A groundbreaking study led by Cornell University researchers has unveiled a new diagnostic tool that leverages machine learning and cell-free RNA analysis to enhance the early detection of pediatric inflammatory conditions. This innovative approach can effectively differentiate between Kawasaki disease (KD), Multisystem Inflammatory Syndrome in Children (MIS-C), viral infections, and bacterial infections, while also monitoring the health of vital organs.
Inflammatory diseases pose a significant risk to children, as their symptoms—such as fever and rash—are often nonspecific, leading to frequent misdiagnoses. If left untreated, MIS-C can result in serious complications, including swelling of the heart, lungs, and brain. Similarly, KD, the leading cause of acquired heart disease in children, can lead to severe cardiac issues like aneurysms and heart attacks. The introduction of a cell-free RNA-based diagnostic test represents a pivotal advancement, enabling clinicians to identify these inflammatory conditions at critical early stages.
The findings of this research were published in the Proceedings of the National Academy of Sciences. The study was led by Iwijn De Vlaminck, an associate professor of biomedical engineering at Cornell, with Conor Loy, an Ignite Fellow for New Ventures, serving as the lead author.
This research builds on a collaboration that began four years ago, initially focusing on the severe cases of COVID-19 and MIS-C that surged during the pandemic. While the team initially explored the potential of cell-free DNA as a diagnostic tool, they shifted their focus to cell-free RNA due to its rich informational content. Although cell-free RNA has been recognized as an effective biomarker in contexts such as pregnancy and cancer, its application in pediatric inflammatory conditions has been relatively underexplored.
“Analyzing RNA in plasma allows us to capture RNA from dying cells as well as from cells throughout the body,” explained Loy. “In inflammatory conditions, there is significant cell death, and as cells disintegrate, their RNA is released into the plasma. By isolating and sequencing this RNA, we can identify disease biomarkers and trace their origins to assess cell death.”
The research team analyzed 370 plasma samples from pediatric patients with various inflammatory conditions. They converted the RNA into DNA and performed DNA sequencing to examine the protein-coding regions of the genome. Loy dedicated a year to experimenting with machine-learning algorithms to identify disease signatures in the samples, effectively creating a comprehensive analytical pipeline for interpreting cell-free RNA data.
In addition to developing a robust diagnostic model, the researchers demonstrated that cell-free RNA sequencing can quantify injury to specific tissues and organs, including the liver, heart, endothelium, nervous system, and upper respiratory tract.
“The novelty and technical innovation of this research lie in our data analysis techniques,” said De Vlaminck. “We can quantify the origin of the RNA, determining how much is derived from the liver or vascular epithelial cells. This quantification helps us understand injury processes that are likely immune-related and occurring in vascularized tissues.”
This important research was supported by the National Institutes of Health’s National Institute of Child Health and Human Development, highlighting its potential impact on pediatric healthcare.
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