Back

Efficient Techniques for Comprehensive Tissue Sampling in Adult Xenopus

Jonas-Closs, R. A.; Anderson, C. E.; Peshkin, L.

2025-01-28 zoology
10.1101/2025.01.27.635174 bioRxiv
Show abstract

Xenopus has long been a pivotal model organism for investigating vertebrate development and disease, offering deep insights into cellular processes and gene function. Despite the wealth of information on embryonic Xenopu,s there remains a significant gap in standardized methods for adult tissue sampling, especially for modern approaches like quantitative proteomics. This study introduces a comprehensive protocol for rapid, precise, and efficient sampling of multiple tissues in adult Xenopu.sThe protocol addresses challenges associated with the subtle anatomical differences compared to other anurans, ensuring reproducibility even for those with limited experience in frog dissection. This protocol is optimized for high- quality biochemical analyses by prioritizing sample freshness. We are facilitating the rapid collection of up to 18 tissues within an hour. Additionally, the methods apply to perfused and unperfused conditions, providing flexibility for a range of experimental needs. This work not only fills a critical methodological gap for Xenopus laevisand tropicalisbut also serves as a valuable resource for researchers adapting techniques to similar amphibian models, thereby enhancing the scope and reliability of comparative biological and evolutionary studies. SummaryThis is part one of a comprehensive Xenopus sampling protocol. The tissues sampled are the heart ventricle, arterial trunk, left liver lobe, gallbladder, lung, pancreas, spleen, larynx, esophagus, stomach, intestines, testes, fat bodies, oviduct, paired kidneys, sciatic plexus, skin, thymus, and whole eye.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.