Essential properties and pitfalls of colorimetric Reverse Transcription Loop-mediated Isothermal Amplification as a point-of-care test for SARS-CoV-2 diagnosis

Abstract

Background SARS-CoV-2 Reverse Transcription Loop-mediated Isothermal Amplification (RT-LAMP) colorimetric detection is a sensitive and specific point-of-care molecular biology technique used to detect the virus in only 30 min. In this manuscript we have described a few nuances of the technique still not properly described in the literature: the presence of three colors clusters; the correlation of the viral load with the color change; and the importance of using an internal control to avoid false-negative results. Methods To achieve these findings, we performed colorimetric RT-LAMP assays of 466 SARS-CoV-2 RT-qPCR validated clinical samples, with color quantification measured at 434 nm and 560 nm. Results First we determinate a sensitivity of 93.8% and specificity of 90.4%. In addition to the pink (negative) and yellow (positive) produced colors, we report for the first time the presence of an orange color cluster that may lead to wrong diagnosis. We also demonstrated using RT-qPCR and RT-LAMP that low viral loads are related to Ct values > 30, resulting in orange colors. We also demonstrated that the diagnosis of COVID-19 by colorimetric RT-LAMP is efficient until the fifth symptoms day when the viral load is still relatively high. Conclusion This study reports properties and indications for colorimetric RT-LAMP as point-of-care for SARS-CoV-2 diagnostic, reducing false results, interpretations and optimizing molecular diagnostics tests application.

Clinical validation of colorimetric RT-LAMP, a fast, highly sensitive and specific COVID-19 molecular diagnostic tool that is robust to detect SARS-CoV-2 variants of concern

“…Yang et al, 2021)(39,52,56,57) and eventually with a higher threshold Ct 35 as well(27,39). Indeed, we confirm that up to Ct 30 RT-LAMP returned 100% sensitivity for SARS-CoV-2 detection, reaching 97% and 88% when considering Ct values up to 32 and 34 respectively.…”

“…Another important (almost) neglected point, is the fact that, although inspired by RT-qPCR target selection, few SARS-CoV-2 RT-LAMP approaches reported an internal control target to confirm the presence of human RNA and monitor sampling or extraction process (39). Wilson-Davies and colleagues (2021) pointed out that the lack of amplification can happen for different reasons concerning the whole reaction, a specific well or due to inhibitory substances; highlighting the importance of including internal control even before nucleic acid extraction, in order to be considered a reliable SARS-CoV-2 LAMP assay (62).…”

“…Single laboratories using different platforms and targets in SARS-CoV-2 molecular testing can represent a potential variability on Ct-values (55). Considering previous convergent reports and presuming different targets and platforms, the data from the literature shows that with a RT-qPCR Ct 30 cut-off where RT-LAMP sensitivity for SARS-CoV-2 detection is close to 100% (Ben-Assa et al, 2020; Lamb et al, 2020; L’Helgouach et al, 2020; Smyrlaki et al, 2020; Lalli et al, 2021; Newman et al, 2021; Yang et al, 2021)(39,52,56,57) and eventually with a higher threshold Ct 35 as well (27,39). Indeed, we confirm that up to Ct 30 RT-LAMP returned 100% sensitivity for SARS-CoV-2 detection, reaching 97% and 88% when considering Ct values up to 32 and 34 respectively.…”

“…However, in the current OmniLAMP ® assay, we included human β-actin RNA (rACTB) as internal control. Other constitutive targets for SARS-CoV-2 RT-LAMP include BPIFA1 (29), human 18S RNA (39) and Statherin RNA (51) (Table 2).…”

A paper-based colorimetric molecular test for SARS-CoV-2 in saliva

“…Recent findings suggest this ambiguity is due to a third, intermediate color cluster (along with positive/negative clusters), that is not adequately addressed in colorimetric assays. ( Aoki et al, 2021 ; de Oliveira Coelho et al, 2021 ). Since invalid results were discarded from further analysis, this elevated false invalid rate may artificially influence the specificity and accuracy metrics of our device.…”