| DOI | Resolve DOI: https://doi.org/10.1016/j.snb.2023.134378 |
|---|
| Author | Search for: Hao, Xingkai; Search for: Wang, Zhong; Search for: St-Pierre, Jean-Philippe; Search for: Dan, Hanhong; Search for: Lin, Min; Search for: Zou, Shan1ORCID identifier: https://orcid.org/0000-0002-2480-6821; Search for: Cao, Xudong |
|---|
| Affiliation | - National Research Council of Canada. Metrology Research Centre
|
|---|
| Funder | Search for: China Scholarship Council; Search for: Natural Sciences and Engineering Research Council of Canada |
|---|
| Format | Text, Article |
|---|
| Subject | rolling circle amplification; E. coli detection; dendrimers; surface modification; nonfouling |
|---|
| Abstract | We develop a multivalent aptamer meshed open pore membrane with a signal amplification method as a simple and robust platform for sensitive, robust, and high-flux detection of E. coli O157:H7 whole cells in food samples. Nylon open pore membranes are surface modified by multi-handled poly(amidoamine) dendrimers as templates that are subsequently functionalized with multiple branches of capturing RCA (cRCA). The resulting cRCA networks are composed of tandem repeating aptamers that can specifically recognize and capture E. coli O157:H7 cells. The captured cells are then conjugated with signaling RCA (sRCA) to amplify the fluorescent detection signal. Our results demonstrate that the cRCA-based aptamer meshwork significantly enhances the surfacecapturing performance of E. coli O157:H7 cells while achieving high-fluxes. Moreover, the membrane surface modification using dendrimers renders the resulting membrane nonfouling thus significantly reducing the background noises by approximately 96 times while the sRCA signal intensification enhances the fluorescent detection signals by approximately 27 times. We show that the membranes exhibit excellent performances in detecting E.coli O157:H7 in milk and orange juice samples with limit of detection of 10 cells per 250 mL with excellent specificity, offering a robust and practical approach for rapid and sensitive detection of foodborne bacteria. |
|---|
| Publication date | 2023-07-29 |
|---|
| Publisher | Elsevier |
|---|
| In | |
|---|
| Language | English |
|---|
| Peer reviewed | Yes |
|---|
| Identifier | S0925400523010936 |
|---|
| Export citation | Export as RIS |
|---|
| Report a correction | Report a correction (opens in a new tab) |
|---|
| Record identifier | 42c64799-1534-46da-8776-5c4a49f39106 |
|---|
| Record created | 2023-08-01 |
|---|
| Record modified | 2023-08-01 |
|---|
