Microfabricated Polyacrylamide Devices for the Controlled Culture of Growing Cells and Developing Organisms

Nghe, Philippe; Boulineau, Sarah; Gude, Sebastian; Recouvreux, Pierre; Zon, Jeroen S. van; Tans, Sander J.

doi:10.1371/journal.pone.0075537

Cited by 27 publications

(26 citation statements)

References 60 publications

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“…S6 in the supplemental material), demonstrating that that the imaging process does not significantly affect the measured cellular behavior. Similarly shaped peaks of fluorescence were observed in both the agarose pad system and in a microfluidics device (45) in which H 2 O 2 was constantly applied to the cells over the duration of the imaging (see Fig. S7 in the supplemental material), indicating that the shape of the fluorescence curve is not due to the degradation of H 2 O 2 over time.…”

Section: Construction Of a Reporter Strain For Dps Productionsupporting

confidence: 53%

Single-Cell Analysis of the Dps Response to Oxidative Stress

et al. 2016

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Microorganisms have developed an elaborate spectrum of mechanisms to respond and adapt to environmental stress conditions. Among these is the expression of dps, coding for the DNA-binding protein from starved cells. Dps becomes the dominant nucleoid-organizing protein in stationary-phase Escherichia coli cells and is required for robust survival under stress conditions, including carbon or nitrogen starvation, oxidative stress, metal exposure, and irradiation. To study the complex regulation of Dps in E. coli, we utilized time-lapse fluorescence microscopy imaging to examine the kinetics, input encoding, and variability of the Dps response in single cells. In the presence of an oxidative stressor, we observed a single pulse of activation of Dps production. Bacteria encounter many stresses during their development, and they need to be able to adapt quickly to the environment to survive. Bacterial response mechanisms frequently involve specific sets of genes that are activated to help the cell adapt to stress. Alternative sigma factors, of which Escherichia coli has seven, are a frequently used regulatory mechanism (1). While housekeeping genes expressed during exponential growth are controlled by the transcription factor 70 (2, 3), alternative sigma factors act as transcription initiation factors to control the activation of specialized regulons under specific growth or stress conditions (4). The general stress response sigma factor S activates the transcription of Ͼ70 genes, conferring resistance to carbon/phosphate/nitrogen starvation, heat shock, high/low pH, UV radiation, and oxidative stress, among others (5, 6).Microorganisms living in an aerobic environment unavoidably encounter oxidative stress as a by-product of their aerobic metabolism (7). The resultant formation of reactive oxygen species (ROS) can lead to damage to cellular components, including membranes, DNA, and proteins (8). As an adaptation to this condition, bacteria produce enzymes, such as superoxide dismutases and reductases, to scavenge these toxic components (9). Additionally, cells also face external sources of oxidative stress: macrophages produce superoxide and nitric oxide to kill invading bacteria (10), following perception of pathogens, plants also induce the synthesis of organic peroxides (11), certain communities of microorganisms excrete ROS to inhibit the growth of their competitors (12), and exposure to environmental redox cycling compounds can cause damaging intracellular redox reactions (13).In this challenging environment, bacteria have developed refined molecular mechanisms of defense. The DNA-binding protein from starved cells (Dps) plays a crucial role during stress exposure. Escherichia coli dps mutants experience a severe reduction in survival when exposed to any of several different stressors, including oxidative stress, heat shock, metal exposure, UV and gamma irradiation, or extreme pH (14-16). Additionally, Dps was shown to protect cells against DNA strand breakage (17). In E. coli, the protective effect of...

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Section: Construction Of a Reporter Strain For Dps Productionsupporting

confidence: 53%

Single-Cell Analysis of the Dps Response to Oxidative Stress

et al. 2016

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“…The analysis of single-cell growth curves has recently produced a number of insights into the mechanism of bacterial growth, including the identification of the adder model for bacterial growth and the observation of metabolic co-dependence within biofilms [20, 55–58]. The images for single-cell growth curves are typically generated by growing bacteria in a patterned microfluidic device, although agar pads and other microfluidic devices have also been used [59–61]. The generation of these growth curves has typically been difficult because of the close proximity of the growing cells, although recent efforts using standard approaches have shown some success [11, 17].…”

Section: Resultsmentioning

confidence: 99%

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments

et al. 2016

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Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a microscope image correspond to which individual cells. Current approaches require many hours of manual curation and depend on approaches that are difficult to share between labs. They are also unable to robustly segment the cytoplasms of mammalian cells. Here, we show that deep convolutional neural networks, a supervised machine learning method, can solve this challenge for multiple cell types across the domains of life. We demonstrate that this approach can robustly segment fluorescent images of cell nuclei as well as phase images of the cytoplasms of individual bacterial and mammalian cells from phase contrast images without the need for a fluorescent cytoplasmic marker. These networks also enable the simultaneous segmentation and identification of different mammalian cell types grown in co-culture. A quantitative comparison with prior methods demonstrates that convolutional neural networks have improved accuracy and lead to a significant reduction in curation time. We relay our experience in designing and optimizing deep convolutional neural networks for this task and outline several design rules that we found led to robust performance. We conclude that deep convolutional neural networks are an accurate method that require less curation time, are generalizable to a multiplicity of cell types, from bacteria to mammalian cells, and expand live-cell imaging capabilities to include multi-cell type systems.

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“…The critical step of bonding the two components, i.e. the PDMS cast and the substratecontaining glass slide, was previously achieved by customized sample holders, [18][19][20] adhesives 21 or plasma oxidizing of the PDMS structure followed by press bonding. 22 However, the addition of adhesives may change the height of the channel by the thickness of the adhesive layer, which would have negative ramifications because of less controlled flow conditions.…”

Section: Introductionmentioning

confidence: 99%