Learning objectives
Introduction
Microarrays, the basics
Evolution of microarray technology over time
Type of array | Number of probes or probe sets | Target spots | Manufacturer | Year invented |
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Nylon | 5,000 | cDNA | n/a | 1996 |
Glass | 40,000 | cDNA | Stanford | 1996 |
Glass | 54,000 | 25mer oligonucleotides | Affymetrix | 2000 |
Glass | 46,000 | 60mer oligonucleotides | Agilent | 2004 |
Exon Array | 1 million exons | 123mer oligonucleotides | Affymetrix | 2005 |
BeadChip | 50,000 | 79mer oligonucleotides | Illumina | 2005 |
The importance of microarrays in human biology
Microarray-based insights for the transplant physician
Author | Journal | Array type | Tissue | Phenotype | Key findings |
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Human studies | |||||
Brouard et al. [49] | Proc Natl Acad Sci U S A 2007 | cDNA | Lymphocyte | TOL, AR, CAN, stable, MIS | AKR1C1, AREG, BRRN1, C1S, CCL20, CDC2, CDH2, CHEK1, DHRS2, DEPDC1, ELF3, HBB, IGFBP3, LTB4DH, MS4A1, MTHFD2, PARVG, PLXNB1, PODXL, PPAP2C, RAB30, RASGRP1, RBM9, RHOH, SLC29A, SMILE, SOX3, SPON1, TK1 and TLE4 |
Li et al. [44] | Physiol Genomics 2007 | U133plus GeneChip | Blood | STA, AR | Globin genes onfounders in biomarker discovery from PAX gene samples for AR |
Nagarajan et al. [89] | Clin Transplant 2007 | cDNA | Peripheral blood | HTN, RVA, EPO | Hemoglobin zeta, G2, E1, CTGF, PLA2 G2A, PDGF-A, VEGF, CDH5, GDF1, TIE, TBRG1, EPS8, FIBP, EPOR, TFRC, STAT5, Jak2 and CLK1 |
Park et al. [90] | Transplantation 2007 | U133A 2.0 GeneChip | Kidney biopsy | Fibrotic | STAT1, STAT2, proteasome subunit [beta]-type-8, Col1A1, FN1, phosphoinositide-3-kinase regulatory subunit-3, VCAM1, GRZMA, GBP1, IER3, HLA-DRbeta, IL-10, TGFB, IFNG, IL-6 and FoxP3 |
Mas et al. [91] | Transplantation 2007 | U133A 2.0 GeneChip | Kidney biopsy, peripheral blood, urine | CAN | TGF-beta, laminin, gamma 2, metalloproteinases-9, collagen type IX alpha 3, immunoglobulins, cytokine, chemokines receptors, EGFR, FGFR2, AGT, EGFR and TGFB |
Morgun et al. [47] | Circ Res 2006 | Oligonucleotide | Heart biopsy, kidney and lung | AR, infection | CCL18, TRB, LTB, ITGB2, HA-1, CORO1A, IGKC, RARRES3, CCL5, HLADRB3, STAT1, C1QA, GMFG, CD74, CD14, PSCD4, BTN3A3, HLA-F and UBE2L6 |
Hotchkiss et al. [55] | Transplantation 2006 | U133A GeneChip | Kidney biopsy | CAN | TGF-B, thrombospondin 1, PDGF, integrins, MMP7, C4B, properdin, VCAM1, Annexins, VEGF, EGF and FGF |
Kurian et al. [54] | Transplantation 2005 | U133A GeneChip | Kidney biopsy | LDN | HIF1a, HIF1B, TNF, TNFR, TGF-B, FGF, integrins, MMP, elastin, GHRH and VEGF |
Eikmans et al. [53] | J Am Soc Nephrol 2005 | HG U95Av2 GeneChip | Kidney cortex | CAN | Surfactant protein-C (SP-C), S100 calcium-binding protein A8 (S100A8), S100A9 and immuno-globulin genes |
Melk et al. [62] | Kidney Int 2005 | cDNA | Kidney cortex | Renal aging | NADH dehydrogenase, APO, kynureninase PAH, dynein, CLDN8, MMP7, fibulin, tenascin, CSPG2, SERPINA3, immunoglobulins, somatostatin receptor, THY1, natriuretic peptide receptor and SLC solute transporter family |
Zhang et al. [92] | Clin Transplant 2004 | HG U95Av2 GeneChip | Lymphocyte | Stable transplant | Membrane-type matrix metalloproteinase 1, SH3 binding protein, MEA6, TOB family 4, RBP2, IL-1A, Argininosuccinate synthetase, Brain and nasopharyngeal carcinoma, NSG-x, hVH-5 and Eosinophil Charcot-Leyden crystal protein |
Mansfield et al. [14] | Am J Transplant 2004 | cDNA | Kidney biopsy | AR sub-types | MIP-1, CCR5, CX3CR1, DARC, SCYB10, SCYA5,SCYA3, SCYA13, SCYA2, IL2RB, IL6R, IL16, 1L15R, DEFA1, DEFB1, SCYA2, SCYA5, MST1, STAT1, STAT6, CD69, MAL, NFATC3, Annexins, CASP10, PECAM1 and VCAM1 |
Hauser et al. [37] | Lab Invest 2004 | cDNA | Kidney biopsy | Donor source | Complements, LTF, NK4, VCAM1, interleukins, HLA, BCL6, GPX2,FBP1, PCK2, SORD, APOA4, CYP3A7, FABP1, APOM, CYP3A4, HIF1A, STAT1,TIMP1, ADAMTS1, TNFSF10 and CDC25B |
Kainz et al. [93] | Am J Transplant 2004 | cDNA | Kidney biopsy | Donor source | Osteopontin, SOD2, RARRES1, chemokine ligand 1, antileukoproteinase, STAT1, CDH6, SPP1, SERPINA3 and GPX2 |
Flechner et al. [56] | Am J Transplant 2004 (a) | Oligonucleotide | Kidney biopsy | CAN, drug effect | TGFB, TNFA, PDGF, ICAM, VCAM1, integrin B, MCP-1, CCR2, MPI-3B, MHC, MMP, TIMP1, RANTES, VEGF, collagen III, Angiotensin II receptor, TSP and FN1 |
Flechner et al. [94] | Am J Transplant 2004 (b) | HG U95Av2 GeneChip | Kidney biopsy, peripheral blood | AR | AIF, CD14, CD163, CD2, CD3D, CD48, CD53, chemokines, interleukins, C1q, immunoglobulins, INFG, TCR TNF, and HLA |
Donauer et al. [57] | Transplantation 2003 | cDNA array | CAN | AQP2, AQP3, lipoprotein lipase, PML-2, Napsin 1, precursor, Flotillin-1, Type IV collagenase, Hepatocyte growth factor activator inhibitor, RIG-like 7–1, MECI-1, PGER, TEM8, MHC class I, C1s and immunoglobulins | |
Higgins et al. [76] | Mol Biol Cell 2004 | cDNA | Cortex, medulla, papillary tips, | Normal | Identify patterns of gene expression in discrete portions of the normal kidney |
Sarwal et al. [1] | N Engl J Med 2003 | cDNA | Kidney biopsy, pediatrics | AR, CAN, DT and infection | TCR, HLA class II, HLA class I, immunoglobulins, lactotransferrin, chemokines, CD20, CD34, IGF1R, TNFR, MST1, NK4, duffy antigen/chemokine, receptor, STAT1, TGFR1, granzyme A, perforin, IL2R, CD53, lymphotoxin, lymphotoxin R, NFKB1, CD59, IFNGR1 and annexins |
Scherer et al. [58] | Transplantation 2003 | HG U95Av2 GeneChip | Kidney biopsy | CAN | Keratin tumor suppressor candidate 7, OS9(APRIL), G-protein gamma7, protein/cell adhesion molecule-like, GRB2-associated binding protein 1, and PRLR |
Chua et al. [95] | Am J Transplant 2003 | cDNA | Kidney biopsy | AR/anemia | Hb-zeta, Hb-beta, Hb-alpha2, FOLR2, FOLR3, CAH1, immunoglobulins, GPX1, and lactotransferrin |
Zhang et al. [96] | Transplant Proc 2002 | Oligonucleotide | Lymphocyte | Stable transplant | CD80, interleukins, CD44, CD40L, CD40, VLA-5, LFA-1, TCR alpha, Lck, calcineurin, PKC, IFNG, LFA-1, TCR alpha, Lck, calcineurin, PKC, IFNG, TGFB, TNF-alpha, TNFR1, G-CSFR and PDGF receptor, |
Akalin et al. [97] | Transplantation 2001 | Hu6800 GeneChip | Kidney biopsy | AR | HuMig, TCR RING4, ISGF-3, CD18 |
Animal studies | |||||
Kusaka et al. [98] | Transplantation 2007 | Agilent rat oligonucleotide array G4130A | Kidney allografts, T lymphocytes | Brain death donor | Gro1, IP-10, p53, NF kappa B, Myc, Jun, c-fos, LCN2 and SPP1 |
Berthier et al. [99] | Kidney Int 2006 | 230 A GeneChip | Kidney allografts | CAN | MMP-11,-12,-14, ADAM-17, TIMP-1,-2 TGF-B, MMP-9, meprin and MMP-24 |
Djamali et al. [100] | Transplantation 2005 | mouse stress toxicity GEArray | Kidney allografts | CAN | ANXA5, CASP1, CASP8, TNFRII, TRAIL, FASL, BAX, inducible nitric oxide synthase, cytochrome p450 4A, [alpha]-crystalline B, heme-oxygenase II, SOD, HSP60, HSP27, BCL-X and metallothionein |
Schuurs et al. [101] | Am J Transplant 2004 | Oligonucleotide | Kidney allografts | HTN brain death | Water channel AQP-2, selectins, IL-6, oc-B-fibrinogen, KIM-1, HO-1, Hsp70, MnSOD2, ATF-3, EGR-1 and PIK3R1 |
Einecke et al. [43] | Am J Transplant 2007 | Oligonucleotide | Mouse kidney | Rejection | SLC2a2, SLC1a1 |
Leonard et al. [102] | FASEB J 2006 | HG U95Av2 GeneChip, murine U77A | Mouse kidney, human proximal tubular epithelial cells | Ischemia reperfusion injury | In mouse model: ALDH1A1, ALDH1A7, GSTM5, GSTA2, GSTP1, NQO1 and Nrf2. In human: Nrf2 is up-regulated on reoxygenation |
Famulski et al. [60] | Am J Transplant 2006 | Oligonucleotide | Mouse kidney | AR | Define IFNG-dependent, rejection-induced transcripts (GRITs) in mouse kidney allografts. IFNG inducible: CXCl9, UBD and MHC |
Einecke et al. [43] | Am J Transplant 2007 | Oligonucleotide | Kidney allografts, T lymphocytes | Rejection | Cytotoxic T lymphocyte-associated transcripts (CATs): CD2, CD3g, GZMB, TCRB, MES |
Current unmet biological questions in transplantation: how can we address them?
Chronic allograft nephropathy what are the early injury pathways?
Acute rejection prediction and immunosuppression customization
Limited donor source—how can we expand this?
Methods of applying microarrays to clinical practice
Identification of differentially expressed genes between sample groups
What limits the use of microarrays in clinical practice?
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Quality controlThe high-throughput nature of this technology, combined with the expected large numbers of data, result in a high risk for error. With the increasing use of genomic studies in transplantation, there is a need to control for various confounder effects that obscure biomarker discovery in graft rejection. In view of many concerns raised, the US Food and Drug Administration (FDA) lunched the Microarray Quality Control (MAQC) project. An excellent correlation of gene expression of human reference RNA (Stratagene) and human brain reference RNA (Ambion), across seven different array platforms, across five different laboratories, using three different amplification protocols [75], was shown in this study. Thus, while the need for quality control is a limitation for array studies, recognition of means to address this could turn this around as a benefit, resulting in the generation of robust datasets that could be queried with confidence by multiple users.
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High costAt present, because of the sophistication of microarrays, this is a costly technology available only in selected laboratories. Microarray technologies, however, are rapidly improving, and the costs of the technique continue to fall, thus paving the way for wider access and more generalized usage.
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Sampling variabilityParticularly for renal transplant biopsies, differing amounts of cortex vs medulla represented in a sample can affect the pattern of gene expression of a sample. Therefore, one can cross-reference a publicly available gene list specific for different compartments of kidney to minimize false clustering of samples [76]. There is also the problem of variable sample pathology. If only one biopsy core is being used for microarray analysis, it will be necessary to identify transcriptional changes that are more global and robust than patchy cellular interstitial infiltration, such as effects of cytokines on the renal tissue or global interstitial changes. mRNA is a very fragile molecule that can be degraded within minutes of surgical procedure [77], drastically affecting the interpretation of microarray data [78]. Moreover, subtle variations in biopsy handling and method of RNA extraction from samples can result in different levels of gene expression [78].
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Difficulty in detecting some disease processes in transplantation by microarraysExisting collagen, readily visible to the pathologist, is not necessarily associated with mRNA changes if the process of active fibrogenesis is complete. Small cell populations that make a major contribution to disease might give only a weak signal in transcriptome studies of whole biopsies or unseparated blood. Antibodies produced in lymphoid tissues could damage the kidney without any mRNA being detectable in the kidney. Microarray analysis cannot offer insights into these critical cellular and molecular processes in the tissues.
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Discrepancy in array studiesWeak overlap exists between gene lists from individual studies of similar phenotypes in transplantation. The disparity among microarray data can be attributed to several factors: differences in microarray platforms with differing gene sets; weak statistical power and small sample sizes; biological variance because of variability in patient characteristics; experimental variance including lack of uniform protocols for study design, sample collection, RNA processing, and sample labeling and hybridization; different tools for data processing and statistical analysis (Table 3); variable thresholds for data filtering; varying stringencies for false discovery rates and statistical significance; and different data analysis methods. Nevertheless, a recent study compared microarray data for rejection across platforms, samples, and laboratories with some success. A gene set for acute rejection prediction generated from a heart biopsy [47] was used to predict previous published data for kidney biopsy [1, 56] and lung broncho-alveolar lavage cells [79].
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Confounders exist in microarray experimentsPrevious studies have demonstrated that the abundance of globin genes in whole blood may mask the underlying biological differences in whole-blood samples. In a comparison of gene-expression profiles of peripheral blood, using different protocols of sample preparation, amplification and hybridization on the Affymetrix platform, we demonstrated that the globin reduction method is not sufficient to unmask clinically relevant, rejection-specific, transcriptome profiles in whole blood. Additional mathematical application for globin gene depletion improves the efficacy of globin reduction but cannot remove the confounding influence of globin gene hybridization [44]. Other problems of analysis of blood may be more serious than the globin issue: the massive changes in cell populations caused by illness, surgery or infection make it difficult to define small changes in specific mRNA levels. It will be challenging to distinguish the blood signal for the alloimmune response from such common non-specific changes.Table 3List of pitfalls in microarray analyses and solutions (SVD singular value decomposition, Cy cyanine, qPCR quantitative polymerase chain reaction)Pitfalls in microarray analysisSolutionsData variability, particularly for genes with low expression levelsUse replicate arrays to reduce false positivesSmall sample amounts which limit replicationUse of amplified RNA (aRNA)Expression bias due to amplificationUse improved protocols with single-roundamplificationDifficult to control input RNA amounts accuratelyUse of normalization standard and two-color labelingstrategy to minimizeSpot quality may varyUse stringent data-filtering criteria to assess signal/noise ratio and spot signal consistencyLot-to-lot variation in PCR yield on cDNA arraysUse data-filtering methods such as SVD to reduce batch biases (see text)Hybridization efficiency varies with different probesUse long-oligonucleotide arrays to minimize selected hybridization artifactsUnequal labeling efficiency of Cy3 and Cy5 dyesUse reciprocal labeling to confirm observations or use single-dye labeling systemSmall numbers of samples and very large numbers of genes analyzed may contribute to false discoveryConfirm mRNA measurements using independent test methods such as qPCR and independent samplesHeterogeneity within study groups may contribute to false discoveryUse statistical modeling such as logistic regression to combine multiple genesProtein expression levels and function not measuredConform with protein expression methods (e.g. immunohistochemistry, protein arrays)
The black-box of microarray data analysis
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The Gene Expression Profile Analysis Suite (GEPAS; https://doi.org/www.gepas.org)
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The Institute for Genomic Research (TIGR; https://doi.org/www.tigr.org/software/microarray.shtml)
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Significance analysis of microarrays (SAM; https://doi.org/www-stat.stanford.edu/tibs/SAM/),
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Prediction analysis of microarrays (PAM; https://doi.org/www-stat.stanford.edu/tibs/PAM/),
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Expression Profiler: next generation (EP:NG; https://doi.org/www.ebi.ac.uk/expressionprofiler),
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Bioconductor (https://doi.org/www.bioconductor.org),
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Cancer gene expression data analyzer (caGEDA; https://doi.org/bioinformatics.upmc.edu/GE2/GEDA.html),
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Analysis of microarray data (AMIADA; https://doi.org/dambe.bio.uottawa.ca/amiada.asp),