To facilitate studying the dynamic induction of gene expression, we stably integrated the LightOn expression system (Wang et al., 2012; Figure 1A) into HeLa cells using the PiggyBac transposon (Lu and Huang, 2014). The LightOn system consists of a synthetic transcriptional activator GAVPO and the mRuby3 (Bajar et al., 2016) reporter driven by a synthetic 5xUAS promoter consisting of five GAL4 binding elements and a TATA box (plasmid B1 in Figure 1—figure supplement 1). To assess the contribution of variation in GAVPO expression to the mRuby noise, the expression of GAVPO is driven by a noise reduction circuit of pCMV-tetO2-tetR-GFP-T2A-GAVPO (Nevozhay et al., 2013), where GFP provides an indirect measurement of GAVPO expression in individual cells (plasmid A1 in Figure 1—figure supplement 1). This approach also facilitates the regulation of mean GAVPO expression. The engineered HeLa cell clone is referred to as HeLa-AB1. In the following experiments, a doxycycline concentration of 1 μg/mL was chosen to ensure the cells were responsive to moderate light intensities. We measured expression of the fluorescent proteins by flow cytometry. At a constant light illumination (AM, illustrated in Figure 1B) with intensities between 0 and 100 μW/cm², the average level of mRuby expression in the population increased with increasing light intensity above 1 μW/cm² (blue line in Figure 1C). The gene expression noise was quantified by the coefficients of variation (CV). The cell population exhibited a bell-shaped noise profile across both the range of light intensities (blue line in Figure 1D) and the range of mean mRuby expression levels (blue line in Figure 1E). The highest noise level (CV ~2.5) was induced at intermediate light intensities (~10 μW/cm²) (Figure 1D). The single-cell expression distributions showed a bimodal pattern, and an increasing number of cells transited from the ‘low’ to the ‘high’ state when the light intensity increased from 10 to 45 μW/cm² (solid lines in Figure 1F). A similar phenomenon was observed in the dox-inducible (Tet-On) system (solid line in Figure 2—figure supplement 1B).

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Benzinger and Khammash proposed that in Saccharomyces cerevisiae, noise in light-induced gene expression is caused by noise transmission from transcription factor concentration to gene expression (Benzinger and Khammash, 2018). This noise can be modeled as a combination of a deterministic single-valued propagation function and an added phenomenological noise contribution, described as Equation 1 in the Materials and methods section. The detailed calculations are described in the Materials and methods section and in Appendix 2. For AM induction at 25 μW/cm² (Figure 1G), the GFP signal exhibited a unimodal distribution with a CV of 0.47, shown in the lower panel of Figure 1H. Assuming GAVPO exhibits noise similar to that seen for GFP, a hypothetical sigmoid GAVPO-mRuby response curve with high cooperativity (black dashed line in the main panel in Figure 1H) could lead to high variability and a bimodal distribution in mRuby (shown as the black dashed line in the right panel in Figure 1H). To assess the contributions of GAVPO noise to mRuby distribution dispersion, we split the flow cytometry data (Figure 1G) into 20 bins based on the GFP signal. We then plotted a dose–response curve of the mean mRuby versus mean GFP-GAVPO expression for these 20 subpopulations (red circles) and fit these data to an empirical function (red dashed line in the main plot in Figure 1H). Using this shallow curve as the deterministic propagation function, Benzinger and Khammash’s hypothesis would predict a unimodal distribution of mRuby with a relatively low CV value of 0.9, shown as the red dashed line in the right panel of Figure 1H. Therefore, the shape of the bimodal distribution and the high noise level (CV = 1.73) of actual mRuby expression in HeLa-AB1 cells (Figure 1—figure supplement 2B) cannot be explained by Benzinger and Khammash's hypothesis for Saccharomyces cerevisiae, at least if the assumption of similar noise for GFP and GAVPO in our system is correct. We further looked at the mRuby distributions of the 20 subpopulations, assessing both the experimental and the predicted data. The CVs for experimentally measured mRuby expressions exhibited a bell-shaped function and ranged between 1.0 and 1.7 (Figure 1—figure supplement 2A). The distributions for subpopulations transitioned from unimodal to bimodal and back to unimodal as mean GFP increased (Figure 1—figure supplement 2C). On the other hand, Benzinger and Khammash's model predictions with two single-valued propagation functions can only exhibit unimodal distributions (Figure 1—figure supplement 2D–I), which cannot explain our observations. This analysis suggested that, given a narrowly defined GFP-GAVPO level, the 5xUAS promoter could exhibit distinct ‘high’ and ‘low’ transcription states.

‘Low’ and ‘high’ noise levels might be related to epigenetic states at the 5xUAS promoter. The concept of epigenetic bistability was theorized by Sneppen et al. (Dodd et al., 2007; Sneppen and Ringrose, 2019). Still, experimental studies have mainly been concerned with long-term epigenetic memory (Hathaway et al., 2012; Singer et al., 2014) instead of fast gene expression fluctuations. Bintu et al., 2016 demonstrated that the kinetics of different histone modifiers vary from hours to days in CHO cells. We hypothesized that by modulating the binding of the transcriptional activator periodically, with time scales similar to those of histone modification dynamics, the transcription dynamics might be ‘resonated’ with histone modification dynamics. In dynamic system theory, this type of resonance might reduce stochasticity (Collins et al., 1995; Ding et al., 1994). To explore this possibility, we use a pulse-width modulation (PWM, illustrated in Figure 1B) regiment with light switching off and on (between 0 and 100 μW/cm²), and the mean light intensity controlled by the fraction of ‘on’ time (duty cycle) with a fixed period. The period of 400 min results in minimal changes in mean gene expression (purple line in Figure 1C), but produces a significant reduction in gene expression noise across the intermediate mean light intensities and intermediate mean gene expression levels (purple lines in Figure 1D–E). For the intermediate mean light intensities, distribution spreading is reduced by ‘eliminating’ the ‘low’ and ‘high’ states, resulting in the generation of an intermediate peak, which increases with mean light intensity (dashed lines in Figure 1F). When the period of PWM is shortened, the noise levels (Figure 1D-E) and distributions gradually revert towards the AM scenario (Figure 1—figure supplement 3A–F). Therefore, we were able to modulate gene expression level and noise independently by altering the mean light intensity and the period of PWM within the range sandwiched between the blue and purple lines in Figure 1D-E. A preliminary lookup table is computed by fitting expression level and distribution spreading (quantified as the ratio of mRuby intensities at 90th and 10th percentiles) to mean light intensity and period of PWM (Figure 1—figure supplement 3H,I).

To confirm the discovery, we stably transfected HeLa and F9 mouse embryonal carcinoma cells with a LightOn-GFP circuit (plasmids A2, B2 in Figure 1—figure supplement 1) to obtain HeLa-AB2 and F9-AB2 clones. We observed similar phenomena of high noise with AM and reduced noise with PWM with HeLa-AB2 (Figure 1—figure supplement 4A–C) and F9-AB2 (Figure 1—figure supplement 4D–F) cells, suggesting that the modulation of LightOn gene expression noise by PWM could be common in mammalian cells. Mean light intensity and period of PWM do not act as orthogonal parameters to control gene expression mean and noise. In F9 cells especially, expression level is often lower with PWM than with AM of the same mean intensity. To maintain the mean expression while adjusting noise, we needed to simultaneously adjust both mean light intensity and period of PWM (Figure 1—figure supplement 3H).

We further generated multiple clones of HeLa-A1B1B3 cells from Hela-AB1, each with similar GAVPO expression but different copy numbers of 5xUAS-mCardinal insertion sites (ranging in number from 3 to 11); these clones exhibited similar distribution dispersions with AM light (Figure 1—figure supplement 5A B). This observation suggested that bimodality is a robust phenomenon. To validate whether similar phenomena occur with chemical-induced p65 transactivator binding, we replaced the VP16 domain of rtTA on the classic tetON system with p65AD. We found that this system exhibited phenomena similar to those in LightOn systems (Figure 1—figure supplement 5D). We further reduced the tetO number from seven to one. However, this decreased the dynamic range of expression by ~200 to ~six fold, to levels too low to allow a clear definition of the existence of bimodality (Figure 1—figure supplement 5C). As singe-cell analysis of histone modifications is able to provide greater sensitivity than protein analysis, further developments in single-cell ChIP-seq assays could help to resolve this technical problem.

To characterize the dynamic noise reductions with PWM, we illuminated the HeLa-AB1 cell with AM at 100 μW/cm² or 25 μW/cm², or with PWM with a period of 400 min, and performed flow cytometry analysis over time. As shown in Figure 1I, the mean expression level of mRuby started to increase at 2–300 min and reached a plateau at around 2000 min for PWM and 2800 min for both AM intensities. The delayed onset of mRuby fluorescence is partially due to its maturation time of approximately 150 min. The noise levels remain relatively constant from 1000 min onwards (Figure 1J). Interestingly, for AM with 100 μW/cm², the noise level spiked initially before settling to a low level. These observations suggest that PWM with a long period is needed to maintain a low level of noise.

Previous studies of histone modification kinetics in CHO cells suggested that histone deacetylase 4 (HDAC4) could be involved in our observed reduction in gene expression noise , as its rate constants are closer to the 400 min of PWM than those of other histone modifying enzyme (Bintu et al., 2016). To identify histone modifications that contributed to the noise in gene expression levels, we tested the effects of LMK-235, a selective inhibitor for HDAC4/5 (Marek et al., 2013). We also tested Vorinostat, a broad-spectrum inhibitor of HDACs (Finnin et al., 1999), and PF-06726304, a selective inhibitor of a Polycomb Repressive Complex 2 component and of H3K27me3 (Kung et al., 2016). We added these inhibitors to the HeLa-AB1 cell and performed light inductions for two days with AM at 25 μW/cm² before flow cytometry analysis. Only LMK-235 significantly increased the expression of mRuby (Figure 2A–B). To compare levels of mRuby expression at the same GAVPO levels, we calculated the population average mRuby versus GFP-GAVPO response curves for cells treated with these inhibitors (Figure 2C). The mRuby versus GFP-GAVPO curve for LMK-235 treated cells (red) is significantly steeper than that of the control cells (blue). Furthermore, this curve also demonstrated that LMK-235 explicitly increases the mRuby expression of cells with the same GAVPO level. When comparing the effects on AM and PWM with a 400 min period, we found that LMK-235 increased mRuby expression but had only a minor impact on noise reduction (Figure 2—figure supplement 2A–F). LMK-235 also increased gene expression levels for F9-AB2 cells and HeLa-Tet-on cells (Figure 2—figure supplement 1A–B).

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The p65AD of the transcriptional activator GAVPO is reported to recruit CBP/p300. The dual-functional proteins CBP/p300 serve both as coactivators to recruit components of the mediator and transcriptional preinitiation complex (PIC) and as a histone acetyltransferase (HAT) (Black et al., 2006; Gerritsen et al., 1997; Ogryzko et al., 1996). A recent CBP/p300 acetylome study revealed their rapid acetylation kinetics at many loci in the mouse genome (Weinert et al., 2018). Putting this information together, we propose a mechanistic model for transcription-histone acetylation coupling of the light-inducible expression system (Figure 2D) in which: (a) chromatin stochastically switches between silent and active states; (b) light induces GAVPO dimerization and enable its binding to UAS elements of the 5xUAS promoter in the active chromatin; (c) bound GAVPO recruits CBP/p300; (d) CBP/p300 acetylates nearby histones, and recruits PIC and mediator complex to facilitate transcription initiation; and (e) a locus with high histone acetylation would tend to stay in the active state and have an increased chance of binding of GAVPO dimer. In summary, the DNA-GAVPO-HAT complex increases the probability of chromatin accessibility and GAVPO binding to DNA by increasing histone acetylation.

This hypothetical positive feedback loop could generate bistability, probably with intermediate GAVPO dimer concentration (or light intensity) (between the dashed cyan lines in Figure 2E). HDACs deacetylate histones and contribute to setting the boundaries between bistability and monostability. For some cells, the local promoter-GAVPO dimer interaction is sufficiently high as to initiate the positive feedback loop and to elevate local histone acetylation and induce high gene expression (red line). Other cells fail to start the positive feedback loop, which leads to low histone acetylation and transcription (blue line). Under this condition, the isogenic cells in a homogenous environment would exhibit large noise due to the history-dependent occupation of each state and stochastic switches between the two states (Isaacs et al., 2003). This could explain the wide distributions of mRuby expression with narrow GFP-GAVPO inputs (Figure 1—figure supplement 2C). The mechanism for PWM-induced noise reduction could be that the cell is switching between a high unimodal expression state (red) at 100 μW/cm² and a low unimodal expression state (blue) in the dark. In each state, the noise is low, and cells pass through the bimodal region rather quickly. For PWM with a shorter period, a cell doesn't have enough time to settle at either of two unimodal states, which leads to high noise.

We assessed the existence of high and low mRuby states by tracking the single-cell mRuby expression dynamics of HeLa-AB1 cells in 25 μW/cm² blue light induction using live-cell fluorescent microscopy. As both GFP and mRuby were tagged with three repeats of the SV40 nuclear localization signal, the GFP-GAVPO channel is used to segment and track cell nuclei over time. Single-cell expressions of GFP-GVPO and mRuby were integrated over the segmented nucleus. A representative time-lapse image series of the overlayed GFP and mRuby signals is shown in Figure 2F. The dashed and dotted circles in Figure 2F indicate that two cells remained in a low mRuby state through cell divisions, whereas two other cells had an elevated mRuby state which was maintained through cell division. The solid circle indicates that two offspring cells of one cell had different mRuby states after cell division. The mRuby expression states were designated as low (<0.1) or high (>0.6) after around 24 hr. The single-cell mRuby trajectories for 100 cells each from low (blue) and high states (red), normalized by cell-cycle stage, are shown in Figure 2G. The normalization process is illustrated in Figure 2—figure supplement 3. Most of the cells at low mRuby expression state at 24 hr remain low through 40 hr, with the mRuby levels of a few cells being elevated toward the high state. Most of the cells at high mRuby state at 24 hr were elevated before 12 hr and remained in the high state through 40 hr. A small fraction of these cells show a drop in mRuby expression, moving them towards the low state. The GFP-GAVPO expression levels were within a factor of two and indistinguishable between the low-state and high-state cells (Figure 2H). These data indicate that the cells mostly maintain their mRuby expression states, with only a small fraction of them stochastically switching mRuby expression states. These single-cell dynamics are consistent with the bistability hypothesis, which assumes a low probability of random transitions. To validate bistability, longer live-cell imaging is required to observe sufficient occurrences of transition between the two states, partially because of the long half-life of the mRuby protein. At present, it is a challenge to maintain longer cell tracking without interruptions for periodic replacements of the culture medium.

To further assess the possibility of bistability, we examined its main characteristic, history-dependence. A cell in a bistable regime remains in the high or low state in a deterministic system, which is dependent only on the previous state of that cell. Stochastic fluctuations induce random transitions between the two states. When compared to ‘naïve’ cells that have not been exposed to blue light before the experiment, ‘primed’ cells that were exposed to 100 μW/cm² blue light immediately before the experiment would exhibit tilted distribution toward ‘high’ states under intermediate AM light induction. However, this priming would produce mRuby expression that partially remains after 48 hr. We constructed another plasmid B3 (Figure 1—figure supplement 1) containing destabilized mCardinal (mCardinal-PEST, shorter half-life) (Li et al., 1998) under the control of the 5xUAS promoter to increase its temporal response, and stably transfected this plasmid into HeLa-AB1 cell to generate HeLa-A1B1B4 clones. When compared to ‘naïve’ cells, the cells primed with 8 hr of 100 μW/cm² blue light exhibited visible but minor distribution shifts toward the high state after 48 hr of further AM blue light inductions at 10 to 25 μW/cm² before mRuby (Figure 2I) and mCardinal (Figure 2J) measurements. The relatively short time scale of CBP/p300 H3K27 acetylation increases stochastic transitions between the postulated ‘low’ and ‘high’ states and prevents observation of a more significant shift toward the high state as a result of priming.

We propose that a high level of noise in gene expression is the consequence of epigenetic bimodality induced by the interplay among promoter-bound GAVPO, HATs, and histone acetylation. Consequently, a reduction in the occurrence of bimodality could be realized by weakening CBP/p300 HAT activities and breaking the postulated positive feedback. We validated this prediction by utilizing A-485, a highly selective and potent inhibitor of the HAT activities of CBP/p300 (Lasko et al., 2017; Weinert et al., 2018), to disrupt the dynamic regulation of these proteins without changing their transcriptional initiation function. When A-485 was added to HeLa-AB1 cells under AM light induction for 48 hr, the CV values for mRuby expression were significantly reduced, especially at intermediate light intensities and mean mRuby expression levels (Figure 3B–C). It seems that A-485 eliminates the ‘low’ and ‘high’ expression states and generates an intermediate state that has a narrower dispersion of mRuby expression (Figure 3E). This experiment also indirectly confirms that CBP/p300 are the prominent coactivators involved in transcription and histone modifications. The mean levels of mRuby expression were also reduced (Figure 3A), possible because HDACs now tilt the balance towards lower histone acetylation, leading to lower chromatin accessibility and less transcription. To validate this explanation, we added both A-485 and LMK-235 to cells under AM light induction for 48 hr. As shown in Figure 3A-C, and F, the addition of the HDAC4/5 inhibitor increased the expression level of these cells back to those seen in the light induction control, while the reduction in noise was retained. Hence, simultaneous inhibition of CBP/p300 HAT activity and HDAC4/5 could be used to reduce noise without changing mean gene expression. However, their effect on either synthetic or endogenous gene expression has never been examined.

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To exclude the global effects of A485 and LMK-235, we generated a GAVPO mutant in which the direct binding of the p65 activation domain to CBP/p300 was greatly reduced by double point mutations corresponding to L449A/F473A in the TA2 section of the activation domain of mouse p65 (Mukherjee et al., 2013). We then constructed a plasmid A3 (PB5-CAG-GAVPO-mutant-PB3), and generated a HeLa-A3B2 cell line. With an intermediate level of AM light induction for 48 hr, the disruption of the direct interaction between GAVPO and CBP/p300 also eliminated the ‘low’ and ‘high’ expression states. Instead, this treatment resulted in an intermediate state with narrow GFP expression dispersion (Figure 3G). Further inhibition of HDAC4/5 using LMK-235 increases GFP expression and its sensitivity to light intensity (Figure 3H). These two experiments suggested that p65-CBP/p300 interaction and CBP/p300 activity in the proximity of the 5xUAS promoter are necessary for transcriptional bimodality. An additional mutation (F542A) in the TA1 section of the p65 activation domain disrupts the interaction of this domain with the Kix domain of CBP (Lecoq et al., 2017). However, this mutation also disrupts interactions with other critical transcriptional factor complexes, such as TFIIH, which might explain our inability to isolate HeLa cells with the p65AD mutants L449A/F473A/F542A.

To assess the existence of epigenetic bimodality directly, we exposed HeLa-AB1 cells to AM light of 25 μW/cm²for one day to induce large expression dispersion. We then sorted the mRuby-low and mRuby-high populations (Figure 3I). We used the two cell populations and dark control cells to perform ChIP-seq assays for H3K27ac and mapped the reads to the whole genome sequence assembly of this HeLa-AB1 clone containing LightOn expression cassettes at nine loci (Figure 3—figure supplement 1A–B). The reads from these loci were combined using the TSS as the reference (Figure 3J and Figure 3—figure supplement 1A). We observe a H3K27ac peak spanning from the 5xUAS promoter (−200 bp) to the N-terminus of the mRuby gene. The signals for the mRuby-low and the dark control populations are essentially the same, whereas the signal for the mRuby-high population is about four times higher. These signals demonstrate that the mRuby-high population from intermediate light induction is in a more epigenetically active state than the mRuby-low and dark control populations. We also performed an ATAC-seq assay on the same cell populations. As shown in Figure 3K, chromatin accessibility is higher in close vicinity to the TSS (approximately from −200 to +200 bp). The hierarchy of chromatin accessibility is mRuby-high, mRuby-low, and dark control, with less separation between the high and low populations than H3K27ac and a clear difference between mRuby-low and dark control. Hence H3K27ac is correlated with the LightOn gene expression state, consistent with a critical role of CBP/p300.

To observe the effect of PWM induction on chromatin accessibility, we performed a single-cell ATAC-seq analysis of the dynamic process of PWM-induced noise reduction. Specifically, we set up a PWM regimen with 100 μW/cm² maximum light, 600 min period, and 25% duty cycle (150 min ‘on’ time). We collected cells at six timepoints corresponding to different light cycle stages (as illustrated in Figure 4A), and sorted 384 tagged nuclei of each population for sequencing and analysis. The mean number of reads spanning −200 to +200 bp exhibited a gradual increase over each light induction cycle, (Figure 4B). The single-cell reads for the six populations are plotted as heatmap images in Figure 4C. As the opening and closing of chromatin is a dynamic process, we only observed approximately 30% of cells with reads at most. As expected, the fraction of cells with open chromatin was lowest for the dark control. Each cycle of ‘on’ light (150, 750, 1350 min) leads to more cells with detected reads. At the end of each ‘on-off’ cycle (600, 1200 min), the fraction of cells with detected reads did not go all the way down to the level of the dark control. To obtain quantitative measurements, we took the mean reads over 400 bp for each cell and calculated the means and CVs for all populations, which are plotted against time in Figure 4D–E. The mean number of reads started at around 0.7, increased after each ‘on’ phase, and decreased to a lesser extent after each 'off' phase. The CV for the mean reads was initially at a high level of 3.8 (high noise of chromatin openness) but fell drastically to 2.6 following just 150 min of illumination. The CV increased after each further ‘off’ phase and decreased after each additional ‘on’ phase to a lesser extent. These data indicated that the basal (dark) chromatin openness is very heterogeneous and that exposure of cells to a short period (150 min) of 100 μW/cm² light initiated transcription and reduced the heterogeneity of chromatin openness. Further increases in the chromatin openness and reduction in heterogeneity occurred after each consequent ‘on’ phase. The cells also maintained part of their epigenetic ‘memory’ during each 'off' period. Therefore, PWM induced an incremental reduction in the variability and an increase in the mean level of chromatin accessibility in each cycle of ‘on’ and ‘off’ phases. The periodic fluctuations in chromatin accessibility did not directly translate into single-cell protein mRuby dynamics, which did not exhibit such periodic fluctuations (Figure 5—figure supplement 1). The protein dynamics data indicated that the mean expression level reached a plateau at around 2000 min for PWM (Figure 1l). We postulate that chromatin openness will also eventually reach a plateau for both the ‘on’ and ‘off’ phases.

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To connect the observed PWM-induced reduction in noise between the epigenetic and protein levels, we measured nuclear mRNA dynamics for single cells under AM and PWM light induction scenarios utilizing the MS2-MCP system (Tutucci et al., 2018). HeLa-AB2 cells, which exhibit PWM-induced noise reduction at 400 and 200 min period (Figure 1—figure supplement 4B–C), were further stably transfected with the LightOn mRNA imaging circuit (plasmid C2, shown in Figure 1—figure supplement 1) to obtain HeLa-ABC2 cells. We used live-cell spinning disk confocal microscopy to image the single mRNAs. When multiple MCP-tdTomato molecules are bound to an mRNA containing 24 MS2 hairpins, they form a punctum with a diameter of around 500 nm above the background fluorescence (illustrated as single mRNA puncta in the two nuclei in Figure 5A). We plotted the single-cell mRNA counts for all the tracked cells against time as heatmap images for AM (100 μW/cm²), PWM (100 μW/cm², 200 min, 25% duty cycle), and AM (25 μW/cm²), as shown in Figure 5B–D, respectively. For quantitative analysis, the means (Figure 5E) and CVs (Figure 5F) for single-nuclear mRNA numbers were plotted against time. For the AM with 100 μW/cm² (blue circles and line), the cells exhibited high variation at the beginning of the light-induction period that reduced over time, while the mean nuclear mRNA counts gradually increased and seemed to reach a high plateau after 1000 min (Figure 5B, E–F). For the AM with 25 μW/cm² (yellow circles and line), the cells exhibited more variation in mRNA numbers over time and a mean that approached a lower plateau (Figure 5D–F). These observations are consistent with the time course of flow cytometry analysis of mRuby protein (Figure 1I-J). For the PWM treatment, there is a clear pulsatile nuclear mRNA count with a period of 200 min, but the mean mRNA count appeared with a time delay of about 40 min after each ‘on’ time point, and as a population, these cells maintained a high mRNA count for the next 100 min or so before dropping down (Figure 5C,E). The variation in mRNA count was maintained at a more or less constant level that was intermediate between the levels for 25 and 100 μW/cm² AM light inductions (Figure 5F). There are also fewer cells without nuclear mRNA with PWM. Although sporadic nuclear mRNA counts might overshadow the periodicity of mRNA accumulation in the PWM case (Figure 5C), we observed oscillatory nuclear mRNA dynamics within the majority of cells by averaging over subpopulations of these cells with different mean RNA (Figure 5G). A period of 200 min, rather than 400 min, was chosen for PWM in the mRNA imaging experiment so that the periodicity of noisy nuclear mRNA dynamics could be recognized with more temporal repeats while noticeable noise reduction was still exhibited.

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There are two noticeable phenomena concerning nuclear mRNA counts during PWM light induction. The heterogeneity of mRNA count remains steady over time and is consistently lower than that seen with AM of 25 μW/cm², no matter whether the cells were in the light ‘on’ or ‘off’ phases (Figure 5F). This suggests that the cells were likely to be alternating between high and low epigenetic states and not trapped in the bistable region (Figure 2E). The mean count exhibited a periodic oscillation. We did not observe periodic oscillation for the single-cell mRuby protein dynamics under PWM with 400 min duty cycle, shown in Figure 5—figure supplement 1, probably due in part to the longer half-life of protein.

Technical challenges that limited this type of long-term statistical analysis of single-cell mRNA dynamics remain. When compared to spinning disk confocal microscopy, light-sheet microscopy could increase the signal-to-noise ratio of these images, improve the fidelity of single mRNA detection, and ensure that an adequate number of repeating images are captured before significant photobleaching. Cell motility prevents the tracking of a sufficient number of cells over a long period, as most of the cells moved out of the imaging field at some time points. Further development of light-sheet microscopy technology is needed to provide the capability to image a large number of large optical fields while maintaining sufficient optical and temporal resolutions over a long period.

CBP/p300 is prominently implicated in regulating enhancer-dependent cell-type-specific gene expression (Ogryzko et al., 1996; Weinert et al., 2018). Compiled analysis of vast public ChIP-seq data from both human and mouse cell experiments suggests that there are more than 10,000 potential CBP/p300 target genes (Oki et al., 2018) (https://chip-atlas.org). Most of the target genes have complex mechanisms for gene regulation that overshadow the effects of the potential positive feedback loop formed by the transcriptional and histone acetylation functions of CBP/p300. Nevertheless, dual-functional CBP/p300 could increase heterogeneity in the expression of a subset of endogenous genes. To test whether CBP/p300 could upregulate heterogeneity in endogenous gene expression, we used A-485 and LMK-235 to inhibit CBP/p300 and HDAC4/5 and to disrupt the potential for positive feedback. In mES cells, the master transcriptional factors Nanog, Oct4 (Pou5f1), and Sox2 recruit CBP/p300 to facilitate ES cell-specific gene expression (Chen et al., 2008). mES cells that are cultured with LIF (but not with 2i cocktail) exhibit subpopulations with high and low expression levels of Rex1 (Zfp42) and Nanog, and with dynamic transitions between the two states (Singer et al., 2014; Toyooka et al., 2008). To test whether CBP/p300 could contribute to bistability in mES cells, we constructed a Rex1-GFP/Oct4-mCherry knock-in cell line of mES D3 cells. This line exhibited bimodal Rex1-GFP expression, with 97% high and 3% low subpopulations, and unimodal Oct4-mCherry expression (Figure 6A). After treatment with LMK-235 and A-485 for two days, the expression of Rex1-GFP converged to an expression pattern centered between the high and low states (Figure 6B), similar to 5xUAS-mRuby expression in HeLa-AB1 cells treated with similar concentration of these inhibitors and AM light induction (Figure 3D and F). Although the global inhibition of CBP/p300 may also indirectly affect Rex1 expression, these results suggest that CBP/p300 may contribute to gene regulation in mES cells.

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We further examined the effects of inhibitors of CBP/p300 and HDAC4/5 on global gene expression heterogeneity in HeLa-AB1 and F9-AB2 cells by performing scRNA-seq analysis. HeLa-AB1 cells were treated with either LMK-235 or A-485 and blue light for two days (A + L) or with light only (control). The library construction, sequencing, and data acquisition processes are described in detail in the Materials and methods section. As shown in Figure 6C, when the CV was plotted versus mean expression, most of the genes concentrated on a narrow band which could be fitted by a power function with an exponent of −0.48 (solid magenta line), similar to the predicted exponent of −0.5 of a Poisson distribution. Outlier genes that significantly deviated from this band (blue dots above the dashed red line) were further selected to assess the effects of A-485 and LMK-235. For the filtered gene set with lower CV values (less than two in control samples), there is a slight but statistically significant increase in mean CV from 1.30 to 1.45 (Figure 6D (magenta Pentastar) and E). On the other hand, for the filtered gene set with higher CV, there are many more genes for which large reductions in CV result from the inhibition of both CBP/p300 and HDAC4/5 (shown in Figure 6D (red triangles)). For these genes, the mean CV exhibits a large (3.49 to 2.55) and significant reduction in cells treated with the inhibitors. Within this set, genes that are targeted by CBP/p300 or by p65 show similar mean CV reduction (Figure 6E). There is much less deviation between the two biological repeats of control samples, especially for the high CV gene sets (Figure 6D and F). The reduction in CV resulting from inhibition of both CBP/p300 and HDAC4/5 can be visualized with narrower plots and computed from unique molecular identifier (UMI) counts, without or with the SAVER denoising process (Huang et al., 2018; Luecken and Theis, 2019; Figure 6G). Similar phenomena were observed with F9-AB2 cells (Figure 6—figure supplement 1). These results provide the first indirect evidence that CBP/p300 positively contributes to noise in global gene expression, especially for genes in human and mouse cells that show highly heterogenous expression. It could also contribute to the heterogeneous expression states in embryonic stem cells.

Every version of the LightOn system is constructed with two plasmids: (1) an activator plasmid A coding for promoter-driven expression of the synthetic light-sensing transcriptional activator GAVPO, a fusion protein of the GAL4 DNA-binding domain, an engineered light-sensing VVD domain and the p65AD activation domain; and (2) a response plasmid B containing the LightOn promoter (5xUAS)-driven reporter genes encoding fluorescent proteins (Figure 1A and Figure 1—figure supplement 1). The plasmids containing the 5xUAS promoter and GAVPO (Wang et al., 2012) were gifts from Prof. Yi Yang. The plasmid for the tetR negative feedback circuit (similar to that described by Nevozhay et al., 2013) containing pCMV-tetO2-tetR-GFP-nuc was a gift from Prof. Jiandong Huang. The plasmids containing mRuby3 and mCardinal were gifts from Prof. Jun Chu. For the HeLa cell line used in flow cytometry, live-cell imaging, and epigenetic assays, the transcriptional activator expression plasmid A1 was constructed by subcloning pCMV-tetO2-tetR-GFP-nuc with GAVPO into a plasmid with a PiggyBac transposon backbone (Lu and Huang, 2014) to form PB5-pCMV-tetO2-tetR-GFP-nuc-2A-GAVPO-2A-Zeo^r-βGpA-PB3. The LightOn plasmid B1 was constructed by assembling 5xUAS, mRuby3, and subcloning it into a plasmid with a PiggyBac transposon backbone (Lu and Huang, 2014) to form PB5-5xUAS-mRuby-nuc-2A-Bla^r-βGpA-PB3. For F9 and HeLa cell transformation, the transcriptional activator plasmid A2 was constructed by subcloning GAVPO into another PiggyBac backbone to form PB5-pCAG- GAVPO-2A-Zeo^r-βGpA-PB3. The LightOn plasmid B2 (or B3) was constructed by subcloning 5xUAS, GFP-nuc (or mCardinal-nuc) into the PiggyBac backbone plasmid to form PB5-5xUAS-GFP-nuc-2A-Bla^r-βGpA-PB3 (or PB5-5xUAS-mCardinal-nuc-2A-Bla^r-βGpA-PB3).

To reduce the half-life of the reporter fluorescent protein and to increase its temporal response, we fused amino acids 422–461 of the degradation domain of mouse ornithine decarboxylase (MODC) (a PEST sequence) (Li et al., 1998) to the C-terminal end of mCardinal to form plasmid B4 (PB5-5xUAS-mCardinal-PEST-2A-Bla^r-βGpA-PB3). To abrogate the interaction between the transactivator GAVPO and CBP/p300, we generated L449A/F473A double mutations in the activation domain of p65 on plasmid A2 to obtain plasmid A3 (Mukherjee et al., 2013). For the HeLa cell line used in mRNA imaging, in addition to transcriptional activator plasmid A2 and LightOn plasmid B2, an additional plasmid, C2, was constructed by subcloning 5xUAS-tagBFP-nuc with 24 MS2 hairpin repeats (synthesized by Wuxi Qinglan Biotech), the SV40 promoter, and MCP-tdTomato-nuc (synthesized by Wuxi Qinglan Biotech) into a PiggyBac backbone plasmid to form PB5-pSV40- MCP-tdTomato-nuc-2A-Puro^r -SV40pA-HS4-5xUAS-BFP-nuc-24xMS2-βGpA-PB3. In all of these constructs, 2A stands for a 2A ‘self-cleaving’ peptide T2A, derived from Thosea asigna virus 2A, which enables the same levels of transcription and translation for the two proteins. The nuc tag consists of three tandem repeats of the SV40 nuclear localization sequence, which facilitate the localization of the protein in the nucleus. The UAS is the specific GAL4-binding element. Puro^r, Zeo^r, and Bla^r are resistance genes for the antibiotics puromycin, zeocin, and blasticidin, respectively. HS4 is the Chicken hypersensitive site 4 (cHS4) insulator. The components of the MS2-MCP system (MS2 RNA loop and MS2 coat protein) were adapted from Tutucci et al., 2018. To modify the transactivator rtTA in the classic tetON system, we replaced its VP16 domain with p65AD to form plasmid A4 (PB5-pCAG-rtetR-p65AD-2A-Zeo^r-βGpA-PB3). A response plasmid B5 for the tetON system was constructed using the 5xUAS promoter in plasmid B2 with TRE-3G promoter containing seven tetO sites. The first six tetO sites were further removed to construct plasmid B6.

To generate the knock-in Rex1-GFP and Oct4-mCherry mES cell line, we constructed two donor plasmids D1 and D2. For construction of the Rex1-GFP knock-in donor plasmid D1, a T2A-GFP-nuc cassette was in-frame fused with a 5' 1000 bp homologous arm, including the C-terminus of the Rex1 gene, and a 1000 bp 3' arm including the 3'UTR sequence of the Rex1 (Zfp42) gene. For the Oct4-mCherry knock-in donor plasmid D2, an ATG-mCherry-linker cassette was in-frame fused with an 800 bp 3' arm, including the N-terminus of the Oct4 coding sequence, and an 800 bp 5' arm, including the 5'UTR sequence of Oct4. The gRNA sequences for Rex1 and Oct4, TCCTAACCCACGCAAAGGCC and CAGGTGTCCAGCCATGGGGA, respectively, were cloned into a pX330 gRNA-Cas9 plasmid (Zhang Feng lab) to obtain Oct4-gRNA-Cas9 and Rex1-gRNA-Cas9 plasmids. All other components, unless otherwise specified, were adapted from Lu and Huang, 2014. All of the plasmid constructions were confirmed by Sanger sequencing. The sequences of the plasmids and oligos used for the construction of plasmids will be provided upon request.

To generate HeLa cells that are stably transfected with GFP-GAVPO and LightOn-mRuby, we cotransfected HeLa cells with 400 ng plasmid A1, 400 ng plasmid B1, and 200 ng PGK-transposase plasmids using Lipofectamine 3000 (Catalog no. L3000-015; Invitrogen) following the manufacturer's protocol. The cells were transfected in one well of a 24-well plate and selected with 100 μg/mL zeocin (Catalog no. R25001; Gibco) for six days, starting one day after transfection. The medium was replaced with DMEM supplemented with 10% FBS and 1 μg/mL doxycycline (Catalog no. D9891; Sigma), and the cells were illuminated with blue light (100 μW/cm²) for 48 hr. Single cells expressing both EGFP and mRuby were sorted into 96-well plates using BD FACSAria SORP. Each clone was verified using flow cytometry two weeks later. One clone, named HeLa-AB1, was chosen for further analysis. Similarly, F9 and HeLa cells were stably transfected with 400 ng plasmid A2, 400 ng plasmid B2, and 200 ng PGK-transposase plasmids. The cells were illuminated with blue light for two days before sorting single cells that expressed GFP. Single clones of F9 cells were analyzed ten days after sorting. One F9 clone, named F9-AB2, was chosen for further analysis. Another HeLa clone, named HeLa-AB2, was further transfected with the mRNA imaging plasmid C2 and PGK-transposase plasmid, and was selected by culturing with 1.5 μg/mL puromycin (Catalog no. A1113803; Gibco) for three days. Cells were illuminated for two days with blue light and sorted by positive GFP, tdTomato, and BFP expression. One clone, named HeLa-ABC2, was chosen for mRNA imaging. To generate HeLa cells that were stably transfected with pTet-GFP, we transfected HeLa cells with a pBX-123 plasmid (Lu and Huang, 2014) and PGK-transposase plasmid, and selected the stable clones with 1.5 μg/mL puromycin (Catalog no. A1113803; Gibco) for three days. One clone, named HeLa-Tet-On, was chosen for further analysis. HeLa cells were stably transfected with 400 ng plasmid A3, 400 ng plasmid B2, and 200 ng PGK-transposase plasmid, selected with 100 μg/mL zeocin, and illuminated with blue light (100 μW/cm²) for six days. GFP-positive single cells were then sorted, expanded, and genotyped to obtain the HeLa-A3B2 cell line. HeLa cells were stably transfected with 400 ng plasmid A4, 400 ng plasmid B5 (or B6), and 200 ng PGK-transposase plasmids, selected with 100 μg/mL zeocin, induced with 1000 ng/mL doxycycline for six days. GFP-positive single cells were sorted, expanded, and genotyped to obtain the HeLa-A4B5 and HeLa-A4B6 cell lines. In addition, HeLa-AB1 cells were further transfected with 400 ng plasmid B4 and 200 ng PGK-transposase plasmid, and illuminated with blue light (100 μW/cm²) for six days. Single cells with mCardinal, GFP, and mRuby signals were then sorted expanded, and genotyped to obtain the HeLa-A1B1B4 cell line.

In addition, HeLa-AB1 cells were transfected with 200 ng PGK-transposase plasmid and a reduced amount of plasmid B3 (40 ng) to obtain 5xUAS-mCardinal clones with fewer integration sites, thereby generating HeLa-A1B1B3 clones. The integration sites were quantified by qPCR of genomic DNA with primers for mCardinal 5'-cttcatcaaccacacccag-3' and 5'- ccatgtcgcatctgccttc-3', and TB Green Premix Ex Taq II (Catalog No. RR820A; Takara). Genomic DNA was purified using the PureLink Genomic DNA Mini Kit (K1820-01, Invitrogen), and 100 ng per reaction was used. A standard curve was generated by serial dilution of plasmid B3 using the same concentration of wild-type HeLa genomic DNA. The total genome size of HeLa cells was estimated to be approximately 1.3-fold that of the normal human genome (Adey et al., 2013).

To generate knock-in Rex1-GFP and Oct4-mCherry mES cell lines, mES cell line D3 (a gift from Prof. Jianbo Yue) was cultured on a 0.1% gelatin-coated 24-well plate with mESC medium (DMEM containing 15% ES FBS, 1000 U/ml LIF, 1X GlutaMax, 1X non-essential amino acid (NEAA) and 0.1 mM 2-mercaptoethanol) until it reached 50% confluence. D3 cells were transfected with 500 ng Oct4-mCherry donor plasmid (D1) and 500 ng Oct4-gRNA-Cas9 plasmids with Lipofectamine 3000 and diluted one day later into a 6-well plate, in which they were maintained for four days in the mESC medium containing 2i cocktail (3 μM CHIR99021 and 1 μM PD0325901). Single cells expressing mCherry were sorted, expanded, and genotyped by PCR and sequencing. One validated Oct4-mCherry clone was used to repeat the knock-in experiments with the Rex1-GFP donor plasmid (D2) and the Rex-gRNA-Cas9 plasmid.

HeLa-AB1 cells and their derivatives were plated in an ibidi 24-well μ-plate at a density of 3 × 10⁴ cells/well. The medium was supplemented with 1 μg/mL doxycycline (dox) on day −1. On day 0, the dox-containing medium was replenished, and the μ-plate was mounted on a customized 24-channel illumination apparatus placed in a Thermo Heracell 150i CO2 incubator. A water-cooling pump was turned on to ensure that cells were cultured at 37°C. A CSV file containing the designed illumination intensities and dynamics for the 24 wells was loaded into the custom Python code to control the apparatus for 48 hr unless otherwise specified. The procedure was similar for F9-AB2, HeLa-AB2 cells, and other light-inducible cell lines, except that doxycycline was not added.

We recorded flow cytometry data for HeLa-AB1 cells, with AM (period = 0) and PWM with different periods (see Figure 1—figure supplement 3A–F), calculated the mean mRuby emission to mean light intensity curve, and distribution spreading (defined as the ratio of mRuby emission at the 90th and 10th percentiles) to mean light intensity. We then fitted the data using a cubic smoothing spline function (csaps) with MATLAB 2018b to generate response-surface plots for the common logarithms of mean value and distribution spreading against period and mean light intensity (Figure 1—figure supplement 3H–I). We chose the distribution spreading ratio as read-out instead of the coefficient of variation, because this ratio provides a more intuitive picture of how diverse the gene expressions were and is not affected by instrumentation settings.

HeLa-AB1 cells were plated in an ibidi μ-plate 24-well plate at a density of 3 × 10⁴ cells/well. The medium was supplemented with 1 μg/mL doxycycline on day −1. On day 0, the medium was supplemented with 0.333 μM (unless otherwise specified) LMK-235 (Catalog no. S7569; SelleckChem), 0.333 μM Vorinostat (Catalog no. S1047; SelleckChem), 0.333 μM PF-06726304 (Catalog no. S8494; SelleckChem), or 1 μM of A-485 (Catalog no. S8740; SelleckChem). On day 0, the μ-plate was mounted on the 24-channel illumination apparatus located in the CO₂ incubator and illuminated for two days before flow cytometry analysis.

For Oct4-mCherry-Rex1-GFP mES cells, the 2i cocktail was removed from the mESC medium for at least four days earlier to establish heterogenous mES cell culture with Rex1-GFP-high and Rex1-GFP-low populations. Cells were then treated with 0.5 μM LMK-235 and 1 μM A-485 for two days before performing flow cytometry analysis.

Cells in one well of a 24-well plate were monodispersed using 125 μL of 1X TrypLE Express Enzyme (Gibco), neutralized with 250 μL of culture medium, filtered through a 40 μm cell strainer (BD Falcon) to remove clumps, and analyzed using a Beckman Cytoflex S cytometer (Brea, US) equipped with 405/488/561/640 nm lasers at high speed (60 μL/min) for 2 min per sample. The remaining samples were kept on ice. To ensure the reproducibility of the measurements, we used fluorescent calibration beads (Sphero Rainbow calibration beads six peaks; Catalog no. RCP-60–5; Spherotech) to adjust the instrumentation parameters.

The flow cytometry data were gated for single cells and exported as CSV files using FlowJo X (Ashland, US). A custom MATLAB (Mathworks, Natick, US) script was used to load the CSV files for quantitative analysis, including mean, CV, distribution of expression, etc. The relevant fluorescent channel was normalized to the mean value of the fifth peak of the Rainbow calibration beads. There was no detectable spillover between the GFP (FITC-A), mRuby (ECD-A), mCardinal (APC-A), and BFP (PB450A) channels, so compensation was not needed.

The expression of two fluorescent proteins linked by a 2A peptide are well-correlated at the single-cell level (Quarton et al., 2020). Thus, single-cell tetR-GFP expression is a close approximation of GAVPO expression as these two genes are linked with a T2A peptide in HeLa-AB1 cells. At a specific AM light induction, the activated GAVPO concentration is estimated to be proportional to total GAVPO concentration in single cells and limited by the expression level of GAVPO. Therefore, single-cell tetR-GFP expression is a reasonable surrogate for single-cell active GAVPO concentration at a specific AM light condition. To estimate the empirical propagation function of GAVPO to mRuby, we extracted single-cell GFP and mRuby expression and sorted the cell population by rising GFP expression. The total population of cells (Figure 1G) was divided into 20 subpopulations with incremental mean GFP values and narrow ranges. An empirical function (red dashed line in the center panel of Figure 1H) was fitted to the mean mRuby values vs. mean GFP values for the subpopulations (red circles in the center panel of Figure 1H). A hypothetical propagation function with high cooperativity was formulated with a Hill function with a Hill coefficient of 8 (black dashed line in the center panel of Figure 1H). A phenomenological model was then used to compute a single-cell mRuby value from the single-cell GFP-GAVPO value using a single-valued propagation function f:

in which the function f can be treated as the fraction of pre-initiation complex recruitment to the promoter, and the ‘noise’ represents the stochastic contributions from transcription, translation, etc. We added a lognormal noise with an amplitude of 0.2 and proportional to the predicted mean mRuby expression, contributing an additional 0.48 to the CV. The detailed algorithm is described in the MATLAB code in Appendix 2.

HeLa-ABC2 cells were plated on 3.5 cm glass-bottom FluoroDishes (FD35PDL, MPI) at a density of 3 × 10⁵ cells/dish one day in advance. Immediately before imaging, the medium was supplemented with 10 mM HEPES, pH 7.4, and 1% penicillin/streptomycin, and the dish was placed on an Andor CSU-X1 spinning disk confocal microscope with a Hamamatsu Orca-Fusion sCMOS camera, Nikon 60X NA1.49 TIRF objective, and Metamorph software. The blue-light illumination was set up by inserting an Arduino-controlled shutter (Daheng Optics GCI-7103) and a blue band pass filter (Omega 465/40DF) in the light path of the bright field illumination. The bright field illumination was adjusted to a blue light spot of 7 mm diameter centered at the bottom of the 35 mm dish, with light intensities of 25 or 100 μW/cm². Multiple XY positions within a 3 mm region were chosen, so that every cell in the optical field was always within the 7 mm blue illumination circle and the positions were separated enough to avoid overlapping in excitation and photobleaching. At each XY-position, 20 z-slices with a spacing of 0.5 μm were recorded with 200 ms exposure per slice. A Prior piezo-z stage was used to ensure z precision. Each XY position was imaged every 10 min for 1000 min at 37°C and 5% CO₂. A 561 nm laser was chosen for excitation to ensure the detection of single mRNA punctae while minimizing photobleaching over 100 3-D imaging loops. A 617/73 emission filter was used to collect tdTomato fluorescence. The AM light of 100 or 25 μW/cm² or PWM with 200 min period, 25% on duty, and 100 μW/cm² max intensity was set up and started immediately before the start of the live-cell confocal imaging. The period of 200 min was chosen over 400 min to enable multiple cycles of light induction without significant photobleaching. This periodicity also significantly reduces noise over the AM (Figure 1—figure supplement 4B–C).

MCP-tdTomato-nuc fluorescence is mostly localized inside the nucleus, which enabled us to segment and track individual nuclei, and to detect mRNA in a single channel (Figure 5A). Each optical field was captured in a 4D XYZT image with 1300 pixels × 1900 pixels × 20 slices × 100 timepoints. With a camera pixel size of 6.5 μm, a 60X NA1.49 TIRF objective, thickness of 0.5 μm per z-slice, and 10 min imaging frequency, with each 4D image recorded 140 × 205 × 9.5 μm³ over 1000 min. The cells moved significantly during this 1000 min period, and most of the nuclei moved outside of the imaging boundary at one time or another. To find all nuclei that stayed entirely inside the boundary, we performed nuclear segmentation and tracking before using Imaris 9.4 to identify and count mRNAs. The first step in this process is the generation of a z-axis maximum projection. A medium filter (window size = 5) and Laplacian of Gaussian filter (sigma = 40) were applied sequentially to projected XYT images, and a threshold was chosen to binarize the images and identify the centroids of the nuclei. Connected components were searched in these binary images over time to track the nuclei that remained inside the imaging boundary. In addition, for each z-projection image, using the identified nuclei centroids as seeds, a watershed algorithm was applied to find the ‘basin’ occupied by each cell at every time point. For all the tracked nuclei, the entire cell region was cropped from the original XYZT images based on its ‘basin’ at each time point, and re-centered to generate a 512 × 512 × 20 × 100 box. The nucleus was finally segmented using Otsu's algorithm in the cropped image. These XYZT images were normalized to the mean nuclear fluorescent intensity to compensate for MCP-tdTomato-nuc expression heterogeneity and possible photobleaching, as Imaris doesn't incorporate adaptive thresholding. The single mRNAs were identified using Imaris's spot model with an estimated x-y diameter of 0.54 μm and z diameter of 1.5 μm. All of the code scripts were written in Julia 1.4.1 using the JuliaImages v0.20 package.

A customized 6-channel LED illumination apparatus was built to implement dynamic blue-light induction without interference from live-cell fluorescent imaging (Appendix 1—figure 3). HeLa-AB1 cells were plated in an ibidi μ-plate 24-well plate at a density of 1 × 10⁴ cells/well, and the medium was supplemented with 1 μg/mL doxycycline on day −1. On day 0, the cells were placed on a Ti-E microscope with a Lumencor SpectraX solid-state light source, a Nikon Plan Apo 20 × 0.75 objective, a Andor Zyla 5.5 sCMOS camera, a Nikon motorized stage, and PFS autofocus systems. The cells were maintained at 37°C and 5% CO₂ in a customized environmental chamber with blue-light illumination from the top. GFP (filter set: excitation FF01-485/20, emission FF01-525/30, dichroic Di01-R405/488/561/635; Semrock) and mRuby (filter set: excitation FF01-560/25, emission FF01-590/20, dichroic Di01-R405/488/561/635; Semrock) channels were imaged every 90 min using Nikon NIS software. The illumination nonuniformity was corrected by normalization against images of a well with media only. The nuclei of cells were segmented and binarized using the GFP images by searching for and filtering local maxima in the Gaussian blurred image as seeds, then applying the watershed algorithm to split individual cells. Each nucleus was extracted by thresholding with Otsu's algorithm in each split region. The tracking was carried out in a reverse time sequence. In any pair of temporally adjacent images, connected cells were identified by closest proximity within a minimal search range of 20 μm. The entire track for each cell was validated with the criteria of continuity in GFP expression (less than 30% relative change, except for cell division events) and manually inspected. For quantification, the local background signal (median signal of nearby 400 × 400 pixels that don't contain any nucleus) was subtracted from the signal of a segmented nucleus and the resulting signal was integrated. To represent continuous expression dynamics, we normalized single-cell expression dynamics with the cell-cycle state for that cell (Figure 2—figure supplement 3C). All the code scripts were written either in Julia 1.4.1 using the JuliaImages v0.20 package or in MATLAB 2018b.

The whole-genome sequencing (50X) of the HeLa-AB1 clone was performed by GENEWIZ (Suzhou, China). The reads were processed to call potential plasmid insertion sites by GENEWIZ (Suzhou, China). The reads were trimmed with cutadapt 1.9.1 to remove dual ends with a base quality cutoff of 20 and to remove adapters. The trimmed reads were filtered with cutadapt 1.9.1 to discard reads containing more than 10% N bases and reads that were shorter than 75 bp. The filtered reads were mapped to the reference genome (hg19) and plasmid insertion sequences using bwa 0.7.12-r1039. The alignments were sorted by leftmost coordinates using samtools 1.6. The duplicated reads in sorted alignments were marked with the MarkDuplicates function of Picard 2.2.1. The break sites between the hg19 and plasmid were called from the alignments using VIFI 0.2.13. These sites were potential plasmid insertion sites in hg19 coordinates. PCR and Sanger sequencing were used to confirm the plasmid insertion sites. We confirmed nine insertion sites for plasmid B1 and five for plasmid A1 (Figure 3—figure supplement 1B). The plasmid insertion sequences were inserted into the human reference genome (hg19) according to the validated insertion sites to build the single cloned cell line reference genome (HeLa-AB1) using a custom Python3 script. Every insertion site was updated after the front sites in the same chromosome had been inserted.

HeLa-AB1 cells were plated in a 10 cm dish at a density of 1.5 × 10⁶ cells/dish. The medium was supplemented with 1 μg/mL doxycycline on day −1 to induce GAVPO expression. On day 0, cells were illuminated by 25 μW/cm² blue light for 24 hr to generate large mRuby distribution dispersion (expression heterogeneity). Cells were monodispersed and filtered through a strainer to remove clumps. FACS Aria SORP was used to sort cells into high and low mRuby populations. Cells were kept on chilled water before and during sorting. Single cells were selected on the basis of their side and forward scattering properties. Manual gates were imposed on the mRuby fluorescence to collect approximately 20% of the lowest and highest mRuby-expression cells (Figure 3I). For each population, 5 × 10⁶ cells were collected. A fraction of the cells (~2×10⁵) were cryopreserved with DMEM supplemented with 40% FBS (Gibco) and 10% DMSO (Catalog no. D8418; Sigma) for ATAC-seq analysis (GENEWITZ, Suzhou, China). The rest of the cells were washed twice in cold PBS buffer and cross-linked with 1% formaldehyde for 10 min at room temperature, and then quenched by adding glycine (125 mM final concentration) for subsequent ChIP-seq experiments.

The following ChIP-seq experiment was performed at IGENBOOK Biotech (Wuhan, China). Samples were lysed, and chromatins was sonicated to obtain soluble sheared chromatin (average DNA length of 200–500 bp). Twenty microliters of chromatin were saved at –20°C for input DNA, and 100 μL chromatin was used for immunoprecipitation. The antibody used for H3K27me3 was ab4729 (Abcam). Ten micrograms of antibody were used in the immunoprecipitation reactions at 4°C overnight. The next day, 30 μL of protein-A beads were added, and the samples were further incubated for three hours. The beads were then washed once with 20 mM Tris/HCL (pH 8.1), 50 mM NaCl, 2 mM EDTA, 1% Triton X-100 and 0.1% SDS, then twice with 10 mM Tris/HCL (pH 8.1), 250 mM LiCl, 1 mM EDTA, 1% NP-40 and 1% deoxycholic acid; and twice with 1 × TE buffer (10 mM Tris-Cl at pH 7.5, 1 mM EDTA). Bound material was then eluted from the beads in 300 μL of elution buffer (100 mM NaHCO₃, 1% SDS), treated first with RNase A (final concentration 8 μg/mL) for six hours at 65°C and then with proteinase K (final concentration 345 μg/mL) overnight at 45°C. Immunoprecipitated DNA was used to construct sequencing libraries following the protocol provided by the NEXTFLEX ChIP-Seq Library Prep Kit for Illumina Sequencing (NOVA-514120, PerkinElmer Applied Genomics) and sequenced on Illumina Xten with the PE150 method.

The following ATAC-seq experiment was performed at GENWITZ (Suzhou, China). The cell pellet was resuspended in 50 μL of tagmentation mix (12.5 μL 4X THS-seq TD buffer, 5 μL 0.1% digitonin (Catalog no. G9441; Promega), 5 μL TTE Mix V50 (Catalog no. TD501; Vazyme), and 27.5 μl H₂O). The tagmentation reaction was performed on a thermomixer (Eppendorf 5384000039) at 800 rpm, 37°C, 30 min. The tagmented DNA was purified following Qiagen MinElute PCR purification kit protocols. The library was constructed using 10 μL purified tagmented DNA, 2.5 μL S5XX and 2.5 μL N7XX indexing primers, 25 μL of NEBNext 2X PCR MasterMix and 10 μL H₂O, amplified at 72°C for 5 min, 98°C for 1 min, then [98°C for 10 s, 63°C for 30 s, 72°C for 20 s] × 12. The product was purified using the SPRI beads method (Beckman Coulter). Sequencing was performed on an Illumina HiSeq with the PE150 method.

The period of 600 min was chosen for PWM light induction as it reduces noise and was better suited for covering chromatin dynamics studies from 0 to 1 day (Figure 4A). Briefly, 50,000 cells were centrifuged at 500 × g, 4°C, 5 min. Cell pellets were resuspended in 50 μL tagmentation mix (33 mM Tris-acetate, pH 7.8, 66 mM potassium acetate, 10 mM magnesium acetate, 16% dimethylformamide (DMF), 0.01% digitonin, and 5 μL of Tn5 from the Nextera kit from Illumina (Cat. No. FC-121–1030)). The tagmentation reaction was performed on a thermomixer at 800 rpm, 37°C, 30 min. The reaction was then stopped by adding an equal volume (50 μL) of tagmentation stop buffer (10 mM Tris-HCl, pH 8.0, 20 mM EDTA, pH 8.0) and left on ice for 10 min. A volume of 200 μL 1X DPBS with 0.5% BSA was added, and the nuclei suspension was transferred to a FACS tube. Tagmented single nuclei were sorted on the basis of GFP signal (nuclear-localized tetR-GFP-nuc) using BD FACSAria SORP in one prepared 384-well plate containing 4 μL lysis buffers (50 mM Tris, pH 8.0, 50 mM NaCl, 20 ug/ml Proteinase K, 0.2% SDS, and 10 μM S5xx/N7xx Nextera index primer mix (5 µM each)). After sorting, the plate was briefly centrifuged and incubated at 65°C for 30 min. Then, 4 μL 10% Tween-20, 2 μL H₂O and 10 μL NEBNext High-Fidelity 2 × PCR Master Mix were added to each well sequentially. Libraries were amplified with 72°C for 5 min, 98°C for 5 min, [98°C for 10 s, 63°C for 30 s, 72°C for 20 s] × 18. Finally, all reactions were pooled together and purified with a PCR minElute purification column (Qiagen). Libraries were sequenced at HaploX Biotech (Shenzhen, China) with a HiseqX machine with 40G per one 384-well plate.

HeLa-AB1 and F9-AB2 cells were seeded into an ibidi 24-well μ-plate at 30,000 cells per well one day before the experiments. At day 0, two wells (A2, A3, control) and one well of HeLa-AB1 cells (C2, inhibitors) were replenished with regular medium or with regular medium containing 0.333 μM LMK-235 and 1 μM A-485, respectively, and illuminated with blue lights for two days at 21 and 9 μW/cm², respectively. At the same time, two wells (A1, B1, control) and one well (C1, inhibitors) of F9-AB2 cells with regular medium and regular medium containing 1 μM LMK-235 and 1 μM A-485, respectively, were cultured in the dark for 2 days. F9-AB2 cells (three samples named A1, B1 and C1, respectively) and HeLa-AB1 cells (three samples named A2, A3 and C2, respectively) in an ibidi 24-well μ-plate were washed three times with 500 μL DMEM, followed by incubation in 125 μL TrypLE Express Enzyme at 37 °C for 5 min. This enzyme was deactivated by 250 μL DMEM + 10% FBS, and the cells were transferred into 1.5 mL centrifuge tubes and centrifuged at 200 × g, 4 °C for 5 min. The resulting cell pellets were washed with 1 mL PBS containing 0.1% w/v BSA. After discarding as much PBS as possible and resuspending the cell pellets with an additional 100 μL PBS containing 0.1% BSA. The cells were filtered through the filter cap of the flow tube (Catalog no. 352235; BD Falcon). Aliquots of 10 μL of filtered cells were mixed with equal volumes of trypan blue solution for cell counting with the Countstar analyzer (RuiYu Biotech, Shanghai, China). Finally, the human cells (HeLa-AB1 A2, A3, and B2) and mouse cells (F9-AB2, A1, B1, and C1) were mixed at a 1:1 ratio to form three samples (A1/A2, B1/A3, C1/C2). Subsequently, according to the 10 × Genomics user guide, scRNA-seq libraries were constructed in three steps: GEM Generation and Barcoding, Post GEM-RT Cleanup and cDNA Amplification, and 3' Gene Expression Library Construction. In the first step, to obtain a final recovery of 10,000 cells/sample, 16,500 cells/sample were loaded into the 10 × machine (Chromium Next GEM Single Cell 3' Library and Gel Bead Kit v3.1). In the third step, after the scRNA-seq libraries were constructed, each library was split into two sets. One set was sequenced on the HiseqX system at HaploX Biotech, whereas the other was sequenced on the MGI2000 platform at BGI.

Reads were preprocessed using the fastp 0.20.0 software, with the default setting and correct low-quality mismatched base pairs in overlapping regions of paired-end reads. The preprocessed reads were mapped to the single cloned cell line reference genome (HeLa-AB1) using bowite2 2.3.5.1. The alignments were preprocessed with samtools 1.9 and then deduplicated using the MarkDuplicates function of Picard 2.22.2. The deduplicated alignments were filtered using samtools 1.9 with SAM flag (-F 1804 f 2) to select reads mapped in the proper pair. These reads were filtered using custom bash code to remove the reads that were mapped to mitochondria. For population sequencing data, all sample filtered reads were processed using the multiBamSummary function of deeptools 3.4.2 to calculate the scale factor for each sample. The scale factor and filtered reads for each sample were processed using the bamCoverage function of deeptools 3.4.2 to calculate the normalized coverage track. For single-cell sequencing data, the filtered reads were processed using the bamCoverage function of deeptools 3.4.2 to calculate the coverage track. The coverage tracks were processed using custom python3 scripts to calculate the coverage around the TSS sites.

The human and mouse mixed reference file was from the 10x genomics website (https://cf.10xgenomics.com/supp/cell-exp/refdata-cellranger-hg19-and-mm10-3.0.0.tar.gz). The coding region of vector CEG was added to the reference file, and a new reference was made using the mkref function of cellranger 3.1.0. The human and mouse mixed reads were processed by cellranger 3.1.0 to obtain the UMI count matrix. The matrix files were processed by R package scater 1.16.2 using the function quickPerCellQC with the default set to filter low-quality cells. The cells were identified as human cells if the total UMI counts mapped to the mouse genome were less than 0.01 times the total UMI counts mapped to the human genome. The cells were identified as mouse cells if the total UMI counts mapped to the human genome were less than 0.018 times the total UMI counts mapped to the mouse genome. The UMI counts were normalized by the library size. All of the following computations in this section were performed using MATLAB R2018b and associated toolboxes. Genes with mean counts under 1 CPM (counts per million) in any sample were neglected. Coefficients of variance (CV) and means for the remaining genes were computed for all three samples. The CV versus mean dot scatter plot was fitted to a power function (‘power1’) using the fit function. A cutoff line for identifying genes with CV outliers was defined as three σ above the fitted curve. The σ for each mean level was computed as the standard deviation of the CVs for the genes with a similar mean. The genes above this cutoff line (mean >5 CPM and CV >1.0), in any of the three samples, were filtered out for further analysis. The normalized count matrices from two control samples were pooled together as the control. If the CV of a gene was smaller than two in the control samples, it was grouped in the ‘low CV’ group. Otherwise, it was grouped in the ‘high CV’ group. Transcription factors (p300, CBP, and p65) for the target gene sets were identified from the ChIP-Atlas potential target genes with the option of ±1 kb distance from TSS. Genome assembly mm10 was selected as mouse reference in ChIP-Atlas. Genome assembly hg19 was chosen as a human reference in ChIP-Atlas. The UMI counts of filtered genes were processed by the R package saver 1.1.2 (Huang et al., 2018) to obtain a smooth distribution (Figure 6G).

Data are described by the sample mean and standard deviation (SD). All statistical analyses were performed using the MATLAB 2018b statistics toolbox. Sample sizes and details of statistical tests are provided in the corresponding figure legends.

To enable dynamic and quantitative modulation of blue-light induction with minimized cross-sample interference, we constructed a 24-channel blue LED illumination apparatus for the black-wall transparent bottom 24-well plate (24-well μ-Plate: Catalog no. 82406; ibidi) (Appendix 1—figure 1). The 1-watt blue CREE LED (465 nm) and matching acrylic lens were from Zhongshan Jieneng LED lighting (Zhongshan, China). The current-intensity curve for each LED was calibrated with a SANWA laser power meter LP1, then fitted with a smoothing spline curve function (caps) using the curve-fitting toolbox in MATLAB 2018b, and saved as a lookup curve (Appendix 1—figure 2). The metal frame was computer numerical control (CNC)-machined from aluminum alloy, sandblasted, and darkened. It was assembled with screws and springs to allow tight fitting to the μ-Plate, which prevents cross-contamination of light between wells and easy assembly (Appendix 1—figure 1). A water-cooling system was used to maintain cell culture temperature at 37°C, independent of light intensity. A computer programmable 24-channel constant currents source (0–10 mA in 4096 steps) provided precise and independent control of currents for each LED. The control code was written in Python3 and modulated the currents at 1 Hz or slower. The light intensities at ‘dark’ were adjusted at the electric board to maintain them within 0.2 μW/cm².

Appendix 1—figure 1

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Appendix 1—figure 2

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We built a 6-channel LED illumination apparatus similar to the 24-channel ones used in the incubator, except that each LED was equipped with a 465/40 bandpass filter (Omega) and assembled on the cover of the environment chamber for multi-well plates (Appendix 1—figure 3). Matching blackout cloths with six holes of 16 mm in diameter, corresponding to six illuminating wells, were inserted between the cover of the 24-well μ-plate and the glass window of the cover for the environmental chamber to minimize cross-well light contamination further.

Appendix 1—figure 3

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

This work was financially supported by the National Key R&D plan project (2019YFA0906002), by National Natural Science Foundation of China projects (31971181, 31770928), by the Shenzhen Peacock Plan (KQTD2016053117035204), by Science and Technology Planning Project of Guangdong Province, China (2016A050503010), and by Shenzhen Science and Technology Innovation Commission (ZDSYS20200811144002008). We thank Professors Qing Nie, Chunhui Hou, Wei Chen, and Liang Fang for stimulating discussions and suggestions, Messrs Wang Yuhao, Yi Zhang, Jiashun Xiao, and other members of the Huang group for technical assistance, and Miss Yifan Zhu for help with graphic illustration. We thank Professors Yi Yang, Jun Chu, and Jiandong Huang, and Jianbo Yue for providing plasmids for LightOn, mRuby3, and mCardinal tetR negative feedback plasmids, and mouse ES D3 cells. We acknowledge the technical support of Dr Wenjie Wei from the SUSTech Research Core Facility.

© 2021, Tan et al.

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