Osmotic pressure regulates DNA labelling and transcription with dCas9-SunTag system in live cells

Domain: physics
Published: 2025-11-26
Editorial outcome: major_revision

Abstract
Extracellular osmotic pressure is a key modulator of intracellular biophysical properties and cellular functions. However, its impact on the cell nucleus remains elusive, largely due to the challenges in real-time measurement of local environmental properties and reaction kinetics at specific loci within the nucleus. Here, we employ the dCas9-SunTag system to investigate the biophysical response at target DNA loci to osmotic pressure alterations. We reveal that variations in extracellular osmotic pressure modulate the efficiency of dCas9-SunTag-mediated fluorescent labelling rapidly and reversibly, with hypoosmotic condition increasing and hyperosmotic condition decreasing the number and fluorescence intensity of foci for telomeres and genes. Strikingly, osmotic pressure also regulates gene transcription with the dCas9-SunTag system, mirroring its effects on fluorescent labelling, as evidenced by changes in mRNA burst frequency. The underlying mechanism is that osmotic pressure shifts the binding-unbinding equilibrium of specific proteins to dCas9-SunTag complex by altering intranuclear crowding. These findings not only highlight the role of mechanical cues in modulating DNA-related processes within the nucleus, but also establish the dCas9-SunTag system as a sensitive probe for intranuclear crowding in response to extracellular cues, notably osmotic pressure.

Introduction
Cells are continually subjected to fluctuations of extracellular environment in vivo, which significantly impact intracellular biophysical properties, thereby influencing cellular functions1–4. Among diverse extracellular environmental factors, osmotic pressure is of great importance involved in such fundamental cellular behaviours as cell migration2,5, mitosis3 and differentiation4, as well as in tissue homeostasis and development6,7. Previous biophysical studies have revealed that the osmotic pressure impacts intracellular properties in various aspects, including the modification of plasma membrane tension as well as lipid liquid-liquid phase separations8, the rearrangement of cytoskeletal networks and their elastic mechanics9, the alternation of cell volume and morphology10, and the modulation of intracellular molecular crowding and diffusion11–13. While the osmotic pressure has been extensively studied for its influence on the intracellular properties, its effect on the cell nucleus remains largely unclear, which is particularly significant as the nucleus is the central location for DNA-related functions14.

Alterations of extracellular osmotic pressure are known to modulate nuclear volume15–17 and intranuclear structural features18–20, and are implicated in DNA repair21, chromosome replication22 and gene expression9,23–25. The mechanism of osmotic pressure impacting DNA-related functions is proposed to involve changes in chromatin architecture and nuclear compartmentalisation due to altered intranuclear molecular crowding and induced phase separation18–20. Additionally, osmotic pressure may modulate crowding and thereby influence molecular diffusion dynamics and reaction kinetics19,26–29, directly affecting DNA-related processes. However, understanding the precise mechanisms by which osmotic pressure affects these processes within complicated nuclear environment remains elusive, primarily due to the challenge in real-time measurement of local environmental properties and reaction kinetics at specific genomic loci.

To address this gap, a sensitive method that probes molecular interactions at specific DNA sequences is essential. The CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR associated) system is renowned for its ability to target specific DNA sequences with high precision30. By integrating the SunTag31, which presents multiple binding sites for various proteins, including fluorescence proteins (FPs) and transcription activation domains (ADs), we may be able to translate the effect of intranuclear environmental factors, such as molecular crowding, into measurable fluorescent signals or transcriptional responses. This combined system hold promise as a potential tool to assess how osmotic pressure influences specific DNA sites and their associated gene functions. Yet, the precise response of this system to intranuclear environment and osmotic pressure variations has not been characterised.

Here, we have investigated the impact of extracellular osmotic pressure on the dCas9-SunTag system in live cells, particularly its capacity in DNA labelling and gene transcription. We discover that the extracellular osmotic pressure variations modulate the efficiency of dCas9-SunTag-mediated fluorescent labelling in a rapid, reversible and repeatable fashion, with hypoosmotic condition raising and hyperosmotic condition diminishing the number and fluorescence intensity of foci, for both telomeres and protein-coding genes. Our experimental research combined with theoretical analysis suggests that osmotic pressure shifts the binding-unbinding equilibrium between fluorescence proteins and dCas9-SunTag complex, by altering intranuclear local crowding. More importantly, we find that osmotic pressure also regulates gene transcription with the dCas9-SunTag system in a manner that parallels its effects on fluorescent labelling. This is further supported by observations of the changes in the mRNA burst frequency under varying osmotic conditions, consistent with our ensemble-level measurements. Our results not only underscore the crucial role of osmotic pressure in regulating the dCas9-SunTag system for DNA labelling and gene expression, and but also highlight how mechanical cues influence intranuclear biological processes. Furthermore, the dCas9-SunTag system serves as a sensitive probe for investigating intranuclear crowding at specific genomic loci, offering insights into cellular responses to complex extracellular stimuli.

Results
Osmotic pressure regulates fluorescent labelling of target DNA with dCas9-SunTag system
The dCas9-SunTag system is a versatile platform for target DNA labelling and gene expression31. As illustrated in Fig. 1a (left), the SunTag consists of an array of repetitive peptide sequence (GCN4 peptide epitope), to which the cognate GCN4 single-chain variable fragment (scFv) antibodies can be attached. The SunTag system can recruit multiple copies of a protein that is fused to the scFv antibody, e.g. fluorescence protein or transcription activation domain, to achieve signal amplification. The complex of the dCas9 protein and the SunTag (dCas9-SunTag) is directed by a single guide RNA (sgRNA) to the target DNA locus with the complementary sequence for imaging or transcription regulation.

We first study the effect of extracellular osmotic pressure on the telomere fluorescence labelling with the dCas9-SunTag system in live cells. A bright green fluorescence protein fused with scFv, scFv-mNeonGreen32, is expressed in cells as the binding protein to SunTag. Under confocal laser scanning microscopy, we observe fluorescent spots in cell nuclei, as expected. The identity of these spots as telomere loci is confirmed through fluorescence in situ hybridisation (FISH) assays using a Cy5-tagged telomere probe32 (Supplementary Fig. 1). To decrease or increase the extracellular osmotic pressure, we add water or 200-Da polyethylene glycol (PEG) to cell culture medium, respectively4,10,16, with the actual extracellular osmotic pressure being calculated (Supplementary Fig. 2). Interestingly, as the hypoosmotic pressure is exerted by adding 30% water, both the number and the fluorescence intensity of telomere foci in a cell increase apparently (Fig. 1b, c). In contrast, the hyperosmotic pressure by the addition of 10% PEG decreases the number and the fluorescence intensity of telomere foci significantly (Fig. 1b, c).

Our results show that the telomere labelling by the dCas9-SunTag system is changed with the extracellular osmotic pressure, yet it could be resulted from two ways: the quantity of binding proteins on dCas9-SunTag, and the quantity of dCas9-SunTag on telomere. We thus repeat experiments on the telomere labelling with the dCas9 directly fused with a fluorescence protein33 for comparison. The results show that the number and the fluorescence intensity of telomere foci in a cell almost remain constant in this case (Supplementary Fig. 3), indicating that the extracellular osmotic pressure has a negligible influence on the binding between dCas9 and telomeres, but plays a critical role in the binding of fluorescence proteins to dCas9-SunTag. It also verifies our assumption that the dCas9-SunTag system is sensitive to the change of osmotic pressure.

Given the dynamic nature of extracellular osmotic pressure, which can fluctuate and return to isotonic conditions, we seek to determine if these variations are mirrored in the fluorescent labelling of telomeres. Notably, when the osmotic pressure returns to its initial isotonic state, the telomere foci, as indicated by fluorescence intensity and number, also revert to their initial levels (Fig. 1b, c). Noted that despite a slight decrease in the number and total intensity of telomere foci due to photobleaching, the effect of osmotic pressure remains clear and significant (Supplementary Fig. 4). Encouraged by the reversible response, we proceed to investigate the cyclic stability of this response. Cells are subjected to a non-isotonic medium for 15 min, followed by a recovery period in isotonic medium for 30 min, with this treatment regimen repeated three times. Throughout these cycles, the number and the fluorescence intensity of telomere foci exhibit a consistent and reversible response (Supplementary Fig. 5). These results not only confirm the reversibility of fluorescent labelling changes induced by osmotic pressure alterations but also establish the repeatability of these responses under controlled perturbations.

To determine the sensitivity of telomere fluorescence labelling to osmotic pressure, we vary extracellular osmolarity using water or PEG of different concentrations. We observe that nuclear volume changes inversely with extracellular osmotic pressures (Fig. 1d), confirming the presence of a gradual osmotic gradient. Our statistical analysis indicates that the telomere fluorescent labelling efficiency is inversely related to external osmotic pressure within a certain range (Fig. 1d). We notice that both the number and the intensity of telomere foci labelled with dCas9-SunTag-FP vary among cells under isotonic condition, reflecting cell-to-cell heterogeneity. We confirm that such variations do not affect the effect of osmotic pressure on telomere labelling, as the results remain consistent across cells with varying initial (Supplementary Fig. 6).

Having established the influence of extracellular osmotic pressure at telomeres, which are highly repetitive and chromosomally distal31–34, we seek to assess whether the response of dCas9-SunTag system to extracellular osmotic pressure is consistent with other genomic regions, especially protein-coding genes that contain low-repeat sequences. We choose MUC4 gene, an epithelial mucin gene with low-repeat characteristics35. Consistent with the telomere labelling results, the MUC4 gene labelling efficiency is elevated under hypoosmotic condition and reduced under hyperosmotic condition (Supplementary Fig. 7). We further confirm that the relationship between MUC4 labelling efficiency and osmotic pressure remains reliable, unaffected by the initial fluorescence intensity of foci (Supplementary Fig. 8). These results imply that the DNA labelling with the dCas9-SunTag system in response to osmotic pressure may not be dependent on the specific DNA sequence locus, indicating a potentially universal effect across various genomic elements.

Osmotic pressure modulates binding–unbinding equilibrium of fluorescence proteins to dCas9-SunTag
Our observation that osmotic pressure can dynamically modulate the fluorescent labelling of target DNA with the dCas9-SunTag system. We explore the underlying mechanism of this modulation. We first perform fluorescence recovery after photobleaching (FRAP) experiments focusing on the telomere foci labelled with the dCas9-SunTag system, to examine whether these interactions are indeed dynamic. After photobleaching, the telomere foci reappear within half a minute, while FRAP experiments conducted in areas lacking telomere foci show a much quicker recovery of fluorescence intensity within a few seconds (Fig. 2a, b). This disparity suggests that the binding of fluorescence proteins to dCas9-SunTag is markedly slower compared to the diffusion of unbound fluorescence proteins. Furthermore, telomere spots labelled with dCas9-FP show no recovery within the duration of the FRAP experiments (Fig. 2a, b, and Supplementary Fig. 9). Collectively, these FRAP findings suggest the highly dynamic equilibrium of fluorescence proteins binding and unbinding to dCas9-SunTag, in contrast to a relatively stable interaction between telomeres and dCas9. This implies that the interaction between fluorescence proteins and dCas9-SunTag is more sensitive to environmental perturbations.

Then, we reason that the osmotic shock could disrupt the equilibrium between the binding and the unbinding of fluorescence proteins to the dCas9-SunTag. Our hypothesis is that hypoosmotic shock favours binding over unbinding, leading to macroscopic fluorescence condensation, whereas hyperosmotic shock promotes the opposite effect, resulting in fluorescence dispersion. To explore this, we meticulously monitor the telomere foci upon osmotic shock, to directly quantify the real-time effect of osmotic pressure on the binding dynamics of fluorescence proteins to dCas9-SunTag. Upon hypoosmotic shock, existing telomere foci intensify gradually and new foci may emerge, while hyperosmotic shock leads to a dimming and potential disappearance of these telomere foci (Fig. 2c, d, and Supplementary Figs. 10, 11).

To further understand the condensation or dispersion of fluorescence proteins, we develop a physical model to describe these processes. Hypothesised for simplicity that the diffusion of fluorescence proteins and the binding or unbinding of them to dCas9-SunTag are uncoupled, the spatiotemporal evolution of fluorescence proteins is depicted as:

\partial c_m(r,t) / \partial t = D \nabla^2 c_m(r,t) - k_b c_m(r,t) + k_u c_f(r,t),
\partial c_f(r,t) / \partial t = k_b c_m(r,t) - k_u c_f(r,t),

where r is the position vector, t is the time, c_m is the concentration of mobile fluorescence protein, c_f is the concentration of fixed fluorescence protein which is bound to the dCas9-SunTag on a target locus, D is the diffusion coefficient of mobile fluorescence protein, k_b is the binding rate and k_u is the unbinding rate. Noted that in alive cell, the movement of fluorescence proteins is enhanced by active fluctuations36. Thus, the parameter D is interpreted as the apparent diffusion coefficient which integrate the effect of active fluctuations. In an equilibrium state under isotonic condition, k_{bi} c_m - k_{ui} c_f = 0 (the subscript ‘i’ refers to ‘isotonic’).

Our experiment results reveal three key insights: 1) diffusion is significantly faster than binding-unbinding dynamics, 2) the fluorescence intensity of a target focus approximates a Gaussian distribution, and 3) k_b = k_{bi} + \Delta k_b and \Delta k_b c_m \gg |k_{bi} c_m - k_{ui} c_f| upon hypoosmotic shock, while k_u = k_{ui} + \Delta k_u and \Delta k_u c_f \gg |k_{bi} c_m - k_{ui} c_f| upon hyperosmotic shock. \Delta k_b is the apparent binding rate and \Delta k_u is the apparent unbinding rate, reflecting the effective rate of reaction as it transitions from one equilibrium state to another due to environmental changes. Based on these premises, we derive a simplified solution to Equation (1), which effectively interprets our experimental outcomes. The fluorescence intensity of a bright target focus with background subtraction is

I(r, \Delta t) = I_0 e^{-r^2 / 2R_0^2} + I_{m0} (1 - e^{-\Delta k_b \Delta t}), \Delta k_b c_m \gg |k_{bi} c_m - k_{ui} c_f|,
I(r, \Delta t) = I_0 e^{-r^2 / 2R_0^2} e^{-\Delta k_u \Delta t}, \Delta k_u c_f \gg |k_{bi} c_m - k_{ui} c_f|,

where r is the distance from a focus centre, \Delta t is the relative time, I_0 represents the fluorescence intensity at r=0 and \Delta t=0, R_0 represents the standard deviation of the fluorescence radial distribution at \Delta t=0, I_{m0} represents the intensity corresponding to mobile fluorescence proteins at \Delta t=0, and v is the volume ratio of target spot to nucleus.

We then employ Eq. (2) to fit the experimental fluorescence distribution of foci within cells, as depicted in Supplementary Fig. 12. This approach allows us to determine the apparent binding rate \Delta k_b during hypoosmotic shock and the apparent unbinding rate \Delta k_u during hyperosmotic shock. The apparent binding rate increases as osmotic pressure decreases (Fig. 2e), and the apparent unbinding rate correlates positively with osmotic pressure (Fig. 2f), in agreement with the observation. The close alignment between our experiment data and our theoretic model indicates that the macroscopic changes in telomere focus fluorescence are probably attributed to the microscopic perturbation of the binding-unbinding equilibrium between fluorescence proteins and dCas9-SunTag in response to osmotic shock. This suggests that osmotic pressure may modulate the availability of binding sites on the SunTag, likely by affecting intracellular molecular crowding37. Our simplified model also underscores the persistence of fundamental physical and chemical principles in the complicated nuclear environment.

Osmotic pressure alters intranuclear dynamics and crowding to modulate binding–unbinding equilibrium of fluorescence proteins to dCas9-SunTag
Next, we seek to elucidate the mechanism underlying the effect of extracellular osmotic pressure on the intranuclear binding-unbinding equilibrium. We hypothesise that changes in osmotic pressure would alter the intranuclear molecular crowding, which in turn would affect the interactions between fluorescent proteins and dCas9-SunTag. Our observations of nuclear volume alterations under various osmotic conditions has supported this (Fig. 1d), as nuclear volume is a reflection of the overall intranuclear molecular crowding, given that the amount of intranuclear macromolecules remains relatively constant over a short period of osmotic shock15,17,38,39. Yet considering the heterogeneity of the complicated intranuclear environment, we need to assess the effect of extracellular osmotic pressure on intranuclear molecular crowding.

Recognising that molecular crowding can influence chromatin dynamics, we observe the motion of chromatin in the nucleus as an indicator. Using particle image velocimetry (PIV) algorithms40 to analyse the videos of nucleus with H2B-mCherry expression at a 1-s time interval, we map the velocity of chromatin motion within the nucleus (Fig. 3a). Our findings reveal that hypoosmotic pressure enhances the overall motion of chromatin, suggesting a reduction in global crowding within the nucleus, while hyperosmotic pressure has the opposite effect (Fig. 3a, b).

After establishing the influence of extracellular osmotic pressure on overall chromatin dynamics, we further investigate if the dynamics of DNA loci is altered by the local crowding variations. We employ single-particle tracking methods41,42 to analyse the movement of telomeres labelled with dCas9-SunTag system (Fig. 3c). Our analysis reveals that telomere trajectories in a cell under hypoosmotic pressure exhibit reduced confinement and increased mobility, compared to their initial state, while hyperosmotic pressure restricts their movement (Fig. 3d). The histograms of telomere displacement from the mean position show a broader spread under hypoosmotic pressure, indicating less local crowding, and a narrower distribution under hyperosmotic pressure, suggesting increased local crowding (Supplementary Fig. 13). The mean squared displacement (MSD) curves further substantiate these findings, demonstrating an increase in telomere motion under hypoosmotic pressure and a decrease under hyperosmotic pressure (Fig. 3e). Collectively, these results indicate that hypoosmotic pressure promotes motion of telomeres, possibly due to reduced local crowding, while hyperosmotic pressure constrains telomere movement, likely due to enhanced local crowding.

To quantify the correlation between local crowding and fluorescent labelling with the dCas9-SunTag system, we calculate the diffusion exponent \alpha for each telomere trajectory from MSD curves, which characterises the motion type and is correlated with the crowding41,42. We observe a positive correlation between \alpha and the fluorescence intensity of a telomere focus under isotonic condition (Fig. 3f), suggesting that less crowded loci tend to bind more fluorescence proteins. More importantly, we find that this positive correlation is maintained under non-isotonic conditions (Fig. 3f), with both the average \alpha and the total fluorescence intensity of telomere foci in a cell synchronously increasing under hypoosmotic pressure and decreasing under hyperosmotic pressure (Fig. 3g). Collectively, our experimental results of intranuclear dynamics support the notion that osmotic pressure regulates the binding of fluorescence proteins to dCas9-SunTag by altering intranuclear local crowding.

Given that chromatin is the main component of intranuclear volume43, intranuclear molecular crowding is largely attributed to chromatin density. To assess the chromatin density, we utilise H2B fluorescently labelled with mCherry as a proxy. Our observations reveal that hypoosmotic pressure leads to a decrease in H2B-mCherry signal intensity coincident with an increase in telomere-associated fluorescence intensity at corresponding loci, while hyperosmotic pressure yields the converse results (Fig. 3h, i). These findings suggest that the local modulation of chromatin crowding around telomeres plays a role in the observed variations in telomere focus fluorescence intensity labelled with dCas9-SunTag system within the nucleus.

Furthermore, we examine whether alternative methods of modulating intranuclear crowding could similarly regulate dCas9-SunTag labelling efficiency. By adding trypsin-EDTA to detach adherent cells under isotonic condition, we induce cellular rounding and nuclear expansion (Supplementary Fig. 14)44. Consistent with hypoosmotic effect, this decreased crowding increases both the number and the total intensity of telomere foci (Supplementary Fig. 14), supporting our hypothesis that reduced crowding enhances the binding of fluorescence proteins to dCas9-SunTag. Moreover, the modulation of intranuclear molecular crowding is concomitant with changes in ion concentration. The ions are known to modulate protein interaction through electrostatic45 or Hofmeister46 effects. Notably, the total intracellular ion concentration decreases under hypoosmotic condition15, while it increases under trypsin treatment due to ion influx through ion channels and pumps on plasma membranes44. Therefore, the elevation of telomere labelling efficiency under both conditions suggests that the crowding alternations rather than ion changes primarily drive the observed effect.

Osmotic pressure regulates transcription of target genes with dCas9-SunTag system
We have demonstrated that extracellular osmotic pressure modulates the binding of fluorescence proteins to dCas9-SunTag, thereby regulating target DNA labelling. Extending this observation, we question whether osmotic pressure could also influence gene transcription in a similar manner. We hypothesise that hypoosmotic pressure would enhance transcription efficiency by increasing the recruitment of transcription activation domains, while hyperosmotic pressure would have the opposite effect. To test this, we utilise sgRNA targeting the promoter of endogenous gene HBG1, and fuse synthetic transcription activation domain VPR47 or VP6448 to scFv-FP for transcription activation with the dCas9-SunTag system (Fig. 4a, and Supplementary Fig. 15). After 1-h osmotic treatment, the mRNA expression of HBG1 is measured through quantitative reverse transcription polymerase chain reaction (RT-qPCR). Consistent with our hypothesis, the relative mRNA expression of HBG1 increases under hypoosmotic treatment and decreases under hyperosmotic condition, with mRNA expression gradients corresponding to the extent of water or PEG addition within a certain range (Fig. 4b).

With the findings that osmotic pressure regulates transcription, we next investigate whether this regulation would further impact downstream protein expression. We employ a CRISPRTag-TRE-luciferase-24\timesMS2 reporter cell line49, where the expression of exogenous luciferase serves as a more accurate and accessible proxy for protein expression than endogenous proteins. In this reporter cell line, a DNA fragment consisting of a CRISPR targetable sequence (CRISPRTag) and a Tet-inducible promoter (TRE)-driven luciferase-24\timesMS2 expression cassette is inserted into the ACTB site in an HEK293T cell49 (Fig. 4c). The specific targeting and efficient transcription activation are verified by the luciferase expression observed exclusively with the complete dCas9-SunTag-FP-AD/sgRNA system, as robust transcription activation requires precise spatial assembly of the dCas9-SunTag-FP-AD complex at the target site (Supplementary Fig. 16). Then the osmotic effect is investigated. As anticipated, the hypoosmotic pressure enhances the luciferase expression, while the hyperosmotic pressure has a repressive consequence (Fig. 4d). And the luciferase expression level exhibits a gradient with the concentration gradient of water or PEG within a certain span (Fig. 4d). These results confirm that the regulation of transcription with dCas9-SunTag system by osmotic pressure subsequently affects the downstream protein expression.

After establishing the regulatory effects of osmotic pressure on gene expression at ensemble level, we delve deeper into the transcription process at the molecular level. We label nascent MS2-tagged luciferase mRNA with MCP-mCherry, enabling real-time visualisation of transcription events (Fig. 4c). Under isotonic condition, the MCP-mCherry fluorescence intensity at the target locus shows mRNA synthesis occurring in a bursting mode (Fig. 4e), with a burst frequency of approximately 0.1 min^{-1} (Fig. 4f), consistent with previous studies50. Notably, the frequency of transcription burst is found to increase under hypoosmotic condition and decrease under hyperosmotic condition (Fig. 4f), aligning with our ensemble-level findings. These findings indicate that osmotic pressure modulates transcription activation frequency, thereby affecting gene expression with the dCas9-SunTag system.

Methods
Plasmids, cell culture and transient transfection
The plasmids used in this work and the corresponding sources are listed in Supplementary Table 1.
HEK293T (including the variant cell lines, H2B-mCherry reporter cell line and CRISPRTag-TRE-luceferase-24\timesMS2 reporter cell line49) cells were cultured in Dulbecco’s modified Eagle medium (Gibco) with high glucose supplemented with 10% foetal bovine serum (Gibco) and 1% penicillin-streptomycin (Gibco). They were incubated at 37 °C and 5% CO_2.
The cells were transfected with plasmids using LipofectamineTM 3000 (Invitrogen). About 24 h after the transfection, the transfection mixture was substituted with fresh growth medium. Around 48 h after the transfection, the cells were observed and applied in the experiments of osmotic effect.

Microscopy
Adherent cells were grown in glass-bottom dishes (Cellvis) and imaged on Leica TCS SP8 laser scanning confocal microscope system equipped with a chamber maintained at 37 °C and 5% CO_2. The accompanying software was Leica Application Suite X. Images were acquired using a 40\times oil immersion objective (1.30 NA) and excitation lasers of 405 nm for Hoechst 33342, 488 nm for green fluorescence proteins (mNeonGreen and sfGFP) and 552 nm for red fluorescence proteins (mCherry and 3\timesmRuby3). The scanning format was 512\times512 and the pixel size was 253 nm \times 253 nm. For z-stack scanning, the step size along z axis was 0.60 \mu m. For intranuclear 2D dynamics tracking, the time interval was 1 s and the recording duration was 1 min. Imaging positions were accurately recorded by the system to ensure that the same individual cells were measured before and after osmotic treatment and other drug treatment. Subsequent image visualisation was performed on ImageJ or Imaris.

Fluorescence in situ hybridisation (FISH)
Cells were fixed with 4% paraformaldehyde, permeabilized with 0.7% Triton X-100 and 0.1% saponin in 2\times SSC (sodium saline citrate) buffer, and treated with RNase A at 37 °C for 1 h. Then, cells underwent dehydration in a graded ethanol series (70%, 85% and 100%) for 5 min each. Subsequently, cells were treated with 70% formamide in 2\times SSC at 85 °C for 30 min. Then cells were sequentially dehydrated again in 70%, 80% and 95% ethanol for 5 min each in an ice bath. Next, Cells were incubated with 2 ng/mL Cy5-tagged telomere probe32 in a hybridisation mixture (10% dextran sulfate, 50% formamide and 500 ng/mL salmon sperm DNA in 2\times SSC) at 37 °C overnight. Finally, cells were washed with 2\times SSC three times.

Osmotic treatment and other drug treatment
For the hypoosmotic or hyperosmotic treatment, the initial isotonic cell culture medium was substituted with a mixture of culture medium and water or 200-Da PEG (Sigma-Aldrich), respectively, at a certain volumetric ratio. The osmotic pressure applied to cells was estimated by diluting the osmolarity due to water or adding the osmolarity due to PEG (5.62 Osm/L) to that of isotonic medium (300 mOsm/L)4,10,16 (Supplementary Fig. 2). For the recovery procedure, the non-isotonic solution was removed and fresh medium was added.
For the procedure of detaching adherent cells from glass substrate under isotonic condition, 0.05% trypsin-EDTA (Gibco) was added into the dish right after the initial medium was discarded.
During imaging experiments, all the operations of aspirating and adding solutions were meticulously performed on the sample stage of the microscope without moving the dishes, ensuring that the same individual cells were observed before and after drug treatment. For all the data analysis in the imaging experiments of osmotic effect or trypsin effect, the same individual cells were analysed across the treatment.

3D image processing and analysis
Nuclei in 3D images (identified by Hoechst 33342 or H2B-mCherry) were detected by the Surface tool on Imaris. Target fluorescence foci in 3D images were detected by the Spots tool on Imaris, where the estimated focus was modelled as a prolate spheroid with xy diameter of 0.7 \mu m and z diameter of 1.4 \mu m, and the filter criterion was the quality of a focus, defined as the centre fluorescence intensity with background subtraction (the intensity of the channel Gaussian filtered by 3/4 of spot radius minus the intensity of the original channel Gaussian filtered by 8/9 of spot radius). To quantify the intensity of target foci in a nucleus, the intensity of nuclear overall background (outside the identified focus spheroids in the nucleus) was firstly subtracted from the original signal to extract the background-corrected intensity. The total fluorescence intensity of target foci in a cell was calculated as the background-corrected intensity sum of all the identified focus spheroids. The quantification was performed on Matlab.

Analysis for fluorescence recovery after photobleaching (FRAP)
The normalised fluorescence intensity inside the photobleached area over time t (Fig. 2b) was calculated as
I_{normalized}(t) = (I(t) - I_{pre}) / (I_0 - I_{pre})
where I(t) was the original intensity at time t, I_{pre} was the original intensity right before photobleaching while I_0 was the one at the first time point after photobleaching.

Particle image velocimetry (PIV)
PIV40 for 2D images of H2B-mCherry was carried out with PIVlab on Matlab. The algorithm of FFT window deformation was employed. Three successive passes were run, with the respective interrogation areas of 16, 8, 4 and the respective steps of 8, 4, 2. Then 2D Gaussian function was used to find a displacement peak with sub-pixel precision. During post-processing, velocity limits were set with standard deviation filter threshold of 8 and local median filter threshold of 3. Data smoothing of the least strength was performed to obtain the final reliable results.

Analysis for single-particle tracking
The 2D trajectories of telomere foci in a cell were tracked by the Spots tool on Imaris, where the estimated focus was modelled as a circle with a diameter of 0.7 \mu m and the filter criterion was the quality of a focus with background subtraction. The tracking algorithm was Brownian motion with a maximum distance of 0.7 \mu m and a maximum time gap of three frames. The translational and rotational drift was subtracted to obtain the final corrected trajectories, with main steps as follows: firstly, the mass centre of all telomere foci in each frame is subtracted from the trajectories, which corrects for translational drift; next, the average rotation matrix in the xy plane for all foci around the mass centre is calculated for each time point; finally, each trajectory is multiplied by the inverse rotation matrix.
The MSD of a telomere trajectory as a function of time lag \tau was calculated as
MSD(\tau) = <|r(t+\tau) - r(t)|^2>,
where t was the acquisition time and r was the 2D coordinate. The MSD curve within a range of 0 < \tau \le 15 s was fitted to
MSD(\tau) = 4D\tau^\alpha,
where D was the diffusion coefficient and \alpha was the diffusion exponent41,42. The MSD calculation as well as the fitting was performed on Matlab.

Analysis for nascent mRNA at target loci
For the analysis on nascent mRNA tagged with MS2-MCP-mCherry, the target site was identified by the target sfGFP focus. The region inside a prolate spheroid with xy diameter of 1 \mu m and z diameter of 2 \mu m was denoted as \Omega_1. The region inside a prolate spheroid with xy diameter of 4 \mu m and z diameter of 8 \mu m and within a distance of 1 \mu m from the centre along z axis was denoted as \Omega_2. The MCP-mCherry effective intensity at the target sfGFP focus was calculated as
I_{effective} = I_{original} - I_{background} \times 100%,
where I_{original} was the original intensity mean in \Omega_1, I_{background} was the original intensity mean in \Omega_2 - \Omega_1. If I_{effective} at a time point was greater than 10%, it was considered that an mRNA burst was occurring.

Measurement of mRNA expression: quantitative reverse transcription polymerase chain reaction (RT-qPCR)
RNA was isolated using Trizol Reagent (Vazyme, R411-02-AA) and reverse-transcribed into cDNA using HiScript II Q RT SuperMix for qPCR (+gDNA wiper) (Vazyme, R223-01). Quantitative real-time PCR (qPCR) was performed on LightCycler 96 System using 2xTaq pro universal SYBR qPCR Master Mix (Vazyme, R712-02-AA). The \Delta\Delta Ct method was employed to obtain the relative gene expression normalised by the glyceraldehyde phosphate dehydrogenase (GAPDH) expression. For the qPCR primers, please refer to the source literature49.
For the experiment of osmotic effect, mRNA expression was measured after the cells were treated under the corresponding osmotic condition for about 1 h. The relative mRNA expression for each osmotic condition shown in Fig. 4b was normalised by the mRNA expression of blank group under the same osmotic condition. The blank group referred to the cells transfected with empty plasmid vectors (pBluescript SK+).

Luciferase assay
HEK293T CRISPRTag-TRE-luceferase-24\timesMS2 reporter cells were seeded in a 12- or 24-well plate. 45 \mu g/mL D-luciferin (Beyotime) was added to each well, and then the cells were incubated at 37 °C and 5% CO_2 for 5 min. Subsequently, the plate was imaged on IVIS Lumina III In Vivo Imaging System. Bioluminescence analysis was performed on Living Image. The luciferase expression was represented by the detected bioluminescence in a well, normalised by the blank well where the cells were transfected with empty plasmid vectors.
For the experiment of osmotic effect, luciferase assay was performed after the cells were treated under the corresponding osmotic condition for about 3 h. The relative luciferase expression for each osmotic condition shown in Fig. 4d was obtained by subtracting the luciferase expression of blank group under the same osmotic condition.

Statistics and reproducibility
Detailed information on study design, experimental implementations and statistics analysis is provided in the corresponding subsections of Methods and figure captions. No statistical method was used to predetermine sample size. Cell sampling was random. Investigators were blinded to allocation during experiments and outcome assessment. Data analyses were performed on Matlab or Origin, unless stated otherwise. For the calculation of change percentage, values of infinity were not included in the statistics. For two-sample t-test, equal variance was not assumed and Welch correction was employed.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In general, I think this is a very well written paper, reporting exciting results. Particularly I find their integration of CRISPR to study nuclear crowding impressive, and it’s a timely matter. The results are also somewhat consistent with recent work using traditional biochemical assays, where crowded cell population resulted in crowded nuclei. I recommend it’s publication.\nI just have a few questions that I suggest the authors to address before its publication.:\nEq. 1 is a simple reaction-diffusion equation, and once the authors assumed changes in recovery (as a result of osmotic pressure change) is due to changes in reaction rate, it is bound to give results in Fig. 2e and f. Therefore I don’t think they can conclude from Fig. 2e and f that “The close alignment between our experiment data and our theoretic model confirms that the macroscopic changes in telomere focus fluorescence are indeed a result of perturbed binding-unbinding equilibrium between fluorescence proteins and dCas9-SunTag in response to osmotic shock at the microscopic level.” Until this point, the model does not yield any useful prediction beyond the assumption that osmotic pressure changes reaction rate. To claim that it is indeed the reaction rate that is changed, perhaps the authors should explore a more direct measurement of reaction rate, or “rescue” experiments where they change the reaction rate using a classical approach in hypo/hyper-osmotic conditions and if the observation is rescued. An alternative would be to use the model with fitted parameters to make some new predictions. It is also up to the authors to just weaken this claim.\nA few minor comments:\n1. “The translational and rotational drift was subtracted to obtain the final corrected trajectories.” Is “correlated” what they meant?\n2. Some of the \\mu m in the figures need to have consistent font.\n3. Can the authors comment on the out-of-equilibrium-ness of intracellular movement, given that an equilibrium reaction-diffusion picture is drawn here? How might activity play a role?", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "In this manuscript, Wei et al. explore the effects of osmotic pressure on dCas9-SunTag-mediated fluorescent labeling, extending this system to study the impact of osmotic pressure on gene expression. Using a well-designed experimental setup, the authors demonstrate that osmotic conditions can influence fluorescence intensity by modulating the binding-unbinding equilibrium of fluorescent proteins to the dCas9-SunTag system. Additionally, the study offers a theoretical framework to quantify the binding and unbinding changes in response to varying osmotic conditions. This work provides fundamental insights into the molecular-level effects of osmotic pressure and its potential applications in characterizing intranuclear properties, making it highly suitable for publication in Nature Communications. Below, I provide several suggestions and comments for the authors to consider during revision:\n(1) The authors modify osmotic pressure by adjusting the concentrations of water or PEG in the medium. While I understand the intent to mimic extracellular osmotic conditions, the manuscript would benefit from a more thorough discussion of how accurately these changes in water or PEG concentrations represent changes in osmotic pressure. As it stands, the argument is not fully convincing. A more detailed explanation of how these specific concentrations compare to physiological osmotic variations would enhance the manuscript.\n(2) The authors propose that “hypoosmotic shock favors binding over unbinding, leading to macroscopic fluorescence condensation, whereas hyperosmotic shock promotes the opposite effect, resulting in fluorescence dispersion.” This hypothesis seems to contradict conventional understanding, where crowding (an entropic effect) typically favors binding over unbinding, particularly under hyperosmotic conditions (i.e., during crowding caused by osmotic stress). It was surprising that the model, which is based on this hypothesis, aligns well with experimental data. To improve clarity and avoid confusion, I suggest the authors provide a more detailed explanation of this hypothesis and its physical underpinnings, especially in relation to the effects of osmotic pressure on protein binding.\n(3) In Figures 2e and 2f, only the binding rates for hypoosmotic shock and the unbinding rates for hyperosmotic shock are presented. Since the discussion revolves around equilibrium, both binding and unbinding rates for each condition should be shown. Presenting both rates would also help to verify the claim made in point (2). Furthermore, when comparing the binding and unbinding rates for hypoosmotic and hyperosmotic conditions, the unbinding rates appear to be an order of magnitude higher than the binding rates. While I understand that these cannot be directly compared as they correspond to different experimental conditions, it raises a question: Does this mean that binding occurs with a very low probability? A discussion on this point, based on the thermodynamic stability of binding, would be useful.", "reviewer_4": "This study integrates CRISPR-based live-cell imaging with biophysical modeling to investigate how osmotic pressure influences nuclear processes such as DNA-protein interactions and transcription. While the study provides insights into the osmotic regulation of nuclear crowding, the experimental design, data analysis, and interpretation require further clarification and stronger evidence to support the conclusions.\n1. Telomere labeling efficiency: Under standard conditions, telomere labeling should yield dozens of observable foci per nucleus, yet the provided figure displays fewer than ten. This raises concerns about labeling efficiency. If a substantial fraction of telomeres remains unlabeled, the conclusions regarding changes in the number of signal spots upon H₂O or PEG treatment may be less reliable. Additionally, the dCas9-SunTag system is known to form phase-separated aggregates in the nucleus independent of DNA binding, raising the possibility that the observed foci do not necessarily correspond to telomere binding sites. Further validation is needed to confirm that the detected signals represent true telomere loci rather than nonspecific nuclear aggregates.\n2. The term \"small guide RNA\" should be corrected to \"single guide RNA\" to maintain consistency with standard CRISPR nomenclature.\n3. The key experimental procedures for H₂O, PEG and other treatment are not clearly described.\n4. Figure 1c – Intensity increment analysis: The notation \"n = 6\" requires clarification—does this refer to six individual spots within a single cell or the average of all spots across six cells? Furthermore, continuous imaging typically results in fluorescence signal decay due to photobleaching. The study does not provide sufficient details on whether this factor was accounted for in the analysis, which could impact the interpretation of fluorescence intensity changes.\n5. Figures 1 and S2 – Please provide additional information to clarify whether the same cells were analyzed across these figures. In Figure S2, the relationship between the initially displayed single cell and the six cell pairs shown after treatment and reversal is unclear, which may cause confusion.\n6. Figures S2a, S2b, and S3a – The foci in H₂O-treated cells appear dimmer than those in pre-PEG-treated cells, suggesting significant variation in intensity across different cells. Given this variability, it remains unclear whether the observed changes result from osmotic pressure effects or intrinsic differences between cells. Additional controls and a more rigorous approach to data selection and processing are needed to ensure that the observed trends are biologically meaningful rather than artifacts of cell-to-cell heterogeneity.\n7. Figure 2e and 2f – Insufficient sample size: The statistical analysis is based on n = 3 cells, which is inadequate for drawing reliable conclusions. The low sample size raises concerns about the robustness of the findings, and a larger dataset is needed to provide stronger statistical support.\n8. Figure 4e – Lack of clear GFP loci: The provided figure does not clearly display GFP spots, raising concerns about signal detectability. The authors should clarify whether this is due to low expression levels, imaging limitations, or potential technical artifacts.\n9. The study attempts to distinguish macromolecular crowding effects from changes in ionic concentration using trypsin/EDTA treatment. This need to be better clarified."}, "author_response": "We thank the reviewers sincerely for reviewing our paper carefully and giving meaningful comments. We have addressed all the comments point by point as shown below. For convenience, the reviewers’ comments are shown in blue, our replies are in black, and the modifications in our revised paper are emphasized in red.\nResponse to Reviewer 1\nIn general, I think this is a very well written paper, reporting exciting results. Particularly I find their integration of CRISPR to study nuclear crowding impressive, and it’s a timely matter. The results are also somewhat consistent with recent work using traditional biochemical assays, where crowded cell population resulted in crowded nuclei. I recommend it’s publication. I just have a few questions that I suggest the authors to address before its publication.\nResponse:\nWe truly appreciate your approval of our work. We have carefully addressed your concerns in the revised manuscript.\nEq. 1 is a simple reaction-diffusion equation, and once the authors assumed changes in recovery (as a result of osmotic pressure change) is due to changes in reaction rate, it is bound to give results in Fig. 2e and f. Therefore I don’t think they can conclude from Fig. 2e and f that “The close alignment between our experiment data and our theoretic model confirms that the macroscopic changes in telomere focus fluorescence are indeed a result of perturbed binding-unbinding equilibrium between fluorescence proteins and dCas9-SunTag in response to osmotic shock at the microscopic level.” Until this point, the model does not yield any useful prediction beyond the assumption that osmotic pressure changes reaction rate. To claim that it is indeed the reaction rate that is changed, perhaps the authors should explore a more direct measurement of reaction rate, or “rescue” experiments where they change the reaction rate using a classical approach in hypo/hyper-osmotic conditions and if the observation is rescued. An alternative would be to use the model with fitted parameters to make some new predictions. It is also up to the authors to just weaken this claim.\nResponse:\nWe are grateful for this valuable comment. Indeed, by fitting our experiment data to our theoretic model, we have determined the apparent binding or unbinding rate. We attempted to directly measure the reaction rate, but found it challenging to image single-molecule binding/unbinding in the complex nucleus due to current technological limitations. Additionally, we made efforts but were unable to find suitable methods for the “rescue” experiments, as changing the reaction rate without adding new variables was difficult. For instance, regulating chromosome compaction with drugs takes hours to days, which would introduce other biological effects unlike the rapid osmotic pressure changes.\nIn the revised manuscript, we refined our theoretical model to better explain the observations. We added a discussion about the binding and unbinding rate, clarifying that the apparent binding or unbinding rate was derived by fitting the experiment data to the model.\nAccordingly, we have added a statement after presenting Equation 1 in the revised manuscript (Page 8):\nIn an equilibrium state under isotonic condition, 𝑘 𝑐 −𝑘 𝑐 = 0 (the subscript “i” refers to “isotonic”).\nAnd we have modified the third premise for deriving Equation 2 in the revised manuscript (Page 8):\n𝑘 = 𝑘 +∆𝑘 and ∆𝑘 𝑐 ≫ |𝑘 𝑐 −𝑘 𝑐 | upon hypoosmotic shock, while 𝑘 = 𝑘 +\n∆𝑘 and ∆𝑘 𝑐 ≫ |𝑘 𝑐 −𝑘 𝑐 | upon hyperosmotic shock. ∆𝑘 is the apparent binding rate and\n∆𝑘 is the apparent unbinding rate, reflecting the effective rate of reaction as it transitions from one\nequilibrium state to another due to environmental changes.\nThe Equation 2 is revised as\n𝐼(𝑟,∆𝑡) = 𝐼 0 e − 2 𝑟 𝑅 2 0 2 + 𝐼 m 𝑣 0 (1−e−𝑣∆𝑘 b ∆𝑡) , ∆𝑘 b 𝑐 m ≫ |𝑘 bi 𝑐 m −𝑘 ui 𝑐 f | ,\n𝑟2\n−\n{ 𝐼\n0\ne 2𝑅0 2e−∆𝑘u∆𝑡 , ∆𝑘\nu\n𝑐\nf\n≫ |𝑘\nbi\n𝑐\nm\n−𝑘\nui\n𝑐\nf\n| .\nAnd all of the corresponding names of the apparent binding rate and the apparent unbinding rate in text and Figure 2e,f have been revised.\nMoreover, we have revised our claim to a more rigorous statement in the revised manuscript (Page 8): “The close alignment between our experiment data and our theoretic model indicates that the macroscopic changes in telomere focus fluorescence are probably attributed to the microscopic perturbation of the binding-unbinding equilibrium between fluorescence proteins and dCas9-SunTag in response to osmotic shock.”\nThank you again for the meaningful suggestions. This provides an important research direction for us in the future.\nA few minor comments:\n1. “The translational and rotational drift was subtracted to obtain the final corrected trajectories.” Is “correlated” what they meant?\nResponse:\nThank you very much for paying attention to the details in our methods of data processing. The original trajectories of telomere foci in a cell from the original images might have drift, from the translation and rotation of the nucleus. To obtain a more reliable result, we correct the trajectories in a cell by subtracting the translational and rotational drift in Imaris Software. The main steps are as follows. Firstly, the mass center of all telomere foci in each frame is subtracted from the trajectories. This corrects for translational drift. Next, the average rotation matrix in the xy plane for all foci around the mass center is calculated for each time point. Finally, each trajectory is multiplied by the inverse rotation matrix.\nIn the revised manuscript, we have clearly described these methods in Section Methods (Page 14):\nThe translational and rotational drift was subtracted to obtain the final corrected trajectories, with main steps as follows: firstly, the mass center of all telomere foci in each frame is subtracted from the trajectories, which corrects for translational drift; next, the average rotation matrix in the xy plane for all foci around the mass center is calculated for each time point; finally, each trajectory is multiplied by the inverse rotation matrix.\n2. Some of the \\mu m in the figures need to have consistent font.\nResponse:\nWe are sorry about the mistake. We have revised the font of “μm” in all figures in the revised manuscript.\n3. Can the authors comment on the out-of-equilibrium-ness of intracellular movement, given that an equilibrium reaction-diffusion picture is drawn here? How might activity play a role?\nResponse:\nThank you for this insightful comment. In a live cell, the movement of fluorescence proteins is enhanced by active fluctuations [Guo et al., Cell 158, 822-832 (2014)]. Thus, the parameter D in Equation 1 should be interpreted as the apparent diffusion coefficient which integrate additional active effect. We solve Equation 1 based on the premise that the diffusion of fluorescence proteins is significantly faster than the binding and unbinding of them to the dCas9-SunTag. Since the movement of proteins is enhanced by active fluctuations, the apparent diffusion coefficient is even larger than the thermal diffusion coefficient, which is consistent with our premise. This allows us to derive Equation 2 and obtain the apparent binding or unbinding rate upon hypoosmotic or hyperosmotic shock.\nAccordingly, we have added a relevant elucidation after the reaction-diffusion equation in our updated manuscript (Page 7~8):\nNoted that in a live cell, the movement of fluorescence proteins is enhanced by active fluctuations [30]. Thus, the parameter D is interpreted as the apparent diffusion coefficient which integrate the effect of active fluctuations.\nResponse to Reviewer 2\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse:\nWe thank you very much for reviewing our paper.\nResponse to Reviewer 3\nIn this manuscript, Wei et al. explore the effects of osmotic pressure on dCas9-SunTag-mediated fluorescent labeling, extending this system to study the impact of osmotic pressure on gene expression. Using a well-designed experimental setup, the authors demonstrate that osmotic conditions can influence fluorescence intensity by modulating the binding-unbinding equilibrium of fluorescent proteins to the dCas9-SunTag system. Additionally, the study offers a theoretical framework to quantify the binding and unbinding changes in response to varying osmotic conditions. This work provides fundamental insights into the molecular-level effects of osmotic pressure and its potential applications in characterizing intranuclear properties, making it highly suitable for publication in Nature Communications. Below, I provide several suggestions and comments for the authors to consider during revision:\nResponse:\nWe are sincerely grateful to you for reviewing our paper and approving our work. We have revised our paper according to your suggestions and comments.\n(1) The authors modify osmotic pressure by adjusting the concentrations of water or PEG in the medium. While I understand the intent to mimic extracellular osmotic conditions, the manuscript would benefit from a more thorough discussion of how accurately these changes in water or PEG concentrations represent changes in osmotic pressure. As it stands, the argument is not fully convincing. A more detailed explanation of how these specific concentrations compare to physiological osmotic variations would enhance the manuscript.\nResponse:\nThank you for this helpful advice. We have supplemented the calculated osmotic pressure applied to cells corresponding to the addition of water or PEG (please see new Figure S2 in the revised paper). The osmotic pressure was estimated by diluting the osmolarity due to water, or adding the osmolarity due to 200-Da PEG (5.62 Osm/L) to that of isotonic medium (300 mOsm/L) [Zhou et al., Proc. Natl. Acad. Sci. 106, 10632-10637 (2009); Guo et al., Proc. Natl. Acad. Sci. 114, E8618-E8627 (2017); Roffay et al., Proc. Natl. Acad. Sci. 118, e2103228118 (2021)].\nAccordingly, we have included the calculated actual extracellular osmotic pressures in the revised manuscript (Page 4):\nTo decrease or increase the extracellular osmotic pressure, we add water or 200-Da polyethylene glycol (PEG) to the cell culture medium, respectively [4, 8, 12], with the actual extracellular osmotic pressure being calculated (Figure S2).\nIn addition, we have added the procedure of osmotic treatment and the method of estimating osmotic pressure in Section Methods (Page 13):\nFor the hypoosmotic or hyperosmotic treatment, the initial isotonic cell culture medium was substituted with a mixture of culture medium and water or 200-Da PEG (Sigma-Aldrich), respectively, at a certain volumetric ratio. The osmotic pressure applied to cells was calculated by diluting the osmolarity due to water or adding the osmolarity due to PEG (5.62 Osm/L) to that of isotonic medium (300 mOsm/L) [4, 8, 12] (Figure S2). For the recovery procedure, the non-isotonic solution was removed and fresh medium was added.\nFigure S1 The actual extracellular osmotic pressure applied to cells at different concentrations of H O or PEG.\n2\n(2) The authors propose that “hypoosmotic shock favors binding over unbinding, leading to macroscopic fluorescence condensation, whereas hyperosmotic shock promotes the opposite effect, resulting in fluorescence dispersion.” This hypothesis seems to contradict conventional understanding, where crowding (an entropic effect) typically favors binding over unbinding, particularly under hyperosmotic conditions (i.e., during crowding caused by osmotic stress). It was surprising that the model, which is based on this hypothesis, aligns well with experimental data. To improve clarity and avoid confusion, I suggest the authors provide a more detailed explanation of this hypothesis and its physical underpinnings, especially in relation to the effects of osmotic pressure on protein binding.\nResponse:\nWe deeply appreciate the valuable suggestion. Indeed, our results differ from previous studies reporting protein condensation in more crowded environments via phase separation [Hancock, J. Struct. Biol. 146, 281-290, (2004); Jalihal et al., Mol. Cell 79, 978-990.e975, (2020); Gao et al., Cell Rep. 40, 111086, (2022)]. To address this point, we first conducted a fluorescence in situ hybridization (FISH) assay (Figure S1 in the revised paper) and verified that most of the foci correspond to target DNA loci, rather than non-specific phase-separated aggregates. Based on these results, we propose a possible physical explanation: the association between the specific scFv-fused proteins and the dCas9-SunTag likely reflects the accessibility of binding sites on the SunTag. In a sparse environment, the SunTag peptide chain might expose more sites for the specific scFv-fused proteins to bind. Conversely, in a more crowded environment, the binding sites might be less accessible due to spatial confinement of the SunTag chain.\nAccordingly, we have included this explanation in the Discussion section of revised manuscript (Page 12):\nThe colocalization in the labelling specificity assessment (Figure S1) suggests that most of the foci correspond to target DNA loci rather than non-specific phase-separated aggregates. The association is possibly a reflection of the accessibility of binding sites on the SunTag. In a sparse environment, the SunTag peptide chain might expose more sites for the specific scFv-fused proteins to bind on. On the contrary, in a more crowded environment, the binding sites might be less accessible for the specific proteins due to spatial confinement of the SunTag chain.\nFigure S2 Specificity evaluation of telomere labelling with dCas9-SunTag system. a, Colocalization of telomeres labelled with dCas9-SunTag-FP (green) and FISH (red) in a cell. Scale bar, 5 µm. b, Ratio of the overlap number between dCas9-SunTag-FP foci and FISH foci to the dCas9-SunTag-FP focus number in a cell. Statistics for 11 cells. Box chart: box body, 25%~75%; whisker, range within 1.5IQR; solid line, median; solid circle, mean.\n(3) In Figures 2e and 2f, only the binding rates for hypoosmotic shock and the unbinding rates for hyperosmotic shock are presented. Since the discussion revolves around equilibrium, both binding and unbinding rates for each condition should be shown. Presenting both rates would also help to verify the claim made in point (2). Furthermore, when comparing the binding and unbinding rates for hypoosmotic and hyperosmotic conditions, the unbinding rates appear to be an order of magnitude higher than the binding rates. While I understand that these cannot be directly compared as they correspond to different experimental conditions, it raises a question: Does this mean that binding occurs with a very low probability? A discussion on this point, based on the thermodynamic stability of binding, would be useful.\nResponse:\nThank you sincerely for the interest in our theoretic model. We apologize for not being clear about the binding rate and the unbinding rate in our previous manuscript. We use Equation 1 to describe both the equilibrium state under isotonic condition and the process of disrupting the equilibrium upon osmotic shock:\n∂𝑐 (𝒓,𝑡)\nm = 𝐷∇2𝑐 (𝒓,𝑡)−𝑘 𝑐 (𝒓,𝑡)+𝑘 𝑐 (𝒓,𝑡) ,\n∂𝑡 m b m u f\n{ (1)\n∂𝑐 (𝒓,𝑡)\nf\n= 𝑘 𝑐 (𝒓,𝑡)−𝑘 𝑐 (𝒓,𝑡) ,\n∂𝑡 b m u f\nIn the equilibrium state under isotonic condition, 𝑘 𝑐 −𝑘 𝑐 = 0, where 𝑘 and 𝑘 refer to the intrinsic binding and unbinding rate under isotonic condition. According to our experiment results, we hypothesize that 𝑘 = 𝑘 +∆𝑘 and ∆𝑘 𝑐 ≫ |𝑘 𝑐 −𝑘 𝑐 | upon hypoosmotic shock, while 𝑘 = 𝑘 +∆𝑘 and ∆𝑘 𝑐 ≫ |𝑘 𝑐 −𝑘 𝑐 | upon hyperosmotic shock. ∆𝑘 is the apparent binding rate and ∆𝑘 is the apparent unbinding rate, reflecting the effective rate of reaction as it transitions from one equilibrium state to another due to environmental changes. Thus, the fluorescence intensity of a bright target focus with background subtraction is\n𝐼(𝑟,∆𝑡) = 𝐼 0 e − 2 𝑟 𝑅 2 0 2 + 𝐼 m 𝑣 0 (1−e−𝑣∆𝑘 b ∆𝑡) , ∆𝑘 b 𝑐 m ≫ |𝑘 bi 𝑐 m −𝑘 ui 𝑐 f | , (2)\n𝑟2\n−\n{ 𝐼\n0\ne 2𝑅0 2e−∆𝑘u∆𝑡 , ∆𝑘\nu\n𝑐\nf\n≫ |𝑘\nbi\n𝑐\nm\n−𝑘\nui\n𝑐\nf\n| .\nFitting the experimental fluorescence distribution of foci within cells to the equation above, we obtain the apparent binding rate ∆𝑘 during hypoosmotic shock and the apparent unbinding rate ∆𝑘 during hyperosmotic shock.\nMoreover, the equilibrium reaction rate under isotonic condition might be estimated from our FRAP experiments. From our FRAP curve of dCas9-SunTag-FP (Figure 2b, green), the equilibrium binding and unbinding rates are approximately on the scale of 10 min-1, substantially greater than ∆𝑘 during hypoosmotic shock and ∆𝑘 upon hyperosmotic shock. It indicates that the fluorescence condensation and dispersion due to the change of osmotic pressure are relatively slow, compared with the highly dynamic intrinsic turnover of fluorescence protein on the dCas9-SunTag.\nAccordingly, we have added a statement after presenting Equation 1 in the revised manuscript (Page 8):\nIn an equilibrium state under isotonic condition, 𝑘 𝑐 −𝑘 𝑐 = 0 (the subscript “i” refers to “isotonic”).\nAnd we have modified the third premise for deriving Equation 2 in the revised manuscript (Page 8):\n𝑘 = 𝑘 +∆𝑘 and ∆𝑘 𝑐 ≫ |𝑘 𝑐 −𝑘 𝑐 | upon hypoosmotic shock, while 𝑘 = 𝑘 +\n∆𝑘 and ∆𝑘 𝑐 ≫ |𝑘 𝑐 −𝑘 𝑐 | upon hyperosmotic shock. ∆𝑘 is the apparent binding rate and\n∆𝑘 is the apparent unbinding rate, reflecting the effective rate of reaction as it transitions from one\nequilibrium state to another due to environmental changes.\nThe Equation 2 is revised as\n𝐼(𝑟,∆𝑡) = 𝐼 0 e − 2 𝑟 𝑅 2 0 2 + 𝐼 m 𝑣 0 (1−e−𝑣∆𝑘 b ∆𝑡) , ∆𝑘 b 𝑐 m ≫ |𝑘 bi 𝑐 m −𝑘 ui 𝑐 f | ,\n𝑟2\n−\n{ 𝐼\n0\ne 2𝑅0 2e−∆𝑘u∆𝑡 , ∆𝑘\nu\n𝑐\nf\n≫ |𝑘\nbi\n𝑐\nm\n−𝑘\nui\n𝑐\nf\n| .\nAnd all of the corresponding names of the apparent binding rate and the apparent unbinding rate in text and Figure 2e,f have been revised.\nResponse to Reviewer 4\nThis study integrates CRISPR-based live-cell imaging with biophysical modeling to investigate how osmotic pressure influences nuclear processes such as DNA-protein interactions and transcription. While the study provides insights into the osmotic regulation of nuclear crowding, the experimental design, data analysis, and interpretation require further clarification and stronger evidence to support the conclusions.\nResponse:\nWe would like to express our sincere appreciation to you for reviewing our paper. We have revised our paper according to your suggestions and comments point by point.\n1. Telomere labeling efficiency: Under standard conditions, telomere labeling should yield dozens of observable foci per nucleus, yet the provided figure displays fewer than ten. This raises concerns about labeling efficiency. If a substantial fraction of telomeres remains unlabeled, the conclusions regarding changes in the number of signal spots upon H₂O or PEG treatment may be less reliable. Additionally, the dCas9-SunTag system is known to form phase-separated aggregates in the nucleus independent of DNA binding, raising the possibility that the observed foci do not necessarily correspond to telomere binding sites. Further validation is needed to confirm that the detected signals represent true telomere loci rather than nonspecific nuclear aggregates.\nResponse:\nThank you for these valuable comments. Indeed, the number of telomere foci labelled with the dCas9-SunTag-FP varies in different cells under isotonic conditions, reflecting the intrinsic cell-to-cell heterogeneity. To assess the result reliability about the effect of osmotic pressure across cells with varying initial number of foci, the cells considered in Figure 1d are categorized into two groups based on the number of telomere foci under initial isotonic condition: one group with fewer than 20 foci and the other with 20 or more. Then the number increment of telomere foci in a cell under diverse osmotic treatments are measured for each group (please see the new Figure S6a). Our results show that the effects of osmotic pressure are consistent for both groups. In addition, the influence of hypoosmotic pressure on the focus number in the cells with fewer initial foci is more significant, while the impact of hyperosmotic pressure in the cells with more initial foci is greater. Overall, despite the intrinsic differences between cells, we note that the response of telomere labelling with dCas9-SunTag to osmotic pressure remains stable.\nTo evaluate the specificity of telomere labelling with the dCas9-SunTag system, we have performed the fluorescence in situ hybridization (FISH) assay employing a Cy5-tagged telomere probe. The results show a high proportion of overlap between the FISH foci and the dCas9-SunTag-FP foci (please see the new Figure S1), verifying that most of the signal foci of dCas9-SunTag-FP correspond to telomere loci.\nAccordingly, we have added more discussion about the result reliability across cells with varying initial labelling efficiency in the revised manuscript (Page 4,6):\nWe notice that both the number and the intensity of telomere foci labelled with dCas9-SunTag-FP vary among cells under isotonic condition, reflecting cell-to-cell heterogeneity. We confirm that such variations do not affect the effect of osmotic pressure on telomere labelling, as the results remain consistent across cells with varying initial cases (Figure S6).\nWe have inserted a description about the specificity evaluation of telomere labelling with dCas9-SunTag system in the revised manuscript (Page 4):\nUnder confocal laser scanning microscopy, we observe fluorescent spots in cell nuclei, as expected. The identity of these spots as telomere loci is confirmed through fluorescence in situ hybridization (FISH) assays using a Cy5-tagged telomere probe [26] (Figure S1).\nAnd we have added the method of FISH in Section Method (Page 13):\nCells were fixed with 4% paraformaldehyde, permeabilized with 0.7% Triton X-100 and 0.1% saponin in 2×SSC (sodium saline citrate) buffer, and treated with RNase A at 37 ℃ for 1 hour. Then cells underwent dehydration in a graded ethanol series (70%, 85% and 100%) for 5 minutes each. Subsequently, cells were treated with 70% formamide in 2×SSC at 85 ℃ for 30 minutes. Then cells were sequentially dehydrated again in 70%, 80% and 95% ethanol for 5 minutes each in an ice bath. Next, Cells were incubated with 2 ng/mL Cy5-tagged telomere probe [26] in a hybridization mixture (10% dextran sulfate, 50% formamide and 500 ng/mL salmon sperm DNA in 2×SSC) at 37 ℃ overnight. Finally, cells were washed with 2×SSC for three times.\nMoreover, our results differ from previous studies reporting protein condensation in more crowded environments via phase separation. We have added more words to clarify this point in the Discussion section of revised manuscript (Page 12):\nThe colocalization in the labelling specificity assessment (Figure S1) suggests that most of the foci correspond to target DNA loci rather than non-specific phase-separated aggregates. The association is possibly a reflection of the accessibility of binding sites on the SunTag. In a sparse environment, the SunTag peptide chain might expose more sites for the specific scFv-fused proteins to bind on. On the contrary, in a more crowded environment, the binding sites might be less accessible for the specific proteins due to spatial confinement of the SunTag chain.\nFigure S6 Grouped statistics for effect of osmotic pressure on the number increment of telomere foci labelled with dCas9-SunTag system. a, The cells are categorized into two groups based on the number of telomere foci under initial isotonic condition: one group with fewer than 20 foci and the other with 20 or more. Left: 30% H O, n = 43; 20% H O, n = 36; 10% H O, n = 29; ctrl (control), n = 17; 2.5% PEG, n = 21; 5% PEG, n = 14; 10% PEG, n = 19. Right: 30% H O, n = 11; 20% H O, n = 19; 10% H O, n = 33; ctrl, n = 25; 2.5% PEG, n = 18; 5% PEG, n = 46; 10% PEG, n = 18. b, The cells are categorized into two groups based on the average intensity of telomere foci under initial isotonic conditions: one group with an average intensity lower than 400 (AU) and the other with 400 (AU) or more. Left: 30% H O, n = 24; 20% H O, n = 25; 10% H O, n = 37; ctrl, n = 24; 2.5% PEG, n = 20; 5% PEG, n = 27; 10% PEG, n = 12. Right: 30% H O, n = 30; 20% H O, n = 30; 10% H O, n = 25; ctrl, n = 18; 2.5% PEG, n = 19; 5% PEG, n = 33; 10% PEG, n = 25. Statistics for cells are presented as mean ± SE.\nFigure S3 Specificity evaluation of telomere labelling with dCas9-SunTag system. a, Colocalization of telomeres labelled with dCas9-SunTag-FP (green) and FISH (red) in a cell. Scale bar, 5 µm. b, Ratio of the overlap number between dCas9-SunTag-FP foci and FISH foci to the dCas9-SunTag-FP focus number in a cell. Statistics for 11 cells. Box chart: box body, 25%~75%; whisker, range within 1.5IQR; solid line, median; solid circle, mean.\n2. The term \"small guide RNA\" should be corrected to \"single guide RNA\" to maintain consistency with standard CRISPR nomenclature.\nResponse:\nThank you for your careful review of our manuscript. We have revised it in the revised manuscript (Page 4).\n3. The key experimental procedures for H₂O, PEG and other treatment are not clearly described.\nResponse:\nThank you for the comment. We have supplemented the calculated osmotic pressure applied to cells corresponding to the addition of water or PEG (please see Page 4 in the revised manuscript and the new Figure S2):\nTo decrease or increase the extracellular osmotic pressure, we add water or 200-Da polyethylene glycol (PEG) to the cell culture medium, respectively [4, 8, 12], with the actual extracellular osmotic pressure being calculated (Figure S2).\nFigure S4 The actual extracellular osmotic pressure applied to cells at different concentrations of H O or PEG.\nFor the hypoosmotic or hyperosmotic treatment, the initial isotonic cell culture medium was substituted with a mixture of culture medium and water or 200-Da PEG (Sigma-Aldrich), respectively, at a certain volumetric ratio. The osmotic pressure applied to cells was calculated by diluting the osmolarity due to water or adding the osmolarity due to PEG (5.62 Osm/L) to that of isotonic medium (300 mOsm/L) [4, 8, 12] (Figure S2). For the recovery procedure, the non-isotonic solution was removed and fresh medium was added.\nFor the procedure of detaching adherent cells from glass substrate under isotonic condition, 0.05% trypsin-EDTA (Gibco) was added into the dish right after the initial medium was discarded.\n4. Figure 1c – Intensity increment analysis: The notation \"n = 6\" requires clarification—does this refer to six individual spots within a single cell or the average of all spots across six cells? Furthermore, continuous imaging typically results in fluorescence signal decay due to photobleaching. The study does not provide sufficient details on whether this factor was accounted for in the analysis, which could impact the interpretation of fluorescence intensity changes.\nResponse:\nWe appreciate your great attention to our data analysis and presentation. The sample size n refers to the number of cells. We have emphasized it in the caption of Figure 1 in the revised manuscript. In addition, we have increased the sample size. Now the sample size in Figure 1c is 10 for both hypoosmotic and hyperosmotic conditions in the revised manuscript.\nWe agree that the fluorescence signal decay due to photobleaching might have an impact on the results. To assess the influence of photobleaching, we image the cells with telomeres labelled with dCas9-SunTag-FP under isotonic condition for 30 min, with a time interval of 1 minute (the same interval as in Figure 1c). The results (Figure S4) show a slight decrease in both the number and the total intensity of telomere foci over time, which is consistent with the fact of photobleaching. However, we note that the effect of osmotic pressure is obvious beyond the influence of the continuous imaging.\nAccordingly, we have included a discussion of the influence of photobleaching in the main text of revised manuscript (Page 4):\nNoted that despite a slight decrease in the number and total intensity of telomere foci due to photobleaching, the effect of osmotic pressure remains clear and significant (Figure S4).\nFigure S4 Impact of continuous imaging on telomere labelling with dCa9-SunTag system. The cells are imaged under isotonic condition for half an hour with a time interval of 1 minute (the same interval as in Figure 1c). The number increment (top) and the total fluorescence intensity increment (bottom) of telomere foci in a cell are measured. Statistics for 16 cells are presented as mean ± SE.\n5. Figures 1 and S2 – Please provide additional information to clarify whether the same cells were analyzed across these figures. In Figure S2, the relationship between the initially displayed single cell and the six cell pairs shown after treatment and reversal is unclear, which may cause confusion.\nResponse:\nThank you very much for the useful advice. We have observed and analyzed the same cells both before and after osmotic treatment. As suggested, we have noted this point in the legends of Figure 1 and revised Figure S5 (corresponding to Figure S2 in our previous version of paper). And we have annotated the time point for each cell image in the revised Figure S5 to be clear.\nMoreover, we have emphasized in “Microscopy” of Section Methods (Page 13) that “Imaging positions were accurately recorded by the system to ensure that the same individual cells were measured before and after osmotic treatment and other drug treatment.” And in “Osmotic treatment and other drug treatment” of Section Methods (Page 13~14), we have elucidated that “During imaging experiments, all the operations of aspirating and adding solutions were meticulously performed on the sample stage of the microscope without moving the dishes, ensuring that the same individual cells were observed before and after drug treatment. For all the data analysis in the imaging experiments of osmotic effect or trypsin effect, the same individual cells were analyzed across the treatment.”\nFigure S5 Response of telomere fluorescent labelling with dCas9-SunTag system to cycling osmotic perturbation. a,b, Snapshots of a cell with telomeres labelled with dCas9-SunTag-FP (green) and H2B labelled with mCherry (red) under the cycling hypoosmotic (a) or hyperosmotic (b) treatment. The images are from 3D orthogonal view along z axis. Scale bars, 5 µm. c, Number increment (top) and total fluorescence intensity increment (bottom) of telomere foci in a cell labelled with dCas9-SunTag system versus time during the cycling hypoosmotic (n = 19) or hyperosmotic (n = 5) treatment. The same cells are analyzed before and after osmotic treatment. Statistics for cells are presented as mean ± SE. The time points of the images in a and b correspond with the temporal coordinates of the data symbols in c.\n6. Figures S2a, S2b, and S3a – The foci in H₂O-treated cells appear dimmer than those in pre-PEG-treated cells, suggesting significant variation in intensity across different cells. Given this variability, it remains unclear whether the observed changes result from osmotic pressure effects or intrinsic differences between cells. Additional controls and a more rigorous approach to data selection and processing are needed to ensure that the observed trends are biologically meaningful rather than artifacts of cell-to-cell heterogeneity.\nResponse:\nWe are grateful to you for raising this issue. Indeed, similar to the variation in the number of telomere foci, there is a broad distribution in the intensity of target foci across different cells under initial isotonic conditions, which can be attributed to cell-to-cell heterogeneity. To evaluate the reliability of our results regarding the effect of osmotic pressure on telomere labelling, we categorized the cells considered in Figure 1d into two groups based on the average intensity of foci under initial isotonic conditions: one group with an average intensity lower than 400 (AU) and the other with 400 (AU) or more. We then measured the number increment of foci in each group under various osmotic treatments (Figure S6b). The results show that the effect of osmotic pressure is consistent for both groups. Additionally, the influence of hypoosmotic pressure on the number increment in cells with lower initial average focus intensity is more significant, while the impact of hyperosmotic pressure in cells with higher initial intensity is greater. This indicates the cell-to-cell heterogeneity in the response to osmotic pressure, as you pointed out.\nMoreover, we assess the reliability of our results regarding MUC4 labelling (Figure S8), and the results are in agreement with the case of telomere labelling.\nOverall, despite the intrinsic differences between cells, we note that the response of DNA labelling with dCas9-SunTag to osmotic pressure remains stable.\nAccordingly, we have added a discussion about assessing the reliability of telomere labelling regulation in the revised manuscript (Page 4,6):\nWe notice that both the number and the intensity of telomere foci labelled with dCas9-SunTag-FP vary among cells under isotonic condition, reflecting cell-to-cell heterogeneity. We confirm that such variations do not affect the effect of osmotic pressure on telomere labelling, as the results remain consistent across cells with varying initial cases (Figure S6).\nAlso, we have added a discussion about MUC4 labelling regulation in the revised manuscript (Page 6):\nWe further confirm that the relationship between MUC4 labelling efficiency and osmotic pressure remains reliable, unaffected by the initial fluorescence intensity of foci (Figure S8).\nFigure S6 Grouped statistics for effect of osmotic pressure on the number increment of telomere foci labelled with dCas9-SunTag system. a, The cells are categorized into two groups based on the number of telomere foci under initial isotonic condition: one group with fewer than 20 foci and the other with 20 or more. Left: 30% H O, n = 43; 20% H O, n = 36; 10% H O, n = 29; ctrl (control), n = 17; 2.5% PEG, n = 21; 5% PEG, n = 14; 10% PEG, n = 19. Right: 30% H O, n = 11; 20% H O, n = 19; 10% H O, n = 33; ctrl, n = 25; 2.5% PEG, n = 18; 5% PEG, n = 46; 10% PEG, n = 18. b, The cells are categorized into two groups based on the average intensity of telomere foci under initial isotonic conditions: one group with an average intensity lower than 400 (AU) and the other with 400 (AU) or more. Left: 30% H O, n = 24; 20% H O, n = 25; 10% H O, n = 37; ctrl, n = 24; 2.5% PEG, n = 20; 5% PEG, n = 27; 10% PEG, n = 12. Right: 30% H O, n = 30; 20% H O, n = 30; 10% H O, n = 25; ctrl, n = 18; 2.5% PEG, n = 19; 5% PEG, n = 33; 10% PEG, n = 25. Statistics for cells are presented as mean ± SE.\nFigure S8 Grouped statistics for effect of osmotic pressure on the number increment of MUC4 foci labelled with dCas9-SunTag system. The cells are categorized into two groups based on the average intensity of MUC4 foci under initial isotonic condition. Statistics for cells are presented as mean ± SE. Left, 30% H O, n = 15; ctrl (control), n = 22; 10% PEG, n = 12. Right, 30% H O, n = 11; ctrl, n = 23; 10% PEG, n = 6.\n7. Figure 2e and 2f – Insufficient sample size: The statistical analysis is based on n = 3 cells, which is inadequate for drawing reliable conclusions. The low sample size raises concerns about the robustness of the findings, and a larger dataset is needed to provide stronger statistical support.\nResponse:\nThank you for the helpful comment. As suggested, we have increased the sample sizes in Figure 2e and 2f in the revised manuscript to provide a stronger statistical support. For Figure 2e, n = 10 cells; for Figure 2f, n = 7 cells. Each cell has more than 10 telomere foci. The results after increasing the sample sizes are consistent with that of the previous version.\nFigure 2 Osmotic pressure modulates binding-unbinding equilibrium of fluorescence proteins to dCas9-SunTag. a, Snapshots of FRAP experiments on the area with a telomere focus labelled with dCas9-SunTag-FP (top, green) as well as on the blank area without a telomere focus (middle) in a cell, and on the area with a telomere focus labelled with dCas9-FP (bottom, red, with blue nucleus stained by Hoechst 33342). The photobleached area is marked by a white circle. b, Normalized fluorescence intensity inside the photobleached area over time. Statistics for cells with n = 23 for dCas9-SunTag-FP, n = 9 for dCas9-SunTag-FP (blank) and n = 11 for dCas9-FP. c, Snapshots of the fluorescence condensation of telomere spots labelled with dCas9-SunTag-FP in a cell upon hypoosmotic shock and the fluorescence dispersion upon hyperosmotic shock, presented from 3D orthogonal view along z axis. The region of interest marked by a white square is enlarged and shown below the image. d, 2D radial distribution of normalized fluorescence intensity of a telomere focus during the condensation upon hypoosmotic shock and during the dispersion upon hyperosmotic shock. Statistics for telomere foci in the same cells shown in c with n = 14 for 30% H O and n = 18 for 10% PEG. e,f, Apparent binding rate of fluorescence protein to dCas9-SunTag upon hypoosmotic shock (e) and apparent unbinding rate upon hyperosmotic shock (f). The apparent binding rate is obtained over 6~10 minutes after adding H O into medium (n = 10 cells) while the apparent unbinding rate is calculated over 1~5 minutes after adding PEG (n = 7 cells). Scale bars, 5 μm. All statistics above are presented as mean ± SE.\n8. Figure 4e – Lack of clear GFP loci: The provided figure does not clearly display GFP spots, raising concerns about signal detectability. The authors should clarify whether this is due to low expression levels, imaging limitations, or potential technical artifacts.\nResponse:\nWe sincerely thank you for the concerns. In Figure 4e, we imaged the CRISPRTag with low-repetitive sequences, which results in less clear fluorescence labelling foci compared to telomere foci. However, the CRISPRTag labelling foci are still detectable. To address this concern, we have added a snapshot of the entire nucleus in Figure 4e in the revised manuscript, which clearly distinguishes the target green fluorescence signal from the background.\nFigure 4 …… e, Left, snapshot of a CRISPRTag-TRE-luciferase-24×MS2 reporter cell transfected with the plasmids of dCas9-SunTag-FP-AD and MCP-mCherry, presented from 3D orthogonal view along z axis. Scale bar, 5 μm. Right, enlarged 2D snapshots at the target locus (marked by a white square on the left image) over time. Scale bar, 500 nm. The 2D radial distributions of mCherry intensity are shown at the bottom.\n9. The study attempts to distinguish macromolecular crowding effects from changes in ionic concentration using trypsin/EDTA treatment. This need to be better clarified.\nResponse:\nWe thank you for the meaningful advice. Since osmotic pressure not only alters intranuclear crowding but also influences intracellular ion concentrations, we seek for an alternative method of modulating intranuclear crowding. This approach aims to further elucidate the regulation of telomere labelling efficiency by the intranuclear crowding. We modulate the nuclear volume by adding trypsin-EDTA into the medium under isotonic condition. We observe an increase in the nuclear volume and the telomere labelling efficiency under trypsin treatment (Figure S14), consistent with the hypoosmotic effect. This experiment corroborates our findings from the hypoosmotic experiment, further demonstrating that decreased intranuclear crowding enhances the binding of fluorescent proteins to dCas9-SunTag. More importantly, given that the intracellular ion concentration increases under trypsin treatment [Lordon et al., Biophysical Journal 123, 2476-2483 (2024)] while decreases under hypoosmotic treatment, the trypsin treatment experiment suggests that ion concentration changes may not significantly influence telomere labelling efficiency.\nAccordingly, we have clarified this point regarding the trypsin treatment experiment in the revised manuscript (Page 10):\nWe observe an increase in the number and the total fluorescence intensity of telomere foci within a cell under trypsin treatment (Figure S14), consistent with the hypoosmotic effect. This supports our hypothesis that decreased intranuclear crowding enhances the binding of fluorescent proteins to dCas9-SunTag. More importantly, since intracellular ion concentration increases under trypsin treatment [37] while it decreases under hypoosmotic condition, our results suggest that ion concentration changes do not significantly influence telomere labelling efficiency.\nWe thank the reviewer sincerely for reviewing our paper carefully and giving meaningful comments. We have addressed all the comments point by point as shown below. For convenience, the reviewers’ comments are shown in blue, our replies are in black, and the modifications in our revised paper are emphasized in red.\nResponse to Reviewer 1\nThe authors adequately addressed all my comments. I believe this paper will make an excellent contribution to Nature Communications.\nResponse:\nWe are deeply grateful to you for approving our work."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors adequately addressed all my comments. I believe this paper will make an excellent contribution to Nature Communications.", "reviewer_3": "I thank the authors for addressing my concerns. I recommend its publication in Nature Communications.", "reviewer_4": "This manuscript by Wei et al. investigates the effects of osmotic pressure on dCas9-SunTag-mediated fluorescent labeling, extending the application of this system to study the impact of osmotic pressure on gene expression. The authors’ responses have addressed many of the concerns raised in the review, and the revisions have improved the manuscript to a certain extent. However, some issues still require further clarification and refinement. Below is a point-by-point assessment of the responses.\n1. The author classified cells based on the initial number of telomere imaging. The results show that whether the initial number is less or more than 20, the trend of increase or decrease in the number of imaging after the change in osmotic pressure is consistent. This classification makes the conclusion more reliable. However, when using the Suntag system for imaging, the number of telomeres should be higher. A typical human cell has 92 telomeres, and previous study (DOI: 10.1007/s13238-017-0460-0) reported a peak distribution between 60 and 80 foci. The low number of foci observed in this study may result from the relatively large Z-step (0.6 µm) used during imaging; a finer Z-step is recommended.\nAdditionally, the authors performed FISH validation for telomere specificity, which is appreciated. It is also suggested to use a similar approach to verify specificity in gene locus imaging shown in Fig. 4.\n2. In the snapshot of the entire nucleus, the GFP signal appears more like a patch than a point (Figure 4e). It is recommended to provide additional validation to confirm the specificity of the signal points. Additionally, multiple red signal points (right panel) can be observed within the nucleus. How are the red signal points that do not colocalize with GFP generated? Please clarify the nature of these non-overlapping signals.\n3. The authors supplemented their study with a trypsin treatment experiment to validate macromolecular crowding effects, showing consistent results with hypoosmotic treatment and strengthening the conclusions. It is recommended that the potential ionic changes from trypsin treatment and their possible impact be more clearly addressed in the main text.\nOverall, the authors have addressed most of the concerns with appropriate revisions and additional experiments, such as increasing sample size and providing further validation for specificity. These efforts have improved the reliability and clarity of the results. However, some issues, such as the relatively low number of detected telomere foci and the need for further clarification on non-colocalized signals and potential ionic effects, still require additional attention. The manuscript has been strengthened, but a few aspects merit further refinement."}, "author_response": "Response:\nWe are grateful to you for the insightful comments. We sincerely appreciate your recognition that our previous revisions have improved the manuscript and addressed many concerns. In this revision, we have addressed all the remaining issues. Below is our detailed point-by-point response.\n1. The author classified cells based on the initial number of telomere imaging. The results show that whether the initial number is less or more than 20, the trend of increase or decrease in the number of imaging after the change in osmotic pressure is consistent. This classification makes the conclusion more reliable. However, when using the Suntag system for imaging, the number of telomeres should be higher. A typical human cell has 92 telomeres, and previous study (DOI: 10.1007/s13238-017-0460-0) reported a peak distribution between 60 and 80 foci. The low number of foci observed in this study may result from the relatively large Z-step (0.6 µm) used during imaging; a finer Z-step is recommended.\nAdditionally, the authors performed FISH validation for telomere specificity, which is appreciated. It is also suggested to use a similar approach to verify specificity in gene locus imaging shown in Fig. 4.\nResponse:\nWe sincerely thank you for recognizing the reliability of our results, particularly the consistent osmotic pressure response observed across cells classified by initial telomere focus number. As demonstrated, cells with both high and low initial focus numbers exhibit consistent trends following osmotic perturbation, reinforcing the robustness of our conclusions.\nRegarding the concern about z-step size during imaging, we have performed imaging of a cell, at 0.2 µm, 0.4 µm, and 0.6 µm increments. The results (Figure R1) show that finer z-steps do not increase detectable focus counts. This is likely because the diffraction-limited axial resolution in our microscopy is estimated to be δ𝑧 = 2𝑛𝜆 / 𝑁𝐴2 ≈ 0.9 μm, which is larger than the z-step size we used. (λ is the light wavelength, NA is the numerical aperture of the objective, and n is the refractive index of imaging medium.)\nFigure R1 Dependency of the detectable number of telomere foci in a cell on the z step size. Statistics for 15 cells are presented as mean ± SE.\nCompared to our previous work (DOI: 10.1007/s13238-017-0460-0), the average labelling number of telomere foci in this work is less, probably due to the plasmid expression variability and intrinsic cellular heterogeneity. We indeed observe a broad distribution in telomere labelling numbers, consistent with natural cell-cycle and transcriptional variations in unsynchronized populations.\nImportantly, we also observe cells exhibiting >60 telomere foci, demonstrating that our system is capable of detecting higher counts. Crucially, all cells - including those with high focus counts - are included in our analysis regardless of focus count and consistently showed identical osmotic pressure response dynamics.\nTo make it more representative, we have updated Figure 1b with the images showing higher-focus-count cells in the revised manuscript.\nRevised Figure 1b. Snapshots of the cells with telomeres labelled with dCas9-SunTag-FP (green) and H2B labelled with mCherry (red) under different osmotic conditions. The images are from 3D orthogonal view along z axis. Scale bars, 5 μm.\nWe appreciate your suggestion on verifying gene locus specificity in Fig. 4. The system of labelling CRISPRTag DNA locus with dCas9 has been developed and applied in previous studies [Chen et al., Nat. Commun. 9, 5065 (2018); Ma et al., Nucleic Acids Res. 51, 5271-5284 (2023)], validating its reliability. In our manuscript, we have further verified the target specificity. The luciferase assay (Fig. S16) provides conclusive functional evidence of targeting specificity: cells with the complete dCas9-SunTag-FP-AD/sgRNA system show an 8-fold luciferase induction versus controls, whereas omission of sgRNA (while retaining dCas9-SunTag-FP-AD) reduces expression to baseline levels. This binary response demonstrates strict dependence on sequence-specific recruitment because transcriptional activation requires precise localization of the scFv-FP-AD activator to the CRISPRTag locus via guided dCas9 binding. We consider that this functional readout provides equivalent validation to FISH by demonstrating strict spatial specificity, as transcriptional activation requires accurate spatial assembly of the dCas9-SunTag-FP-AD complex at the target site.\nAccordingly, we have added a description about the target specificity and efficient transcription activation of luciferase gene with dCas9-SunTag-FP-AD in the revised manuscript (Page 11~12):\nThe specific targeting and efficient transcription activation are verified by the luciferase expression observed exclusively with the complete dCas9-SunTag-FP-AD/sgRNA system, as robust transcription activation requires precise spatial assembly of the dCas9-SunTag-FP-AD complex at the target site (Figure S16).\nFigure S16 Efficient transcription activation of luciferase gene with dCas9-SunTag-FP-VP64. The luciferase expression is represented by the detected bioluminescence in a well, normalized by the blank well where the cells were transfected with empty plasmid vectors. Statistics for 2 independent experiments are presented as mean ± SE.\n2. In the snapshot of the entire nucleus, the GFP signal appears more like a patch than a point (Figure 4e). It is recommended to provide additional validation to confirm the specificity of the signal points. Additionally, multiple red signal points (right panel) can be observed within the nucleus. How are the red signal points that do not colocalize with GFP generated? Please clarify the nature of these non-overlapping signals.\nResponse:\nThank you very much for this comment. Indeed, the sfGFP signal at the target locus occasionally manifests as a non-ideal point, likely resulting from focal-plane deviation during the confocal scan acquisition. Nevertheless, these signals remain readily distinguishable from the background fluorescence, as illustrated by additional examples in Figure R2. In these reporter cells, the nascent MS2-tagged luciferase mRNA is labelled with MCP-mCherry (red fluorescence). Since mRNA may diffuse around after synthesis at target locus, red fluorescence appears elsewhere within the nucleus, distinct from the target site labelled with sfGFP. Critically, only the real-time colocalization of red fluorescence signal with the target locus indicates transcription occurrence.\nFigure R2 CRISPRTag-TRE-luciferase-24×MS2 DNA locus labelled with dCas9-SunTag-FP-AD.\nTo clearly illustrate the position of target locus, we have provided the radial distribution of green fluorescence centered at the target locus in Figure 4e. We have further supplemented Figure 4e with the merged sfGFP/mCherry channel images, demonstrating the colocalization events that report transcription bursts. (please see the revised Figure 4e below)\nRevised Figure 4 …… e, Left, snapshot showing the CRISPRTag-TRE-luciferase-24×MS2 locus labelled with dCas9-SunTag-sfGFP-AD. Presented from 3D orthogonal view along z axis. Scale bar, 5 μm. Right, enlarged 2D snapshots at the target locus (marked by a white square on the left image) over time, indicating the transcription burst. Scale bar, 500 nm. The 2D radial distributions of sfGFP and mCherry intensity are shown at the bottom.\nRegarding the target specificity, our luciferase assay (Figure S16) provides validation: The 8-fold transcriptional activation observed exclusively with sgRNA-guided recruitment (vs. baseline expression without sgRNA) demonstrates strict spatial specificity. This functional evidence confirms that scFv-FP-AD localization is precisely restricted to the CRISPRTag locus, as robust transcriptional activation requires precise spatial assembly of the dCas9-SunTag-FP-AD complex at the target site.\nFigure S16 Efficient transcription activation of luciferase gene with dCas9-SunTag-FP-VP64. The luciferase expression is represented by the detected bioluminescence in a well, normalized by the blank well where the cells were transfected with empty plasmid vectors. Statistics for 2 independent experiments are presented as mean ± SE.\n3. The authors supplemented their study with a trypsin treatment experiment to validate macromolecular crowding effects, showing consistent results with hypoosmotic treatment and strengthening the conclusions. It is recommended that the potential ionic changes from trypsin treatment and their possible impact be more clearly addressed in the main text.\nResponse:\nWe sincerely appreciate this advice. It has been reported that as cells are treated with trypsin, ion influx through ion channels and pumps on plasma membranes is triggered [Lordon et al., Biophys. J. 123, 2476-2483 (2024)]. Thus, intracellular ion concentration increases within several minutes.\nIt is known that ions in solution impact protein binding through the electrostatic interaction [Zhou & Pang, Chem. Rev. 118, 1691-1741 (2018)] and the Hofmeister effect [Gregory et al., Phys. Chem. Chem. Phys. 24, 12682-12718 (2022)]. Notably, the total intracellular ion concentration decreases under hypoosmotic condition [Finan & Guilak, J. Cell. Biochem. 109, 460-467 (2010)], while it increases under trypsin treatment. Therefore, the elevation of telomere labelling efficiency under both conditions suggests that to a certain extent, the total ion concentration alterations may not play a dominant role in the binding of fluorescence proteins to dCas9-SunTag. Nevertheless, the precise influence of a broader change range of ion strength and specific ion effects remains to be further investigated.\nWe have attempted to assess the influence of intracellular ion concentration more accurately through directly modulating intracellular ion concentration without changing molecular crowding. However, it is hard to accomplish in live cells due to current technological limitations. The further exploration of ion effect provides an important research direction for us in the future.\nAccordingly, we have modified the statement about the trypsin treatment and the ion effect to make it more clear in the revised manuscript (Page 10):\nFurthermore, we examine whether alternative methods of modulating intranuclear crowding could similarly regulate dCas9-SunTag labelling efficiency. By adding trypsin-EDTA to detach adherent cells under isotonic condition, we induce cellular rounding and nuclear expansion (Figure S14) [41]. Consistent with hypoosmotic effect, this decreased crowding increases both the number and the total intensity of telomere foci (Figure S14), supporting our hypothesis that reduced crowding enhances the binding of fluorescence proteins to dCas9-SunTag. Moreover, the modulation of intranuclear molecular crowding is concomitant with changes in ion concentration. The ions are known to modulate protein interaction through electrostatic [42] or Hofmeister [43] effects. Notably, the total intracellular ion concentration decreases under hypoosmotic condition [14], while it increases under trypsin treatment due to ion influx through ion channels and pumps on plasma membranes [41]. Therefore, the elevation of telomere labelling efficiency under both conditions suggests that the crowding alternations rather than ion changes primarily drive the observed effect.\nOverall, the authors have addressed most of the concerns with appropriate revisions and additional experiments, such as increasing sample size and providing further validation for specificity. These efforts have improved the reliability and clarity of the results. However, some issues, such as the relatively low number of detected telomere foci and the need for further clarification on non-colocalized signals and potential ionic effects, still require additional attention. The manuscript has been strengthened, but a few aspects merit further refinement.\nResponse:\nWe truly appreciate your time and effort in reviewing our manuscript. Your comments have been invaluable in helping us improve our work. We hope that our revisions have adequately addressed your concerns and that our manuscript now meets the high standards required for publication."}, {"round": 3, "reviewer_comments": {"reviewer_4": "The authors' response has adequately addressed my questions. Only a minor issue remains: in Figure 4e, the enlarged image (right) seems not to originate from the same cell as the original image (left). I support the manuscript’s publication."}, "author_response": "Response:\nWe are deeply grateful to you for approving our work. We have double-checked the images in Figure 4e and confirmed that the enlarged images (right) are from the same cell as the cell shown in the left image. They seem different because the cell (left) is displayed from 3D orthogonal view, while the enlarged target loci (right) are displayed from 2D view. And we have stated about this in the caption of Figure 4e."}]

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