Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Technology Insight: the application of proteomics in gastrointestinal disease

Nature Clinical Practice Gastroenterology & Hepatology volume 4, pages 372–385 (2007)Cite this article

Abstract

Analysis of the human genome has increased our knowledge of the genes that are associated with disease. At the same time, however, it has become clear that having complete DNA sequences alone is not sufficient to elucidate the biological functions of the proteins that they encode. For this reason, proteomics—the analysis of proteins—has become increasingly attractive, because the proteome reflects both the intrinsic genetic programming of a cell and the impact of its immediate environment. The principal goals of clinical proteomics are to identify biomarkers for the early diagnosis of disease and potential targets for therapeutic intervention. Other goals include the identification of biomarkers for the early detection of disease recurrence (relapse) and how they might be combined with diagnostic imaging techniques to improve the sensitivity for detecting disease. This Review describes conventional proteomic technologies, their strengths and limitations, and demonstrates their application to clinical practice, with specific reference to their use in the gastroenterology field.

Key Points

  • Proteomics has become increasingly attractive, because the proteome reflects the intrinsic genetic program of the cell and the impact of its immediate environment

  • The principal goals of clinical proteomics are to identify biomarkers for the early diagnosis of disease and potential targets for therapeutic intervention; other goals include the identification of biomarkers for the early detection of disease recurrence and their combination with diagnostic imaging techniques to improve the sensitivity for detecting disease

  • The development of automated, high-throughput proteomic technologies has enabled large numbers of clinical samples to be analyzed simultaneously, which is a prerequisite for the application of proteomics to clinical practice

  • Conventional proteomics techniques include protein separation (e.g. two-dimensional gel electrophoresis, differential in-gel electrophoresis), protein identification (mass spectrometry) and protein functional analysis (e.g. X-ray crystallography, protein arrays)

  • Limitations concerning reliability, reproducibility and minimization of pre-analysis variables need to be addressed before large-scale proteomics can be applied in routine clinical practice

  • Large-scale prospective studies are needed to validate and prove the utility of the biomarkers identified using proteomic technology

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Protein synthesis
Figure 2: Two-dimensional gel electrophoresis of whole cell lysates from normal squamous esophageal mucosa and from Barrett's columnar metaplasia
Figure 3: Matrix-assisted laser desorption ionization mass spectrometry
Figure 4: Surface-enhanced laser desorption/ionization mass spectrometry spectra
Figure 5: Surface-enhanced laser desorption/ionization serum-based proteomic profiling

Similar content being viewed by others

References

  1. Lander ES et al. (2001) Initial sequencing and analysis of the human genome. Nature 409: 860–921

    Article  CAS  PubMed  Google Scholar 

  2. Venter JC et al. (2001) The sequence of the human genome. Science 291: 1304–1351

    Article  CAS  PubMed  Google Scholar 

  3. Wilkins MR et al. (1996) Current challenges and future applications for protein maps and post-translational vector maps in proteome projects. Electrophoresis 17: 830–838

    Article  CAS  PubMed  Google Scholar 

  4. Graves PR and Haystead TA (2002) Molecular biologist's guide to proteomics. Microbiol Mol Biol Rev 66: 39–63

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Anderson NG et al. (2001). Back to the future: the human protein index (HPI) and the agenda for post-proteomic biology. Proteomics 1: 3–12

    Article  CAS  PubMed  Google Scholar 

  6. Huber LA (2003) Is proteomics heading in the wrong direction? Nat Rev Mol Cell Biol 4: 74–80

    Article  CAS  PubMed  Google Scholar 

  7. Pandey A and Mann M (2000) Proteomics to study genes and genomes. Nature 405: 837–846

    Article  CAS  PubMed  Google Scholar 

  8. Anderson L and Seilhamer J (1997) A comparison of selected mRNA and protein abundances in human liver. Electrophoresis 18: 533–537

    Article  CAS  PubMed  Google Scholar 

  9. Eisenberg D et al. (2000) Protein function in the post-genomic era. Nature 405: 823–826

    Article  CAS  PubMed  Google Scholar 

  10. National Institutes of Health and Food and Drug Administration Clinical Proteomics Program Databank. http://home.ccr.cancer.gov/ncifdaproteomics/ppatterns.asp

  11. Lazaridis KN and Juran BD (2005) American Gastroenterological Association future trends committee report: the application of genomic and proteomic technologies to digestive disease diagnosis and treatment and their likely impact on gastroenterology clinical practice. Gastroenterology 129: 1720–1752

    Article  CAS  PubMed  Google Scholar 

  12. O'Farrell PH (1975) High resolution two-dimensional electrophoresis of proteins. J Biol Chem 250: 4007–4021

    CAS  PubMed  Google Scholar 

  13. Plebani M (2005) Proteomics: the next revolution in laboratory medicine? Clin Chim Acta 357: 113–122

    Article  CAS  PubMed  Google Scholar 

  14. Rabilloud T (2002) Two-dimensional gel electrophoresis in proteomics: old, old fashioned, but it still climbs up the mountains. Proteomics 2: 3–10

    Article  CAS  PubMed  Google Scholar 

  15. Jungblut PR et al. (1999) Proteomics in human disease: cancer, heart and infectious diseases. Electrophoresis 20: 2100–2110

    Article  CAS  PubMed  Google Scholar 

  16. Cross SS et al. (2005) Expression of S100 proteins in normal human tissues and common cancers using tissue microarrays: S100A6, S100A8, S100A9 and S100A11 are all overexpressed in common cancers. Histopathology 46: 256–269

    Article  CAS  PubMed  Google Scholar 

  17. Costa F (2003) Role of faecal calprotectin as non-invasive marker of intestinal inflammation. Dig Liver Dis 35: 642–647

    Article  CAS  PubMed  Google Scholar 

  18. Pardi DS and Sandborn WJ (2005) Predicting relapse in patients with inflammatory bowel disease: what is the role of biomarkers? Gut 54: 321–322

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Weichart D et al. (2006) Analysis of NOD2-mediated proteome response to muramyl dipeptide in HEK293 cells. J Biol Chem 281: 2380–2389

    Article  CAS  PubMed  Google Scholar 

  20. Marouga R et al. (2005) The development of the DIGE system: 2D fluorescence difference gel analysis technology. Anal Bioanal Chem 382: 669–678

    Article  CAS  PubMed  Google Scholar 

  21. Zhou G et al. (2002) 2D differential in-gel electrophoresis for the identification of esophageal scans cell cancer-specific protein markers. Mol Cell Proteomics 1: 117–124

    Article  CAS  PubMed  Google Scholar 

  22. Chait BT (2006) Chemistry. Mass spectrometry: bottom-up or top-down? Science 314: 65–66

    Article  CAS  PubMed  Google Scholar 

  23. Aebersold R and Mann M (2003) Mass spectrometry-based proteomics. Nature 422: 198–207

    Article  CAS  PubMed  Google Scholar 

  24. Aldred S et al. (2004) The use of proteomics for the assessment of clinical samples in research. Clin Biochem 37: 943–952

    Article  CAS  PubMed  Google Scholar 

  25. Meng F et al. (2002) Processing complex mixtures of intact proteins for direct analysis by mass spectrometry. Anal Chem 74: 2923–2929

    Article  CAS  PubMed  Google Scholar 

  26. Srinivas PR et al. (2005) Proteomics for cancer biomarker discovery. Clin Chem 48: 1160–1169

    Google Scholar 

  27. Wysocki VH et al. (2005) Mass spectrometry of peptides and proteins. Methods 35: 211–222

    Article  CAS  PubMed  Google Scholar 

  28. Forbes AJ et al. (2004) Targeted analysis and discovery of posttranslational modifications in proteins from methanogenic archaea by top-down MS. Proc Natl Acad Sci USA 101: 2678–2683

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Barcelo-Batllori S et al. (2002) Proteomic analysis of cytokine induced proteins in human intestinal epithelial cells: implications for inflammatory bowel diseases. Proteomics 2: 551–560

    Article  CAS  PubMed  Google Scholar 

  30. Hsieh SY et al. (2006) Comparative proteomic studies on the pathogenesis of human ulcerative colitis. Proteomics 6: 5322–5331

    Article  CAS  PubMed  Google Scholar 

  31. Gygi SP et al. (1999) Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol 17: 994–999

    Article  CAS  PubMed  Google Scholar 

  32. Li C et al. (2004) Accurate qualitative and quantitative proteomic analysis of clinical hepatocellular carcinoma using laser capture microdissection coupled with isotope-coded affinity tag and two-dimensional liquid chromatography mass spectrometry. Mol Cell Proteomics 3: 399–409

    Article  CAS  PubMed  Google Scholar 

  33. Zhou H et al. (2002) Quantitative proteome analysis by solid-phase isotope tagging and mass spectrometry. Nat Biotechnol 20: 512–515

    Article  CAS  PubMed  Google Scholar 

  34. Molloy MP et al. (2005) Large-scale evaluation of quantitative reproducibility and proteome coverage using acid cleavable isotope coded affinity tag mass spectrometry for proteomic profiling. Proteomics 5: 1204–1208

    Article  CAS  PubMed  Google Scholar 

  35. Chen R et al. (2005) Pancreatic cancer proteome: the proteins that underlie invasion, metastasis, and immunologic escape. Gastroenterology 129: 1187–1197

    Article  CAS  PubMed  Google Scholar 

  36. Hutchens TY et al. (1993) New description strategies for the mass spectrometric analysis of micromolecules. Rapid Communication in Mass Spectrometry 7: 576–580

    Article  CAS  Google Scholar 

  37. Chapman K (2002) The ProteinChip Biomarker System from Ciphergen Biosystems: a novel proteomics platform for rapid biomarker discovery and validation. Biochem Soc Trans 30: 82–87

    Article  CAS  PubMed  Google Scholar 

  38. Petricoin EF et al. (2002) Clinical proteomics: translating benchside promise into bedside reality. Nat Rev Drug Discov 1: 683–695

    Article  CAS  PubMed  Google Scholar 

  39. Issaq HJ et al. (2002) The SELDI-TOF MS approach to proteomics: protein profiling and biomarker identification. Biochem Biophys Res Commun 292: 587–592

    Article  CAS  PubMed  Google Scholar 

  40. Merchant M and Weinberger SR (2000) Recent advancements in surface-enhanced laser desorption/ionization-time of flight-mass spectrometry. Electrophoresis 21: 1164–1177

    Article  CAS  PubMed  Google Scholar 

  41. Petricoin EF et al. (2002) Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359: 572–577

    Article  CAS  PubMed  Google Scholar 

  42. Koopmann J et al. (2004) Serum diagnosis of pancreatic adenocarcinoma using surface-enhanced laser desorption and ionization mass spectrometry. Clin Cancer Res 10: 860–868

    Article  CAS  PubMed  Google Scholar 

  43. Rosty C et al. (2002) Identification of hepatocarcinoma-intestine-pancreas/pancreatitis-associated protein I as a biomarker for pancreatic ductal adenocarcinoma by protein biochip technology. Cancer Res 62: 1868–1875

    CAS  PubMed  Google Scholar 

  44. Liotta LA et al. Clinical proteomics: written in blood. Nature 425: 905

  45. Diamandis EP (2003) Point: Proteomic patterns in biological fluids: do they represent the future of cancer diagnostics? Clin Chem 49: 1272–1275

    Article  CAS  PubMed  Google Scholar 

  46. Ebert MP et al. (2004) Identification of gastric cancer patients by serum protein profiling. J Proteome Res 3: 1261–1266

    Article  CAS  PubMed  Google Scholar 

  47. Melle C et al. (2005) Characterization of pepsinogen C as a potential biomarker for gastric cancer using a histo-proteomic approach. J Proteome Res 4: 1799–1804

    Article  CAS  PubMed  Google Scholar 

  48. Poon TC et al. (2006) Diagnosis of gastric cancer by serum proteomic fingerprinting. Gastroenterology 130: 1858–1864

    Article  CAS  PubMed  Google Scholar 

  49. Bhattacharyya S et al. (2004) Diagnosis of pancreatic cancer using serum proteomic profiling. Neoplasia 6: 674–686

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Scarlett CJ et al. (2006) Proteomic classification of pancreatic adenocarcinoma tissue using protein chip technology. Gastroenterology 130: 1670–1678

    Article  CAS  PubMed  Google Scholar 

  51. Yu Y et al. (2005) Prediction of pancreatic cancer by serum biomarkers using surface-enhanced laser desorption/ionization-based decision tree classification. Oncology 68: 79–86

    Article  CAS  PubMed  Google Scholar 

  52. Poon TC et al. (2005) Prediction of liver fibrosis and cirrhosis in chronic hepatitis B infection by serum proteomic fingerprinting: a pilot study. Clin Chem 51: 328–335

    Article  CAS  PubMed  Google Scholar 

  53. Zhu XD et al. (2004) New serum biomarkers for detection of HBV-induced liver cirrhosis using SELDI protein chip technology. World J Gastroenterol 10: 2327–2329

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Poon TC et al. (2003) Comprehensive proteomic profiling identifies serum proteomic signatures for detection of hepatocellular carcinoma and its subtypes. Clin Chem 49: 752–760

    Article  CAS  PubMed  Google Scholar 

  55. Wang JX et al. (2005) Application of serum protein fingerprinting coupled with artificial neural network model in diagnosis of hepatocellular carcinoma. Chin Med J (Engl) 118: 1278–1284

    CAS  Google Scholar 

  56. Paradis V et al. (2005) Identification of a new marker of hepatocellular carcinoma by serum protein profiling of patients with chronic liver diseases. Hepatology 41: 40–47

    Article  CAS  PubMed  Google Scholar 

  57. Schwegler EE et al. (2005) SELDI-TOF MS profiling of serum for detection of the progression of chronic hepatitis C to hepatocellular carcinoma. Hepatology 41: 634–642

    Article  PubMed  Google Scholar 

  58. Melle C et al. (2005) Discovery and identification of alpha-defensins as low abundant, tumor-derived serum markers in colorectal cancer. Gastroenterology 129: 66–73

    Article  CAS  PubMed  Google Scholar 

  59. Albrethsen J et al. (2005) Upregulated expression of human neutrophil peptides 1, 2 and 3 (HNP 1-3) in colon cancer serum and tumours: a biomarker study. BMC Cancer 5: 8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Chen YD et al. (2004) Artificial neural networks analysis of surface-enhanced laser desorption/ionization mass spectra of serum protein pattern distinguishes colorectal cancer from healthy population. Clin Cancer Res 10: 8380–8385

    Article  CAS  PubMed  Google Scholar 

  61. Yu JK et al. (2004) An integrated approach to the detection of colorectal cancer utilizing proteomics and bioinformatics. World J Gastroenterol 10: 3127–3131

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Landuyt B et al. (2004) Differential protein expression profile in gastrointestinal stromal tumors. Amino Acids 27: 335–337

    Article  CAS  PubMed  Google Scholar 

  63. Chen R et al. (2006) Quantitative proteomic profiling of pancreatic cancer juice. Proteomics 6: 3871–3879

    Article  CAS  PubMed  Google Scholar 

  64. Das S et al. (2005) Differential protein expression profiles of gastric epithelial cells following Helicobacter pylori infection using ProteinChips. J Proteome Res 4: 920–930

    Article  CAS  PubMed  Google Scholar 

  65. Su Y et al. (2006) Diagnosis of gastric cancer using decision tree classification of mass spectral data. Cancer Sci 98: 37–43

    Article  CAS  Google Scholar 

  66. Liang Y et al. (2006) Serum proteomic patterns for gastric lesions as revealed by SELDI mass spectrometry. Exp Mol Pathol 81: 176–180

    Article  CAS  PubMed  Google Scholar 

  67. Song J et al. (2006) Quantification of fragments of human serum inter-alpha-trypsin inhibitor heavy chain 4 by a surface-enhanced laser desorption/ionization-based immunoassay. Clin Chem 52: 1045–1053

    Article  CAS  PubMed  Google Scholar 

  68. Melle C et al. (2004) Proteomic profiling in microdissected hepatocellular carcinoma tissue using ProteinChip technology. Int J Oncol 24: 885–891

    CAS  PubMed  Google Scholar 

  69. El-Aneed A and Banoub J (2006) Proteomics in the diagnosis of hepatocellular carcinoma: focus on high risk hepatitis B and C patients. Anticancer Res 26: 3293–3300

    CAS  PubMed  Google Scholar 

  70. Ward DG et al. (2006) Identification of serum biomarkers for colon cancer by proteomic analysis. Br J Cancer 94: 1898–1905

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  72. Koomen JM et al. (2005). Direct tandem mass spectrometry reveals limitations in protein profiling experiments for plasma biomarker discovery. J Proteome Res 4: 972–981

    Article  CAS  PubMed  Google Scholar 

  73. Roche S et al. (2006) Interest of major serum protein removal for Surface-Enhanced Laser Desorption/Ionization - Time Of Flight (SELDI-TOF) proteomic blood profiling. Proteome Sci 4: 20

    Article  PubMed  PubMed Central  Google Scholar 

  74. Yocum AK et al. (2005) Effect of immunoaffinity depletion of human serum during proteomic investigations. J Proteome Res 4: 1722–1731

    Article  CAS  PubMed  Google Scholar 

  75. Petricoin EF et al. (2006) The blood peptidome: a higher dimension of information content for cancer biomarker discovery. Nat Rev Cancer 6: 961–967

    Article  CAS  PubMed  Google Scholar 

  76. Lowenthal MS et al. (2005) Analysis of albumin-associated peptides and proteins from ovarian cancer patients. Clin Chem 51: 1933–1945

    Article  CAS  PubMed  Google Scholar 

  77. Terracciano R et al. (2006) Selective binding and enrichment for low-molecular weight biomarker molecules in human plasma after exposure to nanoporous silica particles. Proteomics 6: 3243–3250

    Article  CAS  PubMed  Google Scholar 

  78. Sugiyama Y et al. (2005) Analysis of differential gene expression patterns in colon cancer and cancer stroma using microdissected tissues. Gastroenterology 128: 480–486

    Article  CAS  PubMed  Google Scholar 

  79. Shekouh AR et al. (2003) Application of laser capture microdissection combined with two-dimensional electrophoresis for the discovery of differentially regulated proteins in pancreatic ductal adenocarcinoma. Proteomics 3: 1988–2001

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  81. Ransohoff DF (2005) Lessons from controversy: ovarian cancer screening and serum proteomics. J Natl Cancer Inst 97: 315–319

    Article  CAS  PubMed  Google Scholar 

  82. White CN et al. (2005) Quality control for SELDI analysis. Clin Chem Lab Med 43: 125–126

    CAS  PubMed  Google Scholar 

  83. Adam BL et al. (2002) Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res 62: 3609–3614

    CAS  PubMed  Google Scholar 

  84. Qu Y et al. (2002) Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. Clin Chem 48: 1835–1843

    CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  86. Villanueva J et al. (2004) Serum peptide profiling by magnetic particle-assisted, automated sample processing and MALDI-TOF mass spectrometry. Anal Chem 76: 1560–1570

    Article  CAS  PubMed  Google Scholar 

  87. 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: 1637–1649

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  89. Smith FM et al. (2007) Combination of SELDI-TOF-MS and data mining provides early-stage response prediction for rectal tumors undergoing multimodal neoadjuvant therapy. Ann Surg 245: 259–266

    Article  PubMed  PubMed Central  Google Scholar 

  90. Petricoin E, 3rd and Liotta LA (2003) Counterpoint: The vision for a new diagnostic paradigm. Clin Chem 49: 1276–1278

    Article  CAS  PubMed  Google Scholar 

  91. Rogers MA et al. (2003) Proteomic profiling of urinary proteins in renal cancer by surface enhanced laser desorption ionization and neural-network analysis: identification of key issues affecting potential clinical utility. Cancer Res 63: 6971–6983

    CAS  PubMed  Google Scholar 

  92. Munro NP et al. (2006) Urinary biomarker profiling in transitional cell carcinoma. Int J Cancer 119: 2642–2650

    Article  CAS  PubMed  Google Scholar 

  93. Cordingley HC et al. (2003) Multifactorial screening design and analysis of SELDI-TOF ProteinChip array optimization experiments. Biotechniques 34: 364–373

    Article  CAS  PubMed  Google Scholar 

  94. Semmes OJ et al. (2005) Evaluation of serum protein profiling by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry for the detection of prostate cancer: I. Assessment of platform reproducibility. Clin Chem 51: 102–112

    Article  CAS  PubMed  Google Scholar 

  95. Rai AJ et al. (2005) 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 5: 3467–3474

    Article  CAS  PubMed  Google Scholar 

  96. Li J et al. (2005) Independent validation of candidate breast cancer serum biomarkers identified by mass spectrometry. Clin Chem 51: 2229–2235

    Article  CAS  PubMed  Google Scholar 

  97. Mathelin C et al. (2006) Serum biomarkers for detection of breast cancers: a prospective study. Breast Cancer Res Treat 96: 83–90

    Article  CAS  PubMed  Google Scholar 

  98. Hu J et al. (2005) The importance of experimental design in proteomic mass spectrometry experiments: some cautionary tales. Brief Funct Genomic Proteomic 3: 322–331

    Article  CAS  PubMed  Google Scholar 

  99. Zhu W et al. (2003) Detection of cancer-specific markers amid massive mass spectral data. Proc Natl Acad Sci USA 100: 14666–14671

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Sorace JM and Zhan M (2003) A data review and re-assessment of ovarian cancer serum proteomic profiling. BMC Bioinformatics 4: 24

    Article  PubMed  PubMed Central  Google Scholar 

  101. Baggerly KA et al. (2004) Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments. Bioinformatics 20: 777–785

    Article  CAS  PubMed  Google Scholar 

  102. Hong H et al. (2005) Quality control and quality assessment of data from surface-enhanced laser desorption/ionization (SELDI) time-of flight (TOF) mass spectrometry (MS). BMC Bioinformatics 6 (Suppl 2): S5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Grizzle WE and Meleth S (2004) Clarification in the point/counterpoint discussion related to surface-enhanced laser desorption/ionization time-of-flight mass spectrometric identification of patients with adenocarcinomas of the prostate. Clin Chem 50: 1475–1477

    Article  CAS  PubMed  Google Scholar 

  104. Dhingra V et al. (2005) New frontiers in proteomics research: a perspective. Int J Pharm 299: 1–18

    Article  CAS  PubMed  Google Scholar 

  105. MacBeath G (2002) Protein microarrays and proteomics. Nat Genet 32 (Suppl): 526–532

    Article  CAS  PubMed  Google Scholar 

  106. Cutler P (2003) Protein arrays: the current state-of-the-art. Proteomics 3: 3–18

    Article  CAS  PubMed  Google Scholar 

  107. Basset C et al. (2004) Helicobacter pylori infection: anything new should we know? Aliment Pharmacol Ther 20 (Suppl 2): 31–41

    Article  PubMed  Google Scholar 

  108. Ellmark P et al. (2006) Identification of protein expression signatures associated with Helicobacter pylori infection and gastric adenocarcinoma using recombinant antibody microarrays. Mol Cell Proteomics 5: 1638–1646

    Article  CAS  PubMed  Google Scholar 

  109. Paweletz CP et al. (2001) Reverse phase protein microarrays which capture disease progression show activation of pro-survival pathways at the cancer invasion front. Oncogene 20: 1981–1989

    Article  CAS  PubMed  Google Scholar 

  110. Nam MJ et al. (2003) Molecular profiling of the immune response in colon cancer using protein microarrays: occurrence of autoantibodies to ubiquitin C-terminal hydrolase L3. Proteomics 3: 2108–2115

    Article  CAS  PubMed  Google Scholar 

  111. States DJ et al. (2006) Challenges in deriving high-confidence protein identifications from data gathered by a HUPO plasma proteome collaborative study. Nat Biotechnol 24: 333–338

    Article  CAS  PubMed  Google Scholar 

  112. Pohler E et al. (2004) The Barrett's antigen anterior gradient-2 silences the p53 transcriptional response to DNA damage. Mol Cell Proteomics 3: 534–547

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

S Din is funded by a Cancer Research UK Clinical Fellowship. Research within the Gastrointestinal Unit is supported by the Wellcome Trust, CORE, the National Association for Crohn's and Colitis, the Medical Research Council, Action Medical Research and the Scottish Executive. None of the funding agencies had any input into the production of this manuscript.

Author information

Authors and Affiliations

  1. S Din is a Cancer Research UK Clinical Research Fellow, AM Lennon is a Specialist Registrar in Gastroenterology, IDR Arnott is a Consultant Physician and J Satsangi is a Professor of Gastroenterology in the Gastrointestinal Unit, Molecular Medicine Centre, University of Edinburgh, Western General Hospital, Edinburgh, UK.,

    Shahida Din, Anne Marie Lennon, Ian DR Arnott & Jack Satsangi

  2. T Hupp is a Professor of Cancer Biology and Science Director at the Edinburgh Cancer Research Centre, University of Edinburgh, Edinburgh, UK.,

    Ted Hupp

Authors
  1. Shahida Din
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anne Marie Lennon
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ian DR Arnott
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ted Hupp
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jack Satsangi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jack Satsangi.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Din, S., Lennon, A., Arnott, I. et al. Technology Insight: the application of proteomics in gastrointestinal disease. Nat Rev Gastroenterol Hepatol 4, 372–385 (2007). https://doi.org/10.1038/ncpgasthep0872

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ncpgasthep0872

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing