Literature DB >> 27725306

An experimental guideline for the analysis of histologically heterogeneous tumors by MALDI-TOF mass spectrometry imaging.

Sha Lou1, Benjamin Balluff2, Arjen H G Cleven3, Judith V M G Bovée3, Liam A McDonnell4.   

Abstract

Mass spectrometry imaging (MSI) has been widely used for the direct molecular assessment of tissue samples and has demonstrated great potential to complement current histopathological methods in cancer research. It is now well established that tissue preparation is key to a successful MSI experiment; for histologically heterogeneous tumor tissues, other parts of the workflow are equally important to the experiment's success. To demonstrate these facets here we describe a matrix-assisted laser desorption/ionization MSI biomarker discovery investigation of high-grade, complex karyotype sarcomas, which often have histological overlap and moderate response to chemo-/radio-therapy. Multiple aspects of the workflow had to be optimized, ranging from the tissue preparation and data acquisition protocols, to the post-MSI histological staining method, data quality control, histology-defined data selection, data processing and statistical analysis. Only as a result of developing every step of the biomarker discovery workflow was it possible to identify a panel of protein signatures that could distinguish between different subtypes of sarcomas or could predict patient survival outcome. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.
Copyright © 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Biomarker discovery; High grade sarcoma; Mass spectrometry imaging; Protocol optimization

Mesh:

Substances:

Year:  2016        PMID: 27725306     DOI: 10.1016/j.bbapap.2016.09.020

Source DB:  PubMed          Journal:  Biochim Biophys Acta Proteins Proteom        ISSN: 1570-9639            Impact factor:   3.036


  3 in total

1.  Murine stroma adopts a human-like metabolic phenotype in the PDX model of colorectal cancer and liver metastases.

Authors:  Arnaud Blomme; Gaetan Van Simaeys; Gilles Doumont; Brunella Costanza; Justine Bellier; Yukihiro Otaka; Félicie Sherer; Pierre Lovinfosse; Sébastien Boutry; Ana Perez Palacios; Edwin De Pauw; Touko Hirano; Takehiko Yokobori; Roland Hustinx; Akeila Bellahcène; Philippe Delvenne; Olivier Detry; Serge Goldman; Masahiko Nishiyama; Vincent Castronovo; Andrei Turtoi
Journal:  Oncogene       Date:  2017-12-15       Impact factor: 9.867

2.  The metabolic landscape in chronic rotator cuff tear reveals tissue-region-specific signatures.

Authors:  Cyriel Sebastiaan Olie; René van Zeijl; Salma El Abdellaoui; Arjen Kolk; Celeste Overbeek; Rob G H H Nelissen; Bram Heijs; Vered Raz
Journal:  J Cachexia Sarcopenia Muscle       Date:  2021-12-05       Impact factor: 12.910

3.  Deep multiple instance learning classifies subtissue locations in mass spectrometry images from tissue-level annotations.

Authors:  Dan Guo; Melanie Christine Föll; Veronika Volkmann; Kathrin Enderle-Ammour; Peter Bronsert; Oliver Schilling; Olga Vitek
Journal:  Bioinformatics       Date:  2020-07-01       Impact factor: 6.937

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.