Fig. 4: Remote NIR identification of bacteria.
From: Remote near infrared identification of pathogens with multiplexed nanosensors
a A simple NIR stand-off setup enables remote (25 cm) imaging of the NIR fluorescent sensors embedded in a hydrogel (HG) array. b Photograph (in the visible spectrum) of the HG nanosensor array and its corresponding NIR fluorescence image (scale bar 0.5 cm). c Arrangement and functionality of the 9 sensors in the HG array. d Remote NIR fluorescence image of a sensor array incorporated in a microbiological agar plate, inoculated with S. aureus. During bacterial growth the sensor pattern changes (scale bar 0.5 cm). e Corresponding sensor response normalized to the EB-NS signal during S. aureus growth, from 0 to 72 h (n = 3 independent experiments, mean ± SD). f Representative fluorescence response fingerprint of six pathogens, monitored after 72 h. g Fluorescence SWCNT array fingerprint of all bacteria and strains, evaluated after 72 h. (ΔISR—sensor response: IS1/IR1/IS0/IR0; IS—intensity sensor, IR—intensity reference (EB-NS)) (n = 3 independent experiments, mean ± SD). h PCA (principal component analysis) of the fluorescence fingerprint of all analyzed strains, plotted for different timepoints (12–72 h). Each point represents one bacterial sample including clinical isolates from different patients. Control = medium only. i Mean sensor array fingerprint from diverse clinical isolates of each S. aureus (n = 21 biologically independent samples) and S. epidermidis (n = 22 biologically independent samples) 72 h after incubation. (error = SE). j Corresponding PCA for the array fingerprint after 72 h growth of the clinical isolates of S. aureus and S. epidermidis. k Time resolved fluorescence change of the nanosensor array after addition of liquid culture supernatant from P. aeruginosa. (24 h incubation in LB-medium, I—intensity sensor at t = x; I0—intensity sensor at t = 0) (mean of n = 3 independent experiments). l Stochastic simulation that predicts how bacteria discrimination improves with number of sensors. The simulation is based on experimental responses and selectivities as range for novel sensors and uses PCA as well as mean linear discrimination analysis (LDA) to distinguish bacteria. Mean values are plotted and the dashed lines/transparent area represent the SD from 25 independent simulations. Ellipses in (h, j) indicate the 0.68 bivariate confidence interval.
