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Title: Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress}
Article:
Abstract:
From bacteria to humans, individual cells within isogenic populations can show significant variation in stress tolerance, but the nature of this heterogeneity is not clear. To investigate this, we used single-cell RNA sequencing to quantify transcript heterogeneity in single Saccharomyces cerevisiae cells treated with and without salt stress to explore population variation and identify cellular covariates that influence the stress-responsive transcriptome. Leveraging the extensive knowledge of yeast transcriptional regulation, we uncovered significant regulatory variation in individual yeast cells, both before and after stress. We also discovered that a subset of cells appears to decouple expression of ribosomal protein genes from the environmental stress response in a manner partly correlated with the cell cycle but unrelated to the yeast ultradian metabolic cycle. Live-cell imaging of cells expressing pairs of fluorescent regulators, including the transcription factor Msn2 with Dot6, Sfp1, or MAP kinase Hog1, revealed both coordinated and decoupled nucleocytoplasmic shuttling. Together with transcriptomic analysis, our results suggest that cells maintain a cellular filter against decoupled bursts of transcription factor activation but mount a stress response upon coordinated regulation, even in a subset of unstressed cells.
Introduction:
When adversity strikes, it is often the case that some cells in an isogenic population survive whereas others do not. Such phenotypic heterogeneity has been observed in isogenic microbes exposed to environmental stress as well as normal and malignant human cells surviving chemotherapy drugs[1–7]. While genetic mutations can produce cells with heritably high stress tolerance, in many cases, the heterogeneity is transiently induced by epigenetic processes[8, 9]. For example, some isogenic cells within cultures of Saccharomyces cerevisiae can survive extreme heat stress, whereas most cells in the culture cannot[10]. Stress-tolerant individuals may be in an altered state, because they often display transiently reduced growth and markers of the stress response[1, 10–12]. But whether this state mimics that of stress-treated cells that have fully mounted a stress response or instead emerges from partial responses or stochastic events is unclear. Understanding what gives rise to cell-to-cell heterogeneity in stress survival has broad applications, from treating pathogenic microbial infections to blocking drug-resistant human metastases. In several systems, variation in stress tolerance can be traced to heterogeneous expression of defense genes. Graded expression of the stress-responsive TSL1 gene in unstressed yeast quantitatively predicts how well individual cells in a culture will survive severe heat[10]. In some cases, such variation is “intrinsic” to the gene promoter: many defense genes are transcribed through TATA-dependent promoters[13, 14], which produce stochastic transcriptional bursts proposed to play a role in bet-hedging[15–19]. Stochastic fluctuations can also be intrinsic to specific regulators, such that their target genes vary coherently but without a widespread cellular response[20]. But “extrinsic” variation in the cellular system, e. g., activation of the broader upstream signaling response or transition through other physiological states, likely plays an important role. Several studies have interrogated tagged protein abundance in yeast to explore noise in single or paired protein abundances[15, 19, 21–23]. Stewart-Ornstein et al. showed that targets of the yeast “general-stress” responsive transcription factor Msn2 often behave coordinately in single cells, suggesting concerted activation of the entire regulon even in the absence of added stress[22, 23]. Targets of several other transcription factors also showed coordinate behavior across single cells, suggesting that the variation may emerge from stochastic activation of Protein Kinase A( PKA), a common upstream regulator of those factors[22, 23]. In response to acute stress, Msn2 is activated as part of a broader signaling network that regulates the Environmental Stress Response( ESR), a common transcriptomic response triggered by diverse stresses[24, 25]. The ESR includes induced expression of approximately 300 defense genes, regulated in part by Msn2 and its paralog Msn4, which is coordinated with repression of approximately 600 genes encoding ribosomal proteins( RPs) and factors involved in ribosome biogenesis( RiBi) and other processes. RP and RiBi genes are thought to be highly transcribed in actively growing cells, but repressed in response to stress through release of the RP activator Sfp1 or recruitment of the RiBi transcriptional repressors Dot6/Tod6 and histone deacetylases[26–30]. Activation of the ESR after mild stress can impart increased tolerance to subsequent stress, known as acquired stress resistance[31–33]. In some cases, the ESR program also correlates with reduced growth rate, most notably in nutrient-restricted chemostats and in slow-growing mutants potentially experiencing internal stress[34–38]. In fact, O’Duibhir proposed that the ESR may simply be a byproduct of cell-cycle phase, since slow-growing mutants with prolonged G1 phase display an ESR-like transcriptome profile[38]. In nearly all studies to date, increased expression of the induced-Environmental Stress Response( iESR) genes is coupled to reduced expression of RP and RiBi genes in the repressed-Environmental Stress Response( rESR) gene set. Whether regulation of the iESR and rESR genes can be decoupled in wild-type cells is unclear. Msn2 overexpression is sufficient to induce multistress tolerance in yeast cells[39]. Thus, cell-to-cell variation in Msn2 activation could explain the heterogeneity in stress tolerance in an actively growing culture. But it remains unclear if this variation correlates with broader transcriptome changes, if the magnitude of the response in unstressed cells mimics that seen in stressed cells, or if fluctuations in the response correlate with cell-cycle phase. Here, we addressed these questions through single-cell RNA-sequencing( scRNA-seq) coupled with single-cell profiling of transcription factor activation dynamics before and after stress. Our results reveal variable activation of both the ESR and condition-specific regulators after stress and heterogeneity in ESR activation in unstressed cells due to both coordinated and discordant induction of ESR regulators. While ESR activation shows no relation to cell-cycle phase in unstressed cultures, we found that some cells appear to decouple regulation of RP transcripts in a manner linked to S-phase but apparently unrelated to expression changes associated with the yeast metabolic cycle.
Discussion:
Both the scRNA-seq results and fluorescent TF profiling suggest that a subset of cells mediate mild activation of the ESR in the absence of added stress. Four percent of cells showed mildly higher iESR and lower RP mRNA abundance( Fig 1C), consistent with the 4% of cells estimated to coregulate iESR/rESR regulators Msn2 and Dot6( Fig 7C). We propose that this mild activation contributes to the heterogeneity in single-cell survival of extreme stress doses. Although we observed decoupled Msn2 and Dot6 pre-stress nuclear fluctuations by microscopy, we did not observe decoupled activation of their combined targets in individual unstressed cells( Fig 1). The amplitude and duration of pre-stress Msn2/Dot6 pulses were significantly smaller and shorter than immediately after NaCl stress. In the case of Msn2, relocalization is influenced by both nuclear import and export that together produce distinct temporal profiles[76, 83–85]. One prediction is that genes with more Msn2 binding sites are more sensitive to brief pulses of nuclear Msn2[23, 86, 87]. However, our data did not support this: genes with many Msn2 binding sites showed no more evidence of concerted pre-stress fluctuations than genes with few binding sites( S5 Fig). This suggests that cells maintain a filtering system to distinguish a true upstream signal from noisy TF activation. This system could emerge from chromatin regulation[86, 88] or other regulatory signals( e. g., post-translational TF modification) that act as gatekeepers to the transcriptome response. A remaining question is why some unstressed cells activate the ESR program. One model is that stochastic fluctuations in a common upstream regulator produce stochastic but coordinated activation of the downstream factors. A candidate is PKA, which phosphorylates and suppresses several stress-activated regulators( including Msn2 and Dot6), promotes expression of RP transcription[89, 90], and has been implicated in stochastic Msn2 regulation[22, 70, 85, 91]. Whether PKA fluctuations represent random events or a response to some cellular signal is not clear. A second, compatible model is that cells with mild ESR activation are actually experiencing, and thus actively responding to, internal stress. Such stress could emerge from normal cellular processes, e. g., damage from bursts of oxidative metabolism or during DNA replication. A third model that our data discounts is that the ESR fluctuates with the cell cycle in normally dividing cells[38]. We did see a milder ESR activation in post-stress cells in S-phase, but we believe this is due to acclimation-dependent re-entry into the cell cycle. We propose that the previously reported correlation between ESR activation and prolonged G1 in mutants is likely a response to deleterious gene deletions rather than an inherent coupling of the ESR to G1 phase. In many bulk transcriptomic yeast studies to date, RP expression is inversely proportional to stress-defense transcripts in the iESR, and these gene groups display opposing responses during rapid growth versus environmental stress[24, 37, 39]. Indeed, these gene groups are controlled by the same upstream signaling pathways, including PKA, TOR, and stress-activated regulators[92–94]. But studying individual cells expands knowledge of the regulatory system: although RP and iESR transcripts are anticorrelated in most cells in our analysis, RP expression appears decoupled in a subset of unstressed individuals. The reason and mechanisms remain unclear. The link between low RP transcripts and S-phase is appealing: although we found no clear evidence for the same YMC transcriptome seen in nutrient-synchronized cultures[59], metabolic genes are regulated during the G1/S transition[95, 96]. Furthermore, the yeast cyclin dependent kinase, Cdc28, also binds to RP promoters[97], and an imbalance of ribosome components can trigger G1/S delay[98, 99]. Future work will be required to decipher this regulation, as well as the mechanisms that give rise to exquisite control minimizing variation and ensuring cell presence of RP mRNAs. Heterogeneity in microbial stress tolerance has been proposed to serve as a bet-hedging mechanism, ensuring that a minimal fraction of the population survives in the event of catastrophic environmental events[6, 7, 10]. But the phenomenon is also observed in multicelled mammalian systems[2–4, 100] and at least partly influenced by variable activation of the p53 tumor suppressor. Like Msn2, inactive p53 resides in the cytoplasm but upon stress rapidly relocates to the nucleus with transient pulses, where it activates target–gene transcription and has been reported to repress ribosome-producing polymerase I and III[94, 101–103]. p53 gene targets that harbor multiple and high-affinity binding sites are most sensitive to transient nuclear bursts, as reported previously for Msn2 targets, whereas other genes require prolonged p53 activation for full induction[101, 104, 105]. And as is the case with Msn2 activation, prior induction of p53 leads to subsequent tolerance to what would otherwise be lethal drug doses[106]. p53 also shows heterogeneous nuclear pulses in proliferating cells without exogenous stress. Unlike Msn2, which shows quantitatively shorter and weaker pre-stress nuclear pulses, the amplitude and duration of pre-stress p53 pulses is reported to be similar to that seen after inflicted stress; but like the yeast factor, these pre-stress bursts do not necessarily alter gene expression[107]. Instead, layers of post-translational p53 modification can filter potential noise in the regulatory system. A better understanding of the regulatory networks that control heterogeneity in transcription and stress tolerance is likely to open new avenues to control population behavior. Our results present new insights into heterogeneity in the yeast stress response, but many unanswered questions remain. One is how dynamic changes in TF activation relate to dynamic changes in transcript abundance and, in particular, how the frequency and amplitude of TF relocalization quantitatively impact mRNA output. Another is the extent to which cellular dynamics vary across cells, e. g., in terms of how variable cells are in their acclimation to stress. Single-molecule approaches in living single cells will be an important avenue to dissect these questions. In terms of physiology, it will be interesting to dissect how variations in cellular states and systems influence the transcriptome and stress responses more broadly—why do some unstressed cells have lower RP expression and other stressed cells activate unique TF responses? As the accuracy of scRNA-seq improves, so too will the ability to infer physiological differences in the cellular system that influence heterogeneity in stress tolerance and susceptibility.
Genetically identical cells growing in the same environment can vary in their cellular state and behavior . Such heterogeneity may explain why some cells in an isogenic population can survive sudden severe environmental stress whereas other cells succumb . Cell-to-cell variation in gene expression has been linked to variable stress survival , but how and why transcript levels vary across the transcriptome in single cells is only beginning to emerge . Here , we used single-cell RNA sequencing ( scRNA-seq ) to measure cell-to-cell heterogeneity in the transcriptome of budding yeast ( Saccharomyces cerevisiae ) . We find surprising patterns of variation across known sets of transcription factor targets , indicating that cells vary in their transcriptome profile both before and after stress exposure . scRNA-seq analysis combined with live-cell imaging of transcription factor activation dynamics revealed some cells in which the stress response was coordinately activated and other cells in which the traditional response was decoupled , suggesting unrecognized regulatory nuances that expand our understanding of stress response and survival .