3D visualization and quantification of microvessels in the whole ischemic mouse brain using solvent-based clearing and light sheet microscopy

Lugo-Hernandez, Erlen; Squire, Anthony; Hagemann, Nina; Brenzel, Alexandra; Sardari, Maryam; Schlechter, Jana; Sánchez-Mendoza, Eduardo H.; Gunzer, Matthias; Faissner, Andréas; Hermann, Dirk M.

doi:10.1177/0271678x17698970

Cited by 110 publications

(133 citation statements)

References 37 publications

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“…Importantly, our findings on vasculature length are in line with the predictions in the literature obtained in small volumes, confirming the robustness of our method. For example, previous studies quantifying small patches estimated the density of cortical blood vessels as 0.922 m/mm³, 0.444 m/mm³ or 0.471 m/mm³ in the cortex 12,17,37 . Using VesSAP, calculating centerline density as described above, and accounting for the 30 % isotropic tissue shrinkage in DISCO clearing 38 , we found an average vascular density of 0.473 ± 0.161 m/mm³ over the whole mouse cortex.…”

Section: Vessap Provides a Reference Map Of The Whole Brain Vasculatumentioning

confidence: 99%

“…Fluorescent microscopy, on the other hand, provides a higher resolution but can typically be applied to 1-200 μm thin tissue slices, which does not pre-serve the structure of an end-to-end vascular network. Recent advances in tissue clearing could overcome this problem, but so far there has been no demonstration of all vessels of all sizes in an entire brain in three dimensions (3D) 12 .…”

Section: Introductionmentioning

confidence: 99%

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Automated analysis of whole brain vasculature using machine learning

Schoppe

et al. 2019

Preprint

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Tissue clearing methods enable imaging of intact biological specimens without sectioning. However, reliable and scalable analysis of such large imaging data in 3D remains a challenge. Towards this goal, we developed a deep learning-based framework to quantify and analyze the brain vasculature, named Vessel Segmentation & Analysis Pipeline (VesSAP). Our pipeline uses a fully convolutional network with a transfer learning approach for segmentation. We systematically analyzed vascular features of the whole brains including their length, bifurcation points and radius at the micrometer scale by registering them to the Allen mouse brain atlas. We reported the first evidence of secondary intracranial collateral vascularization in CD1-Elite mice and found reduced vascularization in the brainstem as compared to the cerebrum. VesSAP thus enables unbiased and scalable quantifications for the angioarchitecture of the cleared intact mouse brain and yields new biological insights related to the vascular brain function.

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Section: Vessap Provides a Reference Map Of The Whole Brain Vasculatumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated analysis of whole brain vasculature using machine learning

Schoppe

et al. 2019

Preprint

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“…We found that individual cryptococcal cells were almost exclusively trapped in the narrow inter-segmental and brain vessels, it is noteworthy that these vessels are similar in size to mouse brain blood vessels (Fig. 1B; (19,20)). Studies using intravital imaging in mice have previously noted cryptococcal cell trapping but due to the limitations of this model the effect of this phenomenon on disease could not be established.…”

Section: Resultsmentioning

confidence: 63%

Blood vessel occlusion byCryptococcus neoformansis a mechanism for haemorrhagic dissemination of infection

Jf¹,

Bojarczuk²,

Rl³

et al. 2020

Preprint

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24Meningitis caused by infectious pathogens are associated with vessel damage and infarct 25 formation, however the physiological cause is unknown. Cryptococcus neoformans, is a 26 human fungal pathogen and causative agent of cryptococcal meningitis, where vascular 27 events are observed in up to 30% of cases, predominantly in severe infection. Therefore, we 28 aimed to investigate how infection may lead to vessel damage and associated pathogen 29 dissemination using a zebrafish model for in vivo live imaging. We find that cryptococcal 30 cells become trapped within the vasculature (dependent on there size) and proliferate there 31 resulting in vasodilation. Localised cryptococcal growth, originating from a single or small 32 number of cryptococcal cells in the vasculature was associated with sites of dissemination 33 and simultaneously with loss of blood vessel integrity. Using a cell-cell junction tension 34 reporter we identified dissemination from intact blood vessels and where vessel rupture 35 occurred. Finally, we manipulated blood vessel stifness via cell junctions and found 36 increased stiffness resulted in increased dissemination. Therefore, global vascular 37 vasodilation occurs following infection, resulting in increased vessel tension which 38 subsequently increases dissemination events, representing a positive feedback loop. Thus, 39 we identify a mechanism for blood vessel damage during cryptococcal infection that may 40 represent a cause of vascular damage and cortical infarction more generally in infective 41 meningitis. 42 43 44 45 46 47 48 Introduction 49 Life threatening systemic infection commonly results from tissue invasion requiring 50 dissemination of microbes, usually via the blood stream. Blood vessel damage and blockage 51 are commonly associated with blood infection, as exemplified by mycotic (infective) 52 aneurisms or sub-arachnoid haemorrhage (1). Indeed, both bacterial and fungal meningitis 53 are associated with vascular events including vasculitis, aneurisms and infarcts (1-5). 54The mechanisms of dissemination to the brain in meningitis have been extensively studied in 55 vitro and in vivo. Experimental studies suggest three potential mechanisms: passage of the 56 pathogen between cells of the blood brain barrier, polarised endocytosis and exocytosis of 57 the pathogen by brain vascular endothelial cells, and passage through the blood brain 58 barrier inside immune cells. However, we hypothesised that blood vessel blockage and 59 haemorrhagic dissemination might be an alternative mechanism. 60Cryptococcus neoformans is an opportunistic fungal pathogen causing life threatening 61 cryptococcal meningitis in severely immunocompromised patients. C. neoformans is a 62 significant pathogen of HIV/AIDs positive individuals with cryptococcal meningitis ultimately 63 responsible for 15% of all AIDS related deaths worldwide (6). C . neoformans has 64 previsously been suggested to disseminate from the blood stream into the brain through 65 different routes, including transcytosis, and by...

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“…The perfusion protocol have been described elsewhere [20,21,22] and is briefly summarized ProHance. Brains were transferred to PBS containing 2 mM ProHance and 0.02 % sodium azide for at least one week [23].…”

Section: Brain Sample Preparationmentioning

confidence: 99%

“…), Boero et al[52] from thin sections (700 -1200 mm/mm3 ), andLugo-Hernandez et al[21] from whole brain microvascular reconstructions (922 mm/mm 3 ). Tsaiet al [22] report smaller capillary diameters at 3.5 -4.0 µm, compared to a mean of 4.5 µm, despite usage of identical perfusion materials.…”

mentioning

confidence: 99%

Acute and chronic stage adaptations of vascular architecture and cerebral blood flow in a mouse model of TBI

Steinman

Cahill

Koletar

et al. 2018

Preprint

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max 200 words):The 3D organization of cerebral blood vessels determines the overall capacity of the cerebral circulation to meet the metabolic requirements of the brain. This study used Arterial Spin Labeling (ASL) MRI with a hypercapnic challenge and ex vivo Serial Two-Photon Tomography (STPT) to examine the relationship between blood flow and 3D microvascular structure following traumatic brain injury (TBI) in a mouse. Mice were exposed to a controlled cortical impact TBI and allowed to recover for either 1 day or 4 weeks. At each time point, ASL MRI was performed to quantify cerebral perfusion and the brain vasculature was imaged in 3D with STPT. Registration of ASL to STPT enabled flow changes to be related to the underlying microvascular structure in each ASL voxel. Hypoperfusion under rest and hypercapnia was observed both 1 day and 4 weeks post-TBI. Vessel density and vascular volume were reduced 1 day post-TBI, recovering by 4 weeks; however, the reorganized vasculature at the latter time point possessed an abnormal radial pattern. Our findings demonstrate functionally significant long-term changes in the vascular architecture following injury and illustrate why metrics beyond traditional measures of vessel density are required to understand the impact of vascular structure on function.

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3D visualization and quantification of microvessels in the whole ischemic mouse brain using solvent-based clearing and light sheet microscopy

Cited by 110 publications

References 37 publications

Automated analysis of whole brain vasculature using machine learning

Automated analysis of whole brain vasculature using machine learning

Blood vessel occlusion byCryptococcus neoformansis a mechanism for haemorrhagic dissemination of infection

Acute and chronic stage adaptations of vascular architecture and cerebral blood flow in a mouse model of TBI

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