This work was supported in part by grant R00AG031911 from the National Institute on Aging of the National Institutes of Health to P. H. M. The content of this article does not necessarily reflect official views of the National Institutes of Health.

1. Preparation of Media and Solutions
<ol>
	<li>Grow cells using yeast extract-peptone-glucose (YPD) rich medium for the standard protocol. Prepare a 2% bacto-peptone, 1% yeast extract, 2% glucose solution for YPD medium and add 2% bacto-agar for solid medium19. Autoclave to sterilize. Use an alternative medium, if needed, to test a particular growth condition or mutant yeast strain.</li>
	<li>Make solid synthetic complete medium lacking arginine with 60 mg/L canavanine (SC-arg + canavanine) to select for canavanine resistant mutants. Prepare a solution that is 0.67% bacto-yeast nitrogen base without amino acids, 2% glucose, 0.2% complete supplement mixture without arginine (dropout mix), and 2% bacto-agar19. Autoclave and cool prior to adding canavanine from a 20 mg/ml stock solution (filter-sterilized, stored at 4 &#176;C)19.
	<ol>
		<li>Make solid synthetic complete medium containing 5-fluoroorotic acid (5-FOA). Prepare 500 ml of a solution that is 1.34% bacto-yeast nitrogen base without amino acids, 4% glucose, 0.4% complete supplement mixture, and 0.2% 5-FOA and filter-sterilize19. Autoclave 500 ml of a 4% bacto-agar solution, cool briefly, and then mix with the 500 ml filter-sterilized nutrient solution. Use appropriate alternative media for other mutation assays.</li>
	</ol>
	</li>
	<li>For biotin labeling, make 500 ml 1x phosphate buffered saline (PBS) solution containing 137 mM sodium chloride, 2.7 mM potassium chloride, 10.14 mM sodium phosphate dibasic, and 1.77 mM potassium phosphate dibasic, pH 8. Store at 4 &#176;C and keep on ice during use.</li>
	<li>Make a fresh 5 mM biotin stock in sterile, ultrapure water.</li>
	<li>To quench biotin labeling, make 500 ml of 1x PBS, 100 mM glycine. Store at 4 &#176;C and keep on ice during use.</li>
	<li>Depending on the number of cells used and duration of the experiment, make 1-2 L of bead-labeling buffer containing 1x PBS, 2 mM EDTA, 0.5% bovine serum albumin. Filter sterilize and de-gas buffer. Store at 4 &#176;C and keep on ice during use.</li>
	<li>Prepare a 0.4% trypan blue solution in 1x PBS, filter sterilize, and store at RT for use in cell viability measurements.</li>
	<li>Aliquot small volumes (~100 &#181;l) of a wheat germ agglutinin (WGA) fluorescent conjugate to store at -20 &#176;C in foil wrapped (or opaque) microcentrifuge tubes for use in determining cell age.</li>
</ol>
2. Determination of Mutation Rate and Frequency
<ol>
	<li>Establish a baseline mutation rate per cell generation using fluctuation tests for cells grown under standard culture conditions. Grow seven to eleven replicate 1 ml cultures from an initial density of 1,000-5,000 cells/ml to early stationary phase (~108 cell/ml).
	<ol>
		<li>For each replicate culture, dilute cells 1:2,000 and spread 1-4 &#181;l onto non-selective medium (YPD) to determine colony-forming-units/ml. Pellet remaining cells at ~2,400 x g for 1 min in a microcentrifuge and resuspend in 100 &#181;l water to spread onto selective medium to identify mutants. Incubate plates for three days at 30 &#176;C prior to counting colonies (adjust incubation time as needed based on growth rate of cells).</li>
		<li>Determine the number of independent mutations (m) that occurred in the trial based on the number of mutant colonies (r) obtained on selective medium. Use the Lea-Coulson median estimator to find m by substituting the median number of mutant colonies for r in the formula below9. Then substitute values for m until the left side of the equation is virtually zero. 
		<img alt="Equation 1" fo:content-width="1in" src="/files/ftp_upload/51850/51850eq1.jpg" /></li>
		<li>To determine mutation rate, first calculate the average number of viable cells spread onto selective medium by multiplying the average colony-forming-units/ml of the replicate cultures and the volume of culture in ml spread onto selective medium. Divide the m value from step 2.1.2 by this average number of viable cells to obtain the mutation rate per cell generation.</li>
	</ol>
	</li>
	<li>Individually calculate the number of viable cells spread onto selective medium for each replicate culture in a trial as described in step 2.1.3. Divide the number of mutant colonies (r) obtained for a culture by the number of viable cells spread onto selective medium to obtain the mutation frequency. Use the average or median of the individual mutation frequencies from the replicate cultures in step 2.1 as a baseline mutation frequency.</li>
	<li>Make frequency measurements before and after biotin labeling cells (steps 3.2 and 3.4) to determine if the labeling step results in any change in the frequency of mutations that will need to be considered when testing for age-specific changes in mutation accumulation.</li>
</ol>
3. Biotin Labeling
<ol>
	<li>Streak S. cerevisiae from a glycerol stock at -80 &#176;C onto YPD medium and incubate at 30 &#176;C for 1-3 days.</li>
	<li>Using a sterile pipette tip, transfer cells from the streak plate to 1 ml (or more) of water keeping in mind that 1-1.5 cm of a thickly grown portion of the streak will give approximately 108 cells. Determine exact cell density using a hemocytometer.</li>
	<li>Label 108 cells per strain (or treatment condition) per collection point at which mutation frequency will be determined or a population size sufficient to obtain at least 5-10 mutant colonies per sampling on selective medium (based on mutation frequency from step 2.2). Include an additional 108 cells for baseline measurements per strain/condition.</li>
	<li>Biotin label according to standard procedure from the manufacturer, except use a 5 mM biotin reagent stock and gently rock during incubation. Spin for 5 min at 4 &#176;C at 3,000 x g for all centrifugation steps, since spinning at temperatures higher than 4 &#176;C may lead to increased cell loss. After the final wash, suspend cells to a concentration of 108 labeled cells/ml in water.</li>
	<li>Check viability of cells using trypan blue staining. Dilute 10 &#181;l of cells with 23 &#181;l of water and 67 &#181;l of 0.4% trypan blue in 1x PBS. Incubate for at least 40 min at RT before scoring live (unstained) and dead (stained) cells using a hemocytometer.</li>
	<li>Measure baseline values for genome instability, cell age, and other relevant characteristics (see sections 5, 6, and 7).</li>
	<li>Transfer biotin-labeled cells to 20 ml of YPD medium per 108 cells in an Erlenmeyer flask (approximately 5 x 106 cells/ml). Grow culture(s) 16-18 hr at 20 &#176;C to an approximate density of 108 cells/ml (four to five cell generations), but do not allow cells to reach stationary phase. Use a shorter incubation time for growth at 30 &#176;C. Keep biotin-labeled cells at 4 &#176;C for up to several hours prior to addition to medium, if needed to optimize timing of the growth period and collection.</li>
</ol>
4. Bead Labeling and Magnetic Sorting
<ol>
	<li>After the growth phase, dilute an aliquot of culture to determine the cell density using a hemocytometer. Pellet cells by centrifugation for 5 min at 4 &#176;C at 3,000 x g. Pour off supernatant.</li>
	<li>Wash cells by vortexing to suspend pellet in 30 ml of bead-labeling buffer, then centrifuging and pouring off supernatant as in section 4.1. Repeat wash.</li>
	<li>Fully resuspend cells in 50 &#181;l bead-labeling buffer per 108 total cells by vortexing. Add 2 &#181;l magnetic beads per 108 total cells and vortex briefly to mix. Incubate on ice for 10 min, inverting periodically to mix (modified from an online protocol at <a href="http://www.sysbio.org/dataresources/usermanual031113.pdf">http://www.sysbio.org/dataresources/usermanual031113.pdf</a>).</li>
	<li>Wash cells three times with 30 ml of bead-labeling buffer.</li>
	<li>Add 0.5 ml bead-labeling buffer per 108 total cells and briefly vortex pellet on medium setting just until cells are resuspended. Pour through a 40 &#181;m cell strainer into a 50 ml tube to remove any remaining cell clumps. If needed, leave cells on ice for 1-2 hr prior to loading on the column.</li>
	<li>Follow the manufacturer&#8217;s recommended protocol for the magnetic columns to bind, wash, and elute the magnetic bead-labeled mother cells. For increased recovery of labeled cells, use at least one column per 2 x 109 total cells.
	<ol>
		<li>Remove any bubbles that occur when loading columns, as they will block flow. Remove and replace the column on the separator between washes to prevent cells from becoming trapped between the beads (modified from an online protocol at <a href="http://www.sysbio.org/dataresources/usermanual031113.pdf">http://www.sysbio.org/dataresources/usermanual031113.pdf</a>).</li>
		<li>Save flow through from binding and washing steps to analyze young cells produced by the mother cells, if desired. Concentrate cells as described in step 4.7.</li>
		<li>Use multi-column separators for processing multiple samples at once. Elute multiple columns of the same sample into the same collection tube to reduce handling time and decrease loss of cells.</li>
	</ol>
	</li>
	<li>Concentrate eluted mother cells by centrifuging at 3,000 x g for 5 min at 4 &#176;C. Aspirate all but 1 ml buffer per 108 original biotin-labeled cells. Vortex briefly to suspend cells in remaining buffer.</li>
	<li>Dilute an aliquot of cells and stain with trypan blue to determine the cell density and cell viability using a hemocytometer (see step 3.5). Calculate the total number of cells recovered.
	<ol>
		<li>If the number of cells is substantially higher than expected, pass the elution sample through another column to try to remove contaminating young cells. If the number of cells is substantially lower than expected, pass the saved flow through sample through another column to try to improve yield of mother cells.</li>
	</ol>
	</li>
	<li>Use cells for further analyses, as described in sections 5 and 6. Alternatively, grow the cells in YPD broth again to increase their replicative age (step 3.7) and follow by bead labeling and sorting (steps 4.1-4.8.1), without the need to repeat the biotin labeling.</li>
</ol>
5. Determination of Replicative Age
<ol>
	<li>Spin cells for 2 min at 5,000 x g at RT for all steps in this section. Put 25 &#181;l of recovered cells (~108 cells/ml) into a 1.5 ml centrifuge tube and wash twice with 1 ml 1x PBS. Aspirate off supernatant.
	<ol>
		<li>Thaw an aliquot of WGA-conjugate, vortex to mix, and spin briefly to eliminate protein aggregates. Suspend cells in 180 &#181;l 1x PBS and 20 &#181;l of WGA conjugated to a fluorescent molecule to label bud scars on the surface of the cells. Cover the 1.5 ml centrifuge tube with foil and incubate with gentle rocking at 30 &#176;C for 30 min.</li>
		<li>Wash three times with 1 ml 1X PBS.</li>
	</ol>
	</li>
	<li>For fluorescent microscopy, label slides and mount per standard protocol using an anti-fade agent. Fully cure slides in the dark (up to a few days) before sealing coverslip to avoid cell movement during imaging. Count bud scars on at least 50 cells per sample to obtain a representative measure of cell age. Store slides for up to 1 month for viewing bud scars, if needed.
	<ol>
		<li>Alternatively, determine the intensity of the fluorescent signal from the WGA-conjugate by performing flow cytometry on cells suspended in 1 ml 1x PBS after step 5.1.2. Use a 405 nm excitation laser and 530/30 nm emission filter with a 505 nm long pass filter for WGA-Alexa 488. Include an unstained control for each collection point.</li>
	</ol>
	</li>
	<li>Graph bud scars on young cells and aged cells counted by microscopy on the y-axis against the normalized geometric mean (stained geometric mean/unstained geometric mean) of the WGA-conjugate fluorescence from the same cell populations on the x-axis. Obtain the equation of a linear trend line for this relationship. For subsequent experiments, substitute the normalized geometric mean of WGA-conjugate fluorescence intensity of a cell population for x in the equation and solve for y to determine the average cell age of a sample.</li>
</ol>
6. Mutation Frequency
<ol>
	<li>Spread sorted mother cells resuspended in buffer from step 4.7 onto YPD medium and selective medium as described in step 2.1.1. Use 1 ml of resuspended mother cells for selective medium and spin cells at 5,000 x g for 1 min. 
	NOTE: Spread larger volumes of diluted cells onto YPD medium as cells increase in age to compensate for increasing numbers of non-viable and senescent cells in older populations.
	<ol>
		<li>Incubate YPD medium and selective medium plates from step 6.1 at 30 &#176;C for 3 and 4 days, respectively. Increase incubation time up to one week for very old cells or slowly growing strains. Calculate mutation frequency as described in step 2.2.</li>
	</ol>
	</li>
</ol>
7. Calculating Predicted Mutation Frequency for a Given Replicative Age
<ol>
	<li>Calculate the average increase in replicative age of the biotin-labeled cells over the course of the experiment by subtracting the average age of the initial cell population from the average age of the sorted mother cells for the relevant sort. Use the cell ages calculated in step 5.2 or step 5.2.2.</li>
	<li>Multiply the mutation rate obtained in step 2.1.3 by the increase in average cell age calculated in step 7.1. Add this value to the baseline mutation frequency for the initial population calculated in step 2.2 to determine the predicted mutation frequency for cells of the relevant replicative age.</li>
</ol>

<ol>
	<li>Maslov, A. Y., Vijg, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome+instability,+cancer+and+aging.">Genome instability, cancer and aging.</a> Biochim Biophys Acta. 1790, 963-969 (2009).</li><li>Maher, R. L., Branagan, A. M., Morrical, S. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Coordination+of+DNA+replication+and+recombination+activities+in+the+maintenance+of+genome+stability.">Coordination of DNA replication and recombination activities in the maintenance of genome stability.</a> J Cell Biochem. 112, 2672-2682 (2011).</li><li>Longo, V. D., Shadel, G. S., Kaeberlein, M., Kennedy, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Replicative+and+chronological+aging+in+Saccharomyces+cerevisiae.">Replicative and chronological aging in Saccharomyces cerevisiae.</a> Cell Metab. 16, 18-31 (2012).</li><li>Madia, F., Gattazzo, C., Fabrizio, P., Longo, V. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+simple+model+system+for+age-dependent+DNA+damage+and+cancer.">A simple model system for age-dependent DNA damage and cancer.</a> Mech Ageing Dev. 128, 45-49 (2007).</li><li>Madia, F., 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=Oncogene+homologue+Sch9+promotes+age-dependent+mutations+by+a+superoxide+and+Rev1/Polzeta-dependent+mechanism.">Oncogene homologue Sch9 promotes age-dependent mutations by a superoxide and Rev1/Polzeta-dependent mechanism.</a> J Cell Biol. 186, 509-523 (2009).</li><li>Lee, S. S., Avalos Vizcarra, I., Huberts, D. H., Lee, L. P., Heinemann, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Whole+lifespan+microscopic+observation+of+budding+yeast+aging+through+a+microfluidic+dissection+platform.">Whole lifespan microscopic observation of budding yeast aging through a microfluidic dissection platform.</a> Proc Natl Acad Sci U S A. 109, 4916-4920 (2012).</li><li>Xie, Z., 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=Molecular+phenotyping+of+aging+in+single+yeast+cells+using+a+novel+microfluidic+device.">Molecular phenotyping of aging in single yeast cells using a novel microfluidic device.</a> Aging Cell. 11, 599-606 (2012).</li><li>McMurray, M. A., Gottschling, D. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+age-induced+switch+to+a+hyper-recombinational+state.">An age-induced switch to a hyper-recombinational state.</a> Science. 301, 1908-1911 (2003).</li><li>Foster, P. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Methods+for+determining+spontaneous+mutation+rates.">Methods for determining spontaneous mutation rates.</a> Methods Enzymol. 409, 195-213 (2006).</li><li>Boeke, J. D., LaCroute, F., Fink, G. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+positive+selection+for+mutants+lacking+orotidine-5&amp;#34;-phosphate+decarboxylase+activity+in+yeast:+5-fluoro-orotic+acid+resistance.">A positive selection for mutants lacking orotidine-5&amp;#34;-phosphate decarboxylase activity in yeast: 5-fluoro-orotic acid resistance.</a> Mol Gen Genet. 197, 345-346 (1984).</li><li>Whelan, W. L., Gocke, E., Manney, T. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+CAN1+locus+of+Saccharomyces+cerevisiae:+fine-structure+analysis+and+forward+mutation+rates.">The CAN1 locus of Saccharomyces cerevisiae: fine-structure analysis and forward mutation rates.</a> Genetics. 91, 35-51 (1979).</li><li>Lindstrom, D. L., Gottschling, D. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+mother+enrichment+program:+a+genetic+system+for+facile+replicative+life+span+analysis+in+Saccharomyces+cerevisiae.">The mother enrichment program: a genetic system for facile replicative life span analysis in Saccharomyces cerevisiae.</a> Genetics. 183, 413-422 (2009).</li><li>Lindstrom, D. L., Leverich, C. K., Henderson, K. A., Gottschling, D. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Replicative+age+induces+mitotic+recombination+in+the+ribosomal+RNA+gene+cluster+of+Saccharomyces+cerevisiae.">Replicative age induces mitotic recombination in the ribosomal RNA gene cluster of Saccharomyces cerevisiae.</a> PLoS Genet. 7, e1002015 (2011).</li><li>Feser, 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=Elevated+histone+expression+promotes+life+span+extension.">Elevated histone expression promotes life span extension.</a> Mol Cell. 39, 724-735 (2010).</li><li>Afonso, B., Silver, P. A., Ajo-Franklin, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+synthetic+circuit+for+selectively+arresting+daughter+cells+to+create+aging+populations.">A synthetic circuit for selectively arresting daughter cells to create aging populations.</a> Nucleic Acids Res. 38, 2727-2735 (2010).</li><li>Smeal, T., Claus, J., Kennedy, B., Cole, F., Guarente, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Loss+of+transcriptional+silencing+causes+sterility+in+old+mother+cells+of+S.+cerevisiae.">Loss of transcriptional silencing causes sterility in old mother cells of S. cerevisiae.</a> Cell. 84, 633-642 (1996).</li><li>Lam, Y. T., Aung-Htut, M. T., Lim, Y. L., Yang, H., Dawes, I. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Changes+in+reactive+oxygen+species+begin+early+during+replicative+aging+of+Saccharomyces+cerevisiae+cells.">Changes in reactive oxygen species begin early during replicative aging of Saccharomyces cerevisiae cells.</a> Free Radic Biol Med. 50, 963-970 (2011).</li><li>Dang, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Histone+H4+lysine+16+acetylation+regulates+cellular+lifespan.">Histone H4 lysine 16 acetylation regulates cellular lifespan.</a> Nature. 459, 802-807 (2009).</li><li>Amberg, D. C., Burke, D. J., Strathern, J. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Methods in Yeast Genetics: A Cold Spring Harbor Laboratory Course Manual. , (2005).</li><li>Brachmann, C. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Designer+deletion+strains+derived+from+Saccharomyces+cerevisiae+S288C:+a+useful+set+of+strains+and+plasmids+for+PCR-mediated+gene+disruption+and+other+applications.">Designer deletion strains derived from Saccharomyces cerevisiae S288C: a useful set of strains and plasmids for PCR-mediated gene disruption and other applications.</a> Yeast. 14, 115-132 (1998).</li><li>Maxwell, P. H., Burhans, W. C., Curcio, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Retrotransposition+is+associated+with+genome+instability+during+chronological+aging.">Retrotransposition is associated with genome instability during chronological aging.</a> Proc Natl Acad Sci U S A. 108, 20376-20381 (2011).</li><li>Hall, B. M., Ma, C. X., Liang, P., Singh, K. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fluctuation+analysis+CalculatOR:+a+web+tool+for+the+determination+of+mutation+rate+using+Luria-Delbruck+fluctuation+analysis.">Fluctuation analysis CalculatOR: a web tool for the determination of mutation rate using Luria-Delbruck fluctuation analysis.</a> Bioinformatics. 25, 1564-1565 (2009).</li><li>Wu, X., 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+robust+estimator+of+mutation+rates.">A robust estimator of mutation rates.</a> Mutat Res. 661, 101-109 (2009).</li><li>Aguilaniu, H., Gustafsson, L., Rigoulet, M., Nystr&#246;m, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Asymmetric+inheritance+of+oxidatively+damaged+proteins+during+cytokinesis.">Asymmetric inheritance of oxidatively damaged proteins during cytokinesis.</a> Science. 299, 1751-1753 (2003).</li></ol>

The authors have nothing to disclose.

This protocol combines multiple methods to enable resources and approaches available in the Saccharomyces cerevisiae model system to be efficiently applied to the study of genome instability during mitotic cell aging. A wide variety of assays to quantify different forms of genome instability through phenotypic selection could be combined with magnetic sorting to study possible age-specific changes in accumulation of particular forms of mutations or chromosome rearrangements. While whole-genome sequencing approaches could provide more information about the overall types of changes occurring in a genome with age, the ability to use phenotypic selection systems to obtain mutation rate measurements and to preferentially isolate specific types of genetic changes are important advantages for studying mechanistic aspects of aging-related genome instability. Streamlining the determination of cell age and the potential to make parallel measurements of physiological changes through flow cytometry provide efficient means to correlate genome instability with other cellular changes during specific age ranges. Determination of potential age-dependent changes in rates of accumulating mutations requires: 1) careful determination of rates in young cell populations, 2) accurate determination of cell age, and 3) the ability to reliably enrich sufficiently large populations of mother cells to reproducibly measure genome instability in old age. This approach allows the yeast model system to contribute to defining underlying mechanisms responsible for age-dependent changes in rates and/or frequencies of genome instability in mitotically active cells. Furthermore, genetic and environmental factors can be rapidly assessed for contributions to age-dependent genome instability. Ultimately, these characterizations could lead to development of models that account for the manner in which mutations accumulate during replicative aging.
This method depends on the ability to detect mutations or other types of genome instability through the appearance of selectable phenotypes to measure rates of such events. However, creativity in assay design can permit many aspects of genome instability to be investigated. For instance, the URA3 and CAN1 genes can be placed at different genomic sites to test any influences of genomic location on results. Reversion of specific non-functional gene alleles to functional gene alleles could test particular mutational processes. Also, introduction of multiple inactive alleles of a gene or flanking a gene with repeat sequences could be used to examine recombination events through restoration or loss of gene function, respectively. Fluctuation tests are the standard approach for determining rates of these various genome instability events9. The protocol is written using the well-accepted and relatively straightforward Lea-Coulson median estimator to determine mutation rate to make it more accessible to diverse researchers, even though the MSS-maximum likelihood estimator method is considered the best approach for determining mutation rates9. The MSS-maximum likelihood estimator has been previously reviewed in detail9, and can be used as an alternative method, but requires more sophisticated calculations. Alternatively, free software to calculate mutation rates with this method has been made available22. Obtaining reproducible measures of mutation rates requires attention to a few aspects of experimental design, including using sufficient replicate cultures, inoculating cultures at low enough initial cell densities that population size increases by 10,000-fold or more, and choosing appropriate culture volumes so that the entire population of cells can be spread onto selective medium. For the latter point, a previously described formula can be used to adjust the mutation rate based on the fraction of culture tested9. Variation in growth rate of cultures can complicate the determination of a reproducible mutation rate, and a modified median-based estimator that may yield more reproducible results in such situations has been recently described23.
The ability to accurately measure cell age is another factor that is important for establishing whether genome instability frequencies in old cells are different from the values expected due to the rate of events in young cells. We have found WGA staining to be preferable to calcofluor white staining, both in terms of background and flexibility, since WGA is available conjugated to different fluorescent molecules. This flexibility can provide more opportunities for simultaneous measurements of other cell characteristics with fluorescent reagents. Initial work to establish a relationship between signal intensity for the WGA staining and the corresponding number of bud scars on cells can then lead to a more rapid determination of cell age through flow cytometry. This approach also allows use of larger population sizes than would be typically examined by microscopy for improved reproducibility of measurements. While all age determinations could be made through microscopic examination of the cells, we feel that the flow cytometry approach facilitates rapid screening of factors that influence age-dependent genome instability.
In addition to measuring cell age reliably, consideration needs to be given to the number of cell divisions mother cells are allowed to undergo with each successive round of growing and sorting cells. Use of a smaller initial population size or a larger culture volume could allow cells to undergo more cell divisions before reaching the desired cell density. For example, other researchers have used only two rounds of sorting to obtain very old yeast cells16, which has the obvious advantage of obtaining old cells with fewer experimental steps. When considering whether to increase culture volume to allow the cells to complete more cell divisions prior to sorting, keep in mind the limit on the total number of cells that can be processed in a single column. Use of a larger culture volume may necessitate the use of multiple columns to sort one cell population. Also, if cells undergo fewer rounds of cell division between sorts/collection points, then there are more opportunities to observe deviations from expected mutation frequencies at distinct time points during lifespan. For instance, sampling only at early and late time points during lifespan may produce data indicating a steady increase in mutation frequency, but sampling at additional intermediate times during lifespan may show that mutation frequency increases quickly and then plateaus. However, since this protocol isolates old mother cells, there is an opportunity to obtain a more direct measure of changes in mutation rates with age. Fluctuation tests could be performed using mother cells of different ages to determine whether the mutation rate will differ from the rate obtained using young cells. While the cultures for these fluctuation tests will become a mixture of old and young cells as they grow, any deviations from rates obtained starting with young cells could be attributed to the use of an older starting population.
The ability to reproducibly obtain a large enough population of aged mother cells to accurately measure genome instability is critical to the success of this approach. While the protocol describes the use of a particular magnetic sorting system for this purpose, other groups have sorted yeast mother cells with alternative systems14,18(see Table of Materials). Such alternative systems should also work with this method, though some optimization of the manufacturers&#8217; protocols could be required. Regardless of the specific system used, the population size required differs depending on the frequency of the type of mutation or genome rearrangement chosen for study. At minimum, there must be enough mother cells to obtain at least one mutant colony per population to calculate a mutation frequency. In practice, results can be quite variable if only zero to five mutant colonies are expected from the population size, and even a small increase in population size, so that five to ten mutant colonies are expected, can greatly improve reproducibility. When biotin labeling a starting population, the recovery efficiency for each sort needs to be taken into account to help identify the appropriate population size necessary to measure mutation frequency in old mother cells. With 90% recovery of mother cells after each sort, only 59% of the original population would be recovered after five rounds of growth and sorting. This drops to ~33% recovery of the original population after five rounds of growth and sorting if only 80% of labeled mother cells are recovered during each sort. Measuring and maximizing recovery is crucial when measuring low frequency events, since a two to three fold decrease in population size could easily lead to unreliable numbers of mutant colonies per population.
There are numerous options for expanding on or modifying this protocol to obtain a broader understanding of genome instability during mitotic cell aging. Simple examples include analyzing how alterations in gene functions, media changes, or other environmental condition changes alter the accumulation of mutations with age. Mutation rates for young cells with altered gene function or in the appropriate condition would be measured and used to determine whether mutations accumulate differently than predicted by the rate values and differently than in control cell populations. Cell populations at different points during replicative aging could be exposed to specific stressors, such as reactive oxygen species or DNA damaging agents. A transient and mild exposure to oxidative stress did not substantially alter the ability to perform cell sorting (Figure 3), but harsher stresses may complicate recovery of cells. Preliminary experiments would be necessary to verify that mother cells can still be recovered efficiently after specific stresses. Additionally, the cellular characteristics of isolated mother cells could be analyzed in detail, including levels of reactive oxygen species, mitochondrial and vacuolar morphology, and other physiological characteristics that are associated with aging3. Furthermore, the mother cells could be a starting population for a further treatment or manipulation. Since mother and daughter cells are physically separated during the procedure, the daughter cells are also still available for analysis. For example, changes in physiological characteristics of daughter cells or the asymmetric inheritance of damaged macromolecules between yeast mother and daughter cells24 could be compared to the timing of any interesting changes in mutation frequencies/rates. Mutation frequencies for daughter cell populations could also be obtained in order to try to model whether there is asymmetric inheritance of mutations during replicative aging. In summary, combining enrichment of mother cells through magnetic cell sorting with fluctuation tests enables the advantages of the S. cerevisiae model system to be efficiently applied to investigating mechanisms responsible for accumulation of genetic changes during mitotic cell aging.

Microorganisms, such as the yeast Saccharomyces cerevisiae, are excellent models for investigating mutation rates, but these models have not been fully utilized to investigate the accumulation of mutations during mitotic aging of cells. Mutation accumulation in the nuclear genome has been hypothesized to contribute to aging by resulting in progressive loss of (or variation in) gene functions1. The ability to use a microbial system to study the mechanisms and consequences of mutation accumulation during aging could greatly accelerate the identification of genetic and environmental factors influencing this process, because of facile genetic systems and the ease of quantifying rare phenotypic changes in microorganisms. DNA repair factors and DNA damage response proteins are well conserved across diverse species2, and fundamental similarities in organismal aging have been identified for organisms as diverse as S. cerevisiae and mammals3, making it likely that results obtained from studies in yeast will be relevant to aging in many organisms.
Studies of aging in S. cerevisiae make use of two aging models that measure chronological aging or replicative aging of cells. Chronological aging is characterized by the progressive loss of viability in yeast cell populations that are in a non-dividing state (stationary phase) due to nutrient depletion3. The chronological aging model has been used to demonstrate that mutations accumulate with increasing age4, since large cell populations are easily obtained in this model to perform phenotypic selections for mutant cells. In this case, mutation accumulation appears to be influenced by growth-signaling pathways and oxidative stress5, and each of these factors is relevant to understanding aging in multicellular eukaryotes3. The replicative aging model exploits the asymmetric division between S. cerevisiae mother and daughter cells for investigating aging that occurs with successive cell generations3. Yeast replicative aging has often been measured through micromanipulation of small populations of mother and daughter cells, or more recently, through video microscopy of small populations of yeast cells in microfluidic devices6,7. Limited population sizes make these approaches poorly suited for investigating mutation accumulation during mitotic aging unless whole-genome information is obtained from single cells or very high frequency genetic changes are measured8. Whole-genome sequencing of single cells provides substantial data sets for investigating mutation accumulation, but phenotypic selection systems have the important advantage of providing a means of measuring mutation rates to study mechanisms underlying mutation accumulation. Studies to examine mutation frequencies and rates through phenotypic selections in haploid yeast strains during replicative aging have not yet been reported.
Mutation rates are commonly measured using fluctuation tests9. Mutation frequency values reflect the total number of cells harboring a mutation in a gene sequence in a population. This includes cells in which independent mutations occur and any progeny of those cells that simply inherit pre-existing mutations. In contrast, mutation rates measured through fluctuation tests represent the number of independent mutations that newly arise with each cell generation. These tests typically involve inoculating many replicate cultures at low cell densities to reduce the likelihood of adding cells with pre-existing mutations in a target sequence, growing the cultures to near saturation, and identifying mutant cells by growth on selective medium. The numbers of mutant cells identified in the replicate populations are then used in one of several mathematical models to estimate the number of independent mutations that arose during growth9. Comparing the number of independent mutations to the average population size provides a measure of the mutation rate. In S. cerevisiae, mutation frequencies and rates are frequently measured using the URA3 and CAN1 genes, since loss-of-function mutations in these genes produce selectable phenotypes. Loss of URA3 function renders cells resistant to 5-fluoroorotic acid (5-FOA), since the protein encoded by URA3 converts 5-FOA into a toxic molecule10. Loss of function of CAN1 allows cells to become resistant to canavanine, a toxic arginine analog, since the protein encoded by CAN1 transports arginine and canavanine into cells11.
Both genetic and physical sorting strategies have been successfully employed to obtain larger populations of S. cerevisiae mother cells to facilitate investigations of replicative aging. Genetic strategies include clever approaches that select against daughter cell survival in a population. The mother enrichment program (MEP) involves beta-estradiol induction of Cre recombinase expression only in daughter cells, leading to daughter-specific disruption of two essential genes that were engineered to contain loxP sites12. MEP strains can be grown normally in the absence of the inducer, but only the mother cells will continue to divide following induction, while daughter cells will arrest at M-phase typically during their first progression through the cell cycle12. While this system has not been used to broadly study mutation accumulation during aging, it has been used to study loss of heterozygosity, a relatively frequent form of genome instability in diploid yeast strains, as well as biochemical changes in aging mother cells13,14. Similarly, the daughter-arrester system involves daughter-specific expression of the S. cerevisiae URA3 gene15. Daughter-arrester strains grow normally until 5-FOA is added to the medium, at which point daughter cells expressing URA3 will die, while mother cells continue to grow15. These are useful systems to enrich for mother cells, but they depend on maintenance of the gene functions that constitute the selection system. Random mutations in one or more components of the selection system could result in daughter cells that escape the selection and proliferate exponentially. During development of the MEP strains, appropriate population sizes were determined to avoid the appearance of mutant daughter cells that could escape the selection12. However, this limit in population size compromises the detection of rare forms of genome instability during replicative aging. This restriction on population size could be partly overcome by using diploid yeast strains with two copies of each gene contributing to the selection system, since most mutations would likely be recessive12. However, use of diploid strains constrains the types of mutational events that can be easily detected compared to those that can be easily detected in haploid yeast cells.
Physical sorting strategies include isolation of mother cells with a labeled cell surface from unlabeled daughter cells to enrich for large populations of mother cells. The observation that cell surface proteins (cell wall proteins) of S. cerevisiae daughter cells are newly synthesized during budding has been exploited to label mother cells without labeling the daughter cells that they produce16. For example, an initial population of yeast cells labeled on their surface with biotin can be grown and then isolated from their daughter cells using anti-biotin microbeads and magnetic sorting16 because the daughter cells generated by the initial population will not contain biotin on their surface. Serial growth and sorting allows progressively older populations of mother cells to be obtained and analyzed. This procedure has been used successfully to examine changes in cell physiology and gene expression during replicative aging of yeast13,14,17,18. The current method adapts this biotin labeling and magnetic sorting technique to enrich for mother cells with the purpose of measuring the accumulation of potentially rare mutations or other forms of genome instability assayed through phenotypic selections. Serial growth and physical sorting allow analysis of mutation accumulation in progressively older mother cells, as well as in their daughter cell populations. Determination of mutation rates per generation allows a predicted mutation frequency to be calculated for mother cells that have undergone a particular number of cell divisions. Since predicted frequency values are based on the expected increase due to additional rounds of cell division, substantial deviations in the observed mutation frequencies compared to the predicted values provide evidence for age-specific changes in the rate of accumulating mutations. Using this protocol, fluorescent staining of bud scars that correspond to sites of cell divisions on mother cells coupled with analysis by flow cytometry can be used to quickly determine the age distribution of sorted and unsorted populations. Additional fluorescent reagents, such as those used for detecting reactive oxygen species, can also be utilized during this protocol. Overall, this protocol enables efficient analysis of age-dependent changes in genome instability with the potential for correlation to age-related cell physiological changes in a model organism well suited to studying the genetic and environmental factors that influence aging-related genome instability.

<table border="0" cellpadding="0" cellspacing="0" width="802">
	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Name of Material/ Equipment</td>
			<td style="width:108px;">Company</td>
			<td style="width:119px;">Catalog Number</td>
			<td style="width:359px;">Comments/Description</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">EZ-Link Sulfo-NHS-LC-Biotin</td>
			<td style="width:108px;">Thermo Scientific</td>
			<td style="width:119px;">21335</td>
			<td style="width:359px;">100mg</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Anti-Biotin Microbeads</td>
			<td style="width:108px;">Miltenyi Biotec</td>
			<td style="width:119px;">130-090-485</td>
			<td style="width:359px;">2ml MicroBeads conjugated to monoclonal mouse anti-biotin antibodies (isotype: mouse IgG1)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">MACS LS columns</td>
			<td style="width:108px;">Miltenyi Biotec</td>
			<td style="width:119px;">130-042-401</td>
			<td style="width:359px;">25 columns suitable for QuadroMACS separator</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">QuadroMACS separator</td>
			<td style="width:108px;">Miltenyi Biotec</td>
			<td style="width:119px;">130-090-976</td>
			<td style="width:359px;">Separator Only</td>
		</tr>
		<tr height="126">
			<td height="126" style="height:126px;width:216px;">QuadroMACS Starting Kit</td>
			<td style="width:108px;">Miltenyi Biotec</td>
			<td style="width:119px;">130-091-051</td>
			<td style="width:359px;">Includes:&#160; QuadroMACS Separator (130-090-976), MACS MultiStand (130-042-303), LS Columns (130-042-401), MACS 15ml Tube Rack (130-091-052), One 2ml unit of MACS MicroBeads. Note: Alternative systems include Dynabeads Biotin Binder and MagnaBind Magnetic Beads</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">BD Falcon Cell Strainer, 40 &#181;m</td>
			<td style="width:108px;">BD Biosciences</td>
			<td style="width:119px;">352340</td>
			<td style="width:359px;">40 &#181;m for use with BD Falcon 50 ml Conical Tubes.&#160; 50 per pack</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Trypan Blue</td>
			<td style="width:108px;">Sigma-Aldrich</td>
			<td style="width:119px;">T6146</td>
			<td style="width:359px;">Powder, filtering after dissolving important to remove particulates</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Wheat germ agglutinin Alexa Fluor488</td>
			<td style="width:108px;">Life Technologies</td>
			<td style="width:119px;">W11261</td>
			<td style="width:359px;">Conjugates with different excitation/emission spectra could also be used</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">ProLong Gold antifade reagent</td>
			<td style="width:108px;">Life Technologies</td>
			<td style="width:119px;">P36930</td>
			<td>10 ml, other antifade reagents could also be used</td>
		</tr>
	</tbody>
</table>

combining magnetic sorting of mother cells and fluctuation tests to analyze genome instability during mitotic cell aging in saccharomyces cerevisiae

Saccharomyces cerevisiae has been an excellent model system for examining mechanisms and consequences of genome instability. Information gained from this yeast model is relevant to many organisms, including humans, since DNA repair and DNA damage response factors are well conserved across diverse species. However, S. cerevisiae has not yet been used to fully address whether the rate of accumulating mutations changes with increasing replicative (mitotic) age due to technical constraints. For instance, measurements of yeast replicative lifespan through micromanipulation involve very small populations of cells, which prohibit detection of rare mutations. Genetic methods to enrich for mother cells in populations by inducing death of daughter cells have been developed, but population sizes are still limited by the frequency with which random mutations that compromise the selection systems occur. The current protocol takes advantage of magnetic sorting of surface-labeled yeast mother cells to obtain large enough populations of aging mother cells to quantify rare mutations through phenotypic selections. Mutation rates, measured through fluctuation tests, and mutation frequencies are first established for young cells and used to predict the frequency of mutations in mother cells of various replicative ages. Mutation frequencies are then determined for sorted mother cells, and the age of the mother cells is determined using flow cytometry by staining with a fluorescent reagent that detects bud scars formed on their cell surfaces during cell division. Comparison of predicted mutation frequencies based on the number of cell divisions to the frequencies experimentally observed for mother cells of a given replicative age can then identify whether there are age-related changes in the rate of accumulating mutations. Variations of this basic protocol provide the means to investigate the influence of alterations in specific gene functions or specific environmental conditions on mutation accumulation to address mechanisms underlying genome instability during replicative aging.

Watch this Scientific Journal Video about Combining Magnetic Sorting of Mother Cells and Fluctuation Tests to Analyze Genome Instability During Mitotic Cell Aging in Saccharomyces cerevisiae at JoVE.c

Combining Magnetic Sorting of Mother Cells and Fluctuation Tests to Analyze Genome Instability During Mitotic Cell Aging in Saccharomyces cerevisiae

Mutation rates in young Saccharomyces cerevisiae cells measured through fluctuation tests are used to predict mutation frequencies for mother cells of different replicative ages. Magnetic sorting and flow cytometry are then used to measure actual mutation frequencies and age of mother cells to identify any deviations from predicted mutation frequencies.

Combining Magnetic Sorting of Mother Cells and Fluctuation Tests to Analyze Genome Instability During Mitotic Cell Aging in Saccharomyces cerevisiae

Saccharomyces cerevisiae has been an excellent model system for examining mechanisms and consequences of genome instability. Information gained from this ...

combining-magnetic-sorting-mother-cells-fluctuation-tests-to-analyze

Nanyang Technological University

Research

JoVE Journal

Biology

12.5K Views.  Rensselaer Polytechnic Institute. Mutation rates in young Saccharomyces cerevisiae cells measured through fluctuation tests are used to predict mutation frequencies for mother cells of different replicative ages. Magnetic sorting and flow cytometry are then used to measure actual mutation frequencies and age of mother cells to identify any deviations from predicted mutation frequencies. 

Video: Combining Magnetic Sorting of Mother Cells and Fluctuation Tests to Analyze Genome Instability During Mitotic Cell Aging in Saccharomyces cerevisiae

Dissection of Saccharomyces Cerevisiae Asci

Yeast is a highly tractable model system that is used to study many different cellular processes. The common laboratory strain Saccharomyces cerevisiae exists in either a haploid or diploid state. The ability to combine alleles from two haploids and the ability to introduce modifications to the genome requires the production and dissection of asci. Asci production from haploid cells begins with the mating of two yeast haploid strains with compatible mating types to produce a diploid strain. This can be accomplished in a number of ways either on solid medium or in liquid. It is advantageous to select for the diploids in medium that selectively promotes their growth compared to either of the haploid strains. The diploids are then allowed to sporulate on nutrient-poor medium to form asci, a bundle of four haploid daughter cells resulting from meiotic reproduction of the diploid. A mixture of vegetative cells and asci is then treated with the enzyme zymolyase to digest away the membrane sac surrounding the ascospores of the asci. Using micromanipulation with a microneedle under a dissection microscope one can pick up individual asci and separate and relocate the four ascopores. Dissected asci are grown for several days and tested for the markers or alleles of interest by replica plating onto appropriate selective media.

Dissection of Saccharomyces Cerevisiae Asci

Micromanipulation of yeast cells is needed for meiotic genetic analysis or to select diploid zygotes. These micromanipulations are carried out using the microneedle of a dissection microscope. The microneedle is used to relocate cells and is controlled by a micromanipulator which are available with various degrees of automation.

Yeast is a highly tractable model system that is used to study many different cellular processes. The common laboratory strain Saccharomyces cerevisiae ...

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 ...

Microarray Analysis for Saccharomyces cerevisiae

In this protocol, gene expression in yeast (Saccharomyces cerevisiae) is changed after exposure to oxidative stress induced by the addition of hydrogen peroxide (H2O2), an oxidizing agent. In the experiment, yeast is grown for 48 hours in 1/2X YPD broth containing 3X glucose. The culture is split into a control and treated group. The experiment culture is treated with 0.5 mM H2O2 in Hanks Buffered Saline (HBSS) for 1 hour. The control culture is treated with HBSS only. Total RNA is extracted from both cultures and is converted to a biotin-labeled cRNA product through a multistep process. The final synthesis product is taken back to the UVM Microarray Core Facility and hybridized to the Affymetrix yeast GeneChips. The resulting gene expression data are uploaded into bioinformatics data analysis software.

Microarray Analysis for Saccharomyces cerevisiae

In this protocol, gene expression in yeast (Saccharomyces cerevisiae) is changed after exposure to oxidative stress induced by the addition of hydrogen peroxide (H2O2), an oxidizing agent.

In this protocol, gene expression in yeast (Saccharomyces cerevisiae) is changed after exposure to oxidative stress induced by the addition of hydrogen ...

Lipid Vesicle-mediated Affinity Chromatography using Magnetic Activated Cell Sorting (LIMACS): a Novel Method to Analyze Protein-lipid Interaction

The analysis of lipid protein interaction is difficult because lipids are embedded in cell membranes and therefore, inaccessible to most purification procedures. As an alternative, lipids can be coated on flat surfaces as used for lipid ELISA and Plasmon resonance spectroscopy. However, surface coating lipids do not form microdomain structures, which may be important for the lipid binding properties. Further, these methods do not allow for the purification of larger amounts of proteins binding to their target lipids.
 To overcome these limitations of testing lipid protein interaction and to purify lipid binding proteins we developed a novel method termed lipid vesicle-mediated affinity chromatography using magnetic-activated cell sorting (LIMACS). In this method, lipid vesicles are prepared with the target lipid and phosphatidylserine as the anchor lipid for Annexin V MACS. Phosphatidylserine is a ubiquitous cell membrane phospholipid that shows high affinity to the protein Annexin V. Using magnetic beads conjugated to Annexin V the phosphatidylserine-containing lipid vesicles will bind to the magnetic beads. When the lipid vesicles are incubated with a cell lysate the protein binding to the target lipid will also be bound to the beads and can be co-purified using MACS. This method can also be used to test if recombinant proteins reconstitute a protein complex binding to the target lipid. 
 We have used this method to show the interaction of atypical PKC (aPKC) with the sphingolipid ceramide and to co-purify prostate apoptosis response 4 (PAR-4), a protein binding to ceramide-associated aPKC. We have also used this method for the reconstitution of a ceramide-associated complex of recombinant aPKC with the cell polarity-related proteins Par6 and Cdc42. Since lipid vesicles can be prepared with a variety of sphingo- or phospholipids, LIMACS offers a versatile test for lipid-protein interaction in a lipid environment that resembles closely that of the cell membrane. Additional lipid protein complexes can be identified using proteomics analysis of lipid binding protein co-purified with the lipid vesicles.

Lipid Vesicle-mediated Affinity Chromatography using Magnetic Activated Cell Sorting LIMACS: a Novel Method to Analyze Protein-lipid Interaction

To test the interaction of a protein with its target lipid we used MACS and Annexin V-conjugated magnetic beads and lipid vesicles synthesized from the target lipid and Annexin V-binding phosphatidylserine. Proteins bound to the target lipid are co-purified and analyzed after elution from the beads.

The analysis of lipid protein interaction is difficult because lipids are embedded in cell membranes and therefore, inaccessible to most purification ...

A Fluorescence Microscopy Assay for Monitoring Mitophagy in the Yeast Saccharomyces cerevisiae

Autophagy is important for turnover of cellular components under a range of different conditions. It serves an essential homeostatic function as well as a quality control mechanism that can target and selectively degrade cellular material including organelles1-4. For example, damaged or redundant mitochondria (Fig. 1), not disposed of by autophagy, can represent a threat to cellular homeostasis and cell survival. In the yeast, Saccharomyces cerevisiae, nutrient deprivation (e.g., nitrogen starvation) or damage can promote selective turnover of mitochondria by autophagy in a process termed mitophagy 5-9.
We describe a simple fluorescence microscopy approach to assess autophagy. For clarity we restrict our description here to show how the approach can be used to monitor mitophagy in yeast cells. The assay makes use of a fluorescent reporter, Rosella, which is a dual-emission biosensor comprising a relatively pH-stable red fluorescent protein linked to a pH-sensitive green fluorescent protein. The operation of this reporter relies on differences in pH between the vacuole (pH ~ 5.0-5.5) and mitochondria (pH ~ 8.2) in living cells. Under growing conditions, wild type cells exhibit both red and green fluorescence distributed in a manner characteristic of the mitochondria. Fluorescence emission is not associated with the vacuole. When subjected to nitrogen starvation, a condition which induces mitophagy, in addition to red and green fluorescence labeling the mitochondria, cells exhibit the accumulation of red, but not green fluorescence, in the acidic vacuolar lumen representing the delivery of mitochondria to the vacuole. Scoring cells with red, but not green fluorescent vacuoles can be used as a measure of mitophagic activity in cells5,10-12.

A Fluorescence Microscopy Assay for Monitoring Mitophagy in the Yeast Saccharomyces cerevisiae

A robust approach to monitor the delivery of organelles to the acidic lumen of the yeast vacuole for degradation and recycling is described. The method relies on the specific labeling of target organelles with a genetically encoded dual-emission fluorescence pH-biosensor, and visualization of individual cells using fluorescence microscopy.

Autophagy is important for turnover of cellular components under a range of different conditions. It serves an essential homeostatic function as well as a ...

Expression and Purification of the Cystic Fibrosis Transmembrane Conductance Regulator Protein in Saccharomyces cerevisiae

The cystic fibrosis transmembrane conductance regulator (CFTR) is a chloride channel, that when mutated, can give rise to cystic fibrosis in humans.There is therefore considerable interest in this protein, but efforts to study its structure and activity have been hampered by the difficulty of expressing and purifying sufficient amounts of the protein1-3. Like many 'difficult' eukaryotic membrane proteins, expression in a fast-growing organism is desirable, but challenging, and in the yeast S. cerevisiae, so far low amounts were obtained and rapid degradation of the recombinant protein was observed 4-9. Proteins involved in the processing of recombinant CFTR in yeast have been described6-9 .In this report we describe a methodology for expression of CFTR in yeast and its purification in significant amounts. The protocol describes how the earlier proteolysis problems can be overcome and how expression levels of CFTR can be greatly improved by modifying the cell growth conditions and by controlling the induction conditions, in particular the time period prior to cell harvesting. The reagants associated with this protocol (murine CFTR-expressing yeast cells or yeast plasmids) will be distributed via the US Cystic Fibrosis Foundation, which has sponsored the research. An article describing the design and synthesis of the CFTR construct employed in this report will be published separately (Urbatsch, I.; Thibodeau, P. et al., unpublished). In this article we will explain our method beginning with the transformation of the yeast cells with the CFTR construct - containing yeast plasmid (Fig. 1). The construct has a green fluorescent protein (GFP) sequence fused to CFTR at its C-terminus and follows the system developed by Drew et al. (2008)10. The GFP allows the expression and purification of CFTR to be followed relatively easily. The JoVE visualized protocol finishes after the preparation of microsomes from the yeast cells, although we include some suggestions for purification of the protein from the microsomes. Readers may wish to add their own modifications to the microsome purification procedure, dependent on the final experiments to be carried out with the protein and the local equipment available to them. The yeast-expressed CFTR protein can be partially purified using metal ion affinity chromatography, using an intrinsic polyhistidine purification tag. Subsequent size-exclusion chromatography yields a protein that appears to be &gt;90% pure, as judged by SDS-PAGE and Coomassie-staining of the gel.

Expression and Purification of the Cystic Fibrosis Transmembrane Conductance Regulator Protein in Saccharomyces cerevisiae

Attempts to express the cystic fibrosis transmembrane conductance regulator (CFTR) in Saccharomyces cerevisiae have, until now, yielded relatively low amounts of protein. This protocol and the associated reagents distributed via the Cystic Fibrosis Foundation should allow the preparation of milligram amounts of this 'difficult' eukaryotic membrane protein.

The  cystic fibrosis transmembrane conductance regulator (CFTR) is a chloride  channel, that when mutated, can give rise to cystic fibrosis in  ...

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 ...

Coupled Assays for Monitoring Protein Refolding in Saccharomyces cerevisiae

Proteostasis, defined as the combined processes of protein folding/biogenesis, refolding/repair, and degradation, is a delicate cellular balance that must be maintained to avoid deleterious consequences 1. External or internal factors that disrupt this balance can lead to protein aggregation, toxicity and cell death. In humans this is a major contributing factor to the symptoms associated with neurodegenerative disorders such as Huntington's, Parkinson's, and Alzheimer's diseases 10. It is therefore essential that the proteins involved in maintenance of proteostasis be identified in order to develop treatments for these debilitating diseases. This article describes techniques for monitoring in vivo protein folding at near-real time resolution using the model protein firefly luciferase fused to green fluorescent protein (FFL-GFP). FFL-GFP is a unique model chimeric protein as the FFL moiety is extremely sensitive to stress-induced misfolding and aggregation, which inactivates the enzyme 12. Luciferase activity is monitored using an enzymatic assay, and the GFP moiety provides a method of visualizing soluble or aggregated FFL using automated microscopy. These coupled methods incorporate two parallel and technically independent approaches to analyze both refolding and functional reactivation of an enzyme after stress. Activity recovery can be directly correlated with kinetics of disaggregation and re-solubilization to better understand how protein quality control factors such as protein chaperones collaborate to perform these functions. In addition, gene deletions or mutations can be used to test contributions of specific proteins or protein subunits to this process. In this article we examine the contributions of the protein disaggregase Hsp104 13, known to partner with the Hsp40/70/nucleotide exchange factor (NEF) refolding system 5, to protein refolding to validate this approach.

Coupled Assays for Monitoring Protein Refolding in Saccharomyces cerevisiae

This article describes the use of a firefly luciferase-GFP fusion protein to investigate in vivo protein folding in Saccharomyces cerevisiae. Using this reagent, refolding of a model heat-denatured protein can be monitored simultaneously by fluorescence microscopy and an enzymatic assay to probe the roles of proteostasis network components in protein quality control.

Proteostasis, defined as the combined processes of protein folding/biogenesis, refolding/repair, and degradation, is a delicate cellular balance that must ...

Isolating Intestinal Stem Cells from Adult Drosophila Midguts by FACS to Study Stem Cell Behavior During Aging

Aging tissue is characterized by a continuous decline in functional ability. Adult stem cells are crucial in maintaining tissue homeostasis particularly in tissues that have a high turnover rate such as the intestinal epithelium. However, adult stem cells are also subject to aging processes and the concomitant decline in function. The Drosophila midgut has emerged as an ideal model system to study molecular mechanisms that interfere with the intestinal stem cells&#8217; (ISCs) ability to function in tissue homeostasis. Although adult ISCs can be easily identified and isolated from midguts of young flies, it has been a major challenge to study endogenous molecular changes of ISCs during aging. This is due to the lack of a combination of molecular markers suitable to isolate ISCs from aged intestines. Here we propose a method that allows for successful dissociation of midgut tissue into living cells that can subsequently be separated into distinct populations by FACS. By using dissociated cells from the esg-Gal4, UAS-GFP fly line, in which both ISCs and the enteroblast (EB) progenitor cells express GFP, two populations of cells are distinguished based on different GFP intensities. These differences in GFP expression correlate with differences in cell size and granularity and represent enriched populations of ISCs and EBs. Intriguingly, the two GFP-positive cell populations remain distinctly separated during aging, presenting a novel technique for identifying and isolating cell populations enriched for either ISCs or EBs at any time point during aging. The further analysis, for example transcriptome analysis, of these particular cell populations at various time points during aging is now possible and this will facilitate the examination of endogenous molecular changes that occur in these cells during aging.

Isolating Intestinal Stem Cells from Adult Drosophila Midguts by FACS to Study Stem Cell Behavior During Aging

Understanding the endogenous molecular changes in adult stem cells during aging requires isolating the cells of interest. The method described here presents a simple and robust approach to enrich for and isolate Drosophila intestinal stem cells and the enteroblast progenitor cells by FACS at any time point during aging.

Aging tissue is characterized by a continuous decline in functional ability. Adult stem cells are crucial in maintaining tissue homeostasis particularly ...

Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae

Determining the mode of action of bioactive chemicals is of interest to a broad range of academic, pharmaceutical, and industrial scientists. Saccharomyces cerevisiae, or budding yeast, is a model eukaryote for which a complete collection of ~6,000 gene deletion mutants and hypomorphic essential gene mutants are commercially available. These collections of mutants can be used to systematically detect chemical-gene interactions, i.e. genes necessary to tolerate a chemical. This information, in turn, reports on the likely mode of action of the compound. Here we describe a protocol for the rapid identification of chemical-genetic interactions in budding yeast. We demonstrate the method using the chemotherapeutic agent 5-fluorouracil (5-FU), which has a well-defined mechanism of action. Our results show that the nuclear TRAMP RNA exosome and DNA repair enzymes are needed for proliferation in the presence of 5-FU, which is consistent with previous microarray based bar-coding chemical genetic approaches and the knowledge that 5-FU adversely affects both RNA and DNA metabolism. The required validation protocols of these high-throughput screens are also described.

Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae

Here we present a cost-effective method for defining chemical-genetic interactions in budding yeast. The approach is built on fundamental techniques in yeast molecular biology and is well suited for the mechanistic interrogation of small to medium collections of chemicals and other media environments.