Background

Neuroinflammation, a main mechanism behind neuronal death and dysfunction in various pathologies, involves microglia as key players patrolling immune cells [1]. These cells, known for their morphological heterogeneity and dynamic processes, play diverse roles in different neurodegenerative disorders [2,3]. It is well-known that the functional and morphological alterations in response to intracellular or extracellular cues are highly interconnected. For example, microglia often exhibit increased volume and altered dendrite architecture when performing phagocytic functions in damaged loci, as opposed to a ramified, homeostatic morphology in healthy brain tissue [4,5].

Given the fundamental role of microglia as resident immune cells in the central nervous system, understanding their behavior in pathological contexts is crucial. In this regard, light microscopy–based immunohistochemical (IHC) analyses of ex vivo brain sections remain a core tool for neuropathology research centers worldwide. However, the dense and intricate microglial architecture poses challenges for 3D morphological studies, necessitating a workflow that builds on standard techniques and tools to facilitate standardization and widespread adoption in neuropathology research. Previous methods for 3D IHC analyses have inherent limitations that result in high error rates when measuring numerous small parameters in large and complex brain samples. This often requires significant time investments, specialized analyses, and multiple software tools. Moreover, commonly used techniques for quantitative microglial assessment lack data standardization, resulting in non-comparable results [5]. This is exemplified by the inherent limitations of common open-source tools, such as ImageJ, which either lack the capability for 3D assessment or require complex pipelines involving non-standardized plug-ins to obtain 3D data [3,6–9].

To overcome these challenges and capture the continuum of microglial morphologies under various physiological and pathological states, we describe a straightforward and repeatable custom approach for 3D microglial morphology analyses in mouse brain sections. Our pipeline is based on 3D image acquisition workflows built into the standard Imaris software, followed by custom Python scripts for data analysis. Similar approaches have been successfully employed for 3D analyses of cell volumes and morphological features, albeit in distinct research fields from neuroscience [10,11]. The protocol described here was used to study neuroinflammation and treatment response in primary mitochondrial disorders, specifically in a mouse model of Leigh syndrome that presents distinct patterns of inflammatory neurodegeneration associated with treatment-resistant epilepsy [12,13].

This workflow may be applicable to multiple conditions, providing biological plausibility for the association between gene disruptions and neuropathological phenotypes. It also enables the assessment of the effects of various pharmacological treatments, thereby informing pre-clinical drug development.