Skip to main content
SpringerLink
Log in

Direct Assessment of Plasma/Serum Sample Quality for Proteomics Biomarker Investigation

  • Protocol
  • First Online:
Serum/Plasma Proteomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1619))

Abstract

Blood proteome analysis for biomarker discovery represents one of the most challenging tasks to be achieved through clinical proteomics due to the sample complexity, such as the extreme heterogeneity of proteins in very dynamic concentrations, and to the observation of proper sampling and storage conditions. Quantitative and qualitative proteomics profiling of plasma and serum could be useful both for the early detection of diseases and for the evaluation of pathological status. Two main sources of variability can affect the precision and accuracy of the quantitative experiments designed for biomarker discovery and validation. These sources are divided into two categories, pre-analytical and analytical, and are often ignored; however, they can contribute to consistent errors and misunderstanding in biomarker research. In this chapter, we review critical pre-analytical and analytical variables that can influence quantitative proteomics. According to guidelines accepted by proteomics community, we propose some recommendations and strategies for a proper proteomics analysis addressed to biomarker studies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Geyer PE et al (2016) Plasma proteome profiling to assess human health and disease. Cell Syst 2(3):185–195

    Article  CAS  PubMed  Google Scholar 

  2. Pieragostino D et al (2010) Pre-analytical factors in clinical proteomics investigations: impact of ex vivo protein modifications for multiple sclerosis biomarker discovery. J Proteome 73(3):579–592

    Article  CAS  Google Scholar 

  3. Poulsen K et al (2012) Characterization and stability of transthyretin isoforms in cerebrospinal fluid examined by immunoprecipitation and high-resolution mass spectrometry of intact protein. Methods 56(2):284–292

    Article  CAS  PubMed  Google Scholar 

  4. Hubel A et al (2011) State of the art in preservation of fluid biospecimens. Biopreserv Biobank 9(3):237–244

    Article  CAS  PubMed  Google Scholar 

  5. Schoonenboom NS et al (2005) Effects of processing and storage conditions on amyloid β (1–42) and tau concentrations in cerebrospinal fluid: implications for use in clinical practice. Clin Chem 51(1):189–195

    Article  CAS  PubMed  Google Scholar 

  6. Aldred S, Grant MM, Griffiths HR (2004) The use of proteomics for the assessment of clinical samples in research. Clin Biochem 37(11):943–952

    Article  CAS  PubMed  Google Scholar 

  7. Yi J, Craft D, Gelfand CA (2011) Minimizing preanalytical variation of plasma samples by proper blood collection and handling. Methods Mol Biol:137–149

    Google Scholar 

  8. Plebani M (2006) Errors in clinical laboratories or errors in laboratory medicine? Clin Chem Lab Med 44(6):750–759

    CAS  PubMed  Google Scholar 

  9. Del Boccio P et al (2007) Cleavage of cystatin C is not associated with multiple sclerosis. Ann Neurol 62(2):201–204

    Article  PubMed  CAS  Google Scholar 

  10. Greco V et al (2014) Direct analytical sample quality assessment for biomarker investigation: qualifying cerebrospinal fluid samples. Proteomics 14(17–18):1954–1962

    Article  CAS  PubMed  Google Scholar 

  11. Tuck MK et al (2008) Standard operating procedures for serum and plasma collection: early detection research network consensus statement standard operating procedure integration working group. J Proteome Res 8(1):113–117

    Article  CAS  Google Scholar 

  12. Rodak BF, Fritsma GA, Keohane E (2013) Hematology: clinical principles and applications. Elsevier Health Sciences, Amsterdam

    Google Scholar 

  13. Thomson JM (1984) Specimen collection for blood coagulation testing. Lab Hematol 2:833–863

    Google Scholar 

  14. Lista S, Faltraco F, Hampel H (2013) Biological and methodical challenges of blood-based proteomics in the field of neurological research. Prog Neurobiol 101-102:18–34

    Article  CAS  PubMed  Google Scholar 

  15. Ray S et al (2011) Proteomic technologies for the identification of disease biomarkers in serum: advances and challenges ahead. Proteomics 11(11):2139–2161

    Article  CAS  PubMed  Google Scholar 

  16. Tammen H (2008) Specimen collection and handling: standardization of blood sample collection. Methods Mol Biol (Clifton, NJ) 428:35–42

    Article  Google Scholar 

  17. Tammen H, Hess R (2011) Collection and handling of blood specimens for peptidomics. Methods Mol Biol (Clifton, NJ) 728:151–159

    Article  CAS  Google Scholar 

  18. Luque-Garcia JL, Neubert TA (2007) Sample preparation for serum/plasma profiling and biomarker identification by mass spectrometry. J Chromatogr A 1153(1–2):259–276

    Article  CAS  PubMed  Google Scholar 

  19. Thadikkaran L et al (2005) Recent advances in blood-related proteomics. Proteomics 5(12):3019–3034

    Article  CAS  PubMed  Google Scholar 

  20. Adkins JN et al (2002) Toward a human blood serum proteome: analysis by multidimensional separation coupled with mass spectrometry. Mol Cell Proteomics 1(12):947–955

    Article  CAS  PubMed  Google Scholar 

  21. Liotta LA, Ferrari M, Petricoin E (2003) Clinical proteomics: written in blood. Nature 425(6961):905

    Article  CAS  PubMed  Google Scholar 

  22. Diamandis EP (2004) Mass spectrometry as a diagnostic and a cancer biomarker discovery tool – opportunities and potential limitations. Mol Cell Proteomics 3(4):367–378

    Article  CAS  PubMed  Google Scholar 

  23. Diamandis EP (2004) Analysis of serum proteomic patterns for early cancer diagnosis: drawing attention to potential problems. J Natl Cancer Inst 96(5):353–356

    Article  PubMed  Google Scholar 

  24. Tammen H et al (2005) Peptidomic analysis of human blood specimens: comparison between plasma specimens and serum by differential peptide display. Proteomics 5(13):3414–3422

    Article  CAS  PubMed  Google Scholar 

  25. Greening DW, Simpson RJ (2011) Low-molecular weight plasma proteome analysis using centrifugal ultrafiltration. Methods Mol Biol 728:109–124

    Article  CAS  PubMed  Google Scholar 

  26. Anderson NL, Anderson NG (2002) The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics 1(11):845–867

    Article  CAS  PubMed  Google Scholar 

  27. Hu S, Loo JA, Wong DT (2006) Human body fluid proteome analysis. Proteomics 6(23):6326–6353

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Hortin GL, Sviridov D (2010) The dynamic range problem in the analysis of the plasma proteome. J Proteome 73(3):629–636

    Article  CAS  Google Scholar 

  29. Cetın N et al (2016) Serum albumin and von Willebrand factor: possible markers for early detection of vascular damage in children undergoing peritoneal dialysis. Clin Investig Med 39(4):E111

    Article  Google Scholar 

  30. Can B et al (2016) Serum markers of inflammation and oxidative stress in sarcopenia. Aging Clin Exp Res:1–8

    Google Scholar 

  31. ABBRITTI RV et al (2016) Meningiomas and proteomics: focus on new potential biomarkers and molecular pathways. Cancer Genomics Proteomics 13(5):369–379

    CAS  PubMed  PubMed Central  Google Scholar 

  32. LeVine SM (2016) Albumin and multiple sclerosis. BMC Neurol 16(1):1

    Article  Google Scholar 

  33. Greifenhagen U et al (2016) Plasma proteins modified by advanced Glycation end products (AGEs) reveal site-specific susceptibilities to glycemic control in patients with type 2 diabetes. J Biol Chem 291(18):9610–9616

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Chung L et al (2014) Novel serum protein biomarker panel revealed by mass spectrometry and its prognostic value in breast cancer. Breast Cancer Res 16(3):R63

    Article  PubMed  PubMed Central  Google Scholar 

  35. Suman S et al (2016) Quantitative proteomics revealed novel proteins associated with molecular subtypes of breast cancer. J Proteome 148:183–193

    Article  CAS  Google Scholar 

  36. Li D, Chan DW (2014) Proteomic cancer biomarkers from discovery to approval: it’s worth the effort. Expert Rev Proteomics 11(2):135–136

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Crutchfield CA et al (2016) Advances in mass spectrometry-based clinical biomarker discovery. Clin Proteomics 13(1):1

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Percy AJ et al (2016) Clinical translation of MS-based, quantitative plasma proteomics: status, challenges, requirements, and potential. Expert Rev Proteomics 13(7):673–684

    Article  CAS  PubMed  Google Scholar 

  39. Pant P et al (2016) Serum sodium dodecyl sulfate-polyacrylamide gel electrophoresis analysis of patients with membranous nephropathy and focal and segmental glomerulosclerosis. Saudi J Kidney Dis Transpl 27(3):539

    Article  PubMed  Google Scholar 

  40. Sun Y et al (2016) Identification of apolipoprotein CI peptides as a potential biomarker and its biological roles in breast cancer. Med Sci Monitor 22:1152

    Article  Google Scholar 

  41. Streng AS et al (2016) Development of a targeted selected ion monitoring assay for the elucidation of protease induced structural changes in cardiac troponin T. J Proteome 136:123–132

    Article  CAS  Google Scholar 

  42. Zhao W et al (2015) Screening and identification of apolipoprotein AI as a potential hepatoblastoma biomarker in children, excluding inflammatory factors. Oncol Lett 10(1):233–239

    PubMed  PubMed Central  Google Scholar 

  43. Janvilisri T et al (2015) Novel serum biomarkers to differentiate cholangiocarcinoma from benign biliary tract diseases using a proteomic approach. Dis Markers 2015

    Google Scholar 

  44. Mondal G et al (2016) Altered glycosylation, expression of serum haptoglobin and alpha-1-antitrypsin in chronic hepatitis C, hepatitis C induced liver cirrhosis and hepatocellular carcinoma patients. Glycoconj J 33(2):209–218

    Article  CAS  PubMed  Google Scholar 

  45. Piras C et al (2014) Serum protein profiling of early and advanced stage Crohn’s disease. EuPA Open Proteom 3:48–59

    Article  CAS  Google Scholar 

  46. Christensen B et al (2015) Serum proteomic changes after randomized prolonged erythropoietin treatment and/or endurance training: detection of novel biomarkers. PLoS One 10(2):e0117119

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. de Seny D et al (2016) Biomarkers of inflammation and innate immunity in atrophic nonunion fracture. J Transl Med 14(1):258

    Article  PubMed  PubMed Central  Google Scholar 

  48. Heywood WE et al (2016) Global serum glycoform profiling for the investigation of dystroglycanopathies & congenital disorders of glycosylation. Mol Genet Metab Rep 7:55–62

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Wu F et al (2015) Identification of phosphorylated MYL12B as a potential plasma biomarker for septic acute kidney injury using a quantitative proteomic approach. Int J Clin Exp Pathol 8(11):14409

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Okano T et al (2016) Identification of haptoglobin peptide as a novel serum biomarker for lung squamous cell carcinoma by serum proteome and peptidome profiling. Int J Oncol 48(3):945–952

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Viennois E et al (2015) Longitudinal study of circulating protein biomarkers in inflammatory bowel disease. J Proteome 112:166–179

    Article  CAS  Google Scholar 

  52. Nguyen VA et al (2016) A pathway proteomic profile of ischemic stroke survivors reveals innate immune dysfunction in association with mild symptoms of depression-a pilot study. Front Neurol 7:85

    Article  PubMed  PubMed Central  Google Scholar 

  53. Ruland T et al (2016) Molecular serum signature of treatment resistant depression. Psychopharmacology 233(15–16):3051–3059

    Article  CAS  PubMed  Google Scholar 

  54. Lin Q et al (2015) Analysis of colorectal cancer glyco-secretome identifies laminin β-1 (LAMB1) as a potential serological biomarker for colorectal cancer. Proteomics 15(22):3905–3920

    Article  CAS  PubMed  Google Scholar 

  55. Gu H et al (2016) Quantitative profiling of post-translational modifications by immunoaffinity enrichment and LC-MS/MS in cancer serum without immunodepletion. Mol Cell Proteomics 15(2):692–702

    Article  CAS  PubMed  Google Scholar 

  56. Boichenko AP et al (2014) A panel of regulated proteins in serum from patients with cervical intraepithelial neoplasia and cervical cancer. J Proteome Res 13(11):4995–5007

    Article  CAS  PubMed  Google Scholar 

  57. Tonry CL et al (2015) Discovery and longitudinal evaluation of candidate protein biomarkers for disease recurrence in prostate cancer. J Proteome Res 14(7):2769–2783

    Article  CAS  PubMed  Google Scholar 

  58. McArdle A et al (2015) Developing clinically relevant biomarkers in inflammatory arthritis: a multiplatform approach for serum candidate protein discovery. Proteomics Clin Appl 10(6):691–698

    Article  PubMed  CAS  Google Scholar 

  59. Lin C et al (2016) ITRAQ-based quantitative proteomics reveals apolipoprotein AI and transferrin as potential serum markers in CA19-9 negative pancreatic ductal adenocarcinoma. Medicine 95(31):e4527

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Wang X et al (2016) iTRAQ-based proteomics screen identifies LIPOCALIN-2 (LCN-2) as a potential biomarker for colonic lateral-spreading tumors. Sci Rep 6

    Google Scholar 

  61. Wang Q et al (2016) iTRAQ technology-based identification of human peripheral serum proteins associated with depression. Neuroscience 330:291–325

    Article  CAS  PubMed  Google Scholar 

  62. Lee SE et al (2015) Plasma proteome biomarkers of inflammation in school aged children in Nepal. PLoS One 10(12):e0144279

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Xiao H et al (2016) Differential proteomic analysis of human saliva using tandem mass tags quantification for gastric cancer detection. Sci Rep 6

    Google Scholar 

  64. Cominetti O et al (2015) Proteomic biomarker discovery in 1000 human plasma samples with mass spectrometry. J Proteome Res 15(2):389–399

    Article  PubMed  CAS  Google Scholar 

  65. Rosenling T et al (2009) The effect of preanalytical factors on stability of the proteome and selected metabolites in cerebrospinal fluid (CSF). J Proteome Res 8(12):5511–5522

    Article  CAS  PubMed  Google Scholar 

  66. Takehana S et al The effects of pre-analysis sample handling on human plasma amino acid concentrations. Clin Chim Acta 2016;455:68–74.

    Google Scholar 

  67. Mirjanic-Azaric B et al (2015) The impact of time of sample collection on the measurement of thyroid stimulating hormone values in the serum. Clin Biochem 48(18):1347–1349

    Article  CAS  PubMed  Google Scholar 

  68. Banks RE et al (2005) Influences of blood sample processing on low-molecular-weight proteome identified by surface-enhanced laser desorption/ionization mass spectrometry. Clin Chem 51(9):1637–1649

    Article  CAS  PubMed  Google Scholar 

  69. Caisey JD, King DJ (1980) Clinical chemical values for some common laboratory animals. Clin Chem 26(13):1877–1879

    CAS  PubMed  Google Scholar 

  70. Percy AJ, Parker CE, Borchers CH (2013) Pre-analytical and analytical variability in absolute quantitative MRM-based plasma proteomic studies. Bioanalysis 5(22):2837–2856

    Article  CAS  PubMed  Google Scholar 

  71. Lundblad RL (2005) Considerations for the use of blood plasma and serum for proteomic analysis. Int J Gastroenterol 1:1–11

    Google Scholar 

  72. Omenn GS (2004) The human proteome organization plasma proteome project pilot phase: reference specimens, technology platform comparisons, and standardized data submissions and analyses. Proteomics 4(5):1235–1240

    Article  CAS  PubMed  Google Scholar 

  73. Omenn GS (2007) THE HUPO human plasma proteome project. Proteomics Clin Appl 1(8):769–779

    Article  CAS  PubMed  Google Scholar 

  74. Omenn GS et al (2005) Overview of the HUPO plasma proteome project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database. Proteomics 5(13):3226–3245

    Article  CAS  PubMed  Google Scholar 

  75. Lippi G et al (2006) Influence of hemolysis on routine clinical chemistry testing. Clin Chem Lab Med 44(3):311–316

    CAS  PubMed  Google Scholar 

  76. Lippi G et al (2005) Preanalytical variability in laboratory testing: influence of the blood drawing technique. Clin Chem Lab Med 43(3):319–325

    Article  CAS  PubMed  Google Scholar 

  77. Rodriguez AD, Gonzalez PA (2009) Diurnal variations in biomarkers used in cardiovascular medicine: clinical significance. Rev Esp Cardiol 62(11):1340–1341

    Article  PubMed  Google Scholar 

  78. Capila I, Linhardt RJ (2002) Heparin-protein interactions. Angew Chem Int Ed Engl 41(3):391–412

    Article  PubMed  Google Scholar 

  79. Dammann CE et al (2006) Protein detection in dried blood by surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF MS). Biol Neonate 89(2):126–132

    Article  CAS  PubMed  Google Scholar 

  80. Rai AJ et al (2005) HUPO plasma proteome project specimen collection and handling: towards the standardization of parameters for plasma proteome samples. Proteomics 5(13):3262–3277

    Article  CAS  PubMed  Google Scholar 

  81. Ahn S-M, Simpson RJ (2007) Proteomic strategies for analyzing body fluids. In: Thongboonkerd V (ed) Proteomics of human body fluids. Springer, New York, NY, pp 3–30

    Chapter  Google Scholar 

  82. Jambunathan K, Galande AK (2014) Sample collection in clinical proteomics—Proteolytic activity profile of serum and plasma. Proteomics Clin Appl 8(5–6):299–307

    Article  CAS  PubMed  Google Scholar 

  83. White JG (2000) EDTA-induced changes in platelet structure and function: clot retraction. Platelets 11(1):49–55

    Article  CAS  PubMed  Google Scholar 

  84. Drake SK et al (2004) Potential interferences from blood collection tubes in mass spectrometric analyses of serum polypeptides. Clin Chem 50(12):2398–2401

    Article  CAS  PubMed  Google Scholar 

  85. Hsieh SY et al (2006) Systematical evaluation of the effects of sample collection procedures on low-molecular-weight serum/plasma proteome profiling. Proteomics 6(10):3189–3198

    Article  CAS  PubMed  Google Scholar 

  86. Villanueva J et al (2005) Correcting common errors in identifying cancer-specific serum peptide signatures. J Proteome Res 4(4):1060–1072

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Zimmerman LJ et al (2012) Global stability of plasma proteomes for mass spectrometry-based analyses. Mol Cell Proteomics 11(6):M111.014340

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  88. Mateos J et al (2017) Multicentric study of the effect of pre-analytical variables in the quality of plasma samples stored in biobanks using different complementary proteomic methods. J Proteome 150:109–120

    Article  CAS  Google Scholar 

  89. Martino TA et al (2007) Diurnal protein expression in blood revealed by high throughput mass spectrometry proteomics and implications for translational medicine and body time of day. Am J Phys Regul Integr Comp Phys 293(3):R1430–R1437

    CAS  Google Scholar 

  90. Robles MS, Mann M (2013) Proteomic approaches in circadian biology, in Circadian clocks. Springer, New York, NY, pp 389–407

    Google Scholar 

  91. Apweiler R et al (2009) Approaching clinical proteomics: current state and future fields of application in fluid proteomics. Clin Chem Lab Med 47(6):724–744

    Article  CAS  PubMed  Google Scholar 

  92. Rosenling T et al (2011) The impact of delayed storage on the measured proteome and Metabolome of human cerebrospinal fluid. Clin Chem 57(12):1703–1711

    Article  CAS  PubMed  Google Scholar 

  93. Pieragostino D et al (2013) Oxidative modifications of cerebral transthyretin are associated with multiple sclerosis. Proteomics 13(6):1002–1009

    Article  CAS  PubMed  Google Scholar 

  94. Marshall J et al (2003) Processing of serum proteins underlies the mass spectral fingerprinting of myocardial infarction. J Proteome Res 2(4):361–372

    Article  CAS  PubMed  Google Scholar 

  95. West-Nielsen M et al (2005) Sample handling for mass spectrometric proteomic investigations of human sera. Anal Chem 77(16):5114–5123

    Article  CAS  PubMed  Google Scholar 

  96. Rai AJ, Vitzthum F (2006) Effects of preanalytical variables on peptide and protein measurements in human serum and plasma: implications for clinical proteomics. Expert Rev Proteomics 3(4):409–426

    Article  CAS  PubMed  Google Scholar 

  97. Whiteaker JR et al (2010) An automated and multiplexed method for high throughput peptide immunoaffinity enrichment and multiple reaction monitoring mass spectrometry-based quantification of protein biomarkers. Mol Cell Proteomics 9(1):184–196

    Article  CAS  PubMed  Google Scholar 

  98. Whiteaker JR et al (2011) Evaluation of large scale quantitative proteomic assay development using peptide affinity-based mass spectrometry. Mol Cell Proteomics 10(4):110.005645

    Article  CAS  Google Scholar 

  99. Ahmed N et al (2003) An approach to remove albumin for the proteomic analysis of low abundance biomarkers in human serum. Proteomics 3(10):1980–1987

    Article  CAS  PubMed  Google Scholar 

  100. Björhall K, Miliotis T, Davidsson P (2005) Comparison of different depletion strategies for improved resolution in proteomic analysis of human serum samples. Proteomics 5(1):307–317

    Article  PubMed  CAS  Google Scholar 

  101. Steel LF et al (2003) Efficient and specific removal of albumin from human serum samples. Mol Cell Proteomics 2(4):262–270

    Article  CAS  PubMed  Google Scholar 

  102. Gong Y et al (2006) Different immunoaffinity fractionation strategies to characterize the human plasma proteome. J Proteome Res 5(6):1379–1387

    Article  CAS  PubMed  Google Scholar 

  103. Fu Q et al (2005) A robust, streamlined, and reproducible method for proteomic analysis of serum by delipidation, albumin and IgG depletion, and two-dimensional gel electrophoresis. Proteomics 5(10):2656–2664

    Article  CAS  PubMed  Google Scholar 

  104. Barquinero NC (2016) Identification and quantitation of proteins in human plasma and serum by LC-MS/MS. Nat Methods PMCID:PMC3943160

    Google Scholar 

  105. Horn A et al (2006) Multidimensional proteomics of human serum using parallel chromatography of native constituents and microplate technology. Proteomics 6(2):559–570

    Article  CAS  PubMed  Google Scholar 

  106. Tang HY et al (2005) A novel four-dimensional strategy combining protein and peptide separation methods enables detection of low-abundance proteins in human plasma and serum proteomes. Proteomics 5(13):3329–3342

    Article  CAS  PubMed  Google Scholar 

  107. Olsen JV, Ong S-E, Mann M (2004) Trypsin cleaves exclusively C-terminal to arginine and lysine residues. Mol Cell Proteomics 3(6):608–614

    Article  CAS  PubMed  Google Scholar 

  108. Olsen, J.V., S.-E. Ong, and M. Mann, Trypsin cleaves exclusively C-terminal to arginine and lysine residues. Molecular & Cellular Proteomics, 2004. 3(6): p. 608-614.

    Google Scholar 

  109. Proc JL et al (2010) A quantitative study of the effects of chaotropic agents, surfactants, and solvents on the digestion efficiency of human plasma proteins by trypsin. J Proteome Res 9(10):5422–5437

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Zhou J et al (2006) Evaluation of the application of sodium deoxycholate to proteomic analysis of rat hippocampal plasma membrane. J Proteome Res 5(10):2547–2553

    Article  CAS  PubMed  Google Scholar 

  111. Burkhart JM et al (2012) Systematic and quantitative comparison of digest efficiency and specificity reveals the impact of trypsin quality on MS-based proteomics. J Proteome 75(4):1454–1462

    Article  CAS  Google Scholar 

  112. Hoofnagle AN, Wener MH (2009) The fundamental flaws of immunoassays and potential solutions using tandem mass spectrometry. J Immunol Methods 347(1):3–11

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Percy AJ et al (2013) Absolute quantitation of proteins in human blood by multiplexed multiple reaction monitoring mass spectrometry. Methods Mol Biol:167–189

    Google Scholar 

  114. Keshishian H et al (2007) Quantitative, multiplexed assays for low abundance proteins in plasma by targeted mass spectrometry and stable isotope dilution. Mol Cell Proteomics 6(12):2212–2229

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Lehmann S et al (2013) Quantitative clinical chemistry proteomics (qCCP) using mass spectrometry: general characteristics and application. Clin Chem Lab Med 51(5):919–935

    Article  CAS  PubMed  Google Scholar 

  116. Hoofnagle AN (2010) Quantitative clinical proteomics by liquid chromatography–tandem mass spectrometry: assessing the platform. Clin Chem 56(2):161–164

    Article  CAS  PubMed  Google Scholar 

  117. Karsan A et al (2005) Analytical and preanalytical biases in serum proteomic pattern analysis for breast cancer diagnosis. Clin Chem 51(8):1525–1528

    Article  CAS  PubMed  Google Scholar 

  118. Bae YJ et al (2012) Degree of ionization in MALDI of peptides: thermal explanation for the gas-phase ion formation. J Am Soc Mass Spectrom 23(8):1326–1335

    Article  CAS  PubMed  Google Scholar 

  119. Westmacott G et al (2002) The influence of laser fluence on ion yield in matrix-assisted laser desorption ionization mass spectrometry. Int J Mass Spectrom 221(1):67–81

    Article  CAS  Google Scholar 

  120. Frey BL et al (2005) Controlling gas-phase reactions for efficient charge reduction electrospray mass spectrometry of intact proteins. J Am Soc Mass Spectrom 16(11):1876–1887

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Bae YJ, Park KM, Kim MS (2012) Reproducibility of temperature-selected mass spectra in matrix-assisted laser desorption ionization of peptides. Anal Chem 84(16):7107–7111

    Article  CAS  PubMed  Google Scholar 

  122. O'Rourke MB, Djordjevic SP, Padula MP (2016) The quest for improved reproducibility in MALDI mass spectrometry. Mass Spectr Rev. doi:10.1002/mas.21515

    Google Scholar 

  123. Gallien S, Domon B (2015) Detection and quantification of proteins in clinical samples using high resolution mass spectrometry. Methods 81:15–23

    Article  CAS  PubMed  Google Scholar 

  124. Gallien, S. and B. Domon, Detection and quantification of proteins in clinical samples using high resolution mass spectrometry. Methods, 2015. 81: p. 15-23.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Proteomics and metabonomics unit, Fondazione Santa Lucia, IRCCS, Rome, Italy

    Viviana Greco, Luisa Pieroni & Andrea Urbani Ph.D.

  2. Department of Veterinary Medicine, University of Milan, Milan, Italy

    Cristian Piras

  3. Institute of Biochemistry and Clinical Biochemistry, Catholic University of Sacred Heart, Rome, Italy

    Andrea Urbani Ph.D.

Authors
  1. Viviana Greco
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cristian Piras
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luisa Pieroni
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrea Urbani Ph.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrea Urbani Ph.D. .

Editor information

Editors and Affiliations

  1. Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria, Australia

    David W. Greening

  2. Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria, Australia

    Richard J. Simpson

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media LLC

About this protocol

Cite this protocol

Greco, V., Piras, C., Pieroni, L., Urbani, A. (2017). Direct Assessment of Plasma/Serum Sample Quality for Proteomics Biomarker Investigation. In: Greening, D., Simpson, R. (eds) Serum/Plasma Proteomics. Methods in Molecular Biology, vol 1619. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7057-5_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7057-5_1

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7056-8

  • Online ISBN: 978-1-4939-7057-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation