1. The study assessed the reliability and linearity of the Arf6 G-LISA assay, which quantifies activated Arf6 protein levels. 2. A linear regression analysis found the assay to be highly linear from 0-15ng of recombinant Arf6 protein, explaining 98% of variance. 3. A multiple regression model found that the absorbance signal correlated significantly with the amount of Arf6 protein, primary antibody dilution, and secondary antibody dilution used. The model explained 96% of variance in assay results.

1. Reliability Analysis of the Arf6 G-LISA Assay
Kenneth Hee
30 June 2015

2. 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:

3. (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

4. 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

5. 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 1
Delimitations for the 0ng – 15ng linearity experiment (N = 96)
Variable Delimitations
Amount of Arf6 (ng) 0, 1, 3, 6, 9, 12, 15
Primary anti-body dilution 1:40
Secondary 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

6. 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 2
Delimitations for the regression model experiment
Variable Delimitations
Amount of Arf6 (ng) 0, 2, 3, 6, 8
Primary anti-body dilution 1:20, 1:30, 1:40, 1:60
Secondary 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

7. 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 3
Required Materials
Material or Device
Three 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 3
Descriptive Data for the 0ng – 15ng Arf6 Linear Regression (N = 94)
Variable N M SD Skewness
Dependent

8. A490 94 .76 .49 .19
Independent
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 4
Linear 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 1
The linearity results from 0ng to 15ng of A6CA

9. 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,

10. 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 5
Means, standard deviations and inter-correlations for the Arf6 assay (N=192)
Variable M SD 1 2 3
A490 0.66 0.40 0.95** 0.17* 0.20*
Predictor Variables
1. A6CA 3.92 2.70 - 0.00 0.01
2. Primary Dilution 0.03 0.012 - 0.00
3. Secondary Dilution 0.01 0.005 -
** p < .001, * p < .05
Table 6
Model 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)

11. 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

12. 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

13. 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.

14. 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.

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

16. APPENDIX A
Table 1
The 96-well set up for the linearity experiment
Well # 1 2 3 4 5 6 7 8 9 10 11 12
A 0ng 1ng 1ng 3ng 3ng 6ng 6ng 9ng 9ng 12ng 12ng 15ng
B 0ng 1ng 1ng 3ng 3ng 6ng 6ng 9ng 9ng 12ng 12ng 15ng
C 0ng 1ng 1ng 3ng 3ng 6ng 6ng 9ng 9ng 12ng 12ng 15ng
D 0ng 1ng 1ng 3ng 3ng 6ng 6ng 9ng 9ng 12ng 12ng 15ng
E 0ng 1ng 1ng 3ng 3ng 6ng 6ng 9ng 9ng 12ng 12ng 15ng
F 0ng 1ng 1ng 3ng 3ng 6ng 6ng 9ng 9ng 12ng 12ng 15ng
G 0ng 1ng 1ng 3ng 3ng 6ng 6ng 9ng 9ng 12ng 12ng 15ng
H 0ng 1ng 1ng 3ng 3ng 6ng 6ng 9ng 9ng 12ng 12ng 15ng
Table 2
The re-suspension and dilutions of A6CA for the linearity experiment
ng A6CA .60ng/μL A6CA stock (μL) Lysis buffer (μL) Binding buffer (μL)
0 0 600 600
1 40 560 600
3 120 480 600
6 240 360 600
9 360 240 600
12 480 120 600
15 600 0 600
Note: Re-suspend 12 tubes of A6CA (100ng/tube) in 2000μL of lysis buffer

17. APPENDIX B
Table 1
The A6CA well pattern for plate 1 of the multiple regression model experiment
Well # 1 2 3 4 5 6 7 8 9 10 11 12
A 0ng 0ng 2ng 2ng 3ng 3ng 3ng 6ng 6ng 6ng 8ng 8ng
B 0ng 0ng 2ng 2ng 3ng 3ng 3ng 6ng 6ng 6ng 8ng 8ng
C 0ng 0ng 2ng 2ng 3ng 3ng 3ng 6ng 6ng 6ng 8ng 8ng
D 0ng 0ng 2ng 2ng 3ng 3ng 3ng 6ng 6ng 6ng 8ng 8ng
E 0ng 0ng 2ng 2ng 3ng 3ng 3ng 6ng 6ng 6ng 8ng 8ng
F 0ng 0ng 2ng 2ng 3ng 3ng 3ng 6ng 6ng 6ng 8ng 8ng
G 0ng 0ng 2ng 2ng 3ng 3ng 3ng 6ng 6ng 6ng 8ng 8ng
H 0ng 0ng 2ng 2ng 3ng 3ng 3ng 6ng 6ng 6ng 8ng 8ng
Table 2
The A6CA well pattern for plate 2 of the multiple regression model experiment
Well # 1 2 3 4 5 6 7 8 9 10 11 12
A 0ng 0ng 2ng 2ng 3ng 3ng 3ng 6ng 6ng 6ng 8ng 8ng
B 0ng 0ng 2ng 2ng 3ng 3ng 3ng 6ng 6ng 6ng 8ng 8ng
C 0ng 0ng 2ng 2ng 3ng 3ng 3ng 6ng 6ng 6ng 8ng 8ng
D 0ng 0ng 2ng 2ng 3ng 3ng 3ng 6ng 6ng 6ng 8ng 8ng
E 0ng 0ng 2ng 2ng 3ng 3ng 3ng 6ng 6ng 6ng 8ng 8ng
F 0ng 0ng 2ng 2ng 3ng 3ng 3ng 6ng 6ng 6ng 8ng 8ng
G 0ng 0ng 2ng 2ng 3ng 3ng 3ng 6ng 6ng 6ng 8ng 8ng
H 0ng 0ng 2ng 2ng 3ng 3ng 3ng 6ng 6ng 6ng 8ng 8ng

18. APPENDIX B
Table 3
The 96-well set up for the primary antibody dilutions in plate 1
Well # 1 2 3 4 5 6 7 8 9 10 11 12
A 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20
B 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20
C 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20
D 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20 1:20
E 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30
F 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30
G 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30 1:30
H 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 4
The 96-well set up for the primary antibody dilutions in plate 2
Well # 1 2 3 4 5 6 7 8 9 10 11 12
A 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40
B 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40
C 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40
D 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40 1:40
E 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60
F 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60
G 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60
H 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60 1:60

19. APPENDIX B
Table 5
The 96-well set up for the secondary antibody dilutions in plate 1
Well # 1 2 3 4 5 6 7 8 9 10 11 12
A 1:50 1:50 1:50 1:50 1:50 1:50 1:100 1:150 1:50 1:50 1:50 1:50
B 1:100 1:100 1:100 1:100 1:100 1:50 1:100 1:150 1:100 1:100 1:100 1:100
C 1:100 1:100 1:100 1:100 1:100 1:50 1:100 1:150 1:100 1:100 1:100 1:100
D 1:150 1:150 1:150 1:150 1:150 1:50 1:100 1:150 1:150 1:150 1:150 1:150
E 1:50 1:50 1:50 1:50 1:50 1:50 1:100 1:150 1:50 1:50 1:50 1:50
F 1:100 1:100 1:100 1:100 1:100 1:50 1:100 1:150 1:100 1:100 1:100 1:100
G 1:100 1:100 1:100 1:100 1:100 1:50 1:100 1:150 1:100 1:100 1:100 1:100
H 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 6
The 96-well set up for the secondary antibody dilutions in plate 2
Well # 1 2 3 4 5 6 7 8 9 10 11 12
A 1:50 1:50 1:50 1:50 1:50 1:50 1:100 1:150 1:50 1:50 1:50 1:50
B 1:100 1:100 1:100 1:100 1:100 1:50 1:100 1:150 1:100 1:100 1:100 1:100
C 1:100 1:100 1:100 1:100 1:100 1:50 1:100 1:150 1:100 1:100 1:100 1:100
D 1:150 1:150 1:150 1:150 1:150 1:50 1:100 1:150 1:150 1:150 1:150 1:150
E 1:50 1:50 1:50 1:50 1:50 1:50 1:100 1:150 1:50 1:50 1:50 1:50
F 1:100 1:100 1:100 1:100 1:100 1:50 1:100 1:150 1:100 1:100 1:100 1:100
G 1:100 1:100 1:100 1:100 1:100 1:50 1:100 1:150 1:100 1:100 1:100 1:100
H 1:150 1:150 1:150 1:150 1:150 1:50 1:100 1:150 1:150 1:150 1:150 1:150

20. APPENDIX C
Table 1
Re-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 1200
2 300 900 1200
3 600 1000 1600
6 1200 400 1600
8 1200 0 1200
Note: Re-suspend 12 tubes of A6CA in 3750μL of lysis buffer for a .32ng/μL stock
Table 2
Dilutions for the primary and secondary antibodies
Antibody (μL) Dilution Buffer (μL)
Primary Antibody
1:20 150 3000
1:30 100 3000
1:40 75 3000
1:6 50 3000
Secondary Antibody
1:50 60 3000
1:100 50 5000
1:150 20 3000