Antibodies 101: Normalization and Loading Controls for Western Blots

Ah, the notorious western blot: we meet again. So useful, yet so finicky to design and optimize. Today we’ll cover the normalization and loading controls needed for relative quantification of a western blot — and why you might want to be careful relying on so called “housekeeping proteins.”

Fully quantifying a western blot requires a standard curve with a pure sample of your protein of interest (POI). This approach is reliable, but it’s overkill in many cases. Often, you may not need to know the exact quantity of your POI, but simply whether its concentration changes in response to experimental parameters. This is a job for a semi-quantitative western blot.

Most western blots benefit from a positive control — a loading control — that you know is present in every lane of your gel. At their most basic, loading controls help you rule out errors like uneven pipetting or irregular membrane transfer.

Figure 1: A loading control is a type of positive control necessary to demonstrate a POI’s absence. It also helps confirm that lanes are loaded evenly. Created with BioRender.com.

Loading controls are even more important for semi-quantitative western blots. No pipetter is perfect, and proteins may not transfer precisely evenly across the entire blot. So how can you accurately compare different lanes? In relative quantification, the POI bands are normalized to the loading control: the intensity of each POI band is divided by the intensity of the loading control in the same lane, and the resulting fraction is reported.

Figure 2: Normalization with loading controls. Under each lane, the fraction of the POI band over the loading control is shown. In the left gel, the loading controls are equally strong, so the normalized value simply reports which POI band is stronger. The right gel has exactly the same POI bands as the left gel, but because the loading control band is weaker in condition 1 than condition 2, the normalized values for the two conditions are equal. Created with BioRender.com.

This kind of normalization is a nearly ubiquitous tool, but proceed with caution! Loading controls only address one type of error that can impact your western blot, and depending too much on normalization can amplify other errors in experimental design and gel quantification.

Figure 3: In a perfect world, protein concentration in your sample would directly correlate to protein abundance on your membrane, which would be directly reported by band intensity. Researchers use later steps in this chain (blue) to infer or calculate things about earlier ones. In practice, multiple sources of error (red) influence these relationships and must be addressed with separate controls (green). Created with BioRender.com.

Researchers often rely on assumptions that their loading controls are independent of these other sources of error. It’s important to validate these assumptions for your particular experiment and choice of loading control. First, let’s explore why these assumptions are key to using loading controls accurately.

Assumption #1: The loading control is not perturbed by the experiment

The loading control is supposed to control for, well, loading errors. When using it to normalize your results, you are assuming that all differences in the loading control are due to loading and transfer errors — not to any factors from before the proteins even hit the gel.

But what if your experiment changes the expression of your loading control without your knowledge? If you can’t trust Assumption #1, any change in the normalized intensity of your POI could reflect either a genuine change in your POI or an opposite change in your loading control.

Assumption #2: The loading control can be accurately quantified

In western blotting, the relationship between protein abundance and band intensity is an S-shaped curve. Sparse proteins may not be detected at all, while overloaded proteins may saturate the detector. In between these extremes lies the linear detection range.

Figure 4: The linear detection range of the assay is between the two dotted lines. Created with BioRender.com.

To see why this S-shaped relationship poses problems for proteins of very different abundance, consider the following example gel.

Figure 5: An example gel (left) with bands corresponding to points on the plot of protein abundance versus band intensity (right). Created with BioRender.com.

On this gel, the protein of interest band is significantly lighter in “condition 1” than “condition 2.” By eye, the loading control appears about the same between the two conditions. But it isn’t! (No really, I promise it isn’t.) The intuitive, qualitative interpretation of this gel is misleading.

We can see why on the S-shaped curve. On the protein abundance axis, both blue “condition 1” points (POI and loading control) are smaller than the analogous green “condition 2” points by the exact same value. For the two solid POI points, this difference is reflected on the band intensity axis. But the loading control points are outside of the linear detection range. As a result, they have almost the same value on the band intensity axis.

This effect causes misleading qualitative conclusions, and it’s also problematic for computational quantification. Outside the linear detection range, band intensity measurements become extremely susceptible to error, since tiny changes in intensity can reflect massive differences in protein abundance.

Validating the assumptions for different loading controls

So how can you address these errors without making incorrect assumptions? The answer depends on the loading control you choose.

Housekeeping proteins as loading controls

Ubiquitously expressed proteins like β-actin or GAPDH are popular choices for loading controls. These are often called “housekeeping proteins” and are detected with an antibodies, just like the POI.

This approach is so common that we’ve used it for our cartoon gels so far. However, it throws both assumptions discussed above into question.

Assumption #1: The loading control is not perturbed by the experiment

Unfortunately, multiple reports suggest that common housekeeping proteins are influenced by a variety of experimental conditions (Ghosh et al., 2014). Although this assumption is likely true in many cases, you should validate it for your particular setup.

We recommend starting by searching the literature to see what is already known about your housekeeping protein in your context. If you don’t find anything, you’ll need to conduct a control experiment to demonstrate that expression of your reference protein does not vary under your experimental conditions. Otherwise, you might want to consider a different loading control.

Assumption #2: The loading control can be accurately quantified

Most housekeeping proteins are highly expressed in cells. This means that at sample concentrations appropriate to detect a less-abundant POI, the loading control will almost certainly be overloaded. One paper called this problem “the most common error associated with Western blotting quantification” (Gilda & Gomes, 2013).

To address this source of error, it’s important to determine the linear detection range of all proteins that will be measured in your assay. This is influenced by sample concentration as well as by antibody dilution — both factors we’ve covered in our previous blog posts (1, 2) on western blotting. If the abundance of your POI and loading control aren’t too far apart, they may have overlapping linear detection ranges, allowing you to accurately quantify both on the same blot.  

Total protein measurements as loading controls

Total protein measurements are an accurate alternative loading control option. Total protein stains or stain-free imaging systems are both effective for measuring the total protein present in a lane (Aldridge et al., 2008; Gilda & Gomes, 2013). Depending on the approach you choose, your loading control measurement will take place either before or after blotting for your POI.

Figure 6: Total protein measurements are taken on the same gel or membrane (depending on your approach) as your final blot. Quantifying the entire lane of protein provides a loading control that can be used for normalization. Created with BioRender.com.

Assumption #1: The loading control is not perturbed by the experiment

By relying on the total protein in the lane, your loading control won’t be strongly influenced by unexpected variations in any individual protein. This ensures you are truly comparing the abundance of your POI as a fraction of the total sample.

Assumption #2: The loading control can be accurately quantified

Because the total protein is measured in a separate step from the POI blot, you don’t need to worry about the linear detection ranges of multiple targets. Convenient!

Comparison of loading control approaches

Cartoons are nice and all, but we’ll leave you with this real-world comparison of different loading control approaches from Gilda & Gomes, 2013.

Figure 7: Semi-quantification of liver lysate using different loading controls. A) Western bot of liver lysate (10–40 µg) probed with β-actin (1:2000). Representative of three independent blots. B–C) Graphs showing the relative intensity of the β-actin (B) or total protein (C) on the membrane versus the amount of protein loaded on the gel (n=3). Data is presented as mean ± SEM. Adapted from Analytical Biochemistry, vol 440, Gilda, J. E., & Gomes, A. V., “Stain-Free total protein staining is a superior loading control to β-actin for Western blots,” pages 186–188, copyright 2013, with permission from Elsevier.

In this figure, panels A–B show the housekeeping protein approach. By eye, it’s difficult to see an increasing concentration of β-actin in panel A. Indeed, the quantification in panel B is not especially linear.

In panel C, two different methods of quantifying total protein are compared. While a higher slope indicates higher sensitivity, linearity is the most important feature of a loading control, and both total protein measurements are linear. Note the different y-axes between panels B and C, indicating that β-actin is less sensitive as well as less linear than the total protein quantification. This illustrates why total protein measurement is often more accurate as a loading control.

Thanks for joining us in this exploration of western blot normalization. May your blots be clear and your detection range be linear!

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Resources and References

References

Aldridge, G. M., Podrebarac, D. M., Greenough, W. T., & Weiler, I. J. (2008). The use of total protein stains as loading controls: An alternative to high-abundance single-protein controls in semi-quantitative immunoblotting. Journal of Neuroscience Methods, 172(2), 250–254. https://doi.org/10.1016/j.jneumeth.2008.05.003

Ghosh, R., Gilda, J. E., & Gomes, A. V. (2014). The necessity of and strategies for improving confidence in the accuracy of western blots. Expert Review of Proteomics, 11(5), 549–560. https://doi.org/10.1586/14789450.2014.939635

Gilda, J. E., & Gomes, A. V. (2013). Stain-Free total protein staining is a superior loading control to β-actin for Western blots. Analytical Biochemistry, 440(2), 186–188. https://doi.org/10.1016/j.ab.2013.05.027

Additional Resources on the Addgene Blog

• Antibodies 101: The Basics of Western Blotting
• Technical Design of a Western Blot
• Troubleshooting and Optimizing a Western Blot

Resources on Addgene.org

• Addgene's Western Blot Protocol