Supporting Information. Super Resolution Imaging of Nanoparticles Cellular Uptake and Trafficking

Transcription

1 Supporting Information Super Resolution Imaging of Nanoparticles Cellular Uptake and Trafficking Daan van der Zwaag 1,2, Nane Vanparijs 3, Sjors Wijnands 1,4, Riet De Rycke 5, Bruno G. De Geest 2* and Lorenzo Albertazzi 1,6* 1 Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands 2 Department of Chemical Engineering and Chemistry, Eindhoven University of Technology, Eindhoven, The Netherlands 3 Department of Pharmaceutics, Ghent University, Ghent, Belgium 4 Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands 5 VIB Department for Molecular Biomedical Research, Technologiepark 927, 9052 Ghent, Belgium 6 Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain these authors equally contributed as first authors * Prof.Dr. Bruno De Geest Department of Pharmaceutics, Ghent University, Ghent Belgium br.degeest@ugent.be * Dr. Lorenzo Albertazzi Institute for Bioengineering of Catalonia (IBEC), Barcelona, Spain lalbertazzi@ibecbarcelona.eu S1

2 Single Molecule Data Analysis Software 2D Bead Analysis Localization datasets corresponding to two-color, 2D-STORM images acquired as outlined before were imported into Matlab as.txt files. The datasets contain membrane localizations and bead localizations in different channels (see Chart 1 for an example), and were plotted to find regions of interest (ROI) containing a high density of beads inside a cell. These ROIs were automatically processed to efficiently obtain significant amounts of nanoparticles images. Chart 1 Raw data of a 2-color STORM image of nanoparticles (red) internalized in HeLa cells (membrane stained in green). A density-based clustering algorithm (dbscan.m) was applied to the NP channel to remove background localizations and identify individual objects in an image. In this algorithm with parameters k and ε, localizations which have at least k neighboring localization within ε nanometers are clustered together with these neighbors. Iteratively, neighboring points for these newly added neighbors are also detected, until no points are added anymore and a cluster is completely identified. In this way, background localizations due to detector noise, background fluorescence or other causes were eliminated and the individual clusters saved separately (see Chart 2) S2

3 Chart 2 Background removal from the nanoparticle channel (left) and isolation of localization clusters Subsequently, the clusters were fitted as circles to determine the exact location and size of each NP. Depending on the quality of reconstruction, size of the NP, microscope resolution and other experimental factors, this could be done using a fit to either an empty circle (using a closed expression in circfit.m) or a filled circle (optimization using a custom script). The procedure yields the midpoint and radius of every individual bead, which can be used for single-bead analysis. Each cluster was subjected to three quality checks to ensure only well-reconstructed single NP were taken into account in the analysis (see chart 3): - Sphericity. The sphericity was assessed by calculating the aspect ratio (AR) of the localization points cloud, defined as the ratio of the standard deviations in orthogonal directions: AR i = σ i σ, i Here, σ i is the standard deviation of the points cloud in direction i, and σ,i is the standard deviation of the points cloud in the orthogonal direction. If any of the AR i (or AR -1 i if σ,i > σ i ) exceeds a threshold value (1.5 in the 2D overlay), the cluster is rejected. - Size. The NP size was checked by comparing the found radius to the median value of the beads in an entire image, or to the radius given by the supplier. NP that are much smaller (R > 0.5*R ref ) or larger (R > 1.5*R ref ) than this reference radius are rejected. - Number of localizations. NP were rejected if the corresponding cluster contained too few localizations for accurate reconstruction, or too many to be due to normal dye behavior. S3

4 Image overlay was performed by aligning all beads according to their found midpoint and adding all localizations in a finely pixelated grid. For visualization, the Matlab function imagesc was used. Chart 3 2D quality check of localization clusters S4

5 3D Bead Analysis Analysis of 3D-STORM images consisted of largely similar steps to the 2D overlay, adapted for three-dimensional data. After plotting the localizations and selecting an ROI, densitybased clustering was applied (dbscan.m) to yield separated beads. Chart 4 Raw data (left), background removal (middle) and cluster identification (right) of 3D data for nanoparticles. Beads were fitted using hollow (spherefit.m) or filled (custom script) spheres, and identifying appropriate individual beads involved the three quality controls: - Sphericity. For the three-dimensional data, the aspect ratio (AR) was defined as: AR i maxσ j= x, y, z = min σ j= x, y, z If the AR i in any direction i exceeds a threshold value (1.8 in the 3D overlay), the cluster is rejected. Directions i are picked homogeneously in three dimensions using the script psphere.m. - Size. Same as 2D - Number of localizations. Same as 2D i, j i, j S5

6 Chart 5 3D quality check of localization clusters Image overlay was again performed by aligning all beads and adding up localization in a 3Dvoxel grid. Visualization of 2D-planes used the Matlab function slice, and the 3D-cutouts were created using the patch and isonormals commands. External Matlab scripts (via MathWorks) -dbscan.m (M. Daszykowski, University of Silesia) -circfit.m (I. Bucher) -psphere.m (J. Bowman) -spherefit.m (A. Jennings, University of Dayton) S6

7 Figure S1 - Confocal imaging of internalized PS nanoparticles. 80nm NPs are imaged with a confocal microscope to compare with the STORM image reported in Fig.1B. Subdiffraction NPs appears broadened due to the lack of resolution of confocal microscopy. Figure S2 3D reconstruction of internalized PS nanoparticles. 3D STORM imaging of PS nanoparticles and plasma membrane (top panel). Side view of PS nanoparticles (red) and cell membrane (green) highlighting membrane curvature. S7

8 Figure S3 Z sectioning of 330nm nanoparticles in HeLa cells. The 3D imaged reported in Fig. 1E and here in the left panel has been z-sectioned (z-sections of 50nm) to better solve the cluster of NPs internalized. On the right panels two sections (distance about 300nm) are reported highlighting the precise number, a cluster of three in this case, and positioning of the beads in 3D. Figure S4 Confocal z-stacking of internalized PS nanoparticles. 330nm particles internalized in HeLa cells are imaged by confocal microscopy. Z-stacking 3D rendering shows the reconstruction of the particles that due to the lack of z-resolution appears axially elongated. S8

9 Figure S5 STORM colocalization. HeLa cells that internalized 80nm PS nanoparticles (red) are co-stained for specific organelles. The colocalization of the two super resolution channels allows to determine with high accuracy the localization of NPs during intracellular trafficking. Figure S6 Imaging Macropynocytosis events. Conventional (left) and STORM (right) imaging of 220nm NPs interactions with the plasma membrane of HeLa cells. Macropynocytosis membrane ruffles engulfing the NP are imaged. S9

10 Figure S7 Dynamic light scattering and zeta potential. Dynamic light scattering data for the unlabeled and labeled nanoparticles used in this work. For all the particles labeling does not significantly affect NP size. Average size and zeta potentials for all studied NPs are reported in the table. Figure S8 In vitro NP STORM. Super resolution imaging of the NPs presented in this work after adsorption on a glass coverslip. Image size is 4µm. S10

11 Figure S9 Histogram of localizations. Histogram of number of localization per OVAparticle internalized in DC cells. Figure S10 TEM imaging in DC cells. TEM images of individual nanoparticles type internalized in DC cells. S11