A fully integrated, three-dimensional fluorescence to electron microscopy correlative workflow

Claudia S. Lopez, Cedric Bouchet-Marquis, Christopher P. Arthur, Jessica L. Riesterer, Gregor Heiss, Guillaume Thibault, Lee Pullan, Sunjong Kwon, Joe W. Gray - Oregon Health and Sciences University, Portland, US; Thermo Fisher Scientific, Hillsboro, US; Genentech, San Francisco, US

While fluorescence microscopy provides tools for highly specific labeling and sensitive detection, its resolution limit and lack of general contrast has hindered studies of cellular structure and protein localization. Recent advances in correlative light and electron microscopy (CLEM), including the fully integrated CLEM workflow instrument, the Thermo Scientific CorrSight with MAPS, have allowed for a more reliable, reproducible, and quicker approach to correlate three-dimensional time-lapse confocal fluorescence data, with three-dimensional focused ion beam–scanning electron microscopy data. Here we demonstrate the entire integrated CLEM workflow using fluorescently tagged MCF7 breast cancer cells.

How Amira-Avizo Software is used

(…) both LM and 3D FIB-SEM data sets were loaded into the Thermo Scientific Amira Software to allow for easy manipulation of the volumes and correlate information between both imaging modalities.

The EM data were first processed through the DualBeam 3D Wizard to register, crop, and filter the images from the stack. The LM data were intensity normalized across images, aligned, and filtered (AlignSlices; Gaussian filtering, Amira) before being volume rendered. To make the registration between LM and EM data sets easier, the LM data set was resampled to match the EM data set pixel size (x: 5 nm, y: 5 nm, z: 5 nm). LM and EM data were first registered manually in the multiplanar room, which allows loading and manipulating both data sets simultaneously from multiple viewing orientations. During that process, the LM data were loaded as the primary data and the EM data loaded as the overlay data. After a good coarse alignment was found, a refinement of the registration was initiated using the autoregistration options tab (Metric: Mutual Information; Transform: Rigid; Options: Extensive direction). For better results, an optimizer step equivalent to 1e5 voxels should be used. After registration, the volume rendering of the EM data was created, and a portion of the m-Slide where the cells were grown, the plasma membrane of the cells interacting with each other, two mitochondria, and a nucleus were segmented using the Amira Segmentation editor. Segmentation was done using the magic wand segmentation tool and later cleaned up when needed with the brush tool. The registered, volume rendered, and segmented data sets were finally animated using the Amira Animation Director room.