Modeling single-cell phenotypes links yeast stress acclimation to transcriptional repression and pre-stress cellular states

  1. Andrew C Bergen
  2. Rachel A Kocik
  3. James Hose
  4. Megan N McClean
  5. Audrey P Gasch  Is a corresponding author
  1. Center for Genomic Science Innovation, University of Wisconsin-Madison, United States
  2. Department of Biomedical Engineering, University of Wisconsin-Madison, United States
  3. University of Wisconsin Carbone Cancer Center, University of Wisconsin School of Medicine and Public Health, United States
  4. Department of Medical Genetics, University of Wisconsin-Madison, United States

Abstract

Stress defense and cell growth are inversely related in bulk culture analyses; however, these studies miss substantial cell-to-cell heterogeneity, thus obscuring true phenotypic relationships. Here, we devised a microfluidics system to characterize multiple phenotypes in single yeast cells over time before, during, and after salt stress. The system measured cell and colony size, growth rate, and cell-cycle phase along with nuclear trans-localization of two transcription factors: stress-activated Msn2 that regulates defense genes and Dot6 that represses ribosome biogenesis genes during an active stress response. By tracking cells dynamically, we discovered unexpected discordance between Msn2 and Dot6 behavior that revealed subpopulations of cells with distinct growth properties. Surprisingly, post-stress growth recovery was positively corelated with activation of the Dot6 repressor. In contrast, cells lacking Dot6 displayed slower growth acclimation, even though they grow normally in the absence of stress. We show that wild-type cells with a larger Dot6 response display faster production of Msn2-regulated Ctt1 protein, separable from the contribution of Msn2. These results are consistent with the model that transcriptional repression during acute stress in yeast provides a protective response, likely by redirecting translational capacity to induced transcripts.

Editor's evaluation

This paper addresses an important question in the field: the cell-to-cell heterogeneity in stress response and the functional relevance to stress adaptation. The experimental approaches are timely and their clustering and correlation analyses suggest some interesting relationships between phenotypic factors and growth adaptation.

https://doi.org/10.7554/eLife.82017.sa0

Introduction

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
Experimental approach.

(A) Schematic of Msn2 and Dot6 localization in the absence (left) and presence (right) of stress. (B) Diagram of microfluidic device used for time-lapse microscopy. (C) Representative nuclear localization scores (see Methods) for pre-stress growth, the acute-stress response, and the acclimation phase. (D) Cell or two-cell colony size was estimated by the number of pixels within the mask for each colony, and growth rates were calculated based of regression of those points during the pre- or post-stress phases. Cell volume change was reflected in the difference in pixel number before and after stress.

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.

Results

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.

Cell-to-cell heterogeneity in the NaCl stress response.

(A-C) Shown are the distributions of the natural log of (A) colony growth rates before stress, (B) the change in growth rate after NaCl stress compared to before stress, and (C) the maximum change in cell pixel size during the acute-stress response versus during the pre-stress phase.

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.

Nuclear translocation dynamics of Msn2 and Dot6 are more coordinated before stress.

(A) Representative traces of Msn2 and Dot6 in the same cell. (B) The average number of coordinated peaks for Msn2 and Dot6, i.e. peaks called within 6 min (1 timepoint) of each other. (C) The average number of nuclear localization peaks per cell for Msn2 (red) and Dot6 (blue) during pre-stress and acclimation phases. (D–E) The average (black line)+/- one standard deviation (colored spread) of Msn2 (D) and Dot6 (E) nuclear localization during the time course. (F) Trace of the standard deviation of nuclear localization over the time course for Msn2 (red) and Dot6 (blue).

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
Subpopulations of cells show distinct Msn2 and Dot6 translocation dynamics.

221 cells passing quality control metrics were partitioned into sub clusters based on their population-centered nuclear translocation dynamics shown on the right. Each row represents a cell and each column in a block represents a single timepoint; time of NaCl addition is indicated with an arrow. Data on the left show the log2 ratio of nuclear versus total Msn2 (left) or Dot6 (right) according to the orange-scale key, see Methods. Data on the right show the same data normalized to the population median at each timepoint: yellow values indicate higher-than-median nuclear localization levels and blue indicates lower-than-median nuclear localization. Cell clusters identified by the package mclust are labeled to the right.

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

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
Cell subpopulations display different growth rates before and after stress.

(A) Correlation between the natural log of pre- and post-stress growth rates for each cell, colored according to its cell cluster in Figure 4. (B–C) Distribution of median-centered growth rates before (B) and after (C) NaCl addition, for cell clusters shown in Figure 4. Boxes are colored yellow or blue if the distribution was significantly higher or lower, respectfully, from all other cells in the analysis (Wilcoxon Rank Sum test, FDR < 0.022). Dashed line indicates the median of all cells analyzed.

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
A multi-factor model best explains variation in post-stress growth rate.

(A) A representation of the nuclear localization measurements used in the multi-factor linear regression model. (B) Factors considered in the multi-factor linear regression model; those with significant contributions are highlighted with ***. (C) The variance in ln(post-stress growth rate) explained by the multi-factor linear regression model. P-value and R2 are shown at the top of the plot and cell subcluster is indicated according to the key, showing that no single cluster dominates the correlation. (D) Principal component (PC) regression of post-stress growth rate and deconvolution of contributing factors according to the key. Variance explained is listed at the top of each bar (where PC2 does not contribute to post-stress growth rate).

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
Dot6 activation correlates with faster Ctt1 production.

(A) The average and standard deviation (n=4) of growth rates of wild-type (black lines) and dot6∆tod6∆ cells (blue lines) in the absence (solid) and presence (dashed) of 0.7 M NaCl added at 75 min (arrow). (B) Representative traces of single-cell Ctt1 production for pairs of cells that reach similar levels of Ctt1. (C) Correlation of Ctt1 production timing (time to change 5%) versus acute-stress peak heights. (D) The two-factor model correlates with measured Ctt1 production time, with only marginal contribution of Msn2 peak height (p=0.053). Adjusted R2 is shown in both figures.

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).

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.

Complexities in Msn2 dynamics reflect diversity in stress-responsive states

Past studies focusing on aggregate analysis of all single cells in the population reported condition-specific dynamical behavior of Msn2, such as prolonged nuclear pulsing after glucose starvation versus a burst of activation before acclimating to osmotic stress (Hao and O’Shea, 2012; Petrenko et al., 2013; AkhavanAghdam et al., 2016). Elegant studies by Hansen et al. used artificial activation of Msn2 (through chemical inhibition of PKA activity) to show that these differences in Msn2 nuclear translocation dynamics produce different transcriptional outputs (Hansen and O’Shea, 2013; Hansen and O’Shea, 2015a; Hansen and O’Shea, 2015b; Hansen and O’Shea, 2016). Target-gene promoters display different dependencies on the amplitude, frequency, and duration of Msn2 nuclear translocation, such that distinctions in Msn2 behavior activate different sets of genes. Comparable studies of regulators in mammalian systems also reported stress-specific differences in the dynamics of nuclear translocation, which correspond to differences in gene activation (Purvis et al., 2012; Kracikova et al., 2013; Paek et al., 2016). One limitation of the approach of Hansen et al. is that activating Msn2 by wholesale inhibition of PKA likely loses much of the heterogeneity seen in natural responses. Our study thus provides an important complement to artificial system activation.

In fact, our analysis revealed highly varied responses across subpopulations of cells responding to the same stress stimulus. Some cells responded to the osmotic/ionic stress induced by NaCl with a large nuclear pulse of Msn2 followed by near complete acclimation, as previously reported for sorbitol-induced osmotic stress – but other cells showed extensive and prolonged Msn2 fluctuations during the acclimation phase, akin to what has been reported for glucose starvation. These subpopulations are obscured by aggregate analysis but have important implications, since the different dynamics of Msn2 (and likely also Dot6) activation produce different transcriptomic outputs, even for cells responding to the same stressor in the same environment. This hypothesis is consistent with past work from our lab investigating single-cell transcriptomics, in which isogenic cells in the same culture displayed different transcriptomes upon NaCl stress, including for ESR genes, indicating that they experience the stress differently (Gasch et al., 2017).

The Dot6 repressor provides a protective response during stress

Although the variety in Msn2 responses likely has important consequences on downstream gene expression, we were surprised to find little connection to growth rate, at least in the short time frames studied here. Instead, the response of Dot6 explained a much larger fraction of the variance in post-stress growth rate, when considered alone or in the multi-factor linear model (Figure 6 and Supplementary file 3). Cells with a larger Dot6 response during the acute-stress phase showed faster production of Ctt1, separable from Msn2 activity (Figure 7), and faster growth recovery during the acclimation phase. In contrast, cells completely lacking Dot6 and its paralog show delayed Ctt1 accumulation despite having more transcript (Ho et al., 2018) and dramatically reduced post-stress acclimation (Figure 7A).

These results are consistent with our working model of Dot6 activity. At least in response to NaCl treatment, transcriptional repression does not lead to reduced abundance of the encoded proteins (Lee et al., 2011). Instead, we proposed that transcriptional repression helps to deplete the pool of RiBi transcripts that are normally highly transcribed and translated in actively growing cells (Lee et al., 2011; Ho et al., 2018). In the absence of Dot6 repression, aberrantly abundant RiBi transcripts compete with induced mRNAs for available translational machinery, thereby delaying translation of stress-defense transcripts. In the case of NaCl, the limiting factor is unlikely to be ribosomes: we previously showed that this yeast strain exposed to the same dose of NaCl removes a population of ribosomes from the translating pool immediately after stress (Ho et al., 2018). This is consistent with bacterial models of growth regulation, in which cells preserve some ribosomes for later stress acclimation (also indicating that growth rate under these conditions is not limited by ribosome availability) (Mori et al., 2017; Kim et al., 2018; Korem Kohanim et al., 2018; Remigi et al., 2019; Wu et al., 2022). Evidence from bacteria and incidental results in yeast suggest that other features related to translation elongation may limit cell growth in this situation (Dai et al., 2018; Ho et al., 2018; Wu et al., 2022), a limitation that may be alleviated by removing some ribosomes from the translating pool. How all of this fits into broader cellular states is discussed below.

Differences in pre-stress cellular states influence stress acclimation

Many studies have found significant variation in how cells respond to acute stress. Using our system and the conditions studied here, upwards of 35% of the variance in post-stress growth rate could be explained by a multi-factorial model that includes both pre-stress and acute-stress phenotypes. It will be interesting to see as technology develops for improved growth rate measurements if this fit improves further. The remaining unexplained variation is likely influenced by additional features of the cellular state, as well as stochastic effects. We found no connection to cell-cycle phase or cell size, although the lack of correlation could be masked by other confounders (Barber et al., 2021). But one likely contributor is differences in pre-stress metabolic or mitochondrial states as implicated in several studies (Fehrmann et al., 2013; Gasch et al., 2017; Laporte et al., 2018; Dhar et al., 2019; Bagamery et al., 2020). Bagamery et al. showed that pre-stress fluctuations in fermentative versus respirative metabolism influence how cells recover from glucose starvation, with antagonistic fitness effects depending on the situation (Bagamery et al., 2020). Other studies linked variation in mitochondrial function and morphology to cell age and the ability to enter quiescence, which could also influence stress responsiveness (Fehrmann et al., 2013; Laporte et al., 2018). An interesting avenue for future investigation would be to measure metabolic and mitochondrial states along with features studied here.

Regardless, our results are consistent with the fact that pre-stress cellular states influence how cells will respond to future stress. Some cells in our study were fast growing before stress, showed a larger Dot6 response during stress, and acclimated faster in terms of post-stress growth rate; in turn, cells that were slow growing before stress had lower pre-stress Dot6 activity, lower Dot6 activation during the acute phase, and a slower growth acclimation. One hypothesis is fast-growing cells may have higher biosynthetic capacity, and thus more need for ribosomes and higher transcription of RiBi genes. These cells may therefore need to slam on the brakes of RiBi production more strongly in order to free up translational capacity. Repression of RiBi transcripts in and of itself need not impact subsequent growth recovery, if cells already harbor ample ribosomes at the time of stress.

On the other hand, the size of the Dot6 acute-stress peak correlates with post-stress growth acclimation in a way that can be separated from pre-stress growth rate (Figure 6C, D andFigure 6—figure supplement 2). Thus, some cells may be growing at average rates but still require a large Dot6 response, for example if they are already somewhat limited in translational capacity for other reasons and therefore require a strong Dot6 response. Interestingly, pre-stress growth rate did not correlate with the time to cross the Ctt1 threshold (p=0.24), indicating that the correlation with Dot6 is independent. Future studies will be required to test these hypotheses. Interestingly, the Dot6 acute-stress peak height can be fairly well predicted by the relative pre-stress activity of Msn2 versus Dot6 (R2=0.42, Figure 7—figure supplement 1), again linking acute-stress behavior to pre-stress cell states.

Our work adds to a growing body investigating the relationship between stress defense and growth rate. While we expected a relationship between coordinated Msn2/Dot6 activation and growth rate based on past studies (Brauer et al., 2008; Ho et al., 2018), we instead discovered unexpected discordance in the factors’ behavior and an unexpected association of acclimation growth rate and Dot6 activity, the opposite of what several past models predict (Regenberg et al., 2006; Castrillo et al., 2007; Brauer et al., 2008; Airoldi et al., 2009). These results highlight the complexities of eukaryotic growth control and set the stage for further dissection of the driving regulators of growth rate and how best to predict growth under fluctuating conditions.

Methods

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:

Nuclear localization peaks

Temporal peaks of nuclear localization were identified using the MATLAB findpeaks function, where a peak height is called from a local maximum to the nearest minimum (‘valley’) on either side of the peak. In order to estimate a threshold for a true peak of nuclear localization versus background noise, a linear regression was done on pre-stress nuclear localization time points to calculate the difference of each point from the regression line, resulting in a baseline standard deviation of localization values. Since from visual inspection of traces and cells there were many more true peaks for Dot6, the standard deviation for the Msn2-mCherry channel was used to calculate this baseline threshold for both Msn2 and Dot6. Specifically, two standard deviations from the mean of the distribution of was used as a threshold. This threshold appeared to be accurate by visual inspection of cells, where the threshold distinguished what looked like true nuclear localization from the images.

Area under the curve (AUC) of nuclear localization

For pre-stress time points, AUC was calculated by summing the first 9 measurements of nuclear localization scores (top brightest 5% of pixels over the median cellular signal). This summation represents the total relative levels of nuclear localization between all cells. The same AUC calculation was done for the acclimation phase using time points 24–37. The difference in AUC between the two signals (Msn2 – Dot6 AUC in Figures 6 and 7) is simply the difference of the two individual AUC measurements.

Acute stress peak height

The acute stress peak height was calculated by taking the maximum of nuclear localization score during the acute stress response (time points 13–20) and then subtracting the minimum of the nuclear localization scores just before stress (time points 11–13).

iRFP fluorescence was recorded as the median pixel intensity within cell masks, divided by the background fluorescence measured for each image using ImageJ (Abràmoff et al., 2004). Maximum Ctt1 levels were taken as the maximum fluorescence signal from T12-T43 timepoints minus the median of pre-stress (T1-T11) signal. Threshold analysis was done by identifying the time it took each cell to cross a 5% change in Ctt1 abundance. Cells that did not cross that threshold were not included in the timing analysis (but were included in correlations with maximum Ctt1 production).

Cell clustering to identify subpopulations

Nuclear localization scores were log2 transformed, and for each cell and each factor, the value at each timepoint was normalized to the median of all cells for that factor and time point (Figure 4, blue/yellow scale data). The population-median-normalized vector for Msn2 and Dot6 were concatenated and clustered by mclust (Scrucca et al., 2016) using model EII and k=30 (which was collapsed to k=11 by mclust for data shown in Figure 4 and k=9 for data shown in Figure 4—figure supplement 1). The log2 of unnormalized nuclear traces for each cell was added for display in Figure 4 and supplement (orange/white scale data). Relationships with logged growth rate data before and after stress, calculated as described above, were scored for each cluster of cells compared to all other cells in the data (Figure 5 and supplements, Wilcoxon Rank Sum test).

Visual inspection of cells within mclust clusters 2 and 3 indicated that some Msn2 signal was focused but outside the nucleus (evidence after NaCl treatment), likely in the vacuole. There were 18 cells were this was observed visually. Since the impact of this signal was uncertain, these cells were excluded from subsequent regression modeling (i.e. Figures 5A7).

Probabilities of the number cells from each of the three biological replicates

Binomial probabilities were used to determine if each cluster contained more cells from one of the three biological replicates than would be expected by chance. Specifically, if x is the number of cells from a given biological replicate present in a cluster, then probability of having x cells or more in the cluster is

Pr(X x)=k=Xnnkpk(1-p)n-k

where n is the number of cells in the cluster and p is the expected probability of having a cell from a given replicates (that is, the total number of cells in the replicate divided by the total number of cells in all three replicates). Since clusters 8 and 10 had a total of 5 and 6 cells, respectively, they lacked statistical power and were excluded from the analysis.

The Holm-Bonferroni method was used for multiple hypotheses correction, where there were n=27 (9 clusters and testing the number of cells from 3 biological replicates in each cluster) and = 0.05. Using this threshold, only Cluster 9 showed strong enrichment for cells from one replicate compared to expected after multiple-test correction. Of note, this was the only cluster (besides Clusters 8 and 10), that had zero cells from a given replicate (Supplementary file 3).

Permutations of nuclear localization peak matches

To identify if matched peaks of Msn2 and Dot6 were more coordinated than expected by chance, permutations were performed where a random Msn2 and Dot6 trace, including the time points of the called peaks, were randomly paired from the entire dataset. Coordinated peaks were then calculated from these random Msn2/Dot6 trace pairs. These permutations indicated that the number of matched Msn2/Dot6 peaks per cell was much higher than expected by random combinations (zero permuted datasets out of 100,000 total had 0.21 matched pre-stress peaks per cell or more). The same test was done for the matched peaks during the acclimation phase, and although the number of matched peaks per cell was significant for the acclimation time points (a fraction of 4x10–4 of permuted datasets had 0.07 matched peaks per cell or more), this was significantly less than that for the pre-stress time points. This again demonstrated that there was more coordination in nuclear localization between Msn2 and Dot6 during the before stress compared to after stress.

Permutations of nuclear localization peak correlations between cells in two-cell colonies

There were 56 two-cell colonies in the dataset. Of these, 15 colonies showed coordinated Dot6 peaks between the two cells, defined as peaks, occurring within one time point of each other. Permutations were performed where the 112 cells from these 56 colonies were randomly assigned in pairs and the same coordinated peak measurements were performed. Similarly, permutations were performed on matched peaks of Msn2. Results are shown in Supplementary file 1.

Linear models

Multiple linear regressions shown in Figure 6 and Supplementary file 3 were performed using fitlm in MATLAB. Each model was represented by

y = β0+β1x1+ β2x2 ++ βnxn +ϵ

where the dependent variable y is the post-stress growth rate, β0 is the intercept, each subsequent β is the estimate of the slope for each independent variable x , and is the error term. A list of independent variables is shown in Supplementary file 3 for each of the multiple linear regression performed. The p-values shown in Supplementary file 3 were determined from the t-statistic of each β coefficient was not equal to zero. In Supplementary file 3, Model 1 included all variables in the model. Model 2 only included the significant independent variables from Model 1. Model 3 excluded Msn2 acclimation AUC and cell/colony size from Model 2 as the p-values did not pass Holm-Bonferroni correction (=0.05 and n =14). Since Model 3 gave the four most-significant variables, Model 4 then removed pre-stress growth rate to see the resulting explained variance. Model 5 measured the explained variance of the two most significant variables: Dot6 acute stress peak height and pre-stress growth rate. Trends and significance were the same when analyzing only single-cell colonies, except that the minor contribution of the sum of prestress Msn2 peaks to the original model was no longer significant.

For principle component regression (Figure 6D), principle component analysis (PCA) was performed using pca in MATLAB on the 4 factors that had significant influence on post-stress growth rate (Figure 6B, bold). The resulting PCA coefficients (i.e. the loadings) represent the contribution of each of these 4 factors to each PC. For each PC, the value of each coefficient was divided by the sum of coefficient values to give a fractional contribution of each factor to each PC (Drummond et al., 2006). A linear model was then performed as described above, where the dependent variable, y, was again the post-stress growth rate, but the independent variables, x, were the resulting PCA scores for each of the 4 factors.

Data availability

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.

References

    1. Abràmoff MD
    2. Magalhães PJ
    3. Ram SJ
    (2004)
    Image processing with imagej
    Biophotonics Int 11:36–41.

Decision letter

  1. Naama Barkai
    Senior and Reviewing Editor; Weizmann Institute of Science, Israel

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Decision letter after peer review:

[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]

Thank you for submitting the paper "Integrating multiple single-­cell phenotypes links stress acclimation to prior life history in yeast" for consideration at eLife. Your initial submission has been assessed by a Senior Editor in consultation with members of the Board of Reviewing Editors. Although the work is of interest, we regret to inform you that the findings at this stage are too preliminary for further consideration at eLife.

As you will see from the reviews below, the reviewers found your manuscript potentially interesting but the story incomplete (see for example reviewer #2 – "My concern is that the story seems incomplete and lacks any firm conclusions regarding causality or mechanisms. The paper relies completely on the correlation analyses, which could serve as a good starting point for a story, if followed with experimental validation (e.g. by perturbations) and mechanistic investigation. However, the authors decided to end the paper there, leaving the story incomplete and inconclusive. Therefore, a significant amount of further work will be needed to warrant publication of the paper in eLife."; this was pretty much the oncensus)

if you can fully address this concern through additional experiments (as well as the other concerns expressed by the reviewers), we will be happy to reconsider the paper.

Reviewer #1 (Recommendations for the authors):

In the present paper Bergen, Hose, McClean, and Gasch study the yeast stress response and recovery in the form of cell growth rates. It is well-known that there is great heterogeneity among the responses of otherwise genetically identical single cells to stress. An interesting question therefore is to what extent is the heterogeneity is just random stochastic noise and to what extent is it "hard-coded" into the cell based on its recent prior history (e.g. expression of various proteins, cell-to-cell variation of protein abundances, prior stress response, etc).

Clearly both – random noise and prior life history – contribute. For example, it is well known that stochastic single molecule events can regulate cell fate (Choi, Cai, Frieda, Xie, Science, 2008), but it is also well-known that if you "prime" cells by exposing them to mild stress, then they respond much better to a subsequent high-intensity stress.

An interesting question then is, what is the relative contribution of random noise and prior life history? And for prior life history, what factors are most predictive?

The setup of this paper is pretty simple. They look at two well-known factors, Msn2 and Dot6, using a single type of stress (0.7M NaCl step function) and they then quantify various aspects, the 3 most important of which are: (1) Msn2 nuclear localization; (2) Dot6 nuclear localization; and (3) cell growth rate.

The key finding is that prior Dot6 activation is more predictive than Msn2, and that a model with more variables can predict more of the variance than for example a single factor.

While the authors test multiple factors, the overall amount of the variance that can be explained is modest – around 35% using the full linear model.

I don't have major technical concerns and the work generally seems to be well done. I think the two main issues are (1) I would like to see some control computational analyses to assess how robust their growth rate quantification is and (2) the size of the dataset is pretty small, just 221 cells. This is a concern especially since they have 11 clusters, some of which have very few cells in them, raising doubt about their conclusion.

For the 1st concern, I'd like the authors to do a mock experiment: grow cells without any stress and then arbitrarily set a boundary and do similar plots to Figure 2 to see how much change in growth rate they see without stress. This will allow us to understand how much of Figure 2 is true signal and how much is just quantification noise. This could either be a new experiment or re-analysis of existing data before the stress. I'd also like to see "moving average growth rate" plots for single cells – how meaningful is a single growth rate number? I'd also like to get more detail on how growth rate was calculated. If I understand correctly, the authors use cell size. This is very reasonable, but it is a challenging quantification: since volume scales with radius to the 3rd power, tiny errors in the estimation of the radius can result in massive changes in the estimation of the volume. What was the pixel size and did they do subpixel analysis?

In general, I'd like to see a comprehensive description of how they quantified cell growth as well as a comprehensive supplementary figure "stress testing" the robustness of their quantification.

This is important since the entire paper rests on the cell growth numbers being accurate.

For my second concern, I'd like the authors to test how robust their various results are to cell numbers. I suggest that they do subsampling, where they leave out 50% of their data/cells, repeat their analysis, and then iterate multiple times to assess if all of their conclusions are robust. For example, I am concerned about whether or not they have enough data to support 11 clusters. This type of subsampling approach should be informative, though I would like to see this approach applied to all of the major conclusions and analyses.

Since both of these concerns can be assessed using either purely more computational analysis and/or just with the addition of a very simple experiment, hopefully they should not be onerous to address.

My conceptual concern is whether or not the present paper is a major conceptual advance. The notions that prior life history affects how cells deal with a stress response as well as the observation of substantial heterogeneity in single cell stress responses, are both well-known in the field and similar observations have been made in yeast and other organisms. So, the major novel contributions seem to be the relative important of Dot6 and Msn2 (Dot6 is more predictive than Msn2, which at least I did not know), and a quantification of how much one can predict from Dot6 and Msn2. This is certainly a nice contribution, and the study seems to be generally performed in a thoughtful manner, but it is perhaps a modest conceptual advance given what is already known.

Reviewer #2 (Recommendations for the authors):

In this manuscript, Bergen et al. combined microfluidics and time-lapse imaging to monitor single-cell phenotypes in response to acute osmotic stress. In particular, they measured translocation dynamics of two transcription factors, Msn2 and Dot6, together with a series of cellular phenotypes, e.g. cell growth, cell size, cell cycle phase, etc. To make sense of these data, they classified single cells into clusters based on their Msn2 and Dot6 dynamics, and performed correlation analyses to quantify relative contributions of measured phenotypic factors (alone or in combination) to post-stress growth rate. They found that post-stress growth rate showed a stronger correlation with an integration of multiple factors (rather than each single factor alone), among which pre-stress growth rate and Dot6 peak height seem playing major roles.

The authors focused on an important question in the field – the cell-to-cell heterogeneity in stress response and the functional relevance to stress adaptation. The experimental approaches are not new but timely. Their clustering and correlation analyses suggest some interesting relationships between phenotypic factors and growth adaptation.

My concern is that the story seems incomplete and lacks any firm conclusions regarding causality or mechanisms. The paper relies completely on the correlation analyses, which could serve as a good starting point for a story, if followed with experimental validation (e.g. by perturbations) and mechanistic investigation. However, the authors decided to end the paper there, leaving the story incomplete and inconclusive. Therefore, a significant amount of further work will be needed to warrant publication of the paper in eLife.

Other concerns:

1. I find the title a bit misleading. "Prior life history" sounds like a cell's previous stress encounter, nutrient condition, or age, etc. However, in the paper, it seems referring specifically to Msn2/Dot6 dynamics and pre-stress growth rate during the 72-min baseline (no stress) period immediately before the stress treatment. Maybe "pre-stress cellular state" is more accurate than "prior life history."

2. Figure S2, it seems pre-stress growth rate and Dot6 acute stress peak height are major contributing factors to post-stress growth rate. Is it possible that these two factors are mechanistically connected? Is there any correlation between these two factors? My concern here is whether these two factors can be simply combined into one factor, pre-stress growth rate or biogenesis capacity whereas Dot6 acute stress peak height simply depends on Dot6 protein expression level, reflective of cellular biogenesis capacity. If this is true, then an alternative interpretation of the correlation results will be that cell-to-cell variation in post-stress growth rate largely arises from variations in pre-stress growth rate or biogenesis capacity, which has long been known as a major source of extrinsic noise. Dot6 peak height and other phenotypic factors simply reflect pre-stress biogenesis capacity. This interpretation can also reconcile the contradiction that Dot6 peak height positively correlates with post-stress growth rate whereas Dot6 is a repressor of ribosomal biogenesis. A careful test of this possibility will be needed.

Reviewer #3 (Recommendations for the authors):

Bergen et al. study how the dynamic subcellular localizations of two stress-responsive transcription factors (Dot6 and Msn2) relate to variability in the growth of yeast cells before, during and after high-salt stress.

Previous studies (e.g., by the O'Shea lab) used microfluidics to look at subcellular dynamics of Msn2 and other transcription factors from a more mechanistic point of view. Other studies (e.g., Li et al. 2018 cited in the manuscript) used higher-throughput time-lapse imaging to look at population heterogeneity in growth, gene expression and stress tolerance. The present study is appealing because it sits somewhere in between. It uses microfluidics to track the two transcription factors but asks how their dynamics relate to growth.

One interesting finding, corroborating the authors' 2017 work, is that Msn2 and Dot6 do not show entirely coordinated activity. The authors identify upwards of 10 clusters of cells distinguished by different dynamic patterns of the two TFs. This finding raises the possibility that this kind of approach can find meaningful subpopulations of cells with different physiological properties.

However, the manuscript does not go far in developing understanding of how these subpopulations are generated. The authors describe Msn2 and Dot6 as both being controlled by both PKA and TOR. But that does not necessarily mean that Msn2 and Dot6 should always respond together -- one simple hypothesis is that the discordance that is seen is a result of differences in the relative contributions of these signaling pathways to Msn2 and Dot6 control. There are also counter-intuitive results that remain to be explained. In particular, the authors report that cluster 11, with below-average Dot6 response before and during stress showed slower growth. Because Dot6 represses growth-promoting genes, one would expect low Dot6 response to produce faster growth. In the end, the biggest predictor of post-stress growth rate was pre-stress growth rate, so the authors' conclusion that prior life history states are predictive (to some extent) of future ones is reasonable. But it remains to be seen why the correlation exists.

The results presented in the manuscript will certainly be of interest to those following this line of research, but the impact more generally is moderate because of the lack of deeper mechanistic insight into how this important signaling network generates heterogeneity in growth responses.

One recommendation to strengthen the presentation of the paper would be to include a figure (and/or movies) showing primary data (time-lapse images of fluorescent signal in cells in the microfluidics device), especially since this is not an off-the-shelf microfluidics platform.

Another recommendation is to flesh out more the comparisons to prior experimental work. For example, a reader new to this line of research would not immediately grasp that Li et al. 2018 followed colony growth for much longer time periods, did not use microfluidics, and used cells enriched for slow growth when examining Msn2 dynamics. These details can affect how a reader interprets the observation that Msn2 response does not correlate strongly with growth in this study. Also, the authors do not discuss any connection between growth heterogeneity and mitochondrial function, which featured heavily in the Fehrmann et al. 2013 and Li et al. 2018 papers that were cited, as well as in Dhar et al. (eLife 8:e38904, 2019) that was not cited.

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Modeling single-cell phenotypes links yeast stress acclimation to transcriptional repression and pre-stress cellular states" for further consideration by eLife. Your revised article has been evaluated by Naama Barkai (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

I appreciate that the authors have put in a fair amount of work into the revision and I do believe the paper is strengthened. The authors have responded to my main concerns.

Regarding cell growth calculations, I do remain concerned. They do not do subpixel segmentation, and they only do 2D segmentation as I understand it. Furthermore, they do not quantify growth rate (which I would define as the mass accumulation per unit time). Instead, they take the relative change in the median size, and for much of the paper they then take the logarithm of this number. A lot of these assumptions seem reasonable but arbitrary and as added complications, yeast cells shrink dramatically in size upon osmotic stress and then gradually recover (so changes in cell size reflect both cell growth and osmotic changes) and budding yeast mainly grow at the bud, and the bud is frequently out-of-focus and not quantified.

Accordingly, the main output metric in the entire paper – cell growth rate – and the measured values are associated with very very large uncertainties and caveats. At a minimum, this need to be more explicitly mentioned and caveated in the main text.

Moreover, some of the analysis remains not totally convincing. For example, in Figure 5A there is a mostly random vertical scatter of points, and what seems like a fairly arbitrary straight line is drawn through it. The p-value may be small, but this does not look like a model that very well explains the data.

Overall, I appreciate the great work done by the authors to address the reviewer comments, but I do think both some technical and conceptual concerns remain. That said, it is a challenging question to tackle, and it is not clear a priori how much of the variation we should even expect Msn2 and Dot6 dynamics to explain.

https://doi.org/10.7554/eLife.82017.sa1

Author response

[Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]

Reviewer #1 (Recommendations for the authors):

In the present paper Bergen, Hose, McClean, and Gasch study the yeast stress response and recovery in the form of cell growth rates. It is well-known that there is great heterogeneity among the responses of otherwise genetically identical single cells to stress. An interesting question therefore is to what extent is the heterogeneity is just random stochastic noise and to what extent is it "hard-coded" into the cell based on its recent prior history (e.g. expression of various proteins, cell-to-cell variation of protein abundances, prior stress response, etc).

Clearly both – random noise and prior life history – contribute. For example, it is well known that stochastic single molecule events can regulate cell fate (Choi, Cai, Frieda, Xie, Science, 2008), but it is also well-known that if you "prime" cells by exposing them to mild stress, then they respond much better to a subsequent high-intensity stress.

An interesting question then is, what is the relative contribution of random noise and prior life history? And for prior life history, what factors are most predictive?

The setup of this paper is pretty simple. They look at two well-known factors, Msn2 and Dot6, using a single type of stress (0.7M NaCl step function) and they then quantify various aspects, the 3 most important of which are: (1) Msn2 nuclear localization; (2) Dot6 nuclear localization; and (3) cell growth rate.

The key finding is that prior Dot6 activation is more predictive than Msn2, and that a model with more variables can predict more of the variance than for example a single factor.

While the authors test multiple factors, the overall amount of the variance that can be explained is modest – around 35% using the full linear model.

I don't have major technical concerns and the work generally seems to be well done. I think the two main issues are (1) I would like to see some control computational analyses to assess how robust their growth rate quantification is and (2) the size of the dataset is pretty small, just 221 cells. This is a concern especially since they have 11 clusters, some of which have very few cells in them, raising doubt about their conclusion.

As presented in detail below, we have added these controls and a new experiment with an additional 228 cells that validates all of our conclusions in the original manuscript.

For the 1st concern, I'd like the authors to do a mock experiment: grow cells without any stress and then arbitrarily set a boundary and do similar plots to Figure 2 to see how much change in growth rate they see without stress. This will allow us to understand how much of Figure 2 is true signal and how much is just quantification noise. This could either be a new experiment or re-analysis of existing data before the stress. I'd also like to see "moving average growth rate" plots for single cells – how meaningful is a single growth rate number? I'd also like to get more detail on how growth rate was calculated. If I understand correctly, the authors use cell size. This is very reasonable, but it is a challenging quantification: since volume scales with radius to the 3rd power, tiny errors in the estimation of the radius can result in massive changes in the estimation of the volume. What was the pixel size and did they do subpixel analysis?

In general, I'd like to see a comprehensive description of how they quantified cell growth as well as a comprehensive supplementary figure "stress testing" the robustness of their quantification.

This is important since the entire paper rests on the cell growth numbers being accurate.

We addressed the reviewer’s request in several ways. First, we provide additional detail on how the growth rate was calculated. The reviewer raises an important point about the challenges in calculating cell volume; indeed, small error in estimating diameters leads to large error in volume, and for this reason we did not attempt to estimate volume. Instead, we collapsed the three z-stack images and tracked the pixel area, corresponding to colony size, over time. We note that growth rate is calculated by the change in relative size; the median pre-stressed colony size is 4,829 pixels (where pixel size is 0.1075 x 0.1075 m) and cells changed by a median of 30% area over the course of the experiment. We did not do subpixel analysis. It is true that some calculated growth rates are under-estimated (namely, cases where the bud emerges perpendicular to the plane of analysis). However, the correlations we observe cannot be explained by this subset of cells for which growth rate is under-estimated.

Second, we now include several new analyses validating our approach (Figure 1 – supplement 2). We performed a more thorough analysis on growth rate estimates using sliding windows as requested by the reviewer. The results reveal that the growth rates are well measured and robust. (i) The logged change in cell size is very linearly correlated with time in the majority of pre-stressed cells (median R2 = 0.92, Figure 1 – supplement 2A). We estimated growth rates from subsets of timepoints using a sliding window as requested by the reviewer: the correlations between growth rates measured over all pre-stressed timepoints versus subsets of timepoints in a sliding temporal window is also very high for all windows (Figure 1 – supplement 2B). There is a wider distribution of linear fits post-stress (median R2 = 0.73, Figure 1 – supplement 2C), consistent with the range of acclimation behaviors we investigate in the manuscript. Indeed, cells that recovered growth rate after stress showed an increase in logged colony size that was well estimated by a linear fit (i.e. high R2), whereas cells with a lower post-stress growth rate fit less well and were more influenced by measurement noise (Figure 1 – supplement 2E), as confirmed by manual inspection. Together, these data indicate that our measurements are robust to the particular time window we investigated and that the linear fit to estimate growth rate is very high for the vast majority of cells.

Finally, we performed a mock experiment in which cells were exposed to a simple rich-media switch in the absence of NaCl stress. The vast majority of cells did not show large changes in growth rate (median ln(growth-rate change) = -0.20), compared to cells that experience the NaCl shift (median ln(growth-rate change) = -0.85, Figure 1 – supplement 2F). Together, these new experiments show that our growth rate estimates are robust and trends discussed in the manuscript are specific to NaCl stress.

For my second concern, I'd like the authors to test how robust their various results are to cell numbers. I suggest that they do subsampling, where they leave out 50% of their data/cells, repeat their analysis, and then iterate multiple times to assess if all of their conclusions are robust. For example, I am concerned about whether or not they have enough data to support 11 clusters. This type of subsampling approach should be informative, though I would like to see this approach applied to all of the major conclusions and analyses.

The revised manuscript addresses this concern in several ways. We did not intend to imply that there are precisely 11 meaningful clusters (in fact, we stated in the paper that a few of these patterns were only seen in one replicate and thus unlikely to be reproducible). We de-emphasized that there are 11 clusters in the revised manuscript. The important point here is that several of these clusters that are based entirely on transcription-factor dynamics predict pre-stress or post-stress growth rate, which were not used in the clustering. Indeed, we already showed in the manuscript that these correlations remain if we analyze cells from each replicate individually (i.e. subsample by replicate), as highlighted in the text.

Rather than perform additional subsampling analysis, we instead provide a better validation by adding three new replicate experiments. Although the new experiments (described more below) were done with a different strain and somewhat different microscopy settings, all of the relationships reported in the main text are recapitulated with the new data: We confirm that Dot6 peak height, but not Msn2 peak-height, is correlated with pre-stress growth rate (p = 1e-5) and post-stress growth rate (p = 0.002); modeling of post-stress growth rate is significantly influenced by Dot6 peak-height independent of its correlation with pre-stress growth rate (p = 0.04), and a multi-factorial model best explains post-stress growth rate compared to models with any single factor alone (p = 7e-6). We note that because the microscopy conditions were different, we did not attempt to merge data into a single analysis.

Finally, we show that the important clusters in Figure 4 are recapitulated in an independent mixed model clustering of the new dataset (Figure 4 – supplement 1 and Figure 5 – supplement 2). This analysis identified 6 clusters of >3 cells, all of which can be related to clusters from the original dataset. More importantly, the same subpopulation patterns originally associated with pre- and post-stress growth rate differences have the same statistically significant associations in the new dataset.

These new analyses show without a doubt that the patterns and trends we reported in the original manuscript are all valid, robust, and reproducible.

Since both of these concerns can be assessed using either purely more computational analysis and/or just with the addition of a very simple experiment, hopefully they should not be onerous to address.

My conceptual concern is whether or not the present paper is a major conceptual advance. The notions that prior life history affects how cells deal with a stress response as well as the observation of substantial heterogeneity in single cell stress responses, are both well-known in the field and similar observations have been made in yeast and other organisms. So, the major novel contributions seem to be the relative important of Dot6 and Msn2 (Dot6 is more predictive than Msn2, which at least I did not know), and a quantification of how much one can predict from Dot6 and Msn2. This is certainly a nice contribution, and the study seems to be generally performed in a thoughtful manner, but it is perhaps a modest conceptual advance given what is already known.

We appreciate the concern of the reviewer and decided to add several new experiments that provide direct testing of the hypotheses put forward in the original submission. This makes more a much more satisfying study that presents important new insights into stress defense and recovery.

As this reviewer and the others point out, the connection between Dot6 and post-stress growth recovery seems counterintuitive, since a larger Dot6 stress response is predicted to produce stronger repression of growth-promoting genes. However, this result is consistent with our working model:

We previously showed that transcriptional repression by Dot6 (and its paralog Tod6) is required to redirect translational capacity away from ribosome biogenesis (RiBi) mRNAs and toward stress induced transcripts. Mutant cells lacking DOT6 and TOD6 fail to repress RiBi gene targets; the overabundant transcripts remain associated with ribosomes at the expense of stress-induced transcripts including Msn2-regulated CTT1. As a consequence, cells lacking DOT6 and TOD6 show delayed production of Ctt1 protein despite making more CTT1 transcript (Ho et al. Current Biology, 2018).

The current manuscript expands on this model to explore why we now observe that a stronger Dot6 activation response in wild-type cells is associated with faster post-stress growth recovery. First, we show that a dot6∆tod6∆ mutant in our culture media grows indistinguishably from wild type before stress but shows reduced growth recovery after NaCl treatment (new Figure 7A). Thus, Dot6 provides a protective response during stress and is required for normal stress acclimation.

An unresolved question is if differences in Dot6 activation in a wild-type cells affects Ctt1 production. We tested this here by generating a strain with three fluorescent proteins: Dot6-GFP, Msn2mCherry, and Ctt1-iRFP. This strain allows us to track growth rate, transcription factor localization dynamics, and Ctt1 production.

In fact, we find that wild-type cells with a larger Dot6 translocation peak after NaCl show higher levels and faster change of Ctt1 after NaCl treatment – this is separable from the influence of inducer Msn2: linear modeling shows that both factors contribute to Ctt1 production timing, however the contribution of Dot6 is much more significant (Msn2 activation peak is only marginally significant, p = 0.053 and explains much less of the variance in Ctt1 production timing). This is not due to association of Dot6 and pre-stress growth rate, since differences in pre-stress growth show no correlation with Ctt1production time (p = 0.21)

This is an exciting result that significantly expands our past model and our understanding of how and why cells mount a systemic response. We very much hope these extensive revisions have addressed the reviewer’s points. We believe that we have shown that our results and methods are robust, reproducible, and influential given the new insights the revised manuscript presents.

Reviewer #2 (Recommendations for the authors):

In this manuscript, Bergen et al. combined microfluidics and time-lapse imaging to monitor single-cell phenotypes in response to acute osmotic stress. In particular, they measured translocation dynamics of two transcription factors, Msn2 and Dot6, together with a series of cellular phenotypes, e.g. cell growth, cell size, cell cycle phase, etc. To make sense of these data, they classified single cells into clusters based on their Msn2 and Dot6 dynamics, and performed correlation analyses to quantify relative contributions of measured phenotypic factors (alone or in combination) to post-stress growth rate. They found that post-stress growth rate showed a stronger correlation with an integration of multiple factors (rather than each single factor alone), among which pre-stress growth rate and Dot6 peak height seem playing major roles.

The authors focused on an important question in the field – the cell-to-cell heterogeneity in stress response and the functional relevance to stress adaptation. The experimental approaches are not new but timely. Their clustering and correlation analyses suggest some interesting relationships between phenotypic factors and growth adaptation.

My concern is that the story seems incomplete and lacks any firm conclusions regarding causality or mechanisms. The paper relies completely on the correlation analyses, which could serve as a good starting point for a story, if followed with experimental validation (e.g. by perturbations) and mechanistic investigation. However, the authors decided to end the paper there, leaving the story incomplete and inconclusive. Therefore, a significant amount of further work will be needed to warrant publication of the paper in eLife.

We agree with this reviewer (and Reviewer #1) that the original submission left some key hypotheses open ended. We hope that the revised manuscript has addressed this concern: we now provide several new experiments including perturbation analysis with a dot6∆tod6∆ strain and new insights by following Dot6-GFP and Msn2-mCherry dynamics along with production of downstream Ctt1-iRFP protein. As outlined above and in the revised manuscript, these new insights show without a doubt that Dot6 provides a protective response during stress, is required for normal stress acclimation, and is correlated with the timing of Ctt1 induction in a way that is separable from Msn2. We believe the revised manuscript now provides important mechanistic insights that will be of broad interest and impact.

Other concerns:

1. I find the title a bit misleading. "Prior life history" sounds like a cell's previous stress encounter, nutrient condition, or age, etc. However, in the paper, it seems referring specifically to Msn2/Dot6 dynamics and pre-stress growth rate during the 72-min baseline (no stress) period immediately before the stress treatment. Maybe "pre-stress cellular state" is more accurate than "prior life history."

We changed the title to address this comment and reflect the new results in the paper.

2. Figure S2, it seems pre-stress growth rate and Dot6 acute stress peak height are major contributing factors to post-stress growth rate. Is it possible that these two factors are mechanistically connected? Is there any correlation between these two factors? My concern here is whether these two factors can be simply combined into one factor, pre-stress growth rate or biogenesis capacity whereas Dot6 acute stress peak height simply depends on Dot6 protein expression level, reflective of cellular biogenesis capacity. If this is true, then an alternative interpretation of the correlation results will be that cell-to-cell variation in post-stress growth rate largely arises from variations in pre-stress growth rate or biogenesis capacity, which has long been known as a major source of extrinsic noise. Dot6 peak height and other phenotypic factors simply reflect pre-stress biogenesis capacity. This interpretation can also reconcile the contradiction that Dot6 peak height positively correlates with post-stress growth rate whereas Dot6 is a repressor of ribosomal biogenesis. A careful test of this possibility will be needed.

The reviewer raises an important point, one that we addressed in the original manuscript: although Dot6 peak height and pre-stress growth rate are partly correlated with one another, both contribute separately to explain post-stress growth rate. This is evident in the mixed linear modeling, where a model that incorporates both factors explains significantly more of the variance than either single factor model alone.

To further ensure that these factors are not simply co-variates of the same underlying feature, we added two new analyses. First, we added a principal component (PC) regression analysis. We first applied PC analysis to the four factors that were significant in the mixed linear modeling (Figure 6B). We then performed linear modeling using the PC variables and subsequently deconvoluted each PC into biological features that contribute to them. We found that 21% of the variance in post-stress growth rate is explained by PC1 + PC3, which capture the intertwined contributions of pre-stress growth rate, pre-stress Dot6 AUC, and acute-stress Dot6 peak height. It is true that these features may reflect one aspect of the cellular state, such as biosynthetic capacity (a point we present in the revised Discussion). However, an additional 14% of the variance is explained by PC4, which does not relate to pre-stress growth rate and is predominated by Dot6 behavior. Thus, Dot6 acute-stress peak height has separable explanatory power.

As an additional alternate approach, we added another analysis in Figure 6 – supplement 2: we analyzed a subset of cells that were insignificantly different from one another in pre-stress growth rate. Over this subset, there is no correlation between pre-stress and post-stress growth rate – however, there remains a significant correlation between Dot6 acute-stress peak height and post stress growth rate explaining 12% of the variance (p = 9.7e-5). Thus, without a doubt, there is a significant correlation between Dot6 behavior and the ability to recover from stress. As we report in the new manuscript, Dot6 indeed provides a protective response during stress but not before (new Figure 7) and is correlated with faster production of Msn2 target Ctt1, independent of the explanatory power of Msn2. As we expound on in the Results and Discussion, this is all consistent with and significantly expands past work from our lab showing that transient repression of growth-promoting genes is important to temporarily redirect translational capacity to induced transcripts during acclimation.

Reviewer #3 (Recommendations for the authors):

Bergen et al. study how the dynamic subcellular localizations of two stress-responsive transcription factors (Dot6 and Msn2) relate to variability in the growth of yeast cells before, during and after high-salt stress.

Previous studies (e.g., by the O'Shea lab) used microfluidics to look at subcellular dynamics of Msn2 and other transcription factors from a more mechanistic point of view. Other studies (e.g., Li et al. 2018 cited in the manuscript) used higher-throughput time-lapse imaging to look at population heterogeneity in growth, gene expression and stress tolerance. The present study is appealing because it sits somewhere in between. It uses microfluidics to track the two transcription factors but asks how their dynamics relate to growth.

One interesting finding, corroborating the authors' 2017 work, is that Msn2 and Dot6 do not show entirely coordinated activity. The authors identify upwards of 10 clusters of cells distinguished by different dynamic patterns of the two TFs. This finding raises the possibility that this kind of approach can find meaningful subpopulations of cells with different physiological properties.

However, the manuscript does not go far in developing understanding of how these subpopulations are generated. The authors describe Msn2 and Dot6 as both being controlled by both PKA and TOR. But that does not necessarily mean that Msn2 and Dot6 should always respond together -- one simple hypothesis is that the discordance that is seen is a result of differences in the relative contributions of these signaling pathways to Msn2 and Dot6 control. There are also counter-intuitive results that remain to be explained. In particular, the authors report that cluster 11, with below-average Dot6 response before and during stress showed slower growth. Because Dot6 represses growth-promoting genes, one would expect low Dot6 response to produce faster growth. In the end, the biggest predictor of post-stress growth rate was pre-stress growth rate, so the authors' conclusion that prior life history states are predictive (to some extent) of future ones is reasonable. But it remains to be seen why the correlation exists.

The results presented in the manuscript will certainly be of interest to those following this line of research, but the impact more generally is moderate because of the lack of deeper mechanistic insight into how this important signaling network generates heterogeneity in growth responses.

We hope that the new experiments added to the manuscript completely address these points. Our new experiments, outlined in detail above, show that: (1) Dot6 provides a protective response during stress, since cells lacking DOT6 and its paralog TOD6 show wild-type growth before stress but slower growth acclimation after stress (new Figure 7A) – this is exactly consistent with results presented here, in which wild-type cells with a weaker Dot6 response show a slower growth acclimation. (2) Our past work predicts that transient Dot6-dependent transcriptional repression helps to temporarily redirect translational capacity to stress-induced transcripts. Indeed, cells lacking DOT6 and TOD6 show delays in producing Msn2-dependent target Ctt1. We now show here that wild-type cells with a larger Dot6 acute-stress response show statistically significantly faster Ctt1 production, separable from the contribution of Msn2 (new Figure 7). Indeed, a mixed linear model shows that the Dot6 response is the main contributor to Ctt1 production time. (3) Finally, our new analyses show that all of the patterns reported in the original manuscript are reproduced in our new datasets. We integrate these results into a new section in the Discussion that discusses the different pre-stress cellular states that these patterns may reflect.

The revised manuscripts adds important new insights that are likely to be of broad interest. With the new experiments, insights, and tested hypotheses we believe that this work will make an excellent contribution to eLife.

One recommendation to strengthen the presentation of the paper would be to include a figure (and/or movies) showing primary data (time-lapse images of fluorescent signal in cells in the microfluidics device), especially since this is not an off-the-shelf microfluidics platform.

We added Figure 1 – supplement 1 that shows an example cell tracked over time in the bright field, GPF, and mCherry channels.

Another recommendation is to flesh out more the comparisons to prior experimental work. For example, a reader new to this line of research would not immediately grasp that Li et al. 2018 followed colony growth for much longer time periods, did not use microfluidics, and used cells enriched for slow growth when examining Msn2 dynamics. These details can affect how a reader interprets the observation that Msn2 response does not correlate strongly with growth in this study. Also, the authors do not discuss any connection between growth heterogeneity and mitochondrial function, which featured heavily in the Fehrmann et al. 2013 and Li et al. 2018 papers that were cited, as well as in Dhar et al. (eLife 8:e38904, 2019) that was not cited.

We added several statements throughout the paper to clarify that Li et al. followed much longer time frames and that the lack of connection between stress acclimation and Msn2 response pertains to the conditions studied here. The revised Discussion provides an expanded discussion of other features that could influence the stress response, including a more direct mention of metabolic differences and mitochondrial function. We added the references suggested by the reviewer.

[Editors’ note: what follows is the authors’ response to the second round of review.]

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

I appreciate that the authors have put in a fair amount of work into the revision and I do believe the paper is strengthened. The authors have responded to my main concerns.

We also feel that the manuscript has been significantly improved thanks to the review process, and we thank the editor and this reviewer for the positive feedback on our revisions.

Regarding cell growth calculations, I do remain concerned. They do not do subpixel segmentation, and they only do 2D segmentation as I understand it. Furthermore, they do not quantify growth rate (which I would define as the mass accumulation per unit time). Instead, they take the relative change in the median size, and for much of the paper they then take the logarithm of this number. A lot of these assumptions seem reasonable but arbitrary and as added complications, yeast cells shrink dramatically in size upon osmotic stress and then gradually recover (so changes in cell size reflect both cell growth and osmotic changes) and budding yeast mainly grow at the bud, and the bud is frequently out-of-focus and not quantified.

The reviewer raises several points that we will address in turn. In choosing our image segmentation technique we chose to stay close to accepted methodologies in the literature (Versari, et al. 2017; Li, et al. 2018; http://yeast-image-toolkit.biosim.eu/), which do not use subpixel segmentation (see for example Bagamery, et al. 2020; Plavskim, et al. 2021; Li, et al. 2018; Levy, et al. 2012). Importantly, subpixel segmentation would add little to the accuracy of our approach: the median prestressed yeast colony size is ~4,900 pixels – as a back-of-the-envelope calculation assuming a perfect circle, this corresponds to a radius of 39.5. Extending the radius by one pixel and again assuming a simple circle for calculation, that would change the area by 5%. Thus, using subpixel segmentation would not hugely change our area estimates. The minimal error in calculating area combined with our adherence to accepted image segmentation techniques makes us confident in our analysis choices, despite some necessary assumptions with this method.

In terms of growth rate calculations: as a point of clarification, we did not use any median to calculate growth rates. We used the change in natural log of colony area in collapsed images from z-stack measurements. This is in keeping with prevailing approaches in yeast microscopy studies (e.g. Bagamery, et al. 2020; Levy, et al. 2012), which use maximum projection of colony z-stacks to define the colony area and take the linear fit as growth rate. There is a trade-off between z-resolution (i.e. number of z-stacks) and yeast stress induced by repeated light exposure, which limits the number of z-stacks we can take. Short of a different microscopy system, what we have done extends to the limits of our system, which we believe is accurate enough to reveal important new information, as expanded on below.

It is true that our approach will under-estimate growth rates for cells in which the bud emerges perpendicular to the slide plane; however, this noise is almost certainly leading to under-estimates of the relationship between transcription factor activation (which is well measured in all planes) to growth rate. Furthermore, many cells are well measured in our study: 57% of cells were scored as having a bud at the outset of the experiment (indicated as S, G2, or M phase, available in the data source file), and the range of pre- and post-stress growth rates as we measured them are very similar for cells scored in these different phases. Thus, while there is some noise due to budding out of field of focus, our measurements are accurate for most cells. Nonetheless, we have explicitly revised the manuscript to bring this caveat to the reader’s attention as detailed below.

The reviewer also raises concern about changes in cell volume after NaCl stress, which they argue could confound post-stress growth rate measurements. We point out that volume changes recover to pre-stress levels by 30-40 minutes (here, and also Babazadeh et al. 2013; Miermont et al. 2013), and we calculated post-stress growth rates from timepoints spanning 42 – 96 minutes after NaCl exposure in our study. Thus, we do not expect that recovery of cell volume separate from growth can explain changes in colony area at these later time points.

As an important conclusion to our arguments, we highlight that one of the major predictions made from our microfluidics and growth rate measurements – that Dot6 plays a positive role in acclimation to NaCl – is born out in bulk culture analysis that uses different methods to measure culture growth dynamics. Furthermore, the results agree with independent correlations between Dot6 activity and Ctt1 production, whose measurement is not affected by the concerns of the reviewer. Thus, the trends we report are robust and uncovering new information about stress responses, despite some necessary limitations.

Accordingly, the main output metric in the entire paper – cell growth rate – and the measured values are associated with very very large uncertainties and caveats. At a minimum, this need to be more explicitly mentioned and caveated in the main text.

As detailed in above, we feel that the noise associated with our area and growth rate measurements is reasonable and in keeping with established methodology in the field. However, we agree with the reviewer that necessary assumptions and potential caveats should be highlighted in the text. We made several changes to the manuscript to present this:

On Page 7, first paragraph of the Results: “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.”

On Page 13/14 of the Results: “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.”

Page 20 of the Discussion: “It will be interesting to see as technology develops for improved growth rate measurements if this fit improves further.”

On Page 24/25 of the Methods: “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.”

Moreover, some of the analysis remains not totally convincing. For example, in Figure 5A there is a mostly random vertical scatter of points, and what seems like a fairly arbitrary straight line is drawn through it. The p-value may be small, but this does not look like a model that very well explains the data.

Thank you to this reviewer for making our point here, which is that the correlation shown in Figure 5A (between pre- and post-stress growth rates) is very low. A main point of the paper is that the correlation improves significantly when we incorporate other independent parameters (Figure 6B) in the fit – the point here is that the fit significantly improves from Figure 5A to Figure 6C where we explain 35% of the variance in growth rate as we measured it. As discussed above, this is likely an under-estimate due to the noise in measuring growth rate for a subset of cells, but the trends are real.

Overall, I appreciate the great work done by the authors to address the reviewer comments, but I do think both some technical and conceptual concerns remain. That said, it is a challenging question to tackle, and it is not clear a priori how much of the variation we should even expect Msn2 and Dot6 dynamics to explain.

We agree that this is a challenging problem to study but argue that our approach provides important and unexpected insights into stress biology, even if our explanatory power is under-estimated. We had already added a paragraph to the revised manuscript that discusses the numerous factors that act together to influence stress acclimation, citing other possible features not studied here. However, the relationships between stress acclimatation and Msn2 and Dot6 behavior that we report were not expected at the outset, as commented on by all three of the original reviewers, and the revised manuscript presents new models to explain these unexpected results and the role of transcriptional repression in salt acclimation. This work was just presented to world leaders in the field at the International Symposium on Fungal Stress and it was hugely well received. We believe the revised manuscript will also be very well received and make a strong contribution at eLife.

https://doi.org/10.7554/eLife.82017.sa2

Article and author information

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

Funding

National Science Foundation (1715324)

  • Audrey P Gasch

Burroughs Wellcome Fund (1R35GM128873)

  • Megan N McClean

National Institute of Health and Medical Research (R01CA229532)

  • Audrey P Gasch

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Stephanie Geller, Taylor Scott, and Kieran Sweeney for help on microfluidics and microscopy, Michael Newton and Kirin Hong for statistical discussions, and members of the Gasch Lab for constructive comments. This work was supported by NSF grant 1715324 to APG and NIH 1R35GM128873 to MNM, who holds a Career Award at the Scientific Interface from the Burroughs Welcome Fund.

Senior and Reviewing Editor

  1. Naama Barkai, Weizmann Institute of Science, Israel

Publication history

  1. Preprint posted: September 8, 2021 (view preprint)
  2. Received: July 20, 2022
  3. Accepted: November 8, 2022
  4. Accepted Manuscript published: November 9, 2022 (version 1)
  5. Version of Record published: November 21, 2022 (version 2)

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© 2022, Bergen, Kocik et al.

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  1. Andrew C Bergen
  2. Rachel A Kocik
  3. James Hose
  4. Megan N McClean
  5. Audrey P Gasch
(2022)
Modeling single-cell phenotypes links yeast stress acclimation to transcriptional repression and pre-stress cellular states
eLife 11:e82017.
https://doi.org/10.7554/eLife.82017
  1. Further reading

Further reading

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    β-Arrestins are master regulators of cellular signaling that operate by desensitizing ligand-activated G-protein-coupled receptors (GPCRs) at the plasma membrane and promoting their subsequent endocytosis. The endocytic activity of β-arrestins is ligand dependent, triggered by GPCR binding, and increasingly recognized to have a multitude of downstream signaling and trafficking consequences that are specifically programmed by the bound GPCR. However, only one biochemical ‘mode’ for GPCR-mediated triggering of the endocytic activity is presently known – displacement of the β-arrestin C-terminus (CT) to expose clathrin-coated pit-binding determinants that are masked in the inactive state. Here, we revise this view by uncovering a second mode of GPCR-triggered endocytic activity that is independent of the β-arrestin CT and, instead, requires the cytosolic base of the β-arrestin C-lobe (CLB). We further show each of the discrete endocytic modes is triggered in a receptor-specific manner, with GPCRs that bind β-arrestin transiently (‘class A’) primarily triggering the CLB-dependent mode and GPCRs that bind more stably (‘class B’) triggering both the CT and CLB-dependent modes in combination. Moreover, we show that different modes have opposing effects on the net signaling output of receptors – with the CLB-dependent mode promoting rapid signal desensitization and the CT-dependent mode enabling prolonged signaling. Together, these results fundamentally revise understanding of how β-arrestins operate as efficient endocytic adaptors while facilitating diversity and flexibility in the control of cell signaling.

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    Jie Li, Jiayi Wu ... Eunhee Choi
    Research Article

    The insulin receptor (IR) and insulin-like growth factor 1 receptor (IGF1R) control metabolic homeostasis and cell growth and proliferation. The IR and IGF1R form similar disulfide bonds linked homodimers in the apo-state; however, their ligand binding properties and the structures in the active state differ substantially. It has been proposed that the disulfide-linked C-terminal segment of α-chain (αCTs) of the IR and IGF1R control the cooperativity of ligand binding and regulate the receptor activation. Nevertheless, the molecular basis for the roles of disulfide-linked αCTs in IR and IGF1R activation are still unclear. Here, we report the cryo-EM structures of full-length mouse IGF1R/IGF1 and IR/insulin complexes with modified αCTs that have increased flexibility. Unlike the Γ-shaped asymmetric IGF1R dimer with a single IGF1 bound, the IGF1R with the enhanced flexibility of αCTs can form a T-shaped symmetric dimer with two IGF1s bound. Meanwhile, the IR with non-covalently linked αCTs predominantly adopts an asymmetric conformation with four insulins bound, which is distinct from the T-shaped symmetric IR. Using cell-based experiments, we further showed that both IGF1R and IR with the modified αCTs cannot activate the downstream signaling potently. Collectively, our studies demonstrate that the certain structural rigidity of disulfide-linked αCTs is critical for optimal IR and IGF1R signaling activation.