Classification of Pancreatic Ductal Adenocarcinoma Using MALDI Mass Spectrometry Imaging Combined with Neural Networks.

Despite numerous diagnostic and therapeutic advances, pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate, and is the fourth leading cause of cancer death in developing countries. Besides its increasing prevalence, pancreatic malignancies are characterized by poor prognosis. Omics technologies have potential relevance for PDAC assessment but are time-intensive and relatively cost-intensive and limited by tissue heterogeneity. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can obtain spatially distinct peptide-signatures and enables tumor classification within a feasible time with relatively low cost. While MALDI-MSI data sets are inherently large, machine learning methods have the potential to greatly decrease processing time. We present a pilot study investigating the potential of MALDI-MSI in combination with neural networks, for classification of pancreatic ductal adenocarcinoma. Neural-network models were trained to distinguish between pancreatic ductal adenocarcinoma and other pancreatic cancer types. The proposed methods are able to correctly classify the PDAC types with an accuracy of up to 86% and a sensitivity of 82%. This study demonstrates that machine learning tools are able to identify different pancreatic carcinoma from complex MALDI data, enabling fast prediction of large data sets. Our results encourage a more frequent use of MALDI-MSI and machine learning in histopathological studies in the future.

Evaluation of the Aortopathy in the Ascending Aorta: The Novelty of Using Matrix-Assisted Laser Desorption/Ionization Imaging.

PURPOSE: Histopathological evaluation presents conflicting reports regarding aortic abnormalities. The authors aim to present proof-of-concept study to explore the feasibility of matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) in combination with histopathology for characterizing alterations in the aneurysmal ascending formalin-fixed paraffin-embedded (FFPE) aorta tissue. EXPERIMENTAL DESIGN: The authors assess FFPE specimens from patients with a dilated aorta and bicuspid aortic valve (BAV), those with a standard tricuspid aortic valve (TAV), and those with Marfan syndrome (MFS) via histopathology and grade the conditions for elastic fiber fragmentation (EFF) and MALDI-IMS. The proteins using liquid chromatographic-mass spectrometry are identified and the results are confirmed by immunohistochemistry. RESULTS: There is significant difference in terms of EFF between MFS and BAV, and TAV and BAV. Characteristic peptide signatures and m/z values in the EFF facilitate the characterization among the aortic specimens of BAV, MFS, and TAV. The m/z values from the aortic alpha smooth muscle actin and myosin heavy chains significantly increase in BAV compared with MFS and TAV. These findings are confirmed by immunohistochemistry. CONCLUSION: The results represent a strategy that uses MALDI-IMS in combination with histopathology as promising approaches to characterize spatial alteration in the structure of the aneurysmal ascending aorta.

Registration of Image Modalities for Analyses of Tissue Samples Using 3D Image Modelling.

PURPOSE: Biopsies are a diagnostic tool for the diagnosis of histopathological, molecular biological, proteomic, and imaging data, to narrow down disease patterns or identify diseases. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) provides an emerging state-of-the-art technique for molecular imaging of biological tissue. The aim of this study is the registration of MALDI MSI data sets and data acquired from different histological stainings to create a 3D model of biopsies and whole organs. EXPERIMENTAL DESIGN: The registration of the image modalities is achieved by using a variant of the authors' global, deformable Schatten-q-Norm registration approach. Utilizing a connected-component segmentation for background removal followed by a principal-axis based linear pre-registration, the images are adjusted into a homogeneous alignment. This registration approach is accompanied by the 3D reconstruction of histological and MALDI MSI data. RESULTS: With this, a system of automatic registration for cross-process evaluation, as well as for creating 3D models, is developed and established. The registration of MALDI MSI data with different histological image data is evaluated by using the established global image registration system. CONCLUSIONS AND CLINICAL RELEVANCE: In conclusion, this multimodal image approach offers the possibility of molecular analyses of tissue specimens in clinical research and diagnosis.

MALDI-Imaging for Classification of Epithelial Ovarian Cancer Histotypes from a Tissue Microarray Using Machine Learning Methods.

PURPOSE: Precise histological classification of epithelial ovarian cancer (EOC) has immanent diagnostic and therapeutic consequences, but remains challenging in histological routine. The aim of this pilot study is to examine the potential of matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry in combination with machine learning methods to classify EOC histological subtypes from tissue microarray. EXPERIMENTAL DESIGN: Formalin-fixed-paraffin-embedded tissue of 20 patients with ovarian clear-cell, 14 low-grade serous, 19 high-grade serous ovarian carcinomas, and 14 serous borderline tumors are analyzed using MALDI-Imaging. Classifications are computed by linear discriminant analysis (LDA), support vector machines with linear (SVM-lin) and radial basis function kernels (SVM-rbf), a neural network (NN), and a convolutional neural network (CNN). RESULTS: MALDI-Imaging and machine learning methods result in classification of EOC histotypes with mean accuracy of 80% for LDA, 80% SVM-lin, 74% SVM-rbf, 83% NN, and 85% CNN. Based on sensitivity (69-100%) and specificity (90-99%), CCN and NN are most suited to EOC classification. CONCLUSION AND CLINICAL RELEVANCE: The pilot study demonstrates the potential of MALDI-Imaging derived proteomic classifiers in combination with machine learning algorithms to discriminate EOC histotypes. Applications may support the development of new prognostic parameters in the assessment of EOC.

Unraveling local tissue changes within severely injured skeletal muscles in response to MSC-based intervention using MALDI Imaging mass spectrometry.

Pre-clinical and clinical studies are now beginning to demonstrate the high potential of cell therapies in enhancing muscle regeneration. We previously demonstrated functional benefit after the transplantation of autologous bone marrow mesenchymal stromal cells (MSC-TX) into a severe muscle crush trauma model. Despite our increasing understanding of the molecular and cellular mechanisms underlying MSC's regenerative function, little is known about the local molecular alterations and their spatial distribution within the tissue after MSC-TX. Here, we used MALDI imaging mass spectrometry (MALDI-IMS) in combination with multivariate statistical strategies to uncover previously unknown peptide alterations within severely injured skeletal muscles. Our analysis revealed that very early molecular alterations in response to MSC-TX occur largely in the region adjacent to the trauma and only to a small extent in the actual trauma region. Using "bottom up" mass spectrometry, we subsequently identified the proteins corresponding to the differentially expressed peptide intensity distributions in the specific muscle regions and used immunohistochemistry to validate our results. These findings extend our current understanding about the early molecular processes of muscle healing and highlights the critical role of trauma adjacent tissue during the early therapeutic response upon treatment with MSC.

Imaging Mass Spectrometry for Characterization of Atrial Fibrillation Subtypes.

PURPOSE: Atrial fibrillation (AF) is a cardiac arrhythmia characterized by a rapid and irregular heart rhythm. AF types, paroxysmal (PX), persistent (PE), and long-lasting persistent (LSP), require differences in clinical management. Unfortunately, a significant proportion of AF patients are clinically misclassified. Therefore, the aim of this study is to prove that MALDI-Imaging (IMS) is valuable as a diagnostic aid in AF subtypes' assessment. EXPERIMENTAL DESIGN: Patients are clinically classified according to the guidelines of the European Society of Cardiology. FFPE tissue specimens from PE, PX, and LSP subtypes are analyzed by MALDI-IMS and evaluated by multi-statistical testing. Proteins are subsequently identified using LC-MS/MS and findings are confirmed by immunohistochemistry and through the determination of potential fibrosis via histopathology. RESULT: Determined that characteristic peptide signatures and peptide values facilitate to distinguish between PE, PX, and LSP arterial fibrillation subtypes. In particular, peptide values from alpha 1 type I collagen (CO1A1) are identified that are significantly higher in LSP and PE tissues but not in PX myocardial AF tissue. These findings are confirmed by immunohistochemistry and through the determination of potential fibrosis via histopathology. CONCLUSION AND RELEVANCE: These results represent an improvement in AF risk stratification by using MALDI-IMS as a promising approach for AF tissue assessment.

Integration of 3D multimodal imaging data of a head and neck cancer and advanced feature recognition.

In the last years, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) became an imaging technique which has the potential to characterize complex tumor tissue. The combination with other modalities and with standard histology techniques was achieved by the use of image registration methods and enhances analysis possibilities. We analyzed an oral squamous cell carcinoma with up to 162 consecutive sections with MALDI MSI, hematoxylin and eosin (H&E) staining and immunohistochemistry (IHC) against CD31. Spatial segmentation maps of the MALDI MSI data were generated by similarity-based clustering of spectra. Next, the maps were overlaid with the H&E microscopy images and the results were interpreted by an experienced pathologist. Image registration was used to fuse both modalities and to build a three-dimensional (3D) model. To visualize structures below resolution of MALDI MSI, IHC was carried out for CD31 and results were embedded additionally. The integration of 3D MALDI MSI data with H&E and IHC images allows a correlation between histological and molecular information leading to a better understanding of the functional heterogeneity of tumors. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry.

BACKGROUND: Three-dimensional (3D) imaging mass spectrometry (MS) is an analytical chemistry technique for the 3D molecular analysis of a tissue specimen, entire organ, or microbial colonies on an agar plate. 3D-imaging MS has unique advantages over existing 3D imaging techniques, offers novel perspectives for understanding the spatial organization of biological processes, and has growing potential to be introduced into routine use in both biology and medicine. Owing to the sheer quantity of data generated, the visualization, analysis, and interpretation of 3D imaging MS data remain a significant challenge. Bioinformatics research in this field is hampered by the lack of publicly available benchmark datasets needed to evaluate and compare algorithms. FINDINGS: High-quality 3D imaging MS datasets from different biological systems at several labs were acquired, supplied with overview images and scripts demonstrating how to read them, and deposited into MetaboLights, an open repository for metabolomics data. 3D imaging MS data were collected from five samples using two types of 3D imaging MS. 3D matrix-assisted laser desorption/ionization imaging (MALDI) MS data were collected from murine pancreas, murine kidney, human oral squamous cell carcinoma, and interacting microbial colonies cultured in Petri dishes. 3D desorption electrospray ionization (DESI) imaging MS data were collected from a human colorectal adenocarcinoma. CONCLUSIONS: With the aim to stimulate computational research in the field of computational 3D imaging MS, selected high-quality 3D imaging MS datasets are provided that could be used by algorithm developers as benchmark datasets.

MALDI imaging mass spectrometry: discrimination of pathophysiological regions in traumatized skeletal muscle by characteristic peptide signatures.

Due to formation of fibrosis and the loss of contractile muscle tissue, severe muscle injuries often result in insufficient healing marked by a significant reduction of muscle force and motor activity. Our previous studies demonstrated that the local transplantation of mesenchymal stromal cells into an injured skeletal muscle of the rat improves the functional outcome of the healing process. Since, due to the lack of sufficient markers, the accurate discrimination of pathophysiological regions in injured skeletal muscle is inadequate, underlying mechanisms of the beneficial effects of mesenchymal stromal cell transplantation on primary trauma and trauma adjacent muscle area remain elusive. For discrimination of these pathophysiological regions, formalin-fixed injured skeletal muscle tissue was analyzed by MALDI imaging MS. By using two computational evaluation strategies, a supervised approach (ClinProTools) and unsupervised segmentation (SCiLS Lab), characteristic m/z species could be assigned to primary trauma and trauma adjacent muscle regions. Using "bottom-up" MS for protein identification and validation of results by immunohistochemistry, we could identify two proteins, skeletal muscle alpha actin and carbonic anhydrase III, which discriminate between the secondary damage on adjacent tissue and the primary traumatized muscle area. Our results underscore the high potential of MALDI imaging MS to describe the spatial characteristics of pathophysiological changes in muscle.

2D and 3D MALDI-imaging: conceptual strategies for visualization and data mining.

3D imaging has a significant impact on many challenges in life sciences, because biology is a 3-dimensional phenomenon. Current 3D imaging-technologies (various types MRI, PET, SPECT) are labeled, i.e. they trace the localization of a specific compound in the body. In contrast, 3D MALDI mass spectrometry-imaging (MALDI-MSI) is a label-free method imaging the spatial distribution of molecular compounds. It complements 3D imaging labeled methods, immunohistochemistry, and genetics-based methods. However, 3D MALDI-MSI cannot tap its full potential due to the lack of statistical methods for analysis and interpretation of large and complex 3D datasets. To overcome this, we established a complete and robust 3D MALDI-MSI pipeline combined with efficient computational data analysis methods for 3D edge preserving image denoising, 3D spatial segmentation as well as finding colocalized m/z values, which will be reviewed here in detail. Furthermore, we explain, why the integration and correlation of the MALDI imaging data with other imaging modalities allows to enhance the interpretation of the molecular data and provides visualization of molecular patterns that may otherwise not be apparent. Therefore, a 3D data acquisition workflow is described generating a set of 3 different dimensional images representing the same anatomies. First, an in-vitro MRI measurement is performed which results in a three-dimensional image modality representing the 3D structure of the measured object. After sectioning the 3D object into N consecutive slices, all N slices are scanned using an optical digital scanner, enabling for performing the MS measurements. Scanning the individual sections results into low-resolution images, which define the base coordinate system for the whole pipeline. The scanned images conclude the information from the spatial (MRI) and the mass spectrometric (MALDI-MSI) dimension and are used for the spatial three-dimensional reconstruction of the object performed by image registration techniques. Different strategies for automatic serial image registration applied to MS datasets are outlined in detail. The third image modality is histology driven, i.e. a digital scan of the histological stained slices in high-resolution. After fusion of reconstructed scan images and MRI the slice-related coordinates of the mass spectra can be propagated into 3D-space. After image registration of scan images and histological stained images, the anatomical information from histology is fused with the mass spectra from MALDI-MSI. As a result of the described pipeline we have a set of 3 dimensional images representing the same anatomies, i.e. the reconstructed slice scans, the spectral images as well as corresponding clustering results, and the acquired MRI. Great emphasis is put on the fact that the co-registered MRI providing anatomical details improves the interpretation of 3D MALDI images. The ability to relate mass spectrometry derived molecular information with in vivo and in vitro imaging has potentially important implications. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

MRI-compatible pipeline for three-dimensional MALDI imaging mass spectrometry using PAXgene fixation.

MALDI imaging mass spectrometry (MALDI-imaging) has emerged as a spatially-resolved label-free bioanalytical technique for direct analysis of biological samples and was recently introduced for analysis of 3D tissue specimens. We present a new experimental and computational pipeline for molecular analysis of tissue specimens which integrates 3D MALDI-imaging, magnetic resonance imaging (MRI), and histological staining and microscopy, and evaluate the pipeline by applying it to analysis of a mouse kidney. To ensure sample integrity and reproducible sectioning, we utilized the PAXgene fixation and paraffin embedding and proved its compatibility with MRI. Altogether, 122 serial sections of the kidney were analyzed using MALDI-imaging, resulting in a 3D dataset of 200GB comprised of 2million spectra. We show that elastic image registration better compensates for local distortions of tissue sections. The computational analysis of 3D MALDI-imaging data was performed using our spatial segmentation pipeline which determines regions of distinct molecular composition and finds m/z-values co-localized with these regions. For facilitated interpretation of 3D distribution of ions, we evaluated isosurfaces providing simplified visualization. We present the data in a multimodal fashion combining 3D MALDI-imaging with the MRI volume rendering and with light microscopic images of histologically stained sections. BIOLOGICAL SIGNIFICANCE: Our novel experimental and computational pipeline for 3D MALDI-imaging can be applied to address clinical questions such as proteomic analysis of the tumor morphologic heterogeneity. Examining the protein distribution as well as the drug distribution throughout an entire tumor using our pipeline will facilitate understanding of the molecular mechanisms of carcinogenesis.

New analysis workflow for MALDI imaging mass spectrometry: application to the discovery and identification of potential markers of childhood absence epilepsy.

Childhood absence epilepsy is a prototypic form of generalized nonconvulsive epilepsy characterized by short impairments of consciousness concomitant with synchronous and bilateral spike-and-wave discharges in the electroencephalogram. For scientists in this field, the BS/Orl and BR/Orl mouse lines, derived from a genetic selection, constitute an original mouse model "in mirror" of absence epilepsy. The potential of MALDI imaging mass spectrometry (IMS) for the discovery of potential biomarkers is increasingly recognized. Interestingly, statistical analysis tools specifically adapted to IMS data sets and methods for the identification of detected proteins play an essential role. In this study, a new cross-classification comparative design using a combined discrete wavelet transformation-support vector machine classification was developed to discriminate spectra of brain sections of BS/Orl and BR/Orl mice. Nineteen m/z ratios were thus highlighted as potential markers with very high recognition rates (87-99%). Seven of these potential markers were identified using a top-down approach, in particular a fragment of Synapsin-I. This protein is yet suspected to be involved in epilepsy. Immunohistochemistry and Western Blot experiments confirmed the differential expression of Synapsin-I observed by IMS, thus tending to validate our approach. Functional assays are being performed to confirm the involvement of Synapsin-I in the mechanisms underlying childhood absence epilepsy.

Cerebrospinal fluid proteomic patterns discriminate Parkinson's disease and multiple system atrophy.

The differential diagnosis of Parkinson's disease and multiple system atrophy can be challenging, especially in the early stages of the diseases. We developed a proteomic profiling strategy for parkinsonian diseases using mass spectrometry analysis for magnetic-bead-based enrichment of cerebrospinal fluid peptides/proteins and subsequent multivariate statistical analysis. Cerebrospinal fluid was obtained from 37 patients diagnosed with Parkinson's disease, 32 patients diagnosed with multiple system atrophy, and 26 patients diagnosed with other neurological diseases as controls. The samples were from the first cohort and the second cohort. Cerebrospinal fluid peptides/proteins were purified with C8 magnetic beads, and spectra were obtained by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Principal component analysis and support vector machine methods are used to reduce dimension of the data and select features to classify diseases. Cerebrospinal fluid proteomic profiles of Parkinson's disease, multiple system atrophy, and control were differentiated from each other by principal component analysis. By building a support vector machine classifier, 3 groups were classified effectively with good cross-validation accuracy. The model accuracy was well preserved for both cases, training by the first cohort and validated by the second cohort and vice versa. Receiver operating characteristics proved that the peak of m/z 6250 was the most important to differentiate multiple system atrophy from Parkinson's disease, especially in the early stages of the disease. A proteomic pattern classification method can increase the accuracy of clinical diagnosis of Parkinson's disease and multiple system atrophy, especially in the early stages.

Exploring three-dimensional matrix-assisted laser desorption/ionization imaging mass spectrometry data: three-dimensional spatial segmentation of mouse kidney.

Three-dimensional (3D) imaging has a significant impact on many challenges of life sciences. Three-dimensional matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) is an emerging label-free bioanalytical technique capturing the spatial distribution of hundreds of molecular compounds in 3D by providing a MALDI mass spectrum for each spatial point of a 3D sample. Currently, 3D MALDI-IMS cannot tap its full potential due to the lack efficient computational methods for constructing, processing, and visualizing large and complex 3D MALDI-IMS data. We present a new pipeline of efficient computational methods, which enables analysis and interpretation of a 3D MALDI-IMS data set. Construction of a MALDI-IMS data set was done according to the state-of-the-art protocols and involved sample preparation, spectra acquisition, spectra preprocessing, and registration of serial sections. For analysis and interpretation of 3D MALDI-IMS data, we applied the spatial segmentation approach which is well-accepted in analysis of two-dimensional (2D) MALDI-IMS data. In line with 2D data analysis, we used edge-preserving 3D image denoising prior to segmentation to reduce strong and chaotic spectrum-to-spectrum variation. For segmentation, we used an efficient clustering method, called bisecting k-means, which is optimized for hierarchical clustering of a large 3D MALDI-IMS data set. Using the proposed pipeline, we analyzed a central part of a mouse kidney using 33 serial sections of 3.5 µm thickness after the PAXgene tissue fixation and paraffin embedding. For each serial section, a 2D MALDI-IMS data set was acquired following the standard protocols with the high spatial resolution of 50 µm. Altogether, 512 495 mass spectra were acquired that corresponds to approximately 50 gigabytes of data. After registration of serial sections into a 3D data set, our computational pipeline allowed us to reveal the 3D kidney anatomical structure based on mass spectrometry data only. Finally, automated analysis discovered molecular masses colocalized with major anatomical regions. In the same way, the proposed pipeline can be used for analysis and interpretation of any 3D MALDI-IMS data set in particular of pathological cases.

Proteomic pattern analysis discriminates among multiple sclerosis-related disorders.

OBJECTIVE: To use a new, unbiased biomarker discovery strategy to obtain and assess proteomic data from cerebrospinal fluid (CSF) of patients with multiple sclerosis (MS)-related disorders. METHODS: CSF protein profiles were analyzed from 107 patients with either MS-related disorders (including relapsing remitting MS [RRMS], primary progressive MS [PPMS], anti-aquaporin4 antibody seropositive-neuromyelitis optica spectrum disorder [SP-NMOSD], and seronegative-NMOSD with long cord lesions on spinal magnetic resonance imaging [SN-NMOSD]), amyotrophic lateral sclerosis (ALS), or other inflammatory neurological diseases (used as controls). CSF peptides/proteins were purified with magnetic beads, and directly measured by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. The obtained spectra were analyzed with multivariate statistics and pattern matching algorithms. These analyses were replicated in an independent sample set of 84 patients composed of those with MS-related disorders or with other neurological diseases (the second cohort). RESULTS: MS-related disorders differed considerably in terms of CSF protein profiles. SP-NMOSD and SN-NMOSD, both of which fit within the NMO spectrum, were distinguishable from RRMS with high cross-validation accuracy on a support vector machine classifier, especially in relapse phases. Some peaks derived from samples of relapsed SP-NMOSD can discriminate RRMS with high area under curve scores (>0.95) and this was reproduced on the second cohort. The similarity of proteomic patterns between selected neurological diseases were demonstrated by pattern matching analysis. To our surprise, the spectral differences between RRMS and PPMS were much larger than those of PPMS and ALS. INTERPRETATION: Our findings suggest that CSF proteomic pattern analysis can increase the accuracy of disease diagnosis of MS-related disorders and will aid physicians in appropriate therapeutic decision-making.

On the importance of mathematical methods for analysis of MALDI-imaging mass spectrometry data.

In the last decade, matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS), also called as MALDI-imaging, has proven its potential in proteomics and was successfully applied to various types of biomedical problems, in particular to histopathological label-free analysis of tissue sections. In histopathology, MALDI-imaging is used as a general analytic tool revealing the functional proteomic structure of tissue sections, and as a discovery tool for detecting new biomarkers discriminating a region annotated by an experienced histologist, in particular, for cancer studies. A typical MALDI-imaging data set contains 108 to 109 intensity values occupying more than 1 GB. Analysis and interpretation of such huge amount of data is a mathematically, statistically and computationally challenging problem. In this paper we overview some computational methods for analysis of MALDI-imaging data sets. We discuss the importance of data preprocessing, which typically includes normalization, baseline removal and peak picking, and hightlight the importance of image denoising when visualizing IMS data.

Quantitative statistical analysis of standard and human blood proteins from liquid chromatography, electrospray ionization, and tandem mass spectrometry.

It will be important to determine if the parent and fragment ion intensity results of liquid chromatography, electrospray ionization and tandem mass spectrometry (LC-ESI-MS/MS) experiments have been randomly and independently sampled from a normal population for the purpose of statistical analysis by general linear models and ANOVA. The tryptic parent peptide and fragment ion m/z and intensity data in the mascot generic files from LC-ESI-MS/MS of purified standard proteins, and human blood protein fractionated by partition chromatography, were parsed into a Structured Query Language (SQL) database and were matched with protein and peptide sequences provided by the X!TANDEM algorithm. The many parent and/or fragment ion intensity values were log transformed, tested for normality, and analyzed using the generic Statistical Analysis System (SAS). Transformation of both parent and fragment intensity values by logarithmic functions yielded intensity distributions that closely approximate the log-normal distribution. ANOVA models of the transformed parent and fragment intensity values showed significant effects of treatments, proteins, and peptides, as well as parent versus fragment ion types, with a low probability of false positive results. Transformed parent and fragment intensity values were compared over all sample treatments, proteins or peptides by the Tukey-Kramer Honestly Significant Difference (HSD) test. The approach provided a complete and quantitative statistical analysis of LC-ESI-MS/MS data from human blood.

Combined reversed phase HPLC, mass spectrometry, and NMR spectroscopy for a fast separation and efficient identification of phosphatidylcholines.

In respect of the manifold involvement of lipids in biochemical processes, the analysis of intact and underivatized lipids of body fluids as well as cell and tissue extracts is still a challenging task, if detailed molecular information is required. Therefore, the advantage of combined use of high-pressure liquid chromatography (HPLC), mass spectrometry (MS), and nuclear magnetic resonance (NMR) spectroscopy will be shown analyzing three different types of extracts of the ubiquitous membrane component phosphatidylcholine. At first, different reversed phase modifications were tested on phosphatidylcholines (PC) with the same effective carbon number (ECN) for their applicability in lipid analysis. The results were taken to improve the separation of three natural PC extract types and a new reversed phase (RP)-HPLC method was developed. The individual species were characterized by one- and two-dimensional NMR and positive or negative ion mode quadrupole time of flight (q-TOF)-MS as well as MS/MS techniques. Furthermore, ion suppression effects during electrospray ionisation (ESI), difficulties, limits, and advantages of the individual analytical techniques are addressed.

The feasibility of MS and advanced data processing for monitoring Schistosoma mansoni infection.

PURPOSE: Sensitive diagnosis, monitoring of disease progression and the evaluation of chemotherapeutic interventions are of prime importance for the improvement of control and prevention strategies for Schistosomiasis. The aim of the present study was to identify novel markers of Schistosoma mansoni infection and disease using urine samples from a large cohort from an area endemic for S. mansoni. EXPERIMENTAL DESIGN: Urine samples were collected and processed on an automated sample clean-up and fractionation system combining strong cation exchange and reversed phase, and analyzed by MS (MALDI ToF MS). The ClinPro Tools(™) (CPT) software and the Discrete Wavelet Transformation-Support Vector Machine (DWT-SVM) procedure were used for classification and statistical analysis. RESULTS: We observed a large difference in urinary peptide profiles between children and adults but classification based on infection was possible only for children. Here, in the external validation data set, 93% of the infected children were classified correctly with DWT-SVM (versus 76% for CPT). In addition 91% of low-infected children were classified correctly using DWT-SVM (versus 85% for CPT). The discriminating peptides were identified as fragments of collagen 1A1 and 1A3, and uromodulin. CONCLUSIONS AND CLINICAL RELEVANCE: In conclusion, we provide the usefulness of a peptidomics profiling approach combined with DWT-SVM in the monitoring of S. mansoni infection.