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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

Detection limits for weakly labeled fibers. Microscopic images of individual fibers labeled with Pha-L (A,B) and BDA (C,D). Labeling detected by the automatic procedure is shown on corresponding labeling maps (A’–D’). In the original images, weakly labeled fibers are perceived as continuous, but with low contrast against background tissue (B,D). After processing, most detected fibers appear fragmented (B’,D’) with the weakest parts not detected. Noise filtering (active in image B’ but not in D’) eliminates fragments smaller than a specified size, preventing some of the weakly labeled fibers from reaching the detection limit. Scale bar: 0.5 mm
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Figure 4: Detection limits for weakly labeled fibers. Microscopic images of individual fibers labeled with Pha-L (A,B) and BDA (C,D). Labeling detected by the automatic procedure is shown on corresponding labeling maps (A’–D’). In the original images, weakly labeled fibers are perceived as continuous, but with low contrast against background tissue (B,D). After processing, most detected fibers appear fragmented (B’,D’) with the weakest parts not detected. Noise filtering (active in image B’ but not in D’) eliminates fragments smaller than a specified size, preventing some of the weakly labeled fibers from reaching the detection limit. Scale bar: 0.5 mm

Mentions: Fully reliable detection (no false negatives) for high and modest amounts of labeling, for both tracers (BDA and Pha-L) and independent of counterstaining method (Neutral Red or thionine), was also seen in the other cases (Figures 3 and 4, case R606). Low amounts of labeling could be detected at about the same levels as reported for the case shown in Table 1. Thus, some instances of very low amounts of labeling reported by Zakiewicz et al. (2014) were not detected with the automatic method.


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)

Detection limits for weakly labeled fibers. Microscopic images of individual fibers labeled with Pha-L (A,B) and BDA (C,D). Labeling detected by the automatic procedure is shown on corresponding labeling maps (A’–D’). In the original images, weakly labeled fibers are perceived as continuous, but with low contrast against background tissue (B,D). After processing, most detected fibers appear fragmented (B’,D’) with the weakest parts not detected. Noise filtering (active in image B’ but not in D’) eliminates fragments smaller than a specified size, preventing some of the weakly labeled fibers from reaching the detection limit. Scale bar: 0.5 mm
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4835481&req=5

Figure 4: Detection limits for weakly labeled fibers. Microscopic images of individual fibers labeled with Pha-L (A,B) and BDA (C,D). Labeling detected by the automatic procedure is shown on corresponding labeling maps (A’–D’). In the original images, weakly labeled fibers are perceived as continuous, but with low contrast against background tissue (B,D). After processing, most detected fibers appear fragmented (B’,D’) with the weakest parts not detected. Noise filtering (active in image B’ but not in D’) eliminates fragments smaller than a specified size, preventing some of the weakly labeled fibers from reaching the detection limit. Scale bar: 0.5 mm
Mentions: Fully reliable detection (no false negatives) for high and modest amounts of labeling, for both tracers (BDA and Pha-L) and independent of counterstaining method (Neutral Red or thionine), was also seen in the other cases (Figures 3 and 4, case R606). Low amounts of labeling could be detected at about the same levels as reported for the case shown in Table 1. Thus, some instances of very low amounts of labeling reported by Zakiewicz et al. (2014) were not detected with the automatic method.

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