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A molecular multi-gene classifier for disease diagnostics

Nature Chemistry volume 10, pages 746–754 (2018)Cite this article

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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.

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Fig. 1: Universal framework for rapid prototyping of molecular classifiers for gene expression diagnostics.
Fig. 2: Implementation of classifier weights by targeting of multiple adjacent regions in a transcript.
Fig. 3: Molecular implementation of a two-gene classifier for cancer diagnostics.
Fig. 4: In silico training of a minimal linear classifier to discriminate viral from bacterial infections based on host gene expression data.
Fig. 5: A molecular classifier of host gene expression for respiratory infections diagnostics.

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Acknowledgements

The authors thank Y.-J. Chen, S. Chen, G. Chatterjee and D.Y. Zhang for their support and discussions. This work was supported by NSF grants CCF-171449 and CCF-1317653.

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Authors and Affiliations

  1. Department of Bioengineering, University of Washington, Seattle, WA, USA

    Randolph Lopez

  2. Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA, USA

    Randolph Lopez & Georg Seelig

  3. Department of Biology, University of Washington, Seattle, WA, USA

    Ruofan Wang

  4. Department of Microbiology, University of Washington, Seattle, WA, USA

    Ruofan Wang

  5. Department of Electrical Engineering, University of Washington, Seattle, WA, USA

    Georg Seelig

  6. Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA

    Georg Seelig

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  1. Randolph Lopez
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Contributions

R.L.B. and G.S. designed the experiments and wrote the paper. R.L.B. and R.W. performed the experiments.

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Correspondence to Georg Seelig.

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Lopez, R., Wang, R. & Seelig, G. A molecular multi-gene classifier for disease diagnostics. Nature Chem 10, 746–754 (2018). https://doi.org/10.1038/s41557-018-0056-1

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