We conclude that this quantitative high-resolution FIB-SEM approach will open many new possibilities for visualizing the cellular organization of different cell and tissue types and complement other existing high-resolution approaches such as TEM tomography (19) and serial block face imaging (17). The segmentation of selected organelles and cellular components revealed their 3D spatial relationship with unprecedented z-resolution by FIB-SEM, approaching the gold standard of TEM tomography. Furthermore, the determination of organellar inter-relationships and connectivity (32, 34), improvements in nano-scale volumetric quantitative measurements/comparisons as well as the generation of targeted sub-cellular molecular distribution/localization maps using correlative microscopy (35) including photooxidation (36) are natural extensions of this methodology. Our overall goal to optimize conditions for high-resolution 3D reconstructions of entire single cells allowed us to quantify and understand the spatial distribution of numerous sub-cellular structures (Figure 2, Table 1). For the greatest accuracy of volumetric measurement with 3D FIB data sets, reduced milling intervals provide a distinct advantage (37) and for the yeast cell features, we found that this goal was best achieved by acquiring isotropic pixels at ~5 nm or less. As with any approach which involves significant sample preparation and image processing (i.e., fixation, dehydration, resin embedment, image acquisition and analysis routines) consideration must be given to artifacts and limitations of each and interpretation adjusted accordingly. While S. cerevisiae served as a challenging biological specimen for optimizing sample preparation and FIB-SEM validation, we see no technical limitation for extending this strategy to larger cells at comparable pixel resolutions. However, significant improvements in FIB-SEM 3D resolution may likely require a technological breakthrough in SEM resolution itself, as well as improvements in sample stabilization and other environmental influences in the microscope and/or a method for tracking and correcting sample drift.
This project was supported by the Delaware INBRE program, with grants from the National Center for Research Resources-NCRR (5P20RR016472-12) and the National Institute of General Medical Sciences-NIGMS (8 P20 GM103446-12) from the National Institutes of Health.
Dongguang Wei is an employee of Carl Zeiss Microscopy, LLC a manufacturer of the Auriga 60 FIB-SEM used in this study. Shuang Zhang is an employee of Visualization Sciences Group, Inc. a developer of Avizo, the high-performance visualization software framework used in this study. All other authors declare that there is no conflict of interest.
Address correspondence to Kirk J. Czymmek, Department of Biological Sciences, University of Delaware, 330 Wolf Hall, Newark, DE, USA. Email: firstname.lastname@example.org
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