First Ever Molecular Diagnostic Tool Could Enable Early Diagnosis of Inflammatory Diseases in Children

Inflammatory diseases pose a particular threat to children, as symptoms like fever and rash are often nonspecific, leading to frequent misdiagnoses. Conditions like Multisystem Inflammatory Syndrome in Children (MIS-C) can cause inflammation in vital organs such as the heart, lungs, and brain if not promptly treated. Similarly, Kawasaki disease (KD), the leading cause of acquired heart disease in children, can result in cardiac aneurysms and heart attacks. Now, a new cell-free RNA-based test could become the first molecular diagnostic tool to help clinicians detect these inflammatory conditions in children at an early stage.

Cell-free RNA, which is released into the bloodstream through cell death or active secretion, has been harnessed by a research collaboration led by Cornell University (Ithaca, NY, USA). The team developed machine learning models that utilize these molecular RNA fragments to diagnose complex pediatric inflammatory conditions. This diagnostic tool can accurately distinguish between Kawasaki disease, MIS-C, viral infections, or bacterial infections, while also monitoring the patient’s organ health. These results, published in the Proceedings of the National Academy of Sciences, build on earlier work that began four years ago, focusing on severe cases of COVID-19 and MIS-C in children, which surged during the pandemic.

Initially, the researchers were investigating the potential of cell-free DNA in studying these diseases, but their focus shifted to cell-free RNA due to the wealth of information it provides. While cell-free RNA has already been identified as a valuable biomarker for pregnancy and cancer, it is far less studied than cell-free DNA. The research team analyzed 370 plasma samples from pediatric patients with various inflammatory conditions. They converted the RNA to DNA and then used DNA sequencing to explore the protein-coding regions of the genome. The team spent a year refining machine learning algorithms to identify disease signatures in the samples, effectively creating a suite of tools to interpret the cell-free RNA data. In addition to developing an accurate diagnostic model, the researchers demonstrated that cell-free RNA sequencing could also be used to assess damage to specific tissues and organs, including the heart, liver, nervous system, endothelium, and upper respiratory tract.

“When you analyze RNA in plasma, what you’re looking at is RNA from dying cells, and also RNA that’s been released from cells anywhere in the body,” said lead author Conor Loy. “This gives you a huge advantage. In inflammatory conditions, there’s lots of cell death. Cells are, in some cases, exploding and their RNA gets released into plasma. By isolating that RNA and sequencing it, we can discover biomarkers for disease and backtrack where the RNA is coming from to measure cell death.”

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