MALDI mass spectrometry imaging analysis of pituitary adenomas for near-real-time tumor delineation
| Autor: | Ian F. Dunn, Olutayo Olubiyi, Daniel R. Feldman, Armen Changelian, Edward R. Laws, Sandro Santagata, Nathalie Y. R. Agar, Matthew L. Vestal, David Calligaris, Revaz Machaidze, Isaiah Norton |
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| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
medicine.medical_specialty
Pathology Adrenocorticotropic hormone Mass spectrometry imaging Imaging Three-Dimensional Thyroid-stimulating hormone Computer Systems Internal medicine medicine Humans Pituitary Neoplasms Principal Component Analysis Multidisciplinary Molecular pathology Chemistry Pituitary tumors Reproducibility of Results Biological Sciences medicine.disease Prolactin Maldi msi Neoplasm Proteins Endocrinology Pituitary Gland Spectrometry Mass Matrix-Assisted Laser Desorption-Ionization Hormone |
| Popis: | We present a proof of concept study designed to support the clinical development of mass spectrometry imaging (MSI) for the detection of pituitary tumors during surgery. We analyzed by matrix-assisted laser desorption/ionization (MALDI) MSI six nonpathological (NP) human pituitary glands and 45 hormone secreting and nonsecreting (NS) human pituitary adenomas. We show that the distribution of pituitary hormones such as prolactin (PRL), growth hormone (GH), adrenocorticotropic hormone (ACTH), and thyroid stimulating hormone (TSH) in both normal and tumor tissues can be assessed by using this approach. The presence of most of the pituitary hormones was confirmed by using MS/MS and pseudo-MS/MS methods, and subtyping of pituitary adenomas was performed by using principal component analysis (PCA) and support vector machine (SVM). Our proof of concept study demonstrates that MALDI MSI could be used to directly detect excessive hormonal production from functional pituitary adenomas and generally classify pituitary adenomas by using statistical and machine learning analyses. The tissue characterization can be completed in fewer than 30 min and could therefore be applied for the near-real-time detection and delineation of pituitary tumors for intraoperative surgical decision-making. |
| Databáze: | OpenAIRE |
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