Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore, Singapore.
Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore.
Nat Biomed Eng. 2019 Sep;3(9):684-694. doi: 10.1038/s41551-019-0417-0. Epub 2019 Jul 8.
Massively parallel DNA sequencing is established, yet high-throughput protein profiling remains challenging. Here, we report a barcoding approach that leverages the combinatorial sequence content and the configurational programmability of DNA nanostructures for high-throughput multiplexed profiling of the subcellular expression and distribution of proteins in whole cells. The barcodes are formed by in situ hybridization of tetrahedral DNA nanostructures and short DNA sequences conjugated with protein-targeting antibodies, and by nanostructure-assisted ligation (either enzymatic or chemical) of the nanostructures and exogenous DNA sequences bound to nanoparticles of different sizes (which cause these localization sequences to differentially distribute across subcellular compartments). Compared with linear DNA barcoding, the nanostructured barcodes enhance the signal by more than 100-fold. By implementing the barcoding approach on a microfluidic device for the analysis of rare patient samples, we show that molecular subtypes of breast cancer can be accurately classified and that subcellular spatial markers of disease aggressiveness can be identified.
大规模平行 DNA 测序已经建立，然而高通量蛋白质谱分析仍然具有挑战性。在这里，我们报告了一种基于条形码的方法，该方法利用 DNA 纳米结构的组合序列内容和构象可编程性，对整个细胞中蛋白质的亚细胞表达和分布进行高通量、多重分析。条形码由四面体 DNA 纳米结构与与蛋白质靶向抗体偶联的短 DNA 序列的原位杂交，以及纳米结构辅助的连接（酶促或化学）形成，连接的是与不同大小的纳米颗粒结合的外源性 DNA 序列（这些定位序列会在亚细胞区室中产生不同的分布）。与线性 DNA 条形码相比，纳米结构条形码将信号增强了 100 多倍。通过在微流控设备上实施条形码方法来分析罕见的患者样本，我们表明可以准确地对乳腺癌的分子亚型进行分类，并且可以识别疾病侵袭性的亚细胞空间标志物。
