Impact of Formalin- and Cryofixation on Raman Spectra of Human Tissues and Strategies for Tumor Bank Inclusion

Autor: Giulia Mirizzi, Finn Jelke, Michel Pilot, Karoline Klein, Gilbert Georg Klamminger, Jean-Jacques Gérardy, Marily Theodoropoulou, Laurent Mombaerts, Andreas Husch, Michel Mittelbronn, Frank Hertel, Felix Bruno Kleine Borgmann
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Molecules, Vol 29, Iss 5, p 1167 (2024)
Druh dokumentu: article
ISSN: 1420-3049
DOI: 10.3390/molecules29051167
Popis: Reliable training of Raman spectra-based tumor classifiers relies on a substantial sample pool. This study explores the impact of cryofixation (CF) and formalin fixation (FF) on Raman spectra using samples from surgery sites and a tumor bank. A robotic Raman spectrometer scans samples prior to the neuropathological analysis. CF samples showed no significant spectral deviations, appearance, or disappearance of peaks, but an intensity reduction during freezing and subsequent recovery during the thawing process. In contrast, FF induces sustained spectral alterations depending on molecular composition, albeit with good signal-to-noise ratio preservation. These observations are also reflected in the varying dual-class classifier performance, initially trained on native, unfixed samples: The Matthews correlation coefficient is 81.0% for CF and 58.6% for FF meningioma and dura mater. Training on spectral differences between original FF and pure formalin spectra substantially improves FF samples’ classifier performance (74.2%). CF is suitable for training global multiclass classifiers due to its consistent spectrum shape despite intensity reduction. FF introduces changes in peak relationships while preserving the signal-to-noise ratio, making it more suitable for dual-class classification, such as distinguishing between healthy and malignant tissues. Pure formalin spectrum subtraction represents a possible method for mathematical elimination of the FF influence. These findings enable retrospective analysis of processed samples, enhancing pathological work and expanding machine learning techniques.
Databáze: Directory of Open Access Journals
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