Capture, Concentration, and Detection of Salmonella in Foods Using Magnetic Ionic Liquids and Recombinase Polymerase Amplification

重组酶聚合酶扩增 化学 色谱法 检出限 萃取(化学) 沙门氏菌 离子强度 环介导等温扩增 细菌 DNA 水溶液 生物化学 有机化学 遗传学 生物
作者
Stephanie A. Hice,Kevin D. Clark,Jared L. Anderson,Byron F. Brehm‐Stecher
出处
期刊:Analytical Chemistry [American Chemical Society]
卷期号:91 (1): 1113-1120 被引量:53
标识
DOI:10.1021/acs.analchem.8b04751
摘要

We previously investigated the extraction and concentration of bacteria from model systems using magnetic ionic liquid (MIL) solvents while retaining their viability. Here, we combine MIL-based sample preparation with isothermal amplification and detection of Salmonella-specific DNA using recombinase polymerase amplification (RPA). After initial developmental work with Serratia marcescens in water, Salmonella Typhimurium ATCC 14028 was inoculated in water, 2% milk, almond milk, or liquid egg samples and extracted using one of two MILs, including trihexyl(tetradecyl)phosphonium cobalt(II) hexafluoroacetylacetonate ([P66614+][Co(hfacac)3–]) and trihexyl(tetradecyl)phosphonium nickel(II) hexafluoroacetylacetonate ([P66614+][Ni(hfacac)3–]). Viable cells were recovered from the MIL extraction phase after the addition of modified LB broth, followed by a 20 min isothermal RPA assay. Amplification was carried out using supersaturated sodium acetate heat packs and results compared to those using a conventional laboratory thermocycler set to a single temperature. Results were visualized using either gel electrophoresis or nucleic acid lateral flow immunoassay (NALFIA). The combined MIL-RPA approach enabled detection of Salmonella at levels as low as 103 CFU mL–1. MIL-based sample preparation required less than 5 min to capture and concentrate sufficient cells for detection using RPA, which (including NALFIA or gel-based analysis) required approximately 30–45 min. Our results suggest the utility of MILs for the rapid extraction and concentration of pathogenic microorganisms in food samples, providing a means for physical enrichment that is compatible with downstream analysis using RPA.
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