Name: Mapping Absolute DNA Density in Cell Nuclei using Single-molecule Localization Microscopy (Video) | JoVE Journal
Uploaded: 2025-11-11T14:00:00.000+00:00
Duration: 10 min 57 s
Description: Discover absolute DNA density mapping in cell nuclei using Voronoi tessellation of single-molecule localization microscopy data. This computational method enables precise chromatin architecture analysis.

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5 minutes

We thank Dr. Sandra Ritz for letting us use the IMB Imaging Core Facility, Dr. Shih-Ya Chen for letting us use her custom-built SMLM microscope, Dr. Leonard Kubben (IMB) for providing human fibroblasts, Dr. Christof Niehrs (IMB) for providing the C3H10T1/2 cell line, and Dr. Jan Neumann for the MATLAB-Script that we have modified for this work. We also want to thank Dr. Marion Cremer, Dr. Thomas Cremer, and Dr. Christoph Cremer for fruitful discussions.

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</ol>

The authors have no conflicts of interest to disclose.

This article outlines how to measure absolute DNA densities in mammalian cell nuclei using SMLM. In addition, we have demonstrated how to determine the cell cycle stage of cultured cells and how to use this information to estimate the amount of DNA present in a light optical ultra-thin section. Also described in detail are the preparation of adherent cells for fBALM SMLM microscopy and how to process SMLM data to generate super-resolved microscopic images of genomic DNA in cell nuclei. Finally, the protocol shows how the...

Since the early days of cell biology, the cell nucleus-the seat of genetic information-has fascinated biologists. After applying self-developed cytological staining methods, Emil Heitz discovered, in 1928, differently, intensely stained areas within the cell nucleus1. He called the more intensely stained, dense areas &#34;heterochromatin&#34;, whereas he named the less strongly stained, less dense areas &#34;euchromatin&#34;. Over time, it became apparent that active genes are mostly located in euchromatin, while denser areas were found to be rich in repetitive elements and silent genes. The terms heterochromatin and euchromatin have survived to the present day although their definition has changed from structural to molecular properties (see below). Today we know that the cell nucleus is divided into two main phases. One liquid (the interchromatin compartment), which houses the nuclear bodies, the splicing compartment, and the nucleoplasm, and the other solid, hydrogel-like-the chromatin domain, which includes the chromosome territories and the nucleoli2. At the interface to the interchromatin space, chromatin is less dense, and this is where mostly the genetically active processes such as transcription, replication, and repair take place3,4,5, while adjacent to the lamina, around the nucleoli, and near the centromeres, chromatin shows high compaction levels and is in general transcriptionally rather inactive6.
At the molecular level, chromatin is built from DNA wrapped around nucleosomes (an octamer of core histone proteins). The histones possess intrinsically disordered tail domains that can be modified post-translationally by adding several chemical groups (methyl-, acetyl, phosphor-, biotin), amino-acids (arginine), or even proteins (SUMO, Ubiquitin)7. These modifications can be read and attract other proteins (e.g., chromatin remodeler, HP1, repair proteins, RNA) and thereby define the physiological state of the DNA sequence (transcriptionally permissive/restrictive), without directly changing its sequence&#160;(therefore&#160;&#34;epi&#34;genetic)7. The type and number of modifications around a specific sequence can differ profoundly between cell types, is&#160;reversible, and can change throughout development, senescence, and disease8,9,10. There are modifications of histone tails that are more prevalent in highly transcribed regions and modifications that predominate in regions of the genome that have little or no transcriptional activity.
For example, highly transcribed regions that are rich in&#160;H3K4 modifications or whose histone tails are acetylated are usually described as euchromatin, whereas areas rich in repressive histone modifications (H3K9me3, H3K27me3, or H4K20me3) are referred to as heterochromatin, which is further divided into constitutive heterochromatin (transcriptionally inactive in all cells) (e.g., H4K20me3) and facultative heterochromatin (chromatin silenced depending on cell type, e.g., H3K27me3)6. Although biochemical and molecular biological methods are very well suited for measuring chemical changes at the sequence level, it is much more challenging using them to make statements about spatial properties at the mesoscale using these methods.
For example, attempts have been made using proximity information from Hi-C experiments, which detect and map close proximities of DNA between sequence regions, to reconstruct spatial models of the genome11. However, direct comparison with high-resolution light and electron microscopy images shows that this is only possible to a limited extent (Figure 1). Although methods such as Hi-C12, can detect chromosome territories in interphase cells correctly as separate entities, these models are rather &#34;near-sighted&#34;, because DNA sequence proximities alone are blind to reflect spatial relationships between more distant parts of the genome and thus are not very well suited for a proper 3D genome reconstruction. Therefore, density differences also cannot be reflected properly by contact frequency information. This leads to &#34;spaghettified&#34; representations of the genome in the nucleus (see Figure 1B), which appear to occur in a ball of wool-like, freely accessible loops.
To pave the way for an integrative, more realistic picture of the nucleus that combines biochemical information with biophysical and structural data, methods need to be developed that allow generating data on different aspects of nuclear organization. Until recently, it has&#160;been difficult, too, to determine absolute DNA densities in cell nuclei from imaging&#160;data. The reasons for this were the limited resolution of conventional light microscopes, problems in electron microscopy to specifically stain DNA, and the small observation volumes in electron microscopy.
The resolution of conventional fluorescence microscopes is limited by the diffraction of light. The image of a point source of light is spread out due to the diffraction limit and can be described by the Point-Spread-Function (PSF). According to the PSF, the image of a fluorophore, which can be regarded in good approximation as a point source of light, occupies a volume of a certain size around its source of origin (fluorophore)13. When many fluorophores, located much closer to each other than the dimensions of their diffraction-limited images, are excited simultaneously, the imaged intensity distributions are superimposed, and the location of the single fluorophores cannot be resolved. Sequential, stochastic light emission from fluorophores (blinking) allows optical isolation of individual molecules and, thus, finding their exact locations by determining the intensity gravity centers of their signals.
This can be used to reconstruct structural information of samples by the accumulation of fluorophore localization data from many recorded images. This method is generally referred to as single-molecule localization microscopy (SMLM) (for further information see14). The more photons a fluorophore emits during its &#39;on-time,&#39; the more precisely can the intensity gravity centers be determined. Astigmatic lenses in the beam path transform signals of fluorophores above or below the optical focal plane into ellipses, which can be used to determine their position along the optical axis. The long axes of the ellipses originating from fluorescent signals below the focal plane are rotated at 90&#176; compared to the axes of the ellipses originating from fluorophores above the focal plane. Further, the axes-ratio of these ellipses allows determining the molecule&#39;s position along the optical axis relative to the focal plane within a range of &#177;300 nm15.
The quality of super-resolution images generated by the reconstruction of stochastic blinking events strongly depends on the labeling density and the number of blinking events. The latter depend on the photostability of the fluorophores and the number of blinking events before they eventually fail (number of on/off cycles). The method described here for obtaining super-resolution images of intranuclear DNA distribution is termed fBALM (DNA structure fluctuating-assisted binding-activated localization microscopy). It is based on fluorophores that transiently intercalate into nucleic acids and only fluoresce once they are bound to the DNA16,17. Owing to the charged residues of its phosphor-diester backbone, DNA is a highly negatively charged polymer. Stabilization of the complementary DNA strands in living cells requires neutralization by positively charged proteins (e.g., histones) and ions. By lowering the pH, the stability of the complementary pairing of the bases is reduced to allow intercalating dyes to diffuse in and out16,17.
Depending on the intercalating fluorescent dye, this state can be reached within a certain pH range. Fluorescent dyes such as YoYo-1 and SYTOX Orange only fluoresce when bound to DNA and because intercalating dyes (in contrast to dyes that bind the minor groove of the DNA such as Hoechst and 4&#39;,6-diamidino-2-phenylindole [DAPI]) usually bind in a sequence-independent manner, they are well-suited for mapping the distribution of DNA within cell-nuclei by localization microscopy.
Voronoi tessellation is a mathematical method allowing the subdivision of space into different partitions based on the location of the points. In 2D-Space, the size of the resulting tiles reflects the inverse of the density of points18. Because localization microscopy reconstructs images as a set of points that represent the location of fluorophores, Voronoi tessellation can help determining&#160;the density of localization signals (Figure 2). Thus, using a DNA-specific dye as a fluorophore allows for measuring DNA densities.
A priori knowledge of the DNA content (number of base pairs) and the spatial dimension of the nucleus allows the transformation of the relative DNA densities into absolute DNA densities. The following protocol shows the mapping of absolute DNA densities in adherent cells using SMLM at a very high resolution and demonstrates that these densities are subject to large variations.

<table><tbody><tr><td>&lt;strong&gt;Cell Culture&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>&amp;micro;-Dish 35 mm, high Grid-500 Glass Bottom</td><td>Ibidi</td><td>81168</td><td></td></tr><tr><td>C3H 10T1/2</td><td>IMB (Niehrs Lab)</td><td></td><td></td></tr><tr><td>DMEM</td><td>ThermoFisher</td><td>12320032</td><td></td></tr><tr><td>dPBS</td><td>ThermoFisher</td><td>14190144</td><td></td></tr><tr><td>FBS</td><td>Life Technologies</td><td>16000-044</td><td></td></tr><tr><td>HeLa</td><td>Microscopy Core Facility (IMB)</td><td></td><td></td></tr><tr><td>HFB</td><td>IMB (Kubben Lab)</td><td></td><td></td></tr><tr><td>L-Glutamine</td><td>Sigma-Aldrich</td><td>G7513</td><td></td></tr><tr><td>Nonessential Aminoacids and vitamins for HFB</td><td></td><td></td><td></td></tr><tr><td>Sodium Pyruvate</td><td>S8636</td><td></td><td></td></tr><tr><td>&lt;strong&gt;Sample Prep&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Catalase</td><td>Merck</td><td>2593710</td><td></td></tr><tr><td>Glucose</td><td>ThermoFisher</td><td>241922500</td><td></td></tr><tr><td>Glucose Oxidase</td><td>Merck</td><td>49180</td><td></td></tr><tr><td>Paraformaldehyde</td><td>Sigma-Aldrich</td><td>158127</td><td></td></tr><tr><td>RNase Cocktail</td><td>ThermoFisher</td><td>AM2286</td><td></td></tr><tr><td>SYTOX Orange</td><td>ThermoFisher</td><td>S11368</td><td></td></tr><tr><td>TetraSpeck Fluorescent Microspheres Sampler Kit</td><td>ThermoFisher</td><td>T7284</td><td></td></tr><tr><td>Triton X-100</td><td>ThermoFisher</td><td>327372500</td><td></td></tr><tr><td>&lt;strong&gt;Software&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>BioFormats</td><td>OpenMicroscopy.org</td><td></td><td>open source software https://www.openmicroscopy.org/bio-formats/</td></tr><tr><td>CellProfiler v4.2.1</td><td>CellProfiler.org</td><td></td><td>open source software https://cellprofiler.org</td></tr><tr><td>FiJI</td><td>nih.gov</td><td></td><td>open source software https://imagej.net/software/fiji/?Downloads</td></tr><tr><td>LAND</td><td>nih.gov</td><td></td><td>open source software https://github.com/Jan-NM/LAND</td></tr><tr><td>MatLab 2021</td><td>Math Works</td><td></td><td>commercial software - requires &quot;Image Processing Toolbox&quot;</td></tr><tr><td>R v.4..0.3</td><td>r-project.org</td><td></td><td>open source software https://www.r-project.org</td></tr><tr><td>ThunderSTORM v1.3</td><td></td><td></td><td>open source software https://zitmen.github.io/thunderstorm/</td></tr><tr><td>&lt;strong&gt;Microscopes:&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>AF 7000</td><td>Leica</td><td></td><td></td></tr><tr><td>Leica GSD</td><td>Leica</td><td></td><td></td></tr><tr><td>SMLM Microscope</td><td>Cremer Lab</td><td></td><td>custom-built by Dr. S-Y. Chen</td></tr></tbody></table>

mapping absolute dna density in cell nuclei using single-molecule localization microscopy

Mapping Absolute DNA Density in Cell Nuclei using Single-molecule Localization Microscopy

Within the cell nucleus, silent genes are generally located in chromatin areas of high density called heterochromatin, whereas active genes can be mostly found at the interface between chromatin and the interchromatin space called euchromatin. At present, the characterization of eu- and heterochromatin is mostly based on epigenetic modifications of&#160;histone proteins along the DNA sequence, while little is known about absolute DNA densities across the cell nucleus and their functional implications. Models of the nucleus solely based on biochemical data and assumptions about the nature of chromatin as a polymer still differ fundamentally from imaging data generated by high-resolution microscopy. This indicates that there is still important structurally relevant information missing. We believe that spatial constraints might be involved in gene regulation and have therefore developed a method that allows the measurement of absolute DNA densities in mammalian cell nuclei by transforming super-resolution localization data into true-to-scale density maps by Voronoi tessellation.

The HeLa nucleus shown in Figure 5 was chosen from the table generated by CellProfiler4.2.1 in section 3 to have an integrated fluorescence intensity close to the first peak in the histogram shown in Figure 5 that represents nuclei in the G1 phase. Given its small size and pronounced inner structure, it is likely to be an early G1 nucleus that is still in the process of decondensing its chromatin after mitosis. Being in G1 means ...

The present protocol describes a method measuring absolute DNA densities within adherent cell nuclei using Voronoi tessellation of single-molecule localization microscopy data, known volume, genome size, and cell cycle stage.

Within the cell nucleus, silent genes are generally located in chromatin areas of high density called heterochromatin, whereas active genes can be mostly ...

The present method allows absolute DNA density measurements in cell nuclei to investigate the spatial constraints in chromatin structures that are otherwise only characterized by post-translation histone modifications. Voronoi tessellation combined with SMLM allows absolute density estimations of DNA in terms of base pair per square micrometer. The only apriori knowledge needed is the total DNA content of the measured cell nucleus.In future experiments, it would be interesting to investigate whether absolute density differences correlate with biological information from other methods, such as immunofluorescence of epigenetic modifications or Hi-C data. Begin by reconstructing the scanned area by tiling the prerecorded single images together. Open CellProfiler and then load the pipeline cellcycleanalysis.cpproj.In the first step images of the pipeline, drag and drop the images, including the reference image. Next, define a location where the reference images will be stored. Then click on the Start Test Mode icon followed by Run.Once the histogram of the nuclei in the image to be analyzed is displayed, determine the intensity intervals for the G1, S and G2 phases, and make sure that they are entered in the subsequent filter object steps of the pipeline and that these steps are active. Then click the Run button again to continue with the second half of the pipeline. Look at the three pictures with the overlays representing the different cell cycle stages, G1, S or G2, and select the nuclei in G1 or G2 for further imaging so that the amount of DNA in base pairs of the image nuclei is known.Relocate the cells on the single molecule localization microscope, and ensure proper blinking of the FPALM process. First, record a 3D stack of the chosen nucleus only using 500 frames per slice. This allows determining the relative amount of DNA in the midsection, which is later imaged with many more frames to reveal its ultrastructure.Begin the image series acquisition using an exposure time of 50 milliseconds, resulting in a frame rate of approximately 20 frames per second. Take a Z-stack through the nucleus with 200 nanometer step intervals and only 500 frames per light optical section. After the stack was acquired, return to the initial z position and acquire the main data set of 50, 000 frames with the same 50 milliseconds exposure time.Open the FPALM dataset in ImageJ. To optimize the settings for the detection of blinking signals, click on ThunderSTORM, then Run analysis. Now, choose Camera setup.Enter the correct pixel size of the image data, the A/D count, quantum efficiency of the used camera, base level, and EM gain, then click OK.Choose the algorithm to determine the localization coordinates by fitting the blinking signals. In the Image filtering section, use the Wavelet filter B-Spline with a B-Spline order 3 and a B-Spline scale 2. Then, set the Approximate localization of molecules Method to Local maximum, set the Peak intensity threshold and Connectivity to 8-neighborhood.In the Sub-pixel localization of molecules section, as a Method, choose PSF integrated Gaussian, set Fitting radius px to 3, select Fitting method as Maximum likelihood, and set Initial sigma to 1.6. Then click OK to start the detection of the signals and reconstruction of the image. If the acquisition is partitioned into different image stacks, concatenate the localization tables in ThunderSTORM.To do so, click Import in the popup window. Ensure that the Append to current table option is activated and that the correct starting number is used to avoid overriding localizations in the table. Then select the file path and click OK to import one file after another.To prevent over-counting signals across consecutive frames, merge the localization data using maximum distance of a 20 nanometer, 1 maximum off frames, and 0 maximum frames per molecule, then click Merge. To measure and correct drift by cross-correlating data subsections, open the Drift correction menu and click the arrows. Add 3 bins and set the Magnification to 5.Then click Apply, and the window showing the x and y drift will appear. To determine the amount of signal which is proportional to DNA content in each slice of the prerecorded 3D stack with 500 frames for each slice, choose Plugins, then ThunderSTORM, followed by Import/Export, and import the results table for the first slice of the stack. To avoid over-counting signals from other image planes of the 3D stack, signals from outside the 200-nanometer step interval in z between the slices need to be removed.Press Plot histogram and in the open Distribution dialogue, choose z and press OK.Determine the position of the peak and use the filter field to select the signals with the optical step size of 200 nanometers. Click on Visualization and choose the Histograms option, then click OK.Save the resulting image in a folder in the TIFF format. Open all slices and combine them using Image followed by Stacks and then Images to Stack.After selecting the whole image area, go to Analyze, click Tools and select the ROI Manager. Click on Add, then click on Analyze, followed by Set Measurements and select Integrated density. Now, choose the More option, select Multi Measure, check Measure all stacks as well as One row per slice.Click OK, and the results will appear. The sum of the intensities of all slices is proportional to the DNA content of the whole nucleus in the same way as the intensities of individual slices are proportional to their fraction of the whole genome. After opening the results table of the center plane that contains the signals of 50, 000 frames, filter it to a thickness of 100 nanometers.Create the histogram of the resulting signals as demonstrated earlier, and measure the integrated density of the center section. Convert the large ThunderSTORM localization table containing the ultrastructural information from the center section from the csv format into the ORTE format by running the MATLAB script TS2orte. m, which transforms the localization table into a MATLAB matrix and saves it in the mat format.Go into the LAND-voronoi folder, and in the coreAlgorithm sub folder, open the file voronoicluster. m and adjust the DNA content, fraction of the observed center section of the whole nucleus fraction DNA and localizations, and the conversion factor according to the used cell type and cell cycle stage for the absolute density calculations. Edit the script that starts the analysis voronoi.m.Adjust the file path to the orte localization data and the output folder for the result files. Specify the coordinates defining the area in which the tessellation should be done. Define multiple areas to compute within the same input dataset.After running the script, look for the image showing the absolute DNA densities together with other files containing graphs showing the histogram of the area distribution, and a densities. m file containing the densities of every single calculated Voronoi cell in the output folder. The FPALM measurement consisting of 50, 000 frames resulted in 2.68 times 10 to the power of 6 detected localizations within a 100-nanometer thick midsection.The localization accuracy of the reconstructed image was 10 nanometers. The large number of localizations allowed the combination of super resolution imaging while showing absolute DNA densities. It was apparent that the measured DNA densities were not uniformly distributed across the nucleus and covered an extensive dynamic range.Higher magnifications of areas within the nucleus showing larger Voronoi cells indicated low DNA densities, while smaller cells indicated high DNA densities. DNA densities measured in a G1C3H10T half nucleus showed the absolute DNA density distribution within the nucleus of a different type and species. Although the basic organization looked like the early G1 HeLa nucleus, it additionally possessed constitutive clusters of paracentric heterochromatin.No dramatic architectural changes were observed in a G2 nucleus despite a slightly increased nuclear size. The HeLa nucleus treated with TSA showed remarkable differences with respect to nuclear topography and DNA density. Except for the peripheral heterochromatin, which showed islands of higher DNA density, the rest of the chromatin looked much more homogeneous and decondensed.

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JoVE Journal

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