Molecular Imaging of Endometriosis Tissues using Desorption Electrospray Ionization Mass Spectrometry

Endometriosis is a pathologic condition affecting approximately 10% of women in their reproductive years. Characterized by abnormal growth of uterine endometrial tissue in other body areas, endometriosis can cause severe abdominal pain and/or infertility. Despite devastating consequences to patients...

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Bibliographic Details
Published in:Scientific reports 2019-10, Vol.9 (1), p.15690-11, Article 15690
Main Authors: Feider, Clara L, Woody, Spencer, Ledet, Suzanne, Zhang, Jialing, Sebastian, Katherine, Breen, Michael T, Eberlin, Livia S
Format: Article
Language:English
Subjects:
Adult
Biomarkers - metabolism
Choristoma - diagnostic imaging
Choristoma - metabolism
Choristoma - pathology
Desorption
Endometriosis
Endometriosis - diagnostic imaging
Endometriosis - pathology
Endometrium
Endometrium - diagnostic imaging
Endometrium - metabolism
Fatty acids
Female
Humans
Infertility
Ions
Mass spectrometry
Mass spectroscopy
Mathematical models
Middle Aged
Molecular Imaging - methods
Quality of Life
Scientific imaging
Spectrometry, Mass, Electrospray Ionization
Statistical analysis
Uterus
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Summary:Endometriosis is a pathologic condition affecting approximately 10% of women in their reproductive years. Characterized by abnormal growth of uterine endometrial tissue in other body areas, endometriosis can cause severe abdominal pain and/or infertility. Despite devastating consequences to patients' quality of life, the causes of endometriosis are not fully understood and validated diagnostic markers for endometriosis have not been identified. Molecular analyses of ectopic and eutopic endometrial tissues could lead to enhanced understanding of the disease. Here, we apply desorption electrospray ionization (DESI) mass spectrometry (MS) imaging to chemically and spatially characterize the molecular profiles of 231 eutopic and ectopic endometrial tissues from 89 endometriosis patients. DESI-MS imaging allowed clear visualization of endometrial glandular and stromal regions within tissue samples. Statistical models built from DESI-MS imaging data allowed classification of endometriosis lesions with overall accuracies of 89.4%, 98.4%, and 98.8% on training, validation, and test sample sets, respectively. Further, molecular markers that are significantly altered in ectopic endometrial tissues when compared to eutopic tissues were identified, including fatty acids and glycerophosphoserines. Our study showcases the value of MS imaging to investigate the molecular composition of endometriosis lesions and pinpoints metabolic markers that may provide new knowledge on disease pathogenesis.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-019-51853-y