Dynamic Imaging of RNA in Living Cells by CRISPR-Cas13 Systems

Technology

Dynamic Imaging of RNA in Living Cells by CRISPRCas13 Systems Graphical Abstract

Authors Liang-Zhong Yang, Yang Wang, Si-Qi Li, ..., Huang Wu, Gordon G. Carmichael, Ling-Ling Chen

Correspondence [email protected]

In Brief Yang et al. show that the dCas13 system is capable of labeling RNAs. Applying orthogonal dCas13s or combining with dCas9 allows simultaneous visualization of RNA-RNA and DNA-RNA in living cells. The dCas13 system is user friendly in real-time RNA imaging without requiring genetic manipulation.

Highlights d

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Identification of CRISPR-dPspCas13b and -dPguCas13b for RNA imaging Robust tracking of NEAT1 with dPspCas13b and single gRNAs in living cells Tracking of NEAT1 reveals ‘‘kiss-and-run’’ and ‘‘fusion’’ models of paraspeckle dynamics Simultaneous RNA-RNA/DNA labeling by orthogonal dCas13s or combined with dCas9

Yang et al., 2019, Molecular Cell 76, 1–17 December 19, 2019 ª 2019 Elsevier Inc. https://doi.org/10.1016/j.molcel.2019.10.024

Please cite this article in press as: Yang et al., Dynamic Imaging of RNA in Living Cells by CRISPR-Cas13 Systems, Molecular Cell (2019), https:// doi.org/10.1016/j.molcel.2019.10.024

Molecular Cell

Technology Dynamic Imaging of RNA in Living Cells by CRISPR-Cas13 Systems Liang-Zhong Yang,1,4 Yang Wang,1,4 Si-Qi Li,1 Run-Wen Yao,1 Peng-Fei Luan,1 Huang Wu,1 Gordon G. Carmichael,2 and Ling-Ling Chen1,3,5,* 1State Key Laboratory of Molecular Biology, Shanghai Key Laboratory of Molecular Andrology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai 200031, China 2Department of Genetics and Developmental Biology, University of Connecticut Stem Cell Institute, University of Connecticut Health Center, Farmington, CT 06030-3301, USA 3School of Life Science and Technology, ShanghaiTech University, 100 Haike Road, Shanghai 201210, China 4These authors contributed equally 5Lead Contact *Correspondence: [email protected] https://doi.org/10.1016/j.molcel.2019.10.024

SUMMARY

Visualizing the location and dynamics of RNAs in live cells is key to understanding their function. Here, we identify two endonuclease-deficient, single-component programmable RNA-guided and RNA-targeting Cas13 RNases (dCas13s) that allow robust realtime imaging and tracking of RNAs in live cells, even when using single 20- to 27-nt-long guide RNAs. Compared to the aptamer-based MS2-MCP strategy, an optimized dCas13 system is user friendly, does not require genetic manipulation, and achieves comparable RNA-labeling efficiency. We demonstrate that the dCas13 system is capable of labeling NEAT1, SatIII, MUC4, and GCN4 RNAs and allows the study of paraspeckle-associated NEAT1 dynamics. Applying orthogonal dCas13 proteins or combining dCas13 and MS2-MCP allows dual-color imaging of RNAs in single cells. Further combination of dCas13 and dCas9 systems allows simultaneous visualization of genomic DNA and RNA transcripts in living cells.

INTRODUCTION Functions of RNAs are associated with their unique subcellular localizations, and effective imaging techniques for localization are needed. Single-molecule fluorescence in situ hybridization (smFISH) of RNA is most often used to visualize RNAs in fixed cells (Femino et al., 1998; Raj et al., 2008). Some studies have used 5-ethynyl uridine (EU) (Sirri et al., 2016) or bromouridine (BrU) (Koberna et al., 2002) to track the dynamic changes of total RNAs in time-lapse experiments. However, in living cells, conventional FISH techniques are not suitable, owing to the inability to wash away unbound probes coupled with probe instability, leading to the inability to visualize and track single RNA species.

Stem-loop labeling and fluorescence protein tagging by the MS2-MCP system has been mostly used to label RNAs of interest in live cells (Ben-Ari et al., 2010; Larson et al., 2011; Wu et al., 2012), but such genetic insertion of dozens of MS2 aptamers into particular RNA gene loci makes it inconvenient for widespread application, and a common concern is whether such insertions affect RNA structure, dynamics, expression, or function. Other approaches utilizing fluorogenic RNA aptamers, such as Spinach (Paige et al., 2011) and Broccoli (Filonov et al., 2014), have been most successful in bacteria. Molecular beacons, fluorogenic oligonucleotide probes, provide researchers a way to visualize RNA (Chen et al., 2017; Tyagi and Kramer, 1996); however, their use is costly and requires repeats within transcripts to improve the signal-to-noise ratio (SNR) of detected signals. Recent studies showed that nuclear-localized RNA-targeting Cas9 (RCas9) can target RNA and can be used to visualize highly abundant mRNAs, such as b-actin in live cells (Batra et al., 2017; Nelles et al., 2016). However, it is not clear whether it is sufficient and effective for labeling other types of RNAs. To date, a system to visualize specific RNAs in live cells in an effective and user-friendly manner has been lacking. CRISPR-Cas13 is a recently identified RNA-guided and RNAtargeting RNase protein family, including Cas13a, Cas13b, Cas13c, and Cas13d, which are single-component programmable RNases with functions in RNA processing and programmed RNA cleavage (Abudayyeh et al., 2016; East-Seletsky et al., 2016; Konermann et al., 2018; Smargon et al., 2017; Yan et al., 2018). Recent engineering of CRISPR-Cas13 has achieved precise RNA targeting and editing in mammalian cells (Abudayyeh et al., 2017; Cox et al., 2017; Konermann et al., 2018), thus providing a promising new suite of tools for RNA imaging in live cells (Yang and Chen, 2017). However, so far, only RNA endonucleolytic-activity-deficient (d)LwaCas13a has been shown to have the ability to label the abundant mRNA b-actin after stress (Abudayyeh et al., 2017). Can less abundant RNAs be visualized with a dCas13/gRNA (guide RNA) system? Are there dCas13 proteins that work better than dLwaCas13a for tracking RNA dynamics in living cells? Which dCas13 works best to achieve the highest specificity and the most robust SNR? Can Molecular Cell 76, 1–17, December 19, 2019 ª 2019 Elsevier Inc. 1

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multi-color labeling be achieved for RNA in living cells? How efficient can dCas13/gRNA-mediated RNA labeling be, compared to the traditional MS2-MCP system? Answering these questions will broaden CRISPR-dCas13 applications for directly imaging RNA dynamics in live cells. DESIGN To explore the utility of the CRISPR-dCas13 system for real-time RNA imaging, we screened a panel of catalytically dead (d) Cas13 homologs fused with enhanced green fluorescent protein (EGFP) by targeting the long noncoding RNA (lncRNA) NEAT1 with different single gRNAs, followed by examination of EGFP signals that colocalize with paraspeckle marker proteins in different types of human cells (Figures 1A–1E). NEAT1 is a structural RNA component of nuclear domains called paraspeckles (Chen and Carmichael, 2009; Clemson et al., 2009; Naganuma et al., 2012; West et al., 2016), and each paraspeckle in HeLa cells contains on average
50–60 copies of NEAT1 (Chujo et al., 2017). We postulated that this would allow us to identify effective dCas13 proteins, if any, suitable for binding and tracking RNA with medium level of abundance in live cells. Once such dCas13s are identified, we would further optimize this system to achieve enhanced efficiency for RNA labeling in real time and in space, ideally, to a comparable level as the traditional MS2-MCP system. Finally, we wished to explore the optimized dCas13 system to track coding and noncoding RNAs, to study dynamics of different types of nuclear domain-related lncRNAs, and to explore the feasibility of dual-color RNA/RNA and RNA/DNA tracking in single living cells with orthogonal dCas13 proteins, combined dCas13 and MS2MCP systems, or dCas13 and dCas9 systems. Together, we reasoned that the dCas13/gRNA system would provide a useful tool for labeling and tracking RNAs in live cells with high efficiency, convenience, and robustness. RESULTS CRISPR-dCas13 RNA Labeling Systems Enable Visualization of NEAT1 in Live Cells Using Single gRNAs To screen for CRISPR-dCas13 homologs suitable for RNA visualization in live cells, we chose eight Cas13 proteins—Lachno-

spiraceae bacterium (Lba)Cas13a, Leptotrichia wadei (Lwa) Cas13a, Prevotella sp. P5-125 (Psp)Cas13b, Porphyromonas gulae (Pgu)Cas13b, Porphyromonas gulae (Ran)Cas13b, Eubacterium siraeum DSM15702 (Es)Cas13d, Anaerobic digester metagenome 15706 (Adm)Cas13d, and Ruminoccocus flavefaciens XPD3002 (Rfx)Cas13d (Abudayyeh et al., 2016; East-Seletsky et al., 2016; Konermann et al., 2018; Smargon et al., 2017; Yan et al., 2018)—as candidates to visualize NEAT1. NEAT1 contains two isoforms, long NEAT1_2 and short NEAT1_1, that overlap at their 50 ends, and both isoforms are localized to nuclear paraspeckles (Chen and Carmichael, 2009; Clemson et al., 2009; Naganuma et al., 2012; West et al., 2016). Since NEAT1 is a structural RNA that exists in compact, phase-separated paraspeckles (Yamazaki et al., 2018), finding accessible Cas13 binding sites might be problematic. We first designed two gRNAs targeting NEAT1 based on antisense oligonucleotide sequences previously shown to have more than 60% knockdown efficiency (Figure 1A) (Sunwoo et al., 2009). These gRNAs target both NEAT1_1 and the 50 region of NEAT1_2 (Figure 1A). Here, we used HeLa cells as our screening cell line due to the high number of paraspeckles (
10–20) per cell among several cell lines we examined (Figure S1A). mRuby3 was knocked into the NONO locus in HeLa cells (mRuby3-NONOKI cells) to indicate paraspeckles. Next, we fused EGFP to each dCas13 protein and delivered the dCas13-EGFP proteins along with gRNAs to mRuby3-NONO-KI cells (Figure 1B). Three among eight expressed proteins (Figure S1B), dPspCas13b, dPguCas13b, and dRfxCas13d, showed signals that colocalized with NONO-mRuby3 (Figure 1C). However, dRfxCas13d accumulated with NONO in the gNC (non-targeting control guide RNA) group (Figure 1C), but NEAT1 RNA FISH showed that most dRfxCas13d-tagged signals were not NEAT1 RNA (Figure S1C). This indicated that dRfxCas13d may nonspecifically accumulate with NONO, excluding it for RNA labeling. Next, we compared the SNR (Figures S1D and S1E) of these dCas13 proteins to evaluate labeling efficiencies. Results showed that dPspCas13b is the most efficient dCas13 protein to label RNA, followed by dPguCas13b (Figure S1E). Further analysis confirmed colocalization between NEAT1, labeled by the dCas13 system, with NONO (Figure S1F). Of note, although dLwaCas13a was shown to successfully label actin mRNAs

Figure 1. CRISPR-dCas13 Enables Visualization of NEAT1 in Live Cells with Single gRNAs

(A) Illustration of gRNAs used in labeling NEAT1 by CRISPR-dCas13. These gRNAs were designed to specifically target 50 , mid, and 30 regions of NEAT1_2. Note that 50 -targeted gRNAs can also target NEAT1_1. (B) Overview of CRISPR-dCas13-mediated NEAT1 labeling. The NONO-mRuby3 knocked-in HeLa cell line was used to confirm paraspeckle signals labeled by each CRISPR-dCas13 protein combined with gRNAs. (C) Representative images of different dCas13 subtype proteins (including 2 dCas13a, 3 dCas13b, and 3 dCas13d proteins) used in NEAT1 labeling with g50 -1 and -2 (top) or gNC (guide non-targeting control, bottom) in the NONO-mRuby3 knocked-in HeLa cell line. (D) Representative images of colocalizations between dPspCas13b-labeled NEAT1 signals (green) and paraspeckle proteins mCherry-RBM14 (red) and mCherry-FUS (red) in live HeLa cells. (E) Representative images of dPspCas13b-mediated NEAT1 labeling (green) that is colocalized with mCherry-NONO (red) in both live HT29 and U2OS cells. (F) Representative images of dPspCas13b-mediated NEAT1 labeling in HeLa cells with gRNAs targeting 50 , mid, or 30 regions of NEAT1_2. (G) Statistics of signal-to-noise ratio (SNR) of dPspCas13b-labeled NEAT1 signals with gRNAs shown in (F). Data are represented as mean ± SD of SNR from 62, 33, 30, 45, and 37 puncta, respectively; n.s., no significance. (H) Left, smFISH confirmed the colocalization of dPspCas13b signals using g50 -1 and -2 to target NEAT1 in fixed HeLa cells. Right, line scan of the relative fluorescence intensity of the signal indicated as the dotted line in the left panel. (I) Colocalization analysis between dPspCas13b-labeled NEAT1 and smFISH-labeled NEAT1 quantified per cell revealed by Pearson’s correlation. Data are represented as mean ± SD from 34 and 13 cells, respectively.

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under stress (Abudayyeh et al., 2017), this protein did not exhibit detectable signals for NEAT1 (Figures 1C and S1E). CRISPR-dCas13 RNA Labeling Is Compatible with Protein Visualization and Has High Labeling Efficiency Most RNAs have roles in gene regulation and are associated with RNA-binding proteins. NEAT1-based paraspeckles contain more than 40 proteins, including NONO, RBM14, and FUS (Naganuma et al., 2012). We further verified the signals tagged by dPspCas13b through associations with other paraspeckle marker proteins, RBM14 and FUS. The colocalization of dPspCas13b-EGFP-tagged NEAT1 with RBM14 or FUS is similar to NONO (Figure 1D). In two other cell lines, HT29 and U2OS, we found CRISPR-dPspCas13b was also able to label NEAT1 as shown by colocalization with NONO (Figure 1E). Since NEAT1 in paraspeckles is highly organized into ‘‘coreshell’’ structures, with the 50 region of NEAT1 as well as 30 regions of NEAT1_2 in shell regions and the middle region of NEAT1_2 in core regions of paraspeckles (West et al., 2016), we next designed another 28 gRNAs specifically targeting NEAT1_2, with 16 in the middle region and 12 in the 30 region (Figure 1A). Each gRNA was tested for labeling NEAT1 with dPspCas13b. Several single gRNAs, regardless of their targeting regions, could label NEAT1 (Figures 1F and S1G). SNR analysis of these gRNA-generated NEAT1 signals in live cells showed that the 50 region of NEAT1 is more easily targeted (Figure 1G). This might be due to the fact that the 50 region of NEAT1 is most abundant in NEAT1 transcripts and the shell region of the spherical structure is less compact, thus facilitating dPspCas13b-EGFP targeting to NEAT1. Native RNA immunoprecipitation (RIP) assays of dPspCas13b showed that dPspCas13b could target NEAT1 with single gRNAs g50 -1 and g50 -2 but not with a control gRNA (Figures S1H and S1I). smFISH of NEAT1 confirmed that the dPspCas13b system labeled NEAT1 in HeLa cells (Figures 1H and 1I). Optimization of the CRISPR-dPspCas13b System Previous studies of CRISPR-Cas13-mediated RNA cleavage reported that the length and mismatch position of gRNAs contribute to diverse RNA cleavage efficiencies (Abudayyeh

et al., 2017; Cox et al., 2017). In addition, tiling screening assays emphasized the importance of gRNA targeting positions (Abudayyeh et al., 2017; Cox et al., 2017). Similarly, in characterization of the target sequence requirements for sgRNAs for DNA labeling, the length and mismatch positions of sgRNAs also strongly impact labeling efficiency (Chen et al., 2016). These results suggest that features of the gRNA sequence might influence RNA-binding activity and specificity. Therefore, we characterized determinants of gRNA sequences for dPspCas13b imaging. We first tested different guide lengths from 18 to 38 nucleotides (nt) of g50 -1 (Figure 2A), which exhibits the best NEAT1 signal in live HeLa cells (Figures 1F and 1G). Using the same metrics as described earlier, we observed that dPspCas13b could efficiently label NEAT1 with spacer lengths of 22–30 nt (Figure 2A). The shortest guide RNA (18 nt) yielded no specific paraspeckle signals but exhibited a uniform pattern, which might be attributed to unstable binding of dPspCas13b-gRNA to the target RNA or nonspecific binding to other RNAs (Figure 2B). With longer guide lengths, 34 nt and 38 nt, we also could not detect any paraspeckle puncta but did note an obvious nucleolar signal (Figure 2B), perhaps due to insufficient pairing with the target RNA. However, optimizations of gRNAs targeting other regions showed that shorter gRNAs achieved better signals as compared to the original 30-nt gRNAs, indicating gRNA-binding activity is sequence and length dependent (Figures 2C–2E). Shifting the targeting site of gRNA g50 -2 by several base pairs reduced the signal with dPspCas13b, indicating the importance of specific gRNA targeting position (Figures 2C and 2E). Other useful gRNAs, including gmid-7, g30 -2, and g30 -10, were also optimized for length and position. Some of these optimized gRNAs achieved better signals (Figures S2A–S2F). Collectively, these results indicated that the length as well as position of gRNAs for dPspCas13b are critical, and in general shorter gRNAs of 20–27 nt are more suitable. Of note, 30-nt gRNA was used for Cas13b-mediated RNA cleavage in previous studies (Cox et al., 2017; Smargon et al., 2017). To further investigate the specificity of dPspCas13b for labeling NEAT1, we next assessed dPspCas13b RNA-binding specificity.

Figure 2. Optimization of CRISPR-dPspCas13b for NEAT1 Labeling

(A) Schematic view of the length optimization of g50 -1 (left) and SNR statistics of NEAT1 signals labeled with different lengths of gRNAs (right). n = 28, 20, 62, 31, 52, 33. (B) Representative images of CRISPR-dPspCas13b-labeled NEAT1 with different lengths of gRNAs shown in (A). (C) Schematic view of the position and length optimization of g50 -2. (D) Representative image of CRISPR-dPspCas13b-labeled NEAT1 with g50 -2-v4. (E) SNR statistics of NEAT1 signals labeled by different variations of g50 -2. n = 39, 28, 21, 43. (F) Schematic view of the single nucleotide gRNA-RNA mismatch position within the g50 -1 targeting site (top) and SNR statistics (bottom) showing effects of the corresponding single nucleotide mismatches on dPspCas13b-targeting efficiency. n = 81, 53, 50, 54, 48, 39, 33, 51, 42. (G) Representative images of CRISPR-dPspCas13b-labeled NEAT1 with gRNAs bearing different single nucleotide mismatches shown in (F). (H) Schematic view of the double nucleotide gRNA-RNA mismatch position within the g50 -1 targeting site (top) and SNR statistics (bottom) showing effects of corresponding double nucleotide mismatches on the dPspCas13b-targeting efficiency. n = 50, 41, 32, 20, 40, 26, 42, 34. (I) Representative images of CRISPR-dPspCas13b-labeled NEAT1 with gRNAs bearing different double nucleotide mismatches shown in (H). (J) Schematic of 10 optimized gRNAs from 21 to 24 nt in length targeting 50 of NEAT1. (K) Representative images of NEAT1-labeled by dPspCas13b-EGFP with gRNAs shown in (J). (L) Illustration of the optimization of green fluorescent proteins fused with dPspCas13b. (M) Representative images of NEAT1 labeled by CRISPR-dPspCas13b fused with different green fluorescent proteins with g50 -1 and -2; see also Figure S2K. (N) Bar graph showing the SNR of each identified paraspeckle labeled by CRISPR-dPspCas13b fused with different green fluorescent proteins shown in (M); see also Figure S2K. n = 30, 49, 46, 50, 24, 32. In (A), (E), (F), (H) and (N), data are represented as mean ± SD.

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We designed variant g50 -1 versions bearing one or two nucleotide mismatches in the targeting region and examined the abilities of these gRNAs to guide dPspCas13b-EGFP to NEAT1 (Figures 2F–2I). Single nucleotide mismatches for most gRNAs were tolerated but with reduced labeling efficiencies (Figures 2F and 2G). Mismatches in the middle regions of gRNA spacers tended to be less efficient in labeling than those in the 30 region, and mismatches at the 50 side significantly attenuated the labeling ability (Figures 2F and 2G). Moreover, double nucleotide mismatches were less tolerated than single nucleotide mismatches, and mismatches at both positions 17 and 18 completely abolished labeling ability (Figures 2H and 2I). These results suggested that dPspCas13b RNA-binding activity is much more sensitive to gRNA-RNA pairing in the middle and direct repeat (DR)-distal regions, which are similar to previous findings for PspCas13bmediated RNA cleavage (Cox et al., 2017). Having characterized determinants that impact gRNA efficiencies (Figures 2A–2I and S2A–S2F), we designed another 10 gRNAs targeting 50 NEAT1 and transfected them individually into the NONO-mRuby3-KI HeLa cells expressing dPspCas13b-EGFP. Seven out of 10 gRNAs yielded efficient labeling of NEAT1 (Figures 2J, 2K, and S2G), suggesting that our gRNA optimization improved the dPspCas13b system for RNA labeling. After screening for the best gRNAs targeting different layers of paraspeckles and NEAT1 labeling by dPspCas13b, we asked whether a combination of gRNAs would generate better signals. We classified gRNAs into three groups, 50 region, 30 region, and both regions (Figure S2H). Although transfection of these gRNA groups did not generate noticeably stronger signals for NEAT1 (Figures S2I and S2J), we did observe that gRNA groups could reveal the core-shell structure in wide-field imaging (Figure S2I). Furthermore, since different fluorescence tags might influence the quality of signals, we tagged dPspCas13b with several different green fluorescence proteins, including sfGFP, EGFP, and mNeonGreen, as well as tandem repeats of these proteins to obtain better signals (Figure 2L). The SNR analysis showed 3 3 EGFP works best when fused with dPspCas13b (Figures 2M, 2N, and S2K). Finally, the optimized dPspCas13b system with a single gRNA (g30 -2-v3) remarkably revealed core-shell structures associated with NONO under super-resolution microscopy structured illumination microscopy (SIM) (Figures S2L and S2M).

Labeling MUC4 mRNAs and GCN4 Repeats by CRISPRdPspCas13b We next asked about the possibility of using this system to image mRNAs. MUC4 mRNA contains repeat regions in exon 2 (Nollet et al., 1998). We designed three gRNAs targeting the repeats region of MUC4 mRNA (Figure 3A) and all displayed detectable signals by recruiting dPspCas13b-3 3 EGFP to this endogenous mRNA (Figure 3B). smFISH further confirmed the accuracy and efficiency of the dPspCas13b system in labeling MUC4 mRNA (Figure 3C). Of note, similar to NEAT1 (Figure 1C), only dPspCas13b (Figures 3B, 3C, and S3B) and dPguCas13b (Figures S3A and S3B) could be targeted to MUC4 mRNA by gRNAs, while dRfxCas13d generated abnormal signals (Figure S3C). Next, we evaluated the sensitivity of dPspCas13b and single gRNAs in labeling RNA in living cells using constructs containing 4 3, 8 3, 12 3, 16 3, or 24 3 GCN4 elements (Figure 3D) (Tanenbaum et al., 2014). All three designed gRNAs targeting the GNC4 element could successfully recruit dPspCas13b-3 3 EGFP to 24 3 GCN4 elements (Figure 3D). smFISH of the GCN4 elements confirmed the labeling of GCN4 by the dPspCas13b system (Figure S3D). Focusing on one such gRNA, we showed that 16 copies of the GCN4 elements are the minimum number needed to generate convincing signals by dPspCas13b-3 3 EGFP in living cells (Figure 3D). We observed nucleoplasm-localized patterns of MUC4 mRNA (Figures 3B and 3C) and GCN4 elements (Figure 3D) by dPspCas13b-3 3 EGFP/gRNAs. This might due to the fact that we originally selected nuclear-localized dPspCas13b-fused proteins (Figures 2M and 2J). We therefore asked whether fusion of different fluorescent proteins to dPspCas13b would allow targeting to cytoplasmic RNAs. We found that dPspCas13b-3 3 sfGFP and dPspCas13b-2 3 mNeonGreen fused with 1–3 copies of nuclear localization signals (NLSs) successfully targeted GCN4 elements (Figure 3E). Further, smFISH of GCN4 elements confirmed both cytoplasmic and nuclear localization of these transcripts by these different dPspCas13b systems (Figure 3F). Statistical analysis of smFISH of GCN4 supports high specificity and efficiency of dPspCas13b-3 3 sfGFP-3 3 NLS when labeling GCN4 elements (Figures S3E and S3F). Importantly, the minimum number of copies of GCN4 elements to produce reliable signals was reduced to 8 copies (Figure 3G). Of note, compared to 2 3 NLS, one NLS fused with dPspCas13b-2 3 mNeonGreen

Figure 3. Labeling mRNAs by CRISPR-dPspCas13b (A) Schematic of applying dPspCas13b-3 3 EGFP to label MUC4 mRNAs. Three gRNAs targeting repeats in MUC4 are shown. (B) Representative images showing MUC4 mRNAs labeled by dPspCas13b-3 3 EGFP with individual single gRNAs shown in (A). (C) Representative images showing colocalization of dPspCas13b-labeled signals and smFISH-labeled signals using gMUC4-2. (D) Determination of the minimum GCN4 repeats visualized by dPspCas13b-3 3 EGFP with single gRNAs. Left, schematic of experimental design. Right, upper panels, the sequence of GCN4 repeats and three designed gRNAs; middle panels, representative images of the 24 3 GCN4 labeled by dPspCas13b-3 3 EGFP using each individual gRNAs; bottom panels, representative images showing the minimum copy number of GCN4 elements that can be visualized by dPspCas13b-3 3 EGFP with gGCN4-2. (E) Optimization of fluorescently labeled dPspCas13b for visualization of GCN4 elements in the cytoplasm. Left, schematic of experimental design. Right, representative images of 24 3 GCN4 in the cytoplasm by dPspCas13b fused with different fluorescent proteins; nuclei are shown by white dotted lines. (F) smFISH confirms 24 3 GCN4 signals labeled by dPspCas13b-3 3 sfGFP-3 3 NLS and dPspCas13b-2 3 mNeonGreen-NLS with gGCN4-2. (G) Determination of the minimum number of GCN4 elements visualized by dPspCas13b-3 3 sfGFP-3 3 ;LS with 8 3 , 12 3, or 16 3 GCN4 elements and gGCN4-2, nuclei are shown by white dotted lines. (H) Optimization of fluorescently labeled dPspCas13b for visualization of MUC4 mRNAs in the cytoplasm. Left, schematic of experimental design; right, representative images of MUC4 mRNAs using gMUC4-2 and dPspCas13b fused with 3 3 sfGFP-3 3 NLS or 2 3 mNeonGreen-NLS; nuclei are shown by white dotted lines.

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Figure 4. Dynamics of Paraspeckles in Live Cells Revealed by CRISPR-dPspCas13b, MS2-MCP-Labeled NEAT1, or Paraspeckle Proteins (A) Left, representative images showing the colocalization of 24 3 MS2-MCP-labeled NEAT1 and NONO. Right, line scan of the relative fluorescence intensity of the signal indicated by the dotted line in the left panel. (B) Effects of the dPspCas13b system and MS2-KI on NEAT1 expression, shown by RT-qPCR. Data are represented as mean ± SD from 3 independent experiments with similar results. (C) Bar graph showing SNR statistics of NEAT1 signals labeled by dPspCas13b, MS2-MCP, NONO, and TDP43. Data are represented as mean ± SD from 32, 68, 132, and 43 puncta. (D) Bar graph indicating comparison of labeling efficiency distribution between the dPspCas13b (31 cells) and MS2-MCP (30 cells) systems. (E) Schematic of labeling paraspeckles by four methods: labeling with the peripheral localized NEAT1 by the dPspCas13b (1) and MS2-MCP (2) systems, as well as labeling with core-localized NONO-EGFP (3) and shell-localized TDP43-EGFP (4). (F) Short-term imaging of paraspeckles in HeLa cells and trajectories of paraspeckles based on NONO-EGFP, the dPspCas13b system, MS2-MCP, or TDP43EGFP. The trajectory lengths are 60 frames with 1 s per frame. (legend continued on next page)

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showed increased cytoplasmic GCN4 elements (Figure 3E). Similar observations were found for the endogenous MUC4 mRNA in the cytoplasm (Figures 3H and S3G). Collectively, these results clearly revealed that the dPspCas13b system can label repeat-containing mRNAs both in the nucleus and cytoplasm. Dynamics of Paraspeckles/NEAT1 Revealed by CRISPRdPspCas13b and MS2-MCP Labeling Systems Aptamer-based strategies have been widely used in labeling and tracking RNAs (Coulon et al., 2014; Horvathova et al., 2017; Lenstra et al., 2015; Mao et al., 2011). We constructed 24 3 MS2 (24 copies of the MS2 stem loop)-NEAT1-KI HeLa cell lines to compare these two labeling systems (Figure S4A). Northern blots and smFISH showed that the MS2 sequence was knocked into the NEAT1 locus (Figures S4B and S4C). Similar to previous studies (Mao et al., 2011), MS2-NEAT1 tagged with MCP-3 3 GFP largely colocalized with NONO under wide-field microscopy (Figure 4A). Interestingly, overexpression of the dPspCas13b system had no detectable effect on NEAT1 expression (Figure 4B), but knockin (KI) of 24 3 MS2 led to reduced NEAT1 expression by an unknown mechanism (Figures 4B and S4B), raising the concern of using MS2-MCP in studying the behavior of endogenous RNAs. Compared to the dPspCas13b-3 3 EGFP labeling system, MS2-MCP-3 3 EGFP generated about 2-fold higher SNR (Figure 4C). However, colocalization statistics data of NEAT1 smFISH in MS2-NEAT1 KI cells transfected with MCP-3 3 GFP revealed labeling efficiency of only about 30%, while CRISPR-dPspCas13b reached about 80% (Figure 4D), suggesting a higher efficiency of the dCas13b system-mediated targeting to RNA than that of the MS2-MCP system. The lower RNA-labeling efficiency of MS2-MCP system is likely because not all NEAT1 loci in these aneuploid cells were knocked in by MS2 (Figures 4A and S4B). Next, we used NONO-EGFP- (the core region of the spherical paraspeckles), TDP43-EGFP- (the shell region of the spherical paraspeckles), dCas13b-, and MS2-MCP-labeled NEAT1 (the peripheral region of the spherical paraspeckles) to study dynamics of different regions of single paraspeckles in live cells (Figure 4E). All four types of labeling of paraspeckles achieved reliable labeling signals (Figure 4C). We performed 1 s per frame time-lapse microscopy to track the movement of NONO-EGFP, TDP43-EGFP, or NEAT1 in live HeLa cells and analyzed the movement kinetics through characterization of each paraspeckle step displacement in two-dimensional space (Figures 4F–4H). Single-particle tracking showed that movement of paraspeckles tracked by NONO is more constrained than that of either CRISPR-dPspCas13b or MS2-MCP signals for NEAT1 RNA, as well as the shell-region-localized TDP43EGFP within 60 s short-term scales (Figure 4F). Further step distance analysis also strengthened the conclusion that paraspeckles labeled by NONO exhibited more confined movement

dynamics than that seen in both dPspCas13b and MS2-MCP systems that label the 50 regions of NEAT1, and TDP43-EGFP as well (Figures 4G and 4H). These results are consistent with known paraspeckle organization in fixed cells (West et al., 2016) and further reveal that NONO, which is more confined to the core region of paraspeckles, is more constrained in its movement, while the shell region, labeled by TDP43-EGFP, and the dPspCas13b- and MS2-MCP-labeled 50 region of NEAT1, is less constrained (Figure 4E). Models of Paraspeckle Dynamics in Living Cells Previous studies using EM and SIM demonstrated a flexible morphology of paraspeckles between globular and elongated appearance (Hirose et al., 2014; Wang et al., 2018; West et al., 2016), but the morphology dynamics were unclear. We performed long-term time-lapse imaging of paraspeckles labeled by different approaches (Figure 4E) and recorded both fusion and fission events (Figures 5A–5E and S5A–S5F; Videos S1, S2, S3, and S4). With 5-min-interval time-lapse imaging of the CRISPR-dPspCas13b labeling, the relative fluorescence intensity analysis of NEAT1/paraspeckles showed clear fusion progress (Figures 5A and 5B, left; Video S1). To limit biases in 2D (two dimensions) imaging and to obtain a more direct description of the fusion events, we rendered paraspeckles during the fusion events in 3D (three dimensions) (see Method Details). The 3D rendering of paraspeckle objects as described in 2D before articulated the fusion events in both x-z plane (Figure 5C, left) and x-y plane (Figure 5D, left). Moreover, the total intensity of separated paraspeckles labeled by CRISPR-dPspCas13b during the long-term time-lapse imaging also revealed fusion events within 5 min (Figure 5E, left). Similar fusion events were also accurately observed when we shortened the time interval to 1 min (Figures 5A–5E, right; Video S2). Moreover, short-timeinterval-mediated NEAT1 tracking revealed paraspeckle interaction events to be consistent with a ‘‘kiss-and-run’’ model (Figure 5F), where some interactions failed to form a continuous fused signal (Figure 5E, right). Using NONO-EGFP to track paraspeckle protein or the MS2MCP system for NEAT1 with 1-min intervals for imaging also supported this model, shown in Figure 5F (Figures S5A–S5F; Videos S3 and S4). For example, in MS2-MCP time-lapse imaging, the smaller paraspeckle (paraspeckle 3) split from paraspeckle 2 and eventually fused with paraspeckle 1 after several random collisions (Figures S5B, S5D, and S5F; Video S3). These findings together suggest that paraspeckles undergo a ‘‘kissand-run/fusion’’ mode of dynamics in live cells after de novo formation, and this depends on NEAT1 transcription (Mao et al., 2011) and the seeding assembly pattern of PSPs on NEAT1_2 (Figure 5F) (Yamazaki et al., 2018). Quantification revealed about 25% of ‘‘kissing’’ ultimately resulted in elongated paraspeckles (Figure 5G). Under stress conditions after sodium arsenate (SA) treatment (Wang et al., 2018), we found more elongated

(G) Scatterplots of the step displacement (dx, dy) of NONO-labeled, dPspCas13b-labeled, MS2-MCP-labeled, and TDP43-labeled paraspeckles show the shortterm paraspeckle dynamics of each second within 120 s. dxt = ðxt xt1 Þ and dyt = ðyt  yt1 Þ, where (xt, yt) is the coordinate of puncta at time t. Movements were tracked every 1 s for 120 s and calculated every 1 s. (H) Comparison of the average step distance of NONO- (6,575 steps), dPspCas13b- (7,278 steps), MCP- (2,653 steps), and TDP43-labeled (3,363 steps) paraspeckles.

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paraspeckles under SIM (Figure S5G) and observed an enhanced ‘‘fusion’’ mode (Figures 5G and S5H; Video S5). Dual-Color RNA Labeling Using Different dCas13b Systems and a Combination of dCas13b and MS2-MCP Systems Next, we tested whether dPspCas13b and MS2-MCP could label RNAs simultaneously. We transfected dPspCas13b-3 3 EGFP, gRNAs for NEAT1, and MCP-mRuby3 into MS2-NEATKI HeLa cells (Figure 6A). Microscopic images showed paraspeckles tagged with both EGFP and mRuby3, corresponding to the labeling with either the dPspCas13b or the MCP-MS2 system (Figures 6B and 6C). Similar to previous observations (Figure 4D), dPspCas13b targeted more paraspeckles than MS2-MCP (Figures 6B and 6C). In addition to the combination of two different labeling methods at the same time for labeling NEAT1, we asked whether CRISPR-dCas13 could be applied to observe other RNA-based nuclear bodies. SatIII RNA aggregates and appears in nuclear stress bodies (nSBs) after stresses such as heat shock, UV-C (ultraviolet C), and SA treatments (Valgardsdottir et al., 2008). We treated the HAP-mEmerald- and HSF1-mRuby3 (marker proteins of nSBs)-transfected cells with SA and heat shock followed by SatIII FISH. The imaging data showed strong association among these two proteins and SatIII RNA (Figure S6A). For CRISPR-dCas13 labeling of SatIII, we designed two gRNAs based on the annotated sequences of pHuR98 (verified in SatIII transcripts) (Jolly et al., 2002) (Figure S6B). Under SA or heat shock treatments, we observed the aggregated signals of dPspCas13b-3 3 EGFP in nSBs associated with HSF1mRuby3 mediated by either of these two gRNAs (Figures 6D–6F) but not control gRNA (Figure S6C). The dPspCas13generated SNR of SatIII signals is higher than that of NEAT1 (Figures 4C and 6F), likely due to the repeated sequence in SatIII (Figure S6B) and consistent with results shown in Figure 4. Intriguingly, nSBs identified by SatIII tagged with both dPspCas13b and HSF1 showed different morphologies under SA or heat shock treatment (Figure S6D), perhaps indicating the hyperactive transformation of nSBs during their formation. By targeting these two sites, dPguCas13b-3 3 EGFP could also label SatIII in nSBs that are associated with HSF1 and exhibited similar signals with dPspCas13b-3 3 EGFP (Figure S6E). We further confirmed signals labeled by dPspCas13b and dPguCas13b with smFISH of SatIII (Figures S6F and S6G). Previous computational analysis (Smargon et al., 2017) showed that PspCas13b and PguCas13b belong to different

Cas13b subtypes according to protein sequence similarities and putative accessory proteins. In addition, the sequence and structure of DRs in gRNAs of these two Cas13b proteins are quite different. Thus, we tested whether dPspCas13b and dPguCas13b could label the same transcripts simultaneously (Figure 6G). The imaging data showed dual-colorlabeled SatIII in single cells after SA or heat shock treatment (Figure 6H). In addition to dual colors for labeling single RNAs, we could also label two different RNAs using these two orthogonal CRISPR-dCas13 proteins. We performed NEAT1 labeling with dPspCas13b as well as SatIII labeling with dPguCas13b in HeLa cells (Figure 6I). In the untreated cells, we only observed NEAT1 signals but no SatIII signals (Figure 6J). After treatment with SA or heat shock, we succeeded in tracking both nuclear bodies simultaneously (Figure 6J). As controls, these two dCas13b systems do not non-specifically bind each other’s corresponding gRNAs using gRNAs targeting MUC4, NEAT1, and SatIII (Figure 6K), showing that these two systems are sufficiently orthogonal to discriminate between different RNAs. Combining dCas9 and dCas13 Enables Simultaneous Labeling of Genomic DNA and Its Transcripts in Living Cells Fluorescently labeled nuclease-deficient Cas9 (dCas9) protein assembled with various single-guide RNAs (sgRNAs) is robust in labeling DNA repeats (Chen et al., 2013). We asked whether combining the dCas9 and dCas13 systems could achieve simultaneous labeling of genomic DNA and transcribe RNAs in living cells. dCas9-mEmerald achieved successful labeling of the MUC4 and SatIII genomic loci using sgRNAs targeting these repeated loci (Jolly et al., 2002; Nollet et al., 1998) (Figure S7A). After stress treatment, we observed that the stress body marker proteins were localized to the dCas9-labeled SatIII DNA (Figure S7B). Co-transfection of dPspCas13b-mScarlet and their corresponding gRNAs targeting MUC4 and SatIII RNAs (Figure 7A) enabled the labeling of their genomic DNAs and the transcribed RNAs at the same time (Figures 7B and 7C). Of note, in the untreated cells, only the SatIII genomic loci were labeled with dCas9-mEmerald; upon heat shock or SA treatments, the transcribed SatIII RNAs were visualized as shown by the recruitment of dPspCas13b-mScarlet and single gRNA targeting SatIII RNAs (Figure 7C). To exclude the possibility of the dPspCas13b system targeting the gRNA transcription sites and thus introducing false-positive signals, we designed a plasmid that contains gRNA-NEAT1

Figure 5. Model of Paraspeckle Dynamics Revealed by the CRISPR-dPspCas13b System (A) Real-time microscopic images showing paraspeckle dynamics in HeLa cells tracked by the dPspCas13b system with 5-min (left) or 1-min (right) intervals. (B) Line scans showing the relative intensity of the dotted lines in (A) during the fusion events. (C and D) 3D reconstructions of the corresponding paraspeckles shown in (A) in both x-z axis (C) and x-y axis (D). (E) Relative fluorescence intensity of each paraspeckle object during kiss-and-fusion/run events. Arrows indicate the corresponding images shown in (A)–(D). (F) Schematic showing that transcription of NEAT1 (Mao et al., 2011) initiates the seeding assembly mode of PSPs on NEAT1_2, followed by phase separation into paraspeckle formation (Yamazaki et al., 2018). NEAT1 tracking in live cells displayed the kiss-and-run and fusion modes of paraspeckle dynamics post-de novo assembly. (G) Quantification statistics of kiss-and-run and kiss-and-fusion events with or without SA treatment. 18 events were tracked in normal and 36 events were tracked under 4–6 h of SA treatment.

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sequences as well as 24 3 GCN4 elements (Figure 7D) and transfected it into HeLa cells expressing the dPspCas13bmScarlet system as well as the dCas9-mEmerald system. We found no colocalization between the dCas9-mEmerald-labeled GCN4 DNAs and the dPspCas13b-mScarlet-labeled NEAT1 RNAs (Figure 7E). This might be due to the possibilities that dPspCas13b does not efficiently bind unmatured gRNA transcripts and that the level of such nascent transcripts is low. Pre-assembly of the dCas13/gRNA Complex Allows Efficient Targeting to RNAs It has been shown that the dCas9/sgRNA binary complex is stable and binds its target DNA with high affinity, allowing probing of multiple targets by fluorescently labeled arrays of sgRNAs (Deng et al., 2015). We indeed obtained similar observations by pre-assembling the purified dPspCas13b and the Cy3-labeled oligos of gRNAs that target SatIII RNAs (fRNPs) followed by transfection of such fRNPs into cells (Figure 7F). These Cy3-labeled SatIII RNAs labeled by the dPspCas13bgRNA-Cy3 fRNPs were confirmed by smFISH with probes recognizing endogenous SatIII RNAs (Figure 7G). Collectively, all applications with the dCas13 system we present here highlight the utility of this method to track RNA localization and dynamics in live cells. DISCUSSION Visualizing the dynamics of RNAs in live cells is key to understanding their functions. Here, we have identified dPspCas13b and dPguCas13b for direct visualization of NEAT1, MUC4, GCN4, and SatIII transcripts (Figures 1, 3,4, 5, 6, S1, S3, and S6). We demonstrate that fluorescently labeled dPspCas13b with optimized gRNAs targeting NEAT1 achieved a robust (comparable signals to the classical MS2-MCP), rapid (about a week), and efficient (labeling 80% NEAT1) RNA tracking in live cells (Figures 2, 4, 5, S2, S4, and S5). Applying orthogonal dCas13 systems (Figures 6G–6K) or combining dCas13 and MS2-MCP (Figures 6A–6C) achieved dual-color labeling of RNAs in single cells. Combination of dCas13 and dCas9 systems allows simultaneous visualization of genomic DNA and RNA transcripts (Figures 7A–7E). Further, even the in vitro assembly dPspCas13b with fluorescent oligos of gRNAs could

also allow visualization of RNAs in living cells (Figures 7F and 7G). All these applications of the dCas13 system (Figure 7H) provide significant advantages over other RNA imaging approaches. Future studies may identify additional Cas13 proteins to further enhance the utility of this system in real-time RNA imaging in cells. Most paraspeckles are spherically shaped (Wang et al., 2018; West et al., 2016). We observed the peripheral shell region of paraspeckles using gRNAs targeting the 30 region of NEAT1_2 that surrounds the paraspeckle-core-localized NONO (Figures S2L and S2M). Paraspeckle expression and morphology undergo changes in response to different cellular stresses (Adriaens et al., 2016; Hirose et al., 2014; Imamura et al., 2014; Wang et al., 2018), ultimately leading to altered consequences on gene expression. Three models have been proposed for dynamic interactions between individual components required in the assembly of nuclear bodies: seeding assembly and either hierarchical or non-hierarchical stochastic assembly models (Dundr and Misteli, 2010; Mao et al., 2011; Matera et al., 2009). Using inducible transcription of NEAT1 fused with 24 3 MS2 revealed that both NEAT1 transcription activity and NEAT1 itself are needed for de novo formation of paraspeckles, followed by the recruitment of paraspeckle proteins such as NONO and SFPQ (Mao et al., 2011). Functional dissection of NEAT1 showed that the middle region of NEAT1_2 initiates the assembly of phase-separated paraspeckles (Yamazaki et al., 2018). These findings suggest a seeding pattern of de novo paraspeckle assembly that is initiated by recruiting proteins via the NEAT1_2 subdomain and then phase separated into spherical paraspeckles (Figure 5F). Using the CRISPR-dCas13 labeling system to track paraspeckles at the RNA level, we have visualized the dynamics of paraspeckles post-de novo assembly (Figures 4, 5, S4, and S5). Time-lapse images recorded in short or long intervals and periods revealed constrained dynamics of the core paraspeckle protein NONO and NEAT1 within single paraspeckles in seconds (Figures 4E–4H) and further fusion events among paraspeckles in minutes (Figures 5A–5E; Videos S1 and S2), respectively. These dynamics recorded by the dCas13 system are consistent with those tracked by the MS2-MCP system fused at the NEAT1 locus and labeling of NONO (Figures S5A–S5F; Videos S3 and S4). With 1-min time intervals by imaging in live cells, we

Figure 6. Simultaneous Visualization of Different RNAs in Nuclear Bodies (A) Overview of NEAT1 imaging with dual colors tagged by MS2-MCP and CRISPR-dPspCas13b. (B and C) Dual-color labeling of NEAT1 by MS2-MCP and dPspCas13b in single cells. Left panels, representative images showing colocalization of NEAT1 labeled by MCP-mRuby3 and dPspCas13b-3 3 EGFP with gRNAs targeting NEAT1 (g50 -1, g50 -2, gmid-7, g30 -2, and g30 -10) (B) or the 30 (g30 -2 and g30 -10) NEAT1 (C) in the MS2-KI cells. Right panels, line scans of the relative fluorescence intensity of signals indicated by the dotted line in each left panel. (D) Overview of dPspCas13b-mediated imaging of nSBs by targeting SatIII under stresses. HSF1-mRuby3 is the nSB marker protein to confirm the dPspCas13blabeled signals. (E) Representative images of dPspCas13b-labeled SatIII by different gRNAs (green) and HSF1-mRuby3 (red) upon SA (100 mM, 6 h, top rows) or heat shock (42 C 1 h and 37 C 1 h, bottom rows) treatments. See also Figure S6C. (F) Bar graph showing SNR statistics of the dPspCas13b-labeled SatIII shown in (E). Data are represented as mean ± SD and collected from 34, 25, 27, 31, 51, 61, 29, and 48 puncta, respectively. (G and H) Overview (G) and representative images (H) of SatIII RNA visualized by dPspCas13b (green) and dPguCas13b (red) under SA (100 mM, 6 h) or heat shock (42 C 1 h and 37 C 1 h) treatments in living cells. (I and J) Overview (I) and representative images (J) of NEAT1 RNA labeled by dPspCas13b (green) and SatIII RNA labeled by dPguCas13b (red) under SA (100 mM, 6 h) or heat shock (42 C 1 h and 37 C 1 h) treatments in living cells. (K) gRNA replacement assays showing that dPspCas13b and dPguCas13b cannot label MUC4, NEAT1, or SatIII using the each other’s gRNA.

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Figure 7. Simultaneous Visualization of Genomic DNA and RNA Using dCas9 and dCas13 Systems (A) Schematic of DNA/RNA dual-color labeling by the dCas9 and dCas13 systems. (B) Visualization of the MUC4 DNA (green) and RNA (red) by the dCas9 and dCas13 systems simultaneously in living cells. (C) Visualization of the SatIII DNA (green) and RNA (red) by the dCas9 and dCas13 systems simultaneously upon heat shock (42 C 1 h and 37 C 1 h) or SA (100 mM, 6 h) treatments. (D) Schematic experimental design for distinguishing gRNA transcription sites from dPspCas13b-labeled signals. (E) CRISPR-dCas13 does not bind to the gRNA transcription sites. Left, representative images show that the 24 3 GCN4-tagged gNEAT1 plasmid labeled by CRISPR-dCas9 but not CRISPR-dPspCas13b. The CRISPR-dPspCas13b system only specifically labels endogenous NEAT1 RNAs; right, line scan of the relative fluorescence intensity of signals indicated by the dotted line in the left panels. (F) Left, schematic of delivering in vitro assembled dPspCas13b and fluorescent gRNA-Cy3 (fRNPs) into cells for RNA labeling. Right, a representative image showing SatIII RNAs by this method in living cells, nuclei are shown by white dotted lines. (G) smFISH of SatIII (SatIII-Cy5) confirming the SatIII signals labeled by the fRNPs shown in (F). (H) Summary of applications of the CRISPR-dCas13 system in living cells.

captured paraspeckle fusion and fission events (Figures 5A–5E; Video S2). Quantitative analysis of these events showed
30% stochastic collisions that could lead to elongated paraspeckles (Figure 5G). Our observations thus suggest kiss-and-run and fusion models of paraspeckle dynamics in cells (Figures 5F

14 Molecular Cell 76, 1–17, December 19, 2019

and 5G), which are consistent with previous reports that paraspeckle proteins rapidly move into and out of paraspeckles as shown by fluorescence loss in photobleaching (Fox et al., 2002) and fluorescence recovery after photobleaching (Mao et al., 2011; Wang et al., 2018) assays.

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The CRISPR-dCas13 system is feasible for the study of the same and different RNAs or genomic DNAs simultaneously by combining two orthogonal dCas13 systems (Figures 6G–6K), dCas13 and MS2-MCP (Figures 6A–6C) or dCas13 and dCas9 systems (Figures 7A–7E). With gRNAs that recognize the repeats in SatIII (Jolly et al., 2002; Jones et al., 1973) (Figure S6B), we visualized SatIII RNA with both dPspCas13b and dPguCas13b (Figures 6G–6K) in living cells. Nuclear bodies are thought to concentrate specific proteins and RNAs that undergo constant exchanges with nucleoplasmic factors as well as interactions with other types of nuclear bodies (Fox et al., 2002; Shav-Tal et al., 2005; Shen et al., 2019) in response to cellular stresses like DNA damage (Shav-Tal et al., 2005) and apoptosis activation (Shen et al., 2019), but their functions still remain to be explored. Future multiple-color labeling using orthogonal dCas13b systems, combined with traditional aptamers, or even with the dPspCas13b-gRNA fRNP complex for real-time imaging, will expand our capability to study the spatiotemporal dynamics of different transcripts and cellular bodies and offer great potential to advance our understanding of their functions in gene regulation. Limitations There are no current guidelines to design efficient gRNAs targeting an RNA of interest. Structure/conformation of the targeted RNA should be taken into consideration when gRNAs for the dCas13 imaging system are designed. We have observed that gRNAs of 20–22 nt targeting NEAT1 (Figures 2A–2K and S2) displayed reliable SNR signals, suggesting that efficient shRNA or RNAi targeting sites might serve to guide gRNA design. Further, the dCas13b system has lower SNR compared to the MS2-MCP system. Combination of multiple gRNAs targeting an RNA of interest may enhance the SNR, as evidenced by the reliable signals observed by the dCas13 system for the repeat containing MUC4, GCN4, and SatIII RNAs (Figures 3, 6, S3, and S6). STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: d d d d

KEY RESOURCES TABLE CONTACT FOR REAGENT AND RESOURCE SHARING EXPERIMENTAL MODEL AND SUBJECT DETAILS B Human Cell Lines METHOD DETAILS B Cell culture B Plasmid construction B Construction of MS2-NEAT1 knock-in and mRuby3NONO knock-in cell lines by CRISPR/Cas9 B Plasmid transfection for live cell imaging and native RNA immunoprecipitation B Western Blotting B Live cells imaging procedure B Widefield microscopy procedure B Structured Illumination Microscopy (SIM) procedure B Paraspeckle movement tracking analysis procedure

B

d

d

Single molecule RNA Fluorescence in situ Hybridization (smFISH) and proteins visualization B RNA isolation, RT-qPCR and Northern Blotting B Native RNA immunoprecipitation (RIP) B Protein purification B fRNP assembly QUANTIFICATION AND STATISTICAL ANALYSIS B Signal-to-noise (SNR) and co-localization analysis B Particle number analysis B Short-term NEAT1 tracking B Statistical analysis DATA AND SOFTWARE AVAILABILITY

SUPPLEMENTAL INFORMATION Supplemental Information can be found online at https://doi.org/10.1016/j. molcel.2019.10.024. ACKNOWLEDGMENTS This work was supported by the Chinese Academy of Sciences (XDB19020104), the Ministry of Science and Technology of China (2016YFA0100701), the National Natural Science Foundation of China (31725009, 31830108, 31821004, and 31861143025), and the Howard Hughes Medical Institute (55008728). AUTHOR CONTRIBUTIONS L.-L.C. conceived this study. L.-L.C., L.-Z.Y., and Y.W. designed experiments. L.-Z.Y., Y.W., and P.-F.L. performed all experiments and data analysis. S.-Q.L. and H.W. helped with preparing dCas13 plasmids. R.-W.Y. helped with livingcell image analysis. L.-Z.Y., Y.W., G.G.C., and L.-L.C. wrote the manuscript with help from all authors. DECLARATION OF INTERESTS L.-Z.Y., Y.W., and L.-L.C. have filed a patent for the dCas13b RNA labeling. Received: June 25, 2019 Revised: August 25, 2019 Accepted: October 15, 2019 Published: November 19, 2019 REFERENCES Abudayyeh, O.O., Gootenberg, J.S., Konermann, S., Joung, J., Slaymaker, I.M., Cox, D.B., Shmakov, S., Makarova, K.S., Semenova, E., Minakhin, L., et al. (2016). C2c2 is a single-component programmable RNA-guided RNAtargeting CRISPR effector. Science 353, aaf5573. Abudayyeh, O.O., Gootenberg, J.S., Essletzbichler, P., Han, S., Joung, J., Belanto, J.J., Verdine, V., Cox, D.B.T., Kellner, M.J., Regev, A., et al. (2017). RNA targeting with CRISPR-Cas13. Nature 550, 280–284. Adriaens, C., Standaert, L., Barra, J., Latil, M., Verfaillie, A., Kalev, P., Boeckx, B., Wijnhoven, P.W., Radaelli, E., Vermi, W., et al. (2016). p53 induces formation of NEAT1 lncRNA-containing paraspeckles that modulate replication stress response and chemosensitivity. Nat. Med. 22, 861–868. Batra, R., Nelles, D.A., Pirie, E., Blue, S.M., Marina, R.J., Wang, H., Chaim, I.A., Thomas, J.D., Zhang, N., Nguyen, V., et al. (2017). Elimination of Toxic Microsatellite Repeat Expansion RNA by RNA-Targeting Cas9. Cell 170, 899–912.e10. Ben-Ari, Y., Brody, Y., Kinor, N., Mor, A., Tsukamoto, T., Spector, D.L., Singer, R.H., and Shav-Tal, Y. (2010). The life of an mRNA in space and time. J. Cell Sci. 123, 1761–1774.

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Chen, L.L., and Carmichael, G.G. (2009). Altered nuclear retention of mRNAs containing inverted repeats in human embryonic stem cells: functional role of a nuclear noncoding RNA. Mol. Cell 35, 467–478. Chen, B., Gilbert, L.A., Cimini, B.A., Schnitzbauer, J., Zhang, W., Li, G.W., Park, J., Blackburn, E.H., Weissman, J.S., Qi, L.S., and Huang, B. (2013). Dynamic imaging of genomic loci in living human cells by an optimized CRISPR/Cas system. Cell 155, 1479–1491. Chen, B., Hu, J., Almeida, R., Liu, H., Balakrishnan, S., Covill-Cooke, C., Lim, W.A., and Huang, B. (2016). Expanding the CRISPR imaging toolset with Staphylococcus aureus Cas9 for simultaneous imaging of multiple genomic loci. Nucleic Acids Res. 44, e75. Chen, M., Ma, Z., Wu, X., Mao, S., Yang, Y., Tan, J., Krueger, C.J., and Chen, A.K. (2017). A molecular beacon-based approach for live-cell imaging of RNA transcripts with minimal target engineering at the single-molecule level. Sci. Rep. 7, 1550. Chujo, T., Yamazaki, T., Kawaguchi, T., Kurosaka, S., Takumi, T., Nakagawa, S., and Hirose, T. (2017). Unusual semi-extractability as a hallmark of nuclear body-associated architectural noncoding RNAs. EMBO J. 36, 1447–1462. Clemson, C.M., Hutchinson, J.N., Sara, S.A., Ensminger, A.W., Fox, A.H., Chess, A., and Lawrence, J.B. (2009). An architectural role for a nuclear noncoding RNA: NEAT1 RNA is essential for the structure of paraspeckles. Mol. Cell 33, 717–726. Coulon, A., Ferguson, M.L., de Turris, V., Palangat, M., Chow, C.C., and Larson, D.R. (2014). Kinetic competition during the transcription cycle results in stochastic RNA processing. eLife 3. Cox, D.B.T., Gootenberg, J.S., Abudayyeh, O.O., Franklin, B., Kellner, M.J., Joung, J., and Zhang, F. (2017). RNA editing with CRISPR-Cas13. Science 358, 1019–1027. Deng, W., Shi, X., Tjian, R., Lionnet, T., and Singer, R.H. (2015). CASFISH: CRISPR/Cas9-mediated in situ labeling of genomic loci in fixed cells. Proc. Natl. Acad. Sci. USA 112, 11870–11875. Dundr, M., and Misteli, T. (2010). Biogenesis of nuclear bodies. Cold Spring Harb. Perspect. Biol. 2, a000711. East-Seletsky, A., O’Connell, M.R., Knight, S.C., Burstein, D., Cate, J.H., Tjian, R., and Doudna, J.A. (2016). Two distinct RNase activities of CRISPR-C2c2 enable guide-RNA processing and RNA detection. Nature 538, 270–273. Femino, A.M., Fay, F.S., Fogarty, K., and Singer, R.H. (1998). Visualization of single RNA transcripts in situ. Science 280, 585–590. Filonov, G.S., Moon, J.D., Svensen, N., and Jaffrey, S.R. (2014). Broccoli: rapid selection of an RNA mimic of green fluorescent protein by fluorescence-based selection and directed evolution. J. Am. Chem. Soc. 136, 16299–16308. Fox, A.H., Lam, Y.W., Leung, A.K., Lyon, C.E., Andersen, J., Mann, M., and Lamond, A.I. (2002). Paraspeckles: a novel nuclear domain. Curr. Biol. 12, 13–25. Hirose, T., Virnicchi, G., Tanigawa, A., Naganuma, T., Li, R., Kimura, H., Yokoi, T., Nakagawa, S., Be´nard, M., Fox, A.H., and Pierron, G. (2014). NEAT1 long noncoding RNA regulates transcription via protein sequestration within subnuclear bodies. Mol. Biol. Cell 25, 169–183. Horvathova, I., Voigt, F., Kotrys, A.V., Zhan, Y., Artus-Revel, C.G., Eglinger, J., Stadler, M.B., Giorgetti, L., and Chao, J.A. (2017). The Dynamics of mRNA Turnover Revealed by Single-Molecule Imaging in Single Cells. Mol. Cell 68, 615–625.e9. Imamura, K., Imamachi, N., Akizuki, G., Kumakura, M., Kawaguchi, A., Nagata, K., Kato, A., Kawaguchi, Y., Sato, H., Yoneda, M., et al. (2014). Long noncoding RNA NEAT1-dependent SFPQ relocation from promoter region to paraspeckle mediates IL8 expression upon immune stimuli. Mol. Cell 53, 393–406. Jolly, C., Konecny, L., Grady, D.L., Kutskova, Y.A., Cotto, J.J., Morimoto, R.I., and Vourc’h, C. (2002). In vivo binding of active heat shock transcription factor

16 Molecular Cell 76, 1–17, December 19, 2019

1 to human chromosome 9 heterochromatin during stress. J. Cell Biol. 156, 775–781. Jones, K.W., Prosser, J., Corneo, G., and Ginelli, E. (1973). The chromosomal location of human satellite DNA 3. Chromosoma 42, 445–451. Koberna, K., Malı´nsky´, J., Pliss, A., Masata, M., Vecerova, J., Fialova´, M., Bedna´r, J., and Raska, I. (2002). Ribosomal genes in focus: new transcripts label the dense fibrillar components and form clusters indicative of ‘‘Christmas trees’’ in situ. J. Cell Biol. 157, 743–748. Konermann, S., Lotfy, P., Brideau, N.J., Oki, J., Shokhirev, M.N., and Hsu, P.D. (2018). Transcriptome Engineering with RNA-Targeting Type VI-D CRISPR Effectors. Cell 173, 665–676.e14. Larson, D.R., Zenklusen, D., Wu, B., Chao, J.A., and Singer, R.H. (2011). Realtime observation of transcription initiation and elongation on an endogenous yeast gene. Science 332, 475–478. Lenstra, T.L., Coulon, A., Chow, C.C., and Larson, D.R. (2015). SingleMolecule Imaging Reveals a Switch between Spurious and Functional ncRNA Transcription. Mol. Cell 60, 597–610. Mao, Y.S., Sunwoo, H., Zhang, B., and Spector, D.L. (2011). Direct visualization of the co-transcriptional assembly of a nuclear body by noncoding RNAs. Nat. Cell Biol. 13, 95–101. Matera, A.G., Izaguire-Sierra, M., Praveen, K., and Rajendra, T.K. (2009). Nuclear bodies: random aggregates of sticky proteins or crucibles of macromolecular assembly? Dev. Cell 17, 639–647. Naganuma, T., Nakagawa, S., Tanigawa, A., Sasaki, Y.F., Goshima, N., and Hirose, T. (2012). Alternative 30 -end processing of long noncoding RNA initiates construction of nuclear paraspeckles. EMBO J. 31, 4020–4034. Nelles, D.A., Fang, M.Y., O’Connell, M.R., Xu, J.L., Markmiller, S.J., Doudna, J.A., and Yeo, G.W. (2016). Programmable RNA Tracking in Live Cells with CRISPR/Cas9. Cell 165, 488–496. Nollet, S., Moniaux, N., Maury, J., Petitprez, D., Degand, P., Laine, A., Porchet, N., and Aubert, J.P. (1998). Human mucin gene MUC4: organization of its 50 -region and polymorphism of its central tandem repeat array. Biochem. J. 332, 739–748. Paige, J.S., Wu, K.Y., and Jaffrey, S.R. (2011). RNA mimics of green fluorescent protein. Science 333, 642–646. Raj, A., and Tyagi, S. (2010). Detection of individual endogenous RNA transcripts in situ using multiple singly labeled probes. Methods Enzymol. 472, 365–386. Raj, A., van den Bogaard, P., Rifkin, S.A., van Oudenaarden, A., and Tyagi, S. (2008). Imaging individual mRNA molecules using multiple singly labeled probes. Nat. Methods 5, 877–879. Shav-Tal, Y., Blechman, J., Darzacq, X., Montagna, C., Dye, B.T., Patton, J.G., Singer, R.H., and Zipori, D. (2005). Dynamic sorting of nuclear components into distinct nucleolar caps during transcriptional inhibition. Mol. Biol. Cell 16, 2395–2413. Shen, W., De Hoyos, C.L., Migawa, M.T., Vickers, T.A., Sun, H., Low, A., Bell, T.A., 3rd, Rahdar, M., Mukhopadhyay, S., Hart, C.E., et al. (2019). Chemical modification of PS-ASO therapeutics reduces cellular protein-binding and improves the therapeutic index. Nat. Biotechnol. 37, 640–650. Sirri, V., Jourdan, N., Hernandez-Verdun, D., and Roussel, P. (2016). Sharing of mitotic pre-ribosomal particles between daughter cells. J. Cell Sci. 129, 1592–1604. Smargon, A.A., Cox, D.B.T., Pyzocha, N.K., Zheng, K., Slaymaker, I.M., Gootenberg, J.S., Abudayyeh, O.A., Essletzbichler, P., Shmakov, S., Makarova, K.S., et al. (2017). Cas13b Is a Type VI-B CRISPR-Associated RNA-Guided RNase Differentially Regulated by Accessory Proteins Csx27 and Csx28. Mol. Cell 65, 618–630.e7. Sunwoo, H., Dinger, M.E., Wilusz, J.E., Amaral, P.P., Mattick, J.S., and Spector, D.L. (2009). MEN epsilon/beta nuclear-retained non-coding RNAs are up-regulated upon muscle differentiation and are essential components of paraspeckles. Genome Res. 19, 347–359.

Please cite this article in press as: Yang et al., Dynamic Imaging of RNA in Living Cells by CRISPR-Cas13 Systems, Molecular Cell (2019), https:// doi.org/10.1016/j.molcel.2019.10.024

Tanenbaum, M.E., Gilbert, L.A., Qi, L.S., Weissman, J.S., and Vale, R.D. (2014). A protein-tagging system for signal amplification in gene expression and fluorescence imaging. Cell 159, 635–646.

per-resolution microscopy analysis of paraspeckle nuclear body organization. J. Cell Biol. 214, 817–830.

Tyagi, S., and Kramer, F.R. (1996). Molecular beacons: probes that fluoresce upon hybridization. Nat. Biotechnol. 14, 303–308.

Wu, B., Chao, J.A., and Singer, R.H. (2012). Fluorescence fluctuation spectroscopy enables quantitative imaging of single mRNAs in living cells. Biophys. J. 102, 2936–2944.

Valgardsdottir, R., Chiodi, I., Giordano, M., Rossi, A., Bazzini, S., Ghigna, C., Riva, S., and Biamonti, G. (2008). Transcription of Satellite III non-coding RNAs is a general stress response in human cells. Nucleic Acids Res. 36, 423–434.

Yamazaki, T., Souquere, S., Chujo, T., Kobelke, S., Chong, Y.S., Fox, A.H., Bond, C.S., Nakagawa, S., Pierron, G., and Hirose, T. (2018). Functional Domains of NEAT1 Architectural lncRNA Induce Paraspeckle Assembly through Phase Separation. Mol. Cell 70, 1038–1053.e7.

Wang, Y., Hu, S.B., Wang, M.R., Yao, R.W., Wu, D., Yang, L., and Chen, L.L. (2018). Genome-wide screening of NEAT1 regulators reveals crossregulation between paraspeckles and mitochondria. Nat. Cell Biol. 20, 1145–1158.

Yan, W.X., Chong, S., Zhang, H., Makarova, K.S., Koonin, E.V., Cheng, D.R., and Scott, D.A. (2018). Cas13d Is a Compact RNA-Targeting Type VI CRISPR Effector Positively Modulated by a WYL-Domain-Containing Accessory Protein. Mol. Cell 70, 327–339.e5.

West, J.A., Mito, M., Kurosaka, S., Takumi, T., Tanegashima, C., Chujo, T., Yanaka, K., Kingston, R.E., Hirose, T., Bond, C., et al. (2016). Structural, su-

Yang, L., and Chen, L.L. (2017). Enhancing the RNA engineering toolkit. Science 358, 996–997.

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Methods S1

Other Detailed protocol on how to perform CRISPRdCas13 labeling experiments in live cells

CONTACT FOR REAGENT AND RESOURCE SHARING Further information and requests for reagents should be directed to and will be fulfilled by the Lead Contact, Ling-Ling Chen ([email protected]). All materials generated in this study are available from the Lead Contact with a completed Materials Transfer Agreement. EXPERIMENTAL MODEL AND SUBJECT DETAILS Human Cell Lines Human cell lines including HeLa and HEK293 cells were purchased from the American Type Culture Collection (ATCC; https://www. atcc.org). Human cells HeLa S3, HT2, HCT116, SW48, SW480, MCF7, and U2OS were kindly provided by Stem Cell Bank, Chinese Academy of Sciences (http://www.sibcb.ac.cn/). METHOD DETAILS Cell culture Human HeLa, HEK293, HeLa S3, SW48, SW480 and U2OS cell lines were cultured in Dulbecco’s Modified Eagle medium (DMEM) supplemented with 10% Fetal Bovine Serum (FBS); MCF7 was cultured in Eagle’s Minimum Essential Medium (MEM) supplemented with 10% FBS; HCT116 and HT29 cell lines were cultured in McCoy’s 5a Medium supplemented with 10% FBS. All cells were maintained in Heracell 150i CO2 Incubators (Thermo Scientific) at 37 C, 5% CO2. Plasmid construction To construct tandem repeats of fluorescent proteins fused with dCas13 proteins, including dPspCas13b-EGFP, dPspCas13b3xEGFP, dPspCas13b-mNeongreen, dPspCas13b-2 3 mNeongreen, dPspCas13b-3 3 mRuby3, dPguCas13b-3 3 EGFP and dPguCas13b-3 3 mRuby3, diverse dCas13 sequences with NLS were cloned into pHAGE-EF1⍺-IRES-puro using one-step clone method then followed by one-step clone (Yeasen) of fluorescent proteins and another NLS. To construct dPspCas13b-sfGFP, dPspCas13b-2 3 sfGFP and dPspCas13b-3 3 sfGFP, the sequences of NLS-dPspCas13b and fluorescent proteins with NLS were cloned into pHAGE-EF1⍺-IRES-puro simultaneously using one-step clone method (Yeasen). To construct different dCas13 proteins fused with EGFP, including dPguCas13b-EGFP, dRanCas13b-EGFP, dRfxCas13d-EGFP, dEsCas13d-EGFP, dAdmCas13d-EGFP, dLwaCas13a-EGFP and dLbaCas13a-EGFP, the sequence of each dPspCas13b in pHAGE-EF1⍺-dPspCas13b-IRES-puro was replaced with the corresponding dCas13 sequence using NotI and XbaI. To construct mCherry-RBM14, mCherry-FUS, mRuby3-HSF1 TagRFP-HSF1, mEmerald-HAP and mRuby3-HAP plasmids, the coding sequences of RBM14, FUS, HSF1, HAP were amplified from HeLa cDNAs followed by fusion into pmCherry-c1, pmRuby3-c1 or pmEmerald-c1 plasmids digested by EcoRI and BamHI using the one-step clone method (Yeasen). To construct dCas9-mEmerald, mEmerald was fused into c-terminal of dCas9-NLS using one-step clone method (Yeasen). To construct plasmids expressing mRNA with different repeated sequence, 4 3 GCN4, 8 3 GCN4, 12 3 GCN4, 16 3 GCN4 and 24 3 GCN4 were inserted into pmRuby3-c1 plasmid using restriction enzyme HindIII and BamHI. The sequences of backbone plasmids for expressing gRNAs are the same as mentioned (Abudayyeh et al., 2017; Cox et al., 2017; Konermann et al., 2018). Targeted sequences of NEAT1 and SatIII were listed in Table S1. Construction of MS2-NEAT1 knock-in and mRuby3-NONO knock-in cell lines by CRISPR/Cas9 To construct plasmids used in generating these knock-in cell lines, DNA sequences for left homology arm and right homology arm of targeted genes (NEAT1 and NONO) were amplified from the HeLa cell genomic DNA using primer pairs listed in Table S1. The donor sequence was introduced silent mutations within the Cas9 nuclease binding region of the homology arm. For MS2-NEAT1, the repeating units of the MS2 cassettes are composed of six-stem loop blocks and then multimerized 4 3 to obtain the 24 3 stem loops. Below is the 6 3 MS2 sequences:

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CTAGATAGCTCTAGCTGTAGAAAACATGAGGATCACCCATGTCTGCTGGACTACTGTAGAAAACATGAGGATCACCCATGTCTG CTGTCTAGCTGTAGAAAACATGAGGATCACCCATGTCTGCTGGACGACTGTAGAAAACATGAGGATCACCCATGTCTGCTGTCTA GCTGTAGAAAACATGAGGATCACCCATGTCTGCTGGACGACTGTAGAAAACATGAGGATCACCCATGTCTGCTGG, the loops themselves are underlined. The coding sequences of mRuby3 and EGFP were amplified from the plasmids of pmRuby3-c1 and pEGFP-c1. Overlapping PCR was performed to generate the left homology-24 3 MS2/mRuby3/EGFP-right homology arm sequences. The PCR products were purified, digested with XbaI/NheI and cloned into a pCRII vector digested with XbaI/NheI by a standard two fragments ligation. Design of the guide RNAs for CRISPR/Cas9 mediated knock-in was carried out using the CRISPR Design Tool (http://zlab.bio/guide-design-resources) to minimize potential off-target effects. Oligonucleotide pairs (Table S1) were cloned into the vector pX330A (Addgene) and the final bicistronic vector encodes the gRNAs and the Cas9 nuclease. To obtain the knock-in cell lines, 1 3 106 cells were seeded in a 3.5 cm dish with supplemented DMEM+10% FBS at 37 C, 5% CO2 followed by transfection after 16-18 h with the bicistronic nuclease plasmid and corresponding donor plasmids at the ratio of 2 to 1 (2 mg plasmids in total) using Lipofectamine 3000 Transfection Reagent. 24 h later, puromycin (1mg/mL) was added to the cells to increase the KI efficiency. MS2-NEAT1 knock-in cells were confirmed by Northern Blot and single molecular FISH (smFISH) were carried out to confirm MS2-NEAT1 expression. To generate mRuby3-NONO-KI and EGFP-NONO-KI cells, mRuby3/EGFP positive cells were first selected by expression of mRuby3 or EGFP under microscopy and then validated by genomic DNA PCR with the primers listed in Table S1. Plasmid transfection for live cell imaging and native RNA immunoprecipitation To transfect plasmids for live cell imaging and native RNA immunoprecipitation, Lipofectamine 3000 (Invitrogen) transfection was performed with the general ratio of 3 reagents as 1 mg plasmids, 2 mL P3000 and 2 mL lipo3000 in each 12-well plate at 70% cell confluence. After 16-18 h transfection, cells were passage into 35 mm no. 1.5 glass-bottomed dishes (Cellvis) for live cell imaging or passage followed by native RNA immunoprecipitation assay. For each types of transfection, usage of every plasmids was listed as below:

Assay

Plasmid usage

Related to Figures

dCas13 proteins screening in NONO-mRuby3-KI cells

0.2 mg dCas13-EGFP + 0.4 mg g50 -1 + 0.4 mg g50 -2; or 0.2 mg dCas13-EGFP + 0.8 mg gNC

Figure 1C

Colocalization between dCas13 and RBM14, NONO or FUS

0.2 mg dCas13-EGFP + 0.4 mg g50 -1 + 0.4 mg g50 -2 + 0.2 mg mCherry-RBM14/ FUS/NONO

Figures 1D and 1E

Colocalization between dCas13 and NEAT1

0.2 mg dCas13-EGFP + 0.4 mg g50 -1 + 0.4 mg g50 -2; or 0.2 mg dCas13-EGFP + 0.8 mg gNC

Figure 1H

native RNA immunoprecipitation

0.75 mg dPspCas13b; 1.5 mg g50 -1, g50 -2 or gNC

Figures S1H and S1I

Optimizations of gRNAs and fluorescent proteins or MUC4 labeling

0.3 mg dCas13 + 0.7mg gRNA

Figures 2, 3B, 3C, 3H, S3A, and S3G

Repeats labeling test and optimizations of fluorescent proteins for cytoplasmic RNA labeling

0.3 mg dCas13-EGFP + 0.6mg gRNA + 0.6 mg mRuby3-N 3 GCN4 (N = 4,8,12, 16,24)

Figures 3D–3G and S3D

SatIII labeling

0.3 mg dCas13-EGFP + 0.6mg gRNA + 0.3mg mRuby3-HSF1

Figures 6D–6F, S6D, and S6E

RNA/RNA dual labeling

0.3 mg dPspCas13b-3 3 EGFP + 0.6mg gRNA (Psp) + 0.3 mg dPguCas13b-3 3 mRuby3 + 0.6 mg gRNA (Pgu)

Figures 6G–6J

DNA/RNA dual-color labeling

0.3 mg dCas13-mRuby3 + 0.6mg gRNA + 0.3mg dCas9-mEmerald + 0.6mg sgRNA

Figures 7A–7E

Western Blotting Cells were lysed in 1 3 SDS sample buffer and resolved in 10% SDS-SPAGE Gels. After transferring, dCas13 proteins expression were detected by using flag mouse mAb (1:500) and goat anti-mouse IgG-HRP antibody (1:10,000), loading control ACTIN was detected by using b-Actin mouse mAb (1:20,000). Standard substrate buffer was used for visualization against film.

Molecular Cell 76, 1–17.e1–e7, December 19, 2019 e4

Please cite this article in press as: Yang et al., Dynamic Imaging of RNA in Living Cells by CRISPR-Cas13 Systems, Molecular Cell (2019), https:// doi.org/10.1016/j.molcel.2019.10.024

Live cells imaging procedure To visualize paraspeckles labeled by NONO, TDP43, CRISPR-dPspCas13b and MS2-MCP in living cell, cells (transfected or not) were cultured on 35 mm no. 1.5 glass-bottomed dishes (Cellvis). Cells were washed once with PBS and the medium was replaced by FluoroBrite DMEM (GIBCO) supplemented with 10% FBS and placed back in the incubator for 1 h followed by Widefield microscopy or SIM imaging. Widefield microscopy procedure All widefield microscopy images were performed on a DeltaVision Elite imaging system (GE Healthcare) equipped with a 60 3 /1.42 NA Plan Apo oil-immersion objective, or a 100 3 /1.40 NA Plan Apo oil-immersion objective (Olympus), as well as the CoolSnap HQ2 camera (Photometrics) equipped with the live cell imaging environment control system (Live Cell Instrument). Raw data of all presented figures were deconvolved by softWoRx 6.5 using the enhanced ratio method. Structured Illumination Microscopy (SIM) procedure All SIM experiments were performed on a DeltaVision OMX V4 system (GE Healthcare) equipped with a 60 3 /1.42 NA Plan Apo oilimmersion objective (Olympus) and six laser beams (405, 445, 488, 514, 568 and 642nm; 100mW). The microscope was routinely calibrated with a special image registration slide and algorithm provided by GE healthcare. Cells were cultured on 35 mm no. 1.5 glass-bottomed dishes for SIM (Labtide). To obtain optimal images, immersion oil with refractive indices of 1.520 at 37 C for living cells imaging or 1.518 at room temperature (about 25 C) for paraspeckles morphology observation. SIM image stacks were captured with a z-distance of 0.125 mm and with 5 phases, 3 angles, 15 raw images per plane. The raw data were reconstructed with channel specific OTFs and a Wiener filter was set to optimum value by using softWoRx 6.5 package (GE Healthcare). Images were registered with alignment parameters obtained from calibration measurements with 100 nm diameter TetraSpeck Microspheres with four colors (Molecular Probes). Paraspeckle movement tracking analysis procedure 1 s per frame for short-term paraspeckle movement tracking and 1 min per frame for paraspeckles fusion events tracking were performed in time-lapse imaging. Raw data of all presented figures were deconvoluted by softWoRx 6.5 using the enhanced ratio method. Single particle tracking and 3D rendering of NEAT1 labeled by NONO, MS2-MCP and CRISPR-dPspCas13b were produced by using Imaris (Bitplane) with surface building function modules. The parameters set in surface building were as below: surface was defined with diameter of largest sphere at 0.5 mm and surface grain size at 0.05 mm; linking max distance was set to 1 mm, max distance was set to 1 mm, and max gap size was set to 1. Single molecule RNA Fluorescence in situ Hybridization (smFISH) and proteins visualization All smFISH probes were designed via Stellaris Probe Designer and labeled with Cy3 or Cy5 on the 30 ends (Table S1). RNA FISH was carried out as described before (Raj and Tyagi, 2010). Briefly, cells were fixed with 4% PFA for 15 min and washed with DPBS for 3 3 5 min, followed by permeabilization with 0.5% Triton X-100 for 5 min and washed with DPBS for 3 3 5 min. Cells were incubated in 10% formamide/2 3 SSC for 10min at room temperature followed by hybridization at 37 C for 16 h. After hybridization, samples were mounted in VECTASHIELD antifade mounting medium (Vector Lab) and for samples labeled with Cy5, ProLong Diamond antifade reagent (Thermo Fisher) was used. RNA isolation, RT-qPCR and Northern Blotting RNAs from native RIP were extracted with Trizol Reagent (Invitrogen) according to the manufacturer’s protocol. For RT–qPCR, after treatment with DNase I (Ambion, DNA-free kit), cDNA synthesis was carried out using SuperScript III (Invitrogen) with oligo (dT) and random hexamers. qPCR was performed using SYBR Green Realtime PCR Master Mix (TOYOBO) and a StepOnePlus real-time PCR system (Applied Biosystems). Each sample was determined with triplicate independent experiments. To analysis NEAT1 expression, actin mRNA was used for normalization. The relative fold enrichment in RIP assay was calculated with respective input and normalized to the EV. Primers used in qPCR are listed in Table S1. Northern blotting was carried out according to the manufacturer’s protocol (DIG Northern Starter Kit, Roche). 10 mg total RNA of HeLa WT cells or MS2-NEAT1 cells were loaded on 1% native Agarose gels. NEAT1 antisense probe sequence was amplified from cDNA of HeLa cells and MS2 antisense probe sequence was amplified form 12 3 MS2 sequence (construction by 2 3 six-stem loop blocks showed as above) by using 2 3 Hieff PCR Master Mix (Yeasen). Digoxigenin (Roche) labeled NEAT1 and MS2 antisense probes were generated using T7 RNA polymerase by in vitro transcription with the RiboMAX Large Scale RNA Production System (Promega).

e5 Molecular Cell 76, 1–17.e1–e7, December 19, 2019

Please cite this article in press as: Yang et al., Dynamic Imaging of RNA in Living Cells by CRISPR-Cas13 Systems, Molecular Cell (2019), https:// doi.org/10.1016/j.molcel.2019.10.024

Native RNA immunoprecipitation (RIP) HeLa cells (2 3 107) were rinsed twice with ice-cold PBS and suspended in 1 mL RIP buffer (50 mM Tris pH 8.0, 150 mM NaCl, 0.5% Igepal, 1 mM PMSF, 1 3 protease inhibitor cocktail (Roche) and 2 mM VRC) followed by sonication. Cell lysates were centrifuged at 1,000 g for 10 min at 4 C and the supernatants were pre-cleared with 10 mL Dynabeads Protein G (Invitrogen). Then, the pre-cleared supernatants were divided into two parts equally and incubated with 20 mL Dynabeads Protein G (Invitrogen) with antibodies for Flag or mouse IgG2b for 2 h at 4 C followed by washing with three times with high salt buffer (RIP buffer with 0.5 M NaCl, 0.5% sodium deoxycholate and 0.1% Igepal) and two times with RIP buffer. Then, the beads were incubated with elution buffer (100 mM Tris pH 6.8, 4% SDS, and 10mM EDTA) at RT for 10 min. Eluted sample was followed by RNA extraction and RT-qPCR and the primers used are listed in Table S1. Protein purification Expression plasmids for His-tagged dPspCas13, dPspCas13b-EGFP, dPguCas13b-EGFP in pET-28a were individually transformed into E. coli expression strain BL21 [Transetta (DE3) chemically competent cell (Transgen Biotech, CD801)]. After transformation, a single clone was inoculated in 5 mL LB media supplemented with 100 mg/L kanamycin at 250 rpm, 37 C. After overnight growth, the culture was diluted 100-fold into 500 mL LB medium supplemented with 100 mg/L kanamycin. Absorbance was monitored at a wavelength of 600 nm, and upon reaching an optical density (OD600) of 0.6 - 0.8, Isopropyl b-D-Thiogalactoside (IPTG) was added to LB medium at the concentration of 0.5 mM for the induction of protein expression. After overnight incubation at 16 C, 180 rpm, cell pellets were harvested by centrifugation (5,000 rpm, 10 min, 4 C), resuspended in lysis buffer (20 mM Tris-HCI pH 7.5-8.0, 500 mM NaCl, 12 mM b-mercaptoethanol, 0.5 mM PMSF) with 1 mg/mL lysozyme rotated at 4 C for 30 min, and fragmented by high-pressure homogenizer (Ultrahigh pressure cell crusher UH-06; Union-biotech) at 4 C. After centrifugation at 10,000 rpm for 30 min at 4 C, the supernatant cell lysates were filtered through a 0.45 mm filter and then incubated with Ni Sepharose (GE healthcare, 17-5318-01) for 2 h at 4 C. The Sepharose beads were washed with wash buffer (20 mM Tris-HCI pH 7.5 - 8.0, 500 mM NaCl, 20 mM imidazole 0.5 mM PMSF), and bound proteins were eluted with elution buffer (20 mM Tris-HCI pH 7.5 - 8.0, 500 mM NaCl, 250 mM imidazole 0.5 mM PMSF) for twice. Then the protein further purified over the gel filtration chromatography (Superdex-200; GE Healthcare) equilibrated with storage buffer (20 mM Tris-HCI pH 7.5 - 8.0, 500 mM NaCl, and 5% Glycerol). The concentration of purified protein was determined by using Modified Bradford Protein Assay Kit (Sangon Biotech, C503041) and checked by Coomassie blue staining. fRNP assembly 10-20 pmol the Cy3 labeled crRNA were mixed with equal molar amount of dPspCas13b in final volume of 10 mL buffer (100 mM NaCl, 20 mM Trish-HCl, 1 mM MgCl2) to assemble fRNPs. After assembly, fRNPs were transfected by nucleofection (Lonza) with the program I-013 followed by observation with DeltaVision OMX V4 system (GE Healthcare). QUANTIFICATION AND STATISTICAL ANALYSIS Signal-to-noise (SNR) and co-localization analysis SNR defined as the ratio of the intensity of a fluorescent signal and the power of background noise and brief steps of SNR calculation were showed in the Figure S1D. For NEAT1 (SatIII is similar) SNR calculation, spot colocalized with NONO was selected first, then a circle with diameter of 3
4 mm and with center of the spot (exclude the spot that are colocalized with NONO) was selected as background followed by calculated with following formula: SNR = Psignal/Pbackground = (Max intensity of spots signal – Mean intensity of background GFP spot) / Std. dev. of background signal. All the fluorescence imaging data were analyzed by Fiji/ImageJ. Co-localization analysis of NEAT1 signals labeled by dPspCas13b or dPguCas13b with NONO in live imaging and NEAT1 signals labeled by dPspCas13b with NEAT1 in smFISH assay were calculated by the two separated channels signals per cell using Pearson’s correlation coefficient with Coloc 2 plugins in Fiji/ImageJ. Particle number analysis Number of GCN4-elments labeled by dPspCas13b and smFISH were calculated by Imaris with the particle building system. Short-term NEAT1 tracking Signal of NEAT1 labeled with NONO, MS2-MCP and CRSIPR-dPspCas13b was tracked by using the Imaris with the surface building system. For tracking NEAT1, the surface was built in Imaris as described above. Positions of each locus (xt, yt) at different time points (t) were measured, analyzed in Excel and plotted in GraphPad Prism 8. The movement step (dx, dy) was calculated by subtracting the position of a previous time point from the new position: dxt = ðxt xt1 Þ and dyt = ðyt  yt1 Þ, where (xt, yt) is the coordinate of puncta qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi at time t, and (xt-1, yt-1) was the position of the locus at the previous time point (t-1). Step distance = ðxt  xt1 Þ2 + ðyt  yt1 Þ2 was calculated by how far a NEAT1/paraspeckle moved away from its position at the previous time point.

Molecular Cell 76, 1–17.e1–e7, December 19, 2019 e6

Please cite this article in press as: Yang et al., Dynamic Imaging of RNA in Living Cells by CRISPR-Cas13 Systems, Molecular Cell (2019), https:// doi.org/10.1016/j.molcel.2019.10.024

To compare step distances, 6576 step distances of 48 NEAT1 labeled by NONO, 3365 step distances of 30 NEAT1 labeled by TDP43, 7278 step distances of 65 NEAT1 labeled by CRISPR-dPspCas13b and 2654 step distances of 25 NEAT1 labeled by MS2-MCP were analyzed. Statistical analysis Data were analyzed by GraphPad Prism 8. For the comparisons, Unpaired Student’s t test was used in Figures 1I, S1F, S2G, and S3E, correlation analysis was used in Figure S3F. p < 0.05 was considered significant and p > 0.05 was considered no significance. Error bars represent as standard deviation (SD) from data in at least triplicate experiments and these have stated in the corresponding legends. See Method Details and figure legends for details. DATA AND SOFTWARE AVAILABILITY Raw data of key experiments can be accessed on Mendeley Data: https://doi.org/10.17632/8t2pzynkw6.1

e7 Molecular Cell 76, 1–17.e1–e7, December 19, 2019