Figure 1.
Schematic representation of the intensity distribution by an one-dimensional scan through the surface.
A ‘rolling ball’ moves along (arrow) the underside of the intensity curve and thereby identifies the background, which is subsequently subtracted. Remaining peaks of low intensities are removed by an intensity cut-off (dashed line). In the algorithm, the ball moves along a three-dimensional landscape, identifying peaks with a two-dimensional bottom and the intensity as the third dimension.
Figure 2.
Detection of recPrP particles in the presence of blood plasma.
To better resolve grayscales, images are colorized with ‘fire’ lookup table (cf. right bar). Each sample contained 50 ng recPrP aggregates in 100 µl sample volume. All samples were analyzed by surface-FIDA assay using SAF-32 as capture and detection antibody. One image corresponds to 1% of the total well bottom area. RecPrP aggregates (A) in PBS, (B) in ovine plasma, (C) in plasma and 2% sarkosyl, (D) in plasma after sarkosyl and lipase treatment, (E) in plasma after sarkosyl and lipase treatment and PTA-precipitation, (F) in plasma after PTA precipitation. (G) negative control: non-spiked plasma after sarkosyl, lipase and PTA steps.
Figure 3.
Reproducibility of plasma analyses.
Replicate samples of a plasma pool prepared from sheep symptomatic for scrapie and from a pool prepared from uninfected control sheep were processed independently by two experimenters (A and B). Discriminability of positive and negative samples is expressed as fluorescence intensity (positive, p) minus intensity (negative, n) divided by intensity (positive, p). MAb SAF-32 was used as capture, mAb L42 as probe.
Figure 4.
Detection of PrP particles in blood plasma of scrapie-infected sheep in a blinded study.
PrP aggregates from 15 plasma samples were prepared, applied to surface-FIDA using mAb SAF-32 as capture and detection probe and evaluated using a rolling ball radius of 10 px. and intensity cut-off 200. After decoding samples were assigned as scrapie-positive (red) or uninfected controls (green).
Figure 5.
Optimized image processing and evaluation.
(A) Image raw data of control sample no. 6 (top) and a scrapie-positive sample no. 7 (bottom). Depicted is one representative out of nine images taken for each sample. Scale bar = 50 µm. (B) Optimization of ‘rolling ball’ radius. After applying rolling ball background subtraction an intensity cutoff (500) was applied. Smaller radii of 1 and 2 pixels (arrows) allow for differentiation of the positive and the control sample (negative). (C) Optimized background removal parameters were applied to the complete panel of samples.