Ratiometric 3D DNA Machine Combined with Machine Learning Algorithm for Ultrasensitive and High-Precision Screening of Early Urinary Diseases
Na Wu, Xin-Yu ZhangXin-Yu Zhang
General Hospital of Northern Theater Command, Shenyang 110015, China
Dalian Medical University, Dalian 116044, China
More by Xin-Yu Zhang
, Jie Xia, Xin Li, Ting Yang*, and Jian-Hua Wang
Cite this: ACS Nano 2021, 15, 12, 19522–19534
Publication Date:November 23, 2021

https://doi.org/10.1021/acsnano.1c06429

Abstract
Urinary extracellular vesicles (uEVs) have received considerable attention as a potential biomarker source for the diagnosis of urinary diseases. Present studies mainly focus on the discovery of biomarkers based on high-throughput proteomics, while limited efforts have been paid to applying the uEVs’ biomarkers for the diagnosis of early urinary disease. Herein, we demonstrate a diagnosis protocol to realize ultrasensitive detection of uEVs and accurate classification of early urinary diseases, by combing a ratiometric three-dimensional (3D) DNA machine with machine learning (ML). The ratiometric 3D DNA machine platform is constructed by conjugating a padlock probe (PLP) containing cytosine-rich (C-rich) sequences, anchor strands, and nucleic-acid-stabilized silver nanoclusters (DNAAgNCs) onto the magnetic nanoparticles (MNPs). The competitive binding of uEVs with the aptamer releases the walker strand, thus the ratiometric 3D DNA machine was activated to undergo an accurate amplification reaction and produce a ratiometric fluorescence signal. The present biosensor offers a detection limit of 9.9 × 103 particles mL–1 with a linear range of 104–108 particles mL–1 for uEVs. Two ML algorithms, K-nearest neighbor (KNN) and support vector machine (SVM), were subsequently applied for analyzing the correlation between the sensing signals of uEV multibiomarkers and the clinical state. The disease diagnostic accuracy of optimal biomarker combination reaches 95% and 100% by analyzing with KNN and SVM, and the disease type classification exhibits an accuracy of 94.7% and 89.5%, respectively. Moreover, the protocol results in 100% accurate visual identification of clinical uEV samples from individuals with disease or as healthy control by a t-distributed stochastic neighbor embedding (tSNE) algorithm.

尿细胞外囊泡 (uEV) 作为泌尿系统疾病诊断的潜在生物标志物来源受到了广泛关注。目前的研究主要集中在基于高通量蛋白质组学的生物标志物的发现上，而将uEVs的生物标志物应用于早期泌尿系统疾病诊断的努力有限。在这里，我们展示了一种诊断协议，通过将比率三维 (3D) DNA 机器与机器学习 (ML) 相结合，实现对 uEV 的超灵敏检测和早期泌尿疾病的准确分类。通过将包含富含胞嘧啶（C-rich）序列、锚链和核酸稳定的银纳米簇（DNAAgNCs）的挂锁探针（PLP）结合到磁性纳米粒子（MNP）上，构建了比率 3D DNA 机器平台。 uEVs 与适配体的竞争性结合会释放 walker 链，因此激活比率 3D DNA 机器以进行准确的扩增反应并产生比率荧光信号。目前的生物传感器提供 9.9 × 103 个粒子 mL-1 的检测限，uEV 的线性范围为 104-108 个粒子 mL-1。随后应用了两种 ML 算法，K-最近邻 (KNN) 和支持向量机 (SVM)，用于分析 uEV 多生物标志物的传感信号与临床状态之间的相关性。通过KNN和SVM分析，最佳生物标志物组合的疾病诊断准确率达到95%和100%，疾病类型分类准确率分别达到94.7%和89.5%。此外，该协议可通过 t 分布随机邻域嵌入 (tSNE) 算法对患有疾病或作为健康控制的个体的临床 uEV 样本进行 100% 准确的视觉识别。