Normalization Is Critical for Quantitative Immunoblotting

webinar-2-for-facebookInternal loading controls and normalization are critical for quantitative immunoblotting. An accurate loading control will display a linear relationship between signal intensity and sample concentration. An effective normalization strategy should correct for variability at all stages of the immunoblotting process, including the transfer of sample proteins to membrane. It should also be compatible with immunodetection of target proteins.

As researchers detect and interpret subtle changes in protein samples, accurate normalization is becoming increasingly important.

Odyssey CLx Infrared Imaging SystemThe best normalization strategy is one that fits the context and biology of your experiment. No matter what normalization strategy you choose, an Odyssey® Imaging System can provide quantitative results.

Learn more from the full paper on normalization: Western Blot Normalization: Challenges and Considerations for Quantitative Analysis

Ready to find out if the Odyssey CLx Imaging System is right for your lab? Request a demo.

Total Protein Stain as an Internal Loading Control

Using a total protein stain to detect the total protein in each lane of your gel or blot is becoming more popular. Total protein staining is a direct measure of the total amount of sample protein in each lane. For each lane, the sum of all the signal intensities of all the proteins in the lane is used for normalization.

This more direct approach may increase the accuracy of normalization. Unlike housekeeping proteins, total protein staining does not require validation for each experimental context.

A total protein stain should produce a linear increase in signal intensity in response to increasing protein concentration. It should also correct for variation at all points in the Western blot process, including gel loading and transfer to membrane. It must be compatible with downstream immunodetection of your blot. You should make sure that the signal intensity of the total protein stain is moderate, without saturation or low signal-to-noise ratios.

REVERT™ Total Protein Stain provides linear, proportional signal across a broad range of sample concentrations.

REVERT Total Protein Stain

Learn more about total protein controls in the full paper on normalization: Western Blot Normalization: Challenges and Considerations for Quantitative Analysis

Signaling Proteins as Internal Loading Controls

Besides housekeeping proteins and total protein controls, signaling proteins are another option for normalization. This approach is particularly useful for relative analysis of post-translational modifications such as phosphorylation. The method combines two primary antibodies raised in different hosts: a phospho-specific antibody (or other modification-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 with digital imaging. Phospho-signal is then normalized against the total level of target protein, using the target protein as its own internal control.

This is a great strategy to use if you’re studying protein modifications. Bakkenist et al. examined the possibility of binding interference from combined phospho-specific and pan antibodies, but detected little or no effect.
Advantages of Phospho-Analysis with Signaling Proteins:

  • You can detect both unmodified and modified forms of your target protein on the same blot, in the same lane.
  • No error is introduced by stripping and reprobing. Stripping and reprobing of blots can introduce detection artifacts and cause loss of blotted proteins from the membrane.
  • Accuracy is improved by correcting for loading and sampling error

Find out more about multiplex analysis using signaling proteins: Western Blot Normalization: Challenges and Considerations for Quantitative Analysis

Housekeeping Proteins as Internal Loading Controls

Housekeeping proteins such as tubulin, actin, and GAPDH are often used to normalize. In the past, researchers assumed that these proteins were constant in every cell type, because these proteins maintain basic cellular function. Housekeeping proteins are acceptable loading controls if expression is stable, but expression of these proteins can vary depending on your cellular context.

Housekeeping proteins won’t effectively normalize in every experiment, but that doesn’t mean they won’t work for any experiment. If you choose to use a housekeeping protein as your normalization strategy, be sure to validate it to confirm stable expression for your experimental context. As cell types, tissue types, disease states, and experimental treatments change, your internal loading control should remain constant.

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Here are some things to keep in mind:

  • Gene expression levels do not reliably predict protein abundance. Just because mRNA levels are constant, this does not mean protein levels will be similarly constant.
  • Biological factors, like tissue type, growth conditions, stage of development, and disease, may influence expression of housekeeping proteins. Without constant expression, housekeeping proteins are an unreliable way to normalize.
  • Housekeeping proteins are typically very abundant. The resulting strong bands frequently cause signal saturation, which reduces the accuracy of detection.

If you have validated that your housekeeping proteins are constant across all your experimental treatments, you can use them as a reliable loading control. Actin, tubulin, and COX IV primary antibodies can be used for two-color normalization.

Find out more about housekeeping proteins as internal loading controls in Western Blot Normalization: Challenges and Considerations for Quantitative Analysis.

Types of Normalization Strategies

Western Story - GlovedScientistsWhat normalization strategies are available to you when performing Western blots? The next four blog posts in this series will discuss the options that exist and considerations for use of each strategy.

An ideal internal loading control would have a linear, proportional response, be stably expressed in all experimental conditions, correct for variation throughout the whole process of immunoblotting, and be compatible with detection of your target proteins.

If you have the time to validate your loading control for each experiment, a housekeeping protein may work for you. You must validate all your housekeeping proteins to ensure stable expression.

If you’re studying protein modifications, like phosphorylation, ubiquitination, or glycosylation, then a multiplex normalization strategy with a signaling protein is recommended.

Total protein controls use all proteins present in the sample, and include total protein stains. If you don’t have time to validate, a total protein stain is best.

There are many ways to normalize. The best way depends on you and your experimental context. Watch for future blog posts about housekeeping proteins, signaling proteins, and total protein stains.

Find out more about normalization: Western Blot Normalization: Challenges and Considerations for Quantitative Analysis

Are You Experiencing Detection System Saturation?

An effective loading control will display a linear relationship between signal intensity and sample concentration. Saturation can often prevent this linear response, especially for highly abundant proteins. A quick recap: saturation is when strong band intensities appear different, but relative signal intensity plateaus. Check out a previous blog post on how saturation limits accurate Western blot normalization.

Linear range is the region over which signals are directly proportional to the amount of protein present. A wider dynamic range makes it easier to get data within the linear range today, as well as next year – increasing reproducibility.

Film Exposure of Chemiluminescent Blots

While film might be the method of choice for some researchers, it has fundamental limitations that affect the analysis and reproducibility of your data. It provides an extremely narrow linear range of detection, roughly 4-10 fold. Also, rapid saturation of strong signals makes it difficult to accurately determine the upper limit of detection. Film exaggerates small differences in abundance and masks sample-to-sample changes in strong bands.

Western Blot - fig1-detection
Figure 1. Odyssey® data are linear across a much wider range than ECL and film. Pure recombinant p53, Hdm2, and Hdmx protein of known concentration were serially diluted and run in duplicate, followed by Western blot analysis. Proteins were detected by IR fluorescence or standard ECL. Signal intensities were quantified with Odyssey software or, for ECL, densitometry of developed films. Reprinted from Wang, YV et al. Proc Natl Acad Sci USA. 104(30): 12365-70 (2007). Copyright (2007) National Academy of Sciences, U.S.A.

CCD Imaging of Chemiluminescent Blots

Digital imaging of chemiluminescent blots typically offers a wider linear range of detection than film. Many CCD systems are able to detect faint signals without saturating strong signals. Sensitivity and linear range depend on which CCD system you choose.

Even with a digital imager, chemiluminescent Western blot signals are still the result of an enzymatic reaction. The time-dependent enzymatic reaction may still lead to saturation and inaccurate results.

Digital Imaging of Fluorescent Blots

Fluorescent immunoblotting is best performed with near-infrared fluorescent dyes and imaging systems. Background autofluorescence of membranes and biological samples is low in the near-infrared region, enabling high sensitivity. To detect faint signals without saturating strong signals, use an imaging system with a wide linear dynamic range.

Are you experiencing detection system saturation? Find more information about saturation in this full review article:
Western Blot Normalization: Challenges and Considerations for Quantitative Analysis

Saturation Limits Accurate Western Blot Normalization

An effective loading control has a linear, proportional response, meaning the signal intensity of the internal control should accurately reflect sample concentration and abundance of loading control over a wide range. If your loading control doesn’t meet the requirement of a linear response, it affects your accuracy and reproducibility.

Saturation limits the accuracy of normalization, especially if you’re using a housekeeping protein. Housekeeping proteins are often highly abundant in samples, which can lead to strong, saturated signals.

Let’s look at what saturation is and where it can happen.

What is Saturation?

Saturation is when strong signals don’t accurately reflect protein levels. It can come from your membrane, your detection chemistry, and the way you image your blot.

Saturated bands are deceptive (Fig. 1). They hide actual variation in protein levels and underestimate the amount of protein present. The similar apparent intensities of saturated bands may lead you to think your protein levels are equal.
Blog Post 4 - Normalization
Figure 1. Strong bands become saturated and underestimate protein abundance. Strong signals (box) exhibit saturation because they fall outside the linear range of detection. Band intensity can no longer increase proportionately to indicate protein abundance. As a result, the signal intensity of the saturated bands appears similar. High-intensity data points should not be used as controls for normalization.

Membrane Saturation

If you’ve overloaded the samples on your gel, that problem doesn’t go away once you transfer to the membrane. You may lose protein while transferring to the membrane, if overloaded samples exceed membrane capacity.

In addition, highly abundant proteins might stack on top of each other. When primary antibodies can only access the top layer of the protein stack, they can’t detect the rest of the proteins. This leads to underestimation of strong signals, hurting accurate quantitation.

How can you prevent membrane overloading? It’s best to run a dilution series to determine the upper limit of how much sample you should be loading on your gel. Membrane overloading is tricky to avoid, because different proteins generally have different upper limits in the same sample. Because it arises from the binding chemistry of proteins and blotting membranes, membrane saturation can happen with any detection chemistry or imaging method.

Detection Chemistry Saturation

When internal loading control bands are detected outside the linear range of detection, increases in protein level won’t produce a proportional increase in signal intensity. For accurate normalization, both the internal loading control and the target must be detected within the linear range of the method used. The type of detection chemistry you use affects the linear range of detection for your sample proteins.

Enhanced chemiluminescence (ECL) is an indirect, enzymatic method. Secondary antibodies are labeled with horseradish peroxidase (HRP) as an enzymatic reporter. The enzyme produces light after you apply substrate and produces an unstable, time-dependent signal. Because these signals are the result of the kinetics of an enzymatic reaction, the signal doesn’t reflect its protein abundance. Saturation is likely with ECL, because it amplifies signals.

Fluorescence, on the other hand, is direct detection. Fluorophores label secondary antibodies and then generate stable signals. This type of detection chemistry doesn’t depend on enzyme kinetics, so fluorescent detection is more reproducible than ECL detection. Fluorescence is also less likely to saturate, because the signals are directly proportional to the amount of protein.

How can you prevent detection chemistry saturation? The simplest way is to use fluorescence detection instead of ECL, because fluorescence is less likely to saturate.

Blog post 4 - direct-indirect

For more information about saturation, check out the full review article:
Western Blot Normalization: Challenges and Considerations for Quantitative Analysis

Requirements for Internal Loading Controls

Western blots are packed with potential sources of variability. Variability that isn’t accounted for limits reproducibility and threatens your chances for publication-quality data. Normalization corrects for variability introduced during the process of Western blotting.

So what should you do to get more reproducible data? Use an internal loading control for each blot. Internal loading controls are endogenous sample proteins that are stably expressed and unaffected by experimental conditions.

Requirements for an Effective Internal Loading Control:

  • Linear, proportional response. Signal intensity of the internal control should accurately reflect sample con¬centration and abundance of loading control over a wide range.
  • Low biological variability. Your experimental treatments should not affect the expression of your internal loading control. For example, expression of some housekeeping proteins may vary in response to experimental conditions.
  • Corrects for variation at all stages of immunoblotting. Your internal control should correct for variation that occurs throughout the Western blot process, including gel loading and transfer.
  • Compatible with immunodetection. The strategy you choose shouldn’t interfere with effective down¬stream detection of your target proteins.

For more information about internal loading controls, check out the full review article:
Western Blot Normalization: Challenges and Considerations for Quantitative Analysis

What Factors Affect Normalization?

Do you know what factors affect normalization? Routine steps in the Western blotting process such as sample preparation, sample loading, and the detection of multiple proteins can introduce unwanted variability. You should plan to reduce error in every step of the Western blotting process. Without planning, you might get pseudo-quantitative results that don’t reflect the biology of your samples.

Sample Preparation

Blog - Sample prepThe way you prepare your samples can significantly change the results of your experiment. Even small changes in plating, cell lysis, reagent volume, and other technical details can have a surprising impact.

For example, how you lyse your cells affects protein extraction, solubilization, and modification status. The insoluble fraction may retain relevant proteins, affecting your quantitative analysis. Some experimental treatments shift fractions between soluble and insoluble.

For these reasons, it’s important to be consistent when preparing your samples. It’s also good practice to estimate the total protein concentration of each sample after preparation. Bradford, BCA, and Lowry assays are widely used to estimate the total protein concentration. Then it’s possible to adjust gel loading to the estimated protein concentration.

Sample Loading

Blog - sample loadingOverloaded gels create problems. Although strong bands may appear similar, the bands could be saturating either the membrane capacity or the dynamic range of detection. To avoid saturation and inaccurate results, run a standard curve with two-fold serial dilutions of cell lysate. You can then identify the linear range for each target protein.

Detection of Multiple Proteins

You may need to detect multiple proteins to compare relative protein levels, especially if you’re using a housekeeping protein or signaling protein to normalize. Stripping and reprobing is often used to compare different proteins on the same blot, but it can introduce error. Leftover antibodies from incomplete stripping result in artifacts. Overly harsh stripping may result in a loss of sample proteins from the membrane.

If, however, you use near-infrared fluorescent detection, there’s no need to strip and reprobe. Multiplexing is when you detect two different proteins with spectrally-distinct secondary antibodies. Multiplexing is convenient and saves time. It is also more accurate than stripping and reprobing, because no artifacts are introduced, and there’s no possibility for protein loss. With multiplexing, you can use co-migrating proteins, as well as easily identify antibody cross-reactivity.

For more details about factors that affect normalization, check out the full review article:
Western Blot Normalization: Challenges and Considerations for Quantitative Analysis

Understanding Western Blot Normalization

chess piece - kingResearchers rely on Western blotting to detect target proteins in complex samples. This trusted technique is widely used to compare relative protein levels.

However, variability can creep into your Western blots through differences in sample preparation, sample loading, and transfer from gel to membrane. That’s why normalization is important. Normalization is the process of using internal loading controls to mathematically correct for sample-to-sample variation. These internal loading controls verify whether or not samples are uniformly loaded across the gel, confirm consistent transfer from gel to membrane, and enable comparison of relative protein levels between samples.

Normalization is meant to correct for small variation between samples, and can’t completely remove variability. If large data corrections are applied, accuracy may be affected. Normalization is a strategy to apply throughout your experiment, rather than a last step in the protocol. The more sources of variability you can reduce or eliminate, the more reproducible your experiment will be.

The role of an internal loading control is always to confirm the changes you see on the blot reflect actual change in the biology of your samples. To demonstrate statistically significant changes in the abundance of target protein, you need a reliable normalization strategy that fits the context and biology of your experiment. Effective, carefully-planned normalization will more accurately reflect the amount of protein in each lane.

chess piece - bishopUnderstanding Western blot normalization will help you choose a strategy that fits the context and biology of your experiment.

This paper describes important considerations, strengths, and limits of commonly used normalization strategies:

Western Blot Normalization: Challenges and Considerations for Quantitative Analysis