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Brain-Wide Mapping of Axonal Connections: Workflow for Automated Detection and Spatial Analysis of Labeling in Microscopic Sections.

Papp EA, Leergaard TB, Csucs G, Bjaalie JG - Front Neuroinform (2016)

Bottom Line: Parameters from a set of representative images were used to automate detection of labeling in image series covering the entire brain, resulting in binary maps of the distribution of labeling.For high to medium labeling densities, automatic detection was found to provide reliable results when compared to manual analysis, whereas weak labeling required manual curation for optimal detection.Based on the alignment, WHS coordinates were obtained for labeled elements and transformed to stereotaxic coordinates.

View Article: PubMed Central - PubMed

Affiliation: Institute of Basic Medical Sciences, University of Oslo Oslo, Norway.

ABSTRACT
Axonal tracing techniques are powerful tools for exploring the structural organization of neuronal connections. Tracers such as biotinylated dextran amine (BDA) and Phaseolus vulgaris leucoagglutinin (Pha-L) allow brain-wide mapping of connections through analysis of large series of histological section images. We present a workflow for efficient collection and analysis of tract-tracing datasets with a focus on newly developed modules for image processing and assignment of anatomical location to tracing data. New functionality includes automatic detection of neuronal labeling in large image series, alignment of images to a volumetric brain atlas, and analytical tools for measuring the position and extent of labeling. To evaluate the workflow, we used high-resolution microscopic images from axonal tracing experiments in which different parts of the rat primary somatosensory cortex had been injected with BDA or Pha-L. Parameters from a set of representative images were used to automate detection of labeling in image series covering the entire brain, resulting in binary maps of the distribution of labeling. For high to medium labeling densities, automatic detection was found to provide reliable results when compared to manual analysis, whereas weak labeling required manual curation for optimal detection. To identify brain regions corresponding to labeled areas, section images were aligned to the Waxholm Space (WHS) atlas of the Sprague Dawley rat brain (v2) by custom-angle slicing of the MRI template to match individual sections. Based on the alignment, WHS coordinates were obtained for labeled elements and transformed to stereotaxic coordinates. The new workflow modules increase the efficiency and reliability of labeling detection in large series of images from histological sections, and enable anchoring to anatomical atlases for further spatial analysis and comparison with other data.

No MeSH data available.


Related in: MedlinePlus

Workflow module for assigning anatomical location to labeling. We demonstrate the alignment process by verifying the anatomical position of terminal fields reported in Zakiewicz et al. (2014) in axonal tracing material with an injection in the whisker representation of the primary somatosensory cortex (case R602, Zakiewicz et al., 2011). Dense clusters of BDA labeling are clearly visible in the thalamus in the original BDA-NR section (A); Global affine alignment of the section to the Waxholm Space atlas using a custom-angle atlas plate matching the AP position and coronal angle of the section (B); 3D surface model of the thalamus showing the position of the cluster indicated in B within the brain (front/side view; C,D); After fine-tuned local alignment of the thalamic region (E), WHS coordinates are acquired for the centroids of the terminal fields (* in E), and transformed to stereotaxic coordinates (Paxinos and Watson, 2007; atlas plate reproduced with permission from Elsevier) (F–G). AP, anteroposterior; ML, mediolateral; DV, dorsoventral. Scale bar: 0.5 mm.
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Figure 5: Workflow module for assigning anatomical location to labeling. We demonstrate the alignment process by verifying the anatomical position of terminal fields reported in Zakiewicz et al. (2014) in axonal tracing material with an injection in the whisker representation of the primary somatosensory cortex (case R602, Zakiewicz et al., 2011). Dense clusters of BDA labeling are clearly visible in the thalamus in the original BDA-NR section (A); Global affine alignment of the section to the Waxholm Space atlas using a custom-angle atlas plate matching the AP position and coronal angle of the section (B); 3D surface model of the thalamus showing the position of the cluster indicated in B within the brain (front/side view; C,D); After fine-tuned local alignment of the thalamic region (E), WHS coordinates are acquired for the centroids of the terminal fields (* in E), and transformed to stereotaxic coordinates (Paxinos and Watson, 2007; atlas plate reproduced with permission from Elsevier) (F–G). AP, anteroposterior; ML, mediolateral; DV, dorsoventral. Scale bar: 0.5 mm.

Mentions: Workflow for acquiring and processing of section-based image data for brain-wide mapping of axonal connections. Steps 1–4 represent the core workflow for collection of experimental data (Zakiewicz et al., 2011), extended by new modules for automated detection of labeling in series of images (5), anatomical anchoring of images to a 3D reference atlas (6), and spatial analysis of detected labeling (7). The logic and output of the new modules are further explained in Figures 2–5. Metadata are collected at each step and archived together with the original and processed image data.


Brain-Wide Mapping of Axonal Connections: Workflow for Automated Detection and Spatial Analysis of Labeling in Microscopic Sections.

Papp EA, Leergaard TB, Csucs G, Bjaalie JG - Front Neuroinform (2016)

Workflow module for assigning anatomical location to labeling. We demonstrate the alignment process by verifying the anatomical position of terminal fields reported in Zakiewicz et al. (2014) in axonal tracing material with an injection in the whisker representation of the primary somatosensory cortex (case R602, Zakiewicz et al., 2011). Dense clusters of BDA labeling are clearly visible in the thalamus in the original BDA-NR section (A); Global affine alignment of the section to the Waxholm Space atlas using a custom-angle atlas plate matching the AP position and coronal angle of the section (B); 3D surface model of the thalamus showing the position of the cluster indicated in B within the brain (front/side view; C,D); After fine-tuned local alignment of the thalamic region (E), WHS coordinates are acquired for the centroids of the terminal fields (* in E), and transformed to stereotaxic coordinates (Paxinos and Watson, 2007; atlas plate reproduced with permission from Elsevier) (F–G). AP, anteroposterior; ML, mediolateral; DV, dorsoventral. Scale bar: 0.5 mm.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
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Figure 5: Workflow module for assigning anatomical location to labeling. We demonstrate the alignment process by verifying the anatomical position of terminal fields reported in Zakiewicz et al. (2014) in axonal tracing material with an injection in the whisker representation of the primary somatosensory cortex (case R602, Zakiewicz et al., 2011). Dense clusters of BDA labeling are clearly visible in the thalamus in the original BDA-NR section (A); Global affine alignment of the section to the Waxholm Space atlas using a custom-angle atlas plate matching the AP position and coronal angle of the section (B); 3D surface model of the thalamus showing the position of the cluster indicated in B within the brain (front/side view; C,D); After fine-tuned local alignment of the thalamic region (E), WHS coordinates are acquired for the centroids of the terminal fields (* in E), and transformed to stereotaxic coordinates (Paxinos and Watson, 2007; atlas plate reproduced with permission from Elsevier) (F–G). AP, anteroposterior; ML, mediolateral; DV, dorsoventral. Scale bar: 0.5 mm.
Mentions: Workflow for acquiring and processing of section-based image data for brain-wide mapping of axonal connections. Steps 1–4 represent the core workflow for collection of experimental data (Zakiewicz et al., 2011), extended by new modules for automated detection of labeling in series of images (5), anatomical anchoring of images to a 3D reference atlas (6), and spatial analysis of detected labeling (7). The logic and output of the new modules are further explained in Figures 2–5. Metadata are collected at each step and archived together with the original and processed image data.

Bottom Line: Parameters from a set of representative images were used to automate detection of labeling in image series covering the entire brain, resulting in binary maps of the distribution of labeling.For high to medium labeling densities, automatic detection was found to provide reliable results when compared to manual analysis, whereas weak labeling required manual curation for optimal detection.Based on the alignment, WHS coordinates were obtained for labeled elements and transformed to stereotaxic coordinates.

View Article: PubMed Central - PubMed

Affiliation: Institute of Basic Medical Sciences, University of Oslo Oslo, Norway.

ABSTRACT
Axonal tracing techniques are powerful tools for exploring the structural organization of neuronal connections. Tracers such as biotinylated dextran amine (BDA) and Phaseolus vulgaris leucoagglutinin (Pha-L) allow brain-wide mapping of connections through analysis of large series of histological section images. We present a workflow for efficient collection and analysis of tract-tracing datasets with a focus on newly developed modules for image processing and assignment of anatomical location to tracing data. New functionality includes automatic detection of neuronal labeling in large image series, alignment of images to a volumetric brain atlas, and analytical tools for measuring the position and extent of labeling. To evaluate the workflow, we used high-resolution microscopic images from axonal tracing experiments in which different parts of the rat primary somatosensory cortex had been injected with BDA or Pha-L. Parameters from a set of representative images were used to automate detection of labeling in image series covering the entire brain, resulting in binary maps of the distribution of labeling. For high to medium labeling densities, automatic detection was found to provide reliable results when compared to manual analysis, whereas weak labeling required manual curation for optimal detection. To identify brain regions corresponding to labeled areas, section images were aligned to the Waxholm Space (WHS) atlas of the Sprague Dawley rat brain (v2) by custom-angle slicing of the MRI template to match individual sections. Based on the alignment, WHS coordinates were obtained for labeled elements and transformed to stereotaxic coordinates. The new workflow modules increase the efficiency and reliability of labeling detection in large series of images from histological sections, and enable anchoring to anatomical atlases for further spatial analysis and comparison with other data.

No MeSH data available.


Related in: MedlinePlus