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Multiplexed lipid arrays of ligand-induced changes The Alliance for Cell Signaling 2003 Meeting- Pasadena, CA Lipidomics Laboratory H. Alex Brown, director.

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Presentation on theme: "Multiplexed lipid arrays of ligand-induced changes The Alliance for Cell Signaling 2003 Meeting- Pasadena, CA Lipidomics Laboratory H. Alex Brown, director."— Presentation transcript:

1 Multiplexed lipid arrays of ligand-induced changes The Alliance for Cell Signaling 2003 Meeting- Pasadena, CA Lipidomics Laboratory H. Alex Brown, director Vanderbilt University Medical Center

2 Glycerol-3-phosphateDihydroxyacetone phosphateCholine 1-acyl-G-3-P1-acyl-dhAP PA CDP-DG PI PIP PIP 2 PGpPG Cardiolipin LPP DG CPT CDP-Choline PC PE EPT CDP-Ethanolamine ET Ethanolamine-P CK/EK Ethanolamine Choline-P CK/EK CT SM PEMT PSD PSS SerineEthanolamine CO 2 PS Phospholipid Biosynthetic Pathways PIP 3 PI3K PLC IP3 + DG DGK CLS LPA Lyso-bis-PA

3 Conduct lipid analysis on instrumentation with selective ion scanning capability and at least moderate throughput capacity (greater than 100 samples/week). Construct a working phospholipids fragmentation library for B-lymphocytes (subsequently refocused by Steering Cmte to WEHI-231 cells). Generate new computer program software to achieve computational analysis of mass spectrometry data to detect qualitative changes in membrane lipid composition (cellular lipid analysis program). Initiate LIPID ARRAYS on ligand stimulated WEHI-231 cells. Determine feasibility of measuring poly-phosphatidylinositol species as part of the LIPID ARRAYS.      Review of Aims (Lipidomics Lab)

4 Identification of lipid species Conduct lipid analysis on instrumentation with selective ion scanning capability and moderate throughput capacity (greater than 100 samples/week).

5 Electrospray Mass Spectrometry Detector Q1 Electrospray Ion Source Q3Collision Cell (Q2) Syringe Pump Tandem Mass Spectrometer CID: Monitor product formation in Q3 from a specific parent in Q1 SIM: Monitor specific m/z in Q1 SRM: Monitor transition of a specific m/z in Q1 to a specific product in Q3 D. Hachey

6 Adaptation to triple-quadrupole ESI-MS Collision gas into Q2 Q1Q3 electrospray ion beam detector m/z % MS M+M+ M + frag Q1 Q2 and Q3 set to allow all ions to pass M+M+ M + frag Q1 M+M+ Q2 fragmentation Ar (collison gas) Q3 daughter fragment ions MS 3 M+M+ M + frag Q1 M + frag Q2 fragmentation Ar granddaughter fragment ions Q3 mass spectrum m/z % M + frag M+M+ M+M+ m/z % M + frag daughter fragment ions M + frag granddaughter fragment ions A triple-quadrupole ESI mass spectrometer possesses ion selection and fragmentation capabilities. Each quadrupole has a separate function: The first quadrupole (Q1) is used to scan across a preset m/z range or to select an ion of interest. The second quadrupole (Q2), also known as the collision cell, transmits the ions while introducing a collision gas (argon) into the flight path of the selected ion, and the third quadrupole (Q3) serves to analyze the fragment ions generated in the collision cell (Q2). An MS 3 experiment can be performed if a daughter fragment ion is generated in electrospray ionization, i.e., M + frag. Q1 can be used to select the daughter ion, Q2 will generate granddaughter fragment ions, and Q3 will mass analyze for the granddaughter fragment ions. (Based on G. Siuzdak, 1996) M+M+ MS 2

7 Positive Mode Fragmentation Identification of lipid species

8 m/z Relative Abundance ESI+ Up to 135 PLs

9 m/z Relative Abundance ESI+ Up to 86 PLs

10 PC phosphatidylcholine PCe Plasmanyl phosphocholine PCp Plasmenyl phosphocholine Phosphatidylcholines

11 Sphingomyelin

12 m/z Relative Abundance M+1 Loss of PC headgroup

13 PE phosphatidylethanolamine PEe Plasmanyl phosphoethanolamine PEp Plasmenyl phosphoethanolamine O P O HO CH 2 CH CH 2 RO O O OCH 2 CH 2 NH 2 O CH 2 CH CH 2 RO O P O HO OCH 2 CH 2 NH 2 O CH 2 CH CH 2 RO O P O HO OCH 2 CH 2 NH 2 Phosphatidylethanolamines

14 m/z Relative Abundance M+1 Loss of PE headgroup

15 Negative Mode Fragmentation Identification of lipid species

16 PE PGPS PI GPA

17 34:2 PE 16:1 18:1 M-16:1 M-18:1

18 88 36:3 PS 20:3 M-88-20:3 M-88-16:0 16:0 M-88 18:118:2

19 M-88-18:1 18:1 M-88 M-18:1 18:1 LPS

20 H 2 O 18:0 22:6 M-22:6-163 M-22;6 40:6 PI 163

21 Glycerophospholipid Fragmentation Library Construct a working phospholipids fragmentation library for B-lymphocytes (subsequently refocused by Steering Cmte to WEHI-231 cells).

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26 Cellular Lipid Analysis Program Generate new computer program software to achieve computational analysis of mass spectrometry data to detect qualitative changes in membrane lipid composition.

27 Peak Identification Process: Potential Peaks are identified from raw data as possessing a L-H-L pattern. This list is then parsed to find peaks with an associated “brother peak” Intensity m/z Intensity Mass Spectrometer Output Peak? Analysis of Mass Spectrometry Data Dalton to the right with a smaller intensity. After peaks are identified we compute the Mean and Standard Deviation of the peak Intensity (or a transformation thereof.)

28 Analysis of Mass Spectrometry Data Mean After the peak list has been compiled the mean and standard deviation for this list is computed. For the current data set these values are: Mean = SD = 109,724. The peaks are then standardized by calculating the number of deviations they occur above or below the mean. This is a unit-less number, and a peak that has an intensity equal to the mean intensity will receive a score of zero. Note that peaks which occur below the mean will be assigned a negative number. (See Figure 2 for details.) This process is repeated across all time points and repetitions for the basal and agonist conditions. Figure 2: Selected transformed peak values in the m/z range of 700 to 725. Figure 1: Raw intensity data from the Mass spectrometer.

29 BasalT=1.5 T = 3 T = BasalT = 6 T= 15 T= Analysis of Mass Spectrometry Data Group Mean T = 1.5 T = 6 T = 15T = 240 T = 3 Plots the points with respect to time and the mean within the observational units. Generates Upper and Lower control limits for the means of the observational units. These limits represent the expected variation within the observed means. (Not individual observations!) Performs a set of statistical tests looking for “out-of-control” conditions (i.e. non- random variation around the grand mean.) The signals are then analyzed on a line by line basis to form a profile for a given m/z value. Below is the data from the m/z value of in the basal condition. There are four repetitions in each of 5 time points, 1.5, 3, 6, 15, and 240 minutes. Shewhart Control Chart for m/z = Basal Next a Shewhart Control Chart is constructed for the mean of the transformed signal. This chart:

30 Review of Shewhart Control Theory Test 1: One point beyond Zone A. Test 2: Two out of three points in a row in Zone A or beyond. Test 3: Four out of five points in a row in Zone B or beyond. Facts about Shewhart Control Charts: Various statistical tests exist for looking for special cause variation. These include but are not limited to: Where  is the estimate for the population standard deviation and n is the number of repetitions in a group. The control limits for the mean are calculated with the formula: The various zones, A, B, and C, are then calculated by dividing the are between the grand mean and the control limits into thirds. The formulas for these zones and the expected percentage of means falling in them are given in the figure to the right. n X  ˆ 3 

31 A23187T=1.5 T = 3 T = A23187T = 6 T= 15 T= Analysis of Mass Spectrometry Data T=1.5 T=6T=240 T=15T=3 Out of Control points. (Rule 2 of 3 in Zone A or beyond) Data from the m/z value for 2H3 cells treated with Ionophore. The analysis then uses the limits calculated from the basal condition to analyze the stimulated cells. (Assuming the basal data is in control i.e. only random variation present.) Basal DataLigand Data The program looks for patterns in the stimulated case using the limits from the basal condition. Out of control points are marked and cataloged as in the figure at the right. The results of the analysis are then grouped into arrays giving a comprehensive view of time based lipid changes in the cell.

32 PIP Standards Determine feasibility of measuring poly- phosphatidylinositol species as part of the LIPID ARRAYS.

33 PI PI-5-P PI-3-P PI-4-P PI-3,4-P PI-4,5-P PI-3,4,5-P PI-3,5-P PI4K PI 4-phosphatase PI3K PI 3-phosphatase PI5K PI 5-phosphatase PI4K PI 4-phosphatase PI4K PI 4-phosphatase PI5K PI3K PI 3-phosphatase PI5K PI 5-phosphatase PI3K PI 3-phosphatase PI5K PI 5-phosphatase (PTEN) Poly-phosphatidylinositols

34 m/z Relative Abundance Poly-PIP mixture 32:0 PI-3-phosphate 38:4 PI-4-phosphate 38:4 PI-4,5-phosphate 32:0 PI-3,4,5-phosphate

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36 LIPID ARRAYS Initiate LIPID ARRAYS on ligand stimulated WEHI-231 cells.

37 cAMP 2MA, PGE 2, TER, CGS P-protein CD40L, IL4, IL10, INF  Calcium AIG, MIP3a, SLC LPA BLC, ELC, SDF1, S1P Relationship of 15 ligands with clear responses in B cells Sternweis

38 Regulation of B cell activation by antigen-antibody complexes and Ig Fc receptors Antigen-antibody complexes can simultaneously bind to membrane Ig (via antigen) and the Fc  RIIB receptor via the Fc portion of the antibody. As a consequence of this simultaneous ligation of receptors, phosphatases associated with the cytoplasmic tail of the Fc  RIIB inhibit signaling by the B cell receptor complex and block B cell activation. Fc, crystallizable fragment; Fc  RIIB, Fc  receptor II; Ig, immunoglobulin.

39 Lipid Arrays from WEHI-231 Cells treated with Anti-IgM (ELC030121A – 0219A) Negative ModePositive Mode

40 WEHI Cell Lipid Arrays Summary (Negative Mode)

41 WEHI Cell Lipid Arrays Summary (Positive Mode)

42 CD40 (40L)Anti-IgM (AIG)BLC WEHI Cell Lipid Arrays (Negative Mode)

43 CD40 (40L)Anti-IgM (AIG)BLC WEHI Cell Lipid Arrays (Negative Mode)

44 CD40 (40L)Anti-IgM (AIG)BLC WEHI Cell Lipid Arrays (Negative Mode)

45 CD40 (40L)Anti-IgM (AIG)BLC WEHI Cell Lipid Arrays (Negative Mode)

46 LC Class Separation Future Aim: Transition from qualitative to quantitative analysis.

47 Brugger et al. PNAS, 1997

48 Phosphatidylcholine Class Separation Varian 240 LC Polymer Labs 1000  ELSD Phenomenex Luna Si column 0.3 mL/min 70%ACN, 20%IPA, 10%H 2 O, 25mM ammonium formate

49 PC fraction Total Extract

50 PC Fraction +NH 4 OAc min ESI (-)

51 Isotopic Standards for Quantitative Phospholipid Analysis

52 Ligand cAMP The Cell: A lipocentric view GPCR Calcium Cell morphology (microscopy) Phagocytosis Pinocytosis Chemotaxis Secretion Phosphoproteins Gene arrays Lipid Signaling

53 32:1 PEp 32:1 PE 34:0 PE 34:2 PE 36:5 PE 40:6 PS 40:7 PS 36:4 PI 38:4 PI 16:0 LPE, 16:1 LPE 16:0 LPE 16:1 LPE 22:6 LPS 20:4 LPI 34:1 PC34:1 GPA 30:0 PC16:0 LGPA 32:0 PCp16:0 LGPA 32:1 PC16:0 LGPA 34:0 PCe16:0 LGPA Modeling of lipid signaling: precursor-product relationships

54 Rationale for dual-ligand screens Ligand A  many lipid changes Ligand B  little or no lipid changes Conclusion: Outcomes of dual-ligands are complex PC PADAG PI, PG PE, PS,PC LPP GPCR B X PO 4 2- Ligand B GPCR A Ligand A

55 Modeling lipid pathways: AIG Arrays Apoptosis Substrate-Product 36:4 PC 20:4 lysoPC 16:0 ffa PS lysoPS RNAi PLA 1 /PLA 2

56 LIPID MAPS Glue Grant, Ed Dennis P.I. Macrophage lipids BioinformaticsNeutral Lipids Glycolipids Sterols Fatty Acids/ Eicosanoids Phospholipids Sphingolipids/ Gangliosides Synthetic Lipid Design Mike VanNieuwenhze - UCSD Dale Boger – Scripps Inst. Camille Falck- UTSW Walter Shaw - Avanti Polar Lipids Shankar Subramaniam - UCSD Robert C. Murphy – University of Colorado at Denver Chris Raetz – Duke Med. Cntr. David W. Russell - UTSW Alfred H. Merrill - Georgia Tech. H. Alex Brown – Vanderbilt Med. Cntr. Edward Dennis – UCSD Macrophage Core Laboratory Chris Glass - UCSD

57 Expanded analysis of Cellular Lipids Lipid ClassSpecies Robert Murphy/UC-Denver Neutral LipidsMAG, DAG, TAG. Ed Dennis/ UCSDFatty acids/Eicosanoids Prostanoids; hydroxyl- and hydroperoxy-eicosaenoic acids, and leukotrienes; and epoxyeicosatrienoic acids. Free fatty acids, fatty acid amides. Alex Brown/ Vanderbilt PhospholipidsPC, PE, PG, PS, PI (and polyphospho derivatives), PA, cardiolipin, lysophospholipids, plasmalogens and other ether-linked phospholipids, prostanoid containing phospholipids. Al Merrill/ GA Tech Sphingolipids/GangliosidesSphingomyelin,glycosphingolipids, ceramides,sphingosine. David Russell/ UTSW SterolsIsoprenoids, sterols, and bile acids. Chris Raetz/ Duke GlycolipidsPolyisoprene-linked phosphate sugars, certain fat soluble vitamins and quinines, and theglycolipid precursors of the PIglycans. PI/ Institution

58 RAW264.7 macrophage spectra (positive mode) RestingLPS RMJ001 Sample 5 LPS Pos AV:52NL:9.48E m/z Relative Abundance RMJ001 Sample 2 no LPS Pos AV:53NL:1.24E m/z Relative Abundance

59 Primary murine macrophage spectra (negative mode) Vehicle controlAcetylated LDL Loaded Fazio005sample1 vehicle control Neg AV:52NL:3.01E m/z Relative Abundance Fazio004 Sample 8 loaded acLDL Neg AV:53NL:1.31E m/z Relative Abundance

60 Lipofectamine Lipid-mediated transfection: time course of membrane recovery?

61  Construct a working phospholipids fragmentation library for RAW 264.7* cells (* or alternative cells selected by AfCS Steering Committee).  Begin adaptation of phospholipid headgroup classes (i. e. PC, DAG) to quantitative analysis by stable isotope dilution (LIPID MAPS).  Achieve quantitative poly-phosphatidylinositol species analysis for selective LIPID ARRAYS.  Adapt cellular lipid analysis program software to quantitative computational analysis of mass spectrometry data. Work closely with Bioinformatics and Data Analysis Group to convert phospholipid species into numerical format compatible with Oracle format to facilitate cluster analysis and modelling.  Initiate LIPID ARRAYS in RAW264.7 or primary macrophages cells using ligand list generated by Steering Cmte.  Based on choice of macrophage processes (i.e. chemotaxis, secretion) use cluster analysis to identify phospholipid signaling species of interest. Lipidomics Lab Specific Aims

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