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 / 476,954, filed on December 22, 2022, and U.S. Provisional Application No. 63 / 476,942, filed on December 22, 2022, the contents of each of which are incorporated herein by reference in their entirety. Technical Field

[0003] The subject matter described herein relates to a platform for tracking single molecules within complex systems. Background Art

[0004] In the crowded environment of living cells, the movement of proteins is profoundly influenced by their interactions with the surrounding environment. Single-molecule tracking (SMT) is a method to capture protein motion as a reporter of activity. In SMT, a fluorescent protein of interest is imaged with high spatiotemporal resolution to track its movement in complex systems such as living cells. The information embedded in these tracks has been used to study various cellular phenomena, including protein-protein interactions, such as those that mediate signal transduction, inter-organelle communication, nuclear organization, and transcriptional regulation. However, the application scale of SMT technology is limited and it has therefore been mainly used to address specific mechanistic hypotheses. For example, SMT has not yet been adapted to a throughput setting that can achieve system-level screening or drug discovery. Summary of the Invention

[0005] In a first aspect, the present disclosure relates to a method for determining the rate of occurrence of a biological interaction between a compound and a target fluorescent protein in living cells, comprising: (a) contacting a sample comprising a population of living cells with the compound, wherein the living cells comprise the target fluorescent protein; (b) tracking the movement of a plurality of individual target fluorescent proteins in the plurality of living cells in the sample at multiple time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the target fluorescent proteins in the living cells fluoresce; and (ii) detecting fluorescence from the plurality of 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 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 (c) determining a change in the movement of the target fluorescent protein in the presence of the compound, wherein the rate at which the change in the movement of the target fluorescent protein occurs is determined by comparing the change in the movement of the target fluorescent protein at multiple time points.

[0006] In a related aspect, the present disclosure relates to a method for determining the rate of occurrence of a biological interaction between a compound and a target fluorescent protein in living cells, comprising: (a) contacting a sample comprising a population of living cells with the compound, wherein the living cells comprise the target fluorescent protein; (b) tracking the movement of a plurality of individual target fluorescent proteins in the plurality of living cells in the sample at multiple time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so as to cause a subset of the target fluorescent proteins in the living cells to fluoresce; wherein the subset of target fluorescent proteins produces up to about 1,000,000 molecular trajectories in a single detection field of view; (ii) detecting fluorescence from the plurality of target fluorescent proteins in the detection field of the sample plane by 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 (c) determining a change in movement of the target fluorescent protein in the presence of the compound, wherein the rate at which the change in movement of the target fluorescent protein occurs is determined by comparing the change in movement of the target fluorescent protein at multiple time points.

[0007] In a related aspect, the present disclosure relates to a method for determining the rate of occurrence of a biological interaction between a compound and a target fluorescent protein in living cells, comprising: (a) contacting a sample comprising a population of living cells with the compound, wherein the living cells comprise the target fluorescent protein; (b) tracking the movement of a plurality of individual target fluorescent proteins in the plurality of living cells in the sample at multiple time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so as to cause a subset of the target fluorescent proteins in the living cells to fluoresce, wherein the field of view is illuminated using a stroboscopic laser pulse of 0.1 to 1 millisecond; and (ii) detecting fluorescence from the plurality of target fluorescent proteins in the field of view of the sample plane by 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 (c) determining a change in movement of the target fluorescent protein after addition of the compound, wherein the rate at which the change in movement of the target fluorescent protein occurs is determined by comparing the change in movement of the target fluorescent protein at multiple time points.

[0008] In a related aspect, the present disclosure relates to a method for determining the rate of occurrence of a biological interaction between a compound and a target fluorescent protein in living cells, comprising: (a) contacting a sample comprising a population of living cells with the compound, wherein the living cells comprise the target fluorescent protein; (b) tracking the movement of a plurality of individual target fluorescent proteins in the plurality of living cells in the sample at multiple time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so as to cause a subset of the target fluorescent proteins in the living cells to fluoresce; (ii) detecting fluorescence from the plurality of 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 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 (iii) detecting fluorescence from the plurality of target fluorescent proteins in the detection field of view of the sample plane at a rate of more than about 100,000 detection fields per day; and (c) determining changes in the movement of the target fluorescent proteins in the presence of the compound, wherein the rate at which the changes in the movement of the target fluorescent proteins occur is determined by comparing the changes in the movement of the target fluorescent proteins at multiple time points.

[0009] In a first aspect, the present disclosure relates to a method for determining the rate of occurrence of a biological interaction between a compound and a target fluorescent protein in living cells, comprising: (a) contacting a sample comprising a population of living cells with the compound, wherein the living cells comprise the target fluorescent protein; (b) tracking the movement of a plurality of individual target fluorescent proteins in the plurality of living cells in the sample at multiple time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the target fluorescent proteins in the living cells fluoresce; and (ii) detecting fluorescence from the plurality of 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 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 achieves sufficient laser illumination to track protein movement; and (c) determining a change in movement of the target fluorescent protein in the presence of the compound, wherein the rate at which the change in movement of the target fluorescent protein occurs is determined by comparing the change in movement of the target fluorescent protein at multiple time points.

[0010] In some cases of the above aspects, the detected change in motion is an increase in the immobile trajectory, indicating that the binding (f 结合) increase in target fluorescent proteins. In some cases of the above aspects, the detected change in motion is a change in: (a) the median of the jump length distribution; (b) the 3rd quartile of the jump length distribution; (c) the median radius of gyration; (d) the mean posterior diffusion coefficient; (e) the geometric mean posterior diffusion coefficient; (f) the mean square displacement; (g) the median bond angle; (h) the maximum likelihood estimator of the diffusion coefficient; and / or (i) the state occupancy by inference. In some cases of the above aspects, the subset of target fluorescent proteins is located in at most about 80 living cells illuminated within the field of view of the sample plane. In some cases of the above aspects, the target fluorescent proteins interact in a larger molecular assembly. In some cases of the above aspects, the target fluorescent protein is a ligand. In some cases of the above aspects, the target fluorescent protein is a receptor. In some cases of the above aspects, the biological interaction is a direct interaction. In some cases of the above aspects, the direct interaction includes binding of the compound to the target fluorescent protein. In some 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 protein.

[0011] In a related aspect, the present disclosure relates to a method for determining the dosage of a compound, wherein the compound induces a change in the motion of a target fluorescent protein in living cells, the method 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 target fluorescent protein, and (iii) wherein the plurality of samples are contacted with the compound at different concentrations within a range of compound concentrations; and (b) tracking the motion of each target fluorescent protein in the plurality of living cells of 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 so that at least a portion of the target fluorescent proteins in the living cells are induced to move. (ii) detecting, by a detector device, fluorescence from one or more target fluorescent proteins in a detection field of view in the sample plane, wherein the detection field of view has a size in a first dimension of about 150 μm to about 250 μm and a size in a second dimension of about 100 μm to about 210 μm; and (c) determining the rate at which the change in motion of the target fluorescent protein occurs in the presence of the compound; and (d) repeating steps (b)-(c) for each of a plurality of samples over a range of compound concentrations; wherein the rate at which the change in motion of the target fluorescent protein occurs is determined by comparing the change in motion of the target fluorescent protein at a plurality of time points; and wherein the dose is determined by the rate.

[0012] In a related aspect, the present disclosure relates to a method for determining the dosage of a compound that induces a change in the motion of a target fluorescent protein in living cells, the method 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 target fluorescent protein, and (iii) wherein the plurality of samples are contacted with the compound at different concentrations over a range of compound concentrations; and (b) tracking the motion of each target fluorescent protein in the plurality of living cells of 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 so as to cause at least a portion of the target fluorescent proteins in the living cells to fluoresce, wherein a subset of the target fluorescent proteins is detected in a single detection field of view. (b) generating up to about 1,000,000 molecular trajectories in a field; (ii) detecting, by a detector device, fluorescence from one or more target fluorescent proteins in a detection field of view in 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 (c) determining the rate at which the change in motion of the target fluorescent protein occurs in the presence of the compound; and (d) repeating steps (b)-(c) for each of a plurality of samples over a range of compound concentrations; wherein the rate at which the change in motion of the target fluorescent protein occurs is determined by comparing the change in motion of the target fluorescent protein at a plurality of time points; and wherein the dose is determined by the rate.

[0013] In a related aspect, the present disclosure relates to a method for determining a dose response of a compound that induces a change in the motion of a target fluorescent protein in living cells, the method 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 target fluorescent protein, and (iii) wherein the plurality of samples are contacted with the compound at different concentrations within a range of compound concentrations; and (b) tracking the motion of each target fluorescent protein in the plurality of living cells of 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 so as to cause at least a portion of the target fluorescent proteins in the living cells to fluoresce, wherein the field of view is illuminated using a 0. (b) to (c) repeating steps (b) to (c) for each of a plurality of samples over a range of compound concentrations; wherein the rate at which the change in motion of the target fluorescent protein occurs is determined by comparing the change in motion of the target fluorescent protein at a plurality of time points; and wherein the dose is determined by the rate.

[0014] In a related aspect, the present disclosure relates to a method for determining a dose response of a compound that induces a change in the motion of a target fluorescent protein in living cells, the method 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 target fluorescent protein, and (iii) wherein the plurality of samples are contacted with the compound at different concentrations over a range of compound concentrations; and (b) tracking the motion of each target fluorescent protein in the plurality of living cells of 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 so as to cause at least a portion of the target fluorescent proteins in the cells to fluoresce; and (ii) detecting the motion of the sample plane in the field of view by a detector device. (c) determining the rate at which the change in motion of the target fluorescent protein occurs in the presence of the compound; and (d) repeating steps (b)-(c) for each of a plurality of samples over a range of compound concentrations; wherein the rate at which the change in motion of the target fluorescent protein occurs is determined by comparing the change in motion of the target fluorescent protein at a plurality of time points; and wherein the dose is determined by the rate.

[0015] In a related aspect, the present disclosure relates to a method for determining the dosage of a compound, wherein the compound induces a change in the motion of a target fluorescent protein in living cells, the method 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 target fluorescent protein, and (iii) wherein the plurality of samples are contacted with the compound at different concentrations within a range of compound concentrations; (b) tracking the motion of each target fluorescent protein in the plurality of living cells of 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 so that at least a portion of the target fluorescent proteins in the living cells emit fluorescence; (ii) detecting the fluorescence of the target fluorescent protein in the living cells by a detector device; (c) determining the rate at which the change in motion of the target fluorescent protein occurs in the presence of the compound; and (d) repeating steps (b)-(c) for each of a plurality of samples over a range of compound concentrations; wherein the rate at which the change in motion of the target fluorescent protein occurs is determined by comparing the change in motion of the target fluorescent protein at a plurality of time points; and wherein the dose is determined by the rate.

[0016] In some cases of the above aspects, the detected change in motion is an increase in the immobile trajectory, indicating that the binding (f 结合 ) increase in target fluorescent proteins. In some cases of the above aspects, the detected change in motion is a change in: (a) the median of the jump length distribution; (b) the 3rd quartile of the jump length distribution; (c) the median radius of gyration; (d) the mean posterior diffusion coefficient; (e) the geometric mean posterior diffusion coefficient; (f) the mean square displacement; (g) the median bond angle; (h) the maximum likelihood estimator of the diffusion coefficient; and / or (i) the state occupancy by inference. In some cases of the above aspects, the subset of target fluorescent proteins is located in at most about 80 living cells illuminated within the field of view of the sample plane. In some cases of the above aspects, the target fluorescent proteins interact in a larger molecular assembly. In some cases of the above aspects, the target fluorescent protein is a ligand. In some cases of the above aspects, the target fluorescent protein is a receptor. In some cases of the above aspects, the biological interaction is a direct interaction. In some cases of the above aspects, the direct interaction includes binding of the compound to the target fluorescent protein. In some 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 protein.

[0017] In an interrelated aspect, the present disclosure relates to a microscope system configured to detect the occurrence rate of biological interactions between a compound and a target fluorescent protein 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 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 lens for focusing the light beam onto the sample in the 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 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; (e) a detector device for monitoring the photo-based reaction of the target fluorescent protein in the presence of the compound at multiple time points; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining changes in the movement of the target fluorescent protein in the presence of the compound at multiple time points, thereby detecting the occurrence rate of biological interactions between the compound and the target fluorescent protein in the living cells.

[0018] In an interrelated aspect, the present disclosure relates to a microscope system configured to detect the occurrence of biological interactions between a compound and a target fluorescent protein in a living cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population, 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 lens for focusing the light beam onto the sample in the 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 a single photoreceptor in the sample plane. (d) a detector device for monitoring the light-based reaction of the target fluorescent protein in the presence of the compound at multiple time points; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining changes in the movement of the target fluorescent protein in the presence of the compound at multiple time points to detect the occurrence rate of biological interactions between the compound and the target fluorescent protein in living cells.

[0019] In a related aspect, the present disclosure relates to a microscope system configured to detect the occurrence of biological interactions between a compound and a target fluorescent protein in a living cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population, 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, wherein a field of view is illuminated using 0.1 to 1 millisecond strobed laser pulses; and (c) an objective lens for focusing the light beam onto the sample in a sample plane, wherein a subset of the target fluorescent proteins in the sample are positioned in a field of view of the sample plane. (d) a detector device for monitoring the light-based reaction of the target fluorescent protein in the presence of the compound at multiple time points; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining the change in movement of the target fluorescent protein in the presence of the compound relative to the absence of the compound at multiple time points, thereby detecting the occurrence rate of biological interactions between the compound and the target fluorescent protein in living cells.

[0020] In a related aspect, the present disclosure relates to a microscope system configured to detect the occurrence of biological interactions between a compound and a target fluorescent protein in a living cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population, 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 lens for focusing the light beam onto the sample in the sample plane, wherein the plurality of 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 first dimension of about 150 μm to about 250 μm. m in size and having a second dimension of about 100 μm to about 210 μm; (d) a detector device for monitoring the light-based reaction of the target fluorescent protein in the presence of the compound at multiple time points, wherein the monitoring includes detecting fluorescence from multiple target fluorescent proteins in a detection field of view in a sample plane at a rate of more than about 100,000 detection fields 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 changes in the movement of the target fluorescent protein in the presence of the compound at multiple time points, thereby detecting the occurrence rate of biological interactions between the compound and the target fluorescent protein in living cells.

[0021] In a related aspect, the present disclosure relates to a microscope system configured to detect the occurrence of biological interactions between a compound and a target fluorescent protein in a living cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of living cells, and wherein the living 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 lens for focusing the light beam onto the sample in the 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 first dimension. having a dimension of about 150 μm to about 250 μm and a dimension 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 achieves sufficient laser illumination to track protein movement; (e) a detector device for monitoring the light-based reaction of the target fluorescent protein in the presence of the compound at multiple time points; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining changes in the movement of the target fluorescent protein in the presence of the compound at multiple time points, thereby detecting the occurrence rate of biological interactions between the compound and the target fluorescent protein in living cells.

[0022] In some cases of the above aspects, the detected change in motion is an increase in the immobile trajectory, indicating that the binding (f 结合 ) increase in target fluorescent proteins. In some cases of the above aspects, the detected motion is a change in: (a) the median of the jump length distribution; (b) the 3rd quartile of the jump length distribution; (c) the median radius of gyration; (d) the mean posterior diffusion coefficient; (e) the geometric mean posterior diffusion coefficient; (f) the mean square displacement; (g) the median bond angle; (h) the maximum likelihood estimator of the diffusion coefficient; and / or (i) the state occupancy by inference. In some cases of the above aspects, the subset of target fluorescent proteins is located in at most about 80 living cells illuminated within the field of view of the sample plane. In some cases of the above aspects, the target fluorescent proteins interact in a larger molecular assembly. In some cases of the above aspects, the target fluorescent protein is a ligand. In some cases of the above aspects, the target fluorescent protein is a receptor. In some cases of the above aspects, the biological interaction is a direct interaction. In some cases of the above aspects, the direct interaction includes binding of the compound to the target fluorescent protein. In some 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 protein. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] This patent or application file contains at least one drawing drawn 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.

[0024] Figure 1 Schematic diagram depicting the htSMT workflow.

[0025] Figures 2A-2F An exemplary image acquisition system of the present disclosure is depicted, wherein the XZ sampling plane is visible ( Figure 2A and 2D ) or YZ sampling plane visible ( Figure 2B and 2C ), and details of the beams associated with the HILO-based approach ( Figure 2E , OLS on the left, HILO on the right) and an example of combining a camera rolling shutter ( Figure 2F ).

[0026] Figures 3A-3E Various measurement results are depicted, demonstrating that the disclosed image acquisition system and workflow are suitable for robust htSMT analysis. Figure 3A Depicted is a laser titration experiment showing the relationship between laser power (mW) at the sample and signal-to-noise ratio (SNR) (left), as well as the average SNR at the well level for four image acquisition systems, each measuring six different 384-well plates (right). Figure 3B Depicts the difference in spatial SNR heterogeneity between the disclosed OLS system and HILO-based methods. The top figure compares the spatial standard deviation observed in the OLS and HILO-based methods. The bottom figure illustrates the difference in FOV between HILO and OLS-based methods (left figure), as well as a comparison of the spatial heterogeneity between the FOVs of the HILO-based method (center figure) and the OLS-based method (right figure). Figure 3C Dose-response experiments using established and well-characterized compounds against Halo-tagged proteins are depicted to assess inter-plate and day-to-day reproducibility (top panel) and the corresponding EC50s are presented (bottom panel). Figure 3D Demonstrates that the system described herein is configured to capture comparable protein diffusion coefficients per FOV per well, where each point represents the average single FOV position per plot per concentration (top panel), and both EC50 and Z-factor are presented (bottom panel). Figure 3E Data consistency across multiple wells and experiments is depicted, where each point represents one FOV from 14 independently generated dose-response curves.

[0027] Figure 4 Depicts the Figure 3D and Figure 3EComparison of the Z-factors associated with the data presented in with data collected using a HILO-based method.

[0028] Figure 5 Depicted is a schematic diagram of an exemplary sample processing system of the present disclosure.

[0029] Figure 6 An exemplary system of a high-throughput single-molecule imaging platform for measuring protein motion within living cells is illustrated.

[0030] Figure 7 The data flow through an exemplary system of a high-throughput single-molecule imaging platform for measuring protein movement within living cells is illustrated.

[0031] Figure 8 Several images are depicted illustrating the difference between class masks and instance or semantic masks.

[0032] Figure 9 An example computer-implemented environment related to the subject matter described herein is described.

[0033] Figure 10 is a diagram illustrating an example computing device architecture for implementing various aspects described herein.

[0034] Figures 11A-11G The OLS provides nearly full-field uniform illumination, enabling a wide range of SMTs. Figure 11A A simplified schematic diagram describing the OLS implementation is depicted. Briefly, a collimated beam is formed into an optical light 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 is depicted that relies on Halo tagging of a protein target of interest. 549 or JF 646 Organic fluorophores detect individual emitters with appropriate signals 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. Figure 11C Depicted are 20-point dose-response curves from 6-7 different 384-well plates per microscope imaged on the Eikon high-throughput SMT platform. 72 FOVs were captured for each concentration from 12 wells on a random plate, and error bars represent standard deviation. Figure 11D Representative sampled regions illuminated by Halo-Keap1-containing U2OS cells in HILOs and OLs are depicted. Trajectories are plotted over a 1.5 s acquisition and color-coded according to the measured diffusion coefficient, with the nuclear mask outline overlaid with a black dashed line. Figure 11E Quantification of the number of tracks captured per FOV using HILO and OLS is described, with OLS capturing a 6x improvement. Figure 11FDepicted are representative average spatial SNR plots per pixel calculated for 1,232 FOVs of a plate imaged with either HILO or OLS. OLS provides a 6x larger FOV while also improving illumination uniformity. Figure 11G The mean FOV-level standard deviation of the SNR for the 308 well samples is provided.

[0035] Figures 12A-12F An exemplary schematic diagram of an OLS microscope for single-molecule tracking is depicted. Figure 12A depicts an exemplary schematic diagram of an OLS microscope, which is based on scanning an oblique 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 via a single-mode fiber coupled by a collimator. The beam shaper converts the incident Gaussian-shaped light excitation into an optical light sheet, which is focused along the linear axis of the light sheet onto the back focal plane of the microscope objective and scanned along the scanning axis using a galvanometer mirror. The generated 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. The 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. Synchronization of optical excitation, scanning and acquisition is achieved by a custom control unit (MIC). Figure 12B depicts an exemplary schematic diagram of an autofocus unit based on detecting reflections from a 780 nm LED on the top surface of the sample holding glass bottom and repositioning the objective to ensure proper focal plane positioning within the sample. Figure 12C depicts an exemplary schematic diagram of an optical confocal scanning mode achieved by scanning a tilted and focused light sheet through the objective focal plane. Background suppression is achieved by the confocal arrangement of the tilted light sheet (green), the depth of field of the objective, and synchronized rolling shutter detection (orange). Figure 12D depicts an exemplary schematic diagram of a beam shaping subassembly that projects along a linear axis (x) and a scanning axis (y), shaping 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, and then encounters a galvanometer scanning mirror. The inset depicts the beam profile at various positions. Figure 12E depicts an exemplary schematic diagram of a light scanning framework based on a tilted light sheet in the sample plane, achieved by focusing the light excitation along a linear axis in the objective back focal plane and positioning the light excitation along the scanning axis at an offset position relative to the objective optical axis. The corresponding optically aligned fluorescence detections are projected onto the camera sensor. Figure 12F depicts an exemplary schematic of an OLS acquisition mode that relies on detecting fluorescence by matching the exposed pixel areas of the camera and synchronizing the camera's rolling shutter with the optically projected intensity lines of the fluorescence excited by the tilted light sheet.

[0036] Figures 13A-13FCharacterization of motion-induced blur and confocality between OLS and HILO illumination. Figure 13A Depicted is a bar graph comparing HILO and OLS diffusion coefficients measured in 72 FOVs from 12 individual wells for Halo-KEAP1 treated with DMSO or 1 mM KI-696. Despite extensive sampling, the standard deviation of the HILO measurements was larger. Figure 13B Depicted are estimated point spread functions (PSFs) obtained 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 is plotted as a function of integration time. HILO requires a five-times longer integration time to achieve comparable PSF detection and speckle density as OLS, which results in more noticeable motion-induced blur in HILO. Figure 13D PSF width measurements measured in Halo-KEAP1 cells treated with 1 mM KI-696 are plotted versus JF 549 Functional relationship. Figure 13E The mean SNR measured in Halo-KEAP1 cells treated with 1 mM KI-696 is plotted versus JF. 549 Functional relationship. Figure 13F It shows that Halo-JF in solution 549 The number of spots detected when the concentration increases is JF 549 Functional relationship.

[0037] Figures 14A-14D This shows that OLS can achieve repeatable and robust SMT measurements. Figure 14A The EC calculated from each average dose-response curve for each plate per microscope is depicted. 50 Value, the black line represents the median EC 50 . Figure 14B Violin plots of the signal-to-noise ratio (SNR) for each microscope are depicted, with the thick dashed line indicating the median value. Figure 14C Violin plot depicting SNR as a function of FOV position within the acquisition aperture. Figure 14D Depicted are 20-point dose-response curves of Halo-KEAP1 U2OS sampled at the full OLS FOV (purple) versus a 768×768 pixel cropped FOV (black) representing the HILO-sized FOV. Error bars represent the standard deviation between FOVs.

[0038] Figures 15A-15CThis demonstrates that OLS can capture rapid protein diffusion in living cells. Figure 15A Representative images of FOV sizes for five frame rates ranging from 100 to 1250 Hz are depicted. Trajectories are superimposed on the mean projection of the Hoechst channel (blue) and colored according to their maximum likelihood diffusion coefficient. Figure 15B Depicts diffusion coefficients >10 μm 2 The fraction of trajectories with a frame rate of 100 / s was calculated from the posterior mean occupancy of the state array as a function of the frame rate for DMSO- and KI-696-treated cells, respectively. Figure 15C Plotted are the accuracy of state profile recovery for optical dynamic simulations of SMT at several frame rates for three different state mixtures. Error bars represent standard deviation.

[0039] Figures 16A-16E This shows that the frame rate determines the SMT dynamic range. Figure 16A A schematic depicting the role of localization and tracking errors for a hypothetical fast-moving protein is shown. A rolling shutter in OLS captures the position of the dye molecule at discrete time points. If these time points are too close together, the apparent motion will be dominated by localization errors. If the time points are too far apart, reconstructing the trajectory becomes challenging and is dominated by misalignment. Figure 16B A schematic diagram describing the dynamic range of an SMT is depicted, which is limited by positioning error at one end and tracking error at the other end. An approximation of this range for Brownian motion is where σ 2 is the localization error variance, Δt is the frame interval, R is the search radius, and D is the diffusion coefficient. Figure 16C A diagram depicting the simulation method used to test the effects of frame rate. Movies were simulated using real-world effects, including defocus, motion blur, shot noise, and readout noise. Figure 16D Plotting the effect of frame rate on link accuracy and trajectory length. Link 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 Depict the state array posterior mean occupancy of three simulated dynamic mixings at increased frame rates. The red line corresponds to the simulated discrete hybrid 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 repetitions.

[0040] Figures 17A-17C Depicted are diagnostic traces of experimental KEAP1-HaloTagJF549 SMTs in U2OS cells with different frame rates. Figure 17A The average track length is plotted as a function of the frame rate. Track length is defined as the number of spots per track. Figure 17B The average SNR is plotted as a function of frame rate. The SNR is described in Example 2. Figure 17C The average ERLB is plotted as a function of frame rate.

[0041] Figure 18 Provided are state array analyses plotted as a function of frame rate, comparing Keap1-HaloTag U2OS cells treated with DMSO and 1 mM KI-696. The number of FOV replicates 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 mean of all FOVs for the corresponding condition, and the error bars are the standard deviation of the FOV levels.

[0042] Figure 19 An evaluation of the bleaching rate of KEAP1-HaloTag SMT at variable frame rates is provided. The remaining detection fraction is plotted against the frame rate for a given time series. The remaining detection fraction is defined as the number of detections in each frame divided by the number of detections in the first frame. The model f(t) = c0 + (1-c0)e was fitted using iterative least squares. -kt An exponential fit was performed (blue text below the frame rate), where t is the frame index, k is the bleaching rate, and c0 is the unbleached fraction. The number of FOV repetitions per frame rate was as follows: n = 88 (100 Hz), n = 88 (200 Hz), n = 132 (400 Hz), n = 198 (800 Hz), and n = 264 (1250 Hz).

[0043] Figures 20A-20F We demonstrate that OLS can be used to capture inter- and intracellular heterogeneity in the dynamics of individual proteins. Figure 20A Figure 3 Analysis of sources of variation in KEAP1 SMT measured under OLS or HILO illumination. The contribution of cell-to-cell variation is 17- to 32-fold higher than that of FOV-level or well-to-well variation, 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 based on a machine learning (ML) model were used to color cells by cycle stage (bottom). Figure 20C Depicts the response to Figure 20B Quantification of the fraction of cells in each cell phase following cycle blockade treatment in Figure 5. 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 20EArray analysis depicting the state of the cell at each stage predicted by the ML model. Figure 20F Depicted is a heat map of 4,801 individual cells classified using a continuous classification score plotted against PCNA diffusion coefficient.

[0044] Figures 21A-21D The characteristics of the cell cycle prediction model based on PCNA are described. Figure 21A Example images of a time-lapse of PCNA captured on the OLS at 5-minute frame intervals for 12 hours are provided. Figure 21B Schematic depicting a neural network trained to simultaneously perform nucleus segmentation, nucleus cell cycle classification, and nucleus cell cycle regression. Figure 21C The confusion matrix describing the cell cycle classification performance is depicted. Figure 21D Representative images of cell cycle progression of four selected cells over a 12-hour window are provided (left), along with regression-based predictions of cell cycle progression using a 5-frame moving average (right).

[0045] Figures 22A-22D Depicted are PCNA cell line validation using Western blot and cell proliferation assays. Figure 22A Depicted are capillary-based Western blots comparing WT U2OS and N-terminally tagged hybrid PCNA clones with anti-PCNA antibodies (left) and anti-Halo antibodies (right). Figure 22B Depicted are the relative WT and Halo-labeled PCNA levels of WT and Halo-edited U2OS cells normalized to β-actin. Figure 22C Growth curves of WT U2OS and N-terminally Halo-tagged PCNA are depicted. Figure 22D Depicted using JF 549 Cells labeled with both CCR and PCNA were analyzed to measure the spatial colocalization between the two markers during the cell cycle.

[0046] Figures 23A-23M This shows that OLS is applicable to a variety of SMLM techniques and acquisition schemes. Figure 23A Depicts the JF imaged within the same FOV 549 and JF 646 Halo-labeled KEAP1U2OS cells. Figure 23B Depicts the relationship with JF 549 and JF 646 10-point dose response of co-labeled KI-696-treated Halo-KEAP1 U2OS cells. Figure 23C Depicted is a diffraction-limited image of the complete OLS FOV of immunofluorescently labeled tubulin with AF647-conjugated secondary antibody. Figure 23D Depicts Figure 23CMagnified view of the region of interest. Figure 23E Depicted as Figure 23C STORM reconstruction of the full OLS FOV marked. Figure 23F Depicted as Figure 23D of Figure 23E Magnified view of the region of interest. Figure 23G Depicts Figure 23D The yellow line and Figure 23F Line profiles of grey values ​​(au) to compare the spatial resolution of microtubules. Figure 23H Depicted are histograms of localization accuracy for AF647- and CF568-labeled secondary antibodies, respectively, used to stain microtubules with OLS illumination at 0.4 ms integration time. Figure 23I Depicted are representative images of correlated FRAP / SMT, where the central region was bleached using OLS line scans prior to spot recovery after photobleaching. Regions outside and inside the FRAP region were used to measure SMT. Figure 23J The results of the experiments with DMSO or 1 mM KI-696 (using 400 μM JF 549 T cells of U2OS cells treated with Halo-KEAP1 (-Halo ligand labeling) 1 / 2 FRAP, black line indicates the median, and each spot represents an individual FOV. Figure 23K and Figure 23L Depicts DMSO ( Figure 23K ) and 1 mM KI-696 ( Figure 23L ) Spot density after recovery over time. Standard deviations are shown with confidence bands of 8-10 FOVs per condition. Figure 23M Depicts 400pMJF 549 -SMT diffusion coefficients in bleached (inside) and unbleached (outside) regions as a function of Halo ligand concentration.

[0047] Figures 24A-24C The characteristics of dye properties and FRAP as dye concentration increases are illustrated. Figure 24A Depicts JF 646 With JF 549 SNR comparison between . Figure 24B Depicts JF 646 With JF 549 ERLB comparison between. Figure 24C The T measured in the bleached region is depicted. 1 / 2 Sampling as a function of dye concentration for DMSO and 1 mM KI-696 within 6-10 FOV.

[0048] Figures 25A-25BThe contributions of inter-well, inter-FOV, and inter-cell biases to 2D jump length, assessed using jump resampling, are illustrated. Figure 25A Plotted the variance of the sample mean as a function of sample size for different resampling procedures. The straight line with a slope of -1 is the expectation from the law of large numbers; sublinearity is due to residual variance of the well, FOV, or cell. Figure 25B The number of jumps for each well, FOV, or cell used in these analyses is depicted. DETAILED DESCRIPTION

[0049] The subject matter of the present disclosure relates to the development of industrial-scale, high-throughput SMT (htSMT) techniques employing oblique line scanning (OLS) illumination; systems incorporating such OLS htSMT techniques; hardware and software associated with 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 movement in millions of cells per day. In addition to being able to capture a large number of cells per field of view, OLS also benefits from improved spatial uniformity of the 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 2-fold improvement).

[0050] Table 1.

[0051] parameter OLS HILO Spatial SNR uniformity +++ + Confocality +++ + Temporal resolution +++ + FOV size ++++ +

[0052] The OLS htSMT technology described herein can be used for a variety of applications, including but not limited to drug discovery activities such as compound library screening and structure-activity relationship (SAR) elucidation. Importantly, the OLS htSMT technology described herein can be used to characterize the contributions of known and novel pathways to larger molecular assemblies containing targets, such as protein signaling interaction networks.

[0053] refer to Figure 1 , various aspects of the current subject matter can be implemented using the OLS htSMT workflow. The workflow can include various stages, as described in further detail below, such as (i) sample preparation including reagent treatment; (ii) image acquisition using sample imaging to generate a series of images and / or videos; (iii) image analysis by processing these images and videos, such as using various analyses, single emitter detection and sub-pixel localization (i.e., "super-resolution imaging"), tracking, computer vision, and machine learning algorithms; (iv) storage of information extracted from or otherwise representing or comprising 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 can be provided additionally or alternatively using various analyses, tracking, computer vision, and machine learning algorithms).

[0054] The subject matter of the present disclosure is described with reference to the accompanying drawings, in which reference numbers are used throughout to indicate similar or equivalent elements. The drawings are not drawn to scale and are provided solely for the purpose of illustrating the aspects disclosed herein. Several disclosed aspects will be described below with reference to exemplary hardware, software, and applications for illustration. It should be understood that many specific details, relationships, and methods are set forth in order to provide a more complete understanding of the subject matter disclosed herein. For the purpose of clarity of disclosure and not for limitation, the detailed description is divided into the following subsections:

[0055] 1. Definition

[0056] 2.OLS htSMT hardware

[0057] 3. OLS htSMT software

[0058] 4. Specific OLS htSMT applications

[0059] 5. Exemplary Implementation

[0060] 6. Examples

[0061] 1. Definition

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those of ordinary skill in the art are generally understood. In the event of conflict, this document (including definitions) shall prevail. Preferred methods and materials are described below, although methods and materials similar or equivalent to those described herein may be used when practicing or testing the subject matter of the present disclosure. 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 are not intended to be limiting.

[0063] As used herein, the terms "include," "comprising," "having," "has," "may," "containing," and variations thereof are intended to serve as open transitional phrases, terms, or words that do not exclude the possibility of additional actions or structures. Unless the context clearly dictates otherwise, the singular forms "a," "an," and "the" include plural referents. This disclosure also contemplates other instances of "including," "consisting of," and "consisting essentially of" the instances or elements presented herein, whether or not explicitly stated.

[0064] For the recitation of numerical ranges herein, each intervening number within the range is expressly contemplated with equal precision. For example, for a range of 6 to 9, the numbers 7 and 8 are contemplated in addition to 6 and 9, and for 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 contemplated.

[0065] As used herein, the term "about" or "approximately" means that a particular value is within an acceptable error range as determined by one skilled 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, as practiced in the art. Alternatively, "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. Alternatively, particularly with respect to 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.

[0066] As used herein, the term "track" refers to a temporally linked set of spatial coordinates corresponding to the observed positions of a fluorescent protein. In some cases, multiple tracks can be algorithmically constructed by linking multiple fluorescent proteins whose positions are determined at consecutive time points. In some cases, when no other linking is feasible, multiple tracks can be constructed conservatively by linking only spots within a fixed search radius. In some cases, multiple tracks can be constructed probabilistically.

[0067] As defined herein, protein motion refers to the change in position of multiple fluorescent proteins. In some cases, protein motion can be quantified by analyzing changes in spatial coordinates at consecutive time points. Motion characterized in this manner may include, but is not limited to, measurement of 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 protein motion. In some cases, the quantile used is the median of the jump length distribution. In some cases, the quantile used is the 3rd quartile of the jump length distribution. In some cases, protein motion can be quantified by trajectory analysis. Motion characterized in this manner may include, but is not limited to, measurement of mean square displacement, which is defined as the average of the squares of all displacements in a trajectory averaged over multiple trajectories. Motion characterized in this manner may also include, but is not limited to, measurement of trajectory length or trajectory length distribution. Motion characterized in this manner may also include, but is not limited to, measurement of the mean radius of gyration, which is defined as 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 multiple trajectories. Motion characterized in this manner may also include, but is not limited to, measurement of an average bond angle, defined as the angle formed by three consecutive spatial coordinates averaged over multiple trajectories. Motion characterized in this manner may also include, but is not limited to, measurement of a maximum likelihood estimator of the diffusion coefficient, defined as an estimate of the maximum likelihood diffusion coefficient for multiple trajectories under a single-state diffusion model with constant positioning error. In some cases, protein motion may be measured by analyzing the product of a link generation algorithm. Motion characterized in this manner may include, but is not limited to, the average posterior diffusion coefficient, the average of the posterior probability distribution of the coefficients from the probabilistic link algorithm. Motion characterized in this manner may include, but is not limited to, the geometric mean posterior diffusion coefficient, the average of the logarithmic scale posterior probability distribution of the coefficients from the probabilistic link algorithm. In some cases, protein motion may be measured by performing model correlation analysis on multiple trajectories. Motion characterized in this manner may include, but is not limited to, the fraction of immobile molecules ("f") defined by a two-state model fit. 结合 ”).

[0068] As used herein, the term "motion" encompasses changes in the direction of travel of a target as well as changes in speed (increase or decrease). Thus, in some cases, tracking motion can include determining that the target has not moved, for example, when the target is in or substantially in a static binding 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 the jump length corresponds to the observed distance traveled by the target fluorescent protein in consecutive frames); (b) the 3rd quartile of the jump length distribution; (c) the median radius of gyration; (d) the mean posterior diffusion coefficient; (e) the geometric mean posterior diffusion coefficient; (f) the mean square displacement; (g) the median bond angle; (h) the maximum likelihood estimator of the diffusion coefficient; (i) the trajectory length; and / or (j) the state occupancy by inference.

[0069] 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, and not by way of limitation, motion or lack thereof can be induced by: (A) addition of a compound; (B) temperature change; (C) change in oxygen concentration, such as introduction of hypoxic conditions; (D) mechanical stress; (E) pH change; and / or (F) change in light (e.g., increasing or decreasing intensity).

[0070] As used herein, the term "fluorescent protein" refers to any protein that emits a fluorescent signal. In some cases, fluorescent emission occurs under irradiation with light of a specific wavelength. An example of a naturally occurring fluorescent protein is green fluorescent protein (GFP). However, in some cases, the protein of interest may be adapted to emit a fluorescent signal by introducing an encoded fluorescent tag, that is, the protein sequence is fused to the protein of interest so that it emits fluorescence. In some cases, the protein of interest can emit a fluorescent signal by binding to a fluorescent ligand. Non-limiting examples of such encoded fluorescent tags include, but are not limited to, Halo tags, SNAP tags, CLIP tags, TMP tags, and SunTags. Additionally or alternatively, the protein of interest may be adapted to emit a fluorescent signal by coupling to a fluorescent dye molecule (e.g., an amine or thiol-reactive dye).

[0071] As used herein, the term "compound" refers to any chemically defined entity. In some cases, a compound can be a molecule less than 1000 Da, i.e., a "small molecule". In some cases, a compound can be a macromolecule, such as a nucleic acid. In some cases, a nucleic acid can have a defined sequence. In some cases, nucleic acids include: (A) ribonucleic acid (RNA), including, for example, modified RNA; (B) deoxyribonucleic acid (DNA), including, for example, modified DNA; and (C) a combination of (A) and (B). In some cases, the nucleic acid will be a single-stranded or double-stranded small interfering nucleic acid (e.g., 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 limited to, the protein compounds of the present disclosure encompass signal transduction proteins, such as 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, such as a mixture with a defined composition.

[0072] As used herein, the term "uniform intensity" with respect to the intensity of light (eg, light directed toward a sample plane) means that the light intensity does not vary by more than 5% in some cases, 10% in some cases, or 15% in some cases.

[0073] As used herein, the term "uniform intensity" in relation to signal-to-noise ratio (SNR) refers to the pixel-by-pixel SNR within the field of view (FOV), where the range of possible values ​​is between 0.5 and 1 standard deviation of the mean SNR.

[0074] 2. OLS htSMT hardware

[0075] Image acquisition system

[0076] refer to Figure 1 Various aspects of the current subject matter can be implemented using an htSMT workflow, wherein such a workflow is combined with a system for image acquisition. For example, such image acquisition can be combined with imaging of a sample to generate a series of images and / or a video. Figure 2A Depicted is a schematic diagram of an exemplary image acquisition system of the present disclosure, with the XZ sample plane visible. Figure 2BThe same exemplary image acquisition system is depicted, but with the YZ sample plane visible. An 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), the optical relay being 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 a uniform intensity over the longer dimension of the line shape; an optical element, such as a galvanometer (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 a sample plane (2-130) is illuminated by the tilted light beam (2-125), and the resulting light emitted from the sample, such as fluorescent emission, is focused by the objective lens (2-120), passes through a series of optical elements, such as a lens (2-155) and an emission filter (2-160), and reaches an image collection system (2-165).

[0077] 2.1.1. Light source

[0078] refer to Figure 2A An exemplary image acquisition system is provided, the system comprising a light source (2-005) configured to emit light. In certain implementations of the image acquisition system disclosed herein, the light source (2-005) may be configured to emit light of a single wavelength. In certain implementations of the image acquisition system disclosed herein, the light source (2-005) may be configured to emit light of two, three, four, five or more separate wavelengths. In certain implementations, the wavelength of the light emitted by the light source is predetermined. For example, but not by way of limitation, the wavelength may be predetermined so that the emitted light induces fluorescence emission when irradiating a sample (e.g., a sample comprising a fluorescent protein). In some cases, the wavelength employed in connection with the methods described herein will be within the range of 400nm to 650nm. In some cases, the light source (2-005) will emit light with a wavelength between 400nm and 408nm, between 550nm and 565nm, or between 638nm and 650nm. In certain non-limiting implementations, the light source (2-005) is configured to include three lasers having nominal center wavelengths of 405 nm, 560 nm, and 640 nm, respectively, which can be varied within the absorption band of the fluorophore used. In some cases, the 405 nm wavelength is used to excite the Hoechst dye. In some cases, the 560 nm wavelength is used to excite the dye attached to the HaloTag (e.g., JF549). In some cases, the 642 nm or 646 nm wavelength is used to excite the dye attached to the HaloTag (e.g., JF 646 ).

[0079] In certain non-limiting implementations, the light source (2-005) is configured to catalyze a photochemical reaction. For example, and not by way of limitation, the wavelength and intensity of the illumination can cause chemical bond breakage. As an additional example, and not by way of limitation, the wavelength and intensity of the illumination can induce the adoption of a non-radiative dark state (i.e., "photobleaching" the molecule). As an additional example, and not by way of limitation, the wavelength and intensity of the illumination can induce radiative or non-radiative energy transfer between fluorophores within the sample.

[0080] 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 a power greater than 10 mW for certain wavelengths (e.g., 405 nm) and / or delivers a power greater than 150 mW 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.

[0081] 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) may 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 synchronously with the start of image acquisition. In certain non-limiting implementations, the light source (2-005) will pulse at specific time intervals based on the number of frames captured per second. For example, but not by way of limitation, if the detector (2-165) captures 100 frames per second (FPS), the laser is on for 9 milliseconds and off for 1 millisecond. In contrast, in 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 approximately 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 approximately 0.2 milliseconds.

[0082] In certain implementations of the image acquisition system disclosed herein, a single-mode optical fiber can be used to transmit light from the light source (2-005) and to direct the light to the optical relay (2-010). Alternatively, a multimode optical fiber can be used in certain implementations of the image acquisition system disclosed herein. For example, but not by way of limitation, the multimode optical fiber can be configured to have a predetermined shape for sample illumination.

[0083] In certain implementations of the image acquisition systems described herein, such as systems configured for high-throughput sample analysis, the light source (2-005) can be configured to exhibit low power output drift. In certain implementations, this low drift configuration improves the consistency of sample processing to facilitate high-throughput analysis. For example, but not by way of limitation, this low drift power output configuration maintains the power output within a variation of about 0% to about 15%, a variation of about 0% to about 10%, a variation of about 10%, a variation of about 9%, a variation of about 8%, a variation of about 7%, a variation of about 6%, a variation of about 5%, a variation of about 4%, a variation of about 3%, a variation of about 2%, or a variation of about 1%.

[0084] In some cases, such low-drift power output configurations maintain power output within a range of about 0% to about 15%, about 0% to about 10%, about 10%, about 9%, about 8%, about 7%, about 6%, about 5%, about 4%, about 3%, about 2%, or about 1% over a range of ambient (room) temperature (e.g., 17°C + / - 5°C). In some cases, this is achieved by using temperature sensors and / or closed-loop heaters to maintain a stable temperature within the internal light source (e.g., laser engine), thereby reducing output power drift. For example, but not by way of limitation, an insulated housing design can be used to isolate the light source from ambient temperature fluctuations. Additionally or alternatively, closed-loop heaters can be strategically placed at specific locations within the system, such as at the fiber coupler, to reduce output drift. Additionally or alternatively, a water jacket and / or chiller can be used to reduce heat buildup in the laser head. Furthermore, these thermal controls, used alone or in combination, can reduce 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.

[0085] 2.1.2. Optical components and sample illumination

[0086] refer to Figure 2AAn exemplary image acquisition system includes 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), the optical relay being configured to shape the light emitted from the light source to form a shaped light beam (2-065). The specific optical elements implemented in any particular optical relay (2-010) can be selected and configured to produce a suitably shaped light beam (2-065) and provide appropriate translation of the light beam.

[0087] 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 an optical element, such as a collimator (2-020), for collimating the emitted light from the light source (2-005). Additionally or alternatively, the optical relay (2-010) will include additional optical elements, such as a Powell lens (2-025) or other element suitable for generating a beam fan, one or more cylindrical lenses ((2-045) and (2-055)), one or more slits for adjusting the range of the light sheet ((2-050) and (2-095)), 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 may be a galvanometer mirror (2-085) capable of translating light. The specific properties of the optical elements will be predetermined to produce an appropriately shaped beam. For example, but not by way of limitation, the OLS htSMT system of the present disclosure can achieve a uniform horizontal FOV as well as a uniform vertical FOV. This uniformity of horizontal and vertical FOV contrasts with other strategies that provide a non-uniform horizontal FOV and / or a non-uniform vertical FOV (see Table 2).

[0088] Table 2. Technology comparison

[0089]

[0090]

[0091] To achieve a uniform horizontal FOV and a uniform vertical FOV, the optical relay (2-010) of the OLS htSMT system described herein includes an optical element or assembly 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 over the longer dimension of the line shape. 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 so that it has uniform intensity over the longer dimension of the line shape (2-065). The optical relay (2-010) of the OLS htSMT system described herein may include additional or alternative optical elements or assemblies to shape the light beam so that it has uniform intensity over the longer dimension of the line shape (2-065). For example, but not by way of limitation, the optical relay (2-010) of the OLS htSMT system described herein may include a diffractive element or assembly configured to shape the light beam so that it has uniform intensity across the longer dimension of the line shape.

[0092] 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, for example, 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 may 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 may include a computer-controlled motor.

[0093] refer to Figure 2A An exemplary image acquisition 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 guided by an optical element (2-100), such as a dichroic mirror, and the optical element is configured to guide the shaped light beam to an objective lens (2-120), whereby a sample plane (2-130) is illuminated by an inclined light beam (2-125).

[0094] In certain non-limiting implementations of the image acquisition system of the present disclosure, the objective lens (2-120) directs the tilted light 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 enables high-throughput sample analysis by eliminating the oil associated with the use of an oil immersion objective lens, thereby allowing higher image quality and less distortion. The presence of oil is not only problematic in the case of automated systems, where the oil can spread to components, including optical elements that may become dirty due to contact with the oil, but the water immersion objective lens also provides a better refractive index match to the imaging unit, resulting in less distortion and therefore higher image quality compared to an oil immersion objective lens. In certain non-limiting implementations, the objective lens is a 60X 1.27NA water immersion objective lens (Nikon). In certain implementations of the workflow 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 (2-120) at a temperature sufficient to avoid causing temperature changes in a sample contained in the sample plate (2-021).

[0095] Image acquisition

[0096] 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 illumination provided by the tilted light 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 matched to the spectrum of the observed fluorophore and mounted in a high-speed filter wheel (Finger Lakes Instruments), and is collected by the detector device (2-165). In certain non-limiting implementations, the objective lens focuses the fluorescence emission and is directed to an optical relay before being collected by the detector device (2-165). For example, but not by way of limitation, such an optical relay may 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 fluorescent emission focused by the objective is directed through another dichroic mirror to split the emission across multiple regions of a detector (2-165). In certain non-limiting implementations, the fluorescent emission focused by the objective is directed through another dichroic mirror to split the emission across multiple detectors (2-165).

[0097] In certain non-limiting implementations of the image acquisition system of the present disclosure, the detector arrangement is configured to synchronize detection with translation of the tilted light beam (2-125) on the sample plane (2-130). This synchronization is schematically depicted in Figure 2F For example, but not by way of limitation, the detection device may be a CMOS camera, such as a back-illuminated CMOS camera (Hamamatsu Fusion BT).

[0098] In certain implementations of the image acquisition system of the present disclosure, the CMOS camera can be operated so that a series of SMT frames are collected for each field of view. For example, but not limited to, 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 for each 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 may be configured to operate at a frame rate of 100 Hz to 1250 Hz, as shown in Figures 15, 17, 18, and 19. For example, but not by way of limitation, certain cell SMT implementations may operate at 100 Hz. In certain embodiments, certain cell SMT implementations may operate at 200 Hz. In certain embodiments, certain cell SMT implementations may operate at 400 Hz. In certain embodiments, certain cell SMT implementations may operate at 800 Hz. In certain embodiments, certain cell SMT implementations may operate at 1000 Hz. In certain embodiments, certain cell SMT implementations may operate at 1200 Hz. In certain embodiments, certain cell SMT implementations may operate at 1250 Hz. In certain embodiments, certain cell SMT implementations may operate at 1400 Hz. In certain embodiments, certain cell SMT implementations may operate at 1600 Hz. In certain embodiments, certain cell SMT implementations may operate at 1800 Hz. In certain embodiments, certain cell SMT implementations may operate at 2000 Hz. In certain embodiments, certain cellular SMT implementations may be performed at a frame rate of about 100 Hz or higher, about 200 Hz or higher, about 400 Hz or higher, about 600 Hz or higher, about 800 Hz or higher, about 1000 Hz or higher, about 1200 Hz or higher, about 1400 Hz or higher, about 1600 Hz or higher, or about 1800 Hz or higher. In certain embodiments, certain cellular SMT implementations may be performed at a frame rate of at most about 1200 Hz. In certain embodiments, certain cellular SMT implementations may be performed at a frame rate of at most about 1400 Hz. In certain embodiments, certain cellular SMT implementations may be performed at a frame rate of at most about 1600 Hz. In certain embodiments, certain cellular SMT implementations may be performed at a frame rate of at most about 1800 Hz. In certain embodiments, certain cellular SMT implementations may be performed at a frame rate of at most about 2000 Hz.

[0099] 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 components of the imaging system. For example, but not by way of limitation, the detector device can trigger illumination from the light source (2-005) to collect fluorescence emission associated with a stroboscopic laser pulse. For example, but not by way of limitation, such fluorescence emission collection is associated with a frame of 10 to 100 milliseconds and a stroboscopic laser pulse of 2 milliseconds. In certain embodiments, the fluorescence emission collection is associated with a stroboscopic laser pulse of about 0.1 to about 1 millisecond. In certain embodiments, fluorescence emission collection is associated with 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 milliseconds, about 0.1 to about 0.8 milliseconds, about 0.1 to about 0.7 milliseconds, about 0.1 to about 0.6 milliseconds, about 0.1 to about 0.5 milliseconds, about 0.1 to about 0.4 milliseconds, 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, fluorescence emission collection is associated with a stroboscopic laser pulse of about 0.1 to about 0.6 milliseconds. In certain embodiments, fluorescence emission collection is associated with a stroboscopic laser pulse of about 0.1 to about 0.5 milliseconds. In certain embodiments, fluorescence emission collection is associated with a stroboscopic laser pulse of about 0.2 to about 0.4 milliseconds. In certain embodiments, fluorescence emission collection is associated with a stroboscopic laser pulse of about 0.2 milliseconds. In certain embodiments, fluorescence emission collection is associated with strobed laser pulses of approximately 0.4 milliseconds, such as Figure 13C shown.

[0100] In certain implementations, the imaging acquisition system may 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) may have 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. In certain embodiments, the FOV (e.g., the detected FOV) may have a size of about 200 μm to about 250 μm in a first dimension and a size of about 150 μm to about 210 μm in a second dimension, or the FOV (e.g., the detected FOV) may have a size of about 225 μm to about 250 μm in a first dimension and a size of about 175 μm to about 210 μm in a second dimension. For example, but not by way of limitation, the FOV (e.g., the detected FOV) may have a size of about 250 μm in a first dimension and a size of about 190 μm in a second dimension, for example, as disclosed in Example 1.

[0101] In some embodiments, a certain percentage of the FOV (e.g., the detected FOV) provides usable data. In some 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 some embodiments, at least 75% of the FOV (e.g., the detected FOV) provides usable data. In some embodiments, at least 80% of the FOV (e.g., the detected FOV) provides usable data. In some embodiments, at least 85% of the FOV (e.g., the detected FOV) provides usable data. In some embodiments, at least 90% of the FOV (e.g., the detected FOV) provides usable data. In some embodiments, at least 95% of the FOV (e.g., the detected FOV) provides usable data. In some embodiments, at least 96% of the FOV (e.g., the detected FOV) provides usable data. In certain embodiments, at least 97% of the FOV (e.g., the detected FOV) provides usable data. In certain embodiments, at least 98% of the FOV (e.g., the detected FOV) provides usable data. In certain embodiments, at least 99% of the FOV (e.g., the detected FOV) provides usable data. In certain embodiments, 100% of the FOV (e.g., the detected FOV) provides usable data. In certain embodiments, a percentage equal to or greater than about 75% of the FOV provides usable data, 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 provides usable data. In certain embodiments, a certain percentage of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. 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 motion. In certain embodiments, at least 75% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 80% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion.In certain embodiments, at least 85% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 90% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 95% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 96% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 97% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 98% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 99% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, 100% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In some embodiments, a percentage of the FOV equal to or greater than about 75% achieves sufficient laser illumination to track protein motion, for example, a percentage of the FOV equal to or greater than about 80%, a percentage of the FOV equal to or greater than about 85%, a percentage of the FOV equal to or greater than about 90%, a percentage of the FOV equal to or greater than about 95%, a percentage of the FOV equal to or greater than about 96%, a percentage of the FOV equal to or greater than about 97%, a percentage of the FOV equal to or greater than about 98%, or a percentage of the FOV equal to or greater than about 99% achieves sufficient laser illumination to track protein motion.

[0102] 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 × 1728 pixels will define the ROI, which is equivalent to 248.832 × 186.624 microns in the sample plane. In contrast, at 200 FPS, 2304 × 768 pixels will define the ROI, which is equivalent to 248.832 × 82.944 microns in the sample plane.

[0103] In some 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 μm / 9 ms, equivalent to 20.8 μm / ms, equivalent to 2.08 cm / s. In contrast, at 200 FPS, the scan rate can be 82.94 μm / 4 ms, equivalent to 20.7 μm / ms, equivalent to 2.07 cm / s.

[0104] In some implementations, the detector assembly can be used to collect fluorescence emissions at multiple wavelengths. For example, but not by way of limitation, fluorescence emissions from additional fluorophores within the same field of view can be collected at the same frame rate or at different frame rates to provide downstream registration of SMT tracks with other cellular components (e.g., the nucleus). Additional channels of the detector assembly can be used as needed to expand the number of fluorescence emissions captured simultaneously within the same field of view to provide downstream registration of SMT tracks with other cellular components (e.g., the nucleus).

[0105] Sample processing

[0106] refer to Figure 1 , various aspects of the current subject matter can be implemented using htSMT workflows, where such workflows incorporate systems for sample preparation, including reagent handling. For example, and not by way of limitation, Figure 5 A schematic diagram of a sample plate (2-021) is provided, comprising a plurality of wells (2-016) in which samples may be prepared and analyzed. Figure 5 A schematic diagram of sample components, such as cells (2-018) and fluorescent target proteins (2-017) within the cells is also provided. However, as described herein, Figure 5 It is not intended to convey scale, for example, each sample present in wells (2-016) may contain thousands of cells, and each cell may contain many fluorescent target proteins. Figure 5 The ability of the sample processing system of the present disclosure to add additional reagents to the sample (2-019) is also schematically illustrated. Such reagent addition can be handled by robotic operations, such as, but not limited to, translation of the robotic fluid handling system relative to the individual wells (2-016) of the 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 an environmentally controlled area (2-020). For example, but not by way of limitation, the sample 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 an environmentally controlled area (2-020). For example, but not by way of limitation, the sample can be maintained at a humidity of 20%-95%. In certain implementations of the image acquisition system, the sample plate (2-021) can be maintained in a defined gas environment by an environmentally controlled area (2-020). For example, but not by way of limitation, the sample can be maintained at 5% CO2.

[0107] Cell lines and cell culture

[0108] See Figure 5A particular advantage of the htSMT system described herein is that it can be assayed in living cells (2-016), allowing for tracking the activity, mobility, and diffusion behavior of proteins within the crowded environment of living cells. Figure 11B As shown, the htSMT system of the present disclosure can be used to track fluorescently labeled proteins in a sample containing multiple cells. If the sample (e.g., containing such cells) can be focused by the objective lens (2-120) for a long enough time to direct the fluorescent emission of the fluorophore to the detector (2-165), then consider the example cells (e.g., cell lines) used in conjunction with the htSMT system described herein. For example, but not by way of limitation, the cells can be directly adhered to the coverslip. As an additional example, but not by way of limitation, the cells can be induced to adhere to the coverslip after the coverslip is treated with an extracellular matrix material (e.g., fibronectin, collagen, poly-D-lysine, laminin, matrigel, vitronectin, etc.).

[0109] Exemplary cells (e.g., cell lines) may be selected so as to minimize non-fluorophore emission reaching the detector. In certain embodiments, the cells used in the present disclosure may be mammalian, bacterial, or fungal cells. In certain embodiments, the cells are mammalian cells. In certain embodiments, the cells may be obtained from preserved tissue (e.g., fixed tissue), frozen tissue (e.g., frozen tissue sample), or fresh tissue (e.g., fresh tissue sample). In certain embodiments, cells and / or samples containing cells may be obtained from a subject. In certain embodiments, cells may be obtained from a malignant tumor of a tissue or tumor, for example, cells may be present in a tumor sample (e.g., a section of a tumor). In certain embodiments, cells may be obtained from a cell line. For example, but not as a limitation, specific cell lines that can 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 certain embodiments, cells may be present in a three-dimensional structure, such as an organoid or a spheroid. In certain embodiments, the cells may be present in organoids.

[0110] In certain implementations of the htSMT system disclosed herein, cells to be used are cultured as needed to provide sufficient cell numbers to achieve the desired high-throughput analysis. For example, but not limitation, cells such as U2OS 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 with 5% CO2, with subculture performed approximately every two to three days. Additional culture strategies suitable for the cell lines and uses outlined herein are known to those skilled in the relevant art.

[0111] In certain implementations of the htSMT system disclosed herein, cells contain one or more fluorescent target proteins. The choice of specific proteins to be labeled and the specific labeling method may vary depending on the specifics of the particular study. For example, but not by way of limitation, one method for labeling proteins that can be used in conjunction with the htSMT system described herein is a HaloTag fusion strategy. For example, but not by way of limitation, one method for labeling proteins is a SNAPtag fusion. For example, but not by way of limitation, one method for labeling proteins is a CLIPtag fusion. For example, but not by way of limitation, one method for labeling proteins is a fluorophore ligase system. For example, but not by way of limitation, one method for labeling proteins is via a FlAsH or ReAsH tetracysteine ​​motif. For example, but not by way of limitation, one method for labeling proteins is a strain-promoted alkyne-azide cycloaddition reaction of a fluorophore. For example, but not by way of limitation, one method for labeling proteins is by inducing cellular uptake of a separately produced fluorescent target protein. In certain implementations of the htSMT system disclosed herein, cells contain one or more fluorescently labeled glycoproteins. In certain embodiments, one method for labeling proteins uses a gene editing system, such as a CRISPR-based editing system. For example, without limitation, a nucleic acid encoding a fluorescent protein (e.g., a fluorescent tag, such as HaloTag) can be inserted into a gene or upstream or downstream of a gene encoding a protein to be labeled to produce a protein fluorescently labeled with HaloTag (e.g., at its C-terminus or N-terminus), for example as described in Example 2.

[0112] Although the HaloTag fusion method can be implemented in a variety of ways by those skilled in the art, one exemplary method is to transfect a mammalian expression vector in a cell line of interest (e.g., U2OS cells) containing a fusion gene (i.e., a protein of interest fused in frame with the HaloTag sequence) under the control of a weak L30 promoter and containing a neomycin resistance marker. In certain implementations, such transfection can be accomplished using FuGENE 6 (Catalog No. E2691, Promega) when the cell confluence reaches 70%. In certain implementations, the transfected cells can then be selected with an appropriate selection agent, such as G418 (Catalog No. 10131027, Thermo Fisher), at an appropriate concentration of, for example, 500 μg / mL. In certain implementations, the cells can then be cloned and isolated. The cells can first be isolated by rinsing with 100 nM JF 549 -HTL (Cat. No. GA1110, Promega) and 50 nM Hoechst 33342 were used to stain and identify cells with the expected JF 549 The clones of the signal distribution are used to determine the clones expressing the desired fusion gene. Another exemplary method is to transfect cells with a ribonucleoprotein (RNP) complex, which includes sgRNA and Cas9 protein targeting the genomic sequence of the N-terminal or C-terminal region encoding the target protein, and combined with one or more linear dsDNA donors. In certain embodiments, each donor consists of a 200-300bp homology arm specific for each target, a codon-optimized HaloTag sequence, and a TEV linker (ENLYFQG) between the target and the HaloTag. In certain implementations, the SMT conditions can then be used to test the reaction of three to six clones to the control compound, and the most homogeneous clones can then be expanded for further testing.

[0113] While the htSMT workflow of this application is generally described with respect to the implementation of tracking compounds' effects on target fluorescent proteins, the htSMT workflow described herein is equally applicable to tracking and analyzing fluorescent target compounds. For example, but not by way of limitation, the compounds described herein can themselves be fluorescent or modified to facilitate fluorescence detection. Furthermore, changes in the motion of fluorescent compounds can be exploited to determine the SMT spectrum of the compound itself. Therefore, all analytical strategies described herein for tracking target fluorescent proteins also apply to the results obtained by tracking the compound itself.

[0114] 2.2.2. Single-molecule tracking sample preparation

[0115] refer to Figure 5, various aspects of the current subject matter can be implemented using the htSMT workflow, wherein cells (2-018) are seeded on a plate (2-021), such as a 384-well tissue culture treated glass bottom plate, although other types of culture plates can also 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 JF 549 、JF 646 In some embodiments, cells can be incubated with about 0.1-100 pM of JF 549 -HTL (Cat. No. GA1110, Promega) or about 0.1-100 pM JF 646 Incubation with 50 nM Hoechst 33342 (for labeling cell nuclei), for example, for one hour in complete culture medium, may provide ideal results.

[0116] In certain implementations of the htSMT strategy described herein, the cells are then washed, for example, three times in DPBS and twice in imaging medium. In certain implementations, imaging medium is prepared to promote fluorescence emission, such as fluoroBrite DMEM medium (Cat. No. A1896701, Thermo Fisher), and may be supplemented with GlutaMAX (Cat. No. 35050079, Thermo Fisher) and the same serum and antibiotics as the growth medium.

[0117] Where appropriate, the compound can be added to the sample to test its effect on a specific marker protein by SMT. In some implementations, the compound can be serially diluted in an Echo Qualified 384-well low dead volume source microplate (0018544, Beckman Coulter) to generate a dose titration source material. The compound can then be administered in a cell culture medium at a final dilution of, for example, 1:1000. In some implementations of the htSMT strategy described herein, each dose of compound will have at least two replicates per plate and three replicates per plate. In addition, in some implementations of the htSMT strategy described herein, 20 DMSO control wells and two dye-free control wells can be randomly distributed on each sample plate (2-020). In some implementations, the compound can be incubated for 0 to 48 hours before image acquisition, for example, at 37°C for 1 hour.

[0118] 3. OLS htSMT software

[0119] Figure 6 An example system 600 of a high-throughput single-molecule imaging platform for measuring the movement of molecules within living cells is illustrated. An experiment 602 can be performed to collect a large amount of data from a plurality of living cells (e.g., using an imaging system 624 to identify a compound 626 and / or a target 622). The experiment 602 can include applying various identifiers to molecules of interest, such as tags that can then fluoresce or be detected in other ways (e.g., using a laser or other light source). A biological sample forming part of such an experiment 602 can be organized into a plate 604 having a plurality of wells 606. Each well 606 can have one or more associated fields of view (FOV) 610. The FOV 610 can be located within or correspond to a single well 606. A series of images can be generated for the FOV 610 to produce one or more movies 612, which can include SMT movies as well as non-SMT movies. The SMT movie can be used to track the path of a single labeled molecule (e.g., a protein), thereby generating multiple tracks. Each track can be composed of a plurality of spots 614, which include the spatiotemporal coordinates of the labeled molecule at a particular time (e.g., Figure 7 610 ). Separately from tracking, and in some cases in parallel with tracking, the movie 612 can be used to identify molecules by using machine learning and / or computer vision-based image segmentation to generate masks 618. Masks 618 are spatial regions within the FOV 610 generated by segmentation. Each mask 618 can belong to a mask category, which is Figure 8 and Figure 20B A more detailed description is given in .

[0120] Data associated with the two channels (e.g., the tracking channel and the segmentation / masking channel) can be combined to generate a plurality of metrics 620 associated with various aspects of the sample. In other words, the trajectory 616 (e.g., trajectory data) can be combined with the 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 metrics 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.

[0121] Figure 7 The data flow through an example system 700 for a high-throughput single-molecule imaging platform for measuring protein movement in living cells is illustrated. Experimental specifications 704 defining experiments 602 may be provided as data input via one or more clients 702. For example, each experiment 602 may be collected with accompanying stains (e.g., Hoechst or Potomac Red) for downstream analysis including segmentation 618. The experimental specifications 704 may define various parameters of the experiment 602, such as stains, dyes, compounds, treatments, etc. As previously described in Figure 6 As described in , 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 segmented movies 708 (e.g., movie 612) that characterize the motion of molecules. SMT movie 711 can characterize the motion of a single fluorescent molecule and / or an image containing a single fluorescent molecule. Segmented movie 708 can include a series of images that characterize the motion of labeled molecules and / or their components. It should be understood that Hoechst staining is only one technique that can be used to label molecules, and different and / or multiple labeling techniques, such as Potomoc Red, can be utilized depending on the desired configuration. For example, MitoTracker Deep Red can be used to label mitochondria, concanavalin A-dye conjugate can be used to label the endoplasmic reticulum, SYTO 14 can be used to label nucleoli, phalloidin can be used to label actin, etc.

[0122] The SMT movie 711 can be analyzed to perform operations related to molecular tracking 710, which can include detection 712, sub-pixel localization 713, and linking 714 to identify molecular tracks 715 across the various images within the SMT movie 711. More specifically, during detection 712, one or more spots within the SMT movie 711 can be detected or recovered. Each spot can be assigned spatiotemporal coordinates. These spatiotemporal coordinates can be estimated using sub-pixel localization techniques 713. Linking 714 can be performed on these spots to ultimately identify tracks 715.

[0123] As used herein, a link is a potential association between two blobs. Each link is directed, starting from one blob and ending at another. A "correct link" connects two blobs generated 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. The links mentioned herein are of the form a:i→j. This means: link α, starting from blob i and ending at blob j. Links satisfy at least the following three constraints: (a) links advance in time, (b) links must not connect two blobs that are more than a certain limit apart (referred to herein as the "search radius"), and (c) links must not connect two blobs that are more than a certain limit apart in time (referred to herein as the "gap limit"). The blob link graph is a graph of blobs and links for an SMT movie 711. The blobs are the vertices of the graph, and the links are the edges of the graph. Since links advance in time, the blob link graph is a directed acyclic graph. A match is a subset of links in the blob link graph such that no two links in the subset start or end at the same blob. Trajectory 715 is used herein to refer to a continuous (end-to-end) sequence of links in the same match. Multiple trajectories may be used to determine dynamic indicators 730. Such parameters may include properties of the spots that characterize the motion of the spots. Such parameters may include one or more of the velocity, diffusion coefficient, or anomaly parameters of each spot. The dynamic parameters of spot i are referred to herein as θ i The set of dynamic parameters of all blobs in the blob link graph is referred to here as Θ.

[0124] Separately from the processing of the SMT movie 711, and in some variations in parallel therewith, the segmentation movie 708 can be segmented, thereby generating one or more masks 720. The masks can be of various types, including but not limited to nucleus, cytoplasm, and / or irrelevant masks, which will be described in detail in the following sections. Figure 8 . An instance mask is an individual segmented object (e.g., a cell, a nucleus, a mitochondria, M phase, G1 phase, early S phase, mid S phase, late phase, G2 phase). The FOV 610 may contain any number of instance masks of a mask class. A semantic mask is the union of all instance masks of a mask class corresponding to a FOV (e.g., all cells, all nuclei, or all mitochondria of a FOV, etc.). Irrelevant masks may contain portions of the non-SMT movie 708 that are excluded from any downstream data analysis. For example, these irrelevant masks may correspond to portions of the non-SMT movie 708 that are out of focus or contain autofluorescent cell debris that prevents accurate tracking. During the segmentation process, molecules in the segmented movie 708 may be assigned to one or more masks. Image metrics 740 may be evaluated based on the masked molecules (e.g., cell health, focus quality, etc.).

[0125] Experiment information, such as dynamic metrics 730, image metrics 740, and any data derived from any of the metrics (e.g., segmentation information), can be provided to a data repository 770 for storage. Such a data repository 770 can store, for example, any results of an experiment 602, such as dynamic metrics 730, image metrics 740, and / or any data derived from any of the metrics. The data repository can include local persistence and / or dedicated servers accessed locally or via the cloud. The data repository 770 can also store metadata associated therewith and / or metadata associated with the experiment specification 704. Experiment information (e.g., results and metadata from historical experiments, etc.) can be provided to the data repository 770 via a repository application program interface (API) 750. The repository 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.

[0126] In some variations, the segmentation information can be used to identify subcellular compartments, such as the nucleus, nucleolus, cytoplasm, etc. The segmentation information can also be used to distinguish one cell from another. The segmentation information can be stored in a specific format (e.g., a multi-image file format such as TIFF).

[0127] The example dynamic index 730 may also include a state array. The state array is a framework for learning interpretable dynamic models from SMT trajectories and can be used to further understand the movement of the target protein and where in the cell the movement occurs. In some variations, the state array can be generated / populated using segmentation information. The output of the state array can be returned at the subcellular compartment level, allowing scientists to distinguish the dynamics of different subcellular compartments. In addition, the state array can be calculated for each individual subcellular compartment (e.g., each cell nucleus).

[0128] To facilitate application access to data (including but not limited to state arrays), processed SMT data can be stored in formats that allow for the following: (a) representation of processed tracks and associated properties, such as the SNR and spot shape characteristics for each SMT movie; (b) representation of mask objects, including mask categories (e.g., the associated subcellular organelle for each mask object, the cell cycle stage for each mask object, etc.); (c) association of tracks with mask objects (e.g., the nucleus in which each track was observed, or the cell cycle stage in which each track was observed); and (d) association of all SMT movies with metadata about the original experiments, such as compound treatment, acquisition time, and imaging system name. Formats (a) and (c) can be protocol buffer schemas that define the storage format for tracks and associated mask objects. Format (b) can be a specialized image file format that includes the mask object to which each pixel in the FOV belongs. Format (d) can be a PostgreSQL database that records all captured experiments / movies. As a client of processed SMT data, the state array can leverage these data schemas to report the dynamic characteristics of tracks for each mask category or each mask object.

[0129] Figure 8 800 are multiple images illustrating the difference between mask categories and instance or semantic masks. As previously described, non-SMT movies or segmentation movies can be assigned to multiple categories. These categories may include cell nuclei (e.g., category A), cytoplasm (e.g., category B) and / or irrelevant masks (e.g., category C). Unique, individual masks can be applied to biological samples. For example, image 810 is a unique, individual instance mask applied to a cell nucleus (e.g., category A). Image 812 is a unique, individual instance mask applied to the cytoplasm (e.g., category B). Image 820 illustrates multiple instance masks applied to one or more cell nuclei, where a single color represents a different, unique individual instance mask. Image 822 illustrates multiple 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 cell nuclei, which is the 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 explained. Figure 20B As shown, a single instance mask can be applied to cells in M ​​phase, a single instance mask can be applied to cells in G1 phase, a single instance mask can be applied to cells in early S phase, a single instance mask can be applied to cells in mid S phase, a single instance mask can be applied to cells in late S phase and / or a single instance mask can be applied to cells in G2 phase.

[0130] Figure 9An example computer-implemented environment 900 is illustrated in which an imaging system 910 can interact with a computing architecture to execute the various algorithms described herein. Figure 9 As shown, the imaging system 910 can interface with one or more clients 950 (e.g., client 702 via a web application with a graphical user interface). The one or more clients 950 can interface with one or more servers 920 accessible via a network 930. The one or more clients 950 can host a frame grabber that captures images (e.g., movie 612) from a camera. These images can be temporarily stored on the one or more clients 950 and periodically transmitted to the one or more servers 920 via the network 930 for remote storage. The one or more servers 920 can also contain or have access to one or more data stores 940 for storing data collected and / or extracted from the sample by the imaging system 910. In some variations, the network 930 can include or be interfaced with one or more network storage arrays 960 for storing data such as captured images (e.g., movie 612).

[0131] Figure 10 1000 is a diagram illustrating an example computing device architecture for implementing various aspects described herein. In some variations, the sample computing device architecture may be the architecture of client 950 and / or server 920, and some components described with respect to diagram 1000 may be optional for client 950 and / or server 920. Bus 1004 may serve as an information highway interconnecting the 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), may perform the computations and logical operations required to execute a program. Optionally or in addition, processing system 1012, labeled GPU (graphics processing unit) (e.g., one or more computer processors / data processors on a given computer or multiple computers), may perform the computations 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, the program instructions may be stored on a non-transitory computer-readable storage medium, such as a magnetic disk, an optical disk, a recordable memory device, a flash memory, a solid-state drive, or other physical storage medium.

[0132] 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, a solid-state drive, or an external hard drive. The local storage 1052 can be an internal hard drive and / or memory. As previously described, the various examples of removable storage 1056, local storage 1052, and disk controller 1048 are all optional devices. The system bus 1004 can also include at least one communication interface 1024 to allow communication with external devices (such as cloud storage and remote services) that are physically connected to the computing system or obtained externally via a wired or wireless network. In some cases, at least one communication interface 1024 includes or additionally comprises a network interface.

[0133] In some variations, such as for client 950, to provide for user interaction, the subject matter described herein may be implemented on a computing device having a display device 1044 (e.g., an LCD (liquid crystal display) or LED (light emitting diode) monitor) for displaying information obtained from bus 1004 to the user via display interface 1040, and an input device 1032 (e.g., a keyboard and / or pointing device (e.g., a mouse or trackball) and / or a touch screen) through which the user can provide input to the computer. Other types of input devices 1032 may also be used to provide for user interaction; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback via microphone 1036, or tactile feedback); and input from the user may be received in any form, including sound, voice, or tactile input. Input device 1032 and microphone 1036 may be coupled to bus 1004 via input device interface 1028 and communicate information via the bus. For example, input device 1032 may be imaging system 910 configured with the capability to capture the series of images described herein. The frame grabber 1058 may capture or grab a single frame from the analog or digital data that encapsulates a series of images obtained from the bus 1004. The frame grabber 1058 may include a memory capable of storing a single frame or multiple frames. The frame grabber 1058 may also provide the single frame or multiple frames to the bus 1004 for further storage, for example, on the local memory 1052 and / or the removable memory 1056. Other computing devices (e.g., dedicated servers) may omit the incorporation of Figure 10 Describes one or more components.

[0134] One or more aspects or features of the subject matter described herein may be implemented in digital electronic circuits, integrated circuits, 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 may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system comprising at least one programmable processor, which may be dedicated or general purpose, coupled to receive data and instructions from a storage system, at least one input device, and at least one output device, and to transmit data and instructions to the storage system, at least one input device, and at least one output device. A programmable system or computing system may include a client and a server. The client and server are typically remote from each other and typically interact via a communication network. The relationship of client and server arises through computer programs running on respective computers that cause each other to have a client-server relationship.

[0135] These computer programs, which may also be referred to as programs, software, software applications, applications, components or codes, comprise machine instructions for a programmable processor and may be implemented in high-level procedural languages, object-oriented programming languages, functional programming languages, logic programming languages ​​and / or assembly / machine languages. As used herein, the term "machine-readable medium" refers to any computer program product, device and / or apparatus for providing machine instructions and / or data to a programmable processor, such as a disk, an optical disk, a memory and a programmable logic device (PLD), including a machine-readable medium that receives machine instructions in the form of a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor. A machine-readable medium may store such machine instructions non-temporarily, such as a non-transitory solid-state memory or a magnetic hard disk or any equivalent storage medium. A machine-readable medium may alternatively or additionally store such machine instructions in a transient manner, such as a processor cache or other random access memory associated with one or more physical processor cores.

[0136] 4. Specific OLS htSMT applications

[0137] Many, perhaps most, pathways that regulate fundamental cellular biochemistry rely on the transient interaction of protein sensors with protein effectors that trigger changes in cellular physiology. Although the fundamental principles of this process have long been recognized, biochemical studies of these protein interactions typically require in vitro reconstitution or interrogation via pull-down assays after cell permeabilization. The htSMT workflow described here provides a method for visualizing protein movements in large numbers of living cells, while allowing for quantitative assessment of the effects of added compounds, such as small molecule inhibitors.

[0138] refer to Figure 1Various aspects of the OLS htSMT workflow disclosed herein include, but are not limited to: (i) sample preparation (including reagent handling), (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). With respect to biological interpretation, the htSMT workflows described herein can provide specific insights, as described below, depending on the specific workflow employed, such as (i) OLS htSMT screening; (ii) OLS htSMT binding; and / or (iii) OLS KineticSMT.

[0139] 4.1 OLS htSMT screening

[0140] In certain implementations of the OLS htSMT workflows described herein, the systems and methods are applicable to interrogate the ability of one or more compositions (e.g., "test" compounds) to affect the SMT spectrum associated with a labeled protein. For example, such an htSMT workflow would screen for changes in the SMT spectrum, e.g., an increase or decrease in the movement of a protein of interest in the presence of the composition relative to the SMT spectrum in the absence of the composition, e.g., when the addition of the composition is replaced by a control such as, but not limited to, DMSO. It will be understood that higher order comparisons can also be made where compounds are multiplexed, including where multiple proteins fluoresce. In addition, as described above, the htSMT screening strategies described herein are equally applicable to screening SMT spectra associated with fluorescent compounds, such as compounds that are naturally fluorescent or compounds that have been modified to fluoresce or linked to a fluorophore.

[0141] The basis of such htSMT screening strategies is that the htSMT workflow described herein is capable of extracting accurate molecular trajectories on a large scale. Exemplary OLS htSMT workflows include the following individual strategies and 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 so as to cause a subset of fluorescent target proteins in living cells to fluoresce, 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 so as to cause a subset of fluorescent target proteins in living cells to fluoresce, wherein the subset of fluorescent target proteins includes proteins in the range of about 1000 to about 1,000,000. Similarly, illuminating the sample plane to illuminate about 30 to about 80 living cells per FOV and / or causing about 1000 to about 1,000,000 proteins to fluoresce can be combined with any other strategic requirements disclosed herein, for example, determining an average change in the motion of a fluorescent target protein of about 1% to about 5% or about 10% in the presence of a compound relative to the absence of the compound, detecting fluorescence of multiple fluorescent target proteins in the sample plane field of view at a rate of about 10,000 to about 18,000 per system per day, and achieving a z-factor >0.5 based on a single field of view.

[0142] In certain implementations of the OLS htSMT screening workflow described herein, the workflow may include identifying a biological interaction between a compound and a fluorescent target protein 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, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the fluorescent target proteins in the living cells fluoresces, wherein the subset of fluorescent target proteins is present in about 30 to about 80 living cells illuminated in the sample plane field of view, depending on the specific 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, by a detector device, the fluorescence of the plurality of fluorescent target proteins in the sample plane field of view; and (c) determining a change in the movement of the fluorescent target proteins in the presence of the compound; wherein the change in the movement of the fluorescent target proteins in the presence of the compound relative to the movement of the fluorescent target proteins in the absence of the compound identifies a biological interaction between the compound and the fluorescent target proteins.

[0143] In certain implementations of the OLS htSMT screening workflow described herein, the workflow may include identifying a biological interaction between a compound and a fluorescent target protein 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, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the fluorescent target proteins in the living cells fluoresces, 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 a dye concentration deemed sufficient to label the subset of proteins for robust SMT, both of which can be calculated and / or configured by a person skilled in the art based on the disclosure of this application; (ii) detecting, by a detector device, the fluorescence of the plurality of fluorescent target proteins in the field of view of the sample plane; and (c) determining a change in the movement of the fluorescent target proteins in the presence of the compound; wherein the change in the movement of the fluorescent target proteins in the presence of the compound relative to the movement of the fluorescent target proteins in the absence of the compound identifies a biological interaction between the compound and the fluorescent target proteins.

[0144] In certain implementations of the OLS htSMT screening workflow described herein, the workflow may include identifying a biological interaction between a compound and a fluorescent target protein 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 multiple individual fluorescent target proteins in multiple 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 so that a subset of the fluorescent target proteins in the living cells emit fluorescence; (ii) detecting the fluorescence of multiple 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 protein in the presence of the compound, wherein the average change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound is about 1% to about 5% or to about 10%; wherein the change in the movement of the fluorescent target protein in the presence of the compound relative to the movement of the fluorescent target protein in the absence of the compound identifies a biological interaction between the compound and the fluorescent target protein.

[0145] In certain implementations of the OLS htSMT screening workflow described herein, the workflow may include identifying a biological interaction between a compound and a fluorescent target protein 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 multiple individual fluorescent target proteins in multiple 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 so that a subset of the fluorescent target proteins in the living cells emit fluorescence; (ii) detecting the fluorescence of the multiple fluorescent target proteins in the sample plane field of view by a detector device; (iii) wherein the tracking comprises detecting the fluorescence of the multiple fluorescent target proteins in the sample plane field of view at a rate of approximately 10,000 to approximately 18,000 per system per day; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound; wherein the change in the movement of the fluorescent target protein in the presence of the compound relative to the movement of the fluorescent target protein in the absence of the compound identifies a biological interaction between the compound and the fluorescent target protein.

[0146] In certain implementations of the OLS htSMT screening workflow described herein, the workflow may include identifying a biological interaction between a compound and a fluorescent target protein 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 multiple individual fluorescent target proteins in multiple 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 so that a subset of the fluorescent target proteins in the living cells emit fluorescence; (ii) detecting the fluorescence of multiple fluorescent target proteins within the field of view of the sample plane by a detector device; and (iii) achieving a z-factor >0.5 based on a single field of view; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound; wherein the change in the movement of the fluorescent target protein in the presence of the compound relative to the movement of the fluorescent target protein in the absence of the compound identifies a biological interaction between the compound and the fluorescent target protein.

[0147] In certain implementations of the OLS htSMT screening workflow described herein, the workflow may include determining a dose response of a compound that induces a change in the motion 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 the fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of compound concentrations; and (b) tracking the motion of each of the fluorescent target proteins in the plurality of living 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 so that at least a subset of the fluorescent target proteins in the living cells emit fluorescence, wherein the subset of fluorescent target proteins is present (i) detecting fluorescence of one or more fluorescent target proteins in the sample plane by a detector device; and (c) determining a change in motion of the fluorescent target protein in the presence of a compound; and (d) repeating steps (b)-(c) for each of the plurality of samples over a range of compound concentrations; wherein the change in motion of the fluorescent target protein in the presence of the compound over the range of concentrations is indicative of a dose response of the compound.

[0148] 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 motion 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 the fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of compound concentrations; and (b) tracking the motion of each of the fluorescent target proteins in the plurality of living 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 so that at least a subset of the fluorescent target proteins in the living cells emit fluorescence, wherein the subset of fluorescent target proteins comprises approximately 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 concentration of dye that is considered sufficient to label the subset proteins to achieve robust SMT, both of which can be calculated and / or configured by a person skilled in the art based on the disclosure of the present application; (ii) detecting the fluorescence of one or more fluorescent target proteins in the sample plane by a detector device; and (c) determining the change in the movement of the fluorescent target protein in the presence of the compound; (d) repeating steps (b)-(c) for each of the multiple samples within the compound concentration range; wherein the change in the movement of the fluorescent target protein in the presence of the compound over the concentration range is indicative of a dose response of the compound.

[0149] 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 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 compound concentrations; (b) tracking the motion of each of the fluorescent target proteins in the plurality of living 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 so that at least a subset of the fluorescent target proteins in the living cells fluoresces, (ii) detecting fluorescence of one or more fluorescent target proteins in the sample plane by a detector device; and (c) determining the change in motion of the fluorescent target protein in the presence of the compound, wherein the average change in motion of the fluorescent target protein in the presence of the compound relative to the absence of the compound is about 1% to about 5% or to about 10%; and (d) repeating steps (b)-(c) for each of the plurality of samples within the range of compound concentrations; wherein the change in motion of the fluorescent target protein in the presence of the compound over the concentration range is indicative of a dose response of the compound.

[0150] 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 motion 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 the fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of compound concentrations; and (b) tracking the motion of each of the 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 so that the fluorescent target protein in the cell (ii) detecting, by a detector device, the fluorescence of one or more fluorescent target proteins in the sample plane; (iii) wherein the tracking comprises detecting the 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; and (d) repeating steps (b)-(c) for each of the plurality of samples over a range of compound concentrations; wherein the 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.

[0151] In certain implementations of the OLS htSMT screening workflow described herein, the workflow may include determining a dose response of a compound that induces a change in motion 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 compound concentrations; (b) tracking the motion of each fluorescent target protein in a plurality of living 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 so that at least a subset of the fluorescent target proteins in the cells fluoresce, (ii) detecting fluorescence of one or more fluorescent target proteins in the sample plane by a detector device; and (iii) achieving a z-factor >0.5 based on a single field of view; and (c) determining the change in motion of the fluorescent target protein in the presence of the compound; (d) repeating steps (b)-(c) for each of the plurality of samples within the range of compound concentrations; wherein the change in motion of the fluorescent target protein over the concentration range in the presence of the compound indicates a dose response of the compound.

[0152] 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 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 a 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; and (c) an objective for focusing the light beam onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample is positioned in a field of view of the sample plane, and 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, depending on the specific cells used. type, for example, for U2OS 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 a light-based reaction of a fluorescent target protein in the presence of a compound, wherein the detector device is configured to: (i) block light received from a light source outside the sample plane where the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein; and (ii) track the movement of each fluorescent target protein, (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 protein in the presence of the compound relative to the absence of the compound.

[0153] 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 a 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 onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample is positioned 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 protein of interest. (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 a light source outside the sample plane where the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein; and (ii) track the movement of each fluorescent target protein, (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining changes in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0154] In this paper, the OLS In certain implementations of the htSMT screening workflow, the workflow may include using a microscope system configured to identify biological interactions between a compound and a fluorescent target protein in living cells, comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population and wherein the cells comprise a 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 onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample are positioned in a field of view of the sample plane; (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 the sample plane in which the fluorescent target proteins are positioned, thereby tracking the position of the fluorescent target proteins; and (ii) track the movement of each fluorescent target protein, wherein the average change in the movement 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 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 the change in the movement of the fluorescent target proteins in the presence of the compound relative to the absence of the compound.

[0155] In certain implementations of the OLS htSMT screening workflow described herein, the workflow may include using a microscope system configured to identify biological interactions between a compound and a fluorescent target protein in living cells, comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population, and wherein the cells comprise a 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 onto the sample in a sample plane, wherein the plurality of fluorescent target proteins in the sample are disposed in the sample plane; and (d) a microscope for monitoring the fluorescent target proteins in the presence of the compound. A detector device for a light-based reaction, wherein the detector device is configured to: (i) block light received from a light source outside a sample plane in which a fluorescent target protein is located, thereby tracking the position of the fluorescent target protein; and (ii) track the movement of each fluorescent target protein, wherein the tracking comprises detecting fluorescence of a 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 protein in the presence of a compound relative to the absence of the compound.

[0156] In certain implementations of the OLS htSMT screening workflow described herein, the workflow may include using a microscope system configured to identify biological interactions between a compound and a fluorescent target protein in living cells, comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population and wherein the cells comprise a 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 lens for focusing the light beam onto 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 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 the sample plane in which the fluorescent target proteins are disposed, thereby tracking the position of the fluorescent target proteins; (ii) track the movement of each fluorescent target protein; and (iii) achieve a z-factor >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.

[0157] 4.2OLS htSMT combination

[0158] In certain implementations of the OLS htSMT workflow described herein, the systems and methods are adapted to differentiate between off This would result in the f observed in the htSMT screening assay. 结合 Importantly, neither FRAP nor htSMT could distinguish the difference between the off decrease) or increase in chromatin binding rate (k* on increase) and the recovery of the drive, both of which will lead to f 结合 By changing the SMT acquisition conditions to reduce illumination intensity and collect long frame exposures, only immobile proteins form spots. Under these imaging conditions, the distribution of track lengths provides a measure of relative residence time.

[0159] Exemplary OLS htSMT combined workflows include the following individual strategies and combinations of the following strategies, wherein requirements of two or more strategies are combined. For example, but not by way of limitation, the workflows disclosed herein include illuminating a field of view of a sample plane disposed within a sample with a light beam such that a subset of fluorescent target proteins in living cells fluoresce, 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 the sample with a light beam such that a subset of fluorescent target proteins in living cells fluoresces, wherein the subset of fluorescent target proteins includes proteins in a range of about 1000 to about 1,000,000. Similarly, illuminating the sample plane to illuminate about 30 to about 80 living cells per FOV and / or causing about 1000 to about 1,000,000 proteins to fluoresce can be combined with any other strategic requirements disclosed herein, for example, determining an average change in the motion of a fluorescent target protein of 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 multiple fluorescent target proteins in the sample plane field of view at a rate of about 10,000 to about 18,000 per system per day, and achieving a z-factor >0.5 based on a single field of view.

[0160] In certain implementations of the OLS htSMT binding workflow described herein, the workflow will include determining whether a compound that induces a change in binding of a fluorescent target protein in living cells reduces the K of the fluorescent target protein. off, 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 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 so that at least a subset of the fluorescent target proteins in the living cells fluoresce, 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, depending on the specific cell type used, e.g., for U2OS 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 fluorescent target proteins in the sample plane by a detector device, wherein the method is adapted to selectively detect localized fluorescence; and (c) determining a change in movement of the fluorescent target protein in the presence of the compound; wherein an increase in the signal detected from the fluorescent target protein in the presence of the compound relative to the signal of the fluorescent target protein in the absence of the compound (e.g., when addition of the composition is replaced by a control such as, but not limited to, DMSO) indicates that the compound induces a K of the fluorescent target protein. off reduce.

[0161] In certain implementations of the OLS htSMT binding workflow described herein, the workflow can include determining whether a compound that induces a change in binding of a fluorescent target protein in living cells reduces the K of the fluorescent target protein. off , which comprises: (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 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 so as to cause at least a subset of the fluorescent target proteins in the living cells to fluoresce, wherein the subset of fluorescent target proteins comprises from 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 a dye concentration deemed sufficient to label the subset proteins for robust SMT, both of which can be calculated and / or configured by one skilled in the art based on the disclosure of this application; (ii) detecting fluorescence of one or more fluorescent target proteins in the sample by a detector device, wherein the method is adapted to selectively detect localized fluorescence; and (c) determining a change in movement of the fluorescent target protein in the presence of the compound; wherein an increase in the signal detected from the fluorescent target protein in the presence of the compound relative to the signal of the fluorescent target protein in the absence of the compound indicates that the compound induces a K of the fluorescent target protein. off reduce.

[0162] In certain implementations of the OLS htSMT binding workflow described herein, the workflow can include determining whether a compound that induces a change in binding of a fluorescent target protein in living cells reduces the K of the fluorescent target protein. off , 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 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 so as to cause at least a subset of the fluorescent target proteins in the living cells to fluoresce; (ii) detecting fluorescence of one or more fluorescent target proteins in the sample by a detector device, wherein the method is adapted to selectively detect localized fluorescence; and (c) determining a change in movement of the fluorescent target proteins in the presence of the compound, wherein the average change in movement of the fluorescent target proteins in the presence of the compound relative to the absence of the compound is from about 1% to about 5% or to about 10%; 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 K of the fluorescent target proteins. off reduce.

[0163] In certain implementations of the OLS htSMT binding workflow described herein, the workflow can include determining whether a compound that induces a change in binding of a fluorescent target protein in living cells reduces the K of the fluorescent target protein. off , 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 individual fluorescent target 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 so as to cause at least a subset of the fluorescent target proteins in the living cells to fluoresce; (ii) detecting fluorescence of one or more fluorescent target proteins in the sample by a detector device, wherein the method is adapted to selectively detect localized fluorescence; and (iii) achieving a z-factor of >0.5 based on a single field of view; and (c) determining a change in movement of the fluorescent target protein in the presence of the compound; wherein an increase in the signal detected from the fluorescent target protein in the presence of the compound relative to the signal of the fluorescent target protein in the absence of the compound indicates that the compound induces a K of the fluorescent target protein. off reduce.

[0164] In certain implementations of the OLS htSMT binding workflow described herein, the workflow may include determining whether a compound reduces the K of a fluorescent target protein. offThe method of claim 1 , wherein the method comprises: (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 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 so that at least a subset of the fluorescent target proteins in the living cells fluoresce, 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, depending on the specific cell type used, for example, about 30 to about 40 cells per FOV for U2OS cells and about 50 to about 80 cells per FOV for HCT116 cells, taking into account their area differences; (ii) detecting fluorescence of one or more fluorescent target proteins in the sample plane by a detector device, wherein the method is adapted to selectively detect localized fluorescence; and (c) determining the change in movement of the fluorescent target protein in the presence of the compound; wherein an increase in the signal detected from the fluorescent target protein in the presence of the compound relative to the signal of the fluorescent target protein in the absence of the compound indicates that the compound induces a K of the fluorescent target protein. off decreased and suggests that the dose increase is due to decreased drug metabolism caused by increased residence time.

[0165] In certain implementations of the OLS htSMT binding workflow described herein, the workflow may include determining whether a compound reduces the K of a fluorescent target protein. off Determining a dose of a compound that induces a change in binding of a fluorescent target protein in living cells comprises: (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 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 so as to cause at least a subset of the fluorescent target proteins in the living cells to fluoresce, wherein the subset of fluorescent target proteins comprises from 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 a dye concentration deemed sufficient to label the subset of proteins for robust SMT, both of which can be calculated and / or configured by one skilled in the art based on the disclosure of this application; (ii) detecting fluorescence of one or more fluorescent target proteins in the sample by a detector device, wherein the method is adapted to selectively detect localized fluorescence; and (c) determining a change in movement of the fluorescent target protein in the presence of the compound; wherein an increase in the signal detected from the fluorescent target protein in the presence of the compound relative to the signal of the fluorescent target protein in the absence of the compound indicates that the compound induces a K of the fluorescent target protein. off Lowering and, in some cases, increasing the dose due to decreased drug metabolism due to increased residence time.

[0166] In certain implementations of the OLS htSMT binding workflow described herein, the workflow may include determining whether a compound reduces the K of a fluorescent target protein. off The method of claim 1, wherein the method comprises: (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 individual fluorescent target 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 so as to cause at least a subset of the fluorescent target proteins in the living cells to fluoresce; (ii) detecting fluorescence of one or more fluorescent target proteins in the sample by a detector device, wherein the method is adapted to selectively detect localized fluorescence; and (c) determining the change in movement of the fluorescent target proteins in the presence of the compound; wherein the average change in movement of the fluorescent target proteins in the presence of the compound relative to the absence of the compound is from about 1% to about 5% or to about 10%; and 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 K of the fluorescent target proteins. off decrease, indicating an increase in dose due to decreased drug metabolism caused by increased residence time.

[0167] In certain implementations of the OLS htSMT binding workflow described herein, the workflow may include determining whether a compound reduces the K of a fluorescent target protein. off The method of claim 1, wherein the method comprises: (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 individual fluorescent target 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 so as to cause at least a subset of the fluorescent target proteins in the living cells to fluoresce; (ii) detecting fluorescence of one or more fluorescent target proteins in the sample by a detector device, wherein the method is adapted to selectively detect localized fluorescence; and (iii) achieving a z-factor of >0.5 based on a single field of view; and (c) determining the change in movement of the fluorescent target protein in the presence of the compound; wherein: an increase in the signal detected from the fluorescent target protein in the presence of the compound relative to the signal of the fluorescent target protein in the absence of the compound indicates that the compound induces a K of the fluorescent target protein. off Lowering and, in some cases, increasing the dose due to decreased drug metabolism caused by increased residence time.

[0168] In certain implementations of the OLS htSMT binding workflow described herein, the workflow can include using a microscopy system configured to determine whether a compound that induces a change in binding of a fluorescent target protein in a cell reduces the K of the fluorescently labeled target. off , comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population, and wherein the cells comprise fluorescent target proteins; (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; and (c) an objective lens for focusing the light beam onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample is positioned in a field of view of the sample plane, and 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, depending on the specific cell type used, e.g., for U2OS cells, the range is about 30 to about 40 cells per FOV, and for HCT116 cells, the range is about (d) a detector device for monitoring a light-based response of a fluorescent target protein in the presence of a compound, wherein the detector device is configured to: (i) block light received from a light source outside the sample plane in which the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein; and (ii) track the movement of each fluorescent target protein, wherein the tracking is suitable for selectively detecting 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 movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0169] In certain implementations of the OLS htSMT screening workflow described herein, the workflow can include using a microscopy system configured to determine whether a compound that induces a change in binding of a fluorescent target protein in a cell reduces the K of the fluorescently labeled target. off, comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population, 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 lens for focusing the light beam onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample is positioned in a field of view of the sample plane, and wherein the subset of fluorescent target proteins comprises from 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 a dye concentration that is deemed sufficient to label the subset proteins to achieve robust SMT, and a person skilled in the art can determine the amount of the protein in the sample based on the present invention. The disclosed content of the application calculates and / or configures both; (d) a detector device for monitoring the light-based reaction of the fluorescent target protein in the presence of a compound, wherein the detector device is configured to: (i) block light received from a light source outside the sample plane where the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein; and (ii) track the movement of each fluorescent target protein, wherein the tracking is suitable for selectively detecting 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 movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0170] In certain implementations of the OLS htSMT binding workflow described herein, the workflow can include using a microscopy system configured to determine whether a compound that induces a change in binding of a fluorescent target protein in a cell reduces the K of the fluorescently labeled target. off , comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population, and wherein the cells comprise fluorescent target proteins; (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 onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample are positioned in a field of view of the sample plane; (d) a detector arrangement for monitoring the light-based response of the fluorescent target proteins in the presence of a compound, wherein the detector arrangement is configured to: (i) block light received from a light source outside the sample plane in which the fluorescent target proteins are positioned, thereby tracking the position of the fluorescent target proteins; and (ii) track the movement of each fluorescent target protein, wherein the tracking is adapted to selectively detect local fluorescence relative to dynamic fluorescence, and wherein an average change in the movement of the fluorescent target proteins in the presence of the compound relative to the absence of the compound is from about 1% to about 5% or to about 10%; (e) a memory; and (f) a processor in communication with the memory and the detector arrangement, 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.

[0171] In certain implementations of the OLS htSMT binding workflow described herein, the workflow can include using a microscopy system configured to determine whether a compound that induces a change in binding of a fluorescent target protein in a cell reduces the K of the fluorescently labeled target. off , comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population, and wherein the cells comprise fluorescent target proteins; (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 lens for focusing the light beam onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample are positioned in a field of view of the sample plane; (d) a detector arrangement for monitoring the light-based response of the fluorescent target proteins in the presence of a compound, wherein the detector arrangement is configured to: (i) block light received from a light source outside the sample plane in which the fluorescent target proteins are positioned, thereby tracking the position of the fluorescent target proteins; and (ii) track the movement of each fluorescent target protein, wherein the tracking is adapted to selectively detect localized fluorescence relative to dynamic fluorescence; 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 arrangement, 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.

[0172] 4.3OLS KineticSMT

[0173] Because SMT identifies the rate of biological interactions between a compound and its target, it can be used to distinguish between direct and indirect effects on target activity, among other parameters. Given SMT's live cell setup, data collection modes can be configured to measure protein motion (kinetic SMT or kSMT) at set time intervals after compound addition to determine the rate of biological interactions between the compound and its target.

[0174] Exemplary OLS KineticSMT workflows include the following individual strategies and combinations of the following strategies, wherein requirements of two or more strategies are combined. For example, but not by way of limitation, the workflows disclosed herein include illuminating a field of view of a sample plane disposed within a sample with a light beam such that a subset of fluorescent target proteins in living cells fluoresces, 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 the sample with a light beam such that a subset of fluorescent target proteins in living cells fluoresces, wherein the subset of fluorescent target proteins includes proteins in a range of about 1000 to about 1,000,000. Similarly, illuminating the sample plane to illuminate about 30 to about 80 living cells per FOV and / or causing about 1000 to about 1,000,000 proteins to fluoresce can be combined with any other strategic requirements disclosed herein, for example, determining an average change in the motion of a fluorescent target protein of 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 multiple fluorescent target proteins in the sample plane field of view at a rate of about 10,000 to about 18,000 per system per day, and achieving a z-factor >0.5 based on a single field of view.

[0175] In certain implementations of the OLS kinetic htSMT combined workflow described herein, the workflow may include determining the occurrence rate of biological interactions 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 the 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 so as to cause a subset of the fluorescent target proteins in the living cells to emit fluorescence, wherein the subset of fluorescent target proteins is present in the sample; (ii) detecting, by a detector arrangement, fluorescence of a plurality of fluorescent target proteins within the sample planar field of view; and (c) determining changes 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 indicates the rate of occurrence of biological interactions between the compound and the target.

[0176] In certain implementations of the OLS kinetic htSMT combined workflow described herein, the workflow may include determining the occurrence rate of biological interactions 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 multiple individual fluorescent target proteins in multiple living cells in the sample at multiple time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the fluorescent target proteins in the living cells emit fluorescence, 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 proteins for robust SMT, both of which can be calculated and / or configured by a person skilled in the art based on the disclosure of this application; (ii) detecting the fluorescence of multiple fluorescent target proteins in the field of view of the sample plane by a detector device; and (c) determining the change in movement of the fluorescent target proteins in the presence of the compound; wherein the rate at which the change in movement of the fluorescent target proteins in the presence of the compound indicates the occurrence rate of biological interactions between the compound and the target.

[0177] In certain implementations of the OLS kinetic htSMT combined workflow described herein, the workflow may include determining the incidence of biological interactions 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 multiple individual fluorescent target proteins in multiple living cells in the sample at multiple time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the fluorescent target proteins in the living cells emit fluorescence; (ii) detecting the fluorescence of multiple 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 relative to the absence of the compound is about 1% to about 5% or to about 10%; wherein the rate at which the change in the movement of the fluorescent target proteins in the presence of the compound indicates the incidence of biological interactions between the compound and the target.

[0178] In certain implementations of the OLS kinetic htSMT combined workflow described herein, the workflow may include determining the rate of occurrence of biological interactions between a compound and a target, and direct and indirect biological interactions between a compound and a fluorescent target protein 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 multiple individual fluorescent target proteins in multiple living cells in the sample at multiple time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the fluorescent target proteins in the living cells emit fluorescence; (ii) detecting the fluorescence of the multiple fluorescent target proteins in the field of view of the sample plane by a detector device; (iii) wherein the tracking comprises detecting the fluorescence of the multiple fluorescent target proteins in the field of view of the sample plane at a rate of approximately 10,000 to approximately 18,000 per system per day; and (c) determining the change in movement of the fluorescent target protein in the presence of the compound; wherein the rate at which the change in movement of the fluorescent target protein in the presence of the compound indicates the rate of occurrence of biological interactions between the compound and the target.

[0179] In certain implementations of the OLS kinetic htSMT combined workflow described herein, the workflow may include determining the rate of occurrence of biological interactions 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 multiple individual fluorescent target proteins in multiple living cells in the sample at multiple time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the fluorescent target proteins in the living cells emit fluorescence; (ii) detecting the fluorescence of the multiple 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 the movement of the fluorescent target protein in the presence of the compound; wherein the rate at which the movement of the fluorescent target protein changes in the presence of the compound indicates the rate of occurrence of biological interactions between the compound and the target.

[0180] In certain implementations of the OLS kinetics htSMT combined workflow described herein, the workflow may include determining a dose of a compound that induces a change in the motion 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 the compound at different concentrations within a range of compound concentrations; and (b) tracking the motion of each of the 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 so that at least a subset of the fluorescent target proteins in the living cells emit fluorescence, wherein the subset of the fluorescent target proteins (i) detecting fluorescence of one or more fluorescent target proteins in the sample plane by a detector device; and (c) determining the rate at which the motion of the fluorescent target protein changes in the presence of the compound; and (d) repeating steps (b)-(c) for each of the plurality of samples over the range of compound concentrations; wherein the rate at which the motion of the fluorescent target protein changes in the presence of the compound indicates the rate at which a biological interaction between the compound and the target occurs.

[0181] In certain implementations of the OLS kinetics htSMT combined workflow described herein, the workflow may include determining a dose of a compound that induces a change in the motion 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 the fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of compound concentrations; and (b) tracking the motion of each of the 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 so that at least a subset of the fluorescent target proteins in the living cells emit fluorescence, wherein the subset of the fluorescent target proteins comprising from 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 concentration of dye deemed sufficient to label the subset proteins to achieve robust SMT, both of which can be calculated and / or configured by a person skilled in the art based on the disclosure of the present application; (ii) detecting the fluorescence of one or more fluorescent target proteins in the sample plane by a detector device; and (c) determining the rate at which the movement of the fluorescent target protein changes in the presence of a compound; (d) repeating steps (b)-(c) for each of the plurality of samples within the compound concentration range; wherein the rate at which the movement of the fluorescent target protein changes in the presence of the compound indicates the occurrence rate of biological interaction between the compound and the target.

[0182] In certain implementations of the OLS kinetics htSMT combined workflow described herein, the workflow can include determining a dose response of a compound that induces a change in the motion 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 the fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of compound concentrations; and (b) tracking the motion of each of the 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 so that at least a subset of the fluorescent target proteins in the living cells fluoresce, (ii) detecting the fluorescence of one or more fluorescent target proteins in the sample plane by a detector device; and (c) determining the 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 relative to the absence of the compound is from about 1% to about 5% or to about 10%; and (d) repeating steps (b)-(c) for each of the plurality of samples over the range of compound concentrations; wherein the rate at which the motion of the fluorescent target proteins changes in the presence of the compound indicates the occurrence rate of biological interaction between the compound and the target.

[0183] In certain implementations of the OLS kinetics htSMT combined workflow described herein, the workflow may include determining a dose response of a compound that induces a change in the motion 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 the fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of compound concentrations; and (b) tracking the motion of each of the 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 so that the fluorescent target protein in the cells at least a subset of the fluorescent target proteins fluoresce, (ii) detecting the fluorescence of one or more fluorescent target proteins in a sample plane by a detector device; (iii) wherein the tracking comprises detecting the fluorescence of a 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; and (c) determining the rate at which the motion of the fluorescent target proteins changes in the presence of the compound; and (d) repeating steps (b)-(c) for each of the plurality of samples over a range of compound concentrations; wherein the rate at which the motion of the fluorescent target proteins changes in the presence of the compound indicates the rate of occurrence of biological interactions between the compound and the target.

[0184] In certain implementations of the OLS kinetic htSMT combined workflow described herein, the workflow may include determining a dose of a compound that induces a change in motion 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 the compound at different concentrations within a range of compound concentrations; (b) tracking the motion of each fluorescent target protein in the plurality of living cells of 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 so that at least a subset of the fluorescent target proteins in the living cells emit fluorescence, (ii) detecting fluorescence of one or more fluorescent target proteins in 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 rate at which the motion of the fluorescent target protein changes in the presence of the compound; (d) repeating steps (b)-(c) for each of the plurality of samples within the range of compound concentrations; wherein the rate at which the motion of the fluorescent target protein changes in the presence of the compound indicates a rate of occurrence of a biological interaction between the compound and the target.

[0185] In certain implementations of the OLS kinetics htSMT combined workflow described herein, the workflow can include using a microscope system configured to determine the occurrence rate of biological interactions 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 response from a plurality of fluorescent target proteins in the sample; and (c) an objective for focusing the light beam onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample is positioned in a field of view of the sample plane, and 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, depending on the microscope used. for specific cell types, 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 differences in area; (d) a detector device for monitoring a light-based response of a fluorescent target protein in the presence of a compound, wherein the detector device is configured to: (i) block light received from a light source outside the sample plane where the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein at multiple time points; and (ii) track the movement of each fluorescent target protein, (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 protein in the presence of the compound relative to the absence of the compound.

[0186] In certain implementations of the OLS kinetics htSMT combined workflow described herein, the workflow can include using a microscope system configured to determine the occurrence rate of biological interactions between a compound and a target in living cells, 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 response from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample is positioned 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 expression levels of the protein of interest and a dye concentration deemed sufficient to label a subset of proteins to achieve robust SMT, both of which can be calculated and / or configured by a person skilled in the art based on the disclosure of the present application; (d) a detector device for monitoring the light-based response of the fluorescent target protein in the presence of the compound, wherein the detector device is configured to: (i) block light received from a light source outside the sample plane where the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein at multiple time points; and (ii) track the movement of each fluorescent target protein, (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining changes in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0187] In certain implementations of the OLS kinetics htSMT combined workflow described herein, the workflow may include using a microscope system configured to determine the occurrence rate of biological interactions between a compound and a target in living cells, 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 response from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample are positioned in a field of view of the sample plane; and (d) a microscope for monitoring the occurrence rate of biological interactions between a compound and a target in living cells in the presence of the compound. A detector device for a light-based reaction of a fluorescent target protein, wherein the detector device is configured to: (i) block light received from a light source outside of a sample plane where the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein at multiple time points; and (ii) track the movement of each fluorescent target protein, wherein the average change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound is about 1% to about 5% or 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 the change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0188] In certain implementations of the OLS kinetics htSMT combined workflow described herein, the workflow may include using a microscope system configured to determine the occurrence rate 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 cell population, and wherein the cells comprise a 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 onto the sample in a sample plane, wherein the plurality of fluorescent target proteins in the sample are disposed in the sample plane; and (d) a microscope for monitoring the occurrence rate of a biological interaction between a compound and a target in a living cell. A detector device for detecting a light-based reaction of a protein, wherein the detector device is configured to: (i) block light received from a light source outside a sample plane in which the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein at multiple time points; and (ii) track the movement of each fluorescent target protein, wherein the tracking includes detecting the fluorescence of multiple 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 protein in the presence of a compound relative to the absence of the compound.

[0189] In certain implementations of the OLS kinetics htSMT combined workflow described herein, the workflow may include using a microscope system configured to determine the occurrence rate of biological interactions 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 response from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample are positioned in a field of view of the sample plane; d) a detector device for monitoring a light-based reaction of a fluorescent target protein in the presence of a compound, wherein the detector device is configured to: (i) block light received from a light source outside the sample plane where the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein at multiple time points; and (ii) track the movement of each fluorescent target protein; and (iii) achieve a z-factor >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 protein in the presence of the compound relative to the absence of the compound.

[0190] 5. Exemplary Implementation

[0191] A. The present disclosure provides a method for determining the occurrence rate of biological interactions between a compound and a target fluorescent protein in living cells, comprising: (a) contacting a sample comprising a population of living cells with the compound, wherein the living cells comprise the target fluorescent protein; (b) tracking the movement of multiple individual target fluorescent proteins in the multiple living cells in the sample at multiple time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the target fluorescent proteins in the living cells emit fluorescence; and (ii) detecting fluorescence from the multiple 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 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 (c) determining changes in the movement of the target fluorescent protein in the presence of the compound; wherein the rate at which the changes in the movement of the target fluorescent protein occur is determined by comparing the changes in the movement of the target fluorescent protein at multiple time points.

[0192] B. The present disclosure provides a method for determining the occurrence rate of biological interactions between a compound and a target fluorescent protein in living cells, which comprises: (a) contacting a sample comprising a population of living cells with the compound, wherein the living cells comprise the target fluorescent protein; (b) tracking the movement of a plurality of individual target fluorescent proteins in the plurality of living cells in the sample at multiple time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the target fluorescent proteins in the living cells fluoresce; wherein the subset of target fluorescent proteins produces up to about 1,000,000 molecular trajectories in a single detection field of view; (ii) detecting fluorescence from the plurality of target fluorescent proteins in the detection field of the sample plane by 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 (c) determining changes in the movement of the target fluorescent proteins in the presence of the compound; wherein the rate at which the changes in the movement of the target fluorescent proteins occur is determined by comparing the changes in the movement of the target fluorescent proteins at multiple time points.

[0193] C. The present disclosure provides a method for determining the occurrence rate of biological interactions between a compound and a target fluorescent protein in living cells, which comprises: (a) contacting a sample comprising a population of living cells with the compound, wherein the living cells comprise the target fluorescent protein; (b) tracking the movement of a plurality of individual target fluorescent proteins in the plurality of living cells in the sample at multiple time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the target fluorescent proteins in the living cells emit fluorescence; (ii) detecting fluorescence from the plurality of 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 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 (c) determining the change in movement of the target fluorescent protein after the addition of the compound; wherein the rate at which the change in movement of the target fluorescent protein occurs is determined by comparing the change in movement of the target fluorescent protein at multiple time points.

[0194] D. The present disclosure provides a method for determining the occurrence rate of biological interactions between a compound and a target fluorescent protein in living cells, comprising: (a) contacting a sample comprising a population of living cells with the compound, wherein the living cells comprise the target fluorescent protein; (b) tracking the movement of multiple individual target fluorescent proteins in the multiple living cells in the sample at multiple time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the target fluorescent proteins in the living cells emit fluorescence; (ii) detecting fluorescence from the multiple target fluorescent proteins in the detection field of the sample plane by a detector device, wherein the detection field 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 (iii) detecting fluorescence from the multiple target fluorescent proteins in the detection field of the sample plane at a rate of more than 100,000 detection fields per day; and (c) determining changes in the movement of the target fluorescent proteins in the presence of the compound, wherein the rate at which the changes in the movement of the target fluorescent proteins occur is determined by comparing the changes in the movement of the target fluorescent proteins at multiple time points.

[0195] E. The present disclosure provides a method for determining the occurrence rate of biological interactions between a compound and a target fluorescent protein in living cells, comprising: (a) contacting a sample comprising a population of living cells with the compound, wherein the living cells comprise the target fluorescent protein; (b) tracking the movement of multiple individual target fluorescent proteins in the multiple living cells in the sample at multiple time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the target fluorescent proteins in the living cells emit fluorescence; and (ii) detecting fluorescence from the multiple 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 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 achieves sufficient laser illumination to track protein movement; and (c) determining changes in the movement of the target fluorescent protein in the presence of the compound, wherein the rate at which the changes in the movement of the target fluorescent protein occur is determined by comparing the changes in the movement of the target fluorescent protein at multiple time points.

[0196] E1. The method of any one of AE, wherein the detected change in movement is an increase in the immobility trajectory, indicating binding (f 结合 ) Increase of target fluorescent protein.

[0197] E2. A method as described in any of AE, wherein the detected motion change is a change in: (a) the median of the jump length distribution; (b) the 3rd quartile of the jump length distribution; (c) the median radius of gyration; (d) the mean posterior diffusion coefficient; (e) the geometric mean posterior diffusion coefficient; (f) the mean square displacement; (g) the median bond angle; (h) the maximum likelihood estimator of the diffusion coefficient; and / or (i) the state occupancy by inference.

[0198] E3. The method of any one of AE, wherein the target fluorescent proteins interact with each other in a larger molecular assembly.

[0199] E4. The method of E3, wherein the target fluorescent protein is a ligand.

[0200] E5. The method of E3, wherein the target fluorescent protein is a receptor.

[0201] E6. The method of any of AE, wherein the biological interaction is a direct interaction.

[0202] E7. The method of E6, wherein the direct interaction comprises binding of the compound to the target fluorescent protein.

[0203] E8. The method of any of AE, wherein the biological interaction is an indirect interaction.

[0204] E9. The method of E8, wherein the indirect interaction comprises the compound agonizing or antagonizing a larger molecular assembly comprising the target fluorescent protein.

[0205] F. The present disclosure provides a method for determining the dosage of a compound, wherein the compound induces a change in the movement of a target fluorescent protein in living cells, the method 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 target fluorescent protein, and (iii) wherein the plurality of samples are contacted with the compound at different concentrations within a range of compound concentrations; and (b) tracking the movement of each target fluorescent protein in the plurality of living cells of 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 so that at least a portion of the target fluorescent proteins in the living cells are (ii) detecting fluorescence from one or more target fluorescent proteins in the sample plane by a detector device, wherein the detection field of view has a size in a first dimension of about 150 μm to about 250 μm and a size in a second dimension of about 100 μm to about 210 μm; and (c) determining the rate at which the change in motion of the target fluorescent protein occurs in the presence of the compound; (d) repeating steps (b)-(c) for each of a plurality of samples over a range of compound concentrations; wherein the rate at which the change in motion of the target fluorescent protein occurs is determined by comparing the change in motion of the target fluorescent protein at a plurality of time points; and wherein the dose is determined by the rate.

[0206] G. The present disclosure provides a method for determining the dosage of a compound that induces a change in the movement of a target fluorescent protein in living cells, the method 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 target fluorescent protein, and (iii) wherein the plurality of samples are contacted with the compound at different concentrations within a range of compound concentrations; and (b) tracking the movement of each target fluorescent protein in the plurality of living cells of 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 so that at least a portion of the target fluorescent proteins in the living cells emit fluorescence, wherein a subset of the target fluorescent proteins is detected in a single detection field of view. generating up to about 1,000,000 molecular trajectories; (ii) detecting, by a detector device, fluorescence from one or more target fluorescent proteins in a detection field of view in 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 (c) determining the rate at which the change in motion of the target fluorescent protein occurs in the presence of the compound; (d) repeating steps (b)-(c) for each of a plurality of samples over a range of compound concentrations; wherein the rate at which the change in motion of the target fluorescent protein occurs is determined by comparing the change in motion of the target fluorescent protein at a plurality of time points; and wherein the dose is determined by the rate.

[0207] H. The present disclosure provides a method for determining the dose response of a compound that induces a change in the motion of a target fluorescent protein in living cells, the method 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 target fluorescent protein, and (iii) wherein the plurality of samples are contacted with the compound at different concentrations within a range of compound concentrations; and (b) tracking the motion of each target fluorescent protein in the plurality of living cells of 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 so that at least a portion of the target fluorescent proteins in the living cells emit fluorescence (ii) detecting, by a detector device, fluorescence from one or more target fluorescent proteins in a detection field of view in the sample plane, wherein the detection field of view has a size in a first dimension of about 150 μm to about 250 μm and a size in a second dimension of about 100 μm to about 210 μm; and (c) determining the rate at which the change in motion of the target fluorescent protein occurs in the presence of the compound; and (d) repeating steps (b)-(c) for each of a plurality of samples over a range of compound concentrations; wherein the rate at which the change in motion of the target fluorescent protein occurs is determined by comparing the change in motion of the target fluorescent protein at a plurality of time points; and wherein the dose is determined by the rate.

[0208] I. The present disclosure provides a method for determining the dose response of a compound that induces a change in the motion of a target fluorescent protein in living cells, the method 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 target fluorescent protein, and (iii) wherein the plurality of samples are contacted with the compound at different concentrations within a range of compound concentrations; (b) tracking the motion of each target fluorescent protein in the plurality of living cells of 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 so that at least a portion of the target fluorescent proteins in the cells emit fluorescence; (ii) detecting, by a detector device, the motion of the target fluorescent protein in the detection field of the sample plane from a plurality of target fluorescent proteins in the detection field of the sample plane. (c) determining the rate at which the change in motion of the target fluorescent protein occurs in the presence of the compound; and (d) repeating steps (b)-(c) for each of a plurality of samples over a range of compound concentrations; wherein the rate at which the change in motion of the target fluorescent protein occurs is determined by comparing the change in motion of the target fluorescent protein at a plurality of time points; and wherein the dose is determined by the rate.

[0209] J. The present disclosure provides a method for determining the dosage of a compound, wherein the compound induces a change in the movement of a target fluorescent protein in living cells, the method 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 target fluorescent protein, and (iii) wherein the plurality of samples are contacted with the compound at different concentrations within a range of compound concentrations; (b) tracking the movement of each target fluorescent protein in the plurality of living cells of 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 so that at least a portion of the target fluorescent proteins in the living cells emit fluorescence; (ii) detecting the movement of the target fluorescent proteins in the living cells by a detector device. (c) determining the rate at which the change in motion of the target fluorescent protein occurs in the presence of the compound; and (d) repeating steps (b)-(c) for each of a plurality of samples over a range of compound concentrations; wherein the rate at which the change in motion of the target fluorescent protein occurs is determined by comparing the change in motion of the target fluorescent protein at a plurality of time points; and wherein the dose is determined by the rate.

[0210] J1. A method as described in any of FJ, wherein the detected change in movement is an increase in the immobility track, indicating that the binding (f 结合 ) Increase of target fluorescent protein.

[0211] J2. A method as described in any of FJ, wherein the detected motion change is a change in: (a) the median of the jump length distribution; (b) the 3rd quartile of the jump length distribution; (c) the median radius of gyration; (d) the mean posterior diffusion coefficient; (e) the geometric mean posterior diffusion coefficient; (f) the mean square displacement; (g) the median bond angle; (h) the maximum likelihood estimator of the diffusion coefficient; and / or (i) the state occupancy by inference.

[0212] J3. The method of any one of FJ, wherein the target fluorescent proteins interact with each other in a larger molecular assembly.

[0213] J4. The method of J3, wherein the target fluorescent protein is a ligand.

[0214] J5. The method of J3, wherein the target fluorescent protein is a receptor.

[0215] J6. The method of any of FJ, wherein the biological interaction is a direct interaction.

[0216] J7. The method of J6, wherein the direct interaction comprises binding of the compound to the target fluorescent protein.

[0217] J8. A method as described in any of FJ, wherein the biological interaction is an indirect interaction.

[0218] J9. A method as described in J8, wherein the indirect interaction includes the compound agonizing or antagonizing a larger molecular assembly comprising the target fluorescent protein.

[0219] K. The present disclosure provides a microscope system configured to detect the occurrence rate of biological interactions between a compound and a target fluorescent protein 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 the target fluorescent protein; (b) a light source for emitting a light beam capable of inducing a photoreactive reaction of a plurality of the 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 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 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; (e) a detector device for monitoring the photoreactive reaction of the target fluorescent protein in the presence of the compound at multiple time points; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining changes in the movement of the target fluorescent protein in the presence of the compound at multiple time points, thereby detecting the occurrence rate of biological interactions between the compound and the target fluorescent protein in the living cells.

[0220] L. The present disclosure provides a microscope system configured to detect the occurrence of biological interactions between a compound and a target fluorescent protein in a living cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population, 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 lens for focusing the light beam onto the sample in the 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 first dimension of approximately 150 μm. (d) a detector device for monitoring the light-based reaction of the target fluorescent protein in the presence of the compound at multiple time points; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining changes in the movement of the target fluorescent protein in the presence of the compound at multiple time points, thereby detecting the occurrence rate of biological interactions between the compound and the target fluorescent protein in living cells.

[0221] M. The present disclosure provides a microscope system configured to detect the occurrence rate of biological interactions between a compound and a target fluorescent protein in living cells, comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population, and wherein the cells comprise the target fluorescent protein; (b) a light source for emitting a light beam capable of inducing a photoreactive reaction of a plurality of the 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 proteins in the sample are disposed in a 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 photoreactive reaction of the target fluorescent protein in the presence of the compound at multiple time points; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining changes in the movement of the target fluorescent protein in the presence of the compound relative to the absence of the compound at multiple time points, thereby detecting the occurrence rate of biological interactions between the compound and the target fluorescent protein in living cells.

[0222] N. The present disclosure provides a microscope system configured to detect the occurrence rate of biological interactions between a compound and a target fluorescent protein in a living cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population, 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 lens for focusing the light beam onto the sample in the sample plane, wherein the plurality of target fluorescent proteins in the sample are disposed in a detection field of view of the sample plane; (d) a detector device for monitoring the photo-based reaction of the target fluorescent protein in the presence of the compound at multiple time points, wherein the monitoring comprises detecting fluorescence from the plurality of target fluorescent proteins in the detection field of view of the sample plane at a rate of more than 100,000 detection fields 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 changes in the movement of the target fluorescent protein in the presence of the compound at multiple time points, thereby detecting the occurrence rate of biological interactions between the compound and the target fluorescent protein in the living cell.

[0223] O. The present disclosure provides a microscope system configured to detect the occurrence rate of biological interactions between a compound and a target fluorescent protein in a living cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of living cells, and wherein the living 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 lens for focusing the light beam onto the sample in the 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 first dimension of about 15 (e) a detector device for monitoring the light-based reaction of the target fluorescent protein in the presence of the compound at multiple time points; (f) a memory; and (g) a processor in communication with the memory and the detector device, wherein the processor is capable of determining changes in the movement of the target fluorescent protein in the presence of the compound at multiple time points to detect the occurrence rate of biological interactions between the compound and the target fluorescent protein in living cells.

[0224] O1. A system as described in any of KO, wherein the detected change in movement is an increase in immobility trajectory, indicating binding (f 结合 ) Increase of target fluorescent protein.

[0225] O2. A system as described in any of KO, wherein the detected motion is a change in: (a) the median of the jump length distribution; (b) the 3rd quartile of the jump length distribution; (c) the median radius of gyration; (d) the mean posterior diffusion coefficient; (e) the geometric mean posterior diffusion coefficient; (f) the mean square displacement; (g) the median bond angle; (h) the maximum likelihood estimator of the diffusion coefficient; and / or (i) state occupancy by inference.

[0226] O3. A system as described in any of KO, wherein the target fluorescent proteins interact with each other in a larger molecular assembly.

[0227] O4. A system as described in O3, wherein the target fluorescent protein is a ligand.

[0228] O5. The system of O3, wherein the target fluorescent protein is a receptor.

[0229] O6. The system of any one of KO, wherein the biological interaction is a direct interaction.

[0230] O7. A system as described in O6, wherein the direct interaction includes binding of the compound to the target fluorescent protein.

[0231] O8. The system of any one of KO, wherein the biological interaction is an indirect interaction.

[0232] O9. A system as described in O8, wherein the indirect interaction includes the compound agonizing or antagonizing a larger molecular assembly comprising the target fluorescent protein.

[0233] 6. Examples

[0234] The presently disclosed subject matter will be better understood by reference to the following examples, which are intended to be illustrative only and not limiting of the presently disclosed subject matter.

[0235] Example 1: Light Scanning System

[0236] A. Introduction

[0237] Single-molecule localization microscopy (SMLM) techniques, such as single-molecule tracking (SMT), enable in situ measurements in both living and fixed cells, from which data-rich metrics can be extracted. SMT has been successfully applied to address a variety of biological questions and model systems, aiming to reveal the spatiotemporal regulation of molecular mechanisms that control protein function, downstream pathway effects, and cellular function under healthy or pathological conditions. While powerful, SMLM often suffers from low throughput, uneven illumination, and technical biases induced by both the microscope and the user. Due to technical limitations in scaling SMLM techniques, trade-offs must be made between spatial resolution, temporal resolution, and throughput, limiting these techniques to a small number of research groups.

[0238] 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 produces a scannable oblique light sheet that can cover the entire FOV of a water-immersion high-NA objective ( Figure 11A and Figures 12A-12F ). More details about this exemplary OLS system are provided below.

[0239] B. Exemplary OLS System

[0240] SMT image acquisition for the OLS dataset was performed on a custom microscope based on a Nikon Ti2, equipped with a motorized stage, a stage-top environmental chamber (OKO Laboratories), a quad-band filter (Chroma), and custom laser emitters with wavelengths of 405 nm, 561 nm, and 642 nm, delivering >10 mW, >150 mW, and >150 mW to the back focal plane of the objective, respectively. The custom laser emitters consist of three externally triggerable free-space laser sources (Cobolt 06-MLD; Huebner Photonics; 2RU-VFL-P-2000-560-M; MBP Communications Inc.; and VFL-P-2000-642-M; MBP Communications Inc.).

[0241] An oblique line scanning (OLS; Figures 12A and 12D) unit is attached to the rear port of the microscope and provides optical excitation and scanning. The OLS unit receives collimated Gaussian-shaped optical excitation through a polarization-maintaining single-mode fiber coupled to a laser beam coupler. The laser excitation is sent through a combination of a Powell lens, a custom-designed cylindrical lens, and an achromatic lens to shape the beam into a laser line. The beam passes through a set of two adjustable right-angle prisms and then through an aspheric achromatic lens to position the beam and focus the scan axis onto the galvanometer scanning mirror. The beam position is adjusted so that the beam is offset by 3.8 mm from the central optical axis of the objective rear focal plane to achieve an illumination light sheet at a 60-degree oblique angle in the sample (Figure 12E).

[0242] Fluorescence emission was collected through a high-speed filter wheel (Sutter Instruments) using a backlit sCMOS camera (ORCA-Fusion BT, Hamamatsu). The sCMOS camera was operated in progressive mode with an exposure time of 407 μs and an internal line spacing of 4.87 μs to achieve a virtual rolling slit with a light excitation and fluorescence line width of approximately 200% (Figure 12F). Images were collected using a 60×1.27NA water immersion objective (Nikon). The environmental chamber was set at 37°C, 95% humidity, and 5% CO2.

[0243] 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 MicroManager using a custom-designed and user-configurable acquisition script for raster scanning of 384-well plates, along with a custom-designed autofocus routine (Figure 12B). One frame of the Hoechst and Potomac Red channels was collected at the same frame rate for registration of downstream trajectories to the nucleus and cytoplasm, respectively.

[0244] C. Discussion

[0245] This example discloses OLS, a robust illumination and detection mode based on a single objective light sheet, which achieves nanometer-scale spatial resolution and sub-millisecond temporal resolution within a 250×190μm field of view, thus overcoming the limitations of other SMLM techniques. OLS was developed 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 relatively simple optical configuration used in OLS makes this approach easily implementable on inverted microscopes equipped with water- or oil-immersion high numerical aperture (NA) objectives and sCMOS cameras with light sheet mode capability.

[0246] Example 2: OLS high-throughput single-molecule tracking (htSMT)

[0247] A. Introduction

[0248] This example describes an exemplary industrial-scale OLS htSMT technique using the exemplary OLS system of Example 1, and a comparison of such an OLS system with a highly tilted and laminated optical sheet (HILO) system. This example further describes a system incorporating such OLS htSMT technique, hardware and software associated with such OLS htSMT technique, and methods of using such OLS htSMT technique. For example, the OLS htSMT technique described herein is capable of measuring protein movement in millions of cells per day. The OLS htSMT technique described herein exhibits specific, robust, and reproducible results. The OLS htSMT technique 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 the elucidation of SAR. Importantly, the OLS htSMT technique described herein can be used to characterize the contributions of known and novel pathways to interaction networks, such as protein signaling interaction networks.

[0249] B. Results

[0250] a. Creation and verification of htSMT system

[0251] A robotic system was developed that can handle reagents, collect high-quality, rapid SMT image series, process time-sequenced raw images to generate molecular trajectories, and extract biologically interesting features within defined cellular compartments. Figure 1 To examine the performance of the htSMT system, we performed various measurements that demonstrated that the disclosed image acquisition system and workflow are suitable for robust htSMT analysis. For example, Figure 3ADepicted is a laser titration experiment showing the relationship between laser power (mW) at the sample and signal-to-noise ratio (SNR) (left), as well as the average SNR at the well level for four image acquisition systems, each measuring six different 384-well plates (right). Figure 3C Depicted are dose-response experiments using established and well-characterized compounds targeting halo-tagged proteins to assess inter-plate and day-to-day reproducibility (top panel), and the corresponding EC50s are presented (bottom panel). Figure 3D Demonstrates that the system described herein is configured to capture comparable protein diffusion coefficients per FOV per well, where each point represents the average single FOV position per plot per concentration (top panel), and both EC50 and z-factor are presented (bottom panel). Figure 3E Data consistency across multiple wells and experiments is depicted, where each point represents one FOV from 14 independently generated dose-response curves.

[0252] In addition to confirming that the OLS workflow described herein is suitable for robust htSMT analysis, experiments were performed to compare the OLS-based workflow described herein with HILO-based methods. Figure 3D and Figure 3E The comparison of the Z factors associated with the OLS-based data presented in with those collected using the HILO-based method clearly illustrates the improved performance of the OLS-based method. Figure 3B This is particularly evident in the figure that depicts the difference in spatial SNR heterogeneity between the disclosed OLS system and the HILO-based method. The top figure compares the spatial standard deviation observed in the OLS and HILO-based methods. The bottom figure illustrates the difference in FOV between the HILO and OLS-based methods (left figure), as well as a comparison of the spatial heterogeneity between the FOVs of the HILO-based method (center figure) and the OLS-based method (right figure).

[0253] Additional experiments were performed to demonstrate the improved performance of OLS-based methods compared to HILO-based methods. For this comparison, a U2OS cell line with HaloTag genome edited into the amino terminus of the KEAP1 gene (Halo-KEAP1) was used. 549 Initial imaging of sparsely labeled Halo-KEAP1 yielded sharp single-molecule resolution, enabling analysis of spot detection, localization, and tracking ( Figure 11B The performance of the OLS system was benchmarked against the HILO implementation. 1.5 seconds of SMT data were collected in HILO and OLS, and the resulting trajectories were plotted ( Figure 11DThe average number of trajectories collected across the entire 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 of 1,224 FOVs were collected 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 a spatial SNR map was plotted ( Figure 11F The standard deviation and mean SNR per FOV for 308 apertures for both OLS and HILO were then summarized, demonstrating improved SNR consistency and performance when comparing the two illumination modes ( Figure 11G ).

[0254] For HILO, the sample was illuminated for 2 milliseconds by pulsing the laser within a subset of the camera exposure time. For OLS, given the scan rate of the light sheet, each fluorophore was calculated to be exposed for only 400 milliseconds. Given this shorter integration time for fluorophores with different diffusion rates, it was expected that the point spread functions (PSFs) would be more consistent. This hypothesis was tested by analyzing the average spot width of KEAP1 with and without KI-696 ( Figure 13A Under HILO illumination, the average 2σ radius of the single-molecule PSF increases by 4.4%, while for OLS it decreases to 1.4% ( Figure 13B and 13D While a strobe time of 400 μs allows for direct comparison of motion-induced blur performance in OLS, it was found that single-molecule detection was not possible with HILO within this integration time, as the vast majority of PSFs did not pass the noise threshold ( Figure 13C ).

[0255] One of the key advantages offered by OLS is that during scanning of the oblique light sheet, the out-of-focus illumination emitters are outside the pixel band recorded by the camera. To characterize this superior illumination-based optical sectioning method, samples consisting of increasing concentrations of His-HaloTag in solution were prepared to titrate the protein labeling density and the downstream effects on SNR and PSF detection. This experiment surprisingly captured the expected improvement in sectioning capability offered by OLS. A rapid drop in the number of detected localizations was observed in HILO, which correlated with a drop in SNR ( Figure 13E and 13F These results emphasize that under OLS illumination, individual PSFs are better detected, regardless of whether increased dye or protein concentrations cause local PSF overlap. Combined with the reduction in motion blur, OLS enables tracking of single particles at high densities with high resolution.

[0256] To further evaluate the reproducibility of the illumination quality of the disclosed OLS optical system, side-by-side SMT measurements were performed on four different OLS-equipped microscopes using a previously described automated system. Six to seven 384-well plates were tested per microscope. Halo-KEAP1 was treated with 20 concentrations of KI-696 (a small molecule known to disrupt the interaction between KEAP1 and its binding partner NRF2), thereby increasing the fraction of Halo-KEAP1 that rapidly diffused. Each concentration was randomly assigned to 12 replicate wells on the plate, with six FOVs per well. The average dose-response curves for each microscope were highly consistent, with a median increase in diffusion of 47-51%, and a median EC value of 0.05, which was generated across four independent microscopes. 50 The values ​​were between 7.37 and 8.58 nM ( Figure 11C and 14A The average FOV-level SNR for each microscope was compared, 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 perturbation of the well as a whole when imaging a specific FOV ( Figure 14C ). This means that position effects within the well do not appear to exist in this set of measurements. Additionally, the effect of the large OLS FOV size on SMT sampling can be directly characterized by comparing a cropped region of the same FOV to the FOV of a large OLS size. A significant increase in variance was observed when the number of cells captured was reduced to an area of ​​83 × 83 μm ( Figure 14D ).

[0257] C. Method

[0258] a. Cell lines

[0259] U2OS (ATCC catalog number HTB-96) can be grown in DMEM (catalog number 1056601, Gibco DMEM, high glucose, GlutaMAX supplement, Thermofisher) supplemented with 10% fetal bovine serum (catalog number 16000044, Thermofisher) and 1% penicillin-streptomycin (catalog number 15140122, Thermo Fisher) and maintained in a humidified 37°C incubator with 5% CO2, and subcultured approximately every two to three days.

[0260] b. HaloTag-expressing cell lines

[0261] For specific Target-HaloTag fusions, a mammalian expression vector containing the appropriate fusion gene under the control of the weak L30 promoter and a neomycin resistance marker can be transfected into 70% confluent U2OS cells using FuGENE 6 (Cat. No. E2691, Promega). Transfected cells can be selected with 500 μg / mL of G418 (Cat. No. 10131027, Thermo Fisher) and then cloned. 549 -HTL (Cat. No. GA1110, Promega) and 50 nM Hoechst 33342 staining, and identification of cells with expected JF 549 The signal distribution of clones is then used to identify clones expressing the desired fusion gene. Multiple clones can then be tested for response to control compounds using SMT conditions, and the most homogeneous clones can then be expanded for further testing.

[0262] To generate certain KEAP1-HaloTag cell lines (e.g., Figures 14A-14D The cell lines used in the study were transfected with a ribonucleoprotein (RNP) complex containing sgRNA targeting the N-terminal or C-terminal region (Integrated DNA Technologies-IDT) and Cas9 protein (PNA bio, catalog number CP01) along with a linear dsDNA donor (IDT) using the Lonza nucleofection method. Each donor consisted of 200-300 bp homology arms specific for each target, a codon-optimized HaloTag sequence, and a TEV linker (ENLYFQG) between the target and the HaloTag. After transfection, the cells were incubated with the Halo ligand JF 646 (In-house) and imaged using the ImageXpress system (Molecular Devices) to confirm HaloTag integration. Cells were then sorted into 384-well plates. Clonal cells were expanded and imaged using the ImageXpress system and genotyped by Sanger sequencing to confirm homogeneous HaloTag integration.

[0263] c. Western blotting

[0264] Cells can be grown under the same conditions as previously described. 1.5 × 10 cells can be grown per well in a 6-well plate. 6Cells were seeded in DMEM overnight and then treated with compounds (DMSO or 100 nM fulvestrant) for 24 hours the next day. The cells were then lysed in 200 μL 1X cell lysis buffer (catalog number 9803, Cell Signaling). The BCA protein assay kit (catalog number 23225, Pierce TM Protein lysate concentrations were determined using a BCA protein assay kit. Capillary Western immunoassays were then performed using Jess Protein Simple following the manufacturer's instructions (Protein Simple, USA). Anti-target antibody levels were normalized to the loading control β-tubulin (1:100, NC0244815 LI-COR 92642213, Thermo Fisher). Peak values ​​were analyzed using Compass software (Protein Simple, USA).

[0265] d. OLS single-molecule tracking sample preparation

[0266] The cells can then be seeded at 4500-6000 cells per well in 384-well tissue culture treated glass bottom plates. The seeded cells can then be incubated overnight at 37°C and 5% CO2 to allow them to adhere. For all SMT experiments, the cells can be incubated with 5-100 pM JF 549 -HTL (catalog number GA1110, Promega) and 50nM Hoechst 33342 were cultivated together in complete medium for one hour. The cells were then washed three times in DPBS and twice in imaging medium, which was supplemented with GlutaMAX (catalog number 35050079, Thermo Fisher) and fluoroBrite DMEM medium (catalog number A1896701, Thermo Fisher) supplemented with the same serum and antibiotics as the growth medium. Where appropriate, the compound can be serially diluted in Echo Qualified 384-well low dead volume source microplates (0018544, Beckman Coulter) to generate dose titration source material. The compound can be applied in cell culture medium at a final dilution of 1:1000. Each dose of compound can have at least 3 replicates per plate, and up to 3 plate replicates are prepared continuously, with 20 DMSO control wells and 2 dye-free control wells randomly assigned on each plate. Before collecting images, the compound can be cultivated at 37°C for one hour.

[0267] e. Image acquisition

[0268] Unless otherwise stated, all image acquisition using SMT was performed on a custom microscope, motorized stage, stage-top environmental chamber, four-band filter lens (Chroma), and a custom laser engine with wavelengths of 405 nm and 561 nm reaching the back focal plane of the objective lens. Fluorescence emission was collected through a high-speed filter wheel (Sutter Instruments) and a backlit CMOS camera (Hammamatsu Orca Fusion running in light sheet mode). Images were collected using a 60X1.27NA water immersion objective lens (Nikon). The environmental chamber was set to 37°C, 95% humidity, and 5% CO2. In some implementations, each pixel was exposed for 400 microseconds, and the entire region of interest (ROI) required a total of 9 milliseconds. The galvanometer position could then be reset within 1 millisecond (e.g., with the laser turned off) before another image was recorded. In such an implementation, 100 frames per second could be recorded. Additionally or alternatively, a second setup can be employed that uses a smaller ROI to record 200 frames per second with the same 400 microseconds / pixel exposure and 4 milliseconds image recording time. Additionally or alternatively, the galvo mirror reset can be accomplished more quickly.

[0269] f. Image Analysis

[0270] Image acquisition generates a JF for each field of view 549 Film and a Hoechst. JF 549 Movies can be used to track individual JFs 549 Molecular motion is captured, while Hoechst movies can be used for nuclear segmentation. Using a combination of existing methods, tracking is accomplished in three sequential steps: detection, subpixel localization, and linking. Briefly, blobs are detected using a generalized log-likelihood ratio detector. Following detection, the estimated position of each emitter is refined to subpixel resolution using Levenberg-Marquardt fitting and an integrated 2D Gaussian blob model, starting from an initial guess provided by a radially symmetric method. Detected blobs are linked into tracks using a custom modification of the hill climbing algorithm. The same detection, subpixel localization, and linking settings are used for all movies.

[0271] For nuclear segmentation, all frames of the Hoechst movie can be averaged to generate an average projection. This average projection can then be segmented using a neural network trained on human labeled nuclei. Each spot can then be assigned to at most one nucleus using its sub-pixel coordinates.

[0272] To recover motion information from the trajectory, the state array can be used. For example, a Bayesian inference method can be used with the "RBME" likelihood function and a range of 0.01 to 100.0 μm. 2 s-1 A grid of 100 diffusion coefficients and 31 localization error amplitudes ranging from 0.02 to 0.08 μm is used. After inference, the localization errors can be marginalized, resulting in a one-dimensional distribution of diffusion coefficients for each field of view. For single-cell analysis, SMT and nuclear segmentation can be performed, for example on a mixture of U2OS 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 for each cell can be sorted by cluster index to produce a heat map. In order to estimate the binding fraction (f 结合 ), can be used for less than 0.1μm 2 s -1 In order to estimate the free diffusion coefficient (D 自由 ), can be calculated to 0.1μm 2 s -1 The mean of the posterior distribution above.

[0273] g. Single-molecule tracking method

[0274] Single-molecule tracking (SMT) data were processed by a custom pipeline operating on image sequences produced by the microscope. Briefly, individual emitters were detected by applying a generalized log-likelihood ratio test to each 11 × 11 subwindow in the image, as described above (Signal-to-noise ratio definition and quantification section below). Emitters were detected by identifying pixels with a log-likelihood ratio exceeding 14. Detected emitters were localized to subpixel accuracy in a two-stage procedure. First, the subpixel position was estimated by calculating the point of maximum radial symmetry. Second, this estimate was used to implant an iterative Levenberg-Marquardt fitting procedure into a 2D integrated Gaussian within an 11 × 11 pixel subwindow centered on the detection.

[0275] Local emitters are linked in time to generate trajectories using a modification of the Sbalzerini hill climbing algorithm, which uses Gibbs sampling to estimate the uncertainty of data association. Among all SMTs, cSMT prohibits links longer than 1.25 μm and links with more than two interframe gaps to limit association errors. Emitters are assigned to segmentation classes (nucleus, cytoplasm) by comparing their subpixel positions with semantic masks generated by the segmentation procedure.

[0276] h. Data Analysis

[0277] The trace results of the automated processing pipeline can be analyzed using KNIME or Spotfire (TIBCO). 结合 or D自由 Measurements can be associated with experiment metadata and aggregated conditionally. 结合 The change in f can be calculated for each well. 结合 The median f of DMSO in the same plate 结合 The difference between the two. Wells with no cells in the field of view or where the field of view is out of focus can be omitted from further analysis. The median fluorescence intensity of the tracking channel can be used to assess the assay interference of the compound, 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 deviate significantly from the performance of the rest of the screen, the plate can be removed from further analysis. Finally, compounds with a variance more than three standard deviations above the mean compound variance can be removed from downstream analysis. The Z' factor between the active control and DMSO on the plate can be calculated. EC can be calculated in Prism (GraphPad) by first logarithmically transforming the molecular concentration and then fitting to a four-parameter logistic curve 50 value.

[0278] i. Active molecule clustering

[0279] Molecules identified as active can be clustered based on chemical structure. Molecular frameworks can be calculated as known in the art and as implemented in Pipeline Pilot. Molecular frameworks can be clustered using functional class fingerprints (FCFP_4), for example, with a similarity threshold cutoff of 0.3 Tanimoto distance.

[0280] j. Kinetic experiments

[0281] Cells can be seeded into 384-well plates the day before, stained, and washed as described above. One well with multiple FOVs per well can be used as a baseline reading. Compounds can then be added to each well manually or robotically while imaging to a final concentration of 100 nM. Data for the wells can then be collected. Pauses can be included between each FOV so that the entire imaging protocol covers the detection window. The f of each well can be determined relative to t=0. 结合 change.

[0282] For assays up to 4 hours long, the plate can be imaged twice with multiple FOVs per well, with each reading being done at a different FOV position to prevent photobleaching from affecting the data.

[0283] k. Dwell time imaging

[0284] Sample preparation and dwell time imaging experiments can be performed in a similar manner to the single molecule tracking assays described above, with a few exceptions. Samples can be prepared with 1-10 pM JF 549(Promega) and 50 nM Hoechst 33342 for one hour. Multiple frames per field of view were collected by setting the camera integration time to the desired milliseconds and reducing the laser source at the objective to the desired milliwatts. The laser was left on continuously during image acquisition. Compound incubation time ranged from 1 to 4 hours.

[0285] 1. Residence time analysis

[0286] Image processing, including spot detection, localization, and track reconnection, can be performed using the same methods described above. Because dwell time imaging selectively tracks slowly diffusing molecules, individual localizations can be constrained to the maximum displacement distance of a single jump reconnection. The set of tracks for each field of view can be separated into 1-CDF distributions as previously described and fitted to a biexponential decay model.

[0287] m. Fluorescence recovery after photobleaching

[0288] Images can be collected on a custom OLS microscope using a Spectra Light Engine RS-232 as described herein (e.g., in Example 1). Stimulation can be guided using a microscanner coupled to a coherent OBIS 561 nm 100 mW laser. All imaging can be performed using a 60X 1.27NA 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 and stained with 50 nM HTL-JF. 549 Labeled and washed as described above. The compound can be added to a final concentration of 100nM one hour before imaging. Then, by averaging 10 consecutive images, the image before bleaching can be collected. Then 8-10 areas (2 backgrounds, 6-8 cells) can be bleached, and 2 areas in the cell can be left unbleached. The bleached areas are bleached at 10% power without scanning. Over the next 30 seconds, an image can be collected every 200 milliseconds, followed by an image every 1 second for 2 minutes. The background-subtracted average intensity that varies over time in the area of ​​interest can be measured and normalized to the average value of the fluorescence in the baseline image, followed by normalization to the unbleached area to explain the photobleaching of the fluorophore caused by the readout. For three biological experiments, data from multiple cells can be collected in each experiment.

[0289] HILO microscope

[0290] SMT image acquisition for the HILO dataset was performed on a custom microscope based on a Nikon Ti2, a motorized stage, a stage-top environmental chamber (OKO Laboratories), a quad-band filter (Chroma), and custom laser emitters with wavelengths of 405 nm and 561 nm, delivering >10 mW and >150 mW to the back focal plane of the objective, respectively. Fluorescence emission was passed through a high-speed filter wheel (Finger Lakes Instruments) and collected with a backlit sCMOS camera (ORCA-Fusion BT, Hamamatsu). Images were acquired using a 60X 1.27NA water immersion objective (Nikon). The environmental chamber was set to 37°C, 95% humidity, and 5% CO2. For each field of view, 150 SMT frames were collected at a frame rate of 100 Hz using 2 ms stroboscopic laser pulses.

[0291] o.Track measurement

[0292] When reporting the number of trajectories, singlets (trajectories with 1 detection) are excluded as they do not contribute information to most dynamics estimates.

[0293] The average diffusion coefficient was calculated using the mean square displacement method (D est =MSD 2D / 4Δt), it is expected that the estimator will loc 2 / Δt overestimates the diffusion coefficient, where σ loc 2 is the variance of the 1D localization error, and Δt is the frame interval.

[0294] To resolve trajectories in multiple dynamic states, the coefficients of the Brownian mixture model on a grid of diffusion coefficient values ​​and localization error values ​​were inferred using a state array (a variational Bayesian procedure based on a mixture of Dirichlet processes). The mixture components were selected as 100 logarithmically spaced values ​​between 0.01 and 100 μm. 2 The Cartesian product of diffusion coefficients between 1 / s and 31 localization error values ​​(1D standard deviation) between 0.02 and 0.08 μm was used. Occupancy was reported as the average posterior probability of each diffusion coefficient marginalized over all localization error values. To make inference tractable, inference was restricted to 10,000 trajectories randomly sampled from each well.

[0295] Bias estimation in a single population sample is performed using analytical calculations that capture the probability of false links and truncation of the jump length distribution due to the finite search radius.

[0296] p. Empirical estimate of link accuracy

[0297] To estimate the accuracy of the linkage algorithm, a bootstrapping procedure was used. Detection results from the first and second halves of the movie were superimposed, and the tracking algorithm was run on the resulting set of detections, blinded to the source of each detection. From this, a resulting linkage score was calculated, where detections were combined from different parts of the movie. Because this score neither accounts for false linkages between detections in the same half of the movie nor the effects of photobleaching, it forms a lower bound on the error rate of linkage (ERLB).

[0298] q. Definition and quantification of signal-to-noise ratio

[0299] The signal-to-noise ratio (SNR) is defined as the likelihood ratio of a hypothesis test comparing the condition in which the target is not present, where the local image is modeled by the sum of a constant offset and independent Gaussian distributed noise, and the condition in which the target is present, where the local image is modeled by the sum of a centrally located Gaussian peak (of known width but unknown amplitude), independent Gaussian distributed noise, and a constant offset. SNR is expressed as:

[0300]

[0301] in:

[0302] A is the image cropped to the current region of interest (ROI);

[0303] w s is the side length of the square ROI (in pixels);

[0304] is the inner product operator;

[0305] h G is a zero-mean detection kernel that matches the expected Gaussian target profile, and The ROI is summed;

[0306] h u is a uniform kernel (i.e., its value is 1 across the entire ROI).

[0307] D. Discussion

[0308] 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 for characterizing the motion of rapidly moving proteins at high labeling densities. These data demonstrate how OLS, as a novel illumination scheme, enhances several features of SMLM and SMT-based techniques. Compared to the established HILO technique, OLS offers a larger FOV, finer sectioning capabilities, superior SNR, uniform illumination, and higher spatiotemporal resolution. Results from OLS illumination modules implemented on four different microscopes demonstrate the robustness of OLS and the resulting consistent reproducibility of results. This robustness enables SMT measurements to be performed agnostic to any microscope, enabling testing of large compound libraries for drug screening. Furthermore, improved suppression of out-of-focus light enables better single-molecule detection and localization, making OLS suitable for SMT across a variety of cell systems and protein targets, where background fluorescence has previously limited the ability to perform SMT. Consistent with this concept, high-SNR SMT results were achieved using OLS in spheroid cultures of immortalized cancer cells in a more complex cell system.

[0309] Example 3: OLS allows for rapid capture of SMT data, enabling tracking of fast-moving proteins

[0310] This example shows the impact of higher frame rate acquisition on the measurement window improvement and key imaging metrics of the OLS system of Example 1.

[0311] A. Results

[0312] It is assumed that there is a set of optimal acquisition parameters for a given protein of interest, given the range and specificity of protein motion in living cells. The 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 dynamics simulations with a complex mixture of Brownian motions ( Figure 16C ), and then tracked these simulated movies. As the frame rate increased, both average trajectory length and tracking fidelity improved, highlighting that the sampled FOV size was the only significant tradeoff ( Figure 16D ). The underlying dynamical model for each simulation is then estimated using state array analysis, a variational Bayesian method for recovering mixture models from observed trajectories. Increasing the frame rate improves recovery of faster states, but ultimately degrades recovery of slower states ( Figure 16E ). Note also that the lower and upper bounds of the mean squared displacement (MSD) estimator of the diffusion coefficient are determined by the localization error on the one hand and the search radius used in tracking on the other hand, roughly approximating this dynamic range ( Figure 16E , green dashed line). These results indicate that adjustable frame rate is a highly desirable feature in SMT imaging systems.

[0313] Turning to the experimental Halo-KEAP1 system described in Example 2, the OLS-enabled SMT was run at frame rates between 100 and 1250 Hz ( Figure 15A Similar to the simulation results, both the average trajectory length and the estimated link accuracy improve at higher frame rates ( Figure 17A and 17C Furthermore, the OLS illuminator achieves this without degrading the average SNR ( Figure 17B ) or the bleaching rate per frame ( Figure 19 ). Analyses of state arrays were run, and faster motion was recovered as the frame rate was increased until the estimated value of DMSO-treated Halo-KEAP1 stabilized at approximately 9 μm at 400 Hz. 2 / s, and the estimated value of KI-696-treated Halo-KEAP1 stabilized at approximately 14 μm 2 / s( Figure 18 , Figure 15B It is interesting to note that 400 Hz may represent a point of diminishing returns, where the sampling frequency may be suitable for capturing the faster diffusing subset of KEAP1 under DMSO and KI-696 treatment ( Figure 15B Furthermore, when simulating the measured diffusion coefficients of Halo-KEAP1+ / -KI-696 treatment at different frame rates, the simulation results closely matched the measurements ( Figure 15C Together, these results demonstrate that the ability to increase frame rates using line scanning in OLS can facilitate accurate measurements of fast protein diffusion in cellular environments. While 400 Hz appears to be an appropriate sampling rate for KEAP1, it is anticipated that other important biochemical processes in living cells will only be adequately captured at significantly higher frame rates.

[0314] B. Methods

[0315] a. Estimation of SMT dynamic range

[0316] In order to estimate the effect of frame rate on the dynamic range of SMT (e.g. Figure 15C and Figure 16), taking into account the diffusion coefficient of Brownian particles with Gaussian positioning errors The MSD estimator is based on the possible values ​​of Upward bias due to positioning error, where is the variance of the 1D localization error, and Δt is the frame interval. Since D is non-negative, we have In the opposite limit, when the true jump of a particle is 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. Taken together, this gives an estimated dynamic range of Therefore, the effect of changing the frame rate is a pan This simple model of dynamic range does not account for the effects of trajectory misconnection (which could shift the upper limit) or non-MSD estimates of the diffusion coefficient in the array of states (which could degrade the lower limit).

[0317] 2. Optical dynamic simulation

[0318] To evaluate the impact of tracking method and frame rate on the dynamic range of SMT, optical dynamic simulations were performed. These simulations used the scalar diffraction approximation of a paraxial imaging system with NA = 1.2. Briefly, a discrete mixture of Brownian motion without state transitions was simulated in a cube of dimensions 45 × 45 × 8 μm (XYZ) at frame rates of 12.5, 25, 50, 100, 200, 400, 800, or 1600 Hz. The particles were initialized with a density of 0.31 or 0.62 particles per cubic micron (depending on the simulation) and photobleached with a probability of 0.03 per frame. The particle positions coincident with a 500 μs pulse (simulating stroboscopic illumination in HILO or a rolling shutter in OLS) were accumulated onto a simulated 2D camera by convolving with the system's 3D point spread function. This produced a probability distribution of photons arriving at 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, a Gaussian readout noise of 3 photons RMS was added, multiplied by a gain factor of 4.3 per photon counts, and the movie was discretized to 16 bits. This movie was then tracked and subjected to state array inference using the same setup as for Halo-KEAP1 tracking.

[0319] C. Discussion

[0320] OLS is capable of achieving frame rates of at least 1250 Hz without compromising SNR and tracking fidelity. As demonstrated with Halo-KEAP1, a frame rate of 400 Hz was required to fully characterize the increased diffusion of NRF2 drug-induced release. Many biological processes are expected to involve rapid protein motions that were previously impossible to measure with other SMT illumination methods. OLS provides an opportunity to resolve novel protein and cellular mechanisms that may occur on the submillisecond timescale.

[0321] Example 4: OLS captures cell-to-cell heterogeneity in single protein dynamics

[0322] This example demonstrates that the OLS system of Example 1 can be used to analyze cell-to-cell heterogeneity in a sample.

[0323] A. Results

[0324] When evaluating the consistency of SMT measurements, it was determined that cell-to-cell heterogeneity was the largest source of variation, exceeding FOV, well, plate, or microscope-level variation. Cell-to-cell variation was at least an order of magnitude greater than FOV-to-well or well-to-well variation ( Figures 25A-25B This strongly suggests that biological heterogeneity is more dominant than technical variation in the OLS-based SMT measurement ( Figure 20A Examples of cellular heterogeneity include cell cycle, cellular stress states, and genetic variation. Single-cell measurements, such as large-FOV SMT, enable more detailed measurements to better understand this heterogeneity. This example illustrates this single-cell analysis by measuring the effects of the cell cycle on proliferating cell nuclear antigen (PCNA) protein dynamics. PCNA is involved in DNA replication and, as such, relocalizes to replication foci during S phase, exhibiting unique and specific protein dynamics throughout the cell cycle.

[0325] Halo-PCNA was introduced into isolated U2OS clones expressing sub-endogenous levels of marker protein ( Figure 22A and 22B ), and found that the growth rate was not significantly affected by Halo-PCNA expression ( Figure 22C The correct localization of Halo-PCNA was confirmed by co-localization analysis of RFP-labeled anti-PCNA nanobody ( Figure 22D ).

[0326] 50nM JFX 650 Time-lapse microscopy of Halo-PCNA was performed at 12 frames per hour for 12 hours to achieve near saturation labeling ( Figure 21A The manually assigned cell stages and time-based progressions were then used to train a machine learning model, resulting in G1, early S, metaphase S, late S, G2, and mitosis classifiers and regression predictions across the cell cycle continuum ( Figure 21B ). The predicted cell cycle classification performed well compared to manual annotation ( Figure 21C When the regression predictions are plotted over the time series, a clear progression of individual cells through the cell cycle can be observed ( Figure 21D To further validate the cell cycle phase partitioning model, thymidine was used to block cell cycle progression in the S phase, or the CDK-1 inhibitor RO-3306 was used to block the G2-M transition. Both treatments resulted in the expected enrichment of the fraction of cells assigned to the corresponding cell phase ( Figure 20B and 20C). Then 10 pM JF 549 and 50 nM JFX 650 Cells were labeled to enable cell cycle assignment based on near-saturating labeling in conjunction with SMT measurements. 2 Two migration peaks were observed at 100 nm / s, which may represent PCNA associated with DNA replication sites and free PCNA ( Figure 20D When cells in each cell phase were analyzed separately, it became clear that the slow-moving PCNA population was only present in cells predicted to be in S phase, while the fast-moving population was significantly reduced ( Figure 20E ). We then measured the average diffusion coefficient of PCNA in 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 large-FOV SMT enabled by OLS can capture and elucidate cellular-level heterogeneity in protein dynamics.

[0327] B. Methods

[0328] b. CRISPR-mediated Halo-tagged protein knock-in cell line engineering

[0329] All U2OS cells were cultured at 37° C. and 5% CO 2 in Dulbecco's modified Eagle's medium (DMEM) (gibco) supplemented with 10% fetal bovine serum (Corning), 100 units / mL penicillin, and 100 μg / mL streptomycin (Gibco).

[0330] To generate the Halotag-PCNA cell line, a ribonucleoprotein (RNP) complex containing sgRNA (Integrated DNA Technologies-IDT) targeting the N-terminal or C-terminal region and Cas9 protein (PNA bio, catalog number CP01) was transfected with a linear dsDNA donor (IDT) using the Lonza nuclear transfection method. Each donor consists of 200-300bp homology arms specific for each target, a codon-optimized Halotag sequence, and a TEV linker (ENLYFQG) between the target and the Halotag.

[0331] After transfection, cells were incubated with Halo ligand JF 646 (In-house) and imaged using the ImageXpress system (Molecular Devices) to confirm HaloTag integration. Cells were then sorted into 384-well plates. Clonal cells were expanded and imaged using the ImageXpress system and genotyped by Sanger sequencing to confirm homogeneous HaloTag integration.

[0332] b. Cell proliferation

[0333] 6-Well Plate Cell Proliferation—Cells were seeded at 150,000 cells / well in 6-well plates (Fisher Scientific, 07-200-83) and allowed to settle at ambient temperature for 20 minutes to ensure uniform settling. Plates were imaged using an Incucyte (Sartorius), with nine images per well taken every four hours. Cell confluence was determined using a phase masking algorithm applied using Incucyte software v.S3 2019A.

[0334] c. Western Blot Analysis

[0335] Protein was extracted from 1-2 million cells using 1× cell lysis buffer (CST, #9803) diluted in ultrapure sterile water (Intermountain Life Sciences, 20804225) containing 1× 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 the automated Western blotting system Jess (ProteinSimple, 004-650) according to the manufacturer's protocol. A 12-230 kDa separation module was used for detection of PCNA (CST, D3H8P, rabbit, 1:100 dilution, #13110), β-actin (CST, D6A8, rabbit, 1:50 dilution, #8457S), and Halo (Promega, mouse, 1:10 dilution, #G9211). Samples were diluted to 0.3 mg / mL with 0.1× lysis buffer and 5× master mix and heat denatured at 95°C for 5 minutes. All primary antibodies were diluted in Antibody Diluent 2. All reagents were loaded into a microplate, and 13 capillary cartridges were loaded onto a 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 normalized to the loading control, β-actin.

[0336] d. Machine Learning Architecture

[0337] You can use images (e.g., JF 549The image of the PCNA label) automatically determines 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 ( Figures 21A-21D ). The decoder of the architecture may include a set of three independent decoders. D = {D1, D2, D3} Although the encoder E consumes the image θ Shared between D, but each decoder can be configured with a different set of parameters and trained separately to perform three different tasks: 1) nuclear 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.

[0338] Each loss can be based on the class-balanced cross entropy, defined as:

[0339]

[0340] in:

[0341] Z is the model’s predicted output for all categories

[0342] C is the total number of categories

[0343] b is a hyperparameter between 0 and 1

[0344] ·n y is the number of training samples for each category y

[0345] The first loss optimizes the nuclear segmentation task and is defined for an input x representing a 2D image of fluorescently labeled PCNA:

[0346] L 分割 =CB softmax (D1(E θ (x)),y1)

[0347] Where D1(E θ (x)) Generate pixel-level predictions for three categories: nucleus, nucleus edge, and background.

[0348] The second loss optimizes the cell cycle classification task and is defined as:

[0349] L 分类 =CB softmax (D2(E θ (x)),y2)

[0350] Among them, D2(E θ (x)) Generate pixel-level predictions for seven categories: background, mitosis, G1, early S, metaphase S, late S, and G2.

[0351] To generate a continuous representation of the cell cycle classes (M, G1, early S, metaphase S, late S, and G2), a target encoding method is used so that the classes can be linearly arranged from 0-1, thereby assigning cells classified as M to 0 and cells classified as G2 to 0.8. To minimize the complexity of aggregating the three losses, a similar cross-entropy is used to train the regression task. The regression decoder D3 produces 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 optimizes the cell cycle regression task and is defined as:

[0352] L 回归 =CB softmax (D3(E θ (x)),y3)

[0353] Finally, the total loss of the network is the average of these three losses:

[0354]

[0355] Once the model is trained, nuclear segmentation can be performed and each nucleus can be assigned a categorical or continuous representation of the cell cycle based on the mean pixel value that defines the corresponding nucleus.

[0356] e. Evaluation of the highest variance sources in the experiment: jump resampling experiment

[0357] To assess the contribution of inter-well, inter-FOV, and inter-cell biases to the estimated mean 2D jump length, DMSO-treated KEAP1-HaloTag (using JF 549 HILO and OLS data were collected at 100 Hz for the entire plate (308 wells) using a 384-well plate (labeled). Excluding the outer wells in the 384-well plate, this resulted in a dataset containing 308 wells, 12 FOVs per well, and an average of approximately 44 cells per FOV (for OLS) or approximately 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

[0358] Y = B hole + BFOV + B cell + X

[0359] B 孔 、B FOV and B 细胞 is a random variable that models the bias at the pore, FOV, or cell level, and X models the intrinsic randomness of the jump length that depends on the specific pore, FOV, and cell. Simplified, 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 )+Var(X). A more physically realistic model would take into account the potential dependencies between these random variables.

[0360] In order to estimate Var(B 孔 )、Var(B FOV )、Var(B 细胞 ) and Var(X), the sample mean and variance of four different jump resampling schemes are calculated:

[0361] Sample N jumps out of the entire plate. The resulting sample mean is the sum of all sources of variation (B 孔 、B FOV 、B 细胞 and X) are averaged.

[0362] A hole is sampled, and then N samples jump out of the hole. The sample mean is B FOV 、B 细胞 and X, and as N gets larger, the variance of these sample means is expected to approach Var(B 孔 ).

[0363] Sample a hole, sample the FOV of the hole, and then sample N out of the FOV. The resulting sample mean is B 细胞 and X, and as N gets larger, the variance of these sample means is expected to approach Var(B 孔 )+Var(B FOV ).

[0364] Sample a hole, sample the FOV of the hole, sample the cell of the FOV, and then sample N out of the cell. The resulting sample means are simply averaged over X. As N gets larger, the variance of these sample means is expected to approach Var(B 孔 )+Var(B FOV )+Var(B 细胞 ).

[0365] Each sampling scheme was used for 1000 rounds of sampling, and the deviation B 孔 、B FOV and B 细胞 The variance of is estimated by the difference in the variance of the sample means produced by each resampling scheme. As expected, only Var(X) depends on the sample size N, while the other sources of variation remain stable with respect to the sample size after about 100 jumps ( Figures 25A-25B ).

[0366] C. Discussion

[0367] This analysis revealed that the largest source of variation in SMT measurements stems from cellular heterogeneity in protein movement. The large FOV achieved by OLS allows for simultaneous capture of over 50 U2OS cells in culture, enabling the capture of intercellular heterogeneity. This is exemplified by PCNA, which has the potential to simultaneously assign cell cycle phase and monitor protein dynamics. These results clearly demonstrate that PCNA protein dynamics are slower during S phase, correlating with protein enrichment at sites of DNA replication. A dramatic increase in dynamics is observed during G1, G2, and M phases, corresponding to a uniform distribution of most PCNA throughout the nucleus. These findings are consistent with previous characterizations of PCNA dynamics, but the approach presented here offers important improvements. PCNA localization, rather than manual assignment, is used to computationally predict cell cycle progression via a machine learning model. Furthermore, the OLS platform enables rapid and automated capture and analysis of thousands of cells, whereas manual SMT methods typically only allow for analysis of dozens of cells per condition. Automated cell sorting and scalable SMT data collection and analysis are crucial for achieving comparable performance with flow cytometry and other single-cell analysis techniques when characterizing protein movement in heterogeneous cell populations and potentially rare cell subtypes.

[0368] Example 5: OLS is applicable to various SMLM techniques and acquisition schemes

[0369] This embodiment provides an application extension of the OLS system using embodiment 1, which exploits the advantages of OLS through uniform illumination, robustness, high spatiotemporal resolution, and imaging speed.

[0370] A. Results

[0371] The uniform illumination, high spatiotemporal resolution, imaging speed, and overall robustness of the OLS system may have broad advantages in a variety of biological microscopy techniques. To demonstrate the ability to image SMTs with two spectrally distinct fluorophores, JF 549 and JF 646 time series of Halo-Keap1 labeled, resulting in clear single-molecule resolution at two wavelengths ( Figure 23A KI-696 was dose-titrated and JF was measured. 549 and JF 646 The reaction of labeled Halo-Keap1 on both to demonstrate the ability to measure changes in protein motion at two wavelengths ( Figure 23B Although the quantum efficiency of sCMOS decreases in the far-red spectrum, resulting in a decrease in SNR ( Figure 24A ), but using Redshift's JF 646 Captured SMT data, its EC 50 Value and brighter JF 549 Dyes are highly consistent, JF 549 and JF646 The measured EC 50 The robust measurement of protein dynamics at lower SNRs may be explained by the minimal decrease in the error rate lower limit (ERLB) ( Figure 24B Taken together, OLS-based SMT may find applications in multicolor imaging and facilitate the imaging of lower quantum yield fluorophores, thereby broadening the range of available fluorophores for measuring protein motion in biological applications.

[0372] Because of the short integration times produced by OLS line scans, we investigated whether dyes commonly used for STORM imaging of fixed cells would produce images with high x,y resolution within a large FOV. Cells labeled with an anti-tubulin primary antibody and either AlexaFluor 647 (AF647) or CF568-conjugated secondary antibodies were imaged using STORM at 60x magnification for a total imaging time of approximately 60 seconds ( Figures 23C-23F It was observed that despite the short integration time of 400 μs used in the OLS, spontaneous light switching occurs as long as a sufficient number of photons are collected, achieving a lateral positioning accuracy of approximately 15 nm using AF647 or CF568 ( Figure 23G and 23H These results demonstrate the potential for high-speed, high-throughput phenotypic screening using STORM or other super-resolution microscopy techniques with OLS illumination.

[0373] Next, the line scan component of the OLS was used to scan the JF 549 The SMT / FRAP experiments were performed on cells labeled with Halo-KEAP1. Before acquiring the full FOV under normal SMT acquisition, a 240 × 40 μm region was bleached by focusing the scan area on a narrow subset of the FOV for 100–200 scans. This resulted in each frame consisting of a bleached region (FRAP region) and an unbleached region ( Figure 23I ). Instead of capturing the time-dependent recovery of intensity after photobleaching, the normalized spot density after recovery was measured ( Figure 23K and 23L ). At 400 pM JF 549 The average T 1 / 2 (DMSO) was 3.52 (SD=0.38), and T 1 / 2 (KI-696) was 2.27 (SD = 0.43), which seemed to be the best for separating the two conditions ( Figure 23JThis result is consistent with the reported SMT measurements, indicating that KI-696 significantly increases 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 across the perturbation range of 1 μM KI-696 dye concentration ( Figure 23M and 24C ). These results highlight and confirm the advantageous properties of OLS combined with the proposed tracking algorithm to achieve sensitive blob detection and SMT.

[0374] B. Methods

[0375] c. 384-well plate coating and cell seeding for SMT

[0376] 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 incubated overnight at 37°C and 5% CO2. The cells were then washed with DPBS (3×) and Fluorobrite DMEM (2×) using JF. 549 and JF 646 The cells were labeled for SMTs for one hour using Hoechst 33342 and organelle-specific dyes.

[0377] 2. Line FRAP Acquisition

[0378] The FRAP dataset U2OS-KEAP1 was recorded by sequentially imaging five pre-bleach frames, bleaching a subregion of the imaging FOV, and capturing fluorescence recovery at an operating frame rate of 25 fps. Bleaching of a subregion of the FOV was achieved by scanning the excitation laser at high laser power (300 mW) across a subregion of the FOV (16-25% FOV height, 100% FOV width) 100-200 times to bleach localized fluorescence. After the bleaching step at low laser power (70 mW), fluorescence recovery was acquired at 400-500 frames. 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 bleaching and imaging power at the system's 560 nm excitation.

[0379] 3. STORM and PALM datasets

[0380] 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 three times with PBS again. The cells were then blocked in 2% BSA for 1 hour at room temperature and then incubated overnight with anti-α tubulin antibody (ab7291) at 4°C. The primary antibody was removed by washing three times with PBS and the cells were blocked again with 2% BSA for 1 hour at room temperature. The secondary antibody coupled to Alexa Fluorophore 647 was diluted 1:1000 in blocking buffer and incubated at room temperature for 2 hours. The cells were then washed three times with PBS to remove excess secondary antibody. Hoechst33342 at a concentration of 7 μM was added together with the secondary antibody for visualization of the nucleus. The light switch buffer for STORM imaging was prepared as previously described. Buffer A was prepared by 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 HO. GLOX solution was prepared by mixing 14 mg glucose oxidase (G2133) and 17 mg / mL catalase (C40) with 200 μL buffer A. The final photoswitch buffer was prepared by combining 100 μL 1 M MEA (M9768) with 10 μL GLOX and 1 mL buffer B. This buffer was added to the cells in the 384-well plate prior to imaging.

[0381] Use low laser power (300 mW) to capture a diffraction-limited image and identify the relevant focal plane. Increase the laser power to reach approximately 500 mW (x kW / cm) at the back focal plane. 2 ) to activate the optical switch. 500–5000 frames were acquired with a pixel integration time of 0.4 ms.

[0382] C. Discussion

[0383] These results highlight and confirm the advantageous properties of OLS combined with the proposed tracking algorithm, enabling sensitive blob detection and SMT even when marker sparsity is not optimized. In particular, these results demonstrate that OLS is suitable for fast acquisition of STORM datasets despite very short illumination integration times and for AF 647 and CF 568 Both achieved a lateral resolution of approximately 30 nm, highlighting the versatility of OLS. The results further demonstrate a related SMT / FRAP method for studying protein diffusion.

[0384] This example demonstrates that a large FOV, OLS scanning, high SNR, and fast integration time enable 2-color SMT, STORM, and FRAP to be achieved rapidly and consistently on a large scale. It is expected that this platform will be compatible with other methods such as fluorescence correlation spectroscopy (FCS) and image correlation spectroscopy (ICS), enabling the utilization of this advanced microscopy approach in high-content applications within systems biology and drug screening.

[0385] ********

[0386] Although the presently disclosed subject matter and its advantages have been described in detail, it should be understood that various changes, substitutions, and alterations may be made without departing from the spirit and scope of the present disclosure. Furthermore, the scope of this application is not intended to be limited to the particular embodiments of the processes, machines, manufactures, and compositions of matter, devices, methods, and steps described in the specification. Therefore, the appended claims are intended to encompass within their scope such processes, machines, manufactures, compositions of matter, devices, methods, or steps.

[0387] Throughout this application, various patents, patent applications, publications, product descriptions, and protocols are cited, the disclosures of which are incorporated herein by reference in their entirety for all purposes.

Claims

1. A method for determining the occurrence rate of a biological interaction between a compound and a target fluorescent protein in a living cell, comprising: (a) contacting a sample comprising a population of living cells with the compound, wherein the living cells comprise the target fluorescent protein; (b) tracking the movement of a plurality of individual target fluorescent 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 at a sample plane disposed within the sample with a light beam such that a subset of the target fluorescent proteins in the living cells fluoresces; as well as (ii) detecting, by a detector arrangement, the fluorescence of a plurality of said target fluorescent proteins in a detection field of view of said sample plane, wherein said 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 (c) determining a change in the movement of the target fluorescent protein in the presence of the compound; The rate at which the movement change of the target fluorescent protein occurs is determined by comparing the movement changes of the target fluorescent protein at the multiple time points.

2. A method for determining the occurrence rate of a biological interaction between a compound and a target fluorescent protein in a living cell, comprising: (a) contacting a sample comprising a population of living cells with the compound, wherein the living cells comprise the target fluorescent protein; (b) tracking the movement of a plurality of individual target fluorescent 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 target fluorescent proteins in the living cells to fluoresce, 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, by a detector arrangement, the fluorescence of a plurality of said target fluorescent proteins in a detection field of view of said sample plane, wherein said 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 (c) determining a change in the movement of the target fluorescent protein in the presence of the compound; The rate at which the movement change of the target fluorescent protein occurs is determined by comparing the movement changes of the target fluorescent protein at the multiple time points.

3. A method for determining the occurrence rate of a biological interaction between a compound and a target fluorescent protein in a living cell, comprising: (a) contacting a sample comprising a population of living cells with the compound, wherein the living cells comprise the target fluorescent protein; (b) tracking the movement of a plurality of individual target fluorescent 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 at a sample plane disposed within the sample with a light beam such that a subset of the target fluorescent proteins in the living cells fluoresces; (ii) detecting, by a detector arrangement, the fluorescence of a plurality of said target fluorescent proteins in a detection field of view of said sample plane, wherein said 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 (c) determining the change in the movement of the target fluorescent protein after adding the compound; The rate at which the movement change of the target fluorescent protein occurs is determined by comparing the movement changes of the target fluorescent protein at the multiple time points.

4. A method for determining the occurrence rate of a biological interaction between a compound and a target fluorescent protein in a living cell, comprising: (a) contacting a sample comprising a population of living cells with the compound, wherein the living cells comprise the target fluorescent protein; (b) tracking the movement of a plurality of individual target fluorescent 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 at a sample plane disposed within the sample with a light beam such that a subset of the target fluorescent proteins in the living cells fluoresces; (ii) detecting, by a detector arrangement, the fluorescence of a plurality of said target fluorescent proteins in a detection field of view of said sample plane, wherein said 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 (iii) detecting fluorescence of a plurality of said target fluorescent proteins in said detection fields of said sample plane at a rate exceeding 100,000 detection fields per day; and (c) determining a change in the movement of a target fluorescent protein in the presence of the compound; The rate at which the movement change of the target fluorescent protein occurs is determined by comparing the movement changes of the target fluorescent protein at the multiple time points.

5. A method for determining the occurrence rate of a biological interaction between a compound and a target fluorescent protein in a living cell, comprising: (a) contacting a sample comprising a population of living cells with the compound, wherein the living cells comprise the target fluorescent protein; (b) tracking the movement of a plurality of individual target fluorescent 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 at a sample plane disposed within the sample with a light beam such that a subset of the target fluorescent proteins in the living cells fluoresces; and (ii) detecting, by a detector arrangement, fluorescence of a plurality of said target fluorescent proteins in a detection field of view of said sample plane, wherein said 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 said detection field of view achieves sufficient laser illumination to track protein motion; and (c) determining a change in the movement of the target fluorescent protein in the presence of the compound; The rate at which the movement change of the target fluorescent protein occurs is determined by comparing the movement changes of the target fluorescent protein at the multiple time points.

6. The method of any one of claims 1 to 5, wherein the detected change in motion is an increase in immobility trajectory, indicating that the binding (f 结合 ) Increase of target fluorescent protein.

7. The method of any one of claims 1 to 5, wherein the detected change in motion is a change in: (a) Median of jump length distribution; (b) the third quartile of the jump length distribution; (c) median radius of gyration; (d) average posterior diffusion coefficient; (e) Geometric mean posterior diffusion coefficient; (f) mean square displacement; (g) median bond angle; (h) a maximum likelihood estimator of the diffusion coefficient; and / or (i) Occupation through the state of reasoning.

8. The method of any one of claims 1 to 5, wherein the target fluorescent proteins interact with each other in a larger molecular assembly.

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

10. The method of claim 8, wherein the target fluorescent protein is a receptor.

11. The method of any one of claims 1 to 5, wherein the biological interaction is a direct interaction.

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

13. The method of any one of claims 1 to 5, wherein the biological interaction is an indirect interaction.

14. The method of claim 13, wherein the indirect interaction comprises the compound agonizing or antagonizing a larger molecular assembly comprising the target fluorescent protein.

15. A method for determining the dosage of a compound, wherein the compound induces a change in the movement of a target fluorescent protein in a living cell, the method comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of viable cells, (ii) wherein the living cell comprises the target fluorescent protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound within a range of compound concentrations; (b) tracking the movement of individual target fluorescent proteins in a plurality of living cells of 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 so as to cause at least a subset of the target fluorescent proteins in the living cells to fluoresce; (ii) detecting fluorescence of one or more of said target fluorescent proteins in said sample plane by a detector arrangement, wherein said 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 (c) determining the rate at which the change in movement of the target fluorescent protein occurs in the presence of the compound; (d) repeating steps (b)-(c) for each of the plurality of samples within the range of compound concentrations; The rate at which the movement change of the target fluorescent protein occurs is determined by comparing the movement changes of the target fluorescent protein at the multiple time points; and The dosage is determined by the rate.

16. A method for determining the dosage of a compound that induces a change in the motility of a target fluorescent protein in a living cell, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of viable cells, (ii) wherein the living cell comprises the target fluorescent protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound within a range of compound concentrations; (b) tracking the movement of individual target fluorescent proteins in a plurality of living cells of 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 at least a subset of the target fluorescent proteins in the living cells to fluoresce, 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, by a detector arrangement, the fluorescence of one or more of the target fluorescent proteins 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 (c) determining the rate at which the change in movement of the target fluorescent protein occurs in the presence of the compound; (d) repeating steps (b)-(c) for each of the plurality of samples within the range of compound concentrations; The rate at which the movement change of the target fluorescent protein occurs is determined by comparing the movement changes of the target fluorescent protein at the multiple time points; and The dosage is determined by the rate.

17. A method for determining the dose response of a compound that induces a change in the motility of a target fluorescent protein in a living cell, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of viable cells, (ii) wherein the living cell comprises the target fluorescent protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound within a range of compound concentrations; (b) tracking the movement of individual target fluorescent proteins in a plurality of living cells of 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 so as to cause at least a subset of the target fluorescent proteins in the living cells to fluoresce; (ii) detecting, by a detector arrangement, the fluorescence of one or more of the target fluorescent proteins in a 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 (c) determining the rate at which the change in movement of the target fluorescent protein occurs in the presence of the compound; and (d) repeating steps (b)-(c) for each of the plurality of samples within the range of compound concentrations; The rate at which the movement change of the target fluorescent protein occurs is determined by comparing the movement changes of the target fluorescent protein at the multiple time points; and The dosage is determined by the rate.

18. A method for determining the dose response of a compound that induces a change in the motility of a target fluorescent protein in a living cell, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of viable cells, (ii) wherein the living cell comprises the target fluorescent protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound within a range of compound concentrations; (b) tracking the movement of individual target fluorescent proteins in a plurality of living cells of 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 such that at least a subset of the target fluorescent proteins in the cells fluoresce; (ii) detecting, by a detector arrangement, the fluorescence of one or more of the target fluorescent proteins in a 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; iii) wherein the tracking comprises detecting fluorescence of a plurality of the target fluorescent proteins in a detection field of the sample plane at a rate exceeding 100,000 detection fields per day; and (c) determining the rate at which the change in movement of the target fluorescent protein occurs in the presence of the compound; (d) repeating steps (b)-(c) for each of the plurality of samples within the range of compound concentrations; The rate at which the movement change of the target fluorescent protein occurs is determined by comparing the movement changes of the target fluorescent protein at the multiple time points; and The dosage is determined by the rate.

19. A method for determining the dosage of a compound, wherein the compound induces a change in the movement of a target fluorescent protein in a living cell, the method comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of viable cells, (ii) wherein the living cell comprises the target fluorescent protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound within a range of compound concentrations; (b) tracking the movement of individual target fluorescent proteins in a plurality of living cells of 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 so as to cause at least a subset of the target fluorescent proteins in the living cells to fluoresce; (ii) detecting fluorescence of one or more of the target fluorescent proteins in the sample plane by a detector arrangement, 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 achieves sufficient laser illumination to track protein motion; and (c) determining the rate at which the change in movement of the target fluorescent protein occurs in the presence of the compound; (d) repeating steps (b)-(c) for each of the plurality of samples within the range of compound concentrations; The rate at which the movement change of the target fluorescent protein occurs is determined by comparing the movement changes of the target fluorescent protein at the multiple time points; and The dosage is determined by the rate.

20. The method of any one of claims 15 to 19, wherein the detected change in motion is an increase in immobility trajectory, indicating that the binding (f 结合 ) Increase of target fluorescent protein.

21. The method of any one of claims 15 to 19, wherein the detected change in motion is a change in: (a) Median of jump length distribution; (b) the third quartile of the jump length distribution; (c) median radius of gyration; (d) average posterior diffusion coefficient; (e) Geometric mean posterior diffusion coefficient; (f) mean square displacement; (g) median bond angle; (h) a maximum likelihood estimator of the diffusion coefficient, and / or (i) Occupation through the state of reasoning.

22. The method of any one of claims 15 to 19, wherein the target fluorescent proteins interact with each other in a larger molecular assembly.

23. The method of claim 22, wherein the target fluorescent protein is a ligand.

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

25. The method of any one of claims 15 to 19, wherein the biological interaction is a direct interaction.

26. The method of claim 25, wherein the direct interaction comprises binding of the compound to the target fluorescent protein.

27. The method of any one of claims 15 to 19, wherein the biological interaction is an indirect interaction.

28. The method of claim 27, wherein the indirect interaction comprises the compound agonizing or antagonizing a larger molecular assembly comprising the target fluorescent protein.

29. A microscopy system configured to detect the occurrence of a biological interaction between a compound and a target fluorescent protein in a living cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of living cells, and wherein the living cells comprise the target fluorescent protein; (b) a light source for emitting a light beam capable of inducing a light-based reaction of the plurality of target fluorescent proteins in the sample; (c) an objective lens for focusing the light beam onto the sample in the sample plane, wherein the subset of target fluorescent proteins in the sample is disposed in a detection field of view in 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; (e) a detector device for monitoring said light-based reaction of said target fluorescent protein in the presence of said compound at a plurality of time points; (e) memory; as well as (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining changes in the movement of the target fluorescent protein in the presence of the compound at multiple time points to detect the occurrence of biological interactions between the compound and the target fluorescent protein in living cells.

30. A microscopy system configured to detect the occurrence of a biological interaction between a compound and a target fluorescent protein in a living cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population, 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 the plurality of target fluorescent proteins in the sample; (c) an objective for focusing the light beam onto the sample in the sample plane, wherein the subset of target fluorescent proteins in the sample is disposed in a detection field of view in 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, and wherein the subset of target fluorescent proteins produces at most about 1,000,000 molecular trajectories in a single detection field of view; (d) a detector device for monitoring said light-based reaction of said target fluorescent protein in the presence of said compound at a plurality of time points; (e) memory; as well as (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining changes in the movement of the target fluorescent protein in the presence of the compound at multiple time points to detect the occurrence of biological interactions between the compound and the target fluorescent protein in living cells.

31. A microscopy system configured to detect the occurrence of a biological interaction between a compound and a target fluorescent protein in a living cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population, 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 the plurality of target fluorescent proteins in the sample; (c) an objective lens for focusing the light beam onto the sample in the sample plane, wherein the subset of target fluorescent proteins in the sample is positioned in a detection field of view in 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 said light-based reaction of said target fluorescent protein in the presence of said compound at a plurality of time points; (e) memory; as well as (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining changes in the movement of the target fluorescent protein in the presence of the compound relative to the absence of the compound at multiple time points, thereby detecting the occurrence rate of biological interaction between the compound and the target fluorescent protein in living cells.

32. A microscopy system configured to detect the occurrence of a biological interaction between a compound and a target fluorescent protein in a living cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population, 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 the plurality of target fluorescent proteins in the sample; (c) an objective lens for focusing the light beam onto the sample in the sample plane, wherein a plurality of the target fluorescent proteins in the sample are positioned in a detection field of view of the sample plane; (d) a detector device for monitoring said light-based reaction of said target fluorescent protein in the presence of said compound at a plurality of time points, wherein said monitoring comprises detecting fluorescence of a plurality of said target fluorescent proteins in said detection field of view of said sample plane at a rate exceeding 100,000 detection fields per day; (e) memory; as well as (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining changes in the movement of the target fluorescent protein in the presence of the compound at multiple time points to detect the occurrence of biological interactions between the compound and the target fluorescent protein in living cells.

33. A microscopy system configured to detect the occurrence of a biological interaction between a compound and a target fluorescent protein in a living cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of living cells, and wherein the living cells comprise the target fluorescent protein; (b) a light source for emitting a light beam capable of inducing a light-based reaction of the plurality of target fluorescent proteins in the sample; (c) an objective for focusing the light beam onto the sample in the sample plane, wherein a subset of the target fluorescent proteins in the sample is disposed in a detection field of view in 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, and wherein greater than or equal to 95% of the detection field of view achieves sufficient laser illumination to track protein motion; (e) a detector device for monitoring said light-based reaction of said target fluorescent protein in the presence of said compound at a plurality of time points; (e) memory; as well as (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining changes in the movement of the target fluorescent protein in the presence of the compound at multiple time points to detect the occurrence of biological interactions between the compound and the target fluorescent protein in living cells.

34. The system of any one of claims 29 to 33, wherein the detected change in motion is an increase in immobility trajectory, indicating a combination of (f 结合 ) Increase of target fluorescent protein.

35. The system of any one of claims 29 to 33, wherein the detected motion is a change in: (a) Median of jump length distribution; (b) the third quartile of the jump length distribution; (c) median radius of gyration; (d) average posterior diffusion coefficient; (e) Geometric mean posterior diffusion coefficient; (f) mean square displacement; (g) median bond angle; (h) a maximum likelihood estimator of the diffusion coefficient; and / or (i) Occupation through the state of reasoning.

36. The system of any one of claims 29 to 33, wherein the target fluorescent proteins interact with each other in a larger molecular assembly.

37. The system of claim 36, wherein the target fluorescent protein is a ligand.

38. The system of claim 36, wherein the target fluorescent protein is a receptor.

39. The system of any one of claims 29 to 33, wherein the biological interaction is a direct interaction.

40. The system of claim 39, wherein the direct interaction comprises binding of the compound to the target fluorescent protein.

41. The system of any one of claims 29 to 33, wherein the biological interaction is an indirect interaction.

42. The system of claim 41, wherein the indirect interaction comprises the compound agonizing or antagonizing a larger molecular assembly comprising the target fluorescent protein.

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