The trial verified the reduction in symptom frequency and severity by evaluating the proportion of Responders and Non-Responder patients after 6 weeks of treatment, compared to baseline. The effects of Gerdoff® + PPI treatment on extraesophageal symptoms were also evaluated after a 12-week follow up only in Responder patients. The analysis of the change in total Reflux Symptoms Index (RSI) score from baseline to the other time points showed that the extent of the decrease from baseline was higher in the Gerdoff® + PPI group than in the PPI group at any time point. However, the comparison between groups did not show statistically significant differences at any time point. A statistically significant difference, in favor of the Gerdoff® + PPI group, was observed for individual RSI items. Even if the trial showed some limitations, this is the first published study on the efficacy of a medical device containing hyaluronic acid and chondroitin sulphate with antacid in the treatment of extraesophageal reflux symptoms.Extraesophageal reflux symptoms are increasingly common in the Western population and their clinical management is still controversial. Although therapy with proton-pump inhibitors (PPIs) represents the gold standard, to date, many patients are refractory to this treatment. The aim of this study was to evaluate, in patients with a recent diagnosis of GERD experiencing extraesophageal symptoms, the efficacy and safety of a 6-week treatment with PPI acid suppression in combination with Gerdoff Gastroesophageal reflux disease (GERD) is associated with the reflux of gastric acid or intestinal bile contents into the esophagus. According to the Montreal Consensus Conference, typical GERD symptoms are heartburn and regurgitation . The extAn association between gastroesophageal reflux and laryngeal symptoms is supported by frequent observations of these symptoms in patients with GERD. An Italian study demonstrated that 74.4% of patients with GERD present at least one extra-esophageal symptom and laryngeal symptoms are present in a high proportion of patients 19.9–38.7%) [8.7% [3].It was estimated that between 4% and 10% of patients attending ear, nose, and throat (ENT) specialist consultations report signs and symptoms associated with GERD  and thatAcid suppression therapy with proton pomp inhibitors (PPI) is the current standard of care for patients with GERD with typical and atypical presentations. However, the effectiveness of PPI therapy in patients with extraesophageal symptoms is less robust than in subjects with typical GERD manifestations. It has been demonstrated that 50% of patients with atypical GERD symptoms do not respond to 8–12 weeks of PPI therapy and 15% show only partial response ,8.Moreover, it is widely accepted that the mucosal lesions caused by GERD have to be treated with PPI therapy combined with other active substances or devices able to potentiate the effects of the PPI and, thus, improve mucosal defenses ,9. These® is a class-3 CE-marked medical device containing CS, HA, and aluminum hydroxide (HA:AH:CS present with a ratio of 1:20:40) with a melt-in-mouth tablet formulation.Emerging data are emphasizing the role of new medical devices containing hyaluronic acid (HA) and chondroitin sulphate (CS) with an antacid component in the treatment of GERD. Gerdoff® compared to PPI monotherapy. Furthermore, the long-term therapeutic efficacy of Gerdoff® on extraesophageal symptoms was evaluated over a 12-week follow-up period. Patients included in the follow-up period (Gerdoff® + PPI responders only) were randomized to receive Gerdoff® or no treatment.The aim of this study was to evaluate, in patients with a recent diagnosis of GERD experiencing extraesophageal symptoms, the efficacy and safety of a 6-week treatment with PPI acid suppression in combination with GerdoffPatients with extraesophageal symptoms and a recent diagnosis of GERD were enrolled in the study. The diagnosis of GERD with extraesophageal symptoms was based on clinical presentation  and positivity on a validated questionnaire, the Reflux Symptoms Index (RSI), with a total score of >20 . The RSIFrequency of extraesophageal symptoms was evaluated using a self-administrated Likert scale questionnaire. A satisfaction judgment on the treatment’s effect on extraesophageal symptoms was also obtained using a 4-item scale .Extraesophageal GERD symptoms had to have been present for at least 3 months and could be associated or not with typical GERD symptoms . Patients had to have been free from anti-secretory medication (either PPI or histamine 2 receptor antagonists (H2RA)), antacids, and alginate-containing formulations for at least 4 weeks prior to enrolment in the trial. The patients enrolled were of both sexes and aged between 18 and 75.Exclusion criteria were presence of infective or chemical esophagitis, acute or chronic nasosinusitis, chronic bronchitis, and diagnosis of gastrointestinal cancer. Patients on chronic therapy with drugs affecting salivary secretion  were not enrolled.This was a multicenter, prospective, randomized, open-label, two-parallel-group study, followed by a follow-up period.Two Italian hospitals were involved in the study and each of them obtained the approval of the competent ethics committee. The study was registered at ClinicalTrials.gov as NCT03793556. The trial was performed in accordance with Good Clinical Practice (GCP) guidelines  and the ® three times a day plus Omeprazole 40 mg (PPI) once a day (Group 1) or Omeprazole 40 mg (PPI) once a day alone (Group 2) for 6 weeks.Eligible patients gave informed and written consent and were randomized (1:1) to receive open-label treatment with GerdoffDuring the treatment period, no rescue therapy with anti-secretory medications (either PPI or H2RA), antacids, or alginate-containing formulations was permitted, and any use of these drugs had to be registered.® + PPI Group were classified as Responders and Non-Responders on the basis of the RSI questionnaire (responders = at least a 50% reduction in RSI score compared to baseline). To assess the maintenance of the therapeutic effect, responders were randomized 1:1 to open-label treatment with Gerdoff® or to the control group, which did not receive any treatment for 12 weeks. During this study phase, rescue therapy with Omeprazole according to a standard and constant dose regimen was permitted.At the end of this short-course therapy, patients enrolled in the GerdoffSafety and tolerability were assessed during each visit by recording all adverse events, defined as any unfavorable or unintended symptom and/or sign, considered to have a causal relationship with the drugs used in the study. The study design and the detailed assessment schedule are shown in t-test for independent data. Sensitivity analyses were conducted on the primary performance variable. The study of the temporal profile of the RSI questionnaire was performed using repeated measures analysis of variance (ANOVA). In addition to the treatment effect, the time effect and the treatment by time interaction were included in the model.The primary endpoint of the study was treatment efficacy, calculated as the variation in the total RSI score between the baseline and week 6 in the two groups. In the analysis of the primary performance variable, the two treatment groups were compared using Student’s The secondary endpoints of the study were: (I) change in total RSI score between baseline and the other intermediate time points; (II) change in individual RSI item scores between baseline and any time point; (III) number and percentage of responder/non-responder patients at end of treatment (visit V4); (IV) change in upper symptoms, assessed using RSI score and the Likert scale, between baseline and end of treatment (visit V4) and end of follow-up (visit V6); (V) use of rescue medication during the follow-up period: administered therapy, frequency of administration, timing of administration, and administered dose; (VI) patient satisfaction with treatment, rated at intermediate visits, and at the end of treatment visit, by means of a semiquantitative ordinal scale, where: 0 = poor, 1 = fair, 2 = good, 3 = excellent.Yates’ chi-squared test was used to compare the distribution of the proportions of responders and non-responders in the two treatment groups.In the first 6 weeks of treatment, the intake of permitted or prohibited rescue medication in each treatment group was calculated as the frequency of administration of the specific drug and the comparison between groups was performed using Fisher’s exact test. In the first 6 weeks of treatment, the median dose of rescue medication in the two study groups was compared using the non-parametric (distribution-free) median test. If a mean intake value could also be calculated, the comparison between the two treatment groups was performed using the Mann–Whitney U test. The judgment frequencies were compared between the groups using the Mantel–Haenszel chi-squared test.® + PPI versus PPI alone assuming a 3.6-point change in RSI score from baseline between treatments with 5 points in SD for two-sided unpaired Student’s t-test after 6 weeks of treatment.A sample size of 62 patients (78 patients after 20% drop-out rate correction) would provide 80% power to demonstrate the superiority of Gerdoffp-values reported were two-sided. Results were considered to be statistically significant if the two-sided p-value was <0.05.All ® + omeprazole and 36 omeprazole monotherapy (® + omeprazole group and 2 (5.6%) in the omeprazole group dropped out of the study during the 6-week treatment period due to withdrawal of consent. There were no dropouts due to adverse events. In the follow-up period, 18 patients were randomized to the two groups: 10 patients received Gerdoff® and 8 received no treatment. Two patients who were randomized to treatment with Gerdoff® did not attend all the visits scheduled for the follow-up period  than the omeprazole group (11.1%) (® + omeprazole group) and were excluded from the sensitivity analysis on the primary endpoint  . Seven pendpoint . All patWith regard to the primary endpoint, ® + omeprazole group and −13.7 (10.58) in the omeprazole group. Although the adjusted mean change in total RSI score between baseline and week 6 was slightly higher in the Gerdoff® + omeprazole group than in the omeprazole group, this difference was not statistically significant  in the Gerdoff® group and did not change substantially in the untreated group  showed that the decrease from baseline to any time point was greater in the Gerdoffle group .p = 0.0157) and a statistically significant visit effect , whereas the treatment-by-visit interaction was not statistically significant . The analysis performed by stratifying the treatment effect by visit resulted in statistically significant treatment effects at week 1 (V2)  and week 6 (V4) , whereas no statistically significant treatment effects were observed at baseline (V1)  and week 3 (V3) . In the analysis performed by stratifying the treatment effect according to overall visits, a statistically significant effect was observed in both treatment groups (p < 0.0001 in both groups).A further analysis, performed using an ANOVA model to examine the entire curve profile, showed a statistically significant treatment effect , ‘troublesome or annoying cough’ (p = 0.0443 between groups), and ‘heart burn, chest pain, indigestion, or stomach acid in the mouth’ (p = 0.0240 between groups) at week 6 (V4). Other investigated items  were not found to be significantly different  in the Gerdoff® + omeprazole group and 21 (58%) in the omeprazole group and the difference between groups was statistically significant (p = 0.0496 in the Chi-squared test) (The Week-6 responder rate was significantly higher in the Gerdoffed test) .p = 0.0547 Student’s t-test with Cochran correction) (p < 0.0001).The results of the extraesophageal GERD symptom assessments using the Likert scale were consistent with those reported for the RSI questionnaire. More specifically, the mean and median values or total Likert scale score progressively decreased from baseline to the end of the 6-week treatment period in both groups without evidence of significant differences between the groups (rection) . The res® + omeprazole group (68.6%) during the 6-week treatment period. Seven patients  in the Gerdoff® group and five  in the untreated group used omeprazole as rescue medication during the follow-up period. None of the patients (0.0%) in the Gerdoff® + omeprazole group and four patients (11.4%) in the omeprazole group used other drugs as rescue medication.More patients used omeprazole as rescue medication in the omeprazole group (77.8%) than in the GerdoffThe majority of patients in both groups gave an excellent or a good satisfaction judgment for treatment at any post-baseline timepoint .® + omeprazole combination and omeprazole monotherapy were very well tolerated. The number of AEs and the percentage of patients who experienced AEs were similar in the two groups. There was no evidence of important changes in any vital parameters from baseline to any post-baseline time-point in either group. A total of 28 AEs were reported in 14 patients (40.0%) in the Gerdoff® + omeprazole group and 29 AEs were reported in 12 patients (33.3%) in the omeprazole group. No fatal adverse events occurred in any patient (® + omeprazole group and three AEs (10.3%) in the omeprazole group, and influenza-like illness, with four AEs  in the Gerdoff® + omeprazole group and two AEs (5.6%) in the omeprazole group. None of the reported AEs were considered related to treatment with Gerdoff® in either treatment group.Overall, both the Gerdoff patient . The mos® + omeprazole leads to a greater, though not significantly so, reduction in symptoms than with PPI therapy alone. The primary endpoint results showed that the adjusted mean change in total RSI score from baseline to visit V4 (week 6) was slightly higher in the Gerdoff® + omeprazole group than in the omeprazole group. However, the hypothesis of the superiority of Gerdoff® + omeprazole over omeprazole monotherapy was not confirmed. The adjusted mean difference between the Gerdoff® + omeprazole group and the omeprazole group was −2.5 (9.27) and the 95% CI of the difference in adjusted means was −7.03 to 2.04, showing that the difference between groups was not statistically significant . The results of the sensitivity analysis excluding the seven patients who took non-permitted rescue medication (one in the Gerdoff® + omeprazole group and six in the omeprazole group) were consistent with those observed in the primary analysis. The analysis of the change in total RSI score from baseline to the other time points showed that the extent of the decrease from baseline was higher in the Gerdoff® + omeprazole group than in the omeprazole group at any time point. However, the comparison between groups did not show statistically significant differences at any time point. In the interpretation of results, it should be considered that, during the 6-week treatment period, a considerable proportion of patients in both treatment groups used omeprazole or other drugs as rescue medication (this proportion was higher in the omeprazole monotherapy group than in the combination group), which might have reduced the potential difference between groups in the primary and secondary performance endpoints. This result supports the hypothesis that PPI alone is not sufficient for the treatment of extraesophageal GERD symptoms. Although the anticipated number of patients to be enrolled was 78, due to difficulties in identifying eligible patients, recruitment was interrupted at 72. The standard deviation of the difference in adjusted means between the Gerdoff® + omeprazole group and the omeprazole group was 9.27, which was 4.27 points higher than expected. These factors may have contributed to the statistical insignificance of the primary performance endpoint.The results of this study showed that in patients with extraesophageal symptoms and a recent diagnosis of GERD, treatment with Gerdoff® + omeprazole group, in which an effect was observed after just one week of treatment (V2), before coming to an equilibrium phase (V3) and a subsequent further decrease (V4). Considering patients with an at least 50% decrease in RSI score from baseline as Responders (V4), a significant difference could be observed between the two groups in favor of treatment with Gerdoff® + omeprazole. The analysis of the total RSI score results in the follow-up period showed that the mean total RSI score decreased slightly between the start and the end of the follow-up phase in the Gerdoff® group and did not change substantially in the untreated group. The effect observed during the treatment period was maintained or further improved during the follow-up phase in both groups, except in one patient in the untreated group, in whom the total RSI scores were 10 and 24 at the start and at the end of the follow-up period, respectively. It is likely that the change in this patient accounted for the apparent maintenance of the mean total RSI score between the start and the end of the follow-up period. The trend in the results for the individual RSI items showed a progressive decrease in the mean and median scores for all items from baseline to the end of the 6-week treatment period in both groups. A statistically significant difference, in favor of the Gerdoff® + omeprazole group, was observed for the items relating to cough and typical GERD symptoms. The results for the upper GERD symptom assessments using the Likert scale were consistent with those reported using the RSI questionnaire. As with the RSI score, the mean and median total Likert scale score values decreased progressively between baseline and the end of the 6-week treatment period in both groups. Similarly, the trend in the results for the individual items of the Likert scale showed a progressive decrease in the mean and median values of all item scores between baseline and the end of the 6-week treatment period in both groups, without any evidence of substantial differences between them. Six patients in the omeprazole group and one in the Gerdoff® + omeprazole group used other rescue medications. The distribution of patient-reported satisfaction with treatment was similar in the two groups. The majority of patients in both groups reported excellent or good satisfaction with treatment at any post-baseline time-point  and endoscopic examination findings , whereasA recent systematic review demonstrated that six out of the nine systematic reviews/meta-analyses concluded that PPI therapy is not superior to placebo and three concluded that PPI therapy significantly improved LPR symptoms although they did not identify any difference in the post-treatment laryngoscopic findings .Additionally, no significant events were observed in either of the two Cochrane reviews on the effect of PPIs on individual throat symptoms—cough  and dyspA need is emerging from the literature to characterize patients accurately, since only a minority respond to antisecretory therapy, usually those with typical symptoms associated with extraesophageal GERD. With this aim, a recent multicenter study evaluated patients with chronic laryngeal symptoms and identified distinct phenotypes of patients, which included LPR/GERD with a hiatal hernia, LPR with mild GERD, no LPR or GERD, reflex cough, and mixed/possible obstructive esophagogastric junction. The authors demonstrated that individuals with LPR/GERD with hiatal hernia would likely be most responsive to PPI, followed by LPR and LPR with mild GERD and reflux cough . This stMoreover, non-response to PPIs does not make it possible to exclude gastroesophageal reflux (GER) as a causative agent, especially considering the possibility of weakly acidic and biliary refluxes, which can be demonstrated by means of esophageal 24-h pH-impedance monitoring. In light of the increasing number of studies that identify acid and nonacid reflux as important causes of extraesophageal symptoms, our therapeutic approach has to evolve. The use of PPIs alone can be called into question, since these agents are less effective on nonacid or mixed reflux. Given the alkaline pH required for trypsin activity, the administration of high doses of PPIs may be associated with a worsening of complaints [In this regard, in recent years, few studies have evaluated the efficacy of alginate therapy in patients with extraesophageal reflux symptoms. Alginates are an oral pharmacologic therapy that creates a barrier at the esophagogastric junction and a mechanical raft above the gastric contents to prevent gastroesophageal reflux events, whether acidic or nonacidic. In addition, alginates inhibit pepsin and bile salts. For this reason, alginates are particularly well-suited in the case of nonacid or mixed reflux or for patients with postprandial symptoms and, according to a recent article, could have similar efficiency to that of PPIs + alginate ,26. Howe® could effectively treat GER symptoms in patients not responding to PPI or alginate-based formulation [To date, few published studies have shown the effects of products containing CS and HA in providing relief from typical GERD symptoms. Two small prospective placebo-controlled studies have shown that short-term treatment achieved significant and rapid symptom relief in patients with both erosive reflux disease  and NERDmulation .In this study, patients with extraesophageal symptoms and a recent diagnosis of gastroesophageal reflux disease were enrolled. The diagnosis of reflux disease with typical and atypical symptoms was based on the symptoms reported by the patients. No objective assessment of the association between reflux and extraesophageal symptoms was performed. Our study may be limited by the fact that no objective assessment of the association between reflux and extraesophageal symptoms was made and that diagnosis was based only on clinical presentations.In clinical practice, the role of a gastroenterologist vis-à-vis patients referred for evaluation of suspected extraesophageal symptoms is to assess the possible association between reflux and symptoms. Non-GI investigations for ENT, pulmonary, and/or allergic conditions are essential, and in many cases, they should be the first diagnostic procedures performed, as extraesophageal symptoms often have a multifactorial or non-esophageal etiology. Combined pH and impedance monitoring is considered the best tool for characterizing gastroesophageal reflux because it is able to detect all types of reflux events  and determine the proximal extent of the refluxate within the esophagus. In our study, patients were not evaluated with 24-h pH monitoring, high-resolution manometry, and upper-GI endoscopy to assess the cause of the extraesophageal symptoms. Another possible limitation of the study is the great heterogeneity of the enrolled population. As a matter of fact, only some of the patients with extraesophageal symptoms enrolled in the study also showed typical reflux symptoms . An emerging fact in the literature is that patients with typical and atypical reflux symptoms respond better to PPI therapy than patients with extraesophageal symptoms alone . It may ® + omeprazole leads to a greater, although not significantly so, reduction in symptoms than PPI treatment alone. The results of our study therefore suggest that medical devices containing combinations of hyaluronic acid and chondroitin sulphate may play an important role in the treatment of GERD with extraesophageal symptoms. We hope that our results will provide a starting point for—and stimulate—more experimentally robust RCTs going forward.Extraesophageal reflux symptoms are increasingly common in the Western population and their clinical management is still controversial. Although therapy with PPIs represents the gold standard, to date, many patients are refractory to this treatment. It is therefore necessary to identify new drugs that can be used in the treatment of this clinical condition. This is the first published study on the efficacy of a medical device containing hyaluronic acid and chondroitin sulphate with antacid in the treatment of extraesophageal reflux symptoms and it shows that, in patients with extraesophageal symptoms and a recent diagnosis of GERD, treatment with Gerdoff


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. 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 . SimilarOne marker of heterogeneity in stress responses is dynamic localization of stress-activated transcription factors. Several canonical factors, including p53 in mammalian cells  and Msn2Cell-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 : whereasThe 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 . Part ofHere, we monitored dynamic localization changes of both Msn2 and Dot6 in the same yeast cells, along with a panel of other single-cell measurements, to dissect local and systemic variation and illuminate the relationship between ESR activation and growth rate. We optimized a microfluidics system that can monitor single-cell localization levels and dynamics of both Msn2-mCherry and Dot6-GFP along with single-cell and colony growth rates, size, shape, cell-cycle phase and size changes before and after an acute dose of sodium chloride (NaCl) as a model stressor. Our results revealed several insights, including surprising levels of discordance in Msn2 and Dot6 activation that partly explained variation in post-stress growth rate. We developed a multi-factorial model explaining cell growth rate after stress acclimation to demonstrate that stress acclimation is partly predictable based on prior cellular states. Remarkably, one of the important predictors is the activation level of the Dot6 repressor, which counterintuitively is associated with faster growth acclimation and faster production of stress-induced catalase Ctt1. We discuss implications of this work for understanding how cellular state and transcriptional repression influence stress responses.We optimized a microfluidics system that could measure nuclear localization dynamics as well as one- and two-cell colony growth rates before and after exposure to 0.7 M NaCl . We usedThis system captured variation in all of the features measured. As expected based on previous studies , there wWe 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 . RoughlyIn 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 . PermutaComparisons 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  of the pThe 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 . A seconAre 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 . We testOverall, there was a positive correlation between pre-stress growth rate compared to post-stress growth rate ; howeverThe 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 We next investigated other features that could explain differences in post-stress growth rate . PairwisWe 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  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  regression. We first analyzed the four statistically-significant model-input variables in DOT6 and its paralog TOD6 fail to repress hundreds of genes in the RiBi regulon  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  regulon . These tociation . Despitenscripts .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  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  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 .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 . Cells wThese 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 . InsteadMany 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 . But oneRegardless, 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.relative pre-stress activity of Msn2 versus Dot6 -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  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 , 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 , and 2% Glucose. Cells were grown in LFM shake flasks at 30°C for data shown in Strains used include AGY1328  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.An FCS2 chamber  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  was used for time-lapse microscopy. The GFP signal was captured using a ET-EGFP single band filter cube . The mCherry signal was captured using a ET/mCH/TR single band filter cube . 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 .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.simpletracker function  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 . SpecifiNuclear 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 . The folfindpeaks function, where a peak height is called from a local maximum to the nearest minimum  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.Temporal peaks of nuclear localization were identified using the MATLAB For pre-stress time points, AUC was calculated by summing the first 9 measurements of nuclear localization scores . 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  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 . Maximum2 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 , 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. —7.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 where The Holm-Bonferroni method was used for multiple hypotheses correction, where there were n=27  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 .–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.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 . 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 As you will see from the reviews below, the reviewers found your manuscript potentially interesting but the story incomplete  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 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 .Clearly both – random noise and prior life history – contribute. For example, it is well known that stochastic single molecule events can regulate cell fate , 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 , 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  to post-stress growth rate. They found that post-stress growth rate showed a stronger correlation with an integration of multiple factors , 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.eLife.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 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  used microfluidics to look at subcellular dynamics of Msn2 and other transcription factors from a more mechanistic point of view. Other studies  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 , especially since this is not an off-the-shelf microfluidics platform.eLife 8:e38904, 2019) that was not cited.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.  and a Reviewing Editor.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 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. [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 .Clearly both – random noise and prior life history – contribute. For example, it is well known that stochastic single molecule events can regulate cell fate , 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.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 . However, the correlations we observe cannot be explained by this subset of cells for which growth rate is under-estimated.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 2 = 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 . There is a wider distribution of linear fits post-stress , 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 , 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.Second, we now include several new analyses validating our approach . 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  = -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.i.e. subsample by replicate), as highlighted in the text.The revised manuscript addresses this concern in several ways. We did not intend to imply that there are precisely 11 meaningful clusters . 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  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 . 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 , 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: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 .We previously showed that transcriptional repression by Dot6  is required to redirect translational capacity away from ribosome biogenesis (RiBi) mRNAs and toward stress induced transcripts. Mutant cells lacking dot6∆tod6∆ mutant in our culture media grows indistinguishably from wild type before stress but shows reduced growth recovery after NaCl treatment . Thus, Dot6 provides a protective response during stress and is required for normal stress acclimation.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 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.An unresolved question is if differences in Dot6 activation in a 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 . 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  to post-stress growth rate. They found that post-stress growth rate showed a stronger correlation with an integration of multiple factors , 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.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.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 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 . 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  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  used microfluidics to look at subcellular dynamics of Msn2 and other transcription factors from a more mechanistic point of view. Other studies  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.DOT6 and its paralog TOD6 show wild-type growth before stress but slower growth acclimation after stress  – 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 . 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.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 eLife.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 One recommendation to strengthen the presentation of the paper would be to include a figure (and/or movies) showing primary data , 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.  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.et al. 2017; Li, et al. 2018; http://yeast-image-toolkit.biosim.eu/), which do not use subpixel segmentation . 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.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 , 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.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  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 , 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.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.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  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.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) 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.eLife.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