Memory Sequencing Reveals Heritable Single-Cell Gene Expression Programs Associated with Distinct Cellular Behaviors.

Non-genetic factors can cause individual cells to fluctuate substantially in gene expression levels over time. It remains unclear whether these fluctuations can persist for much longer than the time of one cell division. Current methods for measuring gene expression in single cells mostly rely on single time point measurements, making the duration of gene expression fluctuations or cellular memory difficult to measure. Here, we combined Luria and Delbrück's fluctuation analysis with population-based RNA sequencing (MemorySeq) for identifying genes transcriptome-wide whose fluctuations persist for several divisions. MemorySeq revealed multiple gene modules that expressed together in rare cells within otherwise homogeneous clonal populations. These rare cell subpopulations were associated with biologically distinct behaviors like proliferation in the face of anti-cancer therapeutics. The identification of non-genetic, multigenerational fluctuations can reveal new forms of biological memory in single cells and suggests that non-genetic heritability of cellular state may be a quantitative property.

Transcription factor antagonism regulates heterogeneity in embryonic stem cell states.

Gene expression heterogeneity underlies cell states and contributes to developmental robustness. While heterogeneity can arise from stochastic transcriptional processes, the extent to which it is regulated is unclear. Here, we characterize the regulatory program underlying heterogeneity in murine embryonic stem cell (mESC) states. We identify differentially active and transcribed enhancers (DATEs) across states. DATEs regulate differentially expressed genes and are distinguished by co-binding of transcription factors Klf4 and Zfp281. In contrast to other factors that interact in a positive feedback network stabilizing mESC cell-type identity, Klf4 and Zfp281 drive opposing transcriptional and chromatin programs. Abrogation of factor binding to DATEs dampens variation in gene expression, and factor loss alters kinetics of switching between states. These results show antagonism between factors at enhancers results in gene expression heterogeneity and formation of cell states, with implications for the generation of diverse cell types during development.

A heritable iron memory enables decision-making in Escherichia coli.

The importance of memory in bacterial decision-making is relatively unexplored. We show here that a prior experience of swarming is remembered when Escherichia coli encounters a new surface, improving its future swarming efficiency. We conducted >10,000 single-cell swarm assays to discover that cells store memory in the form of cellular iron levels. This "iron" memory preexists in planktonic cells, but the act of swarming reinforces it. A cell with low iron initiates swarming early and is a better swarmer, while the opposite is true for a cell with high iron. The swarming potential of a mother cell, which tracks with its iron memory, is passed down to its fourth-generation daughter cells. This memory is naturally lost by the seventh generation, but artificially manipulating iron levels allows it to persist much longer. A mathematical model with a time-delay component faithfully recreates the observed dynamic interconversions between different swarming potentials. We demonstrate that cellular iron levels also track with biofilm formation and antibiotic tolerance, suggesting that iron memory may impact other physiologies.

Genome-wide screening reveals metabolic regulation of stop-codon readthrough by cyclic AMP.

Translational fidelity is critical for microbial fitness, survival and stress responses. Much remains unknown about the genetic and environmental control of translational fidelity and its single-cell heterogeneity. In this study, we used a high-throughput fluorescence-based assay to screen a knock-out library of Escherichia coli and identified over 20 genes critical for stop-codon readthrough. Most of these identified genes were not previously known to affect translational fidelity. Intriguingly, we show that several genes controlling metabolism, including cyaA and crp, enhance stop-codon readthrough. CyaA catalyzes the synthesis of cyclic adenosine monophosphate (cAMP). Combining RNA sequencing, metabolomics and biochemical analyses, we show that deleting cyaA impairs amino acid catabolism and production of ATP, thus repressing the transcription of rRNAs and tRNAs to decrease readthrough. Single-cell analyses further show that cAMP is a major driver of heterogeneity in stop-codon readthrough and rRNA expression. Our results highlight that carbon metabolism is tightly coupled with stop-codon readthrough.

Dynamics and consequences of the HTLV-1 proviral plus-strand burst.

Expression of the transcriptional transactivator protein Tax, encoded on the proviral plus-strand of human T-cell leukaemia virus type 1 (HTLV-1), is crucial for the replication of the virus, but Tax-expressing cells are rarely detected in fresh blood ex vivo. The dynamics and consequences of the proviral plus-strand transcriptional burst remain insufficiently characterised. We combined time-lapse live-cell imaging, single-cell tracking and mathematical modelling to study the dynamics of Tax expression at single-cell resolution in two naturally-infected, non-malignant T-cell clones transduced with a short-lived enhanced green fluorescent protein (d2EGFP) Tax reporter system. Five different patterns of Tax expression were observed during the 30-hour observation period; the distribution of these patterns differed between the two clones. The mean duration of Tax expression in the two clones was 94 and 417 hours respectively, estimated from mathematical modelling of the experimental data. Tax expression was associated with a transient slowing in cell-cycle progression and proliferation, increased apoptosis, and enhanced activation of the DNA damage response pathways. Longer-term follow-up (14 days) revealed an increase in the proportion of proliferating cells and a decrease in the fraction of apoptotic cells as the cells ceased Tax expression, resulting in a greater net expansion of the initially Tax-positive population. Time-lapse live-cell imaging showed enhanced cell-to-cell adhesion among Tax-expressing cells, and decreased cell motility of Tax-expressing cells at the single-cell level. The results demonstrate the within-clone and between-clone heterogeneity in the dynamics and patterns of HTLV-1 plus-strand transcriptional bursts and the balance of positive and negative consequences of the burst for the host cell.

Exact Distribution of the Quantal Content in Synaptic Transmission.

During electrochemical signal transmission through synapses, triggered by an action potential (AP), a stochastic number of synaptic vesicles (SVs), called the "quantal content," release neurotransmitters in the synaptic cleft. It is widely accepted that the quantal content probability distribution is a binomial based on the number of ready-release SVs in the presynaptic terminal. But the latter number itself fluctuates due to its stochastic replenishment, hence the actual distribution of quantal content is unknown. We show that exact distribution of quantal content can be derived for general stochastic AP inputs in the steady state. For fixed interval AP train, we prove that the distribution is a binomial, and corroborate our predictions by comparison with electrophysiological recordings from MNTB-LSO synapses of juvenile mice. For a Poisson train, we show that the distribution is nonbinomial. Moreover, we find exact moments of the quantal content in the Poisson and other general cases, which may be used to obtain the model parameters from experiments.

Decoding the dynamics of multilayered stochastic antiviral IFN-I responses.

Type I Interferon (IFN-I) responses were first recognized for their role in antiviral immunity, but it is now widely appreciated that IFN-Is have many immunomodulatory functions, influencing antitumor responses, autoimmune manifestations, and antimicrobial defenses. Given these pivotal roles, it may be surprising that multilayered stochastic events create highly heterogeneous, but tightly regulated, all-or-nothing cellular decisions. Recently, mathematical models have provided crucial insights into the stochastic nature of antiviral IFN-I responses, which we critically evaluate in this review. In this context, we emphasize the need for innovative single-cell technologies combined with mathematical models to further reveal, understand, and predict the complexity of the IFN-I system in physiological and pathological conditions that may be relevant to a plethora of diseases.

Corrigendum: Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance.

This corrects the article DOI: 10.1038/nature22794.

Reduction in gene expression noise by targeted increase in accessibility at gene loci.

Many eukaryotic genes are expressed in randomly initiated bursts that are punctuated by periods of quiescence. Here, we show that the intermittent access of the promoters to transcription factors through relatively impervious chromatin contributes to this "noisy" transcription. We tethered a nuclease-deficient Cas9 fused to a histone acetyl transferase at the promoters of two endogenous genes in HeLa cells. An assay for transposase-accessible chromatin using sequencing showed that the activity of the histone acetyl transferase altered the chromatin architecture locally without introducing global changes in the nucleus and rendered the targeted promoters constitutively accessible. We measured the gene expression variability from the gene loci by performing single-molecule fluorescence in situ hybridization against mature messenger RNAs (mRNAs) and by imaging nascent mRNA molecules present at active gene loci in single cells. Because of the increased accessibility of the promoter to transcription factors, the transcription from two genes became less noisy, even when the average levels of expression did not change. In addition to providing evidence for chromatin accessibility as a determinant of the noise in gene expression, our study offers a mechanism for controlling gene expression noise which is otherwise unavoidable.

Cell volume homeostatically controls the rDNA repeat copy number and rRNA synthesis rate in yeast.

The adjustment of transcription and translation rates to the changing needs of cells is of utmost importance for their fitness and survival. We have previously shown that the global transcription rate for RNA polymerase II in budding yeast Saccharomyces cerevisiae is regulated in relation to cell volume. Total mRNA concentration is constant with cell volume since global RNApol II-dependent nascent transcription rate (nTR) also keeps constant but mRNA stability increases with cell size. In this paper, we focus on the case of rRNA and RNA polymerase I. Contrarily to that found for RNA pol II, we detected that RNA polymerase I nTR increases proportionally to genome copies and cell size in polyploid cells. In haploid mutant cells with larger cell sizes, the rDNA repeat copy number rises. By combining mathematical modeling and experimental work with the large-size cln3 strain, we observed that the increasing repeat copy number is based on a feedback mechanism in which Sir2 histone deacetylase homeostatically controls the amplification of rDNA repeats in a volume-dependent manner. This amplification is paralleled with an increase in rRNA nTR, which indicates a control of the RNA pol I synthesis rate by cell volume.

Out-of-equilibrium gene expression fluctuations in the presence of extrinsic noise.

Cell-to-cell variability in protein concentrations is strongly affected by extrinsic noise, especially for highly expressed genes. Extrinsic noise can be due to fluctuations of several possible cellular factors connected to cell physiology and to the level of key enzymes in the expression process. However, how to identify the predominant sources of extrinsic noise in a biological system is still an open question. This work considers a general stochastic model of gene expression with extrinsic noise represented as fluctuations of the different model rates, and focuses on the out-of-equilibrium expression dynamics. Combining analytical calculations with stochastic simulations, we characterize how extrinsic noise shapes the protein variability during gene activation or inactivation, depending on the prevailing source of extrinsic variability, on its intensity and timescale. In particular, we show that qualitatively different noise profiles can be identified depending on which are the fluctuating parameters. This indicates an experimentally accessible way to pinpoint the dominant sources of extrinsic noise using time-coarse experiments.

PyEcoLib: a python library for simulating stochastic cell size dynamics.

Recently, there has been an increasing need for tools to simulate cell size regulation due to important applications in cell proliferation and gene expression. However, implementing the simulation usually presents some difficulties, as the division has a cycle-dependent occurrence rate. In this article, we gather a recent theoretical framework inPyEcoLib, a python-based library to simulate the stochastic dynamics of the size of bacterial cells. This library can simulate cell size trajectories with an arbitrarily small sampling period. In addition, this simulator can include stochastic variables, such as the cell size at the beginning of the experiment, the cycle duration timing, the growth rate, and the splitting position. Furthermore, from a population perspective, the user can choose between tracking a single lineage or all cells in a colony. They can also simulate the most common division strategies (adder, timer, and sizer) using the division rate formalism and numerical methods. As an example of PyecoLib applications, we explain how to couple size dynamics with gene expression predicting, from simulations, how the noise in protein levels increases by increasing the noise in division timing, the noise in growth rate and the noise in cell splitting position. The simplicity of this library and its transparency about the underlying theoretical framework yield the inclusion of cell size stochasticity in complex models of gene expression.

Characterizing non-exponential growth and bimodal cell size distributions in fission yeast: An analytical approach.

Unlike many single-celled organisms, the growth of fission yeast cells within a cell cycle is not exponential. It is rather characterized by three distinct phases (elongation, septation, and reshaping), each with a different growth rate. Experiments also showed that the distribution of cell size in a lineage can be bimodal, unlike the unimodal distributions measured for the bacterium Escherichia coli. Here we construct a detailed stochastic model of cell size dynamics in fission yeast. The theory leads to analytic expressions for the cell size and the birth size distributions, and explains the origin of bimodality seen in experiments. In particular, our theory shows that the left peak in the bimodal distribution is associated with cells in the elongation phase, while the right peak is due to cells in the septation and reshaping phases. We show that the size control strategy, the variability in the added size during a cell cycle, and the fraction of time spent in each of the three cell growth phases have a strong bearing on the shape of the cell size distribution. Furthermore, we infer all the parameters of our model by matching the theoretical cell size and birth size distributions to those from experimental single-cell time-course data for seven different growth conditions. Our method provides a much more accurate means of determining the size control strategy (timer, adder or sizer) than the standard method based on the slope of the best linear fit between the birth and division sizes. We also show that the variability in added size and the strength of size control in fission yeast depend weakly on the temperature but strongly on the culture medium. More importantly, we find that stronger size homeostasis and larger added size variability are required for fission yeast to adapt to unfavorable environmental conditions.

Concentration fluctuations in growing and dividing cells: Insights into the emergence of concentration homeostasis.

Intracellular reaction rates depend on concentrations and hence their levels are often regulated. However classical models of stochastic gene expression lack a cell size description and cannot be used to predict noise in concentrations. Here, we construct a model of gene product dynamics that includes a description of cell growth, cell division, size-dependent gene expression, gene dosage compensation, and size control mechanisms that can vary with the cell cycle phase. We obtain expressions for the approximate distributions and power spectra of concentration fluctuations which lead to insight into the emergence of concentration homeostasis. We find that (i) the conditions necessary to suppress cell division-induced concentration oscillations are difficult to achieve; (ii) mRNA concentration and number distributions can have different number of modes; (iii) two-layer size control strategies such as sizer-timer or adder-timer are ideal because they maintain constant mean concentrations whilst minimising concentration noise; (iv) accurate concentration homeostasis requires a fine tuning of dosage compensation, replication timing, and size-dependent gene expression; (v) deviations from perfect concentration homeostasis show up as deviations of the concentration distribution from a gamma distribution. Some of these predictions are confirmed using data for E. coli, fission yeast, and budding yeast.

Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance.

Therapies that target signalling molecules that are mutated in cancers can often have substantial short-term effects, but the emergence of resistant cancer cells is a major barrier to full cures. Resistance can result from secondary mutations, but in other cases there is no clear genetic cause, raising the possibility of non-genetic rare cell variability. Here we show that human melanoma cells can display profound transcriptional variability at the single-cell level that predicts which cells will ultimately resist drug treatment. This variability involves infrequent, semi-coordinated transcription of a number of resistance markers at high levels in a very small percentage of cells. The addition of drug then induces epigenetic reprogramming in these cells, converting the transient transcriptional state to a stably resistant state. This reprogramming begins with a loss of SOX10-mediated differentiation followed by activation of new signalling pathways, partially mediated by the activity of the transcription factors JUN and/or AP-1 and TEAD. Our work reveals the multistage nature of the acquisition of drug resistance and provides a framework for understanding resistance dynamics in single cells. We find that other cell types also exhibit sporadic expression of many of these same marker genes, suggesting the existence of a general program in which expression is displayed in rare subpopulations of cells.

Single mammalian cells compensate for differences in cellular volume and DNA copy number through independent global transcriptional mechanisms.

Individual mammalian cells exhibit large variability in cellular volume, even with the same absolute DNA content, and so must compensate for differences in DNA concentration in order to maintain constant concentration of gene expression products. Using single-molecule counting and computational image analysis, we show that transcript abundance correlates with cellular volume at the single-cell level due to increased global transcription in larger cells. Cell fusion experiments establish that increased cellular content itself can directly increase transcription. Quantitative analysis shows that this mechanism measures the ratio of cellular volume to DNA content, most likely through sequestration of a transcriptional factor to DNA. Analysis of transcriptional bursts reveals a separate mechanism for gene dosage compensation after DNA replication that enables proper transcriptional output during early and late S phase. Our results provide a framework for quantitatively understanding the relationships among DNA content, cell size, and gene expression variability in single cells.

Development of synaptic fidelity and action potential robustness at an inhibitory sound localization circuit: effects of otoferlin-related deafness.

Sound localization involves information analysis in the lateral superior olive (LSO), a conspicuous nucleus in the mammalian auditory brainstem. LSO neurons weigh interaural level differences (ILDs) through precise integration of glutamatergic excitation from the cochlear nucleus (CN) and glycinergic inhibition from the medial nucleus of the trapezoid body (MNTB). Sound sources can be localized even during sustained perception, an accomplishment that requires robust neurotransmission. Virtually nothing is known about the sustained performance and the temporal precision of MNTB-LSO inputs after postnatal day (P)12 (time of hearing onset) and whether acoustic experience guides development. Here we performed whole-cell patch-clamp recordings to investigate neurotransmission of single MNTB-LSO fibres upon sustained electrical stimulation (1-200 Hz/60 s) at P11 and P38 in wild-type (WT) and deaf otoferlin (Otof) knock-out (KO) mice. At P11, WT and KO inputs performed remarkably similarly. In WTs, the performance increased drastically between P11 and P38, e.g. manifested by an 8 to 11-fold higher replenishment rate (RR) of synaptic vesicles and action potential robustness. Together, these changes resulted in reliable and highly precise neurotransmission at frequencies ≤100 Hz. In contrast, KO inputs performed similarly at both ages, implying impaired synaptic maturation. Computational modelling confirmed the empirical observations and established a reduced RR per release site for P38 KOs. In conclusion, acoustic experience appears to contribute massively to the development of reliable neurotransmission, thereby forming the basis for effective ILD detection. Collectively, our results provide novel insights into experience-dependent maturation of inhibitory neurotransmission and auditory circuits at the synaptic level. KEY POINTS: Inhibitory glycinergic inputs from the medial nucleus of the trapezoid body (MNTB) to the lateral superior olive (LSO) are involved in sound localization. This brainstem circuit performs reliably throughout life. How such reliability develops is unknown. Here we investigated the role of acoustic experience on the functional maturation of MNTB-LSO inputs at juvenile (postnatal day P11) and young adult ages (P38) employing deaf mice lacking otoferlin (KO). We analysed neurotransmission at single MNTB-LSO fibres in acute brainstem slices employing prolonged high-frequency stimulation (1-200 Hz/60 s). At P11, KO inputs still performed normally, as manifested by normal synaptic attenuation, fidelity, replenishment rate, temporal precision and action potential robustness. Between P11 and P38, several synaptic parameters increased substantially in wild-type mice, collectively resulting in high-fidelity and temporally precise neurotransmission. In contrast, maturation of synaptic fidelity was largely absent in KOs after P11. Collectively, reliable neurotransmission at inhibitory MNTB-LSO inputs develops under the guidance of acoustic experience.

The dopamine membrane transporter plays an active modulatory role in synaptic dopamine homeostasis.

Modulatory mechanisms of neurotransmitter release and clearance are highly controlled processes whose finely tuned regulation is critical for functioning of the nervous system. Dysregulation of the monoamine neurotransmitter dopamine can lead to several neuropathies. Synaptic modulation of dopamine is known to involve pre-synaptic D2 auto-receptors and acid sensing ion channels. In addition, the dopamine membrane transporter (DAT), which is responsible for clearance of dopamine from the synaptic cleft, is suspected to play an active role in modulating release of dopamine. Using functional imaging on the Caenorhabditis elegans model system, we show that DAT-1 acts as a negative feedback modulator to neurotransmitter vesicle fusion. Results from our fluorescence recovery after photo-bleaching (FRAP) based experiments were followed up with and reaffirmed using swimming-induced paralysis behavioral assays. Utilizing our numerical FRAP data we have developed a mechanistic model to dissect the dynamics of synaptic vesicle fusion, and compare the feedback effects of DAT-1 with the dopamine auto-receptor. Our experimental results and the mechanistic model are of potential broader significance, as similar dynamics are likely to be used by other synaptic modulators including membrane transporters for other neurotransmitters across species.

Exact Distribution of Threshold Crossing Times for Protein Concentrations: Implication for Biological Timekeeping.

Stochastic protein accumulation up to some concentration threshold sets the timing of many cellular physiological processes. Here we obtain the exact distribution of first threshold crossing times of protein concentration, in either Laplace or time domain, and its associated cumulants: mean, variance, and skewness. The distribution is asymmetric, and its skewness nonmonotonically varies with the threshold. We study lysis times of E. coli cells for holin gene mutants of bacteriophage-λ and find a good match with theory. Mutants requiring higher holin thresholds show more skewed lysis time distributions as predicted. The theory also predicts a linear relationship between infection delay time and host doubling time for lytic viruses, that has recently been experimentally observed.

Noise suppression in stochastic genetic circuits using PID controllers.

Inside individual cells, protein population counts are subject to molecular noise due to low copy numbers and the inherent probabilistic nature of biochemical processes. We investigate the effectiveness of proportional, integral and derivative (PID) based feedback controllers to suppress protein count fluctuations originating from two noise sources: bursty expression of the protein, and external disturbance in protein synthesis. Designs of biochemical reactions that function as PID controllers are discussed, with particular focus on individual controllers separately, and the corresponding closed-loop system is analyzed for stochastic controller realizations. Our results show that proportional controllers are effective in buffering protein copy number fluctuations from both noise sources, but this noise suppression comes at the cost of reduced static sensitivity of the output to the input signal. In contrast, integral feedback has no effect on the protein noise level from stochastic expression, but significantly minimizes the impact of external disturbances, particularly when the disturbance comes at low frequencies. Counter-intuitively, integral feedback is found to amplify external disturbances at intermediate frequencies. Next, we discuss the design of a coupled feedforward-feedback biochemical circuit that approximately functions as a derivate controller. Analysis using both analytical methods and Monte Carlo simulations reveals that this derivative controller effectively buffers output fluctuations from bursty stochastic expression, while maintaining the static input-output sensitivity of the open-loop system. In summary, this study provides a systematic stochastic analysis of biochemical controllers, and paves the way for their synthetic design and implementation to minimize deleterious fluctuations in gene product levels.