Cluster-Enhanced Nanopore Sensing of Ovarian Cancer Marker Peptides in Urine

Thomas W. Rockett, Mohammed Almahyawi, Madhav L. Ghimire, Aashna Jonnalagadda, Victoria Tagliaferro, Sarah J. Seashols-Williams, Massimo F. Bertino, Gregory A. Caputo, Joseph E. Reiner

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The development of novel methodologies that can detect biomarkers from cancer or other diseases is both a challenge and a need for clinical applications. This partly motivates efforts related to nanopore-based peptide sensing. Recent work has focused on the use of gold nanoparticles for selective detection of cysteine-containing peptides. Specifically, tiopronin-capped gold nanoparticles, trapped in the cis-side of a wild-type α-hemolysin nanopore, provide a suitable anchor for the attachment of cysteine-containing peptides. It was recently shown that the attachment of these peptides onto a nanoparticle yields unique current signatures that can be used to identify the peptide. In this article, we apply this technique to the detection of ovarian cancer marker peptides ranging in length from 8 to 23 amino acid residues. It is found that sequence variability complicates the detection of low-molecular-weight peptides (<10 amino acid residues), but higher-molecular-weight peptides yield complex, high-frequency current fluctuations. These fluctuations are characterized with chi-squared and autocorrelation analyses that yield significantly improved selectivity when compared to traditional open-pore analysis. We demonstrate that the technique is capable of detecting the only two cysteine-containing peptides from LRG-1, an emerging protein biomarker, that are uniquely present in the urine of ovarian cancer patients. We further demonstrate the detection of one of these LRG-1 peptides spiked into a sample of human female urine.

Original languageEnglish (US)
Pages (from-to)860-869
Number of pages10
JournalACS Sensors
Volume9
Issue number2
DOIs
StatePublished - Feb 23 2024

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Instrumentation
  • Process Chemistry and Technology
  • Fluid Flow and Transfer Processes

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