用深度学习区分单分子脯氨酸与羟基脯氨酸，准确率达96.6%
Label-free SERS Discrimination of Proline from Hydroxylated Proline at Single-molecule Level Assisted by a Deep Learning Model
- 通过分析SERS峰频次直方图提取特征，抑制信号波动
- 洗脱柠檬酸后实现脯氨酸与羟基脯氨酸的清晰区分
- 适合需要高精度单分子检测的生物医学研究
区分低丰度的羟基脯氨酸对疾病监测和疗效评估至关重要，需依赖单分子传感器。尽管等离子体纳米孔传感器可通过表面增强拉曼光谱（SERS）实现单分子灵敏度检测羟基化，但其信号存在固有波动且受柠檬酸强烈干扰。本文利用单分子SERS峰出现频率直方图提取整体数据集光谱特征，克服信号波动，并研究了柠檬酸被分析物替换后的等离子体纳米孔传感器，以获得清洁、可区分的脯氨酸与羟基脯氨酸信号。通过配体交换使柠檬酸被分析物分子取代，随着孵育时间延长，柠檬酸的特征峰逐渐减弱，证明分析物占据等离子体热点。最终，采用卷积神经网络模型实现了脯氨酸与羟基脯氨酸单分子SERS信号的判别，准确率达96.6%。
原文摘要 · Abstract (English)
Discriminating the low-abundance hydroxylated proline from hydroxylated proline is crucial for monitoring diseases and eval-uating therapeutic outcomes that require single-molecule sensors. While the plasmonic nanopore sensor can detect the hydrox-ylation with single-molecule sensitivity by surface enhanced Raman spectroscopy (SERS), it suffers from intrinsic fluctuations of single-molecule signals as well as strong interference from citrates. Here, we used the occurrence frequency histogram of the single-molecule SERS peaks to extract overall dataset spectral features, overcome the signal fluctuations and investigate the citrate-replaced plasmonic nanopore sensors for clean and distinguishable signals of proline and hydroxylated proline. By ligand exchange of the citrates by analyte molecules, the representative peaks of citrates decreased with incubation time, prov-ing occupation of the plasmonic hot spot by the analytes. As a result, the discrimination of the single-molecule SERS signals of proline and hydroxylated proline was possible with the convolutional neural network model with 96.6% accuracy.
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