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Issue 26, 2016
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Integrated microfluidic aptasensor for mass spectrometric detection of vasopressin in human plasma ultrafiltrate

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Abstract

We present a microfluidic aptamer-based biosensor for detection of low-molecular-weight biomarkers in patient samples. Using a microfluidic device that integrates aptamer-based specific analyte extraction, isocratic elution, and detection by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry, we demonstrate rapid, sensitive and label-free detection of arginine vasopressin (AVP) in human plasma ultrafiltrate. AVP molecules in complex matrices are specifically captured by an aptamer that is immobilized on microbeads via affinity binding in a microchamber. After the removal of unbound, contaminating molecules through washing, aptamer–AVP complexes are thermally disrupted via on-chip temperature control. Released AVP molecules are eluted with purified water and transferred to a separate microchamber, and deposited onto a single spot on a MALDI plate via repeated, piezoelectrically actuated ejection, which enriches AVP molecules over the spot area. This integrated on-chip sample processing enables the quantitative detection of low-abundance AVP by MALDI-TOF mass spectrometry in a rapid and label-free manner. Our experimental results show the detection of AVP in human plasma ultrafiltrate as low as physiologically relevant picomolar concentrations via aptamer-based selective preconcentration, demonstrating the potential of our approach as a rapid (∼1 h), sensitive clinical AVP assay.

Graphical abstract: Integrated microfluidic aptasensor for mass spectrometric detection of vasopressin in human plasma ultrafiltrate

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Publication details

The article was received on 13 Nov 2015, accepted on 05 May 2016 and first published on 11 May 2016


Article type: Paper
DOI: 10.1039/C5AY02979A
Citation: Anal. Methods, 2016,8, 5190-5196
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    Integrated microfluidic aptasensor for mass spectrometric detection of vasopressin in human plasma ultrafiltrate

    J. Yang, J. Zhu, R. Pei, J. A. Oliver, D. W. Landry, M. N. Stojanovic and Q. Lin, Anal. Methods, 2016, 8, 5190
    DOI: 10.1039/C5AY02979A

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