Image processing and supervised machine learning for retinal microglia characterization in senescence
Soyoung Choi1, Daniel Hill2, Jonathan Young3
1UCL Institute of Ophthalmology, London, United Kingdom; Novai Ltd, Reading, United Kingdom.
Abstract:
The process of senescence impairs the function of cells and can ultimately be a key factor in the development of disease. With an aging population, senescence-related diseases are increasing in prevalence. Therefore, understanding the mechanisms of cellular senescence within the central nervous system (CNS), including the retina, may yield new therapeutic pathways to slow or even prevent the development of neuro- and retinal degenerative diseases. One method of probing the changing functions of senescent retinal cells is to observe retinal microglial cells. Their morphological structure may change in response to their surrounding cellular environment. In this chapter, we show how microglial cells in the retina, which are implicated in aging and diseases of the CNS, can be identified, quantified, and classified into five distinct morphotypes using image processing and supervised machine learning algorithms. The process involves dissecting, staining, and mounting mouse retinas, before image capture via fluorescence microscopy. The resulting images can then be classified by morphotype using a support vector machine (SVM) we have recently described showing high accuracy. This SVM model uses shape metrics found to correspond with qualitative descriptions of the shape of each morphotype taken from existing literature. We encourage more objective and widespread use of methods of quantification such as this. We believe automatic delineation of the population of microglial cells in the retina, could potentially lead to their use as retinal imaging biomarkers for disease prediction in the future.
Insights
Cellular senescence contributes to disease, especially in aging populations. This study introduces a machine learning method to classify retinal microglial cell morphotypes, aiding in understanding neurodegenerative diseases.
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.
