Author response: Modeling single-cell phenotypes links yeast stress acclimation to transcriptional repression and pre-stress cellular states
Year: 2022
Type: peer-review
Abstract: Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Methods Data availability References Decision letter Author response Article and author information Metrics 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 Decision letter eLife's review process 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 Download asset Open asset 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. Figure 2 Download asset Open asset 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. Figure 3 Download asset Open asset 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 Download asset Open asset 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 Download elife-82017-fig4-data1-v2.zip The subpopulations were differentiated by a combination of transcription-factor phenotypes. One distinguishing feature was the level of Dot6 activation during the acute-stress phase. Cluster 11 was characterized by lower than population-median magnitude of acute-stress Dot6 nuclear translocation, whereas cells in Clusters 6 and 7 showed higher-than-median Dot6 response. These results are consistent with the wider variance of Dot6 nuclear translocation levels during the acute phase (Figure 3D–F). A second distinguishing feature was the level of nuclear Msn2 and Dot6 before stress. Cluster 11 cells showed low levels of Dot6 before stress, whereas cells in Clusters 9 and 6 displayed higher-than-median nuclear Msn2 and Dot6 during this phase. Finally, the behavior of Msn2 during the post-stress acclimation phase was significantly different across subpopulations. Whereas Clusters 11 and to some extent 7 showed low levels of post-stress Msn2 nuclear localization, cells in multiple clusters showed high levels and/or pulsatile nuclear Msn2 as cells acclimated. We noticed that cells in Clusters 2 and 3 showed elevated levels of mCherry that persisted over time compared to other cells. Closer inspection of the microscopy images suggested that some of the signal may not reflect nuclear translocation but instead was likely vacuolar signal (see more below). As mentioned above, the variation in nuclear localization dynamics captured within these clusters occurred in all three biological replicates and in a separate experiment (See Supplementary file 2 and Figure 4—figure supplement 1), indicating reproducible distinctions in transcription factor behavior. Together, this analysis revealed important differences in cellular behavior across the phases of the NaCl response that are obscured by aggregate analysis of all cells in the population. Cell subpopulations show different relationships with cell growth Are subpopulations of cells identified above biologically meaningful? We turned to the other cellular measurements to look for co-variates in cellular behavior that reflect on higher-order relationships (Figure 5). We tested each of the cell subpopulations for statistically significant differences in pre-stress growth rate, post-stress growth rate, starting size, volume change, and cell-cycle phase at the time of NaCl exposure (inferred by visual inspection of bud size and nucleus location in the cell, see Methods). We found no significant correlations with cell volume changes or cell-cycle phase (although there was a minor signal for cell cycle, Figure 5—figure supplement 1). This is consistent with the lack of strong connection between cell-cycle phase and stress response found in several other studies (Paek et al., 2016; Gasch et al., 2017; Bagamery et al., 2020). In contrast, several clusters showed significant differences in growth rates. Figure 5 with 2 supplements see all Download asset Open asset 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 of growth rates before (B) and after (C) NaCl addition, for cell clusters shown in Figure are colored yellow or blue the was significantly or from all other cells in the analysis indicates the median of all cells there was a correlation between pre-stress growth rate compared to post-stress growth rate (Figure however, the was different for subpopulations of cells. in Cluster which were characterized by Dot6 response before and during stress, showed slower growth rates before and after NaCl treatment (Figure and the slower growth was consistent when biological replicates were analyzed and across multiple (Figure 5—figure supplement 2). In contrast, cells in Cluster 7 showed than average recovery growth rates these cells were characterized by Dot6 nuclear localization responses and nuclear translocation of Msn2 during the phase. The relationships between post-stress growth rate and Dot6 response during the acute phase the possibility that this activation is more to growth rate than Msn2, even when both factors are activated in a systemic response. cells in Cluster 2 that high mCherry before stress displayed slow growth recovery after stress, the biological of the and raising the possibility of stress acclimation in these cells. that cells with vacuolar signal were from subsequent multiple the to post-stress growth rate The above results that how well cells acclimate to NaCl stress, as indicated by post-stress growth rate, may be by cellular responses both before and during the stress response. on the work of Li et al., we expected a negative correlation between Msn2 nuclear localization and growth rate they over time there was no correlation with pre-stress growth rate we a negative correlation between pre-stress Msn2 activation as the area under the for pre-stress and post-stress growth however, the correlation explained only of the variance indicating that the pre-stress behavior of Msn2 has little to post-stress growth rate in our We next investigated other features that could differences in post-stress growth rate (Figure correlations revealed that some individual such as the magnitude of Dot6 acute-stress response, correlated well with post-stress growth rate but others not (Figure supplement 1). However, the most single factor pre-stress growth rate explained only of the variance in post-stress growth rate (Supplementary file Figure 6 with 2 supplements see all Download asset Open asset A model variation in post-stress growth rate. (A) A of the nuclear localization measurements used in the regression (B) in the regression those with significant are with (C) The variance in growth explained by the regression and are shown at the of the and cell is indicated according to the key, that no single cluster the (D) regression of post-stress growth rate and of factors according to the explained is at the of each not to post-stress growth We next cellular phenotypes into a single multiple model could more of the variance in growth. We multiple metrics for pre-stress nuclear localization, including is a measure of the nuclear and the of called translocation is by the magnitude and of pre-stress along with acute-stress translocation and during the acclimation phase. The model also other cell features including pre-stress growth rate, cell-cycle phase at the time of NaCl exposure, and cell size factors (See in Supplementary file 3 for all that not significantly were the variance (Supplementary file The regression identified factors that significantly to post-stress rate in Supplementary file pre-stress of Dot6 nuclear localization, the of pre-stress Msn2 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 of the variance in post-stress growth rate (Figure the of single feature We that noise in the measurements is likely the true such that the is an One is that several of these phenotypes could be of an or cellular For example, both pre-stress growth rate and Dot6 acute-stress correlate with post-stress growth rate, but they also correlate with each cells growing faster before stress have a larger Dot6 The model reports that both factors separable and indeed they more of the variance in stress acclimation than factor to further their we We analyzed the in Figure by and used the as factors in a model of post-stress growth rates (see Methods). and explained of the variance in post-stress growth both captured co-variation in pre-stress growth rate, acute-stress Dot6 response, and pre-stress transcription factor indicating that these features likely reflect the same of the cellular state (Figure However, that is by Dot6 behavior but not by pre-stress growth rate explained an of growth acclimation A was by pre-stress Msn2 behavior but showed no to post-stress growth acclimation rates. Thus, behavior of the Dot6 repressor with post-stress growth rate. As further we analyzed the correlation between Dot6 acute-stress and post-stress acclimation in a of cells with pre-stress growth rates. pre-stress growth rate no for this of cells, whereas Dot6 explained of the variance Figure supplement 2). Thus, the behavior of the Dot6 repressor during acute NaCl stress is associated with growth recovery as cells acclimate (see Dot6 activation is associated with faster production of Ctt1 Dot6 is the transcriptional repressor of growth-promoting ribosome biogenesis
Source: eLife
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
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