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Lipids Analytical Tool (LipidAT): automated analysis of lipidomic mass spectrometry data Jun Ma Advisor: Dr. Haixu Tang Co-Advisor: Dr. David Wild Co-Advisor.

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Presentation on theme: "Lipids Analytical Tool (LipidAT): automated analysis of lipidomic mass spectrometry data Jun Ma Advisor: Dr. Haixu Tang Co-Advisor: Dr. David Wild Co-Advisor."— Presentation transcript:

1 Lipids Analytical Tool (LipidAT): automated analysis of lipidomic mass spectrometry data Jun Ma Advisor: Dr. Haixu Tang Co-Advisor: Dr. David Wild Co-Advisor : Dr. Predrag Radivoja School of Informatics, Indiana University, Bloomington, Indiana

2 Outline Introduction to lipidomics and mass spectrometry Objectives Data and methods Results Future work Acknowledgements

3 Lipidomics Definition: Large-scale study of pathways and networks of cellular lipids in biological systems Methodology: Identification and quantitation of the thousands of cellular lipid molecular species and their interactions with other lipids, proteins, and other metabolites

4 Introduction to Lipidomics GenomeDNA Transcriptome Proteome Metabotome RNA Proteins SugarsNucleotides Amino acids Lipids (Lipidome) Metabolites Phenotype/Function Ref: Wikipedia

5 Phospholipids biological functions Key participants in the regulation and control of cellular function Bioenergetics, signal transduction Cell recognition

6 Structures of phospholipid and membrane bilayer Phospholipids are amphiphilic with hydrophilic head group and hydrophobic fatty acid chain. Phospholipids are building blocks of cellular membranes. Ref: Children's Hospital Oakland Research Institute

7 Alteration of Phospholipids structures Ref: Phil. Trans. R. Soc. A (2006) 364,

8 Significance of lipidomics Change in phospholipid, besides affecting the absolute amounts of phospholipids per cell, also affect the relative ratios of the various phospholipids species in the membrane, which in turn should lead to changes in the membrane structure and consequently the function. Altered phospholipid metabolism has been reported in a variety of diseases, such as anemias, malaria, cancer, nuscular dystrophy, ischemia, diabetes, lung diseases, and liver diseases.

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10 Tandem mass spectrometry approach for phospholipids analysis Advantage – Detection, separation and identification of various phospholipids – Quantization analysis of complex individual phospholipids in complex mixtures

11 Phospholipid MS/MS

12 Objectives of LipidAT Identifying and quantifying individual phospholipid species in mixture Obtaining a comprehensive picture of the differences in membrane phospholipids between contrasting biological conditions

13 Workflow of automated processing of MS/MS data by LipidAT MS 2 data Identification - Generation of peak list - Peak identification by matching m/z of candidate species Report - Identified lipids species - m/z of precursor ion - m/z and intensity of peak Quantification -Normalization to define multiple internal standards -Combination of multiple runs Global Visualization Heatmap Species comparison show Local Visualization Customer- Defined Visualization Show - std of user-defined peak in multiple - difference in the control and sample Data Preprocessing Phospholipids Database -Lipid species library -Fragmentation Information

14 Loading the raw data Read raw data (.raw or.mzData file) – Scan number (integer) – Precursor ion(m/z) – Retention time(min) – Product ions m/z (mass-to-charge ratio) intensity (abundance)

15 Data preprocessing Baseline subtraction – m/z < 100 – Normalization – Similarity of structures and species – Reference peak picking – Absolute quantification Ref: Journal of Lipid Research Vol. 42, 2001 A(X): original intensity of peak x I(PX): intensity of reference peak px

16 Data integration Reduction the noise and error MS Data from a series of replicate runs Weighted moving averaging filter – Reducing random noise at high masses – Retaining a sharp step response – Fitting for time domain encoded signals – Equation: W i : the intensity of peak i M i : the m/z of peak i

17 Phospholipids Identification Build-in fragmentation ion database – 9 phospholipids species (GPA,GPCho,GPIns,GPEtn,GPGro,GPSer,Sphingomyelin,Cardiolipin, Lysophospholipids) – carbon atoms – 0-6 double bonds – Neutral loss – Allowing negative and positive mode of MS Identification standards – Peak must be above threshold – Corresponding peak must have high intensity also in nearby spectra

18 Visualization Heatmap – x-axis: m/z of precursor ion or retention time – y-axis: m/z of fragments – z-axis: color scale coded ratio value of peak intensity in two contrasting condition Comprehensive picture – Difference of components – Difference of absolute quantity of species – Difference of fragments

19 Heatmap

20 Heatmap click-on

21 Fragment ion lookup Error bar Visualize the intensity distribution of specific ion in the sample and control  User-defined ions  Build-in ions for different PLs species Visualize the intensity deviation of the specific ion across several runs – Decide if the combination of multiple runs are feasible

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23 Maintenance of in–house database Database operations – Search data – Insert data – Delete data Database integration – Allow biological experts to integrate their prior data with LipidAT database

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25 Applications & functionalities Load and view.mzData or.raw data format Perform batch processing Display separations, survey scans, and MS/MS data in a single interface Access sample reproducibility, evaluate sample quality and instrument performance Identify the individual phospholipids in large and complex datasets View change of whole phospholipids mixture and specific peaks in contracting biological conditions Customize layout to meet the users needs

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27 Future work Incorporate other lipids species into database Identify minor components of lipids mixtures

28 Acknowledgements Dr. Haixu Tang Dr. David Wild Dr. Predrag Radivojac Lab mates – – Quanhu Sheng – Yong Li – Chuanyih Yu Linda Hostetter Cheminformatics and Bioinformatics faculty School of Informatics Eli Lilly and Company (funder)


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