Blood Transcriptional Fingerprints to Assess the Immune Status of Human Subjects

  • Damien Chaussabel
  • Nicole Baldwin
  • Derek Blankenship
  • Charles Quinn
  • Esperanza Anguiano
  • Octavio Ramilo
  • Ganjana Lertmemongkolchai
  • Virginia Pascual
  • Jacques Banchereau


The blood transcriptome affords a comprehensive view of the status of the human immune system. Global changes in transcript abundance have been measured in the blood of patients with a wide range of diseases. This chapter presents an overview of the advances that have led to the identification of therapeutic targets and biomarker signatures in the field of autoimmunity and infectious disease. It also provides technology and data analysis primers as means of introducing blood transcriptome research to a broad readership. Specifically, we compare microarrays with some of the most recent digital gene expression profiling technologies available to date, including RNA sequencing. Furthermore, in addition to the basic steps involved in the analysis of microarray data we also present more advanced data mining approaches for blood transcriptional fingerprinting.


Transcript Abundance Single Nucleotide Polymorphism Human Immune System Class Comparison Dimension Reduction Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The work of the author is supported by the Baylor Health Care System Foundation and the National Institutes of Health (U19 AIO57234-02, U01 AI082110, P01 CA084512).


  1. Aaroe J, Lindahl T, Dumeaux V, Saebo S, Tobin D, Hagen N, Skaane P, Lonneborg A, Sharma P, Borresen-Dale AL: Gene expression profiling of peripheral blood cells for early detection of breast cancer. Breast Cancer Res 2010, 12(1):R7.PubMedCrossRefGoogle Scholar
  2. Abbas AR, Wolslegel K, Seshasayee D, Modrusan Z, Clark HF: Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus. PLoS One 2009, 4(7):e6098.PubMedCrossRefGoogle Scholar
  3. Achiron A, Gurevich M, Friedman N, Kaminski N, Mandel M: Blood transcriptional signatures of multiple sclerosis: unique gene expression of disease activity. Ann Neurol 2004, 55(3):410–417.PubMedCrossRefGoogle Scholar
  4. Achiron A, Gurevich M, Snir Y, Segal E, Mandel M: Zinc-ion binding and cytokine activity regulation pathways predicts outcome in relapsing-remitting multiple sclerosis. Clin Exp Immunol 2007, 149(2):235–242.PubMedCrossRefGoogle Scholar
  5. Achiron A, Feldman A, Mandel M, Gurevich M: Impaired expression of peripheral blood apoptotic-related gene transcripts in acute multiple sclerosis relapse. Ann N Y Acad Sci 2007, 1107:155–167.PubMedCrossRefGoogle Scholar
  6. Aderem A, Ulevitch RJ: Toll-like receptors in the induction of the innate immune response. Nature 2000, 406(6797):782–787.PubMedCrossRefGoogle Scholar
  7. Alakulppi N, Seikku P, Jaatinen T, Holmberg C, Laine J: Feasibility of diagnosing subclinical renal allograft rejection in children by whole blood gene expression analysis. Transplantation 2008, 86(9):1222–1228.PubMedCrossRefGoogle Scholar
  8. Alcorta DA, Barnes DA, Dooley MA, Sullivan P, Jonas B, Liu Y, Lionaki S, Reddy CB, Chin H, Dempsey AA et al: Leukocyte gene expression signatures in antineutrophil cytoplasmic autoantibody and lupus glomerulonephritis. Kidney Int 2007, 72(7):853–864.PubMedCrossRefGoogle Scholar
  9. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X et al: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000, 403(6769):503–511.PubMedCrossRefGoogle Scholar
  10. Allantaz F, Chaussabel D, Stichweh D, Bennett L, Allman W, Mejias A, Ardura M, Chung W, Wise C, Palucka K et al: Blood leukocyte microarrays to diagnose systemic onset juvenile idiopathic arthritis and follow the response to IL-1 blockade. J Exp Med 2007, 204(9):2131–2144.PubMedCrossRefGoogle Scholar
  11. Allantaz F, Chaussabel D, Banchereau J, Pascual V: Microarray-based identification of novel biomarkers in IL-1-mediated diseases. Curr Opin Immunol 2007, 19(6):623–632.PubMedCrossRefGoogle Scholar
  12. Allison DB, Cui X, Page GP, Sabripour M: Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet 2006, 7(1):55–65.PubMedCrossRefGoogle Scholar
  13. Ardura MI, Banchereau R, Mejias A, Di Pucchio T, Glaser C, Allantaz F, Pascual V, Banchereau J, Chaussabel D, Ramilo O: Enhanced monocyte response and decreased central memory T cells in children with invasive Staphylococcus aureus infections. PLoS One 2009, 4(5):e5446.PubMedCrossRefGoogle Scholar
  14. Asare AL, Kolchinsky SA, Gao Z, Wang R, Raddassi K, Bourcier K, Seyfert-Margolis V: Differential gene expression profiles are dependent upon method of peripheral blood collection and RNA isolation. BMC Genomics 2008, 9:474.PubMedCrossRefGoogle Scholar
  15. Baechler EC, Batliwalla FM, Karypis G, Gaffney PM, Ortmann WA, Espe KJ, Shark KB, Grande WJ, Hughes KM, Kapur V et al: Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc Natl Acad Sci U S A 2003, 100(5):2610–2615.PubMedCrossRefGoogle Scholar
  16. Baechler EC, Bauer JW, Slattery CA, Ortmann WA, Espe KJ, Novitzke J, Ytterberg SR,Gregersen PK, Behrens TW, Reed AM: An interferon signature in the peripheral blood of dermatomyositis patients is associated with disease activity. Mol Med 2007, 13(1–2):59–68.PubMedGoogle Scholar
  17. Barnes MG, Grom AA, Thompson SD, Griffin TA, Pavlidis P, Itert L, Fall N, Sowders DP, Hinze CH, Aronow BJ et al: Subtype-specific peripheral blood gene expression profiles in recent-onset juvenile idiopathic arthritis. Arthritis Rheum 2009, 60(7):2102–2112.PubMedCrossRefGoogle Scholar
  18. Batliwalla FM, Li W, Ritchlin CT, Xiao X, Brenner M, Laragione T, Shao T, Durham R, Kemshetti S, Schwarz E et al: Microarray analyses of peripheral blood cells identifies unique gene expression signature in psoriatic arthritis. Mol Med 2005, 11(1–12):21–29.PubMedGoogle Scholar
  19. Batliwalla FM, Baechler EC, Xiao X, Li W, Balasubramanian S, Khalili H, Damle A, Ortmann WA, Perrone A, Kantor AB et al: Peripheral blood gene expression profiling in rheumatoid arthritis. Genes Immun 2005, 6(5):388–397.PubMedCrossRefGoogle Scholar
  20. Bauer JW, Petri M, Batliwalla FM, Koeuth T, Wilson J, Slattery C, Panoskaltsis-Mortari A, Gregersen PK, Behrens TW, Baechler EC: Interferon-regulated chemokines as biomarkers of systemic lupus erythematosus disease activity: a validation study. Arthritis Rheum 2009, 60(10):3098–3107.PubMedCrossRefGoogle Scholar
  21. Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Statist Soc Ser B 1995, 57:289–300.Google Scholar
  22. Bennett L, Palucka AK, Arce E, Cantrell V, Borvak J, Banchereau J, Pascual V: Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J Exp Med 2003, 197(6):711–723.PubMedCrossRefGoogle Scholar
  23. Berchtold LA, Larsen CM, Vaag A, Faulenbach M, Workman CT, Kruhoffer M, Donath M, Mandrup-Poulsen T: IL-1 receptor antagonism and muscle gene expression in patients with type 2 diabetes. Eur Cytokine Netw 2009, 20(2):81–87.PubMedGoogle Scholar
  24. Berry MP, Graham CM, McNab FW, Xu Z, Bloch SA, Oni T, Wilkinson KA, Banchereau R, Skinner J, Wilkinson RJ, Quinn C, Blankenship D, Dhawan R, Cush JJ, Mejias A, Ramilo O, Kon OM, Pascual V, Banchereau J, Chaussabel D, O’Garra A. Nature. 2010 Aug 19;466(7309):973-7.PMID: 20725040 [PubMed – in process].Google Scholar
  25. Bomprezzi R, Ringner M, Kim S, Bittner ML, Khan J, Chen Y, Elkahloun A, Yu A, Bielekova B, Meltzer PS et al: Gene expression profile in multiple sclerosis patients and healthy controls: identifying pathways relevant to disease. Hum Mol Genet 2003, 12(17):2191–2199.PubMedCrossRefGoogle Scholar
  26. Borovecki F, Lovrecic L, Zhou J, Jeong H, Then F, Rosas HD, Hersch SM, Hogarth P, Bouzou B, Jensen RV et al: Genome-wide expression profiling of human blood reveals biomarkers for Huntington’s disease. Proc Natl Acad Sci U S A 2005, 102(31):11023–11028.PubMedCrossRefGoogle Scholar
  27. Brouard S, Mansfield E, Braud C, Li L, Giral M, Hsieh SC, Baeten D, Zhang M, Ashton-Chess J, Braudeau C et al: Identification of a peripheral blood transcriptional biomarker panel associated with operational renal allograft tolerance. Proc Natl Acad Sci U S A 2007, 104(39):15448–15453.PubMedCrossRefGoogle Scholar
  28. Bunnag S, Einecke G, Reeve J, Jhangri GS, Mueller TF, Sis B, Hidalgo LG, Mengel M, Kayser D, Kaplan B et al: Molecular correlates of renal function in kidney transplant biopsies. J Am Soc Nephrol 2009, 20(5):1149–1160.PubMedCrossRefGoogle Scholar
  29. Burczynski ME, Peterson RL, Twine NC, Zuberek KA, Brodeur BJ, Casciotti L, Maganti V, Reddy PS, Strahs A, Immermann F et al: Molecular classification of Crohn’s disease and ulcerative colitis patients using transcriptional profiles in peripheral blood mononuclear cells. J Mol Diagn 2006, 8(1):51–61.PubMedCrossRefGoogle Scholar
  30. Bushel PR, Heinloth AN, Li J, Huang L, Chou JW, Boorman GA, Malarkey DE, Houle CD, Ward SM, Wilson RE et al: Blood gene expression signatures predict exposure levels. Proc Natl Acad Sci U S A 2007, 104(46):18211–18216.PubMedCrossRefGoogle Scholar
  31. Butte AJ, Kohane IS: Creation and implications of a phenome-genome network. Nat Biotechnol 2006, 24(1):55–62.PubMedCrossRefGoogle Scholar
  32. Cappuzzello C, Napolitano M, Arcelli D, Melillo G, Melchionna R, Di Vito L, Carlini D, Silvestri L, Brugaletta S, Liuzzo G et al: Gene expression profiles in peripheral blood mononuclear cells of chronic heart failure patients. Physiol Genomics 2009, 38(3):233–240.PubMedCrossRefGoogle Scholar
  33. Cassidy JT, Ross E: Juvenile Rheumatoid Arthritis. In: Textbook of Pediatric Rheumatology. 4th edn;W.B. Saunders Company, Philadelphia, 2001:218–321.Google Scholar
  34. Chaussabel D, Semnani RT, McDowell MA, Sacks D, Sher A, Nutman TB: Unique gene expression profiles of human macrophages and dendritic cells to phylogenetically distinct parasites. Blood 2003, 102(2):672–681.PubMedCrossRefGoogle Scholar
  35. Chaussabel D, Allman W, Mejias A, Chung W, Bennett L, Ramilo O, Pascual V, Palucka AK, Banchereau J: Analysis of significance patterns identifies ubiquitous and disease-specific gene-expression signatures in patient peripheral blood leukocytes. Ann N Y Acad Sci 2005, 1062:146–154.PubMedCrossRefGoogle Scholar
  36. Chaussabel D, Quinn C, Shen J, Patel P, Glaser C, Baldwin N, Stichweh D, Blankenship D, Li L, Munagala I et al: A modular analysis framework for blood genomics studies: application to systemic lupus erythematosus. [see comment]. Immunity 2008, 29(1):150–164.PubMedCrossRefGoogle Scholar
  37. Cole J, Tsou R, Wallace K, Gibran N, Isik F: Comparison of normal human skin gene expression using cDNA microarrays. Wound Repair Regen 2001, 9(2):77–85.PubMedCrossRefGoogle Scholar
  38. Connolly PH, Caiozzo VJ, Zaldivar F, Nemet D, Larson J, Hung SP, Heck JD, Hatfield GW, Cooper DM: Effects of exercise on gene expression in human peripheral blood mononuclear cells. J Appl Physiol 2004, 97(4):1461–1469.PubMedCrossRefGoogle Scholar
  39. Crow MK, Wohlgemuth J: Microarray analysis of gene expression in lupus. Arthritis Res Ther 2003, 5(6):279–287.PubMedCrossRefGoogle Scholar
  40. de Jongh GJ, Zeeuwen PL, Kucharekova M, Pfundt R, van der Valk PG, Blokx W, Dogan A, Hiemstra PS, van de Kerkhof PC, Schalkwijk J: High expression levels of keratinocyte antimicrobial proteins in psoriasis compared with atopic dermatitis. J Invest Dermatol 2005, 125(6):1163–1173.PubMedCrossRefGoogle Scholar
  41. Debey S, Zander T, Brors B, Popov A, Eils R, Schultze JL: A highly standardized, robust, and cost-effective method for genome-wide transcriptome analysis of peripheral blood applicable to large-scale clinical trials. Genomics 2006, 87(5):653–664.PubMedCrossRefGoogle Scholar
  42. Deonarine K, Panelli MC, Stashower ME, Jin P, Smith K, Slade HB, Norwood C, Wang E, Marincola FM, Stroncek DF: Gene expression profiling of cutaneous wound healing. J Transl Med 2007, 5:11.PubMedCrossRefGoogle Scholar
  43. Dobbin KK, Zhao Y, Simon RM: How large a training set is needed to develop a classifier for microarray data? Clin Cancer Res 2008, 14(1):108–114.PubMedCrossRefGoogle Scholar
  44. Edwards CJ, Feldman JL, Beech J, Shields KM, Stover JA, Trepicchio WL, Larsen G, Foxwell BM, Brennan FM, Feldmann M et al: Molecular profile of peripheral blood mononuclear cells from patients with rheumatoid arthritis. Mol Med 2007, 13(1–2):40–58.PubMedGoogle Scholar
  45. Emamian ES, Leon JM, Lessard CJ, Grandits M, Baechler EC, Gaffney PM, Segal B, Rhodus NL, Moser KL: Peripheral blood gene expression profiling in Sjogren’s syndrome. Genes Immun 2009, 10(4):285–296.PubMedCrossRefGoogle Scholar
  46. Fall N, Barnes M, Thornton S, Luyrink L, Olson J, Ilowite NT, Gottlieb BS, Griffin T, Sherry DD, Thompson S et al: Gene expression profiling of peripheral blood from patients with untreated new-onset systemic juvenile idiopathic arthritis reveals molecular heterogeneity that may predict macrophage activation syndrome. Arthritis Rheum 2007, 56(11):3793–3804.PubMedCrossRefGoogle Scholar
  47. Findeisen P, Rockel M, Nees M, Roder C, Kienle P, Von Knebel Doeberitz M, Kalthoff H, Neumaier M: Systematic identification and validation of candidate genes for detection of circulating tumor cells in peripheral blood specimens of colorectal cancer patients. Int J Oncol 2008, 33(5):1001–1010.PubMedGoogle Scholar
  48. Flanagan JM, Steward S, Hankins JS, Howard TM, Neale G, Ware RE: Microarray analysis of liver gene expression in iron overloaded patients with sickle cell anemia and beta-thalassemia. Am J Hematol 2009, 84(6):328–334.PubMedCrossRefGoogle Scholar
  49. Flechner SM, Kurian SM, Head SR, Sharp SM, Whisenant TC, Zhang J, Chismar JD, Horvath S, Mondala T, Gilmartin T et al: Kidney transplant rejection and tissue injury by gene profiling of biopsies and peripheral blood lymphocytes. Am J Transplant 2004, 4(9):1475–1489.PubMedCrossRefGoogle Scholar
  50. Franklin BS, Parroche P, Ataide MA, Lauw F, Ropert C, de Oliveira RB, Pereira D, Tada MS, Nogueira P, da Silva LH et al: Malaria primes the innate immune response due to interferon-gamma induced enhancement of toll-like receptor expression and function. Proc Natl Acad Sci U S A 2009, 106(14):5789–5794.PubMedCrossRefGoogle Scholar
  51. Frueh FW, Hayashibara KC, Brown PO, Whitlock JP, Jr.: Use of cDNA microarrays to analyze dioxin-induced changes in human liver gene expression. Toxicol Lett 2001, 122(3):189–203.PubMedCrossRefGoogle Scholar
  52. Geiss GK, Bumgarner RE, Birditt B, Dahl T, Dowidar N, Dunaway DL, Fell HP, Ferree S, George RD, Grogan T et al: Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat Biotechnol 2008, 26(3):317–325.PubMedCrossRefGoogle Scholar
  53. Glatt SJ, Everall IP, Kremen WS, Corbeil J, Sasik R, Khanlou N, Han M, Liew CC, Tsuang MT: Comparative gene expression analysis of blood and brain provides concurrent validation of SELENBP1 up-regulation in schizophrenia. Proc Natl Acad Sci U S A 2005, 102(43):15533–15538.PubMedCrossRefGoogle Scholar
  54. Greco JA, III, Pollins AC, Boone BE, Levy SE, Nanney LB: A microarray analysis of temporal gene expression profiles in thermally injured human skin. Burns 2010, 36:192–204.PubMedCrossRefGoogle Scholar
  55. Greenberg SA, Pinkus JL, Pinkus GS, Burleson T, Sanoudou D, Tawil R, Barohn RJ, Saperstein DS, Briemberg HR, Ericsson M et al: Interferon-alpha/beta-mediated innate immune mechanisms in dermatomyositis. Ann Neurol 2005, 57(5):664–678.PubMedCrossRefGoogle Scholar
  56. Griffiths MJ, Shafi MJ, Popper SJ, Hemingway CA, Kortok MM, Wathen A, Rockett KA, Mott R, Levin M, Newton CR et al: Genomewide analysis of the host response to malaria in Kenyan children. J Infect Dis 2005, 191(10):1599–1611.PubMedCrossRefGoogle Scholar
  57. Gurevich M, Tuller T, Rubinstein U, Or-Bach R, Achiron A: Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells. BMC Med Genomics 2009, 2:46.PubMedCrossRefGoogle Scholar
  58. Han GM, Chen SL, Shen N, Ye S, Bao CD, Gu YY: Analysis of gene expression profiles in human systemic lupus erythematosus using oligonucleotide microarray. Genes Immun 2003, 4(3):177–186.PubMedCrossRefGoogle Scholar
  59. Hayashi T, Tsujii S, Iburi T, Tamanaha T, Yamagami K, Ishibashi R, Hori M, Sakamoto S, Ishii H, Murakami K: Laughter up-regulates the genes related to NK cell activity in diabetes. Biomed Res 2007, 28(6):281–285.PubMedCrossRefGoogle Scholar
  60. Hayes DC, Secrist H, Bangur CS, Wang T, Zhang X, Harlan D, Goodman GE, Houghton RL, Persing DH, Zehentner BK: Multigene real-time PCR detection of circulating tumor cells in peripheral blood of lung cancer patients. Anticancer Res 2006, 26(2B):1567–1575.PubMedGoogle Scholar
  61. Horvath S, Dong J: Geometric interpretation of gene coexpression network analysis. PLoS Comput Biol 2008, 4(8):e1000117.PubMedCrossRefGoogle Scholar
  62. Huang Q, Liu D, Majewski P, Schulte LC, Korn JM, Young RA, Lander ES, Hacohen N: The plasticity of dendritic cell responses to pathogens and their components. Science 2001, 294(5543):870–875.PubMedCrossRefGoogle Scholar
  63. Jacobsen M, Repsilber D, Gutschmidt A, Neher A, Feldmann K, Mollenkopf HJ, Ziegler A, Kaufmann SH: Candidate biomarkers for discrimination between infection and disease caused by Mycobacterium tuberculosis. J Mol Med 2007, 85(6):613–621.PubMedCrossRefGoogle Scholar
  64. Janeway CA, Jr., Medzhitov R: Innate immune recognition. Annu Rev Immunol 2002, 20:197–216.PubMedCrossRefGoogle Scholar
  65. Johnson SB, Lissauer M, Bochicchio GV, Moore R, Cross AS, Scalea TM: Gene expression profiles differentiate between sterile SIRS and early sepsis. Ann Surg 2007, 245(4):611–621.PubMedCrossRefGoogle Scholar
  66. Jorstad TS, Midelfart H, Bones AM: A mixture model approach to sample size estimation in two-sample comparative microarray experiments. BMC Bioinformatics 2008, 9:117.PubMedCrossRefGoogle Scholar
  67. Kaizer EC, Glaser CL, Chaussabel D, Banchereau J, Pascual V, White PC: Gene expression in peripheral blood mononuclear cells from children with diabetes. J Clin Endocrinol Metab 2007, 92(9):3705–3711.PubMedCrossRefGoogle Scholar
  68. Kawai T, Morita K, Masuda K, Nishida K, Sekiyama A, Teshima-Kondo S, Nakaya Y, Ohta M, Saito T, Rokutan K: Physical exercise-associated gene expression signatures in peripheral blood. Clin J Sport Med 2007, 17(5):375–383.PubMedCrossRefGoogle Scholar
  69. Kawasaki M, Iwasaki M, Koshiba T, Fujino M, Hara Y, Kitazawa Y, Kimura H, Uemoto S, Li XK, Tanaka K: Gene expression profile analysis of the peripheral blood mononuclear cells from tolerant living-donor liver transplant recipients. Int Surg 2007, 92(5):276–286.PubMedGoogle Scholar
  70. Koczan D, Guthke R, Thiesen HJ, Ibrahim SM, Kundt G, Krentz H, Gross G, Kunz M: Gene expression profiling of peripheral blood mononuclear leukocytes from psoriasis patients identifies new immune regulatory molecules. Eur J Dermatol 2005, 15(4):251–257.PubMedGoogle Scholar
  71. Lahdesmaki H, Shmulevich L, Dunmire V, Yli-Harja O, Zhang W: In silico microdissection of microarray data from heterogeneous cell populations. BMC Bioinformatics 2005, 6:54.PubMedCrossRefGoogle Scholar
  72. Lequerre T, Gauthier-Jauneau AC, Bansard C, Derambure C, Hiron M, Vittecoq O, Daveau M, Mejjad O, Daragon A, Tron F et al: Gene profiling in white blood cells predicts infliximab responsiveness in rheumatoid arthritis. Arthritis Res Ther 2006, 8(4):R105.PubMedCrossRefGoogle Scholar
  73. Lin D, Hollander Z, Ng RT, Imai C, Ignaszewski A, Balshaw R, Freue GC, Wilson-McManus JE, Qasimi P, Meredith A et al: Whole blood genomic biomarkers of acute cardiac allograft rejection. J Heart Lung Transplant 2009, 28(9):927–935.PubMedCrossRefGoogle Scholar
  74. Lovrecic L, Kastrin A, Kobal J, Pirtosek Z, Krainc D, Peterlin B: Gene expression changes in blood as a putative biomarker for Huntington’s disease. Mov Disord 2009, 24(15):2277–2281.PubMedCrossRefGoogle Scholar
  75. Lu P, Nakorchevskiy A, Marcotte EM: Expression deconvolution: a reinterpretation of DNA microarray data reveals dynamic changes in cell populations. Proc Natl Acad Sci U S A 2003, 100(18):10370–10375.PubMedCrossRefGoogle Scholar
  76. Maes OC, Xu S, Yu B, Chertkow HM, Wang E, Schipper HM: Transcriptional profiling of Alzheimer blood mononuclear cells by microarray. Neurobiol Aging 2007, 28(12):1795–1809.PubMedCrossRefGoogle Scholar
  77. Martin KJ, Graner E, Li Y, Price LM, Kritzman BM, Fournier MV, Rhei E, Pardee AB: High-sensitivity array analysis of gene expression for the early detection of disseminated breast tumor cells in peripheral blood. Proc Natl Acad Sci U S A 2001, 98(5):2646–2651.PubMedCrossRefGoogle Scholar
  78. Martinez-Llordella M, Lozano JJ, Puig-Pey I, Orlando G, Tisone G, Lerut J, Benitez C, Pons JA, Parrilla P, Ramirez P et al: Using transcriptional profiling to develop a diagnostic test of operational tolerance in liver transplant recipients. J Clin Invest 2008, 118(8):2845–2857.PubMedGoogle Scholar
  79. McHale CM, Zhang L, Lan Q, Li G, Hubbard AE, Forrest MS, Vermeulen R, Chen J, Shen M, Rappaport SM et al: Changes in the peripheral blood transcriptome associated with occupational benzene exposure identified by cross-comparison on two microarray platforms. Genomics 2009, 93(4):343–349.PubMedCrossRefGoogle Scholar
  80. Moore DF, Li H, Jeffries N, Wright V, Cooper RA, Jr., Elkahloun A, Gelderman MP, Zudaire E, Blevins G, Yu H et al: Using peripheral blood mononuclear cells to determine a gene expression profile of acute ischemic stroke: a pilot investigation. Circulation 2005, 111(2):212–221.PubMedCrossRefGoogle Scholar
  81. Mootha VK, Bunkenborg J, Olsen JV, Hjerrild M, Wisniewski JR, Stahl E, Bolouri MS, Ray HN, Sihag S, Kamal M et al: Integrated analysis of protein composition, tissue diversity, and gene regulation in mouse mitochondria. Cell 2003, 115(5):629–640.PubMedCrossRefGoogle Scholar
  82. Mueller TF, Einecke G, Reeve J, Sis B, Mengel M, Jhangri GS, Bunnag S, Cruz J, Wishart D, Meng C et al: Microarray analysis of rejection in human kidney transplants using pathogenesis-based transcript sets. Am J Transplant 2007, 7(12):2712–2722.PubMedCrossRefGoogle Scholar
  83. Nakayama M, Kudoh T, Kaikita K, Yoshimura M, Oshima S, Miyamoto Y, Takeya M, Ogawa H: Class A macrophage scavenger receptor gene expression levels in peripheral blood mononuclear cells specifically increase in patients with acute coronary syndrome. Atherosclerosis 2008, 198(2):426–433.PubMedCrossRefGoogle Scholar
  84. Nakou M, Knowlton N, Frank MB, Bertsias G, Osban J, Sandel CE, Papadaki H, Raptopoulou A, Sidiropoulos P, Kritikos I et al: Gene expression in systemic lupus erythematosus: bone marrow analysis differentiates active from inactive disease and reveals apoptosis and granulopoiesis signatures. Arthritis Rheum 2008, 58(11):3541–3549.PubMedCrossRefGoogle Scholar
  85. Nascimento EJ, Braga-Neto U, Calzavara-Silva CE, Gomes AL, Abath FG, Brito CA, Cordeiro MT, Silva AM, Magalhaes C, Andrade R et al: Gene expression profiling during early acute febrile stage of dengue infection can predict the disease outcome. PLoS One 2009, 4(11):e7892.PubMedCrossRefGoogle Scholar
  86. Nau GJ, Richmond JF, Schlesinger A, Jennings EG, Lander ES, Young RA: Human macrophage activation programs induced by bacterial pathogens. Proc Natl Acad Sci U S A 2002, 99(3):1503–1508.PubMedCrossRefGoogle Scholar
  87. Nikpour M, Dempsey AA, Urowitz MB, Gladman DD, Barnes DA: Association of a gene expression profile from whole blood with disease activity in systemic lupus erythaematosus. Ann Rheum Dis 2008, 67(8):1069–1075.PubMedCrossRefGoogle Scholar
  88. Nomura I, Gao B, Boguniewicz M, Darst MA, Travers JB, Leung DY: Distinct patterns of gene expression in the skin lesions of atopic dermatitis and psoriasis: a gene microarray analysis. J Allergy Clin Immunol 2003, 112(6):1195–1202.PubMedCrossRefGoogle Scholar
  89. Ogilvie EM, Khan A, Hubank M, Kellam P, Woo P: Specific gene expression profiles in systemic juvenile idiopathic arthritis. Arthritis Rheum 2007, 56(6):1954–1965.PubMedCrossRefGoogle Scholar
  90. Panelli MC, Stashower ME, Slade HB, Smith K, Norwood C, Abati A, Fetsch P, Filie A, Walters SA, Astry C et al: Sequential gene profiling of basal cell carcinomas treated with imiquimod in a placebo-controlled study defines the requirements for tissue rejection. Genome Biol 2007, 8(1):R8.PubMedCrossRefGoogle Scholar
  91. Pankla R, Buddhisa S, Berry M, Blankenship DM, Bancroft GJ, Banchereau J, Lertmemongkolchai G, Chaussabel D: Genomic transcriptional profiling identifies a candidate blood biomarker signature for the diagnosis of septicemic melioidosis. Genome Biol 2009, 10(11):R127.PubMedCrossRefGoogle Scholar
  92. Pascual V, Allantaz F, Arce E, Punaro M, Banchereau J: Role of interleukin-1 (IL-1) in the pathogenesis of systemic onset juvenile idiopathic arthritis and clinical response to IL-1 blockade. J Exp Med 2005, 201(9):1479–1486.PubMedCrossRefGoogle Scholar
  93. Pascual V, Allantaz F, Patel P, Palucka AK, Chaussabel D, Banchereau J: How the study of children with rheumatic diseases identified interferon-alpha and interleukin-1 as novel therapeutic targets. Immunol Rev 2008, 223:39–59.PubMedCrossRefGoogle Scholar
  94. Pascual V, Chaussabel D, Banchereau J: A genomic approach to human autoimmune diseases. Annu Rev Immunol 2010, 28:535–571.PubMedCrossRefGoogle Scholar
  95. Pawitan Y, Michiels S, Koscielny S, Gusnanto A, Ploner A: False discovery rate, sensitivity and sample size for microarray studies. Bioinformatics 2005, 21(13):3017–3024.PubMedCrossRefGoogle Scholar
  96. Payen D, Lukaszewicz AC, Belikova I, Faivre V, Gelin C, Russwurm S, Launay JM, Sevenet N: Gene profiling in human blood leucocytes during recovery from septic shock. Intensive Care Med 2008, 34(8):1371–1376.PubMedCrossRefGoogle Scholar
  97. Peretz A, Peck EC, Bammler TK, Beyer RP, Sullivan JH, Trenga CA, Srinouanprachnah S, Farin FM, Kaufman JD: Diesel exhaust inhalation and assessment of peripheral blood mononuclear cell gene transcription effects: an exploratory study of healthy human volunteers. Inhal Toxicol 2007, 19(14):1107–1119.PubMedCrossRefGoogle Scholar
  98. Petri M, Singh S, Tesfasyone H, Dedrick R, Fry K, Lal P, Williams G, Bauer J, Gregersen P, Behrens T et al: Longitudinal expression of type I interferon responsive genes in systemic lupus erythematosus. Lupus 2009, 18(11):980–989.PubMedCrossRefGoogle Scholar
  99. Popper SJ, Watson VE, Shimizu C, Kanegaye JT, Burns JC, Relman DA: Gene transcript abundance profiles distinguish Kawasaki disease from adenovirus infection. J Infect Dis 2009, 200(4):657–666.PubMedCrossRefGoogle Scholar
  100. Potti A, Bild A, Dressman HK, Lewis DA, Nevins JR, Ortel TL: Gene-expression patterns predict phenotypes of immune-mediated thrombosis. Blood 2006, 107(4):1391–1396.PubMedCrossRefGoogle Scholar
  101. Ramilo O, Allman W, Chung W, Mejias A, Ardura M, Glaser C, Wittkowski KM, Piqueras B, Banchereau J, Palucka AK et al: Gene expression patterns in blood leukocytes discriminate patients with acute infections. Blood 2007, 109(5):2066–2077.PubMedCrossRefGoogle Scholar
  102. Reghunathan R, Jayapal M, Hsu LY, Chng HH, Tai D, Leung BP, Melendez AJ: Expression profile of immune response genes in patients with severe acute respiratory syndrome. BMC Immunol 2005, 6:2.PubMedCrossRefGoogle Scholar
  103. Repsilber D, Kern S, Telaar A, Walzl G, Black GF, Selbig J, Parida SK, Kaufmann SH, Jacobsen M: Biomarker discovery in heterogeneous tissue samples – taking the in-silico deconfounding approach. BMC Bioinformatics, 11(1):27.PubMedCrossRefGoogle Scholar
  104. Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A, Chinnaiyan AM: Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci U S A 2004, 101(25):9309–9314.PubMedCrossRefGoogle Scholar
  105. Ruan J, Dean AK, Zhang W: A general co-expression network-based approach to gene expression analysis: comparison and applications. BMC Syst Biol 2010, 4(1):8.PubMedCrossRefGoogle Scholar
  106. Sandrin-Garcia P, Junta CM, Fachin AL, Mello SS, Baiao AM, Rassi DM, Ferreira MC, Trevisan GL, Sakamoto-Hojo ET, Louzada-Junior P et al: Shared and unique gene expression in systemic lupus erythematosus depending on disease activity. Ann N Y Acad Sci 2009, 1173:493–500.PubMedCrossRefGoogle Scholar
  107. Sarwal M, Chua MS, Kambham N, Hsieh SC, Satterwhite T, Masek M, Salvatierra O, Jr.: Molecular heterogeneity in acute renal allograft rejection identified by DNA microarray profiling. N Engl J Med 2003, 349(2):125–138.PubMedCrossRefGoogle Scholar
  108. Scherer A, Krause A, Walker JR, Korn A, Niese D, Raulf F: Early prognosis of the development of renal chronic allograft rejection by gene expression profiling of human protocol biopsies. Transplantation 2003, 75(8):1323–1330.PubMedCrossRefGoogle Scholar
  109. Segal E, Yelensky R, Koller D: Genome-wide discovery of transcriptional modules from DNA sequence and gene expression. Bioinformatics 2003, 19 Suppl 1:i273–i282.PubMedCrossRefGoogle Scholar
  110. Segal E, Shapira M, Regev A, Pe’er D, Botstein D, Koller D, Friedman N: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet 2003, 34(2):166–176.PubMedCrossRefGoogle Scholar
  111. Shi L, Reid LH, Jones WD, Shippy R, Warrington JA, Baker SC, Collins PJ, de Longueville F, Kawasaki ES, Lee KY et al: The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol 2006, 24(9):1151–1161.PubMedCrossRefGoogle Scholar
  112. Shi L, Perkins RG, Fang H, Tong W: Reproducible and reliable microarray results through quality control: good laboratory proficiency and appropriate data analysis practices are essential. Curr Opin Biotechnol 2008, 19(1):10–18.PubMedCrossRefGoogle Scholar
  113. Singh MK, Scott TF, LaFramboise WA, Hu FZ, Post JC, Ehrlich GD: Gene expression changes in peripheral blood mononuclear cells from multiple sclerosis patients undergoing beta-interferon therapy. J Neurol Sci 2007, 258(1–2):52–59.PubMedCrossRefGoogle Scholar
  114. Snyder M, Weissman S, Gerstein M: Personal phenotypes to go with personal genomes. Mol Syst Biol 2009, 5:273.PubMedCrossRefGoogle Scholar
  115. Staratschek-Jox A, Classen S, Gaarz A, Debey-Pascher S, Schultze JL: Blood-based transcriptomics: leukemias and beyond. Expert Rev Mol Diagn 2009, 9(3):271–280.PubMedCrossRefGoogle Scholar
  116. Stoeckman AK, Baechler EC, Ortmann WA, Behrens TW, Michet CJ, Peterson EJ: A distinct inflammatory gene expression profile in patients with psoriatic arthritis. Genes Immun 2006, 7(7):583–591.PubMedCrossRefGoogle Scholar
  117. Sultan M, Schulz MH, Richard H, Magen A, Klingenhoff A, Scherf M, Seifert M, Borodina T, Soldatov A, Parkhomchuk D et al: A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 2008, 321(5891):956–960.PubMedCrossRefGoogle Scholar
  118. Suthram S, Dudley JT, Chiang AP, Chen R, Hastie TJ, Butte AJ: Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets. PLoS Comput Biol 2010, 6(2):e1000662.PubMedCrossRefGoogle Scholar
  119. Takamura T, Honda M, Sakai Y, Ando H, Shimizu A, Ota T, Sakurai M, Misu H, Kurita S, Matsuzawa-Nagata N et al: Gene expression profiles in peripheral blood mononuclear cells reflect the pathophysiology of type 2 diabetes. Biochem Biophys Res Commun 2007, 361(2):379–384.PubMedCrossRefGoogle Scholar
  120. Tan PK, Downey TJ, Spitznagel EL, Jr., Xu P, Fu D, Dimitrov DS, Lempicki RA, Raaka BM, Cam MC: Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res 2003, 31(19):5676–5684.PubMedCrossRefGoogle Scholar
  121. Tan FK, Zhou X, Mayes MD, Gourh P, Guo X, Marcum C, Jin L, Arnett FC, Jr.: Signatures of differentially regulated interferon gene expression and vasculotrophism in the peripheral blood cells of systemic sclerosis patients. Rheumatology (Oxford) 2006, 45(6):694–702.CrossRefGoogle Scholar
  122. Tang Y, Lu A, Aronow BJ, Sharp FR: Blood genomic responses differ after stroke, seizures, hypoglycemia, and hypoxia: blood genomic fingerprints of disease. Ann Neurol 2001, 50(6):699–707.PubMedCrossRefGoogle Scholar
  123. Tang BM, McLean AS, Dawes IW, Huang SJ, Lin RC: Gene-expression profiling of peripheral blood mononuclear cells in sepsis. Crit Care Med 2009, 37(3):882–888.PubMedCrossRefGoogle Scholar
  124. Thach DC, Agan BK, Olsen C, Diao J, Lin B, Gomez J, Jesse M, Jenkins M, Rowley R, Hanson E et al: Surveillance of transcriptomes in basic military trainees with normal, febrile respiratory illness, and convalescent phenotypes. Genes Immun 2005, 6(7):588–595.PubMedCrossRefGoogle Scholar
  125. Thompson LJ, Dunstan SJ, Dolecek C, Perkins T, House D, Dougan G, Hue NT, La TT, Du DC, Phuong LT et al: Transcriptional response in the peripheral blood of patients infected with Salmonella enterica serovar Typhi. Proc Natl Acad Sci USA 2009, 106:22433–22438.PubMedCrossRefGoogle Scholar
  126. Ubol S, Masrinoul P, Chaijaruwanich J, Kalayanarooj S, Charoensirisuthikul T, Kasisith J: Differences in global gene expression in peripheral blood mononuclear cells indicate a significant role of the innate responses in progression of dengue fever but not dengue hemorrhagic fever. J Infect Dis 2008, 197(10):1459–1467.PubMedCrossRefGoogle Scholar
  127. Ulitsky I, Shamir R: Identifying functional modules using expression profiles and confidence-scored protein interactions. Bioinformatics 2009, 25(9):1158–1164.PubMedCrossRefGoogle Scholar
  128. van Baarsen LG, Vosslamber S, Tijssen M, Baggen JM, van der Voort LF, Killestein J, van der Pouw Kraan TC, Polman CH, Verweij CL: Pharmacogenomics of interferon-beta therapy in multiple sclerosis: baseline IFN signature determines pharmacological differences between patients. PLoS One 2008, 3(4):e1927.PubMedCrossRefGoogle Scholar
  129. van der Pouw Kraan TC, Wijbrandts CA, van Baarsen LG, Voskuyl AE, Rustenburg F, Baggen JM, Ibrahim SM, Fero M, Dijkmans BA, Tak PP et al: Rheumatoid arthritis subtypes identified by genomic profiling of peripheral blood cells: assignment of a type I interferon signature in a subpopulation of patients. Ann Rheum Dis 2007, 66(8):1008–1014.Google Scholar
  130. Wang M, Master SR, Chodosh LA: Computational expression deconvolution in a complex mammalian organ. BMC Bioinformatics 2006, 7:328.PubMedCrossRefGoogle Scholar
  131. Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. Nature Rev 2009, 10(1):57–63.CrossRefGoogle Scholar
  132. Whitney AR, Diehn M, Popper SJ, Alizadeh AA, Boldrick JC, Relman DA, Brown PO: Individuality and variation in gene expression patterns in human blood. Proc Natl Acad Sci U S A 2003, 100(4):1896–1901.PubMedCrossRefGoogle Scholar
  133. Wold B, Myers RM: Sequence census methods for functional genomics. Nat Methods 2008, 5(1):19–21.PubMedCrossRefGoogle Scholar
  134. Wong HR, Cvijanovich N, Allen GL, Lin R, Anas N, Meyer K, Freishtat RJ, Monaco M, Odoms K, Sakthivel B et al: Genomic expression profiling across the pediatric systemic inflammatory response syndrome, sepsis, and septic shock spectrum. Crit Care Med 2009, 37(5):1558–1566.PubMedCrossRefGoogle Scholar
  135. Wright G, Tan B, Rosenwald A, Hurt EH, Wiestner A, Staudt LM: A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma. Proc Natl Acad Sci U S A 2003, 100(17):9991–9996.PubMedCrossRefGoogle Scholar
  136. Yang MC, Yang JJ, McIndoe RA, She JX: Microarray experimental design: power and sample size considerations. Physiol Genomics 2003, 16(1):24–28.PubMedCrossRefGoogle Scholar
  137. Yao Y, Richman L, Higgs BW, Morehouse CA, de los Reyes M, Brohawn P, Zhang J, White B, Coyle AJ, Kiener PA et al: Neutralization of interferon-alpha/beta-inducible genes and downstream effect in a phase I trial of an anti-interferon-alpha monoclonal antibody in systemic lupus erythematosus. Arthritis Rheum 2009, 60(6):1785–1796.PubMedCrossRefGoogle Scholar
  138. York MR, Nagai T, Mangini AJ, Lemaire R, van Seventer JM, Lafyatis R: A macrophage marker, Siglec-1, is increased on circulating monocytes in patients with systemic sclerosis and induced by type I interferons and toll-like receptor agonists. Arthritis Rheum 2007, 56(3):1010–1020.PubMedCrossRefGoogle Scholar
  139. Zaas AK, Chen M, Varkey J, Veldman T, Hero AO, III, Lucas J, Huang Y, Turner R, Gilbert A, Lambkin-Williams R et al: Gene expression signatures diagnose influenza and other symptomatic respiratory viral infections in humans. Cell Host Microbe 2009, 6(3):207–217.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Damien Chaussabel
    • 1
  • Nicole Baldwin
  • Derek Blankenship
  • Charles Quinn
  • Esperanza Anguiano
  • Octavio Ramilo
  • Ganjana Lertmemongkolchai
  • Virginia Pascual
  • Jacques Banchereau
  1. 1.Baylor Institute for Immunology ResearchBaylor Research InstituteDallasUSA

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