Article Figures & Data
Figures
- Figure 1.
Specificity testing. Each purified antigen was biotinylated at 1,000 ng/ml in 1X PBS. Biotinylated antigen was diluted 10-fold with blocking buffer and incubated with array slide. After extensive wash, fluorescence conjugated streptavidin was added to reveal signals.
- Figure 2.
The reproducibility assay was tested using serum samples. Scatter plot of normalized intensity from serum sample intra-slide on log scale. The log base 2 values of the signal intensities for duplicates experiments are plotted. R2 is equal to 0.9240, suggesting a good reproducibility of two repeated experiments.
- Figure 3.
Cell culture supernatant was diluted 2-, 10- and 20-fold with blocking buffer and then incubated with human label-based antibody arrays.
- Figure 4.
Spiking test. A corresponding recombinant antigen was serially diluted into a human serum solution at 50,000, 5,000, 500, 50 and 5 pg/ml. The serum containing different concentrations of spiking antigen was then labeled with biotin. The biotinylated samples were diluted 5-fold with blocking buffer and incubated with each array slide.
- Figure 5.
Validation assay. A: The conditioned medium was prepared from human glioblastoma cells (U251) stimulated with or without TNF-alpha. Both biotin label-based antibody arrays and ELISA were performed and the result were compared. B: The conditioned medium prepared from human breast cancer cells (MDA-MB-157 and T47D) were assayed with both biotin label-based antibody arrays and ELISA. C: The mini map of antibody arrays.
- Figure 6.
ELISA confirmation. The correlation of BDNF (A) and Acrp-30 (B) between biotin label-based antibody arrays and ELISA were compared. The overall R value is larger than 0.9, suggesting a good correlation between the two assays.
- Figure 7.
Classification tree analysis. Proteins used in the classification tree analysis and their cut-off signal are listed on the left. The range of data specified at each split represents the subset of data which is further subdivided by branches to the right.
- Figure 8.
Split-point score analysis. A: The six markers used in split-point score classification analysis. Misidentified samples were in the bracket using individual markers. B: Dot histogram plot with six analyte split-point score classification of serum samples from healthy controls (N) and individuals with ovarian cancer (CA). Correctly classified normal serum samples should have a score of 0 to 2, whereas samples from ovarian cancer patients should have a score of 3-6. False-negative sample and false positive samples can easily be detected. C: The ROC curve for 5 marker panel of split-score analysis of ovarian cancer vs. healthy controls. The ROC is the curve plotted of sensitivity (true positive) against 1–specificity (false positive) values. D: Table using six-marker split-point score to diagnose ovarian cancer patients. A cut-off score of 3 was used.
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