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Maas DA, Manot-Saillet B, Bun P, Habermacher C, Poilbout C, Rusconi F, Angulo MC. Versatile and automated workflow for the analysis of oligodendroglial calcium signals. Cell Mol Life Sci 2024; 81:15. [PMID: 38194116 PMCID: PMC11073395 DOI: 10.1007/s00018-023-05065-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 11/20/2023] [Accepted: 11/27/2023] [Indexed: 01/10/2024]
Abstract
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.
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Affiliation(s)
- Dorien A Maas
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, "Team: Interactions Between Neurons and Oligodendroglia in Myelination and Myelin Repair", 75014, Paris, France
| | - Blandine Manot-Saillet
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, "Team: Interactions Between Neurons and Oligodendroglia in Myelination and Myelin Repair", 75014, Paris, France
| | - Philippe Bun
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, "NeurImag Platform", 75014, Paris, France
| | - Chloé Habermacher
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, "Team: Interactions Between Neurons and Oligodendroglia in Myelination and Myelin Repair", 75014, Paris, France
- SynapCell, Bâtiment Synergy Zac Isiparc, 38330, Saint Ismier, France
| | - Corinne Poilbout
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, "Team: Interactions Between Neurons and Oligodendroglia in Myelination and Myelin Repair", 75014, Paris, France
| | - Filippo Rusconi
- IDEEV, GQE, Université Paris-Saclay, CNRS, INRAE, AgroParisTech, 12, Route 128, 91272, Gif-sur-Yvette, France
- Centre de Recherche des Cordeliers, Sorbonne Université, INSERM, Université Paris Cité, 75006, Paris, France
| | - Maria Cecilia Angulo
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, "Team: Interactions Between Neurons and Oligodendroglia in Myelination and Myelin Repair", 75014, Paris, France.
- GHU PARIS Psychiatrie and Neurosciences, 75014, Paris, France.
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Benisty H, Song A, Mishne G, Charles AS. Review of data processing of functional optical microscopy for neuroscience. NEUROPHOTONICS 2022; 9:041402. [PMID: 35937186 PMCID: PMC9351186 DOI: 10.1117/1.nph.9.4.041402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 07/15/2022] [Indexed: 05/04/2023]
Abstract
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.
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Affiliation(s)
- Hadas Benisty
- Yale Neuroscience, New Haven, Connecticut, United States
| | - Alexander Song
- Max Planck Institute for Intelligent Systems, Stuttgart, Germany
| | - Gal Mishne
- UC San Diego, Halıcığlu Data Science Institute, Department of Electrical and Computer Engineering and the Neurosciences Graduate Program, La Jolla, California, United States
| | - Adam S. Charles
- Johns Hopkins University, Kavli Neuroscience Discovery Institute, Center for Imaging Science, Department of Biomedical Engineering, Department of Neuroscience, and Mathematical Institute for Data Science, Baltimore, Maryland, United States
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Fernandez-Alvarez M, Atienza M, Zallo F, Matute C, Capetillo-Zarate E, Cantero JL. Linking Plasma Amyloid Beta and Neurofilament Light Chain to Intracortical Myelin Content in Cognitively Normal Older Adults. Front Aging Neurosci 2022; 14:896848. [PMID: 35783126 PMCID: PMC9247578 DOI: 10.3389/fnagi.2022.896848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/23/2022] [Indexed: 11/29/2022] Open
Abstract
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 (Aβ1–42) 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 Aβ1–42 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 Aβ1–42 and higher plasma NfL were associated with lower myelin content in temporo-parietal-occipital regions and the insular cortex, respectively. Whereas the association with Aβ1–42 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.
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Affiliation(s)
- Marina Fernandez-Alvarez
- Laboratory of Functional Neuroscience, Pablo de Olavide University, Seville, Spain
- Network Center for Biomedical Research in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Mercedes Atienza
- Laboratory of Functional Neuroscience, Pablo de Olavide University, Seville, Spain
- Network Center for Biomedical Research in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Fatima Zallo
- Departamento de Neurociencias, Achucarro Basque Center for Neuroscience, Universidad del País Vasco, Leioa, Spain
| | - Carlos Matute
- Network Center for Biomedical Research in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
- Departamento de Neurociencias, Achucarro Basque Center for Neuroscience, Universidad del País Vasco, Leioa, Spain
| | - Estibaliz Capetillo-Zarate
- Network Center for Biomedical Research in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
- Departamento de Neurociencias, Achucarro Basque Center for Neuroscience, Universidad del País Vasco, Leioa, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
| | - Jose L. Cantero
- Laboratory of Functional Neuroscience, Pablo de Olavide University, Seville, Spain
- Network Center for Biomedical Research in Neurodegenerative Diseases (CIBERNED), Madrid, Spain
- *Correspondence: Jose L. Cantero,
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Moura DMS, Brennan EJ, Brock R, Cocas LA. Neuron to Oligodendrocyte Precursor Cell Synapses: Protagonists in Oligodendrocyte Development and Myelination, and Targets for Therapeutics. Front Neurosci 2022; 15:779125. [PMID: 35115904 PMCID: PMC8804499 DOI: 10.3389/fnins.2021.779125] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 12/17/2021] [Indexed: 12/17/2022] Open
Abstract
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.
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Affiliation(s)
- Daniela M. S. Moura
- Department of Biology, Santa Clara University, Santa Clara, CA, United States
| | - Emma J. Brennan
- Department of Biology, Santa Clara University, Santa Clara, CA, United States
| | - Robert Brock
- Department of Biology, Santa Clara University, Santa Clara, CA, United States
| | - Laura A. Cocas
- Department of Biology, Santa Clara University, Santa Clara, CA, United States
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
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O’Connor OM, Alnahhas RN, Lugagne JB, Dunlop MJ. DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics. PLoS Comput Biol 2022; 18:e1009797. [PMID: 35041653 PMCID: PMC8797229 DOI: 10.1371/journal.pcbi.1009797] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 01/28/2022] [Accepted: 12/25/2021] [Indexed: 12/04/2022] Open
Abstract
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.
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Affiliation(s)
- Owen M. O’Connor
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Biological Design Center, Boston University, Boston, Massachusetts, United States of America
| | - Razan N. Alnahhas
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Biological Design Center, Boston University, Boston, Massachusetts, United States of America
| | - Jean-Baptiste Lugagne
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Biological Design Center, Boston University, Boston, Massachusetts, United States of America
| | - Mary J. Dunlop
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Biological Design Center, Boston University, Boston, Massachusetts, United States of America
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