scholarly journals Histo-molecular differentiation of renal cancer subtypes by mass spectrometry imaging and rapid proteome profiling of formalin-fixed paraffin-embedded tumor tissue sections

Oncotarget ◽  
2020 ◽  
Vol 11 (44) ◽  
pp. 3998-4015
Author(s):  
Uwe Möginger ◽  
Niels Marcussen ◽  
Ole N. Jensen
Keyword(s):  
Mass Spectrometry ◽  
Renal Cancer ◽  
Mass Spectrometry Imaging ◽  
Tumor Tissue ◽  
Cancer Subtypes ◽  
Tissue Sections ◽  
Molecular Differentiation ◽  
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scholarly journals Histo-molecular differentiation of renal cancer subtypes by mass spectrometry imaging and rapid proteome profiling of formalin-fixed paraffin-embedded tumor tissue sections

2020 ◽  
Author(s):  
Uwe Möginger ◽  
Niels Marcussen ◽  
Ole N. Jensen
Keyword(s):  
Mass Spectrometry ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cancer ◽  
Renal Cell ◽  
Mass Spectrometry Imaging ◽  
Tissue Sections ◽  
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Formalin Fixed Paraffin Embedded ◽  
Formalin Fixed

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.

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 ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Ffpe Tissue ◽  
Tissue Sections ◽  
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Novel Method ◽  
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scholarly journals Imaging of N-Linked Glycans from Formalin-Fixed Paraffin-Embedded Tissue Sections Using MALDI Mass Spectrometry

2014 ◽  
Vol 9 (9) ◽  
pp. 2149-2156 ◽  
Author(s):  
Shadi Toghi Eshghi ◽  
Shuang Yang ◽  
Xiangchun Wang ◽  
Punit Shah ◽  
Xingde Li ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Maldi Mass Spectrometry ◽  
Tissue Sections ◽  
Formalin Fixed Paraffin ◽  
Formalin Fixed Paraffin Embedded ◽  
Formalin Fixed
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