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Quantitative Framework for Stochastic Nanopore Sensors Using Multiple Channels.

Robert A Lazenby1, Florika C Macazo1, Richard F Wormsbecher1

  • 1Department of Chemistry and Biochemistry, University of Maryland Baltimore County , Baltimore, Maryland 21250, United States.

Analytical Chemistry
|November 30, 2017
PubMed
Summary

Multiple ion channels enhance stochastic sensor sensitivity for molecular binding detection. Signal complexity is managed by analyzing binding event frequency, enabling precise measurements with improved detection limits.

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Area of Science:

  • Biophysics
  • Nanotechnology
  • Analytical Chemistry

Background:

  • Membrane protein channels serve as stochastic sensors for high-specificity, single-molecule binding measurements.
  • Analyzing current-time traces from single ion channels is straightforward, but signals from multiple channels are complex.
  • Existing methods face challenges in interpreting complex signals from multi-channel systems.

Purpose of the Study:

  • To demonstrate that multiple independent ion channels improve detection sensitivity over single-channel measurements.
  • To show that signal complexity in multi-channel systems can be managed using binding event frequency analysis.
  • To establish an upper limit of quantification for high ligand concentrations and channel numbers.

Main Methods:

  • Utilized current-time traces from membrane protein channels as stochastic sensors.
  • Applied Poisson point process modeling to analyze the leading edge of binding events.
  • Expanded calibration to high ligand concentrations and numerous ion channels.

Main Results:

  • Multiple ion channels provide enhanced detection sensitivity compared to single channels.
  • Binding event frequency analysis effectively accounts for increased signal complexity.
  • An upper limit of quantification exists, determined by measurement time resolution and ligand-protein dissociation times.

Conclusions:

  • Superimposing signals from multiple channels and analyzing association times enable sensitive, quantitative measurements.
  • The identified upper limit of quantification predicts measurement requirements for observing processes as Poisson point processes.
  • Nanopore-based sensing analysis offers broad implications for stochastic sensing platforms with multiple, superimposable signals.