Rapid and accurate nucleobase detection using FnCas9 and its application in COVID-19 diagnosis

Abstract

Rapid detection of DNA/RNA pathogenic sequences or variants through point-of-care diagnostics is valuable for accelerated clinical prognosis, as witnessed during the recent COVID-19 outbreak. Traditional methods relying on qPCR or sequencing are tough to implement with limited resources, necessitating the development of accurate and robust alternative strategies. Here, we report FnCas9 Editor Linked Uniform Detection Assay (FELUDA) that utilizes a direct Cas9 based enzymatic readout for detecting nucleobase and nucleotide sequences without trans-cleavage of reporter molecules. We also demonstrate that FELUDA is 100% accurate in detecting single nucleotide variants (SNVs), including heterozygous carriers, and present a simple web-tool JATAYU to aid end-users. FELUDA is semi-quantitative, can adapt to multiple signal detection platforms, and deploy for versatile applications such as molecular diagnosis during infectious disease outbreaks like COVID-19. Employing a lateral flow readout, FELUDA shows 100% sensitivity and 97% specificity across all ranges of viral loads in clinical samples within 1hr. In combination with RT-RPA and a smartphone application True Outcome Predicted via Strip Evaluation (TOPSE), we present a prototype for FELUDA for CoV-2 detection closer to home.

Keywords: CRISPRDx; FELUDA; FnCas9; LFA; SARS-CoV2; SNV detection.

Fig. 1

Schematic for FELUDA detection. Schematic for FELUDA based discrimination of SNVs. A, Strategy for discrimination of substrates differing by single mismatch using FnCas9. The presence of 2 mismatches (marked in red and green) at defined positions on the sgRNA prevents the enzyme from binding to the target leading to differential binding and cleavage outcomes. B, Left panel shows positions of mismatches sgRNAs containing two mismatches at different positions along their lengths. Representative in-vitro cleavage outcomes on wild type (WT) or sickle cell anemia (SCA) substrates (4.1 kb) are shown on the right. Cleavage with FnCas9 produces 2 products (2.3 kb and 1.8 kb). The red dotted box denotes the sgRNA showing negligible cleavage for WT substrate and maximum cleavage for SCA substrate. C, Binding affinity experiments using Microscale Thermophoresis showing the interaction of dSpCas9-GFP, dSpCas9-HF1-GFP, and dFnCas9-GFP with substrates with 0 (blue) or 2 (red) mismatch (MM). Values are expressed as fraction bound protein (y-axis) with respect to varying concentrations of purified DNA substrate (Molar units, M, x-axis). Error bars represent SEM (2 independent experiments). D, Schematic for fluorescence-based detection of sickle cell anemia mutation (SCA) using FELUDA. Error bars represent SD. Student's t-test p values are shown (n = 3 independent measurements). E, Left panel shows schematic of FELUDA for identifying carriers of SCA mutation. The right panel shows blinded FELUDA results in a mixed cohort of individuals (n = 49, one way ANOVA p-value is shown). SCA, sickle cell anemia individuals; SCT, sickle cell trait individuals; WT, normal subjects; SNV, single nucleotide variation; SEM, standard error of the mean. FELUDA accurately genotypes carriers of Mendelian variants. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2

FELUDA for point-of-care nCOV-2 detection. A, Outline of lateral flow assay using FELUDA showing positions of control and test bands. B, Pipeline of FELUDA based detection for SARS-CoV-2 infection from patient samples showing individual steps involved. C, Plot showing the regions of SARS-CoV-2 RNA genome tested for FELUDA, the number of tested regions are represented in red while successful regions are highlighted in green. D, LOD of FELUDA in purified N gene target RNA. The top panel shows representative LFA readout on strips, and the bottom panel shows Fluorescent intensity ratios. Error bars SEM. (n = 3 independent experiments). E, Smartphone GUI for TOPSE showing representative output from a strip image (left). Positive and NTC preprocessed images are shown on the right. FELUDA shows high concordance with gold standard qRT-PCR in detecting nCoV-2 infection. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3

FELUDA shows high concordance with qRT-PCR for SARS-CoV-2 detection A, FELUDA readouts are semiquantitative. Correlation between Ct values (E gene) and TOPSE values are shown (n = 27). B, Strong reproducibility between repeated FELUDA runs on the same positive or negative sample is shown. C, One gene (N gene) FELUDA on clinical samples (x-axis) showing the distribution of TOPSE values (y-axis). Analyzed results represented on the right. D, Two genes (N and S genes) FELUDA on clinical samples (x-axis) showing the distribution of TOPSE values (y-axis). Analyzed results represented on the right.

Fig. 4

Prototype for FELUDA for possible home-testing A, One-pot RT-RPA FELUDA. The top panel shows minimum requirements, bottom panel shows the outcome for 2 representative samples. B, On-body RT-RPA-FELUDA, left figure shows the variation of temperature in different zones of the body marked in red dots and corresponding RPA-FELUDA for a synthetic RNA fragment as starting material. C, Left panel shows representative FELUDA using on body RT-RPA from samples incubated in two different parts of the body. The right panel represents minimum requirements for FELUDA using on-body RT-RPA. All experiments for on body RT-RPA were done by at least 3 different individuals (n = 3). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)