Enhancing proteomics characterization of thyroid nodules: a pixel-classifier for mass spectrometry imaging analyses

Matrix-assisted Laser-desorption Ionization Mass spectrometry imaging (MALDI-MSI) has shown promise in the molecular characterization of thyroid neoplasms [1]. Yet challenges remain in minimizing signal interferents and improving diagnostic discrimination. In this study, we propose an interdisciplinary approach integrating digital pathology with spatial proteomics to enhance MALDI-MSI analysis of thyroid lesions from formalin fixed-paraffin embedded (FFPE) tissue sections. To do so, a pixel-classifier [2] was built in QuPath [3] and employed to automatically select cell-rich regions of interest (ROIs) from hematoxylin and eosin (H&E) stained tissue microarrays (TMA), reducing interference from colloid-rich areas. Then we compared proteomics signals obtained from the full core (FC) areas, manually1 annotated from the pathologist (PAT) and those obtained with the pixel-classifier (PC). Hence, compared to conventional manual annotation approaches (PAT) and FC data, PC ROIs significantly decreased interfering signals (~15%) while increasing the S/N of tryptic peptides (≈ +37%). This resulted in a greater number of detected m/z signals (+9-24\%) and improved spectral clustering when performing Principal component analysis (PCA) to distinguish different histopathological regions. Receiver operating characteristic (ROC) analysis further confirmed the improved classification power, with a 50% increase in discriminatory m/z features among across different thyroid nodules diagnosis compared to conventional FC and PAT data. Unsing a pixel-classifier to select cell-specific regions globally enhances reproducibility, reduces operator workload, and optimizes MALDI-MSI workflows. Altogether, the approach proposed paves the way for more accurate molecular characterization of thyroid neoplasms and holds potential for improving biomarker discovery and diagnostic precision in clinical pathology. References List reference quoted sequentially in the text and marked as [1]. Reference text 10 pts. Journal title in italics. Paper title is not requested. 1 Piga I, Magni F, Smith A. FEBS Lett. 2024 Mar;598(6):621-634. doi: 10.1002/1873-3468.14795. 2 https://qupath.readthedocs.io/en/stable/docs/tutorials/pixel_classification.html; 3 Bankhead, P., Loughrey, M.B., Fernández, J.A. et al. Sci Rep 7, 16878 (2017);