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      MIC‐MAC: An automated pipeline for high‐throughput characterization and classification of three‐dimensional microglia morphologies in mouse and human postmortem brain samples

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          Abstract

          The phenotypic changes of microglia in brain diseases are particularly diverse and their role in disease progression, beneficial, or detrimental, is still elusive. High‐throughput molecular approaches such as single‐cell RNA‐sequencing can now resolve the high heterogeneity in microglia population for a specific physiological condition, however, the relation between the different microglial signatures and their surrounding brain microenvironment is barely understood. Thus, better tools to characterize the phenotypic variations of microglia in situ are needed, particularly for human brain postmortem samples analysis. To address this challenge, we developed MIC‐MAC, a Microglia and Immune Cells Morphologies Analyser and Classifier pipeline that semiautomatically segments, extracts, and classifies all microglia and immune cells labeled in large three‐dimensional (3D) confocal image stacks of mouse and human brain samples. Our imaging‐based approach enables automatic 3D‐morphology characterization and classification of thousands of individual microglia in situ and revealed species‐ and disease‐specific morphological phenotypes in mouse aging, human Alzheimer's disease, and dementia with Lewy Bodie's samples. MIC‐MAC is a precision diagnostic tool that allows a rapid, unbiased, and large‐scale analysis of microglia morphological states in mouse models and patient brain samples.

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          Most cited references16

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          SeeDB: a simple and morphology-preserving optical clearing agent for neuronal circuit reconstruction.

          We report a water-based optical clearing agent, SeeDB, which clears fixed brain samples in a few days without quenching many types of fluorescent dyes, including fluorescent proteins and lipophilic neuronal tracers. Our method maintained a constant sample volume during the clearing procedure, an important factor for keeping cellular morphology intact, and facilitated the quantitative reconstruction of neuronal circuits. Combined with two-photon microscopy and an optimized objective lens, we were able to image the mouse brain from the dorsal to the ventral side. We used SeeDB to describe the near-complete wiring diagram of sister mitral cells associated with a common glomerulus in the mouse olfactory bulb. We found the diversity of dendrite wiring patterns among sister mitral cells, and our results provide an anatomical basis for non-redundant odor coding by these neurons. Our simple and efficient method is useful for imaging intact morphological architecture at large scales in both the adult and developing brains.
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            Temporal Tracking of Microglia Activation in Neurodegeneration at Single-Cell Resolution

            SUMMARY Microglia, the tissue-resident macrophages in the brain, are damage sensors that react to nearly any perturbation, including neurodegenerative diseases such as Alzheimer’s disease (AD). Here, using single-cell RNA sequencing, we determined the transcriptome of more than 1,600 individual microglia cells isolated from the hippocampus of a mouse model of severe neurodegeneration with AD-like phenotypes and of control mice at multiple time points during progression of neurodegeneration. In this neurodegeneration model, we discovered two molecularly distinct reactive microglia phenotypes that are typified by modules of co-regulated type I and type II interferon response genes, respectively. Furthermore, our work identified previously unobserved heterogeneity in the response of microglia to neurodegeneration, discovered disease stage-specific microglia cell states, revealed the trajectory of cellular reprogramming of microglia in response to neurodegeneration, and uncovered the underlying transcriptional programs.
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              Major Shifts in Glial Regional Identity Are a Transcriptional Hallmark of Human Brain Aging

              Summary Gene expression studies suggest that aging of the human brain is determined by a complex interplay of molecular events, although both its region- and cell-type-specific consequences remain poorly understood. Here, we extensively characterized aging-altered gene expression changes across ten human brain regions from 480 individuals ranging in age from 16 to 106 years. We show that astrocyte- and oligodendrocyte-specific genes, but not neuron-specific genes, shift their regional expression patterns upon aging, particularly in the hippocampus and substantia nigra, while the expression of microglia- and endothelial-specific genes increase in all brain regions. In line with these changes, high-resolution immunohistochemistry demonstrated decreased numbers of oligodendrocytes and of neuronal subpopulations in the aging brain cortex. Finally, glial-specific genes predict age with greater precision than neuron-specific genes, thus highlighting the need for greater mechanistic understanding of neuron-glia interactions in aging and late-life diseases.
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                Author and article information

                Contributors
                david.bouvier@uni.lu
                alexander.skupin@uni.lu
                Journal
                Glia
                Glia
                10.1002/(ISSN)1098-1136
                GLIA
                Glia
                John Wiley & Sons, Inc. (Hoboken, USA )
                0894-1491
                1098-1136
                14 April 2019
                August 2019
                : 67
                : 8 ( doiID: 10.1002/glia.v67.8 )
                : 1496-1509
                Affiliations
                [ 1 ] Luxembourg Centre for Systems Biomedicine University of Luxembourg Belval Luxembourg
                [ 2 ] Swiss Data Science Center, ETH Zürich Zürich Switzerland
                [ 3 ] Douglas Mental Health University Institute Department of Psychiatry, McGill University Montreal Quebec Canada
                [ 4 ] Centre for Research in Neuroscience, Department of Neurology and Neurosurgery The Research Institute of the McGill University Health Centre, Montreal General Hospital Montreal Quebec Canada
                [ 5 ] National Biomedical Computation Resource University California San Diego La Jolla California
                Author notes
                [*] [* ] Correspondence

                Dr. David S. Bouvier, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, Rue du Swing, L‐4367, Belvaux, Luxembourg.

                Email: david.bouvier@ 123456uni.lu

                Dr. Alexander Skupin, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7, Rue du Swing, L‐4367, Belvaux, Luxembourg.

                Email: alexander.skupin@ 123456uni.lu

                [†]

                David S. Bouvier and Alexander Skupin contributed equally to this study.

                Author information
                https://orcid.org/0000-0002-8630-1044
                Article
                GLIA23623
                10.1002/glia.23623
                6617786
                30983036
                aca96498-2786-4fbe-9981-f003e02868a1
                © 2019 The Authors. Glia published by Wiley Periodicals, Inc.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 17 December 2018
                : 21 February 2019
                : 29 March 2019
                Page count
                Figures: 8, Tables: 1, Pages: 14, Words: 8120
                Funding
                Funded by: Auguste et Simone Prévot foundation award
                Funded by: Fonds National de la Recherche Luxembourg
                Award ID: C14/BM/7975668/CaSCAD
                Funded by: Luxembourgish Espoir‐en‐Tête Rotary Club award
                Funded by: National Biomedical Computation Resource (NIH)
                Award ID: NIH P41 GM103426
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                glia23623
                August 2019
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.6.5 mode:remove_FC converted:10.07.2019

                Neurosciences
                alzheimer's disease,classification,heterogeneity,high‐throughput,machine learning,microglia,morphology

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