Polysome Fractionation and Analysis of Mammalian Translatomes on a Genome-wide Scale

1. Sucrose Gradient Preparation

1. Prepare 100 ml of 60% (w/v) sucrose solution in ddH₂O. Solution should be filtered through a 0.22 µm filter to prevent clogging of the tubing during the fractionation step (step 3.5).
2. Prepare 5 ml of 10x sucrose gradient buffer: 200 mM HEPES (pH 7.6), 1 M KCl, 50 mM MgCl₂, 100 µg/ml cycloheximide, 1x protease inhibitor cocktail (EDTA-free), 100 units/ml RNase inhibitor.
3. Using the 60% solution prepared as described in step 1.1, make 40 ml of 5% and 50% sucrose solutions in 1x sucrose gradient buffer.
4. Use the Marker block provided with the gradient maker to mark the half-full point on each polyallomer ultracentrifugation tube for layering (6 ultracentrifugation tubes in total). Using the layering device provided with the gradient maker, add 5% sucrose solution until it reaches the half-full point. Then, add 50% sucrose solution from the bottom until the interface between the two solutions reaches the half-full point. Special care should be taken during this step so that gradients are as similar as possible, as this is essential for obtaining high reproducibility (see below).
5. Seal tubes with rate zonal caps provided with the gradient maker and transfer the sealed tubes to the tube holder on the gradient maker.
6. Run the gradient maker to obtain a linear 5% to 50% gradient. 6 linear gradients will be formed in few minutes by rotation at high tilt angle.
   Note: Sucrose gradients can be used immediately or stored at -80 °C for 6 months.

2. Isolation and Sedimentation of Polysomes

1. Depending on the cell type, seed cells into 1-5 15-cm Petri dishes (~15 x 10⁶ cells) at least one day before lysis. On the day of the experiments cells should be ~80% confluent. <!–
   Note: Optimal confluency is critical because translation and proliferation rates are directly proportional⁸, and therefore polysomes should be analyzed in actively proliferating cells (i.e. translation rates will significantly drop in over confluent cells whereas low confluency of cells may not provide sufficient material for further analysis). Exceptions of this rule can be made if for instance the experimenter is interested in studying the effect of contact inhibition on translation. However, cells should not be kept under over confluent conditions for too long, as this may have inadvertent effects on the translatome.
   If transfection is required, most of the commercially available kits do not affect polysome levels. Because on the day of transfection cells must be 80-90% confluent, it is recommended to propagate cells 24 hr post-transfection and isolate polysomes 48 hr post-transfection from cells ~80% confluent.
2. Incubate cells with cycloheximide at a final concentration of 100 µg/ml in growth media for 5 min at 37 °C and 5% CO₂; and wash cells twice with 10 ml of ice-cold 1x PBS containing 100 µg/ml cycloheximide. Cycloheximide is a translation elongation inhibitor that “freezes” ribosomes on the mRNA, thereby preventing ribosome run off ²⁰.
3. Scrape cells gently, in 5 ml of ice-cold 1x PBS containing 100 µg/ml cycloheximide and collect them in a 50 ml tube. It is important that this step is performed reasonably fast as leaving cells on ice for a prolonged period of time will drastically decrease the quality of the polysome preparation.
4. Collect cells by centrifugation at 200 x g for 5 min at 4 °C.
5. Discard supernatant and resuspend cells in 425 μl of hypotonic buffer [(5 mM Tris-HCl (pH 7.5), 2.5 mM MgCl₂, 1.5 mM KCl and 1x protease inhibitor cocktail (EDTA-free)], add 5 μl of 10 mg/ml CHX, 1 μl of 1 M DTT, 100 units of RNAse inhibitor and vortex for 5 sec followed by addition of 25 μl of 10% Triton X-100 (final concentration 0.5%) and 25 μl of 10% Sodium Deoxycholate (final concentration 0.5%) and vortex for 5 sec. Due to cell swelling, the hypotonic buffer will disrupt the plasma membrane, whereas the mild detergent conditions are used to solubilize cytosolic and endoplasmic reticulum-associated ribosomes without disrupting the nuclear envelope.
6. Centrifuge lysates at 16,000 x g for 7 min at 4 °C and transfer supernatant (~500 µl) to a new pre-chilled 1.5 ml tube. Measure OD at 260 nm for each sample using a spectrophotometer and keep 10% of the lysate as input that will be used to determine cytosolic steady-state mRNA levels. Dilute the input to 750 µl in RNAse free H₂O, add 750 µl of Trizol and flash freeze in liquid nitrogen.
7. Transfer ultracentrifuge tubes containing sucrose gradients in pre-chilled rotor buckets. Remove 500 µl from the top of sucrose gradients. Adjust lysates so that they contain the same OD (10-20 OD at 260 nm) in 500 µl of lysis buffer (described in step 2.5) and load them onto each sucrose gradient.
   Note: It is important to immediately load lysates on the gradients, as this will critically improve the quality of polysome preparations.
8. Weigh and balance each gradient before the ultra-centrifugation.
9. Centrifuge at 222,228 x g (36,000 rpm), for 2 hr at 4 °C using SW41Ti rotor.
   Note: In order not to disrupt sucrose gradients, “low” brake option should be selected.

3. Polysome Fractionation and RNA Extraction

1. Prepare the fraction collector by cleaning it with warm RNAse free water containing RNase ZAP (a few sprays). Repeat this step with warm RNAse free water only. Switch UV lamp and wait for the green light to appear.
2. Carefully remove tubes from the rotor and place them on ice. Switch on computer, pump, UV detector and fraction collector. Set pump at 3 ml/min and fill the tubing with the chasing solution [60% (w/v) sucrose containing 0.02% (w/v) bromophenol blue] until it reaches the needle. Make sure to see at least one drop coming out of the needle, and ascertain that no bubbles are introduced in the pump syringe or tubing.
3. Place 2 ml tubes in a fraction collector. Launch analysis program and set sensitivity to +/- 10 mEV. Set “timebase” to 100 sec.
4. Position each ultracentrifugation tube into the UV detector while making sure that the tube is in the orthogonal position. Pierce the tube with the needle by twisting the knob below the tube holder.
5. Set the pump at 1.5 ml/min and collect the fractions setting the time to 30 sec on the fraction collector (this will result in ~ 750 μl in each fraction). Put the pump in the “remote position”, start the pump and fraction collector, and at the same time start recording using the DAQ tracer. This will start an upward displacement of the sucrose gradient and a simultaneous detection of UV absorbance at 254 nm. Stop collecting as soon as the first drop of chasing solution falls in a 2 ml collecting tube.
6. Save the tracing in .csv format and (once step 3.7 is completed), in a spreadsheet application, do the following to identify the positioning of each fraction in the UV absorbance profile:
   1. Select the column containing values corresponding to absorbance at 254 nm (channel 0).
   2. Create a smooth line scatter plot of the absorbances.
   3. Set the major unit of the X axis to 300 (value obtained by dividing the volume of the 5%-50% sucrose gradient (~12 ml) by the collection time of each fraction (30 sec).
7. Add 750 µl of Trizol in each fraction and flash freeze the fractions in liquid nitrogen.
8. Isolate polysome-associated and cytosolic (from 2.6) RNA using Trizol protocol according to manufacturer’s instructions. To increase RNA yield, proceed with RNA precipitation at -80 °C for 30 min or -20 °C overnight.
   Note: Because the amount of RNA precipitated from polysomal fractions may not be clearly visible it is recommended to add carrier. Add 1 μl of carrier to the tube before the RNA precipitation step during the Trizol protocol.
9. Determine which fractions correspond to mRNA associated with >3 ribosome (using approach described 3.6) and pool these fractions. Use the RNA cleanup kit to perform cleanup of pooled polysome-associated and cytosolic RNA (from 2.6). Submit samples to a facility to determine genome-wide mRNA levels using microarrays or deep-sequencing.
   If translation of specific mRNAs need to be monitored by RT-qPCR, it is recommended to use 500 ng of RNA (from pooled fractions containing >3 ribosomes or total RNA) to synthesize cDNA.
   Note: mRNAs associated with >3 ribosomes is set arbitrarily to represent efficiently translated mRNAs and contain more than 80% of newly synthesized polypeptides ^28,29. Including fractions corresponding to light (1-2), medium (2-3) and heavy polysomes (>3) would be advantageous, but these will increase number of the samples by 2-fold (4 vs 2 per condition) and thus the cost of the experiment. Notwithstanding that it is anticipated that the decrease in cost of deep-sequencing and microarray analysis in future, should favor the latter approach, it is advised that during validation, distribution of individual mRNAs is monitored in each fraction.

4. Genome-wide Analysis of mRNA Translation

1. Install R, bioconductor and the anota package:
   1. Install R (download from r-project.org) and start an R-session.
      Note: R is available for all operating systems and anota will run on all of them.
   2. Install bioconductor (bioconductor.org). R code is interpreted through the R-terminal (e.g. the R console in Windows – which is launched by double clicking the R shortcut). Bioconductor is installed by typing (in the R-terminal):
      
      source("http://bioconductor.org/biocLite.R")
      
      biocLite()
       
   3. Install the anota bioconductor package (and the qvalue package that anota requires) by typing (in the R-terminal):
      
      source("http://bioconductor.org/biocLite.R")
      
      biocLite(c(“anota”, “qvalue”))
   4. Test that the package was successfully installed and open the anota manual by typing (in the R-terminal):
      
      library(“anota”)
      
      vignette(“anota”)
       
   5. Note: Additional help for all functions that are used in the anota package can be found either at the anota web-page (http://www.bioconductor.org/packages/release/bioc/html/anota.html) or by using the help function within R. For example, to get help on the anotaPerformQc function go to the R-terminal and type (note that this only works when the anota package has been loaded):
      
      library(“anota”) #loads the anota package
      
      ?anotaPerformQc #opens help for this function
       
2. Import data to R:
   1. Prepare expression data for analysis. Data should be pre-processed (e.g. normalized and quality controlled) with respect to the technique that was used to measure the mRNA levels. Input values must be log transformed, most common is log2. Compile one table with data for all polysome-associated RNA samples and one for all cytosolic RNA samples. The first column should contain the gene-identifiers followed by one data column per sample; the first row should include names for the samples. It is critical that the tables have identical sample order and identical gene order. Save the files as tab-delimited text files called “myPolysomeData.txt” and “myCytosolicData.txt”.
   2. In this example two sample classes are used (control or diseased) with 3 replicates per class, thereby generating 6 samples with this order: C1, C2, C3, D1, D2, D3 in both the myCytosolicData.txt and myPolysomeData.txt files. Anota requires at least 3 replicates per condition when there are two sample classes. These should be independent biological replicates.
   3. Create a directory that contains the data input files (myPolysomeData.txt and myCytosolicData.txt). Open R from this directory. In Windows/Mac this can be done by copying an R-shortcut into this directory and launching R using this shortcut (the working directly can also be changed using the drop-down menus).
   4. Create a file called myCode.R within the newly created directory and open it using a text editing software. Write the code in this file.
   5. To load the data into R write the following code to the myCode.R file (remember to re-save this file after each addition of code). Below is a step-by-step explanation of the code but all code could also be written initially and the source function (that runs the code, see below) used only once:
      
      ##this loads the anota library
      
      library(anota)
      
      ##This loads input data into R
      
      dataCyto <- as.matrix(read.table(“myCytosolicData.txt”, header=TRUE, row.names=1, sep=”t”))
      
      dataPoly <- as.matrix(read.table(“myPolysomeData.txt”, header=TRUE, row.names=1, sep=”t”))
       
   6. Run the code in R by typing directly into the R terminal:
      
      source(“myCode.R”)
       
   7. Check that the data was loaded successfully by typing (into the R-terminal):
      head(dataCyto) #will show top of the dataCyto table
      head(dataPoly)
       
3. Identification of differentially translated genes:
   1. Generate a vector that describes the sample categories (a vector is a type of object in R). This vector should reflect the sample order in the myPolysomeData.txt and myCytosolicData.txt so that all three objects have an identical order of the samples. The vector will be used when instructing anota about which samples to compare. Add the following to the myCode.R file:
      
      ##Note that the replicate samples have identical sample class
      
      ##descriptions (i.e. “C” or “D”)in this vector.
      
      myPhenotypes <- c(“C”, “C”, “C”, “D”, “D”, “D”)
       
   2. Perform quality control of the data set. There are a number of quality measures that need to be assessed before anota can be used for analysis. These are discussed in detail in the anota manual. Add this code to myCode.R file and save:
      
      anotaQcOut <- anotaPerformQc(dataT=dataCyto, dataP=dataPoly, phenoVec=myPhenotypes)
      
      anotaResidOut <- anotaResidOutlierTest(anotaQcObj=anotaQcOut)
       
   3. Run the code by typing into R-terminal:
      
      source(“myCode.R”)
      
      This will generate a number of output files, which can be examined as instructed in the anota manual.
       
   4. Identify differentially translated genes. Samples will be compared based on the alphabetical order of the names supplied to the “phenoVec” parameter (i.e. the “myPhenotypes” vector) unless they are specified by the user (using the “contrasts” parameter). Add the following code to myCode.R file and finish by using the “source” command inside the R-terminal as described above:
      
      anotaSigOut <- anotaGetSigGenes(dataT=dataCyto, dataP=dataPoly, phenoVec=myPhenotypes, anotaQcObj=anotaQcOut)
   5. Use the help for anotaGetSigGenes to understand how the output is formatted and see how to choose sample categories to compare by typing (in the R-terminal):
      
      ?anotaGetSigGenes
       
   6. Filter and plot genes that are differentially translated. There are multiple thresholds that can be applied within the anotaPlotSigGenes function and using help will simplify a custom combination of such thresholds. To select for reliably analyzed genes apply the minSlope (-0.5), maxSlope (1.5) and slopeP (0.01) settings.
      Note: In addition, settings applying to RVM^23,26,30 false discovery rate (FDR) (0.15) and fold changes (log2[1.5]) may e.g. be used to identify differentially translated genes. Other filters such as “selDeltaPT” can be useful for a stricter filtering (e.g. setting selDeltaPT to log2[1.5]). It is also necessary to specify which comparison should be filtered (by using the selCont argument). Apply the described settings by adding the following line to the myCode.R file:
      
      anotaSigFiltered <- anotaPlotSigGenes(anotaSigObj=anotaSigOut, selContr=1, minSlope=(-0.5), maxSlope=1.5, slopeP=0.01, maxRvmPAdj=0.15, minEff=log2(1.5), selDeltaPT=log2(1.5))
       
   7. Perform the analysis by using the source function from the R-terminal as described above. This will also generate a graphical output. Examining this output is a good starting point to assess the performance of the analysis and identify any necessary changes to the settings.
   8. Generate an output table. Add the following code to the myCode.R file:
      
      write.table(anotaSigFiltered$selectedRvmData, file=“MySignificantGenes.txt”, sep=”t”)
       
   9. Run the code using the source function in the R-terminal. The columns in the resulting table are explained in the help for the anotaPlotSigGenes function.