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Analysis of gst::egfp mRNA Levels by RT-qPCR
LAB SESSION 17 Analysis of gst::egfp mRNA Levels by RT-qPCR: Part 2 Goal: You will analyze RT-qPCR data obtained in the previous session to determine the relative levels of gst::egfp mRNA in E. coli induced with IPTG and lactose compared to no inducer.
Introduction During the qPCR run, fluorescence readings were recorded during the annealing/extension step of every sample at every cycle (in “real-time”). By the end of the run, a complete amplification plot (fluorescence readings versus cycle number) was generated for each sample. Now, you must analyze these plots in order to quantify gene expression levels. Before the types of quantification methods are introduced, we will first take a closer look at the results generated. Recall from Lab Session 3 that PCR amplifies DNA exponentially, with the amount of DNA doubling after each cycle. However, when reagents in the PCR sample become limiting, the amplification reaches a plateau. You cannot accurately quantify cDNA levels if you are analyzing in this plateau phase. Accurate quantification occurs during the early cycles of PCR, during exponential amplification. It is easy to obtain data from the exponential phase using qPCR because data was collected at every cycle, not just at the end. In Bio-Rad’s MyiQ software, each sample’s amplification plot is shown in a different color. Fluorescence versus cycle number can be displayed in either the linear view or the log view (Figure 17.1). In either case, you can clearly see that fluorescence intensity (indicating DNA abundance) increases as cycle number increases. An important concept to keep in mind is that the more input cDNA that went into the PCR, the sooner an increase in fluorescence will occur. In Figure 17.1A (linear view), all samples produce minimal fluorescence during the first 13 cycles. These cycles are considered the baseline. Then, the blue amplification plot begins to increase significantly, followed by the red and green plots at later cycle numbers. Since the blue plot showed an increase in fluorescence at the earliest cycle number, we can conclude that the sample it represents contains more input cDNA (in this case, 23S) than the other samples. To assign numeric values to these observations, a threshold is set, intersecting all the plots during exponential Molecular Biology Techniques. DOI: 10.1016/B978-0-12-385544-2.00017-X © 2012 Elsevier Inc. All rights reserved.
141
Molecular Biology Techniques
amplification. The threshold in Figure 17.1 is indicated by an orange horizontal line. Notice that in the linear view, each plot crosses the threshold in the early cycle numbers. Keeping all settings the same but viewing the data on a logarithmic scale (Figure 17.1B) we see that the threshold is high enough to be above background, but low enough to stay out of the plateau phase. The cycle at which a plot crosses the threshold is called the threshold cycle (CT ). The lower the CT, the more abundant the template is. During exponential amplification, DNA should in theory double with each cycle. So, after 2 cycles, there will be 4 times as much as you started with, and after about 3.3 cycles, there will be 10 times as much. Even though the CTs of the amplicons shown in Figure 17.1B differ by only about three cycles, these represent 10-fold differences in 23S amplicon abundance. If the same reactions were performed using conventional PCR, we may have analyzed amplicon abundance after 30 or more cycles on an agarose gel. Based on the qPCR results shown below (especially in the log view) it becomes clear that by this point in the reaction, the plateau phase has been reached and reagents have become limiting. We would conclude that there is very little difference in 23S rRNA levels between samples. However, by examining (A) 900
900
800
800
700
700
600
600
500
500
400
400
300
300
200
200
100
100
PCR Baseline Subtracted CF RFU
142
0
0 –100
0
2
4
6
FIG. 17.1 Amplification plots. Ten-fold serial dilutions of cDNA were used as templates for RT-qPCR reactions using 23S primers. The horizontal orange line represents the threshold. (A) Linear view. (B) Log view.
PCR Baseline Subtracted CF RFU
(B) 1000
–100 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 Cycle 1000
100
100
10
10
1
0
2
4
6
1 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 Cycle
Analysis of gst::egfp mRNA Levels by RT-qPCR: Part 2
amplicons early in the reaction, in the exponential phase, we are able to determine that there are actually 10-fold differences between each sample. There are two general methods for reporting quantification: absolute and relative. Absolute quantification calculates the quantity of a target gene in your unknown samples by interpolation from an absolute standard curve. This curve is commonly made by in vitro transcribed RNA or plasmid DNA with known copies of a target gene. This method is useful when you want to determine the exact copy number of a gene. The second type of quantification is called relative quantification. This aims to determine the difference in expression of a target gene between two samples. Reporting relative quantities is often sufficient when studying changes in gene expression. Basically, it aims to answer the question “How much of gene X is present in sample 1 compared to sample 2?” Relative quantification reports n-fold changes of gene expression rather than exact quantities: “There is 5-fold more of gene X in sample 1 versus sample 2.” In this example, sample 2 serves as a calibrator (the sample used for the basis of a comparison). In our experiments, we will be comparing gst::egfp levels between no induction, induction with IPTG and induction with lactose. Therefore, samples with no induction will serve as calibrators, and gst::egfp levels of samples induced with IPTG or lactose will be expressed relative to those with no induction. Two methods exist for relative quantification: the relative standard curve method and the comparative CT method. In the relative standard curve method, CT values obtained from experimental samples are compared to CT values from a standard curve made with dilutions of cDNA. The exact quantity of a target gene in the standards need not be known; just known mass amounts are required. The same procedure is also followed for a reference gene. Quantities are still expressed in comparison to a calibrator, unlike absolute quantification. The second method for relative quantification is the comparative CT method (also called the ΔΔ CT method). This is the method that we will use in this lab session. It has an advantage in that no standard curve is required. Instead, mathematical formulas are used to calculate relative gene expression. It should be noted that the comparative CT method assumes that PCR efficiencies are very similar between amplification of the target and reference genes. A validation experiment was performed previously to confirm that the primers used had very similar PCR efficiencies. In our experiment, remember that we amplified not only gst::egfp (our target gene) but also 23S rRNA (our reference gene). This reference gene is needed for the comparative CT method. We will be comparing CT values of egfp reactions to 23S rRNA reactions for normalization purposes. Then, these differences (ΔCT ) will be compared to the calibrator, which is no induction. In this way, gst::egfp levels will be 1 in the no induction control and gst::egfp levels will have a n-fold difference with IPTG and lactose induction.
STEP 1: NORMALIZE TO AN ENDOGENOUS REFERENCE For each cDNA in our experiment (no induction, IPTG and lactose), we will be taking the CT of the egfp reaction minus the CT of the 23S reaction to give us ΔCT . ΔCT = CT target gene − CT reference gene egfp
23S
143
Molecular Biology Techniques
STEP 2: COMPARE TO A CALIBRATOR Once a ΔCT value is calculated for no induction, IPTG and lactose cDNAs, we will report each value relative to the calibrator (no induction) to give us ΔΔCT . Notice that ΔΔCT will always be zero for the calibrator sample itself. ΔΔCT = ΔCT test sample − ΔCT calibrator sample
IPTG or lactose
no inducer
STEP 3: CALCULATE RELATIVE QUANTITY Finally, the amount of target, which is now normalized to an endogenous reference and relative to a calibrator, is calculated to provide us with the relative quantity of gst::egfp. 2 −∆∆C T
144
With these values in hand, you can draw conclusions as to how much gst::egfp mRNA was expressed with IPTG and lactose in your positive clone. The references at the end of this chapter provide detailed guides on performing and analyzing RT-qPCR experiments.1–4
Laboratory Exercise Relative Quantification of gst::egfp Levels Note: The following instructions are specific for analyzing data obtained on the Bio-Rad iCycler using MyiQ Software. If you are using equipment made by another manufacturer, your instructor will provide you with data analysis instructions. Your instructor may perform steps 1–17 for you. 1. 2. 3. 4.
5. 6.
7. 8.
Open MyiQ software on the computer connected to the iCycler. Click the “view post-run data” tab. Double-click the data file (.odm) from your lab session to open it. Click the “select wells” button and select the positions of your station’s reactions. If your group had 12 reactions occupying the same row, you can click on the row’s label (A, B, etc.) to automatically select that entire row. Click the “analyze selected wells” button. Make sure that the “log view” button is displayed. That means that the current view is the normal view. Verify that the amplification plots are running along 0 RFU (parallel to the x-axis) in the early cycles of the experiment. If the plots dip well below 0 or if RFU values vary greatly in the early cycles, you will need to adjust the baseline setting. To do this, determine the cycle at which your first (left-most) plot begins to rise above 0 RFU (amplification). Set the baseline cycles to between 2 and this cycle number. Click the “log view” button. Verify the auto-calculated threshold position (orange line) is high enough on the y-axis to be above any background noise (it should not intersect any sharp peaks seen in the early cycles) but is low enough to intersect the plots in the bottom third of the exponential phase.
Analysis of gst::egfp mRNA Levels by RT-qPCR: Part 2
Table 17.1 Pasting CT values into Excel A
B
C
1
egfp
23S
2
CT
CT
3
No inducer
4
IPTG
5
Lactose
D
E
F
Table 17.2 Comparative CT method formulas A
B
C
1
egfp
23S
2
CT
CT
D
E
F
ΔCT
ΔΔCT
2 ΔΔCT
3
No inducer
B3 C3
D3 D3
2^ E3
4
IPTG
B4 C4
D4 D3
2^ E4
5
Lactose
B5 C5
D5 D3
2^ E5
9. If any adjustments are made in steps 6 and/or 8, you will need to click the “recalculate threshold cycles” button. 10. Click the “Reports” button. 11. In the “Select Report” drop-down menu, select “PCR baseline.” 12. Click “save to file” and save the report to a thumb drive. 13. Click the “print” button to print a copy for your lab notebook. 14. Open Microsoft Word. Open the report you saved (a Rich Text Format file). 15. Copy the well number and CT values for each of your reactions. 16. Open Microsoft Excel. 17. Create a new spreadsheet and paste what you copied from the Word document. You may want to paste the values towards the bottom or right of the screen so that you can organize the rest of your spreadsheet (beginning with cell A1). 18. Create column and row headings as shown in Table 17.1. Enter your CT values from RT reactions only in the appropriate cells by copying and, pasting. 19. Enter the formulas shown in Table 17.2 to calculate Δ CT, ΔΔ CT and 2ΔΔ CT values. 20. Column F now shows the level of gst::egfp in each sample, relative to no inducer. 21. Below the data you have entered, paste in your CT values for the six RT reactions. 22. For each RNA sample (no inducer, IPTG, lactose), locate the CT values corresponding to its RT and RT reactions for each primer pair. Remember that these RT reactions were included to control for DNA contamination. Do the CT values differ between the RT and RT
145
Molecular Biology Techniques
reactions? Why? Remember that ideally DNA quantity doubles with each PCR cycle. 23. To calculate the amount of genomic DNA that is attributable to amplification in your RT samples, perform the Δ CT calculation for each 23S reaction. This time, you will use the RT sample as a calibrator: ∆ C T = C T without RT − C T with RT Amplification with the 23S primers, but not the egfp primers, will indicate genomic DNA contamination. Why? You are not interested in comparing the amount in one RNA sample relative to another, so you can skip the ΔΔ CT calculation. The equation 2ΔCt will calculate the quantity (%) of contaminating DNA. Multiply this value by 100 to obtain a percentage. gDNA contamination IPTG (%) = 2−[( CT
146
without RT )−( C T with RT )]
× 100
24. To calculate the amount of plasmid DNA that is attributable to amplification in your RT samples, perform the calculations described above for each egfp reaction.
References 1 Applied Biosystems. Guide to performing relative quantitation of gene expression using real-time quantitative PCR. http://www3.appliedbiosystems.com/cms/groups/mcb_ support/documents/generaldocuments/cms_042380.pdf/, 2008. 2 Applied Biosystems. User bulletin #2. http://www3.appliedbiosystems.com/cms/ groups/mcb_support/documents/generaldocuments/cms_040980.pdf/, 2001. 3 Applied Biosystems. Essentials of real time PCR. http://www3.appliedbiosystems.com/ cms/groups/mcb_marketing/documents/generaldocuments/cms_039996.pdf/. 4 Schmittgen TD, Livak KJ. Analyzing real-time PCR data by the comparative CT method. Nat. Protoc. 2008;3:1101–1108.
Discussion Questions 1. Why do standard PCR programs often include 30 cycles or more, while qPCR amplification plots (like the one in Figure 17.1B) show that at these later cycles, the plateau phase is often reached? 2. You were able to determine the levels of gst::egfp mRNA in the induced cultures relative to the non-induced. Without an inducing molecule to derepress the lac operator of pET-41a, was any gst::egfp mRNA transcribed? Why or why not?
Introduction During the qPCR run, fluorescence readings were recorded during the annealing/extension step of every sample at every cycle (in “real-time”). By the end of the run, a complete amplification plot (fluorescence readings versus cycle number) was generated for each sample. Now, you must analyze these plots in order to quantify gene expression levels. Before the types of quantification methods are introduced, we will first take a closer look at the results generated. Recall from Lab Session 3 that PCR amplifies DNA exponentially, with the amount of DNA doubling after each cycle. However, when reagents in the PCR sample become limiting, the amplification reaches a plateau. You cannot accurately quantify cDNA levels if you are analyzing in this plateau phase. Accurate quantification occurs during the early cycles of PCR, during exponential amplification. It is easy to obtain data from the exponential phase using qPCR because data was collected at every cycle, not just at the end. In Bio-Rad’s MyiQ software, each sample’s amplification plot is shown in a different color. Fluorescence versus cycle number can be displayed in either the linear view or the log view (Figure 17.1). In either case, you can clearly see that fluorescence intensity (indicating DNA abundance) increases as cycle number increases. An important concept to keep in mind is that the more input cDNA that went into the PCR, the sooner an increase in fluorescence will occur. In Figure 17.1A (linear view), all samples produce minimal fluorescence during the first 13 cycles. These cycles are considered the baseline. Then, the blue amplification plot begins to increase significantly, followed by the red and green plots at later cycle numbers. Since the blue plot showed an increase in fluorescence at the earliest cycle number, we can conclude that the sample it represents contains more input cDNA (in this case, 23S) than the other samples. To assign numeric values to these observations, a threshold is set, intersecting all the plots during exponential Molecular Biology Techniques. DOI: 10.1016/B978-0-12-385544-2.00017-X © 2012 Elsevier Inc. All rights reserved.
141
Molecular Biology Techniques
amplification. The threshold in Figure 17.1 is indicated by an orange horizontal line. Notice that in the linear view, each plot crosses the threshold in the early cycle numbers. Keeping all settings the same but viewing the data on a logarithmic scale (Figure 17.1B) we see that the threshold is high enough to be above background, but low enough to stay out of the plateau phase. The cycle at which a plot crosses the threshold is called the threshold cycle (CT ). The lower the CT, the more abundant the template is. During exponential amplification, DNA should in theory double with each cycle. So, after 2 cycles, there will be 4 times as much as you started with, and after about 3.3 cycles, there will be 10 times as much. Even though the CTs of the amplicons shown in Figure 17.1B differ by only about three cycles, these represent 10-fold differences in 23S amplicon abundance. If the same reactions were performed using conventional PCR, we may have analyzed amplicon abundance after 30 or more cycles on an agarose gel. Based on the qPCR results shown below (especially in the log view) it becomes clear that by this point in the reaction, the plateau phase has been reached and reagents have become limiting. We would conclude that there is very little difference in 23S rRNA levels between samples. However, by examining (A) 900
900
800
800
700
700
600
600
500
500
400
400
300
300
200
200
100
100
PCR Baseline Subtracted CF RFU
142
0
0 –100
0
2
4
6
FIG. 17.1 Amplification plots. Ten-fold serial dilutions of cDNA were used as templates for RT-qPCR reactions using 23S primers. The horizontal orange line represents the threshold. (A) Linear view. (B) Log view.
PCR Baseline Subtracted CF RFU
(B) 1000
–100 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 Cycle 1000
100
100
10
10
1
0
2
4
6
1 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 Cycle
Analysis of gst::egfp mRNA Levels by RT-qPCR: Part 2
amplicons early in the reaction, in the exponential phase, we are able to determine that there are actually 10-fold differences between each sample. There are two general methods for reporting quantification: absolute and relative. Absolute quantification calculates the quantity of a target gene in your unknown samples by interpolation from an absolute standard curve. This curve is commonly made by in vitro transcribed RNA or plasmid DNA with known copies of a target gene. This method is useful when you want to determine the exact copy number of a gene. The second type of quantification is called relative quantification. This aims to determine the difference in expression of a target gene between two samples. Reporting relative quantities is often sufficient when studying changes in gene expression. Basically, it aims to answer the question “How much of gene X is present in sample 1 compared to sample 2?” Relative quantification reports n-fold changes of gene expression rather than exact quantities: “There is 5-fold more of gene X in sample 1 versus sample 2.” In this example, sample 2 serves as a calibrator (the sample used for the basis of a comparison). In our experiments, we will be comparing gst::egfp levels between no induction, induction with IPTG and induction with lactose. Therefore, samples with no induction will serve as calibrators, and gst::egfp levels of samples induced with IPTG or lactose will be expressed relative to those with no induction. Two methods exist for relative quantification: the relative standard curve method and the comparative CT method. In the relative standard curve method, CT values obtained from experimental samples are compared to CT values from a standard curve made with dilutions of cDNA. The exact quantity of a target gene in the standards need not be known; just known mass amounts are required. The same procedure is also followed for a reference gene. Quantities are still expressed in comparison to a calibrator, unlike absolute quantification. The second method for relative quantification is the comparative CT method (also called the ΔΔ CT method). This is the method that we will use in this lab session. It has an advantage in that no standard curve is required. Instead, mathematical formulas are used to calculate relative gene expression. It should be noted that the comparative CT method assumes that PCR efficiencies are very similar between amplification of the target and reference genes. A validation experiment was performed previously to confirm that the primers used had very similar PCR efficiencies. In our experiment, remember that we amplified not only gst::egfp (our target gene) but also 23S rRNA (our reference gene). This reference gene is needed for the comparative CT method. We will be comparing CT values of egfp reactions to 23S rRNA reactions for normalization purposes. Then, these differences (ΔCT ) will be compared to the calibrator, which is no induction. In this way, gst::egfp levels will be 1 in the no induction control and gst::egfp levels will have a n-fold difference with IPTG and lactose induction.
STEP 1: NORMALIZE TO AN ENDOGENOUS REFERENCE For each cDNA in our experiment (no induction, IPTG and lactose), we will be taking the CT of the egfp reaction minus the CT of the 23S reaction to give us ΔCT . ΔCT = CT target gene − CT reference gene egfp
23S
143
Molecular Biology Techniques
STEP 2: COMPARE TO A CALIBRATOR Once a ΔCT value is calculated for no induction, IPTG and lactose cDNAs, we will report each value relative to the calibrator (no induction) to give us ΔΔCT . Notice that ΔΔCT will always be zero for the calibrator sample itself. ΔΔCT = ΔCT test sample − ΔCT calibrator sample
IPTG or lactose
no inducer
STEP 3: CALCULATE RELATIVE QUANTITY Finally, the amount of target, which is now normalized to an endogenous reference and relative to a calibrator, is calculated to provide us with the relative quantity of gst::egfp. 2 −∆∆C T
144
With these values in hand, you can draw conclusions as to how much gst::egfp mRNA was expressed with IPTG and lactose in your positive clone. The references at the end of this chapter provide detailed guides on performing and analyzing RT-qPCR experiments.1–4
Laboratory Exercise Relative Quantification of gst::egfp Levels Note: The following instructions are specific for analyzing data obtained on the Bio-Rad iCycler using MyiQ Software. If you are using equipment made by another manufacturer, your instructor will provide you with data analysis instructions. Your instructor may perform steps 1–17 for you. 1. 2. 3. 4.
5. 6.
7. 8.
Open MyiQ software on the computer connected to the iCycler. Click the “view post-run data” tab. Double-click the data file (.odm) from your lab session to open it. Click the “select wells” button and select the positions of your station’s reactions. If your group had 12 reactions occupying the same row, you can click on the row’s label (A, B, etc.) to automatically select that entire row. Click the “analyze selected wells” button. Make sure that the “log view” button is displayed. That means that the current view is the normal view. Verify that the amplification plots are running along 0 RFU (parallel to the x-axis) in the early cycles of the experiment. If the plots dip well below 0 or if RFU values vary greatly in the early cycles, you will need to adjust the baseline setting. To do this, determine the cycle at which your first (left-most) plot begins to rise above 0 RFU (amplification). Set the baseline cycles to between 2 and this cycle number. Click the “log view” button. Verify the auto-calculated threshold position (orange line) is high enough on the y-axis to be above any background noise (it should not intersect any sharp peaks seen in the early cycles) but is low enough to intersect the plots in the bottom third of the exponential phase.
Analysis of gst::egfp mRNA Levels by RT-qPCR: Part 2
Table 17.1 Pasting CT values into Excel A
B
C
1
egfp
23S
2
CT
CT
3
No inducer
4
IPTG
5
Lactose
D
E
F
Table 17.2 Comparative CT method formulas A
B
C
1
egfp
23S
2
CT
CT
D
E
F
ΔCT
ΔΔCT
2 ΔΔCT
3
No inducer
B3 C3
D3 D3
2^ E3
4
IPTG
B4 C4
D4 D3
2^ E4
5
Lactose
B5 C5
D5 D3
2^ E5
9. If any adjustments are made in steps 6 and/or 8, you will need to click the “recalculate threshold cycles” button. 10. Click the “Reports” button. 11. In the “Select Report” drop-down menu, select “PCR baseline.” 12. Click “save to file” and save the report to a thumb drive. 13. Click the “print” button to print a copy for your lab notebook. 14. Open Microsoft Word. Open the report you saved (a Rich Text Format file). 15. Copy the well number and CT values for each of your reactions. 16. Open Microsoft Excel. 17. Create a new spreadsheet and paste what you copied from the Word document. You may want to paste the values towards the bottom or right of the screen so that you can organize the rest of your spreadsheet (beginning with cell A1). 18. Create column and row headings as shown in Table 17.1. Enter your CT values from RT reactions only in the appropriate cells by copying and, pasting. 19. Enter the formulas shown in Table 17.2 to calculate Δ CT, ΔΔ CT and 2ΔΔ CT values. 20. Column F now shows the level of gst::egfp in each sample, relative to no inducer. 21. Below the data you have entered, paste in your CT values for the six RT reactions. 22. For each RNA sample (no inducer, IPTG, lactose), locate the CT values corresponding to its RT and RT reactions for each primer pair. Remember that these RT reactions were included to control for DNA contamination. Do the CT values differ between the RT and RT
145
Molecular Biology Techniques
reactions? Why? Remember that ideally DNA quantity doubles with each PCR cycle. 23. To calculate the amount of genomic DNA that is attributable to amplification in your RT samples, perform the Δ CT calculation for each 23S reaction. This time, you will use the RT sample as a calibrator: ∆ C T = C T without RT − C T with RT Amplification with the 23S primers, but not the egfp primers, will indicate genomic DNA contamination. Why? You are not interested in comparing the amount in one RNA sample relative to another, so you can skip the ΔΔ CT calculation. The equation 2ΔCt will calculate the quantity (%) of contaminating DNA. Multiply this value by 100 to obtain a percentage. gDNA contamination IPTG (%) = 2−[( CT
146
without RT )−( C T with RT )]
× 100
24. To calculate the amount of plasmid DNA that is attributable to amplification in your RT samples, perform the calculations described above for each egfp reaction.
References 1 Applied Biosystems. Guide to performing relative quantitation of gene expression using real-time quantitative PCR. http://www3.appliedbiosystems.com/cms/groups/mcb_ support/documents/generaldocuments/cms_042380.pdf/, 2008. 2 Applied Biosystems. User bulletin #2. http://www3.appliedbiosystems.com/cms/ groups/mcb_support/documents/generaldocuments/cms_040980.pdf/, 2001. 3 Applied Biosystems. Essentials of real time PCR. http://www3.appliedbiosystems.com/ cms/groups/mcb_marketing/documents/generaldocuments/cms_039996.pdf/. 4 Schmittgen TD, Livak KJ. Analyzing real-time PCR data by the comparative CT method. Nat. Protoc. 2008;3:1101–1108.
Discussion Questions 1. Why do standard PCR programs often include 30 cycles or more, while qPCR amplification plots (like the one in Figure 17.1B) show that at these later cycles, the plateau phase is often reached? 2. You were able to determine the levels of gst::egfp mRNA in the induced cultures relative to the non-induced. Without an inducing molecule to derepress the lac operator of pET-41a, was any gst::egfp mRNA transcribed? Why or why not?