First Steps in Relative Quantification Analysis of Multi-Plate Gene

RealTime ready Application Note June 2011

First Steps in Relative Abstract/Introduction

Quantification Analysis This technical application note describes the first steps in data analysis for relative quantification of gene expression of Multi-Plate Gene experiments performed using RealTime ready Custom Panels. The easy initial setup as well as the “Result Export” Expression Experiments procedure of the LightCycler® 480 Software is described. Further preparation of the data depends on the means of analysis. Three routes to analyze the data are described: 1. Spreadsheet calculation software 2. GenEx Software from MultiD Analysis Heiko Walch and Irene Labaere 3. qbasePLUS 2.0 from Biogazelle Roche Applied Science, Penzberg, Germany Whereas GenEx and qbasePLUS are software packages from third party vendors specialized in RT-qPCR data analysis (1, 2) the spreadsheet calculation approach can be seen as a generic procedure for performing subsequent analysis either in the spreadsheet software or in other sophisticated statistical tools such as R, SigmaPlot, etc. (3, 4)

Note that this article does not focus on qPCR setup, experimental planning or correct selection of reference genes. For details on this important topics, see publications such as the MIQE guidelines (5), or other recently published scientific best practices for qPCR expression studies (6, 7, 8).

For life science research only. Not for use in diagnostic procedures.

RealTime ready Setting Up the Experiment and Selecting the Assays

A thoroughly planned experiment is vital to good scientific The gene list was generated using the focus list of assays for data. The RealTime ready Configurator (9) offers various genes “Amplified/overexpressed genes in cancers”, based on a possibilities to facilitate the creation of a reasonable list of publication from Santarius et al. (10). Additionally, some target genes. For example, the focus lists available under the genes from the NF-kB pathway were added via the “Search “Search by Focus Panel” function comprise RealTime ready by Pathway” function in the RealTime ready Configurator. assay collections for different fields of interest, such as: • Genes involved in various signaling pathways RealTime ready Custom Panel 384 – 96 (e.g., NF-kB, Jun, MAPK, sonic hedgehog, Notch, wnt) • Genes involved in specific biological processes (e.g., angiogenesis, oncology, apoptosis, induction of pluripotency, stem cell differentiation) • Genes belonging to certain families or groupings (e.g., protein kinases, phosphatases, proteases, transcription factors) These lists can be seen as scientifically sound suggestions, and the RealTime ready Configurator also offers the freedom to add and remove assays to the desired layout as needed. To demonstrate the first analytical steps in a multi-plate analysis, we set up an artificial time series experiment with two biological samples (S01 and S02) and user selected Replicates: 4 Reference two time points (T01 and T02) after treatment measured user selected Assays: 93 Genes: in duplicates for the cDNA synthesis step and a single qPCR user selected measurement each. With a total of 93 genes of interest Figure 1: Sample layout for a 384-well plate with 4 replicates of and 3 reference genes, we chose a 384-well plate layout with 93 targets of interest and 3 reference genes each. 4 replicates on each plate (see Figure 1).

Performing the RealTime ready Experiment on the LightCycler® 480 Instrument

RealTime ready Custom Panels can be customized on the the page contains a .zip file that can be downloaded and is RealTime ready Configurator (9). The selected assays are associated with the configuration. The archive contains two pre-plated, and ready to use on LightCycler® 480 Multiwell text files: Plates. Single assays are also available in liquid format. The 1. The sample editor import file with layout information “My Orders” page of the Configurator shows the current (see Figure 2) status of all orders. After an order is produced and shipped 2. The configuration info file with additional assay information (see Figure 3) General : Pos General : Target Name Rel Quant : Combined Sample/Target Type

A1 HS|PRKCI|105941 Target Unknown Figure 2: An excerpt of the sample editor import file. The three columns contain information for the well coordinates (General:Pos), the contained A2 HS|AKT3|105933 Target Unknown assays (General:Target Name), and the type of the assay (Rel Quant: … … … Combined Sample/Target Type). The assay information is represented as a concatenation of the organism, the gene symbol, and the assay ID, all N5 HS|NFKB2|100649 Target Unknown separated by vertical dashes (“|”). The different types of assays are either a gene of interest (Target Unknown), a reference gene (Ref Unknown), or N6 HS|ALAS1|102108 Ref Unknown an assay from the control concept (Unassigned Unknown) not used for this demonstration. The file can be imported directly into the LightCycler® N7 HS|TRAF2|102966 Target Unknown 480 sample editor software or opened and modified in any text editor (for example, to change the reference genes or gene annotations) and then … … … reimported afterwards.

2 RealTime ready: First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments Figure 3: Configuration info file containing assay annotation and Combined with the detailed assay information and annotations sequence information. The example configuration info file shown above available at the RealTime ready Configurator, all information required contains additional information regarding the plate layout, gene information, by the MIQE guidelines for scientific publications is provided (5). and assay details including the primer sequences.

The contents of the archive greatly reduce the time needed The information on how the replicates on the plate are used to annotate and set up the experiments in the is plate-specific and also needs to be edited in the sample LightCycler® 480 Software. After creating a new experiment editor. It is essential to use consistent naming for samples, in the LightCycler® 480 Software (11), the provided file replicates, and any additional information that will be needed can be imported into the sample editor to assign all well and later to combine the data. assay information for this configuration (see Figure 4).

Figure 4: Editing target and sample information using the sample editor import file in the sample editor of the LightCycler® 480 Software.

Preparing and Exporting the Result Data from the LightCycler® 480 Software 1.5

The LightCycler® 480 Software offers several analysis The Cq values are generated using the “Abs. Quant Analysis” modules. For this example we want to evaluate the relative and the “automated second derivative” method (11). Finally expression levels of mRNAs in different biological samples. to do a meaningful comparison of different samples and

To do this we compare the normalized Cq values which genes the Cq values need to be annotated with sample and can be used as a relative measurement for the abundance of gene information accordingly as demonstrated below. cDNA covered by the particular RealTime ready assay.

RealTime ready: First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments 3 Exporting the Result Data from the LightCycler® 480 Software

In order to combine the results of multiple plates with tested with a standard cDNA sample, therefore missing values varying numbers of replicates, use absolute quantification can be interpreted as no expression of the relevant target in conjunction with the second derivative method for or, to be more precise, an expression beyond the level of

Cq-calling. The actual relative quantification is done detection for that particular assay. For data analysis purposes, outside the LightCycler® Software, following standard those missing values can be dealt with in various ways, some RT-qPCR analysis procedures (11). Looking at the analysis of which will be explained briefly in the analysis sections. results, some well positions might be highlighted in green, If, however, there are any clear patterns visible, for example, indicating that no Cq value could be calculated. Such one single plate replicate or one column/row shows no calls “failures” may occur for many reasons and can be expected you should check the starting materials and the pipetting if large amounts of different assays are used with schemas for possible technical errors. uncharacterized samples. RealTime ready assays are function

Figure 5: Export of the result Cq values in the LightCycler® 480 Software by right-clicking in the result table and saving the file to an appropriate location.

After the “Abs. Quant. Analysis”, the result data needs to be exported by right-clicking in the result table in the analysis section of the LightCycler® 480 Software for every individual run (see Figure 5). For the sake of data quality, consider how and where to organize the data. In this context, having a dedicated folder structure, with for example, file names and/ or short experimental descriptions along with the result Figure 6: File and folder structure for Experiment. and IXO files names for a particular round of experiments, 1. Short experiment description; is a good start (see Figure 6). For this workflow example, 2. Sample editor import file; the experiment consisted of 3 LightCycler® 480 Instrument 3. Zip file provided by Roche containing the sample editor import file and runs saved as “result_CONFIG_ID_plateXYZ.txt”. assay annotation/information; 4 & 5. Exported result files for the individual plates.

After exporting the results, the run and data gathering step is finished and the data needs to be cleaned and combined in order to be analyzed.

4 RealTime ready: First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments General Considerations for Relative Quantification Expression Data

Multi-plate multi-parameter analysis can be conducted in several ways; this note covers only the first steps of cleaning and combining the results. These steps are usually constant and independent of the level of sophistication followed in the statistical analysis afterwards. For a “fold change analysis” these steps are: 1. Plausibility check and quality control of the results 2. Normalization versus sample-specific calibrators or references 3. Combining the replicates 4. Calculation of relative ratios of the expression levels between different samples For comparing relative expression ratios, we will focus on the Cq method (12). This simplification of the Pfaffl method (13) assumes that the qPCR efficiencies are more or Figure 7: Distribution of the PCR efficiencies of more than 6,000 less equal and ~100%. The RealTime ready assays are RealTime ready qPCR assays. The mean efficiency of the sample is 1.99 with a standard deviation 0.034. Ninety percent of the efficiencies are located pre-tested and fulfill strict quality criteria (14). One of the within a window from 1.95 to 2.05. requirements is that the assay efficiency is in the range of 90–100% (2.0 +/- 10%). Most assays are located in a very As a second simplification, we will not do any inter-plate narrow efficiency window from 1.95–2.05 (see Figure 7). correction with a universal calibrator sample, although Therefore, a first analysis can be done without efficiency with RealTime ready plate layouts containing replicates the correction and using the Cq method. If, however, one concept is optional. The technical plate-to-plate variance needs to qualify very small expression changes with good using RealTime ready assays in conjunction with the statistical power, or if a sample preparation is known to recommended LightCycler® 480 Probes Master, and have issues regarding qPCR efficiency, carefully estimate the the LightCycler® 480 System is negligible. Therefore, an assay efficiencies in the particular experimental workflow additional inter-plate calibration step is not explained and use the results for efficiency correction in the analysis in detail, and may actually be skipped for many qPCR workflow as described by Pfaffl (13). expression analyses using the LightCycler® 480 System.

Basic Excel Analysis

There are numerous spreadsheet calculation software 1. Importing the LightCycler® 480 Data File into Excel packages available for different platforms (e.g. Excel/ All data values in the previously exported result files are Microsoft Office, Numbers/iWorks or Calc/OpenOffice). separated by tabulators. This tab delimited format is easy to Here Excel which is part of the Microsoft Office package and edit, and most software is able to handle the data correctly available for most prominent operating systems is used for with minor adjustments. In Excel, data can be imported demonstrational purposes. Most of the methods introduced using the text import wizard or by simply copy/pasting from below can either be found identically or they can be a standard text editor. In this example, the files were added substituted by similar functions in the other software underneath each other in one spreadsheet. For the plate_02 packages. Although it can be used for basic to advanced data file, only the data rows were imported and the header analyses, it has some drawbacks and is not as powerful or lines were deleted. During concatenation of the result sets, easy to handle as the specialized qPCR data analysis software it is useful to add an additional column holding the plate packages also mentioned in this note. The descriptions and information (see Figure 8). screenshots shown here were created using a Office 2010 version of Excel on a PC running the Windows 7 operating system.

RealTime ready: First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments 5 Basic Excel Analysis continued

2. Combining qPCR Data with Gene Names and for each row. In the case of the result file and the sample Target Type Information editor import file, the common denominator is the well

The table now contains the Cq values and well positions, but position. The positions are fixed and strictly associated with neither the gene names nor the target type information. the assay for one particular configuration. The VLOOKUP Both of these pieces of information are needed and are part function takes the well coordinate from the result table and of the sample editor import file provided on the Configurator. tries to find it in the lookup table (here the sample editor

Consequently, the next step is to combine the Cq values with sheet). An additional parameter to the function specifies the associated gene names. One quick way to do this is by how many columns the function should look to the right to using the Excel vertical lookup function. The VLOOKUP pass the value of a neighboring cell (for details, see Figure 9, (“SVERWEIS” in German versions) is an invaluable tool for as well as the associated Help in the application). combining different tables having one common identifier

Figure 8: Imported LightCycler® 480 Software result files. The grayed-out columns are not required for later analysis and can be deleted or hidden. During the course of concatenating the files, column “I” was created and the plate information was added manually.

Figure 9: Using VLOOKUP function in Excel for combining data from LC_100012220_result sheet again. This is done automatically by the VLOO- different tables. Here the “General:Target Name” is part of the “SampleE- KUP statement framed in blue: =VLOOKUP (C3,SampleEditorImportFile ditorImportFile” sheet and needs to be added to appropriate lines of the !A:B,2,0). Look up the value from cell C3 in the “SampleEditorImportFile” “LC_100012220_result” sheet. The common identifier or key between the sheet in column A. If found, return the value of the second (2) column to the two tables is the well coordinate (A1 to P24) present in both tables. The right of it. If no perfect match is found, no value is returned (0). This only blue highlighted Excel cell in the “LC_100012220_results” sheet contains needs to be entered once and can be copied to the cells underneath either the coordinate value “I1”. In the “SampleEditorImportFile” sheet, you can by dragging the cell or copying the content, marking the rest of the column, manually scroll down until you find the same well coordinate “I1” in line and pasting the content. Afterwards, every row in the “LC_100012220_result” 194. The second column in the same sheet holds the data for the gene tar- sheet contains the matching target information. The target type can be get name “HS|YWHAB|117063” that can now be manually pasted into the added accordingly.

6 RealTime ready: First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments 3. Normalization against Sample-specific for the various algorithms, (15, 16, 17) used to evaluate the Reference Genes best reference gene set, a good reference gene’s expression After this combination step, the data is ready to be analyzed, should be unaffected by any of the experimental procedures leading to the second step of the Cq procedure, which to be assessed. In concordance with recent publications, we is applying the appropriate sample specific references for recommend using more than one reference gene (3 are used normalization. Normalization against sample-specific as a default for nearly all the RealTime ready layouts). Besides reference genes is important to compensate for the inevitable literature searches, there are tools to narrow down the list intrinsic experimental variations that could otherwise lead of candidate reference genes for such pilot studies (e.g. 18). to biases in the results. A variance in input RNA or different The RealTime ready Reference Gene Focus Panel is a cDNA synthesis efficiencies are two examples of these reasonable and pragmatic selection, having the benefit that confounding experimental variances. The selection of good all included genes are also directly available as designated reference genes is crucial but often neglected, as several reference genes in the RealTime ready Configurator. If the publications have shown over the last several years. Without available references are not sufficient, any gene or assay can going into too much detail, reference gene selection usually be used as reference due to the layout flexibility of the target requires a pilot experiment with a set of candidate reference genes in the Configurator. In our example, we have chosen 3 genes, in which the samples are treated in the same way as reference genes: GUSB (GeneID: 2990), HRPT1 (GeneID: planned for the final experiment. As a plausible general rule 3278), and ALAS1 (GeneID: 211), and we will use the mean

Cq of the references as a normalization factor. Naturally, the normalization to the reference genes has to be performed for every replicate separately. This can be archived multiple ways in Excel. For demonstration purposes, we have chosen a method that additionally exemplifies another powerful and versatile Excel tool – the pivot tables. Looking at our now “enriched” result table, one recognizes that each individual sample or replicate is characterized by the plate ID and the sample name (see Figure 9). As a first intermediate step, we add another column (“Tag”) containing the concatenated values of the name and plate column. Second, we insert a pivot table using the newly created tag as line identifier,

calculating the mean of the Cq values shown and filtering to show only “Ref Unknown” target types (see Figure 10).

In each value line, the pivot table now contains one value

that represents the mean Cq value for the three reference genes for one replicate/sample from one plate. These values are now subtracted from the matching (sample and plate)

raw Cq values in the LC_100012220_result table, either manually or using a few mouse clicks in the VLOOKUP Figure 10: Pivot tables are a versatile tool for creating new “pivoting” function (see Figure 11). views on data tables.

Figure 11: Using VLOOKUP to normalize versus the mean of the references for each replicate on each plate.

RealTime ready: First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments 7 Basic Excel Analysis continued

The “Normalized by reference” column now contains the Depending on the type of planned analysis, it may be useful deviation of the gene of interest’s expression compared to arrange the gene names as columns and the sample/ to the mean reference gene’s expression. Now a direct replicates as individual rows of the result table. In a two-step comparison of individual values from different samples and procedure, the current result table can be transformed into different plates is possible, and basic analyses can begin. this kind of data view. First, another pivot table is created

Keep in mind that the data shows Cq value differences, and (see Figure 13). The data is then copied and pasted using the assuming 100% PCR efficiencies, a value of 1 converts to a “Transpose” option into a new data sheet. The result is 2-fold difference (e.g., FoldDiff =2Cq_target – Cq_meanRef). As an shown in Figure 14. additional QC step, it is reasonable to assess how the different biological replicates on the plates performed. This can be done graphically with a scatterplot, as shown in Figure 12.

Figure 12: Scatterplot of a biological replicate comparison (using Figure 13: A pivoted view of the data using the target name as row S01_T01_plate_01 values on the X-axis and S01_T01_plate_02 on the identifier and the “Normalized by reference” values for each individual Y-axis) shows a very good R2 correlation coefficient of 0.98 and sample. The replicates of each sample over different plates are exemplifies the reproducibility of biological replicates on different aggregated by using the mean value of the calculated differences. plates in this experiment.

Figure 14: In the transposed data view, the individual samples can be plotted against each other more intuitively.

These are only the very first steps of data analysis using initially very informative. However, many of the steps during Excel. The used functionalities will most likely have to be qPCR analysis must be done repeatedly, and there are other applied several times in varying orders to get a good software tools specifically created for analyzing qPCR data. understanding of the experimental results. Excel can be In the following section, we introduce two powerful software used for these analyses, and doing it semi-manually is suites that are easy to use with RealTime ready assay data.

8 RealTime ready: First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments First Analysis Steps Using the GenEx Software Suite

GenEx (developed by MultiD) is a software suite tailored ease-of-use and analysis of multi-plate/multi-parameter around the analysis of qPCR data. The software is available qPCR experiments. Detailed descriptions and tutorials in three different versions, each providing basic to highly describing the usage of the import wizard as well as the advanced statistical analysis capabilities. For additional capabilities and usage of the software are available at the information, visit http://www.multid.se/. Recently, MultiD MultiD website (1). The wizard module is self-explanatory Analysis developed a special import wizard for RealTime and takes the generated and exported result files ready plates that is now available as a separate plug-in (result_CONFIG_ID_plateXYZ.txt), as well as the provided module for the GenEx Software package. The module greatly LightCycler® 480 Software sample editor import file for streamlines the analysis workflow and sets a new standard for creating the assay-to-well position association (see Figure 15).

Figure 15: The RealTime ready import wizard is started from the tools section of the GenEx Software package, and offers an extremely rapid and convenient way to start analyzing multiple-plate qPCR experiments.

Figure 16: The LightCycler® 480 result export files and the sample editor import file must be specified during the wizard workflow.

After specifying the panel type (Focus or Custom Panel), the annotated sample editor import file together with all the format (96 or 384), and the layout of the particular target and sample names. Identical sample names can be configuration (in this case, 384 wells with 4 replicates and used later to identify replicates and may be used downstream 96 genes each), the previously exported data files can be for QC and data analysis. After finishing the wizard, the uploaded. In addition, to add the gene of interest and result data from the three plates is saved as a GenEx-specific reference gene information, the sample editor import file .mdf file, and can be edited and analyzed using the extensive needs to be selected (see Figure 16). The reference genes capabilities of the software package. specified in the configuration are automatically taken from

RealTime ready: First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments 9 First Analysis Steps Using qbasePLUS qbasePLUS is a flexible and powerful qPCR analysis software combination with the sample editor import file containing developed and maintained by Biogazelle (2). The original the target names for every well coordinate. The first step release from 2007 was replaced in 2011 by qbasePLUS 2.0, is again combining the result export and the sample editor which is offered in two license versions with extending import as previously shown in Figure 8. Starting with this feature sets. The premium license is available as time limited table generated in the Excel analysis section we create a data trial version and is required for analyzing the data from the table as shown in Figure 17. qbasePLUS has preconfigured 384-well plate experiments. Biogazelle also offers an layouts for different qPCR machines including the Roche extensive set of tutorials to get started with the software LightCycler® Instrument series. The software expects package at their website (for details see the Biogazelle the data to be in the correct format with the data columns website). qbasePLUS can already handle a predefined in the correct order as described in Figure 17. LightCycler® 480 result export format. This format can be Sample files and importing descriptions are available easily generated with the standard result exports in at the Biogazelle website (2).

Figure 17: Data format required by qbasePLUS for LightCycler® 480 results. The columns A, C, E, F, G and H with dark blue headers are required for correct data import. In order to use the sample and target information later in qbasePLUS the software offers the option to “overload” the “Name” column with the sample information and the target information concatenated by one of various separators. Here we have chosen to use “,” as delimiter and combined columns J, D and K by using the special character “&” in a simple Excel function as seen in the cell E2 above. The added plate information from column “J” creates unique sample names in this experiment. The function can be “dragged” or copied to the remaining cells of column E. After completion column D needs to be hidden before exporting the data as described below.

Figure 18: The “Run Import” dialog and the qbasePLUS main window with the Project Explorer. Right-clicking onto individual items of the navigational tree offers context-dependent options. Right-clicking on “Experiments” allows the addition of a new experiment to the project, and right-clicking on “Runs” leads to the “Import Run” dialog shown here. For the RealTime ready result files as generated in the course of this note, it is important to set the different parameters as shown here (TAB delimited and LightCycler® 480 file type).

After preparing the Excel file one needs to re-export the creating a new project by ‘right-clicking’ in the Project results for each plate which can be easily done by hiding Explorer pane in the main qbasePLUS window as shown in “column D” filtering for the “plate_01” in column “J” and Figure 18. The “context specific right-click” is an intuitive copy and pasting the remaining table to a new text file user interface basis of qbasePLUS. Create a new experiment named “qbase_results_LC_100012220_plate01.txt” to import the Run data again by right-clicking on (for example). The filtering and exporting is repeated for “Experiments”. After expanding the experiments navigation all plates in the experiment. Having prepared the result files tree, right click on “Runs” allow the user to import the one can start importing the results in qbasePLUS. Start by previously prepared qPCR result (see Figure 18).

10 RealTime ready: First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments The samples are identified and all genes are imported as of genes by using the geNorm algorithm (15), which can be “Targets of interest”. The next step is to set the reference accessed in the “Analysis” section of the “Project Explorer” genes for further calculations. In the case of the RealTime pane. An interesting feature in this context is that qbasePLUS ready panels where the references were identified in previous presents the geNorm results graphically as well as in an experiments this can be archived by opening the “Targets of interpreted textual format. Further statistical analysis can be interest” branch, selecting the genes to be set as references started from the analysis section (see Figure 19) starting from and setting the target type to “Reference Target”. Alternatively, “Multi-target bar-charts” to sophisticated statistical tests. qbasePLUS offers to identify suitable references within the set

Figure 19: Basic analysis and the Stat. Wizard in qbasePLUS.

The “Stat. wizard” option helps the user to choose the right the experimental planning. Another feature worth statistical test for the data (t-test, Mann-Whitney, paired or mentioning is the option to annotate the assays with additional unpaired etc.). This is helpful although choosing the information (for example the ordering number or the primer appropriate test for the data should be already clear during sequences as provided with RealTime ready assays).

Summary

Experimental planning and data analysis are vital parts of must not be underestimated, getting the data (and hence Cq sound scientific work including expression analysis by values) is often trivial and routine work. The last big hurdle RT qPCR. One of the crucial and most time-consuming first is usually data analysis and getting the relevant results from steps is designing and validating good qPCR assays for the seemingly complex data. This note demonstrates some basic targets of interest. This burden is removed by using concepts and first steps in different software solutions. RealTime ready assays which are all carefully designed and Although it is feasible to do the analysis in spreadsheet function tested in our lab before being released to the software like Excel, it is advisable to carefully consider the time RealTime ready Configurator (9, 14). The second obstacle in and money spent on doing these analyses. Using one of the the path is designing an experiment that is suited to shine dedicated software packages is often more straightforward and new light on the addressed biological question. Choosing many of the pitfalls that await the entry-level statistician are the assays and targets in an appropriate layout is again addressed and taken care of. These software suits can directly facilitated using the search capabilities and the gene deal with all MIQE compliant information supplied with the annotation in the Configurator. Although good lab work RealTime ready assays, greatly simplifying the workflow.

RealTime ready: First Steps in Relative Quantification Analysis of Multi-Plate Gene Expression Experiments 11 Bibliography

1. MultiD – GenEx: http://www.multid.se. 12. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2. Biogazelle – qbasePLUS 2.0: http://www.biogazelle.com. 2(-Delta Delta C(T)) Method. Methods. 2001 3. R Development Core Team: R – A Language and Dec;25(4):402–8 Environment for Statistical Computing. R Foundation 13. Pfaffl MW. A new mathematical model for relative for Statistical Computing, Vienna Austria 2011. quantification in real-time RT-PCR. Nucleic Acids Res. 4. Sysstat Software Inc. – SigmaPlot: 2001 May 1;29(9):e45 http://www.sigmaplot.com/ 14. Roche Applied Science – RealTime ready Whitepaper: 5. Bustin Stephen A.: The MIQE guidelines: minimum https://www.roche-applied-science.com/sis/ information for publication of quantitative real-time realtimeready/index.jsp?&id=RT_050300 PCR experiments. Clin Chem. 2009 Apr;55(4):611–22. 15. Vandesompele J, De Preter K, Pattyn F, Poppe B, 6. Derveaux S., Vandesompele J., Hellemans J.: How to do Van Roy N, De Paepe A, Speleman F. Accurate successful gene expression analysis using real-time PCR. normalization of real-time quantitative RT-PCR data by Methods. 2010 Apr;50(4):227–30 geometric averaging of multiple internal control genes. Genome Biol. 2002 Jun 18;3(7) 7. Kubista M. & Sindelka R.: The prime technique – Real time PCR analyis. 2007 G.I.T. Laboratory Journal 9–10, 16. Jensen J., Ørntoft T.: Normalization of real-time pp 33–35 quantitative RT-PCR data: a model based variance estimation approach to identify genes suited for 8. Kitchen RR, Kubista M, Tichopad A. Statistical aspects of normalization – applied to bladder- and colon-cancer quantitative real-time PCR experiment design. Methods. data-sets.Cancer Research. 2004 (64): 5245–5250 2010 Apr;50(4):231–6 17. Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. 9. Roche Applied Science – The RealTime ready Determination of stable housekeeping genes, Configurator: differentially regulated target genes and sample integrity: http://configurator.realtimeready.roche.com/ BestKeeper-Excel-based tool using pair-wise correlations. 10. Santarius T, Shipley J, Brewer D, Stratton MR, Cooper Biotechnol Lett. 2004 Mar;26 (6):509–15. CS. A census of amplified and overexpressed human 18. Hruz T, Wyss M, Docquier M, Pfaffl MW, Masanetz S, cancer genes. Nat Rev Cancer. 2010 Jan;10(1):59–64 Borghi L, Verbrugghe P, Kalaydjieva L, Bleuler S, Laule O, 11. Roche Applied Science – LightCycler® Real-Time PCR Descombes P, Gruissem W, Zimmermann P. RefGenes: Systems Application Manual: identification of reliable and condition specific reference https://www.roche-applied-science.com/sis/rtpcr/htc/ genes for RT-qPCR data normalization. BMC Genomics. index.jsp?&id=htc_060000 2011 Mar 21;12:156

For life science research only. Not for use in diagnostic procedures. Published by Roche Diagnostics GmbH Sandhofer Straße 116 LIGHTCYCLER and REALTIME READY are trademarks of Roche. 68305 Mannheim All other product names and trademarks are the property of their Germany respective owners.

License Disclaimer information is subject to change or amendment. © 2011 Roche Diagnostics. For current information on license disclaimers for a particular product, All rights reserved. please refer to the Online Technical Support page (http://technical-support.roche.com).  0611