Automated in vivo Tracking of Cortical Oligodendrocytes.

Yu Kang T Xu,Cody L Call,Dwight E Bergles,Jeremias Sulam

doi:10.3389/fncel.2021.667595

Yu Kang T Xu, Cody L Call + Show 2 more

Open Access

https://doi.org/10.3389/fncel.2021.667595

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Abstract

Oligodendrocytes exert a profound influence on neural circuits by accelerating action potential conduction, altering excitability, and providing metabolic support. As oligodendrogenesis continues in the adult brain and is essential for myelin repair, uncovering the factors that control their dynamics is necessary to understand the consequences of adaptive myelination and develop new strategies to enhance remyelination in diseases such as multiple sclerosis. Unfortunately, few methods exist for analysis of oligodendrocyte dynamics, and even fewer are suitable for in vivo investigation. Here, we describe the development of a fully automated cell tracking pipeline using convolutional neural networks (Oligo-Track) that provides rapid volumetric segmentation and tracking of thousands of cells over weeks in vivo. This system reliably replicated human analysis, outperformed traditional analytic approaches, and extracted injury and repair dynamics at multiple cortical depths, establishing that oligodendrogenesis after cuprizone-mediated demyelination is suppressed in deeper cortical layers. Volumetric data provided by this analysis revealed that oligodendrocyte soma size progressively decreases after their generation, and declines further prior to death, providing a means to predict cell age and eventual cell death from individual time points. This new CNN-based analysis pipeline offers a rapid, robust method to quantitatively analyze oligodendrocyte dynamics in vivo, which will aid in understanding how changes in these myelinating cells influence circuit function and recovery from injury and disease.

Highlights

Advances in genetically encoded fluorescent indicators, CRISPR-mediated gene editing and multiphoton microscopy provide unprecedented opportunities for studying cellular dynamics at single-cell resolution in the brains of living animals
To visualize individual oligodendrocytes in the cerebral cortex, cranial windows were surgically implanted in mice that express EGFP under control of the Mobp promoter/enhancer (Hughes et al, 2018; Orthmann-Murphy et al, 2020; Figure 1A)
Seg-convolutional neural networks (CNN) was used to perform semantic segmentation to identify the position of all oligodendrocyte cell bodies within the imaging volume at each timepoint in the timeseries

Summary

Introduction

Advances in genetically encoded fluorescent indicators, CRISPR-mediated gene editing and multiphoton microscopy provide unprecedented opportunities for studying cellular dynamics at single-cell resolution in the brains of living animals. While these approaches hold the potential for profound discoveries about brain function, they come with a host of quantitative challenges. Automated Tracking of Cortical Oligodendrocytes to delve deeper into the brain in vivo (Horton et al, 2013), the challenges associated with quantification of enormous datasets become more acute. Further advances depend critically on the availability of robust analysis platforms to rapidly extract multidimensional observations about cellular dynamics

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