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Published on: June 5, 2018
An Automated Analysis Pipeline for Microglia Morphology in Nikon NIS-Elements
Grace Garman1,2, Stephanie Gottwals3, Gregory Pearson3
1Department of Biomedical Engineering Rensselaer Polytechnic Institute RensselaerNY.
This study introduces an automated analysis pipeline for quantifying microglial cell morphology in mouse brains. The method enhances data acquisition and reduces bias in assessing microglial activation states.
Area of Science:
- Neuroscience
- Cell Biology
- Computational Biology
Background:
- Microglial cell morphology is crucial for determining activation states and indicating pathologies.
- Automated quantification of microglia in mouse brains (300,000-500,000 cells) offers efficiency over manual methods.
- Existing automated tools lack the modularity and comprehensive analysis required for microglial morphology.
Purpose of the Study:
- To develop and present a customizable, automated analysis pipeline for quantifying microglial cell morphology.
- To enable efficient and less biased assessment of microglial activation states in brain sections.
- To provide a modular pipeline adaptable for other ramified cell types, such as neurons.
Main Methods:
- An annotated analysis pipeline was developed using Nikon NIS-Elements software.
- The pipeline incorporates pre-processing, thresholding, skeletonization, puncta detection, Sholl analysis, and branch classification.
- Batch analysis of image files, including entire microscope slides, is enabled after threshold validation.
Main Results:
- The pipeline automates the quantification of microglia morphology in stained brain sections.
- It generates detailed measurement tables, significantly increasing data output per animal.
- Analysis is rapid and hands-off post-segmentation, minimizing human bias.
Conclusions:
- The developed pipeline offers a fast, modular, and customizable solution for automated microglial morphology analysis.
- It provides a valuable tool for researchers studying neuroinflammation and related pathologies.
- This approach maximizes data generation and reduces experimental time, allowing more focus on research.
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