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Is it necessary to calculate lane normalisation factor when doing western blot data analysis?

Is it necessary to calculate lane normalisation factor when doing western blot data analysis?


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I have recently done my first western blot and I am doing data analysis to quantify my blot. I have labelled my membrane against inactive GSK3 and active GSK3 which are phosphoproteins so I am using total GSK3 as an internal loading control. I have read some guides and handbooks about western blot data analysis and I have seen that some of them calculate a lane normalisation factor to account for variations in signal intensities of the loading control. For all loading control bands in each lane, they divide by the loading control band with highest intensity to get the lane normalisation factor. Then for each band of the target protein of interest they divide by the lane normalisation factor to get the normalised intensity.

I was wondering in general with western blots is it good practice to calculate the lane normalisation factor when doing the data analysis? Any insights are appreciated.


Generally speaking, the proper way to quantify a western blot is to normalize to a loading control such as Actin or GAPDH. In this case it would be (pGSK3/Actin)/(GSK3/Actin) as total GSK3 is not a loading control. A loading control is to protein that is accepted as unvarying in concentration across multiple samples if the same protein amoun is used, and to my knowledge GSK3 would show more biological variability than accepted loading controls. This would be considered the best practice to compare protein concentrations across samples.

That being said, if all you care about is the ratio of active to total, pGSK3/Total GSK3 is probably sufficient to assess if your manipulation changed active/inactive:total ratios. For example, in autophagy assays you can measure autophagic flux by measuring LC3-I to LC3-II, and I've never seen them normalized to a loading control since its a ratiometric readout contained within a single sample.


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Abstract

Western blotting is a commonly used technique in biological research. A major problem with Western blotting is not the method itself, but the use of poor quality antibodies as well as the use of different experimental conditions that affect the linearity and sensitivity of the Western blot. Investigation of some conditions that are commonly used and often modified in Western blotting, as well as some commercial antibodies, showed that published articles often fail to report critical parameters needed to reproduce the results. These parameters include the amount of protein loaded, the blocking solution and conditions used, the amount of primary and secondary antibodies used, the antibody incubation solutions, the detection method and the quantification method utilized. In the present study, comparison of ubiquitinated proteins in rat heart and liver samples showed different results depending on the antibody utilized. Validation of five commercial ubiquitin antibodies using purified ubiquitinated proteins, ubiquitin chains and free ubiquitin showed that these antibodies differ in their ability to detect free ubiquitin or ubiquitinated proteins. Investigating proteins modified with interferon-stimulated gene 15 (ISG15) in young and old rat hearts using six commercially available antibodies showed that most antibodies gave different semi-quantitative results, suggesting large variability among antibodies. Evidence showing the importance of the Western blot buffer and the concentration of antibody used is presented. Hence there is a critical need for comprehensive reporting of experimental conditions to improve the accuracy and reproducibility of Western blot analysis. A Western blotting minimal reporting standard (WBMRS) is suggested to improve the reproducibility of Western blot analysis.

Citation: 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. https://doi.org/10.1371/journal.pone.0135392

Editor: Fenfei Leng, Florida International University Bimolecular Sciences Institute, UNITED STATES

Received: May 7, 2015 Accepted: July 21, 2015 Published: August 19, 2015

Copyright: © 2015 Gilda et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: All relevant data are within the paper.

Funding: This work was supported by National Institutes of Health Grants HL080101 and HL096819 (AVG), UC Davis funds (AVG), and a VA RR&D Merit 1I01RX000673 (SCB). The funders did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.


In statistics, there are many tools to analyze the data in detail and one of the most commonly used formula or method is the Normalization method. Normalization and standardization have been used interchangeably but they have usually different interpretations and different meanings altogether. Normalization in layman terms means normalizing of the data. Normalization refers to a scaling of the data in numeric variables in the range of 0 to 1.

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The formula for Normalization is

  • X: It is a set of the observed values present in X.
  • Xmin: It is the minimum values in X
  • X max: It is the maximum values in X

Examples of Normalization Formula (With Excel Template)

Let’s take an example to understand the calculation of Normalization in a better manner.

Normalization Formula – Example #1

Calculate Normalization for the following data set.

Maximum Value in the data set is calculated as

So 75 is the maximum value in the given data set.

Minimum Value in the data set is calculated as

20 is the minimum value in the given data set.

Normalization is calculated using the formula given below

Similarly, we calculated the normalization for all data value.

Normalization Formula – Example #2

Calculate Normalization for the following data set.

Maximum Value in the data set is calculated as

So 164 is the maximum value in the given data set.

Minimum Value in the data set is calculated as

101 is the minimum value in the given data set.

Normalization is calculated using the formula given below

Similarly, we calculated the normalization for all data value.

Normalization Formula – Example #3

Calculate Normalization for the following data set.

Maximum Value in the data set is calculated as

So 197 is the maximum value in the given data set.

Minimum Value in the data set is calculated as

121 is the minimum value in the given data set.

Normalization is calculated using the formula given below

Similarly, we calculated the normalization for all data value.

Explanation

The normalization formula can be explained in the following below steps: –

Step 1: From the data the user needs to find the Maximum and the minimum value in order to determine the outliners of the data set.

Step 2: Then the user needs to find the difference between the maximum and the minimum value in the data set.

Step 3: Value – Min needs to be determined against each and every data point in the set.

Step 4: After determining all the values in the data set the value needs to be put in the formula i.e. X new = (X – X min) / (X max – X min)

Relevance and Uses of Normalization Formula

  • Normalization is widely used in data mining techniques and data processing techniques. It is usually known as featured scaling under which you try to bring data in a normalized or a standardized form to do analysis on it and draw various interpretations.
  • This formula is also used in prediction modeling and forecasting which makes the model more relevant and user-friendly.
  • This formula and technique is also used in the marking scheme of various entrance examinations where in order to ensure that the candidate is neither benefited nor deprived by the level of difficulty in the examination, as a result, the candidate who has attempted simple or easier questions can get more marks in the test in comparison with the candidates who attempt difficult questions in the thought of getting more marks.
  • Normalization also has its own limitations in the sense if the data set has more outliers then normalization of the data set becomes are tedious and a difficult task to be done to the data.

Normalization Formula Calculator

You can use the following Normalization Calculator

Recommended Articles

This has been a guide to Normalization Formula. Here we discuss how to calculate Normalization along with practical examples. We also provide a Normalization calculator with downloadable excel template. You may also look at the following articles to learn more –


Housekeeping genes as normalization controls

To perform normalization using a housekeeping gene, the blot is probed with antibodies to detect the protein of interest while another set of antibodies is used to detect a separate protein used as a normalization control. For quantitative studies, the ratio of the abundance of the protein of interest to the normalization control is used to quantify the amount of the protein of interest in each sample. For qualitative studies, the loading control is often presented in an image for visual comparison.

Housekeeping genes, such as such as glyceraldehyde 3-phosphate dehydrogenase (GADPH), beta-actin or tubulin, are commonly used for loading controls. These proteins are usually expressed constitutively at high levels due to their role in cell viability. However, several factors limit their utility as normalization controls.


Materials and methods

Sample preparation

Thirty-four neuroblastoma cell lines were grown to subconfluency according to standard culture conditions. RNA was isolated using the RNeasy Midi Kit (Qiagen) according to the manufacturer's instructions. Nine RNA samples from pooled normal human tissues (heart, brain, fetal brain, lung, trachea, kidney, mammary gland, small intestine and uterus) were obtained from Clontech. Blood and fibroblast biopsies were obtained from different normal healthy individuals. Thirteen leukocyte samples were isolated from 5 ml fresh blood using Qiagen's erythrocyte lysis buffer. Fibroblast cells from 20 upper-arm skin biopsies were cultured for a short time (3-4 passages) and harvested at subconfluency as described [22]. Bone marrow samples were obtained from nine patients with no hematological malignancy. Total RNA of leukocyte, fibroblast and bone marrow samples was extracted using Trizol (Invitrogen), according to the manufacturer's instructions.

Real-time RT-PCR

DNase treatment, cDNA synthesis, primer design and SYBR Green I RT-PCR were carried out as described [23]. In brief, 2 μg of each total RNA sample was treated with the RQ1 RNase-free DNase according to the manufacturer's instructions (Promega). Treated RNA samples were desalted (to prevent carry over of magnesium) before cDNA synthesis using Microcon-100 spin columns (Millipore). First-strand cDNA was synthesized using random hexamers and SuperscriptII reverse transcriptase according to the manufacturer's instructions (Invitrogen), and subsequently diluted with nuclease-free water (Sigma) to 12.5 ng/μl cDNA. RT-PCR amplification mixtures (25 μl) contained 25 ng template cDNA, 2x SYBR Green I Master Mix buffer (12.5 μl) (Applied Biosystems) and 300 nM forward and reverse primer. Reactions were run on an ABI PRISM 5700 Sequence Detector (Applied Biosystems). The cycling conditions comprised 10 min polymerase activation at 95°C and 40 cycles at 95°C for 15 sec and 60°C for 60 sec. Each assay included (in duplicate): a standard curve of four serial dilution points of SK-N-SH or IMR-32 cDNA (ranging from 50 ng to 50 pg), a no-template control, and 25 ng of each test cDNA. All PCR efficiencies were above 95%. Sequence Detection Software (version 1.3) (Applied Biosystems) results were exported as tab-delimited text files and imported into Microsoft Excel for further analysis. The median coefficient of variation (based on calculated quantities) of duplicated samples was 6%.

Single control normalization error E

For any given m tissue samples, real-time RT-PCR gene-expression levels a ij of n internal control genes are measured. For every combination of two tissue samples p and q, and every combination of two internal control genes j and k, the single control normalization error E was calculated (Equation 1). This is the fold expression difference between samples p and q when normalized to housekeeping gene j or k.

j,k [1,n], p,q [1,m], jk and pq):

Internal control gene-stability measure M

For every combination of two internal control genes j and k, an array A jk of m elements is calculated which consist of log2-transformed expression ratios a ij/a ik (Equation 2). We define the pairwise variation V jk for the control genes j and k as the standard deviation of the A jk elements (Equation 3). The gene-stability measure M j for control gene j is the arithmetic mean of all pairwise variations V jk (Equation 4).

j,k [1,n] and jk):

V jk = st.dev (A jk) (3)

Normalization of array data

Publicly available raw microarray data [14] were downloaded as tab-delimited files. Eight hybridization data sets were randomly selected and imported into Microsoft Excel software for further manipulation (MCF7, DU-145, 786-0, BC2, K562, A549, U251, and SK-OV-3). For each hybridization array, all spots with Cy3 or Cy5 fluorescence intensities below the average overall background level plus one standard deviation were discarded. Subsequently, a local background correction for each spot was applied. Two scale factors were calculated for each slide on the basis of median ratio normalization (median ratio set to 1) and total intensity normalization (equalized sum of fluorescence intensities for both channels). Nine housekeeping genes were identified by BLAST similarity or keyword search against the database of cDNA clones present on the array (see IMAGE clones listed in Table 1).


The Design of a Quantitative Western Blot Experiment

Western blotting is a technique that has been in practice for more than three decades that began as a means of detecting a protein target in a complex sample. Although there have been significant advances in both the imaging and reagent technologies to improve sensitivity, dynamic range of detection, and the applicability of multiplexed target detection, the basic technique has remained essentially unchanged. In the past, western blotting was used simply to detect a specific target protein in a complex mixture, but now journal editors and reviewers are requesting the quantitative interpretation of western blot data in terms of fold changes in protein expression between samples. The calculations are based on the differential densitometry of the associated chemiluminescent and/or fluorescent signals from the blots and this now requires a fundamental shift in the experimental methodology, acquisition, and interpretation of the data. We have recently published an updated approach to produce quantitative densitometric data from western blots (Taylor et al., 2013) and here we summarize the complete western blot workflow with a focus on sample preparation and data analysis for quantitative western blotting.

1. Introduction

Proteomic technologies such as two-dimensional electrophoresis and mass spectrometry are valuable tools in semiquantitative protein profiling studies in order to identify broad expression patterns enabling a better understanding of molecular events, signaling pathways and mechanisms [1]. The resulting data are typically confirmed by a second, independent method such as western blotting. Western blotting was introduced by Towbin et al. [2] in 1979 and has since become a common technique used in research laboratories globally for the immunodetection and quantitation of specific proteins in complex cell homogenates. Over the past three decades, the sensitivity, robustness, and flexibility of the corresponding indicator systems have increased significantly [3, 4]. In addition, the ongoing development of detection media and reagents has provided the scientific community with ultrasensitive imaging systems giving broad dynamic range of detection enabling precise and accurate quantitation of signals from both low and high expressing proteins from the same blot. Although labs have been quick to purchase the latest detection technologies and reagents for western blotting, the associated techniques used to produce the densitometric data have not evolved leading to published data that are difficult or impossible to interpret or reproduce [5–7].

In order to obtain quantitative data from western blots, a rigorous methodology must be used as previously described [8]. Briefly, the validation of antibodies (Ab) is critical both to assure that the Ab/antigen interaction is specific and correct and to determine the dilution factor of samples that is required for protein loading in the quantitative linear dynamic range for each antibody. Furthermore, the appropriate selection of normalization method (based on reference signals obtained either by housekeeping proteins (HKPs) after immunochemical staining or total protein (TP) intensity on blotting membranes after total protein staining) must be considered to assure that the reported fold changes of the target protein are not an artifact of reference signal. Thus, data normalization is crucial to identify and correct experimental errors where reference instability becomes increasingly important with the measurement of smaller differences in target protein expression between samples [9]. The direct effect of poor normalization is evident when sample loading above 10 μg of a total protein lysate per lane is required because traditional loading HKPs such as GAPDH, actin, and tubulin are grossly overloaded and therefore not serving the purpose of data normalization [8, 9]. Also, these HKPs can be affected by the treatment conditions of the experiment giving skewed results for target protein expression that do not reflect the biology of the tested samples [10–15]. Alternatively, normalization by total, blot-transferred protein has recently been shown to give excellent data for typical total protein lysates [16].

Here, we describe some general techniques to produce good quality protein samples with minimal degradation for improved reproducibility between experiments. Also summarized are the basic steps of quantitative western blotting and a standardized approach to calculating the associated densitometric data from multiple blots.

2. Careful Experimental Design Produces Reliable and Reproducible Data

Unlike DNA-based assays that measure a predictable type of molecule that is typically stable in a variety of conditions, proteins can vary significantly in their expression, stability, conformation, and activity under different buffer and experimental conditions. Furthermore, the presence of contaminating proteins in a homogenate can greatly affect the integrity and activity of target proteins [17]. Care must therefore be taken in the design of any protein-based assay to ensure that the apparent differences between case and control samples are not an artifact of the experimental conditions or sample handling. Factors that can have a major influence on the proteome include incubation time and temperature, as well as the parameters for processing samples such as the amount of time between tissue collection and subsequent freezing or even the conditions and timing for thawing tissue or cell pellets (Table 1).

Experiment procedureControl groupsReplicatesExperiment conditionsSample handling
Disease or treatment groupsTime course study (i.e.,

3. Sample Preparation

There are several pitfalls associated with sample preparation that can directly affect the density of bands on a western blot including: (1) improper handling of tissue or cell specimens resulting in variable degradation and/or expression of proteins between samples, (2) inadequate detergents, salts, and protease inhibitors in the lysis buffer, (3) poor homogenization technique.

Since protein lysates are highly complex with contaminants such as cellular or tissue debris, fats, hydrophobic protein aggregates, nucleic acids, and proteases that can directly and negatively affect the results from western blots, it is important to use cell lysis buffers and homogenization techniques that eliminate their effects [17]. In general, homogenization buffers containing nonionic detergents such as NP-40 and Triton X-100 are less harsh than ionic detergents, such as SDS and sodium deoxycholate. Salts such as NaCl or KCl are typically added to a concentration of 100 to 150 mM to prevent protein aggregation. RIPA buffer (1% NP-40 or Triton X-100, 1% sodium deoxycholate, 0.1% SDS, 150 mM NaCl, 50 mM Tris-HCl, pH 7.8, 1 mM EDTA) and complete mini protease inhibitor cocktail tablets (Roche Applied Science) in combination with either mechanical or manual homogenization instruments have been used to produce homogenates that give solid data for western blot assays.

The proper choice of tissue homogenization technique is a prerequisite for a successful western blot assay and the method employed entirely depends on the sample type (i.e., brain versus muscle versus liver tissue as opposed to plated or suspended cells) [18, 19]. A good example of a tissue lysis protocol is as follows: (1) Snap-freeze the tissue in liquid nitrogen and then dice tissue into 1 mm pieces with a scalpel in a mortar on dry ice. Ensure that the scalpel or grinder is also frozen on dry ice to keep the cut or ground tissue close to the temperature of dry ice throughout the procedure. (2) Add the diced/ground tissue to ice-cold RIPA buffer. (3) Transfer the tissue preparation to an ice-cold Dounce tissue homogenizer (Wheaton) and Dounce 25x on ice. (4) Sonicate (Tekmar Sonic Disrupter) the Dounce-homogenized tissue on ice for

seconds at 50% power and clear the extracts by centrifugation at 34,000 ×g at 4°C for 30 minutes. (5) Transfer the supernatant to a new tube and perform protein assay (see below). (6) Store the supernatants at −80°C or in liquid nitrogen for long term storage.

For cell lysis, add the pelleted cells (in the case of cell suspensions) to ice-cold RIPA buffer or for plated cells, add the ice-cold RIPA buffer directly to the plate after washing the cells, and scrape and pipette the cells up and down. Continue with step (3) above.

The total protein concentration of the homogenate from either cell or tissue lysates should be measured using a detergent compatible protein assay such as the RC DC protein assay from Bio-Rad. Ideally, the homogenates would be diluted to a concentration of at least 2 mg/mL which would permit loading between 10 μg and 80 μg per lane of a 1 mm thick mini polyacrylamide SDS-gel.

4. Determine the Linear Dynamic Range of Protein Loading

Most labs load a random amount of protein in each lane of a gel for western blotting that is typically between 10 μg and 100 μg of total lysate and there is typically no scientific basis for choosing this amount. This often results in the overloading of highly expressed, target proteins and particularly the loading controls that are used to normalize the data. This typically gives uniform band densities between lanes for the housekeeping proteins which is not due to consistent protein loading but rather from overloading the membrane with the target protein (Figure 1). To alleviate the effect of membrane saturation, a standard curve of protein load versus band density should be produced for each target protein. This can be accomplished by making a 1/2 dilution series of a pooled sample from all the lysates in the study group starting from 100 μg protein load over at least 12 dilutions on a TGX stain-free SDS-gel (Bio-Rad). Stain-free detection on the ChemiDoc MP (Bio-Rad) camera system can be used to verify the loading, quality, and separation of the homogenate followed by transfer to a low fluorescent PVDF membrane using the Trans Blot Turbo (Bio-Rad) protein transfer system [8].


Reliable western blot data can only be generated when the proper sample amount of protein is used. Loading too much protein leads to signal saturation in western blots, yet too little produces weak signals. Once the experimental setup and conditions are established for the assay, do not change the sample load, transfer method, transfer time, antibody dilution, antibody incubation time, or temperature in subsequent experiments as these factors may significantly change the detection signals.

A typical methodology for determination of the appropriate loading for protein samples follows: (1) Transfer and blot accordingly by incubating the target protein primary antibody and associated secondary to each blot with at least four, 3-minute wash steps between each incubation. (2) Add an imager-compatible, chemiluminescence substrate such as Clarity (Bio-Rad) to develop the immunochemical signal and capture the signal using a CCD-camera-based imager such as the ChemiDoc MP (Bio-Rad). (3) Image the blots using software that provides accurate, background-subtracted densitometric tools such as Image Lab (Bio-Rad) and produce a plot of relative density versus fold dilution for each primary antibody. (4) Validate the antibodies by determining their linear dynamic range (i.e., the range in which a consistent, 1/2 decrease in density is obtained). (5) Select the protein load for each antibody that corresponds to the middle of the linear dynamic range.

Dilution of the individual samples in the study group to the middle point in the linear dynamic range of the pooled sample for each antibody may mean that the individual protein samples require widely different dilutions for each antibody. This will assure that the densitometric data for each target protein will be within the linear dynamic (quantitative) range to give accurate and reproducible results reflecting the true biology between samples in the study set (Figure 2). Inappropriate loading of samples may result in no quantifiable difference between the samples for a given target simply due to overloading the membrane.


(From [9] with permission from the authors and Bio-Rad.) Linearity comparison of stain-free total protein measurement and immunodetection of three housekeeping proteins in 10–50 μg of HeLa cell lysate. On the left are representative images of (a), stain-free blot and the chemi blots for (b), β-actin (c), β-tubulin and (d), GAPDH. Lane labels correspond to total protein load (μg). Although the actin and tubulin signals appear linear, the densitometric ratio was far below the predicted “quantitative response” of actual loading whereas the stain-free signal correlated to the expected result (e).

5. Determine the Appropriate Reference Signal for Data Normalization

A good reference signal or “loading control” is one that is coexpressed with the target protein within the same sample and consistently expressed between samples. HKPs such as tubulin, β-actin, and GAPDH have traditionally served as loading controls, but there are three potential drawbacks to using such controls. (1) HKPs may not be expressed in a uniform manner between the experimental conditions which will give erroneous results [10–15]. The same issue has been found with reference genes used for qPCR where the selection of unstable targets has led to opposite results when contrasted with stable targets [20, 21]. (2) HKPs are highly abundant in lysates and have typically saturated the membrane for samples loaded in excess of about 4 μg per lane (see previous section) (Figure 2). This would give these proteins the “appearance” of good normalization controls because the densities of the associated bands would all be similar between lanes as an “artifact” of membrane saturation. (3) Data normalization with HKPs relies only on one data point and provides a poor reflection of possible process inconsistencies.

Given the problems that arise with HKP controls, alternative methods for normalization have been sought out by the scientific community and we propose that an excellent loading control (LC) should meet the following criteria. (1) It has good responsiveness (1 : 1) to changes in total protein amount of individual samples. (2) It is insensitive to the influence of various physiological conditions and treatments and therefore must be quantified from the membrane itself to take into account the effect of transfer efficiency [22, 23]. (3) Acquisition of the LC would ideally be possible at all phases of the western blot process (i.e., visualization of protein on the pretransfer gel lanes, posttransfer blot, and posttransfer gel lanes) thus enabling a consistent process control. (4) Acquisition of the LC must be fast, easy, and highly reproducible. (5) No lengthy process should be required for the optimization and establishment of LC. (6) The method for LC detection should be compatible with immunochemical staining.

To address these issues, the scientific community is now adopting the use of total lane density from the blot-transferred protein as a means of data normalization [24–26]. There are a number of stains that can be used to visualize, image, and quantify the transferred protein on the blot including Ponceau S, Coomassie R-350, Amido Black, MemCode, and Deep Purple. However, each of these stains has individual issues of being poorly reproducible on a day-to-day basis, limited dynamic range, and restricted compatibility with blotting membranes and immunochemical staining [25]. More recently the Stain-Free technology (Bio-Rad) has been introduced [24–26] and meets all six of the criteria mentioned above for a good LC with a linear dynamic range between about 10 and 80 μg of total protein load from a typical cell or tissue lysate [8, 9]. This permits the use of total lane density from the stain-free blot for normalization between lanes for most western blot studies.

The technique for total lane normalization using the stain-free assay technology has been well-described but briefly it is as follows: (a) The quality of the electrophoretic separation can be verified within a couple of minutes. After UV-activation, the protein bands are visible in the gel and can be recorded with a camera system. The generated fluorescent signal remains stable over a couple of hours. (b) The blot is imaged immediately after transfer to verify the transfer of protein from each lane. (c) The image data from the total density of all the blot-transferred protein bands per lane is then recorded using Image Lab software by selecting a single band in each lane and stretching the band width to cover all the volume peaks in the lane profile. (d) The background rolling disc is adjusted to a low value (between 1 and 5) for all the lanes to assure that only the total background subtracted density from the sum of all the bands per lane is acquired for normalization.

In addition, Stain-Free technology is compatible with both nitrocellulose and PVDF membranes and data normalization with SF blot images is based on many data points which is superior to HKPs.

6. Data Analysis: The Background Subtraction Problem

Background subtraction is a common reason to obtain variable or incorrect data from western blots [27]. Using traditional densitometric analysis methods such as volume analysis from boxed bands and background, variations in background-subtracted data can arise since the background is not subtracted from the same box in which the band resides. Furthermore, the box in which a band is selected always contains density from both the band and the associated background which becomes more prevalent with low density bands [8]. The combination of these factors can result in highly variable data when testing samples with a differentially expressed target protein using a nonspecific antibody with high background. A good alternative to volume analysis using boxes is a rolling disc background subtraction algorithm coupled with a lane profile tool (Figure 3). Image Lab software is designed with both of these tools that can be used simultaneously to ensure that the appropriate band width and lane background is selected for each lane (Figure 3).


(From [8] with permission from the authors and Bio-Rad.) Image acquisition and densitometric analysis. Image Lab software version 5.0 (Bio-Rad) was used for image acquisition and densitometric analysis of the gels, blots, and film in this study. The software interprets the raw data in three dimensions with the length and width of the band defined by the “Lanes and Bands” tool in concert with the “Lane Profile” tool such that the chemiluminescent signal emitted from the blot is registered in the third dimension as a peak rising out of the blot surface. The density of a given band was measured as the total volume under the three-dimensional peak, which could be viewed in two dimensions using the “Lane Profile” tool to adjust the precise width of the band to account for the area under the shaded peak of interest. Background subtraction was set by using the rolling disc setting in the “Lanes” tool. The rolling disc values were set such that the background was subtracted under the band (i.e., peak) of interest in a uniform manner between the lanes of a given blot. In this case, the rolling disc for the two lanes analyzed was set to 18 and 25, respectively, such that the peaks of interest were cut at a consistent level between the markers shown with an “X”.

7. Computational Analysis of Densitometric Data

There are numerous calculations from densitometric data using formulas buried in EXCEL spreadsheets spanning multiple worksheets and files to obtain quantitative data from western blots. It is often difficult to follow the basis of the calculations and we have found that when lab members are faced with the direct question of how to work up the raw data obtained from western blots to publishable results, there is often confusion. The analysis of western blot data can be accomplished using a very similar methodology to qPCR by calculating relative, normalized protein expression as described in the following steps (Tables 2 and 3). (1) For each blot, multiply the background subtracted density (volume in Image Lab software) of the target protein (TP) in each lane by the ratio of density of the loading control (LC) (either housekeeping protein or total lane density) from a control sample loaded into lane 1 of all the study blots to the other lanes in the gel. This will give the normalized density to the loading control (NDL) (Table 2). The control sample is typically a pooled homogenate from all of the samples in a given study aliquoted into multiple tubes to permit the loading of a fresh control sample in lane 1 of each study blot. (2) Calculate the fold difference (FD) for each biological/technical replicate by dividing NDL from each lane by the NDL from the control sample in lane 1 (Table 2). (3) Determine the average FD and associated

values for the biological replicates by importing the FD from step (2) above into a statistical analysis software package such a PRISM or Analyze IT (Table 3).


Why do we need western blotting?

Besides being an essential analytical tool to identify a protein of interest in a complex mixture, western blot data can also be used as a semi-quantitative method to determine and compare the expression of specific proteins in various cells and tissues [5]. Although the western blotting technique can also be used for absolute quantification [6], this requires a linear standard curve of purified target protein. The target protein in the homogenate must be within the range of the standard curve hence western blotting is very rarely used for absolute quantification. However, semi-quantification of protein levels using western blots is common in most life science laboratories.

The advantages of western blots include the ability to detect picogram levels of protein in a sample [7], allowing the technique to be used for many purposes including as an effective early diagnostic tool [8,9]. The sensitivity and specificity of western blots is due to two main factors: 1) the separation of proteins which are different in size, charge and conformation by gel electrophoresis. For sodium-dodecyl sulfate (SDS)-polyacrylamide gel-electrophoresis (PAGE) the proteins are denatured and given a negative charged by binding to SDS, then separated based on size. The molecular mass of the protein identified by western blot can be determined by using standards of known molecular weights. 2) The specificity of the antibody-antigen interaction. The selective nature of the specific antibody allows the detection of a target protein in complex mixtures containing > 100,000 different proteins. When two-dimensional (2D) electrophoresis is used instead of one dimensional (1D) electrophoresis (2DE westerns), isoforms and post-translationally modified target proteins with similar molecular masses can be identified [10].


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Western blot protocol

Reviewed December 14 2020

Western blotting is a technique that uses specific antibodies to identify proteins that have been separated based on size by gel electrophoresis. The immunoassay uses a membrane made of nitrocellulose or PVDF (polyvinylidene fluoride). The gel is placed next to the membrane and the application of an electrical current induces the proteins to migrate from the gel to the membrane. The membrane can then be further processed with antibodies specific for the target of interest and visualized using secondary antibodies and detection reagents.

Contents

View our western blot protocol video below.

For other video protocols please visit our video protocols library here.

​​If you are looking to build up your skills in western blot analysis, check out our free on-demand western blot training.

​Solutions and reagents: lysis buffers

These buffers may be stored at 4°C for several weeks or aliquoted and stored at -20°C for up to a year.

NP-40 buffer

  • 150 mM NaCl
  • 1.0% NP-40 (possible to substitute with 0.1% Triton X-100)
  • 50 mM Tris-HCl, pH 8.0
  • Protease inhibitors

RIPA buffer (radioimmunoprecipitation assay buffer)

  • 150 mM NaCl
  • 1% IGEPAL CA-630
  • 0.5% sodium deoxycholate
  • 0.1% SDS (sodium dodecyl sulphate)
  • 50 mM Tris-HCl, pH 8.0
  • Protease inhibitors

Tris-HCl

​ Solutions and reagents: running, transfer, and blocking buffers

​Laemmli 2X buffer/loading buffer

  • 4% SDS
  • 10% 2-mercaptoethanol
  • 20% glycerol
  • 0.004% bromophenol blue
  • 0.125 M Tris-HCl

Check the pH and adjust to 6.8

Running buffer (Tris-Glycine/SDS)

Check the pH and adjust to 8.3

Transfer buffer (wet)

  • 25 mM Tris base
  • 190 mM glycine
  • 20% methanol
  • Check the pH and adjust to 8.3

For proteins larger than 80 kDa, we recommend that SDS is included at a final concentration of 0.1%.

Transfer buffer (semi-dry)

  • 48 mM Tris
  • 39 mM glycine
  • 20% methanol
  • 0.04% SDS

Blocking buffer

3–5% milk or BSA (bovine serum albumin)

Add to TBST buffer. Mix well and filter. Failure to filter can lead to spotting, where tiny dark grains will contaminate the blot during color development.

​Sample lysis

​Preparation of lysate from cell culture

  1. Place the cell culture dish on ice and wash the cells with ice-cold PBS.
  2. Aspirate the PBS, then add ice-cold lysis buffer (1 mL per 10 7 cells/100 mm dish/150 cm 2 flask 0.5 mL per 5x10 6 cells/60 mm dish/75 cm 2 flask).
  3. Scrape adherent cells off the dish using a cold plastic cell scraper, then gently transfer the cell suspension into a pre-cooled microcentrifuge tube. Alternatively, cells can be trypsinized and washed with PBS prior to resuspension in lysis buffer in a microcentrifuge tube.
  4. Maintain constant agitation for 30 min at 4°C.
  5. Centrifuge in a microcentrifuge at 4°C. You may have to vary the centrifugation force and time depending on the cell type a guideline is 20 min at 12,000 rpm but this must be determined for your experiment (leukocytes need very light centrifugation).
  6. Gently remove the tubes from the centrifuge and place on ice, aspirate the supernatant and place in a fresh tube kept on ice, and discard the pellet.

​Preparation of lysate from tissues

  1. Dissect the tissue of interest with clean tools, on ice preferably, and as quickly as possible to prevent degradation by proteases.
  2. Place the tissue in round-bottom microcentrifuge tubes or Eppendorf tubes and immerse in liquid nitrogen to snap freeze. Store samples at -80°C for later use or keep on ice for immediate homogenization. For a

Sample preparation

  1. Remove a small volume of lysate to perform a protein quantification assay. Determine the protein concentration for each cell lysate.
  2. Determine how much protein to load and add an equal volume 2X Laemmli sample buffer.​

​Loading and running the gel

  1. Load equal amounts of protein into the wells of the SDS-PAGE gel, along with a molecular weight marker. Load 20–30 μg of total protein from cell lysate or tissue homogenate, or 10–100 ng of purified protein.
  2. Run the gel for 1–2 h at 100 V.

The time and voltage may require optimization. We recommend following the manufacturer’s instructions. A reducing gel should be used unless non-reducing conditions are recommended on the antibody datasheet.

The gel percentage required is dependent on the size of your protein of interest:

Protein size

Gel percentage

Gradient gels can also be used.

​Transferring the protein from the gel to the membrane

The membrane can be either nitrocellulose or PVDF. Activate PVDF with methanol for 1 min and rinse with transfer buffer before preparing the stack. The time and voltage of transfer may require some optimization. We recommend following the manufacturer’s instructions. Transfer of proteins to the membrane can be checked using Ponceau S staining before the blocking step.

Prepare the stack as follows:

Figure 1. Example of prepared stack.

Antibody staining

  1. Block the membrane for 1 h at room temperature or overnight at 4°C using blocking buffer.
  2. Incubate the membrane with appropriate dilutions of primary antibody in blocking buffer. We recommend overnight incubation at 4°C other conditions can be optimized.
  3. Wash the membrane in three washes of TBST, 5 min each.
  4. Incubate the membrane with the recommended dilution of conjugated secondary antibody in blocking buffer at room temperature for 1 h.
  5. Wash the membrane in three washes of TBST, 5 min each.
  6. For signal development, follow the kit manufacturer’s recommendations. Remove excess reagent and cover the membrane in transparent plastic wrap.
  7. Acquire image using darkroom development techniques for chemiluminescence, or normal image scanning methods for colorimetric detection.

Useful links

All lanes: beta Actin antibody - loading control (ab8227) at 1/5000 dilution

Lane 1: HeLa whole cell extract
Lane 2: Yeast cell extract
Lane 3: Mouse brain tissue lysate

Protocols are provided by Abcam “AS-IS” based on experimentation in Abcam’s labs using Abcam’s reagents and products your results from using protocols outside of these conditions may vary.

Webinar transcript​

The purpose of western blotting is to separate proteins on a gel according to the molecular weight. The proteins are then transferred onto a membrane where they can be detected using antibodies. Heat the samples and 95 degrees C for five to 10 minutes in a sample buffer containing a reducing agent such as beta-mercaptoethanol. This results in linearized proteins with a negative charge proportional to their size.

Place a gel into the electrophoresis tank and add in buffer, ensuring the tops of the wells are covered. Acrylamide percentage of the gel being used depends on the molecular weight of the target protein. Node a molecular weight market into the first lane then load the samples into adjacent wells. All the samples which contained equal amounts of protein. Once all the samples are loaded, ad running buffer, place the lid onto the electrophoresis tank. Turn on the power supply and set the voltage recommended by the manufacturer of the gels in the gel tank. You should be able to see bubbles rising through the tank. Run the gel until the die front has moved sufficiently down the gel.

The next stage is to transfer the proteins from the gel onto a membrane. Membranes are usually made from nitrocellulose or PVDF. Remove the gel from the tank and carefully release it from its plastic case. Cut up the wells and the gel foot and place the gel into transfer buffer. Prepare the transfer stack by sandwiching the membrane and gel between filter paper and sponges. The membrane should be traced to the positive electrode and the gel closest to the negative electrode. Use a small roller to remove any bubbles between the gel and the membrane. Cap the transfer case closed and submerge into a transfer tank containing transfer buffer. Add water to the outer chamber to keep the system cool and put on the lid. Turn on the power supply to begin protein transfer. Time and voltage require optimization, so check the manufacturer's instructions for guidance.

Now that the proteins have migrated from the gel onto the nitro cellulose membrane, the protein of interest can be detected as an antibody. The membrane can be removed from the cassette and the molecular weight market should now be visible. If required, the transfer of proteins can be confirmed by staining the membrane with [inaudible 00:04:40] solution. To prevent nonspecific binding of the antibody, the membrane needs to be blocked. Pour blocking buffer onto the membrane and agitate gently on a rocker. Typically, this is done using a solution of five percent milk or bovine serum albumin, BSA, for two hours at room temperature or overnight at four degrees. The time and type of blocking buffer should be optimized, so check the data sheet of the primary antibody you intend to use for details.

After the membrane is blocked, remove the blocking buffer and add the diluted primary antibody in the same solution. Incubate on the rocker as before. Typically primary antibody incubations are for one hour at room temperature or overnight at four degrees C. Antibody concentration and incubation time will need to be optimized. Refer to the antibody datasheet for guidance. Pour off the primary antibody and rinse the membrane twice in wash buffer. Follow with one 15 minute wash and three 10 minute washes on a rocker. The wash buffer is usually Trys buffered saline, TBS, or phosphate buffered, saline, PBS, with 0.1 percent tween 20.

Pour off the wash buffer and incubate the membrane in conjugating secondary antibody which has been diluted in blocking buffer. Usually this is done for one hour at room temperature, but antibody concentration and incubation time will need to be optimized. Pull off the secondary antibody and wash the membrane has shown previously.

There are several different systems for detection. If the secondary antibodies conjugate into an enzyme, incubate the membrane in the appropriate substrate before imaging. If the secondary antibodies are fluorescent counjugates then you can move directly onto the imaging step. Imaging can be carried out with x Ray film or with a digital imaging system. Place the membrane into an imaging tray. Place the imaging tray into imaging system. Exposure times will most likely need to be optimized in order to clearly detect the bands relating to the proteins of interest.


Watch the video: Western Blot - Theory and method (July 2022).


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