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                                                                                1. CCRC-HaunerEN
                                                                                2. Technology platforms
                                                                                3. Mass spectrometry

                                                                                Mass spectrometry

                                                                                About us

                                                                                The Mass Spectrometry (MS) Core Facility is a joint venture established by the department of Paediatrics (Kinderklinik und Kinderpoliklinik im Dr. von Haunerschen Kinderspital) and Internal Medicine (Medizinischen Klinik und Poliklinik IV). It combines the expertise of both groups with a focus on metabolism and nutrition, endocrinology, analytical technology, and evaluation of complex datasets. Our mission is to provide cost effective, state-of-the-art expertise, methodology and instrumentation for high-throughput quantitative analysis of compounds such as metabolites, lipids, steroids, drug molecules and environmental contaminants in biological fluids and complex matrices. We provide our services for faculty and student researchers at Ludwig-Maximilians-Universität Munich, as well as researchers at other universities, institutes and private companies. The staff at the MS-Core Facility is highly qualified and can provide professional support in customizing your projects to your specific needs, such as sample collection, handling and storage, sample preparation, and compound identification via MS/MS fragmentation patterns. Several state-of-the-art mass spectrometers combined with ultra-high-pressure liquid chromatographic allow high-throughput sample analysis of up to a thousand compounds per sample injection. Combining this large data pool with sophisticated bio-statistical data evaluation tools from our statistics team allow visualization and interpretation of highly complex data sets. This combination provides insights into metabolic, nutritional, environmental or disease related modifications in biochemical pathways.

                                                                                Instrumentation

                                                                                Our mass spectrometry laboratory is equipped with four LC-MS/MS systems:

                                                                                Two SCIEX QTRAP 6500+ with 1290 Agilent UHPLCs

                                                                                The SCIEX QTRAP 6500+ (YouTube Video) is a state-of-the-art hybrid triple quadrupole mass spectrometer with a linear ion trap as Q3. Its Turbo V™ source can be used with electrospray ionization (ESI) as well as atmospheric pressure chemical ionization (APCI). The optimized Ion Drive QJet Guide improves sensitivity and ion capture rates; the LINAC® collision cell permits reduced dwell times without loss in sensitivity; the Linear Accelerator™ Trap as Q3 allows ion fragmentations up to MS3 and the IonDrive™ High Energy detector provides an increased linearity range at higher count rates compared to previous models. Overall, the SCIEX QTRAP 6500+ mass spectrometer provides enhanced sensitivity and selectivity for targeted small and large molecule quantitation from 5 to 2000 Da in triple quadrupol mode and 50 to 2000 Da in Linear Ion Trap mode.

                                                                                In addition, one of our two SCIEX QTRAP 6500+ instruments is equipped with a SCIEX SelexION®+ Differention Mobility Separation (DMS) device (YouTube Video), which allows the separation of isobaric compounds in complex matrices and hence provide an orthogonal chromatography-free separation prior to mass separation. All MS-instruments are from Sciex (Ontario, Canada).

                                                                                The Agilent 1290 series UHPLC System (YouTube Video) from Agilent Technologies (Waldbronn, Germany) is equipped with a binary pump allowing a system back-pressure of up to 1200 bar. This is essential for the use of short UHPLC columns with small inner diameters and sub-micron particle sizes, which facilitate fast analyte separation combined with reduced sample consumption. The refrigerated multi-deck auto sampler insures sample integrity prior to sample analysis, while the temperature controlled column compartment provides highly reproducible separation performance for the chosen UHPLC column.

                                                                                The QTRAP 6500+ is equipped for the following measurement types in triple quadrupole modes:

                                                                                MRM (MRM), Precursor Ion (Prec), Product Ion (MS2), Neutral Loss (NL), Q1 MS (Q1), Q1 Multiple Ion (Q1 MI), Q3 MS (Q3), Q3 Multiple Ion (Q3 MI)

                                                                                The use of the Linear Ion Trap allows additional information dependent aquisition (IDA):

                                                                                Enhanced MS (EMS), Enhanced Multi-Charge (EMC), Enhanced Product Ion (EPI), Enhanced Resolution (ER), MS/MS/MS (MS3)

                                                                                SCIEX QTRAP 4000 and Agilent 1260 HPLC

                                                                                The SCIEX QTRAP 4000 instrument can perform the same measurement types as the previously described SCIEX QTRAP 6500+, with addition of Time Delayed Fragmentation (TDF). The Agilent 1260 series HPLC system consists of a temperature controlled auto sampler and column oven as well as two separate gradient binary LC pumps (back pressures up to 600 bar) from Agilent Technologies (Waldbronn, Germany). In addition, this LC system also comprises a MayLab column oven with two column switching valve for up to 6 columns and two solvent switching valves for up to 8 different solvents each from MayLab (Vienna, Austria). This LC-MS system is suitable for fast and high-throughput method development and optimization.

                                                                                SCIEX API 2000 with Agilent 1100 HPLC

                                                                                The SCIEX API 2000 is a triple quadrupol instrument, which is currently used for routine amino acid analysis via ion paring reversed phase chromatography.

                                                                                Other instruments:

                                                                                • 7890A Agilent GC-FID for esterified fatty acid analysis (up to C24)
                                                                                • 3800 DANI GC-FID for very long chain fatty acid analysis (LCFA and VLCFA up to C30)
                                                                                SCIEX QTRAP 6500+
                                                                                SCIEX QTRAP 6500+ with SelexION
                                                                                SCIEX QTRAP 4000
                                                                                SCIEX API 2000
                                                                                MS-Core Facility Analysis Portfolio

                                                                                https://forschungsportal.med.uni-muenchen.de/2019/09/30/core-facility-fuer-massenspektrometrie-am-standort-innenstadt/

                                                                                Current metabolomics, lipidomics and steroidomics analysis via LC-MS:

                                                                                • Amino acid analysis including citrulline and ornithine
                                                                                • Carboxylic acid analysis including Krebs Cycle compounds (TCA method)
                                                                                • Non-esterified fatty acids (NEFA-method) saturated and unsaturated FAs (C4 to C24),
                                                                                • Selected Optimized Flow Injection Analysis (SOFIA-method) for phosphatidylcholines (PCaa and PCae), lysophosphatidylcholines (Lyso-PCa and Lyso-PCe), sphinomyelines (SMa and SMe) and acylcarnitines
                                                                                • Oxidized lipids such as epoxyeicosatrienoic acids (EETs) and dihyroxyeicosatrienoic acids (DHETs)
                                                                                • Cholesterol and cholesterol ester
                                                                                • Bile acids
                                                                                • Biotin
                                                                                • Steroids (standard steroid Panel)

                                                                                GC-FID – Lipid Analysis

                                                                                • Total fatty acid analysis (up to C24)
                                                                                • Very long chain fatty acid analysis (LCFA and VLCFA up to C30)

                                                                                ELISA - Peptide Hormone Analysis

                                                                                • Leptin
                                                                                • Adiponectin

                                                                                New LC-MS methods available in 2020/2021

                                                                                • Short chain fatty acids (SCFA, C1 to C6)
                                                                                • Lipid class separation including glycophospholipid analysis including PC, PE, PS, PI and their lyso-forms
                                                                                • Endocannabinoids (EC)
                                                                                • Oxylipins
                                                                                • Chiral amino acid Analysis
                                                                                • Steroids (18 hydroxy - and 18-oxocortisol, 11 oxygenated androgens)
                                                                                Sample Types, Requirements and Storage

                                                                                Biological fluids such as serum, EDTA-plasma, human milk, bronchoalveolar lavages (BAL), cell cultures supernatant as well as mammalian cells (e.g. peripheral blood mononuclear cells (PBMC)) and tissue (e.g. placenta) can be analyzed. Urine and feces samples only after consultation. For sample types other than those, which are here mentioned, please contact the MS Core Facility team for further information. We will provide assistance and guidance concerning sample collection, sample storage and sample preparation. We perform routinely method development, method optimization and method validation.

                                                                                ll samples should at least be cooled down to 0-4°C after harvest and stored at -80°C for longterm storage or until sample sending.

                                                                                Upon arrival, all samples will be stored at -80°C at the MS-Core Facility until sample analysis.

                                                                                All intermediate sample preparation extracts will be stored at the MS-Core Facility at -20°C until a QC quality check confirms the completion of the analysis.

                                                                                1 ... stated are the sample amounts for a full research study involving all lipidomics and metabolomics platforms with quality control samples and statistical data evaluation. 2 ... minimum sample amount for a reduced number of platform analysis. 3 ... for preparation of matrix specific calibrators, samples of plain cell culture medium are needed


                                                                                Biological fluids such as serum, EDTA-plasma, human milk, bronchoalveolar lavages (BAL), cell cultures supernatant as well as mammalian cells (e.g. peripheral blood mononuclear cells (PBMC)) and tissue (e.g. placenta) can be analyzed. Urine and feces samples only after consultation. For sample types other than those, which are here mentioned, please contact the MS Core Facility team for further information. We will provide assistance and guidance concerning sample collection, sample storage and sample preparation. We perform routinely method development, method optimization and method validation.

                                                                                All samples should at least be cooled down to 0-4°C after harvest and stored at -80°C for longterm storage or until sample sending.

                                                                                Upon arrival, all samples will be stored at -80°C at the MS-Core Facility until sample analysis.

                                                                                All intermediate sample preparation extracts will be stored at the MS-Core Facility at -20°C until a QC quality check confirms the completion of the analysis.

                                                                                Statistical Data Evaluation and Visualization    

                                                                                Metabolomics Data Analysis

                                                                                We offer comprehensive metabolomics data analysis, interpretation, and integration with other omics data services providing a variety of data processing, normalization procedures, and a wide array of functions for statistical, functional, as well as data visualization tasks. We can also offer advice or carry out data processing of the subsequent data.

                                                                                1. Data Preprocessing and Normalization

                                                                                Typical data processing pipeline generally proceeds through multiple stages including filtering, batch effect correction, normalization, and missing value imputation. Raw data are processed to remove any measurement noise or baseline and normalization removes undesired systematic variation between different samples. Missing value imputation can be done using different algorithms as K-Nearest Neighbors (KNN), Probabilistic PCA (PPCA), Bayesian PCA (BPCA) method, and Singular Value Decomposition (SVD) method.

                                                                                2. Statistical Data Analysis and Machine Learning

                                                                                Univariate Analysis: exploratory analysis to identify 'key' features potentially significant in discriminating the conditions under study. It includes:

                                                                                • Fold Change Analysis
                                                                                • T-tests
                                                                                • ANOVA and post-hoc analysis
                                                                                • Correlation Analysis

                                                                                Multivariate Analysis: examine patterns in multidimensional data by considering, at once, several data variables

                                                                                • Principal Component Analysis (PCA)
                                                                                • Partial Least Squares - Discriminant Analysis (PLS-DA)
                                                                                • Integrative sparse Partial Least Squares (sPLS) & sPLS Discriminant Analysis (sPLS-DA)
                                                                                • Multidimensional Scaling

                                                                                Robust Feature Selection Methods

                                                                                • Significance Analysis of Microarray (SAM)
                                                                                • Empirical Bayesian Analysis of Microarray (EBAM)

                                                                                Machine Learning Methods

                                                                                • Linear Regression
                                                                                • Logistic Regression
                                                                                • Clustering analysis: statistical method that involves dividing observed datasets into a few subclasses or clusters using a selected statistical distance function. Common clustering algorithms include:
                                                                                • Hierarchical (dendrogram, heatmap)
                                                                                • Partitional Clustering (K-means, self-organizing map (SOM))
                                                                                • Supervised Classification and Feature Selection methods
                                                                                • Linear discriminant analysis
                                                                                • Support vector machine (SVM)
                                                                                • Artificial neural network (ANN)
                                                                                • Decision tree
                                                                                • k-nearest neighbor (KNN)
                                                                                • Random forest

                                                                                3. Pathway Analysis

                                                                                • Identify biological meaningful patterns from quantitative metabolomics data
                                                                                • Over-Representation Analysis or Enrichment Analysis (ORA)
                                                                                • Functional Class Scoring (FCS)
                                                                                • Pathway Topology (PT)

                                                                                4. Biomarker Analysis

                                                                                • Classical univariate ROC curve analyses
                                                                                • Multivariate ROC curve based exploratory analysis
                                                                                • ROC curve based- model Evaluation


                                                                                Data Interpretation: Our team of metabolomics researchers can help in biological interpretations of the statistical outputs for a better understanding of the biochemical causes and physiological consequences. As a custom service, we also offer to integrate the metabolomics data with your other ‘omics’ data.

                                                                                Study Design Consultation: As metabolomics is becoming a more accessible and widely used tool, methods to ensure proper experimental design are crucial for accurate and robust identification of metabolites linked to disease, drugs, environmental or genetic differences. We offer consultation to help with choosing the right experimental design for your study, so the data are well structured for later interpretation. Power analysis can be calculated from pilot data to determine the minimum sample size used to detect an effect size of interest.

                                                                                Statistical software: We work with a variety of statistical and visualization software including R, Metaboanalyst, Galaxy, smart PLS, Biolayout Express 3D

                                                                                Projects

                                                                                Ongoing Research Projects

                                                                                COVID-19 CORKUM (metabolomics and lipidomics study, steroidomics project)

                                                                                Childhood-Obesity Programme (CHOP, RCT with long term follow up)
                                                                                Randomized trial in infants with long term follow-up; high protein supply promotes weight gain and obesity risk, mediated by higher amino acids, insulin and IGF-1 concentrations

                                                                                Toddler Milk Intervention Trial (TOMI, NCT02907502)
                                                                                Effect of milk protein intake in young children on early growth and obesity risk

                                                                                NutriPROGRAM
                                                                                Multi-omics approach using 1H-NMR and LC-MS metabolomics combined with epigenetic data sets of several cohort studies (CHOP, Generation-R, Candle, etc.) for epigenetic pathway analysis

                                                                                Thailand Breast Milk Project
                                                                                Normalization of milk volume produced by the mother and intake by the infant, using [2H]-labelled water; analysis of serum, cord blood, milk, Fe and Zn 

                                                                                Supplementation of breastfed LBWI in Indonesia
                                                                                Pilot study completed, RCT ongoing

                                                                                ERC Advanced Grant Pathophysiology of Primary Aldosteronism (PAPA)


                                                                                Previous Projects:

                                                                                ERC Advanced Grant Metabolic Regulation of Growth

                                                                                GINI and LISA Study with Helmholz Center Munich, Germany
                                                                                Raine Western Australia study with University Perth, Autralia;
                                                                                Generation R study with Erasmus University Rotterdam, Netherlands
                                                                                Rolo Study with Trinity College Dublin, UK
                                                                                UPBEAT study with Kings College London, UK
                                                                                PREOBE study with University of Granada
                                                                                Prevent CD study with Leiden University, Netherlands;
                                                                                Irvine Mother Child Cohort with the University of California, USA
                                                                                Southampton Women Study with the University of Southampton
                                                                                Ulm birth cohort with the University of Ulm
                                                                                HUMIS study with the NIPH

                                                                                etc..

                                                                                Contact our Team

                                                                                Location of the MS-Core Facility

                                                                                Kinderklinik und Kinderpoliklinik im Dr. von Hauner’schen Kinderspital

                                                                                Klinikum der Universität München

                                                                                Lindwurmstr. 4, 80337 München


                                                                                Contact for sample analysis:

                                                                                Dr. Jeannie Horak and Dr. Hans Demmelmair for metabolomics and lipidomics samples,

                                                                                Dr. Martin Bidlingmair and MSc. Sonja Kunz for steroidomics samples


                                                                                Sample delivery

                                                                                MSc. Zorica Stijepic

                                                                                Room D1.02

                                                                                Kinderklinik und Kinderpoliklinik im Dr. von Hauner’schen Kinderspital

                                                                                Klinikum der Universität München

                                                                                Lindwurmstr. 4, 80337 München

                                                                                Coordinators

                                                                                Prof. Dr. med. Dr. h.c. mult. Berthold Koletzko


                                                                                Prof. Dr. med. Dr. h.c. mult. Berthold Koletzko

                                                                                Division Metabolic and Nutritional Medicine, Dept. of Paediatrics, Dr. von Hauner Children’s Hospital, Lindwurmstr. 4, 80337 Munich

                                                                                ✉office.koletzko@med.uni-muenchen.de


                                                                                ☎
                                                                                089-4400-52826

                                                                                Prof. Dr. med.Martin Reincke


                                                                                Prof. Dr. med. Martin Reincke

                                                                                Department of Internal Medicine IV (Endocrinology, Nephrology), Ziemssenstrasse 1, 80337 Munich

                                                                                ✉Martin.Reincke@med.uni-muenchen.de


                                                                                ☎
                                                                                089-4400-52100

                                                                                Department of Metabolic and Nutritional Medicine

                                                                                Metabolomics and Lipidomics Team

                                                                                Dr. Hans Demmelmair (GC Lab Head)
                                                                                Tel.: +49 89 4400 553692
                                                                                Email: Hans.Demmelmair@med.uni-muenchen.de

                                                                                Dr. Jeannie Horak (LC Lab Head)
                                                                                Tel.: +49 89 4400 57971
                                                                                Email: Jeannie.Horak@med.uni-muenchen.de

                                                                                MSc. Jair Gonzalez Marques Jr. (PhD student)
                                                                                Tel.: +49 89 4400 54613
                                                                                Email: Jair.Gonzalez@med.uni-muenchen.de

                                                                                Stefanie Winterstetter (lab technician)
                                                                                Tel.: +49 89 4400 53485
                                                                                Email: Stefanie.Winterstetter@med.uni-muenchen.de

                                                                                MSc. Zorica Stijepic (lab technician)
                                                                                Tel.: +49 89 4400 53485
                                                                                Email: Zorica.Stijepic@med.uni-muenchen.de

                                                                                 

                                                                                Statistical Data Evaluation & Interpretation Team

                                                                                MSc. Mohammed El Sharkawy (PhD med. student)
                                                                                Tel.: +49 89 4400 53427
                                                                                Email: Mohammed.El_Sharkawy@med.uni-muenchen.de

                                                                                Department of Endocriology

                                                                                Steroidomics Team

                                                                                Dr. med. Martin Bidlingmaier (Head Endocrine Laboratory)
                                                                                Tel.: +49 89 4400 52277
                                                                                Email.: Martin.Bidlingmaier@med.uni-muenchen.de

                                                                                MSc. Sonja Kunz (PhD student)

                                                                                Tel.: +49 89 4400 52310
                                                                                Email.: Sonja.Kunz@med.uni-muenchen.de

                                                                                Publications and Patents

                                                                                Publications

                                                                                All publications of our Researchers can be found here:

                                                                                Prof. Dr. med. Dr. h.c. mult Berthold Koletzko

                                                                                Dr. Jeannie Horak

                                                                                Prof. Dr. med. Martin Reincke

                                                                                Dr. med. Martin Bidlingmaier

                                                                                Patents

                                                                                1. Horak J, Laemmerhofer M. Method for the stereoisomerization of chiral amino acids using tagged, especially 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate tagged amino acids. PCT Int. Appl. (2020), WO 2020065047 A1 20200402. Eur. Pat. Appl. (2020), EP 3628652 A1 20200401.
                                                                                2. Glaser C, Demmelmair H, Koletzko B, Hochdurchsatzmethode zur Analyse der Fettsäurezusammensetzung in Phosphoglyceriden von Zellen und Geweben in Blutproben (Blutplasma bzw. Blutserum) (DE102008046227A1)

                                                                                  Glaser C, Demmelmair H, Koletzko B, High throughput method for analyzing the fatty acid composition of plasma phosphoglycerides’ (US 2011/0136143 A1. PCT Int. Appl. (2009), WO 2009149871 A1 20091217.)
                                                                                3. Zentrifugengefäß mit Halterung für Abstrichgerät (EU PCT/EP2011/003829)

                                                                                Research at CCRC Hauner

                                                                                Contact LMU Klinikum

                                                                                Contact CCRC Hauner

                                                                                Haunersches

                                                                                CCRC Hauner

                                                                                Kinderklinik und Kinderpoliklinik

                                                                                im Dr. von Haunerschen Kinderspital

                                                                                Ludwig Maximilians Universität München

                                                                                Lindwurmstr. 4

                                                                                80337 Munich, Germany

                                                                                ☎ +49-89-4400-57705

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                                                                                en
                                                                                • Deutsch - de
                                                                                • English - en

                                                                                Comprehensiv...

                                                                                • Clinical research
                                                                                  • Interdisciplinary pediatric study center (Hauner iPSC)
                                                                                  • Ped-COVID-19 Study
                                                                                  • Michael Albert
                                                                                  • Scivias Study
                                                                                  • TRACE Study
                                                                                  • ImmGenDC Study
                                                                                  • Hematology/Oncology/Coagulation
                                                                                • Research Labs
                                                                                  • Binder Lab
                                                                                  • Bohlen Lab
                                                                                  • Braun Lab
                                                                                  • Griese Lab
                                                                                  • Hauck Lab
                                                                                  • Hübner Lab
                                                                                  • Jeremias lab
                                                                                  • Kim-Hellmuth Lab
                                                                                  • Klein Lab
                                                                                  • Koletzko Lab
                                                                                  • Kotlarz Lab
                                                                                  • Lange-Sperandio Lab
                                                                                  • von Mutius Lab
                                                                                  • Nußbaum Lab
                                                                                  • Rosenecker Lab
                                                                                  • Schaub Lab
                                                                                  • Schmid Lab
                                                                                  • Schwerd Lab
                                                                                • PhD Program
                                                                                • Technology platforms
                                                                                  • Bioinformatics
                                                                                  • Flow Cytometry
                                                                                  • High throughput sequencing
                                                                                  • Microscopy
                                                                                  • Pre-GMP Facility
                                                                                  • Mass spectrometry
                                                                                  • Hauner Biobank
                                                                                  • Organoid Lab
                                                                                  • Humangenetik am Hauner
                                                                                • News/Events
                                                                                  • News
                                                                                  • Rare Disease Day
                                                                                  • ECHO-Meeting
                                                                                  • Meinhard von Pfaundler-Lectures
                                                                                  • Klaus Betke Symposium
                                                                                • Join us!