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 conditi...

<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. 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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...

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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>General Materials</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>500 mL Bottletop Filter .22 &#181;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 &#956;m pore size, 25 mm diameter; used to filter concanavalin A solution</td>
		</tr>
		<tr>
			<td>Media Components</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>Microscopy Materials</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 reccomended 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 &#176;C</td>
		</tr>
		<tr>
			<td>Strains Used</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&#916;::Scw11P::GFP</td>
		</tr>
		<tr>
			<td>Equipment</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 <sup ...

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 ...