Abstract:The mother machine is a popular microfluidic device that allows long-term time-lapse imaging of thousands of cells in parallel by microscopy. It has become a valuable tool for single-cell level quantitative analysis and characterization of many cellular processes such as gene expression and regulation, mutagenesis or response to antibiotics. The automated and quantitative analysis of the massive amount of data generated by such experiments is now the limiting step. In particular the segmentation and tracking o… Show more
Live cell imaging of microbial cells with microscopy has revolutionised quantitative microbiology. Micrographs are one of the most information-rich data types captured about a microbe, allowing quantification of the size and morphology of individual cells and their gene-expression over time. However, an optical microscope is a diffraction-limited system, and the comparable size of the point spread function of the microscope to the size of a microbial cell can lead to imaging artefacts which corrupt and bias the data. Additionally, the comparable thickness of a microbe to the depth of field of the microscope means that the 2D image contains compressed, projected 3D information. This makes it difficult to extract the underlying 3D distribution of photon emitters. For unknown distributions, the problem can be as ill-posed as a deconvolution problem, usually not having a unique solution. Together, the diffraction and projection effects affect our ability to accurately quantify the size and shape of microbial cells from their images and their contents from intensity measurements. In this paper, we use a mixture of simulations and experiments of microscopic image formation of microbial cells to illustrate the effects of diffraction and projection on cell segmentation and signal quantification. We use targeted experiments to validate the predictions where possible. Finally, we use the knowledge of these effects to design experiments which can help to reduce the errors and biases in our analysis. Awareness of these effects and the approaches towards alleviating them will help to accurately quantify microbiology from microscopy data.
Live cell imaging of microbial cells with microscopy has revolutionised quantitative microbiology. Micrographs are one of the most information-rich data types captured about a microbe, allowing quantification of the size and morphology of individual cells and their gene-expression over time. However, an optical microscope is a diffraction-limited system, and the comparable size of the point spread function of the microscope to the size of a microbial cell can lead to imaging artefacts which corrupt and bias the data. Additionally, the comparable thickness of a microbe to the depth of field of the microscope means that the 2D image contains compressed, projected 3D information. This makes it difficult to extract the underlying 3D distribution of photon emitters. For unknown distributions, the problem can be as ill-posed as a deconvolution problem, usually not having a unique solution. Together, the diffraction and projection effects affect our ability to accurately quantify the size and shape of microbial cells from their images and their contents from intensity measurements. In this paper, we use a mixture of simulations and experiments of microscopic image formation of microbial cells to illustrate the effects of diffraction and projection on cell segmentation and signal quantification. We use targeted experiments to validate the predictions where possible. Finally, we use the knowledge of these effects to design experiments which can help to reduce the errors and biases in our analysis. Awareness of these effects and the approaches towards alleviating them will help to accurately quantify microbiology from microscopy data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.