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Supplementary Information Multicolor Single Particle Reconstruction Of Supplementary Information Multicolor single particle reconstruction of protein complexes Christian Sieben1,3,*,#, Niccolò Banterle2,*, Kyle M. Douglass1, Pierre Gönczy2, Suliana Manley1,3,# 1) Laboratory for Experimental Biophysics, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland 2) Swiss Institute for Experimental Cancer Research, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland 3) Swiss National Centre for Competence in Research (NCCR) in Chemical Biology * authors contributed equally # correspondence should be addressed to: Suliana Manley, [email protected] or Christian Sieben, [email protected] Supplementary Figure 1: Flowchart of the general data analysis workflow Supplementary Figure 2: Single field view STORM reconstruction of purified human centrioles immunolabelled against Cep152 and Cep164 Supplementary Figure 3: Two-color particle segmentation and isolation workflow Supplementary Figure 4: Montage of aligned dual-color reconstructions Supplementary Figure 5: Single particle reconstruction with unknown symmetry Supplementary Figure 6: Single particle reconstructions of purified T4 bacteriophages Supplementary Figure 7: Particle alignment workflow Supplementary Figure 8: 2D particle averaging of classified top view particles Supplementary Figure 9: Axial alignment of multiple centriolar proteins Supplementary Note 1: Choice of fluorophores and imaging buffer Supplementary Note 2: Single particle reconstruction from SMLM images in Scipion Supplementary Note 3: Overview of the used shape descriptors Supplementary Note 4: Software documentation Supplementary Table 1: Primary antibodies used in this study Supplementary Figure 1: Flowchart of the general data analysis workflow. Flowchart of the entire dual-color single particle reconstruction procedure, with approximate processing times indicated on the left in days. Custom software to perform the steps in orange boxes is available as supplementary software. Updates are available on GitHub (https://github.com/christian- 7/MultiColorSPR). *: indicates that more details for this particular step can be found in Supplementary Figure 3. 2 Supplementary Figure 2: Single field of view STORM reconstruction of purified human centrioles immunolabeled against Cep152 and Cep164. Only the Cep152 channel is shown. From this particular field of view, 174 particles could be extracted. A montage of dual-color particles from the same data set is shown in Supplementary Figure 4. Shown in red and green squares (a, b, higher magnification views) are examples of particles that were kept in the high- quality particle library (green) or filtered out during this step (red). See also Supplementary Note 3 for more details on the segmentation procedure. 3 Supplementary Figure 3: Two-color particle segmentation and isolation workflow. Localization clusters corresponding to centrioles labelled for Cep152 (Channel 1) and HsSAS-6 (Channel 2) were segmented using the wide field overview image taken for each field of view before acquiring the STORM stack. For each individual particle, the localizations from both channels (extracted segments) were combined and subsequently clustered using DBSCAN1 (identified clusters). Only clusters with more than 100 localizations were kept (selected clusters), which removed most of the nonspecific localizations. Finally, for both identified particles, the two channels were separated again (final 2C particle) and rendered into a two-color image with a pixel size of 10 nm (bottom panels). We used the same procedure for single-color datasets, omitting the channel separation and recombination steps. 4 Supplementary Figure 4: Montage of aligned dual-color reconstructions. Montage of 324 aligned reconstructions of individual centrioles immunoabeled against Cep152 (magenta) and Cep164 (green) (a). For each particle and in both channels, the resolution was determined using Fourier ring correlation (FRC). A resolution histogram of the full dataset (2762 particles) is shown in b. Magenta: Cep152, Green: Cep164. The median resolution was 44 nm (Cep152) and 49 nm (Cep164). 5 Supplementary Figure 5: Single particle reconstruction with unknown symmetry. We tested our single particle reconstruction pipeline using a simulated asymmetric structure given by a putative combination of two centriolar proteins, Cep152 and HsSAS-6; a simplified version of the structure is reconstructed in Fig. 2. Using a ground truth model, we simulated 6000 particles with a labelling efficiency of 0.4 (Supplementary Note 4), which were classified into 60 classes. Two examples of raw particles with their corresponding class averages are shown in a. The initial model (c1) was refined using projection matching (Xmipp3) (b). 6 Supplementary Figure 6: Single particle reconstructions of purified T4 bacteriophages. T4 bacteriophages have a well-characterized shape (schematic in a) that is preserved during their isolation and purification, as we verified by atomic force microscopy (b). Phages labelled with Alexa 647 NHS ester were imaged, segmented and reconstructed as described for human centrioles. 1794 particles were classified using template-free ML2D2. Shown are two example classes with two raw particles each (c). The best class averages (see Supplementary Note 2) were used to reconstruct the 3D model (c6 symmetry) (d). The shape of the phage is well represented while only the long tail fibers are missing, presumably due to their increased flexibility as can be observed within the raw particle dataset (c). The raw particles have a median resolution of 41 nm (e, left), whereas the 3D particle was reconstructed at an isotropic resolution of 58 nm as determined by Fourier shell correlation (FSC) (e, right inset). 7 Supplementary Figure 7: Particle alignment workflow. In order to position two reconstructed volumes in the correct context, we developed a simple alignment procedure (a). Since the centriole is rotationally symmetric, we can collapse one axis at a time and determine the translation between the reference protein and the protein of interest by looking at top and side view projections. We use supervised machine learning to train computational models to automatically identify top and side view projections using a library of calculated shape descriptors (b, Supplementary Note 3). The models have an accuracy of 94 % (top view) and 79 % (side view), as shown by Receiver operating characteristic (ROC) curves for two trained models (c). The models are trained on small subsets of the dataset (e.g. 200 particles) and can then be applied to the corresponding full or other datasets using the same reference protein. To train a model, we visually classified all 200 particles within the subset into three classes: top view, side view or unclassified. This was then taken as a response to train the model (Classification Learner, MATLAB). The extracted top and side view particles can then be used for 2D averaging (Supplementary Figure 8) and compared to their respective counter particle in the second channel to extract translation (Δz) and rotational alignment parameters. 8 Supplementary Figure 8: 2D particle averaging of classified top view particles. We use a combination of stepwise 1 ° rotations between 0 and 359 °, followed by lateral translations and cross-correlation to find the optimal overlay of a pair of particles. Performed on centriole top view particles, this allows us to study the symmetry properties of the corresponding proteins. The biggest cross-correlation peak provides the best rotation and translation between two particles. For each alignment iteration, individual particles were compared to the sum of all aligned particles from the previous iteration. This 2D averaging procedure revealed a nine-fold symmetrical organization of Cep57 (c) and Cep164 (e), which can be compared to the 9-fold symmetric simulated structure (a). We further tested this visual observation by measuring the angle between neighboring foci (as depicted in a). Histograms of angles between foci and their nearest neighbours and corresponding Gaussian fits are shown in (b, d, f). Besides a mean angle of 39.5 for the simulated particles (b), we found on average 42.4 for Cep57 (d) and 43.3 for Cep164 (f) supporting a 9-fold symmetric organization in both cases. 9 10 Supplementary Figure 9: Axial alignment of multiple centriolar proteins. Top and side view projections were selected using a library of shape descriptors (see Supplementary Note 2) calculated for the reference protein (Cep57 or Cep152). For proteins that share the same principal symmetry axis (xy in a), the side view projections can be used to find the axial translation (Δz) between both imaged proteins. Individual particles were extracted according to the shape of the reference protein. For each reference particle, the translation and rotation was determined for optimal overlap and then applied to both the reference and the protein of interest. The resulting two color images are shown in a. Line plots (b) can be used to find the axial translation. While Cep57 and Cep152 overlap and are centered at the same axial position, we found a translation of ΔzCep152/Cep164 - 2 = 94 nm and ΔzCep57/Cep164 - 1 = 280 nm. The obtained radius for Cep152 is in good agreement with Lucinavičius et al 3 , while we report a smaller radius than Sonnen et al4. This might reflect cell cycle-dependent changes in protein levels and/or distribution, which could result in variability
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