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Extracellular space preservation aids the connectomic analysis of neural circuits.

Pallotto M, Watkins PV, Fubara B, Singer JH, Briggman KL - Elife (2015)

Bottom Line: Any improvement in segmentation error rates would therefore directly reduce the time required to analyze 3D EM data.ECS preserved tissue is easier to segment using machine learning algorithms, leading to significantly reduced error rates.We conclude that preservation of ECS benefits multiple aspects of the connectomic analysis of neural circuits.

View Article: PubMed Central - PubMed

Affiliation: Circuit Dynamics and Connectivity Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States.

ABSTRACT
Dense connectomic mapping of neuronal circuits is limited by the time and effort required to analyze 3D electron microscopy (EM) datasets. Algorithms designed to automate image segmentation suffer from substantial error rates and require significant manual error correction. Any improvement in segmentation error rates would therefore directly reduce the time required to analyze 3D EM data. We explored preserving extracellular space (ECS) during chemical tissue fixation to improve the ability to segment neurites and to identify synaptic contacts. ECS preserved tissue is easier to segment using machine learning algorithms, leading to significantly reduced error rates. In addition, we observed that electrical synapses are readily identified in ECS preserved tissue. Finally, we determined that antibodies penetrate deep into ECS preserved tissue with only minimal permeabilization, thereby enabling correlated light microscopy (LM) and EM studies. We conclude that preservation of ECS benefits multiple aspects of the connectomic analysis of neural circuits.

No MeSH data available.


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Cumulative distributions for 3D hand-labeled volumes and skeletonizations of the LowECS (blue) and HighECS (brown) data volumes.(A) Cumulative distribution frequency (CDF) of number of intracellular ground truth voxels per object. The distribution medians are significantly different (Wilcoxon rank-sum test, p <0.0001). (B) CDF of path length per skeleton, medians 10.2 (LowECS) and 10.1 (HighECS) μm. The distributions are not significantly different (two-sample Kolmogorov–Smirnov (KS) test, p = 0.91). (C) CDF of the number of nodes annotated per skeleton, medians 16 (LowECS) and 17 (HighECS) nodes. The distributions not significantly different (two-sample KS test, p = 0.77).DOI:http://dx.doi.org/10.7554/eLife.08206.017
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fig3s1: Cumulative distributions for 3D hand-labeled volumes and skeletonizations of the LowECS (blue) and HighECS (brown) data volumes.(A) Cumulative distribution frequency (CDF) of number of intracellular ground truth voxels per object. The distribution medians are significantly different (Wilcoxon rank-sum test, p <0.0001). (B) CDF of path length per skeleton, medians 10.2 (LowECS) and 10.1 (HighECS) μm. The distributions are not significantly different (two-sample Kolmogorov–Smirnov (KS) test, p = 0.91). (C) CDF of the number of nodes annotated per skeleton, medians 16 (LowECS) and 17 (HighECS) nodes. The distributions not significantly different (two-sample KS test, p = 0.77).DOI:http://dx.doi.org/10.7554/eLife.08206.017

Mentions: To assess segmentation performance across the entire volumes, we densely skeletonized the neurites within each volume; these skeletons served as a ground truth (GT) measure of neurite continuity (Figure 3A, B). Notably, the total number of skeletons (LowECS=220, HighECS=217), the total skeletonized path length (LowECS = 3.29 mm, HighECS = 3.40 mm) and the median neurite path length (LowECS = 10.2 [7.6 – 13.3] µm, HighECS = 10.1 [6.6 – 12.7] µm, median [IQR]) were similar between the two datasets. The distributions of neurite path lengths were not significantly different between the two datasets (Kolmogorov–Smirnov test, p=0.91). Together these measurements indicate that the basic statistics of 3D neurite continuity are not altered for the HighECS data compared to the LowECS perfused tissue (Figure 3—figure supplement 1).


Extracellular space preservation aids the connectomic analysis of neural circuits.

Pallotto M, Watkins PV, Fubara B, Singer JH, Briggman KL - Elife (2015)

Cumulative distributions for 3D hand-labeled volumes and skeletonizations of the LowECS (blue) and HighECS (brown) data volumes.(A) Cumulative distribution frequency (CDF) of number of intracellular ground truth voxels per object. The distribution medians are significantly different (Wilcoxon rank-sum test, p <0.0001). (B) CDF of path length per skeleton, medians 10.2 (LowECS) and 10.1 (HighECS) μm. The distributions are not significantly different (two-sample Kolmogorov–Smirnov (KS) test, p = 0.91). (C) CDF of the number of nodes annotated per skeleton, medians 16 (LowECS) and 17 (HighECS) nodes. The distributions not significantly different (two-sample KS test, p = 0.77).DOI:http://dx.doi.org/10.7554/eLife.08206.017
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fig3s1: Cumulative distributions for 3D hand-labeled volumes and skeletonizations of the LowECS (blue) and HighECS (brown) data volumes.(A) Cumulative distribution frequency (CDF) of number of intracellular ground truth voxels per object. The distribution medians are significantly different (Wilcoxon rank-sum test, p <0.0001). (B) CDF of path length per skeleton, medians 10.2 (LowECS) and 10.1 (HighECS) μm. The distributions are not significantly different (two-sample Kolmogorov–Smirnov (KS) test, p = 0.91). (C) CDF of the number of nodes annotated per skeleton, medians 16 (LowECS) and 17 (HighECS) nodes. The distributions not significantly different (two-sample KS test, p = 0.77).DOI:http://dx.doi.org/10.7554/eLife.08206.017
Mentions: To assess segmentation performance across the entire volumes, we densely skeletonized the neurites within each volume; these skeletons served as a ground truth (GT) measure of neurite continuity (Figure 3A, B). Notably, the total number of skeletons (LowECS=220, HighECS=217), the total skeletonized path length (LowECS = 3.29 mm, HighECS = 3.40 mm) and the median neurite path length (LowECS = 10.2 [7.6 – 13.3] µm, HighECS = 10.1 [6.6 – 12.7] µm, median [IQR]) were similar between the two datasets. The distributions of neurite path lengths were not significantly different between the two datasets (Kolmogorov–Smirnov test, p=0.91). Together these measurements indicate that the basic statistics of 3D neurite continuity are not altered for the HighECS data compared to the LowECS perfused tissue (Figure 3—figure supplement 1).

Bottom Line: Any improvement in segmentation error rates would therefore directly reduce the time required to analyze 3D EM data.ECS preserved tissue is easier to segment using machine learning algorithms, leading to significantly reduced error rates.We conclude that preservation of ECS benefits multiple aspects of the connectomic analysis of neural circuits.

View Article: PubMed Central - PubMed

Affiliation: Circuit Dynamics and Connectivity Unit, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, United States.

ABSTRACT
Dense connectomic mapping of neuronal circuits is limited by the time and effort required to analyze 3D electron microscopy (EM) datasets. Algorithms designed to automate image segmentation suffer from substantial error rates and require significant manual error correction. Any improvement in segmentation error rates would therefore directly reduce the time required to analyze 3D EM data. We explored preserving extracellular space (ECS) during chemical tissue fixation to improve the ability to segment neurites and to identify synaptic contacts. ECS preserved tissue is easier to segment using machine learning algorithms, leading to significantly reduced error rates. In addition, we observed that electrical synapses are readily identified in ECS preserved tissue. Finally, we determined that antibodies penetrate deep into ECS preserved tissue with only minimal permeabilization, thereby enabling correlated light microscopy (LM) and EM studies. We conclude that preservation of ECS benefits multiple aspects of the connectomic analysis of neural circuits.

No MeSH data available.


Related in: MedlinePlus