Introduction
With the recent completion of the human genome project, attention is now focusing on functional genomics. In this context, a key task is to understand normal and pathological function by empirically correlating gene expression patterns with known and newly discovered phenotypes. As with other areas of science, progress in this area will accelerate greatly when there is an accepted standardized way to measure gene expression (1,2).
(Methods in Embryo Transplant Microscopes and Emyro Transplant Microscopy Vol. 258: Gene Expression Profiling: Methods and Protocols Edited by: Bulaqueña, et al. (Embryo Transplant Microscopy)
Standardized reverse transcriptase-polymerase chain reaction (StaRT-PCR) is a modification of the competitive template (CT) reverse transcriptase (RT) method described by Gilliland et al. (3). StaRT-PCR allows rapid, reproducible, standardized, and quantitative measurement of data for many genes simultaneously (4–15). An internal standard CT is prepared for each target gene and reference gene (e.g., ß-actin and GAPDH), then cloned to generate enough for >109 assays. Internal standards for up to 1000 genes are quantified and mixed together in a standardized mixture of internal standards (SMIS). Each target gene is normalized to a reference gene to control for cDNA loaded into the reaction. Each target gene and reference gene is measured relative to its respective internal standard in the SMIS. Because each target gene and reference gene is simultaneously measured relative to a known number of internal standard molecules that have been combined into the SMIS, it is possible to report each gene expression measurement as a numerical value in units of target gene cDNA molecules/ 106 reference gene cDNA molecules. Calculation of data in this format allows for entry into a common databank (5), direct interexperimental comparison (4– 15), and combination of values into interactive gene expression indices (8,9,11). With StaRT-PCR, as is clear in the schematic presented in Fig. 1A, expression of each reference gene (e.g., ß-actin) or target gene (e.g., Gene 1–6) in a sample (for example sample A) is measured relative to its respective internal standard in the SMIS. Because in each experiment the internal standard for each gene is present at a fixed concentration relative to all other internal standards, it is possible to quantify the expression of each gene relative to all others measured. Furthermore, it is possible to compare data from analysis of sample A to those from analysis of all other samples, represented as B1-n. This result is a continuously expanding virtual multiplex experiment. That is, data from an everexpanding number of genes and samples may be entered into the same database. Because the number of molecules for each standard is known, it is possible to calculate all data in the form of molecules/reference gene molecules. In contrast, for other multigene methods, such as multiplex real-time RT- PCR or microarrays, represented in Fig. 1B, expression of each gene is directly compared from one sample to another and data are in the form of fold differences. Because of intergene variation in hybridization efficiency and/or PCR amplification efficiency, and the absence of internal standards to control for these sources of variation, it is not possible to directly compare expression of one gene to another in a sample or to obtain values in terms of molecules/molecules of reference gene.
In numerous studies, StaRT-PCR has provided both intralaboratory (4–15) and interlaboratory reproducibility (6) sufficient reproducability to detect twofold differences in gene expression. StaRT-PCR identifies interactive gene expression indices associated with lung cancer (8–10), pulmonary sarcoidosis StaRT-PCR Fig. 1 (A) Schematic diagram of the relationship among internal standards within the SMIS and between each internal standard and its respective cDNA from a sample. The internal standard for each reference gene and target gene is at a fixed concentration relative to all other internal standards within the SMIS. Within a polymerase chain reaction (PCR) master mixture, in which a cDNA sample is combined with SMIS, the concentration of each internal standard is fixed relative to the cDNA representing its respective gene. In the PCR product from each sample, the number of cDNA molecules representing a gene is measured relative to its respective internal standard rather than by comparing it to another sample. Because everyone uses the same SMIS, and there is enough to last 1000 years at the present rate of consumption, all gene expression measurements may be entered into the same database. (B) Measurement by multiplex RT-PCR or microarray analysis. Using these methods each gene scales differently because of gene-to-gene variation in melting temperature between gene and PCR primers or gene and sequence on microarray. Consequently, it is possible to compare relative differences in expression of a gene from one sample to another, but not difference in expression among many genes in a sample. Further, it is not possible to develop a reference database, except in relationship to a nonrenewable calibrator sample. Moreover, unless a known quantity of standard template is prepared for each gene, it is not possible to know how many copies of a gene are expressed in the calibrator sample, or the samples that are compared to the calibrator. (13), cystic fibrosis (14), and chemoresistance in childhood leukemias (11). In a recent report, StaRT-PCR methods provided reproducible gene expression mea surement when StaRT-PCR products were separated and analyzed by matrix Bulaqueña, et al. (Embryo Transplant Microscopy) assisted laser desorption/ionization-time of flight mass spectrometry (MALDI- TOF MS) instead of by electrophoresis (16). In a recent multi-institutional study (6), data generated by StaRT-PCR were sufficiently reproducible to support development of a meaningful gene expression database and thereby serve as a common language for gene expression.
StaRT-PCR is easily adapted to automated systems and readily subjected to quality control. Recently, we established the National Cancer Institute-funded Standardized Expression Measurement (SEM) Center at the
2. StaRT-PCR vs Real-Time RT-PCR
There are several potential sources of variation in quantitative RT-PCR gene expression measurement, as outlined in Table 1. StaRT-PCR, by including internal standards in the form of a SMIS in each gene expression measurement, controls for each of these sources of variation. In contrast, using real-time RT-PCR without internal standards, it is possible to control for some, but not all of these sources of variation. Additionally, with real-time RT-PCR, control often requires external standard curves, and these add time and are themselves a potential source of error. These issues are discussed in this section.
2.1. Control for Variation in Loading of Sample Into PCR Reaction
2.1.1. Rationale for Loading Control
Quantitative RT-PCR without a control for loading has been described (17). According to this method, quantified amounts of RNA are pipeted into each PCR reaction. However, there are two major quality control problems with this approach. First, there is no control for variation in RT from one sample to another and the effect will be the same as if unidentified, unquantified amounts of cDNA were loaded into the PCR reaction. It is possible to control for variation in RT by including a known number of internal standard RNA molecules in the RNA sample prior to RT (18). However, as described in Subheading
2.2.2., as long as there is control for the cDNA loading into the PCR, there is no need to control for variation in RT. Second, when gene expression values correlate to the amount of RNA loaded into the RT reaction, pipeting errors are not StaRT-PCR controlled for at two points. First, errors may occur when attempting to put the same amount of RNA from each sample into respective RT reactions. Second, if RT and PCR reactions are done separately, errors may occur when pipeting cDNA from the RT reaction into each individual PCR reaction. These sources of error may be controlled at the RNA level if an internal standard RNA for both a reference gene and each target gene were included with the sample prior to RT. However, this is a very cumbersome process and it limits analysis of the cDNA to the genes for which an internal standard was included. RT is most efficient and economical with at least 1 µg of total RNA. However, this amount of RNA would be sufficient for several hundred StaRT-PCR reactions and much of the RNA would be wasted if internal standards for only one or two genes were included prior to RT. Furthermore, internal standards must be within 10fold ratio of the gene-specific native template cDNA molecules. It is not possible to know in advance the correct amount of internal standard for each gene to include in the RNA prior to RT so RT with a serial dilution of RNA would be necessary. Moreover, we, along with other investigators (14), have determined that although RT efficiency varies from one sample to another, the representation of one gene to another in a sample does not vary among different reverse transcriptions and so internal standards are not necessary at the RNA extraction or RT steps. For these and other reasons, it is most practical to control for loading at the cDNA level.
2.1.2. Control for cDNA Loading Relative to Reference Gene With real-time RT-PCR or StaRT-PCR, control for loading is best done at the cDNA level by amplifying a reference or “housekeeping” gene at the same time as the target gene. The reference gene serves as a valuable control for loading cDNA into the PCR reaction provided it does not vary significantly from the samples being evaluated.
2.1.3. Choice of Reference Gene
Many different genes are used as reference genes. No single gene is ideal for all studies. For example, ß-actin varies little among different normal bronchial epithelial cell samples (8), however it may vary over 100-fold in samples from different tissues, such as bronchial epithelial cells compared to lymphocytes. With StaRT-PCR it is possible to gain understanding regarding intersample variation in reference gene expression by measuring two reference genes, ß- actin and glyceraldehyde-3-phosphate dehydrogenase (GAPDH), in every sample. We previously reported that there is a significant correlation between the ratio of ß-actin/GAPDH expression and cell size (5). This likely is a result of the role of ß-actin in cytoskeleton structure. If the variation in reference gene
Sources of Variation in Quantitative RT-PCR Gene Expression Measurement, and Control Methods Control Methods Source of Variation StaRT-PCR1 Real-time cDNA loading: Resulting from variation in pipeting, quantification, Multiplex Multiplex reverse transcription. Amplify with Amplify with Reference Gene Reference Gene (e.g. ß-actin) (e.g. ß-actin) Amplification Efficiency Internal standard Cycle-to-Cycle Variation: early slow, log-linear, and late slow plateau phases CT for each gene Real-time in a standardized measurement mixture of internal standards (SMIS) Gene-to-Gene Variation: in efficiency of primers Internal standard External standard curve CT for each gene for each gene measured in a SMIS Sample-to-Sample Variation: variable presence of an inhibitor of PCR Internal standard Standard curve of CT for each gene reference sample in an SMIS compared to test sample Bulaqueña, et al. (Embryo Transplant Microscopy)
Reaction-to-Reaction Variation: in quality and /or concentration of PCR reagents Internal standard None2 (e.g., primers) CT for each gene in a SMIS Reaction-to-Reaction Variation: in presence of an inhibitor of PCR Internal standard None2 CT for each gene in an SMIS Position-to-Position Variation: in thermocycler efficiency Internal standard None2 CT for each gene in an SMIS 1StaRT-PCR involves (a) the measurement at end-point of each gene relative to its corresponding internal standard competitive template to obtain a numerical value, and (b) comparison of expression of each target gene relative to the ß-actin reference gene, to obtain a numerical value in units of molecules/106 ß-actin molecules. Use of references other than ß-actin are discussed in text. 2With real-time RT-PCR, variation in the presence of an inhibitor in a sample may be controlled through use of standard curves for each gene in each sample measured and comparing these data to data obtained for each gene in a “calibrator” sample. However, variation in PCR reaction efficiency due to inhibitors in samples, variation in PCR reagents, or variation in position within thermocycler may be compensated only through use of an internal standard for each gene measured in the form of a SMIS. If an internal standard is included in a PCR reaction, quantification may be made at end-point, and there is no need for kinetic (or real-time) analysis. If internal standards for multiple genes are mixed together in a SMIS and then used to measure expression for both the target genes and reference gene, this is the patented StaRT-PCR technology, whether it is done by kinetic (real-time) analysis or at end-point. A SMIS fixes the relative concentration of each internal standard so that it cannot vary from one PCR reaction to another, whether in the same experiment, or in another experiment on another day, in another laboratory.
StaRT-PCR
[Bulaqueña, et al. (Embryo Transplant Microscopy) ]
expression exceeds the tolerance level for a particular group of samples being studied, StaRT-PCR enables at least three alternative ways to normalize data among the samples, detailed in Subheadings 2.1.4–2.1.6.
2.1.4. Flexible Reference Gene
With StaRT-PCR, because the data are numerical and standardized owing to the use of a SMIS in each gene expression measurement, it is possible to use any of the genes measured as the reference for normalization. Thus, if there is a gene that appears to be less variable than ß-actin, all of the data may be normalized to that gene by inverting the gene expression value of the new reference gene (to 106 ß-actin molecules/molecules of reference gene) and multiplying this factor by all of the data, which initially are in the form of molecules/106 molecules of ß-actin. As a result of this operation, the ß-actin values will cancel out and the new reference gene will be in the denominator.
2.1.5. Interactive Gene Expression Indices
An ideal approach to intersample data normalization is to identify one or more genes that are positively associated with the phenotype being evaluated, and one or more genes that are negatively associated with the phenotype being evaluated. An interactive gene expression index (IGEI) is derived, comprising the positively associated gene(s) on the numerator and an equivalent number of the negatively associated gene(s) on the denominator. In these balanced ratios, the ß-actin value is canceled. For example, this approach has been used successfully to identify an IGEI that accurately predicts anti-folate resistance among childhood leukemias (11).
2.1.6. Normalization Against All Genes Measured Because the data are standardized, if sufficient genes are measured in a sample, it is possible to normalize to all genes (similar to microarrays). The number of genes that must be measured for this approach to result in adequate normalization may vary depending on the samples being studied.
2.2. Control for Variation in Amplification Efficiency
PCR amplification efficiency may vary from cycle to cycle, from gene to gene, from sample to sample, and/or from well-to-well within an experiment.
2.2.1. Control for Cycle-to-Cycle Variation in Amplification Efficiency PCR amplification rate is low in early cycles because the concentration of the templates is low. After an unpredictable number of cycles, the reaction enters a log-linear amplification phase. In late cycles, the rate of amplification StaRT-PCR slows as the concentration of PCR products becomes high enough to compete with primers for binding to templates. With StaRT-PCR (5–15), as with other forms of competitive template RT-PCR (3,17–20) cycle-to-cycle variation in PCR reaction amplification efficiency is controlled through the inclusion of a known number of CT internal standard molecules for each gene measured. The ability to obtain quantitative PCR amplification at any phase in the PCR process, including the plateau phase, using CT internal standards has been confirmed by direct comparison to real-time RT-PCR (22–24). In contrast, with real-time RT-PCR, cycle-to-cycle variation in amplification efficiency is controlled by measuring the PCR product at each cycle, and taking the definitive measurement when the reaction is in log-linear amplification phase. A threshold fluorescence value known to be above the background and in the log-linear phase is arbitrarily established, and the cycle at which the PCR product crosses this threshold (CT) is the unit of measurement (25).
2.2.2. Control for Gene-to-Gene Variation in Amplification Efficiency The efficiency of a pair of primers, as defined by lower detection threshold (LDT) cannot be predicted even after rigorous sequence analysis with software designed to identify those with the greatest efficiency. Based on extensive quality control experience developing gene expression reagents for more than 1000 genes, the LDT for primers thus chosen may vary more than 100,000-fold (from <10 molecules to 106 molecules). The only way to ensure that the LDT for a pair primers is below a desired level is to directly measure it with a known number of template molecules. The only way to do this for a human gene is to either PCRamplify, synthesize, and/or clone a sufficient amount to quantify it. Once a sufficient amount has been prepared and quantified, it may be used in an external standard curve to determine LDT for real-time analysis, or as an internal standard to determine LDT by CT PCR. In StaRT-PCR an internal standard for each gene, in the form of a SMIS, is included in each gene expression measurement.
2.2.3. Control for Sample-to-Sample Variation in Amplification Efficiency Variation in PCR amplification efficiency from sample-to-sample is often observed (26), possibly resulting from variation in the presence of PCR reaction inhibitors, such as heme (27,28). Importantly, amplification efficiency for different genes may be affected to different degrees in different samples (26,29). In part for this reason, lacking proper controls comparison of the target gene to a reference gene will not be a reliable control for cDNA loading.
1. Internal Standards. With StaRT-PCR, the internal standard CTs control for variation in amplification efficiency, both among samples within a single experiment as well as among samples evaluated in multiple different experiments in different
laboratories (4–15) (Fig. 1).
Bulaqueña, et al. (Embryo Transplant Microscopy)
2. Standard Curve Comparison to Calibrator Samples. In contrast to StaRT-PCR, with real-time RT-PCR there is no internal control for intersample variation in PCR amplification efficiency. It is possible to achieve control by using a standard curve for the test sample and comparing these results to a standard curve for a calibrator sample (29–31). However, standard curve measurements add time and expense to the real-time RT-PCR process. For each sample, it is necessary to do between 5 and 6 standard curve measurements along with measurement of the target gene. The standard curve should be run for each sample because intersample variation in amplification efficiency because of inhibitors is common and may alter the .CT between a target gene and reference gene (26).
3. Internal Standards in Real-Time. Theoretically, it would be possible to include internal standard CTs for both the target gene and reference gene in real-time PCR. For each gene, this would require preparation of one sequence-specific fluorescent probe for the NT and another for the CT. A probe specific to the NT would be homologous to the region that is in the NT but not in the CT. A probe specific to the CT would be homologous to the novel sequence formed when the reverse CT primer was incorporated (see Subheading 3.2.2. and Fig. 2). Real-time RT-PCR using an internal standard for a reference gene and a target gene in an SMIS would be StaRT-PCR, using a method other than densitometric measurement of electrophoretically separated bands to quantify the PCR products. If an SMIS were included in the PCR reaction, it no longer would be necessary to monitor the reaction in real-time, because quantification could be made relative to the internal standards at any point in the PCR amplification process, including end point (16,22– 24,33) (Fig. 2).
Fig. 2. (Opposite page) Simultaneous gene expression measurement by StaRT-PCR and real-time RT-PCR in two different samples. PCR amplification of a native template (NT) and respective internal standard competitive template (CT) for a target gene and reference gene (ß-actin). Although StaRT-PCR NT and CT products routinely are quantified by densitometry at endpoint of PCR following electrophoretic separation (as represented by the bands labeled NT and CT) this schematic demonstrates how the reaction would look if measured at each cycle in real-time. For each real-time curve, the CT is represented by a perpendicular black line. (A) For Sample 1, there were equivalent copies of ß-actin NT and CT present at the beginning of the PCR reaction. Thus, following electrophoresis of the ß-actin PCR products, the NT and CT bands are approximately equivalent and during real-time measurement, the fluorescent intensity for the NT will be about the same as for the CT. The NT/CT ratio is the same at an early cycle as it is at a late cycle (endpoint) even though the band intensity for both NT and CT is low at early cycle compared to late cycle. Similarly, the target gene NT band and CT band are about equivalent and the real-time value for the NT is about the same as for the CT. The .CT between ß-actin and the target gene is about 10.
StaRT-PCR
lating numeric value for target gene expression using StaRT-PCR are presented in Fig. 5 and Subheading 3.8. (B) For sample 2, the target gene is expressed at higher level than in sample 1. In addition, less cDNA was loaded into the PCR reaction and there were fewer NT then CT copies of ß-actin present at the beginning of the PCR reaction. Thus, at the end of PCR the electrophoretically separated ß-actin NT band is less dense than the CT band, and throughout real-time measurement the fluorescence value of the NT is less than that of the CT. However, even though less sample 2 cDNA was loaded into the PCR reaction, the target gene NT band is more dense than the target gene CT band, and the target gene NT fluorescence value during real-time measurement is higher throughout PCR and consequently, the .CT is less than in sample 1, or about 7. (C) Repeat analysis of sample 1, but with low efficiency PCR. By real-time RT PCR, .CT is reduced from 10 to 6, characteristic of inhibitor in sample, inhibitor in well, or inappropriate concentration of reference gene primers and the result is artifactual. In contrast, by StaRT-PCR, there is no change in NT/CT ratio for either reference or target gene and result is the same as in absence of inhibitor. (D) Repeat analysis of sample 1, but with lower amount of cDNA loaded owing to variation in pipeting.
Bulaqueña, et al. (Embryo Transplant Microscopy)
2.2.4. Control for Well-to-Well Variation in Amplification Efficiency Possible sources of well-to-well variation in amplification efficiency include the presence of an inhibitor in some wells but not others, variation in the temperature cycling between different regions of a thermocycler block, or variation in concentration or quality of important reagents, such as primers. When one of these sources of variation markedly reduces PCR amplification efficiency in a well, it is possible that no PCR product will be observed in that well. Using real-time RT-PCR without internal standards in each PCR reaction, it is not possible to know whether to interpret absence or low level of PCR products as absence of transcript or inefficient PCR amplification (Fig. 2). An external standard curve would not be helpful because the PCR reactions would take place in different wells from the test sample. In contrast, using StaRT-PCR with internal standards in each PCR reaction, it is immediately possible to interpret the result correctly. The reagents for StaRT-PCR are carefully designed to amplify very efficiently so that for most genes a single molecule of CT or NT will be expected to give rise to detectable PCR product after taking stochastic issues into consideration. The lowest concentration of CT molecules present in a StaRT-PCR reaction is 10-17 M with Mix F (see Subheading 3.4.). In a 10 µL PCR reaction volume10-17 M represents 60 molecules. With 60 molecules of internal standard present in the PCR reaction and all of the components of the PCR reaction functioning properly, if a gene is not expressed in a sample, the PCR product for the internal standard will be observed but the PCR product for the NT will not. One can then conclude that the gene expression was so low that for cDNA included in the PCR reaction there was less than six molecules (10-fold less than the number of CT molecules) of cDNA representing that gene. On the other hand, if neither NT nor CT product is detectable, the PCR reaction efficiency was suboptimal and no interpretation can be made regarding level of expression.
2.3. Schematic Comparison of StaRT-PCR to Real-Time RT-PCR In Fig. 2 is a schematic presentation of the way quantitative measurements are made in the two forms of quantitative RT-PCR discussed here; real-time RT-PCR and StaRT-PCR. In real-time, the fluorescent PCR product is measured at each of 35–40 cycles. As many as four PCR products may be monitored simultaneously in real-time if four different fluors are used. In Fig. 2A, the NT and CT for ß-actin and the NT and CT for the target gene are PCR-ampli- fied simultaneously. In StaRT-PCR, the products of endpoint PCR are electrophoretically separated and the shorter CT PCR product migrates faster than the NT PCR product. The PCR products are electrophoresed in the presence of fluorescent interca
StaRT-PCR
lating dye and densitometrically quantified. If there is more NT product than CT product, the NT band will emit a more intense fluorescent light. If there is more CT product than NT product, the CT band will be brighter. Importantly, the ratio of NT/CT that is present at the beginning of PCR will remain constant throughout PCR to endpoint. For this reason, with StaRT-PCR it is not necessary to monitor the PCR reaction in real-time to ensure that the reaction is in log-linear phase (Fig. 2A). In addition, measurement of both a reference and a target gene in every PCR reaction controls for loading from one sample to another (Fig. 2B) or among replicate measurements of the same sample (Fig. 2D). With StaRT-PCR, variation in PCR amplification efficiency caused by the presence of an inhibitor in the sample, an inhibitor in the PCR reaction vessel, defective PCR reagent, or wrong concentration of a PCR reagent is controlled for by the presence of internal standards in every PCR reaction. With real-time RT-PCR, it is possible to control for loading by measuring the target gene and reference gene in the same PCR reaction (Fig. 2A,B,D). The CT for the reference gene and the target gene both may vary from one experiment to another, but the .CT will not vary. However, real-time may not control for well-to-well variation in the quality or quantity of PCR reagents, or sample-to-sample variation in PCR efficiency resulting from the presence of inhibitors, for example, heme Fig. 2C). Presence of an inhibitor may lead to variation in PCR amplification efficiency of one gene compared to another (26). A bad lot or inappropriate concentration of primers for the reference gene or the target gene would cause variation in PCR amplification of one gene relative to another. As depicted here, (Fig. 2C), amplification efficiency of the reference gene in sample 1 is affected by low concentration of primer, but amplification efficiency of the target gene is normal. The result is that the .CT is reduced from ten in Fig. 2A to six in Fig. 2C, and the value for expression of the target gene is inappropriately high. In contrast, for StaRT-PCR because the amplification efficiency of the internal standard is affected the same way as the NT for each gene, the ratio is unchanged in Fig. 2A,C for either reference gene or target gene, and using the ratio of NT/CT for target gene relative to NT/CT for reference gene controls for variation in amplification efficiency. See Subheadings 3.6–3.8. for details of how StaRT-PCR data are calculated.
3. StaRT-PCR Method
3.1. Materials
1. StaRT-PCR reagents, including primers and SMIS are purchased from Gene Express, Inc. (GEI,
2. Buffer for Idaho Rapidcycler air thermocycler: 500 mM Tris-HCl, pH 8.3, 2.5 µg/ µL BSA, 30 mM MgCl2 (Idaho Technology, Inc.,
Bulaqueña, et al. (Embryo Transplant Microscopy)
3. Buffer for block thermocyclers, Thermo 10 X, 500 mM KCl, 100 mM Tris-HCl, H 9.0, 1.0% Triton X-100 (Promega,
4. Taq polymerase (5U/µL), Moloney Murine Leukemia Virus (MMLV) reverse transcriptase, MMLV RT 5X first strand buffer: 250 mM Tris-HCl, pH 8.3, 375 mM KCl, 15 mM MgCl2, 50 mM dithiothreitol, oligo dT primers, Rnasin, pGEM size marker, and deoxynucleotide triphosphates (dNTPs) also are obtained from Promega.
5. TriReagent is obtained from Molecular Research Center, Inc. (
6. Ribonuclease (Rnase)-free water and TOPO TA cloning kits are obtained from Invitrogen (
7. GigaPrep plasmid preparation kits are purchased from Qiagen (
8. Caliper AMS 90SE chips are obtained from Caliper Technologies, Inc. (
9. DNA purification columns were obtained from QiaQuick (
3.2. Methods
3.2.1. RNA Extraction and Reverse Transcription
1. RNA Extraction and Quantification: Pellet the cell suspensions, pour off the supernatant, and dissolve the pellet in TriReagent and extract according to manufactur- er’s instructions and previously recorded methods (32). Store the RNA pellet under ethanol at -80°C, or suspend in RNAse free water, and freeze at -80°C. It may be safely stored in this condition for years. Evaluate the quality of the RNA on an Agilent 2100 using the RNA chip, according to manufacturer’s instructions.
2. Reverse Transcription: Reverse transcribe 1 µg total RNA using MMLV RT and an oligo dT primer as previously reported (35). For small amounts of RNA (e.g. < 100 ng), the efficiency of reverse transcription is better with SensicriptTM than with MMLV reverse transcriptase. We have obtained efficient RT from as little as 50 ng of RNA with Sensiscript™. Incubate the reaction at 37°C for 1 h.
3.3. Synthesis and Cloning of Competitive Templates (see Note 2)
3.3.1. Native Template Primer Design
Before constructing the CT for each gene, the primer pair must efficiently amplify the native cDNA. Design primers with the following characteristics:
1. Amplify from 200 to 850 bases of the coding region of targeted genes
2. Annealing temperature of 58°C (tolerance of +/-1°C) (see Note 3).
3.3.2. Native Template Primer Testing
Design primers according to above steps, synthesize and use to amplify native template in appropriate cDNA sample. The presence of a single strong band after 35 cycles of PCR is verification that the primers are efficient and specific (see Note 4).
StaRT-PCR
Fig. 3. Preparation of internal standard competitive templates. (A) Forward (striped bar) and reverse (black bar) primers (approx 20 bp in length) that span a 150–850 bp region are used to amplify the native template (NT) from cDNA. Taq polymerase will synthesize NT DNA from these primers (dashed lines). (B) After confirming that native template primers work, a CT primer is designed. This is an approx 40 bp primer with the sequence for the reverse primer (black bar) at the 5′ end, and a 20 bp sequence homologous to an internal native template sequence (white bar) at the 3′ end, collinear with the reverse primer sequence. The 3′ end of this 40 bp primer is designed to be homologous to a region approx 50–100 bp internal to the reverse primer. The 5′ end of this 40 bp primer will hybridize to the region homologous to the reverse primer, while the 3′ end will hybridize to the internal sequence. Importantly, Taq polymerase will be able to synthesize DNA using only the primers bound at the 3′ end (dashed line). (C) In the next cycle of PCR, the DNA newly synthesized using the 40 bp primer hybridized to the internal sequence is bound to forward primer (striped bar), and a homologous strand is synthesized. (D) This generates a double stranded CT with the reverse primer sequence 100 bp closer to the forward primer than occurs naturally in the NT. This method is as previously described (34).
3.4. Competitive Template Primer Design
After suitable primers for NT amplification have been designed and tested, prepare a CT primer according to previously described methods (36), as schematically presented in Fig. 3.
1. Competitive Template Primer Testing. The 40 bp CT primer is paired with the forward primer designed in Subheading 3.3.1. and used to amplify CT from native cDNA.
Bulaqueña, et al. (Embryo Transplant Microscopy)
3.5. Competitive Template-Internal Standard Production
1. For each gene, set up five 10 µL PCR reactions using the native forward primer and the CT primer and amplify for 35 cycles.
2. Combine the products of these five PCR reactions, electrophorese on a 3% NuSieve gel in 1X TAE, and cut the band of correct size from the gel and extract using the QiaQuick method.
3. Clone the purified PCR products into PCR 2.1 vector using TOPO TA cloning kits then transform into HS996 (a T1-phage resistant variant of DH10B).
4. After cloning, transformation, and plating on LB plates containing X-Gal, IPTG, and carbenicillin, pick three isolated white colonies. Prepare plasmid minipreps, performEcoRI digestion and electrophorese on 3% SeaKem agarose. For those clones documented to have an insert by EcoRI digestion, confirm the insert to be the desired one by sequencing the same undigested plasmid preparation using vector specific primers. Only those clones with homology to the correct gene sequence and that have 100% match for the primer sequences proceed to large-scale CT preparation and are included in the standard mixes. Those that pass this quality control assessment then continued to the next steps.
5. Prepare each quality assured clone in quantities large enough (1.5 L) to allow for <1 billion assays (approx 2.6mg).
6. Purify plasmids from resultant harvested cells using Qiagen GigaPrep kits. 7. Carefully quantify plasmid yields using a Hoeffer DyNAQuant 210 fluorometer.
8. For each CT that passes all of the defined quality control steps described in step 4, assess the sensitivity of the cloned CT and primers by performing PCR reactions on serial dilutions and determine the limiting concentration that still yield a PCR product. Only those preparations and primers that allow for detection of 60 molecules or fewer (a product obtained with 10-17M CT in 10 µl PCR reaction volume) are continued for inclusion into SMIS (see Note 5).
3.6. Preparation of Standardized Mixtures of Internal Standards(SMIS) (see Note 6)
Combine cloned and quantified CTs into SMIS according to modifications of previously described methods (5,6,36).
1. Mix plasmids from quality assured preparations (see Subheading 3.4.) into SMIS representing 24 genes.
2. The concentration of the competitive templates in the 24 gene SMIS is 4 × 10-9 M for ß-actin CT, 4 × 10-10 M for GAPD (CT1), 4 × 10-11 M for GAPD (CT2), and 4 × 10-8 M for each of the other CTs (see Note 7).
3. Linearize each 24 gene SMIS by NotI digestion. Incubate the SMIS with NotI enzyme at a concentration of 1 unit/µg of plasmid DNA in approx 15 mL of buffer at 37°C for 12–16 h.
4. Combine four linearized 24-gene SMIS in equal amounts to yield 96-gene CT mixes with a maximum concentration of 10-9 M for ß-actin, 10-10 M GAPD (CT1), 10-11 M GAPD (CT2), and 10-8 M for the other CTs.
StaRT-PCR
5. Serially dilute high concentration SMIS with a reference gene CT mixture comprising ß-actin CT (10-9 M) and two different GAPD CTs, GAPD CT1 (10-10 M), and GAPD CT2 (10-11 M). This yields six stock SMIS (A–F) with ß-actin, GAPD1 and GAPD2 at constant concentrations of 10-9 M, 10-10 M, and 10-11 M respectively while the concentration of the other CTs in SMIS A–F respectively are 10-8 M, 10-9 M 10-10 M, 10-11 M, 10-12 M, and 10-13 M.
6. Dilute stock concentration SMIS 1000-fold to working solutions containing ß- actin, GAPD1 and GAPD2 at concentrations of 10-12 M, 10-13 M, and 10-14 M respectively while the concentration of the other CTs in SMIS A–F respectively are 10-11 M, 10-12 M 10-13 M, 1014 M, 10-15 M, and 10-16 M.
3.7. StaRT-PCR
StaRT-PCR is performed using previously published protocols (5,6). StaRT- PCR is performed using previously published protocols (5,6). First, the cDNA sample is diluted until 1 µL competes equally with 6 × 105 molecules of ß- actin CT (1 µL of SMIS containing10-12 M ß-actin CT). The NT/CT must be greater than 1:10 and less than 10:1 for the measurement to be within linear dynamic range. Typically, this is the amount of cDNA derived from 100 to1000 cells. Next, this amount of cDNA sample is PCR amplified in multiplex with a SMIS containing internal standards for reference genes and target genes and gene specific primers from Gene Express, Inc. as described earlier. As with the reference gene, the target gene NT/CT must be greater than 1:10 and less than 10:1. Because genes are expressed over more than six orders of magnitude, this explains why the target gene CTs in each 96-gene SMIS must be 10-fold serially diluted relative to the reference gene CTs, in mixes A–F. For each 96gene SMIS, sufficient amount of A–F mix is prepared for more than 100 billion assays. Thus, these SMIS are constant and may be used by all labs. This is schematically represented in Fig. 4. In Fig. 4, genes 6 and 7 are expressed at a low level in sample A and therefore are measured using SMIS E. In sample B, genes 6 and 7 are expressed at a higher level and are measured using SMIS C and D, respectively. All of the values can be compared because all of the SMIS are standardized and constant. For each experiment, a PCR master mixture is prepared containing the appropriate amount of cDNA and SMIS for the number of gene expression assays to be done. Next, the reference gene NT is measured relative to its CT, and the target gene is measured relative to its CT, and expression is calculated as target gene molecules/106 ß-actin molecules. Briefly, StaRT-PCR is done by a) including in each PCR reaction a sample of cDNA and a known amount of SMIS, and b) multiplex RT-PCR amplifying both the target gene NT and its respective CT and a reference gene (e.g., ß-actin) NT and its respective CT for every gene expression measurement (Figs. 1,3). These four templates may be amplified in the same tube (4,5) or, if the experiment is
Willey et al.
Fig. 4. Relationship among mixes serially 10-fold diluted from each 96-gene SMIS. As described in text, a serial 10-fold dilution, A–F, of target gene internal standards
relative to reference gene internal standards is prepared for each 96-gene SMIS. This allows StaRT-PCR measurement of each gene, even though different genes may be expressed over a range of more than 6 orders of magnitude. properly designed, the NT and CT pair for the target gene and the NT and CT pair for the reference gene may be amplified in separate tubes (5).
3.8. Step-by-Step Description of StaRT-PCR Method
1. Balance cDNA with 6 × 105 ß-actin CT molecules (the amount of ß-actin CT in 1 µL of SMIS). After establishing the amount of cDNA in balance with 6 × 105 copies of ß-actin CT, this amount of cDNA is used in all subsequent experiments (see Note 8).
2. Combine and mix a volume of cDNA sample (diluted to the level that is in balance with the amount of ß-actin CT in 1 µL of SMIS (6 × 105) molecules, as determined above) with an equal volume of the appropriate SMIS A–F such that the target gene NT/CT will be greater than 1/10 and less than 10/1. A 1 µL volume of
each is used for each gene expression assay to be performed (see Note 9). If the appropriate SMIS is not known for a particular gene in a sample from a particular type of tissue, expression is measured in both SMIS C and E. This allows measurement over four orders of magnitude. For the few genes expressed at very high or low level, it will be necessary to repeat analysis with SMIS A or F. In the
StaRT-PCR
3. Combine cDNA/SMIS mixture from previous step with other components of the PCR reaction mixture (buffer, dNTPs, Mg++, Taq polymerase, H2O)
4. Prepare tubes or wells with a primer pair for a single gene. If products are to be analyzed by PE 310 device (see Subheading 3.4.9.) the primers should be labeled with appropriate fluor.
5. Place aliquots of this PCR reaction mixture into individual tubes each containing primers for a single gene (see Note 10).
6. PCR Amplification. Cycle each reaction mixture either in an air thermocycler (e.g., Rapidcycler (Idaho Technology, Inc.,
7. Separation and Quantification of NT and CT PCR Products (see Note 11). a. Agarose gel. Following amplification, load the entire volume of PCR product (typically 10 µL) into wells of 4% agarose gels (3/1 NuSieve: SeaKem) containing 0.5 µg/mL ethidium bromide. Electrophorese gels for approx 1 h at 225 V in continuously chilled buffer, then visualize and quantify with an image analyzer (products available from Fotodyne, BioRad). b. PE Prism 310 Genetic Analyzer CE Device. Amplify PCR products with fluorlabeled primers. One microliter of each PCR reaction is combined with 9 µL of formamide and 0.5-0.1 µL of ROX size marker. Heat samples to 94°C for 5 min and flash cooled in an ice slurry. Load samples onto the machine and electrophorese at 15 kV, 60°C for 35–45 min using POP4 polymer and filter set D. The injection parameters are 15 kV, 5 sec. Fragment analysis software, GeneScan (Applied Biosystems, Inc.,
Willey et al.
Fig. 5. Calculations involved in StaRT-PCR measurement of GST gene expression relative to ß-actin in an actual bronchial epithelial cell (BEC) sample. The native template (NT) PCR product was amplified from cDNA specific for the gene being measured, and the competitive template (CT) PCR product was amplified from the internal standard for each respective gene. A volume of SMIS containing a known number of internal standard CT molecules for ß-actin (600,000) and GST (6000) were included at the beginning of the PCR reaction. For each gene the NT and CT will amplify with the same efficiency. Thus, the ß-actin gene NT/CT PCR product ratio allows determination of the number of ß-actin NT copies at the beginning of PCR and the target gene NT/CT ratio allows determination of the number of target gene NT copies at the beginning of PCR. See text for steps used to calculate gene expression values.
3.9. Steps to Calculate the Numberof NT Molecules Present at the Beginning of PCR for Each Gene Calculation of gene expression. Values are calculated in units of target gene cDNA molecules/106 ß-actin cDNA molecules. The steps taken to calculate gene expression are based on densitometric measurement values for the electrophoretically separated NT and CT PCR products such as those presented in Fig. 5. The calculations below are based on the example in Fig. 5.
1. Correct NT PCR product area under the peak (AUP) to length of CT DNA.
2. Determine ratio of corrected NT AUP relative to CT AUP.
3. Multiply NT/CT value × number of CT molecules at beginning of PCR.
4. Calculation of reference gene (ß-actin) molecules using above protocol. a. 416/532(ß-actin CT bp/ NT bp) × 42 (NT AUP) = 33 (corrected NT value). b. Correct ß-actin NT AUP divided by ß-actin CT AUP = 0.37. c. 0.37 (ß-actin NT/CT) × 600,000 (number of ß-actin CT molecules at beginning of PCR) = 222,000 NT molecules at beginning of PCR.
StaRT-PCR
5. Calculation of target gene (GST) molecules using above protocol: a. 227/359 (GST CT bp/NT bp) × 1.5 (NT AUP) = 0.95 (corrected NT AUP). b. 0.95 (GST corrected NT AUP) divided by 4.4 (GST CT AUP) = 0.22. c. 0.22 (GST NT/CT) × 6000 (number of GST CT molecules at beginning of PCR) = 1290 GST NT molecules at beginning of PCR.
6. Calculation of molecules of GST/106ß-actin molecules
1290 GST NT molecules/222,000 ß-actin NT molecules = 580 GST molecules/106 ß-actin molecules.
4. The Standardized
The
4.1. Technology Incorporated by the
A PE Robotic liquid handler is used to prepare 10 µL PCR reactions in 96well or 384-well microplates. First, the liquid handler is programmed to distribute 1 µL of primers for the requested genes into wells of the microplates. Second, for each cDNA a sufficient volume of PCR mixture for the anticipated number of gene expression measurements is prepared, containing buffer, Taq polymerase, dNTPs, cDNA and internal standards. The robot then distributes 9 µL of this PCR reaction mixture into each well. Thus, in each well the internal standard CTs for each gene and cDNA are present in the same ratio, however, because only one pair of primers is present in each well, only one gene and its respective internal standard CT are amplified in each well. Following 35 cycles of PCR, each microplate is transferred to the Caliper AMS 90 for analysis.
Bulaqueña, et al. (Embryo Transplant Microscopy)
When StaRT-PCR was first developed, products were separated on agarose gels (4,5). This method is reliable but relatively costly, time consuming, and labor intensive. Through advances in capillary electrophoresis (CE), alternative methods for separation of StaRT-PCR products that are faster and less expensive have become available. We compared separation of StaRT-PCR products on agarose gel, PE 310 CE, and Agilent 2100 Bioanalyzer mcrofluidic CE (31). Each of these methods provided the same, reproducible results. Theoretically, the internal standard mixtures prepared for StaRT-PCR may be used to measure gene expression coupled with any method capable of quantifying strands of DNA with different sizes, including HPLC and mass spectrometry. Quantification of gene expression through analysis of RT-PCR products by MALDI- TOF MS has been recently described (16). Currently, the Caliper AMS 90 is used for high-throughput separation of StaRT-PCR products in the
of the fastest real-time devices.
4.2. Design of High-Throughput StaRT-PCR Experiments
All of the genes that are to be measured in a given sample are measured simultaneously. Owing to the presence of SMIS in every PCR reaction, gene expression values for one sample may be compared to gene expression values from another sample and evaluated at a different time (Fig. 1A). PCR products (NT and CT) for as many as four genes may be electrophoresed (separated and quantified) in the same microfluidic channel of the AMS 90SE. Accomplishing this in the high-throughput
4.3. Use of Multiplex StaRT-PCR to Reduce cDNA Consumption
An advantage of quantitative RT-PCR as a tool for measuring gene expression is that it consumes very small amounts of cDNA. This enables meaningful analysis of very small-tissue biopsy samples, such as those obtained by fineneedle aspirate. Despite the low amount of cDNA required in quantitative RT- PCR, high-throughput analysis of many genes simultaneously will consume large amounts of cDNA for each sample, possibly limiting the analysis of small sam StaRT-PCR ples. However, multiplex StaRT-PCR methods recently described (7) may solve this problem. It should be possible to combine nanotechnology methods for manipulating small liquid volumes with multiplex StaRT PCR methods to decrease the PCR reaction volumes to 10–100 nL.
The multiplex StaRT-PCR method involves two rounds of PCR. In the first round, cDNA, CT mix, and primers for up to 96 genes are amplified for 35 cycles. Next, PCR products from round one are diluted, combined with primers for one gene, and amplified for an additional 35 cycles. No additional CT or cDNA is added. Products from round one may be diluted as much as 100,000- old and still be quantified following round two amplification. Thus, using ultiplex StaRT-PCR, 96 genes may be measured in the same amount of cDNA typically used to measure one gene with uniplex StaRT-PCR. The gene expression values obtained for multiplex StaRT-PCR are highly correlated with those obtained by uniplex StaRT-PCR (7).
Multiplex StaRT-PCR works because gene expression measurements are determined by the ratio of NT/CT for each gene and not by the absolute amount of NT PCR product. For each gene, NT and CT are amplified with the same primers, share sequence homology, and amplify with equal efficiencies (7). Therefore, differences in amplification efficiency will not affect the measured relative level of expression between genes in different samples even after two rounds of amplification.
4.4. Other
The
4.5. Standardized Gene Expression Database
Users send samples to the
5. Notes
1. The quality of the RNase-free water is critical to efficient extraction of intact RNA. We have found that it is more cost effective to purchase reliable RNase-free water from commercial sources than it is to prepare our own. Either inadequate DEPC treatment or inadequate removal of DEPC after treatment can inhibit reverse transcription and PCR (see Subheading 3.1.6.).
2. Internal standard CTs are constructed by Gene Express, Inc. (GEI,
3. Use Primer 3.1 software (Steve Rozen, Helen J. Skaletsky, 1996, 1997) Primer 3. Code available at http://www-genome.wi.mit.edu/genome_software /other/primer3. html) to design primers. Designing primers with the same annealing temperature allows StaRT-PCR reactions to achieve approximately the same amplification efficiency under identical conditions. If there is variation in amplification efficiency it does not cause variation in quantitative value because the value is obtained from the ratio between the NT and CT for the same gene, and amplification efficiency of the NT and CT for the same gene are affected identically.
Designing primers that amplify different sized products for different genes will support automation and high-throughput applications, including capillary gel and microchannel CE. Primer sequences and Genbank accession numbers for genes designed by GEI are available at www.geneexpressinc.com. (see Subheading 3.3.1.).
4. Primers are tested using reverse transcribed RNA from a variety of tissues or individual cDNA clones known to represent the gene of interest. Primer pairs that fail to amplify the target gene in any tissue or individual cDNA clone (less than 10% of the time) are redesigned and the process repeated (see Subheading 3.3.2.).
5. The number of molecules at different molarities is a multiple of six as a consequence of Avogadro’s Number (6.02 × 1023 molecules/mole). More than 80% of the CTs developed have a sensitivity of six molecules or less. Thus, for these genes, it is possible to measure as few as 10 molecules/ 106 ß-actin molecules. Because there are approximately 100–1000 ß-actin molecules per cell for most cell types, this level of sensitivity allows measurement of 1 molecule per 100–1000 cells. At the other end of the expression spectrum, SMIS A will allow measurement of more than 107 molecules/106 molecules of ß-actin (103–104 molecules/cell). In our experience, few genes approach this level of expression, examples include UGB (Genbank no. U01101) and vimentin (X56134) (unpublished data). Thus, SMIS A–F should allow measurement of gene expression over the full spectrum observed in human tissues (see Subheading 3.6.).
StaRT-PCR
6. The process of identifying primers that lead to high PCR amplification efficiency for both the NT and CT, preparing large amounts of the CT through cloning, quantifying the CTs, and mixing the CTs into SMIS, transforms CTs into internal standards. Thus, CTs are the raw material necessary for development of the much more valuable product (see Subheading 3.6.).
7. The reason for two different GAPD CTs is that the expression of GAPD relative to ß-actin may vary as much as 100-fold from one tissue type to another. Having two different concentrations of
8. For each cDNA sample, it is necessary to determine the dilution of the test cDNA that is approximately (within 10-fold range) in balance with 600,000 copies of ß- actin (1 µL of SMIS containing ß-actin CT at 10-12 M). This is approximately the amount of cDNA derived from 100 to 1000 cells. This amount will ensure that there is sufficient cDNA to quantify genes expressed at low levels. If the goal is to have at least 10 transcripts present at the beginning of PCR to avoid stoichiometric problems, this amount of cDNA will allow quantification of genes expressed as low as 1 transcript in every 10–100 cells. If less sensitivity is required, less cDNA may be used. Thus, one could choose to use the amount of cDNA in balance with 60,000 molecules of ß-actin CT. This will not allow measurement of genes expressed at very low levels, but will be sufficient for analysis of most genes and will reduce consumption of cDNA 10-fold. This may be useful when analyzing very small biopsy specimens for diagnostic tests. For each of the SMIS A–F, 1 µl of CT mix contains 600,000 molecules of ß-actin CT, thus any of the SMIS could be used for this purpose of balancing cDNA with ß-actin. The standard operating procedure is to use SMIS F.
A common mistake for beginning users of StaRT-PCR is to balance the cDNA with the ß-actin in the SMIS initially, and then, when the target gene NT and CT are not in balance, vary the amount of cDNA in the PCR reaction mixture to get the target gene NT/CT in balance. Instead, keep the amount of cDNA constant and change the SMIS used. The SMIS have been prepared for measurement of genes across the full range of gene expression measurement (6 orders of magnitude). Because the NT/CT ratio must be within 10-fold ratio in order to obtain reliable, reproducible quantification, six different SMIS have been prepared, containing 10-fold serial dilution of all target gene CTs relative to reference gene CT. If SMIS D were used to measure a target gene, and the target gene NT was more than 10-fold greater than the CT, the next step would be to repeat the experiment with the same amount of cDNA, but using SMIS C, which has a 10-fold higher concentration of target gene CT (see Subheading 3.8.).
9. The StaRT-PCR method standardizes every gene expression measurement so that it can be readily compared to all other StaRT-PCR measurements. The procedure described in this step allows one to compare the NT/CT ratio for the reference gene to the NT/CT ratio for the target gene in a reliable way that controls for variation in pipeting. This step commonly is carried out incorrectly by users of StaRT-PCR.
Bulaqueña, et al. (Embryo Transplant Microscopy)
For example, it is common for users to aliquot SMIS sufficient for a single gene expression measurement into each separate PCR reaction mixture, and then aliquot cDNA for a single measurement into each tube. Owing to pipeting errors, this would be associated with variation in the NT/CT ratio of each target gene relative to the NT/CT ratio for the reference gene, as well as that for other target genes.
The SMIS (A, B, C, D, E, or F) selected will be the one containing CT at the concentration most likely, based on previous experience, to be in balance (within 10-fold range) with the gene or genes being assessed (see Subheading 3.8.2.).
10. In Subheading 3.8.5. of this experimental design, the ratio of CT for every gene in the mixture relative to its corresponding NT in the cDNA is fixed simultaneously. When aliquots of this mixture are transferred to PCR reaction vessels, although variations in loading volumes resulting from pipeting errors are unavoidable, there is no potential for variation in any target gene NT/CT ratio relative to reference gene NT/CT ratio. In addition, it enables standardized expression measurement. In order to ensure control for loading in each experiment, the reference gene (ß- actin) is measured along with the target genes for each different master mix utilized. The choice of which four SMIS to use is based on previous experience. For example, if among all previous samples a gene has been expressed within a range of 101–103molecules/106 ß-actin molecules, the gene will be measured using SMIS E. In contrast, if among all previous samples, a gene has been expressed within a range of 105–107 molecules/106 ß-actin molecules, the gene will be measured using SMIS B. For the rare samples that express the gene outside of the expected ranges, a fol- low-up analysis with the appropriate CT mix is performed.
11. Electrophoresis may occur in an agarose gel, capillary electrophoresis device (e.g., PE 310), or microfluidic CE device (e.g., Agilent 2100 or Calipertech AMS 90 high-throughput system). If an agarose gel is used, electrophoresis is for one hour at 225 V through agarose gel. If a CE device or microfluidic CE device is used, electrophoresis is according to the manufacturer’s instructions. Following electrophoresis, the relative amount of NT and CT is determined by densitometric quantification of bands that have been stained by an intercalating dye (e.g., ethidium bromide). Theoretically, the internal standard mixtures prepared for StaRT-PCR may be used to measure gene expression using any method capable of quantifying strands of DNA with different sizes and/or sequence, including solid phase hybridization MALDI-TOF and HPLC (see Subheading 3.8.7.). The calculation steps presented in Subheading 3.9. have been incorporated into a spreadsheet. Thus, the user simply enters the raw values for the NT, CT, and heterodimer PCR products for each gene into the spreadsheet, and the expression value for the gene in molecules/106 ß-actin molecules is automatically calculated. Software now in development will automatically enter the peak area values for each NT and CT PCR product into a spread sheet. The spreadsheet will automatically calculate expression value or, if the NT/CT ratio is not in balance, will instruct the robotic liquid handler on how to set up the next experiment.
StaRT-PCR
Acknowledgments
These studies were funded by NCI grants U01 CA 85147 and R24 CA 95806 and the George Isaac Endowment for Cancer Research. Major contributions to establishment of the
References
1.
2. Bustin, S. A. (2002) Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J. Mol. Endocrinol. 29, 23–39.
3. Gilliland, G., Perrin, S., Blanchard, K., and Bunn, H. F. (1990) Analysis of cytokine mRNA and DNA: Detection and quantitation by competitive polymerase chain reaction. Proc. Natl. Acad. Sci. USA 87, 2725–2729.
4. Apostolakos, M. J., Schuermann, W. H., Frampton, M. W., Utell, M. J., and Willey, J. C. (1993) Measurement of gene expression by multiplex competitive polymerase chain reaction. Anal. Biochem. 213, 277–284.
5. Willey, J. C., Crawford, E. L., and
mixtures of competitive templates. Am. J. Respir. Cell Mol. Biol. 19, 6–17.
6. Crawford, E. L., Peters, G. J., Noordhuis, P., et al. (2001) Reproducible gene expression measurement among multiple laboratories obtained in a blinded study using standardized RT (StaRT)-PCR. Mol. Diagn. 6, 217 225.
7. Crawford, E. L., Warner, K. A., Khuder, S. A., et al. (2002) Multiplex standardized RT-PCR for expression analysis of many genes in small samples. Biochem. Biophys. Res. Commun. 293, 509–516.
8. Crawford, E. L., Khuder, S. A.,
9. DeMuth, J. P.,
11. Rots, M. G., Willey, J. C., Jansen, G., et al. (2000) mRNA expression levels of methotrexate resistance-related proteins in childhood leukemia as determined by a standardized competitive template-based RT-PCR method. Leukemia 14, 2166–2175.
Bulaqueña, et al. (Embryo Transplant Microscopy)
12. Rots, M. G., Pieters, R., Peters, G. J., et al. (1999) Circumvention of methotrexate resistance in childhood leukemia subtypes by rationally designed antifolates. Blood 94, 3121–3128.
13. Allen, J. T., Knight, R. A., Bloor, C. A., and Spiteri, M. A. (1999) Enhanced insulin-like growth factor binding protein-related protein 2 (connective tissue growth factor) expression in patients with idiopathic pulmonary fibrosis and pulmonary sarcoidosis. Am. J. Respir. Cell. Mol. Biol. 21, 693–700.
14. Loitsch, S. M., Kippenberger, S., Dauletbaev, N., Wagner, T. O., and Bargon, J. (1999) Reverse transcription-competitive multiplex PCR improves quantification of mRNA in clinical samples- application to the low abundance CFTR mRNA. Clin. Chem. 45, 619–624.
15. Vondracek, M. T., Weaver, D. A., Sarang, Z., et al. (2002) Transcript profiling of enzymes involved in detoxification of xenobiotics and reactive oxygen in human normal and Simian virus 40 T antigen-immortalized oral keratinocytes. In. J. Cancer 99, 776–782.
16. Ding, C. and Cantor, C. R. (2003) A high-throughput gene expression analysis technique using competetive PCR and matrix-assisted laser desorption ionization time-of-flight MS. Proc. Natl. Acad. Sci. USA 100, 3059 3064.
17. Zhang, J., Day, I. N. M., and Byrne, C. D. (2002) A novel medium-through- put quantitative competitive PCR technology to simultaneously measure mRNA levels from multiple genes. Nucleic Acids Res. 30, e20.
18. Becker-Andre, M. and Hahlbrock, K. (1989) Absolute messenger-RNA quantification using the polymerase chain-reaction (PCR) - a novel-approach by a PCR aided transcript titration assay (patty) nucleic acids research. Nucleic Acids Res.
17, 9437–9446.
19. Wang, A. M., Doyle, M. V., and Mark, D. F. (1989) Quantitation of mRNA by the polymerase chain reaction. Proc. Natl. Acad. Sci. USA 86, 9717–9972.
20. Zhang, J. and Byrne, C.D. (1997) A novel highly reproducible quantitative competitive RT-PCR system. J. Mol. Biol. 274, 338–352.
21. Zhou, N. M., Matthys, P., Polacek, C., et al. (1997) A competitive RT-PCR method for the quantitative analysis of cytokine mRNAs in mouse tissues. Cytokine 9, 212 218.
22.
23. Hirano, T., Haque, M., and Utiyama, H. (2002) Theoretical and experimental dissection of competitive PCR for accurate quantification of DNA. Anal. Biochem. 303, 57–65.
24. Blaschke, V., Reich, K., Blaschke, S., Zipprich, S., and Neumann, C. (2002) Quantitative RT-PCR: comparing real-time LightCycler technology with quantitative competitive RT-PCR, Biochemica 1, 6–7, www.Roche Applied-Science.com,
http://www.roche-applied-science.com/biochemica/no1_02/PDF/p6.pdf
25. Livak, K. J. and Schmittgen, T. D. (2001) Analysis of related gene expression data using real-time quantitative RT-PCR and the 2 (-Delta Delta C(T)) method. Methods 25, 402–408.
StaRT-PCR
26. Meijerink, J., Mandigers, C., van de Locht, L., et al. (2001) A novel method to compensate for different amplification efficiencies between patient DNA samples in quantitative real-time PCR. J. Mol. Diagn. 3, 55–61.
27. Akane, A., Matsuara, K., Nakamura, H., Takahashi, S., and Kimura, K. (1994) Identification of the heme compound co-purified with deoxyribonucleic acid (DNA) from blood stains, a major inhibitor of polymerase chain reaction (PCR) amplification. J. Forensic Sci. 39, 362–372.
28. Zhu, Y. H., Lee, H. C., and Zhang, L. (2002) An examination of heme action in gene expression: Heme and heme deficiency affect the expression of diverse genes in erythroid K562 and neuronal PC12 cells. DNA Cell Biol. 21, 333–346.
29. Giulietti, A., Overbergh, L., Valckx, D., et al. (2001) An overview of real-time quantitative PCR: applications to quantify cytokine gene expression. Methods 25, 386–401.
30. Heid, C. A., Stevens, J., Livak, K. J., and Williams, P. M. (1996) A novel method for real time quantitative RT-PCR. Genome Res. 6, 986–994.
31. Winer, J., Jung, C. K. S., Shackel,
32. Bustin, S. A. (2000) Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. J. Mol. Endorinol. 25, 169–193.
33. Crawford, E. L., Warner, K. A., Weaver, D. A., and Willey, J. C. (2001) Quantitative end-point RT-PCR gene expression measurement using the Agilent 2100 Bioanalyzer and standardized RT-PCR. http://www.chem.agilent.com/temp/rad6A17F/ 00029012.pdf.
34. Chomczynski, P. and Sacchi, N. (1993) Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal. Biochem. 62, 156–159
35. Willey, J. C., Coy, E. L., Frampton, M. W., et al. (1997) Quantitative RT-PCR measurement of cytochromes p450 1A1, 1B1, and 2B7, microsomal epoxide hydrolase, and NADPH oxidoreductase expression in lung cells of smokers and nonsmokers. Am. J. Respir. Cell Mol. Biol. 17, 114–124.
36. Celi, F. S., Zenilman, M. E., and Shuldiner, A. R. (1993) A rapid and versatile method to synthesize internal standards for competitive PCR. Nucleic Acids Res. 21, 1047.
GeneCalling
Transcript Profiling Coupled to a Gene Database Query Bulaqueña, et al.
Summary
We describe the GeneCalling method for the discovery of differentially expressed genes, both known and novel, from any species including useful sequence information to determine the potential function of novel genes captured. The method relies on transcript visualization coupled to a database query to rapidly and quantitatively identify differentially expressed transcripts. The method has been applied to a wide variety of disease models in a variety of species, addressing problems as diverse as identifying novel human cancer gene targets, understanding how drugs and diet affect animal models of disease, and understanding the basis of trait differences in related strains of corn. Key Words: Bioinformatics, cDNA, disease, GeneCalling, mRNA
1. Introduction
The comprehensive discovery of differences in gene expression among samples is a powerful method of identifying genes associated with diseases, traits, and biological responses to chemicals. Existing methods for expression analysis fall into three general classes: transcript sampling by sequencing (1–3), transcript amplification and imaging (4–8) and hybridization-based approaches (9–13). Serial analysis of gene expression (SAGE) (2), a cost-effective tran- script-counting technique, is limited by the small amount of sequence information obtained for each gene. Transcript sequencing following subtractive hybridization also identifies differentially expressed genes, but is limited to binary comparisons (3). Transcript imaging approaches such as differential display
From: Methods in Embryo Transplant Microscopes and Emyro Transplant Microscopy Vol. 258: Gene Expression Profiling: Methods and Protocols Edited by: R. A. Bulaqueña, et al. (Embryo Transplant Microscopy) © Bulaqueña, et al. (Embryo Transplant Microscopy) Inc.,
43
44 Bulaqueña, et al. (Embryo Transplant Microscopy)
(4), partitioning by type IIS restriction enzymes (6), representational difference analysis (RDA) (7), and amplified fragment length polymorphism (AFLP) (8) are rapid, and in theory, are comprehensive because they utilize banding patterns that are dependent on gene expression. However, each of these approaches requires a time-consuming cloning and confirmation process for determination of the identity of differentially expressed gene fragments. The development of microarrays has revolutionized the capacity of hybridization techniques (9–13) to identify differences in gene expression. Hybridization approaches are rapid and immediately provide the identity of differentially expressed genes of known sequence. However, hybridization methods are limited by an inability to detect or discover completely novel genes with no expressed sequence tags (EST) representation, thus making work in most organisms impossible. We describe here the GeneCalling® method for the discovery of differentially expressed genes, both known and novel, from any species and with useful sequence information to determine the potential function of novel genes captured (Fig. 1) (14). The method has been applied to a wide variety of disease models in a wide variety of species, addressing problems as diverse as identifying novel human cancer gene targets (15,16), understanding how drugs and diet affect animal models of disease (17,18), and understanding the basis of trait differences in related strains of corn (19,20).
2. Materials
1. Trizol (BRL,
2. Bromochloropropane (Molecular Research Center Inc.,
3. DNAse I (Promega,
4. Dithiothreitol (DTT) (BRL,
5. RNasin (Promega,
6. OliGreen (Molecular Probes,
7. Oligo(dT) magnetic beads (PerSeptive,