# Multiscale visualization of nucleolar chromatin in yeast
Saccharomyces cerevisiae

Claudie Carron, Sarah Danché, Valdir Gomes Neto, Mickaël Lelek, Nana Kadidia Maiga, Isabelle Léger-Silvestre, Thomas Mangeat, Stéphanie Balor, Carla C Oliveira, Christophe Zimmer, et al.

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► To cite this version:

Claudie Carron, Sarah Danché, Valdir Gomes Neto, Mickaël Lelek, Nana Kadidia Maiga, et al. Multiscale visualization of nucleolar chromatin in yeast Saccharomyces cerevisiae. Journal of Structural Biology, 2025, 217 (3), pp.108228. 10.1016/j.jsb.2025.108228. hal-05261356

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Submitted on 15 Sep 2025

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Research Article

Multiscale visualization of nucleolar chromatin in yeast

*Saccharomyces cerevisiae*

Claudie Carron<sup>a,1,2</sup>, Sarah Danché<sup>a,1</sup>, Valdir Gomes Neto<sup>a,b,1</sup>, Mickaël Lelek<sup>c</sup>, Nana Kadidia Maiga<sup>a</sup>, Isabelle Léger-Silvestre<sup>a</sup>, Thomas Mangeat<sup>d</sup>, Stéphanie Balor<sup>d</sup>, Carla C. Oliveira<sup>b</sup>, Christophe Zimmer<sup>c</sup>, Frédéric Beckouët<sup>a</sup>, Christian Rouvière<sup>d</sup>, Benjamin Albert<sup>a</sup>, Sylvain Cantaloube<sup>d,*</sup>, Olivier Gadal<sup>a,*</sup>

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ARTICLE INFO

Edited by Andreas H. Engel

Keywords:

Saccharomyces cerevisiae

Ribosomal DNA

Interphasic chromosome organization

Nucleolus

A B S T R A C T

Spatial organization of chromosomes is crucial for genome stability, transcription, and proper mitotic segregation. By employing a range of imaging technologies, including random illumination microscopy and single molecule localization microscopy (SMLM), we conducted an in-depth exploration of the chromatin organization in budding yeast, with optical resolutions ranging from 250 nm to 50 nm. *In silico* models based on passively moving polymer chains and local tethering to nuclear landmarks explained much of the experimental data in yeast chromatin. We compared these models with our new imaging data of the nucleoplasmic and nucleolar chromatin. Chromatin fibers observed in the nucleoplasm showed some similarity with model prediction with a resolution of 150 nm. However, we visualized local clustering of chromatin in both the nucleoplasm and nucleolus, rather than the tube-like appearance predicted by polymer chain models. In the nucleolus, local clustering of ribosomal DNA (rDNA) chromatin is consistently observed from 150 nm resolution down to 50 nm. We also observed that actively transcribed rDNA spatially segregates from bulk nucleolar chromatin. Using correlative light and electron microscopy (CLEM), we found that local rDNA clustering is forming a specific nucleolar subdomain visible in transmission electron microscopy, the yeast equivalent of metazoan fibrillar center. We conclude that nucleolar chromatin forms a distinct sub-nucleolar compartment in yeast, supporting the model of a tripartite structural organization of the yeast nucleolus.

1. Introduction

Understanding the principles governing genome organization in
nuclear space is now recognized as a central goal in biology, as this
organization affects genome expression, stability, and replication
(Dekker et al., 2023). In all eukaryotic genomes analyzed to date, this
spatial organization is not random: chromosomes and loci occupy
preferential positions relative to each other and/or to nuclear landmarks
(Hoencamp et al., 2021; Torres et al., 2023). Mechanistic studies are
often first carried out in a simple model organism such as the budding

yeast *Saccharomyces cerevisiae*, using genetic and genomic tools that
have enabled us and others to obtain high-quality quantitative data, and
numerous microscopic observations (Albert et al., 2013; Hajjoul et al.,
2013; Miné-Hattab and Taddei, 2019; Wong et al., 2013).

The 13 megabases (Mb) of the nuclear genome of S. cerevisiae are
distributed across 16 chromosomes. Of these, 12 Mb are located in the
nucleoplasm, while 1 Mb consists of a repetition of ribosomal RNA genes
(rDNA) present on the right arm of chromosome XII, and is confined to
the nucleolus. In interphase, centromeres (CEN), telomeres (TEL), and
the nucleolus are the major players in establishment of genome spatial

<footnote><sup>a</sup> MCD (CBI), Université de Toulouse, CNRS, UPS, 31000 Toulouse, France</footnote>

<footnote><sup>b</sup> Department of Biochemistry, Institute of Chemistry, University of São Paulo, São Paulo, Brazil</footnote>

<footnote><sup>c</sup> Imaging and Modeling Unit, Department of Computational Biology, Institut Pasteur, Paris, France</footnote>

<footnote><sup>d</sup> Centre de Biologie Intégrative (CBI), Université de Toulouse, CNRS, UPS, 31000 Toulouse, France</footnote>

<footnote>* Corresponding authors.</footnote>

<footnote>E-mail addresses: sylvain.cantaloube2@univ-tlse3.fr (S. Cantaloube), olivier.gadal@univ-tlse3.fr (O. Gadal).</footnote>

<footnote n="1">¹ Equal first authors.</footnote>

<footnote n="2">² Present address: Ochs Laboratory, University of Copenhagen; Biotech Research & Innovation Centre (BRIC), Faculty of Health and Medical Science, Ole Maaløes Vej 5, 2200 Copenhagen, Denmark.</footnote>
geometry (Berger et al., 2008; Taddei and Gasser, 2012). Intra-nuclear microtubules organized from the spindle pole body tether the CENs in clusters near the nuclear envelope (Bystricky et al., 2004; Guacci et al., 1997, 1994, Jin et al., 1998, 2000). TELs are forming foci at the nuclear envelope (Gotta et al., 1996; Klein et al., 1992). As a result, yeast chromosomes are constantly tethered by nuclear landmarks, forming a Rabl-like conformation with chromosome arms extending in between CEN and TEL (Jin et al., 2000). Finally, during most phases of the cell cycle, the nucleolus occupies about one-third of the nuclear volume, physically separates the rDNA genes from the rest of the genome, and is located diametrically opposite the SPB/CEN (Bystricky et al., 2005; Yang et al., 1989).

Due to the physical limitation of standard fluorescent microscopy to a resolution of 200 nm at best, the use of fluorescent *in situ* hybridisation (FISH) which requires prior denaturation, in the small yeast nuclei of 2 µm diameter, is challenging. The use of Fluorescent *in situ* hybridization (FISH) techniques in the yeast nucleus of a 2 µm diameter, limited to a standard optical resolution and requiring prior denaturation is challenging. Nevertheless, these FISH approaches have been instrumental in revealing the pivotal role of key genetic elements, such as centromeres (CENs), telomeres (TELs), and the nucleolus, in shaping genome organization. To date, the yeast nucleoplasm remains mostly visually amorphous throughout the cell cycle, using either fluorescence or transmission electron microscopy (TEM) (Jin et al., 1998, 2000; Léger-Silvestre et al., 1999; Lorenz et al., 2002). This amorphous appearance in fluorescence microscopy is a resolution-based artefact, with chromatin fibers blurred by optical resolution. Electron microscopy is not limited by poor resolution and is ideal for studying nuclear condensates such as the nucleolus, but even with sophisticated chromatin staining techniques, it is a challenge to reveal the internal organization of chromosomes (Ou et al., 2017; Sirri et al., 2008). Other tools have been successfully used to quantitatively described genome organization in yeast. On one hand, fluorescence microscopy techniques based on localization microscopy approach were extensively used: labelling individual loci allows to identify the center of a fluorescent spot with an accuracy which is not limited by diffraction (Wang et al., 2016b). The calculation of the positions of the loci and their distances, with measurements sometimes limited to the nuclear envelope (NE) and sometimes extending to both the NE and the nucleolus, enabled the determination of their statistical positions in nuclear space as mapped *in vivo* by imaging hundreds to thousands of cells (Berger et al., 2008; Hediger et al., 2004). Similarly, distributions of distances between pairs of telomeres were accurately computed (Therizols et al., 2010). Finally, large amount of tracking dataset have been generated, allowing to describe with good accuracy loci motion in living cells (Amitai et al., 2015; Backlund et al., 2014; Hajjoul et al., 2013; Socol et al., 2019, 2017). On the other hand, numerous dataset of contact frequencies across the genome, measured by genome-wide chromosome conformation capture of increasing quality are now available (Costantino et al., 2020; Dauban et al., 2020; Duan et al., 2010; Lazar-Stefanita et al., 2017).

With the availability of such high-quality data from a variety of sources, computer modeling of chromatin structure has become the technique of choice to identify principles governing genome organization. Indeed, *in silico* models of entire yeast chromosomes have been built that recapitulate most of the observations made by combining imaging and conformation capture technologies (Tjong et al., 2012; Tokuda et al., 2012; Wong et al., 2013). Simple *in silico* models were highly successful: semi-flexible polymers were used to describe chromosomes with few parameters including rigidity of chromosome and DNA compaction (Wong et al., 2012). Physical tethering of those polymers to SPB and applying a force to maintain TEL near NE, and thickening the rDNA was sufficient to recapitulated Rabl-like configuration, and a crescent shaped nucleolus opposed to the SPB. Likewise, quantitative measure of contacts frequency and overall chromatin properties such as distance measurement and mean-square displacement computed from tracking chromatin motion were recapitulated with good

agreement with the experimental dataset (Albert et al., 2013; Arbona et al., 2017; Herbert et al., 2017; Wong et al., 2012). Note that some notable discrepancies were observed when evaluating the repositioning of active genes toward the nuclear envelope (NE) and the positioning of tRNA genes toward the nucleolus (Albert et al., 2013; Belagal et al., 2016; Casolari et al., 2004; González et al., 2023).

We now know that yeast chromatin is organized at local level by powerful molecular motors, including chromatin remodelers, RNA polymerases and three structural maintenance of chromosome (SMC) complexes: condensin, cohesin and Smc5/6. Along chromosome arms, self-associating domains, named chromosomally interacting domains (CIDs) are detected flanked by promoters of highly transcribed genes and genome position characterized by high frequency of nucleosome free regions (Chapard et al., 2023; Hsieh et al., 2016, 2015). Mitotic yeast chromosomes are also organized as cohesin-dependent loops and domains (Costantino et al., 2020; Dauban et al., 2019b). Furthermore, formation of condensate in the nucleus, well documented for the nucleolus, is also an important driver of chromatin organization (Lawrimore et al., 2021). Molecular motors and formation of nuclear condensates being absent of most simple *in silico* model, it can appear counterintuitive that global genomic properties are recapitulated ignoring such important active processes. However, properties of chromosome depend on length scales probed, and should be evaluated using scale-dependent tools (Hertzog and Erdel, 2023). Indeed, CIDs are typically about 5 kb or smaller, and in wild-type yeast, cohesin-dependent loops are of limited size, ranging from 10 to 20 kb, suggesting local reorganization along the fibers with largely unexplored consequences at larger scales. We now need to figure out how we can see changes in the structure of chromosomes, which are caused by molecular motors.

To address this question, we decided to use high-resolution microscopy techniques to visualize yeast chromatin in both the nucleoplasm and nucleolus with high accuracy (Burgers and Vlijm, 2023; Correll et al., 2024; Miné-Hattab et al., 2017). We then compared our high resolution imaging to virtual imaging data obtained using *in silico* model of the entire yeast genome (Wong et al., 2013, 2012). By assessing the scale at which the model's prediction fails to account for the observed distribution of chromatin in the nucleus, we could then narrow down the scale at which motors and nuclear condensates may act to reorganize the chromosome in budding yeast. The last 20 years have seen the development and dissemination in laboratories of a series of techniques enabling the optical resolution limit to be exceeded. Novel techniques enabled us to observe structures previously inaccessible to conventional microscopy: Structured illumination microscopy (SIM) can halve optical resolution (Gustafsson, 2000; Gustafsson et al., 2008; Heintzmann and Cremer, 1999) and Single Molecule Localization Microscopy (SMLM) is a computational imaging method achieving super-resolution by localizing individual molecules (Betzig et al., 2006; Hess et al., 2006; Rust et al., 2006). Most recently, Random illumination microscopy (RIM) achieved similar resolutions to SIM, but due to the illumination scheme, it is largely insensitive to optical aberrations (Jin and Xi, 2025; Mangeat et al., 2021).

Here, we explored three-dimensional organization of nucleoplasmic and nucleolar chromatin using RIM and SMLM techniques. We compared the imaging dataset with a virtual microscopy image of the yeast chromosomes based on an *in silico* yeast genome model (Wong et al., 2012).

In both the nucleoplasm and the nucleolus, we were able to quantitatively explore differences between the model of chromosomes and the observation. Using super-resolution imaging of yeast chromatin, we were able to reveal regions of high chromatin density alternating with lower density in both rDNA and nucleoplasmic chromatin, reminiscent of structures called mesodomains, nanodomains, or nucleosome clutches, depending on the microscopy resolution used (Fang et al., 2018; Miron et al., 2020; Ricci et al., 2015). Such local clustering is not accounted for by simple model, in which the chromosome appears as a
tube-like structure. Using correlative light and electron microscopy (CLEM), we were able to relate this local clustering of rDNA chromatin to a subnucleolar compartment where rDNA is concentrated. Such local rDNA clustering is associated with a specific nucleolar subdomain visible by transmission electron microscopy, the yeast equivalent of the metazoan fibrillar center.

## 2. Materials and methods

### 2.1. Media, culture conditions, and molecular biology

The yeast strains, plasmids, and oligonucleotides used in this study are listed in Tables S1, S2, and S3, respectively. Yeast media were used as described previously (Rose et al., 1990). Yeast growth was performed in YPD medium (1 % yeast extract, 2 % peptone and 2 % glucose) or YNB medium (0.67 % yeast nitrogen base, 0.5 % (NH4)2SO4 and 2 % glucose or galactose) supplemented with the required amino acids. The plasmids described in this study were constructed using standard cloning techniques: pFAa-mEosEM-KIURA3 vector was constructed by ligation between a PCR-generated fragment using oligos 1932 and 1933 and pRSET-mEosEM (Fu et al., 2020) as a template cut Asci-PacI site, and pFA6-GST-klURA3 was cloned into the same site (Sung et al., 2008). The plasmid pOGT2-pNOP1-NG-LacI was constructed in two in-fusion cloning steps (Takara). The cloning intermediate pOGT1-NG-lacI was constructed by in-fusion cloning into pAFS144 cut Xhol-EcoRI PCR-generated fragment (number 2219 and 2220) with pFA6a-link-ymNeongreen-SphHis5 as template. Next, the PCR-generated fragment (oligo 2221-2222) with pUN100-CFP-Nop1 as template was cloned into pAFS144-NG-lacI cut ScaI-Xhol by in-fusion cloning. Plasmid pOGT4-pNOP1-EOS-LacI was constructed by NEB builder cloning into pOGT2-pNOP1-NG-LacI cut Xhol-NcoI PCR-generated fragment (number 2223 and 2224) with pFAa-mEosEM-KIURA3 as template. Yeast strains were constructed by meiotic crossing and DNA transformation (Sambrook et al., 1989). Strains carrying rDNA-lacO were transformed with pOGT2-pNOP1-NG-LacI or pOGT4-pNOP1-EOS-lacI linearized with NheI and screened for fluorescent signal after selection for histidine prototrophy. Strains carrying genomic insertion of neongreen were constructed by amplifying a PCR cassette containing S. pombe *HIS3* gene orthologs as selectable marker and *ymNeongreen* tag from plasmid pFA6a-link-ymNeongreen-SphHis5. Strains carrying genomic insertion of GFP, mCherry and mEOS-EM were constructed by amplification of a PCR cassette containing the *URA3* gene from K. lactis as selectable marker and GFP-tag, mCherry-tag or mEOS-EM-tag sequence from plasmids pFA6-GFP(S65T)-KIURA3, pFA6a-mCherry-KIURA3, pFA6a-mEosEM-KIURA3. The PCR fragment was inserted by homologous recombination downstream of each wild-type gene in the W303 background or downstream of each TAP-tagged gene in the BY4741 genetic background (Ghaemmaghami et al., 2003) using the Swap-tag method (Sung et al., 2008). Transformants were selected for prototrophy and checked by PCR of the targeted genomic loci, and analyzed by fluorescence microscopy.

### 2.2. Sample preparation for fluorescence imaging

Yeast *S. cerevisiae* were prepared for imaging as described by Mund et collaborators with some modifications (Mund et al., 2014). IbiId® (60 µm dish 35 mm high) were treated with 20 µl of Bio-Conext® (amchro, PSX1055) during 30 min in humid chamber at room temperature(RT). Dish were washed two times with 96 % ethanol and 2 times with water. After 30 min at 65 °C to remove any liquid, 20 µL of concanavaline A were spread on the Bio-Conext zone and incubated 1 h in humid chamber at RT. The dishes were washed 3 times with fresh Milli-Q water and dried at RT. 4 OD of exponential cultures were collected, resuspended in 100 µL of YNB supplemented with all amino acids and 4 % glucose. Yeasts were deposited during 15 min on the IbiId dish previously prepared in humid chamber at RT, and then fixed using 2 mL of 3 % v/v glyoxal (Sigma-Aldrich #128465) during 15 min on orbital shaker. The glyoxal was quenched using 2 mL of 2,5M glycine during 15 min on orbital shaker. Yeasts were rinsed with DPBS (Gibco #1419-094), and permeabilized 1 h on orbital shaker with 2 mL of 0.25 % Triton X-100, 1 % BSA in DBPS. They were delicately rinsed with 1 mL of DPBS 3 times. For DNA labelling, cells were stained using 0.178 µM of Hoechst 33,342 for 10 min, and rinsed with 1 mL of DPBS 3 times (Thermofisher # 62249). Alternatively, yeast cells were washed with SD medium, fixed with 4 mL 3 % v/v glyoxal in PBS pH 8 and quenched with 4 mL 2.5 M glycine. DNA was then stained with Hoechst-33342 (2 µM). Fixed cells were mounted on 1.8 % agarose pads in Gene Frames (Thermo Scientific) for microscopic imaging.

### 2.3. Wide field (WF) microscopy and deconvolution

Wide-field fluorescence microscopy was performed using an automated inverted epifluorescence microscope Nikon Ti-E/B equipped with the “perfect focus system” (PFS, Nikon), a Plan Apo 100x objective (VC 100x Oil DIC N2 NA1. 4), a Lumencor SpectraX as illumination source (Ex:390BP22 for Hoechst, Ex: 475BP34 for GFP and Ex: 575BP33 for mCherry), Semrock Brightline multiband dichroic filters (FF409/493/573/652-Di02 for Hoechst and GFP and FF459/526/596-Di01 for mCherry) and Semrock emission filters (Em: 442BP20 for Hoechst, Em: 536BP40 for GFP and Em: 641BP75 for mCherry), and a Flash4. 0 sCMOS camera (Hamamatsu). Fluorescence images were acquired and processed using Nis-Elements AR software (Nikon). Images were deconvolved using TMHuygens Professional (SVI TM).

### 2.4. 3D random illumination microscopy (RIM) acquisition and reconstruction

3D RIM was used to observe Hoechst, GFP, and mCherry staining using an upgrade of the system described previously (Mangeat et al., 2021). 3D images were acquired using an inverted microscope (TiE Nikon) equipped with a ×100 magnification, 1.49 NA objective (CFI SR APO 100XH ON 1.49 NIKON) and sCMOS camera (ORCA-Fusion, Hamamatsu). A commercial acquisition software (INSCOPER SA) allows multi-color 3D whole cell imaging. Fast diode lasers (Oxxius) with wavelengths centered at 405 nm, 488 nm (LBX-488-200-CSB), and 561 nm were used to generate a TEM00 beam with a diameter of 2.2 mm. The polarized beam was rotated 5° before hitting an X4 beam expander (GBE04-A) to produce an 8.8 mm TEM00 beam. A fast spatial light phase binary modulator (QXGA fourth dimensions) was conjugated to the image plane to generate 400 random illuminations per plane. 3D image reconstruction was then performed as described previously and at GitHub (https://github.com/teamRIM/tutoRIM) (Mangeat et al., 2020). Live cell observation was performed using a 32-frame motif sequence at 1.6 frames per second.

### 2.5. Single molecule localization microscopy (SMLM)

SMLM was performed using a Nikon Ti-E/B automated inverted epifluorescence microscope equipped with the “perfect focus system” (PFS, Nikon), a piezo stage (Nano Z100-N – Mad City Labs), a CFI Apochromat TIRF 100XC oil objective (NA1. 49), a quad-band dichroic mirror (97335 Nikon N-STORM TIRF Filter Set), a TIRF illumination arm (here we use a highly inclined and laminated optical sheet HILO illumination mode), an EM-CCD ANDOR iXon Ultra DU897 camera and a thermostatic chamber at 24 °C to limit the instrumental drift. The excitation is controlled by an AOTF. For PALM, we use a 405 nm laser (CoherentTM – Cube 405-100C) for mEos-EM photoconversion and a 561 nm laser (CoherentTM – Sapphire) for imaging at a power of 1 kW/cm². For the PALM experiment, a 405 nm laser is used at low laser power to increase the photoconversion rate. For PALM image sequence acquisition, the imaging laser (561 nm) was used in continuous mode and the acquisition time was set to 30 ms. Due to the relationship
between resolution and sampling, the number of images depends on the object to be imaged (see Fig. S2-D) (Lelek et al., 2021). Images were acquired and processed using Nis-Elements AR software (Nikon). Point-spread-function (PSF) astigmatism is introduced using adaptive optics with a deformable mirror (MicAO 3DSR – Imagine OpticTM) inserted in the detection path just before the EMCCD camera. First, this deformable mirror is used to correct the main optical aberrations of our microscope in order to maximize the number of photons per PSF. Secondly, it allows us to introduce a controlled astigmatic deformation to shape the PSF and access the axial localization of our single fluorescent molecules. PSF calibrations were determined using commercially available fluorescent beads (FluoSpheresTM carboxylate-modified microspheres, 0.17 µm, orange fluorescent (540/560), 2 % solids, InvitrogenT).

## 2.6. SMLM: localizations and filtering

3D detection is performed using ThunderStorm, an ImageJ plugin (Ovesný et al., 2014). For each time point of the image sequence, raw images are first filtered (wavelet filter (B-spline) order 3, scale 2.0). Then, the approximate detection molecules are obtained thanks to the local maximum method, and then the sub-pixel localization of the molecules is determined using the PSF elliptical Gaussian method (fitting radius 6 pixels – initial sigma 1.6). Once the set of localizations has been determined, ThunderStorm offers the possibility of filtering the data according to different criteria. In our case, we systematically eliminated the localizations of the first 10,000 frames and kept the molecules detected with a localization precision of less than 50 nm.

## 2.7. SMLM: Drift correction

Strict environmental controls are applied to minimize sample drift: air conditioning, thermostatic chamber, Nikon Perfect focus system, x and y controlled stage.

The residual drift, which is mainly due to temperature variations caused by our air conditioning system, was estimated by autocorrelation using the ThunderSTORM software algorithm (Ovesný et al., 2014). The measured drift appears to be periodic and sinusoidal, which is not consistent with the drift correction available in ThunderSTORM. We have developed a proprietary algorithm that accurately corrects our experimental data by performing local linear regressions between two autocorrelation blocks.

## 2.8. Resolution determination using Fourier ring correlation (FRC) and visualization of SMLM dataset

Resolution was determined by Fourier Ring Correlation (FRC) as described in the literature by Nieuwenhuizen and coworkers. They determined that the SMLM image resolution *r* is the inverse of the spatial frequency for which the FRC curve falls below the 1/7 threshold (~0.143) using the BIOP FRC Plugin (Nieuwenhuizen et al., 2013). The visualization of the SMLM datasets is done using the ThunderStorm visualization option. We set the voxel size to have 3 pixels per unit resolution. We then blur the localizations with a theoretical PSF corresponding to the expected resolution.

This method of calculating resolution is for 2D data. Therefore, we underestimate the true value of resolution on our 3D datasets.

## 2.9. Simulations of in silico images of yeast chromatin at different resolutions

Simulated images (WF, RIM, and SMLM) of chromosomes based on polymer simulations of the yeast genome (Wong et al., 2012) were generated as previously described (Hao et al., 2021). Only chromatin positions are calculated in the model. WF, RIM and SMLM virtual images were generated by random sampling. To obtain the most realistic simulation, we assumed a multiple labeling density defined by the

experimentally determined labeling efficiency (70 % of sampling for SMLM and 100 % for WF and RIM), so that a finite number of chromatin positions is initialized from the 3D models. For WF and RIM, a PSF size defined by the resolution of each method is superimposed on the initial position, which is finally given by the simulated image of the DNA. To mimic the SMLM acquisition, blinking number, localization errors and non-specific localizations are added to the simulation. The localizations are distributed according to a normal distribution with mean and standard deviation defined by the initial position of the histone and the localization error determined by the FRC measurement, respectively. Note that in the *in silico* model, since the density of nucleoplasmic chromatin is higher than that of nucleolar chromatin, the sampling for virtual imaging of rDNA was increased to meet the Nyquist limit by adding localization points on the segment between two virtual chromatin segments.

## 2.10. Quantitative analysis of chromatin texture

To analyze the texture of chromatin in the nucleoplasm and nucleus, we use an à-trous wavelet algorithm thanks to an ImageJ plugin developed by A. Fertin (TIMC, Grenoble). The principle of this algorithm is to subsample the signal by introducing holes in the image (Fig. S1-A). At each step, the frequencies are halved. Note that this algorithm computes a full-resolution image at each stage by filling the holes. As described in Fig. S1-B, we applied a threshold to the first frequency obtained with the à trou algorithm. Using ImageJ, we segmented the image and analyzed the resulting objects to determine the area or circularity of each chromatin cluster. Circularity is a ratio that takes into account the area and perimeter of the object. Examples of circularity for different shapes are shown in Fig. S1-C. Circularity and area means, p-values and sample size (n) are shown in table S4. Number of cells analyzed are shown in table S5.

## 2.11. Correlative light electron microscopy (CLEM) of yeast nuclei

Yeast cells were prepared, cryo-fixed, cryo-substituted, and imaged as previously described (Normand et al., 2016). Honeycomb copper finder grids (Maxtaform Reference Finder Grids, Style H6, 230 × 262 pitch, copper) were covered with Formvar and coated with 0.01 % polylysine for 10 s and then with ½ distilled Fluorospheres™ carboxylate-modified 0.2 µm, orange (540/560) for 3 min. Blocks were sectioned with a Leica Ultracut UCT ultramicrotome and placed on a grid prepared as indicated. Ultrathin sections between 130–180 nm (yellow to purple) were made. The grids with sections were then placed on a Citifluor® AF1 drop on an 18 mm round slide and then captured with another 18 mm round slide and placed in the LudinTM chamber for fluorescence imaging. Fluorescence imaging was performed using a Nikon TI-E/B inverted wide field microscope (see above). After fluorescence imaging, the slide was removed from the Ludin chamber and incubated in distilled water for 1 h to separate the two slides without damaging the grid. The grid was recovered and contrasted with slight variations: either with Renold's lead citrate 3 % for 10 min and Uranyless for 5 min, or first, Uranyless for 2 min and Renold's lead citrate 3 % for 2 min in a saturated atmosphere of NaOH. The grids were then imaged using a Jeol-JEM 1400 electron microscope with different zooms: one with the maximum number of beads around the cell and one with the cell in focus. Finally, the images were aligned using ec-CLEM (Paul-Gilloteaux et al., 2017). The first deformation matrix was calculated between the fluorescence image in the GFP channel and the first zoom using either beads or the cell contour. The second matrix was calculated between two TEM images using ribosomes or dense elements present in both images. If necessary and indicated by the plugin, the matrix was rigid or not. Both matrices were then applied to modify the fluorescence image. The modified fluorescence image and the TEM image were then merged.
# 3. Results

## 3.1. Exploring yeast chromatin structure using RIM microscopy

The computational model of the yeast chromosomes provides highly accurate predictions of chromosome positions, motions, and contact frequencies, as determined through experimental means. This includes the prediction of the position of chromosome XII, which bears the ribosomal DNA (rDNA) locus (Albert et al., 2013; Wong et al., 2013, 2012). In the nucleoplasm, the budding yeast genome is predominantly organized as euchromatin, with a small heterochromatin area near the telomeres (TEL) and silenced mating type loci (HML and HMR). To compare microscopy observations to *in silico* model of yeast genome, we first generated virtual microscopic images of yeast chromosomes based on the theoretical resolution of 250 nm (wild field microscopy Fig. 1A, left column-virtual WF) and on the 150 nm theoretical resolution of RIM microscopy (RIM, Fig. 1A right column- Virtual RIM). The chromatin density of rDNA in the nucleolus is much lower than that of nucleoplasmic chromatin. Therefore, when the entire genome is uniformly labeled and the threshold is adjusted to visualize the nucleoplasmic chromatin signal, the rDNA is hardly visible (Fig. 1A, Upper panel, in red). We specifically extracted rDNA from the model and increased contrast of this genomic area (Fig. 1A, middle panel in green) allowing to visualize both nucleoplasmic and nucleolar chromatin (Fig. 1A, bottom panel).

To directly challenge the model prediction with microscopic observation, we labelled bulk chromatin, histone H2A and rDNA with fluorescent markers. Hoechst-33342 was used to fluorescently label genomic DNA, and the histone H2A (here *HTA1* gene) was expressed in the same cells in fusion with mCherry. To specifically label rDNA, we used a yeast strain carrying a modified rDNA with fluorescent operator repressor system (FROS) labelling (Goetze et al., 2010; Miyazaki and Kobayashi, 2011; Shou et al., 2001). FROS-labeled rDNA was generated by Kobayashi laboratory to introduce 50 repeats of lacO, which are recognized by fluorescently labelled LacI (using GFP or mNeonGreen, see table S1) and labels all rDNA genes regardless of their transcriptional status (Dauban et al., 2019a; Miyazaki and Kobayashi, 2011).

We imaged our triple-labelled yeast using WF and RIM microscopy (Fig. 1B). Since rDNA is highly reorganized during the cell cycle, we chose two representative nuclei to show the heterogeneous appearance of rDNA. 400 images were acquired at each focal plan using random speckled illumination. Due to the random nature of the speckled illumination, the sum of the collected fluorescence signals provides wide-field imaging (~250 nm resolution – WF, Fig. 1B, left column of each nucleus). We next used RIM reconstruction method that presents a linear signal detection as a function of the fluorescent signal detected in cells and allows improved lateral and axial resolution by two fold (Mangeat et al., 2021). Using RIM imaging modality, we could see high level of details in yeast nucleoplasm and nucleolus: area of low or no signal, alternating with bright signal (~150 nm resolution; left column Fig. 1B). Structures in nucleoplasmic chromatin were visible in both wavelengths, with an increased resolution when collecting signals of smaller wavelength: (Fig. 1B, Hoechst staining $\lambda$ = 405 nm in grey and mCherry $\lambda$ = 575 nm in red). Due to the much lower signal intensity, co-localization of nucleolar chromatin labeled with FROS-rDNA and both Hoechst and histone H2A detected in the raw data is barely visible when using the full dynamic range of intensity (Fig. 1B).

We observed a global agreement between chromatin imaging and virtual imaging (compare Fig. 1A and B). In both, rDNA chromatin density in the nucleolus was much lower than nucleoplasmic chromatin and required specific enhancement to be detectable. Most chromosome arms were blurred in virtual microscopy. Because RIM improved lateral and axial resolution by a factor of two compared to WF imaging, small

portions of the chromosome axes at the periphery of the nucleoplasm remained visible as “tube-like” structures in virtual RIM, where chromatin density is low (Fig. 1A). Accordingly, at these densities, tube-like structures were visible throughout the rDNA in virtual RIM microscopy. In experimental RIM imaging, tube-like structures were also visible at the periphery of nucleoplasmic chromatin, but the chromatin signal often appeared as local clustering. Due to the lower chromatin density in the nucleolus, a few tube-like structures were also visible, but most rDNA chromatin appears in local clusters (Fig. 1B).

To quantitatively assess such chromatin patterns, we used a wavelet filter segmentation called 'à trou', which has previously been used to analyze chromatin texture in nuclei (Herbomel et al., 2016). We established an image analysis pipeline that allowed segmentation of areas of high chromatin intensity in both virtual and experimental microscopy. We analyzed either the bulk chromatin signal of the nucleoplasm or the rDNA signal individually, due to the highly variable chromatin density (Fig. S1). To decipher tubular versus punctate chromatin domains, we used the circularity index, which ranks each shape from 0 to 1, where 1 is a perfect circle (Fig. S1C). By calculating the circularity of segmented chromatin domains between model and microscopic data in nucleoplasm and rDNA, we were able to assess a significant difference between model and experimental imaging (Fig. 1C and D). In nucleoplasmic chromatin, differences were detected only in RIM imaging, with a higher frequency of circular domains in experimental microscopy than predicted by the model (Fig. 1C). In rDNA, although differences between experimental and virtual microscopy were visible in WF microscopy, we could not detect a significant difference in the circularity of segmented domains. Experimental RIM imaging of rDNA confirmed a lower number of tubular chromatin domains than predicted by the model. Local clustering was evident in RIM imaging (Fig. 1D).

We conclude that RIM imaging achieved a resolution that allowed us to reveal some details of internal chromosome organization; within rDNA, local clustering was easier to detect due to lower chromatin density, but appeared very similar to nucleoplasmic chromatin with our quantitative imaging pipeline. We observed local clustering of all chromatin domains not accounted for by *in silico* models of chromatin fibers. This identification was reminiscent of mesoscale chromatin domains, or chromatin nanodomains previously identified in the nucleoplasm of metazoan cells using SIM microscopy (Miron et al., 2020; Szabo et al., 2020).

## 3.2. Single-molecule localization microscopy (SMLM) reveals nanodomains along chromatin fiber

A large family of imaging techniques is based on the detection of single molecules in fluorescence, either by slow, stochastic temporal activation of the fluorophore followed by rapid bleaching, or by statistical blinking of the dyes during imaging (Liu et al., 2022). SMLM imaging offers high spatial resolution, 50 nm or better. Classically, resolution is not limited by diffraction, but by the signal-to-noise ratio per activated molecule. However, the sampling rate of our molecules of interest is a major limitation (Lelek et al., 2021). To study chromatin with SMLM, we had to maintain a low level of fluorescence events per image to avoid detection of multiple fluorophores, while maintaining a very high number of detections per nucleus. We were able to overcome this apparent contradiction thanks to the relatively low density of chromatin in yeast and the long imaging times per field of view (>120 min).

We used histone fused to mEos-EM fluorescent protein to perform photoactivated localization microscopy (PALM) (Fu et al., 2020). mEos-EM is a photo-switchable fluorophore derived from mEos4 that has been described for use in correlative light and electron microscopy (CLEM). With up to 4.10⁴ individual detections per imaged section of nuclei,
<figure><img src="image_5.png" /><figcaption>B</figcaption></figure>

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<figure><img src="image_7.png" /><figcaption>D</figcaption></figure>

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(caption on next page)
**Fig. 1.** Yeast chromatin in the nucleoplasm and nucleolus. A: *In silico* model of chromatin organization in the yeast S. cerevisiae nucleus (Wong et al., 2012). Virtual imaging is achieved by convolution of the *in silico* model with the point spread function (PSF) of wide-field microscopy (WF – left column) or RIM imaging (right column). Bulk chromatin is shown in red. Note that due to the low density of rDNA, it is nearly impossible to see the chromatin in such a display, so the rDNA chromatin is highlighted in green. Bottom panel shows bulk chromatin (red) and rDNA (green). B: Microscopic observation of chromatin organization in S. cerevisiae. Representative WF and RIM images of two different nuclei are shown. From top to bottom: DNA staining with Hoechst intercalant (Hoechst – gray), histone marker H2A-mCherry (histone – red), NG-LacI expressed in cells carrying modified rDNA-lacO repeats (rDNA – green), and merge image. Scale bars: 1 µm. C: Quantitative comparison of experimental and virtual imaging of bulk chromatin. Representative images of virtual and experimental observations for both WF and RIM imaging are shown in false color (ImageJ Look Up Table: Orange Hot) (Schneider et al., 2012). The imaging pipeline is then used to segment chromatin domains (object mask). Circularity is then calculated for each segmented object, allowing quantitative evaluation of the difference between virtual and experimental imaging. D: Quantitative comparison of experimental and virtual imaging of rDNA. Similar panels are shown, allowing a direct comparison between bulk chromatin and rDNA. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

limited to a depth of 800 nm in our imaging setup, we achieved a reasonable sampling rate with good accuracy. In SMLM, evaluation of resolution and sampling rate is a key quality criterion to ensure proper determination of complex structures such as chromatin (Lelek et al., 2021). We decided to evaluate the resolution, sampling qualities, and detection characteristics used to generate SMLM images of chromatin (Fig. S2). The first criterion used to evaluate the quality of our dataset was the localization precision of each individual H2A-mEos EM detection obtained with the ThunderSTORM plugin, one of the existing SMLM software (Ovesný et al., 2014; Sage et al., 2019). For each individual detection of a histone molecule, the localization accuracy (θloc) was estimated (Fig. S2A). We decided to threshold our experimental data to retain only those molecules whose localization precision was less than 35 nm (Fig. S2A – dark gray), resulting in a mean localization precision estimated at 28 nm (mean θloc), while retaining a sufficient number of localizations to observe our structures of interest.

Determining the resolution of a single-molecule localization microscopy (SMLM) image is not straightforward, but the upper resolution limit should be defined by 2.3 times the localization accuracy (Lelek et al., 2021). To determine the resolution of the 3D images, we chose to use 2D Fourier Ring Correlation (FRC). We compared SMLM imaging using all detected histone molecules (gray) or using the most accurate detection (θloc < 35 nm). Data filtering is expected to alter both resolution and sampling quality. However, even if θloc is improved by keeping only the most accurate detections, the resolution computed by FRC was rather poor (110 nm) and only marginally improved by selecting the most accurate detections (compare FRC in black and gray; Fig. S2B). FRC resolution may be underestimated if photoactivation frequently occurs near previously detected molecules. To evaluate possible non-random activation, we generated two datasets using either the experimental order of detections (beginning vs. end of the temporal sequence of molecules detected in a nucleus) or randomized localization. When calculating the FRC of randomized detection, the resolution of the SMLM is approximately 40 nm (Fig. S2B). To confirm that photoactivation was temporally biased by frequent photoactivation near previously detected molecules, we visualized sampling along chromatin fibers by encoding in two colors (green and red) a sequential or randomization of temporal detections (Fig. S2C). When using the experimental order of detection, most areas are green or red, with very little yellow. Conversely, when using random color-coded SMLM detections, we observed a more robust sampling: most areas are detected in yellow, indicating detected chromatin imaged more than twice. After excluding obvious imaging artifacts such as multiple detections of the same photoactivation events, we propose that mEos-EM SMLM imaging was not spatially random and that the resolution of chromatin imaging estimated using FRC was approximately 40 nm when calculated in two dimensions around the focal plan (Fig. S2B). Importantly, this calculated resolution using the FRC localization accuracy is better than the resolution estimated using the localization accuracy generated for the entire 3D dataset (approximately 800 nm in depth). Finally, sampling quality is another important parameter, but not easy to estimate for chromatin.

With 13 Mb of genomic DNA and approximately one nucleosome per 147 bp and a linker DNA of 15–20 bp in yeast, we estimate 5.10⁴ to 10⁵ nucleosomes per haploid genome (Cole et al., 2012; Zaugg and Luscombe, 2012). Quantitative proteomic studies gave the same order of magnitude, with 0.86 10⁵ molecules of H2A per cell (Ho et al., 2018). Even when we inserted H2A-mEos-EM into one of the two copies of the H2A gene, we had to image a very large number of mEos-EM in a nuclear volume of about 4 µm³, which posed a real challenge for single-molecule detection (Wang et al., 2016a). Here, we addressed sampling quality by calculating the resolution limit as a function of the number of localized molecules (Fig. S2D). Theory predicts that resolution limits decrease rapidly as a function of localized molecules, but reach a plateau, indicating that increased sampling significantly improves resolution (Lelek et al., 2021). We can see here that the sampling rate that allowed us to obtain a resolution of about 50 nm (indicated by a plateau) was about 4.10⁴ localizations when considering all detections. Interestingly, the best sampling was obtained with much fewer detected molecules (2.10⁴) if only the most accurate detection is retained (Fig. S2D comparing resolution limits as a function of the number of localized molecules).

Having quantitatively assessed the relevance of SMLM imaging of H2A-mEos-EM for depicting yeast chromatin structure, we next compared different nuclear sections. In each analyzed nucleus, at each cell cycle phase examined, we detected local clustering along chromatin fibers of the nucleoplasm, characterized by areas of high intensity flanked by nuclear domains of low signal (Fig. 2A). Such a pattern has previously been described in metazoan nuclei as nucleosome clutches or nanodomains (Fang et al., 2018; Ricci et al., 2015). *In silico* modeling was again used to generate virtual images (Fig. 2B, left panel). We used as input a localization accuracy of 25 nm and a labeling density of 70 % of all polymer segments (22813 individual chain segments present in the model), consistent with the number of detections obtained from the experimental PALM data. In virtual microscopy at this resolution, we can see chromosomes as tubes and regions flanking chromosome axes with no detectable signal (Fig. 2B, left panel and zoomed area). Since less than 3 % of the yeast nuclear space is chromatin, this model prediction may be realistic (Miné-Hattab and Taddei, 2019). Experimental PALM imaging of chromatin revealed a more complex picture (compare left and right panels in Fig. 2B). With a localization precision of 25 nm and a similar number of localizations (2 to 3,10⁴) as in virtual imaging, no continuous tubes representing chromosome arms were visible in experimental SMLM. Rather, we saw little or no area without signal in the center of the nucleus. Frequent contacts between adjacent fibers or possible chromatin loops were detected in experimental imaging, but not in virtual imaging at comparable resolution (Fig. 2B, right panel and zoomed area).

To quantitatively assess this difference in chromatin distribution, we repeated the “à trou” wavelet filter segmentation, and compared the circularity of segmented chromatin domains in experimental and virtual SMLM imaging (Fig. 2C). This analysis confirmed that chromatin is organized in local clusters along fibers, interspersed with areas of lower density. This chromatin organization was reminiscent of nanodomains
<caption>A</caption>

Experimental PALM microscopy - H2A

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20163 localizations

28467 localizations

27359 localizations

23258 localizations

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Virtual SMLM - DNA

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Experimental SMLM - H2A

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**Fig. 2.** Single Molecule localization microscopy of yeast chromatin. A: Reconstructions of Photo-Activation Localization Microscopy (PALM) images of histones H2A-mEos-EM. For each nucleus, the top panel shows the transmitted image (right) of the frame in SMLM and is used to visualize bud size. White squares represent the regions shown in fluorescence. The SMLM image of a nuclear section is shown in false color (orange hot; bottom). B: Comparison of virtual and experimental SMLM microscopy. Comparison of experimental and virtual imaging of SMLM chromatin imaging. Representative images of virtual and experimental observations are shown using false color. C: Quantitative comparison of experimental and virtual SMLM chromatin imaging. Representative images of virtual and experimental observations are displayed using false color (ImageJ Look Up Table: Orange Hot) (Schneider et al., 2012). The imaging pipeline is then used to segment chromatin domains (objects mask). Circularity is then calculated for each segmented object, allowing quantitative assessment of the difference between virtual and experimental imaging. Scale bars: trans.: 5 µm; SMLM nuclei: 0.5 µm; Zoom: 0.2 µm. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
or nucleosome clusters previously described in mammalian chromatin (Ricci et al., 2015).

Despite a bias in the temporal activation of the H2A-mEOS-EM fusion protein in SMLM imaging of nuclear sections (see Fig. S2), we were able to achieve a resolution of ~50 nm in SMLM imaging of chromatin with sufficient sampling. This quantitative analysis supported our observation using RIM imaging of the detection of nanodomains along chromatin fibers, which was absent in the *in silico* model when considering a resolution of 50 nm. We conclude that SMLM imaging of yeast chromatin reveals local clustering along chromatin fibers.

### 3.3. Transcriptionally active regions of the rDNA are organized separately from inactive rDNA clusters

After imaging bulk genomic DNA with SMLM in the nucleoplasm, we decided to focus specifically on rDNA because of its unique properties: repetitive, rearranged during the cell cycle, and containing actively transcribed genes, representing approximately 50 % of all rDNA genes (Dammann et al., 1995; Dauban et al., 2019a; Hamperl et al., 2013).

Because of its repetitive nature, ribosomal DNA (rDNA) has been extensively studied using fluorescent *in situ* hybridization (FISH) and live cell imaging. rDNA organization has been readily probed, revealing a “puff,” “line,” and “loop” morphology that was sequentially detected during the cell cycle (Guacci et al., 1994; Lavoie et al., 2004). Since then, rDNA has been used extensively for screening of defects in genome organization (D'Ambrosio et al., 2008b, 2008a; Freeman et al., 2000; Guacci et al., 1997; Lopez-Serra et al., 2013; Thattikota et al., 2018; Wang et al., 2016). When chromatin is labeled with a generic DNA intercalating dye in both human and yeast cells, the appearance of rDNA is different from other genomic regions (Shen and Skibbens, 2017; Smirnov et al., 2016). While nucleosomes and histones are present in rDNA, they are known to be depleted from actively transcribed genes (Hamperl et al., 2013).

To specifically address possible differences in localization between active and inactive rDNA, we chose to combine bulk chromatin labeling (Hoechst-33342) with two fluorescently labeled proteins associated either with transcriptionally active genes or total rDNA. Net1 is a highly

abundant rDNA-associated protein known to be specifically associated with intergenic spacers 1 and 2 flanking transcribed rDNA genes (Goetze et al., 2010; Hannig et al., 2019). Therefore, Net1 fused to red fluorescent protein (mKate or mCherry) was used to detect actively transcribed genes, and FROS labeled rDNA (mNeonGreen or GFP-LacI and rDNA-lacO genes) was used to localize rDNA genes regardless of their transcriptional status (Dauban et al., 2019a; Miyazaki and Kobayashi, 2011). As previously observed using WF imaging, rDNA appears as multiple bright punctate signals interspersed with regions of lower intensity (Fig. 3). Using confocal imaging in live cells, we and others previously reported that we could not detect a major difference between the Net1 and FROS-rDNA fluorescence signal in the strain carrying rDNA-lacO (Dauban et al., 2019a; Lawrimore et al., 2021). Using fixed cells imaged by wide field microscopy and processed by deconvolution, we were able to clearly distinguish FROS-rDNA relative to Net1 (Fig. 3). Notably, we observed a partial colocalization between Net1 and FROS-rDNA, with a significant difference in intensity at some positions of the rDNA (Fig. 3).

To confirm the apparent separation of active rDNA from labeled bulk rDNA, we used the RIM imaging modality (Fig. 4). When labeling bulk genomic material with Hoechst, we were able to reproduce a high level of detail in the nucleoplasm (compare Fig. 1B to 3A) and detected rDNA at the periphery of bulk chromatin, in areas of lower signal intensity positive for FROS-rDNA and Net1 signal. While there is a global colocalization of Net1 and FROS-rDNA, we can identify areas where rDNA associated with Net1 shows a depletion of FROS-rDNA signal. Conversely, there are few areas with FROS-rDNA signal where Net1 signal is not visible (Fig. 4A). Note that similar relative localization of Net1 and FROS-rDNA was observed at each cell cycle phase examined.

We have previously documented rDNA chromatin motion, which is distinct from nucleoplasm chromatin (Albert et al., 2013). To exclude that the partial overlap between LacI and Net1 could also be due to an artifact caused by chemical fixation of the cells, we used RIM imaging on live cells with simultaneous acquisition in both channels, we reconstructed the signal every 0.64 s and generated a short movie of both signals (Fig. 4B and Movie 1). We were able to confirm and extend our previous findings, with only a partial overlap of actively transcribed

<figure><img src="image_14.png" /><figcaption>**Fig. 3.** Rrna organization studied by wide field microscopy. Deconvolved brightfield images of asynchronous cells with double labeling of rDNA; Fluorescent Repressor/Operator System (FROS) to label rDNA (NG-LacI binding to modified rDNA with lacO) and Net1-mKate fusion protein to label active rDNA genes. From left to right: Bright field image of the cells (gray-BF) with a white square representing the fluorescent region of interest. Bulk DNA labeling (here Hoechst 33342-gray), FROS labeling of all rDNA units (NG-LacI, green), Net1-mKate fusion protein (red), merge of LacI-GFP and Net1 (green/red), and merge panel. Scale bar: 5 µm (left panel) and 1 µm (right panel). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
A

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B

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**Fig. 4.** Visualization of rDNA organization during the cell cycle of *S. cerevisiae* using Random Illumination Microscopy (RIM). A rDNA organization in fixed cells using Random Illumination microscopy. Fluorescence images of fixed asynchronous cells obtained by RIM technique. From left to right: Bright field image (BF) of the cells overlayed with Hoechst (Hoechst 33342-blue) fluorescence (gray and blue, respectively), with the region of interest (ROI) shown in white; zoom on Hoechst fluorescence signal, FROS labeling of all rDNA units (LacI-GFP, green), Net1-tDimer fusion protein (Net1, red). The last two panels show the fusion of LacI-GFP and Net1 (green/red) and together with DNA labeling (gray). From top to bottom: one cell in G1 (unbudded), one cell in S phase (small bud) and two cells in G2/M phase (big bud) at different stages of rDNA segregation. Scale bar: 5 µm (left panel). Scale bar: 0.5 µm (right panel). B rDNA organization in living cells. Fluorescence images of live cells obtained by RIM technique. From left to right: five time points (0.64 s step) were shown to illustrate the movement of rDNA labeled with LacI-GFP and Net1-tDimer (green/red) independently or together. Scale bar: 1 µm. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

rDNA genes labeled by Net1 and bulk rDNA, with a rapid reorganization of rDNA on a short time scale (0.64 s).

In conclusion, we observed spatial clustering of Net1 on subregions of rDNA. This observation suggests an additional layer of organization within rDNA chromatin structure *in vivo*, with spatial clustering of active and inactive rDNA genes.

**4. Correlative light and transmission electron microscopy (CLEM) reveals that rDNA chromatin is concentrated in the fibrillar center (FC) of the yeast nucleolus**

Next, we tried to determine in which region of the nucleolus bulk rDNA and active rDNA are located. The nucleolus is best studied by electron microscopy, which can reveal ultrastructural details of nuclear
morphology with nanometer resolution (Léger-Silvestre et al., 1999; Thelen et al., 2021; Thiry and Lafontaine, 2005). As in the metazoan nucleolus, the budding nucleolar ultrastructure was initially divided into three compartments, the fibrillar center (FC), the dense fibrillar component (DFC), and the granular component (GC), based on their morphological aspect in TEM (Léger-Silvestre et al., 1997, 1999a). The yeast FC is reminiscent of the appearance of nucleoplasmic chromatin (Léger-Silvestre et al., 1997, 1999a). Later, this initial proposal was challenged due to the different appearance and composition of the fibrillar center in yeast and metazoans, leading to the model of a two-compartment nucleolus in yeast (Thiry and Lafontaine, 2005). However, nucleolar DNA has been detected in a third nucleolar region near but distinct from the DFC, defined as “heterogeneous areas” (Thelen et al., 2021).

To characterize chromatin localization in yeast by transmitted electron microscopy (TEM), we performed correlative light electron microscopy (CLEM) on the yeast strain with labeled histone H2A and FROS-rDNA already imaged by WF and RIM microscopy in Fig. 1. The CLEM protocol used here consisted of fluorescence imaging of ultrathin sections of cryo-fixed, cryo-substituted yeasts embedded in resin. After fluorescence imaging, the sample was then prepared to allow TEM imaging of the yeast nucleus and nucleolus in optimal conditions (Normand et al., 2016). FC, defined as nucleolar fibrillar areas of low contrast (similar to nucleoplasmic signal) surrounded by or flanking DFC, were visible, with morphological appearance varying according to the section analyzed (Figs. 5 and S3, see TEM imaging). Note that since FC are a small nucleolar domains, this subdomain is not observed in every nucleolar section (Fig. S4). After aligning the fluorescence signal with TEM images, we found a complete overlay of FROS-rDNA and FC (Figs. 5 and S3, see Merge). Most of the histone signal is detected in the nucleoplasm and is largely excluded from the nucleolar region, in agreement with the low rDNA density in the nucleolus (see Fig. 1). Note that variation in histone signal intensity across sections did not correlate with detectable nucleoplasmic structures visible in TEM, showing that the contrast, obtained by Uranyless and lead citrate, is not specific for chromatin.

Next, to assess in which nucleolar subdomains Net1 is concentrated relative to FROS-rDNA localized in the FC, we evaluated the position of the Net1-mKate fusion protein relative to FROS-rDNA using CLEM. As expected from RIM imaging showing distinct locations between Net1 and FROS-rDNA, we detected Net1 in the interface between the FC and DFC regions of the nucleolus (Fig. 6A). Note that some FC associated with FROS-rDNA were not associated with detectable Net1 signal, reinforcing our initial observation of spatial segregation of Net1 from bulk rDNA. To rule out the possibility that FC appearance or Net1 localization could be affected by FROS modification of rDNA, we performed CLEM in a strain carrying only the Net1-GFP fusion protein and no rDNA modification (Fig. S5). Fluorescent beads, visible as very bright signals relative to the Net1-GFP signal in CLEM imaging, were added to allow accurate realignment between fluorescence and ultrastructural images (Paul-Gilloteaux et al., 2017). In such strains, Net1 was associated with or in close proximity to detectable FC (Fig. S5, Merge), demonstrating that the modified FROS-labeled rDNA does not affect the morphological appearance of FC or Net1 localization.

To demonstrate that DFC indeed surrounds FC, we performed CLEM imaging of a yeast strain carrying a Net1-mKate fusion with a known marker of DFC, Nop56, one of the components of small nucleolar ribonucleic particles (snoRNPs), fused to mNeongreen. Dot-like structures of Net1 were flanking FC and surrounded by Nop56-mNeongreen signal (Fig. 6B). Indeed, the Nop56 signal colocalized with the DFC visible by TEM. Therefore, by CLEM we could show that Net1 is located at the interface between the yeast equivalent of FC and DFC (here labeled with Nop56). To strengthen our data showing the localization of active rDNA genes, we also performed CLEM with a strain expressing Fob1 fused to YPET. Fob1 is a replication fork-blocking protein, associated with the RENT complex and was shown to colocalize with Net1 (Huang and

<figure><img src="image_17.png" /><figcaption>**Fig. 5.** Correlative microscopy shows localization of rDNA in the FC of *S. cerevisiae* nucleolus. CLEM images of yeast chromatin. Ultrastructure of the yeast nuclei (gray) is superimposed on H2A (red) and FROS-rDNA (green). Note the appearance of the yeast nucleolus as a crescent-shaped area of the nucleus of heterogeneous appearance in which fibrillar electron clear areas are visible (FC white arrow), surrounded by DFC. Merge shows that rDNA is concentrated in the FC. Scale bar: 0.5 µm. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
A

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B

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**Fig. 6.** Correlative microscopy shows that Net1 is located at the interface between the yeast equivalent of FC and DFC. A: *CLEM images of active* versus *bulk rDNA*. Ultrastructure of the yeast nuclei (gray) is superimposed on Net1 (red) and FROS rDNA (green) showing that Net1 is flanking the FC and partially overlaps with the bulk rDNA which is concentrated in the FC (Merge). Scale bar: 0.2 µm. B: *CLEM images of active rDNA and DFC*. From left to right, TEM images of the cells (gray), fluorescence signal of Net1-mCherry (red) and Nop56-mNG (green), and merged signal are shown. DFC are colocalized with Nop56, a component of snoRNPs, surrounding FC (white arrow). Scale bar: 0.5 µm. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Moazed, 2003). In this strain, Nop56 labelling the DFC was fused to Turquoise2, and Rrp1, a pre-60S biogenesis factor, concentrated in the GC was fused to mCherry. The CLEM pictures confirm that Fob 1 is localized, as Net1 at the interface FC/DFC (Fig. 9C).

In metazoan cells, FC is enriched in rDNA-bound molecules such as UBF and Pol I. To investigate Pol I localization in FC using CLEM, we performed a similar protocol and were able to show that Pol I is indeed present in yeast FC and DFC (Figs. 7A and S6). Note that in the rare mitotic cells identified in some of our CLEM analyses, we could identify FC with similar localization of Net1 and Pol I (Fig. S5, nucleus 3 and S6, nucleus 3). Upon transcriptional repression, the nucleolus is rapidly reorganized in both yeast and metazoans (Correll et al., 2024). To evaluate Pol I localization in the yeast nucleolus upon transcriptional repression, we performed CLEM imaging using the thermosensitive allele of the Pol I initiation factor rrn3-ts cultured at 37 °C.

Upon rRNA inhibition following Rrn3 inactivation, the nucleolus was drastically remodeled: it became smaller, lost its crescent-shaped appearance for a more spherical shape, and dissociated from the nuclear envelope (Fig. 7B). In our CLEM experiments, Pol I was localized relative to the mCherry-Nop1 fusion protein, labeling snoRNPs (Fig. 7C). Upon inhibition of rRNA synthesis, nucleolar subdomains were not clearly visible in TEM, but Pol I and Nop1 were still nucleolar and partially overlapped.

Using CLEM approach, we observed a functional subdivision of the FC and DFC. The FC is enriched in bulk rDNA, while the DFC is enriched in snoRNPs (labeled using Nop1 or Nop56). Active rDNA (Net1) and Pol I are located at the interface between the FC and DFC. This subdivision is reminiscent of, but not identical to, the metazoan organization. We proposed that yeast rDNA is enriched in the FC subdomains of the nucleolus, with actively transcribed rDNA localized at the interface between DFC and FC.

## 4.1. Investigation of rDNA clustering with SMLM

CLEM experiments allowed us to define the yeast FC as electronlucent regions of the nucleolus that are irregular in shape and size, enriched in rDNA, and in close proximity to Net1. To explore the organization of rDNA within the FC in the yeast nucleolus, we used two strains, either carrying mEOS-LacI and rDNA-lacO, allowing detection of all FROS-labeled rDNA repeats in the SMLM, or Net1-mEOS-EM fusion protein labeling actively transcribing rDNA genes. We performed PALM imaging, facilitated by a density of molecules much lower than histone molecules. The signal intensities of mEOS-EM-LacI and Net1-mEOS-EM are roughly comparable, but the number of Net1 molecules per cell is approximately 6.3 × 10³, more than ten times lower than that of histones (Ho et al., 2018). We used a similar localization precision as in Fig. 4, with a threshold of 35 nm, giving an average resolution of about 50 nm (Fig. 8).

As with rDNA imaging by RIM imaging, we saw clusters of rDNA imaged by PALM microscopy separated by more than 100 nm, which is fully consistent with the punctate appearance of rDNA by RIM microscopy (Fig. 8A). Four individual nuclei are shown to illustrate the different FROS rDNA configurations observed. Interestingly, numerous clusters were separated by less than 100 nm and could not be resolved by RIM. We also detected a significant rDNA signal of lower concentration around rDNA clusters indicating a fraction of decompacted rDNA chromatin (Fig. 8A, and Movie 3). Such rDNA morphology is very different from the virtual imaging of rDNA generated using *in silico* model, as shown by comparing the 3D rendering of experimental rDNA versus virtual SMLM (compare Movie 2 and 3). To quantify the presence of chromatin clusters in rDNA, we compared our observations with virtual SMLM (Fig. 8A, lower left panel). Using “à trou” wavelet filter segmentation, we were able to detect tube-like structures in virtual SMLM imaging, but not in experimental SMLM of rDNA. When quantifying the circularity of chromatin domains, we confirm a higher circularity of chromatin domains in experimentally imaged rDNA (Fig. 8A,

lower middle panel). We also observed that the fraction of nucleolus containing chromatin is smaller in experimental imaging compared to model prediction (Fig. 8A, lower right panel).

Next, we evaluated Net1 localization by SMLM in exponentially growing cells and after transcriptional repression achieved by 2 h treatment with rapamycin (Tsang et al., 2003). As for SMLM imaging of FROS rDNA, we showed, for both conditions, Net1 localization in four individual nuclei. As for bulk rDNA, we observed local clustering of Net1. However, the decompacted rDNA signal around Net1 clusters is higher than that of the bulk rDNA. This is consistent with the idea that Net1 is enriched on actively transcribed rDNA. (Fig. 8B). Correspondingly, upon repression, the Net1 SMLM signal is more compacted, with higher clustering of Net1 molecules detected (Fig. 8C).

Because Net1 is associated with both the 5' and 3' ends of actively transcribed genes, we propose that spatial clustering of Net1 and the dispersed signal around these clusters could reveal the location of actively transcribed genes in the nucleolus, at the interface between FC and DFC. Spatial distribution of Net1 is compacted during transcriptional repression.

## 5. Discussion

In this work, we found that yeast chromatin structure can be explored using high-resolution microscopy techniques. These observations led us to reevaluate the predictive value of *in silico* models of yeast chromatin. Our experimental microscopy observations were compared with virtual microscopy generated from *in silico* models of yeast chromatin (Fig. 9A). Surprisingly, we observed local clustering of chromatin in both nucleoplasmic and nucleolar chromatin that was not accounted for by the *in silico* model of the yeast chromosome used here (Fig. 9B). By focusing on nucleolar chromatin, we were able to show that rDNA is concentrated in a subnucleolar domain, the FC. In rDNA, such spatial segregation between transcribed DNA and nascent RNA seems to be conserved in eukaryotes (Miron et al., 2020; Yao et al., 2019). The yeast FC is morphologically smaller than in metazoan cells, with the rDNA-bound proteins Net1, Fob1 and Pol I enriched at the FC/DFC interface. Our CLEM images support the model of a yeast nucleolus defined by three subdomains, FC, DFC and GC (Fig. 9C and D).

### 5.1. Chromatin Structure in Eukaryotes: High-resolution imaging of yeast chromatin

We decided to address an existing paradox in the description of yeast chromatin structure. On the one hand, the structure of chromosomes can be modeled as polymers, ignoring the local effects of such important functions as transcription, chromatin remodelers, and SMC complexes (Tjong et al., 2012; Tokuda et al., 2012; Wong et al., 2013). On the other hand, Hi-C and MicroC technologies show that contacts along the chromosomes depend on chromatin structure and molecular motors (Bastie et al., 2021; Chapard et al., 2023; Dauban et al., 2019b; Hsieh et al., 2015, 2016; Meneu et al., 2025). Indeed, yeast genome organization is largely controlled by the extrusion of chromatin loops by SMCs (Kim et al., 2023). We propose that these two observations are compatible if they reflect organization at different scales. Chromosome properties would then depend on the length scales probed (Hertzog and Erdel, 2023). When we imaged either nucleoplasmic chromatin or rDNA chromatin at resolutions of 150 to 50 nm, we see local clustering that is not captured by the simple biophysical model used here (Fig. 9B). Imaging may suggest heterogeneous chromatin density along chromosome arms and more frequent associations within the chromosome than the *in silico* model would have predicted. This hypothesis is supported by a clear difference between chromatin visualized as a distinct tube-like structure in the model, but with irregular clustering in the experimental data. This observation is reminiscent of our previous study investigating the relevance of the Rouse polymer model to describe chromatin motion in the nucleoplasm, and we found that transient
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**Fig. 7.** Correlative microscopy shows a localization of Pol I in FC/DFC of *S. cerevisiae* nucleolus and a nucleolar re-organization upon inhibition of rRNA synthesis. **A.** CLEM images of Rpa190-mCherry (Polymerase I) and Nop56-Tq2, a DFC marker. From left to right: TEM images of the nuclei, overlay of Nop56/Pol I fluorescent signals, overlay of TEM and Pol I fluorescence, and overlay of TEM, Pol I, Nop56 (Merge). **B.** Morphology of yeast nucleoli upon transcriptional inhibition. Using *rrn3-ts* allele to inhibits Pol I activity, yeast nucleolus observed by TEM is dissociated from nuclear envelope, and is re-arranged into a spherical shape. Six nuclei are shown. **C.** CLEM images of yeast nucleolus following rDNA synthesis inhibition. Inhibition of rRNA synthesis was achieved by inactivation of rrn3-ts at 37 °C for 45 min. From left to right, TEM images of the cells (gray), fluorescence signal of mCherry-Nop1 (red) and Rpa190-GFP (green), and merged signals are shown. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
<figure><img src="image_23.png" /><figcaption>**Fig. 8. Localization of rDNA using PALM imaging.** A: SMLM imaging of bulk rDNA: mEOS-EM-lacI expressed in rDNA-lacO background was used to perform PALM imaging of bulk rDNA. Upper panel: for each of the four nuclei shown, Hoechst (red), SMLM imaging (orange hot) and green fluorescence of mEOS-EM (green) detected before photoconversion to red during imaging are overlaid with SMLM at the top of each panel to show the specificity of the collected SMLM signal. SMLM is shown below to show rDNA morphology. Bottom panel: Virtual SMLM and experimental imaging were compared using the “à trou” wavelet filtering process, and the circularity (middle) and area (right) of the segmented domains are compared. B: SMLM imaging of Net1-mEOS-EM in exponential growth culture and C upon transcriptional repression. Imaging was performed on an asynchronous yeast culture. Four representative cells (yellow rectangle in bright field) were selected. Clusters of Net1 are visible, with significant signal surrounding the visible clusters. Scale bars: 1 µm (Merge) and 0.5 µm (SMLM). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
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**Fig. 9.** Model of yeast chromatin. **A:** *In silico* model predictions are used to challenge microscopic observation. To evaluate the ability of the *in silico* model of the chromosome to recapitulate chromatin organization *in vivo*, we compared super-resolution imaging of chromatin in both nucleoplasm in rDNA with virtual images generated from the model. **B:** *Virtual* versus *experimental SMLM imaging of chromatin*. 3D view of chromatin fibers is shown to highlight the tubular appearance of chromatin predicted by the model versus the nanodomains detected in experimental imaging. The schematic on the right shows chromosome arms visible as tubes in virtual microscopy versus chromatin blobs or nanodomains detected. Note that rDNA also forms nanodomains or chromatin blobs, but with lower chromatin density. **C:** *CLEM imaging of three domains of yeast nucleolus*. The complex organization of the yeast nucleolus is revealed using three fluorescent proteins, each concentrated in a different region of the nucleolus. Fob1 fused to YPET is used to visualize active rDNA (similar to Net1, both members of the RENT complex). Nop56, a component of snoRNPs fused to Turquoise2, labels the DFC and Rrp1 fused to mCherry concentrates in the GC. Note that due to fluorescence loss during sample preparation, only the highest signal intensity can be detected; therefore, the Nop56 fluorescence signal does not overlap with the full DFC defined in the TEM and the Rrp1 fluorescence signal does not overlap with the full GC. **D:** *Tripartite structural organization of the yeast nucleolus*. Schematic representation of the yeast nucleolus. The detection of FC-containing rDNA in the yeast nucleolus supports the model of a tripartite structural organization of the yeast nucleolus. Each of the three compartments is drawn using CLEM imaging of the three fluorescently labeled proteins in panel C as a model.

internal contacts along fibers (Rouse TIC) are an important parameter to describe the scaling of chromatin motion (Socol et al., 2019).

## 5.2. Yeast nucleolar chromatin

In the *in silico* model used here, to account for the high transcriptional activity in the nucleolus, the authors modeled the rDNA region as a thicker chromosome compared to the nucleoplasmic chromatin. This simple assumption in rDNA chromatin property was sufficient to form a nucleolus occupying one third of the nuclear volume by a heteropolymer exclusion effect segregating nucleoplasmic from nucleolar chromatin (Wong et al., 2012). In this simplified modelization, rDNA associated with active RNA polymerase and nascent ribosomal RNA is sufficient to model the entire nucleolus (see Fig. 9). This simple working hypothesis was later challenged. Our microscopic observations showed that changing the rDNA copy number from 200 copies (about 1 Mb) to about 25 copies had little effect on nucleolar size (Dauban et al., 2019a). Subsequently, Lawrimore et al. advanced a biophysical model of the nucleolus, including nucleolar chromatin and a biomolecular condensate (Lawrimore et al., 2021). Here, We demonstrate that rDNA morphology significantly deviates from the model. This suggests that biomolecular condensates, rather than rDNA chromatin structures alone, organize the yeast nucleolar structure. Such discrepancies become even more apparent as the resolution of rDNA imaging is increased from ~ 150 nm (RIM) to ~ 50 nm in SMLM imaging. To improve existing model, contact along the chain, formation of chromatin loops and existence of nuclear condensate, such as the sub-nucleolar compartment DFC and GC are likely to be instrumental to better account for our microscopic observations.

## 5.3. Organization of actively transcribed ribosomal DNA in the nucleolus

The concept of phase separation in macromolecular assembly is a widespread mechanism underlying the spatiotemporal coordination of biological activities in cells, in both normal and pathological contexts (Mehta and Zhang, 2022). Recently, the coexistence of three distinct phases defining three subnucleolar compartments containing rDNA, nascent transcripts, and pre-ribosomes has been proposed to promote efficient ribosome production (Riback et al., 2023; Yao et al., 2019). In such a model, the synthesis of ribosomal RNA at the interface between the FC and the DFC leads to a restricted but directional mobility of nascent transcripts within the DFC.

However, in metazoans and budding yeast, the concept of phase separation in the nucleolus during active ribosome production is still controversial (Tartakoff et al., 2022). Most models of phase separation are based on *in vitro* experiments and highly simplified biochemical setups and biophysical measurements in metazoan cells. We would like to argue that it is important to define nucleolar compartments and their putative functions in budding yeast (Tartakoff et al., 2021). Budding yeast is by far the model organism in which ribosome biogenesis is best understood. However, the existence of two nucleolar subcompartments separating rDNA from nascent transcripts has been debated. Initial TEM observations documented three subnucleolar compartments in yeast:

FC, DFC and GC (Léger-Silvestre et al., 1999). Later, the morphological definition of FC was questioned and a single compartment containing both rDNA and nascent transcript was proposed (Thiry and Lafontaine, 2005). In the yeast nucleolus, it has been proposed that the rDNA is located in the nucleolar interstices (Thelen et al., 2021). Here we show that FC do exist in yeast. Using TEM alone, we observe that the FC has an appearance very similar to the nucleoplasm. However, our finding that this nucleolar domain contains a bulk rDNA cluster and that Net1 as well as Fob1, nucleolar proteins associated with actively transcribed rDNA, and Pol I are both found in these domains and in the adjacent compartment, the DFC, supports the idea that the FC/DFC organization is conserved in eukaryotic cells and is not restricted to amniotes (Lamaye et al., 2011; Thiry and Lafontaine, 2005).

Our finding that FC is present in the yeast nucleolus does not provide mechanistic insight into how ribosome assembly is spatiotemporally coordinated within the nucleolus. However, it does allow us to question the role of compartmentalization and possibly phase separation in ribosome assembly in a genetically amenable eukaryotic cell. The yeast FC is smaller than the metazoan FC. In contrast to most phase-separated compartments, yeast FC, as characterized by both TEM and Net1 (SMLM), is not spherical and has a more heterogeneous appearance. Phase-separated compartments tend to adopt a spherical shape to minimize their surface area and surface tension, as a spherical shape is the most energetically favorable configuration for a given volume. Note that under transcriptional repression, the nucleolar shape is closer to a spherical shape. We now need to investigate how such domains are reorganized upon stress and inhibition of rRNA synthesis to explore whether FC becomes spherical, as the phase separation mechanism would predict.

# 6. Concluding remarks

High-resolution imaging of chromatin will allow us to explore new concepts and organizational principles in the nucleus. The recent definition of mesoscale domains separating chromatin and RNA-rich regions in the nucleoplasm awaits biological explanation (Maslova and Krasi-kova, 2021; Miron et al., 2020). In addition, the partitioning of active gene loci to NE has been proposed to occur by polymer-polymer phase separation of chromatin caused by a dense phase followed by a low density segment forming a dilute phase (González et al., 2023). In this study, we demonstrated that chromatin imaging with 50 nm resolution is now technically feasible with both CLEM and SMLM, allowing future structure-function analysis in different growth conditions and mutant contexts. Furthermore, the observation of a specific nucleolar compartment in the yeast nucleolus, equivalent to the metazoan FC, will allow structure-function analysis to identify the molecular determinant of compartmentalization in the nucleolus and later to investigate the role of the subnucleolar compartments in ribosome biogenesis.

## Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work, the authors used ChatGPT-4,
DeepSeek or DeepL in order to proofread and shorten the text. After using these tools/services, the authors reviewed and edited the content as needed and take full responsibility for the publication's content.

## Funding

Funding was obtained from CBI (Centre de Biologie Integrative – Toulouse), Agence Nationale de la Recherche (ANR) Projet-ANR-23-CE12-0001[DUKKED] and ANR-21-CE12-0008-0 S.D. was supported by a Ph.D. Fellowship from Projet-ANR-23-CE12-0001[DUKKED]. C.C was supported by a Ph.D. Fellowship from the Ministère de l'Éducation Nationale Ph.D. Fellowship. This work was supported by the "Fondation ARC pour la Recherche sur le Cancer" (ARCPJA 20191209547 and ARCPJA22020060002067) and the "Ligue Nationale Contre le Cancer" (3FI14194UPAL). Valdir Gomes Neto is funded by FAPESP grant (22/16740-2). Imaging facilities used are funded by The National Infrastructure France-BioImaging (https://france-bioimaging.org/) supported by the French National Research Agency (ANR-24-INBS-0005 FBI BIOGEN).

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

We would like to thank the LITC imaging platform of Toulouse TRI-Genotoul for their assistance, the METI platform for the TEM acquisitions and Image processing platform for imaging pipeline. We acknowledge TRI-Genotoul platform, member of the national infrastructure France-BioImaging. Christophe Normand strain construction and initial PALM imaging. JF646-Hoechst and JF549- Hoechst used here during calibration of PALM imaging was generously provided by Luke Lavis (Janelia). Arnold Fertin (TIMC, Grenoble) for is help in image analysis and finding the proper tools.

## Appendix A. Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jsb.2025.108228.

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