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Protein Depletion for Plasma and Serum Proteomic Analysis

Human plasma and serum represents an important biological material for disease diagnosis. However, the wide dynamic range in protein concentration remains a major challenge in the development of diagnostic assays for the very low concentration of biomarker proteins in the presence of high abundance proteins. A practical and effective strategy is to remove 99% of the diagnostically uninformative proteins in order to enhance the detection of the low abundance proteins and penetrate deeper into the plasma proteome. Among a number of plasma protein depletion techniques, the ProteoPrep 20 represents the most powerful enabling technology currently available.

1.1 Why blood plasma?
Blood plasma is not only the most studied among biological fluids, but also the primary material for disease diagnosis. Blood plasma contains a very high concentration of proteins, typically in the range of 60-80 mg of protein per ml. Estimates of the number of proteins in blood plasma start from 10,000, but the actual number of distinct proteins may be several orders of magnitude higher [1,2]. This is because each protein has a potential for a variety of post-translational and metabolic modifications [3-6], both in normal and diseased cells.

The global composition of proteins in the blood plasma represents the plasma proteome. Perfusion of blood through the different organs and tissues can result in the addition of new proteins, removal of some proteins, or modification of existing proteins, which may vary according to specific physiological or pathological conditions [7-14]. It is logical to expect correlation between the proteomic profiles of ed the number of proteins analyzed and identified, some of the proteins found in the non-depleted serum were not found in the depleted serum [70,109,112]. This is mostly attributed to the so-called sponge effect, where small proteins and peptides may bind to large proteins that normally serve as their carriers [109,112]. In reality there is no quantitative data to show how much of the non-targeted proteins are non-specifically bound to the specifically depleted proteins, and how much are bound to the depletion matrices. Nevertheless, these observations raise concerns about the validity of the quantitative representation of the whole proteome when only the protein-depleted sample is analyzed. Therefore, for particular applications the specifically depleted bound fraction may also be analyzed to ensure that no important proteins are inadvertently omitted.

2.1 Depletion of albumin and the IgGs
Human serum albumin (HSA) and the various forms of immunoglobulins (IgGs) represent the most abundant proteins in the serum, constituting up to 80% of the total plasma proteins. The classical depletion strategy for albumin involves the use of the hydrophobic dye Cibacron blue, a chlorotriazine dye which has high affinity for albumin [104,105,113-115]. This strategy of removing albumin is still sometimes used in proteomic analyses because of its relatively low cost [52,116-120]. Other small molecules have been designed (e.g. mimetic dyes) which demonstrate greater specificity than Cibacron Blue. Another classical affinity medium is the Protein A/G [121,122], which is used for the removal of the immunoglobulins [123,124]. As a group, the immunoglobulins represent the second most abundant proteins in the plasma or serum. A low cost depletion kit for simultaneous depletion of albumin and immunoglobulins (Cat. No. PROTBA) is available which includes both types of resins.

Comparative studies indicate that using antibody affinity ligands for HSA and IgG result in more specific depletion compared to the traditional Cibacron blue/Protein A or G depletion methods [71,100,106]. Because of this demonstrated specificity, the trend is now towards the use of immunoaffinity media for most proteomic analyses. Affinity media are made up of matrices with covalently attached antibodies to the specific abundant proteins [15,124-126]. An immunoaffinity media for HSA and IgG depletion is available (Cat. No. PROTIA), conveniently packed as spin columns that are compatible with centrifugation.

Despite the efficiency of immunoaffinity media, depletion of more proteins besides HSA and the IgGs is necessary to enhance the detection of very low abundance proteins that are present at the low ng/ml to pg/ml levels. For example, it was estimated that even if 99.9% of albumin were removed, the remaining albumin concentration would be about 50 g/ml, which is still 50,000-fold higher concentration compared to the tumor marker prostate- specific antigen [26,127,128]. In addition, there are still many other highly abundant proteins that can potentially mask the analysis of the low abundance proteins and should, therefore, be removed.

2.2 Depletion of six abundant proteins
While removing HSA and the IgGs has consistently shown improvement in the detection of some low abundance proteins, analytical efficiency is expected to improve even farther by increasing the number of proteins depleted. Depletion of 6 and 12 abu ndant proteins is expected to remove about 85% and 90%, respectively, of the total proteins [71,100]. For example, columns containing affinity ligands for the top six abundant proteins have been shown to improve the visualization, detection and identification of more low abundance proteins [38,70,73,74,99- 101,106,109,112,129-133], when compared to depletion of only HSA and IgGs. In addition, data from the HUPO Plasma Proteome Project clearly showed that depletion of the most abundant proteins in serum, whether only albumin, albumin and IgGs, or the six most abundant proteins, improved detection of some of the low abundance proteins [80]. However, the same report also indicated incomplete sampling of proteins is a dominant feature. Part of the reason is likely the limitation in the amount of sample that can be loaded for analysis, before the remaining high abundance proteins interfere with the analysis. An affinity column designed to remove the 12 most abundant proteins is also available, but experimental data on this product is yet to emerge.

2.3 Depletion of 20 abundant proteins
It has been suggested that removal of 18 to 22 of the most abundant proteins is desirable in order to effect an overall depletion of 98 to 99 percent of the total proteins [100,134]. A new affinity column with high binding capacity has been developed. The ProteoPrep 20 Plasma Immunodepletion Kit (PROT20) is the only commercially available product that contains immunoaffinity ligands designed to remove 20 of the abundant proteins (Table 1) in human plasma or serum [128]. This novel technology is the most power ful tool currently available, and has demonstrated the ability to deplete more proteins to visualize low copy number proteins in plasma samples and subsequently identify them by mass spectrometry [135].

For convenience, the ProteoPrep 20 Plasma Immunodepletion Kit (PROT20) is supplied as a complete kit containing 3 spin columns and the necessary reagents and consumable supplies. The kit also includes protocols that have been optimized for specific applications. Carefully controlled tests [135] indicated that each spin column removed the 20 high abundance proteins with an average depletion of 99.6% when 10 x 8 l plasma depletions were concentrated and depleted twice. This depletion enabled a 38-fold and a 3-fold increase, respectively, in the load of low abundance proteins compared to the sample without depletion and depletion of just 6 proteins. This enrichment consequently enabled the identification of several low abundance proteins that could not be detected either in the non-depleted serum nor the 6-protein depleted serum. Finally, the spin columns have high economic value because they are re-usable for at least 100 times. Ordering information for PROT20 and companion reagents/consumables is shown in Table 2.

As indicated previously, protein depletion can be considered an initial dimension in orthogonal protein separation, the purpose of which is to separate the highly abundant proteins from the low abundance proteins. Since the flow through from ProteoPrep 20 spin column (low abundance proteins) and the fraction derived from the proteins bound to the affinity media (high abundance proteins) are both in solution phase, they are amenable to subsequent protein separation steps. A variety of possible combinations of orthogonal protein separation techniques are shown in the workflow (Figure 1), depending on the application and instrumentation available to the researcher. Finally, the different fractions from the different multi-dimensional separation techniques are subjected to trypsin digestion and analyzed by LC-mass spectrometry. Multi-dimensional analysis and mass spectrometry will be discussed separately elsewhere.

Figure 1. Typical workflow for protein depletion using ProteoPrep 20 Plasma Immunodepletion Kit (PROT20), leading to multidimensional separation, mass spectrometry, and protein identification. The different separation techniques are enclosed in a dotted box to indicate that any combination of these techniques can be used in an orthogonal manner. Abbreviations used: HPLC, High Performance Liquid Chromatography; RP, Reversed Phase; IE, Ion Exchange; SEC, Size Exclusion; AC, Affinity Chromatography; SDS-PAGE, SDS-Polyacrylamide Electrophoresis; CZE, Capillary Zone Electrophoresis; CIEF, Capillary Isoelectric Focusing; CGE, Capillary Gel Electrophoresis.


  1. Apweiler R, Bairoch A, Wu C, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, et al.: UniProt: the Universal Protein knowledgebase Nucl. Acids Res. 2004, 32: D115-D119.
  2. Huber LA: Is proteomics heading in the wrong direction? Nature Reviews/Molecular Cell Biology 2003, 4:74-80.
  3. Han KK, Martinage A: Possible relationship between coding recognition amino acid sequence motif or residue(s) and posttranslational chemical modification of proteins. Int. J. Biochem. 1992, 24:1349-1363.
  4. Han KK, Martinage A: Post-translational chemical modification(s) of proteins. Int. J. Biochem. 1992, 24:19-28.
  5. Han KK, Martinage A: Post-translational chemical modifications of proteins--III. Current developments in analytical procedures of identification and quantitation of post-translational chemically modified amino acid(s) and its derivatives. Int J Biochem 1993, 25:957-970.
  6. Mann M, Jensen ON: Proteomic analysis of post-translational modifications. Nature Biotechnology 2003, 21:255-261.
  7. Petricoin E, Liotta L: The vision for a new diagnostic paradigm. Clin. Chem. 2003, 49:1276-1278.
  8. Petricoin E, Liotta L: Clinical applications of proteomics. J. Nutr. 2003, 133:2476S-2484S.
  9. Liotta LA, Ferrari M, Petricoin EF: Clinical proteomics: Written in blood. Nature 2003, 425:905.
  10. Lathrop JT, Anderson NL, Anderson NG, Hammond DJ: Therapeutic potential of the plasma proteome. Curr. Opin. Mol. Ther. 2003, 5:250-257.
  11. Lathrop J, Hayes T, Carrick K, Hammond D: Rarity gives a charm: evaluation of trace proteins in plasma and serum. Expert Rev. Proteomics 2005, 2:393-406.
  12. Cristea M, Gaskell SJ, Whetton AD: Proteomics techniques and their application to hematolo gy. Blood 2004, 103:3624-3634.
  13. Chan KC, Lucas DA, Hise D, C.F. S, Xiao Z, Janini GM, Buetow KH, Issaq HJ, Veenstra TD, Conrads TP: Analysis of the Human Serum Proteome Clin. Proteomics 2004, 1:101-226.
  14. Rose K, Bougueleret L, Baussant T, Bohm G, Botti P, Colinge J, Cusin I, Gaertner H, Gleizes A, Heller M, et al.: Industrial-scale proteomics: from liters of plasma to chemically synthesized proteins. Proteomics 2004, 4:2125-2150.
  15. Anderson N, Polanski M, Pieper R, Gatlin T, Tirumalai R, Conrads T, Veenstra T, Adkins J, Pounds J, Fagan R, et al.: The human plasma proteome: A non-redundant list developed by combination of four separate sources. Mol. Cell. Proteomics 2004, 3:311-316.
  16. Schaub S, Rush D, Wilkins J, Gibson IW, Weiler T, Sangster K, Nicolle L, Karpinski M, Jeffery J, Nickerson P: Proteomic-based detection of urine proteins associated with acute renal allograft rejection. J. Am. Soc. Nephrol. 2004, 15:219-227.
  17. Vlahou A, Schellhammer PF, Mendrinos S, Patel K, Kondylis FI, Gong L, Nasim S, Wright Jr GL: Development of a novel proteomic approach for the detection of transitional cell carcinoma of the bladder in urine. Am. J. Pathol. 2001, 158:1491-1502.
  18. Characterization of the microheterogeneity of transthyretin in plasma and urine using SELDI-TOF-MS immunoassay on World Wide Web URL:
  19. Chen YC, Li TY, Tsai MF: Analysis of the saliva from patients with oral cancer by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Rapid Commum. Mass Spectrom. 2002, 16:364-369.
  20. Xie H, Rhodus NL, Griffin RJ, Carlis JV, Griffin TJ: A catalogue of human saliva proteins identified by free flow electrophoresis-based peptide separation and tandem mass spectrometry. Mol. Cell. Proteomics 2005, 4:1826-1830.
  21. Hirtz C, Chevalier F, Centeno D, Rofidal V, Egea JC, Rossignol M, Sommerer N, de Periere DD: MS Characterization of multiple forms of alpha-amylase in human saliva. Proteomics 2005, 5:4597-4607.
  22. Celis JE, Gromov P, Cabezon T, Moreira JMA, Ambarrtsumina N, Sandelin K, Rank F, Gromova I: Proteomic characterization of the interstitial fluid perfusing the breast tumor microenvironment. Mol. Cell. Proteomics 2004, 3:327-344.
  23. Nilsson S, Ramstrm M, Palmblad M, Axelsson O, Bergquist J: Explorative study of the protein composition of amniotic fluid by liquid chromatography Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. J. Proteome Res. 2004, 3:884-889.
  24. Gravett MG, Novy MJ, Rosenfeld RG, Reddy AP, Jacob T, Turner M, McCormack AL, Lapidus JA, Hitti J, Eschenbach DA, et al.: Diagnosis of intra-amniotic infection by proteomic profiling and identification of novel biomarkers. JAMA 2004, 292:462-269.
  25. Wang TH, Chang YL, Peng HH, Wang ST, Lu HW, Teng SH, Chang SD, Wang HS: Rapid detection of fetal aneuploidy using proteomics approaches on amniotic fluid supernatant. Prenatal Diagnosis 2005, 25:559-566.
  26. Tsangaris G, Weitzdorfer R, Pollak D, Lubec G, Fountoulakis M: The amniotic fluid cell proteome. Electrophoresis 2005, 26:11681173.
  27. Anahory T, Dechaud H, Bennes R, Marin P, Lamb NJ, Laoudj D: Identification of new proteins in follicular fluid of mature human follicles. Electrophoresis 2002, 23:1197-1202.
  28. Garcia BA, Smalley DM, Cho HJ, Shabanowitz J, Ley K, Hunt DF: The platelet microparticle proteome. J. Proteome Res. 2005, 4:1516-1521.
  29. Cagney G, Park S, Chung C, Tong B, ODuslaine C, Shields DC, Emili A: Human tissue profiling with multidimensional protein identification technology. J. Proteome Res. 2005, 4:1757-1767.
  30. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh E, et al.: Initial sequencing and analysis of the human genome. Nature 2001, 409:860-921.
  31. Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, Smith HO, Yandell M, Evans CA, Holt RA, et al.: The sequence of the human genome. Science 2001, 291:1304-1351.
  32. International Human Genome Sequencing Consortium: Finishing the euchromatic sequence of the human genome. Nature 2004, 431:931-945.
  33. Anderson L, Seilhamer J: A comparison of selected mRNA and protein abundances in human liver. Electrophoresis 1997, 18:533-537.
  34. Anderson NL, Anderson NG: Proteome and proteomics: new technologies, new concepts, and new words. Electrophoresis 1998, 19:185 3-1861.
  35. Futcher B, Latter GI, Monardo P, McLaughlin CS, Garrels JI: A sampling of the yeast proteome. Mol. Cell. Biol. 1999, 19:7357-7368.
  36. Gygi S, Rochon Y, Franza B, Aebersold R: Correlation between protein and mRNA abundance in yeast. Mol. Cell. Biol. 1999, 19:1720-1730.
  37. Strohman R: Epigenesis: The missing beat in biotechnology? Bio/Technology 1994, 12:156-164.
  38. Misek DE, Kuick R, Wang HX, Galchev V, Deng B, Zhao R, Tra J, Pisano MR, Amunugama R, Allen DL, et al.: A wide range of protein isoforms in serum and plasma uncovered by a quantitative intact protein analysis system. Proteomics 2005, 5:3343-3352.
  39. Hoogland C, Sanchez JC, Walther D, Baujard V, Baujard O, Tonella L, Hochstrasser DF, Appel RD: Two dimensional electrophoresis resources available from ExPASy. Electrophoresis 1999, 20:35683571.
  40. Petricoin EF, Paweletz CP, Liotta LA: Clinical applications of proteomics: Proteomic pattern diagnostics. J. Mammary Gland Biol. Neoplasia 2002, 7:433-440.
  41. Petricoin EF, Ardekani AM, Hitt BE, Levine PJ, Fusaro VA, Steinberg SM, Mills GB, Simone C, Fishman DA, Kohn EC, et al.: Use of proteomic patterns in serum to identify ovarian cancer. Lancet 2002, 359:572-577.
  42. Adam BL, Qu Y, Davis JW, Ward MD, Clements MA, Cazares LH, Semmes OJ, Shellhammer PF, Yasui Y, Feng Z, et al.: Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res. 2 blood plasma with the specific physiological or pathological states. A recent extensive compilation of human plasma proteins indicated that most of the major categories of proteins in the human body were represented in the blood plasma [15]. Thus, the plasma proteome is an ideal source of diagnostic markers and therapeutic targets for many human diseases [10,11,15]. A protein, or most likely a set of proteins, that undergo changes in concentration or structural composition (e.g. PTM) as a result of disease or physiological state can potentially be used as diagnostic biomarkers. A biomarker is an identified protein or group of proteins, which change in concentration or structural composition due to a particular disease state.

    When blood is coagulated and centrifuged, a translucent liquid called serum separates as a top layer. The coagulated portion is presumed to be mostly fibrin and other proteins involved in the coagulation process. The serum still contains a very high concentration of proteins. While both plasma and serum have been extensively used for diagnostic purposes, there is an increasing trend towards the use of blood plasma for proteomic profiling to ensure that important proteins are not trapped and lost into the coagulated portion.

    As alternatives to blood plasma and serum, proteomic analyses of other biological fluids such as cerebral spinal fluid (CSF), urine [16-18], saliva [19-21], interstitial fluid [22], amniotic fluid [23-26], follicular fluid [27], and platelet-derived microparticles in blood [28] are also now being investigated for diagnostic biomarker discovery. In addition, proteomic profiles of human tissues like the brain, heart, liver, lung, muscle, pancreas, spleen, 002, 62:3609-3614.

  43. Adam BL, Vlahou A, Semmes OJ, Wright Jr GL: Proteomic approaches to biomarker discovery in prostate and bladder cancers. Proteomics 2001, 1:1264-1270.
  44. Li J, Zhang ZS, Rosenzweig J, Wang YY, Chan DW: Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer. Clin. Chem. 2002, 48:1296- 1304.
  45. Vejda S, Posovszky C, Zelzer S, Peter B, Bayer E, Gelbmann D, Schulte-Hermann R, Gerner C: Plasma from cancer patients featuring a characteristic protein composition mediates protection against apoptosis. Mol. Cell. Proteomics 2002, 1:387-393.
  46. Johnson PJ: A framework for the molecular classification of circulating tumor markers. Ann. NY Acad Sci 2001, 945:8-21.
  47. Qin LX, Tang ZY: The prognostic molecular markers in hepatocellular carcinoma. World J. Gastroenterol 2002, 8:385-392.
  48. Feng JT, Liu YK, Song HY, Dai Z, Qin LX, Almofti R, Fang CY, Lu HJ, Yang PY, Tang ZY: Heat-shock protein 27: A potential biomarker for hepatocellular carcinoma identified by serum proteome analysis. Proteomics 2005, 5:4581-4588.
  49. Kozak K, Su F, Whitelegge J, Faull K, Reddy S, Farias-Eisner R: Characterization of serum biomarkers for detection of early stage ovarian cancer. Proteomics 2005, 5:4589-4596.
  50. Kozak KR, Amneus MW, Pusey SM, Su F, Luong MN, Luong SA, Reddy ST, Farias-Eisner R: Identification of biomarkers for ovarian cancer using strong anion-exchange ProteinChips: Potential use in diagnosis and prognosis. Proc. Natl Acad. Sci. USA 2003, 100:12343-12348.
  51. Ahmed N, Oliva KT, Barker G, Hoffman P, Reeve S, Smith IA, Quinn MA, Rice GE: Proteomic tracking of serum protein isoforms as screening biomarkers of ovarian cancer. Proteomics 2005, 5:4625-4636.
  52. Ahmed N, Barker G, Oliva KT, Hoffmann P, Riley C, Reeve S, Smith A, Kemp BE, Quinn MA, Rice GE: Proteomic-based identification of haptoglobin-1 precursor as a novel circulating biomarker of ovarian cancer. Brit. J. Cancer 2004, 91:129-140.
  53. Imam-Sghiouar N, Laude-Lemaire I, Labas V, Pflieger D, Le Caer JP, Caron M, Nabias DK, Joubert-Caron R: Subproteomics analysis of phosphorylated proteins: Application to the study of B-lymphoblasts from a patient with Scott syndrome. Proteomics 2002, 2:828-838.
  54. Sinz A, Bantscheff M, Mikkat S, Ringel B, Drynda S, Kekow J, Thiesen HJ, Glocker MO: Mass spectrometric proteome analyses of synovial fluids and plasmas from patients suffering from rheumatoid arthritis and comparison to reactive arthritis or osteoarthritis. Electrophoresis 2002, 23:3445-3456.
  55. Berhane BT, Zong C, Liem DA, Huang A, Le S, Edmondson RD, Jones RC, Qiao X, Whitelegg JP, Ping P, et al.: Cardiovascular-related proteins identified in human plasma by the HUPO Plasma Proteome Project Pilot Phase. Proteomics 2005, 5:3520-3530.
  56. Yu KH, Rustgi AK, Blair IA: Characterization of proteins in human pancreatic cancer serum using differential gel electrophoresis and tandem mass spectrometry. J. Proteome Res. 2005, 4:1742-1751.
  57. Petricoin EF, Ornstein DK, Paweletz CP, Ardekani A, Kackett PS, Hitt BA, Velassco A, Trucco C, Wigand L, Wood K, et al.: Serum proteomic patterns for detection of prostate cancer. J. Natl Cancer Inst. 2002, 94:1576-1578.
  58. Jutter G, Sinha P: Proteomics for studying cancer cells and the development of chemoresistance. Proteomics 2001, 1:12331248.
  59. Wu W, Hu W, Kavanagh JJ: Proteomics in cancer research. Int. J. Gynecol. Cancer 2002, 12:409-423.
  60. Zhang Z, Bast Jr RC, Yu C, Li J, Sokoll LJ, Rai AJ, Rosenzweig JM, Cameron B, Wang YY, Meng XY, et al.: Three Biomarkers Identified from Serum Proteomic Analysis for the Detection of Early Stage Ovarian Cancer. Cancer Res. 2004, 64:5882-5890.
  61. Zhu W, Wang X, Ma Y, Rao M, Glimm J, Kovach JS: Detection of cancer-specific markers amid massive mass spectral data. Proc. Natl Acad. Sci. USA 2003, 100:14666-14671.
  62. Qu Y, Adam BL, Yasui Y, Ward MD, Cazares LH, Schellhammer PF, Feng Z, Semmes OJ, Wright Jr GL: Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates postate cancer from noncancer patients. Clin. Chem. 2002, 48:1835-1843.
  63. Banez LL, Prasanna P, Sun L, Ali A, Zou Z, Adam BL, McLeod DG, Moul JW, Srivastava S: Diagnostic potential of serum proteomic patterns in prostate cancer. J. Urol. 2003, 170:442-446.
  64. Rai A, Zhang Z, Rosenzweig J, Shih I, Pham T, Fung E, Sokoll L, Chan D: Proteomic approaches to tumor marker discovery. Arch. Pathol. Lab. Med. 2002, 126:1518-1526.
  65. Rosenblatt KP, Bryant-Greenwood P, Killian JK, Mehta AI, Geho D, Espina V, Petricoin III EF, Liotta LA: Serum proteomics in cancer diagnosis and management. Annu. Rev. Med. 2004, 55:97-112.
  66. Veenstra TD, Conrads TP, Hood BL, Avellino AM, Ellenbogen RG, Morrison RS: Biomarkers: Mining the biofluid proteome. Mol. Cell. Proteomics 2005, 4:409-418.
  67. Lundblad RL: Considerations for the use of blood plasma and serum for proteomic analysis. The Internet Journal of Gastroenterology 2005, 1.
  68. Haab BB, Geierstanger BH, Michailidis G, Vitzhum F, Forrester S, Okon R, Saviranta P, Brinker A, Sorette M, Perlee L, et al.: Immunoassay and antibody microarray analysis of the HUPO Plasma Proteome Project reference specimens: Systematic variation between sample types and calibration of mass spectrometry data. Proteomics 2005, 5:3278-3291.
  69. Tammen H, Schulte I, Hess R, Menzel C, Kellmann M, T. M, Schulz-Knapp P: Peptidomic analysis of human blood specimens: Comparison between plasma specimens and serum by differential peptide display. Proteomics 2005, 5:3414-3422.
  70. Yocum AK, Yu K, Oe T, Blair IA: Effect of immunoaffinity depletion of human serum during proteomic investigations. J. Proteome Res. 2005, 4:1722-1731.
  71. Bjorhall K, Miliotis T, Davidsson P: Comparison of different depletion strategies for improved resolution in proteomic analysis of human serum samples. Proteomics 2005, 5:307-317.
  72. Rai AJ, Stemmer PM, Zhang ZS, Adam BL, Morgan WT, Caffrey RE, Podust VN, Patel M, Lim LY, Shipulina NV, et al.: Analysis of Human Proteome Organization Plasma Proteome Project (HUPO PPP) reference specimens using surface enhanced laser desorption/ionization-time of flight (SELDI-TOF) mass spectrometry: Multi-institution correlation of spectra and identification of biomarkers. Proteomics 2005, 5:34567-33474.
  73. Barnea E, Sorkin R, Ziv T, Beer I, Admon A: Evaluation of pre-fractionation methods as a preparatory step for multidimensional-based chromatography of serum proteins. Proteomics 2005, 5:3367-3375.
  74. Li X, Gong Y, Wang Y, Wu S, Cai Y, He P, Lu Z, Ying W, Zhang Y, Jiao L, et al.: Comparison of alternative analytical techniques for the characterization of the human serum proteome in HUPO Plasma Proteome Project. Proteomics 2005, 5:3423-3441.
  75. Kapp EA, Schutz F, Connolly LM, Chakel JA, Meza JE, Miller CA, Fenyo D, Eng JK, Adkins JN, Omenn GS, et al.: An evaluation, comparison, and accurate benchmarking of several publicly available MS/MS search algorithms: Sensitivity and specifi city analysis. Proteomics 2005, 5:3475-3490.
  76. Hereen RMA: Proteome imaging: A closer look at lifes organization. Proteomics 2005, 5:4316-4326.
  77. Beer I, Barnea E, Admon A: Centralized data analysis of a large interlaboratory proteomics project: A feasibility study. Proteomics 2005, 5:3491-3496.
  78. Martens L, Hermjakob H, Jones P, Adamski M, Taylor C, States D, Gevaert K, Vandekerchove J, Apweiler R: PRIDE: The proteomics identifications database. Proteomics 2005, 5:3537-3545.
  79. Martens L, Nesvizhskii AI, Hermjakob H, Adamski M, Omenn GS, Vandekerchove J, Apweiler R, Gevaert K: Do we want our raw data? Including binary mass spectrometry data in public proteomics data repositories. Proteomics 2005, 5:3501-3505.
  80. Omenn GS, States DJ, Adamski M, Blackwell TW, Rajasree M, Hermjakob H, Apweiler R, Haab BB, Simpson RJ, Eddes JS, et al.: Overview of the HUPO Plasma Proteome Project: Results from the pilot phase with 35 collaborating laboratories and multiple analytical groups generating a core data set of 3020 proteins and a publicly-available database. Proteomics 2005, 5:3226-3245.
  81. Muthusamy B, Hanumanthu G, Suresh S, Rekha B, Srinivas D, Karthick L, Vrushabendra BM, Sharma S, Mishra G, Chatterjee P, et al.: Plasma Proteome Database as a resource for proteomics research. Proteomics 2005, 5:3531-3536.
  82. Deutsch EW, Eng JK, Zhang H, King NL, Nesvizhskii AI, Lin B, Lee HJ, Yi EC, Ossola R, Aebersold R: Human Plasma Peptide Atlas. Proteomics 2005, 5:3497-3500.
  83. Corthals GL, Wasinger VC, Hochstrasser DF, Sanchez JC: The dynamic range of protein expression: A challenge for proteomic research. Electrophoresis 2000, 21:1104-1115.
  84. Gygi AP, Rist B, Aebersold R: Measuring gene expression by quantitative proteome analysis. Curr. Opin. Biotechnol 2000, 11:396-401.
  85. Anderson NL, Anderson NG: The human plasma proteome: History, character, and diagnostic prospects. Mol. Cell. Proteomics 2002, 1:845-867.
  86. Thadikkaran L, Siegenthaler MA, Crettaz D, Queloz PA, Schneider P, Tissot JD: Recent advances in blood-related proteomics. Proteomics 2005, 5:3019-3034.
  87. Merrell K, Southwick K, Graves SW, Esplin MS, Lewis NE, Thulin CD: Analysis of low-abundance, low-molecular-weight serum proteins using mass spectrometry. J. Biomol. Tech. 2004, 15:238-248.
  88. Adkins J, Varnum S, Auberry K, Moore R, Angell N, Smith R, Springer D, Pounds J: Toward a human blood serum proteome: Analysis by multidimensional separation coupled with mass spectrometry. Mol. Cell. Proteomics 2002, 1:947-955.
  89. Richter R, Schulz-Knappe P, Schrader M, Standker L, Jurgens M, Tammen H, Forssmann WG: Composition of the peptide fraction in human blood plasma: database of circulating human peptides. J. Chromatogr. B Biomed. Sci. Appl. 1999, 726:25-35.
  90. Aebersold R, Mann M: Mass spectrometry-based proteomics. Nature 2003, 422:198-207.
  91. Wolters DA, Washburn MP, Yates III JR: An automated multidimensional protein identification technology for shotgun proteomics. Anal. Chem. 2001, 73:5683-5690.
  92. Kelleher NL, Lin HY, Valaskovic GA, Aaserud DJ, Fridriksson EK, McLafferty FW: Top down versus bottom up protein characterization by tandem high resolution mass spectrometry. J. Am. Chem. Soc. 1999, 121:806-812.
  93. McDonald WH, Yates III JR: Shotgun proteomics and biomarker discovery. Dis. Markers 2002, 18:99-105.
  94. Wu SL, Choudhary G, Ramstrom M, Bergquist J, Hancock WS: Evaluation of shotgun sequencing for proteomic analysis of human plasma us ing HPLC coupled with either ion trap or Fourier transform mass spectrometry. J. Proteome Res. 2003, 2:283-293.
  95. Link AJ, Eng JK, Schieltz DM, Carmack E, Mize GJ, Morris DR, Garvik BM, Yates III JR: Direct analysis of protein complexes using mass spectrometry. Nat. Biotechnol. 1999, 17:676-682.
  96. Washburn MP, Wolters D, Yates III JR: Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol. 2001, 19:242-247.
  97. Shen Y, Jacobs JM, Camp DG, Fang R, Moore RJ, Smith RD, Xiao W, Davis RW, Tomkins RG: Ultra-high efficiency strong cation-exchange LC/RPLC/MS/MS for high dynamic range characterization of the human plasma proteome. Anal. Chem. 2004, 76:1134-1144.
  98. Swanson SK, Washburn MP: The continuing evolution of shotgun proteomics. Drug Discov. Today 2005, 10:719-725.
  99. He P, He HZ, Dai J, Wang Y, Sheng QH, Zhou LP, Zhang ZS, Sun YL, Liu F, Wang K, et al.: The human plasma proteome: Analysis of Chinese serum using shotgun strategy. Proteomics 2005, 5:3442-3453.
  100. Echan LA, Tang HY, Ali-Khan N, Lee K, Speicher DW: Depletion of multiple high abundance proteins improves protein profiling capability of human serum and plasma. Proteomics 2005, 5:3292-3303.
  101. Huang HL, Stasyk T, Morandell S, Mogg M, Schreiber M, Feuerstein I, Huck CW, Stecher G, Bonn GK, Huber LA: Enrichment of low-abundant serum proteins by albumin/immunoglobulin G immunoaffinity depletion under partly denaturing conditions. Electrophoresis 2005, 26:2843-2849. < br>
  102. Lowenthal MS, Mehta AI, Frogale K, Bandle RW, Araujo RP, Hood BL, Veenstra TD, Conrads TP, Goldsmith P, Fishman D, et al.: Analysis of albumin-associated peptides and proteins from ovarian cancer patients. Clin. Chem. 2005, 51:1933-1945.
  103. Harper RG, Workman SR, Schuetzner S, Timperman AT, Sutton JN: Low-molecular-weight human serum proteome using ultrafiltration, isoelectric focusing, and mass spectrometry. Electrophoresis 2004, 25:1299-1306.
  104. Travis J, Bowen J, Tewksbury D, Johnson D, Pannell R: Isolation of albumin from whole human plasma and fractionation of albumin-depleted plasma. Biochem. J. 1976, 157:301-306.
  105. Travis J, Pannell R: Selective removal of albumin from plasma by affi nity chromatography. Clin. Chim. Acta 1973, 49:49-52.
  106. Zolotarjova N, Martosella J, Nicol G, Bailey J, Boyes BE, Barrett WC: Differences among techniques for high abundant protein depletion. Proteomics 2005, 5:3304-3313.
  107. Tam SW, Pirro J, Hinerfeld D: Depletion and fractionation technologies in plasma proteomic analysis. Expert Rev. Proteomics 2004, 1:411-420.
  108. Zhou M, Lucas DA, Chan KC, Issaq HJ, Petricoin EF, Liotta LA, Veenstra TD, Conrads TP: An investigation into the human serum interactome. Electrophoresis 2004, 25:1289-1298.
  109. Moritz RL, Clippingdale AB, Kapp EA, Eddes JS, Ji H, Gilbert S, Connolly LM, Simpson RJ: Application of 2-D free-flow electrophoresis/RP-HPLC for proteomic analysis of human plasma depleted of multi-high abundance proteins. Proteomics 2005, 5:3402-3413.
  110. Fountoulakis M, Juranville J, Jiang L, Avila D, Roder D, Jakob P, Berndt P, Evers S, Langen H: Depletion of the high-abundance plasma proteins. Amino Acids 2004, 27:249-259.
  111. Granger J, Siddiqui J, Copeland S, Remick D: Albumin depletion of human plasma also removes low abundance proteins including the cytokines. Proteomics 2005, 5:4713-4718.
  112. Moritz R, Ji H, F S, Connolly L, Kapp E, Speed T, Simpson R: A proteome strategy for fractionating proteins and peptides using continuous free-flow electrophoresis coupled to rapid reversed-phase high-performance liquid chromatography. Anal. Chem. 2004, 76:811-824.
  113. Leatherbarrow RJ, Dean PD: Studies on the mechanism of binding of serum albumin to immobilized Cibacron Blue F3GA. Biochem. J. 1980, 189:27-34.
  114. Gianazza E, Arnaud P: A general method for fractionation of plasma proteins. Dye-ligand affinity chromatography on immobilized Cibacron blue F3-GA. Biochem. J. 1982, 201:129-136.
  115. Gianazza E, Arnaud P: Chromatography of plasma proteins on immobilized Cibacron Blue F3-GA. Mechanism of the molecular interaction. Biochem. J. 1982, 203:637-641.
  116. Kubo K, Honda M, Imoto M, Morishima Y: Capillary zone electrophoresis of albumin-depleted human serum using a linear polyacrylamide-coated capillary: Separation of serum - and globulins into individual components. Electrophoresis 2000, 21:396-402.
  117. Ahmed N, Barker G, Oliva K, Garfin D, Talmadge K, Georgiou H, Qui and testis are now being explored [29]. While the usefulness of these alternative biological fluids or tissues has not yet been clearly established, it is very conceivable that their profiles will complement or supplement those obtained from blood plasma or serum proteomics.

    1.2 Why proteomics?
    There are two important biomolecular disciplines used in identifying disease- associated biomarkers: genomics and proteomics. In the genomics approach, genes that are associated with specific diseases or physiological processes are identified and studied. The Human Genome Project (HGP) led to the successful sequencing of the human genome [30,31], which resulted in the identification of about 20,000 25,000 genes in the human body [32]. In various diseased states the expression of specific genes may either be enhanced (turned on) or suppressed (turned off). Thus, the levels of mRNA generated from the relative expression of these genes have been thought to correlate to specific diseased states.

    However, there are still questions about the correlation between the expression levels of mRNAs and the corresponding changes in expression levels of proteins expressed, whether in human tissues [12,33,34] or in yeast cells [35,36]. In addition, one gene may express multiple proteins [35,37], with multiple biological functions. Finally, the proteins expressed from the genes may undergo a variety of post-translational modifications [4,5], as well as isoforms [38], some of which may be important in disease processes. For example, human plasma has been shown to contain 22 different forms of &alpha -1-antitrypsin [39]. In many cases, the processes that regulate post-translational protein modifications are ind nn M, Rice G: An approach to remove albumin for the proteomic analysis of low abundance biomarkers in human serum. Proteomics 2003, 3:1980-1987.

  118. Shaw MM, Riedere BM: Sample preparation for two-dimensional gel electrophoresis. Proteomics 2003, 3:1408-1417.
  119. Hammack BN, Owens GP, Burgoon MP, Gilden DH: Improved resolution of human cerebrospinal fluid proteins on two-dimensional gels. Multiple Sclerosis 2003, 9:472-475.
  120. Li C, Lee KH: Affinity depletion of albumin from human cerebrospinal fluid using Cibacron-blue-3GA-derivatized photopatterned copolymer in a microfluidic device. Anal. Biochem. 2004, 333:381-388.
  121. Bjorck L, Kronvall D: Analysis of bacterial cell wall proteins and human serum proteins bound to bacterial cell surfaces. Acta. Pathol. Microbiol. Scand. B 1981, 89:1-6.
  122. Guss B, Eliasson M, Olsson A, Uhlen M, Jornvall H, Flock JI, Lindberg M: Structure of the IgG-binding regions of streptococcal protein G. EMBO J 1986, 5:1567-1575.
  123. Greenough C, Jenkins RE, Kiterringham NR, Pirmohamed M, Park BK, Pennington SR: A method for the rapid depletion of albumin and immunoglobulin from human Plasma. Proteomics 2004, 4:3107-3111.
  124. Wang YY, Cheng P, Chan DW: A simple affinity spin tube filter method for removing high-abundant common proteins or enriching low-abundant biomarkers for serum proteomic analysis. Proteomics 2003, 3:243-248.
  125. Govorukhina NI, Keizer-Gunnink A, van der Zee AGJ, de Jong S, de Bruijn HWA, Bischoff R: Sample preparation of human serum for the analysis of tumor markers. Comparison of different approaches for albumin and gamma-globulin depletion. J. Chromatogr A 2003, 1009:171-178.
  126. 126. Pieper R, Su Q, Gatlin CL, Huang ST, Anderson NL, Steiner S: Multi-component immunoaffinity subtraction chromatography: an innovative step towards a comprehensive survey of the human plasma proteome. Proteomics 2003, 3:422-432.
  127. Zolg JW, Langen H: How industry is approaching the search for new diagnostic markers and biomarkers. Mol. Cell. Proteomics 2004, 3:345-354.
  128. Pisano MR: Identification of Protein Following Removal of 20 High Abundance Proteins (ProteoPrep). In HUPO - Sigma Luncheon Seminar. Edited by. Munich, Germany.; 2005.
  129. Tang HY, Ali-Khan N, Echan LA, Levenkova N, Rux JJ, Speicher DW: A novel four-dimensional strategy combining protein and peptide separation methods enables detection of low-abundance proteins in human plasma and serum proteome. Proteomics 2005, 5:3329-3342.
  130. Yang Z, Hancock WS, Richmond-Chew T, Bonilla L: A Study of Glycoproteins in Human Serum and Plasma Reference Standards (HUPO) using Multi-Lectin Affi nity Chromatography Coupled with RPLC-MS/MS. Proteomics 2005:3353-3366.
  131. Cho SY, Lee EY, Chun YW, Lee JS, Kim HY, Park JM, Kwon MS, Park YK, Lee HJ, Kang MJ, et al.: Efficient prefractionation of low abundance proteins in human plasma and construction of a two-dimensional map. Proteomics 2005, 5:3386-3396.
  132. Martosella J, Solotarjova N, Liu H, Nicol G, Boyes BE: Reversed-phase high performance liquid chromatographic prefractionation of immunodepleted human serum proteins to enhance mass spectrometry identification of lower abundant proteins. J. Proteome Res 2005, 4:1522-1537.
  133. Brzeski H, Katenhusen RA, Sullivan AG, Russell S, George A, Somiari RI, Shriver C: Albumin depletion method of improved plasma glycoprotein analysis by two-dimensional difference gel electrophoresis. BioTechniques 2003, 35:1128-1132.
  134. Tirumalai RS, Chan KC, Prieto DA, Isaaq HJ, Conrads T, Veenstra TD: Characterization of the low molecular weight human serum proteome. Mol Cell Proteomics 2003, 2:1096-1103.
  135. Schuchard MD, Melm CD, Crawford AS, Chapman HA, Cockrill SL, Ray KB, Mehigh RJ, Kappel WK, Scott GB: Immunoaffinity depletion of 20 high abundance human plasma proteins. Removal of approximately 97% of total plasma protein improves identification of low abundance proteins. Origins 2005, 21:17-23.

back to top ependent of gene function. Thus, despite the abundance of scientific data, diagnostic approaches based on genomic studies are still limited and are not always practical for clinical use.

The obvious alternative is the proteomics approach since, as the final form of the gene product, proteins are most directly related with biological function. The proteome is also more responsive to physiological and diseased states, as well as external stimuli. The dynamic nature of the proteome, as opposed to the static nature of the genome, makes the proteome a real time indicator of physiological processes. The proteomes of normal and diseased states are quantitatively compared, and biomarker proteins are then identified based on their relative abundance or structural form (i.e. PTM state) [7,8,10,11,40-56]. Once identified, these biomarker proteins are utilized for developing diagnostic tools, and the processes that regulate their expression, processing and functions can be used as therapeutic targets for drug candidates. Proteomic analyses have been used to investigate potential biomarkers for such diseases as cancer [7,8,22,40-52,56-65], hemophilia [53], osteoarthritis [54], and cardiovascular diseases [55].

The major goal of plasma and serum proteomics is to obtain the most reliable information possible for diagnostic and therapeutic purposes. This requires the establishment of accurate and comprehensive baseline data of the serum proteome, including as many of the low abundance proteins as possible, against which subsequent data from a variety of serum samples can be compared. A baseline profile includes both the identification and quantitation of different proteins. Such a baseline would permit better detection o f significant changes in biomarker levels as a result of specific physiological conditions or disease, as well as indicate whether the condition warrants further investigation. Highly sensitive and accurate biomarkers are very important in detecting the early onset of diseases, since these biomarker proteins are usually present at very low concentrations.

Although simple in principle, obtaining reliable baseline information is extremely difficult in practice [2,66]. Major issues include variability in sample collection and handling [66-69], a lack of standardized protocols and instrumentation [64,70-74], and differences in handling, processing and interpreting the data [68,75-79]. The recognition of the enormity of the problem and potential benefits of success has brought international cooperation and coordination within the research community, [e.g. Human Proteome Organisation (HUPO)]. HUPO was organized in an attempt to provide a comprehensive analysis of the proteins of human plasma and serum, annotate the entire human proteome, and make the data publicly accessible. An initial set of data generated from the Plasma Proteome Project (PPP) of HUPO identified 9504 proteins with one or more peptides, and 3020 proteins with two or more peptides and were taken to represent their Core Dataset [80]. A similar database has annotated gene products encoded by 3778 distinct genes [81]. Current data from HUPO and elsewhere have successfully mapped 6342 peptides to EnsEMBL 29.35b genome build [82]. As more sensitive procedures are developed, the number of proteins identified will likely increase. However, the present results indicate that the number of proteins identified is still below the predicted number of protei ns present in the plasma or serum.

Proteomics is certainly a promising approach to revolutionize clinical diagnostics, improve prognosis, and lead to potentially life-saving medical treatments. However, it is very likely that genomics and proteomics will complement each other in establishing the most comprehensive approach to biomarker discovery and identification of therapeutic targets that will ultimately find clinical applications in the bedside.

1.3 The analytical challenge: Detecting the low abundance proteins
The presence of a large number of proteins in blood plasma makes human plasma an excellent material for discovering biomarkers for potential clinical diagnostics and therapeutics. However, it also represents a tremendous analytical challenge because the estimated dynamic range of protein concentrations in human serum may be up to 12 orders of magnitude [83-86]. Albumin, the most abundant protein, constitutes over half of the plasma proteins and is present at 30-50 mg/ml concentration. In contrast, most of the potential biomarkers are secreted into the blood stream at very low copy number [11,26,86-89], especially in the early onset of diseases [7,8,40,85,88]. For example, the cytokines and the prostate specific antigen (PSA) are present in the low pg/ml levels. Based on this wide dynamic range, quantitation of all proteins simultaneously in a single assay is enormously difficult. The more abundant proteins will certainly mask the detection of the very low abundance proteins.

The analytical challenge is further increased when we consider that the very low concentrations of potential biomarker proteins in raw samples are beyond the detection limit of most analytical instrume nts [90]. For example, while mass spectrometry (MS) represents the most sophisticated and sensitive analytical tool currently available, the current dynamic range of detection is only about 103 when analyzed in a single spectrum. Even when MS is combined with an on-line separation such as HPLC, enhancement of the dynamic range will only be in the 104 to 106 ranges.

Innovations in both sample preparation and protein analysis are therefore necessary to push the analytical capabilities towards the required 1012 dynamic range. In sample preparation, depletion of the abundant, mostly high molecular weight proteins is a necessity to enable loading of a much higher amount of the low copy and/or low molecular weight proteins for analysis. This strategy has been shown to effect a general enhancement of the intensity of the low abundance proteins, as discussed in greater detail in Section 2.

Innovations in protein analysis consist of a large group of multidimensional separation technologies that are applied orthogonally to fractionate the proteins and peptides prior to mass spectrometric analysis. These multidimensional technologies for protein and peptide separation vary in principle and instrumentation, and include such techniques as electrophoresis (1D-PAGE, 2D-PAGE, capillary, free-flow, etc.), chromatography (reversed-phase, ion exchange, size exclusion, affinity, etc), ultrafiltration, solvent precipitation, and other less common fractionation techniques. Traditionally, each orthogonal separation technique is a separate process step. However, a significant innovation was developed and termed Multi-Dimensional Protein Identification Technology (MuDPIT), wh ere two separation techniques are achieved in a single column packed with two different separation matrices [91]. Typically MudPIT uses a strong cation exchange and a reversed phase resin in single column that can be interfaced directly with the mass spectrometer. This technology allows a higher level of automation in sample handling, analysis and data processing.

Different combinations of these multidimensional separation technologies are used in both top down and bottom up proteomic analysis. In the top down approach [92] a mixture of proteins in a sample are separated into individual spots or fractions using different separation techniques, and the individual proteins are then analyzed by mass spectrometry to establish their identity. This is accomplished by determining the mass of the whole protein ion and then fragmenting the ionized protein to yield relatively large segments whose masses can then be deconvoluted and compared against known proteins in protein databases. On the other hand, the bottom up approach can be performed by using either of two strategies: In one strategy, samples containing a mixture of different proteins are subjected to multidimensional separation techniques and the individual protein spots or fractions are digested with trypsin to yield peptide fragments. With or without another separation step, the tryptic peptides are analyzed by mass spectrometry to establish their identify, either based on their peptide mass fingerprints or by further mass fragmentation to obtain sequence information. Recently, most bottom up proteomics employ the shotgun strategy [91,93- 99] where, without prior separation, entire samples containing a mixture of a large number of different proteins, such as plasma or serum, are proteolytically digested into peptides. The peptides in the tryptic digest are then separated by multidimensional separation techniques and then analyzed by mass spectrometry to establish the identities of the proteins present in the sample. In other words, the top down approach utilizes the mass spectral information from the whole protein for identification, while in the bottom up approach the mass spectral data of the peptides are used to identify their source proteins. In both top down and bottom up proteomics, the combination of protein depletion and multidimensional separation technologies offer significant enhancement in sensitivity for low abundance proteins by removing the masking effect of the highly abundant proteins, thereby enabling deeper penetration into the plasma proteomes.

Since protein depletion is becoming a common choice as the first dimension in orthogonal protein separation strategies, this subject will be emphasized in this review. Depletion of plasma proteins can be accomplished using different strategies, but the final goal is to separate the high abundance, non-diagnostic proteins from the low abundance proteins.

In the past, the fractions containing the most abundant proteins were presumed to be diagnostically unimportant and usually not analyzed. However, recent proteomic analyses indicate that other proteins may be concomitantly removed during depletion due to non-specific binding to the depleted proteins [26,70,73,74,100-111]. For example, comparative experiments between non-depleted serum and serum depleted of the six most abundant proteins have shown that while depletion significantly increas


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