Cancer prognostics by direct detection of p53-antibodies on gold surfaces by impedance measurements

Elisabet Prats-Alfonso*, Xavier Sisquella, Nadia Zine, Gemma Gabriel, Anton Guimerà, F. Javier Del Campo, Rosa Villa, Adam H. Eisenberg, Milan Mrksich, Abdelhamid Errachid, Jordi Aguiló, Fernando Albericio

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

The identification and measurement of biomarkers is critical to a broad range of methods that diagnose and monitor many diseases. Serum auto-antibodies are rapidly becoming interesting targets because of their biological and medical relevance. This paper describes a highly sensitive, label-free approach for the detection of p53-antibodies, a prognostic indicator in ovarian cancer as well as a biomarker in the early stages of other cancers. This approach uses impedance measurements on gold microelectrodes to measure antibody concentrations at the picomolar level in undiluted serum samples. The biosensor shows high selectivity as a result of the optimization of the epitopes responsible for the detection of p53-antibodies and was validated by several techniques including microcontact printing, self-assembled-monolayer desorption ionization (SAMDI) mass spectrometry, and adhesion pull-off force by atomic force microscopy (AFM). This transduction method will lead to fast and accurate diagnostic tools for the early detection of cancer and other diseases.

Original languageEnglish (US)
Pages (from-to)2106-2115
Number of pages10
JournalSmall
Volume8
Issue number13
DOIs
StatePublished - Jul 9 2012

Keywords

  • biomedical applications
  • biosensors
  • cancer prognostics
  • characterization tools
  • self-assembly

ASJC Scopus subject areas

  • Biotechnology
  • Biomaterials
  • Chemistry(all)
  • Materials Science(all)

Fingerprint Dive into the research topics of 'Cancer prognostics by direct detection of p53-antibodies on gold surfaces by impedance measurements'. Together they form a unique fingerprint.

Cite this