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

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)

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:

GC-FID – Lipid Analysis

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)

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.

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

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

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..

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

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

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

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)