcytosine ( 5m C) level and methylation ratio in DNA samples. This method utilizes the plasmonic Pickering emulsions as the biosensing platform for label‐free SERS detection, formed upon the addition of a sub‐microliter DNA sample to the hydrophobic Au nanostar‐containing n ‐decane. Distinct spectral signatures of monophosphates of canonical deoxyribonucleotides (dNMPs) and the common methylation modification 5‐methyl‐2′‐deoxycytidine (d 5m CMP) are identified and distinguished by the iMeSERS biosensor. The deep learning algorithms trained with SERS signatures of dNMPs and d 5m CMP are then applied to the quantitative analysis of global DNA methylation. The exceptional capability of the deep learning‐driven approach is demonstrated for simultaneous quantification of the methylation ratio and level using a sub‐microliter volume of DNA samples. This work shows the power of label‐free SERS techniques combined with deep learning algorithms for quantitative analysis of epigenetic DNA modifications with great promises for clinical diagnosis.