Figure 3.

Principles of image correlation spectroscopy on heterogeneous cluster shapes and heterogeneous image contents. (A) With a home-made MATLAB script, 20 random image series were generated containing either white noise (randomly scattered pixel; left panel), one size of round particles (0.78 μm² surface area on average in image series; second panel), a mix of noise (random pixels), or two sizes of round particles (10 particles of 0.78 μm² surface area on average in image series and 100 round particles of 0.13 μm² surface area in image series; third panel) or the latter set of images after filtering the noise (right panel) by removing a group of pixels with fewer than seven pixels. Gaussian fits (down row of figures) were figured out on the averaged ACF. Averaged cluster surface area and averaged cluster density were calculated by ICS. Pixel size: 100 nm. (B) Cropped size-filtered E_FRET image of CT1-mT2+RPB1-Reach2 is shown on the left. Red circle describes the average cluster surface area calculated by FICS and reported in Table 1. Right insets display the same-cropped area showing one noncompact E_FRET cluster and the different ways ICS could partition this E_FRET cluster in different subclusters (pink-line limited areas). Original image size: 128 × 128 pixels. Scale bars, 1 μm. (C) Examples of 128 × 128 pixels images generated with triangle particles of two different surfaces and at two different densities. These figures illustrate the formation of noncompact clusters with irregularly serrated borders from overlapping particles. (D) Graph plot showing ICS-mediated error in cluster surface area and cluster density as a function of the particle overlap occurring upon image generation. The particle overlap was calculated as the division of the product of implemented particle number and implemented particle surface area by the integrated surface area of the signal in images. ICS-mediated error in cluster density estimation was calculated as the ratio between ICS values and the values implemented in 108 image series. These series of 20 images were implemented with circles, squares, or triangles, with six possible object surface areas (3-, 5-, 7-, 9-, 11-, and 13-pixel) and six possible object densities (20, 70, 120, 170, 220, and 270 clusters per image). (E) Graph plot showing the linear relationship between ICS-mediated error in cluster surface area and cluster density as a function of the particle density. Values are extracted from the same dataset as in (D). Tendency curves (solid lines) and SD (dotted lines) are represented. To see this figure in color, go online.