Issue 18, 2024

Electrostatic microfiltration (EM) enriches and recovers viable microorganisms at low-abundance in large-volume samples and enhances downstream detection

Abstract

Rapid and sensitive detection of pathogens in various samples is crucial for disease diagnosis, environmental surveillance, as well as food and water safety monitoring. However, the low abundance of pathogens (<10 CFU) in large volume (1 mL−1 L) samples containing vast backgrounds critically limits the sensitivity of even the most advanced techniques, such as digital PCR. Therefore, there is a critical need for sample preparation that can enrich low-abundance pathogens from complex and large-volume samples. This study develops an efficient electrostatic microfiltration (EM)-based sample preparation technique capable of processing ultra-large-volume (≥500 mL) samples at high throughput (≥10 mL min−1). This approach achieves a significant enrichment (>8000×) of extremely-low-abundance pathogens (down to level of 0.02 CFU mL−1, i.e., 10 CFU in 500 mL). Furthermore, EM-enabled sample preparation facilitates digital amplification techniques sensitively detecting broad pathogens, including bacteria, fungi, and viruses from various samples, in a rapid (≤3 h) sample-to-result workflow. Notably, the operational ease, portability, and compatibility/integrability with various downstream detection platforms highlight its great potential for widespread applications across diverse settings.

Graphical abstract: Electrostatic microfiltration (EM) enriches and recovers viable microorganisms at low-abundance in large-volume samples and enhances downstream detection

Supplementary files

Article information

Article type
Paper
Submitted
13 May 2024
Accepted
15 Aug 2024
First published
19 Aug 2024
This article is Open Access
Creative Commons BY-NC license

Lab Chip, 2024,24, 4275-4287

Electrostatic microfiltration (EM) enriches and recovers viable microorganisms at low-abundance in large-volume samples and enhances downstream detection

Y. Liu, J. J. Raymond, X. Wu, P. W. L. Chua, S. Y. H. Ling, C. C. Chan, C. Chan, J. X. Y. Loh, M. X. Y. Song, M. Y. Y. Ong, P. Ho, M. E. Mcbee, S. L. Springs, H. Yu and J. Han, Lab Chip, 2024, 24, 4275 DOI: 10.1039/D4LC00419A

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