Rapid Detection of Urinary Tract Infection in 10 min by Tracking Multiple Phenotypic Features in a 30 s Large-Volume Scattering Video of Urine Microscopy

Abstract

Rapid point-of-care (POC) diagnosis of bacterial infection diseases provides clinical benefits of prompt initiation of antimicrobial therapy and reduction of the overuse/misuse of unnecessary antibiotics for nonbacterial infections. We present here a POC compatible method for rapid bacterial infection detection in 10 min. We use a large-volume solution scattering imaging (LVSi) system with low magnifications (1-2×) to visualize bacteria in clinical samples, thus eliminating the need for culture-based isolation and enrichment. We tracked multiple intrinsic phenotypic features of individual cells in a short video. By clustering these features with a simple machine learning algorithm, we can differentiate Escherichia coli from similar-sized polystyrene beads, distinguish bacteria with different shapes, and distinguish E. coli from urine particles. We applied the method to detect urinary tract infections in 104 patient urine samples with a 30 s LVSi video, and the results showed 92.3% accuracy compared with the clinical culture results. This technology provides opportunities for rapid bacterial infection diagnosis at POC settings.

Keywords: UTI screening; bacteria detection; machine learning; multiple phenotypic features; solution scattering imaging.

Figure 1.. Principle of single cell phenotypic feature tracking for rapid E. coli detection and differentiation from 1 μm polystyrene beads.

(A) Schematic illustration of the experimental setup for single E. coli imaging and temporal trajectory tracking. (B) E. coli rotation-induced scattering intensity fluctuation tracking compared to 1 μm beads. (C) Sub-pixel motion tracking of single E. coli compared to 1 μm beads. (D) Characterization of E. coli cell population from single cell phenotypic features including intensity fluctuation (Normalized Intensity Standard Deviation, NISD) (from panel B) and micro-motion (MSD, mean square displacement) (from panel C).

Figure 2.. Differentiation of E. coli from polystyrene beads by phenotypic features tracking.

(A) Single cell motion and intensity mapping for E. coli and 1 μm polystyrene beads. (B) Comparison of the corresponding micro motion (top panel) and intensity fluctuation (lower panel) of single E. coli cell and 1 μm polystyrene bead. (C) Training results with machine learning classification (Support Vector Machine, SVM) based on mean squared displacement (MSD) of single cell motion and normalized intensity standard deviation (NISD) of single cell intensity. (D) Classification of a 4:1 mixed sample of E. coli: polystyrene beads with the trained SVM model. Scale bar: 5 μm.

Figure 3.. Differentiation of rod-shaped bacteria (E. coli) and spherically-shaped bacteria (S. saprophyticus) by single-cell phenotypic features tracking.

(A) Single cell motion and intensity mapping for E. coli and S. saprophyticus. (B) Comparison of the corresponding micro motion (upper panel) and intensity fluctuation (lower panel) for single E. coli and S. saprophyticus cells. (C) Training results obtained from individual pure cultures of E. coli (n = 267) and S. saprophyticus (n = 211) with machine learning classification (Support Vector Machine, SVM) based on mean squared displacement (MSD) of single cell motion and normalized intensity standard deviation (NISD) of single cell intensity. (D) Testing results obtained from individual pure cultures of both E. coli cells (n = 109) and S. saprophyticus cells (n = 96) with the trained SVM model. Scale bar: 20 μm.

Figure 4.. Differentiation of bacterial cells from urine particles by phenotypic features tracking.

(A) Single cell motion and intensity mapping for cultured E. coli cells and urine particles. (B) Comparison of the corresponding micro motion (top panel) and intensity fluctuation (lower panel) of a single E. coli cell and a single urine particle. (C) The corresponding training results of E. coli (n = 185) and urine particles (n = 155) with machine learning classification (Support Vector Machine, SVM) based on mean squared displacement (MSD) of single cell motion and normalized intensity standard deviation (NISD) of single cell intensity. (D) The corresponding testing results of E. coli (n = 80) and urine particles (n = 66) with the trained SVM model. Scale bar: 20 μm.

Figure 5.. Rapid infection detection with the trained SVM model.

(A) SVM classification result of one representative infection negative sample (Sample #11). (B) SVM classification result of one representative infection positive sample (Sample #12). (C) Comparison of culture-based detection and phenotypic tracking for LVSi-RD of UTIs. T_I indicates the determined infection threshold. (D) The ROC curve for UTI diagnosis evaluation from LVSi-RD. At the threshold of 0.5, the sensitivity and specificity were 84% and 100%, respectively.