ProMoIJ: A new tool for automatic three-dimensional analysis of microglial process motility

Abstract

Microglia, the immune cells of the central nervous system, continuously survey the brain to detect alterations and maintain tissue homeostasis. The motility of microglial processes is indicative of their surveying capacity in normal and pathological conditions. The gold standard technique to study motility involves the use of two-photon microscopy to obtain time-lapse images from brain slices or the cortex of living animals. This technique generates four dimensionally-coded images which are analyzed manually using time-consuming, non-standardized protocols. Microglial process motility analysis is frequently performed using Z-stack projections with the consequent loss of three-dimensional (3D) information. To overcome these limitations, we developed ProMoIJ, a pack of ImageJ macros that perform automatic motility analysis of cellular processes in 3D. The main core of ProMoIJ is formed by two macros that assist the selection of processes, automatically reconstruct their 3D skeleton, and analyze their motility (process and tip velocity). Our results show that ProMoIJ presents several key advantages compared with conventional manual analysis: (1) reduces the time required for analysis, (2) is less sensitive to experimenter bias, and (3) is more robust to varying numbers of processes analyzed. In addition, we used ProMoIJ to demonstrate that commonly performed 2D analysis underestimates microglial process motility, to reveal that only cells adjacent to a laser injured area extend their processes toward the lesion site, and to demonstrate that systemic inflammation reduces microglial process motility. ProMoIJ is a novel, open-source, freely-available tool which standardizes and accelerates the time-consuming labor of 3D analysis of microglial process motility.

Main Points

ProMoIJ allows 3D analysis of cell process motility and presents several advantages compared with manual protocols:

• Reduced analysis time.
• Reduced sensitiveness to experimenter bias.
• Increased robustness to the number of processes analyzed.

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