Quantitative Western Blots
Is Your Question Quantitative or Qualitative?
It all depends on what you want to do. Is your question qualitative or quantitative? You can use a qualitative Western blot to identify the presence or absence of a protein of interest. A quantitative Western is used to detect specific proteins and measure relative changes between different conditions.
Just remember – it’s all relative. Quantitative, qualitative, or semi-quantitative: there are no absolute measurements. The key is to maximize accuracy and precision to make relative comparisons as meaningful as possible. How? By reducing variability, whenever possible.
Qualitative: Yes/NoQuantitative: How Much?
“Although originally a qualitative or at best a semi-quantitative method, with the rise of computational systems biology, Western blotting has become increasingly important for fully quantitative applications.”
A Degasperi et al.1
What is a Quantitative Western Blot?
A quantitative Western blot makes relative comparisons between different treatments possible. The goal of a quantitative Western is to accurately measure changes in protein expression.
Western Blot Uses
- Protein-protein interactions
- Signaling pathways
- Cell surface proteins
- RNAi analysis
See some published examples of quantitative Western blotsView publications
“The development of the immunoblot to detect and characterize a protein with an antisera, even in a crude mixture, was a breakthrough with wide-ranging and unpredictable applications across physiology and medicine.”
AA McDonough et al.2
Why Do We Need Quantitative Western Blots?
Life-altering medicine. Increased crop yields. All of us want to make a difference with our life’s work. Quantitative Westerns can be a powerful tool to advance discovery and make the world a better place.
“With numbers comes great responsibility."
Presenting the Best Blots
Scientific publishers, funding agencies, and biotechnology and pharmaceutical companies are interested in quantitative Western blots. Journal standards for publishing Western blots have become more rigorous in recent years.5, 8, 11, 22, 26 It’s important to understand the theory and technique of Westerns to get accurate protein quantification. With more credible data, we can build a solid foundation for the future.
“Another concern is the current inability of any laboratory to reproduce published western blots since the details published in journals about western blotting are typically minimal.”
R Ghosh et al.4
Quantitative Western Blot Requirements
- Signals are proportional to the amount of protein loaded.
- Proteins of interest (or “targets”) are quantified within the linear range of detection.
- Internal loading controls correct for unavoidable variability from sample preparation, loading, and transfer.
Along with these requirements, experimental variability should be kept low so that replication is achievable.
For a Western to be quantitative, you must validate several conditions. A difference in band intensity can be caused by experimental variability (like using a different antibody or blocker), or by real, honest-to-goodness biological changes. It’s important to determine the sources of variability in your Western blotting procedure, and how best to control for them, before you even load the gel.
“…it is key to convincingly validate the integrity of the sample, the specificity of the antibody, and the linearity of the detection system and to assess sample loading.”
AA McDonough et al.2
“Positive and negative controls, as well as molecular size markers, should be included on each gel and blot—either in the main figure or an expanded data supplementary figure.”
What is Variability?
Your conclusions will always be based on (and tempered by) the amount of variability in your experiment. You might define or measure variability with CV (coefficient of variation), σ (standard deviation), r2 (coefficient of determination), or something else entirely. Reducing variability is the key to maximizing precision.
Determining the significance of experimental results is a common concern for quantitative Westerns. One way is to define the percent change of band signal between treated and untreated samples. The experimental change needs to be greater than your measure of variability, like CV. So, to be more confident of small changes, decrease the CV in your experiment. Easier said than done, right? To get accurate answers consistently, remove sources of error wherever possible.
Reduce variabilityto improve reproducibility
“The premise of immunoblotting is simple, but execution is tricky, and there are many variations in the method that can affect the outcome. Add quantitation to the end of an immunoblot and the complexity of implementations increases even further.”
Questions to Consider
Think about a typical Western blot. Maybe you’re about to attempt your first one. Maybe you’ve been doing Westerns for years. Regardless of your level of expertise, there are multiple questions to answer before you begin.
- Is your detection chemistry based on enzymatic or fluorescent detection?
- What is the dynamic range of your detection system?
- What normalization strategy works best for your experimental context and matches the linear range of your target?
- How will the way you compare and analyze quantified bands affect your results?
- How many technical and biological replicates do you need?
The following sections, broken into five themes related to the above five questions, will help you gain a better understanding of quantitative Western blots and ensure you know how to produce the very best Western blot data.
Using Chemistry to Get Proportional Signals
Why Confirm that Signals Are Proportional?
Because expensive antibodies are wasted every day.
Seem dramatic? Well, “Western blot” and “quick ‘n’ easy protein assay” aren’t exactly synonymous.
The accuracy, integrity, and reproducibility of your blot depends on the details of detection chemistry that you choose. For accurate quantitation, signals need to be proportional to the amount of protein loaded. That means if you double the amount of target protein, the signal should be twice as large as well.
Don’t just assume that your signals are proportional – you must prove it to your boss, reviewers, and colleagues with solid evidence. One thing that affects signal proportionality is your choice of detection chemistry. The two most common Western blotting methods are chemiluminescence detection and fluorescence detection.
“The analysis revealed how seemingly minor variations affect immunoblot linearity and reproducibility, yielding pseudoquantiative numbers that are not directly proportional to the input material.”
Enhanced chemiluminescence (ECL) is an indirect detection method that uses HRP-labeled secondary antibodies. Horseradish peroxidase (HRP) is an enzyme that reacts with substrate to generate photons of light. Light is the signal that the film or digital imager detects.
Be aware of enzyme and substrate dynamics and timing issues. With ECL, the answer you get depends on when you ask. Timing matters.
Because the kinetics of the enzymatic reaction affect signal intensity, signals don’t always accurately reflect target abundance. The proportionality between different target signals is constantly changing.
“Chemiluminescence detection is highly sensitive… However, the enzymatic reaction is dynamic and changes over time making it necessary to optimize reaction times and imaging.”
ST Mathews et al.6
Fluorescence detection is the easiest way to get proportional signals. Direct detection is performed using secondary antibodies labeled with IRDye near-infrared (NIR) fluorescent dyes. These NIR fluorescent signals are stable for months – no enzymes or substrates are used.
The intensity of fluorescent signals is unaffected by exposure times, making fluorescence more consistent and reliable for quantitative results. The long-lasting signals from fluorescent dyes remove one variable from the experimental equation.
With ECL, timing mattersFluorescence is stable for months
“The signal to sample ratio is linear over a broad range of protein concentrations with infrared dye-labeled secondary antibodies.”
D Bond et al.7
Accuracy in Western blot detection chemistry depends on many things, including proper antibody validation. So don’t waste valuable antibodies. Confirm that your antibody recognizes the correct protein target before you begin your experiment. To verify antibody specificity and to identify possible interference from background bands, perform single-color control blots.
“…a detailed characterization that demonstrates not only the specificity of the antibody, but also the range of reactivity of the reagent in the assay, should be published.”
Imaging in the Linear Range
Why Detect in the Linear Range?
Outside the linear range of detection, the relationship between the amount of protein on the blot and the measured signal is unknown. Your relative measurement of protein amount is then not representative of a significant, quantitative value. So what exactly is the linear range, and how is different from dynamic range?
Linear range: the span of signal intensities that display a linear relationship between amount of protein on the membrane and signal intensity recorded by the detector.
Dynamic range: the range of band intensities the detection system can measure in a single capture.
High sensitivity without saturation is the key to a wide linear dynamic range.
Targets shouldn’t be quantified or compared outside the linear range of detection. A wide dynamic range makes it easier to produce Western blot data within the linear range. An ideal detection system picks up faint signals without saturating strong signals.
Wide dynamic range:High sensitivity without saturation
“…authors should clearly explain how quantitative data were obtained, whether signal intensity has a linear relationship with antigen loading, and how protein loading was normalized among lanes.”
The Journal of Biological Chemistry8
With saturation, band intensity may appear different, but relative signal intensity plateaus. Strong signals can exceed the capacity of some detection systems to show differences in band intensity, creating saturated bands. The ideal detection system will be able to accurately capture both strong and faint bands in a single image. Here we give some common causes of saturation, and how to avoid them.
“Saturated immunoblots do not overestimate a change in protein; instead, they can substantially underestimate it.”
- What: Sample proteins have exceeded the maximum binding capacity in an area of the membrane.
- Why: Although more sample protein is present, the membrane literally can’t hold any more and the excess protein is washed away.
- Result: Membrane saturation results in underestimation of strong bands and can compromise data accuracy.
- How to Avoid: Determine protein concentration with a colorimetric assay, to estimate proper loading amounts. Then Western blot a serial dilution to determine the best loading amount.
“Under certain conditions, loading large amounts of protein samples may actually decrease the relative amount of low abundance proteins that bind to nitrocellulose and polyvinylidene difluoride membranes due to saturation of the membrane by highly expressed proteins.”
R Ghosh et al.4
- What: Film can no longer detect additional light (signal).
- Why: Film records signal with silver grains that are activated in response to light. When the light (signal) has already activated the finite number of silver grains in a region of film, saturation occurs.
- Result: Photons given off from the reaction that do not activate silver grains will not be recorded as signal. Even before saturation, the amount of signal detected by film plateaus – underestimating strong signals.
- How to Avoid: Get a better idea of where saturation begins for each sample. Run a serial dilution or make a calibration curve. Film will always have a limited capacity to record signals due to its narrow dynamic range of 4-10-fold, or less than 1.5 logs.
“Using film to perform quantitative immunoblotting was avoided entirely, because the dynamic range of film is so small that quantitative analysis is virtually impossible.”
- What: Signal intensity is too bright for the detector to accurately record.
- Why: Some imagers have a limited dynamic range, due to the way data are captured and stored. For example, a typical CCD camera system is limited to 3.5 logs of dynamic range.
- Result: Some data are lost, resulting in inaccurate, imprecise quantitation.
- How to Avoid: Detect highly abundant targets with a detection system that has a wider linear dynamic range. For example, Odyssey® imaging systems have a linear dynamic range of 6 logs – currently unmatched by any other imaging system on the market. This wide dynamic range results in elimination of detector saturation for Western blotting experiments.
“When chemiluminescence is used cavalierly, there is a danger of wildly exaggerated claims.”
To detect strong signals accurately, the dynamic range of your detection system should exceed both the chemistry and biology of your samples. With a wide dynamic and linear range, your detection system is no longer a limiting factor for generating quantitative data. Odyssey® imaging systems exceed a sample’s ability to saturate the detectors.
“The saturation point of signal is of critical importance when measuring subtle differences in expression levels and can lead to inaccurate measurements.”
SL Eaton et al.9
Sensitivity can be defined as the lower limit of detection. Choosing a detection system with increased sensitivity extends the lower limit of linear dynamic range. Reducing background with an appropriate blocker also increases sensitivity. For example, a blocking buffer optimization kit can help you identify the best blocking choice for your targets.
The weakest bands on any Western blot may be outside the linear range, so be cautious about comparing or quantitating very weak bands. Depending on your detection system, a good rule of thumb to follow is two standard deviations above the limit of detection. That baseline should provide enough separation over and above any noise resulting from the blot background.
Reduced backgroundcauses higher sensitivity
“Infrared imaging is equally sensitive to chemiluminescence and more sensitive to visible fluorescence in part to reduced autofluorescence in the longer infrared wavelength.”
ST Mathews et al.6
Since chemiluminescence relies on enzymatic amplification of signal, it can be considered a sensitive detection method. However, chemiluminescence on film is disproportionately insensitive to signal at low intensities of light, because more than one photon of light is required to activate a single silver grain in the film emulsion. This means that film underestimates faint or weak signals, preventing accurate protein quantification.
“…chemiluminescent exposures consistently yielded stronger band densities. However, the linear dynamic range was very limited, and signals often decreased at high protein inputs.”
Visible fluorescence is limited by high background from plastics, membranes, and biological materials. Cellular proteins autofluoresce strongly in visible fluorescence channels (blue, green, and red). Both nitrocellulose and PVDF membranes also strongly autofluoresce in the 532 nm and 635 nm regions. As a result, sensitivity with visible fluorescence detection is severely limiting.
With near-infrared (NIR) fluorescence detection, membrane and protein autofluorescence is negligible. This means high sensitivity in both the 700 nm and 800 nm channels of near-infrared fluorescence detection. By using these wavelengths, fluorescent Western blotting sensitivity can match, or even exceed, ECL sensitivity.
Some instruments use precise laser excitation along with specialized optics to further enhance sensitivity. Lasers eliminate artificial background, by decreasing the light leakage caused by non-specific, diffuse white light. Using NIR lasers results in higher signal-to-noise ratios and better image quality.
“Furthermore, membranes and biomolecules show little autofluorescence in the longer NIR wavelength region, dramatically lowering background and significantly increasing the associated signal-to-noise ratio.”
CJ Bakkenist et al.10
How to Determine the Linear Range of Detection
- Prepare and load a gel with two-fold serial dilutions of sample
- Perform Western blot detection and quantify signal from your targets
- Plot the target signals to determine the range of sample loading where signals accurately reflect protein abundance
“…it is our experience and opinion that each immunoblot should contain a demonstration of the linearity of the detection system.”
AA McDonough et al.2
The linear range for each target will depend on natural abundance, how you prepare your samples, the primary antibody affinity for your target, and a whole host of other factors. For accurate quantitation, your detection method must have sufficient linear range to capture the brightest and faintest signals from your blot. Ideally, you should be able to get high sensitivity for faint signals without saturating strong signals. Choose a detection method that works best for your research and your experimental conditions.
Normalizing to Correct for Technique Variability
Normalization uses an internal loading control to correct for unavoidable sample-to-sample and lane-to-lane variations.
Without Western blot normalization, you can’t know if changes in band intensity reflect biological change in your samples or variability in sample preparation, loading, and transfer. Normalization provides a baseline to compare changes in protein expression.
Normalization is necessaryfor comparing differences
“Oddly, the most pervasive challenges to published data we see at Cell relate to loading controls. There seems to be some misalignment among scientists regarding the importance and meaning of the actin bands in a standard western blot.”
Variability in Western Blotting Technique
Normalization corrects for some of the technique variability inherent to any Western blotting experiment, including:
- Unequal protein sample concentration. Variation can be minimized by using a protein concentration assay to make sample loading as even as possible.
- Inconsistent loading across the gel. Sample viscosity and pipetting inconsistency can introduce variation from lane to lane.
- Transfer variation. Temperature, membrane binding capacity, membrane position in the transfer tank, and edge effects can all contribute to transfer variability, even when appropriate levels of protein are loaded. You may need to optimize transfer buffer conditions, voltage, transfer time, or other factors.
A variety of normalization methods are available to correct for inherent Western blot variability. Choose and validate your internal loading control based on the context of your experiment.
“…the loading control (the POI/LC ratio) is able to normalize for “loading errors” due to human error, incomplete transfer, or position effect on the blot.”
GM Aldridge et al.12
Internal Loading Control Requirements
The core principle of accurate normalization is that the target and internal loading control signals must vary to the same degree with sample loading.12
Internal loading controls are endogenous proteins used to indicate sample concentration. Loading controls must be unaffected by experimental conditions.
Your normalization strategy should meet these requirements:
- Is unaffected by experimental conditions
- Is detected within the same linear range as the target
- Does not interfere with target detection
Choosing and validating an internal loading control that meets these requirements is fundamental to the design of an accurate Western blot.
“A difference between two samples could be the result of an actual difference in the [target], or a difference in the abundance of the LC.”
GM Aldridge et al.12
Normalize your target to a single, unrelated endogenous protein present in all samples. With this strategy, normalization is dependent on the accurate detection of a single protein. Be sure to validate that your housekeeping protein (HKP) is unaffected by experimental conditions or treatments in all of your samples, and that you’re detecting the housekeeping protein in the linear range of detection.
“House-keeping” proteins should not be used for normalization without evidence that experimental manipulations do not affect their expression.”
The Journal of Biological Chemistry8
- Experimental manipulation (such as temperature, cell confluence, or drug treatments)
- Tissue type
- Cell line
- Age of culture
- Disease or injury state
“A recent mass spectrometry-based approach found that the protein expression levels of HKPs such as GAPDH vary substantially between tissues.”
R Ghosh et al.4
Saturation of Strong Signals
Since many housekeeping proteins are highly expressed, they are at risk for saturation. Ensure that your housekeeping protein and your target have a common linear dynamic range. If your target expresses low signal intensities, it may be very challenging to detect both the housekeeping protein and target accurately. It helps if your detection system has a wide linear dynamic range.
“…in the typical loading range of 10-50 µg of cell lysate, quantitation of housekeeping proteins by immunodetection is not possible because they are in saturating quantities.”
AA McDonough et al.2
Co-migration of Proteins
Proteins with a similar molecular weight will migrate together on a blot. For ECL detection, this is a problem: each protein-antibody complex generates an identical signal (light). Co-migrating proteins can’t be distinguished from each other with ECL detection.
To detect different targets with the same molecular weight, use spectrally distinct fluorescent dyes attached to highly cross-adsorbed secondary antibodies. The signals can be detected simultaneously (often called “multiplexing”), then quantified separately.
Validating Housekeeping Proteins
If you choose to use a housekeeping protein as an internal loading control, validate its stable expression for your experimental conditions, including each tissue, treatment, and cell density. See the table below for some published examples of variability. If you’re using chemiluminescent detection, choose a housekeeping protein with a different molecular weight than your target.
Comparison of Housekeeping Loading Controls
This following table has a few examples of some common housekeeping proteins, their molecular weights, and a few considerations. This list is not complete, and any housekeeping protein you choose should be fully validated for your system under study.
“Although we cannot prove that high-abundance loading controls are inaccurate under all possible conditions, we conclude that the burden of proof should lie with the researcher to demonstrate that their loading control is reflective of quantitative differences in protein concentration.”
GM Aldridge et al.12
|Housekeeping Protein||Molecular Weight||Note|
|COX IV||17 kDa||Excellent choice for low-abundance target proteins.|
|Histone H3||17 kDa||Not suitable for experiments where the nuclear envelope has been removed.|
|PCNA||36 kDa||PCNA is quickly degraded when DNA damage pathways are activated.13|
Not suitable for studies comparing normal and diseased placentas.14
Not suitable for differential expression studies on adipose tissue.15
May not be suitable for cross-tissue comparisons.16
Not suitable for rat retinal development.17May not be suitable for cross-tissue comparisons.18
|β-Tubulin||52-55 kDa||Not suitable for comparing leukocytes from different age groups.19|
|Lamin B1||68 kDa||Not suitable for experiments where the nuclear envelope has been removed.|
|Vinculin||124 kDa||Can be used as a loading control for high molecular weight proteins.|
The target protein is used as its own internal loading control. Normalize a specific modification of your target against all target protein regardless of modification.
A single antibody detects an unmodified epitope on the target protein, accounting for the total amount of target protein present (sometimes referred to as a pan-specific antibody). A modification-specific antibody specific to that modification’s epitope detects just the modified form of the target protein. Modified signal is then normalized to the total level of target protein.
“Signals obtained using antibodies specific for phosphorylated epitopes should be normalized to the total protein level of the target protein.”
The Journal of Biological Chemistry8
“Fluorescent tagged secondaries are also useful to analyze protein phosphorylation because, if they are generated in two different species, one color can be used to detect the total protein and another for the phosphorylated form.”
AA McDonough et al.2
Total Protein Stain
Normalize your target to the total amount of protein sample per lane. With a total protein stain, you can monitor protein transfer across the entire blot at all molecular weights. This will allow you to determine if there are any irregularities that indicate you should run the blot again to get more robust results.
REVERT™ Total Protein Stain is a membrane stain that fluoresces at 700 nm, and can be detected with a near-infrared fluorescence imaging system. With REVERT, you can detect targets in the 800 nm channel on the same blot, in the same lane, and at the same time.
“The stained gel approach is a far more valuable loading control than that obtained by quantitating saturated signals from housekeeping proteins. The latter reveals little more than whether the sample was loaded or not.”
AA McDonough et al.2
Signal from REVERT staining is proportional to sample loading. It stains total protein in individual lanes, with a linear detection range of 1-60 µg when used with cell lysate. However, individual proteins are easily detectable at low nanogram amounts.
Unlike Stain-Free™ technology or the Amersham WB System (CyDye labeling), REVERT Total Protein staining does not covalently modify sample proteins. Irreversible chemical modification (like with covalent labeling) can interfere with antibody binding and detection of your targets.
Unlike other total protein methods,REVERT won't covalently modify samples
“Normalization of signal intensity to total protein loading (assessed by staining membranes using Coomassie blue, Ponceau S or other protein stains) is preferred.”
The Journal of Biological Chemistry8
This method uses a commercial pre-cast gel that contains trihalo compounds. UV light causes these compounds to irreversibly cross-link with tryptophan residues present in your sample. With UV light, you can then visualize the sample proteins either on the gel or on the membrane.
One limitation of this method includes chemical modification of your sample that may affect target detection and accurate normalization. If your target has many tryptophan residues, Stain-Free may block antibody to target binding, making accurate quantitation problematic.
In addition, UV imaging of the transferred membrane is much less sensitive than gel imaging, due to high autofluorescence of the membrane. Finally, many proteins do not contain tryptophan residues, and therefore will not be modified or detected. Stain-Free is more sensitive compared to Ponceau visible staining, but is still not as sensitive as near-infrared stains due to membrane autofluorescence.
“However, quantitative analysis of the band intensity indicates that the blots from gels activated with the Criterion Stain Free system have a reduced chemiluminescent signal when a mAb specific for an epitope that contains a Trp residue is used. Mean reduction of intensities of 79% and 78% were seen for all dilutions of the 12H12 mAb and 3E5 mAb, respectively. This suggests that chemical modification of Trp induced by UV exposure during the activation step of the Criterion Stain Free imaging process interferes with recognition by antibodies when the epitope contains Trp.”
A Elbaggari et al.20
CyDye Labeling (Amersham WB System)
In this method, a fluorescent dye labels all the proteins in your sample. This amine-reactive fluorescent dye covalently binds to the lysine residues in your sample proteins.
Limitations of this method include:
- Possible interference with antibody binding during immunodetection
- Irreversible chemical modification of your proteins
- Variable sample labeling based on amino acid composition
“Furthermore, the order of stains and antibodies should be tested to ensure that prior steps do not affect subsequent binding.”
GM Aldridge et al.12
Replicate Gel Staining
Some researchers run replicate gels and stain one of the gels with a stain, like Coomassie. Staining a gel (rather than a blot) does not account for transfer variability, and is not a direct reflection of what is going on with the membrane containing proteins that will be quantified and analyzed. Replicate gel staining also requires a separate gel run and imaging.
Comparing Bands with Proper Analysis
Why Compare Bands on the Same Blot?
If you’re using a Western blot for a quantitative comparison of different treatments of the same sample(s), you need a way to reproducibly and appropriately look at your experimental outcomes in tandem. That means normalizing signals that are detected within the linear range and are proportional to protein expression. But it also means taking the definition of quantitative a bit further. It’s possible to get two quantitative Western blots and yet still compare them inaccurately. Proper analysis takes all factors into consideration and reduces variability wherever possible. It’s a bit tedious, but absolutely necessary.
“Data comparisons should only be made from comparative experiments, and individual data should not be utilized across multiple figures.”
So if you dare to compare, do so with care.
Proper analysis can solve many headaches. You may be wondering how to normalize samples of two different groups from different animals. Or maybe you’re trying to perform cross-blot comparison from one experiment. Protein quantification is a tricky thing – try to keep consistency in mind.
Compare with consistencyAnalyze bands on the same blot
“Most importantly, it is possible to compare two blots only if they present exactly the same conditions, using different lysates derived from cells cultured and treated in the same way.”
A Degasperi et al.1
Multiple Film Exposures
We advise against comparing multiple film exposures or combining data captured on different pieces of film. Multiple film exposures from the same blot are captured under different time points, meaning the raw data are ever changing. What’s more, film detects signals with a shocking lack of proportionality with both high and low intensity signals. (Seriously, the physics regarding film’s reciprocity failure are fascinating.)
Then, once you’ve got a bunch of pieces of film, you’ll need to get them digitized for protein quantification and eventual publication. No problem, you say. I’ll just pop it in my trusty office scanner. Sorry, but 1970 called, and it wants its densitometry back. Optical density (OD) values underestimate strong signals due to how quickly film saturates. This disappointing dynamic range of film makes densitometry an art rather than a science. Hint: your science should be a science.
“Film can make small differences in abundance appear as large differences in band intensity. When saturated, film exposures can also hide sample-to-sample variations in high-abundance proteins such as loading controls.”
Stripping and Reprobing
Stripping is not uniform across the entire blot, creating additional variability. Light stripping leaves antibodies behind and creates artifacts. Harsh stripping leads to a loss of sample proteins from the membrane. For these reasons, stripping and reprobing is not recommended for quantitative Westerns.
“Membrane stripping and reprobing is a quantitative trade-off between antibody removal and total protein loss.”
You can simultaneously detect two targets in one sample on the same blot, in the same lane with multiplexing. Multiplexing is dual-color near-infrared detection with two distinct dye-labeled secondary antibodies.
Fluorescent immunoblotting with multiple antibodies makes normalization with an internal control simpler, more convenient, and more accurate. The blot is incubated with primary antibodies raised in different hosts. Secondary antibodies labeled with spectrally-distinct fluorescent dyes are then used to simultaneously detect the internal control on the same blot and in the same lanes as the target protein.
- Stripping and reprobing is not required
- Detect co-migrating proteins together with multiple antibodies
- Transfer artifacts affect bands in the same way (unlike replicate blots)
- Identify antibody cross-reactivity easily
- Conserve precious samples
Comparing bands in the same lane with two colors is the most quantitative way to analyze Western blots.
“For proteins, two-color analysis on one blot gives faster, more precise measurements of protein expression, by eliminating the variability due to stripping or comparing separate blots.”
L Picariello et al.21
Comparing Blots that Aren’t Replicates
It’s best not to compare bands between different samples if they’re on different blots. If this is unavoidable, compare only across replicates that are produced in parallel. Avoid comparing blots that aren’t replicates.
For the best analysis, reduce variability wherever you can. That could mean using digital detection instead of film, or multiplexing instead of stripping and reprobing.
“…blots were standardized so that data from replicate blots could be combined.”
GM Aldridge et al.12
Replicating for Better Reproducibility
Even with consistent sample loading and accurate normalization, replication is still necessary. Replication helps you discern if a difference in relative quantitation is due to a real change in protein expression, or perhaps a small difference in Western blotting technique.
Types of Replicates
Both biological and technical replicates are necessary for accurate, reliable results. Technical replicates will help you identify inaccuracies caused by processing variation, while biological replicates will help confirm that biological changes are real and not an irreproducible fluke.
“Information including but not limited to the number of repetitions, cells, or samples analyzed must be provided in the relevant figure legend(s) and/or Materials and methods section.
The Journal of Cell Biology22
Technical replicate: repeated measurements of the same sample that represent independent measures of the random noise associated with protocols or equipment.23
Technical replicates address the reproducibility of the assay or technique, but not the reproducibility of the effect or event being studied. But technical replicates can be very important – they tell us if our measurements are scientifically robust or noisy, and how large the measured effect must be to stand out above that noise.
Biological replicate: parallel measurements of biologically distinct samples that capture random biological variation, which may itself be a subject of study or a source of noise.23
Context is critical, and appropriate replication may depend on how widely the results can be generalized. If it’s possible, demonstrate the effect you’re studying in multiple samples and cell types.
“Ideally, researchers at the bench should be able to identify whether a failure to reproduce published data in their laboratories is based on a valid difference in experimental findings or on the result of changes in production or distribution of research tools. To ensure reproducibility, the reporting of research reagents must be complete and unambiguous.”
M Uhlen et al.24
Replicating Objective and Subjective Decisions
Some choices researchers make are objective (based on data). Others are more subjective and stem more from personal preference. It’s important to be aware of decisions that other researchers may make differently.
For example, Western blotting may encompass a range of experience levels: from undergrads to PIs. A perfectly designed protocol doesn’t help your replication efforts if you don’t follow it.
“An investigation of quantitative western blotting using erythropoietin showed that the interoperator variability was the main error source, accounting for nearly 80% of the total variance.”
R Ghosh et al.4
Even with a validated and appropriate loading control, normalization can’t correct for all sources of error in Western blot technique. Make sure to check that your sample prep, loading, and transfer Western blotting procedures are as free of variability as possible.
Variability in your samples can arise from:
- Differences in cells due to pathology2
- How you handle samples2
- “Cellular debris” from inconsistent or improper fractionation4
Develop a consistent protocol for preparing samples. Then make sure you (and the rest of your lab) follow it as best as you can.
“The conditions of cell lysis have a profound impact on the proteins that are extracted and the condition in which they are preserved.”
Air bubbles, variable amounts of sample loading, and similar errors can happen without careful attention to proper pipetting technique. Inconsistent sample loading cannot be avoided entirely, which is why normalization is so important.
Use a BCA assay (also called “bicinchoninic acid assay” or “Smith assay”) or a Bradford assay to determine the concentration of a protein in solution. A concentration assay, along with a dilution series, will help you determine an appropriate amount of sample to load.
Use a concentration assayto load the right amount of sample
“In our experience, differences due to position on the gel and improper mixing of samples can be more significant than potential loading effects (as long as a BCA test is run first). Therefore it is most important that samples be positioned randomly on the gel, and gels run at least in duplicate (we prefer triplicate).”
GM Aldridge et al.12
Replicates are also important for spotting variation that may be caused by transfer. The transfer process introduces many possible sources of variation:
- Transfer stack position in the tank
- Loading position on the gel
- Inconsistent temperature across the membrane
- Inconsistent electric field across the membrane
- Improperly prepared transfer stack
“One problem that may be encountered is variations in transfer efficiency. Small proteins (<10 kDa) may not be retained by the membrane, large proteins (>140 kDa) may not be transferred to the membrane and varying gel concentrations may affect transfer efficiency.”
R Ghosh et al.4
Chemistry can be tricky to replicate. In an enzymatic reaction (like chemiluminescence), timing greatly affects signal intensity. Consider these sources of variability with ECL:
- Concentration of substrates and enzymes
- Supplier of substrates and secondary antibodies
- Age and storage conditions of reagents and membranes
- Availability of sample protein, substrate, and enzyme across the blot
“Appropriate blocking reagents must be selected to avoid high background and nonspecific binding. Under certain blocking conditions, as much as 60% of proteins have been shown to be lost from membranes.”
R Ghosh et al.4
Some antibodies perform differently depending on their application or assay. It’s important to optimize the amount of antibody to get signals proportional to your targets.
Using too much secondary antibody can cause non-specific binding. One form of non-specific binding is secondary antibody cross-reactivity, which is when the secondary antibody reacts directly with proteins in the sample, or the wrong primary antibody, instead of the intended primary antibody. To get the best results, check for non-specific binding. Ensure that the intended antibody specifically binds to the correct target.
“With limited research funding, identifying poor quality antibodies and poor Western blotting techniques will save money, save researchers time and improve the quality of the results.”
JE Gilda et al.25
Substrate availability affects signal intensity. The amount of substrate available to the HRP enzyme may be variable across the blot, which changes the rate of the enzymatic reaction.
Variable substrate availability in different areas of the blot causes inconsistent signal generation. Factors to watch out for include substrate pooling, bubbles, and non-uniform distribution of substrate, because each can affect signal intensity.
Local depletion is when a high antibody concentration causes higher enzymatic activity in localized areas. It can be avoided by ensuring substrate is in excess, as well as optimizing both primary and secondary antibody concentrations.
It’s best to apply substrate equally across the blot, following vendor recommendations for volume of substrate per cm2 of membrane. Ignoring supplier recommendations, or applying substrate inconsistently to certain areas to save money can result in substrate depletion. In the case of applying “just enough” substrate, the reaction can quickly be over. Once that happens, even substrate diffusion from an adjacent area may not be able to replenish substrate in that depleted area. The amount of substrate becomes the limiting factor on the reaction, instead of the amount of HRP enzyme (secondary antibody). If you’re skimping on substrate to save a buck, that can create additional issues by limiting the enzymatic reaction.
“Addition of too much secondary antibody enzyme conjugate and/or incubation of the secondary enzyme for prolonged periods are major causes of high background, short signal duration, signal variability and low sensitivity.”
R Ghosh et al.4
Timing matters. Keep these timing factors identical between replicates:
- How long you wash your blot
- Incubation time with antibodies and buffer
- Length of exposure to film or digital imager
There’s also a limited window for detection. When the HRP enzyme has consumed all the substrate, the signal (light) fades, and your opportunity for using that blot is over. Signal fades in minutes with ECL. The answer you get depends on when you ask. Because signals are unstable, your results are dependent on timing. ECL will give you different results when you measure at different times.
When using ECL for your detection chemistry, a runaway reaction creates a lack of proportionality. ECL signals may not be proportional to the amount of protein, due to the enzymatic amplification of signal.
Since ECL is an indirect method (the signal detected is light emitted from the HRP reacting with substrate), enzyme and substrate kinetics can really affect quantitation. The alternative is to use a direct method like fluorescence, because dyes labeling the secondary antibodies are the only signals detected.
Using IRDye fluorophores, signals are stable for months. Timing is no longer such a critical part of the experiment. All of the variables associated with enzyme kinetics or substrate availability are eliminated.
“Fluorescent secondary antibodies detected in the infrared spectrum produce a constant amount of light. The static nature of the intensity of light generated by infrared activation improves precision and the ability to differentiate differences in signal intensity produced by antibodies bound to proteins. This allows for a more accurate quantification of protein levels compared with enzyme-labeled secondary antibodies.”
ST Mathews et al.6
There are two types of imaging variability; the kinds of decisions the manufacturer makes (for example, the way the instrument software captures data and stores it), in addition to any decisions the researcher makes. For instance, some researchers may use the same instrument in completely different ways, due to differences in training, experience, or simply personal preference.
What kinds of decisions do instrument manufacturers consider? The instrument software is one area where vendors typically differ. Keep in mind that different imagers will get completely different data from the same blot, due to differences in how data are captured.
Automatic settingsremove human bias
Better Data Capture
For the best ECL data, what you don’t want is a variety of subpar image captures with completely variable raw data associated with each one. Then your decision is simply settling – which of these images is the least awful? Rather, a single image with one set of raw data, capturing the full breadth of bands from a wide linear dynamic range, will be the most useful for protein quantification and comparison.
The Autoscan feature of the Odyssey® CLx imager is designed for getting the right image the first time. The result is one image with both faint (low-intensity) and strong (high-intensity) bands visible and ready for quantitative analysis. That requires an imager that can get high sensitivity without saturation.
“Additionally, the available image analysis software has transformed the once frustrating western blotting of yester years to that which can be imaged accurately and repeatedly with a one-touch, preset or user-defined setting.”
ST Mathews et al.6
Imagers that lack this wide dynamic range use other techniques to artificially get passable results. For example, binning sacrifices image resolution to compensate for low sensitivity. Signals from surrounding areas are combined into one data point. Importantly, pixel values are reassigned. This gives you a fuzzy image and can affect accurate quantitation.
With proper optical design, image capture should be uniform across the entire blot. The entire field of view will ideally have a very low coefficient of variation. When instruments aren’t designed with the best performance in mind, blots may be altered post-capture to adjust for the non-uniform image with flat fielding. Flat fielding is a fix for subpar capture, rather than a real solution.
In any case, an instrument designed for the best image capture possible will require fewer decisions on the part of us mere mortals. The definition of being human is that we mess things up. So where proper instrument development and design has taken place, the only choices left will be those that are dependent on your specific research needs. For example, running a microwell plate instead of a blot on the same scanner. A powerful imager will capture great data the first time, without making you endlessly ponder and control for numerous variables.
As with image capture, Western blot analysis that reduces variability wherever possible is the goal. Journals often discourage altering image appearance too much, and outright ban doctoring images by removing artifacts or unwanted bands. Be wise when changing the brightness or contrast of your blots, and always avoid changing the raw data.
“Linear adjustment of contrast, brightness, or color must be applied to an entire image or plate equally. Nonlinear adjustments must be specified in the figure legend. Selective enhancement or alteration of one part of an image in not acceptable.”
With an imager, your results are already digital and immediately ready for analysis. With film, you need to digitize your results. Once you’ve got a decent film exposure or two, the data concerns don’t stop there – especially considering that the tools available to digitize data aren’t always designed with scientific rigor in mind.
Ever notice how a copy of a copy of a copy makes for a lousy office memo? That’s because office scanners aren’t built for high fidelity capture – they’re built for an office. A dedicated laboratory scanner is designed to capture data in a scientific setting. An office scanner is not, and the differences in design reflect that. For instance, a common office copier has limited dynamic range and uneven illumination of the scan area.
In addition, an automatic gain control can significantly affect the signal in one area due to differences in surrounding areas. A quantitative Western blotting procedure should measure all signals independently – regardless if the rest of the blot has a lot of noise or signal, or not.
“…additional image corrections that could alter the linearity, such as automatic gain control.”
A Degasperi et al.1
Densitometry measures the degree of darkness of the film image, as a function of light transmission through the developed film. For example, if a region transmits one-tenth of the incident light, its density is equal to 1. For Western blot analysis, the useful range of densities is approximately 0.2 - 2.0 (roughly 10-fold). This range represents the shades of grey that can be discriminated by densitometry. Higher optical densities (ODs) (above 2.0) appear black and indicate saturation.
The overall response of film is non-linear, but approaches linearity over narrow ranges. A standard curve can help confirm the linear range of film response for each experiment.
Western Analysis Software
Prepare a Western blot image for publication with analysis software:
- Subtract background (also called “noise”) across the blot
- Select bands and quantify their signal intensities
- Adjust image display (without changing the raw data)
- Export data for statistical analysis or graphical comparisons
To quantify Westerns accurately, you need to use a method of background subtraction. The best method for background correction depends on your image and its background consistency.
“…a larger disc size than necessary leads to only partial removal of the background while a smaller disc size than needed will result in the deletion of the actual target protein signal.”
R Ghosh et al.4
The Rolling ball method from ImageJ software subtracts background from an entire image, which will modify the underlying image data. It also requires you to select a radius for your Rolling ball correction. The correct radius depends on image resolution, shape width, shape separation, and shape overlap – which can all vary throughout an image. Selecting a radius that allows for consistent, accurate background subtraction can be difficult.
Image Studio software changes only how the image is displayed, but never the raw data. This software is designed for Western blot analysis. While you can adjust brightness and contrast to your heart’s content, the raw data remain the same.
Empiria Studio™ software changes only how the image is displayed, but never the raw data. This software is designed for Western blot analysis.
“…programs containing rolling ball algorithms that are applied to the entire image for immunoblots such as found in ImageJ should be avoided.”
R Ghosh et al.4
You can reduce variability in many ways. Here are some Western blot tips for more accurate and reproducible data.
- Prepare samples consistently, paying special attention to sample handling and fractionation
- Perform a concentration assay to determine proper loading amounts
- Hold transfer conditions constant, as much as possible
- Optimize antibody dilutions for the best signals
- Keep incubation, wash, and exposure times identical between replicates
- Capture one image that shows both faint and strong signals without saturation (an Autoscan feature may be helpful)
- Avoid post-capture image manipulations like binning and flat fielding
- Stay away from analysis tweaks that affect your raw data, like adjusting the contrast to make unwanted bands disappear or removing artifacts
- Use imagers designed for digitizing scientific data, rather than general purpose office scanners
- Analyze data with suitable Western blot background subtraction software
Producing Better Quantitative Westerns
We aren’t alone in encouraging more rigorous Western blots. Many journal editors and funding agencies have voiced their support for producing better Western blots that are more quantitative and more reproducible. Part of their reasoning is to enable better reproducibility for exciting new breakthroughs. Another part is how research needs to be considered credible by both peer researchers and the public to foster support and funding. When the average Joe trusts that scientists are diligently following the scientific method, odds are better that his government will agree and dedicate more of its budget to scientific research.
“Although the quantitative use of Western blotting is now widespread, published articles often lack the details of how Western blot results were quantified and how biological replicates were compared to obtain statistics.”
A Degasperi et al.1
Reproducible Data Save Time and Money
Scientific research is not for the sake of knowledge alone. We invest in scientific discovery, in part because we believe that one day something better will come of it. This is why the pharmaceutical and biotechnology industries rely so heavily on peer-reviewed published literature to find new ideas that might have wider application.
If you care enough to have your research built upon in the future, it is on you to make that research robust. If Western blots are a part of your research, being diligent about every experimental detail is imperative for other labs, clinics, and pharmacists who want to stand on your shoulders and take your discovery one step further. Improving research reproducibility will save time and resources spent on validating irreproducible data.
“Just because we can put numbers on an image does not imply that we should – a quantified biomolecule should relate directly to the true quantity of that biomolecule if it is to be meaningful.”
Constant Instead of Variable
To get the very best Western blots, it all comes down to reducing variability. An imaging system and detection chemistry that matches these characteristics are ideal:
- Stable and proportional signals, like those produced by fluorescence detection
- High sensitivity without saturation from a wide linear dynamic range (at least 4 logs)
- Able to adapt to different normalization strategies
- Capable of detecting multiple targets in the same lane simultaneously through multiplexing
- Accurate and precise data capture, for easier replication
- Compatible with software designed for Western blot analysis
“Likewise, the production of light generated from fluorophores detected in the infrared spectrum not only improves quantification and accuracy, but facilitates normalization and comparative analysis months to years later without a loss of signal if membrane imaging is desired for future analyses. Therefore, infrared imaging has the potential to dramatically improve the efficiency of western blotting methods.”
ST Mathews et al.6
Get the Best Blots
Are your blots constant – or a constant source of worry? The best way to increase accuracy and reproducibility in your Western blots is to remove some variables from the equation. Each choice is a chance to reduce variability.
It may be easiest to start with how you compare blots and prepare them for publication. Choosing software designed specifically for analyzing Western blots makes your analysis reliable and effective.
Next, look at whether your signals are within the linear range of detection. This is affected by how you image your blots. Digital imaging is faster and much more consistent than exposing blots to film.
Then move to your chemistry. No matter what chemistry you use, make sure to validate antibodies before running an experimental Western blot. While ECL detection will work with some optimization, your signals will be more proportional and reproducible with stable near-infrared fluorescence detection.
Finally, look at reducing sources of error in technique by choosing an appropriate loading control. REVERT total protein stain is a good normalization strategy, because it has a wide linear range, high sensitivity, and the ability to correct for transfer errors without covalent modification of your target proteins.
What steps can you take today to improve your Western blot results?
LI-COR provides products, protocols, and support for Western blotting and a range of other protein assays. Let us know how we can help you.
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- Degasperi A, Birtwistle MR, Volinsky N, Rauch J, Kolch W, Kholodenko BN (2014) Evaluating Strategies to Normalise Biological Replicates of Western Blot Data. PLoS ONE 9(1): e87293. doi:10.1371/journal.pone.0087293
- McDonough AA, Veiras LC, Minas JN, Ralph DL. (2015) Considerations when quantitating protein abundance by immunoblot. Am J Physiol Cell Physiol. 308:C426-33.
- Janes KA. (2015) An analysis of critical factors for quantitative immunoblotting. Sci Signal. 8(371): rs2.
- Ghosh R, Gilda JE, Gomes AV. (2014) The necessity of and strategies for improving confidence in the accuracy of western blots. Expert review of proteomics. 11(5):549-560. doi:10.1586/14789450.2014.939635.
- Image Integrity. Nature. Macmillan Publishers Limited, 2016. Web. 3 March 2016.
- Mathews ST, Plaisance EP, Kim T. (2009) Imaging systems for westerns: chemiluminescence vs. infrared detection. Methods Mol Biol. 536:499-513. doi: 10.1007/978-1-59745-542-8_51.
- Bond D, Primrose DA, Foley E. (2008) Quantitative Evaluation of Signaling Events in Drosophila S2 Cells. Biol Proced Online. 10:20-28.
- Instructions for Authors. The Journal of Biological Chemistry. American Society for Biochemistry and Molecular Biology. Web. 3 March 2016.
- Eaton SL, Hurtado ML, Oldknow KJ, Graham LC, Marchant TW, Gillingwater TH, Farquharson C, Wishart TM. (2014) A Guide to Modern Quantitative Fluorescent Western Blotting with Troubleshooting Strategies. J Vis Exp. (93): e52099. doi:10.3791/52099.
- Bakkenist CJ, Czambel RK, Hershberger PA, Tawbi H, Beumer JH, Schmitz JC. (2015) A quasi-quantitative dual multiplexed immunoblot method to simultaneously analyze ATM and H2AX Phosphorylation in human peripheral blood mononuclear cells. Oncoscience. 2(5): 542–554.
- Cell Press Digital Image Guidelines. Cell. CellPress. Web. 3 Marc
- Aldridge GM, Podrebarac DM, Greenough WT, Weiler IJ. (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. doi:10.1016/j.jneumeth.2008.05.00.
- Cazzalini O, Sommatis S, Tillhon M, Dutto I, Bachi A, Rapp A, Nardo T, Scovassi AI, Necchi D, Cardoso MC, Stivala LA, Prosperi E. (2014) CBP and p300 acetylate PCNA to link its degradation with nucleotide excision repair synthesis. Nucleic Acids Res. 42(13): 8433-48.
- Lanoix D, St-Pierre J, Lacasse A-A, Viau M, Lafond J, Vaillancourt C. (2012) Stability of reference proteins in human placenta: General protein stains are the benchmark. Placenta 33: 151–156.
- Pérez-Pérez R, López JA, García-Santos E, Camafeita E, Gómez-Serrano M, Ortega-Delgado FJ, Ricart W, Fernández-Real JM, Peral B. (2012) Uncovering Suitable Reference Proteins for Expression Studies in Human Adipose Tissue with Relevance to Obesity. PLoS ONE 7(1): e30326.
- Barber RD, Harmer DW, Coleman RA, Clark BJ. (2005) GAPDH as a housekeeping gene: analysis of GAPDH mRNA expression in a panel of 72 human tissues. Physiol Genomics. 21(3): 389-95.
- Rocha-Martins M, Njaine B, Silveira MS. (2012) Avoiding Pitfalls of Internal Controls: Validation of Reference Genes for Analysis by qRT-PCR and Western Blot throughout Rat Retinal Development. PLoS ONE 7(8): e43028.
- Eaton SL, Roche SL, Llavero Hurtado M, Oldknow KJ, Farquharson C, Gillingwater TH, Wishart TM. (2013) Total Protein Analysis as a Reliable Loading Control for Quantitative Fluorescent Western Blotting. PLoS ONE 8(8): e72457.
- Yu HR, Kuo HC, Huang HC, Huang LT, Tain YL, Chen CC, Liang CD, Sheen JM, Lin IC, Wu CC, Ou CY, Yang KD. (2011) Glyceraldehyde-3-phosphate dehydrogenase is a reliable internal control in western blot analysis of leukocyte subpopulations from children. Anal Biochem. 413(1): 24–9.
- Elbaggari A, Choe J, McDonald K, Alburo A. (2008) Evaluation of the Criterion Stain Free™ Gel Imaging System for Use in Western Blotting Applications. Bio-Rad Laboratories, Inc. Tech Note 5781 Rev A.
- Picariello L, Carbonell Sala S, Martineti V, Gozzini A, Aragona P, Tognarini I, Paglierani M, Nesi G, Brandi ML, Tonelli F. (2006) A comparison of methods for the analysis of low abundance proteins in desmoid tumor cells. Anal Biochem. 354(2): 205-12.
- Instructions for Authors. The Journal of Cell Biology. The Rockefeller University Press. Web. 3 March 2016.
- Blainey P, Krzywinski M, and Altman N. (2014) Points of Significance: Replication. Nature Methods 11(9): 879-880. doi:10.1038/nmeth.3091
- Uhlen M, Bandrowski A, Carr S, Edwards A, Ellenberg J, Lundberg E, Rimm DL, Rodriguez H, Hiltke T, Snyder M, Yamamoto T. (2016) A proposal for validation of antibodies. Nature Methods 13: 823-827. doi: 10.1038/nmeth.3995
- Gilda JE, Ghosh R, Cheah JX, West TM, Bodine SC, Gomes AV (2015) Western Blotting Inaccuracies with Unverified Antibodies: Need for a Western Blotting Minimal Reporting Standard (WBMRS). PLoS ONE 10(8): e0135392. doi:10.1371/journal.pone.0135392
- Instructions for preparing an initial manuscript. Science. American Association for the Advancement of Science. Web. 3 March 2016.