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PubMed | University of Bremen, Proteopath GmbH, SCiLS GmbH, TU Munich and University of Heidelberg
Type: | Journal: Biochimica et biophysica acta | Year: 2016

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) shows a high potential for applications in histopathological diagnosis, and in particular for supporting tumor typing and subtyping. The development of such applications requires the extraction of spectral fingerprints that are relevant for the given tissue and the identification of biomarkers associated with these spectral patterns. We propose a novel data analysis method based on the extraction of characteristic spectral patterns (CSPs) that allow automated generation of classification models for spectral data. Formalin-fixed paraffin embedded (FFPE) tissue samples from N=445 patients assembled on 12 tissue microarrays were analyzed. The method was applied to discriminate primary lung and pancreatic cancer, as well as adenocarcinoma and squamous cell carcinoma of the lung. A classification accuracy of 100% and 82.8%, resp., could be achieved on core level, assessed by cross-validation. The method outperformed the more conventional classification method based on the extraction of individual m/z values in the first application, while achieving a comparable accuracy in the second. LC-MS/MS peptide identification demonstrated that the spectral features present in selected CSPs correspond to peptides relevant for the respective classification. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.


News Article | November 14, 2016
Site: www.eurekalert.org

We leave behind trace chemicals, molecules and microbes on every object we touch. By sampling the molecules on cell phones, researchers at University of California San Diego School of Medicine and Skaggs School of Pharmacy and Pharmaceutical Sciences were able to construct lifestyle sketches for each phone's owner, including diet, preferred hygiene products, health status and locations visited. This proof-of-concept study, published November 14 by Proceedings of the National Academy of Sciences, could have a number of applications, including criminal profiling, airport screening, medication adherence monitoring, clinical trial participant stratification and environmental exposure studies. "You can imagine a scenario where a crime scene investigator comes across a personal object -- like a phone, pen or key -- without fingerprints or DNA, or with prints or DNA not found in the database. They would have nothing to go on to determine who that belongs to," said senior author Pieter Dorrestein, PhD, professor in UC San Diego School of Medicine and Skaggs School of Pharmacy and Pharmaceutical Sciences. "So we thought -- what if we take advantage of left-behind skin chemistry to tell us what kind of lifestyle this person has?" In a 2015 study , Dorrestein's team constructed 3D models to illustrate the molecules and microbes found at hundreds of locations on the bodies of two healthy adult volunteers. Despite a three-day moratorium on personal hygiene products before the samples were collected, the researchers were surprised to find that the most abundant molecular features in the skin swabs still came from hygiene and beauty products, such as sunscreen. "All of these chemical traces on our bodies can transfer to objects," Dorrestein said. "So we realized we could probably come up with a profile of a person's lifestyle based on chemistries we can detect on objects they frequently use." Thirty-nine healthy adult volunteers participated in Dorrestein's latest study. The team swabbed four spots on each person's cell phone -- an object we tend to spend a lot of time touching -- and eight spots on each person's right hand, for a total of nearly 500 samples. Then they used a technique called mass spectrometry to detect molecules from the samples. They identified as many molecules as possible by comparing them to reference structures in the GNPS database , a crowdsourced mass spectrometry knowledge repository and annotation website developed by Dorrestein and co-author Nuno Bandeira, PhD, associate professor at the Jacobs School of Engineering and Skaggs School of Pharmacy and Pharmaceutical Sciences at UC San Diego. With this information, the researchers developed a personalized lifestyle "read-out" from each phone. Some of the medications they detected on phones included anti-inflammatory and anti-fungal skin creams, hair loss treatments, anti-depressants and eye drops. Food molecules included citrus, caffeine, herbs and spices. Sunscreen ingredients and DEET mosquito repellant were detected on phones even months after they had last been used by the phone owners, suggesting these objects can provide long-term composite lifestyle sketches. "By analyzing the molecules they've left behind on their phones, we could tell if a person is likely female, uses high-end cosmetics, dyes her hair, drinks coffee, prefers beer over wine, likes spicy food, is being treated for depression, wears sunscreen and bug spray -- and therefore likely spends a lot of time outdoors -- all kinds of things," said first author Amina Bouslimani, PhD, an assistant project scientist in Dorrestein's lab. "This is the kind of information that could help an investigator narrow down the search for an object's owner." There are limitations, Dorrestein said. First of all, these molecular read-outs provide a general profile of person's lifestyle, but they are not meant to be a one-to-one match, like a fingerprint. To develop more precise profiles and for this method to be more useful, he said more molecules are needed in the reference database, particularly for the most common foods people eat, clothing materials, carpets, wall paints and anything else people come into contact with. He'd like to see a trace molecule database on the scale of the fingerprint database, but it's a large-scale effort that no single lab will be able to do alone. Moving forward, Dorrestein and Bouslimani have already begun extending their study with an additional 80 people and samples from other personal objects, such as wallets and keys. They also hope to soon begin gathering another layer of information from each sample -- identities of the many bacteria and other microbes that cover our skin and objects. In a 2010 study , their collaborator and co-author, Rob Knight, PhD, professor in the UC San Diego School of Medicine and Jacobs School of Engineering and director of the Center for Microbiome Innovation at UC San Diego, contributed to a study in which his team found they could usually match a computer keyboard to its owner just based on the unique populations of microbes the person left on it. At that time, they could make the match with a fair amount of accuracy, though not yet precisely enough for use in an investigation. Beyond forensics, Dorrestein and Bouslimani imagine trace molecular read-outs could also be used in medical and environmental studies. For example, perhaps one day physicians could assess how well a patient is sticking with a medication regimen by monitoring metabolites on his or her skin. Similarly, patients participating in a clinical trial could be divided into subgroups based on how they metabolize the medication under investigation, as revealed by skin metabolites -- then the medication could be given only to those patients who can metabolize it appropriately. Skin molecule read-outs might also provide useful information about a person's exposure to environmental pollutants and chemical hazards, such as in a high-risk workplace or a community living near a potential pollution source. Study co-authors also include: Alexey V. Melnik, Zhenjiang Zech Xu, Amnon Amir, Ricardo R. da Silva, Mingxun Wang, UC San Diego; and Theodore Alexandrov, UC San Diego, European Molecular Biology Laboratory and SCiLS GmbH.


News Article | November 16, 2016
Site: www.medicalnewstoday.com

We leave behind trace chemicals, molecules and microbes on every object we touch. By sampling the molecules on cell phones, researchers at University of California San Diego School of Medicine and Skaggs School of Pharmacy and Pharmaceutical Sciences were able to construct lifestyle sketches for each phone's owner, including diet, preferred hygiene products, health status and locations visited. This proof-of-concept study, published by Proceedings of the National Academy of Sciences, could have a number of applications, including criminal profiling, airport screening, medication adherence monitoring, clinical trial participant stratification and environmental exposure studies. "You can imagine a scenario where a crime scene investigator comes across a personal object - like a phone, pen or key - without fingerprints or DNA, or with prints or DNA not found in the database. They would have nothing to go on to determine who that belongs to," said senior author Pieter Dorrestein, PhD, professor in UC San Diego School of Medicine and Skaggs School of Pharmacy and Pharmaceutical Sciences. "So we thought - what if we take advantage of left-behind skin chemistry to tell us what kind of lifestyle this person has?" In a 2015 study , Dorrestein's team constructed 3D models to illustrate the molecules and microbes found at hundreds of locations on the bodies of two healthy adult volunteers. Despite a three-day moratorium on personal hygiene products before the samples were collected, the researchers were surprised to find that the most abundant molecular features in the skin swabs still came from hygiene and beauty products, such as sunscreen. "All of these chemical traces on our bodies can transfer to objects," Dorrestein said. "So we realized we could probably come up with a profile of a person's lifestyle based on chemistries we can detect on objects they frequently use." Thirty-nine healthy adult volunteers participated in Dorrestein's latest study. The team swabbed four spots on each person's cell phone - an object we tend to spend a lot of time touching - and eight spots on each person's right hand, for a total of nearly 500 samples. Then they used a technique called mass spectrometry to detect molecules from the samples. They identified as many molecules as possible by comparing them to reference structures in the GNPS database , a crowdsourced mass spectrometry knowledge repository and annotation website developed by Dorrestein and co-author Nuno Bandeira, PhD, associate professor at the Jacobs School of Engineering and Skaggs School of Pharmacy and Pharmaceutical Sciences at UC San Diego. With this information, the researchers developed a personalized lifestyle "read-out" from each phone. Some of the medications they detected on phones included anti-inflammatory and anti-fungal skin creams, hair loss treatments, anti-depressants and eye drops. Food molecules included citrus, caffeine, herbs and spices. Sunscreen ingredients and DEET mosquito repellant were detected on phones even months after they had last been used by the phone owners, suggesting these objects can provide long-term composite lifestyle sketches. "By analyzing the molecules they've left behind on their phones, we could tell if a person is likely female, uses high-end cosmetics, dyes her hair, drinks coffee, prefers beer over wine, likes spicy food, is being treated for depression, wears sunscreen and bug spray - and therefore likely spends a lot of time outdoors - all kinds of things," said first author Amina Bouslimani, PhD, an assistant project scientist in Dorrestein's lab. "This is the kind of information that could help an investigator narrow down the search for an object's owner." There are limitations, Dorrestein said. First of all, these molecular read-outs provide a general profile of person's lifestyle, but they are not meant to be a one-to-one match, like a fingerprint. To develop more precise profiles and for this method to be more useful, he said more molecules are needed in the reference database, particularly for the most common foods people eat, clothing materials, carpets, wall paints and anything else people come into contact with. He'd like to see a trace molecule database on the scale of the fingerprint database, but it's a large-scale effort that no single lab will be able to do alone. Moving forward, Dorrestein and Bouslimani have already begun extending their study with an additional 80 people and samples from other personal objects, such as wallets and keys. They also hope to soon begin gathering another layer of information from each sample - identities of the many bacteria and other microbes that cover our skin and objects. In a 2010 study , their collaborator and co-author, Rob Knight, PhD, professor in the UC San Diego School of Medicine and Jacobs School of Engineering and director of the Center for Microbiome Innovation at UC San Diego, contributed to a study in which his team found they could usually match a computer keyboard to its owner just based on the unique populations of microbes the person left on it. At that time, they could make the match with a fair amount of accuracy, though not yet precisely enough for use in an investigation. Beyond forensics, Dorrestein and Bouslimani imagine trace molecular read-outs could also be used in medical and environmental studies. For example, perhaps one day physicians could assess how well a patient is sticking with a medication regimen by monitoring metabolites on his or her skin. Similarly, patients participating in a clinical trial could be divided into subgroups based on how they metabolize the medication under investigation, as revealed by skin metabolites - then the medication could be given only to those patients who can metabolize it appropriately. Skin molecule read-outs might also provide useful information about a person's exposure to environmental pollutants and chemical hazards, such as in a high-risk workplace or a community living near a potential pollution source. Study co-authors also include: Alexey V. Melnik, Zhenjiang Zech Xu, Amnon Amir, Ricardo R. da Silva, Mingxun Wang, UC San Diego; and Theodore Alexandrov, UC San Diego, European Molecular Biology Laboratory and SCiLS GmbH. Article: Lifestyle chemistries from phones for individual profiling, Pieter C. Dorrestein et al., Proceedings of the National Academy of Sciences, doi: 10.1073/pnas.1610019113, published online 14 November 2016.


Alexandrov T.,University of Bremen | Alexandrov T.,Steinbeis Innovation Center i Research | Alexandrov T.,SCiLS GmbH | Alexandrov T.,University of California at San Diego | Bartels A.,University of Bremen
Bioinformatics | Year: 2013

Motivation: Imaging mass spectrometry has emerged in the past decade as a label-free, spatially resolved and multi-purpose bioanalytical technique for direct analysis of biological samples. However, solving two everyday data analysis problems still requires expert judgment: (i) the detection of unknown molecules and (ii) the testing for presence of known molecules.Results: We developed a measure of spatial chaos of a molecular image corresponding to a mass-to-charge value, which is a proxy for the molecular presence, and developed methods solving considered problems. The statistical evaluation was performed on a dataset from a rat brain section with test sets of molecular images selected by an expert. The measure of spatial chaos has shown high agreement with expert judges. The method for detection of unknown molecules allowed us to find structured molecular images corresponding to spectral peaks of any low intensity. The test for presence applied to a list of endogenous peptides ranked them according to the proposed measure of their presence in the sample. © The Author 2013.


Ernst G.,Friedrich - Schiller University of Jena | Guntinas-Lichius O.,Jena University Hospital | Hauberg-Lotte L.,University of Bremen | Trede D.,SCiLS GmbH | And 4 more authors.
Head and Neck | Year: 2015

Background Despite efforts in localization of key proteins using immunohistochemistry, the complex proteomic composition of pleomorphic adenomas has not yet been characterized. Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI imaging) allows label-free and spatially resolved detection of hundreds of proteins directly from tissue sections and of histomorphological regions by finding colocalized molecular signals. Spatial segmentation of MALDI imaging data is an algorithmic method for finding regions of similar proteomic composition as functionally similar regions. Methods We investigated 2 pleomorphic adenomas by applying spatial segmentation to the MALDI imaging data of tissue sections. Results The spatial segmentation subdivided the tissue in a good accordance with the tissue histology. Numerous molecular signals colocalized with histologically defined tissue regions were found. Conclusion Our study highlights the cellular transdifferentiation within the pleomorphic adenoma. It could be shown that spatial segmentation of MALDI imaging data is a promising approach in the emerging field of digital histological analysis and characterization of tumors. © 2014 Wiley Periodicals, Inc.


Laouirem S.,University Paris Diderot | Le Faouder J.,University Paris Diderot | Alexandrov T.,University of Bremen | Alexandrov T.,Steinbeis Innovation Center i Research | And 11 more authors.
Journal of Pathology | Year: 2014

Cirrhosis is a lesion at risk of hepatocellular carcinoma (HCC). Identifying mechanisms associated with the transition from cirrhosis to HCC and characterizing biomarkers of cirrhosis at high risk of developing into cancer are crucial for improving early diagnosis and prognosis of HCC. We used MALDI imaging to compare mass spectra obtained from tissue sections of cirrhosis without HCC, cirrhosis with HCC, and HCC, and a top-down proteomics approach to characterize differential biomarkers. We identified a truncated form of monomeric ubiquitin lacking the two C-terminal glycine residues, Ubi(1-74), the level of which increased progressively, from cirrhosis without HCC to cirrhosis with HCC to HCC. We showed that kallikrein-related peptidase 6 (KLK6) catalysed the production of Ubi(1-74) from monomeric ubiquitin. Furthermore, we demonstrated that KLK6 was induced de novo in cirrhosis and increased in HCC in parallel with accumulation of Ubi(1-74). We investigated in vitro the possible consequences of Ubi(1-74) accumulation and demonstrated that Ubi(1-74) interferes with the normal ubiquitination machinery in what is likely to be a kinetic process. Our data suggest that de novo KLK6 expression during early liver carcinogenesis may induce production of Ubi(1-74) by post-translational modification of ubiquitin. Given the deleterious effect of Ubi(1-74) on protein ubiquitination and the major role of ubiquitin machinery in maintenance of cell homeostasis, Ubi(1-74) might severely impact a number of critical cellular functions during transition from cirrhosis to cancer. Ubi(1-74) and KLK6 may serve as markers of cancer risk in patients with cirrhosis. Copyright © 2014 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.


Alexandrov T.,University of Bremen | Alexandrov T.,SCiLS GmbH | Alexandrov T.,Steinbeis Innovation Center i Research | Alexandrov T.,University of California at San Diego | And 5 more authors.
Analytical Chemistry | Year: 2013

Imaging mass spectrometry (imaging MS) has emerged in the past decade as a label-free, spatially resolved, and multipurpose bioanalytical technique for direct analysis of biological samples from animal tissue, plant tissue, biofilms, and polymer films.1,2 Imaging MS has been successfully incorporated into many biomedical pipelines where it is usually applied in the so-called untargeted mode-capturing spatial localization of a multitude of ions from a wide mass range.3 An imaging MS data set usually comprises thousands of spectra and tens to hundreds of thousands of mass-to-charge (m/z) images and can be as large as several gigabytes. Unsupervised analysis of an imaging MS data set aims at finding hidden structures in the data with no a priori information used and is often exploited as the first step of imaging MS data analysis. We propose a novel, easy-to-use and easy-to-implement approach to answer one of the key questions of unsupervised analysis of imaging MS data: what do all m/z images look like? The key idea of the approach is to cluster all m/z images according to their spatial similarity so that each cluster contains spatially similar m/z images. We propose a visualization of both spatial and spectral information obtained using clustering that provides an easy way to understand what all m/z images look like. We evaluated the proposed approach on matrix-assisted laser desorption ionization imaging MS data sets of a rat brain coronal section and human larynx carcinoma and discussed several scenarios of data analysis. © 2013 American Chemical Society.


PubMed | University of Bremen, Bruker, SCiLS GmbH, Friedrich - Schiller University of Jena and Jena University Hospital
Type: Journal Article | Journal: Head & neck | Year: 2015

Despite efforts in localization of key proteins using immunohistochemistry, the complex proteomic composition of pleomorphic adenomas has not yet been characterized. Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI imaging) allows label-free and spatially resolved detection of hundreds of proteins directly from tissue sections and of histomorphological regions by finding colocalized molecular signals. Spatial segmentation of MALDI imaging data is an algorithmic method for finding regions of similar proteomic composition as functionally similar regions.We investigated 2 pleomorphic adenomas by applying spatial segmentation to the MALDI imaging data of tissue sections.The spatial segmentation subdivided the tissue in a good accordance with the tissue histology. Numerous molecular signals colocalized with histologically defined tissue regions were found.Our study highlights the cellular transdifferentiation within the pleomorphic adenoma. It could be shown that spatial segmentation of MALDI imaging data is a promising approach in the emerging field of digital histological analysis and characterization of tumors.


Grant
Agency: European Commission | Branch: H2020 | Program: RIA | Phase: PHC-32-2014 | Award Amount: 3.00M | Year: 2015

Metabolomics is recognized as a crucial scientific domain, promising to advance our understanding of biology, physiology, and medicine. The emergence of high-resolution imaging mass spectrometry (HR imaging MS) opened doors to spatial profiling of hundreds of metabolites directly from tissue sections. However, clinical use of HR imaging MS is hampered by a lack of clinically-oriented bioinformatics tools for molecular interpretation of the complex and information-rich data produced. Our goal is to address this bottleneck. We will develop algorithms for high-throughput putative annotation of hundreds of metabolites, knowledge-based downstream analysis, and validation of biologically-relevant leads. We will create the METASPACE engine, an open online platform providing these tools integrated into validated workflows for clinical use. This engine will be evaluated in clinical case studies on metabolic phenotyping of tumor response to chemotherapy and polymicrobial infections in cystic fibrosis. This demonstration will raise awareness and build trust among potential end-users. METASPACE will create a research ecosystem for exploitation of spatial metabolomic data that benefits both academics and industry. An open-source approach will stimulate developments in this field and provide a sustainable platform capable of incorporating future bioinformatics. Our user-centred tools, linked to existing molecular databases, will enable researchers without mass spectrometry or bioinformatics experience to turn big and complex HR imaging MS data into molecular knowledge. A considerable outreach effort, alongside constant interaction with the clinical metabolomics community, will maximize impact and dissemination. By engaging and educating envisaged end-users, METASPACE will facilitate future clinical discoveries in studies that require untargeted metabolic profiling and imaging. Our project will drive innovation and create a novel bioinformatics research field centred in Europe.


PubMed | J. Craig Venter Institute, San Diego State University, University of Heidelberg, University of British Columbia and 4 more.
Type: Journal Article | Journal: mSystems | Year: 2016

Microbes are commonly studied as individual species, but they exist as mixed assemblages in nature. At present, we know very little about the spatial organization of the molecules, including natural products that are produced within these microbial networks. Lichens represent a particularly specialized type of symbiotic microbial assemblage in which the component microorganisms exist together. These composite microbial assemblages are typically comprised of several types of microorganisms representing phylogenetically diverse life forms, including fungi, photosymbionts, bacteria, and other microbes. Here, we employed matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) imaging mass spectrometry to characterize the distributions of small molecules within a

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