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>

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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><tbody><tr><td>EZ-Link Sulfo-NHS-LC-Biotin</td><td>Thermo Scientific</td><td>21335</td><td>100 mg</td></tr><tr><td>Anti-Biotin Microbeads</td><td>Miltenyi Biotec</td><td>130-090-485</td><td>2 ml MicroBeads conjugated to monoclonal mouse anti-biotin antibodies (isotype: mouse IgG1)</td></tr><tr><td>MACS LS columns</td><td>Miltenyi Biotec</td><td>130-042-401</td><td>25 columns suitable for QuadroMACS separator</td></tr><tr><td>QuadroMACS separator</td><td>Miltenyi Biotec</td><td>130-090-976</td><td>Separator Only</td></tr><tr><td>QuadroMACS Starting Kit</td><td>Miltenyi Biotec</td><td>130-091-051</td><td>Includes: QuadroMACS Separator (130-090-976), MACS MultiStand (130-042-303), LS Columns (130-042-401), MACS 15 ml Tube Rack (130-091-052), One 2 ml unit of MACS MicroBeads. Note: Alternative systems include Dynabeads Biotin Binder and MagnaBind Magnetic Beads</td></tr><tr><td>BD Falcon Cell Strainer, 40 &amp;micro;m</td><td>BD Biosciences</td><td>352340</td><td>40 &amp;micro;m for use with BD Falcon 50 ml Conical Tubes. 50 per pack</td></tr><tr><td>Trypan Blue</td><td>Sigma-Aldrich</td><td>T6146</td><td>Powder, filtering after dissolving important to remove particulates</td></tr><tr><td>Wheat germ agglutinin Alexa Fluor488</td><td>Life Technologies</td><td>W11261</td><td>Conjugates with different excitation/emission spectra could also be used</td></tr><tr><td>ProLong Gold antifade reagent</td><td>Life Technologies</td><td>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

Universidad de Cartagena UDEC

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

Continuous High-resolution Microscopic Observation of Replicative Aging in Budding Yeast

We demonstrate the use of a simple microfluidic setup, in which single budding yeast cells can be tracked throughout their entire lifespan. The microfluidic chip exploits the size difference between mother and daughter cells using an array of micropads. Upon loading, cells are trapped underneath these micropads, because the distance between the micropad and cover glass is similar to the diameter of a yeast cell (3-4 &mu;m). After the loading procedure, culture medium is continuously flushed through the chip, which not only creates a constant and defined environment throughout the entire experiment, but also flushes out the emerging daughter cells, which are not retained underneath the pads due to their smaller size. The setup retains mother cells so efficiently that in a single experiment up to 50 individual cells can be monitored in a fully automated manner for 5 days or, if necessary, longer. In addition, the excellent optical properties of the chip allow high-resolution imaging of cells during the entire aging process.

We describe here the operation of a microfluidic device that allows continuous and high-resolution microscopic imaging of single budding yeast cells during their complete replicative and/or chronological lifespan.

We demonstrate the use of a simple microfluidic setup, in which single budding yeast cells can be tracked throughout their entire lifespan. The ...

Bioengineering

Quantifying Yeast Chronological Life Span by Outgrowth of Aged Cells

The budding yeast Saccharomyces cerevisiae has proven to be an important model organism in the field of aging research 1. The replicative and chronological life spans are two established paradigms used to study aging in yeast. Replicative aging is defined as the number of daughter cells a single yeast mother cell produces before senescence; chronological aging is defined by the length of time cells can survive in a non-dividing, quiescence-like state 2. We have developed a high-throughput method for quantitative measurement of chronological life span. This method involves aging the cells in a defined medium under agitation and at constant temperature. At each age-point, a sub-population of cells is removed from the aging culture and inoculated into rich growth medium. A high-resolution growth curve is then obtained for this sub-population of aged cells using a Bioscreen C MBR machine. An algorithm is then applied to determine the relative proportion of viable cells in each sub-population based on the growth kinetics at each age-point. This method requires substantially less time and resources compared to other chronological lifespan assays while maintaining reproducibility and precision. The high-throughput nature of this assay should allow for large-scale genetic and chemical screens to identify novel longevity modifiers for further testing in more complex organisms.

Chronological aging in yeast refers to the loss of cell viability associated with time in stationary phase. Here we describe a high-throughput method for quantitatively determining yeast chronological life span.

The budding yeast Saccharomyces cerevisiae has proven to be an important model organism in the field of aging research 1. The replicative and ...

A Suppressor Screen for the Characterization of Genetic Links Regulating Chronological Lifespan in Saccharomyces cerevisiae

Aging is the time dependent deterioration of an organism&#8217;s normal biological processes that increases the probability of death. Many genetic factors contribute to alterations in the normal aging process. These factors intersect in complex ways, as evidenced by the wealth of documented links identified and conserved in many organisms. Most of these studies focus on loss-of-function, null mutants that allow for rapid screening of many genes simultaneously. There is much less work that focuses on characterizing the role that overexpression of a gene in this process. In the present work, we present a straightforward methodology to identify and clone genes in the budding yeast, Saccharomyces cerevisiae, for study in suppression of the short-lived chronological lifespan phenotype seen in many genetic backgrounds. This protocol is designed to be accessible to researchers from a wide variety of backgrounds and at various academic stages. The SIR2 gene, which codes for a histone deacetylase, was selected for cloning in the pRS315 vector, as there have been conflicting reports on its effect on the chronological lifespan. SIR2 also plays a role in autophagy, which results when disrupted via the deletion of several genes, including the transcription factor ATG1. As a proof of principle, we clone the SIR2 gene to perform a suppressor screen on the shortened lifespan phenotype characteristic of the autophagy deficient atg1&#916; mutant and compare it to an otherwise isogenic, wild type genetic background.

A Suppressor Screen for the Characterization of Genetic Links Regulating Chronological Lifespan in Saccharomyces cerevisiae

Here is a protocol to identify genetic interactions through an increased copy number suppressor screen in Saccharomyces cerevisiae. This method allows researchers to identify, clone, and test suppressors in short-lived yeast mutants. We test the effect of the copy number increase of SIR2 on lifespan in an autophagy null mutant.

Aging is the time dependent deterioration of an organism&#8217;s normal biological processes that increases the probability of death. Many genetic factors ...

Genetics

A high-throughput method to globally study the organelle morphology in S. cerevisiae

High-throughput methods to examine protein localization or organelle morphology is an effective tool for studying protein interactions and can help achieve an comprehensive understanding of molecular pathways. In Saccharomyces cerevisiae, with the development of the non-essential gene deletion array, we can globally study the morphology of different organelles like the endoplasmic reticulum (ER) and the mitochondria using GFP (or variant)-markers in different gene backgrounds. However, incorporating GFP markers in each single mutant individually is a labor-intensive process. Here, we describe a procedure that is routinely used in our laboratory. By using a robotic system to handle high-density yeast arrays and drug selection techniques, we can significantly shorten the time required and improve reproducibility. In brief, we cross a GFP-tagged mitochondrial marker (Apc1-GFP) to a high-density array of 4,672 nonessential gene deletion mutants by robotic replica pinning.  Through diploid selection, sporulation, germination and dual marker selection, we recover both alleles. As a result, each haploid single mutant contains Apc1-GFP incorporated at its genomic locus. Now, we can study the morphology of mitochondria in all non-essential mutant background. Using this high-throughput approach, we can conveniently study and delineate the pathways and genes involved in the inheritance and the formation of organelles in a genome-wide setting.

GFP-fusion proteins are widely used to visualize organelles by confocal microscopy. However, screening for mutations that affect the morphology of organelles generally requires individual mutagenesis and is time consuming. Here, we demonstrate a method to simultaneously incorporate organelle-GFP markers in almost 5,000 non-essential genes in yeast.

High-throughput methods to examine protein localization or organelle morphology is an effective tool for studying protein interactions and can help ...

Live Cell Imaging of Chromosome Segregation During Mitosis

Chromosomes must be reliably and uniformly segregated into daughter cells during mitotic cell division. Fidelity of chromosomal segregation is controlled by multiple mechanisms that include the Spindle Assembly Checkpoint (SAC). The SAC is part of a complex feedback system that is responsible for prevention of a cell progress through mitosis unless all chromosomal kinetochores have attached to spindle microtubules. Chromosomal lagging and abnormal chromosome segregation is an indicator of dysfunctional cell cycle control checkpoints and can be used to measure the genomic stability of dividing cells. Deregulation of the SAC can result in the transformation of a normal cell into a malignant cell through the accumulation of errors during chromosomal segregation. Implementation of the SAC and the formation of the kinetochore complex are tightly regulated by interactions between kinases and phosphatase such as Protein Phosphatase 2A (PP2A). This protocol describes live cell imaging of lagging chromosomes in mouse embryonic fibroblasts isolated from mice that had a knockout of the PP2A-B56&#947; regulatory subunit. This method overcomes the shortcomings of other cell cycle control imaging techniques such as flow cytometry or immunocytochemistry that only provide a snapshot of a cell cytokinesis status, instead of a dynamic spatiotemporal visualization of chromosomes during mitosis.

This protocol describes an easy and convenient method to label and visualize live chromosomes in mitotic cells using Histone2B-GFP BacMam 2.0 label and a spinning disc confocal microscopy system.

Chromosomes must be reliably and uniformly segregated into daughter cells during mitotic cell division. Fidelity of chromosomal segregation is controlled ...

Developmental Biology

Evaluation of the Spindle Assembly Checkpoint Integrity in Mouse Oocytes

Aneuploidy is the leading genetic abnormality causing early miscarriage and pregnancy failure in humans. Most errors in chromosome segregation that give rise to aneuploidy occur during meiosis in oocytes, but why oocyte meiosis is error-prone is still not fully understood. During cell division, cells prevent errors in chromosome segregation by activating the spindle assembly checkpoint (SAC). This control mechanism relies on detecting kinetochore (KT)-microtubule (MT) attachments and sensing tension generated by spindle fibers. When KTs are unattached, the SAC is activated and prevents cell-cycle progression. The SAC is activated first by MPS1 kinase, which triggers the recruitment and formation of the mitotic checkpoint complex (MCC), composed of MAD1, MAD2, BUB3, and BUBR1. Then, the MCC diffuses into the cytoplasm and sequesters CDC20, an anaphase-promoting complex/cyclosome (APC/C) activator. Once KTs become attached to microtubules and chromosomes are aligned at the metaphase plate, the SAC is silenced, CDC20 is released, and the APC/C is activated, triggering the degradation of Cyclin B and Securin, thereby allowing anaphase onset. Compared to somatic cells, the SAC in oocytes is not as effective because cells can undergo anaphase despite having unattached KTs. Understanding why the SAC is more permissive and if this permissiveness is one of the causes of chromosome segregation errors in oocytes still needs further investigation. The present protocol describes the three techniques to comprehensively evaluate SAC integrity in mouse oocytes. These techniques include using nocodazole to depolymerize MTs to evaluate the SAC response, tracking SAC silencing by following the kinetics of Securin destruction, and evaluating the recruitment of MAD2 to KTs by immunofluorescence. Together these techniques probe mechanisms needed to produce healthy eggs by providing a complete evaluation of SAC integrity.

Error in chromosome segregation is a common feature in oocytes. Therefore, studying the spindle assembly checkpoint gives important clues about the mechanisms needed to produce healthy eggs. The present protocol describes three complementary assays to evaluate spindle assembly checkpoint integrity in mouse oocytes.

Aneuploidy is the leading genetic abnormality causing early miscarriage and pregnancy failure in humans. Most errors in chromosome segregation that give ...

Studying Age-dependent Genomic Instability using the S. cerevisiae Chronological Lifespan Model

Studies using the Saccharomyces cerevisiae aging model have uncovered life span regulatory pathways that are partially conserved in higher eukaryotes1-2. The simplicity and power of the yeast aging model can also be explored to study DNA damage and genome maintenance as well as their contributions to diseases during aging. Here, we describe a system to study age-dependent DNA mutations, including base substitutions, frame-shift mutations, gross chromosomal rearrangements, and homologous/homeologous recombination, as well as nuclear DNA repair activity by combining the yeast chronological life span with simple DNA damage and mutation assays. The methods described here should facilitate the identification of genes/pathways that regulate genomic instability and the mechanisms that underlie age-dependent DNA mutations and cancer in mammals.

Studying Age-dependent Genomic Instability using the S. cerevisiae Chronological Lifespan Model

Here we describe a set of DNA mutation assays that can be combined with the yeast chronological life span model to study the genes/pathways that regulate or contribute to genomic DNA instability during aging.

Studies using the Saccharomyces cerevisiae aging model have uncovered life span regulatory pathways that are partially conserved in higher eukaryotes1-2. ...

Flow Cytometry-based Purification of S. cerevisiae Zygotes

Zygotes are essential intermediates between haploid and diploid states in the life cycle of many organisms, including yeast (Figure 1) 1. S. cerevisiae zygotes result from the fusion of haploid cells of distinct mating type (MATa, MATalpha) and give rise to corresponding stable diploids that successively generate as many as 20 diploid progeny as a result of their strikingly asymmetric mitotic divisions 2. Zygote formation is orchestrated by a complex sequence of events: In this process, soluble mating factors bind to cognate receptors, triggering receptor-mediated signaling cascades that facilitate interruption of the cell cycle and culminate in cell-cell fusion. Zygotes may be considered a model for progenitor or stem cell function.
Although much has been learned about the formation of zygotes and although zygotes have been used to investigate cell-molecular questions of general significance, almost all studies have made use of mating mixtures in which zygotes are intermixed with a majority population of haploid cells 3-8. Many aspects of the biochemistry of zygote formation and the continuing life of the zygote therefore remain uninvestigated.
Reports of purification of yeast zygotes describe protocols based on their sedimentation properties 9; however, this sedimentation-based procedure did not yield nearly 90% purity in our hands. Moreover, it has the disadvantage that cells are exposed to hypertonic sorbitol. We therefore have developed a versatile purification procedure. For this purpose, pairs of haploid cells expressing red or green fluorescent proteins were co-incubated to allow zygote formation, harvested at various times, and the resulting zygotes were purified using a flow cytometry-based sorting protocol. This technique provides a convenient visual assessment of purity and maturation. The average purity of the fraction is approximately 90%. According to the timing of harvest, zygotes of varying degrees of maturity can be recovered. The purified samples provide a convenient point of departure for "-omic" studies, for recovery of initial progeny, and for systematic investigation of this progenitor cell.

Flow Cytometry-based Purification of S. cerevisiae Zygotes

To purify zygotes of S. cerevisiae, haploid cells of opposite mating type were engineered to express red or green fluorescent proteins, co-incubated to allow zygote formation, and fractionated using a flow cytometry-based protocol. The highly-enriched fraction enables subsequent "-omic" studies, recovery of initial progeny, and systematic investigation of zygote morphogenesis.

Zygotes are essential intermediates between haploid and diploid states in the life cycle of many organisms, including yeast (Figure 1) 1. S. cerevisiae ...

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 ...

Using Microfluidic Devices to Measure Lifespan and Cellular Phenotypes in Single Budding Yeast Cells

Budding yeast Saccharomyces cerevisiae is an important model organism in aging research. Genetic studies have revealed many genes with conserved effects on the lifespan across species. However, the molecular causes of aging and death remain elusive. To gain a systematic understanding of the molecular mechanisms underlying yeast aging, we need high-throughput methods to measure lifespan and to quantify various cellular and molecular phenotypes in single cells. Previously, we developed microfluidic devices to track budding yeast mother cells throughout their lifespan while flushing away newborn daughter cells. This article presents a method for preparing microfluidic chips and for setting up microfluidic experiments. Multiple channels can be used to simultaneously track cells under different conditions or from different yeast strains. A typical setup can track hundreds of cells per channel and allow for high-resolution microscope imaging throughout the lifespan of the cells. Our method also allows detailed characterization of the lifespan, molecular markers, cell morphology, and the cell cycle dynamics of single cells. In addition, our microfluidic device is able to trap a significant amount of fresh mother cells that can be identified by downstream image analysis, making it possible to measure the lifespan with higher accuracy.

This article presents a protocol optimized for the production of microfluidic chips and the setup of microfluidic experiments to measure the lifespan and cellular phenotypes of single yeast cells.

Budding yeast Saccharomyces cerevisiae is an important model organism in aging research. Genetic studies have revealed many genes with conserved effects ...

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

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Using Microfluidic Devices to Measure Lifespan and Cellular Phenotypes in Single Budding Yeast Cells