Progenesis QI for proteomics: a first-rate solution for relative and absolute quantitative workflows Richard R. Sprenger, PhD

Transcription

1 Progenesis QI for proteomics: a first-rate solution for relative and absolute quantitative workflows Richard R. Sprenger, PhD Lipid Research Group Institute for Biochemistry and Molecular Biology University of Southern Denmark, dense, Denmark 2015

2 Amsterdam, 383 years ago: no mass spectrometers Doctor Tulp s lesson, by Rembrandt van Rijn

3 but the small city of dense, Denmark is big on mass spectrometers University of Southern Denmark (SDU, dense)

4 The Department of Biochemistry and Molecular Biology SDU - dense a large variety of around 17 mass spectrometers as part of the VILLUM Center for Bioanalytical Sciences the center supports various genomics, metabolomics, proteomics and lipidomics groups Both for method development and research: from fundamental to aging and disease-related topics in a wide range of model organisms

5 Label-free quantitative proteomics: what should be in the workflow toolbox? proper study design and sample preparation unbiased and efficient digestions reproducible (1D or 2D) LC-MS/MS software for data processing and protein identification software for data analysis, including label-free relative and/or absolute quantitation

6 Label-free quantitative proteomics: what should be in the workflow toolbox? proper study design and sample preparation unbiased and efficient digestions reproducible (1D or 2D) LC-MS/MS software for data processing and protein identification time per step Software for data analysis, including label-free relative and/or absolute quantitation

7 Label-free quantitative proteomics: what should be in the workflow toolbox? proper study design and sample preparation unbiased and efficient digestions reproducible (1D or 2D) LC-MS/MS software for data processing and protein identification time per step Software for data analysis, including label-free relative and/or absolute quantitation

8 Progenesis QI for proteomics no software is magic or can turn lead to gold (garbage in garbage out) but Progenesis QI does offer a few magical features: DIA-like data completeness (>99%) for DDA-based methods currently the most accurate label-free estimation of absolute protein abundance all common instruments supported both 1D and fractionation (2D) workflows straightforward visual workflow with automation QC metrics and statistics

9 Case 1: which protein digestion protocol is the best (and how do you determine that?) Molecular & Cellular Proteomics (2013) IR Leon et al MCP 2013

10 Which protein digestion protocol is the best (and how do you determine that?) we performed a quantitative comparative study to compare 9 existing and novel protein digestion procedures to identify methods with highest efficiency and least bias Membrane protein enriched rat liver mitochondrial fractions Waters 1D UPLC Synapt G1 QTF PLGS v2.4 Progenesis LC-MS IR Leon et al MCP 2013

11 Which protein digestion protocol is the best (and how do you determine that?) Resulting data completeness: 99% and 96% between all protocols and replicates, for resp. proteins and peptides Technical CV, ~5% on average Traditionally, the protocol yielding the most proteins and peptides would be the winner, but with 99% data completeness? Quantitative analysis of abundance and physicochemical properties revealed two optimal digestion protocols IR Leon et al MCP 2013

12 Which protein digestion protocol is the best two optimal (SDC-based) digestion protocols were identified, which have until now proven to be most optimal for the efficient analysis of a large variety of proteome samples, with the least quantitative bias standard in-solution digestion (SDC ISD) - simple and straightforward, scalable - high recovery, zero loss spin filter in-solution digestion (SDC SF-ISD) - applicable to any sample (SDS -> Urea -> SDC) - more time consuming - potential minor losses IR Leon et al MCP 2013

13 Identified proteins ( 1pep/prot) Identified proteins ( 1pep/prot) Identified proteins ( 1pep/prot) DIA-like data completeness (>99%) for DDA-based methods any (free) alternatives? from our comparative test: Progenesis LC-MS generated the highest quality datasets with the least missing values and lowest variation (CV) Proteome Discoverer average 28.2% missing values Proteome Discoverer (average top3) AVG 28.2% missing values MaxQuant average 7.7% missing values (28.7% without match-between-runs ) MaxQuant (ibaq) AVG 7.7% missing values (28.7% without 'match between runs' Progenesis average 0.02% missing values Progenesis LC-MS (sum unique) AVG 0.02% missing values Sample 1 (n=4) Sample 2 (n=4) Sample 3 (n=4) Total 0 Sample 1 (n=4) Sample 2 (n=4) Sample 3 (n=4) Total 0 Sample 1 (n=4) Sample 2 (n=4) Sample 3 (n=4) Total only matching identified ions matching all ions/features comparison of data-processing software and missing values Sprenger et al. ASMS

14 Case 2: Proteome and lipidome dynamics in yeast (proteolipidomics: combined quantitative proteomics and lipidomics) Chemistry & Biology 2015 Casanovas et al. Chemistry & Biology 2015

15 Proteome and lipidome dynamics in yeast (quantitative proteolipidomics) Yeast metabolic adaption (diauxic shift) Proteome Discoverer 1.4 Thermo rbitrap XL Progenesis LC-MS Waters online RP/RP 2D-LC (5 fractions x 180min runs) Casanovas et al. Chemistry & Biology 2015

16 Proteome and lipidome dynamics in yeast (quantitative proteolipidomics) Resulting data completeness: >99% between all conditions and 5 biological replicates. Biological CV ~15% on average (median ~7%) ur dataset now among the most comprehensive and reproducible yeast proteomes reported in a comparative quantitative study Casanovas et al. Chemistry & Biology 2015

17 Proteome and lipidome dynamics in yeast (quantitative proteolipidomics) Data completeness crucial for linking temporal lipid profiles with protein data Casanovas et al. Chemistry & Biology 2015

18 How about absolute protein quantification? until recently, only relative quantitation was available in Progenesis (using unique peptides only) Added benefits of (accurate) absolute protein quantification: Determine the actual on-column load Calculate digestion efficiency (by including a known, unlabelled protein standard prior to digestion) Calculate the absolute amount/concentration of all identified proteins, isoforms and homologous, in a single sample (without comparing) Calculate cellular protein concentrations and copies/cell Know the relative inter-sample abundance (importance) of regulated proteins (e.g. 10-fold high abundant vs. 2-fold low abundant protein) Functional analysis at the protein complex level using known stoichiometry information or specific activity

19 Hi3 is the preferred absolute quantification mehod Ahrne et al. Proteomics 2013

20 Hi3 is the preferred absolute quantification mehod the combination of Progenesis with Hi3 provides the highest precision and best accuracy Proteome Discoverer MaxQuant Progenesis Sprenger et al. ASMS

21 Hi3-based absolute protein quantification The intensity response under ESI conditions of the 3 most intense peptides is directly related to the molar amount infused this allows relative quantitation: [ADH] = x mol [BSA] = x mol [HBA] = 0.5 x mol [HBB] = 0.5 x mol co-infusion of a known, unlabelled protein standard allows for absolute quantitation: 3 peptideintensity A i 1 3 peptideintensity B i 1 X fmol/µl A = protein of interest B = internal standard protein X = concentration of internal standard Silva et al, Mol. Cell Proteomics 5 (2006) 144

22 Accurate label-free absolute quantitation is however not a trivial task.. Protein Quantification - which method? - correct for homologues & isoforms? Database search - use matured databases (signal peptides, chains) Charge deconvolution - sum all charge-states (also single charged) Deisotoping - sum areas of all ion isotopes Protein sample - BiBo principle - amount (AAA) - cell number label-free (absolute) protein quantification the ideal scenario Ion areas - need sufficient data points/peak - fast MS/MS or DIA Digestion - efficient - unbiased Separation - reproducible 1D/2D LC Ionization - efficiency is variable (peptide sequence dependent) In-source changes - sum up any insource fragments, water-loss, etc.

23 Accurate label-free absolute quantitation is however not a trivial task.. Protein Quantification - which method? - correct for homologues & isoforms? Protein sample - BiBo principle - amount (AAA) - cell number Database search - use matured databases (signal peptides, chains) Charge deconvolution - sum all charge-states (also single charged) Progenesis QI 2.0 label-free (absolute) protein quantification the ideal scenario Deisotoping - sum areas of all ion isotopes Ion areas - need sufficient data points/peak - fast MS/MS or DIA Digestion - efficient - unbiased Separation - reproducible 1D/2D LC Ionization - efficiency is variable (peptide sequence dependent) In-source changes - sum up any insource fragments, water-loss, etc.

24 New in Progenesis QI 2.0: absolute quantification absolute protein quantification is now available, based on the Hi-3 (or Hi-n) method and, uniquely: platform independent, with deconvolution of all charge states, as well as corrected quantification of homologous proteins and isoforms (which share Hi3 peptides) ADH1 ADH2 ADH3 15 pmol 15 pmol 15 pmol Sum is 45 pmol? No, sum should be 15 pmol How to correct/distribute?

25 Amount correction for proteins sharing Hi3 peptides unique peptides are used to redistribute the total absolute amount

26 New in Progenesis QI 2.0: absolute quantification ADH1 ADH2 ADH3 15 pmol 15 pmol 15 pmol Sum is 45 pmol? 100fmol No, sum should be 15 pmol -> redistribute: 10 pmol 5 pmol 100 fmol

27 protein copies/cell Examples of Hi3 absolute protein quantification 3,374,611 copies 6 orders of dynamic range 2 peptides/protein 8 peptides/protein on average 24.3% average coverage >30,000 unique peptides >700,000 PSMs - 5 fractions / rbitrap Velos yeast proteins absolutely quantified Protein ID 18 copies 24.3% average coverage 85.4% with with 2 peptides 8 peptides/protein average >30,000 unique peptides >700,000 PSMs Sprenger et al. ASMS

28 Examples of Hi3 absolute protein quantification Stoichiometry IR Leon et al MCP 2013

29 Examples of Hi3 absolute protein quantification Regulation and Stoichiometry average nm ratio diet 2 diet 3 diet2/3 Hadha Trifunctional enzyme subunit alpha 5.94 (±0.78) (±0.59) 1.88 Hadhb Trifunctional enzyme subunit beta 6.37 (±0.95) (±0.62) 2.18 Atp5a1 ATP synthase subunit alpha (±2.83) (±2.69) Atp5b ATP synthase subunit beta (±0.59) (±2.55) Atp5c1 ATP synthase gamma chain 5.3 (±0.71) 2.72 (±0.43) Atp5d ATP synthase subunit delta 3.06 (±0.54) 1.63 (±0.38) Atp5e ATP synthase subunit epsilon 1.54 (±0.31) 0.75 (±0.14) Atp5f1 ATP synthase subunit b 5.12 (±0.54) 2.8 (±0.35) Atp5h ATP synthase subunit d 5.96 (±0.08) 3.72 (±0.94) Atp5l ATP synthase 2.04 (±1.31) 1.22 (±0.57) Atp5o ATP synthase subunit 6.45 (±0.12) 3.85 (±0.9) Data from K Wrzesinski et al. - J Proteomics 2013

30 mol% PC species Intensity P - - P - - Intensity Examples of Hi3 absolute protein quantification large scale circadian proteolipidomics in mouse LIPIDMICS PRTEMICS N + - H H P H H H H H H3N + P Lipid extraction tissue lysates Protein digestion* P H Lipid extract Robotic sample infusion internal standards internal standards Peptide mixture Peptide separation (1D and online 2D-LC ) MS analysis MS analysis m/z DAY 1 ZT (h) food + food m/z Data analysis 38 Data analysis (PD 1.4 / Progenesis QI v2.0) Identification Time (h) and quantification of lipid species Identification and quantification of proteins Sprenger et al. figure adapted from A Casanovas et al. 2015

31 Circadian rhythm and feeding response profiles - food + food - food + food - food + food - food + food - food + food - food + food Sprenger et al.

32 Circadian rhythm and feeding response profiles - food + food - food + food - food + food - food + food - food + food - food + food Sprenger et al.

33 Quantitative proteomics of 50 mouse liver replicates 1D-LC-MS analysis of 50 samples (16 time points) in 3 days on the rbitrap Fusion ~4400 label-free quantified proteins with very high data completeness (>98%) - food + food - food + food circadian proteins data completeness Sprenger et al.

34 In-depth absolute quantitative mouse proteomics deep analysis of pooled liver replicates (9 time points) using online 2D-LC and rbitrap Fusion ~8500 label-free quantified proteins in < 3days with very high data completeness of >99% - food + food circadian proteins data completeness Sprenger et al.

35 In summary Progenesis QI for proteomics 2.0 provides a robust platform for: Stacking of runs and identifications, resulting in very high data completeness (>99%) currently the most accurate label-free estimation of absolute protein abundance all common instruments supported

36 In summary Progenesis QI for proteomics 2.0 provides a robust platform for: Stacking of runs and identifications, resulting in very high data completeness (>99%) currently the most accurate label-free estimation of absolute protein abundance all common instruments supported support for both 1D and fractionation (2D) workflows straightforward visual workflow with automation QC metrics and statistics

37 In summary Progenesis QI for proteomics 2.0 provides a robust platform for: Stacking of runs and identifications, resulting in very high data completeness (>99%) currently the most accurate label-free estimation of absolute protein abundance all common instruments supported support for both 1D and fractionation (2D) workflows straightforward visual workflow with automation QC metrics and statistics Is it perfect?

38 Acknowledgements Lipid group: Færgeman lab Nils Færgeman Ditte Nees Signe Bek Lipid group: Ejsing lab Christer Ejsing Albert Casanovas Elena Sokol Josch Pauling Protein Research group le Jensen Ileana Leon Veit Schwammle Institute for Biochemistry and Molecular Biology SDU, dense, Denmark