Nature Methods: doi: /nmeth Supplementary Figure 1

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1 Supplementary Figure 1 File Hierarchy, Single Molecule Profiler software interface and datamining using Cell Profiler Analyst The Single Molecule Profiler (SMP) software automatically generates a database from the single molecule localization files that have been organized in a plate layout format, as illustrated by the tree folder hierarchy. ASCII metadata text files (i.e. DetectionsFile.txt, TracksFile.txt, ClusterFile.txt, etc.) must contain the same number of header lines, identical column separators and descriptors for all wells and all positions, all of which can be specified by the user. A single metadata text file is used as a model for the data format and is parsed to generate descriptors for each column in the file. The user may select which descriptors, as well as the maximum number of objects (detections, tracks or clusters ), to be included in the database to reduce the final database size. The Single Molecule Profiler automatically generates the all files (red rectangle) necessary for importing into Cell Profiler Analyst (

2 Supplementary Figure 2 Acquisition template and 3D calibration for astigmatic-based 3D dstorm of a complete 96-well plate (a) Snake-like acquisition template corresponding to a travel length of 85 cm. Dark-grey wells containing adsorbed nanodiamonds have been used to compute the astigmatic-based 3D calibration (Z distance of 1 µm with 50 nm step size). (b) Mean of SigmaY-SigmaX of the astigmatic PSF as a function of Z position computed from 15 nanodiamonds Z-stacks (left) and representative images of nanodiamonds as a function of Z position in 4 extreme wells of a plate (right). Error bars represent S.D. We can notice the excellent reproducibility of the astigmatic calibration across entire plate.

3 Supplementary Figure 3 Single-molecule signal quality control across entire p96-well plate for 3D dstorm acquisition (a) Heat maps of the total number of localizations (top), mean of Gaussian fit Chi² (middle), and median of the centroid Z coordinate for all the localizations (bottom). We can notice that there is no correlation between the position inside the plate and these quantitative metadata, illustrating that this p96-well plate is suitable to achieve reproducible 3D super-resolution imaging. (b) Normalized histoplots of integrated intensity per detection (I 0 ) grouped by well. A similar distribution can be observed from the first well (A1) until the last well (H3). (c) Comparison between extreme positions on a plate (A1 and H12) acquired automatically using HCS-SMLM with a manual acquisition done in the middle of the plate (E6). These quality control experiments are coherent with the 3D super-resolution images acquired in Figure 3, demonstrating the possibility to automatically acquire hundreds of images in 3D SMLM with the same quality compared to manual acquisitions.

4 Supplementary Figure 4 dstorm buffer efficiency over time (a) (Top) Heatmap representation of frame_50, the median frame at which 50% of the localized have been detected. In a perfect dstorm regime, the median frame should be close to the middle frame (4,000 for an acquisition of 8,000 frames) as for well A1. (Bottom) Boxplots displaying the median values and IQR of 8 groups of 12 wells grouped per hour after buffer incubation (corresponding to an entire line of the plate, as color box next to the heatmap). (b) Normalized histoplots of the number of localizations per frame in wells separated by 1 hour. Even if the total number of detections and the quality of the single molecule detections are suitable during the complete 96-well plate acquisition (8 hours, see Fig. 3 and Fig. 4), we can observe that the buffer efficiency for Alexa 647 starts being affected already after 1 hour of incubation (well A10 as compared to well A1). A continuous decrease of the frame_50 metadata correlates with the buffer incubation time. (c) 3D dstorm images taken 6 hours, 10 hours and 14 hours after the first acquisition. Images have been acquired automatically in the same plate in different wells. We can observe that, after 14 hours of buffer incubation, the total number of localizations became too small (less than 1 million in a FOV of 20.5X20.5 µm 2 ) to properly reconstruct continuous microtubules with the buffer 200 mm of thiols at ph7.2.

5 Supplementary Figure 5 3D DNA-PAINT acquisitions of microtubules in a p96-well plate (a) Left: Normalized histoplots of the number of detections per frame (for the first 8,000 frames) in 3 different wells. In contrast to the dstorm acquisition, we can observe a constant number of localizations over the course of the 9-hour acquisition. Right: Normalized histoplots of integrated intensity per detection (I 0 ) grouped by well showing a similar distribution from the first well (A1) until the last well (B7). (b) 18 astigmatism-based 3D DNA-PAINT SMLM images (FOV 20.5 x 20.5 µm, 40 nm/px) of microtubules in Cos-7 cells automatically gathered using our HCS-SMLM approach. It corresponds to an acquisition time of 9 hours (30 min per cell), illustrating (1) the capability to efficiently acquire DNA-PAINT data using our HCS-SMLM pipeline, and (2) the time-consuming process of DNA-PAINT as compared to dstorm. In 9 hours, the HCS-dSTORM pipeline acquired 5-fold more positions with similar microtubule reconstruction quality compared to HCS-DNA-PAINT (only 18 cells in 9 hours). The DNA-PAINT acquisition generated also a larger database as more molecules were detected (2 to 5 fold) than in a dstorm acquisition due to a higher duty cycle of DNA probes than organic dyes. (c) Examples of astigmatism-based 3D DNA-PAINT images in well A1, after 4 hours (well A8), and after 9 hours (well B7). Color codes for the Z-position. Scale bar: 5 µm.

6 Supplementary Figure 6 Stepbleaching analysis of isolated Alexa 647 fluorophores inside a P96-well plate and acquisition scheme for fluorophore photophysics study (a) Isolated single fluorophore conditions of the coated 96-well plate were controlled using a stepbleaching experiment using the PIF software 1. For stepbleaching analysis, fluorophores were imaged at 5 different positions per well in Tris-HCl ph7.5 buffer (10 kw/cm², 20ms exposure time for 20 sec). Left: Maximum intensity projection of a stack of Alexa 647 fluorophores (AF 647). Top right: representative intensity time trace for one detected fluorophore showing one single bleaching step. Bottom right: stepbleaching distribution for all detected molecules (n = 214). (b) dstorm acquisition template for organic dyes well plate: a pumping phase with 100% of laser power was applied (640 nm: 40 kw/cm², 532 nm: 20 kw/cm² both for 10 sec) in order to excite a maximum of molecules to the triplet state. Immediately after the pumping phase, the single-molecule sequence ( STORM regime ) was automatically launched for a duration of 2 minutes at 50% laser power. For meos3.2 isolated proteins, no pumping phase was needed and the 405 nm and 561 nm lasers were used at constant power during the acquisition phase. 16 buffers on 3 different fluorophores (48 conditions) were tested.

7 Supplementary Figure 7 2 color dstorm acquisition Microtubules labelled with Alexa 532 and Vimentin labelled with Alexa 647 in Cos-7 cells using the same dstorm buffer (200mM bmea at ph7.2) selected from the characterization of isolated fluorophores (same as Figure 2c). Super-resolution (SR) reconstructions were compared to high resolution (HR) images. Focus position was set at the bottom of the cell (approx. 300 nm above the coverslip surface in TIRF illumination at 50fps).

8 Supplementary Figure 8 Comparison of buffers for 2 color dstorm acquisition LaminB1 labelled with Alexa 532 and microtubules labelled with Alexa 647 in Cos-7 cells using 2 different buffers (Top panel: 25mM bmea at ph7.2; Bottom panel: 25 mm bmea at ph5.2) displaying poor photophysics characteristics for both fluorophores (see Fig. 2b). Focus was at the top of the cell, approximately 5 µm above the coverslip surface. Acquisitions were achieved in the same conditions as for Figure 2c. As described by the photophysics parameters in Figure 2b, Alexa 647 detection was clearly affected by the decrease in thiols concentration (Top Panel) and of the ph (Bottom Panel). Also and as previously described, Alexa 532 detection is slightly affected with an apparent smaller number of localized single molecules, especially for the buffer with 25 mm bmea, ph 5.2 (Bottom Panel).

9 Supplementary Figure 9 Cell viability and culture medium (a) Plate layout with 3 types of culture medium tested on a p96-well plate. (b) Example of cells imaged by widefield time-lapse microscopy for at least 8 hours. After more than 4 hours of acquisition, HeLa cells clearly required serum to stay alive and spread correctly on the support. After 8 hours of acquisition on the setup in the Fluorobrite+Glutamax+serum medium, a similar number of cells and identical shapes were obtained as compared to classical medium (DMEM+Glutamax+SVF), validating the use of Fluorobrite medium as an imaging medium for our living cells experiments. (c) No difference in background fluorescence (yellow boxes, quantification on right) or single molecule intensity (green boxes) was noticed between Fluorobrite+Glutamax+serum medium and PBS.

10 Supplementary Figure 10 Single molecule signal quality control across an entire 96-well plate for PALM imaging (a) Example of single molecule localization quality control. Here, acquired positions whose photon distributions demonstrate a strong (above 10%) population of multi-emitter detections are automatically excluded. The limit between single and multi-emitter is defined on 10 distributions of isolated proteins. White arrows indicate multi-emitter signals due to a too high density of proteins inside the cell. (b) Plate layout of a control assay on p96-well plate with purified meos3.2 protein adsorbed to the glass (4 dark grey wells on plate corners). Ten random positions per well were acquired and the histogram represents the cumulative diffusion coefficient metadata. The threshold between mobile and immobile molecules was set to 10-2 um²/sec (see Online Methods). Only a weak fraction of mobile tracks (10%) were measured, indicating good stability of the plate on the stage (no drift, no deformation) during the screening process. (c) Left: Plate layout of a control assay with living HeLa cells expressing SEP::GluA1::mEos2 across 60 wells of a p96-well plate. Three positions per well were acquired and the diffusion coefficients pooled in one histogram per well. Right: Histogram of 6 representative wells (corresponding to dark grey wells), showing similar DCoef distributions and fractions of immobile tracks (N=180 cells).

11 Supplementary Figure 11 Different modes of acquisition sequences 4 different concentrations of Ab crosslinker (Polyclonal Ab anti-sep) were tested (0, 1/100, 1/300, 1/1000) on HeLa cells expressing SEP::GluA1::mEos2.1, and 3 types of acquisition sequences were performed: I/ Sequential acquisition: antibodies were incubated well by well, requiring manual intervention between each well. II/ Serial acquisition: all antibody concentrations were loaded at the same time at the beginning of the acquisition (no user intervention during acquisition). The acquisition order was the well without Ab first, then the wells with 1/100, 1/300 and 1/1000 of Ab concentration. III/ Random acquisition: same as II/ except that the positions were randomly acquired between all wells.

12 Supplementary Figure 12 Percentage of immobile tracks and variability depending of the acquisition sequence. Cumulative histograms of DCoefs per well, (N=10 cells per well), and corresponding percentage of immobile molecules. The dose response of Ab crosslinkers were similar for the 3 acquisition modes.

13 SUPPLEMENTARY TABLE Metadata 1 and 2 : Localization and Tracking (ON-LINE) Metadata 3 Individual Fluorescent Molecule Photophysics (OFF-LINE) Metadata 4 Cluster Analysis (OFF-LINE) Supplementary Table 1: Metadata definitions Name Definition Units Applications XY localization XY sub-diffraction position of the detection inside the field of view Intensity I Integrated intensity calculated from a Gaussian fit of each 0 detection Sigma Sigma of each detection computed by bi-dimensional Gaussian fitting D coef Global diffusion coefficient : fitting the first four points of the MSD to r 2 = 4*Dcoef*t InstantD Same as Dcoef but calculated for each time point of the trajectory and fitting the 4 following points of the MSD % of mobile tracks (> 10-² µm²/sec) vs. the % immobile tracks % of mobile vs. immobile tracks (< 10-2 µm²/sec) nm photons nm µm²/sec µm²/sec on (ON phase duration) msec off (OFF phase duration) msec Duty Cycle on/( on+ off) _ Number of blinks (#blinks) number of OFF phases _ Intensity (intensity of all detections for single molecule) photons Initial time Total life time Time of the first detection of the fluorophore s first ON phase (after the optional pumping phase) Total duration between the initial time and the last detection of the fluorophore s last ON phase % msec msec Shape descriptors Area, perimeter, Feret diameter (i.e) of each clusters nm², nm % of clustered vs. isolated localizations % of localizations inside vs. outside clusters % Density Number of molecules divided by the area of each cluster nm - ² Number of localizations (#locs) Number of localizations of each molecule inside a cluster _ Intensity (intensity of each detection of all molecules in one cluster) photons Orientation Angle of the Feret Diameter ⁰ Barycenter XY Position XY of the barycenter of the cluster nm Mandatory parameters: quality control i.e. dynamics of single proteins in living cells in response to drugs i.e. screening of new fluorescent probes designed for SMLM i.e. structural analysis of proteins in living or fixed cells

14 SUPPLEMENTARY NOTES 1. Acquisition workflow We have developed several acquisition strategies depending on the study. A first approach, adapted to densely distributed objects of interest, consists of using the same high NA oil immersion objective to identify cells/objects of interest and to screen the SMLM acquisition at the recorded positions. This is combined with either randomly or manually moving the stage to the position of interest. A second approach is dedicated to the case of sparsely distributed structures of interest inside the wells. In this case, we implemented a more integrative two-step approach using two objectives: in a pre-screening step, a low magnification lens is quickly used to automatically or manually identify the regions of interest. It is then followed by the screening step, where a high magnification and high NA objective is used to perform SMLM acquisitions at the identified positions. For multiwell plate acquisition, the immersion oil must be carefully pre-dispensed on the well plate bottom surface before being loaded on the microscope stage. This oil painting step avoids user intervention and loss of focus during stage displacement or objective switching. In order to maintain the focus at each position of the plate without damaging the objective, we have developed a two-step routine that first identifies the position of the glass coverslip surface by moving the focus along the axial (Z) direction to obtain the highest reflection of a tilted infrared diode beam onto the coverslip surface. Second, a fixed axial offset from this coverslip surface is introduced to perform the acquisition at the interface between the coverslip and the sample. Once the focal position is set, it is maintained during the entire screening process, involving lateral (XY) displacements at fixed axial position. If the focus is lost during lateral displacement, the autofocusing routine restarts automatically. A manual preset of the focus is also possible. For each position, SMLM data are collected in streaming mode using either an EM-CCD or scmos camera. Exposure time, laser intensities and acquisition duration per position can be adjusted depending on the acquisition protocol (e.g. (spt)palm, dstorm, DNA-PAINT, etc.), fluorophore photophysical properties, biological sample and statistics. 2. Metadata computation An important step to achieve efficient HCS-SMLM is real-time processing and analysis. This is a key step in order to deal with the massive amount of data generated by the screening process. Indeed, a typical SMLM-HCS acquisition of a 96-well plate can lead to a huge amount of data, ranging from 100 GB to 2 TB of data organized in thousands of image stacks. The post-acquisition processing of such a huge amount of data, including loading and analyzing, can easily represent few days of delay before accessing the localization metadata (Fig. 1d). To overcome this limit, we have implemented single-molecule localization and tracking in real-time. First, high accuracy single molecule localization is performed onthe-fly, in parallel with the streaming acquisition process, using a combination of GPU-based wavelet filtering and Gaussian fitting 53. Briefly, each image of the streams is split into small overlapping regions to permit parallel wavelet filtering. Once the parallel filtering is achieved, the resulting image is stitched back and thresholded automatically. A watershed algorithm is used to separate molecules in close proximity one to each other. The localization coordinates of each identified molecule are then extracted from their centroid and stored into the memory. At the end of the acquisition, the position of the fluorophores and the number of photons per localization event can be refined using GPU-based Gaussian fitting algorithm. When quantitative information about the dynamics is required, single molecule trajectories and diffusive behavior parameters (mean square displacements and diffusion

15 coefficients) 15 are computed from the list of refined molecule coordinates using a simulated annealing algorithm 55. To avoid delaying the HCS-SMLM acquisition pipeline, the tracking process is launched on a separate thread, allowing the screening process to continue uninhibited. If needed, only the quantitative metadata can be stored and the SMLM raw images can be discarded, saving an enormous amount of space on the storage media. Depending on the molecule density, image raw data can represent more than 90% of the total space including metadata and database. While online processing is important to gain time (Fig. 1d), it is not mandatory to perform HCS-SMLM since Single Molecule Profiler software can handle metadata generated from conventional SMLM software. Offline metadata are computed directly from the online metadata (Fig. 1b). Depending on the biological question to address, these metadata can be molecular organization (clustering), photophysical properties of the fluorophores, or anything else which can be computed directly from the online metadata. As an example, we used the tessellation-based segmentation method SR-Tesseler 46 to compute both the photophysics of various fluorophores under different conditions and to quantify membrane receptor clustering in response to a cross-linking agent. 3. Raw data organization to be used with Single Molecule Profiler (SMP) The database is composed of several tables: the image table, which contains the image data information (path, name, well, condition, total number of localizations, etc.) as well as a several tables containing the quantitative single molecule metadata (e.g. trajectories, diffusion coefficients, photophysics parameters, cluster characteristics, etc.). The metadata tables and the image table are linked through a unique imageid identificator (Fig. 1c). The free database creation tool SMP has been designed for use with any single molecule localization dataset obtained from conventional software, such as ThunderSTORM. The data must respect the following checklist to be converted into a database by SMP (see Supplementary Fig.1). 1/ Folder hierarchy: The localization text files must be organized into the hierarchy described in Supplementary Fig. 1: The top level plate folder contains a number folders for each individual well. Each well represents an individual condition to be compared in CellProfiler Analyst software. These well folders must follow the labelling scheme for a 96-well or 384-well plate. For the 96-well plate layout, wells are arranged in an 8x12 grid, with each row denoted by a letter A-H and each column denoted by a number For the 384-well plate layout, wells are arranged in a 16x24 grid, with each row denoted by a letter A-P and each column denoted by a number Note the leading 0 for single digit row numbers and no spaced between the column letter and row number, i.e. A01, A10. Each well folder contains a number of position folders. Each position represents a different acquisition under the same condition of its parent well. These position folders can be labelled as P with a 2 digit identifier. For example, the well A01 may contain a number of position folders, P 01, P 02, and P 03. This is a strong suggestion to clearly label position folders with a numerical identifier but any names are tolerated for position folders. Note that all folders that are presents inside a well folder will be considered like a position folders. Example file hierarchy for a single 96-well plate (plate \ well \ position):

16 MyPlate\A01\P 01 MyPlate\A01\P 02 MyPlate\H12\P 09 2/ Single molecule localization data files: Each position folder contains the localization text files relevant to that particular position. A single position folder must contain at least a single: DetectionsFile.txt containing the list of localizations The position folder may contain additional data files for supplemental analyses: TracksFile.txt ClusterFile.txt AnyThingElse.txt containing the list of trajectories. containing the list of clusters containing the list of any objects of interest Example of localization and tracking data files : MyPlate\A01\P 01\DetectionsFile.txt MyPlate\A01\P 01\TracksFile.txt MyPlate\A01\P 02\DetectionsFile.txt MyPlate\A01\P 02\TracksFile.txt MyPlate\H12\P 09\DetectionsFile.txt MyPlate\ H12\P 09\TracksFile.txt 3/ Single molecule localization data file structure: Individual single molecule localization data text files must adhere to a specific file structure in order to be properly recognized by SMP and converted into a functional database, but some flexibility in the formatting is accepted. The user may specify the column separator (tab, space, ), decimal separator (dots, comma, ), and number of header lines for each data text file type. Note that all files, from the same data type, must have the same formatting, i.e. the same separators and number of header lines. Each detections file must contain at least 2 columns: Xloc Yloc The x-coordinate of the localization The y-coordinate of the localization The data files may optionally contain any additional descriptors, for example intensity, quality of fit, z- position, etc. Note: SMP automatically detects the header of each additional column, and it is therefore recommended to use unique and comprehensive header names. Example: DetectionFile.txt : ID, Xloc, Yloc, Zloc, IntGaus, Sigma, Chi2, (red: mandatory) TracksFile.txt: ID, Xtrack, Ytrack, Frame, ClusterFile.txt: ID, ClusterSize, DetectionsCount,

17 4. User manual for SMP 1/ Select the plate dimensions (96 or 384 wells) 2/ Click on the load button to choose the plate directory for your data (example: \MyPlate). SMP will parse your data layout and visualize its structure as a multi-well plate according to the folder hierarchy outlined above. (Optional) Users may select/deselect wells to be combined into the database by clicking on the wellplate layout (green: selected; yellow: rejected). Mousing over will show the number of positions in every selected well. 3/ Click on the Add button to add datasets for the single molecule localization text data files in each of the positions of each well. Specify the data type, column separator and decimal limiter, and the number of header lines and the first line of data. Once the file format has been defined, a template of the column structure must be defined by loading a file model using the button. The columns are then parsed into individual descriptors, which may be removed from the final database by unchecking them from the list. The x- and y- localization column names must be selected. (Optional) To make large datasets manageable and reduce the size of the final database, use the fetch option to randomly select a subset of lines per dataset. 10,000 detections per image is typically sufficient for control quality. (Optional) Select images to be loaded by CellProfiler Analyst. Note that these images must all have the same file name for each position in every well and must be in TIFF format. Click Validate to save the file parsing structure for this type of dataset. Repeat this process for each type of dataset, for example detections, tracks, and clusters. These file parsing parameters may be modified using the Edit button. 4/ Once all of the data types have been added, click on Create SR-HCS database. A progress bar will appear during the database creation process. Once completed, at least 4 new files (per_image.csv, per_object.csv, MyPlate.sql, MyPlate.PROPERTIES) will have been generated in in the plate directory, ready to use with the Cell Profiler Analyst (CPA) software ( You have to import the file MyPlate.PROPERTIES in CPA. The table per_image contains global descriptors (i.e total number of detections) whereas per_object table contains all descriptors of the detection files.