Machine learning enabled acoustic detection of sub-nanomolar concentration of trypsin and plasmin in solution
Abstract
Here, 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 κ-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 κ-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.
- Authors:
-
- Comenius Univ., Bratislava (Slovakia)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1482462
- Alternate Identifier(s):
- OSTI ID: 1591683
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Sensors and Actuators. B, Chemical
- Additional Journal Information:
- Journal Volume: 272; Journal Issue: C; Journal ID: ISSN 0925-4005
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 47 OTHER INSTRUMENTATION; Casein; Plasmin; Trypsin; Protease; QCM; Biosensor; Machine learning
Citation Formats
Tatarko, Marek, Muckley, Eric S., Subjakova, Veronika, Goswami, Monojoy, Sumpter, Bobby G., Hianik, Tibor, and Ivanov, Ilia N. Machine learning enabled acoustic detection of sub-nanomolar concentration of trypsin and plasmin in solution. United States: N. p., 2018.
Web. doi:10.1016/j.snb.2018.05.100.
Tatarko, Marek, Muckley, Eric S., Subjakova, Veronika, Goswami, Monojoy, Sumpter, Bobby G., Hianik, Tibor, & Ivanov, Ilia N. Machine learning enabled acoustic detection of sub-nanomolar concentration of trypsin and plasmin in solution. United States. doi:https://doi.org/10.1016/j.snb.2018.05.100
Tatarko, Marek, Muckley, Eric S., Subjakova, Veronika, Goswami, Monojoy, Sumpter, Bobby G., Hianik, Tibor, and Ivanov, Ilia N. Sat .
"Machine learning enabled acoustic detection of sub-nanomolar concentration of trypsin and plasmin in solution". United States. doi:https://doi.org/10.1016/j.snb.2018.05.100. https://www.osti.gov/servlets/purl/1482462.
@article{osti_1482462,
title = {Machine learning enabled acoustic detection of sub-nanomolar concentration of trypsin and plasmin in solution},
author = {Tatarko, Marek and Muckley, Eric S. and Subjakova, Veronika and Goswami, Monojoy and Sumpter, Bobby G. and Hianik, Tibor and Ivanov, Ilia N.},
abstractNote = {Here, 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 κ-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 κ-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.},
doi = {10.1016/j.snb.2018.05.100},
journal = {Sensors and Actuators. B, Chemical},
number = C,
volume = 272,
place = {United States},
year = {2018},
month = {5}
}
Web of Science
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Works referencing / citing this record:
Machine Learning‐Enabled Correlation and Modeling of Multimodal Response of Thin Film to Environment on Macro and Nanoscale Using “Lab‐on‐a‐Crystal”
journal, January 2020
- Muckley, Eric S.; Collins, Liam; Srijanto, Bernadeta R.
- Advanced Functional Materials, Vol. 30, Issue 10
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