Protocol to analyze 1D and 2D mass spectrometry data from glioblastoma tissues for cancer diagnosis and immune cell identification
Year: 2024
Type: article
Abstract: In context of cancer diagnosis-based mass spectrometry (MS), the classification model created is crucial. Moreover, exploration of immune cell infiltration in tissues can offer insights within the tumor microenvironment. Here, we present a protocol to analyze 1D and 2D MS data from glioblastoma tissues for cancer diagnosis and immune cells identification. We describe steps for training the most optimal model and cross-validating it, for discovering robust biomarkers and obtaining their corresponding boxplots as well as creating an immunoscore based on MS-imaging data. For complete details on the use and execution of this protocol, please refer to Zirem et al. 1 • Protocol for analyzing MS data for cancer diagnosis and immune cell identification • Training and cross-validating an accurate and reliable classification model • Steps for generating statistical plots corresponding to robust biomarkers • Localizing immune cells in 2D MS data Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. In context of cancer diagnosis-based mass spectrometry (MS), the classification model created is crucial. Moreover, exploration of immune cell infiltration in tissues can offer insights within the tumor microenvironment. Here, we present a protocol to analyze 1D and 2D MS data from glioblastoma tissues for cancer diagnosis and immune cells identification. We describe steps for training the most optimal model and cross-validating it, for discovering robust biomarkers and obtaining their corresponding boxplots as well as creating an immunoscore based on MS-imaging data.
Source: STAR Protocols
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,500
Funders: Ohio Cancer Research, Université de Lille, Agence Nationale de la Recherche, Institut National de la Santé et de la Recherche Médicale, National Radio Research Agency, Ministère de l'Enseignement Supérieur et de la Recherche, Institut Universitaire de France, Région Hauts-de-France, European Regional Development Fund
OpenAlex ID: W4402222299