Abstract
Obtaining single cell data from time-lapse microscopy images is critical for quantitative biology, but bottlenecks in cell identification and segmentation must be overcome. We propose a novel, versatile method that uses machine learning classifiers to identify cell morphologies from z-stack bright-field microscopy images. We show that axial information is enough to successfully classify the pixels of an image, without the need to consider in focus morphological features. This fast, robust method can be used to identify different cell morphologies, including the features of E. coli, S. cerevisiae and epithelial cells, even in mixed cultures. Our method demonstrates the potential of acquiring and processing Z-stacks for single-layer, single-cell imaging and segmentation.
Highlights
Thanks to the development of microfluidics and microscopy, it is possible to measure the dynamics of single cells over time[1,2]
E. coli cells were loaded into a microfluidic device where they were cultured in narrow chambers (Fig. 1C)
It appears as a versatile method that can be used to facilitate complex segmentation problems
Summary
Thanks to the development of microfluidics and microscopy, it is possible to measure the dynamics of single cells over time[1,2]. Research groups design and tweak image analysis software[5,6,7,8,9,10,11,12,13] to match their specific segmentation problem This is a considerable waste of time and energy, and highlights the need for a simple, versatile strategy to segment cells, irrespective of experimental design or cellular characteristics. Segmentation critically depends on obtaining high-quality images with a constant focus that outlines the borders and main morphological features of the cell. This is an important constraint, which – in practice – requires periodic auto-focusing or a control system to automatically maintain perfect focus. The method is simple, robust, can be run in real-time and – importantly – gives excellent results for E. coli (rod-shaped), S. cerevisiae (round), mammalian epithelial (HeLa) cells, and even a mixture of bacteria and yeast cells
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