Utilizing supervised machine learning to identify microglia and astrocytes in situ: implications for large-scale image analysis and quantification

Background: The evaluation of histological tissue samples plays a crucial role in deciphering preclinical disease and injury mechanisms. High-resolution images can be obtained quickly however data acquisition are often bottlenecked by manual analysis methodologies.

New method: We describe and validate a pipeline for a novel machine learning-based analytical method, using the Opera High-Content Screening system and Harmony software, allowing for detailed image analysis of cellular markers in histological samples.

Results: To validate the machine learning pipeline, analyses of single proteins in mouse brain sections were utilized. To demonstrate adaptability of the pipeline for multiple cell types and epitopes, the percent brain coverage of microglial cells, identified by ionized calcium binding adaptors molecule 1 (Iba1), and of astrocytes, by glial fibrillary acidic protein (GFAP) demonstrated no significant differences between automated and manual analyses protocols. Further to examine the robustness of this protocol for multiple proteins simultaneously labeling of rat brain sections were utilized; co-localization of astrocytic endfeet on blood vessels, using aquaporin-4 and tomato lectin respectively, were efficiently identified and quantified by the novel pipeline and were not significantly different between the two analyses protocols. Comparison with Existing Methods: The automated platform maintained the sensitivity and accuracy of manual analysis, while accomplishing the analyses in 1/200^th of the time.

Conclusions: We demonstrate the benefits and potential of adapting an automated high-throughput machine-learning analytical approach for the analysis ofin situ tissue samples, show effectiveness across different animal models, while reducing analysis time and increasing productivity.