With the discovery of fluorescent proteins and improvements of fluorescent reporters, it has become possible to specifically label particular components of cells and follow cellular functions using microscopy (Specht, Braselmann and Palmer 2017). A key advantage of microscopy over other techniques for characterizing microbes is that it can acquire data for living cells with high spatial resolution. Optical microscopy has long been an important technique for characterizing and understanding the microbial world. We also discuss recent advances in image analysis of microbial cells and communities, and how these advances open up opportunities for quantitative studies of spatiotemporal processes in microbiology, based on image cytometry and adaptive microscope control.īiofilm, microbial community, single cell, segmentation, phenotyping, machine learning, data science INTRODUCTION
#Cellprofiler tracking how to#
Here, we provide a brief introduction to core concepts of automated image processing, recent software tools and how to validate image analysis results. The rapidly evolving progress in computational image analysis has recently enabled the quantification of a large number of properties of single cells and communities, based on traditional analysis techniques and convolutional neural networks. Fluorescence microscopy techniques are widely used to measure spatial structure inside living cells and communities, which often results in large numbers of images that are difficult or impossible to analyze manually. Similarly, the spatial arrangement of genotypes and phenotypes in microbial communities has important consequences for cooperation, competition, and community functions. The cellular morphology and sub-cellular spatial structure critically influence the function of microbial cells.
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