Semi-automated protocol to quantify and characterize fluorescent three-dimensional vascular images.

The microvasculature facilitates gas exchange, provides nutrients to cells, and regulates blood flow in response to stimuli. Vascular abnormalities are an indicator of pathology for various conditions, such as compromised vessel integrity in small vessel disease and angiogenesis in tumors. Traditional immunohistochemistry enables the visualization of tissue cross-sections containing exogenously labeled vasculature. Although this approach can be utilized to quantify vascular changes within small fields of view, it is not a practical way to study the vasculature on the scale of whole organs. Three-dimensional (3D) imaging presents a more appropriate method to visualize the vascular architecture in tissue. Here we describe the complete protocol that we use to characterize the vasculature of different organs in mice encompassing the methods to fluorescently label vessels, optically clear tissue, collect 3D vascular images, and quantify these vascular images with a semi-automated approach. To validate the automated segmentation of vascular images, one user manually segmented one hundred random regions of interest across different vascular images. The automated segmentation results had an average sensitivity of 83±11% and an average specificity of 91±6% when compared to manual segmentation. Applying this procedure of image analysis presents a method to reliably quantify and characterize vascular networks in a timely fashion. This procedure is also applicable to other methods of tissue clearing and vascular labels that generate 3D images of microvasculature.

Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages.

Cerebral microhemorrhages (CMHs) are associated with cerebrovascular disease, cognitive impairment, and normal aging. One method to study CMHs is to analyze histological sections (5-40 µm) stained with Prussian blue. Currently, users manually and subjectively identify and quantify Prussian blue-stained regions of interest, which is prone to inter-individual variability and can lead to significant delays in data analysis. To improve this labor-intensive process, we developed and compared three digital pathology approaches to identify and quantify CMHs from Prussian blue-stained brain sections: (1) ratiometric analysis of RGB pixel values, (2) phasor analysis of RGB images, and (3) deep learning using a mask region-based convolutional neural network. We applied these approaches to a preclinical mouse model of inflammation-induced CMHs. One-hundred CMHs were imaged using a 20 × objective and RGB color camera. To determine the ground truth, four users independently annotated Prussian blue-labeled CMHs. The deep learning and ratiometric approaches performed better than the phasor analysis approach compared to the ground truth. The deep learning approach had the most precision of the three methods. The ratiometric approach has the most versatility and maintained accuracy, albeit with less precision. Our data suggest that implementing these methods to analyze CMH images can drastically increase the processing speed while maintaining precision and accuracy.

Simple methodology to visualize whole-brain microvasculature in three dimensions.

Significance: To explore brain architecture and pathology, a consistent and reliable methodology to visualize the three-dimensional cerebral microvasculature is beneficial. Perfusion-based vascular labeling is quick and easily deliverable. However, the quality of vascular labeling can vary with perfusion-based labels due to aggregate formation, leakage, rapid photobleaching, and incomplete perfusion. Aim: We describe a simple, two-day protocol combining perfusion-based labeling with a two-day clearing step that facilitates whole-brain, three-dimensional microvascular imaging and characterization. Approach: The combination of retro-orbital injection of Lectin-Dylight-649 to label the vasculature, the clearing process of a modified iDISCO+ protocol, and light-sheet imaging collectively enables a comprehensive view of the cerebrovasculature. Results: We observed ∼ threefold increase in contrast-to-background ratio of Lectin-Dylight-649 vascular labeling over endogenous green fluorescent protein fluorescence from a transgenic mouse model. With light-sheet microscopy, we demonstrate sharp visualization of cerebral microvasculature throughout the intact mouse brain. Conclusions: Our tissue preparation protocol requires fairly routine processing steps and is compatible with multiple types of optical microscopy.

Use of magnetic capture to identify elevated levels of CCL2 following intra-articular injection of monoiodoacetate in rats.

PURPOSE: Synovial fluid biomarkers help evaluate osteoarthritis (OA) development. Magnetic capture, our new magnetic nanoparticle-based technology, has proven to be effective for determining extracellular matrix fragment levels in two rat OA models. Here, the feasibility of magnetic capture for detecting monocyte chemoattractant protein-1 (MCP-1 or CCL2) is demonstrated after intra-articular injection of monoiodoacetate (MIA) in the rat knee. METHODS: Forty-eight male Lewis rats received a right hind limb, intra-articular injection of MIA (1 mg in 25 µl of saline) or 25 µl of saline. Magnetic capture and lavage were performed at 7 days after injection (n = 6 per treatment per procedure), with magnetic capture additionally performed at 14 and 28 days post-injection (n = 6 per treatment per time point). CCL2 was also assessed in serum. RESULTS: Serum CCL2 levels revealed no difference between MIA and saline animals (p = 0.0851). In contrast, magnetic capture and lavage detected a significant increase of CCL2 in the MIA-injected knee, with the MIA-injected knee having elevated CCL2 compared to contralateral and saline-injected knees (p = 0.00016 (contralateral) and p = 0.00016 (saline) for magnetic capture; p = 0.00023 (contralateral) and p = 0.00049 (saline) for lavage). CONCLUSIONS: Magnetic capture of CCL2 was successfully developed and applied to determine levels of CCL2 in a rat knee. Magnetic capture detected a statistically significant increase of CCL2 in MIA-injected knees compared to controls, and CCL2 levels stayed relatively stable from week 1 through week 4 post-MIA injection.