Easing diagnosis and pushing the detection limits of SARS-CoV-2

Abstract

Rigorous testing is the way forward to fight the coronavirus disease 2019 pandemic. Here we show that the currently used and most reliable reverse transcription-polymerase chain reaction-based severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) procedure can be further simplified to make it faster, safer, and economical by eliminating the RNA isolation step. The modified method is not only fast and convenient but also at par with the traditional method in terms of accuracy, and therefore can be used for mass screening. Our method takes about half the time and is cheaper by ∼40% compared to the currently used method. We also provide a variant of the new method that increases the efficiency of detection by ∼30% compared to the existing procedure. Taken together, we demonstrate a more effective and reliable method of SARS-CoV-2 detection.

Keywords: COVID-19; SARS-CoV-2 diagnosis; cost-effective; direct RT-PCR; dry swab; false-negative.

Figure 1:

RNA extracted from TE buffer outperform other methods. (A) Schematic of the entire protocol for TE-based sample extraction and RT-PCR. (B) Heatmap representing the C_T values of E and RdRP genes obtained in two replicates (Reps.1 and 2) of RT-PCR using TE extract, VTM-extracted RNA (VTM-RNA), and TE-extracted RNA (TE-RNA) as templates (n = 40). Details in Supplementary data, Table S2. Samples D1–D40 are represented as 1–40. Dark purple shade represents no signal detection.

Figure 2:

Increased sensitivity of COVID-19 diagnosis using TE-extracted RNA. (A) and (B) Scatter plot of the C_T values for each sample obtained using different methods as labeled in the Figure and the data points represents the average of C_T values of E gene (A) and RdRP (B); error bars represent the standard error of mean for the data obtained from two experiments. (C) Bar graph representing the difference in C_T values of E gene and RdRP between TE-based RNA and traditional (VTM-RNA) method. Negative and positive values indicate increased and reduced sensitivity, respectively. The data points are average of two technical replicates; error bars represent the standard error of mean for the data obtained from two experiments.