Karyotypic Determinants of Chromosome Instability Black arrow indicates a chromosome with a non-integer copy number in the population. (E) Representative DNA FACS profile of the g20 population of an aneuploid strain for which the modal ploidy could not be reliably determined and that was hence excluded from further study. doi:10.1371/journal.pgen.1002719.g001 deviants, a part of each g20 population sample was further cultured in liquid for 5 and 10 more generations to yield g25 and g30 population samples, respectively, and the time when each of the cultures reached these numbers of generations was recorded. To estimate the number of cell divisions required for detecting karyotypic deviants, we performed computer simulations to calculate the fraction of cells with deviant karyotypes based on different chromosome mis-segregation rates at different generation times. We found that .10% of a cell population is expected to display a deviant karyotype after 20 generations (cell divisions) in the presence of a very high CIN level (161023 chromosome mis- segregation per generation) or after 30 generations with a lower CIN level (561024 chromosome mis-segregation per generation, Figure S2A and S2B). For a comparison, wild-type diploid and tetraploid yeasts were reported to have an artificial chromosome loss rate of 2.661024 and 5.761022 per generation, respectively [21]. Again, to determine karyotype diversity within each population, ,200 cells from each g25 or g30 population were spread onto YPD plates, and 11 colonies from each plate were randomly selected and stored for later karyotyping by FACS and qPCR (Figure 1A). These were referred to as the g25 and g30 colony samples, and a total of 2647611 = 1034 such samples were obtained. We did not extend the above procedure beyond generation 30 due to the increasing effect of growth competition on karyotype diversity within each population. Nevertheless, our experimental design was likely to somewhat under-estimate the karyotype diversity for the aneuploid populations as a result of growth competition. Having obtained and stored away all samples as described above, we first performed FACS analysis on all g20 population samples (Figure S3). One population (strain 221) displayed an apparent ploidy of 2.1 by FACS but subsequent qPCR karyotyp- ing indicated that it was a hypo-diploid. Another g20 aneuploid population (strain 242) had a ploidy over 2N, possibly due to a whole-genome duplication event, and was not included in further analysis (Figure S1). Five of the g20 populations exhibited FACS profiles suggesting an extreme level of DNA content heterogeneity in the population, characterized by the presence of multiple broad peaks with no clear G1 and G2 peaks (Figure 1E and Figure S3). These 5 strains were not included in further analysis due to the difficulty to determine the karyotype makeup of the population (see Figure S1). Conversely, the FACS profiles of the remaining 41 g20 population samples displayed a more homogeneous, albeit aneuploid, DNA content between 1N and 2N with clearly identifiable G1 and G2 peaks (Figure 1B–1D and Figure S3). The ploidy distribution of these 41 populations are more uniform compared to the binomial distribution expected from triploid meiosis (Figure 1B), with significantly fewer than expected viable strains with a ploidy ,1.5 (Kolmogorov-Smirnov test between observed and expected cumulative distribution function P = 1.5861022, Figure S4). This result suggests that the viability of aneuploid strains may be biased toward those with ploidy close to a euploid state (haploid or diploid) compared to those with ploidy equidistant to the two nearby euploid states. We next subjected the above 41 g20 population samples to qPCR karyotyping analysis in order to determine the modal karyotype of each population by combining the ploidy estimate from FACS with the chromosome stoichiometry data from qPCR karyotyping assays. This was successfully accomplished for 36 of the g20 populations where the dominant karyotype could be clearly determined (Figure 1C, showing one such example, and Figure S5, showing all qPCR data for the g20 populations). The remaining 5 g20 populations were simply too heterogeneous in qPCR profiles for us to determine the modal karyotype (Figure S5 ‘‘too heterogeneous’’). These initial observations already indicate that different aneuploid strains exhibit different levels of CIN. Karyotype dynamics in aneuploid populations In order to associate specific CIN level with specific aneuploid karyotypes, we observed karyotype dynamics in each of the aneuploid strain populations by determining the karyotypes of the 11 randomly selected colonies plated from the population culture at one of the three (g20, g25 and g30) time points (see Figure 1A). Because qPCR karyotyping was of significant cost, only 27 of the 36 strains described above were subjected to this analysis while 9 were excluded due to contamination or redundancy in karyotype with other aneuploid strains in the collection (see Figure S1). We first used FACS data from the colonies to help select the time point most appropriate for karyotype analysis. For those strains that appeared to be most stable (g20 and g25 colonies showing ploidy variation similar to a wild-type control), colonies from the g30 populations were chosen to maximize the chance of observing some karyotypic deviants, whereas for those strains displaying the greatest apparent instability by FACS (colonies showing ploidy variation substantially larger than a wild-type control) colonies of populations from the earlier time points (g20 and g25) were chosen for qPCR analysis. Figure S6 displays representative examples of a haploid control (A), relatively stable (B) and unstable (C) aneuploid strain. The karyotyping data from the g20 population samples and g20, g25 or g30-derived colony samples allowed us to examine the relationship among the observed aneuploid karyotypes of all analyzed samples originated from the same spore. We next used a haplotype mapping approach to determine the minimum number of chromosome gain or loss events sufficient to explain the diverse karyotypes revealed by the 11 analyzed colonies of a given population. Haplotype maps are typically used to display genetic variation based on SNP loci and help to study the genotypic variation between populations of individuals [22]. A parsimony approach is used during the reconstruction of the relationship between the observed genotypes and do not require a priori information regarding the phylogeny of the individuals in the population. In adopting this approach, the 16 yeast chromosomes were treated as independent loci, each of which can exist in, and change between, two states defined by copy numbers (analogous to alleles) – 1 or 2 copies (as the ploidy of these strains varied between 1N and 2N). The algorithm proceeded by attempting to connect all 12 karyotypes (the g20 population karyotype +11 colony karyotypes) in a single haplotype map (in this case, a ‘karyotype map’) by minimizing the total number of mutational events (in this case, copy number changes) in the entire map. It has to be noted that the parsimony approach underlying this algorithm allows distinguishing those colony karyotypes that most likely originated directly from a CIN event in the ancestral spore karyotype from those that arose as secondary events from already deviant karyotypes accumulated in the population. This step was important to not over-estimate the level of CIN that could be attributed to the original spore karyotypes. Figure 2 and Figure S7 display the resulting karyotype maps in the 27 aneuploid strains. Based on the number of independent CIN events that could be directly linked back to the original PLoS Genetics | www.plosgenetics.org 4 May 2012 | Volume 8 | Issue 5 | e1002719