Article | Published:

A molecular multi-gene classifier for disease diagnostics

Nature Chemistryvolume 10pages746754 (2018) | Download Citation

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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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.

Author information

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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Contributions

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

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Georg Seelig.

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DOI

https://doi.org/10.1038/s41557-018-0056-1