Live Imaging of Nascent RNA Dynamics Reveals Distinct Types of Transcriptional Pulse Regulation

Home , Housekeeping gene, Transcriptional bursting

Live imaging of nascent RNA dynamics reveals distinct types of transcriptional pulse regulation

Tetsuya Muramotoa,1, Danielle Cannona,2, Marek Gierlinski b,c, Adam Corrigana,2, Geoffrey J. Bartonb,c, and Jonathan R. Chubba,2,3

aDivision of Cell and Developmental Biology, bDivision of Biological Chemistry and Drug Discovery, and cWellcome Trust Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dundee DD1 5EH, United Kingdom

Edited by Sanjay Tyagi, University of Medicine and Dentistry of New Jersey, Newark, NJ, and accepted by the Editorial Board March 21, 2012 (received for review October 25, 2011) Transcription of genes can be discontinuous, occurring in pulses or have different fluctuation kinetics. However, available data on bursts. It is not clear how properties of transcriptional pulses vary transcription bursts and pulses imply active transcriptional states between different genes. We compared the pulsing of five house- last more in the range of minutes, even for strongly transcribed keeping and five developmentally induced genes by direct imaging genes (1). To describe the dynamics of transcription pulses it is of single gene transcriptional events in individual living Dictyoste- therefore necessary to directly observe pulses of RNA production, lium cells. Each gene displayed its own transcriptional signature, which requires using high-affinity RNA–protein interactions to differing in probability of firing and pulse duration, frequency, and deliver fluorescent signals to nascent RNA (15, 16). The resultant intensity. In contrast to the prevailing view from both prokary- accumulation of fluorescence at the site of transcription is viewed otes and eukaryotes that transcription displays binary behavior, under a microscope as a fluorescent spot, which appears and dis- strongly expressed housekeeping genes altered the magnitude of appears (pulses) at irregular intervals when imaged in living cells. their transcriptional pulses during development. These nonbinary How is pulsing different for genes with different functions and “tunable” responses may be better suited than stochastic switch expression requirements? How is pulsing reactive to cell context behavior for housekeeping functions. Analysis of RNA synthesis for different genes? To address these questions, we compared

kinetics using fluorescence recovery after photobleaching implied pulsing of a set of housekeeping and developmentally induced BIOLOGY modulation of housekeeping-gene pulse strength occurs at the genes in Dictyostelium cells. Dictyostelium are social amoebae, DEVELOPMENTAL level of transcription initiation rather than elongation. In addition, existing as single feeding cells that, upon starvation, initiate a disparities between single cell and population measures of tran- developmental program resulting in chemotactic cell aggrega- script production suggested differences in RNA stability between tion, followed by differentiation and remodeling of the aggregate gene classes. Analysis of stability using RNAseq revealed no major into a spore-containing mass suspended above the substrate by a global differences in stability between developmental and house- stalk. As with many differentiation steps in disease and devel- keeping transcripts, although strongly induced RNAs showed un- opment, from prokaryotes to stem cells, initial differentiation is usually rapid decay, indicating tight regulation of expression. scattered or stochastic, not determined by cell position (7, 17). We measured pulsing of five developmental and five house- transcriptional bursting | RNA turnover | stochastic gene expression keeping genes at different stages during preaggregative development. Each gene showed its own pulsing properties, measured ranscription is not adequately described by the smooth, seam- directly using a variety of parameters. Housekeeping genes Tless process implied by standard measures of RNA level. strongly modulate pulsing strength during development, allowing Within individual cells, transcription occurs as a series of irregular greater tunability of transcription at the single-cell level. In con- pulses, interspersed by long, irregular periods of inactivity. Pulsing trast, most developmental genes showed binary behavior. Dif- (or bursting) is a fundamental feature of transcription, conserved ferences in pulsing at different developmental time points are from prokaryotes to mammalian cells (1–5). These phenomena controlled at the level of transcription initiation. Finally, we have strong implications for our understanding of transcriptional demonstrate strongly induced developmental transcripts tend to mechanism and may provide a major source of stochasticity in show higher turnover than other transcripts, indicative of tight gene expression (6), a driver of cell diversity in differentiation and control of expression. disease (7, 8). However, it is unclear how pulsing behaves for Results and Discussion different genes with different functional properties. Pulsing dynamics, and therefore deeper understanding of un- To visualize transcriptional dynamics of single housekeeping and derlying transcriptional mechanics and regulation of different developmentally induced genes, we used MS2 stem loops for na- genes, are masked when averaged over millions of dead cells, as scent RNA detection (15). A 1.3-kb array of 24 MS2 loops was ′ occurs with standard bulk RNA measurement techniques, from integrated into 5 coding sequences of genes in Dictyostelium cells Northern blotting to RNA sequencing (RNAseq). The readout (Fig. 1A). Selection of recombinants used blasticidin resistance from these methods also has a variable contribution from RNA stability. Although strong inferences can be made from hetero- geneities in transcript number using hybridization against RNA in Author contributions: T.M. and J.R.C. designed research; T.M. and D.C. performed re- – search; T.M., M.G., G.J.B., and J.R.C. contributed new reagents/analytic tools; T.M., D.C., single cells (RNA-FISH) (9 11), an erroneous inference of strong M.G., A.C., and J.R.C. analyzed data; and T.M., D.C., M.G., and J.R.C. wrote the paper. fi transcription from both bulk and xed-cell RNA techniques The authors declare no conflict of interest. emerges if RNA is stable. To appreciate how transcription is This article is a PNAS Direct Submission. S.T. is a guest editor invited by the Editorial Board. regulated and how the process differs between different genes, it 1Present address: Quantitative Biology Center, RIKEN, Osaka 565-0874, Japan. is crucial to look at the process itself in living cells at the single- fl 2Present address: Department of Cell and Developmental Biology and Medical Research gene level. Live-cell methods using uorescent proteins or lucif- Council Laboratory for Molecular Cell Biology, University College London, London WC1E erase have been very useful in giving us a sense of the instability 6BT, United Kingdom. of transcriptional states, with expression heterogeneity between 3To whom correspondence should be addressed. E-mail: [email protected]. fl cells and slow uctuations (hours to days) between different This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. states (12–14). These methods also reveal that different genes 1073/pnas.1117603109/-/DCSupplemental.

www.pnas.org/cgi/doi/10.1073/pnas.1117603109 PNAS Early Edition | 1of6 Downloaded by guest on October 2, 2021 A MS2-GFP gene

bsr MS2 RNA 24xMS2 (1.3kb) (1.3kb) B act5-MS2

carA-MS2

0:00 2:30 5:00 7:30 10:00 12:30

Fig. 1. Visualization of nascent RNA pulses during development. (A) Schematic of RNA detection system. MS2-GFP protein binds with high afﬁnity to MS2 RNA. Twenty-four MS2 repeats and selection cassette (bsr) were knocked into 5′ regions of target genes by homologous recombination in Dictyostelium cells. (B) Examples of cells displaying pulses for housekeeping (act5) and development (carA) genes are shown. Arrows indicate transcription spots. Timing (minute: second). Maximum-intensity projections of 3D stacks. Cells can move several microns between frames. Field of view in each image is 36 × 36 μm.

cassettes (bsr) downstream of MS2 repeats. To ensure similar (Fig. 2A), from 90% of cells for act5 and scd, to 15% for abpE transcript length between genes, termination of transcription and rpl15. The reverse was true for development genes, with 3% after MS2 repeats used the bsr terminator, which directly fol- of cells with carA spots at 0 h increasing to 80% at 5 h of de- lows the repeats. The RNA contained only the 1.3-kb MS2 array, velopment (Fig. 2A). Weaker expressed development genes, plus standard short 3′ and short variable 5′ extensions. We studied cofC and zfaA, barely reached 1% of cells showing spots, al- transcription of five housekeeping genes [abpE (actin-binding), though to preserve viability we did not image with single-mole- act5 (β-actin), cinD (putative transcription factor), rpl15 (ribosome cule sensitivity, so would not detect weak transcription. Under no protein), and scd (fatty acid desaturase)] and five development conditions did any gene show spots in 100% of cells. Four of five genes [carA (chemoattractant receptor), csaA (cell adhesion), development genes showed spots in a small percentage (<5%) of cofC (actin-severing), hspF (heat-shock), and zfaA (early devel- undifferentiated cells. Do these rare events imply all cells ex- opment marker)]. Developmental genes show strong increases in pressed, masked by use of a limited capture period? Or do they fi mRNA abundance during the rst 5 h of development. House- reflect distinct subpopulations of cells? keeping genes were selected for strong expression in undiffer- Both possibilities were identified. MS2 repeats were replaced in entiated (vegetative) cells, although transcripts can be detected to targeting vectors with a GFP gene, which allowed GFP expression varying degrees during development. Dictyostelium is haploid, so to minimize effects of targeting we selected, where possible, genes from a duplicated region of chromosome 2 in the parental strain. A Housekeeping genes Developmental genes Act5, zfaA,andcsaA genes are not duplicated, but act5 is one of 100 100 90 abpE 90 carA 17 actin genes encoding identical proteins, csaA mutants have a act5 cofC 80 cinD 80 csaA minimal developmental phenotype (18), and zfaA-MS2 cells show 70 rpl15 70 hspF no obvious defects. Expression of MS2 RNA was similar to en- 60 scd 60 zfaA 50 50 dogenous RNA in wild-type cells during early development (Fig. 40 40 S1). Small differences observed likely relate to differences in 30 30 20 20 % Expressing of Cells % Expressing of Cells turnover between MS2 and parental RNAs (see below). The 10 10 MS2-GFP expression vector was transformed into recombinants 0 0 to obtain relatively uniform MS2-GFP expressing cell lines, with 012345 012345 comparable intensities for each gene (Fig. S2A). B Development Time (hrs) Development Time (hrs) Nascent transcripts were detected in living cells for all 10 genes, and spots appeared and disappeared in pulses (Fig. 1B). It was immediately clear that pulsing behavior, from spot intensity to the proportion of cells with pulses, was highly variable de- fi pendent on gene and development stage. Blind-selected elds act5-GFP carA-GFP hspF-GFP WT were captured as 3D stacks every 2.5 min, for 30 min for undifferentiated (0 h), starvation (3 h), and preaggregation (5 h) Fig. 2. Changing proportions of expressing cells during development. (A) cells, with around 1,000 cells studied per time point per gene for Numbers of cells displaying nascent RNA spots for different housekeeping all 10 selected genes over 3–4 experimental days (Fig. S2B). (Left) and development (Right) genes at different stages of development. Bars show SD of replicates over 3–4 experimental days. Around 1,000 cells were imaged per time point (Fig. S2B). (B) Variation of protein levels between Pulsing Dynamics of Developmental and Housekeeping Genes. Dur- cells. GFP was targeted (instead of MS2) at endogenous loci and fluorescence ing development, each gene showed characteristic pulsing be- levels observed in undifferentiated cells. Parental (WT) cells were imaged havior. In line with bulk expression data (19), housekeeping under identical conditions to assess autofluorescence. Field of view in each genes showed a higher proportion of cells with RNA spots at 0 h image is 194 × 194 μm.

2of6 | www.pnas.org/cgi/doi/10.1073/pnas.1117603109 Muramoto et al. Downloaded by guest on October 2, 2021 from the same native promoters (Fig. 2B). GFP half-life is several A hours in Dictyostelium (20), allowing a readout of accumulated transcription. GFP expressed from housekeeping promoter, act5, Duration showed strong, relatively even expression, implying over time- scales of several hours that all cells transcribe act5. The same Intensity ﬁ ﬂ

nding was true for carA. Compared with auto uoresence levels Spot Intensity of parental cells, carA-GFP cells display even GFP, with fewer Time than 1/1,000 cells expressing at levels two- to threefold greater ﬁ Pulse duration Pulse duration than the mean. This nding implies the 3% of 0 h cells showing B 15 C 15 carA-MS2 spots are not partially differentiated “founders,” but Housekeeping genes abpE carA Developmental genes act5 cofC that all cells have low-level transcription. CarA encodes the ag- 12 cinD 12 csaA rpl15 hspF gregation stage cAMP receptor. It might be advantageous for scd zfaA undifferentiated cells to perceive cAMP, a differentiation signal 9 9 and chemoattractant, to insure against changing nutritional conditions. In contrast, GFP expressed from the hspF promoter 6 6

showed variegated expression, with most cells showing only DurationPulse (min) 3 DurationPulse (min) 3 autoﬂuorescence, and around 1% of cells with strong GFP expression (Fig. 2B). Heterogeneity for hspF may relate to it being 0 0 a stress-response gene. These genes display high expression noise 012345 012345 in yeast, perhaps a hedging strategy against environmental ﬂuc- Pulse frequency Pulse frequency D 10 E 10 tuations (21, 22). Housekeeping genes abpE carA Developmental genes act5 cofC Dynamics of nascent RNA production were assessed within 8 cinD 8 csaA individual cells displaying spots. We describe dynamics in terms of rpl15 hspF scd zfaA pulse duration, frequency, and intensity (Fig. 3A). Pulse duration 6 6 was highly heterogeneous between pulses, cells, and genes. Pulses/h Shorter pulses (<10 min) were most common for all genes, but 4 Pulses/h 4

some pulses began before and ended after capture (>30 min). BIOLOGY 2 2

Pulse-duration distributions approximate to exponential (Fig. DEVELOPMENTAL S3A). Deviation from exponential appeared greater for strong 0 0 pulsers (act5, scd, carA, and csaA). To measure durations we in- 012345 012345 cluded incomplete pulses, to avoid the capture-window skewing Pulse intensity Pulse intensity data for strong transcribers. With pulse durations from complete F G 1200 Housekeeping genes abpE 1200 carA Developmental genes pulses, distributions for these genes tended more to exponential act5 cofC 1000 cinD 1000 csaA (Fig. S3B) and goodness-of-fit tests find exponential behavior rpl15 hspF scd zfaA rejected at the 1% level in only one case (csaA 5 h). Behavior of 800 800 csaA might be explained by responsiveness of the gene to extra- cellular cAMP, which oscillates during development. Periodic 600 600 Pulse IntensityPulse stimulation might override the more irregular transitions of IntensityPulse 400 400 bursting. Intervals between pulses were also distributed expo- 200 200 nentially for development and housekeeping genes (Fig. S4). 0 0 The effect of development on pulse duration showed distinct 012345 012345 behaviors for development and housekeeping genes (Fig. 3 B and H I Integrated Intensity Integrated Intensity C). For development genes, pulse duration did not alter greatly 5000 5000 during development. Durations were similar at 5 h, when the Housekeeping genes abpE carA Developmental genes act5 cofC genes were expressed most strongly, and 0 h, when few cells dis- 4000 cinD 4000 csaA rpl15 hspF played spots. This finding implies the transcriptional response for scd zfaA developmental genes is binary or all-or-nothing, with parameters 3000 3000 of pulses remaining relatively constant but the number of pulsing cells changing. This finding has been observed at the protein level 2000 2000 in viral (23), prokaryotic (24), and synthetic (25, 26) gene-in- 1000 1000 duction contexts, and is apparent for pulsing of two additional Summed Intensity per Pulse Summed Intensity per Pulse developmental genes in Dictyostelium (1, 27). In contrast, pulse 0 0 012345 012345 durations for two strongly expressed housekeeping genes, act5 Development Time (hrs) Development Time (hrs) and scd, diminished strongly during development, with pulses two- to threefold shorter for these genes at 5 h than at 0 h. This Fig. 3. Dynamics of pulsing behavior during development. (A) Cartoon finding indicates an alternative, nonbinary response, where in- depicting pulse parameters. Dashes on horizontal axis indicate image cap- dividual cells tune the level of transcript produced per tran- ture. Zero on the vertical axis corresponds to detection threshold. Pulse scriptional event during differentiation. duration is the number of consecutive frames a pulse can be detected above Measurements of other pulse parameters showed similar bi- background. Intensity is the mean intensity value measured at each frame in a pulse (averaged over all pulses in a cell). Pulse frequency is pulses per hour. nary and nonbinary behaviors for development and housekeeping For each housekeeping and developmental gene, the pulse duration (B and genes. Pulse frequency and intensity showed little or no change C), frequency (D and E), intensity (F and G), and integrated intensity (H and I) for most developmental genes during differentiation (Fig. 3). (product of duration and intensity of single pulses) were measured at dif- Pulse frequency for carA increased (Fig. 3E), evidence of non- ferent developmental time points. Bars show SEM. binary behavior, offset by decreasing pulse intensity between 3 and 5 h (Fig. 3G). In contrast, all five housekeeping genes showed decreasing pulse intensity (Fig. 3F) and three housekeeping genes and scd. Summing intensity values for each time point within a showed clear decreases in pulse frequency during development pulse gave integrated pulse strengths (Fig. 3 H and I). For act5, (Fig. 3D), most apparent for the two strongest expressers, act5 this parameter decreased 8.1-fold during development. Decreases

Muramoto et al. PNAS Early Edition | 3of6 Downloaded by guest on October 2, 2021 in pulse strength were also marked for scd and cinD, although similar rates. This transcription rate is within the 1- to 6-kb range occurred for all housekeeping genes. For developmental genes of estimates made using bulk approaches or multicopy array (Fig. 3I), carA showed an increase in integrated intensity of 2.5- FRAP (30), two- to fourfold higher than estimates from single fold, dominated by a small outlier group of very strong long pulses alleles at heterologous loci in human HEK-293 cells (31), and (Fig S5A, 4 of 100). GFP expression in carA-GFP knock-in cells similar to estimates from fluorescence fluctuation measurements at 5 h of development was, as at 0 h, relatively homogeneous, at a single yeast gene (16). Transcription from HIV promoters − implying the outliers are not a distinct population of carA ex- can be 50–100 kb·min 1 (29). pression. An alternative explanation is the outliers reflect poor The binary tendency for developmental genes might result clearance of transcript, or a “traffic jam” behind stalled poly- from polymerase preloading, so even in undifferentiated cells, merase (28). strong transcription occurs because polymerase is primed. To test this possibility, we performed ChIP using antibodies against RNA Polymerase Kinetics at Transcription Sites. How does the changing Polymerase II (Pol II). Pol II initiation is marked by phosphory- spot behavior during development relate to polymerase dynam- lation of serine 5 (S5) on its carboxy terminal domain. S5 also ics at transcription sites? To address this question, we performed marks poised or primed polymerase. Pol II then enters pro- fluorescence recovery after photobleaching (FRAP) on spots in gressive transcript elongation characterized by serine 2 phos- undifferentiated (0 h) and 4-h developed act5-MS2 cells. Tran- phorylation (S2) (32). ChIP using modification-specific antibodies scription sites were bleached with localized laser pulses, and 3D revealed S5 and S2 were high in undifferentiated cells for images captured every 5 s after bleaching (Fig. 4A). Turnover of housekeeping genes, declining during development (Fig. S5E). MS2 protein on stem loops is negligible (29), so spot recovery In contrast, development genes showed increases in S2 and S5 reflects new transcription. Postbleach nuclear background in- during differentiation. Increases were weak for hspF, perhaps tensities for both 0- and 4-h cells were 85% of original intensity evidence of a preloaded state in undifferentiated cells. This (Fig. S5B). The small fraction of bleached MS2-GFP will affect finding was not apparent for carA and csaA, implying no pop- spot intensity, but bleaching is the same for different time points, ulation-wide Pol II preloading. so we can compare recovery rates and relate these to estimates of transcription rates. Comparing Single-Cell Dynamics and Steady-State RNA Level. How Of 46 spots in 0-h cells, 87% of spots recovered within 120 s do single-cell nascent RNA dynamics relate to RNA levels as- postbleach. High proportions of recovery imply spots are highly sessed by traditional measures? To address this question, we dynamic, not dominated by long-term stalled polymerase (28). compared pulse properties to RNA levels quantified from North- For 85 4-h cells, 52% of spots recovered. These recovery data are ern blots of developmental time courses. Data comparing spot in line with nonbinary pulse behavior for housekeeping genes. incidence and pulse duration are plotted against relative RNA Pulses are shorter and less intense during differentiation, so less level in Fig. 5. RNA levels showed correlations with both spot in- likely to show detectable fresh transcription postbleach. cidence (Fig. 5A) and pulse duration (Fig. 5B). Fig. 5 A and B show Normalized spot recovery rates were similar in undifferentiated housekeeping RNAs were more abundant than developmental (0 h) and differentiated cells (4 h) (Fig. 4B). Because both 0- and RNAs for a given level of nascent RNA. This may be, in part, 4-h spots take the same time to repopulate with nascent RNA, because housekeeping RNAs are already present at the beginning this indicates the time for a polymerase to traverse the gene (the of development. There will be a delay before developmental RNAs elongation rate) is similar. Raw spot intensities were greater at 0 h accumulate. These data may also be explained by housekeeping (Fig. 4C) and initial and final spot intensities were correlated (Fig. RNAs being more stable than developmentally induced RNAs. S5C). This finding led to raw final intensities and recovery rates We tested this possibility by treating 5-h developed MS2 (Fig. − being greater at 0 h (Fig. S5D). However, with recovery data S6A) and parental (Fig. S6B) cell lines with 125 μg·mL 1 acti- normalized by prebleach intensity (29) (Fig. 4B), trajectories in 0- nomycin D (actD), to block new transcription (33). After treat- and 4-h cells were very similar. These data imply the differences in ment, spots disappeared for act5 after 10 min, and scd, carA, and raw intensities between developmental time points (nonbinary csaA after 20 min. After actD, MS2 RNAs from housekeeping behavior) are simply because of differences in the loading of genes act5, cinD, and scd were degraded gradually, with mean polymerase on the gene (the initiation rate). lifetimes of 57, 46, and 36 min, respectively (Fig. S6C). MS2 With an awareness that trains of polymerases only move as fast RNAs from developmental genes, carA, csaA, and hspF were as the slowest, we approximated a lower-bound transcription degraded more quickly (lifetimes 18, 25, and 25 min). Similar rate. Four-hour recoveries plateau ∼60 s postbleach. The MS2 trends were seen for untagged genes in 5-h developed parental array is 1.3 kb, and it takes 1 min to repopulate the locus, giving cells (Fig. S6B), where overall, housekeeping mRNAs were more − a transcription rate for a native single locus of 1.3 kb·min 1. The stable than developmentally induced mRNAs, apart from scd plateau for 0-h cells is less distinct, but similar trajectories imply (housekeeping) and dscA (development). Comparing data on

A B C 1.0 100 0H 0H 0.8 4H 80 4H y(a.u) t 0.6 60 ntensi 0:00 0:30 I d

e 0.4 40 s li a 0.2 20 Norm

0 Intensity of transcription site 0 0 20 40 60 80 100 120 0 20 40 60 80 100 120 1:00 1:30 2:00 Time (Seconds) Time (seconds)

Fig. 4. Dynamics of RNA turnover at a single native gene. (A) Recovery of ﬂuorescence at act5 transcription site after photobleaching. Undifferentiated cell, 2D maximum projection of 3D stack. The spot became detectable 30 s postbleach. Box in ﬁrst image shows bleached region. Timing (minute:second). (B) Trajectories of FRAP curves from normalized data from undifferentiated (0 h, n = 20) and 4-h developed (n =14)act5 cells. Data were normalized to prebleach spot intensities. (C) Raw FRAP trajectories for act5 transcription spots in 0- and 4-h cells. Bars show SEM. Field of view for each image is a 17.5 × 17.5 μm.

4of6 | www.pnas.org/cgi/doi/10.1073/pnas.1117603109 Muramoto et al. Downloaded by guest on October 2, 2021 We defined induction, Rg, of a gene as the ratio of intensity A B fi 100 12 (RNA abundance) at 5 h to that at 0 h. We de ned degradation, House. House. Develop. 10 Develop. Dg, of a gene as the ratio of intensity at 5 h and 5 h + 60 min. The 80 relationship between induction and degradation is shown in Fig. 8 −14 60 5C. There is a weak (r = 0.13), but significant (P ∼ 10 ) cor- 6 relation between R and D . The basis of this trend appears to be 40 g g 4 high levels of degradation for very highly induced genes (Rg >

20 Duration (min) Pulse fi % of Cells Expressing % of Cells 2 10). To assess the signi cance of this trend, we divided data into bins of 30 datapoints along the horizontal axis. Individually, only 0 0 50 3025201510 50 3025201510 the last bin (Rg > 50) showed significant increase above Dref , the Relative RNA level Relative RNA level mean degradation of genes with low induction (Rg < 10). How- C ever, the rising trend starts at lower Rg, and the combined sig- a) −10 nificance of the last four bins (Rg > 9) is P < 10 . In summary, 0 1 these data show a subset of highly induced genes are, on average, less stable than genes with low levels of developmental induction. The dispersion in degradation is large and there are points

1 with high induction and low degradation. In addition, the effect is somewhat dependent on selection of “good” datapoints, where Degradation all replicates show a level of expression signiﬁcantly different α α 1 . from zero ( s = 0.05). Choosing s = 0.01 for accepting points 0 leaves only 463 “good” genes and too few high-induction data-

α=0.001 points to assess the increase in degradation. On the other hand, b) α=0.01 α=0.1 with αs = 0.1, “good” data are noisier, although the trend com- Z −7 bined for Rg > 10 is still signiﬁcant (P < 10 ). ′ -2 0 2 4 6 Although RNA stability determinants appeared to be at 5 ends 10.0 1.0 1 01 001 0001 ﬁ Induction of genes for several loci described here, we were unable to nd

speciﬁc motifs related to general mRNA stability or instability BIOLOGY Fig. 5. Transcriptional dynamics and RNA stability. Comparison of single-

from whole-genome data. There was a clear correlation between DEVELOPMENTAL cell (MS2) and ensemble (Northern blot) measures of RNA production with transcript length and degradation (Fig. S7A) but not between GC (A) percentage of cells expressing and (B) pulse duration. Closed circles and content and degradation (Fig. S7B). We extracted stability data crosses show housekeeping and development genes, respectively. (C) Plot of for 28 “core” cAMP pathway genes and plotted this against in- RNAseq data showing the distribution of fold-induction (between 0 and 5 h) and fold-degradation after actD treatment (for 60 min at 5-h development). duction and expression level. Degradation was not correlated to (a) Gray points show degradation versus induction for all 10,232 nonzero fold-induction (Fig. S7C) or expression level (Fig. S7D) for these genes. Darker green points represent data reproducible between three bi- genes. The act8 family is a group of 17 genes (including act5) ological replicates. Data were divided into bins of 30 points along the hor- encoding identical β-actin proteins (36). Why do cells need so izontal axis. The mean degradation (black line) and its 95% confidence many genes encoding identical proteins? Perhaps 5′ and 3′ UTRs interval (blue lines) are shown for each bin. The red horizontal line shows allow specialization, for example by regulating RNA stability. Fig. the Reference Level, the mean degradation of genes with low induction, S7E shows a plot of induction vs. degradation for the 17 mRNAs. < Rg 10. (b) Statistical assessment of this trend. The three gray dashed lines None show extremes of degradation or induction, although two fi α show the Bonferonni-corrected statistical signi cance levels of = 0.1, 0.01, outliers are apparent-act1, the most stable and highly induced and and 0.001. The last four bins combined (induction greater than ∼9) are − ′ above Reference Level at high significance level, P < 10 10. act7, the least stable and most weakly induced. 3 UTRs of act mRNAs show distinct motifs. Most of the family have a motif with a core GATGAAAG 25 nt downstream of the TAA. Both act1 tagged and untagged alleles indicated MS2-tagged RNAs were and act7 lack this motif. A second motif, GTTGTTGATC, is less stable than parental RNAs (lifetimes two- to threefold conserved 140 nt downstream of TAA, although not in act7. Both lower). However, with the exception of scd, relative stability of act1 and act7 are the least-expressed members of the family (Fig. transcripts was retained after tagging (Fig. S6 A–C). As tran- S7F), so these may be genes no longer under selection, or used at a specific developmental state. scripts from tagged genes only differ by their 5′ ends (terminators ′ are identical), these data imply 5 sequences carry strong deter- Concluding Remarks. Live-cell analysis of nascent RNA dynamics minants of RNA stability. This finding may parallel recent yeast has revealed differences in pulsing behavior between different studies revealing regulation of RNA stability at the promoter genes. Each gene has characteristic pulsing kinetics, but dis- (34, 35). Overall, this small-scale analysis provides evidence that tinctions between housekeeping and developmentally induced RNAs from developmentally induced genes tend to be less stable genes were identified. In keeping with the single-cell literature than housekeeping RNAs. for prokaryote, eukaryote, viral, and synthetic systems, devel- To address whether this tendency is general, we quantified opmental gene pulsing was observed to be predominantly binary. RNA decay using RNAseq, to measure RNA stability at the Individual cells have a varying probability of firing, but properties genome level. Five-hour–developed cells were treated with actD. of pulses within transcribing cells largely fall within a standard RNA was collected from the starting population (0 h) the 5-h range, despite external conditions. In contrast, housekeeping population and 30 and 60 min after actD treatment. RNAseq genes showed a strong capacity for intracellular modulation of transcription, with shorter, weaker pulses occurring as cells enter was carried out on three biological replicates for each time point. differentiation. Binary responses might be explained by a high At least two-million reads per time point were obtained (Dataset cooperativity and positive feedback in upstream signaling, or an ’ S1). Similarity between replicates was high (Pearson s correla- obstacle creating an “off” state, such as a recalcitrant nucleo- fi > tion coef cients r 0.94). Eleven RNAs revealed by RNAseq to some. Noisy binary behaviors necessitate noise-reduction strat- be stable or unstable were tested by blotting of different extracts egies to allow precision in cell response, or alternative programs (Fig. S6D). Degradation measured by Northern and RNAseq to compensate for heterogeneity, which may be a driver for the showed a clear correlation (R2 = 0.75) (Fig. S6E). evolution of so-called “redundancy” if ordered cell behavior is

Muramoto et al. PNAS Early Edition | 5of6 Downloaded by guest on October 2, 2021 a requirement. That individual cells can tune the expression level at 40% power (50 iterations over 1.8 μm2) on a Zeiss LSM 710 confocal with of some genes more precisely implies a modicum of control in an a63×/1.4 NA objective. Stacks (13 slices; step 0.78 μm) were acquired im- otherwise noisy process. Stochastic switch-like behavior is well mediately before bleaching and every 5 s for 120 s during recovery. Images suited to cell choices in development or stress responses (37); it are displayed as 2D maximum-intensity projections of 3D stacks. may be less suitable for housekeeping functions, where measured responses, rather than probabilistic on-or-off decisions, seem Data Analysis for RNAseq. Short-read data were obtained for four conditions more appropriate. If the absence or presence of a conserved ri- (0 h, 5 h, 5 h + 30 min, and 5 h + 60 min actD), in three replicates. Reads were single-end and 50-bp long. We quality-clipped reads, mapped them to Dic- bosomal protein were effectively a coin-toss decision, this would tyostelium discoideum genome release 9 and corresponding gene models not be in the interests of the cell. It will be interesting to address from Ensembl, and obtained normalized gene-expression levels (intensities) how modulation of housekeeping transcription relates to the de- quantified as number of fragments (reads) per kilobase of exon per million rived nonbinary behavior in synthetic systems (38, 39). fragments mapped (FPKM). Intensity errors were substantial for many genes; For a subset of induced genes, tight control of RNA level was therefore, we removed noise by rejecting intensities statistically different also apparent in our RNA stability analysis. Whereas the bulk of from zero. The majority of rejected “noisy” data had low expression levels RNAs are largely unstable or stable irrespective of their degree (Fig. S7G). We normalized each gene profile to a calibration reference and of induction during development, a subset of strongly induced selected ribosomal protein genes, which have roughly constant, high ex- developmental RNAs showed unusually high turnover. Insta- pression levels. We calculated mean profiles of all calibration genes (Fig. bility allows flexibility, permitting more rapid transition to the S7H), then divided all gene profiles by this profile normalized to its own next developmental stage, or bet-hedging for rapid access back to mean. Examples of normalized expression profiles are shown in Fig. S7I. fi vegetative growth. We de ned induction, Rg,ofageneg as the ratio of intensities at 5 h to that at 0-h development. We define degradation, Dg,ofageneg as the Materials and Methods ratio of intensities at 5 h and 5 h + 60 min actD. These two quantities de- Cell Lines and Imaging. Detailed information can be found in SI Materials and scribe increase in expression level during differentiation and amount of Methods. MS2 repeats were targeted into 5′ coding regions of 10 Dictyos- mRNA degradation, respectively. Errors were propagated from intensities telium genes. Targeting vectors were transformed into Dictyostelium AX3 to find SEs on Rg and Dg. To assess the significance of the trend between Rg −1 cells and recombinants selected with 10 μg·mL blasticidinS. Correct knock- and Dg, we divided data into bins of 30 datapoints along the horizontal fi in clones were identi ed by Southern blotting then transformed with MS2- axis. For each bin, i, we calculated the mean degradation, Di ,itsSE,εi,and GFP expression vector (1) and selected for stable clones with relatively uni- the 95% confidenceintervalofthemean.Wedefined a reference level, the fl form uorescence. To generate GFP knock-in cell lines, we used in-frame GFP mean degradation level at low-induction, Dref, as the mean of all points sequences, inserted into the same targeting fragments. with Rg < 10. For imaging, we used agar overlay (1) and an imaging station optimized for rapid acquisition from photosensitive samples (40). Three-dimensional ACKNOWLEDGMENTS. This work was supported by a Japan Society for the stacks (41 slices; 250-nm z-step) were captured at multiple positions every 2.5 Promotion of Science Postdoctoral Fellowship for Research Abroad (to T.M.) min with 30-ms exposures. Cells were imaged for 30 min without prior and a Wellcome Trust Senior Research Fellowship and Medical Research fluorescence exposure. For FRAP, spots were bleached using a 488-nm laser Council project grant (to J.R.C.).

1. Chubb JR, Trcek T, Shenoy SM, Singer RH (2006) Transcriptional pulsing of a de- 22. Newman JR, et al. (2006) Single-cell proteomic analysis of S. cerevisiae reveals the velopmental gene. Curr Biol 16:1018–1025. architecture of biological noise. Nature 441:840–846. 2. Golding I, Paulsson J, Zawilski SM, Cox EC (2005) Real-time kinetics of gene activity in 23. Ko MS, Nakauchi H, Takahashi N (1990) The dose dependence of glucocorticoid-in- individual bacteria. Cell 123:1025–1036. ducible gene expression results from changes in the number of transcriptionally ac- 3. Raj A, Peskin CS, Tranchina D, Vargas DY, Tyagi S (2006) Stochastic mRNA synthesis in tive templates. EMBO J 9:2835–2842. mammalian cells. PLoS Biol 4:e309. 24. Novick A, Weiner M (1957) Enzyme induction as an all-or-none phenomenon. Proc 4. Paré A, et al. (2009) Visualization of individual Scr mRNAs during Drosophila em- Natl Acad Sci USA 43:553–566. bryogenesis yields evidence for transcriptional bursting. Curr Biol 19:2037–2042. 25. Becskei A, Séraphin B, Serrano L (2001) Positive feedback in eukaryotic gene networks: 5. Lionnet T, et al. (2011) A transgenic mouse for in vivo detection of endogenous la- Cell differentiation by graded to binary response conversion. EMBO J 20:2528–2535. – beled mRNA. Nat Methods 8:165 170. 26. Walters MC, et al. (1995) Enhancers increase the probability but not the level of gene 6. Blake WJ, et al. (2006) Phenotypic consequences of promoter-mediated transcrip- expression. Proc Natl Acad Sci USA 92:7125–7129. – tional noise. Mol Cell 24:853 865. 27. Stevense M, Muramoto T, Müller I, Chubb JR (2010) Digital nature of the immediate- 7. Balázsi G, van Oudenaarden A, Collins JJ (2011) Cellular decision making and bi- early transcriptional response. Development 137:579–584. – ological noise: From microbes to mammals. Cell 144:910 925. 28. Voliotis M, Cohen N, Molina-París C, Liverpool TB (2008) Fluctuations, pauses, and 8. Miller-Jensen K, Dey SS, Schaffer DV, Arkin AP (2011) Varying virulence: Epigenetic backtracking in DNA transcription. Biophys J 94:334–348. – control of expression noise and disease processes. Trends Biotechnol 29:517 525. 29. Maiuri P, et al. (2011) Fast transcription rates of RNA polymerase II in human cells. 9. So LH, et al. (2011) General properties of transcriptional time series in Escherichia coli. EMBO Rep 12:1280–1285. Nat Genet 43:554–560. 30. Ardehali MB, Lis JT (2009) Tracking rates of transcription and splicing in vivo. Nat 10. Zenklusen D, Larson DR, Singer RH (2008) Single-RNA counting reveals alternative Struct Mol Biol 16:1123–1124. modes of gene expression in yeast. Nat Struct Mol Biol 15:1263–1271. 31. Yunger S, Rosenfeld L, Garini Y, Shav-Tal Y (2010) Single-allele analysis of transcrip- 11. Taniguchi Y, et al. (2010) Quantifying E. coli proteome and transcriptome with single- tion kinetics in living mammalian cells. Nat Methods 7:631–633. molecule sensitivity in single cells. Science 329:533–538. 32. Wade JT, Struhl K (2008) The transition from transcriptional initiation to elongation. 12. Sigal A, et al. (2006) Variability and memory of protein levels in human cells. Nature Curr Opin Genet Dev 18:130–136. 444:643–646. 33. Firtel RA, Baxter L, Lodish HF (1973) Actinomycin D and the regulation of enzyme 13. Suter DM, et al. (2011) Mammalian genes are transcribed with widely different biosynthesis during development of Dictyostelium discoideum. J Mol Biol 79:315–327. bursting kinetics. Science 332:472–474. 34. Bregman A, et al. (2011) Promoter elements regulate cytoplasmic mRNA decay. Cell 14. Harper CV, et al. (2010) Dynamic organisation of prolactin gene expression in living – pituitary tissue. J Cell Sci 123:424–430. 147:1473 1483. 15. Bertrand E, et al. (1998) Localization of ASH1 mRNA particles in living yeast. Mol Cell 35. Trcek T, Larson DR, Moldón A, Query CC, Singer RH (2011) Single-molecule mRNA 2:437–445. decay measurements reveal promoter-regulated mRNA stability in yeast. Cell 147: – 16. Larson DR, Zenklusen D, Wu B, Chao JA, Singer RH (2011) Real-time observation of tran- 1484 1497. scription initiation and elongation on an endogenous yeast gene. Science 332:475–478. 36. Joseph JM, et al. (2008) The actinome of Dictyostelium discoideum in comparison to 17. Thompson CR, Reichelt S, Kay RR (2004) A demonstration of pattern formation actins and actin-related proteins from other organisms. PLoS ONE 3:e2654. without positional information in Dictyostelium. Dev Growth Differ 46:363–369. 37. Acar M, Mettetal JT, van Oudenaarden A (2008) Stochastic switching as a survival 18. Harloff C, Gerisch G, Noegel AA (1989) Selective elimination of the contact site A strategy in ﬂuctuating environments. Nat Genet 40:471–475. protein of Dictyostelium discoideum by gene disruption. Genes Dev 3(12A):2011–2019. 38. Nevozhay D, Adams RM, Murphy KF, Josic K, Balázsi G (2009) Negative autoregulation 19. Parikh A, et al. (2010) Conserved developmental transcriptomes in evolutionarily di- linearizes the dose-response and suppresses the heterogeneity of gene expression. vergent species. Genome Biol 11:R35. Proc Natl Acad Sci USA 106:5123–5128. 20. Deichsel H, et al. (1999) Green ﬂuorescent proteins with short half-lives as reporters in 39. Takahashi S, Pryciak PM (2008) Membrane localization of scaffold proteins promotes Dictyostelium discoideum. Dev Genes Evol 209:63–68. graded signaling in the yeast MAP kinase cascade. Curr Biology 18:1184–1191. 21. Bar-Even A, et al. (2006) Noise in protein expression scales with natural protein 40. Muramoto T, Muller I, Thomas G, Melvin A, Chubb JR (2010) Methylation of H3K4 is abundance. Nat Genet 38:636–643. required for inheritance of active transcriptional states. Curr Biology 20(5):397–406.

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