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Analysis of gst::egfp mRNA Levels by RT-qPCR: Part II
Lab Session 15
Analysis of gst::egfp mRNA Levels by RT-qPCR: Part II Goal: You will analyze RT-qPCR data obtained in the previous session to determine the relative levels of gst::egfp mRNA in Escherichia coli induced with IPTG and lactose compared to those with no inducer.
I.
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 these types of quantification methods are introduced, we will first take a closer look at the results generated in this type of experiment. Recall from Lab Session 3 that PCR amplifies DNA exponentially, with the amount of DNA doubling each cycle. Depending on the amount of starting template DNA, it will take a number of cycles to produce detectable fluorescence. Quantification cannot take place this early in the experiment. Later, when reagents in the PCR become limiting, the amplification reaches a plateau; therefore, you cannot accurately quantify cDNA levels in this plateau phase. Accurate quantification can only occur 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. Typically, 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 (Fig. 15.1). 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 Fig. 15.1A (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 amplification. The threshold in Fig. 15.1 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 (Fig. 15.1B) we see that the threshold is high enough to be above background noise, 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 two cycles, there will be four 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 Fig. 15.1B 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 (especially in the log view) it becomes clear that by this point in the reaction, the plateau phase has been reached and the reagents have become limiting. We would conclude that there is little difference in 23S rRNA levels between samples. However, by examining amplicons early in the reaction, in the exponential phase, we are able to determine that there are actually a 10-fold difference between each sample.
Molecular Biology Techniques. DOI: https://doi.org/10.1016/B978-0-12-815774-9.00015-0 © 2019 Elsevier Inc. All rights reserved.
121
122
PART | IV Analysis of mRNA Levels
FIGURE 15.1 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. The x-axis displays cycle number and the y-axis displays relative fluorescence units (RFU) (A) Linear view. (B) Log view.
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 fivefold 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.
Lab Session | 15
123
Two methods exist for relative quantification: the relative standard curve method and the comparative CT method (also called the ΔΔCT method). The comparative CT method is what we will use in this lab session. It has the advantage 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 PCR amplification efficiencies are extremely similar between amplification of the test and reference genes. Not all reactions will have 100% amplification efficiency, where the DNA amount exactly doubles each cycle. Most reactions will be slightly lower or higher than 100%, and this can ultimately affect quantification. A validation experiment was performed previously to confirm that the primers have similar amplification efficiencies. In the last experiment, you amplified not only gst::egfp (our test 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 (two comparisons, so ΔΔCT). In this way, gst::egfp levels will be 1 3 in the no induction control and gst::egfp levels will have an n-fold difference with IPTG and lactose induction.
STEP 1: NORMALIZE TO AN ENDOGENOUS REFERENCE ΔCT 5 CT
egfp
2 CT
23S
For each cDNA in our experiment (no induction, IPTG, and lactose), you will be taking the CT of the egfp reaction minus the CT of the 23S reaction to get a ΔCT.
STEP 2: COMPARE TO A CALIBRATOR ΔΔCT 5 ΔCT
IPTG
2 ΔCT
no
Once a ΔCT value is calculated for no induction, IPTG, and lactose cDNAs, you will report each value relative to the calibrator (no induction) to get a ΔΔCT. Notice that ΔΔCT will always be zero for the calibrator sample itself.
STEP 3: CALCULATE RELATIVE QUANTITY 22ΔΔCT Finally, the amount of the test gene (gst::egfp with IPTG or lactose), which is now normalized to an endogenous reference (23s with IPTG or lactose) and relative to a calibrator (no induction), is calculated to provide us with the relative quantity of gst::egfp mRNA. 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.13
II.
LABORATORY EXERCISE
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 117 for you. 1. 2. 3. 4.
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 group’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. 5. Make sure that the “log view” button is displayed. That means that the current view is the normal view.
124
PART | IV Analysis of mRNA Levels
6. Verify that the amplification plots are running along 0 relative fluorescence units (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 two and this cycle number. 7. Click the “log view” button. 8. 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. 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 with column and row headings shown in Fig. 15.2. Paste your CT values from 1 RT reactions only in the appropriate cells. 18. Enter the formulas shown in Fig. 15.2 to calculate ΔCT, ΔΔCT, and 22ΔΔCT values. 19. The last column (relative quantity) now shows the level of gst::egfp in each sample, relative to no inducer. 20. Below the data you have entered, create the column and row headings shown in Fig. 15.3. Paste your CT values for the six 2 RT reactions and six 1 RT reactions. 21. For each RNA sample (no inducer, IPTG, lactose), locate the CT values corresponding to its 2 RT and 1 RT reactions for each primer pair (you will analyze egfp first). Remember that these 2 RT reactions were included to control for DNA contamination. 22. Amplification with the egfp primers will indicate plasmid DNA contamination. To calculate the amount of plasmid DNA that is attributable to amplification in your 1 RT samples, perform the ΔCT calculation for each egfp reaction. This time, you will use the 1 RT sample as a calibrator: ΔCT 5 CT
without RT
2 CT
with RT
You are not interested in performing a second comparison between the CT in one RNA sample relative to another in this step, so you will not use ΔΔCT values. You stop at one comparison (ΔCT). Finally, the equation 22ΔCT will calculate the quantity (in percent) of contaminating plasmid DNA. Multiply this value by 100 to obtain a percentage. DNA contaminationIPTG ð%Þ 5 22½ðCT
without RTÞ2ðCT with RTÞ
3 100
23. Amplification with the 23S primers will indicate genomic DNA contamination. Perform the calculations described in Step 22 for each 23S reaction. Refer to Fig. 15.3. 24. To summarize, these results should be collected for your notebook: a. Relative quantity of gst::egfp mRNA in uninduced, IPTG-induced, and lactose-induced samples. This should be expressed as a fold-change (Steps 1719). b. Plasmid DNA contamination for each sample (Step 22) c. Genomic DNA contamination for each sample (Step 23)
FIGURE 15.2 Excel example. Screenshot shows the layout and formulas used in the comparative CT method to calculate relative quantity.
Lab Session | 15
125
FIGURE 15.3 Excel example. Screenshot shows the layout and formulas used to calculate the percentage of DNA contamination.
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. Schmittgen TD, Livak KJ. Analyzing real-time PCR data by the comparative CT method. Nat Protoc 2008;3:11018.
DISCUSSION QUESTIONS 1. Why do standard PCR programs often include 30 cycles or more, while qPCR amplification plots (like the one in Fig. 15.1B) 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?
Analysis of gst::egfp mRNA Levels by RT-qPCR: Part II Goal: You will analyze RT-qPCR data obtained in the previous session to determine the relative levels of gst::egfp mRNA in Escherichia coli induced with IPTG and lactose compared to those with no inducer.
I.
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 these types of quantification methods are introduced, we will first take a closer look at the results generated in this type of experiment. Recall from Lab Session 3 that PCR amplifies DNA exponentially, with the amount of DNA doubling each cycle. Depending on the amount of starting template DNA, it will take a number of cycles to produce detectable fluorescence. Quantification cannot take place this early in the experiment. Later, when reagents in the PCR become limiting, the amplification reaches a plateau; therefore, you cannot accurately quantify cDNA levels in this plateau phase. Accurate quantification can only occur 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. Typically, 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 (Fig. 15.1). 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 Fig. 15.1A (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 amplification. The threshold in Fig. 15.1 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 (Fig. 15.1B) we see that the threshold is high enough to be above background noise, 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 two cycles, there will be four 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 Fig. 15.1B 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 (especially in the log view) it becomes clear that by this point in the reaction, the plateau phase has been reached and the reagents have become limiting. We would conclude that there is little difference in 23S rRNA levels between samples. However, by examining amplicons early in the reaction, in the exponential phase, we are able to determine that there are actually a 10-fold difference between each sample.
Molecular Biology Techniques. DOI: https://doi.org/10.1016/B978-0-12-815774-9.00015-0 © 2019 Elsevier Inc. All rights reserved.
121
122
PART | IV Analysis of mRNA Levels
FIGURE 15.1 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. The x-axis displays cycle number and the y-axis displays relative fluorescence units (RFU) (A) Linear view. (B) Log view.
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 fivefold 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.
Lab Session | 15
123
Two methods exist for relative quantification: the relative standard curve method and the comparative CT method (also called the ΔΔCT method). The comparative CT method is what we will use in this lab session. It has the advantage 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 PCR amplification efficiencies are extremely similar between amplification of the test and reference genes. Not all reactions will have 100% amplification efficiency, where the DNA amount exactly doubles each cycle. Most reactions will be slightly lower or higher than 100%, and this can ultimately affect quantification. A validation experiment was performed previously to confirm that the primers have similar amplification efficiencies. In the last experiment, you amplified not only gst::egfp (our test 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 (two comparisons, so ΔΔCT). In this way, gst::egfp levels will be 1 3 in the no induction control and gst::egfp levels will have an n-fold difference with IPTG and lactose induction.
STEP 1: NORMALIZE TO AN ENDOGENOUS REFERENCE ΔCT 5 CT
egfp
2 CT
23S
For each cDNA in our experiment (no induction, IPTG, and lactose), you will be taking the CT of the egfp reaction minus the CT of the 23S reaction to get a ΔCT.
STEP 2: COMPARE TO A CALIBRATOR ΔΔCT 5 ΔCT
IPTG
2 ΔCT
no
Once a ΔCT value is calculated for no induction, IPTG, and lactose cDNAs, you will report each value relative to the calibrator (no induction) to get a ΔΔCT. Notice that ΔΔCT will always be zero for the calibrator sample itself.
STEP 3: CALCULATE RELATIVE QUANTITY 22ΔΔCT Finally, the amount of the test gene (gst::egfp with IPTG or lactose), which is now normalized to an endogenous reference (23s with IPTG or lactose) and relative to a calibrator (no induction), is calculated to provide us with the relative quantity of gst::egfp mRNA. 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.13
II.
LABORATORY EXERCISE
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 117 for you. 1. 2. 3. 4.
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 group’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. 5. Make sure that the “log view” button is displayed. That means that the current view is the normal view.
124
PART | IV Analysis of mRNA Levels
6. Verify that the amplification plots are running along 0 relative fluorescence units (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 two and this cycle number. 7. Click the “log view” button. 8. 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. 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 with column and row headings shown in Fig. 15.2. Paste your CT values from 1 RT reactions only in the appropriate cells. 18. Enter the formulas shown in Fig. 15.2 to calculate ΔCT, ΔΔCT, and 22ΔΔCT values. 19. The last column (relative quantity) now shows the level of gst::egfp in each sample, relative to no inducer. 20. Below the data you have entered, create the column and row headings shown in Fig. 15.3. Paste your CT values for the six 2 RT reactions and six 1 RT reactions. 21. For each RNA sample (no inducer, IPTG, lactose), locate the CT values corresponding to its 2 RT and 1 RT reactions for each primer pair (you will analyze egfp first). Remember that these 2 RT reactions were included to control for DNA contamination. 22. Amplification with the egfp primers will indicate plasmid DNA contamination. To calculate the amount of plasmid DNA that is attributable to amplification in your 1 RT samples, perform the ΔCT calculation for each egfp reaction. This time, you will use the 1 RT sample as a calibrator: ΔCT 5 CT
without RT
2 CT
with RT
You are not interested in performing a second comparison between the CT in one RNA sample relative to another in this step, so you will not use ΔΔCT values. You stop at one comparison (ΔCT). Finally, the equation 22ΔCT will calculate the quantity (in percent) of contaminating plasmid DNA. Multiply this value by 100 to obtain a percentage. DNA contaminationIPTG ð%Þ 5 22½ðCT
without RTÞ2ðCT with RTÞ
3 100
23. Amplification with the 23S primers will indicate genomic DNA contamination. Perform the calculations described in Step 22 for each 23S reaction. Refer to Fig. 15.3. 24. To summarize, these results should be collected for your notebook: a. Relative quantity of gst::egfp mRNA in uninduced, IPTG-induced, and lactose-induced samples. This should be expressed as a fold-change (Steps 1719). b. Plasmid DNA contamination for each sample (Step 22) c. Genomic DNA contamination for each sample (Step 23)
FIGURE 15.2 Excel example. Screenshot shows the layout and formulas used in the comparative CT method to calculate relative quantity.
Lab Session | 15
125
FIGURE 15.3 Excel example. Screenshot shows the layout and formulas used to calculate the percentage of DNA contamination.
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. Schmittgen TD, Livak KJ. Analyzing real-time PCR data by the comparative CT method. Nat Protoc 2008;3:11018.
DISCUSSION QUESTIONS 1. Why do standard PCR programs often include 30 cycles or more, while qPCR amplification plots (like the one in Fig. 15.1B) 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?