DOI: 10.5061/dryad.xd2547dsw
title: Example single molecule tracking data from: A high-throughput platform for single-molecule tracking identifies drug interaction and cellular mechanisms

description: The regulation of cell physiology depends largely upon interactions of
 functionally distinct proteins and cellular components. These interactions
 may be transient or long-lived, but often affect protein motion.
 Measurement of protein dynamics within a cellular environment,
 particularly while perturbing protein function with small molecules, may
 enable dissection of key interactions and facilitate drug discovery;
 however, current approaches are limited by throughput with respect to data
 acquisition and analysis. As a result, studies using super-resolution
 imaging are typically drawing conclusions from tens of cells and a few
 experimental conditions tested. We addressed these limitations by
 developing a high-throughput single-molecule tracking (htSMT) platform for
 pharmacologic dissection of protein dynamics in living cells at an
 unprecedented scale (capable of imaging &gt; 106 cells/day
 and screening &gt; 104 compounds). We applied htSMT to measure the
 cellular dynamics of fluorescently tagged estrogen receptors (ER) and
 screened a diverse library to identify small molecules that perturbed ER
 function in real-time. With this one experimental modality, we determined
 the potency, pathway selectivity, target engagement, and mechanism of
 action for identified hits. Kinetic htSMT experiments were capable of
 distinguishing between on-target and on-pathway modulators of ER
 signaling. Integrated pathway analysis recapitulated the network of known
 ER interaction partners and suggested potentially novel, kinase-mediated
 regulatory mechanisms. The sensitivity of htSMT revealed a new correlation
 between ER dynamics and the ability of ER antagonists to suppress cancer
 cell growth. Therefore, measuring protein motion at scale is a powerful
 method to investigate dynamic interactions among proteins and may
 facilitate the identification and characterization of novel therapeutics.
description: All image acquisition using SMT was performed on a custom-built
 HILO microscope based on a Nikon Ti2, motorized stage, stage top
 environmental chamber (OKO labs), quad-band filter cube (Chroma), custom
 laser launch with 405 nm, and 561 nm wavelengths, coupled to a Nikon TIRF
 illumination module by fiber optic element (KineFlex
 HPV-P-3-S-405..640-0.7-APC-P2) delivering &gt;10 mW and &gt;600 mW
 of power in a gaussian beam with a FWHM of approximately 250 µm to the
 back focal plane of the objective. Angle of inclination and beam direction
 were adjusted by micrometer on the TIRF illuminator and empirically set to
 maximize and flatten the signal across the camera field of view.
 Fluorescence emission was passed through a high-speed filter wheel (Finger
 Lakes Instruments) and collected with a backlit CMOS camera (Prime 95b,
 Teledyne). Images were acquired with a 60X 1.27 NA water immersion
 objective (Nikon). Environmental chamber was set to 37° Celsius, 95%
 humidity, and 5% CO<sub>2</sub>. For each field of view, 200
 SMT frames were collected at a frame rate of 100 Hz, with a 2 msec
 stroboscopic laser pulse. 10 frames of the Hoechst channel were collected
 at the same frame rate for downstream registration of trajectories to
 nuclei. Sample focus was maintained using the reflection-based Perfect
 Focus System to determine the position of the coverglass and apply an
 empirically-determined offset to focus into the
 sample.<br><br> Image acquisition produced
 one JF<sub>549</sub> movie and one Hoechst per field of view.
 The JF<sub>549</sub> movie was used to track the motion of
 individual JF<sub>549</sub> molecules, while the Hoechst movie
 was used for nuclear segmentation. Tracking was accomplished in three
 sequential steps – detection, subpixel localization, and linking – using a
 combination of existing methods. Briefly, spots were detected using a
 generalized log likelihood ratio detector to test every 11×11 pixel window
 using a gaussian kernel with a radius of 1.25 pixels and with a log
 likelihood detection threshold 14<sup>68</sup>. After detection, the estimated position of each emitter was refined to subpixel resolution using Levenberg-Marquardt fitting<sup>69–71</sup> with an integrated 2D Gaussian spot model<sup>72</sup> starting from an initial guess afforded by the radial symmetry method<sup>73</sup>. Detected spots were linked into trajectories using a custom modification of a hill-climbing algorithm with a maximum linking radius of 1.25 µm and allowing a maximum of 2 gap frames where a spot may go undetected but still be linked within the same trajectory<sup>10,74</sup>. The same detection, subpixel localization, and linking settings were used for all movies used in this manuscript. For nuclear segmentation, all frames of the Hoechst movie were averaged to generate a mean projection. This mean projection was then segmented with a neural network trained on human-labeled nuclei, the output of which is a mask assigning a semantic category to each pixel in the image<sup>75</sup>. Each spot was assigned to at most one nucleus using its subpixel coordinates. To recover dynamical information from trajectories, we used state arrays<sup>35</sup>, a Bayesian inference approach, with the “RBME” likelihood function and a grid of 100 diffusion coefficients from 0.01 to 100.0 µm<sup>2</sup> s<sup>-1</sup> and 31 localization error magnitudes from 0.02 to 0.08 µm. For each assay well, 10,000 trajectories were randomly sampled from the aggregated pool of nuclear trajectories, and this set of trajectories was used for state inference. After inference, localization error was marginalized out to yield a one-dimensional distribution over the diffusion coefficient for each field of view. For single-cell analysis, we performed SMT and nuclear segmentation on a mixture of U2OS cells bearing H2B-HaloTag, HaloTag-CaaX, or free HaloTag. We then evaluated the marginal likelihood of each of a set of 100 diffusion coefficients on the set of trajectories within each segmented nucleus<sup>30</sup>. These marginal likelihood functions were clustered with k-means (3 clusters), and the marginal likelihood functions for each cell were ordered by their cluster index to produce the heat map. To estimate the fraction bound (<em>f</em><sub>bound</sub>), we integrated the state array posterior distribution below 0.1 µm<sup>2</sup> s<sup>-1</sup>. To estimate the free diffusion coefficient (<em>D</em><sub>free</sub>), we computed the mean of the posterior distribution above 0.1 µm<sup>2</sup> s<sup>-1</sup> by renormalizing the distribution density of the bins &gt;0.1 µm<sup>2</sup> s<sup>-1 </sup>and computing the bin-weighted mean.
description: # Example single molecule tracking data from: A high-throughput platform
 for single-molecule tracking identifies drug interaction and cellular
 mechanisms
 [https://doi.org/10.5061/dryad.xd2547dsw](https://doi.org/10.5061/dryad.xd2547dsw) ## Description of the data and file structure Each unique field of view is grouped based on the file name and the format `{plate identifier}_{well address}_{field of view number}` For each field of view, there are two raw `.nd2` files from the microscope, one corresponding to the single molecule tracking movie (`chSPT.nd2`) and the other an image of hoechst-stained nuclei (`chMask1.nd2`). The `chMask1.nd2` file is segmented to produce a tiff file  `_ChMask1_corr_binary_mask.tiff` After segmentation of the mask images, particle detection and tracking are performed as described in McSwiggen et al 2024 and Driouchi et al 2024. This produces the csv output with sub-pixel locations and tracks identities in the file `_spots_and_tracks.csv` . In this file, a mask index of 0 means the spot was detected outside of the nuclei, whereas any positive integer corresponds to a unique instance in that field of view.  Lastly, these tracks are used as the input to the State Arrays algorithm as described in Heckert et. al. 2022, the output of which is the csv file ending in `_StateArray.csv` ### Files and variables #### File: 910357-U2OS-0005\_WellD17\_Point01\_spots\_and\_tracks.csv **Description:** Output of detection and tracking step. ##### Variables * frame: image index in the X,Y,T raw files * y0_pixel: sub-pixel location of detected particle in the y direction * x0_pixel: sub-pixel location of detected particle in the x direction * trajectory: integer identifier for each unique trajectory as it is detected in subsequent frames * mask_index.NUCLEUS: integer identfier for each nucleus. A 0 indicates the spot was detected outside of a nucleus mask. #### File: 910357-U2OS-0005\_WellD16\_Point03\_spots\_and\_tracks.csv **Description:** Output of detection and tracking step. ##### Variables * frame: image index in the X,Y,T raw files * y0_pixel: sub-pixel location of detected particle in the y direction * x0_pixel: sub-pixel location of detected particle in the x direction * trajectory: integer identifier for each unique trajectory as it is detected in subsequent frames * mask_index.NUCLEUS: integer identfier for each nucleus. A 0 indicates the spot was detected outside of a nucleus mask. #### File: 910357-U2OS-0005\_WellD16\_StateArray.csv **Description:** quantification of state occupation using bayesian state inference method with a uniform prior. ##### Variables * diff_coef_value: Center of the bin used in the state array diffusion coefficient grid * posterior_occupation: Number of jumps inferred to be in that bin * normalized_posterior_occupation: Relative number of jumps in that bin normalized to total number of jumps #### File: 910357-U2OS-0005\_WellD17\_StateArray.csv **Description:** quantification of state occupation using bayesian state inference method with a uniform prior. ##### Variables * diff_coef_value: Center of the bin used in the state array diffusion coefficient grid * posterior_occupation: Number of jumps inferred to be in that bin * normalized_posterior_occupation: Relative number of jumps in that bin normalized to total number of jumps #### File: 716634-U2OS-0010\_WellB19\_StateArray.csv **Description:** quantification of state occupation using bayesian state inference method with a uniform prior. ##### Variables * diff_coef_value: Center of the bin used in the state array diffusion coefficient grid * posterior_occupation: Number of jumps inferred to be in that bin * normalized_posterior_occupation: Relative number of jumps in that bin normalized to total number of jumps #### File: 910357-U2OS-0005\_WellD17\_Point01\_ChMask1\_corr\_binary\_mask.tiff **Description:** Binary segmented file. #### File: 716634-U2OS-0010\_WellB19\_Point01\_spots\_and\_tracks.csv **Description:** Output of detection and tracking step. ##### Variables * frame: image index in the X,Y,T raw files * y0_pixel: sub-pixel location of detected particle in the y direction * x0_pixel: sub-pixel location of detected particle in the x direction * trajectory: integer identifier for each unique trajectory as it is detected in subsequent frames * mask_index.NUCLEUS: integer identfier for each nucleus. A 0 indicates the spot was detected outside of a nucleus mask. #### File: 910357-U2OS-0005\_WellD16\_Point03\_ChMask1\_corr\_binary\_mask.tiff **Description:** Binary segmented file. #### File: 716634-U2OS-0010\_WellB19\_Point01\_ChMask1\_corr\_binary\_mask.tiff **Description:** Binary segmented file. #### File: 910357-U2OS-0005\_WellD16\_Point03\_ChMask1.nd2 **Description:** original Hoechst image #### File: 910357-U2OS-0005\_WellD17\_Point01\_ChMask1.nd2 **Description:** original Hoechst image #### File: 716634-U2OS-0010\_WellB19\_Point01\_ChMask1.nd2 **Description:** original Hoechst image #### File: 910357-U2OS-0005\_WellD16\_Point03\_ChSPT.nd2 **Description:** original single molecule movie #### File: 910357-U2OS-0005\_WellD17\_Point01\_ChSPT.nd2 **Description:** original single molecule movie #### File: 716634-U2OS-0010\_WellB19\_Point01\_ChSPT.nd2 **Description:** original single molecule movie ## Access information Data was solely generated and analyzed at Eikon Therapeutics Inc. For additional data requests related to the McSwiggen et al 2024 manuscript, please refer to the contact information in that manuscript.