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Objective: To investigate the in vivo biological effects of leukocyte-poor platelet-rich plasma (LpPRP) treatment in human synovial layer to establish the cellular basis for a prolonged clinical improvement., Methods: Synovial tissues ( n  = 367) were prospectively collected from patients undergoing arthroscopic surgery. Autologous-conditioned plasma, LpPRP, was injected into the knees of 163 patients 1-7 days before surgery to reduce operative trauma and inflammation, and to induce the onset of regeneration. A total of 204 patients did not receive any injection. All samples were analyzed by mass spectrometry imaging. Data analysis was evaluated by clustering, classification, and investigation of predictive peptides. Peptide identification was done by tandem mass spectrometry and database matching., Results: Data analysis revealed two major clusters belonging to LpPRP-treated (LpPRP-1) and untreated (LpPRP-0) patients. Classification analysis showed a discrimination accuracy of 82%-90%. We identified discriminating peptides for CD45 and CD29 receptors (receptor-type tyrosine-protein phosphatase C and integrin beta 1), indicating an enhancement of musculoskeletal stem cells, as well as an enhancement of lubricin, collagen alpha-1-(I) chain, and interleukin-receptor-17-E, dampening the inflammatory reaction in the LpPRP-1 group following LpPRP injection., Conclusions: We could demonstrate for the first time that injection therapy using "autologic-conditioned biologics" may lead to cellular changes in the synovial membrane that might explain the reported prolonged beneficial clinical effects. Here, we show in vivo cellular changes, possibly based on muscular skeletal stem cell alterations, in the synovial layer. The gliding capacities of joints might be improved by enhancing of lubricin, anti-inflammation by activation of interleukin-17 receptor E, and reduction of the inflammatory process by blocking interleukin-17., Competing Interests: RC and JK were employed by Proteopath GmbH. JK received payment for speaker honorarium from Astrazeneca (dates: June 2020, November 2020, and December 2021) and Molecular Health GmbH (date: November 2020). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Baltzer, Casadonte, Korff, Baltzer, Kriegsmann, Kriegsmann and Kriegsmann.)

Mutations of the oncogenes v-raf murine sarcoma viral oncogene homolog B1 ( BRAF ) and neuroblastoma RAS viral oncogene homolog ( NRAS ) are the most frequent genetic alterations in melanoma and are mutually exclusive. BRAF V600 mutations are predictive for response to the two BRAF inhibitors vemurafenib and dabrafenib and the mitogen-activated protein kinase kinase (MEK) inhibitor trametinib. However, inter- and intra-tumoral heterogeneity and the development of acquired resistance to BRAF inhibitors have important clinical implications. Here, we investigated and compared the molecular profile of BRAF and NRAS mutated and wildtype melanoma patients' tissue samples using imaging mass spectrometry-based proteomic technology, to identify specific molecular signatures associated with the respective tumors. SCiLSLab and R-statistical software were used to classify peptide profiles using linear discriminant analysis and support vector machine models optimized with two internal cross-validation methods (leave-one-out, k-fold). Classification models showed molecular differences between BRAF and NRAS mutated melanoma, and identification of both was possible with an accuracy of 87-89% and 76-79%, depending on the respective classification method applied. In addition, differential expression of some predictive proteins, such as histones or glyceraldehyde-3-phosphate-dehydrogenase, correlated with BRAF or NRAS mutation status. Overall, these findings provide a new molecular method to classify melanoma patients carrying BRAF and NRAS mutations and help provide a broader view of the molecular characteristics of these patients that may help understand the signaling pathways and interactions involving the altered genes.

Sample processing of formalin-fixed specimens constitutes a major challenge in molecular profiling efforts. Pre-analytical factors such as fixative temperature, dehydration, and embedding media affect downstream analysis, generating data dependent on technical processing rather than disease state. In this study, we investigated two different sample processing methods, including the use of the cytospin sample preparation and automated sample processing apparatuses for proteomic analysis of multiple myeloma (MM) cell lines using imaging mass spectrometry (IMS). In addition, two sample-embedding instruments using different reagents and processing times were considered. Three MM cell lines fixed in 4% paraformaldehyde were either directly centrifuged onto glass slides using cytospin preparation techniques or processed to create paraffin-embedded specimens with an automatic tissue processor, and further cut onto glass slides for IMS analysis. The number of peaks obtained from paraffin-embedded samples was comparable between the two different sample processing instruments. Interestingly, spectra profiles showed enhanced ion yield in cytospin compared to paraffin-embedded samples along with high reproducibility compared to the sample replicate.

Artificial intelligence (AI) has shown potential for facilitating the detection and classification of tumors. In patients with non-small cell lung cancer, distinguishing between the most common subtypes, adenocarcinoma (ADC) and squamous cell carcinoma (SqCC), is crucial for the development of an effective treatment plan. This task, however, may still present challenges in clinical routine. We propose a two-modality, AI-based classification algorithm to detect and subtype tumor areas, which combines information from matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) data and digital microscopy whole slide images (WSIs) of lung tissue sections. The method consists of first detecting areas with high tumor cell content by performing a segmentation of the hematoxylin and eosin-stained (H&E-stained) WSIs, and subsequently classifying the tumor areas based on the corresponding MALDI MSI data. We trained the algorithm on six tissue microarrays (TMAs) with tumor samples from N = 232 patients and used 14 additional whole sections for validation and model selection. Classification accuracy was evaluated on a test dataset with another 16 whole sections. The algorithm accurately detected and classified tumor areas, yielding a test accuracy of 94.7% on spectrum level, and correctly classified 15 of 16 test sections. When an additional quality control criterion was introduced, a 100% test accuracy was achieved on sections that passed the quality control (14 of 16). The presented method provides a step further towards the inclusion of AI and MALDI MSI data into clinical routine and has the potential to reduce the pathologist's work load. A careful analysis of the results revealed specific challenges to be considered when training neural networks on data from lung cancer tissue.

Subtyping of the most common non-small cell lung cancer (NSCLC) tumor types adenocarcinoma (ADC) and squamous cell carcinoma (SqCC) is still a challenge in the clinical routine and a correct diagnosis is crucial for an adequate therapy selection. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) has shown potential for NSCLC subtyping but is subject to strong technical variability and has only been applied to tissue samples assembled in tissue microarrays (TMAs). To our knowledge, a successful transfer of a classifier from TMAs to whole sections, which are generated in the standard clinical routine, has not been presented in the literature as of yet. We introduce a classification algorithm using extensive preprocessing and a classifier (either a neural network or a linear discriminant analysis (LDA)) to robustly classify whole sections of ADC and SqCC lung tissue. The classifiers were trained on TMAs and validated and tested on whole sections. Vital for a successful application on whole sections is the extensive preprocessing and the use of whole sections for hyperparameter selection. The classification system with the neural network/LDA results in 99.0%/98.3% test accuracy on spectra level and 100.0%/100.0% test accuracy on whole section level, respectively, and, therefore, provides a powerful tool to support the pathologist's decision making process. The presented method is a step further towards a clinical application of MALDI MSI and artificial intelligence for subtyping of NSCLC tissue sections., (© 2022 The Authors. Proteomics - Clinical Applications published by Wiley-VCH GmbH.)

Many studies have demonstrated that tissue phenotyping (tissue typing) based on mass spectrometric imaging data is possible; however, comprehensive studies assessing variation and classifier transferability are largely lacking. This study evaluated the generalization of tissue classification based on Matrix Assisted Laser Desorption/Ionization (MALDI) mass spectrometric imaging (MSI) across measurements performed at different sites. Sections of a tissue microarray (TMA) consisting of different formalin-fixed and paraffin-embedded (FFPE) human tissue samples from different tumor entities (leiomyoma, seminoma, mantle cell lymphoma, melanoma, breast cancer, and squamous cell carcinoma of the lung) were prepared and measured by MALDI-MSI at different sites using a standard protocol (SOP). Technical variation was deliberately introduced on two separate measurements via a different sample preparation protocol and a MALDI Time of Flight mass spectrometer that was not tuned to optimal performance. Using standard data preprocessing, a classification accuracy of 91.4% per pixel was achieved for intrasite classifications. When applying a leave-one-site-out cross-validation strategy, accuracy per pixel over sites was 78.6% for the SOP-compliant data sets and as low as 36.1% for the mistuned instrument data set. Data preprocessing designed to remove technical variation while retaining biological information substantially increased classification accuracy for all data sets with SOP-compliant data sets improved to 94.3%. In particular, classification accuracy of the mistuned instrument data set improved to 81.3% and from 67.0% to 87.8% per pixel for the non-SOP-compliant data set. We demonstrate that MALDI-MSI-based tissue classification is possible across sites when applying histological annotation and an optimized data preprocessing pipeline to improve generalization of classifications over technical variation and increasing overall robustness.

Among neonates, tested positive for SARS-CoV-2, the majority of infections occur through postpartum transmission. Only few reports describe intrauterine or intrapartum SARS-CoV-2 infections in newborns. To understand the route of transmission, detection of the virus or virus nucleic acid in the placenta and amniotic tissue are of special interest. Current methods to detect SARS-CoV-2 in placental tissue are immunohistochemistry, electron microscopy, in-situ hybridization, polymerase chain reaction (PCR) and next-generation sequencing. Recently, we described an alternative method for the detection of viral ribonucleic acid (RNA), by combination of reverse transcriptase-PCR and mass spectrometry (MS) in oropharyngeal and oral swabs. In this report, we could detect SARS-CoV-2 in formal-fixed and paraffin-embedded (FFPE) placental and amniotic tissue by multiplex RT-PCR MS. Additionally, we could identify the British variant (B.1.1.7) of the virus in this tissue by the same methodology. Combination of RT-PCR with MS is a fast and easy method to detect SARS-CoV-2 viral RNA, including specific variants in FFPE tissue.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is an established tool for the investigation of formalin-fixed paraffin-embedded (FFPE) tissue samples and shows a high potential for applications in clinical research and histopathological tissue classification. However, the applicability of this method to serial clinical and pharmacological studies is often hampered by inevitable technical variation and limited reproducibility. We present a novel spectral cross-normalization algorithm that differs from the existing normalization methods in two aspects: (a) it is based on estimating the full statistical distribution of spectral intensities and (b) it involves applying a non-linear, mass-dependent intensity transformation to align this distribution with a reference distribution. This method is combined with a model-driven resampling step that is specifically designed for data from MALDI imaging of tryptic peptides. This method was performed on two sets of tissue samples: a single human teratoma sample and a collection of five tissue microarrays (TMAs) of breast and ovarian tumor tissue samples ( N = 241 patients). The MALDI MSI data was acquired in two labs using multiple protocols, allowing us to investigate different inter-lab and cross-protocol scenarios, thus covering a wide range of technical variations. Our results suggest that the proposed cross-normalization significantly reduces such batch effects not only in inter-sample and inter-lab comparisons but also in cross-protocol scenarios. This demonstrates the feasibility of cross-normalization and joint data analysis even under conditions where preparation and acquisition protocols themselves are subject to variation.

The discrimination of malignant melanoma from benign nevi may be difficult in some cases. For this reason, immunohistological and molecular techniques are included in the differential diagnostic toolbox for these lesions. These methods are time consuming when applied subsequently and, in some cases, no definitive diagnosis can be made. We studied both lesions by imaging mass spectrometry (IMS) in a large cohort ( n = 203) to determine a different proteomic profile between cutaneous melanomas and melanocytic nevi. Sample preparation and instrument setting were tested to obtain optimal results in term of data quality and reproducibility. A proteomic signature was found by linear discriminant analysis to discern malignant melanoma from benign nevus ( n = 113) with an overall accuracy of >98%. The prediction model was tested in an independent set ( n = 90) reaching an overall accuracy of 93% in classifying melanoma from nevi. Statistical analysis of the IMS data revealed mass-to-charge ratio ( m/z) peaks which varied significantly (Area under the receiver operating characteristic curve > 0.7) between the two tissue types. To our knowledge, this is the largest IMS study of cutaneous melanoma and nevi performed up to now. Our findings clearly show that discrimination of melanocytic nevi from melanoma is possible by IMS.

Subtyping of non-small cell lung cancer (NSCLC) is paramount for therapy stratification. In this study, we analyzed the largest NSCLC cohort by mass spectrometry imaging (MSI) to date. We sought to test different classification algorithms and to validate results obtained in smaller patient cohorts. Tissue microarrays (TMAs) from including adenocarcinoma (ADC, n = 499) and squamous cell carcinoma (SqCC, n = 440), were analyzed. Linear discriminant analysis, support vector machine, and random forest (RF) were applied using samples randomly assigned for training (66%) and validation (33%). The m/z species most relevant for the classification were identified by on-tissue tandem mass spectrometry and validated by immunohistochemistry (IHC). Measurements from multiple TMAs were comparable using standardized protocols. RF yielded the best classification results. The classification accuracy decreased after including less than six of the most relevant m/z species. The sensitivity and specificity of MSI in the validation cohort were 92.9% and 89.3%, comparable to IHC. The most important protein for the discrimination of both tumors was cytokeratin 5. We investigated the largest NSCLC cohort by MSI to date and found that the classification of NSCLC into ADC and SqCC is possible with high accuracy using a limited set of m/z species.

Background: While subtyping of the majority of malignant chromophobe renal cell carcinoma (cRCC) and benign renal oncocytoma (rO) is possible on morphology alone, additional histochemical, immunohistochemical or molecular investigations are required in a subset of cases. As currently used histochemical and immunohistological stains as well as genetic aberrations show considerable overlap in both tumors, additional techniques are required for differential diagnostics. Mass spectrometry imaging (MSI) combining the detection of multiple peptides with information about their localization in tissue may be a suitable technology to overcome this diagnostic challenge. Patients and Methods: Formalin-fixed paraffin embedded (FFPE) tissue specimens from cRCC (n=71) and rO (n=64) were analyzed by MSI. Data were classified by linear discriminant analysis (LDA), classification and regression trees (CART), k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) algorithm with internal cross validation and visualized by t-distributed stochastic neighbor embedding (t-SNE). Most important variables for classification were identified and the classification algorithm was optimized. Results: Applying different machine learning algorithms on all m/z peaks, classification accuracy between cRCC and rO was 85%, 82%, 84%, 77% and 64% for RF, SVM, KNN, CART and LDA. Under the assumption that a reduction of m/z peaks would lead to improved classification accuracy, m/z peaks were ranked based on their variable importance. Reduction to six most important m/z peaks resulted in improved accuracy of 89%, 85%, 85% and 85% for RF, SVM, KNN, and LDA and remained at the level of 77% for CART. t-SNE showed clear separation of cRCC and rO after algorithm improvement. Conclusion: In summary, we acquired MSI data on FFPE tissue specimens of cRCC and rO, performed classification and detected most relevant biomarkers for the differential diagnosis of both diseases. MSI data might be a useful adjunct method in the differential diagnosis of cRCC and rO., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Purpose: Differential diagnosis of ulcerative colitis (UC) and Crohn's disease (CD) is of utmost importance for the decision making of respective therapeutic treatment strategies but in about 10-15% of cases, a clinical and histopathological assessment does not lead to a definite diagnosis. The aim of the study is to characterize proteomic differences between UC and CD., Experimental Design: Microproteomics is performed on formalin-fixed paraffin-embedded colonic tissue specimens from 9 UC and 9 CD patients. Protein validation is performed using immunohistochemistry (IHC) (n UC =51, n CD =62, n CTRL =10) followed by digital analysis., Results: Microproteomic analyses reveal eight proteins with higher abundance in CD compared to UC including proteins related to neutrophil activity and damage-associated molecular patterns. Moreover, one protein, Aldo-keto reductase family 1 member C3 (AKR1C3), is present in eight out of nine CD and absent in all UC samples. Digital IHC analysis reveal a higher percentage and an increased expression intensity of AKR1C3-positive epithelial cells in CD compared to UC and in controls compared to inflammatory bowel disease (IBD)., Conclusion and Clinical Relevance: Overall, the results suggest that microproteomics is an adequate tool to highlight protein patterns in IBD. IHC and digital pathology might support future differential diagnosis of UC and CD., (© 2020 The Authors. Proteomics - Clinical Applications published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is an established tool for the investigation of formalin fixed paraffin embedded (FFPE) tissue samples and shows a high potential for applications in clinical research and histopathological diagnosis. The applicability and accuracy of this method, however, heavily depends on the quality of the acquired data, and in particular mass misalignment in axial time-of-flight (TOF) MSI continues to be a serious issue. We present a mass alignment and recalibration method that is specifically designed to operate on MALDI peptide imaging data. The proposed method exploits statistical properties of the characteristic chemical noise background observed in peptide imaging experiments. By comparing these properties to a theoretical peptide mass model, the effective mass shift of each spectrum is estimated and corrected. The method was evaluated on a cohort of 31 FFPE tissue samples, pursuing a statistical validation approach to estimate both the reduction of relative misalignment, as well as the increase in absolute mass accuracy. Our results suggest that a relative mass precision of approximately 5 ppm and an absolute accuracy of approximately 20 ppm are achievable using our method.

Purpose: Identification of proteolytic peptides from matrix-assisted laser desorption/ionization (MALDI) imaging remains a challenge. The low fragmentation yields obtained using in situ post source decay impairs identification. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is an alternative to in situ MS/MS, but leads to multiple identification candidates for a given mass. The authors propose to use LC-MS/MS-based biomarker discovery results to reliably identify proteolytic peptides from MALDI imaging., Experimental Design: The authors defined m/z values of interest for high grade squamous intraepithelial lesion (HSIL) by MALDI imaging. In parallel the authors used data from a biomarker discovery study to correlate m/z from MALDI imaging with masses of peptides identified by LC-MS/MS in HSIL. The authors neglected candidates that were not significantly more abundant in HSIL according to the biomarker discovery investigation., Results: The authors assigned identifications to three m/z of interest. The number of possible identifiers for MALDI imaging m/z peaks using LC-MS/MS-based biomarker discovery studies was reduced by about tenfold compared using a single LC-MS/MS experiment. One peptide identification candidate was validated by immunohistochemistry., Conclusion and Clinical Relevance: This concept combines LC-MS/MS-based quantitative proteomics with MALDI imaging and allows reliable peptide identification. Public datasets from LC-MS/MS biomarker discovery experiments will be useful to identify MALDI imaging m/z peaks., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Purpose: To present matrix-assisted laser desorption/ionization (MALDI) imaging as a powerful method to highlight various tissue compartments., Experimental Design: Formalin-fixed paraffin-embedded (FFPE) tissue of a uterine cervix, a pancreas, a duodenum, a teratoma, and a breast cancer tissue microarray (TMA) are analyzed by MALDI imaging and by immunohistochemistry (IHC). Peptide images are visualized and analyzed using FlexImaging and SCiLS Lab software. Different histological compartments are compared by hierarchical cluster analysis., Results: MALDI imaging highlights tissue compartments comparable to IHC. In cervical tissue, normal epithelium can be discerned from intraepithelial neoplasia. In pancreatic and duodenal tissues, m/z signals from lymph follicles, vessels, duodenal mucosa, normal pancreas, and smooth muscle structures can be visualized. In teratoma, specific m/z signals to discriminate squamous epithelium, sebaceous glands, and soft tissue are detected. Additionally, tumor tissue can be discerned from the surrounding stroma in small tissue cores of TMAs. Proteomic data acquisition of complex tissue compartments in FFPE tissue requires less than 1 h with recent mass spectrometers., Conclusion and Clinical Relevance: The simultaneous characterization of morphological and proteomic features in the same tissue section adds proteomic information for histopathological diagnostics, which relies at present on conventional hematoxylin and eosin staining, histochemical, IHC and molecular methods., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Purpose: Matrix assisted laser desorption/ionization time-of-flight mass spectrometry imaging (MALDI-MSI) is a powerful tool to analyze the spatial distribution of peptides in tissues. Digital PCR (dPCR) is a method to reliably detect genetic mutations. Biopsy material is often limited due to minimally invasive techniques, but information on diagnosis, prognosis, and prediction is required for subsequent clinical decision making. Thus, saving tissue material during diagnostic workup is highly warranted for best patient care. The possibility to combine proteomic analysis by MALDI-MSI and mutational analysis by dPCR from the same tissue section is evaluated., Experimental Design: Ten 0.5 × 0.5 cm formalin-fixed paraffin embedded tissue samples of pulmonary adenocarcinomas with known EGFR or KRAS mutations are analyzed by MALDI-MSI. Subsequently, DNA is extracted from the analyzed tissue material and tested for the respective driver mutation by dPCR., Results: Detection of driver gene mutations after MALDI MSI analysis is successful in all analyzed samples. Determined mutant allele frequencies are in good agreement with values assessed from untreated serial tissue sections with a mean absolute deviation of 0.16., Conclusion and Clinical Relevance: It has been demonstrated that MALDI-MSI can be combined with genetic analysis, like dPCR. Workflows enabling the subsequent analysis of proteomic and genetic markers are particularly promising for the analysis of limited sample material such as biopsy specimen., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Objective: Tissue slides analyzed by MS imaging (MSI) are stained by H&E (Haematoxylin and Eosin) to identify regions of interest. As it can be difficult to identify specific cells of interest by H&E alone, data analysis may be impaired. Immunohistochemistry (IHC) can highlight cells of interest but single or combined IHC on tissue sections analyzed by MSI have not been performed., Methods: We performed MSI on bone marrow biopsies from patients with multiple myeloma and stained different antibodies (CD38, CD138, MUM1, kappa- and lambda). A combination of CK5/6/TTF1 and Napsin-A/p40 is stained after MSI on adenocarcinoma and squamous cell carcinoma of the lung. Staining intensities of p40 after MSI and on a serial section are quantified on a tissue microarray (n = 44) by digital analysis., Results: Digital evaluation reveals weaker staining intensities after MSI as compared to serial sections. Staining quality and quantity after MSI enables to identify cells of interest. On the tissue microarray, one out of 44 tissue specimens shows no staining of p40 after MSI, but weak nuclear staining on a serial section., Conclusion: We demonstrated that single and double IHC staining is feasible on tissue sections previously analyzed by MSI, with decreased staining intensities., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Purpose: To facilitate the transition of MALDI-MS Imaging (MALDI-MSI) from basic science to clinical application, it is necessary to analyze formalin-fixed paraffin-embedded (FFPE) tissues. The aim is to improve in situ tryptic digestion for MALDI-MSI of FFPE samples and determine if similar results would be reproducible if obtained from different sites., Experimental Design: FFPE tissues (mouse intestine, human ovarian teratoma, tissue microarray of tumor entities sampled from three different sites) are prepared for MALDI-MSI. Samples are coated with trypsin using an automated sprayer then incubated using deliquescence to maintain a stable humid environment. After digestion, samples are sprayed with CHCA using the same spraying device and analyzed with a rapifleX MALDI Tissuetyper at 50 µm spatial resolution. Data are analyzed using flexImaging, SCiLS, and R., Results: Trypsin application and digestion are identified as sources of variation and loss of spatial resolution in the MALDI-MSI of FFPE samples. Using the described workflow, it is possible to discriminate discrete histological features in different tissues and enabled different sites to generate images of similar quality when assessed by spatial segmentation and PCA., Conclusions and Clinical Relevance: Spatial resolution and site-to-site reproducibility can be maintained by adhering to a standardized MALDI-MSI workflow., (© 2018 The Authors. Proteomics - Clinical Application published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Background: In matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) standardized sample preparation is important to obtain reliable results. Herein, the impact of section thickness in formalin-fixed paraffin embedded (FFPE) tissue microarrays (TMA) on spectral intensities is investigated., Patients and Methods: TMAs consisting of ten different tissues represented by duplicates of ten patients (n = 200 cores) are cut at 1, 3, and 5 μm. MSI analysis is performed and mean intensities of all evaluable cores are extracted. Measurements are merged and mean m/z intensities are compared., Results: Visual inspection of spectral intensities between 1, 3, and 5 μm reveals generally higher intensities in thinner tissue sections. Specifically, higher intensities are observed in the vast majority of peaks (98.6%, p < 0.01) in 1 μm compared with 5 μm sections. Note that 28.4% and 2.1% of m/z values exhibit a at least two- and threefold intensity difference (p < 0.01) in 1 μm compared to 5 μm sections, respectively., Conclusion: A section thickness of 1 μm results in higher spectral intensities compared with 5 μm. The results highlight the importance of standardized protocols in light of recent efforts to identify clinically relevant biomarkers using MSI. The use of TMAs for comparative analysis seems advantageous, as section thickness displays less variability., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Purpose: To develop a mass spectrometry imaging (MSI) based workflow for extracting m/z values related to putative protein biomarkers and using these for reliable tumor classification., Experimental Design: Given a list of putative breast and ovarian cancer biomarker proteins, a set of related m/z values are extracted from heterogeneous MSI datasets derived from formalin-fixed paraffin-embedded tissue material. Based on these features, a linear discriminant analysis classification model is trained to discriminate the two tumor types., Results: It is shown that the discriminative power of classification models based on the extracted features is increased compared to the automatic training approach, especially when classifiers are applied to spectral data acquired under different conditions (instrument, preparation, laboratory)., Conclusions and Clinical Relevance: Robust classification models not confounded by technical variation between MSI measurements are obtained. This supports the assumption that the classification of the respective tumor types is based on biological rather than technical differences, and that the selected features are related to the proteomic profiles of the tumor types under consideration., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Purpose: Using a recently developed matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) method, human breast cancer formalin-fixed paraffin-embedded (FFPE) tissue sections and tissue microarrays (TMA) are evaluated for N-linked glycan distribution in the tumor microenvironment., Experimental Design: Tissue sections representing multiple human epidermal growth factor receptor 2 (HER2) receptor-positive and triple-negative breast cancers (TNBC) in both TMA and FFPE slide format are processed for high resolution N-glycan MALDI-IMS. An additional FFPE tissue cohort of primary and metastatic breast tumors from the same donors are also evaluated., Results: The cumulative N-glycan MALDI-IMS analysis of breast cancer FFPE tissues and TMAs indicate the distribution of specific glycan structural classes to stromal, necrotic, and tumor regions. A series of high-mannose, branched and fucosylated glycans are detected predominantly within tumor regions. Additionally, a series of polylactosamine glycans are detected in advanced HER2+, TNBC, and metastatic breast cancer tissues. Comparison of tumor N-glycan species detected in paired primary and metastatic tissues indicate minimal changes between the two conditions., Conclusions and Clinical Relevance: The prevalence of tumor-associated polylactosamine glycans in primary and metastatic breast cancer tissues indicates new mechanistic insights into the development and progression of breast cancers. The presence of these glycans could be targeted for therapeutic strategies and further evaluation as potential prognostic biomarkers., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Purpose: To define proteomic differences between pancreatic ductal adenocarcinoma (pDAC) and pancreatic neuroendocrine tumor (pNET) by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI)., Experimental Design: Ninety-three pDAC and 126 pNET individual tissues are assembled in tissue microarrays and analyzed by MALDI MSI. The cohort is separated in a training (52 pDAC and 83 pNET) and validation set (41 pDAC and 43 pNET). Subsequently, a linear discriminant analysis (LDA) model based on 46 peptide ions is performed on the training set and evaluated on the validation cohort. Additionally, two liver metastases and a whole slide of pDAC are analyzed by the same LDA algorithm., Results: Classification of pDAC and pNET by the LDA model is correct in 95% (39/41) and 100% (43/43) of patients in the validation cohort, respectively. The two liver metastases and the whole slide of pDAC are also correctly classified in agreement with the histopathological diagnosis., Conclusion and Clinical Relevance: In the present study, a large dataset of pDAC and pNET by MALDI MSI is investigated, a class prediction model that allowed separation of both entities with high accuracy is developed, and differential peptide peaks with potential diagnostic, prognostic, and predictive values are highlighted., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Purpose: High-grade squamous intraepithelial lesion (HSIL) is a known precursor for squamous cell carcinoma of uterine cervix. Although it is known that SILs are associated to infection by human papillomavirus, downstream biological mechanisms are still poorly described. In this study, we compared the microproteomic profile of HSIL to normal tissues: ectocervix (ectoC) and endocervix (endoC)., Experimental Design: Tissue regions of endoC, ectoC, and HSlL were collected by laser microdissection (3500 cells each) from five patients. Samples were processed and analyzed using our recently developed laser microdissection-based microproteomic method. Tissues were compared in order to retrieve HSIL's proteomic profile. Potentially interesting proteins for pathology were stained by immunohistochemistry., Results: We identified 3072 proteins among the fifteen samples and 2386 were quantified in at least four out of the five biological replicates of at least one tissue type. We found 236 proteins more abundant in HSIL. Gene ontology enrichments revealed mechanisms of DNA replication and RNA splicing. Despite the squamous nature of HSIL, a common signature between HSIL and endoC could be found. Finally, potential new markers could support diagnosis of dysplasia in SILs., Conclusion and Clinical Relevance: This microproteomic investigation of HSIL gives insights into the biology of cervical precancerous lesions., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Mass spectrometric (MS) techniques are applied in various areas of medical diagnostics. For the detection of microbiological germs and genetic mutations, MS is a method used in routine. Since MS also allows the analysis of proteins and peptides, it seems an ideal candidate to supplement histopatholological diagnostics. Matrix-assisted laser desorption/ionization time-of-flight Imaging MS links molecular analysis of numerous analytes with morphological information about their spatial distribution in cells or tissues. Herein, we review principle MS techniques as well as potential applications in pathology and discuss our vision for a future workflow., (Copyright © 2018 Elsevier GmbH. All rights reserved.)

Dysregulation of neuropeptides may play an important role in aging-induced impairments. Among them, pituitary adenylate cyclase-activating polypeptide (PACAP) is a potent cytoprotective peptide that provides an endogenous control against a variety of tissue-damaging stimuli. We hypothesized that the progressive decline of PACAP throughout life and the well-known general cytoprotective effects of PACAP lead to age-related pathophysiological changes in PACAP deficiency, supported by the increased vulnerability to various stressors of animals partially or totally lacking PACAP. Using young and aging CD1 PACAP knockout (KO) and wild type (WT) mice, we demonstrated pre-senile amyloidosis in young PACAP KO animals and showed that senile amyloidosis appeared accelerated, more generalized, more severe, and affected more individuals. Histopathology showed age-related systemic amyloidosis with mainly kidney, spleen, liver, skin, thyroid, intestinal, tracheal, and esophageal involvement. Mass spectrometry-based proteomic analysis, reconfirmed with immunohistochemistry, revealed that apolipoprotein-AIV was the main amyloid protein in the deposits together with several accompanying proteins. Although the local amyloidogenic protein expression was disturbed in KO animals, no difference was found in laboratory lipid parameters, suggesting a complex pathway leading to increased age-related degeneration with amyloid deposits in the absence of PACAP. In spite of no marked inflammatory histological changes or blood test parameters, we detected a disturbed cytokine profile that possibly creates a pro-inflammatory milieu favoring amyloid deposition. In summary, here we describe accelerated systemic senile amyloidosis in PACAP gene-deficient mice, which might indicate an early aging phenomenon in this mouse strain. Thus, PACAP KO mice could serve as a model of accelerated aging with human relevance. © 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland., (© 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.)

Motivation: Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Since mass spectra exhibit certain structural similarities to image data, deep learning may offer a promising strategy for classification of IMS data as it has been successfully applied to image classification., Results: Methodologically, we propose an adapted architecture based on deep convolutional networks to handle the characteristics of mass spectrometry data, as well as a strategy to interpret the learned model in the spectral domain based on a sensitivity analysis. The proposed methods are evaluated on two algorithmically challenging tumor classification tasks and compared to a baseline approach. Competitiveness of the proposed methods is shown on both tasks by studying the performance via cross-validation. Moreover, the learned models are analyzed by the proposed sensitivity analysis revealing biologically plausible effects as well as confounding factors of the considered tasks. Thus, this study may serve as a starting point for further development of deep learning approaches in IMS classification tasks., Availability and Implementation: https://gitlab.informatik.uni-bremen.de/digipath/Deep&#95;Learning&#95;for&#95;Tumor&#95;Classification&#95;in&#95;IMS., Contact: jbehrmann@uni-bremen.de or christianetmann@uni-bremen.de., Supplementary Information: Supplementary data are available at Bioinformatics online.

Proteomic approaches are of growing importance in the biologist's toolbox. It greatly benefited from past and recent advances in sampling, chemical processing, mass spectrometry (MS) instrumentation, and data processing. MS-based analysis of proteins is now in the process of being translated in pathology for objective diagnoses. In this viewpoint, we present the workflows that we think are the most promising for applications in pathology. We also comment what we think are prerequisites for a successful translational implementation., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Tissue microarrays (TMAs) are commonly used in biomarker research. To enhance the efficacy of TMAs and to avoid floating or folding of tissue cores, various improvements such as the application of carriers and melting techniques have been proposed. Compared with classical TMAs (cTMAs), carrier-based TMAs (cbTMAs) have been shown to have several advantages including sample handling and sectioning. Up to now, little is known about the efficacy and quality of cbTMAs compared with cTMAs. Thus, we set out to compare both types systematically. We constructed 5 spleen-based TMAs and 5 cTMAs with 10×10 different tissue types each. The total number of available cores, the number of folded cores, and the total core area was measured and evaluated by digital pathology. About 2% of cores got lost due to floating in both, cbTMAs and cTMAs, respectively. The remaining cores showed significant differences with regard to core integrity as about 1% of cbTMA cores and 9% of cTMA cores were folded (P<0.01). Folding or rolling was associated with specific tissue types. The size of the cores was smaller and less variable in cbTMAs (0.86±0.06 mm) compared with cTMAs (0.97±0.14 mm). The application of cbTMAs is an easy, inexpensive, and effective way to improve TMA-based research.

The histopathological synovitis score evaluates the immunological and inflammatory changes of synovitis in a graduated manner generally customary for diagnostic histopathological scores. The score results from semiquantitative evaluation of the width of the synovial surface cell layer, the cell density of the stroma and the density of the inflammatory infiltration into 4 semiquantitative levels (normal 0, mild 1, moderate 2, severe 3). The addition of these values results in a final score of 0-9 out of 9. On the basis of this summation the condition is divided into low-grade synovitis and high-grade synovitis: A synovitis score of 1 to≤4 is called low-grade synovitis (arthrosis-associated/OA synovitis, posttraumatic synovitis, meniscopathy-associated synovitis and synovitis with haemochromatosis). A synovitis score of≥5 to 9 is called high-grade synovitis (rheumatoid arthritis, psoriatic arthritis, Lyme arthritis, postinfection/reactive arthritis and peripheral arthritis with Bechterew's disease). By means of the synovitis score it is therefore possible to distinguish between degenerative/posttraumatic diseases (low-grade synovitis) and inflammatory rheumatic diseases (high-grade synovitis) with a sensitivity of 61.7% and a specificity of 96.1%. The diagnostic accuracy according to ROC analysis (AUC: 0.8-0.9) is good. Since the first publication (2002) and an associated subsequent publication (2006), the synovitis score has nationally and internationally been accepted for histopathological assessment of the synovitis. In a PubMed data analysis (status: 14.02.2017), the following citation rates according to Cited by PubMed Central articles resulted for the two synovitis score publications: For DOI: 10.1078/0344-0338-5710261 there were 29 Cited by PubMed Central articles and for the second extended publication DOI:10.1111/j.1365-2559.2006.02508 there were 44 Cited by PubMed Central articles. Therefore a total of 73 PubMed citations are observed over a period of 15 years, which demonstrates an international acceptance of the score. This synovitis score provides for the first time a diagnostic, standardised and reproducible histopathological evaluation method enabling a contribution to the differential diagnosis of chronic inflammatory general joint diseases. This is particularly the case by incorporation into the joint pathology algorithm. To specify the synovitis score an immunohistochemical determination of various inflammation-relevant CD antigens is proposed to enable a risk stratification of high-grade synovitis (e.g.: progression risk and sensitivity for biologicals)., (Copyright © 2017 Elsevier GmbH. All rights reserved.)

In advanced tumor stages, diagnosis is frequently made from metastatic tumor tissue. In some cases, the identification of the tumor of origin may be difficult by histology alone. In this setting, immunohistochemical and molecular biological methods are often required. In a subset of tumors definite diagnosis cannot be achieved. Thus, additional new diagnostic methods are required for precise tumor subtyping. Mass spectrometric methods are of special interest for the discrimination of different tumor types. We investigated whether it is possible to discern adenocarcinomas of colon and lung using high-throughput imaging mass spectrometry on formalin-fixed paraffin-embedded tissue microarrays. 101 primary adenocarcinoma of the colon and 91 primary adenocarcinoma of the lung were used to train a Linear Discriminant Analysis model. Results were validated on an independent set of 116 colonic and 75 lung adenocarcinomas. In the validation cohort 109 of 116 patients with colonic and 67 of 75 patients with lung adenocarcinomas were correctly classified. The ability to define proteomic profiles capable to discern different tumor types promises a valuable tool in cancer diagnostics and might complement current approaches. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann., (Copyright © 2016 Elsevier B.V. All rights reserved.)