Western blot analysis is susceptible to error and variation in more ways than one, whether it be from the technique itself, or from the reagents, samples, and materials used in the assay. While it is impossible to eliminate all variation and error, by accounting for its sources and following good Western blotting best practices, you can minimize variation and error and generate accurate and replicable results. Let’s consider some common sources of variation, and best practices to minimize their impact on the accuracy of results.
1. Cell Lines and Cell Culture Practices
Cell lines, the very source of samples for Western blotting, can introduce error and variability in assay and analysis. There is a risk of cross-contamination between different cell lines and infection with bacteria, viruses, or other agents, when working in shared cell culture hoods and incubators1. Repeated propagation of cell lines can also result in a cell line drift, causing changes in the genetic makeup of cells, and possibly also in protein expression1. Cell culture media, serum, reagents, and glassware have an impact on the growth of cells and the overall experimental conditions, potentially affecting assay results1,3.
How to Reduce Variation Associated with Cell Lines
For human cell lines, use cells authenticated using Short Tandem Repeat (STR) profiling, and check animal-derived cell lines for mycoplasma and viral contamination1. Develop a growth profile of your cell line before initiating experiments, so you know when to harvest cells, perform assays, or start a fresh culture3. Microscopic assessment and analysis of expression data also helps identify any changes in cell behavior3.
2. Primary Antibodies
Western blotting results depend heavily upon the quality of primary antibodies used. Variability between batches, as well as cross-reactivity of the antibody with different isoforms of the target or entirely different protein targets can lead to non-specific binding and background signal, yielding results that are difficult to replicate4.
How to Reduce Variation Associated with Primary Antibodies
Antibody validation for confirming binding and specificity of antibodies to the protein of interest is essential prior to their use in assays. Confirm antibody data such as batch and lot numbers, cross-reactivity, and characterization assays from antibody vendors4. By including positive and negative controls in your experiment, you can check for any non-specific binding of the antibody to other proteins present in your samples4. Use the antibodies in applications recommended by the vendor, as functionality in different types of experiments (e.g., Western blots and immunofluorescence applications) might vary3.
3. Loading and Normalization
Measuring changes in the expression levels of proteins is a relative assessment. So, if you inadvertently load unequal amounts of sample across wells and compare protein levels, it can throw your results off. Similarly, normalizing data to a single housekeeping protein whose expression may have been affected by experimental treatments, or normalizing without validating the housekeeping protein antibody expression, will also introduce error in your analysis.
How to Reduce Variation Associated with Loading and Normalization
Check for equal sample loading across lanes and uniform housekeeping protein expression using a loading indicator. For an even more airtight analysis, consider normalizing data to total protein loading (use this protocol for normalization using Revert™ 700 Total Protein Stain).
In contrast to a single housekeeping protein, total protein normalization reduces the impact of biological variability on data, by accounting for all proteins loaded in the lane and allows you to evaluate the efficiency of transfer prior to the immunodetection.
4. Range of Detection
Are you comparing data captured at different exposures or on separate blots? Think about some of the sources of variability in this comparison: experimental conditions, reagents used, and exposure times. Can you have confidence in your comparative analysis?
How to Reduce Variation Associated with Detection Range
5. Detection Chemistry
You are eager to see the results of your experiment, but depending upon how you choose to visualize them, you could be introducing a whole new set of variables into your data. X-ray film has a very narrow detection range in which its response to light is linear5. Signals above and below this range cannot accurately be documented on film, so band intensities are not proportional to light emitted during the chemiluminescence reaction. In addition, the enzymatic reaction signal varies with substrate incubation time, type, amount, and temperature, to name a few. Acquiring accurate signals within the combined linear range of film and the enzymatic chemiluminescence reaction is just that much more challenging.
How to Reduce Variation Associated with Detection Chemistry
Consider detection using near-infrared fluorescence imaging. Digital imaging provides a much wider dynamic range compared to film. Direct detection using dye-conjugated antibodies eliminates variation of the enzymatic reaction. As a result, you can acquire signals within the combined linear range, proportional to the amount of protein present on your blot.6. Data Analysis
Beware of conclusions based on data from a single experimental run. Variation in the biology of the experimental system, as well as in assay, technique, and equipment needs to be accounted for using replicate samples. Similarly, analyzing data images using unsupported software programs leaves your data vulnerable to error. Certain image enhancement features like gamma correction or conversion to other file formats can cause non-linear adjustments to the image and/or loss of data depth needed for accurate analysis.
How to Reduce Variation Associated with Data Analysis
Include both technical and biological replicate samples in your experimental design. Technical replicates are repeated measurements of the sample, representing independent measurements of the noise in equipment and technique6. For instance, loading multiple wells with the same amount of sample or repeating blots with the same samples on different days are a few ways to take variation in technique into consideration. On the other hand, biological replicates capture random biological variation by measuring responses in biologically distinct samples6. So, you could repeat your assay with independently generated samples taken from different cell or tissue types, to confirm that your observations are not an irreproducible fluke. See more tips on replicate samples.
Technical replicates help identify variation in technique. Biological replicates derived from independent samples capture random biological variation. As for data analysis, always use software programs that are compatible with your imaging system and designed for your specific assay. Minimize image processing, as not all software packages indicate whether the original data is modified. Avoid converting and transferring files between software programs.
Now that you know some of the experimental factors that could be influencing your Western blot results, how will you implement these best practices in your protocols, detection, and data analysis? Get a refresher on the basics of Western blotting at Lambda U® On-Demand Western Blot Education Portal.
References:
- Freedman LP, Venugopalan G, Wisman R. Reproducibility2020: Progress and priorities. F1000Research. 2017;6:604. doi:10.12688/f1000research.11334.1.
- Cell Line Authentication. The Global Biological Standards Institute™. Web. Accessed December 20, 2017.
- Baker M. Reproducibility: Respect your cells! Nature 537, 433–435; 15 September 2016. doi:10.1038/537433a
- Baker M. Reproducibility crisis: Blame it on the antibodies. Nature 521, 274–276; 21 May 2015. doi:10.1038/521274a
- Laskey, R.A. Efficient detection of biomolecules by autoradiography, fluorography or chemiluminescence. Methods of detecting biomolecules by autoradiography, fluorography and chemiluminescence. Amersham Life Sci. Review 23:Part II (1993).
- Blainey P, Krzywinski M, and Altman N. (2014) Points of Significance: Replication. Nature Methods 11(9): 879-880. doi:10.1038/nmeth.30
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