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Pp 90505-lc-ms-serum-profiling-biomarker-msacleu2019-pp90505-en
- 1. 1 The world leader in serving science Alexander Boychenko1, Natalia Govorukhina2, Runsheng Zheng1 1Thermo Fisher Scientific, Germering, Germany 2University of Groningen, Groningen, The Netherlands Precise Quantitative Serum LC-MS/MS Profiling: The Impact of Sample Preparation and Sample Source on Biomarker Discovery Studies For Research Use Only. Not for diagnostic purposes.
- 3. 3 Blood Complexity https://www.proteinatlas.org/hu manproteome/blood 94% 6% > 3000 proteins identified with MS > 94 % of total protein amount in serum is occupied by 14 proteins Protein concentrations in blood Protein abundances in blood 78 107 228 498 670 27 838 776 10-100 ug/mL 1-10 ug/mL 0.10-1 ug/mL 0.01-0.1 ug/mL 1e-2 - 1e-3 ug/mL 100-1000 ug/mL 1e-3 - 1e-4 ug/mL < 1e-4 ug/mL
- 4. 4 Protein Biomarkers in Blood S.S. Hori, Sci. Transl. Med. 3 109-116 (2011) Tumor cells Normal cells Specific biomarkersNatural biological variation of protein concentration FDA Approved Biomarkers (mostly nonspecific) Aberrant secretions, 6% Receptor ligands, 18% Tissue leakage, 25% Immunogobulins, 6% Function on plasma, 45% N. Leigh Anderson, Clinical Chemistry 56:2 177–185 (2010) > 80% proteins have less than 2- fold abundance variation 51% - normal functions of blood 25% - tissue leakage 18% - receptor ligands 6% - tumor markers Log2 (Protein Abundance) Frequency
- 6. 6 Experimental Design: “Triangular” Biomarker Discovery and Validation Pipeline Analytical Variability1 Biological Variability2 Protein regulation: cases vs. controls 3 Verification 100’s samples 100’s analytes Validation 1000’s samples 10’s analytes • Lower analytical variability gives more confidence in discovered protein abundance changes in cases and controls • Reliably defined biological variability is the key to unbiased selection of biomarker candidates • Deeper proteome profiling increases chance of discovering specific biomarkers
- 7. 7 “The Devil is in the Detail”: Multicentric Study and Sample Preparation SeraLab (UK) – 1 group (Healthy) Bleed bag unit (not specified) O/N clotting; T +4 ºC 2,800g; 20min; 5 ºC UMCG (NL) – 2 groups (CIN0, Healthy) Serum tube: BD 367953 2 - 8 hours clotting; +20 ºC 3,000g, 10 min; RT Lund (SE) – 1 group (Healthy) Serum tube: BD 367615 30 min - 2 hours clotting; +20 ºC 2,000g, 10 min; RT How reliable is your analytical and data processing pipeline? Can you identify biomarkers in samples from healthy subjects? For Research Use Only. Not for diagnostic purposes.
- 8. 8 Fast, Deep, Quantitative Proteomics Platform Flow, µL/min Samples per 24 hours MS utilization, % Average PWHM, sec Average PW base, sec Asymmetry 60 min 0.300 24 95 10 19 1.23 48 min 0.600 30 90 9 18 1.21 24 min 0.800 60 87 7 13 1.17 14.4 min 1.000 100 85 4 7 1.13 8 min 1.500 180 75 3 6 1.16 75 µm x 150 mm, 3 µm PN ES800 Thermo Scientific™ UltiMate™ 3000 RSLCnano system Thermo Scientific™ Q Exactive™ HF-X Hybrid Quadrupole-Orbitrap™ mass-spectrometer 5049 10468 14107 18261 24932 1069 1708 2258 2720 3432 0 500 1000 1500 2000 2500 3000 3500 4000 0 5000 10000 15000 20000 25000 30000 35000 8 min 14.4 min 24 min 48 min 60 min Proteingroups Peptidegroups HeLa digest, 200 ng 1071 1703 2063 2474 2767 138 178 217 230 265 0 50 100 150 200 250 300 0 500 1000 1500 2000 2500 3000 3500 4000 8 min 14.4 min 24 min 48 min 60 min Proteingroups Peptidegroups Serum digest, 0.05 uL Standardized low-flow LCMS methods 60 min low-flow LCMS method was used for analysis of 15 crude serum samples For Research Use Only. Not for diagnostic purposes.
- 9. 9 Analytical Variability: Precision of Low-Flow LCMS Analysis Analytical Variability1 Biological Variability2 Proteins regulation: cases vs. controls 3 RSD, % log2 Protein abundance ratios • 3 LCMS replicates per sample • Label-free quantification Proteins identified (1% FDR): 265 Proteins quantified: 248 • RSD < 15% 218 (88%) • 95% of proteins have abundance variation < 50% For Research Use Only. Not for diagnostic purposes.
- 10. 10 Experimental Design: Biological Variability Analytical Variability1 Biological Variability2 Proteins regulation: cases vs. controls 3 Analytical variability Biological variability Sweden UMCGCIN0 Seralab CIN0 Seralab UMCG Sweden • Biological variability is significantly larger in comparison to analytical variability • Still 80% of proteins have less than a 2-fold change For Research Use Only. Not for diagnostic purposes.
- 11. 11 Experimental Design: Regulated Proteins Analytical Variability1 Biological Variability2 Proteins regulation: cases vs. controls 3 Seralab CIN UMCG CIN Sweden CIN UMCG Seralab Sweden Seralab Sweden UMCG UP and DOWN regulated proteins Protein ratio distribution, selection criteria 80% 90% 1-1 Cauchy σ=0.310 Reliability of quantification, Biological functions Data Analysis pipeline 2-2 Boichenko et al. J Proteome Res. 2014 Nov 7;13(11):4995-5007 Selection criteria • 94 proteins had at least one abundance ratio > 2 • 22 proteins had abundance ratio > 4 • 16 of them can be mapped to blood atlas For Research Use Only. Not for diagnostic purposes.
- 12. 12 Why Did We Find Proteins Regulated in Healthy Groups? KRT20 FGG LPA PPIA APOD SERPINA5 KRT2PF4V1 PPBP HBD CNDP1 HBB HBA1 ORM1 THBS1 CAT -6 -5 -4 -3 -2 -1 0 1 2 3 4 0 500 1000 1500 2000 2500 3000 3500 LOG2(PROTEINCONCENTRATION) PROTEIN NO.(SORTEDBY ABUNDANCE) Protein Concentration (Human Blood Atlas) Regulated (p >0.8), fold change > 2 Regulated (p > 0.9), fold change > 4 Significant regulation of classical blood proteins in healthy groups is very unlikely and can lead to • Artificial protein abundance alteration in one or multiple individual samples (storage, analysis error, etc.) • Artefacts arising from sample preparation KRT20 FGG LPA PPIA APOD SERPINA5 KRT2 PF4V1 PPBP HBD CNDP1 HBB HBA1 ORM1 THBS1 CAT -3 -2 -1 0 1 2 3 4 0 200 400 600 800 1000 LOG2(PROTEINCONCENTRATION) PROTEIN NO. (SORTED BY ABUNDANCE) Protein Concentration (Human Blood Atlas) Regulated (p >0.8), fold change > 2 Regulated (p > 0.9), fold change > 4 For Research Use Only. Not for diagnostic purposes.
- 13. 13 Protein Regulation in Individual Samples HBD IGKV1D-13 APOD IGKV2D-30 KRT2 CAT HBB HBA1 PF4V1 PPBP P0DOX6 CNDP1 THBS1 ORM1 IGHG3 IGLV2-23 PPIA KRT20 SERPINA5 LPA IGHD–noref. FGG CIN0_01 CIN0_02 CIN0_03 Seralab_01 Seralab_02 Seralab_03 Seralab_04 Seralab_05 Sweden_01 Sweden_02 Sweden_03 UMCG_01 UMCG_02 UMCG_03 -6.0 -3.0 0.0 3.0 6.0 Log2(ProteinAbundance) Proteins downregulated in Seralab samples represent classical blood proteins expressed in specific blood cells released during the serum preparation process A deeper dive into the proteome is required to find specific biomarkers PCA of 15 individual samples For Research Use Only. Not for diagnostic purposes.
- 14. 14 Deep Profiling of Individual Samples
- 15. 15 Deeper Proteome Profiling with Comprehensive Online-2D Low-Flow LC-MS/MS 75 µm x 150 mm, 3 µm PN ES800 UltiMate 3000 RSLCnano Online 2D configuration Thermo Scientific™ Orbitrap Exploris™ 480 2nd Dimension 1st Dimension PepSwift, Monolithic Column 100 µm x 250 mm 2 fractions (135 min) 4 fractions (225 min) 8 fractions (405 min) 2292 407 1600 • Automated sample analysis (no manual sample handling) • Orthogonal selectivity in 1st and 2nd dimension • Low overlap of peptides elution between fractions • Deeper complex proteome profiling 810 1403 1519 782810 1221 1266 595 810 2031 3297 3892 0 500 1000 1500 2000 2500 3000 3500 4000 4500 0 200 400 600 800 1000 1200 1400 1600 1 2 3 4 Fraction # UniPep New.unipep Acc.unipep For Research Use Only. Not for diagnostic purposes.
- 16. 16 Increased Dynamic Range with Online 2D Low-Flow LC-MS/MS 2840 2884 3764 4825309 265 329 377 0 50 100 150 200 250 300 350 400 0 1000 2000 3000 4000 5000 6000 Merged 15 samples x 60 min, 15 hours 0.25uL serum, 2 frac, 2.25 hours 0.25uL serum, 4 frac, 3.75 hours 0.50uL serum, 8 frac, 6.75 hours Peptide Groups Protein Groups -6 -4 -2 0 2 4 0 500 1000 1500 2000 2500 3000 3500 LOG10(PROTEINCONCENTRATION) PROTEIN NO. (SORTED BY ABUNDANCE) Human Blood Atlas Merged 15 samples x 60 min, 24 hours 2 fractions, Online 2D, 2.25 hours 4 fractions, Online 2D, 3.75 hours 8 fractions, Online 2D, 6.75 hours • Higher loading and peak capacity of online 2D low-flow LC-MS/MS analysis results in deeper serum proteome coverage • Protein and peptide identifications increase linearly with number of fractions • Automated on-line 2D LC-MS/MS analysis can be easily adjusted to any number of fractions and combined with DDA and DIA MS acquisition techniques For Research Use Only. Not for diagnostic purposes.
- 17. 17 From Groups to Individual Samples: Focus on Precision Medicine • 15 individual samples were analysed with low-flow online 2D LC-MS/MS • 442 proteins were quantified based on 3536 unique peptides • 110 protein ratio combinations were computed for 15 samples • 237 proteins had at least one abundance ratio > 2 • 151 of these proteins were mapped to the Human Blood Atlas • PCA shows clear separation of samples obtained from Seralab • There is no clear differentiation between other sample groups For Research Use Only. Not for diagnostic purposes.
- 18. 18 Pathway Mapping for Serum Regulated Proteins https://reactome.org/ Most UP and DOWN regulated proteins in individual samples were related to clotting or generic immune system response For Research Use Only. Not for diagnostic purposes.
- 19. 19 Protein Regulation in Individual Samples CIN0_01 CIN0_02 CIN0_03 Seralab_01 (ref.) Seralab_02 Seralab_03 Seralab_04 Seralab_05 Sweden_01 Sweden_02 Sweden_03 UMCG_01 UMCG_02 UMCG_03 UMCG_04 C4A SAA2 CA1 CD5L ACTC1 LYZ KRT14 CFHR5 MBL2 PCOLCE ICAM2 PFN1 PRDX2 PEPD CAT MASP2 LCN2 PTPRJ LAMP2 NRP1 LTF TAGLN2 PRSS3 SERPINA11 BLVRB TPM3 MAN1A1 CACNA2D1 MMRN1 PCSK9 COL6A1 PDLIM1 PRG2 TREML1 PGD SRGN SPTA1 ADAMTS13 CD99 APP INHBC PRL HBB HBD HBA2 PF4V1 APMAP CRP PPBP SPARC SAA1 APOD ALDOB AZGP1 ORM2 PRTN3 APOC3 PZP TIMP1 MCAM PON3 CHL1 RBP4 FLNA TSKU APOC2 COLEC11 APCS TPM4 ADIPOQ KRT2 NCAM1 AGT PROZ SERPINA6 APOC4 F11 EFEMP1 GPX3 ALDOA TLN1 PPIA KRT10 H6PD ORM1 HIST1H1C NID1 AOC3 IGF1 ENPP2 PRG4 APOM MMRN2 KNG1 GP1BA LUM PI16 VNN1 TFRC IL1RAP FCN3 DBH LCP1 DSG2 PRKCSH CRTAC1 COLEC10 TUBB3 OAF FYN LGALS3BP CFHR4 FBLN1 CNTN1 CFHR1 KRT9 VCAM1 PLA2G7 HSPD1 MASP1 YWHAZ TNXB VASN SERPINA5 BPIFB1 MASP1 SERPING1 LDHB LPA PLXDC2 PRDX1 PIGR F13A1 SHBG HIST1H4A POSTN GGH ICAM1 ENG CPS1 MUC5B COMP CA2 ANG HSP90AB1 PKM FGB FGG THBS4 FCGR3A KRT17 The analysis of individual samples is complicated by a broad variation in expression across healthy individuals, sample preparation, analytical variability A number of proteins in Seralab samples show significant abundance alternations in comparison with other groups For Research Use Only. Not for diagnostic purposes.
- 20. 20 Significant Sample Specific Regulations: Potential Biomarkers Comparison of Targeted Mass Spectrometry Techniques with an Immunoassay: A Case Study for HSP90α Guzel C., et al Proteomics Clin. Appl. 2018, 12, 1700107 HSP90AB PKM Deeper blood proteome profiling is required to retrieve sample protein abundance changes for disease specific biomarkers HSP90AB PKM Low tissue, cell specificity Low cancer specificity CIN0 Seralab Sweden UMCG CIN0 Seralab Sweden UMCG Low tissue, cell specificity Low cancer specificity-4 -3 -2 -1 0 1 2 3 4 CIN0_1 CIN_02 CIN_03 Seralab_01 Seralab_02 Seralab_03 Seralab_04 Seralab_05 Sweden_01 Sweden_02 Sweden_03 UMCG_01 UMCG_02 UMCG_03 UMCG_04 PZP For Research Use Only. Not for diagnostic purposes.
- 21. 21 • Standardized high-throughput low-flow LC-MS methods can be used for routine profiling of TOP 200 proteins or development targeted assays with high sensitivity • Standardized sample preparation of biofluids and critical analysis of potential validation targets is essential for biomarkers discovery studies. • Profiling of high abundant blood (serum, plasma) proteins has limited benefit for the discovery of new biomarkers or population related changes due to high stability of the blood proteome and non- specific changes related to the immune system response • Comprehensive high pH RP x low pH RP low-flow online 2D LC-MS/MS analysis shows great potential for deep, automated quantitative blood proteome profiling required to discover cases specific biomarkers Conclusions For Research Use Only. Not for diagnostic purposes.
- 22. 22 Acknowledgements RUG, Groningen, NL UMCG, Groningen NL R. Bischoff A. van der Zee N. Govorukhina K. ten Hoor H. Klip University of Groningen Erasmus MC, Rotterdam Dr. T. Luider C. Guzel Germering, Germany Bremen, Germany T. Arrey P. Jehle W. Decrop C. Pynn R. Zheng For Research Use Only. Not for diagnostic purposes.