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A correlative approach for combining microCT, light and transmission electron microscopy in a single 3D scenario.

Handschuh S, Baeumler N, Schwaha T, Ruthensteiner B - Front. Zool. (2013)

Bottom Line: Since every imaging method is physically limited by certain parameters, a correlative use of complementary methods often yields a significant broader range of information.We found structures typical for mollusc excretory systems, including ultrafiltration sites at the pericardial wall, and ducts leading from the pericardium towards the kidneys, which exhibit a typical basal infolding system.Classical TEM serial section investigations are extremely time consuming, and modern methods for 3D analysis of ultrastructure such as SBF-SEM and FIB-SEM are limited to very small volumes for examination.

View Article: PubMed Central - HTML - PubMed

Affiliation: VetImaging, VetCore Facility for Research, University of Veterinary Medicine, Veterinärplatz 1, 1210, Vienna, Austria. stephan.handschuh@vetmeduni.ac.at.

ABSTRACT

Background: In biomedical research, a huge variety of different techniques is currently available for the structural examination of small specimens, including conventional light microscopy (LM), transmission electron microscopy (TEM), confocal laser scanning microscopy (CLSM), microscopic X-ray computed tomography (microCT), and many others. Since every imaging method is physically limited by certain parameters, a correlative use of complementary methods often yields a significant broader range of information. Here we demonstrate the advantages of the correlative use of microCT, light microscopy, and transmission electron microscopy for the analysis of small biological samples.

Results: We used a small juvenile bivalve mollusc (Mytilus galloprovincialis, approximately 0.8 mm length) to demonstrate the workflow of a correlative examination by microCT, LM serial section analysis, and TEM-re-sectioning. Initially these three datasets were analyzed separately, and subsequently they were fused in one 3D scene. This workflow is very straightforward. The specimen was processed as usual for transmission electron microscopy including post-fixation in osmium tetroxide and embedding in epoxy resin. Subsequently it was imaged with microCT. Post-fixation in osmium tetroxide yielded sufficient X-ray contrast for microCT imaging, since the X-ray absorption of epoxy resin is low. Thereafter, the same specimen was serially sectioned for LM investigation. The serial section images were aligned and specific organ systems were reconstructed based on manual segmentation and surface rendering. According to the region of interest (ROI), specific LM sections were detached from the slides, re-mounted on resin blocks and re-sectioned (ultrathin) for TEM. For analysis, image data from the three different modalities was co-registered into a single 3D scene using the software AMIRA®. We were able to register both the LM section series volume and TEM slices neatly to the microCT dataset, with small geometric deviations occurring only in the peripheral areas of the specimen. Based on co-registered datasets the excretory organs, which were chosen as ROI for this study, could be investigated regarding both their ultrastructure as well as their position in the organism and their spatial relationship to adjacent tissues. We found structures typical for mollusc excretory systems, including ultrafiltration sites at the pericardial wall, and ducts leading from the pericardium towards the kidneys, which exhibit a typical basal infolding system.

Conclusions: The presented approach allows a comprehensive analysis and presentation of small objects regarding both the overall organization as well as cellular and subcellular details. Although our protocol involves a variety of different equipment and procedures, we maintain that it offers savings in both effort and cost. Co-registration of datasets from different imaging modalities can be accomplished with high-end desktop computers and offers new opportunities for understanding and communicating structural relationships within organisms and tissues. In general, the correlative use of different microscopic imaging techniques will continue to become more widespread in morphological and structural research in zoology. Classical TEM serial section investigations are extremely time consuming, and modern methods for 3D analysis of ultrastructure such as SBF-SEM and FIB-SEM are limited to very small volumes for examination. Thus the re-sectioning of LM sections is suitable for speeding up TEM examination substantially, while microCT could become a key-method for complementing ultrastructural examinations.

No MeSH data available.


Workflow of TEM section 3D registration. A. MicroCT stack. B. LM image stack. C. LM image stack inverted and Gaussian filtered to enhance similarity with the microCT stack. D. Untreated LM stack with co-registration parameters adopted from the previously co-registered LM stack that was inverted and filtered. E. TEM section. F. Template of LM section that was re-sectioned for TEM. G. TEM section with 3D co-registration parameters. H. Final 3D scenario with co-registered data of microCT, LM and TEM. Processes: 1, co-registration of the modified LM image stack with the microCT stack; 2, adoption of the co-registration parameters to the untreated LM stack; 3, 2D registration of TEM images in LM image templates with the help of Photoshop. 4, adoption of the of the 3D co-registration parameters to the 2D registered TEM image. Blue background: 2D environment; peach colored background: 3D environment.
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Figure 4: Workflow of TEM section 3D registration. A. MicroCT stack. B. LM image stack. C. LM image stack inverted and Gaussian filtered to enhance similarity with the microCT stack. D. Untreated LM stack with co-registration parameters adopted from the previously co-registered LM stack that was inverted and filtered. E. TEM section. F. Template of LM section that was re-sectioned for TEM. G. TEM section with 3D co-registration parameters. H. Final 3D scenario with co-registered data of microCT, LM and TEM. Processes: 1, co-registration of the modified LM image stack with the microCT stack; 2, adoption of the co-registration parameters to the untreated LM stack; 3, 2D registration of TEM images in LM image templates with the help of Photoshop. 4, adoption of the of the 3D co-registration parameters to the 2D registered TEM image. Blue background: 2D environment; peach colored background: 3D environment.

Mentions: For co-registration of image stack data from two different sources (microCT and LM serial section images, Figure 4A,B) both stacks were loaded into the AMIRA® Pool and saved in amira mesh (AM) format. The microCT stack was used as reference dataset, as it is free from both geometric distortions and misalignments. The LM stack (the same as previously processed for segmentation – see above) was then inverted using the Arithmetic module (expr: A*–1+255) and filtered using Gauss-Smoothing (3D, Kernel: 3/3/3) (Figure 4C). These steps enhanced similarity to the CT dataset and thereby facilitated co-registration based on correlation metrics for use with the AffineRegistration module. Subsequently the LM image stack was coarsely aligned with the reference (microCT) stack by hand (while displaying both stacks with a Voltex module) using the Transform Editor. This was followed by fine co-registration, which was performed automatically with the AffineRegistration module (Additional file 2). This module was connected to the LM stack and the Reference port was connected to the microCT stack. The only parameter changed from the default settings was Correlation (at metric). Thus, the registration process was rigid and included subsequent steps of rotation and translation. After registration, the transformed section image stack was saved. The same transformation parameters were subsequently applied (copy/paste in the Transform Editor dialog) to the original (non-inverted, unfiltered) LM stack (Figure 4D) and to the segmentation stack. Accordingly, both LM stacks, the segmentation stack as well as the surfaces resulting from the segmentation dataset became co-registered with the microCT stack in the 3D scene.


A correlative approach for combining microCT, light and transmission electron microscopy in a single 3D scenario.

Handschuh S, Baeumler N, Schwaha T, Ruthensteiner B - Front. Zool. (2013)

Workflow of TEM section 3D registration. A. MicroCT stack. B. LM image stack. C. LM image stack inverted and Gaussian filtered to enhance similarity with the microCT stack. D. Untreated LM stack with co-registration parameters adopted from the previously co-registered LM stack that was inverted and filtered. E. TEM section. F. Template of LM section that was re-sectioned for TEM. G. TEM section with 3D co-registration parameters. H. Final 3D scenario with co-registered data of microCT, LM and TEM. Processes: 1, co-registration of the modified LM image stack with the microCT stack; 2, adoption of the co-registration parameters to the untreated LM stack; 3, 2D registration of TEM images in LM image templates with the help of Photoshop. 4, adoption of the of the 3D co-registration parameters to the 2D registered TEM image. Blue background: 2D environment; peach colored background: 3D environment.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750762&req=5

Figure 4: Workflow of TEM section 3D registration. A. MicroCT stack. B. LM image stack. C. LM image stack inverted and Gaussian filtered to enhance similarity with the microCT stack. D. Untreated LM stack with co-registration parameters adopted from the previously co-registered LM stack that was inverted and filtered. E. TEM section. F. Template of LM section that was re-sectioned for TEM. G. TEM section with 3D co-registration parameters. H. Final 3D scenario with co-registered data of microCT, LM and TEM. Processes: 1, co-registration of the modified LM image stack with the microCT stack; 2, adoption of the co-registration parameters to the untreated LM stack; 3, 2D registration of TEM images in LM image templates with the help of Photoshop. 4, adoption of the of the 3D co-registration parameters to the 2D registered TEM image. Blue background: 2D environment; peach colored background: 3D environment.
Mentions: For co-registration of image stack data from two different sources (microCT and LM serial section images, Figure 4A,B) both stacks were loaded into the AMIRA® Pool and saved in amira mesh (AM) format. The microCT stack was used as reference dataset, as it is free from both geometric distortions and misalignments. The LM stack (the same as previously processed for segmentation – see above) was then inverted using the Arithmetic module (expr: A*–1+255) and filtered using Gauss-Smoothing (3D, Kernel: 3/3/3) (Figure 4C). These steps enhanced similarity to the CT dataset and thereby facilitated co-registration based on correlation metrics for use with the AffineRegistration module. Subsequently the LM image stack was coarsely aligned with the reference (microCT) stack by hand (while displaying both stacks with a Voltex module) using the Transform Editor. This was followed by fine co-registration, which was performed automatically with the AffineRegistration module (Additional file 2). This module was connected to the LM stack and the Reference port was connected to the microCT stack. The only parameter changed from the default settings was Correlation (at metric). Thus, the registration process was rigid and included subsequent steps of rotation and translation. After registration, the transformed section image stack was saved. The same transformation parameters were subsequently applied (copy/paste in the Transform Editor dialog) to the original (non-inverted, unfiltered) LM stack (Figure 4D) and to the segmentation stack. Accordingly, both LM stacks, the segmentation stack as well as the surfaces resulting from the segmentation dataset became co-registered with the microCT stack in the 3D scene.

Bottom Line: Since every imaging method is physically limited by certain parameters, a correlative use of complementary methods often yields a significant broader range of information.We found structures typical for mollusc excretory systems, including ultrafiltration sites at the pericardial wall, and ducts leading from the pericardium towards the kidneys, which exhibit a typical basal infolding system.Classical TEM serial section investigations are extremely time consuming, and modern methods for 3D analysis of ultrastructure such as SBF-SEM and FIB-SEM are limited to very small volumes for examination.

View Article: PubMed Central - HTML - PubMed

Affiliation: VetImaging, VetCore Facility for Research, University of Veterinary Medicine, Veterinärplatz 1, 1210, Vienna, Austria. stephan.handschuh@vetmeduni.ac.at.

ABSTRACT

Background: In biomedical research, a huge variety of different techniques is currently available for the structural examination of small specimens, including conventional light microscopy (LM), transmission electron microscopy (TEM), confocal laser scanning microscopy (CLSM), microscopic X-ray computed tomography (microCT), and many others. Since every imaging method is physically limited by certain parameters, a correlative use of complementary methods often yields a significant broader range of information. Here we demonstrate the advantages of the correlative use of microCT, light microscopy, and transmission electron microscopy for the analysis of small biological samples.

Results: We used a small juvenile bivalve mollusc (Mytilus galloprovincialis, approximately 0.8 mm length) to demonstrate the workflow of a correlative examination by microCT, LM serial section analysis, and TEM-re-sectioning. Initially these three datasets were analyzed separately, and subsequently they were fused in one 3D scene. This workflow is very straightforward. The specimen was processed as usual for transmission electron microscopy including post-fixation in osmium tetroxide and embedding in epoxy resin. Subsequently it was imaged with microCT. Post-fixation in osmium tetroxide yielded sufficient X-ray contrast for microCT imaging, since the X-ray absorption of epoxy resin is low. Thereafter, the same specimen was serially sectioned for LM investigation. The serial section images were aligned and specific organ systems were reconstructed based on manual segmentation and surface rendering. According to the region of interest (ROI), specific LM sections were detached from the slides, re-mounted on resin blocks and re-sectioned (ultrathin) for TEM. For analysis, image data from the three different modalities was co-registered into a single 3D scene using the software AMIRA®. We were able to register both the LM section series volume and TEM slices neatly to the microCT dataset, with small geometric deviations occurring only in the peripheral areas of the specimen. Based on co-registered datasets the excretory organs, which were chosen as ROI for this study, could be investigated regarding both their ultrastructure as well as their position in the organism and their spatial relationship to adjacent tissues. We found structures typical for mollusc excretory systems, including ultrafiltration sites at the pericardial wall, and ducts leading from the pericardium towards the kidneys, which exhibit a typical basal infolding system.

Conclusions: The presented approach allows a comprehensive analysis and presentation of small objects regarding both the overall organization as well as cellular and subcellular details. Although our protocol involves a variety of different equipment and procedures, we maintain that it offers savings in both effort and cost. Co-registration of datasets from different imaging modalities can be accomplished with high-end desktop computers and offers new opportunities for understanding and communicating structural relationships within organisms and tissues. In general, the correlative use of different microscopic imaging techniques will continue to become more widespread in morphological and structural research in zoology. Classical TEM serial section investigations are extremely time consuming, and modern methods for 3D analysis of ultrastructure such as SBF-SEM and FIB-SEM are limited to very small volumes for examination. Thus the re-sectioning of LM sections is suitable for speeding up TEM examination substantially, while microCT could become a key-method for complementing ultrastructural examinations.

No MeSH data available.