Abstract
Extracting quantitative measurements from time-lapse images is necessary in external feedback control applications, where segmentation results are used to inform control algorithms. We describe ChipSeg, a computational tool that segments bacterial and mammalian cells cultured in microfluidic devices and imaged by time-lapse microscopy, which can be used also in the context of external feedback control. The method is based on thresholding and uses the same core functions for both cell types. It allows us to segment individual cells in high cell density microfluidic devices, to quantify fluorescent protein expression over a time-lapse experiment, and to track individual mammalian cells. ChipSeg enables robust segmentation in external feedback control experiments and can be easily customized for other experimental settings and research aims.
Highlights
Live-cell imaging by automated microscopy enables the collection of large-scale data useful to study the link between cellular dynamics and emerging phenotypes.In the context of synthetic biology, the combination of control engineering algorithms with live-cell imaging has been shown to successfully enable the automatic regulation of gene expression across cellular chassis.[1−8] If employing microfluidics/microscopy platforms, the external feedback control action is implemented by measuring the relevant control output throughout the time-lapse experiment by means of automatic segmentation
We present here ChipSeg, a thresholding-based algorithm that automatically segments both bacterial and mammalian cells cultured in microfluidic devices
ChipSeg performs global thresholding to segment a bacterial or mammalian cell cluster in a region of interest within the microfluidic device used for cell culturing; pre-processing, such as filtering and contrast enhancement, can be applied (Supporting Information)
Summary
In the context of synthetic biology, the combination of control engineering algorithms with live-cell imaging has been shown to successfully enable the automatic regulation of gene expression across cellular chassis.[1−8] If employing microfluidics/microscopy platforms, the external feedback control action is implemented by measuring the relevant control output (e.g., fluorescent reporter expression in cells grown within microfluidic devices) throughout the time-lapse experiment by means of automatic segmentation. This measurement informs a control algorithm that computes the control input to minimize the control error (i.e., the difference between the control target and output). The algorithm is easy to use and shows robust segmentation results in external feedback control experiments with microfluidics/microscopy platforms;[5−10] ChipSeg can be adapted for open-loop experiments and other cell types/experimental settings
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