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.

Bio-Technical Methods Section (BTS)

Optimization of quantitative real-time RT-PCR parameters for the study of lymphoid malignancies

Abstract

Real-time quantitative reverse transcription polymerase chain reaction (RT-PCR) is a powerful method for measurement of gene expression for diagnostic and prognostic studies of non-Hodgkin's lymphomas (NHL). In order for this technique to gain wide applicability, it is critically important to establish a uniform method for normalization of RNA input. In this study, we have determined the best method to quantify the RNA/cDNA input per reaction and searched for the most useful endogenous control genes for normalization of the measurements, based on their abundance and lowest variability between different types of lymphoid cells. To accomplish these aims, we have analyzed the RNA expression of 11 potential endogenous control genes (glyceraldehyde-3-phosphate dehydrogenase, β-actin, peptidylprolyl isomerase A, β2 microglobulin, protein kinase cGMP-dependent, type I, hypoxanthine phosphoribosyltransferase 1, TATA box binding protein, transferrin receptor, large ribosomal protein, β-glucoronidase and 18S ribosomal RNA). In all, 12 different B- and T-cell lymphoma/leukemia cell lines, 80 B- and T-cell NHL specimens, and resting and activated normal B and T lymphocytes were screened. Normalization of the nucleic acid input by spectrophotometric OD260 measurement of RNA proved more reliable than spectrophotometric or fluorometric measurements of cDNA or than electrophoretic estimation of the ribosomal and mRNA fractions. The protein kinase cGMP-dependent, type I (PRKG1) and the TBP genes were expressed at common abundance and exhibited the lowest variability among the cell specimens. We suggest that for further lymphoma studies based on the real-time RT-PCR quantification of gene expression, that RNA input in each reaction be equalized between the specimens by spectrophotometric OD260 measurements. The expression of the gene of interest in different samples should be normalized by concomitant measurement of the PRKG1 and/or the TBP gene products.

Introduction

Measurement of gene expression is becoming increasingly important in the study of diverse biological processes and understanding of disease pathogenesis. Traditional methods, such as Northern blots and RNA protection assays, are limited by their requirement for large amounts of RNA and their time-consuming nature. By contrast, real-time reverse transcription-polymerase chain reaction (RT-PCR) requires minute amounts of RNA and is rapid and quanti-tative.1 These features are especially valuable in the clinical setting. Indeed, multiple assays based on real-time quanti-tative RT-PCR have been developed for diagnosis, predic-tion of outcome and monitoring of disease in lymphoma patients.2,3,4,5,6

Clinical applicability of these assays requires high reproducibility and precision, which may be confounded by inconsistencies in the procedures used to collect tissues and to isolate the RNA. A common method for minimizing these errors is to standardize the input amount of RNA or cDNA and to simultaneously measure a cellular RNA whose expression level is constant between samples.7,8 This RNA serves as an endogenous control and allows comparison of the data on the genes of interest from different samples.

Ideally, the RNA target used as an endogenous control should be expressed at a constant level among different tissues and at all stages of development as well as be unaffected by experimental treatments.7,8 Some investigators also require that the endogenous control should be expressed at roughly the same level as the RNA under study.8 A constitutively expressed ‘housekeeping’ gene would, therefore, appear to be a good choice for an endogenous control. Unfortunately, there is no one single RNA whose expression is constant in all situations.8 It is, therefore, imperative to identify the most appropriate reference RNA for the particular set of experimental samples and to validate its use. In recent lymphoma studies various ‘housekeeping’ genes (glyceraldehyde-3-phosphate dehydrogenase (GAPD), β2 microglobulin (B2m), β-actin (ACTB), ribosomal proteins and others) have been used as endogenous controls, frequently without demonstration of their validity for this purpose.2,5,9,10,11 Inconsistent results may occur when ‘housekeeping genes’ do not exhibit the mandatory constancy of expression throughout all the test samples. The aims of this study, therefore, were to identify the best endogenous control genes and to establish general guidelines for RNA normalization in the real-time quantitative RT-PCR technique with particular emphasis on lymphoid cells and their malignant counterparts.

Materials and methods

A detailed and comprehensive description of the methods and protocols is presented in the Appendix.

Patient material and cell lines

Six B-cell non-Hodgkin's lymphoma (NHL) cell lines (Raji, SU-DHL6, HF1, OCI-Ly3, OCI-Ly10 and RC-K8,) and six T-cell lymphoma and leukemia cell lines (SU-DHL5, SUP-T13, PEER, HPB-ALL, MOLT-4 and Jurkatt) were selected for this study. All cell lines, except OCI-Ly3 and OCI-LY10, were grown in RPMI 1640 medium (Fisher Scientific Co., LLC, Santa Clara, CA, USA), supplemented with 10% fetal calf serum, 2 mM/L glutamine (GIBCO BRL, Grand Island, NY, USA), and penicillin/streptomycin (GIBCO BRL, Grand Island, NY, USA). The OCI-Ly3 and OCI-Ly10 cell lines were grown in IMDM essential medium (Fisher Scientific Co., LLC, Santa Clara, CA, USA), supplemented with 20% fresh human plasma and 50 μ M β-mercaptoethanol.

Biopsy specimens from two normal tonsils and 80 NHL patients either with follicle center B-cell lymphoma (FCL) (nine patients), diffuse large B-cell cell lymphoma (DLBCL) (66 patients) or T-cell lymphoma (five patients), classified according to the Revised European-American Lymphoma Classification,12 were used in this study. All FCL specimens but one had been stored in liquid nitrogen as a viable cell suspension. One FCL specimen was a fresh tumor sample obtained during diagnostic biopsy from which a single cell suspension was prepared. All T-cell lymphoma and seven DLBCL specimens were also stored in liquid nitrogen as a viable cell suspension. Additional 59 DLBCL specimens were embedded in Tissue-Tek optimal cutting Temperature (OCT) compound 4583 (Miles Inc., Elkhart, IN, USA) and preserved at −80°.

Peripheral blood mononuclear cells from healthy volunteers were isolated by Ficoll-Isopaque density centrifugation (Amersham Pharmacia Biotech, Piscataway, NJ, USA). B and T cells were enriched to more than 90% purity by human B- and T-cell enrichment cocktails, respectively (StemCell Technologies, Vancouver, BC, Canada). The enriched B and/or T cells were cultured in complete media at 5×106 cells per well in six-well plates (Corning Inc., Corning, NY, USA) and were stimulated with IL-4 100 U/ml (R&D Systems Inc., Minneapolis, MN, USA) or with IL-2 50 U/ml (Chiron, Emeryville, CA, USA) for 6 and 24 h.

RNA isolation and quantification

Total cellular RNA was isolated from 5 to 1.0×107 cells or from OCT embedded tissues using the RNeasy® Mini Kit (Qiagen, Valencia, CA, USA) according to the manufacturer's instructions. The amount of total RNA isolated from the cells was quantified using spectrophotometric OD260 measurements. The quality and relative composition of the RNA (percentage of mRNA and rRNA) were assessed by the Agilent 2100 Bioanalyzer (Palo Alto, CA, USA). cDNA was also quantified using spectrophotometric OD260 measurements or by fluorescence after the addition of PicoGreen® (Molecular Probes, Eugene, OR, USA).

Quantitative real-time RT-PCR

The expression levels of the 11 candidate endogenous control genes: GAPD, ACTB, peptidylprolyl isomerase A (PPIA, also known as cyclophilin A), B2M, protein kinase cGMP-dependent, type I (PRKG1, also known as PGK), hypoxanthine phosphoribosyltransferase 1 (HPRT1), TATA box binding protein (TBP), transferrin receptor (TFRC), large ribosomal protein-RPLPO, β-glucoronidase (GUSB) and 18S rRNA (18S) were measured by real-time quantitative RT-PCR using the 5′ nuclease technology on an ABI PRISM® 7900HT Sequence Detection System (Applied Biosystems, Foster City, CA, USA) and the Human TaqMan® pre-developed assay reagents (PDARs) endogenous controls (Applied Biosystems, Foster City, CA, USA). The sequences of the probes labeled with VIC® dye-MGB and primers for the Human TaqMan® PDAR endogenous controls are as follows: (GAPD (P/N 4326317E, forward primer 5′- IndexTermAGCCGAGCCACATCGCT-3′, probe 5′-IndexTermCCCTGGTGACCAGGC-3′, reverse primer 5′-IndexTermTGGCAACAATATCCACTTTACCAGAGT-3′)); (ACTB (P/N 4326315E, forward primer 5′-IndexTermCCCCGCGAGCACAGA-3′, probe 5′-IndexTermCTTTGCCGATCCGC-3′, reverse primer 5′-IndexTermCCACGATGGAGGGGAAGAC-3′)); (PPIA (P/N 4326316E, forward primer 5′-IndexTermCTGGACCCAACACAAATGGT-3′, probe 5′-IndexTermTGCACTGCCAAGACT-3′, reverse primer 5′-IndexTermGCCTTCTTTCACTTTGCCAAAC-3′)); (B2M (P/N 4326319E, forward primer 5′-IndexTermTGACTTTGTCACAGCCCAAGATA-3′, probe 5′-IndexTermACATGTCTCGATCCCAC-3′, reverse primer 5′-IndexTermCGGCATCTTCAAACCTCCA-3′)); (PRGK1 (P/N 4326318E, forward primer 5′-IndexTermGGGAAAAGATGCTTCTGGGAA-3′, probe 5′-IndexTermAAGGTTAAAGCCGAGCCA-3′, reverse primer 5′-IndexTermTTGGAAAGTGAAGCTCGGAAA-3′)); (HPRT (P/N 4326321E, forward primer 5′-IndexTermTGGTCAGGCAGTATAATCCAAAGA-3′, probe 5′-IndexTermAGCTTGCGACCTTGAC-3′, reverse primer 5′-IndexTermTCAAATCCAACAAAGTCTGGCTTA-3′)); (TBP (P/N 4326322E, forward primer 5′-IndexTermGCACAGGAGCCAAGAGTGAA-3′, probe 5′-IndexTermCTTAGCTGGAAAACC-3′, reverse primer 5′-IndexTermTCACAGCTCCCCACCATGTT-3′)); (TFRC (P/N 4326323E, forward primer 5′-IndexTermGGATAAAGCGGTTCTTGGTACCA-3′, probe 5′-IndexTermTGCCAGCCCACTGTT-3′, reverse primer 5′-IndexTermCCAGTAACCGGATGCTTCACA-3′)); (RPLPO (P/N 4326314E, forward primer 5′-IndexTermCCAACTACTTCCTTAAGATCATCCAACTA-3′, probe 5′-IndexTermTGCTCCCACAATGAA-3′, reverse primer 5′-IndexTermACATGCGGATCTGCTGCA-3′)); (GUSB (P/N 4326320E, forward primer 5′-IndexTermCTCATTTGGAATTTTGCCGATT-3′, probe 5′-IndexTermCGTCGGTGACTGTTC-3′, reverse primer 5′-IndexTermCCGAGTGAAGATCCCCTTTTTA-3′)); (18S (P/N 4319413, forward primer 5′-IndexTermCGGCTACCACATCCAAGGAA-3′, probe 5′-IndexTermCACCAGACTTGCCCTC-3′, reverse primer 5′-IndexTermGCTGGAATTACCGCGGCT-3′)).

The RNA was reverse transcribed using the High-Capacity cDNA Archive Kit (Applied Biosystems, Foster City, CA, USA) according to the manufacturer's protocol with a minor modification, the addition of RNase inhibitor (Applied Biosystems, Foster City, CA, USA) at a final concentration of 1 U/μl. Samples were incubated at 25°C for 10 min and 37°C for 120 min. PCR reactions were prepared in a final volume of 20 μl, with final concentrations of 1× TaqMan® Universal PCR Master Mix (Applied Biosystems, Foster City, CA, USA) and cDNA derived from 20 ng of input RNA or 20 ng of cDNA as determined by spectrophotometric OD260 or PicoGreen® (Molecular Probes, Eugene, OR, USA) measurements. Thermal cycling conditions comprised an initial UNG incubation at 50°C for 2 min, AmpliTaq Gold® DNA Polymerase activation at 95°C for 10 min, 40 cycles of denaturation at 95°C for 15 s, and annealing and extension at 60°C for 1 min. Each measurement was performed in triplicate and the threshold cycle (Ct), the fractional cycle number at which the amount of amplified target reached a fixed threshold, was determined. Gene expression was concomitantly measured in Raji cells (calibrator) as suggested in ABI PRISM® 7700 SDS User Bulletin #2 (Applied Biosystems, Foster City, CA, USA) to allow comparison across all the tested specimens.

Statistical analysis

Interrun variability and intersample variability were assessed by variance using the Excel computer software. Comparison of variances for the expression of distinct genes was performed with Bartlett's test (GraphPad Prism® Software, version2, San Diego, CA, USA).

Results

To identify the most appropriate endogenous control genes for the quantification of RNA expression in lymphomas, we initially assessed the expression of 11 commonly used housekeeping genes (18S, RPLPO, GAPD, PPIA, PRKG1, TBP, B2M, GUSB, HPRT1, β-actin and TFRC) in six B-cell and six T-cell NHL and leukemia cell lines (Figure 1). The specific genes demonstrated different inter-cell line variability in their RNA expression. There was similarity in expression level of each of these genes between the B- and T-cell lines and within each cellular subset (B and T cells, respectively). The least variability between the T-cell lines was observed in the expression of 18S and RPLPO. The least variability between the B-cell lines was observed in expression of 18S, RPLPO, GAPD, PRKG1, PPIA and TBP. Consequently, these six genes were chosen for more extensive evaluation in NHL samples and control lymphocytes. Overall, these genes were found to exhibit differences in abundance in the specimens studied: 18S is a very high abundance gene (defined by Ct<10), GAPD, PPIA and RPLPO are high abundance genes (defined by Ct between 15 and 20) and PRKG1 and TBP are intermediate abundance genes (defined by Ct between 20 and 30).

Figure 1
figure1

mRNA expression of candidate endogenous control genes in human lymphoma and leukemia cell lines. (a) B-cell lines and (b) T-cell lines. Each cell line is represented by a different color or filling pattern. Numbers above the bars represent variance of expression of particular gene among the cell lines.

Many sources of intrinsic variability exist, including differences in efficiencies of RT reactions, pipetting inaccuracies and differences in handling and storage of the cells. Therefore, we assessed the intrinsic variability of gene expression levels for each of these six genes. Cells from an FCL biopsy were divided into eight aliquots containing 107 cells each. Each aliquot was treated as a distinct sample. In three aliquots, RNA preparation was performed after the removal of dead cells and cellular debris by Ficoll-Isopaque density centrifugation, while in the remaining five aliquots RNA preparation was not preceded by this density centrifugation. An identical amount of cDNA corresponding to 20 ng of RNA quantified by spectrophotometric OD260 measurement was used in each reaction. The expression of the 18S, RPLPO, GAPD, PPIA, PRKG1, and TBP genes in these aliquots is presented in Figure 2a. For each gene, the expression was very similar between the aliquots and not affected by the density separation procedure. All the genes exhibited interassay Variance 0.10.

Figure 2
figure2

Interassay variability and the effect of nucleic acid quantification by measurement of RNA input by spectophotometric OD260 measurements (a) or by measurement of cDNA input by PicoGreen® (b) on the expression of 18S, PRKG1, PO, TBP and GAPD genes. Cells from an FCL biopsy were divided into eight aliquots containing 107 cells each. Each aliquot was treated as a distinct sample. In three aliquots, RNA preparation was performed after the removal of dead cells and cellular debris by Ficoll-Isopaque density centrifugation (hatched bars), while in the remaining five aliquots RNA preparation was not preceded by density centrifugation. Numbers above the bars represent variance of expression of particular gene among the specimens.

We next assessed the best quantification method to control input RNA and/or cDNA per reaction. Four different quantification methods were assessed: (a) spectrophotometric OD260 measurement of RNA; (b) proportion of mRNA and rRNA (for 18S) derived from the electrophoretic separation with Agilent 2100 Bioanalyzer; (c) spectrophotometric OD260 measurements of cDNA and (d) fluorometric measurement of cDNA after addition of PicoGreen®. RNA and cDNA concentrations were measured by these methods and accordingly cDNA derived from 20 ng of input RNA or 20 ng of cDNA were added to each reaction. For the six evaluated genes, the smallest variability was observed in reactions in which the RNA input was quantified and equalized according to spectrophotometric OD260 measurements (Figure 2a). Equalization of input according to fluorometric measurement with PicoGreen® resulted in higher interassay variability (P<0.001, Figure 2b). Equalization of the RNA input according to the proportion of mRNA and rRNA (for 18S) or of the cDNA input according to spectrophotometric OD260 measurements, respectively, did not improve the interassay variation (data not shown). Therefore, RNA quantification by spectrophotometric OD260 measurements prior to RT is the best method to insure identical RNA input into RT-PCR reactions.

In clinical practice, the time from the biopsy acquisition to RNA preparation may vary from sample to sample. Extension of this time period may result in RNA degradation and might differentially affect the measured gene expression levels. To examine the effect of time variation in RNA preparation, a fresh FCL biopsy was divided into six portions from which RNA was prepared either immediately or after 2, 4, 6 or 24 h, during which time the cell specimens were kept on ice. RNA was also prepared from a portion of cells after freezing in 10% dimethyl sulfoxide (DMSO) for 24 h (Figure 3). For each gene, the variability in mRNA expression was similar or slightly above the expected intrinsic interassay variability. Consequently, these experiments demonstrated that differences in time between the biopsy and RNA preparation have only minimal effects on the measurement of mRNA expression of these six genes, as long as the specimens are kept on ice. However, if RNA degradation occurs, it may differentially affect distinct endogenous control genes. To test this hypothesis, a DLBCL specimen was divided into two parts, one part stored in liquid nitrogen until RNA extraction, while the second part was left at room temperature to allow cell death and partial RNA degradation, confirmed by analysis on the Agilent 2100 Bioanalyzer. Next we compared the expression of the 18S, RPLPO, GAPD, PPIA, PRKG1 and TBP genes in the preserved and degraded portions of the specimen (Table 1). The expression of all the genes decreased upon degradation; however, the extent of decrease varied between the genes with smallest decrease in expression observed for 18S.

Figure 3
figure3

Effect of delay in RNA preparation on the expression of 18S, PRKG1, PO, TBP and GAPD genes in FCL. A fresh biopsy was divided into six portions from which RNA was prepared immediately or after 2, 4, 6 and 24 h, during which time the specimens were kept on ice. RNA was also prepared from a portion of cells after freezing in 10% DMSO for 24 h (hatched bars). Numbers above the bars represent variance of expression of particular gene among the specimens.

Table 1 Gene expression upon tissue degradation

Subsequently, we have examined the expression of these genes in randomly chosen eight FCL specimens and two tonsils (Figure 4a), 7 DLBCL specimens (Figure 4b), five T-cell lymphomas (Figure 4c) as well as in normal resting and stimulated B and T lymphocytes (Figures 5a and b). Although there was no marked change in the expression of these housekeeping genes upon stimulation of B and T cells (Figure 5), the tumor experiments demonstrate that there is at least slight variability between specimens within one group as well as variability across the groups. This suggests that there is no perfect endogenous control gene for lymphoid tumors. While the Raji cell line, used as a calibrator, exhibited identical gene expression in all the experiments (data not shown), the abundance of these genes was lower in real specimens compared to the cell lines, as demonstrated by the higher Ct values (Figure 4 and Figure 5 vs Figure 1). However, the differences in the expression of these five genes between the samples within each disease or within the stimulation subcategories were relatively small and quite comparable.

Figure 4
figure4

mRNA expression of 18S, PO, GAPD, PRKG1 and TBP in tumor samples: B-cell FCL (a), DLBCL (b) and T-cell lymphoma (c). Numbers above the bars represent variance of expression of particular gene among the specimens. Hatched bars in (a) represent normal tonsils.

Figure 5
figure5

mRNA expression of 18S, PO, GAPD, PRKG1 and TBP in normal peripheral blood B (a) and T (b) cells. (a) Gene expression in un-stimulated (solid bars) and IL-4 stimulated (hatched bars) B -cells. Black, - stimulation for 6 h; gray, stimulation for 24 h. (b) Gene expression in un-stimulated (solid bars) and IL-2 stimulated (hatched bars) T-cells. Numbers above the bars represent variance of expression of particular gene among the specimens.

Between all the actual tumor specimens, normal tonsils and B and T cells studied, the gene with the lowest variance (0.13) was 18S RNA (Table 2). However, this gene is more stable and is less affected by degradation than most other genes and it is not expressed as mRNA thereby limiting its usage as an endogenous control. PRKG1 and TBP exhibited low variance in expression (0.72 and 0.68, respectively, Table 2a) compared to the other genes and thus represent good endogenous control genes. The lower abundance of TBP compared to PRKG1 might favor the usage of PRKG1 as a uniform endogenous control for lymphoid malignancy studies. Overall, the genes most suitable to serve as internal RNA quality and quantity controls in both B- and T-cell specimens are PRKG1 and TBP (Table 2a). To confirm this observation in a larger number of samples, we have analyzed PRKG1, TBP and GAPDH expression in an additional group of 59 DLBCL specimens (Figure 6). The gene with the lowest variance was PRKG1, followed by TBP and GAPDH, thus confirming our initial observations.

Table 2 (a) Comparison of statistical variance across different samples. (b) Comparison of mean Ct values across different samples
Figure 6
figure6

mRNA expression of GAPD, PRKG1 and TBP in 59 DLBCL tumor samples. Numbers above the bars represent variance of expression of particular gene among the specimens.

Discussion

The aim of this study was to establish guidelines for quantitative real-time RT-PCR studies of normal lymphocytes and lymphomas by (a) establishing the optimal technique for quantification of the input amount of RNA and/or cDNA and (b) identifying the appropriate endogenous control genes for RNA quality and quantity normalization.

Recent applications of DNA microarray techniques to assess global RNA expression patterns in tumor specimens have resulted in the accumulation of valuable data, which can be used to identify target genes for design of diagnostic and prognostic assays using real-time PCR methodology.13,14,15 The small amounts of RNA needed for these real-time PCR assays and the absence of any requirement for post-PCR processing make this method an ideal tool for clinical applications. However, introduction of these methods into clinical practice requires methodological standardization and identification of proper controls for RNA input normalization.

Previous attempts to identify ideal ‘housekeeping’ genes useful to control RNA input in all tissues demonstrated that such a gene does not exist.7,8,16,17 For example, the previously used ACTB and B2M genes are expressed at moderately abundant levels in most cells, similar to many other genes. However, alterations in cell morphology associated with tumor progression or transformation, dictated by cytoskeletal components, is associated with changes in the expression of ACTB.18 Indeed, in the present study (Figure 1) this gene demonstrated high variability between the tested cell lines. B2M is an integral part of the HLA class I complex. However, its expression has been reported to vary in different lymphomas.19 Therefore, experimental establishment of genes most suitable to be used as endogenous controls needs to be performed for each cell type, tumor type and each experimental design. Only limited and inconclusive attempts have been made to investigate the expression of housekeeping genes in lymphomas by Northern blot quantification.17 GAPD has commonly been used as a reference gene in lymphoma studies. However, confirmation of its usefulness as an endogenous control in studies of lymphoid malignancies has been lacking. Moreover, GAPD has been shown to exhibit marked variability of expression among normal colon epithelium,8 human prostate carcinoma20 and during the cell cycle.21

In the current study, we tested the most appropriate method to measure the RNA/cDNA input and evaluated the best intrinsic control gene for lymphoma RNA normalization. Our study demonstrates that RNA spectrophotometric measurement is the best method to insure identical RNA input into RT-PCR reactions. This result implied that the RT reaction goes to completion and that input mRNA is directly proportional to the amount of cDNA produced. It also indicated very good RT reproducibility across different transcripts within a given sample. We also demonstrated that, as long as the biopsy specimens are kept on ice, delays in RNA preparation do not significantly affect gene expression measurements assessed by real-time quantitative RT-PCR. However once partial degradation of RNA occurs, it affects distinct genes differentially with 18S being least affected thus confirming previous reports.22,23 Consequently, genes that exhibit marked resistance to degradation compared to most other genes may not serve as a good endogenous control.

Our study demonstrates that there is most probably no perfect housekeeping gene that can be used as a universal control for quantitative RT-PCR experiments. However, the genes most suitable for RNA quality and quantity control in both B and T cells and tumors are PRKG1 and TBP. Although 18S exhibited lowest variation in expression, its very high abundance, resistance to degradation compared to other genes and its inability to serve as a control for mRNA limit its universal applicability. Therefore, we suggest, based on our investigations, that spectophotometric OD260 RNA measurements be made and the test samples be equalized according to these determinations. Expression of the genes of interest in different samples should be normalized by concomitant measurement of the PRKG1 and/or the TBP gene products.

References

  1. 1

    Heid CA, Stevens J, Livak KJ, Williams PM . Real time quantitative PCR. Genome Res 1996; 6: 986–994.

    CAS  Article  Google Scholar 

  2. 2

    Lossos IS, Jones KD, Warnke R, Natkunam Y, Kaizer H, Zehnder JL et al. The expression of a single gene, BCL-6, strongly predicts survival in patients with diffuse large B-cell lymphoma. Blood 2001; 98: 945–951.

    CAS  Article  Google Scholar 

  3. 3

    Bruggemann M, Droese J, Bolz I, Luth P, Pott C, von Neuhoff N et al. Improved assessment of minimal residual disease in B cell malignancies using fluorogenic consensus probes for real-time quantitative PCR. Leukemia 2000; 14: 1419–1425.

    CAS  Article  Google Scholar 

  4. 4

    Medeiros LJ, Hai S, Thomazy VA, Estalilla OC, Romaguera J, Luthra R . Real-time RT-PCR assay for quantifying cyclin D1 mRNA in B-cell NON-Hodgkin's lymphomas. Mod Pathol 2002; 15: 556–564.

    Article  Google Scholar 

  5. 5

    Maes B, Vanhentenrijk V, Wlodarska I, Cools J, Peeters B, Marynen P et al. The NPM–ALK and the ATIC–ALK fusion genes can be detected in non-neoplastic cells. Am J Pathol 2001; 158: 2185–2193.

    CAS  Article  Google Scholar 

  6. 6

    Snider JV, Wechser MA, Lossos IS . Human disease characterization: real-time quantitative PCR analysis of gene expression. Drug Discov Today 2001; 6: 1062–1067.

    CAS  Article  Google Scholar 

  7. 7

    Suzuki T, Higgins PJ, Crawford DR . Control selection for RNA quantitation. Biotechniques 2000; 29: 332–337.

    CAS  Article  Google Scholar 

  8. 8

    Bustin SA . Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J Mol Endocrinol 2000; 25: 169–193.

    CAS  Article  Google Scholar 

  9. 9

    Baseggio L, Bienvenu J, Charlot C, Picollet J, Felman P, Coiffier B et al. Higher LPS-stimulated TNF-αmRNA levels in peripheral blood mononuclear cells from non-Hodgkin's lymphoma patients. Exp Hematol 2001; 29: 330–338.

    CAS  Article  Google Scholar 

  10. 10

    Bijwaard KE, Aguilera NS, Monczak Y, Trudel M, Taubenberger JK, Lichy JH . Quantitative real-time reverse transcription-PCR assay for cyclin D1 expression: utility in the diagnosis of mantle cell lymphoma. Clin Chem 2001; 47: 195–201.

    CAS  PubMed  Google Scholar 

  11. 11

    Husson H, Carideo EG, Neuberg D, Schultze J, Munoz O, Marks PW et al. Gene expression profiling of follicular lymphoma and normal germinal center B cells using cDNA arrays. Blood 2002; 99: 282–289.

    CAS  Article  Google Scholar 

  12. 12

    Harris NL, Jaffe ES, Stein H, Banks PM, Chan JK, Cleary ML et al. A revised European-American classification of lymphoid neoplasms: a proposal from the International Lymphoma Study Group [see comments]. Blood 1994; 84: 1361–1392.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000; 403: 503–511.

    CAS  Article  Google Scholar 

  14. 14

    Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, Fisher RI et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med 2002; 346: 1937–1947.

    Article  Google Scholar 

  15. 15

    Shipp MA, Ross KN, Tamayo P, Weng AP, Kutok JL, Aguiar RC et al. Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med 2002; 8: 68–74.

    CAS  Article  Google Scholar 

  16. 16

    Feroze-Merzoug F, Berquin IM, Dey J, Chen YQ . Peptidylprolyl isomerase A (PPIA) as a preferred internal control over GAPDH and β-actin in quantitative RNA analyses. Biotechniques 2002; 32: 776–778,780,782.

    CAS  Article  Google Scholar 

  17. 17

    Finnegan MC, Goepel JR, Hancock BW, Goyns MH . Investigation of the expression of housekeeping genes in non-Hodgkin's lymphoma. Leukemia Lymphoma 1993; 10: 387–393.

    CAS  Article  Google Scholar 

  18. 18

    Lossos IS, Alizadeh AA, Diehn M, Warnke R, Thorstenson Y, Oefner PJ et al. Transformation of follicular lymphoma to diffuse large-cell lymphoma: alternative patterns with increased or decreased expression of c-myc and its regulated genes. Proc Natl Acad Sci USA 2002; 99: 8886–8891.

    CAS  Article  Google Scholar 

  19. 19

    Aviles A, Zepeda G, Diaz-Maqueo JC, Rodriguez L, Guzman R, Garcia EL et al. β2 microglobulin level as an indicator of prognosis in diffuse large cell lymphoma. Leukemia Lymphoma 1992; 7: 135–138.

    CAS  Article  Google Scholar 

  20. 20

    Ripple MO, Wilding G . Alteration of glyceraldehyde-3-phosphate dehydrogenase activity and messenger RNA content by androgen in human prostate carcinoma cells. Cancer Res 1995; 55: 4234–4236.

    CAS  PubMed  Google Scholar 

  21. 21

    Mansur NR, Meyer-Siegler K, Wurzer JC, Sirover MA . Cell cycle regulation of the glyceraldehyde-3-phosphate dehydrogenase/uracil DNA glycosylase gene in normal human cells. Nucleic Acids Res 1993; 21: 993–998.

    CAS  Article  Google Scholar 

  22. 22

    Noguchi I, Arai H, Iizuka R . A study on postmortem stability of vasopressin messenger RNA in rat brain compared with those in total RNA and ribosomal RNA. J Neural Transm Gen Sect 1991; 83: 171–178.

    CAS  Article  Google Scholar 

  23. 23

    Santiago TC, Purvis IJ, Bettany AJ, Brown AJ . The relationship between mRNA stability and length in Saccharomyces cerevisiae. Nucleic Acids Res 1986; 14: 8347–8360.

    CAS  Article  Google Scholar 

Download references

Acknowledgements

This work was supported by Grants CA33399 and CA34233 from the USPHS-NIH.

Author information

Affiliations

Authors

Corresponding author

Correspondence to R Levy.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Lossos, I., Czerwinski, D., Wechser, M. et al. Optimization of quantitative real-time RT-PCR parameters for the study of lymphoid malignancies. Leukemia 17, 789–795 (2003). https://doi.org/10.1038/sj.leu.2402880

Download citation

Keywords

  • real-time quantitative RT-PCR
  • endogenous controls
  • lymphoma

Further reading

Search

Quick links