Combined Mass Spectrometry Imaging and Top-down Microproteomics Reveals Evidence of a Hidden Proteome in Ovarian Cancer

Recently, it was demonstrated that proteins can be translated from alternative open reading frames (altORFs), increasing the size of the actual proteome. Top-down mass spectrometry-based proteomics allows the identification of intact proteins containing post-translational modifications (PTMs) as well as truncated forms translated from reference ORFs or altORFs. Top-down tissue microproteomics was applied on benign, tumor and necrotic-fibrotic regions of serous ovarian cancer biopsies, identifying proteins exhibiting region-specific cellular localization and PTMs. The regions of interest (ROIs) were determined by MALDI mass spectrometry imaging and spatial segmentation. Analysis with a customized protein sequence database containing reference and alternative proteins (altprots) identified 15 altprots, including alternative G protein nucleolar 1 (AltGNL1) found in the tumor, and translated from an altORF nested within the GNL1 canonical coding sequence. Co-expression of GNL1 and altGNL1 was validated by transfection in HEK293 and HeLa cells with an expression plasmid containing a GNL1-FLAG(V5) construct. Western blot and immunofluorescence experiments confirmed constitutive co-expression of altGNL1-V5 with GNL1-FLAG. Taken together, our approach provides means to evaluate protein changes in the case of serous ovarian cancer, allowing the detection of potential markers that have never been considered. •Top-down spatially-resolved microproteomics associated to MALDI-Mass spectrometry imaging•Identification of proteoform markers of ovarian cancer•Identification of proteins translated from alternative open reading frames Mass spectrometry (MS) imaging is a molecular imaging tool which can be used to map distribution of molecules and distinguish between normal and diseased states based on molecular content. In this work, MALDI MS imaging was combined with intact protein identification by top-down microproteomics analysis. Using ovarian cancer tissues, tumor and benign zones can be distinguished from each other with great correlation and in accord with standard pathological examination. We then extracted proteins in each of the regions and identified several potential ovarian cancer biomarkers as well as non-canonical proteins translated from alternative open reading frames. In summary, our approach allows molecular classification of the tissue and identification of protein biomarkers including proteins issued from alternative open reading frames.