A Systematic Approach to Quantitative Western Blot Analysis T ∗ Lakshmi Pillai-Kastoori , Amy R

Analytical Biochemistry 593 (2020) 113608

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Analytical Biochemistry

journal homepage: www.elsevier.com/locate/yabio

A systematic approach to quantitative Western blot analysis T ∗ Lakshmi Pillai-Kastoori , Amy R. Schutz-Geschwender, Jeﬀ A. Harford

LI-COR Biosciences, 4647 Superior Street, Lincoln, NE, 68504, USA

ABSTRACT

Attaining true quantitative data from WB requires that all the players involved in the procedure are quality controlled including the user. Appropriate protein extraction method, electrophoresis, and transfer of proteins, immunodetection of blotted protein by antibodies, and the ultimate step of imaging and analyzing the data is nothing short of a symphony. Like with any other technology in life-sciences research, Western blotting can produce erroneous and irreproducible data. We provide a systematic approach to generate quantitative data from Western blot experiments that incorporates critical validation steps to identify and minimize sources of error and variability throughout the Western blot process.

1. Introduction results [6,11–13]. However, reproducibility does not guarantee accuracy; an assay Western blotting is a simple yet powerful procedure to investigate may produce precise data that are inaccurate [13,18 ,19]. Accuracy and the presence, relative abundance, relative mass, presence of post- precision are important but distinct aspects of reproducibility. Accuracy translational modifications (PTM) as well as to study protein-protein indicates how closely the measured values represent the true value of interactions. These applications of Western blot provide valuable in- the target or analyte in a sample. Do these values reflect a genuine formation in both academic research, diagnostic and therapeutic change or difference in the experimental sample? Precision describes testing. This workhorse method, first described by Towbin et al. [1] and the repeatability, variation, and error of assay measurements. If the test Burnette [2], relies on the specific interaction of antibodies with target is repeated many times, how similar are the results? quantitative ana- antigens present in the sample mixture. After sample proteins are se- lysis of Western blot data should strive for both precision and accuracy, parated on a protein gel and transferred to the membrane, primary and to help ensure that the reported observations convey meaningful in- secondary antibodies are used to bind and visualize the target protein. formation about the experimental samples [14,19–22]. The Western blot was originally intended to provide a yes/no an- This review aims at providing a step-by-step breakdown of the ap- swer about the presence of the target protein in a protein sample. This proach to perform and gather quantifiable data from Western blots qualitative method confirms the presence of target bands by simple (Fig. 1). visual assessment [3–8]. With the surge in the field of systems biology and the need to understand complex biological systems, Western Blot is 2. Quantitative Western Blot analysis: core concepts no longer limited to generating qualitative data. The reproducibility of Western blot analysis and other im- At the heart of protein detection via Western Blotting is the use of munoassays is an ongoing source of concern in the scientific community quality reagents and correct methodology. While there are several re- [6,9–11]. The immunoblotting process involves a complex series of sources available to overcome the technical limitations of Western interdependent steps that are influenced by subjective choices and user Blotting, there is limited information available on how data can be expertise [6,11–14]. Variations in experimental design, methodology, quantified and the correlation between the combined linear range of and technique can be substantial sources of error. This variability is detection and normalization strategy. particularly troubling for quantitative analysis of Western blot data, where an error could lead to misinterpretation of data [6,14,15]. See- 2.1. Break it right: “Fit for purpose” mingly minor or insignificant differences in the reagents and parameters used for each experiment may have a surprisingly strong influ- The quality of the data gathered from a Western blot is as good as ence on the results [6,9,13,14,16–18]. Careful experimental design and the quality of the sample used. Protein extracts need to be prepared, well-characterized methods are essential to avoid common pitfalls and purified and quantified using reagents that best suit the sample type. determine which assay parameters are most critical for reproducible How would you choose from the numerous extractions, fractionation,

∗ Corresponding author. E-mail addresses: [email protected], [email protected] (L. Pillai-Kastoori). https://doi.org/10.1016/j.ab.2020.113608 Received 1 August 2019; Received in revised form 16 December 2019; Accepted 27 January 2020 Available online 31 January 2020 0003-2697/ © 2020 LI-COR BIOSCIENCES, LINCOLN, NEBRASKA 68504. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/). L. Pillai-Kastoori, et al. Analytical Biochemistry 593 (2020) 113608

Fig. 2. Linear relationship between sample concentration and band intensity. The signal derived from the protein bands on a Western blot varies with the amount of sample extract loaded onto the protein gel. The illustrated graph depicts a linear and proportional relationship between amount of sample loaded (x,2x,5x) and the relative ﬂuorescence units (R.F.U) captured from the target bands (y,2y,5y). Tail and shoulder end of the data curve capture noise and saturated signal, respectively. μg, micrograms; R.F.U, Relative ﬂuorescence units.

Fig. 1. Quantitative approach to Western Blotting. Understanding of the physiology and cellular location of the target protein will allow for optimal protein sample can improve outcomes in Western Blotting [15,29,32]. protein extraction outcomes from various sample source. Quantification of For accurate quantitative analysis, sample loading must be evaluated protein extracts prior to Western blotting is crucial first step in generation of and calibrated experimentally by the user. How do you determine the accurate data. Validation of antibodies is required both by the vendor as well as correct loading concentration for the sample of interest? the end-user in the context of the experimental system under study. Internal For accurate quantitative data analysis, the relationship between loading controls (ILC) needs to identify and validated within the context of the sample loading and band intensity must be evaluated and calibrated to experimental study. Both target and internal loading control needs to detect and determine the linear range of detection for the assay [3,6,7,29]. The analyzed within the combined linear range (CLR) of detection. Normalization of linear range of detection is the range of sample loading where band the target protein to appropriate ILC will provide an accurate representation of how target protein abundance is affected in the experimental study. Finally, intensity increases in proportion to sample loading or target abundance. biological and technical replicates not only provide statistical power but also Within this range, a change in sample input will produce a linear and rule out user bias and accounts for biological variability. proportional response in signal output which is determined coefficient of determination R2. Closer the R2 value is to more the linear regression reflects the data. For example, a two-fold or five-fold increase in sample and purification methods? Several comprehensive reports have been loading should theoretically induce an equivalent increase in relative published by researchers including Bass et al. [23], Pandey et al. [24], fluorescence units which would be recorded as band intensity (Fig. 2). Wingfield et al. [25], Peach et al. [26], Gavini et al. [27], and Miskiewiz Analyzing data outside of the linear range may result in inaccurate and et al. [28], describe in great details various considerations for preparing non-reproducible data. All quantification must be performed within the quality protein extracts. Work published by Murphy and the group also range where an increase in sample input (x,2x, and 5x) produces a describes the implication of sample fractionation on downstream pro- linear and proportional response in signal output (y, 2y, and 5y). Above tein detection [29]. Users' experimental workflow influences the the linear range of detection (shoulder), strong bands exceed the ca- strategy for extraction and solubilization of target protein. It is critical, pacity of the assay and become saturated; the expected, proportional however, to standardize the sample preparation method in alignment increase in band intensity does not occur and strong signals will be with the experimental design and using consistent protocols throughout underestimated. Below the linear range (tail end), faint bands are dif- the duration of the study. In summary, the user needs to consider the ficult to reliably distinguish from membrane background and do not type of target protein (nuclear, transmembrane, mitochondrial, etc.) as reflect actual differences in band intensity. well as the type of tissue/cell that houses the target in order to choose We strongly emphasize the dual importance of linearity and pro- the extraction, purification, and quantification strategy. portionality in the quantitative analysis of Western blot data. Linear regression can be used to fit a data set to a straight line, but this does 2 2.2. Sample: loaded or overloaded? not imply or guarantee proportionality. An R value equal to 1 indicates that the regression line represents the data perfectly, but this line may The next important question is; how much sample needs to be or may not represent a proportional signal response. A linear and loaded for separation using two-dimensional electrophoresis. Sodium proportional relationship is represented by the equation y=mx, which dodecyl sulfate-polyacrylamide gel (SDS-PAGE) matrix, electroblotting describes a straight line passing through the origin. However, it may be (transfer), and protein binding capabilities of membranes influence possible to identify regions or subsets of the data that do display a together with the type of target protein influence the overall perfor- proportional response. mance of Western Blotting technique. Overloading of samples is a widespread problem that often goes unrecognized and may compromise 2.3. Combined linear range (CLR) of detection the accuracy of quantitative analysis [3,4,6,7,30,31]. Murphy and others have successfully shown that in fact, loading low amounts of The linear range of detection is central to quantitative analysis of

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Western blot data; the accuracy and validity of analysis are based on Adoption of HKPs as ILCs for immunoblotting was inspired by this foundation. Because it is influenced by numerous experimental housekeeping genes, the stably expressed transcripts used as en- factors, the linear range should be determined empirically for each dogenous controls for analysis of gene expression analysis. Stable ex- Western Blot assay. A combined linear range denotes the linear range pression of actin, tubulin, and other housekeeping genes in many cell for both the target protein as well as a loading protein [also called types and tissues initially indicated that their protein products should Internal Loading Control (ILC)]. Internal loading control as the name be similarly appropriate for Western blot normalization. However, the suggests is present internal to the test sample itself and could be a single expression of some HKPs is now known to be altered in response to Housekeeping protein (HKP) or total cellular protein. Serial dilutions of certain experimental conditions. Recent studies have demonstrated biological samples can be used to identify the appropriate linear range regulated expression of actin, tubulin, and other HKPs in response to of sample loading for both the target protein(s) and internal loading conditions such as cell confluence, disease state, developmental stage, control. It is important to note that this linear range will likely be dif- hormonal state, drug treatment, and cell or tissue type ferent for each target protein and loading control combinations and [33,35,37,41–49]. Manceau et al. [43], demonstrate the variability in may be affected by the detection chemistry and/or imaging platform. β-tubulin and β-actin across different tissues extracts from mouse The relationship between sample input and band intensity must be both neonates. Because the variability of HKP expression in experimental linear and proportional for both target protein and internal loading samples will introduce error in quantitative analysis, validation is an control to enable quantitative analysis. Choosing an appropriate ILC important first step. Before an HKP or other single-protein loading may seem simple, but it is an important and sometimes obscure aspect control is used for quantitative analysis of Western blot data, its stable of experimental design for accurate quantitative analysis of Western expression should be demonstrated and verified for the relevant ex- blot data [6,10]. perimental samples and manipulations [3,29,33–35,37,47].

2.3.1. Internal loading controls 2.5. Internal loading control: total cellular protein The ILC must meet the two requirements when evaluated in the specific conditions, treatments, cell or tissue types, and experimental In recent years, total cellular protein staining of the blotted mem- context where it will be used. Because experimental manipulations may brane has emerged as the preferred method for normalization of sample influence the expression of the ILC, biological stability should be vali- loading in quantitative analysis of Western blot data dated in all samples that represent the intended conditions and treat- [35,37,41,44,47,50–53]. Prior to the immunodetection of the blot, a ments. Biological variability in expression of the ILC is a source of error fluorescent total protein stain is used to visualize and assess actual that may undermine the accuracy of the analysis; particularly for ana- sample loading across the blot. The membrane is then processed for lysis of small differences in target abundance [3,11,33–35]. The antibody incubations and detection. Total protein loading controls are variability introduced by technical limitations (gel matrix, transfer much more resistant to biological variability than an HKP or other conditions, buffers, etc.) of the Western blot method should also be single-protein loading control [3,15,31,35,44,47,50,51] and may pro- considered when choosing and validating an ILC. Either type of varia- vide a wider linear range of detection than an HKP – and because of its bility may make it more difficult to reliably detect small differences and resistance to biological variability, it requires less validation prior to effects, leading to false-negative results [4,15,36,37]. use [3,4,7,15,35,41,44,50,51]. Because variation is often introduced Normalization of a target protein is most accurate when the target during electrophoretic transfer from the gel to a membrane, total pro- protein and ILC are detected in the same lane on a single blot tein staining of the blot is preferable to staining of the gel prior to [6,8,38,39]. To achieve this goal, detection must be performed in the transfer or staining of a duplicate gel loaded with the same samples combined linear range – the range of sample loading that produces a [3,35,47,51]. Any method used for the detection of total protein linear, proportional response in band intensity for both the target and loading should not damage the bound sample proteins or interfere with the ILC [3,6,7,29, 33]. If they cannot be accurately detected in the same subsequent antibody-based detection of the blot [6,31,35,54,55]. A range of sample loading, the results will not be meaningful; a different total protein stain should meet a number of criteria, including an ap- ILC should be selected for the experiment. This may occur for a variety propriate linear range of detection that is compatible with the target of of reasons, such as the use of an abundant HKP as an ILC for a low- interest; stable signals and low sample-to-sample variability; lack of abundance target, a pan/PTM analysis experiment where an abundance interference with immunodetection and other downstream analysis; of unmodified and modified forms varies dramatically, if primary an- suitability for membrane staining, to correct for variability during tibody specificity and affinity are poor, or if transfer methods require transfer; and compatibility with multiplex detection and quantification further optimization. [3,6,35,41,47,51–53,55,56]. Several fluorescent membrane stains have recently been described in the literature, including Ponceau Stain [55], 2.4. Internal loading control: housekeeping protein Coomassie Brilliant Blue, SYPRO Ruby stain, Blot FastStain [57], and Revert 700 Total Protein Stain [35,58–62]. A single endogenous reference protein, such as a housekeeping protein, is often used as an ILC. Members of Actin, tubulin families and 2.6. Detection chemistry glyceraldehyde‐3‐phosphate dehydrogenase (GAPDH) are common examples. This method is referred to in the literature as a single-protein Two main types of detection chemistry are used to visualize signals loading control [3]. Although this method is widely used and can be on Western blots. Enhanced chemiluminescence (ECL) is a popular quite accurate, it is uniquely vulnerable to biological variability [40]. enzymatic method that uses horseradish peroxidase (HRP) as an in- An HKP or other single-protein loading control alters and reformulates direct reporter of secondary antibody binding. This HRP reporter pro- the experimental hypothesis, as described by Aldridge et al. [3]. Rather duces photons of light as it consumes a luminol-based substrate. than evaluating the abundance of the target protein relative to total Fluorescence detection is a non-enzymatic method that uses fluor- sample protein or cell number, the experiment now examines target ophore-labeled secondary antibodies as a direct reporter of antibody abundance relative to that particular single-protein loading control [3]. binding. Although chemiluminescence is widely used, its inherent en- If the expression of that single protein is stable and unaffected by the zyme/substrate kinetics are a source of variability and often fail to relevant experimental conditions and samples, this ILC meets the first produce the linear, proportional signal response required for quantita- requirement and the hypothesis is valid. But if the expression of that tive analysis of Western blot data [5,6,8,11,63]. The rate of the che- single-protein loading control is variable, the reformulated hypothesis miluminescent reaction is dependent on the local concentration of en- will fail. zyme and substrate. Substrate availability is a dynamic property, and

3 L. Pillai-Kastoori, et al. Analytical Biochemistry 593 (2020) 113608 the reaction rate will vary continuously over time and across the surface 2.7. Normalized Western blot data of the blot. A low-intensity band will consume the available substrate much more slowly than a strong band with a high local concentration of Normalization mathematically corrects for small, unavoidable var- HRP reporter that may rapidly deplete the available substrate. If the iations in sample loading and protein transfer by comparing the target HRP concentration is too high, a signal may be lost in areas where the protein to the ILC in each lane. The target protein signal in each lane is substrate is rapidly consumed and exhausted; this is the cause of ghost divided (normalized) by the signal value for the ILC in that lane. When bands (reverse banding) and “burned-in” bands with brown or yellow target signals are normalized to sample loading, relative levels of the precipitate [6,64]. Different chemiluminescent substrate formulation target protein can be compared across the blot to determine if changes can be specifically used to detect femtograms (ultrasensitive), pico- in band intensity represent biological differences between samples. grams or milligrams of target protein within a lysate [65,66]. As such Normalization is based on the fundamental assumption that both the simultaneous detection of high and low concentration of proteins in the target and ILC signals are dependent on sample concentration. For this same blot can be challenging with one formulation ECL substrate assumption to hold true, as mentioned above the ILC must meet two [67–69] thereby, resulting in a non-linear and narrow dynamic range of requirements: it must be expressed at a generally constant level across detection. However, titration of protein amount, the primary antibody all relevant experimental samples and conditions; and the resulting can help in mitigating all the above-mentioned issues, provided the signal intensity must be proportional to abundance, without saturation scientist is cognizant [52,70]. effects or technical limitations. A loading control that does not meet We and others developed fluorescent Western blot methods as a both requirements is not an accurate indicator of sample loading and more convenient and quantitative alternative to indirect chemilumi- should not be used for normalization and quantitative analysis of nescence [63,71]. Fluorophores are retained at the site of antibody Western blot data [3,31,47,51]. binding and emit photons upon exposure to the appropriate wavelength Without normalization, an apparent difference in target abundance of excitation light, typically in the near-infrared (NIR) spectrum on a Western blot cannot be accurately interpreted. This concept is il- [5,6,8,38,71–73]. Signals are very stable and much more reproducible lustrated in Fig. 4. Detection of the target protein is seen in Fig. 4A because they are unaffected by timing, substrate availability, and other (green). Plotting the raw intensity values for the target may suggest that variables that limit the usefulness of chemiluminescent methods for target abundance is variable among the different samples (Fig. 4C). quantitative analysis [5,6,8,11,74]. Fluorescence imaging also enables However, the observed difference between target bands may be the multiplex detection of a target protein and ILC in the same lane on the result of inconsistent loading of total protein extract (Fig. 4B). Sample same blot, for more accurate correction of sample-to-sample and lane- #4 appears to have reduced levels of target protein compared to rest of to-lane variation [6,8,38,63]. Stripping of Western blots for re-probing the samples, however, after normalization to correct variations in can cause substantial loss of sample proteins from the membrane and is sample loading; it is evident that sample extracts were loaded unequally an avoidable source of error [6,8,30,31]. Fluorescence is generally re- in each lane and therefore, any changes in target protein observed may cognized as the most accurate and reliable method for quantitative be due to improper loading of protein extracts and not due to experi- analysis of Western blot data [4–8,41,73,74]. Source of species in which mental intervention (Fig. 4D). antibodies were raised, the wavelength of secondary antibody tagged Normalization is appropriate for the correction of small and un- fluorophore, and imaging system needs are critical to performing avoidable differences in cell number, sample concentration, loading fluorescence-based Western blot detection of proteins. error, and position or edge effects from electrophoresis and transfer. Saturation artifacts are not readily apparent in a Western blot image However, it should not be relied on to correct for preventable error and and may go undetected if serial dilutions of a sample are not examined variability in the quantitative analysis of the Western blot process [3,6]. to define the linear range of detection. Two types of saturation are Normalization should be used in addition to careful experimental de- frequently observed in quantitative analysis of Western blot data. sign and sample preparation, not in place of it. Minimizing or elim- Membrane saturation is caused by the overloading of the sample inating sources of variation throughout the experiment is critical for protein. During the transfer of an overloaded gel to a blotting mem- reproducible quantitative analysis of Western blot data (see Refs. brane, abundant sample proteins bind in layers on the surface of the [6,7,29,78] for excellent information about protein extraction, sample membrane [11,15,31,76]. If highly abundant proteins exceed the local handling, and other error-prone aspects of the Western blot method). binding capacity of the membrane, they will be washed away. The Total protein normalization is an aggregate method that combines layering of abundant proteins also limits antibody access and may only the band intensities from many different sample proteins in each lane – allow antibodies to bind the top layer of protein. This layering effect essentially using multiple ILC proteins to provide a more accurate causes underestimation of strong bands and may also interfere with the readout of sample loading. This multi-protein approach is also called detection of low-abundance proteins on the blot. Membrane saturation normalization by sum [36]. This normalization method also offers a is common may be difficult to identify without the evaluation of a di- more direct readout of the cellular material loaded than antibody-based lution series of the sample. This type of saturation can affect any detection of a single endogenous protein [3,4,31,35,36 ,41,44]. Total Western blot assay, regardless of the detection chemistry or imaging protein normalization is now increasingly viewed as the “gold stan- method [11,15,77]. dard” for performing quantitative analysis of Western blot data [52,53]. Signal saturation occurs when the intensity of a strong band ex- The Journal of Biological Chemistry recommends normalization to total ceeds the capacity of the detection chemistry or imaging system. At this protein loading, clearly stating their preference “that signal intensities point, strong signals begin to plateau and increasing amounts of target are normalized to total protein by staining membranes with Coomassie no longer produce the expected increase in signal (Fig. 3). Chemilu- Blue, Ponceau S, or other protein stains” [52,53]. minescent detection is highly susceptible to saturation; the generation of a signal is restricted by substrate availability, and the linear range is 3. Background subtraction quite limited, even when detected by digital imaging [5–7,11,15,74]. Fluorescent Western blot methods are relatively resistant to saturation Western blots not only contain the signal generated by the im- and provide a much broader linear range, particularly when imaged munodetection of the target protein but also signal generated as a result with a digital system that provides an appropriately wide dynamic of nonspecific bands, smears, and the inherent signal produced by the range [5–8,73,74]. However, in heavily loaded samples, self-quenching membrane itself. When a target band is quantified the background of tightly packed fluorophores may be possible [6]. signal around the band is also included in the data analysis, and the goal is to reduce/remove the confounding error introduced by such background signal [23,79,80]. Currently, several Western blot analysis

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Fig. 3. Saturation of strong bands obscures actual differences between samples. Increasing amounts of C32 (sc-2205) whole cell lysate (1–60 μgs) were loaded on 4–12% Bis-Tris PAGE gels and transferred onto a PVDF membrane. Fluorescent detection of Actin was detected on three replicate blots performed on different days. A) Actin bands were analyzed and quantified with Image Studio™ analysis software (LI-COR Biosciences) and the detected band intensities compared to the predicted signal intensity, based on the known amount of sample protein loaded in each lane. Results of three replicate experiments are shown (blue line indicates mean values; dotted line shows predicted band intensity for a linear and proportional response. Above 10 μg of cell lysate per lane, actin signals begin to plateau, and increased sample loading does not produce the expected increase in band intensity. B) Differences between high-intensity bands were underestimated by quantitative analysis. Pink bars show the actual mean band intensity of replicate blots [ ± Standard Deviation (STDEV.)]; blue bars indicate the predicted increase in band intensity. A comparison of the 5 μg and 20 μg sample lanes indicates a 3.1-fold increase in signal, lower than the predicted 4-fold increase. Comparison of the 10 μg and 30 μg sample lanes indicates a larger discrepancy in band intensity: a 1.6-fold increase is observed, roughly half of the expected 3-fold change. At or above 30 μg of sample, increases in band intensity are minimal and differences between lanes are not reproducibly detected (saturated). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) software packages offer multiple options to subtract background signal works well with real-life Western blots that have oddly-shaped mis- and quantify target bands. Each Western blot has a unique profile with shapen bands and smears, and the data generated has greater accuracy respect to artifacts, uniformity of background, positioning of lanes and than local and global subtraction methods (For more details read [85]; bands and requires a background subtraction algorithm that can adapt Fig. 5). to the said variations. There are several methods to subtract the background from the 4. A systematic approach to accurate quantitative analysis of – Western Blot image [23,79,81 84] either using the Shape-Based Western blot data methods for local and/or global background subtraction; Lane-Based methods such as Rolling Ball subtraction. A new patent-pending Here, we describe a five-step systematic strategy to increase the method called Adaptive Background Subtraction (ABS) removes user- precision and reliability of the quantitative data generated from the fi bias by taking into consideration both shape as well as lane pro les to Western blotting technique. This process incorporates critical valida- generate reproducible and accurate quantitative Western Blot data. ABS tion steps that address common, but sometimes unrecognized, sources background subtraction algorithm present in Empiria Studio Software of error and variation. Because we and others strongly prefer and

Fig. 4. Normalization enables accurate interpretation of differences in protein expression. This is a simulated Western blot representing unequal loading of sample extracts to visualize a target protein and total protein extract via antibody and Total Protein Stain respectively. Total protein will be used as an ILC in the simulated data analysis. A) This illustration depicts Western blot results for a target protein in lanes 1–5 (green bands). Lanes 3 and 4 appear to have higher signal for the target protein compared to lane 4. B) The same blot from panel A is presented to depict the total protein extract in lanes 1 through 5 (red). Lanes 3 and 4 appear to have higher total protein extract compared to lane 4. C) Raw intensity values for the target protein bands plotted against the sample number suggest varying expression levels of the target protein. D) Upon normalization of the target protein bands in panel A with the total protein signal in panel B it is evident that sample extracts were loaded unequally in each lane and therefore, any changes in target protein observed may be due to improper loading of protein extracts and not due to experimental intervention. Such a Western blot would not provide accurate and reproducible qualitative data. R.F.U; Relative fluorescence units. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Fig. 5. Quantitative analysis with Adaptive Background Subtraction (ABS) is more reproducible than local and global background subtraction. Multiple users analyzed the designated bands on each blot (arrowheads on images) using local, global/user-defined, and Empiria Studio ABS background subtraction. Variability of background-corrected band intensity, expressed as the mean % CV of the quantitative results for that image, was compared for each background subtraction method. A) Blot A displayed oddly shaped bands and smeared trails in sample lanes. Graph shows the mean CV of quantitative analysis for each background method (error bars show standard deviation, n = 6). B) On Blot B, faint bands were surrounded by background in sample lanes. Graph shows the mean CV for each method (error bars show standard deviation, n = 6). C) The designated bands on Blot C were smeared and misshapen. Graph shows the mean CV for each method (error bars show standard deviation, n = 5). recommend fluorescent Western blot methods for quantitative analysis preference to bind the target antigen in the presence of a heterogeneous [5–8,52,63,74], this strategy was specifically developed for use with mixture of sample proteins. These characteristics should be verified and fluorescence-based detection. validated for each primary antibody. Because antibody performance is greatly influenced by the assay context and parameters, validation 4.1. Validate the primary antibody should be performed by the user in the intended assay and relevant experimental context [18,86–94]. The reproducibility of results pro- After sample proteins are separated on a protein gel and transferred duced with the primary antibody should also be verified [18,76,88,95]. to the membrane, primary and secondary antibodies are used to bind and visualized the target protein. This process relies on two key prop- 4.1.1. Specificity erties of the primary antibody: specificity, the antibody's ability to re- The specificity of antibody within the realm of a Western blot means cognize and bind to the target antigen; and selectivity, the antibody's that the antibody recognizes the target protein(s), either as a single

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distinct band or a set of bands of the correct molecular mass. Detection of a single band at the expected molecular weight is an important first step but is not sufficient to prove antibody specificity. It is wise to re- member that the presence of “one band” on a blot does not mean “one target” [7,53,86,87,96,97]. Antibodies can be validated by testing the performance of antibodies on either genetic knockout, knockdown, positive, and negative protein samples (see for more details [92,98–104]). The specificity of the antibodies against Pan and Phospho-EGFR was validated in a 2-pronged approach by using a peptide blocking strategy. A single blot (Fig. 6A and B) is divided into 2 halves (yellow line) and each half was then incubated with stained with a Total Protein Stain (Fig. 6A; Revert 700 stain) which was detected in the 700 nm channel and Anti-Phospho-EGF Receptor (EGFR) antibody (Fig. 6B) which was detected in the 800 nm channel. The portion of the blot with the blocked antibody cocktail (Anti-Phospho EGFR + Phospho-EGFR immunogen peptide) has Revert 700 signal but no visible signal in the 800 nm channel (Fig. 6B; Right of the yellow line). Staining with a total protein stain ensures that equal protein was loaded in all the lanes and it is not a lack of protein lysate that con- tributes to absent bands in the blocked portion of the membrane. This data suggests that antibody against Phospho-EGFR protein detects a band ~175 kDa which is absent in the blocked blot. A separate blot containing equal amounts of protein extracts was divided into 2 halves (Fig. 6C and D) and multi-color detection of Pan-EGF (Fig. 6C) and Phospho-EGF (Fig. 6D) was performed. The portion of the blot with the blocked antibody cocktail (Anti-Phospho EGFR + Phospho-EGFR immunogen peptide) has protein signal in the 700 nm channel from im- Fig. 6. Validation of antibody against Phospho-EGFR in both untreated munodetection of Anti-Pan-EGFR (Fig. 6C) but no visible signal in the and Etoposide treated A431 extracts via peptide blocking method. Untreated and Etoposide treated A431 cell extracts were loaded onto 4–12% 800 nm channel (Fig. 6D; Right of the yellow line). Bis-Tris gel (Life Technologies NP0303; Lot# 16010670) and run under MOPS buffer system. A single blot was split through the center (yellow line) with each 4.1.2. Selectivity blot piece containing protein ladder. (A) After wet tank transfer and prior to In a typical quantitative analysis of the Western blot data, target blocking with a blocking buffer, the nitrocellulose blot was stained with Revert protein abundance may be lower relative to a large excess of unrelated 700 Total Protein Stain to visualize total protein extract loaded on each lane. sample proteins. The antibody must be able to overcome this imbalance The blot displays equal amounts of total protein loaded on each lane in the and selectively bind the target antigen in a complex mixture, without ′ 700 nm channel (red). (A ) The same blot in (A) was split into 2 pieces and the interference from off-target binding [90,92]. For this reason, selectivity piece on the left-hand side was incubated with Phospho-EGF Receptor (EGFR) should be verified with endogenous levels of target expression in the (Tyr1068) (D7A5) XP® Rabbit mAb (CST #3777; Lot #13; 1 μg/ml) and anti- complex sample [76,88]. Puri fied or overexpressed target protein alters body binding was detected using IRDye® 800CW Goat (polyclonal) Anti-Rabbit the balance of protein abundance in the sample, creating an artificial IgG (H + L) at 1/15000 dilution. The blot of the left was incubated with a fl solution of pre-incubated mixture of Rabbit Phospho-EGFR antibody and context that may not re ect actual antibody selectivity and expected ff custom synthesized blocking peptide (twice the molar ration of primary anti- o -target binding [88,105,106]. body) corresponding to the antibody epitope of CST #3777 (CST #Y1068; Lot Optimization of certain assay conditions may improve antibody #5; 2 μg/ml). Both the blot pieces were incubated for one hour at room tem- selectivity. Insufficient antibody dilution and extended incubation perature with gentle shaking. Evident lack of signal in the blocked portion of times may promote off-target binding and detection of undesired, blot suggests that signal observed in the left blot is indeed specific to CST nonspecific bands [30,47,76,92]. Storage and reuse (“recycling”)of #3777. (B) CST #3777 antibody was further validated in A431 lysates by diluted primary antibodies is not recommended; this practice results in probing the same blot with Mouse Anti-Human EGFR antibody cocktail inconsistent antibody quality, titer, and stability that may introduce fi (ThermoFisher Scienti c AHR5062; Lot# 73759137A) and antibody binding error and make a quantitative analysis of Western blot data results less was detected using IRDye® 800CW Goat (polyclonal) Anti-Mouse IgG (H + L) at reproducible [76]. Blocking buffer can also have a dramatic impact on 1/15000 dilution in the 700 nm channel to ensure that lanes have an equal antibody selectivity; an inappropriate blocking agent may greatly in- amount of protein extracts as well stability of Pan-EGFR in the A431 extracts. ff Signal for Pan-EGFR detected by AHR5062 antibody is observed on both sides crease o -target binding in Western blot analysis [107,108]. of the blot suggesting that the blocking peptide is specific to CST #3777. (B′) The same blot in (A) was split into 2 pieces and the piece on the left-hand side 4.1.3. Publication guidelines was incubated with Phospho-EGF Receptor (EGFR) (Tyr1068) (D7A5) XP® Detailed reporting of antibody details and experimental methods is Rabbit mAb (CST #3777; Lot #13; 1 μg/ml). The blot of the left was incubated an important aspect of quantitative analysis of Western blot data with a solution of pre-incubated mixture of Rabbit Phospho-EGFR antibody and [53,87,109,110]. Recommendations for reporting of antibody in- custom synthesized blocking peptide (twice the molar ration of primary anti- formation are addressed elsewhere (including [76,87,110]). The au- body) corresponding to the antibody epitope of CST #3777 (CST #Y1068; Lot thors should describe how antibody specificity was validated, including μ #5; 2 g/ml). Both the blot pieces were incubated for one hour at room tem- the methods used, assay parameters, positive and negative controls, and perature with gentle shaking. Evident lack of signal in the blocked portion of fi sample type (cell or tissue type, source, lysate, overexpressed or pur- blot suggests that signal observed in the left blot is indeed speci c to CST fi #3777. (For interpretation of the references to colour in this figure legend, the i ed target protein) The authors should be prepared to submit the raw reader is referred to the web version of this article.) validation data and unprocessed images during review [76,87,110].

4.2. Validate the internal loading control

An ILC must be present in all experimental samples at a stable level

7 L. Pillai-Kastoori, et al. Analytical Biochemistry 593 (2020) 113608

Fig. 7. Identification and validation of an appropriate HKP in the MAPK/ERK pathway. Cell extracts prepared from untreated (UT) or Epidermal growth factor (EGF) (ET) treated A431 cells were loaded onto 10% 10% Bis-Tris gel (NP0303BOX) and run under the MOPS buffer system. All the blots were incubated with Intercept Blocking Buffer for one hour at room temperature. Technical replicates for both UT and ET categories are presented. Proteins belonging to MAPK/ERK pathway were selected to be tested via Western blot method. Total protein extract was visualized in each blot (a, a’,a”) using Revert 700 Total Protein Stain (LI-COR P/N 926-11010). Blots; b, b’, and b” were incubated with Anti-Ras antibody (CST #3965; Lot #1); Anti-CDK4 (D9G3E) antibody (CST #12790; Lot#14); and Anti- CDK6 (DCS83) antibody (CST #3136; Lot #4). Merged images of the blots (c, c’,c”) imaged under different acquisition channels; 700 nm for Revert 700 (a, a’,a”) and 800 nm for Ras, CDK4, and CDK6 (b, b’,b”) highlight uniform loading of sample in each lane. Ras, CDK4, and CDK6 levels appear to be unchanged between untreated and EGF treated A431 cell extracts (d, d’,d”). F test to compare variance, P value > 0.05). Sample loaded: 5 micrograms; Blocking buffer: Intercept Blocking Buffer (TBS); Intercept T20 (TBS) Antibody Diluent; Protein ladder: Chameleon duo ladder (LI-COR P/N 928-70000; Lot# C70803-03). Imager: Odyssey® CLx; resolution: 169 μm; Intensity: auto mode.

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Fig. 8. Determination of the combined linear range of detection for CPARP, β-ACTIN (ACTB), GAPDH, and COXIV. Jurkat cell extracts harvested from cells treated with 25 μM Etoposide for 5 h at 37 °C was loaded on 4–12% Bis-Tris NuPage gels and electrophoresed under the MOPS buffer system. The blots were blocked with Odyssey Blocking buffer- TBS for one hour at room temperature and subsequently incubated with primary antibodies against CPARP (CST #5625; Lot#13), ACTB (LI-COR P/N 926-42210; Lot #C80322-01), GAPDH (CST #5174; Lot #2), and COXIV (LI-COR P/N 926-42214; Lot #C5033324-02) (~1 μg/ml) at 4 °C overnight with gentle shaking. Subsequently, the primary antibody binding was visualized by using IRDye 800CW Goat anti-Rabbit IgG (H + L). Sample loaded: 1.25–40 micrograms; Blocking buffer: Odyssey Blocking Buffer (TBS); Protein ladder: Chameleon duo ladder (LI-COR P/N 928-70000; Lot# C70803-03). Imager: Odyssey® CLx; resolution: 169 μm; Intensity: auto mode. Linear range of detection for all the proteins are plotted in (A). Linear range of CPARP and COXIV (B) appears to have overlapping range of detection compared to CPARP and ACTB (C) and CPARP and GAPDH (D). to be used as a valid indicator or proxy of sample loading protein loaded in each lane (Fig. 7a, a’,a”). No significant changes in [3,29,33–35,37]. Stability should be demonstrated before an HKP or protein expression were observed for Ras, CDK4, and CDK6 proteins other single protein is used for quantitative analysis of Western blot (Fig. 7b, b’,b”;d,d’,d”), and therefore, is suitable to be used as HKP for data normalization, to ensure that expression of the ILC is not affected the samples under consideration. by experimental treatments or manipulations [29,33,34,47,53,111]. 4.3. Two-sides of the same coin: Post-Translationally modified (PTM) 4.2.1. Stable or Unstable: ILC? proteins Expression of the HKP or other single-protein control should be carefully examined in samples that represent the desired experimental Western blots generated to detect PTM proteins utilize an ILC that conditions. A comparison of HKP levels to total protein loading in these accounts for the total presence of the unchanged protein (also known as samples is a straightforward way to demonstrate the stability of ex- Pan-protein). Here, a modification-specific primary antibody is multi- pression. Alternatively, HKP levels may be compared to an unrelated, plexed with a pan-specific primary antibody that recognizes the target endogenous protein already shown to be stably expressed in the re- protein in any modification state [6,38,71,72,112 –114]. The pan- and levant experimental samples and conditions. If changes are made in modification-specific antibodies should be derived from different host experimental treatments, cell line, tissue type, cell density, or other species, so they can be discriminated by secondary antibodies labeled relevant experimental parameters, stable HKP expression, as well as a with spectrally distinct fluorophores [63]. Pan/Phospho and other linear range of detection, must be re-validated for the new conditions types of pan/PTM analysis are widely used to monitor and compare [33,34,47]. Several proteins belonging to the MAPK pathway were relative changes in protein modification across a group of samples. Pan/ tested for stability in both vehicles treated and etoposide treated Jurkat Phospho analysis is specifically recommended by the Journal of Biolo- cell extracts (Fig. 7c, c’,c”). Each blot was also stained to visualize total gical Chemistry, which stipulates that “phospho-specific antibody signals

9 L. Pillai-Kastoori, et al. Analytical Biochemistry 593 (2020) 113608

Fig. 9. Combined Linear Range (CLR) detection of Total protein extract and OCT-4 in HEK293T cells. 4-fold dilution of cell extracts from HEK293T untreated control (a’-a”) as well as knockdown HEK293T (experimental) (b-b”) category were loaded onto 10% Bis-Tris gel (NP0303BOX) and run under the MOPS buffer system. All the blots were incubated with Revert 700 Total Protein Stain (LI-COR P/N 926-11010) to visualize total protein extract in each lane. Subsequently, the blots were incubated in Intercept Blocking Buffer for one hour at room temperature and thereafter in Anti-Oct-4 antibody (CST #2750; Lot #4) and IRDye 800CW Goat anti-Rabbit IgG (H + L). (a-a”) Individual linear range of detection for total protein and OCT-4 was determined for control cell extracts (a - a”) as well as for experimental cell extracts (b – b”). Combined linear range for total protein extract and OCT-4 protein detection was plotted for both cell extract types (c, d). CLR was identified at 4.5 micrograms for both cell types. Blocking buffer: Intercept Blocking Buffer (TBS); Intercept T20 (TBS) Antibody Diluent; Protein ladder: Chameleon duo ladder (LI-COR P/N 928-70000; Lot# C70803-03). Imager: Odyssey® CLx; resolution: 169 μm; Intensity: auto mode. should be normalized to total levels of the target protein” [97,111]. 4.4. Define the linear range of detection Although pan/phospho analysis is the most commonly performed type of pan/PTM analysis, other protein modifications are also studied with Using the validated antibodies, it is important to determine the this method (ubiquitination and palmitoylation analysis). appropriate amount of sample to load to generate data that can be Pan-protein normalization accounts for changes in expression of the quantified. Normalization and analysis must be performed in the linear target protein, which might confound analysis of the abundance of the range of detection, where a linear and proportional response is ob- modified form. It also eliminates the stripping and reprobing of blots, served between sample loading and band intensity. which introduces error by causing loss of blotted proteins from the membrane [6,31,38]. Because the unmodified and modified target 4.4.1. Finding the combined linear range for a target protein and internal protein is detected on the same blot and in the same lane, normalization loading control can correct for transfer artifacts in the blot. Pan/PTM analysis should Careful examination of the linear range is particularly important for only be used for comparison of relative abundance; it does not provide HKPs and other single-protein loading controls. The strong bands pro- information about the stoichiometry of the modification [77]. duced by highly abundant ILC are frequently affected by saturation. If the abundance of a target is substantially lower than the single-protein loading control, the combined linear range may be very narrow. Total 4.3.1. Publication guidelines protein normalization typically provides a wider combined linear If an HKP is used for quantitative analysis of Western blot data range, without the rapid saturation observed for HKPs normalization, authors should be prepared to provide evidence that its [35,44,50,51,55,56]. This wider combined linear range is very helpful expression was not affected by the experimental treatments applied for low-abundance target proteins, which often require the loading of [53,111]. The editors of the Journal of Biological Chemistry have ex- large amounts of sample protein and cannot be used with a highly pressed a preference for total protein normalization in quantitative abundant HKP. analysis of Western blot data [53]. The experiment in Fig. 8 demonstrates the differences in a linear range that can be observed between different HKPs. Cleaved PARP (c-

10 L. Pillai-Kastoori, et al. Analytical Biochemistry 593 (2020) 113608

PARP) and three different HKPs (β-actin, COX IV, and GAPDH) were normalized signalExperimental sample Fold change = detected in lysates from Jurkat cells treated with 25 μM Etoposide for normalized signalControl sample 5 h at 37 °C (check material and method section for more details). Because the target and HKPs span a range of MW and are well resolved A ratiometric analysis is often performed to enable a relative com- by electrophoresis, all four proteins could be detected simultaneously parison of target abundance across a group of samples. The normalized on a single Western blot. This facilitates comparison by eliminating target signal for each sample is divided by the normalized target signal blot-to-blot inconsistencies in sample loading, electrophoresis, and observed in the control sample. These ratios express the abundance of transfer. Fig. 8A shows a combined linear range for poly (ADP-ribose) the target protein as a fold or percentage change, relative to the control. polymerase cleavage (CPARP), GAPDH, COXIV, and β-actin (ACTB). In A comparison of all samples to the experimental control produces re- Fig. 8B, the combined linear range for CPARP and COX IV is linear lative values that are unitless, proportional, and independent of the raw between ~7.5 and 30 micrograms. However, there is no useable com- signal intensity. Fold change values > 1.0 indicate increased abun- bined linear range for CPARP, ACTB, and GAPDH (Fig. 8C and D). In dance relative to the control, and values < 1.0 denote decreased these samples, band intensity for GAPDH and ACTB begins to saturate abundance. Percentage change expresses the same information as a and plateau at ~15 μg of sample protein loading. percentage, with a positive value indicating increased relative abun- Working in the middle of the combined linear range helps to limit dance and a negative percentage indicating decreased abundance error and variability introduced by high- and low-intensity data points. (Table 1). Fig. 10 demonstrates the normalization of CPARP protein to With an HKP or other single-protein loading control, high-intensity data Total protein extracts from Jurkat cells exposed to three different ex- points introduce error and should not be used for normalization; these perimental conditions. Untreated, 33% spiked Etoposide lysate, and data points will increase the mean Coefficient of Variance (CV) of the 100% Etoposide treated lysates are loaded onto a PAGE gel in technical normalized data and should be avoided [15,36]. From a statistical replicates (n = 3). Signal values for total protein, CPARP abundance perspective, variability introduced by a single-protein loading control from each category of cell extracts are represented in 10A′-B’. Fig. 10C’ may increase the frequency of false-negative results (small, but statis- reveals the fold change in expression of CPARP in 33% (~2 fold) and tically significant, differences between samples that are not identified 100% (~3.3 fold) Etoposide exposed extracts. during data analysis). If validation experiments fail to identify a combined linear range of 4.5.1. Publication guidelines detection, as in Fig. 8C–D, a different ILC may be required. The use of Methods used to quantify and normalize signal intensities should be total protein for normalization decreases the variability of high-in- disclosed, along with data analysis methods and software tools used for tensity data points but does have the potential to increase variation for quantification and analysis. Raw data showing image analysis and low-intensity measurements [36]. Fig. 9 demonstrates the combined quantification, including original digital images of Western blots, may linear ranges for OCT-4 protein in HEK293T cell extracts derived from be requested during peer review [13,53,111,115]. control and experimental categories. For both lysates, 4.5 micrograms appear to be in the middle of the combined linear range of detection for 4.6. Replicate and reproduce Total protein extracts and OCT-4 protein. Optimization of transfer methods, sample loading, antibody choice, detection chemistry, or Replication of quantitative analysis of Western blot data results other assay parameters may help to improve the linear range of de- confirms the validity and reproducibility of any observed changes tection for the selected ILC. In general, increasing the number of in- [13,14,18,20,21]. Replicate measurements help to ensure that the ex- ternal control proteins will reduce the mean CV and improve the ac- perimental effects we report are reproducible – that they represent curacy of normalization, making it easier to reliably detect small or actual differences between samples, rather than artifacts of experi- subtle differences between samples [3,6,35,36,51]. mental variability or noise [12,18]. Replication is essential when analyzing small or subtle differences between samples, to characterize and understand the contribution of error and the limits of quantitative 4.4.2. Publication guidelines analysis [12,19,20]. Two types of replicates, technical and biological, When quantitative analysis of Western blot data results are re- are commonly used to address different questions in quantitative ana- ported, authors should disclose how signal intensity was quantified as lysis of Western blot data. well as how the linear relationship between sample loading and signal intensity was confirmed for each antigen [53,111]. 4.6.1. Technical and Biological Replicates Technical replicates are repeated measurements of the same sample, used to characterize the precision and variability of any assay or 4.5. Data analysis method [19,20 ,116]. Common examples include loading the same sample in multiple lanes on a Western blot, running replicate blots in Data analysis of Western blot data sets should be performed on parallel, or running the same kind of gel multiple times on different biological as well as technical replicates generated using the validated days. High variability between technical replicates makes it more dif- antibodies, combined linear range of detection, and valid ILC estab- ficult to separate an observed experimental effect from the inherent lished in the prior steps. After quantification of target and ILC signals, variation of the Q WB assay [12,19,20,53,117]. As a result, small but data analysis is performed. genuine differences between samples may not be detected. Analysis typically begins with the designation of the experimental Biological replicates are parallel measurements of independent and control sample that will be used for relative comparison. The lane biologically distinct samples that are intended to control for random normalization factor is then calculated for each lane, using the HKP biological variation [9,19–21]. A biologically relevant effect should be band intensity values or combined total protein signal values for each observed reproducibly in independent samples. Analysis of samples lane (Fig. 10A). This factor is calculated by dividing the signal intensity derived from multiple individuals or from multiple, independent cell value for each experimental sample by the signal intensity observed for cultures are common examples. In some cases, a similar experimental the control. To apply the normalization factors, the signal intensity of effect may be demonstrated in a different biological system or context the target band in each lane is divided by the lane normalization factor [13,20,86,117]. Biological variability is generally expected to be higher for that lane. This process generates the normalized signal intensity than technical variability [12,16,20]. Considerations for choosing ap- value for each sample. propriate replicates are described by others [19–21]. The replication strategy should be developed prior to the generation of quantitative

11 L. Pillai-Kastoori, et al. Analytical Biochemistry 593 (2020) 113608

Fig. 10. Normalization and analysis of PARP cleavage after treatment with etoposide. Jurkat cell extracts harvested from cells with the following treatments; untreated Jurkat lysate; 33% (untreated lysate spiked with etoposide-treated lysate at a final concentration of 33%); 100% (etoposide- treated Jurkat lysate only). (treated with 25 μM Etoposide for 5 h at 37 °C) was loaded on 4–12% Bis-Tris NuPage gels and electrophoresed under the MOPS buffer system. A. Three blots with biological replicates were incubated with Revert 700 Total Protein Stain (LI-COR P/N 926-11010) to visualize total protein extract in each lane. B–C. Subsequently, the blots were blocked with Odyssey Blocking buffer- TBS for one hour at room temperature and then incubated with primary antibodies against CPARP (CST #5625; Lot#13), Jurkat cells were treated with etoposide to induce cleavage of PARP. Cleaved PARP (c-PARP) was detected in cell lysates by Western blotting (green). A′ Total protein staining (red) was used as an internal loading control for quantitative analysis. B′) Etoposide- induced cleavage of PARP was detected on the membrane (graph shows raw data and normalized result for each treatment). C′) Relative fold induction of c-PARP was calculated for n = 3 biological replicates and n = 3 technical replicate per biological replicate, using the normalized values (graph shows individual fold-change values and mean). (n = 3 technical replicates for each sample; n = 3 biological replicates). CLR for total protein and CPARP was identified at 5 micrograms for all three cell types. Blocking buffer: Odyssey Blocking Buffer (TBS); Protein ladder: Chameleon duo ladder (LI-COR P/N 928-70000). Imager: Odyssey® CLx; resolution: 169 μm; Intensity: auto mode. Image Studio™ software was used for protein band detection. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Table 1 confidently report a small effect, it may be necessary to minimize Abundance of target protein, expressed as fold or per- sources of variation in the experimental protocol and increase the centage change relative to the control sample. number of replicates to reduce the mean CV. Fold Change Percentage change Normalization of Western blot data should always be performed before the analysis of individual replicate samples. Raw signal in- 1.0 0% tensities should never be directly compared between blots. A multitude 1.25 +25% of experimental factors influence the raw signal intensities observed on 1.5 +50% 2.0 +100% each blot, and direct comparison of band intensity is meaningless. Raw 0.75 - 25% band intensity is useful and informative only when analyzed by re- 0.5 - 50% lative, ratiometric comparison to an appropriate control sample on the 0.1 - 100% same blot. To minimize error introduced by position effects, we recommend loading replicate samples in random order for gel electrophoresis. Consistent placement of certain samples in the same position analysis of Western blot data [19,31] and it may be helpful to consult a (for example, always loading the positive control in the farthest left statistician. lanes of the gel) may consistently produce edge effects that will be inadvertently propagated throughout the data analysis. 4.6.2. Using replicates to characterize error and noise in quantitative analysis of Western blot data 4.6.3. Publication guidelines The coefficient of variation (CV) of replicate measurements can be The number of replicates performed for each quantitative analysis of used to evaluate the precision of quantitative analysis of Western blot Western blot data measurement should be clearly indicated, with suf- data results. A low CV indicates high precision of measurement and low ficient information to clearly distinguish technical replicates from in- signal variability in the assay; a larger CV denotes reduced precision dependent biological replicates [13,19,53,111]. Figure legends should and greater assay variability. The CV can be used to determine if the address experimental uncertainty and explicitly define n for each ex- magnitude of a relative change in target abundance is large enough to periment. Some journals also request that authors present a quantitative be reliably detected above the assay noise. As a general rule of thumb, analysis of Western blot data and other small datasets as scatter plots the magnitude of an observed effect should be at least twice as large as with error bars. This format more clearly depicts the spread and dis- the mean CV of the replicate measurements. In other words, for an tribution of data points than the traditional bar-and-plunger presenta- observed difference of 25% between samples (a 0.75-fold or 1.25-fold tion [53,109,118,119]. Fig. 10 illustrates how different data distribu- change), the mean CV of replicate samples should be less than 12%. To tions can produce the same bar graph – a graph that may suggest

12 L. Pillai-Kastoori, et al. Analytical Biochemistry 593 (2020) 113608 different conclusions than the full dataset [118,119]. Weissgerber et al. 6. Materials and methods [119] recently introduced free, online interactive tools designed to improve data visualization for small datasets. The “interactive dot plot” 6.1. Cell culture tool allows different graphs to be viewed as univariate scatterplots, box plots, and violin plots. It also enables visualization of clustered non- A431 (ATCC® CRL-1555™), Jurkat (ATCC® TIB-152™) were cultured independent data, such as technical replicates. The “interactive re- at 37 °C in a humidified atmosphere containing 5% CO2 as per ATCC peated experiments” is designed for visualization and comparison of guidelines. Cells were cultured up to 80% confluence before being data from repeated, independent experiments. treated or harvested to be stored at −80 °C.

6.2. Cell lysis 5. Discussion Cells were resuspended in radioimmunoprecipitation assay (RIPA) Accurate, reproducible quantitative analysis of Western blot data buffer (Pierce #89900) supplemented with Halt™ Protease and requires careful experimental design and execution. The core concepts Phosphatase Inhibitor Cocktail (100×) (Thermo Scientific #878440) described here should be considered during experimental design and and incubated on ice for 20 min with intermittent vortexing. Lysates planning. Before a sample is lysed for protein extraction, careful con- were sonicated twice at 1-min intervals until no pellet was visible and sideration must be given to habitually disregarded aspects such as the then clarified by centrifugation at 10,000 RCF for 15 min at 4 °C. The cellular location of the target protein, purification strategy, as well as supernatant was taken and protein concentration determined by bi- how the extract will be quantified. Loading of the quantified sample cinchoninic acid (BCA) assay as previously described before being needs further attention into the gel and buffer composition, and elec- stored at −80 °C. trophoresis and protein transfer methods. Validation of antibodies used for immunodetection of the target protein should be undertaken by the 6.3. Commercial cell lysates user and data shared with the readers. Identification of an appropriate housekeeping protein that is stable is crucial and operating within the C32 Whole Cell Lysate: sc-2205, POU5F1 overexpression lysate combined linear range of the HKP and the target protein is even more (Origene # LY400950; Lot #O1AF4D); HEK293T lysate (Origene essential. #LY500001 Lot #O1AF4D); were mixed with either 2× protein loading In recent years, standards and checklists have been proposed to buffer (PLB) (LICOR# 928-40004) OR 2× SDS buffer (OriGene) and enhance reproducibility by improving the transparency of reporting, denatured by boiling at 97 °C for 5 min. The aliquots were then im- increasing recognition of common experimental pitfalls, and raising mediately placed on ice for 5 min before being spun down. awareness about the importance of validation [13, 14, 109, 115, 120–123]. These measures have stimulated conversation about these 6.4. Western Blot important topics, which is critical for the development of the nomen- clature and resources needed to reach a consensus on guidelines and Lysates were loaded onto NuPAGE 4–12% 15 well Bis-Tris gels best practice. Such guidelines must be a community effort and can be (Invitrogen; NP0323BOX), NuPAGE™ 10% Bis-Tris, 1.0 mm, 10-well helpful to both new and established investigators in the field protein gels (ThermoFisher Scientific; NP0301BOX) and run at 200 V [13,14,121]. Standards and guidelines can also clarify and simplify the for 37 min. Gels were wet transferred using 20% methanol onto either peer review process. Han et al. [120] recently examined the impact of a Nitrocellulose membranes’ μM; 7 cm × 8.5 cm (P/N 926-31090). manuscript submission checklist on the transparency and quality of Membranes were blocked for one hour at room temperature in Odyssey reporting in preclinical biomedical research. They evaluated the re- Blocking Buffer (TBS) (LI-COR 927-50000). Primary antibodies were porting of methodological and analytical information in two journals: diluted with their respective blocking buffers (see figure legends) and Nature (which implemented a mandatory checklist in 2013) and Cell incubated overnight at 4 °C. Washes were performed with TBS 0.1% (which does not require a checklist). Comparison of 2013 (pre-check- Tween-20 (TBST) before the addition of secondary antibody for one list) and 2015 (post-checklist) data shows that a required submission hour at room temperature. Washes were performed with 1× TBST checklist is associated with improved reporting of methodological de- before imaging on Odyssey CLx. protein detection was performed using tails [120]. Image Studio Ver 5.2. To be truly useful, guidelines cannot be overly rigid; in the laboratory, one size does not fit all [13]. But fl exible and appropriate 6.5. Antibodies guidelines can call attention to common mistakes, raise awareness, and make researchers stop and think about the strengths and limitations of Primary antibodies include β-Actin (LI-COR 926-42212; Lot the methods they use. The systematic workflow we present here seeks #C503324-02), CPARP (CST #5625; Lot#13), ACTB (LI-COR P/N 926- to minimize the error introduced by common methodological limita- 42210; Lot #C80322-01), GAPDH (CST #5174; Lot #2), and COXIV (LI- tions and improve the overall usefulness of quantitative analysis of COR P/N 926-42214; Lot #C5033324-02), Phospho EGFR (CST #3777; Western blot data. We believe this flexible and adaptable approach has Lot #13); Pan EGFR (Thermo Fisher Scientific AHR5062; Lot # the potential to improve the quality, reproducibility, and precision of 737509137A), Ras (CST #3965; Lot #1), CDK4 (CST # 12790; Lot quantitative analysis of Western blot data. It will also help to ensure #14), CDK6 (CST #3136; Lot #4), OCT-4 (CST #2750; Lot #4). that the data generated in today's quantitative analysis of Western blot Primary antibodies were detected using IRDye 800CW Goat (poly- data experiments will be able to meet increasingly rigorous publication clonal) anti-Mouse IgG (H + L) highly cross-adsorbed (LI-COR#925- standards in the future. The ultimate goal, of course, is to improve the 32210), IRDye 800CW Goat (polyclonal) anti-Rabbit IgG (H + L) overall accuracy of quantitative analysis of Western blot data by in- highly cross-adsorbed (LI-COR# 926-32211). creasing the likelihood that the changes we observe, and report are reproducible and reflect real and meaningful differences between bio- 6.6. Imaging logical samples. All blots were imaged wet on the Odyssey® CLx imaging system using 680 nm and 780 nm channels. Protein detection was performed using Image Studio Ver 5.2. Adobe Photoshop Elements 13 was used to prepare image panels and annotations.

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Author contributions and is it significant? A brief discussion of statistics and experimental design, EMBO Rep. 13 (2012) 291–296. [23] J.J. Bass, D.J. Wilkinson, D. Rankin, B.E. Phillips, N.J. Szewczyk, K. Smith, A.R.S.G co-wrote the main manuscript text and prepared Figures. P.J. Atherton, An overview of technical considerations for Western blotting ap- J.A.H supervised, reviewed, and proof-read the manuscript. LPK de- plications to physiological research, Scand. J. Med. Sci. Sports 27 (2017) 4–25. signed and conducted the experiments, wrote the main manuscript text [24] A. Pandey, K. Shin, R.E. Patterson, X.-Q. Liu, J.K. Rainey, Current strategies for fi fi protein production and purification enabling membrane protein structural and nalized the Figures. ARSG and LPK are co- rst authors of this biology, Biochem. Cell. Biol. 94 (2016) 507–527. manuscript. [25] P.T. Wingfield, Overview of the purification of recombinant proteins, Curr. Protein Pept. Sci. 80 (2015) 6.1.1-6.1.35. 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