Synthesis and Application of Superabsorbent Polymer Microspheres for Rapid Concentration and Quantification of Microbial Pathogens in Ambient Water
Abstract
Even though numerous methods have been developed for the detection and quantification of waterborne pathogens, the application of these methods is often hindered by the very low pathogen concentrations in natural waters. Therefore, rapid and efficient sample concentration methods are urgently needed. Here we present a novel method to pre-concentrate microbial pathogens in water using a portable 3D-printed system with super-absorbent polymer (SAP) microspheres, which can effectively reduce the actual volume of water in a collected sample. The SAP microspheres absorb water while excluding bacteria and viruses by size exclusion and charge repulsion. To improve the water absorption capacity of SAP in varying ionic strength waters (0-100 mM), we optimized the formulation of SAP to 180 g∙L⁻¹ Acrylamide, 75 g∙L⁻¹ Itaconic Acid and 4.0 g∙L⁻¹ Bis-Acrylamide for the highest ionic strength water as a function of the extent of cross-linking and the concentration of counter ions. Fluorescence microscopy and double-layer agar plating respectively showed that the 3D-printed system with optimally-designed SAP microspheres could rapidly achieve a 10-fold increase in the concentration of Escherichia coli (E. coli) and bacteriophage MS2 within 20 minutes with concentration efficiencies of 87% and 96%, respectively. Fold changes between concentrated and original samples from qPCR and RT-qPCR results were found to be respectively 11.34-22.27 for E. coli with original concentrations from 10⁴ to 10⁶ cell·mL⁻¹, and 8.20-13.81 for MS2 with original concentrations from 10⁴-10⁶ PFU·mL⁻¹. Furthermore, SAP microspheres can be reused for 20 times without performance loss, significantly decreasing the cost of our concentration system.
Additional Information
© 2020 The Authors. Published by Elsevier. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Received 26 September 2019, Revised 11 December 2019, Accepted 9 January 2020, Available online 11 January 2020. The authors acknowledge the financial support provided by the Bill and Melinda Gates Foundation (grant no. OPP1111252). The authors thank Dr. Katharina Urmann, Dr. Xingyu Lin and Dr. Xing Xie for their helpful advices and discussions. CRediT authorship contribution statement: Xunyi Wu: Conceptualization, Methodology, Investigation, Formal analysis, Writing - original draft. Xiao Huang: Conceptualization, Methodology, Writing - review & editing. Yanzhe Zhu: Writing - review & editing. Jing Li: Methodology, Writing - review & editing. Michael R. Hoffmann: Conceptualization. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Attached Files
Published - 1-s2.0-S1383586619343874-main.pdf
Supplemental Material - 1-s2.0-S1383586619343874-mmc1.xml
Supplemental Material - 1-s2.0-S1383586619343874-mmc2.docx
Files
Additional details
- PMCID
- PMC7045201
- Eprint ID
- 100671
- Resolver ID
- CaltechAUTHORS:20200113-095556475
- Bill and Melinda Gates Foundation
- OPP1111252
- Created
-
2020-01-13Created from EPrint's datestamp field
- Updated
-
2022-02-15Created from EPrint's last_modified field