Name: high-throughput live imaging of microcolonies to measure heterogeneity in growth and gene expression
Uploaded: 2021-04-18T15:00:00.000+00:00
Duration: 12 min 52 s
Description: Watch this Scientific Journal Video about High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression at JoVE.com

We thank Naomi Ziv, Sasha Levy and Shuang Li for their contributions to developing this protocol, David Gresham for shared equipment, and Marissa Knoll for help with video production. This work was supported by National Institutes of Health grant R35GM118170.

1. Preparation of Randomized Plates (Prior to Experiment Day)
<ol>
	<li>Plan the strains and conditions to be tested with the growth assay. At this point, randomly assign strains and conditions to any well. 
	NOTE: When considering plate setup, it is advisable to include more than one replicate per strain and growth condition on a single plate to account for well-related noise in measurements. See Discussion for more details.</li>
	<li>Computationally randomize the location of each strain and environmental condition for plate replicates that will be run on different days.</li>
	<li>Grow all cells that will be used in the experiment to saturation in yeast extract-peptone-dextrose (YEPD; 2% glucose) medium in a shaker at 30 &#176;C (or any other appropriate temperature).</li>
	<li>Create the randomized stock plates either manually or with a liquid-handling robot. Add 10 &#181;L of the designated saturated cells to each well of a sterile U-bottom tissue culture plate. If multiple strains will be tested in a single well, do not combine them at this point; this combining will be done just prior to cell dilutions on the day of the experiment to ensure all strains are at the correct concentrations when plated as microcolony founder cells.</li>
	<li>Add 10 &#181;L of 30% glycerol to each well of each plate. Pipet up and down so that the cells and the glycerol become well mixed.</li>
	<li>Seal each plate with a foil cover and freeze down immediately at -70 &#176;C until ready to use. 
	&#8203;NOTE: It is important to create all randomized plates on the same day, and freeze them, so that the pre-growth conditions of the cells in each plate will have been identical and will not generate technical variation in the growth-rate assay.</li>
</ol>
2. Pre-Growth of Yeast
NOTE: Typically, this starts prior to experiment day and is highly dependent on the experimental question. See Discussion for details.
<ol>
	<li>Remove a stock plate (10 &#181;L yeast, 10 &#181;L glycerol per well) from the -70 &#176;C freezer and add 180 &#181;L of the media to be used for the experiment. If the experiment will be conducted using nutrient-limiting media, do not pre-grow yeast to saturation in the nutrient-limiting media as sporulation of yeast can occur. Instead, pre-grow in non-limiting media.</li>
	<li>Grow yeast while shaking at 30 &#176;C. Consider whether to run the assay starting with cells in log phase or in stationary phase to determine if diluting the cells multiple times prior to the experiment will be necessary. If the yeast strains or conditions in the assay are expected to have significantly different growth rates, then a two-day pre-growth period will be necessary in order for all the different conditions to reach stationary phase.</li>
</ol>
3. Microscope Setup
<ol>
	<li>Microscope plate preparation
	<ol>
		<li>Ensure that the microscope incubator is on and heating the microscope chamber to the desired growth temperature for the experimental conditions. For standard experiments using Saccharomyces cerevisiae cells, the incubator should heat the microscope chamber to 30 &#176;C to ensure that the growth conditions for the cells will be correct during the growth rate assay.</li>
		<li>Sanitize the workbench, pipettes, and other tools with 70% ethanol. Retrieve a microscope plate and place it on the bench on top of a lint- and static-free wipe. 
		NOTE: Never touch the bottom of the microscope plate, even with gloves on, and always set the microscope plate down on top of a lint- and static-free wipe any time it touches any surface. This prevents smudges or scratches from impeding growth-rate measurements once the experiment is being imaged.</li>
		<li>Thaw 5 mL of 5x concanavalin A solution, dilute to 1x with water, and filter sterilize through a syringe fitted with a 0.2-&#181;m filter.</li>
		<li>Filter sterilize all other liquids that will be used in the assay with a 0.2 &#181;m filter, including experimental media, to remove any crystals or debris that may have materialized in the solutions. The presence of crystals would reduce the quality of the microscopy images.</li>
		<li>Pipet 200 &#181;L of concanavalin A solution into each well of the microscope plate.</li>
		<li>Centrifuge the plate for 2 min at 411 x gravity (g) with a lint- and static-free wipe under the plate, to ensure that the concanavalin A solution evenly covers the bottom of each well and that there are no air bubbles.</li>
		<li>Cover the plate with its lid and let it sit for 1-2 h. The precise time the plate sits is flexible, but it is important to be consistent between different runs of the experiment.</li>
		<li>Remove all of the concanavalin A solution from the plate either by suction or by forcefully discharging it out into the sink or a receptacle. Be careful not to touch the glass part of the plate. It is acceptable if some drops of concanavalin A solution remain in the wells.</li>
		<li>Wash the microscope plate wells by adding 400 &#181;L of sterile water. Remove the water as done with the concanavalin A in the previous step. Do not let the plate sit dry.</li>
		<li>Immediately add 185 &#181;L experimental growth media into the plate. 15 &#181;L of correctly diluted cells will be added to this plate.</li>
	</ol>
	</li>
	<li>Yeast Cell Dilution 
	NOTE: The steps below describe a dilution of yeast from a saturated culture (approximately 108 cells/mL) 400-fold to achieve a concentration of 250,000 cells/mL, 15 &#181;L of which will be diluted into 400 &#181;L in the glass-bottom plate, giving a final number of approximately 4000 cells per well in a 96-well plate. If using a 384-well plate the final number of cells per well should be approximately 700 and the dilutions should be adjusted accordingly. This ratio should be adjusted for cells collected in log phase, growing in richer or poorer pre-growth media, or from different strains. The final density of cells per well should be reduced when running the growth rate assay for time periods longer than 10 h.
	<ol>
		<li>Set up two 96-well culture plates for serial dilutions: label as plate 1 and 2, and add 90 &#181;L experimental growth media (i.e., the media that the yeast will grow in on the microscope) to each serial dilution plate. 
		NOTE: Regardless of what final dilution is used, at least two serial dilutions of cells are recommended, in each of which a small volume of yeast is pipetted into a bigger volume of experimental media and then a large volume of experimental media is mixed in vigorously with a pipet (as in steps 3.2.5 and 3.2.6 below).</li>
		<li>Retrieve the plate of cells from pre-growth and centrifuge the plate for 2 min at 411 x g. 
		NOTE: It is very important not to cross-contaminate different wells in the plate. The purpose of this centrifugation step before removing the foil covering from plates is to ensure that yeast-filled droplets from one well do not fly off the foil and end up in other wells. Be careful never to tilt or agitate the plates to avoid yeast coming into contact with the foil covering after centrifugation.</li>
		<li>Carefully peel back the foil and resuspend cells by vigorously pipetting cells with a pipet set to approximately one-half the total volume in the plate while moving the pipet around the well to mix. Check that all cells have been resuspended from the bottom of the wells.</li>
		<li>If multiple strains within individual wells will be used, strains should be mixed at this time at the ratio necessary for the experiment. If a reference strain will be used to generate growth-rate measurements, the ratio of reference to test strain should be 1:1.</li>
		<li>Pipet 10 &#181;L of yeast from growth media into dilution plate 1. Add 100 &#181;L of experimental growth media to each well to a final volume of 200 &#181;L per well. Pipet up and down vigorously.</li>
		<li>Pipet 10 &#181;L of yeast from plate 1 into plate 2. Add 100 &#181;L of experimental growth media to each well, and pipet up and down vigorously to mix. 
		NOTE: These dilution steps are critical to help separate clusters of yeast that are stuck together at the end of the pre-growth stage and ensure that approximately equal numbers of yeast cells end up in each well. Having consistent numbers of yeast in each well helps remove experimental noise and biases in growth-rate measurements (see Representative Results).</li>
	</ol>
	</li>
	<li>Sonication 
	&#8203;NOTE: Sonication is optional, and only needs to be performed for yeast strains that have a high propensity to adhere to one another (e.g., some wild strains). For lab strains, sonication is generally not necessary and may be skipped by proceeding to step 3.4.
	<ol>
		<li>Sanitize a 96-pin sonicator head with 70% ethanol by placing it in a 96-well plate filled with 70% ethanol and dry with a lint- and static-free wipe.</li>
		<li>Set a sonication program that is sufficiently strong to break apart flocculated yeast cells, but does not kill cells or cause elevated stress responses. Some testing may be required to identify the best sonication program for a given experiment. The sonication program used in this experiment is: amplitude = 10, process time = 10 s, pulse-on = 1 s, pulse-off = 1 s. This exact program is likely not applicable to all sonicators so testing is suggested prior to the experiment day.</li>
		<li>Mix the yeast in serial dilution plate 2 once more by pipetting up and down vigorously five times.</li>
		<li>Place dilution plate 2 on the platform and secure it with the sonicator pins in the cell suspension but not touching the bottom of the plate. Run the sonication program using appropriate ear protection.</li>
		<li>After the program runs, clean the sonicator head with 70% ethanol and then with water, and then immediately proceed to microscope plate preparation so that the cells do not flocculate again.</li>
	</ol>
	</li>
	<li>Prepare Plate for Microscope:
	<ol>
		<li>Pipet 15 &#181;L of yeast from serial dilution plate 2 into the microscope plate to a volume of 200 &#181;L. Add 200 &#181;L of experimental growth media to each well to a final volume of 400 &#181;L per well, and pipet up and down vigorously to mix.</li>
		<li>Cover the plate with a breathable membrane. It is important to seal the plate well with this membrane, for example using a rubber roller.</li>
		<li>To adhere the yeast cells to the concanavalin A on the glass surface, centrifuge the plate with a lint- and static-free wipe beneath it for 2 min at 411 x g.</li>
		<li>At the microscope, wipe the top and bottom of the plate with a lint- and static-free wipe, and blow compressed air onto the plate to get rid of debris.</li>
		<li>Place the plate onto the microscope, making sure it is level and that the A1 well is in the top left corner.</li>
	</ol>
	</li>
</ol>
4. Time-lapse Microscopy Growth-rate Measurements
NOTE: During time-lapse microscopy the following features are computer controlled: x, y, and z position, shutters, and fluorescence filters. A hardware-based auto-focus system is optimal to prevent focal plane drift during time-lapse imaging. Alternatively, a software-based auto-focusing loop can be used. To maintain humidity in the microscope chamber, it is advised to keep a beaker with purified water in the chamber throughout the duration of the experiment.
<ol>
	<li>Create a list of positions (x,y) to image, so that each microscope-plate well is fully imaged. Avoid overlapping images so that no cell is analyzed multiple times.</li>
	<li>Image in brightfield with diascopic illumination (DIA) at a magnification of 15x. Set exposure to ~5 ms.</li>
	<li>Zoom in on the image digitally so that cells are clearly visible. Use the focusing knobs to identify the ideal focus for the experiment in the four wells on the corner of the plate and in one well at the center of the plate. Focus in such a way as to get maximum contrast of the cells.</li>
	<li>Set the z position (or autofocus position) for the experiment to be an average of the z/autofocus positions identified for each of these wells. If the microscope plate is well made and the glass bottom does not have defects, the ideal focus positions should be similar for each well. 
	NOTE: When analyzing images with the Processing Images Easily (PIE) image analysis pipeline11, 12, it is helpful for the cells to be slightly out of focus on the microscope so that there is a dark rim outside of the cell and a light-colored interior, which aids in accurate colony recognition and size estimates.</li>
	<li>If using fluorescent strains, identify the channels and the exposures with which to image, ensuring that no pixels are overexposed. When setting exposure time for fluorescent channels, turn off the &#34;live capture&#34; mode on the microscope to avoid exposing cells to fluorescence excitation for long periods of time, as this can both photobleach the cells and cause stress.</li>
	<li>Set up the time-sequence acquisition to capture images at the desired time interval for the desired length of time.</li>
	<li>Run the experiment.</li>
</ol>

<ol>
	<li>Geiler-Samerotte, K. A., Hashimoto, T., Dion, M. F., Budnik, B. A., Airoldi, E. M., Drummond, D. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantifying+condition-dependent+intracellular+protein+levels+enables+high-precision+fitness+estimates.">Quantifying condition-dependent intracellular protein levels enables high-precision fitness estimates.</a> PloS one. 8 (9), 75320 (2013).</li><li>Kussell, E., Leibler, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Phenotypic+diversity,+population+growth,+and+information+in+fluctuating+environments.">Phenotypic diversity, population growth, and information in fluctuating environments.</a> Science. 309 (5743), 2075-2078 (2005).</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=Stochastic+gene+expression+in+fluctuating+environments.">Stochastic gene expression in fluctuating environments.</a> Genetics. 167 (1), 523-530 (2004).</li><li>King, O. D., Masel, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+evolution+of+bet-hedging+adaptations+to+rare+scenarios.">The evolution of bet-hedging adaptations to rare scenarios.</a> Theoretical population biology. 72 (4), 560-575 (2007).</li><li>Acar, M., Mettetal, J. T., 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=Stochastic+switching+as+a+survival+strategy+in+fluctuating+environments.">Stochastic switching as a survival strategy in fluctuating environments.</a> Nature genetics. 40 (4), 471-475 (2008).</li><li>Avery, S. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microbial+cell+individuality+and+the+underlying+sources+of+heterogeneity.">Microbial cell individuality and the underlying sources of heterogeneity.</a> Nature reviews. Microbiology. 4 (8), 577-587 (2006).</li><li>Levy, S. F., Ziv, N., Siegal, M. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bet+hedging+in+yeast+by+heterogeneous,+age-correlated+expression+of+a+stress+protectant.">Bet hedging in yeast by heterogeneous, age-correlated expression of a stress protectant.</a> PLoS biology. 10 (5), 1001325 (2012).</li><li>van Dijk, 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=Slow-growing+cells+within+isogenic+populations+have+increased+RNA+polymerase+error+rates+and+DNA+damage.">Slow-growing cells within isogenic populations have increased RNA polymerase error rates and DNA damage.</a> Nature communications. 6, 7972 (2015).</li><li>Ziv, N., Shuster, B. M., Siegal, M. L., Gresham, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Resolving+the+Complex+Genetic+Basis+of+Phenotypic+Variation+and+Variability+of+Cellular+Growth.">Resolving the Complex Genetic Basis of Phenotypic Variation and Variability of Cellular Growth.</a> Genetics. 206 (3), 1645-1657 (2017).</li><li>Li, S., Giardina, D. M., Siegal, M. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Control+of+nongenetic+heterogeneity+in+growth+rate+and+stress+tolerance+of+Saccharomyces+cerevisiae+by+cyclic+AMP-regulated+transcription+factors.">Control of nongenetic heterogeneity in growth rate and stress tolerance of Saccharomyces cerevisiae by cyclic AMP-regulated transcription factors.</a> PLoS genetics. 14 (11), 1007744 (2018).</li><li>Plavskin, Y., Li, S., Ziv, N., Levy, S. F., Siegal, M. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Robust+colony+recognition+for+high-throughput+growth+analysis+from+suboptimal+low-magnification+brightfield+micrographs.">Robust colony recognition for high-throughput growth analysis from suboptimal low-magnification brightfield micrographs.</a> bioRxiv. , (2018).</li><li>Ziv, N., Siegal, M. L., Gresham, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genetic+and+nongenetic+determinants+of+cell+growth+variation+assessed+by+high-throughput+microscopy.">Genetic and nongenetic determinants of cell growth variation assessed by high-throughput microscopy.</a> Molecular biology and evolution. 30 (12), 2568-2578 (2013).</li><li>Hall, D. W., Joseph, S. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+high+frequency+of+beneficial+mutations+across+multiple+fitness+components+in+Saccharomyces+cerevisiae.">A high frequency of beneficial mutations across multiple fitness components in Saccharomyces cerevisiae.</a> Genetics. 185 (4), 1397-1409 (2010).</li><li>Saleemuddin, M., Husain, Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Concanavalin+A:+a+useful+ligand+for+glycoenzyme+immobilization--a+review.">Concanavalin A: a useful ligand for glycoenzyme immobilization--a review.</a> Enzyme and microbial technology. 13 (4), 290-295 (1991).</li><li>Geiler-Samerotte, K. A., Bauer, C. R., Li, S., Ziv, N., Gresham, D., Siegal, M. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+details+in+the+distributions:+why+and+how+to+study+phenotypic+variability.">The details in the distributions: why and how to study phenotypic variability.</a> Current opinion in biotechnology. 24 (4), 752-759 (2013).</li><li>Nakagawa, S., Schielzeth, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Repeatability+for+Gaussian+and+non-Gaussian+data:+a+practical+guide+for+biologists.">Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists.</a> Biological reviews of the Cambridge Philosophical Society. 85 (4), 935-956 (2010).</li><li>Bolker, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exemplary+and+surrogate+models:+two+modes+of+representation+in+biology.">Exemplary and surrogate models: two modes of representation in biology.</a> Perspectives in biology and medicine. 52 (4), 485-499 (2009).</li></ol>

The authors have nothing to disclose.

The protocol described here is a versatile assay that allows cell growth and gene expression to be monitored simultaneously at the level of individual microcolonies. Combining these two modalities yields unique biological insights. For example, previous work has used this assay to show a negative correlation between expression of the TSL1 gene and microcolony growth rate in isogenic wildtype cells by measuring both simultaneously7,10. It is also possible to monitor the relationship between growth rate and subcellular localization dynamics of fluorescently tagged proteins with the described assay. For example, a negative relationship between growth rate and the nuclear occupancy of RFP-tagged transcription factor Msn2&#160;was identified by imaging fluorescence in cells every minute for 30 min before initializing the growth assay10. Finally, this protocol allows monitoring of cell responses to environmental stresses and perturbations. Treatments such as heat shock can be administered part-way through the growth assay. Such studies have revealed, for example, that slow-growing cells expressing high levels of Tsl1 are more tolerant of heat shock 7,10.
In addition to the common pitfalls described in the Representative Results section (Fig 4), several key factors must be considered when designing a growth-assay experiment. The phenotypic variation that is measured with the assay is affected by multiple factors, some of which are repeatable and of inherent interest, such as genotype or environment, whereas others are the result of technical variation13. Therefore, the first important consideration when designing a microcolony assay is to carry out the experiment in a way that facilitates the separation of effects of interest from the effects resulting from technical variation; key to this separation are randomizing treatments or strains on experimental plates, and including controls in every experiment. Appropriate statistical methods, such as linear mixed modeling (LMM), must be applied to data after they are collected, to account for different sources of variation in downstream analysis17,18.
In order to employ statistical methods to separate variation in growth phenotypes due to experimental variables of interest from variation due to random factors such as batch effects, it is necessary to run multiple experiments on different days and with randomized well locations of genotype and growth conditions. In addition, to increase the power of the experiment to detect growth differences between strains or treatment conditions, it is also important to include multiple replicates of each tested genotype or growth condition, on a single experimental plate where possible.
One key advantage of the microcolony growth assay lies in the amount of data it can collect: a single experimental plate typically generates data for ~105 individual microcolonies, which is several orders of magnitude greater than typical experiments using microfluidic devices instead of multiwell plates. Software that automatically tracks colonies and calculates relevant data (e.g. lag times, growth rates, and mean fluorescent intensities) is key to taking full advantage of this assay. This software should not only robustly identify colony bounds and track colonies through time, but also correctly measure growth in colonies with a lag phase13. One option developed for this purpose is PIE, open-source software that tracks colonies over time (including accounting for shifts in colony position), calculates growth rates, and allows integration of fluorescent imaging data into experimental measurements11,12.
The microcolony growth assay can be used to gain insight into fundamental questions relating to yeast fitness and evolution, including growth phenotype variability, changes in different environments or in response to stress, and the relationship between growth and protein expression or subcellular localization. The assay has been used extensively in studies of both laboratory and wild strains of S. cerevisiae7,9,10,13.

Growth phenotypes contribute critically to yeast fitness. Natural selection can efficiently distinguish between lineages with growth rates differing by the inverse of the effective population size, which can exceed 108 individuals1. Furthermore, variability of growth rates among individuals within a population is an evolutionarily relevant parameter, as it can serve as the basis for survival strategies such as bet hedging2,3,4,5,6. Therefore, assays that allow for highly accurate measurements of growth phenotypes and their distributions are pivotal for the study of microorganisms. The microcolony growth assay described here can generate individual growth-rate measurements for ~105 microcolonies per experiment. This assay therefore provides a powerful protocol to study yeast evolutionary genetics and genomics. It lends itself particularly well to testing how variability within populations of genetically identical single cells is generated, maintained, and contributes to population fitness7,8,9,10.
The method described here (Figure 1) uses periodically captured, low-magnification brightfield images of cells growing in liquid media on a 96- or 384-well glass-bottom plate to track growth into microcolonies. The cells adhere to the lectin concanavalin A, which coats the bottom of the microscope plate, and form two-dimensional colonies. Because the microcolonies grow in a monolayer, microcolony area is highly correlated with cell number7. Therefore, accurate estimates of microcolony growth rate and lag time can be generated with custom image-analysis software that tracks the rate of change of the area of each microcolony. Furthermore, the experimental setup can monitor the abundances and even the subcellular localizations of fluorescently labeled proteins expressed in these microcolonies. Downstream processing of data from this microcolony growth assay can be achieved by custom analysis or by existing image-analysis software, such as Processing Images Easily (PIE)11, an algorithm for robust colony area recognition and high-throughput growth analysis from low-magnification, brightfield images, which is available via GitHub12.
Because growth-rate estimates derived from the microcolony-growth assay are generated from a large number of single-colony measurements, they are extremely accurate, with standard errors several orders of magnitude smaller than the estimates themselves for a reasonably sized experiment. Therefore, the power of the assay to detect growth-rate differences between different genotypes, treatments, or environmental conditions is high. The multiwell-plate format allows numerous different environment and genotype combinations to be compared in a single experiment. If strains constitutively express different fluorescent markers, they may be mixed in the same well and distinguished by subsequent image analysis, which could increase power further by allowing well-by-well data normalization.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62038/62038fig1v5.jpg" /> 
Figure 1: Schematic representation of the protocol. This protocol follows two main steps, which are the preparation of the experimental plate and the preparation of the cells to image. Randomization of plates and growth of cells should be conducted before and leading up to the experiment day. Repeated mixing of cells at each step during dilution is imperative in the steps until plating, and therefore preparing the experimental plate first is recommended so that it is ready for plating immediately upon the completion of cell dilution. <a href="https://www.jove.com/files/ftp_upload/62038/62038fig1v5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table><tbody><tr><td>&lt;strong&gt;General Materials&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>500 mL Bottletop Filter .22 &amp;micro;m PES Sterilizing, Low Protein Binding, w/45mm Neck</td><td>Fisher</td><td>CLS431154</td><td>used to filter the media</td></tr><tr><td>BD Falcon*Tissue Culture Plates, microtest u-bottom</td><td>Fisher</td><td>08-772-54</td><td>96-well culture tubes used to freeze cells, pre-grow cells, and dilutions</td></tr><tr><td>BD Syringes without Needle, 50 mL</td><td>Fisher</td><td>13-689-8</td><td>Used to filter the Concanavalin A</td></tr><tr><td>Costar Sterile Disposable Reagent Reservoirs</td><td>Fisher</td><td>07-200-127</td><td>reagent reservoirs used to pipette solutions with multichannel pipette</td></tr><tr><td>Costar Thermowell Aluminum Sealing Tape</td><td>Fisher</td><td>07-200-684</td><td>96-well plate seal for pre-growth and freezing</td></tr><tr><td>lint and static free Kimwipes</td><td>Fisher</td><td>06-666A</td><td>lint and static free wipes to keep microscope plate bottom free of debris and scratches</td></tr><tr><td>Nalgene Syringe Filters</td><td>ThermoFisher Scientific</td><td>199-2020</td><td>0.2 &amp;mu;m pore size, 25 mm diameter; used to filter concanavalin A solution</td></tr><tr><td>&lt;strong&gt;Media Components&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Minimal chemically defined media (MD; 2% glucose)</td><td></td><td></td><td>alternative microscopy media used for yeast pre-growth and growth during microscopy</td></tr><tr><td>Synthetic Complete Media (SC; 2% glucose)</td><td></td><td></td><td>microscopy media used for yeast pre-growth and growth during microscopy</td></tr><tr><td>Yeast extract-peptone-dextrose (YEPD; 2% glucose) medium</td><td></td><td></td><td>cell growth prior to freezing down randomized plates</td></tr><tr><td>&lt;strong&gt;Microscopy Materials&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Breathe-Easy sealing membrane</td><td>Millipore Sigma</td><td>Z380059-1PAK</td><td>breathable membranes used to seal plate during microscopy experiment. At this stage breathable membranes are recommended because they prevent condensation in the wells and allow for better microscopy images</td></tr><tr><td>Brooks 96-well flat clear glass bottom microscope plate</td><td>Dot Scientific</td><td>MGB096-1-2-LG-L</td><td>microscope plate</td></tr><tr><td>Concanavalin A from canavalia ensiformis (Jack Bean), lyophilized powder</td><td>Millipore Sigma</td><td>45-C2010-1G</td><td>Make 5x concanavalin A solution and freeze 5ml of 5x concanavalin A in 50 mL conical tubes at -80 &amp;deg;C</td></tr><tr><td>&lt;strong&gt;Strains Used&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>MAH.5, MAH.96, MAH.52, MAH.66, MAH.11, MAH.58, MAH.135, MAH.15, MAH.44, MAH.132</td><td></td><td></td><td>Haploid mutation accumulation strains in a laboratory background, described in Hall and Joseph 2010</td></tr><tr><td>EP026.2A-2C</td><td></td><td></td><td>Progeny of the ancestral Hall and Joseph 2010 mutation accumulation strain, transformed with YFR054c&amp;Delta;::Scw11P::GFP</td></tr><tr><td>&lt;strong&gt;Equipment&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Misonix Sonicator S-4000 with 96-pin attachment</td><td></td><td></td><td>Sonicator https://www.labx.com/item/misonix-inc-s-4000-sonicator/4771281</td></tr><tr><td>Nikon Eclipse Ti-E with Perfect Focus System</td><td></td><td></td><td>Inverted microscope with automated stage and autofocus system</td></tr></tbody></table>

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.

The novelty of this protocol is that growth rate can be calculated for individual cells within a population by tracking their growth into microcolonies through time-lapse imaging (Figure 2A). Because microcolonies grow for many hours in a planar manner due to the presence of concanavalin A, their areas can be tracked throughout the experiment, and a linear fit to the change in the natural log of the area over time can be used to calculate growth rate for each individual colony observed 7,9,10,13. Differences in microcolony growth rate for individual isogenic cells in the same environment are clearly recorded (Figures 2A, 2B). Image-analysis software should be used to automatically track changes in microcolony size and fluorescence (Figure 2C); the colony tracking shown in Figure 2A was done using PIE11,12.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/62038/62038fig02.jpg" /> 
Figure 2: Quantification of growth rate and fluorescence in yeast microcolonies.&#160;(A) A portion of a single imaging field showing microcolonies growing at the start of the experiment, after 3 h, and after 6 h, showing colony tracking using the PIE colony tracking software. Colonies are visibly growing in a monolayer for the duration of the experiment. (B) Change in ln(area) over time for the tracked colonies in panel A. If sufficient timepoint data exist for a colony, its growth rate and pre-growth lag time can be calculated. (C) Image showing GFP fluorescence intensity for the colonies in panel A, taken after the growth portion of the experiment is complete, with the colony outlines from the 6-h timepoint overlaid. Here, Scw11P::GFP marks a reference strain included in every experimental well. Calculating the GFP fluorescence level of each colony allows determination of which colonies originate from the reference strain, and which from the non-reference strains in each well. Each scale bar is 50 &#181;m. <a href="https://www.jove.com/files/ftp_upload/62038/62038fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Typically, ~105 microcolony growth rates per experiment can be collected using this assay. These data can be used to observe both differences between strains/growth conditions, and variation across genetically identical microcolonies grown in a shared condition. For example, Figure 3A shows the distributions of growth rates for ~12,000 colonies from a mutation-accumulation line (MAH.44)14 and GFP-marked reference strain grown in the same wells, with both differences between the strains and high within-strain variability visible. In Figure 3B, individual growth rates and summary statistics for 10 mutation-accumulation strains, paired with their in-well GFP-marked controls, are shown; the collected data allow precise calculation of small average growth-rate differences.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/62038/62038fig03.jpg" /> 
Figure 3. Large sample sizes of individual microcolony growth rates allow for precise quantification of within-strain and between-strain growth variation.&#160;(A) The distributions of growth rates of ~12,000 colonies from two strains. Note differences between the modes of the distributions, as well as the long tail of slow-growing colonies present in each one; the latter is only detectable due to individual microcolony measurements. (B) Distributions of individual microcolony growth rates (black dots) and summary statistics (boxplots showing median, as well as lower and upper 25% quantiles) for ~120,000 colonies from 11 strains. As in (A), each strain was grown in an individual well with a spiked-in GFP-marked reference strain; data shown here represent 14 experimental wells per MAH-reference strain pair. <a href="https://www.jove.com/files/ftp_upload/62038/62038fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The microcolony growth assay can also be used to simultaneously measure growth and gene expression by imaging both brightfield and fluorescence channels. In the experiment shown in Figure 2, fluorescent imaging of GFP expression after the completion of the growth phase of the experiment allowed identification of colonies belonging to a GFP-marked in-well reference strain with weak GFP expression (Figure 2C); however, measurement of intercolony differences in gene expression levels across timepoints is also possible (see Discussion).
A number of common pitfalls can prevent collection of accurate growth-rate data or the correct analysis of these data. One key point is that growth-rate measurements rely on the formation of immobile, two-dimensional microcolonies from single founder yeast cells. Concanavalin A interacts noncovalently with polysaccharides on the surfaces of yeast cells to immobilize microcolonies. The bond between concanavalin A and yeast cells can be reversed by competition with sugars or by low pH15. Therefore, strongly acidic media or media containing lectin-binding components such as yeast extract (YEPD) or phthalate (Edinburgh Minimal Media), cannot be used for this assay without modifications to the immobilization technique (Figure 4A).
If the growth assay is being conducted with yeast strains that flocculate, the optional sonication step should be used to break apart aggregated cells so single cells are immobilized on the microscope plate at the beginning of the experiment (see Figure 4B for an example of flocculating cells post-plating if the sonication step is omitted). Any microcolonies that are founded by a cluster of multiple cells should be excluded in downstream analysis, as growth-rate measurements are no longer derived from a single founder cell, and cells may not be growing in two dimensions. Microcolonies that are founded by a budding cell are admissible. The formation of two-dimensional microcolonies is impeded by yeast that flocculate very strongly, even if colonies are founded by a single cell, and therefore the ability of a yeast strain to grow in a single layer on concanavalin A should be tested before conducting a growth assay.
Another important set of considerations comes during both the planning and analysis stages of the experiment, when determining how many timepoints to include in analysis. First, it is important to include data for enough timepoints across a sufficient period of time to accurately track growth: it is recommended that assays are run in such a way that yeast have time to go through ~5 doublings, with at least 10 timepoints collected over that period. However, simply including every collected timepoint in the growth-rate calculation will result in a bias that artificially lowers growth rate for many colonies. This bias can occur when cells go through a lag phase before beginning growth (Figure 4C). Pre-growth lag of microcolonies is common and varies between experimental conditions and strains9,13. Experimental analysis methods must be able to differentiate timepoints in the &#34;active growth&#34; period from pre-growth lag; one approach is to use a pre-set number of timepoints in a window and find the window that corresponds to the highest growth rate (Figure 4C, solid line)11,13.
Finally, one of the most critical steps of the microcolony growth assay is the yeast-cell dilution step. The concentration of cells and the ratio of different genotypes within microscope-plate wells must be carefully controlled. For statistical analysis it is important that experiments be balanced such that approximately equal numbers of cells for each genotype and condition are tested and compared16. In addition, useful growth rates typically cannot be measured after neighboring colonies merge because the growth rates of the individual colonies can no longer be discerned; therefore, more densely plated cells will yield growth data from fewer timepoints (Figure 4D). Importantly, if cell density is high or uneven at the start of an experiment, filtering out merged microcolonies will disproportionately exclude faster growing microcolonies from downstream analyses because fast growers will merge more frequently than slow growers. Therefore, final growth measurements will be biased towards slower-growing sub-populations. Additionally, different numbers of colonies may be filtered out for different treatments, precluding a balanced experiment. It is recommended to plate around 4000 cells per well in a 96-well plate (or 700 cells per well in a 384-well plate). Thorough pipet mixing throughout the yeast-dilution portion of the protocol is imperative to ensure that the correct number of cells is present in each well, and that cells are evenly dispersed throughout the well. It is also advisable to remove any microcolonies from analysis whose centers are within ~25&#160;cell diameters of each other.
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/62038/62038fig04.jpg" /> 
Figure 4. Experimental pitfalls.&#160;(A) Colonies growing in YEPD media, which prevents efficient binding of cells to concanavalin A. The appearance of new cells far from any colony (arrowheads), as well as the appearance of many out-of-focus cells at the edge of the colony, are the result of cells failing to adhere to the glass surface and drifting away from the colony during growth. (B) Cells from a flocculating strain right after plating without sonication; notice the presence of large numbers of clusters of multiple cells (arrowheads), and cells within the clusters in different focal planes. (C) Change in ln(area) over time for a colony with a long lag phase. Note that if all timepoints are used for growth-rate estimation, the estimated growth rate is significantly depressed; an accurate measurement is produced only when the subset of n timepoints (here n=6) that results in the highest growth rate estimate is used. (D) Cells that were plated too densely shown at the start of the experiment and after 7 h of growth. Colors track individual colonies until they merge with neighbors; here, by the 7-h mark, the majority of colonies have merged, with only a small number of individual slow-growing colonies remaining. (Tracking for a small subset of the colonies shown here is lost for reasons unrelated to colony merging.) Each scale bar is 50 &#181;m. <a href="https://www.jove.com/files/ftp_upload/62038/62038fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about High-Throughput Live Imaging of Microcolonies to Measure Heterogeneity in Growth and Gene Expression at JoVE.com

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

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 ...

high-throughput-live-imaging-microcolonies-to-measure-heterogeneity

Nanyang Technological University

Research

JoVE Journal

Biology

4.7K Views.  New York University. 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. 

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

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations

Microbes provide an intriguing system to study social interaction among individuals within a population. The short generation times and relatively simple genetic modification procedures of microbes facilitate the development of the sociomicrobiology field. To assess the fitness of certain microbial species, selected strains or their genetically modified derivatives within one population, can be fluorescently labelled and tracked using microscopy adapted with appropriate fluorescence filters. Expanding colonies of diverse microbial species on agar media can be used to monitor the spatial distribution of cells producing distinctive fluorescent proteins.
Here, we present a detailed protocol for the use of green- and red-fluorescent protein producing bacterial strains to follow spatial arrangement during surface colonization, including flagellum-driven community movement (swarming), exopolysaccharide- and hydrophobin-dependent growth mediated spreading (sliding), and complex colony biofilm formation. Non-domesticated isolates of the Gram-positive bacterium, Bacillus subtilis can be utilized to scrutinize certain surface spreading traits and their effect on two-dimensional distribution on the agar-solidified medium. By altering the number of cells used to initiate colony biofilms, the assortment levels can be varied on a continuous scale. Time-lapse fluorescent microscopy can be used to witness the interaction between different phenotypes and genotypes at a certain assortment level and to determine the relative success of either.

The role of assortment (spatial segregation) in evolutionary scenarios can be examined using simple microbial systems in the laboratory that allow controlled adjustment of spatial distribution. By modifying the founder cell density, various assortment levels can be visualized using fluorescently labelled bacterial strains in the colony biofilms of Bacillus subtilis.

Microbes provide an intriguing system to study social interaction among individuals within a population. The short generation times and relatively simple ...

Genetics

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

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 ...

Time-lapse Imaging of Mitosis After siRNA Transfection

Changes in cellular organization and chromosome dynamics that occur during mitosis are tightly coordinated to ensure accurate inheritance of genomic and cellular content. Hallmark events of mitosis, such as chromosome movement, can be readily tracked on an individual cell basis using time-lapse fluorescence microscopy of mammalian cell lines expressing specific GFP-tagged proteins. In combination with RNAi-based depletion, this can be a powerful method for pinpointing the stage(s) of mitosis where defects occur after levels of a particular protein have been lowered. In this protocol, we present a basic method for assessing the effect of depleting a potential mitotic regulatory protein on the timing of mitosis. Cells are transfected with siRNA, placed in a stage-top incubation chamber, and imaged using an automated fluorescence microscope. We describe how to use software to set up a time-lapse experiment, how to process the image sequences to make either still-image montages or movies, and how to quantify and analyze the timing of mitotic stages using a cell-line expressing mCherry-tagged histone H2B. Finally, we discuss important considerations for designing a time-lapse experiment. This strategy is complementary to other approaches and offers the advantages of 1) sensitivity to changes in kinetics that might not be observed when looking at cells as a population and 2) analysis of mitosis without the need to synchronize the cell cycle using drug treatments. The visual information from such imaging experiments not only allows the sub-stages of mitosis to be assessed, but can also provide unexpected insight that would not be apparent from cell cycle analysis by FACS.

Here we describe a basic protocol to image and quantify the mitotic timing of live mammalian tissue culture cells after siRNA transfection.

Changes in cellular organization and chromosome dynamics that occur during mitosis are tightly coordinated to ensure accurate inheritance of genomic and ...

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations

Understanding the mechanisms that control critical biological events of neural cell populations, such as proliferation, differentiation, or cell fate decisions, will be crucial to design therapeutic strategies for many diseases affecting the nervous system. Current methods to track cell populations rely on their final outcomes in still images and they generally fail to provide sufficient temporal resolution to identify behavioral features in single cells. Moreover, variations in cell death, behavioral heterogeneity within a cell population, dilution, spreading, or the low efficiency of the markers used to analyze cells are all important handicaps that will lead to incomplete or incorrect read-outs of the results. Conversely, performing live imaging and single cell tracking under appropriate conditions represents a powerful tool to monitor each of these events. Here, a time-lapse video-microscopy protocol, followed by post-processing, is described to track neural populations with single cell resolution, employing specific software. The methods described enable researchers to address essential questions regarding the cell biology and lineage progression of distinct neural populations.

A robust protocol to monitor neural populations by time-lapse video-microscopy followed by software-based post-processing is described. This method represents a powerful tool to identify biological events in a selected population during live imaging experiments.

Understanding the mechanisms that control critical biological events of neural cell populations, such as proliferation, differentiation, or cell fate ...

Neuroscience

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).

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.

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

Bioengineering

Monitoring Plasmid Replication in Live Mammalian Cells over Multiple Generations by Fluorescence Microscopy

Few naturally-occurring plasmids are maintained in mammalian cells. Among these are genomes of gamma-herpesviruses, including Epstein-Barr virus (EBV) and Kaposi's Sarcoma-associated herpesvirus (KSHV), which cause multiple human malignancies 1-3. These two genomes are replicated in a licensed manner, each using a single viral protein and cellular replication machinery, and are passed to daughter cells during cell division despite their lacking traditional centromeres 4-8.
Much work has been done to characterize the replications of these plasmid genomes using methods such as Southern blotting and fluorescence in situ hybridization (FISH). These methods are limited, though. Quantitative PCR and Southern blots provide information about the average number of plasmids per cell in a population of cells. FISH is a single-cell assay that reveals both the average number and the distribution of plasmids per cell in the population of cells but is static, allowing no information about the parent or progeny of the examined cell.
Here, we describe a method for visualizing plasmids in live cells. This method is based on the binding of a fluorescently tagged lactose repressor protein to multiple sites in the plasmid of interest 9. The DNA of interest is engineered to include approximately 250 tandem repeats of the lactose operator (LacO) sequence. LacO is specifically bound by the lactose repressor protein (LacI), which can be fused to a fluorescent protein. The fusion protein can either be expressed from the engineered plasmid or introduced by a retroviral vector. In this way, the DNA molecules are fluorescently tagged and therefore become visible via fluorescence microscopy. The fusion protein is blocked from binding the plasmid DNA by culturing cells in the presence of IPTG until the plasmids are ready to be viewed.
This system allows the plasmids to be monitored in living cells through several generations, revealing properties of their synthesis and partitioning to daughter cells. Ideal cells are adherent, easily transfected, and have large nuclei. This technique has been used to determine that 84% of EBV-derived plasmids are synthesized each generation and 88% of the newly synthesized plasmids partition faithfully to daughter cells in HeLa cells. Pairs of these EBV plasmids were seen to be tethered to or associated with sister chromatids after their synthesis in S-phase until they were seen to separate as the sister chromatids separated in Anaphase10. The method is currently being used to study replication of KSHV genomes in HeLa cells and SLK cells. HeLa cells are immortalized human epithelial cells, and SLK cells are immortalized human endothelial cells. Though SLK cells were originally derived from a KSHV lesion, neither the HeLa nor SLK cell line naturally harbors KSHV genomes11. In addition to studying viral replication, this visualization technique can be used to investigate the effects of the addition, removal, or mutation of various DNA sequence elements on synthesis, localization, and partitioning of other recombinant plasmid DNAs.

A method of observing individual DNA molecules in live cells is described. The technique is based on the binding of a fluorescently tagged lac repressor protein to binding sites engineered into the DNA of interest. This method can be adapted to follow many recombinant DNAs in live cells over time.

Few naturally-occurring plasmids are maintained in mammalian cells.  Among these are genomes of gamma-herpesviruses, including Epstein-Barr virus  (EBV) ...

Single-molecule Imaging of Gene Regulation In vivo Using Cotranslational Activation by Cleavage (CoTrAC)

We describe a fluorescence microscopy method, Co-Translational Activation by Cleavage (CoTrAC) to image the production of protein molecules in live cells with single-molecule precision without perturbing the protein's functionality. This method makes it possible to count the numbers of protein molecules produced in one cell during sequential, five-minute time windows. It requires a fluorescence microscope with laser excitation power density of ~0.5 to 1 kW/cm2, which is sufficiently sensitive to detect single fluorescent protein molecules in live cells. The fluorescent reporter used in this method consists of three parts: a membrane targeting sequence, a fast-maturing, yellow fluorescent protein and a protease recognition sequence. The reporter is translationally fused to the N-terminus of a protein of interest. Cells are grown on a temperature-controlled microscope stage. Every five minutes, fluorescent molecules within cells are imaged (and later counted by analyzing fluorescence images) and subsequently photobleached so that only newly translated proteins are counted in the next measurement.
Fluorescence images resulting from this method can be analyzed by detecting fluorescent spots in each image, assigning them to individual cells and then assigning cells to cell lineages. The number of proteins produced within a time window in a given cell is calculated by dividing the integrated fluorescence intensity of spots by the average intensity of single fluorescent molecules. We used this method to measure expression levels in the range of 0-45 molecules in single 5 min time windows. This method enabled us to measure noise in the expression of the &lambda; repressor CI, and has many other potential applications in systems biology.

Single-molecule Imaging of Gene Regulation In vivo Using Cotranslational Activation by Cleavage CoTrAC

We describe a fluorescence microscopy method, Co-Translational Activation by Cleavage (CoTrAC), to image the production of protein molecules in live cells with single-molecule precision without perturbing the protein's functionality. This method has been used to follow the stochastic expression dynamics of a transcription factor, the &lambda; repressor CI 1.

We describe a fluorescence microscopy method, Co-Translational Activation by Cleavage (CoTrAC) to image the production of protein molecules in live cells ...

Real-time Imaging of Single Engineered RNA Transcripts in Living Cells Using Ratiometric Bimolecular Beacons

The growing realization that both the temporal and spatial regulation of gene expression can have important consequences on cell function has led to the development of diverse techniques to visualize individual RNA transcripts in single living cells. One promising technique that has recently been described utilizes an oligonucleotide-based optical probe, ratiometric bimolecular beacon (RBMB), to detect RNA transcripts that were engineered to contain at least four tandem repeats of the RBMB target sequence in the 3&#8217;-untranslated region. RBMBs are specifically designed to emit a bright fluorescent signal upon hybridization to complementary RNA, but otherwise remain quenched. The use of a synthetic probe in this approach allows photostable, red-shifted, and highly emissive organic dyes to be used for imaging. Binding of multiple RBMBs to the engineered RNA transcripts results in discrete fluorescence spots when viewed under a wide-field fluorescent microscope. Consequently, the movement of individual RNA transcripts can be readily visualized in real-time by taking a time series of fluorescent images. Here we describe the preparation and purification of RBMBs, delivery into cells by microporation and live-cell imaging of single RNA transcripts.

Ratiometric bimolecular beacons (RBMBs) can be used to image single engineered RNA transcripts in living cells. Here, we describe the preparation and purification of RBMBs, delivery of RBMBs into cells by microporation and fluorescent imaging of single RNA transcripts in real-time.

The growing realization that both the temporal and spatial regulation of gene expression can have important consequences on cell function has led to the ...

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein&#8217;s function, regulation and localization often depend on its interactions with other proteins. Here, we describe a protocol for the yeast protein-fragment complementation assay (PCA), a powerful method to detect direct and proximal associations between proteins in living cells. The interaction between two proteins, each fused to a dihydrofolate reductase (DHFR) protein fragment, translates into growth of yeast strains in presence of the drug methotrexate (MTX). Differential fitness, resulting from different amounts of reconstituted DHFR enzyme, can be quantified on high-density colony arrays, allowing to differentiate interacting from non-interacting bait-prey pairs. The high-throughput protocol presented here is performed using a robotic platform that parallelizes mating of bait and prey strains carrying complementary DHFR-fragment fusion proteins and the survival assay on MTX. This protocol allows to systematically test for thousands of protein-protein interactions (PPIs) involving bait proteins of interest and offers several advantages over other PPI detection assays, including the study of proteins expressed from their endogenous promoters without the need for modifying protein localization and for the assembly of complex reporter constructs.

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells

Proteins interact with each other and these interactions determine in a large part their functions. Protein interaction partners can be identified at high-throughput in vivo using a yeast fitness assay based on the dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA).

Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein&#8217;s function, regulation and localization often ...

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

Summary

Explore More Videos

Chapters in this video

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations

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

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

Time-lapse Imaging of Mitosis After siRNA Transfection

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations

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

Monitoring Plasmid Replication in Live Mammalian Cells over Multiple Generations by Fluorescence Microscopy

Single-molecule Imaging of Gene Regulation In vivo Using Cotranslational Activation by Cleavage (CoTrAC)

Real-time Imaging of Single Engineered RNA Transcripts in Living Cells Using Ratiometric Bimolecular Beacons

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells