Annexin A2 and alpha actinin 4 expression correlates with metastatic potential of primary endometrial cancer.

The prediction of lymph node metastasis using clinic-pathological data and molecular information from endometrial cancers lacks accuracy and is therefore currently not routinely used in patient management. Consequently, although only a small percentage of patients with endometrial cancers suffer from metastasis, the majority undergo radical surgery including removal of pelvic lymph nodes. Upon analysis of publically available data and published research, we compiled a list of 60 proteins having the potential to display differential abundance between primary endometrial cancers with versus those without lymph node metastasis. Using data dependent acquisition LC-ESI-MS/MS we were able to detect 23 of these proteins in endometrial cancers, and using data independent LC-ESI-MS/MS the differential abundance of five of those proteins was observed. The localization of the differentially expressed proteins, was visualized using peptide MALDI MSI in whole tissue sections as well as tissue microarrays of 43 patients. The proteins identified were further validated by immunohistochemistry. Our data indicate that annexin A2 protein level is upregulated, whereas annexin A1 and α actinin 4 expression are downregulated in tumours with lymph node metastasis compared to those without lymphatic spread. Moreover, our analysis confirmed the potential of these markers, to be included in a statistical model for prediction of lymph node metastasis. The predictive model using highly ranked m/z values identified by MALDI MSI showed significantly higher predictive accuracy than the model using immunohistochemistry data. In summary, using publicly available data and complementary proteomics approaches, we were able to improve the prediction model for lymph node metastasis in EC.

Classification of MALDI-MS imaging data of tissue microarrays using canonical correlation analysis-based variable selection.

Applying MALDI-MS imaging to tissue microarrays (TMAs) provides access to proteomics data from large cohorts of patients in a cost- and time-efficient way, and opens the potential for applying this technology in clinical diagnosis. The complexity of these TMA data-high-dimensional low sample size-provides challenges for the statistical analysis, as classical methods typically require a nonsingular covariance matrix that cannot be satisfied if the dimension is greater than the sample size. We use TMAs to collect data from endometrial primary carcinomas from 43 patients. Each patient has a lymph node metastasis (LNM) status of positive or negative, which we predict on the basis of the MALDI-MS imaging TMA data. We propose a variable selection approach based on canonical correlation analysis that explicitly uses the LNM information. We apply LDA to the selected variables only. Our method misclassifies 2.3-20.9% of patients by leave-one-out cross-validation and strongly outperforms LDA after reduction of the original data with principle component analysis.

Lymph node metastasis of primary endometrial cancers: Associated proteins revealed by MALDI imaging.

Metastasis is a crucial step of malignant progression and is the primary cause of death from endometrial cancer. However, clinicians presently face the challenge that conventional surgical-pathological variables, such as tumour size, depth of myometrial invasion, histological grade, lymphovascular space invasion or radiological imaging are unable to predict with accuracy if the primary tumour has metastasized. In the current retrospective study, we have used primary tumour samples of endometrial cancer patients diagnosed with (n = 16) and without (n = 27) lymph node metastasis to identify potential discriminators. Using peptide matrix assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI), we have identified m/z values which can classify 88% of all tumours correctly. The top discriminative m/z values were identified using a combination of in situ sequencing and LC-MS/MS from digested tumour samples. Two of the proteins identified, plectin and α-Actin-2, were used for validation studies using LC-MS/MS data independent analysis (DIA) and immunohistochemistry. In summary, MALDI-MSI has the potential to identify discriminators of metastasis using primary tumour samples.

MALDI Mass Spectrometry Imaging Reveals Decreased CK5 Levels in Vulvar Squamous Cell Carcinomas Compared to the Precursor Lesion Differentiated Vulvar Intraepithelial Neoplasia.

Vulvar cancer is the fourth most common gynecological cancer worldwide. However, limited studies have been completed on the molecular characterization of vulvar squamous cell carcinoma resulting in a poor understanding of the disease initiation and progression. Analysis and early detection of the precursor lesion of HPV-independent vulvar squamous cell carcinoma (VSCC), differentiated vulvar intraepithelial neoplasia (dVIN), is of great importance given dVIN lesions have a high level of malignant potential. Here we present an examination of adjacent normal vulvar epithelium, dVIN, and VSCC from six patients by peptide Matrix-assisted laser desorption/ionization Mass Spectrometry Imaging (MALDI-MSI). The results reveal the differential expression of multiple peptides from the protein cytokeratin 5 (CK5) across the three vulvar tissue types. The difference observed in the relative abundance of CK5 by MALDI-MSI between the healthy epithelium, dVIN, and VSCC was further analyzed by immunohistochemistry (IHC) in tissue from eight VSCC patients. A decrease in CK5 immunostaining was observed in the VSCC compared to the healthy epithelium and dVIN. These results provide an insight into the molecular fingerprint of the vulvar intraepithelial neoplasia that appears to be more closely related to the healthy epithelium than the VSCC.

MALDI imaging mass spectrometry of N-linked glycans on formalin-fixed paraffin-embedded murine kidney.

Recent developments in spatial proteomics have paved the way for retrospective in situ mass spectrometry (MS) analyses of formalin-fixed paraffin-embedded clinical tissue samples. This type of analysis is commonly referred to as matrix-assisted laser desorption/ionization (MALDI) imaging. Recently, formalin-fixed paraffin-embedded MALDI imaging analyses were augmented to allow in situ analyses of tissue-specific N-glycosylation profiles. In the present study, we outline an improved automated sample preparation method for N-glycan MALDI imaging, which uses in situ PNGase F-mediated release and measurement of N-linked glycans from sections of formalin-fixed murine kidney. The sum of the presented data indicated that N-glycans can be cleaved from proteins within formalin-fixed tissue and characterized using three strategies: (i) extraction and composition analysis through on-target MALDI MS and liquid chromatography coupled to electrospray ionization ion trap MS; (ii) MALDI profiling, where N-glycans are released and measured from large droplet arrays in situ; and (iii) MALDI imaging, which maps the tissue specificity of N-glycans at a higher resolution. Thus, we present a complete, straightforward method that combines MALDI imaging and characterization of tissue-specific N-glycans and complements existing strategies.

Raw N-glycan mass spectrometry imaging data on formalin-fixed mouse kidney.

Provided is the annotated raw data for N-glycan mass spectrometry imaging (MSI) annotations in thin cross-sections of formalin-fixed and paraffin-embedded murine kidney. Relevant meta-data have been provided in this brief and the raw MSI data can be accessed using ProteomeXchange with the PRoteomics IDEntifications (PRIDE) identifier PXD009808. This brief is the first in a set of submissions from our group which will make raw data publicly accessible for existing and future MSI studies.

Balancing sufficiency and impact in reporting standards for mass spectrometry imaging experiments.

Reproducibility, or a lack thereof, is an increasingly important topic across many research fields. A key aspect of reproducibility is accurate reporting of both experiments and the resulting data. Herein, we propose a reporting guideline for mass spectrometry imaging (MSI). Previous standards have laid out guidelines sufficient to guarantee a certain quality of reporting; however, they set a high bar and as a consequence can be exhaustive and broad, thus limiting uptake.To help address this lack of uptake, we propose a reporting supplement-Minimum Information About a Mass Spectrometry Imaging Experiment (MIAMSIE)-and its abbreviated reporting standard version, MSIcheck. MIAMSIE is intended to improve author-driven reporting. It is intentionally not exhaustive, but is rather designed for extensibility and could therefore eventually become analogous to existing standards that aim to guarantee reporting quality. Conversely, its abbreviated form MSIcheck is intended as a diagnostic tool focused on key aspects in MSI reporting.We discuss how existing standards influenced MIAMSIE/MSIcheck and how these new approaches could positively impact reporting quality, followed by test implementation of both standards to demonstrate their use. For MIAMSIE, we report on author reviews of four articles and a dataset. For MSIcheck, we show a snapshot review of a one-month subset of the MSI literature that indicated issues with data provision and the reporting of both data analysis steps and calibration settings for MS systems. Although our contribution is MSI specific, we believe the underlying approach could be considered as a general strategy for improving scientific reporting.

Proteomic responses to gold(iii)-toxicity in the bacterium Cupriavidus metallidurans CH34.

The metal-resistant ß-proteobacterium Cupriavidus metallidurans drives gold (Au) biomineralisation and the (trans)formation of Au nuggets largely via unknown biochemical processes, ultimately leading to the reductive precipitation of mobile, toxic Au(i/iii)-complexes. In this study proteomic responses of C. metallidurans CH34 to mobile, toxic Au(iii)-chloride are investigated. Cells were grown in the presence of 10 and 50 µM Au(iii)-chloride, 50 µM Cu(ii)-chloride and without additional metals. Differentially expressed proteins were detected by difference gel electrophoresis and identified by liquid chromatography coupled mass spectrometry. Proteins that were more abundant in the presence of Au(iii)-chloride are involved in a range of important cellular functions, e.g., metabolic activities, transcriptional regulation, efflux and metal transport. To identify Au-binding proteins, protein extracts were separated by native 2D gel electrophoresis and Au in protein spots was detected by laser absorption inductively coupled plasma mass spectrometry. A chaperon protein commonly understood to bind copper (Cu), CupC, was identified and shown to bind Au. This indicates that it forms part of a multi-metal detoxification system and suggests that similar/shared detoxification pathways for Au and Cu exist. Overall, this means that C. metallidurans CH34 is able to mollify the toxic effects of cytoplasmic Au(iii) by sequestering this Au-species. This effect may in the future be used to develop CupC-based biosensing capabilities for the in-field detection of Au in exploration samples.