PhD student Jennifer Kim
When faced with a daunting challenge early in her PhD, Jennifer Kim, a graduate student in the Ciernia lab, turned a roadblock into an innovative solution. Tasked with characterizing microglia morphology changes across multiple brain regions from large cohorts of samples, she encountered limitations in existing tools.
“I was discouraged to find that the most commonly used analysis methods at the time required manual segmentation of individual microglia to obtain measures down to a single cell resolution and that many studies reported individual morphology measures in isolation,” she recalls. “This motivated me to develop a protocol that would be more time-efficient and comprehensive in its approach to identify how microglia shift their cellular state.”
Over the last few years, Jennifer has worked in collaboration with Drs. Paul Pavlidis and Annie Ciernia to create a standardized method for conducting morphology (shape) analysis on individual microglia in a quick and non-biased manner, while considering multiple measures of morphology together to better understand how their form relates to their function. The result is the creation of MicrogliaMorphology and its companion R package, MicrogliaMorphologyR, which are both free and open source for the scientific community.
Creating a user-friendly tool
“As the immune cells of the brain, microglia exhibit a dynamic range of cellular shapes that allow them to rapidly adapt to local changes in the brain environment,” Jennifer explains. “Although microglia exist on a continuum of morphologies, we can group them into common forms to measure changes in their populations.”
The MicrogliaMorphology tool is a user-friendly ImageJ program that helps to efficiently identify and measure 27 shape features from hundreds to thousands of microglia cells. The companion R package, MicrogliaMorphologyR, processes this data, allowing users to explore, visualize, and analyze microglia shapes, identify patterns, track changes and perform statistical tests. As Jennifer highlights in a recent paper published in eNeuro, these tools will help researchers systematically study microglia diversity and how their shapes change in different experiments.
Figure 1: Analysis toolset overview
A unique approach to analyzing microglia
Figure 2: Morphology features
Many tools currently exist to analyze microglia morphology, but often involve significant effort to extract meaningful information at a larger scale. Most existing methods rely on manually selecting and segmenting individual cells in images to measure their shapes. This process can introduce bias in cell selection and significantly limit the number of cells that can be analyzed. Microglia shapes can also be described using features like branch length, branching complexity, territory span and circularity—features that usually change together. However, studies often focus on only a few of these features, which can give an incomplete or biased representation of changes in microglia shape.
Figure 2 shows how MicrogliaMorphology automates the analysis of morphology measures using ImageJ plugins FracLac and AnalyzeSkeleton.
“Our analysis approach eliminates manual bias and considers contributions of many feature measures together,” Dr. Pavlidis notes. “This is necessary to better understand a microglia’s actual morphological state and relationship to cellular function.”
New directions in brain research
Dr. Annie Ciernia
The tool is already proving invaluable in several diverse research projects. It has been used to identify how gut inflammation and the microbiome influence microglial morphology in the developing brain and to profile changes in models of Alzheimer disease.
“We have also been contacted by more than a dozen labs from all over the world who are applying our toolset to a wide variety of projects,” says Dr. Ciernia.
Recent technologies like single-cell sequencing and spatial transcriptomics have advanced the understanding of microglial diversity, revealing molecular processes across different states such as normal brain function, development and disease. However, these studies often don’t connect molecular patterns to the wide range of microglial shapes or their specific functions, beyond the basic “resting” or “activated” categories.
“Microglia are incredibly diverse, varying in shape, molecular makeup, and behaviour depending on their environment and function,” says Dr. Ciernia. “While we know their shape reflects their role in the brain, studies have only scratched the surface of understanding how specific shapes relate to what microglia do.”
This new tool helps to address this gap by classifying microglia based on their shapes and mapping them to their precise locations within brain tissue samples, allowing researchers to link molecular activity with physical shape at the single-cell level. Together, these tools provide a powerful framework to study how the shape of microglia relates to their function in the brain.
Figure 3: Colour By Cluster feature allows for examination of the spatial distribution of microglia morphologies across brain regions. Individual microglia in the frontal cortex, striatum and hippocampus brain regions coloured by their morphology clusters.
Commitment to open science
Dr. Pavlidis
“While there has been considerable progress in the field to develop automated microglia morphology segmentation tools, the majority of published tools are not openly available nor well-documented and there has been limited transparency about the methods used to analyze the resulting morphological measures,” says Dr. Pavlidis. “To support open science, we share all our raw image data, code and tools, enabling the broader scientific community to build upon and extend on our findings.”
With its open source foundation, this toolset will help neuroscientists from around the world gain new insights on microglia morphology and ultimately help to accelerate breakthroughs across diverse research questions.
Banner image: MicrogliaMorphology’s Colour By Cluster feature allows for examination of the spatial distribution of microglia morphologies within brain regions. Individual microglia in the hippocampus coloured by their morphology clusters (yellow = hypertrophic, light green = ameboid, dark green = rod-like, salmon = ramified).
