scholarly journals A novel method for analyzing formalin-fixed paraffin embedded (FFPE) tissue sections by mass spectrometry imaging

2007 ◽  
Vol 83 (7) ◽  
pp. 205-214 ◽  
Author(s):  
Yutaka AOKI ◽  
Atsuhiko TOYAMA ◽  
Takashi SHIMADA ◽  
Tetsuyoshi SUGITA ◽  
Chikage AOKI ◽  
...  
2020 ◽  
Author(s):  
Uwe Möginger ◽  
Niels Marcussen ◽  
Ole N. Jensen

AbstractPathology differentiation of renal cancer types is challenging due to tissue similarities or overlapping histological features of various tumor (sub)types. As assessment is often manually conducted outcomes can be prone to human error and therefore require high-level expertise and experience. Mass spectrometry can provide detailed histo-molecular information on tissue and is becoming increasingly popular in clinical settings. Spatially resolving technologies such as mass spectrometry imaging and quantitative microproteomics profiling in combination with machine learning approaches provide promising tools for automated tumor classification of clinical tissue sections.In this proof of concept study we used MALDI-MS imaging (MSI) and rapid LC-MS/MS-based microproteomics technologies (15 min/sample) to analyze formalin-fixed paraffin embedded (FFPE) tissue sections and classify renal oncocytoma (RO, n=11), clear cell renal cell carcinoma (ccRCC, n=12) and chromophobe renal cell carcinoma (ChRCC, n=5). Both methods were able to distinguish ccRCC, RO and ChRCC in cross-validation experiments. MSI correctly classified 87% of the patients whereas the rapid LC-MS/MS-based microproteomics approach correctly classified 100% of the patients.This strategy involving MSI and rapid proteome profiling by LC-MS/MS reveals molecular features of tumor sections and enables cancer subtype classification. Mass spectrometry provides a promising complementary approach to current pathological technologies for precise digitized diagnosis of diseases.


2015 ◽  
Vol 11 (6) ◽  
pp. 1507-1514 ◽  
Author(s):  
Gabriele De Sio ◽  
Andrew James Smith ◽  
Manuel Galli ◽  
Mattia Garancini ◽  
Clizia Chinello ◽  
...  

The paper shows a new method for the application of Matrix Assisted Laser Desorption/Ionisation (MALDI) Mass Spectrometry Imaging (MSI) technology on formalin-fixed paraffin-embedded (FFPE) tissue samples.


2016 ◽  
Vol 54 (11) ◽  
pp. 2798-2803 ◽  
Author(s):  
Elham Salehi ◽  
Mohammad T. Hedayati ◽  
Jan Zoll ◽  
Haleh Rafati ◽  
Maryam Ghasemi ◽  
...  

In a retrospective multicenter study, 102 formalin-fixed paraffin-embedded (FFPE) tissue specimens with histopathology results were tested. Two 4- to 5-μm FFPE tissue sections from each specimen were digested with proteinase K, followed by automated nucleic acid extraction. Multiple real-time quantitative PCR (qPCR) assays targeting the internal transcribed spacer 2 (ITS2) region of ribosomal DNA, using fluorescently labeled primers, was performed to identify clinically important genera and species of Aspergillus , Fusarium , Scedosporium , and the Mucormycetes . The molecular identification was correlated with results from histological examination. One of the main findings of our study was the high sensitivity of the automated DNA extraction method, which was estimated to be 94%. The qPCR procedure that was evaluated identified a range of fungal genera/species, including Aspergillus fumigatus , Aspergillus flavus , Aspergillus terreus , Aspergillus niger , Fusarium oxysporum , Fusarium solani , Scedosporium apiospermum , Rhizopus oryzae , Rhizopus microsporus , Mucor spp., and Syncephalastrum . Fusarium oxysporum and F. solani DNA was amplified from five specimens from patients initially diagnosed by histopathology as having aspergillosis. Aspergillus flavus , S. apiospermum , and Syncephalastrum were detected from histopathological mucormycosis samples. In addition, examination of four samples from patients suspected of having concomitant aspergillosis and mucormycosis infections resulted in the identification of two A. flavus isolates, one Mucor isolate, and only one sample having both R. oryzae and A. flavus . Our results indicate that histopathological features of molds may be easily confused in tissue sections. The qPCR assay used in this study is a reliable tool for the rapid and accurate identification of fungal pathogens to the genus and species levels directly from FFPE tissues.


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