To monitor transcriptional dynamics in living cells, we integrated an array of MS2 stem loops after the promoter of the endogenous actin5 locus, a strongly expressed actin gene in undifferentiated Dictyostelium cells. Transcription continues after the MS2 loops into the coding sequence, then native terminator, to generate a full length transcript of around 2.5 kb. We visualized the resulting transcription dynamics using time-lapse fluorescence microscopy and extracted time series of spot intensities using custom-built software integrating both cell tracking and spot detection (Corrigan and Chubb, 2014). The movie sequence in Figure 1A illustrates the tracking of a typical cell, showing the fluctuations in spot intensity over time. Figure 1B shows the measured transcription spot intensity for the cell in Figure 1A, with a kymograph of the spot fluctuations. For most genes studied, the durations for which a spot is present or absent are often measured to have approximately exponential distributions, which is the case for act5 (Figure 1—figure supplement 1) (Muramoto et al., 2012). Exponential timescales have been inferred to represent modulation of gene activity, between the ON and OFF states of the two-state model, with a rate-limiting step determining switching between states (Golding and Cox, 2006). A more simple possibility is that stochastic fluctuations of a gene with no OFF state - the one state model - could give rise to pulses and intervals between pulses with the experimentally observed lifetimes.

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To test if a one state model, where the probability of a polymerase initiating is constant over time, can give rise to the distributions of spot durations observed experimentally, we constructed a Monte-Carlo simulation framework describing the MS2 system, incorporating polymerase initiation (the rate at which polymerases begin transcription) and elongation (the velocity of the polymerase while transcribing) (Figure 1C,D). Additional features, such as the number of gene states, and termination/release rate (the rate at which the RNA leaves the gene) were omitted at this stage. By defining a signal detection threshold above which a spot is determined to be present, we calculated the pulse and interval length distributions from the simulated data. The average pulse duration depends strongly on the initiation rate, but was also substantially sensitive to the detection threshold and frame interval specified in the simulations. The pulse duration distribution had exponential distributions at long timescales, with deviations at very short timescales (Figure 1E). This was surprising, as the exponential distribution was previously inferred to support the two-state model. Here, our simulations clearly show such a distribution can arise from a more simple one-state scenario.

An exponential distribution of pulse durations has one parameter, a characteristic timescale, and arises from there being a constant probability of the pulse ending at any given time. This probability is independent of the duration for which the pulse has been sustained. It is tempting to infer from this probability the rate of switching from an active to an inactive state of a two state gene. However, with exponential behaviour observed for our simulation of constant polymerase initiation rates (this is only one state), it is necessary to consider additional contributions to the likelihood of a spot disappearing from one imaging frame to the next.

To investigate the origins of exponential behaviour, we constructed a theory of spot appearance and disappearance for the live MS2 system. By considering the rate of initiation of polymerases at the start of the gene and the number of RNA stem loops produced by a polymerase as it moves along the gene, we calculate the joint probability distribution linking the spot intensity in the previous frame with the current frame. (Figure 1F, full details in the appendix). The theory successfully predicts the characteristic exponential timescale of pulse duration in terms of the one state model, incorporating the polymerase elongation rate, the frame interval and threshold of spot detection. In simple terms, the pulse duration reflects the balance between polymerase initiation and elongation or termination - in order for a spot to remain present in the next frame, a sufficient number of new polymerases must be initiated to replace the loss of spot intensity from cleaved RNA leaving the transcription site. Intuitively, high initiation rates are likely to give rise to large numbers of initiations per frame, giving long pulses. Increasing the threshold of spot detection decreases the likelihood of sufficient initiations and thus decreases the pulse duration. These predictions are in agreement with the Monte Carlo simulations of the MS2 system (Figure 1G and Figure 1—figure supplement 2). The initiation and elongation rates have approximately inverse effects, with initiation scaling directly with spot intensity (as expected intuitively) and elongation scaling inversely. The effect of slow elongation increasing spot intensity can be thought of in terms of a polymerase 'traffic jam', with several polymerases building up behind a slower one, causing a build up of nascent RNA on the gene (Darzacq et al., 2007). Additionally, the frame interval of observation influences the effective pulse duration, as longer frame intervals increase the likelihood of a gap between pulses being missed between frames. Overall, this theory shows that the length of time for which a spot is visible is not simply related to switches between proposed gene states in two state models and explains the dependence of the pulse duration on the imaging signal-to-noise (Muramoto et al., 2010) and frame interval (Masaki et al., 2013).

Since the theory shows that the simple presence of a spot does not provide insight into what activity state the gene is currently in, we must make precise measurements of transcription site intensity in order to detect initiation rate changes (changes in gene state). As depicted by the cartoon in Figure 2A, a fluctuation in intensity might be consistent with either the one state (Poisson, top) or multi-state (bottom) models. Without calibration of the intensity fluctuations in terms of numbers of RNAs, it is difficult to discount either model. One strategy that can be used is an autocorrelation analysis, which measures the magnitude and timescale of intensity fluctuations (Larson et al., 2011). The autocorrelation measures the ratio of intensity fluctuations relative to the mean intensity. For a Poisson (one state) gene, where initiations behave independently of the previous or subsequent polymerase, this ratio is related to the average number of bound polymerases, allowing the initiation rate to be estimated. This was previously used to infer the initiation and elongation rates from a POL1 promoter inserted upstream of the GLT1 gene in budding yeast (Larson et al., 2011), specifying the time for the correlation to decay to zero as the time for which a single polymerase contributes to the spot intensity.

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Applying a similar analysis to transcription of the Dictyostelium act5 gene (Figure 2B), we find that inference of polymerase properties is complicated by an additional long-timescale decay of several minutes to tens of minutes (black dotted line). The additional slow decay means the RNA level at the gene is correlated beyond the dwell time at the gene of a single RNA. This argues against a Poisson model and indicates a model with two or more transcription states would better describe act5 transcription. If the autocorrelation is recalculated using only frames where a spot is present, the slow decay remains, suggesting that the active periods where a spot is present are not defined by a single constant initiation rate; in other words there are multiple 'ON' states. By subtracting a linear estimate of the long timescale decay from the autocorrelation, we calculated estimates of the RNA dwell time and polymerase load on the gene (Larson et al., 2011). The point where the autocorrelation deviates from the long decay gives an approximation of the dwell time as 190 ± 20 s. The average number of polymerases contributing to the intensity is estimated from the magnitude of the deviation at zero lag time as 20–27 polymerases. The cells are predominantly in G2 (Muramoto and Chubb, 2008), so these autocorrelation-based estimates are for the polymerase load across 2 alleles of the act5 gene comprising the transcription spot.

As described above, the observed pulsing properties also depend on the RNA dwell time- the time the RNA spends at the transcription site during elongation and termination before it is released from the gene. As an independent measure of dwell time, we used fluorescence recovery after photobleaching (FRAP) measurements on MS2 spots (Muramoto et al., 2012). The MCP-RNA interaction is stable (Maiuri et al., 2011), so recovery of the spot intensity after bleaching is determined by transcription of new MS2 loops. In the absence of photobleaching, spot intensities fluctuate over time, therefore a bleached spot is not expected to recover to its pre-bleach intensity. We therefore normalized the curves based on the recovered intensity. Recovery of a single endogenous gene is highly stochastic, as expected from simulations of photobleaching and recovery (see below). Therefore, we averaged over 20–30 cells in order to extract the typical RNA dwell time. The average recovery curve was fit to a processive polymerase model of the intensity recovery to estimate the elongation rate and termination time (full details in the appendix). We calculated the dwell time for three different gene constructs, with the MS2 loops integrated into the 5’ gene region, the 3’ region, and replacing the entire actin5 coding region (Figure 2C). The dwell time for the 5’ insertion was longer than for the other insertions, as expected as the polymerase will continue to transcribe after the MS2 array, whereas with the other two cell lines, termination will occur immediately upon completion of synthesis of the array. Using the difference in dwell times and assuming a constant rate of elongation across the gene, we calculate the elongation rate as 22 nt.s^-1 and termination time as 60-70 s. This gives a total dwell time of around 170 s, for the 5’ MS2 insertion, roughly in agreement with the autocorrelation-derived approximation on the same allele.

As shown by our pulsing theory, spot intensity variations may reflect either fluctuations in the initiation rate or dwell time. If the dwell time is systematically different for spots of different intensity, we would observe this by studying the recovery times of high and low intensity spots. We divided the FRAP data into three equal groups based on their recovery intensity (inset of Figure 2D) and calculated the average recovery curve for each group. Figure 2D shows that no significant differences in recovery time are observed for the different intensity groups, suggesting dwell time variations are not the primary source of the spot intensity variability.

We then calibrated live RNA spot intensity in terms of the number of MS2 stem loops by comparison with smFISH using a probe for the MS2 RNA. We used FISH-quant (Mueller et al., 2013) to estimate the intensity of one mature cytoplasmic RNA then used this information to calibrate the number of nascent MS2 loops at the transcription site (Figure 2E). We compared the distribution of these nascent RNA counts with the intensity distribution of transcription site MCP-GFP intensity measurements from live cells immediately prior to fixation for smFISH (Figure 2F). The two distributions are aligned by calculating the percentiles of each distribution and using these values as calibration points (Figure 2G). Due to the differing precision with which the intensity can be calculated in live and fixed samples, there is some deviation from a linear relationship at the extreme ends of the calibration curve, and a kink at low intensities where the detection threshold of live spots is higher than in fixed spots, however the overall trend can be used to calibrate the spot intensities measured in live cells in terms of the number of MS2 stem loops present.

After calibrating the system, we used our pulsing theory outlined above to estimate the initiation rate. We measured how the mean pulse duration changes as the detection threshold is varied. For a one-state (constant activity) gene, the pulse duration would lie on a contour calculated using our theory. Figure 2—figure supplement 1 shows that the experimental data does not lie on a single contour of constant initiation rate, consistent with the autocorrelation results in Figure 2B. Similarly, contours calculated from a simple two-state implementation, with on- and off-rates chosen to produce the experimentally observed spot frequency (details in the appendix), cannot match the variation of pulse duration and spot frequency as the detection threshold is varied (Figure 2—figure supplement 2). Instead, the behaviour at high thresholds suggests a short-lived higher initiation rate.

Dictyostelium cells are almost exclusively in G2, owing to a very short S phase and complete absence of G1 (Muramoto and Chubb, 2008). Therefore almost all cells will have replicated the MS2-tagged gene and have two genes, held together by sister chromatid cohesion, contributing to a single resolved spot. Transcription of the unreplicated gene will not confuse analysis, as act5 transcription is not robustly detected in the first 15 min after mitosis (Muramoto et al., 2010), when euchromatin is replicated (Muramoto and Chubb, 2008). A higher initiation rate state is possible in the situation where both copies are simultaneously active - giving a potential third state with double the initiation rate of a single copy.

To test the possibility of this third state, we applied probabilistic modelling to our quantitative data to assess the extent to which three state or higher models can describe the observed transcription dynamics. Since a two-state model of transcriptional activity, with an ‘off’ state and an ‘on’ state of constant initiation rate, does not describe the live data adequately, we asked whether an improvement could be made by including additional active states with different initiation rates. It is not possible to directly observe polymerase initiations using the MS2 system, so we used hidden Markov modelling to infer the likelihoods of different numbers of active states from the measured live cell intensity data.

In simple terms, a hidden Markov model (HMM) describes a sequence of observations in terms of a sequence of underlying, 'hidden' states. By extension from the two-state model, we define a 'state' as a configuration with a single constant (Poisson) initiation rate. In terms of transcription dynamics, the hidden state corresponds to the underlying initiation rate of the system. This rate cannot be observed directly and instead must be inferred from measurements of the transcription site intensity, which is related probabilistically to the initiation rate. Using standard HMM techniques, the magnitudes and dynamics of the underlying initiation rate can be optimized to maximize the probability of generating the experimental data from the model. For the case of the MS2 system, the spot intensity does not depend only on the instantaneous initiation rate, because polymerases initiated in previous frames are still present on the gene and also contribute to the spot intensity. To take account of these polymerase contributions, we constructed a model (full details in the appendix, architecture depicted in Figure 3A) with hidden states representing the initiation rate (blue circles) and the number of polymerases initiated (orange circles), inferred from the sequence of transcription site intensities (green squares). A benefit of our two-layer HMM framework is that we explicity model the polymerases uncoupled from transitions between states of gene activity. This means that the distribution of polymerases initiated in each state can be estimated (see below).

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To test how well models with discrete states of activity describe the data, we calculate the probability of obtaining the experimental data using the model parameters. This probability is known as the likelihood. We use a standard approach to compare models of different complexities, by calculating the Akaike Information Criterion (AIC) from the likelihood (Singer et al., 2014). The AIC is used to find the best approximation in situations where the real scenario is likely to be highly complex. Models are penalized by their number of free parameters, preventing overfitting by excessively complex models. The AIC estimates the prediction error of the candidate model, giving a lower numerical value for models which more accurately describe the experimental data.

To test the validity of the use of the HMMs and associated algorithms, we tested the framework on simulated data, with known numbers of promoter states, and known timescales of switching between states (Figure 3B). In these cases, the 'hidden' initiation states and numbers of initiated polymerases are known, allowing the accuracy of the probabilistic methods to be measured. Applying the hidden Markov approach to a simulated 3 state model accurately estimated 3 discrete initiation states as the point at which AIC is minimized (Figure 3C). 1 and 2 states give a higher AIC (lower likelihood), whilst 4 shows no improvement over 3 states. In addition, this approach robustly measured the initiation rate of each state, as well as the timescale of switching between the activity states (Figure 3—figure supplement 1).

We next applied the model fitting to experimental data for transcription of the actin5 gene. We carried out 4 independent time lapse experiments, with multiple fields of view in each experiment, capturing data on 44–145 cells per experiment, giving 1686–6350 individual cell-frames, per experiment. Figure 3D shows the AIC for models with discrete numbers of initiation rate states. The 2-state model shows a strong reduction in AIC compared to a 1-state model, with the 3-state model better still. The curve does not plateau after the 3 state model, instead, the fit to the data is continually and significantly improved by adding further initiation states. The continually improving fit does not depend on the permitted mechanisms of switching allowed from state to state (Figure 3D). This continual improvement in fit occurs if the gene constrained to switching between states one step at a time (Figure 3D- 'ladder'), in steps of up to two states ('Jump 1'), or is unconstrained ('Free'). The most realistic interpretation of the continually improving fit with more states is that the gene switches in activity over a spectrum or continuum of states, rather than a small number of discrete activity states.

In Figure 3D the largest improvement in fitting occurs up to 3 states; therefore, is a model with three initiation rate states appropriate for the act5 gene? Although the slow rate of improvement in fit for higher numbers of states may suggest three states are adequate, closer analysis of the optimal 3-state fit reveals its inability to capture the full variability of the transcription site intensities. The values of the estimated initiation rates are not uniformly spaced (as would be expected for two alleles undergoing random telegraph transcription), but have a low state and a very high state in addition to an off-state. Furthermore, Figure 3E shows strongly non-Poisson distributions for the predicted number of polymerases initiated per frame in each state of the three state model, inconsistent with a constant initiation rate within each state. In finding the best 3 state fit, the modelling process effectively forced outlying data into non-Poissonian states. The wide polymerase distributions imply either that the initiation rate is varying within a state, or that transitions are occurring between states on a timescale significantly faster than the frame interval.

To determine the dynamics of the gene state and number of initiated polymerases, we used a Gibbs sampling algorithm (see appendix). The estimated number of polymerases initiated in each frame is approximately independent of the number of states chosen for the model. The initiation rate, the number of events per unit of time, is difficult to define as an instantaneous measurement; instead, by plotting the cumulative number of polymerase initiations as a function of time the initiation rate can be taken from the gradient. For a typical cell in the experimental data, the cumulative polymerase plot is composed of linear segments (Figure 3—figure supplement 2) representing periods of time over which the initiation rate is constant. We measured the initiation rate using an edge-preserving smoothing filter and found a diversity of such gradients, indicating a spectrum of initiation rates, again implying the transcriptional behaviour is not adequately described by a few discrete levels of activity. The most realistic model to account for this additional complexity in initiation fluctuations is a ladder or continuum of activity states. The timescale of initiation rate variation through this spectrum of activity states can be determined using an autocorrelation analysis of non-zero initiation rates. Figure 3F shows such a plot, which reveals the initiation rate fluctuates with an average timescale of around 5–6 min, although it is possible for a roughly constant initiation rate to be sustained for up to 15 min (Figure 3—figure supplement 2).

To what extent can fluctuations in elongation rate contribute to the complexity of the ladder or continuum of transcriptional states? A transiently slower rate of elongation may cause a build-up in the number of polymerases on the gene and therefore contribute to non-Poisson variations in spot intensity. To test this possibility, we use simulations with three initiation rate states – with average initiation and elongation rates matched to the experimental estimates – to address whether the additional complexity shown experimentally beyond a three state model (Figure 3D) could be accounted for by adding elongation rate fluctuations to the system.

In addition to increasing the spot intensity, slower elongation rates lead to increased dwell time, which in FRAP measurements, would lead to the brighter spots taking longer to recover. However, the experimental data (Figure 2D) showed no observable difference in recovery between groups of different intensity. It remains possible that fluctuations in elongation rate may occur over a timescale which would not be resolved by FRAP. To investigate the timescale of dwell time fluctuations that might be invisible to FRAP, we developed simulations of the FRAP protocol, matching the experimental procedure as closely as possible. We incorporated temporal fluctuations in the polymerase elongation rate acting either globally (affecting every polymerase on the gene in the same way) or in a polymerase-by-polymerase manner. Since very little is known about the type of elongation fluctuations possible in vivo, we implemented a system which switches randomly between 10 and 30 nucleotides/s, and varied the timescale of fluctuation (how long the system remains in either state).

The FRAP curves produced by individual simulated cells are treated in the same way as experimental data - dividing the cells into three groups based on their recovery intensity and rejecting cells showing no recovery – to determine the largest fluctuation timescale which shows no difference between intensity groups. As shown in Figure 4A, elongation rates fluctuating independently for each polymerase cannot produce any differences in the recovery curves. For the density of polymerases on the gene for typical spot intensities, fluctuations of individual polymerases are suppressed by catching up and being blocked by a polymerase immediately downstream. This results in a bulk polymerase elongation rate close to the lower speed of 10 nucleotides/s. Thus independent fluctuations in polymerase elongation cannot account for the fluctuations in spot intensity in the continuum model.

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In contrast, global fluctuations in the elongation rate give rise to different recovery curves for different spot intensities (Figure 4B), as the brightest spots tend to be caused by slow-moving polymerases staying at the transcription site for longer, while the intermediate spots have a bias towards faster moving polymerases. As in experiments, the lowest intensity recoveries show a high degree of noise, due to the small normalisation factor. As the timescale of the elongation fluctuations becomes shorter, the difference between the recoveries is reduced, as variability in the elongation rate is blurred out by faster transitions. The point at which the simulated data becomes consistent with the experimental traces (Figure 2D) is around the lower two panels of Figure 4B, that is, an elongation fluctuation timescale with an upper limit of around 10-40 s. What this effectively means is that gene-wide fluctuations in the elongation rate may be present on a timescale of up to around 40 s without being apparent in experimental FRAP recovery curves. The exact timescale depends also on the magnitude of the fluctuations, taken here to be around 50% of the mean elongation rate, nevertheless, some degree of elongation fluctuation may contribute to the temporal variation of the transcription spot intensity.

We then asked whether adding elongation fluctuations of the magnitude identified above to the three-state model is sufficient to produce the decrease in AIC value beyond 3 states seen in hidden Markov modelling. The difference, ΔAIC, between AIC (3-state) and the minimal AIC value (for a model up to 8-states) represents the additional complexity present in the data – essentially the information missing from a three-state fit. We applied our HMM fitting to simulations of the three-state model with different timescales of elongation fluctuation, and measured ΔAIC to estimate if the addition of elongation fluctuations is sufficient to recapitulate the additional complexity measured experimentally.

Figure 4C compares the ΔAIC value between a representative experiment and simulations of elongation fluctuations (adjusted to match experimental sample size). For no fluctuations, no improvement is seen as expected, since a three-state model successfully describes the simulated data. For 13 s elongation fluctuations a small increase in complexity is observed. For the 43 s timescale, the magnitude of the improvement is comparable to that observed experimentally. This suggests these elongation fluctuations can potentially recapitulate the variability in spot intensities observed in experiments, with two important caveats. Firstly, in the simulations the 43 s timescale data plateau at 5 states, rather than around 7–8 for the experimental data. Secondly, training a three-state model on experimental data yielded distributions of polymerase initiations in each state with variances greater than expected for Poisson distributions (Figure 3E). This super-Poissonian behaviour of the experimental data is not reproduced by training the same three state model on in silico elongation fluctuation data (Figure 4D, Figure 4—figure supplement 1), indicating further complexity in the experimental data not fully explained by the simple elongation fluctuations.

To test the effects of perturbing the initiation rate on transcriptional bursting, we generated point mutations in the TATA box of the act5 promoter, T1A and A2C (Figure 5A and Figure 5—figure supplement 1 and 2), which have strong effects on transcriptional output in yeast genes and mammalian expression plasmids (Patwardhan et al., 2009; Raser and O'Shea, 2004). Both TATA mutant lines displayed a slight reduction in overall spot intensity (Figure 5B), with FRAP experiments suggesting a similar dwell time to wild type (Figure 5—figure supplement 3). Analysis of time-lapse experiments using the polymerase HMM framework found that the TATA mutants spend less time in medium and high initiation rate states, and more time at lower initiation rates, compared to control cells (Figure 5C). The overall timescale of initiation rate variability, measured through autocorrelation, was not substantially changed (Figure 5D) with both the wild-type and TATA mutations showing fluctuation timescales of several minutes. In addition, we observed no clear difference in switching rates between the inactive and active states. The rate of switching to the inactive state, k(off), was unchanged between wild-type and TATA mutants (Figure 5E). The tendency of spots to appear, k(on), showed a slight impairment in A2C mutants, although this was not statistically significant. This subtle effect might be also interpreted as the enhanced occupancy of active states of unobservably low intensity in the A2C mutation, rather than simply the absence of transcription. We then addressed the likelihood of the gene switching up or down in initiation rate based upon its current state (Figure 5—figure supplement 4). For all cell lines, both wild-type and TATA mutant, high initiation rates had a tendency to revert to lower initiation rates, and lower initiation rates had a tendency to revert to higher initiation rates. In the TATA mutants, the initiation rate is less likely to switch to a higher activity state, resulting in reduced time spent in high activity states. Together these observations suggest that perturbing the TATA box does not affect the duration or frequency of active states, but rather modulates the initiation rates that are possible.

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The standard analysis of transcriptional bursts using smFISH measurements can extract estimates of the burst size and burst frequency. Implicit in the assumptions of the random telegraph (two state) model is the idea that cells undergoing a burst in a particular gene all have the same underlying initiation rate. However, it seems unlikely, based on our data, that the active initiation rate will be constant over time during a burst, or that it will be the same for all cells in a population. Rather, the rate of initiation will depend on the binding of molecular factors, which turn over on the timescale of seconds to tens of seconds, based upon measured residence times of transcription factors (Chen et al., 2014; Izeddin et al., 2014). The cytoplasmic lifetime of mRNA means that these fast fluctuations are blurred out in static measurements, such as smFISH. In most such cases only broad differences in overall transcript content between cells can be determined, which accounts for the success of models with a few numbers of discrete states in fitting the data. Measurements using the MS2 system are integrated over the dwell time of the nascent RNA, a timescale of 2–3 min. The dynamic measurements can begin to resolve systematic variability in the initiation rate over time, which is difficult to assign to a small number of discrete states. Our proposed continuum model is summarized in Figure 6A. This model may also be approximated by a discrete state model with more states than can be effectively detected (Figure 6—figure supplement 1).

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Our continuum model may be consistent with a recent theoretical model (Rieckh and Tkačik, 2014) which proposed rapid switching between states as an improvement over the standard 2-state model in terms of information transmission (Figure 6—figure supplement 1iii). In this view the initiation rate fluctuations can be thought of as the fraction of time for which the factors required for initiation are bound to the promoter. The rate of binding depends on the spatiotemporal variability in local concentrations, producing a continuum of activities when integrated over the MS2 RNA dwell time. When the TATA box is mutated, the overall decreased initiation rate in the active state may reflect the reduced rate of binding of necessary transcription factors, and therefore reduced occupancy of the initiating state (Figure 6—figure supplement 2). Illustrating the potential equivalence of this rapid switching model with our continuum model, our data show that the optimal discrete 3-state model consigns polymerase initiations to non-Poissonian states (Figure 3E), which implies either multiple initiation rates within a state (in other words more than 3 states) or switching between the discrete states faster than the imaging frame interval.

Is this continuum view in conflict with the existing smFISH data on 2-state models? To answer this question, we simulated steady state levels of RNA derived from a gene where the binding rate varies continuously over time, overlaid with longer periods of inactivity (Figure 6B, upper). We incorporated autocorrelation-derived timescales and initiation rates extracted from the hidden Markov modelling together with a mRNA lifetime of 40 min, similar to measured lifetime values of act5-MS2 using actinomycinD-treated cells. The distribution of cytoplasmic mRNA molecules was well fit to a standard two-state bursting model (Singer et al., 2014), using a negative binomial function (see appendix) capturing primarily the long timescale activity intervals (Figure 6C). The shorter timescale of the MS2 measurements (Figure 6B, lower) is able to discriminate fluctuations within active regions. In other words, when measured in the steady state, using methods such as RNA FISH, a continuum of transcriptional states will appear to consist of far fewer states than can be detected using live cell approaches, highlighting the different timescales measured by different methods. The two-state model approximates the magnitude and broad timescale of transcriptional variability, although it should be noted that the bursting parameters extracted may not directly correspond to the physical quantities of burst size and frequency.

For targeting of MS2 repeats into the actin 5 gene, we utilized a BsrGI-SpeI restriction fragment containing 24 MS2 repeats (1.3 kb) upstream of the blasticidin resistance (bsr) cassette (Faix et al., 2004). The resistance cassette is flanked by loxP sites for CRE-mediated removal of the marker, allowing transcription to terminate at the natural 3’ sequence of the gene. For the 5’ tagging of act5, the MS2-bsr was cloned between a promoter fragment of the gene (-680 to +21) and a gene fragment (+108 to +1313), using BsrGI and SpeI sites. Cells derived using this 5’ tagging vector were used for all experiments described in this paper, unless indicated otherwise. For the gene replacement vector, the MS2-bsr was cloned between the same promoter fragment and a fragment from the 3’ coding sequence and terminator of the gene (+1092 to +1899). For the 3’ targeting vector, we used this same 3’ region combined with a 5’ region derived from the act5 coding sequence (+259 to +1113). The ATG corresponds to +1. The translational STOP, TAA is at +1129. The first clear polyadenylation motif (AATAAA) starts at +1193. Sequences were checked at each cloning step to ensure plasmid stocks retained the correct sequences (as specified by dictyBase). For TATA mutations, the T1A (AATAAAT) and A2C (TCTAAAT) mutant promoter fragments were generated by gene synthesis, then spliced into the wild-type promoter sequence using the BstEII site upstream of the TATA box, prior to inserting the MS2-bsr fusion, again using BsrGI and SpeI sites at the same positions.

Targeting fragments were released from cloning vectors by digestion with polylinker enzymes ClaI and NotI. These targeting fragments were transformed into a Dictyostelium AX3 clone previously engineered to express a red fluorescent nuclear marker, H2Bv3-Cherry, under the control of the endogenous promoter of the rps30 gene. Targeted clones were identified by PCR, then genomic DNA Southern blotted to ensure MS2 repeat integrity and single correct insertions in the targeted clones. Correct targeting of TATA mutations to the act5 promoter was checked by sequencing of PCR products from recombinant clones. Positive clones were then transiently transfected with a plasmid expressing the CRE recombinase, to remove the bsr cassette, allowing the MS2 RNA to fall under the control of the natural act5 terminator. Clones were then transformed with an extrachromosomal vector, based on pDM1096 (from Dr. Douwe Veltman) expressing the MCP-GFP fusion protein, to permit detection of nascent RNA in living cells. Selection used 20 µg/ml G418.

Cell culture preparation for imaging was carried using cells grown in HL5 medium (FORMEDIUM) supplemented with penicillin+streptomycin. 20 µg/ml G418 selection was added 72 hr after thawing frozen stocks. 18 hr prior to imaging, cells were split into imaging chambers (NUNC LabTek-II) at the appropriate density for imaging the next day at around 20% confluency, drug selection was removed and HL5 was replaced with imaging medium (75% LoFlo medium (Formedium), 10% FBS, 15% HL5). Imaging media was refreshed 1.5 hr before imaging. Imaging was performed using an UltraView Vox spinning disc confocal microscope. Objective, laser lines, camera (Hamamatsu C9100-13 EM-CCD) settings, laser powers and exposure times were optimized to minimize photobleaching and ensure negligible photo-toxicity (measured in terms of the average transcription spot intensity - in trials we found transcription spots were attenuated before reduced cell motility or cell rounding is observed). Data were analysed using custom-built software integrating both cell tracking and spot detection. Code can be accessed at http://www.ucl.ac.uk/lmcb/sites/default/files/Corrigan2016MatlabFiles.zip.

Cells were imaged live as described above for a single time point and then immediately fixed and prepared for smFISH measurements following the procedure outlined in Raj et al. (2006), using a single probe (CATGGGTGATCCTCATGT; Biosearch, Petaluma, CA) against the repeated spacer between the each MS2 loop sequence, end-labelled with Quasar 570 fluorophore. Nuclei were co-stained with DAPI. Image stacks were acquired using the spinning disc using exposure times of 3 s for single molecule sensitivity. Individual cytoplasmic RNA molecules and nascent transcription sites were detected using FISH-quant software v2 (Mueller et al., 2013), augmented with a custom-written dual-threshold algorithm to segment nuclei and cytoplasm of cells based on the DAPI signal.

A Monte Carlo simulation framework for the MS2 system was constructed using custom-written tools in MATLAB. For simulation of FRAP experiments, binding and potential unbinding of MCP was simulated with first order kinetics. Tools for analysis of n^th-order hidden Markov models, including the forward-backward and Baum-Welch algorithms and Gibbs sampling, were custom-written using MATLAB. Full details of the simulation and probabilistic analysis procedures are presented in the appendix.

Fluorescence recovery after photobleaching (FRAP) experiments were performed following previously described procedures (Muramoto et al., 2012). Briefly, a cell with an active transcription site was identified and a single z-stack captured prior to bleaching. Bleaching parameters (bleach size, number of bleaching cycles, bleach power) were optimized empirically to give reliable bleaching, reproducible recovery and minimal photodamage. Two seconds after bleaching, z-stacks were acquired with a frame interval of 10 s for 5 min. The location of bleached spots is difficult to robustly measure automatically, therefore after automated segmentation and spot detection, spot locations were verified manually using a custom-built interface before spot intensity calculation. For each cell, the spot intensity and the relative local level of background fluorescence was recorded for every frame.

To estimate the RNA dwell time, we use a robust weighted averaging scheme to generate a smooth recovery curve, excluding outliers in an unbiased manner. Traces were weighted according to the difference from the mean, averaged over all frames of each trace (one weight value per cell), and the weighted average recovery calculated.

Weights were recalculated using distance from the new average recovery, and this process was iterated until the weights no longer changed with a relative tolerance of 10⁻³. This approach assigns low weights to cells that behave significantly differently to the majority of cells, for example cells where the transcription spot disappears or does not recover. The number of such cells is in agreement with the observed frequency of transcription becoming inactive in a five minute window.

The average recovery curve is then fit to a model recovery which takes as inputs the time to produce a full complement of loops, t_elong, the time spent with a full complement of loops, t_full, and the normalised recovery of the background fluorescent MCP level, f(t). Approximating the gene to be uniformly occupied by polymerases, the rate of change of transcription spot intensity is calculated as the balance between the gain of newly produced loops being bound by unbleached MCP (A) and the loss of unbleached loops when RNA leaves the site, from fluorophores binding newly produced loops (B). If the bleach does not completely remove the spot fluorescence, a further source of fluorophore loss arises from the pre-existing, partially bleached RNA leaving the transcription site C). This contribution is proportional to the unbleached spot fraction Γ. By calculating the number of loops per polymerase as a function of position on the gene, the rate of intensity loss due to incompletely bleached RNA leaving the TS is:

The rate of intensity loss due to newly produced loops and unbleached fluorophore leaving the TS depends on the level of the background fluorescence at the point that each loop was produced (i.e. loops produced shortly after the bleaching event will be more likely to be bound by bleached MCP, since the background level takes a finite time to recover). Therefore when an RNA leaves at time t, we integrate the background fluorescence over the range of times when the loops of this RNA were produced:

where:

The rate of intensity increase due to newly produced loops being bound by unbleached fluorophore depends on the recovery of the background level of unbleached fluorophore, f(t):

The simplicity of this expression stems from the assumption that polymerases are uniformly spread over the gene. The time evolution of the transcription site intensity is then calculated by numerically integrating the rates of fluorescence loss and gain:

Least-squares optimization is then performed to calculate the values of t_elong and t_full which are consistent with the observed recovery, using the observed background level to estimate the fraction of unbleached MCP available. In practice, the value of (t_elong + t_full) can be calculated robustly.

To track cells from one frame to the next, we use the coordinates of the nuclear centroid as the spatial coordinates as the nucleus tends to more robustly segmented than the cell outline. For dense fields of view a greedy algorithm, which assigns a cell to the closest cell in the previous frame, performs very poorly. Instead, we minimize the global sum of squared displacements from one frame to the next, adding penalties for unmatched cells in either frame. Because of the flux of cells entering and leaving the field of view, we reduce the unmatched cost as the coordinate approaches the edge of the field of view. This modification prevents a cell leaving the field of view being matched with a nearby cell entering in the next frame. The optimal configuration of frame-to-frame matches is found using the Munkres algorithm (Munkres, 1957). To facilitate tracking of highly motile cells, where the frame-to-frame displacement can be comparable to the cell-cell separation, where necessary we include the mCherry and GFP fluorescence levels within the nucleus as additional coordinate dimensions. While differences in x- and y-coordinates are weighted quadratically, we use an alternative functional form for intensity differences, reflecting the increased measurement error for the fluorescence compared with the centroids. The function used has no cost for small differences in intensity, and becomes saturated at large intensity differences. This saturation value prevents a noisy intensity measurement, caused for example by poor segmentation, adversely influencing the matching.

A final consideration is that there may be several matching configurations with very similar global costs, and the configuration with the lowest absolute cost might not be the ground truth, due to high cell motility. To find matches which are robust and do not change in slightly sub-optimal configurations, we add an amount of randomness to each element of the cost matrix and recalculate the matches. This is repeated a large number of times, and only matches which appear in greater than a threshold fraction of configurations are recorded. Although this can lead to an increase in false negatives, trackable cells not being matched, it is an important step to ensure that false positives, incorrect matches giving rise to anomalous transcription dynamics, are minimized. Therefore, we set the confidence threshold to a large value such as 0.8 or 0.9; cell-frames which do not meet this level are flagged for efficient manual tracking using a custom designed user interface written in MATLAB (The Mathworks, Natick, MA).

The MS2 system was constructed in silico, beginning with probabilistic modelling of polymerase initiation, elongation and termination using a modification of the well-known Gillespie algorithm (Gillespie, 1977). In the first stage, the promoter is modelled as switching across a ‘ladder’ of discrete initiation rate states, with single, exponentially-distributed steps for switches in the initiation rate. Typically, transitions are allowed only between adjacent or next-adjacent states. Using the generated switches in initiation rate, polymerase dynamics are simulated using single rate limiting steps for polymerase binding and single nucleotide elongation steps. Optionally, additional steps for the transition from transcription initiation to elongation, and for termination, poly-adenylation and RNA cleavage, can be included. The time for a cleaved RNA to leave the transcription site is typically in the timescale of seconds, and can be subsumed into the termination time. Polymerases that move within a specified ‘blocking’ distance of a downstream polymerase cannot make an elongation step until the downstream polymerase (in the 3’ direction) has moved. Similarly, until a polymerase has moved more than the blocking distance from the binding site, the rate of an initiation event is set to zero.

For comparison with experiments, the binding of fluorescent MCP to the nascent RNA loops and measurement of the transcription site intensity are modelled. If the loop binding is assumed to be fast and irreversible (Maiuri et al., 2011), then the measurement error can be approximated by addition of random Gaussian noise. For simulation of photobleaching experiments, the concentration and diffusivity of the fluorophores is included. When the bleaching event is triggered, each bound MCP is bleached with a probability depending on the laser strength. Bleaching of the background MCP level is modelled with a Gaussian depletion profile centred on the transcription site to allow the time evolution of the fraction of fluorescent MCP at the site to be calculated analytically as a Gaussian solution of the 3D diffusion equation. For this purpose, the nuclear boundary and the finite number of fluorophores in the cell are neglected.

To calculate the recovery of the transcription spot, the total concentration of MCP is assumed to be constant over time, with the fraction of unbleached MCP recovering over time as described above. For simplicity it is assumed that the concentration of MCP (both bleached and unbleached) is not affected by RNA binding and unbinding events. We calculate the probabilities of an unbound loop becoming bound by bleached and unbleached fluorophores, and of a bound loop experiencing an unbinding event during one timestep, using estimated values for binding and unbinding rate constants and the MCP concentration and assuming first order reaction kinetics.

In our model of MS2 transcription, a polymerase must move away from the binding site by a threshold distance before a subsequent polymerase can attach. This effectively means that polymerase attachment is no longer a Poisson process. However, by considering the distribution of time interval between successive initiations, a correction factor can be applied to calculate the effective initiation rate.

In a Poisson process with mean rate λ the time interval between successive events has an exponential distribution with mean τ = 1/λ. To correct for the polymerase blocking distance, D_block, we modify the interval distribution to incorporate the probability that the binding site has been cleared by time t. For a polymerase taking single exponential steps, each of time 1/ν_el, where ν_el is the polymerase elongation rate, the time taken to take D_block steps is approximated by:

by combining random variables using a Gaussian approximation. This expression can be used to modify the interval distribution:

However to a very good approximation we can use a deterministic clearance time of D_block/v_el to calculate the correction to the initiation rate:

The average time interval between polymerase initiations is given by:

Therefore the effective initiation rate is given by:

In the case of a two-state model of promoter activity, in addition to Poisson initiations in the active state, the promoter switches stochastically to and from an inactive state with no initiation. Thus a spot can disappear from stochastic fluctuations or gene switching, and hence P(off) and the pulse duration need to be modified accordingly. To calculate the joint intensity distribution for a two-state model, we break the gene into regions, corresponding to the distance moved by a polymerase in one frame, and build the JID combinatorically from components where the gene was off or on for each segment (Appendix 3—figure 1).

Appendix 3—figure 1

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Hidden Markov models represent a discrete Bayesian approach to inferring the dynamics of an underlying (or hidden) state, which cannot be directly observed, from a sequence of observable emissions. In this respect, they are well-suited for studying transcription, where the transcription spot intensity fluctuates over time and is related probabilistically to the underlying configuration of nascent polymerases. The uses and methods of hidden Markov models have been described in detail previously by a large number of texts, and so we will focus on the specific changes or additions to the standard methods that are required for their application to the MS2 system.

We will denote the observable spot intensity as I, such that the emission state in frame k is I_k. The underlying gene state, represented by g_k, is quantized into discrete levels of promoter activity. From one frame to the next, the probability of the hidden state switching from state i to state j is encoded by the transition matrix, T_ij, where T_ij = P(g_k+1 = j|g_k = i). The observable emissions are related probabilistically to the hidden state using continuous distributions: E_i(x) = P(I_k = x|g_k = i). Often a single Gaussian distribution or a superposition of Gaussians is used as each emission distribution; for the MS2 system, the properties of the Gaussian can be calculated from the initiation rate using Equations 3.31 and 3.32.

The utility of hidden Markov models relies on the assumptions that the probability of observing the emission state I_k and the probability of switching to a state in frame (k + 1) depend only on the current state g_k. While introducing some restrictions, these assumptions allow the probability of observing a long sequence of observations to be calculated efficiently (or indeed within astronomical timescales). By defining the probability P(g_k+1  = j|I_k+1 = x, g_k = i) = T_ijE_j(x), it is possible to calculate the conditional probability of an observed sequence P(data|Θ) given the system parameters Θ = {T_ij, E_i(x)} in a single pass through the sequence, using the so-called forward algorithm. A similar definition of the conditional probabilities moving backwards through the sequence allows the likelihood of an individual underlying state P(g_i|data, Θ) to be calculated (the forward-backward algorithm). The suitability of a given model parameter set Θ is describing the data can be assessed using P(data|Θ), referred to as the likelihood, which is calculated using the forward algorithm. Therefore, an aim of using this approach is to find the parameter set with the maximum likelihood. This can be achieved using the Baum-Welch algorithm, an expectation maximisation (EM) method which iteratively converges towards a maximum of the likelihood in parameter space.

The clearest issue with a Hidden Markov approach in modelling the MS2 system of live transcription is the finite retention time of polymerases at the site of transcription. Not only do polymerases make different contributions to the intensity depending on their location on the gene, but the processivity of polymerases means that the intensity depends on the rate of initiation in the previous 2-3 min, rather than depending solely on the current promoter state, violating the Markov assumption. One approach to incorporate the history of the hidden state sequence is to add further hidden states encoding the promoter state in the previous n frames, as far back as required. For the so-called nth-order model, the hidden state configuration is {g_0,k,g_−1,k,g_−2,k …}, and the transition matrix is then modified so that g_0,k+1 is sampled from T_ij using g_0,k, and the higher order history states are deterministically passed from the previous frame: g_−1,k+1 = g_0,k, g_−2,k+1 = g_−1,k, etc.

For the MS2 system, it is not the promoter state per se that determines the spot intensity, but rather the number of polymerases initiated, therefore we construct a hidden state architecture encoding the current promoter state and the number of polymerases initiated in previous frames that are still attached and therefore contribute to the intensity: {g_k, p_0,k, p_−1,k, p_−2,k, p_−2,k}. The dimensionality of the hidden states required depends on the retention time of a polymerase’s RNA at the TS and the frame interval of imaging. The quantities p_m,k effectively represent the number of polymerases present on different segments of the gene. Transitions from one frame to the next are determined by a set of transition matrices, with the promoter state randomly switching as before, the number of polymerases in the first segment being sampled from a Poisson variable of the current promoter state, and subsequent polymerase segments passed processively from the previous segment in the previous frame:

If the polymerases are not processive, that is, there is a non-zero probability of premature termination and cleavage, Equation 4.3 may be modified appropriately to reflect the probabilistic passing of polymerases from one segment to the next.

The segments are related to the physical location on the gene, and so polymerases in different segments will contribute a different number of loops to the overall intensity. In this way, the emission state is constructed by summing the number of polymerases in each segment, multiplied by the number of loops per polymerase, c_−m, for that segment, combined with additive random measurement noise:

In practice the uncertainty arising from the variation in loop number across a segment is smaller than the measurement noise and can be subsumed into this.

The complexity of the hidden state architecture for the polymerase hidden Markov model (pHMM) means that the vast majority of transitions from one hidden state to another have zero probability. Therefore, we developed a custom versions of the forward-backward and Baum-Welch algorithms using sparse matrix methods. During Baum-Welch optimization, the emission states are held fixed rather than being recalculated in each iteration, to prevent overfitting of the data and convergence to non-physical values for the measurement noise σ_noise and segment loop number c_m. Therefore the measurement error and segment loop number must be known a priori and cannot be inferred using the expectation maximization (EM) of the Baum-Welch method. Furthermore, at each iteration we impose the constraint of Poisson activity within each promoter state, effectively calculating the best-fit value of the initiation rate of each hidden state, λ(g_k). As shown by the results of the forward-backward algorithm (main text Figure 1E) and Gibbs sampling (see below), this does not prevent non-Poisson behaviour being observed when a model of long-lived discrete states is not appropriate.

The inputs to our Baum-Welch algorithm are the system parameters, σ_noise, calculated from autocorrelation measurements, the coefficients c_m calculated from FRAP experiments, initial guesses for the initiation rate for each promoter state and the rates of transition between these states, and experimental data calibrated in terms of the number of loops at the transcription site. The algorithm produces estimates for the most likely values for the initiation and transition rates, and the likelihood P(data|Θ), which can be used to compare different hidden state architectures. The algorithm is run until the relative change in each of the parameters in Θ is smaller than a tolerance of 10⁻⁵, up to a maximum of 500 iterations.

The HMM framework was tested on simulated data of varying numbers of states, of known initiation and transition rates. As starting values, physically feasible values matching the spread in initiation rates and timescale of variability were supplied to the Baum-Welch algorithm, and the most likely system parameters were compared with the ground truth values of number of states, initiation rates and transition rates which are known as inputs to the simulations. The accuracy of the initial estimates influences the number of iterations required for convergence, but for physically realistic values did not affect the final convergence.

To estimate the underlying initiation state and number of new polymerases in each frame of each cell, a Gibbs sampling algorithm was designed, taking account of the fact that a change in a polymerase sample affects the likelihood of multiple frames. Briefly, cells were considered one at a time. For each cell, the hidden states are initialized at random by sampling the next state based on P(next|prev, intensity). A time point is chosen at random, and the hidden state for this time point is resampled using the probability of each hidden state P(H(k)|I(k),H(k-1),H(k+1)), which takes account of the spot intensity, the transition from the previous frame and the transition to the next frame. This resampling procedure was repeated many times before recording a sample of the hidden state sequence. The interval between taking samples of the hidden state sequence was defined such that each time point is resampled 15 times on average between each recorded sample. The distribution of polymerases initiated in each state calculated using Gibbs sampling were consistent with the data obtained using the forward-backward algorithm.

The utility of a particular model can be assessed by comparing simulations using the model to experiments. If the ground truth model has been identified then simulations of the model should fully reproduce the experimental data. Analysis of the hidden Markov fits for simple models with small numbers of discrete states indicates such models do not sufficiently account for the complexity of the data (Figure 1E). In order to distinguish between competing models we looked into standard methods of balancing goodness of fit and model complexity. Those most widely used are the Akaike Information Criterion (AIC) (Singer et al., 2014) and Bayesian Information Criterion (BIC), which both penalize the number of free parameters in the model, albeit with different weights (Aho et al., 2014). The AIC is a standard approach for finding the most accurate model when the real situation is likely to be highly complex (Burnham and Anderson, 2002). The BIC is most effective for simple systems and assumes the ground truth model is one of the candidate models to be tested. Since our data was more complex than any of the models we fit, the AIC was deemed the most appropriate for our study.

For a gene with a constant Poisson rate of mRNA production, the number of mRNA molecules in the cytoplasm is a balance between the rates of production and decay. When the mRNA lifetime is a simple exponential distribution (one rate-limiting step for degradation) with lifetime τ_RNA, the number of cytoplasmic mRNAs is a well-characterized Poisson distribution. The effect of transcriptional bursting is to broaden the distribution of cytoplasmic mRNA number, increasing the ratio of the variance to the mean. In this situation, the distribution of mRNA per cell, x, has been shown to be characterized by a negative binomial (NB) distribution (Paulsson et al., 2000).

The Poisson distribution is a limiting case of the negative binomial distribution as r → ∞. In the context of transcriptional bursting, the NB parameters r and p can be related to bursting characteristics, with the frequency of bursts given by rτ_RNA and the average number of mRNA produced per burst by (1 − p)/p.

The parameters r and p and their confidence intervals were calculated from simulated smFISH distributions using the MATLAB function nbinfit.

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

© 2016, Corrigan et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.