Optimization of Reverse Transcription Loop-Mediated Isothermal Amplification for In Situ Detection of SARS-CoV-2 in a Micro-Air-Filtration Device Format

The Coronavirus disease 2019 (COVID-19) pandemic has supercharged innovation in the field of molecular diagnostics and led to the exploration of systems that permit the autonomous identification of airborne infectious agents. Airborne virus detection is an emerging approach for determining exposure risk, although current methods limit intervention timeliness. Here, we explore reverse transcription loop-mediated isothermal amplification (RT-LAMP) assays for one-pot detection of Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) (SCV2) run on membrane filters suitable for micro-air-filtration of airborne viruses. We use a design of experiments statistical framework to establish the optimal additive composition for running RT-LAMP on membrane filters. Using SCV2 liquid spike-in experiments and fluorescence detection, we show that single-pot RT-LAMP on glass fiber filters reliably detected 0.10 50% tissue culture infectious dose (TCID<sub>50</sub>) SCV2 per reaction (3600 E-gene copies) and is an order of magnitude more sensitive than conventional RT-LAMP.

Authors

Institutions

Publication Details

Journal
ACS Omega
Published
2024-09-20
DOI
https://doi.org/10.1021/acsomega.4c05784
Citations
3
Primary Topic
Biosensors and Analytical Detection
Type
article
Field-Weighted Citation Impact
0.55

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimization of Reverse Transcription Loop-Mediated Isothermal Amplification for In Situ Detection of SARS-CoV-2 in a Micro-Air-Filtration Device Format

Ian R. Monk, N.J. FitzGerald, Jacob Fry, Timothy P. Stinear et al.
3 citations
ACS Omega
Biosensors and Analytical Detection
0.55
article

Optimization of Reverse Transcription Loop-Mediated Isothermal Amplification for In Situ Detection of SARS-CoV-2 in a Micro-Air-Filtration Device Format

Ian R. Monk, N.J. FitzGerald, Jacob Fry, Timothy P. Stinear, David J. Collins, Jean Lee, Julie McAuley, Gregory J. Barbante, Jessica L. Porter, Samuel T. Martin
article en
3 citations

Abstract

The Coronavirus disease 2019 (COVID-19) pandemic has supercharged innovation in the field of molecular diagnostics and led to the exploration of systems that permit the autonomous identification of airborne infectious agents. Airborne virus detection is an emerging approach for determining exposure risk, although current methods limit intervention timeliness. Here, we explore reverse transcription loop-mediated isothermal amplification (RT-LAMP) assays for one-pot detection of Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) (SCV2) run on membrane filters suitable for micro-air-filtration of airborne viruses. We use a design of experiments statistical framework to establish the optimal additive composition for running RT-LAMP on membrane filters. Using SCV2 liquid spike-in experiments and fluorescence detection, we show that single-pot RT-LAMP on glass fiber filters reliably detected 0.10 50% tissue culture infectious dose (TCID<sub>50</sub>) SCV2 per reaction (3600 E-gene copies) and is an order of magnitude more sensitive than conventional RT-LAMP.

ACS OmegaVol. 9(39)
Defence Science and Technology Group (AU), The University of Melbourne (AU), Peter Doherty Institute (AU)
U.S. Department of Defense, Australian Government, Department of Defence, Australian Government, University of Melbourne, DMTC, Defence Science and Technology Group, Institute of Infection and Immunity, Centre of Excellence in Exciton Science
Clean water and sanitation
Openalex Percentile: Top 37%
Biosensors and Analytical Detection
0.55
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.