TITLE: No Title
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[DESCRIPTION]
<div title="Page 2">
<p>Housekeeping proteins (HKPs) are routinely used as loading controls for Western blot normalization. Accurate normalization requires stable expression of the HKP across all experimental conditions and treatments. Because HKP normalization relies on a single indicator of sample loading, variation in HKP expression leads to inconsistent estimation of sample loading and introduces experimental error that may alter data analysis.</p>
<p> </p>
<div title="Page 3">
<div>
<div>
<p>This protocol describes how to use a housekeeping protein for Western blot normalization and quantitative analysis.</p>
<p> </p>
<p><strong>Note:</strong> This protocol is intended for use with near-infrared fluorescent Western blots.</p>
</div>
</div>
</div>
</div>

[BEFORE_START]
<div title="Page 3">
<div>
<div>
<div>
<p>Important: Before an HKP is used for Western blot normalization, stable expression must be validated for the specific experimental context and treatments. For more information, see the <a href="https://www.protocols.io/view/housekeeping-protein-validation-protocol-g75bzq6" target="_blank">Housekeeping Protein Validation Protocol</a>.</p>
</div>
</div>
</div>
</div>

[GUIDELINES]
<p><strong>I. Introduction</strong></p>
<p> </p>
<div title="Page 2">
<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>
<p><strong>More Info:</strong> For more normalization related resources, see 'Further Reading' in section VI. of these guidelines. </p>
<p> </p>
<p><strong>Using a Housekeeping Protein (HKP) as an Internal Loading Control </strong></p>
<p> </p>
<div title="Page 3">
<p>Housekeeping proteins (HKPs) are routinely used as loading controls for Western blot normalization. Accurate normalization requires stable expression of the HKP across all experimental conditions and treatments. Because HKP normalization relies on a single indicator of sample loading, variation in HKP expression leads to inconsistent estimation of sample loading and introduces experimental error that may alter data analysis.</p>
<p> </p>
<p>For widely-used HKPs (such as actin, tubulin, and GAPDH), stable expression has generally been assumed. However, expression of common HKPs is now known to vary in response to certain experimental conditions, including cell confluence, disease state, drug treatment, and cell or tissue type.</p>
<p> </p>
<div title="Page 3">
<p><strong>Important:</strong> Before an HKP is used for Western blot normalization, stable expression must be validated for the specific experimental context and treatments. For more information, see the <a href="https://www.protocols.io/view/housekeeping-protein-validation-protocol-g75bzq6" target="_blank">Housekeeping Protein Validation Protocol</a>.</p>
<p> </p>
<div title="Page 3">
<p>This protocol describes how to use a housekeeping protein for Western blot normalization and quantitative analysis.</p>
<p> </p>
<div title="Page 3">
<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>
<div title="Page 4">
<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 VI.</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> Two-color Western blot detection requires careful selection of primary and secondary antibodies to prevent cross-reactivity. Always perform single-color control blots first to verify antibody specificity, and to identify possible interference from background bands.</p>
<p> </p>
<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>
<p><strong>III. Required Reagents</strong></p>
</div>
</div>
</div>
<p> </p>
<p>1. <strong>Treated and untreated samples</strong></p>
<p>    Protein concentration must be determined for all samples.</p>
<p> </p>
<p>2.<strong> Electrophoresis reagents</strong></p>
<p> </p>
<p>3. <strong>Transfer reagents</strong></p>
<p> </p>
<p>4. <strong>Western blot detection reagents (near-infrared fluorescence)</strong></p>
<p>    Perform near-infrared Wester 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. Protocol</strong></p>
<p> </p>
<p>See 'STEPS'</p>
<p> </p>
<p><strong>V. Target Protein and HKP Quantification</strong></p>
<p> </p>
<div title="Page 5">
<div>
<div>
<p>Use Image Studio™ Software (download the free version of Image Studio - <a href="https://www.licor.com/bio/products/software/image_studio_lite/download.html" target="_blank">Image Studio Lite</a>) to quantify the fluorescent signals of the HKP (700 nm) and target protein (800 nm).</p>
</div>
</div>
</div>
<p> </p>
See 'STEPS' (11-20)
<p> </p>
<p><strong>VI. Normalization Calculations and Analysis of Replicates</strong></p>
<p> </p>
<div title="Page 7">
<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 (1, 2).</p>
<p> </p>
<div title="Page 7">
<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>
<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 (HKP, 700 nm)</strong></p>
<div title="Page 8"> <br />1. Prepare a spreadsheet that contains the HKP and target protein quantification data.</div>
<div title="Page 8"> </div>
<div title="Page 8">2. Using the HKP data from the normalization channel, calculate the Average (arithmetic mean), Standard Deviation, and % Coefficient of Variation (% CV) of the replicate samples.</div>
<div title="Page 8"> </div>
<div title="Page 8">'Average' formula in Excel = AVERAGE(rep1, rep2, ...)</div>
<div title="Page 8"> </div>
<div title="Page 8">Standard Deviation formula in Excel = STDEV(rep 1 value, rep 2 value, ...)</div>
<div title="Page 8"> </div>
<div title="Page 8">% Coefficient of Variation =  <img src="https://s3.amazonaws.com/pr-journal/id2c2w6.png" width="200" data-src="https://s3.amazonaws.com/pr-journal/id2c2w6.png" data-ofn="Screen Shot 2017-02-15 at 8.49.38 AM.png" /></div>
<div title="Page 8"> </div>
<div title="Page 8">REVERT Staining (700 nm)</div>
<div title="Page 8"> </div>
<div title="Page 8">
<table>
<tbody>
<tr>
<td>Lane</td>
<td>Sample Treatment</td>
<td>Replicate</td>
<td>700 nm signal</td>
<td>Average Signal</td>
<td>St Dev</td>
<td>CV</td>
</tr>
<tr>
<td>1</td>
<td>none</td>
<td>1</td>
<td>1,000</td>
<td>900</td>
<td>100</td>
<td>11%</td>
</tr>
<tr>
<td>2</td>
<td> </td>
<td>2</td>
<td>800</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>3</td>
<td> </td>
<td>3</td>
<td>900</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>4</td>
<td>UV</td>
<td>1</td>
<td>800</td>
<td>833</td>
<td>58</td>
<td>7%</td>
</tr>
<tr>
<td>5</td>
<td> </td>
<td>2</td>
<td>900</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>6</td>
<td> </td>
<td>3</td>
<td>800</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
</tbody>
</table>
<p><em>Example values shown for illustration only.</em></p>
<p> </p>
<p>3. Calculate the Lane Normalization Factor (LNF) for each lane.</p>
<p> </p>
<p>    a. Identify the lane with the highest signal for HKP.</p>
<p>    b. Use this value to calculate the Lane Normalization Factor for each lane. </p>
<p> </p>
<p><em><img id="s-mce-img" class="s-mce-img" src="https://s3.amazonaws.com/pr-journal/id3c2w6.png" data-src="https://s3.amazonaws.com/pr-journal/id3c2w6.png" data-ofn="Screen Shot 2017-02-15 at 8.54.10 AM.png" /></em></p>
<p> </p>
<p>HKP (700 nm)</p>
<p> </p>
<table>
<tbody>
<tr>
<td>Lane</td>
<td>
<p>Sample</p>
<p>Treatment</p>
</td>
<td>Replicate</td>
<td>700 nm signal</td>
<td>Highest Signal</td>
<td>
<p>Lane Normalization</p>
<p>factor</p>
</td>
</tr>
<tr>
<td>1</td>
<td>none</td>
<td>1</td>
<td>1,000</td>
<td>1,000</td>
<td>1</td>
</tr>
<tr>
<td>2</td>
<td> </td>
<td>2</td>
<td>800</td>
<td>1,000</td>
<td>0.8</td>
</tr>
<tr>
<td>3</td>
<td> </td>
<td>3</td>
<td>900</td>
<td>1,000</td>
<td>0.9</td>
</tr>
<tr>
<td>4</td>
<td>UV</td>
<td>1</td>
<td>800</td>
<td>1,000</td>
<td>0.8</td>
</tr>
<tr>
<td>5</td>
<td> </td>
<td>2</td>
<td>900</td>
<td>1,000</td>
<td>0.9</td>
</tr>
<tr>
<td>6</td>
<td> </td>
<td>3</td>
<td>800</td>
<td>1,000</td>
<td>0.8</td>
</tr>
</tbody>
</table>
<p> </p>
<p><strong>Important:</strong> Normalization factors must be calculated for each blot. Normalization factors and standard curves cannot be reused between blots. </p>
<p> </p>
<p><strong>Calculate the Normalized Target Protein Signals (800 nm)</strong></p>
<p> </p>
<p>1. Using the Target Protein data from the 800 nm channel, calculate the Average, Standard Deviation, and % Coefficient of Variation of the replicate samples.</p>
<p> </p>
<p>Target protein (800 nm), not normalized</p>
<p> </p>
<table>
<tbody>
<tr>
<td>Lane</td>
<td>Sample Treatment</td>
<td>Replicate</td>
<td>Target 800 nm</td>
<td>Average Signal</td>
<td>St Dev</td>
<td>CV</td>
</tr>
<tr>
<td>1</td>
<td>none</td>
<td>1</td>
<td>650</td>
<td>600</td>
<td>50</td>
<td>8%</td>
</tr>
<tr>
<td>2</td>
<td> </td>
<td>2</td>
<td>550</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>3</td>
<td> </td>
<td>3</td>
<td>600</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>4</td>
<td>UV</td>
<td>1</td>
<td>450</td>
<td>477</td>
<td>25</td>
<td>5%</td>
</tr>
<tr>
<td>5</td>
<td> </td>
<td>2</td>
<td>500</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>6</td>
<td> </td>
<td>3</td>
<td>480</td>
<td> </td>
<td> </td>
<td> </td>
</tr>
</tbody>
</table>
<div title="Page 10">
<p><em>Example values shown for illustration only.</em></p>
<p> </p>
<p>2. Calculate the Normalized Target Signal for each target band by applying the LNF 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/id5c2w6.png" width="300" data-src="https://s3.amazonaws.com/pr-journal/id5c2w6.png" data-ofn="Screen Shot 2017-02-15 at 9.02.16 AM.png" /></p>
<p> </p>
<p>    b. Calculate the Average, Standard Devation, and % Coefficient of Variation of the replicates.</p>
<p> </p>
<table>
<tbody>
<tr style="height: 20px;">
<td style="height: 20px;" colspan="4">
<p>Target Protein (not normalized)</p>
</td>
<td style="height: 20px;" colspan="2">Normalization</td>
<td style="height: 20px;" colspan="4">Normalized to total protein</td>
</tr>
<tr style="height: 32px;">
<td style="height: 32px;">Lane</td>
<td style="height: 32px;">
<p>Sample</p>
<p>Treatment</p>
</td>
<td style="height: 32px;">Replicate</td>
<td style="height: 32px;">
<p>Target</p>
<p>(800 nm)</p>
</td>
<td style="height: 32px;">
<p>LNF</p>
<p>(HKP)</p>
</td>
<td style="height: 32px;">
<p>Norm. of</p>
<p>Target</p>
</td>
<td style="height: 32px;">
<p>Norm.</p>
<p>Target</p>
</td>
<td style="height: 32px;">
<p>Average </p>
<p>signal</p>
</td>
<td style="height: 32px;">
<p>St</p>
<p>Dev</p>
</td>
<td style="height: 32px;">
<p>CV</p>
</td>
</tr>
<tr style="height: 20px;">
<td style="height: 20px;">1</td>
<td style="height: 20px;">none</td>
<td style="height: 20px;">1</td>
<td style="height: 20px;">650</td>
<td style="height: 20px;">1</td>
<td style="height: 20px;">650/1</td>
<td style="height: 20px;">650</td>
<td style="height: 20px;">668</td>
<td style="height: 20px;">19</td>
<td style="height: 20px;">3%</td>
</tr>
<tr style="height: 20px;">
<td style="height: 20px;">2</td>
<td style="height: 20px;"> </td>
<td style="height: 20px;">2</td>
<td style="height: 20px;">550</td>
<td style="height: 20px;">0.8</td>
<td style="height: 20px;">550/0.8</td>
<td style="height: 20px;">688</td>
<td style="height: 20px;"> </td>
<td style="height: 20px;"> </td>
<td style="height: 20px;"> </td>
</tr>
<tr style="height: 20px;">
<td style="height: 20px;">3</td>
<td style="height: 20px;"> </td>
<td style="height: 20px;">3</td>
<td style="height: 20px;">600</td>
<td style="height: 20px;">0.9</td>
<td style="height: 20px;">600/0.9</td>
<td style="height: 20px;">667</td>
<td style="height: 20px;"> </td>
<td style="height: 20px;"> </td>
<td style="height: 20px;"> </td>
</tr>
<tr style="height: 20px;">
<td style="height: 20px;">4</td>
<td style="height: 20px;">UV</td>
<td style="height: 20px;">1</td>
<td style="height: 20px;">450</td>
<td style="height: 20px;">0.8</td>
<td style="height: 20px;">450/0.8</td>
<td style="height: 20px;">563</td>
<td style="height: 20px;">573</td>
<td style="height: 20px;">44</td>
<td style="height: 20px;">4%</td>
</tr>
<tr style="height: 20px;">
<td style="height: 20px;">5</td>
<td style="height: 20px;"> </td>
<td style="height: 20px;">2</td>
<td style="height: 20px;">500</td>
<td style="height: 20px;">0.9</td>
<td style="height: 20px;">500/0.9</td>
<td style="height: 20px;">556</td>
<td style="height: 20px;"> </td>
<td style="height: 20px;"> </td>
<td style="height: 20px;"> </td>
</tr>
<tr style="height: 20px;">
<td style="height: 20px;">6</td>
<td style="height: 20px;"> </td>
<td style="height: 20px;">3</td>
<td style="height: 20px;">480</td>
<td style="height: 20px;">0.8</td>
<td style="height: 20px;">480/0.8</td>
<td style="height: 20px;">600</td>
<td style="height: 20px;"> </td>
<td style="height: 20px;"> </td>
<td style="height: 20px;"> </td>
</tr>
</tbody>
</table>
<p> </p>
<p><strong>VII. Data Interpretation</strong></p>
<p> </p>
<p>1. Use the normalized Target Protein values for relative comparison of samples.</p>
<div title="Page 11">
<p>    In the example above, Target Protein level is 14% lower in UV-treated samples than in untreated samples.</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 measurement precision.</li>
<li>A larger % CV indicates greater variation in signal and reduced precision</li>
</ul>
b. On a Western blot, a change in band intensity is meaningful only if its magnitude substantially exceeds the % CV.<br />
<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), a CV of 10% or less would be recommended for replicate samples.<br />For a specific measurement, this threshold for the magnitude of change would correspond to the mean + 2 SD.</li>
</ul>
<ul>
<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>
</ul>
<div title="Page 11">
<ul>
<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> </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>
</div>
<p> </p>
<p><strong>VIII. Further Reading</strong></p>
<p> </p>
<div title="Page 12">
<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="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="%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>
<p> </p>
<div title="Page 12">
<p><strong>Pan/Phospho Analysis For Western Blot Normalization</strong></p>
<p><a href="licor.com/PanProteinNormalization" target="_blank">licor.com/PanProteinNormalization</a></p>
<p>This protocol describes how to use pan-specific antibodies as an internal loading control for normalization.</p>
<p> </p>
<div title="Page 12">
<p><strong>Housekeeping Protein Validation Protocol</strong></p>
<p><a href="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>
<div title="Page 12">
<p> </p>
<p><strong>References</strong></p>
<p> </p>
<div title="Page 12">
<p>1. Robasky, K, Lewis NE, and Church GM. Nat. Rev. Genet. 15: 56–62 (2014). <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>
<p> </p>
<p>2. Naegle K, Gough NR, and Yaffe MB. Sci Signal. 8:fs7 (2015).</p>
<p><a href="https://www.ncbi.nlm.nih.gov/pubmed/25852186" target="_blank">https://www.ncbi.nlm.nih.gov/pubmed/25852186</a></p>
</div>
</div>
</div>
<p> </p>
<p> </p>
</div>
</div>
</div>
</div>
</div>
</div>
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</div>
</div>
<p> </p>
</div>
</div>
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</div>
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</div>

[STEPS]
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