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. 2019 Oct 30;9(1):15690.
doi: 10.1038/s41598-019-51853-y.

Molecular Imaging of Endometriosis Tissues Using Desorption Electrospray Ionization Mass Spectrometry

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Free PMC article

Molecular Imaging of Endometriosis Tissues Using Desorption Electrospray Ionization Mass Spectrometry

Clara L Feider et al. Sci Rep. .
Free PMC article

Abstract

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.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Negative ion mode DESI-MS imaging data acquired from endometriosis and eutopic endometrium tissues obtained from different patients. (a) Selected DESI-MS profiles from the endometrial glands within ectopic endometrial tissue collected from endometriosis lesions (top) and eutopic endometrial tissue from inside the uterus (bottom). The spectra shown are an average of 10 scans. (b) Selected DESI-MS ion images of endometriosis and endometrium tissues. Regions of endometrial glands and stroma within the lesions are outlined in black on the optical images of the H&E stained tissue sections. (c) High magnification view of an endometriosis tissue showing outlined regions of endometrial glands and stroma that spatially correlate to the distributions of various molecular ions detected by DESI-MS imaging, as exemplified by m/z 303. 233 and m/z 723.478 shown.
Figure 2
Figure 2
Intra-patient analysis of eutopic endometrium and four endometriosis tissues collected from ovary, rectal, bladder and endometrioma (patient #98). (a) Selected DESI mass spectra obtained from three samples, including endometriosis from both the right ovary and an ovarian cyst (endometrioma), and eutopic endometrium. (b) PCA score plots of the per-pixel data extracted from all endometriosis tissues (yellow) versus eutopic endometrium tissue (black). (c) PCA results of the per-pixel data extracted from endometriosis tissues per region of excision (ovary in orange; rectal in pink, bladder in green, endometrioma in blue) versus eutopic endometrium tissue (black). Ellipses are calculated by a one-sigma ellipse (68% probability) of an estimated bivariate Gaussian distribution for each group.
Figure 3
Figure 3
Results of statistical analyses performed on DESI-MS imaging data. (a) A total of 98 tissues including 76 tissues with endometriosis lesions and 22 eutopic endometrium tissues were prospectively collected from 51 different patients. Art provided by Viktoriia Tymoshenoko/Shutterstock.com. (b) Lasso per-pixel accuracy results for the training, test, and validation sample sets. (c) Features selected by lasso as discriminatory for endometriosis and endometrial tissue, where negative weights are more indicative of endometriosis and positive weights are more indicative of endometrial tissue. (d) Features selected by bootstrap analysis of the same sample set that are indicative of endometriosis or endometrial tissue, where negative z-scores indicate increase abundance in endometriosis and positive z-scores indicate increased abundance in endometrial tissue.

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