Cartilage collagen matrix loss and osteophytes were not found in any histological image; 2 therefore, these measures were not included in the table. Other values are shown as mean 3 +/-95% confidence interval (C.I.). Comparison for MIA versus saline animals at each week 4 are from the Tukey HSD test, whereas the comparison for MIA and saline animals for all 5

. 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 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.

Objective: In osteoarthritis (OA) models, histology is commonly used to evaluate the severity of joint damage. Unfortunately, semi-quantitative histological grading systems include some level of subjectivity, and quantitative grading systems can be tedious to implement. The objective of this work is to introduce an open source, graphic user interface (GUI) for quantitative grading of knee OA. Methods: Inspired by the 2010 OARSI histopathology recommendations for the rat, our laboratory has developed a GUI for the evaluation of knee OA, nicknamed GEKO. In this work, descriptions of the quantitative measures acquired by GEKO are presented and measured in 42 histological images from a rat knee OA model. Using these images, across-session and within-session reproducibility for individual graders is evaluated, and inter-grader reliability across different levels of OA severity is also assessed. Results: GEKO allowed histological images to be quantitatively scored in less than 1 min per image. In addition, intra-class coefficients (ICCs) were largely above 0.8 for across-session reproducibility, within- session reproducibility, and inter-grader reliability. These data indicate GEKO aided in the reproducibility and repeatability of quantitative OA grading across graders and grading sessions. Conclusions: Our data demonstrate GEKO is a reliable and efficient method to calculate quantitative histological measures of knee OA in a rat model. GEKO reduced quantitative grading times relative to manual grading systems and allowed grader reproducibility and repeatability to be easily assessed within a grading session and across time. Moreover, GEKO is being provided as a free, open-source tool for the OA research community.

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.