We gratefully acknowledge all members of our laboratory and the Centre for Integrated System Biology of Ageing and Nutrition (CISBAN) for support and helpful discussions. This study was supported by Biotechnology and Biological Sciences Research Council (BBSRC) (BB/C008200/1) and the Wellcome Trust (075294, 093088).

Manual QFA Protocol (for S. cerevisiae strains)
1. Culturing Yeast Strains
<ol>
 <li>Up to 96 independent yeast strains are cultured in 200 &mu;l of rich liquid media in a 96-well culture dish. The strains are grown to saturation in a temperature controlled incubator.</li>
 <li>A 96 pin, 1/8" diameter manual replica plater (SIGMA R-2508) is sterilized by first dipping the replica plater in 100% ethanol, flaming and then leaving to cool.</li>
 <li>200 &mu;l of sterile water is arrayed in a 96-well culture plate. The saturated cultures are vortexed (Eppendorf MixMate, 30 sec, 750 rpm) and diluted by dipping the sterilized pin tool into the saturated strains three times then into the plate containing the water once.</li>
 <li>The pin tool is re-sterilized as per 1.2.</li>
 <li>The sterilized pin tool is used to spot the diluted culture onto sold agar plates by dipping into the plate containing the diluted culture three times then transferring the pin tool to a rectangular solid agar plate.</li>
 <li>The rectangular solid agar plates are incubated at an appropriate temperature before being imaged manually using an S&amp;P Robotics spImager. Alternative image capture methods may be also be used such as a FujiFilm LAS4000, or a less expensive digital SLR camera if care is taken to ensure even illumination and consistent plate positioning relative to the imaging camera.</li>
 <li>The method is adaptable to smaller numbers of parallel cultures, such as 48, which can be fitted onto round Petri dishes if care is taken to ensure the same angle relative to the imaging camera during each photograph.</li>
</ol>
Fully Automated QFA Protocol (for S. cerevisiae strains)
2. Culturing Yeast Strains
<ol>
 <li>Start with a rectangular array of independent yeast strains growing on solid agar. In this example the starting strains are the result of crossing a temperature sensitive query mutation with the Yeast Deletion Collection (YDC) by Synthetic Genetic Array (SGA). Final SGA plates are in 1536 format. This is 16 rows by 24 columns of colonies/cultures representing four replicates of 384 independent yeast strains. See Figure 1 for plate layouts.</li>
 <li>384 strains out of 1536 are transferred robotically using an S&amp;P Robotics Inc BM3-SC robot with a 96 pin, 1-mm pin diameter sterile pintool into four 96-well culture plates containing 200 &mu;l of selective media (Figure 1). Pintool cleanliness and sterility is achieved by sequentially washing in sterile water twice (once with a revolving brush to remove debris), 70% ethanol (with sonication) and finally 100% ethanol.</li>
 <li>The cultures are grown to saturation (for three days at 20 &deg;C in this example, see Figure 1).</li>
</ol>
3. Spotting Yeast Cultures
<ol>
 <li>Using a Beckman Biomek FX, saturated cultures are resuspended by vortexing on a Variomag Teleshake (anticlockwise at 1000 rpm for 20 sec) and diluted approximately 1:70 by pinning into 200 &mu;l sterile water using a sterile 96 pin robotic pin tool (V&amp;P Scientific magnetic mounting pintool, 2 mm pin diameter). Pin tool cleanliness and sterility is achieved by washing in sterile water, washing in 70% ethanol (with a brush to remove debris) and finally dipping in 70% ethanol followed by drying.</li>
 <li>Diluted cultures are spotted onto solid agar plates in 384 format using the same robotic pin tool (see Figure 1) after cleaning and sterilisation.</li>
 <li>After pinning, plates are transferred to a Cytomat temperature controlled, humidified incubator with automatic carousel and access hatch.</li>
</ol>
4. Image Capture
<ol>
 <li>An S&amp;P Robotics automated imager repeatedly removes the plates from the Cytomat incubator, removes the plate lid, places them precisely under an enclosed, uniformly illuminated space beneath a Canon EOS Rebel Ti 35mm DSLR, captures an image at 5184 x 3456 px resolution, replaces the plate lid, and returns them to the incubator carousel. One image is captured immediately following initial transfer to the incubator to allow for a zero time point growth measurement (background). Robot handling and photography takes 2 min per plate.</li>
 <li>With a fully loaded carousel, the maximum achievable plate image capture frequency is one photograph every 6 hr. Plates are incubated for up to six days (until colony growth saturation. Figure 1 shows three typical images from a time-course at 20 &deg;C.</li>
 <li>For tracking purposes, plates are named according to incubator name, temperature, unique sequential batch number and position in the incubator. Image names are a concatenation of plate name and timestamp and are automatically constructed upon image capture (e.g. K000011_030_010_2011-09-22_09-47-54.jpg, File Type FT1, Table1).</li>
</ol>
5. Capture of Experimental Metadata
The metadata files described in this section are tab-delimited text files which are manually generated (e.g. using spreadsheet software). The Experimental Description file (FT2, Table 1) is unique to each experiment, but the Library Description (FT3, Table 1) can be re-used extensively once constructed.
<ol>
 <li>The experiment is described in the Experimental Description file (FT2, Table1) containing columns for barcode (or automatically generated plate name), experiment starting timestamp, plate treatment, contents of solid agar medium, screen name, library, plate number (for libraries with multiple plates) and a repeat-quadrant number (see Figure 1) for scaling down from 1536 format to 384 format.</li>
 <li>The yeast strain library is described with a Library Description file (FT3, Table1) stating the genotype grown in each culture location in each plate of a library. It contains columns for: library name, ORF, plate number, plate row, plate column and an optional notes column.</li>
 <li>An optional Standard Gene Name file (FT4, Table 1) describing the standard gene name (e.g. RAD9) associated with each systematic ORF y-number (e.g. YDR217C) identifying strains being screened can be provided. This file contains two columns: ORF &amp; Gene name.</li>
</ol>
6. Data Analysis
The QFA computational workflow requires access to a reasonably powerful multicore computer workstation (e.g. a Dell Precision T3500 with Xeon quad-core 2.67 GHz processor and 12Gb RAM) on which is installed the Colonyzer image analysis software tool 3,4 and the QFA R package7, both of which are documented online, are freely available and run on a range of operating systems.
<ol>
 <li>Run Colonyzer with each of the captured plate images as input, generating one Colonyzer Output file (FT5 Table 1) for each image captured. Colonyzer Output files specify culture density estimates, culture area, shape and color for each of the 384 locations on the imaged plate. Output filename is automatically copied from the source image filename (e.g. the plate photograph K000011_030_010_2011-09-22_09-47-54.jpg (FT1, Table 1) corresponds to the Colonyzer output file K000011_030_010_2011-09-22_09-47-54.dat (FT5, Table 1)).</li>
 <li>Using the QFA R package, load experimental metadata (FT2, Table1) and Colonyzer output files (FT5 Table 1). These data are merged into R data frames (which can be exported as QFA Raw Data text files (FT6, Table 1)) for further analysis.</li>
 <li>The QFA R package contains functions to gather cell density timecourses for each culture, to fit generalized logistic population models to observations and to plot both (see Figures 2 and 3 for examples). Fitted parameter values are written to QFA Logistic Parameter files (FT7 Table 1). See QFA R package documentation for further details.</li>
 <li>The QFA R package also contains functions for quality control: edge colonies are discarded due to the greater availability of nutrients at the plate edges and difficulty with image analysis near plate walls, cultures which failed the SGA and genotypes displaying linkage with screen-specific marker genes are stripped from analysis.</li>
 <li>Several quantitative fitness measures are derived from logistic population model parameters for each culture. These include maximum population doubling rates (MDR) and number of divisions from inoculation to saturation (MDP). For each set of replicate cultures (of unique genotype for example) and for each fitness definition, several summary statistics of fitness estimates are computed: mean and median fitness, fitness standard deviation and number of replicates observed. Summary statistics are output to QFA Fitness Summary files (FT8, Table 1) for further analysis, e.g. calculation of genetic interaction scores (FT9 Table 1).</li>
</ol>

7. Representative Results
Figure 2 shows one typical growth curve out of 384 QFA cultures growing at 20 &deg;C on solid agar as quantified by Colonyzer, with increase in cell density first becoming detectable after roughly 2 days. This delay is likely a combined effect of a culture lag phase after inoculation onto solid medium and a lower limit of detection for cell density. Figure 3 shows 308 similar growth curves captured from a single plate. Figure 4 demonstrates the comparison of two genome-wide QFA screens to infer genetic interaction strengths (adapted from 1). The QFA R package 7 also includes functions to produce Figure 3 and to output ranked lists of examined genotypes and estimates of genetic interaction strength (GIS), together with a q-value (False Discovery Rate (FDR) corrected p-value) for the significance of observed GIS.
<img alt="Figure 1" src="/files/ftp_upload/4018/4018fig1.jpg" fo:content-width="6in" fo:src="/files/ftp_upload/4018/4018fig1highres.jpg" /> 
Figure 1. Scheme for robotic inoculation of yeast strains in 384-spot format. This procedure begins with 1536 independent cultures per plate (left). In this typical example, colonies at positions 1,1; 1,2; 2,1 and 2,2 (colored red) are four replicates of the same genotype. his3::KANMX cultures in yellow, growing on the edge of the plate, have a growth advantage due to lack of competition and are therefore not examined by QFA. One of these replicates (e.g. 1,1) is inoculated into liquid growth media in 96-well plates using a 96-pin tool which inoculates 96 out of 1536 colonies each time. In order to inoculate one replicate for each of 384 gene deletions, four different "quadrants" (indicated as red, blue, green and purple) are inoculated into four different 96-well plates containing growth media. After growth to saturation (e.g. 3 days at 20 &deg;C), cultures are diluted in water, then the four quadrants from one repeat are spotted in 384-format onto a solid agar plate (right) in the same pattern as the original SGA plate (as indicated by color). The process can be repeated to test other replicates: 1,2; 2,1 and 2,2. Example time-lapse images on the right were captured 0.5, 2 and 3.5 days after inoculation. Adapted from Supplementary Figure 1 2. <a href="https://www.jove.com/files/ftp_upload/4018/4018fig1large.jpg" target="_blank">Click here to view larger figure</a>.
 
<img alt="Figure 2" src="/files/ftp_upload/4018/4018fig2.jpg" fo:content-width="6in" fo:src="/files/ftp_upload/4018/4018fig2highres.jpg" /> 
Figure 2. A single robot-captured QFA growth curve. A) QFA growth curve of a yeast strain growing on agar containing galactose at 20 &deg;C. Images were captured robotically approximately every 2 hours. Exponential phase at this temperature was observable for approximately 1.5 days, with culture density increase first becoming detectable approximately 2 days after inoculation. A generalized logistic model was fit to the observed data (grey curve). Model parameters automatically fitted by the QFA R package are presented: K (carrying capacity (AU)), r (growth rate (d-1)), g (inoculum density (AU)), v (growth symmetry). B) As for panel A, plotted with cell density on a log scale. <a href="https://www.jove.com/files/ftp_upload/4018/4018fig2large.jpg" target="_blank">Click here to view larger figure</a>.
<img alt="Figure 3" src="/files/ftp_upload/4018/4018fig3.jpg" /> 
Figure 3. Automatically generated growth curves captured in parallel from a single 384-format plate. Each panel shows cell density estimates (red crosses) and model fit (black curves) for independent cultures labeled by gene name or ORF y-number grown in a QFA procedure. This example plate was imaged by robot and grown in an automated incubator at 20 &deg;C. Experimental metadata are automatically included in the figure title: plate name, plate treatment, agar medium and library plate number. Fitted model parameter values are also automatically printed on each panel (see Figure 2). Edge cultures were stripped from QFA analysis, leaving the 308 cultures in this plot (see QFA Protocol 5.4). Since edge cultures are not analyzed in QFA, these culture locations are typically filled with neutral control strains (pGAL-HIS3 in this case). The original version of this figure, output by the QFA R package, is in .pdf format and is therefore infinitely zoomable and searchable by text queries. <a href="https://www.jove.com/files/ftp_upload/4018/4018fig3.pdf" target="_blank">Click here to view larger figure</a>.

<img alt="Figure 4" src="/files/ftp_upload/4018/4018fig4.jpg" fo:content-width="6in" fo:src="/files/ftp_upload/4018/4018fig4highres.jpg"/> 
Figure 4. Comparing fitnesses from two QFA screens to deduce genetic interaction. This plot shows comparison of the fitness of budding yeast deletions (yfg&Delta;) combined with the neutral ura3&Delta; mutation or the temperature sensitive cdc13-1 mutation and grown at the semi-permissive temperature for cdc13-1 (27 &deg;C). Fitness was calculated as the product of maximum doubling rate and maximum doubling potential 1. Most deletion strains grow well when combined with the neutral ura3&Delta; mutation, as expected and have high fitness, but deletions combined with cdc13-1 have a wide range of fitnesses. The light blue lines cross at the location of the neutral his3&Delta; mutation, the solid line is a linear regression model of genetic independence (expected fitness of cdc13-1 yfg&Delta; mutants given fitness of ura3&Delta; yfg&Delta; mutants) and the dashed line is the line of equal fitness. Deletions colored green significantly enhance the fitness defect of cdc13-1 strains. Deletions colored red significantly suppress the same defect. Vertical distance of any mutation cdc13-1 yfg&Delta; from the solid line indicates the strength of genetic interaction between cdc13-1 and yfg&Delta; under the plate conditions. Note that some of the strongest suppressors of cdc13-1 are previously identified checkpoint genes, RAD17, DDC1 and RAD24 and the nuclease EXO1 near the top right. Genes whose deletion reduces fitness include YKU80 (encoding the DNA repair and telomere capping protein Yku80) near the bottom right. Also note that some groups of genes that function together tend to be co-located on this plot. Three example clusters are highlighted in blue SPE1, SPE2 and SPE3: spermidine synthesis genes, RAD17, DDC1 and RAD24: encoding components of the checkpoint sliding clamp and checkpoint clamp loader, MRE11, RAD50 and XRS2: the MRX complex, involved in DDR and EST1 &amp; EST3: genes involved in telomere length regulation. Adapted from Supplementary Figure 1B 1, raw data from which this plot was generated can be downloaded from here: <a href="http://research.ncl.ac.uk/colonyzer/AddinallQFA/" target="_blank">http://research.ncl.ac.uk/colonyzer/AddinallQFA/</a>. <a href="https://www.jove.com/files/ftp_upload/4018/4018fig4.pdf" target="_blank">Click here to view larger figure</a>.
 
 
<table border="1">
 <tbody>
 <tr>
 <td>File ID</td>
 <td>File Type</td>
 <td>Manually Constructed</td>
 <td>Update Frequency</td>
 <td>Prerequisite</td>
 </tr>
 <tr>
 <td>FT1</td>
 <td>Plate photograph</td>
 <td>No</td>
 <td>Per plate &amp; timepoint</td>
 <td>Yes</td>
 </tr>
 <tr>
 <td>FT2</td>
 <td>Experimental Description</td>
 <td>Yes</td>
 <td>Per experiment</td>
 <td>Yes</td>
 </tr>
 <tr>
 <td>FT3</td>
 <td>Library Description</td>
 <td>Yes</td>
 <td>Per library</td>
 <td>Yes</td>
 </tr>
 <tr>
 <td>FT4</td>
 <td>Standard Gene Names</td>
 <td>Yes</td>
 <td>Per library</td>
 <td>No</td>
 </tr>
 <tr>
 <td>FT5</td>
 <td>Colonyzer Output</td>
 <td>No</td>
 <td>Per timepoint per plate</td>
 <td>Yes</td>
 </tr>
 <tr>
 <td>FT6</td>
 <td>QFA Raw Data</td>
 <td>No</td>
 <td>Per experiment</td>
 <td>-</td>
 </tr>
 <tr>
 <td>FT7</td>
 <td>QFA Logistic Parameters</td>
 <td>No</td>
 <td>Per experiment</td>
 <td>-</td>
 </tr>
 <tr>
 <td>FT8</td>
 <td>QFA Fitness Summary</td>
 <td>No</td>
 <td>Per experiment</td>
 <td>-</td>
 </tr>
 <tr>
 <td>FT9</td>
 <td>QFA Genetic Interaction Hitlist</td>
 <td>No</td>
 <td>Per GIS study (2 expts.)</td>
 <td>-</td>
 </tr>
 </tbody>
</table>
Table 1. Electronic QFA Files. All files listed (excepting FT1) are tab-delimited text files and can be constructed or read using any text editor or spreadsheet software. For further details about constructing QFA metadata files and precise interpretation of QFA output please see the QFA R package documentation 7 and the Colonyzer software documentation 3.
<img src="/files/ftp_upload/4018/4018table2.jpg" alt="Table 2" /> 
Table 2. Methods for assessing culture fitness. A summary of the features and requirements of a number of methods for assessing microbial culture fitness. A review by Blomberg 8 contains more detailed comparison of some of these. <a href="https://www.jove.com/files/ftp_upload/4018/4018table2large.jpg" target="_blank">Click here to view larger figure</a>.

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	<li>Addinall, S. G., Holstein, E., Lawless, C., Yu, M., Chapman, 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=Quantitative+Fitness+Analysis+shows+that+NMD+proteins+and+many+other+protein+complexes+suppress+or+enhance+distinct+telomere+cap+defects.">Quantitative Fitness Analysis shows that NMD proteins and many other protein complexes suppress or enhance distinct telomere cap defects.</a> PLoS Genetics. 7 (4), e1001362 (2011).</li><li>Addinall, S. G., Downey, M., Yu, M., Zubko, M. K., Dewar, 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+genomewide+suppressor+and+enhancer+analysis+of+cdc13-1+reveals+varied+cellular+processes+influencing+telomere+capping+in+Saccharomyces+cerevisiae.">A genomewide suppressor and enhancer analysis of cdc13-1 reveals varied cellular processes influencing telomere capping in Saccharomyces cerevisiae.</a> Genetics. 180, 2251-2266 (2008).</li><li>Lawless, C., Wilkinson, D., Young, A., Addinall, S., Lydall, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Colonyzer:+automated+quantification+of+micro-organism+growth+characteristics+on+solid+agar.">Colonyzer: automated quantification of micro-organism growth characteristics on solid agar.</a> BMC Bioinformatics. 11, 287-28 (2010).</li><li>Tong, A. H., Evangelista, M., Parsons, A. B., Xu, H., Bader, G. 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=Systematic+genetic+analysis+with+ordered+arrays+of+yeast+deletion+mutants.">Systematic genetic analysis with ordered arrays of yeast deletion mutants.</a> Science. 294, 2364-2368 (2001).</li><li>Costanzo, M., Baryshnikova, A., Bellay, J., Kim, Y., Spear, E. 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=The+genetic+landscape+of+a+cell.">The genetic landscape of a cell.</a> Science. 327, 425-431 (2010).</li><li>Blomberg, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+growth+rate+in+high-throughput+growth+phenotyping.">Measuring growth rate in high-throughput growth phenotyping.</a> Curr. Opin. 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No conflicts of interest declared.

QFA is in many senses a direct descendent of three established bench-scale microbiological techniques: culture dilution series spot tests 9, growth curve determination in aerated liquid cultures 9 and replica plating 13. These three methods are summarized and compared with QFA and other high-throughput techniques in Table 2. Whereas dilution series spot tests define a strain's fitness as its ability to form colonies in a series of cultures grown from a range of inoculum dilutions, QFA measures strain fitness by repeated observations of a single culture to construct a growth curve. Quantifying fitness from single cultures allows many more strains to be examined simultaneously, under identical conditions. Replicating arrays of cultures allows repeated testing of strain collections, most usefully when testing for culture fitness differences in different environments or genetic backgrounds. The QFA protocol presented here uses expensive robotic equipment to replicate, inoculate, incubate and image agar plates, appropriate for observing growth of thousands of independent cultures (robotic QFA, Table 2). However, since QFA is based on established, traditional lab techniques, it can also be carried out much more cheaply by replacing robot assistance with manual steps (manual QFA, Table 2). Manual QFA involves replication and inoculation of cultures onto agar plates using a manual pin tool and manual transfer of plates to and from an incubator for photography. The same computational analysis workflow can be applied to generate growth curves from either experimental design.
QFA fitness estimates seem to be comparatively precise. In Figure 4, the mean fitnesses of four example clusters of functionally related gene deletions are highlighted. The close proximity of members of each functionally related gene class in two independent genetic backgrounds (ura3&Delta; and cdc13-1) indicates the reproducibility of QFA fitness estimates. For example, only 3 gene deletions (out of a possible 4,300) separate the three members of the conserved MRX complex.
High-throughput alternatives to QFA for testing growth characteristics of microbial strains include SGA 5,6, competition screens in barcoded libraries 11,12 and screens capturing optical density kinetics in spectrophotometric plate readers 10 which are summarized in Table 2 and described more fully elsewhere 8. QFA and SGA plates, where cultures grow on solid agar surfaces (agar based methods, Table 2) can be handled quickly and easily by robot. Solid agar surface cultures are well aerated throughout growth and cells can grow in fixed cultures, allowing sociable microbes to grow in a community 14. Left intact, microbial communities affect their own micro-environments, secreting toxins such as ethanol, and possibly signaling between cells. However, continuous mixing of liquid cultures, necessary to achieve adequate aeration, artificially disrupts microbial communities and their micro-environments which may affect their modes of growth. Cross-contamination is less of a concern in solid agar methods since there is less opportunity for liquid splashes carrying cells between cultures. If contamination occurs by foreign air-borne microbes in solid agar assays, it can often be detected by visual inspection of solid agar plates and accounted for or removed.
QFA throughput is significantly higher than that of parallel liquid methods in two ways. First of all, on QFA (and SGA) plates, inoculated cultures are packed together more densely, giving more independent cultures per plate. In QFA there are typically 308 cultures, not counting non-experimental edge cultures (Figure 1) compared to parallel liquid cultures with 96 or 100 cultures per plate. Secondly, QFA experiments can be scaled to a much larger number of plates. Whereas the number of plates analyzed in a single QFA experiment is limited only by the space available in an incubator or warm room, the minimum allowable image capture frequency and the maximum achievable capture frequency, the number of plates in a liquid growth experiment is strongly limited by the capacity the plate reader (typically one or two plates), or of the stacker attached to the plate reader (typically 25-50 plates). We have recently carried out a fully automated QFA experiment on 123 plates, each with 308 experimental cultures, giving 37,884 simultaneous cultures. We estimate that the maximum achievable number of independent cultures growing in liquid is 96 (cultures/plate) x 50 (plates/stacker) = 4800, which is around ten fold less than our QFA throughput. An alternative for liquid culture screens is to utilise many automated growth devices in parallel (Table 1 from the review by Blomberg 8), each having a capacity of one or two plates. Temperature control in each device is independent, and so conditions are not identical, but assuming that they are, this workflow would require at least 190 such devices to match QFA throughput (assuming 200 liquid cultures per device).
In summary, QFA is a high-quality workflow that can be usefully applied to gather quantitative growth phenotypes in both small scale focussed experiments and high-throughput screens. It is flexible enough to be applied effectively with varying requirements for robotic equipment. The computational component of the QFA workflow is based on freely available, open source code.

<table border="1">
	<tbody>
		<tr>
			<td>Name of reagent/equipment</td>
			<td>Company</td>
			<td>Catalogue number</td>
			<td>Comments </td>
		</tr>
		<tr>
			<td>Replica Plater</td>
			<td>Sigma</td>
			<td>R-2508</td>
			<td>96 pin manual pintool with 1/8 " diameter pins</td>
		</tr>
		<tr>
			<td>Mix Mate</td>
			<td>Eppendorf</td>
			<td>5353 000.014</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Biomek FX</td>
			<td>Beckman</td>
			<td>A31842</td>
			<td>&nbsp;</td>
		</tr>
		<tr>
			<td>Teleshake</td>
			<td>Thermo Scientific</td>
			<td>50095890</td>
			<td>Installed on Biomek FX</td>
		</tr>
		<tr>
			<td>BM3-SC</td>
			<td>S&amp;P Robotics Inc</td>
			<td>BM3-SC</td>
			<td>192 shelf rotating carousel</td>
		</tr>
		<tr>
			<td>spImager</td>
			<td>S&amp;P Robotics Inc</td>
			<td>spImager</td>
			<td>High resolution manual imaging</td>
		</tr>
		<tr>
			<td>spImager with Cytomat</td>
			<td>S&amp;P Robotics Inc</td>
			<td>Custom</td>
			<td>Temperature controlled automated high resolution imaging</td>
		</tr>
		<tr>
			<td>Cytomat 6001 with heat exchanger</td>
			<td>Thermo Scientific</td>
			<td>51022222</td>
			<td>Attached to spImager</td>
		</tr>
		<tr>
			<td>Ecoline RE207</td>
			<td>Lauda</td>
			<td>RE207</td>
			<td>Attached to Cytomat</td>
		</tr>
		<tr>
			<td>spImager with carousel</td>
			<td>S&amp;P Robotics Inc</td>
			<td>Custom</td>
			<td>Automated high resolution imaging</td>
		</tr>
		<tr>
			<td>Robot pin tool fixture for FP12pins</td>
			<td>V&amp;P Scientific</td>
			<td>AFIX96FP12</td>
			<td>N/A</td>
		</tr>
		<tr>
			<td>96 x FP12 pins</td>
			<td>V&amp;P Scientific</td>
			<td>FP12</td>
			<td>50.4 mm long, 17 mm exposed pin length</td>
		</tr>
		<tr>
			<td>Docking Station for Pin Tool</td>
			<td>V&amp;P Scientific</td>
			<td>VP425</td>
			<td>Docking station base removed to allow fan drying of pins</td>
		</tr>
		<tr>
			<td>Pin Cleaning Brush</td>
			<td>V&amp;P Scientific</td>
			<td>VP425</td>
			<td>N/A</td>
		</tr>
		<tr>
			<td>Replica Plater</td>
			<td>Sigma</td>
			<td>R-2508</td>
			<td>Manual pin tool</td>
		</tr>
		<tr>
			<td>Nunc OmniTray with Lid</td>
			<td>Nunc</td>
			<td>734-0490</td>
			<td>N/A</td>
		</tr>
		<tr>
			<td>96 well sterile polystyrene plates</td>
			<td>Greiner BioOne</td>
			<td>655161</td>
			<td>N/A</td>
		</tr>
		<tr>
			<td>96 well sterile polystyrene lids</td>
			<td>Greiner BioOne</td>
			<td>656171</td>
			<td>N/A</td>
		</tr>
	</tbody>
</table>
Table 3. Specific reagents and equipment.

a quantitative fitness analysis workflow

Quantitative Fitness Analysis (QFA) is an experimental and computational workflow for comparing fitnesses of microbial cultures grown in parallel1,2,3,4. QFA can be applied to focused observations of single cultures but is most useful for genome-wide genetic interaction or drug screens investigating up to thousands of independent cultures. The central experimental method is the inoculation of independent, dilute liquid microbial cultures onto solid agar plates which are incubated and regularly photographed. Photographs from each time-point are analyzed, producing quantitative cell density estimates, which are used to construct growth curves, allowing quantitative fitness measures to be derived. Culture fitnesses can be compared to quantify and rank genetic interaction strengths or drug sensitivities. The effect on culture fitness of any treatments added into substrate agar (e.g. small molecules, antibiotics or nutrients) or applied to plates externally (e.g. UV irradiation, temperature) can be quantified by QFA.
The QFA workflow produces growth rate estimates analogous to those obtained by spectrophotometric measurement of parallel liquid cultures in 96-well or 200-well plate readers. Importantly, QFA has significantly higher throughput compared with such methods. QFA cultures grow on a solid agar surface and are therefore well aerated during growth without the need for stirring or shaking.
QFA throughput is not as high as that of some Synthetic Genetic Array (SGA) screening methods5,6. However, since QFA cultures are heavily diluted before being inoculated onto agar, QFA can capture more complete growth curves, including exponential and saturation phases3. For example, growth curve observations allow culture doubling times to be estimated directly with high precision, as discussed previously1.
Here we present a specific QFA protocol applied to thousands of S. cerevisiae cultures which are automatically handled by robots during inoculation, incubation and imaging. Any of these automated steps can be replaced by an equivalent, manual procedure, with an associated reduction in throughput, and we also present a lower throughput manual protocol. The same QFA software tools can be applied to images captured in either workflow.
We have extensive experience applying QFA to cultures of the budding yeast S. cerevisiae but we expect that QFA will prove equally useful for examining cultures of the fission yeast S. pombe and bacterial cultures.

Watch this Scientific Journal Video about A Quantitative Fitness Analysis Workflow at JoVE.com

A Quantitative Fitness Analysis Workflow

Quantitative Fitness Analysis (QFA) is a complementary series of experimental and computational methods for estimating microbial culture fitnesses. QFA estimates the effect of genetic mutations, drugs or other applied treatments on microbe growth. Experiments scaling from focussed analysis of single cultures to thousands of parallel cultures can be designed.

Quantitative Fitness Analysis (QFA) is an experimental and computational workflow for comparing fitnesses of microbial cultures grown in parallel1,2,3,4. ...

Research

JoVE Journal

Biology

14.3K Views.  Newcastle University Medical School. Quantitative Fitness Analysis (QFA) is a complementary series of experimental and computational methods for estimating microbial culture fitnesses. QFA estimates the effect of genetic mutations, drugs or other applied treatments on microbe growth. Experiments scaling from focussed analysis of single cultures to thousands of parallel cultures can be designed.

Video: A Quantitative Fitness Analysis Workflow

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing measurement of multiple cellular phenotypes within a single assay. In this study, we utilized HCA to develop a novel assay for neurotoxicity. Neurotoxicity assessment represents an important part of drug safety evaluation, as well as being a significant focus of environmental protection efforts. Additionally, neurotoxicity is also a well-accepted in vitro marker of the development of neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Recently, the application of HCA to neuronal screening has been reported. By labeling neuronal cells with &beta;III-tubulin, HCA assays can provide high-throughput, non-subjective, quantitative measurements of parameters such as neuronal number, neurite count and neurite length, all of which can indicate neurotoxic effects. However, the role of astrocytes remains unexplored in these models. Astrocytes have an integral role in the maintenance of central nervous system (CNS) homeostasis, and are associated with both neuroprotection and neurodegradation when they are activated in response to toxic substances or disease states. GFAP is an intermediate filament protein expressed predominantly in the astrocytes of the CNS. Astrocytic activation (gliosis) leads to the upregulation of GFAP, commonly accompanied by astrocyte proliferation and hypertrophy. This process of reactive gliosis has been proposed as an early marker of damage to the nervous system. The traditional method for GFAP quantitation is by immunoassay. This approach is limited by an inability to provide information on cellular localization, morphology and cell number. We determined that HCA could be used to overcome these limitations and to simultaneously measure multiple features associated with gliosis - changes in GFAP expression, astrocyte hypertrophy, and astrocyte proliferation - within a single assay. In co-culture studies, astrocytes have been shown to protect neurons against several types of toxic insult and to critically influence neuronal survival. Recent studies have suggested that the use of astrocytes in an in vitro neurotoxicity test system may prove more relevant to human CNS structure and function than neuronal cells alone. Accordingly, we have developed an HCA assay for co-culture of neurons and astrocytes, comprised of protocols and validated, target-specific detection reagents for profiling &beta;III-tubulin and glial fibrillary acidic protein (GFAP). This assay enables simultaneous analysis of neurotoxicity, neurite outgrowth, gliosis, neuronal and astrocytic morphology and neuronal and astrocytic development in a wide variety of cellular models, representing a novel, non-subjective, high-throughput assay for neurotoxicity assessment. The assay holds great potential for enhanced detection of neurotoxicity and improved productivity in neuroscience research and drug discovery.

This article describes a novel protocol and reagent set designed for sensitive measurement of neurotoxic effects of compounds and treatments on co-cultures of neurons and astrocytes using high content analysis. Results demonstrate that high content analysis represents an exciting novel technology for neurotoxicity assessment.

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing ...

Quantitative Phosphoproteomics in Fatty Acid Stimulated Saccharomyces cerevisiae

This protocol describes the growth and stimulation, with the fatty acid oleate, of isotopically heavy and light S. cerevisiae cells. Cells are ground using a cryolysis procedure in a ball mill grinder and the resulting grindate brought into solution by urea solubilization. This procedure allows for the lysis of the cells in a metabolically inactive state, preserving phosphorylation and preventing reorientation of the phosphoproteome during cell lysis.  Following reduction, alkylation, trypsin digestion of the proteins, the samples are desalted on C18 columns and the sample complexity reduced by fractionation using hydrophilic interaction chromatography (HILIC). HILIC columns preferentially retain hydrophilic molecules which is well suited for phosphoproteomics.  Phosphorylated peptides tend to elute later in the chromatographic profile than the non phosphorylated counterparts. After fractionation, phosphopeptides are enriched using immobilized metal chromatography, which relies on charge-based affinities for phosphopeptide enrichment. At the end of this procedure the samples are ready to be quantitatively analyzed by mass spectrometry.

Quantitative Phosphoproteomics in Fatty Acid Stimulated Saccharomyces cerevisiae

Description of a quantitative phosphorylation procedure using cryolysis, urea solubilziation, HILIC fractionation and IMAC enrichment of phosphorylated peptides.

This protocol describes the growth and stimulation, with the fatty acid oleate, of isotopically heavy and light S. cerevisiae cells. Cells are ground ...

Live-cell Imaging and Quantitative Analysis of Embryonic Epithelial Cells in Xenopus laevis

Embryonic epithelial cells serve as an ideal model to study morphogenesis where multi-cellular tissues undergo changes in their geometry, such as changes in cell surface area and cell height, and where cells undergo mitosis and migrate. Furthermore, epithelial cells can also regulate morphogenetic movements in adjacent tissues1. A traditional method to study epithelial cells and tissues involve chemical fixation and histological methods to determine cell morphology or localization of particular proteins of interest. These approaches continue to be useful and provide "snapshots" of cell shapes and tissue architecture, however, much remains to be understood about how cells acquire specific shapes, how various proteins move or localize to specific positions, and what paths cells follow toward their final differentiated fate. High resolution live imaging complements traditional methods and also allows more direct investigation into the dynamic cellular processes involved in the formation, maintenance, and morphogenesis of multicellular epithelial sheets. Here we demonstrate experimental methods from the isolation of animal cap tissues from Xenopus laevis embryos to confocal imaging of epithelial cells and simple measurement approaches that together can augment molecular and cellular studies of epithelial morphogenesis.

Live-cell Imaging and Quantitative Analysis of Embryonic Epithelial Cells in Xenopus laevis

Xenopus embryonic epithelia are an ideal model system to study cell behaviors such as polarity development and shape change during epithelial morphogenesis. Traditional histology of fixed samples is increasingly being complemented by live-cell confocal imaging. Here we demonstrate methods to isolate frog tissues and visualize live epithelial cells and their cytoskeleton using live-cell confocal microscopy.

Embryonic epithelial cells serve as an ideal model to study morphogenesis where multi-cellular tissues undergo changes in their geometry, such as changes ...

Quantitative Live Cell Fluorescence-microscopy Analysis of Fission Yeast

Several microscopy techniques are available today that can detect a specific protein within the cell. During the last decade live cell imaging using fluorochromes like Green Fluorescent Protein (GFP) directly attached to the protein of interest has become increasingly popular 1. Using GFP and similar fluorochromes the subcellular localisations and movements of proteins can be detected in a fluorescent microscope. Moreover, also the subnuclear localisation of a certain region of a chromosome can be studied using this technique. GFP is fused to the Lac Repressor protein (LacR) and ectopically expressed in the cell where tandem repeats of the lacO sequence has been inserted into the region of interest on the chromosome2. The LacR-GFP will bind to the lacO repeats and that area of the genome will be visible as a green dot in the fluorescence microscope. Yeast is especially suited for this type of manipulation since homologous recombination is very efficient and thereby enables targeted integration of the lacO repeats and engineered fusion proteins with GFP 3. Here we describe a quantitative method for live cell analysis of fission yeast. Additional protocols for live cell analysis of fission yeast can be found, for example on how to make a movie of the meiotic chromosomal behaviour 4. In this particular experiment we focus on subnuclear organisation and how it is affected during gene induction. We have labelled a gene cluster, named Chr1, by the introduction of lacO binding sites in the vicinity of the genes. The gene cluster is enriched for genes that are induced early during nitrogen starvation of fission yeast 5. In the strain the nuclear membrane (NM) is labelled by the attachment of mCherry to the NM protein Cut11 giving rise to a red fluorescent signal. The Spindle Pole body (SPB) compound Sid4 is fused to Red Fluorescent Protein (Sid4-mRFP) 6. In vegetatively growing yeast cells the centromeres are always attached to the SPB that is embedded in the NM 7. The SPB is identified as a large round structure in the NM. By imaging before and 20 minutes after depletion of the nitrogen source we can determine the distance between the gene cluster (GFP) and the NM/SPB. The mean or median distances before and after nitrogen depletion are compared and we can thus quantify whether or not there is a shift in subcellular localisation of the gene cluster after nitrogen depletion.

The fission yeast, Schizosaccharomyces pombe, is a good model system to study basic cellular processes. Here we describe a method to perform quantitative live cell analysis of fission yeast. In this particular experiment we focus on organisation of the genome within the cell nucleus, but the method can also be used to study cytosolic factors.

Several microscopy techniques are available today that can detect a specific protein within the cell. During the last decade live cell imaging using ...

Quantitative Analysis of Random Migration of Cells Using Time-lapse Video Microscopy

Cell migration is a dynamic process, which is important for embryonic development, tissue repair, immune system function, and tumor invasion 1, 2. During directional migration, cells move rapidly in response to an extracellular chemotactic signal, or in response to intrinsic cues 3 provided by the basic motility machinery. Random migration occurs when a cell possesses low intrinsic directionality, allowing the cells to explore their local environment.
Cell migration is a complex process, in the initial response cell undergoes polarization and extends protrusions in the direction of migration 2. Traditional methods to measure migration such as the Boyden chamber migration assay is an easy method to measure chemotaxis in vitro, which allows measuring migration as an end point result. However, this approach neither allows measurement of individual migration parameters, nor does it allow to visualization of morphological changes that cell undergoes during migration.
Here, we present a method that allows us to monitor migrating cells in real time using video - time lapse microscopy. Since cell migration and invasion are hallmarks of cancer, this method will be applicable in studying cancer cell migration and invasion in vitro. Random migration of platelets has been considered as one of the parameters of platelet function 4, hence this method could also be helpful in studying platelet functions. This assay has the advantage of being rapid, reliable, reproducible, and does not require optimization of cell numbers. In order to maintain physiologically suitable conditions for cells, the microscope is equipped with CO2 supply and temperature thermostat. Cell movement is monitored by taking pictures using a camera fitted to the microscope at regular intervals. Cell migration can be calculated by measuring average speed and average displacement, which is calculated by Slidebook software.

This method allows monitoring of cells in real time and quantitative measurements of different cell migration parameters such as speed, displacement, and velocity. Unlike the traditional methods, this real time approach is not based on endpoint quantitative migration measurements; instead it allows monitoring and calculating different parameters continuously.

Cell migration is a dynamic process, which is important for embryonic development, tissue repair, immune system function, and tumor invasion 1, 2. During ...

Quantitative Analysis of Autophagy using Advanced 3D Fluorescence Microscopy

Prostate cancer is the leading form of malignancies among men in the U.S. While surgery carries a significant risk of impotence and incontinence, traditional chemotherapeutic approaches have been largely unsuccessful. Hormone therapy is effective at early stage, but often fails with the eventual development of hormone-refractory tumors. We have been interested in developing therapeutics targeting specific metabolic deficiency of tumor cells. We recently showed that prostate tumor cells specifically lack an enzyme (argininosuccinate synthase, or ASS) involved in the synthesis of the amino acid arginine1. This condition causes the tumor cells to become dependent on exogenous arginine, and they undergo metabolic stress when free arginine is depleted by arginine deiminase (ADI)1,10. Indeed, we have shown that human prostate cancer cells CWR22Rv1 are effectively killed by ADI with caspase-independent apoptosis and aggressive autophagy (or macroautophagy)1,2,3. Autophagy is an evolutionarily-conserved process that allows cells to metabolize unwanted proteins by lysosomal breakdown during nutritional starvation4,5. Although the essential components of this pathway are well-characterized6,7,8,9, many aspects of the molecular mechanism are still unclear - in particular, what is the role of autophagy in the death-response of prostate cancer cells after ADI treatment? In order to address this question, we required an experimental method to measure the level and extent of autophagic response in cells - and since there are no known molecular markers that can accurately track this process, we chose to develop an imaging-based approach, using quantitative 3D fluorescence microscopy11,12.
Using CWR22Rv1 cells specifically-labeled with fluorescent probes for autophagosomes and lysosomes, we show that 3D image stacks acquired with either widefield deconvolution microscopy (and later, with super-resolution, structured-illumination microscopy) can clearly capture the early stages of autophagy induction. With commercially available digital image analysis applications, we can readily obtain statistical information about autophagosome and lysosome number, size, distribution, and degree of colocalization from any imaged cell. This information allows us to precisely track the progress of autophagy in living cells and enables our continued investigation into the role of autophagy in cancer chemotherapy.

Autophagy is a ubiquitous process that enables cells to degrade and recycle proteins and organelles. We apply advanced fluorescence microscopy to visualize and quantify the small, but essential, physical changes associated with the induction of autophagy, including the formation and distribution of autophagosomes and lysosomes, and their fusion into autolysosomes.

Prostate cancer is the leading form of malignancies among men in the U.S. While surgery carries a significant risk of impotence and incontinence, ...

Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae

The Fluorescence in situ Hybridization (FISH) method allows one to detect nucleic acids in the native cellular environment. Here we provide a protocol for using FISH to quantify the number of mRNAs in single yeast cells. Cells can be grown in any condition of interest and then fixed and made permeable. Subsequently, multiple single-stranded deoxyoligonucleotides conjugated to fluorescent dyes are used to label and visualize mRNAs. Diffraction-limited fluorescence from single mRNA molecules is quantified using a spot-detection algorithm to identify and count the number of mRNAs per cell. While the more standard quantification methods of northern blots, RT-PCR and gene expression microarrays provide information on average mRNAs in the bulk population, FISH facilitates both the counting and localization of these mRNAs in single cells at single-molecule resolution.

Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae

This protocol describes an experimental procedure for performing Fluorescence in situ Hybridization (FISH) for counting mRNAs in single cells at single-molecule resolution.

The Fluorescence in situ Hybridization (FISH) method allows one to detect nucleic acids in the native cellular environment. Here we provide a protocol for ...

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Biochemical assays with recombinant human MHC II molecules can provide rapid, quantitative insights into immunogenic epitope identification, deletion, or design1,2. Here, a peptide-MHC II binding assay is scaled to 384-well format. The scaled down protocol reduces reagent costs by 75% and is higher throughput than previously described 96-well protocols1,3-5. Specifically, the experimental design permits robust and reproducible analysis of up to 15 peptides against one MHC II allele per 384-well ELISA plate. Using a single liquid handling robot, this method allows one researcher to analyze approximately ninety test peptides in triplicate over a range of eight concentrations and four MHC II allele types in less than 48 hr. Others working in the fields of protein deimmunization or vaccine design and development may find the protocol to be useful in facilitating their own work. In particular, the step-by-step instructions and the visual format of JoVE should allow other users to quickly and easily establish this methodology in their own labs.

Biochemical assays with recombinant human MHC II molecules can provide rapid, quantitative insights into immunogenic epitope identification, deletion, or design. &#160;Here, a peptide-MHC II binding assay scaled to 384-well plates is described. This cost effective format should prove useful in the fields of protein deimmunization and vaccine design and development.

Biochemical assays with recombinant human MHC II molecules can provide rapid, quantitative insights into immunogenic epitope identification, deletion, or ...

A New Technique for Quantitative Analysis of Hair Loss in Mice Using Grayscale Analysis

Alopecia is a common form of hair loss which can occur in many different conditions, including male-pattern hair loss, polycystic ovarian syndrome, and alopecia areata. Alopecia can also occur as a side effect of chemotherapy in cancer patients. In this study, our goal was to develop a consistent and reliable method to quantify hair loss in mice, which will allow investigators to accurately assess and compare new therapeutic approaches for these various forms of alopecia. The method utilizes a standard gel imager to obtain and process images of mice, measuring the light absorption, which occurs in rough proportion to the amount of black (or gray) hair on the mouse. Data that has been quantified in this fashion can then be analyzed using standard statistical techniques (i.e., ANOVA, T-test). This methodology was tested in mouse models of chemotherapy-induced alopecia, alopecia areata and alopecia from waxing. In this report, the detailed protocol is presented for performing these measurements, including validation data from C57BL/6 and C3H/HeJ strains of mice. This new technique offers a number of advantages, including relative simplicity of application, reliance on equipment which is readily available in most research laboratories, and applying an objective, quantitative assessment which is more robust than subjective evaluations. Improvements in quantification of hair growth in mice will improve study of alopecia models and facilitate evaluation of promising new therapies in preclinical studies.

Alopecia is a common form of hair loss which can occur in many different conditions, including as a side-effect of chemotherapy. We have developed a method to quantify hair loss in mice, utilizing a standard gel imager to perform a grayscale analysis, to facilitate study of promising new alopecia therapies.

Alopecia is a common form of hair loss which can occur in many different conditions, including male-pattern hair loss, polycystic ovarian syndrome, and ...

Complete Workflow for Analysis of Histone Post-translational Modifications Using Bottom-up Mass Spectrometry: From Histone Extraction to Data Analysis

Nucleosomes are the smallest structural unit of chromatin, composed of 147 base pairs of DNA wrapped around an octamer of histone proteins. Histone function is mediated by extensive post-translational modification by a myriad of nuclear proteins. These modifications are critical for nuclear integrity as they regulate chromatin structure and recruit enzymes involved in gene regulation, DNA repair and chromosome condensation. Even though a large part of the scientific community adopts antibody-based techniques to characterize histone PTM abundance, these approaches are low throughput and biased against hypermodified proteins, as the epitope might be obstructed by nearby modifications. This protocol describes the use of nano liquid chromatography (nLC) and mass spectrometry (MS) for accurate quantification of histone modifications. This method is designed to characterize a large variety of histone PTMs and the relative abundance of several histone variants within single analyses. In this protocol, histones are derivatized with propionic anhydride followed by digestion with trypsin to generate peptides of 5 - 20 aa in length. After digestion, the newly exposed N-termini of the histone peptides are derivatized to improve chromatographic retention during nLC-MS. This method allows for the relative quantification of histone PTMs spanning four orders of magnitude.

This protocol outlines a fully integrated workflow for characterizing histone post-translational modifications using mass spectrometry (MS). The workflow includes histone purification from cell cultures or tissues, histone derivatization and digestion, MS analysis using nano-flow liquid chromatography and instructions for data analysis. The protocol is designed for completion within 2 - 3 days.

Nucleosomes are the smallest structural unit of chromatin, composed of 147 base pairs of DNA wrapped around an octamer of histone proteins. Histone ...