Contributions of low molecule number and chromosomal positioning to stochastic gene expression

Transcriptional priming and chromatin regulation during stochastic cell fate specification

Stochastic cell fate specification, in which a cell chooses between two or more fates with a set probability, diversifies cell subtypes in development. Although this is a vital process across species, a common mechanism for these cell fate decisions remains elusive. This review examines two well-characterized stochastic cell fate decisions to identify commonalities between their developmental programmes. In the fly eye, two subtypes of R7 photoreceptors are specified by the stochastic ON/OFF expression of a transcription factor, spineless. In the mouse olfactory system, olfactory sensory neurons (OSNs) randomly select to express one copy of an olfactory receptor (OR) gene out of a pool of 2800 alleles. Despite the differences in these sensory systems, both stochastic fate choices rely on the dynamic interplay between transcriptional priming, chromatin regulation and terminal gene expression. The coupling of transcription and chromatin modifications primes gene loci in undifferentiated neurons, enabling later expression during terminal differentiation. Here, we compare these mechanisms, examine broader implications for gene regulation during development and posit key challenges moving forward. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Gene expression bias between the subgenomes of allopolyploid hybrids is an emergent property of the kinetics of expression

Hybridization coupled to polyploidy, or allopolyploidy, has dramatically shaped the evolution of flowering plants, teleost fishes, and other lineages. Studies of recently formed allopolyploid plants have shown that the two subgenomes that merged to form that new allopolyploid do not generally express their genes equally. Instead, one of the two subgenomes expresses its paralogs more highly on average. Meanwhile, older allopolyploidy events tend to show biases in duplicate losses, with one of the two subgenomes retaining more genes than the other. Since reduced expression is a pathway to duplicate loss, understanding the origins of expression biases may help explain the origins of biased losses. Because we expect gene expression levels to experience stabilizing selection, our conceptual frameworks for how allopolyploid organisms form tend to assume that the new allopolyploid will show balanced expression between its subgenomes. It is then necessary to invoke phenomena such as differences in the suppression of repetitive elements to explain the observed expression imbalances. Here we show that, even for phenotypically identical diploid progenitors, the inherent kinetics of gene expression give rise to biases between the expression levels of the progenitor genes in the hybrid. Some of these biases are expected to be gene-specific and not give rise to global differences in progenitor gene expression. However, particularly in the case of allopolyploids formed from progenitors with different genome sizes, global expression biases favoring one subgenome are expected immediately on formation. Hence, expression biases are arguably the expectation upon allopolyploid formation rather than a phenomenon needing explanation. In the future, a deeper understanding of the kinetics of allopolyploidy may allow us to better understand both biases in duplicate losses and hybrid vigor.

Leveraging gene correlations in single cell transcriptomic data

BACKGROUND

Many approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data-looking for rare cell types, subtleties of cell states, and details of gene regulatory networks-there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data when ground truth about biological variation is unknown (i.e., usually).

RESULTS

We approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization-a step that skews distributions, particularly for sparse data-and calculate p-values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene-gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships.

CONCLUSIONS

New insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene-gene correlations.

Heritable transcriptional defects from aberrations of nuclear architecture

Transcriptional heterogeneity due to plasticity of the epigenetic state of chromatin contributes to tumour evolution, metastasis and drug resistance1-3. However, the mechanisms that cause this epigenetic variation are incompletely understood. Here we identify micronuclei and chromosome bridges, aberrations in the nucleus common in cancer4,5, as sources of heritable transcriptional suppression. Using a combination of approaches, including long-term live-cell imaging and same-cell single-cell RNA sequencing (Look-Seq2), we identified reductions in gene expression in chromosomes from micronuclei. With heterogeneous penetrance, these changes in gene expression can be heritable even after the chromosome from the micronucleus has been re-incorporated into a normal daughter cell nucleus. Concomitantly, micronuclear chromosomes acquire aberrant epigenetic chromatin marks. These defects may persist as variably reduced chromatin accessibility and reduced gene expression after clonal expansion from single cells. Persistent transcriptional repression is strongly associated with, and may be explained by, markedly long-lived DNA damage. Epigenetic alterations in transcription may therefore be inherently coupled to chromosomal instability and aberrations in nuclear architecture.

Epigenetic fluctuations underlie gene expression timescales and variability

Isogenic populations of mammalian cells exhibit significant gene expression variability. This variability can be separated into two sources, cis, or allele-specific sources, and trans and global processes. Furthermore, each source of variability has its own timescale. Fast timescales will result in rapid fluctuation of gene expression whereas slow timescales will result in longer persistence of gene expression levels over time. Here we investigated sources of gene expression that are intrinsic, i.e. coming from cis-regulatory factors and follow slow timescales. To do so, we developed a reporter system that isolates allele-specific variability and measures its persistence in imaging and long-term fluctuation analysis experiments. Our results identify a new source of gene expression variability that is allele-specific but that is fluctuating on timescales of days. We hypothesized that allele-specific fluctuations of epigenetic regulatory factors are responsible for the newly discovered allele-specific and slow source of gene expression variability. Using mathematical modeling we showed that the addition of this effect to the two-state model is sufficient to account for all empirical observation. Furthermore, using direct assays of chromatin markers we find fluctuation in H3K4me3 levels that match the observed changes in gene expression levels providing direct experimental support of our model. Collectively, our work shows that slow fluctuations of regulatory chromatin modifications contribute to the variability in gene expression.

Kinetic characteristics of transcriptional bursting in a complex gene model with cyclic promoter structure

While transcription often occurs in a bursty manner, various possible regulations can lead to complex promoter patterns such as promoter cycles, giving rise to an important question: How do promoter kinetics shape transcriptional bursting kinetics? Here we introduce and analyze a general model of the promoter cycle consisting of multi-OFF states and multi-ON states, focusing on the effects of multi-ON mechanisms on transcriptional bursting kinetics. The derived analytical results indicate that burst size follows a mixed geometric distribution rather than a single geometric distribution assumed in previous studies, and ON and OFF times obey their own mixed exponential distributions. In addition, we find that the multi-ON mechanism can lead to bimodal burst-size distribution, antagonistic timing of ON and OFF, and diverse burst frequencies, each further contributing to cell-to-cell variability in the mRNA expression level. These results not only reveal essential features of transcriptional bursting kinetics patterns shaped by multi-state mechanisms but also can be used to the inferences of transcriptional bursting kinetics and promoter structure based on experimental data.

Regulatory mechanisms ensuring coordinated expression of functionally related genes

Coordinated spatiotemporal expression of large sets of genes is required for the development and homeostasis of organisms. To achieve this goal, organisms use myriad strategies where they form operons, utilize bidirectional promoters, cluster genes, share enhancers among genes by DNA looping, and form topologically associated domains and transcriptional condensates. Coexpression achieved by these different strategies is hypothesized to have functional importance in minimizing gene expression variability, establishing dosage balance to ensure stoichiometry of protein complexes, and minimizing accumulation of toxic intermediate metabolites. By combining gene-editing tools with computational modeling, recent studies tested the advantages of adjacent genes located in pairs and clusters. We propose that with the advancement of gene editing, single-cell sequencing, and imaging tools, one could readily test the functional importance of different coexpression strategies in a variety of biological processes.

Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network

Although it is well appreciated that gene expression is inherently noisy and that transcriptional noise is encoded in a promoter’s sequence, little is known about the extent to which noise levels of individual promoters vary across growth conditions. Using flow cytometry, we here quantify transcriptional noise in Escherichia coli genome-wide across 8 growth conditions and find that noise levels systematically decrease with growth rate, with a condition-dependent lower bound on noise. Whereas constitutive promoters consistently exhibit low noise in all conditions, regulated promoters are both more noisy on average and more variable in noise across conditions. Moreover, individual promoters show highly distinct variation in noise across conditions. We show that a simple model of noise propagation from regulators to their targets can explain a significant fraction of the variation in relative noise levels and identifies TFs that most contribute to both condition-specific and condition-independent noise propagation. In addition, analysis of the genome-wide correlation structure of various gene properties shows that gene regulation, expression noise, and noise plasticity are all positively correlated genome-wide and vary independently of variations in absolute expression, codon bias, and evolutionary rate. Together, our results show that while absolute expression noise tends to decrease with growth rate, relative noise levels of genes are highly condition-dependent and determined by the propagation of noise through the gene regulatory network.

Genome-wide flow cytometry measurements reveal that gene expression noise in bacteria is highly condition-dependent; while absolute noise levels of all genes decrease with growth-rate, theoretical modeling shows that the relative noise levels of different genes are determined by the propagation of noise through the gene regulatory network (GRN). Thus GRN structure controls not only mean expression but also noise levels.

Multiplexed Single-Cell in situ RNA Profiling

The ability to quantify a large number of varied transcripts in single cells in their native spatial context is crucial to accelerate our understanding of health and disease. Bulk cell RNA analysis masks the heterogeneity in the cell population, while the conventional RNA imaging approaches suffer from low multiplexing capacity. Recent advances in multiplexed fluorescence in situ hybridization (FISH) methods enable comprehensive RNA profiling in individual cells in situ. These technologies will have wide applications in many biological and biomedical fields, including cell type classification, signaling network analysis, tissue architecture, disease diagnosis and patient stratification, etc. In this minireview, we will present the recent technological advances of multiplexed single-cell in situ RNA profiling assays, discuss their advantages and limitations, describe their biological applications, highlight the current challenges, and propose potential solutions.

Systematic analysis of noise reduction properties of coupled and isolated feed-forward loops

Cells can maintain their homeostasis in a noisy environment since their signaling pathways can filter out noise somehow. Several network motifs have been proposed for biological noise filtering and, among these, feed-forward loops have received special attention. Specific feed-forward loops show noise reducing capabilities, but we notice that this feature comes together with a reduced signal transducing performance. In posttranslational signaling pathways feed-forward loops do not function in isolation, rather they are coupled with other motifs to serve a more complex function. Feed-forward loops are often coupled to other feed-forward loops, which could affect their noise-reducing capabilities. Here we systematically study all feed-forward loop motifs and all their pairwise coupled systems with activation-inactivation kinetics to identify which networks are capable of good noise reduction, while keeping their signal transducing performance. Our analysis shows that coupled feed-forward loops can provide better noise reduction and, at the same time, can increase the signal transduction of the system. The coupling of two coherent 1 or one coherent 1 and one incoherent 4 feed-forward loops can give the best performance in both of these measures.

Cellular behavior can be affected by noise in molecular interactions. Signaling pathways should process noisy input signals and support cellular decision making by properly transducing the signals, while removing noise from them. Three component networks of feed-forward loops (FFLs) have been proposed to serve as ideal noise reducers, while linear pathways were shown to be good signal transducers. These signaling units do not work in isolation, so there is a possibility that a combination of various feed-forward loops can provide good noise reduction, while maintaining good signal transduction. To test this hypothesis, we have systematically tested the noise reducing and signal transducing capabilities of all possible combinations of feed-forward loops and compared them with the performance of individual FFLs. We built mathematical models of all these systems and compared their capabilities at reducing noise in the input signal while maintaining responses to meaningful changes in the incoming signal. We found that a combination of two copies of a special type of fully positive signaling FFLs is the best noise reducer, while a combination of two incoherent (one positive, one negative signal) FFLs can provide the best signal transduction. The combination of these two FFLs could provide good signal processing where both noise reduction and signal transduction are achieved.

Independent control of mean and noise by convolution of gene expression distributions

Gene expression noise can reduce cellular fitness or facilitate processes such as alternative metabolism, antibiotic resistance, and differentiation. Unfortunately, efforts to study the impacts of noise have been hampered by a scaling relationship between noise and expression level from individual promoters. Here, we use theory to demonstrate that mean and noise can be controlled independently by expressing two copies of a gene from separate inducible promoters in the same cell. We engineer low and high noise inducible promoters to validate this result in Escherichia coli, and develop a model that predicts the experimental distributions. Finally, we use our method to reveal that the response of a promoter to a repressor is less sensitive with higher repressor noise and explain this result using a law from probability theory. Our approach can be applied to investigate the effects of noise on diverse biological pathways or program cellular heterogeneity for synthetic biology applications.

Clustering single-cell RNA-seq data by rank constrained similarity learning

MOTIVATION

Recent breakthroughs of single-cell RNA sequencing (scRNA-seq) technologies offer an exciting opportunity to identify heterogeneous cell types in complex tissues. However, the unavoidable biological noise and technical artifacts in scRNA-seq data as well as the high dimensionality of expression vectors make the problem highly challenging. Consequently, although numerous tools have been developed, their accuracy remains to be improved.

RESULTS

Here, we introduce a novel clustering algorithm and tool RCSL (Rank Constrained Similarity Learning) to accurately identify various cell types using scRNA-seq data from a complex tissue. RCSL considers both local similarity and global similarity among the cells to discern the subtle differences among cells of the same type as well as larger differences among cells of different types. RCSL uses Spearman's rank correlations of a cell's expression vector with those of other cells to measure its global similarity, and adaptively learns neighbour representation of a cell as its local similarity. The overall similarity of a cell to other cells is a linear combination of its global similarity and local similarity. RCSL automatically estimates the number of cell types defined in the similarity matrix, and identifies them by constructing a block-diagonal matrix, such that its distance to the similarity matrix is minimized. Each block-diagonal submatrix is a cell cluster/type, corresponding to a connected component in the cognate similarity graph. When tested on 16 benchmark scRNA-seq datasets in which the cell types are well-annotated, RCSL substantially outperformed six state-of-the-art methods in accuracy and robustness as measured by three metrics.

AVAILABILITY

The RCSL algorithm is implemented in R and can be freely downloaded at https://cran.r-project.org/web/packages/RCSL/index.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Gene Families With Stochastic Exclusive Gene Choice Underlie Cell Adhesion in Mammalian Cells

Exclusive stochastic gene choice combines precision with diversity. This regulation enables most T-cells to express exactly one T-cell receptor isoform chosen from a large repertoire, and to react precisely against diverse antigens. Some cells express two receptor isoforms, revealing the stochastic nature of this process. A similar regulation of odorant receptors and protocadherins enable cells to recognize odors and confer individuality to cells in neuronal interaction networks, respectively. We explored whether genes in other families are expressed exclusively by analyzing single-cell RNA-seq data with a simple metric. This metric can detect exclusivity independently of the mean value and the monoallelic nature of gene expression. Chromosomal segments and gene families are more likely to express genes concurrently than exclusively, possibly due to the evolutionary and biophysical aspects of shared regulation. Nonetheless, gene families with exclusive gene choice were detected in multiple cell types, most of them are membrane proteins involved in ion transport and cell adhesion, suggesting the coordination of these two functions. Thus, stochastic exclusive expression extends beyond the prototypical families, permitting precision in gene choice to be combined with the diversity of intercellular interactions.

Multiplexed In Situ Protein Profiling with High-Performance Cleavable Fluorescent Tyramide

Understanding the composition, function and regulation of complex cellular systems requires tools that quantify the expression of multiple proteins at their native cellular context. Here, we report a highly sensitive and accurate protein in situ profiling approach using off-the-shelf antibodies and cleavable fluorescent tyramide (CFT). In each cycle of this method, protein targets are stained with horseradish peroxidase (HRP) conjugated antibodies and CFT. Subsequently, the fluorophores are efficiently cleaved by mild chemical reagents, which simultaneously deactivate HRP. Through reiterative cycles of protein staining, fluorescence imaging, fluorophore cleavage, and HRP deactivation, multiplexed protein quantification in single cells in situ can be achieved. We designed and synthesized the high-performance CFT, and demonstrated that over 95% of the staining signals can be erased by mild chemical reagents while preserving the integrity of the epitopes on protein targets. Applying this method, we explored the protein expression heterogeneity and correlation in a group of genetically identical cells. With the high signal removal efficiency, this approach also enables us to accurately profile proteins in formalin-fixed paraffin-embedded (FFPE) tissues in the order of low to high and also high to low expression levels.

Interplay between genetic, epigenetic, and gene expression variability: Considering complexity in evolvability

Genetic variability, epigenetic variability, and gene expression variability (noise) are generally considered independently in their relationship with phenotypic variation. However, they appear to be intrinsically interconnected and influence it in combination. The study of the interplay between genetic and epigenetic variability has the longest history. This article rather considers the introduction of gene expression variability in its relationships with the two others and reviews for the first time experimental evidences over the four relationships connected to gene expression noise. They show how introducing this third source of variability complicates the way of thinking evolvability and the emergence of biological novelty. Finally, cancer cells are proposed to be an ideal model to decipher the dynamic interplay between genetic, epigenetic, and gene expression variability when one of them is either experimentally increased or therapeutically targeted. This interplay is also discussed in an evolutionary perspective in the context of cancer cell drug resistance.

Highly Sensitive and Multiplexed Protein Imaging With Cleavable Fluorescent Tyramide Reveals Human Neuronal Heterogeneity

The ability to comprehensively profile proteins in intact tissues in situ is crucial for our understanding of health and disease. However, the existing methods suffer from low sensitivity and limited sample throughput. To address these issues, here we present a highly sensitive and multiplexed in situ protein analysis approach using cleavable fluorescent tyramide and off-the-shelf antibodies. Compared with the current methods, this approach enhances the detection sensitivity and reduces the imaging time by 1-2 orders of magnitude, and can potentially detect hundreds of proteins in intact tissues at the optical resolution. Applying this approach, we studied protein expression heterogeneity in a population of genetically identical cells, and performed protein expression correlation analysis to identify co-regulated proteins. We also profiled >6,000 neurons in a human formalin-fixed paraffin-embedded (FFPE) hippocampus tissue. By partitioning these neurons into varied cell clusters based on their multiplexed protein expression profiles, we observed different sub-regions of the hippocampus consist of neurons from distinct clusters.

Noise and the molecular processes underlying cell fate decision-making

Cell fate decision-making events involve the interplay of many molecular processes, ranging from signal transduction to genetic regulation, as well as a set of molecular and physiological feedback loops. Each aspect offers a rich field of investigation in its own right, but to understand the whole process, even in simple terms, we need to consider them together. Here we attempt to characterise this process by focussing on the roles of noise during cell fate decisions. We use a range of recent results to develop a view of the sequence of events by which a cell progresses from a pluripotent or multipotent to a differentiated state: chromatin organisation, transcription factor stoichiometry, and cellular signalling all change during this progression, and all shape cellular variability, which becomes maximal at the transition state.

Pairing of segmentation clock genes drives robust pattern formation

Gene expression is an inherently stochastic process ^1,2; however, organismal development and homeostasis require that cells spatiotemporally coordinate the expression of large sets of genes. Coexpressed gene pairs in metazoans often reside in the same chromosomal neighborhood, with gene pairs representing 10% - 50% of all genes depending on species ^3–6. As shared upstream regulators can ensure correlated gene expression, the selective advantage of maintaining adjacent gene pairs remains unknown ⁶. Here, using two linked zebrafish segmentation clock genes, her1 and her7, and combining single-cell transcript counting, genetic engineering, real-time imaging and computational modeling, we reveal that gene pairing boosts correlated transcription and provides phenotypic robustness for developmental pattern formation. Our results demonstrate that disrupting gene pairing disrupts oscillations and segmentation, identifying the selective pressure retaining correlated transcription to sustain a robust and rapid developmental clock. We anticipate that these findings will inspire investigating advantages of gene pairing in other systems and engineering precise synthetic clocks in embryos and organoids.

In Vivo Feedback Control of an Antithetic Molecular-Titration Motif in Escherichia coli Using Microfluidics

We study both in silico and in vivo the real-time feedback control of a molecular titration motif that has been earmarked as a fundamental component of antithetic and multicellular feedback control schemes in E. coli. We show that an external feedback control strategy can successfully regulate the average fluorescence output of a bacterial cell population to a desired constant level in real-time. We also provide in silico evidence that the same strategy can be used to track a time-varying reference signal where the set-point is switched to a different value halfway through the experiment. We use the experimental data to refine and parametrize an in silico model of the motif that can be used as an error computation module in future embedded or multicellular control experiments.

Extrinsic noise prevents the independent tuning of gene expression noise and protein mean abundance in bacteria

It is generally accepted that prokaryotes can tune gene expression noise independently of protein mean abundance by varying the relative levels of transcription and translation. Here, we address this question quantitatively, using a custom-made library of 40 Bacillus subtilis strains expressing a fluorescent protein under the control of different transcription and translation control elements. We quantify noise and mean protein abundance by fluorescence microscopy and show that for most of the natural transcription range of B. subtilis, expression noise is equally sensitive to variations in the transcription or translation rate because of the prevalence of extrinsic noise. In agreement, analysis of whole-genome transcriptomic and proteomic datasets suggests that noise optimization through transcription and translation tuning during evolution may only occur in a regime of weak transcription. Therefore, independent control of mean abundance and noise can rarely be achieved, which has strong implications for both genome evolution and biological engineering.

Inter-embryo gene expression variability recapitulates the hourglass pattern of evo-devo

BACKGROUND

The evolution of embryological development has long been characterized by deep conservation. In animal development, the phylotypic stage in mid-embryogenesis is more conserved than either early or late stages among species within the same phylum. Hypotheses to explain this hourglass pattern have focused on purifying the selection of gene regulation. Here, we propose an alternative-genes are regulated in different ways at different stages and have different intrinsic capacities to respond to perturbations on gene expression.

RESULTS

To eliminate the influence of natural selection, we quantified the expression variability of isogenetic single embryo transcriptomes throughout fly Drosophila melanogaster embryogenesis. We found that the expression variability is lower at the phylotypic stage, supporting that the underlying regulatory architecture in this stage is more robust to stochastic variation on gene expression. We present evidence that the phylotypic stage is also robust to genetic variations on gene expression. Moreover, chromatin regulation appears to play a key role in the variation and evolution of gene expression.

CONCLUSIONS

We suggest that a phylum-level pattern of embryonic conservation can be explained by the intrinsic difference of gene regulatory mechanisms in different stages.

SIR2 Expression Noise Can Generate Heterogeneity in Viability but Does Not Affect Cell-to-Cell Epigenetic Silencing of Subtelomeric URA3 in Yeast

Chromatin structure clearly modulates gene expression noise, but the reverse influence has never been investigated, namely how the cell-to-cell expression heterogeneity of chromatin modifiers may generate variable rates of epigenetic modification. Sir2 is a well-characterized histone deacetylase of the Sirtuin family. It strongly influences chromatin silencing, especially at telomeres, subtelomeres and rDNA. This ability to influence epigenetic landscapes makes it a good model to study the largely unexplored interplay between gene expression noise and other epigenetic processes leading to phenotypic diversification. Here, we addressed this question by investigating whether noise in the expression of SIR2 was associated with cell-to-cell heterogeneity in the frequency of epigenetic silencing at subtelomeres in Saccharomyces cerevisiae Using cell sorting to isolate subpopulations with various expression levels, we found that heterogeneity in the cellular concentration of Sir2 does not lead to heterogeneity in the epigenetic silencing of subtelomeric URA3 between these subpopulations. We also noticed that SIR2 expression noise can generate cell-to-cell variability in viability, with lower levels being associated with better viability. This work shows that SIR2 expression fluctuations are not sufficient to generate cell-to-cell heterogeneity in the epigenetic silencing of URA3 at subtelomeres in Saccharomyces cerevisiae but can strongly affect cellular viability.

Single-cell proteomic analysis

The ability to comprehensively profile proteins in every individual cell of complex biological systems is crucial to advance our understanding of normal physiology and disease pathogenesis. Conventional bulk cell experiments mask the cell heterogeneity in the population, while the single-cell imaging methods suffer from the limited multiplexing capacities. Recent advances in microchip-, mass spectrometry-, and reiterative staining-based technologies have enabled comprehensive protein profiling in single cells. These approaches will bring new insights into a variety of biological and biomedical fields, such as signaling network regulation, cell heterogeneity, tissue architecture, disease diagnosis, and treatment monitoring. In this article, we will review the recent advances in the development of single-cell proteomic technologies, describe their advantages, discuss the current limitations and challenges, and propose potential solutions. We will also highlight the wide applications of these technologies in biology and medicine. This article is categorized under: Cancer > Molecular and Cellular Physiology.

Predictive features of gene expression variation reveal mechanistic link with differential expression

For most biological processes, organisms must respond to extrinsic cues, while maintaining essential gene expression programmes. Although studied extensively in single cells, it is still unclear how variation is controlled in multicellular organisms. Here, we used a machine-learning approach to identify genomic features that are predictive of genes with high versus low variation in their expression across individuals, using bulk data to remove stochastic cell-to-cell variation. Using embryonic gene expression across 75 Drosophila isogenic lines, we identify features predictive of expression variation (controlling for expression level), many of which are promoter-related. Genes with low variation fall into two classes reflecting different mechanisms to maintain robust expression, while genes with high variation seem to lack both types of stabilizing mechanisms. Applying this framework to humans revealed similar predictive features, indicating that promoter architecture is an ancient mechanism to control expression variation. Remarkably, expression variation features could also partially predict differential expression after diverse perturbations in both Drosophila and humans. Differential gene expression signatures may therefore be partially explained by genetically encoded gene-specific features, unrelated to the studied treatment.

Absolutely robust controllers for chemical reaction networks

In this work, we design a type of controller that consists of adding a specific set of reactions to an existing mass-action chemical reaction network in order to control a target species. This set of reactions is effective for both deterministic and stochastic networks, in the latter case controlling the mean as well as the variance of the target species. We employ a type of network property called absolute concentration robustness (ACR). We provide applications to the control of a multisite phosphorylation model as well as a receptor-ligand signalling system. For this framework, we use the so-called deficiency zero theorem from chemical reaction network theory as well as multiscaling model reduction methods. We show that the target species has approximately Poisson distribution with the desired mean. We further show that ACR controllers can bring robust perfect adaptation to a target species and are complementary to a recently introduced antithetic feedback controller used for stochastic chemical reactions.

Molecular basis and biological function of variability in spatial genome organization

The complex three-dimensional organization of genomes in the cell nucleus arises from a wide range of architectural features including DNA loops, chromatin domains, and higher-order compartments. Although these features are universally present in most cell types and tissues, recent single-cell biochemistry and imaging approaches have demonstrated stochasticity in transcription and high variability of chromatin architecture in individual cells. We review the occurrence, mechanistic basis, and functional implications of stochasticity in genome organization. We summarize recent observations on cell- and allele-specific variability of genome architecture, discuss the nature of extrinsic and intrinsic sources of variability in genome organization, and highlight potential implications of structural heterogeneity for genome function.

Insights about collective decision-making at the genetic level

By living in a collective, individuals can share and aggregate information to base their decisions on the many rather than on the one, thereby increasing accuracy. But a collective can also be defined at the molecular level. In the following, we reason that genes, by working collectively, share fundamental features with social organisms, which ends, without invoking cognition, in wiser responses. For that, we compile into a single picture the terms redundancy, stochastic resonance, intrinsic and extrinsic noise, and cross-regulation.

Noise in the Vertebrate Segmentation Clock Is Boosted by Time Delays but Tamed by Notch Signaling

Taming cell-to-cell variability in gene expression is critical for precise pattern formation during embryonic development. To investigate the source and buffering mechanism of expression variability, we studied a biological clock, the vertebrate segmentation clock, controlling the precise spatiotemporal patterning of the vertebral column. By counting single transcripts of segmentation clock genes in zebrafish, we show that clock genes have low RNA amplitudes and expression variability is primarily driven by gene extrinsic sources, which is suppressed by Notch signaling. We further show that expression noise surprisingly increases from the posterior progenitor zone to the anterior segmentation and differentiation zone. Our computational model reproduces the spatial noise profile by incorporating spatially increasing time delays in gene expression. Our results, suggesting that expression variability is controlled by the balance of time delays and cell signaling in a vertebrate tissue, will shed light on the accuracy of natural clocks in multi-cellular systems and inspire engineering of robust synthetic oscillators.

Heterogeneous Timing of Gene Induction as a Regulation Strategy

In response to environmental changes, cells often adapt by up-regulating genes to synthesize proteins that generate a benefit in the new environment. Several such cases of gene induction have been reported where the timing was heterogeneous, with some cells responding early and others responding late, although the microbial population was genetically homogeneous and the environment was well mixed. Here, we explore under which conditions heterogeneous timing of gene induction could be advantageous for the population as a whole. We base our study on a mathematical model that accounts for the cost of protein synthesis in terms of resources, which cells must provide immediately, whereas the associated benefit accumulates only slowly over the protein lifetime. Due to this delayed benefit, gene induction can be a risky investment, if resources are scarce and the environment fluctuates rapidly and unpredictably. Unprofitable gene induction then depletes the remaining limiting resource needed for maintenance of cell viability. We show that whenever gene induction is associated with a transient risk but beneficial in the long run, the stochastic timing of gene induction maximizes the reproductive success of a population. In particular, in an environment of stochastic periods of famine and feast, an optimum emerges from a trade-off between short-term growth, favoring rapid and homogeneous responses, and long-term survival, favoring a broadly heterogeneous response. Our analysis suggests that the optimal variability of induction times is just as large as the time required for the amortization of the initial investment into protein synthesis.

Drugs modulating stochastic gene expression affect the erythroid differentiation process

To better understand the mechanisms behind cells decision-making to differentiate, we assessed the influence of stochastic gene expression (SGE) modulation on the erythroid differentiation process. It has been suggested that stochastic gene expression has a role in cell fate decision-making which is revealed by single-cell analyses but studies dedicated to demonstrate the consistency of this link are still lacking. Recent observations showed that SGE significantly increased during differentiation and a few showed that an increase of the level of SGE is accompanied by an increase in the differentiation process. However, a consistent relation in both increasing and decreasing directions has never been shown in the same cellular system. Such demonstration would require to be able to experimentally manipulate simultaneously the level of SGE and cell differentiation in order to observe if cell behavior matches with the current theory. We identified three drugs that modulate SGE in primary erythroid progenitor cells. Both Artemisinin and Indomethacin decreased SGE and reduced the amount of differentiated cells. On the contrary, a third component called MB-3 simultaneously increased the level of SGE and the amount of differentiated cells. We then used a dynamical modelling approach which confirmed that differentiation rates were indeed affected by the drug treatment. Using single-cell analysis and modeling tools, we provide experimental evidence that, in a physiologically relevant cellular system, SGE is linked to differentiation.

Discontinuous transcription

Numerous studies based on new single-cell and single-gene techniques show that individual genes can be transcribed in short bursts or pulses accompanied by changes in pulsing frequencies. Since so many examples of such discontinuous or fluctuating transcription have been found from prokaryotes to mammals, it now seems to be a common mode of gene expression. In this review we discuss the occurrence of the transcriptional fluctuations, the techniques used for their detection, their putative causes, kinetic characteristics, and probable physiological significance.

Chromosome-wide co-fluctuation of stochastic gene expression in mammalian cells

Gene expression is subject to stochastic noise, but to what extent and by which means such stochastic variations are coordinated among different genes are unclear. We hypothesize that neighboring genes on the same chromosome co-fluctuate in expression because of their common chromatin dynamics, and verify it at the genomic scale using allele-specific single-cell RNA-sequencing data of mouse cells. Unexpectedly, the co-fluctuation extends to genes that are over 60 million bases apart. We provide evidence that this long-range effect arises in part from chromatin co-accessibilities of linked loci attributable to three-dimensional proximity, which is much closer intra-chromosomally than inter-chromosomally. We further show that genes encoding components of the same protein complex tend to be chromosomally linked, likely resulting from natural selection for intracellular among-component dosage balance. These findings have implications for both the evolution of genome organization and optimal design of synthetic genomes in the face of gene expression noise.

Quantitative analysis of the yeast pheromone pathway

The pheromone response pathway of the yeast Saccharomyces cerevisiae is a well-established model for the study of G proteins and mitogen-activated protein kinase (MAPK) cascades. Our longstanding ability to combine sophisticated genetic approaches with established functional assays has provided a thorough understanding of signalling mechanisms and regulation. In this report, we compare new and established methods used to quantify pheromone-dependent MAPK phosphorylation, transcriptional induction, mating morphogenesis, and gradient tracking. These include both single-cell and population-based assays of activity. We describe several technical advances, provide example data for benchmark mutants, highlight important differences between newer and established methodologies, and compare the advantages and disadvantages of each as applied to the yeast model. Quantitative measurements of pathway activity have been used to develop mathematical models and reveal new regulatory mechanisms in yeast. It is our expectation that experimental and computational approaches developed in yeast may eventually be adapted to human systems biology and pharmacology.

The emergence of the two cell fates and their associated switching for a negative auto-regulating gene

Background

Decisions in the cell that lead to its ultimate fate are important for fundamental cellular functions such as proliferation, growth, differentiation, development, and death. These cell fate decisions can be influenced by both the gene regulatory network and also environmental factors and can be modeled using simple gene feedback circuits. Negative auto-regulation is a common feedback motif in the gene circuits. It can act to reduce gene expression noise or induce oscillatory expression and is thought to lead to only one cell fate. Here, we present experimental and modeling data to suggest that a self-repressor circuit can lead to two cell fates under specific conditions.

Results

We show that the introduction of inducers capable of binding and unbinding to a self-repressing gene product (protein), thus regulating the associated gene, can lead to the emergence of two cell states. We suggest that the inducers can alter the effective regulatory binding and unbinding speed of the self-repressor regulatory protein to its destination DNA without changing the gene itself. The corresponding simulation results are consistent with the experimental findings. We propose physical and quantitative explanations for the origin of the two phenotypic cell fates.

Conclusions

Our results suggest a mechanism for the emergence of multiple cell fates. This may explain the heterogeneity often observed among cell states, while illustrating that altering gene regulation strength can influence cell fates and their decision-making processes without genetic changes.

Electronic supplementary material

The online version of this article (10.1186/s12915-019-0666-0) contains supplementary material, which is available to authorized users.

Hyperosmotic Stress Response Memory is Modulated by Gene Positioning in Yeast

Cellular memory is a critical ability that allows microorganisms to adapt to potentially detrimental environmental fluctuations. In the unicellular eukaryote Saccharomyces cerevisiae, cellular memory can take the form of faster or slower responses within the cell population to repeated stresses. Using microfluidics and fluorescence time-lapse microscopy, we studied how yeast responds to short, pulsed hyperosmotic stresses at the single-cell level by analyzing the dynamic behavior of the stress-responsive STL1 promoter (pSTL1) fused to a fluorescent reporter. We established that pSTL1 exhibits variable successive activation patterns following two repeated short stresses. Despite this variability, most cells exhibited a memory of the first stress as decreased pSTL1 activity in response to the second stress. Notably, we showed that genomic location is important for the memory effect, since displacement of the promoter to a pericentromeric chromatin domain decreased the transcriptional strength of pSTL1 and led to a loss of memory. This study provides a quantitative description of a cellular memory that includes single-cell variability and highlights the contribution of chromatin structure to stress memory.

Analysis of Cell Lineage Trees by Exact Bayesian Inference Identifies Negative Autoregulation of Nanog in Mouse Embryonic Stem Cells

Many cellular effectors of pluripotency are dynamically regulated. In principle, regulatory mechanisms can be inferred from single-cell observations of effector activity across time. However, rigorous inference techniques suitable for noisy, incomplete, and heterogeneous data are lacking. Here, we introduce stochastic inference on lineage trees (STILT), an algorithm capable of identifying stochastic models that accurately describe the quantitative behavior of cell fate markers observed using time-lapse microscopy data collected from proliferating cell populations. STILT performs exact Bayesian parameter inference and stochastic model selection using a particle-filter-based algorithm. We use STILT to investigate the autoregulation of Nanog, a heterogeneously expressed core pluripotency factor, in mouse embryonic stem cells. STILT rejects the possibility of positive Nanog autoregulation with high confidence; instead, model predictions indicate weak negative feedback. We use STILT for rational experimental design and validate model predictions using novel experimental data. STILT is available for download as an open source framework from http://www.imsb.ethz.ch/research/claassen/Software/stilt---stochastic-inference-on-lineage-trees.html.

Cytoplasmic Amplification of Transcriptional Noise Generates Substantial Cell-to-Cell Variability

Transcription is an episodic process characterized by probabilistic bursts, but how the transcriptional noise from these bursts is modulated by cellular physiology remains unclear. Using simulations and single-molecule RNA counting, we examined how cellular processes influence cell-to-cell variability (noise). The results show that RNA noise is higher in the cytoplasm than the nucleus in ∼85% of genes across diverse promoters, genomic loci, and cell types (human and mouse). Measurements show further amplification of RNA noise in the cytoplasm, fitting a model of biphasic mRNA conversion between translation- and degradation-competent states. This multi-state translation-degradation of mRNA also causes substantial noise amplification in protein levels, ultimately accounting for ∼74% of intrinsic protein variability in cell populations. Overall, the results demonstrate how noise from transcriptional bursts is intrinsically amplified by mRNA processing, leading to a large super-Poissonian variability in protein levels.

Development of a Metabolite Sensor for High-Throughput Detection of Aldehydes in Escherichia Coli

We have developed a fluorescence-based metabolite sensor enabling in vivo detection of various aldehydes of biotechnological interest in Escherichia coli. YqhC is a transcriptional regulator that is known to be involved in the upregulation of the yqhD-dgkA operon in the presence of aldehydes. We took advantage of this property by constructing a bi-modular biosensor, in which a sensing module constitutively expresses yqhC while a reporter module drives the expression of the syfp2 reporter gene that is put under control of the yqhD promoter. The sensitivity of the sensor has been optimized by engineering the 5′-UTRs of both the sensing and the reporter modules resulting in a 70-fold gain of fluorescence in response to the model compound glycolaldehyde at 5 mM. The optimized sensor further responded to other aldehydes when supplemented to the cultivation medium at concentrations of 1–10 mM. We furthermore showed that this metabolite sensor was functional in vivo as it responded to the presence of glycoladehyde that is specifically produced upon induction of a synthetic xylulose-1-phosphate pathway expressed in E. coli. This bi-modular sensor can therefore be employed as an exquisite tool for FACS-based ultra-high-throughput screening of aldehyde (over) producing enzymes.

Clonal dynamics studied in cultured induced pluripotent stem cells reveal major growth imbalances within a few weeks

Background

Human induced pluripotent stem (iPS) cells have revolutionised research and spark hopes for future tissue replacement therapies. To obtain high cell numbers, iPS cells can be expanded indefinitely. However, as long-term expansion can compromise cell integrity and quality, we set out to assess potential reduction of clonal diversity by inherent growth imbalances.

Methods

Using red, green, blue marking as a lentiviral multi-colour clonal cell tracking technology, we marked three different iPS cell lines as well as three other cell lines, assigning a unique fluorescent colour to each cell at one point in culture. Subsequently, we followed the sub-clonal distribution over time by flow cytometry and fluorescence microscopy analysis in regular intervals.

Results

In three human iPS cell lines as well as primary human fibroblasts and two widely used human cell lines as controls (K562 and HEK 293 T), we observed a marked reduction in sub-clonal diversity over time of culture (weeks). After 38 passages, all iPS cultures consisted of less than 10 residual clones. Karyotype and function, the latter assessed by cardiomyocyte differentiation and tissue engineering, did not reveal obvious differences.

Conclusions

Our results argue for a quick selection of sub-clones with a growth advantage and flag a normally invisible and potentially undesired behaviour of cultured iPS cells, especially when using long-term cultured iPS cells for experiments or even in-vivo applications.

Electronic supplementary material

The online version of this article (10.1186/s13287-018-0893-2) contains supplementary material, which is available to authorized users.

Transcriptional timing and noise of yeast cell cycle regulators-a single cell and single molecule approach

Gene expression is a stochastic process and its appropriate regulation is critical for cell cycle progression. Cellular stress response necessitates expression reprogramming and cell cycle arrest. While previous studies are mostly based on bulk experiments influenced by synchronization effects or lack temporal distribution, time-resolved methods on single cells are needed to understand eukaryotic cell cycle in context of noisy gene expression and external perturbations. Using smFISH, microscopy and morphological markers, we monitored mRNA abundances over cell cycle phases and calculated transcriptional noise for SIC1, CLN2, and CLB5, the main G1/S transition regulators in budding yeast. We employed mathematical modeling for in silico synchronization and for derivation of time-courses from single cell data. This approach disclosed detailed quantitative insights into transcriptional regulation with and without stress, not available from bulk experiments before. First, besides the main peak in G1 we found an upshift of CLN2 and CLB5 expression in late mitosis. Second, all three genes showed basal expression throughout cell cycle enlightening that transcription is not divided in on and off but rather in high and low phases. Finally, exposing cells to osmotic stress revealed different periods of transcriptional inhibition for CLN2 and CLB5 and the impact of stress on cell cycle phase duration. Combining experimental and computational approaches allowed us to precisely assess cell cycle progression timing, as well as gene expression dynamics.

Post-transcriptional bursting in genes regulated by small RNA molecules

Gene expression programs in living cells are highly dynamic due to spatiotemporal molecular signaling and inherent biochemical stochasticity. Here we study a mechanism based on molecule-to-molecule variability at the RNA level for the generation of bursts of protein production, which can lead to heterogeneity in a cell population. We develop a mathematical framework to show numerically and analytically that genes regulated post transcriptionally by small RNA molecules can exhibit such bursts due to different states of translation activity (on or off), mostly revealed in a regime of few molecules. We exploit this framework to compare transcriptional and post-transcriptional bursting and also to illustrate how to tune the resulting protein distribution with additional post-transcriptional regulations. Moreover, because RNA-RNA interactions are predictable with an energy model, we define the kinetic constants of on-off switching as functions of the two characteristic free-energy differences of the system, activation and formation, with a nonequilibrium scheme. Overall, post-transcriptional bursting represents a distinctive principle linking gene regulation to gene expression noise, which highlights the importance of the RNA layer beyond the simple information transfer paradigm and significantly contributes to the understanding of the intracellular processes from a first-principles perspective.

Highly multiplexed single-cell in situ RNA and DNA analysis with bioorthogonal cleavable fluorescent oligonucleotides

The ability to profile transcripts and genomic loci comprehensively in single cells in situ is essential to advance our understanding of normal physiology and disease pathogenesis. Here we report a highly multiplexed single-cell in situ RNA and DNA analysis approach using bioorthogonal cleavable fluorescent oligonucleotides. In this approach, oligonucleotides tethered to fluorophores through an azide-based cleavable linker are used to detect their nucleic acids targets by in situ hybridization. After fluorescence imaging, the fluorophores in the whole specimen are efficiently cleaved in 30 minutes without loss of RNA or DNA integrity. Through reiterative cycles of hybridization, imaging, and cleavage, this method has the potential to quantify hundreds to thousands of different RNA species or genomic loci in single cells in situ at the single-molecule sensitivity. Applying this approach, we demonstrate that different nucleic acids can be detected in each hybridization cycle by multi-color staining, and at least ten continuous hybridization cycles can be carried out in the same specimen. We also show that the integrated single-cell in situ analysis of DNA, RNA and protein can be achieved using cleavable fluorescent oligonucleotides combined with cleavable fluorescent antibodies. This highly multiplexed imaging platform will have wide applications in systems biology and biomedical research.

Glucose repression can be alleviated by reducing glucose phosphorylation rate in Saccharomyces cerevisiae

Microorganisms commonly exhibit preferential glucose consumption and diauxic growth when cultured in mixtures of glucose and other sugars. Although various genetic perturbations have alleviated the effects of glucose repression on consumption of specific sugars, a broadly applicable mechanism remains unknown. Here, we report that a reduction in the rate of glucose phosphorylation alleviates the effects of glucose repression in Saccharomyces cerevisiae. Through adaptive evolution under a mixture of xylose and the glucose analog 2-deoxyglucose, we isolated a mutant strain capable of simultaneously consuming glucose and xylose. Genome sequencing of the evolved mutant followed by CRISPR/Cas9-based reverse engineering revealed that mutations in the glucose phosphorylating enzymes (Hxk1, Hxk2, Glk1) were sufficient to confer simultaneous glucose and xylose utilization. We then found that varying hexokinase expression with an inducible promoter led to the simultaneous utilization of glucose and xylose. Interestingly, no mutations in sugar transporters occurred during the evolution, and no specific transporter played an indispensable role in simultaneous sugar utilization. Additionally, we demonstrated that slowing glucose consumption also enabled simultaneous utilization of glucose and galactose. These results suggest that the rate of intracellular glucose phosphorylation is a decisive factor for metabolic regulations of mixed sugars.

Highly Multiplexed Single-Cell Protein Analysis

Single-cell proteomic analysis is crucial to advance our understanding of normal physiology and disease pathogenesis. The comprehensive protein profiling in individual cells of a heterogeneous sample can provide new insights into many important biological issues, such as the regulation of inter- and intracellular signaling pathways or the varied cellular compositions of normal and diseased tissues. With highly multiplexed molecular imaging of many different protein biomarkers in patient biopsies, diseases can be accurately diagnosed to guide the selection of the ideal treatment. In this Minireview, we will describe the recent technological advances of single-cell proteomic assays, discuss their advantages and limitations, highlight their applications in biology and precision medicine, and present the current challenges and potential solutions.

Intrinsic adaptive value and early fate of gene duplication revealed by a bottom-up approach

The population genetic mechanisms governing the preservation of gene duplicates, especially in the critical very initial phase, have remained largely unknown. Here, we demonstrate that gene duplication confers per se a weak selective advantage in scenarios of fitness trade-offs. Through a precise quantitative description of a model system, we show that a second gene copy serves to reduce gene expression inaccuracies derived from pervasive molecular noise and suboptimal gene regulation. We then reveal that such an accuracy in the phenotype yields a selective advantage in the order of 0.1% on average, which would allow the positive selection of gene duplication in populations with moderate/large sizes. This advantage is greater at higher noise levels and intermediate concentrations of the environmental molecule, when fitness trade-offs become more evident. Moreover, we discuss how the genome rearrangement rates greatly condition the eventual fixation of duplicates. Overall, our theoretical results highlight an original adaptive value for cells carrying new-born duplicates, broadly analyze the selective conditions that determine their early fates in different organisms, and reconcile population genetics with evolution by gene duplication.

The Evolution of Gene-Specific Transcriptional Noise Is Driven by Selection at the Pathway Level

Biochemical reactions within individual cells result from the interactions of molecules, typically in small numbers. Consequently, the inherent stochasticity of binding and diffusion processes generates noise along the cascade that leads to the synthesis of a protein from its encoding gene. As a result, isogenic cell populations display phenotypic variability even in homogeneous environments. The extent and consequences of this stochastic gene expression have only recently been assessed on a genome-wide scale, owing, in particular, to the advent of single-cell transcriptomics. However, the evolutionary forces shaping this stochasticity have yet to be unraveled. Here, we take advantage of two recently published data sets for the single-cell transcriptome of the domestic mouse Mus musculus to characterize the effect of natural selection on gene-specific transcriptional stochasticity. We show that noise levels in the mRNA distributions (also known as transcriptional noise) significantly correlate with three-dimensional nuclear domain organization, evolutionary constraints on the encoded protein, and gene age. However, the position of the encoded protein in a biological pathway is the main factor that explains observed levels of transcriptional noise, in agreement with models of noise propagation within gene networks. Because transcriptional noise is under widespread selection, we argue that it constitutes an important component of the phenotype and that variance of expression is a potential target of adaptation. Stochastic gene expression should therefore be considered together with the mean expression level in functional and evolutionary studies of gene expression.

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

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.

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.