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Published May 1, 2020 | Supplemental Material
Journal Article Open

Self-partitioning SlipChip for slip-induced droplet formation and human papillomavirus viral load quantification with digital LAMP

Abstract

Human papillomavirus (HPV) is one of the most common sexually transmitted infections worldwide, and persistent HPV infection can cause warts and even cancer. Nucleic acid analysis of HPV viral DNA can be very informative for the diagnosis and monitoring of HPV. Digital nucleic acid analysis, such as digital PCR and digital isothermal amplification, can provide sensitive detection and precise quantification of target nucleic acids, and its utility has been demonstrated in many biological research and medical diagnostic applications. A variety of methods have been developed for the generation of a large number of individual reaction partitions, a key requirement for digital nucleic acid analysis. However, an easily assembled and operated device for robust droplet formation without preprocessing devices, auxiliary instrumentation or control systems is still highly desired. In this paper, we present a self-partitioning SlipChip (sp-SlipChip) microfluidic device for the slip-induced generation of droplets to perform digital loop-mediated isothermal amplification (LAMP) for the detection and quantification of HPV DNA. In contrast to traditional SlipChip methods, which require the precise alignment of microfeatures, this sp-SlipChip utilized a design of "chain-of-pearls" continuous microfluidic channel that is independent of the overlapping of microfeatures on different plates to establish the fluidic path for reagent loading. Initiated by a simple slipping step, the aqueous solution can robustly self-partition into individual droplets by capillary pressure-driven flow. This advantage makes the sp-SlipChip very appealing for the point-of-care quantitative analysis of viral load. As a proof of concept, we performed digital LAMP on an sp-SlipChip to quantify human papillomaviruses (HPVs) 16 and 18 and tested this method with fifteen anonymous clinical samples.

Additional Information

© 2020 Published by Elsevier B.V. Received 23 October 2019, Revised 5 February 2020, Accepted 17 February 2020, Available online 18 February 2020. We thank Dr. Rebecca R. Pompano and Ms. Jing Ling for editing the manuscript. This work is supported by the National Natural Science Foundation of China (no. 21705109), the Innovation Research Plan supported by Shanghai Municipal Education Commission (no. ZXWF082101), the Natural Science Foundation of Shanghai (no. 19ZR1475900), the interdisciplinary program of Shanghai Jiao Tong University (no. YG2015ZD11), and supported by Shanghai Jiao Tong University Scientific and Technological Innovation Funds. Z. Y., W. L., M. Y., and Q. W. contributed equally. CRediT authorship contribution statement Ziqing Yu: Formal analysis, Writing - original draft. Weiyuan Lyu: Writing - original draft. Mengchao Yu: Formal analysis, Writing - original draft. Qian Wang: Writing - original draft. Haijun Qu: Formal analysis, Writing - original draft. Rustem F. Ismagilov: Conceptualization, Writing - original draft. Xu Han: Writing - original draft. Dongmei Lai: Writing - original draft. Feng Shen: Conceptualization, Writing - original draft. 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.

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