Xenograpsus testudinatus in the laboratory that were contrasted by the offering of non-vent cues. This crab species is endemic and dominates the vent fauna of Turtle Island off the NE coast of Taiwan. HV crabs were separately and in combination offered the following vent-specific cues: (1) sulfuric sediment, (3) air-bubbling, (4) elevated temperature, (5) dead settled zooplankton, (7) other crabs, and (8) shade. The non-vent-specific cues were: (2) quarz sediment, (6) dead fish, (8) light. These cues were provided on either side of a two-choice chamber. The movement of individual crabs was monitored: as initial and final choices, and as the proportion of time the crabs spent in each compartment (resident time). Cues were offered alone and no such cue as a control in the same set-up. Sulfuric sediments and dead fish were significantly more attractive to females, and other crabs irrespective of gender were significantly more attractive to males. When compared to expected distributions, crabs, irrespective of gender, significantly avoided light and tended to select other crabs, air-bubbling, sulfuric sediment, elevated temperature, dead fish, dead zooplankton, and quarz sediments in the order of decreasing importance. Data do not support the hypothesis that dead settled zooplankton was particularly attractive nor that the other gender was selected. A combination of several vent-associated cues  facilitated the strongest attraction to the crabs as reflected by all response variables. The ‘first choice’ responses were always consistent with the side of the choice compartment in which they spent the longest amount of time (resident time), but not with the ‘final choice’ of crabs, suggesting that the ‘resident time’ in addition to their ‘first choice’ is more reliable than just the ‘final choice’. The present results provide the first indication that several vent-associated habitat cues function as attractors for HV crabs. Habitat choice is also reflected by crab larval distribution in the field which tend to stay near the bottom not to be carried away from their specific habitat.Hydrothermal vent organisms are adapted to their extreme and patchily distributed habitats. They are expected to have evolved mechanisms that keep them in their specific habitation. Since little is known about the recruitment or habitat selection of HV organisms such as brachyurans, we examined the properties of several hydrothermal vent-associated cues on the behavior of the hydrothermal vent (HV) crab  Hydrothermal vents (HVs) are extreme habitats with unique physicochemical and geological conditions . They arXenograpsus testudinatus [Biological communities associated with HVs show behavioral, physiological, morphological, and reproductive adaptations –9. This udinatus  in the l2 and HCl but high C02 and H2S contents of a mantle source region without significant crust contamination [Xenograpsus testudinatus Ng et al. 2000 [X. testudinatus was discovered as the third species of the genus Xenograpsus Takeda and Kurata, 1977 (Bythograeidae) from shallow water hydrothermal vents off the east coast of Taiwan [Turtle Island is a volcanic island originating from the Holocene close offshore at the NE coast of Taiwan . The HVsmination . There amination . McClay mination  summarizmination  listed 1al. 2000  describeal. 2000 . X. testf Taiwan . Meanwhif Taiwan . Large nXenograpsus were found in all the known hydrothermal vents—in the western Pacific Ocean [Representatives of ic Ocean . Informaic Ocean –31. Crabic Ocean  and ecolic Ocean , and theic Ocean . But litX. testudinatus larvae in the shallow HVs of Turtle Island (2) adult behavior in two-choice experiments designed to test towards different HV-associated single cues or a combination of cues (3).The objective of the present study is to evaluate aspects of the supply side ecology of HV crabs. Specifically, we hypothesize that there will be adaptations towards larval retainment and cues for home finding in crabs. Hence, we studied: (1) the spatiotemporal field distribution of The Ministry of Science and Technology of Taiwan, ROC, granted permission. Medicine, Kaohsiung Medical University (KMU), the Asia-Pacific Ocean Research Center of the Department of Oceanography (No. 76211194) in the frame of the KMU/NSYSU cooperation, and MOST 105-2621-M-037-001 to T.H. Shih.Meroplanktonic crab larvae were collected by oblique tows with a standard North Pacific zooplankton net (mouth diameter 45 cm and a mesh size of 333 μm) from surface waters (0-1m depth), midwater (6m depth), and above ground (12-15m depth) at an experimental vent site A of Turtle Island in 4 replicates . A HydroX. testudinatus were collected for behavioral experimentation from the vent site of Turtle Island by SCUBA dives at a depth of about 18m. The crabs were rapidly transferred to aquaria filled with aerated surface seawater from the vent site as soon as they reached the Research Vessel. We also provided air-supply during the rapid and cautious transport of the crabs to the laboratory.Adults of the vent crab X. testudinatus to different cues in a 2-choice experiment.A binary-choice tank (95 x 60 x 45 cm)  was usedIndividual crabs were introduced in the middle of the chamber in a round untransparent beaker with an opening at the inverted bottom. The beaker was manually lifted for each individual run of the experiment. The crabs then could choose to move to either the left or the right side where alternating different materials were deposited behind a glass wall that allowed water communication through a gap at the bottom. This gab also allowed physical (warmer or colder water) or physical   cues to (1) Sulfuric sediment of 0.8 mm mean grain size was collected from around the chimney of the vent site, sieved through a 0.9 and 0.7 mm geological sieve and kept airtight in plastic bags in the refrigerator until experimentation. On the treatment side 10g of sulfuric sediment was added prior to the choice test.(2) Quartz sediment of 0.8 mm grain size was obtained from a commercial aquarium supplyer and kept dry until experimentation. On the treatment side 10g were added prior to the choice test.(4) Elevated/ low temperature. Elevated temperature was provided by a 100W commercial aquarium heater that provided a gradient of 10°C from the treatment compartment (35°C) to the main test arena (25°C). Low temperature provided a similar gradient of 10°C by adding seawater ice cubes to the treatment compartment.(5) Dead settled zooplankton (5 g) was obtained alive from off Turtle Island waters, frozen, and defrosted prior to experimentation.(6) Muscle meat of mackerel fish (5 g in 3 pieces of tail muscle) obtained from Badouzih fish-market were defrosted prior to experimentation.(7) Light/ shade. Light from 2 swan neck cold lamps was provided from above. Shade was created by covering the entire treatment side half of the experimental tank by black velvet.(8) Male/ female. A single male or female was exposed in the treatment compartment to the opposite sex in the choice area.(9) Group of 6: six crabs  were exposed in the treatment compartment to either, a single male and a single female in the choice area.(10) Air-bubbling was provided by a commercial air pump from regular silicon tube without air stone at mild bubbling of 5 L air per minute in the treatment compartment.(11) Mix of factors I (HV condition): Sulfur sediment (5g), elevated temperature, air-bubbling, shade were provided on the treatment side.(12) Mix of factors II (non-HV condition): Quartz sediment, low temperature, no air-bubbling, light was provided on the treatment side.Sixteen individual crabs of balanced gender ratio were tested individually between 9.00 and 18.00 hours of the day within 48 hours after capture. The position of the treatment and control side of the two-choice apparatus was reversed to avoid position bias after every five batches of crabs had been tested. The setup was cleaned after 10 batches had been tested. Each crab spent 5 min. in the two-choice chamber. A "no choice" was recorded for the rare case when a crab remained inactive for the whole experimental duration. A crab was considered to have made a ‘first choice’ when it moved >5 cm to either side . The ‘final choice’ of crab was the side they chose at the end of a 5-min experimental period. In addition was the amount of time crabs spent on each side of the device recorded. The experimental protocol followed otherwise the criteria provided by Train .Statistical testing for crab larvae distribution and crab habitat choice behavior were using SPSS version 13.0 for Windows software package . For the analyses of spatial distribution of crab larvae, one-way analysis of variance (ANOVA) with post-hoc Tukey’s honestly test was applied for abundance check among three depth samples following Zar . A Chi-SX. testudinatus that they tend to stay near the bottom even when swimming. The field distribution of larvae clearly indicates that its 4 zoea and 1 megalopa stages are distributed throughout the water column . The abundance of five stages larvae were not significantly different in the surface water (3). All zoea and megalopa crab larvae were higher near the bottom layer than in samples collected from the surface and from 6 m depth  (p = 0.029). In particular were densities of zoea 3 and zoea 4 larvae significantly higher in samples of 12-15m depth than at the surface, 0-1m (p < 0.001) and at 6m (p < 0.001) (p < 0.001) and middle zone (p < 0.001)  . Zoea 2 < 0.001) . Megalop< 0.001) . Finally< 0.001) . PreviouX. testudinatus always significantly preferred vent-associated cues . Furthermore, a combination of the HV-associated cues  was significantly more attractive .Despite the observed differences in crabs responding to the seven cues between the first choice and the final choice, the time they spent in the compartment containing each of the seven compounds was significantly longer than that in the control compartment  became established. Taiwan also plans to increase the size of Taiwan's MPAs to 20% of its total ocean territory within this decade and Kueishan Island has to be one of the focus points of protection also to ensure the sustainability of a future ocean with all its diverse ocean landscapes and biotic adaptations from molecular to ecosystem scale.


Pseudomonas aeruginosa PAO1, a medically important and innately multi-drug resistant bacterium. We systematically mapped RNA cleavage and dephosphorylation sites that result in 5′-monophosphate terminated RNA (pRNA) using monophosphate RNA-Seq (pRNA-Seq). Transcriptional start sites (TSS) were also mapped using differential RNA-Seq (dRNA-Seq) and both datasets were compared to conventional RNA-Seq performed in a variety of growth conditions.Understanding the RNA processing of an organism’s transcriptome is an essential but challenging step in understanding its biology. Here we investigate with unprecedented detail the transcriptome of E. coli RNase E site previously established in vitro. Using the dRNA-Seq library, we performed an operon analysis and predicted 3159 potential TSS. A correlation analysis uncovered 105 antiparallel pairs of TSS that were separated by 18 bp from each other and were centered on single palindromic TAT(A/T)ATA motifs (likely − 10 promoter elements), suggesting that, consistent with previous in vitro experimentation, these sites can initiate transcription bi-directionally and may thus provide a novel form of transcriptional regulation. TSS and RNA-Seq analysis allowed us to confirm expression of small non-coding RNAs (ncRNAs), many of which are differentially expressed in swarming and biofilm formation conditions.The pRNA-Seq library revealed known tRNA, rRNA and transfer-messenger RNA (tmRNA) processing sites, together with previously uncharacterized RNA cleavage events that were found disproportionately near the 5′ ends of transcripts associated with basic bacterial functions such as oxidative phosphorylation and purine metabolism. The majority (97%) of the processed mRNAs were cleaved at precise codon positions within defined sequence motifs indicative of distinct endonucleolytic activities. The most abundant of these motifs corresponded closely to an Pseudomonas aeruginosa and discover extensive transcript processing not previously appreciated. We have also gained novel insight into RNA maturation and turnover as well as a potential novel form of transcription regulation.This study uses pRNA-Seq, a method that provides a genome-wide survey of RNA processing, to study the bacterium www.pseudomonas.com.NOTE: All sequence data has been submitted to the NCBI sequence read archive. Accession numbers are as follows: . The sequence data is viewable using Jbrowse on The online version of this article (10.1186/s12864-018-4538-8) contains supplementary material, which is available to authorized users. Pseudomonas aeruginosa is a medically important γ-proteobacterium that is noted for causing opportunistic infections in hospitalized patients and chronic lung infections in cystic fibrosis patients , as, asssrA ria Fig.  showed ernpB and crcZ) also showed evidence of cleavage. In addition, we found clear evidence of cleavage in transcripts for a wide range of proteins associated with critical cellular functions other than protein translation. For example, cleavage in PA3648/opr86 was detected, particularly in the central region of the gene. PA3648 encodes the only essential integral outer membrane protein in P. aeruginosa PAO1 – a protein critical for outer membrane biogenesis , wherein nucleotides present in 10–30% of the sequences are depicted in lower case letters, nucleotides present in 31–64% of the sequences are depicted in uppercase letters and nucleotides present in 65% or more of the sequences are depicted in bold upper case letters. The position of the predominant cleavage site is indicated by the downward arrow ↓], where the downward arrow indicates the cleavage site. RNAs that contained this cleavage motif were located in genes that disproportionately belonged to the KEGG categories “oxidative phosphorylation”  and “purine metabolism”  (Table K-means clustering decomposed the RNA digestion patterns into five distinct classes (plus a ‘noisy’ class) based on peak shape that correlated with specific RNA sequence motifs ↓] and was consistent with either two adjacent cut sites or an initial cleavage event followed by the removal of an additional nucleotide in the 5′- > 3′ direction. The motif for this cluster shared properties with the predominant Sharp motif but was notably lacking in sequence conservation at the + 2 and + 3 position. The Tail L motif also frequently (> 30%) had a U at the + 1 position that was absent in the predominant Sharp motif. Transcripts containing this cleavage motif included those from genes belonging to the KEGG categories “RNA polymerase” , “protein export”  and “purine metabolism” .The remaining cleaved RNAs sorted into RNA digestion patterns that showed asymmetries in their cleavage patterns Fig. . When viU,c)] and was quite different from the Sharp and Tail L motifs  and “protein export”  among others  had a shoulder immediately 3′ of its main peak and was named “Tail R” Fig. . This moifs Fig. . RNAs coA subset of the pRNA-Seq-determined cleavage sites were found to localize exactly with our determined TSS locations. In total 131 sites (92 in coding genes + 39 in rRNA) met this criterion and were thus likely to be due to dephosphorylation of transcripts whereby the triphosphate was removed from the 5′ end of the RNA molecule and a 5′ monophosphate remained  that represented 36% of the strain PAO1 genome. Strikingly, just 54% (1695) of these TSS lay outside of ORFs. The remaining 1467 TSS lay within ORFs implying a potential regulatory role for such transcripts. We compared our predicted TSS to a set of 51 previously described TSS from strain PAO1 grown under various conditions  for our RNA-Seq datasets  were PCR confirmed here , characterized the associated promoters, identified RNA cleavage motifs, and further characterized ncRNAs.The continual turnover of RNA transcripts in a bacterial cell is a highly dynamic process that has been fine tuned by evolution. Our analysis of RNA post-transcriptional processing revealed three important steps involved in the eventual destruction of RNA species. Each process has a significant ramification for understanding the regulation of RNA turnover in the γ-proteobacteria.Of the 240 most abundantly transcribed protein-coding genes, the transcripts from 111 (46%) were found as cleaved products in the dRNA-Seq library  that possessed recognizable RBS produced transcripts that were cleaved such that their RBS were removed, even though the associated gene remained intact, implying a further level of translational suppression.E. coli, would be predicted to be cleaved by RNase E: [N(G) ↓ N]  . Our dats enzyme , 7, 39.P. aeruginosa has 66.6% G + C in its genome, therefore the third codon positions in this organism tend to be occupied by guanine and cytosine. Our analysis indicated that cleavage sites tended to be located between the first and second codon positions in annotated protein coding genes. Thus most of the cleavage sequence motifs showed a preponderance of G and C residues in their third codon positions   were present in the dRNA-Seq library, but not in the TSS library. It is possible that some TSS were identified incorrectly due to incomplete enzyme digestion during library preparation. However, since we do not see any tRNAs or tmRNA in the TSS libraries, we can conclude that enzyme digestion (and elimination of transcripts possessing 5′ monophosphates) from the TSS library was successful. Therefore, we can also conclude that the overlapping sites from the dRNA and pRNA-Seq libraries exist within the cell, and that such transcripts are processed by pyrophosphatases after transcription. Triphosphates at the 5′ end of E. coli transcripts can be removed by the pyrophosphatase RppH; thus it seems possible that the homolog YgdP in P. aeruginosa (67% identity to E. coli RppH), may be responsible for this activity in Pseudomonas.A total of 119 peaks corresponded exactly to a TSS found in our dRNA-Seq library. As mentioned above, we believe that these are mRNAs that have been matured by a pyrophosphatase and are consequently bona fide entities within P. aeruginosa, involved in outer membrane biogenesis. This gene and many other coding genes were not known to be subject to RNA-cleavage based regulation. Notably, the impacted protein-encoding genes tended to code for essential cellular functions. Our analysis represents a starting point for more in-depth characterization of the important role of RNA cleavage in transcriptional regulation and overall cell stability.RNA cleavage resulting in RNA with a 5′ monophosphate was associated with a surprisingly diverse class of genes that had not been previously thought to be subject to such regulation and was found at notable levels in a surprisingly high number of protein-coding genes. Genes of note included PA3648/opr86 (an Omp85 homolog), which encodes the only known essential integral outer membrane protein in E. coli RNA polymerase in association with DNA probes.Our observation of 105 back-to-back TSS spaced by 18 bp and often containing a palindromic − 10 A/T motif at the precise center of the back-to-back TSS has at least two mechanistic explanations. First, an individual polymerase holoenzyme complex could bind to either motif and form an open-form transcriptional bubble in the palindromic region through sigma factor specific interactions. Such complexes might transiently prevent transcription in the opposite direction and/or provide a competitive mechanism for transcriptional initiation. This first model is supported by numerous lines of biochemical and structural evidence . SecHelicobacter pylori, Anabaena sp. PCC7120, Trichodesmium erythraeum IMS101 and Burkholderia cenocepacia J2315 [H. pylori group who pioneered dRNA-Seq methodology [600 at harvest) resulting in the use of different TSS. P. aeruginosa, with its notably larger genome and number of transcriptional regulators cf. H. pylori, might have a more complex transcriptome. Overall, our predicted TSS showed a mean deviation of 5.0 bases from previously published TSS. Most of this variation was due to the presence of 6 outliers that had differences from published TSS ranging from 8 to 66 nt. Interestingly 3 of the 6 outliers were involved in transcriptional regulation, and may have multiple promoters enabling different transcriptional hierarchies depending on the growth conditions.Previously, dRNA-Seq studies have been shown to be an accurate method for determining prokaryotic TSS in ia J2315 –51. In tP. aeruginosa strain PA14 by employing a 5′ triphosphate transcript mapping strategy. Strains PA14 and PAO1 are highly similar organisms, with genomes differing by only roughly 200 genes [Wurtzel et al.  greatly 00 genes . In both00 genes  identifi00 genes ). This g00 genes .H. pylori, they represented 27% of the transcriptome [P. aeruginosa and H. pylori, or reflect the fact that the P. aeruginosa genome is 3-fold larger than that of H. pylori.In many bacterial RNA-Seq studies, it appears to be fairly common to find transcripts generated from the DNA strands opposite to those on which ORFs are located. This has been termed “antisense transcription” , 24. In criptome . The larP. aeruginosa PA14 focused on the changes in gene expression between cells grown in planktonic vs. biofilm conditions [Helicobacter pylori [A RNA-Seq study of nditions . This grnditions  reportedr pylori . The useListeria monocytogenes transcribes at least 98% of its genes [P. aeruginosa [P. aeruginosa lives requires the complex interplay of genes expressed under different conditions to ensure survival and competitiveness.Under the standard growth conditions used, only 34.5% of genes in PAO1 were found to have an upstream TSS, including genes found in predicted operons. In contrast, Toledo-Arana et al. report that under all conditions studied, the firmicute ts genes . This diruginosa  and nearP. aeruginosa [In addition to the novel layers of transcriptional complexity revealed by our dRNA-Seq analysis, this study detected other impacts on transcription. For example, data from the RNA-Seq library analysis indicated a trend towards a reduction in the level of transcription around the terminus of replication Fig. . This isruginosa .Pseudomonas genome database [P. aeruginosa transcriptome investigations have sought to more thoroughly annotate ncRNAs. A recent RNA-Seq study [P. aeruginosa PA14. Many of the ncRNAs defined in that study employed a very low threshold for TSS prediction , as well as a small number of total RNA-Seq reads spanning the region of the predicted ncRNA. Therefore, these ncRNAs predicted by others should be further verified using additional sequencing data, or an additional method such as Northern blotting or PCR, to confirm their existence and expression under different conditions, as well as looking at their potential regulatory roles in the cell. Our current investigation utilized high stringency methods and confirmatory RT-PCR to define 31 reliable ncRNAs that were produced under the investigated conditions, and not all of these were identified in the above-mentioned RNA-Seq studies. Intriguingly 30 of these were found to be dysregulated under the adaptive lifestyle conditions of biofilm formation and/or swarming motility. This indicates that it is important to consider different growth conditions when confirming ncRNA species and implies that translational regulation mediated through ncRNAs may be an important element in determining adaptive lifestyle changes. Certainly this is true of the ncRNA crc [rsmYZ [prrF1,2 and phrS as critical regulatory elements in one or both of these lifestyle changes [Small, non-coding RNA  transcripts are emerging as a major mechanism for regulating translational expression in bacteria and were also investigated here. At the time of the current study there were 140 annotated ncRNAs in PAO1, based on the database  and the database . All buteq study  on straieq study  identifycRNA crc  and rsmYc [rsmYZ  and we h changes .Here, we use pRNA-Seq to identify RNA transcript 5′ monophosphate cleavage/processing sites in a genome-wide manner. Cleavage sites were predominately located between the first and second codon positions within protein-coding genes. Further examination of cleavage sites has revealed that they can be classified into five distinct categories, based on their cleavage peak shape and associated sequence motifs. The cleaved transcripts occur in genes associated with specific KEGG categories, but a much wider set of categories and genes was observed than initially anticipated. We also identified a correlation between TSS that lie ~ 18 bp apart on opposite strands of the transcriptome. These sites are separated by a distinct motif, and transcription may be initiated here by RNA polymerase dimers. This combination of pRNA-Seq, dRNA-Seq and RNA-Seq thus provided us with a more extensive view of the transcriptome.Pseudomonas can undertake, it would be necessary to perform similar analyses, under these conditions, to those described here in order to define the transcriptional complexity that underpins diversity in this organism. Nevertheless, this study has provided a new window into a previously unappreciated level of complexity in RNA processing in a bacterial transcriptome. It involves a greater extent of transcript cleavage than previously anticipated, evidently occurring in a regulated fashion through enzymatically-controlled processes. However, this is only the start, as we expand, in the future, our understanding of the role and significance of RNA processing events in maintaining a dynamic, flexible and robust bacterial transcriptome.Due to the diversity of lifestyles and complex adaptations that Pseudomonas aeruginosa strain PAO1 was grown in Luria Broth (LB) medium at 37 °C  genome using 4 different tools: Bowtie , BWA 6060, mrsFAP. aeruginosa strain PAO1 was grown in LB medium at 37 °C  or 34 °C (library PA0001), synthetic cystic fibrosis medium (SCFM) [600 and rates of shaking, see Table 600 for the samples grown in ASM media was not determined due to biofilm formation in this medium; these samples were grown for 48 h before harvesting. ASM cultures were stabilized by adding an equal volume of RNA Later (Ambion), incubated at 23 °C for 10 min, then centrifuged for 30 min at 3200 x g, 4 °C. The pellet was resuspended in Sputasol (Oxoid) in order to break down the biofilm structure and incubated at 37 °C for 25 min with shaking. Two volumes of RNA protect were added, then the mixture was incubated at 23 °C for 5 min and centrifuged for 30 min at 3200 x g, 4 °C. The supernatant was removed, and the pellet was further extracted using the RNeasy kit beginning at step #6 of the manufacturer’s protocol.m (SCFM) , and artThe pRNA-Seq library construction process is depicted in Additional file The RNA used to construct library SN865 (LB 37 °C) was prepared as described previously  by VertiThe RNA used to construct libraries PA0001 (LB 34 °C), PA0004 (LB 37 °C), A03674 (SCFM) and A06026 (ASM) was reverse transcribed using random hexamers and the SuperscriptTM Double Stranded cDNA synthesis kit (Invitrogen). cDNA was sheared and size fractionated using SDS-PAGE. The 190-210 bp area was excised, eluted and purified with a QIAquick purification kit (Qiagen). Libraries were constructed using the Illumina Genome Analyzer protocol and paired-end 50 bp sequence reads were obtained using an Illumina Genome Analyzer II as per the manufacturer’s instructions. All reads were checked for passage of Illumina quality standards. Reads organized into FASTQ files were aligned using Bowtie  to the PPlease see Supplemental Information for a detailed description of how peak height cutoffs were determined for the dRNA-Seq and pRNA-Seq data.Once mapped, mate pairs of the 5′ monophosphate reads that were from nuclease-cleaved ends were discarded. Remaining reads from both the pRNA-Seq and dRNA-Seq libraries were trimmed of adapter sequence up to the first base pair. Genomic locations with coverage over the statistically determined cutoff  were marked as a peak, which represents possible cleavage sites or TSS, respectively. Regions encoding rRNA or tmRNA and their respective upstream intergenic regions were subjected to a higher cutoff threshold since the coverage in these regions was disproportionally higher. For the pRNA-Seq library, the peak height cut-off on both strands in these areas was 2000. For the dRNA-Seq library, the cutoff on the + strand was 30,000 and on the – strand was 2000. If multiple peaks were observed within 5 nucleotides of one another, only the location with the highest coverage was used as a predicted cleavage site. The TSS peaks were categorized according to their locations. Peaks in an intergenic region and on the same strand as the closest downstream gene = primary. Peaks within gene boundaries and on the same strand as the gene = internal. Peaks within gene boundaries and on the opposite strand from the gene = antisense. Peaks within an intergenic region = primary antisense and peaks within an area where 2 gene boundaries overlap = internal genes overlap.http://www.r-project.org/). The parameter k was estimated to be around 15 by plotting within-group variance for a number of the clusters. Therefore an initial k value of 20 was used and clusters with similar profiles were merged for subsequent analysis. An over-representation gene function analysis was calculated using hypergeometric tests based on KEGG pathway categories [Cleavage site motifs were calculated using MEME software  with deftegories  and HolmNovel regulon member identification, RpoN binding site identification, Ribosomal binding site identification, small ncRNA identification and Functional analysis of ncRNAs by RT-PCR methods are described in Supplementary Information.Additional File 1:Figure S1. Measures of cleavage site motif category sequence member distance within and between categories. Figure S2A and B. Additional cleavage site motifs. Figure S3. While our data is more comprehensive, 86% of previously published transcription start sites lie within ±3 nt of our dRNA-Seq set. Figure S4. A Promoter motif associated with several virulence factors and two hypothetical proteins is highly similar to the RpoN binding motif. Figure S5. Small non-coding RNA gene expression in biofilms (A) and cells undergoing swarming motility (B) compared to expression in planktonic cells. Figure S6. Proposed polymerase dimer binding to a palindromic antiparallel TSS. Figure S7. Step by step construction of the pRNA-Seq library. Figure S8. Peak height threshold determination for generating cutoffs for site predictions (Transcription start sites (TSS) and RNA cleavage sites). Table S1A. SNPs found in our sequenced isolate of PAO1 cf. the reference strain. Table S1B. Indels found in our sequenced isolate of PAO1 vs. the reference strain. Table S2. The 240 most abundant transcripts from the RNA-Seq study of cells grown in LB at 37 °C. Table S3. Sequence motifs associated with cleavage sites. Table S4.p-values for promoters matching motif associated with virulence factors.Table S5A. Summary of predicted RpoN binding sites and adjacent downstream genes in Pseudomonas aeruginosa PAO1. Table S5B. Function classes associated with genes downstream of predicted RpoN binding sites. Table S6. Fold change in RNA-Seq read density throughout the PAO1 genome. Table S7. Sequences of the oligonucleotides used in the preparation of the pRNA-Seq library. Table S8. Fitted slopes and intercepts for data from H = 1..100XX. Fit to G(H) = H0XS. Table S9. List of primers utilized for confirmation of sRNAs by RT-qPCR. The Perl script used for 5′ end trimming on library. (DOCX 1536 kb)Supplementary results, figures and methods.


In MERFISH, RNAs are identified via a combinatorial labeling approach that encodes RNA species with error-robust barcodes followed by sequential rounds of single-molecule FISH (smFISH) to read out these barcodes. The accuracy of RNA identification relies on spatially separated signals from individual RNA molecules, which limits the density of RNAs that can be measured and makes the multiplexed imaging of a large number of high-abundance RNAs challenging. Here we report an approach that combines MERFISH and expansion microscopy to substantially increase the total density of RNAs that can be measured. Using this approach, we demonstrate accurate identification and counting of RNAs, with a near 100% detection efficiency, in a ~130-RNA library composed of many high-abundance RNAs, the total density of which is more than 10 fold higher than previously reported. In parallel, we demonstrate the combination of MERFISH with immunofluorescence in expanded samples. These advances increase the versatility of MERFISH and will facilitate its application to a wide range of biological problems.As an image-based single-cell transcriptomics approach, multiplexed error-robust fluorescence  In situ imaging-based approaches to single-cell transcriptomics allow not only the expression profile of individual cells to be determined but also the spatial positions of individual RNA molecules to be localized. These approaches provide a powerful means to map the spatial organizations of RNAs inside cells and the transcriptionally distinct cells in tissues1. Multiplexed fluorescence in situ hybridization (FISH)7 and in situ sequencing9 have been used to profile the expressions of a large number of  RNA species in single cells. In particular, MERFISH, a massively multiplexed form of smFISH, enables RNA imaging at the transcriptomic scale with high accuracy and detection efficiency7. By imaging single RNA molecules, smFISH provides the precise copy number and spatial distribution of individual RNA species in single cells11. MERFISH multiplexes smFISH measurements by labeling RNAs combinatorically with oligonucleotide probes which contain error-robust barcodes and then measuring these barcodes through sequential rounds of smFISH imaging. Using this approach, we demonstrated simultaneous imaging of hundreds to a thousand of RNA species in individual cells using barcoding schemes capable of detecting and/or correcting errors7. Recently, we have increased the measurement throughput of MERFISH to tens of thousands of cells per single-day-long measurement12. In addition, we developed a matrix-imprinting-based sample clearing approach that substantially reduces the fluorescence background and increases the signal-to-background ratio by anchoring RNA molecules to a polymer matrix and removing other cellular components that give rise to fluorescence background13. This clearing approach enabled high-quality MERFISH measurement of tissue samples13.15, by analysis methods to address partially overlapping fluorophores19, or by sample expansion21. In particular, since neighboring RNA molecules may physically overlap in space, expansion microscopy (ExM), which uses sample expansion to effectively increase the distances between neighboring molecules20, may provide an especially effective means to increase the RNA density limit accessible by MERFISH. In ExM, the desired signal is conjugated to an expandable polyelectrolyte gel, and then the gel is physically expanded by changing the ionic strength of the buffer20. ExM has recently been combined with smFISH to help better resolve highly expressed RNAs, with either single-round or multiple rounds of smFISH to measure one or several genes22. In addition, RNAs have been anchored to a polyacrylamide matrix to facilitate sample clearing and background removal in RNA FISH24 and improve the signal-to-background ratio in MERFISH measurements13. Thus, we reason that ExM should also be compatible with MERFISH and may help substantially increase the RNA density measurable by MERFISH.In order to accurately identify RNA molecules, MERFISH, as well as other multiplexed smFISH-based RNA profiling methods, requires non-overlapping signals from individual RNAs. However, when two molecules are sufficiently close to each other, the signal from one molecule will overlap with that from the other molecule, diminishing our ability to identify these RNAs and, thus, limiting the density of RNAs that can be profiled. Indeed, in MERFISH experiments, we often find this density limit a major limiting factor in our choice of genes to profile, both in terms of the total number of genes and their RNA expression levels. This problem could potentially be mitigated by super-resolution optical imaging13, in cultured human osteosarcoma (U-2 OS) cells. Unlike our previous MERFISH measurements of lower-density RNA libraries, in which we demonstrated 80–90% detection efficiency, the RNAs in this high-density library are not well resolved and hence are detected with a low detection efficiency of ~20% without gel expansion. In contrast, individual RNA molecules become well resolved in expanded samples, leading to a substantial increase in their detection efficiency. Comparison with smFISH and bulk sequencing results demonstrate that these RNAs in the expanded sample are detected with high accuracy and near 100% detection efficiency. In addition, we also demonstrate the ability to perform simultaneous MERFISH RNA imaging and immunofluorescence imaging of proteins in these expanded samples. This combination can be used to provide cellular context for the RNA expression analysis, and can potentially enable simultaneous profiling of transcriptomic and proteomic expression.In this paper, we report an approach that combines MERFISH and ExM to greatly increase the molecular density of RNA libraries accessible to MERFISH. We anchor mRNAs to an expandable polyelectrolyte gel via acrydite-modified poly(dT) locked nucleic acid (LNA) probe hybridized to the poly(A) tail of mRNAs. We demonstrate the efficacy of this approach by imaging a high-abundance RNA library, which contains ~130 RNA species with a total RNA abundance that is 14-fold higher than what has been previously demonstrated with MERFISH imaging13. This code includes 140 unique barcodes and we used 129 of them to encode RNAs and kept 11 of them as blank controls that did not correspond to any RNA. Among the 129 targeted RNAs, 106 RNAs were in the abundance range of 40–250 copies per cell to increase the total abundance of the library, and the remaining 23 spanned an abundance range of 1–1000 copies per cell to quantify performance across different abundances. The total abundance of RNAs in this library was 14-fold higher than the 130-RNA libraries that we have previously measured using the MHD4 code with ~80–90% detection efficiency13.To illustrate the effect of RNA density on multiplexed smFISH measurements, we measured a high-abundance, ~130-RNA library using our previously published 16-bit modified Hamming distance 4 (MHD4) binary code, which allows error detection and correction13, we labeled the RNAs with two sets of oligonucleotide probes. In the first step, each cellular RNA was hybridized with a complex library of oligonucleotide probes termed encoding probes, each encoding probe containing a targeting sequence that binds the cellular RNA and multiple readout sequences; the collection of readout sequences associated with the cellular RNA determine the barcode of this RNA. In the second step, the readout sequences, and hence the barcodes, were detected through a series of smFISH measurements, each round with one or more readout probes complementary to one or more readout sequences. We carried out MERFISH measurements in U-2 OS cells using the matrix-imprinting-based clearing method to reduce the fluorescence background as previously published13. Briefly, the cells were fixed, permeabilized, labeled simultaneously with encoding probes for the 129 RNA species as well as acrydite-modified poly(dT) LNA probes that target polyadenylated RNAs. We then embedded the cells in a non-expandable, polyacrylamide gel and the poly(A)-containing mRNAs were anchored to the gel through the poly(dT) probes. Next, we removed cellular proteins and lipids by Proteinase K digestion and detergent extraction to remove fluorescence background and clear the samples. After this clearing, eight rounds of two-color smFISH measurements were performed to read out the 16-bit barcode for each RNA. Because the heights of the cells were greater than the depth of a single optical section, we imaged the sample with multiple z-sections to ensure that >90% of RNA molecules within the cells were detected.As in our previous MERFISH measurements13. The average copy number per cell detected for the RNAs in this high-abundance library by MERFISH correlated with the RNA abundance measured by bulk RNA sequencing with a Pearson correlation coefficient (r) of 0.6  or 16% (median) of the RNA copies determined by smFISH  tail. This anchoring geometry should allow the separation between molecules during expansion without concurrent stretching of the molecules, and could potentially improve separation of the nearby RNA molecules. Second, we found that MERFISH encoding probes did not reliably penetrate the gel after expansion and stabilization, perhaps due to the increased length of these probes compared to those used for conventional smFISH. Thus, we stained the samples with encoding probes before embedding in the gel. By contrast, we found that the short MERFISH readout probes penetrated this gel without a problem, allowing the sequential rounds of smFISH to be performed in expanded samples. Third, to stabilize the already bound encoding probes on RNAs, we utilized a low salt buffer ), instead of water, for the expansion of the gel. Our measurement of the gel volume showed that dialysis in 0.5× SSC produced 2.3-fold expansion in each dimension and 12-fold expansion in volume, which was smaller than that reported previously for expansion in water21. We note that as long as the expansion is sufficient to separate neighboring RNA molecules, a lower expansion factor has the advantage of allowing faster imaging.We reasoned that the reduced MERFISH detection efficiency due to overlapping single-molecule signals could be alleviated by sample expansion, which will increase the distance between molecules and reduce overlap. To test this idea, we fixed and permeabilized the cells, and labeled them with the encoding probes and poly(dT) anchoring probes as described above. Then we embedded the cells in an expandable polyelectrolyte gel using a protocol modified from the published ExM methodsate Fig. 20,21. ThNotably, in the expanded samples, individual RNA molecules became substantially better resolved Fig.  and manyTo quantify the improvement in detection efficiency with expansion, we compared the average MERFISH counts for individual RNA species per cell between expanded and unexpanded samples. We found that the copy numbers per cell for the 129 RNA species detected in expanded samples correlated with those in the unexpanded samples with a high Pearson correlation coefficient of 0.88 Fig. . However27.Our ability to quantify the copy numbers of RNAs in a high-density library also allowed us to characterize the cell-to-cell variability in the expression levels of these RNAs. We determined the variance in RNA copy number and the Fano factor, defined as the ratio of the variance to the mean RNA copy number, for the 129 genes in our library. Both the variance and the Fano factor increased with the RNA expression level  anchor probes, and then embedded the labeled sample in an expandable gel. We added acrydite modification to the oligo on the antibodies so that it can be incorporated into the polymer gel during the embedding step. After digestion, gel expansion and second embedding in a non-expandable gel as described in the previous section, we performed the MERFISH readout procedure to first detect the readout sequences in the RNA encoding probes and then with an additional round of FISH detection to read out the oligo sequence representing the protein target.For demonstration purposes, we immunostained cadherin in cultured U-2 OS cells, which were also stained for the same high-abundance RNA library described in the earlier sections. We observed both specific staining of cadherin on the cell periphery Fig.  as well In this paper, we have demonstrated an approach to combine MERFISH and ExM to measure high-abundance RNA libraries. We showed that when the total RNA density is high, the overlap between signals from nearby RNA molecules substantially reduced the MERFISH detection efficiency and that sample expansion can overcome this overlapping problem and greatly increase the detection efficiency. Specifically, for an RNA library with total RNA abundance that is 14-fold higher than our previously measured libraries, the MERFISH detection efficiency dropped to <20% in unexpanded samples, and expansion recovered the near 100% detection efficiency of MERFISH.7. To increase the number of RNA species that can be simultaneously imaged in single cells while maintaining a similar number of imaging rounds, the density of RNAs imaged per round will increase and will become an important limiting factor in the number of RNAs that can be imaged. Likewise, our ability to profile RNAs with high expression levels will also be limited by this factor. We anticipate that the expansion MERFISH approach reported here should facilitate a substantial increase in the number of RNA species and the total RNA abundance level that can be measured in single cells. In this work, we measured 129 RNA species, the total abundance of which is ~13,000 molecules per cell, with ~100% detection efficiency in expanded U2-OS cell with a 12-fold volume expansion factor. It is possible that a substantial increase in the total RNA copy number will lead to a reduction in detection efficiency even in expanded samples. To assess this limitation, we simulated MERFISH images of RNA molecules at higher total abundance based on our experimental measured RNA spot size and pixel size. The simulation results suggest that an RNA density of ~3–4 molecules per µm2 will lead to significant signal overlap between neighboring molecules that results in a 50% reduction in MERFISH detection efficiency. This molecular density, with an imaging depth of ~1 µm per optical section, corresponds to ~70,000–100,000 molecules per cell after expansion based on the measured U2-OS cell cytoplasm volume. For a measurement of a randomly chosen 1,000 RNA species, there is only a ~2–5% probability that their total copy number will reach this density range. We thus expect our current approach to combine MERFISH and ExM to be adequate for many applications of MERFISH. However, a typical mammalian cell expresses ~50,000 to 300,000 of RNA molecules in total when all genes are considered32 and, in addition, certain subcellular regions can have higher RNA density than others. Hence, we anticipate that further improvements are still needed for some applications that involve a very large number of genes and/or very high-abundance RNAs. This could potentially be achieved through decreasing the ionic strength of the expansion buffer or using multiple rounds of expansion33 to further increase the volume expansion factor or combining expansion-MERFISH with other super-resolution imaging methods15.Each mammalian cell can express thousands of different RNA species. We have previously demonstrated MERFISH imaging of up to 1,000 RNA species in single cells35 are expected to be sufficiently long to ensure high capture efficiency36, and we did not observe a significant effect of the poly(A) tail length on the RNA detection efficiency. However, for RNAs with too short a poly(A) tail or without a poly(A) tail, alternative anchoring strategies will need to be used. Potential alternative strategies include using acrydite-modified oligos with complementary sequences to the RNAs of interest to link these RNAs to the gel matrix, or directly linking the RNAs to the gel through cross-linking molecules such as LabelX21. Additionally, one can also explore protocols to expand the gel without strong proteinase digestion31, in which case RNAs can be linked to the gel through proteins by chemicals such as EDC24.Here, we linked RNAs with a poly(A) tail to the gel matrix using an acrydite-labeled LNA poly(dT) anchoring probes. For the RNAs measured in this work, the poly(A) tail lengths (between 30–100 nt)40. We thus envision such combination of MERFISH and immunofluorescence imaging may allow a combined transcriptomic and proteomic measurements simultaneously in single cells.In addition to enabling the measurement of high-density RNA libraries, we also demonstrated the ability to combine MERFISH with immunofluorescence in expanded samples. Such combination can substantially expand the scope of MERFISH applications. For example, immunofluorescence allows us to measure cellular structures and compartments, such as endoplasmic reticulum, mitochondria, and microtubules, among others, which can provide important information on the cellular context for the measured RNAs. In addition, although individual cells in the system studied in this work can be clearly segmented just based on the total RNA image itself, it is possible that the total RNA signal alone is not adequate for cell segmentation in some other systems. In such cases, immunofluorescence imaging of cell surface markers could be helpful. Moreover, because the expression levels of mRNA and proteins are not always correlated in a simple manner, but could be complicated by post-transcriptional regulation, a combined RNA and protein expression profiling of single cells can provide useful insights that may not be deciphered from mRNA profiling alone. In conventional immunofluorescence with fluorophore-conjugated antibodies, the number of spectrally-distinct fluorophores limits the number of protein targets that can be studied simultaneously. The use of oligo-conjugated antibodies potentially allows visualization of many proteins in sequential rounds of hybridization using just one or a few spectrally distinct fluorophores12. In this encoding scheme, all barcodes used are separated by a Hamming distance of at least 4, and hence at least four bits must be read incorrectly to change one valid barcode to another. A constant Hamming weight (i.e. the number of “1” bits in each barcodes) of 4 is used to avoid potential measurement bias due to differential rates of “1” to “0” and “0” to “1” errors12. The encoding probe set that we used contained 92 encoding probes per RNA. Each encoding probe was comprised of a 30-nt target region designed using a pipeline published previously12, flanked by two 20-nt readout sequences randomly selected out of the four ones assigned to each RNA, one 20-nt priming region at the 5′ end and another 20-nt priming region using the reverse complement of the T7 promoter at the 3′ end. Additional adenosine nucleotide spacers were added between readout sequences and target regions to prevent target regions from combining with Gs from adjacent sequences to form G quadruplets. The priming region at the 5′ end had a thymine at the end, which was put at the junction of the priming regions at the 5′ end and the encoding region. This was designed to incorporate a uracil on the forward primer used in the reverse transcription step of probe construction, which can be cleaved by Uracil-Specific Excision Reagent (USER) Enzyme41. The cleavable primer design together with using the reverse complement of the T7 promoter as the second primer allowed us to remove these priming regions from the final probes, producing encoding probes with a length of 72 nt compared to 112 nt using previous approaches13. The reduction in probe length may facilitate penetration of probes and reduce non-specific binding. To include some abundant but relatively short RNAs in our library, instead of designing probes targeting distinct regions of RNAs, we allowed probes to share up to 20 nt with another probe. 92 target regions per RNA were selected randomly out of all potential target regions of an RNA. By allowing up to a 20-nt overlap between neighboring encoding probes, this design allowed RNAs as short as 1,200 nt to be targeted by 92 encoding probes with a 30-nt targeting sequence. Because a given cellular RNA is typically bound by less than one third of the 92 encoding probes , we reason that the encoding probes with overlapping targeting regions would not substantially interfere with each other but would partially compensate for reduced binding due to local inaccessible regions on the target RNA (e.g. secondary structure) or loss of probes during synthesis. The same 20-nt, three-letter readout sequences were used as previously published12.The MERFISH encoding probes were designed using the same 16-bit Hamming-weight-4 Hamming-distance-4 code with 140 possible barcodes as previously publishedin vitro transcription templates, converted these templates into RNA via in vitro transcription (New England Biolabs), and converted the RNA back to DNA via reverse transcription  as previously published12. The probes were then digested by USER Enzyme41 (New England Biolabs) at a dilution of 1:30 (vol/vol) incubated at 37 °C for 24 h to cleave off the priming region at the site of a uracil between a priming region at the 5′ end and the target region. After that, DNA was purified via alkaline hydrolysis to remove RNA and column purification (Zymo Research). The final probes were resuspended in RNAase-free water and stored at −20 °C.The encoding probe set was amplified from complex oligonucleotide pools. Briefly, we amplified the oligopools (CustomArray) via limited-cycle PCR to make 42. Readout probes conjugated to the desired dye via a disulfide linkage were synthesized and purified by Bio-synthesis, Inc., resuspended immediately in Tris-EDTA (TE) buffer, pH 8 (Thermo Fisher) to a concentration of 100 μM and stored at −20 °C. To reduce the number of freeze-thaw cycles, 1-μM aliquots were made in TE buffer and stored at −20 °C.The three-letter readout sequences were designed as published previously43. First, secondary antibodies were labeled with a copper-free click crosslinking agent using NHS-ester chemistry. Specifically, azide preservative was removed from the unconjugated Donkey Anti-Rabbit secondary antibodies (Thermo Fisher Scientific) using a spin-column based dialysis membrane  according to the manufacturer’s instructions. DBCO-PEG5-NHS Ester (Kerafast) was diluted to a concentration of 10 μM in anhydrous dimethyl sulfoxide (DMSO) (Thermo Fisher Scientific). 2 μL of the solution was then combined with 100 μL of 2 mg/mL of the antibody in 1× phosphate-buffered saline (PBS). This reaction was incubated at room temperature for 1 hour and then terminated via a second round of purification using the Amicon columns as described above. The average number of DBCO crosslinkers per antibody was determined via the relative absorption of the sample at 280 nm (antibody) and 309 nm (DBCO). On average the procedure described above produced ~7 DBCO per antibody.Oligonucleotides containing the complementary sequence of the desired readout probe were conjugated to secondary antibodies via a combination of NHS-ester and copper-free click chemistries similar to a published methodOligonucleotide probes containing the desired sequence as well as a 5′-acrydite, to allow cross-linking to the polymer gel, and a 3′-azide, to allow cross-linking to the DBCO-labeled antibodies, were ordered from IDT and suspended to 100 μM in 1× PBS. 20 μL of the appropriate oligonucleotide was then added to 100 μL of the DBCO-labeled antibodies at a final concentration of ~2 mg/mL. This reaction was incubated at 4 C for at least 12 hours. Labeled antibodies were not further purified as residual oligonucleotides, not conjugated to antibodies, were readily washed away from samples.2 for 48 h. Cells were fixed for 15 min in 4% (vol/vol) paraformaldehyde (PFA) (Electron Microscopy Sciences) in 1× PBS at room temperature, washed three times with 1× PBS, permeabilized for 10 min with 0.5% (vol/vol) Triton X-100 (Sigma) in 1× PBS at room temperature, and washed once with 1× PBS.U-2 OS cells (ATCC) were cultured with Eagle’s Minimum Essential Medium (ATCC) containing 10% (vol/vol) fetal bovine serum (FBS) (Thermo Fisher Scientific). Cells were plated on 40-mm-diameter, no.1.5 coverslips (Bioptechs) at 350,000 cells per coverslip and were incubated in Petri dishes at 37 °C with 5% COPermeabilized cells were incubated for 5 min in encoding wash buffer comprising 2× saline-sodium citrate (SSC) (Ambion) and 30% (vol/vol) formamide (Ambion). Then 30 μL of ∼300 μM encoding probes and 3.3 μM of poly(dT) LNA anchor probe  with a 20-nt reverse complement of a readout sequence and a 5′-acrydite modification (Integrated DNA Technologies)) in encoding hybridization buffer was added to the surface of Parafilm (Bemis) and was covered with a cell-containing coverslip. Encoding hybridization buffer was composed of encoding wash buffer supplemented with 0.1% (wt/vol) yeast tRNA (Life Technologies), 1% (vol/vol) murine RNase inhibitor (New England Biolabs), and 10% (wt/vol) dextran sulfate (Sigma). Samples were incubated in a humid chamber inside a hybridization oven at 37 °C for 40 h. Cells then were washed with encoding wash buffer and incubated at 47 °C for 30 min; this washing step was repeated once.For immunofluorescence only, cells were fixed and permeabilized as described in the “Cell culture and fixation” Section. Samples were first blocked at room temperature for 30 min in blocking buffer consisted of 4% (wt/vol) UltraPure BSA (Thermo Fisher Scientific) in 2× SSC supplemented with 3% (vol/vol) RNasin Ribonuclease inhibitor (Promega), 6% (vol/vol) murine RNAase inhibitor and 1 mg/ml yeast tRNA. Samples were then incubated with primary antibodies  in blocking buffer at a concentration of 2 μg/ml for 1 h at room temperature, and washed three times with 2× SSC for 10 min each. Samples were then incubated with oligo-labeled secondary antibodies in blocking buffer at a concentration of 3.75 μg/ml for 1 h at room temperature, then washed with 2× SSC three times for 10 min each. Samples were fixed again with 4% (vol/vol) PFA in 2× SSC for 10 min and washed three times with 2× SSC.To combine MERFISH with immunofluorescence, samples were first stained with encoding probes and washed as described in the “Encoding probe staining” section above. To stabilize these probes, samples were then briefly post-fixed with 4% (vol/vol) PFA in 2× SSC at room temperature for 10 min and washed three times with 2× SSC. Samples were then stained for immunofluorescence as described above.Stained samples on silanized coverslips treated as in “Silanization of coverslips for unexpanded samples” Section (see below), were first incubated for 5 min with a de-gassed polyacrylamide solution, consisted of 4% (vol/vol) of 19:1 acrylamide/bis-acrylamide (BioRad), 60 mM Tris⋅HCl pH 8 (Thermo Fisher Scientific), 0.3 M NaCl (Thermo Fisher Scientific), 0.2% (vol/vol) Tetramethylethylenediamine (TEMED) (Sigma) and a 1:25,000 dilution of 0.1-µm-diameter carboxylate-modified orange fluorescent beads . The beads served as fiducial markers for the alignment of images taken across multiple rounds of smFISH imaging. The polyacrylamide solution was kept on ice and further supplemented with ammonium persulfate (Sigma) at a final concentration of 0.2% (wt/vol).21. We used 2% (wt/vol) SDS to facilitate lipid removal. After the wash, the gel was covered with digestion buffer supplemented with 1% (vol/vol) Proteinase K (New England Biolabs). The sample was digested in this buffer for >12 h in a humidified, 37 °C incubator and then washed three times with 2× SSC for 15 min each on a rocker. MERFISH measurements were either performed immediately or the sample was stored in 2× SSC supplemented with 0.1% (vol/vol) murine RNase inhibitor at 4 °C for no longer than 48 h.50 µL of this gel solution was added to the surface of a glass plate (TED Pella) that had been pretreated for 5 min with 1 mL GelSlick (Lonza) so as not to stick to the polymer gel. Samples were aspirated, dried quickly with KimWipes (Kimtech) from the edge of the coverslips, and gently inverted onto this 50-µL droplet to form a thin layer of solution between the coverslip and the glass plate. The solution was then allowed to polymerize for 2 h at room temperature in a home-built chamber filled with nitrogen. The coverslip and the glass plate were then gently separated, and the PA film was washed once with a digestion buffer consisted of 2% (wt/vol) Sodium dodecyl sulfate (SDS) (Thermo Fisher Scientific), 0.5% (vol/vol) Triton X-100 in 2× SSC. Our digestion buffer is different from published previously44. Briefly, 40-mm-diameter #1.5 coverslips (Bioptechs) were washed for 30 min via immersion in a 1:1 mixture of 37% (vol/vol) hydrochloric acid (Sigma) and methanol (Sigma) at room temperature. Coverslips were then rinsed three times in deionized water and once in 70% (vol/vol) ethanol. Coverslips were blown dry with nitrogen gas and then immersed in 0.1% (vol/vol) triethylamine (Millipore) and 0.2% (vol/vol) allyltrichlorosilane (Sigma) in chloroform for 30 min at room temperature. Coverslips were washed once each with chloroform and ethanol and then blow dry with nitrogen gas. Silanized coverslips were then stored at room temperature in a desiccated chamber overnight before use to dehydrate the silane layer.To stabilize the polymer film, coverslips were silanized as published previously21. Monomer solution consisted of 2 M NaCl, 7.7% (wt/wt) sodium acrylate (Sigma), 4% (vol/vol) of 19:1 acrylamide/bis-acrylamide and 60 mM Tris-HCl pH 8 was prepared and frozen in aliquots at −20 °C. Monomer solution was thawed, degassed and cooled to 4 °C on ice before use. TEMED with a final concentration of 0.2% (vol/vol) and a 1:5,000 dilution of 0.1-µm-diameter carboxylate-modified orange fluorescent beads was added to the solution. The beads served as fiducial markers for the alignment of images taken across multiple rounds of smFISH imaging. Stained samples were incubated in the solution for 5 min at room temperature. The solution was then kept on ice and further supplemented with ammonium persulfate at a final concentration of 0.2% (wt/vol).The embedding and expansion protocol was modified based on previously published methodsThe casting of a thin polymer film and polymerization was performed the same as described in the “Gel embedding, digestion and clearing for unexpanded samples” Section. After polymerization, the coverslip and the glass plate were gently separated. The gel film on the coverslip was washed once with the digestion buffer and trimmed to desired sizes using a razor blade. Digestion was performed the same as described in the “Gel embedding, digestion and clearing for unexpanded samples” Section. The gel would expand ~1.5 fold during digestion.After digestion, samples were expanded in 0.5× SSC buffer supplemented with 0.2% (vol/vol) Proteinase K at room temperature. Proteinase K was added to maintain samples in an RNAase free environment and to digest away any newly exposed proteins. The buffer was changed every 30 min until samples no longer expanded . Expanded gels were re-embedded in polyacrylamide gel to stabilize the gel for sequential rounds of readout probe hybridization and imaging. Briefly, samples were incubated in re-embedding solution composed of 4% (vol/vol) of 19:1 acrylamide/bis-acrylamide with 30–75 mM NaCl, 6–15 mM Tris ⋅HCl pH 8 and 0.2% (vol/vol) of TEMED for 20 min at room temperature on a rocker. The re-embedding solution was then kept on ice and further supplemented with ammonium persulfate at a final concentration of 0.2% (wt/vol). Gels were placed on a bind-silane-treated coverslip prepared using the protocol described in “Bind-silane treatment of coverslips” section (see below), rinsed with the solution and dried quickly with KimWipes. Coverslips with gels were put in a home-built nitrogen chamber, covered a glass plate and allowed to polymerize at room temperature for 1 h. The salt concentrations in the buffers utilized for expansion and re-embedding were determined so that the encoding probes were maintained on the RNA during these processes. The salt concentration may need to be adjusted when different probe sets are used.45. Briefly, coverslips were immersed in a solution composed of 5% (vol/vol) glacial acetic acid (Sigma) and 0.38% (vol/vol) bind-silane  in 99% (vol/vol) ethanol for 1 h at room temperature. After being quickly washed with 70% (vol/vol) ethanol three times, coverslips were put into a 60 °C oven until dried completely. Coverslips can be stored in seal containers with desiccants for up to a month.40-mm-diameter, no.1.5 coverslips (Bioptechs) were sonicated in 1 M potassium hydroxide for 30 min, wash three times with deionized water and sonicated again in 70% (vol/vol) ethanol for 30 min. Coverslips were silanized using a modified version of published protocolsTo facilitate choosing the right focal plane for imaging, embedded samples were hybridized in dish with the first pair of two-color readout probes in hybridization buffer composed of 2× SSC, 5% (vol/vol) ethylene carbonate (Sigma), 0.1% (vol/vol) murine RNase inhibitor in nuclease-free water, and 3 nM of the appropriate readout probes, for 30 min (expanded samples) or 10 min (unexpanded samples) at room temperature and washed for 20 min (expanded samples) or 7 min (unexpanded samples) in wash buffer composed of 2× SSC and 10% (vol/vol) ethylene carbonate in nuclease-free water. Samples were then washed with 2× SSC once, stained with 4′,6-Diamidino-2-Phenylindole, Dihydrochloride (DAPI) (Thermo Fisher Scientific) at 10 µg/ml in 2× SSC for 10 min, and washed 3 times in 2× SSC for 5 min each.12. Flow and incubation time was increased for expanded samples phosphine hydrochloride (TCEP) (Sigma) incubation time was increased to 30 min and all buffer exchange time to 7 min) to allow diffusion to reach equilibrium inside of the gel. After each MERFISH imaging round, the fluorescence of the readout probes was extinguished by incubating the sample in a reductive cleavage buffer composed of 2 × SSC and 50 mM TCEP.Samples were then mounted into a flow chamber and buffer exchange through this chamber was controlled via a home-built fluidics system composed of three computer-controlled eight-way valves (Hamilton) and a computer-controlled peristaltic pump (Gilson) as published previouslyIn order to accurately compare the counts per cell between different samples, we performed z-scanning to image more than 90% of target RNAs in a cell. Specifically, we performed optical sectioning at discrete imaging planes across the cell with a step size of 0.5 µm and quantified distribution of smFISH signals as a function of z position. We found >90% of RNAs located in the first 5 µm-depth volume in unexpanded samples and in the first 12 µm-depth volume in expanded samples from the surface of coverslips. Thus, we performed z-stack imaging by scanning a 5 µm-depth volume for unexpanded samples and a 12 µm-depth volume for expanded samples with a step size of 1 µm. The scanning in z direction was controlled by a Nano-F200 nanopositioner (Mad City Labs).12. Briefly, after hybridization of readout probes and exchange of imaging buffer, samples were imaged with a FOV area of 223 × 223 µm utilizing a 2,048 × 2,048 pixel, scientific complementary metal-oxide semiconductor (sCMOS) camera  in combination with a high numerical aperture (NA = 1.3) and a high-magnification (60×) silicone oil objective . The imaging (and corresponding fluidic exchange) process was repeated eight times for MERFISH-only samples and nine times for MERFISH + IF samples, with 405-nm DAPI channel imaged in conjunction with the first round of readout imaging.Sequential probe imaging and signal removal was carried out on a high-throughput imaging platform as published previously100–400 FOVs were measured per sample. We measured a total of ~1,000 cells for expanded samples in two independent sample preparations and measurements (one containing ~750 cells and the other containing ~250 cells), and results from the two independent experiments were compared with each other in Fig. 12. Briefly, z-stacks at each location from different imaging rounds were corrected for lateral offsets based on the location of fiducial beads. Each focal plane of the corrected z-stacks was high-pass filtered to remove background, deconvolved to tighten RNA spots, and then Gaussian-pass filtered to facilitate connecting signals from one imaging round to another. To correct for differences in the brightness between color channels, images were first normalized by equalizing their intensity histograms and refined further via iterative decoding trials to remove substantial variation in the fluorescence intensity between different bits. The set of 16 normalized intensity values (corresponding to 16 readout imaging) observed for each pixel in each FOV at each focal plane represented a vector in a 16-dimensional space. The pixel vector was normalized and compared to each of the 140 barcodes in the 16-bit MHD4 code. A pixel was assigned to a barcode if the Euclidean distance between the vector and a barcode was smaller than a given threshold defined by the distance of a single-bit error. Adjacent pixels in all focal planes of the z-stacks were combined into a single putative RNA using a 3-D connectivity array with maximal neighborhood connectivity.Registration of images of the same FOV across imaging rounds as well as decoding of the RNA barcodes was conducted using a previously published analysis pipeline12. Briefly, the segmentation boundaries separating cells were calculated using the watershed algorithm based on the inverted RNA density with DAPI stained regions as initial seeds. Because the neighboring cells were not necessarily in physical contact, the segmentation boundaries that separate cells do not necessary coincide with the physical boundaries of the cells, but might encompass slightly more area than that occupied physically by cells.Cells were segmented using a previously published approachComputations were split between the Odyssey cluster supported by the FAS Division of Science, Research Computing Group at Harvard University and a desktop server that contained two 10-core Intel Xeon E5–2680 2.8 GHz CPUs and 256 GB of RAM.12, and a 20-nt custom-designed readout sequence12. We designed 48 probes for each gene. The probes were synthesized from IDT, resuspended at 100 μM per probe in TE buffer, and pooled together for each gene. After permeabilization, cells on silanized coverslips treated as in “Silanization of coverslips for unexpanded samples” Section were incubated for 5 min in encoding wash buffer comprising 2× SSC and 30% (vol/vol) formamide. Then 30 μL of 2 μM smFISH probes and 3.3 μM of poly(dT) LNA anchor probe in encoding hybridization buffer was added to the surface of Parafilm and was covered with a cell-containing coverslip. Samples then were incubated in a humid chamber inside a hybridization oven at 37 °C for 24 h. Encoding hybridization buffer was the same as the one used for MERFISH. Cells then were washed with encoding wash buffer and were incubated at 47 °C for 30 min; this washing step was repeated once. Cells were then embedded and cleared using the matrix-imprinting-based clearing method described in “Gel embedding, digestion and clearing for unexpanded samples” Section. Embedded samples were hybridized in dish with a Cy5 20-nt readout probe in hybridization buffer, washed and stained with DAPI as described in “Sequential rounds of readout probe staining and automatic buffer exchange” Section. Images were collected scanning of a 5 µm-depth volume at a step size of 1 µm and 100 FOVs were collected for each smFISH probe set. smFISH spots were detected using the multi-emitter fitting routine 3D-DAOSTORM46 for each z section and spots appearing in adjacent z sections at the same x,y location were combined into one. ~1,000 cells per gene were measured in each smFISH measurement.Each smFISH probe contains a 30-nt target region, designed using a pipeline published previouslyWe performed simulation using parameters based on real MERFISH measurement conditions and encoding scheme. In each simulated image, the pixel size is 109 nm and the RNA spots have a Gaussian intensity profile with a standard deviation of 1.6 pixels. RNAs were distributed randomly across the field of view. The decoding of the simulated images was performed in the same way as the standard MERFISH decoding procedure described earlier and decoded results at different simulated RNA density were compared with the ground truth to determine the detection efficiency.The estimation of copy numbers per cell for the randomly chosen 1,000 RNA species was done by computing the total FPKM of the randomly chosen RNA species multiplied by the ratio of copy number to FPKM value as calculated from expanded MERFISH measurements.The datasets that support the finding of this paper are available from the corresponding author upon request.The software used to analyze the datasets are available from the corresponding author upon request.Supplementary Information


RNA acts not only as an information bearer in the biogenesis of proteins from genes, but also as a regulator that participates in the control of gene expression. In bacteria, small RNA molecules (sRNAs) play controlling roles in numerous processes and help to orchestrate complex regulatory networks. Such processes include cell growth and development, response to stress and metabolic change, transcription termination, cell-to-cell communication, and the launching of programmes for host invasion. All these processes require recognition of target messenger RNAs by the sRNAs. This review summarizes recent results that have provided insights into how bacterial sRNAs are recruited into effector ribonucleoprotein complexes that can seek out and act upon target transcripts. The results hint at how sRNAs and their protein partners act as pattern-matching search engines that efficaciously regulate gene expression, by performing with specificity and speed while avoiding off-target effects. The requirements for efficient searches of RNA patterns appear to be common to all domains of life. Like its eukaryotic and archaeal counterparts, bacterial gene expression is regulated by a multitude of mechanisms. At the level of mRNA synthesis, regulation is mediated by transcription factors and their partners, whose actions are often organized into co-operative networks. These networks generate co-ordinated patterns of expression and underpin the orchestrated responses to signals and changing metabolic conditions or help to launch multistep programmes, such as the generation of enormous multicomponent assemblies  .Although transcriptional networks have regulatory power and versatility, additional processes must be involved in achieving stable control. One indication that there is more to the control of gene expression than the regulation of transcription alone is the often-noted discrepancy in abundances of mRNAs and cellular proteins –6. The aVibrio cholera, the cholera-causing bacterium [Escherichia coli and the closely related Salmonella. These enteric gamma-proteobacteria have been shown to encode hundreds of sRNAs that bear a region of partial or complete sequence complementarity to target transcripts, known as the ‘seed region’ [Many elegant experimental studies over the last decade have revealed that small regulatory RNAs (sRNAs) are central factors for post-transcriptional control in diverse bacterial and archaeal species, in which they influence RNA stability, processing, and translation . The actacterium ,22. Evenacterium , their m region’ . When mR region’ ,24. Many region’ . The comRNA-mediated control (‘riboregulation’) bears loose analogy to information retrieval by an internet search engine, whereby a user-defined query text will return web page hits ranked based on probabilistic matches to that text. sRNAs, much like a search query, can be used to scan the mRNA population for matches through complementary base-pairing. The quality of the resulting matches is based on sequence similarity, the length and number of sRNA complementary regions, as well as structural features and cellular abundances of both sRNA and mRNA species. One of the puzzles concerning RNA-mediated regulation is how specificity is achieved, and off-target effects are avoided, given that the seed region often pairs to a target with imperfect base-pairing complementarity over, sometimes, very short segments that are not thermodynamically stable in isolation, and considering the astronomical numbers of potential interaction sites with entirely wrong RNA partners. Part of the solution rests in the properties of the ribonucleoprotein effector complexes involving sRNAs. In bacteria, RNA chaperones, ribonucleases, and other helpers facilitate the search to match sRNA to target and deliver the optimal functional output. These helper proteins accelerate the pairing between sRNA and mRNA and provide an additional level of specificity ,26,27, min vivo. We will explore a representative RNA degradation machinery that can be programmed by RNA regulators and describe the process of sRNA-mediated decay. The RNA chaperones that facilitate the process of RNA action will be presented.Here, we will describe recent advances in understanding how sRNAs are recognized and paired with mRNAs in bacteria, E. coli and related bacterial species, an important regulatory hub of post-transcriptional gene regulation is the RNA degradosome, a multienzyme machine that functions in RNA processing and turnover as well as in sRNA-mediated target silencing. In many ways, it is functionally analogous to the miRNA/siRNA RISC complex (the RNA-induced silencing complex) of eukaryotes [In karyotes ,29.E. coli RNA degradosome comprises the following enzymes: the hydrolytic endonuclease RNase E, which is the main component and scaffold; the phosphorolytic exoribonuclease polynucleotide phosphorylase (PNPase); an ATP-dependent helicase RhlB, and enolase, a glycolytic enzyme [Bacillus subtilis and the pathogenic species Staphylococcus aureus [The composition of the bacterial RNA degradosome varies in the course of evolution and, even within one species, changes with growth phase and environmental conditions . The canc enzyme –33. Thesc enzyme . The funs aureus –37).E. coli mRNAs, turnover rapidly follows after cleavage by the main degradosome component, RNase E. The enzymatic activity of RNase E can be substantially boosted if the 5′-end of the substrate is mono-phosphorylated. Nascent transcripts are produced with a 5′-triphosphate group, but this can be converted into a monophosphate by an RNA pyrophosphohydrolase . Followas RppH; . The ATPas RppH; ,39. Thusas RppH; ,41,42 anas RppH; .E. coli [S. aureus, RNase Y, is also membrane-associated [E. coli, the membrane localization of RNA degradosomes is not ubiquitous in bacteria. For instance, in Caulobacter crescentus, the degradosome assembles into foci close to the bacterial chromosome [The C-terminal portion of RNase E serves as a recruiting scaffold for the other components of the degradosome. The catalytic, globular N-terminal domain forms a homotetramer that is organized as a dimer of dimers . AdjacenE. coli ,45 and isociated . Althougromosome .Figure E. coli, S. aureus, and other bacterial species could, in principle, add an additional layer of organization to RNA-mediated regulation. Insights into the spatial organization of cellular RNA and its potential relationship to post-transcriptional control have recently been provided using super-resolution microscopy [E. coli. Moreover, these mRNAs are subject to higher degradation rates, which are abolished when the RNA degradosome is dissociated from the membrane via point mutations in the membrane attachment amphipathic helix. As for the features that might induce membrane enrichment, it was shown that localization is conferred co-translationally by signal peptides that direct the mRNA species to the membrane. Artificially inducing membrane localization of several mRNAs by incorporation of sequences, encoding such signal peptides, reduced their stability significantly [E. coli, whereas a mRNA control (gfp) is rarely present in the nucleoid, most likely because of its larger size compared with sRNAs [E. coli. Conceivably, this could be due to rapid degradation of sRNA–mRNA pairs at the membrane. In light of the membrane association of the degradosome, it is possible that mRNAs that are transcribed at the periphery of the E. coli genome  may be more prone to degradation than RNA species in which the corresponding genes are localized more centrally .A sequencing-based study has elucidated the role of RNase E in the maturation of Hfq-dependent sRNAs in himurium . By alighimurium . RNase EompD sRNA–mRNA duplex [Further insights into the physical proximity of protein and RNA molecules involved in sRNA regulation were achieved with a variant on RNA sequencing, the CLASH methodology  ,72,73. BA duplex ,63.rpoS, which encodes the stress factor σS, is strongly increased via this sRNA-mediated antitermination mechanism upon entry into the stationary growth phase, conceivably to help accommodate the associated metabolic changes within the cell. The sequencing results suggest sRNA-mediated antitermination as a widespread transcriptional control mechanism of Rho-dependent termination, adding another mode to the repertoire of sRNA-mediated riboregulation.A novel sRNA-mediated mechanism for gene activation on a transcriptional level has also been revealed through deep-sequencing studies . In someRNA sequencing-based techniques have led to a tremendous increase in our understanding of the sRNA interactome both on a global level, with the discovery of numerous new sRNAs and their targets, and at ‘high-resolution’ levels, with the elucidation of the exact mechanisms and recognition patterns that drive the sRNA-mediated decay.Salmonella virulence factors [The role of Hfq as a global sRNA chaperone in bacteria has been well established, but given its estimated cellular numbers and anticipated workload, it is not expected to be the sole protein fulfilling a chaperone function. Many sRNAs are associated with members of the CsrA/RsmA protein family. CsrA, a translational repressor that targets hundreds of mRNAs, is sequestered by the sRNAs, McaS, and CsrB/C ,76. CsrA factors .E. coli and S. enterica lack Hfq or CsrA recognition motifs [S. enterica, most of which are Hfq-independent, suggesting that ProQ associates with a new class of highly structured sRNAs [hupA mRNA. As such, ProQ forms a ternary complex with the RaiZ–hupA duplex, whereas in certain proposed decay models Hfq is believed to only bind single-stranded RNA species , an sRNed sRNAs ), and seed sRNAs ,83). In ed sRNAs  on the Pcies see . ProQ hoachinery .E. coli and S. enterica [in vivo, and, based on functional experiments, have proposed a protective role for PNPase for some sRNA species. Andrade et al. [in vivo, in which PNPase is unable to degrade sRNAs. As such, sRNAs could gain cumulative protection from both Hfq and PNPase in this ternary complex, being fully protected only when both modulators are present. Both the S1 RNA-binding domain and the active site of PNPase play a role in binding sRNAs, although more determinants are likely to be involved. As to how PNPase would interact with sRNAs in the ternary complex, one possibility is that the enzyme starts degrading the 3′-end of a Hfq-associated sRNA, but is stalled at this end, forming a shielding Hfq–PNPase–sRNA complex [Surprisingly, the exoribonuclease PNPase may play a cryptic chaperone role, even though the main role of PNPase is to degrade RNAs in enterica –88. De Lenterica  found the et al. . Finally complex ,91.in vivo. There has been much progress towards explaining how the key ribonuclease of riboregulation, RNase E, recruits and acts upon sRNA:mRNA pairs. Numerous molecular genetics studies have revealed that RNase E and the RNA chaperone Hfq physically associate in vivo and work in conjunction in numerous cases of sRNA-mediated target silencing [The interactions between sRNAs, ribosomes, ribonucleases, RNA chaperones, and target mRNAs affect the kinetics and efficiency of riboregulation ilencing . Moreoveilencing . The choilencing , and thiin vivo and the machinery involved. These underpin the accurate and fast search modes that enable bacteria to make rapid and robust responses to environmental and developmental stimuli. As these data become increasingly complemented with in vivo kinetic studies [The expanding studies into bacterial riboregulation are rapidly providing a detailed atlas of the dynamic pairings that occur  studies , a compr studies .