Unbiased Data Analysis for the Parameterization of Fast Translocation Events through Nanopores

Florian L. R. Lucas*, Kherim Willems, Matthijs J. Tadema, Katarzyna M. Tych, Giovanni Maglia, Carsten Wloka*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
5 Downloads (Pure)

Abstract

Single-molecule nanopore electrophysiology is an emerging technique for the detection of analytes in aqueous solutions with high sensitivity. These detectors have proven applicable for the enzyme-assisted sequencing of oligonucleotides. There has recently been an increased interest in the use of nanopores for the fingerprinting of peptides and proteins, referred to as single-molecule nanopore spectrometry. However, the analysis of the resulting electrophysiology traces remains complicated due to the fast unassisted translocation of such analytes, usually in the order of micro-to milliseconds, and the small ion current signal produced (in the picoampere range). Here, we present the application of a generalized normal distribution function (gNDF) for the characterization of short-lived ion current signals (blockades). We show that the gNDF can be used to determine if the observed blockades have adequate time to reach their maximum current plateau while also providing a description of each blockade based on the open pore current (I-O), the difference caused by the pore blockade (delta I-B), the position in time (mu), the standard deviation (sigma), and a shape parameter (beta), leaving only the noise component. In addition, this method allows the estimation of an ideal range of low-pass filter frequencies that contains maximum information with minimal noise. In summary, we show a parameter-free and generalized method for the analysis of short-lived ion current blockades, which facilitates single-molecule nanopore spectrometry with minimal user bias.

Original languageEnglish
Pages (from-to)26040–26046
Number of pages7
JournalACS Omega
Volume7
Early online date19-Jul-2022
DOIs
Publication statusPublished - 2-Aug-2022

Keywords

  • ACCURATE DATA PROCESS
  • DNA
  • NOISE

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