Morphological investigation of astrocyte brain cells using quantitative phase imaging

Area of Science:

• Neuroscience
• Biophysics
• Optical Engineering

Background:

• Astrocytes are crucial glial cells supporting neuronal function and brain homeostasis.
• Comprehensive morphological analysis of astrocytes is essential for understanding their roles.
• Existing imaging techniques often require labels or lack real-time, high-resolution capabilities.

Purpose of the Study:

• To present a quantitative imaging technique for comprehensive morphological analysis of astrocytes.
• To enable label-free, real-time 3D visualization and quantification of astrocyte somata and branches.
• To integrate digital holography with AI for advanced astrocyte segmentation and morphometric analysis.

Main Methods:

• Development of a custom-built common-path quantitative phase microscope using a grating and dual-lens system.
• Label-free, real-time 3D imaging of astrocyte cells by measuring optical path length difference (OPD).
• Multistage deep learning segmentation of astrocyte cells and optical thickness-based clustering for soma and branch segmentation.

Main Results:

• Successful label-free, real-time 3D visualization and quantification of astrocyte morphometric properties.
• Accurate segmentation of astrocyte somata and branches using deep learning and clustering on OPD maps.
• Correlation of phase-derived and geometrical morphological parameters for segmented astrocyte components.

Conclusions:

• The developed quantitative phase microscope offers a powerful tool for label-free, real-time astrocyte morphology analysis.
• The integration of digital holography and AI provides a novel approach for detailed astrocyte segmentation and morphometrics.
• This technique advances our ability to study astrocyte structure-function relationships in neuroscience research.