In contrast to other studies using the MS2 system for mRNA detection, in this work, we used only one or two MS2-binding sites instead of six or more (Nevo-Dinur et al., 2011; van Gijtenbeek et al., 2016) and MS2 coat protein (Peabody, 1993; Stockley et al., 1995) fused to the bright, monomeric GFP derivate mVenus (Kremers et al., 2006). Rather than using fixed cells (Femino et al., 1998) and bulky MS2-GFP tags or slow acquisition speeds (4 Hz) (Golding and Cox, 2004), we imaged using slim field illumination and stream acquisition in the milliseconds range (Rosch et al., 2018; Hernandez-Tamayo et al., 2019; Burghard-Schrod et al., 2020). We reasoned that at an integration time of 75 ms, we would not track freely diffusing MS2-mVenus molecules, for which ≤ 20 ms would be required [5 ms is necessary to track free mVenus, (Schibany et al., 2018)], but predominantly track MS2 molecules bound to their target mRNA molecules. To test this assumption, we imaged MS2-mVenus via SMT with two different exposure times. We chose 8 ms to capture freely diffusive molecules, or 75 ms, where we would likely miss out freely diffusive molecules.

Jump distance (JD) analysis (Figures 1A,C,E) is based on squared displacement analyses (Weimann et al., 2013; Rösch et al., 2018), showing the probability of particles taking certain steps during a given time interval, assuming two-dimensional Brownian motion. We used several tests in order to evaluate if a single or more populations with an average diffusion constant can explain the observed JD distribution, using Rayleigh distributions. Probability–probability plots shown in Figures 1B,D,F plot the deviation between observed data (blue line) and modeled data (red dotted line), and lack of deviation indicates a high quality for the goodness of fit (Rösch et al., 2018). Based on tests and the probability plots, MS2-mVenus tracked with 8-ms exposure time showed two populations, which best explain the data (Figures 1A,B), having diffusion coefficients (DCs) of D₂ with 0.55 μm² s^–1, likely freely diffusive MS2-mVenus, and D₁ of 0.11 μm² s^–1, that is, a slow-mobile population, likely consisting of MS2 bound to mRNA (Figures 1G,H). A DC of 0.55 μm² s^–1 is surprisingly low for a small protein fused to mVenus, but at the acquisition speed used, we do not believe that our analyses are hampered by technical limitations. Supplementary Figure 1 shows that assuming a third population does not increase the goodness of fit (compare panels B and D) and was therefore discarded.

On the other hand, for MS2-mVenus tracked with an exposure time of 75 ms, three populations can explain the data better than assuming two populations (Supplementary Figures 1E–H). The most mobile population of MS2-mVenus at 75 ms had a diffusion constant (D) of 0.094 μm² s^–1, very similar to that of the slower population tracked with 8 ms (Figure 1G). The intermediate population D₂ had a D of 0.012 μm² s^–1 and a size of 43.7%, similar to the fast-mobile population with 43.6%. D₁ with 0.003 μm² s^–1 weighed only 12.7%. Most likely, the latter population represents MS2 bound to mRNA being translated by polysomes, the largest structure expected for mRNA to be present in. Very similar DCs were obtained for cells expressing ypbR-ypzF mRNA having two MS2-binding sites (Figures 1E,F, 2E–H). Here, the triple fit covered best the whole data set. These data suggest that MS2 protein binds to mRNA other than its target mRNA with considerable affinity. Although this caveat compromises specific claims, we found that the static fraction is much larger for cells carrying mRNA with MS2-binding sites, 25% of MS2-mVenus molecules showed static motion compared to 13% in cells lacking the MS2-binding site, and likewise, the intermediate mobile population was larger (49% vs. 44%), whereas the high mobility fraction was smaller (26% vs. 44% in cells lacking MS2-binding sites). These findings suggest that the high mobility fraction represents mostly MS2 bound to non-specifically recognized targets, whereas the other two fractions contain a large portion of MS2 bound to its specifically recognized mRNAs.

As a further argument for a considerable degree of specificity, we analyzed the presence of MS2-mVenus molecules in an average size cell of 3 × 1 μm, into which all tracks obtained from hundreds of cells are projected. For example, a “heat map,” where white areas show a low probability of distribution and red to black a high concentration of molecules, shows that MS2-mVenus diffuses throughout the cell with no clear preference to a certain area (Figures 1I,J), whereas the presence of the MS2-binding sites on ybpR-ypzF mRNA shows a preferential localization toward the periphery of cells (Figure 1K). Of note, expression of MS2-mVenus at low level does not strongly affect exponential growth, but cells reached a lower density than cells lacking the fusion protein (Supplementary Figure 2). We speculate that MS2 might bind to one ribosomal RNA, a highly abundant molecule that would strongly compete with few mRNAs carrying two MS2-binding sites. With this caveat in mind, we went on to analyze if differences might be observed for different mRNA molecules.

We analyzed four different mRNAs, all of the operons. The ypbR-ypzF operon comprises open reading frames (ORFs) for soluble and membrane-associated proteins (Table 1). ypbR encodes for DynA, a soluble protein involved in membrane fusion after lesions (Sawant et al., 2016), and appears to aid in membrane fusion of the invaginating division septum (Dempwolff et al., 2012). The products of ypbS and ypzF are of presently unknown function. FtsY is the membrane-associated receptor for the signal recognition particle (Braig et al., 2009) and localizes at the membrane, but also diffuses through the cell (Mayer et al., 2021); RNase Y is also membrane-associated (Hamouche et al., 2020), whereas SMC localizes to the nucleoids (Schibany et al., 2018). The mreB-minD operon comprises genes for cell shape-determining proteins MreB (soluble), MreC, and MreD (membrane proteins), part of the Rod complex for lateral cell wall synthesis (Dominguez-Escobar et al., 2011; Dersch et al., 2020). MreB forms filamentous structures underneath the cell membrane (Reimold et al., 2013). MinC and MinD, encoded at the 3′ end of the operon, are part of the membrane-associated Min system (Yu et al., 2020) and function as cell division inhibitors (Strahl and Hamoen, 2010), localizing at the septum and cell poles. The fourth mRNA, operon spoIIIE-ymfC, encodes for YmfC, a transcription factor of the GntR family (Resch et al., 2010) and membrane protein SpoIIIE, an ATP-dependent dsDNA translocase (Fleming et al., 2010; El Najjar et al., 2018). MreB and MreC are essential proteins, and the deletion of FtsY leads to extremely slow growth of B. subtilis. Addition of two MS2-binding sites did not considerably slow down growth of cells compared with cells expressing only MS2-mVenus (Supplementary Figure 2), indicating that both mRNAs retain functionality. Although we cannot test for functionality of the other two tagged mRNAs during exponential growth (note that SpoIIIE is constitutively expressed and plays an additional role during sporulation), we assume that the two binding sites do not cause a major defect in terms of mRNA dynamics.

Figures 2A–G reinforce the idea that the distribution of ypbR-ypzF tracks is best explained assuming the presence of three populations tagged mRNAs (Figures 2I–K). The same holds true for the other three tagged mRNAs, indicating that three distinct diffusion constants could be a general property for mRNA molecules.

For all tagged mRNAs, the diffusion constants for the most mobile populations D₃ are similar between MS2-mVenus alone and bound to the mRNAs. In MS2-mVenus, the mobile fraction has D = 0.094 μm² s^–1, ypbR-ypzF 0.09 μm² s^–1, rnc-ftsY 0.083 μm² s^–1, mreB-minD 0.078 μm² s^–1, and spoIIIE-ymfC 0.09 μm² s^–1 (Figure 3). Note that errors stated in Figure 3B refer to fitting errors, whereas all data are combined from tracks of three independent biological replicates (for statistics, see Supplementary Table 1). The intermediate mobile diffusion constant (D₂) is roughly one order of magnitude lower, with 0.019 μm² s^–1 for MS2-mVenus and ypbR-ypzF. For rnc-ftsY, D is 0.016 μm² s^–1 for mreB-minD 0.017 μm² s^–1, and for spoIIIE-ymfC, D = 0.015 μm² s^–1 (Figure 3). Those two populations diffuse faster or slower compared to a study about mRNA diffusion, suggesting 0.05 μm² s^–1 for mRNAs moving out of the nucleoid into the ribosome-rich area in E. coli (Castellana et al., 2016). Thus, for ypbR-ypzF, rnc-ftsY, mreB-minD, and spoIIIE-ypzF, the high mobility population could represent free mRNA, and the intermediate-mobile population could consist of partially assembled ribosomes, where the mRNA is already bound to the smaller subunit S30, before the large subunit starts to associate (Gualerzi and Pon, 2015). We will discuss SMT of the ribosomal protein L1 later on. The static population D₃ does not differ considerably between MS2-mVenus expressed alone or with mRNA containing binding sites (Figure 3). The diffusive coefficient with 0.003 μm² s^–1 for MS2-mVenus, ypbR-ypzF, rnc-ftsY, and spoIIIE-ymfC and/or with 0.0042 μm² s^–1 for mreB-minD is extremely low, suggesting engagement of mRNA with polysomes. Of note, our measured diffusion constants are in good agreement with those found for eukaryotic cells (Katz et al., 2016; Yan et al., 2016).

MS2-mVenus alone has a large high-mobility population of 43.6%, and a large intermediate mobile fraction (43.7%), but a small static fraction (12.7%). For ypbR-ypzF compared with MS2-mVenus control, the highly mobile population decreases to 25.6% ± 0.002%, whereas the static population increases to 25.3% ± 0.002%. Also, the intermediate mobile population with 49.1% is larger than that for “MS2-mVenus-only.” Static populations of the other mRNAs were also more than twofold larger than that of MS2-mVenus alone (Figure 3), suggesting that static fractions are dominated by MS2-mVenus specifically bound to its cognate mRNA. Based on this, we can assume that approximately one-third to half of our data in the strains expressing tagged mRNA are tracked MS2 coat protein bound to the binding site on the chosen mRNA, and leaving a large but not overwhelming degree of background noise of MS2 coat protein binding to unspecific targets, most likely RNA, in the cells.

Considering the size of mRNAs, the two mRNAs operonsypbR-ypzF with 4,059 bp and mreB-minD with 3,894 bp, and larger mRNAs rnc-ftsY with 5,477 bp (or 4,627 bp whenthe smc promoter is used) and the smaller operon spoIIIE-ymfC with 3191 bp (Table 1), show similar diffusion constants (Figure 3). Thus, to a first approximation, the size of mRNA does not appear to play a decisive role for mRNA diffusion, or for the mobility of assembled polysomes.

To address the nature of the different populations of MS2 tagged mRNA, we performed SMT of the ribosomal protein L1, which is part of the large subunit of the ribosome (Akanuma et al., 2012; Rosenberg et al., 2012). L1-BFP has been shown to be fully functional and to localize mainly at the cell poles and around the nucleoids using epifluorescence (Mascarenhas et al., 2001). We performed SMT using 75-ms stream acquisition for comparison with the mRNA data and 20 ms to be able to detect possibly freely diffusive L1. Highest density of L1-mVenus molecules was observed at the cell poles, septum area, and at the membrane (Figures 4A,B), in agreement with earlier epifluorescence experiments (Mascarenhas et al., 2001).

Jump distance (JD) analyses suggest that assuming two populations for L1-mVenus can explain the observed data well, but three populations increase the quality of the overall fit (Supplementary Figure 3). square displacement analysis (SQD) analysis shows that L1, tracked with 20-ms exposure time, has a high-mobile population of 40.2%, with D = 0.34 μm² s^–1, a slow mobile population of 48.7%, with D = 0.06 μm² s^–1, and a static population of only 11%, with D = 0.016 μm² s^–1 (Figures 4E,F). We can assume that the high-mobile fraction consists of L1 within diffusing large subunits, as D₃ is similar to that determined by the Elf group for free subunits that diffuse through the entire cell (Sanamrad et al., 2014). The other two populations likely represent L1 bound to 70S subunits plus mRNA. In E. coli, a DC of 0.04 μm² s^–1 was assumed for a translating ribosome (Bakshi et al., 2012). Our investigation suggests that more than a single form of translating ribosomes exists or different forms of ribosome/mRNA complexes with distinct mobilities. In comparison, L1-mVenus tracked with longer integration time, DCs as all fractions decreased, likely because many fast-diffusing L1 molecules (e.g., in the free large subunit) are not captured at 75-ms integration time. The static population has a lower DC at 75 ms compared to 25 ms, with D = 0.003 μm² s^–1, because it can be more accurately determined at slower stream acquisition.

Interestingly, at 75-ms integration time, the DC of the static population of L1 with 0.003 μm² s^–1 and of the mRNAs with 0.003 μm² s^–1 for ypbR-ypzF, rnc-ftsY, and spoIIIE-ymfC and 0.004 μm² s^–1 for mreB-minD were very similar (Figure 4F). Thus, we propose that this population might be polysomes in full swing and/or mRNA for membrane proteins being translated at the SecYEG translocon after delivery through the SRP system.

Heat maps for all four analyzed mRNAs showed a tendency for a preferential localization toward the cell periphery (Figures 5A–D), different from the relatively even distribution of MS2-mVenus only (Figure 1J), with the ypbR-ypzF transcript revealing the strongest accumulation at the cell poles (Figure 5A). In order to gain spatial information of different dynamics within the cell, we set up confinement maps, which visualize low molecule mobility in cells. Confined motion was assumed for molecules that stay within in a circle of 120 nm (roughly three times the localization error in this study) for a minimum of eight steps and longer. Such tracks represent molecules that stay associated with a defined subcellular position, likely bound to a large, static complex and are represented by red tracks in Figures 5E–H. Of note, these molecules will largely overlap with the population of molecules showing the slowest diffusion constant and thus to 50% and more represent MS2-mVenus bound to its specific mRNA molecule rather than non-specifically bound MS2-mVenus (see above). While freely diffusing molecules, indicated by blue tracks, are found throughout the cells, confined motion is enriched at the cell periphery and is depleted from the central places of the cell (Figures 5E–H). The exception is the cell middle, whereas in large cells (tracks of all sizes of cells are projected into the standardized cell), the segregated nucleoids make space for the invaginating septum, and where ribosomes are also accumulated. The trend for depletion at spaces of nucleoids is also seen for L1 protein representing the ribosomes (Figures 4C,D). As confined motion likely represents translating polysomes, that of labeled mRNAs likewise will account for transcripts being actively translated.

According to the model of the Amster–Choder group (Nevo-Dinur et al., 2011), mRNAs may be enriched near the area where an encoded protein will be active. For the four mRNA molecules, we found small but noticeable differences in the probability of confined motion, as apparent from the histograms of the short (y) cell axis or long (x) axis distributions (Figures 5I–L). ypbR-ypzF mRNA showed a relatively even distribution with depletion of confined tracks from the cell center (Figure 5J). For rnc-ftsY, enrichment at the septal area can be seen in the x-axis histogram (Figure 5I). FtsY has been shown to diffuse through the entire cell in Shewanella putrefaciens, but to move in confined motion close to the cell membrane (Mayer et al., 2021). Thus, while enrichment at the septal area might suggest preferred translation at this site in large cells commencing division, freely diffusive FtsY can easily reach this place within few seconds. For mreB-minD and for spoIIIE-ymfC, we observed polar enrichment (Figures 5K,L), as judged from the x-axis distribution.

Mixed behavior between mobile and confined localization, indicated by green tracks in Figures 5E–H, is much more rarely observed than either confined or free mobility. This indicates that molecules only rarely switch between free diffusion and confinement, but rather stay in either state of mobility for an extended time.

As further test for the idea that the MS2 tag with two repeats of the MS2-binding site is useful for SMT, we tracked molecule fluorescence under rifampicin stress. We used concentrations of rifampicin where not all RNAP molecules might be inhibited, so some transcription should remain active. Figure 6 and Supplementary Figure 4 show a similar change in the localization pattern of MS2-mVenus expressed without any binding site, and L1-mVenus, which showed exclusive accumulations at the cell poles (Figures 6A,B,E,F). mRNA spoIIIE-ymfC showed the strongest effect. Under normal conditions, spoIIIE-ymfC had a higher density at the cell periphery (Figure 6C), whereas following rifampicin treatment, membrane-proximal accumulation was lost; only some increased signal at the poles and the cell center remained (Figure 6D). With regard to the confinement maps, MS2-mVenus only and L1-mVenus changed their pattern by stronger polar accumulation, whereas spoIIIE-ymfC mRNA visually lost peripheral localization in favor of more central positioning (Figures 6G–L). Interestingly, histograms of confined tracks revealed stronger polar accumulation of MS2-mVenus only, L1-mVenus and spoIIIE-ymfC following RNA depletion, as judged from the x-axis scans (Figures 6M–R), whereas y-axis histograms reveal a shift of tracks away from the periphery of the cell toward the cell center, most pronounced for specifically labeled mRNA (Figures 6O,P). Strongest changes seen for spoIIIE-ymfC mRNA in response to mRNA depletion support the idea of significant specificity in mRNA labeling versus non-specific MS2-mVenus binding.

Not only the localization pattern changed following downshift of RNAP activity, but also the single-molecule dynamics (Figure 7). Here the effect was even more pronounced for spoIIIE-ymfC and L1 than for MS2 tag-only. D’s for the three populations remained similar for all three constructs (Figure 7B). Population size of the static fraction decreased for MS2-mVenus only; the intermediate mobile fraction also decreased under mRNA depletion, whereas the high-mobile population increased (Figure 7A). Thus, mRNA depletion led to higher mobility of MS2-mVenus. L1-mVenus showed a similar but stronger trend of increased mobility; the static fraction was less than halved, whereas the fastest (“free 50S subunits”) fraction almost doubled (Figure 7A). Interestingly, spoIIIE-ymfC dynamics changed differently from those of L1. The static mRNA population halved under rifampicin treatment, and the high-mobile population increased from 48.9 to 63.6%, whereas the intermediate-mobile population changed only little. This would fit to our idea that the static and intermediate mobile populations of L1 and the tested mRNAs are different stages of assembled ribosomes, in which the static population could be the translating ribosome, which are similarly affected by a reduction in transcription activity.

As shown, the MS2 tag apparently binds to an unspecific target in B. subtilis. Because of a fortuitous mistake in our initial design, plasmids containing mRNA and (initially a single) MS2 site did not contain a terminator sequence shortly after the MS2-binding site, like for the shown samples previously. A downstream terminator sequence in the plasmid, approximately 1,700 bp after the MS2-binding site, terminated the artificial mRNA constructs. We took advantage of this collection of artificial mRNAs (original mRNA at 5′ end, MS2 sequence in the middle, plasmid-derived 1,700 bp including terminator at the 3′ end) and observed the behavior of mRNAs having different 5′ portions. All constructs grew similarly as MS2-mVenus only–expressing cells (Supplementary Figure 2), suggesting that the extended mRNAs did not lead to any major problems for the cells. Even with one MS2 dimer bound to the MS2-binding site, we were able to track mRNA constructs (for JD analyses, see Supplementary Figure 5), whose static fractions were all considerably larger than for the cells expressing MS2-mVenus only (Figure 8Q), again suggesting that we receive enough specific labeling of tagged mRNAs. Regarding the mRNAs encoding for mostly soluble proteins (Figures 8A–D and Table 1), we see the highest density in the heat map near the membrane and also a small amount at the pole, whereas confined localization of the mRNAs of ribosomal proteins rplJ-rplL and rplK-rplA can be detected in the cytoplasm, probably around the nucleoid (Sohmen et al., 2015). The mRNA hag, which is monocistronic and encodes for flagellin, the largest extracellular part of the flagellum, is an exception (Holscher et al., 2018). Its mRNA is most evenly distributed and shows a strong midcell accumulation (Figure 8A and Supplementary Figure 6A), whereas the other mRNAs showed different variations of the peripherally and polarly accumulation theme (Figures 8B–K and Supplementary Figure 6). Interestingly, the heat maps for the two ypbR-ypzF constructs with two MS2 sites (Figures 5A,E) and one site and artificial 3′ end (Figures 8D,L) are visually similar to each other, and likewise for mRNAs rnc-ftsY and the operon mreB-minD (Figures 5, 8). This finding suggests that our observations made with the artificial mRNAs bear close resemblance to the ones made with the native constructs including only the two MS2-binding sites.

ComN is a transcription factor localized at the cell poles, whose transcript has also been shown to localize to the poles using FISH (dos Santos et al., 2012), and SecDF are integral membrane proteins involved in protein membrane insertion (Furukawa et al., 2017; Neef et al., 2020). ylxM-rplS encodes for SRP and ribosomal proteins, but also a tRNA methyltransferase (Bystrom and Bjork, 1982; Sohmen et al., 2015; RNase for 16S rRNA processing – rimM gene, Lovgren et al., 2004). Thus, upper panels of the confinement maps from Figure 8 show mRNAs for operons containing ORFs for soluble proteins (or membrane-associated DynA for the ypbR operon), and lower panels represent mRNAs encoding membrane proteins (or membrane-associated FtsY, Table 1). For all mRNAs, freely diffusive tracks are found throughout the cells, that is, also including movement through the nucleoid-containing cellular spaces. Although histograms of confined tracks show visual differences in membrane enrichment (depletion from the cell center), as judged from the y-axis scans, overall, there is no convincing general trend for mRNAs containing ORFs for membrane proteins toward the cell membrane (Supplementary Figures 6E–H), compared to those encoding soluble proteins (Figures 6A–D); a majority of mRNAs tend to be depleted from the central cellular area (Supplementary Figure 6).

Concerning diffusion constants, for both rnc-ftsY constructs, these are comparable among all three populations, but the population size of the mobile population for two binding sites with 42.6% is approximately 50% larger than of rnc-ftsY with one binding site (27.8%), and the static population shrinks by 50% from 30.2 to 15%. This change in dynamics could reflect lowered translation efficiency of the artificially enlarged mRNA. For mreB-minD, no significant changes in the population sizes were observed, but DCs differ. Interestingly, with one binding site, D₁ (static) and D₂ (slow mobile) are less mobile than with two repeats. Despite several other differences between the constructs, the general concept of three diffusive states for mRNAs holds true.

The largest differences between D’s were found for the slow mobile populations, the lowest of 0.013 μm² s^–1 for mreB/minD and the highest of 0.023 μm² s^–1 for ypbR-ypzF. Monocistronic mRNA hag, possibly containing the lowest number of translating ribosomes (we assume), appears to diffuse almost fastest (Figures 8Q,R). D₃ of the different mRNAs varied between 0.081 and 0.104 μm² s^–1 (hag with the lowest and rplJ-rplL with the highest DC). Thus, assuming that the fast-mobile fraction corresponds to freely moving mRNA, size variations cause minor changes in mobility.

Each B. subtilis construct is in the wild-type NCIB 3610, which possesses a plasmid for increased transformation efficiency with a comI mutation (Konkol et al., 2013). For cloning, the E. coli strain DH5α was used. Both strains were cultivated in Luria–Bertani medium, for B. subtilis only for overnight cultures. E. coli was grown at 200 revolutions/min (rpm) and 37°C, whereas B. subtilis was cultivated at 250 rpm and 30°C. For SMT, B. subtilis was grown in S7₅₀ minimal medium [1% (wt/vol) fructose, 0.1% (wt/vol) glutamate, 0.004% (wt/vol)] Casamino acids (Jaacks et al., 1989) with the same temperature and speed. For the determination of the growth rate, it was measured with an optical density at 600 nm (OD₆₀₀). An OD of 0.7 was used for microscopy. Selection of the strains was accomplished by using the antibiotics ampicillin (100 μg/mL) for E. coli and chloramphenicol (5 μg/mL) and spectinomycin (100 μg/mL) for B. subtilis. For mRNA depletion, commonly 200 μg/mL is used, which also rapidly induces cell death in the culture. We used a lower concentration of 50 μg/mL, where dead cells were rarely seen during the first 40 min after drug addiction. An even lower concentration of 25 μg/mL was also used (Supplementary Figure 4).

First, the fusion protein MS2-mVenus was constructed in E. coli DH5α with the plasmid pSG1193 (ECE153). With this plasmid, the coat protein is set between the flanks of the alpha amylase amyE. After transformation in B. subtilis, a double crossover occurs with the amyE gene section between plasmid and bacterial chromosome, and therefore, this gene is interrupted and no longer functional in B. subtilis. With this plasmid, a xylose promoter is set before the MS2-mVenus and therefore can be induced with xylose, something that was not done here. The strain MS2-mVenus in B. subtilis NCIB 3610 was then later on used to get the different mRNA constructs transformed into it.

For the mRNA constructs, the plasmid pHJDS (Defeu Soufo and Graumann, 2004) with a C-terminal fusion of one or two MS2-binding sites was used. After having the monocistronic and polycistronic mRNA constructs cloned with the MS2-binding site in E. coli, the constructs were cloned via a single-crossover event into B. subtilis MS2-mVenus at the original locus. Selection for the correct constructs was done by antibiotic resistance selection and test PCR. L1-mVenus was made by using the plasmid pSG1164 with a C-terminal mVenus. With this plasmid, also a single-crossover event occurs at the original locus in B. subtilis.

Bacillus subtilis NCIB 3610 cells with only MS2-mVenus, MS2-mVenus + mRNA constructs, and L1-mVenus constructs were grown in S7₅₀ minimal medium at 30°C under shaking conditions to an OD of 0.7. For the cells, stressed with 25 and 50 μg/mL rifampicin, it was added to the constructs and incubated for 40 min. Cells were spotted on coverslips (25 mm, Marienfeld) and covered with an agarose pad [1% (wt/vol)], made of S7₅₀ Medium and a smaller coverslip (12 mm, Marienfeld).

Imaging was performed with a Nikon Eclipse Ti microscope equipped with a high numerical aperture objective (CFI Apochromat TIRF 100XC Oil, NA 1.49), an EM-CCD camera (ImagEM X2, Hamamatsu), and a YFP filter set (BrightLine 500/24, Beamsplitter 520 and BrightLine 542/27). mVenus fluorophores were excited by the central part of a laser beam (TOPTICA Beam Smart, 515 nm, maximum power 100 mW) with a laser intensity of 20 mW. Each movie consists of 3,000 frames and was recorded with an integration time of 8, 20, or 75 ms, using Nikon NIS-Elements BR.

First, the videos were cut with Fiji (ImageJ) (Schindelin et al., 2012), and the first 500 to 1000 frames were cut off, to a point where only one or two signals were present in cells. Afterward, the cell meshes were set with oufti (Paintdakhi et al., 2016). For particle detection, U-track (Jaqaman et al., 2008), a MATLAB software, was used. Here, the minimal length of tracks was set to 8, to avoid analyzing freely diffusive molecules that diffuse slowly for a short time, and to link to points, no gaps for the particle detection was allowed. The Brownian search radius was set with the lower bound of 0 and the upper bound of 3. Data were analyzed using SMTracker (Rösch et al., 2018). Here, the Stationary Localization Analysis panel for the dwell time analysis and the SQD panel were used. R² was as a measure for goodness, and statistical tests such as Kolmogorov–Smirnov goodness of fit and null hypothesis significance were used to find the best fit to Rayleigh distributions. Bayesian information criterion was used to avoid overfitting of data. Although, at present, SMTracker allows only to differentiate up to 3 populations, assuming more than 3 population presented overfitting of data from this study, based on an R value of “1” for using three populations.

With regard to the number of tracks (Supplementary Table 1), the MS2 system with two binding sites might be a better option to collect data for mRNA dynamics; however, the dynamics and localization patterns were quite similar between the one and two MS2-binding sites constructs. Thus, motion of artificial mRNAs does not appear to differ much between native and synthetic mRNAs.