#FEMSmicroBlog: Image analysis of bacterial cells and communities

10-12-2020

A picture is worth a thousand words – but how do we make sure to extract all relevant information from images in an unbiased and reproducible manner? The new review “Advances and opportunities in image analysis of bacterial cells and communities” in FEMS Microbiology Reviews is aimed at scientists interested in introducing automated image analysis into their workflows. Co-author Hanna Jeckel summarizes the review for the #FEMSmicroBlog. #FascinatingMicrobes 

 

On the importance of automated image analysis

In microbiology, optical microscopy is an important technique used to capture the behaviour and development of microbial organisms and communities in space and time. Over the past few decades, technological advancements have sped up the process of image acquisition, which together with the possibility to conveniently store terabytes of data on hard-drives and servers has led to a tremendous increase in the amount of imaging data captured.

While in the past, manual or semi-manual extraction of qualitative and quantitative information from images was possible, a single experiment can nowadays contain more data than could possibly be manually reviewed by a human researcher in a reasonable amount of time.

The importance of automated image analysis is therefore rapidly growing in microbiology, requiring researchers to familiarize themselves with different image analysis techniques and keep track of the increasing number of available software tools developed to facilitate image quantification.

The review “Advances and opportunities in image analysis of bacterial cells and communities” in FEMS Microbiology Reviews is aimed at scientists interested in introducing automated image analysis into their workflows. It provides an overview of the current capabilities and limitations of image analysis methods and tools available to extract quantitative information from imaging data.

A single experiment can nowadays contain more data than could possibly be manually reviewed by a human researcher in a reasonable amount of time.

 

From the basics to new techniques

The review guides the reader through the basics of object detection in images, including the terminology and principles of traditional methods as well as neural network-based techniques, which have received increased attention in recent years and produce promising results. The review also includes a list of software tools available for microbial image analysis together with a number of helpful tips and guidelines regarding their usage and quality control, in order to facilitate the search for an appropriate software and lower the hurdle of getting started.

The review also includes a list of software tools available for microbial image analysis together with a number of helpful tips and guidelines.

After a successful object detection, image cytometry is the next step towards gaining insight into biological processes based on imaging data. During this process, quantitative information is gathered about each object detected in the field of view, capturing the dynamics and diversity of properties such as cell shape or fluorescence intensities. When taken in context of a microbial community rather than single cells alone, spatial coordinates serve as an additional layer of information to be taken into account.

The review discusses recent advances in the field of image cytometry both in two-dimensional and three-dimensional systems and the concept of cube-cytometry, which allows users to gather spatial information in images without single-cell level resolution. The result of image cytometry is a list of properties for each object, which can, similar to the data sets obtained via RNA-sequencing, be interpreted as a high-dimensional data space. This space may serve as a basis for phenotyping, and we discuss different methods of dimensionality reduction and clustering that may be used to achieve this goal.

A rendered biofilm microcolony of Vibrio cholerae. Automated image analysis was used to detect all individual cells, and to measure cytometric parameters for each cell. (c) The authors

 

The advances in the field of image analysis and automated phenotyping also give rise to new experimental techniques. For example, image augmentation and image restoration allow for faster and longer imaging of living samples, by breaching the limits imposed by phototoxicity and photobleaching. Furthermore, live image analysis can be used to set up an adaptive microscopy approach, where experimental settings are automatically adapted during the experiment to identify and track events, increasing both the speed and specificity of data acquisition.

Image analysis can not only to be applied after an experiment has already been completed, but also serve as a tool to actively shape experiments and improve their outcome.

The review highlights some of the recent implementations of these new methods as examples of how image analysis can not only to be applied after an experiment has already been completed, but also serve as a tool to actively shape experiments and improve their outcome.

 

About the author of this blog

Hannah Jeckel received her Master’s degree in Physics and Mathematics from the Philipps-University Marburg (Germany) and is currently a PhD student at the Max Planck Institute for Terrestrial Microbiology in Marburg. In the Biophysics lab of Prof. Dr. Knut Drescher she studies the development of bacterial communitites and interactions within them via optical microscopy. Owing to her quantiative background, Hannah is passionate about using computational approaches to optimize and automatize experiments and their analyses.

About this blog section

The section #FascinatingMicrobes for the #FEMSmicroBlog explains the science behind a paper and highlights the significance and broader context of a recent finding. One of the main goals is to share the fascinating spectrum of microbes across all fields of microbiology.

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