Institute of Molecular Medicine, Department of Urology, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Frontiers Science Center for Transformative Molecules, School of Chemistry and Chemical Engineering, Zhangjiang Institute for Advanced Study, and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
Nat Nanotechnol. 2023 Jun;18(6):677-686. doi: 10.1038/s41565-023-01348-9. Epub 2023 Mar 27.
A molecular classification of diseases that accurately reflects clinical behaviour lays the foundation of precision medicine. The development of in silico classifiers coupled with molecular implementation based on DNA reactions marks a key advance in more powerful molecular classification, but it nevertheless remains a challenge to process multiple molecular datatypes. Here we introduce a DNA-encoded molecular classifier that can physically implement the computational classification of multidimensional molecular clinical data. To produce unified electrochemical sensing signals across heterogeneous molecular binding events, we exploit DNA-framework-based programmable atom-like nanoparticles with n valence to develop valence-encoded signal reporters that enable linearity in translating virtually any biomolecular binding events to signal gains. Multidimensional molecular information in computational classification is thus precisely assigned weights for bioanalysis. We demonstrate the implementation of a molecular classifier based on programmable atom-like nanoparticles to perform biomarker panel screening and analyse a panel of six biomarkers across three-dimensional datatypes for a near-deterministic molecular taxonomy of prostate cancer patients.
疾病的分子分类准确反映临床行为，为精准医学奠定了基础。基于 DNA 反应的计算分类器的开发是更强大的分子分类的一个关键进展，但处理多个分子数据类型仍然是一个挑战。在这里，我们介绍了一种 DNA 编码的分子分类器，它可以物理地实现多维分子临床数据的计算分类。为了在异质分子结合事件中产生统一的电化学传感信号，我们利用基于 DNA 框架的可编程原子样纳米粒子，具有 n 个价态，开发价态编码的信号报告器，使任何生物分子结合事件到信号增益的转换都具有线性关系。计算分类中的多维分子信息因此被精确地分配了生物分析权重。我们演示了基于可编程原子样纳米粒子的分子分类器的实现，用于进行生物标志物面板筛选，并分析了三个维度数据类型的六个生物标志物面板，以对前列腺癌患者进行近乎确定性的分子分类。
