Paper-based lateral flow immunoassays (LFIAs) using conventional sandwich-type immunoassays are one of the most commonly used point-of-care (PoC) tests. However, the application of gold nanoparticles (AuNPs) in LFIAs does not meet sensitivity requirements for the detection of infectious diseases or biomarkers present at low concentrations in body fluids because of the limited number of AuNPs that can bind to the target. To overcome this problem, we first developed a single-stranded DNA binding protein (RPA70A, DNA binding domain A of human Replication Protein A 70 kDa) conjugated to AuNPs for a sandwich assay using a capture antibody immobilized in the LFIA and an aptamer as a detection probe, thus, enabling signal intensity enhancement by attaching several AuNPs per aptamer. We applied this method to detect the influenza nucleoprotein (NP) and cardiac troponin I (cTnI). We visually detected spiked targets at a low femtomolar range, with limits of detection for NP in human nasal fluid and for cTnI in serum of 0.26 and 0.23 pg·mL–1, respectively. This technique showed significantly higher sensitivity than conventional methods that are widely used in LFIAs involving antibody-conjugated AuNPs. These results suggest that the proposed method can be universally applied to the detection of substances requiring high sensitivity and can be used in the field of PoC testing for early disease diagnosis.

In this paper, we introduce an effective method for selecting aptamer that increases the signal-to-noise ratio in a heterogenous sandwich-type immunosensor and confirm the efficiency of selected aptamer candidates in the colorimetric assay. Using the proposed method, four aptamer candidates with Kd values ranging from 77.6 nM to 125.7 nM were obtained.

The detection of trace protein biomarkers is essential in the diagnostic field. Protein detection systems ranging from widely used enzyme-linked immunosorbent assays to simple, inexpensive approaches, such as lateral flow immunoassays, play critical roles in medical and drug research. Despite continuous progress, current systems are insufficient for the diagnosis of diseases that require high sensitivity. In this study, we developed a heterogeneous sandwich-type sensing platform based on recombinase polymerase amplification using DNA aptamers specific to the target biomarker. Only the DNA bound to the target in the form of a heterogeneous sandwich was selectively amplified, and the fluorescence signal of an intercalating dye added before the amplification reaction was detected, thereby enabling high specificity and sensitivity. We applied this method for the detection of protein biomarkers for various infectious diseases including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and observed attomolar-level detection of biomarkers and low cross-reactivity between different viruses. We also confirmed detection efficiency of the proposed method using clinical samples. These results demonstrate that the proposed sensing platform can be used to diagnose various diseases requiring high sensitivity, specificity, and accuracy.

Several types of biosensors have been developed to detect a wide variety of human diseases. Immunosensors are classified as the most representative of all biosensors. They are based on antibodies that selectively recognize specific analytes and have high specificity and sensitivity. However, there are limitations to the types of substances that can be detected, and it is sometimes difficult to achieve sufficient sensitivity without additional amplification steps.To overcome these problems, novel immunosensors are being developed that combine DNA-based high signal amplification systems. These technologies ameliorate the low sensitivity of existing immunosensors by using DNA probes that can bind directly to targets as bioreceptors or act as signal amplifiers. In this review, we will discuss immunodetection methods that use DNA-based technologies on laboratory-scale and advanced point-of-care testing (POCT) that employ these technologies for high performance analyses.