Fig. 3: Ultra-sensitivity diagnosis of pathogens by TCC. | Nature Communications

Fig. 3: Ultra-sensitivity diagnosis of pathogens by TCC.

From: Ultrasensitive detection of clinical pathogens through a target-amplification-free collateral-cleavage-enhancing CRISPR-CasΦ tool

Fig. 3

a Thermolysis-TCC detection workflow for pathogens (using S. aureus as an example). b Fluorescence detection showing thermolysis by DEPC H2O leads to the best signal-to-noise ratio. H2O NC, negative control by thermolysis of S. Typhi in DEPC H2O. c Sensitivity of TCC detection for S. aureus. d Specificity heatmap of TCC detecting pathogens. TCC testing of thermolysis products (24 CFU/mL) from different bacteria using specific gRNAs for E. coli, K. pneumoniae, Pseudomonas aeruginosa (P. aeruginosa) and S. aureus. EC, E. coli. KP, K. pneumoniae. PA, P. aeruginosa. SA, S. aureus. e Ultra-sensitivity detection of E. coli chuA gene by TCC. f Ultra-sensitivity of detecting K. pneumoniae rmpA gene by TCC. g Ultra-sensitivity of detecting P. aeruginosa oprD gene by TCC. Unpaired one-way ANOVA and Turkey’s multiple comparisons test were used to statistically analyze each group of independent technical replicates (n = 3), where ns indicates no significant difference (p > 0.05), and asterisks (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001) indicate significant differences. Data are presented as mean values. Error bars represent mean ± SD.

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