Abstract
Early identification of pathogenic bacteria in food, water, and bodily fluids is very important and yet challenging, owing to sample complexities and large sample volumes that need to be rapidly screened. Existing screening methods based on plate counting or molecular analysis present various tradeoffs with regard to the detection time, accuracy/sensitivity, cost, and sample preparation complexity. Here, we present a computational live bacteria detection system that periodically captures coherent microscopy images of bacterial growth inside a 60-mm-diameter agar plate and analyses these time-lapsed holograms using deep neural networks for the rapid detection of bacterial growth and the classification of the corresponding species. The performance of our system was demonstrated by the rapid detection of Escherichia coli and total coliform bacteria (i.e., Klebsiella aerogenes and Klebsiella pneumoniae subsp. pneumoniae) in water samples, shortening the detection time by >12 h compared to the Environmental Protection Agency (EPA)-approved methods. Using the preincubation of samples in growth media, our system achieved a limit of detection (LOD) of ~1 colony forming unit (CFU)/L in ≤9 h of total test time. This platform is highly cost-effective (~$0.6/test) and has high-throughput with a scanning speed of 24 cm2/min over the entire plate surface, making it highly suitable for integration with the existing methods currently used for bacteria detection on agar plates. Powered by deep learning, this automated and cost-effective live bacteria detection platform can be transformative for a wide range of applications in microbiology by significantly reducing the detection time and automating the identification of colonies without labelling or the need for an expert.
Highlights
The rapid and accurate identification of live microorganisms is of great importance for a wide range of applications[1,2,3,4,5,6,7,8], including drug discovery screening assays[1,2,3], clinical diagnoses[4], microbiome studies[5,6], and food and water safety[7,8]
We demonstrated the efficacy of this platform by performing the early detection and classification of three types of bacteria, i.e., E. coli, Klebsiella aerogenes (K. aerogenes), and Klebsiella pneumoniae (K. pneumoniae), and achieved a limit of detection (LOD) of ~1 colony forming unit (CFU)/L in ≤9 h of the total test time
We demonstrated our system by monitoring bacterial colony growth within 60-mm-diameter agar plates and quantitatively analysed the capabilities of the platform for early detection of the bacterial growth and classification of bacterial species
Summary
The rapid and accurate identification of live microorganisms is of great importance for a wide range of applications[1,2,3,4,5,6,7,8], including drug discovery screening assays[1,2,3], clinical diagnoses[4], microbiome studies[5,6], and food and water safety[7,8]. Analytical methods used to detect E. coli and total coliforms are based on culturing the obtained samples on solid agar plates (e.g., the US Environmental Protection Agency (EPA) 1103.1 and EPA 1604 methods) or in liquid media (e.g., Colilert test), followed by visual recognition and counting by an expert, as described in the EPA. The use of solid agar plates is a relatively more cost-effective method and provides flexibility for the volume of the sample to be analysed, which can vary from 100 mL to several litres to enhance the sensitivity. This traditional culture-based detection method requires the colonies to grow to a certain macroscopic size for visibility, which often takes 24–48 h in the case of bacterial samples. There is no EPA-approved nucleic acid-based analytical method[17] for detecting coliforms in water samples
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