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Proof of concept for identifying cystic fibrosis from perspiration samples
Contributed by Richard N. Zare, October 11, 2019 (sent for review June 4, 2019; reviewed by Nathalie Y. R. Agar, Alan K. Jarmusch, and Bineet Sharma)

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Significance
Early diagnosis and characterization of the severity of CFTR mutations carried in cystic fibrosis (CF) impacts life expectancy and quality of life for patients. We demonstrate a testing platform that combines analysis of perspiration samples by desorption electrospray ionization mass spectrometry and a machine-learning method of gradient boosted decision trees, with an accuracy for the correct identification of CF cases of 98 ± 2%. Our sampling method is minimally invasive; it only requires swiping a standard microscope slide across the patient’s forehead, with no sample processing. The whole collection and testing process takes less than 2 min, which suggests a faster alternative with comparable accuracy to the conventional sweat chloride test, which takes up to 3 h.
Abstract
The gold standard for cystic fibrosis (CF) diagnosis is the determination of chloride concentration in sweat. Current testing methodology takes up to 3 h to complete and has recognized shortcomings on its diagnostic accuracy. We present an alternative method for the identification of CF by combining desorption electrospray ionization mass spectrometry and a machine-learning algorithm based on gradient boosted decision trees to analyze perspiration samples. This process takes as little as 2 min, and we determined its accuracy to be 98 ± 2% by cross-validation on analyzing 277 perspiration samples. With the introduction of statistical bootstrap, our method can provide a confidence estimate of our prediction, which helps diagnosis decision-making. We also identified important peaks by the feature selection algorithm and assigned the chemical structure of the metabolites by high-resolution and/or tandem mass spectrometry. We inspected the correlation between mild and severe CFTR gene mutation types and lipid profiles, suggesting a possible way to realize personalized medicine with this noninvasive, fast, and accurate method.
Footnotes
- ↵1To whom correspondence may be addressed. Email: zare{at}stanford.edu.
Author contributions: Z.Z. and R.N.Z. designed research; Z.Z. and D.A. performed research; Z.Z., D.A., C.M., and R.N.Z. analyzed data; and Z.Z., C.M., and R.N.Z. wrote the paper.
Reviewers: N.Y.R.A., Harvard Medical School; A.K.J., University of California, San Diego; and B.S., Renji Hospital.
The authors declare no competing interest.
Data deposition: Raw data can be accessed through the Open Science Framework at https://osf.io/j59h2/?view_only=c0212307a2714d909559550a65db0213, DOI:10.17605/OSF.IO/J59H2.
This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1909630116/-/DCSupplemental.
Published under the PNAS license.
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