Create a Customized List of Journal Articles that Reference LI-COR Imaging Systems

Finding out how other researchers have used LI-COR® imaging systems and reagents can really help when you are trying to decide on which system is best for your lab. With over 10,000 journal citations, the Odyssey® Imaging Systems have a long, proven track record in life science research.

There is now a tool that you can use to customize a list of peer-reviewed references specific to your research and application interests. You can access this new Publications Database through pages on our website, such as Products > Imaging System pages (for example, Odyssey CLx > Who’s Using it) and Application pages (for example, Quantitative Western Blots > Publications) or through the link in the footer, which is on the bottom of all web pages.

You can filter results by four categories. Select at least one filter. Each filter you select narrows the search by making the resulting set a combination of all filter parameters.

Let’s go over the various options you can use to create your customized publications list.

You can filter by Research Area. The list of research areas includes 72 different categories. Choose a single, or multiple, area(s). Remember, the more you choose, the narrower your search will be.

You can filter by Instrument.

You can filter by Application.

You can filter by Country. This is the country of the corresponding author’s email address.

For example, if you select “Apoptosis” in Research Area, “Odyssey CLx” in Instrument, and “Germany” in Country, you will receive a list of three publications that specifically reference apoptosis, the Odyssey CLx, and publications where the corresponding author is from Germany.

You can sort the columns in the results by clicking on the corresponding header. You can also show 10, 25, 50, or all entries. If the list does not suit your needs, you can Clear All Filters and start over.

If the number of publications returned is large, you can refine the set using keywords, such as your protein or disease of interest, separated by a space. Results displayed will contain all the terms in the search field. You can also use the digital object identifier (DOI) prefix unique to each publishing group to search for publications in specific journals. For example, use 10.1074 for the Journal of Biological Chemistry and other ASBMB journals.

We regularly add publications to the database, so check back frequently to see who is using LI-COR imaging systems to get published, and how they are using the instrument in their research.

Create your own customized publication list and, if you have feedback on how to improve this tool, please click on the Feedback button and let us know!

Technical and Biological Replicates are Critical for Quantitative Western Blot Success

Replicates improve the reproducibility and accuracy of experimental findings. They are important because they confirm the validity of observed changes in protein levels. Without replication, it is impossible to know if an effect is real or simply an artifact of experimental noise or variation, which can directly affect conclusions made about experimental findings.

There are two types of replicates: biological and technical. Each type addresses different questions1,2,3. Peer-reviewed journals, such as the Journal of Biological Chemistry, have specific guidelines in regards to replicates.

“Authors must state the number of independent samples (biological replications) and the number of replicate samples (technical replicates) and report how many times each experiment was repeated.”
Instructions for Authors. The Journal of Biological Chemistry

Technical vs. Biological Replicates: Which Do You Need to Include?

Technical Replicates

Technical replicates are repeated measurements used to establish the variability of a protocol or assay, and determine if an experimental effect is large enough to be reliably distinguished from the assay noise1. Examples may include loading multiple lanes with each sample on the same blot, running multiple blots in parallel, or repeating the blot with the same samples on different days.

Figure 1. Technical replicates help identify variation in technique. For example, lysate derived from a mouse and treated under a set of experimental conditions (A, B, C), then run and measured independently three times, will help identify variation in technique.

Technical replicates evaluate the precision and reproducibility of an assay, to determine if the observed effect can be reliably measured. When technical replicates are highly variable, it is more difficult to separate the observed effect from the assay variation. You may need to identify and reduce sources of error in your protocol to increase the precision of your assay.

Technical replicates do not address the biological relevance of the results.

Biological Replicates

Biological replicates are parallel measurements of biologically distinct and independently generated samples, used to control for biological variation and determine if the experimental effect is biologically relevant. The effect should be reproducibly observed in independent biological samples. Demonstration of a similar effect in another biological context or system can provide further confirmation. Examples include analysis of samples from multiple mice rather than a single mouse, or from multiple batches of independently cultured and treated cells.

Figure 2. Biological replicates derived from independent samples help capture random biological variation. For example, lysates derived from 3 mice and treated under the same set of experimental conditions (A, B, C), will help identify variation resulting from the biology.

To demonstrate the same effect in a different experimental context, the experiment might be repeated in multiple cell lines, in related cell types or tissues, or with other biological systems.

An appropriate replication strategy should be developed for each experimental context. Several recent papers discuss considerations for choosing technical and biological replicates1,2,3.

This protocol, Quantitative Western Blot Analysis with Replicates, will guide you in choosing and incorporating technical and biological replicates in your experimental design for reproducible data. It includes calculations for replicate analysis as well as how to interpret the data you obtain.

Additional Resources to Help You Get the Best Data

LI-COR has additional resources that you can use as you plan your quantitative Western blot strategy.


  1. Naegle K, Gough NR, Yaffe MB. Criteria for biological reproducibility: what does “n” mean? Sci Signal. 8 (371): fs7 (2015).
  2. Blainey P, Krzywinski M, Altman N. Replication: quality is often more important than quantity. Nat Meth. 11(9): 879-80 (2014).
  3. Vaux DL, Fidler F, Cumming G. Replicates and repeats – what is the difference and is it significant? EMBO reports 13(4): 291-96 (2012).

Why Can Western Blot Data be Difficult to Reproduce?

Western blot analysis is susceptible to error and variation in more ways than one, whether it be the technique itself, or 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 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.

Cell line 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.

What can you do?
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.

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.

What can you do?
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.

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.

What can you do?

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™ 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.

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?

What can you do?

To accurately detect and compare signals from both protein targets and internal loading controls (whether a housekeeping protein, modified forms of a protein, or total protein) you need to measure data from the same blot, within the combined linear detection range of the assay. The linear range is where signal intensities detected by the imaging system are proportional to protein abundance. So how do you determine the combined linear range? Create a dilution series to determine the linear response range of both the target protein and internal loading control. For quantitative analysis load sample amounts that provide a linear response within the range. This protocol will guide you through the steps.


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.

What can you do?
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.

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.

What can you do?
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™.


  1. Freedman LP, Venugopalan G, Wisman R. Reproducibility2020: Progress and priorities. F1000Research. 2017;6:604. doi:10.12688/f1000research.11334.1.
  2. Cell Line Authentication. The Global Biological Standards Institute™. Web. Accessed December 20, 2017.
  3. Baker M. Reproducibility: Respect your cells! Nature 537, 433–435; 15 September 2016. doi:10.1038/537433a
  4. Baker M. Reproducibility crisis: Blame it on the antibodies. Nature 521, 274–276; 21 May 2015. doi:10.1038/521274a
  5. 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).
  6. Blainey P, Krzywinski M, and Altman N. (2014) Points of Significance: Replication. Nature Methods 11(9): 879-880. doi:10.1038/nmeth.30