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>

<ol>
	<li>Daniel, R., Rubens, J. R., Sarpeshkar, R., Lu, T. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synthetic+analog+computation+in+living+cells.">Synthetic analog computation in living cells.</a> Nature. 497, 619-623 (2013).</li><li>Yang, X. S. Y., L, A. B., Harwood, C., Jensen, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Imaging Bacterial Molecules, Structures and Cells. , (2016).</li><li>Joyce, G., Robertson, B. D., Williams, K. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+modified+agar+pad+method+for+mycobacterial+live-cell+imaging.">A modified agar pad method for mycobacterial live-cell imaging.</a> Biomedcentral Research Notes. , (2011).</li><li>Cotlet, M., Goodwin, P. M., Waldo, G. S., Werner, J. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparison+of+the+fluorescence+dynamics+of+single+molecules+of+a+green+fluorescent+protein:+One-+versus+two-photon+excitation.">A comparison of the fluorescence dynamics of single molecules of a green fluorescent protein: One- versus two-photon excitation.</a> ChemPhysChem. , (2006).</li><li>Andersen, J. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=New+unstable+variants+of+green+fluorescent+protein+for+studies+of+transient+gene+expression+in+bacteria.">New unstable variants of green fluorescent protein for studies of transient gene expression in bacteria.</a> Applied and Environmental Microbiology. 64, 2240-2246 (1998).</li><li>Barger, N., Litovco, P., Li, X., Habib, M., Daniel, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synthetic+metabolic+computation+in+a+bioluminescence-sensing+system.">Synthetic metabolic computation in a bioluminescence-sensing system.</a> Nucleic Acids Research. , (2019).</li><li>Young, J. W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+single-cell+gene+expression+dynamics+in+bacteria+using+fluorescence+time-lapse+microscopy.">Measuring single-cell gene expression dynamics in bacteria using fluorescence time-lapse microscopy.</a> Nature Protocols. , (2012).</li><li>Swain, P. S., Elowitz, M. B., Siggia, E. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intrinsic+and+extrinsic+contributions+to+stochasticity+in+gene+expression.">Intrinsic and extrinsic contributions to stochasticity in gene expression.</a> Proceedings of the National Academy of Science United States. , (2002).</li><li>Elowitz, M. B., Levine, A. J., Siggia, E. D., Swain, P. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stochastic+gene+expression+in+a+single+cell.">Stochastic gene expression in a single cell.</a> Science. , (2002).</li><li>Baumgart, L., Mather, W., Hasty, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synchronized+DNA+cycling+across+a+bacterial+population.">Synchronized DNA cycling across a bacterial population.</a> Nature Genetics. , (2017).</li><li>Arriaga, E. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Determining+biological+noise+via+single+cell+analysis.">Determining biological noise via single cell analysis.</a> Analytical and Bioanalytical Chemistry. , (2009).</li><li>Nielsen, A. A. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genetic+circuit+design+automation.">Genetic circuit design automation.</a> Science. , (2016).</li><li>Ozbudak, E. M., Thattai, M., Kurtser, I., Grossman, A. D., van Oudenaarden, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regulation+of+noise+in+the+expression+of+a+single+gene.">Regulation of noise in the expression of a single gene.</a> Nature Genetics. 31, 69-73 (2002).</li><li>Pedraza, J. H., Van Oudenaarden, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Noise+propagations+in+gene+networks.">Noise propagations in gene networks.</a> Science. , (2005).</li><li>Jennifer, A. N. B., Christopher, A. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Principles+of+genetic+circuit+design.">Principles of genetic circuit design.</a> Nature Methods. , (2014).</li><li>Aoki, S. K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+universal+biomolecular+integral+feedback+controller+for+robust+perfect+adaptation.">A universal biomolecular integral feedback controller for robust perfect adaptation.</a> Nature. , (2019).</li><li>Hanna, H. A., Danial, L., Kvatinsky, S., Daniel, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Cytomorphic Electronics with Memristors for Modeling Fundamental Genetic Circuits. 4545, (2020).</li><li>de Jong, I. G., Beilharz, K., Kuipers, O. P., Veening, J. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Live+cell+imaging+of+Bacillus+subtilis+and+Streptococcus+pneumoniae+using+automated+time-lapse+microscopy.">Live cell imaging of Bacillus subtilis and Streptococcus pneumoniae using automated time-lapse microscopy.</a> Journal of Visualized Experiments. , (2011).</li><li>Eling, N., Morgan, M. D., Marioni, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Challenges+in+measuring+and+understanding+biological+noise.">Challenges in measuring and understanding biological noise.</a> Nature Reviews Genetics. , (2019).</li><li>Thattai, M., Van Oudenaarden, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Attenuation+of+noise+in+ultrasensitive+signaling+cascades.">Attenuation of noise in ultrasensitive signaling cascades.</a> Biophysical Journal. , (2002).</li><li>Thattai, M., Van Oudenaarden, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intrinsic+noise+in+gene+regulatory+networks.">Intrinsic noise in gene regulatory networks.</a> Proceedings of the National Academy of Science United States. , (2001).</li><li>Rosenfeld, N., Young, J. W., Alon, U., Swain, P. S., Elowitz, M. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene+regulation+at+the+single-cell+level.">Gene regulation at the single-cell level.</a> Science. , (2005).</li><li>Van Der Ziel, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Noise in Measurements. , (1976).</li><li>Sambrook, J., Fritsch, E. F., Maniatis, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+cloning:+A+laboratory+manual.+2nd+Edition.">Molecular cloning: A laboratory manual. 2nd Edition.</a> Cold Spring Harbor Laboratory Press. , (1989).</li><li>Carpenter, A. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CellProfiler:+Image+analysis+software+for+identifying+and+quantifying+cell+phenotypes.">CellProfiler: Image analysis software for identifying and quantifying cell phenotypes.</a> Genome Biology. , (2006).</li><li>Paintdakhi, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Oufti:+An+integrated+software+package+for+high-accuracy,+high-throughput+quantitative+microscopy+analysis.">Oufti: An integrated software package for high-accuracy, high-throughput quantitative microscopy analysis.</a> Molecular Microbiology. , (2016).</li><li>Ducret, A., Quardokus, E. M., Brun, Y. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MicrobeJ,+a+tool+for+high+throughput+bacterial+cell+detection+and+quantitative+analysis.">MicrobeJ, a tool for high throughput bacterial cell detection and quantitative analysis.</a> Nature Microbiology. , (2016).</li><li>Stylianidou, S., Brennan, C., Molecular, S. B. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> SuperSegger: robust image segmentation, analysis and lineage tracking of bacterial cells - Stylianidou - 2016 - Molecular Microbiology. , (2016).</li><li>Balomenos, A. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Image+analysis+driven+single-cell+analytics+for+systems+microbiology.">Image analysis driven single-cell analytics for systems microbiology.</a> Biomedcentral System Biology. , (2017).</li><li>Smit, J. H., Li, Y., Warszawik, E. M., Herrmann, A., Cordes, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Colicoords:+A+Python+package+for+the+analysis+of+bacterial+fluorescence+microscopy+data.">Colicoords: A Python package for the analysis of bacterial fluorescence microscopy data.</a> PLoS One. , (2019).</li><li>Guido, N. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+bottom-up+approach+to+gene+regulation.">A bottom-up approach to gene regulation.</a> Nature. , (2006).</li><li>Beal, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Signal-to-noise+ratio+measures+efficacy+of+biological+computing+devices+and+circuits.">Signal-to-noise ratio measures efficacy of biological computing devices and circuits.</a> Frontiers in Bioengineering and Biotechnology. , (2015).</li><li>Marr, D., Hildreth, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Theory+of+edge+detection.">Theory of edge detection.</a> Proceedings of the Royal Society - Biology Science. 207, 187-217 (1980).</li><li>Otsu, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Threshold+selection+method+from+gray-level+histograms.">Threshold selection method from gray-level histograms.</a> IEEE Transactions Systems Man Cybernetics. , (1979).</li><li>Soille, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Morphological Image Analysis: Principles and Applications, Second edition. , (2000).</li><li>Bradley, D., Roth, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Adaptive+Thresholding+using+the+Integral+Image.">Adaptive Thresholding using the Integral Image.</a> Journal of Graphics Tools. , (2007).</li><li>Meyer, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Topographic+distance+and+watershed+lines.">Topographic distance and watershed lines.</a> Signal Processing. , (1994).</li><li>Maurer, C. R., Qi, R., Raghavan, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+linear+time+algorithm+for+computing+exact+Euclidean+distance+transforms+of+binary+images+in+arbitrary+dimensions.">A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions.</a> IEEE Transactions on Pattern Analysis and Machine. , (2003).</li><li>Millo, R., Phillips, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=What+is+the+maturation+time+for+fluorescent+proteins.">What is the maturation time for fluorescent proteins.</a> Cell Biology by Numbers. , (2015).</li></ol>

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 stochastic effects and less affected by cell division. The first circuit constitutively expressed GFP (Figure 10a), and the second circuit constitutively expressed sfGFP fused to a ssrA degradation tag (Figure 10b). In order to quantify the stochastic behavior of the fluorescent proteins, we also recorded the bright field images. The results show that the expression of GFP, specifically at its maturation39, is the dominant noise source. The periodic saw tooth behavior pattern observed in Figure 10a can be explained by the random process of cell division and the long-time scale of GFP maturation (~50 minutes). By contrast, the sfGFP signal of the second circuit was stabilized during the measurement, because the very short maturation time of the sfGFP (~6 minutes). We developed a simple formula that describes the level of GFP in both circuits when we consider only the process of cell division and GFP maturation time. At a given time, t, we assume that there are x copy numbers of proteins, and &#181; copy numbers of plasmids. The protein copy number can be described by:
1.3&#160;<img alt="Equation 4" src="/files/ftp_upload/61290/61290eq4.jpg" style="font-size: 14px; opacity: 0.9;" />
1.4&#160;<img alt="Equation 5" src="/files/ftp_upload/61290/61290eq5.jpg" style="opacity: 0.9; font-size: 14px;" />
When considering only division events; <img alt="Equation 6" src="/files/ftp_upload/61290/61290eq6.jpg" style="opacity: 0.9; font-size: 14px;" />&#160;after protein maturation and so for the specific plasmids we get:
For GFP (<img alt="Equation 8" src="/files/ftp_upload/61290/61290eq8.jpg" style="opacity: 0.9; font-size: 14px;" />): 1.5&#160;<img alt="Equation 9" src="/files/ftp_upload/61290/61290eq9.jpg" style="opacity: 0.9; font-size: 14px;" />
For sfGFP (<img alt="Equation 10" src="/files/ftp_upload/61290/61290eq10.jpg" style="opacity: 0.9; font-size: 14px;" />):1.6&#160;<img alt="Equation 11" src="/files/ftp_upload/61290/61290eq11.jpg" style="opacity: 0.9; font-size: 14px;" />
Then, the developed series of sfGFP:
1.7&#160;<img alt="Equation 12" src="/files/ftp_upload/61290/61290eq12.jpg" style="opacity: 0.9; font-size: 14px;" />
In the case that <img alt="Equation 13" src="/files/ftp_upload/61290/61290eq13.jpg" style="opacity: 0.9; font-size: 14px;" />, we obtain <img alt="Equation 14" src="/files/ftp_upload/61290/61290eq14.jpg" style="opacity: 0.9; font-size: 14px;" />. This result can be explained as follows. When x is small, sfGFP levels increase by a small amount. When x is large, sfGFP is degraded to a steady state. Similarly, for the circuit of GFP, each cell contains about 10 units of GFP, but since maturation time for GFP is longer than the mitosis, degrading saw tooth patterns of fluorescence intensities are observed. In the model, we assumed that replication of the plasmid is fast enough that plasmid distribution remains constant before and after division. The ssrA degradation tag often reduces the protein half-life time from hours to less than an hour5 and leads to a fast steady state. We showed that the method also provides a distribution similar to that measured with a high population flow analyzer and that the CV of this distribution is equal to or smaller than the flow analyzer CV.
While several methods3,7,18 were developed for live cell imaging, the method presented here is tailored specifically to Escherichia coli. This bacterium requires a special media and a slightly different approach. The protocol has the following important features: (1) Establishing a dilution ratio specific to the bacteria and strain at the beginning of exponential growth rather than at the middle stage (Exponential growth without shaking) (2) Escherichia coli best divide at 37 &#176;C, but at 37 &#176;C the minimal media gel loses water rapidly which leads to shrinkage and instability. The protocol here overcomes this challenge (3) We first apply the bacteria sample and then seal it with liquid gel. Since the sample is trapped between the glass bottom and gel we require a gel that (I) allows gas exchange to avoid cutting air pockets, (II) remains liquid at low temperatures as Escherichia coli are sensitive to heat and (III) makes sure the sample will not be mixed inside the liquid gel. The gel remains liquid at 37 &#176;C, however, we recommend use of a protective cap on top of the sample to avoid excessive heat and sample mixing inside the gel (4) This approach requires simple, generic equipment (5) Samples can be measured directly without preheating, so there is no loss of division events (6) The protocol, which includes the wet-lab steps and the customized automated software, can be used to study the stochastic behavior of genetic circuits such as quantifying the SNR and CV of circuit signals. We compared the microscopy method to flow analyzer results in order to validate the use of small populations of cells and establish a baseline for comparison according to CV (7) The software allows us to detect cells on the monolayer without human intervention and analyze total noise. Software tools such as ImageJ18 or Schnitzcells require manual identification of cells and are challenging for adjustments.
When continuously imaging living cell colonies, design the experiment while considering several complex physical parameters, such as cellular stress and toxicity, rate of resources depletion, necrosis and degradation time of inducers and chemicals. The protocol allows reliable measurement of up to five hours. Our experiments suggest that Escherichia coli synchronize when dividing in micro, monolayer colonies (Supplementary Video 3). We assume that the gel is uniform in terms of resources and toughness, the cells have similar behavior, and the field of illumination is slightly bigger from the field of view. Thus, each cell should produce the same signal and statistical data, which can be collected as we have shown by comparing CV obtained in this way to measurement with a flow analyzer. In order to measure noise at constant initial conditions for longer periods of time, we will consider using microfluidic chips in the future. Further benefits of such a device are maintaining fixed positions of cells and stable focus10. Still, design, fabrication of microfluidic chips and priming for experiments require training, specific equipment (custom made at times) and time. For this reason, it is beneficial to use time lapse microscopy as shown in this protocol to acquire a general understanding of the circuit.
The proposed protocol and the developed software allow reproducible measurements, from which it is simple to derive graphs. It also allows testing and comparison of total noise and can be modified for measuring intrinsic and extrinsic noise. The method is based on generic or easy to order materials, openly shared, easy to use software and does not require specific training. We have shown that the population measured by microscopy is big enough to obtain meaningful data by comparing the method CV to that obtained with a flow analyzer. Therefore, we can successfully establish a SNR baseline using this protocol and compare it to more complex gene circuits.

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

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).

Watch this Scientific Journal Video about Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy at JoVE.com

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

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.

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 ...

continuous-measurement-biological-noise-escherichia-coli-using-time

Universidad San Sebastian

Research

JoVE Journal

Bioengineering

3.5K Views.  Technion - Israel Institute of Technology. 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. 

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

Live Cell Imaging of Bacillus subtilis and Streptococcus pneumoniae using Automated Time-lapse Microscopy

During the last few years scientists became increasingly aware that average data obtained from microbial population based experiments are not representative of the behavior, status or phenotype of single cells. Due to this new insight the number of single cell studies rises continuously (for recent reviews see 1,2,3). However, many of the single cell techniques applied do not allow monitoring the development and behavior of one specific single cell in time (e.g. flow cytometry or standard microscopy).
Here, we provide a detailed description of a microscopy method used in several recent studies 4, 5, 6, 7, which allows following and recording (fluorescence of) individual bacterial cells of Bacillus subtilis and Streptococcus pneumoniae through growth and division for many generations. The resulting movies can be used to construct phylogenetic lineage trees by tracing back the history of a single cell within a population that originated from one common ancestor. This time-lapse fluorescence microscopy method cannot only be used to investigate growth, division and differentiation of individual cells, but also to analyze the effect of cell history and ancestry on specific cellular behavior. Furthermore, time-lapse microscopy is ideally suited to examine gene expression dynamics and protein localization during the bacterial cell cycle. The method explains how to prepare the bacterial cells and construct the microscope slide to enable the outgrowth of single cells into a microcolony. In short, single cells are spotted on a semi-solid surface consisting of growth medium supplemented with agarose on which they grow and divide under a fluorescence microscope within a temperature controlled environmental chamber. Images are captured at specific intervals and are later analyzed using the open source software ImageJ.

Live Cell Imaging of Bacillus subtilis and Streptococcus pneumoniae using Automated Time-lapse Microscopy

This protocol provides a step-by-step procedure to monitor single cell behavior of different bacteria in time using automated fluorescence time-lapse microscopy. Furthermore, we provide guidelines how to analyze the microscopy images.

During the last few years scientists became increasingly aware that average data obtained from microbial population based experiments are not ...

Immunology and Infection

Following Cell-fate in E. coli After Infection by Phage Lambda

The system comprising bacteriophage (phage) lambda and the bacterium E. coli has long served as a paradigm for cell-fate determination1,2. Following the simultaneous infection of the cell by a number of phages, one of two pathways is chosen: lytic (virulent) or lysogenic (dormant)3,4. We recently developed a method for fluorescently labeling individual phages, and were able to examine the post-infection decision in real-time under the microscope, at the level of individual phages and cells5. Here, we describe the full procedure for performing the infection experiments described in our earlier work5. This includes the creation of fluorescent phages, infection of the cells, imaging under the microscope and data analysis. The fluorescent phage is a "hybrid", co-expressing wild- type and YFP-fusion versions of the capsid gpD protein. A crude phage lysate is first obtained by inducing a lysogen of the gpD-EYFP (Enhanced Yellow Fluorescent Protein) phage, harboring a plasmid expressing wild type gpD. A series of purification steps are then performed, followed by DAPI-labeling and imaging under the microscope. This is done in order to verify the uniformity, DNA packaging efficiency, fluorescence signal and structural stability of the phage stock. The initial adsorption of phages to bacteria is performed on ice, then followed by a short incubation at 35&deg;C to trigger viral DNA injection6. The phage/bacteria mixture is then moved to the surface of a thin nutrient agar slab, covered with a coverslip and imaged under an epifluorescence microscope. The post-infection process is followed for 4 hr, at 10 min interval. Multiple stage positions are tracked such that ~100 cell infections can be traced in a single experiment. At each position and time point, images are acquired in the phase-contrast and red and green fluorescent channels. The phase-contrast image is used later for automated cell recognition while the fluorescent channels are used to characterize the infection outcome: production of new fluorescent phages (green) followed by cell lysis, or expression of lysogeny factors (red) followed by resumed cell growth and division. The acquired time-lapse movies are processed using a combination of manual and automated methods. Data analysis results in the identification of infection parameters for each infection event (e.g. number and positions of infecting phages) as well as infection outcome (lysis/lysogeny). Additional parameters can be extracted if desired.

Following Cell-fate in E. coli After Infection by Phage Lambda

This article describes the procedure for preparing a fluorescently-labeled version of bacteriophage lambda, infection of E. coli bacteria, following the infection outcome under the microscope, and analysis of infection results.

The system comprising bacteriophage (phage) lambda and the bacterium E. coli has long served as a paradigm for cell-fate determination1,2. Following the ...

In Situ Measurement and Correlation of Cell Density and Light Emission of Bioluminescent Bacteria

There is a considerable number of bacterial species capable of emitting light. All of them share the same gene cluster, namely the lux operon. Despite this similarity, these bacteria show extreme variations in characteristics like growth behavior, intensity of light emission or regulation of bioluminescence. The method presented here is a newly developed assay that combines recording of cell growth and bioluminescent light emission intensity over time utilizing a plate reader. The resulting growth and light emission characteristics can be linked to important features of the respective bacterial strain, such as quorum sensing regulation. The cultivation of a range of bioluminescent bacteria requires a specific medium (e.g., artificial sea water medium) and defined temperatures. The easy to handle, non-bioluminescent standard-research bacterium Escherichia coli (E. coli), on the other hand, can be cultivated inexpensively in high quantities in laboratory scale. Exploiting E. coli by introducing a plasmid containing the whole lux operon can simplify experimental conditions and additionally opens up many possibilities for future applications. The expression of all lux genes utilizing an E. coli expression strain was achieved by construction of an expression plasmid via Gibson cloning and insertion of four fragments containing seven lux genes and three rib genes of the lux-rib operon into a pET28a vector. E. coli based lux gene expression can be induced and controlled via Isopropyl-&#946;-D-thiogalactopyranosid (IPTG) addition resulting in bioluminescent E. coli cells. The advantages of this system are to avoid quorum sensing regulation restrictions and complex medium compositions along with non-standard growth conditions, such as defined temperatures. This system enables analysis of lux genes and their interplay, by the exclusion of the respective gene from the lux operon, or even addition of novel genes, exchanging the luxAB genes from one bacterial strain by another, or analyzing protein complexes, such as luxCDE.

In Situ Measurement and Correlation of Cell Density and Light Emission of Bioluminescent Bacteria

Bioluminescent bacteria regulate light production through a variety of mechanisms, such as quorum sensing. This novel method allows the in situ investigation of bioluminescence and the correlation of light emission to cell density. An artificial bioluminescent Escherichia coli system allows the characterization of lux operons, Lux proteins, and their interplay.

There is a considerable number of bacterial species capable of emitting light. All of them share the same gene cluster, namely the lux operon. Despite ...

Biochemistry

Characterizing Microbiome Dynamics &#8211; Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

The investigation of pure cultures and monitoring of microbial community dynamics is vital to understand and control natural ecosystems and technical applications driven by microorganisms. Next generation sequencing methods are widely utilized to resolve microbiomes, but they are generally resource and time intensive and deliver mostly qualitative information. Flow cytometric microbiome analysis does not suffer from those disadvantages and can provide relative subcommunity abundances and absolute cell numbers at-line. Although it does not deliver direct phylogenetic information, it can enhance the analysis depth and resolution of sequencing approaches. In sharp contrast to medical applications in both research and routine settings, flow cytometry is still not widely used for microbiome analysis. Missing information on sample preparation and data analysis pipelines may create an entry barrier for the researchers facing microbiome analysis challenges that would often be textbook flow cytometry applications. Here, we present three comprehensive workflows for pure cultures, complex communities in clear medium and complex communities in challenging matrices, respectively. We describe individual sampling and fixation procedures and optimized staining protocols for the respective sample sets. We elaborate the cytometric analysis with a complex research centered and an application focused bench top device, describe the cell sorting procedure and suggest data analysis packages. We furthermore propose important experimental controls and apply the presented workflows to the respective sample sets.

Flow cytometric analysis has proven valuable for investigating pure cultures and monitoring microbial community dynamics. We present three comprehensive workflows, from sampling to data analysis, for pure cultures and complex communities in clear medium as well as in challenging matrices, respectively.

The investigation of pure cultures and monitoring of microbial community dynamics is vital to understand and control natural ecosystems and technical ...

Environment

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

Growth dynamics are fundamental characteristics of microorganisms. Quantifying growth precisely is an important goal in microbiology. Growth dynamics are affected both by the doubling time of the microorganism and by any delay in growth upon transfer from one condition to another, the lag. The ScanLag method enables the characterization of these two independent properties at the level of colonies originating each from a single cell, generating a two-dimensional distribution of the lag time and of the growth time. In ScanLag, measurement of the time it takes for colonies on conventional nutrient agar plates to be detected is automated on an array of commercial scanners controlled by an in house application. Petri dishes are placed on the scanners, and the application acquires images periodically. Automated analysis of colony growth is then done by an application that returns the appearance time and growth rate of each colony. Other parameters, such as the shape, texture and color of the colony, can be extracted for multidimensional mapping of sub-populations of cells. Finally, the method enables the retrieval of rare variants with specific growth phenotypes for further characterization. The technique could be applied in bacteriology for the identification of long lag that can cause persistence to antibiotics, as well as a general low cost technique for phenotypic screens.

ScanLag is a high-throughput method for measuring the delay in growth, namely lag time, as well as the growth rate of colonies for thousands of cells in parallel. Screening using ScanLag enables the discrimination between long lag-time and slow growth at the level of a single variant.

Growth dynamics are fundamental characteristics of microorganisms. Quantifying growth precisely is an important goal in microbiology. Growth dynamics are ...

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

A swarm of the &delta;-proteobacterium Myxococcus xanthus contains millions of cells that act as a collective, coordinating movement through a series of signals to create complex, dynamic patterns as a response to environmental cues. These patterns are self-organizing and emergent; they cannot be predicted by observing the behavior of the individual cells. Using a time-lapse microcinematography tracking assay, we identified a distinct emergent pattern in M. xanthus called chemotaxis, defined as the directed movement of a swarm up a nutrient gradient toward its source 1.
In order to efficiently characterize chemotaxis via time-lapse microcinematography, we developed a highly modifiable plate complex (Figure 1) and constructed a cluster of 8 microscopes (Figure 2), each capable of capturing time-lapse videos. The assay is rigorous enough to allow consistent replication of quantifiable data, and the resulting videos allow us to observe and track subtle changes in swarm behavior. Once captured, the videos are transferred to an analysis/storage computer with enough memory to process and store thousands of videos. The flexibility of this setup has proven useful to several members of the M. xanthus community.

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

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.

A swarm of the &delta;-proteobacterium Myxococcus xanthus contains millions of cells that act as a collective, coordinating movement through a series of ...

Biology

Precise, High-throughput Analysis of Bacterial Growth

Bacterial growth is a central concept in the development of modern microbial physiology, as well as in the investigation of cellular dynamics at the systems level. Recent studies have reported correlations between bacterial growth and genome-wide events, such as genome reduction and transcriptome reorganization. Correctly analyzing bacterial growth is crucial for understanding the growth-dependent coordination of gene functions and cellular components. Accordingly, the precise quantitative evaluation of bacterial growth in a high-throughput manner is required. Emerging technological developments offer new experimental tools that allow updates of the methods used for studying bacterial growth. The protocol introduced here employs a microplate reader with a highly optimized experimental procedure for the reproducible and precise evaluation of bacterial growth. This protocol was used to evaluate the growth of several previously described Escherichia coli strains. The main steps of the protocol are as follows: the preparation of a large number of cell stocks in small vials for repeated tests with reproducible results, the use of 96-well plates for high-throughput growth evaluation, and the manual calculation of two major parameters (i.e., maximal growth rate and population density) representing the growth dynamics. In comparison to the traditional colony-forming unit (CFU) assay, which counts the cells that are cultured in glass tubes over time on agar plates, the present method is more efficient and provides more detailed temporal records of growth changes, but has a stricter detection limit at low population densities. In summary, the described method is advantageous for the precise and reproducible high-throughput analysis of bacterial growth, which can be used to draw conceptual conclusions or to make theoretical observations.

Quantitative evaluation of bacterial growth is essential to understanding microbial physiology as a systems-level phenomenon. A protocol for experimental manipulation and an analytical approach are introduced, allowing for precise, high-throughput analysis of bacterial growth, which is a key subject of interest in systems biology.

Bacterial growth is a central concept in the development of modern microbial physiology, as well as in the investigation of cellular dynamics at the ...

Method for Labeling Transcripts in Individual Escherichia coli Cells for Single-molecule Fluorescence In Situ Hybridization Experiments

A method is described for labeling individual messenger RNA (mRNA) transcripts in fixed bacteria for use in single-molecule fluorescence in situ hybridization (smFISH) experiments in E. coli. smFISH allows the measurement of cell-to-cell variability in mRNA copy number of genes of interest, as well as the subcellular location of the transcripts. The main steps involved are fixation of the bacterial cell culture, permeabilization of cell membranes, and hybridization of the target transcripts with sets of commercially available short fluorescently-labeled oligonucleotide probes. smFISH can allow the imaging of the transcripts of multiple genes in the same cell, with limitations imposed by the spectral overlap between different fluorescent markers. Following completion of the protocol illustrated below, cells can be readily imaged using a microscope coupled with a camera suitable for low-intensity fluorescence. These images, together with cell contours obtained from segmentation of phase contrast frames, or from cell membrane staining, allow the calculation of the mRNA copy number distribution of a sample of cells using open-source or custom-written software. The labeling method described here can also be applied to image transcripts with stochastic optical reconstruction microscopy (STORM).

Method for Labeling Transcripts in Individual Escherichia coli Cells for Single-molecule Fluorescence In Situ Hybridization Experiments

This manuscript describes a method for labeling individual messenger RNA (mRNA) transcripts with fluorescently-labeled DNA probes, for use in single-molecule fluorescence in situ hybridization (smFISH) experiments in E. coli. smFISH is a visualization method that allows the simultaneous detection, localization, and quantification of single mRNA molecules in fixed individual cells.

A method is described for labeling individual messenger RNA (mRNA) transcripts in fixed bacteria for use in single-molecule fluorescence in situ ...

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

Single-molecule fluorescence in situ hybridization (smFISH) allows for counting the absolute number of mRNAs in individual cells. Here, we describe an application of smFISH to measure the rates of transcription and mRNA degradation in Escherichia coli. As smFISH is based on fixed cells, we perform smFISH at multiple time points during a time-course experiment, i.e., when cells are undergoing synchronized changes upon induction or repression of gene expression. At each time point, sub-regions of an mRNA are spectrally distinguished to probe transcription elongation and premature termination. The outcome of this protocol also allows for analyzing intracellular localization of mRNAs and heterogeneity in mRNA copy numbers among cells. Using this protocol many samples (~50) can be processed within 8 h, like the amount of time needed for just a few samples. We discuss how to apply this protocol to study the transcription and degradation kinetics of different mRNAs in bacterial cells.

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

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

Single-molecule fluorescence in situ hybridization (smFISH) allows for counting the absolute number of mRNAs in individual cells. Here, we describe an ...

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

Precise measurements of between- and within-strain heterogeneity in microbial growth rates are essential for understanding genetic and environmental inputs into stress tolerance, pathogenicity, and other key components of fitness. This manuscript describes a microscope-based assay that tracks approximately 105&#160;Saccharomyces cerevisiae microcolonies per experiment. After automated time-lapse imaging of yeast immobilized in a multiwell plate, microcolony growth rates are easily analyzed with custom image-analysis software. For each microcolony, expression and localization of fluorescent proteins and survival of acute stress can also be monitored. This assay allows precise estimation of strains&#39; average growth rates, as well as comprehensive measurement of heterogeneity in growth, gene expression, and stress tolerance within clonal populations.

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.

Precise measurements of between- and within-strain heterogeneity in microbial growth rates are essential for understanding genetic and environmental ...

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

Podsumowanie

Przeglądaj więcej filmów

Rozdziały w tym wideo

Live Cell Imaging of Bacillus subtilis and Streptococcus pneumoniae using Automated Time-lapse Microscopy

Following Cell-fate in E. coli After Infection by Phage Lambda

In Situ Measurement and Correlation of Cell Density and Light Emission of Bioluminescent Bacteria

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

ScanLag: High-throughput Quantification of Colony Growth and Lag Time

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

Precise, High-throughput Analysis of Bacterial Growth

Method for Labeling Transcripts in Individual Escherichia coli Cells for Single-molecule Fluorescence In Situ Hybridization Experiments

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

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