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Characterizing the Blood-brain Barrier and Gut Barrier with Super-resolution Imaging: Opportunities and Challenges

Overview
Journal Neurophotonics
Date 2023 Oct 6
PMID 37799760
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Abstract

Brain and gut barriers have been receiving increasing attention in health and diseases including in psychiatry. Recent studies have highlighted changes in the blood-brain barrier and gut barrier structural properties, notably a loss of tight junctions, leading to hyperpermeability, passage of inflammatory mediators, stress vulnerability, and the development of depressive behaviors. To decipher the cellular processes actively contributing to brain and gut barrier function in health and disease, scientists can take advantage of neurophotonic tools and recent advances in super-resolution microscopy techniques to complement traditional imaging approaches like confocal and electron microscopy. Here, we summarize the challenges, pros, and cons of these innovative approaches, hoping that a growing number of scientists will integrate them in their study design exploring barrier-related properties and mechanisms.

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De Koninck Y, De Koninck P, Devor A, Lavoie-Cardinal F Neurophotonics. 2024; 11(1):014401.

PMID: 38550388 PMC: 10973712. DOI: 10.1117/1.NPh.11.1.014401.

References
1.
Arizono M, Inavalli V, Panatier A, Pfeiffer T, Angibaud J, Levet F . Structural basis of astrocytic Ca signals at tripartite synapses. Nat Commun. 2020; 11(1):1906. PMC: 7170846. DOI: 10.1038/s41467-020-15648-4. View

2.
Brightman M, Reese T . Junctions between intimately apposed cell membranes in the vertebrate brain. J Cell Biol. 1969; 40(3):648-77. PMC: 2107650. DOI: 10.1083/jcb.40.3.648. View

3.
Berg S, Kutra D, Kroeger T, Straehle C, Kausler B, Haubold C . ilastik: interactive machine learning for (bio)image analysis. Nat Methods. 2019; 16(12):1226-1232. DOI: 10.1038/s41592-019-0582-9. View

4.
Durand A, Wiesner T, Gardner M, Robitaille L, Bilodeau A, Gagne C . A machine learning approach for online automated optimization of super-resolution optical microscopy. Nat Commun. 2018; 9(1):5247. PMC: 6286316. DOI: 10.1038/s41467-018-07668-y. View

5.
Vicidomini G, Bianchini P, Diaspro A . STED super-resolved microscopy. Nat Methods. 2018; 15(3):173-182. DOI: 10.1038/nmeth.4593. View