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Quantitative 3D structured illumination microscopy of nuclear structures

Nature Protocols volume 12, pages 1011–1028 (2017)Cite this article

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Abstract

3D structured illumination microscopy (3D-SIM) is the super-resolution technique of choice for multicolor volumetric imaging. Here we provide a validated sample preparation protocol for labeling nuclei of cultured mammalian cells, image acquisition and registration practices, and downstream image analysis of nuclear structures and epigenetic marks. Using immunostaining and replication labeling combined with image segmentation, centroid mapping and nearest-neighbor analyses in open-source environments, 3D maps of nuclear structures are analyzed in individual cells and normalized to fluorescence standards on the nanometer scale. This protocol fills an unmet need for the application of 3D-SIM to the technically challenging nuclear environment, and subsequent quantitative analysis of 3D nuclear structures and epigenetic modifications. In addition, it establishes practical guidelines and open-source solutions using ImageJ/Fiji and the TANGO plugin for high-quality and routinely comparable data generation in immunostaining experiments that apply across model systems. From sample preparation through image analysis, the protocol can be executed within one week.

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Figure 1
Figure 2: Biological calibration sample for 3D color channel alignment.
Figure 3: Outline of image segmentation procedure.
Figure 4: Defining proximity values with in vitro and biological calibration samples imaged by 3D-SIM.
Figure 5: Minimal distance and correlation measurements of epigenetic marks and replicating chromatin.

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Acknowledgements

We thank Philip Tinnefeld and Mario Raab (TU Braunschweig, Germany) for the development and assistance with the preparation and image acquisition of DNA-origami rods. This work was funded by the Deutsche Forschungsgemeinschaft (SFB 1064 and Nanosystems Initiative Munich, NIM) and Wellcome Trust Strategic Awards 091911 and 107457, supporting advanced microscopy at Micron Oxford. J.D. was supported by the NIH-Oxford-Cambridge Scholars Program. A.M. was supported by Japan Society for the Promotion of Science (JSPS) KAKENHI grants JP16H01440 ('resonance bio'), JP15K14500 and JP26292169.

Author information

Author notes

  1. Felix Kraus, Tsotne Chitiashvili & Yolanda Markaki

    Present address: Present addresses: Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Melbourne, Australia (F.K.); Department of Biological Chemistry, David Geffen School of Medicine, UCLA, Los Angeles, California, USA (Y.M., T.C.).,

Authors and Affiliations

  1. Department of Biology II, Center for Integrated Protein Science (CIPSM) and Center for Advanced Light Microscopy (CALM), Ludwig Maximilians University (LMU), Martinsried, Germany

    Felix Kraus, Tsotne Chitiashvili, Alexei Budco, Heinrich Leonhardt & Yolanda Markaki

  2. Department of Biochemistry, Micron Advanced Bioimaging Unit, University of Oxford, Oxford, UK

    Ezequiel Miron, Justin Demmerle, Quentin Alle & Lothar Schermelleh

  3. Advanced ICT Research Institute Kobe, National Institute of Information and Communications Technology, Kobe, Japan

    Atsushi Matsuda

  4. Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan

    Atsushi Matsuda

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Contributions

F.K., E.M., A.B., T.C. and Q.A. performed the experiments. F.K. established the image analysis workflows. J.D., F.K., E.M., Y.M. and L.S. wrote the manuscript. A.M. established image registration procedures. Y.M., L.S. and F.K. directed experiments and designed the protocol. H.L., Y.M. and L.S. conceived the project.

Corresponding authors

Correspondence to Lothar Schermelleh or Yolanda Markaki.

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Integrated supplementary information

Supplementary Figure 1 Adaptive image registration for 3D channel alignment.

(a, top) Screenshot of processing a 3D calibration dataset with preview in Chromagnon. View of software after alignment (bottom). The software allows the export of calculated parameters for later use, loading a previously exported parameter file, or continuing immediately to start a batch processing alignment with the currently loaded parameter. (b) Inset from a (after alignment), showing intensity line profiles in the z dimension of an orthogonal image section after only 2D X-Y alignment vs 3D XYZ alignment. As shown by the difference in peak overlaps of the profiles, 3D alignment is required to have no residual z-shift in the image registration.

Supplementary Figure 2 Comparison of segmentation and quantitative analysis of single vs. batch processing.

(a) Workflow of single and batch-processing analysis. In single processing, individual image stacks are manually segmented and analyzed in an iterative manner. In batch mode, emphasis is placed on the definition of segmentation parameters and measurements with subsequent automated segmentation and analysis of all image data of a project. (b) Example for single processing in Fiji. Segmentation of each channel in the input dataset needs to be done sequentially, in an iterative manner. (c) Example program for batch image analysis in TANGO. All datasets of a given project are analyzed in one run, following defined parameters. Pooled results of all data in one project can be exported and analyzed at once.

Supplementary Figure 3 Comparison of nuclear morphological features with different fixation methods.

(a) Images of DAPI stained nuclei after 2 % formaldehyde (FA) or 2% formaldehyde followed by methanol (FA+MeOH) fixation. (a) Nuclear volume, surface and morphology (ratio ellipsoid/volume) (b) as well as average replication foci number/volume (c) do not differ significantly between the two fixation methods. Scale bar 2.5 μm.

Supplementary information

Supplementary Figures and Table

Supplementary Figures 1–3, Supplementary Methods 1 and 2, and Supplementary Tables 1–3 (PDF 7128 kb)

Supplementary Software

Supplementary Software. TANGO processing chains. We provide basic processing chains (PCs) for the TANGO suite for batch-oriented image processing and analysis of 3D-SIM datasets (Supplementary Table 3). These comprise example PCs for nuclear mask segmentation and structure segmentation templates for focal and broad spots. First, the PCs 'nucleus.bson' and 'channel.bson' are imported, together with their metadata files, to TANGO via clicking on the 'Import Processing Chains' button in the 'Connect' tab of TANGO. After importation to TANGO, a variety of PCs can be used for basic image segmentation. For segmentation of the nuclear volume, the 'Nuclear_Mask' PC can be used for nuclear stains with a prominent nuclear rim. Segmentation of less prominent nuclear borders, such as those seen in stem cell nuclear staining, can be achieved with the 'ESC_Mask PC'. Both will create the mask based on the nuclear stain, which will be used to define the volume for subsequent image analysis. Further, we included two more PC templates for segmentation of nuclear structures with either focal distributions, such as short EdU pulses ('Focal_Spot'), or broader signals, such as some dense antibody-labeled histone marks ('Volumetric_Spot'). After image segmentation, the derived objects can be analyzed. Measurements are defined by selecting 'Edit Experiment' > 'Measurements' in TANGO. The Supplementary Software contains the readme file as well as the processing chains and can be downloaded as a zip file. (ZIP 53 kb)

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Kraus, F., Miron, E., Demmerle, J. et al. Quantitative 3D structured illumination microscopy of nuclear structures. Nat Protoc 12, 1011–1028 (2017). https://doi.org/10.1038/nprot.2017.020

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