Sebastian Malkusch 1, # , Walter Muranyi 2, # , Barbara Müller 2 , Hans-Georg Kräusslich 2 , Mike
Heilemann
1, 3, *
1
Biotechnology and Biophysics, Julius-Maximilians-University Würzburg, Würzburg,
Germany;
2
Department of Infectious Diseases, Virology, University Hospital Heidelberg,
Heidelberg, Germany;
3
BIOQUANT Centre, University of Heidelberg, Heidelberg, Germany
*
Corresponding author: m.heilemann@uni-wuerzburg.de, phone +49 931 31 84643, fax +49
931 41 84509
Supplemental Note
Simulation of single-molecule localization data and calculation of Ripley’s K-function
We simulated a single molecule surface with individual clusters that differ in fluorophore distribution and blinking behavior. However, the simulated data represent an ideal case, as all clusters were simulated to have the same size and being equidistant. These parameters would vary in a real experiment, which would lead to an array of K-functions from which exact numbers on cluster sizes cannot be extracted without further information of the system
(Kiskowski et al., 2009). Therefore, the K-function is commonly used only to differentiate spatial distributions or aggregation states of proteins (see e.g. (Williamson et al., 2011)).
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Supplemental Figures
Supplemental figure 1. The spatial resolution of dSTORM imaging was determined by plotting the nearest-neighbor distance for each single-molecule localization (in total, 210812 single-molecule localizations were analyzed). The maximum of the distribution at 18 nm represents the experimental localization accuracy.
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Supplemental figure 2. Simulation of single-molecule localization data and analysis with
Ripley’s K-function. (a) Single-molecule localization data of clusters with a diameter of 140 nm were simulated and background noise was added. The clusters were spaced in a regular pattern with the same average distance (scale bar: 200 nm). (b) For cluster analysis using the
K-function, the simulated data were extended to a 3 x 3 area to minimize edge effects. (c) The normalized K-function was calculated for simulated clusters at different inter-cluster distances, ranging from 200 nm to 1 µm, leading to a shift in the maximum with increased inter-cluster distance. (d) The maximum of the normalized K-function is plotted against the inter-cluster distance. The graph shows that the normalized K-function yields a measure for the cluster size which depends also on the inter-cluster distance, and which generally scales between the cluster radius and the cluster diameter.
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* a)
150
*
#
100
50
#
200 nm
0 b)
0 100 r [nm]
200 300
Supplemental figure S3.
(a) Super-resolution image of HIV-1 assembly sites displaying a clear doughnut-like shape. Arrows indicate two exemplary sites analyzed in (b). (b) Intensity profiles of 2D projections visualizing an Intensity minimum within the center of the cluster.
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Supplemental figure S4. Simulation of 3D single-molecule localization data. Simulation parameters were chosen to match the size of HIV-1 particles, i.e. a diameter of 140 nm. (a-f)
Exemplary representation of simulated 3D single-molecule localization data (left panels) and
2D intensity projection (right panels) ranging from a full sphere (a) to a small cap (f). (g)
Intensity profiles of 2D projections visualizing non-uniform intensity distributions which correlate with the part of the sphere imaged.
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References
Kiskowski MA, Hancock JF, Kenworthy AK (2009) On the use of Ripley's K-function and its derivatives to analyze domain size. Biophys J 97:1095-1103
Williamson DJ, Owen DM, Rossy J, Magenau A, Wehrmann M, Gooding JJ, Gaus K (2011)
Pre-existing clusters of the adaptor Lat do not participate in early T cell signaling events. Nat
Immunol 12:655-662
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