All organisms respond to cellular stress, which can arise from external conditions such as drugs and environmental shifts or internal perturbations including mutation and disease. Thus, at the cellular level, organisms must be able to sense both external and internal signals to mount a proper response. Yet in both single- and multi-celled organisms, there can be large variation in how individual cells respond to environmental stress, even among genetically identical cells in the same environment. For example, cell-to-cell variation in signaling and gene expression have been linked to differential survival of isogenic cancer cells responding to drugs (Lee et al., 2014; Paek et al., 2016; Shaffer et al., 2017; Inde and Dixon, 2018). Similarly, cellular heterogeneity in bacterial growth and gene expression can produce variation in survival upon antibiotic treatment (Balaban et al., 2004; Keren et al., 2004). Understanding the nature of this variation could facilitate the modulation of stress survival, with therapeutic applications.

One marker of heterogeneity in stress responses is dynamic localization of stress-activated transcription factors. Several canonical factors, including p53 in mammalian cells (Purvis et al., 2012; Kracikova et al., 2013; Paek et al., 2016) and Msn2 and its paralog Msn4 in fungi (Görner et al., 1998), reside in the cytosol in the absence of stress but rapidly translocate to the nucleus upon activation. These and other stress-activated factors can vary substantially in their responsiveness, in ways that can impact cellular outputs including gene-expression. For example, Msn2 localization dynamics differ depending on the nature of the stress (Hao and O’Shea, 2012; Petrenko et al., 2013; Granados et al., 2018), and these differences impart distinct effects on different target genes (Hao and O’Shea, 2012; Hansen and O’Shea, 2013; Stewart-Ornstein et al., 2013; Hansen and O’Shea, 2015a; Hansen and O’Shea, 2015b; Hansen and O’Shea, 2016; Hansen and Zechner, 2021). Msn2 targets with highly responsive promoters can be induced even with brief pulses of nuclear Msn2, whereas genes with less responsive promoters require prolonged Msn2 activation (Hansen and O’Shea, 2013; Hansen and O’Shea, 2015a; Hansen and O’Shea, 2015b; Hansen and O’Shea, 2016). Similarly, differences in the dynamics of p53 localization can lead to distinct transcriptional outputs, and these distinctions correlate with differences in stress survival (Purvis et al., 2012). Several studies have observed substantial cell-to-cell heterogeneity in nuclear localization dynamics of these factors (Cai et al., 2008; Cheong et al., 2011; Purvis and Lahav, 2013; Lin et al., 2015; AkhavanAghdam et al., 2016; Gasch et al., 2017; Granados et al., 2018; Li et al., 2018); however, the causes and functional effects of this variation remain poorly understood.

Cell-to-cell variation in transcription factor localization dynamics could arise for several reasons. Changes in the state of a single transcription factor may alter its localization independent of or separable from the cellular system (defined as factor-specific variation). In contrast, activity-state changes in the upstream signaling networks or cellular system itself could produce coordinated activation of the stress response (referred to as systemic variation). Distinguishing between local versus systemic variation has been difficult, since most studies to date have followed only single transcription factors. We recently developed strains in which two differentially tagged transcription factors regulated by the same signaling network are expressed in the same yeast cell. Msn2 activator fused to mCherry is co-expressed with the transcriptional repressor Dot6 fused to GFP. Both factors help to coordinate the yeast environmental stress response (Gasch et al., 2000; Causton et al., 2001): whereas Msn2 activates defense genes that are induced in the ESR (iESR genes), Dot6 represses growth-promoting genes involved in ribosome biogenesis that are correspondingly repressed in the ESR during stress (rESR genes) (Lippman and Broach, 2009; Bergenholm et al., 2018). Both factors are controlled by the Protein Kinase A (PKA) and mTOR pathways, which are generally associated with promoting growth (Figure 1A): PKA/TOR-dependent phosphorylation of Msn2 and Dot6 maintains the factors in the cytosol, whereas Msn2 and Dot6 dephosphorylation after PKA/TOR inhibition leads to their nuclear localization (Görner et al., 1998; Smith et al., 1998; Lippman and Broach, 2009). Thus, we expect the two factors to be coordinated in their localization when the stress response is activated systemically but discordant in response to factor-specific differences in regulation.

Figure 1 with 2 supplements see all

Download asset Open asset

The challenges in distinguishing factor-specific versus systemic variation have obscured how systemic activation of the stress response relates to other physiological responses. One important factor is growth rate. Growth rate and stress tolerance are competing interests in the cell and are often antagonistically regulated: fast growing cells tend to be the most susceptible to stress and toxins, whereas slow growing or quiescent cells generally survive extreme conditions (Balaban et al., 2004; Lu et al., 2009; Zakrzewska et al., 2011; Levy et al., 2012). Part of this antagonistic correlation is thought to be controlled, at least under specific situations, by the RAS-PKA pathway, which promotes growth and suppresses the stress response (Smith et al., 1998; Gasch et al., 2000; Zaman et al., 2008; Zaman et al., 2009). Li et al., 2018 used single-cell microscopy to show that slower growing cells in an isogenic culture displayed lower levels of the PKA allosteric activator cAMP and that artificial activation of PKA diminished the slow growing population (Li et al., 2018). They further showed a slight but statistically significant negative correlation between Msn2 nuclear localization and micro-colony growth over the subsequent 10 hr in the absence of stress. This suggests that activation of Msn2 is coupled to reduced growth rate, a theory put forward and debated in other bulk-culture studies (Regenberg et al., 2006; Castrillo et al., 2007; Brauer et al., 2008; Ho et al., 2018). The inability to distinguish between factor-specific variation and systemic activation of the stress response likely obscures the true relationship with growth.

Here, we monitored dynamic localization changes of both Msn2 and Dot6 in the same yeast cells, along with a panel of other single-cell measurements, to dissect local and systemic variation and illuminate the relationship between ESR activation and growth rate. We optimized a microfluidics system that can monitor single-cell localization levels and dynamics of both Msn2-mCherry and Dot6-GFP along with single-cell and colony growth rates, size, shape, cell-cycle phase and size changes before and after an acute dose of sodium chloride (NaCl) as a model stressor. Our results revealed several insights, including surprising levels of discordance in Msn2 and Dot6 activation that partly explained variation in post-stress growth rate. We developed a multi-factorial model explaining cell growth rate after stress acclimation to demonstrate that stress acclimation is partly predictable based on prior cellular states. Remarkably, one of the important predictors is the activation level of the Dot6 repressor, which counterintuitively is associated with faster growth acclimation and faster production of stress-induced catalase Ctt1. We discuss implications of this work for understanding how cellular state and transcriptional repression influence stress responses.

We optimized a microfluidics system that could measure nuclear localization dynamics as well as one- and two-cell colony growth rates before and after exposure to 0.7 M NaCl (Figure 1B and Methods). Using this system, we characterized the variation in cell responses for 72 min before and 144 min after exposure to NaCl, which induces ionic and osmotic stress, in biological triplicates done on separate days. This time frame captures phenotypic variation in cells growing in the absence of stress, during the acute stress-response phase (from 0 to 54 min after osmotic stress), and over later timepoints as cells acclimate to continuous NaCl. Microscopy imaging and analysis reports on Msn2-mCherry and Dot6-GFP nuclear localization dynamics in the same cells (Figure 1C, Figure 1—figure supplement 1). We used MATLAB scripts to identify nuclear translocation events, which we refer to as ‘peaks’ in the traces (see Methods). We also measured cell and colony growth phenotypes, including colony size, colony growth rates (defined by increase in pixel number of masked colony area and vetted with several analyses, Figure 1—figure supplement 2) both before and after stress, and change in cell size due to volume loss upon NaCl stress (Figure 1D and Methods). We used the relative change in colony area over time, collapsed from multiple z-stack images per time point, as a proxy for growth rate. One limitation is that growth by this estimation will be under-estimated for cells that bud perpendicular to the slide plane, introducing noise into the growth rate measurements for some cells. We limited our analysis to colonies of only one or two cells at the beginning of the time series and to cells that passed several quality-control filters (see Methods). In total, we analyzed 221 cells passing these filters, collected from the three independent biological replicates.

This system captured variation in all of the features measured. As expected based on previous studies (Levy et al., 2012; Fehrmann et al., 2013; Crane et al., 2014; Li et al., 2018; Jin et al., 2019), there was substantial variation in cellular growth rates before NaCl addition, confirming that cells vary considerably in their growth properties in the absence of stress (Figure 2A). Most colonies reduced their growth rate in response to NaCl stress (but not a mock treatment, Figure 1—figure supplement 2F), but once again there was substantial variation: some cells showed dramatic growth reduction upon NaCl, whereas others showed little to no change (Figure 2B). There were even individual colonies that accelerated growth after stress: 11 of 14 of these cells showed a small bud at the time of salt exposure, suggesting a cell cycle connection. NaCl-induced osmotic pressure is expected to produce rapid water loss before cells acclimate, and indeed most cells shrunk immediately after stress despite substantial variation in size changes (Figure 2C). Together, these results highlight the extensive cell-to-cell variation in behavior that is not identified in bulk measures of culture growth.

Figure 2

Download asset Open asset

Msn2 and Dot6 nuclear localization show only partial coordination

We next investigated co-variation in Msn2-mCherry and Dot6-GFP localization dynamics, before and as cells responded to NaCl. Both factors showed sporadic activation in unstressed cells, with brief and typically low levels of nuclear translocation (Figure 3A). Roughly 54% of Msn2 pre-stress peaks and 37% of Dot6 pre-stress peaks were temporally coordinated with the other factor (Figure 3B), which is significantly above chance (p<<0.0001, permutation analysis, see Methods) and suggests systemic activation of the stress response. This reveals both coordinated and independent fluctuations in Msn2 and Dot6 activation in the absence of stress, consistent with our prior results (Gasch et al., 2017). In the vast majority of cells, NaCl provoked a dramatic and coordinated increase in nuclear localization of both Msn2 and Dot6 (acute phase). However, after stress Msn2 and Dot6 behavior deviated: whereas few cells showed post-stress Dot6 nuclear translocation, many cells showed asynchronous pulses of Msn2 (Figure 3C–F), consistent with prior work (Petrenko et al., 2013). This was surprising, since we expected that Msn2 and Dot6 would be highly correlated during and immediately after NaCl treatment.

Figure 3

Download asset Open asset

In the course of this analysis, we realized another key difference between Msn2 and Dot6: the profiles of Dot6 nuclear pulses were often highly correlated between unstressed cells in the same colony, indicated by co-occurring peaks in two-cell colonies (Supplementary file 1). Permutation tests showed that this was highly significant compared to random chance (p=9.3e-4, see Methods). In contrast, the co-occurrence of Msn2 peaks in cells from the same colony was not significantly different from random. Since these cells are in the same local environment and have a shared life history in that one cell is the daughter of the other, it suggests that some feature of Dot6 regulation is predictable but separable from Msn2 behavior.

Reproducible differences in Msn2 versus Dot6 activation reveal subpopulations of cells

Comparisons of Msn2 and Dot6 nuclear localization patterns indicated different localization dynamics across cells, raising the possibility of distinct cell subpopulations. To investigate, we used Gaussian finite mixture modelling (Scrucca et al., 2016) of the population-normalized Msn2 and Dot6 nuclear localization traces to identify populations or ‘clusters’ of cells with distinguishable localization patterns (Figure 4, see Methods). Most clusters captured cells from all three biological replicates, with the exception of cell Cluster 9 and several small clusters that were enriched for cells from one replicate (Supplementary file 2). Six of these patterns were clearly recapitulated in an independent experiment (Figure 4—figure supplement 1). Thus, most of the cell groupings represent reproducible subpopulations with different stress-responsive behaviors.

Figure 4 with 1 supplement see all

Download asset Open asset

Figure 4—source data 1

The text file contains the log₂ of unnormalized nuclear trace values and the population-median-normalized nuclear trace values across the time course for each cell, divided into clusters (for strain AGY1328).

https://cdn.elifesciences.org/articles/82017/elife-82017-fig4-data1-v2.zip

Download elife-82017-fig4-data1-v2.zip

The subpopulations were differentiated by a combination of transcription-factor phenotypes. One distinguishing feature was the level of Dot6 activation during the acute-stress phase. Cluster 11 was characterized by lower than population-median magnitude of acute-stress Dot6 nuclear translocation, whereas cells in Clusters 6 and 7 showed higher-than-median Dot6 response. These results are consistent with the wider variance of Dot6 nuclear translocation levels during the acute phase (Figure 3D–F). A second distinguishing feature was the level of nuclear Msn2 and Dot6 before stress. Cluster 11 cells showed low levels of Dot6 before stress, whereas cells in Clusters 9 and 6 displayed higher-than-median nuclear Msn2 and Dot6 during this phase. Finally, the behavior of Msn2 during the post-stress acclimation phase was significantly different across subpopulations. Whereas Clusters 11 and to some extent 7 showed low levels of post-stress Msn2 nuclear localization, cells in multiple clusters showed high levels and/or pulsatile nuclear Msn2 as cells acclimated. We noticed that cells in Clusters 2 and 3 showed elevated levels of mCherry that persisted over time compared to other cells. Closer inspection of the microscopy images suggested that some of the signal may not reflect nuclear translocation but instead was likely vacuolar signal (see more below). As mentioned above, the variation in nuclear localization dynamics captured within these clusters occurred in all three biological replicates and in a separate experiment (See Supplementary file 2 and Figure 4—figure supplement 1), indicating reproducible distinctions in transcription factor behavior. Together, this analysis revealed important differences in cellular behavior across the phases of the NaCl response that are obscured by aggregate analysis of all cells in the population.

Cell subpopulations show different relationships with cell growth

Are subpopulations of cells identified above biologically meaningful? We turned to the other cellular measurements to look for co-variates in cellular behavior that reflect on higher-order relationships (Figure 5). We tested each of the cell subpopulations for statistically significant differences in pre-stress growth rate, post-stress growth rate, starting size, volume change, and cell-cycle phase at the time of NaCl exposure (inferred by visual inspection of bud size and nucleus location in the cell, see Methods). We found no significant correlations with cell volume changes or cell-cycle phase (although there was a minor signal for cell cycle, Figure 5—figure supplement 1). This is consistent with the lack of strong connection between cell-cycle phase and stress response found in several other studies (Paek et al., 2016; Gasch et al., 2017; Bagamery et al., 2020). In contrast, several clusters showed significant differences in growth rates.

Figure 5 with 2 supplements see all

Download asset Open asset

Overall, there was a positive correlation between pre-stress growth rate compared to post-stress growth rate (Figure 5A); however, the association was different for subpopulations of cells. Cells in Cluster 11, which were characterized by below-average Dot6 response before and during stress, showed slower growth rates before and after NaCl treatment (Figure 5B–C), and the slower growth was consistent when biological replicates were analyzed individually (p<0.02, T-test) and across multiple experiments (Figure 5—figure supplement 2). In contrast, cells in Cluster 7 showed higher than average recovery growth rates – these cells were characterized by larger-than-average Dot6 nuclear localization responses and somewhat below-average nuclear translocation of Msn2 during the acute-response phase. The relationships between post-stress growth rate and Dot6 response during the acute phase raised the possibility that this factor’s activation is more closely tied to growth rate than Msn2, even when both factors are activated in a systemic response. Interestingly, cells in Cluster 2 that had unusually high (and potentially vacuolar) mCherry fluorescence before stress displayed very slow growth recovery after stress, demonstrating the biological validity of the subpopulation and raising the possibility of poor stress acclimation in these cells. (We note that cells with apparent vacuolar signal were excluded from subsequent analyses).

Combining multiple characteristics increases the predictive power to explain post-stress growth rate

The above results hinted that how well cells acclimate to NaCl stress, as indicated by post-stress growth rate, may be predicted by cellular responses both before and during the stress response. Based on the work of Li et al., 2018, we expected a negative correlation between Msn2 nuclear localization and growth rate (which they reported over much longer time frames). While there was no correlation with pre-stress growth rate (p=0.65), we did observe a negative correlation between pre-stress Msn2 activation (taken as the area under the nuclear-localization curve (AUC) for pre-stress timepoints) and post-stress growth rate; however, the correlation explained only 3% of the variance (p=0.016, linear regression), indicating that the pre-stress behavior of Msn2 has little power to predict post-stress growth rate in our study.

We next investigated other features that could explain differences in post-stress growth rate (Figure 6A–B). Pairwise correlations revealed that some individual features, such as the magnitude of Dot6 acute-stress response, correlated well with post-stress growth rate but others did not (Figure 6—figure supplement 1). However, the most impactful single factor – pre-stress growth rate – explained only 20% of the variance in post-stress growth rate (Supplementary file 3).

Figure 6 with 2 supplements see all

Download asset Open asset

We next asked if combining cellular phenotypes into a single multiple linear model could explain more of the variance in growth. We considered multiple metrics for summarizing pre-stress nuclear localization, including AUC (which is a measure of the overall nuclear abundance) and the sum of called translocation peak heights (which is influenced by the magnitude and frequency of pre-stress pulses), along with acute-stress translocation peak height and AUC during the acclimation phase. The model also incorporated other cell features including pre-stress growth rate, cell-cycle phase at the time of NaCl exposure, and cell size factors (See ‘Model 1’ in Supplementary file 3 for all parameters used). Factors that did not contribute significantly (adjusted p>0.05) were progressively removed until the variance explain decreased (Supplementary file 3). The final regression identified four factors that contributed significantly to explain post-stress rate (‘Model 3’ in Supplementary file 3): pre-stress AUC of Dot6 nuclear localization, the sum of pre-stress Msn2 peak heights, the pre-stress growth rate of the cells, and the magnitude of Dot6 nuclear localization change immediately after NaCl. Together, these factors – all but one of which represent pre-stress cellular phenotypes – explained 35% of the variance in post-stress growth rate (Figure 6C), nearly doubling the explanatory power of any single feature alone. We note that noise in the growth-rate measurements is likely diminishing the true fit, such that the explanatory power reported here is actually an under-estimate.

One challenge is that several of these phenotypes could be co-variants of an underlying hidden variable or cellular state. For example, both pre-stress growth rate and Dot6 acute-stress peak height correlate with post-stress growth rate, but they also correlate with each other: cells growing faster before stress have a larger Dot6 stress-response. The mixed-linear model reports that both factors contribute separable predictive power, and indeed together they explain more of the variance in stress acclimation than either factor alone. Nonetheless, to further disentangle their co-variation, we applied principal component (PC) regression. We first analyzed the four statistically-significant model-input variables in Figure 6B by PCA and then used the resulting components as factors in a linear model of post-stress growth rates (see Methods). PC1 and PC3 together explained 21% of the variance in post-stress growth rate: both captured co-variation in pre-stress growth rate, acute-stress Dot6 response, and pre-stress transcription factor behaviors, indicating that these features likely reflect the same aspects of the cellular state (Figure 6D). However, PC4 that is dominated by Dot6 behavior but not influenced by pre-stress growth rate explained an additional 14% of growth acclimation (p=1e-4). A fourth component, PC2, was dominated by pre-stress Msn2 behavior but showed no power to predict post-stress growth acclimation rates. Thus, behavior of the Dot6 repressor independently correlates with post-stress growth rate. As further confirmation, we analyzed the correlation between Dot6 acute-stress peak height and post-stress acclimation in a subset of cells with similar pre-stress growth rates. Indeed, pre-stress growth rate had no predictive power for this subset of cells, whereas Dot6 peak height explained 12% of the variance (p=1e-4, Figure 6—figure supplement 2). Thus, the behavior of the Dot6 repressor during acute NaCl stress is associated with growth recovery as cells acclimate (see Discussion).

Dot6 activation is associated with faster production of Ctt1 protein

Dot6 is the transcriptional repressor of growth-promoting ribosome biogenesis (RiBi) genes; thus, its positive association with post-stress growth rate may seem counterintuitive. However, this result is consistent with past work from our lab: in response to NaCl stress, cells lacking DOT6 and its paralog TOD6 fail to repress hundreds of genes in the RiBi regulon (Lee et al., 2011; Ho et al., 2018). These transcripts remain associated with ribosomes, whereas stress-induced transcripts including Msn2-regulated CTT1 show reduced ribosome association (Ho et al., 2018). Despite producing more CTT1 mRNA, the dot6∆tod6∆ mutant shows delayed production of Ctt1 protein. We proposed that transcriptional repression of otherwise highly transcribed mRNAs is important to free up translational capacity to translate stress-induced transcripts (Ho et al., 2018).

To investigate on a cellular level, we attempted microscopy in a dot6∆tod6∆ strain; however, whereas the strain grew fine in the device before stress, it was unable to recover growth after NaCl treatment. Indeed, bulk-culture experiments revealed that the dot6∆tod6∆ mutant grew as wild type before stress, but showed significantly reduced growth rate after NaCl treatment (Figure 7A). This is consistent with our results in wild-type cells, where cells with a weaker Dot6 response show a reduced post-stress growth rate. Thus, bulk-culture experiments reinforce the trends of the microfluidic analysis, showing that Dot6 provides a protective response during NaCl stress.

Figure 7 with 1 supplement see all

Download asset Open asset

A major unanswered question is how Dot6 behavior in a wild-type cell relates to growth and Ctt1 production. We therefore generated a strain to track Dot6-GFP, Msn2-mCherry, and Ctt1-iRFP in the same cells. Cellular Ctt1 levels (defined as maximum iRFP signal normalized to pre-stress levels, see Methods) were correlated with both Msn2 and Dot6 peak heights (but not their pre- or post-stress behaviors). However, the explanatory power was significantly higher when considering the timing of Ctt1 production. We defined the time for Ctt1-iRFP levels to cross a change threshold (see Methods). Even for cells that reached the same maximal Ctt1 levels, the time to get there varied (Figure 7B). We found that the time to cross that threshold was correlated with both Msn2 and Dot6 peak heights, which are themselves weakly correlated; however, the variance explained was significantly higher for Dot6 activity (Figure 7C). Indeed, a mixed model considering both factors confirmed that the contribution of Dot6 was significantly more than that of Msn2 behavior, which was only marginally significant in the model (p=0.053, Figure 7D). Dot6 is not known to regulate Ctt1 or bind its promoter (Zhu et al., 2009), and we previously showed that dot6∆tod6∆ cells induce CTT1 transcript to higher levels than wild type during NaCl stress (Ho et al., 2018). Together, this suggests an indirect effect of Dot6 that is separable from Msn2 regulation. In sum, our results indicate that Dot6 provides a protective response during NaCl treatment (Figure 7A), is correlated with faster Ctt1 production in both mutant (Ho et al., 2018) and wild-type cells (Figure 7D), and is associated with faster growth recovery after NaCl treatment (Figure 6, see Discussion).

By following dynamic activation of two different stress-regulated transcription factors, in conjunction with other cellular features including growth rate, cell size, and cell cycle stage, we uncovered previously unrecognized inter-dependencies that present new insights into mechanisms of stress defense. Our results reveal much more complexity in Msn2 and Dot6 behavior than previously recognized, that the relative activation of these factors along with other pre-stress phenotypes can partly predict cellular outcomes including growth acclimation, and that behavior of the Dot6 repressor influences post-stress growth rate and the dynamics of a downstream response. Below we discuss implications of these results.

Strains used include AGY1328 (BY4741 DOT6-GFP(S65T)-His3MX, MSN2-mCherry-HYGMX), AGY1813 (BY47141 DOT6-GFP(S65T)-His3MX, MSN2-mCherry-HYGMX, CTT1-iRFP-KanMX), and AGY1363 (BY4741 dot6::KAN tod6::HYG CTT1-GFP(S65T)-His3MX) (strains available upon request). For microscopy experiments, overnight cultures were grown from single colonies to exponential phase at 30°C (Optical Density, OD₆₀₀ <1) in Low Fluorescent Medium (LFM) before cells were adhered to the microscope slide as described below. LFM consisted of 0.17% Yeast Nitrogen Base without Ammonium Sulfate, Folic Acid, or Riboflavin (#MP114030512, Thermo Fisher Scientific, Waltham, Massachusetts), 0.5% Ammonium Sulfate, 0.2% complete amino acids supplement, where individual amino acids concentrations are as defined in Yeast Synthetic Drop-out Media Supplements (Sigma-Aldrich, Saint Louis, Missouri), and 2% Glucose. Cells were grown in LFM shake flasks at 30°C for data shown in Figure 7A.

An FCS2 chamber (Bioptechs Inc, Butler, Pennsylvania) microfluidic system was used for time-lapse microscopy. In short, a 40 mm round glass coverslip and FCS2 lower gasket were assembled, and Concanavalin A solution (2 mg/mL Concanavalin A, 5 mM MnCl₂, 5 mM CaCl₂) was applied to the coverslip, incubated for 2 min, then aspirated. Next, 350 μL of an ~0.5 OD₆₀₀ culture was placed on the coverslip and incubated 5 min for cells to settle and adhere to the Concanavalin A. 150 μL of the media was then removed and the rest of the FCS2 chamber was assembled.

Media was flown through the FCS2 chamber using gravity flow. Input tubing was attached to elevated bottles containing either LFM or LFM +0.7 M NaCl (See diagram in Figure 1B) with a valve to switch between media with and without 0.7 M NaCl. The outflow tubing was connected to an additional ~1 m of BD Intramedic PE Tubing (#1417012D, Thermo Fisher Scientific, Waltham, Massachusetts) with the smaller inner diameter of 0.86 mm being vital to controlling the gravity flow of media. The entire assembly, including the microscope stand, bottles containing media, and FCS2 chamber, were enclosed in an incubator maintaining internal temperature of 30°C throughout the entire protocol.

A Nikon Eclipse Ti inverted microscope with the Perfect Focus System (Nikon Inc, Melville, New York) was used for time-lapse microscopy. The GFP signal was captured using a ET-EGFP single band filter cube (#49002, Chroma Technology Corp, Bellows Falls, Vermont excitation 470/40 x emission 525/50 m). The mCherry signal was captured using a ET/mCH/TR single band filter cube (#96365, Chroma Technology Corp, Bellows Falls, Vermont excitation 560/40 x emission 630/75 m). In addition, exposure from a halogen lamp was used to capture white-light images of all cells. For experiments using AGY1813, the iRFP signal was captured using a Cy 5.5 filter cube (#49022, Chroma Technology Corp, Bellows Falls, Vermont excitation 650/45 x emission 720/60 m).

Images of each field of view were captured at 6-min intervals. The z-focal plane focus was set on the center of cells, and images were taken 1 μm above, at, and 1 μm below this center of focus, generating a three-image z-stack for each channel. The three-image z-stacks were collapsed into a single image by taking the maximum projection of the 3 images using a custom MATLAB script.

Cells were identified using a MATLAB circle-finding function on the brightfield images. Individual cells were then tracked through all images using the MATLAB simpletracker function (Tinevez, 2019). Cell colonies were defined by segmenting images into a binary black-and-white image, and single colonies were defined as enclosed masks. The number of cells within each colony was determined simply as the number of identified circles that overlapped with a given enclosed white area of the binary images. Pre-stress growth was scored by linear regression on colony size (defined as the total pixel number within the masked area of the colony) for the first twelve 6-min time points and reported as the natural log of the rate of increase. Post-stress growth was measured in the same manner for time points 20–29 (representing resumed growth at the beginning of the acclimation phase: 114–168 min into the time course). We note that our proxy for post-stress growth rate, taken as an indication of how well cells acclimate to salt stress, could also be influenced by differences in volume recovery for some cells, which may also be a feature of successful acclimation.

We applied several quality control filters to insure accuracy of growth rates. First, to ensure that colony growth rates were representative of nuclear localization dynamics within individual cells, we limited our analysis to colonies consisting of no more than two cells at the time points leading up to NaCl exposure. Most of these two-cell colonies represented mother/daughter cells and therefore had clear shared life histories. Second, in some cases a budding daughter cell was lost during the time-course, resulting in a misleading negative growth rate. Consequently, regressions resulting in negative slopes were excluded. Lastly, a visual inspection of individual colonies during the time course excluded colonies where new cells adhered to a given colony. Thirty cells were excluded from post-stress measures due to these cell adhesion issues that skewed colony size measures. Another six cells (2.7% of total cells) had no apparent post-stress growth and the calculated slope was therefore dominated by noise in pixel number. This resulted in either a negative or near zero slope and consequently did not provide an informative growth rate measure when taking the natural log of the change in colony size. Consequently, these six cells were also excluded from post-stress growth rate measures. Experiments with AGY1813 (n=3) had the same quality control filters applied to them, with an additional metric applied to exclude cells expressing persistent, high iRFP signal throughout the time course (11 cells). This resulted in an analysis of 228 cells.

Cell-cycle phase at the time of osmotic stress was measured by visual inspection of cell bud presence/size and nucleus location within the cell in accordance with standard yeast cell-cycle definitions (Howell and Lew, 2012). Specifically, S-phase appearance of a bud but no migration of nucleus, (G2) bud and nucleus migration toward bud, but no nucleus in daughter cell, M-phase nucleus in both cell and bud, and active division of nuclei, (G1) no bud and nucleus is not actively dividing.

Nuclear localization of Msn2 and Dot6 was measured by taking the pixel intensity of the top 5% of pixels in the cell divided by the median pixel intensity within the circle mask identified for each cell, similar to other studies (Cai et al., 2008; Hao and O’Shea, 2012; Petrenko et al., 2013; Lin et al., 2015; AkhavanAghdam et al., 2016; Gasch et al., 2017; Granados et al., 2018). The following nuclear localization metrics were analyzed:

All data generated and analyzed during this study are included in the manuscript and supporting files. Code used to analyze image files and generate data for cellular phenotypes is included in Source Code Files. Supplementary Data Files containing cells and associated phenotypic information are included. Source Data Files have been provided for Figure 4, and Figure 4 - supplement 1.

1. 1. Abràmoff MD
   2. Magalhães PJ
   3. Ram SJ

   (2004)

   Image processing with imagej

   Biophotonics Int 11:36–41.

   • Google Scholar
2. 1. Airoldi EM
   2. Huttenhower C
   3. Gresham D
   4. Lu C
   5. Caudy AA
   6. Dunham MJ
   7. Broach JR
   8. Botstein D
   9. Troyanskaya OG

   (2009) Predicting cellular growth from gene expression signatures

   PLOS Computational Biology 5:e1000257.

   https://doi.org/10.1371/journal.pcbi.1000257

   • PubMed
   • Google Scholar
3. 1. AkhavanAghdam Z
   2. Sinha J
   3. Tabbaa OP
   4. Hao N

   (2016) Dynamic control of gene regulatory logic by seemingly redundant transcription factors

   eLife 5:e18458.

   https://doi.org/10.7554/eLife.18458

   • PubMed
   • Google Scholar
4. 1. Bagamery LE
   2. Justman QA
   3. Garner EC
   4. Murray AW

   (2020) A putative bet-hedging strategy buffers budding yeast against environmental instability

   Current Biology 30:4563–4578.

   https://doi.org/10.1016/j.cub.2020.08.092

   • PubMed
   • Google Scholar
5. 1. Balaban NQ
   2. Merrin J
   3. Chait R
   4. Kowalik L
   5. Leibler S

   (2004) Bacterial persistence as a phenotypic switch

   Science 305:1622–1625.

   https://doi.org/10.1126/science.1099390

   • PubMed
   • Google Scholar
6. 1. Barber F
   2. Min J
   3. Murray AW
   4. Amir A

   (2021) Modeling the impact of single-cell stochasticity and size control on the population growth rate in asymmetrically dividing cells

   PLOS Computational Biology 17:17.

   https://doi.org/10.1371/journal.pcbi.1009080

   • PubMed
   • Google Scholar
7. 1. Bergenholm D
   2. Liu G
   3. Holland P
   4. Nielsen J

   (2018) Reconstruction of a global transcriptional regulatory network for control of lipid metabolism in yeast by using chromatin immunoprecipitation with lambda exonuclease digestion

   MSystems 3:e00215-17.

   https://doi.org/10.1128/mSystems.00215-17

   • PubMed
   • Google Scholar
8. 1. Brauer MJ
   2. Huttenhower C
   3. Airoldi EM
   4. Rosenstein R
   5. Matese JC
   6. Gresham D
   7. Boer VM
   8. Troyanskaya OG
   9. Botstein D

   (2008) Coordination of growth rate, cell cycle, stress response, and metabolic activity in yeast

   Molecular Biology of the Cell 19:352–367.

   https://doi.org/10.1091/mbc.e07-08-0779

   • PubMed
   • Google Scholar
9. 1. Cai L
   2. Dalal CK
   3. Elowitz MB

   (2008) Frequency-Modulated nuclear localization bursts coordinate gene regulation

   Nature 455:485–490.

   https://doi.org/10.1038/nature07292

   • PubMed
   • Google Scholar
10. 1. Castrillo JI
    2. Zeef LA
    3. Hoyle DC
    4. Zhang N
    5. Hayes A
    6. Gardner DC
    7. Cornell MJ
    8. Petty J
    9. Hakes L
    10. Wardleworth L
    11. Rash B
    12. Brown M
    13. Dunn WB
    14. Broadhurst D
    15. O’Donoghue K
    16. Hester SS
    17. Dunkley TP
    18. Hart SR
    19. Swainston N
    20. Li P
    21. Gaskell SJ
    22. Paton NW
    23. Lilley KS
    24. Kell DB
    25. Oliver SG

    (2007) Growth control of the eukaryote cell: a systems biology study in yeast

    Journal of Biology 6:4.

    https://doi.org/10.1186/jbiol54

    • PubMed
    • Google Scholar
11. 1. Causton HC
    2. Ren B
    3. Koh SS
    4. Harbison CT
    5. Kanin E
    6. Jennings EG
    7. Lee TI
    8. True HL
    9. Lander ES
    10. Young RA

    (2001) Remodeling of yeast genome expression in response to environmental changes

    Molecular Biology of the Cell 12:323–337.

    https://doi.org/10.1091/mbc.12.2.323

    • PubMed
    • Google Scholar
12. 1. Cheong R
    2. Rhee A
    3. Wang CJ
    4. Nemenman I
    5. Levchenko A

    (2011) Information transduction capacity of noisy biochemical signaling networks

    Science 334:354–358.

    https://doi.org/10.1126/science.1204553

    • PubMed
    • Google Scholar
13. 1. Crane MM
    2. Clark IBN
    3. Bakker E
    4. Smith S
    5. Swain PS

    (2014) A microfluidic system for studying ageing and dynamic single-cell responses in budding yeast

    PLOS ONE 9:e100042.

    https://doi.org/10.1371/journal.pone.0100042

    • PubMed
    • Google Scholar
14. 1. Dai X
    2. Zhu M
    3. Warren M
    4. Balakrishnan R
    5. Okano H
    6. Williamson JR
    7. Fredrick K
    8. Hwa T

    (2018) Slowdown of translational elongation in Escherichia coli under hyperosmotic stress

    MBio 9:e17.

    https://doi.org/10.1128/mBio.02375-17

    • PubMed
    • Google Scholar
15. 1. Dhar R
    2. Missarova AM
    3. Lehner B
    4. Carey LB

    (2019) Single cell functional genomics reveals the importance of mitochondria in cell-to-cell phenotypic variation

    eLife 8:23.

    https://doi.org/10.7554/eLife.38904

    • PubMed
    • Google Scholar
16. 1. Drummond DA
    2. Raval A
    3. Wilke CO

    (2006) A single determinant dominates the rate of yeast protein evolution

    Molecular Biology and Evolution 23:327–337.

    https://doi.org/10.1093/molbev/msj038

    • PubMed
    • Google Scholar
17. 1. Fehrmann S
    2. Paoletti C
    3. Goulev Y
    4. Ungureanu A
    5. Aguilaniu H
    6. Charvin G

    (2013) Aging yeast cells undergo a sharp entry into senescence unrelated to the loss of mitochondrial membrane potential

    Cell Reports 5:1589–1599.

    https://doi.org/10.1016/j.celrep.2013.11.013

    • PubMed
    • Google Scholar
18. 1. Gasch A.P
    2. Spellman PT
    3. Kao CM
    4. Carmel-Harel O
    5. Eisen MB
    6. Storz G
    7. Botstein D
    8. Brown PO

    (2000) Genomic expression programs in the response of yeast cells to environmental changes

    Molecular Biology of the Cell 11:4241–4257.

    https://doi.org/10.1091/mbc.11.12.4241

    • PubMed
    • Google Scholar
19. 1. Gasch AP
    2. Yu FB
    3. Hose J
    4. Escalante LE
    5. Place M
    6. Bacher R
    7. Kanbar J
    8. Ciobanu D
    9. Sandor L
    10. Grigoriev IV
    11. Kendziorski C
    12. Quake SR
    13. McClean MN

    (2017) Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress

    PLOS Biology 15:28.

    https://doi.org/10.1371/journal.pbio.2004050

    • PubMed
    • Google Scholar
20. 1. Goranov AI
    2. Cook M
    3. Ricicova M
    4. Ben-Ari G
    5. Gonzalez C
    6. Hansen C
    7. Tyers M
    8. Amon A

    (2009) The rate of cell growth is governed by cell cycle stage

    Genes & Development 23:1408–1422.

    https://doi.org/10.1101/gad.1777309

    • PubMed
    • Google Scholar
21. 1. Görner W
    2. Durchschlag E
    3. Martinez-Pastor MT
    4. Estruch F
    5. Ammerer G
    6. Hamilton B
    7. Ruis H
    8. Schüller C

    (1998) Nuclear localization of the C2H2 zinc finger protein Msn2p is regulated by stress and protein kinase A activity

    Genes & Development 12:586–597.

    https://doi.org/10.1101/gad.12.4.586

    • PubMed
    • Google Scholar
22. 1. Granados AA
    2. Pietsch JMJ
    3. Cepeda-Humerez SA
    4. Farquhar IL
    5. Tkačik G
    6. Swain PS

    (2018) Distributed and dynamic intracellular organization of extracellular information

    PNAS 115:6088–6093.

    https://doi.org/10.1073/pnas.1716659115

    • PubMed
    • Google Scholar
23. 1. Hansen AS
    2. O’Shea EK

    (2013) Promoter decoding of transcription factor dynamics involves a trade-off between noise and control of gene expression

    Molecular Systems Biology 9:704.

    https://doi.org/10.1038/msb.2013.56

    • PubMed
    • Google Scholar
24. 1. Hansen AS
    2. O’Shea EK

    (2015a) Cis determinants of promoter threshold and activation timescale

    Cell Reports 12:1226–1233.

    https://doi.org/10.1016/j.celrep.2015.07.035

    • PubMed
    • Google Scholar
25. 1. Hansen AS
    2. O’Shea EK

    (2015b) Limits on information transduction through amplitude and frequency regulation of transcription factor activity

    eLife 4:e559.

    https://doi.org/10.7554/eLife.06559

    • PubMed
    • Google Scholar
26. 1. Hansen AS
    2. O’Shea EK

    (2016) Encoding four gene expression programs in the activation dynamics of a single transcription factor

    Current Biology 26:R269–R271.

    https://doi.org/10.1016/j.cub.2016.02.058

    • PubMed
    • Google Scholar
27. 1. Hansen AS
    2. Zechner C

    (2021) Promoters adopt distinct dynamic manifestations depending on transcription factor context

    Molecular Systems Biology 17:e9821.

    https://doi.org/10.15252/msb.20209821

    • PubMed
    • Google Scholar
28. 1. Hao N
    2. O’Shea EK

    (2012) Signal-dependent dynamics of transcription factor translocation controls gene expression

    Nature Structural & Molecular Biology 19:31–39.

    https://doi.org/10.1038/nsmb.2192

    • PubMed
    • Google Scholar
29. 1. Ho YH
    2. Shishkova E
    3. Hose J
    4. Coon JJ
    5. Gasch AP

    (2018) Decoupling yeast cell division and stress defense implicates mRNA repression in translational reallocation during stress

    Current Biology 28:2673–2680.

    https://doi.org/10.1016/j.cub.2018.06.044

    • PubMed
    • Google Scholar
30. 1. Howell AS
    2. Lew DJ

    (2012) Morphogenesis and the cell cycle

    Genetics 190:51–77.

    https://doi.org/10.1534/genetics.111.128314

    • PubMed
    • Google Scholar
31. 1. Inde Z
    2. Dixon SJ

    (2018) The impact of non-genetic heterogeneity on cancer cell death

    Critical Reviews in Biochemistry and Molecular Biology 53:99–114.

    https://doi.org/10.1080/10409238.2017.1412395

    • PubMed
    • Google Scholar
32. 1. Jin M
    2. Li Y
    3. O’Laughlin R
    4. Bittihn P
    5. Pillus L
    6. Tsimring LS
    7. Hasty J
    8. Hao N

    (2019) Divergent aging of isogenic yeast cells revealed through single-cell phenotypic dynamics

    Cell Systems 8:242–253.

    https://doi.org/10.1016/j.cels.2019.02.002

    • PubMed
    • Google Scholar
33. 1. Keren I
    2. Shah D
    3. Spoering A
    4. Kaldalu N
    5. Lewis K

    (2004) Specialized persister cells and the mechanism of multidrug tolerance in Escherichia coli

    Journal of Bacteriology 186:8172–8180.

    https://doi.org/10.1128/JB.186.24.8172-8180.2004

    • PubMed
    • Google Scholar
34. 1. Kim JS
    2. Yamasaki R
    3. Song S
    4. Zhang W
    5. Wood TK

    (2018) Single cell observations show persister cells wake based on ribosome content

    Environmental Microbiology 20:2085–2098.

    https://doi.org/10.1111/1462-2920.14093

    • PubMed
    • Google Scholar
35. 1. Korem Kohanim Y
    2. Levi D
    3. Jona G
    4. Towbin BD
    5. Bren A
    6. Alon U

    (2018) A bacterial growth law out of steady state

    Cell Reports 23:2891–2900.

    https://doi.org/10.1016/j.celrep.2018.05.007

    • PubMed
    • Google Scholar
36. 1. Kracikova M
    2. Akiri G
    3. George A
    4. Sachidanandam R
    5. Aaronson SA

    (2013) A threshold mechanism mediates p53 cell fate decision between growth arrest and apoptosis

    Cell Death and Differentiation 20:576–588.

    https://doi.org/10.1038/cdd.2012.155

    • PubMed
    • Google Scholar
37. 1. Laporte D
    2. Gouleme L
    3. Jimenez L
    4. Khemiri I
    5. Sagot I

    (2018) Mitochondria reorganization upon proliferation arrest predicts individual yeast cell fate

    eLife 7:22.

    https://doi.org/10.7554/eLife.35685

    • Google Scholar
38. 1. Lee MV
    2. Topper SE
    3. Hubler SL
    4. Hose J
    5. Wenger CD
    6. Coon JJ
    7. Gasch AP

    (2011) A dynamic model of proteome changes reveals new roles for transcript alteration in yeast

    Molecular Systems Biology 7:1–12.

    https://doi.org/10.1038/msb.2011.48

    • PubMed
    • Google Scholar
39. 1. Lee M-CW
    2. Lopez-Diaz FJ
    3. Khan SY
    4. Tariq MA
    5. Dayn Y
    6. Vaske CJ
    7. Radenbaugh AJ
    8. Kim HJ
    9. Emerson BM
    10. Pourmand N

    (2014) Single-cell analyses of transcriptional heterogeneity during drug tolerance transition in cancer cells by RNA sequencing

    PNAS 111:E4726–E4735.

    https://doi.org/10.1073/pnas.1404656111

    • PubMed
    • Google Scholar
40. 1. Levy SF
    2. Ziv N
    3. Siegal ML

    (2012) Bet hedging in yeast by heterogeneous, age-correlated expression of a stress protectant

    PLOS Biology 10:e1001325.

    https://doi.org/10.1371/journal.pbio.1001325

    • PubMed
    • Google Scholar
41. 1. Li S
    2. Giardina DM
    3. Siegal ML
    4. Dudley AM

    (2018) Control of nongenetic heterogeneity in growth rate and stress tolerance of Saccharomyces cerevisiae by cyclic AMP-regulated transcription factors

    PLOS Genetics 14:e1007744.

    https://doi.org/10.1371/journal.pgen.1007744

    • Google Scholar
42. 1. Lin Y
    2. Sohn CH
    3. Dalal CK
    4. Cai L
    5. Elowitz MB

    (2015) Combinatorial gene regulation by modulation of relative pulse timing

    Nature 527:54–58.

    https://doi.org/10.1038/nature15710

    • PubMed
    • Google Scholar
43. 1. Lippman SI
    2. Broach JR

    (2009) Protein kinase A and TORC1 activate genes for ribosomal biogenesis by inactivating repressors encoded by dot6 and its homolog tod6

    PNAS 106:19928–19933.

    https://doi.org/10.1073/pnas.0907027106

    • PubMed
    • Google Scholar
44. 1. Lu C
    2. Brauer MJ
    3. Botstein D

    (2009) Slow growth induces heat-shock resistance in normal and respiratory-deficient yeast

    Molecular Biology of the Cell 20:891–903.

    https://doi.org/10.1091/mbc.E08-08-0852

    • PubMed
    • Google Scholar
45. 1. Mori M
    2. Schink S
    3. Erickson DW
    4. Gerland U
    5. Hwa T

    (2017) Quantifying the benefit of a proteome reserve in fluctuating environments

    Nature Communications 8:1–8.

    https://doi.org/10.1038/s41467-017-01242-8

    • PubMed
    • Google Scholar
46. 1. Paek AL
    2. Liu JC
    3. Loewer A
    4. Forrester WC
    5. Lahav G

    (2016) Cell-To-Cell variation in p53 dynamics leads to fractional killing

    Cell 165:631–642.

    https://doi.org/10.1016/j.cell.2016.03.025

    • PubMed
    • Google Scholar
47. 1. Petrenko N
    2. Chereji RV
    3. McClean MN
    4. Morozov AV
    5. Broach JR

    (2013) Noise and interlocking signaling pathways promote distinct transcription factor dynamics in response to different stresses

    Molecular Biology of the Cell 24:2045–2057.

    https://doi.org/10.1091/mbc.E12-12-0870

    • PubMed
    • Google Scholar
48. 1. Purvis JE
    2. Karhohs KW
    3. Mock C
    4. Batchelor E
    5. Loewer A
    6. Lahav G

    (2012) P53 dynamics control cell fate

    Science 336:1440–1444.

    https://doi.org/10.1126/science.1218351

    • Google Scholar
49. 1. Purvis JE
    2. Lahav G

    (2013) Encoding and decoding cellular information through signaling dynamics

    Cell 152:945–956.

    https://doi.org/10.1016/j.cell.2013.02.005

    • PubMed
    • Google Scholar
50. 1. Regenberg B
    2. Grotkjaer T
    3. Winther O
    4. Fausbøll A
    5. Akesson M
    6. Bro C
    7. Hansen LK
    8. Brunak S
    9. Nielsen J

    (2006) Growth-rate regulated genes have profound impact on interpretation of transcriptome profiling in Saccharomyces cerevisiae

    Genome Biology 7:R107.

    https://doi.org/10.1186/gb-2006-7-11-r107

    • PubMed
    • Google Scholar
51. 1. Remigi P
    2. Ferguson GC
    3. McConnell E
    4. De Monte S
    5. Rogers DW
    6. Rainey PB

    (2019) Ribosome provisioning activates a bistable switch coupled to fast exit from stationary phase

    Molecular Biology and Evolution 36:1056–1070.

    https://doi.org/10.1093/molbev/msz041

    • PubMed
    • Google Scholar
52. 1. Scrucca L
    2. Fop M
    3. Murphy TB
    4. Raftery AE

    (2016) Mclust 5: clustering, classification and density estimation using Gaussian finite mixture models

    The R Journal 8:289–317.

    https://doi.org/10.32614/RJ-2016-021

    • PubMed
    • Google Scholar
53. 1. Shaffer SM
    2. Dunagin MC
    3. Torborg SR
    4. Torre EA
    5. Emert B
    6. Krepler C
    7. Beqiri M
    8. Sproesser K
    9. Brafford PA
    10. Xiao M
    11. Eggan E
    12. Anastopoulos IN
    13. Vargas-Garcia CA
    14. Singh A
    15. Nathanson KL
    16. Herlyn M
    17. Raj A

    (2017) Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance

    Nature 546:431–435.

    https://doi.org/10.1038/nature22794

    • Google Scholar
54. 1. Smith A
    2. Ward MP
    3. Garrett S

    (1998) Yeast PKA represses msn2p/msn4p-dependent gene expression to regulate growth, stress response and glycogen accumulation

    The EMBO Journal 17:3556–3564.

    https://doi.org/10.1093/emboj/17.13.3556

    • PubMed
    • Google Scholar
55. 1. Stewart-Ornstein J
    2. Nelson C
    3. DeRisi J
    4. Weissman JS
    5. El-Samad H

    (2013) Msn2 coordinates a stoichiometric gene expression program

    Current Biology 23:2336–2345.

    https://doi.org/10.1016/j.cub.2013.09.043

    • PubMed
    • Google Scholar
56. Software

    1. Tinevez JY

    (2019) Simpletracker, version de97f90

    Github.

    https://github.com/tinevez/simpletracker
57. 1. Wu C
    2. Balakrishnan R
    3. Braniff N
    4. Mori M
    5. Manzanarez G
    6. Zhang Z
    7. Hwa T

    (2022) Cellular perception of growth rate and the mechanistic origin of bacterial growth law

    PNAS 119:1–9.

    https://doi.org/10.1073/pnas.2201585119

    • Google Scholar
58. 1. Zakrzewska A
    2. van Eikenhorst G
    3. Burggraaff JEC
    4. Vis DJ
    5. Hoefsloot H
    6. Delneri D
    7. Oliver SG
    8. Brul S
    9. Smits GJ

    (2011) Genome-Wide analysis of yeast stress survival and tolerance acquisition to analyze the central trade-off between growth rate and cellular robustness

    Molecular Biology of the Cell 22:4435–4446.

    https://doi.org/10.1091/mbc.E10-08-0721

    • PubMed
    • Google Scholar
59. 1. Zaman S
    2. Lippman SI
    3. Zhao X
    4. Broach JR

    (2008) How Saccharomyces responds to nutrients

    Annual Review of Genetics 42:27–81.

    https://doi.org/10.1146/annurev.genet.41.110306.130206

    • PubMed
    • Google Scholar
60. 1. Zaman S
    2. Lippman SI
    3. Schneper L
    4. Slonim N
    5. Broach JR

    (2009) Glucose regulates transcription in yeast through a network of signaling pathways

    Molecular Systems Biology 5:245.

    https://doi.org/10.1038/msb.2009.2

    • PubMed
    • Google Scholar
61. 1. Zhu C
    2. Byers KJRP
    3. McCord RP
    4. Shi Z
    5. Berger MF
    6. Newburger DE
    7. Saulrieta K
    8. Smith Z
    9. Shah MV
    10. Radhakrishnan M
    11. Philippakis AA
    12. Hu Y
    13. De Masi F
    14. Pacek M
    15. Rolfs A
    16. Murthy T
    17. Labaer J
    18. Bulyk ML

    (2009) High-Resolution DNA-binding specificity analysis of yeast transcription factors

    Genome Research 19:556–566.

    https://doi.org/10.1101/gr.090233.108

    • PubMed
    • Google Scholar

Author details

1. Andrew C Bergen

   Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Rachel A Kocik

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1295-7718

2. Rachel A Kocik

   Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, United States

   Contribution

   Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Writing – review and editing

   Contributed equally with

   Andrew C Bergen

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2422-4538

3. James Hose

   Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, United States

   Contribution

   Resources

   Competing interests

   No competing interests declared

4. Megan N McClean

   1. Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, United States
   2. Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, United States
   3. University of Wisconsin Carbone Cancer Center, University of Wisconsin School of Medicine and Public Health, Madison, United States

   Contribution

   Resources, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

5. Audrey P Gasch

   1. Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, United States
   2. University of Wisconsin Carbone Cancer Center, University of Wisconsin School of Medicine and Public Health, Madison, United States
   3. Department of Medical Genetics, University of Wisconsin-Madison, Madison, United States

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Visualization, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   agasch@wisc.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8182-257X

• 1,183

  views

• 154

  downloads

• 2

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.