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Biology subjects

Ahn, T.

Publications and source records attributed to Ahn, T..

3 recordsLinked to original sources

KneEZ Clear, an Effective Tissue Clearing Protocol to Study Musculoskeletal Tissues in the Mouse

Wholemount, 3-dimensional (3D) tissue imaging holds significant promise for analyzing heterogeneous musculoskeletal tissues, such as knee joints, that demand time- and labor-intensive processing using traditional histological methods. Current musculoskeletal clearing protocols rely on either solvent-based tissue clearing, which substantially alters the size and architecture of cleared tissues, possibly compromising downstream quantification and perhaps more importantly reducing signal from endogenous fluorescent reporters, or on expensive and time-consuming hydrogel-based approaches that requires specialized equipment. While aqueous-based clearing overcomes these challenges, there is a clear need for a method that is optimized for clearing musculoskeletal tissues and that can easily be implemented in a standard lab environment. Here, we present KneEZ Clear, a simple, rapid, and flexible aqueous-based method that renders mineralized and non-mineralized tissues of murine knee joints optically transparent. We show that KneEZ Clear, which is based on the EZ Clear method, is highly flexible, demonstrating efficacy in a wide range of murine musculoskeletal tissues including the vertebral column, hindlimb, skull, and teeth. Critically, KneEZ Clear does not require specialized equipment and retains endogenous signal from fluorophores and fluorescent proteins. Additionally, following clearing and wholemount imaging, precious samples can still be processed for subsequent 2D histological analyses for validation or further study. Finally, we show that KneEZ Clear can be applied to samples of disease models to reveal alterations in tissue architecture and homeostasis. The simplicity, versatility, and efficiency of KneEZ Clear for optical clearing of musculoskeletal tissues will accelerate our understanding of cellular interactions and dynamics in homeostasis and disease.

physiology↗

Optimization of Tissue Clearing Methods and Imaging Conditions for 3D Visualization of the Vasculature of the Adult Murine Knee

The vasculature is an essential regulator of tissue oxygenation, metabolism, and immune surveillance under developmental and homeostatic conditions, as well as in regeneration and disease. Within the musculoskeletal system, blood vessels are involved in bone development and resorption, and they also mediate the inflammatory processes that contribute to diseases affecting the joints, including osteoarthritis. Historically, visualization of vascular networks in joints has been limited to conventional histological approaches due to technical limitations and the inherit structure of the skeletal muscle and bones. Recent advances in optical tissue clearing technologies and 3D fluorescence imaging approaches enabling three-dimensional analysis in whole, intact tissues offer an entree to interrogate musculoskeletal development and disease at a cellular resolution. However, these techniques were not originally developed to image hard mineralized tissues, such as the femur and tibias. To circumvent these challenges, we have optimized tissue decalcification conditions and systematically compared aqueous- and solvent-based tissue clearing approaches to establish an optimal pipeline for clearing and imaging of mineralized tissues with superior resolution of the vasculature. Collectively, this work shows that optical clearing and lightsheet microscopy presents a viable method for generating high-resolution images of the murine hindlimb vasculature, with potential applications in aging and disease modeling.

developmental biology↗

Algorithm for selecting potential SARS-CoV-2 dominant variants based on POS-NT frequency

COVID-19, currently prevalent worldwide, is caused by a novel coronavirus, SARS-CoV-2. Similar to other RNA viruses, SARS-CoV-2 continues to evolve through random mutations, creating numerous variants, such as Alpha, Beta, and Delta. It is, therefore, necessary to predict the mutations constituting the dominant variant before they are generated. This can be achieved by continuously monitoring the mutation trends and patterns. Hence, in the current study, we sought to design a dominant variant candidate (DVC) selection algorithm. To this end, we obtained COVID-19 sequence data from GISAID and extracted position-nucleotide (POS-NT) frequency ratio data by country and date through data preprocessing. We then defined the dominant dates for each variant in the USA and developed a frequency ratio prediction model for each POS-NT. Based on this model, we applied DVC criteria to develop the selection algorithm, verified for Delta and Omicron. Using Condition 3 as the DVC criterion, 69 and 102 DVC POS-NTs were identified for Delta and Omicron an average of 47 and 82 days before the dominant dates, respectively. Moreover, 13 and 44 Delta- and Omicron-defining POS-NTs were recognized 18 and 25 days before the dominant dates, respectively. We identified all DVC POS-NTs before the dominant dates, including soaring and gently increasing POS-NTs. Considering that we successfully defined all POS-NT mutations for Delta and Omicron, the DVC algorithm may represent a valuable tool for providing early predictions regarding future variants, helping improve global health. Author Summary

bioinformatics↗