Coughing and sneezing that often indicates the flu could be traced to a rare genetic disorder related to cilia, the small hairs that protrude from cells throughout the human body.
Though it only affects a small number of people annually, ciliary dyskinesia can mimic the symptoms of less serious diseases. New research published in the journal Science Translational Medicine identifies a new technique to help clinicians make more accurate early diagnoses.
“The disorder causes the cilia to not move properly. Without the proper motion, they can’t clear out mucus and with that you get anything from flu-like symptoms, all the way up to lung scarring necessitating lung transplants,” said the study’s lead author Shannon Quinn, an assistant professor of computer science at the University of Georgia.
The study, “Automated identification of abnormal respiratory ciliary motion in nasal biopsies,” was published Aug. 5. It was part of Quinn’s doctoral research while at the University of Pittsburgh.
Clinicians go through a series of steps to diagnose ciliary dyskinesia, and no single method produces a certain diagnosis. Electron microscopy detects structural abnormalities, measuring the frequency at which the cilia beat. Nasal cultures from patients are plated for biopsy and grown in the lab. Then, their motions are analyzed with video microscopy. From the videos, clinicians or researchers make a determination about whether the motion is normal.
“It’s that last step that we’re focusing on,” Quinn said. “Researchers or clinicians making this determination based on their own training and experience is the current state of the art, but it is subjective, laborious and error prone. There is no cross-institutional commonality for making the diagnoses. So our goal was to provide a quantitative baseline for that particular step in the diagnostic process.”
By providing a baseline for this one step in the diagnostic process for ciliary dyskinesia, the researchers have established a pipeline to take some of the guesswork out of the process.
“To be able to attach numbers to the motion introduces a higher degree of certainty in diagnosing the abnormalities,” Quinn said. “It provides a quantitative definition that is relevant across clinics, across research institutions, and it’s all automated so that we have a direct comparison between motion types.”
The faster, more accurate diagnosis is applicable across the class of disorders that involve cilia dyskinesia. A growing body of research on cilia suggests a litany of other conditions could be implicated by the disorder, from congenital heart disease to early embryonic development in which cilia play a large role in establishing signaling pathways.
“Identifying these abnormalities more rigorously and earlier could help limit the need for more invasive procedures later on,” Quinn said.
The research is a product of the combination of disciplines in the field of computational biology, which uses expertise from mathematics, statistics, computer science, imaging, informatics and the biological sciences. Senior investigators on the study are Chakra Chennubhotla and Cecilia Lo of the University of Pittsburgh School of Medicine. Additional study co-authors are Maliha Zahid, Richard Francis and John Durkin of the University of Pittsburgh School of Medicine.
A video showing normal and abnormal cilia motion is available at https://www.youtube.com/watch?v=5jfxh2SuZGE.
Source: University of Georgia