Scan4CFU: Low-cost, open-source bacterial colony tracking over large areas and extended incubation times

TL;DR

Scan4CFU is an open-source, low-cost system designed for automated, large-area bacterial colony imaging and analysis, enabling detailed study of growth heterogeneity and its implications for antibiotic resistance.

Contribution

It introduces a novel, affordable platform for high-throughput bacterial colony tracking with detailed software and hardware specifications.

Findings

Successfully tracked E. coli colony growth kinetics

Demonstrated system's capability for large-area, extended incubation monitoring

Potential to uncover new insights into bacterial heterogeneity and antibiotic response

Abstract

A hallmark of bacterial populations cultured in vitro is their homogeneity of growth, where the majority of cells display identical growth rate, cell size and content. Recent insights, however, have revealed that even cells growing in exponential growth phase can be heterogeneous with respect to variables typically used to measure cell growth. Bacterial heterogeneity has important implications for how bacteria respond to environmental stresses, such as antibiotics. The phenomenon of antimicrobial persistence, for example, has been linked to a small subpopulation of cells that have entered into a state of dormancy where antibiotics are no longer effective. While methods have been developed for identifying individual non-growing cells in bacterial cultures, there has been less attention paid to how these cells may influence growth in colonies on a solid surface. In response, we have developed a low-cost, open-source platform to perform automated image capture and image analysis of bacterial colony growth on multiple nutrient agar plates simultaneously. The descriptions of the hardware and software are included, along with details about the temperature-controlled growth chamber, high-resolution scanner, and graphical interface to extract and plot the colony lag time and growth kinetics. Experiments were conducted using a wild type strain of Escherichia coli K12 to demonstrate the feasibility and operation of our setup. By automated tracking of bacterial growth kinetics in colonies, the system holds the potential to reveal new insights into understanding the impact of microbial heterogeneity on antibiotic resistance and persistence.