Integrated Graphene Oxide Purification-Lateral Flow Test Strips (iGOP-LFTS) for Direct Detection of PCR Products with Enhanced Sensitivity and Specificity

Abstract

An integrated graphene oxide purification-lateral flow test strip (iGOP-LFTS) was developed for on-strip purifying and visually detecting polymerase chain reaction (PCR) products with an improved sensitivity as well as a more stringent specificity. PCR products amplified with a pair of biotin- and digoxin-labeled primers were directly pipetted onto GO pads, on which graphene oxide selectively adsorbed residual primers and primer-dimers with the aid of a running buffer containing MgCl and Tween 20. By stacking up three GO pads to increase the surface area for adsorption, 83.4% of double-stranded DNA with a length of 30 bp and 98.6% of 20-nt primers could be removed from a 10-μL DNA mixture. Since no primers interfered with detection, the increase of the sample loading volume from 5 to 20 μL could improve the signal-to-noise ratio of the test line 1.6 fold using the iGOP-LFTS while no changes were observed using the conventional LFTS. The limit of detection of the iGOP-LFTS was determined to be 30 copies of bacteriophage λ-DNA with naked eyes and this limit could be further decreased to 3 copies by loading 20 μL of the sample, which corresponded to a 1000-fold improvement compared to that of the LFTS detected by naked eyes. When the ImageJ analysis was employed, a 100-fold decrease of the detection limit can be obtained. In addition, due to the removal of the primer-dimers, the dim test line observed in the negative control of the LFTS was eliminated using the iGOP-LFTS. A mock clinical specimen spiked with defective HIV-1 (human immunodeficiency virus) viruses was successfully analyzed using a two-step reverse transcription-PCR with 30 amplification cycles followed by the iGOP-LFTS detection. These significant improvements were achieved without introducing any additional hands-on operations and instrumentations.

Ultrasensitive and Highly Specific Lateral Flow Assays for Point-of-Care Diagnosis

“…163 For DNA/RNA LFAs with a sample preamplification step, FPs can be produced by the primers/probes and side products from the preamplification step, such as primer dimers. 160,164,165 Such FPs can be reduced by optimizing the primer and probe sequences in the preamplification step 151,152,166,167 and the capture and detection sequences in the LFA. 168 Other special treatments can also be used to prevent FPs.…”

“…Assay optimization to reduce the NSB that results in an FP readout is critical to improving the specificity of LFAs. The NSB of labels in the test region usually arises from the following sources: Presence of NSB substances in a sample that are capable of binding to the affinity molecules in LFAs Nonspecific interactions between the conjugated labels and the capture antibodies and/or membrane ,, Physical trapping of the conjugated labels (especially aggregates of unstable labels) in the porous membrane ,, …”

Point-of-care CRISPR/Cas nucleic acid detection: Recent advances, challenges and opportunities

“…By using tagged primers, the amplicons of interest could be sandwiched between membrane and gold nanoparticle (AuNP) probes for capture and signal read-out. However, proper distinguishing between primer-dimers, incorrect amplicons due to the low temperature amplification, and the amplicons of interest is very difficult, leading to false-positives ( Crannell et al, 2016 ; Li et al, 2017 ). Nucleic acid hybridization on LFAs is more specific, but requires single stranded amplicons and PCR reaction which need sophisticated thermocyclers, making this method unsuitable for POC applications ( Xu et al, 2014 ).…”

Rapid point‐of‐care testing methods/devices for meat species identification: A review

“…(2019) have integrated SERS with competitive LFA and found the system about three orders of magnitude more sensitive than that of a commercially available kit. The sensitivity and specificity of LFA device can also be enhanced by (a) modifying the format of assay, (b) efficient extraction and concentration of analyte, and (c) the optimization of LFA‐based platform using mathematical modeling (Javani, Javadi‐Zarnaghi, & Rasaee, 2017; Kim et al., 2017; Li, Gu, Lyu, Jiang, & Liu, 2017; Lin, Chen, Liang, Liu, & Hou, 2019; Sharma et al., 2019; Yin et al., 2019; Zeng, Wang, Li, Du, & Liu, 2011, 2012; Zhang, Liu, et al., 2020).…”