RNAs relay genetic information from DNA to proteins or function by themself. Live-cell imaging of RNAs at a single-molecule level is crucial to uncovering their roles in gene expression regulation (Buxbaum et al., 2015). Various tools have been developed to visualize RNAs in live cells (Braselmann et al., 2020; Le et al., 2022), including RNA-binding protein–fluorescent protein approaches (Bertrand et al., 1998), CRISPR-based systems (Nelles et al., 2016; Yang et al., 2019), and those utilizing fluorophore–RNA aptamer pairs (Chen et al., 2019; Paige et al., 2011; Sunbul et al., 2021). The MS2-based system is the most widely used and represents the current gold standard for single-molecule RNA imaging in live cells (Braselmann et al., 2020; Bertrand et al., 1998). MS2 is a short RNA stem-loop bound specifically by the bacteriophage MS2 coat protein (MCP). To image RNA, 24xMS2 are placed at the 3′UTR or 5′UTR, and a fluorescent protein is fused to the MCP (MCP-FP). When coexpressed in cells, up to 48 fluorescent proteins (2 × 24, with two MCPs bound to one MS2) will be recruited to the RNA through MS2–MCP binding. This forms a fluorescent spot indicating a single RNA molecule (Braselmann et al., 2020).

The MS2 system has been successfully used to trace the whole mRNA life-cycle from transcription, to nuclear export, subcellular localization, translation, and to final degradation (Braselmann et al., 2020; Le et al., 2022). However, most RNA imaging studies in animal cells have been performed using exogenous mRNAs in cultured cell lines. 24xMS2 (about 1300 nt in length) have to be knocked into a specific genomic locus to image endogenous mRNA. The difficulty and low efficiency of the knock-in of long sequences into the genome represent a significant obstacle toward visualizing endogenous mRNA using the MS2 system. Thus, it is not surprising that less than 10 endogenous mRNAs have been imaged in live animal cells at a single-molecule level, and examples of endogenous mRNAs imaged in live animals remain extremely rare (Halstead et al., 2015; Levo et al., 2022; Dufourt et al., 2021; Park et al., 2014; Das et al., 2018; Zimyanin et al., 2008; Forrest and Gavis, 2003; Lee et al., 2022). Since overexpressed mRNAs may not faithfully recapitulate endogenous mRNA expression and dynamics, the development of more sensitive techniques for endogenous mRNA imaging is of great value.

In this study, we reasoned that combining the MS2 with a signal amplifier may allow the recruitment of more fluorescent proteins to the RNA with fewer MS2 repeats (i.e., 8xMS2 – see Figure 1A). To achieve this, we combined the MS2 and Suntag systems. Suntag is a 19 amino acid protein tag that binds to its specific single-chain variable fragment (scFv) antibody (Tanenbaum et al., 2014). We fused MCP with a 24xSuntag array and linked scFv with sfGFP. When coexpressed in cells, one MS2 interacts with two MCP-24xSuntag molecules, further recruiting 2 × 24 GFP molecules (Figure 1A). As the Suntag serves as a signal amplifier, the combined system was named as MS2-based signal Amplification with Suntag System (MASS). When an 8xMS2 is placed into the 3′UTR, up to 384 (2 × 8 × 24) GFP can then be tethered to a single mRNA through the MCP–Suntag–scFv–sfGFP interaction (Figure 1A). This leads to the formation of an intense GFP spot associated with single mRNA, facilitating live RNA imaging.

Figure 1 with 6 supplements see all

Download asset Open asset

Figure 1—source data 1

qRT-PCR (Quantitative Reverse Transcription PCR), number of foci detected by smFISH and MASS, signal-to-noise ratio, velocity, and intensity of the foci of mRNA detected in HeLa cells.

https://cdn.elifesciences.org/articles/82178/elife-82178-fig1-data1-v3.xlsx

Download elife-82178-fig1-data1-v3.xlsx

As proof of concept, 8xMS2 V1⁴ was fused to the 3′UTR of β-ACTIN mRNA and transfected into HeLa cells. When all the required elements of the MASS (MS2, MCP, 24xSuntag, and scFv antibody) were present, bright GFP foci were readily detected (Figure 1B). As controls, no GFP foci were detected when omitting any one of these elements (Figure 1B). With MCP-24xSuntag, an MCP molecule could be labeled with up to 24 GFPs. Under our imaging conditions (100–500 ms exposure time), MCP-24xSuntag particles were not detected (Figure 1B), probably because MCP-24xSuntag are diffusing too fast to be imaged as a spot. Thus, the GFP foci clearly represent β-ACTIN RNA molecules.

In addition, we performed MASS combined with single-molecule RNA fluorescence in situ hybridization (smFISH) using a probe against the MS2 stem-loop and a probe against the linker region between the MS2 stem-loops (Figure 1C, Figure 1—figure supplement 1A, B). We found that MASS detected a similar number of GFP foci compared to the spots detected by smFISH (Figure 1—figure supplement 1C, Figure 1—source data 1). Moreover, the majority of GFP foci (72%) colocalized with the smFISH spots of β-ACTIN-3′UTR-8xMS2 mRNAs (Figure 1C, D, Figure 1—figure supplement 1B, Figure 1—source data 1). It is reported that not all MS2 stem-loop will be bound by the MCP (Wu et al., 2012). As only 8xMS2 was used in MASS, it is likely that some mRNAs were not fully bound by MCP and were not detected. On the other hand, in theory, only up to 16 probes will be hybridized with the 8xMS2 stem-loops and the linker regions in the smFISH experiment, and it is possible that some mRNAs were miss labeled by smFISH. Therefore, 100% colocalization of MASS foci with the smFISH spots was hard to achieve. Taken together, our data indicated that MASS is able to detect single mRNA molecules and label the majority of mRNAs from a specific gene in live cells.

It was reported that tagging mRNAs with MS2 stem-loops might affect mRNA stability, which could be counteracted by using improved versions of MS2 repeats (Li et al., 2022; Vera et al., 2019). We found GFP foci of β-ACTIN-3′UTR-8xMS2 mRNAs were detected regardless of using MS2-V1⁴ or MS2-V7²¹ with 2× tandem repeats of monomer MCPs (tdMCP) (Figure 1—figure supplement 2A). Therefore, MASS could be performed using different versions of MS2 stem-loops and MCPs. To directly test whether mRNA stability was affected while imaging by MASS, we examined the stability of three mRNAs: MYC, HSPA1A, and KIF18B, which were reported as medium stable mRNAs (Vera et al., 2019; Sharova et al., 2009). We found that the stability of those mRNAs was not significantly affected either by tagging of 8xMS2 V1 or by coexpression of the MASS imaging system (Figure 1—figure supplement 2B, C, Figure 1—source data 1). It is worth noting that we only examined three mRNAs in this study. The stability of specific mRNAs might be affected by MASS. If so, an improved version of MS2 should be used for the imaging experiment.

It has been reported that β-ACTIN mRNAs with 3′UTR can localize to the lamellipodia (Katz et al., 2012). In support of this, we observed that the GFP foci of β-ACTIN-3′UTR-8XMS2 mRNAs were indeed localized to the lamellipodia in HeLa cells (Figure 1—figure supplement 3A and Video 1). To further test whether mRNA localization was affected by MASS, we imaged β-ACTIN-3′UTR mRNA with MASS or with the conventional 24xMS2 system in NIH/3T3 cells, which is a mouse fibroblast cell line. We found that GFP foci of β-ACTIN-3′UTR mRNAs detected by MASS or 24xMS2 system showed similar localization (Figure 1—figure supplement 3B). Thus, these data suggested that MASS did not affect RNA subcellular localization.

Video 1

Download asset

Haven established that MASS with MCP-24xSuntag could image single RNA molecules; we next sought to test whether MASS could be performed with shorter repeats of Suntag arrays (MCP-12xSuntag, MCP-6xSuntag) and compare MASS to the conventional 24xMS2 image system.

With the conventional 24xMS2 mRNA imaging system, a nuclear localization signal (NLS) was usually fused to MCP to localize NLS-MCP-GFP into GFP. NLS-MCP-GFP will be exported into the cytoplasm with mRNAs when binding to mRNAs. This strategy allows the detection of β-ACTIN-3′UTR mRNAs with a signal-to-noise ratio of 1.21 (Figure 1—figure supplement 4A, B, Figure 1—source data 1). MASS with MCP-24xSuntag showed the highest signal-to-ratio of 1.79. MCP-12xSuntag and MCP-6xSuntag labeled β-ACTIN-3′UTR-8xMS2 mRNAs with a similar signal-to-noise ratio of 1.42 and 1.48, which are better than the conventional 24xMS2 system (Figure 1—figure supplement 4C–I, Figure 1—source data 1). Thus, MASS is flexible regarding the length of Suntag repeats used for imaging. MASS with short repeats of Suntag (MCP-6xSuntag) is sufficient for RNA labeling.

One critical concern about MASS is that intense tagging of mRNAs may affect the dynamics of mRNAs. To address this, we performed live-cell imaging of β-ACTIN mRNA using the conventional 24xMS2 system or MASS with different lengths of Suntag arrays (MCP-24xSuntag, MCP-12xSuntag, and MCP-6xSuntag). The velocity of mRNA movement in each imaging condition was measured. We found that compared to the conventional 24xMS2 system, mRNA labeled by MASS with MCP-24xSuntag or MCP-12xSuntag showed a smaller velocity (Figure 1—figure supplement 5A, B, Figure 1—source data 1), indicating that heavier labeling affected mRNA movement speed. In contrast, mRNAs labeled by MASS with MCP-6xSuntag showed a similar velocity to that labeled with the conventional 24xMS2 system (Figure 1—figure supplement 5A, B, Figure 1—source data 1). Those data pointed out that when MASS is used to measure the speed of mRNA movement, a short Suntag array (MCP-6xSuntag) should be used. We next measured the average intensity of each GFP foci of β-ACTIN mRNA labeled using the conventional 24xMS2 system or MASS with different lengths of Suntag arrays (MCP-24xSuntag, MCP-12xSuntag, and MCP-6xSuntag). We found GFP foci detected by MASS showed higher intensity than those detected by the 24xMS2 system (Figure 1—figure supplement 6, Figure 1—source data 1). These data support that with MASS, a single mRNA molecule could be tethered with more GFP molecules, forming a GFP spot of higher fluorescence intensity. Such an application, retains the ability to image mRNA using low-power lasers, thus lowering any unwanted phototoxicity and photobleaching and still allowing the tracking of mRNA dynamics in high temporal resolution.

We then performed time-lapse imaging of the β-ACTIN-3′UTR-8XMS2 mRNA with a time interval of 1 s in HeLa cells (Video 2). We found that foci of mRNAs showed various dynamics: (1) Fusion. GFP spots fused into a more prominent spot (Figure 1E and Video 3); (2) Fission. Large GFP foci split into smaller spots (Figure 1F and Video 4); (3) Transient interaction. GFP foci touched each other briefly, then moved away (Figure 1G and Video 5), suggesting there are dynamic RNA–RNA interactions in cells; (4) Dynamic movement or anchoring. Despite most foci of β-ACTIN-3′UTR-8XMS2 mRNAs showing dynamic movement in cells, other foci were far more static, showing little movement (Figure 1H and Video 6), suggesting that these latter mRNAs may be anchored to subcellular structures.

Video 2

Download asset

Video 3

Download asset

Video 4

Download asset

Video 5

Download asset

Video 6

Download asset

Our ultimate goal was to develop tools for endogenous mRNA imaging in live animals. It has been previously reported that the knock-in of short sequences into the genome is far more efficient than those of longer sequences (Wang et al., 2022; Paix et al., 2017). The MASS exploits this advantage as only 8xMS2 (350 nt) needs to be inserted into a genomic locus, thus overcoming the previous obstacle of the requirement of inserting a long 1300 nt 24xMS2 into the genome for live-cell imaging of endogenous mRNA.

We then used the nematode C. elegans to specifically examine whether the MASS could be used for RNA imaging in live animals. An 8xMS2 was placed into the 3′UTR of cdc-42 mRNA (Figure 2—figure supplement 1). cdc-42-8xMS2, MCP-24xSuntag, and scFv-sfGFP were also expressed in the epidermis of live C. elegans. Consistent with the observations in HeLa cells, bright GFP foci could only be detected when all the required elements were present (Figure 2—figure supplement 1). Similarly, excluding any essential elements resulted in a complete failure of foci formation (Figure 2—figure supplement 1). Thus, the MASS was efficient in imaging exogenous RNAs in live animals.

Next, we set out to visualize gene expression activation and the dynamics of endogenous mRNAs in live animals. We used the skin of C. elegans as a model, which is composed of an epidermal epithelium with multiple nuclei. Upon wounding the epithelium via laser or needle, specific gene expressions and downstream signaling cascades for wound repair are then triggered and activated (Xu and Chisholm, 2014; Figure 2A). To this end, 8xMS2 was knocked into the 3′UTR region of two endogenous genes, C42D4.3 and mai-1 (Figure 2B and Figure 2—figure supplement 2A), the expression levels of which were reported to increase significantly after wounding (Fu et al., 2020). MCP-24xSuntag and scFv-sfGFP were expressed in the epidermis with the tissue-specific promoter semo-1 and col-19. We then used a UV laser to injure an area of the epidermis. Prior to wounding, few sfGFP foci were detected in either wild-type (WT) or in 8xMS2 knock-in animals (Figure 2B and Figure 2—figure supplement 2A). In contrast, numerous GFP foci were formed 15 min after wounding in the C42D4.3 and mai-1 8xMS2 knock-in animals but not in the WT animals (Figure 2B and Figure 2—figure supplement 2A, Videos 7–9). This result suggests that the wounding had activated C42D4.3 and mai-1 mRNA expression. In agreement with this, qRT-PCR showed that C42D4.3 and mai-1 mRNA levels were upregulated more than eightfold 15 min after injury (Figure 2C and Figure 2—figure supplement 2B, Figure 2—source data 1), confirming that GFP foci were able to detect endogenous C42D4.3–8xMS2 and mai-1-8xMS2 mRNAs. In addition, the expression level (Figure 2—figure supplement 2C, Figure 2—source data 1) and stability of C42D4.3 mRNA (Figure 2—figure supplement 2D, Figure 2—source data 1) were similar in WT, C42D4.3 8xMS2 knock-in animals and animals expressing the MASS imaging system, indicating MASS did not affect the expression level and stability of endogenous C42D4.3 mRNAs.

Video 7

Download asset

Video 8

Download asset

Figure 2 with 5 supplements see all

Download asset Open asset

Figure 2—source data 1

qRT-PCR (Quantitative Reverse Transcription PCR), number, and size of foci of mRNAs detected by MASS in C. elegans.

https://cdn.elifesciences.org/articles/82178/elife-82178-fig2-data1-v3.xlsx

Download elife-82178-fig2-data1-v3.xlsx

Video 9

Download asset

As the mRNA expression level of C42D4.3 and mai-1 significantly increased after wounding, we expected a boost in transcription. GFP will form extensive foci in the nuclei with active transcription. However, we failed to detect the appearance of bigger GFP foci in the nucleus. The epidermis of C. elegans is a syncytium with 139 nuclei located in different focal planes. With our microscopy, we could image only one focal plane, in which there are usually 4–10 nuclei. Therefore, it is likely that the nuclei with active transcription were out of focus, and therefore the GFP foci formed at the transcription site were not detected.

Next, we tracked the dynamics of endogenous C42D4.3–8xMS2 mRNA in the C. elegans epidermis after wounding. We found that in proximity to the injury site GFP foci were detected as early as 1 min after wounding (Figure 2D, Figure 2—figure supplement 3, and Videos 7 and 8). As a control, we pretreated the C. elegans with Actinomycin D, which potently inhibits gene transcription. In such cases, no GFP foci could be detected after wounding (Figure 2—figure supplement 4). This indicated that GFP foci are newly synthesized C42D4.3–8xMS2 mRNAs. Our data demonstrated that gene expression activation and transcription occur extremely fast (in this case, within 1 min after the stimulation). In addition, the appearance of GFP foci gradually spreads from the area around the injury site to distal regions. The total foci number steadily increased in the epidermis (Figure 2D, E, Figure 2—figure supplement 3, and Videos 7 and 8). These data suggested that wounding generated a signal to activate downstream gene expression around the injury site. The signal was then diffused to distal regions and able to induce gene expression there. In addition, GFP foci preferentially underwent fusion leading to increased foci size (Figure 2F, G, and Video 10, Figure 2—source data 1). In contrast, when exogenous BFP-8xMS2 was expressed and imaged with MASS in the epidermis (Figure 2—figure supplement 5A, Figure 2—source data 1), such fusion events were not frequently observed (Video 11), and the puncta size of BFP-8xMS2 mRNAs kept constant in 5 min (Figure 2—figure supplement 5A–C, Figure 2—source data 1). In addition, the puncta size of C42D4.3-8xMS2 mRNAs was significantly larger than that of BFP-8xMS2 mRNAs (Figure 2—figure supplement 5C, Figure 2—source data 1). These data suggested that C42D4.3 mRNAs undergo clustering after wounding and form RNA granules in vivo. In agreement with our observation, it has been previously reported that mRNAs formed large clusters and are co-translated in Drosophila embryos (Dufourt et al., 2021).

Video 10

Download asset

Video 11

Download asset

It has been recently reported that the SunRISER and MoonRISER system by combination of PP7 and Suntag or Moontag enables imaging of single exogenous mRNAs in living cells. Through computational and experimental approaches, the authors further optimized the SunRISER system and showed that SunRISER provided an excellent approach for long-term imaging of overexpressed mRNA in living cells (Guo and Lee, 2022). The principle of the SunRISER system and MASS is similar, which uses a protein signal amplifier to generate a higher fluorescence signal for RNA imaging. In this study, we compared MASS to the conventional 24xMS2 mRNA imaging system and characterized the effect of MASS on mRNA stability and dynamics. In addition, we primarily explored the application of MASS to image endogenous RNA in live animals, which has not been tested in the previous study. Here, we showed such signal amplification strategy is valuable for imaging endogenous mRNAs that utilizing only 8xMS2 in comparison to the 24xMS2 used in the classic MS2-based live-cell RNA imaging. The advantage of a short MS2 is of prime benefits for imaging endogenous mRNA as it reduced difficulties involved in inserting a long 1300 nt 24xMS2 into a genomic locus. We expect this tool will help promote studies of RNA transcription, nuclear export, subcellular localization, translation, RNA sensing, and degradation, at the endogenous level in culture cells and live animals.

Videos 1–11 contain source data for figures. Figure 1—source data 1 contains source data for statistical analysis in cells. Figure 2—source data 1 contains source data for statistical analysis in C. elegans.

1. 1. Bertrand E
   2. Chartrand P
   3. Schaefer M
   4. Shenoy SM
   5. Singer RH
   6. Long RM

   (1998) Localization of ash1 mRNA particles in living yeast

   Molecular Cell 2:437–445.

   https://doi.org/10.1016/s1097-2765(00)80143-4

   • PubMed
   • Google Scholar
2. 1. Braselmann E
   2. Rathbun C
   3. Richards EM
   4. Palmer AE

   (2020) Illuminating RNA biology: tools for imaging RNA in live mammalian cells

   Cell Chemical Biology 27:891–903.

   https://doi.org/10.1016/j.chembiol.2020.06.010

   • PubMed
   • Google Scholar
3. 1. Buxbaum AR
   2. Haimovich G
   3. Singer RH

   (2015) In the right place at the right time: visualizing and understanding mRNA localization

   Nature Reviews. Molecular Cell Biology 16:95–109.

   https://doi.org/10.1038/nrm3918

   • PubMed
   • Google Scholar
4. 1. Chen X
   2. Zhang D
   3. Su N
   4. Bao B
   5. Xie X
   6. Zuo F
   7. Yang L
   8. Wang H
   9. Jiang L
   10. Lin Q
   11. Fang M
   12. Li N
   13. Hua X
   14. Chen Z
   15. Bao C
   16. Xu J
   17. Du W
   18. Zhang L
   19. Zhao Y
   20. Zhu L
   21. Loscalzo J
   22. Yang Y

   (2019) Visualizing RNA dynamics in live cells with bright and stable fluorescent RNAs

   Nature Biotechnology 37:1287–1293.

   https://doi.org/10.1038/s41587-019-0249-1

   • PubMed
   • Google Scholar
5. 1. Das S
   2. Moon HC
   3. Singer RH
   4. Park HY

   (2018) A transgenic mouse for imaging activity-dependent dynamics of endogenous Arc mRNA in live neurons

   Science Advances 4:eaar3448.

   https://doi.org/10.1126/sciadv.aar3448

   • PubMed
   • Google Scholar
6. 1. Dufourt J
   2. Bellec M
   3. Trullo A
   4. Dejean M
   5. De Rossi S
   6. Favard C
   7. Lagha M

   (2021) Imaging translation dynamics in live embryos reveals spatial heterogeneities

   Science 372:840–844.

   https://doi.org/10.1126/science.abc3483

   • PubMed
   • Google Scholar
7. 1. Forrest KM
   2. Gavis ER

   (2003) Live imaging of endogenous RNA reveals a diffusion and entrapment mechanism for Nanos mRNA localization in Drosophila

   Current Biology 13:1159–1168.

   https://doi.org/10.1016/s0960-9822(03)00451-2

   • PubMed
   • Google Scholar
8. 1. Fu H
   2. Zhou H
   3. Yu X
   4. Xu J
   5. Zhou J
   6. Meng X
   7. Zhao J
   8. Zhou Y
   9. Chisholm AD
   10. Xu S

   (2020) Wounding triggers Miro-1 dependent mitochondrial fragmentation that accelerates epidermal wound closure through oxidative signaling

   Nature Communications 11:1050.

   https://doi.org/10.1038/s41467-020-14885-x

   • PubMed
   • Google Scholar
9. 1. Guo Y
   2. Lee REC

   (2022) Long-Term imaging of individual mRNA molecules in living cells

   Cell Reports Methods 2:100226.

   https://doi.org/10.1016/j.crmeth.2022.100226

   • PubMed
   • Google Scholar
10. 1. Halstead JM
    2. Lionnet T
    3. Wilbertz JH
    4. Wippich F
    5. Ephrussi A
    6. Singer RH
    7. Chao JA

    (2015) An RNA biosensor for imaging the first round of translation from single cells to living animals

    Science 347:1367–1671.

    https://doi.org/10.1126/science.aaa3380

    • PubMed
    • Google Scholar
11. 1. Katz ZB
    2. Wells AL
    3. Park HY
    4. Wu B
    5. Shenoy SM
    6. Singer RH

    (2012) Β-Actin mRNA compartmentalization enhances focal adhesion stability and directs cell migration

    Genes & Development 26:1885–1890.

    https://doi.org/10.1101/gad.190413.112

    • Google Scholar
12. 1. Le P
    2. Ahmed N
    3. Yeo GW

    (2022) Illuminating RNA biology through imaging

    Nature Cell Biology 24:815–824.

    https://doi.org/10.1038/s41556-022-00933-9

    • PubMed
    • Google Scholar
13. 1. Lee BH
    2. Shim JY
    3. Moon HC
    4. Kim DW
    5. Kim J
    6. Yook JS
    7. Kim J
    8. Park HY

    (2022) Real-time visualization of mrna synthesis during memory formation in live mice

    PNAS 119:e2117076119.

    https://doi.org/10.1073/pnas.2117076119

    • PubMed
    • Google Scholar
14. 1. Levo M
    2. Raimundo J
    3. Bing XY
    4. Sisco Z
    5. Batut PJ
    6. Ryabichko S
    7. Gregor T
    8. Levine MS

    (2022) Transcriptional coupling of distant regulatory genes in living embryos

    Nature 605:754–760.

    https://doi.org/10.1038/s41586-022-04680-7

    • PubMed
    • Google Scholar
15. 1. Li W
    2. Maekiniemi A
    3. Sato H
    4. Osman C
    5. Singer RH

    (2022) An improved imaging system that corrects MS2-induced RNA destabilization

    Nature Methods 19:1558–1562.

    https://doi.org/10.1038/s41592-022-01658-1

    • PubMed
    • Google Scholar
16. 1. Nelles DA
    2. Fang MY
    3. O’Connell MR
    4. Xu JL
    5. Markmiller SJ
    6. Doudna JA
    7. Yeo GW

    (2016) Programmable RNA tracking in live cells with CRISPR/Cas9

    Cell 165:488–496.

    https://doi.org/10.1016/j.cell.2016.02.054

    • PubMed
    • Google Scholar
17. 1. Paige JS
    2. Wu KY
    3. Jaffrey SR

    (2011) RNA mimics of green fluorescent protein

    Science 333:642–646.

    https://doi.org/10.1126/science.1207339

    • PubMed
    • Google Scholar
18. 1. Paix A
    2. Folkmann A
    3. Goldman DH
    4. Kulaga H
    5. Grzelak MJ
    6. Rasoloson D
    7. Paidemarry S
    8. Green R
    9. Reed RR
    10. Seydoux G

    (2017) Precision genome editing using synthesis-dependent repair of cas9-induced DNA breaks

    PNAS 114:E10745–E10754.

    https://doi.org/10.1073/pnas.1711979114

    • PubMed
    • Google Scholar
19. 1. Park HY
    2. Lim H
    3. Yoon YJ
    4. Follenzi A
    5. Nwokafor C
    6. Lopez-Jones M
    7. Meng X
    8. Singer RH

    (2014) Visualization of dynamics of single endogenous mRNA labeled in live mouse

    Science 343:422–424.

    https://doi.org/10.1126/science.1239200

    • PubMed
    • Google Scholar
20. 1. Sharova LV
    2. Sharov AA
    3. Nedorezov T
    4. Piao Y
    5. Shaik N
    6. Ko MSH

    (2009) Database for mRNA half-life of 19 977 genes obtained by DNA microarray analysis of pluripotent and differentiating mouse embryonic stem cells

    DNA Research 16:45–58.

    https://doi.org/10.1093/dnares/dsn030

    • PubMed
    • Google Scholar
21. 1. Sunbul M
    2. Lackner J
    3. Martin A
    4. Englert D
    5. Hacene B
    6. Grün F
    7. Nienhaus K
    8. Nienhaus GU
    9. Jäschke A

    (2021) Super-Resolution RNA imaging using a rhodamine-binding aptamer with fast exchange kinetics

    Nature Biotechnology 39:686–690.

    https://doi.org/10.1038/s41587-020-00794-3

    • PubMed
    • Google Scholar
22. 1. Tanenbaum ME
    2. Gilbert LA
    3. Qi LS
    4. Weissman JS
    5. Vale RD

    (2014) A protein-tagging system for signal amplification in gene expression and fluorescence imaging

    Cell 159:635–646.

    https://doi.org/10.1016/j.cell.2014.09.039

    • PubMed
    • Google Scholar
23. 1. Tinevez JY
    2. Perry N
    3. Schindelin J
    4. Hoopes GM
    5. Reynolds GD
    6. Laplantine E
    7. Bednarek SY
    8. Shorte SL
    9. Eliceiri KW

    (2017) TrackMate: an open and extensible platform for single-particle tracking

    Methods 115:80–90.

    https://doi.org/10.1016/j.ymeth.2016.09.016

    • PubMed
    • Google Scholar
24. 1. Tutucci E
    2. Vera M
    3. Biswas J
    4. Garcia J
    5. Parker R
    6. Singer RH

    (2018) An improved MS2 system for accurate reporting of the mrna life cycle

    Nature Methods 15:81–89.

    https://doi.org/10.1038/nmeth.4502

    • PubMed
    • Google Scholar
25. 1. Vera M
    2. Tutucci E
    3. Singer RH

    (2019) Imaging single mrna molecules in mammalian cells using an optimized MS2-MCP system

    Methods in Molecular Biology 2038:3–20.

    https://doi.org/10.1007/978-1-4939-9674-2_1

    • PubMed
    • Google Scholar
26. 1. Wang J
    2. He Z
    3. Wang G
    4. Zhang R
    5. Duan J
    6. Gao P
    7. Lei X
    8. Qiu H
    9. Zhang C
    10. Zhang Y
    11. Yin H

    (2022) Efficient targeted insertion of large DNA fragments without DNA donors

    Nature Methods 19:331–340.

    https://doi.org/10.1038/s41592-022-01399-1

    • PubMed
    • Google Scholar
27. 1. Wu B
    2. Chao JA
    3. Singer RH

    (2012) Fluorescence fluctuation spectroscopy enables quantitative imaging of single mRNAs in living cells

    Biophysical Journal 102:2936–2944.

    https://doi.org/10.1016/j.bpj.2012.05.017

    • PubMed
    • Google Scholar
28. 1. Xu S
    2. Chisholm AD

    (2014) Methods for skin wounding and assays for wound responses in C. elegans

    Journal of Visualized Experiments 3:51959.

    https://doi.org/10.3791/51959

    • PubMed
    • Google Scholar
29. 1. Yan X
    2. Hoek TA
    3. Vale RD
    4. Tanenbaum ME

    (2016) Dynamics of translation of single mRNA molecules in vivo

    Cell 165:976–989.

    https://doi.org/10.1016/j.cell.2016.04.034

    • PubMed
    • Google Scholar
30. 1. Yang LZ
    2. Wang Y
    3. Li SQ
    4. Yao RW
    5. Luan PF
    6. Wu H
    7. Carmichael GG
    8. Chen LL

    (2019) Dynamic imaging of RNA in living cells by CRISPR-cas13 systems

    Molecular Cell 76:981–997.

    https://doi.org/10.1016/j.molcel.2019.10.024

    • PubMed
    • Google Scholar
31. 1. Zimyanin VL
    2. Belaya K
    3. Pecreaux J
    4. Gilchrist MJ
    5. Clark A
    6. Davis I
    7. St Johnston D

    (2008) In vivo imaging of oskar mRNA transport reveals the mechanism of posterior localization

    Cell 134:843–853.

    https://doi.org/10.1016/j.cell.2008.06.053

    • PubMed
    • Google Scholar

Author details

1. Yucen Hu

   Zhejiang Provincial Key Laboratory of Cancer Molecular Cell Biology, Life Sciences Institute, Zhejiang University, Hangzhou, China

   Contribution

   Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Contributed equally with

   Jingxiu Xu and Erqing Gao

   Competing interests

   No competing interests declared

2. Jingxiu Xu

   International Biomedicine-X research center of the Second Affiliated Hospital, Zhejiang University, Hangzhou, China

   Contribution

   Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Contributed equally with

   Yucen Hu and Erqing Gao

   Competing interests

   No competing interests declared

3. Erqing Gao

   International Biomedicine-X research center of the Second Affiliated Hospital, Zhejiang University, Hangzhou, China

   Contribution

   Formal analysis, Validation, Investigation, Visualization, Methodology

   Contributed equally with

   Yucen Hu and Jingxiu Xu

   Competing interests

   No competing interests declared

4. Xueyuan Fan

   Zhejiang Provincial Key Laboratory of Cancer Molecular Cell Biology, Life Sciences Institute, Zhejiang University, Hangzhou, China

   Contribution

   Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8297-1596

5. Jieli Wei

   Zhejiang Provincial Key Laboratory of Cancer Molecular Cell Biology, Life Sciences Institute, Zhejiang University, Hangzhou, China

   Contribution

   Supervision, Methodology

   Competing interests

   No competing interests declared

6. Bingcheng Ye

   Zhejiang Provincial Key Laboratory of Cancer Molecular Cell Biology, Life Sciences Institute, Zhejiang University, Hangzhou, China

   Contribution

   Methodology

   Competing interests

   No competing interests declared

7. Suhong Xu

   1. International Biomedicine-X research center of the Second Affiliated Hospital, Zhejiang University, Hangzhou, China
   2. Center for Stem Cell and Regenerative Medicine and Department of Burn and wound repair of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Project administration, Writing - review and editing

   For correspondence

   shxu@zju.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4079-340X

8. Weirui Ma

   Zhejiang Provincial Key Laboratory of Cancer Molecular Cell Biology, Life Sciences Institute, Zhejiang University, Hangzhou, China

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   maweirui@zju.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9193-7311

• 6,449

  views

• 687

  downloads

• 8

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.