Automated characterisation of microglia in ageing mice using image processing and supervised machine learning algorithms

Label-Free Imaging of Inflammation at the Level of Single Cells in the Living Human Eye

Purpose

Putative microglia were recently detected using adaptive optics ophthalmoscopy in healthy eyes. Here we evaluate the use of nonconfocal adaptive optics scanning light ophthalmoscopy (AOSLO) for quantifying the morphology and motility of presumed microglia and other immune cells in eyes with retinal inflammation from uveitis and healthy eyes.

Design

Observational exploratory study.

Participants

Twelve participants were imaged, including 8 healthy participants and 4 posterior uveitis patients recruited from the clinic of 1 of the authors (M.H.E.).

Methods

The Pittsburgh AOSLO imaging system was used with a custom-designed 7-fiber optical fiber bundle for simultaneous confocal and nonconfocal multioffset detection. The inner retina was imaged at several locations at multiple timepoints in healthy participants and uveitis patients to generate time-lapse images.

Main Outcome Measures

Microglia and macrophages were manually segmented from nonconfocal AOSLO images, and their morphological characteristics quantified (including soma size, diameter, and circularity). Cell soma motion was quantified across time for periods of up to 30 minutes and their speeds were calculated by measuring their displacement over time.

Results

A spectrum of cell morphologies was detected in healthy eyes from circular amoeboid cells to elongated cells with visible processes, resembling activated and ramified microglia, respectively. Average soma diameter was 16.1 ± 0.9 μm. Cell movement was slow in healthy eyes (0.02 μm/sec on average), but macrophage-like cells moved rapidly in some uveitis patients (up to 3 μm/sec). In an eye with infectious uveitis, many macrophage-like cells were detected; during treatment their quantity and motility decreased as vision improved.

Conclusions

In vivo adaptive optics ophthalmoscopy offers promise as a potentially powerful tool for detecting and monitoring inflammation and response to treatment at a cellular level in the living eye.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Targeted therapy and deep learning insights into microglia modulation for spinal cord injury

Spinal cord injury (SCI) is a devastating condition that can cause significant motor and sensory impairment. Microglia, the central nervous system's immune sentinels, are known to be promising therapeutic targets in both SCI and neurodegenerative diseases. The most effective way to deliver medications and control microglial inflammation is through nanovectors; however, because of the variability in microglial morphology and the lack of standardized techniques, it is still difficult to precisely measure their activation in preclinical models. This problem is especially important in SCI, where the intricacy of the glia response following traumatic events necessitates the use of a sophisticated method to automatically discern between various microglial cell activation states that vary over time and space as the secondary injury progresses. We address this issue by proposing a deep learning-based technique for quantifying microglial activation following drug-loaded nanovector treatment in a preclinical SCI model. Our method uses a convolutional neural network to segment and classify microglia based on morphological characteristics. Our approach's accuracy and efficiency are demonstrated through evaluation on a collection of histology pictures from injured and intact spinal cords. This robust computational technique has potential for analyzing microglial activation across various neuropathologies and demonstrating the usefulness of nanovectors in modifying microglia in SCI and other neurological disorders. It has the ability to speed development in this crucial sector by providing a standardized and objective way to compare therapeutic options.

Physical exercise regulates microglia in health and disease

There is a well-established link between physical activity and brain health. As such, the effectiveness of physical exercise as a therapeutic strategy has been explored in a variety of neurological contexts. To determine the extent to which physical exercise could be most beneficial under different circumstances, studies are needed to uncover the underlying mechanisms behind the benefits of physical activity. Interest has grown in understanding how physical activity can regulate microglia, the resident immune cells of the central nervous system. Microglia are key mediators of neuroinflammatory processes and play a role in maintaining brain homeostasis in healthy and pathological settings. Here, we explore the evidence suggesting that physical activity has the potential to regulate microglia activity in various animal models. We emphasize key areas where future research could contribute to uncovering the therapeutic benefits of engaging in physical exercise.

Astrocyte CCN1 stabilizes neural circuits in the adult brain

Neural circuits in many brain regions are refined by experience. Sensory circuits support higher plasticity at younger ages during critical periods - times of circuit refinement and maturation - and limit plasticity in adulthood for circuit stability. The mechanisms underlying these differing plasticity levels and how they serve to maintain and stabilize the properties of sensory circuits remain largely unclear. By combining a transcriptomic approach with ex vivo electrophysiology and in vivo imaging techniques, we identify that astrocytes release cellular communication network factor 1 (CCN1) to maintain synapse and circuit stability in the visual cortex. By overexpressing CCN1 in critical period astrocytes, we find that it promotes the maturation of inhibitory circuits and limits ocular dominance plasticity. Conversely, by knocking out astrocyte CCN1 in adults, binocular circuits are destabilized. These studies establish CCN1 as a novel astrocyte-secreted factor that stabilizes neuronal circuits. Moreover, they demonstrate that the composition and properties of sensory circuits require ongoing maintenance in adulthood, and that these maintenance cues are provided by astrocytes.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

Microglial morphometric analysis: so many options, so little consistency

Quantification of microglial activation through morphometric analysis has long been a staple of the neuroimmunologist's toolkit. Microglial morphological phenomics can be conducted through either manual classification or constructing a digital skeleton and extracting morphometric data from it. Multiple open-access and paid software packages are available to generate these skeletons via semi-automated and/or fully automated methods with varying degrees of accuracy. Despite advancements in methods to generate morphometrics (quantitative measures of cellular morphology), there has been limited development of tools to analyze the datasets they generate, in particular those containing parameters from tens of thousands of cells analyzed by fully automated pipelines. In this review, we compare and critique the approaches using cluster analysis and machine learning driven predictive algorithms that have been developed to tackle these large datasets, and propose improvements for these methods. In particular, we highlight the need for a commitment to open science from groups developing these classifiers. Furthermore, we call attention to a need for communication between those with a strong software engineering/computer science background and neuroimmunologists to produce effective analytical tools with simplified operability if we are to see their wide-spread adoption by the glia biology community.

A comparison of machine learning approaches for the quantification of microglial cells in the brain of mice, rats and non-human primates

Microglial cells are brain-specific macrophages that swiftly react to disruptive events in the brain. Microglial activation leads to specific modifications, including proliferation, morphological changes, migration to the site of insult, and changes in gene expression profiles. A change in inflammatory status has been linked to many neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease. For this reason, the investigation and quantification of microglial cells is essential for better understanding their role in disease progression as well as for evaluating the cytocompatibility of novel therapeutic approaches for such conditions. In the following study we implemented a machine learning-based approach for the fast and automatized quantification of microglial cells; this tool was compared with manual quantification (ground truth), and with alternative free-ware such as the threshold-based ImageJ and the machine learning-based Ilastik. We first trained the algorithms on brain tissue obtained from rats and non-human primate immunohistochemically labelled for microglia. Subsequently we validated the accuracy of the trained algorithms in a preclinical rodent model of Parkinson's disease and demonstrated the robustness of the algorithms on tissue obtained from mice, as well as from images provided by three collaborating laboratories. Our results indicate that machine learning algorithms can detect and quantify microglial cells in all the three mammalian species in a precise manner, equipotent to the one observed following manual counting. Using this tool, we were able to detect and quantify small changes between the hemispheres, suggesting the power and reliability of the algorithm. Such a tool will be very useful for investigation of microglial response in disease development, as well as in the investigation of compatible novel therapeutics targeting the brain. As all network weights and labelled training data are made available, together with our step-by-step user guide, we anticipate that many laboratories will implement machine learning-based quantification of microglial cells in their research.

Daytime Light Deficiency Leads to Sex- and Brain Region-Specific Neuroinflammatory Responses in a Diurnal Rodent

Seasonal changes in peripheral inflammation are well documented in both humans and animal models, but seasonal changes in neuroinflammation, especially the impact of seasonal lighting environment on neuroinflammation remain unclear. To address this question, the present study examined the effects of environmental lighting conditions on neuroinflammation in a diurnal rodent model, Nile grass rats (Arvicanthis niloticus). Male and female grass rats were housed in either bright (brLD) or dim (dimLD) light during the day to simulate a summer or winter light condition, respectively. After 4 weeks, microglia markers Iba-1 and CD11b, as well as pro-inflammatory cytokines TNF-α and IL-6, were examined in the anterior cingulate cortex (ACC), basolateral amygdala (BLA), and dorsal hippocampus (dHipp). The results revealed that winter-like dim light during the day leads to indicators of increased neuroinflammation in a brain site- and sex-specific manner. Specifically, relatively few changes in the neuroinflammatory markers were observed in the ACC, while numerous changes were found in the BLA and dHipp. In the BLA, winter-like dimLD resulted in hyper-ramified microglia morphology and increased expression of the pro-inflammatory cytokine IL-6, but only in males. In the dHipp, dimLD led to a higher number and hyper-ramified morphology of microglia as well as increased expression of CD11b and TNF-α, but only in females. Neuroinflammatory state is thus influenced by environmental light, differently in males and females, and could play a role in sex differences in the prevalence and symptoms of psychiatric or neurological disorders that are influenced by season or other environmental light conditions. Diurnal Nile grass rats were housed under bright or dim light during the day for 4 weeks, simulating seasonal fluctuations in daytime lighting environment. Dim light housing resulted in hyper-ramified morphology of microglia (scale bar, 15 μm) and altered expression of pro-inflammatory cytokines (TNF-α) in a sex- and brain region-specific manner.

MorphoMacro for in vivo and ex vivo quantitative morphometric analysis of microglia

Microglia cells dynamically survey the central nervous system microenvironment and, in response to tissue damage inflicted by radiation therapy, disease or infection, undergo morphological and functional changes that culminate in microglia activation. Cell shape transformation can be assessed descriptively or, alternatively, it can be quantified as a continuous variable for parameters including total cell size as well as protrusion length, ramification and complexity. The purpose of the MorphoMacro method is to quantitatively profile multiple and single microglia cells using the available ImageJ platform. This method outlines the required steps and ImageJ plugins to convert fluorescence and bright-field photomicrographs into representative binary and skeletonized images and to analyze them using the MorphoMacro software plugin for multiparametric and multilevel description of microglia cell morphology in vivo and ex vivo. Overall, the protocol provides a quantitative and comprehensive tool that can be used to identify, stratify, and monitor diverse microglia morphologies in homeostatic, different disease conditions and subsequent therapeutic monitoring.

Activity of a Novel Anti-Inflammatory Agent F-3,6'-dithiopomalidomide as a Treatment for Traumatic Brain Injury

Traumatic brain injury (TBI) is a major risk factor for several neurodegenerative disorders, including Parkinson's disease (PD) and Alzheimer's disease (AD). Neuroinflammation is a cause of later secondary cell death following TBI, has the potential to aggravate the initial impact, and provides a therapeutic target, albeit that has failed to translate into clinical trial success. Thalidomide-like compounds have neuroinflammation reduction properties across cellular and animal models of TBI and neurodegenerative disorders. They lower the generation of proinflammatory cytokines, particularly TNF-α which is pivotal in microglial cell activation. Unfortunately, thalidomide-like drugs possess adverse effects in humans before achieving anti-inflammatory drug levels. We developed F-3,6'-dithiopomalidomide (F-3,6'-DP) as a novel thalidomide-like compound to ameliorate inflammation. F-3,6'-DP binds to cereblon but does not efficiently trigger the degradation of the transcription factors (SALL4, Ikaros, and Aiolos) associated with the teratogenic and anti-proliferative responses of thalidomide-like drugs. We utilized a phenotypic drug discovery approach that employed cellular and animal models in the selection and development of F-3,6'-DP. F-3,6'-DP significantly mitigated LPS-induced inflammatory markers in RAW 264.7 cells, and lowered proinflammatory cytokine/chemokine levels in the plasma and brain of rats challenged with systemic LPS. We subsequently examined immunohistochemical, biochemical, and behavioral measures following controlled cortical impact (CCI) in mice, a model of moderate TBI known to induce inflammation. F-3,6'-DP decreased CCI-induced neuroinflammation, neuronal loss, and behavioral deficits when administered after TBI. F-3,6'-DP represents a novel class of thalidomide-like drugs that do not lower classical cereblon-associated transcription factors but retain anti-inflammatory actions and possess efficacy in the treatment of TBI and potentially longer-term neurodegenerative disorders.