Smart 3D super-resolution microscopy reveals the architecture of the RNA scaffold in a nuclear body

Domain: physics
Published: 2025-11-27
Editorial outcome: major_revision

Abstract
Small subcellular organelles orchestrate key cellular functions. How biomolecules are spatially organized within these assemblies is poorly understood. Here, we report an automated super-resolution imaging and analysis workflow that integrates confocal microscopy, morphological object screening, targeted 3D super-resolution STED microscopy and quantitative image analysis. Using this smart microscopy workflow, we target the 3D organization of NEAT1, an architectural RNA that constitutes the structural backbone of paraspeckles, a membraneless nuclear organelle. Using site-specific labeling, morphological sorting and particle averaging, we reconstruct the morphological space of paraspeckles along their development cycle from over 10,000 individual particles. Applying spherical harmonics analysis, we report so-far unknown heterotypes of NEAT1 RNA organization. By integrating multi-positional labeling, we determine the coarse conformation of NEAT1 within the organelle and show that the 3’ end forms a loop-like structure at the surface of the paraspeckle. Our study reveals key structural features of paraspeckle structure and growth, as well as the molecular organization of its scaffolding RNA.

Introduction
The precise molecular organization of biomolecules within larger assemblies or sub-cellular structures determines its function and is highly regulated in a cell1. Optical super-resolution microscopy (SRM) has positioned itself as a powerful tool to visualize the nano-scale architecture in cells2,3.

Applying SRM to unravel the nano-scale structure of large protein complexes or small organelles is, however, still challenging. This is due to, for example, imperfect labeling and a low signal-to-noise ratio (SNR), resulting in incomplete information on a particular object. If an object has a high symmetry and is highly abundant in a cell, its nanoscale structure can nevertheless be determined by acquiring a large amount of data and subjecting it to particle averaging analysis4,5. A very prominent example is the nuclear pore complex (NPC), a highly symmetric multi-protein assembly that mediates nucleocytoplasmic exchange6. Various optical SRM studies have contributed structural information on protein organization in NPCs7–9 that complemented information obtained from electron microscopy10. A more challenging situation arises when the target object in a cell shows structural variations or heterogeneities or is part of a dynamic process. In order to obtain nano-scale structural information for such structures, this demands an even larger amount of data, as well as analysis tools to extract structural variations and to group structurally similar instances11. In combination with multi-target labeling, even the structural organization of complex cellular machines such as the endocytotic complex become accessible12. To further generalize the approach to low-abundant cellular objects, correlative imaging workflows that combine fast high-throughput microscopy for cell screening with target-selective high-resolution microscopy13 and equipped with automated selection of objects14 can be employed. However, a super-resolution imaging and analysis workflow that is capable of reporting relevant 3D structural information on low-abundant, structurally heterogeneous sub-cellular objects remains challenging.

Here, we introduce an automated super-resolution imaging and analysis workflow that integrates large field of view (FOV) confocal microscopy, morphological object screening, targeted 3D SRM and quantitative image analysis. We developed this workflow alongside the structural study of a small and membraneless nuclear organelle, the paraspeckle, an RNA-protein condensate with a diameter of ~360 nm and a global shape ranging from spherical to ellipsoidal15. The long non-coding RNA Nuclear Enriched Abundant Transcript 1 (NEAT1) is an architectural RNA that scaffolds paraspeckles and determines their abundance and internal organization. During transcription, the 22.7 kb NEAT1_2 isoform interacts with 40 different paraspeckle proteins, including SFPQ, NONO and PSPC1, in a concerted manner. Through RNA-protein and protein-protein interactions NEAT1 adopts a V-shaped structure when assembled into paraspeckles with a characteristic core-shell structure, whereby the middle of the RNA lies at the core and the 5′ and 3′ ends set the boundary of the particle16,17. In contrast, the short 3.7 kb isoform, NEAT1_1, overlaps the 5′ end of NEAT1_2 and is not a major component of paraspeckles and may have paraspeckle-independent roles18. The molar ratio of the two isoforms can vary greatly depending on the cell type. For example, NEAT1_1 and NEAT1_2 occur in a ratio of roughly 2:1 in U-2OS cells, while their ratio in HeLa cells is around 1:919. Paraspeckles are important mediators of the nuclear stress response through the sequestration of specific proteins and RNAs, and its composition and shape is thought to be stress specific20,21. To understand the shape-function relationship requires 3D structural information on these heterogeneous objects.

The nuclear location of paraspeckles demands a super-resolution method that can cover the 3D depth of a cell, which can be achieved by 3D stimulated emission depletion (STED) microscopy22. The low abundance of 5–20 paraspeckles per nucleus23 and the slow imaging speed of 3D STED microscopy precludes the acquisition of a large amount of data required to extract relevant structural information. Therefore, we combined a large FOV pre-screening step with a targeted 3D STED imaging of identified objects. We first measured the global size and shape heterogeneity of paraspeckles, employing RNA-targeting fluorescence in-situ hybridization (FISH) probes that target the 3′ and 5′ ends of NEAT1_2. Using an automated particle sorting and averaging algorithm, we determined the global growth of paraspeckles in 3D. Furthermore, we developed a spherical harmonics-based image analysis approach to determine the orientation of the 3′ and 5′ ends within a paraspeckle, which allowed us to extract the heterogeneity in NEAT1_2 packaging in paraspeckles. Lastly, we determined the coarse conformation of NEAT1_2 by sequentially labeling 11 different positions along NEAT1_2 and found a region close to its 3′ end that is exposed to the outside of the particle. The resultant findings allow us to draft a model for paraspeckle growth and reveal so-far unknown structural phenotypes of RNA packaging. The developed methods are transferable to other subcellular particles and will contribute to the understanding of how the molecular architecture of these systems exert cell biological function.

Results
Automated, correlative high-resolution microscopy
We developed an automated volumetric 3D SRM workflow combining large FOV confocal microscopy and target-selective STED microscopy coupled to automated post-processing and analysis. First, confocal microscopy is applied for 3D imaging of a large number of cells (Fig. 1A). Next, automated image analysis identifies objects that are then subjected to targeted small volume 3D super-resolution STED microscopy (Fig. 1B). This automated procedure enables the unsupervised acquisition of a large amount of 3D super-resolution data of target objects in regions of interest (ROI) from entire cell volumes (Fig. 1C). Finally, an automated post-processing algorithm aligns and sorts the objects in the 3D super-resolution images based on their size and shape for further analysis (Fig. 1D).

Automated 3D STED imaging of paraspeckles in cells
We applied the imaging workflow to visualize nuclear paraspeckles in fixed HeLa cells, a commonly used cell line for their structural characterization23–26. For this purpose, we targeted NEAT1_2 using RNA-FISH probes extended by a single-stranded DNA handle and targeted by fluorophore-labeled locked nucleic acids (LNA) (Supplementary Table 1)27. Specifically, we labeled defined regions of NEAT1_2 (e.g., the 3′ end) with several RNA-FISH probes, each equipped with the same single-stranded DNA handle and targeted by the same fluorophore-labeled LNA, in order to enhance the fluorescence signal (see “Methods”) (Fig. 2A). We varied this strategy in the following three ways: (1) for a global morphological analysis of paraspeckles, we targeted both the 3′ and 5′ end of NEAT1_2 and labeled these with the same fluorophore; (2) in order to assess the distribution of the 3′ and 5′ ends of NEAT1_2 within paraspeckles, we targeted both the 3′ and 5′ end of NEAT1_2 but labeled these with two different fluorophores; (3) for a coarse conformation of NEAT1_2 in paraspeckles, we targeted the 3′ and 5′ ends of NEAT1_2 with the same fluorophore, and added a third set of RNA-FISH probes that targets an internal position of NEAT1_2 and is labeled with a second color. Next to enhancing the fluorescence signal recorded for specific regions of NEAT1_2, this approach is modular and, as only two fluorophore-labeled LNAs are needed (see “Methods”), cost efficient.

Paraspeckles are low in number and located at different positions in the nucleus. We applied the fully automated imaging workflow (Fig. 1A, B) to increase the efficiency and speed of data collection. In a first step, the full 3D volume of several pre-selected cells was recorded using low-resolution confocal microscopy over a large FOV (100 µm × 100 µm × 8 µm, isotropic pixel size of 250 nm) (Fig. 2B, Ci). Next, this imaging data was subjected to cross-correlation analysis with an ellipsoidal template representing a paraspeckle (Fig. 2Ci), returning a first estimate of the center coordinates of target objects (Fig. 2Cii). In the next step, a high-resolution confocal volume (2 µm × 2 µm × 4 µm, isotropic pixel size of 100 nm) was recorded at the position of identified objects, which was again subjected to cross-correlation analysis and reported more accurate information on the center coordinates of the object (Fig. 2Ciii). These coordinates defined the center of the ROI for super-resolution 3D STED imaging (1.1 µm × 1.1 µm × 1.1 µm, isotropic pixel size of 30 nm; resolution in Supplementary Table 2) (Fig. 2Civ). The last two steps were repeated for each individual identified object within a cell to minimize sample drift between measurements. In the case that multiple objects were identified within the high-resolution confocal volume, the coordinates were discarded and no STED measurement was performed. Supplementary Fig. 1A shows examples of accepted or rejected high-resolution confocal volumes. The ratio of accepted-to-rejected coordinates was around 4:1 (Supplementary Fig. 1B). To validate whether the automatically selected objects are indeed paraspeckles, we visualized NEAT1_2 together with the paraspeckle marker PSPC1 (see “Methods”)28. We performed an automated selection of coordinates using the NEAT1_2 signal and recorded two-color STED images of NEAT1_2 and PSPC1-GFP (Supplementary Fig. 2A). Almost 95% of the automatically selected particles showed a signal of PSPC1 (Supplementary Fig. 2B), indicating that our pipeline is robust.

Paraspeckles are heterogeneous in size and ellipticity
For a first global analysis of the size and morphological heterogeneity of paraspeckles, we labeled the 3′ and 5′ ends of NEAT1_2 using RNA-FISH probes targeted by two LNAs carrying the same fluorophore (Fig. 3A). The 3′ and 5′ ends of NEAT1_2 were reported to be positioned at the surface of paraspeckles16, and thus can serve to measure the 3D shape of the particles.

We recorded 3D STED images of 13,801 identified objects classified as bonafide paraspeckles using our automated imaging workflow (Supplementary Table 3, Supplementary Movie 1, Methods). These 3D volume images were rotated to align the major axis of the particles (i.e., the direction along which the particle is the longest) with the vertical axis of the image frame (Methods) (Supplementary Fig. 3A). The rotationally aligned images (Fig. 3B) were then sorted by their major axis lengths (Methods), binned into 250 groups of equal particle numbers (Supplementary Fig. 3B), were translationally aligned and averaged in each group (Methods) (Supplementary Fig. 3C).

For a quantitative analysis of the shape of paraspeckles, the average signal-to-center distance of the individual aligned particles was calculated along each axis to find the shell-to-center distances (Methods) (Supplementary Fig. 3D). These distances are plotted against particle number, sorted for their size as was done prior to translational alignment (Fig. 3C), and particle averaging and as histograms (Supplementary Fig. 4). The ratio between the major and the minor axis distance (Fig. 3D) represents a measure of elongation and ellipticity of the particle. Together, these two plots (Fig. 3C, D) allow us to group the shape of paraspeckles into three stages of progression: in the first stage, paraspeckles are approximately spherical, with an ellipticity of 1.14 to 1.30 (Fig. 3Ei, Supplementary Movie 2, Supplementary Table 4). In the second stage, the length of paraspeckles is increased along each axis, but at different scales and with the major axis showing the strongest increase, resulting in ellipsoidal paraspeckles with an ellipticity between 1.30 and 1.49 (Fig 3Eii, Supplementary Movie 2, Supplementary Table 4). In the third stage, the minor axis shell-to-center distance plateaued at 162 ± 16 nm (standard error of mean (s.e.m.)), while the intermediate axis increases shallowly between 185 and 215 nm, the major axis increases stronger to 235 and 315 nm, and the ellipticity reaches values of 2.0 (Fig 3Eiii, Supplementary Movie 2, Supplementary Table 4). 3487 out of the 13,801 imaged paraspeckles extended beyond the volume of the STED ROI (out-of-ROI; examples in Supplementary Fig. 5A). These out-of-ROI paraspeckles were analyzed separately, with a length-sorting and binning based on the intensity spread along the major axis (Methods). The averaged images reveal two additional phenotypes for elongated paraspeckles: tube-like paraspeckles with a division through the center (Fig 3Fi, Supplementary Movie 3) and closely neighboring pairs of paraspeckles (Fig 3Fii, Supplementary Movie 3). To assess the robustness of the acquisition pipeline distinguishing between paraspeckles inside and outside of an ROI, we treated HeLa cells with the proteasome inhibitor MG132, which was shown to increase the number of paraspeckles in cells and their size29 (Supplementary Fig. 5B, C). For all 13,801 paraspeckles, we used the fluorescence intensity as a measure for RNA quantity. To determine how the RNA quantity scales with paraspeckle volume, the intensity of images was averaged for each of the 250 sorting bins and plotted against the respective paraspeckle volume (Methods), showing a positive linear correlation (Supplementary Fig. 6, Supplementary Table 5).

NEAT1_2 5′ and 3′ ends frequently localize to opposite ends of the paraspeckle
Next, we sought to extract the position of the 3′ and 5′ ends of NEAT1_2 within paraspeckles. For this purpose, we labeled the 3′ and 5′ ends of NEAT1_2 with two spectrally orthogonal fluorophore-labeled LNAs and performed two-color 3D STED microscopy (Fig. 4A, Supplementary Movie 4, 5). We then rotationally and translationally aligned the two-color STED images and projected the 3D image intensities onto a spherical surface (Fig. 4A, left) (Methods). These surface intensity distributions were expanded into spherical harmonics (Fig. 4A, right) (Methods). The contribution of the first degree spherical harmonics to the power spectrum of the surface intensity distribution was used to measure the extent to which the 5′ and 3′ ends of NEAT1_2 localize to the same or opposite ends of the paraspeckle, expressed as a degree of polarization P.

We next grouped the paraspeckles into two categories based on their ellipticity: approximately spherical paraspeckles with ellipticity ≤1.3 and elongated paraspeckles with ellipticity >1.3. For the nearly spherical particles, the polarization ranges from 0.6% (no polarization) to 89.7% (near complete polarization), with a mean value of 44.1 ± 0.8% (s.e.m.) (Fig. 4B, Supplementary Table 6). This distribution of polarization degrees follows the same pattern as that of simulated spherical paraspeckles with the 5′ and 3′ ends of NEAT1_2 randomly distributed across the paraspeckle shell region (Supplementary Fig. 7). From the experimental data, we generated 3D volume representations showing the orientation of the 3′ and 5′ ends of NEAT1_2 for low (P < 7%), mid (44% < P < 47%) and high polarization (P > 80%) (Fig. 4C, Supplementary Movie 4). Paraspeckles with low polarization exhibit the 3′ and 5′ ends of NEAT1_2 scattered across the shell (Fig. 4C, left). Paraspeckles with high polarization show that the 3′ and 5′ ends of NEAT1_2 localize at opposite sides of the particle, and thus have a dipolar structure (Fig. 4C, right). The intermediate fraction of paraspeckles shows that paraspeckles with intermediate polarization have the 3′ and 5′ ends of NEAT1_2 distributed across the surface with a bias towards the opposite ends of the sphere (Fig. 4C, middle). 2D projections of polarized paraspeckles (P > 80%) show that the dipolar distribution of the 3′ and 5′ ends of NEAT1_2 is obscured (Supplementary Fig. 8), illustrating the need for 3D high-resolution data for meaningful interpretation of the particle structure.

This analysis was repeated for the elongated paraspeckles, with polarization values ranging from 0.9% to 90.1% and a mean value of 39.8 ± 0.6% (s.e.m.) (Fig. 4D, Supplementary Table 6). The 3D volume representations show similar structural phenotypes for the orientation of the 3′ and 5′ ends in the sphere for low (P < 3%), mid (35% < P < 46%) and highly polarized (P > 78%) paraspeckles (Fig. 4E, Supplementary Movie 5). The distribution of the polarization degrees across the elongated paraspeckles (Fig. 4D) follows the same pattern as that of the spherical paraspeckles (Fig. 4B).

In addition to the degree of polarization, we also defined a polarization direction as a vector pointing from the 5′ dominant to the 3′ dominant region (Methods). For elongated paraspeckles (ellipticity >1.3), we can also define the angle α between this polarization vector and the major axis (Methods) (Fig. 4F). For non-polarized elongated paraspeckles, the polarization vector is ill-defined, and we find α = 59 ± 3° (Fig. 4G, blue, Supplementary Table 7), close to the expected value for a random distribution, β = 57.3° (Methods). For polarized elongated paraspeckles, however, we find α = 71.5 ± 1.7° (Fig. 4G, red, Supplementary Table 7), exceeding the random angle. This indicates a non-random relationship between the polarization vector and the elongation of the paraspeckle, with a tendency towards perpendicular orientations. For approximately spherical paraspeckles (ellipticity ≤1.3), the major axis is ill-defined, and we indeed find near-random angles α = 57 ± 3° for both polarized and non-polarized paraspeckles (Supplementary Fig. 9, Supplementary Table 7). To summarize, elongated paraspeckles with polarized 3′ and 5′ ends are more likely to be polarized perpendicular to their major axis than parallel to their major axis.

Revealing the coarse conformation of NEAT1_2 in paraspeckles
Next, we aimed to determine the coarse conformation of the architectural RNA NEAT1_2 in paraspeckles. For this purpose, we labeled NEAT1_2 at the 3′ and 5′ ends with the same fluorophore-labeled LNA, serving as reference position, and at a second position within the RNA sequence with an orthogonal fluorophore-labeled LNA, serving as internal position (Fig. 5A, B). We performed two-color super-resolution STED microscopy of the identified paraspeckles, measuring 11 internal positions in separate imaging experiments. For each of the 11 positions, between 967 and 2992 two-color STED volumes were recorded (Supplementary Table 3). The average fluorescence intensity for each internal position increased linearly with paraspeckle volume (Supplementary Fig. 10, Supplementary Table 5).

The 3D images were rotated to align the paraspeckles along their major axis. For each of the 11 datasets, the images were binned into 20 groups based on paraspeckle major axis length (Supplementary Fig. 3B) (Methods). Next, the images were translationally aligned per bin (Methods) (Supplementary Fig. 3C). We aligned the volumes of the 11 internal positions with the transformation matrix from their corresponding reference channel. To visually assess the results, averages were made of the aligned images in each bin. We show cross-sections of one of these bin averages in Fig. 5C (Supplementary Table 8) for each of the 11 internal positions. Representative raw 2D cross-sections of individual spherical paraspeckles from all 11 RNA-FISH probe sets are shown in Supplementary Fig. 11. We observe that the signal from the RNA regions near the 5′ and 3′ ends average to hollow shells (Fig. 5C); the regions closer to the middle of the RNA produces smaller shells and regions closest to the middle appear as blobs. To quantify these different shapes, we calculated the mean distance to the paraspeckle center of the fluorescence signal from each RNA position (Methods) (Supplementary Fig. 3D; Fig. 5D; Supplementary Table 9). For simplification, we removed the outliers in Fig. 5D. The distribution including all data points is shown in Supplementary Fig. 12A. From nucleotides 1000 to 3000 (1–3K), near the 5′ end, to nucleotides 9–11K, the average fluorescence signal approaches the center of the paraspeckle. From nucleotides 10–12K to 16–18K, the average fluorescence signal approaches the outer boundary of the shell again. Interestingly, for nucleotides 18–20K, the fluorescence signal lies outside of the shell, before again reaching the outer boundary at nucleotides 20–22K. This indicates a loop of NEAT1_2 that is positioned outside of the putative boundary of the paraspeckle. This pattern is conserved across different paraspeckle phenotypes: in Supplementary Fig. 12B–E, the signal distances to the paraspeckle center are depicted for spherical (ellipticity ≤1.3) and elongated (ellipticity >1.3) paraspeckles separately, showing no significant differences (Supplementary Table 9). To further validate the presence of this loop region, we recorded imaging data of cells labeled for the 3′ end (22–22.7K nt) together with the 3′ loop region (18–20K nt), and compared this data to images where the 3′ end and the 5′ end (0–1K nt) were labeled. We found a regional overlap of the 3′ end with the 3′ loop, whereas this less observed for the 3′ end and the 5′ end (Supplementary Fig. 13, Supplementary Table 10). In conclusion, by visualizing different regions of the NEAT1_2 RNA using two-color STED imaging, we report the coarse conformation of the architectural RNA NEAT1_2 in an average paraspeckle with varying compaction levels with the 5′ end to center RNA region exhibiting higher compaction than the center-3′ counterpart, and a small protruding loop right before the 3′ end.

Methods
Sample preparation: cell culture, BAC transfection and drug treatments
HeLa wild type (WT) cells (German Collection of Microorganisms and Cell Cultures GmbH, Germany) were grown in high glucose DMEM supplemented with 1% (v/v) GlutaMAX, 10% (v/v) FBS, 100 µ/mL penicillin, and 100 µg/mL streptomycin (all reagents from Gibco, ThermoFisher, Germany) under humidified conditions at 37 °C and 5% CO2. All cells were passaged every 3–4 days or upon reaching 80% confluency.
For MG132 treatment and controls HeLa WT cells were cultivated on chamber slides (94.6190.802, SARSTEDT) overnight. On the following day, cells were treated with 10 μM MG132 (M7449-200UL, Sigma Aldrich) in fresh DMEM for 4 h. The same volume of DMSO was used as control (1.1 μL of DMSO for each 100 μL of medium). Cells were washed two times with phosphate buffer saline (PBS) prior fixation using 4% PFA (28908, Thermo Scientific).
Bacterial artificial chromosomes (BACs) harboring a sequence encoding for C-terminally GFP-tagged PSPC145 were isolated from E. coli DH10 cells using the NucleoBond™ Xtra Midi EF kit (Macherey-Nagel, Germany). WT HeLa cells were transfected with 1 µg purified BAC DNA per well in 6-well plates using jetPRIME (Polyplus, France). For stable integration of the BACs, cells were selected with 400 µg/mL Geneticin (G418, Gibco, ThermoFisher, Germany), sorted for single-cell clones with near-endogenous expression levels and expanded.

Sample preparation: RNA FISH
For FISH staining RNA, HeLa cells were seeded on 8-well chambered coverglass (Sarstedt, Germany) that were coated with fibronectin (Sigma-Aldrich, Germany) at a density of 30,000 cells per well and were incubated overnight at 37 °C and 5% CO2. Cells were fixed with 4% PFA in 1x PBS for 20 min, washed with PBS and permeabilized with 70% ethanol for at least 3 h at 4 °C. FISH was performed using Stellaris buffers (LGC Biosearch Technologies, UK) following the manufacturer’s protocol. Briefly, coverglasses were washed with Stellaris Wash Buffer A for 5 min at room temperature. Next cells were incubated with Stellaris Hybridization buffer containing the different FISH probes based on their respective positions (1 µmol/L, Supplementary Tables 1, 3, 11), placed in a humidified chamber and hybridized overnight at 37 °C protected from light. After hybridization, the coverslips were incubated with Stellaris Wash Buffer A. Cells were incubated with Wash Buffer B for 5 min and washed three times with PBS. FISH probes were designed using Stellaris Probe Designer (v4.2, LGC Biosearch Technologies, UK) (Supplementary Table 11). The single-stranded LNA were added at the right concentration (diluted in PBS with 500 mM NaCl) right before imaging (P2: 20 nM and P3: 30 nM; Supplementary Table 1).
For co-labeling of NEAT1_2 and PSPC1-GFP, cells were FISH-stained as described above, followed by incubation with an antibody incubation buffer (Massive Photonics, Germany) for 30 min at room temperature. Cells were washed three times with PBS and incubated with an antibody incubation buffer containing FluoTag-x4 anti-GFP Abberior STAR RED nanobodies (NanoTag Biotechnologies, Germany) in a 1:100 dilution for 1 h at room temperature. After washing three times with PBS cells were post-fixated using 4% FA in PBS for 10 min. For imaging 30 nM P3-LNA (Supplementary Table 1) were diluted in PBS with 500 mM NaCl and added right before imaging.
All oligonucleotide sequences used for RNA FISH and imager strands were purchased commercially (Supplementary Table 1).

Microscope
STED imaging was performed on an Abberior expert line microscope (Abberior Instruments, Germany) with an Olympus IX83 body (Olympus Deutschland GmbH, Germany) using a UPLXAPO 60x NA 1.42 oil immersion objective (Olympus Deutschland GmbH, Germany).
Confocal imaging was performed using a 561 nm excitation laser (2.9 µW at the back focal plane). Fluorescence was collected in the spectral range of 571 nm to 630 nm using an avalanche photodiode (APD). The images were acquired with a pinhole diameter of 1.0 AU, a pixel dwell time of 4 µs and a line accumulation of 1. A voxel size of 250 nm was used for overview images, respectively, 100 nm for the smaller ROI confocal volumes.
For 3D STED image acquisition of RNA-FISH stained cells, samples were excited with either a 561 nm or 640 nm pulsed excitation laser (4.1 µW and 3.9 µW at the back focal plane, respectively) and depleted using a 775 nm pulsed laser (212 mW and 275 mW at the back focal plane, respectively) with a 3D top-hat point spread function (PSF) and with an excitation delay of 750 ps and a 8 ns width. Fluorescence was collected in the spectral range of 571–630 nm (561 nm excitation) and 650–763 nm (640 nm excitation) using two APDs. The images were acquired with a pinhole diameter of 0.81 AU, line accumulation of 8, pixel dwell time of 10 µs and an isotropic voxel size of 30 nm.
For 3D STED image acquisition of immuno-stained PSPC1-GFP together with NEAT1_2, samples were excited with either a 561 or a 640 nm pulsed excitation laser (2.0 µW and 3.1 µW at the back focal plane) and depleted using a 775 nm pulsed laser (98 mW and 154 mW at the back focal plane). The voxel size was 35 nm.
For 2D STED image acquisition of RNA-FISH stained cells, samples were excited with either a 561 nm or 640 nm pulsed excitation laser (4.5 µW and 4.25 µW at the back focal plane, respectively) and depleted using a 775 nm pulsed laser (304 mW and 376 mW at the back focal plane, respectively) with a 2D donut shaped PSF and with an excitation delay of 750 ps. Fluorescence was collected in the spectral range of 571–630 nm (561 nm excitation) and 650–763 nm (640 nm excitation) using two APDs. The images were acquired with a pinhole diameter of 0.81 AU, line accumulation of 8, pixel dwell time of 10 µs and a pixel size of 25 nm.

Imaging automation
The microscope hardware is accessed through the dedicated software Imspector46. In the Imspector graphical user interface (GUI), the user can start the image acquisition by opening a measurement window, setting the imaging parameters for this window, and starting the measurement. In the current study, three different measurement windows were used, the parameters for which are given in section 1.2: the confocal overview window, the smaller confocal ROI window, and the 3D STED window.
Imspector has a Python interface, SpecPy, which makes measurement control through Python scripts possible. Using SpecPy, a Python script was written that enables unsupervised imaging (Fig. 2B–E) in a method similar to that proposed by Mol and Vlijm40. After the user has set the desired experimental parameters for the three measurement windows, they can run the script from the Imspector GUI to start the unsupervised 3D STED acquisition of 55 ROIs per hour.
Upon running the Python code from the Imspector GUI, a 100 µm × 100 µm × 8 µm overview is made in the first window with confocal resolution and isotropic 250 nm voxels (Fig. 2B). Normalized cross-correlations47 are performed with multiple ellipsoid templates T (Fig. 2C), which represent a coarse model of diffraction-limited paraspeckles. Each of these templates represents a spherical paraspeckle with a different radius, as chosen by the user upon initialization of the acquisition. For each radius r, a binary template image R, discretized to 250 nm voxels, is made first according to
R(x,y,z) = 1 if x^2 + y^2 + z^2 ≤ r^2, 0 else (1)
where x,y,z run from -r-border to r+border, with the empty border set to 500 nm. Four templates were used in the current work, with radii r = 50, 100, 150, 200 nm. The templates T are then generated by convolving the binary spheres R with a Gaussian function G, representing the microscope’s PSF:
T = R * G, (2)
G(x,y,z) = exp(-x^2/2σ_x^2 - y^2/2σ_y^2 - z^2/2σ_z^2), (3)
where * indicates the convolution, σ_x = σ_y are set at the lateral confocal resolution of 250 nm, and σ_z is set to 400 nm. In the four resulting normalized cross-correlation images, for each chosen template radius, the peaks higher than threshold 0.65 are then selected as the centers for ROIs.
The first ROI is imaged in the second Imspector window, again at confocal resolution but with a 2 µm × 2 µm × 4 µm FOV and isotropic 100 nm voxels (Fig. 2D). This new image is cross-correlated with the four ellipsoid templates, now discretized to finer 100 nm voxels. The ROI is discarded if all peaks in the cross-correlation images are below a threshold of 0.7, or if there are multiple non-neighboring peaks above this threshold. If the ROI is accepted, the highest cross-correlation peak is selected as the new ROI center. In the third Imspector window, a 1.11 µm × 1.11 µm × 1.11 µm voxel STED image is then made around the new ROI center with isotropic 30 nm voxels (Fig. 2E).
This pipeline is repeated for all ROIs found in the large confocal overview. When all accepted ROIs have been imaged at STED resolution, a new large FOV overview is made at a different sample position and the full process is repeated. The experiment lasts until the number of overviews set by the user has been reached, or until the user manually aborts the experiment.

Experiments
A total of 12 two-color imaging experiments was conducted to assess the conformation of the architectural RNA NEAT1_2 inside paraspeckles. In the first 11, Abberior STAR 635P (STAR635P) was used to label one of the 11 consecutive nucleotide regions along the RNA (Supplementary Table 1, Fig. 4A); Abberior STAR 580 (STAR580) was used for the 5′ and 3′ ends of the RNA to provide a consistent reference. In the 12th experiment, STAR580 was used to label the 5′ end and STAR635P to label the 3′ end. To perform the rotational and translational alignment, reference images were created by normalizing then summing each pair of images.
Through the automated imaging pipeline, between 967 and 2992 two-color 3D paraspeckle images were acquired for each experiment during overnight measurements.
Finally, for global shape analysis, the 5′/3′ reference images of all 11 internal probe experiments were combined in one reference image set of 13,801 images.
An additional imaging experiment was performed to validate the pipeline specificity for paraspeckles. STAR580 was used to label the 5′ and 3′ end of NEAT1_2 and STAR635P was used to label PSPC1. 2193D STED images were recorded.

Image analysis
The image analysis was performed using MATLAB (R2023a, MathWorks, USA) with additional functions from the DIPimage toolbox.

Analysis of PSPC1 signal
Two-color 3D STED images were averaged to 2D using a z-projection. NEAT1_2 signal was segmented using an intensity threshold. Mean fluorescence signal in the PSPC1 averages was measured inside and outside of the corresponding NEAT1_2 segment. The mean signal outside was subtracted from the mean signal inside. Furthermore, a 5% crosstalk of the NEAT1_2 signal was assumed and additionally subtracted from the mean signal inside. Remaining values greater than zero were assigned as PSPC1 signals.

Rotational alignment and sorting
We rotationally aligned and sorted the paraspeckle images before further analyzing and comparing them. The alignment was based on a principal component analysis of the reference channel. For each reference image, we calculated the 3×3 covariance matrix K using
K = Σ_k,l,m I(x_k,y_l,z_m) [x_k,y_l,z_m] [x_k,y_l,z_m]^T, (4)
where x,y,z are the center of intensity mean corrected 3D image coordinates (Supplementary Fig. 3D). Now, all paraspeckles are spherical with
[x,y,z] = [x^,y^,z^] - Σ_k,l,m I(x^_k,y^_l,z^_m) [x^_k,y^_l,z^_m], (5)
with x^,y^,z^ the voxel coordinates and I are the image intensity values. Variance relates to how the intensity values are spatially distributed in each dimension, with a smaller object giving lower variances. The transpose of the covariance matrix was then used as rotation matrix, for both the reference and the corresponding internal probe image to align the longest axis of the object with the vertical image axis.

Particle averaging
We used a particle averaging approach to translationally align the rotated images. Considering the heterogeneity among the imaged paraspeckles, the images were first grouped (binned) by the paraspeckles’ major axis length. This length was calculated as the distance between the 50% intensity drop-off points at either end of the image’s major axis (Supplementary Fig. 3B).
The binning was performed through one of two methods: with fixed bin size or with fixed length interval. The former, where a fixed number of images was assigned to each bin, was used for the combined reference images set, comprising the reference images of all 11 internal probe experiments. This set of 13,801 images was sufficiently large to assume inter-image similarity within 250 bins of 55–56 images each. For the smaller image sets of the individual internal probe experiments and the 5′/3′ experiments, this inter-image similarity was achieved by binning by major axis length: 20 linear intervals were set from the lowest to the highest major axis length in the set, and the images were attributed to 20 corresponding bins based on their major axis length.
For each bin, the averaging was initialized with a normalized sum of all rotationally aligned reference images in the bin, centered at the image center. Each individual image was then shifted to maximize the cross-correlation with this sum. This process was repeated once, initialized now with an updated template from the sum of the shifted images. The found translations that maximize the cross-correlations with this updated template were used to align the internal probe images as well. For both the reference and the internal probe images, the aligned images were also summed per bin to generate particle averages.
The calculation of the paraspeckle length based on the 50% drop-off points fails for paraspeckles that are imaged off-center or are larger than the imaged ROI. For these images, the drop-off points would lie beyond the image borders. As a consequence, the sorting and subsequent averaging of these images is less reliable. Images with the drop-off point at the image boundary were therefore excluded from the quantitative analyses described low. To still consider them qualitatively, an alternative sorting was performed on these out-of-ROI paraspeckles based on the eigenvalues of the covariance matrix (Eq. 4), which give the image variance along each axis. The variance along the major axis is then related to the length of the paraspeckle. While it is a more convoluted length measure than the 50% drop-off, it does allow for sorting of paraspeckles larger than the ROI. Sorting, binning, and averaging was therefore additionally performed on the out of ROI paraspeckles with this eigenvalue sorting, giving more reliable averages for larger paraspeckles.

Radial intensity distribution
To measure the distribution of each NEAT1_2 section within the paraspeckle, we calculated its average distance to the paraspeckle center from the individual rotationally and translationally aligned images. To include non-spherical paraspeckles, the images’ major and intermediate axes were first scaled by the ratio between the length of the paraspeckle along these axes and its length along the minor axis (Supplementary Fig. 3D). Now, all paraspeckles are spherical with radius equal to the length along the minor axis. The average signal-to-center distance was then found from the radial intensity distribution I_r, which describes the mean intensity as a function of the distance r to the image center. The radial distance d* for which the radial intensity below and above d* are equal was selected as the average signal-to-center distance:
∫_0^d* I_r dr = ∫_d*^L/2 I_r dr, (6)
where L is the width of the image and r is the radius. The average intermediate and major axis signal-to-center distances were then calculated by scaling this d* with the previously found ratio between the axes. Hence, three average signal-to-center distance values are found for each image: one for each axis.
For paraspeckles, the minor axis is the most well-defined and consistent17. Because of this, the distance along the minor axis was used when comparing the localizations of different NEAT1_2 regions.

Intensity vs. volume
For each paraspeckle image, we calculated the sum intensity by subtracting the background and summing the pixel values. To find the background value, the intensity median of the eight 7×7×7 pixel corners of the image was calculated. We estimated each paraspeckle volume by determining the peak image intensity value, counting the number of pixels with at least half this intensity, and multiplying this number with the pixel volume of 30 nm × 30 nm × 30 nm. To reduce the influence of noise and shape heterogeneity in determining the relationship between the image intensity and the paraspeckle volume, the intensity and volume values were then averaged over the image bins used to align and register the individual paraspeckles.

5′/3′ polarization
To study the relative localizations of the 5′ and the 3′ ends of NEAT1_2, we performed a two-color experiment with a different label on each of the two RNA ends. The resulting images were rotationally and translationally aligned using the normalized sum of the two channels as the reference.
We combined the aligned image pairs by subtracting the normalized 3′ channel from the normalized 5′ channel. We then projected the resulting 3D intensity information, in which negative values indicate 3′ dominant regions and positive values 5′ dominant regions, onto a spherical surface. To perform this projection, we first defined and discretized a spherical cone with a solid angle of 4π and its height equal to the radius of the inscribed sphere. We used this cone to sample the image volume at 512 positions on a highly uniform Fibonacci grid48, using linear interpolation where the cones cut voxels. The intensity function on the spherical surface was then composed by attributing the sum of the intensities inside each cone to the corresponding Fibonacci grid point on the surface. With our chosen sampling method, overlaps and gaps between the cones lead to over- and undercounting of some voxels. Summing the overcount values of all cones and comparing this sum to the analytical volume of the inscribed sphere gives us the total overlap volume of 19%; the summed undercount values give us the total gap volume of 22%. On average, this error per cone is smaller than the 3D PSF volume (Supplementary Table 2). The found intensity function on the spherical surface was expanded into spherical harmonics (Fig. 4A)49. The expansion of the intensity function A(Ω) = A(θ,φ) into spherical harmonics, with θ and φ the polar and azimuthal angles, respectively, is given by
A(Ω) = Σ_l=0^1 Σ_m=-l^l a_l^m Y_l^m(Ω) (7)
where Y_l^m is the spherical harmonic of degree l and order m, and a_l^m is the corresponding expansion coefficient. The spherical harmonic Y_l^m is given by49
Y_l^m(θ,φ) = (-1)^m sqrt((2l+1)/4π * (l-m)!/(l+m)!) P_l^m(cos θ) e^imφ, (8)
where P_l^m is the associated Legendre function. The expansion coefficients a_l^m are then found using:
a_l^m = N_φ N_θ Σ_φ=φ_1^φ_N Σ_θ=θ_1^θ_N Y_l^m(θ,φ)* A(θ,φ), (9)
where N is the number of conical sections, Δφ and Δθ are the azimuthal and polar angular separation between the conical sections, φ_1 and θ_1 are the respective azimuthal and polar angle of the first conical section and φ_N and θ_N are the azimuthal and polar angle of the Nth (512th) conical section. The summation reflects the discrete nature of the computational calculations.
To measure the 5′/3′ polarization, i.e., how strongly the two RNA ends localize to opposite ends of the paraspeckle, we first define the power spectrum of our signal A(Ω) using its spherical harmonics expansion:
Power = ∫ |A(Ω)|^2 dΩ = Σ_l=0^1 Σ_m=-l^l |a_l^m|^2 = Σ_l=0^1 Σ_m=-l^l |a_l^m|^2, (10)
where the last step follows from the orthonormality of spherical harmonics:
∫ Y_l1^m1(Ω) Y_l2^m2*(Ω) dΩ = δ_l1l2 δ_m1m2. (11)
The contribution C_l of each degree of spherical harmonics to this power spectrum is therefore given by:
C_l = 1/512 Σ_m=-l^l |a_l^m|^2. (12)
We are interested in the first degree spherical harmonics, which consist of two poles with opposite signs. We used their contribution to the power spectrum to measure the degree of polarization P:
P = C_1 / Σ_l=0^1 C_l, (13)
with the summed power spectrum included as a normalization factor.
The polarization analysis outlined above was repeated for 1000 simulated paraspeckles. We simulated these particles by randomly distributing 30 ± 5 pairs of points across a spherical surface with a radius of 180 nm. These images were blurred with a Gaussian filter with σ = 110 nm to resemble the experimental data.
For the experimental data, we also calculated the direction of polarization (polarization vector) using the expression for the spherical harmonics of the first degree. On the unit sphere, these expressions are49:
Y_1^-1 = sqrt(3/8π) sin θ e^-iφ = sqrt(3/8π) (x-iy)
Y_1^0 = sqrt(3/4π) cos θ = sqrt(3/4π) z (14)
Y_1^1 = -sqrt(3/8π) sin θ e^iφ = -sqrt(3/8π) (x+iy).
Ignoring the constant sqrt(3/8π), the spherical harmonics expansion Ψ using only degree l=1 becomes
Ψ = a_1^-1 (x-iy) + a_1^0 sqrt(2) z - a_1^1 (x+iy) = (a_1^-1 - a_1^1) x - i(a_1^-1 + a_1^1) y + sqrt(2) a_1^0 z, (15)
giving rise to polarization vector
v = [(a_1^-1 - a_1^1), -i(a_1^-1 + a_1^1), sqrt(2) a_1^0]^T (16)
The angle α_i between the polarization vector and coordinate axis i ∈ {1,2,3} is then found from the on-axis projections using
α_i = arccos(|v_i|). (17)
This angle was compared against the angle β between the z-axis (i=3) and the vector to a random point on a sphere. To find β, we first calculated the angle between the unit orientation vector, parametrized by spherical coordinates, and the unit vector along the z-axis:
arccos(n^ · z^) = arccos([sin θ cos φ, sin θ sin φ, cos θ] · [0,0,1]) = arccos(cos θ) = θ (18)
This angle was averaged over the surface of a hemisphere to find the random angle β:
β = 1/(2π) ∫_0^2π ∫_0^π/2 θ sin θ dθ dφ = 1/(2π) 2π = 1 rad ≈ 57.3°.
(19)

Calculation of Pearson correlation coefficient
The colocalization of the fluorescence signal in two detection channels was calculated using the Pearson correlation coefficient. The single images were preprocessed by normalizing their intensities to a [0,1] scale. For each image pair, the Pearson correlation coefficient was calculated on a pixel-by-pixel basis.

Statistics and reproducibility
No statistical method was used to predetermine sample size, and no data were excluded from the analyses. In addition, no sample size calculation was performed. All super-resolution experiments were performed at least in triplicates or at least 1000 particles were recorded. In the case of control experiments, particle numbers were limited (~70–200). Independent samples were obtained by FISH staining cells that were seeded on different days. Statistical analyses were performed using GraphPad Prism 9 (GraphPad Software, USA). The data was checked for normality using a Shapiro–Wilk test. For non-normally distributed datasets, a Mann–Whitney U test was employed. The significance level **** corresponds to a p value of < 0.0001.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, the Heilemann team developed an innovative super-resolution 3D imaging pipeline combining large field-of-view (FOV) confocal microscopy and target-selective small volume 3D stimulated emission depletion (STED) microscopy coupled to automated post-processing. This image acquisition-analysis workflow aims to achieve quantitative processing of large amounts of imaging data for molecular organization of 3D structures on low-abundant, heterogeneous subcellular objects, which is a prevailing challenge in cell biology thus is much appreciated. To demonstrate the power and value of this method, the authors explored the 3D organization of the NEAT1_2 non-coding RNA in paraspeckles, the architectural backbone of these highly heterogenous membraneless nuclear organelle. FISH probes targeting the 3’ and 5’ ends of NEAT1_2 known to be present at the spherical/ellipsoidal surface， were used to determine the heterogeneity of the size and shape of paraspeckles and NEAT1_2 orientation/polarization. Moreover, FISH probes at 11 sequential internal positions along NEAT1_2 were utilized to explore coarse conformation of NEAT1_2 within paraspeckles in relation to the 5’ and 3’ termini on the surface. The outcome of the studies corroborates many previously reported known features of NEAT1_2 hence showcased the capability of the acquisition-analysis method. In addition, new 3D structural information of NEAT1_2 and quantitative heterogeneity of paraspeckles are provided.\nHowever, there are substantial concerns that need to be addressed:\n1) Most data involved using the FISH probe targeting the 5’ end of NEAT1_2. However, the authors ignored the fact that the 3.7 kb of the 5’ end of NEAT1_2 completely overlaps with NEAT1_1, which is also present in paraspeckles. Thus, the detection of 5’ ends of NEAT1_1 by this FISH probe cannot be excluded. This is a major issue in determining distribution/polarization of NEAT1_2 terminals, especially considering the unknown abundance of NEAT1_1 molecules within the highly heterogenic paraspeckles. Moreover, the authors never mentioned the noise of this 5’ probe in detecting NEAT1_1, which is often more abundant and can also be present in non-paraspeckle nuclear bodies. At least NEAT1_1 needs to be eliminated and relevant experiments repeated. Without such specificity control, conclusions involving the 5’ probe may not reflect the actual 3D structure of NEAT1_2 within paraspeckles.\n2) Current studies of paraspeckle rely on co-labeling of NEAT1_2 and known paraspeckle proteins (e.g. NONO, PSPC1, and PSF) to ensure the identity of paraspeckles and exclude NEAT1 detection in non-paraspeckle nuclear bodies. The authors should also apply their acquisition-analysis method on particles co-labeled by NEAT1_2 FISH and known paraspeckle proteins IF and compare the heterogeneity on size and shape of paraspeckles determined by FISH of NEAT1_2 alone.\nConsidering the fact that the 5’-end probe detects NEAT1_1 as mentioned above, perhaps co-labeling of NEAT1_2 by the 3’ terminal FISH probe with the AGGF1 protein IF, which is located at the outside rim of some paraspeckles (FASEB J. 2022 Jun;36(6):e22366), may further demonstrate NEAT1_2 orientation/polarization and application value of their acquisition-analysis method as a proof of principle.\n3) Whether dynamic changes of paraspeckles can indeed be measured is a critical test of the stated value of the acquisition-analysis method. The authors must demonstrate the ability to quantitatively characterize elongated paraspeckles upon MG132 induced stress compared to controls.\n4) The authors found that “the paraspeckle volume is linearly dependent on the intensity measured for NEAT1_2 RNA” and concluded that the paraspeckle size and shape depend on the number of NEAT1_2 molecules inside. However, this is only an assumption and alternative possibilities exist. Direct evidence is required to support this conclusion, by manipulation of NEAT1_2 abundance (knockdown and increased biogenesis) as shown in many previous reports.\nSpecific comments\na) Among the 11 FISH probes targeting internal NEAT1_2 regions inside of paraspeckles (Fig. 5), again the probes targeting 5’end and 1-3k overlap with NEAT1_1 without demonstration of NEAT1_2 specificity.\nNo information is provided whether these internal probes have equal efficiency as compared to probes targeting terminal regions. In fact, in table 2, the numbers of images recorded and independent measurements for terminal probes appear to be much higher than internal probes. It is crucial to exclude the possibility that differences in calculated diameter arise from differential probe efficiency.\nIn addition to averaged spherical paraspeckles (Fig. 5C), perhaps representative actual images should also be presented. An explanation is needed why the averaged size of paraspeckles shown in Fig. 5C appear smaller than shown in Figure 3.\nb) The authors stated that the signal of the 16-22K probe protrudes beyond the boundary delineated by the 5' and 3' ends, forming a small loop (Fig. 5D). This is an important discovery and further evidence is needed to convince the readers, e.g. clear demonstration of colocalization and co-polarization of this loop with the 3’ end of NEAT1_2 and/or protrusion beyond other surface markers of paraspeckles, Ideally.\nc) Data in Figure 5D should be carefully reviewed. The apparent discrepancy in the numbers of data points between the top and bottom 5th percentiles (probe ) needs to be explained. For example, in the “5-7” group, only 23 data points fall within the 5th percentile, while more than 34 points are located in the bottom 5th percentile.\nMinor concerns:\ni) Light purple color is used to indicate the major axes in shell-to-center distances (Fig. 3C)” and also the “ratio of major/minor axis (Fig. 3D)”. Consider using a different color in Fig. 3D for clarity.\nii) Figure 4C and E show representative image of “44%<P<47%” and “35%<P<46%”, respectively. This is a slightly narrower bin than mentioned in the main text. Justification should be provided. Since the author assumes a certain distribution of the random degree of polarization, they should be able to provide the calculated significance of measured degree of polarization. Also, please clarify whether the scale bar is applied to the entire panel in Fig. 4C, 4D, and 5C.\niii) Regarding the predicted NEAT1_2 orientation, the authors hypothesized that the 5’ and 3’ terminals of NEAT1_2 are distributed end to end radially from straight lines to “V” shapes. Additional possibilities could also be consistent with their observation to explain the polarization, which should be discussed.", "reviewer_2": "In this manuscript, the authors developed an automated confocal-3D STED imaging flow and analysis tools to study the 3D structures of paraspeckles, a sub-nuclear particle thought to have important functions in mediating nuclear stress. These 360 nm particles are sparsely distributed inside nuclei. The 3D STED nanoscopy is perhaps more suitable than electron microscopy in this study because of the molecular specificity and easier 3D imaging that STED can provide. STED has a limited spatial resolution, usually between 30 to 100 nm, which means the number of pixels per particle is limited. However, spatial resolution may be enough to obtain the structural information that the authors want to acquire.\nThe imaging experiments were well-designed. First, they designed the automated imaging flow that allowed them to acquire over 10,000 individual particles at superresolution in 3D. In this workflow, the authors used a second confocal imaging session to carefully recenter a particle for 3D-STED imaging. Second, targeting both the 3’ and 5’ end of NEAT1_2 and labeling these with the same fluorophore, they performed a global morphological analysis of paraspeckles g. Third, in order to assess the distribution of the 3’ and 5’ ends of ends of NEAT1_2 within paraspeckles, they labeled the 3’ and 5’ ends with two different fluorophores. Lastly, to interrogate the course conformation of NEAT1_2, they labeled the 3’ and 5’ ends of NEAT1_2 with the same fluorophore and different internal locations of NEAT1_2 with a second fluorophore.\nThe quantitative analysis using multiple mathematical approaches is very impressive. This analysis allows the author to form a detailed picture of molecular alignments. They used the 3D ellipsoidal pattern to analyze the shape of the particles. They also used spherical harmonics to analyze the distribution of 3’ and 5’ ends and observe different types of distribution quantified by the polarization and polarization angle. According to the quantitative analysis, they propose the 3’ end forming a loop-like structure at the surface of the paraspeckle.\nOverall, this manuscript presents an excellent example for demonstrating the power of 3D STED superresolution microscopy integrated with extensive quantitative analysis to address suitable problems, achieving insights into molecular components and their structural conformation. The writing and presentation of this manuscript is outstanding. This reviewer specifically appreciates the well-thought color scheme used to present complicated data sets to ease understanding.\nTo further improve the quality of the manuscript, please address the following questions.\n1) The spatial resolution. The spatial resolution of the STED microscopy cannot be well defined by settings and optics. A characterization of the spatial resolution in 3D is recommended.\n2) Bleaching effect. The same sample is imaged multiple times. The bleaching effect could be a problem, especially in the case of 3D imaging, where the step size is set to a very small value. Please comment on the bleaching effect that may affect the signal level and accuracy of the quantitative analysis.\n3) In Figure 2 iv), please indicate the meaning of the color\n4) In Paragraph 4 on Page 4, “…and plotted against particle size (Fig 3C) and as histograms (Fig S2).” Please confirm if it is the particle size in Fig. 3D.\n5) In Figure 3C, the x-axis is sorted bin number? Not image number? It seems the data point is for each bin, not for each image.\n6) Figure 5C, please discuss why the internal probe images show bigger particles, especially at 1-3, 18-20, and 20-22.", "reviewer_3": "The authors' work is interesting, but I am not happy with their methodology. In particular, there are several issues in the context of using spherical harmonics.\n- On page 13, you write that you projected the 3D intensity information on a sperical surface. Which projection did you use? It is not uniquely defined based on the information that you provide in the manuscript.\n- Two lines above equation 7 you mention that the spherical surface is expanded into spherical harmonics, but this is not true. The intensity function on the surface is expanded, not the surface itself.\n- In the line below equation 7 you need to present explicitly the equations for the spherical harmonics Y_l^m and expansion coefficients a_l^m as there exist different conventions for defining them. Without mentioning the equations that you used, the analysis cannot be reproduced.\n- There are several problems with equation 8.\n# First, \"relative weights of the first degree ... expansion coefficients\" sounds a bit unspecific. For example, it does not suggest that the coefficients are squared in equation 8.\n# Second, I see no justification for defining the polarization as in equation 8. Here the polarization can even diverge. Why did you not simply calculate the angular power spectrum of the intensity distribution and choose the degree l=1 contribution from the angular power spectrum (or even better its square root, as it is frequently done in the literature) as a value for the polarization? The angular power spectrum is widely used and well known and would be a better choice than your definition.\n# Third, instead of dividing by the sum of all squared expansion coefficients in equation 8 (which is wrong, if I remember correctly, if one does not ensure equal contributions of the different degrees by including appropriate prefactors), you should simply normalize the intensity distribution function before you start to expand it.\n# Fourth, you should have taken the absolute squares of the coefficients and not just their squares. These coefficients have complex values. This means that with your definition of equation 8 you get complex values for the polarization, which does not make sense and is not in line with what you present in the main text.\n- Before equation 9 you cite reference 45. Which reference do you mean here? The list of references for the manuscript ends at reference 40.\n- Did you ensure that reference 45 uses the same convention for the spherical harmonics and corresponding expansion as you? If you used not the same convention, equations 9-11 are wrong in your case.\n- Equation 9 is not needed at all. Instead of calculating first a spherical harmonics expansion and using then a representation of the polarization vector in spherical harmonics, you could simply expand the known intensity function into a Cartesian expansion up to degree 1. The degree 1 contribution is the polarization vector.\n- Below equation 12 you mention a Fibonacci grid. There is no point in using a Fibonacci grid here (and the restriction to the polar angle seems to make is even wrong). When you want to have an expression for the angle between the z axis and a random point on the sphere, you can simply write down the unit orientation vector in 3D parametrized by spherical coordinates, multiply it by the unit vector along the z axis (scalar product), and calculate the arccos from it to obtain the angle between the z axis and another point on the sphere. Afterwards, you just need to average this over the spherical coordinates by a surface integral with a normalizing prefactor 1/(4Pi).\nDue to these issues I cannot recommend publication of this article in its present form.", "reviewer_4": "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."}, "author_response": "Response to Reviewer comments\nReviewer #1 (Remarks to the Author):\n1) Most data involved using the FISH probe targeting the 5’ end of NEAT1_2. However, the\nauthors ignored the fact that the 3.7 kb of the 5’ end of NEAT1_2 completely overlaps with\nNEAT1_1, which is also present in paraspeckles. Thus, the detection of 5’ ends of NEAT1_1\nby this FISH probe cannot be excluded. This is a major issue in determining\ndistribution/polarization of NEAT1_2 terminals, especially considering the unknown\nabundance of NEAT1_1 molecules within the highly heterogenic paraspeckles. Moreover, the\nauthors never mentioned the noise of this 5’ probe in detecting NEAT1_1, which is often more\nabundant and can also be present in non-paraspeckle nuclear bodies. At least NEAT1_1\nneeds to be eliminated and relevant experiments repeated. Without such specificity control,\nconclusions involving the 5’ probe may not reflect the actual 3D structure of NEAT1_2 within\nparaspeckles.\nResponse: We thank the reviewer for this important comment and apologize for not having\ndiscussed NEAT1_1 and the influence its presence would have on our results. Indeed, the first\n3.7 kb of both RNA isoforms are identical; thus, the FISH probes designed for and applied in\nthis study will target both RNAs. We were aware of this; yet in our study design, the presence\nof NEAT1_1 should not interfere with our results. We appreciate the opportunity to explain our\nreasoning in the following:\n1) A study by Chujo et al. quantified NEAT1 molecules in different cell types using an\nimproved RNA extraction method and Northern blotting. They found that in HeLa cells,\nwhich we used in this study, NEAT1_1 and NEAT1_2 isoforms occur in a ratio of almost\n1:9 1. This means the vast majority of detectable NEAT1 RNAs is the long isoform\nNEAT1_2.\n2) Another study by Li and colleagues had demonstrated that NEAT1_1 is localizing to\nparaspeckle-independent foci containing NEAT1_1, but not NEAT1_2. These\nparaspeckle-independent foci have been dubbed microspeckles and are on average\nsmaller than the minimal size of paraspeckles 2. Our automatically selecting and\nmeasuring pipeline accounts for size and should exclude almost all non-paraspeckle\nfoci. To validate this, we performed the colocalization experiment suggested by the\nReviewer. We discuss this in more detail in the later part of the answer to this comment\n(please see below).\n3) We know from electron microscopy analysis using probes for the 5’ end, middle and 3’\nend, that NEAT1_2 RNA extends throughout the core of a paraspeckle, with 5’ and 3’\nends aligned at the periphery 3. This arrangement was further supported by a study by\nWest and colleagues, where super-resolution structured illumination microscopy\nconfirmed the arrangement of both 5′ and 3′ ends of NEAT1_2 at the periphery of\nparaspeckles 4. Therefore our reference FISH probes also exclusively stained the shell\nregion of paraspeckles and are thus very capable of characterizing the global shape of\nthe paraspeckles.\n4) Together with the core/shell distribution of NEAT1_2 RNA West et al. reported that\nNEAT1_2 adopts a V-shape inside paraspeckles, which was obtained from\nmeasurements in FUS KO cells 4. This V-shape of single or bundles of NEAT1_2 is\ndisplayed in several publications 5–9. According to this model we expected a\nhomogeneously distributed signal of both NEAT1_2 ends in the paraspeckle shell\nregion. Surprisingly, some of the paraspeckles we analysed deviated from this model.\nMore precisely we observed a polarized signal for FISH probes that hybridize to the 3’\nand 5’ ends. From this we concluded that this is not explainable by sticking to a V-\nshaped RNA model, instead NEAT1_2 RNAs must follow a straight line through the\nparaspeckle. To avoid a potential corruption of the NEAT1_2 5’ end signal due to\nNEAT1_1 signal, we also investigated the distribution of FISH probes that bind in the\nregion from 5K to 7K of NEAT1_2, which is outside the overlap region but still near to\nthe 5’ end. Response Figure 1A shows paraspeckles with 5K to 7K staining in green\ntogether with the reference staining that binds in the paraspeckle shell. This clean\nNEAT1_2 5’ end signal also shows a polarised distribution in some paraspeckles.\nTogether with the polarised uncorrupted 3’ end signal shown in Figure 4 (main\nmanuscript) our data demonstrate that both ends of NEAT1_2 can occur in a polarized\ndistribution. This finding of a subset of paraspeckles with linearly arranged NEAT1_2\nmolecules is unlikely to originate from the NEAT1_1 signal, which represents only 10%\nof the total NEAT1 population in HeLa cells 1.\nTo further confirm this we investigated whether a different NEAT1_1/NEAT1_2\ncomposition of the paraspeckles influences the polarity values. Response Figure 1B\n(see below) shows the ratio of NEAT1 5’ end signal (which detects both isoforms) to\nNEAT1_2 3’ end signal plotted against the polarity and proves that there is no\ndependence. All taken together we conclude that our findings on the polarity are not\ninfluenced by the potential noise of our FISH probes binding to NEAT1_1.\nNevertheless, we apologize for the lack of classification and have therefore revised the\nmanuscript accordingly (introduction p.2 §3; discussion p.6 §2).\nResponse Figure 1: A Paraspeckles with “clean” NEAT1_2 5’ end (green) together with “reference” staining\n(magenta). Schematic illustration of RNA FISH probes staining NEAT1_1 and NEAT1_2 on the left and\nrepresentative two-color volumes of paraspeckles with polarised 5’ end signal. Scale bar = 500 nm. B Plot of\nintensity ratios between possibly corrupted NEAT1_2 5’ end signal and uncorrupted NEAT1_2 3’ end signal. Data\nis obtained from Figure 4. In each instance, a total of 100 data points were summarised. Error bars represent the\nstandard error of the mean (SEM).\n2) Current studies of paraspeckle rely on co-labeling of NEAT1_2 and known paraspeckle\nproteins (e.g. NONO, PSPC1, and PSF) to ensure the identity of paraspeckles and exclude\nNEAT1 detection in non-paraspeckle nuclear bodies. The authors should also apply their\nacquisition-analysis method on particles co-labeled by NEAT1_2 FISH and known paraspeckle\nproteins IF and compare the heterogeneity on size and shape of paraspeckles determined by\nFISH of NEAT1_2 alone.\nConsidering the fact that the 5’-end probe detects NEAT1_1 as mentioned above, perhaps co-\nlabeling of NEAT1_2 by the 3’ terminal FISH probe with the AGGF1 protein IF, which is located\nat the outside rim of some paraspeckles (FASEB J. 2022 Jun;36(6):e22366), may further\ndemonstrate NEAT1_2 orientation/polarization and application value of their acquisition-\nanalysis method as a proof of principle.\nResponse: We thank the reviewer for this important comment. We followed the suggestion of\nthe reviewer and conducted additional experiments to validate our experimental approach. We\nmeasured the colocalization of the identified RNA signal with the known paraspeckle protein\nPSPC1 to ensure highest selection quality. For this we used HeLa cells stably expressing a\nPSPC1-GFP fusion protein, which we labeled with a nanobody against GFP, together with\nFISH probes we termed “reference” in the manuscript (binding to NEAT1_2 in the regions 0 to\n1K and 22K to 22.7K nts). Response Figure 2A shows the resultant two colour 3D STED\nimages. Nearly 95% of the automatically selected particles showed a signal that can be\nassigned to PSPC1 (Response Figure 2B) proving the high specificity of our pipeline. After\naligning and sorting along the major axis, the curve of the elongation (Response Figure 2C)\nis similar to that in Figure 3C. We included this validation experiment of our automated\nmicroscope for the unsupervised measurement of paraspeckles in the manuscript (Figure S2;\nresults p.4 §1; discussion p.7 §3; methods p.10 §1, p.10 §4, p.11 §3, p.12 §8, p.12 §10).\nThese experiments were conducted with the support of Mrs. Ellen Kazumi Okuda. Because\nthis was a significant work package, we included Mrs. Okuda as a co-author.\nWe also thank the reviewer for suggesting to co-stain NEAT1_2 3’ ends with a protein (AGGF1)\nlocalizing to the shell region of paraspeckles to address the specificity of the FISH probes\nbinding near the 5’ end of NEAT1_2. The polarity analysis presented in our manuscript requires\na high degree of labeling of our targets. Unfortunately, classical immunostaining suffers from\na degree of labeling issues 10 relative to RNA-FISH. Hence, we used our FISH data presented\nalready in our manuscript. As shown earlier in comment 1 we used NEAT1_2 staining from 5K\nto 7K along with our reference staining as an alternative to staining a protein in the shell region.\nWe believe that this analysis provides a better solution for this issue (Response Figure 1A).\nResponse Figure 2: A Examples of automatically selected NEAT1 foci. Shown is a signal of NEAT1 stained with\n“reference” probes binding to the very 3’ and 5’ end of NEAT1_2 (magenta), immunofluorescence signal of PSPC1-\nGFP (green) and composites of both signals. Scale bar = 500 nm. B Number of automatically selected and\nmeasured particles showing overlap with PSPC1-GFP immunofluorescence signal and with no overlap. 219\nparticles were measured. C Ratio of the paraspeckles’ major to minor shell-to-center distance, obtained from\nparaspeckles showing overlap with PSPC1 signal. In each instance, a total of 10 data points were summarized.\nError bars represent the standard error of the mean (s.e.m.).\n3) Whether dynamic changes of paraspeckles can indeed be measured is a critical test of the\nstated value of the acquisition-analysis method. The authors must demonstrate the ability to\nquantitatively characterize elongated paraspeckles upon MG132 induced stress compared to\ncontrols.\nResponse: We thank the reviewer for this suggestion. MG132 treatment is known to increase\nparaspeckle size and number dramatically 11. HeLa cells treated with MG132 contained more\nand bigger paraspeckles than control cells (treated with DMSO), often accumulating together\n(Response Figure 3A). Responsively, a ~3 fold increase of NEAT1 RNA signal per cell was\ndetected (Response Figure 3B).\nOur intention for this manuscript was to follow the macroscopic size and shape of round to\nelliptical paraspeckles with the goal to further identify microscopic features within. For this\nreason, our pipeline excluded volumes with paraspeckles packed too densely (example in\nResponse Figure 3C) during the initial confocal screening. We also excluded paraspeckles\nexceeding the measured STED volume in the analysis (example in Response Figure 3D). By\napplying our pipeline to MG132 treated cells, we observed that the pipeline correctly rejects\nmore paraspeckles that are either too densely packed or too big (Response Figure 3E). For\naccepted paraspeckles we only find minor changes in the distribution of their axis length\ncompared to control cells (Response Figure 3F, G).\nTo analyze elongated paraspeckles expected after MG132 treatment, the overall volume\nmeasured by STED (currently 1.1µm x 1.1µm x 1.1µm) would have to be increased. In addition,\nthe analysis routine would have to be extended to a model capable of treating rod shaped\nparaspeckles and even shapes of higher complexity. This would be beyond the scope of this\nmanuscript.\nWe have revised the manuscript to better explain the acquisition and analysis pipeline (Figure\nS1; results p.4 §1; Fig S5; results p.5 §1).\nResponse Figure 3: Pipeline performance when utilised on MG132 treated cells.\nA Confocal measurements of HeLa cells treated with DMSO (left) and MG132 (right). Cells were treated with 10\nµM of MG132 (M7449-200UL, Sigma Aldrich) or similar volume of DMSO for 4 hours, fixed and stained using RNA-\nFISH probes hybridising to the very 3’ and 5’ end of NEAT1_2. Scale bar = 20 µm. B Mean NEAT1 fluorescence\nsignal per cell for DMSO (gray) and MG132 (light red) treated HeLa cells. N = 50. C Examples of confocal screened\nROIs containing paraspeckles. ROIs matching with a single ellipsoidal template are accepted for a subsequent\nSTED measurement (green). ROIs containing multiple paraspeckles are rejected (red). Scale bar = 500 nm. D\nExamples of STED volumes containing paraspeckles stained with FISH probes binding to the very 3’ and 5’ end of\nNEAT1_2. Volumes containing paraspeckles confined within the volume of the STED ROI were accepted and\nfurther analysed; volumes containing paraspeckles that protruded outside the STED ROI volume were rejected.\nScale bar = 300 nm. E Fractions of automatically found paraspeckles that are measured with STED and further\nanalysed (green), that were measured with STED but extended beyond the volume of the STED ROI (“out of ROI”;\norange) and multiple paraspeckles packed densely that were not measured with STED (red). Shown is data for\nDMSO (gray) and MG132 (light red) treated cells. F Measured shell-to-center distances of the paraspeckles along\ntheir minor, intermediate and major axes for STED volumes acquired for DMSO (black, gray and light gray) and\nMG132 (red, light red and very light red) treated cells. The points indicate the averages over 75 raw images; the\ncorresponding s.e.m. is indicated by the bars. G Ratio of the paraspeckles’ major to minor shell-to-center distance\nfor DMSO (black) and MG132 (red) treated cells. 1500 cells were measured each.\n4) The authors found that “the paraspeckle volume is linearly dependent on the intensity\nmeasured for NEAT1_2 RNA” and concluded that the paraspeckle size and shape depend on\nthe number of NEAT1_2 molecules inside. However, this is only an assumption and alternative\npossibilities exist. Direct evidence is required to support this conclusion, by manipulation of\nNEAT1_2 abundance (knockdown and increased biogenesis) as shown in many previous\nreports.\nResponse: We thank the reviewer for this suggestion. We measured the 3D surface of PS\nparticles from the 5’ fluorescence signal detected with 3D STED microscopy. From this, we\ncalculated the 3D volume of the PS sphere, and the fluorescence intensity recorded for this\nPS. Plotting both values for a large number of particles yielded the almost linear dependency\nthat we show in (Figure S3 and S6; Table 4). We agree with the reviewer that this is a\ncorrelation, and not a causation – and softened our statement in the Discussion section\naccordingly (discussion p.7 §3).\nSpecific comments\na) Among the 11 FISH probes targeting internal NEAT1_2 regions inside of paraspeckles (Fig.\n5), again the probes targeting 5’end and 1-3k overlap with NEAT1_1 without demonstration of\nNEAT1_2 specificity.\nNo information is provided whether these internal probes have equal efficiency as compared\nto probes targeting terminal regions. In fact, in table 2, the numbers of images recorded and\nindependent measurements for terminal probes appear to be much higher than internal\nprobes. It is crucial to exclude the possibility that differences in calculated diameter arise from\ndifferential probe efficiency.\nIn addition to averaged spherical paraspeckles (Fig. 5C), perhaps representative actual\nimages should also be presented. An explanation is needed why the averaged size of\nparaspeckles shown in Fig. 5C appear smaller than shown in Figure 3.\nResponse: We thank the reviewer for these insightful comments. We addressed the presence\nof NEAT1_1 and NEAT1_2 in an earlier response (please see above).\nEach internal probe was measured with a terminal probe as a reference. The images shown\nin Table 2 (updated Table 3) are the pooled data of all terminal probes measured during each\ninternal probe acquisition. Hence the disparity in the amounts between internal and terminal\nprobes. Importantly, our image analysis procedure alleviates possible effects of incomplete\nlabeling by (i) referencing the probe signal to a signal in the PS shell (3’/5’) and by (ii) employing\nparticle averaging (Figure 5C). To clarify this, we added a short explanation on the averaging\nprocess (results p.6§3).\nAll probes have been designed using Stellaris Designer and the maximum number of probes\nreceived was used for the experiments. Although the efficiency of the individual FISH probes\n(521 different probes were used) is not known and difficult to determine, all probe sets provided\na comparable signal intensity in the different NEAT1 regions (Figure S6 and S10; Table 4).\nResponse Figure 4 shows individual raw images for all probe sets used in this study. The\nintensity distribution in these images do not hint at obvious signs of incomplete labeling. It is\nto be noted that lower signal to noise ratios would lead to a broadened intensity distribution\nwith a lesser influence on the mean of the distribution.\nWe thank the reviewer for the suggestion to present representative actual images and added\nthese to the manuscript (Figure S11, results p.6 §3)\nWe thank the reviewer for drawing our attention to the fact that selected examples in Figure\n5C appear smaller. We indeed selected the wrong images and have corrected this error in the\nrevised manuscript (correction in Figure 5C).\nResponse figure 4: Representative 2D cross-sections of individual spherical paraspeckles all 11 RNA-FISH\nprobe sets. For visualisation all two-color 3D STED volumes were aligned rotationally and translationally using a\ntransformation matrix calculated from Reference channel signal. Subsequently images are obtained by summing\nup the three centre XY layers. The top row shows fluorescence images of RNA-FISH probes hybridizing to the very\n3’ and 5’ end of NEAT1_2 (Reference; magenta). The middle row shows fluorescence images of RNA-FISH probes\nwhich each hybridize to the corresponding internal position of NEAT1_2 (Internal; green). The bottom row shows\nthe composite of Reference and Internal. Scale bar = 300 nm.\nb) The authors stated that the signal of the 16-22K probe protrudes beyond the boundary\ndelineated by the 5' and 3' ends, forming a small loop (Fig. 5D). This is an important discovery\nand further evidence is needed to convince the readers, e.g. clear demonstration of\ncolocalization and co-polarization of this loop with the 3’ end of NEAT1_2 and/or protrusion\nbeyond other surface markers of paraspeckles, Ideally.\nResponse: We are pleased that the reviewer highlights the importance of our discovery of an\nadditional loop of NEAT1_2 in paraspeckles in the region of about 16K to 22K nts, which also\nrepresents the largest distance to the paraspeckle center.\nOur experiments were designed to rigorously map multiple distances along NEAT1_2 with\nrespect to a reference signal. As a reference signal, we chose the 5’ and 3’ ends of NEAT1_2,\nwhich define the shell region of paraspeckles 3,4 and are qualified as surface markers.\nSpecifically, a small region at the very 5’ (0 to 1K nts) and 3’ end (22K to 22.7K nts) of NEAT1_2\nwere chosen to act as a “reference” to describe the global size and shape of the paraspeckles.\nAll of the other 11 probe sets stretching over the remaining ~93% of NEAT1_2 dubbed\n“internal” were recorded together with the “reference” probes, and all distances were evaluated\nin comparison to the “reference”. Only the “internal” probes binding NEAT1_2 in the regions\nfrom 18K to 20K protrude the shell boundary (defined by the “reference” signal) and excel the\ndistances measured for all other “internal” regions. In addition, this finding is flanked by the\ndistances measured for the adjacent regions 16-18K and 20-22K. Furthermore, the distribution\nshown in Figure 5D, again taking into account the reference signal, shows a continuous curve.\nTaken together, we are convinced that this proves beyond doubt the existence of the loop\nstructure of NEAT1_2 in the range 16K to 22K nts.\nFollowing the suggestion of the reviewer, we added images of individual dual-labeled\nparaspeckles (Response Figure 4 and Figure S11).\nc) Data in Figure 5D should be carefully reviewed. The apparent discrepancy in the numbers\nof data points between the top and bottom 5th percentiles (probe ) needs to be explained.\nResponse: The plot shown in Figure 5D is a zoom in and the remaining points are outside the\nrange shown, which is why the number above and below may vary. The scale was chosen to\ndirect the attention to the main part of the points and their median than to the outliers. After all,\nthere are more than 690 data points per plot shown. We would like to thank the reviewer for\nnoticing this and allowing us to maintain the highest scientific standards. For correction we\namended Figure 5D by removing the outliers and adding a new panel A to Figure S12 that\nincludes all data points and proceeding the same way with the two plots in Figure S12.\nCaptions of Figure 5 and S12 have been adapted accordingly.\nMinor concerns:\ni) Light purple color is used to indicate the major axes in shell-to-center distances (Fig. 3C)”\nand also the “ratio of major/minor axis (Fig. 3D)”. Consider using a different color in Fig. 3D for\nclarity.\nResponse: We thank the reviewer for his suggestion to enhance clarity. We changed the color\nin Figure 3D to black.\nii) Figure 4C and E show representative image of “44%<P<47%” and “35%<P<46%”,\nrespectively. This is a slightly narrower bin than mentioned in the main text. Justification should\nbe provided. Since the author assumes a certain distribution of the random degree of\npolarization, they should be able to provide the calculated significance of measured degree of\npolarization. Also, please clarify whether the scale bar is applied to the entire panel in Fig. 4C,\n4D, and 5C.\nResponse: We thank the reviewer for drawing our attention to the inconsistency between the\nfigures given in the main text and those in Figure 4. The numbers in the main text were\nincorrect and have been corrected (results p.5 §4).\nRegarding the polarization, we have two interpretations of this comment and decided to\naddress both:\n(i) the distribution of polarization values was derived from analyzing the intensity distribution of\n5’ and 3’ signals on the surface of single PS particles and is shown in Figure 4BD. There is\nno assumption on a random distribution underlying this analysis. However, and in response to\na similar question raised by reviewer #3, we conducted a simulation of random distribution of\n3’ and 5’ signals on the surface of PS particles (see Response Figure 5; and a detailed\ndescription on that simulation in the response to reviewer #3). Comparing this simulated\ndistribution with the experimental distribution using a two-tailed Mann-Whitney-U-test yielded\nno significant difference in the populations (p=0.59).\nResponse figure 5: Compared distributions for polarization degree of 3’ and 5’ ends. Degree of polarization\namong spherical paraspeckles for experimental and simulated data.\n(ii) Should the reviewer have referred to a random orientation (not polarization), it would be\nrelated to the data shown in Figure 4G. The random orientation of two vectors in 3D space is\ncalculated as 1 radian ≈ 57° and shown as a thin line in the graph. We added a short summary\nat the end of the paragraph that conveys the main message of the result. To enhance clarity,\nwe have included a summarizing sentence at the end of this section (results p.6 §1).\nThe description of scale bars for the subpanels mentioned were clarified.\niii) Regarding the predicted NEAT1_2 orientation, the authors hypothesized that the 5’ and 3’\nterminals of NEAT1_2 are distributed end to end radially from straight lines to “V” shapes.\nResponse possibilities could also be consistent with their observation to explain the\npolarization, which should be discussed.\nResponse: Considering a minor contribution from NEAT1_1 (see above), and taking into\naccount our experimental observations of non-polarised, partially polarised and fully polarised\nparaspeckles, more than one configuration must be possible. The very likely hypothesis is that\nnext to V-shape, a straight-across configuration occurs. Yet we agree with the reviewer that\nother configurations, of intermediate shape, might occur and for example explain partially\npolarized particles. For these reasons, and to leave room for other interpretations (which we\ndo not have experimental access to yet, though), we phrased this rather broad statement in\nthe discussion of the manuscript.\nReviewer #2 (Remarks to the Author):\n1) The spatial resolution. The spatial resolution of the STED microscopy cannot be well defined\nby settings and optics. A characterization of the spatial resolution in 3D is recommended.\nResponse: We agree with the reviewer that a characterization of the spatial resolution should\nbe given. Therefore we calculated the axial and lateral resolution of STED images acquired\nwith Abberior STAR 580 (STAR 580) and Abberior STAR 635P (STAR 635P) using single\nimage Fourier ring correlation 12. Results are shown in Response Table 1 and are added to\nthe manuscript (Table 2; results p.4 §1)\nResponse Table 1: Median Resolution of measured STED images. Resolutions were determined by\nperforming single image Fourier ring correlation on 100 central cross sections (sum of 3 central slices) of images\nacquired using P3-STAR 580 and P3-STAR 635P. Errors are given as standard deviation.\nImager strand Cross section view Resolution / nm\nP3-STAR 580 XZ 133±15\nXY 112±13\nYZ 123±15\nP3-STAR 635P XZ 109±13\nXY 100±16\nYZ 106±12\n2) Bleaching effect. The same sample is imaged multiple times. The bleaching effect could be\na problem, especially in the case of 3D imaging, where the step size is set to a very small\nvalue. Please comment on the bleaching effect that may affect the signal level and accuracy\nof the quantitative analysis.\nResponse: We agree with the reviewer that a bleaching effect may affect the results of the\nquantitative analysis. We didn't expect a significant bleaching effect influencing the presented\ndata because of the following reasons: For both of the screening steps very low laser intensities\nwere used (571 nm; 2.9 µW at the back focal plane). This and the high amount of fluorophores\nper stained RNA region makes us very confident that there isn’t any noticeable photobleaching\noccurring during the screening steps. For the 3D STED imaging we choose to record XZY\nstacks over XYZ due to recommendation 13,14. For 3D STED recording with a top-hat point\nspread function it is supposed to avoid a decreasing signal in later recorded sheets.\nTo check for bleaching, we studied the image intensities summed along the Y-direction, over\nthe XZ planes. Since we recorded XZY stacks, bleaching would cause a sharp drop-off along\nthe Y-direction. We compare this to a control of sums along the X-direction, with the ZY planes\nsummed. We found only a minor drop-off along the Y-direction (Response Figure 6). We can\nconclude that we did not experience detrimental bleaching.\nResponse Figure 6: Summed up intensities of recorded image planes along the X- e.g. Y-direction.\n3) In Figure 2 iv), please indicate the meaning of the color\nResponse: In Figure 2 vi) the colors indicate the stained part of the exemplary paraspeckle.\nThe shell region is indicated by magenta and the core region by green color. Clarification has\nbeen added to Figure 2 caption.\n4) In Paragraph 4 on Page 4, “…and plotted against particle size (Fig 3C) and as histograms\n(Fig S2).” Please confirm if it is the particle size in Fig. 3D.\nResponse: We thank the reviewer for spotting this. It is the particle number sorted for size as\nwas done prior to the translational alignment and particle averaging. The sentence in the\nmanuscript has been revised accordingly (results p.4 §4).\n5) In Figure 3C, the x-axis is sorted bin number? Not image number? It seems the data point\nis for each bin, not for each image.\nResponse: The lines in Figure 3C indicate the envelope around the single image points. For a\nmore clear figure, points were added that each correspond to the mean of 100 single images.\nPlease note that these are not the same bins as were used for the translational alignment and\nparticle averaging.\n6) Figure 5C, please discuss why the internal probe images show bigger particles, especially\nat 1-3, 18-20, and 20-22.\nResponse: Reviewer #1 also raised a question very similar to this. Due to miscommunication\non our part we selected the wrong images to present. More precisely, the averages for the\nreference probes were displayed with the wrong zoom settings. Therefore all reference\nparticles appeared too small. The Figure 5C is now corrected in the manuscript.\nReviewer #3 (Remarks to the Author):\n1. On page 13, you write that you projected the 3D intensity information on a spherical\nsurface. Which projection did you use? It is not uniquely defined based on the\ninformation that you provide in the manuscript.\nTwo lines above equation 7 you mention that the spherical surface is expanded into\nspherical harmonics, but this is not true. The intensity function on the surface is\nexpanded, not the surface itself.\nResponse: Thank you for bringing this detail to our attention, this is indeed what we meant.\nWe added an explanation of the projection method and rewrote the paragraph for clarity\n(methods p.15 §3):\n“We combined the aligned image pairs by subtracting the normalized 3' channel from the\nnormalized 5' channel. We then projected the resulting 3D intensity information, in which\nnegative values indicate 3' dominant regions and positive values 5’ dominant regions, onto a\nspherical surface. To do so, the image volume was first divided into 512 spherical cones of\nequal volume. The intensity was summed in each cone. The spherical surface was then\ncomposed by attributing these sums to the surface points corresponding to the centres of the\ncorresponding cones. Subsequently, the found intensity function on the spherical surface was\nexpanded into spherical harmonics. The expansion of the intensity function A(Ω)=A(θ,ϕ) into\nspherical harmonics, with θ and ϕ the polar and azimuthal angles, respectively, is given by…”\n2. In the line below equation 7 you need to present explicitly the equations for the\nspherical harmonics Y_l^m and expansion coefficients a_l^m as there exist different\nconventions for defining them. Without mentioning the equations that you used, the\nanalysis cannot be reproduced.\nResponse: We included the definition in the main text (methods p.15 §3):\n“The expansion of the intensity function A(Ω)=A(θ,ϕ) into spherical harmonics, with θ and ϕ the\npolar and azimuthal angles, respectively, is given by\nwhere 𝑌\" is the spherical harmonic of degree l and order m, and 𝑎\" is the corresponding\n! !\nexpansion coefficient. The spherical harmonic 𝑌\" is given by Arfken et al., 2013. 15\n!\nwhere 𝑃\" is the associated Legendre function. The expansion coefficients 𝑎\" are then found\n! !\nusing:\nThe summation reflects the discrete nature of the computational calculations.”\n3. There are several problems with equation 8.\nFirst, \"relative weights of the first degree ... expansion coefficients\" sounds a bit\nunspecific. For example, it does not suggest that the coefficients are squared in\nequation 8.\nResponse: Thank you for addressing this. We have updated the manuscript to be more explicit\nabout our measure of the degree of polarisation (methods p.15-16). See the discussion below\nfor more details on this measure.\nSecond, I see no justification for defining the polarization as in equation 8. Here the\npolarization can even diverge. Why did you not simply calculate the angular power\nspectrum of the intensity distribution and choose the degree l=1 contribution from the\nangular power spectrum (or even better its square root, as it is frequently done in the\nliterature) as a value for the polarization? The angular power spectrum is widely used\nand well known and would be a better choice than your definition.\nThird, instead of dividing by the sum of all squared expansion coefficients in equation\n8 (which is wrong, if I remember correctly, if one does not ensure equal contributions\nof the different degrees by including appropriate prefactors), you should simply\nnormalize the intensity distribution function before you start to expand it.\nFourth, you should have taken the absolute squares of the coefficients and not just\ntheir squares. These coefficients have complex values. This means that with your\ndefinition of equation 8 you get complex values for the polarization, which does not\nmake sense and is not in line with what you present in the main text.\nResponse: We thank the reviewer for noticing the typo in the original equation regarding the\nabsolute values. We have corrected this (methods p.16 eq.12). We hereby expand on our\nchoice of measure of the degree of polarisation.\nWe used the spherical harmonics 𝑌\" in their orthonormal definition (eq. 8), with\n!\nThe orthonormality is useful when expressing the power spectrum of the intensity distribution\non the spherical surface, A(Ω), using the spherical harmonics expansion:\nHence, the contribution of each degree to the power spectrum is given by the sum of the\nsquared moduli of the expansion coefficients:\nWe are interested in the relative contribution of the first-degree spherical harmonics. To\nmeasure the degree of polarisation, we normalised the contribution of these first-degree\nspherical harmonics to the total power:\nThis degree of polarisation takes values between 0 (no polarisation) and 1 (fully polarised). It\ndoes not diverge, as C_1 is included in the normalisation sum. We have added the four\nexpressions above to the manuscript (methods p.16 eq.10-13). We also updated Figure 4A\nand the corresponding caption to include equation 13, the detailed expression for the degree\nof polarization.\nTo allow for intuitive interpretation of this measure, it should follow roughly a normal distribution\nbetween 0 and 1, centred at 0.5, for a completely random distribution of the 5’ and 3’ ends of\nNEAT1_2 across the paraspeckles surface. To verify that our measure fulfills this, we\nsimulated 1000 such paraspeckles and found the following distribution (with the degree\nmultiplied by 100 to get a percentage). Histogram and representative examples are in\nResponse Figure 7. Of note, this is in line with the findings from our experimental data (Figure\n4B, C). We can conclude that our measure for the degree of polarisation accurately represents\nthe contribution of the first-degree spherical harmonics to the power spectrum of our signal.\nFurthermore, it follows an approximately random normal distribution between 0 and 1, with a\nmean around 0.5, for a randomly distributed signal. We added the simulated data of spherical\nparaspeckles to the manuscript (Figure S7; results p.5 §4; methods p.16 §5).\nResponse Figure 7: Polarisation degrees for simulated paraspeckles with random distributed 3’ and 5’ ends\nof NEAT1_2. A Distribution of measured degree of polarization for 1000 simulated paraspeckles. B Examples of\nsimulated spherical paraspeckles with the 5’ and 3’ ends of NEAT1_2 randomly distributed across the paraspeckle\nshell region (Methods).\n4. Before equation 9 you cite reference 45. Which reference do you mean here? The list\nof references for the manuscript ends at reference 40.\nResponse: We thank the reviewer for noticing this issue with a few citations. We have checked\nthe references and corrected the errors.\n5. Did you ensure that reference 45 uses the same convention for the spherical harmonics\nand corresponding expansion as you? If you used not the same convention, equations\n9-11 are wrong in your case.\nResponse: The same convention was used as in the reference.\n6. Equation 9 is not needed at all. Instead of calculating first a spherical harmonics\nexpansion and using then a representation of the polarization vector in spherical\nharmonics, you could simply expand the known intensity function into a Cartesian\nexpansion up to degree 1. The degree 1 contribution is the polarization vector.\nResponse: There are indeed different ways to estimate the polarisation vector. We have\nalready calculated the spherical harmonics expansion to find the degree of polarisation. We\nuse the same expansion to find the polarisation vector to avoid doing additional calculations.\n7. Below equation 12 you mention a Fibonacci grid. There is no point in using a Fibonacci\ngrid here (and the restriction to the polar angle seems to make is even wrong). When\nyou want to have an expression for the angle between the z axis and a random point\non the sphere, you can simply write down the unit orientation vector in 3D parametrized\nby spherical coordinates, multiply it by the unit vector along the z axis (scalar product),\nand calculate the arccos from it to obtain the angle between the z axis and another\npoint on the sphere. Afterwards, you just need to average this over the spherical\ncoordinates by a surface integral with a normalizing prefactor 1/(4Pi).\nResponse: Following the proposed method (but integrating over a hemisphere, as the\npolarisation vector falls between 0 and pi/2):\nOf course, the result is the same as with the Fibonacci grid. We have replaced the Fibonacci\ngrid method with the suggested derivation for simplicity (methods p.17 eq.18-19).\nDue to these issues I cannot recommend publication of this article in its present form.\nReviewer #3 (Remarks on code availability):\nAs my report mentions some issues with the analysis, the code needs to be revised\naccordingly.\nWe have updated the code according to the discussion above.\nReferences\n1. Chujo, T. et al. Unusual semi-extractability as a hallmark of nuclear body-associated\narchitectural noncoding RNAs. The EMBO Journal (2017)\n2. Li, R., Harvey, A. R., Hodgetts, S. I. & Fox, A. H. Functional dissection of NEAT1 using\ngenome editing reveals substantial localization of the NEAT1_1 isoform outside\nparaspeckles. RNA 23, 872–881 (2017).\n3. Souquere, S., Beauclair, G., Harper, F., Fox, A. & Pierron, G. Highly ordered spatial\norganization of the structural long noncoding NEAT1 RNAs within paraspeckle nuclear\nbodies. Mol Biol Cell 21, 4020–4027 (2010).\n4. West, J. A. et al. Structural, super-resolution microscopy analysis of paraspeckle\nnuclear body organization. J. Cell Biol. 214, 817–830 (2016).\n5. McCluggage, F. & Fox, A. H. Paraspeckle nuclear condensates: Global sensors of cell\nstress? BioEssays 43, 2000245 (2021).\n6. Unveiling the intricacies of paraspeckle formation and function. Current Opinion in Cell\nBiology 90, 102399 (2024).\n7. Naveed, A., Fortini, E., Li, R. & Fox, A. H. Long Non-coding RNAs and Nuclear Body\nFormation and Function. Molecular Biology of Long Non-coding RNAs 65–84 (2019).\n8. Nakagawa, S., Yamazaki, T., Mannen, T. & Hirose, T. ArcRNAs and the formation of\nnuclear bodies. Mamm Genome 33, 382–401 (2022).\n9. Hirose, T., Ninomiya, K., Nakagawa, S. & Yamazaki, T. A guide to membraneless\norganelles and their various roles in gene regulation. Nat Rev Mol Cell Biol 24, 288–304\n(2023).\n10. Hellmeier, J. et al. Quantification of absolute labeling efficiency at the single-protein\nlevel. Nature Methods 21, 1702–1707 (2024).\n11. Hirose, T. et al. NEAT1 long noncoding RNA regulates transcription via protein\nsequestration within subnuclear bodies. Mol Biol Cell 25, 169–183 (2014).\n12. Rieger, B., Droste, I., Gerritsma, F., Ten Brink, T. & Stallinga, S. Single image Fourier\nring correlation. Opt. Express 32, 21767–21782 (2024).\n13. Velicky, P. et al. Dense 4D nanoscale reconstruction of living brain tissue. Nature\nMethods 20, 1256–1265 (2023).\n14. Mol, F. N. & Vlijm, R. Automated STED nanoscopy for high-throughput imaging of\ncellular structures. bioRxiv 2022.09.29.510126 (2022) doi:10.1101/2022.09.29.510126.\n15. Arfken, G. B., Weber, H. J. & Harris, F. E. Mathematical Methods for Physicists A\nComprehensive Guide. 715–772 (Academic Press, 2013)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have made extensive efforts to address the questions raised in the last round of review and indeed answered/explained many of the questions raised in the rebuttal and the text. However, there are still substantial concerns that remain and need to be further addressed.\n1. The authors cited previous reports and argued that Hela cells harbor NEAT1_1:NEAT1_2 at 1:9 ratio, thus the common 5’ probe used in this manuscript “should not interfere with the results”. However, these previous studies still do not exclude NEAT1_1 in paraspeckles (PSP). In fact, it is unknown whether 1:9 ratio of NEAT1_1/NEAT1_2 from total RNA on northern indeed represents the ratio of these two NEAT1 isoforms in PSP. The new data in Response Figure 1 used a probe for the NEAT1_2-specific 5-7 kb region in a polarized PSP example, which is localized more in the center relevant to the reference 5’-3’ probe. Nonetheless, this still does not definitively address the 5’-3’ probes are indeed from NEAT1_2. Whether NEAT1_1 abundance could corrupt the 'reference' signal, thereby compromising the fundamental interpretation of PSP outlines, is critical for NEAT1/PSP field, which should be clearly demonstrated. The ideal experiment is to eliminate NEAT1_1, or at least apply this method to U2OS cells that express abundant NEAT1_1 and show whether NEAT1_1 abundance can interfere with the analysis/conclusion. This will address whether the method is limited only to cells have negligible NEAT1_1 but could not be applied to many other cells that express abundant NEAT1_1. If this is the case, the limitation of using this method for studying paraspeckles in cells with abundant NEAT1_1, should be clearly indicated.\n2. Response Fig.2/S2: The text indicates 93% whereas the rebuttal indicates 95% of PSP contains PSPC1. Such colocalization is convincing but which is correct? PSPC1 appears to be localized in the center of PSPs (panel A). What is the shell-to-center distances of PSPC1 compared to the 3’-5’ probe at the surface?\n3. The MG132 data is a direct test for the method. This reviewer suggests including this data in supplemental info and discusses the results relevant to the literature of PSP elongation upon exposure to MG132.\n4. Response Fig. 4 nicely showed 18K to 20K probe protrudes the shell boundary. Does this protrusion exhibit co-polarization with the 3’ end of NEAT1_2? If such co-polarization is not consistently observed, would it undermine the interpretation?\n5. According to the comparison against simulated random distribution in response figure 5, there is no significant polarization of either 5’ or 3’ end of NEAT1 on paraspeckles. Nonetheless, the subsequent analysis is a major claim. It is unclear why to spend large efforts to chase this statistically insignificant phenomenon? Also, the authors should run a similar simulation on elliptical particles to address that the correlation between polarizing angle and major axis is not an artifact from categorizing algorithms.\n6. Nearly all original equations are improperly rendered after the revision (P11-16). Please double check and correct.", "reviewer_2": "The authors have addressed all my questions very well. I have no further questions.\n(Remarks on code availability)\nThe code is published on GitHub with a well-written README file. I am glad the authors mentioned the version of DIPLIB because the current version need to be built from source code. DIPLIB 2.9 can be installed with an executable installation file. However, I was not able to run the demo because of following error message in MATLAB (ver. 2023a):\nUnrecognized function or variable 'PgetLocs'.\nError in PRotation (line 43)\n[locs, Nlocs, leg] = PgetLocs([projDir '\\' headDir]);\nNonetheless, the documentation is very clear and useful for setting up the code.", "reviewer_3": "The authors have stongly improved the presentation of their methodology. The mentioned errors have been corrected. With the added descriptions the previous questions have been answered and the previous confusion has been removed.\nI have only a few further remarks. When these issues get solved, I no longer have any objections to publication of this work.\n- Above Eq. (7), \"...the image volume was first divided into 512 spherical cones of equal volume.\": Using spherical cones it is not possible to divide a volume into pieces without gaps or overlaps. There exist better methods that allow to partition a volume into solid angles without gaps and overlaps. This is equivalent to partitioning the surface of a sphere into areas without gaps and overlaps. The parts might then have different volumes, which is not a problem, but requires to be taken into account by appropriate weight factors.\n- Eq. (9): Introduce the symbols \\Delta\\varphi, \\Delta\\theta, \\varphi_1, \\varphi_N, \\theta_1, \\theta_N\n- Above Eq. (19): replace \"integrated\" by \"averaged\" as you not only integrate, but also divide by 2Pi.", "reviewer_4": "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."}, "author_response": "Response to Reviewer comments\nWe thank all the reviewers for investing time in assessing our revised manuscript and for the\nvaluable feedback. We have addressed all points raised by the reviewers. As part of this\nrevision, new data was generated, analysed and interpreted.\nReviewer #1 (Remarks to the Author):\nThe authors have made extensive efforts to address the questions raised in the last round of\nreview and indeed answered/explained many of the questions raised in the rebuttal and the\ntext. However, there are still substantial concerns that remain and need to be further\naddressed.\n1. The authors cited previous reports and argued that Hela cells harbor NEAT1_1:NEAT1_2\nat 1:9 ratio, thus the common 5’ probe used in this manuscript “should not interfere with the\nresults”. However, these previous studies still do not exclude NEAT1_1 in paraspeckles\n(PSP). In fact, it is unknown whether 1:9 ratio of NEAT1_1/NEAT1_2 from total RNA on\nnorthern indeed represents the ratio of these two NEAT1 isoforms in PSP. The new data in\nResponse Figure 1 used a probe for the NEAT1_2-specific 5-7 kb region in a polarized PSP\nexample, which is localized more in the center relevant to the reference 5’-3’ probe.\nNonetheless, this still does not definitively address the 5’-3’ probes are indeed from\nNEAT1_2. Whether NEAT1_1 abundance could corrupt the 'reference' signal, thereby\ncompromising the fundamental interpretation of PSP outlines, is critical for NEAT1/PSP field,\nwhich should be clearly demonstrated. The ideal experiment is to eliminate NEAT1_1, or at\nleast apply this method to U2OS cells that express abundant NEAT1_1 and show whether\nNEAT1_1 abundance can interfere with the analysis/conclusion. This will address whether\nthe method is limited only to cells have negligible NEAT1_1 but could not be applied to many\nother cells that express abundant NEAT1_1. If this is the case, the limitation of using this\nmethod for studying paraspeckles in cells with abundant NEAT1_1, should be clearly\nindicated.\nResponse: We thank the reviewer for this important comment. We understand the concern\nthat, albeit that the NEAT1_1 content is very low compared to NEAT1_2 in HeLa cells (1:9\nratio), we did not confirm that this ratio holds also true for the paraspeckles. We thank the\nreviewer for suggesting an experiment to address this concern, which we have carried out as\npart of this revision and whose results we discuss below (Response Figure 8). We will begin\nthis response with a short summary on the current literature, which will be helpful as\nbackground information and supports our rationale.\nSasaki et al. reported that NEAT1 is uniquely enriched in the high‐density nucleoplasmic\nfraction (Sasaki et al. 2009). Nishimoto et al. reported that >93% and >96% of all NEAT1_2\nfoci overlapped with the paraspeckle proteins TDP-43 and FUS/TLR, respectively, indicating\nthat almost all NEAT1_2 is localized inside paraspeckles (Nishimoto et al. 2013). In contrast\nto that, NEAT1_1 was reported to accumulate in paraspeckle-independent speckles\n(microspeckles) (Li et al. 2017). Taken together, the fraction of NEAT1_2 in paraspeckles\nmight even be higher than the ratio to NEAT1_1 suggests (of 9:1 in HeLa cells; see\nResponse Figure 9) (Chujo et al. 2017). We agree with the reviewer that the northern blot\ndata does not necessarily reflect the NEAT1_2/NEAT1_1 content in paraspeckles.\nWe followed the suggestion of the reviewer and performed new experiments as part of this\nrevision. We measured paraspeckles in U-2 OS cells using two sets of FISH probes: the first\nprobes bind to the 5’ end (0-1K nt) of NEAT1 (targeting both NEAT1_2 and NEAT1_1), and\nthe second set to the 3’ end (22-22.7K nt) targeting only NEAT1_2. Response Figure 8A\nshows confocal images of both U-2 OS and HeLa cells, where the NEAT1_2 foci (identified\nvia 3’ end signal) are found overlapping with signal originating from the 5’ end probe for both\ncell lines. However, U-2 OS cells exhibited fewer NEAT1_2 foci (~ 3 times less), and the area\nof these foci was smaller (~1.5 times smaller) than in their HeLa counterparts (Response\nFigure 8B, C). This is in line with the previous observation of lower NEAT1_2 expression\nlevels in U-2 OS cells than in HeLa cells (Chujo et al. 2017) (Response Figure 9). Importantly,\nthe mean fluorescence intensity for both 3’ end and 5’end probes showed no significant\ndifference in both cell lines (Response Figure 8D). In addition, no structural differences were\nobserved in STED images of paraspeckles (Response Figure 8E). Mean intensity analysis\nof the higher-resolution STED data of both 3’ and 5’ ends also showed no significant\ndifference (Response Figure 8F). In conclusion, the observation of no apparent differences\nin the NEAT1_1/NEAT1_2 intensities in paraspeckles indicates that the composition of\nparaspeckles in U-2 OS and HeLa cells is very similar, despite the different expression ratio\nof the two isoforms. This is in accordance with literature reporting that NEAT1_1 has a\nPS-independent role (Li et al. 2017).\nThe above data of 5’/3’ intensities in HeLa and U-2 OS indicates that NEAT1_1 is not a\nstructural component of paraspeckles. In conclusion, we can safely assume that the 5’\nreference signal is not corrupted in paraspeckles, and that our structural analysis hence is\nvalid. Moreover, this hints at a general building principle of paraspeckles. However, we agree\nwith the reviewer that for such a statement, it would require a more thorough examination.\nInterestingly, for both U-2 OS and HeLa cells, we observe a similar background signal all\nover the nucleoplasm (excluding PS), which might reflect the NEAT1_1 localization in other\nregions in the nucleus. However, that will require future studies.\nWe made according changes in the manuscript and now (1) emphasize that our study\ntargeted paraspeckles in HeLa cells, and (2) discuss the possible influence of isoform\nexpression level in context with the current literature. We have not included the additional\ndata in the manuscript, since we believe that this requires an extensive and biology-focused\nthorough examination, which would merit a study on its own.\nResponse Figure 8: Comparison of NEAT1_1 and NEAT1_2 in HeLa and U-2 OS cells. A\nConfocal images of HeLa and U-2 OS cells labeled with FISH probes targeting the 5’end of NEAT1\n(0-1K nt; binding both isoforms) and the 3’end (22-22.7K nt; binding NEAT1_2 exclusively) (scale bar =\n20 µm). B Number of NEAT1_2 foci detected per cell. Bars and whiskers indicate mean and s.e.m.\nSignificance was tested using a two-tailed student's t-test. **: p<0.01, N = 201 (HeLa) and N = 156\n(U-2 OS). C Mean size (area) of NEAT1_2 foci in HeLa and U-2 OS cells. Black dotted line represents\nthe median. Significance was tested using a Mann-Whitney-U-test. ****: p<0.0001, N = 796 (HeLa), N\n= 201 (U-2 OS). D Mean fluorescence intensity of the 5’ and 3’ ends of NEAT1_2 foci in HeLa and U-2\nOS cells (confocal data). Black dotted line represents the median. Significance was tested using a\ntwo-tailed student's t-test (for 5’end distributions) and a Mann-Whitney-U-test (for 3’end distributions).\nns: p>0.05. N = 225 (HeLa), N = 145 (U-2 OS). E STED images of HeLa and U-2 OS cells labeled at\nthe 5’ and 3’ ends (scale bar = 300 nm). F Mean fluorescence intensity of the 5’ and 3’ ends of\nNEAT1_2 foci in HeLa and U-2 OS cells (STED). Black dotted line represents the median. Significance\nwas tested using a two-tailed student's t-test. ns: p>0.05. N = 84 (HeLa), N = 73 (U-2 OS).\nResponse Figure 9: Quantitative characterization of NEAT1 (Figure and caption taken from Chujo\net. al, 2017, original Figure 2DE (Chujo et al. 2017)). Left: “Steady‐state levels of NEAT1_2 and\nNEAT1_1 in various human cell lines. An example of RPA using total RNA extracted with the improved\nextraction method is shown in the upper panel. In the lower panel, NEAT1 levels (expressed as\nrelative molarities) are shown relative to NEAT1_2 level in HeLa cells.”; Right: “RNA‐FISH of\nNEAT1_2 (green) in various human cell lines. Nuclei were stained with DAPI (blue).”\n2. Response Fig.2/S2: The text indicates 93% whereas the rebuttal indicates 95% of PSP\ncontains PSPC1. Such colocalization is convincing but which is correct? PSPC1 appears to\nbe localized in the center of PSPs (panel A). What is the shell-to-center distances of PSPC1\ncompared to the 3’-5’ probe at the surface?\nResponse: We apologize for the discrepancy and appreciate the reviewer for pointing this\nout. The correct number is ~95 %. The manuscript has been updated accordingly (p4 §1).\nPSPC1 is a PSP that is known to be located in the centre of PS (Hao et al. 2025), and the\nreviewer is fully correct. Results of the shell-to-centre distance ratio analysis, presented in\nthe submitted manuscript, are plotted in Response Figure 10 together with data from\ndifferent NEAT1_2 regions for comparison. We note that imaging data of PSPC1 and the\nNEAT1 shell data was recorded [as part of the first revision] to demonstrate the robustness of\nthe particle selection procedure, yet not with acquisition settings required for high-resolution\nquantitative distance analysis: the sample size is smaller (n(PSPC1) ~ 200; n(internal FISH\nprobes) ~ 1000), and the spatial resolution is different. This allows for a qualitative\ncomparison (Response Figure 10). A fully quantitative comparison would require a higher\nnumber of particles and isotropic 3D STED imaging settings. Therefore, we did not include\nthis data in the manuscript. Nevertheless, we agree with the reviewer that the investigation of\nthe position of proteins within paraspeckles and relative to NEAT1_2 is an exciting\nopportunity for future research.\nResponse Figure 10: Average localization of NEAT1_2 regions and PSPC1 inside the\nparaspeckle. The average distance of the 4 different NEAT1_2 regions and PSPC1 to the\nparaspeckle center, relative to the to-center distance of the combined 5’/3’ reference positions,\nas calculated from radial intensity distribution. Lines inside the boxes indicate median, the box indicates\nthe 25th and 75th percentile and the whiskers indicate the 5th and 95th percentile.\n3. The MG132 data is a direct test for the method. This reviewer suggests including this data\nin supplemental info and discusses the results relevant to the literature of PSP elongation\nupon exposure to MG132.\nResponse: We thank the reviewer for the suggestion. Following the advice of the reviewer,\nwe have included the MG132 data in the revised manuscript and discussed its implications\n(Fig S5B, C; results p.5 §1; discussion p.9 §2; methods p.10 §2).\n4. Response Fig. 4 nicely showed 18K to 20K probe protrudes the shell boundary. Does this\nprotrusion exhibit co-polarization with the 3’ end of NEAT1_2? If such co-polarization is not\nconsistently observed, would it undermine the interpretation?\nResponse: We thank the reviewer for this question. To verify whether the protruding loop\nco-localizes/co-polarizes with the 3’ end, we labeled paraspeckles in HeLa cells using two\nFISH probes, a first one targeting the 3’ loop region (18-20K nt) and a second one targeting\nthe 3’ end (22-22.7K nt), and conducted STED microscopy experiments (Response Figure\n11A). To answer the question of the reviewer, we particularly selected paraspeckles that\nshowed an incomplete ring in the 3’ end channel, which we interpreted as polarized\nparaspeckles (Response Figure 11B). In these paraspeckles, we observe an overlap of the\n3’ end and 3’ loop regions, indicating close proximity. This overlap might be better described\nas ‘co-arrangement’: since the 3’ loop is located outside the shell, the overlap to the 3’ end\nappears not complete; however, the spatial patterns are almost identical (an inflation of the\nimages of the 3’ end would lead to almost perfect overlap with the images of the 3’ loop).\nTo better contextualise the observation, we compared the 3' end/loop region data with\nparaspeckles with 5' end/3' end staining (see Response Figure 8E, left panel): those\nparaspeckles which show an incomplete ring in the 3' end signal show a 5’ end signal that\ncomplements the structure of the ring (polar, Response Figure 11C). This supports the\nhypothesis that in paraspeckles exhibiting a polarized distribution of the 5’ and 3’ ends, the 3’\nend is in close proximity to the loop region. In addition, we calculated the Pearson correlation\ncoefficient for both 3’ end measured together with loop region (0.67+- 0.15) and 3’ end\nmeasured together with the 5’ end (0.51 +- 0.17) which supports the visual inspection\n(Response Figure 11D).\nWe include this data in the revised manuscript (Figure S13; results p.7 §1; methods p.11\n§5, p.17 §5).\nResponse Figure 11: Two-color STED experiments of paraspeckles labeled at the 3’ loop and 3’\nend, and at the 3’ and 5’ ends, of NEAT1_2. A Co-staining of the protruding 3’ loop region (18-20K\nnt) and the 3’end (22-22.7K nt) (scale bar = 300 nm). B Selected images showing potentially polarized\nparaspeckles. FISH probes hybridized in the protruding loop region (18-20K nt) and positioned directly\nat the 3’end (22-22.7K nt) (scale bar = 300 nm). C Selected images showing potentially polarized\nparaspeckles labeled at the 5’ end (0-1K nt) and the 3’ end (22-22.7K nt) (scale bar = 300 nm). D\nPearson correlation coefficient for paraspeckles labeled at the 3’ end and 3’ loop, and for paraspeckles\nlabeled at the 3’ and 5’ ends. Significance was tested using a two-tailed student's t-test (****:\np<0.0001). N = 199 (3’ end and 3’ loop), N = 208 (3’ end and 5 ’end).\n5. According to the comparison against simulated random distribution in response figure 5,\nthere is no significant polarization of either 5’ or 3’ end of NEAT1 on paraspeckles.\nNonetheless, the subsequent analysis is a major claim. It is unclear why to spend large\nefforts to chase this statistically insignificant phenomenon? Also, the authors should run a\nsimilar simulation on elliptical particles to address that the correlation between polarizing\nangle and major axis is not an artifact from categorizing algorithms.\nResponse: We thank the reviewer for this comment. The key finding of this analysis is that\nparaspeckles are not built in an ordered way, but rather all possible orientations of NEAT1_2\nare observed, that is, all possible configurations that result from a random distribution.\nParaspeckles were predominantly thought to be built with a V-shape arrangement of\nNEAT1_2 (McCluggage and Fox 2021; Ingram and Fox 2024; Naveed et al. 2019; Nakagawa\net al. 2022; Hirose et al. 2023). From our observations of highly or moderately polarized\nparaspeckles that were not reported before (Figure 4C,E), we concluded that the V-shaped\nmodel cannot be correct for a large number of paraspeckles, as this model would only allow\nfor a homogenous distribution (non-polar) of the signal from both RNA ends across the\nparaspeckle surface. The presence of highly polarised paraspeckles necessitates the\npossibility of the two NEAT1_2 ends to adopt different orientations, including the possibility\nthat they are arranged in a straight conformation through the paraspeckle.\nIn order to support this interpretation of our data, we performed simulations where the 5’ and\n3’ ends were free to distribute on a spherical surface (a random distribution of ends, rather\nthan a V-shape distribution). These simulations resulted in particles with signal distributions\nranging from “non-polar” to “polar” (Figure S7B) and were similar in their occurrence to\nexperimentally found structures of paraspeckles. Moreover, the histogram of all possible\npolarization arrangements of simulated particles matched the data of experimentally\nmeasured polarization in paraspeckles very well (Figure 4B, S7A). Taken together, our data\nand simulation indicate that NEAT1_2 5’ and 3’ ends arrange not only into a V-shape, but\nalso into other configurations including those where the RNA spans the entire length of the\nminor axis of the paraspeckle.\nTo address the concern that the relationship between the polarizing angle and the major axis\nis an algorithmic artefact, we extended our simulation and now include ellipsoidal\nparaspeckles with randomly distributed 5’ and 3’ ends over its surface. Response Figure 12\nshows four boxplots, corresponding to the real/simulated and spherical/elongated\nparaspeckles, showing the angles between the polarization axis and the elongation axis for\npolarized particles (polarization > 75%). This analysis shows that the angle between the\npolarisation vector and the elongation axis has a broad range around the random angle of\n~57 degrees (1 rad) in the case of real spherical, simulated spherical and simulated\nelongated paraspeckles; only the real elongated paraspeckles show a more narrow\ndistribution around a non-random angle. This allows us to conclude that the relationship\nfound between the polarisation and the elongation in the real polarised elongated\nparaspeckles is not an analysis artefact. On that note, our results also show that elongated\nparaspeckles exhibit a polarization along the long axis. This hints that this observation might\nbe relevant for growth and division, which would be interesting to address in the future.\nResponse Figure 12: Angle of polarization vector with elongation axis for real and simulated\nparaspeckles. Shown is the angle that was calculated between the polarisation vector and the vector\npointing in the direction of paraspeckle elongation for real and simulated spherical ((ellipticity > 1.3))\nparaspeckles as well as for real and simulated elongated (ellipticity > 1.3). The red line indicates the\nmedian, box indicates the 25th and 75th percentile and the whiskers indicate the 5th and 95th\npercentile. Black line indicates the random angle of ~57 degrees.\n6. Nearly all original equations are improperly rendered after the revision (P11-16). Please\ndouble check and correct.\nResponse: We apologize for this mistake, which was a consequence of incorrect conversion\ninto the .pdf format.\nReviewer #2 (Remarks to the Author):\nThe authors have addressed all my questions very well. I have no further questions.\nResponse: We thank the reviewer for the time and valuable feedback throughout this\nrevision.\nReviewer #2 (Remarks on code):\nThe code is published on GitHub with a well-written README file. I am glad the authors\nmentioned the version of DIPLIB because the current version need to be built from source\ncode. DIPLIB 2.9 can be installed with an executable installation file. However, I was not able\nto run the demo because of following error message in MATLAB (ver. 2023a):\nUnrecognized function or variable 'PgetLocs'.\nError in PRotation (line 43)\n[locs, Nlocs, leg] = PgetLocs([projDir '\\' headDir]);\nNonetheless, the documentation is very clear and useful for setting up the code.\nResponse: We thank the reviewer for testing the software. The presented error likely arose\ndue to an incomplete path. To avoid this issue in the future, the software has been modified\naccordingly (path set in the script instead of manually), such that this error should not occur\nany longer.\nReviewer #3 (Remarks to the Author):\nThe authors have strongly improved the presentation of their methodology. The mentioned\nerrors have been corrected. With the added descriptions the previous questions have been\nanswered and the previous confusion has been removed.\nResponse: We thank the reviewer for the time and valuable feedback throughout this\nrevision.\nI have only a few further remarks. When these issues get solved, I no longer have any\nobjections to publication of this work.\n- Above Eq. (7), \"...the image volume was first divided into 512 spherical cones of equal\nvolume.\": Using spherical cones it is not possible to divide a volume into pieces without gaps\nor overlaps. There exist better methods that allow to partition a volume into solid angles\nwithout gaps and overlaps. This is equivalent to partitioning the surface of a sphere into\nareas without gaps and overlaps. The parts might then have different volumes, which is not a\nproblem, but requires to be taken into account by appropriate weight factors.\nResponse: We thank the reviewer for the opportunity to motivate our projection method. We\nindeed chose to prioritise equal volumes contributing to equal surface areas, allowing us to\nattribute equal image volumes to all 512 equidistant points on the surface. We defined the\ncones such that their surface areas (spherical caps) add up to the total spherical surface\narea. As noted by the reviewer, this came at the cost of gaps and overlaps between the\nsurface elements (spherical caps).\nWe can calculate the overlaps between all 512 spherical caps using an analytical method\n[https://math.stackexchange.com/q/4028073]\n2\n𝐴 = 2𝑟 (π − 2𝐴 𝑐𝑜𝑠(θ ) − 𝐵)\n𝑜 𝑐\n𝑘\n𝐴 = 2 𝑎𝑟𝑐𝑡𝑎𝑛( )\n𝑠𝑖𝑛(𝑠 − θ)\n𝑐\n𝑘\n𝐵 = 2 𝑎𝑟𝑐𝑡𝑎𝑛( )\n𝑠𝑖𝑛(𝑠 − θ )\n𝑑\n2𝑠𝑖𝑛(𝑠 − θ) 𝑠𝑖𝑛(𝑠 − θ )\n𝑘 = 𝑐 𝑑\n𝑠𝑖𝑛(𝑠)\n1\n𝑠 = (2θ + θ )\n2 𝑐 𝑑\nwhere 𝐴 is the area of overlap between the two spherical caps, θ is the half angle of the\n𝑜 𝑐\nspherical caps (in our case, 5 degrees for all cones) and θ is the angle between the caps\n𝑑\n(value dependent on the pair of cones).\nPerforming this calculation for all 512 spherical caps, we find an average overlap area of\n8.7%, which is 70 nm^2 per spherical cap. Considering our pixel area of 30x30 = 900 nm^2\nand resolution of 100-120 nm, we consider this an acceptable error.\nTo verify that our chosen method does not qualitatively affect our results, we repeated the\npolarisation analysis using a different volume-to-surface projection method. In this alternative\nmethod, we again distribute 512 points evenly across a spherical surface. We use these\npoints to construct a 3D grid in spherical coordinate space, with 6 radial sections as the third\ndimension. We then resample our images in this 3D grid, allowing us to sum over the radial\ndimension to get the summed intensity per surface element. With this method, we no longer\nhave overlaps or gaps between the sections.\nBelow we present the resulting degrees of polarisation for four cases: imaged spherical and\nellipsoidal paraspeckles analysed with our existing projection method and with the alternative\nprojection method described above.\nWe observe that there is no qualitative difference between the results from the two projection\nmethods. We therefore have chosen to keep our existing figures and results. We did update\nthe code to include the alternative projection method.\n- Eq. (9): Introduce the symbols \\Delta\\varphi, \\Delta\\theta, \\varphi_1, \\varphi_N, \\theta_1,\n\\theta_N\nResponse: We thank the reviewer for noting these missing symbol introductions. We have\nupdated the manuscript to include them (methods p. 16 § 1, equation 9).\n- Above Eq. (19): replace \"integrated\" by \"averaged\" as you not only integrate, but also divide\nby 2Pi.\nResponse: We thank the reviewer for noting this oversight, this is indeed what we meant.\nWe have updated the manuscript to correct this.\nReviewer #3 (Remarks on code):\nThe code contains a README file, but I did not find sufficient information there on how to\ninstall or run the code.\nResponse: We thank the reviewer for raising this point. The README file referred to the\ndemo manual included in the demo folder. We have now copied the information from the\ndemo manual to the README file.\nEditorial comments:\nTo maximise the reproducibility of research data, we strongly encourage you to provide a file\ncontaining the raw data underlying the following types of display items.\nResponse: A source data file containing all data underlying the display items in the\nmanuscript has been submitted with this revision.\nPlease replace your bar graphs with plots that feature information about the distribution of the\nunderlying data. All data points should be shown for plots with a sample size less than 10.\nFor larger sample sizes, please consider box-and-whisker or violin plots as alternatives.\nMeasures of centrality, dispersion and/or error bars should be plotted and described in the\nfigure legend.\nResponse: We followed this instruction and made changes accordingly. We note that we\nkeep the bar plot in Figure S2B, as it visualizes a single number value (number of particles\ncolocalizing the protein PSPC1 and the structural RNA NEAT1) rather than a distribution."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The previous concerns are well addressed", "reviewer_3": "1) Above Eq. (7), \"...the image volume was first divided into 512 spherical cones of equal volume.\":\nThe authors have explained in their rebuttal why they want to keep their method of dividing the volume into cones and although it is not the best possible method I agree that choosing a better method would not change the results to a relevant extent and is therefore not necessary. However, the authors did not change their manuscript in any way that prevents other readers from raising the same criticism as I did when I read this work. In particular, the authors should mention in their manuscript the following:\n- The method that is used to divide the image volume into cones\n- The diameter (or solid angle) of each cone\n- That there are overlaps between neighboring cones\n- Some numbers that show the readers that using these cones is not a problem:\n-- Give an upper bound for the percentage of the overlap volume of all cones compared to the total volume of all cones.\n-- Give an upper bound for the percentage of the image volume that does not belong to any cone compared to the total image volume.\n2) In the line below Eq. (9) is should read \"azimuthal\" instead of \"azimuth\".\nApart from that, the changes that I have requested in my previous report have been implemented (although the change was not highlighted in the last case).", "reviewer_4": "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."}, "author_response": "Response to Reviewer comments\nWe thank all reviewers for assessing the revised manuscript and for the valuable feedback.\nWe have addressed all points raised by Reviewer #3. As part of this revision, the methods\nsection of the manuscript has been updated.\nReviewer #3 (Remarks to the Author):\n1) Above Eq. (7), \"...the image volume was first divided into 512 spherical cones of equal\nvolume.\":\nThe authors have explained in their rebuttal why they want to keep their method of dividing\nthe volume into cones and although it is not the best possible method I agree that choosing\na better method would not change the results to a relevant extent and is therefore not\nnecessary. However, the authors did not change their manuscript in any way that prevents\nother readers from raising the same criticism as I did when I read this work. In particular, the\nauthors should mention in their manuscript the following:\n- The method that is used to divide the image volume into cones\n- The diameter (or solid angle) of each cone\n- That there are overlaps between neighboring cones\n- Some numbers that show the readers that using these cones is not a problem:\n-- Give an upper bound for the percentage of the overlap volume of all cones compared to\nthe total volume of all cones.\n-- Give an upper bound for the percentage of the image volume that does not belong to any\ncone compared to the total image volume.\nResponse: We thank the reviewer for pointing at this missing information. Following these\nsuggestions, we have updated the manuscript [Methods, p. 16 §5].\n2) In the line below Eq. (9) is should read \"azimuthal\" instead of \"azimuth\".\nResponse: We have corrected the manuscript accordingly [Methods p. 17 §3]."}]

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