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

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.


Background
Mass spectrometry imaging (MSI) is a label-free and untargeted method to generate spatial distribution maps for hundreds to thousands of molecules directly from a single tissue section. The most common MSI technique is based on matrix assisted laser desorption/ionization (MALDI) mass spectrometry and called MALDI MSI or MALDI imaging. It allows spatial resolution in the low micrometer range while preserving the integrity of the measured molecules such as proteins, peptides, metabolites and lipids. After the MALDI measurement, the tissue section remains amenable to histological staining, which can be compared to the measured molecular distributions. Molecular histology impacts many aspects of histopathological diagnostics and research and thus MSI is emerging as a powerful technology in translational studies [1,2]. In particular, the analysis of tumor tissues with pronounced cellular and morphological heterogeneity benefits from the spatially resolved MSI technology [3,4]. Common applications for MSI in cancer studies include tumor typing and subtyping [5][6][7], studying resection margins and tumor heterogeneity [8,9], and finding biomarkers for tumor diagnosis, prognosis or prediction [10][11][12].
The successes seen in translational MSI studies highlight the great potential for MSI in clinical settings, which require thorough quality control, good experimental design as well as standardized and reproducible experiments, analysis and reporting [2,[13][14][15][16]. Despite their general importance for any omics-study, such aspects are only starting to become topics of research and developments in MSI. Recently, two studies emerged, which demonstrated that standardized sample preparation protocols allow for reproducible MSI across several laboratories [14,15]. Suggestions for the inclusion of quality metrics into sample preparation protocols were made by Gustafsson et al. (use of internal peptide standards to measure and re-adjust mass accuracy [17]) and Erich et al. (implementation of quality controls for tryptic digestion efficiency [13]).
In contrast, in most MSI studies the data analysis part is neither standardized, transparent nor reproducible, even though this part of an MSI experiment can be improved with the least effort. It requires publishing raw data and metadata as well as reporting the entire multistep analysis workflow with all fine grained parameters and settings in an accessible way.
We have previously established MSI tools in the Galaxy platform for reproducible MSI analyses [18]. Galaxy represents a highly suitable platform for reproducible biomedical data science allowing to track provenance, store tool names, versions and all set parameters for all analyses in publishable Galaxy history. Galaxy is accessible for every researcher, offers a graphical user interface, and comprises free access to thousands of pre-installed tools and large public computational resources. Galaxy also enables high levels of interoperability by implying tools of different omics domains, which can be connected to build (multi-omics) workflows. Also, analysis histories and workflows can be shared privately, with collaboration partners or publicly, allowing full transparent and reproducible data analyses.
Here, we aim to showcase that a fully transparent and reproducible data analysis of a translational MSI cancer study is possible in the Galaxy framework.
As a use case, we have imaged patient derived urothelial tissue cohort comprising urothelial cancer, precursor lesions and benign tissues for their spatial tryptic peptide composition. Based on these tissues we established a classifier for two different tissue types, urothelial tumor and stroma. Considering urothelial tumor, an additional classifier was built to distinguish between the two clinical relevant groups: muscle-infiltrating urothelial carcinoma and non-muscle infiltrating papillary urothelial carcinoma low grade (pTa low). The latter classifier could be applied to estimate the molecular risk of progression for three non-muscle infiltratingpapillary urothelial carcinoma high-grade (pTa high) tissues. The classification of tumor areas from their surrounding stroma tissue is the key for tumor specific analysis. Currently, most MSI tumor studies rely on the manual annotation of tumor areas by a pathologist, which is a bottleneck in terms of available experts and time constraints, which could be overcome by applying automated classification of the two tissue types. The complete analysis including quality control, image co-registration, filtering regions of interest (ROIs), combining files, pre-processing, classification, and visualization was performed in a single platform: the European Galaxy server [19]. This allowed for the easy sharing of all analysis histories together with all raw and intermediate data to enable FAIR (findable, accessible, interoperable, and re-usable) data sharing and full transparency and reproducibility [20].

Patient cohort
Forty-nine bladder tissue specimens from 47 patients were collected during transurethral resection at the University Medical Center in Freiburg. The study was approved by the Ethics Committee of the University Medical Center Freiburg (no. 491/16). All patients gave written informed consent. Before study inclusion, all patient data were pseudonymized.
Bladder tissue specimens were formalin-fixed directly after surgical removal and paraffin-embedded as described previously [21]. All tissue specimens were reviewed by two experienced pathologists. Biopsie tissue cores with 2 mm diameter were extracted from each formalin-fixed paraffin embedded (FFPE) tissue block and randomly assembled into two FFPE tissue microarrays (TMA) blocks. The following tissue cores were included into the TMA: Muscle-infiltrating urothelial cancer (n = 12), non-muscle infiltrating papillary urothelial carcinoma high-(pTa high, n = 5)/low grade (pTa low, n = 20), carcinoma in situ (pTis, n = 2), and papillary urothelial neoplasm of low malignant potential (PUN-LUMP, n = 2), as well as inflammatory bladder specimens (n = 8). 6 µm thick sections were sliced with a microtome and mounted onto indium tin oxide (ITO) coated glass slides (Bruker Daltonik, Bremen, Germany).

MSI data acquisition
Tissue sections were measured with a 4800 MALDI-TOF/TOF Analyzer (Applied Biosystems, Waltham, MA, USA) using the 4000 Series Explorer software (Novartis and Applied Biosystems) to set instrument parameters. A squared region was imaged with 150 µm raster step size, a laser focus of 100 µm diameter and by averaging 500 laser shots per spectrum in a mass range from 800 to 2300 m/z in positive ion reflectron mode. Before starting the imaging measurement, internal calibrants in a spectrum outside the tissue region were used for m/z re-calibration.

H&E staining and annotation
Matrix was removed from the slides by rinsing with 70% ethanol after MSI measurement. Afterwards, hemalum staining of the measured tissue was performed by immersing the tissue sections in Mayer's acid Hemalum solution (Waldeck, Münster, Germany) for 1 min and rinsing with water for 1 min. Dehydration was performed with four short incubations in 100% ethanol and 2 incubations in xylol. Stained tissues were scanned at × 20 magnification. A pathologist (KEA) annotated a coherent area within the largest tumor and stroma regions in photoshop CS5 (Adobe, San Jose, USA). Only annotated spectra were considered for further analysis.

MSI quality control, data handling and pre-processing
Analyze7.5 files were uploaded to the European Galaxy server [24], where the complete analysis was performed and afterwards published [18,19]. First, a quality control with the MSI qualitycontrol tool (m/z of interest: four internal calibrants, ppm range: 200) was performed to ensure sufficient quality of the data and to find appropriate parameters for the following pre-processing steps. A previously published Galaxy workflow [18] was slightly modified and applied for co-registration of the stained image and the MSI image for each TMA separately. Six visually determined characteristic tissue spots were used as teachmarks for affine transformation. The obtained warping matrix was applied to extract the coordinates corresponding to the annotated regions from the MSI data leading to 2169 tumor and stroma specific spectra, while all pTis and PUNLUMP spectra were removed. Both files were binned in 50 ppm m/z steps and cut to their common m/z range 800-2300 in the 'MSI preprocessing' tool (method: m/z binning, width of bin: 50, unit for bin: ppm, select m/z options: change m/z range, minimum value for m/z: 800, maximum value for m/z: 2300 and combined into one dataset using the 'MSI combine' tool (Optional annotation of pixels with tabular files: TMA1 annotations, TMA2 annotations). The Cardinal (v 2.6.0) [25] based 'MSI preprocessing' tool was used for pre-processing: gaussian smoothing (window: 8, standard deviation: 2), baseline reduction (blocks: 750), m/z alignment (tolerance: 200 ppm), peak picking (signal to noise: 5, blocks: 600, window: 10), alignment (tolerance: 200 ppm) and filtering (frequency: 0.01) to obtain a common m/z peak list. The m/z peak list was used to extract the original peptide intensity from the smoothed and baseline reduced dataset by peak binning (tolerance: 200 ppm) in the 'MSI preprocessing' tool. Mass recalibration (tolerance: 200 ppm) was performed based on the three internal calibrants within the m/z range and the most abundant tryptic autolysis peptide (m/z 405.42) using the align spectra function of the MALDIquant peak detection tool [tolerance: 0.0002, don't throw an error when less than 2 reference m/z were found in a spectrum: yes, If TRUE the intensity values of MassSpectrum or MassPeaks objects with missing (NA) warping functions are set to zero: yes, Should empty spectra be removed: yes]. Afterwards the processed imzML data was converted into a continuous file with the 'MSI preprocessing' tool (Processed imzML file: yes, mass accuracy to which the m/z values will be binned: 0.005, unit of the mass accuracy: mz; preprocessing method: peak filtering, minimum frequency 0.01). Potential contaminant m/z features were removed with the 'MSI filtering' tool (Select m/z feature filtering option: remove m/z, tabular file with m/z features to remove: potential contaminant list, window in which all m/z will be removed: 200, units: ppm). The potential contaminant list was built based on the internal calibrants as well as CHCA matrix peaks and bovine trypsin peptides. The m/z of the latter two were obtained from the MALDI contaminant list published by Keller [26]. Finally, intensity normalization to the total ion current (TIC) of each spectrum was performed in the 'MSI preprocessing' tool. Between and after the pre-processing steps eight times a quality control was performed with the 'MSI qualitycontrol' tool using the three internal calibrants, a 200 ppm range and spectra annotation information to summarize either the properties of each TMA or of each patient tissue core.

MSI statistical modelling, visualizations and identification
The pre-processed file was subjected to spectra classification using Cardinal's spatial shrunken centroids (SSC) algorithm [27] in the 'MSI classification' tool. For tumor vs. stroma classification, stroma of non-malignant tissues and tumor tissues were not separated. All 39 patients were split randomly 80:20 into training (n = 31) and test group (n = 8). The patients of the training group were split into ten random groups. The scikit learn [28] based Split Dataset tool was used for all the patient grouping steps and the 'MSI filtering' tool in order to separate all training and test spectra into separate imzML files. First, tenfold cross validation was performed on the training file in the 'MSI classification' tool (Pixel coordinates and their classes: file from Split Dataset tool that contains the spectra conditions and folds of the training data, select the method for classification: spatial shrunken centroids, analysis step to perform: cvApply, write out best r and s values: yes, r: 2, s: 0, 2, 4, 6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40, method to use to calculate the spatial smoothing kernels: adaptive) to find optimal classification parameters. The optimized parameters (r = 2, s = 18) were applied to build a classifier on the training data with the 'MSI classification' tool (Pixel coordinates and their classes: file from Split Dataset tool that contains the spectra conditions of the training data, select the method for classification: spatial shrunken centroids, analysis step to perform: spatial shrunken centroids, r: 2, s: 18, method to use to calculate the spatial smoothing kernels: adaptive, Results as.RData output: yes). The classifier obtained as.RData file was then applied to the test data in the 'MSI classification' tool (Analysis step to perform: prediction, which classification method was used: SSC_classifier, load annotations: use annotations, load tabular file with pixel coordinates and the new response: file from Split Dataset tool that contains the spectra conditions of the test data).
For muscle-infiltrating vs. non-muscle infiltrating low-grade tumor classification, only tumor ROIs from muscle-infiltrating urothelial cancer and non-muscle infiltrating low-grade papillary urothelial cancer were included into the analysis. Patients were randomly assigned 80:20 into training (n = 20) and test group (n = 6). The training group was further split into five random groups and fivefold cross validation was performed to find optimal classification parameters. Again, the scikit learn based Split Dataset tool was used for all the patient grouping steps and the 'MSI filtering' tool in order to separate all training and test spectra into separate imzML files. First, five-fold cross validation was performed on the training file in the 'MSI classification' tool (Pixel coordinates and their classes: file from Split Dataset tool that contains the spectra conditions and folds of the training data, select the method for classification: spatial shrunken centroids, analysis step to perform: cvApply, write out best r and s values: yes, r: 2, s: 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, method to use to calculate the spatial smoothing kernels: adaptive) to find optimal classification parameters. The optimized parameters (r = 2, s = 4) were used to build a classifier on the training data with the 'MSI classification' tool (Pixel coordinates and their classes: file from Split Dataset tool that contains the spectra conditions of the training data, select the method for classification: spatial shrunken centroids, analysis step to perform: spatial shrunken centroids, r: 2, s: 4, method to use to calculate the spatial smoothing kernels: adaptive, Results as.RData output: yes). The classifier obtained as.RData file was then applied to the test data in the 'MSI classification' tool (Analysis step to perform: prediction, which classification method was used: SSC_classifier, load annotations: use annotations, load tabular file with pixel coordinates and the new response: file from Split Dataset tool that contains the spectra conditions of the test data).
Furthermore, this classifier was applied to the tumor ROIs of the three non-muscle infiltrating high-grade papillary urothelial cancers to predict their infiltration potential by using the 'MSI classification' tool (Analysis step to perform: prediction, which classification method was used: SSC_classifier, load annotations: use annotations, load tabular file with pixel coordinates and the new response: file from Split Dataset tool that contains the spectra conditions of the high-grade tumors). The most discriminative m/z features were selected according to the highest t-statistic values and their abundances in the different groups were visualized. Ion images were plotted with the 'MSI mz images' tool (plusminus m/z: 0.25, contrast enhancement 'histogram') on the binned, filtered, and combined data, which was TIC normalized in the 'MSI preprocessing' tool in a separate step only for visualization purposes. Average mass spectra plots per group were generated from binned, filtered, combined and smoothed MSI data with the 'MSI plot spectra' tool (choose spectra: plot single spectra, load tabular file with pixel coordinates: combined spectra annotations, separate plot per spectrum or overlaid plot with average spectra per annotation group: overlaid spectra plots, zoomed in m/z range: tabular file with mz of interest, m/z value to subtract from m/z values in tabular file: 1, m/z value to add to m/z values in tabular file: 4, load tabular file with m/z values: file with top mz value per condition). All m/z features that were part of one of the two classifiers (t-statistic value above zero) were matched with the Join two files tool (Choose the metrics of your distance: ppm, allowed distance between the two values that will trigger a merge: 200) to the downloaded MSiMass list [29] to obtain putative identifications. For Figs. 2, 3, 4, 5, pdf files from Galaxy were imported into Adobe Illustrator CS2 to arrange subfigures and adjust the label sizes.

Immunohistochemical assessment
Representative images of immunohistochemical stains are given in Figs. 6b-e and 7b-e. All slides were reviewed simultaneously by two experienced pathologists and one natural scientist via the Olympus BX51 microscope using a multi-viewing unit. Immunoreactivity for cytokeratin 7 and vimentin was scored according to percentage of cytoplasmic positively stained cells within the tumor. For histone H2A, immunoreactivity was scored according to percentage of nuclear positively stained cells within the tumor and the tumor stroma separately from each other. Immunostaining was considered positive, when appropriate brown staining was seen in the tumor cell cytoplasm (cytokeratin 7/vimentin) or in the nucleus (histone H2A).

Immunohistological assessment and statistical analyses
Some tissue cores could not be evaluated, either due to being lost during preparation or because the tissue column in the tissue microarray was already empty. A few cores that were lost or damaged during MSI sample preparation could be evaluated immunohistochemically. Wilcoxon rank-sum test and Mann-Whitney-U-Test were used for statistical testing of paired and unpaired data, respectively.

Overview of the urothelial cancer cohort
The urothelial cancer cohort consisted of two TMAs comprising 49 bladder tissue cores derived from 47 patients. Two tissue cores were lost during sample preparation and in three tissue cores, neither tumor nor stroma regions were withdrawn during TMA construction. Due to the insufficient sample size number one pTis and two PUNLUMP were excluded from the analysis. The exclusion of these tissues led to a final cohort of 39 tissue cores from 39 patients (Table 1) and 2169 mass spectra out of which 1076 were annotated as tumor and 1093 as stroma.

Transparency and reproducibility of the MSI data analysis in the Galaxy framework
Both TMAs were imaged for tryptic peptides, hematoxylin and eosin stained and annotated for tumor and stroma ROIs. Raw data and spectra annotation information have been published via the PRIDE repository (identifier:PXD026459) [30]. The complete data analysis was performed on the European Galaxy server and was separated into seven different analysis histories, to keep the histories clearly arranged according to the different analysis steps: co-registrations, data preparation and preprocessing, classifications, visualizations, and identification (Fig. 1a). To achieve full reproducibility and transparency of the study we published all Galaxy histories, which contain raw and intermediate files together Pre-processing Workflow with the tool name, tool version and all set parameters. For each of the first five analysis steps, Galaxy workflows were built and published to enable re-running the same analysis in a standardized and automated way. The preprocessing workflow is depicted as an example in Fig. 1b.

Quality control and preprocessing
The acquired data showed pronounced intensity batch effects and m/z shifts, which could be removed through careful adjustment of the preprocessing steps. Key to observe and overcome these technical issues was the usage of internal calibrants [17] together with the Galaxy 'MSI qualitycontrol' tool, which generated more than 30 different descriptive plots. Both TMAs showed systematically increasing m/z values for the internal calibrants during the course of the measurement (Fig. 2a). This suggests that the TOF tube of the near-antique mass spectrometer, which was not built to acquire tens of thousands of spectra in a row, heated up during the measurement. These m/z shifts could be removed by aligning each spectrum to the mean spectrum and recalibrating the m/z positions via the internal calibrants (Fig. 2b). Intensity batch effects were observed between the two measurements with higher intensities in TMA2 (Fig. 2a). As the baseline was already removed during data acquisition, TIC normalization could not be performed on the raw data as suggested by Deininger et al. [31]. Instead we were able to reduce the batch effects (Fig. 2b) by performing TIC normalization after peak picking and contaminant removal as suggested by Fonville [32].

Classification of tumor and stroma spectra
In several cancers, tumor cells are intermingled or surrounded by connective tissue, the so-called tumor stroma, which is part of the tumor microenvironment. To   Fig. 2 Data properties accessed by the 'MSI qualitycontrol' tool. a Mass and intensity shifts before pre-processing. b Mass and intensity shifts could be reduced through careful adjustment of pre-processing steps and parameters distinguish tumor and stroma tissue types, we have built a classifier, which reached 94% on the training and 99% on the test datasets with high sensitivity and specificity ( Table 2). To avoid overfitting, we generated training and test datasets by splitting patients randomly into the two groups and thus guarantee that all spectra of the same patient are present only in one of the two groups. Despite these precautions, the classification accuracy is likely too optimistic due to the small amount of samples. During classification, feature selection was performed by shrinking the number of m/z features that are included into the classifier to a minimum. 77 m/z features were included in the classifier (t-statistic > 0) out of which 37 were describing tumor and 40 stroma spectra (Fig. 3a). m/z 901.49 and 868.47 had the highest t-statistics for tumor and stroma respectively and were therefore the most discriminative m/z features (Fig. 3b, c).

Classification of infiltration behavior
Next, we were interested in classifying tumors according to their infiltration status. Only spectra corresponding to muscle-infiltrating urothelial cancer (n = 11, 731 spectra) and non-muscle infiltrating low-grade papillary urothelial cancer (n = 15, 312 spectra) were included to compare both tumor subtypes. Classification accuracies for the training data were 96 and 99% for the test data with high sensitivity and specificity (Table 3). Again, the classification accuracy is likely too optimistic due to the small amount of samples. The classifier included 35 m/z features to classify muscle-infiltrating and 36 m/z features to classify non-muscle infiltrating tumors (Fig. 4a). The m/z feature 944.53 was the most discriminative for muscle-infiltrating tumor and m/z 1104.57 for non-infiltrating tumors (Fig. 4b, c).

Prediction of muscle-infiltration potential of high-grade carcinomas
Non-muscle infiltrating high-grade urothelial cancers are not muscle infiltrating but are considered high-risk tumors as their risk of progression ranges from 15 to 40% and is thus much higher compared to Non-muscle infiltrating low-grade cancers [33]. The tissue cohort included three non-muscle infiltrating high-grade papillary urothelial cancer tissues (33 spectra), which were not included into the classification analysis because of their low sample number and being present only in one of the two TMAs.
Instead, we determined their muscle-infiltration potential by classifying them with the previously built classifier into muscle-infiltrating and non-muscle infiltrating cancers. With a total of only three patients, this analysis step is rather illustrative. The majority of spectra of all three tissue cores was classified as non-muscle infiltrating but    19:8 in one tissue 2 out of 15 spectra were classified as muscleinfiltrating and several other spectra were classified only with low probabilities as non-muscle infiltrating, suggesting that this cancer might have the molecular potential to transition into a muscle-infiltrating cancer (Fig. 5). Unfortunately this hypothesis could not be verified by clinical data because the patient was lost to follow up.

Assigning identities to m/z features
To obtain an idea about the identity of the measured peptides, we assigned tentative identifications via the MSiMass list [29]. Out of 123 unique m/z features that were part of the two classifiers (t-statistics > 0), 16 were matched to an entry of the MSiMass list within 200 ppm mass tolerance (Table 4). Most tentative collagen peptides were found in stromal regions and most keratin peptides in tumor regions, which is their expected location in tumor tissues.
The tentative m/z identifications of the tumorstroma classifier point towards ubiquitous peptides that are likely to be found in other solid tumors and their surrounding stroma as well. Histone H2A and H4 were part of the tumor classification and likely indicators of increased cell density in the urothelium (transitional epithelium) compared to stroma tissue, because their abundance is proportional to the amount of DNA [34]. Another potential hit for the tumor classifier was heat shock protein beta-1, which is a member of the heat shock protein family, which has been linked to (urothelial) cancers before [35,36]. Stroma is connective tissue and therefore characterized by protein fibrils made for example out of collagens. This intrinsically corroborates collagen alpha-1(1) and alpha-2(1) chain precursors, which we found to be important for stromal classification. Cytokeratin 16 has been associated with ureter, bladder and urethra and also keratinization of urothelial carcinomas [37]. However, as an epithelial cell specific intermediate filament, it is probably a mis-identification as it was part of the stromal classifier.
The tentative m/z identifications of the infiltration classifier showed that several peptides from intermediate filament proteins such as cytokeratins and vimentin were important for the classification into muscle-infiltrating and non-muscle infiltrating tumors.
Histone H2A refers to a set of closely related proteins that are involved in packaging DNA molecules into chromatin and thus influence gene expression. In our study cohort, 100% of all tumor tissues stained positive and 26 out of 27 stroma samples stained positive for histone H2A.X (Fig. 6). Staining intensity was medium or high in the majority of tissues. The percentage of positively stained cells was on median 100% in tumor and 80% in stroma tissues and the difference statistically significant (p-value of 0.0015 in Wilcoxon Rank Sum test). Together with the much higher cell density in the tumor areas compared to the stromal area this confirms the MSI results of more abundant histone H2A in tumor compared to stroma tissue. However, in contrast to the MSI results, there was a very slight tendency towards more frequent and more intense staining of histone H2A in low grade compared to infiltrating urothelial cancer (p-value of 0.69 in Mann-Whitney-U-test).
Cytokeratine 7 is a type II intermediate filament protein that is present in most urothelial carcinomas [38].

Discussion
We successfully conduct a fully transparent and reproducible analysis of an urothelial cancer MSI study in the Galaxy framework. The complete analysis was performed on a single platform, the European Galaxy Server. The previously established Galaxy MSI tools [18] allowed for all necessary analysis steps to classify different tissue   types and tumor subtypes of an urothelial cancer cohort. This included co-registration of optical and MSI images, thorough quality controls, pre-processing as well as statistical modelling and peptide identification. Technical artifacts such as intensity batch effects and m/z shifts could be observed and removed via the 'MSI qualitycontrol' tool in combination with an adjusted pre-processing. Classification of different tissue types as well as different urothelial tumor subtypes was achieved with very high accuracy.
Despite our best efforts in separating the available cohort into training and testing samples, we expect that these accuracies are likely too optimistic due to the limited sample numbers and the experimentally controlled setting with little variability: all samples derived from one pathological institute, the samples were prepared and measured together and similarly analyzed. This study, despite its little variability shows the potential of the approach, even though lower classification performance is expected in broader and more clinically relevant circumstances, such as multi-site studies on tissue material from different institutions.
The application of the Spatial Shrunken Centroids classification method enabled direct extraction of the m/z features that mostly contributed to the classification. A few of these m/z features could be assigned to tentative identifications, which were mostly in line with the biological context. The differential expression of three proteins was analyzed by immunohistochemistry.
Histone H2A.X could be confirmed for being more expressed in tumor than in stromal tissues. However, in contrast to the MSI results, histone H2A.X IHC showed a tendency towards a more frequent expression in nonmuscle infiltrating low-grade urothelial cancer than muscle-infiltrating cancer. Assuming m/z 944.53 is correctly identified as the peptide AGLQFPVGR of histone H2A, it would be present in the majority of histone H2A proteins. Via IHC we only stained histone H2A.X and the different abundance of this peptide between non-muscle infiltrating and muscle-infiltrating cancer could stem from other histone H2A protein family members.
IHC confirmed higher levels of cytokeratine 7 and lower levels of vimentin in non-muscle infiltrating lowgrade urothelial cancer compared to muscle-infiltrating urothelial carcinoma.
The raster size of 150 µm (with laser spot diameter of 100 µm) applied in this study did not allow for a more detailed investigation of the morphological structures and cell types, for example immune cells that are present in the desmoplastic stroma and the tumor tissue areas. Furthermore, the limited spatial resolution lead to relatively small datasets that could have been processed on a normal personal computer instead of a large cloud infrastructure. However, our findings highlight the general applicability of the Galaxy tools and workflows for reproducible classification analysis in translational MSI experiments. MSI studies with larger raw files due to high mass and spatial resolution will especially benefit from the huge computational resources that are available on the public Galaxy servers as these might be too demanding for computation on local computers.
We have published all Galaxy analysis histories for our study to make it fully transparent and reproducible. The Galaxy histories contain raw, meta and intermediate results data, as well as all tool names, tool versions, and all set tool parameters. Thus, every detail of the performed analysis can be re-traced and reproduced. In a copy of the Galaxy histories, researchers can adjust the analysis procedure according to their interest and inspect how changing different steps or parameters will change the outcome. Even though the Galaxy analysis history alone enables full reproducibility, we published all raw data including pathological annotations of stained tissues in the PRIDE proteomics data repository. This allows reuse of the data for new urothelial carcinoma studies and fosters future bioinformatic investigations since it represents the first human peptide imaging study that contains different disease groups and releases spectra wise pathological annotations for a complete patient cohort. While we have used the European Galaxy server for the analysis, studies with stricter data security restrictions could perform the analysis via ready to use docker containers on their local computing infrastructure [18]. However, to increase the trust in published MSI studies and to forward the MSI field it will become increasingly important to share raw data and analysis code, which may require to include data sharing into ethic approvals and patient consent forms from the beginning on.

Conclusion
We have performed the complete MSI analysis of an urothelial cancer cohort in a single platform, the European Galaxy server. Having used an outdated mass spectrometer, our study shows the importance of quality controls and pre-processing adjustment in order to detect and remove technical artifacts. Afterwards, we were able to classify tumor and stroma tissues as well as muscle-infiltrating and non-muscle infiltrating urothelial carcinomas based on their tryptic peptide composition with high accuracy and biologically explainable peptide identifications. Histone H2A was more abundant in tumor compared to stroma tissues, cytokeratine 7 more abundant and vimentin less abundant in nonmuscle infiltrating than in muscle-infiltrating cancers. In addition to these translational and biological findings, we highlight the potential for translational MSI studies and set new levels in terms of reproducibility and transparency by sharing all raw data and spectra annotations as well as the complete analysis histories. We would like to encourage the community to join our efforts to lay the foundation for advancing MSI towards clinical settings.