Cell culture metabolomics in the diagnosis of lung cancer - The influence of cell culture conditions

Lung cancer is the leading cause of cancer deaths. Unfortunately, lung cancer is often diagnosed only when it becomes symptomatic or at an advanced stage when few treatment options are available. Hence, a diagnostic test suitable for screening widespread populations is required to enable earlier diagnosis. Analysis of exhaled breath provides a non-invasive method for early detection of lung cancer. Analysis of volatile organic compounds (VOCs) by various mass spectral techniques has identified potential biomarkers of disease. Nevertheless, the metabolic origins and the disease specificity of VOCs need further elucidation. Cell culture metabolomics can be used as a bottom-up approach to identify biomarkers of pathological conditions and can also be used to study the metabolic pathways that produce such compounds. This paper summarizes the current knowledge of lung cancer biomarkers in exhaled breath and emphasizes the critical role of cell culture conditions in determining the VOCs produced in vitro. Hypoxic culture conditions more closely mimic the conditions of cancer cell growth in vivo. We propose that since hypoxia influences cell metabolism and so potentially the VOCs that the cancer cells produce, the cell culture metabolomics projects should consider culturing cancer cells in hypoxic conditions

Computational methods for metabolomic data analysis of ion mobility spectrometry data-reviewing the state of the art

Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) is a well known technology for detecting volatile organic compounds (VOCs). We may utilize MCC/IMS for scanning human exhaled air, bacterial colonies or cell lines, for example. Thereby we gain information about the human health status or infection threats. We may further study the metabolic response of living cells to external perturbations. The instrument is comparably cheap, robust and easy to use in every day practice. However, the potential of the MCC/IMS methodology depends on the successful application of computational approaches for analyzing the huge amount of emerging data sets. Here, we will review the state of the art and highlight existing challenges. First, we address methods for raw data handling, data storage and visualization. Afterwards we will introduce de-noising, peak picking and other pre-processing approaches. We will discuss statistical methods for analyzing correlations between peaks and diseases or medical treatment. Finally, we study up-to-date machine learning techniques for identifying robust biomarker molecules that allow classifying patients into healthy and diseased groups. We conclude that MCC/IMS coupled with sophisticated computational methods has the potential to successfully address a broad range of biomedical questions. While we can solve most of the data pre-processing steps satisfactorily, some computational challenges with statistical learning and model validation remain

An integrative clinical database and diagnostics platform for biomarker identification and analysis in ion mobility spectra of human exhaled air

Over the last decade the evaluation of odors and vapors in human breath has gained more and more attention, particularly in the diagnostics of pulmonary diseases. Ion mobility spectrometry coupled with multi-capillary columns (MCC/IMS), is a well known technology for detecting volatile organic compounds (VOCs) in air. It is a comparatively inexpensive, non-invasive, high-throughput method, which is able to handle the moisture that comes with human exhaled air, and allows for characterizing of VOCs in very low concentrations. To identify discriminating compounds as biomarkers, it is necessary to have a clear understanding of the detailed composition of human breath. Therefore, in addition to the clinical studies, there is a need for a flexible and comprehensive centralized data repository, which is capable of gathering all kinds of related information. Moreover, there is a demand for automated data integration and semi-automated data analysis, in particular with regard to the rapid data accumulation, emerging from the high-throughput nature of the MCC/IMS technology. Here, we present a comprehensive database application and analysis platform, which combines metabolic maps with heterogeneous biomedical data in a well-structured manner. The design of the database is based on a hybrid of the entity-attribute-value (EAV) model and the EAV-CR, which incorporates the concepts of classes and relationships. Additionally it offers an intuitive user interface that provides easy and quick access to the platform's functionality: automated data integration and integrity validation, versioning and roll-back strategy, data retrieval as well as semi-automatic data mining and machine learning capabilities. The platform will support MCC/IMS-based biomarker identification and validation. The software, schemata, data sets and further information is publicly available at \urlhttp://imsdb.mpi-inf.mpg.de

Computational methods for breath metabolomics in clinical diagnostics

For a long time, human odors and vapors have been known for their diagnostic power. Therefore, the analysis of the metabolic composition of human breath and odors creates the opportunity for a non-invasive tool for clinical diagnostics. Innovative analytical technologies to capture the metabolic profile of a patient’s breath are available, such as, for instance, the ion mobility spectrometry coupled to a multicapilary collumn. However, we are lacking automated systems to process, analyse and evaluate large clinical studies of the human exhaled air. To fill this gap, a number of computational challenges need to be addressed. For instance, breath studies generate large amounts of heterogeneous data that requires automated preprocessing, peak-detection and identification as a basis for a sophisticated follow up analysis. In addition, generalizable statistical evaluation frameworks for the detection of breath biomarker profiles that are robust enough to be employed in routine clinical practice are necessary. In particular since breath metabolomics is susceptible to specific confounding factors and background noise, similar to other clinical diagnostics technologies. Moreover, spesific manifestations of disease stages and progression, may largely influence the breathomics profiles. To this end, this thesis will address these challenges to move towards more automatization and generalization in clinical breath research. In particular I present methods to support the search for biomarker profiles that enable a non-invasive detection of diseases, treatment optimization and prognosis to provide a new powerful tool for precision medicine.Seit jeher ist bekannt, dass Körpergeruch und der Atem Hinweise zu deren Gesundheitszustand liefern können. Eine Analyse der Atemluft auf molekularer Ebene verspricht daher neue Ansätze zur Diagnose spezifischer Krankheiten. Innovative Technologien wie die Ionen Mobilitäts Spectrometrie in Kombination mit einer Multikapilarsäule, erlauben erstmals hochauflösende metabolische Profile der Atemluft innerhalb kürzester Zeit zu erzeugen. Zur Zeit fehlen jedoch die notwendigen computergestützten Applikationen zur automatischen Organisation und Auswertung der generierten Daten. Eine besondere Herausforderung stellen dabei die großen Mengen heterogenener klinischer und analytischer Daten und deren Verarbeitung. Ähnlich wie andere Hochdurchsatzverfahren unterliegt die Atemluft dem Einfluss von Hintergrundsignalen wie der Umgebungsluft oder Anderen die Ergebnisse verzerrenden Faktoren, wie zum Beispiel Ernährung, Lebensgewohnheiten oder Medikation. Dies erfordert den Einsatz von modernen Methoden der Statistik und des maschinellen Lernens, um robuste und generalisierbare Krankheitsmarker zu identifizieren. Ein besonderer Augenmerk gilt hierbei auch Krankheiten deren metabolischer Fingerabdruck sich im Krankheitsverlauf drastisch verändern können. Das Ziel meiner Arbeit ist es Lösungen für die beschriebenen Probleme zu finden und damit die Suche nach praxistauglichen Krankheitsmarkern mit bioinformatischen Methoden zu unterstützen. Im Rahmen mehrerer Studien und Softwareprojekten wurden grundlegende Methodiken vorgestellt, evaluiert und etabliert, insbesondere im Hinblick auf die Entwicklung computergestützter Systeme zur automatischen Analyse von Atemluftdaten. Die vorgestellten Verfahren legen den Grundstein für die nicht invasive Detektion von Krankheiten, Optimierung und Prognose von Behandlungen und darüber hinaus für ein weiteres Werkzeug der personalisierten Medizin

A low cost gas phase analysis system for the diagnosis of bacterial infection

Drug resistance is becoming a major concern in both the western world and in developing countries. The over use of common anti-bacterial drugs has resulted in a plethora of multi-drug resistant diseases and an ever reducing number of effective treatments - and is now of major concern to the UK government. One of the major reasons behind this is the difficulty in identifying bacterial infections from viral infections, especially in primary care where patients have an expectation of receiving medication. For most viral conditions, there is no effective treatment and the body fights off the disease, thus prescribing anti-bacterial drugs simply results in the proliferation of drugs within the community - increasing the rate of drug resistance. Increasing drug resistance contributed to the rise of superbugs (drug resistant bacteria) which are expected to kill an about 10 million people a year worldwide by the year 2050 and could result to an economic loss of $63 trillion. Increasing drug resistance contributed to the rise of superbugs (drug resistant bacteria) which are expected to kill an about 10 million people a year worldwide by the year 2050 and could result to an economic loss of$63 trillion. Therefore, there is a strong medical and economic need to develop tools that can diagnose bacterial diseases from viral infections, focused towards primary care. One means of achieving this is through the detection of gas-phase biomarkers IX of disease. It is well known that the metabolic activity of bacteria is significantly different from its host. Many studies have shown that it is possible to detect a bacterial infection, identify the strain and its current life-cycle stage simply by measuring bacterial metabolic emissions. In addition, the human body's response to a bacterial infection is significantly different from a viral infection the human body's response to a bacterial infection is significantly different from a viral infection, allowing human stress markers to also be used for differentiating these conditions. Thus, there is evidence that these bio-markers exist and could be detected. However, a major limiting factor inhibiting the wide-spread deployment of this concept is the unit cost of the analytical instrumentation required for gas analysis. Currently, the main preferred methods are GCMS (gas chromatography/mass spectrometry), TOF-MS (time of flight - MS) and SIFT-MS (selective ion flow tube - MS). Though excellent at undertaking this role, the typical unit cost of these instruments is in excess of $100k, making them out of reach of current GP budgets. Therefore, what is required is a low-cost, portable instrument that can detect bacterial infections from viral infections and be applicable to primary care

Gas chromatographic-mass spectrometry analysis of volatile organic compounds from cancer cell cultures - The effect of hypoxia

Early diagnosis of lung cancer improves patient outcomes which has led to a search for non-invasive diagnostic tests suitable for population screening. Volatile organic compounds (VOCs) in exhaled breath have shown potential, however, confirmation of the metabolic origins and disease specificity of candidate markers is required. Cell culture metabolomics can identify disease biomarkers and their origins. To date VOC profiles from in vitro cultured cancer cells have little similarity to cancer breath VOC profiles. In vivo, cancer cells experience hypoxia whereas in vitro cells are cultured under normoxic conditions. Since hypoxia influences cell metabolism, we hypothesize that cancer cells cultured under hypoxic conditions will have altered cell metabolism and produce VOC profiles more typical of cancer breathe. This study investigates the effect of hypoxia on metabolic reprogramming in A549 lung cancer cells cultured under standard normoxic (atmospheric oxygen) or hypoxic (2% oxygen) conditions. Results from quantitative RT-PCR demonstrated a significant upregulation in hypoxia of the glucose transporter (GLUT1) and the key TCA regulatory gene PDHK1, demonstrating that hypoxia plays a pivotal role in regulating metabolism in A549 cells. A ratio-metric assessment of Lipid Peroxidation (LPO) and the production of reactive oxygen species (ROS) showed an increase in LPO and a slight decrease in the production of ROS in hypoxic cultures, the combined effect of which may serve to equip the cells to adapt to and proliferate under low oxygen. Finally, the comparison of endogenous VOCs produced by A549 cells under hypoxic and normoxic conditions identified twelve VOCs unique to cells grown under hypoxic conditions including n-pentane, a marker of LPO and cancer, and 3-methyl hexane, which has been reported as a biomarker of cancer. This data is consistent with the hypothesis that a hypoxic tumour microenvironment may influence cell metabolism leading to a unique and diagnostic cancer VOC profile.Doctor of Philosoph

IMS2- An integrated medical software system for early lung cancer detection using ion mobility spectrometry data of human breath

Baumbach J, Bunkowski A, Lange S, et al. IMS2- An integrated medical software system for early lung cancer detection using ion mobility spectrometry data of human breath. Journal of Integrative Bioinformatics. 2007;4(3):75.IMS2 is an Integrated Medical Software system for the analysis of Ion Mobility Spectrometry (IMS) data. It assists medical staff with the following IMS data processing steps: acquisition, visualization, classification, and annotation. IMS2 provides data analysis and interpretation features on the one hand, and also helps to improve the classification by increasing the number of the pre-classified datasets on the other hand. It is designed to facilitate early detection of lung cancer, one of the most common cancer types with one million deaths each year around the world. After reviewing the IMS technology, we first describe the software architecture of IMS2 and then the integrated classification module, including necessary pre-processing steps and different classification methods. The Lung Hospital Hemer (Germany) provided IMS data of 35 patients suffering from lung cancer and 72 samples of healthy persons. IMS2 correctly classifies 99% of the samples, evaluated using 10-fold cross-validation

Differenzierung von Methicillin-sensiblem und Methicillin-resistentem Staphylococcus aureus anhand flüchtiger organischer Verbindungen mittels Multikapillarsäulen-Ionenmobilitätsspektrometrie und elektronischer Nase „Cyranose 320“

Hintergrund: Staphylococcus aureus besiedelt als Kommensale 20 – 40 % der Bevölkerung und gehört zu den häufigsten Verursachern nosokomialer Infektionen. Bereits frühzeitig sind multiresistente Stämme entstanden, welche in Methicillin-sensible Stämme (MSSA) und Methicillin-resistente Stämme (MRSA) eingeteilt werden. Auch gegen Reserveantibiotika entwickeln sich zunehmend Resistenzen. S. aureus ist für eine erhöhte Morbidität und Mortalität sowie für eine verlängerte Hospitalisationszeit inklusive Isolationsmaßnahmen und damit einhergehender Kosten verantwortlich. Die mikrobiologische Diagnostik mittels Erregerkultivierung stellt den Goldstandard zum Keimnachweis und zur Bestimmung von Resistenzen dar, ist jedoch zeitaufwendig und teuer. Die PCR-basierte Diagnostik ist zwar schneller, jedoch wirtschaftlich nicht effizient. Ein frühzeitiger Keimnachweis durch Screening-Untersuchungen mit darauffolgender Isolation oder Dekolonisierung kann zu einer Reduktion der Keimübertragung und folglich der Morbidität, Mortalität und der assoziierten Kosten führen. Somit besteht die Notwendigkeit für kosten- und zeiteffektive Methoden zur Erkennung von MSSA und MRSA mit hoher diagnostischer Wertigkeit. Ziel: In der vorliegenden Arbeit sollte festgestellt werden, ob sich MRSA und MSSA anhand der Analyse volatiler organischer Verbindungen (volatile organic compounds, VOC), welche durch Stoffwechselprozesse von Organismen exprimiert werden, mittels Multikapillarsäulen-Ionenmobilitätsspektrometrie (MCC-IMS) sowie der elektronischen Nase Cyranose 320 aus dem Nährmedium Brain Heart Infusion Broth (BHI) identifizieren und voneinander differenzieren lassen. Methoden: Es wurden aus routinemäßig entnommenen Screening-Abstrichen je 20 Proben mit MRSA und MSSA ins Flüssignährmedium BHI übertragen und auf eine Konzentration von 108 KBE/ml verdünnt. Aus jeder Probe wurden 500 μl für die Messungen entnommen. Mit dem MCC-IMS wurden mittels Headspace-Messungen je 20 MRSA- und MSSA- Proben und analog dazu 27 Proben des nicht bebrüteten Flüssignährmediums BHI analysiert. Vor jeder Probenmessung erfolgte eine Leermessung der Laborflasche zur Referenz und zur Elimination von Störfaktoren. Die aus den Messungen resultierenden Peaks wurden visualisiert und statistisch analysiert und ermöglichten im Anschluss durch Zuordnung spezifischer Peaks zu den jeweiligen Proben eine Differenzierung der Gruppen. Durch Abgleich mit einer bestehenden Datenbank konnten die Peaks entsprechenden organischen Substanzen zugeordnet werden. Mit der Cyranose 320 wurde der Headspace von je 20 MRSA- und MSSA-Proben und von 20 Proben nicht bebrüteter BHI analysiert. Jede Probe wurde 5-mal hintereinander gemessen, um ein Machine-Learning zu gewährleisten. Im Anschluss erfolgten eine lineare Diskriminanzanalyse und die Berechnung der Mahalanobis-Distanz zur Differenzierung der Gruppen. Eine Leave-One-Out Kreuzvalidierung wurde zur Bestimmung des Kreuzvalidierungswerts durchgeführt. Die Gruppen konnten durch eine Mustererkennung voneinander unterschieden werden. Ergebnisse: Mittels MCC-IMS konnten in der Gegenüberstellung der Gruppen MRSA und BHI 19 hochsignifikante Peaks (p [ 0.001) nachgewiesen werden, welche eine Unterscheidung mit einer Sensitivität und Spezifität von jeweils ] 90 % bis 99.9 % ermöglichen. MSSA konnte aus dem Nährmedium BHI anhand 20 hochsignifikanter Peaks mit einer Sensitivität von 92.6 % bis 96.3 % und einer Spezifität von 90 % bis 99.9 % differenziert werden. MRSA und MSSA konnten anhand 11 hochsignifikanter Peaks mit einer Sensitivität und Spezifität von jeweils 90 % bis 99.9% voneinander differenziert werden. Zwei Peaks waren ausreichend, um anhand eines Entscheidungsbaums in zwei Schritten eine Trennung aller Gruppen zu gewährleisten. Die Cyranose 320 konnte die Gruppen MRSA und BHI mit einer Sensitivität von 96 % und einer Spezifität von 94 % voneinander abgrenzen. Eine Differenzierung von MSSA und BHI konnte mit einer Sensitivität von 81 % und einer Spezifität von 75 % erreicht werden. MRSA und MSSA konnten mit einer Sensitivität von 100 % und einer Spezifität von 91 % voneinander unterschieden werden. Schlussfolgerung: Eine Unterscheidung der Bakterien MRSA und MSSA aus dem Flüssignährmedium BHI und insbesondere eine Differenzierung der Keime voneinander ist mittels MCC-IMS sowie Cyranose 320 mit hoher Sensitivität und Spezifität möglich. Es bedarf weiterer, prospektiver Studien im klinischen Setting, um die Ergebnisse zu verifizieren und somit eine potenzielle zeit- und kostensparende Alternative zur konventionellen Diagnostik zu ermöglichen

Synthesis, Isomer Separation and Quantification of Minor Alkaloids in Pepper Fruits using High Performance Liquid Chromatography

The literature section of this thesis provides an overview of modern ion-mobility spectrometry techniques in context with recent applications in analytical chemistry. While ion-mobility spectrometry is an “old” separation technique, it has received in recent years increasing attention for its unique ability to achieve separation of isomeric molecular species. Ion-mobility spectrometry can be readily hyphenated with chromatographic and mass spectrometric techniques, introducing an additional separation dimension with the unique capability of differentiating isobaric analyte ions based on their collision cross sections. After a brief introduction into the theory of ion-mobility spectrometry, most recent applications in the field are presented with the focus being on the discrimination of small isomeric molecules. The research project section of the thesis reports the synthesis of isomerically pure standard materials of the commercially unavailable pepper alkaloids piperettine and piperettyline, and the qualitative and quantitative analysis of piperettine in selected pepper fruit samples. Strategies for the synthesis of piperettine reported in the literature are reviewed, and critically evaluated in terms of practicability and overall yields. A new, expedient, and operationally convenient synthetic approach to isomerically pure piperettine and piperettyline from inexpensive starting materials is described. In course of stability studies, both alkaloids were found to be stable in the crystalline states and as solutions in a range of organic solvents under exclusion of ambient light. However, diluted solutions of both compounds proved extremely photosensitive, with extensive double bond isomerization occurring within seconds upon sunlight exposure. An analytical method for the quantification of piperettine in pepper fruit samples was developed, involving liquid extraction, extract clean-up by solid-phase extraction, and HPLC-UV analysis. The use of a chiral stationary phase (Chiralpak IB) under optimized reversed phase condition allowed for the first time clean separation of piperettine from its naturally co-occurring isomers, and thus for its unambiguous quantification. Subsequently, this method was employed to quantify piperettine in black, green, white, and red long pepper samples. The observed piperettine content were 1.4 – 3.7 mg/g in the pepper fruit samples, representing 46 – 69% of the total sum of isomers

IMS2 – An integrated medical software system for early lung cancer detection using ion mobility spectrometry data of human breath

IMS2 is an Integrated Medical Software system for the analysis of Ion Mobility Spectrometry (IMS) data. It assists medical staff with the following IMS data processing steps: acquisition, visualization, classification, and annotation. IMS2 provides data analysis and interpretation features on the one hand, and also helps to improve the classification by increasing the number of the pre-classified datasets on the other hand. It is designed to facilitate early detection of lung cancer, one of the most common cancer types with one million deaths each year around the world