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Sample preparation strategies for efficient correlation of 3D SIM and soft X-ray tomography data at cryogenic temperatures

Abstract

3D correlative microscopy methods have revolutionized biomedical research, allowing the acquisition of multidimensional information to gain an in-depth understanding of biological systems. With the advent of relevant cryo-preservation methods, correlative imaging of cryogenically preserved samples has led to nanometer resolution imaging (2–50 nm) under harsh imaging regimes such as electron and soft X-ray tomography. These methods have now been combined with conventional and super-resolution fluorescence imaging at cryogenic temperatures to augment information content from a given sample, resulting in the immediate requirement for protocols that facilitate hassle-free, unambiguous cross-correlation between microscopes. We present here sample preparation strategies and a direct comparison of different working fiducialization regimes that facilitate 3D correlation of cryo-structured illumination microscopy and cryo-soft X-ray tomography. Our protocol has been tested at two synchrotron beamlines (B24 at Diamond Light Source in the UK and BL09 Mistral at ALBA in Spain) and has led to the development of a decision aid that facilitates experimental design with the strategic use of markers based on project requirements. This protocol takes between 1.5 h and 3.5 d to complete, depending on the cell populations used (adherent cells may require several days to grow on sample carriers).

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Fig. 1: Flow chart showing the correlative cryo-light/fluorescence and soft X-ray microscopy fiducialization workflow described in this study.
Fig. 2: Illustration of 2D (cryoSIM only and cryoSIM on X-ray mosaic) and 3D (cryoSXT only and cryoSIM with cryoSXT) correlation by using commercially available fiducial markers and organelle trackers.
Fig. 3: Decision-making scheme for use of fiducials for CLXT.
Fig. 4: Demonstration of correlation of fluorescence and X-ray data in U2OS cells by using green lipid droplets as fiducial markers.
Fig. 5: Optimization of 250-nm gold fiducial dispersion by using sonication and dispersal in different media.
Fig. 6: Examples of equipment used for sample support preparation, fiducial dispersal and sample vitrification through plunge freezing.
Fig. 7: Conventional cryo-microscopy setup, mapping software and sample evaluation.
Fig. 8: Representation of TRE values for different fiducial markers using eC-CLEM.
Fig. 9: Correlation of CLXT data with TetraSpeck microspheres.

Data availability

Original imaging data referenced in the manuscript are deposited at the BioImage Archive (https://www.ebi.ac.uk/biostudies/BioImages) and EMPIAR (https://www.ebi.ac.uk/pdbe/emdb/empiar/). The accession numbers for the data deposited at EMPIAR are EMPIAR-10617, EMPIAR-10618, EMPIAR-10619, EMPIAR-10620, EMPIAR-10621, EMPIAR-10622 and EMPIAR-10624, and the accession numbers for the data deposited at the BioImage Archive are S-BIAD36, S-BIAD37, S-BIAD38, S-BIAD39, S-BIAD40, S-BIAD41 and S-BSST576.

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Acknowledgements

We thank P. Paul-Gilloteaux for her invaluable help with eC-CLEM and previous and current members of the B24 and BL09 teams, with special thanks to M. Spink for instrumentation support and A. Taylor and A. Prescott for technical support. We also thank A. Clayton for suggestions in trying new reagents and M. Dumoux for help with laboratory techniques, advice and training. We acknowledge the support of Micron in the development, application and maintenance of the B24 super-resolution facility. We also thank A. Aires Trapote (CIC BiomaGUNE, San Sebastian, Spain), A. V. Villar, A. R. Palanca, D. Maestro Lavín (IBBTEC, Santander, Spain) and J. Conesa (ALBA and CNB-CSIC). This work was carried out with the support of the Diamond Light Source, instrument B24 (proposals MX18737, MX20321, BI22274, BI23046 and BI25162). We acknowledge ALBA for allocated MISTRAL beamtimes 2018093099 and 2019093739. This project has received funding from the European Commission Horizon 2020 iNEXT-Discovery project and the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement 75439 and Wellcome awards 091911/Z/11/Z and 107457/Z/15/Z. S.B. is supported by ERC AdG670930.

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C.A.O. coordinated and produced the manuscript with the help of I.K., J.G., E.P. and M.H. C.A.O., I.K., J.G., A.L.C., K.L.N. and S.B. provided data, protocols and critical evaluation of results. M.A.K. and T.M.F. provided support with software and protocol development. I.M.D. supported cryoSIM operations and optimization. E.P. and M.H. managed beamline resources, supervised experiments and evaluated applicability and user-friendliness.

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Correspondence to Maria Harkiolaki.

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Key references using this protocol

Kounatidis, I. et al. Cell 182, 1–16 (2020): https://doi.org/10.1016/j.cell.2020.05.051

Phillips, M. et al. Optica 7, 802–812 (2020): https://doi.org/10.1364/OPTICA.393203

Harkiolaki, M. et al. Emerg. Top. Life Sci. 2, 81–92 (2018): https://doi.org/10.1042/ETLS20170086

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Okolo, C.A., Kounatidis, I., Groen, J. et al. Sample preparation strategies for efficient correlation of 3D SIM and soft X-ray tomography data at cryogenic temperatures. Nat Protoc 16, 2851–2885 (2021). https://doi.org/10.1038/s41596-021-00522-4

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