Novel image analysis tool for rapid screening of cell morphology in preclinical animal models of disease

Michelle Guignet; Martin Schmuck; Danielle J Harvey; Danh Nguyen; Donald Bruun; Angela Echeverri; Gene Gurkoff; Pamela J Lein

doi:10.1016/j.heliyon.2023.e13449

Novel image analysis tool for rapid screening of cell morphology in preclinical animal models of disease

Heliyon. 2023 Feb 8;9(2):e13449. doi: 10.1016/j.heliyon.2023.e13449. eCollection 2023 Feb.

Authors

Michelle Guignet¹, Martin Schmuck¹, Danielle J Harvey², Danh Nguyen³, Donald Bruun¹, Angela Echeverri⁴, Gene Gurkoff^{4

5}, Pamela J Lein^{1

6}

Affiliations

¹ Department of Molecular Biosciences, School of Veterinary Medicine, University of California-Davis, 1089 Veterinary Medicine Drive, Davis, CA, 95616, USA.
² Department of Public Health Sciences, University of California-Davis, One Shields Avenue, Davis, CA, 95616, USA.
³ Division of General Internal Medicine, Department of Medicine, School of Medicine, University of California-Irvine, 100 Theory, Suite 120, Irvine, CA, 92617, USA.
⁴ Department of Neurological Surgery, School of Medicine, University of California-Davis, 4800 Y Street, Sacramento, CA, 95817, USA.
⁵ Center for Neuroscience, University of California-Davis, 1544 Newton Court, Davis, CA, 95618, USA.
⁶ MIND Institute, School of Medicine, University of California-Davis, 2825 50th Street, Sacramento, CA, 95817, USA.

Abstract

The field of cell biology has seen major advances in both cellular imaging modalities and the development of automated image analysis platforms that increase rigor, reproducibility, and throughput for large imaging data sets. However, there remains a need for tools that provide accurate morphometric analysis of single cells with complex, dynamic cytoarchitecture in a high-throughput and unbiased manner. We developed a fully automated image-analysis algorithm to rapidly detect and quantify changes in cellular morphology using microglia cells, an innate immune cell within the central nervous system, as representative of cells that exhibit dynamic and complex cytoarchitectural changes. We used two preclinical animal models that exhibit robust changes in microglia morphology: (1) a rat model of acute organophosphate intoxication, which was used to generate fluorescently labeled images for algorithm development; and (2) a rat model of traumatic brain injury, which was used to validate the algorithm using cells labeled using chromogenic detection methods. All ex vivo brain sections were immunolabeled for IBA-1 using fluorescence or diaminobenzidine (DAB) labeling, images were acquired using a high content imaging system and analyzed using a custom-built algorithm. The exploratory data set revealed eight statistically significant and quantitative morphometric parameters that distinguished between phenotypically distinct groups of microglia. Manual validation of single-cell morphology was strongly correlated with the automated analysis and was further supported by a comparison with traditional stereology methods. Existing image analysis pipelines rely on high-resolution images of individual cells, which limits sample size and is subject to selection bias. However, our fully automated method integrates quantification of morphology and fluorescent/chromogenic signals in images from multiple brain regions acquired using high-content imaging. In summary, our free, customizable image analysis tool provides a high-throughput, unbiased method for accurately detecting and quantifying morphological changes in cells with complex morphologies.

Keywords: Automated image analysis; Microglia; Morphology; Organophosphate; Traumatic brain injury; Unbiased image analysis.

Abstract

Grants and funding