We've updated our Privacy Policy to make it clearer how we use your personal data. We use cookies to provide you with a better experience. You can read our Cookie Policy here.


Specific Methylation Patterns Unique to Each Tissue Identified

Three test tubes on a blue background.
Credit: Testalize.me / Unsplash.
Listen with
Register for free to listen to this article
Thank you. Listen to this article using the player above.

Want to listen to this article for FREE?

Complete the form below to unlock access to ALL audio articles.

Read time: 1 minute

A research team led by Xianghong Jasmine Zhou, PhD, professor of Pathology and Laboratory Medicine at David Geffen School of Medicine at UCLA, has made an important advancement to address one of the major challenges in cell-free DNA (cfDNA) testing, also known as liquid biopsy. They’ve identified specific methylation patterns unique to each tissue, potentially helping to Identify the specific tissue or organ associated with cfDNA alterations picked up by testing, a critical challenge for accurate diagnosis and monitoring of diseases.

Cell-free DNA has significant potential in disease detection and monitoring. However, accurately quantifying tissue-derived cfDNA has proven challenging with current methods, among them determining the tissue origin of cfDNA fragments detected in these tests.

Want more breaking news?

Subscribe to Technology Networks’ daily newsletter, delivering breaking science news straight to your inbox every day.

Subscribe for FREE

In a new study, published in Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS), the team developed a comprehensive and high-resolution methylation atlas based on a vast dataset of 521 noncancerous tissue samples representing 29 major types of human tissues. They call the approach cfSort, and showed it successfully identified specific methylation patterns unique to each tissue at the fragment level and validated these findings using additional datasets.

Going further, the team illustrated the clinical applications of cfSort through two potential uses: aiding in disease diagnosis and monitoring treatment side effects. By estimating the tissue-derived cfDNA fraction using cfSort, they were able to assess and predict clinical outcomes in patients.

“We have shown that the cfSort outperformed the existing methods in terms of accuracy and detection limit: making more accurate tissue fraction estimation and distinguishing a lower level of tissue-derived cfDNA,” said first author Shuo Li. “In addition, the cfSort demonstrated nearly perfect robustness toward the unseen local fluctuations of tissue compositions, indicating its wide applicability to diverse individuals.” 

Reference: Li S, Zeng W, Ni X, et al. Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring. Proc Natl Acad Sci USA. 2023;120(28):e2305236120. doi: 10.1073/pnas.2305236120

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.