Fluorescent Sensor Arrays Can Predict and Quantify the Composition of Multicomponent Bacterial Samples

Abstract

Fast and reliable identification of infectious disease agents is among the most important challenges for the healthcare system. The discrimination of individual components of mixed infections represents a particularly difficult task. In the current study we further expand the functionality of a ratiometric sensor array technology based on small-molecule environmentally-sensitive organic dyes, which can be successfully applied for the analysis of mixed bacterial samples. Using pattern recognition methods and data from pure bacterial species, we demonstrate that this approach can be used to quantify the composition of mixtures, as well as to predict their components with the accuracy of ~80% without the need to acquire additional reference data. The described approach significantly expands the functionality of sensor arrays and provides important insights into data processing for the analysis of other complex samples.

Keywords: 3-hydroxyflavone; ESIPT; discriminant analysis; machine learning; multiparametric sensing; pathogenic bacteria; pattern analysis.

Figure 1

(A) Fluorescence spectra of DOAF-loaded nanoparticles upon incubation with E. coli (EC), S. epidermidis (SE), and their mixtures in different proportions each line is the average of four spectra. The isoemissive point at ~470 nm serves as evidence of the mixed samples spectra being linear combinations of the emission spectra of the pure components. (B) Canonical score plot from LDA analysis of response patterns of pure bacteria and their mixtures. Signals from the mixtures are positioned along the line connecting the centroids of the 95% confidence ellipses for the pure bacteria.

Figure 2

Canonical score plot of the results of LDA for pure bacterial samples and their binary mixtures. Signals from pure bacteria are represented by filled dots and solid ellipses; mixtures are represented by circles and dashed ellipses. Yellow dots are centroids of the 95% confidence ellipses. Lines connecting centroids of the mixture components serve as visual guide.

Figure 3

(A) Quantification of the binary mixture of bacteria after the components are identified. The content of S. epidermidis (SE) is proportional to the distance between the 95% confidence ellipse centroids of the mixture and K. pneumoniae (KP). (B) Canonical score plot of the sensor's response to an equal mixture of S. aureus (SA), E. coli (EC) and K. pneumoniae (KP) related to the signals of pure components. The signal of the mixture represents an almost perfect linear combination, being located very close to the center of the triangle (marked with an orange cross).

Figure 4

Example of a positive (A) and negative (B) decision regarding a potential component of an unknown mixture in the “one against the rest” analysis. The decision is made based on the overlap between the 95% confidence ellipses of the mixture and the complex of “other” bacteria, and the angle between the vectors connecting the ellipse centroid of the “other” aggregate group and the signal of the component under question, respectively.