Moving translational mass spectrometry imaging towards transparent and reproducible data analyses: a case study of an urothelial cancer cohort analyzed in the Galaxy framework

Abstract

Background Mass spectrometry imaging (MSI) derives spatial molecular distribution maps directly from clinical tissue specimens and thus bears great potential for assisting pathologists with diagnostic decisions or personalized treatments. Unfortunately, progress in translational MSI is often hindered by insufficient quality control and lack of reproducible data analysis. Raw data and analysis scripts are rarely publicly shared. Here, we demonstrate the application of the Galaxy MSI tool set for the reproducible analysis of a urothelial carcinoma dataset. Methods Tryptic peptides were imaged in a cohort of 39 formalin-fixed, paraffin-embedded human urothelial cancer tissue cores with a MALDI-TOF/TOF device. The complete data analysis was performed in a fully transparent and reproducible manner on the European Galaxy Server. Annotations of tumor and stroma were performed by a pathologist and transferred to the MSI data to allow for supervised classifications of tumor vs. stroma tissue areas as well as for muscle-infiltrating and non-muscle infiltrating urothelial carcinomas. For putative peptide identifications, m/z features were matched to the MSiMass list. Results Rigorous quality control in combination with careful pre-processing enabled reduction of m/z shifts and intensity batch effects. High classification accuracy was found for both, tumor vs. stroma and muscle-infiltrating vs. non-muscle infiltrating urothelial tumors. Some of the most discriminative m/z features for each condition could be assigned a putative identity: stromal tissue was characterized by collagen peptides and tumor tissue by histone peptides. Immunohistochemistry confirmed an increased histone H2A abundance in the tumor compared to the stroma tissues. The muscle-infiltration status was distinguished via MSI by peptides from intermediate filaments such as cytokeratin 7 in non-muscle infiltrating carcinomas and vimentin in muscle-infiltrating urothelial carcinomas, which was confirmed by immunohistochemistry. To make the study fully reproducible and to advocate the criteria of FAIR (findability, accessibility, interoperability, and reusability) research data, we share the raw data, spectra annotations as well as all Galaxy histories and workflows. Data are available via ProteomeXchange with identifier PXD026459 and Galaxy results via https://github.com/foellmelanie/Bladder_MSI_Manuscript_Galaxy_links. Conclusion Here, we show that translational MSI data analysis in a fully transparent and reproducible manner is possible and we would like to encourage the community to join our efforts.

The journey towards clinical adoption of MALDI‐MS‐based imaging proteomics: from current challenges to future expectations

“…This non‐linear and mass‐dependent approach not only serves to reduce batch effects in inter‐laboratory scenarios but also in cross‐protocol scenarios. This desire for reproducible methods has also been supported by a trend towards more accessible and transparent data being shared among various centres, for example through the emergence of the Galaxy framework which has integrated 18 dedicated mass spectrometry imaging tools to facilitate this process [19] and has been shown to be a feasible platform that can be used to classify clinical specimens of FFPE tissue [20].…”

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

“…Recently, MALDI imaging along with deep learning has been employed in multi-class cancer subtyping in salivary gland carcinomas [ 97 ]. In translational MSI, to improve transparent and reproducible data analysis, implementation of the Galaxy framework has been shown for the urothelial carcinoma dataset [ 98 ].…”

Cardinal v3 - a versatile open source software for mass spectrometry imaging analysis

“…Tryptic peptides were imaged in urothelial tissues with a MALDI-TOF device at 150 µm spatial resolution 20 . The study cohort consisted of two tissue microarrays (TMAs) containing 39 patient's urothelial tissue specimens with different types of urothelial cancer or benign diagnoses and annotations for tumor and stroma tissue regions.…”

“…In the following, we illustrate how Cardinal v3 enables new, and principally different research. We highlight four reproducible case studies based on open datasets from the PRIDE repository 17 : 1) Segmentation of a high resolution phospholipid imaging data set 18 , 2) Single ion segmentation and concentration curves of a very large sized peptide imaging data set 19 , 3) Supervised and 4) Semi-supervised classification of a multiple replicate peptide imaging data set 20 . The raw data were transferred to the MassIVE database and the R Markdown files containing the R code for the case studies were added as re-analysis via MassIVE.quant (MassIVE identifier: MSV000086099, MSV000086102, MSV000089594).…”