Department of Bioengineering, University of Washington, Seattle, WA, USA.
Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA, USA.
Nat Chem. 2018 Jul;10(7):746-754. doi: 10.1038/s41557-018-0056-1. Epub 2018 Apr 30.
Despite its early promise as a diagnostic and prognostic tool, gene expression profiling remains cost-prohibitive and challenging to implement in a clinical setting. Here, we introduce a molecular computation strategy for analysing the information contained in complex gene expression signatures without the need for costly instrumentation. Our workflow begins by training a computational classifier on labelled gene expression data. This in silico classifier is then realized at the molecular level to enable expression analysis and classification of previously uncharacterized samples. Classification occurs through a series of molecular interactions between RNA inputs and engineered DNA probes designed to differentially weigh each input according to its importance. We validate our technology with two applications: a classifier for early cancer diagnostics and a classifier for differentiating viral and bacterial respiratory infections based on host gene expression. Together, our results demonstrate a general and modular framework for low-cost gene expression analysis.
尽管基因表达谱分析在诊断和预后方面具有早期的应用前景，但它仍然成本高昂，并且在临床环境中实施具有挑战性。在这里，我们引入了一种分子计算策略，用于分析复杂基因表达谱中包含的信息，而无需昂贵的仪器。我们的工作流程首先在标记的基因表达数据上训练计算分类器。然后，在分子水平上实现这个计算分类器，以实现对以前未表征样本的表达分析和分类。分类是通过 RNA 输入和工程化 DNA 探针之间的一系列分子相互作用来实现的，这些探针旨在根据其重要性对每个输入进行差异化加权。我们使用两个应用程序验证了我们的技术：一个用于早期癌症诊断的分类器，以及一个基于宿主基因表达区分病毒和细菌呼吸道感染的分类器。总之，我们的结果展示了一种用于低成本基因表达分析的通用和模块化框架。
