Combined Amplification and Molecular Classification for Gene Expression Diagnostics

  • Gokul Gowri
  • Randolph Lopez
  • Georg SeeligEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11648)


RNA expression profiles contain information about the state of a cell and specific gene expression changes are often associated with disease. Classification of blood or similar samples based on RNA expression can thus be a powerful method for disease diagnosis. However, basing diagnostic decisions on RNA expression remains impractical for most clinical applications because it requires costly and slow gene expression profiling based on microarrays or next generation sequencing followed by often complex in silico analysis. DNA-based molecular classifiers that perform a computation over RNA inputs and summarize a diagnostic result in situ have been developed to address this issue, but lack the sensitivity required for use with actual biological samples. To address this limitation, we here propose a DNA-based classification system that takes advantage of PCR-based amplification for increased sensitivity. In our initial scheme, the importance of a transcript for a diagnostic decision is proportional to the number of molecular probes bound to that transcript. Although probe concentration is similar to that of the RNA input, subsequent amplification of the probes with PCR can dramatically increase the sensitivity of the assay. However, even slight biases in PCR efficiency can distort weight information encoded by the original probe set. To address this concern, we developed and mathematically analyzed multiple strategies for mitigating the bias associated with PCR-based amplification. We evaluate these amplified molecular classification strategies through simulation using two distinct gene expression data sets and associated disease categories as inputs. Through this analysis, we arrive at a novel molecular classifier framework that naturally accommodates PCR bias and also uses a smaller number of molecular probes than required in the initial, naive implementation.



G. G. was supported by Caltech’s Summer Undergraduate Research Fellowship program. R. L. and G. S. were supported by NSF grant CCF-1714497.


  1. 1.
    Tsalik, E.L., et al.: Host gene expression classifiers diagnose acute respiratory illness etiology. Sci. Transl. Med. 8, 322ra11 (2016)CrossRefGoogle Scholar
  2. 2.
    Best, M.G., et al.: RNA-Seq of tumor-educated platelets enables blood-based pan-cancer, multiclass, and molecular pathway cancer diagnostics. Cancer cell 28, 666–676 (2015)CrossRefGoogle Scholar
  3. 3.
    Benenson, Y., Gil, B., Ben-Dor, U., Adar, R., Shapiro, E.: An autonomous molecular computer for logical control of gene expression. Nature 429, 423–429 (2004)CrossRefGoogle Scholar
  4. 4.
    Lopez, R., Wang, R., Seelig, G.: A molecular multi-gene classifier for disease diagnostics. Nat. Chem. 10, 746–754 (2018)CrossRefGoogle Scholar
  5. 5.
    Zhang, D.Y.: Cooperative hybridization of oligonucleotides. J. Am. Chem. Soc. 133, 1077–1086 (2010)CrossRefGoogle Scholar
  6. 6.
    Turberfield, A.J., Mitchell, J., Yurke, B., Mills Jr., A.P., Blakey, M., Simmel, F.C.: DNA fuel for free-running nanomachines. Phys. Rev. Lett. 90, 118102 (2003)CrossRefGoogle Scholar
  7. 7.
    Dirks, R.M., Pierce, N.A.: Triggered amplification by hybridization chain reaction. Proc. Nat. Acad. Sci. USA 101, 15275–15278 (2004)CrossRefGoogle Scholar
  8. 8.
    Seelig, G., Yurke, B., Winfree, E.: Catalyzed relaxation of a metastable DNA fuel. J. Am. Chem. Soc. 128, 12211–12220 (2006)CrossRefGoogle Scholar
  9. 9.
    Zhang, D.Y., Turberfield, A.J., Yurke, B., Winfree, E.: Engineering entropy-driven reactions and networks catalyzed by DNA. Science 318, 1121–1125 (2007)CrossRefGoogle Scholar
  10. 10.
    Zhang, D.Y., Seelig, G.: DNA-based fixed gain amplifiers and linear classifier circuits. In: Sakakibara, Y., Mi, Y. (eds.) DNA 2010. LNCS, vol. 6518, pp. 176–186. Springer, Heidelberg (2011). Scholar
  11. 11.
    Chen, S.X., Seelig, G.: A DNA neural network constructed from molecular variable gain amplifiers. In: Brijder, R., Qian, L. (eds.) DNA 2017. LNCS, vol. 10467, pp. 110–121. Springer, Cham (2017). Scholar
  12. 12.
    Li, H., Qiu, J., Fu, X.D.: RASL-seq for massively parallel and quantitative analysis of gene expression. Curr. Protoc. Mol. Biol. 98, 4–13 (2012)CrossRefGoogle Scholar
  13. 13.
    Cherry, K.M., Qian, L.: Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks. Nature 559, 370 (2018)CrossRefGoogle Scholar
  14. 14.
    Burchill, S.A., Perebolte, L., Johnston, C., Top, B., Selby, P.: Comparison of the RNA-amplification based methods RT-PCR and NASBA for the detection of circulating tumour cells. Br. J. Cancer 86, 102 (2002)CrossRefGoogle Scholar
  15. 15.
    Deng, R., Zhang, K., Sun, Y., Ren, X., Li, J.: Highly specific imaging of mRNA in single cells by target RNA-initiated rolling circle amplification. Chem. Sci. 8, 3668–3675 (2017)CrossRefGoogle Scholar
  16. 16.
    Song, T., Garg, S., Mokhtar, R., Bui, H., Reif, J.: Analog computation by DNA strand displacement circuits. ACS Synth. Biol. 5, 898–912 (2016)CrossRefGoogle Scholar
  17. 17.
    Stougaard, M., Juul, S., Andersen, F.F., Knudsen, B.R.: Strategies for highly sensitive biomarker detection by Rolling Circle Amplification of signals from nucleic acid composed sensors. Integr. Biol. 3, 982–992 (2011)CrossRefGoogle Scholar
  18. 18.
    Takahashi, H., Matsumoto, A., Sugiyama, S., Kobori, T.: Direct detection of green fluorescent protein messenger RNA expressed in Escherichia coli by rolling circle amplification. Anal. Biochem. 401, 242–249 (2010)CrossRefGoogle Scholar
  19. 19.
    Stahlberg, A., Krzyzanowski, P.M., Jackson, J.B., Egyud, M., Stein, L., Godfrey, T.E.: Simple, multiplexed, PCR-based barcoding of DNA enables sensitive mutation detection in liquid biopsies using sequencing. Nucleic Acids Res. 44, e105–e105 (2016)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.California Institute of TechnologyPasadenaUSA
  2. 2.University of WashingtonSeattleUSA

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