Publications

@article{fincher2021p,

title = {Molecular Mapping of Neutral Lipids Using Silicon Nanopost Arrays and TIMS Imaging Mass Spectrometry},

author = {Jarod A Fincher and Katerina V Djambazova and Dustin R Klein and Martin Dufresne and Lukasz G Migas and Raf Van de Plas and Richard M Caprioli and Jeffrey M Spraggins},

url = {https://doi.org/10.1021/jasms.1c00159},

doi = {10.1021/jasms.1c00159},

year  = {2021},

date = {2021-10-01},

journal = {Journal of the American Society for Mass Spectrometry},

volume = {32},

number = {10},

pages = {2519-2527},

abstract = {We demonstrate the utility of combining silicon nanopost arrays (NAPA) and trapped ion mobility imaging mass spectrometry (TIMS IMS) for high spatial resolution and specificity mapping of neutral lipid classes in tissue. Ionization of neutral lipid species such as triglycerides (TGs), cholestryl esters (CEs), and hexosylceramides (HexCers) from biological tissues has remained a challenge for imaging applications. NAPA, a matrix-free laser desorption ionization substrate, provides enhanced ionization efficiency for the above-mentioned neutral lipid species, providing complementary lipid coverage to matrix-assisted laser desorption ionization (MALDI). The combination of NAPA and TIMS IMS enables imaging of neutral lipid species at 20 μm spatial resolution while also increasing molecular coverage greater than 2-fold using gas-phase ion mobility separations. This is a significant improvement with respect to sensitivity, specificity, and spatial resolution compared to previously reported imaging studies using NAPA alone. Improved specificity for neutral lipid analysis using TIMS IMS was shown using rat kidney tissue to separate TGs, CEs, HexCers, and phospholipids into distinct ion mobility trendlines. Further, this technology allowed for the separation of isomeric species, including mobility resolved isomers of Cer(d42:2) (m/z 686.585) with distinct spatial localizations measured in rat kidney tissue section.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{tideman2021y,

title = {Automated biomarker candidate discovery in imaging mass spectrometry data through spatially localized Shapley additive explanations},

author = {Leonoor E M Tideman and Lukasz G Migas and Katerina V Djambazova and Nathan Heath Patterson and Richard M Caprioli and Jeffrey M Spraggins and Raf Van de Plas},

url = {https://doi.org/10.1016/j.aca.2021.338522},

doi = {10.1016/j.aca.2021.338522},

year  = {2021},

date = {2021-09-01},

journal = {Analytica Chimica Acta},

volume = {1177},

pages = {338522},

abstract = {The search for molecular species that are differentially expressed between biological states is an important step towards discovering promising biomarker candidates. In imaging mass spectrometry (IMS), performing this search manually is often impractical due to the large size and high-dimensionality of IMS datasets. Instead, we propose an interpretable machine learning workflow that automatically identifies biomarker candidates by their mass-to-charge ratios, and that quantitatively estimates their relevance to recognizing a given biological class using Shapley additive explanations (SHAP). The task of biomarker candidate discovery is translated into a feature ranking problem: given a classification model that assigns pixels to different biological classes on the basis of their mass spectra, the molecular species that the model uses as features are ranked in descending order of relative predictive importance such that the top-ranking features have a higher likelihood of being useful biomarkers. Besides providing the user with an experiment-wide measure of a molecular species' biomarker potential, our workflow delivers spatially localized explanations of the classification model's decision-making process in the form of a novel representation called SHAP maps. SHAP maps deliver insight into the spatial specificity of biomarker candidates by highlighting in which regions of the tissue sample each feature provides discriminative information and in which regions it does not. SHAP maps also enable one to determine whether the relationship between a biomarker candidate and a biological state of interest is correlative or anticorrelative. Our automated approach to estimating a molecular species' potential for characterizing a user-provided biological class, combined with the untargeted and multiplexed nature of IMS, allows for the rapid screening of thousands of molecular species and the obtention of a broader biomarker candidate shortlist than would be possible through targeted manual assessment. Our biomarker candidate discovery workflow is demonstrated on mouse-pup and rat kidney case studies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{neumann2021d,

title = {Protocol for multimodal analysis of human kidney tissue by imaging mass spectrometry and CODEX multiplexed immunofluorescence},

author = {Elizabeth K Neumann and Nathan Heath Patterson and Jamie L Allen and Lukasz G Migas and Haichun Yang and Maya Brewer and David M Anderson and Jennifer Harvey and Danielle B Gutierrez and Raymond C Harris and Mark P deCaestecker and Agnes B Fogo and Raf Van de Plas and Richard M Caprioli and Jeffrey M Spraggins},

url = {https://doi.org/10.1016/j.xpro.2021.100747},

doi = {10.1016/j.xpro.2021.100747},

year  = {2021},

date = {2021-09-01},

journal = {STAR Protocols},

volume = {2},

number = {3},

pages = {100747},

abstract = {Here, we describe the preservation and preparation of human kidney tissue for interrogation by histopathology, imaging mass spectrometry, and multiplexed immunofluorescence. Custom image registration and integration techniques are used to create cellular and molecular atlases of this organ system. Through careful optimization, we ensure high-quality and reproducible datasets suitable for cross-patient comparisons that are essential to understanding human health and disease. Moreover, each of these steps can be adapted to other organ systems or diseases, enabling additional atlas efforts.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{patterson2021h,

title = {Autofluorescence microscopy as a label-free tool for renal histology and glomerular segmentation},

author = {Nathan Heath Patterson and Elizabeth K Neumann and Kavya Sharman and Jamie Allen and Raymond Harris and Agnes B Fogo and Mark Caestecker and Richard M Caprioli and Raf Van de Plas and Jeffrey M Spraggins},

url = {https://doi.org/10.1101/2021.07.16.452703},

doi = {10.1101/2021.07.16.452703},

year  = {2021},

date = {2021-01-01},

journal = {bioRxiv},

abstract = {Functional tissue units (FTUs) composed of multiple cells like the glomerulus in the kidney nephron play important roles in health and disease. Histological staining is often used for annotation or segmentation of FTUs, but chemical stains can introduce artefacts through experimental factors that influence analysis. Secondly, many molecular -omics techniques are incompatible with common histological stains. To enable FTU segmentation and annotation in human kidney without the need for histological staining, we detail here the use of widefield autofluorescence (AF) microscopy as a simple, label-free modality that provides detailed renal morphology comparable to periodic acid-Schiff (PAS) stained tissue in both formalin-fixed paraffin-embedded (FFPE) and fresh frozen samples and with no tissue processing beyond sectioning. We demonstrate automated deep learning-based glomerular unit recognition and segmentation on PAS and AF images of the same tissue section from 9 fresh frozen samples and 9 FFPE samples. All training comparisons were carried out using registered AF microscopy and PAS stained whole slide images originating from the same section, and the recognition models were built with the exact same training and test examples. Measures of recognition performance, such as the Dice-Sorensen coefficient, the true positive rate, and the positive predictive value differed less than 2% between standard PAS and AF microscopy for both preservation methods. These results demonstrate that AF is a potentially powerful tool to study human kidney tissue, that it can serve as a label-free source for automated and manual annotation of tissue structures.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{djambazova2021z,

title = {MALDI TIMS IMS of Disialoganglioside Isomers–GD1a and GD1b in Murine Brain Tissue},

author = {Katerina Djambazova and Martin Dufresne and Lukasz Migas and Angela Kruse and Raf Van de Plas and Richard Caprioli and Jeffrey Spraggins},

url = {https://doi.org/10.26434/chemrxiv-2021-tkzc6},

doi = {10.26434/chemrxiv-2021-tkzc6},

year  = {2021},

date = {2021-01-01},

journal = {ChemRxiv},

abstract = {Gangliosides are classified as acidic glycosphingolipids, containing ceramide moieties and oligosaccharide chains with one or multiple sialic acid residue(s). The presence of multiple sialylation sites gives rise to highly diverse isomeric structures with distinct biological roles. Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) enables the untargeted spatial analysis of gangliosides, among other biomolecules, directly from tissue sections. Integrating trapped ion mobility mass spectrometry (TIMS), a gas-phase separation technology, with MALDI IMS allows for the investi-gation of isomeric lipid structures in situ. Here we demonstrate the gas-phase separation of disialoganglioside isomers GD1a and GD1b that differ in the position of a sialic acid residue, in a standard mixture of both isomers, a total ganglioside extract, and directly from thin tissue sections. The unique spatial distributions of GD1a/b (d36:1) and GD1a/b (d38:1) were deter-mined from rat hippocampus, as well as in a spinal cord tissue section.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{verbeeck2020a,

title = {Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry},

author = {Nico Verbeeck and Richard M Caprioli and Raf Van de Plas},

url = {https://doi.org/10.1002/mas.21602},

doi = {10.1002/mas.21602},

year  = {2020},

date = {2020-05-01},

journal = {Mass Spectrometry Reviews},

volume = {39},

number = {3},

pages = {245-291},

abstract = {Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high-dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data-driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry-based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1-47, 2019.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{neumann2020l,

title = {Spatial Metabolomics of the Human Kidney using MALDI Trapped Ion Mobility Imaging Mass Spectrometry},

author = {Elizabeth K Neumann and Lukasz G Migas and Jamie L Allen and Richard M Caprioli and Raf Van de Plas and Jeffrey M Spraggins},

url = {https://doi.org/10.1021/acs.analchem.0c02051},

doi = {10.1021/acs.analchem.0c02051},

year  = {2020},

date = {2020-01-01},

journal = {Analytical Chemistry},

volume = {92},

number = {19},

pages = {13084-13091},

abstract = {Low molecular weight metabolites are essential for defining the molecular phenotypes of cells. However, spatial metabolomics tools often lack the sensitivity, specify, and spatial resolution to provide comprehensive descriptions of these species in tissue. MALDI imaging mass spectrometry (IMS) of low molecular weight ions is particularly challenging as MALDI matrix clusters are often nominally isobaric with multiple metabolite ions, requiring high resolving power instrumentation or derivatization to circumvent this issue. An alternative to this is to perform ion mobility separation before ion detection, enabling the visualization of metabolites without the interference of matrix ions. Additional difficulties surrounding low weight metabolite visualization include high resolution imaging, while maintaining sufficient ion numbers for broad and representative analysis of the tissue chemical complement. Here, we use MALDI timsTOF IMS to image low molecular weight metabolites at higher spatial resolution than most metabolite MALDI IMS experiments (20 μm) while maintaining broad coverage within the human kidney. We demonstrate that trapped ion mobility spectrometry (TIMS) can resolve matrix peaks from metabolite signal and separate both isobaric and isomeric metabolites with different distributions within the kidney. The added ion mobility data dimension dramatically increased the peak capacity for spatial metabolomics experiments. Through this improved sensitivity, we have found >40 low molecular weight metabolites in human kidney tissue, such as argininic acid, acetylcarnitine, and choline that localize to the cortex, medulla, and renal pelvis, respectively. Future work will involve further exploring metabolomic profiles of human kidneys as a function of age, sex, and race.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{djambazova2020a,

title = {Resolving the Complexity of Spatial Lipidomics Using MALDI TIMS Imaging Mass Spectrometry},

author = {Katerina V Djambazova and Dustin R Klein and Lukasz G Migas and Elizabeth K Neumann and Emilio S Rivera and Raf Van de Plas and Richard M Caprioli and Jeffrey M Spraggins},

url = {https://doi.org/10.1021/acs.analchem.0c02520},

doi = {10.1021/acs.analchem.0c02520},

year  = {2020},

date = {2020-01-01},

journal = {Analytical Chemistry},

volume = {92},

number = {19},

pages = {13290-13297},

abstract = {Lipids are a structurally diverse class of molecules with important biological functions including cellular signaling and energy storage. Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) allows for direct mapping of biomolecules in tissues. Fully characterizing the structural diversity of lipids remains a challenge due to the presence of isobaric and isomeric species, which greatly complicates data interpretation when only m/z information is available. Integrating ion mobility separations aids in deconvoluting these complex mixtures and addressing the challenges of lipid IMS. Here, we demonstrate that a MALDI quadrupole time-of-flight (Q-TOF) mass spectrometer with trapped ion mobility spectrometry (TIMS) enables a >250% increase in the peak capacity during IMS experiments. MALDI TIMS-MS separation of lipid isomer standards, including sn backbone isomers, acyl chain isomers, and double-bond position and stereoisomers, is demonstrated. As a proof of concept, in situ separation and imaging of lipid isomers with distinct spatial distributions were performed using tissue sections from a whole-body mouse pup.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{jones2020x,

title = {Discovering New Lipidomic Features Using Cell Type Specific Fluorophore Expression to Provide Spatial and Biological Specificity in a Multimodal Workflow with MALDI Imaging Mass Spectrometry},

author = {Marissa A Jones and Sung Hoon Cho and Nathan Heath Patterson and Raf Van de Plas and Jeffrey M Spraggins and Mark R Boothby and Richard M Caprioli},

url = {https://doi.org/10.1021/acs.analchem.0c00446},

doi = {10.1021/acs.analchem.0c00446},

year  = {2020},

date = {2020-01-01},

journal = {Analytical Chemistry},

volume = {92},

number = {10},

pages = {7079-7086},

abstract = {Identifying the spatial distributions of biomolecules in tissue is crucial for understanding integrated function. Imaging mass spectrometry (IMS) allows simultaneous mapping of thousands of biosynthetic products such as lipids but has needed a means of identifying specific cell-types or functional states to correlate with molecular localization. We report, here, advances starting from identity marking with a genetically encoded fluorophore. The fluorescence emission data were integrated with IMS data through multimodal image processing with advanced registration techniques and data-driven image fusion. In an unbiased analysis of spleens, this integrated technology enabled identification of ether lipid species preferentially enriched in germinal centers. We propose that this use of genetic marking for microanatomical regions of interest can be paired with molecular information from IMS for any tissue, cell-type, or activity state for which fluorescence is driven by a gene-tracking allele and ultimately with outputs of other means of spatial mapping.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{perry2020t,

title = {Integrated molecular imaging technologies for investigation of metals in biological systems: A brief review},

author = {William J Perry and Andy Weiss and Raf Van de Plas and Jeffrey M Spraggins and Richard M Caprioli and Eric P Skaar},

url = {https://doi.org/10.1016/j.cbpa.2020.01.008},

doi = {10.1016/j.cbpa.2020.01.008},

year  = {2020},

date = {2020-01-01},

journal = {Current Opinion in Chemical Biology},

volume = {55},

pages = {127-135},

abstract = {Metals play an essential role in biological systems and are required as structural or catalytic co-factors in many proteins. Disruption of the homeostatic control and/or spatial distributions of metals can lead to disease. Imaging technologies have been developed to visualize elemental distributions across a biological sample. Measurement of elemental distributions by imaging mass spectrometry and imaging X-ray fluorescence are increasingly employed with technologies that can assess histological features and molecular compositions. Data from several modalities can be interrogated as multimodal images to correlate morphological, elemental, and molecular properties. Elemental and molecular distributions have also been axially resolved to achieve three-dimensional volumes, dramatically increasing the biological information. In this review, we provide an overview of recent developments in the field of metal imaging with an emphasis on multimodal studies in two and three dimensions. We specifically highlight studies that present technological advancements and biological applications of how metal homeostasis affects human health.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{spraggins2019g,

title = {High-Performance Molecular Imaging with MALDI Trapped Ion-Mobility Time-of-Flight (timsTOF) Mass Spectrometry},

author = {Jeffrey M Spraggins and Katerina V Djambazova and Emilio S Rivera and Lukasz G Migas and Elizabeth K Neumann and Arne Fuetterer and Juergen Suetering and Niels Goedecke and Alice Ly and Raf Van de Plas and Richard M Caprioli},

url = {https://doi.org/10.1021/acs.analchem.9b03612},

doi = {10.1021/acs.analchem.9b03612},

year  = {2019},

date = {2019-01-01},

journal = {Analytical Chemistry},

volume = {91},

number = {22},

pages = {14552-14560},

abstract = {Imaging mass spectrometry (IMS) enables the spatially targeted molecular assessment of biological tissues at cellular resolutions. New developments and technologies are essential for uncovering the molecular drivers of native physiological function and disease. Instrumentation must maximize spatial resolution, throughput, sensitivity, and specificity, because tissue imaging experiments consist of thousands to millions of pixels. Here, we report the development and application of a matrix-assisted laser desorption/ionization (MALDI) trapped ion-mobility spectrometry (TIMS) imaging platform. This prototype MALDI timsTOF instrument is capable of 10 μm spatial resolutions and 20 pixels/s throughput molecular imaging. The MALDI source utilizes a Bruker SmartBeam 3-D laser system that can generate a square burn pattern of <10 × 10 μm at the sample surface. General image performance was assessed using murine kidney and brain tissues and demonstrate that high-spatial-resolution imaging data can be generated rapidly with mass measurement errors <5 ppm and ∼40 000 resolving power. Initial TIMS-based imaging experiments were performed on whole-body mouse pup tissue demonstrating the separation of closely isobaric [PC(32:0) + Na]+ and [PC(34:3) + H]+ (3 mDa mass difference) in the gas phase. We have shown that the MALDI timsTOF platform can maintain reasonable data acquisition rates (>2 pixels/s) while providing the specificity necessary to differentiate components in complex mixtures of lipid adducts. The combination of high-spatial-resolution and throughput imaging capabilities with high-performance TIMS separations provides a uniquely tunable platform to address many challenges associated with advanced molecular imaging applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{hubmap-consortium2019l,

title = {The human body at cellular resolution: the NIH Human Biomolecular Atlas Program},

author = {HuBMAP Consortium and Raf Van de Plas},

url = {https://doi.org/10.1038/s41586-019-1629-x},

doi = {10.1038/s41586-019-1629-x},

year  = {2019},

date = {2019-01-01},

journal = {Nature},

volume = {574},

number = {7777},

pages = {187-192},

abstract = {Transformative technologies are enabling the construction of three-dimensional maps of tissues with unprecedented spatial and molecular resolution. Over the next seven years, the NIH Common Fund Human Biomolecular Atlas Program (HuBMAP) intends to develop a widely accessible framework for comprehensively mapping the human body at single-cell resolution by supporting technology development, data acquisition, and detailed spatial mapping. HuBMAP will integrate its efforts with other funding agencies, programs, consortia, and the biomedical research community at large towards the shared vision of a comprehensive, accessible three-dimensional molecular and cellular atlas of the human body, in health and under various disease conditions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{patterson2018c,

title = {Next Generation Histology-Directed Imaging Mass Spectrometry Driven by Autofluorescence Microscopy},

author = {Nathan Heath Patterson and Michael Tuck and Adam Lewis and Alexis Kaushansky and Jeremy L Norris and Raf Van de Plas and Richard M Caprioli},

url = {https://doi.org/10.1021/acs.analchem.8b02885},

doi = {10.1021/acs.analchem.8b02885},

year  = {2018},

date = {2018-01-01},

journal = {Analytical Chemistry},

volume = {90},

number = {21},

pages = {12404-12413},

abstract = {Histology-directed imaging mass spectrometry (IMS) is a spatially targeted IMS acquisition method informed by expert annotation that provides rapid molecular characterization of select tissue structures. The expert annotations are usually determined on digital whole slide images of histological stains where the staining preparation is incompatible with optimal IMS preparation, necessitating serial sections: one for annotation, one for IMS. Registration is then used to align staining annotations onto the IMS tissue section. Herein, we report a next-generation histology-directed platform implementing IMS-compatible autofluorescence (AF) microscopy taken prior to any staining or IMS. The platform enables two histology-directed workflows, one that improves the registration process between two separate tissue sections using automated, computational monomodal AF-to-AF microscopy image registration, and a registration-free approach that utilizes AF directly to identify ROIs and acquire IMS on the same section. The registration approach is fully automated and delivers state of the art accuracy in histology-directed workflows for transfer of annotations (∼3-10 μm based on 4 organs from 2 species) while the direct AF approach is registration-free, allowing targeting of the finest structures visible by AF microscopy. We demonstrate the platform in biologically relevant case studies of liver stage malaria and human kidney disease with spatially targeted acquisition of sparsely distributed (composing less than one tenth of 1% of the tissue section area) malaria infected mouse hepatocytes and glomeruli in the human kidney case study.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{patterson2018q,

title = {Advanced Registration and Analysis of MALDI Imaging Mass Spectrometry Measurements through Autofluorescence Microscopy},

author = {Nathan Heath Patterson and Michael Tuck and Raf Van de Plas and Richard M Caprioli},

url = {https://doi.org/10.1021/acs.analchem.8b02884},

doi = {10.1021/acs.analchem.8b02884},

year  = {2018},

date = {2018-01-01},

journal = {Analytical Chemistry},

volume = {90},

number = {21},

pages = {12395-12403},

abstract = {The correlation of imaging mass spectrometry (IMS) with histopathology can help relate novel molecular findings obtained through IMS to the well-characterized and validated histopathology knowledge base. The quality of correlation between these two modalities is limited by the quality of the spatial mapping that is obtained by registration of the two image types. In this work, we develop novel workflows for MALDI IMS-to-microscopy data registration and analysis using nondestructive IMS-compatible wide field autofluorescence (AF) microscopy combined with computational image registration. First, a substantially automated procedure for high-accuracy registration between IMS and microscopy data of the same section is described that explicitly links the MALDI laser ablation pattern imaged by microscopy to its corresponding IMS pixel. Subsequent examination of the registered data allows for high-confidence colocalization of image features between the two modalities, down to single-cell scales within tissue. Building on this IMS-microscopy spatial mapping, we furthermore demonstrate the automated spatial correlation between IMS measurements from serial sections. This AF-registration-driven inter-section analysis, using a combination of nonlinear AF-to-AF and IMS-to-AF image registrations, can be applied to tissue sections that are prepared and imaged with different sample preparations (e.g., lipids vs proteins) and/or that are measured using different spatial resolutions. Importantly, all registrations, whether within a single section or across serial sections, are entirely independent of the IMS intensity signal content and thus unbiased by it.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{prentice2018l,

title = {Enhanced Ion Transmission Efficiency up to m/ z 24 000 for MALDI Protein Imaging Mass Spectrometry},

author = {Boone M Prentice and Daniel J Ryan and Raf Van de Plas and Richard M Caprioli and Jeffrey M Spraggins},

url = {https://doi.org/10.1021/acs.analchem.7b05105},

doi = {10.1021/acs.analchem.7b05105},

year  = {2018},

date = {2018-01-01},

journal = {Analytical Chemistry},

volume = {90},

number = {8},

pages = {5090-5099},

abstract = {The molecular identification of species of interest is an important part of an imaging mass spectrometry (IMS) experiment. The high resolution accurate mass capabilities of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) have recently been shown to facilitate the identification of proteins in matrix-assisted laser desorption/ionization (MALDI) IMS. However, these experiments are typically limited to proteins giving rise to ions of relatively low m/ z due to difficulties transmitting and measuring large molecular weight ions of low charge states. Herein we have modified the source gas manifold of a commercial MALDI FT-ICR MS to regulate the gas flow and pressure to maximize the transmission of large m/ z protein ions through the ion funnel region of the instrument. By minimizing the contribution of off-axis gas disruption to ion focusing and maximizing the effective potential wall confining the ions through pressure optimization, the signal-to-noise ratios (S/N) of most protein species were improved by roughly 1 order of magnitude compared to normal source conditions. These modifications enabled the detection of protein standards up to m/ z 24 000 and the detection of proteins from tissue up to m/ z 22 000 with good S/N, roughly doubling the mass range for which high quality protein ion images from rat brain and kidney tissue could be produced. Due to the long time-domain transients (>4 s) required to isotopically resolve high m/ z proteins, we have used these data as part of an FT-ICR IMS-microscopy data-driven image fusion workflow to produce estimated protein images with both high mass and high spatial resolutions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cassat2018v,

title = {Integrated molecular imaging reveals tissue heterogeneity driving host-pathogen interactions},

author = {James E Cassat and Jessica L Moore and Kevin J Wilson and Zach Stark and Boone M Prentice and Raf Van de Plas and William J Perry and Yaofang Zhang and John Virostko and Daniel C Colvin and Kristie L Rose and Audra M Judd and Michelle L Reyzer and Jeffrey M Spraggins and Caroline M Grunenwald and John C Gore and Richard M Caprioli and Eric P Skaar},

url = {https://doi.org/10.1126/scitranslmed.aan6361},

doi = {10.1126/scitranslmed.aan6361},

year  = {2018},

date = {2018-01-01},

journal = {Science Translational Medicine},

volume = {10},

number = {432},

abstract = {Diseases are characterized by distinct changes in tissue molecular distribution. Molecular analysis of intact tissues traditionally requires preexisting knowledge of, and reagents for, the targets of interest. Conversely, label-free discovery of disease-associated tissue analytes requires destructive processing for downstream identification platforms. Tissue-based analyses therefore sacrifice discovery to gain spatial distribution of known targets or sacrifice tissue architecture for discovery of unknown targets. To overcome these obstacles, we developed a multimodality imaging platform for discovery-based molecular histology. We apply this platform to a model of disseminated infection triggered by the pathogen Staphylococcus aureus, leading to the discovery of infection-associated alterations in the distribution and abundance of proteins and elements in tissue in mice. These data provide an unbiased, three-dimensional analysis of how disease affects the molecular architecture of complex tissues, enable culture-free diagnosis of infection through imaging-based detection of bacterial and host analytes, and reveal molecular heterogeneity at the host-pathogen interface.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{verbeeck2017k,

title = {Connecting imaging mass spectrometry and magnetic resonance imaging-based anatomical atlases for automated anatomical interpretation and differential analysis},

author = {Nico Verbeeck and Jeffrey M Spraggins and Monika J M Murphy and Hui-Dong Wang and Ariel Y Deutch and Richard M Caprioli and Raf Van de Plas},

url = {https://doi.org/10.1016/j.bbapap.2017.02.016},

doi = {10.1016/j.bbapap.2017.02.016},

year  = {2017},

date = {2017-07-01},

journal = {Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics},

volume = {1865},

number = {7},

pages = {967-977},

abstract = {Imaging mass spectrometry (IMS) is a molecular imaging technology that can measure thousands of biomolecules concurrently without prior tagging, making it particularly suitable for exploratory research. However, the data size and dimensionality often makes thorough extraction of relevant information impractical. To help guide and accelerate IMS data analysis, we recently developed a framework that integrates IMS measurements with anatomical atlases, opening up opportunities for anatomy-driven exploration of IMS data. One example is the automated anatomical interpretation of ion images, where empirically measured ion distributions are automatically decomposed into their underlying anatomical structures. While offering significant potential, IMS-atlas integration has thus far been restricted to the Allen Mouse Brain Atlas (AMBA) and mouse brain samples. Here, we expand the applicability of this framework by extending towards new animal species and a new set of anatomical atlases retrieved from the Scalable Brain Atlas (SBA). Furthermore, as many SBA atlases are based on magnetic resonance imaging (MRI) data, a new registration pipeline was developed that enables direct non-rigid IMS-to-MRI registration. These developments are demonstrated on protein-focused FTICR IMS measurements from coronal brain sections of a Parkinson's disease (PD) rat model. The measurements are integrated with an MRI-based rat brain atlas from the SBA. The new rat-focused IMS-atlas integration is used to perform automated anatomical interpretation and to find differential ions between healthy and diseased tissue. IMS-atlas integration can serve as an important accelerator in IMS data exploration, and with these new developments it can now be applied to a wider variety of animal species and modalities. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{norris2017e,

title = {Integrated, High-Throughput, Multiomics Platform Enables Data-Driven Construction of Cellular Responses and Reveals Global Drug Mechanisms of Action},

author = {Jeremy L Norris and Melissa A Farrow and Danielle B Gutierrez and Lauren D Palmer and Nicole Muszynski and Stacy D Sherrod and James C Pino and Jamie L Allen and Jeffrey M Spraggins and Alex L R Lubbock and Ashley Jordan and William Burns and James C Poland and Carrie Romer and M Lisa Manier and Yuan-Wei Nei and Boone M Prentice and Kristie L Rose and Salisha Hill and Raf Van de Plas and Tina Tsui and Nathaniel M Braman and M Ray Keller and Stacey A Rutherford and Nichole Lobdell and Carlos F Lopez and D Borden Lacy and John A McLean and John P Wikswo and Eric P Skaar and Richard M Caprioli},

url = {https://doi.org/10.1021/acs.jproteome.6b01004},

doi = {10.1021/acs.jproteome.6b01004},

year  = {2017},

date = {2017-01-01},

journal = {Journal of Proteome Research},

volume = {16},

number = {3},

pages = {1364-1375},

abstract = {An understanding of how cells respond to perturbation is essential for biological applications; however, most approaches for profiling cellular response are limited in scope to pre-established targets. Global analysis of molecular mechanism will advance our understanding of the complex networks constituting cellular perturbation and lead to advancements in areas, such as infectious disease pathogenesis, developmental biology, pathophysiology, pharmacology, and toxicology. We have developed a high-throughput multiomics platform for comprehensive, de novo characterization of cellular mechanisms of action. Platform validation using cisplatin as a test compound demonstrates quantification of over 10 000 unique, significant molecular changes in less than 30 days. These data provide excellent coverage of known cisplatin-induced molecular changes and previously unrecognized insights into cisplatin resistance. This proof-of-principle study demonstrates the value of this platform as a resource to understand complex cellular responses in a high-throughput manner.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{marien2016g,

title = {Phospholipid profiling identifies acyl chain elongation as a ubiquitous trait and potential target for the treatment of lung squamous cell carcinoma},

author = {Eyra Marien and Michael Meister and Thomas Muley and Teresa Gomez Del Pulgar and Rita Derua and Jeffrey M Spraggins and Raf Van de Plas and Frank Vanderhoydonc and Jelle Machiels and Maria Mercedes Binda and Jonas Dehairs and Jami Willette-Brown and Yinling Hu and Hendrik Dienemann and Michael Thomas and Philipp A Schnabel and Richard M Caprioli and Juan Carlos Lacal and Etienne Waelkens and Johannes V Swinnen},

url = {https://doi.org/10.18632/oncotarget.7179},

doi = {10.18632/oncotarget.7179},

year  = {2016},

date = {2016-03-01},

journal = {Oncotarget},

volume = {7},

number = {11},

pages = {12582-97},

abstract = {Lung cancer is the leading cause of cancer death. Beyond first line treatment, few therapeutic options are available, particularly for squamous cell carcinoma (SCC). Here, we have explored the phospholipidomes of 30 human SCCs and found that they almost invariably (in 96.7% of cases) contain phospholipids with longer acyl chains compared to matched normal tissues. This trait was confirmed using in situ 2D-imaging MS on tissue sections and by phospholipidomics of tumor and normal lung tissue of the L-IkkαKA/KA mouse model of lung SCC. In both human and mouse, the increase in acyl chain length in cancer tissue was accompanied by significant changes in the expression of acyl chain elongases (ELOVLs). Functional screening of differentially expressed ELOVLs by selective gene knockdown in SCC cell lines followed by phospholipidomics revealed ELOVL6 as the main elongation enzyme responsible for acyl chain elongation in cancer cells. Interestingly, inhibition of ELOVL6 drastically reduced colony formation of multiple SCC cell lines in vitro and significantly attenuated their growth as xenografts in vivo in mouse models. These findings identify acyl chain elongation as one of the most common traits of lung SCC discovered so far and pinpoint ELOVL6 as a novel potential target for cancer intervention.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{anderson2016m,

title = {3-D imaging mass spectrometry of protein distributions in mouse Neurofibromatosis 1 (NF1)-associated optic glioma},

author = {David M G Anderson and Raf Van de Plas and Kristie L Rose and Salisha Hill and Kevin L Schey and Anne C Solga and David H Gutmann and Richard M Caprioli},

url = {https://doi.org/10.1016/j.jprot.2016.02.004},

doi = {10.1016/j.jprot.2016.02.004},

year  = {2016},

date = {2016-01-01},

journal = {Journal of Proteomics},

volume = {149},

pages = {77-84},

abstract = {Neurofibromatosis type 1 (NF1) is a common neurogenetic disorder, in which affected individuals develop tumors of the nervous system. Children with NF1 are particularly prone to brain tumors (gliomas) involving the optic pathway that can result in impaired vision. Since tumor formation and expansion requires a cooperative tumor microenvironment, it is important to identify the cellular and acellular components associated with glioma development and growth. In this study, we used 3-D matrix assisted laser desorption ionization imaging mass spectrometry (MALDI IMS) to measure the distributions of multiple molecular species throughout optic nerve tissue in mice with and without glioma, and to explore their spatial relationships within the 3-D volume of the optic nerve and chiasm. 3-D IMS studies often involve extensive workflows due to the high volume of sections required to generate high quality 3-D images. Herein, we present a workflow for 3-D data acquisition and volume reconstruction using mouse optic nerve tissue. The resulting 3-D IMS data yield both molecular similarities and differences between glioma-bearing and wild-type (WT) tissues, including protein distributions localizing to different anatomical subregions. 

BIOLOGICAL SIGNIFICANCE: The current work addresses a number of challenges in 3-D MALDI IMS, driven by the small size of the mouse optic nerve and the need to maintain consistency across multiple 2-D IMS experiments. The 3-D IMS data yield both molecular similarities and differences between glioma-bearing and wild-type (WT) tissues, including protein distributions localizing to different anatomical subregions, which could then be targeted for identification and related back to the biology observed in gliomas of the optic nerve.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{marien2015l,

title = {Non-small cell lung cancer is characterized by dramatic changes in phospholipid profiles},

author = {Eyra Marien and Michael Meister and Thomas Muley and Steffen Fieuws and Sergio Bordel and Rita Derua and Jeffrey Spraggins and Raf Van de Plas and Jonas Dehairs and Jens Wouters and Muralidhararao Bagadi and Hendrik Dienemann and Michael Thomas and Philipp A Schnabel and Richard M Caprioli and Etienne Waelkens and Johannes V Swinnen},

url = {https://doi.org/10.1002/ijc.29517},

doi = {10.1002/ijc.29517},

year  = {2015},

date = {2015-10-01},

journal = {International Journal of Cancer},

volume = {137},

number = {7},

pages = {1539-48},

abstract = {Non-small cell lung cancer (NSCLC) is the leading cause of cancer death globally. To develop better diagnostics and more effective treatments, research in the past decades has focused on identification of molecular changes in the genome, transcriptome, proteome, and more recently also the metabolome. Phospholipids, which nevertheless play a central role in cell functioning, remain poorly explored. Here, using a mass spectrometry (MS)-based phospholipidomics approach, we profiled 179 phospholipid species in malignant and matched non-malignant lung tissue of 162 NSCLC patients (73 in a discovery cohort and 89 in a validation cohort). We identified 91 phospholipid species that were differentially expressed in cancer versus non-malignant tissues. Most prominent changes included a decrease in sphingomyelins (SMs) and an increase in specific phosphatidylinositols (PIs). Also a decrease in multiple phosphatidylserines (PSs) was observed, along with an increase in several phosphatidylethanolamine (PE) and phosphatidylcholine (PC) species, particularly those with 40 or 42 carbon atoms in both fatty acyl chains together. 2D-imaging MS of the most differentially expressed phospholipids confirmed their differential abundance in cancer cells. We identified lipid markers that can discriminate tumor versus normal tissue and different NSCLC subtypes with an AUC (area under the ROC curve) of 0.999 and 0.885, respectively. In conclusion, using both shotgun and 2D-imaging lipidomics analysis, we uncovered a hitherto unrecognized alteration in phospholipid profiles in NSCLC. These changes may have important biological implications and may have significant potential for biomarker development.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{van-de-plas2015a,

title = {Image fusion of mass spectrometry and microscopy: a multimodality paradigm for molecular tissue mapping},

author = {Raf Van de Plas and Junhai Yang and Jeffrey Spraggins and Richard M Caprioli},

url = {https://doi.org/10.1038/nmeth.3296},

doi = {10.1038/nmeth.3296},

year  = {2015},

date = {2015-04-01},

journal = {Nature Methods},

volume = {12},

number = {4},

pages = {366-72},

abstract = {We describe a predictive imaging modality created by 'fusing' two distinct technologies: imaging mass spectrometry (IMS) and microscopy. IMS-generated molecular maps, rich in chemical information but having coarse spatial resolution, are combined with optical microscopy maps, which have relatively low chemical specificity but high spatial information. The resulting images combine the advantages of both technologies, enabling prediction of a molecular distribution both at high spatial resolution and with high chemical specificity. Multivariate regression is used to model variables in one technology, using variables from the other technology. We demonstrate the potential of image fusion through several applications: (i) 'sharpening' of IMS images, which uses microscopy measurements to predict ion distributions at a spatial resolution that exceeds that of measured ion images by ten times or more; (ii) prediction of ion distributions in tissue areas that were not measured by IMS; and (iii) enrichment of biological signals and attenuation of instrumental artifacts, revealing insights not easily extracted from either microscopy or IMS individually.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{verbeeck2014z,

title = {Automated anatomical interpretation of ion distributions in tissue: linking imaging mass spectrometry to curated atlases},

author = {Nico Verbeeck and Junhai Yang and Bart De Moor and Richard M Caprioli and Etienne Waelkens and Raf Van de Plas},

url = {https://doi.org/10.1021/ac502838t},

doi = {10.1021/ac502838t},

year  = {2014},

date = {2014-09-01},

journal = {Analytical Chemistry},

volume = {86},

number = {18},

pages = {8974-82},

abstract = {Imaging mass spectrometry (IMS) has become a prime tool for studying the distribution of biomolecules in tissue. Although IMS data sets can become very large, computational methods have made it practically feasible to search these experiments for relevant findings. However, these methods lack access to an important source of information that many human interpretations rely upon: anatomical insight. In this work, we address this need by (1) integrating a curated anatomical data source with an empirically acquired IMS data source, establishing an algorithm-accessible link between them and (2) demonstrating the potential of such an IMS-anatomical atlas link by applying it toward automated anatomical interpretation of ion distributions in tissue. The concept is demonstrated in mouse brain tissue, using the Allen Mouse Brain Atlas as the curated anatomical data source that is linked to MALDI-based IMS experiments. We first develop a method to spatially map the anatomical atlas to the IMS data sets using nonrigid registration techniques. Once a mapping is established, a second computational method, called correlation-based querying, gives an elementary demonstration of the link by delivering basic insight into relationships between ion images and anatomical structures. Finally, a third algorithm moves further beyond both registration and correlation by providing automated anatomical interpretation of ion images. This task is approached as an optimization problem that deconstructs ion distributions as combinations of known anatomical structures. We demonstrate that establishing a link between an IMS experiment and an anatomical atlas enables automated anatomical annotation, which can serve as an important accelerator both for human and machine-guided exploration of IMS experiments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dittwald2014w,

title = {Towards automated discrimination of lipids versus peptides from full scan mass spectra},

author = {Piotr Dittwald and Vu Trung Nghia and Glenn A Harris and Richard M Caprioli and Raf Van de Plas and Kris Laukens and Anna Gambin and Dirk Valkenborg},

url = {https://doi.org/10.1016/j.euprot.2014.05.002},

doi = {10.1016/j.euprot.2014.05.002},

year  = {2014},

date = {2014-09-01},

journal = {EuPA Open Proteomics},

volume = {4},

pages = {87-100},

abstract = {Although physicochemical fractionation techniques play a crucial role in the analysis of complex mixtures, they are not necessarily the best solution to separate specific molecular classes, such as lipids and peptides. Any physical fractionation step such as, for example, those based on liquid chromatography, will introduce its own variation and noise. In this paper we investigate to what extent the high sensitivity and resolution of contemporary mass spectrometers offers viable opportunities for computational separation of signals in full scan spectra. We introduce an automatic method that can discriminate peptide from lipid peaks in full scan mass spectra, based on their isotopic properties. We systematically evaluate which features maximally contribute to a peptide versus lipid classification. The selected features are subsequently used to build a random forest classifier that enables almost perfect separation between lipid and peptide signals without requiring ion fragmentation and classical tandem MS-based identification approaches. The classifier is trained on in silico data, but is also capable of discriminating signals in real world experiments. We evaluate the influence of typical data inaccuracies of common classes of mass spectrometry instruments on the optimal set of discriminant features. Finally, the method is successfully extended towards the classification of individual lipid classes from full scan mass spectral features, based on input data defined by the Lipid Maps Consortium.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fassbender2012g,

title = {Combined mRNA microarray and proteomic analysis of eutopic endometrium of women with and without endometriosis},

author = {A Fassbender and N Verbeeck and D Börnigen and C M Kyama and A Bokor and A Vodolazkaia and K Peeraer and C Tomassetti and C Meuleman and O Gevaert and R Van de Plas and F Ojeda and B De Moor and Y Moreau and E Waelkens and T M D'Hooghe},

url = {https://doi.org/10.1093/humrep/des127},

doi = {10.1093/humrep/des127},

year  = {2012},

date = {2012-07-01},

journal = {Human Reproduction},

volume = {27},

number = {7},

pages = {2020-9},

abstract = {BACKGROUND: An early semi-invasive diagnosis of endometriosis has the potential to allow early treatment and minimize disease progression but no such test is available at present. Our aim was to perform a combined mRNA microarray and proteomic analysis on the same eutopic endometrium sample obtained from patients with and without endometriosis. METHODS: mRNA and protein fractions were extracted from 49 endometrial biopsies obtained from women with laparoscopically proven presence (n= 31) or absence (n= 18) of endometriosis during the early luteal (n= 27) or menstrual phase (n= 22) and analyzed using microarray and proteomic surface enhanced laser desorption ionization-time of flight mass spectrometry, respectively. Proteomic data were analyzed using a least squares-support vector machines (LS-SVM) model built on 70% (training set) and 30% of the samples (test set). 

RESULTS: mRNA analysis of eutopic endometrium did not show any differentially expressed genes in women with endometriosis when compared with controls, regardless of endometriosis stage or cycle phase. mRNA was differentially expressed (P< 0.05) in women with (925 genes) and without endometriosis (1087 genes) during the menstrual phase when compared with the early luteal phase. Proteomic analysis based on five peptide peaks [2072 mass/charge (m/z); 2973 m/z; 3623 m/z; 3680 m/z and 21133 m/z] using an LS-SVM model applied on the luteal phase endometrium training set allowed the diagnosis of endometriosis (sensitivity, 91; 95% confidence interval (CI): 74-98; specificity, 80; 95% CI: 66-97 and positive predictive value, 87.9%; negative predictive value, 84.8%) in the test set. 

CONCLUSION: mRNA expression of eutopic endometrium was comparable in women with and without endometriosis but different in menstrual endometrium when compared with luteal endometrium in women with endometriosis. Proteomic analysis of luteal phase endometrium allowed the diagnosis of endometriosis with high sensitivity and specificity in training and test sets. A potential limitation of our study is the fact that our control group included women with a normal pelvis as well as women with concurrent pelvic disease (e.g. fibroids, benign ovarian cysts, hydrosalpinges), which may have contributed to the comparable mRNA expression profile in the eutopic endometrium of women with endometriosis and controls.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fassbender2012b,

title = {Proteomics analysis of plasma for early diagnosis of endometriosis},

author = {Amelie Fassbender and Etienne Waelkens and Nico Verbeeck and Cleophas M Kyama and Attila Bokor and Alexandra Vodolazkaia and Raf Van de Plas and Christel Meuleman and Karen Peeraer and Carla Tomassetti and Olivier Gevaert and Fabian Ojeda and Bart De Moor and Thomas D'Hooghe},

url = {https://doi.org/10.1097/AOG.0b013e31823fda8d},

doi = {10.1097/AOG.0b013e31823fda8d},

year  = {2012},

date = {2012-02-01},

journal = {Obstetrics and Gynecology},

volume = {119},

number = {2 Pt 1},

pages = {276-85},

abstract = {OBJECTIVE: To test the hypothesis that differential surface-enhanced laser desorption/ionization time-of-flight mass spectrometry protein or peptide expression in plasma can be used in infertile women with or without pelvic pain to predict the presence of laparoscopically and histologically confirmed endometriosis, especially in the subpopulation with a normal preoperative gynecologic ultrasound examination. 

METHODS: Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry analysis was performed on 254 plasma samples obtained from 89 women without endometriosis and 165 women with endometriosis (histologically confirmed) undergoing laparoscopies for infertility with or without pelvic pain. Data were analyzed using least squares support vector machines and were divided randomly (100 times) into a training data set (70%) and a test data set (30%). 

RESULTS: Minimal-to-mild endometriosis was best predicted (sensitivity 75%, 95% confidence interval [CI] 63-89; specificity 86%, 95% CI 71-94; positive predictive value 83.6%, negative predictive value 78.3%) using a model based on five peptide and protein peaks (range 4.898-14.698 m/z) in menstrual phase samples. Moderate-to-severe endometriosis was best predicted (sensitivity 98%, 95% CI 84-100; specificity 81%, 95% CI 67-92; positive predictive value 74.4%, negative predictive value 98.6%) using a model based on five other peptide and protein peaks (range 2.189-7.457 m/z) in luteal phase samples. The peak with the highest intensity (2.189 m/z) was identified as a fibrinogen β-chain peptide. Ultrasonography-negative endometriosis was best predicted (sensitivity 88%, 95% CI 73-100; specificity 84%, 95% CI 71-96) using a model based on five peptide peaks (range 2.058-42.065 m/z) in menstrual phase samples. 

CONCLUSION: A noninvasive test using proteomic analysis of plasma samples obtained during the menstrual phase enabled the diagnosis of endometriosis undetectable by ultrasonography with high sensitivity and specificity. 

LEVEL OF EVIDENCE: II.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{signoretto2011a,

title = {Tensor Versus Matrix Completion: A Comparison With Application to Spectral Data},

author = {Marco Signoretto and Raf Van de Plas and Bart De Moor and Johan AK Suykens},

url = {https://doi.org/10.1109/LSP.2011.2151856},

doi = {10.1109/LSP.2011.2151856},

year  = {2011},

date = {2011-07-01},

journal = {IEEE Signal Processing Letters},

volume = {18},

number = {7},

pages = {403–406},

abstract = {Tensor completion recently emerged as a generalization of matrix completion for higher order arrays. This problem formulation allows one to exploit the structure of data that intrinsically have multiple dimensions. In this work, we recall a convex formulation for minimum (multilinear) ranks completion of arrays of arbitrary order. Successively we focus on completion of partially observed spectral images; the latter can be naturally represented as third order tensors and typically exhibit intraband correlations. We compare different convex formulations and assess them through case studies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kyama2011k,

title = {Evaluation of endometrial biomarkers for semi-invasive diagnosis of endometriosis},

author = {Cleophas M Kyama and Attila Mihalyi and Olivier Gevaert and Etienne Waelkens and Peter Simsa and Raf Van de Plas and Christel Meuleman and Bart De Moor and Thomas M D'Hooghe},

url = {https://doi.org/10.1016/j.fertnstert.2010.06.084},

doi = {10.1016/j.fertnstert.2010.06.084},

year  = {2011},

date = {2011-03-01},

journal = {Fertility and Sterility},

volume = {95},

number = {4},

pages = {1338-43.e1-3},

abstract = {OBJECTIVE: To test the hypothesis that specific proteins and peptides are expressed differentially in eutopic endometrium of women with and without endometriosis and at specific stages of the disease (minimal, mild, moderate, or severe) during the secretory phase. 

DESIGN: Patients with endometriosis were compared with controls. 

SETTING: University hospital. 

PATIENT(S): A total of 29 patients during the secretory phase were selected for this study on the basis of cycle phase and presence or absence of endometriosis. 

INTERVENTION(S): Endometriosis was confirmed laparoscopically and histologically in 19 patients with endometriosis of revised American Society for Reproductive Medicine stages (9 minimal-mild and 10 moderate-severe), and the presence of a normal pelvis was documented by laparoscopy in 10 controls. 

MAIN OUTCOME MEASURE(S): Protein expression of endometrium was evaluated with use of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. The differential expression of protein mass peaks was analyzed with use of support vector machine algorithms and logistic regression models. 

RESULT(S): Data preprocessing resulted in differential expression of 73, 30, and 131 mass peaks between controls and patients with endometriosis (all stages), with minimal-mild endometriosis, and with moderate-severe endometriosis, respectively. Endometriosis was diagnosed with high sensitivity (89.5%) and specificity (90%) with use of five down-regulated mass peaks (1.949 kDa, 5.183 kDa, 8.650 kDa, 8.659 kDa, and 13.910 kDa) obtained after support vector machine ranking and logistic regression classification. With use of a similar analysis, minimal-mild endometriosis was diagnosed with four mass peaks (two up-regulated: 35.956 kDa and 90.675 kDa and two down-regulated: 1.924 kDa and 2.504 kDa) with maximal sensitivity (100%) and specificity (100%). The 90.675-kDa and 35.956-kDa mass peaks were identified as T-plastin and annexin V, respectively. 

CONCLUSION(S): Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry analysis of secretory phase endometrium combined with bioinformatics puts forward a prospective panel of potential biomarkers with sensitivity of 100% and specificity of 100% for the diagnosis of minimal to mild endometriosis.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fassbender2010i,

title = {TRIzol treatment of secretory phase endometrium allows combined proteomic and mRNA microarray analysis of the same sample in women with and without endometriosis},

author = {Amelie Fassbender and Peter Simsa and Cleophas M Kyama and Etienne Waelkens and Attila Mihalyi and Christel Meuleman and Olivier Gevaert and Raf Van de Plas and Bart Moor and Thomas M D'Hooghe},

url = {https://doi.org/10.1186/1477-7827-8-123},

doi = {10.1186/1477-7827-8-123},

year  = {2010},

date = {2010-10-01},

journal = {Reproductive Biology and Endocrinology},

volume = {8},

pages = {123},

abstract = {BACKGROUND: According to mRNA microarray, proteomics and other studies, biological abnormalities of eutopic endometrium (EM) are involved in the pathogenesis of endometriosis, but the relationship between mRNA and protein expression in EM is not clear. We tested for the first time the hypothesis that EM TRIzol extraction allows proteomic Surface Enhanced Laser Desorption/Ionisation Time-of-Flight Mass Spectrometry (SELDI-TOF MS) analysis and that these proteomic data can be related to mRNA (microarray) data obtained from the same EM sample from women with and without endometriosis. METHODS: Proteomic analysis was performed using SELDI-TOF-MS of TRIzol-extracted EM obtained during secretory phase from patients without endometriosis (n = 6), patients with minimal-mild (n = 5) and with moderate-severe endometriosis (n = 5), classified according to the system of the American Society of Reproductive Medicine. Proteomic data were compared to mRNA microarray data obtained from the same EM samples. 

RESULTS: In our SELDI-TOF MS study 32 peaks were differentially expressed in endometrium of all women with endometriosis (stages I-IV) compared with all controls during the secretory phase. Comparison of proteomic results with those from microarray revealed no corresponding genes/proteins. 

CONCLUSION: TRIzol treatment of secretory phase EM allows combined proteomic and mRNA microarray analysis of the same sample, but comparison between proteomic and microarray data was not evident, probably due to post-translational modifications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{luts2010n,

title = {A tutorial on support vector machine-based methods for classification problems in chemometrics},

author = {Jan Luts and Fabian Ojeda and Raf Van de Plas and Bart De Moor and Sabine Van Huffel and Johan A K Suykens},

url = {https://doi.org/10.1016/j.aca.2010.03.030},

doi = {10.1016/j.aca.2010.03.030},

year  = {2010},

date = {2010-04-01},

journal = {Analytica Chimica Acta},

volume = {665},

number = {2},

pages = {129-45},

abstract = {This tutorial provides a concise overview of support vector machines and different closely related techniques for pattern classification. The tutorial starts with the formulation of support vector machines for classification. The method of least squares support vector machines is explained. Approaches to retrieve a probabilistic interpretation are covered and it is explained how the binary classification techniques can be extended to multi-class methods. Kernel logistic regression, which is closely related to iteratively weighted least squares support vector machines, is discussed. Different practical aspects of these methods are addressed: the issue of feature selection, parameter tuning, unbalanced data sets, model evaluation and statistical comparison. The different concepts are illustrated on three real-life applications in the field of metabolomics, genetics and proteomics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{lemaire2009i,

title = {Insulin crystallization depends on zinc transporter ZnT8 expression, but is not required for normal glucose homeostasis in mice},

author = {K Lemaire and M A Ravier and A Schraenen and J W M Creemers and R Van de Plas and M Granvik and L Van Lommel and E Waelkens and F Chimienti and G A Rutter and P Gilon and P A Veld and F C Schuit},

url = {https://doi.org/10.1073/pnas.0906587106},

doi = {10.1073/pnas.0906587106},

year  = {2009},

date = {2009-09-01},

journal = {Proceedings of the National Academy of Sciences of the United States of America},

volume = {106},

number = {35},

pages = {14872-7},

abstract = {Zinc co-crystallizes with insulin in dense core secretory granules, but its role in insulin biosynthesis, storage and secretion is unknown. In this study we assessed the role of the zinc transporter ZnT8 using ZnT8-knockout (ZnT8(-/-)) mice. Absence of ZnT8 expression caused loss of zinc release upon stimulation of exocytosis, but normal rates of insulin biosynthesis, normal insulin content and preserved glucose-induced insulin release. Ultrastructurally, mature dense core insulin granules were rare in ZnT8(-/-) beta cells and were replaced by immature, pale insulin "progranules," which were larger than in ZnT8(+/+) islets. When mice were fed a control diet, glucose tolerance and insulin sensitivity were normal. However, after high-fat diet feeding, the ZnT8(-/-) mice became glucose intolerant or diabetic, and islets became less responsive to glucose. Our data show that the ZnT8 transporter is essential for the formation of insulin crystals in beta cells, contributing to the packaging efficiency of stored insulin. Interaction between the ZnT8(-/-) genotype and diet to induce diabetes is a model for further studies of the mechanism of disease of human ZNT8 gene mutations.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cadron2009k,

title = {The use of laser microdissection and SELDI-TOF MS in ovarian cancer tissue to identify protein profiles},

author = {Isabelle Cadron and Toon Van Gorp and Frederic Amant and Ignace Vergote and Philippe Moerman and Etienne Waelkens and Anneleen Daemen and Raf Van de Plas and Bart De Moor and Robert Zeillinger},

url = {https://ar.iiarjournals.org/content/29/4/1039.long},

year  = {2009},

date = {2009-04-01},

journal = {Anticancer Research},

volume = {29},

number = {4},

pages = {1039-45},

abstract = {BACKGROUND: There is a strong need for prognostic biomarkers in ovarian cancer patients due to the heterogeneous responses on current treatment modalities. 

MATERIALS AND METHODS: This study investigates the feasibility of combining laser microdissection (LMD) and surface enhanced laser desorption ionization-time of flight mass spectrometry (SELDI-TOF MS) in ovarian cancer tissue to obtain protein profiles. 

RESULTS: Ideal conditions for preparing a protein lysate were determined and subsequently analysed on SELDI-TOF MS. Applying these protocols on tissue of 9 ovarian cancer patients showed different protein profiles between platinum sensitive and resistant patients. 

CONCLUSION: This shows that combining optimised protocols for LMD with SELDI-TOF MS can be used to obtain discriminatory protein profiles. However, studies with large patient numbers and validation sets are essential to identify reliable biomarkers using this approach.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}