Cell-type specific gene expression profiles of leukocytes in human peripheral blood

Chana Palmer, Maximilian Diehn, Ash A Alizadeh, Patrick O Brown, Chana Palmer, Maximilian Diehn, Ash A Alizadeh, Patrick O Brown

Abstract

Background: Blood is a complex tissue comprising numerous cell types with distinct functions and corresponding gene expression profiles. We attempted to define the cell type specific gene expression patterns for the major constituent cells of blood, including B-cells, CD4+ T-cells, CD8+ T-cells, lymphocytes and granulocytes. We did this by comparing the global gene expression profiles of purified B-cells, CD4+ T-cells, CD8+ T-cells, granulocytes, and lymphocytes using cDNA microarrays.

Results: Unsupervised clustering analysis showed that similar cell populations from different donors share common gene expression profiles. Supervised analyses identified gene expression signatures for B-cells (427 genes), T-cells (222 genes), CD8+ T-cells (23 genes), granulocytes (411 genes), and lymphocytes (67 genes). No statistically significant gene expression signature was identified for CD4+ cells. Genes encoding cell surface proteins were disproportionately represented among the genes that distinguished among the lymphocyte subpopulations. Lymphocytes were distinguishable from granulocytes based on their higher levels of expression of genes encoding ribosomal proteins, while granulocytes exhibited characteristic expression of various cell surface and inflammatory proteins.

Conclusion: The genes comprising the cell-type specific signatures encompassed many of the genes already known to be involved in cell-type specific processes, and provided clues that may prove useful in discovering the functions of many still unannotated genes. The most prominent feature of the cell type signature genes was the enrichment of genes encoding cell surface proteins, perhaps reflecting the importance of specialized systems for sensing the environment to the physiology of resting leukocytes.

Figures

Figure 1
Figure 1
Overview of hierarchical clustering of all samples. Hierarchically clustered gene expression profiles of 28 mixed and purified leukocyte samples. Results for ~741 genes with high variation in transcript levels among these samples. Genes and blood samples are organized by hierarchical clustering based on overall similarity in expression patterns. Expression levels are represented by a color key in which bright red represents the highest levels and bright green represents the lowest levels, and less saturated shades represent intermediate levels of expression. Values were centered across all samples to a mean of zero. Clusters of genes are labeled according to which samples have the highest relative gene expression.
Figure 2
Figure 2
B-cell signature genes. Relative expression of B-cell signature genes is shown for all samples. Genes (rows) are sorted in descending order of correlation of gene expression with relative abundance of B-cells. Samples (columns) are ordered in decreasing order of estimated relative abundance of B-cells from left to right. The grayscale bar on top shows the estimated relative abundance of B-cells in each class of samples – black indicates 100% relative abundance of B-cells, white indicates 0% relative abundance of B-cells, and grey indicates intermediate relative abundance of B-cells. Gene expression values are centered across all samples to a median of zero. All genes mentioned in the text are listed in order of correlation with B-cell abundance and black bars indicate their positions in the figure.
Figure 3
Figure 3
T-cell signature genes. Relative expression of T-cell signature genes is shown for all samples. Genes (rows) are sorted in descending order of correlation of gene expression with relative abundance of T-cells. Samples (columns) are ordered in decreasing relative abundance from left to right. Gene expression values are centered across all samples to a median of zero. The grayscale bar on top shows the estimated relative abundance of T-cells in each class of samples – black indicates 100% relative abundance of T-cells, white indicates 0% relative abundance of T-cells, and grey indicates intermediate relative abundance of T-cells. All genes mentioned in the text are listed in order of correlation with T-cell abundance, and black bars indicate their positions in the figure.
Figure 4
Figure 4
CD8+ T-cell signature genes. Relative expression of CD8+ T-cell signature genes is shown for all samples. Genes (rows) are sorted in descending order of correlation of gene expression with relative abundance of T-cells. Samples (columns) are ordered in decreasing relative abundance from left to right. The grayscale bar on top shows the estimated relative abundance of CD8+ T-cells in each class of samples – black indicates 100% relative abundance of CD8+ T-cells, white indicates 0% relative abundance of CD8+ T-cells, and grey indicates intermediate relative abundance of CD8+ T-cells. Gene expression values are centered across all samples to a median of zero. All genes mentioned in the text are listed and their position is marked with a black bar.
Figure 5
Figure 5
Granulocyte signature genes. Relative expression of granulocyte signature genes is shown for all samples. Genes (rows) are sorted in descending order of correlation of gene expression with relative abundance of granulocytes. Samples (columns) are ordered in decreasing relative abundance from left to right. The grayscale bar on top shows the estimated relative abundance of granulocytes in each class of samples – black indicates 100% relative abundance of granulocytes, white indicates 0% relative abundance of granulocytes, and grey indicates intermediate relative abundance of granulocytes. Gene expression values are centered across all samples to a median of zero. All genes mentioned in the text are listed in order of correlation with granulocyte abundance, and black bars indicate their positions in the figure.
Figure 6
Figure 6
Lymphocyte signature genes. Relative expression of lymphocyte signature genes is shown for all samples. Genes (rows) are sorted in ascending order of descending correlation of gene expression with relative abundance of lymphocytes. Samples (columns) are ordered in increasing relative abundance from left to right. The grayscale bar on top shows the estimated relative abundance of lymphocytes in each class of samples – black indicates 100% relative abundance of lymphocytes, white indicates 0% relative abundance of lymphocytes, and grey indicates intermediate relative abundance of lymphocytes. Gene expression values are centered across all samples to a median of zero. All genes mentioned in the text are listed and their position is marked with a black bar.

References

    1. Parks D, Herzenberg L. Fluorescence-activated cell sorting: Theory, experimental optimization, and applications in lymphoid cell biology. Methods Enzymol. 1984;108:197–241.
    1. Bomprezzi R, Ringner M, Kim S, Bittner ML, Khan J, Chen Y, Elkahloun A, Yu A, Bielekova B, Meltzer PS, Martin R, McFarland HF, Trent JM. Gene expression profile in multiple sclerosis patients and healthy controls: identifying pathways relevant to disease. Hum Mol Genet. 2003;12:2191–2199. doi: 10.1093/hmg/ddg221.
    1. 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;97:1461–1469. doi: 10.1152/japplphysiol.00316.2004.
    1. Lampe JW, Stepaniants SB, Mao M, Radich JP, Dai H, Linsley PS, Friend SH, Potter JD. Signatures of environmental exposures using peripheral leukocyte gene expression: tobacco smoke. Cancer Epidemiol Biomarkers Prev. 2004;13:445–453.
    1. Moore DF, Li H, Jeffries N, Wright V, Cooper RA, Jr, Elkahloun A, Gelderman MP, Zudaire E, Blevins G, Yu H, Goldin E, Baird AE. Using peripheral blood mononuclear cells to determine a gene expression profile of acute ischemic stroke: a pilot investigation. Circulation. 2005;111:212–221. doi: 10.1161/01.CIR.0000152105.79665.C6.
    1. Rubins KH, Hensley LE, Jahrling PB, Whitney AR, Geisbert TW, Huggins JW, Owen A, Leduc JW, Brown PO, Relman DA. The host response to smallpox: analysis of the gene expression program in peripheral blood cells in a nonhuman primate model. Proc Natl Acad Sci USA. 2004;101:15190–15195. doi: 10.1073/pnas.0405759101.
    1. Tang Y, Schapiro MB, Franz DN, Patterson BJ, Hickey FJ, Schorry EK, Hopkin RJ, Wylie M, Narayan T, Glauser TA, Gilbert DL, Hershey AD, Sharp FR. Blood expression profiles for tuberous sclerosis complex 2, neurofibromatosis type 1, and Down's syndrome. Ann Neurol. 2004;56:808–814. doi: 10.1002/ana.20291.
    1. Twine NC, Stover JA, Marshall B, Dukart G, Hidalgo M, Stadler W, Logan T, Dutcher J, Hudes G, Dorner AJ, Slonim DK, Trepicchio WL, Burczynski ME. Disease-associated expression profiles in peripheral blood mononuclear cells from patients with advanced renal cell carcinoma. Cancer Res. 2003;63:6069–6075.
    1. Radich JP, Mao M, Stepaniants S, Biery M, Castle J, Ward T, Schimmack G, Kobayashi S, Carleton M, Lampe J, Linsley PS. Individual-specific variation of gene expression in peripheral blood leukocytes. Genomics. 2004;83:980–988. doi: 10.1016/j.ygeno.2003.12.013.
    1. Sharma A, Sharma VK, Horn-Saban S, Lancet D, Ramachandran S, Brahmachari SK. Assessing natural variations in gene expression in humans by comparing with monozygotic twins using microarrays. Physiol Genomics. 2005;21:117–123. doi: 10.1152/physiolgenomics.00228.2003.
    1. 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 USA. 2003;100:1896–1901. doi: 10.1073/pnas.252784499.
    1. Kasper DLaH , Tinsley Randolph . Harrison's principles of internal medicine. 16. New York : McGraw-Hill, Medical Pub. Division; 2005.
    1. Kuby J. Immunology. 3. W. H. Freeman and Company; 1997.
    1. Reichert T, DeBruyere M, Deneys V, Totterman T, Lydyard P, Yuksel F, Chapel H, Jewell D, Van Hove L, Linden J, et al. Lymphocyte subset reference ranges in adult Caucasians. Clin Immunol Immunopathol. 1991;60:190–208. doi: 10.1016/0090-1229(91)90063-G.
    1. Shahabuddin S. Quantitative differences in CD8+ lymphocytes, CD4/CD8 ratio, NK cells, and HLA-DR(+)-activated T cells of racially different male populations. Clin Immunol Immunopathol. 1995;75:168–170. doi: 10.1006/clin.1995.1067.
    1. Murray JI, Whitfield ML, Trinklein ND, Myers RM, Brown PO, Botstein D. Diverse and specific gene expression responses to stresses in cultured human cells. Mol Biol Cell. 2004;15:2361–2374. doi: 10.1091/mbc.E03-11-0799.
    1. Sayama K, Diehn M, Matsuda K, Lunderius C, Tsai M, Tam S-Y, Botstein D, Brown P, Galli S. Transcriptional response of human mast cells stimulated via the FcepsilonRI and identification of mast cells as a source of IL-11. BMC Immunology. 2002;3:5. doi: 10.1186/1471-2172-3-5.
    1. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA. 2001;98:5116–5121. doi: 10.1073/pnas.091062498.
    1. Hosack DA, Dennis G, Jr, Sherman BT, Lane HC, Lempicki RA. Identifying biological themes within lists of genes with EASE. Genome Biol. 2003;4:R70. doi: 10.1186/gb-2003-4-10-r70.
    1. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–29. doi: 10.1038/75556.
    1. Kurosaki T. Regulation of B-cell signal transduction by adaptor proteins. Nat Rev Immunol. 2002;2:354–363. doi: 10.1038/nri801.
    1. Deneys V, Mazzon AM, Marques JL, Benoit H, De Bruyere M. Reference values for peripheral blood B-lymphocyte subpopulations: a basis for multiparametric immunophenotyping of abnormal lymphocytes. J Immunol Methods. 2001;253:23–36. doi: 10.1016/S0022-1759(01)00338-6.
    1. Matthias P, Rolink AG. Transcriptional networks in developing and mature B cells. Nat Rev Immunol. 2005;5:497–508. doi: 10.1038/nri1633.
    1. Weil R, Israel A. T-cell-receptor- and B-cell-receptor-mediated activation of NF-[kappa]B in lymphocytes. Curr Opin Immunol. 2004;16:374–381. doi: 10.1016/j.coi.2004.03.003.
    1. Rothenberg EV, Taghon T. Molecular genetics of T cell development. Annu Rev Immunol. 2005;23:601–649. doi: 10.1146/annurev.immunol.23.021704.115737.
    1. Cobb JP, Mindrinos MN, Miller-Graziano C, Calvano SE, Baker HV, Xiao W, Laudanski K, Brownstein BH, Elson CM, Hayden DL, Herndon DN, Lowry SF, Maier RV, Schoenfeld DA, Moldawer LL, Davis RW, Tompkins RG, Bankey P, Billiar T, Camp D, Chaudry I, Freeman B, Gamelli R, Gibran N, Harbrecht B, Heagy W, Heimbach D, Horton J, Hunt J, Lederer J, Mannick J, McKinley B, Minei J, Moore E, Moore F, Munford R, Nathens A, O'Keefe G, Purdue G, Rahme L, Remick D, Sailors M, Shapiro M, Silver G, Smith R, Stephanopoulos G, Stormo G, Toner M, Warren S, West M, Wolfe S, Young V. Application of genome-wide expression analysis to human health and disease. Proc Natl Acad Sci USA. 2005;102:4801–4806. doi: 10.1073/pnas.0409768102.
    1. Obata-Onai A, Hashimoto S, Onai N, Kurachi M, Nagai S, Shizuno K, Nagahata T, Matsushima K. Comprehensive gene expression analysis of human NK cells and CD8(+) T lymphocytes. Int Immunol. 2002;14:1085–1098. doi: 10.1093/intimm/dxf086.
    1. Glimcher LH, Townsend MJ, Sullivan BM, Lord GM. Recent developments in the transcriptional regulation of cytolytic effector cells. Nat Rev Immunol. 2004;4:900–911. doi: 10.1038/nri1490.
    1. Hashimoto S, Nagai S, Sese J, Suzuki T, Obata A, Sato T, Toyoda N, Dong HY, Kurachi M, Nagahata T, Shizuno K, Morishita S, Matsushima K. Gene expression profile in human leukocytes. Blood. 2003;101:3509–3513. doi: 10.1182/blood-2002-06-1866.
    1. Filion LG, Izaguirre CA, Garber GE, Huebsh L, Aye MT. Detection of surface and cytoplasmic CD4 on blood monocytes from normal and HIV-1 infected individuals. J Immunol Methods. 1990;135:59–69. doi: 10.1016/0022-1759(90)90256-U.
    1. Biswas P, Mantelli B, Sica A, Malnati M, Panzeri C, Saccani A, Hasson H, Vecchi A, Saniabadi A, Lusso P, Lazzarin A, Beretta A. Expression of CD4 on human peripheral blood neutrophils. Blood. 2003;101:4452–4456. doi: 10.1182/blood-2002-10-3056.
    1. Cliff JM, Andrade IN, Mistry R, Clayton CL, Lennon MG, Lewis AP, Duncan K, Lukey PT, Dockrell HM. Differential gene expression identifies novel markers of CD4+ and CD8+ T cell activation following stimulation by Mycobacterium tuberculosis. J Immunol. 2004;173:485–493.
    1. Kwakkenbos MJ, Chang G-W, Lin H-H, Pouwels W, de Jong EC, van Lier RAW, Gordon S, Hamann J. The human EGF-TM7 family member EMR2 is a heterodimeric receptor expressed on myeloid cells. J Leukoc Biol. 2002;71:854–862.
    1. Itoh K, Okubo K, Utiyama H, Hirano T, Yoshii J, Matsubara K. Expression profile of active genes in granulocytes. Blood. 1998;92:1432–1441.
    1. Bertrand G, Coste J, Segarra C, Schved J-F, Commes T, Marti J. Use of serial analysis of gene expression (SAGE) technology reveals new granulocytic markers. J Immunol Methods. 2004;292:43–58. doi: 10.1016/j.jim.2004.06.012.
    1. Shyamsundar R, Kim YH, Higgins JP, Montgomery K, Jorden M, Sethuraman A, van de Rijn M, Botstein D, Brown PO, Pollack JR. A DNA microarray survey of gene expression in normal human tissues. Genome Biol. 2005;6:R22. doi: 10.1186/gb-2005-6-3-r22.
    1. Wang E, Miller LD, Ohnmacht GA, Liu ET, Marincola FM. High-fidelity mRNA amplification for gene profiling. Nat Biotechnol. 2000;18:457–459. doi: 10.1038/74546.
    1. Eisen MB, Brown PO. DNA arrays for analysis of gene expression. Methods Enzymol. 1999;303:179–205.
    1. Alizadeh A, Eisen M, Davis RE, Ma C, Sabet H, Tran T, Powell JI, Yang L, Marti GE, Moore DT, Hudson JR, Jr, Chan WC, Greiner T, Weisenburger D, Armitage JO, Lossos I, Levy R, Botstein D, Brown PO, Staudt LM. The lymphochip: a specialized cDNA microarray for the genomic-scale analysis of gene expression in normal and malignant lymphocytes. Cold Spring Harb Symp Quant Biol. 1999;64:71–78. doi: 10.1101/sqb.1999.64.71.
    1. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudson J, Jr, Lu L, Lewis DB, Tibshirani R, Sherlock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Levy R, Wilson W, Grever MR, Byrd JC, Botstein D, Brown PO, Staudt LM. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000;403:503–511. doi: 10.1038/35000501.
    1. Diehn M, Sherlock G, Binkley G, Jin H, Matese JC, Hernandez-Boussard T, Rees CA, Cherry JM, Botstein D, Brown PO, Alizadeh AA. SOURCE: a unified genomic resource of functional annotations, ontologies, and gene expression data. Nucleic Acids Res. 2003;31:219–223. doi: 10.1093/nar/gkg014.

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