Arf6 Reliability Paper - LinkedIn

Reliability Analysis of the Arf6 G-LISA Assay

Kenneth Hee

30 June 2015

Introduction

Traditional pull-down assays are a widely popular tool for studying protein-protein

interactions. Immunoprecipitations that bind to native protein structures are extremely useful

in biomedical research (Suzuki, H., Ogawa, C., Usui, K. & Hayashizaki, Y., 2004; Craig, T., Ciufo, L.

& Morgan, A. 2004). However, traditional pull-down assays are time consuming and the results

are prone to error (Craig et al., 2004). In order solve the reproducibility and time issues

associated with the pull-down method, a variety of techniques have been developed. One

particular method is the G-LISA assay developed by Cytoskeleton Incorporated (Cheng, L., Davis,

A. & Middleton, K., 2010; Middleton, K., Law, A. & Koch, K., 2013). This paper aims to

determine the internal reliability and linearity of the Arf6 G-LISA assay.

The G-LISA is an ELISA based assay that is used to quantitate the amount of activate GTP

bound Arf6 protein, in a particular cell lysate. During the initial incubation of the cell lysate, the

activated Arf6 protein binds to an effector protein that is bound to the wells of a 96-well plate.

The Arf-6 and effector complex is then incubated in the presence of an Arf6 specific primary

antibody. Then subsequently incubated in the presence of an HRP labeled secondary antibody,

and detected by an HRP conjugation system. The amount of active Arf-6 protein and antibody

dilutions, regulate the signal of the G-LISA assay (Cheng et al. 2010, Middleton et al., 2013).

Which in this case, is the colorimetric absorbance at 490nm. In order to get reliable results, the

signal of the assay must fall within the linear range.

Therefore the goal of this study assesses the affects of these three variables on the

colorimetric signal: (a) amount of Arf-6 protein, (b) the primary antibody dilution and (c) the

secondary antibody dilution. The overall goal of the study aims to answer these research

questions:

(a) Is the assay linear within the 0 to 15ng range?

(b) How can the colorimetric signal be altered to fall within the linear range?

(c) Do alterations in the primary or secondary antibody affect the signal more? And how

does this relate the assays reliability and validity?

In essence, the assay can be adapted according to a predictive model to increase the

reliability for a given set of assay conditions. In order to validate the Arf6 G-LISA, a linearity

assay was performed from a range of 0ng to 15ng of recombinant Arf6 (A6CA) protein provided

with the kit. Results concluded the Arf6 G-LISA to be linear from a range of 0ng to 15ng of the

recombinant A6CA, F(1, 92) = 3790.27, p < .001, adjusted R2 = .98. Secondly, a multiple

regression model was produced using the amount of A6CA, primary antibody dilution and

secondary antibody dilution as predictor variables, F(3, 188) = 1637.39, p < .001, adjusted R2

= .96. This model provides insight on how the experimental conditions can be altered to fall

within the linear range of the assay. Thereby allowing the researcher to conduct more reliable

testing.

Literature Review

The Arf6 cell-signaling pathway plays an important role in breast cancer and Alzheimer’s

(Hashimoto et al, 2004; Woonjin et al., 2006). According to World Health Organization

statistics, female breast cancer had an estimated 1.4 million new cases worldwide in 2008

(World Health Organization, 2011). Therefore, potential therapies involving the Arf6 signaling

pathway are certainly of interest in biomedical research.

Biomedical research relies on quality antibodies and validated assays in order to provide

reliable clinical diagnostics. In turn, the field of clinical diagnostics relies on statistically sound

and valid research tools to provide outstanding health care results (Diller, R. & Kellar, R., 2014;

Holger, L., 2011; Krecsák et al., 2011; Walker, R., 2006). An issue plaguing the field, is inevitably

the high cost of processing large sample sizes and providing data to support assay validity.

Traditional assays, such as the pull-down method, require large amounts of cell lysate

and yield variable results (Craig, T., Ciufo, L. & Morgan, A., 2004; Nina, L. & Dagnino, L., 2009).

Furthermore, issues like conformational specific antibodies producing false results are of

concern in immuno-based assays (Reich et al., 1983; Suzuki et al., 2004). Clinical diagnostic

assay and reagent development is highly dependent on proper statistical evaluations. The need

for antibody validation services will continue to grow with the seemingly ever-expanding

antibodies available for research (Nassar et al., 2011; Walker, R., 2006).

Limitations and Delimitations

The G-LISA assay was originally developed for the Rho family of GTPases and was

determined to be linear within 1ng to 30ng of recombinant His-RhoA protein (Cheng, L., Davis,

A. & Middleton, K., 2010). Importantly, the recombinant His-RhoA protein results were

validated against a titration of wild type RhoA protein. Importantly, the mutant Arf6 control

protein (A6CA) is assumed to behave like the wild type Arf6 protein found in cell lysates. This

assumption was based on the validity evidence for the RhoA G-LISA. A titration of calpeptin

treated and serum-starved lysates both exhibited a linear increase in signal (Cheng et al., 2010).

Furthermore, the RhoA G-LISA was validated against Swiss 3T3 (mouse), A431 (human) and

HeLa (human) cell lysates. This is a critical assumption for this study. Interestingly, certain

antibodies are more specific to the native conformations of their antigen than to the denatured

species (Reich et al., 1983; Suzuki et al., 2004). Such as those species probed for under the

denaturing conditions used in western blotting systems.

The delimitations for the linearity experiment are outlined in Table 1. The manual

recommended antibody dilutions of a 1:40 primary and 1:100 secondary were chosen to delimit

the linearity experiment (Middleton et al., 2013). In order to observe the high and low plateau

regions of the assay, a range of 0 – 15ng was chosen. The linearity of the assay was

hypothesized to have a threshold level of 12ng and therefore the range was expanded to 15ng.

Table 1Delimitations for the 0ng – 15ng linearity experiment (N = 96)Variable DelimitationsAmount of Arf6 (ng) 0, 1, 3, 6, 9, 12, 15Primary anti-body dilution 1:40Secondary anti-body dilution 1:100

Likewise the multiple regression experiment assumed the assay signal (A490) correlates

with the endogenous protein levels found in cell lysates and tissues. Again this study assumes

that the recombinant Arf-6 protein provided with the assay behaves similarly to the GTP bound

activated Arf-6 protein in endogenous systems. Since cell lysates are costly and limited

reagents this study was delimited to the recombinant Arf6 protein. This assumption is a critical

limitation of this study, that must be addressed in future experiments before validating the Arf6

G-LISA kit. Antibody recognition is susceptible to post-translational modifications and

conformational differences, which could lead to false results (Craig et al., 2010; Reich et al.,

1983; Suzuki et al., 2004.)

The multiple regression experiment study was delimited to a range of 1 to 8ng of the

recombinant Arf6 protein. The primary antibody dilution was delimited to a range of 1:20 to

1:60, and the secondary antibody dilution was delimited to a range of 1:50 to 1:150.

Additionally the results assumed no human experimental error was introduced. The

delimitations for the regression model experiment are presented in Table 2. The primary and

secondary dilutions were designed to straddle the recommended dilution factors. The

recommended dilutions for the primary and secondary antibodies are 1:40 and 1:100,

respectively (Middleton et al., 2013).

Table 2Delimitations for the regression model experiment

Variable DelimitationsAmount of Arf6 (ng) 0, 2, 3, 6, 8Primary anti-body dilution 1:20, 1:30, 1:40, 1:60Secondary anti-body dilution 1:50, 1:100, 1:150

Materials and Data Collection

Although the manual only claims the Arf6 G-LISA to be linear within the 1ng to 9ng

range, the linearity experiment covered a range of 0ng to 15ng. This was done to find the

upper threshold for the assay’s linearity. Each level of A6CA contained a minimum of N = 8, in

order to provide an accurate representation of the variance. This design was used to increase

the sample size to a minimum of N = 16 for amounts of protein falling within the 1ng to 9ng

published range. Appendix A outlines the 96-well plate set up for the linearity experiment.

The multiple regression model experiment was delimited to a range of 0ng to 8ng. The

maximum of A6CA was reduced in order to increase the sample size of the various conditions.

This was necessary because the primary and secondary dilution patterns delimited each

condition to limited number of wells. The antibody dilution patterns are presented in Appendix

B. The recipes for re-suspension and dilutions of A6CA and the antibodies are outlined

Appendix C.

The assay manual was followed in all respects (Middleton et al., 2013). Except for the

antibody dilutions that were altered to produce the multiple regression model. The results

were collected using a Molecular Devices, SpectraMax250. The materials for data collection are

outlined in Table 3. The raw absorbance data from the Spectramax250 was exported into a

Microsoft Excel document, and then exported into IBM SPSS Statistics, Version 21. Both the

linearity and multiple regression were done using the general linear modeling function in IBM

SPSS Statistics.

Table 3Required Materials

Material or DeviceThree Arf6 kits (Cytoskeleton Inc., Cat. # BK133).PBS buffer (10 mM phosphate buffer pH 7.4, 140 mM NaCl, 3 mM KCl)Multi-channel pipettor (VWR, Cat. # 89000-552)Multi-channel pipettor solution basins (VWR, Cat. # 21007-970)Vortex mixer (VWR, Cat. #58816-121 )Two orbital microplate shakers (VWR, Cat. # 57019).Microplate spectrophotometer (Molecular Devices, SpectraMax 250)Concentrated sulfuric acid (VWR, Cat. #AA88568-LQ)

Results

A simple linear regression was done to investigate the Arf6 G-LISA assay linearity. The

linear regression experiment ranged from 0ng to 15ng of recombinant Arf6 protein provided

with the kit. Appendix A outlines the 96-well plate set up for the linearity experiment. After

the removal of outliers the normality assumption was not violated for any of the 7 nominal

protein amounts. The readings of the outlier wells were affected by experimental error and the

introduction of air bubbles during the incubation. Also the assumption of linearity was not

violated. Table 3 outlines the sample sizes, means, standard deviation and skewness values for

each variable.

Table 3Descriptive Data for the 0ng – 15ng Arf6 Linear Regression (N = 94)

Variable N M SD SkewnessDependent A490 94 .76 .49 .19Independent 0ng 8 .14 .01 .39 1ng 15 .19 .01 .57 3ng 16 .39 .02 -.56 6ng 16 .72 .06 -.73 9ng 16 1.08 .05 -.13 12ng 15 1.41 .06 -1.0 15ng 8 1.49 .10 .56

The amount of Arf6 protein (M = 7.65, SD = 4.78) significantly predicted the absorbance

Arf6 G-LISA assay, F(1, 92) = 3790.27, p < .001, adjusted R2 = .98. According to the literature in

the field this is a large effect size. Meaning that the model predicted 98% of the variance in the

assay. Table 4 outlines the simple linear regression analysis summary. The beta weights

indicate that for every 1ng increase in A6CA the signal increases by .102 units. Figure 1

demonstrates the linearity of the Af6 G-LISA graphically. The equation for the model was:

A490 = .113 + .102 A6CA + error

Table 4Linear Regression for Predicting the Absorbance Signal of the Arf6 G-LISA (N=94)

Variable B SEB β R2 A6CA 0.102** 0.002 0.988 .98 Constant 0.113 0.013** p < .001; * p < .05; F(1, 92) = 3790.27

Figure 1The linearity results from 0ng to 15ng of A6CA

Next a multiple regression was performed to determine how each of the three predictor

variables (amount of Arf6, primary antibody dilution and secondary antibody dilution) affected

the signal of the Arf6 G-LISA assay. The following assumptions for a multiple regression were

checked: (a) independence of observations (b) normality (c) linearity and (d) that the errors are

normally distributed. The independence of observations was assumed since each reaction was

performed in different wells of the plate. Normality was checked against the skewness values

and none violated the normality assumption. Note an N=94, as some well reads were outliers

caused by user error and removed from the analysis. Linearity was checked using a matrix

scatterplot. All variables exhibited a near linear relationship and therefore the assumption of

linearity was not violated. Finally the residuals plot displayed a pattern and the homogeneity of

variances assumption was violated. Therefore the results should be viewed with caution.

The experimental set up, outlined in Appendix B and C, yielded an overall significant

model for predicting the colorimetric signal, F(3, 188) = 1637.39, p < .001. The predictor

variables were the amount of Arf6 protein (t = 67.56, p < .001), the primary antibody dilution (t

= 11.85, p < .001) and the secondary antibody dilution (t = 13.65, p < .001). The means,

standard deviation, inter-correlations tables can be found in Table 5. The multiple regression

model summary is outlined in Table 6.

The model had an adjusted R2 = .96, meaning that 96% of the variance was explained by

the model. According to the literature in the field this is a large effect size. The results for the

multiple regression are presented graphically in Figure 2. The equation for the model was:

A490 = -.22 + .14A6CA + 5.36Primary + 14.21Secondary + error

All three predictor variables significantly contributed to the model. However, the beta

weights presented in Table 6 suggest that for every 1ng increase in A6CA, the signal increases

by .14 units. Holding everything else constant.

Table 5Means, standard deviations and inter-correlations for the Arf6 assay (N=192)

Variable M SD 1 2 3A490 0.66 0.40 0.95** 0.17* 0.20* Predictor Variables1. A6CA 3.92 2.70 - 0.00 0.012. Primary Dilution 0.03 0.012 - 0.003. Secondary Dilution 0.01 0.005 -** p < .001, * p < .05

Table 6Model Summary for the Multiple Regression (N=192)

Variable B SEB β t R2

Model 1 0.96 A6CA 0.14** 0.002 0.946 67.56 Primary Dilution 5.36** 0.452 0.166 11.85 Secondary Dilution 14.21** 1.042 0.191 13.65 Constant -0.22 0.021 ** p < .001, * p < .05

Figure 2:Graphical Results for the Multiple Regression (N=192)

Discussion

First, the linearity experiment ranging from 0ng to 15ng produced a significant model

for predicting the colorimetric signal, p < .001. The large adjusted R2 value of .98 indicates the

assay is in fact linear from 0ng to 15ng of A6CA. However, the assay does not exhibit linearity

at the high (12-15ng) and low (0-1ng) amounts of A6CA. Notice the tails of the plot begin to

plateau, following a cubic pattern. This indicates the best OD for acquiring reliable results is in

the 1 – 6ng range. The Arf6 G-LISA exhibits significant linearity within the published 1ng to 9ng

range. Additionally, the threshold range of the assay was determined to be 12ng. Meaning

that any results with an OD490 greater than 1.41 will be more susceptible to error. Notice the

large standard deviation (SD = .10) at the 15ng level of Arf6 protein.

Likewise, the multiple regression experiment produced a significant model for predicting

the assay signal based on the manipulation of the amount of recombinant Arf6, primary dilution

and secondary antibody dilutions, p < .05. The model explained 96% of the variance in the

assay’s results, which is high. Meaning the Arf6 G-LISA assay is highly reliable. Certainly worth

explaining is the use of the model:

A490 = -.22 + .14A6CA + 5.36Primary + 14.21Secondary

Notice the signal was more closely correlated to secondary antibody dilution (r = .20)

than the primary antibody (r = .17). This indicates the secondary antibody dilution factor has

greater effect on the colorimetric signal of the assay. Notice that the antibody dilution factors

are entered as their reciprocal:

A490 (6ng, 1:40, 1:100) = -.22 + .14(6ng) + 5.36(1/40) + 14.21(1/100)

A490 (6ng, 1:40, 1:100) = .90

Now consider a particular cell lysate with only .5ng of active Arf6 protein. Using the

recommended antibody dilutions the A490 (.5ng, 1:40, 1:100) = .126. The signal in this case

does not fall within the linear range of the experiment. However if the antibody concentrations

are increased the signal is shifted into the linear range. A490 (.5ng, 1:20, 1:50) = .40. Under

these conditions, the .5ng of A6CA behaves like 3ng of A6CA. The alteration in conditions can

be used to discriminate between lysates with low protein concentrations. On the other hand,

the dilution factors can be increased to lower the signal for lysates with high protein

concentrations. In applications such as immunohistochemistry, antibody dilutions are critical to

obtaining reliable results for use in clinical diagnostics (Walker, R., 2006). Importantly, Figure 2

indicates that all combinations of antibody dilution still yields reliable results with an adjusted

R2 = .99.

Certainly, future experiments should validate these results by titrating lysate

concentrations from various sources against the recombinant control protein. Similar to the

validation process followed for the RhoA G-LISA, which was validated against Swiss 3T3, A431

and HeLa cell lysates (Cheng et al., 2010). As mentioned, the field of biomedical research is

moving towards increased sample sizes and the proper validation of assays (Diller, R. & Kellar,

R., 2014; Holger, L., 2011; Krecsák et al., 2011; Walker, R., 2006). Those on the forefront of

biomedical research are using statics to conduct valid research. Certainly, biostatics will become

an increasingly important field in biomedical research.

Works Cited

Cheng, L., Davis, A. & Middleton, K. (2010). U.S. Patent No. 7,763, 418 B2. United States Trademark and Patent Office: Washington, DC.

Diller, Robert & Kellar, Robert (2014). Validating whole slide digital morphometric analysis as a microscopy tool. Microscopy and Microanalysis, 21(1): 249-255.

Hashimoto, S., Onodera, Y., Hashimoto, A., Tanaka, M., Hamaguchi, M., Yamada, A. & Sabe, H. (2004). Requirement for Arf6 in breast cancer invasive activities. PNAS, 101(17), 6647-6652.

Kim, W., Kim, Y., Min, J., Kim, D.J., Chang, Y. & Hecht, M. (2006). A high-throughput screen for compounds that inhibit aggregation of the Alzheimer’s peptide. ACS Chemical Biology, 1(7), 461-469.

Krecsák, L., Micsik, T., Kiszler, G., Krenács, T., Szabó, D., Jónás, V., … Molnár, B. (2011). Technical note on the validation of a semi-automated image analysis software application for estrogen and progesterone receptor detection in breast cancer. Diagnostic Pathology, 6(6).

Lange, Holger (2011). Digital Pathology: A regulatory Overview. Lab Medicine, 42(10), 588-591.

Middleton, K., Law, A. & Koch, K. (2013). G-LISA Arf6 activation assay. Retrieved from http://www.cytoskeleton.com/datasheets/. Retrieved: 4 April 2013.

Nassar, A., Cohen, C., Agersborg, S., Zhou, W., Lynch, K., Albitar, M., Barker, E., Vanderbilt, B., Thompson, J., Heyman, E., Lange, H., Olson, A. & Siddiqui, M. (2011). Arch Pathol Lab Med, 135, 896-902.

Nina, Lylia & Dagnino, Lina (2009). Accurate and reproducible measurements of RhoA activation in small samples of primary cells. Analytical Biochemistry, (398), 135-137.

Reich, N., Sarnow, P., Duprey, E. & Levine, A. (1983). Monoclonal antibodies which recognize native and denatured forms of the adenovirus DNA-binding protein. Virology, 128(2), 480-484.

Samaroo, H., Opsahl, A., Schreiber J., O’Neill, S., Marconi, M., Qian J., Carvajal-Gonzalez, S. Tate, B., Milici, A., Bales, K. & Stephenson, D. (2012). High throughput object-based image analysis of beta-amyloid plaques in human and transgenic mouse brain. Journal of Neuroscience Methods, 204(1), 179-88.

Suzuki, H., Ogawa, C., Usui, K. & Hayashizaki, Y. (2004). In vitro pull-down assay with expression constructs. BioTechniques, (37), 918-920.

Walker, R.A. (2006). Quantification of immunohistochemistry – issues concerning methods, utility and semiquantitative assessment I. Histopathology, 49, 406-410.

World Health Organization (2011). Global cancer facts and figures, 2nd edition. Retrieved from: http://www.cancer.org/acs/groups/content/@epidemiologysurveilance/documents/document/acspc-027766.pdf. Retrieved: 15 May 2015.

APPENDIX A

Table 1The 96-well set up for the linearity experiment

Well # 1 2 3 4 5 6 7 8 9 10 11 12A 0ng 1ng 1ng 3ng 3ng 6ng 6ng 9ng 9ng 12ng 12ng 15ngB 0ng 1ng 1ng 3ng 3ng 6ng 6ng 9ng 9ng 12ng 12ng 15ngC 0ng 1ng 1ng 3ng 3ng 6ng 6ng 9ng 9ng 12ng 12ng 15ngD 0ng 1ng 1ng 3ng 3ng 6ng 6ng 9ng 9ng 12ng 12ng 15ngE 0ng 1ng 1ng 3ng 3ng 6ng 6ng 9ng 9ng 12ng 12ng 15ngF 0ng 1ng 1ng 3ng 3ng 6ng 6ng 9ng 9ng 12ng 12ng 15ngG 0ng 1ng 1ng 3ng 3ng 6ng 6ng 9ng 9ng 12ng 12ng 15ngH 0ng 1ng 1ng 3ng 3ng 6ng 6ng 9ng 9ng 12ng 12ng 15ng

Table 2The re-suspension and dilutions of A6CA for the linearity experiment

ng A6CA .60ng/μL A6CA stock (μL) Lysis buffer (μL) Binding buffer (μL)0 0 600 6001 40 560 6003 120 480 6006 240 360 6009 360 240 600

12 480 120 60015 600 0 600

Note: Re-suspend 12 tubes of A6CA (100ng/tube) in 2000μL of lysis buffer

APPENDIX B

Table 1The A6CA well pattern for plate 1 of the multiple regression model experiment


Table 2The A6CA well pattern for plate 2 of the multiple regression model experiment


APPENDIX B

Table 3The 96-well set up for the primary antibody dilutions in plate 1

Well # 1 2 3 4 5 6 7 8 9 10 11 12A 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20B 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20C 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20D 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20E 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30F 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30G 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30H 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30

Table 4The 96-well set up for the primary antibody dilutions in plate 2

Well # 1 2 3 4 5 6 7 8 9 10 11 12A 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40B 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40C 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40D 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40E 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60F 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60G 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60H 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60

APPENDIX B

Table 5The 96-well set up for the secondary antibody dilutions in plate 1

Well # 1 2 3 4 5 6 7 8 9 10 11 12A 1:50 1:50 1:50 1:50 1:50 1:50 1:100 1:150 1:50 1:50 1:50 1:50B 1:100 1:100 1:100 1:100 1:100 1:50 1:100 1:150 1:100 1:100 1:100 1:100C 1:100 1:100 1:100 1:100 1:100 1:50 1:100 1:150 1:100 1:100 1:100 1:100D 1:150 1:150 1:150 1:150 1:150 1:50 1:100 1:150 1:150 1:150 1:150 1:150E 1:50 1:50 1:50 1:50 1:50 1:50 1:100 1:150 1:50 1:50 1:50 1:50F 1:100 1:100 1:100 1:100 1:100 1:50 1:100 1:150 1:100 1:100 1:100 1:100G 1:100 1:100 1:100 1:100 1:100 1:50 1:100 1:150 1:100 1:100 1:100 1:100H 1:150 1:150 1:150 1:150 1:150 1:50 1:100 1:150 1:150 1:150 1:150 1:150

Table 6The 96-well set up for the secondary antibody dilutions in plate 2

Well # 1 2 3 4 5 6 7 8 9 10 11 12A 1:50 1:50 1:50 1:50 1:50 1:50 1:100 1:150 1:50 1:50 1:50 1:50B 1:100 1:100 1:100 1:100 1:100 1:50 1:100 1:150 1:100 1:100 1:100 1:100C 1:100 1:100 1:100 1:100 1:100 1:50 1:100 1:150 1:100 1:100 1:100 1:100D 1:150 1:150 1:150 1:150 1:150 1:50 1:100 1:150 1:150 1:150 1:150 1:150E 1:50 1:50 1:50 1:50 1:50 1:50 1:100 1:150 1:50 1:50 1:50 1:50F 1:100 1:100 1:100 1:100 1:100 1:50 1:100 1:150 1:100 1:100 1:100 1:100G 1:100 1:100 1:100 1:100 1:100 1:50 1:100 1:150 1:100 1:100 1:100 1:100H 1:150 1:150 1:150 1:150 1:150 1:50 1:100 1:150 1:150 1:150 1:150 1:150

APPENDIX C

Table 1Re-suspension and dilutions of A6CA for the multiple regression experiment

ng A6CA .32ng/μL A6CA stock (μL) Lysis buffer (μL) Binding buffer (μL)0 0 1200 12002 300 900 12003 600 1000 16006 1200 400 16008 1200 0 1200

Note: Re-suspend 12 tubes of A6CA in 3750μL of lysis buffer for a .32ng/μL stock

Table 2Dilutions for the primary and secondary antibodies

Antibody (μL) Dilution Buffer (μL)Primary Antibody

1:20 150 30001:30 100 30001:40 75 30001:6 50 3000

Secondary Antibody1:50 60 3000

1:100 50 50001:150 20 3000

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