Machine learning analysis of whole mouse brain vasculature

MBV-Pipe: A One-Stop Toolbox for Assessing Mouse Brain Morphological Changes for Cross-Scale Studies

Mouse models are crucial for neuroscience research, yet discrepancies arise between macro- and meso-scales due to sample preparation altering brain morphology. The absence of an accessible toolbox for magnetic resonance imaging (MRI) data processing presents a challenge for assessing morphological changes in the mouse brain. To address this, we developed the MBV-Pipe (Mouse Brain Volumetric Statistics-Pipeline) toolbox, integrating the methods of Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL)-Voxel-based morphometry (VBM) and Tract-Based Spatial Statistics (TBSS) to evaluate brain tissue volume and white matter integrity. To validate the reliability of MBV-Pipe, brain MRI data from seven mice at three time points (in vivo, post-perfusion, and post-fixation) were acquired using a 9.4T ultra-high MRI system. Employing the MBV-Pipe toolbox, we discerned substantial volumetric changes in the mouse brain following perfusion relative to the in vivo condition, with the fixation process inducing only negligible variations. Importantly, the white matter integrity was found to be largely stable throughout the sample preparation procedures. The MBV-Pipe source code is publicly available and includes a user-friendly GUI for facilitating quality control and experimental protocol optimization, which holds promise for advancing mouse brain research in the future.

Applications of Lightsheet Fluorescence Microscopy by High Numerical Aperture Detection Lens

This Review explores the evolution, improvements, and recent applications of Light Sheet Fluorescence Microscopy (LSFM) in biological research using a high numerical aperture detection objective (lens) for imaging subcellular structures. The Review begins with an overview of the development of LSFM, tracing its evolution from its inception to its current state and emphasizing key milestones and technological advancements over the years. Subsequently, we will discuss various improvements of LSFM techniques, covering advancements in hardware such as illumination strategies, optical designs, and sample preparation methods that have enhanced imaging capabilities and resolution. The advancements in data acquisition and processing are also included, which provides a brief overview of the recent development of artificial intelligence. Fluorescence probes that were commonly used in LSFM will be highlighted, together with some insights regarding the selection of potential probe candidates for future LSFM development. Furthermore, we also discuss recent advances in the application of LSFM with a focus on high numerical aperture detection objectives for various biological studies. For sample preparation techniques, there are discussions regarding fluorescence probe selection, tissue clearing protocols, and some insights into expansion microscopy. Integrated setups such as adaptive optics, single objective modification, and microfluidics will also be some of the key discussion points in this Review. We hope that this comprehensive Review will provide a holistic perspective on the historical development, technical enhancements, and cutting-edge applications of LSFM, showcasing its pivotal role and future potential in advancing biological research.

Modified histological staining for the identification of arterial and venous segments of brain microvessels

BACKGROUND

This study aimed to develop a modified histochemical staining technique to successfully identify arterial and venous segments of brain microvessels.

NEW METHOD

Gelatin/red ink-alkaline phosphatase-oil red O (GIAO) staining was developed from the traditional gelatin-ink perfusion method. Oil red Chinese ink for brush writing and painting mixed with gelatin was used to label cerebral vascular lumens. Subsequently, alkaline phosphatase staining was used to label endothelial cells on the arterial segments of cerebral microvessels. Thereafter, the red ink color in vessel lumens was highlighted with oil red O staining.

RESULTS

The arterial segments of the brain microvessels exhibited red lumens surrounded by dark blue walls, while the venous segments were bright red following GIAO staining. Meanwhile, the nerve fiber bundles were stained brownish-yellow, and the nuclei appeared light green under light microscope. After cerebral infarction, we used GIAO staining to determine angiogenesis features and detected notable vein proliferation inside the infarct core. Moreover, GIAO staining in conjunction with hematoxylin staining was performed to assess the infiltration of foamy macrophages.

COMPARISON WITH EXISTING METHOD

Red Chinese ink enabled subsequent multiple color staining on brain section. Oil red O was introduced to improved the resolution and contrast between arterial and venous segments of microvessels.

CONCLUSION

With excellent resolution, GIAO staining effectively distinguished arterial and venous segments of microvessels in both normal and ischemic brain tissue. GIAO staining, as described in the present study, will be useful for histological investigations of microvascular bed alterations in a variety of brain disorders.

Noninvasive microvascular imaging in newborn rats using high-frequency ultrafast ultrasound

Ultrasound imaging stands as the predominant modality for neonatal health assessment, with recent advancements in ultrafast Doppler (μDoppler) technology offering significant promise in fields such as neonatal brain imaging. Combining μDoppler with high-frequency ultrasound (HF-μDoppler) presents a potential efficient avenue to enhance in vivo microvascular imaging in small animals, notably newborn rats, a crucial preclinical animal model for neonatal disease and development research. It is necessary to verify the imaging performance of HF-μDoppler in preclinical trials. This study investigates the microvascular imaging capabilities of HF-μDoppler using a 30 MHz high-frequency linear array probe in newborn rats. Results demonstrate the clarity of cerebral microvascular imaging in rats aged 1 to 7 postnatal days, extending to whole-body microvascular imaging, encompassing the central nervous system, including the brain and spinal cord. In conclusion, HF-μDoppler technology emerges as a reliable imaging tool, offering a new perspective for preclinical investigations into neonatal diseases and development.

Capillary connections between sensory circumventricular organs and adjacent parenchyma enable local volume transmission

Among contributors to diffusible signaling are portal systems which join two capillary beds through connecting veins (Dorland 2020). Portal systems allow diffusible signals to be transported in high concentrations directly from one capillary bed to the other without dilution in the systemic circulation. Two portal systems have been identified in the brain. The first was discovered almost a century ago and connects the median eminence to the anterior pituitary gland (Popa & Fielding 1930). The second was discovered a few years ago, and links the suprachiasmatic nucleus to the organum vasculosum of the lamina terminalis, a sensory circumventricular organ (CVO) (Yao et al. 2021). Sensory CVOs bear neuronal receptors for sensing signals in the fluid milieu (McKinley et al. 2003). They line the surface of brain ventricles and bear fenestrated capillaries, thereby lacking blood brain barriers. It is not known whether the other sensory CVOs, namely the subfornical organ (SFO), and area postrema (AP) form portal neurovascular connections with nearby parenchymal tissue. This has been difficult to establish as the structures lie at the midline and protrude into the ventricular space. To preserve the integrity of the vasculature of CVOs and their adjacent neuropil, we combined iDISCO clearing and light-sheet microscopy to acquire volumetric images of blood vessels. The results indicate that there is a portal pathway linking the capillary vessels of the SFO and the posterior septal nuclei, namely the septofimbrial nucleus and the triangular nucleus of the septum. Unlike the latter arrangement, the AP and the nucleus of the solitary tract share their capillary beds. Taken together, the results reveal that all three sensory circumventricular organs bear specialized capillary connections to adjacent neuropil, providing a direct route for diffusible signals to travel from their source to their targets.

Aging drives cerebrovascular network remodeling and functional changes in the mouse brain

Aging is frequently associated with compromised cerebrovasculature and pericytes. However, we do not know how normal aging differentially impacts vascular structure and function in different brain areas. Here we utilize mesoscale microscopy methods and in vivo imaging to determine detailed changes in aged murine cerebrovascular networks. Whole-brain vascular tracing shows an overall ~10% decrease in vascular length and branching density with ~7% increase in vascular radii in aged brains. Light sheet imaging with 3D immunolabeling reveals increased arteriole tortuosity of aged brains. Notably, vasculature and pericyte densities show selective and significant reductions in the deep cortical layers, hippocampal network, and basal forebrain areas. We find increased blood extravasation, implying compromised blood-brain barrier function in aged brains. Moreover, in vivo imaging in awake mice demonstrates reduced baseline and on-demand blood oxygenation despite relatively intact neurovascular coupling. Collectively, we uncover regional vulnerabilities of cerebrovascular network and physiological changes that can mediate cognitive decline in normal aging.

Functional ultrasound imaging and neuronal activity: how accurate is the spatiotemporal match?

Over the last decade, functional ultrasound (fUS) has risen as a critical tool in functional neuroimaging, leveraging hemodynamic changes to infer neural activity indirectly. Recent studies have established a strong correlation between neural spike rates (SR) and functional ultrasound signals. However, understanding their spatial distribution and variability across different brain areas is required to thoroughly interpret fUS signals. In this regard, we conducted simultaneous fUS imaging and Neuropixels recordings during stimulus-evoked activity in awake mice within three regions the visual pathway. Our findings indicate that the temporal dynamics of fUS and SR signals are linearly correlated, though the correlation coefficients vary among visual regions. Conversely, the spatial correlation between the two signals remains consistent across all regions with a spread of approximately 300 micrometers. Finally, we introduce a model that integrates the spatial and temporal components of the fUS signal, allowing for a more accurate interpretation of fUS images.

Pia-FLOW: Deciphering hemodynamic maps of the pial vascular connectome and its response to arterial occlusion

The pial vasculature is the sole source of blood supply to the neocortex. The brain is contained within the skull, a vascularized bone marrow with a unique anatomical connection to the brain meninges. Recent developments in tissue clearing have enabled detailed mapping of the entire pial and calvarial vasculature. However, what are the absolute flow rate values of those vascular networks? This information cannot accurately be retrieved with the commonly used bioimaging methods. Here, we introduce Pia-FLOW, a unique approach based on large-scale transcranial fluorescence localization microscopy, to attain hemodynamic imaging of the whole murine pial and calvarial vasculature at frame rates up to 1,000 Hz and spatial resolution reaching 5.4 µm. Using Pia-FLOW, we provide detailed maps of flow velocity, direction, and vascular diameters which can serve as ground-truth data for further studies, advancing our understanding of brain fluid dynamics. Furthermore, Pia-FLOW revealed that the pial vascular network functions as one unit for robust allocation of blood after stroke.

Mapping the blood vasculature in an intact human kidney using hierarchical phase-contrast tomography

The architecture of the kidney vasculature is essential for its function. Although structural profiling of the intact rodent kidney vasculature has been performed, it is challenging to map vascular architecture of larger human organs. We hypothesised that hierarchical phase-contrast tomography (HiP-CT) would enable quantitative analysis of the entire human kidney vasculature. Combining label-free HiP-CT imaging of an intact kidney from a 63-year-old male with topology network analysis, we quantitated vasculature architecture in the human kidney down to the scale of arterioles. Although human and rat kidney vascular topologies are comparable, vascular radius decreases at a significantly faster rate in humans as vessels branch from artery towards the cortex. At branching points of large vessels, radii are theoretically optimised to minimise flow resistance, an observation not found for smaller arterioles. Structural differences in the vasculature were found in different spatial zones of the kidney reflecting their unique functional roles. Overall, this represents the first time the entire arterial vasculature of a human kidney has been mapped providing essential inputs for computational models of kidney vascular flow and synthetic vascular architectures, with implications for understanding how the structure of individual blood vessels collectively scales to facilitate organ function.

3DVascNet: An Automated Software for Segmentation and Quantification of Mouse Vascular Networks in 3D

BACKGROUND

Analysis of vascular networks is an essential step to unravel the mechanisms regulating the physiological and pathological organization of blood vessels. So far, most of the analyses are performed using 2-dimensional projections of 3-dimensional (3D) networks, a strategy that has several obvious shortcomings. For instance, it does not capture the true geometry of the vasculature and generates artifacts on vessel connectivity. These limitations are accepted in the field because manual analysis of 3D vascular networks is a laborious and complex process that is often prohibitive for large volumes.

METHODS

To overcome these issues, we developed 3DVascNet, a deep learning-based software for automated segmentation and quantification of 3D retinal vascular networks. 3DVascNet performs segmentation based on a deep learning model, and it quantifies vascular morphometric parameters such as vessel density, branch length, vessel radius, and branching point density. We tested the performance of 3DVascNet using a large data set of 3D microscopy images of mouse retinal blood vessels.

RESULTS

We demonstrated that 3DVascNet efficiently segments vascular networks in 3D and that vascular morphometric parameters capture phenotypes detected by using manual segmentation and quantification in 2 dimension. In addition, we showed that, despite being trained on retinal images, 3DVascNet has high generalization capability and successfully segments images originating from other data sets and organs.

CONCLUSIONS

Overall, we present 3DVascNet, a freely available software that includes a user-friendly graphical interface for researchers with no programming experience, which will greatly facilitate the ability to study vascular networks in 3D in health and disease. Moreover, the source code of 3DVascNet is publicly available, thus it can be easily extended for the analysis of other 3D vascular networks by other users.

Imaging brain tissue architecture across millimeter to nanometer scales

Mapping the complex and dense arrangement of cells and their connectivity in brain tissue demands nanoscale spatial resolution imaging. Super-resolution optical microscopy excels at visualizing specific molecules and individual cells but fails to provide tissue context. Here we developed Comprehensive Analysis of Tissues across Scales (CATS), a technology to densely map brain tissue architecture from millimeter regional to nanometer synaptic scales in diverse chemically fixed brain preparations, including rodent and human. CATS uses fixation-compatible extracellular labeling and optical imaging, including stimulated emission depletion or expansion microscopy, to comprehensively delineate cellular structures. It enables three-dimensional reconstruction of single synapses and mapping of synaptic connectivity by identification and analysis of putative synaptic cleft regions. Applying CATS to the mouse hippocampal mossy fiber circuitry, we reconstructed and quantified the synaptic input and output structure of identified neurons. We furthermore demonstrate applicability to clinically derived human tissue samples, including formalin-fixed paraffin-embedded routine diagnostic specimens, for visualizing the cellular architecture of brain tissue in health and disease.

Deep 3D histology powered by tissue clearing, omics and AI

To comprehensively understand tissue and organism physiology and pathophysiology, it is essential to create complete three-dimensional (3D) cellular maps. These maps require structural data, such as the 3D configuration and positioning of tissues and cells, and molecular data on the constitution of each cell, spanning from the DNA sequence to protein expression. While single-cell transcriptomics is illuminating the cellular and molecular diversity across species and tissues, the 3D spatial context of these molecular data is often overlooked. Here, I discuss emerging 3D tissue histology techniques that add the missing third spatial dimension to biomedical research. Through innovations in tissue-clearing chemistry, labeling and volumetric imaging that enhance 3D reconstructions and their synergy with molecular techniques, these technologies will provide detailed blueprints of entire organs or organisms at the cellular level. Machine learning, especially deep learning, will be essential for extracting meaningful insights from the vast data. Further development of integrated structural, molecular and computational methods will unlock the full potential of next-generation 3D histology.

Virtual reality-empowered deep-learning analysis of brain cells

Automated detection of specific cells in three-dimensional datasets such as whole-brain light-sheet image stacks is challenging. Here, we present DELiVR, a virtual reality-trained deep-learning pipeline for detecting c-Fos+ cells as markers for neuronal activity in cleared mouse brains. Virtual reality annotation substantially accelerated training data generation, enabling DELiVR to outperform state-of-the-art cell-segmenting approaches. Our pipeline is available in a user-friendly Docker container that runs with a standalone Fiji plugin. DELiVR features a comprehensive toolkit for data visualization and can be customized to other cell types of interest, as we did here for microglia somata, using Fiji for dataset-specific training. We applied DELiVR to investigate cancer-related brain activity, unveiling an activation pattern that distinguishes weight-stable cancer from cancers associated with weight loss. Overall, DELiVR is a robust deep-learning tool that does not require advanced coding skills to analyze whole-brain imaging data in health and disease.

Synthetic Optical Coherence Tomography Angiographs for Detailed Retinal Vessel Segmentation Without Human Annotations

Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases. Segmenting the visible blood vessels is a common first step when extracting quantitative biomarkers from these images. Classical segmentation algorithms based on thresholding are strongly affected by image artifacts and limited signal-to-noise ratio. The use of modern, deep learning-based segmentation methods has been inhibited by a lack of large datasets with detailed annotations of the blood vessels. To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data. However, the previously proposed simulations fail to faithfully model the retinal vasculature and do not provide effective domain adaptation. Because of this, current methods are unable to fully segment the retinal vasculature, in particular the smallest capillaries. In this work, we present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We then introduce three contrast adaptation pipelines to decrease the domain gap between real and artificial images. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets that compare our method to traditional computer vision algorithms and supervised training using human annotations. Finally, we make our entire pipeline publicly available, including the source code, pretrained models, and a large dataset of synthetic OCTA images.

Revealing the MRI-Contrast in Optically Cleared Brains

The current consensus holds that optically-cleared specimens are unsuitable for Magnetic Resonance Imaging (MRI); exhibiting absence of contrast. Prior studies combined MRI with tissue-clearing techniques relying on the latter's ability to eliminate lipids, thereby fostering the assumption that lipids constitute the primary source of ex vivo MRI-contrast. Nevertheless, these findings contradict an extensive body of literature that underscores the contribution of other features to contrast. Furthermore, it remains unknown whether non-delipidating clearing methods can produce MRI-compatible specimens or whether MRI-contrast can be re-established. These limitations hinder the development of multimodal MRI-light-microscopy (LM) imaging approaches. This study assesses the relation between MRI-contrast, and delipidation in optically-cleared whole brains following different tissue-clearing approaches. It is demonstrated that uDISCO and ECi-brains are MRI-compatible upon tissue rehydration, despite both methods' substantial delipidating-nature. It is also demonstrated that, whereas Scale-clearing preserves most lipids, Scale-cleared brain lack MRI-contrast. Furthermore, MRI-contrast is restored to lipid-free CLARITY-brains without introducing lipids. Our results thereby dissociate between the essentiality of lipids to MRI-contrast. A tight association is found between tissue expansion, hyperhydration and loss of MRI-contrast. These findings then enabled us to develop a multimodal MRI-LM-imaging approach, opening new avenues to bridge between the micro- and mesoscale for biomedical research and clinical applications.

Revealing intact neuronal circuitry in centimeter-sized formalin-fixed paraffin-embedded brain

Tissue-clearing and labeling techniques have revolutionized brain-wide imaging and analysis, yet their application to clinical formalin-fixed paraffin-embedded (FFPE) blocks remains challenging. We introduce HIF-Clear, a novel method for efficiently clearing and labeling centimeter-thick FFPE specimens using elevated temperature and concentrated detergents. HIF-Clear with multi-round immunolabeling reveals neuron circuitry regulating multiple neurotransmitter systems in a whole FFPE mouse brain and is able to be used as the evaluation of disease treatment efficiency. HIF-Clear also supports expansion microscopy and can be performed on a non-sectioned 15-year-old FFPE specimen, as well as a 3-month formalin-fixed mouse brain. Thus, HIF-Clear represents a feasible approach for researching archived FFPE specimens for future neuroscientific and 3D neuropathological analyses.

A robust vessel-labeling pipeline with high tissue clearing compatibility for 3D mapping of vascular networks

The combination of vessel-labeling, tissue-clearing, and light-sheet imaging techniques provides a potent tool for accurately mapping vascular networks, enabling the assessment of vascular remodeling in vascular-related disorders. However, most vascular labeling methods face challenges such as inadequate labeling efficiency or poor compatibility with current tissue clearing technology, which significantly undermines the image quality. To address this limitation, we introduce a vessel-labeling pipeline, termed Ultralabel, which relies on a specially designed dye hydrogel containing lysine-fixable dextran and gelatins for double enhancement. Ultralabel demonstrates not only excellent vessel-labeling capability but also strong compatibility with all tissue clearing methods tested, which outperforms other vessel-labeling methods. Consequently, Ultralabel enables fine mapping of vascular networks in diverse organs, as well as multi-color labeling alongside other labeling techniques. Ultralabel should provide a robust and user-friendly method for obtaining 3D vascular networks in different biomedical applications.

NEATmap: a high-efficiency deep learning approach for whole mouse brain neuronal activity trace mapping

Quantitative analysis of activated neurons in mouse brains by a specific stimulation is usually a primary step to locate the responsive neurons throughout the brain. However, it is challenging to comprehensively and consistently analyze the neuronal activity trace in whole brains of a large cohort of mice from many terabytes of volumetric imaging data. Here, we introduce NEATmap, a deep learning-based high-efficiency, high-precision and user-friendly software for whole-brain neuronal activity trace mapping by automated segmentation and quantitative analysis of immunofluorescence labeled c-Fos+ neurons. We applied NEATmap to study the brain-wide differentiated neuronal activation in response to physical and psychological stressors in cohorts of mice.

Enhanced capillary delivery with nanobubble-mediated blood-brain barrier opening and advanced high resolution vascular segmentation

Overcoming the blood-brain barrier (BBB) is essential to enhance brain therapy. Here, we utilized nanobubbles with focused ultrasound for targeted and improved BBB opening in mice. A microscopy technique method assessed BBB opening at a single blood vessel resolution employing a dual-dye labeling technique using green fluorescent molecules to label blood vessels and Evans blue brain-impermeable dye for quantifying BBB extravasation. A deep learning architecture enabled blood vessels segmentation, delivering comparable accuracy to manual segmentation with a significant time reduction. Segmentation outcomes were applied to the Evans blue channel to quantify extravasation of each blood vessel. Results were compared to microbubble-mediated BBB opening, where reduced extravasation was observed in capillaries with a diameter of 2-6 μm. In comparison, nanobubbles yield an improved opening in these capillaries, and equivalent efficacy to that of microbubbles in larger vessels. These results indicate the potential of nanobubbles to serve as enhanced agents for BBB opening, amplifying bioeffects in capillaries while preserving comparable opening in larger vessels.

Vascularized organoid-on-a-chip: design, imaging, and analysis

Vascularized organoid-on-a-chip (VOoC) models achieve substance exchange in deep layers of organoids and provide a more physiologically relevant system in vitro. Common designs for VOoC primarily involve two categories: self-assembly of endothelial cells (ECs) to form microvessels and pre-patterned vessel lumens, both of which include the hydrogel region for EC growth and allow for controlled fluid perfusion on the chip. Characterizing the vasculature of VOoC often relies on high-resolution microscopic imaging. However, the high scattering of turbid tissues can limit optical imaging depth. To overcome this limitation, tissue optical clearing (TOC) techniques have emerged, allowing for 3D visualization of VOoC in conjunction with optical imaging techniques. The acquisition of large-scale imaging data, coupled with high-resolution imaging in whole-mount preparations, necessitates the development of highly efficient analysis methods. In this review, we provide an overview of the chip designs and culturing strategies employed for VOoC, as well as the applicable optical imaging and TOC methods. Furthermore, we summarize the vascular analysis techniques employed in VOoC, including deep learning. Finally, we discuss the existing challenges in VOoC and vascular analysis methods and provide an outlook for future development.

Development of an Automated Morphometric Approach to Assess Vascular Outcomes following Exposure to Environmental Chemicals in Zebrafish

BACKGROUND

Disruptions in vascular formation attributable to chemical insults is a pivotal risk factor or potential etiology of developmental defects and various disease settings. Among the thousands of chemicals threatening human health, the highly concerning groups prevalent in the environment and detected in biological monitoring in the general population ought to be prioritized because of their high exposure risks. However, the impacts of a large number of environmental chemicals on vasculature are far from understood. The angioarchitecture complexity and technical limitations make it challenging to analyze the entire vasculature efficiently and identify subtle changes through a high-throughput in vivo assay.

OBJECTIVES

We aimed to develop an automated morphometric approach for the vascular profile and assess the vascular morphology of health-concerning environmental chemicals.

METHODS

High-resolution images of the entire vasculature in Tg(fli1a:eGFP) zebrafish were collected using a high-content imaging platform. We established a deep learning-based quantitative framework, ECA-ResXUnet, combined with MATLAB to segment the vascular networks and extract features. Vessel scores based on the rates of morphological changes were calculated to rank vascular toxicity. Potential biomarkers were identified by vessel-endothelium-gene-disease integrative analysis.

RESULTS

Whole-trunk blood vessels and the cerebral vasculature in larvae exposed to 150 representative chemicals were automatically segmented as comparable to human-level accuracy, with sensitivity and specificity of 95.56% and 95.81%, respectively. Chemical treatments led to heterogeneous vascular patterns manifested by 31 architecture indexes, and the common cardinal vein (CCV) was the most affected vessel. The antipsychotic medicine haloperidol, flame retardant 2,2-bis(chloromethyl)trimethylenebis[bis(2-chloroethyl) phosphate], and tert-butylphenyl diphenyl phosphate ranked as the top three in vessel scores. Pesticides accounted for the largest group, with a vessel score of ≥ 1 , characterized by a remarkable inhibition of subintestinal venous plexus and delayed development of CCV. Multiple-concentration evaluation of nine per- and polyfluoroalkyl substances (PFAS) indicated a low-concentration effect on vascular impairment and a positive association between carbon chain length and benchmark concentration. Target vessel-directed single-cell RNA sequencing of f l i 1 a + cells from larvae treated with λ -cyhalothrin , perfluorohexanesulfonic acid, or benzylbutyl phthalate, along with vessel-endothelium-gene-disease integrative analysis, uncovered potential associations with vascular disorders and identified biomarker candidates.

DISCUSSION

This study provides a novel paradigm for phenotype-driven screenings of vascular-disrupting chemicals by converging morphological and transcriptomic profiles at a high-resolution level, serving as a powerful tool for large-scale toxicity tests. Our approach and the high-quality morphometric data facilitate the precise evaluation of vascular effects caused by environmental chemicals. https://doi.org/10.1289/EHP13214.

Tissue clearing method in visualization of cancer progression and metastasis

Since various imaging modalities have been developed, cancer metastasis can be detected from an early stage. However, limitations still exist, especially in terms of spatial resolution. Tissue-clearing technology has emerged as a new imaging modality in cancer research, which has been developed and utilized for a long time mainly in neuroscience field. This method enables us to detect cancer metastatic foci with single-cell resolution at whole mouse body/organ level. On top of that, 3D images of cancer metastasis of whole mouse organs make it easy to understand their characteristics. Recently, further applications of tissue clearing methods were reported in combination with reporter systems, labeling, and machine learning. In this review, we would like to provide an overview of this technique and current applications in cancer research and discuss their potentials and limitations.

Deep learning-based vessel extraction in 3D confocal microscope images of cleared human glioma tissues

Comprehensive visualization and accurate extraction of tumor vasculature are essential to study the nature of glioma. Nowadays, tissue clearing technology enables 3D visualization of human glioma vasculature at micron resolution, but current vessel extraction schemes cannot well cope with the extraction of complex tumor vessels with high disruption and irregularity under realistic conditions. Here, we developed a framework, FineVess, based on deep learning to automatically extract glioma vessels in confocal microscope images of cleared human tumor tissues. In the framework, a customized deep learning network, named 3D ResCBAM nnU-Net, was designed to segment the vessels, and a novel pipeline based on preprocessing and post-processing was developed to refine the segmentation results automatically. On the basis of its application to a practical dataset, we showed that the FineVess enabled extraction of variable and incomplete vessels with high accuracy in challenging 3D images, better than other traditional and state-of-the-art schemes. For the extracted vessels, we calculated vascular morphological features including fractal dimension and vascular wall integrity of different tumor grades, and verified the vascular heterogeneity through quantitative analysis.

An Energy Matching Vessel Segmentation Framework in 3-D Medical Images

Accurate vascular segmentation from High Resolution 3-Dimensional (HR3D) medical scans is crucial for clinicians to visualize complex vasculature and diagnose related vascular diseases. However, a reliable and scalable vessel segmentation framework for HR3D scans remains a challenge. In this work, we propose a High-resolution Energy-matching Segmentation (HrEmS) framework that utilizes deep learning to directly process the entire HR3D scan and segment the vasculature to the finest level. The HrEmS framework introduces two novel components. Firstly, it uses the real-order total variation operator to construct a new loss function that guides the segmentation network to obtain the correct topology structure by matching the energy of the predicted segment to the energy of the manual label. This is different from traditional loss functions such as dice loss, which matches the pixels between predicted segment and manual label. Secondly, a curvature-based weight-correction module is developed, which directs the network to focus on crucial and complex structural parts of the vasculature instead of the easy parts. The proposed HrEmS framework was tested on three in-house multi-center datasets and three public datasets, and demonstrated improved results in comparison with the state-of-the-art methods using both topology-relevant and volumetric-relevant metrics. Furthermore, a double-blind assessment by three experienced radiologists on the critical points of the clinical diagnostic processes provided additional evidence of the superiority of the HrEmS framework.

An MR-based brain template and atlas for optical projection tomography and light sheet fluorescence microscopy in neuroscience

Introduction

Optical Projection Tomography (OPT) and light sheet fluorescence microscopy (LSFM) are high resolution optical imaging techniques, ideally suited for ex vivo 3D whole mouse brain imaging. Although they exhibit high specificity for their targets, the anatomical detail provided by tissue autofluorescence remains limited.

Methods

T1-weighted images were acquired from 19 BABB or DBE cleared brains to create an MR template using serial longitudinal registration. Afterwards, fluorescent OPT and LSFM images were coregistered/normalized to the MR template to create fusion images.

Results

Volumetric calculations revealed a significant difference between BABB and DBE cleared brains, leading to develop two optimized templates, with associated tissue priors and brain atlas, for BABB (OCUM) and DBE (iOCUM). By creating fusion images, we identified virus infected brain regions, mapped dopamine transporter and translocator protein expression, and traced innervation from the eye along the optic tract to the thalamus and superior colliculus using cholera toxin B. Fusion images allowed for precise anatomical identification of fluorescent signal in the detailed anatomical context provided by MR.

Discussion

The possibility to anatomically map fluorescent signals on magnetic resonance (MR) images, widely used in clinical and preclinical neuroscience, would greatly benefit applications of optical imaging of mouse brain. These specific MR templates for cleared brains enable a broad range of neuroscientific applications integrating 3D optical brain imaging.

A deep learning classification task for brain navigation in rodents using micro-Doppler ultrasound imaging

Positioning and navigation are essential components of neuroimaging as they improve the quality and reliability of data acquisition, leading to advances in diagnosis, treatment outcomes, and fundamental understanding of the brain. Functional ultrasound imaging is an emerging technology providing high-resolution images of the brain vasculature, allowing for the monitoring of brain activity. However, as the technology is relatively new, there is no standardized tool for inferring the position in the brain from the vascular images. In this study, we present a deep learning-based framework designed to address this challenge. Our approach uses an image classification task coupled with a regression on the resulting probabilities to determine the position of a single image. To evaluate its performance, we conducted experiments using a dataset of 51 rat brain scans. The training positions were extracted at intervals of 375 μm, resulting in a positioning error of 176 μm. Further GradCAM analysis revealed that the predictions were primarily driven by subcortical vascular structures. Finally, we assessed the robustness of our method in a cortical stroke where the brain vasculature is severely impaired. Remarkably, no specific increase in the number of misclassifications was observed, confirming the method's reliability in challenging conditions. Overall, our framework provides accurate and flexible positioning, not relying on a pre-registered reference but rather on conserved vascular patterns.

Current and future applications of light-sheet imaging for identifying molecular and developmental processes in autism spectrum disorders

Autism spectrum disorder (ASD) is identified by a set of neurodevelopmental divergences that typically affect the social communication domain. ASD is also characterized by heterogeneous cognitive impairments and is associated with cooccurring physical and medical conditions. As behaviors emerge as the brain matures, it is particularly essential to identify any gaps in neurodevelopmental trajectories during early perinatal life. Here, we introduce the potential of light-sheet imaging for studying developmental biology and cross-scale interactions among genetic, cellular, molecular and macroscale levels of circuitry and connectivity. We first report the core principles of light-sheet imaging and the recent progress in studying brain development in preclinical animal models and human organoids. We also present studies using light-sheet imaging to understand the development and function of other organs, such as the skin and gastrointestinal tract. We also provide information on the potential of light-sheet imaging in preclinical drug development. Finally, we speculate on the translational benefits of light-sheet imaging for studying individual brain-body interactions in advancing ASD research and creating personalized interventions.

Noise learning of instruments for high-contrast, high-resolution and fast hyperspectral microscopy and nanoscopy

The low scattering efficiency of Raman scattering makes it challenging to simultaneously achieve good signal-to-noise ratio (SNR), high imaging speed, and adequate spatial and spectral resolutions. Here, we report a noise learning (NL) approach that estimates the intrinsic noise distribution of each instrument by statistically learning the noise in the pixel-spatial frequency domain. The estimated noise is then removed from the noisy spectra. This enhances the SNR by ca. 10 folds, and suppresses the mean-square error by almost 150 folds. NL allows us to improve the positioning accuracy and spatial resolution and largely eliminates the impact of thermal drift on tip-enhanced Raman spectroscopic nanoimaging. NL is also applicable to enhance SNR in fluorescence and photoluminescence imaging. Our method manages the ground truth spectra and the instrumental noise simultaneously within the training dataset, which bypasses the tedious labelling of huge dataset required in conventional deep learning, potentially shifting deep learning from sample-dependent to instrument-dependent.

Therapy-induced modulation of tumor vasculature and oxygenation in a murine glioblastoma model quantified by deep learning-based feature extraction

Glioblastoma presents characteristically with an exuberant, poorly functional vasculature that causes malperfusion, hypoxia and necrosis. Despite limited clinical efficacy, anti-angiogenesis resulting in vascular normalization remains a promising therapeutic approach. Yet, fundamental questions concerning anti-angiogenic therapy remain unanswered, partly due to the scale and resolution gap between microscopy and clinical imaging and a lack of quantitative data readouts. To what extend does treatment lead to vessel regression or vessel normalization and does it ameliorate or aggravate hypoxia? Clearly, a better understanding of the underlying mechanisms would greatly benefit the development of desperately needed improved treatment regimens. Here, using orthotopic transplantation of Gli36 cells, a widely used murine glioma model, we present a mesoscopic approach based on light sheet fluorescence microscopic imaging of wholemount stained tumors. Deep learning-based segmentation followed by automated feature extraction allowed quantitative analyses of the entire tumor vasculature and oxygenation statuses. Unexpectedly in this model, the response to both cytotoxic and anti-angiogenic therapy was dominated by vessel normalization with little evidence for vessel regression. Equally surprising, only cytotoxic therapy resulted in a significant alleviation of hypoxia. Taken together, we provide and evaluate a quantitative workflow that addresses some of the most urgent mechanistic questions in anti-angiogenic therapy.

Circuit mechanism for suppression of frontal cortical ignition during NREM sleep

Conscious perception is greatly diminished during sleep, but the underlying circuit mechanism is poorly understood. We show that cortical ignition-a brain process shown to be associated with conscious awareness in humans and non-human primates-is strongly suppressed during non-rapid-eye-movement (NREM) sleep in mice due to reduced cholinergic modulation and rapid inhibition of cortical responses. Brain-wide functional ultrasound imaging and cell-type-specific calcium imaging combined with optogenetics showed that activity propagation from visual to frontal cortex is markedly reduced during NREM sleep due to strong inhibition of frontal pyramidal neurons. Chemogenetic activation and inactivation of basal forebrain cholinergic neurons powerfully increased and decreased visual-to-frontal activity propagation, respectively. Furthermore, although multiple subtypes of dendrite-targeting GABAergic interneurons in the frontal cortex are more active during wakefulness, soma-targeting parvalbumin-expressing interneurons are more active during sleep. Chemogenetic manipulation of parvalbumin interneurons showed that sleep/wake-dependent cortical ignition is strongly modulated by perisomatic inhibition of pyramidal neurons.

Deep learning in mesoscale brain image analysis: A review

Mesoscale microscopy images of the brain contain a wealth of information which can help us understand the working mechanisms of the brain. However, it is a challenging task to process and analyze these data because of the large size of the images, their high noise levels, the complex morphology of the brain from the cellular to the regional and anatomical levels, the inhomogeneous distribution of fluorescent labels in the cells and tissues, and imaging artifacts. Due to their impressive ability to extract relevant information from images, deep learning algorithms are widely applied to microscopy images of the brain to address these challenges and they perform superiorly in a wide range of microscopy image processing and analysis tasks. This article reviews the applications of deep learning algorithms in brain mesoscale microscopy image processing and analysis, including image synthesis, image segmentation, object detection, and neuron reconstruction and analysis. We also discuss the difficulties of each task and possible directions for further research.

Vasculature of the Suprachiasmatic Nucleus: Pathways for Diffusible Output Signals

Transplant studies demonstrate unequivocally that the suprachiasmatic nucleus (SCN) produces diffusible signals that can sustain circadian locomotor rhythms. There is a vascular portal pathway between the SCN and the organum vasculosum of the lamina terminalis in mouse brain. Portal pathways enable low concentrations of neurosecretions to reach specialized local targets without dilution in the systemic circulation. To explore the SCN vasculature and the capillary vessels whereby SCN neurosecretions might reach portal vessels, we investigated the blood vessels (BVs) of the core and shell SCN. The arterial supply of the SCN differs among animals, and in some animals, there are differences between the 2 sides. The rostral SCN is supplied by branches from either the superior hypophyseal artery (SHpA) or the anterior cerebral artery or the anterior communicating artery. The caudal SCN is consistently supplied by the SHpA. The rostral SCN is drained by the preoptic vein, while the caudal is drained by the basal vein, with variations in laterality of draining vessels. In addition, several key features of the core and shell SCN regions differ: Median BV diameter is significantly smaller in the shell than the core based on confocal image measurements, and a similar trend occurs in iDISCO-cleared tissue. In the cleared tissue, whole BV length density and surface area density are significantly greater in the shell than the core. Finally, capillary length density is also greater in the shell than the core. The results suggest three hypotheses: First, the distinct arterial and venous systems of the rostral and caudal SCN may contribute to the in vivo variations of metabolic and neural activities observed in SCN networks. Second, the dense capillaries of the SCN shell are well positioned to transport blood-borne signals. Finally, variations in SCN vascular supply and drainage may contribute to inter-animal differences.

3D imaging and morphometric descriptors of vascular networks on optically cleared organs

The vascular system is a multi-scale network whose functionality depends on its structure, and for which structural alterations can be linked to pathological shifts. Though biologists use multiple 3D imaging techniques to visualize vascular networks, the 3D image processing methodologies remain sources of biases, and the extraction of quantitative morphometric descriptors remains flawed. The article, first, reviews the current 3D image processing methodologies, and morphometric descriptors of vascular network images mainly obtained by light-sheet microscopy on optically cleared organs, found in the literature. Second, it proposes operator-independent segmentation and skeletonization methodologies using the freeware ImageJ. Third, it gives more extractable network-level (density, connectivity, fractal dimension) and segment-level (length, diameter, tortuosity) 3D morphometric descriptors and how to statistically analyze them. Thus, it can serve as a guideline for biologists using 3D imaging techniques of vascular networks, allowing the production of more comparable studies in the future.

U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets

Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (μMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer's. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile. Recently, several reasonably automated approaches have emerged in the preclinical context but still require significant manual input and are less accurate than the deep learning approach presented in this paper-quantified by the Dice score. In this work, the implementation of a shallow, three-dimensional U-Net architecture for the segmentation of vessels in murine brains is presented, which is (1) open-source, (2) can be achieved with a small dataset (in this work only 8 μMRI imaging stacks of mouse brains were available), and (3) requires only a small subset of labelled training data. The presented model is evaluated together with two post-processing methodologies using a cross-validation, which results in an average Dice score of 61.34% in its best setup. The results show, that the methodology is able to detect blood vessels faster and more reliably compared to state-of-the-art vesselness filters with an average Dice score of 43.88% for the used dataset.

Laminin-coated electronic scaffolds with vascular topography for tracking and promoting the migration of brain cells after injury

In the adult brain, neural stem cells are largely restricted into spatially discrete neurogenic niches, and hence areas of neuron loss during neurodegenerative disease or following a stroke or traumatic brain injury do not typically repopulate spontaneously. Moreover, understanding neural activity accompanying the neural repair process is hindered by a lack of minimally invasive devices for the chronic measurement of the electrophysiological dynamics in damaged brain tissue. Here we show that 32 individually addressable platinum microelectrodes integrated into laminin-coated branched polymer scaffolds stereotaxically injected to span a hydrogel-filled cortical lesion and deeper regions in the brains of mice promote neural regeneration while allowing for the tracking of migrating host brain cells into the lesion. Chronic measurements of single-unit activity and neural-circuit analyses revealed the establishment of spiking activity in new neurons in the lesion and their functional connections with neurons deeper in the brain. Electronic implants mimicking the topographical and surface properties of brain vasculature may aid the stimulation and tracking of neural-circuit restoration following injury.

The role of leptomeningeal collaterals in redistributing blood flow during stroke

Leptomeningeal collaterals (LMCs) connect the main cerebral arteries and provide alternative pathways for blood flow during ischaemic stroke. This is beneficial for reducing infarct size and reperfusion success after treatment. However, a better understanding of how LMCs affect blood flow distribution is indispensable to improve therapeutic strategies. Here, we present a novel in silico approach that incorporates case-specific in vivo data into a computational model to simulate blood flow in large semi-realistic microvascular networks from two different mouse strains, characterised by having many and almost no LMCs between middle and anterior cerebral artery (MCA, ACA) territories. This framework is unique because our simulations are directly aligned with in vivo data. Moreover, it allows us to analyse perfusion characteristics quantitatively across all vessel types and for networks with no, few and many LMCs. We show that the occlusion of the MCA directly caused a redistribution of blood that was characterised by increased flow in LMCs. Interestingly, the improved perfusion of MCA-sided microvessels after dilating LMCs came at the cost of a reduced blood supply in other brain areas. This effect was enhanced in regions close to the watershed line and when the number of LMCs was increased. Additional dilations of surface and penetrating arteries after stroke improved perfusion across the entire vasculature and partially recovered flow in the obstructed region, especially in networks with many LMCs, which further underlines the role of LMCs during stroke.

Dynamic rewiring of neurovasculature in health and disease

Brain vasculature is chiefly considered a support network responsible for delivering signaling molecules and nutrients to neural cells. Several central disorders exhibit disruptions in functional and structural plasticity of this network. Considering this vasculature as structurally dynamic, it challenges the field's view and may be important for brain-directed therapeutic strategies.

D-LMBmap: a fully automated deep-learning pipeline for whole-brain profiling of neural circuitry

Recent proliferation and integration of tissue-clearing methods and light-sheet fluorescence microscopy has created new opportunities to achieve mesoscale three-dimensional whole-brain connectivity mapping with exceptionally high throughput. With the rapid generation of large, high-quality imaging datasets, downstream analysis is becoming the major technical bottleneck for mesoscale connectomics. Current computational solutions are labor intensive with limited applications because of the exhaustive manual annotation and heavily customized training. Meanwhile, whole-brain data analysis always requires combining multiple packages and secondary development by users. To address these challenges, we developed D-LMBmap, an end-to-end package providing an integrated workflow containing three modules based on deep-learning algorithms for whole-brain connectivity mapping: axon segmentation, brain region segmentation and whole-brain registration. D-LMBmap does not require manual annotation for axon segmentation and achieves quantitative analysis of whole-brain projectome in a single workflow with superior accuracy for multiple cell types in all of the modalities tested.

Microporous Polylactic Acid Scaffolds Enable Fluorescence-Based Perfusion Imaging of Intrinsic In Vivo Vascularization

In vivo tissue engineering (TE) techniques like the AV loop model provide an isolated and well-defined microenvironment to study angiogenesis-related cell interactions. Functional visualization of the microvascular network within these artificial tissue constructs is crucial for the fundamental understanding of vessel network formation and to identify the underlying key regulatory mechanisms. To facilitate microvascular tracking advanced fluorescence imaging techniques are required. We studied the suitability of microporous polylactic acid (PLA) scaffolds with known low autofluorescence to form axial vascularized tissue constructs in the AV loop model and to validate these scaffolds for fluorescence-based perfusion imaging. Compared to commonly used collagen elastin (CE) scaffolds, the total number of vessels and cells in PLA scaffolds was lower. In detail, CE-based constructs exhibited significantly higher vessel numbers on day 14 and 28 (d14: 316 ± 53; d28: 610 ± 74) compared to the respective time points in PLA-based constructs (d14: 144 ± 18; d28: 327 ± 34; each p < 0.05). Analogously, cell counts in CE scaffolds were higher compared to corresponding PLA constructs (d14: 7661.25 ± 505.93 and 5804.04 ± 716.59; d28: 11211.75 + 1278.97 and 6045.71 ± 572.72, p < 0.05). CE scaffolds showed significantly higher vessel densities in proximity to the main vessel axis compared to PLA scaffolds (200-400 µm and 600-800 µm on day 14; 400-1000 µm and 1400-1600 µm on day 28). CE scaffolds had significantly higher cell counts on day 14 at distances from 800 to 2000 µm and at distances from 400 to 1600 µm on day 28. While the total number of vessels and cells in PLA scaffolds were lower, both scaffold types were ideally suited for axial vascularization techniques. The intravascular perfusion of PLA-based constructs with fluorescence dye MHI148-PEI demonstrated dye specificity against vascular walls of low- and high-order branches as well as capillaries and facilitated the fluorescence-based visualization of microcirculatory networks. Fluorophore tracking may contribute to the development of automated quantification methods after 3D reconstruction and image segmentation. These technologies may facilitate the characterization of key regulators within specific subdomains and add to the current understanding of vessel formation in axially vascularized tissue constructs.

Protocol for fine casting, imaging, and analysis of murine vascular networks with VALID

The majority of fluorescent vessel labeling techniques currently available are limited by their expense, incomplete labeling, or complexity. Here, we present VALID (vessel labeling via gelatin-based lipophilic dye solution)-a protocol for complete labeling of different vascular networks. We describe steps for preparing different dye hydrogels, murine vascular casting and tissue harvesting, immunolabeling, tissue clearing, and imaging, as well as detailed analysis of the vascular networks. This protocol is helpful for evaluating vascular lesions in studying different vessel-associated diseases. For complete details on the use and execution of this protocol, please refer to Zhu et al.1.

DEEP-squared: deep learning powered De-scattering with Excitation Patterning

Limited throughput is a key challenge in in vivo deep tissue imaging using nonlinear optical microscopy. Point scanning multiphoton microscopy, the current gold standard, is slow especially compared to the widefield imaging modalities used for optically cleared or thin specimens. We recently introduced "De-scattering with Excitation Patterning" or "DEEP" as a widefield alternative to point-scanning geometries. Using patterned multiphoton excitation, DEEP encodes spatial information inside tissue before scattering. However, to de-scatter at typical depths, hundreds of such patterned excitations were needed. In this work, we present DEEP2, a deep learning-based model that can de-scatter images from just tens of patterned excitations instead of hundreds. Consequently, we improve DEEP's throughput by almost an order of magnitude. We demonstrate our method in multiple numerical and experimental imaging studies, including in vivo cortical vasculature imaging up to 4 scattering lengths deep in live mice.

Application of fluorescence micro-optical sectioning tomography in the cerebrovasculature and applicable vascular labeling methods

Fluorescence micro-optical sectioning tomography (fMOST) is a three-dimensional (3d) imaging method at the mesoscopic level. The whole-brain of mice can be imaged at a high resolution of 0.32 × 0.32 × 1.00 μm3. It is useful for revealing the fine morphology of intact organ tissue, even for positioning the single vessel connected with a complicated vascular network across different brain regions in the whole mouse brain. Featuring its 3d visualization of whole-brain cross-scale connections, fMOST has a vast potential to decipher brain function and diseases. This article begins with the background of fMOST technology including a widespread 3D imaging methods comparison and the basic technical principal illustration, followed by the application of fMOST in cerebrovascular research and relevant vascular labeling techniques applicable to different scenarios.

Deep self-learning enables fast, high-fidelity isotropic resolution restoration for volumetric fluorescence microscopy

One intrinsic yet critical issue that troubles the field of fluorescence microscopy ever since its introduction is the unmatched resolution in the lateral and axial directions (i.e., resolution anisotropy), which severely deteriorates the quality, reconstruction, and analysis of 3D volume images. By leveraging the natural anisotropy, we present a deep self-learning method termed Self-Net that significantly improves the resolution of axial images by using the lateral images from the same raw dataset as rational targets. By incorporating unsupervised learning for realistic anisotropic degradation and supervised learning for high-fidelity isotropic recovery, our method can effectively suppress the hallucination with substantially enhanced image quality compared to previously reported methods. In the experiments, we show that Self-Net can reconstruct high-fidelity isotropic 3D images from organelle to tissue levels via raw images from various microscopy platforms, e.g., wide-field, laser-scanning, or super-resolution microscopy. For the first time, Self-Net enables isotropic whole-brain imaging at a voxel resolution of 0.2 × 0.2 × 0.2 μm3, which addresses the last-mile problem of data quality in single-neuron morphology visualization and reconstruction with minimal effort and cost. Overall, Self-Net is a promising approach to overcoming the inherent resolution anisotropy for all classes of 3D fluorescence microscopy.

Diameter-dependent assessment of microvascular leakage following ultrasound-mediated blood-brain barrier opening

Blood brain barrier disruption (BBBD) using focused ultrasound (FUS) and microbubbles (MB) is an effective tool for therapeutic delivery to the brain. BBBD depends to a great extent on MB oscillations. Because the brain vasculature is heterogenic in diameter, reduced MB oscillations in smaller blood vessels, together with a lower number of MBs in capillaries, can lead to variations in BBBD. Therefore, evaluating the impact of microvasculature diameter on BBBD is of great importance. We present a method to characterize molecules extravasation following FUS-mediated BBBD, at a single blood vessel resolution. Evans blue (EB) leakage was used as marker for BBBD, whereas blood vessels localization was done using FITC labeled Dextran. Automated image processing pipeline was developed to quantify the extent of extravasation as function of microvasculature diameter, including a wide range of vascular morphological parameters. Variations in MB vibrational response were observed in blood vessel mimicking fibers with varied diameters. Higher peak negative pressures (PNP) were required to initiate stable cavitation in fibers with smaller diameters. In vivo in the treated brains, EB extravasation increased as a function of blood vessel diameter. The percentage of strong BBBD blood vessels increased from 9.75% for 2-3 μm blood vessels to 91.67% for 9-10 μm. Using this method, it is possible to conduct a diameter-dependent analysis that measures vascular leakage resulting from FUS-mediated BBBD at a single blood vessel resolution.

Digital labeling for 3D histology: segmenting blood vessels without a vascular contrast agent using deep learning

Recent advances in optical tissue clearing and three-dimensional (3D) fluorescence microscopy have enabled high resolution in situ imaging of intact tissues. Using simply prepared samples, we demonstrate here "digital labeling," a method to segment blood vessels in 3D volumes solely based on the autofluorescence signal and a nuclei stain (DAPI). We trained a deep-learning neural network based on the U-net architecture using a regression loss instead of a commonly used segmentation loss to achieve better detection of small vessels. We achieved high vessel detection accuracy and obtained accurate vascular morphometrics such as vessel length density and orientation. In the future, such digital labeling approach could easily be transferred to other biological structures.

Machine-learning-assisted single-vessel analysis of nanoparticle permeability in tumour vasculatures

The central dogma that nanoparticle delivery to tumours requires enhanced leakiness of vasculatures is a topic of debate. To address this, we propose a single-vessel quantitative analysis method by taking advantage of protein-based nanoprobes and image-segmentation-based machine learning (nano-ISML). Using nano-ISML, >67,000 individual blood vessels from 32 tumour models were quantified, revealing highly heterogenous vascular permeability of protein-based nanoparticles. There was a >13-fold difference in the percentage of high-permeability vessels in different tumours and >100-fold penetration ability in vessels with the highest permeability compared with vessels with the lowest permeability. Our data suggest passive extravasation and transendothelial transport were the dominant mechanisms for high- and low-permeability tumour vessels, respectively. To exemplify the nano-ISML-assisted rational design of nanomedicines, genetically tailored protein nanoparticles with improved transendothelial transport in low-permeability tumours were developed. Our study delineates the heterogeneity of tumour vascular permeability and defines a direction for the rational design of next-generation anticancer nanomedicines.

Aging drives cerebrovascular network remodeling and functional changes in the mouse brain

Aging is the largest risk factor for neurodegenerative disorders, and commonly associated with compromised cerebrovasculature and pericytes. However, we do not know how normal aging differentially impacts the vascular structure and function in different brain areas. Here we utilize mesoscale microscopy methods (serial two-photon tomography and light sheet microscopy) and in vivo imaging (wide field optical spectroscopy and two-photon imaging) to determine detailed changes in aged cerebrovascular networks. Whole-brain vascular tracing showed an overall ~10% decrease in vascular length and branching density, and light sheet imaging with 3D immunolabeling revealed increased arteriole tortuosity in aged brains. Vasculature and pericyte densities showed significant reductions in the deep cortical layers, hippocampal network, and basal forebrain areas. Moreover, in vivo imaging in awake mice identified delays in neurovascular coupling and disrupted blood oxygenation. Collectively, we uncover regional vulnerabilities of cerebrovascular network and physiological changes that can mediate cognitive decline in normal aging.

Near-lifespan longitudinal tracking of brain microvascular morphology, topology, and flow in male mice

In age-related neurodegenerative diseases, pathology often develops slowly across the lifespan. As one example, in diseases such as Alzheimer's, vascular decline is believed to onset decades ahead of symptomology. However, challenges inherent in current microscopic methods make longitudinal tracking of such vascular decline difficult. Here, we describe a suite of methods for measuring brain vascular dynamics and anatomy in mice for over seven months in the same field of view. This approach is enabled by advances in optical coherence tomography (OCT) and image processing algorithms including deep learning. These integrated methods enabled us to simultaneously monitor distinct vascular properties spanning morphology, topology, and function of the microvasculature across all scales: large pial vessels, penetrating cortical vessels, and capillaries. We have demonstrated this technical capability in wild-type and 3xTg male mice. The capability will allow comprehensive and longitudinal study of a broad range of progressive vascular diseases, and normal aging, in key model systems.