Electrostatic microfiltration (EM) enriches and recovers viable microorganisms at low-abundance in large-volume samples and enhances downstream detection
Name
d4lc00419a.pdf
Description
Published version
Size
3.08 MB
Format
Adobe PDF
Checksum (MD5)
246a17e60874ebda494cf27bd30c54a2
Author(s)
Liu, Yaoping
Raymond, Joshua J
Wu, Xiaolin
Chua, Patrina Wei Lin
Ling, Sharon Yan Han
Chan, Chia Ching
Chan, Cheryl
Loh, Joanne Xin Yi
Song, Melody Xing Yen
Ong, Matilda Yu Yan
Date Issued
September 10, 2024
Journal
Lab on a Chip
Publisher
Royal Society of Chemistry
Citation
Liu, Yaoping, Raymond, Joshua J, Wu, Xiaolin, Chua, Patrina Wei Lin, Ling, Sharon Yan Han et al. 2024. "Electrostatic microfiltration (EM) enriches and recovers viable microorganisms at low-abundance in large-volume samples and enhances downstream detection." Lab on a Chip, 24 (18).
Version
Final published version
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.
MIT Department
Singapore-MIT Alliance in Research and Technology (SMART)
Massachusetts Institute of Technology. Center for Biomedical Innovation
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution-Noncommercial
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DOI of Published Version
https://doi.org/10.1039/d4lc00419a
