Name: Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy (Video) | JoVE Journal
Uploaded: 2021-04-27T12:30:00.000+00:00
Duration: 8 min 25 s
Description: Learn continuous measurement of biological noise in Escherichia coli using time-lapse microscopy. This cost-effective method enables live imaging of synthetic gene circuits to assess signal variability and circuit performance.

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5 minutes

We thank Mr. Gil Gelbert (Faculty of electric Engineering, Technion) for assisting with the MATLAB code. We thank Dr. Ximing Li (Faculty of bio-medical Engineering, Technion) for assisting with proofing this article. This research was partially supported by the Neubauer Family Foundation and Israel Ministry of Science, grant 2027345.

1.&#160;Media and culture preparation
<ol>
	<li>Prepare stock solution of 1,000x Carbenicillin (50 mg/mL) or relevant antibiotic.
	<ol>
		<li>.Weigh 0.5 g of carbenicillin. Add 10 mL of sterile H2O. Dissolve completely.</li>
		<li>Sterilize carbenicillin stock through a 0.22 &#181;m syringe filter. Aliquot the antibiotic solution and store at -20 &#176;C.</li>
	</ol>
	</li>
	<li>To prepare lysogeny&#160;broth (LB) plates, mix 5 g of tryptone, 5 g of NaCl, 2.5 g of yeast extract and 7.5 g of Bacto agar with 0.5 L of sterile H2O. Autoclave the solution at 121 &#176;C for 20 min.
	<ol>
		<li>Partially submerge the molten gel-mix in a 50 &#176;C water bath.Add 1,000 &#181;L of carbenicillin (50 mg/mL).</li>
		<li>Prepare Petri dishes in a sterile environment. Leave the plates to set before storing them in the fridge.</li>
	</ol>
	</li>
	<li>For M9 minimal media, prepare separate stock solutions of the following: 5x M9 salts (56.4 g/L), 2 M glucose and 2% biotin-free casamino acids. Autoclave the solutions at 121 &#176;C for 20 min.</li>
	<li>To prepare 5 Petri plates, mix 1125 mg of low melting agar and 400 mg of agar with 89.2 mL of minimal media (1x M9). Add 10 mL of 2% casamino acids (2% [vol/vol]) in a 250 mL Erlenmeyer flask. 
	NOTE: Make sure to pour the media on the inner lips of the flask.</li>
	<li>Microwave the solution in short bursts of 3 to 4 seconds. Repeat until the solution is clear. 
	NOTE: Make sure not to reach boiling point.</li>
	<li>Place the 250 mL Erlenmeyer flask in a hot water bath (60 &#176;C) to further mix by diffusion, and leave it to cool until its temperature falls to about 45-50&#176;C.</li>
	<li>Light the flame at the plate-pouring bench.</li>
	<li>Add quickly all the solutions in the following order: 
	800 &#181;L of 50% glycerol (0.4% [vol/vol]) 
	100 &#181;L of thiamine (B1) 
	1100 &#181;L of Glucose (2M) 
	100 &#181;L of Carbenicillin (50 mg/mL).</li>
	<li>Swirl the Erlenmeyer flask to ensure even distribution of all ingredients throughout the agar.</li>
	<li>Open one plate at a time next to the flame and begin pouring.
	<ol>
		<li>Leave the plates on the bench for a few minutes until initial solidification.</li>
		<li>Turn the plates upside down to prevent water condensation from dripping onto the gel.</li>
		<li>Leave the plates to solidify at room temperature for about 2 h.</li>
		<li>Once the plates have solidified and dried, they can be stored at 4 &#176;C for about 3 months.</li>
	</ol>
	</li>
</ol>
2.&#160;Bacterial strains and plasmids construction
NOTE:The genetic circuit contains one part; a Green Fluorescent Protein (GFP) driven by a PtetO promoter resulting in constitutive expression. All the plasmids in this work were constructed using basic molecular cloning techniques and were transformed into Escherichia coli 10&#946;, using a standard heat shock protocol24. The final construct was transformed into Escherichia coli MG1655 wild type strain for testing.
<ol>
	<li>Transform the desired plasmid into Escherichia coli MG1655 cells with the standard heat shock protocol24.</li>
	<li>Grow the transformed cells on an LB agar plate overnight at 37&#176;C. 
	NOTE: It is possible to keep the Petri dishes from step 2.2 up to 3 days for microscopy use.</li>
	<li>Inoculate a single colony into 5 mL of LB broth supplemented with the relevant antibiotics in a glass tube.</li>
	<li>Grow cells at 37 &#176;C with 250 rpm shaking speed in incubator for 2 h until liquid is cloudy.</li>
	<li>Prepare 1 mL of dilution solution as follows: 
	892 &#181;L of minimal media (1x M9) 
	8 &#181;L of 50% glycerol (0.4% [vol/vol]) 
	100 &#181;L of 2% Casamino acids (0.2% [wt/vol]) 
	1 &#181;L of thiamine (B1) 
	11 &#181;L of Glucose (2 M) 
	&#8203;1 &#181;L of relevant antibiotics
	<ol>
		<li>Mix and spin down.</li>
	</ol>
	</li>
	<li>Dilute the cell culture (1:30) from step 2.4 into a 2 mL tube by adding 30 &#181;L of Escherichia coli growth to 1000 &#181;L of dilution solution (step 2.5).</li>
	<li>Incubate the tube for 1 h with shaking (250 rpm) at 37 &#176;C.</li>
	<li>Grow 40 &#181;L - 60 &#181;L on M9 plates prepared in step 1.10. Incubate the plate at 37 &#176;C overnight. 
	NOTE: The Optical Density (OD600nm) for plating should be around 0.1.</li>
	<li>Place up to three 35 mm glass bottom plates on the bench. 
	NOTE: Make sure the plate&#39;s glass has the correct thickness for the microscope lenses used.</li>
	<li>Prepare the culture for seeding on gel plates, repeat steps 2.3 to 2.6 for colonies from plate prepared at step 2.9 microscope measurements.</li>
	<li>Prepare the following solution to make three microscope plates.
	<ol>
		<li>Preheat the water bath to 60 &#176;C.</li>
		<li>Mix 112.5 mg of low melting agar and 40 mg of agar with 8.92 mL of minimal media (1x M9) and add 1 mL of 2% casamino acids (0.2% [vol/vol]) in a 25 mL Erlenmeyer flask. 
		NOTE: Make sure to pour the media on the inner lips of the flask.</li>
	</ol>
	</li>
	<li>Microwave the solution in short bursts of 2 to 3 seconds. Repeat until the solution is clear. 
	NOTE:Make sure not to reach boiling point.</li>
	<li>Place the 25 mL Erlenmeyer flask in a hot water bath (60 &#176;C) to further mixing by diffusion, and leave it to cool on the bench until its temperature falls to about 45-50 &#176;C.</li>
</ol>
3.&#160;Preparation of agarose pads
<ol>
	<li>Clean bench with 70% ethanol. Stretch tape on the cleaned bench. Make sure the tape is smooth and leveled.</li>
	<li>Prepare two coverslips: one on the tape and a second one nearby. Prepare a coverlid.</li>
	<li>Remove flask (prepared at step 2.14) from water bath, wipe the outside of the flask clean and leave it to cool until its temperature falls to about 45-50 &#176;C.</li>
	<li>To make the gel solution, mix quickly all the solutions in the following order: 
	80 &#181;L of 50% glycerol (0.4% [vol/vol]) 
	1 mL of 2% casamino acids (0.2% [wt/vol]) 
	10 &#181;L of thiamine (B1) 
	110 &#181;L of glucose (2 M) 
	10 &#181;L of relevant antibiotics.</li>
	<li>Pour 1.5 mL of the gel on the coverslip and cover it with the second piece making a &#34;sandwich&#34;.</li>
	<li>Return the Erlenmeyer to the hot water bath. Cover the sandwich with the lid and set timer for 20 minutes.</li>
	<li>At the same time, incubate the tube from step 2.11 for 1 h at 37 &#176;C with shaking (250 rpm)</li>
	<li>After 20 minutes flip the sandwich (step 3.5), cover it and leave to rest for 1 h. 
	NOTE: For better results leave the &#34;sandwich&#34; to rest at 4 &#176;C for the duration of step 3.8.</li>
	<li>Seed Escherichia coli culture from step 3.7 by pipetting the sample onto the 35 mm dish. 
	NOTE: Pipetting 6 &#181;L of cells as separate small drops gives the best results.</li>
	<li>Allow the drops to dry, for at least 15 minutes and up to 30 minutes.</li>
	<li>Expose the solidified gel of the sandwich from step 3.8 by sliding the coverslip away.</li>
	<li>Cut sandwich into small individual pads with biopsy punch or tip.</li>
	<li>Lean it gently on the sample (step 3.9). Leave the dish for 20 minutes on the bench.</li>
</ol>
4.&#160;Preparing the sample for microscopy imaging
<ol>
	<li>Remove the flask from water bath from step 3.6. Wipe the outside of the flask clean and let cool to room temperature (25 &#176;C). 
	NOTE: Remove the flask at step 4.1 about 3 minutes before the timer ends.</li>
	<li>Pour 3 mL of the gel constantly to the plate perimeter in a circular motion. Leave to solidify for a few minutes.</li>
	<li>Seal plates with tape and pierce several holes with 25 G needle. Flip all the dishes to prevent water condensation dripping onto the gel.</li>
	<li>Incubate all the dishes at 4 &#176;C for 30 min to allow full solidification while preventing cell mitosis. 
	NOTE:&#160;Leave samples at 4 &#176;C for successive measurements, no more than one day.</li>
	<li>Start the microscope per manufacturer instructions.</li>
	<li>Find the initial focus using lowest amplification lens and engage automatic focus system (AFS).</li>
	<li>Use oil if needed, drown the lens with oil and spread it carefully by moving the plate with a platform controller (not manually) and AFS can be engaged again.</li>
	<li>Use relative cross-section in Z direction, following default suggestion for Z step cross-sections. 
	NOTE: We took bright field images every 5 minutes and fluorescent images every 20 minutes. Sometimes the focus needs to be adjusted during the first 30 minutes. Preheating the microscope incubator box and oil, while refrigerating the sample tends to help reduce the initial loss of focus.</li>
</ol>
5.&#160;Data analysis
NOTE: In order to process microscopy data, we designed a computer-based software in MATLAB. This software facilitates identification of cell boundaries from bright field tiff images and segments and sorts cells by area. The output of this image analysis can be used as a mask on fluorescent tiff images to derive cell intensity levels and cancel artifacts in the fluorescent domain such as cell halo due to microscope resolution limits. The software developed was inspired from similar works7,25,26,27,28,29,30 and provides an elegant solution tailored for the lab.
<ol>
	<li>First, define the following parameters in main_code.m.
	<ol>
		<li>Define the folder of the acquisition images.</li>
		<li>Define the image time period - bright field channel time step in minutes.</li>
		<li>Define the GFP frequency - rate of acquisition of GFP images.</li>
		<li>Define the microscope resolution (i.e., how many pixels equals 1 &#181;m).</li>
		<li>Define histogram bin range - cell area range. 
		NOTE: The process of classifying the data according to cell area is similar to the principles of gating in flow cytometry data analysis. Gates are placed around populations of cells with common characteristics, usually forward scattered and side scattered, to isolate and quantify these populations of interest. Microscopy allows to gate, investigate and quantify several cell groups.</li>
		<li>Define convolution kernel (3,3) - this parameter detects cell boundaries by global thresholding (count_cells.m). 
		NOTE: This setup is needed only when changing the lab or microscope. Software requires the input bright field channel to be labeled with index c1, fluorescence channel labeled as c2.</li>
	</ol>
	</li>
	<li>Run main_code.m, which will run all other scripts automatically (count_cells.m, cell_growth_rate.m).
	<ol>
		<li>Program automatically segments bright field images (see Supplementary Figure 2-4).</li>
		<li>Combine the segmentation image with fluorescent image (GFP) to extract intensity level per cell.</li>
		<li>Calculate the graph of amount of cells by time and fit according to exponential growth.</li>
		<li>Calculate the mean and standard deviation (STD) for each cell area range.</li>
		<li>Calculate the signal to noise ratio (SNR) for each cell area range.</li>
		<li>Plot and fit the distribution of amount of cells by intensity.</li>
		<li>Calculate the coefficient of Variance (CV) and compare to flow analyzer data. 
		NOTE: Software will give as output the final segmentation images for adjusting conv_kernel in a new folder &#34;yourfolder/Segmented&#34;. Software will give as output the graphs in a new folder &#34;yourfolder/Graphs.</li>
	</ol>
	</li>
	<li>In order to compare between experiments, use compare_experiments.m.
	<ol>
		<li>Define directories for the saved graphs and the address for the \CompareResult directory.</li>
		<li>Run the file.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

In this work, we developed a protocol that enables computer tracing of Escherichia coli live cells, following division and fluorescent levels over a period of hours. This protocol allows us to quantify the stochastic dynamics of genetic circuits in Escherichia coli by measuring the CV and SNR in real time. In this protocol, we compared the stochastic behaviors of two different circuits as shown in Figure 10. It has been shown that plasmids with low copy numbers are more prone to stochas...

Synthetic biology is a multidisciplinary field that has emerged in the past decade and aims to translate engineering design principles into rational biological design1,2,3, in an effort to achieve multi-signal integration and processing in living cells for understanding the basic science4,5, diagnostic, therapeutic and biotechnological applications6,7,8,9,10. Our ability to quantify the input-output response of synthetic gene circuits has been revolutionized by recent advances in single-cell technology, including the flow analyzer and live cell imaging using automated time-lapse microscopy11. A flow analyzer is often used to measure the response of these circuits at the steady state1,12, and inverted microscopy is used to measure the dynamic response of synthetic gene circuits at the level of a single cell3. For example, one of the early works in synthetic biology involved the construction of genetic oscillator networks in living cells using negative feedback loops with a delay3. Later on, the genetic oscillator circuits were applied to understand metabolic control in the dynamic environment of living cells4. Automated time-lapse microscopy is one method to characterize such circuits. We hypothesize that the host cells, Escherichia coli, synchronize when forming micro colonies, allowing measurement of signal and calculation of noise without tracking exact mother-daughter relations.
Noise is a fundamental, inherent aspect of biological systems often arising from multiple sources. Consider for example, biochemical reactions involving signals that originate from the transport of discrete random carriers such as diffusion of proteins13. These signals propagate with random fluctuations14. Other noise sources are resource availability, cell division and variations in environmental conditions such as temperature, humidity and pressure. Biological signals that propagate in synthetic gene circuits often have a very low signal to noise ratio (SNR), which disturbs the performance of such circuits. Therefore, genetic circuit design remains one of the most challenging aspects of genetic engineering15. For example, in contrast to most approaches which calculate only the mean gene expression (measured over the entire cell population), the variance of the measured signal is considered in order to engineer predictable behavior through synthetic gene networks12. As such, the levels of variability or noise in the protein expression play a dominant role in design and performance of analog and digital gene circuits1,16,17.
Many approaches has been developed to quantify cell-to-cell variability, including in Escherichia coli3,7,18. These methods are often used to study gene activation and metabolic pathways, however with less focus on the study of stochastic noise dynamics, like measuring and disentangling specific noise sources, especially for genetic circuits in living cells where this is a fundamental challange19,20,21. Several factors, both inherited to the circuit itself (intrinsic) and derived from the host cells (extrinsic), can disturb the continuous performance of genetic circuits. In this paper, we developed a protocol that aims to quantify the total noise in Escherichia coli&#160;cells, including the intrinsic and extrinsic noise sources6,22. By quantifying the total noise and then evaluating the SNR23, the design of gene circuits can be improved. This method can be modified to measure independent noise sources separately, by monitoring several fluorescent proteins6,20. For the protocol described here, we keep the environmental conditions well controlled and continuously measure the activity of cells without the influence of external factors. We measure the signal from fluorescent proteins in single cells over time and simultaneously image them under an agarose substrate. The resulting images are analyzed using the laboratory&#39;s custom MATLAB.
Ideally, continuous measurement of the real time activity of fluorescent proteins inside a cell will produce accurate data through the growth and division of the cells. However, it is challenging to acquire such data. This is due to degradation of fluorescent proteins, known as photo-bleaching7, when they are exposed to radiation in the excitation process. Furthermore, Escherichia coli cells are also sensitive for the excitation, which might lead to phototoxicity7. Both issues limit the amount of photo frames that can be acquired and the time between acquisitions. The substrate and medium types (e.g., lysogeny broth) that is used to grow the cells during imaging also have a critical role. We strongly recommend using minimal medium, which minimizes non-fluorescent background and extends cell division time.
Moreover, the sample needs to be prepared considering the following requirements (1) Low cell division rate allows for less frequent exposures for closely imaging the division cycle and reducing the probability of phototoxicity and photobleaching. We set the acquisition time to about half of the predicted mitosis time (2) Low cell density at the beginning of the experiment allows for better uniformity and trackability of division. Cell density is affected by the dilution ratio of the Escherichia coli cells, which is a significant parameter for the success of this protocol and needs to be determined for every lab. In order to establish the ratio, each new Escherichia coli strain or media used should be fitted with growth rate graphs (Supplementary Figure 1). An appropriate ratio has been achieved if cells can grow without additional shaking after a short incubation from an initial density of about OD600nm = 0.1. Cells at this phase will divide according to the environment temperature only (3) Restriction of cell movement: cell movement strongly depends on substrate (agarose pad) firmness. The substrate firmness depends on the amount of total agarose and the gel solidification time. Gels cannot be left to solidify overnight at room temperature, as the Escherichia coli will undergo mitosis. Other factors that affect substrate stability include the amount of water in the sample and humidity. Additional issues are discussed in detail in the&#160;Representative Results. This protocol provides many details and gradually moves from one step to another. The protocol offers long stability for imaging experiments and provides a basic image processing tool.&#160;

<table><tbody><tr><td>35mm glass dish</td><td>mattek</td><td>P35G-0.170-14-C</td><td>thickness corresponding with microscope lense.</td></tr><tr><td>Agarose&amp;nbsp;</td><td>Lonza</td><td>5004</td><td>LB preperation</td></tr><tr><td>AHL&amp;nbsp;</td><td>Sigma-Aldrich</td><td>K3007</td><td>inducer</td></tr><tr><td>Bacto tryptone&amp;nbsp;</td><td>BD - Becton, Dickinson and Company&amp;nbsp;</td><td>211705</td><td>LB preperation</td></tr><tr><td>Carb</td><td>Invitrogen</td><td>10177-012</td><td>antibiotic</td></tr><tr><td>Carb</td><td>Formedium</td><td>CAR0025</td><td>antibiotic</td></tr><tr><td>Casamino acids&amp;nbsp;</td><td>BD - Becton, Dickinson and Company&amp;nbsp;</td><td>223050</td><td>minimal media solution</td></tr><tr><td>eclipse Ti</td><td>nikon</td><td></td><td>inverted microscope</td></tr><tr><td>Glucose</td><td>Sigma-Aldrich</td><td>G5767</td><td>minimal media solution</td></tr><tr><td>Glyserol</td><td>Bio-Lab</td><td>000712050100</td><td>minimal media substrate</td></tr><tr><td>Immersol 518F</td><td>zeiss</td><td>4449600000000</td><td>immersion oil</td></tr><tr><td>M9 salt solution&amp;nbsp;</td><td>Sigma-Aldrich</td><td>M6030</td><td>minimal media solution</td></tr><tr><td>NaCl</td><td>Bio-Lab</td><td>214010</td><td>LB preperation</td></tr><tr><td>Noble agar</td><td>Sigma-Aldrich</td><td>A5431</td><td>minimal media substrate</td></tr><tr><td>parafilm tape</td><td>Bemis</td><td>PM-996</td><td>refered to as tape in text</td></tr><tr><td>Seaplaque GTG Agarose</td><td>Lonza</td><td>50111</td><td>minimal media substrate</td></tr><tr><td>thaymine B1</td><td>Sigma-Aldrich</td><td>T0376&amp;nbsp;</td><td>minimal media solution</td></tr><tr><td>Yeast Extract</td><td>BD - Becton, Dickinson and Company&amp;nbsp;</td><td>212750</td><td>LB preperation</td></tr></tbody></table>

continuous measurement of biological noise in escherichia coli using time-lapse microscopy

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

The protocol developed here offers a tool to enable computer tracking of Escherichia coli division and fluorescent levels over several hours. The process starts by screening for colonies that survive on minimal media, assuming that only Escherichia coli harboring the correct plasmid will be able to thrive in the specific conditions. Since the process of building large genetic circuits, requiring the assembly of many DNA parts, is challenging, circuit components are often distributed between multiple plasmids at different copy numbers requiring the use of several antibiotics. Mutations in the plasmid can destroy transcription of the antibiotic resistance genes and interject with resources management in the cell leading to necrosis. The selected colony is set on a glass-bottom Petri dish and a few focus planes are selected for microscopy tracking in both bright field and fluorescent domains. The protocol maintains the image focus for more than 12 hours under initial conditions that cannot be regulated, creating a few difficulties. For example, dead cells start to accumulate in the lenses' field of focus after a few hours of imaging, which causes toxins to buildup and the signal to blur and decay. Depletion of nutrients introduces new metabolic processes and hinder the desired response of the circuit. The experiment's temperature lowers the effectivity of inducers and antibiotics, which can further damage the reliability of the signal. The minimal media gel shrinks and dries, and as a result the optical focus changes over time. We developed this method to overcome these challenges in Escherichia coli, similar to previous works developing analogous methods for other micro-organisms. In addition, this method offers an algorithm to quantify the total stochastic noise in unaltered and altered cells, finding that the results are consistent with flow analyzer predictions as shown by a similar coefficient of variation (CV).

The software analyzes bright field domain images that are off-white and black. The Escherichia coli will look like black oblong shapes on an off-white background and dynamic range of luminance should show a spike at its center (Figure 1). In fluorescent images cells may have a small halo but individual cells with oblong shapes can still be resolved. A mitosis event should be first detected after 30 minutes. Microscope focus should remain stable over time and although cells might mov...

This work presents a microscopy method that allows live imaging of a single cell of Escherichia coli for analysis and quantification of the stochastic behavior of synthetic gene circuits.

The protocol developed here offers a tool to enable computer tracking of Escherichia coli division and fluorescent levels over several hours. The process ...

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JoVE Journal

Bioengineering

To study Myxococcus xanthus swarm behavior, we have designed a time-lapse microcinematography protocol that can be modified for different assays. It employs standard growth conditions adapted for microscopy, and yields reproducible results by the use of inexpensive, reusable silicone gaskets. We have used this method to quantify multicellular chemotaxis.

Recording Multicellular Behavior in Myxococcus xanthus Biofilms using Time-lapse Microcinematography

Source: Valentine, M. E., et al. Generation of In-Frame Gene Deletion Mutants in Pseudomonas aeruginosa and Testing for Virulence Attenuation in a Simple Mouse Model of Infection. J. Vis. Exp. (2020)This video demonstrates the use of bioluminescence to visualize and compare the in vivo behavior of virulent and attenuated bacterial strains in mice. It outlines the preparation of bioluminescent bacterial suspensions and their intraperitoneal injection into mice. The virulent strain exhibits...

Tracking Bacterial Infection Dynamics in a Mouse Using Bioluminescence

Source: Oms, T., et al. Population and Single-Cell Analysis of Antibiotic Persistence in Escherichia coli. J. Vis. Exp. (2023)This video presents a protocol for monitoring individual bacterial cells expressing a fluorescently labeled DNA-binding protein within a microfluidic system. It illustrates how antibiotics inhibit most cells, while a subpopulation of persister cells survives the treatment and regrows following antibiotic removal.

Microfluidic Time-Lapse Microscopy to Study Antibiotic Persistence in Bacteria

Source: Cabeen, M. T., et al. Single-cell Microfluidic Analysis of Bacillus subtilis. J. Vis. Exp. (2018)The video demonstrates the use of a microfluidic device to observe bacterial growth at the single-cell level. It showcases how non-motile bacterial cells, confined within micro-scale trenches, are continuously nourished and imaged under a fluorescence microscope. The setup enables real-time visualization of cell division and lineage tracking across multiple generations.

Tracking Bacterial Growth at Single-Cell Resolution in a Microfluidic System

Source: Chaimovitz, E. A., et al. Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy. J. Vis. Exp. (2021)This video demonstrates real-time visualization of biological noise in E. coli by tracking fluctuations in GFP fluorescence during bacterial growth and division using time-lapse microscopy.

Continuous Monitoring of Biological Noise in Bacteria Using Time-Lapse Microscopy

Source: Schumacher, D., and Sogaard-Andersen, L. Fluorescence Live-cell Imaging of the Complete Vegetative Cell Cycle of the Slow-growing Social Bacterium Myxococcus xanthus. J. Vis. Exp. (2018)This video demonstrates the use of live-cell imaging to track fluorescently tagged chromosome origins during bacterial cell division. It captures chromosome segregation dynamics using time-lapse phase-contrast and fluorescence microscopy.

Time-Lapse Imaging of Chromosome Segregation in Fluorescently Labeled Bacteria

Source: Makowski, Ł., et al. Live-Cell Imaging of the Life Cycle of Bacterial Predator Bdellovibrio bacteriovorus using Time-Lapse Fluorescence Microscopy. J. Vis. Exp. (2020)This video demonstrates time-lapse fluorescence imaging of Bdellovibrio bacteriovorus preying on immobilized E. coli beneath an agarose pad, enabling real-time visualization of intracellular replication and progeny release for studying bacterial predation and host-pathogen interactions.

Live-Cell Time-Lapse Imaging of Bdellovibrio bacteriovorus Predation on E. coli

In this article, we demonstrate live imaging of individual worms employing a custom microfluidic device. In the device, multiple worms are individually confined to separate chambers, allowing multiplexed longitudinal surveillance of various biological processes.

A Microfluidic Platform for Longitudinal Imaging in Caenorhabditis elegans

This protocol describes an application of single-molecule fluorescence in situ hybridization (smFISH) to measure the in vivo kinetics of mRNA synthesis and degradation.

Probing mRNA Kinetics in Space and Time in Escherichia coli using Two-Color Single-Molecule Fluorescence In Situ Hybridization

Yeast growth phenotypes are precisely measured through highly parallel time-lapse imaging of immobilized cells growing into microcolonies. Simultaneously, stress tolerance, protein expression, and protein localization can be monitored, generating integrated datasets to study how environmental and genetic differences, as well as gene-expression heterogeneity among isogenic cells, modulate growth.

High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression

Antibiotic persistence describes the ability of small subpopulations within a sensitive isogenic population to transiently tolerate high doses of bactericidal antibiotics. The present protocol combines approaches to characterize the antibiotic persistence phenotype at the molecular and cellular levels after exposing Escherichia coli to lethal doses of ofloxacin.

Population and Single-Cell Analysis of Antibiotic Persistence in Escherichia coli

Learn continuous measurement of biological noise in Escherichia coli using time-lapse microscopy. This cost-effective method enables live imaging of synthetic gene circuits to assess signal variability and circuit performance.

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

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