Three-Dimensional Cell and Tissue Reconstruction by Serial Block Face SEM

National Institute Of Biomedical Imaging And Bioengineering, Bethesda

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Abstract

Our laboratory is equipped with (1) a serial block-face scanning electron microscope comprising a Zeiss SIGMA-VP SEM coupled with a Gatan 3View in situ ultramicrotomy system to determine the 3D ultrastructure of cells and tissues at a lateral (x,y) resolution of 5 to 10 nanometers and a z-resolution of 25 nm to 50 nm; and (2) a recently installed Zeiss Crossbeam 550 focused ion beam scanning electron. We have applied SBEM extensively to determine the 3-D ultrastructure of human and mouse blood platelets that were rapidly fixed prior to purification to minimize activation (1-3). One objective was to determine the 3D organization of granules, dense granules, mitochondria, and canalicular system in resting human platelets and map their spatial relationships. We found that granule number correlated linearly with platelet size, whereas dense granule and mitochondria number had little correlation with platelet size. 3D data from 30 platelets indicated only limited spatial intermixing of the different organelle classes. Interestingly, almost 70% of granules came within 35 nm of each other, a distance associated in other cell systems with protein mediated contact sites. Size and shape analysis of the 1,500 granules analyzed revealed no more variation than that expected for a Gaussian distribution (Pokrovskaya et al., Platelets 32(1): 97-104; 2021). Although mouse platelets provide an experimental model for hemostasis and thrombosis, important physiological data from this model have received little quantitative, 3D ultrastructural analysis. We have obtained SBEM data from resting mouse platelets. Quantitative analysis revealed that mouse alpha-granules typically had a variable, elongated, rod shape, different from the round/ovoid shape of human granules. This variation in length was confirmed qualitatively by higher-resolution, focused ion beam (FIB) SEM at a nominal 5 nm z-step size. The unexpected alpha-granule shape raises novel questions regarding alpha-granule biogenesis and dynamics, about whether the variation arise at the level of the megakaryocyte and alpha-granule biogenesis or from differences in alpha-granule dynamics and organelle fusion/fission events within circulating platelets (Pokrovskaya et al., Platelets 32(5): 608-617; 2021). Furthermore, quantitative analysis revealed that the two major organelles in circulating platelets, alpha-granules and mitochondria, displayed a stronger linear relationship between organelle number/volume and platelet size, i.e., a scaling in number and volume to platelet size, than found in human platelets suggestive of a tighter mechanistic regulation of their inclusion during platelet biogenesis. The overall spatial arrangement of organelles within mouse platelets resembled that of resting human platelets, with mouse alpha-granules clustered closely together with little space for inter-digitation of other organelles. We have used SBF-SEM to investigate the organization and activation of platelets in the formation of blood clots in a mouse model. It has been established that primary hemostasis after vascular injury results in a platelet-rich thrombus, which has been assumed to form a solid plug. Unexpectedly, our 3D electron microscopy of mouse jugular vein puncture wounds revealed that the resulting thrombi were structured about localized, nucleated platelet aggregates, containing pedestals and columns, that produced a vaulted thrombus capped by extravascular platelet adherence. Pedestal and column surfaces were lined by pro-coagulant platelets. Furthermore, early steps in thrombus assembly were sensitive to the ADP chemoreceptor P2Y12 inhibition as well as to thrombin inhibition. Based on these results, a cap and build paradigm has been proposed, which might have translational implications for bleeding control and hemostasis (Rhee et al., Commun. Biol. 4: Article 1090; 2021). Further work revealed that initial platelet anchoring to the exposed adventitia resulted in localized patches of degranulated, highly activated, procoagulant-like platelets bound directly to collagen (initiation step). Activation to a procoagulant state was sensitive to dabigatran, a direct protease-activated receptor (PAR) inhibitor, but not to cangrelor, a P2Y12 receptor inhibitor. Sustained thrombus growth (propagation step) was accompanied by the capture of strings of loosely associated discoid platelets tethered one to another. Spatial examination indicated that staged platelet activation resulted in a discoid platelet tethering zone that was pushed progressively outward. Overall thrombus growth was sensitive to cangrelor. As thrombus growth slowed, discoid platelet recruitment became rare and loosely adherent intravascular platelets failed to convert to tightly adherent platelets. Our 3D image data support a model in which initial high platelet activation is limited to 5 microns distance from the adventitia; subsequent tethered discoid platelet recruitment is followed by platelet activation to produce tightly adherent platelets; and decreased signaling intensity over time results in self-limiting, intravascular platelet activation (Pokrovskaya et al., Journal of Thrombosis and Haemostasis, in press). Based on our long-term collaboration with the Storrie laboratory, University of Arkansas for Medical Sciences, and the Whiteheart laboratory, University of Kentucky, we are performing an ultrastructural study of human blood platelets from hospitalized COVID-19 patients at University of Kentucky HealthCare, Lexington, KY. We are also analyzing blood platelets obtained from heathy volunteer donors, collected under the same conditions as platelets from the COVID-19 patients, to provide a baseline for the ultrastructure of normal resting platelets. Both, the SBF-SEM and the FIB-SEM are being used to obtain the structural data. SBF-SEM produces large 3D images of cellular ultrastructure, but the labor required to manually segment EM images into their semantic components severely limits further data analysis. Currently, software pipelines incorporating deep neural networks offer state-of-the-art performance for automated segmentation. However, even state-of-the-art automated segmentation tools require extensive manual correction for many data sets of interest to the structural biology and systems biology communities, and are therefore impractical for image analysis. Our lab is designing novel neural networks and incorporating them into a segmentation software pipeline to improve automated segmentation performance for EM data sets taken from multiple biological systems. We are beginning to develop a design framework and software for constructing segmentation neural networks, and to test these methods on large 3D data sets generated in our laboratory. Our approach aims to segment intracellular volumes into multiple classes of organelles for a diverse range of cell types. Preliminary results for blood platelets show considerable promise (Guay, M.D. et al., Sci. Rep. 11: 2561; 2021).