Direct comparison of colorimetric signal amplification techniques in lateral flow immunoassays

The lateral flow immunoassay (LFIA) is widely adopted for point-of-care testing, but its limit of detection (LoD) falls short of that of laboratory-based immunoassays. Several techniques have been proposed to enhance the LoD of LFIAs using visual colorimetric readouts, yet a direct comparison of the LoDs achieved by these techniques has not been performed. In this work, we measure the LoDs of LFIAs designed for the detection of a malaria protein, PfHRP2, using four different colorimetric signal generation techniques: (i) AuNP(40 nm)-tagged detection antibodies (base case), (ii) AuNP-based enhancement of AuNP(40 nm) signal, (iii) oxidation of chloronaphthol/diaminobenzidine (CN/DAB) using HRP-tagged detection antibodies, and (iv) oxidation of CN/DAB using polyHRP(400)-tagged detection antibodies. The LoDs and the 95% confidence intervals of the LoDs achieved by the 4 techniques were 19.34 (13.37-27.62) ng mL-1, 9.57 (6.76-13.28) ng mL-1, 21.57 (14.26-32.18) ng mL-1, and 6.09 (2.23-13.47) ng mL-1, respectively. Contrary to popular perception, enzymatic signal generation using HRP-tagged detection antibodies did not improve the LoD compared to the base case of AuNP-based signal generation. Further studies revealed that the very high extinction coefficient of gold nanoparticles renders them an excellent choice for colorimetric detection, surpassing the performance of enzymatic signal generation using HRP-tagged antibodies. However, enzymatic signal generation using polyHRP-tagged antibodies improved the LoD compared to the base case. These results show that enzymatic signal amplification should not be a priori assumed to be superior to AuNP-based signal generation; and provide a reference point to LFIA developers to select an appropriate signal generation modality. This study reveals that enzymatic signal amplification isn't always the silver bullet for boosting lateral flow assay signals; a careful evaluation is essential for achieving optimal performance.