Oblique scanner system and method for high throughput single molecule tracking in living cells

Abstract

High throughput single molecule tracking (htSMT) systems and methods are described in which the htSMT workflow is adapted to characterize the contribution of known and new pathways to interaction networks in living cells, such as protein signaling interaction networks.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to U.S. Provisional Application No. 63 / 476,953, filed December 22, 2022, and U.S. Provisional Application No. 63 / 476,942, filed December 22, 2022, the contents of which are incorporated herein by reference in their entirety. Technical Field

[0003] The topic described in this article involves platforms for tracking individual molecules within complex systems. Background Technology

[0004] In the crowded environment of living cells, protein movement is profoundly influenced by interactions with the surrounding environment. Single-molecule tracking (SMT) is a method for capturing protein movement as an activity reporter. In SMT, fluorescent proteins of interest are imaged at high spatiotemporal resolution to track their movement within complex systems such as living cells. The information embedded in these tracks has been used to study a variety of cellular phenomena, including protein-protein interactions such as those mediating signal transduction, inter-organelle communication, nuclear organization, and transcriptional regulation. However, the application scale of SMT is limited, thus it is primarily used to address specific mechanistic assumptions. For example, SMT has not yet been adapted to throughput settings that enable system-level screening or drug discovery. Summary of the Invention

[0005] In a first aspect, this disclosure relates to whether a compound that induces changes in the binding of a target fluorescent protein in living cells reduces the K-value of the target fluorescent protein. off The method includes: (a) contacting a sample containing a population of live cells with a compound, wherein the live cells contain a target fluorescent protein; (b) tracking the movement of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking includes: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least one subset of the fluorescent target proteins in the live cells to fluoresce; (ii) detecting the fluorescence of one or more target fluorescent proteins in a detection field of view of the sample plane by means of a detector device, wherein the detection field of view has a size of about 150 μm to about 250 μm in a first dimension and a size of about 100 μm to about 210 μm in a second dimension, and wherein the method is adapted to selectively detect local fluorescence; and (c) determining changes in the movement of the target fluorescent protein in the presence of the compound, wherein an increase in the signal detected from the target fluorescent protein in the presence of the compound, relative to the signal of the target fluorescent protein in the absence of the compound, indicates that the compound induces K-wave activity in the target fluorescent protein. off reduce.

[0006] In related aspects, the present disclosure relates to a method of determining whether a compound that induces a change in binding of a target fluorescent protein in a live cell reduces the K off of the target fluorescent protein, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a target fluorescent protein; (b) tracking motion of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of fluorescent target proteins in the live cells to emit fluorescence, wherein the subset of target fluorescent proteins produces at most about 1,000,000 molecular tracks in a single detection field of view; (ii) detecting fluorescence of one or more target fluorescent proteins in a detection field of view of the sample plane by a detector device, wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension and a size of about 100 pm to about 210 pm in a second dimension, and wherein the method is adapted to selectively detect local fluorescence; and (c) determining a change in motion of the target fluorescent protein in the presence of the compound, wherein an increase in signal detected from the target fluorescent protein in the presence of the compound relative to signal of the target fluorescent protein in the absence of the compound indicates that the compound induces a reduction in the K off of the target fluorescent protein.

[0007] In related aspects, the present disclosure relates to a method of determining whether a compound that induces a change in binding of a target fluorescent protein in a live cell reduces the K off of the target fluorescent protein, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a target fluorescent protein; (b) tracking motion of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of fluorescent target proteins in the live cells to emit fluorescence; (ii) detecting fluorescence of one or more target fluorescent proteins in a detection field of view of the sample plane by a detector device, wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension and a size of about 100 pm to about 210 pm in a second dimension, and wherein the method is adapted to selectively detect local fluorescence-related; and (c) determining a change in motion of the target fluorescent protein in the presence of the compound, wherein an increase in signal detected from the target fluorescent protein in the presence of the compound relative to signal of the target fluorescent protein in the absence of the compound indicates that the compound induces a reduction in the K off of the target fluorescent protein.

[0008] In related aspects, the present disclosure relates to a method of determining whether a compound that induces a change in binding of a target fluorescent protein in a live cell reduces the K offMethods of the above aspects include: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a target fluorescent protein; (b) tracking motion of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of fluorescent target proteins in the live cells to emit fluorescence; (ii) detecting fluorescence of one or more target fluorescent proteins in a detection field of view of the sample plane by a detector device, wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension and a size of about 100 pm to about 210 pm in a second dimension, wherein greater than or equal to 95% of the detection field of view achieves sufficient laser illumination to track protein motion, and wherein the method is adapted to selectively detect local fluorescence; and (c) determining a change in motion of the target fluorescent proteins in the presence of the compound, wherein an increase in signal detected from the target fluorescent proteins in the presence of the compound relative to signal of the target fluorescent proteins in the absence of the compound indicates that the compound induces a K off decrease in the target fluorescent proteins.

[0009] In certain cases of the above aspects, the detected change in motion is an increase in immotile trajectories, indicating an increase in occupancy or duration of a bound state (f 结合 ) of the target fluorescent proteins. In certain cases of the above aspects, the detected change in motion is a change in: (a) median of the jump length distribution; (b) 3rd quartile of the jump length distribution; (c) median turnaround radius; (d) mean posterior diffusion coefficient; (e) geometric mean posterior diffusion coefficient; (f) mean squared displacement; (g) median bond angle; (h) diffusion coefficient maximum likelihood estimator; and / or (i) state occupancy by inference. In certain cases of the above aspects, the target fluorescent proteins interact in a larger molecular assembly. In certain cases of the above aspects, the target fluorescent proteins are ligands. In certain cases of the above aspects, the target fluorescent proteins are receptors. In certain cases of the above aspects, the biological interaction is a direct interaction. In certain cases of the above aspects, the direct interaction comprises binding of the compound to the target fluorescent proteins. In certain cases of the above aspects, the biological interaction is an indirect interaction. In certain cases of the above aspects, the indirect interaction comprises the compound agonizing or antagonizing a larger molecular assembly comprising the target fluorescent proteins.

[0010] In interrelated aspects, the disclosure relates to a method of determining that a compound decreases a K offA method of determining a dose of a compound that induces a change in binding of a target fluorescent protein in a live cell by determining whether the compound decreases the K off of the target fluorescent protein, comprising: (a) contacting a sample comprising a population of live cells with the compound, wherein the live cells comprise the target fluorescent protein; (b) tracking movement of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence; (ii) detecting fluorescence of one or more target fluorescent proteins in a detection field of view of the sample plane by a detector device, wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension and a size of about 100 pm to about 210 pm in a second dimension, and wherein the method is adapted to selectively detect local fluorescence; and (c) determining the dose by determining a change in movement of the target fluorescent protein in the presence of the compound, and wherein an increase in signal detected from the target fluorescent protein in the presence of the compound relative to signal of the target fluorescent protein in the absence of the compound indicates that the compound induces a decrease in the K

[0011] In related aspects, the disclosure relates to a method of determining a dose of a compound that induces a change in binding of a target fluorescent protein in a live cell by determining whether the compound decreases the K off of the target fluorescent protein, comprising: (a) contacting a sample comprising a population of live cells with the compound, wherein the live cells comprise the target fluorescent protein; (b) tracking movement of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence, wherein the subset of target fluorescent proteins produces at most about 1,000,000 molecular tracks in a single detection field of view; (ii) detecting fluorescence of one or more target fluorescent proteins in a detection field of view of the sample plane by a detector device, wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension and a size of about 100 pm to about 210 pm in a second dimension, and wherein the method is adapted to selectively detect local fluorescence; and (c) determining the dose by determining a change in movement of the target fluorescent protein in the presence of the compound, and wherein an increase in signal detected from the target fluorescent protein in the presence of the compound relative to signal of the target fluorescent protein in the absence of the compound indicates that the compound induces a decrease in the K off of the target fluorescent protein.

[0012] In related aspects, the disclosure relates to a method of determining a dose of a compound that induces a change in binding of a target fluorescent protein in a live cell by determining whether the compound decreases the K offA method of determining a dose of a compound that induces a change in binding of a target fluorescent protein in live cells by determining that the compound reduces the K off of the target fluorescent protein, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a target fluorescent protein; (b) tracking the motion of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence; (ii) detecting the fluorescence of one or more target fluorescent proteins in a detection field of view of the sample plane by a detector device, wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension and a size of about 100 pm to about 210 pm in a second dimension, and wherein the method is adapted to selectively detect local fluorescence; and (c) determining the dose by determining a change in motion of the target fluorescent protein in the presence of the compound; and wherein an increase in signal detected from the target fluorescent protein in the presence of the compound relative to the signal of the target fluorescent protein in the absence of the compound indicates that the compound induces a change in binding of the target fluorescent protein in live cells.

[0013] In a related aspect, the disclosure relates to a method of determining a dose of a compound that induces a change in binding of a target fluorescent protein in live cells by determining that the compound reduces the K off of the target fluorescent protein, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a target fluorescent protein; (b) tracking the motion of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence; (ii) detecting the fluorescence of one or more target fluorescent proteins in a detection field of view of the sample plane by a detector device, wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension and a size of about 100 pm to about 210 pm in a second dimension, wherein greater than or equal to 95% of the detection field of view achieves sufficient laser illumination to track protein motion, and wherein the method is adapted to selectively detect local fluorescence; and (c) determining the dose by determining a change in motion of the target fluorescent protein in the presence of the compound; and wherein an increase in signal detected from the target fluorescent protein in the presence of the compound relative to the signal of the target fluorescent protein in the absence of the compound indicates that the compound induces a change in binding of the target fluorescent protein in live cells. off of the target fluorescent protein, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a target fluorescent protein; (b) tracking the motion of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence; (ii) detecting the fluorescence of one or more target fluorescent proteins in a detection field of view of the sample plane by a detector device, wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension and a size of about 100 pm to about 210 pm in a second dimension, wherein greater than or equal to 95% of the detection field of view achieves sufficient laser illumination to track protein motion, and wherein the method is adapted to selectively detect local fluorescence; and (c) determining the dose by determining a change in motion of the target fluorescent protein in the presence of the compound; and wherein an increase in signal detected from the target fluorescent protein in the presence of the compound relative to the signal of the target fluorescent protein in the absence of the compound indicates that the compound induces a change in binding of the target fluorescent protein in live cells.

[0014] In certain cases of the above aspects, the change in motion detected is an increase in immotile trajectories, indicating a change in binding (f 结合) an increase in a target fluorescent protein. In certain instances of the above aspect, the detected change in motion is a change in (a) median of jump length distribution; (b) 3rd quartile of jump length distribution; (c) median turn radius; (d) mean posterior diffusion coefficient; (e) geometric mean posterior diffusion coefficient; (f) mean squared displacement; (g) median bond angle; (h) diffusion coefficient maximum likelihood estimator; and / or (i) state occupancy by inference. In certain instances of the above aspect, the target fluorescent protein interacts in a larger molecular assembly. In certain instances of the above aspect, the target fluorescent protein is a ligand. In certain instances of the above aspect, the target fluorescent protein is a receptor. In certain instances of the above aspect, the biological interaction is a direct interaction. In certain instances of the above aspect, the direct interaction comprises binding of the compound to the target fluorescent protein. In certain instances of the above aspect, the biological interaction is an indirect interaction. In certain instances of the above aspect, the indirect interaction comprises the compound agonizing or antagonizing a larger molecular assembly comprising the target fluorescent protein.

[0015] In related aspects, the disclosure relates to a microscope system configured to determine whether a compound that induces a change in target fluorescent protein binding within a cell reduces the K off , comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise the target fluorescent protein; (b) a light source for emitting a light beam capable of inducing a photo-based reaction of a plurality of the target fluorescent proteins in the sample; (c) an objective for focusing the light beam onto the sample in a sample plane, wherein a subset of the target fluorescent proteins in the sample is disposed in a detection field of view of the sample plane, and wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension, and a size of about 100 pm to about 210 pm in a second dimension; (d) a detector device for monitoring the photo-based reaction of the target fluorescent proteins in the presence of the compound; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in motion of the target fluorescent proteins in the presence of the compound.

[0016] In related aspects, the disclosure relates to a microscope system configured to determine whether a compound that induces a change in target fluorescent protein binding within a cell reduces the K off, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise the target fluorescent protein; (b) a light source for emitting a light beam capable of inducing a photo-based reaction of a plurality of the target fluorescent proteins in the sample; (c) an objective for focusing the light beam onto the sample in a sample plane, wherein a subset of the target fluorescent proteins in the sample is disposed in a detection field of view of the sample plane, and wherein the subset of target fluorescent proteins produces at most about 1,000,000 molecular trajectories in a single detection field of view, and wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension, and a size of about 100 pm to about 210 pm in a second dimension; (d) a detector device for monitoring the photo-based reaction of the target fluorescent proteins in the presence of the compound; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in motion of the target fluorescent proteins in the presence of the compound.

[0017] In related aspects, the present disclosure relates to a microscope system configured to determine whether a compound that induces a change in binding of a target fluorescent protein within a cell will decrease the K off , comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise the target fluorescent protein; (b) a light source for emitting a light beam capable of inducing a photo-based reaction of a plurality of the target fluorescent proteins in the sample; (c) an objective for focusing the light beam onto the sample in a sample plane, wherein a subset of the target fluorescent proteins in the sample is disposed in a detection field of view of the sample plane, wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension, and a size of about 100 pm to about 210 pm in a second dimension; (d) a detector device for monitoring the photo-based reaction of the target fluorescent proteins in the presence of the compound; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in motion of the target fluorescent proteins in the presence of the compound relative to the absence of the compound.

[0018] In related aspects, the present disclosure relates to a microscope system configured to determine whether a compound that induces a change in binding of a target fluorescent protein within a cell will decrease the K off, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise the target fluorescent protein; (b) a light source for emitting a light beam capable of inducing a photo-based reaction of a plurality of the target fluorescent proteins in the sample; (c) an objective for focusing the light beam onto the sample in a sample plane, wherein a subset of the target fluorescent proteins in the sample is disposed in a detection field of view of the sample plane, wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension, a size of about 100 pm to about 210 pm in a second dimension, and wherein greater than or equal to 95% of the detection field of view achieves sufficient laser illumination to track protein motion; (d) a detector device for monitoring the photo-based reaction of the target fluorescent proteins in the presence of the compound; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in motion of the target fluorescent proteins in the presence of the compound.

[0019] In certain instances of the foregoing aspects, the detected change in motion is an increase in immotile trajectories indicative of binding of the compound to the target fluorescent protein (f 结合 ) In certain instances of the foregoing aspects, the detected change in motion is a change in: (a) median of jump length distribution; (b) 3rd quartile of jump length distribution; (c) median turnaround radius; (d) mean posterior diffusion coefficient; (e) geometric mean posterior diffusion coefficient; (f) mean squared displacement; (g) median bond angle; (h) diffusion coefficient maximum likelihood estimator; and / or (i) state occupancy by inference. In certain instances of the foregoing aspects, the target fluorescent protein interacts in a larger molecular assembly. In certain instances of the foregoing aspects, the target fluorescent protein is a ligand. In certain instances of the foregoing aspects, the target fluorescent protein is a receptor. In certain instances of the foregoing aspects, the biological interaction is a direct interaction. In certain instances of the foregoing aspects, the direct interaction comprises binding of the compound to the target fluorescent protein. In certain instances of the foregoing aspects, the biological interaction is an indirect interaction. In certain instances of the foregoing aspects, the indirect interaction comprises agonism or antagonism of a larger molecular assembly comprising the target fluorescent protein by the compound. BRIEF DESCRIPTION OF DRAWINGS

[0020] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0021] Figure 1 A schematic diagram depicting the htSMT workflow.

[0022] Figures 2A-2FAn exemplary image acquisition system of the present disclosure is depicted, where the X-Z sampling plane is visible Figure 2A and 2D or the Y-Z sampling plane is visible Figure 2B and 2C , as well as details of the light beam in relation to the HILO-based method Figure 2E , OLS on the left and HILO on the right) and an example of the combination with a camera rolling shutter Figure 2F .

[0023] Figures 3A-3E Various measurement results are depicted, indicating that the image acquisition system and workflow of the present disclosure are suitable for robust htSMT analysis. Figure 3A A laser titration experiment is depicted, showing the relationship between laser power at the sample (mW) and signal-to-noise ratio (SNR) (left panel), as well as the average SNR per well across four image acquisition systems, each measuring six different 384-well plates (right panel). Figure 3B Differences in spatial SNR heterogeneity between the OLS system of the present disclosure and the HILO-based method are depicted. The top panel compares the spatial standard deviation observed in OLS and HILO-based methods. The bottom panel illustrates the FOV difference between HILO and OLS-based methods (left panel), as well as a comparison of spatial heterogeneity between the FOV of the HILO-based method (middle panel) and the OLS-based method (right panel). Figure 3C A dose response experiment on Halo-tagged proteins using an established and well-characterized compound is depicted to assess inter-plate and daily reproducibility (top panel) and the corresponding EC50 presented (bottom panel). Figure 3D The system described herein is shown to be configured to capture comparable protein diffusion coefficients per FOV per well, where each dot represents the average single FOV position per plate per concentration (top panel), and both EC50 and Z-factor are presented (bottom panel). Figure 3E Data consistency across multiple wells and multiple experiments is depicted, where each dot represents one FOV in 14 independently generated dose response curves.

[0024] Figure 4 A comparison of Z-factors related to data presented in Figure 3D and Figure 3E and data collected using the HILO-based method is depicted.

[0025] Figure 5 A schematic diagram of an exemplary sample processing system of the present disclosure is depicted.

[0026] Figure 6 An exemplary system for a high-throughput single-molecule imaging platform for measuring protein motion in live cells is illustrated.

[0027] Figure 7 Data flow through an exemplary system for a high-throughput single molecule imaging platform for measuring protein motion in live cells is illustrated.

[0028] Figure 8 Multiple images are depicted, illustrating the difference between mask classes and instance or semantic masks.

[0029] Figure 9 An exemplary computer-implemented environment related to the subject matter described herein is illustrated.

[0030] Figure 10 is a diagram illustrating an exemplary computing device architecture for implementing various aspects described herein.

[0031] Figures 11A-11G OLS provides near full-field uniform illumination, enabling a wide range of SMT. Figure 11A A simplified schematic depicting OLS implementation is illustrated. In brief, a collimated light beam is formed into an optical sheet, which is sent to a water-immersion objective, and the emitted light is projected onto a high-speed sCMOS camera. Figure 11B An exemplary SMT workflow relying on Halo-tagging of the protein target of interest is illustrated. Using JF 549 or JF 646 Organic fluorophore detects individual emitters with proper signal for inter-frame linking and track generation. From these coordinates and tracks, a variety of metrics can be extracted, including protein diffusion and spatial localization, among others. Figure 11C 20-point dose response curves for 6-7 different 384-well plates imaged on the Eikon high-throughput SMT platform are illustrated. 72 FOVs of 12 wells were captured for each concentration on random plates, and error bars represent standard deviation. Figure 11D Representative sampled regions illuminated by Halo-Keapl -containing U2OS cells in HILO and OLS are illustrated. Trajectories are plotted over a 1.5 second acquisition and color-coded according to measured diffusion coefficients, with a black dashed line overlaid on the nuclear mask outline. Figure 11E Quantification of the number of trajectories captured per FOV using HILO and OLS is illustrated, where OLS captures a 6-fold improvement. Figure 11F Representative average spatial SNR maps per pixel computed for 1,232 FOVs of a plate imaged with HILO or OLS are illustrated. OLS provides a 6-fold improvement in FOVs, while also improving illumination uniformity. Figure 11G Average FOV-level standard deviation of SNR is provided for 308-well sampling.

[0032] Figure 12 A-12F depicts an exemplary schematic of an OLS microscope for single molecule tracking.Figure 12 A depicts an exemplary schematic of an OLS microscope based on scanning a tilted excitation light sheet over a sample placed in an inverted microscope using a galvanometer scanning mirror. The OLS microscope is based on multi-wavelength light excitation provided by a laser engine module (LEM) and coupled to a beam shaper through a collimator-coupled single-mode optical fiber. The beam shaper converts the incoming Gaussian-shaped light excitation into an optical light sheet that is focused along the line axis in the light sheet onto the back focal plane of the microscope objective and scanned along the scan axis using a galvanometer mirror. The resulting oblique light sheet is fed into a water-immersion coupled and environmentally controlled sample holding plate while the relative position of the microscope focal plane is controlled by an autofocus unit. Excited fluorescence is spectrally filtered from the excitation light by a dichroic filter and an emission filter and projected onto a high-speed sCMOS camera. The synchronization of the optical excitation, scanning, and acquisition is achieved through a custom control unit (MIC). Figure 12 B depicts an exemplary schematic of an autofocus unit based on detecting 780 nm-LED reflection off the top surface of the sample holding glass bottom and repositioning the objective to ensure proper focal plane positioning within the sample. Figure 12 C depicts an exemplary schematic of an optical confocal scanning mode achieved by scanning a tilted and focused light sheet through the objective focal plane. Background rejection is achieved by confocally aligning the tilted light sheet (green), the depth of field of the objective, and the synchronized rolling shutter detection (orange). Figure 12 D depicts an exemplary schematic of a beam shaping sub-assembly that projects along the line axis (x) and the scan axis (y) and shapes the collimated light excitation into a light sheet through a series of lenses consisting of a Powell lens, a cylindrical lens, a spherical lens, and a plano-convex lens before encountering the galvanometer scanning mirror. The inset depicts the light beam profile at various positions. Figure 12 E depicts an exemplary schematic of a light sheet scanning framework based on a tilted light sheet in the sample plane achieved by focusing the light excitation along a line axis in the objective back focal plane and positioning the light excitation at an offset position relative to the objective optical axis along the scan axis. The corresponding optically aligned fluorescence detection is projected onto the camera sensor. Figure 12 F depicts an exemplary schematic of an OLS acquisition mode that relies on detecting fluorescence by matching the camera's exposed pixel area and synchronizing the camera's rolling shutter with the optical projection intensity line of the tilted light sheet excitation's fluorescence.

[0033] Figures 13A-13F A bar graph depicting the characteristics of motion-induced blurring and confocality between OLS and HILO illumination. Figure 13A A bar graph comparing the HILO and OLS diffusion coefficients measured in 72 FOVs of 12 individual wells of Halo-KEAP1 treated with DMSO or 1 mM KI-696. Despite extensive sampling, the measurement standard deviation of HILO remains large.Figure 13B Estimate point spread functions are plotted by averaging all detections in a representative 150 frame acquisition. The following number of PSFs were detected in each condition: n = 123,596 (OLS-DMSO), n = 3,897 (HILO-DMSO), n = 113,276 (OLS 0.33 mM KI-696), n = 13,620 (HILO 0.33 mM KI-696), from one representative FOV. Figure 13C PSF detection as a function of integration time is plotted. HILO requires 5x longer integration times to achieve comparable PSF detection and spot density to OLS, which results in more pronounced motion blur in HILO. Figure 13D PSF width measurements measured in Halo-KEAP1 cells treated with 1 mM KI-696 as a function of JF 549 are plotted. Figure 13E Average SNR measured in Halo-KEAP1 cells treated with 1 mM KI-696 as a function of JF 549 are plotted. Figure 13F Spot detection number measured in solution as a function of Halo-JF 549 concentration is plotted. 549 Spot detection number measured in solution as a function of Halo-JF 549 concentration is plotted.

[0034] Figures 14A-14D OLS enables reproducible and robust SMT measurements. Figure 14A EC 50 values calculated from each average dose response curve per plate per microscope are plotted, with median EC 50 values represented by black lines. Figure 14B Violin plots of signal-to-noise ratio (SNR) per microscope are plotted, with medians indicated by thick dashed lines. Figure 14C Violin plots of SNR as a function of FOV position within the acquisition well are plotted. Figure 14D 20-point dose response curves of Halo-KEAP1 U2OS sampled in full OLS FOV (purple) versus 768x768 pixel cropped FOV representative of HILO size FOV (black) are plotted. Error bars indicate standard deviation between FOVs.

[0035] Figures 15A-15C OLS enables capturing fast protein diffusion in live cells. Figure 15A Representative images of FOV size for each of five frame rates in the 100-1250 Hz range are plotted. Trajectories are overlaid onto the mean projection of the Hoechst channel (blue) and colored according to their maximum likelihood diffusion coefficient. Figure 15B Diffusion coefficients >10 pm2 Trajectory scores of S as a function of frame rate for DMSO and KI-696 treated cells, calculated from state array posterior mean occupancy. Figure 15C Accuracy of state profile recovery for SMT performed optical dynamic simulation at several frame rates for three different state mixtures is depicted. Error bars represent standard deviation.

[0036] Figures 16A-16E Frame rate determines SMT dynamic range. Figure 16A A schematic depicting the role of localization and tracking errors for hypothetical fast moving proteins is depicted. A rolling shutter in OLS captures the position of dye molecules at discrete time points. If these time points are too close, the motion will be dominated by localization error. If the time points are too far apart, reconstructing the trajectory becomes challenging and is dominated by false linking. Figure 16B A schematic depicting the SMT dynamic range, which is limited at one end by localization error and at the other end by tracking error, is depicted. An approximation of this range for Brownian motion is where σ 2 is the localization error variance, At is the frame interval, R is the search radius, and D is the diffusion coefficient. Figure 16C A schematic of the simulation method to test the role of frame rate is depicted. The movie is simulated with real-world effects including defocus, motion blur, shot noise, and readout noise. Figure 16D The effect of frame rate on linking accuracy and trajectory length is depicted. Linking accuracy is defined as the fraction of correct links produced by the tracking algorithm; trajectory length is the number of points in each trajectory. The quantile is the over-simulated movie. Figure 16E State array posterior mean occupancy for three simulated dynamic mixtures at increasing frame rates is depicted. The red line corresponds to the simulated discrete mixture model, the blue line corresponds to the state array posterior mean, and the green line corresponds to the expected SMT dynamic range defined in (B). Each condition includes ten simulation repeats.

[0037] Figures 17A-17C Tracking diagnostics for experimental KEAP1-HaloTag JF549 SMT in U2OS cells with different frame rates are depicted. Figure 17A Mean trajectory length as a function of frame rate is depicted. Trajectory length is defined as the number of spots in each trajectory. Figure 17B Mean SNR as a function of frame rate is depicted. SNR is described in Example 2. Figure 17C Mean ERLB as a function of frame rate is depicted.

[0038] Figure 18State array analysis plotted as a function of frame rate is provided comparing DMSO and 1 mM KI-696 treated Keap1-HaloTag U2OS cells. The number of FOVs repeated per frame rate is as follows: n = 88 (100 Hz), n = 88 (200 Hz), n = 132 (400 Hz), n = 198 (800 Hz), and n = 264 (1250 Hz). The line is the average of all FOVs under the respective condition, and the error band is the standard deviation at the FOV level.

[0039] Figure 19 An evaluation of the bleaching rate of KEAP1-HaloTag SMT at variable frame rates is provided. The fraction of remaining detections is plotted over the frame rate of a given time series. The fraction of remaining detections is defined as the number of detections per frame divided by the number of detections in the first frame. A model f(t) = c0 + (1 - c0)e -kt An exponential fit is performed (blue text below frame rate), where t is the frame index, k is the bleaching rate, and c0 is the unbleached fraction. The number of FOVs repeated per frame rate is as follows: n = 88 (100 Hz), n = 88 (200 Hz), n = 132 (400 Hz), n = 198 (800 Hz), and n = 264 (1250 Hz).

[0040] Figures 20A-20F It is illustrated that OLS can be used to capture intercellular and intracellular heterogeneity of single protein kinetics. Figure 20A An analysis of KEAP1 SMT variant origin measured under OLS or HILO illumination is depicted. The contribution of cell-to-cell differences is 17-32 times higher than the contribution of FOV-level or well-to-well differences, respectively. Figure 20B Representative images of Halo-PCNA labeled cells treated with 2 mM thymidine or 10 mM RO-3306 are depicted (top). Cell cycle predictions according to a machine learning (ML) model are used to color cells by cycle phase (bottom). Figure 20C Quantification of the fraction of each cell phase cells in response to Figure 20B circumvention treatment in is depicted. The following number of cells were analyzed for each condition: 34,067 for DMSO, 3,831 for RO-3306, and 6,044 for thymidine. Figure 20D State array analysis of the total population of sparsely labeled PCNA cells is depicted. Figure 20E State array analysis of cells in each phase predicted by the ML model is depicted. Figure 20F A heatmap of 4,801 single cells classified using a continuous classification score plotted against PCNA diffusion coefficient is depicted.

[0041] Figures 21A-21DFeatures of the PCNA-based cell cycle prediction model are illustrated. Figure 21A Example images of PCNA time-lapse photography captured on the OLS at 5 minute intervals for 12 hours are provided. Figure 21B A schematic of a neural network trained to perform both nucleus segmentation, nucleus cell cycle classification, and nucleus cell cycle regression is depicted. Figure 21C A confusion matrix depicting cell cycle classification performance is depicted. Figure 21D Representative images of cell cycle progression of 4 selected cells over a 12 hour window (left), and regression-based cell cycle progression prediction plot drawn with a 5 frame moving average (right) are provided.

[0042] Figures 22A-22D PCNA cell line validation using western blot and cell proliferation assay is depicted. Figure 22A Capillary-based western blots comparing WT U2OS and N-terminally tagged hybrid PCNA clones with anti-PCNA antibody (left) and anti-Halo antibody (right) are depicted. Figure 22B Relative WT and Halo-tagged PCNA levels normalized to beta-actin WT and Halo-edited U2OS cells are depicted. Figure 22C Growth curves of WT U2OS and N-terminally Halo-tagged PCNA are depicted. Figure 22D JF 549 and CCR PCNA-labeled cells to measure spatial co-localization between the two labels during cell cycle progression.

[0043] Figures 23A-23M The OLS is illustrated as suitable for multiple SMLM techniques and acquisition schemes. Figure 23A JF 549 and JF 646 labeled Halo-KEAP1 U2OS cells imaged within the same FOV are depicted. Figure 23B A 10-point dose response of KI-696 treated Halo-KEAP1 U2OS cells co-labeled with JF 549 and JF 646 are depicted. Figure 23C Diffraction-limited images of a full OLS FOV immunofluorescence-labeled microtubulin protein with AF647-coupled secondary antibody are depicted. Figure 23D A zoomed-in view of a region of interest in Figure 23C is depicted. Figure 23E A STORM reconstruction of a full OLS FOV as in Figure 23C is depicted. Figure 23F A zoomed-in view of a region of interest in Figure 23D is depicted. Figure 23E is depicted.Figure 23G Depiction of Figure 23D the yellow line in Figure 23F and line profiles of the gray value (a.u) to compare the spatial resolution of microtubules. Figure 23H Depiction of the localization precision histograms for AF647 and CF568 secondary antibodies respectively, staining microtubules with OLS illumination at 0.4 millisecond integration time. Figure 23I Depiction of representative images of the related FRAP / SMT, where the central region was bleached using OLS line-scan before spot recovery after photobleaching. Regions outside and inside the FRAP region were used to measure SMT. Figure 23J Depiction of T 549 -Halo ligand labeling) treated Halo-KEAP1 U2OS cells. 1 / 2 FRAP, black lines indicate the median, and each spot represents one individual FOV. Figure 23K and Figure 23L Depiction of the spot density after recovery over time for DMSO Figure 23K ) and 1 mM KI-696 Figure 23L ) respectively. Standard deviation is shown as confidence bands for 8-10 FOVs under each condition. Figure 23M Depiction of 400 pM JF 549 -Halo ligand concentration in the bleached (inside) and unbleached (outside) regions.

[0044] Figures 24A-24C Illustration of dye performance and characteristics of FRAP changes with increasing dye concentration. Figure 24A Depiction of the SNR comparison between JF 646 and JF 549 . Figure 24B Depiction of the ERLB comparison between JF 646 and JF 549 . Figure 24C Depiction of the sampling of T 1 / 2 measured in the bleached region as a function of dye concentration for DMSO and 1 mM KI-696 within 6-10 FOVs.

[0045] Figures 25A-25B Illustration of the contribution of inter-well, inter-FOV, and inter-cell bias to 2D hop length evaluated using jump resampling. Figure 25A Depiction of the variance of sample means as a function of sample size in different resampling procedures. The straight line with a slope of -1 is the expectation of the law of large numbers; sublinearity is due to residual variance of wells, FOVs, or cells. Figure 25B Depiction of the number of jumps per well, FOV, or cell used in these analyses. Detailed Implementation

[0046] This disclosure relates to the development of industrial-scale, high-throughput SMT (htSMT) technologies employing oblique line scan (OLS) illumination; systems incorporating such OLS htSMT technologies; hardware and software associated with such OLS htSMT technologies; and methods for using such OLS htSMT technologies. For example, the OLS htSMT technologies described herein are capable of measuring protein motion in millions of cells per day. In addition to capturing a large number of cells in each field of view, OLS benefits from improved spatial uniformity of signal-to-noise ratio (SNR) on the camera chip, better confocality (less out-of-focus signal and less motion blur), and higher temporal resolution, as shown in Table 1 (where each "+" represents a 2x improvement).

[0047] Table 1.

[0048] Parameter OLS HILO Spatial SNR uniformity +++ + Confocality +++ + Temporal resolution +++ + FOV size ++++ +

[0049] The OLS htSMT technique described in this article can be used for a variety of applications, including but not limited to drug discovery activities such as compound library screening and elucidation of structure-activity relationships (SAR). Importantly, the OLS htSMT technique described in this article can be used to characterize the contributions of known and novel pathways to the assembly of larger molecules containing targets, such as protein-protein signaling interaction networks.

[0050] refer to Figure 1 The OLS htSMT workflow can be used to implement various aspects of the current topic. This workflow may include various stages, as will be described in further detail below, such as (i) sample preparation including reagent treatment; (ii) image acquisition to generate a series of images and / or videos using sample imaging; (iii) image analysis by processing these images and videos, such as using various analyses, single-emitter detection and subpixel localization (i.e., “super-resolution imaging”), tracking, computer vision, and machine learning algorithms; (iv) storing information extracted from or otherwise characterized or contained in the images and videos (i.e., features, original images, modified images, etc.); and (v) using the stored information to provide insights, including biological interpretations (which may be provided additionally or alternatively using various analyses, tracking, computer vision, and machine learning algorithms).

[0051] The subject matter of the present disclosure is described with reference to the accompanying drawings, of which the use along with reference numbers to indicate like or equivalent elements in which the reference numbers used in the drawings are commonly referred to as being referred to throughout the specification. The drawings are not to scale and are provided merely to illustrate disclosed aspects. Several disclosed aspects will be described with reference to exemplary hardware, software, and applications for illustrative purposes. It should be understood that the numerous specific details, relationships, and methods are set forth to provide a full understanding of the subject matter disclosed herein. The detailed description is divided into subsections for the sake of clarity of disclosure, not limitation.

[0052] 1. Definitions

[0053] 2. OLS htSMT Hardware

[0054] 3. OLS htSMT Software

[0055] 4. Specific OLS htSMT Applications

[0056] 5. Exemplary Embodiments

[0057] 6. Examples

[0058] 1. Definitions

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. In case of conflict, the present document, including definitions, controls. Preferred methods and materials are described below, although methods and materials similar or equivalent to those described herein can be used in practice or testing of the present subject matter. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. The materials, methods, and examples disclosed herein are illustrative only and not intended to be limiting.

[0060] The terms “comprise,” “comprising,” “having,” “has,” “can,” “contain,” and variants thereof, as used herein, are intended to be open-ended transitional phrases, terms, or words that do not preclude the possibility of additional acts or structures. The singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. The present disclosure also contemplates the alternative “comprising” situations or elements presented herein, “consisting of’ and “consisting essentially of’ whether expressly set forth or not.

[0061] With respect to recitations of numerical ranges herein, each intervening number within the range is explicitly included. For example, with respect to a range of 6 to 9, the numbers 7 and 8 are included in addition to 6 and 9, and with respect to a range of 6.0 to 7.0, the numbers 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, and 7.0 are expressly included.

[0062] As used herein, the term "about" or "approximately" means within an acceptable error range for the particular value as determined by one of ordinary skill in the art, which will depend in part on how the value is measured or determined, i.e., the limitations of the measurement system. For example, "about" can mean within 3 or more standard deviations, per the practice in the art. Or, "about" can mean a range of up to 20%, preferably up to 10%, more preferably up to 5% and more preferably up to 1% of a given value. Or, especially for biological systems or processes, the term can mean within an order of magnitude, preferably within 5-fold and more preferably within 2-fold of a value.

[0063] As used herein, the term "trajectory" refers to a set of spatial coordinates linked in time corresponding to fluorescent protein observation locations. In some cases, trajectories can be algorithmically constructed by linking multiple fluorescent proteins that determine locations at consecutive time points. In some cases, trajectories can be conservatively constructed by linking only spots within a fixed search radius when no other linkage is feasible. In some cases, trajectories can be probabilistically constructed.

[0064] Protein motion, as defined herein, refers to changes in the position of a plurality of fluorescent proteins. In certain instances, protein motion can be quantified by analyzing changes in spatial coordinates between successive time points. Motion characterized in this way can include, but is not limited to, measurement of the jump length distribution: given a set of protein displacements between one time point and a subsequent time point, a histogram of the probability of each displacement length (“jump length”) can be constructed. Quantiles of this distribution can be used to describe the motion of the protein. In certain instances, the quantile used is the median of the jump length distribution. In certain instances, the quantile used is the 3rd quartile of the jump length distribution. In certain instances, protein motion can be quantified by trajectory analysis. Motion characterized in this way can include, but is not limited to, measurement of the mean squared displacement, defined by the average of the squares of all displacements in a plurality of trajectories, averaged over the trajectories. Motion characterized in this way can also include, but is not limited to, measurement of the trajectory length or trajectory length distribution. Motion characterized in this way can also include, but is not limited to, measurement of the average radius of gyration, defined by the root mean square distance between all coordinates in a trajectory and the centroid of the set of points contained in the trajectory, averaged over a plurality of trajectories. Motion characterized in this way can also include, but is not limited to, measurement of the average bond angle, defined by the angle formed by three consecutive spatial coordinates, averaged over a plurality of trajectories. Motion characterized in this way can also include, but is not limited to, measurement of the diffusion coefficient maximum likelihood estimator, defined as the estimate of the maximum likelihood diffusion coefficient over a plurality of trajectories under a single state diffusion model with constant localization error. In certain instances, protein motion can be measured by analyzing the product of a link generation algorithm. Motion characterized in this way can include, but is not limited to, the average posterior diffusion coefficient, the average of the posterior probability distribution of the coefficient from a probabilistic linking algorithm. Motion characterized in this way can include, but is not limited to, the geometric mean posterior diffusion coefficient, the average of the log-scale posterior probability distribution of the coefficient from a probabilistic linking algorithm. In certain instances, protein motion can be measured by model-related analysis of a plurality of trajectories. Motion characterized in this way can include, but is not limited to, the fraction of immobile molecules defined by a two-state model fit (“f 结合 ”)).

[0065] As used herein, the term "motion" encompasses changes in direction of travel as well as changes in velocity (increases or decreases) of a target. Thus, in certain instances, tracking motion can include determining that a target is not moving, e.g., when the target is in or substantially in a static bound state. Motion can be characterized in a variety of ways, including but not limited to quantifying: (a) the median of the jump length distribution (where jump length corresponds to the observed distance a target fluorescent protein travels in successive frames); (b) the 3rd quartile of the jump length distribution; (c) the median turn radius; (d) the mean posterior diffusion coefficient; (e) the geometric mean posterior diffusion coefficient; (f) the mean squared displacement; (g) the median bond angle; (h) the diffusion coefficient maximum likelihood estimator; (i) the trajectory length; and / or (j) the state occupancy by inference.

[0066] As used herein, detected motion (including but not limited to any change in motion) can occur in response to any environmental or other factor. For example, but not by way of limitation, motion or lack of motion can be induced by: (A) addition of a compound; (B) change in temperature; (C) change in oxygen concentration, e.g., introduction of anoxic conditions; (D) mechanical stress; (E) change in pH; and / or (F) change in illumination (e.g., increase or decrease in intensity).

[0067] As used herein, the term "fluorescent protein" refers to any protein that emits a fluorescent signal. In certain instances, the fluorescent emission occurs upon irradiation with light of a particular wavelength. One example of a naturally occurring fluorescent protein is green fluorescent protein (GFP). However, in certain instances, a protein of interest can be adapted to emit a fluorescent signal by introduction of a coded fluorescent tag, i.e., a protein sequence is fused to the protein of interest to cause it to emit fluorescence. In certain instances, a protein of interest can be adapted to emit a fluorescent signal by binding to a fluorescent ligand. Non-limiting examples of such coded fluorescent tags include but are not limited to: Halo tags, SNAP tags, CLIP tags, TMP tags, and SunTags. Additionally or alternatively, a protein of interest can be adapted to emit a fluorescent signal by coupling to a fluorescent dye molecule (e.g., an amine- or thiol-reactive dye).

[0068] As used herein, the term "compound" refers to any chemically defined entity. In some cases, a compound can be a molecule of less than 1000 Da, i.e., a "small molecule." In some cases, a compound can be a macromolecule, e.g., a nucleic acid. In some cases, a nucleic acid can have a defined sequence. In some cases, nucleic acids include: (A) ribonucleic acids (RNAs), including, e.g., modified RNAs; (B) deoxyribonucleic acids (DNAs), including, e.g., modified DNAs; and (C) combinations of (A) and (B). In some cases, a nucleic acid will be a single- or double-stranded small interfering nucleic acid (e.g., a double-stranded siRNA), an antisense oligonucleotide, a ribozyme, a microRNA, or an aptamer. In some cases, a compound can be a protein. For example, but not by way of limitation, proteinaceous compounds of the present disclosure encompass signaling proteins, e.g., protein hormones, cytokines, kinases, phosphatases and other enzymes and transcription factors, as well as antibodies, contractile proteins, structural proteins, storage proteins, and transport proteins. In some cases, a compound can refer to a mixture of molecules, e.g., a mixture with a defined composition.

[0069] As used herein, the term "uniform intensity" in relation to the intensity of light (e.g., light directed toward a sample plane) means that the difference in light intensity is in some cases no more than 5%, in some cases no more than 10%, or in some cases no more than 15%.

[0070] As used herein, the term "uniform intensity" in relation to the signal-to-noise ratio (SNR) means the pixel-wise SNR within a field of view (FOV), where the range of possible values is between 0.5 and 1 standard deviation of the mean SNR.

[0071] 2. OLS htSMT hardware

[0072] 2.1. Image acquisition system

[0073] Reference Figure 1 Various aspects of the current subject matter can be implemented using an htSMT workflow, where such a workflow incorporates a system for image acquisition. For example, such image acquisition can incorporate imaging of a sample to generate a series of images and / or video. Figure 2A A schematic diagram depicting an exemplary image acquisition system of the present disclosure, where the X-Z sample plane is visible. Figure 2BThe same exemplary image acquisition system is depicted, but with the Y-Z sample plane visible. Exemplary image acquisition system (2-001) includes a light source (2-005) configured to emit light that is relayed by one or more optical elements in an optical relay (2-010) configured to shape the light emitted from the light source to form a shaped light beam (2-065) such that the shaped light beam has uniform intensity over the longer dimension of the linear shape; an optical element, such as a galvanometer mirror (2-085), configured to translate the shaped light beam; and one or more optical elements, such as a dichroic mirror (2-100), configured to direct the shaped light beam to an objective lens (2-120), whereby a portion of the sample plane (2-130) is illuminated by the oblique light beam (2-125), the resulting light emitted from the sample, such as fluorescence emission, is focused by the objective lens (2-120) through a series of optical elements, such as a lens (2-155) and an emission filter (2-160), to an image collection system (2-165).

[0074] 2.1.1. Light source

[0075] With reference to Figure 2A exemplary image acquisition system, the system includes a light source (2-005) configured to emit light. In certain implementations of the image acquisition systems disclosed herein, the light source (2-005) can be configured to emit light at a single wavelength. In certain implementations of the image acquisition systems disclosed herein, the light source (2-005) can be configured to emit light at two, three, four, five, or more separate wavelengths. In certain implementations, the wavelength of light emitted by the light source is predetermined. For example, but not by way of limitation, the wavelength can be predetermined such that the emitted light, when illuminating a sample (e.g., a sample containing a fluorescent protein), elicits a fluorescence emission. In certain cases, the wavelength employed in connection with the methods described herein will be in the range of 400 nm to 650 nm. In certain cases, the light source (2-005) will emit light at a wavelength between 400 nm and 408 nm, between 550 nm and 565 nm, or between 638 nm and 650 nm. In certain non-limiting implementations, the light source (2-005) is configured to include three lasers with nominal center wavelengths of 405 nm, 560 nm, 640 nm, respectively, which can be varied within the absorption bands of the fluorophores used. In certain cases, the 405 nm wavelength is used to excite Hoechst dye. In certain cases, the 560 nm wavelength is used to excite a dye (e.g., JF549) attached to a HaloTag. In certain cases, the 642 nm or 646 nm wavelength is used to excite a dye (e.g., JF 646 ) attached to a HaloTag.

[0076] In certain non-limiting implementations, the light source (2-005) is used to catalyze photochemical reactions. For example, but not by way of limitation, the wavelength and illumination intensity can cause a chemical bond to break. As an additional example, but not by way of limitation, the wavelength and illumination intensity can induce a non-radiative dark state (i.e., a “photobleaching molecule”). As an additional example, but not by way of limitation, the wavelength and illumination intensity can induce radiative or non-radiative energy transfer between fluorophores within the sample.

[0077] In certain implementations of the image acquisition systems described herein, the light source (2-005) can be configured to deliver a predetermined amount of power to the back focal plane of the objective lens (2-105). For example, but not by way of limitation, the light source (2-005) delivers more than 10 mW of power for certain wavelengths (e.g., 405 nm), and / or more than 150 mW of power for other wavelengths (e.g., 640 nm). Additionally or alternatively, where the light source (2-005) includes three lasers emitting at wavelengths of 405 nm, 560 nm, and 640 nm, respectively, the light source (2-005) can be configured to deliver a predetermined amount of power to the back focal plane of the objective lens (2-105). For example, but not by way of limitation, 405 nm can be configured to deliver > 10 mW; 560 nm can be configured to deliver > 150 mW; and 640 nm can be configured to deliver > 50 mW.

[0078] In certain implementations of the image acquisition systems described herein, the light source (2-005) is configured to emit pulsed light. For example, but not by way of limitation, the light source (2-005) can be configured to emit stroboscopic pulsed light. In certain implementations of the image acquisition systems described herein, the light source (2-005) is configured to emit pulsed light in synchronization with the start of image acquisition. In certain non-limiting implementations, the light source (2-005) will pulse at specific time intervals depending on the number of frames captured per second. For example, but not by way of limitation, if the probe (2-165) captures 100 frames per second (FPS), the laser is on for 9 milliseconds and off for 1 millisecond. In contrast, in a 200 FPS mode, the laser is on for 4 milliseconds and off for 1 millisecond. In certain implementations of the OLS htSMT workflow, the light source is configured to change from 90% power to 10% power in less than about 0.4 milliseconds. In certain implementations of the OLS htSMT workflow, the light source is configured to change from 90% power to 10% power in less than about 0.2 milliseconds.

[0079] In certain implementations of the image acquisition systems disclosed herein, a single mode optical fiber can be used to implement the light emission of the light source (2-005) and to guide the light to the optical relay (2-010). Alternatively, a multi-mode optical fiber can be employed in certain implementations of the image acquisition systems disclosed herein. For example, but not by way of limitation, the multi-mode optical fiber can be configured to have a predetermined shape for sample illumination.

[0080] In certain implementations of the image acquisition systems described herein, such as with respect to systems configured for high-throughput sample analysis, the light source (2-005) can be configured to exhibit low power output drift. In certain implementations, such a low drift configuration improves consistency in sample processing to facilitate high-throughput analysis. For example, but not by way of limitation, such a low drift power output configuration maintains the power output within about 0% to about 15% variation, about 0% to about 10% variation, about 10% variation, about 9% variation, about 8% variation, about 7% variation, about 6% variation, about 5% variation, about 4% variation, about 3% variation, about 2% variation, or about 1% variation.

[0081] In certain instances, such a low drift power output configuration maintains the power output within about 0% to about 15% variation, about 0% to about 10% variation, about 10% variation, about 9% variation, about 8% variation, about 7% variation, about 6% variation, about 5% variation, about 4% variation, about 3% variation, about 2% variation, or about 1% variation, in the case of environmental temperature (room temperature) variation (e.g., 17°C + / - 5°C). In certain instances, this is achieved by using temperature sensors and / or closed loop heaters to keep the internal light source (e.g., laser engine) temperature stable, thereby reducing output power drift. For example, but not by way of limitation, a thermally insulated housing design can be used to insulate the light source from environmental temperature fluctuations. Additionally or alternatively, a closed loop heater can be strategically placed in the system at a specific location, such as an optical fiber coupler, to reduce output drift. Additionally or alternatively, a water jacket and / or chiller can be used to reduce heat build-up in the laser head. Furthermore, these thermal controls, used individually or in combination, can shorten the warm-up time to reach a stable operating state and maintain a more stable internal operating temperature when the laser is turned off and on.

[0082] 2.1.2. Optical elements and sample illumination

[0083] Reference Figure 2Aan exemplary image acquisition system of the present disclosure, the system including a light source (2-005) configured to emit light, the light being relayed by one or more optical elements in an optical relay (2-010) configured to shape the light emitted from the light source to form a shaped light beam (2-065). The particular optical elements implemented by any particular optical relay (2-010) can be selected and configured to produce a light beam (2-065) of the appropriate shape and to provide the appropriate translation of the light beam.

[0084] In certain non-limiting implementations of the optical relay (2-010) of the presently disclosed image acquisition system, the optical relay (2-010) will include one or more lenses and / or other optical elements. For example, but not by way of limitation, the selection and orientation of the lenses and other optical elements in the optical relay (2-010) will be configured to appropriately shape the light beam directed toward the sample. In certain non-limiting implementations, the optical relay (2-010) will include optical elements for collimating the emitted light from the light source (2-005), such as a collimator (2-020). Additionally or alternatively, the optical relay (2-010) will include additional optical elements, such as, for example, a Powell lens (2-025) or other element adapted to produce a fan of light beams, one or more cylindrical lenses ((2-045) and (2-055)), one or more slits ((2-050) and (2-095)) for adjusting the range of light sheets, one or more achromatic lenses ((2-060) and (2-080)), and / or one or more mirrors ((2-070), (2-075), and (2-085)), one or more of which can be a galvanometer (2-085) capable of translating the light. The particular properties of the optical elements will be predetermined to produce a light beam of the appropriate shape. For example, but not by way of limitation, the OLS htSMT system of the present disclosure can implement a uniform horizontal FOV as well as a uniform vertical FOV. This uniformity of the horizontal and vertical FOVs is in contrast to other strategies that provide a non-uniform horizontal FOV and / or a non-uniform vertical FOV (see Table 2).

[0085] Table 2. Technology Comparison

[0086]

[0087]

[0088] To achieve a uniform horizontal FOV as well as a uniform vertical FOV, the optical relay (2-010) of the OLS htSMT system described herein includes optical elements or components capable of producing a light beam that is elongated along the X-plane and narrowed along the Y-plane, and wherein the light beam has uniform intensity across the longer dimension of the line. In certain non-limiting implementations, the optical relay (2-010) of the OLS htSMT system described herein will include a Powell lens (2-025) to shape the light beam to have uniform intensity across the longer dimension of the line (2-065). The optical relay (2-010) of the OLS htSMT system described herein can include additional or alternative optical elements or components to shape the light beam to have uniform intensity across the longer dimension of the line (2-065). For example, but not by way of limitation, the optical relay (2-010) of the OLS htSMT system described herein can include a diffractive element or component configured to shape the light beam to have uniform intensity across the longer dimension of the line.

[0089] In certain non-limiting implementations of the optical relay (2-010) of the presently disclosed image acquisition system, the optical relay (2-010) will include one or more optical elements or components configured to translate the light beam relative to the sample plane of the sample to be analyzed, e.g., in a direction orthogonal to the longer dimension of the light beam. For example, but not by way of limitation, such optical elements or components configured to translate the light beam relative to the sample plane of the sample to be analyzed can include a galvanometer (2-085) or a piezoelectric element configured to translate the light beam. Additionally or alternatively, such optical elements or components configured to translate the light beam relative to the sample plane of the sample to be analyzed can include a computer-controlled motor.

[0090] With reference to Figure 2A an exemplary image acquisition system, the system includes an optical relay (2-010) configured to shape light emitted from a light source to form a shaped light beam (2-065), which is then directed by an optical element (2-100), e.g., a dichroic mirror, which is configured to direct the shaped light beam to an objective lens (2-120), whereby a sample plane (2-130) is illuminated by the oblique light beam (2-125).

[0091] In certain non-limiting implementations of the image acquisition system of the present disclosure, the objective lens (2-120) directs the oblique beam (2-125) onto the sample plane (2-130) to be analyzed. In certain non-limiting implementations of the image acquisition system of the present disclosure, the objective lens (2-120) is a water immersion objective lens. The use of a water immersion objective lens allows for higher throughput sample analysis by eliminating the oil associated with the use of an oil immersion objective lens, thereby allowing for higher image quality and less distortion. Not only is the presence of oil problematic in the context of automated systems, where oil can spread onto components, including optical elements that can become dirty from contact with the oil, but water immersion objective lenses are better matched to the refractive index of the imaging unit, resulting in less distortion and thus higher image quality compared to oil immersion objective lenses. In certain non-limiting implementations, the objective lens is a 60X 1.27 NA water immersion objective lens (Nikon). In certain implementations of the workflows described herein, the water immersion objective lens (2-120) will be heated by a heating element. For example, such a heating element will maintain the water immersion objective lens (2-120) at a temperature sufficient to avoid causing a change in the temperature of the samples contained in the sample plate (2-021).

[0092] 2.1.3. Image Acquisition

[0093] In certain non-limiting implementations of the image acquisition system of the present disclosure, the objective lens (2-0120) is also used to focus fluorescence emitted by the sample (2-145) in response to the illumination provided by the oblique beam (2-125). In certain non-limiting implementations, the objective lens focuses the fluorescence emission (2-145) through emission filters ((2-150) and (2-160)), for example, bandpass emission filters that are matched to the spectrum of the fluorophores being observed and mounted in a high-speed filter wheel (Finger Lakes Instruments), and collected by a detector device (2-165). In certain non-limiting implementations, the objective lens focuses the fluorescence emission to an optical relay before it is collected by the detector device (2-165). For example, but not by way of limitation, such an optical relay can include one or more lenses (2-155) and one or more additional optical elements, for example, elements configured to reject additional scattered light before collection by the detector device (2-165). In certain non-limiting implementations, the objective lens focused fluorescence emission is directed through another dichroic mirror to split the emission over multiple areas of a detector (2-165). In certain non-limiting implementations, the objective lens focused fluorescence emission is directed through another dichroic mirror to split the emission over multiple detectors (2-165).

[0094] In certain non-limiting implementations of the image acquisition system of the present disclosure, the detector device is configured to synchronize detection with the translation of the oblique beam (2-125) over the sample plane (2-130). This synchronization is schematically depicted inFigure 2F The detection device can be any device capable of detecting the presence of a substance. For example, but not by way of limitation, the detection device can be a CMOS camera, such as a back-illuminated CMOS camera (Hamamatsu Fusion BT).

[0095] In certain implementations of the image acquisition system of the present disclosure, the CMOS camera can be operated such that for each field of view, a series of SMT frames are collected. For example, but not by way of limitation, 1-20,000 SMT frames, 1-15,000 SMT frames, 1-10,000 SMT frames, 1-5,000 SMT frames, 1-1,000 SMT frames, 2-500 SMT frames, 5-250 SMT frames, 10-200 SMT frames, 100-200 SMT frames, or 200 SMT frames are collected per field of view. In certain implementations, the CMOS camera can be configured to operate at a frame rate of about 0.5 to about 2000 Hz. In certain implementations, the CMOS camera can be configured to operate at a frame rate of 0.5 to 1000 Hz, or in certain implementations at a frame rate of 100 Hz. In certain embodiments, the CMOS camera can be configured to operate at a frame rate of 100 Hz to 1250 Hz, as shown in FIGS. 15, 17, 18, and 19. For example, but not by way of limitation, certain cellular SMT implementations can be performed at 100 Hz. In certain embodiments, certain cellular SMT implementations can be performed at 200 Hz. In certain embodiments, certain cellular SMT implementations can be performed at 400 Hz. In certain embodiments, certain cellular SMT implementations can be performed at 800 Hz. In certain embodiments, certain cellular SMT implementations can be performed at 1000 Hz. In certain embodiments, certain cellular SMT implementations can be performed at 1200 Hz. In certain embodiments, certain cellular SMT implementations can be performed at 1250 Hz. In certain embodiments, certain cellular SMT implementations can be performed at 1400 Hz. In certain embodiments, certain cellular SMT implementations can be performed at 1600 Hz. In certain embodiments, certain cellular SMT implementations can be performed at 1800 Hz. In certain embodiments, certain cellular SMT implementations can be performed at 2000 Hz. In certain embodiments, certain cellular SMT implementations can be performed at a frame rate of about 100 Hz or more, about 200 Hz or more, about 400 Hz or more, about 600 Hz or more, about 800 Hz or more, about 1000 Hz or more, about 1200 Hz or more, about 1400 Hz or more, about 1600 Hz or more, or about 1800 Hz or more. In certain embodiments, certain cellular SMT implementations can be performed at a frame rate of up to about 1200 Hz. In certain embodiments, certain cellular SMT implementations can be performed at a frame rate of up to about 1400 Hz. In certain embodiments, certain cellular SMT implementations can be performed at a frame rate of up to about 1600 Hz. In certain embodiments, certain cellular SMT implementations can be performed at a frame rate of up to about 1800 Hz. In certain embodiments, certain cellular SMT implementations can be performed at a frame rate of up to about 2000 Hz.

[0096] In certain non-limiting implementations of the image acquisition system of the present disclosure, the detector device is configured to transmit a signal with each frame to trigger other elements of the imaging system. For example, but not by way of limitation, the detector device can trigger illumination from a light source (2-005) to collect fluorescent emission associated with a stroboscopic laser pulse. For example, but not by way of limitation, such fluorescent emission collection is associated with a frame of 10 to 100 milliseconds and a stroboscopic laser pulse of 2 milliseconds. In certain embodiments, the fluorescent emission collection is associated with a stroboscopic laser pulse of about 0.1 to about 1 millisecond. In certain embodiments, the fluorescent emission collection is associated with a stroboscopic laser pulse of about 0.2 to about 0.6 milliseconds, about 0.2 to about 0.5 milliseconds, about 0.2 to about 0.4 milliseconds, or about 0.3 to about 0.5 milliseconds. In certain embodiments, the fluorescent emission collection is associated with a stroboscopic laser pulse of about 0.1 to about 0.6 milliseconds. In certain embodiments, the fluorescent emission collection is associated with a stroboscopic laser pulse of about 0.1 to about 0.5 milliseconds. In certain embodiments, the fluorescent emission collection is associated with a stroboscopic laser pulse of about 0.2 to about 0.4 milliseconds. In certain embodiments, the fluorescent emission collection is associated with a stroboscopic laser pulse of about 0.2 milliseconds. In certain embodiments, the fluorescent emission collection is associated with a stroboscopic laser pulse of about 0.4 milliseconds, as shown, for example. Figure 13C

[0097] In certain implementations, the imaging acquisition system can be configured to acquire a predetermined field of view (FOV), such as a detected FOV. In certain embodiments, the FOV (e.g., the detected FOV) can have a size of about 150 pm to about 250 pm in a first dimension and a size of about 100 pm to about 210 pm in a second dimension. In certain embodiments, the FOV (e.g., the detected FOV) can have a size of about 200 pm to about 250 pm in a first dimension and a size of about 150 pm to about 210 pm in a second dimension, or the FOV (e.g., the detected FOV) can have a size of about 225 pm to about 250 pm in a first dimension and a size of about 175 pm to about 210 pm in a second dimension. For example, but not by way of limitation, the FOV (e.g., the detected FOV) can have a size of about 250 pm in a first dimension and a size of about 190 pm in a second dimension, for example, as disclosed in Example 1.

[0098] ​In certain embodiments, a certain percentage of the FOV (e.g., detected FOV) provides usable data. In certain embodiments, at least 75% of the FOV, at least 80% of the FOV, at least 85% of the FOV, at least 90% of the FOV, at least 95% of the FOV, at least 96% of the FOV, at least 97% of the FOV, at least 98% of the FOV, at least 99% of the FOV, or 100% of the FOV provides usable data. In certain embodiments, at least 75% of the FOV (e.g., detected FOV) provides usable data. In certain embodiments, at least 80% of the FOV (e.g., detected FOV) provides usable data. In certain embodiments, at least 85% of the FOV (e.g., detected FOV) provides usable data. In certain embodiments, at least 90% of the FOV (e.g., detected FOV) provides usable data. In certain embodiments, at least 95% of the FOV (e.g., detected FOV) provides usable data. In certain embodiments, at least 96% of the FOV (e.g., detected FOV) provides usable data. In certain embodiments, at least 97% of the FOV (e.g., detected FOV) provides usable data. In certain embodiments, at least 98% of the FOV (e.g., detected FOV) provides usable data. In certain embodiments, at least 99% of the FOV (e.g., detected FOV) provides usable data. In certain embodiments, 100% of the FOV (e.g., detected FOV) provides usable data. In certain embodiments, a percentage equal to or greater than about 75% of the FOV provides usable data, e.g., a percentage equal to or greater than about 80% of the FOV, a percentage equal to or greater than about 85% of the FOV, a percentage equal to or greater than about 90% of the FOV, a percentage equal to or greater than about 95% of the FOV, a percentage equal to or greater than about 96% of the FOV, a percentage equal to or greater than about 97% of the FOV, a percentage equal to or greater than about 98% of the FOV, or a percentage equal to or greater than about 99% of the FOV provides usable data. In certain embodiments, a certain percentage of the FOV (e.g., detected FOV) achieves sufficient laser illumination to track protein movement. For example, but not by way of limitation, at least 75% of the FOV, at least 80% of the FOV, at least 85% of the FOV, at least 90% of the FOV, at least 95% of the FOV, at least 96% of the FOV, at least 97% of the FOV, at least 98% of the FOV, at least 99% of the FOV, or 100% of the FOV achieves sufficient laser illumination to track protein movement. In certain embodiments, at least 75% of the FOV (e.g., detected FOV) achieves sufficient laser illumination to track protein movement. In certain embodiments, at least 80% of the FOV (e.g., detected FOV) achieves sufficient laser illumination to track protein movement.In certain embodiments, at least 85% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein movement. In certain embodiments, at least 90% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein movement. In certain embodiments, at least 95% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein movement. In certain embodiments, at least 96% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein movement. In certain embodiments, at least 97% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein movement. In certain embodiments, at least 98% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein movement. In certain embodiments, at least 99% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein movement. In certain embodiments, 100% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein movement. In certain embodiments, a percentage equal to or greater than about 75% of the FOV achieves sufficient laser illumination to track protein movement, for example, a percentage equal to or greater than about 80% of the FOV, a percentage equal to or greater than about 85% of the FOV, a percentage equal to or greater than about 90% of the FOV, a percentage equal to or greater than about 95% of the FOV, a percentage equal to or greater than about 96% of the FOV, a percentage equal to or greater than about 97% of the FOV, a percentage equal to or greater than about 98% of the FOV, or a percentage equal to or greater than about 99% of the FOV achieves sufficient laser illumination to track protein movement.

[0099] In certain implementations, the imaging acquisition system can be configured to acquire a predetermined image size per frame, referred to herein as a region of interest (ROI). In certain implementations, the ROI will vary depending on the frame rate employed. For example, at 100 FPS, 2304 x 1728 pixels will define the ROI, which corresponds to 248.832 x 186.624 microns in the sample plane. In contrast, at 200 FPS, 2304 x 768 pixels will define the ROI, which corresponds to 248.832 x 82.944 microns in the sample plane.

[0100] In certain implementations, the imaging acquisition system can be configured to perform a predetermined scan rate at a predetermined frame rate. For example, but not by way of limitation, at 100 FPS: the scan rate can be 186.624 microns / 9 milliseconds, which corresponds to 20.8 microns / millisecond, which corresponds to 2.08 centimeters / second. In contrast, at 200 FPS, the scan rate can be 82.94 microns / 4 milliseconds, which corresponds to 20.7 microns / millisecond, which corresponds to 2.07 centimeters / second.

[0101] In certain implementations, the detector device can be used to collect fluorescent emissions at multiple wavelengths. For example, but not by way of limitation, fluorescent emissions of additional fluorophores can be collected at the same frame rate or at different frame rates of the same field of view to provide downstream registration of the SMT tracks with other cellular components, such as the nucleus. Additional channels of the detector device can be used as needed to expand the number of fluorescent emissions captured simultaneously within the same field of view to provide downstream registration of the SMT tracks with other cellular components, such as the nucleus.

[0102] 2.2. Sample processing

[0103] Reference Figure 1 Various aspects of the current subject matter can be implemented using an htSMT workflow, where such workflows incorporate systems for sample preparation, including reagent processing. For example, but not by way of limitation, Figure 5 A schematic of a sample plate (2-021) is provided, which includes a plurality of wells (2-016) in which samples can be prepared and analyzed. Figure 5 A schematic of sample components, such as cells (2-018) and fluorescent target proteins within the cells (2-017) is also provided. However, as described herein, Figure 5 It is not intended to convey scale, for example, each sample present in a well (2-016) can comprise thousands of cells, and each cell can comprise many fluorescent target proteins. Figure 5 The ability of the sample processing system of the present disclosure to add additional reagents to samples (2-019) is also schematically illustrated. Such reagent additions can be processed through robotic manipulation, such as but not limited to translation of a robotic fluid handling system relative to the individual wells (2-016) of a sample plate (2-021), translation of the sample plate (2-021) itself, or a combination of both. In certain implementations of the image acquisition system, the sample plate (2-021) can be maintained in a temperature-controlled environment by the environmental control region (2-020). For example, but not by way of limitation, samples can be maintained at 22-50 °C. In certain implementations of the image acquisition system, the sample plate (2-021) can be maintained in a humidity-controlled environment by the environmental control region (2-020). For example, but not by way of limitation, samples can be maintained at 20-95% humidity. In certain implementations of the image acquisition system, the sample plate (2-021) can be maintained in a defined gaseous environment by the environmental control region (2-020). For example, but not by way of limitation, samples can be maintained under 5% C02.

[0104] 2.2.1. Cell lines and cell culture

[0105] Reference Figure 5A particular advantage of the htSMT system described herein is its ability to measure live cells (2-016) to facilitate the tracking of protein activity, mobility, and diffusion behavior within crowded live cellular environments. Figure 11B As shown, the htSMT system of this disclosure can be used to track fluorescently labeled proteins in samples containing multiple cells. Example cells (e.g., cell lines) for use in conjunction with the htSMT system described herein are considered if the sample (e.g., containing such cells) can be focused by the objective lens (2-120) for a sufficiently long time to direct the fluorescence emission of the fluorophore to the detector (2-165). For example, but not as a limitation, cells can adhere directly to the coverslip. As an additional example, but not as a limitation, cell adhesion to the coverslip can be induced after treatment with an extracellular matrix material (e.g., fibronectin, collagen, poly-D-lysine, laminin, matrix adhesive, hydrin, etc.).

[0106] Exemplary cells (e.g., cell lines) may be selected to minimize non-fluorophore emission reaching the detector. In some embodiments, the cells used in this disclosure may be mammalian, bacterial, or fungal cells. In some embodiments, the cells are mammalian cells. In some embodiments, the cells may be obtained from preserved tissue (e.g., fixed tissue), frozen tissue (e.g., frozen tissue samples), or fresh tissue (e.g., fresh tissue samples). In some embodiments, cells and / or samples containing cells may be obtained from a subject. In some embodiments, the cells may be obtained from a malignant tumor of tissue or tumor; for example, the cells may be present within a tumor sample (e.g., a slice of tumor). In some embodiments, the cells may be obtained from a cell line. For example, but not as a limitation, specific cell lines that may be used in conjunction with the htSMT system described herein include: U2OS cells (ATCC catalog number HTB-96), MCF7 cells (ATCC catalog number HTB-22), T47d cells (ATCC catalog number HTB-133), and SK-BR-3 cells (ATCC catalog number HTB-30). In some embodiments, the cells may be present in a three-dimensional structure, such as an organoid or spheroid. In some implementations, the cells may be present in organoids.

[0107] In certain implementations of the htSMT system of the present disclosure, cells to be used are cultured as needed to provide sufficient cell numbers to enable the desired high-throughput analysis. For example, but not by way of limitation, cells such as U20S cells (ATCC Catalog No. HTB-96), MCF7 cells (ATCC Catalog No. HTB-22), T47d cells (ATCC Catalog No. HTB-133), and SK-BR-3 cells (ATCC Catalog No. HTB-30) can be grown in DMEM (Catalog No. 1056601, Gibco DMEM, high glucose, GlutaMAX supplement, Thermofisher) supplemented with 10% fetal bovine serum (Catalog No. 16000044, Thermofisher) and 1% penicillin-streptomycin (Catalog No. 15140122, Thermo Fisher) and maintained in a humidified 37 °C incubator at 5% CO2, with subculturing approximately every two to three days. Additional culturing strategies suitable for the cell lines and uses outlined herein are known to those of skill in the relevant art.

[0108] In certain implementations of the htSMT system of the present disclosure, the cells comprise one or more fluorescent target proteins. The selection of the particular protein to be labeled and the specific labeling method can vary depending on the particularities of the specific study. For example, but not by way of limitation, one method of labeling proteins that can be used in conjunction with the htSMT system described herein is the HaloTag fusion strategy. For example, but not by way of limitation, one method of labeling proteins is the SNAPtag fusion. For example, but not by way of limitation, one method of labeling proteins is the CLIPtag fusion. For example, but not by way of limitation, one method of labeling proteins is through a fluorophore ligation enzyme system. For example, but not by way of limitation, one method of labeling proteins is via the FlAsH or ReAsH tetra-cysteine motif. For example, but not by way of limitation, one method of labeling proteins is through a strain-promoted alkyne-azide cycloaddition reaction of a fluorophore. For example, but not by way of limitation, one method of labeling proteins is through inducing cellular uptake of a separately produced fluorescent target protein. In certain implementations of the htSMT system of the present disclosure, the cells comprise one or more fluorescently labeled glycoproteins. In certain embodiments, one method of labeling proteins uses a gene editing system, such as a CRISPR-based editing system. For example, but not by way of limitation, a nucleic acid encoding a fluorescent protein (e.g., a fluorescent tag such as a HaloTag) can be inserted into a gene or upstream or downstream of a gene encoding a protein to be labeled to produce a protein that is fluorescently labeled with a HaloTag (e.g., at its C- or N-terminus), for example as described in Example 2.

[0109] While one skilled in the art can implement the HaloTag fusion approach in a variety of ways, one exemplary approach is to transfect a mammalian expression vector containing the fusion gene (i.e., the protein of interest fused in-frame to the HaloTag sequence) under the control of a weak L30 promoter and containing a neomycin resistance marker in a cell line of interest (e.g., U20S cells). In certain implementations, such transfection can be accomplished using FuGENE 6 (Cat. No. E2691, Promega) when the cells have reached 70% confluency. In certain implementations, the transfected cells can then be selected with an appropriate selection agent, such as G418 (Cat. No. 10131027, Thermo Fisher) at an appropriate concentration, such as 500 pg / mL. In certain implementations, the isolated cells can then be cloned. Clones expressing the desired fusion gene can be initially determined by staining with 100 nM JF 549 -HTL (Cat. No. GA1110, Promega) and 50 nM Hoechst 33342 and identifying clones with the expected distribution of JF 549 signals. Another exemplary approach is to transfect cells with a ribonucleoprotein (RNP) complex comprising an sgRNA targeting a genomic sequence encoding an N-terminal or C-terminal region of the target protein and a Cas9 protein in combination with one or more linear dsDNA donors. In certain embodiments, each donor consists of 200-300 bp homology arms specific to each target, a codon-optimized HaloTag sequence, and a TEV linker (ENLYFQG) between the target and HaloTag. In certain implementations, three to six clones can subsequently be tested for their response to a control compound using SMT conditions, and the most homogenous clone can then be expanded for further testing.

[0110] While the htSMT workflow of the present application is generally described with respect to implementations that track the effect of a compound on a target fluorescent protein, the htSMT workflow described herein is equally applicable to the tracking and analysis of fluorescent target compounds. For example, and without limitation, the compounds described herein can themselves be fluorescent or can be modified to facilitate fluorescent detection. In addition, changes in the movement of the fluorescent compound can be utilized to determine the SMT profile of the compound itself. Thus, all of the analysis strategies described herein with respect to tracking a target fluorescent protein are also applicable to results obtained by tracking the compound itself.

[0111] 2.2.2. Single molecule tracking sample preparation

[0112] References Figure 5Aspects of the current subject matter can be implemented using an htSMT workflow in which cells (2-018) are seeded on plates (2-021), such as a tissue culture treated 384-well glass bottom plate, although other types of culture plates can be used with the methods outlined herein, including but not limited to single chamber, 9-well glass bottom plates, 24-well glass bottom plates, 96-well glass bottom plates, 1536-well glass bottom plates, and 3456-well glass bottom plates, as well as plates made of alternative materials, such as plates made partially or entirely of plastic. In certain implementations, cells (2-018) are seeded at 1 to 20,000 cells per well (2-016), such as 50 to 10,000, 100 to 9,000, 250 to 8500, 500 to 7500, 750 to 7000, 2500 to 6500, or 6000 cells per well. The seeded cells can then be incubated under conditions suitable for adhesion, such as overnight at 37 °C and 5% CO2. To enable fluorescence emission, the cells can be incubated with a sufficient amount of a tag (e.g., one or more cell permeable fluorophores). For example, but not by way of limitation, in the case of a HaloTag fusion, the cells can be incubated with about 0.1 to about 100 pM of JF649 (Promega), JF646 (Promega), or other similar cell permeable fluorophores. In certain embodiments, the cells can be incubated with about 0.1-100 pM of JF649 (Promega), JF646 (Promega), or about 0.1-100 pM of JF 549 , JF 646 646 (Promega), or other similar cell permeable fluorophores. In certain embodiments, the cells can be incubated with about 0.1-100 pM of JF 549 649 (Promega), JF646 (Promega), or about 0.1-100 pM of JF 646 and / or 50 nM Hoechst 33342 (to label the nuclei), such as for one hour in complete media, can provide desirable results.

[0113] In certain implementations of the htSMT strategy described herein, the cells are subsequently washed, such as three times in DPBS, and twice in imaging media. In certain implementations, the imaging media is prepared to promote fluorescence emission, such as fluoroBrite DMEM media (catalog number A1896701, Thermo Fisher), and can be supplemented with GlutaMAX (catalog number 35050079, Thermo Fisher) and the same serum and antibiotics as the growth media.

[0114] Where appropriate, compounds can be added to samples to test their effect on specific marker proteins by SMT. In certain implementations, compounds can be serially diluted in Echo Qualified 384-well low-dead-volume source microplates (0018544, Beckman Coulter) to generate a dose titration source material. Compounds can then be administered in cell culture media at, for example, a final 1:1000 dilution. In certain implementations of the htSMT strategy described herein, each compound will have at least two replicates per plate and three plate replicates. Additionally, in certain implementations of the htSMT strategy described herein, 20 DMSO control wells and two no-dye control wells can be randomly distributed across each sample plate (2-020). In certain implementations, compounds can be incubated for 0 to 48 hours prior to image acquisition, for example, 1 hour at 37 °C.

[0115] 3. OLS htSMT software

[0116] Figure 6 An example system 600 for a high-throughput single-molecule imaging platform for measuring molecular motion within living cells is illustrated. Experiments 602 can be conducted to collect a large amount of data from a plurality of living cells (e.g., using imaging system 624 to identify compounds 626 and / or targets 622). Experiments 602 can include applying various identifiers to molecules of interest, such as tags that can subsequently emit fluorescence or otherwise be detected (e.g., using a laser or other light source). Biological samples forming part of such experiments 602 can be organized into plates 604 having a plurality of wells 606. Each well 606 can have one or more associated fields of view (FOV) 610. FOVs 610 can be located within or correspond to individual wells 606. A series of images can be generated for FOVs 610 to produce one or more movies 612, which can include SMT movies as well as non-SMT movies. SMT movies can be used to track the paths of individual labeled molecules (e.g., proteins), resulting in a plurality of tracks. Each track can be composed of a plurality of spots 614, which include the spatiotemporal coordinates of a labeled molecule at a particular time (e.g., as described in further detail in Figure 7 Separately from, and in some cases in parallel with, tracking, movies 612 can be used to identify molecules to generate masks 618 by using machine learning and / or computer vision-based image segmentation. Masks 618 are spatial regions within FOVs 610 that result from segmentation. Each mask 618 can belong to a mask class, which is described in further detail in Figure 8 and Figure 20B .

[0117] Data associated with two channels (e.g., tracking channel and segmentation / masking channel) can be combined to generate a plurality of indicators 620 associated with various aspects of the sample. In other words, trajectories 616 (e.g., trajectory data) can be combined with machine learning processed image segmentation data and further analyzed using statistical / machine learning methods. The processing of the combined data can be used to generate indicators 620, such as hit scores associated with compounds and / or targets in the biological sample, which can be stored in a database structure, such as Figure 9 Further described.

[0118] Figure 7 Data flow through an example system 700 for a high-throughput single-molecule imaging platform for measuring protein motion within live cells is illustrated. Experiment specifications 704 defining an experiment 602 can be provided as data input by one or more clients 702. For example, each experiment 602 can be collected with a concomitant stain (e.g., Hoechst or Potomac Red) for downstream analysis including segmentation 618. Experiment specifications 704 can define various parameters for an experiment 602, such as stains, dyes, compounds, treatments, etc. As previously described in Figure 6 An imaging system 706 (e.g., imaging system 624) can capture a series of images that generate one or more SMT movies 711 and / or non-SMT movies or segmentation movies 708 (e.g., movies 612) characterizing molecular motion, as described previously in

[0119] SMT movies 711 can be analyzed to perform operations associated with molecular tracking 710, which can include detection 712, sub-pixel localization 713, and linking 714 to identify molecular trajectories 715 across images within SMT movies 711. More specifically, during detection 712, one or more spots within SMT movies 711 can be detected or recovered. Each spot can be equipped with spatiotemporal coordinates. These spatiotemporal coordinates can be estimated by using sub-pixel localization techniques 713. Linking 714 can be performed on these spots to ultimately identify trajectories 715.

[0120] A link as used herein is a potential association between two spots. Each link is directional, starting at one spot and ending at another. A "correct link" connects two spots produced by the same emitter in different frames; otherwise, the link is "incorrect." One goal of the linking algorithm is to estimate which links are correct. Links are referred to herein in the format a:i→j. This means: link a, starting at spot i and ending at spot j. Links satisfy at least the following three constraints: (a) links progress in time, (b) links must not connect two spots that are more than a certain limit apart (referred to herein as the "search radius"), and (c) links must not connect two spots that are more than a certain limit apart in time (referred to herein as the "gap limit"). A spot-link graph is a graph of spots and links of an SMT movie 711. Spots are the vertices of the graph, and links are the edges of the graph. Since links progress in time, a spot-link graph is a directed acyclic graph. A match is a subset of links in a spot-link graph such that no two links in the subset start or end at the same spot. Trajectories 715 are used herein to refer to a sequence of consecutive (end-to-end) links in the same match. Multiple trajectories can be used to determine dynamic indicators 730. Such parameters can include properties of spots characterizing spot motion. Such parameters can include one or more of the velocity, diffusion coefficient, or anomaly parameter of each spot. The dynamic parameter of spot i is referred to herein as θ i . The set of dynamic parameters of all spots in a spot-link graph is referred to herein as Θ.

[0121] Separately from, and in some variants in parallel with, the processing of the SMT movie 711, the segmented movie 708 can undergo segmentation, generating one or more masks 720. Masks can be of various categories, including but not limited to nucleus, cytoplasm, and / or irrelevant masks, which will be further described in Figure 8 . Example masks are individual segmented objects (e.g., one cell, one nucleus, one mitochondrion, M phase, G1 phase, early S phase, mid-S phase, late phase, G2 phase). A FOV 610 can contain any number of example masks of one mask category. A semantic mask is the union of all example masks of one mask category corresponding to one FOV (e.g., all cells, all nuclei, or all mitochondria of one FOV, etc.). Irrelevant masks can contain parts of the non-SMT movie 708 that are excluded from any downstream data analysis. For example, these irrelevant masks can correspond to parts of the non-SMT movie 708 that are out of focus or contain autofluorescent cell debris that hinders accurate tracking. During segmentation, molecules in the segmented movie 708 can be assigned to one or more masks. Image indicators 740 can be evaluated according to the masked molecules (e.g., cell health, focus quality, etc.).

[0122] Experimental information, such as dynamic metrics 730, image metrics 740, and any data from which either is derived (e.g., segmentation information), can be provided to a data store 770 for storage. Such a data store 770 can store, for example, any results of an experiment 602, such as dynamic metrics 730, image metrics 740, and / or any data from which either is derived. The data store can include local persistence and / or a dedicated server accessed locally or through the cloud. The data store 770 can also store metadata associated therewith and / or metadata associated with the experiment specification 704. Experimental information (e.g., results and metadata from historical experiments, etc.) can be provided to the data store 770 through a store application program interface (API) 750. The store API 750 can also interface with a web-based graphical user interface front end 760 that provides such information for display on the client 702.

[0123] In some variations, segmentation information can be used to identify subcellular compartments, such as the nucleus, nucleolus, cytoplasm, etc. Segmentation information can also be used to distinguish one cell from another. Segmentation information can be stored in a particular format (e.g., TIFF or other multi-image file format).

[0124] Example dynamic metrics 730 can also include a state array. State arrays are a framework for learning interpretable dynamic models from SMT trajectories and can be used to further understand the motion of a target protein and where that motion occurs in the cell. In some variations, segmentation information can be used to generate / populate a state array. The output of a state array can be returned at the subcellular compartment level, enabling scientists to distinguish the dynamics of different subcellular compartments. In addition, a state array can be computed for each individual subcellular compartment (e.g., each nucleus).

[0125] To facilitate application access to data, including but not limited to the state array, the processed SMT data can allow the following formats to be stored: (a) representing processed trajectories and associated attributes, such as SNR and spot shape characteristics for each SMT movie, (b) representing mask objects, including mask categories (e.g., associated organelle for each mask object, cell cycle stage for each mask object, etc.), (c) associations of trajectories with mask objects (e.g., observation of the nucleus for each trajectory or observation of the cell cycle stage for each trajectory), and (d) associations of all SMT movies with metadata about the original experiment, such as compound treatment, acquisition time, and imaging system name. Formats (a) and (c) can be protocol buffer schemas that define the storage format of trajectories and associated mask objects. Format (b) can be a specialized image file format that includes, for each pixel in the FOV, the mask object to which it belongs. Format (d) can be a PostgreSQL database that records all captured experiments / movies. As a client of the processed SMT data, the state array can utilize these data schemas to report dynamic features of trajectories for each mask category or each mask object.

[0126] Figure 8 A plurality of images 800 illustrating the difference between mask categories and instances or semantic masks. As previously described, non-SMT movies or segmented movies can be assigned to a plurality of categories. These categories can include nuclei (e.g., category A), cytoplasm (e.g., category B), and / or irrelevant masks (e.g., category C). Unique, individual mask instances can be applied to a biological sample. For example, image 810 is a unique, individual instance mask applied to a nucleus (e.g., category A). Image 812 is a unique, individual instance mask applied to cytoplasm (e.g., category B). Image 820 illustrates a plurality of instance masks applied to one or more nuclei, where a single color represents a different, unique individual instance mask. Image 822 illustrates a plurality of masks applied to one or more cytoplasms, where a single color represents a different, unique individual instance mask. Image 830 illustrates a semantic mask applied to one or more nuclei, which is a union of all instance masks. Image 832 illustrates a semantic mask applied to one or more cytoplasms. Figure 20B The use of mask categories is further illustrated. As shown in Figure 20B a single instance mask can be applied to a cell in M phase, a single instance mask can be applied to a cell in G1 phase, a single instance mask can be applied to a cell in early S phase, a single instance mask can be applied to a cell in mid-S phase, a single instance mask can be applied to a cell in late S phase, and / or a single instance mask can be applied to a cell in G2 phase.

[0127] Figure 9An example computer implementation environment 900 is described, in which the imaging system 910 can interact with the computing architecture to execute the various algorithms described herein. Figure 9 As shown, the imaging system 910 may interface with one or more clients 950 (e.g., client 702 via a web application with a graphical user interface). One or more clients 950 may interface with one or more servers 920 accessible via network 930. One or more clients 950 may host frame captures of images (e.g., video 612) captured from a camera. These images may be temporarily stored on one or more clients 950 and periodically transferred via network 930 to one or more servers 920 for remote storage. One or more servers 920 may also contain or have access to one or more data storage units 940 for storing data collected and / or extracted from samples by the imaging system 910. In some variations, network 930 may include or interface with one or more network storage arrays 960 for storing data such as captured images (e.g., video 612).

[0128] Figure 10 Figure 1000 illustrates an example computing device architecture for implementing the various aspects described herein. In some variations, the example computing device architecture may be the architecture of client 950 and / or server 920, and some components described with respect to Figure 1000 may be optional for client 950 and / or server 920. Bus 1004 may serve as an information highway for interconnecting other illustrated components of the hardware. Processing system 1008, labeled CPU (Central Processing Unit) (e.g., one or more computer processors / data processors on a given computer or multiple computers), performs computational and logical operations required to execute a program. Optionally or additionally, processing system 1012, labeled GPU (Graphics Processing Unit) (e.g., one or more computer processors / data processors on a given computer or multiple computers), performs computational and logical operations required to execute a program. Non-transitory processor-readable storage media (e.g., read-only memory (ROM) 1016 and random access memory (RAM) 1020) may communicate with processing system 1008 and / or processing system 1012 and may include one or more programming instructions for the operations specified herein. Optionally, program instructions may be stored on a non-transitory computer-readable storage medium, such as a disk, optical disk, recordable storage device, flash memory, solid-state drive, or other physical storage medium.

[0129] In one example, the disk controller 1048 can interface one or more optional removable storage 1056 or local storage 1052 with the system bus 1004. The removable storage 1056 can be an external or internal disk drive, or solid state drive, or external hard disk drive. The local storage 1052 can be an internal hard disk and / or memory. As mentioned previously, the various examples of removable storage 1056, local storage 1052, and disk controller 1048 are optional devices. The system bus 1004 can also include at least one communication interface 1024 to allow for communication with external devices (e.g., cloud storage and remote services) that are physically connected to the computing system, or that are accessed through a wired or wireless network over the air. In some cases, the at least one communication interface 1024 includes or otherwise comprises a network interface.

[0130] In some variations, such as for the client 950, to provide for interaction with a user, the subject matter described herein can be implemented on a computing device having a display 1044, e.g., an LCD (liquid crystal display) or LED (light emitting diode) monitor, for displaying information to the user and a display interface 1040, e.g., a web browser, for allowing the user to interact with or provide input to the computing device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. The input device 1032 and microphone 1036 can be coupled to the bus 1004 through an input device interface 1028 and communicate information to and from the bus 1004, e.g., in the form of analog or digital signals. For example, the input device 1032 can be an imaging system 910 configured with the ability to capture a series of images described herein. A frame grabber 1058 can capture or grab individual frames from analog or digital data obtained from the bus 1004 that encapsulates a series of images. The frame grabber 1058 can include a memory capable of storing a single or multiple frames. The frame grabber 1058 can also provide the single or multiple frames to the bus 1004 for further storage on, e.g., the local storage 1052 and / or the removable storage 1056. Other computing devices, such as a dedicated server, can omit the combination of the display 1044, display interface 1040, input device 1032, and microphone 1036. Figure 10 One or more components described.

[0131] One or more aspects or features of the subject matter described herein can be realized in digital electronic circuitry, integrated circuitry, specially designed application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) computer hardware, firmware, software, and / or combinations thereof. These various aspects or features can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device. The programmable system or computing system can include clients and servers. The clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server can arise by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

[0132] These computer programs, also referred to as programs, software, software applications, applications, components, or code, include machine instructions for a programmable processor, and can be implemented in a high-level procedural language, an object-oriented programming language, a functional programming language, a logical programming language, and / or in assembly / machine language. As used in this paper, the term "machine-readable medium" refers to any computer program product, apparatus and / or device, e.g., magnetic discs, optical disks, memory, and Programmable Logic Devices (PLDs), used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor. The machine-readable medium can store such machine instructions non-transitorily, such as a non-transient solid-state memory or a magnetic hard drive or any other

[0133] 4. Specific OLS htSMT applications

[0134] Many, perhaps most, pathways that regulate the basic biochemistry of a cell rely on the interaction of a protein sensor that transiently engages a protein effector to trigger a physiological change in the cell. While the basic principles of this process have been recognized for some time, biochemical studies of these protein interactions have generally required reconstitution in vitro or interrogation by pull-down assays after permeabilization of the cell. The htSMT workflow described herein provides a method to visualize protein movements in a large number of living cells and with the ability to quantitatively assess the effect of added compositions, such as small molecule inhibitors.

[0135] References Figure 1Aspects of the OLS htSMT workflow of the present disclosure include, but are not limited to: (i) sample preparation (including reagent processing), (ii) image acquisition using sample imaging to generate a series of images and / or videos, (iii) image analysis by processing these images and videos, (iv) information storage, and (v) providing insights using the stored information (including biological interpretation). In terms of biological interpretation, the htSMT workflow described herein can provide specific insights, as described below, depending on the particular workflow employed, such as (i) OLS htSMT screening; (ii) OLS htSMT binding; and / or (iii) OLS KineticSMT.

[0136] In certain embodiments, the workflow of the present disclosure can include detecting fluorescence of a plurality of target fluorescent proteins in a field of view of a sample plane, wherein the field of view (e.g., the detected field of view) has a size of about 150 pm to about 250 pm in a first dimension and a size of about 100 pm to about 210 pm in a second dimension. In certain embodiments, the FOV (e.g., the detected FOV) can have a size of about 150 pm to about 250 pm in a first dimension and a size of about 100 pm to about 210 pm in a second dimension. In certain embodiments, the FOV (e.g., the detected FOV) can have a size of about 200 pm to about 250 pm in a first dimension and a size of about 150 pm to about 210 pm in a second dimension, or the FOV (e.g., the detected FOV) can have a size of about 225 pm to about 250 pm in a first dimension and a size of about 175 pm to about 210 pm in a second dimension. For example, but not by way of limitation, the FOV (e.g., the detected FOV) can have a size of about 250 pm in a first dimension and a size of about 190 pm in a second dimension, e.g., as disclosed in Example 1.

[0137] In certain embodiments, a percentage of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein movement. For example, but not by way of limitation, at least 75% of the FOV, at least 80% of the FOV, at least 85% of the FOV, at least 90% of the FOV, at least 95% of the FOV, at least 96% of the FOV, at least 97% of the FOV, at least 98% of the FOV, at least 99% of the FOV, or 100% of the FOV achieves sufficient laser illumination to track protein movement. In certain embodiments, at least 75% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein movement. In certain embodiments, at least 80% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein movement. In certain embodiments, at least 85% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein movement. In certain embodiments, at least 90% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein movement. In certain embodiments, at least 95% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein movement. In certain embodiments, at least 96% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein movement. In certain embodiments, at least 97% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein movement. In certain embodiments, at least 98% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein movement. In certain embodiments, at least 99% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein movement. In certain embodiments, 100% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein movement. In certain embodiments, a percentage equal to or greater than about 75% of the FOV achieves sufficient laser illumination to track protein movement, for example, a percentage equal to or greater than about 80% of the FOV, a percentage equal to or greater than about 85% of the FOV, a percentage equal to or greater than about 90% of the FOV, a percentage equal to or greater than about 95% of the FOV, a percentage equal to or greater than about 96% of the FOV, a percentage equal to or greater than about 97% of the FOV, a percentage equal to or greater than about 98% of the FOV, or a percentage equal to or greater than about 99% of the FOV achieves sufficient laser illumination to track protein movement. In certain embodiments, a percentage equal to or greater than about 90% achieves sufficient laser illumination to track protein movement. In certain embodiments, a percentage equal to or greater than about 95% achieves sufficient laser illumination to track protein movement.

[0138] In certain embodiments, the workflow of the present disclosure comprises detecting the field of view at a frame rate of up to about 2000 Hz. In certain embodiments, the workflow of the present disclosure comprises detecting the field of view at a frame rate of about 100 Hz or more, about 200 Hz or more, about 400 Hz or more, about 600 Hz or more, about 800 Hz or more, about 1000 Hz or more, about 1200 Hz or more, about 1400 Hz or more, about 1600 Hz or more, or about 1800 Hz or more. In certain embodiments, the workflow of the present disclosure comprises detecting the field of view at a frame rate of up to about 1200 Hz. In certain embodiments, the workflow of the present disclosure comprises detecting the field of view at a frame rate of up to about 1400 Hz. In certain embodiments, the workflow of the present disclosure comprises detecting the field of view at a frame rate of up to about 1600 Hz. In certain embodiments, the workflow of the present disclosure comprises detecting the field of view at a frame rate of up to about 1800 Hz.

[0139] In certain embodiments, the workflow of the present disclosure comprises illuminating the field of view using a stroboscopic laser pulse of 0.1 to 1 millisecond. In certain embodiments, the disclosed workflow comprises illuminating the field of view using a stroboscopic laser pulse of about 0.2 to about 1 millisecond, about 0.3 to about 1 millisecond, about 0.4 to about 1 millisecond, about 0.1 to about 0.9 millisecond, about 0.1 to about 0.8 millisecond, about 0.1 to about 0.7 millisecond, about 0.1 to about 0.6 millisecond, about 0.1 to about 0.5 millisecond, about 0.1 to about 0.4 millisecond, about 0.2 to about 0.6 millisecond, about 0.2 to about 0.5 millisecond, about 0.2 to about 0.4 millisecond, or about 0.3 to about 0.5 millisecond. In certain embodiments, the workflow of the present disclosure comprises illuminating the field of view using a stroboscopic laser pulse of about 0.1 to about 0.6 millisecond. In certain embodiments, the workflow of the present disclosure comprises illuminating the field of view using a stroboscopic laser pulse of about 0.1 to about 0.5 millisecond. In certain embodiments, the workflow of the present disclosure comprises illuminating the field of view using a stroboscopic laser pulse of about 0.2 to about 0.4 millisecond. In certain embodiments, the workflow of the present disclosure comprises illuminating the field of view using a stroboscopic laser pulse of about 0.2 millisecond.

[0140] In certain embodiments, the workflow of the present disclosure includes illuminating a field of view disposed in a sample plane within a sample with a light beam to cause a subset of fluorescent target proteins in live cells to emit fluorescence, thereby imaging a plurality of molecular tracks. In certain embodiments, up to about 1,000,000 molecular tracks in a single detection field of view can be imaged, such as up to about 900,000, up to about 800,000, up to about 700,000, up to about 600,000, up to about 500,000, up to about 400,000, up to about 300,000, up to about 200,000, or up to about 100,000. In certain embodiments, the number of tracks imaged in a single detection field of view can be from about 30,000 to about 1,000,000, such as from about 30,000 to about 250,000. For example, but not by way of limitation, the number of tracks imaged in a single detection field of view can be from about 50,000 to about 200,000, from about 100,000 to about 200,000, from about 100,000 to about 500,000, or from about 100,000 to about 150,000. In certain embodiments, the number of tracks imaged in a single detection field of view can be up to about 1,000,000. In certain embodiments, the number of tracks imaged in a single detection field of view can be from about 100,000 to about 1,000,000. In certain embodiments, the number of tracks imaged in a single detection field of view can be from about 200,000 to about 1,000,000. In certain embodiments, the number of tracks imaged in a single detection field of view can be from about 100,000 to about 500,000. In certain embodiments, the number of tracks imaged in a single detection field of view can be from about 200,000 to about 500,000.

[0141] In certain embodiments, the field of view can include a plurality of cells. In certain embodiments, the number of cells imaged in the field of view is related to the size of the cells being imaged. For example, but not by way of limitation, the smaller the size of the cells, the greater the number of cells that can be imaged in the field of view. In certain embodiments, depending on the size of the cells being imaged, the field of view can include about 30 to about 200 live cells, for example, can include about 30 to about 80 cells or about 50 to about 80 cells. In certain embodiments, depending on the size of the cells being imaged, the field of view can include up to about 80 live cells, for example, mammalian cells. In certain embodiments, for U2OS cells, the range is about 30 to about 40 cells per field of view, while for HCT116 cells, the range is about 50 to about 80 cells per field of view, taking into account their size difference. In certain embodiments, the field of view can include about 30 to about 80 live cells, for example, mammalian cells. In certain embodiments, the field of view can include about 50 to about 80 live cells, for example, mammalian cells. In certain embodiments, the field of view can include about 55 to about 80 live cells, for example, mammalian cells. In certain embodiments, the field of view can include about 60 to about 80 live cells, for example, mammalian cells.

[0142] In certain embodiments, the workflow of the present disclosure can include analyzing a subset (e.g., subpopulation) of cells present within the field of view, for example, analyzing and / or tracking trajectories of fluorescent target proteins in the subset (e.g., subpopulation) of cells present within the field of view. For example, but not by way of limitation, the workflow of the present disclosure can include analyzing about 1% to about 99% of the cells present within the field of view, for example, about 1% to about 50% of the cells present within the field of view.

[0143] In certain embodiments, the workflow of the present disclosure includes illuminating the field of view disposed in the sample plane within the sample with a light beam to cause a plurality of fluorescent target proteins in the live cells to emit fluorescence. In certain embodiments, the plurality of fluorescent target proteins can include about 1,000 to about 1,000,000 fluorescent target proteins, for example, about 10,000 to about 1,000,000 or about 100,000 to about 1,000,000.

[0144] In certain embodiments, the workflow of the present disclosure can include detecting fluorescence of the plurality of fluorescent target proteins in the field of view of the sample plane at a rate of more than about 100,000 detected fields of view per day. For example, but not by way of limitation, the rate of detected fields of view per day is about 100,000 to about 1,000,000. In certain embodiments, the workflow of the present disclosure can include detecting fluorescence of the plurality of fluorescent target proteins in the field of view of the sample plane at a rate of about 100,000 to about 500,000 detected fields of view per day.

[0145] In certain embodiments, the workflow of the present disclosure can include determining a change in the movement of the fluorescently labeled target protein in the presence of the compound. For example, but not by way of limitation, the average change in movement of the fluorescent target protein in the presence of the compound is about 1% to about 5% or to about 10% relative to the baseline movement in the absence of the compound. In certain embodiments, the average change in movement of the fluorescent target protein in the presence of the compound is about 1% to about 5%. In certain embodiments, the average change in movement of the fluorescent target protein in the presence of the compound is about 1% to about 10%.

[0146] In certain embodiments, exemplary OLS htSMT workflows include each of the above strategies, as well as combinations of these strategies, wherein two or more strategic requirements are combined.

[0147] 4.1 OLS htSMT screening

[0148] In certain implementations of the OLS htSMT workflows described herein, the systems and methods are adapted to interrogate the ability of one or more compositions (e.g., “test” compounds) to affect the SMT profile associated with a labeled protein. For example, such htSMT workflows will screen for changes in the SMT profile, e.g., an increase or decrease in the movement of a protein of interest in the presence of the composition relative to the SMT profile in the absence of the composition, e.g., when the addition of the composition is replaced with a control, e.g., but not limited to DMSO. It will be appreciated that higher order comparisons can also be made, including where multiple proteins are fluorescing, in the context of the multiplexing of compounds. Moreover, as described above, the htSMT screening strategies described herein are equally applicable to screening the SMT profile associated with a fluorescent compound, e.g., a compound that is naturally fluorescent or that has been modified to fluoresce or that is linked to a fluorophore.

[0149] The basis for such htSMT screening strategies is that the htSMT workflows described herein are capable of extracting accurate molecular trajectories at scale. Exemplary OLS htSMT workflows include each of the following strategies, as well as combinations of the following strategies, wherein two or more strategic requirements are combined. For example, but not by way of limitation, the workflows of the present disclosure include illuminating a field of view of a sample plane disposed within a sample with a light beam to cause a subset of fluorescent target proteins in live cells to fluoresce, wherein the subset of fluorescent target proteins is present in about 30 to about 80 live cells illuminated in the field of view of the sample plane, and illuminating a field of view of a sample plane disposed within a sample with a light beam to cause a subset of fluorescent target proteins in live cells to fluoresce, wherein the subset of fluorescent target proteins comprises in the range of about 1,000 to about 1,000,000 proteins. Similarly, illuminating a sample plane to illuminate about 30 to about 80 live cells per FOV and / or to cause about 1,000 to about 1,000,000 proteins to fluoresce can be combined with any other strategic requirement disclosed herein, e.g., determining that the average change in motion of the fluorescent target proteins in the presence of the compound is in the range of about 1% to about 5% or to about 10% relative to the motion of the fluorescent target proteins in the absence of the compound, detecting fluorescence of the plurality of fluorescent target proteins in the field of view of the sample plane at a rate of about 10,000 to about 18,000 per system per day, and achieving a z-factor of >0.5 based on a single field of view.

[0150] In certain implementations of the OLS htSMT screening workflows described herein, the workflows can include identifying a biological interaction between a compound and a fluorescent target protein in a live cell, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise the fluorescent target protein; (b) tracking motion of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause a subset of fluorescent target proteins in live cells to fluoresce, wherein the subset of fluorescent target proteins is present in about 30 to about 80 live cells illuminated in the field of view of the sample plane, depending on the particular cell type used, e.g., for U2OS cells, the range is about 30 to about 40 cells per FOV, while for HCT116 cells, the range is about 50 to about 80 cells, taking into account their area differences; (ii) detecting fluorescence of the plurality of fluorescent target proteins within the field of view of the sample plane by a detector device; and (c) determining a change in motion of the fluorescent target proteins in the presence of the compound; wherein the change in motion of the fluorescent target proteins in the presence of the compound identifies a biological interaction between the compound and the fluorescent target protein relative to the motion of the fluorescent target proteins in the absence of the compound.

[0151] In certain implementations of the OLS htSMT screening workflow described herein, the workflow can comprise identifying a biological interaction between a compound and a fluorescent target protein in a live cell, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a fluorescent target protein; (b) tracking motion of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause a subset of the fluorescent target proteins in the live cells to fluoresce, wherein the subset of fluorescent target proteins comprises about 1000 to about 1,000,000 proteins, wherein the number of proteins within the subset depends on the expression level of the protein of interest and the concentration of dye deemed sufficient to label a subset of proteins to enable robust SMT, both of which can be calculated and / or configured by one of skill in the art in light of the disclosure herein; (ii) detecting fluorescence of the plurality of fluorescent target proteins within the field of view of the sample plane by a detector device; and (c) determining a change in motion of the fluorescent target proteins in the presence of the compound; wherein a change in motion of the fluorescent target proteins in the presence of the compound relative to the motion of the fluorescent target proteins in the absence of the compound identifies a biological interaction between the compound and the fluorescent target protein.

[0152] In certain implementations of the OLS htSMT screening workflow described herein, the workflow can comprise identifying a biological interaction between a compound and a fluorescent target protein in a live cell, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a fluorescent target protein; (b) tracking motion of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause a subset of the fluorescent target proteins in the live cells to fluoresce; (ii) detecting fluorescence of the plurality of fluorescent target proteins within the field of view of the sample plane by a detector device; and (c) determining a change in motion of the fluorescent target proteins in the presence of the compound, wherein the average change in motion of the fluorescent target proteins in the presence of the compound relative to the absence of the compound is about 1% to about 5% or to about 10%; wherein a change in motion of the fluorescent target proteins in the presence of the compound relative to the motion of the fluorescent target proteins in the absence of the compound identifies a biological interaction between the compound and the fluorescent target protein.

[0153] In certain implementations of the OLS htSMT screening workflow described herein, the workflow can comprise identifying a biological interaction between a compound and a fluorescent target protein in a living cell, comprising: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking motion of a plurality of individual fluorescent target proteins in a plurality of living cells in the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause a subset of the fluorescent target proteins in the living cells to emit fluorescence; (ii) detecting fluorescence of the plurality of fluorescent target proteins within the field of view of the sample plane by a detector device; (iii) wherein the tracking comprises detecting fluorescence of the plurality of fluorescent target proteins in the field of view of the sample plane at a rate of about 10,000 to about 18,000 per system per day; and (c) determining a change in motion of the fluorescent target proteins in the presence of the compound; wherein a change in motion of the fluorescent target proteins in the presence of the compound relative to motion of the fluorescent target proteins in the absence of the compound identifies a biological interaction between the compound and the fluorescent target protein.

[0154] In certain implementations of the OLS htSMT screening workflow described herein, the workflow can comprise identifying a biological interaction between a compound and a fluorescent target protein in a living cell, comprising: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking motion of a plurality of individual fluorescent target proteins in a plurality of living cells in the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause a subset of the fluorescent target proteins in the living cells to emit fluorescence; (ii) detecting fluorescence of the plurality of fluorescent target proteins within the field of view of the sample plane by a detector device; and (iii) achieving a z-factor of >0.5 based on a single field of view; and (c) determining a change in motion of the fluorescent target proteins in the presence of the compound; wherein a change in motion of the fluorescent target proteins in the presence of the compound relative to motion of the fluorescent target proteins in the absence of the compound identifies a biological interaction between the compound and the fluorescent target protein.

[0155] In certain implementations of the OLS htSMT screening workflow described herein, the workflow can include determining a dose response of a compound that induces a change in the movement of a fluorescent target protein in living cells, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of living cells, (ii) wherein the living cells comprise a fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of concentrations of the compound; (b) tracking the movement of individual fluorescent target proteins in a plurality of living cells of the samples, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the living cells to fluoresce, wherein the subset of the fluorescent target proteins are present in about 30 to about 80 living cells illuminated in the field of view of the sample plane, depending on the particular cell type used, for example, for U20S cells, the range is about 30 to about 40 cells per FOV, and for HCT116 cells, the range is about 50 to about 80 cells, taking into account their area differences; (ii) detecting the fluorescence of one or more of the fluorescent target proteins in the sample plane by a detector device; and (c) determining a change in the movement of the fluorescent target proteins in the presence of the compound; (d) repeating steps (b)-(c) for each of the plurality of samples over the range of concentrations of the compound; wherein a change in the movement of the fluorescent target proteins over the range of concentrations in the presence of the compound indicates a dose response of the compound.

[0156] In certain implementations of the OLS htSMT screening workflow described herein, the workflow can include determining a dose response of a compound that induces a change in the movement of a fluorescent target protein in live cells, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of live cells, (ii) wherein the live cells comprise a fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of concentrations of the compound; (b) tracking the movement of individual fluorescent target proteins in a plurality of live cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence, wherein the subset of the fluorescent target proteins comprises about 1000 to about 1,000,000 proteins, wherein the number of proteins within the subset depends on the expression level of the protein of interest and the concentration of dye deemed sufficient to label the subset of proteins to enable robust SMT, both of which can be calculated and / or configured by one of skill in the art in view of the disclosure herein; (ii) detecting the fluorescence of the one or more fluorescent target proteins in the sample plane by a detector device; and (c) determining a change in the movement of the fluorescent target proteins in the presence of the compound; (d) repeating steps (b)-(c) for each of the plurality of samples over the range of concentrations of the compound; wherein a change in the movement of the fluorescent target proteins over the range of concentrations in the presence of the compound is indicative of a dose response of the compound.

[0157] In certain implementations of the OLS htSMT screening workflow described herein, the workflow can include determining a dose response of a compound that induces a change in the movement of a fluorescent target protein in live cells, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of live cells, (ii) wherein the live cells comprise a fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of concentrations of the compound; (b) tracking the movement of individual fluorescent target proteins in a plurality of live cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence, (ii) detecting the fluorescence of the one or more fluorescent target proteins in the sample plane by a detector device; and (c) determining a change in the movement of the fluorescent target proteins in the presence of the compound, wherein the average change in the movement of the fluorescent target proteins in the presence of the compound is about 1% to about 5% or to about 10% relative to the absence of the compound; and (d) repeating steps (b)-(c) for each of the plurality of samples over the range of concentrations of the compound; wherein a change in the movement of the fluorescent target proteins over the range of concentrations in the presence of the compound is indicative of a dose response of the compound.

[0158] In certain implementations of the OLS htSMT screening workflow described herein, the workflow can include determining a dose response of a compound that induces a change in motion of a fluorescent target protein in live cells, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of live cells, (ii) wherein the live cells comprise a fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of concentrations of the compound; (b) tracking motion of individual fluorescent target proteins in a plurality of live cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the cells to emit fluorescence, (ii) detecting fluorescence of the one or more fluorescent target proteins in the sample plane by a detector device; (iii) wherein the tracking comprises detecting fluorescence of a plurality of fluorescent target proteins in the field of view of the sample plane at a rate of about 10,000 to about 18,000 per system per day; and (c) determining a change in motion of the fluorescent target proteins in the presence of the compound; (d) repeating steps (b)-(c) for each of the plurality of samples over the range of concentrations of the compound; wherein a change in motion of the fluorescent target proteins over the range of concentrations in the presence of the compound is indicative of a dose response of the compound.

[0159] In certain implementations of the OLS htSMT screening workflow described herein, the workflow can include determining a dose response of a compound that induces a change in motion of a fluorescent target protein in live cells, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of live cells, (ii) wherein the live cells comprise a fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of concentrations of the compound; (b) tracking motion of individual fluorescent target proteins in a plurality of live cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the cells to emit fluorescence, (ii) detecting fluorescence of the one or more fluorescent target proteins in the sample plane by a detector device; and (iii) achieving a z-factor of >0.5 based on a single field of view; and (c) determining a change in motion of the fluorescent target proteins in the presence of the compound; (d) repeating steps (b)-(c) for each of the plurality of samples over the range of concentrations of the compound; wherein a change in motion of the fluorescent target proteins over the range of concentrations in the presence of the compound is indicative of a dose response of the compound.

[0160] In certain implementations of the OLS htSMT screening workflow described herein, the workflow can include using a microscope system configured to identify a biological interaction between a compound and a fluorescent target protein in a live cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of live cells, and wherein the live cells comprise the fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample are disposed in a field of view of the sample plane, and wherein the subset of the fluorescent target proteins are present in about 30 to about 80 live cells illuminated in the field of view of the sample plane, depending on the particular cell type used, for example, for U20S cells, the range is about 30 to about 40 cells per FOV, and for HCT116 cells, the range is about 50 to about 80 cells, taking into account their area differences; (d) a detector device for monitoring the light-based response of the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from the light source outside of the sample plane where the fluorescent target proteins are disposed, thereby tracking the location of the fluorescent target proteins; and (ii) track the motion of individual fluorescent target proteins, (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the motion of the fluorescent target proteins in the presence of the compound relative to the absence of the compound.

[0161] In certain implementations of the OLS htSMT screening workflow described herein, the workflow can include using a microscope system configured to identify biological interactions between a compound and a fluorescent target protein in a live cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise the fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based reaction from a plurality of the fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample is disposed in a field of view of the sample plane; and wherein the subset of fluorescent target proteins comprises about 1000 to about 1,000,000 proteins, wherein the number of proteins in the subset depends on the expression level of the protein of interest and the dye concentration deemed sufficient to label the subset of proteins to enable robust SMT, both of which can be calculated and / or configured by one of skill in the art in light of the disclosure herein; (d) a detector device for monitoring the light-based reaction of the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from the light source outside of the sample plane where the fluorescent target proteins are disposed, thereby tracking the location of the fluorescent target proteins; and (ii) track the motion of individual fluorescent target proteins, (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the motion of the fluorescent target proteins in the presence of the compound relative to the absence of the compound.

[0162] In certain implementations of the OLS htSMT screening workflow described herein, the workflow can include using a microscope system configured to identify biological interactions between a compound and a fluorescent target protein in a live cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise the fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based reaction from a plurality of the fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample is disposed in a field of view of the sample plane; (d) a detector device for monitoring the light-based reaction of the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from the light source outside of the sample plane where the fluorescent target proteins are disposed, thereby tracking the location of the fluorescent target proteins; and (ii) track the motion of individual fluorescent target proteins, wherein the average change in the motion of the fluorescent target proteins in the presence of the compound relative to the absence of the compound is about 1% to about 5% or to about 10%, (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the motion of the fluorescent target proteins in the presence of the compound relative to the absence of the compound.

[0163] In certain implementations of the OLS htSMT screening workflow described herein, the workflow can include using a microscope system configured to identify biological interactions between a compound and a fluorescent target protein in a living cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise the fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based reaction from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in a sample plane, wherein the plurality of fluorescent target proteins in the sample are disposed in the sample plane; (d) a detector device for monitoring the light-based reaction of the fluorescent target protein in the presence of the compound, wherein the detector device is configured to: (i) block light received from the light source outside of the sample plane where the fluorescent target proteins are disposed, thereby tracking the location of the fluorescent target proteins; and (ii) track the movement of individual fluorescent target proteins, wherein the tracking comprises detecting fluorescence of the plurality of fluorescent target proteins in a field of view of the sample plane at a rate of about 10,000 to about 18,000 per system per day; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0164] In certain implementations of the OLS htSMT screening workflow described herein, the workflow can include using a microscope system configured to identify biological interactions between a compound and a fluorescent target protein in a living cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise the fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based reaction from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in a sample plane, wherein the plurality of fluorescent target proteins in the sample are disposed in the sample plane; (d) a detector device for monitoring the light-based reaction of the fluorescent target protein in the presence of the compound, wherein the detector device is configured to: (i) block light received from the light source outside of the sample plane where the fluorescent target proteins are disposed, thereby tracking the location of the fluorescent target proteins; (ii) track the movement of individual fluorescent target proteins; and (iii) achieve a z-factor of >0.5 based on a single field of view; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0165] OLS htSMT binding

[0166] In certain implementations of the OLS htSMT workflows described herein, the systems and methods are adapted to distinguish between recovery from exposure to a compound driven by an increase in residence time (k off decrease) that would result in an increase in f 结合 observed in the htSMT screening assay. Importantly, neither FRAP nor htSMT can distinguish between recovery driven by an increase in residence time (k off decrease) or an increase in chromatin binding rate (k on increase), both of which would result in an increase in f 结合 By changing the SMT acquisition conditions to reduce the illumination intensity and collect long frame exposures, only immobile proteins will form spots. Under these imaging conditions, the distribution of track lengths provides a measure of relative residence time.

[0167] Exemplary OLS htSMT binding workflows include each of the following strategies, as well as combinations of the following strategies, wherein two or more strategic requirements are combined. For example, but not by way of limitation, the workflows of the present disclosure include illuminating a field of view of a sample plane disposed within a sample with a light beam to cause a subset of fluorescent target proteins in living cells to emit fluorescence, wherein the subset of fluorescent target proteins is present in about 30 to about 80 living cells illuminated in the field of view of the sample plane, and illuminating a field of view of a sample plane disposed within a sample with a light beam to cause a subset of fluorescent target proteins in living cells to emit fluorescence, wherein the subset of fluorescent target proteins comprises in the range of about 1,000 to about 1,000,000 proteins. Similarly, illuminating a sample plane to illuminate about 30 to about 80 living cells per FOV and / or to cause about 1,000 to about 1,000,000 proteins to emit fluorescence can be combined with any other strategic requirement disclosed herein, for example, determining that the average change in motion of the fluorescent target proteins in the presence of a compound (e.g., when addition of the compound is replaced by a control such as, but not limited to, DMSO) is in the range of about 1% to about 5% or to about 10% relative to the absence of the compound, detecting fluorescence of a plurality of fluorescent target proteins in the field of view of the sample plane at a rate of about 10,000 to about 18,000 per system per day, and achieving a z-factor of >0.5 based on single fields of view.

[0168] In certain implementations of the OLS htSMT binding workflows described herein, the workflows will include determining whether a compound that induces a change in binding of a fluorescent target protein in living cells decreases the K off, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a fluorescent target protein; (b) tracking the motion of individual fluorescent target proteins in a plurality of cells in the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence, wherein the subset of the fluorescent target proteins is present in about 30 to about 80 live cells illuminated in the field of view of the sample plane, depending on the particular cell type used, e.g., for U2OS cells, the range is about 30 to about 40 cells per FOV, while for HCT116 cells, the range is about 50 to about 80 cells, taking into account their area differences; (ii) detecting the fluorescence of the one or more fluorescent target proteins in the sample plane by a detector device, wherein the method is adapted to selectively detect local fluorescence; and (c) determining a change in the motion of the fluorescent target proteins in the presence of the compound; wherein an increase in the signal detected from the fluorescent target proteins in the presence of the compound relative to the signal of the fluorescent target proteins in the absence of the compound (e.g., when the addition of the composition is replaced with a control such as, but not limited to, DMSO) indicates that the compound induces a decrease in the K off of the fluorescent target proteins in the live cells.

[0169] In certain implementations of the OLS htSMT binding workflow described herein, the workflow can comprise determining whether a compound that induces a change in the binding of a fluorescent target protein in live cells will decrease the K off of the fluorescent target proteins, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a fluorescent target protein; (b) tracking the motion of individual fluorescent target proteins in a plurality of cells in the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence, wherein the subset of the fluorescent target proteins comprises about 1,000 to about 1,000,000 proteins, wherein the number of proteins within the subset depends on the expression level of the protein of interest and the dye concentration deemed sufficient to label the subset of proteins to enable robust SMT, both of which can be calculated and / or configured by one of skill in the art in light of the disclosure herein; (ii) detecting the fluorescence of the one or more fluorescent target proteins in the sample by a detector device, wherein the method is adapted to selectively detect local fluorescence; and (c) determining a change in the motion of the fluorescent target proteins in the presence of the compound; wherein an increase in the signal detected from the fluorescent target proteins in the presence of the compound relative to the signal of the fluorescent target proteins in the absence of the compound indicates that the compound induces a decrease in the K off of the fluorescent target proteins.

[0170] In certain implementations of the OLS htSMT binding workflows described herein, the workflows can include determining whether a compound that induces a change in binding of a fluorescent target protein in a live cell reduces the Kd of the fluorescent target protein off , comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a fluorescent target protein; (b) tracking motion of individual fluorescent target proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view disposed at a sample plane within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence; (ii) detecting fluorescence of one or more of the fluorescent target proteins in the sample by a detector device, wherein the method is adapted to selectively detect local fluorescence; and (c) determining a change in motion of the fluorescent target proteins in the presence of the compound, wherein the average change in motion of the fluorescent target proteins in the presence of the compound is about 1% to about 5% or to about 10% relative to the absence of the compound; wherein an increase in signal detected from the fluorescent target proteins in the presence of the compound relative to the signal of the fluorescent target proteins in the absence of the compound indicates that the compound induces a reduction in the Kd of the fluorescent target protein off .

[0171] In certain implementations of the OLS htSMT binding workflows described herein, the workflows can include determining whether a compound that induces a change in binding of a fluorescent target protein in a live cell reduces the Kd of the fluorescent target protein off , comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a fluorescent target protein; (b) tracking motion of individual fluorescent target proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view disposed at a sample plane within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence; (ii) detecting fluorescence of one or more of the fluorescent target proteins in the sample by a detector device, wherein the method is adapted to selectively detect local fluorescence; and (iii) achieving a z-factor of >0.5 based on individual fields of view; and (c) determining a change in motion of the fluorescent target proteins in the presence of the compound; wherein an increase in signal detected from the fluorescent target proteins in the presence of the compound relative to the signal of the fluorescent target proteins in the absence of the compound indicates that the compound induces a reduction in the Kd of the fluorescent target protein off .

[0172] In certain implementations of the OLS htSMT binding workflows described herein, the workflows can include determining whether a compound that induces a change in binding of a fluorescent target protein in a live cell reduces the Kd of the fluorescent target protein offTo determine a dose of a compound that induces a change in binding of a fluorescent target protein in live cells, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a fluorescent target protein; (b) tracking motion of individual fluorescent target proteins in a plurality of cells in the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to fluoresce, wherein the subset of the fluorescent target proteins is present in about 30 to about 80 live cells illuminated in the field of view of the sample plane, depending on the particular cell type used, for example, for U20S cells, the range is about 30 to about 40 cells per FOV, while for HCT116 cells, the range is about 50 to about 80 cells, taking into account their area differences; (ii) detecting fluorescence of the one or more fluorescent target proteins in the sample plane by a detector device, wherein the method is adapted to selectively detect local fluorescence; and (c) determining a change in motion of the fluorescent target proteins in the presence of the compound; wherein an increase in signal detected from the fluorescent target proteins in the presence of the compound relative to the signal of the fluorescent target proteins in the absence of the compound indicates that the compound induces a decrease in K off of the fluorescent target protein, and in certain cases, a dose increase due to decreased drug metabolism as a result of increased residence time.

[0173] In certain implementations of the OLS htSMT binding workflow described herein, the workflow can comprise determining a dose of a compound that induces a change in binding of a fluorescent target protein in live cells by determining whether the compound will decrease K off of the fluorescent target protein, and in certain cases, a dose increase due to decreased drug metabolism as a result of increased residence time. To determine a dose of a compound that induces a change in binding of a fluorescent target protein in live cells, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a fluorescent target protein; (b) tracking motion of individual fluorescent target proteins in a plurality of cells in the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to fluoresce, wherein the subset of the fluorescent target proteins comprises about 1000 to about 1,000,000 proteins, wherein the number of proteins within the subset depends on the expression level of the protein of interest and the dye concentration deemed sufficient to label the subset of proteins to enable robust SMT, both of which can be calculated and / or configured by one skilled in the art in light of the disclosure herein; (ii) detecting fluorescence of the one or more fluorescent target proteins in the sample by a detector device, wherein the method is adapted to selectively detect local fluorescence; and (c) determining a change in motion of the fluorescent target proteins in the presence of the compound; wherein an increase in signal detected from the fluorescent target proteins in the presence of the compound relative to the signal of the fluorescent target proteins in the absence of the compound indicates that the compound induces a decrease in K off of the fluorescent target protein, and in certain cases, a dose increase due to decreased drug metabolism as a result of increased residence time.

[0174] In certain implementations of the OLS htSMT binding workflow described herein, the workflow can include determining a compound dose that induces a change in binding of a fluorescent target protein in live cells by determining whether the compound decreases the K off of the fluorescent target protein, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a fluorescent target protein; (b) tracking motion of individual fluorescent target proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view disposed at a sample plane within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence; (ii) detecting fluorescence of one or more of the fluorescent target proteins in the sample by a detector device, wherein the method is adapted to selectively detect local fluorescence; and (c) determining a change in motion of the fluorescent target proteins in the presence of the compound; wherein the average change in motion of the fluorescent target proteins in the presence of the compound relative to the absence of the compound is about 1% to about 5% or to about 10%; wherein an increase in signal detected from the fluorescent target proteins in the presence of the compound relative to the signal of the fluorescent target proteins in the absence of the compound indicates that the compound induces a decrease in the K off of the fluorescent target protein, and in certain cases, an increase in dose due to decreased drug metabolism resulting from increased residence time.

[0175] In certain implementations of the OLS htSMT binding workflow described herein, the workflow can include determining a compound dose that induces a change in binding of a fluorescent target protein in live cells by determining whether the compound decreases the K off of the fluorescent target protein, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a fluorescent target protein; (b) tracking motion of individual fluorescent target proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view disposed at a sample plane within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence; (ii) detecting fluorescence of one or more of the fluorescent target proteins in the sample by a detector device, wherein the method is adapted to selectively detect local fluorescence; and (iii) achieving a z-factor of >0.5 based on individual fields of view; and (c) determining a change in motion of the fluorescent target proteins in the presence of the compound; wherein: an increase in signal detected from the fluorescent target proteins in the presence of the compound relative to the signal of the fluorescent target proteins in the absence of the compound indicates that the compound induces a decrease in the K off of the fluorescent target protein, and in certain cases, an increase in dose due to decreased drug metabolism resulting from increased residence time.

[0176] In certain implementations of the OLS htSMT binding workflow described herein, the workflow can include using a microscope system configured to determine whether a compound that induces a change in binding of a fluorescent target protein in a cell would reduce the Kd of a fluorescently labeled target off , comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise a fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based reaction from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample are disposed in a field of view of the sample plane, and wherein the subset of the fluorescent target proteins are present in about 30 to about 80 live cells illuminated in the sample plane field of view, depending on the particular cell type used, for example, for U2OS cells, the range is about 30 to about 40 cells per FOV, while for HCT116 cells, the range is about 50 to about 80 cells, taking into account their area differences; (d) a detector device for monitoring the light-based reaction of the fluorescent target proteins in the presence of a compound, wherein the detector device is configured to: (i) block light received from the light source outside the sample plane where the fluorescent target proteins are disposed, thereby tracking the location of the fluorescent target proteins; and (ii) track the motion of individual fluorescent target proteins, wherein the tracking is adapted to selectively detect local fluorescence relative to dynamic fluorescence; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the motion of the fluorescent target proteins in the presence of the compound relative to the absence of the compound.

[0177] In certain implementations of the OLS htSMT screening workflow described herein, the workflow can include using a microscope system configured to determine whether a compound that induces a change in binding of a fluorescent target protein in a cell would reduce the Kd of a fluorescently labeled target off, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise fluorescent target proteins; (b) a light source for emitting a light beam capable of inducing a light-based reaction from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample is disposed in a field of view of the sample plane, and wherein the subset of fluorescent target proteins comprises about 1000 to about 1,000,000 proteins, wherein the number of proteins in the subset depends on the expression level of the protein of interest and the dye concentration deemed sufficient to label the subset of proteins to enable robust SMT, both of which can be calculated and / or configured by one of skill in the art in light of the disclosure herein; (d) a detector device for monitoring the light-based reaction of the fluorescent target proteins in the presence of a compound, wherein the detector device is configured to: (i) block light received from the light source outside of the sample plane where the fluorescent target proteins are disposed, thereby tracking the location of the fluorescent target proteins; and (ii) track the motion of individual fluorescent target proteins, wherein the tracking is adapted to selectively detect local fluorescence relative to dynamic fluorescence; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in motion of the fluorescent target proteins in the presence of the compound relative to the absence of the compound.

[0178] In certain implementations of the OLS htSMT binding workflow described herein, the workflow can comprise using a microscope system configured to determine whether a compound that induces a change in binding of a fluorescent target protein in a cell reduces the Kd of the fluorescently labeled target off , comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise fluorescent target proteins; (b) a light source for emitting a light beam capable of inducing a light-based reaction from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample is disposed in a field of view of the sample plane; (d) a detector device for monitoring the light-based reaction of the fluorescent target proteins in the presence of a compound, wherein the detector device is configured to: (i) block light received from the light source outside of the sample plane where the fluorescent target proteins are disposed, thereby tracking the location of the fluorescent target proteins; and (ii) track the motion of individual fluorescent target proteins, wherein the tracking is adapted to selectively detect local fluorescence relative to dynamic fluorescence, and wherein the average change in motion of the fluorescent target proteins in the presence of the compound relative to the absence of the compound is about 1% to about 5% or to about 10%; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in motion of the fluorescent target proteins in the presence of the compound relative to the absence of the compound.

[0179] In certain implementations of the OLS htSMT binding workflow described herein, the workflow can include using a microscope system configured to determine whether a compound that induces a change in fluorescent target protein binding in a cell will decrease the Kd of the fluorescently labeled target off comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise a fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based reaction from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample are disposed in a field of view of the sample plane; (d) a detector device for monitoring the light-based reaction of the fluorescent target proteins in the presence of a compound, wherein the detector device is configured to: (i) block light received from the light source outside of the sample plane where the fluorescent target proteins are disposed, thereby tracking the location of the fluorescent target proteins; and (ii) track the motion of individual fluorescent target proteins, wherein the tracking is adapted to selectively detect local fluorescence relative to dynamic fluorescence; (iii) achieve a z-factor of >0.5 based on a single field of view; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the motion of the fluorescent target proteins in the presence of the compound relative to the absence of the compound.

[0180] 4.3 OLS KineticSMT

[0181] Since SMT can identify the occurrence of a biological interaction between a compound and a target, SMT can be used to distinguish between direct and indirect effects on target activity, among other parameters. Given the live cell setting of SMT, data collection modes can be configured that allow for the measurement of protein motion (KineticSMT or kSMT) at set time intervals after compound addition to determine the occurrence of a biological interaction between a compound and a target.

[0182] An exemplary OLS Kinetic SMT workflow includes each of the following strategies and combinations of the following strategies, wherein two or more strategy requirements are combined. For example, but not by way of limitation, the workflow of the present disclosure includes illuminating a field of view of a sample plane disposed within a sample with a light beam to cause a subset of fluorescent target proteins in live cells to emit fluorescence, wherein the subset of fluorescent target proteins is present in about 30 to about 80 live cells illuminated in the field of view of the sample plane, and illuminating a field of view of a sample plane disposed within a sample with a light beam to cause a subset of fluorescent target proteins in live cells to emit fluorescence, wherein the subset of fluorescent target proteins comprises in the range of about 1,000 to about 1,000,000 proteins. Similarly, illuminating a sample plane to illuminate about 30 to about 80 live cells per FOV and / or to cause about 1,000 to about 1,000,000 proteins to emit fluorescence can be combined with any other strategy requirement disclosed herein, for example, determining that the average change in motion of the fluorescent target proteins is about 1% to about 5% or to about 10% in the presence of a compound relative to the absence of the compound (e.g., when the addition of the compound is replaced by a control such as, but not limited to, DMSO), detecting fluorescence of a plurality of fluorescent target proteins in the field of view of the sample plane at a rate of about 10,000 to about 18,000 per system per day, and achieving a z-factor of >0.5 based on a single field of view.

[0183] In certain implementations of the OLS Kinetic htSMT binding workflow described herein, the workflow can include determining the occurrence rate of a biological interaction between a compound and a target in live cells, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a fluorescent target protein; (b) tracking motion of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample at a plurality of time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause a subset of fluorescent target proteins in live cells to emit fluorescence, wherein the subset of fluorescent target proteins is present in about 30 to about 80 live cells illuminated in the field of view of the sample plane, depending on the particular cell type used, for example, for U2OS cells, the range is about 30 to about 40 cells per FOV, while for HCT116 cells, the range is about 50 to about 80 cells, taking into account their area differences; (ii) detecting fluorescence of the plurality of fluorescent target proteins within the field of view of the sample plane by a detector device; and (c) determining a change in motion of the fluorescent target proteins in the presence of the compound; wherein the rate at which the motion of the fluorescent target proteins changes in the presence of the compound is indicative of the occurrence rate of the biological interaction between the compound and the target.

[0184] In certain implementations of the OLS kinetic htSMT binding workflow described herein, the workflow can comprise determining the occurrence of a biological interaction between a compound and a target in living cells, comprising: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of living cells in the sample at a plurality of time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause a subset of the fluorescent target proteins in the living cells to fluoresce, wherein the subset of fluorescent target proteins comprises about 1000 to about 1,000,000 proteins, wherein the number of proteins within the subset depends on the expression level of the protein of interest and the concentration of dye deemed sufficient to label the subset of proteins to enable robust SMT, both of which can be calculated and / or configured by one of skill in the art in light of the disclosure herein; (ii) detecting the fluorescence of the plurality of fluorescent target proteins within the field of view of the sample plane by a detector device; and (c) determining a change in the movement of the fluorescent target proteins in the presence of the compound; wherein the rate at which the movement of the fluorescent target proteins changes in the presence of the compound is indicative of the occurrence of a biological interaction between the compound and the target.

[0185] In certain implementations of the OLS kinetic htSMT binding workflow described herein, the workflow can comprise determining the occurrence of a biological interaction between a compound and a target in living cells, comprising: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of living cells in the sample at a plurality of time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause a subset of the fluorescent target proteins in the living cells to fluoresce; (ii) detecting the fluorescence of the plurality of fluorescent target proteins within the field of view of the sample plane by a detector device; and (c) determining a change in the movement of the fluorescent target proteins in the presence of the compound, wherein the average change in the movement of the fluorescent target proteins in the presence of the compound is about 1% to about 5% or to about 10% relative to the absence of the compound; wherein the rate at which the movement of the fluorescent target proteins changes in the presence of the compound is indicative of the occurrence of a biological interaction between the compound and the target.

[0186] In certain implementations of the OLS kinetic htSMT binding workflow described herein, the workflow can include determining the occurrence rate of a biological interaction between a compound and a target in live cells, direct and indirect biological interactions between a compound and a fluorescent target protein, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a fluorescent target protein; (b) tracking movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample at a plurality of time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause a subset of the fluorescent target proteins in the live cells to emit fluorescence; (ii) detecting fluorescence of the plurality of fluorescent target proteins within the field of view of the sample plane by a detector device; (iii) wherein the tracking comprises detecting fluorescence of the plurality of fluorescent target proteins in the field of view of the sample plane at a rate of about 10,000 to about 18,000 per system per day; and (c) determining a change in movement of the fluorescent target proteins in the presence of the compound; wherein a rate at which the movement of the fluorescent target proteins changes in the presence of the compound is indicative of the occurrence rate of the biological interaction between the compound and the target.

[0187] In certain implementations of the OLS kinetic htSMT binding workflow described herein, the workflow can include determining the occurrence rate of a biological interaction between a compound and a target in live cells, direct and indirect biological interactions between a compound and a fluorescent target protein, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a fluorescent target protein; (b) tracking movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample at a plurality of time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause a subset of the fluorescent target proteins in the live cells to emit fluorescence; (ii) detecting fluorescence of the plurality of fluorescent target proteins within the field of view of the sample plane by a detector device, wherein detection based on a single field of view is associated with a z-factor of > 0.5; and (c) determining a change in movement of the fluorescent target proteins in the presence of the compound; wherein a rate at which the movement of the fluorescent target proteins changes in the presence of the compound is indicative of the occurrence rate of the biological interaction between the compound and the target.

[0188] In certain implementations of the OLS kinetic htSMT binding workflow described herein, the workflow can include determining a dose of a compound that induces a change in motion of a fluorescent target protein in live cells, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of live cells, (ii) wherein the live cells comprise a fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of concentrations of the compound; (b) tracking motion of individual fluorescent target proteins in a plurality of live cells of the samples at a plurality of time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence, wherein the subset of the fluorescent target proteins are present in about 30 to about 80 live cells illuminated in the field of view of the sample plane, depending on the particular cell type used, for example, for U20S cells, the range is about 30 to about 40 cells per FOV, and for HCT116 cells, the range is about 50 to about 80 cells, taking into account their area differences; (ii) detecting fluorescence of one or more of the fluorescent target proteins in the sample plane by a detector device; and (c) determining a rate at which the motion of the fluorescent target proteins changes in the presence of the compound; (d) repeating steps (b)-(c) for each of the plurality of samples over the range of concentrations of the compound; wherein the rate at which the motion of the fluorescent target proteins changes in the presence of the compound is indicative of a rate of occurrence of a biological interaction between the compound and the target.

[0189] In certain implementations of the OLS kinetic htSMT binding workflow described herein, the workflow can comprise determining a dose of a compound that induces a change in motion of a fluorescent target protein in live cells, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of live cells, (ii) wherein the live cells comprise a fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of concentrations of the compound; (b) tracking motion of individual fluorescent target proteins in a plurality of live cells of the samples at a plurality of time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence, wherein the subset of the fluorescent target proteins comprises about 1000 to about 1,000,000 proteins, wherein the number of proteins within the subset depends on the expression level of the protein of interest and the concentration of dye deemed sufficient to label the subset of proteins to enable robust SMT, both of which can be calculated and / or configured by one of skill in the art in light of the disclosure herein; (ii) detecting fluorescence of the one or more fluorescent target proteins in the sample plane by a detector device; and (c) determining a rate at which the motion of the fluorescent target proteins changes in the presence of the compound; (d) repeating steps (b)-(c) for each of the plurality of samples over the range of concentrations of the compound; wherein the rate at which the motion of the fluorescent target proteins changes in the presence of the compound is indicative of an occurrence rate of a biological interaction between the compound and the target.

[0190] In certain implementations of the OLS kinetic htSMT binding workflow described herein, the workflow can comprise determining a dose of a compound that induces a change in motion of a fluorescent target protein in live cells, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of live cells, (ii) wherein the live cells comprise a fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of concentrations of the compound; (b) tracking motion of individual fluorescent target proteins in a plurality of live cells of the samples at a plurality of time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence, (ii) detecting fluorescence of the one or more fluorescent target proteins in the sample plane by a detector device; and (c) determining a rate at which the motion of the fluorescent target proteins changes in the presence of the compound, wherein the average change in the motion of the fluorescent target proteins in the presence of the compound is about 1% to about 5% or to about 10% relative to the absence of the compound; and (d) repeating steps (b)-(c) for each of the plurality of samples over the range of concentrations of the compound; wherein the rate at which the motion of the fluorescent target proteins changes in the presence of the compound is indicative of an occurrence rate of a biological interaction between the compound and the target.

[0191] In certain implementations of the OLS kinetic htSMT binding workflow described herein, the workflow can comprise determining a dose response of a compound that induces a change in motion of a fluorescent target protein in live cells, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of live cells, (ii) wherein the live cells comprise a fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of concentrations of the compound; (b) tracking motion of individual fluorescent target proteins in a plurality of live cells of the samples at a plurality of time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence, (ii) detecting fluorescence of the one or more fluorescent target proteins in the sample plane by a detector device; (iii) wherein the tracking comprises detecting fluorescence of a plurality of fluorescent target proteins in the field of view of the sample plane at a rate of about 10,000 to about 18,000 per system per day; and (c) determining a rate at which the motion of the fluorescent target proteins changes in the presence of the compound; (d) repeating steps (b)-(c) for each of the plurality of samples over the range of concentrations of the compound; wherein the rate at which the motion of the fluorescent target proteins changes in the presence of the compound is indicative of an occurrence rate of a biological interaction between the compound and the target.

[0192] In certain implementations of the OLS kinetic htSMT binding workflow described herein, the workflow can comprise determining a dose response of a compound that induces a change in motion of a fluorescent target protein in live cells, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of live cells, (ii) wherein the live cells comprise a fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of concentrations of the compound; (b) tracking motion of individual fluorescent target proteins in a plurality of live cells of the samples at a plurality of time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence, (ii) detecting fluorescence of the one or more fluorescent target proteins in the sample plane by a detector device; (iii) wherein the tracking comprises detecting fluorescence of a plurality of fluorescent target proteins in the field of view of the sample plane at a rate of about 10,000 to about 18,000 per system per day; and (c) determining a rate at which the motion of the fluorescent target proteins changes in the presence of the compound; (d) repeating steps (b)-(c) for each of the plurality of samples over the range of concentrations of the compound; wherein the rate at which the motion of the fluorescent target proteins changes in the presence of the compound is indicative of an occurrence rate of a biological interaction between the compound and the target.

[0193] In certain implementations of the OLS kinetic htSMT binding workflow described herein, the workflow can include using a microscope system configured to determine the occurrence of a biological interaction between a compound and a target in living cells, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of living cells, and wherein the living cells comprise fluorescent target proteins; (b) a light source for emitting a light beam capable of inducing a light-based reaction from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample are disposed in a field of view of the sample plane, and wherein the subset of the fluorescent target proteins are present in about 30 to about 80 living cells illuminated in the sample plane field of view, depending on the particular cell type used, for example, for U20S cells, the range is about 30 to about 40 cells per FOV, and for HCT116 cells, the range is about 50 to about 80 cells, taking into account their area differences; (d) a detector device for monitoring the light-based reaction of the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from the light source outside the sample plane where the fluorescent target proteins are disposed, thereby tracking the location of the fluorescent target proteins at multiple time points; and (ii) track the motion of individual fluorescent target proteins, (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the motion of the fluorescent target proteins in the presence of the compound relative to the absence of the compound.

[0194] In certain implementations of the OLS kinetic htSMT binding workflow described herein, the workflow can include using a microscope system configured to determine the occurrence of a biological interaction between a compound and a target in a living cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise fluorescent target proteins; (b) a light source for emitting a light beam capable of inducing a light-based reaction from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample are disposed in a field of view of the sample plane; and wherein the subset of fluorescent target proteins comprises about 1000 to about 1,000,000 proteins, wherein the number of proteins in the subset depends on the expression level of the protein of interest and the dye concentration deemed sufficient to label the subset of proteins to enable robust SMT, both of which can be calculated and / or configured by one of skill in the art in light of the disclosure herein; (d) a detector device for monitoring the light-based reaction of the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from the light source outside of the sample plane where the fluorescent target proteins are disposed, thereby tracking the location of the fluorescent target proteins at a plurality of time points; and (ii) track the motion of individual fluorescent target proteins, (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the motion of the fluorescent target proteins in the presence of the compound relative to the absence of the compound.

[0195] In certain implementations of the OLS kinetic htSMT binding workflow described herein, the workflow can include using a microscope system configured to determine the occurrence of a biological interaction between a compound and a target in a living cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise fluorescent target proteins; (b) a light source for emitting a light beam capable of inducing a light-based reaction from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample are disposed in a field of view of the sample plane; (d) a detector device for monitoring the light-based reaction of the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from the light source outside of the sample plane where the fluorescent target proteins are disposed, thereby tracking the location of the fluorescent target proteins at a plurality of time points; and (ii) track the motion of individual fluorescent target proteins, wherein the average change in the motion of the fluorescent target proteins in the presence of the compound relative to the absence of the compound is about 1% to about 5% or to about 10%, (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the motion of the fluorescent target proteins in the presence of the compound relative to the absence of the compound.

[0196] In certain implementations of the OLS kinetic htSMT binding workflow described herein, the workflow can include using a microscope system configured to determine the occurrence of a biological interaction between a compound and a target in a live cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise fluorescent target proteins; (b) a light source for emitting a light beam capable of inducing a light-based reaction from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in a sample plane, wherein the plurality of fluorescent target proteins in the sample are disposed in the sample plane; (d) a detector device for monitoring the light-based reaction of the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from the light source outside of the sample plane where the fluorescent target proteins are disposed, thereby tracking the location of the fluorescent target proteins at a plurality of time points; and (ii) track the movement of individual fluorescent target proteins, wherein the tracking comprises detecting fluorescence of the plurality of fluorescent target proteins in a field of view of the sample plane at a rate of about 10,000 to about 18,000 per system per day; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the movement of the fluorescent target proteins in the presence of the compound relative to the absence of the compound.

[0197] In certain implementations of the OLS kinetic htSMT binding workflow described herein, the workflow can include using a microscope system configured to determine the occurrence of a biological interaction between a compound and a target in a live cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of live cells, and wherein the live cells comprise fluorescent target proteins; (b) a light source for emitting a light beam capable of inducing a light-based reaction from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample are disposed in a field of view of the sample plane; (d) a detector device for monitoring the light-based reaction of the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from the light source outside of the sample plane where the fluorescent target proteins are disposed, thereby tracking the location of the fluorescent target proteins at a plurality of time points; and (ii) track the movement of individual fluorescent target proteins; and (iii) achieve a z-factor of >0.5 based on a single field of view; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the movement of the fluorescent target proteins in the presence of the compound relative to the absence of the compound.

[0198] 5. Exemplary embodiments

[0199] A.The present disclosure provides a method of determining whether a compound that induces a change in binding of a target fluorescent protein in a living cell reduces the K off of the target fluorescent protein, comprising: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a target fluorescent protein; (b) tracking motion of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the living cells to emit fluorescence; (ii) detecting fluorescence of one or more target fluorescent proteins in a detection field of view of the sample plane by a detector device, wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension and a size of about 100 pm to about 210 pm in a second dimension, and wherein the method is adapted to selectively detect local fluorescence; and (c) determining a change in motion of the target fluorescent protein in the presence of the compound, wherein an increase in signal detected from the target fluorescent protein in the presence of the compound relative to signal of the target fluorescent protein in the absence of the compound indicates that the compound induces a reduction in the K off of the target fluorescent protein.

[0200] B.The present disclosure provides a method of determining whether a compound that induces a change in binding of a target fluorescent protein in a living cell reduces the K off of the target fluorescent protein, comprising: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a target fluorescent protein; (b) tracking motion of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the living cells to emit fluorescence, wherein the subset of target fluorescent proteins produces at most about 1,000,000 molecular tracks in a single detection field of view; (ii) detecting fluorescence of one or more target fluorescent proteins in a detection field of view of the sample plane by a detector device, wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension and a size of about 100 pm to about 210 pm in a second dimension, and wherein the method is adapted to selectively detect local fluorescence; and (c) determining a change in motion of the target fluorescent protein in the presence of the compound, wherein an increase in signal detected from the target fluorescent protein in the presence of the compound relative to signal of the target fluorescent protein in the absence of the compound indicates that the compound induces a reduction in the K off of the target fluorescent protein.

[0201] C.The present disclosure provides a method of determining whether a compound that induces a change in binding of a target fluorescent protein in a living cell reduces the K offA method of determining whether a compound that induces a change in binding of a target fluorescent protein in live cells reduces the K off of the target fluorescent protein, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a target fluorescent protein; (b) tracking the movement of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence; (ii) detecting the fluorescence of one or more target fluorescent proteins in a detection field of view of the sample plane by a detector device, wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension and a size of about 100 pm to about 210 pm in a second dimension, and wherein the method is adapted to selectively detect local fluorescence correlation; and (c) determining a change in movement of the target fluorescent proteins in the presence of the compound, wherein an increase in signal detected from the target fluorescent proteins in the presence of the compound relative to the signal of the target fluorescent proteins in the absence of the compound indicates that the compound induces a reduction in the K

[0202] D. The present disclosure provides a method of determining whether a compound that induces a change in binding of a target fluorescent protein in live cells reduces the K off of the target fluorescent protein, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a target fluorescent protein; (b) tracking the movement of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence; (ii) detecting the fluorescence of one or more target fluorescent proteins in a detection field of view of the sample plane by a detector device, wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension and a size of about 100 pm to about 210 pm in a second dimension, wherein greater than or equal to 95% of the detection field of view achieves sufficient laser illumination to track protein movement, and wherein the method is adapted to selectively detect local fluorescence; and (c) determining a change in movement of the target fluorescent proteins in the presence of the compound, wherein an increase in signal detected from the target fluorescent proteins in the presence of the compound relative to the signal of the target fluorescent proteins in the absence of the compound indicates that the compound induces a reduction in the K off of the target fluorescent protein.

[0203] E1. The method of any one of A-E, wherein the change in detected movement is an increase in immotile trajectories, indicating an increase in occupancy or duration of the bound state (f 结合 ) of the target fluorescent protein.

[0204] E2. The method of any one of A-E, wherein the detected change in motion is a change in: (a) median of the jump length distribution; (b) 3rd quartile of the jump length distribution; (c) median turn radius; (d) mean posterior diffusivity; (e) geometric mean posterior diffusivity; (f) mean squared displacement; (g) median bond angle; (h) diffusivity maximum likelihood estimator; and / or (i) state occupancy by inference.

[0205] E3. The method of any one of A-E, wherein the target fluorescent protein interacts in a larger molecular assembly.

[0206] E4. The method of E3, wherein the target fluorescent protein is a ligand.

[0207] E5. The method of E3, wherein the target fluorescent protein is a receptor.

[0208] E6. The method of any one of A-E, wherein the biological interaction is a direct interaction.

[0209] E7. The method of E6, wherein the direct interaction comprises binding of the compound to the target fluorescent protein.

[0210] E8. The method of any one of A-E, wherein the biological interaction is an indirect interaction.

[0211] E9. The method of E8, wherein the indirect interaction comprises the compound agonizing or antagonizing a larger molecular assembly comprising the target fluorescent protein.

[0212] F. The present disclosure provides a method of determining whether a compound reduces the K offA method of determining a dose of a compound that induces a change in binding of a target fluorescent protein in a live cell, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a target fluorescent protein; (b) tracking movement of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence; (ii) detecting fluorescence of one or more target fluorescent proteins in a detection field of view of the sample plane by a detector device, wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension and a size of about 100 pm to about 210 pm in a second dimension, and wherein the method is adapted to selectively detect localized fluorescence; and (c) determining the dose by determining a change in movement of the target fluorescent proteins in the presence of the compound; and wherein an increase in signal detected from the target fluorescent proteins in the presence of the compound relative to signal of the target fluorescent proteins in the absence of the compound indicates that the compound induces a K off decrease in the target fluorescent protein.

[0213] G. The present disclosure provides a method of determining a dose of a compound that induces a change in binding of a target fluorescent protein in a live cell by determining whether the compound decreases a K off of the target fluorescent protein, comprising: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a target fluorescent protein; (b) tracking movement of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of the fluorescent target proteins in the live cells to emit fluorescence, wherein the subset of target fluorescent proteins produces at most about 1,000,000 molecular tracks in a single detection field of view; (ii) detecting fluorescence of one or more target fluorescent proteins in a detection field of view of the sample plane by a detector device, wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension and a size of about 100 pm to about 210 pm in a second dimension, and wherein the method is adapted to selectively detect localized fluorescence; and (c) determining the dose by determining a change in movement of the target fluorescent proteins in the presence of the compound; and wherein an increase in signal detected from the target fluorescent proteins in the presence of the compound relative to signal of the target fluorescent proteins in the absence of the compound indicates that the compound induces a K off decrease in the target fluorescent protein.

[0214] H. The present disclosure provides a method of determining a dose of a compound that induces a change in binding of a target fluorescent protein in a live cell by determining whether the compound decreases a K offA method for determining a dose of a compound that induces a change in binding of a target fluorescent protein in a live cell by: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a target fluorescent protein; (b) tracking motion of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of fluorescent target proteins in the live cells to emit fluorescence; (ii) detecting fluorescence of one or more target fluorescent proteins in a detection field of view of the sample plane by a detector device, wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension and a size of about 100 pm to about 210 pm in a second dimension, and wherein the method is adapted to selectively detect local fluorescence; and (c) determining the dose by determining a change in motion of the target fluorescent proteins in the presence of the compound; and wherein an increase in signal detected from the target fluorescent proteins in the presence of the compound relative to signal of the target fluorescent proteins in the absence of the compound indicates that the compound induces a decrease in K off of the target fluorescent proteins.

[0215] I. The present disclosure provides a method for determining a dose of a compound that induces a change in binding of a target fluorescent protein in a live cell by determining that the compound decreases K off of the target fluorescent proteins. A method for determining a dose of a compound that induces a change in binding of a target fluorescent protein in a live cell by: (a) contacting a sample comprising a population of live cells with a compound, wherein the live cells comprise a target fluorescent protein; (b) tracking motion of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to cause at least a subset of fluorescent target proteins in the live cells to emit fluorescence; (ii) detecting fluorescence of one or more target fluorescent proteins in a detection field of view of the sample plane by a detector device, wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension and a size of about 100 pm to about 210 pm in a second dimension, wherein greater than or equal to 95% of the detection field of view achieves sufficient laser illumination to track protein motion, and wherein the method is adapted to selectively detect local fluorescence; and (c) determining the dose by determining a change in motion of the target fluorescent proteins in the presence of the compound; and wherein an increase in signal detected from the target fluorescent proteins in the presence of the compound relative to signal of the target fluorescent proteins in the absence of the compound indicates that the compound induces a decrease in K off of the target fluorescent proteins.

[0216] I1. The method of any of F-I, wherein the change in motion detected is an increase in immotile trajectories, indicating an increase in binding of the target fluorescent protein. 结合 .

[0217] I2. The method of any one of F-I, wherein the detected change in motion is a change in: (a) median of a jump length distribution; (b) 3rd quartile of a jump length distribution; (c) median turn radius; (d) mean posterior diffusion coefficient; (e) geometric mean posterior diffusion coefficient; (f) mean squared displacement; (g) median bond angle; (h) diffusion coefficient maximum likelihood estimator; and / or (i) state occupancy by inference.

[0218] I3. The method of any one of F-I, wherein the target fluorescent protein interacts in a larger molecular assembly.

[0219] I4. The method of I3, wherein the target fluorescent protein is a ligand.

[0220] I5. The method of I3, wherein the target fluorescent protein is a receptor.

[0221] I6. The method of any one of F-I, wherein the biological interaction is a direct interaction.

[0222] I7. The method of I6, wherein the direct interaction comprises binding of the compound to the target fluorescent protein.

[0223] I8. The method of any one of F-I, wherein the biological interaction is an indirect interaction.

[0224] I9. The method of I8, wherein the indirect interaction comprises the compound agonizing or antagonizing a larger molecular assembly comprising the target fluorescent protein.

[0225] J. The present disclosure provides a microscope system configured to determine whether a compound that induces a change in binding of a target fluorescent protein within a cell reduces the K off of the target fluorescent protein, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise the target fluorescent protein; (b) a light source for emitting a light beam capable of inducing a photochemical reaction of a plurality of the target fluorescent proteins in the sample; (c) an objective for focusing the light beam onto the sample in a sample plane, wherein a subset of the target fluorescent proteins in the sample are disposed in a detection field of view of the sample plane, and wherein the detection field of view has a dimension of about 150 pm to about 250 pm in a first dimension and a dimension of about 100 pm to about 210 pm in a second dimension; (d) a detector device for monitoring the photochemical reaction of the target fluorescent proteins in the presence of the compound; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in motion of the target fluorescent proteins in the presence of the compound.

[0226] K. The present disclosure provides a microscope system configured to determine whether a compound that induces a change in target fluorescent protein binding within a cell reduces the K off , comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise the target fluorescent protein; (b) a light source for emitting a light beam capable of inducing a light-based reaction of a plurality of the target fluorescent proteins in the sample; (c) an objective for focusing the light beam onto the sample in a sample plane, wherein a subset of the target fluorescent proteins in the sample are disposed in a detection field of view of the sample plane, and wherein the subset of target fluorescent proteins produces at most about 1,000,000 molecular trajectories in a single detection field of view, and wherein the detection field of view has a dimension of about 150 pm to about 250 pm in a first dimension, and a dimension of about 100 pm to about 210 pm in a second dimension; (d) a detector device for monitoring the light-based reaction of the target fluorescent proteins in the presence of the compound; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in motion of the target fluorescent proteins in the presence of the compound.

[0227] L. The present disclosure provides a microscope system configured to determine whether a compound that induces a change in target fluorescent protein binding within a cell reduces the K off , comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise the target fluorescent protein; (b) a light source for emitting a light beam capable of inducing a light-based reaction of a plurality of the target fluorescent proteins in the sample; (c) an objective for focusing the light beam onto the sample in a sample plane, wherein a subset of the target fluorescent proteins in the sample are disposed in a detection field of view of the sample plane, wherein the detection field of view has a dimension of about 150 pm to about 250 pm in a first dimension, and a dimension of about 100 pm to about 210 pm in a second dimension; (d) a detector device for monitoring the light-based reaction of the target fluorescent proteins in the presence of the compound; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in motion of the target fluorescent proteins in the presence of the compound relative to the change in motion of the target fluorescent proteins in the absence of the compound.

[0228] M. The present disclosure provides a microscope system configured to determine whether a compound that induces a change in target fluorescent protein binding within a cell reduces the K off, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise the target fluorescent protein; (b) a light source for emitting a light beam capable of inducing a photo- based reaction of a plurality of the target fluorescent proteins in the sample; (c) an objective for focusing the light beam onto the sample in a sample plane, wherein a subset of the target fluorescent proteins in the sample is disposed in a detection field of view of the sample plane, wherein the detection field of view has a size of about 150 pm to about 250 pm in a first dimension, a size of about 100 pm to about 210 pm in a second dimension, and wherein greater than or equal to 95% of the detection field of view achieves sufficient laser illumination to track protein motion; (d) a detector device for monitoring the photo-based reaction of the target fluorescent proteins in the presence of the compound; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in motion of the target fluorescent proteins in the presence of the compound.

[0229] M1. The system of any one of claims 27-30, wherein the detected change in motion is an increase in immotile trajectories indicative of binding of the compound to the target fluorescent protein. 结合 ) target fluorescent protein.

[0230] M2. The system of any one of claims 27-30, wherein the detected change in motion is a change in: (a) median of jump length distribution; (b) 3rd quartile of jump length distribution; (c) median turnaround radius; (d) mean posterior diffusion coefficient; (e) geometric mean posterior diffusion coefficient; (f) mean squared displacement; (g) median bond angle; (h) diffusion coefficient maximum likelihood estimator; and / or (i) state occupancy by inference.

[0231] M3. The system of any one of J-M, wherein the target fluorescent protein interacts in a larger molecular assembly.

[0232] M4. The system of M3, wherein the target fluorescent protein is a ligand.

[0233] M5. The system of M3, wherein the target fluorescent protein is a receptor.

[0234] M6. The system of any one of J-M, wherein the biological interaction is a direct interaction.

[0235] M7. The system of M6, wherein the direct interaction comprises binding of the compound to the target fluorescent protein.

[0236] M8. The system of any one of J-M, wherein the biological interaction is an indirect interaction.

[0237] M9. The system of M8, wherein the indirect interaction comprises the compound agonizing or antagonizing a larger molecule assembly comprising the target fluorescent protein.

[0238] 6. Examples

[0239] The disclosed subject matter will be better understood by reference to the following examples, which are given solely as examples of the disclosed subject matter and are not intended to be limiting.

[0240] Example 1: Light sheet scanning system

[0241] Introduction

[0242] Single-molecule localization microscopy (SMLM) techniques, such as single-molecule tracking (SMT), enable in situ measurements in live and fixed cells, from which data-rich indicators can be extracted. SMT has been successfully applied to address a variety of biological questions and model systems aimed at revealing the spatiotemporal regulation of molecular mechanisms that control protein function, downstream pathway effects, and cellular function under healthy or pathological conditions. While SMLM is powerful, it is often plagued by low throughput, uneven illumination, and technical biases introduced by the microscope and user. Due to technical limitations of scaling SMLM techniques, tradeoffs must be made between spatial resolution, temporal resolution, and throughput, thereby limiting these techniques to a small number of research groups.

[0243] This example describes the development of the OLS system disclosed herein to overcome the limitations of other SMLM techniques. Briefly, a thin optical light sheet is shaped and focused onto the back focal plane of a microscope objective and scanned using a galvanometer mirror. This optical configuration results in a scannable oblique light sheet that can cover the entire FOV of a water-immersion high-NA objective (A-12F). More detailed information about this exemplary OLS system is provided below. Figure 11A and Figure 12 A-12F). More detailed information about this exemplary OLS system is provided below.

[0244] B. Exemplary OLS system

[0245] SMT image acquisition for the OLS dataset was performed on a custom microscope based on a Nikon Ti2, motorized stage, stage-top environmental chamber (OKO Lab), four-band filter cube (Chroma), custom laser emitter with wavelengths of 405 nm, 561 nm, and 642 nm delivering >10 mW, >150 mW, and >150 mW of power, respectively, to the back focal plane of the objective lens. The custom laser emitter was composed of three externally triggerable free-space laser sources (CoBolt 06-MLD; Huebner Photonics; 2RU-VFL-P-2000-560-M; MBP Communications Inc.; VFL-P-2000-642-M; MBP Communications Inc.).

[0246] Oblique Line Scanning (OLS; Figure 12 The A and 12D) units were attached to the rear port of the microscope, providing optical excitation and scanning. The OLS unit received the collimated Gaussian-shaped light excitation through a polarization maintaining single mode fiber coupled to a laser beam coupler. The laser excitation was sent through a combination of Powell lens, custom designed cylindrical lens, and achromatic lens to shape the beam into a laser line. The beam was then positioned through a set of two position adjustable right angle prisms, followed by an aspheric achromatic lens to position the beam and focus the scanning axis onto the galvanometer scanning mirror, adjusting the beam position to offset the beam 3.8 mm from the center optical axis of the back focal plane of the objective lens to achieve the illumination light sheet in the sample at a 60-degree angle of inclination Figure 12 E).

[0247] Fluorescence emission was collected through a high-speed filter wheel (Sutter Instruments) and with a back-illuminated sCMOS camera (ORCA-Fusion BT, Hamamatsu). The sCMOS camera was operated in progressive mode with an exposure time of 407 ps and an internal line interval of 4.87 ps to achieve a virtual rolling slit of approximately 200% of the fluorescence line width Figure 12 F). Images were acquired using a 60X 1.27 NA water immersion objective lens (Nikon). The environmental chamber was set to 37 °C, 95% humidity, and 5% CO2.

[0248] System hardware control was implemented in a custom designed, user-configurable circuit board for software interfacing, synchronization, and device control. Data acquisition control was implemented in custom designed and user-configurable acquisition scripts for raster scanning 384-well plates in MicroManager, a custom designed autofocus program Figure 12 B). One frame each of Hoechst and Potomac Red channels were collected at the same frame rate for downstream tracking registration to the nucleus and cytoplasm, respectively.

[0249] Discussion

[0250] The present embodiments disclose OLS, a robust illumination and detection modality based on single objective light sheet, which achieves nanometer spatial resolution and sub-millisecond temporal resolution within a 250 x 190 pm field of view, overcoming the limitations of other SMLM techniques. The development of OLS aims to expand the effective imaging area while homogenizing the SNR across the entire camera chip to produce high-quality SMLM and SMT raw image files without compromising the achieved spatiotemporal resolution. The optical configuration used in OLS is relatively simple, making this approach easily implementable on an inverted microscope equipped with a water- or oil-immersion high numerical aperture (NA) objective and a sCMOS camera with light sheet mode functionality.

[0251] Example 2: OLS high-throughput single-molecule tracking (htSMT)

[0252] Introduction

[0253] The present embodiments describe an exemplary industrial-scale OLS htSMT technique using the exemplary OLS system of Example 1, and a comparison of such an OLS system to a highly inclined and laminated optical sheet (HILO) system. The present example further describes systems incorporating such OLS htSMT techniques, hardware and software related to such OLS htSMT techniques, and methods of using such OLS htSMT techniques. For example, the OLS htSMT techniques described herein are capable of measuring protein motions of millions of cells per day. The OLS htSMT techniques described herein exhibit specific, robust, and reproducible results. The OLS htSMT techniques described herein can be used for a variety of applications, including but not limited to classical drug discovery activities such as compound library screening and elucidation of SAR. Importantly, the OLS htSMT techniques described herein can be used to characterize the contribution of known and new pathways to interaction networks, such as protein signaling interaction networks.

[0254] Results

[0255] Creation and validation of the htSMT system

[0256] A robotic system was developed that is capable of handling reagents, collecting high-quality, fast SMT image series, processing time-ordered raw images to produce molecular tracks, and extracting features of biological interest within defined cellular compartments. Figure 1 To examine the htSMT system performance, we performed various measurements that demonstrate that the image acquisition system and workflow of the present disclosure are suitable for robust htSMT analysis. For example, Figure 3AA laser titration experiment is depicted showing the relationship of laser power at the sample (mW) versus signal-to-noise ratio (SNR) (left panel), and the average SNR per well for four image acquisition systems, each measuring six different 384-well plates (right panel). Figure 3C A dose response experiment using an established and well-characterized compound to halo-tagged protein is depicted to assess inter-plate and day-to-day reproducibility (upper panel) and the corresponding EC50 presented (lower panel). Figure 3D The system described herein is shown configured to capture comparable protein diffusion coefficients per FOV per well, where each dot represents the average single FOV position per plate per concentration (upper panel), and both EC50 and z-factor are presented (lower panel). Figure 3E Data consistency across multiple wells and multiple experiments is depicted, where each dot represents one FOV in 14 independently generated dose response curves.

[0257] In addition to determining that the OLS workflow described herein is suitable for robust htSMT analysis, experiments were performed to compare the OLS-based workflow described herein to the HILO-based method. For example, the OLS-based data presented in Figure 3D and Figure 3E A comparison of the z-factor associated with the OLS-based data presented in Figure 3B and the data collected using the HILO-based method clearly illustrates the improved performance of the OLS-based method. These differences between the OLS-based method and the HILO-based method are particularly evident in

[0258] Additional experiments were performed to illustrate the improved performance of the OLS-based method compared to the HILO-based method. To perform such comparisons, a U2OS cell line was used that had the HaloTag gene edited to the amino-terminal end of the KEAP1 gene (Halo-KEAP1). Initial imaging of the Halo-KEAP1 sparsely labeled with the rhodamine dye Janelia Fluorophore 549 (JF 549 ) produced clear single-molecule resolution, from which spot detection, localization, tracking analysis could be applied Figure 11B The performance of the OLS system was benchmarked against the HILO implementation. SMT data was collected for 1.5 seconds in both HILO and OLS, and the resulting tracks Figure 11D). The average number of trajectories collected across the FOV increased from 25,765 ± 4838 for HILO to 167,479 ± 46,324 for OLS, matching the calculated 6-fold increase in imaging field Figure 11E ). SMT data was collected for 1,224 FOVs on a 384-well plate and the average signal-to-noise ratio (SNR) of all spots located within each pixel of the FOV was calculated and plotted as a spatial SNR map Figure 11F ). The standard deviation and average SNR of each FOV for the 308 wells of OLS and HILO were then summarized, demonstrating both the consistency and performance improvement in SNR when comparing the two illumination modes Figure 11G

[0259] For HILO, the sample was illuminated for 2 milliseconds by pulsing the excitation laser for a subset of the camera exposure time. For OLS, given the scan rate of the light sheet, each fluorophore was exposed for only 400 milliseconds. Given this shorter fluorophore integration time, it was expected that the point spread function (PSF) of different diffusion rates would be more consistent. This hypothesis was tested by analyzing the average spot width of KEAP1 with and without KI-696 Figure 13A ). The average 2s radius of the single molecule PSF increased by 4.4% under HILO illumination and decreased to 1.4% for OLS Figure 13B and 13D ). While the 400 microsecond strobe time directly compares the motion-induced blur performance in OLS, it was found that HILO was unable to perform single molecule detection at this integration time because the vast majority of PSFs did not pass the noise threshold Figure 13C

[0260] One of the main advantages provided by OLS is that the out-of-focus illumination emitter is located outside the pixel band recorded by the camera during the scan of the tilted light sheet. To characterize this illumination-based superior optical sectioning method, a sample composed of increasing concentrations of His-HaloTag in solution was prepared to titrate the protein label density and the downstream effects on SNR and PSF detection. This experiment surprisingly captured the expected improvement in the sectioning capability provided by OLS. A rapid decrease in the number of detected localizations was observed in HILO, which correlated with a decrease in SNR Figure 13E and 13F ). These results highlight that under OLS illumination, single PSFs are better detected regardless of whether an increase in dye or protein concentration would cause local PSF overlap. In combination with the reduction in motion blur, OLS is able to track high density of single particles with high resolution performance.

[0261] ​​To further assess the reproducibility of illumination quality of the OLS optical system of the present disclosure, side-by-side SMT measurements were performed on four different OLS-equipped microscopes using the previously described automation system (McSwiggen et al., bioRxiv: 2023.2001.2005.522916 (2023)). Six to seven 384-well plates were tested per microscope, with Halo-KEAP1 treated with 20 concentrations of KI-696, a small molecule known to disrupt the KEAP1 interaction with its binding partner NRF2, increasing the fraction of rapidly diffusing Halo-KEAP1, with 12 well replicates randomly assigned across the plate for each concentration, and 6 FOVs per well. The average dose response curves were highly consistent across each microscope, with a median increase in diffusion of 47-51%, and median EC 50 values between 7.37 and 8.58 nM Figure 11C and 14A The average FOV-level SNR was compared across each microscope, and all four microscopes provided an average SNR ranging between 28.08 and 28.89 Figure 14B No variation was observed between subsequent FOVs captured within a single well, indicating minimal disturbance to the entire well when imaging a particular FOV Figure 14C This means that in this set of measurements, a position effect within the well did not appear to exist. Additionally, by comparing the cropped area of the same FOV to the large OLS-sized FOV, the impact of the large OLS FOV size on SMT sampling could be directly characterized. When the number of cells captured was scaled down to an 83x83 pm area, a significant increase in variance was observed Figure 14D

[0262] Methods

[0263] Cell lines

[0264] U2OS (ATCC Catalog # HTB-96) can be grown in DMEM (Catalog # 1056601, Gibco DMEM, high glucose, GlutaMAX supplement, Thermofisher) supplemented with 10% fetal bovine serum (Catalog # 16000044, Thermofisher) and 1% penicillin-streptomycin (Catalog # 15140122, Thermo Fisher) and maintained in a humidified 37 °C incubator with 5% CO2, with approximately biweekly to triweekly passaging.

[0265] HaloTag-expressing cell lines

[0266] ​For a particular Target-HaloTag fusion, a mammalian expression vector containing the appropriate fusion gene under weak L30 promoter control and containing a neomycin resistance marker can be transfected into U20S cells at 70% confluency using FuGENE 6 (Cat# E2691, Promega). Transfected cells can be selected with 500 pg / mL of G418 (Cat# 10131027, Thermo Fisher) followed by clonal isolation. Clones expressing the desired fusion gene can be determined by first staining with 100 nM JF 549 -HTL (Cat# GA1110, Promega) and 50 nM Hoechst 33342 and identifying clones with the expected JF 549 signal profile. Multiple clones can then be tested for response to control compounds using SMT conditions, and the most homogenous clone can then be expanded for further testing.

[0267] To generate certain KEAP1-HaloTag cell lines (e.g., Figures 14A-14D used in the Examples), ribonucleoprotein (RNP) complexes containing sgRNAs targeting either the N-terminal or C-terminal region (Integrated DNA Technologies - IDT) and Cas9 protein (PNA bio, Cat# CP01) were transfected with linear dsDNA donors (IDT) using the Lonza nucleofection method. Each donor consisted of 200-300 bp homology arms specific to each target, a codon-optimized HaloTag sequence, and a TEV linker (ENLYFQG) between the target and HaloTag. Following transfection, cells were incubated with Halo ligand JF 646 (internal) and imaged with an ImageXpress system (Molecular Device) to confirm HaloTag integration. Cells were then sorted into 384-well plates. Clonal cells were imaged for expansion and genotyped by Sanger sequencing to confirm homogenous HaloTag integration using the ImageXpress system.

[0268] Western Blot

[0269] Cells can be grown under the same conditions as previously described. 1.5 x 105cells per well can be plated in 6-well plates. Cells can be transfected with 1.5 pg of siRNA per well using Lipofectamine RNAiMax (Cat# 13778-100, Thermo Fisher) and incubated for 48 hours. Cells can then be harvested and lysed in RIPA buffer (Cat# 89900, Thermo Fisher) and protein concentration can be determined using the BCA assay (Cat# 23225, Thermo Fisher). 20 pg of protein can be loaded per lane and run on a 4-12% Bis-Tris gel (Cat# NP0332BOX, Invitrogen) and transferred to a PVDF membrane (Cat# IPVH00010, Millipore). Membranes can be blocked in 5% milk in TBS-T for 1 hour at room temperature and then incubated with primary antibody overnight at 4°C. Membranes can be washed in TBS-T and incubated with secondary antibody for 1 hour at room temperature. Membranes can be washed in TBS-T and imaged using an Odyssey CLx Imaging System (Cat# LI-COR# 929002, LI-COR Biosciences). 6Cells can be seeded in DMEM overnight, then compound treatment (DMSO or 100 nM fulvestrant) can be performed the next day for 24 hours. Cells can then be lysed in 200 pL IX cell lysis buffer (Cat# 9803, Cell Signaling). Protein lysate concentration can then be determined following the manufacturer’s instructions using BCA Protein Assay Kit (Cat# 23225, Pierce TM BCA Protein Assay Kit). Capillary Western can then be performed using Jess Protein Simple following the manufacturer’s instructions (Protein Simple, USA). The level of anti-target antibody can be normalized to the loading control b-tubulin (1:100, NC0244815 LI-COR 92642213, Thermo Fisher). Peak analysis can be performed using Compass software (Protein Simple, USA).

[0270] OLS single molecule tracking sample preparation

[0271] Cells can then be seeded at 4500-6000 cells per well onto tissue culture treated 384 well glass bottom plates. Seeded cells can then be incubated overnight at 37°C and 5% CO2 to allow adhesion. For all SMT experiments, cells can be incubated with 5-100 pM JF 549 -HTL (Cat# GA1110, Promega) and 50 nM Hoechst 33342 in complete media for one hour. Cells can then be washed three times in DPBS and twice in imaging media, which is fluoroBrite DMEM media (Cat# A1896701, Thermo Fisher) supplemented with GlutaMAX (Cat# 35050079, Thermo Fisher) and the same serum and antibiotics as the growth media. Compounds can be serially diluted in Echo Qualified 384 well low dead volume source microplates (0018544, Beckman Coulter) as appropriate to generate dose titration source material. Compounds can be administered in cell culture media at a final 1:1000 dilution. Each compound can have at least 3 replicates per plate, and up to 3 plate replicates can be prepared in series, with 20 DMSO control wells and 2 no-dye control wells can be randomly assigned on each plate. Compounds can be incubated for one hour at 37°C prior to image acquisition.

[0272] Image acquisition

[0273] Unless otherwise noted, all image acquisition using SMT was performed on a custom microscope, motorized stage, stage-top environmental chamber, four-band filter cubes (Chroma), custom laser engine with wavelengths of 405 nm and 561 nm reaching the back focal plane of the objective lens. Fluorescence emission was passed through a high-speed filter wheel (Sutter Instruments) and collected with a back-illuminated CMOS camera (Hammamatsu Orca Fusion running in light sheet mode). Images were acquired using a 60X 1.27 NA water immersion objective (Nikon). The environmental chamber was set to 37 °C, 95% humidity, and 5% C02. In certain implementations, each pixel was exposed for 400 microseconds, and the entire region of interest (ROI) required 9 milliseconds total. The galvanometer position could then be reset in 1 millisecond (e.g., with the laser turned off), and another image could be recorded. In such implementations, 100 frames per second could be recorded. Additionally or alternatively, a second setup could be employed that uses a smaller ROI in order to record 200 frames per second with the same 400 microseconds per pixel exposure and 4 millisecond image recording time. Additionally or alternatively, the galvanometer reset could be accomplished more quickly.

[0274] Image Analysis

[0275] Image acquisition produces one JF per field of view 549 Movies and one Hoechst. JF 549 Movies can be used to track individual JF 549 molecules, while Hoechst movies can be used for nuclear segmentation. Using a combination of existing methods, tracking can be accomplished in three successive steps: detection, sub-pixel localization, and linking. Briefly, spots can be detected using a generalized log-likelihood ratio detector. After detection, the estimated position of each emitter can be refined to sub-pixel resolution using a Levenberg-Marquardt fit starting from an initial guess provided by the radially symmetric method. A custom modification of the hill-climbing algorithm can be used to link detected spots into tracks. All movies can use the same detection, sub-pixel localization, and linking settings.

[0276] For nuclear segmentation, all frames of the Hoechst movie can be averaged to generate an average projection. This average projection can then be segmented with a neural network trained on human-labeled cell nuclei. Next, each spot can be assigned to at most one nucleus using its sub-pixel coordinates.

[0277] To recover motion information from the tracks, a state array can be used. For example, a Bayesian inference method can be used with an “RBME” likelihood function and 0.01 to 100.0 pm 2 s-1 grid of 100 diffusion coefficients and 0.02 to 0.08 pm of 31 localization error amplitudes. By inference, localization errors can be marginalized to yield a one-dimensional distribution of diffusion coefficients per field of view. For single-cell analysis, SMT and nuclear segmentation can be performed, for example, on a mixture of U20S cells with H2B-HaloTag, HaloTag-CaaX, or free HaloTag. The marginal likelihood of a set of 100 diffusion coefficients on the set of trajectories within each segmented nucleus can then be evaluated. These marginal likelihood functions can be clustered using k-means, and the marginal likelihood functions of each cell can be ordered by cluster index to yield a heatmap. To estimate the binding fraction (f 结合 ), the integral of the posterior distribution of the state array below 0.1 pm 2 s -1 can be evaluated. To estimate the free diffusion coefficient (D 自由 ), the mean of the posterior distribution of 0.1 pm 2 s -1 above can be computed.

[0278] Single molecule tracking method

[0279] Single molecule tracking (SMT) data is processed by a custom pipeline that operates on the sequence of images produced by the microscope. Briefly, single emitters are detected by applying a generalized log-likelihood ratio test to each 11x11 sub-window in the image, as described above (see the signal-to-noise ratio definition and quantification section below). Emitters are detected by identifying pixels with log-likelihood ratios exceeding 14. Detected emitters are localized to sub-pixel accuracy in a two-stage procedure. First, a sub-pixel position is estimated by computing the maximum radial symmetry point. Second, this estimate is used to seed an iterative Levenberg-Marquardt fitting procedure to detect a 2D integrated Gaussian within the 11x11 pixel sub-window centered on the estimate.

[0280] Local emitters can be linked in time to yield trajectories using a modification of the Sbalzerini hill-climbing algorithm that uses Gibbs sampling to estimate the uncertainty of data associations. In all SMT, cSMT prohibits links longer than 1.25 pm and prohibits links with more than 2 gap frames to limit association errors. Emitters are assigned to segmentation classes (nucleus, cytoplasm) by comparing their sub-pixel positions to the semantic mask produced by the segmentation procedure.

[0281] Data analysis

[0282] The tracking results of the automated processing pipeline can be analyzed using KNIME or Spotfire (TIBCO). Individual f 结合 or D 自由Measurements can be associated with experimental metadata and aggregated by condition.f 结合 Changes in f 结合 from the median f 结合 between DMSO wells in the same plate. Wells with no cells in the field of view or out of focus can be omitted from further analysis. Assay interference by a compound can be assessed using the median fluorescence intensity of the track channel, and if the compound is more than 3 standard deviations above the median intensity of the DMSO wells, it can be omitted. Similarly, if the active and negative controls cannot be clearly distinguished, or have a significant deviation from the performance of the rest of the screen, the plate can be removed from further analysis. Finally, compounds with a variance more than 3 standard deviations above the average compound variance can be removed from downstream analysis. The Z' factor between the active control and DMSO on a plate can be calculated. EC 50 values can be calculated in Prism (GraphPad) by first taking a log transformation of the molecular concentrations, and then fitting to a four parameter logistic curve.

[0283] Active molecule clustering

[0284] Molecules identified as active can be clustered based on chemical structure. Molecule frameworks can be calculated as known in the art, and as implemented in Pipeline Pilot. Molecule frameworks can be clustered using functional class fingerprints (FCFP_4), for example, with a similarity threshold cutoff of 0.3 Tanimoto distance.

[0285] Kinetic experiments

[0286] Cells can be seeded into 384 well plates the day before, stained and washed as described above. One well per plate with multiple FOVs per well can be used as a baseline read. Then, compounds can be added to each well, either manually or by robot, while imaging, until a final concentration of 100 nM is reached. Data for the well can then be collected. Pauses can be included between each FOV so that the entire imaging protocol covers the detection window. Changes in f 结合 for each well can be determined relative to t=0.

[0287] For detection times up to 4 hours, plates can be imaged twice, with multiple FOVs per well, and the FOV positions read differently each time to prevent photobleaching from affecting the data.

[0288] Residence time imaging

[0289] Sample preparation and performance of residence time imaging experiments can be performed in a similar manner to the single molecule tracking assay described above, with a few exceptions. Samples can be prepared with 1-10 pM JF 549Cells can be stained with 50 nM Hoechst 33342 for one hour. Multiple frames per field of view can be collected by setting the camera integration time to the desired time in milliseconds and reducing the laser source to the desired milliwatts at the objective. The laser can be left on during image acquisition. Compound incubation times can be 1 to 4 hours.

[0290] Dwell time analysis

[0291] Image processing can be performed using the same methods described above, including spot detection, localization, and track reconnection. Because dwell time imaging selectively tracks slowly diffusing molecules, individual localizations can be restricted to a maximum displacement distance of a single hop reconnection. The set of trajectories per field of view can be binned into 1-CDF distributions and fitted to a bi-exponential decay model as previously described

[0292] Fluorescence recovery after photobleaching

[0293] Images can be acquired on a custom OLS microscope using a Spectra Light Engine RS-232 as described herein (e.g., in Example 1). Stimulation can be directed using a microscop using a coherent OBIS 561 nm 100 mW laser coupled to a microscop. All imaging can be performed using a 60X 1.27 NA water immersion objective (Nikon). All experiments can be performed at 37 °C. For FRAP experiments, cells can be seeded into 384 well plates the day before, labeled with 50 nM HTL-JF 549 , and washed as described above. Compounds can be added to a final concentration of 100 nM one hour prior to imaging. Pre-bleach images can then be acquired by averaging 10 consecutive images. 8-10 regions can then be bleached (2 background, 6-8 cells), and 2 regions in a cell can not be bleached. Bleached regions can be bleached at 10% power without scanning. Images can be acquired every 200 milliseconds for the next 30 seconds, then every 1 second for 2 minutes. The background-subtracted average intensity in the region of interest as a function of time can be measured and normalized to the average value of fluorescence in the baseline image, then to the unbleached regions to account for photobleaching of the fluorophores caused by readout. For three biological experiments, data from multiple cells can be pooled for each experiment.

[0294] HILO microscopy

[0295] SMT image acquisition for the HILO dataset was performed on a custom microscope based on a Nikon Ti2, motorized stage, stage-top environmental chamber (OKO Lab), four-band filter cube (Chroma), custom laser emitters at wavelengths 405 nm and 561 nm delivering >10 mW and >150 mW of power to the back focal plane of the objective lens, respectively. Fluorescence emission was passed through a high-speed filter wheel (Finger Lakes Instruments) and collected with a back-illuminated sCMOS camera (ORCA-Fusion BT, Hamamatsu). Images were acquired using a 60X 1.27 NA water-immersion objective (Nikon). The environmental chamber was set to 37°C, 95% humidity, and 5% C02. For each field of view, 150 SMT frames were collected at a frame rate of 100 Hz using a 2 ms strobed laser pulse.

[0296] Trajectory measurements

[0297] In reporting the number of trajectories, singletons (trajectories with 1 detection) were excluded as they do not contribute information for most dynamic estimates.

[0298] The mean squared displacement method was used to calculate the average diffusion coefficient (D est = MSD 2D / 4Δt), which is expected to overestimate the diffusion coefficient by σ loc 2 / Δt, where σ loc 2 is the variance of the 1D localization error and Δt is the frame interval.

[0299] To resolve trajectories in multiple dynamic states, a state array (a variational Bayesian procedure based on Dirichlet process mixture) was used to infer the coefficients of a Brownian mixture model on a grid of diffusion coefficient values and localization error values. The mixture components were chosen to be the Cartesian product of 100 logarithmically spaced diffusion coefficients between 0.01 and 100 pm 2 / s and 31 0.02 to 0.08 pm localization error values (1D standard deviation). The occupancy was reported as the mean posterior probability of each diffusion coefficient marginalizing over all localization error values. To make the inference tractable, the inference was limited to 10,000 trajectories randomly sampled from each well.

[0300] Bias estimation in single-population samples was performed using an analytical calculation that captures the probability of false links and truncation of the jump length distribution due to a finite search radius.

[0301] Empirical estimation of linking accuracy

[0302] To estimate the accuracy of the linking algorithm, a bootstrap procedure was used. The results of the detections in the first and second halves of the movie were superimposed and the resulting set of detections was run through the tracking algorithm without knowledge of the source of each detection. From this, the resulting linking scores were computed, where individual detections were merged from different parts of the movie. Since this score does not take into account either the false links between detections in the same half of the movie or the effects of photobleaching, it forms a lower bound on the error rate of linking (ERLB).

[0303] Signal-to-noise ratio definition and quantification

[0304] The signal-to-noise ratio (SNR) is defined in terms of the likelihood ratio of a hypothesis test comparing: the null condition where the local image is modeled as the sum of a constant offset and independent Gaussian-distributed noise; and the alternative condition where the local image is modeled as the sum of a centered Gaussian peak (width known but amplitude unknown), independent Gaussian-distributed noise, and a constant offset. The SNR is expressed as:

[0305]

[0306] where:

[0307] A is the image cropped to the current region of interest (ROI);

[0308] w s is the side length of the square ROI (in pixels);

[0309] is the inner product operator;

[0310] h G is a zero-mean detection kernel that matches the expected Gaussian target profile, and where the sum is taken over the ROI;

[0311] h u is a uniform kernel (i.e., it has a value of 1 over the entire ROI).

[0312] D. Discussion

[0313] Taken together, these results highlight the robustness and reproducibility of SMT measurements using OLS illumination within a large FOV. These results further demonstrate the superior performance of OLS over HILO in characterizing the motion of fast-moving proteins at high labeling densities. These data demonstrate how OLS, as a new illumination scheme, enhances several properties of SMLM and SMT-based techniques. Compared to the established technique of HILO, OLS offers a larger FOV, finer slicing capability, superior SNR, uniform illumination, and higher spatiotemporal resolution. By reporting results achieved on four different microscopes with the OLS illumination module, the consistency of the robustness of OLS and the reproducibility of the resulting data is demonstrated. This robustness enables SMT measurements to be performed agnostically on any microscope to test large compound libraries for drug screening. Additionally, the improved rejection of out-of-focus light enables better single-molecule detection and localization, making OLS suitable for SMT on a variety of cellular systems and protein targets that were previously limited by background fluorescence. Consistent with this idea, high-SNR SMT results in more complex cellular systems were achieved using OLS in spheroid cultures of immortalized cancer cells.

[0314] Example 3: OLS allows fast acquisition of SMT data, enabling tracking of fast-moving proteins

[0315] This example shows the effect of higher frame rate acquisition on the determination window and key imaging metrics for the OLS system of Example 1.

[0316] A. Results

[0317] It is hypothesized that given the range and specificity of protein motion in living cells, there exists an optimal set of acquisition parameters for a given protein of interest. Frame rate is related to other experimental factors such as localization error and tracking error to determine the information that can be recovered from SMT Figure 16A and 16B ). To understand these effects, we performed optical dynamic simulations with a complex mixture of Brownian motions Figure 16C ), which were then tracked. As frame rate was increased, both the average trajectory length and tracking fidelity improved, highlighting that the size of the FOV being sampled was the only significant tradeoff Figure 16D ). State array analysis, a variational Bayesian method for recovering the underlying dynamic model from observed trajectories, was then used to estimate the underlying dynamic model for each simulation. Increasing frame rate improved recovery of faster states but ultimately decreased recovery of slower states Figure 16E ). It was also noted that the lower and upper bounds of the mean squared displacement (MSD) estimator of the diffusion coefficient were determined by localization error on one hand and the search radius used in tracking on the other, which roughly approximated the dynamic rangeFigure 16E (Green dashed line). These results indicate that adjustable frame rate is a highly desirable feature for SMT imaging systems.

[0318] The experimental Halo-KEAP1 system described in Example 2 supports OLS SMT operation at frame rates from 100 to 1250 Hz. Figure 15A Similar to the simulation results, both the average trajectory length and the estimated link accuracy improved at higher frame rates. Figure 17A and 17C Furthermore, OLS illuminators achieve this without reducing the average SNR (Short-to-Narrow Radiator). Figure 17B ) or bleaching rate per frame ( Figure 19 ) Array analysis during operation showed that as the frame rate increased, faster motion was recovered until the estimated value of Halo-KEAP1 processed by DMSO stabilized at approximately 9 μm at 400 Hz. 2 / s, and the estimated value of Halo-KEAP1 treated with KI-696 stabilized at approximately 14 μm. 2 / s( Figure 18 , Figure 15B Interestingly, it's worth noting that 400Hz might represent a point of diminishing returns, where the sampling frequency might be suitable for capturing a subset of KEAP1 that spreads faster under DMSO and KI-696 processing. Figure 15B Furthermore, when simulating the measured diffusion coefficient of Halo-KEAP1+ / -KI-696 processing at different frame rates, the simulation results closely matched the measurement results. Figure 15C In summary, these results demonstrate that the ability to increase frame rates using line scans in OLS facilitates accurate measurement of rapid protein diffusion in the cellular environment. While 400 Hz appears to be a suitable sampling rate for KEAP1, other important biochemical processes in living cells are expected to be adequately captured only at significantly higher frame rates.

[0319] method

[0320] SMT dynamic range estimation

[0321] To estimate the impact of frame rate on SMT dynamic range (e.g.) Figure 15C (As shown in Figure 16), the diffusion coefficient of Brownian particles with Gaussian positioning error is considered. The bounds of possible values ​​for the mean square displacement (MSD) estimator. The MSD estimator is based on... The upward offset is due to positioning error, among which Δt is the variance of the 1D positioning error, and Δt is the frame interval. Since D is non-negative, we obtain... In the opposite limit, when the true jumps of the particles are much larger than the search radius R used for tracking, the jumps are uniformly distributed within the tracking range gate (a circle of radius R), and the MSD estimator of the diffusion coefficient is upper bounded by R 2 / 8Δt. In combination, this yields a dynamic range estimate Thus, the effect of changing the frame rate is to shift the dynamic range in the domain. This simple dynamic range model does not account for the effects of trajectory misconnections (which can change the upper bound) or non-MSD estimates of the diffusion coefficient in the state array (which can lower the lower bound). Optical dynamic simulations

[0322]

[0323] To assess the effects of the tracking method and frame rate on the SMT dynamic range, optical dynamic simulations were performed. These simulations used a scalar diffraction approximation of a paraxial imaging system with NA = 1.2. Briefly, discrete mixtures of Brownian motion without state transitions were simulated in a cubic volume of size 45 x 45 x 8 pm (XYZ) at frame rates of 12.5, 25, 50, 100, 200, 400, 800, or 1600 Hz. The particles were initialized at a density of 0.31 or 0.62 particles per cubic micrometer (depending on the simulation) and photobleached with a probability of 0.03 per frame. Particle positions coinciding with 500-microsecond pulses (simulating stroboscopic illumination in HILO or rolling shutter in OLS) were accumulated onto simulated 2D cameras by convolution with the 3D point spread function of the system. This produced a probability distribution of photon arrivals to all simulated camera pixels. Next, photon arrivals from this distribution were sampled as a Poisson process until there were an average of 90 or 125 photons per particle (depending on the simulation). Finally, RMS 3-photon Gaussian readout noise was added, multiplied by a 4.3 counts per photon gain factor, and the movie was discretized to 16 bits. This movie was then tracked and state array inferred with the same settings as Halo-KEAP1 tracking.

[0324] Discussion

[0325] OLS enables frame rates of at least 1250 Hz without compromising SNR and tracking fidelity. As shown in Halo-KEAP1, a frame rate of 400 Hz is needed to adequately characterize the increase in release diffusion induced by NRF2 drugs. It is expected that there are many biological processes involving fast protein motions that were previously unmeasurable by other SMT illumination methods. OLS offers an opportunity to address new protein and cellular mechanisms that can occur on the sub-millisecond scale.

[0326] Example 4: OLS captures intercellular heterogeneity in single protein kinetics ​

[0327] This example demonstrates that the OLS system of Example 1 can be used to analyze intercellular heterogeneity in a sample.

[0328] Results

[0329] In assessing the consistency of SMT measurements, it was determined that intercellular heterogeneity was the largest source of variation, exceeding variation at the FOV, well, plate, or microscope level. Intercellular bias was at least an order of magnitude larger than inter-FOV or inter-well bias Figures 25A-25B ). This strongly suggests that biological heterogeneity dominates over technical variation in OLS-based SMT measurements Figure 20A ). Examples of cellular heterogeneity include cell cycle, cellular stress state, and genetic variation. Single-cell measurements such as large-FOV SMT enable more nuanced measurements to better understand this heterogeneity. This example illustrates this single-cell analysis by measuring the effect of cell cycle on the protein kinetics of proliferating cell nuclear antigen (PCNA). PCNA is involved in DNA replication and thus relocates to replication foci during S phase, exhibiting a unique and specific protein dynamics throughout the cell cycle.

[0330] Halo-PCNA was introduced into a U2OS clonal isolate expressing a sub-endogenous level of the marker protein Figure 22A and 22B ), and it was found that growth rate was not significantly affected by Halo-PCNA expression Figure 22C ). Co-localization analysis by RFP-labeled anti-PCNA nanobodies confirmed proper localization of Halo-PCNA Figure 22D .

[0331] Time-lapse microscopy was performed on Halo-PCNA labeled with 50 nM JFX 650 for 12 hours at 12 frames per hour to achieve near-saturation labeling Figure 21A ). A machine learning model was then trained using manually assigned cell phases and time-based progression to yield G1, early S, mid-S, late S, G2, and mitotic classifiers as well as regression predictions across the cell cycle continuum Figure 21B ). The predicted cell cycle classifications performed well compared to manual annotations Figure 21C ). When the regression predictions were plotted over time, clear progression of individual cells through the cell cycle was observed Figure 21D . To further validate the cell phase assignment model, S phase progression was blocked with thymidine or G2-M transition was blocked with the CDK-1 inhibitor RO-3306. Both treatments resulted in the expected enrichment of cells assigned to the corresponding cell phase Figure 20B and 20C). Cells were then labeled with 10 pM JF 549 and 50 nM JFX 650 Cells were labeled to enable simultaneous cell cycle distribution based on near-saturation labeling along with SMT measurements. Two migration peaks were observed at 0.043 and 9.88 pm 2 / s, likely representing PCNA associated with DNA replication sites and free PCNA, respectively Figure 20D When analyzing cells in each cell phase separately, it became clear that the slowly migrating PCNA population was only present in cells predicted to be in S phase, while the fast migrating population was significantly reduced Figure 20E The average diffusion coefficient of PCNA was then measured for 4,081 individual cells to characterize the heterogeneity of the cell population, which is largely described by the cell cycle Figure 20F Taken together, these data provide an example of how the large-FOV SMT supported by OLS can capture and elucidate cell-level heterogeneity in protein kinetics.

[0332] B. Methods

[0333] Engineering of cell lines with CRISPR knock-in of Halo-tagged proteins

[0334] All U2OS cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) (gibco) supplemented with 10% fetal bovine serum (Corning), 100 units / mL penicillin, 100 pg / mL streptomycin (Gibco) at 37 °C and 5% CO2.

[0335] To generate Halotag-PCNA cell lines, ribonucleoprotein (RNP) complexes containing sgRNAs targeting either the N-terminal or C-terminal region (Integrated DNA Technologies - IDT) and Cas9 protein (PNA bio, cat# CP01) were transfected with linear dsDNA donors (IDT) using the Lonza nucleofection method. Each donor consisted of 200-300 bp homology arms specific to each target, a codon-optimized Halotag sequence, and a TEV linker (ENLYFQG) between the target and Halotag.

[0336] Following transfection, cells were incubated with Halo ligand JF 646 (internal) and imaged with the ImageXpress system (Molecular Device) to confirm HaloTag integration. Cells were then sorted into 384-well plates. Clonal cells were imaged for expansion using the ImageXpress system and genotyped by Sanger sequencing to confirm homogenous HaloTag integration.

[0337] b. Cell Proliferation

[0338] 6-well plate cell proliferation - Cells were seeded into 6-well plates (Fisher Scientific, 07-200-83) at 150,000 cells / well and left to settle for 20 minutes at ambient temperature to ensure even settling. Plates were imaged using Incucyte (Sartorius) with 9 images taken per well every 4 hours and the Incucyte software v.S3 2019A was used to apply a phase mask algorithm to determine cell confluency.

[0339] c. Western Blot Analysis

[0340] Protein was extracted from 1-2 million cells by lysing in lx cell lysis buffer (CST, #9803) diluted in ultrapure sterile water (Intermountain Life Sciences, 20804225) containing lx Halt protease and phosphatase inhibitor cocktail (Thermo Fisher Scientific, 78440). Samples were collected using a cell scraper (Fisher Scientific, 08-100-241) and placed on ice for 15 minutes followed by centrifugation at 15,000 RPM for 10 minutes. The supernatant was transferred to a new tube. Protein concentration was determined using the Pierce BCA Protein Assay Kit (Thermo Fisher Scientific, A55864) according to the manufacturer’s protocol. Samples were run on an automated western blot system Jess (ProteinSimple, 004-650) according to the manufacturer’s protocol. The 12-230 kDa separation module was used for PCNA (CST, D3H8P, rabbit, 1 : 100 dilution, #13110), b-actin (CST, D6A8, rabbit, 1 :50 dilution, #8457S), and Halo (Promega, mouse, 1 : 10 dilution, #G9211) detection. Samples were diluted to 0.3 mg / mL with 0.1 x lysis buffer and 5x master mix, and heat denatured at 95 °C for 5 minutes, and all primary antibodies were diluted with antibody diluent 2. All reagents were loaded into a microplate, and 13 capillary cartridges were loaded onto the Jess western blot instrument. Run parameters were set using COMPASS software v6.0.0 (ProteinSimple). The corresponding bands for each protein were visualized using COMPASS software, and bands were normalized to the loading control b-actin.

[0341] d. Machine Learning Architecture

[0342] Images (e.g., JF 549images of labeled PCNA) automatically determine the cell cycle as provided above. A machine learning model such as a neural network based on the U-Net architecture can be trained to assign a label to each pixel Figures 21A-21D The decoder of the architecture can include a set of three independent decoders. D = {D1, D2, D3} While the encoder e θ are shared among D, but each decoder can be configured to have a different set of parameters and trained separately to perform three different tasks: 1) nucleus segmentation, 2) cell cycle classification, and 3) cell cycle regression. The network can be trained end-to-end using the average of three different losses for these three tasks.

[0343] Each loss can be based on class-balanced cross-entropy, defined as:

[0344]

[0345] where:

[0346] Z is the model’s predicted output for all classes

[0347] C is the total number of classes

[0348] b is a hyperparameter between 0 and 1

[0349] n y is the number of training samples for each class y

[0350] The first loss can optimize the nucleus segmentation task and is defined for an input x representing a 2D image of fluorescently labeled PCNA as:

[0351] L 分割 = CB softmax (D1(E θ (x)), y1)

[0352] where D1(E θ (x)) produces pixel-level predictions for the three classes of nucleus, nucleus edge, and background.

[0353] The second loss can optimize the cell cycle classification task and is defined as:

[0354] L 分类 = CB softmax (D2(E θ (x)), y2)

[0355] where D2(E θ (x)) produces pixel-level predictions for the seven classes of background, mitosis, G1, early S, mid S, late S, and G2.

[0356] To produce a continuous representation of the cell cycle classes (M, Gl, early S, mid-S, late S, and G2), a target coding approach can be used so that the classes can be linearly arranged from 0-1, such that cells classified as M are assigned to 0 and cells classified as G2 are assigned to 0.8. To minimize the complexity of aggregating three losses, a cross-entropy can similarly be used to train the regression task. The regression decoder D3 can produce two outputs, and the training data can be represented using a binary vector (λ, 1 - λ), where λ represents the 0-1 cell cycle value. The third loss can optimize the cell cycle regression task, and is defined as:

[0357] L 回归 = CB softmax ( D3( E θ (x)), y3)

[0358] Finally, the total loss for the network is the average of these three losses:

[0359]

[0360] Once the model is trained, nuclei segmentation can be performed, and the classification or continuous representation of the cell cycle can be assigned to each nucleus according to the defined average pixel value of the corresponding nucleus.

[0361] e. Evaluation of the highest variance source in the experiment: the jump resampling experiment

[0362] To evaluate the contribution of well-to-well, FOV-to-FOV, and cell-to-cell bias to the estimated average 2D jump length, KEAP1-HaloTag (labeled with JF 549 ) treated with DMSO was acquired at 100 Hz for the entire plate (308 wells) for both HILO and OLS data. Excluding the outer ring of wells in the 384-well plate, this resulted in a dataset containing 308 wells, 12 FOVs per well, and an average of about 44 cells per FOV (for OLS) or about 15 cells per FOV (for HILO). A simple model of jump length was considered. Let Y be the observed 2D jump length, then Y is modeled as the sum

[0363] Y = Bwell + BFOV + Bcell + X

[0364] B 孔 , B FOV , and B 细胞 are random variables modeling the bias at the well, FOV, or cell level, and X models the inherent randomness of the jump length depending on the specific well, FOV, and cell. A simplification is that B 孔 , B FOV , B 细胞 , and X are assumed to be independent. Under this simplification, Var(Y) = Var(B 孔)+Var(B FOV )+Var(B 细胞 A more physically realistic model would consider the potential dependencies between these random variables.

[0365] To estimate Var(B) 孔 ), Var(B FOV ), Var(B 细胞 Using Var(X) and Var(X), the sample mean and variance of four different skip resampling schemes were calculated:

[0366] Sample N jumps out of the entire plate. The resulting sample mean is relative to all sources of variation (B). 孔 B FOV B 细胞 Average X.

[0367] A sample is taken from a hole, and then sample N jumps out of that hole. The resulting sample mean is compared with B. FOV B 细胞 The variance of these sample means is expected to approach Var(B) as N increases, and is averaged with X. 孔 ).

[0368] A sample is taken from a hole, and the field of view (FOV) of that hole is also sampled. Then, sample N is removed from that FOV. The resulting sample mean is compared to B. 细胞 The variance of these sample means is expected to approach Var(B) as N increases, and is averaged with X. 孔 )+Var(B FOV ).

[0369] A sample is taken from a well, its field of view (FOV) is sampled, and the cells within that FOV are sampled. Then, sample N is taken from these cells. The resulting sample means are averaged only over X. As N increases, the variance of these sample means is expected to approach Var(B). 孔 )+Var(B FOV )+Var(B 细胞 ).

[0370] Each sampling scheme underwent 1000 rounds of sampling, with a bias B. 孔 B FOV and B 细胞 The variance is estimated by the difference between the sample mean and variance produced by each resampling scheme. As expected, only Var(X) depends on the sample size N, while other sources of variation remain stable relative to the sample size after approximately 100 jumps. Figures 25A-25B ).

[0371] discuss

[0372] This analysis shows that the largest variation in SMT measurements originates from cell-to-cell heterogeneity in protein motion. The large FOV enabled by OLS allows for the simultaneous capture of over 50 U20S cells in culture, enabling the capture of intercellular heterogeneity. This is exemplified by PCNA, which can simultaneously assign cell cycle phase and monitor protein dynamics. These results clearly show that PCNA protein dynamics are slower in S phase, which correlates with protein enrichment at DNA replication sites. Within G1, G2, and M phases, a sharp increase in dynamics is observed, corresponding to the uniform distribution of most PCNA throughout the nucleus. These findings are consistent with previous characterizations of PCNA dynamics, but the method presented here has important improvements. Using a machine learning model, PCNA localization is used instead of manual assignment to calculate the predicted cell cycle. Additionally, the OLS platform is capable of rapidly and automatically capturing and analyzing thousands of cells, whereas manual SMT methods typically only allow for the analysis of tens of cells per condition. Automated cell classification, along with the ability to scale SMT data collection and analysis, is critical for achieving comparable work to flow cytometry and other single-cell analysis techniques when characterizing protein motion in heterogeneous cell populations and potentially rare cell subtypes.

[0373] Example 5: OLS is applicable to multiple SMLM techniques and acquisition schemes

[0374] This example provides an application extension of the OLS system of Example 1, which takes advantage of OLS through uniform illumination, robustness, high spatiotemporal resolution, and imaging speed.

[0375] Results

[0376] The uniform illumination, high spatiotemporal resolution, imaging speed, and overall robustness of the OLS system can have broad advantages in multiple biological microscopy techniques. To demonstrate the ability to image SMT on two spectrally distinct fluorophores, time series of JF 549 and JF 646 labeled Halo-Keap1 were captured sequentially, enabling clear single-molecule resolution at both wavelengths Figure 23A . KI-696 was dose titrated and the response on both JF 549 and JF 646 labeled Halo-Keap1 was measured to demonstrate the ability to measure changes in protein motion at two wavelengths Figure 23B . Despite the decreased sCMOS quantum efficiency in the far-red spectrum leading to reduced SNR Figure 24A , SMT data was captured using the red-shifted JF 646 with EC 50 values highly consistent with the brighter JF 549 dye, JF 549 and JF646 Measured EC 50 4.96 nM and 6.45 nM, respectively. Robust measurement of protein kinetics at lower SNR can be explained by minimal decrease in lower error rate (ERLB) Figure 24B In summary, OLS-based SMT can find applications in multicolor imaging and facilitate imaging of lower quantum yield fluorophores, thus broadening the range of available fluorophores to measure protein motions in biological applications.

[0377] Since the integration time is shorter for OLS line scans, it was investigated whether dyes commonly used for fixed cell STORM imaging would produce images with high x,y resolution within a large FOV. Cells labeled with anti-tubulin primary antibody and Alexa Fluor 647 (AF647) or CF568 conjugated secondary antibody were imaged using STORM within a 60x field of view with a total imaging time of ~60 seconds Figures 23C-23F It was observed that despite the use of a short integration time of 400 ps in OLS, spontaneous light switching occurred as long as a sufficient number of photons were collected, thus achieving lateral localization precision of approximately 15 nm using AF647 or CF568 Figure 23G and 23H These results demonstrate the possibility of high-throughput phenotyping at high speed using STORM or other super-resolution microscopy techniques with OLS illumination.

[0378] Next, KI-696 treated Halo-KEAP1 cells labeled with JF 549 were subjected to related SMT / FRAP experiments using the line scan component of OLS. A 240x40 pm region was bleached by focusing the scan region on a narrow subset of the FOV for 100-200 scans before the full FOV was collected under normal SMT acquisition Figure 23I Instead of capturing the recovery of intensity over time after photobleaching, the normalized spot density was measured after recovery Figure 23K and 23L At a JF 549 dye concentration of 400 pM, the measured average T 1 / 2 (DMSO) was 3.52 (SD=0.38) and T 1 / 2 (KI-696) was 2.27 (SD=0.43), which appeared to be optimal for separating the two conditions Figure 23J). This result is consistent with the reported SMT measurements, indicating that KI-696 significantly increased Halo-KEAP1 local protein dynamics. To further confirm the consistency of the FRAP measurements, SMT was applied to both bleached and unbleached regions of the FOV. In both cases, an increase in Halo-KEAP1 dynamics was measured over the range of perturbation of the 1 mM KI-696 dye concentration Figure 23M and 24C ). These results highlight and confirm the advantageous properties of the combination of OLS with the proposed tracking algorithm to enable sensitive spot detection and SMT.

[0379] Methods

[0380] 384-well plate coating and cell seeding for SMT

[0381] Cells were seeded at 4000-6000 cells per well in tissue culture treated 384-well glass bottom plates (Cellvis #1.5 coverslips) and allowed to adhere and incubate overnight at 37°C and 5% CO2. Prior to washing with DPBS (3x) and Fluorobrite DMEM media (2x), cells were labeled for SMT using JF 549 and JF 646 and Hoechst 33342 and organelle specific dyes for one hour.

[0382] Line FRAP acquisition

[0383] The FRAP data set U2OS-KEAP1 was recorded by continuous imaging of 5 pre-bleach frames, bleaching a sub-region of the imaged FOV and capturing fluorescence recovery at an operational frame rate of 25 fps. Bleaching of the local fluorescence was achieved by scanning the excitation laser at high laser power (300 mW) over the sub-region of the FOV (16-25% FOV height, 100% FOV width) for 100-200 times. Fluorescence recovery was acquired in 400-500 frames after the bleaching step at low laser power (70 mW). Laser power was adjusted by implementing an acousto-optic tunable filter in the laser engine module (AOTF-Ed 2018-1; OptoElectronics) which was tuned for the bleaching and imaging power of the system at 560 nm excitation.

[0384] STORM and PALM data sets

[0385] U2OS cells were fixed in 4% PFA for 10 minutes, washed three times with PBS, permeabilized with 0.1% Triton-X for 10 minutes, and washed again three times with PBS. Cells were then blocked in 2% BSA for 1 hour at room temperature, followed by overnight incubation with anti-a tubulin antibody (ab7291) at 4°C. Primary antibody was removed by washing three times with PBS, and cells were again blocked with 2% BSA for 1 hour at room temperature. Alexa Fluorophore 647-conjugated secondary antibody was diluted 1 : 1000 in blocking buffer and incubated for 2 hours at room temperature. Cells were then washed three times with PBS to remove excess secondary antibody. Hoechst 33342 at a concentration of 7 mM was added with the secondary antibody for nucleus visualization. The light-switching buffer for STORM imaging was prepared as previously described (Dempsey et al., Nat Methods 8(12): 1027-1036 (2011)). Buffer A was prepared from 0.5 mL 1 M Tris (pH 8.0), 0.146 g NaCl, and 50 mL H2O. Buffer B was prepared from 2.5 mL 1 M Tris (pH 8.0), 0.029 g NaCl, 5 g glucose, and 47.5 mL H2O. GLOX solution was prepared by mixing 14 mg glucose oxidase (G2133) and 17 mg / mL catalase (C40) with 200 pL buffer A. The final light-switching buffer was prepared by combining 100 pL 1 M MEGA (M9768) with 10 pL GLOX and 1 mL buffer B. This buffer was added to cells in a 384-well plate prior to imaging.

[0386] Low laser power (300 mW) was used to capture diffraction-limited images and identify the relevant focal plane. Laser power was increased to reach -500 mW (x kW / cm2) at the back focal plane to initiate the photo-switching. 500-5000 frames were acquired with a pixel integration time of 0.4 milliseconds. 2 ), to initiate the photo-switching. 500-5000 frames were acquired with a pixel integration time of 0.4 milliseconds.

[0387] C. Discussion

[0388] These results highlight and demonstrate the advantageous properties brought by the combination of OLS with the proposed tracking algorithm, enabling sensitive spot detection and SMT even in the case of sub-optimal marker sparsity. In particular, these results highlight the versatility of OLS by demonstrating that it is suitable for fast acquisition of STORM datasets, despite very short illumination integration times, and for both AF 647 and CF 568 achieving a lateral resolution of -30 nm. The results further demonstrate a related SMT / FRAP method by which to study protein diffusion.

[0389] This example demonstrates that large FOV, OLS scanning, and high SNR with fast integration times enable 2-color SMT, STORM, and FRAP to be implemented on a large scale, rapidly, and consistently. It is expected that this platform will be compatible with other methods such as fluorescence correlation spectroscopy (FCS) and image correlation spectroscopy (ICS) and enable the use of this advanced microscopy approach in high content applications within systems biology and drug screening.

[0390] ********

[0391] While the presently disclosed subject matter and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the disclosure. Moreover, the scope of the present application is not intended to be limited to the particular embodiments of the process, machine, manufacture, and composition of matter, means, methods and steps described in the specification. Accordingly, the appended claims are intended to cover all such processes, machines, manufactures, compositions of matter, means, methods, or steps.

[0392] Throughout this application various patents, patent applications, publications, product descriptions, and protocols are referenced. The disclosures of the publications in their entireties are hereby incorporated by reference for all purposes.

Claims

1. A compound for determining whether inducing changes in the binding of a target fluorescent protein in living cells reduces the Kc of the target fluorescent protein. off The methods include: (a) Contacting a sample containing a population of live cells with the compound, wherein the live cells contain the target fluorescent protein; (b) Tracking the movement of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking includes: (i) Illuminate the field of view of the sample plane placed within the sample with a light beam so that at least one subset of the target fluorescent proteins in the living cells fluoresce; (ii) Detecting the fluorescence of one or more of the target fluorescent proteins in a detection field of view of the sample plane using a detector device, wherein the detection field of view has a size of about 150 μm to about 250 μm in a first dimension and a size of about 100 μm to about 210 μm in a second dimension, and wherein the method is adapted to selectively detect local fluorescence; and (c) Determine the motion changes of the target fluorescent protein in the presence of the compound; The increase in the signal detected from the target fluorescent protein in the presence of the compound, relative to the signal in the absence of the compound, indicates that the compound induces K-wave activity in the target fluorescent protein. off reduce.

2. A compound that determines whether inducing changes in the binding of a target fluorescent protein in living cells reduces the Kc of the target fluorescent protein. off The methods include: (a) Contacting a sample containing a population of live cells with the compound, wherein the live cells contain the target fluorescent protein; (b) Tracking the movement of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking includes: (i) Illuminate the field of view of a sample plane disposed within the sample with a light beam to cause at least one subset of the target fluorescent proteins in the live cells to fluoresce, wherein the subset of the target fluorescent proteins produces up to about 1,000,000 molecular trajectories in a single detection field of view. (ii) Detecting the fluorescence of one or more target fluorescent proteins in the detection field of view of the sample plane using a detector device, wherein the detection field of view has a size of about 150 μm to about 250 μm in a first dimension and a size of about 100 μm to about 210 μm in a second dimension, and wherein the method is adapted to selectively detect local fluorescence; and (c) Determine the motion changes of the target fluorescent protein in the presence of the compound; The increase in the signal detected from the target fluorescent protein in the presence of the compound, relative to the signal in the absence of the compound, indicates that the compound induces K-wave activity in the target fluorescent protein. off reduce.

3. A compound that determines whether inducing changes in the binding of a target fluorescent protein in living cells reduces the Kc of the target fluorescent protein. off The methods include: (a) Contacting a sample containing a population of live cells with the compound, wherein the live cells contain the target fluorescent protein; (b) Tracking the movement of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking includes: (i) Illuminate the field of view of the sample plane placed within the sample with a light beam so that at least one subset of the target fluorescent proteins in the living cells fluoresce; (ii) Detecting the fluorescence of one or more of the target fluorescent proteins in a detection field of view of the sample plane using a detector device, wherein the detection field of view has a size of about 150 μm to about 250 μm in a first dimension and a size of about 100 μm to about 210 μm in a second dimension, and wherein the method is adapted to selectively detect local fluorescence correlations; and (c) Determine the motion changes of the target fluorescent protein in the presence of the compound; The increase in the signal detected from the target fluorescent protein in the presence of the compound, relative to the signal in the absence of the compound, indicates that the compound induces K-wave activity in the target fluorescent protein. off reduce.

4. A compound that determines whether inducing changes in the binding of a target fluorescent protein in living cells reduces the Kc of the target fluorescent protein. off The methods include: (a) Contacting a sample containing a population of live cells with the compound, wherein the live cells contain the target fluorescent protein; (b) Tracking the movement of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking includes: (i) Illuminate the field of view of the sample plane placed within the sample with a light beam so that at least one subset of the target fluorescent proteins in the living cells fluoresce; (ii) Detecting the fluorescence of one or more of the target fluorescent proteins in a detection field of view of the sample plane using a detector device, wherein the detection field of view has a size of about 150 μm to about 250 μm in a first dimension and a size of about 100 μm to about 210 μm in a second dimension, wherein greater than or equal to 95% of the detection field of view provides sufficient laser illumination to track protein movement, and wherein the method is suitable for selectively detecting local fluorescence; and (c) Determine the motion changes of the target fluorescent protein in the presence of the compound; The increase in the signal detected from the target fluorescent protein in the presence of the compound, relative to the signal in the absence of the compound, indicates that the compound induces K-wave activity in the target fluorescent protein. off reduce.

5. The method of any one of claims 1 to 4, wherein the detected change in motion is an increase in the stationary trajectory, indicating the binding state of the target fluorescent protein (f 结合 The occupation or duration of ) increases.

6. The method of any one of claims 1 to 4, wherein the detected change in motion is a change in the following: (a) The median of the jump length distribution; (b) The third quartile of the jump length distribution; (c) Median radius of gyration; (d) Mean posterior diffusion coefficient; (e) Geometric mean posterior diffusion coefficient; (f) Mean square displacement; (g) Median bond angle; (h) Maximum likelihood estimate of the diffusion coefficient; and / or (i) The state is occupied through reasoning.

7. The method of any one of claims 1 to 4, wherein the target fluorescent protein interacts in the assembly of a larger molecule.

8. The method of claim 7, wherein the target fluorescent protein is a ligand.

9. The method of claim 7, wherein the target fluorescent protein is a receptor.

10. The method of any one of claims 1 to 4, wherein the biological interaction is a direct interaction.

11. The method of claim 10, wherein the direct interaction comprises the binding of the compound to the target fluorescent protein.

12. The method of any one of claims 1 to 4, wherein the biological interaction is an indirect interaction.

13. The method of claim 12, wherein the indirect interaction comprises the compound activating or antagonizing a larger molecular assembly containing the target fluorescent protein.

14. A method for reducing the K-ray intensity of a target fluorescent protein by identifying compounds. off A method for determining the dosage of the compound that induces changes in the binding of the target fluorescent protein in living cells, comprising: (a) Contacting a sample containing a population of live cells with the compound, wherein the live cells contain the target fluorescent protein; (b) Tracking the movement of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking includes: (i) Illuminate the field of view of the sample plane placed within the sample with a light beam so that at least one subset of the target fluorescent proteins in the living cells fluoresce; (ii) Detecting the fluorescence of one or more of the target fluorescent proteins in a detection field of view of the sample plane using a detector device, wherein the detection field of view has a size of about 150 μm to about 250 μm in a first dimension and a size of about 100 μm to about 210 μm in a second dimension, and wherein the method is adapted to selectively detect local fluorescence; and (c) Determining the dosage by determining the motility changes of the target fluorescent protein in the presence of the compound; and The increase in the signal detected from the target fluorescent protein in the presence of the compound, relative to the signal in the absence of the compound, indicates that the compound induces K-wave activity in the target fluorescent protein. off reduce.

15. A method for determining whether a compound reduces the K-ray intensity of a target fluorescent protein. off A method for determining the dosage of the compound that induces changes in the binding of the target fluorescent protein in living cells, comprising: (a) Contacting a sample containing a population of live cells with the compound, wherein the live cells contain the target fluorescent protein; (b) Tracking the movement of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking includes: (i) Illuminate the field of view of a sample plane disposed within the sample with a light beam to cause at least one subset of the target fluorescent proteins in the live cells to fluoresce, wherein the subset of the target fluorescent proteins produces up to about 1,000,000 molecular trajectories in a single detection field of view. (ii) Detecting the fluorescence of one or more target fluorescent proteins in the detection field of view of the sample plane using a detector device, wherein the detection field of view has a size of about 150 μm to about 250 μm in a first dimension and a size of about 100 μm to about 210 μm in a second dimension, and wherein the method is adapted to selectively detect local fluorescence; and (c) Determining the dosage by determining the motility changes of the target fluorescent protein in the presence of the compound; and The increase in the signal detected from the target fluorescent protein in the presence of the compound, relative to the signal in the absence of the compound, indicates that the compound induces K-wave activity in the target fluorescent protein. off reduce.

16. A method for reducing the K-ray intensity of a target fluorescent protein by identifying compounds. off A method for determining the dosage of the compound that induces changes in the binding of the target fluorescent protein in living cells, comprising: (a) Contacting a sample containing a population of live cells with the compound, wherein the live cells contain the target fluorescent protein; (b) Tracking the movement of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking includes: (i) Illuminate the field of view of the sample plane placed within the sample with a light beam so that at least one subset of the target fluorescent proteins in the living cells fluoresce; (ii) Detecting the fluorescence of one or more of the target fluorescent proteins in a detection field of view of the sample plane using a detector device, wherein the detection field of view has a size of about 150 μm to about 250 μm in a first dimension and a size of about 100 μm to about 210 μm in a second dimension, and wherein the method is adapted to selectively detect local fluorescence; and (c) Determining the dosage by determining the motility changes of the target fluorescent protein in the presence of the compound; and The increase in the signal detected from the target fluorescent protein in the presence of the compound, relative to the signal in the absence of the compound, indicates that the compound induces K-wave activity in the target fluorescent protein. off reduce.

17. A method for reducing the K-ray intensity of a target fluorescent protein by identifying compounds. off A method for determining the dosage of the compound that induces changes in the binding of the target fluorescent protein in living cells, comprising: (a) Contacting a sample containing a population of live cells with the compound, wherein the live cells contain the target fluorescent protein; (b) Tracking the movement of individual target fluorescent proteins in a plurality of cells of the sample, wherein the tracking includes: (i) Illuminate the field of view of the sample plane placed within the sample with a light beam so that at least one subset of the target fluorescent proteins in the living cells fluoresce; (ii) Detecting the fluorescence of one or more of the target fluorescent proteins in a detection field of view of the sample plane using a detector device, wherein the detection field of view has a size of about 150 μm to about 250 μm in a first dimension and a size of about 100 μm to about 210 μm in a second dimension, wherein greater than or equal to 95% of the detection field of view provides sufficient laser illumination to track protein movement, and wherein the method is suitable for selectively detecting local fluorescence; and (c) Determining the dosage by determining the motility changes of the target fluorescent protein in the presence of the compound; and The increase in the signal detected from the target fluorescent protein in the presence of the compound, relative to the signal in the absence of the compound, indicates that the compound induces K-wave activity in the target fluorescent protein. off reduce.

18. The method of any one of claims 14 to 17, wherein the detected change in motion is an increase in the stationary trajectory, indicating a combination (f 结合 Increase in target fluorescent proteins.

19. The method of any one of claims 14 to 17, wherein the detected change in motion is a change in the following: (a) The median of the jump length distribution; (b) The third quartile of the jump length distribution; (c) Median radius of gyration; (d) Mean posterior diffusion coefficient; (e) Geometric mean posterior diffusion coefficient; (f) Mean square displacement; (g) Median bond angle; (h) Maximum likelihood estimate of the diffusion coefficient; and / or (i) The state is occupied through reasoning.

20. The method of any one of claims 14 to 17, wherein the target fluorescent protein interacts in the assembly of a larger molecule.

21. The method of claim 20, wherein the target fluorescent protein is a ligand.

22. The method of claim 21, wherein the target fluorescent protein is a receptor.

23. The method of any one of claims 14 to 17, wherein the biological interaction is a direct interaction.

24. The method of claim 23, wherein the direct interaction comprises the binding of the compound to the target fluorescent protein.

25. The method of any one of claims 14 to 17, wherein the biological interaction is an indirect interaction.

26. The method of claim 25, wherein the indirect interaction comprises the compound activating or antagonizing a larger molecular assembly containing the target fluorescent protein.

27. A microscope system configured to determine whether a compound that induces changes in the binding of an intracellular target fluorescent protein reduces the Kc of the target fluorescent protein. off ,include: (a) A stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise the target fluorescent protein; (b) A light source for emitting a light beam capable of inducing photogenic responses of multiple target fluorescent proteins in the sample; (c) An objective lens for focusing the light beam onto the sample in the sample plane, wherein a subset of the target fluorescent protein in the sample is disposed in the detection field of view of the sample plane, and wherein the detection field of view has a size of about 150 μm to about 250 μm in a first dimension and a size of about 100 μm to about 210 μm in a second dimension. (d) A detector device for monitoring the photogenic response of the target fluorescent protein in the presence of the compound; (e) Memory; as well as (f) A processor that communicates with the memory and the detector device, wherein the processor is capable of determining the motion changes of the target fluorescent protein in the presence of the compound.

28. A microscope system configured to determine whether a compound that induces changes in the binding of an intracellular target fluorescent protein reduces the Kc of the target fluorescent protein. off ,include: (a) A stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise the target fluorescent protein; (b) A light source for emitting a light beam capable of inducing photogenic responses of multiple target fluorescent proteins in the sample; (c) An objective lens for focusing the light beam onto the sample in the sample plane, wherein a subset of the target fluorescent protein in the sample is disposed in a detection field of view of the sample plane, and wherein the subset of the target fluorescent protein generates up to about 1,000,000 molecular trajectories in a single detection field of view, and wherein the detection field of view has a size of about 150 μm to about 250 μm in a first dimension and a size of about 100 μm to about 210 μm in a second dimension; (d) A detector device for monitoring the photogenic response of the target fluorescent protein in the presence of the compound; (e) Memory; as well as (f) A processor that communicates with the memory and the detector device, wherein the processor is capable of determining the motion changes of the target fluorescent protein in the presence of the compound.

29. A microscope system configured to determine whether a compound that induces changes in the binding of an intracellular target fluorescent protein reduces the Kc of the target fluorescent protein. off ,include: (a) A stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise the target fluorescent protein; (b) A light source for emitting a light beam capable of inducing photogenic responses of multiple target fluorescent proteins in the sample; (c) An objective lens for focusing the light beam onto the sample in the sample plane, wherein a subset of the target fluorescent protein in the sample is disposed in the detection field of view of the sample plane, wherein the detection field of view has a size of about 150 μm to about 250 μm in a first dimension and a size of about 100 μm to about 210 μm in a second dimension. (d) A detector device for monitoring the photogenic response of the target fluorescent protein in the presence of the compound; (e) Memory; as well as (f) A processor communicating with the memory and the detector device, wherein the processor is capable of determining the motion changes of the target fluorescent protein in the presence of the compound relative to the absence of the compound.

30. A microscope system configured to determine whether a compound that induces changes in the binding of an intracellular target fluorescent protein reduces the K+ of the target fluorescent protein. off ,include: (a) A stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise the target fluorescent protein; (b) A light source for emitting a light beam capable of inducing photogenic responses of multiple target fluorescent proteins in the sample; (c) An objective lens for focusing the light beam onto the sample in the sample plane, wherein a subset of the target fluorescent protein in the sample is disposed in the detection field of view of the sample plane, wherein the detection field of view has a size of about 150 μm to about 250 μm in a first dimension and a size of about 100 μm to about 210 μm in a second dimension, and wherein greater than or equal to 95% of the detection field of view provides sufficient laser illumination to track protein motion; (d) A detector device for monitoring the photogenic response of the target fluorescent protein in the presence of the compound; (e) Memory; as well as (f) A processor that communicates with the memory and the detector device, wherein the processor is capable of determining the motion changes of the target fluorescent protein in the presence of the compound.

31. The system as described in any one of technical solutions 27 to 30, wherein the detected change in motion is an increase in the stationary trajectory, indicating a combination (f 结合 Increase in target fluorescent proteins.

32. The system of any one of claims 27 to 30, wherein the detected change in motion is a change in the following: (a) The median of the jump length distribution; (b) The third quartile of the jump length distribution; (c) Median radius of gyration; (d) Mean posterior diffusion coefficient; (e) Geometric mean posterior diffusion coefficient; (f) Mean square displacement; (g) Median bond angle; (h) Maximum likelihood estimate of the diffusion coefficient; and / or (i) The state is occupied through reasoning.

33. The system of any one of claims 27 to 30, wherein the target fluorescent protein interacts in the assembly of a larger molecule.

34. The system of claim 33, wherein the target fluorescent protein is a ligand.

35. The system of claim 33, wherein the target fluorescent protein is a receptor.

36. The system of any one of claims 27 to 30, wherein the biological interaction is a direct interaction.

37. The system of claim 36, wherein the direct interaction comprises the binding of the compound to the target fluorescent protein.

38. The system of any one of claims 27 to 30, wherein the biological interaction is an indirect interaction.

39. The system of claim 38, wherein the indirect interaction comprises the compound activating or antagonizing a larger molecular assembly containing the target fluorescent protein.

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