PLoS ONE (Jan 2015)

Multidimensional mapping method using an arrayed sensing system for cross-reactivity screening.

  • Sheryl E Chocron,
  • Bryce M Weisberger,
  • Hadar Ben-Yoav,
  • Thomas E Winkler,
  • Eunkyoung Kim,
  • Deanna L Kelly,
  • Gregory F Payne,
  • Reza Ghodssi

DOI
https://doi.org/10.1371/journal.pone.0116310
Journal volume & issue
Vol. 10, no. 3
p. e0116310

Abstract

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When measuring chemical information in biological fluids, challenges of cross-reactivity arise, especially in sensing applications where no biological recognition elements exist. An understanding of the cross-reactions involved in these complex matrices is necessary to guide the design of appropriate sensing systems. This work presents a methodology for investigating cross-reactions in complex fluids. First, a systematic screening of matrix components is demonstrated in buffer-based solutions. Second, to account for the effect of the simultaneous presence of these species in complex samples, the responses of buffer-based simulated mixtures of these species were characterized using an arrayed sensing system. We demonstrate that the sensor array, consisting of electrochemical sensors with varying input parameters, generated differential responses that provide synergistic information of sample. By mapping the sensing array response onto multidimensional heat maps, characteristic signatures were compared across sensors in the array and across different matrices. Lastly, the arrayed sensing system was applied to complex biological samples to discern and match characteristic signatures between the simulated mixtures and the complex sample responses. As an example, this methodology was applied to screen interfering species relevant to the application of schizophrenia management. Specifically, blood serum measurement of antipsychotic clozapine and antioxidant species can provide useful information regarding therapeutic efficacy and psychiatric symptoms. This work proposes an investigational tool that can guide multi-analyte sensor design, chemometric modeling and biomarker discovery.