8 Research Areas Where Housekeeping Proteins Can Compromise Western Blot Data

Western blotting enables the detection and measurement of target proteins in a complex sample. As a result, it is a valuable tool that has become a staple of scientific research, spanning various disciplines.

One of the key considerations for generating reliable and reproducible data with western blotting is normalization of the samples to control for technical errors, such as variations in sample loading or transfer.

Traditionally, researchers have used housekeeping proteins (HKPs), such as GAPDH, β-actin, and tubulin, as loading controls, assuming their expression remains constant across conditions. However, a growing body of evidence suggests this is, in fact, not the case, and relying on HKPs as normalization controls can impact the reliability of your results.

In this blog, we discuss the problems that arise with the use of HKPs in different research contexts and how total protein normalization (TPN) could provide a solution.

Housekeeping Proteins vs. Total Protein Normalization

As experimental models grow more intricate, the likelihood of normalization artifacts also rises. Researchers in areas like cancer biology, neuroscience, immunology, and aging often study pathways that directly influence cellular metabolism, structure, or stress responses, many of which regulate key housekeeping proteins.

Since a growing body of evidence points to the fluctuating nature of HKP expression in various conditions and cell types, the scientific community has been shifting towards another method of normalization—TPN.

In TPN, the entire protein content in each lane is measured, and the signal intensity for your target protein is compared to the total protein content of the sample. This way, the data produced are less susceptible to shifts in the expression of individual proteins, leading to more reproducible results and robust conclusions.

How Housekeeping Protein Variability Affects Different Research Areas

To put things into perspective, let’s dive a bit deeper into the specific risks associated with the use of HKPs in various research contexts.

Neuroscience

It is now known that the expression of common HKPs can vary significantly across the different brain regions, cell types, and disease models. For example, studies have shown that GAPDH levels are significantly increased in patients with Alzheimer’s disease, correlating with disease severity, and that β-actin increases by more than 2-fold in spinal cord injury (Itakura et al. 2015, Liu and Xu 2006). Therefore, using HKPs for normalization can lead to inaccurate results and false conclusions.

Using TPN to analyze total protein content helps to overcome this by accounting for the heterogeneity of brain tissue. 

Cancer Biology

Because cancer cells undergo extensive metabolic rewiring, shifts in protein synthesis, and structural changes affecting cytoskeletal proteins (such as β-actin and β-tubulin), it is well established that HKP expression is not stable under these conditions (Hu et al. 2016).

When employing HKP analysis, observed changes in protein expression could result from variations in the normalization control rather than genuine biological differences. TPN minimizes the risk of normalization artifacts, providing a more robust foundation for data analysis.

Stem Cell and Developmental Biology

HKPs are generally associated with functions such as maintaining cellular structure (β-actin and β-tubulin) or supporting cellular metabolism and energy production (GAPDH), processes that are commonly altered during cell differentiation or maturation.

One study showed that during neuronal differentiation, GAPDH and β-actin maintained stable expression during the early stages; however, at the later stages of differentiation, when morphological and biochemical changes were more dramatic, their expression was significantly altered (Zhou et al. 2010).

In contrast, TPN provides more stable comparisons across developmental stages.

Immunology and Inflammation

During an immune response, as energy needs increase, activated immune cells, such as T cells, transition from baseline energy production via oxidative phosphorylation to glycolysis (Shi et al. 2025). This can cause shifts in the expression of metabolic enzymes, like GAPDH.

What’s more, immune activation is often associated with cell migration, for example, leukocytes migrating to the site of infection. Migration causes structural reorganization of the cell, changing the levels of proteins such as β-actin and β-tubulin.

Using these HKPs as controls can misinterpret results in studies involving cytokine stimulation, infection, or autoimmunity models, whereas TPN enables consistency throughout immune cell activation.

Metabolic Disease Research

Not only does immune activation lead to changes in energy metabolism, but this is also regulated by factors such as insulin, glucose, and hypoxia. For example, in liver and fat cells, insulin has been shown to increase GAPDH mRNA levels to up to 10-fold (Beisswenger et al. 2003).

Metabolic diseases, such as diabetes and obesity, are often associated with altered levels of these factors, meaning that your loading control could vary more than your target protein. TPN allows normalization independent of metabolic state.

Organoids and 3D Culture Models

Organoids and 3D cell culture models are beneficial in research as they typically mimic tissue architecture more accurately than conventional monolayer cultures. However, cell-type heterogeneity in the culture can complicate HKP use, as each cell type expresses the proteins differently. This could lead to pseudo-loading consistency across samples.

On the other hand, TPN provides a more accurate representation of input even across complex samples.

Aging and Senescence

Aging often brings about dramatic metabolic and structural changes to a cell, and as a result, the commonly used housekeeping proteins can fluctuate significantly during senescence and age-related biological processes (McLoughlin et al. 2019).

This can result in normalization errors in studies investigating longevity, age-related diseases, or the efficacy of senolytics—a class of drugs that selectively target senescent cells in the body.

TPN helps ensure that observed differences represent true biological changes rather than age-related variations in housekeeping protein levels.

Hypoxia and Oxidative Stress

Hypoxia and the subsequent oxidative stress that it induces have been shown to significantly impact the expression of housekeeping proteins such as GAPDH (Gyongyosi et al. 2018).

Consequently, proteins traditionally used for normalization may be affected by the very treatments being studied. Total protein normalization avoids this issue by reducing dependence on stress-sensitive loading controls.

Making the Switch to Total Protein Normalization

While conventional methods of normalization using HKPs have been utilized extensively in research, recent research indicates that their expression is inconsistent and can vary greatly across experimental conditions in various areas of science, leading to inaccurate data and incorrect conclusions being drawn.

On the other hand, TPN via Stain-Free imaging or total protein stains provides a practical alternative that can improve accuracy, reproducibility, and confidence in your western blot data.

So, now may be the time to revisit your western blot normalization approach and make the switch to TPN for data you can trust.