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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.

尿細胞外囊泡 (uEV) 作為泌尿系統(tǒng)疾病診斷的潛在生物標志物來源受到了廣泛關注。目前的研究主要集中在基于高通量蛋白質組學的生物標志物的發(fā)現(xiàn)上，而將uEVs的生物標志物應用于早期泌尿系統(tǒng)疾病診斷的努力有限。在這里，我們展示了一種診斷協(xié)議，通過將比率三維 (3D) DNA 機器與機器學習 (ML) 相結合，實現(xiàn)對 uEV 的超靈敏檢測和早期泌尿疾病的準確分類。通過將包含富含胞嘧啶（C-rich）序列、錨鏈和核酸穩(wěn)定的銀納米簇（DNAAgNCs）的掛鎖探針（PLP）結合到磁性納米粒子（MNP）上，構建了比率 3D DNA 機器平臺。 uEVs 與適配體的競爭性結合會釋放 walker 鏈，因此激活比率 3D DNA 機器以進行準確的擴增反應并產生比率熒光信號。目前的生物傳感器提供 9.9 × 103 個粒子 mL-1 的檢測限，uEV 的線性范圍為 104-108 個粒子 mL-1。隨后應用了兩種 ML 算法，K-最近鄰 (KNN) 和支持向量機 (SVM)，用于分析 uEV 多生物標志物的傳感信號與臨床狀態(tài)之間的相關性。通過KNN和SVM分析，最佳生物標志物組合的疾病診斷準確率達到95%和100%，疾病類型分類準確率分別達到94.7%和89.5%。此外，該協(xié)議可通過 t 分布隨機鄰域嵌入 (tSNE) 算法對患有疾病或作為健康控制的個體的臨床 uEV 樣本進行 100% 準確的視覺識別。