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Localization-based super-resolution imaging meets high-content screening

Abstract

Single-molecule localization microscopy techniques have proven to be essential tools for quantitatively monitoring biological processes at unprecedented spatial resolution. However, these techniques are very low throughput and are not yet compatible with fully automated, multiparametric cellular assays. This shortcoming is primarily due to the huge amount of data generated during imaging and the lack of software for automation and dedicated data mining. We describe an automated quantitative single-molecule-based super-resolution methodology that operates in standard multiwell plates and uses analysis based on high-content screening and data-mining software. The workflow is compatible with fixed- and live-cell imaging and allows extraction of quantitative data like fluorophore photophysics, protein clustering or dynamic behavior of biomolecules. We demonstrate that the method is compatible with high-content screening using 3D dSTORM and DNA-PAINT based super-resolution microscopy as well as single-particle tracking.

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Figure 1: HCS-SMLM acquisition setup and analysis workflow.
Figure 2: HCS-SMLM allows fully automated 3D-dSTORM imaging of a complete 96-well plate.
Figure 3: Effect of dSTORM buffer on fluorophore photophysics.
Figure 4: Quantification of mobility and clustering of membrane proteins.

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Acknowledgements

This work was supported by the advanced ERC grant ADOS to D.C., the regional council of Aquitaine grant Dynascreen to D.C. and J.-B.S., the FranceBioImaging infrastructure ANR-10-INBS-04 to J.-B.S., the LabEx BRAIN and the IdEx Bordeaux to J.-B.S., and the Fondation pour la Recherche Médicale DEI20151234402 and ANR-Integractome to G.G.

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Authors

Contributions

A.B. performed all the experiments with the help of R.G., C.B. and M.C. A.K. developed the software for online processing and Single Molecule Profiler. F.L. and C.B. performed quantitative data analysis. M.C. developed the HCS-SMLM acquisition interface. O.R. and G.G. designed some biological control experiments. All the authors contributed to the manuscript. J.-B.S. and D.C. came up with the original idea. J.-B.S. supervised this work.

Corresponding author

Correspondence to Jean-Baptiste Sibarita.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

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 (http://cellprofiler.org/cp-analyst/).

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.

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 (I0) 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.

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 μm2) to properly reconstruct continuous microtubules with the buffer 200 mM of thiols at pH7.2.

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 (I0) 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.

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.

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).

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).

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.

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).

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.

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.

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Beghin, A., Kechkar, A., Butler, C. et al. Localization-based super-resolution imaging meets high-content screening. Nat Methods 14, 1184–1190 (2017). https://doi.org/10.1038/nmeth.4486

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