Machine learning enabled acoustic detection of sub-nanomolar concentration of trypsin and plasmin in solution

Marek Tatarko; Eric S. Muckley; Veronika Subjakova; Monojoy Goswami; Bobby G. Sumpter; Tibor Hianik; Ilia N. Ivanov

doi:10.1016/j.snb.2018.05.100

Machine learning enabled acoustic detection of sub-nanomolar concentration of trypsin and plasmin in solution

Marek Tatarko, Eric S. Muckley, Veronika Subjakova, Monojoy Goswami, Bobby G. Sumpter, Tibor Hianik, Ilia N. Ivanov

Research output: Contribution to journal › Article › peer-review

26 Scopus citations

Abstract

We demonstrate a machine learning enabled low-cost acoustic detection of protease which may find application in assuring quality and safety of dairy products, drug screening, molecular profiling, and disease diagnostics. A hydrophilic SiO₂-coated quartz crystal microbalance (QCM) acts as a substrate to assemble α-, β-, and ĸ-casein layers (protease reporters) and as a transducer for measuring changes in frequency as casein is removed by protease. We demonstrate that α-, β-, and ĸ-caseins can form stable assembly on SiO₂ from phosphate-buffered solution (PBS) solution. Exposure to protease results in cleaving of casein which changes the frequency of the 1st–11th odd harmonics of QCM. Monitoring β-casein cleavage allows ∼0.2 nM detection of trypsin and ∼0.5 nM detection of plasmin and enables differentiation between trypsin and plasmin after <2 min of protease exposure. The casein-coated QCM allows sub-nanomolar detection and classification of protease.

Original language	English
Pages (from-to)	282-288
Number of pages	7
Journal	Sensors and Actuators, B: Chemical
Volume	272
DOIs	https://doi.org/10.1016/j.snb.2018.05.100
State	Published - Nov 1 2018

Funding

This research was conducted at the Center for Nanophase Materials Sciences, which is a US Department of Energy Office of Science User Facility. MT, VS, TH acknowledge funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 690898 . This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).

Keywords

Biosensor
Casein
Machine learning
Plasmin
Protease
QCM
Trypsin

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title = "Machine learning enabled acoustic detection of sub-nanomolar concentration of trypsin and plasmin in solution",

abstract = "We demonstrate a machine learning enabled low-cost acoustic detection of protease which may find application in assuring quality and safety of dairy products, drug screening, molecular profiling, and disease diagnostics. A hydrophilic SiO2-coated quartz crystal microbalance (QCM) acts as a substrate to assemble α-, β-, and ĸ-casein layers (protease reporters) and as a transducer for measuring changes in frequency as casein is removed by protease. We demonstrate that α-, β-, and ĸ-caseins can form stable assembly on SiO2 from phosphate-buffered solution (PBS) solution. Exposure to protease results in cleaving of casein which changes the frequency of the 1st–11th odd harmonics of QCM. Monitoring β-casein cleavage allows ∼0.2 nM detection of trypsin and ∼0.5 nM detection of plasmin and enables differentiation between trypsin and plasmin after <2 min of protease exposure. The casein-coated QCM allows sub-nanomolar detection and classification of protease.",

keywords = "Biosensor, Casein, Machine learning, Plasmin, Protease, QCM, Trypsin",

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AU - Muckley, Eric S.

AU - Subjakova, Veronika

AU - Goswami, Monojoy

AU - Sumpter, Bobby G.

AU - Hianik, Tibor

AU - Ivanov, Ilia N.

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AB - We demonstrate a machine learning enabled low-cost acoustic detection of protease which may find application in assuring quality and safety of dairy products, drug screening, molecular profiling, and disease diagnostics. A hydrophilic SiO2-coated quartz crystal microbalance (QCM) acts as a substrate to assemble α-, β-, and ĸ-casein layers (protease reporters) and as a transducer for measuring changes in frequency as casein is removed by protease. We demonstrate that α-, β-, and ĸ-caseins can form stable assembly on SiO2 from phosphate-buffered solution (PBS) solution. Exposure to protease results in cleaving of casein which changes the frequency of the 1st–11th odd harmonics of QCM. Monitoring β-casein cleavage allows ∼0.2 nM detection of trypsin and ∼0.5 nM detection of plasmin and enables differentiation between trypsin and plasmin after <2 min of protease exposure. The casein-coated QCM allows sub-nanomolar detection and classification of protease.

KW - Biosensor

KW - Casein

KW - Machine learning

KW - Plasmin

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ER -

Machine learning enabled acoustic detection of sub-nanomolar concentration of trypsin and plasmin in solution

Abstract

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Keywords

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