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[DESCRIPTION]
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<p>QWB is often used for relative analysis of protein phosphorylation and other post- translational modifications (PTMs). This method combines two primary antibodies raised in different hosts: a phospho-specific or other PTM-specific antibody, and a pan-specific antibody that recognizes the target protein regardless of its modification state. Fluorescently-labeled secondary antibodies are used to simultaneously detect and discriminate the two signals. Phospho-specific signals are then normalized against the total level of the target protein, using that protein as its own internal loading control for maximum accuracy.</p>
<p> </p>
<p>This multiplex fluorescent method is used to analyze changes in target protein phosphorylation, glycosylation, acetylation, ubiquitination, and other PTMs.</p>
<p> </p>
<p><strong>Note:</strong> This protocol is intended for use with near-infrared fluorescent Western blots.</p>
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</div>
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[GUIDELINES]
<p><strong>I. Introduction</strong></p>
<p> </p>
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<p>In quantitative Western blotting (QWB), normalization mathematically corrects for unavoidable sample-to-sample and lane-to-lane variation by comparing the target protein to an internal loading control. The internal loading control is used as an indicator of sample protein loading, to correct for loading variation and confirm that observed changes represent actual differences between samples.</p>
<p> </p>
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<p><strong>More Info:</strong> For more normalization related resources, see 'Further Reading' in section IX.</p>
<p> </p>
<p><strong>Pan-Specific Antibodies as an Internal Loading Control</strong></p>
<p> </p>
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<p>QWB is often used for relative analysis of protein phosphorylation and other post- translational modifications (PTMs). This method combines two primary antibodies raised in different hosts: a phospho-specific or other PTM-specific antibody, and a pan-specific antibody that recognizes the target protein regardless of its modification state. Fluorescently-labeled secondary antibodies are used to simultaneously detect and discriminate the two signals. Phospho-specific signals are then normalized against the total level of the target protein, using that protein as its own internal loading control for maximum accuracy.</p>
<p> </p>
<p>This multiplex fluorescent method is used to analyze changes in target protein phosphorylation, glycosylation, acetylation, ubiquitination, and other PTMs.</p>
<p> </p>
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<p><strong>Note:</strong> This protocol is intended for use with near-infrared fluorescent Western blots.</p>
<p> </p>
<p><strong>II. Keys for Success</strong></p>
<p> </p>
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<p><strong>Saturation and linear range.</strong> Saturated bands and sample overloading frequently compromise the accuracy of QWB. Use a dilution series to verify that you are working within the linear range of detection, and signal intensity is proportional to sample loading. See the protocol: <a href="https://www.protocols.io/view/determining-the-linear-range-for-quantitative-west-g78bzrw" target="_blank">Determining the Linear Range for Quantitative Western Blot Detection</a> for more information.</p>
<p> </p>
<p><strong>Replication.</strong> Replicate samples provide information about the inherent variability of your methods, to determine if the changes you see are meaningful and significant. A minimum of three technical replicates is recommended for each sample. Replicates are discussed further in section VII.</p>
<p> </p>
<p><strong>Uniform sample loading.</strong> Uniform loading of total sample protein across the gel is critical for accurate QWB analysis. A protein concentration assay (BCA, Bradford, or similar assay) must be used to adjust sample concentration and load all samples as consistently as possible.</p>
<p> </p>
<p><strong>Antibody validation.</strong> Verify specificity of the phospho-antibody to ensure that it does not cross-react with the unmodified target protein, and to identify possible interference from background bands. Important guidelines are provided in Section IV.</p>
<p> </p>
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<p><strong>More Info:</strong> The Antibody Publication Database can help you find antibody pairs that work for your experiment (<a href="https://www.licor.com/antibodyrequest" target="_blank">licor.com/antibodyrequest</a>).</p>
<p> </p>
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<p><strong>Phosphorylation stoichiometry.</strong> This protocol is intended for relative comparison of pan- protein and phospho-protein signals, and results do not indicate the stoichiometry of phosphorylation (1).</p>
<p> </p>
<p><strong>III. Required Reagents</strong></p>
<p> </p>
<p><strong>1. Treated and untreated samples</strong></p>
<p>Protein concentration must be determined for all samples</p>
<p> </p>
<p><strong>2. Electrophoresis reagents</strong></p>
<p> </p>
<p><strong>3. Transfer reagents</strong></p>
<p> </p>
<p><strong>4. Pan-specific and modificiation-specific antibodies against target protein</strong></p>
<p>Antibody selection and validation are described in Section IV.</p>
<p> </p>
<p><strong>5. Western blot detection reagents (near-infrared fluorescence)</strong></p>
<p>Perform near-infrared Western blot detection according to the Near-Infrared Western Blot Detection Protocol</p>
<p><a href="https://www.protocols.io/view/near-infrared-nir-western-blot-detection-pvdf-memb-g7rbzm6" target="_blank">LI-COR NIRWesternProtocol with PVDF Membrane</a></p>
<p><a href="https://www.protocols.io/view/near-infrared-nir-western-blot-detection-nitrocell-g7nbzme" target="_blank">LI-COR NIRWestern Protocol with Nitrocellulose Membrane</a></p>
<p> </p>
<p><strong>IV. Selection of pan- and phospho-specific antibodies</strong></p>
<p> </p>
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<p>Antibody selection and validation are critical for pan/phospho analysis and pan/PTM analysis of other modifications. Follow these important guidelines:</p>
<p> </p>
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<ul>
<li>Pan- and modification-specific primary antibodies must be raised in different host species, so they can be discriminated by the secondary antibodies. For example, a rabbit polyclonal pan-antibody can be used with a mouse monoclonal phospho-antibody.</li>
<li>Use highly cross-adsorbed secondary antibodies labeled with spectrally distinct NIR fluorescent dyes.</li>
<li>The primary antibodies must recognize different epitopes on the protein, to avoid binding interference. Polyclonal pan-specific antibodies less likely to demonstrate interference.</li>
<li>To validate the primary antibody pair, three Western blots are recommended. Perform a separate Western blot with each individual primary antibody, and a third blot with the two primary antibodies combined. These controls will characterize the banding patterns and verify that the primary antibodies can be multiplexed.</li>
</ul>
<p> </p>
<p><strong>V. Protocol</strong></p>
<p> </p>
<p>See 'STEPS'</p>
<p> </p>
<p><strong>VI. Pan Protein and Phospho-Protein Quantification</strong></p>
<p> </p>
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<p>Use Image Studio™ Software (free a free version of the analysis software, <a href="https://www.licor.com/bio/products/software/image_studio_lite/download.html" target="_blank">download Image Studio Lite</a>) to quantify the fluorescent signals for the pan protein (700 nm) and phosphorylated target protein (800 nm).</p>
<p> </p>
<p>See 'STEPS'</p>
<p> </p>
<p><strong>VII. Normalization Calculations and Analysis of Replicates</strong></p>
<p> </p>
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<p>Replication is an important part of QWB analysis, and is used to confirm the validity of observed changes in protein levels. Biological and technical replicates are both important, but meet different needs (2, 3).</p>
<p> </p>
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<p><strong>Biological replicates:</strong> Parallel measurements of biologically distinct samples, used to control for biological variation and examine the generalizability of an experimental observation.</p>
<p> </p>
<p><strong>Technical replicates:</strong> Repeated measurements used to establish the variability of a protocol or assay, and determine if an experimental effect is large enough to be reliably distinguished from the assay noise.</p>
<p> </p>
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<p>Technical replication can be performed by testing the sample multiple times on the same gel or membrane (intra-assay variation) or by testing the sample multiple times in several Western blot experiments. This procedure describes the normalization and analysis of technical replicates that were tested multiple times on the same membrane.</p>
<p> </p>
<p><strong>Calculate the Lane Normalization Factor for Each Lane (pan protein, 700 nm)</strong></p>
<p> </p>
<p>1. Prepare a spreadsheet containing the pan protein and phospho-protein quantification data.</p>
<p> </p>
<p>2. Using the pan-antibody data from the normalization channel (700 nm), calculate the Average (arithmetic mean), Standard Deviation, and % Coefficient of Variation (% CV) of the replicate samples.</p>
<p> </p>
<div title="Page 8">“Average” formula in Excel = AVERAGE(rep 1, rep 2, ......)</div>
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<div title="Page 8">Standard Deviation formula in Excel = STDEV(rep 1 value, rep 2 value, .....)</div>
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<div title="Page 8">% Coefficient of Variation =  <img id="s-mce-img" class="s-mce-img" src="https://s3.amazonaws.com/pr-journal/ifnc2w6.png" width="210" height="49" data-src="https://s3.amazonaws.com/pr-journal/ifnc2w6.png" data-ofn="Screen Shot 2017-02-16 at 9.51.57 AM.png" /></div>
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<div title="Page 8">Pan Protein (700 nm)</div>
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<table>
<tbody>
<tr>
<td>Treatment</td>
<td>Replicate</td>
<td>
<p>Primary</p>
<p>Antibody</p>
</td>
<td>Signal</td>
<td>Average</td>
<td>St Dev</td>
<td>% CV</td>
</tr>
<tr>
<td>-</td>
<td>1</td>
<td>pan</td>
<td>990</td>
<td>950</td>
<td>36</td>
<td>4%</td>
</tr>
<tr>
<td>-</td>
<td>2</td>
<td>pan</td>
<td>940</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>-</td>
<td>3</td>
<td>pan</td>
<td>920</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>+</td>
<td>1</td>
<td>pan</td>
<td>930</td>
<td>943</td>
<td>51</td>
<td>5%</td>
</tr>
<tr>
<td>+</td>
<td>2</td>
<td>pan</td>
<td>900</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>+</td>
<td>3</td>
<td>pan</td>
<td>1000</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
</tbody>
</table>
<p><em>Example values shown for illustration only</em></p>
<p> </p>
<p>3.<em> </em>Calculate the Lane Normalization Factor (LNF) for each lane.</p>
<p> </p>
<p>a. Identify the lane with the highest signal for total protein staining.</p>
<p>b. Use this value to calculate the Lane Normalization Factor for each lane.</p>
<p> </p>
<p><img id="s-mce-img" class="s-mce-img" src="https://s3.amazonaws.com/pr-journal/ifqc2w6.png" width="390" height="52" data-src="https://s3.amazonaws.com/pr-journal/ifqc2w6.png" data-ofn="Screen Shot 2017-02-16 at 9.55.16 AM.png" /></p>
<table>
<tbody>
<tr>
<td>Treatment</td>
<td>Replicate</td>
<td>
<p>Primary</p>
<p>Antibody</p>
</td>
<td>Signal</td>
<td>
<p>Highest</p>
<p>Signal</p>
</td>
<td>LNF</td>
</tr>
<tr>
<td>-</td>
<td>1</td>
<td>pan</td>
<td>990</td>
<td>1,000</td>
<td>0.99</td>
</tr>
<tr>
<td>-</td>
<td>2</td>
<td>pan</td>
<td>940</td>
<td>1,000</td>
<td>0.94</td>
</tr>
<tr>
<td>-</td>
<td>3</td>
<td>pan</td>
<td>920</td>
<td>1,000</td>
<td>0.92</td>
</tr>
<tr>
<td>+</td>
<td>1</td>
<td>pan</td>
<td>930</td>
<td>1,000</td>
<td>0.93</td>
</tr>
<tr>
<td>+</td>
<td>2</td>
<td>pan</td>
<td>900</td>
<td>1,000</td>
<td>0.90 </td>
</tr>
<tr>
<td>+</td>
<td>3</td>
<td>pan</td>
<td>1,000</td>
<td> 1,000</td>
<td>1.00 </td>
</tr>
</tbody>
</table>
<p> </p>
<p> </p>
<p><strong>Calculate the Normalized Phospho-Target Signals (phospho antibody, 800 nm)</strong></p>
<p> </p>
<p>1. Using the phospho-antibody data from the 800 nm channel, calculate the Average (arithmetic mean), Standard Deviation, and % Coefficient of Variation (% CV) of the replicate samples.</p>
<p> </p>
<p>Phospho-target (800 nm)</p>
<p> </p>
<table>
<tbody>
<tr>
<td>Treatment</td>
<td>Replicate</td>
<td>
<p>Primary </p>
<p>Antibody</p>
</td>
<td>Signal</td>
<td>Average</td>
<td>St Dev</td>
<td>% CV</td>
</tr>
<tr>
<td>-</td>
<td>1</td>
<td>phospho</td>
<td>160</td>
<td>182</td>
<td>26</td>
<td>14%</td>
</tr>
<tr>
<td>-</td>
<td>2</td>
<td>phospho</td>
<td>175</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>-</td>
<td>3</td>
<td>phospho</td>
<td>210</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>+</td>
<td>1</td>
<td>phospho</td>
<td>19,500</td>
<td>18,833</td>
<td>1,607</td>
<td>9%</td>
</tr>
<tr>
<td>+</td>
<td>2</td>
<td>phospho</td>
<td>17,000</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>
<p>+</p>
</td>
<td>3</td>
<td>phospho</td>
<td>20,000</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
</tbody>
</table>
<p><em>Example values shown for illustration only. </em></p>
<p> </p>
<p>2. Calculate the Normalized Phospho-Target Signal for each target band by applying the Lane Normalization Factor for that lane.</p>
<p> </p>
<p>a. Divide the Target Signal for each lane by the corresponding LNF.</p>
<p> </p>
<p><img id="s-mce-img" class="s-mce-img" src="https://s3.amazonaws.com/pr-journal/ifrc2w6.png" width="362" height="38" data-src="https://s3.amazonaws.com/pr-journal/ifrc2w6.png" data-ofn="Screen Shot 2017-02-16 at 10.02.03 AM.png" /></p>
<p> </p>
<p>Phospho-target (800 nm) - Normalization</p>
<p> </p>
<table>
<tbody>
<tr>
<td>Treatment</td>
<td>Replicate</td>
<td>
<p>Primary</p>
<p>Antibody</p>
</td>
<td>Signal</td>
<td>LNF (pan - Ab)</td>
<td>St Dev</td>
<td>
<p>Norm.</p>
<p>Signal</p>
</td>
</tr>
<tr>
<td>-</td>
<td>1</td>
<td>phospho</td>
<td>160</td>
<td>0.99</td>
<td>160/0.99</td>
<td>162</td>
</tr>
<tr>
<td>-</td>
<td>2</td>
<td>phospho</td>
<td>175</td>
<td>0.94</td>
<td>175/0.94</td>
<td>186</td>
</tr>
<tr>
<td>-</td>
<td>3</td>
<td>phospho</td>
<td>210</td>
<td>0.92</td>
<td>210/0.92</td>
<td>228</td>
</tr>
<tr>
<td>+</td>
<td>1</td>
<td>phospho</td>
<td>19,500</td>
<td>0.93</td>
<td>19,500/0.93</td>
<td>20,968</td>
</tr>
<tr>
<td>+</td>
<td>2</td>
<td>phospho</td>
<td>17,000</td>
<td>0.9</td>
<td>17,000/0.9</td>
<td>18,889</td>
</tr>
<tr>
<td>+</td>
<td>3</td>
<td>phospho</td>
<td>20,000</td>
<td>1</td>
<td>20,000/1</td>
<td>20,000 </td>
</tr>
</tbody>
</table>
<p> </p>
<p>b. Use this value to calculate the Lane Normalization Factor for each lane</p>
<p> </p>
<table>
<tbody>
<tr>
<td>Treatment</td>
<td>Replicate</td>
<td>
<p>Primary</p>
<p>Antibody</p>
</td>
<td>Signal</td>
<td>
<p>Norm</p>
<p>Signal</p>
</td>
<td>Average</td>
<td>
<p>St Dev</p>
</td>
<td>
<p>% CV</p>
</td>
</tr>
<tr>
<td>-</td>
<td>1</td>
<td>phospho</td>
<td>160</td>
<td>162</td>
<td>192</td>
<td>34</td>
<td>18%</td>
</tr>
<tr>
<td>-</td>
<td>2</td>
<td>phospho</td>
<td>175</td>
<td>186</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>-</td>
<td>3</td>
<td>phospho</td>
<td>210</td>
<td>228</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>+</td>
<td>1</td>
<td>phospho</td>
<td>19,500</td>
<td>20,968</td>
<td>19,952</td>
<td>1,040</td>
<td>5%</td>
</tr>
<tr>
<td>+</td>
<td>2</td>
<td>phospho</td>
<td>17,000</td>
<td>18,889</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>+</td>
<td>3</td>
<td>phospho</td>
<td>20,000</td>
<td>20,000</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
</tbody>
</table>
<p> </p>
<p> </p>
<p><strong>VIII. Data Interpretation</strong></p>
<p> </p>
<div title="Page 10">
<p style="display: inline !important;">1. Use the normalized Target Protein values for relative comparison of samples.</p>
<p>In the example above, treatment induced a 100-fold increase in phosphorylation of the target protein.</p>
<p> </p>
<p>2. % CV can be used to evaluate the robustness of QWB results, and determine if the magnitude of observed changes in Target Protein levels is large enough to be reliably distinguished from assay variability.</p>
<p> </p>
<p>a. The percent coefficient of variation (% CV) describes the spread or variability of measured signals by expressing the standard deviation (SD) as a percent of the average value (arithmetic mean). Because % CV is independent of the mean and has no unit of measure, it can be used to compare the variability of data sets and indicate the precision and repeatability of an assay.</p>
<ul>
<li>A low % CV value indicates low signal variability and high precision of measurement.</li>
<li>A larger % CV indicates greater variation in signal and reduced precision</li>
</ul>
<p>b. On a Western blot, a change in band intensity is meaningful only if its magnitude substantially exceeds the % CV.</p>
<div title="Page 11">
<ul>
<li>Generally speaking, the magnitude of the reported change should be at least 2X greater than the % CV.</li>
<li><strong>Example:</strong> To report a 20% difference between samples (0.8-fold or 1.2-fold change in band intensity), % CV 10% would be recommended for replicate samples.</li>
<li>For a specific measurement, this threshold for the magnitude of change would correspond to the mean + 2 SD.</li>
<li>Faint bands or subtle changes in band intensity are more difficult to detect reliably. In these situations, QWB analysis requires more extensive replication and low % CV.</li>
<li>This is a general guideline only. Replication needs and data interpretation are specific to your experiment, and you may wish to consult a statistician.</li>
</ul>
<p> </p>
<p>3. Compare the % CV of Target Protein replicates before and after normalization.</p>
<p> </p>
<p>a. Normalization should not greatly increase the % CV of replicate samples.</p>
<p>b. The purpose of normalization is to reduce the variability between replicate samples by correcting for lane-to-lane variation. A large increase in % CV after normalization of replicates is a warning sign that the normalization method is not sufficiently robust, and may be a source of experimental error.</p>
<p> </p>
<p><strong>IX. Further Reading</strong></p>
<p> </p>
<p>Please see the following for more information about normalization.</p>
<p> </p>
<div title="Page 12">
<p><strong>Western Blot Normalization Handbook</strong></p>
<p><a href="https://www.licor.com/documents/gtk1a4mrphretj1gvmzld0f920g7h8sh" target="_blank">licor.com/handbook</a></p>
<p>The Normalization Handbook describes how to choose and validate an appropriate internal loading control for normalization.</p>
<p> </p>
<p><strong>Good Normalization Gone Bad</strong></p>
<div title="Page 12">
<p><a href="https://www.licor.com/documents/r8b0v1iuo2lkvdonha1toxx778aap3tr" target="_blank">licor.com/GNGB</a></p>
<p>Good Normalization Gone Bad presents examples of normalization that have been adversely affected by common pitfalls and offers potential solutions.</p>
<p> </p>
<div title="Page 12">
<p><strong>Western Blot Normalization White Paper</strong></p>
<p><a href="https://www.licor.com/documents/fwf274p9k35hwym6p2keq397p4edho5o" target="_blank">licor.com/normalizationreview</a></p>
<p>This white paper comprehensively reviews the literature of Western blot normalization.</p>
<p> </p>
<div title="Page 12">
<p><strong>Determining the Linear Range for Quantitative Western Blot Detection</strong></p>
<p><a href="https://www.protocols.io/view/licor.com/LinearRange" target="_blank">licor.com/LinearRange</a></p>
<p>This protocol explains how to choose an appropriate amount of sample to load for QWB analysis.</p>
<p> </p>
<div title="Page 12">
<p><strong>REVERT™ Total Protein Stain Normalization Protocol</strong></p>
<p><a href="https://www.protocols.io/view/%20licor.com/RevertNormalization" target="_blank">licor.com/RevertNormalization</a></p>
<div title="Page 12">
<p>This protocol describes how to use REVERT™ Total Protein Stain for Western blot normalization and analysis.</p>
<div title="Page 12">
<p> </p>
<div title="Page 12">
<p><strong>Housekeeping Protein Validation Protocol</strong></p>
<p><a href="https://www.protocols.io/view/licor.com/HKP-Validation" target="_blank">licor.com/HKP-Validation</a></p>
<p>This protocol explains how to validate an HKP for use as an internal loading control, by demonstrating that HKP expression is stable in the relevant experimental samples.</p>
<p> </p>
<div title="Page 12">
<p><strong>Housekeeping Protein Normalization Protocol</strong></p>
<p><a href="https://www.protocols.io/view/licor.com/HKP-Normalization" target="_blank">licor.com/HKP-Normalization</a></p>
<p>This protocol describes how to use a housekeeping protein for Western blot normalization and quantitative analysis.</p>
<p><strong> </strong></p>
<p><strong>References</strong></p>
<p> </p>
<div title="Page 12">
<p>1. Janes KA (2015) <a href="http://stke.sciencemag.org/content/8/371/rs2" target="_blank">An analysis of critical factors for quantitative immunoblotting</a>. Sci Signal. 8(371): rs2.</p>
<p>2. Robasky, K, Lewis NE, and Church GM. Nat. Rev. Genet. 15: 56–62 (2014).</p>
<p><a href="http://www.nature.com/nmeth/journal/v11/n9/pdf/nmeth.3091.pdf" target="_blank">http://www.nature.com/nmeth/journal/v11/n9/pdf/nmeth.3091.pdf</a></p>
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<p><a href="https://www.ncbi.nlm.nih.gov/pubmed/25852186" target="_blank">https://www.ncbi.nlm.nih.gov/pubmed/25852186</a></p>
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