Implementation of automated behavior metrics to evaluate voluntary wheel running effects on inflammatory-erosive arthritis and interstitial lung disease in TNF-Tg mice.

BACKGROUND: Although treatment options and algorithms for rheumatoid arthritis (RA) have improved remarkably in recent decades, there continues to be no definitive cure or pharmacologic intervention with reliable long-term efficacy. For this reason, the combination of medications and healthy lifestyle modifications are essential for controlling joint disease, and extra-articular manifestations of RA, such as interstitial lung disease (ILD) and other lung pathologies, which greatly impact morbidity and mortality. Generally, exercise has been deemed beneficial in RA patients, and both patients and clinicians are motivated to incorporate effective non-pharmacologic interventions. However, there are limited evidence-based and specific exercise regimens available to support engagement in such activities for RA patients. Here, we provided the continuous opportunity for exercise to mice and implemented automated recording and quantification of wheel running behavior. This allowed us to describe the associated effects on the progression of inflammatory-erosive arthritis and ILD in the tumor necrosis factor transgenic (TNF-Tg) mouse model of RA. METHODS: Wild-type (WT; males, n=9; females, n=9) and TNF-Tg (males, n=12; females, n=14) mice were singly housed with free access to a running wheel starting at 2 months until 5 to 5.5 months of age. Measures of running included distance, rate, length, and number of run bouts, which were derived from continuously recorded data streams collected automatically and in real-time. In vivo lung, ankle, and knee micro-computed tomography (micro-CT), along with terminal micro-CT and histology were performed to examine the association of running behaviors and disease progression relative to sedentary controls. RESULTS: TNF-Tg males and females exhibited significantly reduced running distance, rate, length, and number of run bouts compared to WT counterparts by 5 months of age (p<0.0001). Compared to sedentary controls, running males and females showed increased aerated lung volumes (p<0.05) that were positively correlated with running distance and rate in female mice (WT: Distance, ρ=0.705/rate, ρ=0.693 (p<0.01); TNF-Tg: ρ=0.380 (p=0.06)/ρ=0.403 (p<0.05)). Talus bone volumes were significantly reduced in running versus sedentary males and negatively correlated with running distance and rate in TNF-Tg mice (male: ρ=-903/ρ=-0.865; female: ρ=-0.614/ρ=-0.594 (p<0.001)). Histopathology validated the lung and ankle micro-CT findings. CONCLUSIONS: Implementation of automated wheel running behavior metrics allows for evaluation of longitudinal activity modifications hands-off and in real-time to relate with biomarkers of disease severity. Through such analysis, we determined that wheel running activity increases aerated lung volumes, but exacerbates inflammatory-erosive arthritis in TNF-Tg mice. To the end of a clinically relevant model, additional functional assessment of these outcomes and studies of pain behavior are warranted.

A high-throughput semi-automated bone segmentation workflow for murine hindpaw micro-CT datasets.

INTRODUCTION: Micro-computed tomography (µCT) is a valuable imaging modality for longitudinal quantification of bone volumes to identify disease or treatment effects for a broad range of conditions that affect bone health. Complex structures, such as the hindpaw with up to 31 distinct bones in mice, have considerable analytic potential, but quantification is often limited to a single bone volume metric due to the intensive effort of manual segmentation. Herein, we introduce a high-throughput, user-friendly, and semi-automated method for segmentation of murine hindpaw µCT datasets. METHODS: In vivo µCT was performed on male (n = 4; 2-8-months) and female (n = 4; 2-5-months) C57BL/6 mice longitudinally each month. Additional 9.5-month-old male C57BL/6 hindpaws (n = 6 hindpaws) were imaged by ex vivo µCT to investigate the effects of resolution and integration time on analysis outcomes. The DICOMs were exported to Amira software for the watershed-based segmentation, and watershed markers were generated automatically at approximately 80% accuracy before user correction. The semi-automated segmentation method utilizes the original data, binary mask, and bone-specific markers that expand to the full volume of the bone using watershed algorithms. RESULTS: Compared to the conventional manual segmentation using Scanco software, the semi-automated approach produced similar raw bone volumes. The semi-automated segmentation also demonstrated a significant reduction in segmentation time for both experienced and novice users compared to standard manual segmentation. ICCs between experienced and novice users were >0.9 (excellent reliability) for all but 4 bones. DISCUSSION: The described semi-automated segmentation approach provides remarkable reliability and throughput advantages. Adoption of the semi-automated segmentation approach will provide standardization and reliability of bone volume measures across experienced and novice users and between institutions. The application of this model provides a considerable strategic advantage to accelerate various research opportunities in pre-clinical bone and joint analysis towards clinical translation.