Name: polysome fractionation and analysis of mammalian translatomes on a genome-wide scale
Uploaded: 2014-05-17T14:00:00
Duration: 10 min 56 s
Description: Watch this Scientific Journal Video about Polysome Fractionation and Analysis of Mammalian Translatomes on a Genome-wide Scale at JoVE.com

This research was supported by the Canadian Institutes of Health Research grant (CIHR MOP-115195) and FRQ-S to I.T., who is also a recipient of CIHR Young Investigator Award; and the Swedish Research Council and the Swedish Cancer Society to O.L.

1. Sucrose Gradient Preparation
<ol>
	<li>Prepare 100 ml of 60% (w/v) sucrose solution in ddH2O. Solution should be filtered through a 0.22 &#181;m filter to prevent clogging of the tubing during the fractionation step (step 3.5).</li>
	<li>Prepare 5 ml of 10x sucrose gradient buffer: 200 mM HEPES (pH 7.6), 1 M KCl, 50 mM MgCl2, 100 &#181;g/ml cycloheximide, 1x protease inhibitor cocktail (EDTA-free), 100 units/ml RNase inhibitor.</li>
	<li>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.</li>
	<li>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).</li>
	<li>Seal tubes with rate zonal caps provided with the gradient maker and transfer the sealed tubes to the tube holder on the gradient maker.</li>
	<li>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 &#176;C for 6 months.</li>
</ol>
2. Isolation and Sedimentation of Polysomes
<ol>
	<li>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&#160;post-transfection from cells ~80% confluent.</li>
	<li>Incubate cells with cycloheximide at a final concentration of 100 &#181;g/ml in growth media for 5 min at 37 &#176;C and 5% CO2; and wash cells twice with 10 ml of ice-cold 1x PBS containing 100 &#181;g/ml cycloheximide. Cycloheximide is a translation elongation inhibitor that &#8220;freezes&#8221; ribosomes on the mRNA, thereby preventing ribosome run off 20.</li>
	<li>Scrape cells gently, in 5 ml of ice-cold 1x PBS containing 100 &#181;g/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.</li>
	<li>Collect cells by centrifugation at 200 x g for 5 min at 4 &#176;C.</li>
	<li>Discard supernatant and resuspend cells in 425 &#956;l 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 &#956;l of 10 mg/ml CHX, 1 &#956;l of 1 M DTT, 100 units of RNAse inhibitor and vortex for 5 sec followed by addition of 25 &#956;l of 10% Triton X-100 (final concentration 0.5%) and 25 &#956;l 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.</li>
	<li>Centrifuge lysates at 16,000 x g for 7 min at 4 &#176;C and transfer supernatant (~500 &#181;l) 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 &#181;l in RNAse free H2O, add 750 &#181;l of Trizol and flash freeze in liquid nitrogen.</li>
	<li>Transfer ultracentrifuge tubes containing sucrose gradients in pre-chilled rotor buckets. Remove 500 &#181;l from the top of sucrose gradients. Adjust lysates so that they contain the same OD (10-20 OD at 260 nm) in 500 &#181;l 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.</li>
	<li>Weigh and balance each gradient before the ultra-centrifugation.</li>
	<li>Centrifuge at 222,228 x g (36,000 rpm), for 2 hr at 4 &#176;C using SW41Ti rotor. 
	Note: In order not to disrupt sucrose gradients, &#8220;low&#8221; brake option should be selected.</li>
</ol>
3. Polysome Fractionation and RNA Extraction
<ol>
	<li>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.</li>
	<li>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.</li>
	<li>Place 2 ml tubes in a fraction collector. Launch analysis program and set sensitivity to +/- 10 mEV. Set &#8220;timebase&#8221; to 100 sec.</li>
	<li>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.</li>
	<li>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 &#956;l in each fraction). Put the pump in the &#8220;remote position&#8221;, 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.</li>
	<li>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:
	<ol>
		<li>Select the column containing values corresponding to absorbance at 254 nm (channel 0).</li>
		<li>Create a smooth line scatter plot of the absorbances.</li>
		<li>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).</li>
	</ol>
	</li>
	<li>Add 750 &#181;l of Trizol in each fraction and flash freeze the fractions in liquid nitrogen.</li>
	<li>Isolate polysome-associated and cytosolic (from 2.6) RNA using Trizol protocol according to manufacturer&#8217;s instructions. To increase RNA yield, proceed with RNA precipitation at -80 &#176;C for 30 min or -20 &#176;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 &#956;l of carrier to the tube before the RNA precipitation step during the Trizol protocol.</li>
	<li>Determine which fractions correspond to mRNA associated with &#62;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 &#62;3 ribosomes or total RNA) to synthesize cDNA. 
	Note: mRNAs associated with &#62;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&#160;(&#62;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.</li>
</ol>
4. Genome-wide Analysis of mRNA Translation
<ol>
	<li>Install R, bioconductor and the anota package:
	<ol>
		<li>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.</li>
		<li>Install bioconductor (bioconductor.org). R code is interpreted through the R-terminal (e.g. the R console in Windows &#8211; which is launched by double clicking the R shortcut). Bioconductor is installed by typing (in the R-terminal): 
		 
		source(&#34;http://bioconductor.org/biocLite.R&#34;) 
		 
		biocLite() 
		&#160;</li>
		<li>Install the anota bioconductor package (and the qvalue package that anota requires) by typing (in the R-terminal): 
		 
		source(&#34;http://bioconductor.org/biocLite.R&#34;) 
		 
		biocLite(c(&#8220;anota&#8221;, &#8220;qvalue&#8221;))</li>
		<li>Test that the package was successfully installed and open the anota manual by typing (in the R-terminal): 
		 
		library(&#8220;anota&#8221;) 
		 
		vignette(&#8220;anota&#8221;) 
		&#160;</li>
		<li>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(&#8220;anota&#8221;) #loads the anota package 
		 
		?anotaPerformQc #opens help for this function 
		&#160;</li>
	</ol>
	</li>
	<li>Import data to R:
	<ol>
		<li>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 &#8220;myPolysomeData.txt&#8221; and &#8220;myCytosolicData.txt&#8221;.</li>
		<li>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.</li>
		<li>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).</li>
		<li>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.</li>
		<li>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 &#60;- as.matrix(read.table(&#8220;myCytosolicData.txt&#8221;, header=TRUE, row.names=1, sep=&#8221;\t&#8221;)) 
		 
		dataPoly &#60;- as.matrix(read.table(&#8220;myPolysomeData.txt&#8221;, header=TRUE, row.names=1, sep=&#8221;\t&#8221;)) 
		&#160;</li>
		<li>Run the code in R by typing directly into the R terminal: 
		 
		source(&#8220;myCode.R&#8221;) 
		&#160;</li>
		<li>Check that the data was loaded successfully by typing (into the R-terminal): 
		head(dataCyto) #will show top of the dataCyto table 
		head(dataPoly) 
		&#160;</li>
	</ol>
	</li>
	<li>Identification of differentially translated genes:
	<ol>
		<li>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. &#8220;C&#8221; or &#8220;D&#8221;)in this vector. 
		 
		myPhenotypes &#60;- c(&#8220;C&#8221;, &#8220;C&#8221;, &#8220;C&#8221;, &#8220;D&#8221;, &#8220;D&#8221;, &#8220;D&#8221;) 
		&#160;</li>
		<li>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 &#60;- anotaPerformQc(dataT=dataCyto, dataP=dataPoly, phenoVec=myPhenotypes) 
		 
		anotaResidOut &#60;- anotaResidOutlierTest(anotaQcObj=anotaQcOut) 
		&#160;</li>
		<li>Run the code by typing into R-terminal: 
		 
		source(&#8220;myCode.R&#8221;) 
		 
		This will generate a number of output files, which can be examined as instructed in the anota manual. 
		&#160;</li>
		<li>Identify differentially translated genes. Samples will be compared based on the alphabetical order of the names supplied to the &#8220;phenoVec&#8221; parameter (i.e. the &#8220;myPhenotypes&#8221; vector) unless they are specified by the user (using the &#8220;contrasts&#8221; parameter). Add the following code to myCode.R file and finish by using the &#8220;source&#8221; command inside the R-terminal as described above: 
		 
		anotaSigOut &#60;- anotaGetSigGenes(dataT=dataCyto, dataP=dataPoly, phenoVec=myPhenotypes, anotaQcObj=anotaQcOut)</li>
		<li>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 
		&#160;</li>
		<li>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 RVM23,26,30 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 &#8220;selDeltaPT&#8221; 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 &#60;- 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)) 
		&#160;</li>
		<li>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.</li>
		<li>Generate an output table. Add the following code to the myCode.R file: 
		 
		write.table(anotaSigFiltered$selectedRvmData, file=&#8220;MySignificantGenes.txt&#8221;, sep=&#8221;\t&#8221;) 
		&#160;</li>
		<li>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.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Maier, T., Guell, M., Serrano, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Correlation+of+mRNA+and+protein+in+complex+biological+samples.">Correlation of mRNA and protein in complex biological samples.</a> FEBS Lett. 583, 3966-3973 (2009).</li><li>de Sousa Abreu, R., Penalva, L. O., Marcotte, E. M., Vogel, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+signatures+of+protein+and+mRNA+expression+levels.">Global signatures of protein and mRNA expression levels.</a> Mol Biosyst. 5, 1512-1526 (2009).</li><li>Keene, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA+regulons:+coordination+of+post-transcriptional+events.">RNA regulons: coordination of post-transcriptional events.</a> Nat Rev Genet. 8, 533-543 (2007).</li><li>Komili, S., Silver, P. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Coupling+and+coordination+in+gene+expression+processes:+a+systems+biology+view.">Coupling and coordination in gene expression processes: a systems biology view.</a> Nat Rev Genet. 9, 38-48 (2008).</li><li>Schwanhausser, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+quantification+of+mammalian+gene+expression+control.">Global quantification of mammalian gene expression control.</a> Nature. 473, 337-342 (2011).</li><li>Sonenberg, N., Hinnebusch, A. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regulation+of+translation+initiation+in+eukaryotes:+mechanisms+and+biological+targets.">Regulation of translation initiation in eukaryotes: mechanisms and biological targets.</a> Cell. 136, 731-745 (2009).</li><li>Rolfe, D. F., Brown, G. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cellular+energy+utilization+and+molecular+origin+of+standard+metabolic+rate+in+mammals.">Cellular energy utilization and molecular origin of standard metabolic rate in mammals.</a> Physiol Rev. 77, 731-758 (1997).</li><li>Johnson, L. F., Levis, R., Abelson, H. T., Green, H., Penman, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Changes+in+RNA+in+relation+to+growth+of+the+fibroblast.+IV.+Alterations+in+theproduction+and+processing+of+mRNA+and+rRNA+in+resting+and+growing+cells.">Changes in RNA in relation to growth of the fibroblast. IV. Alterations in theproduction and processing of mRNA and rRNA in resting and growing cells.</a> J Cell Biol. 71, 933-938 (1976).</li><li>Fabian, M. R., Sonenberg, N., Filipowicz, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regulation+of+mRNA+translation+and+stability+by+microRNAs.">Regulation of mRNA translation and stability by microRNAs.</a> Annu Rev Biochem. 79, 351-379 (2010).</li><li>De Benedetti, A., Graff, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=eIF-4E+expression+and+its+role+in+malignancies+and+metastases.">eIF-4E expression and its role in malignancies and metastases.</a> Oncogene. 23, 3189-3199 (2004).</li><li>Roux, P. P., Topisirovic, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regulation+of+mRNA+translation+by+signaling+pathways.">Regulation of mRNA translation by signaling pathways.</a> Cold Spring Harb Perspect Biol. 4, (2012).</li><li>Ron, D., Harding, H. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein-folding+homeostasis+in+the+endoplasmic+reticulum+and+nutritional+regulation.">Protein-folding homeostasis in the endoplasmic reticulum and nutritional regulation.</a> Cold Spring Harb Perspect Biol. 4, (2012).</li><li>Silvera, D., Formenti, S. C., Schneider, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Translational+control+in+cancer.">Translational control in cancer.</a> Nat Rev Cancer. 10, 254-266 (2010).</li><li>Proud, C. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=mTOR+Signalling+in+Health+and+Disease.">mTOR Signalling in Health and Disease.</a> Biochem Soc Trans. 39, 431-436 (2011).</li><li>Topisirovic, I., Sonenberg, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=mRNA+Translation+and+Energy+Metabolism+in+Cancer:+The+Role+of+the+MAPK+and+mTORC1+Pathways.">mRNA Translation and Energy Metabolism in Cancer: The Role of the MAPK and mTORC1 Pathways.</a> Cold Spring Harb Symp Quant Biol. , (2011).</li><li>Pavitt, G. D., Proud, C. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein+synthesis+and+its+control+in+neuronal+cells+with+a+focus+on+vanishing+white+matter+disease.">Protein synthesis and its control in neuronal cells with a focus on vanishing white matter disease.</a> Biochem Soc Trans. 37, 1298-1310 (2009).</li><li>Abe, M., Bonini, N. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MicroRNAs+and+neurodegeneration:+role+and+impact.">MicroRNAs and neurodegeneration: role and impact.</a> Trends Cell Biol. 23, 30-36 (2013).</li><li>Kasinath, B. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regulation+of+mRNA+translation+in+renal+physiology+and+disease.">Regulation of mRNA translation in renal physiology and disease.</a> Am J Physiol Renal Physiol. 297, 1153-1165 (2009).</li><li>Zoncu, R., Efeyan, A., Sabatini, D. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=mTOR:+from+growth+signal+integration+to+cancer,+diabetes+and+ageing.">mTOR: from growth signal integration to cancer, diabetes and ageing.</a> Nat Rev Mol Cell Biol. 12, 21-35 (2011).</li><li>Schneider-Poetsch, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inhibition+of+eukaryotic+translation+elongation+by+cycloheximide+and+lactimidomycin.">Inhibition of eukaryotic translation elongation by cycloheximide and lactimidomycin.</a> Nat Chem Biol. 6, 209-217 (2010).</li><li>Larsson, O., Tian, B., Sonenberg, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Toward+a+genome-wide+landscape+of+translational+control.">Toward a genome-wide landscape of translational control.</a> Cold Spring Harb Perspect Biol. 5, (2013).</li><li>Ingolia, N. T., Ghaemmaghami, S., Newman, J. R., Weissman, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+analysis+in+vivo+of+translation+with+nucleotide+resolution+using+ribosome+profiling.">Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling.</a> Science. 324, 218-223 (2009).</li><li>Larsson, O., Sonenberg, N., Nadon, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identification+of+differential+translation+in+genome+wide+studies.">Identification of differential translation in genome wide studies.</a> Proc Natl Acad Sci U S A. , (2010).</li><li>Larsson, O., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Distinct+perturbation+of+the+translatome+by+the+antidiabetic+drug+metformin.">Distinct perturbation of the translatome by the antidiabetic drug metformin.</a> Proc Natl Acad Sci U S A. 109, 8977-8982 (2012).</li><li>Colman, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+analysis+of+host+mRNA+translation+during+hepatitis+C+virus+infection.">Genome-wide analysis of host mRNA translation during hepatitis C virus infection.</a> J Virol. 87, 6668-6677 (2013).</li><li>Larsson, O., Sonenberg, N., Nadon, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=anota:+Analysis+of+differential+translation+in+genome-wide+studies.">anota: Analysis of differential translation in genome-wide studies.</a> Bioinformatics. 27, 1440-1441 (2011).</li><li>Gandin, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Degradation+of+Newly+Synthesized+Polypeptides+by+Ribosome-Associated+RACK1/c-Jun+N-Terminal+Kinase/Eukaryotic+Elongation+Factor+1A2.">Degradation of Newly Synthesized Polypeptides by Ribosome-Associated RACK1/c-Jun N-Terminal Kinase/Eukaryotic Elongation Factor 1A2.</a> Complex. Mol Cell Biol. 33, 2510-2526 (2013).</li><li>Warner, J. R., Knopf, P. M., Rich, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+multiple+ribosomal+structure+in+protein+synthesis.">A multiple ribosomal structure in protein synthesis.</a> Proc Natl Acad Sci U S A. 49, 122-129 (1963).</li><li>Gierer, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Function+of+aggregated+reticulocyte+ribosomes+in+protein+synthesis.">Function of aggregated reticulocyte ribosomes in protein synthesis.</a> J Mol Biol. 6, 148-157 (1963).</li><li>Wright, G. W., Simon, R. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+random+variance+model+for+detection+of+differential+gene+expression+in+small+microarray+experiments.">A random variance model for detection of differential gene expression in small microarray experiments.</a> Bioinformatics. 19, 2448-2455 (2003).</li><li>Eggers, D. K., Welch, W. J., Hansen, W. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Complexes+between+nascent+polypeptides+and+their+molecular+chaperones+in+the+cytosol+of+mammalian+cells.">Complexes between nascent polypeptides and their molecular chaperones in the cytosol of mammalian cells.</a> Mol Biol Cell. 8, 1559-1573 (1997).</li><li>Grosso, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PKCbetaII+modulates+translation+independently+from+mTOR+and+through+RACK1.">PKCbetaII modulates translation independently from mTOR and through RACK1.</a> Biochem J. 415, 77-85 (2008).</li><li>Oh, W. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=mTORC2+can+associate+with+ribosomes+to+promote+cotranslational+phosphorylation+and+stability+of+nascent+Akt+polypeptide.">mTORC2 can associate with ribosomes to promote cotranslational phosphorylation and stability of nascent Akt polypeptide.</a> EMBO J. 29, 3939-3951 (2010).</li><li>Zinzalla, V., Stracka, D., Oppliger, W., Hall, M. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Activation+of+mTORC2+by+association+with+the+ribosome.">Activation of mTORC2 by association with the ribosome.</a> Cell. 144, 757-768 (2011).</li><li>Bjur, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Distinct+translational+control+in+CD4++T+cell+subsets.">Distinct translational control in CD4+ T cell subsets.</a> PLoS Genet. 9, e13 (2013).</li><li>Leek, J. T., Storey, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Capturing+heterogeneity+in+gene+expression+studies+by+surrogate+variable+analysis.">Capturing heterogeneity in gene expression studies by surrogate variable analysis.</a> PLoS Genet. 3, 1724-1735 (2007).</li><li>Guttman, M., Russell, P., Ingolia, N. T., Weissman, J. S., Lander, E. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ribosome+Profiling+Provides+Evidence+that+Large+Noncoding+RNAs+Do+Not+Encode+Proteins.">Ribosome Profiling Provides Evidence that Large Noncoding RNAs Do Not Encode Proteins.</a> Cell. 154, 240-251 (2013).</li><li>Hsieh, A. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+translational+landscape+of+mTOR+signalling+steers+cancer+initiation+and+metastasis.">The translational landscape of mTOR signalling steers cancer initiation and metastasis.</a> Nature. 485, 55-61 (2012).</li><li>Thoreen, C. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+unifying+model+for+mTORC1-mediated+regulation+of+mRNA+translation.">A unifying model for mTORC1-mediated regulation of mRNA translation.</a> Nature. 485, 109-113 (2012).</li></ol>

The authors declare no competing financial interests.

This article describes a well-established polysome fractionation protocol followed by an in-house developed analysis method for capturing qualitative and quantitative changes in translational activity on a genome-wide scale in mammalian cells. For successful completion of this protocol special attention should be paid to 1) Cell confluency (as the proliferation rates and nutrient availability correlate with mRNA translation activity and can affect the composition of the translatome, cell confluency should be consistent across replicates and experimental conditions), 2) Rapid cell lysis (Notwithstanding the presence of cycloheximide and RNAse inhibitors in buffers, lysis should be swift and cellular extracts should be overlaid on sucrose gradients immediately after lysis to prevent RNA degradation and dissociation of polysomes),&#160;3) Gradient preparation (to ensure high data reproducibility, sucrose gradients should be prepared using the gradient maker and special care should be taken when handling them).
In addition to studying the changes in translational activity, this protocol can be used to isolate ribosome-associated protein complexes and establish their physiological functions. To this end, levels and phosphorylation status of various ribosome-associated proteins can be determined by TCA precipitation, followed by Western blotting, whereas ribosome-associated protein complexes can be immunoprecipitated from gradient fractions and analyzed by Western blotting or mass-spectrometry. A shortcoming of this technique is that the slow gradient fraction method could result in dissociation of even tightly bound proteins whose binding kinetics are in rapid association/dissociation. Factors associated to newly synthesized polypeptides are typical examples. Newly synthesized polypeptides emerging form the ribosomes can be immobilized on protein complexes associated with ribosomes using chemical cross-linkers such as 3,3&#180;-dithiobis[sulfosuccinimidylpropionate] (DTSSP)31. This approach revealed that the Receptor for Activated C Kinase 1 (RACK1)/c-Jun N-terminal kinase (JNK)/eukaryotic translation elongation factor 1A2 (eEF1A2) complex regulates degradation of newly synthesized polypeptides in response to stress27. In addition, similar methodology was used in studies showing that the protein kinase C bII (PKCbII) is recruited to ribosomes by RACK132 and that activation of mTOR complex 2 (mTORC2) occurs on the ribosomes where it phosphorylates newly synthesized AKT polypeptides and regulates their stability33,34.
Major limitations of the polysome fractionation method followed by anota analysis are: 1) requirement for a relatively high number of cells (~15 x 106 cells), 2) lack of positional information regarding localization of ribosome on a given mRNA molecule, and 3) issues related to cellular and molecular heterogeneity of normal and tumor tissues. Issues related to required cell number can be resolved by aligning absorbance spectra peaks corresponding to monosomes (80S) of cells whose amounts are limiting with those obtained from high-abundance cells (e.g. HeLa cells) 35. Ribosome profiling technique (see below) can be used to determine exact position of the ribosome on a given mRNA molecule, whereas issues related to confounding effects of tissue heterogeneity on the interpretation of changes in gene expression obtained from complex systems such as human tissues are discussed in detail in a publication by Leek and Storey 36.
Recently, a novel ribosome profiling technique was developed, wherein ribosome protected fragments (RPFs) are generated by RNAse I treatment and analyzed by deep-sequencing22. This technique allows determination of the ribosome position at a single nucleotide resolution, thereby providing hitherto unprecedented insights into ribosome biology. For example, ribosome profiling can be used for determination of ribosome density on a given mRNA molecule or the identification of elements that influence translation initiation rates such as the alternative initiation sites, initiation at non-AUG codons and regulatory elements such as uORFs. However, there are several methodological limitations that restrict the ability of ribosome profiling to accurately estimate mRNA translation efficiency. These include independent biases introduced by random fragmentation and RNAse I digestion, biases introduced by translation inhibitors (e.g. elongation inhibitors such as emetine and cycloheximide are likely to induce ribosome accumulation at translation initiation sites), a large number of false positive and false negative results associated with TE scores as well as their inaccuracy in predicting the reads that originate from protein coding mRNAs37. Perhaps most importantly, whereas ribosome profiling allows direct identification of ribosome position on a given mRNA molecule, the number of ribosomes that is associated with a given mRNA is indirectly estimated by normalizing frequencies of reads in RPFs (ribosome-associated mRNA) over those observed in randomly fragmented mRNAs (total mRNA). For instance, in a simple setting where four &#8220;B&#8221; mRNA molecules (Ba, Bb, Bc and Bd) are occupied by four ribosomes at the positions 1, 2, 3 and 4, an inherent shortcoming of the ribosome profiling technique will not permit a distinction between a scenario where all 4 ribosomes associate only with a Ba mRNA in positions 1, 2, 3, and 4 and a scenario where Ba, Bb, Bc and Bd mRNA are each occupied by a single ribosome at the position 1, 2, 3, and 4, respectively. In contrast, during polysome fractionation, polysome integrity is preserved, thereby allowing isolation of pools of mRNAs associated with a defined number of ribosomes (Figure 1). This important distinction between polysome fractionation and ribosome profiling suggests that whereas the former method can be used to directly compare mRNAs in mRNP, light and heavy polysome fractions, the latter method will likely fail to capture changes in the translatome that are caused by mRNAs that transition from light to heavy polysomes, while overestimating the contribution of those that shift from the mRNP fraction to heavy polysomes. The biological significance of these discrepancies between the aforementioned methods is underscored by a large body of data showing that translational activation of a subset of mRNAs such as those that are eIF4E-sensitive, transition from light to heavy polysomes, whereas others, such as those harboring 5&#8217;TOP elements, are recruited to heavy polysomes directly from pools of ribosome-free mRNA6. Interestingly, the mTOR pathway has been shown to concomitantly modulate translation of &#8220;eIF4E-sensitive&#8221; and 5&#8217;TOP mRNAs11. Therefore, methodological differences that are discussed above may explain the apparent discordance of the conclusion between studies using ribosome profiling38,39 and polysome fractionation24 to assess the effects of mTOR inhibition on the translatome.
In conclusion, polysome fractionation and ribosome profiling are complementary methods that primarily provide information regarding the number of ribosomes associated with mRNA and position of the ribosome on mRNA, respectively. Importantly, notwithstanding the shortcomings and advantages of these methods, it remains essential that the genome-wide data obtained by both procedures are adequately analyzed and functionally and biochemically validated.

The regulatory networks that control gene expression have been extensively studied in the last two decades. The vast majority of these research efforts focused on transcriptional regulation, whereby changes in the steady-state mRNA levels on a genome-wide scale were used to determine so-called &#8220;gene expression&#8221; profiles. Recent studies reveal that steady-state mRNA levels only loosely correspond to the composition of the proteome1,2, thereby indicating that the post-transcriptional mechanisms, including mRNA translation, play a major role in regulation of gene expression3,4. Indeed, it has been estimated that ~50% of protein levels are determined at the level of mRNA translation in immortalized mouse fibroblasts5.
mRNA translation is a highly regulated process during which mRNAs are translated into proteins via orchestrated action of ribosomes, transfer RNAs (tRNAs) and accessory factors commonly referred to as translation factors (TFs)6. Protein synthesis is the most energy consuming process in mammalian cells7 and therefore global mRNA translation rates are adjusted to accommodate nutrient availability and cell proliferation rates8. In addition to alterations in global mRNA translation rates, various extracellular stimuli (e.g. hormones and growth factors), intracellular cues (e.g. amino acid levels) and different types of stress (e.g. ER-stress) induce selective changes in the pools of mRNAs that are being translated (translatome)6.&#160;Depending on the type of stimulus, translation activity of some, but not all mRNAs are dramatically affected, thereby resulting in changes in the proteome that are required to mount a rapid cellular response6. These qualitative and quantitative changes in the translatome are thought to be mediated by the interplay between trans-acting factors (e.g. components of translational machinery, auxiliary factors or miRNAs) and cis-elements (RNA elements or features) present in selected subsets of mRNAs6,9. For instance, changes in the levels and/or activity of rate-limiting translation initiation factors eIF4E and eIF2 selectively modulate translation of transcripts based on the specific 5&#8217;UTR features6. eIF4E and eIF2 are required for the recruitment of mRNA and initiator tRNA to the ribosome, respectively6. An increase in eIF4E activity selectively bolsters translation of mRNAs harboring long and highly structured 5&#8217;UTRs including those encoding proliferation and survival stimulating proteins including cyclins, c-myc and Bcl-xL10. In turn, inactivation of eIF2 leads to a global protein synthesis shut-down, whilst selectively up regulating translation of mRNAs containing short inhibitory upstream open reading frames (uORFs) in their 5&#8217;UTRs, such as those encoding master transcriptional regulators of the unfolded protein response (e.g. ATF4). In response to various stimuli including nutrients, growth factors and hormones, the mechanistic/mammalian target of rapamycin (mTOR) pathway stimulates eIF4E activity by inactivating the 4E-binding protein (4E-BP) family of translation suppressors, whereas the MAPK pathway directly phosphorylates eIF4E6,11. In turn, eIF2&#945; kinases (i.e. PERK, PKR, HRI and GCN2) inhibit eIF2 by phosphorylating its eIF2&#945; regulatory subunit in response to nutrient deprivation, ER-stress and virus infection6,12. Alterations in the activity and/or expression of TFs, other components of the translational machinery and regulatory factors including miRNAs have been observed in various pathological states including cancer, metabolic syndromes, neurological and psychiatric disorders, and renal and cardiovascular diseases13-19. Collectively, these data indicate that translational mechanisms play a pivotal role in maintaining cell homeostasis and that their abrogation plays a central role in the etiology of various human diseases.
Herein, a protocol for fractionation of polysomes by sucrose density gradient centrifugation in mammalian cells, which is used to separate polysomes from monosomes, ribosomal subunits and messenger ribonucleoprotein particles (mRNPs) is described. This enables discrimination between efficiently translated (associated with heavy polysomes) from poorly translated (associated with light polysomes) mRNAs. In this assay, ribosomes are immobilized on the mRNA using translation elongation inhibitors such as cycloheximide20 and cytosolic extracts are separated on 5-50% linear sucrose density gradients by ultracentrifugation. Subsequent fractionation of sucrose gradients allows isolation of mRNAs according to the number of ribosomes they bind to. RNA extracted from each fraction can be then used to determine changes in the distributions of mRNAs across the gradient between different conditions, whereby translational efficiency increases from the top to the bottom of the gradient. Northern Blotting or quantitative reverse transcription polymerase chain reaction (qRT-PCR) are used to determine levels of mRNAs in each fraction.
Alternatively, fractions containing heavy polysomes (typically more than 3 ribosomes) are pooled and genome-wide polysome-associated mRNA levels are determined using microarray or deep-sequencing. It is of utmost importance to emphasize that the levels of polysome-associated mRNAs are, in addition to translation, affected by transcriptional and post-transcriptional mechanisms that influence cytosolic mRNAs levels21. Therefore, to determine differences in translation using genome-wide data from polysome-associated mRNA it is necessary to correct for the effects from steps in the gene expression pathway that are upstream of translation21. To allow such a correction, cytosolic RNA is prepared in parallel with polysome-associated RNA from each sample and the genome-wide steady-state mRNA levels are determined21. Currently, so-called &#8220;translation-efficiency&#8221; (TE) scores (i.e. the log-ratio between polysome-associated mRNA data and cytosolic mRNA data) are often employed to correct for the effects of changes in cytosolic mRNA&#160;levels on translation efficiency of a given mRNA22. However, using TE scores to identify differential translation is associated with substantial numbers of false positive and false negative findings due to a mathematical property of the TE scores commonly referred to as spurious correlation27. Indeed a survey of data sets from multiple labs indicated that such spurious correlations seem to be inevitable when analyzing changes in the translatomes23. This prompted development of the &#8220;analysis of differential translation&#8221; (anota) algorithm, which does not suffer from the aforementioned shortcomings23. During anota-analysis a regression model is used to derive measures of translational activity independent of cytosolic RNA levels. Such measures are then compared between conditions and a statistics is calculated. The user has the option of applying a variance shrinkage method, which improves statistical power and reduces the occurrence of false positive findings in studies with few replicates 24. Significantly, it was recently demonstrated that perturbations in the translatome captured by anota, but not TE scores, correlate with changes in the proteome25. Therefore, it is highly recommended to apply anota analysis for identification of changes in translation on a genome-wide scale. While the theoretical underpinnings of the anota algorithm were discussed in detail previously21,23,26, here focus is on how to apply it in practice.
In addition to studying changes in mRNA translation activity in the cell, this ribosome fractionation protocol can be used to isolate and biochemically and functionally characterize ribosome- and polysome-associated protein complexes. This approach has successfully been deployed in the past to identify complexes that regulate the stability of newly synthesized polypeptides27 and/or are involved in the phosphorylation of components of the translational machinery. This application of the polysome fractionation method will also be briefly discussed.

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		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">HEPES</td>
			<td style="width:84px;">Biobasic</td>
			<td style="width:119px;">HB0264</td>
			<td style="width:223px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">MgCl 2</td>
			<td style="width:84px;">Sigma</td>
			<td style="width:119px;">M8266</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">KCl</td>
			<td style="width:84px;">VWR</td>
			<td>CABDH4532</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">Dithiothreitol (DTT)</td>
			<td style="width:84px;">Biobasic</td>
			<td style="width:119px;">DB0058</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">Tris</td>
			<td style="width:84px;">Biobasic</td>
			<td style="width:119px;">TB0196</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">Glycoblue</td>
			<td style="width:84px;">Ambion</td>
			<td style="width:119px;">AM9515</td>
			<td>RNA precipitation carrier</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">Sodium Deoxycholate&#160;</td>
			<td style="width:84px;">Sigma</td>
			<td style="width:119px;">D6750</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">Triton X-100</td>
			<td style="width:84px;">Sigma</td>
			<td style="width:119px;">X100</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">Cycloheximide</td>
			<td style="width:84px;">Sigma</td>
			<td style="width:119px;">C1988</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">Protease inhibitor cocktail&#160;</td>
			<td style="width:84px;">Roche</td>
			<td style="width:119px;">04693132001</td>
			<td>Tablets (EDTA-free)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">RNase inhibitor&#160;</td>
			<td style="width:84px;">Promega</td>
			<td style="width:119px;">N2515</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">0.45 &#181;m Filter System 150 ml</td>
			<td style="width:84px;">Corning</td>
			<td style="width:119px;">431155</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">Sucrose</td>
			<td style="width:84px;">Sigma</td>
			<td style="width:119px;">S0389</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">RNeasy MinElute Cleanup kit&#160;</td>
			<td style="width:84px;">Qiagen</td>
			<td style="width:119px;">74204</td>
			<td>RNA cleanup kit</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:269px;">Thin wall polyallomer ultracentrifuge tubes&#160;</td>
			<td style="width:84px;">Beckman Coulter</td>
			<td style="width:119px;">331372</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">Equipments</td>
			<td style="width:84px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:269px;">SW41Ti Rotor Package with accessories</td>
			<td style="width:84px;">Beckman Coulter</td>
			<td style="width:119px;">331336</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:269px;">Optima L100 XP ultra centrifuge</td>
			<td style="width:84px;">Beckman Coulter</td>
			<td>DS-9340A</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">ISCO fraction collector&#160;</td>
			<td style="width:84px;">Brandel</td>
			<td style="width:119px;">FC-176-R1</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">Type II Detector with Chart Recorder</td>
			<td style="width:84px;">Brandel</td>
			<td style="width:119px;">UA-6</td>
			<td>UV detector</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:269px;">Tube Piercer w/Flow Cell and Syringe Pump</td>
			<td style="width:84px;">Brandel</td>
			<td style="width:119px;">BR-A86-5</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">UA-6 Detector Cable</td>
			<td style="width:84px;">Brandel</td>
			<td style="width:119px;">60-1020-211</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">FC-176 Communication Cable</td>
			<td style="width:84px;">Brandel</td>
			<td style="width:119px;">FCC-176</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">Gradient Master Base Unit</td>
			<td style="width:84px;">Biocomp</td>
			<td style="width:119px;">108-1</td>
			<td>Gradient Maker</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">Gradient Forming Attachments</td>
			<td style="width:84px;">Biocomp</td>
			<td style="width:119px;">105-914A-1R</td>
			<td>Gradient Maker attachment</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:269px;">Measurement Computing 8-channel 50kHz Data Acquisition Device</td>
			<td style="width:84px;">MicroDAQ</td>
			<td style="width:119px;">USB-1208FS</td>
			<td>Analysis software attachment</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:269px;">Measurement Computing Data Acquisition Software</td>
			<td style="width:84px;">MicroDAQ</td>
			<td style="width:119px;">TracerDAQ Pro</td>
			<td>Analysis software (Download only)</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;"></td>
			<td style="width:84px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">Buffers</td>
			<td style="width:84px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">10X buffer for sucrose gradients:</td>
			<td style="width:84px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">200 mM HEPES (pH7.6)</td>
			<td style="width:84px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">1 M KCl</td>
			<td style="width:84px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">50 mM MgCl2</td>
			<td style="width:84px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">100 &#181;g/ml cycloheximide</td>
			<td style="width:84px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">1x protease inhibitor cocktail (EDTA-free)</td>
			<td style="width:84px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">100 units/ml RNase inhibitor</td>
			<td style="width:84px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;"></td>
			<td style="width:84px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:269px;">Lysis buffer</td>
			<td style="width:84px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">5 mM Tris-HCl (pH7.5)</td>
			<td style="width:84px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="25">
			<td height="25" style="height:25px;">2.5 mM MgCl2</td>
			<td style="width:84px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">1.5 mM KCl&#160;</td>
			<td style="width:84px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">1x protease inhibitor cocktail (EDTA-free)]</td>
			<td style="width:84px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="84">
			<td height="84" style="height:84px;width:269px;">Then add cycloheximide (CHX), DTT, RNAse inhibitor, Triton and Sodium Deoxycholate as indicated in the main text</td>
			<td style="width:84px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
	</tbody>
</table>

polysome fractionation and analysis of mammalian translatomes on a genome-wide scale

mRNA translation plays a central role in the regulation of gene expression and represents the most energy consuming process in mammalian cells. Accordingly, dysregulation of mRNA translation is considered to play a major role in a variety of pathological states including cancer. Ribosomes also host chaperones, which facilitate folding of nascent polypeptides, thereby modulating function and stability of newly synthesized polypeptides. In addition, emerging data indicate that ribosomes serve as a platform for a repertoire of signaling molecules, which are implicated in a variety of post-translational modifications of newly synthesized polypeptides as they emerge from the ribosome, and/or components of translational machinery. Herein, a well-established method of ribosome fractionation using sucrose density gradient centrifugation is described. In conjunction with the in-house developed &#8220;anota&#8221; algorithm this method allows direct determination of differential translation of individual mRNAs on a genome-wide scale. Moreover, this versatile protocol can be used for a variety of biochemical studies aiming to dissect the function of ribosome-associated protein complexes, including those that play a central role in folding and degradation of newly synthesized polypeptides.

mTOR is a major node of the cellular network that coordinates global protein synthesis rates with nutrient availability19. mRNA translation is regulated mainly at the rate-limiting initiation step6. The proportion of ribosomes engaged in polysomes positively correlates with translation initiation rates28. An example of applying the polysome fractionation method to investigate the role of mTOR signaling in mediating the effects of insulin on mRNA translation is presented. To this end, MCF7 human breast cancer cells were maintained in low serum and then stimulated with insulin alone or in combination with the active-site mTOR inhibitor Torin1. Non-stimulated cells that were continuously kept in low serum, served as a control. mRNPs, monosome (80S) and polysome fractions were separated using the polysome fractionation method. Relative to the control cells, insulin induced an increase in absorbance in gradient fractions corresponding to polysomes, accompanied by a concomitant decrease in absorbance in the monosome fraction (Figure 1). These findings show that the proportion of ribosomes engaged in polysomes is increased in insulin treated as compared to control cells, therefore indicating that, as expected, insulin stimulates global translation initiation rates. Torin1 reversed the effects of insulin on absorbance profiles (Figure 1), thereby corroborating the findings that mTOR signaling plays a major role in mediating the effects of insulin on the translation machinery19.
Based on a tenet that the inconsistencies in gradient preparation cannot be avoided, questions have been raised regarding the reproducibility of the data obtained using the polysome fractionation method22. To empirically test this potentially deleterious issue, cytosolic and heavy polysome-associated RNA (mRNA associated with 4 and more ribosomes) were isolated from MCF7 cells treated with insulin alone or insulin in combination with Torin1 from 4 independent biological replicates (Figure 2). The effects of insulin and Torin1 on the composition of cytosolic and heavy polysome-associated mRNA in each replicate was determined on a genome-wide scale using a microarray approach. To determine the reproducibility of the polysome fractionation method the principal component analysis (PCA) was applied. Such analysis showed that samples belonging to each condition were tightly positioned within the two first components while the different conditions were well separated (Figure 2). These findings show that the polysome fractionation method as described here is highly reproducible and therefore appropriate to study quantitative and qualitative changes in translation on a genome-wide level.
<img alt="Figure 1" fo:content-width="5in" fo:src="/files/ftp_upload/51455/51455fig1highres.jpg" src="/files/ftp_upload/51455/51455fig1.jpg" /> 
Figure 1. Polysomal profiles showing the effects of serum starvation, insulin and mTOR signaling on global translation in MCF7 cells. MCF7 cells were deprived of nutrients (maintained in 0.1% FBS) for 16 hours and treated with 5 nM insulin (Ins) alone or in combination with 250 nM Torin1 (Ins + Torin1) for 4 hours. Untreated cells that were continuously deprived of nutrients (0.1% FBS) were used as a control. The corresponding cytosolic extracts were sedimented by centrifugation on 5-50% sucrose gradients. Free ribosomal subunits (40S and 60S), monosomes (80S) and number of ribosomes in the polysome fractions are indicated.
<img alt="Figure 2" fo:content-width="5in" fo:src="/files/ftp_upload/51455/51455fig2highres.jpg" src="/files/ftp_upload/51455/51455fig2.jpg" /> 
Figure 2. Genome-wide data obtained using polysome profiling is highly reproducible. MCF7 cells were treated as in Figure 1. Cytosolic and polysome-associated RNA (&#62;3 ribosomes) was extracted from 4 independent biological replicates and their genome-wide mRNA levels were determined using GeneTitan arrays (Affymetrix). PCA was used to assess the reproducibility of the resulting data. Shown are the first two PCA components for all treatments (T1 = Torin 1; ctrl =control; Ins = insulin), origin of RNA (C = cytosolic; P = polysome-associated) and replicates. Samples from the same condition and RNA origin are closely positioned indicating high reproducibility.

Watch this Scientific Journal Video about Polysome Fractionation and Analysis of Mammalian Translatomes on a Genome-wide Scale at JoVE.com

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

Ribosomes play a central role in protein synthesis. Polyribosome (polysome) fractionation by sucrose density gradient centrifugation allows direct determination of translation efficiencies of individual mRNAs on a genome-wide scale. In addition, this method can be used for biochemical analysis of ribosome- and polysome-associated factors such as chaperones and signaling molecules.

mRNA translation plays a central role in the regulation of gene expression and represents the most energy consuming process in mammalian cells. ...

polysome-fractionation-analysis-mammalian-translatomes-on-genome-wide

Research

JoVE Journal

Biology

Intranuclear Microinjection of DNA into Dissociated Adult Mammalian Neurons

Primary neuronal cell cultures are valuable tools to study protein function since they represent a more biologically relevant system compared to immortalized cell lines. However, the post-mitotic nature of primary neurons prevents effective heterologous protein expression using common procedures such as electroporation or chemically-mediated transfection. Thus, other techniques must be employed in order to effectively express proteins in these non-dividing cells.
In this article, we describe the steps required to perform intranuclear injections of cDNA constructs into dissociated adult sympathetic neurons. This technique, which has been applied to different types of neurons, can successfully induce heterologous protein expression. The equipment essential for the microinjection procedure includes an inverted microscope to visualize cells, a glass injection pipet filled with cDNA solution that is connected to a N2(g) pressure delivery system, and a micromanipulator. The micromanipulator coordinates the injection motion of microinjection pipet with a brief pulse of pressurized N2 to eject cDNA solution from the pipet tip. 
This technique does not have the toxicity associated with many other transfection methods and enables multiple DNA constructs to be expressed at a consistent ratio. The low number of injected cells makes the microinjection procedure well suited for single cell studies such as electrophysiological recordings and optical imaging, but may not be ideal for biochemical assays that require a larger number of cells and higher transfection efficiencies. Although intranuclear microinjections require an investment of equipment and time, the ability to achieve high levels of heterologous protein expression in a physiologically relevant environment makes this technique a very useful tool to investigate protein function.

Direct intranuclear injection of cDNA is an effective transfection technique for post-mitotic cells. This method provides high levels of heterologous protein expression from single or multiple cDNA constructs and enables protein function to be studied in a physiologically relevant environment with a variety of single cell assays.

Primary neuronal cell cultures are valuable tools to study protein function since they represent a more biologically relevant system compared to ...

In situ Subcellular Fractionation of Adherent and Non-adherent Mammalian Cells

Protein function is intimately coupled to protein localization. Although some proteins are restricted to a specific location or subcellular compartment, many proteins are present as a freely diffusing population in free exchange with a sub-population that is tightly associated with a particular subcellular domain or structure. In situ subcellular fractionation allows the visualization of protein compartmentalization and can also reveal protein sub-populations that localize to specific structures. For example, removal of soluble cytoplasmic proteins and loosely held nuclear proteins can reveal the stable association of some transcription factors with chromatin. Subsequent digestion of DNA can in some cases reveal association with the network of proteins and RNAs that is collectively termed the nuclear scaffold or nuclear matrix. 
Here we describe the steps required during the in situ fractionation of adherent and non-adherent mammalian cells on microscope coverslips. Protein visualization can be achieved using specific antibodies or fluorescent fusion proteins and fluorescence microscopy. Antibodies and/or fluorescent dyes that act as markers for specific compartments or structures allow protein localization to be mapped in detail. In situ fractionation can also be combined with western blotting to compare the amounts of protein present in each fraction. This simple biochemical approach can reveal associations that would otherwise remain undetected.

In situ Subcellular Fractionation of Adherent and Non-adherent Mammalian Cells

In situ subcellular fractionation of mammalian cells on microscope coverslips allows the visualisation of protein localisation.

Protein function is intimately coupled to protein localization. Although some proteins are restricted to a specific location or subcellular compartment, ...

Genome-wide Analysis of Aminoacylation (Charging) Levels of tRNA Using Microarrays

tRNA aminoacylation, or charging, levels can rapidly change within a cell in response to the environment[1]. Changes in tRNA charging levels in both prokaryotic and eukaryotic cells lead to translational regulation which is a major cellular mechanism of stress response. Familiar examples are the stringent response in E. coli and the Gcn2 stress response pathway in yeast ([2-6]). Recent work in E. coli and S. cerevisiae have shown that tRNA charging patterns are highly dynamic and depends on the type of stress experienced by cells [1, 6, 7]. The highly dynamic, variable nature of tRNA charging makes it essential to determine changes in tRNA charging levels at the genomic scale, in order to fully elucidate cellular response to environmental variations. In this review we present a method for simultaneously measuring the relative charging levels of all tRNAs in S. cerevisiae . While the protocol presented here is for yeast, this protocol has been successfully applied for determining relative charging levels in a wide variety of organisms including E. coli and human cell cultures[7, 8].

Genome-wide Analysis of Aminoacylation Charging Levels of tRNA Using Microarrays

We describe a method for microarray analysis to determine relative aminoacylation levels of all tRNAs from S. cerivisiae.

tRNA aminoacylation, or charging, levels can rapidly change within a cell in response to the environment[1]. Changes in tRNA charging levels in both ...

Genome-wide Analysis using ChIP to Identify Isoform-specific Gene Targets

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the protein domains that bind to specific histone modifications. One such domain is the plant homeodomain (PHD), found in several chromatin-binding proteins. The epigenetic factor RBP2 has multiple PHD domains, however, they have different functions (Figure 4). In particular, the C-terminal PHD domain, found in a RBP2 oncogenic fusion in human leukemia, binds to trimethylated lysine 4 in histone H3 (H3K4me3)1. The transcript corresponding to the RBP2 isoform containing the C-terminal PHD accumulates during differentiation of promonocytic, lymphoma-derived, U937 cells into monocytes2. Consistent with both sets of data, genome-wide analysis showed that in differentiated U937 cells, the RBP2 protein gets localized to genomic regions highly enriched for H3K4me33. Localization of RBP2 to its targets correlates with a decrease in H3K4me3 due to RBP2 histone demethylase activity and a decrease in transcriptional activity. In contrast, two other PHDs of RBP2 are unable to bind H3K4me3. Notably, the C-terminal domain PHD of RBP2 is absent in the smaller RBP2 isoform4. It is conceivable that the small isoform of RBP2, which lacks interaction with H3K4me3, differs from the larger isoform in genomic location. The difference in genomic location of RBP2 isoforms may account for the observed diversity in RBP2 function. Specifically, RBP2 is a critical player in cellular differentiation mediated by the retinoblastoma protein (pRB). Consistent with these data, previous genome-wide analysis, without distinction between isoforms, identified two distinct groups of RBP2 target genes: 1) genes bound by RBP2 in a manner that is independent of differentiation; 2) genes bound by RBP2 in a differentiation-dependent manner.
To identify differences in localization between the isoforms we performed genome-wide location analysis by ChIP-Seq. Using antibodies that detect both RBP2 isoforms we have located all RBP2 targets. Additionally we have antibodies that only bind large, and not small RBP2 isoform (Figure 4). After identifying the large isoform targets, one can then subtract them from all RBP2 targets to reveal the targets of small isoform. These data show the contribution of chromatin-interacting domain in protein recruitment to its binding sites in the genome.

Here we are presenting a chromatin immunoprecipitation (ChIP) procedure for genome-wide location analysis of protein isoforms that differ in a histone-binding domain. We are applying it to ChIP-Seq analysis to identify the targets of the KDM5A/JARID1A/RBP2 histone demethylase.

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the ...

Mutagenesis and Functional Analysis of Ion Channels Heterologously Expressed in Mammalian Cells

We will demonstrate how to study the functional effects of introducing a point mutation in an ion channel. We study G protein-gated inwardly rectifying potassium (referred to as GIRK) channels, which are important for regulating the excitability of neurons. There are four different mammalian GIRK channel subunits (GIRK1-GIRK4) - we focus on GIRK2 because it forms a homotetramer. Stimulation of different types of G protein-coupled receptors (GPCRs), such as the muscarinic receptor (M2R), leads to activation of GIRK channels. Alcohol also directly activates GIRK channels. We will show how to mutate one amino acid by specifically changing one or more nucleotides in the cDNA for the GIRK channel. This mutated cDNA sequence will be amplified in bacteria, purified, and the presence of the point mutation will be confirmed by DNA sequencing. The cDNAs for the mutated and wild-type GIRK channels will be transfected into human embryonic kidney HEK293T cells cultured in vitro. Lastly, whole-cell patch-clamp electrophysiology will be used to study the macroscopic potassium currents through the ectopically expressed wild-type or mutated GIRK channels. In this experiment, we will examine the effect of a L257W mutation in GIRK2 channels on M2R-dependent and alcohol-dependent activation.

We will demonstrate how to study the effect of a single point mutation on the function of an ion channel.

We will demonstrate how to study the functional effects of introducing a point mutation in an ion channel. We study G protein-gated inwardly rectifying ...

Scale-Up of Mammalian Cell Culture using a New Multilayered Flask

A growing number of cell-based applications require large numbers of cells. Usage of single layer T-flasks, that are adequate during small-scale expansion, may become cumbersome, laborious and time-consuming when large numbers of cells are required. To address this need, the performance of a new multi-layered cell culture vessel to facilitate easy scale up of cells from single layered T-flasks will be discussed. The flasks tested are available in 3- and 5-layer format and enable culture and complete recovery of three and five times the number of cells respectively, compared to T-175 flasks. A key feature of the BD Multi-Flask is a mix/equilibration port that allows rapid in-vessel mixing as well as uniform distribution of cells and reagents within and 
between layers of each vessel and consistently produce cells that can be cultured in an environment that is congruent to T-175 flasks.
The design of these Multi-Flasks also allows for convenient pipette access for adding reagents and cells directly into the flasks as well as efficient recovery of valuable cells and reagents and reduces risk of contamination due to pouring. For applications where pouring is preferred over pipetting, the design allows for minimal residual liquid retention so as to reduce wastage of valuable cells and reagents.

Cells play an instrumental and increasing role in research, and the discovery and development of new therapeutics. With this increasing need for greater number of cells we need more efficient and effective ways for growing and harvesting attachment dependent cells. A Multilayered flask with the right features can serve this purpose.

A growing number of cell-based applications require large numbers of cells. Usage of single layer T-flasks, that are adequate during small-scale ...

Analysis of Cell Cycle Position in Mammalian Cells

The regulation of cell proliferation is central to tissue morphogenesis during the development of multicellular organisms. Furthermore, loss of control of cell proliferation underlies the pathology of diseases like cancer. As such there is great need to be able to investigate cell proliferation and quantitate the proportion of cells in each phase of the cell cycle. It is also of vital importance to indistinguishably identify cells that are replicating their DNA within a larger population. Since a cell&prime;s decision to proliferate is made in the G1 phase immediately before initiating DNA synthesis and progressing through the rest of the cell cycle, detection of DNA synthesis at this stage allows for an unambiguous determination of the status of growth regulation in cell culture experiments.
DNA content in cells can be readily quantitated by flow cytometry of cells stained with propidium iodide, a fluorescent DNA intercalating dye. Similarly, active DNA synthesis can be quantitated by culturing cells in the presence of radioactive thymidine, harvesting the cells, and measuring the incorporation of radioactivity into an acid insoluble fraction. We have considerable expertise with cell cycle analysis and recommend a different approach. We Investigate cell proliferation using bromodeoxyuridine/fluorodeoxyuridine (abbreviated simply as BrdU) staining that detects the incorporation of these thymine analogs into recently synthesized DNA. Labeling and staining cells with BrdU, combined with total DNA staining by propidium iodide and analysis by flow cytometry1 offers the most accurate measure of cells in the various stages of the cell cycle. It is our preferred method because it combines the detection of active DNA synthesis, through antibody based staining of BrdU, with total DNA content from propidium iodide. This allows for the clear separation of cells in G1 from early S phase, or late S phase from G2/M. Furthermore, this approach can be utilized to investigate the effects of many different cell stimuli and pharmacologic agents on the regulation of progression through these different cell cycle phases.
In this report we describe methods for labeling and staining cultured cells, as well as their analysis by flow cytometry. We also include experimental examples of how this method can be used to measure the effects of growth inhibiting signals from cytokines such as TGF-&beta;1, and proliferative inhibitors such as the cyclin dependent kinase inhibitor, p27KIP1. We also include an alternate protocol that allows for the analysis of cell cycle position in a sub-population of cells within a larger culture5. In this case, we demonstrate how to detect a cell cycle arrest in cells transfected with the retinoblastoma gene even when greatly outnumbered by untransfected cells in the same culture. These examples illustrate the many ways that DNA staining and flow cytometry can be utilized and adapted to investigate fundamental questions of mammalian cell cycle control.

Determining the cell cycle position of a population of cells, or understanding how signals affect proliferation, can be readily measured by flow cytometry using this protocol. We report a simple experimental approach to staining cells and quantifying their position in the cell cycle.

The regulation of cell proliferation is central to tissue morphogenesis during the development of multicellular organisms. Furthermore, loss of control of ...

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of protein-bound DNA and aligning the short reads to a reference genome. Enriched regions are revealed as peaks, which often differ dramatically in shape, depending on the target protein1. For example, transcription factors often bind in a site- and sequence-specific manner and tend to produce punctate peaks, while histone modifications are more pervasive and are characterized by broad, diffuse islands of enrichment2. Reliably identifying these regions was the focus of our work.
Algorithms for analyzing ChIPseq data have employed various methodologies, from heuristics3-5 to more rigorous statistical models, e.g. Hidden Markov Models (HMMs)6-8. We sought a solution that minimized the necessity for difficult-to-define, ad hoc parameters that often compromise resolution and lessen the intuitive usability of the tool. With respect to HMM-based methods, we aimed to curtail parameter estimation procedures and simple, finite state classifications that are often utilized.
Additionally, conventional ChIPseq data analysis involves categorization of the expected read density profiles as either punctate or diffuse followed by subsequent application of the appropriate tool. We further aimed to replace the need for these two distinct models with a single, more versatile model, which can capably address the entire spectrum of data types.
To meet these objectives, we first constructed a statistical framework that naturally modeled ChIPseq data structures using a cutting edge advance in HMMs9, which utilizes only explicit formulas-an innovation crucial to its performance advantages. More sophisticated then heuristic models, our HMM accommodates infinite hidden states through a Bayesian model. We applied it to identifying reasonable change points in read density, which further define segments of enrichment. Our analysis revealed how our Bayesian Change Point (BCP) algorithm had a reduced computational complexity-evidenced by an abridged run time and memory footprint. The BCP algorithm was successfully applied to both punctate peak and diffuse island identification with robust accuracy and limited user-defined parameters. This illustrated both its versatility and ease of use. Consequently, we believe it can be implemented readily across broad ranges of data types and end users in a manner that is easily compared and contrasted, making it a great tool for ChIPseq data analysis that can aid in collaboration and corroboration between research groups. Here, we demonstrate the application of BCP to existing transcription factor10,11 and epigenetic data12 to illustrate its usefulness.

Our Bayesian Change Point (BCP) algorithm builds on state-of-the-art advances in modeling change-points via Hidden Markov Models and applies them to chromatin immunoprecipitation sequencing (ChIPseq) data analysis. BCP performs well in both broad and punctate data types, but excels in accurately identifying robust, reproducible islands of diffuse histone enrichment.

ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of ...

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

Transposon mutagenesis and single-gene deletion are two methods applied in genome-wide gene knockout in bacteria 1,2. Although transposon mutagenesis is less time consuming, less costly, and does not require completed genome information, there are two weaknesses in this method: (1) the possibility of a disparate mutants in the mixed mutant library that counter-selects mutants with decreased competition; and (2) the possibility of partial gene inactivation whereby genes do not entirely lose their function following the insertion of a transposon. Single-gene deletion analysis may compensate for the drawbacks associated with transposon mutagenesis. To improve the efficiency of genome-wide single gene deletion, we attempt to establish a high-throughput technique for genome-wide single gene deletion using Streptococcus sanguinis as a model organism. Each gene deletion construct in S. sanguinis genome is designed to comprise 1-kb upstream of the targeted gene, the aphA-3 gene, encoding kanamycin resistance protein, and 1-kb downstream of the targeted gene. Three sets of primers F1/R1, F2/R2, and F3/R3, respectively, are designed and synthesized in a 96-well plate format for PCR-amplifications of those three components of each deletion construct. Primers R1 and F3 contain 25-bp sequences that are complementary to regions of the aphA-3 gene at their 5' end. A large scale PCR amplification of the aphA-3 gene is performed once for creating all single-gene deletion constructs. The promoter of aphA-3 gene is initially excluded to minimize the potential polar effect of kanamycin cassette. To create the gene deletion constructs, high-throughput PCR amplification and purification are performed in a 96-well plate format. A linear recombinant PCR amplicon for each gene deletion will be made up through four PCR reactions using high-fidelity DNA polymerase. The initial exponential growth phase of S. sanguinis cultured in Todd Hewitt broth supplemented with 2.5% inactivated horse serum is used to increase competence for the transformation of PCR-recombinant constructs. Under this condition, up to 20% of S. sanguinis cells can be transformed using ~50 ng of DNA. Based on this approach, 2,048 mutants with single-gene deletion were ultimately obtained from the 2,270 genes in S. sanguinis excluding four gene ORFs contained entirely within other ORFs in S. sanguinis SK36 and 218 potential essential genes. The technique on creating gene deletion constructs is high throughput and could be easy to use in genome-wide single gene deletions for any transformable bacteria.

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

An efficient genome-wide single gene mutation method has been established using Streptococcus sanguinis as a model organism. This method has achieved via high throughput recombinant PCRs and transformations.

Transposon  mutagenesis and single-gene deletion are two methods applied in genome-wide  gene knockout in bacteria 1,2. Although transposon mutagenesis is ...

Analysis of Translation Initiation During Stress Conditions by Polysome Profiling

Precise control of mRNA translation is fundamental for eukaryotic cell homeostasis, particularly in response to physiological and pathological stress. Alterations of this program can lead to the growth of damaged cells, a hallmark of cancer development, or to premature cell death such as seen in neurodegenerative diseases. Much of what is known concerning the molecular basis for translational control has been obtained from polysome analysis using a density gradient fractionation system. This technique relies on ultracentrifugation of cytoplasmic extracts on a linear sucrose gradient. Once the spin is completed, the system allows fractionation and quantification of centrifuged zones corresponding to different translating ribosomes populations, thus resulting in a polysome profile. Changes in the polysome profile are indicative of changes or defects in translation initiation that occur in response to various types of stress. This technique also allows to assess the role of specific proteins on translation initiation, and to measure translational activity of specific mRNAs. Here we describe our protocol to perform polysome profiles in order to assess translation initiation of eukaryotic cells and tissues under either normal or stress growth conditions.

Here, we describe a method to analyze changes in the initiation of mRNA translation of eukaryotic cells in response to stress conditions. This method is based on the velocity separation on sucrose gradients of translating ribosomes from non-translating ribosomes.

Precise control of mRNA translation is fundamental for eukaryotic cell homeostasis, particularly in response to physiological and pathological stress. ...