中国科学院化学所苏萌团队报道了基于印刷异链的机器学习辅助高通量鉴定和定量蛋白质生物标志物。相关研究成果发表在2024年7月1日出版的《美国化学会杂志》。

先进的体外诊断技术在疾病的早期检测、预后和进展监测方面是非常可取的。

该文中，研究人员设计了一种基于多材料杂环链的可调液体限制自组装的多重蛋白质生物传感策略，与标准ELISA试剂盒相比，该策略显示出更高的灵敏度、吞吐量和准确性。通过控制材料组合和配体纳米颗粒（NPs）的数量，研究人员在聚合物-半导体杂环链中观察到强大的近场增强以及强电磁共振。特别地，它们的光信号在宽范围内显示出对半导体NPs的配位数的线性响应。

因此，通过对中心聚合物链上的抗体进行功能化，开发了一种可见的纳米光子生物传感器，该生物传感器可以识别附着在半导体NPs上的靶蛋白。这能够在一个步骤中以超低的检测极限（1pg/mL）特异性检测来自健康人和癌症患者的多种蛋白质生物标志物。此外，通过结合神经网络算法，实现了对缓冲液、尿液和血清等不同临床样本中蛋白质表达水平的快速高通量量化，平均准确率为97.3%。

该项工作表明，基于异链的生物传感器是构建下一代诊断工具的示例性候选者，适用于许多临床环境。

研究人员表示，先进的体外诊断技术在疾病的早期发现、预后和进展监测方面是非常需要的。

附：英文原文

Title: Machine Learning-Assisted High-Throughput Identification and Quantification of Protein Biomarkers with Printed Heterochains

Author: Xiangyu Pan, Zeying Zhang, Yang Yun, Xu Zhang, Yali Sun, Zixuan Zhang, Huadong Wang, Xu Yang, Zhiyu Tan, Yaqi Yang, Hongfei Xie, Bogdan Bogdanov, Georgii Zmaga, Pavel Senyushkin, Xuemei Wei, Yanlin Song, Meng Su

Issue&Volume: July 1, 2024

Abstract: Advanced in vitro diagnosis technologies are highly desirable in early detection, prognosis, and progression monitoring of diseases. Here, we engineer a multiplex protein biosensing strategy based on the tunable liquid confinement self-assembly of multi-material heterochains, which show improved sensitivity, throughput, and accuracy compared to standard ELISA kits. By controlling the material combination and the number of ligand nanoparticles (NPs), we observe robust near-field enhancement as well as both strong electromagnetic resonance in polymer–semiconductor heterochains. In particular, their optical signals show a linear response to the coordination number of the semiconductor NPs in a wide range. Accordingly, a visible nanophotonic biosensor is developed by functionalizing antibodies on central polymer chains that can identify target proteins attached to semiconductor NPs. This allows for the specific detection of multiple protein biomarkers from healthy people and pancreatic cancer patients in one step with an ultralow detection limit (1 pg/mL). Furthermore, rapid and high-throughput quantification of protein expression levels in diverse clinical samples such as buffer, urine, and serum is achieved by combining a neural network algorithm, with an average accuracy of 97.3%. This work demonstrates that the heterochain-based biosensor is an exemplary candidate for constructing next-generation diagnostic tools and suitable for many clinical settings.

DOI: 10.1021/jacs.4c04460

Source: https://pubs.acs.org/doi/abs/10.1021/jacs.4c04460