Tracing the Footsteps of the Data Reproducibility Crisis

Have you found it challenging to replicate the results of your own or somebody else’s experiments? You are not alone. A member survey conducted by the American Society for Cell Biology (ASCB) revealed that out of 869 respondents, 72% had trouble reproducing the findings of at least one publication1. In a more comprehensive study by the Nature Publishing Group, over 60% and 70% of researchers surveyed in medicine and biology, respectively, reported failure in replicating other researchers’ results2. And out of the 1,576 scientists surveyed in various fields, 90% agreed that there is a reproducibility crisis in scientific literature2.

In case you are wondering, both surveys were conducted between 2014 and 2015, and there is a growing consensus about data reproducibility challenges. But how did we get here?

Beginnings of a Crisis

In 2011, scientists at Bayer HealthCare in Germany published an article in Nature Reviews Drug Discovery, reporting inconsistencies between published data and in-house target validation studies3. Out of the 67 target identification and validation projects they had analyzed for data reproducibility, 43 had shown inconsistencies and had resulted in the termination of projects3. Through this review, the Bayer researchers attempted to raise awareness about the challenges in reproducing published data and called for confirmatory studies prior to investing in downstream drug development projects3.

Close on the heels of Bayer’s report, researchers at Amgen described their attempts at replicating the results of published oncology studies, in a 2012 Nature commentary4. While reporting success at confirming the findings of only 6 out of the 53 landmark publications reviewed, the Amgen scientists outlined recommendations to improve replicability of pre-clinical studies4.

These publications spurred data reproducibility conversations within the biomedical research community, giving way to a wave of initiatives to analyze and address the problem.

Data Reproducibility Gaining Momentum

Reproducibility of research data depends, in part, on the specific materials used in the experiment. But how often are research reagents referenced in sufficient detail? A study found that 54% of resources reported in publications, including model organisms, antibodies, reagents, constructs, and cell lines, were not uniquely identifiable5. In order to promote proper reporting of research materials used, the NIH has recommended that journals expand or eliminate the limits on the length of methods sections17.

When you had challenges reproducing data in your lab, were you able to identify what caused them? In another publication, study design, biological reagents and reference materials, laboratory protocols, and data analysis and reporting were attributed as the four primary causes of experimental irreproducibility6. In effect, an estimated 50% of the U.S. preclinical research budget, or $28 billion a year, was reportedly being spent on data that is not reproducible6.

Based on feedback from researchers in academia, biopharmaceutical companies, journal editors, and funding agency personnel, the Global Biological Standards Institute (GBSI) developed a report highlighting the need for a standards framework in life sciences7.

Changes Instituted by Granting Agencies and Policy Makers

In the face of data reproducibility challenges, government agencies that fund research, including the National Institutes of Health (NIH) and National Science Foundation (NSF) developed action plans to improve the reproducibility of research8,9. The NIH also revised criteria for grant applications8. That means researchers will need to report details of experimental design, biological variables, and authenticate research materials when applying for grants8.

The Academy of Medical Sciences (UK), the German Research Foundation (DFG), and the InterAcademy Partnership for Health (IAP for Health) identified specific activities to improve reproducibility of published data10,11,12.

Recommendations on Use of Standards, Best Practices, and Reagent Validation

Among the organizations championing the development of standards and best practices to improve the reproducibility of biomedical research are:

  • Federation of American Societies for Experimental Biology (FASEB) with recommendations regarding the use of mouse models and antibodies13
  • American Statistical Association’s (ASA) report on statistical analysis best practices when publishing data14
  • Global Biological Standards Institute with recommendations regarding the additional standards in life science research; antibody validation and cell line authentication groups in partnership with life science vendors, academia, industry, and journal publishers15
  • Science Exchange’s efforts at validation of experimental results16

Changes to Publication Guidelines

Journal groups have been revising author instructions and publication policies to encourage scientists to publish data that is robust and replicable. That means important changes regarding reporting of study design, replicates, statistical analyses, reagent identification and validation, are coming your way.

  • The NIH and journal publishing groups including Nature, Science, Cell, Journal of Biological Chemistry, Journal of Cell Biology, and Public Library of Science (PLOS), among others, have developed and endorsed principles and guidelines for reporting preclinical research. These guidelines include statistical analysis, transparency in reporting, data and material sharing, refutations, screening for image-based data (e.g. Western blots) and unique identification of research resources (antibodies, cell lines, animals)17
  • The Center for Open Science (COS) developed Transparency and Openness Promotion (TOP) guidelines framework for journal publishers. Signatories include journal publication groups like AAAS, ASCB, Biomed Central, F1000, Frontiers, Nature, PLOS, Springer, and Wiley, among others18

Emphasis on Training

To train scientists in proper study design and data analysis, the NIH has developed training courses and modules19. A number of universities also offer courses in study design and statistics20.

In the face of revisions to grant applications and publication guidelines, use of standards, reagent validation, and need for consistent training in methods and technique, changes are coming your way. Is your lab prepared? Let us help you get there. See what has changed for publishing Western blot data and get your entire lab trained to generate consistent and reproducible Western blot data at Lambda U™.

References:

  1. How Can Scientists Enhance Rigor in Conducting Basic Research and Reporting Research Results? American Society for Cell Biology. Web. Accessed October 6, 2017.
  2. Baker M. 1,500 scientists lift the lid on reproducibility. Nature (News Feature) 533, 452-454.
  3. Prinz F, Schlange T, Asadullah K. 2011. Believe it or not: how much can we rely on published data on potential drug targets? Nat Rev Drug Discov 10, 712-713. doi:10.1038/nrd3439-c1
  4. Begley GC, Ellis LM. 2012. Drug development: Raise standards for preclinical cancer research. Nature 483, 531-533. doi:10.1038/483531a
  5. Vasilevsky NA, Brush MH, Paddock H, et al. On the reproducibility of science: unique identification of research resources in the biomedical literature. Abdullah J, ed. PeerJ. 2013;1:e148. doi:10.7717/peerj.148.
  6. Freedman LP, Cockburn IM, Simcoe TS (2015). The Economics of Reproducibility in Preclinical Research. PLOS Biology 13(6): e1002165.
  7. The Case for Standards in Life Science Research – Seizing Opportunities at a Time of Critical Need. The Global Biological Standards Institute. Web. Accessed November 16, 2017.
  8. Enhancing Reproducibility through Rigor and Transparency. National Institutes of Health. Web. Accessed October 6, 2017.
  9. A Framework for Ongoing and Future National Science Foundation Activities to Improve Reproducibility, Replicability, and Robustness in Funded Research. December 2014. National Science Foundation. Web. Accessed October 6, 2017.
  10. Reproducibility and Reliability of Biomedical Research. The Academy of Medical Sciences (UK). Web. Accessed October 6, 2017.
  11. DFG Statement on the Replicability of Research Results. The Deutsche Forschungsgemeinschaft (DFG – German Research Foundation). Web. Accessed October 6, 2017.
  12. A Call for Action to Improve the Reproducibility of Biomedical Research. The InterAcademy Partnership for Health. Accessed October 6, 2017.
  13. Enhancing Research Reproducibility: Recommendations from the Federation of American Societies for Experimental Biology. Federation of American Societies for Experimental Biology. Web. Accessed October 6, 2017.
  14. Recommendations to Funding Agencies for Supporting Reproducible Research. American Statistical Association. Web. Accessed October 6, 2017.
  15. Reproducibility2020. The Global Biological Standards Institute™. Web. Accessed October 6, 2017.
  16. Validation by the Science Exchange network. Science Exchange. Web. Accessed November 16, 2017.
  17. Principles and Guidelines for Reporting Preclinical Research. Rigor and Reproducibility. National Institutes of Health. Web. Accessed November 16, 2017.
  18. Transparency and Openness Promotion (TOP). Center for Open Science. Web. Accessed October 6, 2017.
  19. Training. Rigor and Reproducibility. National Institutes of Health. Web. Accessed November 16, 2017.
  20. Freedman LP, Venugopalan G and Wisman R. Reproducibility2020: Progress and priorities [version 1; referees: 2 approved]. F1000Research 2017, 6:604 (doi:10.12688/ f1000research.11334.1)

10 Tips for Reproducible Odyssey® Western Blots

When your results depend upon reproducible measurements of protein expression changes, minimize error and variation to maximize the accuracy of your data. Get the most out of your Odyssey Imaging System with these 10 tips for robust and replicable analysis of Western blots.

1. Use the Right Membrane

It is important that you consider a few factors before choosing the appropriate membrane for your experiment. Both PVDF and nitrocellulose membranes are available in two different pore sizes, 0.2 µm for proteins less than 20 kDa, and 0.45 µm for most Western blotting applications.

Consider other experimental conditions, as well, such as:

Condition PVDF Membranes Nitrocellulose
Membranes
Protein
Binding
Capacity
150-200 µg of protein/cm2 which
might result in increased background signal
80-100 µg of protein/cm2
Membrane
Integrity
Less fragile and a better choice for
experiments that require stripping and re-probing
of membrane
Fragile
Transfer
Buffer
Conditions
PVDF membranes must be pre-wetted with
methanol, but can be used with methanol-free
transfer buffer
Transfer buffer must contain methanol
Detection Low-fluorescence PVDF must be used
for near-infrared fluorescence detection to
avoid high background resulting from
autofluorescence of standard PVDF membranes.
It is recommended that you cut a small sample
of membrane and image it both wet and dry,
to check for autofluorescence and background.
All nitrocellulose membranes are
suitable for near-infrared detection


LI-COR has evaluated and compared different transfer membranes types, and overall, nitrocellulose membranes offer the lowest membrane autofluorescence.

Figure 1. Membrane autofluorescence from PVDF affects Western blot performance. Transferrin was detected by Western blotting, using various vendors and brands of PVDF membrane. Blots were imaged with the Odyssey Classic Infrared Imaging System in both the 700 and 800 nm channels.

2. Dry Membrane after Transfer

Once the transfer of proteins from the gel to membrane has been completed, it is recommended that you air-dry the membrane, before proceeding to the blocking step. By letting the membrane air-dry, you are essentially allowing the protein to get “fixed” in place. This helps ensure that proteins are not lost from the membrane during the subsequent processing steps like washes, blocking, and probing. It will also help retain low abundance proteins, giving you better sensitivity. Also, proteins at higher concentrations will not smear when the membrane is allowed to air-dry.

3. Optimize Blocking Conditions

The right blocking buffer can greatly enhance sensitivity of near-infrared Western blots by reducing background interference, promoting specific binding of primary antibody to target, and yielding high signal-to-noise ratios with minimal non-specific signals. However, there isn’t a universal blocking buffer suitable for all experimental conditions, so optimization is important.

Blocking reagents can influence antibody binding and specificity. For example, milk-based blockers can cause high background when using anti-goat antibodies, streptavidin-biotin based detection, or when probing phosphorylated target proteins.

Also consider the buffering system used in the experiment. Washing, blocking, and antibody dilutions must be performed using either Phosphate Buffered Saline (PBS) or Tris Buffered Saline (TBS) consistently throughout the protocol.

Additionally, exposure to detergent should be avoided until the blocking step is complete, as it may cause high membrane background.

Figure 2. Effect of various blocking agents on detection of pAkt and total Akt in Jurkat lysate after stimulation by calyculin A. Total and phosphorylated Akt were detected in calyculin A-stimulated (+) and non-stimulated (-) Jurkat lysate at 10 µg; 5 µg; and 2.5 µg/well. Blots were probed with pAkt Rabbit mAb (Santa Cruz P/N sc-135650) and Akt mAb (CST P/N 2967) and detected with IRDye® 800CW Goat anti-Rabbit IgG (LI-COR P/N 926-32211) and IRDye 680RD Goat anti-Mouse IgG (LI-COR P/N 926-68070); scanned on Odyssey® CLx (auto scan 700 & 800). pAkt (green) is only detected with Odyssey Blocking Buffer (TBS).

For more optimization tips, see the Odyssey Blocking Buffer optimization protocol.

4. Optimize the Dilution of Secondary Antibodies

Using secondary antibodies at the right concentration is critical to Western blotting success. Higher dilutions provide lower membrane background and fewer background bands. On the other hand, too much secondary antibody can result in strong bands and signal saturation. Therefore, it is recommended that you optimize the dilution range for your IRDye® 800CW and IRDye 680RD conjugated secondary antibodies within 1:10,000 to 1:40,000. Ideally, begin with a 1:20,000 dilution and then optimize according to primary antibody and preferred appearance of the blot.

5. Validate Primary Antibodies

As primary antibodies bind directly to the molecule of interest to enable detection, it is critical to ensure that the antibodies are specific and bind with high affinity to the target (and isoform) of interest. A positive and negative control sample can identify non-specific interactions of the antibody. In addition, you may want to knockout the expression of your target to see if the antibody binds to any other proteins within the sample. Treating cells with growth factors that induce or inhibit expression of the target, or using a blocking peptide to inhibit binding of the antibody to the target protein are some of the other methods used to confirm antibody specificity.

When performing validation assays, do not use purified or overexpressed target protein. Also, examine different cell lines or tissues with known levels of expression of the target protein.

6. Determine the Combined Linear Range of Detection


For accurate quantitation of Western blots, it is essential that both the target protein and the internal loading control (whether total protein, housekeeping protein, or modified form of the target) are measured within the combined linear range of detection.

First, the linear range for target and internal loading control need to be determined separately. This can be performed using a dilution series of the sample and the appropriate internal loading control. The individual loading ranges obtained can then be combined to identify a loading amount within the combined linear range of detection. See the complete step-by-step protocol.

7. Use Proper Experimental Controls

Control samples are essential for generating reliable and reproducible data. Including both a positive and a negative control in your experimental design will serve as helpful checkpoints for accurate target detection. A positive control will help you confirm your antibody specificity to target within the experimental conditions. On the other hand, a negative control will help you identify any non-specific binding. Most journals recommend including a molecular size marker in Western blot data images submitted for publication. Markers aid in identifying and confirming the target within the expected molecular weight range.

“Positive and negative controls, as well as molecular size markers, should be included on each gel and blot…”
– Image Integrity, Authors and Referees, Nature; Author Guidelines, EMBO Molecular Medicine

8. Normalize Data to Internal Loading Controls

Normalization corrects for sample-to-sample and lane-to-lane variation by measuring data with reference to internal loading controls. If you do not normalize your samples, any observed changes in band intensity could be a result of error in sample preparation, loading, transfer, or actual experimental conditions.

Housekeeping proteins that have been validated for stable expression, total protein loading amounts, or modified forms of target proteins (e.g., phosphorylated and total) can all be used as internal loading controls. Housekeeping proteins or modified forms of target protein use a single or a few endogenous proteins as reference. On the other hand, total protein control takes into account the sum total of all proteins loaded within the lane. Learn more about Western blot normalization.

9. Perform Measurements in Replicates

Taking replicate measurements of experimental data are necessary for accurate, reliable results. Both technical and biological replicates help address different questions about data reproducibility.

Technical replicates are repeated measurements of the same sample that represent independent measures of the noise associated with protocols or equipment1. For example, by loading replicate lanes for each sample on a blot or repeating blots with same samples on different days, you can address the reproducibility of the technique.

Biological replicates are parallel measurements of biologically distinct samples that capture biological variation within the system1. For example, using samples derived from different cell types, tissue types, or organisms, you can evaluate if similar results can be observed, or whether your finding is an anomaly. This protocol – Quantitative Western Blot Analysis with Replicate Samples – will help you define and design your experimental replicate strategy.

10. Use Software That Does Not Modify Raw Data

Accurate data measurement and analysis is the foundation of your research findings. Image file modifications using unsupported image editing and analysis software programs can compromise the integrity of your data. Ensure that you are using a software program designed to analyze the results of your Western blot experiments that is compatible with your detection system, like Image Studio Software.

Image Studio only affects how raw data pixels are mapped to the screen, leaving your original experimental results secure. With data capture and analysis integrated in a single interface, you can keep variability from file transfers and digital adjustments from affecting your data.

Keep these ten tips for near-infrared fluorescent Western blots handy. Download this wallpaper for your computer or this flyer for quick references.

Reference:
1. Blainey P, Krzywinski M, and Altman N. (2014) Points of Significance: Replication. Nature Methods 11(9): 879-880. doi:10.1038/nmeth.30

Validate Housekeeping Proteins Before Using Them for Western Blot Normalization


Why is it important to validate housekeeping proteins before using them for Western blot normalization?

Housekeeping proteins (HKPs) are routinely used for Western blot normalization. For common HKPs (such as actin, tubulin, or GAPDH), stable protein expression is generally assumed. However, expression of several HKPs is now known to vary in response to certain experimental conditions, including cell confluence, disease state, drug treatment, and cell or tissue type. Because HKP normalization uses a single indicator of sample loading, changes in HKP expression will introduce error and may alter data analysis and interpretation.

In the instructions to authors for the Journal of Biological Chemistry, they state:

Before using a housekeeping protein for Western blot normalization, it is critical that you validate that its expression is constant across all samples and unaffected by your experimental context and conditions, especially if you have plans to publish.

How do I validate housekeeping proteins?

The Housekeeping Protein Validation Protocol gives you step-by-step instructions on how to validate a housekeeping protein for use as an internal loading control. It also provides information on how to analyze the data in Image Studio™ software (download free Image Studio Lite) and how to quantitate housekeeping proteins. Detailed calculations and information on how to interpret the data will allow you to be confident in your validation process – and make the right decision for your Western blot normalization strategy.

When you have completed your housekeeping protein validation and have determined that the HKP expression is unaffected by your experimental conditions, you can use the Housekeeping Protein Normalization Protocol and proceed with using your validated HKPs for Western blot normalization and quantitative analysis.

There you go! Unlike what other vendors may be telling you, you CAN use housekeeping proteins for Western blot normalization – as long as you validate that their expression is not changing under your experimental conditions. Download your copies of Housekeeping Protein Validation Protocol and Housekeeping Protein Normalization Protocol and get started today.

Other Protocols to Support Western Blot Normalization

LI-COR has several other protocols to help you meet new stringent publication guidelines and requirements. These are detailed protocols and include information on how to analyze and interpret your data.

With all of these protocols and our scientific experts, we can help you collect accurate, reliable Western blotting data. You will be confident in your results and your conclusions. When you submit your data for publication, you will be confident that you are meeting even the toughest publication standards. Protocols are also available in an online format at protocols.io

The Gold Standard for Western Blot Normalization: Total Protein Staining



In the instructions to authors for the Journal of Biological Chemistry, they state:

While you have choices for your Western blot normalization strategy – you can still use housekeeping proteins as long you have validated that their expression is not changing – total protein staining detection is becoming the “gold standard” for normalization of protein loading.

After transfer, but prior to immunodetection, the membrane is treated with a total protein stain to assess actual sample loading across the blot. Because this internal loading control uses the combined signal from many different sample proteins in each lane, error and variability are minimized. This antibody-independent method corrects for variation in both sample protein loading and transfer efficiency, and monitors protein transfer across the blot at all molecular weights. The figure at the left shows that REVERT Total Protein Stain provides highly efficient protein staining on nitrocellulose or Immobilon®-FL PVDF membranes in under 10 minutes. Complete figure legend.

REVERT™ Total Protein Stain is a near-infrared fluorescent membrane stain used for total protein detection and normalization. REVERT staining is imaged at 700 nm, and fluorescent signals are proportional to sample loading.

The REVERT Total Protein Stain Normalization protocol describes how to use REVERT Total Protein Stain for Western blot normalization and quantitative analysis. It includes step-by-step instructions on how to use REVERT stain. There is also detailed information on normalization calculations, analysis of replicates, and data interpretation.

Replication is an important part of quantitative Western blot analysis and is used to confirm the validity of observed changes in protein levels. Biological and technical replications should both be done, since they are both important but meet different needs.

LI-COR has several other protocols to help you meet publication guidelines and requirements. In all of them, key factors for success, data analysis and interpretation are covered as well as links to additional educational resources.

With these protocols and our scientific experts, we can help you collect accurate, reliable data that will meet even the toughest publication standards. Protocols are also available in an online format at protocols.io

Download your copy of REVERT Total Protein Stain Normalization protocol and use the gold standard to determine your protein loading concentrations. Let us help you be confident in the Western blotting data you submit for publication.

The Importance of Detecting in the Combined Linear Range for Western Blots



In the instructions to authors for the Journal of Biological Chemistry, they state:

What is the linear range of detection?

In quantitative Western blot analysis, the linear range of detection is the range of sample loading that produces a linear relationship between the amount of target on the membrane and the band intensity recorded by the detector.

Within the linear range of detection, band intensity should be proportional to the amount of target. A change in target abundance should produce an equivalent change in signal response. At the upper and lower ends of the linear range, this proportional relationship is lost. Band intensity no longer reflects the abundance of target, and quantification is not possible.

Quantitative Western blot analysis is only accurate if the target protein and internal loading control can both be detected within the same linear range – a range that must be determined experimentally for each target and loading control. The combined linear range is then used to determine how much sample should be loaded to produce a linear signal response for both the target protein and the internal loading control.

Are YOU detecting your target protein and your internal loading control in the combined linear range?

How is the combined linear range determined?

Help has arrived! The protocol “Determining the Linear Range for Quantitative Western Blot Detection” from LI-COR explains how to use serial dilutions of sample protein to determine the linear ranges of detection for a target and internal loading control, and choose an appropriate amount of sample to load for quantitative Western blot analysis.

This protocol also explains key factors for success, required reagents, data analysis and interpretation. Two methods for determining the linear range are included in the protocol:

  • Determining the Linear Range for a Target Protein and REVERT™ Total ProteinStain. Follow these instructions if total protein staining of the membrane will be used as the internal loading control for quantitative Western blot normalization.
  • Determining the Linear Range for a Target Protein and a Housekeeping Protein. Follow these instructions if a housekeeping protein will be used as the internal loading control for quantitative Western blot normalization. This method also applies to normalization with a pan-specific antibody for analysis of phosphorylation or other post-translational modifications.

LI-COR has several other protocols to help you get published. In all of them, key factors for success, data analysis and interpretation are covered as well as links to additional educational resources.

With these protocols and our scientific experts, we can help you collect rock-solid data that will meet even the toughest publication standards. Protocols are also available in an online format at protocols.io

Download your copy of Determining the Linear Range for Quantitative Western Blot Detection so that you can accurately determine the linear range for your quantitative western blot detection. Let us help you be confident in the Western blotting data you submit for publication.

New Protocols for Western Blot Normalization to Help You Get Published



Western blotting is the most widely used method for the detection and characterization of proteins. Although the basic elements of Western blotting remain unchanged, journal standards for publishing Western blots (e.g., JBC’s Instructions for Authors) have become more rigorous in recent years.

Are you interested in quantifying your proteins on your Western blot but are not sure how to manage Western blot variability and increase the accuracy of your results?

The key is to maximize Western blot accuracy and precision. This makes relative comparisons meaningful. How can you accomplish this? By reducing variability whenever possible with good experimental design. You can also correct for variability by using the appropriate internal loading controls for your Western blot normalization.

Normalization Protocols

LI-COR developed a series of protocols to help improve the quality of quantitative Western blots. Whether you are a beginner or a seasoned user, we can help you collect rock-solid data that will meet even the toughest publication standards.

The protocols cover key factors for success, data analysis and interpretation, and include links to additional educational resources for quantitative Western blotting.

Do you need help determining the linear range of your target protein and internal loading control, or validating your housekeeping protein, using REVERT total protein stain for normalization or using total and post-translationally modified proteins for normalization? If so, our tools, products, and services can help you get published.

These protocols are also available in an online format at protocols.io

Normalization Is Critical for Quantitative Immunoblotting

Normalization Webinar InvitationFor more information on Western blot normalization, watch these webinars:


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

Normalization Webinar InvitationFor more information on Western blot normalization, watch these webinars:


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

Normalization Webinar InvitationFor more information on Western blot normalization, watch these webinars:


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.
signaling-protein
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

Normalization Webinar InvitationFor more information on Western blot normalization, watch these webinars:


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.

post 7 image
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 freque[marketo-fat form=”1644″]ntly 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.