Study of RNA Polymerase II Clustering inside Live-Cell Nuclei Using Bayesian Nanoscopy Xuanze Chen,†,‡,⊥ Mian Wei,†,⊥ M. Mocarlo Zheng,†,§,⊥ Jiaxi Zhao,∥ Huiwen Hao,† Lei Chang,† Peng Xi,‡ and Yujie Sun*,† †
State Key Laboratory of Membrane Biology, Biodynamic Optical Imaging Center (BIOPIC), School of Life Sciences, Peking University, Beijing 100871, China ‡ Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China § School of Physics, Peking University, Beijing 100871, China ∥ Department of Physics, Tsinghua University, Beijing 100084, China S Supporting Information *
ABSTRACT: Nanoscale spatiotemporal clustering of RNA polymerase II (Pol II) plays an important role in transcription regulation. However, dynamics of individual Pol II clusters in live-cell nuclei has not been measured directly, prohibiting in-depth understanding of their working mechanisms. In this work, we studied the dynamics of Pol II clustering using Bayesian nanoscopy in live mammalian cell nuclei. With 50 nm spatial resolution and 4 s temporal resolution, Bayesian nanoscopy allows direct observation of the assembly and disassembly dynamics of individual Pol II clusters. The results not only provide quantifications of Pol II clusters but also shed light on the understanding of cluster formation and regulation. Our study suggests that transcription factories form on-demand and recruit Pol II molecules in their pre-elongation phase. The assembly and disassembly of individual Pol II clusters take place asynchronously. Overall, the methods developed herein are also applicable to studying a wide realm of real-time nanometer-scale nuclear processes in live cells. KEYWORDS: Bayesian nanoscopy, RNA polymerase II, cluster analysis, nanostructure dynamics, live cell super-resolution
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Quantitative characterization of transcription factories is essential for deciphering their working mechanisms. Various techniques, including immunofluorescence, RNA fluorescence in situ hybridization (FISH), and electron microscopy (EM), have been applied to the quantification of number, size, and constituent stoichiometry of transcription factories.3,11,15−17 It is found that the number and size of transcription factories vary greatly in different studies, which on the one hand seems to depend on the cell type and on the other hand is partly due to different measurement approaches. In addition, component analysis has revealed that a transcription factory tends to have a porous, protein-rich core containing transcription complexes and regulation factors with chromatin DNA and transcripts being held on the surface.16,18 How transcription factories are formed has been under large debate. Studies have suggested that promoter identity, sharing
ranscription is a fundamental cellular process. In eukaryotes, it is proposed that transcription occurs in discrete nanoscale RNA polymerase II (Pol II)clustered foci termed “transcription factories”.1−4 Previous studies have suggested that Pol II clustering participates in a wide range of stages in regulation of gene expression, from genome organization,5−7 transcriptional regulation,8−11 premRNA processing,12,13 down to RNA transport.9 On the level of genome structure, Pol II clustering mediates the formation of chromatin loops.6,7 On the level of transcriptional regulation, Pol II clustering was proposed to enhance the efficiency of transcription initiation through molecular crowding effect8 and regulates cotranscription of functionally associated genes.9−11 On the level of pre-mRNA splicing, it was reported that Pol II clusters colocalize with nuclear speckles, which are enriched in pre-mRNA splicing factors.12,13 On the level of RNA export, Pol II clustering could allow functionally associated transcripts to be exported together.9 In addition, the association of genes at Pol II clusters may increase the susceptibility of chromosomal translocation and cause genome instability, which could lead to cancer.14 © XXXX American Chemical Society
Received: November 17, 2015 Accepted: February 8, 2016
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Figure 1. Bayesian super-resolution imaging reveals spatial organization of Pol II clusters in living cell nuclei. (a) The conventional fluorescence image of Pol II (Dendra2-RPB1 green emission). (b) Bayesian super-resolution image obtained through analysis of 200 raw frames (50 fps). (c and d) Zoom-in view of boxed region 1 and 2, respectively, in panels a and b. (e) Pol II clusters identified with DBSCAN analysis of (b), the color bar denotes identity of different clusters, and the different color marks Pol II clusters with different areas (the smallest to largest cluster area corresponding to violet to red color). (f) The distribution of area for Pol II clusters (n = 10 cells). (g) The distribution of cluster−cluster nearest neighbor distance (NND) for Pol II clusters (n = 10 cells).
optical diffraction limit have sprung up. The major techniques can be divided into two categories: (i) patterned light modulation microscopy, such as stimulated emission depletion microscopy (STED),23 reversible saturable optical fluorescence transitions (RESOLFT) microscopy,24 and (saturated) structure illumination microscopy ((S)SIM);25 (ii) single-molecule localization microscopy, such as photoactivated localization microscopy (PALM),26 fluorescence photoactivation localization microscopy (FPALM),27 and stochastic optical reconstruction microscopy (STORM).28 Using STORM, Zhao et al. carried out precise counting of Pol II molecules in transcription factories.29 Cisse et al. developed time-correlated PALM (tcPALM) to study transient formation of Pol II clusters. Yet, tcPALM only allows us to observe the assembly process of Pol II clusters based on the single molecule accumulation principle of tcPALM.8 It is not clear whether Pol II clusters disassemble and, if so, how fast it occurs. Moreover, tcPALM uses 405 nm light for activation, which may cause phototoxicity and prevent long-term live cell imaging.
of specific transcription factors and RNA splicing may be responsible for the formation of transcription factories.6,10,19−21 Additional evidence further implies that these factories might be associated with some nuclear scaffolds.18,22 However, in spite of those findings and implications, the dynamic features of transcription factories are still elusive. Generally, imaging of fixed cells by either immunofluorescence or electron microscopy can provide high spatial resolution but is also prone to fixation and labeling artifacts. In contrast, live cell imaging can provide dynamic information at physiological condition. However, it has been challenging to directly visualize individual transcription factories in live eukaryotic cells due to high fluorescence background and poor spatial resolution limited by the optical diffraction. Therefore, in order to settle the debates on transcription factories, it is essential to visualize them in realtime at high spatial resolution. Given the high density of transcription factories in the nucleus, super-resolution fluorescence imaging has turned out to be an inevitable choice. In the past decade, various superresolution techniques (nanoscopy) aiming at breaking the B
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Figure 2. Live cell Bayesian super-resolution temporal sequence reveals dynamic assembly and disassembly processes of Pol II clustering. (a) Average image of whole nucleus raw data. (b−d) Conventional fluorescence images of the boxed region in (a) at different time points. Prominent Pol II clusters are marked by ellipses. (e) The merged image of (b−d). (f−h) Corresponding Bayesian super-resolution reconstructed images of (b−d). (i) The merged image of (f−h). Scale bar is 10 μm in (a) and 2.5 μm in (b−i).
molecule density were captured using low intensity 488 nm excitation under 50 fps (frames per second) frame rate (Figure 1a and Movie S1). Blinking and bleaching of Dendra2 molecules both occurred during image acquisition and allowed Bayesian analysis to obtain super-resolution images using 200 raw frames (Figure 1b). Owing to the dramatically improved spatial resolution, Bayesian nanoscopy was able to provide much more information about the spatial organization of Pol II clusters than the diffraction-limited conventional fluorescence microscopy. As an example, the two prominent Pol II clusters in the Bayesian super-resolution image (Figure 1b) could hardly be discerned by the conventional fluorescence microscopy (Figure 1a). Moreover, the super-resolution images revealed a size of approximately 500 nm and an irregular spherical geometry for these two clusters (Figure 1c,d). These structural details provide implications for the organization of Pol II molecules in transcription factories. To quantify the overall properties of Pol II clusters in the live nucleus, DBSCAN analysis was performed on the Bayesian superresolution images to identify Pol II clusters using a threshold of 50 nm-diameter circular area (see Materials and Methods). This threshold was chosen mainly based on the spatial resolution of Bayesian nanoscopy in our experiments. Note that the reported size range of Pol II clusters (40−200 nm in mean diameter) also favors the selected threshold.11,14−17 The analysis shows that in the living cell there were 271 Pol II clusters distributed evenly in the equatorial plane of the nucleus (Figure 1e). To confirm these clusters are functionally organized Pol II molecules, we performed a simulation in which the same number of spots in Figure 1b were placed
In this work, we study Pol II clustering dynamics in live mammalian cell nuclei using a novel fast super-resolution technique recently developed by Cox et al., named Bayesian analysis of blinking and bleaching (3B or Bayesian nanoscopy). 30 Bayesian nanoscopy models the most likely distribution of label locations by analyzing the fluctuation/ blinking and bleaching of all fluorophores, even when these fluorophores are highly overlapping. This method greatly reduces the amount of raw data required to construct a superresolution image and is able to resolve dynamic processes with 4 s temporal resolution.30−32 Taking advantage of the low phototoxicity and high spatiotemporal resolution of Bayesian nanoscopy, we are able to directly observe both spatial organization and temporal dynamics of Pol II clusters. For the first time, both assembly and disassembly processes of individual Pol II clusters are visualized in live cell nuclei. Our study provides important insights for in-depth understanding of transcription factories, including their asynchronous nature and on-demand assembly, as well as the relations between Pol II cluster formation and transcription stages. This work also exemplifies a general approach to study complex nanoscale dynamic processes in the crowded intranuclear space.
RESULTS AND DISCUSSION A U2OS cell line that stably expresses Dendra2-fused Pol II catalytic subunit RPB1 (Dendra2-RPB1) was constructed for the study of Pol II clustering dynamics using Bayesian nanoscopy (Figure S1). To demonstrate the ability of Bayesian nanoscopy in quantification of Pol II clusters in live cell nuclei, 200 live-cell frames of Dendra2-RPB1 green emission with high C
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Figure 3. Live cell Bayesian nanoscopy time lapse imaging reveals spatial organization dynamics of Pol II clusters under serum stimulation. (a) The conventional image of Pol II clusters inside the live-cell nucleus. (b−d) Bayesian nanoscopy results at different time points. Prominent Pol II clusters are marked by dash boxes. (e−g) Pol II clusters identified with DBSCAN analysis of images in (b−d), respectively. (h) The distribution of Pol II cluster area at different time points in (e−g). (i and j) Size changes of three prominent Pol II clusters in (b−g). Scale bar: 5 μm in (a−g) and 500 nm in (i).
randomly in the same area (Figure S2a). DBSCAN analysis of the simulated data with the same setting as that in Figure 1e identified 65 clusters, much less than that in Figure 1e. Moreover, as shown in Figure S2b, the spot−spot nearest neighbor distance distribution also reveals clear differences between the experimental data (Figure 1b) and the simulated data (Figure S2a). Analysis of 10 cells shows that on average there were 252 ± 66 Pol II clusters in the equatorial plane of a nucleus. To calculate the total number of Pol II clusters in a nucleus, we approximated the thickness of the image plane as 600 nm, which is defined by the optical sectioning thickness of 3B imaging, and assumed the radius of semimajor axis and semiminor axis of the ellipsoidal nucleus is, respectively, 6 and 4 μm. These approximations yielded a total number of Pol II clusters in a living cell nucleus to be 2324, consistent with the reported range between 500 and 8000, which were mainly measured in fixed cells.11,14−17 In living cells, Pol II clusters also exhibited different sizes (cluster areas) with a median cluster area of 29 655 ± 2478 nm2 or 194.4 ± 7.9 nm (N = 2519 clusters, n = 10 cells) in diameter if the cluster is assumed as a circular shape (Figure 1f). The nearest neighbor distance between Pol II clusters indicates that, in the living cell, Pol II clusters are spaced 595.3 ± 43.2 nm (n = 10 cells) apart (Figure 1g), which is similar to the results demonstrated in previous works.29,33
In addition to quantitating Pol II clusters in living cells, Bayesian nanoscopy is also able to directly visualize their dynamics at high spatiotemporal resolution. To monitor Pol II clustering processes, 200-frame raw data (50 fps) were acquired to reconstruct a corresponding Bayesian super-resolution image and the time lapse super-resolution movie was obtained with a sliding window of 4 s (Figure 2). It is shown that some Pol II clusters were assembled within 8 s, such as the one marked by ellipse 1 (Figure 2b−i). This assembly time scale is similar to previously reported values.8 It is also worth noting that although for large, prominent clusters like the one marked by ellipse 1, the change of their sizes may be inferred by the change of the fluorescence intensity in the conventional time lapse images, it is not possible to do so for small clusters (Figure 2b−e). In contrast, with subdiffraction resolution offered by Bayesian nanoscopy, the dynamic change of cluster sizes can be directly observed (Figure 2f−i). More importantly, Bayesian nanoscopy was also able to capture the disassembly process of Pol II clusters which is inaccessible by either conventional imaging or tcPALM.8 An example of such cluster disassembly events is marked by ellipse 2, where a Pol II cluster became about 50% smaller in 8 s (Figure 2f−i). We superimposed the 3 time-lapse images to check the dynamics of multiple Pol II clusters in the same period of time (Figure 2e,i). The results show that while no meaningful information can be extracted from the conventional image (Figure 2e), the D
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ACS Nano Bayesian nanoscopy result clearly indicates the asynchronous nature of Pol II clusters (Figure 2i). To the best of our knowledge, this is the first time that the dynamic processes of both assembly and disassembly of Pol II clusters were imaged directly, with a spatial resolution of ∼50 nm and a temporal resolution of 4 s. Observation of Pol II clustering dynamics with high spatiotemporal resolution opens a new avenue to the understanding of transcription regulation in live cells. Regarding the formation of transcription factories, three main models have been proposed, namely preassembled model, ondemand model and steady-state model. We set to study these models using Bayesian nanoscopy by measuring the dynamics of Pol II clusters under serum stimulation. Previously, Cisse et al. studied the assembly dynamics of Pol II clusters using tcPALM and reported that the burst size and lifetime of Pol II clusters increased dramatically at the condition of serum stimulation, which can coordinately enhance the expression of serum-responsive genes.8 Our Bayesian nanoscopy recapitulated this phenomenon in a direct and dynamic fashion with additional information on the disassembly dynamics (Figure 3). For serum-stimulated cells, we found that the median cluster area was 29 295 ± 2514 nm2 (N = 3862 clusters, n = 12 cells), which was similar to that in normal cells, whereas the number of clusters in serum-stimulated cells (322 ± 61, n = 12 cells) was larger than that in normal cells (252 ± 66, n = 10 cells). We quantified Pol II clusters at 3 time points of the Bayesian superresolution images (Figure 3b−g). The results show that upon serum stimulation, the number of Pol II clusters went through a quick increase from 4 to 18 s (Figure 3h). Meanwhile, the median size of Pol II clusters did not change from 4 to 18 s. These data suggest that serum stimulates a burst of de novo Pol II cluster formation, in favor of the on-demand model. From 18 to 32 s, both the number and size of Pol II clusters decreased, indicating that some Pol II clusters were undergoing disassembly process (Figure 3h). Further analysis of three prominent clusters (marked by the dash boxes in Figure 3b−g) shows that all three clusters survived the total imaging time of 32 s (Figure 3i), which accorded with the previously reported lifetime (∼48 s) of Pol II clusters after serum stimulation.8 As shown in Figure 3i, from 4 to 18 s, clusters R1 and R2 underwent a tardy growth in size, while cluster R3 went through a decrease in size. Then all three clusters reduced its size from 18 to 32 s. These results suggest that, even in the same nucleus, distinct Pol II clusters behaved in different manners, reflecting the complexity of transcription regulation. Next, we interrogated Pol II clusters in different transcriptional states of cells using Bayesian nanoscopy (Supplementary Note 1 in Supporting Information). Specifically, we studied Pol II clusters in cells that were treated with transcriptional inhibitors, actinomycin D (ActD) and 5,6-dichloro-1-β-Dribofuranosylbenzimidazole (DRB) and cells that underwent serum stimulation. DRB inhibits transcriptional elongation by targeting positive elongation factor b (P-TEFb),34,35 while ActD intercalates into DNA at the transcription initiation complex and stalls Pol II elongation.35,36 We investigated how DRB treatment, ActD treatment, and serum stimulation affect Pol II clustering by analyzing the size and density of Pol II clusters in living cells. As shown in Figure 4a, the distribution of Pol II cluster size in DRB treated cells is similar to that in normal cells. Interestingly, the other transcription inhibitor, ActD, reduced the number of giant clusters, and serumstimulated cells had evidently more giant clusters than ActD
Figure 4. (a) Proportion distribution of cluster area of all clusters. The number of giant clusters (cluster area >3.5 × 1005 nm2) among all clusters is given in the box plot (inset, the “+” denotes mean value, n = 10 cells). (b) Histogram of cluster densities under different treatment conditions (n = 10 cells).
treated cells (Figure 4a, inset), suggesting that serum stimulation promotes formation of large clusters. The number of Pol II clusters may also indicate the transcription activity. We analyzed the relationship between the cluster density (cluster number divided by nucleus area) and the transcription activity of cells under different treatment conditions. As shown in Figure 4b, the Pol II cluster density in serum-stimulated cells (31.4 ± 1.5 μm−2, N = 2519 clusters, n = 10 cells) is significantly higher than that in normal cells (26.1 ± 3.7 μm−2, N = 3255 clusters, n = 10 cells) and DRB/ActD treated cells (24.6 ± 4.4 μm−2, N = 2488 clusters, n = 10 DRB treated cells and 24.8 ± 5.3 μm−2, N = 2553 clusters, n = 10 ActD treated cells, respectively), while no significant difference is observed between normal and DRB/ActD treated cells. Considering that DRB and ActD inhibit transcription elongation, these results suggest that formation of Pol II clusters is likely to take place before the transcription elongation step, consistent with the previous findings.8,37
CONCLUSIONS Understanding the organization and functions of transcription factories requires observation of both static characteristics and dynamics of Pol II clusters inside live cell nuclei. Optical diffraction-limited spatial resolution has been preventing direct observation of individual Pol II clusters using fluorescence microscopy given the high density of Pol II molecules in the nucleus.33 Moreover, the dynamic nature of transcription factories adds another layer of complexity for the investigation. Although a number of EM studies have described Pol II clusters E
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MATERIALS AND METHODS
in terms of number, size, and constituents, observation of their dynamics is still largely lacking, which calls for live cell superresolution nanoscopy capable of imaging transcription factories at a high spatiotemporal resolution. The dynamic information on Pol II clusters with high spatiotemporal resolution has the potential to unravel the principles of transcription regulation in Pol II clusters. In this work, both spatial organization and temporal dynamics of Pol II clusters were directly observed and quantified using Bayesian nanoscopy and cluster analysis. With ∼50 nm subdiffraction limit spatial resolution offered by Bayesian nanoscopy, the size and number of Pol II clusters were measured in living cell nuclei. The values are consistent with those obtained previously in fixed cells. More importantly, with a temporal resolution of 4 s, it is for the first time that the dynamic processes of Pol II clusters, including both cluster assembly and disassembly, were directly observed. As Bayesian nanoscopy is intrinsically compatible with live cells, it allows us to study transcription states in response to stimulations and repressions in a timely and physiological manner. Previously, a number of studies suggest that transcription factories are preassembled stable structures that recruit genes upon transcription activation.38−42 In contrast, the on-demand model suggests that transcription factories are dynamic structures that form de novo via recruitment of transcription machineries by coassociated active genes.35,43−46 In another scenario, transcription factories are thought to be structures in steady state with constant flux of transcription regulators and factors.47 This model is supported by fluorescence recovery after photobleaching (FRAP) assays in living cells and it is not exclusive with the other two models. The inconsistency among these models seems more likely caused by the differences in sample processing and experimental approaches. While most observations in fixed cells support the preassembled stable structure model, live cell studies are in favor of the dynamic ondemand model.36,37,47 Our live-cell Bayesian nanoscopy studies shed light on indepth understanding of transcription factories. First, directly visualization of appearing, disappearing, and size changes of Pol II clusters in the live cell super-resolution images reveals the asynchronous nature of Pol II clusters. Second, the abrupt increase in the number of Pol II clusters upon serum activation supports the on-demand model, which may underlie the enhanced transcription efficiency of serum responsive genes and also imply an important role of transcription factors, e.g., serum response factor (SRF), for transcription factory formation. However, it is beyond the scope of this work to study the role of SRF. Third, using DRB and ActD to block the elongation phase of transcription, we proved that Pol II clusters are likely to form before the transcription elongation step, possibly playing a role in the formation of preinitiation complex and the regulation of transcription initiation. In the future, with the development of novel reversibly switchable fluorescent proteins (RSFPs) with high blinking ranges, brightness, and photostability,48,49 Bayesian nanoscopy would enable characterization of Pol II with single molecule sensitivity and long-term, real time observation, whereby kinetics of individual Pol II clustering may be quantified more completely, deepening our understanding of transcription regulation through Pol II clusters. In conclusion, our method exemplifies a general approach for studies of complex nanostructure dynamics in the crowded space of a live cell nucleus.
Dendra2-Pol II Cell Line Engineering. A U2OS cell line that stably expresses the Pol II catalytic subunit RPB1 fused with Dendra2 was constructed as previously described.8 Plasmids coding Dendra2 fused to the N-terminus of an α-amanitin-resistant mutant of RPB1 (Dendra2-RPB1Amr) was a kind gift of Xavier Darzacq. U2OS cells were cultured to ∼70% confluence in a 6 cm Petri dish and then transfected with the Dendra2-RPB1Amr plasmid using Fugene 6 (E2691) from Promega, according to the supplier’s recommendations. The next day, cells were passaged to three new 10 cm dishes, with 70%, 20%, and 10% of cells, respectively. Selection was carried out 10 h after passage by adding 2 μg/mL α-amanitin (A2263 from SigmaAldrich) to the culture media. Single colonies were picked and cultured separately in DMEM Low Glucose media with 1 μg/mL αamanitin. Cells were selected by microscopic observation and Western blot. Optical Setup for Pol II 3B Imaging. Pol II 3B imaging used an Olympus inverted microscope equipped with a 100 X oil objective (Olympus, NA 1.49) and EMCCD (Andor-897). A 488 nm laser (MPB Communications, 500 mW) was used to excite the fluorophore Dendra2. A GFP filter was used to detect the green emission of Dendra2. The intensity of 488 nm laser was set high enough to produce blinking but not strong enough to completely bleach the fluorophores during the acquisition. The maximum power near the back pupil of the objective was about 1 mW for the 488 nm laser. Pol II 3B Analysis. The Bayesian nanoscopy, Bayesian analysis of Bleaching and Blinking (3B), is a localization microscopy analysis method that addresses the high-density fluorophore data extracted from live cells with standard fluorescent proteins.30 In 3B analysis, the entire image sequences are modeled as a set of blinking and bleaching “3B fluorophores” (3B spots), and the properties of blinking and bleaching are utilized by hybridizing two hidden Markov model inference methods to improve the obtained accuracy of fluorophore positions.50 Owing to the extremely slow speed during 3B reconstruction for a whole large image at a time, we first generate many small overlapping masks that indicate which area of the image to analyze. Then, we launch the 3B analysis program for each area and get a list of 3B spots’ 2D coordinates. Next, we stitch all areas together and average all the overlapping areas. Finally, we endow every 3B spot with the same Gaussian point spread function (PSF) and give the density image a false RGB colormap (formula provided in the Supplementary Note 2 in Supporting Information). The 3B analysis program is downloaded from the open-source code online,30 and other assistant programs for reconstruction are written in MATLAB. Taking calculation resolution into consideration, 200 iteration cycles were required to reconstruct one Bayesian nanoscopy image (Figure S3). In addition, the Bayesian nanoscopy analysis of tcPALM data shows evident colocalization with the tcPALM image (Figure S4). See more details in Supporting Information. Identification of Pol II Clusters. Admittedly, spots obtained from the 3B analysis are far from the real fluorophores in terms of the total number and localization precision, but there is an obvious positive correlation between the degree of 3B spot clustering and the possibility of existence of a real Pol II cluster. Thus, we appropriately clustered together the 3B spots via DBSCAN and the neighborhood radius (Eps)51 was chosen as 50 nm based on the spatial resolution of Bayesian nanoscopy and the previously reported size of Pol II clusters. Once a set of 3B clusters was generated, we coarse-grained the whole imaging region as 5 nm × 5 nm bins (equivalent to the pixel size of 3B reconstruction images) and calculated the area of each 3B cluster by counting the number of bins that contain 3B spots in the identified cluster. Note that there is no significant difference between the 3B reconstruction results (with 5 nm × 5 nm pixel size) of 200 iteration cycles and 300 iteration cycles (Figure S3). For each identified 3B cluster, in order to fill the possible vacant bins among those 3B-spotdistributed bins, we remapped its area using 30 nm × 30 nm bins. The “30 nm” bin size was chosen mainly based on the mean and median F
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ACS Nano NND of randomly distributed spots within the whole nucleus region (see simulation results in Figure S2). Statistical Analysis. Statistically significant differences between different treatment conditions were identified using Student’s t test. Significance levels were indicated as *p < 0.05, **p < 0.01, ***p < 0.001. The error bar was indicated as standard deviation.
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ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsnano.5b07257. Additional experimental data (PDF) Movie S1 (AVI)
AUTHOR INFORMATION Corresponding Author
*E-mail:
[email protected]. Author Contributions ⊥
X.C., M.W., and M.M.Z. contributed equally to this work. X.C. and Y.S. designed the project. X.C. and M.W. performed experiments and collected the raw data. M.W. prepared the samples. X.C, M.Z., and Y.S. performed Bayesian reconstruction and data analysis. X.C., M.W., M.Z., and Y.S. wrote the manuscript. All authors participated in discussion and editing of the manuscript.
Notes
The authors declare no competing financial interest.
ACKNOWLEDGMENTS We thank Mr. F. Xu and Dr. F. Zhang (Institute of Computing Technology, Chinese Academy of Sciences) for helpful discussion and providing the computing platform, Dr. S. Cox (King’s College London), Dr. H. Cang (Waitt Advanced Biophotonics Center) for the open-source code and helpful guidance about the 3B algorithm. Especially, X. Z. Chen thanks Dr. S. Cox (King’s College London), Dr. G. E Jones (King’s College London) and Dr. P. F. Roberts (King’s College London) for Bayesian nanoscopy instruction during the 2015 Croucher Summer Course in Hong Kong University. M. Wei thanks Dr. X. Darzacq (University of California, Berkeley) and Dr. I. I. Cisse (Massachusetts Institute of Technology) for useful discussions. This work is supported by grants from the National Science Foundation of China 21573013, 21390412, 31271423, and 31327901, 863 Program SS2015AA020406 and CAS Interdisciplinary Innovation Team for Y.S. REFERENCES (1) Jackson, D. A.; Hassan, A. B.; Errington, R. J.; Cook, P. R. Visualization of Focal Sites of Transcription within Human Nuclei. EMBO J. 1993, 12, 1059−1065. (2) Wansink, D. G.; Schul, W.; van der Kraan, I.; van Steensel, B.; van Driel, R.; de Jong, L. Fluorescent Labeling of Nascent RNA Reveals Transcription by RNA Polymerase II in Domains Scattered throughout the Nucleus. J. Cell Biol. 1993, 122, 283−293. (3) Iborra, F. J.; Pombo, A.; Jackson, D. A.; Cook, P. R. Active RNA Polymerases Are Localized within Discrete Transcription Factories. J. Cell Sci. 1996, 109, 1427−1436. (4) Rieder, D.; Trajanoski, Z.; McNally, J. G. Transcription Factories. Front. Genet. 2012, 3, 221. (5) Papantonis, A.; Cook, P. R. Transcription Factories: Genome Organization and Gene Regulation. Chem. Rev. 2013, 113, 8683−8705. (6) Li, G.; Ruan, X.; Auerbach, R. K.; Sandhu, K. S.; Zheng, M.; Wang, P.; Poh, H. M.; Goh, Y.; Lim, J.; Zhang, J.; Sim, H. S.; Peh, S. Q.; Mulawadi, F. H.; Ong, C. T.; Orlov, Y. L.; Hong, S.; Zhang, Z.; G
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