Automated in vivo Tracking of Cortical Oligodendrocytes

FAST: Fast, Free, Consistent, and Unsupervised Oligodendrocyte Segmentation and Tracking System

To develop reparative therapies for neurological disorders like multiple sclerosis (MS), we need to better understand the physiology of loss and replacement of oligodendrocytes, the cells that make myelin and are the target of damage in MS. In vivo two-photon fluorescence microscopy allows direct visualization of oligodendrocytes in the intact brain of transgenic mouse models, promising a deeper understanding of the longitudinal dynamics of replacing oligodendrocytes after damage. However, the task of tracking the fate of individual oligodendrocytes requires extensive effort for manual annotation and is especially challenging in three-dimensional images. While several models exist for annotating cells in two-dimensional images, few models exist to annotate cells in three-dimensional images and even fewer are designed for tracking cells in longitudinal imaging. Notably, existing options often come with a substantial financial investment, being predominantly commercial or confined to proprietary software. Furthermore, the complexity of processes and myelin formed by individual oligodendrocytes can result in the failure of algorithms that are specifically designed for tracking cell bodies alone. Here, we propose a fast, free, consistent, and unsupervised beta-mixture oligodendrocyte segmentation system (FAST) that is written in open-source software, and can segment and track oligodendrocytes in three-dimensional images over time with minimal human input. We showed that the FAST model can segment and track oligodendrocytes similarly to a blinded human observer. Although FAST was developed to apply to our studies on oligodendrocytes, we anticipate that it can be modified to study four-dimensional in vivo data of any brain cell with associated complex processes.

Mitochondrial network reorganization and transient expansion during oligodendrocyte generation

Oligodendrocyte precursor cells (OPCs) give rise to myelinating oligodendrocytes of the brain. This process persists throughout life and is essential for recovery from neurodegeneration. To better understand the cellular checkpoints that occur during oligodendrogenesis, we determined the mitochondrial distribution and morphometrics across the oligodendrocyte lineage in mouse and human cerebral cortex. During oligodendrocyte generation, mitochondrial content expands concurrently with a change in subcellular partitioning towards the distal processes. These changes are followed by an abrupt loss of mitochondria in the oligodendrocyte processes and myelin, coinciding with sheath compaction. This reorganization and extensive expansion and depletion take 3 days. Oligodendrocyte mitochondria are stationary over days while OPC mitochondrial motility is modulated by animal arousal state within minutes. Aged OPCs also display decreased mitochondrial size, volume fraction, and motility. Thus, mitochondrial dynamics are linked to oligodendrocyte generation, dynamically modified by their local microenvironment, and altered in the aging brain.

Versatile and automated workflow for the analysis of oligodendroglial calcium signals

Although intracellular Ca2+ signals of oligodendroglia, the myelin-forming cells of the central nervous system, regulate vital cellular processes including myelination, few studies on oligodendroglia Ca2+ signal dynamics have been carried out and existing software solutions are not adapted to the analysis of the complex Ca2+ signal characteristics of these cells. Here, we provide a comprehensive solution to analyze oligodendroglia Ca2+ imaging data at the population and single-cell levels. We describe a new analytical pipeline containing two free, open source and cross-platform software programs, Occam and post-prOccam, that enable the fully automated analysis of one- and two-photon Ca2+ imaging datasets from oligodendroglia obtained by either ex vivo or in vivo Ca2+ imaging techniques. Easily configurable, our software solution is optimized to obtain unbiased results from large datasets acquired with different imaging techniques. Compared to other recent software, our solution proved to be fast, low memory demanding and faithful in the analysis of oligodendroglial Ca2+ signals in all tested imaging conditions. Our versatile and accessible Ca2+ imaging data analysis tool will facilitate the elucidation of Ca2+-mediated mechanisms in oligodendroglia. Its configurability should also ensure its suitability with new use cases such as other glial cell types or even cells outside the CNS.

Review of data processing of functional optical microscopy for neuroscience

Functional optical imaging in neuroscience is rapidly growing with the development of optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. We cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges.

Linking Plasma Amyloid Beta and Neurofilament Light Chain to Intracortical Myelin Content in Cognitively Normal Older Adults

Evidence suggests that lightly myelinated cortical regions are vulnerable to aging and Alzheimer’s disease (AD). However, it remains unknown whether plasma markers of amyloid and neurodegeneration are related to deficits in intracortical myelin content, and whether this relationship, in turn, is associated with altered patterns of resting-state functional connectivity (rs-FC). To shed light into these questions, plasma levels of amyloid-β fragment 1–42 (Aβ_1–42) and neurofilament light chain (NfL) were measured using ultra-sensitive single-molecule array (Simoa) assays, and the intracortical myelin content was estimated with the ratio T1-weigthed/T2-weighted (T1w/T2w) in 133 cognitively normal older adults. We assessed: (i) whether plasma Aβ_1–42 and/or NfL levels were associated with intracortical myelin content at different cortical depths and (ii) whether cortical regions showing myelin reductions also exhibited altered rs-FC patterns. Surface-based multiple regression analyses revealed that lower plasma Aβ_1–42 and higher plasma NfL were associated with lower myelin content in temporo-parietal-occipital regions and the insular cortex, respectively. Whereas the association with Aβ_1–42 decreased with depth, the NfL-myelin relationship was most evident in the innermost layer. Older individuals with higher plasma NfL levels also exhibited altered rs-FC between the insula and medial orbitofrontal cortex. Together, these findings establish a link between plasma markers of amyloid/neurodegeneration and intracortical myelin content in cognitively normal older adults, and support the role of plasma NfL in boosting aberrant FC patterns of the insular cortex, a central brain hub highly vulnerable to aging and neurodegeneration.

Neuron to Oligodendrocyte Precursor Cell Synapses: Protagonists in Oligodendrocyte Development and Myelination, and Targets for Therapeutics

The development of neuronal circuitry required for cognition, complex motor behaviors, and sensory integration requires myelination. The role of glial cells such as astrocytes and microglia in shaping synapses and circuits have been covered in other reviews in this journal and elsewhere. This review summarizes the role of another glial cell type, oligodendrocytes, in shaping synapse formation, neuronal circuit development, and myelination in both normal development and in demyelinating disease. Oligodendrocytes ensheath and insulate neuronal axons with myelin, and this facilitates fast conduction of electrical nerve impulses via saltatory conduction. Oligodendrocytes also proliferate during postnatal development, and defects in their maturation have been linked to abnormal myelination. Myelination also regulates the timing of activity in neural circuits and is important for maintaining the health of axons and providing nutritional support. Recent studies have shown that dysfunction in oligodendrocyte development and in myelination can contribute to defects in neuronal synapse formation and circuit development. We discuss glutamatergic and GABAergic receptors and voltage gated ion channel expression and function in oligodendrocyte development and myelination. We explain the role of excitatory and inhibitory neurotransmission on oligodendrocyte proliferation, migration, differentiation, and myelination. We then focus on how our understanding of the synaptic connectivity between neurons and OPCs can inform future therapeutics in demyelinating disease, and discuss gaps in the literature that would inform new therapies for remyelination.

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze images of single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0 retains all the functionality of the original version, which was optimized for bacteria growing in the mother machine microfluidic device, but extends results to two-dimensional growth environments. Two-dimensional environments represent an important class of data because they are more straightforward to implement experimentally, they offer the potential for studies using co-cultures of cells, and they can be used to quantify spatial effects and multi-generational phenomena. However, segmentation and tracking are significantly more challenging tasks in two-dimensions due to exponential increases in the number of cells. To showcase this new functionality, we analyze mixed populations of antibiotic resistant and susceptible cells, and also track pole age and growth rate across generations. In addition to the two-dimensional capabilities, we also introduce several major improvements to the code that increase accessibility, including the ability to accept many standard microscopy file formats as inputs and the introduction of a Google Colab notebook so users can try the software without installing the code on their local machine. DeLTA 2.0 is rapid, with run times of less than 10 minutes for complete movies with hundreds of cells, and is highly accurate, with error rates around 1%, making it a powerful tool for analyzing time-lapse microscopy data.