A molecular multi-gene classifier for disease diagnostics

Abstract

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.

Effective design principles for leakless strand displacement systems

“…However, the sequences that we used are short and unstructured, while biological sequences usually have significant intrinsic secondary structures, which could affect desired kinetic behavior (54) or impose sequence constraints and limit the design space. Despite these challenges, there is a body of work demonstrating strand displacement cascades with natural DNA and RNA inputs, including logic computation both in vitro (9) and in vivo (25), a molecular multigene classifier (21), regulation of cellular gene expression (26,27), and in situ fluorescence imaging of mRNA expression (55). Destabilizing organic solvents (56) and the hybridization probe method (21) can be generally adopted to overcome secondary structure in strand displacement.…”

“…Strand displacement reactions underlie molecular motors (4,6), robots (7,8), logic circuits capable of signal restoration, amplification, digital (9,10) and neural network-like computation (11), and controllers that implement prescribed time-varying dynamics (12,13) based on a systematic theory (14,15). Strand displacement-based molecular amplifiers (16)(17)(18)(19) are not only an essential component for signal restoration in digital circuits but also, useful for diagnostic applications (20,21). The successful implementation of synthetic cell-free systems also leads to additional applications at the interface with biology: nucleic acid-based nanodevices have been delivered in vivo (22) as imaging probes (23) or to perform logical computation (24,25).…”

Programmable patterns in a DNA-based reaction–diffusion system

“…29,[34][35][36][37] Toehold-mediated DNA strand displacement has proved to be a convenient framework for implementing complex reaction sequences using synthetic DNA in well-mixed test tubes. 38,39 Using the principles of strand displacement, researchers have created sophisticated reaction networks that perform computation like neural networks, 40,41 diagnostic classifiers, 42 dynamic 3D nanostructures 43,44 and even approximate the dynamics of formal, mathematically specified chemical reaction networks (CRNs). [45][46][47][48] Building on these results, theoretical work has argued that a wide range of patterns is achievable if DNA-based CRNs are embedded in a spatial reactor.…”

Distributed DNA-based Communication in Populations of Synthetic Protocells

“…This important feature could, for example, aid the design of multi-layer DNA-based winner-take-all circuits 47 capable of classifying complex information patterns. Such circuits would be highly valuable in disease diagnostics 48 .…”