Issue 12, 2019

A hydrogel sensor-based microfluidic platform for the quantitative and multiplexed detection of fertility markers for point-of-care immunoassays

Abstract

We report a new microfluidics-based immunoassay platform based on 3D porous hydrogel particle sensors with planar rectangular geometries. Large numbers of sensors are made off-chip before being embedded into an injection molded microfluidic chip. This allows for both multiplexing and scalability of manufacturing processes. A specially designed adapter containing detection reagents and buffers is interfaced with a microfluidic chip allowing for an automated immunoassay to be performed. All antibody reagents required for the test are stored in a dried form on the chip, enabling stability for resource-limited settings. Fluorescence-based immunoassays are performed by a sequential flow of the sample, detection reagents and wash buffer over the sensors. A compact instrument that integrates microfluidic flow dispensing and pumping with highly sensitive array-based detection is used to conduct and read the assay. A multiplexed set of 4 important fertility-linked tests, beta-human chorionic gonadotropin (β-hCG), follicle stimulating hormone (FSH), luteinizing hormone (LH) and prolactin (PRL), that are commonly required at gynecology/IVF settings were developed and validated using discarded clinical samples. Detailed data on accuracy, precision, linearity, effects of interference and cross-reactivity have been studied, rendering the platform ready for clinical use.

Graphical abstract: A hydrogel sensor-based microfluidic platform for the quantitative and multiplexed detection of fertility markers for point-of-care immunoassays

Supplementary files

Article information

Article type
Paper
Submitted
03 Dec 2018
Accepted
05 Feb 2019
First published
08 Feb 2019

Anal. Methods, 2019,11, 1639-1650

A hydrogel sensor-based microfluidic platform for the quantitative and multiplexed detection of fertility markers for point-of-care immunoassays

S. Kalme, S. Kandaswamy, A. Chandrasekharmath, R. Katiyar, G. P. Rajamanickam, S. Kumar and D. Dendukuri, Anal. Methods, 2019, 11, 1639 DOI: 10.1039/C8AY02641F

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