Whole-genome detection using multivalent DNA-coated colloids

Author:

Xu Peicheng1,Cao Ting23ORCID,Fan Qihui1ORCID,Wang Xiaochen23,Ye Fangfu123ORCID,Eiser Erika45ORCID

Affiliation:

1. Beijing National Laboratory for Condensed Matter Physics and Laboratory of Soft Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China

2. Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China

3. School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China

4. Porelab, Department of Physics, Norwegian University of Science and Technology, Trondheim NO-7491, Norway

5. Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, United Kingdom

Abstract

To minimize the incorrect use of antibiotics, there is a great need for rapid and inexpensive tests to identify the pathogens that cause an infection. The gold standard of pathogen identification is based on the recognition of DNA sequences that are unique for a given pathogen. Here, we propose and test a strategy to develop simple, fast, and highly sensitive biosensors that make use of multivalency. Our approach uses DNA-functionalized polystyrene colloids that distinguish pathogens on the basis of the frequency of selected short DNA sequences in their genome. Importantly, our method uses entire genomes and does not require nucleic acid amplification. Polystyrene colloids grafted with specially designed surface DNA probes can bind cooperatively to frequently repeated sequences along the entire genome of the target bacteria, resulting in the formation of large and easily detectable colloidal aggregates. Our detection strategy allows “mix and read” detection of the target analyte; it is robust and highly sensitive over a wide concentration range covering, in the case of our test target genome Escherichia coli bl21-de3, 10 orders of magnitude from 10 1 to 10 10 copies/mL. The sensitivity compares well with state-of-the-art sensing techniques and has excellent specificity against nontarget bacteria. When applied to real samples, the proposed technique shows an excellent recovery rate. Our detection strategy opens the way to developing a robust platform for pathogen detection in the fields of food safety, disease control, and environmental monitoring.

Funder

MOST | National Key Research and Development Program of China

Strategic Priority Research Program of Chinese Academy of Sciences

CAS | Youth Innovation Promotion Association

Research Council of Norway

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

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