Expression of nano-engineered RNA organelles in bacteria

Domain: neuroscience
Published: 2026-02-14
Editorial outcome: minor_revision

Abstract
Designing synthetic biomolecular condensates, or membraneless organelles, offers insights into the functions of their natural counterparts and is equally valuable for cellular and metabolic engineering. Choosing E. coli for its biotechnological relevance, we deploy RNA nanotechnology to design and express non-natural membraneless organelles in vivo. The designer condensates assemble co-transcriptionally from branched RNA motifs interacting via base-pairing. Exploiting binding selectivity, we express orthogonal, non-mixing condensates, and by embedding a protein-binding aptamer, we achieve selective protein recruitment. Condensates can be made to dissolve and reassemble upon thermal cycling, thereby reversibly releasing and recapturing protein clients. The synthetic organelles are expressed robustly across the cell population and remain stable despite enzymatic RNA processing. Compared with existing solutions based on peptide building blocks or repetitive RNA sequences, these nanostructured RNA motifs enable algorithmic control over interactions, affinity for clients, and condensate microstructure, opening further directions in synthetic biology and biotechnology.

Introduction
Membraneless organelles (MLOs), emerging from condensation of proteins and nucleic acids, are found in most living cells, where they orchestrate biochemical reactions by sequestering and co-localising enzymes, substrates, and metabolites, and by maintaining homoeostasis1–6. Historically thought not to contain intracellular compartments, prokaryotes are now recognised to leverage membraneless organelles, such as BR-bodies, carboxysomes, polyP granules, ParB condensates, and RNA polymerase clusters, to modulate processes including division, polarisation, nucleoid replication, transcription, and protein homoeostasis5–8.

Engineering non-natural, designer MLOs both in vitro and in vivo is key to unravelling the biophysics and functions of natural condensates9–11, and equally valuable as a tool to programme metabolism and cellular functions in synthetic biology and biomanufacturing12,13. To this end, establishing robust strategies to engineer biomolecular condensation in widely adopted prokaryotic chassis is particularly urgent.

Synthetic MLOs have been constructed from peptides and proteins, demonstrating remarkable functionalities14–28. Condensate-forming proteins, however, often assemble through non-specific interactions between Intrinsically Disordered Regions (IDRs), commonly combining electrostatic, π−π, and hydrophobic interactions between amino acid residues29,30. The complexity of these interactions makes them difficult to prescribe by design31–33, with modelling often requiring advanced numerical simulations34–36.

Alternative to proteins, naturally occurring RNA repeats can be deployed for MLO assembly37–39, but their simple structure limits the scope for more advanced engineering.

Different from proteins, however, nucleic acids benefit from highly predictable base-pairing interactions, which have led to the emergence of robust, algorithmic strategies to design nanostructures and materials40–44. DNA nanotechnology has been successfully deployed to construct synthetic condensates with precisely controlled size and microstructure45–50, and displaying advanced functions ranging from reversible assembly and disassembly51–53, to large-scale morphological re-structuring54–60, capture and release of functional payloads51,61,62, and spatial organisation of biocatalytic pathways63–66. Despite this versatility, the use of synthetic DNA building blocks is limited to in vitro applications, as DNA nanostructures cannot be expressed by living cells67.

To overcome this roadblock, here we deploy RNA nanostructures, which retain much of the programmability of DNA constructs68–71, but can be synthesised in vivo72 and in crowded environments that mimic cellular conditions73. We engineer E. coli to express branched RNA motifs that assemble into synthetic MLOs when featuring complementary base-pairing domains, while remaining largely soluble when designed to be non-interacting. Base-pairing selectivity enables the expression of orthogonal nanostructures, producing non-mixing, co-existing MLOs, while embedding aptamers facilitates specific protein capture. MLOs can be made to assemble and disassemble upon thermal cycling, reversibly releasing and re-capturing payloads in the process. Our results demonstrate the potential of co-transcriptional RNA nanotechnology for engineering non-natural MLOs, combining a robust and programmatic design paradigm with genetic encodability.

Results
Design and expression of condensate-forming RNA motifs in E. coli

The condensate-forming motifs consist of branched RNA nanostars (NS), with four double stranded (ds) ‘arms’ emanating from a central junction (Fig. 1A)69. Each nanostar arm terminates with a 9-nucleotide (nt) kissing loop (KL), consisting of a 6 nt recognition domain asymmetrically flanked by adenines (Fig. 1A). NS designs A and B feature palindromic, self-complementary domains in the KLs, producing affinity between same-type NSs and leading to condensation69. A and B KLs are mutually orthogonal, resulting in negligible cross-binding69. NS designs A' and B' are identical to A and B, respectively, but have KL sequences scrambled to eliminate self-complementarity and prevent condensation69. Fluorescent Light-Up Aptamers (FLAPs) are embedded in one of the NS arms to facilitate detection and visualisation. Designs A and A' include a ‘Pepper aptamer’ binding the HBC620 dye74 (Fig. 1A(i) and Supplementary Fig. 1), while B and B' feature a ‘Broccoli aptamer’75 binding the TBI dye (Fig. 1A(ii) and Supplementary Fig. 1)76. All NS sequences are reported in Supplementary Table 1.

For all NS designs, correct expression, condensate formation (or lack thereof) and interaction orthogonality were confirmed with in vitro transcription experiments (Supplementary Figs. 2–8, Methods). Encouragingly, for in vivo expression robust condensation was observed in buffers mimicking the intracellular ionic conditions encountered in E. coli (200 mM K+ and 1 mM Mg2+77,78) (Supplementary Fig. 9).

NS sequences, followed by transcription termination domains79,80, were then cloned into plasmids for expression in the E. coli BL21(DE3) strain with the IPTG-inducible T7 transcription system, as outlined in the Methods. Plasmid maps are reported in Supplementary Figs. 10, 11, while plasmid sequences are reported in Supplementary Data 1. E. coli were grown from a single colony in supplemented M9 medium to mid-log phase (OD 0.7) before inducing NS expression. Cells were characterised by confocal microscopy one hour post-induction (Methods).

WefirsttestedE.coliexpressingindividualNSs:A,B,A'orB'.Forboth“sticky”motifs,AandB,visualinspectionconfirmsMLOformation(Fig.1B(i),C(i)andSupplementaryFigs.12,13).Condensatesappeartobeoverwhelminglylocatedatthepolesofthecell,acommonpatterninproteinMLOs8.Thisobservationisconfirmedbyaveragingthefluorescencedistributionsofindividualcellsacrossthepopulations,producingthe2DmapsinFig.1B(ii),C(ii).Polelocalisationislikelytoresultfromsterichindrancebythenucleoidlocatedinthecentralsectionofthecell81.Totestthishypothesis,inSupplementaryNote1,weusesimplepolymerphysicsargumentstoestimatetheenergycostofformingasmallcondensate(100nmindiameter)withinthenucleoid82.Wefindthispenaltytobeashighas4.2×10−20J,or10k T,whichjustifiestheexclusionofthecondensatesfromthecentralsegmentofthecells.Inadditiontotheeffectofthenucleoid,anisotropicconfinementhasalsobeenhypothesisedascontributingtopoleaccumulationofaggregates8.PoleaccumulationconsistentlyemergesfromthedistributionofMLOspositionsthat,however,alsorevealsasparsethirdclusterinthecentralsectionofthecell(SupplementaryFig.14).Imageanalysisconfirmsthatmostcells,78%forAand88%forB,containtwoMLOs,whilesomefeaturethree(20%forAand8%forB,Fig.1B(iii),C(iii)).Rarely,cellshaveasingleMLO,andanegligiblefractioniscondensate-free.DetailedstatisticsoncondensatenumbersarereportedinTable1.RT-qPCRwasusedtoquantifyNSexpressionlevels,yieldinganintracellularconcentrationof16±5μMforNSB1hourpost-induction(SupplementaryFigs.15,16,Methods).ByassumingthatmostNSsaccumulateinthecondensates,andapproximatingtheper-NSvolumeinthecondensedphaseasthatofaspherewithradiusequaltotheNSarmlength(~8nm69),wecanroughlyestimatethetotalcondensatevolumefromtheNSconcentration.Byfurtherassumingthatthisvolumeisequallydividedbetweentwocondensates,weestimateameancondensatediameterof~400nm,compatiblewithmicroscopyobservations(Fig.1B(i),C(i)).Intra-cellularNSconcentrationiscomparablewithinvitroexpressionyields,measuredas13±4μM3hoursfromstartingthereaction(SupplementaryFig.16).

Through image segmentation applied to single cells, we determined the integrated fluorescence intensity of individual condensates, I_cond, expected to be proportional to their volume. Similarly, we computed the average intensity in the condensate-free region, i_disp, proportional to the concentration of the dispersed NS phase, and the average intensity across the entire cell, i_tot, proportional to the overall NS concentration (Methods). In Supplementary Fig. 17, we show that i_disp is weakly correlated with I_cond (R2 = 0.21 and 0.24 for A and B, respectively), consistent with the notion that the NS behaves similarly to an equilibrium phase-separating system, in which the concentration of the dispersed phase is independent on the volume of the dense phase. In addition, consistently with the lever rule, i_tot is strongly linearly correlated with I_cond (R2 = 0.70 and 0.83 for A and B, respectively). When identical staining and imaging settings are applied, typical fluorescence intensity recorded from condensates expressed in vivo closely matches the one measured in vitro, suggesting similar FLAP response and NS concentration in the condensed phase (Supplementary Fig. 18). Inspection of the time evolution of individual cells reveals occasional instances in which small condensates form in the central section, before merging with one of the polar MLOs, suggesting a pathway for condensate accretion alternative to single-monomer addition (Fig. 1D(i) and Supplementary Fig. 19A), as previously observed for repeat-RNA MLOs38. We propose that the rapid migration of mid-cell nascent condensates toward the poles is driven by the energetic cost associated with nucleoid deformation (Supplementary Note 1). These coalescence events hint at a liquid-like state of the MLOs. Consistent with the liquid-like state, Fluorescence Recovery After Photobleaching (FRAP) experiments revealed partial recovery (Supplementary Fig. 19B, C). It should however, be noted that recovery rates may be enhanced by the exchange of FLAP dyes69.

Time lapses additionally suggest that a third MLO may appear near the equator of cells with two polar condensates as they approach division, when the central section is free from the nucleoid (Fig. 1D(ii) and Supplementary Fig. 20). Consistent with this picture, for both A and B designs, we find a significant correlation between the length of the cells and the number of hosted MLOs (Supplementary Fig. 21). In some instances, the central condensates then appear to split between the two daughter cells, which emerge with two polar condensates of asymmetric intensity (Fig. 1D(ii) and Supplementary Fig. 20(i)). Over time, smaller condensates grow toward parity with the larger ones, progressively reducing size asymmetry (Supplementary Fig. 22). This evidence excludes Ostwald ripening as a dominant mechanism for condensate coarsening, consistent with observations made on DNA nanostar condensates in vitro83. In other cases, the newly formed MLO partitions entirely in one of the daughter cells, leaving the other with a single condensate (Supplementary Fig. 20(ii)). Evidence of division confirms the viability of the MLO-expressing cells.

Expressing NS designs with scrambled KLs, A' and B', largely results in uniformly distributed fluorescence (Fig. 1B(i)-(ii), C(i)-(ii) and Supplementary Figs. 23, 24), with a small fraction of the cells (23% for A' and 28% for B, Fig. 1B(iii)-C(iii)) displaying regions of increased signal. These clusters likely arise from non-sequence-specific interactions between NSs or with other cellular RNA or proteins84,85. Average fluorescence maps (Fig. 1B(ii)-C(ii)) and location analysis (Supplementary Fig. 14) show that, like specific MLOs, non-specific NS clusters are preferentially located at the cell poles.

Owing to imaging noise and cell-to-cell variation, it is sometimes challenging to distinguish slight non-uniformities in the fluorescent signal from NS condensates, which may lead to false-positive MLO detections for A' and B' (Methods). To discriminate more reliably, we analyse the frequency distribution of pixel values within single cells and extract values for the kurtosis (Methods). A positive kurtosis reflects highly localised fluorescence, characteristic of MLO formation, whereas a negative kurtosis reflects a more uniform fluorescence distribution (Supplementary Fig. 25). Consistently, we observe positive kurtosis for NS A and B, and negative values for A' and B' (Fig. 1E, and Supplementary Tables 3, 4). Alternative to the kurtosis, fluorescence-distribution skewness also discriminates well between MLO-forming and scrambled NS designs (Supplementary Figs. 25, 26).

Optical density measurements performed in bulk cultures reveal an expected decrease in growth rate upon induction, which is marginally more prominent in E. coli expressing MLO-forming NSs compared to those producing non-sticky designs (Supplementary Figs. 27, 28A). Simultaneous detection of NS fluorescence shows the anticipated signal growth upon induction, with stronger emission observed for A and B NSs compared to A' and B' (Supplementary Figs. 28B). Uninduced controls do not show significant induction leakage.

Given the limited half-life of mRNA in E. coli86, it is expected that nanostars may also be progressively degraded. We thus investigated stability and processing of the expressed A and B NSs with nanopore RNA sequencing and Polyacrylamide Gel Electrophoresis (PAGE) (Supplementary Figs. 29, 30). We find that both constructs are cleaved at specific sites, located on two of the arms of NS A and on one arm of NS B. For both nanostructures, fragments corresponding to all sections of the designed transcript are detected, implying that the observed fragmentation is likely the result of endonucleases acting on folded constructs, rather than abortive transcription. In this scenario, cleavage of one of the dsRNA arms would reduce NS valency without impacting the folded construct downstream of the restriction site. This picture is consistent with the evidence of robust MLO formation: while a fraction of the NS population may have reduced valency, a sufficient proportion of the constructs must retain three or four KLs to support phase separation. Despite occurring at two distinct sites, enzymatic degradation is less pronounced for NS A compared to NS B (Supplementary Figs. 29B, 30B). Denaturing PAGE (Supplementary Figs. 29C, 30C) shows substantial quantities of the full, uncleaved transcript for NS A, which are not detected for NS B. Native PAGE shows the full-length construct as the dominant species for NS A, indicating that most cleavage events detected by sequencing may only nick the dsRNA arms, rather than fully detaching them, allowing the construct to retain the designed secondary structure (Supplementary Fig. 29D). For NS B, a moderate amount of the complete, folded construct is present (Supplementary Fig. 30D), likely featuring a nicked but not fully cleaved arm. The most abundant species, however, has lower molecular weight and likely corresponds to the NS lacking one KL and having, therefore, reduced valency (Supplementary Fig. 30D). This observation highlights the advantage of using tetravalent rather than trivalent NS designs: given that a minimum valency of three is required to sustain phase separation69, the built-in redundancy of tetravalent constructs makes them intrinsically more resilient. In both NS designs, cleavage was not detected on NS arms that include the FLAP or the T7 terminator, possibly due to these structural features hindering RNase access87. Both NSs, however, feature a cleavage site in the terminator loop itself, which is thus likely to be detached in many transcripts (Supplementary Figs. 29A, B, 30A, B). The apparent protective effect of altering the duplex structure of NS arms could be leveraged to design against degradation. In addition, stability may be enhanced by circularising the constructs to prevent digestion by exonucleases88.

Expression of orthogonal membraneless organelles in E. coli

KL orthogonality between A and B designs should enable co-expression of non-mixing MLOs. We test this hypothesis by designing plasmids containing two distinct NS sequences (Fig. 2A and Supplementary Fig. 11). Different T7 terminator sequences80 are used for the two NSs to minimise the potential for non-specific affinity between the constructs induced by the terminator loops.

When co-expressing A and B, confocal microscopy confirms the formation of non-mixing MLOs (Fig. 2B(i), Supplementary Figs. 31, 32). Similar to single-NS systems, condensates are preferentially located towards the poles, as evident from both average fluorescence maps (Fig. 2B(ii), Supplementary Fig. 33) and the locations of individual MLOs (Fig. 2C). Of the cells containing one A-type and B-type MLO (60% of all cells co-expressing NSs A and B), 66% had both MLOs positioned at the same pole. This asymmetric distribution may stem from the cells’ division history, as newly formed poles are less likely to harbour condensates. Alternatively, or in addition, it may be driven by weak, non-specific affinity between A and B condensates. We also observe a clear asymmetry between the exact positioning of A and B condensates: the former are nearly always in contact with a pole, while the latter may occupy more central locations (Supplementary Fig. 14). Inspection of individual micrographs confirms that B condensates tend to accumulate around polar A MLOs (Supplementary Fig. 31). We ascribe this behaviour to the tendency of A NSs to form larger condensates (Supplementary Fig. 34), which may appear faster than B MLOs and grow to occupy the entire polar region, pushing smaller B condensates more towards the central segment of the cell. Slower condensation kinetics for the B design may be linked to its greater susceptibility to degradation and consequent reduced valency.

Most cells express one MLO of each kind, with a smaller percentage containing two A and/or two B MLOs (Fig. 2B(iii) and Table 1). The overall number of MLOs in cells co-expressing A and B (averaging 2.3) is similar to that of cells expressing only A (2.2) or only B (2.0), indicating that co-expression does not significantly alter condensate formation.

Cells co-expressing one sticky NS and one non-sticky NS, A+B' or A'+B, behave as expected, with MLOs of the intended NS species (A or B) coexisting with a largely diffuse phase of the soluble NSs (B' or A', Fig. 2B(i) and Supplementary Figs. 35, 36). The fluorescent signal distribution (Fig. 2B(ii)) and condensate locations (Supplementary Fig. 14) of A and B MLOs are similar to those observed for cells that do not simultaneously express B' or A' (Fig. 1B(ii), C(ii) and Supplementary Fig. 14), indicating that soluble NSs do not interfere with the condensation of sticky motifs. Condensate numbers are, however, lower, with a greater proportion of cells expressing a single A or B MLO (Fig. 2B(iii)), arguably due to a lower overall expression of the MLO-forming motif due to the simultaneous production of the soluble NSs.

The fluorescence distribution of B', when co-expressed with A, is largely uniform, with a small fraction of the cells (27%) displaying regions of increased B signal at the poles (Fig. 2B(ii)-(iii) and Table 1), similar to that observed in cells expressing B' only (28%). The fluorescence distribution of A', when co-expressed with B, is almost completely uniform, with only 8% of cells displaying regions of increased A signal (Fig. 2B(ii) and Table 1), compared to 25% for E. coli expressing only A'. Cells co-expressing the two soluble NSs, A' and B', display near-uniform fluorescence distribution (Fig. 2B(ii) and Supplementary Fig. 37), with non-specific A' and B' clusters detected only in 5% and 2% of the cells, respectively (Fig. 2B(iii) and Table 1).

The lesser tendency of A' and B' to form non-specific clusters is consistent with a reduced NS concentration caused by the simultaneous expression of two constructs. The greater abundance of non-specific clusters seen when non-sticky NS are co-expressed with sticky motifs, particularly for A'+B expression, may be due to MLO-templated clustering of the otherwise soluble NSs. The soluble NSs may indeed weakly partition within, or coat the specifically-assembled MLO, or the condensates may serve as heterogeneous nucleation sites for non-specific aggregation. Either process may be facilitated by weak affinity between the soluble and sticky NSs due to base pairing, misfolding, enzymatic degradation, or the action of native RNA-binding proteins. This interpretation is supported by the evidence of weak partitioning of soluble NS B in NS A condensates observed in vitro (Supplementary Fig. 2), and quantified in Supplementary Fig. 8 (Methods).

As done for single-NS systems, we use the kurtosis of pixel-intensity distributions to assess the tendency of individual NS components to condense. In line with observations made on Fig. 2B, for all combinations tested, cell populations expressing A or B NSs have positive kurtosis in the relevant fluorescent channels, while those expressing A' or B' have negative kurtosis (Fig. 2D and Supplementary Tables 5, 6). Skewness analysis can similarly distinguish between cells expressing condensate-forming and non-sticky NSs (Supplementary Fig. 26).

To further assess the relative distribution of co-expressed NS components, we study the cross-correlation between Pepper (NS A) and Broccoli (NS B) fluorescence signals (Supplementary Fig. 38A), extracting distributions of the coefficient of determination, R2, across E. coli populations (Fig. 2E and Supplementary Table 2). In cells expressing A and B, the low R2 confirms that MLOs are orthogonal and non-overlapping. When expressing A' and B', R2 is highest, as both NSs are evenly distributed throughout the cell. In E. coli producing A'+B, a low R2 follows from the localised Broccoli signal in a diffused Pepper background. The higher R2 value found for cells expressing A+B' follows from the localised Pepper signal in a diffused Broccoli background. The low R2 for A+B' and A'+B supports the hypothesis that B' may non-specifically partition within MLOs, consistent with observations made in vitro (Supplementary Figs. 2B, 8).

Bulk growth curves collected for E. coli expressing two NS species are similar to those recorded for single-NS expression (Supplementary Figs. 39A). Samples show no significant differences in the trend and magnitude of the fluorescent traces recorded simultaneously (Supplementary Figs. 39B). We also observe that some cells still contain MLOs after a week at room temperature, indicating long-term stability of the condensates despite enzymatic degradation of individual NSs (Supplementary Fig. 40).

Selective protein recruitment in RNA membraneless organelles

Owing to their modularity and controlled structure, the RNA motifs can be straightforwardly modified to feature additional functional motifs, including aptamers enabling the capture of molecular clients. As proof-of-concept, we embedded the GFP-binding AP3 aptamer89 in one of the arms of NS A, opposite to the arm hosting the Pepper FLAP (Fig. 3A). Note that the resulting A_AP3 remains tetravalent, as the kissing loop is inserted between two stem loops of the aptamer.

Confocal micrographs and image analysis confirm that A_AP3 expression in E. coli produces MLOs with similar spatial distribution and numbers as those measured for NS A (Fig. 3B(i)–(iii), Table 2, and Supplementary Fig. 41). Co-expression of GFP results in a clear co-localisation of the protein with the A signal (Fig. 4B(i), (ii) and Supplementary Fig. 42). Cells expressing A_AP3 alone and those co-expressing A_AP3 and GFP both contained an average of 2.0 condensates per cell (Table 2), indicating that GFP expression does not noticeably affect A_AP3 condensate formation.

In contrast, GFP remains evenly distributed when co-expressed with NS A lacking the GFP-binding aptamer, confirming the specificity of the recruitment mechanism (Fig. 3B(i), (ii) and Supplementary Fig. 43). Expressing GFP in the absence of NSs confirms the lack of spontaneous protein aggregation and the formation of inclusion bodies (Fig. 3B(i), (ii) and Supplementary Fig. 44)90. The small number of GFP clusters detected by our segmentation algorithm in the absence of A_AP3 NSs come from segmentation artefacts linked to the 3D shape of the cell91, which results in brighter central sections that may be occasionally misclassified as MLOs.

Partial recovery of the GFP signal in FRAP experiments demonstrates that the recruited protein can exchange with cytosolic GFP (Fig. 3C).

Analysis of the kurtosis of the pixel-fluorescence distribution in Fig. 3D shows the expected trends: positive kurtosis is measured in the Pepper (NS) channel for all MLO-forming NS designs, and in the GFP channel only when the protein-binding A_AP3 is expressed (Supplementary Tables 7, 8). Consistent trends are observed when analysing skewness (Supplementary Fig. 26). Finally, image correlation analysis shows a strong fluorescence co-localisation between GFP and A_AP3 MLOs but not between GFP and A MLOs (Fig. 3E, Supplementary Fig. 38, and Supplementary Table 2). Incidentally, the strong co-localisation between GFP and A_AP3 confirms the limited impact of potential chromatic aberrations between the red fluorescence channel (used for Pepper-labelled NSs) and the green channel (used for GFP and Broccoli-labelled NSs.)

Temperature-dependent dissolution and re-assembly of MLOs

Since the NSs self-assemble through base-pairing interactions, we anticipated that the MLOs would dissolve at sufficiently high temperature, as observed in vitro (Supplementary Figs. 45–47). Consistent with this expectation, heating E. coli containing A MLOs led to their dissolution at 61 °C. This process was reversible, with A MLOs reassembling with a similar polar distribution as the cells were cooled (Fig. 4A(i)-(ii) and Supplementary Fig. 48). Sampling the skewness of the pixel-intensity distribution across the bacteria population provides information on the condensate melting process (Fig. 4A(iii)). At the starting temperature, the skewness is high across the population, reflecting the presence of MLOs. As the temperature is increased, the distribution becomes bimodal, with a second population of cells displaying low skewness following MLO dissolution. The intra-population variability in the onset of melting may be due to cell-to-cell variation in NS expression and, therefore, concentration. At sufficiently high temperature, most cells display low skewness. By fitting the skewness distributions, we can extract a temperature-dependent fraction of cells hosting MLOs, which shows a small hysteresis between heating and cooling ramps. This may be caused by slow assembly kinetics, delays in thermal equilibration, or changes in protein crowding due to protein denaturation during heating, which could alter the local environment of the MLOs (Fig. 4A(iv) and Supplementary Fig. 49). B MLOs showed a reversible disassembly behaviour similar to the A design, but with a lower melting temperature: condensates began to melt at 49 °C and fully dissolve by 58 °C (Fig. 4B(i)-(iv) and Supplementary Figs. 49, 50). Single-cell skewness-temperature curves were used to estimate the melting temperature for each cell. Linear regression between the melting temperature and cellular fluorescence gave low coefficients of determination (R2 < 0.2), indicating no detectable correlation between NS concentration and melting temperature (Supplementary Fig. 51). The difference in melting temperature between the two constructs is unlikely to result from different KL affinity, having observed that NSs with KL A disassemble at slightly lower temperature (39.9 °C) compared to those with KL B in vitro (40.9 °C, Supplementary Figs. 45–47). Instead, it may result from the greater degree of enzymatic degradation experienced by NS B (Supplementary Figs. 29, 30). A reduction in NS valency is also consistent with the broader melting transition seen in NS B compared to the A design. Different from A NSs, a small fraction (~20%) of cells expressing B MLOs do not re-assemble condensates when cooled, possibly due to nanostructure misfolding or failure to refold upon cooling. For both A and B NSs, condensate fluorescence intensity appears to increase after the heating and cooling ramps (Fig. 4A(i), B(i)), which may be attributed to thermally induced alterations in membrane permeability, facilitating greater intracellular accumulation of FLAP dyes. We further note that melting temperatures measured in vivo are substantially higher than those we observe in vitro, where the MLOs melted at 39.9–40.9 °C (Supplementary Fig. 45–47, 52). This deviation likely originates from intracellular molecular crowding promoting condensation92 or difference in ionic conditions between the in vitro transcription buffer and the cytoplasm.

We then demonstrate temperature-triggered release of captured proteins by heating MLOs that had sequestered GFP. When cells expressing the A_AP3 NSs and GFP were briefly heated to 70 °C for 2 minutes, the GFP and nanostar fluorescent signal dispersed throughout the cell. Upon cooling, the MLOs reassembled at the poles, with GFP relocalising accordingly (Fig. 4C(i), (ii) and Supplementary Figs. 53, 54). Approximately 10% of the cells retained the ability to grow and divide following thermal treatment (Supplementary Fig. 55). Growth and division is found to resume after a 3-hour delay, which is likely the time required for bacterial recovery93.

Methods
Chemicals and reagents
All reagents were obtained from Merck unless otherwise specified. E. coli BL21(DE3) and XL10-Gold cells were obtained from Agilent. DH5α cells were purchased from Thermo Fisher Scientific. TBI was purchased from Tocris Bioscience. HBC620 was purchased from MedChem Express. DFHBI-1T was purchased from Cayman Chemical. Recombinant GFP was purchased from Caltag Medsystems. Pico-Surf fluorinated oil was purchased from Sphere Fluidics. CellBrite Fix 640 membrane dye was purchased from Biotium. The T7-FlashScribe™ Transcription Kit was purchased from Cellscript. The RNeasy Mini kit and Bacteria Protect Reagent were obtained from Qiagen. GeneRuler 100 bp DNA Ladder and RiboRuler Low Range RNA Ladder were obtained from Thermo Fisher Scientific. LB-agar plates, glycerol, ultrapure water, and SOC media were obtained from the technical support team of the Department of Chemical Engineering and Biotechnology, University of Cambridge. Linear DNA strands were obtained from Integrated DNA Technologies (IDT) as gBlocks. Geneframes were obtained from Thermo Fisher Scientific. Glass capillaries were purchased from VitroCom. Nunc MicroWell 96-Well Optical-Bottom Plates with Polymer Base (Cell culture, black, Surface Treatment: Cell Culture, Bottom: Flat) were obtained from Thermo Fisher Scientific. The pET plasmid (53137) was obtained from Addgene. The NEBuilder HiFi kit (E5520S) and the Monarch miniprep kit were obtained from New England Biolabs (NEB).

Expression and characterisation of RNA nanostars in vitro
In vitro transcription reactions were prepared at room temperature using the T7-FlashScribe™ Transcription Kit and 40 nM of the relevant linear DNA template with T7 terminator or 5 nM of the relevant plasmid (for samples used in PAGE analysis). The final reaction mixture contained 9 mM of each ribonucleotide triphosphate, 0.05 units per μL RNase inhibitor, and 10 mM dithiothreitol. In addition, the reaction contained 50 μM of the Broccoli aptamer-binding dye DFHBI-1T, and 0.5 μM of the Pepper aptamer-binding dye HBC620. Mixtures were loaded into borosilicate glass capillaries with rectangular cross-section (0.2 mm × 2 mm, VitroCom) and capped with paraffin oil. Capillaries were then immediately placed at 37 °C and incubated for 4 hours, until imaging. Epifluorescence micrographs were obtained using a Nikon Eclipse Ti2-E inverted microscope equipped with a Hamamatsu ORCA-Flash4.0 V3 sCMOS camera, a tuneable light source (Lumencor SPECTRA X LED engine), and a Plan Apo λ 20 × 0.75 N.A. dry objective. The HBC620 dye was excited using a 590 nm LED source with an LED-DA/FI/TR/Cy5-B filter, 10 ms excitation time, and fluorescence was recorded through an FF01-624/40 emission filter. DFHBI-1T was excited using a 470 nm LED source with an LED-DA/FI/TR/Cy5-B filter, 500 ms excitation time, and fluorescence was recorded through an EM510 emission filter. To determine the melting temperature of NS A and NS B, samples were incubated for 15 h at 30 °C and subsequently heated using a Peltier-controlled microscope stage (Temikra Ltd.). The temperature was increased from 30 °C to 50 °C and then cooled back to 30 °C. During this process, the temperature was adjusted by 1 °C every 5 min, with imaging performed after each temperature change. For directly comparing the fluorescence intensity of in vitro condensates with that recorded for E. coli (Supplementary Fig. 18), the in vitro transcription reaction was performed as described above, except for replacing DFHBI-1T with 10 μM TBI, as used for E. coli, followed by a 15 h incubation at 30 °C. For the in vitro transcription of NS A_AP3, the reaction was prepared as described above with the addition of 750 nM recombinant GFP. The transcription mixture was then encapsulated within water-in-oil droplets by mixing 22 μL of the solution with 90 μL of 2% w/w Pico-surf fluorinated oil and vortexing at 2,500 rpm for 30 s. The top layer, containing the emulsion droplets, was extracted, loaded into capillaries, and incubated at 37 °C for 8 h before being imaged on the Nikon Eclipse Ti2-E inverted microscope. The settings described above were used for imaging the Pepper channel (NS A_AP3), while GFP fluorescence was imaged using the 470 nm LED source with an LED-DA/FI/TR/Cy5-B filter and 2 ms excitation time. To induce thermal disassembly of the in vitro A_AP3 + GFP samples, the loaded capillaries were heated using a Peltier microscope stage (Temikra Ltd.) to 20 °C, 37 °C and 40 °C, and subsequently cooled to 20 °C. To test the behaviour of NSs in buffer mimicking ionic conditions in E. coli (Supplementary Fig. 9), transcribed NSs were purified by Monarch Spin RNA Cleanup Kit (NEB), then 500 nM NS was added to 15 μL of buffer (40 mM Tris, pH 8.0, 200 mM KCl, 1 mM MgCl2). The solution was incubated at 37 °C in 384-Well Optical-Bottom Microplate, black, non-treated PS surface (ThermoFisher) for 1 h before epifluorescence imaging.

Image analysis of NS condensates in vitro. Images (16-bit) were extracted from .nd2 (Nikon) or .czi (Zeiss) files. Fluorescence intensities reported in Supplementary Fig. 8 and for the comparison of in vitro and in vivo fluorescence intensities were quantified as the mean pixel intensity on mask-segmented images. Segmentation masks were generated using Otsu thresholding on flattened images, which were then applied to the corresponding raw images for intensity measurements. For melting-temperature analysis (Supplementary Fig. 45), raw 16-bit images were used to calculate the ratio between the standard deviation of the pixel values and the mean pixel intensity (σ/I). The melting and condensate-formation temperature were determined as the point of intersection between the ratio curves and the plateau, defined as the mean of data points from 41 °C onwards, plus a constant of 0.05. To generate the displayed micrographs, images were rescaled to facilitate visualisation by dividing the pixel intensities by the intensity of the brightest pixels (99.9–100th percentile), and then exported as coloured micrographs. For micrographs showing condensate melting, condensate formation, and TBI dye comparisons, the brightest pixels across the entire image set were used for division. For all other micrographs, the brightest pixels within each individual image were used for division. The Python packages tifffile and numpy were used for handling and processing the microscopy images, scikit-image was used for image masking and thresholding, Pillow was used for micrograph export, and Matplotlib was used for creating plots.

Plasmid construction
Nanostar design. RNA NSs A, A', B, and B' were adapted from Ref. 69. Each construct consists of a four-way junction with 25 bp double-stranded RNA arms. One arm in each design contains a FLAP, Pepper for A and A'74, and Broccoli for B and B'76. Each arm in NS A ends with a KL with sequence 5’-AUCGCCAAA-3’, while arms in NS B terminate with KL 5’-AGUCGACAA-3’. The KL sequences were designed in Ref. 69 to ensure similar binding strength while retaining mutual orthogonality and self-complementarity. NSs A' and B' have KL nucleotide sequences scrambled to eliminate self-complementarity. NS A_AP3 additionally features the GFP-binding AP3 aptamer in one of the arms, opposite to the one hosting the Pepper aptamer89. NS arms that include fluorescent or GFP-binding aptamers retain KLs, ensuring that the constructs remain tetravalent. All NS designs were verified with Nupack98 and Kinefold99. Sequences of all NS designs are reported in Supplementary Table 1.

Plasmid design and construction. To construct plasmids expressing single NSs (A, A', B, B', A_AP3), the natural T7 terminator sequence80 was appended to the 3’ end of the NS sequences. The NS+terminator sequences were cloned into a pET plasmid with an ampicillin resistance gene (Addgene #53137), using the NEBuilder HiFi kit, following the manufacturer’s instructions. To construct plasmids expressing two NSs (A+B, A+B', A'+B, or A'+B'), the natural T7 terminator was appended to the 3’ end of NSs A and A', while a T7 terminator variant80 was used for NSs B, and B'. Different terminators were used to reduce the chances of non-specific interactions between the two constructs induced by the terminators. As for single NSs, the resulting sequences were cloned into the pET plasmid (Addgene #53137) using the NEBuilder HiFi kit. A 44 bp linker separates the two NS sequences on the plasmid. All plasmid maps are provided in Supplementary Figs. 10, 11. The GFP-expressing plasmid was constructed by cloning the eGFP sequence from pEGFP-N1 (Takara) into the pACYCDuet-1 backbone (Novagen), from which LacI had been removed. Both DNA fragments were obtained as linear sequences and assembled using the NEBuilder HiFi kit. The plasmids containing ampicillin resistance genes were propagated with XL-10 Gold cells, while plasmids containing chloramphenicol resistance genes were propagated in DH5α cells. Plasmids were extracted from the cloning strains using the Monarch miniprep kit. The sequences of all of the constructed plasmids were verified by whole-plasmid sequencing (Full Circle Labs).

RNA nanostar expression in E. coli
Throughout all experiments, antibiotics matching the relevant resistance genes were used, namely ampicillin (50 μg mL−1), and chloramphenicol (34 μg mL−1).

Transformation of BL21(DE3) strains. To transform BL21(DE3) bacteria with plasmids containing the ampicillin resistance gene, 10 ng of the target plasmid were mixed with 10 μL of BL21(DE3) competent cells and incubated on ice for 15 min. The samples were plated on LB-agar containing carbenicillin and incubated at 37 °C for 16 h. To transform BL21(DE3) bacteria with plasmids containing the chloramphenicol resistance gene, or for co-transformation with plasmids containing different antibiotic resistance genes, 10 ng of each plasmid was added to 10 μL of BL21(DE3) competent cells and incubated on ice for 30 min. The samples were placed in a 42 °C water bath for 30 s, followed by 2 min incubation on ice, and addition of 250 μL Super Optimal broth with Catabolite repression (SOC) medium. The samples were placed in a shaking incubator (Multitron Standard, Infors HT, 37 °C, 225 rpm) for 1 h, before plating on LB-agar with matching antibiotics, and incubating at 37 °C for 16 h. LB-agar plates with transformed cells were stored at 4 °C for up to two weeks before RNA expression experiments.

RNA and protein expression in E. coli. A single colony was transferred to 5 mL M9 minimal medium, supplemented with 0.4% glycerol, 0.2% casamino acids, 2 mM MgCl2, 100 μM CaCl2 (hereby referred to as M9 medium with supplements), and matching antibiotics. The culture was grown for 4 h (225 rpm, 37 °C) in a shaking incubator, to reach an OD 600 around 0.6–0.8, measured by spectrophotometer (SmartSpec Plus, Bio-Rad). IPTG (0.4 mM), TBI (10 μM), HBC620 (5 μM) were added to 2 mL of the sample, and the samples were further incubated for 1 h (225 rpm, 37 °C) in a shaking incubator. TBI dye was used instead of DFHBI-1T for imaging in cells due to its improved photostability in vivo, which makes it better suited for cellular imaging applications76. 10 μL of the sample was diluted with 10 μL of the M9 medium with supplements, including the same concentrations of dyes, inducer, and antibiotics. The samples were stained with 0.2 μL of CellBrite Fix 640 membrane dye (1000×) at 25 °C for 10 minutes, before transferring to agarose pads for imaging.

Extraction of RNA from E. coli. Bacterial samples were prepared as described above. RNA was extracted from bacteria using the Qiagen RNeasy Mini kit with Bacteria Protect Reagent, following the manufacturer’s instructions. In the cell lysis step, the cells were sonicated for three cycles at maximum speed for 30 s, with 1 min rest between the cycles (Soniprep 150, MSE supplies LLC).

Quantifying NS expression in E. coli with RT-qPCR. Extracted RNA samples were reverse-transcribed and amplified using the Takyon No ROX SYBR (Eurogentec) master mix and Takyon One Step converter (Eurogentec) in MicroAmp Optical 96-well reaction plates with optical adhesive covers (Applied Biosystems) on the QuantStudio 6 Pro qPCR (ThermoFisher). 60 ng of total RNA per sample was loaded into each well in a total reaction volume of 7.1 μL. Three biological replicates and three technical replicates were used for each sample. The cycle number at which a sample’s fluorescence signal crosses a predefined threshold (Cq) was recorded for each sample. To quantify the relative amount of target transcripts, a housekeeping gene (adk) was used as a reference. The relative transcript number (FD) is then calculated by: ΔCq = Cq_Gene - Cq_Reference (1); FD = 2^-ΔCq (2).

Primers B_fwd and B_rev targeted NS B, while primers adk_fwd and adk_rev targeted adk. Primers were selected to amplify the first half of the NS, to facilitate the detection of cleaved constructs (Supplementary Fig. 30). The primer sequences were: B_fwd: 5’-GCAGTGCTATGAGTGTC-3’, B_rev: 5’-CGACTGTGGCATACAGCGAC-3’, adk_fwd: 5’-ATTCTGCTTGGCGCTCCGGG-3’, adk_rev: 5’-CCGTCAACTTTCGCGTATTT-3’. The thermocycling parameters were as follows: 48 °C (10 min), followed by 95 °C (3 min), then 40 cycles of 95 °C (10 s) and 60 °C (1 min). The temperature ramp rate was 1.6 °C s−1 for all steps. To construct the calibration curve, in vitro transcribed NS RNA was purified using the Monarch Spin RNA Cleanup Kit (NEB). The concentration of purified NS was quantified by UV absorbance using a Nanodrop One spectrophotometer. Calibration curves were created by measuring the Cq values at different NS concentrations. The intracellular concentration of NS was estimated by dividing the amount of extracted RNA by the overall intracellular volume. The overall intracellular volume calibration curve created using known purified RNA concentrations was used to calculate the concentration of RNA per mL of culture or per cell. To compute the latter, we estimated the total number of cells in the culture using optical density (OD) (1 OD = (1.0 ± 0.3) × 10^8 colony forming units mL−1, based on 4 independent measurements), and multiplied by the expected single-cell volume of 3 μm^3, obtained by approximating the cell as a cuboid with dimensions 1 μm × 1 μm × 3 μm.

Preparation of agarose pads for imaging. For E. coli imaging on agarose pads, we prepared 2% molten low-gelling-temperature agarose in M9 media with supplements and added IPTG (0.4 mM), TBI (10 μM), HBC620 (5 μM), and matching antibiotics. The molten agarose solution was kept at 50 °C, and 100 μL was added to a small gene frame (10 mm × 10 mm) adhered to a microscope slide (Clear ground 0.8–1.0 mm, Fisher Scientific). The molten agarose was sandwiched between two glass slides and left at 25 °C for 10 min to solidify. The solidified agarose was transferred to larger gene frames (17 mm × 28 mm) and adhered to a glass slide. 2 μL of the prepared cells was added to each agarose pad, and after 5 minutes at 25 °C, a coverslip (24 mm × 60 mm, No. 1, Epredia) was adhered to the other side of the gene frame.

Confocal imaging of E. coli
The prepared agarose pads were imaged with a Zeiss LSM800 confocal microscope, using a Plan-Apochromat 63× 1.4 N.A. Oil DIC M27 objective. The fluorescent signal from the Pepper aptamer was acquired using a 561 nm laser set to 1.0% intensity and a Master Gain of 500 V, with an emission window of 580 to 650 nm. The fluorescent signal from the Broccoli aptamer and GFP was acquired using a 488 nm laser set at 2.0% intensity for A+B and 1.0% for all others samples and a Master Gain of 500 V, with an emission window of 470 to 580 nm for all samples other than for A+B', which had a restricted emission window of 470 to 530 nm. The settings for samples A+B' were optimised to minimise bleed-through of the Pepper fluorescence in the Broccoli channel. While this fluorescence is weak and generally not noticeable in other samples, it becomes noticeable here because A is concentrated at MLOs, and B' is mostly evenly distributed (Supplementary Fig. 56). The image frame size was set to 1192 pixels × 1192 pixels with 16-bit depth. Line-averaging was enabled and set to 4×. The scan mode along the x direction was selected to be bidirectional. The scanning rate was set to produce a scanning time of 1.77 μs per pixel. This protocol was also used for imaging the in vitro samples when comparing fluorescence intensities between the in vitro and E. coli samples.

FRAP of E. coli. The 561 nm laser was set at 100% intensity for photobleaching A MLOs, while the 488 nm laser was set at 100% intensity for photobleaching B MLOs and GFP-containing MLOs. A total of 25 A MLOs, 12 B MLOs, and 25 GFP-containing MLOs were chosen for photobleaching, with the entire condensate bleached in each case. A total of 15 A MLOs, 8 B MLOs, 15 GFP-containing MLOs were chosen as non-photobleached controls. Fluorescence recovery was monitored at 10 s intervals. Condensate intensity was quantified as the mean intensity of the 10 brightest pixels within the MLO.

Epifluorescence imaging of E. coli
Time-lapse images of E. coli. Time-lapse images of cells expressing NS A were acquired with a Ti2 Eclipse Epifluorescent microscope (Nikon), equipped with an sCMOS camera (Hamamatsu ORCA-Flash4.0 V3), a tuneable light source (Lumencor SPECTRA X LED engine), and a CFI Plan Apo λ D 60× 1.42 N.A. oil immersion objective. The Pepper aptamer was excited using a 555 nm LED with an LED-DA/FI/TR/Cy5-B filter, and fluorescence was collected through an FF01-624/40 emission filter with a 500 ms exposure time. Images were taken every 5 minutes. Samples were incubated at 37 °C using a heating stage (Temikra Ltd.).

Temperature-dependent dissolution and re-assembly of MLOs. To study the melting and reforming of MLOs in bacteria, bacteria with NS A or NS B were prepared for imaging as described above, except that 2% agarose low EEO was used for the agarose pads. E. coli samples on agarose pads were attached to a Peltier microscope stage (Temikra Ltd.) equipped with a sapphire microscope window to ensure optical transparency and good thermal conduction (UQG Optics), and imaged with a Ti2 Eclipse Epifluorescent microscope (Nikon), equipped with an sCMOS camera (Hamamatsu ORCA-Flash4.0 V3), a tuneable light source (Lumencor SPECTRA X LED engine), and a CFI Plan Apo λ D 60× 1.42 N.A. oil immersion objective. The Pepper aptamer was excited using a 555 nm LED with an LED-DA/FI/TR/Cy5-B filter, and fluorescence was collected through an FF01-624/40 emission filter with a 500 ms exposure time. The Broccoli aptamer was excited using a 470 nm LED with an LED-DA/FI/TR/Cy5-B filter, and fluorescence was collected through an EM510 emission filter with a 2 s exposure time. The samples were heated from 37 °C to either 58 °C or 61 °C with increments of 3 °C every 2 min. Samples were then cooled from their maximum temperature to 37 °C with increments of 3 °C every 2 min. For bacteria co-expressed with NS A_AP3 and GFP, cells were imaged using the same Pepper aptamer settings as for Nanostar A, while the GFP signal was captured on the 470 nm LED with an LED-DA/FI/TR/Cy5-B filter, and fluorescence was collected through an EM510 emission filter with a 50 ms exposure time. Cells were heated to 37 °C, 70 °C and cooled to 37 °C, with 2 min incubation periods between each temperature.

Analysis of E. coli micrographs
Images and metadata were extracted from .czi (Zeiss) or .nd2 (Nikon) files using pylibCZIrw and Nd2Reader, respectively. The brightfield image underwent flat-field correction and non-local means denoising. Initial binary masks of the cells were generated using a calculated threshold and adjusted manually if needed. Small holes were filled, and objects with an area smaller than half of a typical E. coli cell (2 μm × 0.5 μm) were removed. Since the algorithm struggled to segment individual bacteria within microcolonies, bacteria inside these regions were filtered out based on object proximity. Objects on image borders were discarded. The remaining objects were filtered based on eccentricity (higher than 0.85), aspect ratio (between 2.5 and 4.5), area (smaller than 7.07 μm^2), and width of E. coli (smaller than 1.5 μm)100–103. The final segmented set was further filtered manually via an interactive GUI. For R2 calculation, pixel intensities from one fluorescent channel were compared against those from another, yielding the coefficient of determination (R2). To compute kurtosis and skewness for each bacterium, pixel intensities in segmented single cells were normalised so that the top 5% of pixels had a value of 1. These pixels were then removed, and kurtosis and skewness of the pixel-intensity distribution were calculated using Scipy104. To determine the number of MLOs per bacterium, each segmented bacterium was rotated so that its long axis aligned horizontally. To minimise interpolation loss, pixels were subdivided into 16 smaller pixels before rotation. Intensities were then averaged vertically to obtain a linear profile, from which peaks were detected using Scipy. The sensitivity of peak detection was manually adjusted based on noise levels. For heatmaps of fluorescence intensity, horizontally aligned bacteria were scaled to a standardised size of 48 pixels × 130 pixels, and pixel intensities were averaged across aligned positions. For Fig. 2B(ii), cells were aligned with their most fluorescent region oriented upwards. This was determined by the Pepper channel for cells co-expressing nanostar A and B/B', and by the Broccoli channel for cells co-expressing nanostar A' and B. To determine locations of MLOs, the condensates were segmented using adaptive thresholding (cv2)105. MLO positions and sizes were validated by comparing them to peaks detected at similar locations within the cell to prevent misclassification. The MLO position was recorded as the highest-intensity pixel within the mask. For quantification of condensate and dispersed-phase intensities, local intensity maxima were identified in each segmented cell. Only cells in which the detected number of peaks matched the expected condensate count were retained. Around each peak, a 2D Gaussian was fitted and an elliptical condensate mask was generated, which defined the condensate region. The integrated condensate intensity, I_cond, was computed as the sum of pixel intensities within this region. The dispersed-phase intensity, i_disp, was calculated as the mean intensity in the remaining cell area after excluding the condensate part. i_tot was computed as the mean intensity within the entire cell area. All analyses were performed using Python 3.10, and visualisations were generated with Matplotlib106 and Seaborn107. To generate the displayed fluorescence micrographs, images were rescaled to facilitate visualisation by dividing the pixel intensities by the intensity of the brightest pixels (99.9-100th percentile). To generate the displayed bright-field micrographs, intensities were rescaled by subtracting the intensity of the dimmest pixel and dividing by the range between the brightest and dimmest pixels. For micrographs showing condensate melting and condensate formation, the brightest pixels across the entire image set were used for division. For all other micrographs, the brightest pixels within each individual image were used for division.

Analysis of the temperature-dependent dissolution and reassembly of MLOs. Micrographs were analyzed fully automatically to obtain segmented bacteria. Heatmap progression across different temperatures was determined by extracting the maximum intensity along the long axis of standard heatmaps, computed as discussed above. The skewness of the intensity distribution of the pixel intensity within single cells was used to monitor the progression of dissolution and reassembly. To estimate the proportion of cells with and without MLOs, the distribution of skewness values across the cell population at a given temperature was fitted using a mixture of two Gaussians (using skimage), with one Gaussian fitting the sub-population of cells with high skewness (featuring condensates) and the second fitting the sub-population of cells with low skewness (lacking condensates)108. For each cell, the probability of belonging to either sub-population was calculated. If the probability of belonging to the MLO-forming distribution exceeded 0.5, the cell was classified as MLO-forming. To estimate T_m for single cells, cells with skewness above 0.25 at 37 °C (indicating the presence of condensates) were selected, and their skewness-temperature profiles were fitted with a sigmoid curve. The inflection point of the sigmoid fit was taken as the estimated T_m.

Growth and RNA production curves in bulk cultures
To characterise growth and RNA production in bulk cell samples, E. coli from single colonies were grown in M9 media with supplements for 4 h as described above. E. coli samples were then diluted to OD 600 = 0.05. 100 μL of the diluted solutions were loaded in a Nunc MicroWell 96-Well Optical-Bottom well plate, together with TBI (10 μM), HBC620 (5 μM), and matching antibiotics. The samples were cultured at 37 °C in a plate reader (Tecan M2000). Every 7 minutes, the plate was shaken (orbital) for 5 s with 3.5 mm amplitude, followed by absorbance and fluorescence measurements. For absorbance, wavelength was set to 600 nm and bandwidth was set to 9 nm. For detecting Broccoli fluorescence, we set the excitation wavelength to 470 nm, the excitation bandwidth to 9 nm, the emission wavelength to 520 nm and the emission bandwidth to 20 nm. For Pepper fluorescence, we set the excitation wavelength to 570 nm, excitation bandwidth to 9 nm, emission wavelength to 620 nm and emission bandwidth to 20 nm. When the OD 600 reached 0.7, we added 0.4 mM IPTG to the samples requiring induction, before continuing the experiment with unchanged settings.

RNA sequencing of E. coli
E. coli expressing A or B nanostars were induced for 1 h as described above. Cells were pelleted and shipped to Full Circle Labs for RNA extraction, poly(A) tailing, and nanopore sequencing. To identify start and end positions of RNA raw reads109, the reads were aligned to the expected RNA sequence with the Needleman-Wunsch algorithm110, and reads with alignment scores corresponding to ≥7 matched nucleotides were retained for further analysis. Due to known imprecision in base-calling near the 5’ ends of direct nanopore RNA reads111, fragment start positions were defined as the first aligned nucleotide in a stretch of at least 3 consecutive matches between the read and the expected sequence.

Polyacrylamide gel electrophoresis
The gels were prepared with a 19:1 w/v acrylamide:bis-acrylamide solution in Tris-Borate-EDTA (TBE) buffer. Acrylamide fractions (w/w) of 6% and 8% were used for native and denaturing gels, respectively. The denaturing gel contained 7 M urea. For both native and denaturing PAGE, samples transcribed in vitro were diluted 1:10, while for E. coli RNA extracts, 500 ng of RNA were used. Samples were then mixed with 0.83 μL of the DNA Gel Loading Dye (6×) for native PAGE and 2.5 μL of the RNA Gel Loading Dye (2×) for denaturing PAGE before loading a total of 5 μL of mixture into the respective lanes. For the denaturing gels, samples were heated to 70 °C for 10 min and subsequently cooled on ice for 3 min before loading. For native gels, one lane contained 1.25 μL of the GeneRuler 100 bp DNA Ladder, while 1.25 μL of the RiboRuler Low Range RNA Ladder was used for denaturing gels. For both native and denaturing gels, sample stacking was performed by running the gel at 40 V for 20 min immediately after loading. The native gels were then run at 150 V for 60 min, while the denaturing gels were run at 270 V for 60 min. All gels were run in TBE. Gels were stained after electrophoresis with 1 × SYBR Gold nucleic acid gel stain and imaged using a Syngene G:BOX Chemi XRQ gel documentation system. Lane intensities were quantified using ImageJ112. The full, uncropped gel images are shown in Supplementary Fig. 57.

Statistics & reproducibility
No statistical method was used to predetermine sample size. No data were excluded from the analyses, except cells in large clumps that cannot be clearly segmented. The experiments were not randomized. The Investigators were not blinded to allocation during experiments and outcome assessment.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript entitled “Expression of Nano-engineered RNA Organelles in Bacteria” presents a highly interesting study that leverages RNA nanotechnology to construct programmable, orthogonal, membrane-less organelles in E. coli. These organelles assemble co-transcriptionally, recruit proteins via aptamers, and can reversibly dissolve and reassemble through thermal cycling. Notably, this work represents the first demonstration of nanostar RNA condensates expressed in a living system, as well as specific protein recruitment to a designed RNA organelle. While the study is impressive and conceptually novel, several issues require attention before publication.\n1. For the stability of NS transcripts in E. coli, the half-life of NS transcripts is unknown. Circularization is a powerful method to enhance RNA transcript stability. Have you tried this approach to improve stability? What is the local concentration of NS in E. coli? Is it comparable to the level in the in vitro transcriptional NS condensate? Does the NS condensate exhibit a similar Broccoli aptamer intensity when the same TBI concentration is applied?\n2. In Extended Figs. 8 and 9, nanopore sequencing was used to confirm NS transcription in E. coli. However, Extended Figs. 8A and 9A show relatively low and gradually increasing fragment coverage for Arm 1a of both NS A and NS B. Moreover, there is no expected cleavage site at Arm 1a (Extended Figs. 8B and 9B). The author should provide a clearer explanation for this observation.\n3. In the section “Expression of orthogonal membrane-less organelles in E. coli,” Fig. 3 should be referenced as Fig. 2. In the section “Selective protein recruitment in RNA membrane-less organelles,” Fig. 4 should be referenced as Fig. 3.\n4. A magnesium and sodium titration experiment confirmed that NS condensate formation requires concentrations exceeding 20 mM Mg²⁺ or 20 mM Na⁺ (Nat. Commun., 2024, 15, 6244). However, the free ion concentrations in E. coli could be much lower. The author needs to explain why this condensate can form without disassembly.\n5. Regarding the explanation: “In addition, fluorescence bleed-through from the Pepper (NS A) to the Broccoli (NS ¯B) channels, observed in vitro (Extended Data Fig. 1), may contribute to the appearance of ¯B clusters, despite optimizing imaging settings (Methods and Supplementary Fig. 21).” This explanation is inconsistent. When NS A and NS B condensates are co-expressed, these two-color channels work well, so why would applying the same settings now cause crosstalk between them?\n6. For “By fitting the skewness distributions, we can extract a temperature dependent fraction of cells hosting MLOs, which shows a small hysteresis between heating and cooling ramps, likely caused by slow assembly kinetics or delays in thermal equilibration in the samples”, However, this explanation may be more applicable to non-living systems. In living cells, protein crowding is present, and these proteins could denature after a heating–cooling cycle, altering the local environment of the MLOs.\n7. “Importantly, the cells remained viable and were able to grow and divide after heating and cooling.” This conclusion appears to be in stark contrast with previously reported findings (Joumal of Applied Microbiology Symposium Supplement 2000, 88, 798-898; FEMS Microbiol Lett 2006, 260, 100–105.)\n8. A recent publication on such co-transcriptional condensates in crowded environments should be added to the background in the introduction (https://www.nature.com/articles/s41467-025-63445-8).\n9. As a side note, please avoid the ultracompact and narrow formatting of the PDF to simplify reviewing.\nfinally, congratulations to the authors, I enjoyed the manuscript very much.", "reviewer_2": "The manuscript \"Expression of nano-engineered RNA organelles in bacteria\" by Ng et al. presents a novel and compelling study on the development of methods to express unnatural condensates in E. coli cells using RNA nanostructures. The authors provide a thorough characterization of condensate morphologies derived from various nucleic acid designs, demonstrating that specific RNA-RNA base-pairing interactions mediate condensation. They also perform experiments on the co-expression of two distinct types of droplet compartments within the same bacterial cell, the selective sequestration of specific proteins within these droplets, and the thermo-responsive behavior of the condensates that mirrors observations from in vitro analogs.\nTo my knowledge, this work represents the first demonstration of nucleic acid nanotechnology-driven condensates in vivo, opening exciting avenues for nanoscale design to modulate biological function as well as for probing the intricate cellular environment. The results are generally strong and well-supported. However, enhancing the characterization, analysis, and discussion could significantly improve the manuscript’s clarity, rigor, and potential impact. These suggestions are listed roughly in order of priority:\n1. Condensate Dynamics and Equilibration Mechanisms\n• The dynamics of condensate formation during RNA expression remain unclear. A time-resolved analysis of condensation kinetics would elucidate whether condensation approaches near-equilibrium morphologies or is maintained in a far-from-equilibrium state.\n• After cell division, it is noted that droplet sizes are often unequal (one droplet being approximately halved). Does the size difference change over time? Does the smaller droplet dissolve like one would expect for an Ostwald ripening mechanism or are droplets actively maintained at the poles?\n2. Quantitative Characterization Using Thermodynamically Relevant Metrics\n• The authors should clarify why droplet volume fraction is not used as a quantitative measure. In my view, a key strength of nanostar condensates is that they are theoretically very well-described. Estimating condensate volume fraction from measured droplet sizes relative to cell volume and comparing this to expression yields (i.e. bulk average nanostar concentration) would allow for a more complete thermodynamic interpretation.\n• In Extended Figure 7, the data show an average absorbance and fluorescence as a function of time. Could this be used to calibrate the expression yield using fluorescence? More precisely, a gel-based assay at multiple time points to quantify \"complete\" nanostar expression would be even better.\n3. Impact of the Cellular Interior\n• The physical characteristics of the nucleoid and its energetic landscape should be discussed in relation to condensate formation. What is the energetic cost of creating a void within the nucleoid? How might the nucleoid’s properties influence the transport and equilibration of nanostar species across the cell?\n4. Biological Implications and Functional Impact\n• While the introduction highlights the design potential of nucleic acid nanotechnology, the manuscript would benefit from a brief but explicit discussion of potential biological functions that nanostar condensates could modulate in vivo in the conclusion.\n• Additionally, a brief comparison with other nucleic acid nanotechnologies and their in vivo applications would improve the potential impact.\n5. Co-expressed Orthogonal Condensates\n• Figure 2Bii’s microscopy images are hard to interpret, especially in areas where cells are tightly packed, and they do not seem to reflect the diagram that shows one of each condensate at both poles. Additionally, could the authors clarify whether chromatic aberration was corrected and what calibration methods were used?\n• Extended Figure 1B suggests possible mixing of condensate types. Is there direct evidence of mixing or interaction between droplet species in vivo?\n• Extended Figure 10 shows droplets predominantly localized to one side of the cell when coexpressed. What might explain this asymmetric distribution? Could slight condensate mixing provide a nucleation point for B onto A?\n6. Temperature Dependence of Condensates\n• In Figure 4Ai, the uniform RNA control appears brighter than condensate-containing cells after normalization. Since normalization by maximum intensity varies across cells, this may be misleading; raw intensity comparisons would be more informative.\n• What is the in vitro melting temperature of the condensates? Does melting temperature correlate with cellular expression level or fluorescence intensity? Given theoretical predictions, higher nanostar concentration should increase the melting temperature until the critical point, after which it declines. Exploring this relationship experimentally would enhance the thermodynamic analysis."}, "author_response": "WethanktheEditorandtheReviewersfortakingthetimetoconsideroursubmis-sion and for their positive and constructive feedback to improve it. Below, we address point-by-pointthequestionsraised,andweencloseupdatedversionsofthemanuscriptand SI, where modifications have been made accordingly and are highlighted in red.\nIn addition to discussing the points suggested by the Reviewers and providing\nadditional data (figures and tables), we have fixed various typos and standardised the\nmethod through which fluorescence images are normalised (simple linear rescaling),\nto facilitate cross-experiment comparisons. We further replaced the uncropped gel\nimages in Supplementary Fig. 48, as we noted that, in our initial submission, we had\naccidentally included images of gels acquired during the optimisation stages, rather\nthan the final gels produced in optimised conditions and used in the main text. We\napologise for this mistake, which has now been fixed.\nAs requested, a sentence mentioning source data has been included in the data\navailability section, and the source data used to generate the figures are enclosed\nas an Excel spreadsheet. In addition, we also included a link to a permanent, freely\naccessibledatarepositorycontainingrawdata.Thelinkwillbecomeactivebeforethe\nmanuscript is published in its final form.\nIn this document, points raised by the Reviewers are highlighted in blue, while\nour answers are presented in black. In “...” we presented quoted excerpts of the text\nhighlighting in red the modified portions of the revised manuscript. All modified and\nnewly included graphical elements, quoted in this response, are included for quick\nreference.Referencesquotedintheresponsearelistedatthebottomofthedocument,\nand their numbering does not match that used in the main text or SI.\nReviewer 1\nThe manuscript entitled “Expression of Nano-engineered RNA Organelles in Bac-teria” presents a highly interesting study that leverages RNA nanotechnology to\nconstruct programmable, orthogonal, membrane-less organelles in E.coli. These\norganelles assemble co-transcriptionally, recruit proteins via aptamers, and can\nreversibly dissolve and reassemble through thermal cycling. Notably, this work rep-resents the first demonstration of nanostar RNA condensates expressed in a living\nsystem, as well as specific protein recruitment to a designed RNA organelle. While\nthe study is impressive and conceptually novel, several issues require attention before\npublication.\nWe thank the Reviewer for their positive assessment on our work, and for recom-mending publication following the corrections addressed below.\n1. For the stability of NS transcripts in E. coli, the half-life of NS transcripts is\nunknown. Circularisation is a powerful method to enhance RNA transcript stability.\nHave you tried this approach to improve stability?\nBecause we observe robust condensate formation in the vast majority of cells, we\nhave not explored circularisation as a strategy to enhance RNA transcript stability.\nconsider it in subsequent studies, should greater stability be required. We mentioned\nthis possibility in the revised manuscript.\n“In addition, stability may be enhanced by circularising the constructs to prevent\ndigestion by exonucleases [1].”\nWhat is the local concentration of NS in E. coli? Is it comparable to the level in\nthe in vitro transcriptional NS condensate?\nWethanktheReviewerforraisingthisinterestingpoint.Intherevisedmanuscript,\nwe used RT-qPCR to estimate the concentration of NS B expressed in E. coli both\nbefore induction and at 1 h post-induction, the timepoint at which cells were imaged.\nThese results are presented in the new Supplementary Fig. 14, included below,\nwhere we report: A) NS concentrations relative to the mRNA of a housekeeping\ngene; B) Absolute NS concentration in the cell culture; and C) An estimate of the\naverage intra-cellular NS concentration, obtained by using optical density (OD) to\ndetermine cell counts (1OD = (1.0 ± 0.3) × 108 colonyformingunitsmL−1, based\non 4 independent measurements). At 1 h post-induction, RT-qPCR estimates the\nintracellular NS concentration to be 16±5 µM (7 biological replicates). We can use\nthe measured intracellular concentration to roughly estimate the average volume of\nthe condensed phase. Under the simplified assumptions that, within the condensates,\neach NS occupies the volume equal to that of a sphere with radius equal to the NS\narm length (∼ 8 nm, see [2]), the volume per NS is approximately 2.11×10−6µm3.\nApproximating the cell as a cuboid with dimensions 1 µm × 1 µm × 3 µm, a concen-tration of 16 µM corresponds to about 31,000 intracellular NSs. Assuming that most\nexpressed NSs are gathered in condensates, the total condensate volume is roughly\n6.7×10−2µm3. If this volume is equally divided between two spherical condensates\nper cell, this back-of-the-envelope calculation yields an expected condensed diameter\nof ∼ 400 nm. These length scales are not inconsistent with microscopy observations,\nwith the caveat that an accurate determination of condensate size is hindered by\ndiffraction-limited resolution. Intracellular NS concentration is also similar to values\nobtained with IVT expression, namely 13 ± 4 µM for NS B, measured from three\nindependent IVT reactions after 3 hours of incubation (Supplementary Fig. 14D).\nWe have now included the new information in the main text:\n“RT-qPCR was used to quantify NS expression levels, yielding an intra-cellular\nconcentration of 16 ± 5 µM for NS B 1 hour post-induction (Supplementary Figs.\n13, 14, Methods). By assuming that most NSs accumulate in the condensates, and\napproximating the per-NS volume in the condensed phase as that of a sphere with\nradius equal to the NS arm length (∼ 8 nm [2]), we can roughly estimate the total\ncondensate volume from the NS concentration. By further assuming that this volume\nis equally divided between two condensates, we estimate a mean condensate diameter\n2\nof ∼ 400 nm, compatible with microscopy observations. Intra-cellular NS concentra-tion is comparable with in vitro expression yields, measured as 13±4 µM 3 hours\nfrom starting the reaction (Supplementary Fig. 14).”\nRT-qPCR was used here as it is the most widely adopted technique for measuring\nintracellular RNA concentrations due to its sensitivity, specificity, and relative ease of\nuse.Thissaid,RT-qPCRisnotwithoutlimitations,asoutcomescanbeinfluencedby\nvariability in RNA extraction, secondary structure, enzymatic processing (Extended\nData Figs. 8A-B, 9A-B) and post-transcriptional modifications. These considerations\nare included in the caption of Supplementary Fig. 14.\nFull details on the RNA extraction and RT-qPCR protocols are included in the\nrevised Methods section. We also include new Supplementary Fig. 13, reporting\ncalibration data used for absolute RNA quantification. We have commented on these\nresults in the revised main text.\n    \n    \n    \n  \n  \n  \n  \n  \n              \n O Q   1 6   & R Q F H Q W U D W L R Q     Q J   ˜ / 1 \f\n T &\nSupplementary Figure 13: RT-qPCR Nanostar concentration calibration\ncurve. RT-qPCR calibration curve showing the relationship between Cq values and\nthe natural logarithm of RNA concentration for NS B. Linear fitting produced Cq =\n−1.59×ln(NS concentration/ngµL−1)+11.8. See Methods for experimental details.\n3\n        \n      \n      \n      \n  \n     \n , Q G X F W L R Q   W L P H     K \f\n   %   6 1\n \f N G D   R W   H Y L W D O H U    \n $\n      \n      \n      \n      \n  \n     \n , Q G X F W L R Q   W L P H     K \f\n H U X W O X F   Q L   %   6 1  \f1\n / P   J Q    \n %\n    \n    \n    \n    \n  \n  \n     \n , Q G X F W L R Q   W L P H     K \f\n U D O X O O H F D U W Q L   %   6 1\n \f 0 ˜    \n &\n    \n    \n    \n    \n  \n  \n , Q W U D F H O O X O D U  , 9 7\n Q R L W D U W Q H F Q R F   %   6 1\n \f 0 ˜    \n '\nSupplementary Figure 14: Concentration of NSs expressed in E. coli as\nanalysed by RT-qPCR. (A) Bar graphs of the relative concentration of NS B,\nnormalised to the housekeeping gene adk. (B) Absolute concentrations of NS B per\nmL of cell culture. (C) Intracellular concentrations of NS B, estimated by divid-ing the data in (B) by the overall intracellular volume. To compute the latter,\nwe estimated the total number of cells in the culture using optical density (OD)\n(1OD = (1.0±0.3)×108 colonyformingunitsmL−1, based on 4 independent mea-surements), and multiplied by the expected single-cell volume of 3 µm3, obtained\nby approximating the cell as a cuboid with dimensions 1 µm × 1 µm × 3 µm (see\nMethods).Theheightofthebarsarethemeanvaluesofthreetechnicalreplicates(RT-qPCR measurements) performed on each biological replicate grown from independent\ncolonies. For the 1 h induction condition, seven biological replicates were analysed;\nfor the 0 h induction condition, three biological replicates were analyzed. Error bars\nindicate standard error of the mean. (D) Side-by-side comparison of the intracellu-lar concentration of NS B and that of NS B expressed in vitro 3 h after starting\nthereaction.ForRT-qPCR,acalibrationcurveproducedusingknownconcentrations\nof purified RNA was used to calculate the concentrations in (B) and (C) (Supple-mentary Fig. 13). NS concentration in the in vitro sample was determined with UV\nabsorbance using a Nanodrop ONE spectrophotometer, following RNA purification.\nWenotethat,whileRT-qPCRiswidelyadoptedformeasuringintracellularRNAcon-centrationsduetoitssensitivity,specificity,andrelativeeaseofuse,outcomescanstill\nbe influenced by variability in RNA extraction, secondary structure, and enzymatic\nprocessing(ExtendedDataFigs.8A-B,9A-B)andpost-transcriptionalmodifications.\nsame TBI concentration is applied?\nWe thank the Reviewer for this question. To address it, we carried out additional\nIVT experiments where NS B condensates were stained with 10 µM TBI, the same\nconcentration used for the E. coli experiments. The confocal images were acquired\nfollowing the same imaging protocol used for E. coli experiments and subjected\nto the same post-processing steps (Methods). Image segmentation reveals that the\nmean pixel intensity of in vitro NS B condensates is just 23% higher than that of the\ncondensates expressed in E. coli. This suggests comparable fluorescence intensity in\nthe Broccoli channel between the in vitro and in vivo samples that, in turn, hints at\nsimilarities in FLAP response and overall NS concentration in the condensed phase.\nFor additional clarity, micrographs showing side-by-side comparison of in vitro and in\nvivo expressed NS B condensates are included in new Supplementary Fig. 16.\nWe comment on these results in the revised main text:\n“When identical staining and imaging settings are applied, typical fluorescence\nintensity recorded from condensates expressed in vivo closely matches the one\nmeasured in vitro, suggesting similar FLAP response and NS concentration in the\ncondensed phase (Supplementary Fig. 16).”\n5\n(i) (ii)\nSupplementary Figure 16: Confocal micrographs of in vitro and in vivo\nNS B condensates stained using the same TBI concentration (10 µM).The\nmicrographs were obtained using the same imaging protocol and subjected to the\nsame post-processing settings (Methods). The segmented images exhibit comparable\nfluorescence, with the mean pixel intensity of NS B condensates expressed (i) in vitro\nbeing23%higherthanthatofNSBcondensatesexpressed(ii)inE.coli.Thissuggests\nsimilarities in FLAP response and overall NS concentration in the condensed phase.\nScale bars, 20 µm.\n6\n2. In Extended Figs. 8 and 9, nanopore sequencing was used to confirm NS tran-scription in E. coli. However, Extended Figs. 8A and 9A show relatively low and\ngradually increasing fragment coverage for Arm 1a of both NS A and NS B.\nThe relatively low and gradually increasing coverage for Arm 1a—corresponding\nto the 5’ end of the nanostar transcript—is likely attributed to known limitations in\nbasecalling accuracy near the 5’ end of direct RNA nanopore reads [3]. The RNA is\nsequenced from 3’ to 5’, and the helicase enzyme that controls translocation through\nthe pore is at a distance equivalent to 10-15 nt from the pore itself. Thus, when the\nenzyme releases the last nucleotide at the 5’ end, the final 10-15 nucleotides of the\nstrand are rapidly driven through the pore, which prevents their accurate reading. To\nclarify the observed coverage pattern, we have now included a mention of this effect\nin the relevant figure captions.\n“Relatively low coverage at the 5’ end (Arm 1a) is consistent with known base-calling limitations in direct RNA nanopore reads. Specifically, the helicase enzyme\ncontrolling translocation through the nanopore sits at a distance equivalent to 10-15\nnt from the sensing region, causing the final 10-15 nt of a strand being sequenced\nto rapidly translocate once the 5’ is released by the enzyme, preventing accurate\nreading [3].”\nMoreover, there is no expected cleavage site at Arm 1a (Extended Figs. 8B and\n9B). The author should provide a clearer explanation for this observation.\nThe absence of cleavage at Arm 1 is indeed notable. While predictive tools for\nRNase cleavage sites are currently lacking, one possible explanation is that the T7\nterminator, which protrudes from the stem structure, may sterically hinder RNase\naccess and protect this region from degradation. Similarly, cleavage on Arm 2 is not\nobserved, which could be due to the steric hindrance from the FLAP on Arm 2. We\nhave now included a discussion of this in the manuscript.\n“In both NS designs, cleavage was not detected on NS arms that include the\nFLAP or the T7 terminator, possibly due to these structural features hindering\nRNase access [4]. Both NSs, however, feature a cleavage site in the terminator\nloop itself, which is thus likely to be detached in many transcripts (Extended Data\nFigs. 8A-B, 9A-B). The apparent protective effect of altering the duplex structure of\nNS arms could be leveraged to design against degradation.”\n3. In the section “Expression of orthogonal membrane-less organelles in E. coli,”\nFig. 3 should be referenced as Fig. 2. In the section “Selective protein recruitment in\nRNA membrane-less organelles,” Fig. 4 should be referenced as Fig. 3.\nThe typo has now been corrected.\n7\n4. A magnesium and sodium titration experiment confirmed that NS condensate\nformation requires concentrations exceeding 20 mM Mg2+ or 20 mM Na+ (Nat. Com-mun., 2024, 15, 6244). However, the free ion concentrations in E. coli could be much\nlower. The author needs to explain why this condensate can form without disassembly.\nIn E. coli, the total intracellular unbound magnesium concentration is estimated\nto be around 1 mM [5], while potassium levels range from 200 to 400 mM [6]. To test\nfor the stability of the condensates under relevant conditions, we performed control\nexperiments in which we purified in vitro-expressed NSs and redissolved them in a\nbuffer with 1 mM magnesium chloride and 200 mM potassium chloride. As shown in\nthe new Supplementary Figure 8, condensate formation was readily observed. This\nsuggests that the concentration of magnesium and potassium found in E. coli can\nsupport condensate growth. Additional cellular factors, such as molecular crowding,\nmay further promote condensate assembly.\nWe have included the new data and accompanying discussion in the manuscript\nmain text:\n“Encouraginglyforinvivoexpression,robustcondensationwasobservedinbuffers\nmimicking the intracellular ionic conditions encountered in E. coli (200 mM K+ and\n1 mM Mg2+ [5, 6]) (Supplementary Fig. 8).”\n   L L \f\nSupplementaryFigure8:Epifluorescencemicrographsofinvitroexpressed\nNS incubated in buffer mimicking the intracellular ionic environment of\nE. coli. Epifluorescence micrographs of (i) NS A. (ii) NS B, after 1 hour incubation\nin buffer containing 1 mM MgCl and 200 mM KCl. Scale bars, 200µm.\n2\n9\n5. Regarding the explanation: “In addition, fluorescence bleed-through from the\nPepper (NS A) to the Broccoli (NS ¯B) channels, observed in vitro (Extended Data\nFig. 1), may contribute to the appearance of ¯B clusters, despite optimizing imaging\nsettings (Methods and Supplementary Fig. 21).” This explanation is inconsistent. When\nNS A and NS B condensates are co-expressed, these two-colour channels work\nwell, so why would applying the same settings now cause crosstalk between them?\nWethanktheReviewerforthevaluablesuggestionthatfluorescencebleed-through\nfrom the Pepper (NS A) to the Broccoli channel (NS B¯) might not be the most\nplausible explanation for the observed NS B¯ clusters in the A+B¯ samples.\nTo address this issue, we applied image segmentation to micrographs of in vitro-expressed condensates and reported the data in new Supplementary Fig. 7, shown\nbelow for quick reference. First, we quantified fluorescence emission in the Broccoli\nchannel (normally used to image NSs B and B¯) in samples containing only the\ncondensate-forming NS A, labelled with Pepper FLAPs (Supplementary Fig. 7A).\nThe amount of bleed-through from the Pepper channel into the Broccoli channel was\nminimal.\nWe then performed the same analysis on samples expressing A NSs together\nwith dispersed B¯ NSs and, unsurprisingly, we observed substantially higher signal in the\nBroccoli channel (Supplementary Fig. 7B). As noted in our initial submission, the\nsignalwithinAcondensateswashigherthaninthesurroundingbackground,aneffect\ninitially ascribed to bleed-through.\nIn Supplementary Fig. 7C, we then computed the difference, δ, between the Broc-coli signal measured within A condensates and that measured in the surrounding\nbackground. Comparing A-only and A+B¯ samples, δ is substantially higher in the\nlatter. Two effects could contribute to this: (i) bleed-through from the Pepper dye\nbeing stronger within A-type condensates than in the surrounding solution, and (ii)\na difference in B¯ NS concentration between the A-type condensates and the back-ground.If (i)werethe onlyeffect—i.e.if theconcentrationofB¯ withinA condensates\nwere equal to that in the background—then similar δ values would be observed in\nboth A and A+B¯ samples. Instead, the much greater δ in A+B¯ suggests that B¯\naccumulates within A condensates, causing the increased Broccoli signal. We surmise\nthat this accumulation arises from weak affinity between the scrambled KLs on B¯\nand the A KLs.\nSuch non-specific affinity is not observed in condensate-forming B NSs. In A+B\nsamples, Broccoli fluorescence is higher in the background than in A condensates,\nconsistent with excluded volume effects that lower the concentration of the dispersed\nB phase within A condensates relative to the surrounding solution. The absence\nof A-B condensate adhesion further supports the lack of non-specific A-B affinity\n(Supplementary Fig. 7B).\nrelevant, and referred to the new Supplementary Figure.\n“The greater abundance of non-specific clusters seen when non-sticky NS are\nco-expressed with sticky motifs, particularly for A+B¯ expression, may be due to\nMLO-templated clustering of the otherwise soluble NSs. The soluble NSs may indeed\nweakly partition within, or coat the specifically-assembled MLO, or the condensates\nmay serve as heterogeneous nucleation sites for non-specific aggregation. Either pro-cess may be facilitated by weak affinity between the soluble and sticky NSs due to\nbase pairing, misfolding, enzymatic degradation, or the action of native RNA-binding\nproteins. This interpretation is supported by the evidence of weak partitioning of\nsoluble NS B¯ in NS A condensates observed in vitro (Extended Data Fig. 1), and\nquantified in Supplementary Fig. 7 (Methods).”\nWe have included a summary of the discussion provided here in the caption of the\nnew Supplementary Fig. 7.\nA B C\nSupplementary Figure 7: Quantification of the Broccoli signal observed\nin A-type condensates. (A) Mean Broccoli fluorescence within and outside (bulk\nphase) NS A condensates in NS A-only samples. The Broccoli signal in the denser NS\nAcondensatesdiffersonlyslightlyfromthatinthebulkphase,indicatingthatfluores-cencebleed-throughfromthePepperchannelintotheBroccolichannelisminimal.(B)\nMean Broccoli fluorescence within and outside NS A condensates in A+B¯ and A+B\nsamples. In A+B¯ samples, Broccoli fluorescence is higher within NS A condensates\nthan in the bulk, whereas in A+B samples, fluorescence is higher in the bulk phase.\nThe latter observation is consistent with excluded volume effects that lower the con-centrationofNSBwithinAcondensatesrelativetothebulkphase.(C)Difference,δ,\nin mean Broccoli fluorescence intensity between NS A condensates and the surround-ing bulk phase in A-only and A+B¯ samples. The larger δ in A+B¯ samples suggests\naccumulation of NS B¯ within A condensates, likely due to weak affinity between the\nscrambled KLs of NS B¯ and the KLs of NS A.\n11\n6. For “By fitting the skewness distributions, we can extract a temperature depen-dent fraction of cells hosting MLOs, which shows a small hysteresis between heating\nand cooling ramps, likely caused by slow assembly kinetics or delays in thermal equi-libration”, However, this explanation may be more applicable to non-living systems. In living cells, protein crowding is present, and these proteins could denature after a heating–cooling cycle, altering the local environment of the MLOs.\nWe agree with the insightful comment by the Reviewer, and modified the\nmanuscript to include this argument.\n“By fitting the skewness distributions, we can extract a temperature-dependent\nfraction of cells hosting MLOs, which shows a small hysteresis between heating and\ncooling ramps. This may be caused by slow assembly kinetics, delays in thermal equi-libration, or changes in protein crowding due to protein denaturation during heating,\nwhichcouldalterthelocalenvironmentoftheMLOs(Fig.4A(iv)andSupplementary\nFig. 40).”\n7. “Importantly, the cells remained viable and were able to grow and divide after\nheating and cooling.” This conclusion appears to be in stark contrast with previously\nreported findings (Journal of Applied Microbiology Symposium Supplement 2000, 88,\n798-898; FEMS Microbiol Lett 2006, 260, 100–105.)\nWe thank the Reviewer for the comment, which prompted us to re-analyse our\ndata on post-thermal-treatment cell viability. Overall, the cell population resumes\ngrowth, as we had previously reported. However, we have now quantified that only a\nsmall fraction of cells, approximately 10%, retain the ability to grow and divide and\nis responsible for population growth. Those surviving cells required roughly 3 hours\nbefore division was observed, indicating that stress from heat exposure significantly\nslowed down division.\nIn Ref. [7], quoted by the Reviewer, the cell lethality was quantified using a\nlive/dead stain to show compromised cell membranes as well as DAPI to show com-promised nucleoids. Neither are the most accurate measure of cell death, as cells with\ncompromised membrane [8] and nucleoid [9] can still recover. Bacterial cells can be in\na metabolically viable but not culturable state, that over time can be recovered [10],\nconsistent with our observation of delayed division post heating.\nRef. [11], also quoted by the Reviewer, analyses the effect of heating on bacteria\nin conditions quite different from ours. This includes bacteria in homogenised meats\nas well as in skimmed milk, where they note a 6-decimal reduction of cells after\nheating at 70◦C for 2 mins. They note that there might be differences to heat resis-tance when cells are in a different media, if a different strain is used or if a different\nheating treatment is used, which may be the case for our experiments. Additionally\nthis paper does not investigate further the long term recovery of these cells, but does\n12\nreference work from Ref. [12], reporting on the recovery of bacteria when heated at\nhigh temperatures for a short period. Ref. [12] notes that some bacteria can repair\nsublethal damage after 4+ hours, reflecting our results post heating.\nIn summary, we observe that a fraction of the cells are able to survive the heating\ntreatment and later resume division, leading to the observed population growth.\nWe have revised the main text to reflect our quantification of the percentage of\nviable cells, and discuss our observation in the context of literature.\n“Approximately 10% of the cells retained the ability to grow and divide following\nthermal treatment (Supplementary Fig. 46). Growth and division is found to resume\nafter a 3-hour delay, which is likely the time required for bacterial recovery [10].”\n8. A recent publication on such co-transcriptional condensates in\ncrowded environments should be added to the background in the introduction\n(https://www.nature.com/articles/s41467-025-63445-8).\nWe have expanded the introduction to include this relevant literature:\n“To overcome this roadblock, here we deploy RNA nanostructures, which retain\nmuch of the programmability of DNA constructs [2, 13–15], but can be synthesised\nin vivo [16] and in crowded environments that mimic cellular conditions [17].”\n9. As a side note, please avoid the ultracompact and narrow formatting of the\nPDF to simplify reviewing.\nThank you for the feedback. For our initial submission, we used the Springer\nNature LaTex template, which enforces this formatting. In the marked main text\nprepared for this resubmission, we edited the template to enforce double spacing,\nwhich we hope facilitates reviewing. The unmodified, single-spacing template has\nbeen retained for the unmarked version of the main text to facilitate manuscript\nproduction.\nfinally, congratulations to the authors, I enjoyed the manuscript very much.\nThank you. We appreciate your help in further improving the manuscript.\nThe manuscript “Expression of nano-engineered RNA organelles in bacteria” by\nNg et al. presents a novel and compelling study on the development of methods to express\nunnatural condensates in E. colicells using RNA nanostructures. The authors provide\na thorough characterisation of condensate morphologies derived from various nucleic\nacid designs, demonstrating that specific RNA-RNA base-pairing interactions mediate\ncondensation. They also perform experiments on the co-expression of two distinct\ntypes of droplet compartments within the same bacterial cell, the selective sequestra-tion of specific proteins within these droplets, and the thermo-responsive behaviour of\nthe condensates that mirrors observations from in vitro analogs. To my knowledge,\nthis work represents the first demonstration of nucleic acid nanotechnology-driven\ncondensates in vivo, opening exciting avenues for nanoscale design to modulate bio-logical function as well as for probing the intricate cellular environment. The results\nare generally strong and well-supported. However, enhancing the characterisation,\nanalysis, and discussion could significantly improve the manuscript’s clarity, rigor,\nand potential impact. These suggestions are listed roughly in order of priority:\nWe thank the Reviewer for the positive assessment of our manuscript and for\nconsidering it to be written to a high standard.\n1. Condensate Dynamics and Equilibration Mechanisms. The dynamics of con-densate formation during RNA expression remain unclear. A time-resolved analysis\nof condensation kinetics would elucidate whether condensation approaches near-equilibrium morphologies or is maintained in a far-from-equilibrium state. After cell\ndivision, it is noted that droplet sizes are often unequal (one droplet being approxi-mately halved). Does the size difference change over time? Does the smaller droplet\ndissolve like one would expect for an Ostwald ripening mechanism or are droplets\nactively maintained at the poles?\nQuantitative estimation of condensate size and (even more so) morphology are\nchallenging due to condensate dimensions being close to or below the diffraction-limited resolution of our microscope. Yet, under the assumption that the density of\nNSs, and therefore of the fluorescent dye, is uniform within the condensates, the flu-orescence signal integrated across a condensate, I , is expected to be proportional\ncond\nto the condensate volume.\nFollowing to this strategy, we estimated the time-dependent ratio between the\nintegrated fluorescence intensity of the condensates present at the two cell poles\n(smaller over larger condensate, r = Ismall/Ilarge), and used it as a proxy for the\ncond cond\nvolume ratio. We tracked r for a few cells until they approached the division stage,\nas reported in the new Supplementary Fig. 18, included below for quick reference. In\nall cases, r increases over time, starting from ∼ 0.3–0.6 and approaching ∼ 0.6–0.9.\nEvidence that condensate asymmetry originating from cell division is progressively\neliminated suggests that Ostwald ripening is not prominent. Rather, the smaller con-densateformedatthenewcellpoletendstogrowovertime,withitssizeapproaching\n14\nthatofthelarger,oldercondensate.StudiesofsimilarDNAnanostarsystemsinvitro\nalso exclude Ostwald ripening as a dominant mechanism for condensate growth [18].\nWe have included a comment on this observation in the revised main text:\n“In some instances, the central condensates then appear to split between the two\ndaughter cells, which emerge with two polar condensates of asymmetric intensity\n(Fig. 1D(ii) and Extended Data Fig. 4(i)). Over time, smaller condensates grow\ntowards parity with the larger ones, progressively reducing size asymmetry (Supple-mentary Fig. 18). This evidence excludes Ostwald ripening as a dominant mechanism\nfor condensate coarsening, consistent with observations made on DNA nanostar con-densates in vitro [18].”\nSupplementary Figure 18: Ratio of condensate sizes in single cells express-ing NS A over time. The ratio r was calculated as r =Ismall/Ilarge, where Ismall is\ncond cond cond\nthe integrated fluorescence intensity of the smaller MLO and Ilarge is the integrated\ncond\nfluorescence intensity of the larger MLO. Each line represents one cell, tracked until\nthe onset of cell division. The area of MLOs was selected manually using ImageJ.\nImages were acquired every 5 minutes.\n15\n2. Quantitative Characterisation Using Thermodynamically Relevant Metrics.\nThe authors should clarify why droplet volume fraction is not used as a quantitative\nmeasure. In my view, a key strength of nanostar condensates is that they are theo-retically very well-described. Estimating condensate volume fraction from measured\ndroplet sizes relative to cell volume and comparing this to expression yields (i.e. bulk\naverage nanostar concentration) would allow for a more complete thermodynamic\ninterpretation.\nWe thank the Reviewer for the insightful comment. As discussed above, we do not\nfeel we can reliably estimate condensate size, due to the droplets being often close\nto or below the diffraction limit. We can, however, use the integrated fluorescence\nintensity within a condensate, I , as a proxy for its size, as done in point 1. In\ncond\naddition we can, with some accuracy, compute the average fluorescence intensity,\ni , in the centre of the cells away from the condensates, which is proportional to\ndisp\nthe concentration of NSs in the dispersed (gas) phase. In the new Supplementary\nFig. 15A we show a scatter plot of i vs I , and observe that the former is\ndisp cond\nnearly independent on the latter for both A and B-type condensates. This finding\nis consistent with the system being at or near thermodynamic equilibrium, which\nwould imply the concentration of dispersed NSs coexisting with the condensates\nbeing independent of condensate size. Expectedly, the mean fluorescence intensity\nmeasured across the entire cell, i , proportional to the overall NS concentration, is\ntot\nstrongly linearly correlated with I , as shown in Supplementary Figure 15B.\ncond\nIn the revised main text we have commented on these observations, referring to\nthe new Supplementary Figure.\nThe main text now reads:\n“Throughimagesegmentationappliedtosinglecells,wedeterminedtheintegrated\nfluorescence intensity of individual condensates, I , expected to be proportional\ncond\nto their volume. Similarly, we computed the average intensity in the condensate-free\nmid cell region, i , proportional to the concentration of the dispersed NS phase,\ndisp\nand the average intensity across the entire cell, i , proportional to the overall NS\ntot\nconcentration (Methods). In Supplementary Fig. 15, we show that i is weakly\ndisp\ncorrelated with I (R2 =0.21 and 0.24 for A and B, respectively), consistent with\ncond\nthe notion that the NS behave similarly to an equilibrium phase-separating system,\nin which the concentration of the dispersed phase is independent on the volume of\nthe dense phase. In addition, consistently with the lever rule, i is strongly linearly\ntot\ncorrelated with I (R2 =0.70 and 0.83 for A and B, respectively).”\ncond\nSupplementaryFigure15:ProbingofthethermodynamicbehaviourofNSs\nwithin cells. (A) Scatter plot of the average fluorescence intensity measured outside\ncondensates (i ) against the integrated fluorescence inside segmented condensates\ndisp\n(I ). i is proportional to the concentration of dispersed NSs, while I is\ncond disp cond\nexpected to be proportional to condensate volume, under the assumption of uniform\nNS density and brightness in the condensed phase. Linear fits (dashed gray line) are\nshown with the following parameters: NS A (top) (y =6.4×10−5x+4.3×10−3, R2 =\n0.21) and NS B (bottom) (y =5.1×10−5x+1.3×10−2, R2 =0.24). Weak correlation\nbetween i and I is consistent with NS concentration in the dispersed phase\ndisp cond\nbeingindependentoncondensatevolume,asexpectedatthermodynamicequilibrium.\n(B)Scatteroftheaveragefluorescenceofthewholecell,i ,proportionaltotheoverall\ntot\nNS concentration, against I . Linear fits (dashed gray line) are shown with the\ncond\nfollowing parameters: NS A (top) (y =2.0×10−4x+4.9×10−3, R2 =0.70) and NS B\n(bottom) (y = 2.1×10−4x+1.3×10−2, R2 = 0.83). At thermodynamic equilibrium,\na linear dependence of condensate volume on overall NS concentration is consistent\nwith the lever rule.\nfluorescence as a function of time. Could this be used to calibrate the expression yield\nusing fluorescence (total concentration of nanostars)? More precisely, a gel-based\nassay at multiple time points to quantify “complete” nanostar expression would be\neven better.\nWe agree with the Reviewer on the importance of quantifying NS expression\nlevels.RT-qPCRwasselectedastheanalysismethodasitisthemostwidelyadopted\ntechniqueformeasuringintracellularRNAconcentrationsduetoitssensitivity,speci-ficity, and relative ease of use.\nWe considered plate reader measurements less reliable, as one would need to\nperform calibrations with in vitro expressed (or reconstituted) NSs, whose fluores-cence intensity (per NS) may differ from the in vivo case due to differences in dye\navailability or micro-environment.\nThe RT-qPCR results are presented in the new Supplementary Fig. 14. For E.\ncoli expressing NS B, we report: A) NS concentrations relative to the mRNA of a\nhousekeeping gene; B) Absolute NS concentration in the cell culture; and C) An esti-mate of the average intra-cellular NS concentration, calculated using optical density\n(OD) to determine cell counts (1OD = (1.0±0.3)×108 colonyformingunitsmL−1,\nbased on 4 independent measurements).\nAt 1 h post-induction, RT-qPCR yields an estimated intracellular NS concentra-tion to be 16±5 µM (7 biological replicates), similar to the value measured in vitro\n(13±4 µM 3 h from starting the reaction).\nIn the revised manuscript, we describe the new results in the main text:\n“RT-qPCR was used to quantify NS expression levels, yielding an intra-cellular\nconcentration of 16 ± 5 µM for NS B 1 hour post-induction (Supplementary Figs.\n13, 14, Methods). By assuming that most NSs accumulate in the condensates, and\napproximating the per-NS volume in the condensed phase as that of a sphere with\nradius equal to the NS arm length (∼ 8 nm [2]), we can roughly estimate the total\ncondensate volume from the NS concentration. By further assuming that this volume\nis equally divided between two condensates, we estimate a mean condensate diameter\nof ∼ 400 nm, compatible with microscopy observations (Fig. 1B(i)-C(i)). Intra-cellular NS concentration is comparable with in vitro expression yields, measured as\n13±4 µM 3 hours from starting the reaction (Supplementary Fig. 14).”\n18\n    \n    \n    \n  \n  \n  \n  \n  \n              \n O Q   1 6   & R Q F H Q W U D W L R Q     Q J   ˜ / 1 \f\n T &\nSupplementary Figure 13: RT-qPCR Nanostar concentration calibration\ncurve. RT-qPCR calibration curve showing the relationship between Cq values and\nthe natural logarithm of RNA concentration for NS B. Linear fitting produced Cq =\n−1.59×ln(NS concentration/ngµL−1)+11.8. See Methods for experimental details.\n19\n        \n      \n      \n      \n  \n     \n , Q G X F W L R Q   W L P H     K \f\n   %   6 1\n \f N G D   R W   H Y L W D O H U    \n $\n      \n      \n      \n      \n  \n     \n , Q G X F W L R Q   W L P H     K \f\n H U X W O X F   Q L   %   6 1  \f1\n / P   J Q    \n %\n    \n    \n    \n    \n  \n  \n     \n , Q G X F W L R Q   W L P H     K \f\n U D O X O O H F D U W Q L   %   6 1\n \f 0 ˜    \n &\n    \n    \n    \n    \n  \n  \n , Q W U D F H O O X O D U  , 9 7\n Q R L W D U W Q H F Q R F   %   6 1\n \f 0 ˜    \n '\nSupplementary Figure 14: Concentration of NSs expressed in E. coli as\nanalysed by RT-qPCR. (A) Bar graphs of the relative concentration of NS B,\nnormalised to the housekeeping gene adk. (B) Absolute concentrations of NS B per\nmL of cell culture. (C) Intracellular concentrations of NS B, estimated by divid-ing the data in (B) by the overall intracellular volume. To compute the latter,\nwe estimated the total number of cells in the culture using optical density (OD)\n(1OD = (1.0±0.3)×108 colonyformingunitsmL−1, based on 4 independent mea-surements), and multiplied by the expected single-cell volume of 3 µm3, obtained\nby approximating the cell as a cuboid with dimensions 1 µm × 1 µm × 3 µm (see\nMethods).Theheightofthebarsarethemeanvaluesofthreetechnicalreplicates(RT-qPCR measurements) performed on each biological replicate grown from independent\ncolonies. For the 1 h induction condition, seven biological replicates were analysed;\nfor the 0 h induction condition, three biological replicates were analyzed. Error bars\nindicate standard error of the mean. (D) Side-by-side comparison of the intracellu-lar concentration of NS B and that of NS B expressed in vitro 3 h after starting\nthereaction.ForRT-qPCR,acalibrationcurveproducedusingknownconcentrations\nof purified RNA was used to calculate the concentrations in (B) and (C) (Supple-mentary Fig. 13). NS concentration in the in vitro sample was determined with UV\nabsorbance using a Nanodrop ONE spectrophotometer, following RNA purification.\nWenotethat,whileRT-qPCRiswidelyadoptedformeasuringintracellularRNAcon-centrationsduetoitssensitivity,specificity,andrelativeeaseofuse,outcomescanstill\nbe influenced by variability in RNA extraction, secondary structure, and enzymatic\nprocessing(ExtendedDataFigs.8A-B,9A-B)andpost-transcriptionalmodifications.\n20\n3. Impact of the Cellular Interior. The physical characteristics of the nucleoid and\nits energetic landscape should be discussed in relation to condensate formation. What\nis the energetic cost of creating a void within the nucleoid? How might the nucleoid’s\nproperties influence the transport and equilibration of nanostar species across the\ncell?\nWe thank the Reviewer for the insightful question. In the revised submission we\nevaluated the energy cost of forming a condensate in the dense nucleoid region. This\nmay provide a driving force for the migration of nascent condensates from the cells’\ncentre towards the poles, as observed in Extended Data Fig. 3A, and ultimately help\nexplain the overwhelming tendency of condensates to accumulate at the poles. The\nassociated discussion is included in the new Supplementary Note 1:\nSupplementary Note 1: Energetics of condensate formation within the\nnucleoid\nThebacterialnucleoidisadense,dynamic,andviscoelasticpolymericmeshcomposed\nprimarily of DNA and associated proteins [19]. It occupies the central region of the\ncell due to ongoing replication and cohesion of sister chromosomes at the terminus\nregion,whichspatiallyanchorsthenucleoidmid-cell[20].Formingacondensateinthe\ncentral region of the cell would create a void within the nucleoid, and thus have an\nassociated energetic cost. To estimate this energy, we start from the work of Pelletier\net al. [21]. In cells, the chromosome is compressed by the osmotic pressure exerted by\ncytosoliccrowdingagentsthatareexcludedfromthenucleoid.Toimitatetheeffectof\nintracellularcrowding,Pelletieretal.compressedtheE. coli nucleoidinamicrofluidic\nchannelusingabeaddrivenbyopticaltweezers,andshowedthenucleoid’smechanical\nresponse can be modelled as an entropic spring\nf\n(cid:18)\nR\n(cid:19) (cid:18)\nR\n(cid:19)−2\n= − ,\nA R R\n0 0\nwheref isthecompressionforce,A=−2.04pNisascalingconstant,R =10.1µmis\n0\ntheequilibriumlengthofthenucleoidinthemicrofluidicchannelofwidthw =1.6µm,\nand R is the compressed nucleoid length. Here, lengths refer to the spatial extent of\nthe nucleoid along the channel axis.\nThe compression pressure is thus given by\nf A\n(cid:34)(cid:18)\nR\n(cid:19) (cid:18)\nR\n(cid:19)−2 (cid:35)\nP = = − .\nw2 w2 R R\n0 0\nThe energy required to create a void of volume ∆V, given an initial nucleoid volume\nV, can be estimated as\n(cid:90) V+∆V (cid:90) V+∆V A (cid:34)(cid:18) R (cid:19) (cid:18) R (cid:19)−2 (cid:35)\n∆E = P dV = − dV.\nw2 R R\nV V 0 0\nchange, which allows us to simply express the energy cost of void formation as\n∆E ≈P ∆V.\nPelletier et al. report that that a force of 100 pN is required to compress the\nnucleoid to its in vivo size (R/R ≈ 0.1), resulting in P ≈ 80Jm−3 [21]. Using this\n0\nvalue, we can estimate the energy cost of forming a spherical void of 50 nm in diam-eter as ≈ 4.2×10−20 J, or ≈ 130 k T. This energy cost is roughly 500-fold higher\nB\nthan the hybridisation free energy of the KL domain (8.8×10−23 J, estimated using\nNupack [22]). This rough estimate suggests that the formation of nanostar clusters\nwithin the nuceloid region is strongly disfavoured, consistent with the observed,\noverwhelming tendency for pole accumulation.\nBeyond energetic constraints, it is also likely that the nucleoid presents a signif-icant steric barrier for transport of nanostars and nanostar clusters across the cell,\nas previously reported for protein aggregates [23]. Combined with the energy cost\nof deforming the nucleoid, these steric effects would make it unlikely for nanostar\nclusters formed in nucleoid-free regions to diffuse into the nucleoid.\nFinally, we note that cell crowding can be modulated, for instance by using sub-lethal concentrations of antibiotics [24]. Changing cell crowding should impact the\nmechanical characteristics of the nucleoid, which may in turn influence condensate\nlocalisation.\nIn the main text, we comment on these results and refer to the new Supplemen-tary Note 1:\n“Pole localisation is likely to result from steric hindrance by the nucleoid located\nin the central section of the cell [25]. To test this hypothesis, in Supplementary Note\n1, we use simple polymer physics arguments to estimate the energy cost of forming a\nsmall condensate (50 nm in diameter) within the nucleoid [21]. We find this penalty\nto be as high as ∼130 k T, equivalent to ∼ 500× the KL hybridisation free energy,\nB\nwhich justifies the exclusion of the condensates from the central segment of the\ncells. In addition to the effect of the nucleoid, anisotropic confinement has also been\nhypothesised as contributing to pole accumulation of aggregates [26].”\n“We propose that the rapid migration of mid-cell nascent condensates toward the\npoles is driven by the energetic cost associated with nucleoid deformation (Supple-mentary Note 1).”\n4.BiologicalImplicationsandFunctionalImpact.Whiletheintroductionhighlights\nthe design potential of nucleic acid nanotechnology, the manuscript would benefit\nfrom a brief but explicit discussion of potential biological functions that nanostar\ncondensates could modulate in vivo in the conclusion.\n“Beyond industrial applications, the synthetic RNA condensates may be used to\nmodulate biological functions in vivo by influencing gene regulation, buffering stress\nresponses, and generally mimicking native phase-separated assemblies. For example,\nthe condensates could be engineered to sequester transcriptional regulators or RNAs\nto alter gene expression profiles, protect key transcripts under stress, or serve as\nsynthetic analogues of bacterial RNA granules [27].”\nAdditionally, a brief comparison with other nucleic acid nanotechnologies and\ntheir in vivo applications would improve the potential impact.\nWe thank the Reviewer for the suggestion. In the revised main text, we have\nexpanded the conclusion to briefly discuss key functions demonstrating genetically\nencoded, and in-vivo expressed RNA nanostructures (scaffolds, RNA origami),\nwhich are the closest to the class of nucleic acid nanodevices of our condensate-forming\nnanostars:\n“These capabilities will add to the significant opportunities already offered by\ngenetically-encoded RNA nanostructures, which have been deployed to regulate tran-scription [28], translation [29], and metabolic pathways [30, 31].”\n5. Co-expressed Orthogonal Condensates. Figure 2Bii’s microscopy images are\nhard to interpret, especially in areas where cells are tightly packed, and they do not\nseem to reflect the diagram that shows one of each condensate at both poles.\nThank you for the feedback. We agree with the comments and have made several\nimprovements in the revised Fig. 2B, included below.\n1. Microscope images with fewer microcolonies have been selected to improve\nclarity, and included in Fig. 2B (i).\n2. The schematic illustration in Fig. 2B(ii) and 2D has been revised so that the\nnumber of condensates depicted now matches the measured average number of con-densates per cell (Fig. 2B (iii)).\n3. The average fluorescence intensity distribution heatmap (Fig. 2B (ii)) was\noriginally constructed by using cells in random orientation along the longitudinal\naxis. As a result, although most cells contained a single condensate at one pole,\nthe random orientation caused condensates to appear at both poles in the average\nimage - half at the top and half at the bottom- creating an impression of bipolar\ncondensate formation. We have now updated the protocol to align cells with their\nmost fluorescent pole oriented upwards, which led to a more accurate representation\nof the condensate localisation pattern. The new analysis protocol has been outlined\n23\nin the revised methods section:\n“For Fig. 2B(ii), cells were aligned with their most fluorescent pole oriented\nupwards. This was determined by the Pepper channel for cells co-expressing nanostar\nA and B/B¯, and by the Broccoli channel for cells co-expressing nanostar A¯ and B.”\nFor completeness, the heatmaps used for the initial submission, are retained as\nSupplementary Fig. 24, and mentioned in the main text:\n“Similar to single-NS systems, condensates are preferentially located towards the\npoles, as evident from both average fluorescence maps (Fig. 2B(ii), Supplementary\nFig. 24) and the locations of individual MLOs (Fig. 2C).”\n↵\nT7 RNAP\nB\n(i) Pepper Broccoli Merged (ii) A B (iii)\n100\n(ii) AB 50 Broccoli fluorescence\nAB\nE\nAB\nAB\nPepper Broccoli\n(normalised) Number of MLOs\n0 1 0 1 2 3\nsllec\nfo egatnecreP\n100\n50\nsllec\nfo\negatnecreP\n100\n50\nsllec\nfo\negatnecreP\n100\n50\nsllec\nfo\negatnecreP\nA B\nCAGCUG\nFigure 2. Co-expression of orthogonal, non-mixing RNA MLOs in E. coli.\n(A) Schematic of the co-expression of A and B NSs in E. coli. A and B motifs have\ndifferent kissing loop sequences and cannot interact by base-pairing, therefore form-ing separate MLOs in E. coli. Both NS sequences are cloned into a single plasmid for\nT7RNApolymerasetranscription,eachcontrolledbyitsownpromoterandadistinct\nterminator[32].(B)Co-expressionofNSpairsinE. coli.Toptobottom:A+B,A+B¯,\nA¯+B and A¯+B¯ NSs. (i) Confocal micrographs. Left: Pepper fluorescence (NSs A and\nA¯). Middle: Broccoli fluorescence (NSs B and B¯). Right: Merged Pepper fluorescence,\nBroccolifluorescence,andbright-field.Scalebars:10µm.(ii)Schematicrepresentation\n(left)andpopulation-averagedmapsofPepperfluorescencedistribution(middle),and\nBroccoli fluorescence distribution (right) in E. coli (Methods). (iii) Percentage of E.\ncoli containing 0, 1, 2 or 3 A or B MLOs per cell. (C) Scatter plot of the locations of\nMLOs in E. coli co-expressing both A and B NSs. Each symbol represents the posi-tion of a detected MLO (Methods). (D) Box plots of the values of kurtosis extracted\nfrom the pixel-value distribution of fluorescence intensity in single cells. Analysis is\nperformedon(i)Pepperand(ii)BroccolifluorescenceforcellsexpressingA+B,\nA¯+B,A+B¯ and A¯+B¯ NSs (left to right). Outcomes of statistical tests are provided in Sup-plementary Tables 6 and 7. (E) Box plots of the correlation coefficient (R2) values\ncomputed between Broccoli and Pepper fluorescent signals. Outcomes of statistical\ntests are provided in Supplementary Table 3. In D-E, each data point refers to an\nindividual E. coli. In B-E, data are obtained from 3 separate cultures, each starting\nfrom a single colony.\nA\nB\nB\nA\nB\nSupplementary Figure 24: Average fluorescence intensity heatmaps of cells\nco-expressing two NSs (A) Average heatmap generated from cells oriented ran-domly along the longitudinal axis. From left, cells co-expressing NSs A+B, A+B¯,\nA¯+B and A¯+B¯. (B) Average heatmap generated from cells co-expressing NSs A+B\naligned such that the pole with highest Broccoli fluorescence is positioned on the left.\nIn Fig. 2B(ii), maps were generated by aligning cells based on the pole with the high-est Pepper fluorescence. These alternative maps are shown here for completeness.\nand what calibration methods were used?\nBoth objectives used in this study, the Plan-Apochromat 63× 1.4 N.A. Oil DIC\nM27 (Zeiss LSM 800) and the CFI Plan Apo λD 60× 1.42 N.A. oil immersion (Nikon\nTi2 Eclipse), are apochromatic and designed to minimise chromatic aberration. As\nsuch, no additional calibration or correction was deemed necessary. Two fluorescent\nchannels were used in our experiments, one for the Pepper Aptamer (NS A) and one\nfor the Broccoli Aptamer (NS B) and GFP. The absence of significant chromatic\naberrations between these two channels is confirmed by the evidence of near-ideal\nco-localisation of GFP with A-type condensates, demonstrated in Fig. 3B(i) and 3E.\nThe main text has been revised to include this information:\n“Incidentally, the strong co-localisation between GFP and A confirms the lim-AP3\nited impact of potential chromatic aberrations between the red fluorescence channel\n(used for Pepper-labelled NSs) and the green channel (used for GFP and Broccoli-labelled NSs.)”\nExtended Figure 1B suggests possible mixing of condensate types. Is there direct\nevidence of mixing or interaction between droplet species in vivo?\nWe thank the Reviewer for pointing out this matter. This point was also raised\nby Reviewer 1 and has been addressed above; however, we include a response here for\nthe convenience of Reviewer 2.\nAs noted by the Reviewer, Extended Fig. 1B shows fluorescent signal from the\nBroccoli channel co-localised with A NS condensates in samples also containing\nsoluble B¯ NSs. This signal may arise either from weak partitioning of B¯ into A\ncondensates or from bleed-through of the Pepper (NS A) emission into the Broccoli\nchannel. To pinpoint the origin of the effect, in the revised submission we performed\nimage segmentation on micrographs of in vitro-expressed condensates, and present\nthe results in Supplementary Fig. 7.\nFirst,wequantifiedfluorescenceintheBroccolichannel(usedtoimageNSsBand\nB¯) in samples containing only the condensate-forming NS A, labelled with Pepper\nFLAPs (Supplementary Fig. 7A). The amount of bleed-through from the Pepper\nchannel into the Broccoli channel was minimal.\nWe then performed the same analysis on samples expressing A NSs together\nwith dispersed B¯ NSs and, unsurprisingly, observed substantially higher signal in the\nBroccoli channel (Supplementary Fig. 7B). As noted in our initial submission, the\nsignalwithinAcondensateswashigherthaninthesurroundingbackground,aneffect\ninitially ascribed to bleed-through.\nsignal measured within A condensates and that in the surrounding background.\nComparing A-only and A+B¯ samples, δ is substantially higher in the latter. Two\neffects could contribute to this: (i) bleed-through from the Pepper dye being stronger\nwithin A-type condensates than in the surrounding solution, and (ii) a difference in\nB¯ NS concentration between the A-type condensates and the background. If (i) were\nthe only effect—i.e. if the concentration of B¯ within A condensates were equal to that\nin the background—then similar δ values would be observed in both A and A+B¯\nsamples. Instead, the much greater δ in A+B¯ suggests that B¯ accumulates within A\ncondensates,causingtheincreasedBroccolisignal.Wesurmisethatthisaccumulation\narises from weak affinity between the scrambled KLs on B¯ and the A KLs.\nSuch non-specific affinity is not observed in condensate-forming B NSs. In A+B\nsamples, Broccoli fluorescence is higher in the background than in A condensates,\nconsistent with excluded volume effects that lower the concentration of the dispersed\nB phase within A condensates relative to the surrounding solution. The absence of\nA-B condensate adhesion further supports the lack of non-specific A-B affinity.\nIn vivo, we also observed B¯ accumulation within NS A condensates, as shown in\n(Fig. 2B, 2E, and Table 1), and discussed in the main text:\n“The soluble NSs may indeed weakly partition within, or coat the specifically-assembled MLO, or the condensates may serve as heterogeneous nucleation sites for\nnon-specific aggregation. Either process may be facilitated by weak affinity between\nthe soluble and sticky NSs due to base pairing, misfolding, enzymatic degradation, or\nthe action of native RNA-binding proteins.”\nIn the revised manuscript, we have incorporated this new in vitro analysis to\nfurther support partitioning of NS B¯ into NS A condensates.\n“The greater abundance of non-specific clusters seen when non-sticky NS are\nco-expressed with sticky motifs, particularly for A+B¯ expression, may be due to\nMLO-templated clustering of the otherwise soluble NSs. The soluble NSs may indeed\nweakly partition within, or coat the specifically-assembled MLO, or the condensates\nmay serve as heterogeneous nucleation sites for non-specific aggregation. Either pro-cess may be facilitated by weak affinity between the soluble and sticky NSs due to\nbase pairing, misfolding, enzymatic degradation, or the action of native RNA-binding\nproteins. This interpretation is supported by the evidence of weak partitioning of\nsoluble NS B¯ in NS A condensates observed in vitro (Extended Data Fig. 1), and\nquantified in Supplementary Fig. 7 (Methods).”\nWe have included a summary of the discussion provided here in the caption of the\nnew Supplementary Fig. 7.\n28\n“In E. coli producing A and B¯, a low R2 follows from the localised Pepper (NS\nA) signal in a diffused Broccoli (NS B) background. The higher R2 value found for\ncells expressing A+B¯ supports the hypothesis that B¯ may non-specifically partition\nwithin MLOs, consistent with observations made in vitro (Extended Data Fig. 1B,\nSupplementary Fig. 7).”\nA B C\nSupplementary Figure 7: Quantification of the Broccoli signal observed\nin A-type condensates. (A) Mean Broccoli fluorescence within and outside (bulk\nphase) NS A condensates in NS A-only samples. The Broccoli signal in the denser NS\nAcondensatesdiffersonlyslightlyfromthatinthebulkphase,indicatingthatfluores-cencebleed-throughfromthePepperchannelintotheBroccolichannelisminimal.(B)\nMean Broccoli fluorescence within and outside NS A condensates in A+B¯ and A+B\nsamples. In A+B¯ samples, Broccoli fluorescence is higher within NS A condensates\nthan in the bulk, whereas in A+B samples, fluorescence is higher in the bulk phase.\nThe latter observation is consistent with excluded volume effects that lower the con-centrationofNSBwithinAcondensatesrelativetothebulkphase.(C)Difference,δ,\nin mean Broccoli fluorescence intensity between NS A condensates and the surround-ing bulk phase in A-only and A+B¯ samples. The larger δ in A+B¯ samples suggests\naccumulation of NS B¯ within A condensates, likely due to weak affinity between the\nscrambled KLs of NS B¯ and the KLs of NS A.\nwhen coexpressed. What might explain this asymmetric distribution? Could slight\ncondensate mixing provide a nucleation point for B onto A?\nWe thank the reviewer for the insightful observation, which prompted us to anal-yse the localisation of A and B MLOs when co-expressed. The data shows that there\nisindeedanasymmetricdistribution,anditismorelikelyforAandBMLOstobeon\nthe same side of the cell. To estimate this asymmetry, we considered cells containing\nexactly one A-type and one B-type MLO, a sub-population accounting for 60% of all\nsegmentedcells.Inthissub-population,66%ofthecellshadbothoftheMLOsatone\npole, while the remaining 34% had the two MLOs at opposite poles. Cells displaying\na greater number of A and/or B condensates are less interesting to study, as they are\nbound to have A and B condensates co-localised in at least one of the poles.\nThe asymmetric distribution could originate from the division history of the cells,\nas newly formed poles are less likely to contain condensates or the nanostar-coding\nplasmid compared to the old poles. We plan to focus on quantifying and gaining a\nmechanistic insight on condensate redistribution upon division in subsequent studies.\nNon-specific affinity between different nanostar types could also favour condensate\nformation on the same pole although, as discussed in the previous point, this appears\nto occur primarily between soluble B¯ NS and condensate-forming A, and not between\nA and B NSs.\nWe have made multiple changes to the manuscript based on the analysis. Firstly,\nExtended Data Figure 10 has been revised to include cells with condensates on oppo-site poles and on same poles, better reflecting the observed phenotypes. Second, we\nhave expanded main text to discuss this observation:\n“Of the cells containing one A-type and B-type MLO (60% of all cells co-expressing NSs A and B), 66% had both MLOs positioned at the same pole. This\nasymmetric distribution may stem from the cells’ division history, as newly formed\npoles are less likely to harbour condensates. Alternatively, or in addition, it may be\ndriven by weak, non-specific affinity between A and B condensates. We also observe\na clear asymmetry between the exact positioning of A and B condensates: the former\nare nearly always in contact with a pole, while the latter may occupy more central\nlocations (Extended Data Fig. 2).”\n30\nExtended Data Figure 10: Zoomed in micrographs of cells co-expressing\nNSs A and B. Zoomed in confocal micrographs recorded in the Pepper channel (NS\nA, left), the Broccoli channel (NS B, centre) and merging both fluorescence channels\nwith bright-field images (right) of E. coli co-expressing NS A and NS B, 1 h after\nIPTG induction. Scale bars, 2 µm.\n31\n6. Temperature Dependence of Condensates. In Figure 4Ai, the uniform RNA\ncontrol appears brighter than condensate-containing cells after normalisation.\nSince normalisation by maximum intensity varies across cells, this may be misleading;\nraw intensity comparisons would be more informative.\nThe Reviewer has raised a good suggestion to improve data visualisation. We\nhave revised the figure such that now all images now share the same contrast scale.\nIntriguingly,the condensatesbecome brighterafter thetemperature ramp,suggesting\nthatheatingmighthaveincreasedthedyepenetrationintothecell.Wehavetherefore\nincluded a comment on the increase in fluorescence in the main text:\n“For both A and B NSs, condensate fluorescence intensity appears to increase\nafter the heating and cooling ramps (Fig. 4A(i), B(i)), which may be attributed to\nthermally induced alterations in membrane permeability, facilitating greater intracel-lular accumulation of FLAP dyes.”\n32\nreppeP\ndegreM\n37 oC 70 oC 37 oC\nPFG\nA (iii) C\n49 52 55 58 61 58 55 52 49\n(i) (i)\n(ii) (iv)\nB\n(iii)\n46 49 52 55 58 55 52 49 46\n(i)\n(ii) (iv) (ii)\nreppeP\nPFG\n(normalised)\n0 1\nFig. 4: Synthetic RNA MLOs reversibly disassemble and reassemble in E.\ncoli upon temperature cycling. (A) Reversible disassembly and reassembly of A\nMLOs. (i) Merged epifluorescence and bright-field micrographs showing disassembly\nand reassembly of A MLOs in a single cell. Scale bars, 1 µm. (ii) Population-averaged\nmapsofPepper(NS)fluorescenceasafunctionoftemperatureuponheatingandsub-sequent cooling. (iii) Cell-population distribution of the values of skewness computed\nfor the fluorescence-intensity distributions of Pepper signal in individual E. coli on\nheatingandcooling.(iv)PercentageofsinglecellscontainingMLOsasextractedfrom\nthe skewness distributions (Methods). (B) Reversible disassembly and reassembly of\nBMLOs.DataarepresentedasinpanelA,usingBroccolifluorescencetoimageNSs.\n(C) Disassembly and reassembly of A MLOs with captured GFP.\n(i) Confocal\nAP3\nmicrographsofsamplesat37◦C(left),afterheatingto70◦C(middle),andaftercool-ing back to 37◦C (right). Top: Pepper (NS) fluorescence; middle: GFP fluorescence;\nbottom:mergedPepperfluorescence,GFPfluorescence,andbright-field.Scalebars,10\nµm.(ii)Population-averagedmapsofPepper(top)andGFP(bottom)fluorescenceat\nthe temperatures shown in (i). In A–C, fluorescence maps and quantitative analyses\nare based on E. coli from 3 independent cultures, each starting from a single colony.\nWe thank the Reviewer for the interesting question. In our initially submitted\nmanuscript,wereferencedmeasurementsmadebyFabrinietal[2]ofthemeltingtem-perature of similar NS designs. Here, we have implemented design changes compared\nto Fabrini et al., which may impact the melting temperature. Hence, in the revised\nsubmission, we included new measurements to determine the melting temperatures\nof in vitro NS A and NS B condensates (Methods). Following Ref. [33], the melting\ntemperature was determined using a thermal microscope stage, monitoring the sam-ples via epifluorescence imaging during a heating ramp, followed by a cooling ramp,\nas detailed in the updated Methods section. We then analysed the epifluorescence\nframes by computing the standard deviation of the pixel values in the relevant fluo-rescent channel, σ, and the mean pixel intensity I. The quantity σ/I, plotted against\nT, displays a sharp transition when condensates form or melt, which can be used\nto determine the melting (on heating, T ) and condensate-formation (on cooling,\nm\nT ) temperatures [33]. Results are collated in new Supplementary Fig. 36, with Sup-f\nplementary Figs. 37 and 38 containing relevant epifluorescence snapshots. The new\nfigures are included at the end of this document for quick reference. As expected, and\npreviously observed with similar DNA condensates [33], for both A and B T -values\nm\nare slightly higher compared to T . This is due to condensate assembly timescales\nf\nbeing slower than those of condensate melting, and possible small delays in heat\npropagation through the sample cell. We additionally note that T values are slightly\nm\nhigher for NS B compared to NS A, 40.9◦C vs 39.9◦C. This trend is consistent with\nthe observations of Fabrini et al. [2], with the small absolute differences likely due to\ndesign changes and differences in the IVT buffer and assembly protocol used (that\nmay impact NS concentration and KL-KL affinity). More remarkably, as already\nnoted in the initial submission, the in-vitro melting temperatures are substantially\nlower compared to those measured in vivo (Fig. 4), likely due to a combination of\ndifferent ionic conditions, and higher molecular crowding.\nThe main text was revised to describe and discuss the new data:\n“Since the NSs self-assemble through base-pairing interactions, we anticipated\nthat the MLOs would dissolve at sufficiently high temperature, as observed in vitro\n(Supplementary Figs. 36, 37, 38). Consistent with this expectation, heating E. coli\ncontaining A-MLOs led to their dissolution at 61◦C.”\n“The difference in melting temperature between the two constructs is unlikely to\nresult from different KL affinity, having observed that NSs with KL A disassemble at\nslightly lower temperature (39.9◦C) compared to those with KL B in vitro (40.9◦C,\nSupplementary Figs. 36, 37, 38).”\n“ForbothAandBNSs,condensatefluorescenceintensityappearstoincreaseafter\nthe heating and cooling ramps (Fig. 4A(i), B(i)), which may be attributed to ther-mally induced alterations in membrane permeability, facilitating greater intracellular\n34\naccumulation of FLAP dyes. We further note that melting temperatures measured in\nvivo are substantially higher than those we observe in vitro, where the MLOs melted\nat 39.9-40.9◦C (Supplementary Fig. 36, 37, 38, 43). This deviation likely originates\nfrom intracellular molecular crowding promoting condensation [34] or difference in\nionic conditions between the in vitro transcription buffer and the cytoplasm.”\n35\nI/\nSupplementary Figure 36: Thermal transition curves of A and B conden-sates expressed in vitro. The thermal transition curves of NS A condensates are\nshown in orange, and those of NS B condensates in cyan. Solid curves correspond-ing to the heating ramp were used to estimate the melting temperatures (T ), while\nm\ndashedcurvescorrespondingtothecoolingrampwereusedtoestimatethecondensate-formation temperatures (T ). Data show the ratio between the standard deviation of\nf\nthepixelvalues(σ)andthemeanpixelintensity(I)oftheepifluorescencemicrographs.\nThe quantity σ/I, plotted against T displays a sharp transition when condensates\nmelt or form, which can be used to estimate T and T . These were computed from\nm f\nthe intersections of the relevant curve with a threshold value in σ/I, defined as the\nhigh-temperature plateau of the data (mean computed for T >41◦C) augmented by\n0.05units(Methods).ForbothAandB,T values(39.9◦Cand40.9◦C,respectively)\nm\nare slightly higher than their corresponding T values (36.9◦C and 38.6◦C, respec-tively),\nreflectingtheslowertimescalesofcondensateassemblycomparedtocondensate\nmelting. Notably, the calculated T and T values are substantially lower than those\nm f\nobserved in vivo, likely due to a combination of different ionic conditions and molec-ular crowding. Samples were initially incubated at 30◦C for 15 hours, after which the\ntemperature was gradually changed from 30◦C to 50◦C and back to 30◦C, with 1◦C\nsteps introduced every 5 minutes.\n30°C 37 °C 44 °C\n36 °C 43 °C 50 °C\nSupplementary Figure 37: Epifluorescence micrographs of NS A (left) and\nNS B (right) condensate melting upon increasing temperature. After the\ninitial 15 hour incubation at 30◦C, the temperature was gradually increased from\n30◦C to 50◦C in 1◦C steps introduced every 5 minutes, leading to NS condensate\nmelting. Scale bars, 100 µm.\n50 °C 43 °C 36 °C\n44 °C 37 °C 30°C\nSupplementary Figure 38: Epifluorescence micrographs of NS A (left) and\nNS B (right) condensate formation upon cooling. The temperature was grad-ually decreased from 50◦C to 30◦C in 1◦C steps introduced every 5 minutes, leading\nto NS condensate-formation. Scale bars, 100 µm.\nintensity?Giventheoreticalpredictions,highernanostarconcentrationshouldincrease\nthe melting temperature until the critical point, after which it declines. Exploring this\nrelationship experimentally would enhance the thermodynamic analysis.\nWe thank the Reviewer for the insightful suggestion. To address this, we ana-lyzed the temperature-dependent data for the skewness of the fluorescence-intensity\ndistributions recorded in single cells, which we used to determine the population dis-tributions in Fig. 4A(iii) and B(iii). This produced skewness–temperature curves that\ncould, potentially, be used to extract condensate T for single cells. For each curve\nm\nthat showed a skewness above 0.25 at the initial temperature of 37◦C (indicating\nthe presence of condensates), T was estimated as the inflection point of a sigmoid\nm\nfit (Supplementary Fig. 42 Left, included below). We then produced a scatter plot\nof T values against the integrated fluorescence at 37◦C, for each cell (Supplemen-tary Fig. 42 Right). Linear fits of the scatter plots yielded a very low coefficient of\ndetermination (R2 < 0.2), indicating no correlation between fluorescence intensity\nand melting temperature.\nHowever, we point out that several sources of error or uncertainty are likely to\nimpact these data. First, for NS A, skewness-temperature curves did not reach their\nhigh-temperature plateau at the maximum temperature we probed (61 ◦C), making\nthe sigmoid fit imprecise. Furthermore, for both NS types, the reliability of the sig-moid fit is limited by the number of temperatures probed and the intrinsic noise of\nthe measurements. We have included a summary of the discussion in the caption of\nthe new Supplementary Fig. 42, included below for quick reference:\nThe main text was revised to discuss the new data:\n“Single-cell skewness-temperature curves were used to estimate the melting\ntemperature for each cell. Linear regression between the melting temperature and\ncellular fluorescence gave low coefficients of determination (R2 < 0.2), indicating no\ndetectable correlation between NS concentration and melting temperature (Supple-mentary Fig. 42).”\n39\n3\n2\n1\n0\n−1\n40 45 50 55\nTemperature (°C)\nssenwekS\n0.2\n0.1\n40 45 50 55\nMelting temperature (°C)\nC°\n73\nta\necnecseroulf\ndetargetnI\n0.2\n0.1\n0.0\n40 45 50 55\nMelting temperature (°C)\nC°\n73\nta\necnecseroulf\ndetargetnI\n3\n2\n1\n0\n40 45 50 55 60\nTemperature (°C)\nssenwekS\nSupplementary Figure 42: Analysis of the relationship between fluores-cence intensity and melting temperature. Each row contains data from one\nbiologicalreplicateofE. coli expressingNSA(top)orNSB(bottom).Theleftpanels\nshow single-cell skewness as a function of temperature, with the melting temperature\ndeterminedbyfittingasigmoidcurve(indicatedbydots).Therightpanelsdisplaythe\nrelationship between the melting temperature and the integrated cellular fluorescence\natthestartingtemperatureof37◦C.Linearfitswereusedtocalculatethecoefficients\nof determination (R2) for NS A (R2 =0.00) and NS B (R2 =0.18). We note that, for\nNS A, skewness-temperature curves did not reach their high-temperature plateau at\nthe maximum temperature we probed (61 ◦C), making the sigmoid fit imprecise. Fur-thermore,forbothNStypes,thereliabilityofthesigmoidfitislimitedbythenumber\nof temperatures probed and the intrinsic noise of the measurements.\nThe readme file has been updated.\n[1] Wesselhoeft, R. A., Kowalski, P. S. & Anderson, D. G. Engineering circular rna\nfor potent and stable translation in eukaryotic cells. 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Organizationofintracellular\nreactionswithrationallydesignedRNAassemblies. Science 333,470–474(2011).\n[31] Sachdeva, G., Garg, A., Godding, D., Way, J. C. & Silver, P. A. Nucleic Acids\nResearch 42, 9493–9503 (2014).\n[32] Calvopina-Chavez, D. G., Gardner, M. A. & Griffitts, J. S. Engineering effi-cient termination of bacteriophage T7 RNA polymerase transcription. G3\nGenes—Genomes—Genetics 12, jkac070 (2022).\n[33] Tanase, D. A. et al. Internal phase separation in synthetic dna condensates.\nAdvanced Science 12, e06275 (2025).\n[34] Singh, A., Maity, A. & Singh, N. Structure and Dynamics of dsDNA in Cell-like\nEnvironments. Entropy 24, 1587 (2022).\nPoints raised by the Reviewers are highlighted in blue, while our responses are presented in black.\nREVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nGreat effort during the revisions. This is a great study and is ready for publication.\nWe sincerely thank the Reviewer for their positive assessment and for the constructive suggestions\nprovided during the earlier stages, which helped us strengthen the manuscript.\nReviewer #2 (Remarks to the Author):\nI appreciate the authors' detailed and thorough response, which included substantial new\nexperimental work, data analysis, and writing that significantly enhance the quality of the\nmanuscript. My concerns have been addressed, and I support publication.\nWe are grateful to the Reviewer for their careful evaluation and for encouraging us to expand and\nrefine the manuscript. We are pleased that the additional experiments and analyses have resolved\ntheir concerns and that they now support publication."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Great effort during the revisions. This is a great study and is ready for publication.", "reviewer_2": "I appreciate the authors' detailed and thorough response, which included substantial new experimental work, data analysis, and writing that significantly enhance the quality of the manuscript. My concerns have been addressed, and I support publication."}, "author_response": "Response to reviewersʼ comments (Round 2)\nPoints raised by the Reviewers are highlighted in blue, while our responses are presented in black.\nREVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nGreat effort during the revisions. This is a great study and is ready for publication.\nWe sincerely thank the Reviewer for their positive assessment and for the constructive suggestions\nprovided during the earlier stages, which helped us strengthen the manuscript.\nReviewer #2 (Remarks to the Author):\nI appreciate the authors' detailed and thorough response, which included substantial new\nexperimental work, data analysis, and writing that significantly enhance the quality of the\nmanuscript. My concerns have been addressed, and I support publication.\nWe are grateful to the Reviewer for their careful evaluation and for encouraging us to expand and\nrefine the manuscript. We are pleased that the additional experiments and analyses have resolved\ntheir concerns and that they now support publication."}]

Decision letter