Image processing and supervised machine learning for retinal microglia characterization in senescence.

Area of Science:

• Neuroscience
• Cell Biology
• Biomedical Imaging

Background:

• Cellular senescence impairs cell function and contributes to disease development.
• Aging populations are experiencing increased prevalence of senescence-related diseases.
• Understanding senescence in the central nervous system (CNS), including the retina, is crucial for developing therapeutic strategies against neurodegenerative diseases.

Purpose of the Study:

• To investigate the mechanisms of cellular senescence within the retina by analyzing microglial cell morphology.
• To develop and validate an objective method for identifying, quantifying, and classifying senescent retinal microglial cells.
• To explore the potential of retinal microglial cells as biomarkers for predicting neuro- and retinal degenerative diseases.

Main Methods:

• Dissection, staining, and mounting of mouse retinas.
• Image acquisition using fluorescence microscopy.
• Application of image processing and a supervised machine learning algorithm (Support Vector Machine - SVM) for classifying microglial cells into five distinct morphotypes based on shape metrics.

Main Results:

• A Support Vector Machine (SVM) model was developed to accurately classify retinal microglial cells into five morphotypes.
• The SVM model utilizes shape metrics derived from existing literature on microglial morphology.
• The study demonstrates a high accuracy in classifying microglial morphotypes, offering an objective quantification method.

Conclusions:

• The developed image processing and machine learning approach provides an objective method for quantifying retinal microglial cells.
• Automatic delineation of microglial cell populations can serve as a valuable tool for research into aging and CNS diseases.
• Retinal microglial cell morphotype classification holds potential as future imaging biomarkers for early disease prediction.