Learning Objectives. Overview of topics to be discussed 10/25/2013 HIGH RESOLUTION MASS SPECTROMETRY (HRMS) IN DISCOVERY PROTEOMICS

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1 HIGH RESOLUTION MASS SPECTROMETRY (HRMS) IN DISCOVERY PROTEOMICS A clinical proteomics perspective Michael L. Merchant, PhD School of Medicine, University of Louisville Louisville, KY Learning Objectives After this presentation, you should be able to: 1. Provide a working description of the relationship of resolution and mass accuracy as it related to protein identification. 2. Provide an overview of the LCMS based proteomic workflow. 3. Describe factors mitigating the discovery process in clinical proteomics. 4. Provide an example of the application of high resolution mass spectrometry (HRMS) to biomarker discovery and protein post translational modification Overview of topics to be discussed Introduction Overview of the LCMS workflow Sample handling and preparation Data dependent acquisition (DDA) workflow versus the data independent acquisition (DIA) workflow Informatics Current applications Biomarker discovery Quantitative Phosphoproteomics Top down mass spectrometry Conclusions Acknowledgements University of Louisville Jon Klein, MD, PhD Ken McLeish, MD Michael Brier, PhD Jian Cai, PhD James Hribar, PhD Danny Wilkey, BA Ming Li, BS The Ohio State Univ. Brad Rovin, MD University of Pennsylvania Harv Feldman, MD Peter Yang, PhD University of Washington Jonathan Himmelfarb, MD Disclosures NIH (NIDDK) R01 DK U01 DK Owner/Partner Pharos Medicine, LLC. 1

Introduction terms and definitions Resolution Low ( 1000) Medium ( 10,000 20,000) High ( 50,000) Mass

001 Da) Tandem Mass Spectrometry MS2 Precision Proteomics Application of high resolution methods at the

species being studied Data dependent analysis MS2 fragmentation targeting based on MS1 information Data

Qualitative analysis Index of proteins present in the sample Quantitative analysis Associating relative

Mann M, and Kelleher N L PNAS 2008;105:18132-18138 Egertson JD et. Al.

protease Protein Peptides Bench work (Variability) Informatics In Silico Peptide assignment

separation methods Protease digestion Peptide separation methods Sensitivity, mass accuracy, and scan

2 Introduction terms and definitions Resolution Low ( 1000) Medium ( 10,000 20,000) High ( 50,000) Mass Accuracy Low ( Da) Medium ( Da) High ( Da) Tandem Mass Spectrometry MS2 Precision Proteomics Application of high resolution methods at the MS1 and MS2 (or MS n ) level of analysis Proteospecific Amino acid sequence that is specific to the species being studied Data dependent analysis MS2 fragmentation targeting based on MS1 information Data independent analysis MS2 fragmentation data collected systematically and independent of MS1 information Qualitative analysis Index of proteins present in the sample Quantitative analysis Associating relative or absolute values of abundance to the Qual analysis. Mann M, and Kelleher N L PNAS 2008;105: Egertson JD et. Al. Nature Methods (8) Proteomic workflow Traditional Bottom up Approach Bottlenecks protease Protein Peptides Bench work (Variability) Informatics In Silico Peptide assignment (Statistics) Protein assignment Mass Spectrometer Amount of isolated protein Sample complexity Protein separation methods Protease digestion Peptide separation methods Sensitivity, mass accuracy, and scan speed of mass spectrometer Prior knowledge of PTM Protein database False positive assignments Impact of mass spectrometer in contemporary proteomics Precision Proteomics Mann M, and Kelleher N L PNAS 2008;105: by National Academy of Sciences 2

Factors mitigating discovery progress Defining the question that proteomics will answer Informatics Research question Sample handling Sample handling Isolation of protein containing structure Protein

3 Factors mitigating discovery progress Defining the question that proteomics will answer Informatics Research question Sample handling Sample handling Isolation of protein containing structure Protein extraction Protein separation Protein digestion Mass spectrometry analysis Quantification Mass spectrometer Informatics Identification Bioinformatics/Interpretation The research question The research design Expression and quantitative proteomics Define and quantify the protein components Functional proteomics. Define the interactions among proteins yielding knowledge about Protein Interaction Networks (PIN) Define the mechanisms by which proteins communicate with each other yielding knowledge about Protein Signaling Networks (PSN) PIN PSN Expression Minimizing unnecessary variance and systematic errors Power the study correctly Correcting for multiple hypothesis Replicates a) Biological b) Technical Detergents a) SDS or NP 40 b) Protease MAX Chaotropes a) Urea or b) GuanHCL Buffers a) ammonium bicarbonates/acetates or b) TRIS, c) Trizma, d) Hepes, e) MES Inhibitors a) Protease, b) Phosphatase, c) Deacetylase, or d) Bacteriastat Proteases a) Trypsin, b) Arg C, c) Lys C, or d) Asp N Addressing complexity before mass measurement Protein Expression System Lysis Subcellular Fractionation Electrophoresis Chromatography Denaturing Native Reversed Phase Ion Exchange 1 DE 2 DE HILIC Affinity SEC Antibody Lectin Metal Chelate 3

Overview of LCMS workflow Acquisition of parent peptide mass to charge (m/z) and fragment m/z values Algorithmic matching of observed to predicted fragment masses y 3 y 2 y 1 nanolc column nanospray

4 Overview of LCMS workflow Acquisition of parent peptide mass to charge (m/z) and fragment m/z values Algorithmic matching of observed to predicted fragment masses y 3 y 2 y 1 nanolc column nanospray source sample nanolc LTQ Orbitrap ELITE b 1 b 2 b 3 MS1* Curates database Intensity Fragment 0 m/z z 3 z 2 z 1 MS2 c 1 c 2 c 3 Advantages of HRMS in proteomic discovery research Qualitative/quantitative methods using HRMS data dependent analysis (DDA) MS1 based quantification Feature analysis Differential LCMS (dlcms) Enzymatic labeling Oxygen 18 ( 18 O) Chemical labeling Reductive methylation ICAT Chemical synthesis with stable isotope labeled amino acids Metabolic labeling SILAC ( 13 C, 15 N labeled amino acids) 13 C, 15 N labeled amino acids growth media MS2 based quantification Label Free Spectral counting Isobaric chemical tags Reporter ion quantification itraq TMT 4

5 Caveats to increased sensitivity m/z z=+3 Urine proteomic study Acute kidney injury following cardiac surgery Elution of identical peptide in two patient samples m/z z=+3 Single MS spectra can contain >100 peptide features High complexity 1) analysis of only 16 of 100 peptides is common 2) repeat analysis may miss 5 of the 16 3) many will have closely isobaric species Increased sensitivity can contribute to co isolation of closely isobaric peptides m/z z= m/z z=+3 Sequence: VFNNIGADLLTGSESENK, Charge: +3, Monoisotopic m/z: Da, MH+: Da, RT: min, Identified with: Mascot (v1.27); IonScore:48, Exp Value:1.5E 003, Ions matched by search engine: 11/102 Fragment match tolerance used for search: 1.2 Da Fragments used for search: c; y; z; z+2 SCY1 like protein 2 [SCYL2_HUMAN] 5

proteins via top down MS Histones and epigentics URINE PROTEOMICS AND LUPUS NEPHRITIS Efforts to support diagnosis without renal

6 HRMS in clinical proteomic studies Label free identification and quantification of biomarkers of human disease (urine proteomics) Addressing over abundant proteins Post translational modifications Cell signaling and the phosphoproteome Direct analysis of whole proteins via top down MS Histones and epigentics URINE PROTEOMICS AND LUPUS NEPHRITIS Efforts to support diagnosis without renal biopsies Renal disease, urine protein, and depletion Patient No ~80% of urinary MS/MS data is from abundant serum proteins 6

94 Orbitrap Elite Analysis 620 35543 145551 24 Scaffold IDs; 99% Protein Probability, 95% Peptide

7 All Proteins No Decoy IDs Decoy IDs # Proteins # Identified Spectra # Spectra % Identified LTQ Analysis Orbitrap Elite Analysis Scaffold IDs; 99% Protein Probability, 95% Peptide Probability with at Least Two High Confidence Peptides TIC for 0.15M Salt Step of SCX Fractionation LTQ LTQ Orbitrap E+7 E+9 Scan #19133 MS Scan # m/z Zoom from m/z 7

MS2 Spectrum for 1049.5465m/z Peak Observed Matched Peroxiredoxin 6 [PRDX6_HUMAN] Monoisotopic m/z: 1049.54653 Da ( 3.54 mmu/ 3.

8 MS2 Spectrum for m/z Peak Observed Matched Peroxiredoxin 6 [PRDX6_HUMAN] Monoisotopic m/z: Da ( 3.54 mmu/ 3.37 ppm) Mascot IonScore:68 Sequence: PGGLLLGDVAPNFEANTTVGR, Charge: +2 Phosphoproteome Studies Identification of low abundant (rare), protein post translational modifications to gain information on molecular signaling events Studying protein phosphorylation events using affinity enrichment needle in a haystack Engholm Keller, K and Larsen, MR J. Proteomics 75 (2011)

Reductive methylation: an inexpensive and effective quantification strategy Wilson Grady, JT, Haas, W, and Gygi, SP Methods 61 (2013) 277 286 Integrated workflow for ps/pt/py detection and

9 Reductive methylation: an inexpensive and effective quantification strategy Wilson Grady, JT, Haas, W, and Gygi, SP Methods 61 (2013) Integrated workflow for ps/pt/py detection and quantification Wilson Grady, JT, Haas, W, and Gygi, SP Methods 61 (2013) Informatics analysis of phosphoproteome data Wilson Grady, JT, Haas, W, and Gygi, SP Methods 61 (2013)

HRMS AND TOP DOWN PROTEOMICS Analysis of protein post translational modifications from the whole protein level. FTMS selected ion monitoring (932 942m/z) for CAII Z=+31 937.302 CAII 937.

10 HRMS AND TOP DOWN PROTEOMICS Analysis of protein post translational modifications from the whole protein level. FTMS selected ion monitoring ( m/z) for CAII Z= CAII CAII + Na CAII H 2 O CAII + 2Na +1 MS2 Fragmentation Spectrum Analysis of whole protein modification Isolated Cells Extract histones Abs 280nm Isolate/enrich Histones Time (min) HPLC 12% SDS PAGE Histones Modified histones Arg C digestion Top Down Arg C digestion Modified and unmodified histone peptide fragments Bottom Up LCMS Analysis 10

mrp HPLC PTM states Charge states Conclusions The sensitivity and analytic power of current HRMS platforms are pushing the boundaries of discovery work.

With regards to clinical proteomics experiments, maximizing reproducible sample handling is vital to the success of all clinical proteomic projects.

Efficient utilization of proteomic resources are often times best achieved through collaboration. Self Assessment Questions 1.

mass spectrometer D) amount of isolated protein E) none of the above 2.

11 mrp HPLC PTM states Charge states Conclusions The sensitivity and analytic power of current HRMS platforms are pushing the boundaries of discovery work. Given the increased sensitivity of state of the art mass spectrometers, sample (mis )handling can significantly impact the quality of the data regardless of the proteomic end goal. With regards to clinical proteomics experiments, maximizing reproducible sample handling is vital to the success of all clinical proteomic projects. LCMS based proteomics methods are important tools that can be used to aid in the study human health and disease. Efficient utilization of proteomic resources are often times best achieved through collaboration. Self Assessment Questions 1. Which of the following is not a source of variability in proteomic discovery research A) sample complexity B) peptide separation methods C) sensitivity, mass accuracy, and scan speed of the mass spectrometer D) amount of isolated protein E) none of the above 2. Which of the following is not a statistical bottleneck proteomic discovery research A) prior knowledge of PTM B) protein database C) false positive assignments D) peptide separation methods E) none of the above 3. Advantages of high resolution mass spectrometers over older low resolution mass spectrometers include A) detection of lower abundant peptides B) greater coverage of peptide fragmentation data C) better detection of protein post translational modifications D) characterization of whole proteins and post translational patterns E) all of the above 11

Quantification with Proteome Discoverer. Bernard Delanghe

Quantification with Proteome Discoverer Bernard Delanghe Overview: Which approach to use? Proteome Discoverer Quantification Method What When to use Metabolic labeling SILAC Cell culture systems Small