Systems and methods for high-throughput single-molecule tracking in living cells

Abstract

A sequence of images visualizing the motion of molecules is received. The molecules are linked between the images. Using a variational Bayesian optimization algorithm, and based on the linking, possible trajectories of each molecule with associated probabilities are generated. Data characterizing the generated possible trajectories with associated probabilities is provided to a consuming application or process.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Application No. 63 / 476,951, filed December 22, 2022, and U.S. Provisional Application No. 63 / 476,942, filed December 22, 2022, the entire contents of which are each incorporated herein by reference.

[0002] The subject matter described herein relates to a platform for tracking single molecules in complex systems. [Background technology]

[0003] Protein movement within the dense environment of living cells is strongly influenced by interactions with its surroundings. Single-molecule tracking (SMT) is one method for capturing protein movement as a reporter of activity. In SMT, fluorescent proteins of interest are imaged with high spatiotemporal resolution to track their movement within complex systems, such as living cells. The information embedded in these traces has been used to investigate diverse cellular phenomena, including protein-protein interactions, such as those mediating signal transduction, interorganelle communication, nuclear organization, and transcriptional regulation. However, the application of SMT technology is limited in scale and has primarily been used to address specific mechanistic hypotheses. For example, SMT is not amenable to throughput settings that enable systems-level screening or drug discovery. Summary of the Invention

[0004] In a first aspect, a sequence of images visualizing the motion of molecules is received. The molecules are linked between the images. Using a variational Bayesian optimization algorithm, possible trajectories for each molecule, with associated probabilities, are generated based on the linking. Data characterizing the generated possible trajectories with associated probabilities is provided to a consuming application or process.

[0005] In a correlation aspect, a sequence of images visualizing the motion of molecules is received. The molecules are linked between the images. Using a Gibbs sampling algorithm and based on the linking, possible trajectories of each molecule with associated probabilities are generated. Data characterizing the generated possible trajectories with associated probabilities is provided to a consuming application or process.

[0006] In yet another related aspect, a sequence of images visualizing the motion of molecules is received. The molecules are linked between the images. Using an adaptive hill-climbing algorithm and based on the linking, possible trajectories of each molecule with associated probabilities are generated. Data characterizing the generated possible trajectories with associated probabilities is provided to a consuming application or process.

[0007] At least a subset of the image sequence can include at least 100 molecules per image, in other variations there are at least 1000 molecules per image, and in still other variations there are at least 10,000 molecules per image.

[0008] The molecules in some variations have a density of at least 0.01 emitters per square micron per image, whereas in some variations they may have a density of at least 0.1 emitters per square micron per image.

[0009] Molecules within a biological sample can be labeled, the labeled biological sample can fluoresce, and an image sequence can be generated as the biological sample fluoresces.

[0010] Producing the image sequence can be performed using a microscope system.

[0011] Molecules can be imaged within living cells.

[0012] A probabilistic dynamic model can be inferred that includes information characterizing the trajectories of the molecules. The probabilistic dynamic model can include an array of states, and the array of states can be populated with information characterizing the trajectories of the molecules.

[0013] An internal confidence metric based on the associated probability can be generated, and the provided data can include the generated internal confidence metric. The generated internal confidence metric can be a tracking error rate lower bound that defines a lower bound on the rate of misconnections made by linking. The generated internal metric can include calculating a confidence level for each trajectory.

[0014] Furthermore, dynamic metrics can be generated independent of any particular trajectory.

[0015] Linking can include obtaining data with a plurality of statistical data extracted from the total number of detections or the number of detections in a cell.

[0016] Providing the data may include one or more of visualizing at least some of the generated possible trajectories with associated probabilities in a graphical user interface, storing at least some of the generated possible trajectories with associated probabilities in a physical persistent state, loading at least some of the generated possible trajectories with associated probabilities into memory, or transmitting at least some of the generated possible trajectories with associated probabilities over a network to a remote computing device.

[0017] At least some of the image sequences may comprise consecutive images from the corresponding moving images, while in another variation, at least some of the image sequences used to be linked are non-consecutive images from the corresponding moving images.

[0018] In another related aspect, an image sequence visualizing the movement of a molecule may be received. The image sequence may include a first type generated using a first imaging modality and a second type generated using a second, different imaging modality. Spots may be detected within the image sequence of the first type. The detected spots within the image sequence of the first type may be linked to a trajectory using a probabilistic tracking algorithm. The image sequence of the second type may be segmented to generate a plurality of instance masks. A molecule within the image sequence of the second type may be assigned to at least one instance mask of the plurality of instance masks. Data characterizing the linking and assignment may be provided to a consuming application or process.

[0019] Probabilistic tracking algorithms can take different forms, including variational Bayesian optimization algorithms, Gibbs sampling algorithms, or adaptive hill climbing algorithms.

[0020] The first imaging modality and the second imaging modality can include different molecular labeling techniques.

[0021] The first type of image sequence may be a single molecule tracking (SMT) video, and the second type of image sequence may be a non-SMT video.

[0022] The detected spots may contain intracellular components.

[0023] The types of molecules in the first type of image sequence can be labeled with distinct fluorophores.

[0024] A plurality of statistical metrics associated with at least one of the trajectories or at least one instance mask may be generated.

[0025] A hierarchy of instance masks can be stored.

[0026] The detecting may utilize one or more of a generalized log-likelihood ratio spot detector, a difference of Gaussians (DoG) detector, a Laplacian of Gaussians (LoG) detector, or a Hessian determinant (DoH) blob detector.

[0027] The detected spots can be associated with spatiotemporal coordinates using sub-pixel localization.

[0028] Sub-pixel localization can include one or more of a radially symmetric localizer, or maximum likelihood fitting to a candidate spot model using the Levenberg-Marquardt method.

[0029] In some variations, the image sequence can be generated by a fluorescence microscopy apparatus having a first optical element or assembly, a second optical element or assembly, a third optical element or assembly, and a detector device. The first optical element or assembly can be configured to receive a fluorescence excitation light source and generate a collimated light beam having an elongated linear shape in the xy plane, such that the light beam has uniform intensity across the longer dimension of the linear shape. The second optical element or assembly can be configured to tilt the light beam with respect to the z-axis in the xz plane, and the second optical element can be further configured to focus the light beam on a sample surface located in the xy plane, thereby illuminating a portion of the sample surface. The third optical element or assembly can be configured to translate the light beam within the sample surface in a direction perpendicular to the longer dimension of the light beam. The detector device can be configured to receive light from the illuminated sample surface and form one or more projection images based on the light received from the sample surface.

[0030] The first optical component element or assembly may include a Powell lens that produces a collimated light beam having an elongated linear shape in the xy plane.

[0031] The first optical component element or assembly may include one or more diffraction gratings that generate a collimated light beam having an elongated linear shape in the xy plane.

[0032] The first optical component element or assembly may include a combination of lenses that produce a collimated light beam having an elongated linear shape in the xy plane.

[0033] The second optical component element or assembly can include an objective lens.

[0034] The third optical component element or assembly may include a galvo mirror configured to translate the light beam in the sample plane in a direction orthogonal to the longer dimension of the light beam.

[0035] The third optical component element or assembly may include a piezo element configured to translate the light beam in the sample plane in a direction perpendicular to the longer dimension of the light beam.

[0036] The detector device may include a solid-state sensor whereby the detector device supports a shutter mode for synchronizing translation of the light beam at the sample plane with selective activation or readout of the solid-state sensor.

[0037] In some variations, the image sequence can be generated by a microscope system for detecting the position of molecules having a stage, a light source, an objective lens, and a detector device. The stage can support a sample containing the molecules. The light can emit a light beam that can induce a light-based response from molecules in the sample such that the light beam has a linear shape in the sample plane and has uniform intensity across a longer dimension of the linear shape in the sample plane. The objective lens can focus the light beam on the sample in the sample plane such that the molecules are located at the sample plane. The detector device can monitor the light-based response from the molecules and thereby detect the position of the molecules.

[0038] The microscope system may also include a scanning optical element or assembly configured to translate the light beam in the sample plane in a direction perpendicular to the longer dimension of the light beam, thereby enabling the total field of view of the microscope system in the xy plane to be expanded.

[0039] The detector device may include a solid-state sensor whereby the detector device supports a shutter mode for synchronizing translation of the light beam at the sample plane with selective activation or readout of the solid-state sensor.

[0040] The detector device can support a shutter mode for synchronizing the translation of the light beam in the sample plane with the selective activation or readout of the semiconductor sensor.

[0041] The sample can be placed in an open well of a microplate that can contain multiple open wells. Such a variation can provide an xy position controller for changing the field of view of the microscope system, where the changed field of view encompasses a different subset of the multiple open wells.

[0042] The microscope system can include an automated sample handling robot system that enables high-throughput manipulation of multiple samples on a stage, the robot system including a memory, a processor in communication with the memory, and one or more robotic end effectors in communication with the processor, whereby the one or more end effectors manipulate the multiple samples on the stage based on communication with the processor.

[0043] Non-transitory computer program products (i.e., tangibly embodied computer program products) that store instructions, which, when executed by one or more data processors of one or more computing systems, cause at least one data processor to perform the operations described herein, are also described. Similarly, computer systems are described that may include one or more data processors and memory coupled to the one or more data processors. The memory may temporarily or permanently store instructions that cause at least one processor to perform one or more of the operations described herein. Furthermore, methods may be performed by one or more data processors, whether within a single computing system or distributed across two or more computing systems. Such computing systems may be connected via one or more connections, including, but not limited to, connections via a network (e.g., the Internet, a wireless wide area network, a local area network, a wide area network, a wired network, etc.), direct connections between one or more of the computing systems, etc., and may exchange data and / or commands or other instructions, etc.

[0044] The subject matter described herein offers many technical advantages. For example, the subject matter provides a fast, computationally efficient method for tracking targets (e.g., molecules) that separates interpretable information from background and other conflicting noise. The subject matter described herein can be used to analyze complex data that may include thousands to tens of thousands of fast-moving targets in close proximity. Furthermore, the subject matter described herein is advantageous in that it can be implemented with minimal or no human supervision.

[0045] More specifically, the present subject matter offers many technical advantages related to scalability. Current platforms are capable of generating data for over 100 molecules per frame (i.e., image) with multiple imaging systems running continuously. This capability, without human oversight of the raw data, requires tracking methods that are (1) highly scalable and (2) provide a built-in measure of confidence / diagnosis of the tracking results. Furthermore, the probabilistic tracking algorithms provided herein provide a built-in measure of confidence to the consuming application / process without human oversight. Furthermore, the current probabilistic tracking algorithms provide dynamic metrics that can be used for drug screening independent of the specific trajectory.

[0046] The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the description below. Other features and advantages of the subject matter described herein will be apparent from the description and drawings, and from the claims. [Brief explanation of the drawings]

[0047] This patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0048] [Figure 1] A schematic diagram of the htSMT workflow is shown.

[0049] [Figure 2A] 1 illustrates an exemplary image acquisition system of the present disclosure, showing the XZ sample plane. [Figure 2B] 1 illustrates an exemplary image acquisition system of the present disclosure, with the YZ sample plane visible. [Figure 2C] 1 illustrates an exemplary image acquisition system of the present disclosure, showing the XZ sample plane. [Figure 2D] 1 illustrates an exemplary image acquisition system of the present disclosure, with the YZ sample plane visible. [Figure 2E]Details of the light beam compared to the HILO-based approach are shown. [Figure 2F] An example of incorporating a camera roll shutter is shown below.

[0050] [Figure 3A] Figure 1 shows various measurements demonstrating that the disclosed image acquisition system and workflow are suitable for robust htSMT analysis. A laser titration experiment showing the relationship between laser power (mW) at the sample and signal-to-noise ratio (SNR) (left panel) and the average SNR at the well level across four image acquisition systems measuring six different 384-well plates per system (right panel). [Figure 3B] Figure 1 shows various measurements demonstrating the suitability of the disclosed image acquisition system and workflow for robust htSMT analysis. The difference in spatial SNR heterogeneity between the disclosed OLS system and a HILO-based approach is shown. The top panel compares the spatial standard deviation observed with OLS to a HILO-based approach. The bottom panel shows the difference in FOV between the HILO-based and OLS-based approaches (left image) and a comparison of spatial heterogeneity across the FOV for the HILO-based approach (center image) and the OLS-based approach (right image). [Figure 3C] Figure 1 shows various measurements demonstrating that the disclosed image acquisition system and workflow are suitable for robust htSMT analysis. Dose-response experiments performed on Halo-tagged proteins using established and well-characterized compounds to assess plate-to-plate and day-to-day reproducibility are shown (top panel) with the respective EC50s presented (bottom panel). [Figure 3D] Various measurements are shown demonstrating that the image acquisition system and workflow of the present disclosure are suitable for robust htSMT analysis. The system described herein is configured to capture comparable protein diffusion coefficients per FOV per well, with each point representing an individual FOV position averaged per plot for each concentration (top panel), and both EC50 and Z-factor are presented (bottom panel). [Figure 3E] Various measurements are shown demonstrating that the disclosed image acquisition system and workflow are suitable for robust htSMT analysis. Data consistency across multiple wells and multiple experiments is demonstrated, with each point representing one FOV from 14 independently generated dose-response curves.

[0051] [Figure 4] A comparison of the Z-factors associated with the data presented in Figures 3D and 3E and data collected using a HILO-based approach is shown.

[0052] [Figure 5] 1 shows a schematic diagram of an exemplary sample handling system of the present disclosure.

[0053] [Figure 6] An exemplary system for a high-throughput single-molecule imaging platform for measuring protein movement in living cells is shown.

[0054] [Figure 7] FIG. 1 illustrates data flow through an exemplary system for a high-throughput single-molecule imaging platform that measures protein movement in living cells.

[0055] [Figure 8] 10A-10C are multiple images showing the difference between mask categories and instance / semantic masks.

[0056] [Figure 9] 1 illustrates an exemplary computer-implemented environment relevant to the subject matter described herein.

[0057] [Figure 10] FIG. 1 illustrates a sample computing device architecture for implementing various aspects described herein.

[0058] [Figure 11] A diagram showing the pipeline for scalable tracing in htSMT is shown.

[0059] [Figure 12A] We present benchmarks of various tracking algorithms and demonstrate aspects related to optical dynamic simulations. [Figure 12B] We present benchmarks of various tracking algorithms and provide a basis for comparison. [Figure 12C] We present a benchmark of various tracking algorithms and present benchmarking results in terms of recall, precision, and F1 score.

[0060] [Figure 13A] We demonstrate that OLS provides uniform illumination over nearly the entire field of view, enabling extended SMT. A simplified schematic illustrating an embodiment of an OLS is shown. Briefly, a collimated beam is shaped into an optical light sheet, which is directed into a water immersion objective, and the emitted light is projected onto a high-speed sCMOS camera. [Figure 13B] We demonstrate that OLS provides uniform illumination across nearly the entire field of view, enabling extended SMT. We demonstrate an exemplary SMT workflow that relies on Halo tagging of protein targets of interest. Using JF549 or JF646 organic fluorophores, we detected individual emitters with the appropriate signal, performed interframe stitching, and tracked generation. From these coordinates and trajectories, various metrics can be extracted, including protein diffusion and spatial localization, among others. [Figure 13C] This demonstrates that OLS provides uniform illumination across nearly the entire field of view, enabling extended SMT. The results of HIF ablation on rod and cone cell survival and function are shown. This shows 20-point dose-response curves from six to seven different 384-well plates per microscope, imaged on Eikon's high-throughput SMT platform. 72 FOV from 12 wells were captured for each concentration on randomized plates, and error bars indicate standard deviation. [Figure 13D]This shows that OLS provides uniform illumination across nearly the entire field of view, enabling extended SMT. Representative sampling areas in HILO and OLS for illumination from Halo-Keap1 containing U2OS cells are shown. Trajectories were plotted over a 1.5-second acquisition and color-coded based on the measured diffusion coefficients, with the nuclear mask outline overlaid with a black dotted line. [Figure 13E] We show that OLS provides uniform illumination across almost the entire field of view, enabling extended SMT. We show quantification of the number of trajectories captured per FOV using HILO and OLS, with OLS capturing a 6x improvement. [Figure 13F] We demonstrate that OLS provides uniform illumination across nearly the entire field of view, enabling extended SMT. Representative average spatial SNR maps per pixel calculated across a 1,232 FOV are shown for plates imaged with HILO or OLS. OLS provides a 6x larger FOV while improving illumination uniformity. [Figure 13G] Figure 1 shows that the OLS provides uniform illumination across nearly the entire field of view, enabling extended SMT. Figure 2 provides the standard deviation of the mean FOV level in SNR sampled across 308 wells.

[0061] [Figure 14A]An exemplary schematic of an OLS microscope for single-molecule tracking is shown. An exemplary schematic of an OLS microscope based on scanning a tilted excitation light sheet using a galvanometric scanning mirror across a sample placed in an inverted microscope is shown. The OLS microscope is based on multiwavelength optical excitation provided by a laser engine module (LEM) and coupled to a beam shaper by a collimator-coupled single-mode fiber. The beam shaper converts the incident Gaussian-shaped optical excitation into an optical light sheet, which is focused along the light sheet's line axis onto the back focal plane of the microscope objective and scanned along the scan axis using a galvanometric mirror. The resulting oblique light sheet is immersion-coupled and delivered to an environmentally controlled sample-holding plate, while the relative position of the microscope's focal plane is controlled by an autofocus unit. The excitation fluorescence is spectrally filtered from the excitation light by a dichroic filter and an absorption filter and projected onto a high-speed sCMOS camera. Synchronization of optical excitation, scanning, and acquisition is achieved by a custom-built control unit (MIC). [Figure 14B] Figure 1 shows an exemplary schematic of an OLS microscope for single-molecule tracking. Figure 2 shows an exemplary schematic of an autofocus unit based on detecting a 780 nm-LED reflection at the top surface of the sample-holding glass bottom and repositioning the objective lens to ensure proper focal plane positioning within the sample. [Figure 14C] Figure 1 shows an exemplary schematic of an OLS microscope for single-molecule tracking. Figure 2 shows an exemplary schematic of the optical confocal scanning mode, achieved by scanning a tilted and focused light sheet through the focal plane of the objective. Background suppression is achieved by the confocal placement of the tilted light sheet (green), the depth of field of the objective, and synchronized rolling shutter detection (orange). [Figure 14D]Figure 1 shows an exemplary schematic of an OLS microscope for single-molecule tracking. Figure 2 shows an exemplary schematic of a beam-shaping subassembly that shapes the collimated optical excitation into a light sheet by a series of Powell, cylindrical, spherical, and plano-convex lenses projected along the line axis (x) and scan axis (y) before encountering a galvanometric scanning mirror. The inset shows the optical beam profile at each position. [Figure 14E] Figure 1 shows an exemplary schematic of an OLS microscope for single-molecule tracking. Figure 2 shows an exemplary schematic of an optical line-scanning framework based on a tilted light sheet in the sample plane, achieved by focusing the optical excitation along the line axis of the back focal plane of the objective and positioning the optical excitation along the scan axis at an offset relative to the optical axis of the objective. Corresponding detections of the optically aligned fluorescence are projected onto a camera sensor. [Figure 14F] Figure 1 shows an exemplary schematic of an OLS microscope for single-molecule tracking. Figure 2 shows an exemplary schematic of an OLS acquisition mode that relies on detecting fluorescence by matching the area of ​​the camera's exposure pixels and synchronizing the camera's rolling shutter to the optically projected intensity line of fluorescence excited by a tilted light sheet.

[0062] [Figure 15A] Characterization of motion-induced blur and confocality between OLS and HILO illumination is shown. A bar graph comparing the measured diffusion coefficients of Halo-KEAP1 treated with DMSO or 1 mM KI-696 across 72 FOVs from 12 individual wells for HILO and OLS. Despite extensive sampling, the standard deviation of the measurements is still larger for HILO. [Figure 15B]Characterization of motion-induced blur and confocality between OLS and HILO illumination is shown. Estimated point spread functions were determined by averaging all detections over a representative 150-frame acquisition. For each condition, the following numbers of PSFs were detected: n = 123,596 (OLS-DMSO), n = 3,897 (HILO-DMSO), n = 113,276 (OLS 0.33 mM KI-696), and n = 13,620 (HILO 0.33 mM KI-696) from one representative FOV. [Figure 15C] Characterization of motion-induced blur and confocality between OLS and HILO illumination. PSF detection is shown as a function of integration time. HILO required five times longer integration time to achieve comparable PSF detection and spot density as OLS, resulting in more pronounced motion-induced blur with HILO. [Figure 15D] Figure 1 shows characterization of motion-induced blur and confocality between OLS and HILO illumination. Figure 2 shows PSF width measurements as a function of JF549 measured in Halo-KEAP1 cells treated with 1 mM KI-696. [Figure 15E] Figure 1 shows the characterization of motion-induced blur and confocality between OLS and HILO illumination. Figure 2 shows the average SNR plotted as a function of JF549 measured in Halo-KEAP1 cells treated with 1 mM KI-696. [Figure 15F] Figure 1 shows the characterization of motion-induced blurring and confocality between OLS and HILO illumination. Figure 2 shows the number of spot detections plotted as a function of JF549 measured at increasing concentrations of Halo-JF549 in solution.

[0063] [Figure 16A] This demonstrates that OLS enables reproducible and robust SMT measurements. EC50 values ​​calculated from each dose-response curve averaged per plate per microscope are shown. The black line represents the median EC50. [Figure 16B]We demonstrate that OLS enables reproducible and robust SMT measurements. Figure 1 shows violin plots of signal-to-noise ratio (SNR) for each microscope, with the thick dashed line representing the median. [Figure 16C] Figure 1 shows that OLS enables reproducible and robust SMT measurements. Violin plot of SNR as a function of FOV position within an acquired well. [Figure 16D] This demonstrates that OLS enables reproducible and robust SMT measurements. A 20-point dose-response curve for Halo-KEAP1 U2OS sampled across the full OLS FOV (purple) and a cropped FOV of 768 x 768 pixels (black) representing a HILO-sized FOV is shown. Error bars indicate standard deviation across FOVs.

[0064] [Figure 17A] We demonstrate that OLS enables the capture of fast protein diffusion in live cells. Representative images of different FOV sizes for each of five frame rates ranging from 100 to 1250 Hz are shown. Trajectories are superimposed on the average projection of the Hoechst channel (blue) and colored by their maximum likelihood diffusion coefficient. [Figure 17B] We demonstrate that OLS enables the capture of fast protein diffusion in live cells. Figure 1 shows the fraction of trajectories with diffusion coefficients greater than 10 mm / s as a function of frame rate for DMSO- and KI-696-treated cells, calculated from the posterior mean occupancy of state sequences. [Figure 17C] We demonstrate that OLS enables the capture of fast protein diffusion in live cells. We demonstrate the accuracy of state profile recovery from optical dynamic simulations of SMT across several frame rates for three different state mixtures. Error bars indicate standard deviation.

[0065] [Figure 18A]We show that the frame rate determines the dynamic range of SMT. We show a schematic illustrating the role of localization and tracking errors for a hypothetical fast-moving protein. The OLS's rolling shutter captures the position of dye molecules at separate 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, it will be difficult to reconstruct the trajectory and will be dominated by misconnections. [Figure 18B] We show that the frame rate determines the dynamic range of SMT. We show a schematic diagram illustrating the dynamic range of SMT, which is limited by localization error on the one hand and tracking error on the other. An approximation of this range for Brownian motion is

number

[0066] [Figure 19] Figures A-C show experimental KEAP1-HaloTag JF549 SMT tracking diagnostics in U2OS cells using various frame rates. Figure A shows the average track length plotted as a function of frame rate. Track length is defined as the number of spots per track. Figure B shows the average SNR plotted as a function of frame rate. SNR is described in Example 2. Figure C shows the average ERLB plotted as a function of frame rate.

[0067] [Figure 20] Figure 1 shows a state sequence analysis 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 was as follows: n = 88 (100 Hz), n = 88 (200 Hz), n = 132 (400 Hz), n = 198 (800 Hz), and n = 264 (1250 Hz). The lines represent the average across all FOVs in the corresponding condition, and the error bands represent the standard deviation at the FOV level.

[0068] [Figure 21] We provide an assessment of the bleaching rate of KEAP1-HaloTag SMTs at varying frame rates. The remaining fractional detections were plotted across frame rates for a given time series. The remaining fractional detections was defined as the number of detections in each frame divided by the number of detections in the first frame. An exponential fit (blue text below the frame rate) was performed using an iterative least-squares routine to the model f(t) = c0 + (1 - c0)e - kt, where t is the frame index, k is the bleaching rate, and c0 is the unbleached fraction. The number of FOV replicates 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).

[0069] [Figure 22A]We demonstrate that OLS can be applied to capture intercellular and subcellular heterogeneity in the dynamics of single proteins. We demonstrate the analysis of the sources of variability in the SMT of KEAP1 measured under either OLS or HILO illumination. The contribution of intercellular variability is 17-32 times greater than the contribution of inter-FOV or inter-well variability, respectively. [Figure 22B] We demonstrate that OLS can be applied to capture intercellular and intracellular heterogeneity of single protein dynamics. Representative images of Halo-PCNA-labeled cells treated with 2 mM thymidine or 10 mM RO-3306 are shown (top). Cell cycle predictions from a machine learning (ML) model are used to color-code cells by cycle phase (bottom). [Figure 22C] This demonstrates that OLS can be applied to capture the intercellular and intracellular heterogeneity of single protein dynamics. Figure 22B shows quantification of the proportion of cells in each cell phase in response to cycle-blocking treatment. For each condition, the following cell numbers were analyzed: 34,067 for DMSO, 3,831 for RO-3306, and 6,044 for thymidine. [Figure 22D] We demonstrate that OLS can be applied to capture inter- and intracellular heterogeneity of single protein dynamics. We show state sequence analysis of the total population of sparsely labeled PCNA cells. [Figure 22E] We demonstrate that OLS can be applied to capture the inter- and intracellular heterogeneity of single protein dynamics. We show the state sequence analysis of cells at each phase predicted by the ML model. [Figure 22F] Figure 1 shows that OLS can be applied to capture inter- and intracellular heterogeneity of single protein dynamics. Figure 2 shows a heatmap of 4,801 individual cells classified using the continuous classification score plotted against the PCNA diffusion coefficient.

[0070] [Figure 23A] We demonstrate the characterization of a PCNA-based cell cycle prediction model. We provide example images from a time-lapse of PCNA captured over 12 hours with an OLS frame interval of 5 minutes. [Figure 23B] Figure 1 shows the characterization of a PCNA-based cell cycle prediction model. A schematic diagram of a neural network trained to simultaneously perform nuclei segmentation, nuclei cell cycle classification, and nuclei cell cycle regression is shown. [Figure 23C] Figure 1 shows the characterization of the PCNA-based cell cycle prediction model. Figure 2 shows the confusion matrix describing the performance of cell cycle classification. [Figure 23D] Characterization of the PCNA-based cell cycle prediction model. Representative images of cell cycle progression over a 12-hour window for four selected cells are provided (left), along with a graph of the regression-based prediction of cell cycle progression plotted with a 5-frame moving average (right).

[0071] [Figure 24A] Validation of PCNA cell lines using Western blot and cell proliferation analysis. Capillary-based Western blots comparing WT U2OS and N-terminally tagged heterozygous PCNA clones with anti-PCNA (left) and anti-Halo (right) antibodies are shown. [Figure 24B] Validation of PCNA cell lines using Western blot and cell proliferation analysis. Relative WT and Halo-tagged PCNA levels normalized to β-actin WT and Halo-edited U2OS cells are shown. [Figure 24C] Figure 1 shows validation of PCNA cell lines using Western blot and cell proliferation analysis. Growth curves of WT U2OS and N-terminal Halo-tagged PCNA are shown. [Figure 24D] Validation of PCNA cell lines using Western blot and cell proliferation analysis is shown. Cells labeled with both JF549 and CCR PCNA are shown to measure spatial colocalization between the two labels over the course of the cell cycle.

[0072] [Figure 25A] Figure 1 shows that OLS is suitable for various SMLM techniques and acquisition schemes. Halo-KEAP1 U2OS cells labeled with JF549 and JF646 are imaged within the same FOV. [Figure 25B] Figure 1 shows that OLS is suitable for a variety of SMLM techniques and acquisition schemes. Figure 2 shows a 10-point dose response of KI-696-treated Halo-KEAP1 U2OS cells co-labeled with JF549 and JF646. [Figure 25C] Figure 1 shows that OLS is suitable for a variety of SMLM techniques and acquisition schemes. A diffraction-limited image of the complete OLS FOV of immunofluorescently labeled tubulin with AF647-conjugated secondary antibody is shown. [Figure 25D] A magnified region of interest in Figure 25C shows that OLS is suitable for a variety of SMLM techniques and acquisition schemes. [Figure 25E] Showing that OLS is suitable for a variety of SMLM techniques and acquisition schemes, Figure 25C shows a STORM reconstruction of the full OLS FOV labeled as such. [Figure 25F] Figure 25E shows a magnified region of interest similar to Figure 25D, demonstrating that OLS is suitable for a variety of SMLM techniques and acquisition schemes. [Figure 25G] We demonstrate that OLS is suitable for various SMLM techniques and acquisition schemes. To compare the spatial resolution of microtubules, we show line profiles from the yellow line in gray values ​​(au) in Figures 25D and 25F. [Figure 25H] Showing that OLS is suitable for a variety of SMLM techniques and acquisition schemes, we show the respective localization accuracy histograms for AF647- and CF568-labeled secondary antibodies used to stain microtubules using OLS illumination with an integration time of 0.4 ms. [Figure 25I] We demonstrate that OLS is suitable for a variety of SMLM techniques and acquisition schemes. Representative images of correlation FRAP / SMT are shown, where the central region was bleached using OLS line scanning before spot recovery after photobleaching. Regions outside and inside the FRAP region were used to measure SMT. [Figure 25J]Figure 1 shows that OLS is suitable for various SMLM techniques and acquisition schemes. Figure 2 shows T1 / 2FRAP of Halo-KEAP1 U2OS cells treated with 1 mM KI-696 and labeled with DMSO or 400 pM JF549-Halo ligand. The black line indicates the median, and each spot represents an individual FOV. [Figure 25K] Figure 1 shows that OLS is suitable for various SMLM techniques and acquisition schemes. Spot densities after recovery in DMSO over time are shown. Standard deviations are shown for confidence bands of 8–10 FOVs per condition. [Figure 25L] This demonstrates that OLS is suitable for a variety of SMLM techniques and acquisition schemes. Spot densities after recovery over time are shown for 1 m MKI-696. Standard deviations are shown for confidence bands of 8–10 FOVs per condition. [Figure 25M] Figure 1 shows that OLS is suitable for various SMLM techniques and acquisition schemes. Diffusion coefficients from the SMT are shown for a JF549-Halo ligand concentration of 400 pM in the bleached (inner) and unbleached (outer) regions.

[0073] [Figure 26A] Figure 1 shows the dye performance and FRAP characterization with increasing dye concentration. Figure 2 shows the SNR comparison between JF646 and JF549. [Figure 26B] Figure 1 shows the dye performance and FRAP characterization with increasing dye concentration. Figure 2 shows the ERLB comparison between JF646 and JF549. [Figure 26C] Characterization of dye performance and FRAP with increasing dye concentration. Sampling of T measured in the bleached region as a function of dye concentration from 6 to 10 FOV is shown for DMSO and 1 mM KI-696.

[0074] [Figure 27A]Figure 1 shows the contribution of well-to-well, FOV-to-FOV, and cell-to-cell bias to 2D jump length, assessed using jump resampling. Variance relative to the sample mean as a function of sample size is shown for different resampling procedures. A straight line with a slope of -1 is the expected value from the law of large numbers; sublinearity is due to residual variance across wells, FOVs, or cells. [Figure 27B] Figure 1 shows the contribution of well-to-well, FOV-to-FOV, and cell-to-cell bias to 2D jump length, as assessed using jump resampling. The number of jumps per well, FOV, or cell used in these analyses is shown. DETAILED DESCRIPTION OF THE INVENTION

[0075] The subject matter of this disclosure relates to industrial-scale high-throughput SMT (htSMT) techniques using oblique line scanning (OLS) illumination, systems incorporating such OLS htSMT techniques, hardware and software developments related to such OLS htSMT techniques, and methods of using such OLS htSMT techniques. For example, the OLS htSMT techniques described herein are capable of measuring protein motion in millions of cells per day. In addition to the ability to capture a large number of cells per field of view, OLS offers advantages such as improved spatial uniformity of the signal-to-noise ratio (SNR) across the camera chip, improved confocality (fewer out-of-focus signals and reduced motion blur), and improved temporal resolution, as shown, for example, in Table 1 (each "+" represents a 2x improvement). [Table 1]

[0076] The OLS htSMT technology described herein can be used for a variety of applications, including, but not limited to, drug discovery activities such as screening compound libraries and elucidating structure-activity relationships (SAR). Importantly, the OLS htSMT technology described herein can be used to characterize the contributions of both known and novel pathways to larger molecular assemblies that contain targets, such as protein signaling interaction networks.

[0077] 1 , aspects of the present subject matter can be implemented using an OLS htSMT workflow, which can include various stages, such as (i) sample preparation, including reagent handling, (ii) image acquisition using imaging of the sample to generate a series of images and / or videos, (iii) image analysis, for example, using various analytics, single emitter detection and sub-pixel localization (i.e., "super-resolution imaging"), tracking, computer vision, and machine learning algorithms to process these images and videos, (iv) storage of information extracted from or characterizing or constituting the images and videos (i.e., features, raw images, modified images, etc.), and (v) providing insights using the stored information, including biological interpretations (which can additionally or alternatively be provided using various analytics, tracking, computer vision, and machine learning algorithms), as described in more detail below.

[0078] The subject matter of the present disclosure will be described with reference to the figures, wherein reference numerals are used to denote like or equivalent elements throughout. The figures are not drawn to scale and are provided solely to illustrate aspects disclosed herein. Certain disclosed aspects are described below with reference to illustrative example hardware, software, and applications. It should be understood that numerous specific details, relationships, and methods are set forth to provide a more thorough understanding of the subject matter disclosed herein. For clarity of disclosure, and not for purposes of limitation, the detailed description is divided into the following subsections. 1.Definition 2.OLS htSMT hardware 3.OLS htSMT software 4. Specific OLS htSMT Applications 5. Exemplary Embodiments 6. Working Example

[0079] 1.Definition Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. In the case of conflict, the present document, including definitions, will control. Preferred methods and materials are described below; however, methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the subject matter of this disclosure. All publications, patent applications, patents, and other references mentioned herein are incorporated herein by reference in their entirety. The materials, methods, and examples disclosed herein are illustrative only and are not intended to be limiting.

[0080] The terms "comprise," "include," "having," "has," "can," "contain," and variations thereof, as used herein, are intended to be open-ended transitional phrases, terms, or phrases that do not exclude the possibility of additional acts or structures. The singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. The present disclosure also contemplates other instances of "comprising," "consisting of," and "consisting essentially of" the instances or elements presented herein, whether explicitly stated or not.

[0081] In reciting numerical ranges herein, each intervening number in the range is expressly contemplated with the same precision. For example, in the range 6 to 9, the numbers 7 and 8 are contemplated in addition to 6 and 9, and in the range 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.

[0082] As used herein, the term "about" or "approximately" means within an acceptable error range for a particular value as determined by those skilled in the art, which depends in part on the method of measuring or determining the value, i.e., the limitations of the measurement system. For example, "about" can mean within 3 or more standard deviations, according to the practice in the art. Alternatively, "about" can mean a range of up to 20%, preferably up to 10%, more preferably up to 5%, and even more preferably up to 1% of a given value. Alternatively, particularly with respect to biological systems or processes, this term can mean within an order of magnitude, preferably within 5 times, more preferably within 2 times of a value.

[0083] As used herein, the term "trajectory" refers to a set of spatial coordinates corresponding to the observed positions of fluorescent proteins linked in time. In certain cases, multiple trajectories can be constructed algorithmically by linking multiple fluorescent proteins whose positions have been determined at successive time points. In certain cases, multiple trajectories can be constructed conservatively by linking only spots within a fixed search radius when other links are not valid. In certain cases, multiple trajectories can be constructed probabilistically.

[0084] As defined herein, protein motion refers to changes in the positions of multiple fluorescent proteins. In certain cases, protein motion can be quantified by analyzing changes in spatial coordinates at successive time points. Motion characterized in this way can include, but is not limited to, measuring the distribution of jump lengths. That is, 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 certain cases, the quantile used is the median of the jump length distribution. In certain cases, the quantile used is the third quartile of the jump length distribution. In certain cases, protein motion can be quantified by analyzing trajectories. Motion characterized in this way can include, but is not limited to, measurements of mean square displacement, defined as the mean value of the squares of all displacements within a trajectory, averaged over multiple trajectories. Motion characterized in this way can also include, but is not limited to, measurements of trajectory length or measurements of the distribution of trajectory lengths. Such characterized motion may include, but is not limited to, measurements of the average radius of gyration, defined by the root-mean-square distance of all coordinates on a trajectory from the center of mass of the set of points included in the trajectory, averaged over multiple trajectories. Such characterized motion may also include, but is not limited to, measurements of the average bond angle, defined by the angle formed from three consecutive spatial coordinates averaged over multiple trajectories. Such characterized motion may also include, but is not limited to, measurements of the maximum likelihood diffusion coefficient estimate, defined as the maximum likelihood diffusion coefficient estimate for multiple trajectories under a single-state diffusion model with a constant localization error. In certain cases, protein motion may be measured through analysis of the products of a link generation algorithm. Such characterized motion may include, but is not limited to, the mean posterior diffusion coefficient, the average of the posterior probability distribution of coefficients from a probabilistic linkage algorithm. Such characterized motion may include, but is not limited to, the geometric mean posterior diffusion coefficient, the average of the log-scaled posterior probability distribution of coefficients from a probabilistic linkage algorithm.In certain cases, protein motion can be measured through model-dependent analysis of multiple trajectories. Motion characterized in this way is the fraction of immobile molecules ("f") defined by two-state model fitting. bound ") may include, but is not limited to:

[0085] As used herein, the term "motion" encompasses not only changes in the direction in which a target moves, but also both increases and decreases in the speed of movement. Thus, tracking motion may, in certain cases, include determining that a target is not moving, e.g., determining that the target is in a statically constrained state or an essentially statically constrained state. Motion can be characterized in various 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's fluorescent protein in successive frames), (b) the third 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 squared displacement, (g) the median bond angle, (h) the maximum likelihood estimator of the diffusion coefficient, (i) the trajectory length, and / or (j) the inferred state occupancy.

[0086] As used herein, detected movement includes, but is not limited to, any change in movement, which may occur in response to any environmental or other factor. For example, but not limited to, movement, or lack thereof, may be caused by (A) the addition of a compound, (B) a change in temperature, (C) a change in oxygen concentration, e.g., the introduction of hypoxia, (D) mechanical stress, (E) a change in pH, and / or (F) a change in light exposure (e.g., an increase or decrease in intensity).

[0087] As used herein, the term "fluorescent protein" refers to any protein that emits a fluorescent signal. In certain cases, the fluorescent emission occurs in response to irradiation with light of a specific wavelength. An example of a naturally occurring fluorescent protein is green fluorescent protein (GFP). However, in certain cases, a protein of interest can be adapted to emit a fluorescent signal through the introduction of an encoded fluorescent tag. That is, a protein sequence is fused to the protein of interest to make it fluorescent. In certain cases, a protein of interest can be adapted to emit a fluorescent signal through the binding of a fluorescent ligand. Non-limiting examples of such encoded fluorescent tags include Halo tag, SNAP tag, CLIP tag, TMP tag, and SunTag. Additionally or alternatively, a protein of interest can be adapted to emit a fluorescent signal by binding to a fluorescent dye molecule, such as an amine-reactive dye or a sulfhydryl-reactive dye.

[0088] As used herein, the term "compound" refers to any chemically defined entity. In certain cases, a compound may be a molecule less than 1000 Da, i.e., a "small molecule." In certain cases, a compound may be a macromolecule, such as a nucleic acid. In certain cases, a nucleic acid may have a defined sequence. In certain cases, a nucleic acid includes (A) ribonucleic acid (RNA) (e.g., including modified RNA), (B) deoxyribonucleic acid (DNA) (e.g., including modified DNA), and (C) a combination of (A) and (B). In certain cases, a nucleic acid is a single- or double-stranded small interfering nucleic acid (e.g., double-stranded siRNA), an antisense oligonucleotide, a ribozyme, a microRNA, or an aptamer. In certain cases, a compound may be a protein. For example, and not by way of limitation, protein compounds of the present disclosure include signaling 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 certain cases, a compound can refer to a mixture of molecules, for example, a mixture of defined composition.

[0089] The term "uniform intensity" as used herein refers to a difference in intensity of no more than 5%, sometimes no more than 10%, or sometimes no more than 15%, relative to the intensity of light, e.g., light directed at the sample surface.

[0090] The term "uniform intensity" as used herein, in relation to signal-to-noise ratio (SNR), refers to the SNR per pixel within the field of view (FOV) where the range of possible values ​​falls between 0.5 and 1 standard deviation from the mean SNR.

[0091] 2.OLS htSMT hardware 2.1.Image Acquisition System With reference to Figure 1, aspects of the present subject matter can be implemented using an htSMT workflow incorporating a system for image acquisition. For example, such image acquisition can incorporate imaging of a sample to generate a series of images and / or video. Figure 2A shows a schematic diagram of an exemplary image acquisition system of the present disclosure, with the XZ sample plane visible. Figure 2B shows the same exemplary image acquisition system, 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 configured to shape the light emitted from the light source to form a shaped beam (2-065) such that the shaped beam has a uniform intensity across the long dimension of the linear shape, the system further including optical elements, such as a galvo mirror (2-085), configured to translate the shaped beam, and one or more optical elements, such as a dichroic mirror (2-100), configured to direct the shaped beam to an objective lens (2-120) so that a portion of the sample surface (2-130) is illuminated by an oblique beam (2-125) to produce light emission from the sample, such as fluorescent emission, which is focused by the objective lens (2-120) through a series of optical elements, such as a lens (2-155) and an absorption filter (2-160), onto an image collection system (2-165).

[0092] 2.1.1.Light source Referring to the exemplary image acquisition system of FIG. 2A, the system includes a light source (2-005) configured to emit light. The light source (2-005), in certain embodiments of the image acquisition systems disclosed herein, can be configured to emit light at a single wavelength. In certain embodiments of the image acquisition systems disclosed herein, the light source (2-005) can be configured to emit light at two, three, four, five, or more distinct wavelengths. In certain embodiments, the wavelength(s) of light emitted by the light source are predetermined. For example, but not by way of limitation, the wavelength(s) can be predetermined such that, when irradiated onto a sample, e.g., a sample containing a fluorescent protein, the emitted light induces fluorescence. In certain instances, the wavelength(s) used in connection with the methods described herein will fall within the range of 400 nm to 650 nm. In certain instances, the light source (2-005) emits light having a wavelength of 400 nm to 408 nm, 550 nm to 565 nm, or 638 nm to 650 nm. In certain non-limiting embodiments, the light source (2-005) is configured to include three lasers with nominal center wavelengths of 405 nm, 560 nm, and 640 nm, which may vary within the absorption band of the fluorophore used. In certain instances, the 405 nm wavelength is used to excite Hoechst dye. In certain instances, the dye attached to the HaloTag (e.g., JF 549 A wavelength of 560 nm is used to excite .

[0093] In certain non-limiting embodiments, the light source (2-005) is used to catalyze a photochemical reaction. For example, but not by way of limitation, the wavelength(s) and illumination intensity can be such that cleavage of a chemical bond occurs. By way of additional example and not by way of limitation, the wavelength(s) and illumination intensity can induce the adoption of a non-radiative dark state (i.e., "photobleaching molecules"). By way of additional example and not by way of limitation, the wavelength(s) and illumination intensity can induce radiative or non-radiative energy transfer between fluorophores within the sample. In certain instances, a dye (e.g., JF) attached to a HaloTag can be irradiated. 646) wavelengths of 642 or 646 nm are used to excite .

[0094] In certain embodiments of the image acquisition system 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, and without limitation, the light source (2-005) can deliver more than 10 mW of power at a particular wavelength, such as 405 nm, and / or more than 150 mW of power at another wavelength, such as 640 nm. Additionally or alternatively, if 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, and without limitation, 405 nm can be configured to deliver more than 10 mW, 560 nm can be configured to deliver more than 150 mW, and 640 nm can be configured to deliver more than 50 mW.

[0095] In certain embodiments of the image acquisition system described herein, the light source (2-005) is configured to emit pulsed light. For example, but not by way of limitation, the light source (2-005) can be configured to emit strobe pulsed light. In certain embodiments of the image acquisition system described herein, the light source (2-005) is configured to emit pulsed light synchronized with the start of image acquisition. In certain non-limiting embodiments, the light source (2-005) pulses at specific time intervals depending on the number of frames per second being captured. For example, but not by way of limitation, if 100 frames per second (FPS) are being captured by the detector (2-165), the laser will be on for 9 ms and off for 1 ms. In contrast, in a 200 FPS mode, the laser will be on for 4 ms and off for 1 ms. In certain embodiments of the OLS htSMT workflow, the light source is configured to transition from 90% to 10% power in less than about 0.4 ms. In a particular embodiment of the OLS htSMT workflow, the light source is configured to transition from 90% to 10% power in less than about 0.2 ms.

[0096] The emission of light by the light source (2-005) and the directing of that light to the optical relay (2-010) may be facilitated using a single mode fiber in certain embodiments of the image acquisition systems disclosed herein. Alternatively, multimode fiber may be used in certain embodiments of the image acquisition systems disclosed herein. For example, but not by way of limitation, the multimode fiber may be configured in a predetermined shape for sample illumination.

[0097] In certain embodiments of the image acquisition systems described herein, for example, with respect to systems configured for high-throughput sample analysis, the light source (2-005) can be configured to exhibit low drift in output. In certain embodiments, such low-drift configurations increase the consistency of sample processing and facilitate high-throughput analysis. For example, and not by way of limitation, such low-drift output configurations maintain output within about 0% to about 15% variation, about 0% to about 10% variation, about 10% variation, about 9% variation, about 8% variation, about 7% variation, about 6% variation, about 5% variation, about 4% variation, about 3% variation, about 2% variation, or about 1% variation.

[0098] In certain instances, such a low-drift output configuration maintains output power within about 0% to about 15% variation, about 0% to about 10% variation, about 10% variation, about 9% variation, about 8% variation, about 7% variation, about 6% variation, about 5% variation, about 4% variation, about 3% variation, about 2% variation, or about 1% variation across ambient (room) temperature variations of, for example, 17°C + / - 5°C. In certain instances, this is achieved by using a temperature sensor and / or a closed-loop heater to stabilize the temperature of the internal light source (e.g., laser engine) and thereby reduce output power drift. For example, but not by way of limitation, the light source may be thermally isolated from ambient temperature variations using an insulated enclosure design. Additionally or alternatively, closed-loop heaters can be strategically placed in specific locations within the system, such as at the fiber coupler, to reduce output power drift. Additionally or alternatively, a water jacket and / or cooling device can be used to reduce heat buildup from the laser head. Additionally, these thermal controls, used individually or in combination, reduce the warm-up time to reach a steady state of operation and maintain a more stable internal operating temperature as the laser is powered off and on.

[0099] 2.1.2. Optical Elements and Sample Illumination Referring to the exemplary image acquisition system of Figure 2A, the system includes a light source (2-005) configured to emit light, which is relayed by one or more optical elements in an optical relay (2-010) configured to shape the light emitted from the light source to form a shaped beam (2-065). The particular optical elements of any particular optical relay (2-010) embodiment can be selected and configured to not only generate a beam (2-065) of an appropriate shape, but also to provide appropriate translation of that beam.

[0100] In certain non-limiting embodiments of the optical relay (2-010) of the image acquisition system of the present disclosure, the optical relay (2-010) comprises 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) are configured to appropriately shape the light beam directed at the sample. In certain non-limiting embodiments, the optical relay (2-010) includes an optical element, such as a collimator (2-020), for collimating the light emitted from the light source (2-005). Additionally or alternatively, the optical relay (2-010) may include additional optical elements, such as a Powell lens (2-025) or other element adapted to create a beam fan, one or more cylindrical lenses (2-045 and 2-055), one or more slits (2-050 and 2-095) for adjusting the size of the light sheet, 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 galvo mirrors (2-085) that can translate the light. The specific attributes of the optical elements are predetermined to produce an appropriately shaped light beam. For example, but not by way of limitation, the OLS htSMT system of the present disclosure may achieve a uniform horizontal FOV and a uniform vertical FOV. Such uniformity of horizontal and vertical FOV is in contrast to other strategies that provide non-uniform horizontal and / or non-uniform vertical FOV (see Table 2). [Table 2]

[0101] 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 generating a beam that is elongated along the X-plane and narrow along the Y-plane, where the light beam has a uniform intensity across the long dimension of the linear shape. In a specific, non-limiting embodiment, the optical relay (2-010) of the OLS htSMT system described herein includes a Powell lens (2-025) that shapes the light beam to have a uniform intensity across the long dimension of the linear shape (2-065). The optical relay (2-010) of the OLS htSMT system described herein can include additional or alternative optical elements or assemblies that shape the light beam to have a uniform intensity across the long dimension of the linear shape (2-065). For example, but not by way of limitation, the optical relay (2-010) of the OLS htSMT system described herein can include a diffractive element or assembly configured to shape the light beam to have a uniform intensity across the long dimension of the linear shape.

[0102] In certain non-limiting embodiments of the optical relay (2-010) of the image acquisition system of the present disclosure, the optical relay (2-010) comprises one or more optical elements or assemblies configured to translate the light beam relative to the sample surface of the sample being analyzed, e.g., in a direction perpendicular to the long dimension of the light beam. For example, and without limitation, such optical elements or assemblies configured to translate the light beam relative to the sample surface of the sample being analyzed can include a galvo mirror (2-085) or a piezoelectric element configured to translate the light beam. Additionally or alternatively, such optical elements or assemblies configured to translate the light beam relative to the sample surface of the sample being analyzed can comprise a computer-controlled motor.

[0103] Referring to the exemplary image acquisition system of FIG. 2A, the system includes an optical relay (2-010) configured to shape light emitted from a light source to form a shaped beam (2-065), which is directed by an optical element (2-100), such as a dichroic mirror, and directed toward an objective lens (2-120), thereby illuminating a sample plane (2-130) with an oblique beam (2-125).

[0104] In certain non-limiting embodiments of the image acquisition system of the present disclosure, the objective lens (2-120) directs an inclined beam (2-125) onto the sample plane (2-130) to be analyzed. In certain non-limiting embodiments 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 facilitates high-throughput sample analysis by eliminating the oil present in conjunction with the use of an oil-immersion objective lens, thereby enabling higher quality and less distortion. In the context of automated systems, the presence of oil can be problematic, as well as the potential for oil to spread to components such as optical elements that may be exposed to oil and become contaminated. However, water-immersion objective lenses have a refractive index more suited to cellular imaging, resulting in less distortion and improved image quality than oil-immersion objective lenses. In certain non-limiting embodiments, the objective lens is a 60x 1.27 NA water-immersion objective lens (Nikon). In certain embodiments of the workflow described herein, the water-immersion objective lens (2-120) is heated by a heating element. For example, such a heating element maintains the water immersion objective (2-120) at a temperature sufficient to avoid inducing temperature changes in the sample contained in the sample plate (2-021).

[0105] 2.1.3. Image Acquisition In certain non-limiting embodiments of the image acquisition system of the present disclosure, the objective lens (2-0120) is also used to focus the fluorescence emitted by the sample (2-145) in response to the illumination provided by the oblique beam (2-125). In certain non-limiting embodiments, the fluorescence emission (2-145) focused on the objective lens passes through absorption filters (2-150 and 2-160), e.g., bandpass absorption filters that match the spectrum of the fluorophore under observation and are mounted on a high-speed filter wheel (Finger Lakes Instruments), and is collected by a detector device (2-165). In certain non-limiting embodiments, the fluorescence emission focused on the objective lens is directed to an optical relay before collection by the detector device (2-165). For example, but not by way of limitation, such an optical relay can include one or more lenses (2-155) and one or more additional optical elements, e.g., elements configured to reject additional scattered light before collection by the detector device (2-165). In certain non-limiting embodiments, the fluorescent emission focused onto the objective is directed through another dichroic mirror to split the emission across multiple regions of the detector (2-165). In certain non-limiting embodiments, the fluorescent emission focused onto the objective is directed through another dichroic mirror to split the emission across multiple detectors (2-165).

[0106] In certain non-limiting embodiments of the image acquisition system of the present disclosure, the detector device is configured to synchronize detection of the tilted beam (2-125) across the sample plane (2-130) with the movement of the beam. Such synchronization is shown schematically in FIG. 2F. For example, but not by way of limitation, the detector device can be a CMOS camera, such as a back-illuminated CMOS camera (the Hamamatsu Fusion BT).

[0107] In certain embodiments of the image acquisition system of the present disclosure, the CMOS camera can be operated to collect a series of SMT frames for each field of view. For example, and without limitation, 1-20,000 SMT frames, 1-15,000 SMT frames, 1-10,000 SMT frames, 1-5,000 SMT frames, 1-1,000 SMT frames, 2-500 SMT frames, 5-250 SMT frames, 10-200 SMT frames, 100-200 SMT frames, or 200 SMT frames can be collected per field of view. In certain embodiments, the CMOS camera can be configured to operate at a frame rate of about 0.5 to about 2000 Hz. In certain embodiments, the CMOS camera can be configured to operate at a frame rate of 0.5 to 1000 Hz, although in certain embodiments, it can be configured to operate at 100 Hz. In certain embodiments, the CMOS camera may be configured to operate at a frame rate between 100 Hz and 1250 Hz, as shown in FIGS. 17, 19, 20, and 21. For example, and without limitation, certain cell SMT implementations may be performed at 100 Hz. In certain embodiments, certain cell SMT implementations may be performed at 200 Hz. In certain embodiments, certain cell SMT implementations may be performed at 400 Hz. In certain embodiments, certain cell SMT implementations may be performed at 800 Hz. In certain embodiments, certain cell SMT implementations may be performed at 1000 Hz. In certain embodiments, certain cell SMT implementations may be performed at 1200 Hz. In certain embodiments, certain cell SMT implementations may be performed at 1250 Hz. In certain embodiments, certain cell SMT implementations may be performed at 1400 Hz. In certain embodiments, certain cell SMT implementations may be performed at 1600 Hz. In certain embodiments, certain cell SMT implementations may be performed at 1800 Hz. In certain embodiments, certain cellular SMT implementations can run at 2000 Hz.In certain embodiments, certain cell SMT implementations can be performed at frame rates of about 100 Hz or greater, about 200 Hz or greater, about 400 Hz or greater, about 600 Hz or greater, about 800 Hz or greater, about 1000 Hz or greater, about 1200 Hz or greater, about 1400 Hz or greater, about 1600 Hz or greater, or about 1800 Hz or greater. In certain embodiments, certain cell SMT implementations can be performed at frame rates of up to about 1200 Hz. In certain embodiments, certain cell SMT implementations can be performed at frame rates of up to about 1400 Hz. In certain embodiments, certain cell SMT implementations can be performed at frame rates of up to about 1600 Hz. In certain embodiments, certain cell SMT implementations can be performed at frame rates of up to about 1800 Hz. In certain embodiments, certain cell SMT implementations can be performed at frame rates of up to about 2000 Hz.

[0108] In certain non-limiting embodiments of the image acquisition system of the present disclosure, the detector device is configured to transmit a signal with each frame to trigger other elements of the imaging system. For example, but not by way of limitation, the detector device can trigger illumination from the light source (2-005) to collect fluorescence emission associated with a strobe laser pulse. For example, but not by way of limitation, such fluorescence emission collection is associated with a 10-100 millisecond frame and a 2 millisecond strobe laser pulse. In certain embodiments, the fluorescence emission collection is associated with a strobe laser pulse of about 0.1 to about 1 millisecond. In certain embodiments, fluorescence emission collection is associated with a strobe laser pulse of about 0.2 to about 1 millisecond, about 0.3 to about 1 millisecond, about 0.4 to about 1 millisecond, about 0.1 to about 0.9 millisecond, about 0.1 to about 0.8 millisecond, about 0.1 to about 0.7 millisecond, about 0.1 to about 0.6 millisecond, about 0.1 to about 0.5 millisecond, about 0.1 to about 0.4 millisecond, about 0.2 to about 0.6 millisecond, about 0.2 to about 0.5 millisecond, about 0.2 to about 0.4 millisecond, or about 0.3 to about 0.5 millisecond. In certain embodiments, fluorescence emission collection is associated with a strobe laser pulse of about 0.1 to about 0.6 millisecond. In certain embodiments, fluorescence emission collection is associated with a strobe laser pulse of about 0.1 to about 0.5 millisecond. In certain embodiments, fluorescence emission collection is associated with a strobe laser pulse of about 0.2 to about 0.4 millisecond. In certain embodiments, the fluorescence emission collection is associated with a strobe laser pulse of about 0.2 milliseconds. In certain embodiments, the fluorescence emission collection is associated with a strobe laser pulse of about 0.4 milliseconds, as shown in FIG. 15C.

[0109] In certain embodiments, the imaging acquisition system can be configured to acquire a predetermined field of view (FOV), e.g., a detected FOV. In certain embodiments, the FOV, e.g., the detected FOV, can have a size of a first dimension (about 150 μm to about 250 μm) by a second dimension (about 100 μm to about 210 μm). In certain embodiments, the FOV, e.g., the detected FOV, can have a size of a first dimension (about 200 μm to about 250 μm) by a second dimension (about 150 μm to about 210 μm), or the FOV, e.g., the detected FOV, can have a size of a first dimension (about 225 μm to about 250 μm) by a second dimension (about 175 μm to about 210 μm). For example, and not by way of limitation, an FOV, e.g., a detected FOV, may have a size of a first dimension (approximately 250 μm) by a second dimension (approximately 190 μm), e.g., as disclosed in Example 1.

[0110] In certain embodiments, a certain percentage of the FOV, e.g., the detected FOV, provides usable data. In certain embodiments, at least 75% of the FOV, at least 80% of the FOV, at least 85% of the FOV, at least 90% of the FOV, at least 95% of the FOV, at least 96% of the FOV, at least 97% of the FOV, at least 98% of the FOV, at least 99% of the FOV, or 100% of the FOV provides usable data. In certain embodiments, at least 75% of the FOV, e.g., the detected FOV, provides usable data. In certain embodiments, at least 80% of the FOV, e.g., the detected FOV, provides usable data. In certain embodiments, at least 85% of the FOV, e.g., the detected FOV, provides usable data. In certain embodiments, at least 90% of the FOV, e.g., the detected FOV, provides usable data. In certain embodiments, at least 95% of the FOV, e.g., the detected FOV, provides usable data. In certain 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, about 75% or more of the FOV, e.g., about 80% or more of the FOV, about 85% or more of the FOV, about 90% or more of the FOV, about 95% or more of the FOV, about 96% or more of the FOV, about 97% or more of the FOV, about 98% or more of the FOV, or about 99% or more 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, without 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 certain embodiments, about 75% or more of the FOV achieves sufficient laser illumination to track protein motion, e.g., about 80% or more of the FOV, about 85% or more of the FOV, about 90% or more of the FOV, about 95% or more of the FOV, about 96% or more of the FOV, about 97% or more of the FOV, about 98% or more of the FOV, or about 99% or more of the FOV provides sufficient laser illumination to track protein motion.

[0111] In certain embodiments, the imaging acquisition system can be configured to acquire a predetermined image dimension per frame, referred to herein as a region of interest (ROI). In certain embodiments, the ROI varies depending on the frame rate employed. For example, at 100 FPS, 2304 x 1728 pixels define an ROI that is 248.832 x 186.624 microns in the sample plane. In contrast, at 200 FPS, 2304 x 768 pixels define an ROI that is 248.832 x 82.944 microns in the sample plane.

[0112] In certain embodiments, the imaging acquisition system can be configured to perform a predetermined sweep speed at a predetermined frame rate. For example, and not by way of limitation, at 100 FPS, the sweep speed may be 186.624 microns / 9 ms, which corresponds to 20.8 microns / ms, which corresponds to 2.08 cm / s. In contrast, at 200 FPS, the sweep speed may be 82.94 microns / 4 ms, which corresponds to 20.7 microns / ms, which corresponds to 2.07 cm / s.

[0113] In certain embodiments, a detector device can be used to collect fluorescent emissions at multiple wavelengths. For example, but not by way of limitation, fluorescent emissions of additional fluorophores can be collected at the same frame rate or at different frame rates for the same field of view to provide downstream registration of SMT tracks to other cellular components, such as nuclei. Additional channels of the detector device can be used as needed to expand the number of fluorescent emissions simultaneously captured for the same field of view to provide downstream registration of SMT tracks to other cellular components, such as nuclei.

[0114] 2.2. Sample handling Referring to FIG. 1 , embodiments of the present subject matter can be implemented using an htSMT workflow incorporating a system for sample preparation, including reagent processing. For example, and not by way of limitation, FIG. 5 provides a schematic diagram of a sample plate (2-021) containing multiple wells (2-016) in which sample preparation and analysis can occur. FIG. 5 also provides a schematic diagram of the components of a sample, e.g., cells (2-018) and fluorescent target proteins (2-017) within the cells. However, as noted herein, FIG. 5 is not intended to convey scale; for example, each sample present in well (2-016) may contain thousands of cells, each cell containing numerous fluorescent target proteins. FIG. 5 also schematically illustrates the ability of the sample processing system of the present disclosure to add additional reagents to the sample (2-019). The addition of such reagents can be handled by robotic manipulation, including, but not limited to, translation of a 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 embodiments of the image acquisition system, the sample plate (2-021) can be maintained in a temperature-controlled environment via the environmental control area (2-020). For example, but not limited to, the sample may be maintained at 22-50°C. In certain embodiments of the image acquisition system, the sample plate (2-021) can be maintained in a humidity-controlled environment via the environmental control area (2-020). For example, but not limited to, the sample may be maintained at 20%-95% humidity. In certain embodiments of the image acquisition system, the sample plate (2-021) can be maintained in a defined gas environment via the environmental control area (2-020). For example, but not limited to, the sample may be maintained at 5% CO2.

[0115] 2.2.1. Cell Lines and Cell Culture Referring to Figure 5, a particular advantage of the htSMT system described herein is its ability to assay live cells (2-016), facilitating tracking of protein activity, mobility, and diffusion behavior within a dense, live-cell environment. As shown in Figure 13B, the htSMT system of the present disclosure can be used to track fluorescently labeled proteins in a sample containing multiple cells. Exemplary cells (e.g., cell lines) for use in conjunction with the htSMT system described herein are considered if the sample (e.g., containing such cells) can be focused by the objective lens (2-120) for a sufficient period of time so that the fluorescent emission of the fluorophore can be directed toward the detector (2-165). For example, but not by way of limitation, cells can be directly attached to a coverslip. As a further example, but not by way of limitation, cells can be induced to attach to the coverslip after treating the coverslip with an extracellular matrix material (e.g., fibronectin, collagen, poly-D-lysine, laminin, Matrigel, vitronectin, etc.). As an additional non-limiting example, cells can be induced to adhere to a coverslip after treating the coverslip with plasma.

[0116] Exemplary cells, e.g., cell lines, can be selected to minimize non-fluorophore emissions reaching the detector. In certain embodiments, cells for use in the present disclosure can be mammalian cells, bacterial cells, or fungal cells. In certain embodiments, the cells are mammalian cells. In certain embodiments, the cells can be obtained from preserved tissue, e.g., fixed tissue, frozen tissue, e.g., frozen tissue samples, or fresh tissue, e.g., fresh tissue samples. In certain embodiments, the cells and / or samples comprising cells can be obtained from a subject. In certain embodiments, the cells can be obtained from a tissue malignancy or tumor, e.g., the cells can be present within a tumor sample (e.g., a portion of a tumor). In certain embodiments, the cells can be obtained from a cell line. For example, but not by way of limitation, certain cell lines that can be used in connection 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 can be present in three-dimensional structures such as organoids or spheroids.In certain embodiments, cells can be present in organoids.

[0117] In certain embodiments of the htSMT system of the present disclosure, the cells used are cultured as needed to provide sufficient cell numbers to achieve the desired high-throughput analysis. For example, but not limited to, 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 (Cat. No. 1056601, Gibco DMEM, High Glucose, GlutaMAX Supplement, Thermofisher) supplemented with 10% fetal bovine serum (Cat. No. 16000044, Thermofisher) and 1% penicillin-strep (Cat. No. 15140122, Thermofisher), maintained in a humidified 37°C incubator with 5% CO2, and subcultured approximately every 2-3 days. Additional culture strategies that may be suitable for use with the cell lines outlined herein will be known to those of skill in the relevant art.

[0118] In certain embodiments of the htSMT system of the present disclosure, cells contain one or more fluorescent target proteins. The choice of the specific protein(s) to be labeled and the specific labeling approach can vary depending on the particularities of a particular investigation. For example, but not by way of limitation, one approach for labeling proteins used in connection with the htSMT system described herein is the HaloTag fusion strategy. For example, but not by way of limitation, one approach for labeling proteins is SNAPtag fusion. For example, but not by way of limitation, one approach for labeling proteins is to use a fluorophore ligase system. For example, but not by way of limitation, one approach for labeling proteins is to use a tetracysteine ​​motif such as FlAsH or ReAsH. For example, but not by way of limitation, one approach for labeling proteins is by strain-promoted alkyne-azide cycloaddition of a fluorophore. For example, but not by way of limitation, one approach for labeling proteins is by inducing cellular uptake of a separately produced fluorescent target protein. In certain embodiments of the htSMT system of the present disclosure, the cells contain one or more fluorescently labeled glycoproteins. In certain embodiments, one approach to labeling proteins uses a gene editing system, such as a CRISPR-based editing system. For example, but not limited to, a nucleic acid encoding a fluorescent protein (e.g., a fluorescent tag such as HaloTag) can be inserted into a gene encoding the protein to be labeled, or upstream or downstream of the gene, to generate a protein fluorescently labeled with HaloTag (e.g., at its C-terminus or N-terminus), as described, for example, in Example 2.

[0119] While those skilled in the art can implement the HaloTag fusion approach in a variety of ways, one exemplary approach is to transfect a mammalian expression vector containing 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 into a cell line of interest (e.g., U2OS cells). In certain embodiments, such transfection can be achieved when cells are at 70% confluence using FuGENE6 (Cat. No. E2691, Promega). In certain embodiments, transfected cells can then be selected using an appropriate selection agent, e.g., G418 (Cat. No. 10131027, Thermo Fisher), at an appropriate concentration, e.g., 500 μg / mL. In certain embodiments, cells can then be clonally isolated. Clones expressing the desired fusion gene can be initially transfected with 100 nM JF 549 -HTL (Cat. No. GA1110, Promega) and 50 nM Hoechst 33342 staining, JF 549 The distribution of signals can be determined by identifying expected clones. An alternative exemplary approach is to transfect cells with a ribonucleoprotein (RNP) complex containing a target protein and an sgRNA targeting a genomic sequence encoding the N- or C-terminal region of the Cas9 protein, combined with one or more linear dsDNA donors. In certain embodiments, each donor consists of 200-300 bp of homology arms specific for each target, a codon-optimized HaloTag sequence, and a TEV linker (ENLYFQG) between the target and the HaloTag. In certain embodiments, 3-6 clones can be subsequently tested for response to control compounds using SMT conditions, and the most homogeneous clones can then be expanded for further testing.

[0120] Although the htSMT workflow of the present application is generally described with respect to an embodiment tracking the effects of compounds on a target fluorescent protein, 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 may themselves be fluorescent or may be modified to facilitate fluorescent detection. Furthermore, changes in the motion of fluorescent compounds can be used to determine the SMT profile of the compound itself. Thus, all analytical strategies described herein for tracking a target fluorescent protein are also applicable to results obtained by tracking the compound itself.

[0121] 2.2.2. Single Molecule Tracking Sample Preparation Referring to FIG. 5, embodiments of the present subject matter can be implemented using an htSMT workflow whereby cells (2-018) are seeded onto plates (2-021), e.g., tissue culture-treated 384-well glass-bottom plates, although other plate types, 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, e.g., plates made partially or entirely of plastic, can also be used in conjunction with the approach outlined herein. In certain embodiments, cells (2-018) are seeded at 1 to 20,000 cells per well (2-016), e.g., 50 to 10,000 cells, 100 to 9,000 cells, 250 to 8,500 cells, 500 to 7,500 cells, 750 to 7,000 cells, 2,500 to 6,500 cells, or 6,000 cells. The seeded cells can then be incubated under conditions desirable for attachment, e.g., overnight at 37°C and 5% CO2. To allow for fluorescence, the cells can be incubated with a sufficient amount of label, e.g., one or more cell-permeable fluorophores. For example, but not by way of limitation, in the case of HaloTag fusions, cells can be incubated with about 0.1 to about 100 pM of JF 549 , J.F. 646or other equivalent cell-permeable fluorophores. In certain embodiments, cells are incubated with about 0.1-100 pM JF 549 -HTL (catalog no. GA1110, Promega), or approximately 0.1–100 pM JF 646 and / or 50 nM Hoechst 33342 (for labeling nuclei) in complete medium for, for example, 1 hour to achieve the desired results.

[0122] In certain embodiments of the htSMT strategy described herein, the cells are then washed, for example, three times in DPBS and twice in imaging medium. In certain embodiments, the imaging medium is prepared to facilitate fluorescence, for example, fluoroBrite DMEM medium (catalog number A1896701, Thermo Fisher), which can be supplemented with GlutaMAX (catalog number 35050079, Thermo Fisher) and the same serum and antibiotics as the growth medium.

[0123] Where appropriate, compounds can be added to samples to test their effect on specific target proteins via SMT. In certain embodiments, compounds can be serially diluted in an Echo-certified 384-well low-dead-volume source microplate (product number 0018544, Beckman Coulter) to generate source material for dose titration. Compounds can then be administered to cell culture media at a final dilution of, for example, 1:1000. In certain embodiments of the htSMT strategy described herein, each dose of compound has at least two replicates per plate and three plate replicates. Additionally, in certain embodiments of the htSMT strategy described herein, 20 DMSO control wells and two no-dye control wells can be randomized across each sample plate (2-020). In certain embodiments, compounds can be incubated for 0-48 hours, for example, 1 hour at 37°C, before acquiring images.

[0124] 3.OLS htSMT software 3.1. htSMT Software Overview FIG. 6 illustrates an exemplary system 600 for a high-throughput single-molecule imaging platform for measuring molecular movement in living cells. An experiment 602 can be performed to collect large amounts of data from multiple living cells (e.g., using an imaging system 624 to identify compounds 626 and / or targets 622). The experiment 602 can include applying various identifiers, such as labels that can subsequently fluoresce or otherwise be detected, to molecules of interest (e.g., using a laser or other light source). Biological samples forming part of such an experiment 602 can be organized in a plate 604 having multiple wells 606. Each well 606 can have one or more associated fields of view (FOVs) 610. An FOV 610 can be a position within or corresponding to a single well 606. Image sequences can be generated for the FOV 610 to generate one or more movies 612, which can include SMT movies and non-SMT movies. SMT movies can be used to track the path of individual labeled molecules, such as proteins, generating multiple trajectories. Each trajectory may consist of multiple spots 614 containing spatiotemporal coordinates of labeled molecules at a particular time (as described in more detail in FIG. 7). Separately from, and in some examples in parallel with, tracking, the video 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 described in more detail in FIGS. 8 and 22B.

[0125] Data associated with the two channels (e.g., the tracking channel and the segmentation / masking channel) can be combined to generate multiple metrics 620 associated with various aspects of the sample. In other words, the trajectories 616 (e.g., trajectory data) can be combined with the machine learning processed image segmentation data and further analyzed using statistical / machine learning techniques. Processing of the combined data can be used to generate metrics 620, such as hit scores, associated with compounds and / or targets within the biological sample, which may be stored in a database structure, as further described in FIG. 9.

[0126] FIG. 7 illustrates data flow through an exemplary system 700 for a high-throughput single-molecule imaging platform for measuring protein movement in live cells. An experiment specification 704 defining an experiment 602 can be provided as data input via one or more clients 702. For example, each experiment 602 can be collected along with an accompanying stain (e.g., Hoechst or Promac Red) used for downstream analysis, including segmentation 618. The experiment specification 704 can define various parameters for the experiment 602, such as stains, dyes, compounds, and treatments. As previously described in FIG. 6, the imaging system 706 (e.g., imaging system 624) can capture a sequence of images that generate one or more SMT movies 711 and / or non-SMT or segmented movies 708 (e.g., movie 612) that characterize the movement of molecules. The SMT movie 711 can characterize the movement of individual fluorophores and / or can include images of individual fluorophores. The segmented movie 708 can include a sequence of images that characterize the movement of a labeled molecule and / or its components. It will be understood that Hoechst staining is only one technique that may be used to label molecules, and / or multiple labeling techniques, such as Potomoc Red, may be utilized depending on the desired configuration. For example, MitoTracker Deep Red may be used to label mitochondria, Concanavalin A-dye conjugates may be used to label endoplasmic reticulum, SYTO14 may be used to label nucleoli, phalloidin may be used to label actin, etc.

[0127] The SMT movie 711 can be analyzed to perform operations related to molecule tracking 710, which can include detecting 712, sub-pixel localizing 713, and linking 714 to identify trajectories 715 of molecules across various images in the SMT movie 711. More specifically, during detecting 712, one or more spots can be detected or recovered in the SMT movie 711. Each spot can be provided with spatiotemporal coordinates. These spatiotemporal coordinates can be estimated by using sub-pixel localizing techniques 713. Linking 714 can be performed on the spots to ultimately identify trajectories 715.

[0128] As used herein, a link is a potential association between two spots. Each link is directed, starting at one spot and ending at another. A "correct link" connects two spots generated by the same emitter in different frames; otherwise, the link is "incorrect." One goal of the linking algorithm is to estimate which link is correct. In this specification, links are referred to in the form a:i→j, which is interpreted to mean link a starting at spot i and ending at spot j. A link satisfies at least three of the following constraints: (a) the link moves forward in time; (b) the link cannot connect two spots that are more distant than a certain limit (hereinafter referred to as the "search radius"); and (c) the link cannot connect two spots that are more distant in time than a certain limit (hereinafter referred to as the "gap limit"). A spot-link graph is a graph of the spots and links of a single SMT video 711. In this graph, spots are vertices and links are edges. Because links move forward in time, the spot-link graph is a directed acyclic graph. A matching is a subset of links in the spot-link graph, such that no two links in this subset start or end at the same spot. A trajectory 715 is used herein to refer to a sequence of consecutive (end-to-end) links in the same matching. A dynamic metric 730 can be determined using multiple trajectories. Such parameters can include spot attributes that characterize the spot's movement. Such parameters can include one or more of the velocity, diffusion coefficient, or anomaly parameter(s) of each spot. The dynamic parameter(s) of spot i are defined herein as θ i Herein, the set of dynamic parameters of all spots in the spot-link graph is called Θ.

[0129] Separately from, and in some variations in parallel with, the processing of the SMT video 711, the segmented video 708 can undergo segmentation to generate one or more masks 720. The masks can be classified into various categories, including, but not limited to, cell nucleus, cytoplasm, cell cycle phase, and / or extraneous masks, which are further described in FIGS. 8 and 22B. An instance mask is an individually segmented object (e.g., one cell, one nucleus, one mitochondrion, M phase, G1 phase, early S phase, mid-S phase, late S phase, G2 phase). The FOV 610 can include any number of instance masks for one mask category. A semantic mask is the union of all instance masks corresponding to one type of mask category for one FOV (e.g., all cells, all nuclei, or all mitochondria for one FOV). Extraneous masks can include portions of the non-SMT video 708 that are excluded from any downstream data analysis. For example, these extraneous masks may correspond to portions of the non-SMT movie 708 that are out of focus or contain autofluorescent cellular debris that prevents accurate tracking. During segmentation, molecules in the segmented movie 708 can be assigned to one or more masks. Image metrics 740 can be assessed from the masked molecules, such as cell health, focus quality, etc.

[0130] Experiment information, such as dynamic metrics 730, image metrics 740, and any data from which any metrics are derived (e.g., segmentation information), may be provided to a data repository 770 for storage. Such a data repository 770 may store any results of the experiment 602, such as, for example, dynamic metrics 730, image metrics 740, and / or any data from which any metrics are derived. The data repository may include a local persistence server and / or a dedicated server accessed locally or via the cloud. The data repository 770 may also store metadata associated therewith and / or associated with the experiment specification 704. Experiment information (e.g., results and metadata from past experiments, etc.) may be provided to the data repository 770 via a repository application program interface (API) 750. The repository API 750 may also interface with a web-based graphical user interface front end 760 that provides such information for display on the client 702.

[0131] In some variations, the segmentation information can be used to identify subcellular compartments such as the nucleus, nucleolus, cytoplasm, etc. The segmentation information may also be used to distinguish one cell from another. The segmentation information may be stored in a particular format (e.g., a multi-image file format such as TIFF, etc.).

[0132] The exemplary dynamics metric 730 can also include a state array. The state array is a framework for learning interpretable dynamic models from SMT trajectories and can be used to gain additional insight into the movement of a target protein and where that movement occurs within the cell. In some variations, the state array can be generated / added using segmentation information. The state array output can be returned at the subcellular compartment level, allowing researchers to distinguish between dynamics in different subcellular compartments. Additionally, the state array can be calculated for each individual subcellular compartment (e.g., for each nucleus).

[0133] To facilitate data access by applications, including but not limited to state arrays, processed SMT data may be stored in a format that allows for (a) representation of processed trajectories and associated attributes, such as SNR and spot shape characteristics, for each SMT video; (b) representation of mask objects, including mask categories (e.g., the subcellular organelles associated with each mask object, the cell cycle stage for each mask object, etc.); (c) association of trajectories with mask objects (e.g., the cell nuclei in which each trajectory was observed, the cell cycle stage in which each trajectory was observed, etc.); and (d) association of all SMT videos with metadata related to the original experiment, such as compound treatment, acquisition time, and imaging system name. Formats (a) and (c) may be protocol buffer schemas that define the storage format for trajectories and associated mask objects. Format (b) may be a specialized image file format containing the mask object to which each pixel in the FOV belongs. Format (d) may be a PostgreSQL database that records all captured experiments / videos. As a client of the processed SMT data, state arrays can reference these data schemas to report the dynamic properties of trajectories by mask category or by mask object.

[0134] FIG. 8 illustrates a plurality of images 800 illustrating the distinction between mask categories and instance or semantic masks. As previously described, a non-SMT video or segmented video can be assigned to multiple categories. Such categories may include cell nuclei (e.g., Category A), cytoplasm (e.g., Category B), and / or extraneous masks (e.g., Category C). Unique, individual masks can be applied to a biological sample. For example, image 810 illustrates a unique, individual instance mask applied to a cell nucleus (e.g., Category A). Image 812 illustrates a unique, individual instance mask applied to a cell cytoplasm (e.g., Category B). Image 820 illustrates multiple instance masks applied to one or more nuclei, each with a different, unique, individual instance mask. Image 822 illustrates multiple masks applied to one or more cytoplasms, each with a different, unique, individual instance mask. Image 830 illustrates a semantic mask that is the union of all instance masks applied to one or more nuclei. Image 832 illustrates a semantic mask applied to one or more cytoplasms. 22B further illustrates the use of mask categories. As shown in FIG. 22B, an individual instance mask can be applied to cells in M ​​phase, an individual instance mask can be applied to cells in G1 phase, an individual instance mask can be applied to cells in early S phase, an individual instance mask can be applied to cells in mid S phase, an individual instance mask can be applied to cells in late S phase, and / or an individual instance mask can be applied to cells in G2 phase.

[0135] FIG. 9 illustrates an exemplary computer-implemented environment 900 in which an imaging system 910 can interact with a computing architecture to execute various algorithms described herein. As shown in FIG. 9 , the imaging system 910 can interface with one or more clients 950 (e.g., client 702 via a web application having a graphical user interface). The one or more clients 950 can interface with one or more servers 920 accessible via network(s) 930. The one or more clients 950 can host a frame grabber that captures images (e.g., video 612) from a camera. These images can be temporarily stored on the one or more clients 950 and periodically transferred via the network 930 to the one or more servers 920 for remote storage. The one or more servers 920 also include 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 may include or interface with one or more network storage arrays 960 for storing data such as captured images (e.g., video 612).

[0136] FIG. 10 is a diagram 1000 illustrating a sample computing device architecture for implementing various aspects described herein. In some variations, the sample computing device architecture may be that of a client(s) 950 and / or a server(s) 920, and some components described in connection with diagram 1000 may be optional for the client(s) 950 and / or the server(s) 920. A bus 1004 may function as an information highway interconnecting the other illustrated components of hardware. A processing system 1008 (e.g., one or more computer processors / data processors in a given computer or in multiple computers), labeled CPU (Central Processing Unit), may perform the computational and logical operations required to execute a program. Optionally, or additionally, a processing system 1012 (e.g., one or more computer processors / data processors in a given computer or in multiple computers), labeled GPU (Graphics Processing Unit), may perform the computational and logical operations required to execute a program. Non-transitory processor-readable storage media, such as read-only memory (ROM) 1016 and random access memory (RAM) 1020, may be in communication with processing system 1008 and / or processing system 1012 and may contain 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, optical disk, recordable memory device, flash memory, solid-state drive, or other physical storage medium.

[0137] In one example, the disk controller 1048 can interface with one or more optional removable storage 1056 or local storage 1052 via 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 mentioned above, these various examples of the removable storage 1056, the local storage 1052, and the disk controller 1048 are optional devices. The system bus 1004 may also include at least one communication interface 1024 to enable communication with external devices either physically connected to the computing system or externally available via a wired or wireless network, such as cloud storage or a remote service. In some cases, the at least one communication interface 1024 includes or otherwise comprises a network interface.

[0138] In some variations, e.g., client(s) 950, to provide for interaction with a user, the subject matter described herein can 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 retrieved from bus 1004 to a user via display interface 1040, and input devices 1032, such as a keyboard and / or pointing device (e.g., a mouse or trackball) and / or a touch screen, for a user to provide input to the computer. Other types of input devices 1032 can also be used to provide interaction with a user. For example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback via microphone 1036, or tactile feedback), and input from the user can be received in any form, including acoustic, voice, or tactile input. The input devices 1032 and microphone 1036 can be connected to bus 1004 via input device interface 1028 to communicate information. As an example, the input device 1032 may be the imaging system 910 configured with the capability to capture a sequence of images, as described herein. The frame grabber 1058 may capture or grab individual frames from analog or digital data encapsulating the sequence of images acquired from the bus 1004. The frame grabber 1058 may include memory capable of storing single or multiple frames. The frame grabber 1058 may also provide individual frames or multiple frames to the bus 1004 for further storage, for example, in the local storage 1052 and / or the removable storage 1056. Other computing devices, such as dedicated servers, may omit one or more of the components described in connection with FIG. 10 .

[0139] One or more aspects or features of the subject matter described herein may be implemented in digital electronic circuitry, 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 implementation in one or more computer programs executable and / or interpretable by a programmable system including at least one programmable processor, which may be special-purpose or general-purpose, coupled to receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device. The programmable system or computing system may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and by virtue of the client-server relationship they have to each other.

[0140] These computer programs, which may also be referred to as programs, software, software applications, applications, components, or code, contain 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, a "machine-readable medium" refers to any computer program product, apparatus, and / or device (e.g., magnetic disk, optical disk, memory, programmable logic device (PLD), etc.) used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives the machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor. A machine-readable medium may non-transitory store such machine instructions, such as, for example, a non-transitory solid-state memory, a magnetic hard drive, or any equivalent storage medium. Alternatively or additionally, a machine-readable medium may temporarily store such machine instructions, such as, for example, a processor cache or other random access memory associated with one or more physical processor cores.

[0141] 3.2. Probabilistic methods for high-density molecular tracking in living cells One aspect of the htSMT workflow 100 involves the recovery of trajectories 715, or paths of identifiers (e.g., individual fluorescent emitters, etc.), from a recorded image sequence (e.g., an SMT movie 711). This recovery, as described herein, is "tracking."

[0142] There are several challenges associated with htSMT tracking 710. First, each emitter is dim, contributing only 100 photons per frame, which may require highly sensitive detection methods. Second, the absolute intensity and noise characteristics of each video may depend on the original imaging system. These differences may arise from differences in laser power or camera gain and offset. As a result, htSMT tracking methods are desirable to be invariant to changes in the absolute intensity of the video. Third, protein movement within cells is fast, and molecules can move quickly in and out of focus. As a result, the average trajectory length is short, only 3–4 frames, which can severely limit the information available for predicting the molecule's future movement. Fourth, tracking becomes difficult at high label densities due to ambiguity in relating detections to trajectories. For example, tracking methods can modulate their parameters in a density-dependent manner to achieve accurate tracking at various densities.

[0143] As previously described, htSMT tracking 710 can include detection 712, sub-pixel localization 713, and linking 714. First, during detection 712, portions of each video frame of the SMT video 711 containing emitters are identified. Second, sub-pixel localization 713 infers emitter locations with sub-pixel resolution, resulting in spatiotemporal coordinates for each detected emitter. For example, sub-pixel localization 713 may fit the observed light distribution around the emitter to an approximation of the point spread function (PSF) of the imaging system. Third, a linking algorithm associates detected emitters with trajectories. While all steps must be performed to meet the needs of htSMT data processing, linking 714 in particular can become prohibitively expensive as label density and / or the number of detections per frame increase. This can be due to the combinatorial explosion of possible trajectories that can be constructed from a given set of detections.

[0144] Figure 11 shows a diagram 1100 illustrating a pipeline 1110 for scalable tracking in htSMT. Diagram 1100 is a modular structure that allows custom combinations of the tracking 710 (e.g., detection 712, sub-pixel localization 713, and linking 714) described in Figure 7 at runtime as specified in an experiment-specific configuration file 1122. Each tracking pipeline 1110 can have an associated runtime configuration 1120 specified by the experiment-specific configuration file 1122, which can define particular types of detectors 1112, sub-pixel localizers 1113, and linkers 1114.

[0145] The tracking pipeline 1110 may receive as input an image sequence (e.g., an SMT video) 1111. To detect or retrieve one or more spots in the image sequence 1111, a detector 1112 may be applied to the image sequence 1111 using any of the following detector types: a generalized log-likelihood ratio spot detector, a difference of Gaussians (DoG) detector, a Laplacian of Gaussians (LoG) detector, a Hessian determinant (DoH) blob detector, or any combination thereof. Depending on the implementation, other types of detectors may be used.

[0146] The detected spots can be associated with spatiotemporal coordinates using sub-pixel localization 1113. Such sub-pixel localization 1113 can include any of the following localizer types: radially symmetric localizer, maximum likelihood fitting to a candidate spot model using the Levenberg-Marquardt method, etc. Depending on the implementation, other types of localization techniques can also be used.

[0147] Linking of two spots can be performed using a linker 1114. In some variations, the linker 1114 can rely on either heuristics (e.g., nearest neighbor methods) or exact solutions to the assignment problem (e.g., the Hungarian algorithm). Such linker types can utilize separate steps for inferring trajectories and inferring dynamic parameters from the trajectories. In other variations, the linker 1114 can infer a joint probability distribution over possible trajectories and dynamic parameters in a scalable manner. This distribution can be used to make a more informed point estimate of the "correct" trajectory, to estimate the confidence of a particular set of trajectories, or to derive dynamic results completely independent of the trajectories. This approach, referred to herein as "probabilistic linking," can be used to estimate distributions over trajectories in videos containing thousands to tens of thousands of nearby fast-moving targets.

[0148] The tracking pipeline 1110 can output object tracks 1115 in a graphical format that represent possible trajectories. This output can depend on the type of linker utilized by the linker 1114.

[0149] 3.2.1. Probabilistic methods for high-density molecular tracking in living cells Two exemplary probabilistic link types of linker 1114 may utilize different methods, including variational Bayesian inference (referred to herein as "vtrack") or Gibbs sampling (referred to herein as "gibbstrack").

[0150] The dynamic parameters of each spot can be viewed as arguments to a motion model that defines a probability distribution over its future motion. Here, any vector displacement r between spots i and j i→j We can assume that the probability of depends only on the dynamic parameters of spots i and j, and not on the rest of the spot-link graph. Equation 1 expresses this probability and defines the "motion model". f r|θ(r i→j │θ i ,θ j )(1)

[0151] One alternative to Equation 1 is the likelihood function for scaled Brownian motion, which can be characterized by a single kinetic parameter (the diffusion coefficient) per spot. A simplified Bayesian model for scaled Brownian motion is detailed in Section 3.2.6 below.

[0152] The objective function of the link algorithm may be defined as follows: Let M be the number of links in the spot link graph. E∈{0,1} M If link a participates in the matching, then E a = 1, otherwise E a Let w∈R be a vector of 1s and 0s that represent matchings such that =0. M Let be a real-valued weight vector. Let the likelihood of starting or ending a trajectory be w0. Then, the role of the link algorithm is to find the best matching

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[0153] Equation 2 (eg, the solution criterion for the link problem) is an example of an imbalanced assignment problem because each matching cannot contain two links that start or end at the same spot.

[0154] Since each element of the matching vector E corresponds to a link a:i→j, in this specification, each element is represented by its link index a (i.e., E a ) or its spot index (i.e., E i→j ) for convenience. Similarly, vector displacements can be indexed by r a or r i→jcan be expressed as either:

[0155] If the weight vector is constant, Equation 2 can be solved by classical methods for solving assignment problems. These include exact methods such as the Hungarian algorithm and heuristics such as nearest neighbor methods.

[0156] In general, however, the weight vector can be a function of a dynamic parameter Θ, which can be estimated from the trajectory defined by the matching vector E. Since both E and Θ are unknown a priori, they can be jointly estimated.

[0157] The goal of the probabilistic link algorithm is to estimate the conditional distribution p(E,Θ│R), where R = (r1,…,r M ) represents the vector displacements corresponding to each of the M links in the spot-link graph. Optionally, these terms may be generalized to include any additional information relevant to the link problem (spatial location, spot shape characteristics, etc.). Once the distribution p(E,Θ|R) is obtained (e.g., via vtrack or gibbstrack as discussed herein), this distribution can be used to estimate the maximum a posteriori trajectory, or the dynamic parameters, by solving Equation 2, with the marginal link probabilities w=logp(E|R).

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[0158] Bayes' theorem allows us to write the conditional distribution p(E,Θ|R) as Equation 3 (e.g., Bayes' theorem for the linking problem).

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[0159] In Equation 3, the term p(R|E,Θ) is the likelihood of the observed displacement given the dynamic model Θ and the matching vector E. r|θ (r i→j │θ i ,θ j ) is the dynamic parameter θ i and θ j A single displacement r given i→j Since the likelihood function of is (Equation 1), the total likelihood function p(R|E,Θ) is given by the product of the likelihood functions of each link (Equation 4). In Equation 4 (e.g., the likelihood function for the link problem), Pa(i) is the set of parent spots of spot i, which includes the set of all spots j such that j → i is a allowed link.

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[0160] In Equation 3, the term p(E, Θ) is a prior distribution on E and Θ. Herein, we can assume that p(E, Θ) = p(E)p(Θ), where p(E) = constant for all allowed E,

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[0161] In Equation 3, the term p(R) is the so-called "evidence" and can be analytically intractable. As a result, the left-hand side of Equation 3 cannot be evaluated in closed form. The methods described herein, based on Gibbs sampling (gibbstrack) and variational track (vtrack), circumvent this problem. Gibbstrack approximates the left-hand side of Equation 3 by drawing a fixed number of random samples, while vtrack approximates the left-hand side of Equation 3 using a mean-field approximation. Gibbstrack and vtrack are each described in detail below.

[0162] 3.2.2. Probabilistic Linking via Variational Bayesian Optimization (vtrack) Using vtrack, the posterior distribution p(E,Θ|R) can be approximated using variational Bayesian optimization. In this method, by assuming that the posterior distribution is factored in E and Θ, we approximate:

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[0163] By working with the model introduced in Equation 4, a simplified version of vtrack can be derived. This derivation allows for greater freedom in the choice of motion model, as vtrack can accommodate a variety of motion models. As shown below, vtrack can be derived for the Brownian motion model or for more general models.

[0164] We consider the joint probability function over all variables in the model, represented by Equation 4. With this choice of prior distribution, as explained in Section 3.2.1, the joint probability distribution over all parameters can be factored in the form presented by Equation 5 (e.g., the joint probability density for the linking problem). p(R,E,Θ)=p(R│E,Θ)p(E)p(Θ) (5)

[0165] Substituting Equation 4 and the prior distribution p(R, Θ) into Equation 5 and taking the logarithm, we obtain Equation 6 (e.g., the logarithmic joint probability density for the link problem).

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[0166] The variational pursuit approach finds an analytical approximation to the true posterior distribution that satisfies two criteria:

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[0167] Then, q maximizes the lower bound on the evidence (Equations 8 and 9).

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[0168] Any distribution q(E,Θ) that satisfies Equations 7 and 9 must be Equations 10 and 11. In Equation 10 (e.g., the recurrence equation for the factors of q(E)) and Equation 11 (e.g., the recurrence equation for the factor q(Θ)), logp(R,E,Θ) is given by Equation 6, and the expected value

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[0169] Equations 10 and 11 can be solved sequentially to produce a progressively better approximation q(E,Θ), a scheme known as expectation-maximization. This algorithm converges because the lower bound on the evidence (Equation 8) is convex with respect to each factor in q. This method is referred to herein as vtrack, in reference to the tracking model defined by Equation 6.

[0170] Once q(E,Θ) is obtained, we convert the weight vector into the marginal log-probability of each link.

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[0171] Equations 10 and 11 may be solved for any motion model for which there exists a conjugate prior (i.e., any choice of Equation 1) in combination with the log probability density represented by Equation 6. This includes any motion model with a probability density in the exponential family of distributions.

[0172] 3.2.3. Specific Form of vtrack of Brownian Motion As a limited demonstration of vtrack, we can now solve Equations 10 and 11 for the Brownian motion model expressed in Equation 19 using the prior expressed in Equation 20. Under these conditions, the log joint probability (Equation 6) becomes Equation 12 (e.g., the joint probability density of Brownian motion). In Equation 12, m is the spatial dimension and θ i is the diffusion coefficient of spot i, and r i→j is the spatial displacement of link i→j, Δt is the frame interval, and α0 and β0 are a priori parameters.

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[0173] Substituting Equation 12 into Equation 10 and Equation 11 and solving for the respective factors q(E) and q(Θ), we obtain Equation 13 and Equation 14. In these equations, GraphSoftmax is the graphical softmax operator described below, T is temperature, w0 is the trajectory initiation likelihood, and

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[0174] 3.2.4. Extended Variational Pursuit Algorithm The vtrack algorithm above can be generalized to exploit the additional information implicit in the spot-link graph as follows: Given a spot-link graph containing N spots and M links,

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[0175] As in the case of the simple vtrack algorithm, an approximate posterior distribution

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[0176] As an example of a solution, the results can be demonstrated using Brownian motion. To do this, f r|θ (r i→j │θ i ) is a gamma distribution of the form specified in Equation 19, and p(θ i ) can be assumed to be an inverse gamma distribution of the form specified in Equation 19. The posterior distribution specified in Equation 16 and Equation 17 can be obtained. In Equation 16, the term

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[0177] As in the case of the simple vtrack algorithm, once an approximate posterior distribution q(E,Θ) is obtained, the log-marginal link probabilities (

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[0178] 3.2.5. Gibbs Sampling for Probabilistic Links (Gibbstrack) Another approach to estimating p(E,Θ|R) is to draw random samples from this distribution and then take the average of these samples to approximate the mean of the posterior distribution. A simple and flexible way to achieve this sampling scheme is to alternately sample from the conditional distributions of E and Θ (Equation 18). Equation 18 defines Gibbs tracking and can be expressed as follows: E~p(E│R,Θ) (18) Θ~p(Θ│R,E)

[0179] The sampling scheme expressed in Equation 18 can be achieved as follows: Start with an estimate of the dynamic parameters Θ and set E to all zeros (this is always a valid matching vector for any spot-link graph). At each iteration, evaluate the log-likelihood of each link according to the current dynamic model (via Equation 1 for selecting the motion model).

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[0180] E1,E2,…,E n and Θ1, Θ2,…, Θ n If is the sample thus generated from Equation 18, then the peripheral link probability l a teeth

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[0181] 3.2.6. Bayesian Model of Brownian Motion One type of motion model for Vtrack and Gibbstrack is scaled Brownian motion, which characterizes the motion of each spot with a single dynamic parameter (the diffusion coefficient). Under scaled Brownian motion, the likelihood function (Equation 1) becomes a gamma distribution, expressed as Equation 19, where m represents the dimension of the space in which the motion is observed, and θ i is the diffusion coefficient of spot i, and r i→j is the vector displacement corresponding to link i → j. Equation 19 is the likelihood function of Brownian motion in m dimensions, which can be expressed as:

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[0182] A useful prior distribution conjugate to the Brownian likelihood expressed in Equation 19 is the inverse gamma prior, expressed in Equation 20, where α0 and β0 are prior hyperparameters, Δt is the frame interval, and θ is the diffusion coefficient of a single spot. Equation 20 is the prior distribution for Brownian motion and can be expressed as follows:

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[0183] A series of observed displacements r1, r2, ..., r n Given f, the posterior distribution of the diffusion coefficient is given by Equation 21, which forms the basis for Bayesian inference of the diffusion coefficient. θ refers to the inverse gamma distribution of the form given by Equation 20. Equation 21 is the posterior distribution of Brownian motion, which can be expressed as:

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[0184] 3.2.7. Sparse Hill Climbing Algorithm Equation 3 is a weight vector, under the condition that the weights are constant.

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[0185] In the subsequent algorithm, the "pivot" is a matching vector E∈{0,1} that modifies at most four elements, as follows: M Each pivot is a weight change Δw=W that determines whether the pivot is accepted. a -W b -W c +W d Each pivot selects a link a:i → j and a =w a It is started by setting E bIf there exists a link b:i→k such that =1, then W b =w b otherwise, W b = w0. E c If there exists a link c:l→j such that =1, then W c =w c otherwise, W c If both of the above conditions are true and d:k→j is a link, then W d =w d otherwise, W d = w0. The pivot is Δw = W a -W b -W c +W d > 0 is accepted. If the pivot is accepted, E a = 1, if b is a link, E b =0, if c is a link, E c = 0, if d is a link, E d =1 to perform a pivot.

[0186] The lookup required at each pivot (e.g., determining whether links b, c, and d exist) can be performed quickly by keeping in memory a record of each spot's currently assigned forward and reverse links.

[0187] The sparse hill climbing algorithm may be described as follows for a spot-link graph with N spots and M links: M and the known weight vector

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[0188] One way to determine convergence is to check that there is no change in the matching vector E after iterating through all M links in random order.

[0189] The sparse hill-climbing algorithm provides a nearest-neighbor solution to the tracking problem (for each link a:i → j, a =w0-r i→j (r i→j It can be used for a variety of purposes, including estimating σ(σ), ...

[0190] Graphical Softmax One aspect of the vtrack algorithm is to normalize some link log-likelihoods for the incoming and outgoing links in the spot-link graph, taking into account dependencies between links induced by topological constraints on matching (e.g., two links in the same matching cannot start or end at the same spot).

[0191] One way to deal with regularization is to generalize the softmax operator (e.g., the Boltzmann distribution) to doubly stochastic matrices.

[0192] Another way to deal with normalization is to use the "graphical softmax" operator. The input to the graphical softmax operator is a spot-link graph with N spots and M links, where each link

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[0193] Five vectors,

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[0194] The start probability v and end probability w are the probabilities of all links entering and leaving each spot.

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[0195] The convergence rate of this algorithm depends more on the sparsity of the problem than on its size: for htSMT, convergence can occur in about 20 iterations.

[0196] 3.2.9. Measures of confidence in tracking solutions Ensuring high-quality data can increase confidence in the results produced by an htSMT system. However, because htSMTs can be generated rapidly and continuously across multiple imaging systems, the ability of human supervision to uncover problems in the data can be limited. Therefore, a feature of the tracking pipeline described herein is that it provides a built-in measure of confidence in the tracking solution that can be used as a diagnostic for imaging quality in lieu of direct human supervision.

[0197] Equation 22 is the normalized entropy of the posterior distribution over the trajectories, which can be defined as:

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[0198] Equation 22 defines the confidence in the solution of the tracking algorithm, with values ​​closer to 0 indicating higher confidence. Values ​​below 0.4 may reflect a high degree of confidence in the tracking solution. However, tracking particles at very high speeds (especially in the case of Brownian motion) can be prone to errors, and a higher threshold may be acceptable for such use cases.

[0199] The tracking error rate lower bound (ERLB) is a second diagnostic useful for assessing the quality of hSMT results. This metric is designed as a lower bound on the fraction of incorrect links created by the tracking algorithm. For example, a value of 0.1 indicates that at least 10% of the links created by the tracking algorithm are likely to be incorrect. Because it may not be known in advance which links are correct or incorrect, ERLB creates a situation where a subset of links is known to be incorrect.

[0200] The ERLB is computed once per SMT video: the set of detections from the second half of the video is superimposed on the set of detections from the first half of the video. The tracking algorithm is then re-run on this superimposed set of detections, without knowledge of which detections come from which half of the video. Under these conditions, any link created by the tracking algorithm between two detections derived from different halves of an SMT video will be incorrect. Since it may be unclear whether a link between two detections derived from the same half of the video is incorrect, the proportion of such links is a lower bound on the error rate.

[0201] The process of overlapping the two halves of the video is achieved in the following way: Each spot is always associated with a frame index, or the index of the image in the original sequence of images from which the spot was obtained. Let S1 be the set of spots from the first half of the SMT video, and S2 be the set of spots from the second half of the video. Let T be the total number of frames in the video. For each spot in S2, subtract floor(T / 2) from its original frame index. Then, combine S1 and S2 into a new set of detections.

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[0202] 3.2.10. Trajectory-Independent Dynamic Estimation Using Probabilistic Tracking Algorithms The goal of htSMT is to infer the dynamic parameters of a target protein and identify experimental conditions that alter its dynamics. For example, one may estimate the diffusion coefficient of a target protein for each of several compound treatments to identify compounds that perturb the protein's diffusion state (e.g., by creating or disrupting protein-protein interactions).

[0203] These dynamic parameters are typically estimated from the trajectories. However, stochastic tracking provides a posterior distribution over both the dynamic parameters and the trajectories p(E,Θ|R), so that the estimation of the dynamic parameters can be done by marginalizing over the trajectories.

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[0204] As an example, we describe a procedure for estimating the marginal posterior diffusion coefficients over all possible past and all possible future times for each spot.

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[0205] n SMT videos with M possible links and N spots.

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[0206] 3.2.11. Tracking Algorithm Benchmark Figures 12A-12C show benchmarks of various tracking algorithms. Regarding Figure 12A, optical dynamic simulations were used to test the accuracy of several linking algorithms, including vTrack, GibbsTrack, and adaptive hill climbing, as well as other linking algorithms: random, conservative, and nearest neighbor. In the random linking algorithm, which served as a control, each detection was randomly linked to another detection within its distance gate (i.e., the set of detections within its search range and gap limit). The conservative linking algorithm was configured so that each detection was linked to another detection only if there were no other possible detections within the applicable distance gate. The nearest neighbor linking algorithm stipulated that each detection was linked to its nearest neighbor within the applicable distance gate. Three classes of experiments (Benchmark 1, Benchmark 2, and Benchmark 3) were conducted with increasing difficulty. The outputs of these experiments were link recall, link precision, and F1 score (i.e., the harmonic mean of recall and precision). The metrics in this context are shown in Figure 12B. To ensure a fair comparison, the search range (i.e., the maximum distance over which links are considered) and gap limit (i.e., the maximum number of gap frames over which links are considered) were kept constant at 1.25 μm and 2 gaps for all algorithms. The exception is the conservative linking algorithm, which essentially requires 0 gaps (so all links are between consecutive frames). The benchmark results are shown in Figure 12C.

[0207] 4. Specific OLS htSMT Applications Many, perhaps most, pathways that regulate fundamental cellular biochemistry rely on the interaction of protein sensors and protein effectors that transiently engage and trigger changes in cellular physiology. While the fundamentals of this process have long been recognized, biochemical investigation of these protein interactions has typically required in vitro reconstitution or been investigated through pull-down assays after cell permeabilization. The htSMT workflow described herein provides a means to visualize protein movement in large numbers of live cells, and also in contexts where the effects of additive compounds, such as small molecule inhibitors, can be quantitatively assessed.

[0208] 1 , aspects of the OLS htSMT workflow of the present disclosure include, but are not limited to, (i) sample preparation, including reagent handling, (ii) image acquisition, imaging the sample to generate a series of images and / or video, (iii) image analysis, 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 provide the ability to provide specific insights, as outlined below, depending on the particular workflow employed, e.g., (i) OLS htSMT screening, (ii) OLS htSMT binding, and / or (iii) OLS kinetic SMT.

[0209] In certain embodiments, a workflow of the present disclosure can include detecting fluorescence from multiple target fluorescent proteins within a field of view at a sample plane, where the field of view (e.g., a detection field of view) has a size of a first dimension (about 150 μm to about 250 μm) by a second dimension (about 100 μm to about 210 μm). In certain embodiments, the FOV, e.g., the detected FOV, can have a size of a first dimension (about 150 μm to about 250 μm) by a second dimension (about 100 μm to about 210 μm). In certain embodiments, the FOV, e.g., the detected FOV, can have a size of a first dimension (about 200 μm to about 250 μm) by a second dimension (about 150 μm to about 210 μm), or the FOV, e.g., the detected FOV, can have a size of a first dimension (about 225 μm to about 250 μm) by a second dimension (about 175 μm to about 210 μm). For example, and not by way of limitation, an FOV, e.g., a detected FOV, may have a size of a first dimension (approximately 250 μm) by a second dimension (approximately 190 μm), e.g., as disclosed in Example 1.

[0210] In certain embodiments, a certain percentage of the FOV, e.g., the detected FOV, achieves sufficient laser illumination to track protein motion. For example, without 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 certain embodiments, about 75% or more of the FOV achieves sufficient laser illumination to track protein motion, e.g., about 80% or more of the FOV, about 85% or more of the FOV, about 90% or more of the FOV, about 95% or more of the FOV, about 96% or more of the FOV, about 97% or more of the FOV, about 98% or more of the FOV, or about 99% or more of the FOV provides sufficient laser illumination to track protein motion. In certain embodiments, about 90% or more of the FOV achieves sufficient laser illumination to track protein motion. In certain embodiments, about 95% or more of the FOV achieves sufficient laser illumination to track protein motion.

[0211] In certain embodiments, the disclosed workflow includes detecting a field of view at a frame rate of up to about 2000 Hz. In certain embodiments, the disclosed workflow includes detecting a field of view at a frame rate of about 100 Hz or more, about 200 Hz or more, about 400 Hz or more, about 600 Hz or more, about 800 Hz or more, about 1000 Hz or more, about 1200 Hz or more, about 1400 Hz or more, about 1600 Hz or more, or about 1800 Hz or more. In certain embodiments, the disclosed workflow includes detecting a field of view at a frame rate of up to about 1200 Hz. In certain embodiments, the disclosed workflow includes detecting a field of view at a frame rate of up to about 1400 Hz. In certain embodiments, the disclosed workflow includes detecting a field of view at a frame rate of up to about 1600 Hz. In certain embodiments, the disclosed workflow includes detecting a field of view at a frame rate of up to about 1800 Hz.

[0212] In certain embodiments, the disclosed workflow includes illuminating the field of view using a strobe laser pulse of about 0.1 to about 1 millisecond. In certain embodiments, the disclosed workflow includes illuminating the field of view using a strobe laser pulse of about 0.2 to about 1 millisecond, about 0.3 to about 1 millisecond, about 0.4 to about 1 millisecond, about 0.1 to about 0.9 millisecond, about 0.1 to about 0.8 millisecond, about 0.1 to about 0.7 millisecond, about 0.1 to about 0.6 millisecond, about 0.1 to about 0.5 millisecond, about 0.1 to about 0.4 millisecond, about 0.2 to about 0.6 millisecond, about 0.2 to about 0.5 millisecond, about 0.2 to about 0.4 millisecond, or about 0.3 to about 0.5 millisecond. In certain embodiments, the disclosed workflow includes illuminating the field of view using a strobe laser pulse of about 0.1 to about 0.6 millisecond. In certain embodiments, the disclosed workflow includes illuminating the field of view using a strobe laser pulse of about 0.1 to about 0.5 milliseconds. In certain embodiments, the disclosed workflow includes illuminating the field of view using a strobe laser pulse of about 0.2 to about 0.4 milliseconds. In certain embodiments, the disclosed workflow includes illuminating the field of view using a strobe laser pulse of about 0.2 milliseconds.

[0213] In certain embodiments, the disclosed workflow includes illuminating a field of view of a sample surface disposed within the sample with a light beam to induce fluorescence by a subset of fluorescent target proteins in live cells, thereby imaging multiple molecular trajectories. In certain embodiments, up to about 1,000,000 molecular trajectories can be imaged within a single detection field, e.g., up to about 900,000, up to about 800,000, up to about 700,000, up to about 600,000, up to about 500,000, up to about 400,000, up to about 300,000, up to about 200,000, or up to about 100,000 molecular trajectories. In certain embodiments, the number of trajectories imaged within a single detection field can be between about 30,000 and about 1,000,000, e.g., between about 30,000 and about 250,000. For example, and without limitation, the number of trajectories imaged within a single detection field of view may be about 50,000 to about 200,000, about 100,000 to about 200,000, about 100,000 to about 500,000, or about 100,000 to about 150,000. In certain embodiments, the number of trajectories imaged within a single detection field of view may be up to about 1,000,000. In certain embodiments, the number of trajectories imaged within a single detection field of view may be about 100,000 to about 1,000,000. In certain embodiments, the number of trajectories imaged within a single detection field of view may be about 200,000 to about 1,000,000. In certain embodiments, the number of trajectories imaged within a single detection field of view may be about 100,000 to about 500,000. In certain embodiments, the number of trajectories imaged within a single detection field of view can be from about 200,000 to about 500,000.

[0214] In certain embodiments, a field of view can include multiple cells. In certain embodiments, the number of cells imaged within a field of view is related to the size of the cells being imaged. For example, but not by way of limitation, the smaller the cell size, the greater the number of cells that can be imaged within the field of view. In certain embodiments, depending on the size of the cells being imaged, a field of view can include about 30 to about 200 live cells, e.g., about 30 to about 80 cells or about 50 to about 80 cells. In certain embodiments, depending on the size of the cells being imaged, a field of view can include up to about 80 live cells, e.g., mammalian cells. In certain embodiments, for U2OS cells, the range is about 30 to about 40 cells per field, while for HCT116 cells, the range is about 50 to about 80 cells per field, taking into account differences in area. In certain embodiments, a field of view can include 30 to about 80 live cells, e.g., mammalian cells. In certain embodiments, a field of view can include 50 to about 80 live cells, e.g., mammalian cells. In certain embodiments, the field of view can include between 55 and about 80 viable cells, e.g., mammalian cells. In certain embodiments, the field of view can include between 60 and about 80 viable cells, e.g., mammalian cells.

[0215] In certain embodiments, the disclosed workflows may include analyzing a subset (e.g., a subpopulation) of cells present within a field of view, e.g., analyzing and / or tracking the trajectory of a fluorescent target protein within a subset (e.g., a subpopulation) of cells present within a field of view. For example, and without limitation, the disclosed workflows may include analyzing between about 1% and about 99% of the cells present within a field of view, e.g., between about 1% and about 50% of the cells present within a field of view.

[0216] In certain embodiments, the disclosed workflow can include illuminating a field of view of a sample surface disposed within the sample with a light beam to induce fluorescence of multiple fluorescent target proteins within the live cells. In certain embodiments, the multiple fluorescent target proteins can include about 1,000 to about 1,000,000, e.g., about 10,000 to about 1,000,000, or about 100,000 to about 1,000,000 fluorescent target proteins.

[0217] In certain embodiments, the disclosed workflows can include detecting fluorescence from multiple fluorescent target proteins within a field of view at the sample plane at a rate of greater than about 100,000 detection fields per day. For example, and without limitation, fields of view can be detected at a rate of about 100,000 to about 1,000,000 per day. In certain embodiments, the disclosed workflows can include detecting fluorescence from multiple fluorescent target proteins within a field of view at the sample plane at a rate of about 100,000 to about 500,000 detection fields per day.

[0218] In certain embodiments, the workflow of the present disclosure can include determining a change in the movement of a fluorescently labeled target protein in the presence of a compound. For example, but not by way of limitation, the average change in the movement of the fluorescent target protein in the presence of the compound is about 1% to about 5%, or about 10%, relative to baseline movement in the absence of the compound. In certain embodiments, the average change in the movement of the fluorescent target protein in the presence of the compound is about 1% to about 5%. In certain embodiments, the average change in the movement of the fluorescent target protein in the presence of the compound is about 1% to about 10%.

[0219] In certain embodiments, an exemplary OLS htSMT workflow includes the individual strategies described above, as well as combinations of these strategies where two or more of the strategic requirements are combined.

[0220] 4.1.OLS htSMT Screening In certain embodiments of the OLS htSMT workflow described herein, the systems and methods are adapted to examine the ability of one or more compositions (e.g., "test" compounds) to affect the SMT profile associated with a labeled protein. For example, such htSMT workflows screen for changes in the SMT profile, e.g., either an increase or decrease in the movement of the protein of interest, in the presence of a composition compared to the SMT profile in the absence of the composition. It will also be appreciated that higher-order comparisons can be performed when compounds are multiplexed, including when multiple proteins are fluorescent. Furthermore, as outlined above, the htSMT screening strategies described herein are equally applicable to screening SMT profiles associated with fluorescent compounds, e.g., compounds that are naturally fluorescent or compounds that have been modified to fluoresce or linked to a fluorophore.

[0221] Fundamental to such an htSMT screening strategy is the ability of the htSMT workflow described herein to extract large-scale, accurate molecular trajectories. Exemplary OLS htSMT workflows include the following individual strategies and combinations of the following strategies, combining two or more strategic requirements. For example, without limitation, the disclosed workflow includes both illuminating a field of view in a sample plane disposed within a sample with a light beam to induce fluorescence by a subset of fluorescent target proteins in live cells, the subset of fluorescent target proteins being present in about 30 to about 80 live cells illuminated within the field of view in the sample plane, and illuminating a field of view in a sample plane disposed within the sample with a light beam to induce fluorescence by a subset of fluorescent target proteins in live cells, the subset of fluorescent target proteins comprising a range of about 1,000 to about 1,000,000 proteins. Similarly, illuminating the sample plane to illuminate about 30 to about 80 live cells per FOV and / or generating fluorescence from about 1,000 to about 1,000,000 proteins can be combined with any of the other strategic requirements disclosed herein, such as determining that the average change in fluorescent target protein motion in the presence of a compound is about 5% to about 10% compared to baseline motion under DMSO conditions, detecting fluorescence from multiple fluorescent target proteins within the field of view of the sample plane at a rate of about 10,000 to about 18,000 per day per system, and achieving a Z-factor greater than 0.5 based on a single field of view.

[0222] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include identifying biological interactions between a compound and a fluorescent target protein in live cells, the workflow including (a) contacting a sample including a population of live cells with a compound, the live cells including the fluorescent target protein, the workflow further including (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample, the tracking including (i) illuminating with a light beam a field of view in a sample plane disposed within the sample to cause fluorescence by a subset of the target fluorescent proteins in the live cells, the subset of fluorescent target proteins being selected depending on the particular cell type being used. Depending on the method, there are approximately 30 to approximately 80 live cells illuminated within the field of view of the sample plane. For example, for U2OS cells, the range is approximately 30 to approximately 40 cells per FOV, while for HCT116 cells, the range is approximately 50 to approximately 80 cells, taking into account the difference in area. The tracking further includes (ii) detecting fluorescence from multiple fluorescent target proteins within the field of view of the sample plane via a detector device. The workflow further includes (c) determining a change in the movement of the fluorescent target proteins in the presence of a compound. The change in the movement of the fluorescent target proteins in the presence of the compound compared 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.

[0223] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include identifying biological interactions between a compound and a fluorescent target protein in live cells, the workflow including (a) contacting a sample including a population of live cells with a compound, the live cells including the fluorescent target protein, and the workflow further including (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample, the tracking including (i) illuminating with a light beam a field of view in a sample plane disposed within the sample to induce fluorescence by a subset of target fluorescent proteins in the live cells, the subset of fluorescent target proteins including a range of about 1,000 to about 1,000,000 proteins, and The number of proteins in the set depends on the expression level of the protein of interest and the dye concentration deemed appropriate for labeling a subset of proteins for robust SMT, both of which can be calculated and / or configured by one of skill in the art based on the disclosure of the present application; the tracking further includes (ii) detecting fluorescence from multiple fluorescent target proteins within a field of view of the sample plane via a detector device; the workflow further includes (c) determining a change in the movement of the fluorescent target proteins in the presence of a compound; a change in the movement of the fluorescent target proteins in the presence of the compound compared 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.

[0224] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include identifying a biological interaction between a compound and a fluorescent target protein in live cells, the workflow including: (a) contacting a sample including a population of live cells with a compound, the live cells including the fluorescent target protein; and (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample, the tracking including: (i) illuminating a field of view in a sample plane disposed within the sample with a light beam to cause fluorescence by a subset of the target fluorescent proteins in the live cells; and (ii) detecting fluorescence from the plurality of fluorescent target proteins in the field of view in the sample plane via a detector device. The workflow can further include (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 is about 5% to about 10% compared to baseline movement under DMSO conditions; and a change in the movement of the fluorescent target protein in the presence of the compound compared 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.

[0225] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include identifying a biological interaction between a compound and a fluorescent target protein in live cells, the workflow including: (a) contacting a sample including a population of live cells with a compound, the live cells including the fluorescent target protein; and (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample, the tracking including: (i) illuminating a field of view in a sample plane disposed within the sample with a light beam to cause fluorescence by a subset of the target fluorescent proteins in the live cells; and (ii) detecting, via a detector device, the fluorescence from the plurality of fluorescent target proteins in the field of view in the sample plane; and (iii) tracking including detecting the fluorescence from the plurality of fluorescent target proteins in the field of view in the sample plane at a rate of about 10,000 to about 18,000 per system per day; and the workflow further including: (c) determining a change in the movement of the fluorescent target protein in the presence of the compound; and a change in the movement of the fluorescent target protein in the presence of the compound compared 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.

[0226] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include identifying a biological interaction between a compound and a fluorescent target protein in live cells, the workflow including: (a) contacting a sample including a population of live cells with a compound, the live cells including the fluorescent target protein; and (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample, the tracking including: (i) illuminating with a light beam a field of view in a sample plane disposed within the sample to cause fluorescence by a subset of the target fluorescent proteins in the live cells; (ii) detecting via a detector device fluorescence from the plurality of fluorescent target proteins in the field of view in the sample plane; and (iii) achieving a Z-factor of greater than 0.5 based on the single field of view; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound, wherein a change in the movement of the fluorescent target protein in the presence of the compound compared 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.

[0227] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include determining a dose response of a compound to induce a change in the movement of a fluorescent target protein in a live cell, the workflow including: (a) contacting a plurality of samples with a compound, (i) each sample including a population of live cells, (ii) the live cells including a fluorescent target protein, and (iii) the plurality of samples being contacted with different concentrations of the compound over a range of compound concentrations; and (b) tracking the movement of individual target fluorescent proteins in the plurality of live cells of the sample, the tracking including: (i) illuminating with a light beam a field of view in a sample plane located within the sample to induce fluorescence by at least a subset of the fluorescent target proteins in the live cells; The protein subset is present in approximately 30 to approximately 80 live cells illuminated within a field of view of the sample plane, depending on the particular cell type used. For example, for U2OS cells, the range is approximately 30 to approximately 40 per FOV, while for HCT116 cells, the range is approximately 50 to approximately 80 per FOV, taking into account differences in area. The tracking further includes (ii) detecting fluorescence from one or more of the fluorescent target proteins within the sample plane via a detector device. The workflow further includes (c) determining a change in the movement of the fluorescent target protein in the presence of a compound; and (d) repeating steps (b) to (c) for each of a plurality of samples across a range of compound concentrations, wherein the change in the movement of the fluorescent target protein across a range of concentrations in the presence of the compound indicates a dose response of the compound.

[0228] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include determining a dose response of a compound that induces a change in the movement of a fluorescent target protein in a live cell, the workflow including: (a) contacting a plurality of samples with a compound, (i) each sample including a population of live cells; (ii) the live cells including a fluorescent target protein; and (iii) the plurality of samples being contacted with different concentrations of the compound over a range of compound concentrations; the workflow further including: (b) tracking the movement of individual target fluorescent proteins in the plurality of live cells of the sample, the tracking including: (i) illuminating with a light beam a field of view in a sample plane disposed within the sample to induce fluorescence by at least a subset of the fluorescent target proteins in the live cells; The subset includes proteins ranging from 0 to approximately 1,000,000, where the number of proteins in the subset depends on the expression level of the protein of interest and the dye concentration deemed appropriate for labeling the subset proteins for robust SMT, both of which can be calculated and / or configured by one of ordinary skill in the art based on the disclosure of the present application. The tracking further includes (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device, where the workflow further includes (c) determining a change in the movement of the fluorescent target proteins in the presence of a compound; and (d) repeating steps (b)-(c) for each of a plurality of samples across a range of compound concentrations, where the change in the movement of the fluorescent target proteins in the presence of the compound across the concentration range indicates a dose response of the compound.

[0229] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include determining a dose response of a compound to induce a change in the movement of a fluorescent target protein in a live cell, the workflow including: (a) contacting a plurality of samples with a compound, (i) each sample including a population of live cells; (ii) the live cells including a fluorescent target protein; and (iii) the plurality of samples being contacted with different concentrations of the compound over a range of compound concentrations; the workflow further including: (b) tracking the movement of individual target fluorescent proteins in the plurality of live cells of the samples, the tracking including: (i) determining whether the compound induces fluorescence by at least a subset of the fluorescent target proteins in the live cells; (ii) illuminating with a light beam a field of view in a sample plane disposed within the sample; and (ii) detecting fluorescence from one or more fluorescent target proteins in the sample plane via a detector device; the workflow further includes (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 is about 5% to about 10% compared to baseline movement under DMSO conditions; and (d) repeating steps (b)-(c) for each of a plurality of samples across a range of compound concentrations, wherein the change in the movement of the fluorescent target protein across the concentration range in the presence of the compound indicates a dose response of the compound.

[0230] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include determining a dose response of a compound to induce a change in the movement of a fluorescent target protein in a living cell, the workflow including: (a) contacting a plurality of samples with a compound, (i) each sample including a population of living cells; (ii) the living cells including a fluorescent target protein; and (iii) the plurality of samples being contacted with different concentrations of the compound over a range of compound concentrations; the workflow further including: (b) tracking the movement of individual target fluorescent proteins in a plurality of living cells of the samples, the tracking including: (i) detecting changes in the movement of individual target fluorescent proteins in the samples so as to cause fluorescence by at least a subset of the fluorescent target proteins in the cells. (ii) illuminating with a light beam a field of view in the plane of the sample disposed in the sample; and (ii) detecting via a detector device fluorescence from one or more of the fluorescent target proteins in the plane of the sample; (iii) said tracking includes detecting fluorescence from a plurality of fluorescent target proteins in the field of view in the plane of the sample at a rate of about 10,000 to about 18,000 per day per system; the workflow further includes (c) determining a change in the movement of the fluorescent target proteins in the presence of a compound; and (d) repeating steps (b)-(c) for each of a plurality of samples across a range of compound concentrations, wherein the change in the movement of the fluorescent target proteins in the presence of the compound across the range of concentrations indicates a dose response of the compound.

[0231] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include determining a dose response of a compound to induce a change in the movement of a fluorescent target protein in a living cell, the workflow including: (a) contacting a plurality of samples with a compound, (i) each sample including a population of living cells; (ii) the living cells including a fluorescent target protein; and (iii) the plurality of samples being contacted with different concentrations of the compound over a range of compound concentrations; and the workflow further including: (b) tracking the movement of individual target fluorescent proteins in a plurality of living cells of the samples, the tracking including: (i) determining a dose response of at least one fluorescent target protein in the cells; (ii) illuminating a field of view in a sample plane located within the sample with a light beam to cause fluorescence by a subset of the fluorescent target proteins; (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device; and (iii) achieving a Z-factor of greater than 0.5 based on the single field of view, the workflow further including (c) determining a change in the movement of the fluorescent target proteins in the presence of the compound; and (d) repeating steps (b)-(c) for each of a plurality of samples across a range of compound concentrations, wherein the change in the movement of the fluorescent target proteins in the presence of the compound across the concentration range indicates a dose response of the compound.

[0232] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include the use of a microscopy system configured to identify biological interactions between compounds and fluorescent target proteins in live cells, the system including: (a) a stage for supporting a sample, the sample including a population of live cells, the live cells including a fluorescent target protein; the system further including: (b) a light source 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 plane of the sample, wherein a subset of the fluorescent target proteins in the sample is positioned within a field of view in the plane of the sample, the subset of fluorescent target proteins being present in about 30 to about 80 live cells illuminated within the field of view in the plane of the sample, depending on the particular cell type used, e.g., For U2OS cells, the range is about 30 to about 40 cells per FOV, while for HCT116 cells, the range is about 50 to about 80 cells, taking into account differences in area. The system further includes (d) a detector device that monitors a light-based response from the fluorescent target protein in the presence of a compound, the detector device being configured to (i) block light received from a light source other than 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 individual fluorescent target proteins. The system further includes (e) a memory and (f) a processor in communication with the memory and the detector device, the processor being capable of determining a change in the movement of the fluorescent target protein in the presence of the compound compared to the absence of the compound.

[0233] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include use of a microscope system configured to identify biological interactions between compounds and fluorescent target proteins in live cells, the system including: (a) a stage for supporting a sample, the sample including a population of cells, the cells including a fluorescent target protein; the system further including: (b) a light source 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 plane of the sample; a subset of the fluorescent target proteins in the sample is positioned within a field of view in the plane of the sample; the subset of fluorescent target proteins includes proteins in a range of about 1,000 to about 1,000,000; and the number of proteins in the subset is determined based on the expression level of the protein of interest and the number of proteins in the subset to identify a robust SMT. and a dye concentration deemed appropriate for labeling the subset proteins for the compound, both of which can be calculated and / or configured by one of ordinary skill in the art based on the disclosure of the present application; the system further includes (d) a detector device that monitors a light-based response from the fluorescent target proteins in the presence of the compound, the detector device being configured to (i) block light received from a light source other than the sample plane where the fluorescent target proteins are located, thereby tracking the positions of the fluorescent target proteins, and (ii) track the movement of individual fluorescent target proteins; the system further includes (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 compared to the absence of the compound.

[0234] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include the use of a microscope system configured to identify biological interactions between compounds and fluorescent target proteins in live cells, the system including: (a) a stage for supporting a sample, the sample including a population of cells, the cells including a fluorescent target protein; the system further including: (b) a light source 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 plane of the sample, wherein a subset of the fluorescent target proteins in the sample are positioned within a field of view of the sample plane; and the system further including: (d) detecting a light-based response from the fluorescent target proteins in the presence of a compound. The system further includes a detector device that monitors the light-based response of the fluorescent target proteins, the detector device being configured to (i) block light received from a light source other than the sample plane on which the fluorescent target proteins are located, thereby tracking the positions of the fluorescent target proteins, and (ii) track the movement of individual fluorescent target proteins, wherein the average change in the movement of the fluorescent target proteins in the presence of the compound is about 5% to about 10% compared to baseline movement under DMSO conditions, and the system further includes (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 compared to the absence of the compound.

[0235] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include the use of a microscope system configured to identify biological interactions between a compound and a fluorescent target protein in living cells, the system including: (a) a stage for supporting a sample, the sample including a population of cells, the cells including a fluorescent target protein; the system further including: (b) a light source 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 plane of the sample, the plurality of fluorescent target proteins in the sample being disposed in the plane of the sample; and (d) monitoring the light-based response from the fluorescent target proteins in the presence of the compound. the detector device is configured to (i) block light received from a light source other than the sample plane where the fluorescent target proteins are located, thereby tracking the positions of the fluorescent target proteins, and (ii) track the movement of individual fluorescent target proteins, wherein the tracking includes detecting fluorescence from a plurality of fluorescent target proteins within a field of view of the sample plane at a rate of about 10,000 to about 18,000 per day per system, and the system further includes (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 a compound compared to the absence of the compound.

[0236] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include the use of a microscope system configured to identify biological interactions between a compound and a fluorescent target protein in living cells, the system including: (a) a stage for supporting a sample, the sample including a population of cells, the cells including a fluorescent target protein; the system further including: (b) a light source 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 plane of the sample, the plurality of fluorescent target proteins in the sample being disposed in the plane of the sample; and (d) an objective lens for focusing the light beam onto the sample in a plane of the sample, the plurality of fluorescent target proteins in the sample being disposed in the plane of the sample. The system includes a detector device that monitors a light-based response from the fluorescent target proteins, the detector device being configured to (i) block light received from a light source other than the sample plane where the fluorescent target proteins are located, thereby tracking the position of the fluorescent target proteins, (ii) track the movement of individual fluorescent target proteins, and (iii) achieve a Z-factor of greater than 0.5 based on a single field of view, and the system further includes (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 a compound compared to the absence of the compound.

[0237] 4.2.OLS htSMT binding In certain embodiments of the OLS htSMT workflow described herein, the system and method utilizes the f seen in the htSMT screening assay. bound Increase in residence time (k* off Importantly, both FRAP and htSMT are adapted to distinguish between recovery after exposure to a compound caused by an increase in dwell time (k* off decrease in chromatin binding rate (k* on It is not possible to distinguish between recovery caused by boundThis results in an increase in the number of dwell times. By modifying the SMT acquisition conditions to reduce the illumination intensity and collect longer frame exposures, only immobile proteins form spots. Under these imaging conditions, the distribution of track lengths provides a measure of relative dwell times.

[0238] Exemplary OLS htSMT combined workflows include the following individual strategies and combinations of the following strategies that combine two or more strategic requirements: For example, but not by way of limitation, the disclosed workflow includes both illuminating a field of view in a sample plane disposed within the sample with a light beam to induce fluorescence by a subset of fluorescent target proteins in live cells, the subset of fluorescent target proteins being present in about 30 to about 80 live cells illuminated within the field of view in the sample plane, and illuminating a field of view in a sample plane disposed within the sample with a light beam to induce fluorescence by a subset of fluorescent target proteins in live cells, the subset of fluorescent target proteins comprising a range of about 1,000 to about 1,000,000 proteins. Similarly, illuminating the sample plane to illuminate about 30 to about 80 live cells per FOV and / or generating fluorescence from about 1,000 to about 1,000,000 proteins can be combined with any of the other strategic requirements disclosed herein, such as determining that the average change in fluorescent target protein motion in the presence of a compound is about 5% to about 10% compared to baseline motion under DMSO conditions, detecting fluorescence from multiple fluorescent target proteins within the field of view of the sample plane at a rate of about 10,000 to about 18,000 per day per system, and achieving a Z-factor greater than 0.5 based on a single field of view.

[0239] In certain embodiments of the OLS htSMT binding workflow described herein, the workflow determines whether a compound that induces a change in binding of a fluorescent target protein in live cells is a K offand determining whether a compound reduces the activity of a cell of interest, the workflow comprising: (a) contacting a sample comprising a population of live cells with a compound, the live cells comprising a fluorescent target protein; and (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, the tracking comprising: (i) illuminating a field of view in a sample plane disposed within the sample with a light beam to induce fluorescence by at least a subset of the target fluorescent proteins in the live cells, the subset of fluorescent target proteins being present in about 30 to about 80 live cells illuminated in the field of view in the sample plane, depending on the particular cell type used; e.g., U2 For OS cells, the range is about 30 to about 40 per FOV, while for HCT116 cells, the range is about 50 to about 80, taking into account differences in area. The tracking further includes (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device, the method being adapted to selectively detect localized fluorescence. The workflow further includes (c) determining a change in the movement of the fluorescent target protein in the presence of a compound, where an increase in the signal detected from the fluorescent target protein in the presence of the compound compared to the signal of the fluorescent target protein in the absence of the compound indicates that the compound has a K off This indicates that it induces a decrease in

[0240] In certain embodiments of the OLS htSMT binding workflow described herein, the workflow determines whether a compound that induces a change in binding of a fluorescent target protein in live cells is a K offand determining whether a compound reduces a protein of interest, the workflow comprising: (a) contacting a sample comprising a population of live cells with a compound, the live cells comprising a fluorescent target protein; and (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, the tracking comprising: (i) illuminating with a light beam a field of view in a sample plane disposed within the sample so as to induce fluorescence by at least a subset of the target fluorescent proteins in the live cells; the subset of fluorescent target proteins comprising a range of about 1,000 to about 1,000,000 proteins; and the number of proteins in the subset is determined based on the expression level of the protein of interest and the robust S. and a dye concentration deemed appropriate for labeling a subset of proteins for MTs, both of which can be calculated and / or configured by a person skilled in the art based on the disclosure of the present application; the tracking further includes (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device, the method being adapted to selectively detect localized fluorescence; the workflow further includes (c) determining a change in the movement of the fluorescent target proteins in the presence of a compound, wherein an increase in the signal detected from the fluorescent target proteins in the presence of the compound compared to the signal from the fluorescent target proteins in the absence of the compound indicates that the compound has a K off This indicates that it induces a decrease in

[0241] In certain embodiments of the OLS htSMT binding workflow described herein, the workflow determines whether a compound that induces a change in binding of a fluorescent target protein in live cells is a K offthe workflow may include determining whether the compound reduces the K of the fluorescent target protein, the workflow including (a) contacting a sample including a population of live cells with a compound, the live cells comprising a fluorescent target protein; and (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, the tracking including (i) illuminating a field of view in a sample plane disposed within the sample with a light beam to cause fluorescence by at least a subset of the target fluorescent proteins in the live cells; and (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device, the method adapted to selectively detect localized fluorescence; and the workflow further including (c) determining a change in the movement of the fluorescent target protein in the presence of the compound, the average change in the movement of the fluorescent target protein in the presence of the compound being about 5% to about 10% compared to baseline movement under DMSO conditions; and an increase in the signal detected from the fluorescent target protein in the presence of the compound compared to the signal of the fluorescent target protein in the absence of the compound, indicating that the compound reduces the K of the fluorescent target protein. off This indicates that it induces a decrease in

[0242] In certain embodiments of the OLS htSMT binding workflow described herein, the workflow determines whether a compound that induces a change in binding of a fluorescent target protein in live cells is a K offthe workflow may include determining whether a compound reduces K of the fluorescent target protein, the workflow including (a) contacting a sample including a population of live cells with a compound, the live cells comprising a fluorescent target protein, the workflow further including (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, the tracking including (i) illuminating a field of view in a sample plane disposed within the sample with a light beam to cause fluorescence by at least a subset of the target fluorescent proteins in the live cells, and (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device, the method adapted to selectively detect localized fluorescence, the tracking further including (iii) achieving a Z-factor of greater than 0.5 based on a single field of view, the workflow further including (c) determining a change in the movement of the fluorescent target protein in the presence of the compound, wherein an increase in a signal detected from the fluorescent target protein in the presence of the compound compared to a signal from the fluorescent target protein in the absence of the compound indicates that the compound reduces K of the fluorescent target protein. off This indicates that it induces a decrease in

[0243] In certain embodiments of the OLS htSMT binding workflow described herein, the workflow measures the capacity of a compound to induce a change in binding of a fluorescent target protein in live cells by determining whether the compound has a K offThe workflow can include determining whether the compound reduces the activity of the compound by determining whether the compound reduces the activity of the compound, the workflow comprising: (a) contacting a sample comprising a population of live cells with a compound, the live cells comprising a fluorescent target protein; and (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, the tracking comprising: (i) illuminating a field of view in a sample plane disposed within the sample with a light beam to induce fluorescence by at least a subset of the target fluorescent proteins in the live cells, the subset of fluorescent target proteins being present in about 30 to about 80 live cells illuminated in the field of view in the sample plane, depending on the particular cell type used. For example, in the case of U2OS cells, the range is about 30 to about 40 per FOV, while in the case of HCT116 cells, the range is about 50 to about 80, taking into account the difference in area. The tracking further includes (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device, the method being adapted to selectively detect localized fluorescence. The workflow further includes (c) determining a change in the movement of the fluorescent target protein in the presence of a compound, where an increase in the signal detected from the fluorescent target protein in the presence of the compound compared to the signal of the fluorescent target protein in the absence of the compound indicates that the compound has a K off This indicates that increasing the dose induces a decrease in drug metabolism due to increased residence time.

[0244] In certain embodiments of the OLS htSMT binding workflow described herein, the workflow measures the capacity of a compound to induce a change in binding of a fluorescent target protein in live cells by determining whether the compound has a K offand determining whether the compound reduces the activity of the target fluorescent protein in a plurality of cells in the sample, the workflow comprising: (a) contacting a sample comprising a population of live cells with a compound, the live cells comprising a fluorescent target protein; and (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, the tracking comprising: (i) illuminating with a light beam a field of view in a sample plane disposed within the sample to induce fluorescence by at least a subset of the target fluorescent proteins in the live cells, the subset of fluorescent target proteins comprising a range of about 1,000 to about 1,000,000 proteins, the number of proteins in the subset being determined based on the expression level of the protein of interest and the locus of the target fluorescent protein. and a dye concentration deemed appropriate for labeling a 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 the present application; the tracking further includes (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device, the method being adapted to selectively detect localized fluorescence; the workflow further includes (c) determining a change in the movement of the fluorescent target proteins in the presence of a compound, wherein an increase in the signal detected from the fluorescent target proteins in the presence of the compound compared to the signal from the fluorescent target proteins in the absence of the compound indicates that the compound has a K off These studies have shown that increasing the dose induces a decrease in the drug's metabolism, and in certain cases, increases in the drug's residence time, leading to a decrease in drug metabolism.

[0245] In certain embodiments of the OLS htSMT binding workflow described herein, the workflow measures the capacity of a compound to induce a change in binding of a fluorescent target protein in live cells by determining whether the compound has a K offthe workflow may include (a) contacting a sample containing a population of live cells with a compound, the live cells comprising a fluorescent target protein; and (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, the tracking including (i) illuminating a field of view in a sample plane disposed within the sample with a light beam to cause fluorescence by at least a subset of the target fluorescent proteins in the live cells; and (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device, the method adapted to selectively detect localized fluorescence; and (c) determining a change in the movement of the fluorescent target proteins in the presence of the compound, the average change in the movement of the fluorescent target proteins in the presence of the compound being about 5% to about 10% compared to baseline movement under DMSO conditions; and an increase in the signal detected from the fluorescent target proteins in the presence of the compound compared to the signal of the fluorescent target proteins in the absence of the compound, which indicates that the compound has a K of the fluorescent target proteins. off This indicates that increasing the dose induces a decrease in drug metabolism due to increased residence time.

[0246] In certain embodiments of the OLS htSMT binding workflow described herein, the workflow measures the capacity of a compound to induce a change in binding of a fluorescent target protein in live cells by determining whether the compound has a K offthe workflow may include (a) contacting a sample comprising a population of live cells with a compound, the live cells comprising a fluorescent target protein; and (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, the tracking including (i) illuminating with a light beam a field of view in a sample plane disposed within the sample so as to cause fluorescence by at least a subset of the target fluorescent proteins in the live cells; and (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device, the method adapted to selectively detect localized fluorescence; the tracking further including (iii) achieving a Z-factor of greater than 0.5 based on a single field of view; and the workflow may further include (c) determining a change in the movement of the fluorescent target protein in the presence of the compound, wherein an increase in a signal detected from the fluorescent target protein in the presence of the compound compared to a signal from the fluorescent target protein in the absence of the compound indicates that the compound is a signal that corresponds to a K of the fluorescent target protein. off These results demonstrate that the drug induces a decrease in the amount of steroid hormone and, in some cases, an increase in dosage due to increased residence time leading to decreased drug metabolism.

[0247] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow determines whether a compound that induces a change in binding of a fluorescent target protein in a cell is a K of the fluorescently labeled target. offThe method can include using a microscope system configured to determine whether a fluorescent target protein reduces a signal, the system including: (a) a stage for supporting a sample, the sample including a population of cells, the cells including a fluorescent target protein; and (b) a light source 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 plane of the sample, wherein a subset of the fluorescent target proteins in the sample is positioned within a field of view in the plane of the sample, the subset of fluorescent target proteins being present in about 30 to about 80 live cells illuminated within the field of view in the plane of the sample, depending on the particular cell type used; e.g., for U2OS cells, the range is about 30 to about 40 per FOV, while for HCT116 cells, the range is Taking into account differences in area, the number is about 50 to about 80. The system further includes (d) a detector device that monitors a light-based response from the fluorescent target protein in the presence of a compound, the detector device being configured to (i) block light received from a light source other than the sample surface 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, the tracking being adapted to selectively detect localized fluorescence relative to dynamic fluorescence. The system further includes (e) a memory and (f) a processor in communication with the memory and the detector device, the processor being capable of determining a change in the movement of the fluorescent target protein in the presence of the compound compared to the absence of the compound.

[0248] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow determines whether a compound that induces a change in binding of a fluorescent target protein in a cell is a K of the fluorescently labeled target. offThe method can include using a microscope system configured to determine whether a protein of interest is reduced, the system including: (a) a stage for supporting a sample, the sample including a population of cells, the cells including fluorescent target proteins; the system further including: (b) a light source 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 plane of the sample; a subset of the fluorescent target proteins in the sample being positioned within a field of view in the plane of the sample; the subset of fluorescent target proteins including proteins in a range of about 1,000 to about 1,000,000; the number of proteins in the subset being dependent on the expression level of the protein of interest and a dye concentration deemed appropriate for labeling the subset proteins for robust SMT, both of which are dependent on the expression level of the protein of interest and a dye concentration deemed appropriate for labeling the subset proteins for robust SMT. The system can be calculated and / or constructed by one skilled in the art based on the disclosure of the present application, and further includes (d) a detector device that monitors a light-based response from 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 other than 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 individual fluorescent target proteins, wherein the tracking is adapted to selectively detect localized fluorescence versus dynamic fluorescence, and the system further includes (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 compared to the absence of the compound.

[0249] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow determines whether a compound that induces a change in binding of a fluorescent target protein in a cell is a K of the fluorescently labeled target. offThe present invention can include the use of a microscope system configured to determine whether a compound reduces a certain level of ... (i) blocking light received from a light source other than the sample plane where the light is being detected, thereby tracking the position of the fluorescent target proteins; and (ii) tracking the movement of individual fluorescent target proteins, wherein the tracking is adapted to selectively detect localized fluorescence relative to dynamic fluorescence, and the average change in the movement of the fluorescent target proteins in the presence of the compound is about 5% to about 10% compared to baseline movement under DMSO conditions. The system further includes (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 compared to the absence of the compound.

[0250] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow determines whether a compound that induces a change in binding of a fluorescent target protein in a cell is a K of the fluorescently labeled target. offThe present invention can include the use of a microscope system configured to determine whether a compound reduces a light-based response from the fluorescent target proteins, the system including: (a) a stage for supporting a sample, the sample including a population of cells, the cells including fluorescent target proteins; the system further including: (b) a light source 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 plane of the sample, wherein a subset of the fluorescent target proteins in the sample is positioned within a field of view of the plane of the sample; and the system further including: (d) a detector device for monitoring the light-based response from the fluorescent target proteins in the presence of a compound, the detector device detecting (i) the fluorescent target proteins; and (ii) blocking light received from a light source other than the sample plane where the protein is located, thereby tracking the position of the fluorescent target proteins; and (ii) tracking the movement of individual fluorescent target proteins, wherein the tracking is adapted to selectively detect localized fluorescence versus dynamic fluorescence; the detector device is further configured to (iii) achieve a Z-factor of greater than 0.5 based on a single field of view; and the system further includes (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 compared to the absence of the compound.

[0251] 4.3.OLS Kinetic SMT Because SMT can identify the rate of biological interaction between a compound and a target, it can be used to distinguish between direct and indirect effects on target activity, among other parameters. Given the live-cell setting of SMT, a data collection mode (kinetic SMT or kSMT) can be configured that allows protein movement after compound addition to be measured at set intervals to determine the rate of biological interaction between the compound and the target.

[0252] Exemplary OLS kinetic SMT workflows include the following individual strategies and combinations of the following strategies that combine two or more strategic requirements: For example, but not by way of limitation, the disclosed workflow includes both illuminating a field of view in a sample plane disposed within a sample with a light beam to induce fluorescence by a subset of fluorescent target proteins in live cells, the subset of fluorescent target proteins being present in about 30 to about 80 live cells illuminated within the field of view in the sample plane, and illuminating a field of view in a sample plane disposed within the sample with a light beam to induce fluorescence by a subset of fluorescent target proteins in live cells, the subset of fluorescent target proteins comprising a range of about 1,000 to about 1,000,000 proteins. Similarly, illuminating the sample plane to illuminate about 30 to about 80 live cells per FOV and / or generating fluorescence from about 1,000 to about 1,000,000 proteins can be combined with any of the other strategic requirements disclosed herein, such as determining that the average change in fluorescent target protein motion in the presence of a compound is about 5% to about 10% compared to baseline motion under DMSO conditions, detecting fluorescence from multiple fluorescent target proteins within the field of view of the sample plane at a rate of about 10,000 to about 18,000 per day per system, and achieving a Z-factor greater than 0.5 based on a single field of view.

[0253] In certain embodiments of the OLS kinetic htSMT binding workflow described herein, the workflow can include identifying an incidence of biological interactions between a compound and a target in live cells, the workflow including (a) contacting a sample including a population of live cells with a compound, the live cells including a fluorescent target protein, the workflow further including (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample at multiple time points, the tracking including (i) illuminating with a light beam a field of view in a sample plane disposed within the sample to cause fluorescence by a subset of the target fluorescent proteins in the live cells, the fluorescent target proteins The subset of cells is present in approximately 30 to approximately 80 illuminated live cells within the field of view of the sample plane, depending on the specific cell type used. For example, for U2OS cells, the range is approximately 30 to approximately 40 cells per FOV, while for HCT116 cells, the range is approximately 50 to approximately 80 cells, taking into account differences in area. The tracking further includes (ii) detecting fluorescence from multiple fluorescent target proteins within the field of view of the sample plane via a detector device. The workflow further includes (c) determining changes in the movement of the fluorescent target proteins in the presence of a compound, where the rate at which changes in the movement of the fluorescent target proteins occur in the presence of the compound indicates the rate of occurrence of biological interactions between the compound and the target.

[0254] In certain embodiments of the OLS kinetic htSMT binding workflow described herein, the workflow can include identifying a rate of occurrence of a biological interaction between a compound and a target in live cells, the workflow including (a) contacting a sample including a population of live cells with a compound, the live cells including a fluorescent target protein, the workflow further including (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample at multiple time points, the tracking including (i) illuminating with a light beam a field of view in a sample plane disposed within the sample to induce fluorescence by a subset of target fluorescent proteins in the live cells, the subset of fluorescent target proteins ranging from about 1000 to about 1 The subset includes proteins in the range of 100,000,000, where the number of proteins in the subset depends on the expression level of the protein of interest and the dye concentration deemed appropriate for labeling 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 the present application, and the tracking further includes (ii) detecting fluorescence from multiple fluorescent target proteins within a field of view of the sample plane via a detector device, and the workflow further includes (c) determining a change in the movement of the fluorescent target proteins in the presence of a compound, where the rate at which the change in the movement of the fluorescent target proteins occurs in the presence of the compound indicates the occurrence rate of biological interaction between the compound and the target.

[0255] In certain embodiments of the OLS kinetic htSMT binding workflow described herein, the workflow can include identifying a rate of occurrence of a biological interaction between a compound and a target in live cells, the workflow including: (a) contacting a sample including a population of live cells with a compound, the live cells including a fluorescent target protein; and (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample at multiple time points, the tracking including: (i) illuminating a field of view in a sample plane disposed within the sample with a light beam to induce fluorescence by a subset of the target fluorescent proteins in the live cells; and (ii) detecting fluorescence from the plurality of fluorescent target proteins in the field of view in the sample plane via 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 under DMSO conditions is about 5% to about 10% compared to baseline movement in the absence of the compound; and the rate at which the change in the movement of the fluorescent target proteins in the presence of the compound indicates a rate of occurrence of a biological interaction between the compound and the target.

[0256] In certain embodiments of the OLS kinetic htSMT combined workflow described herein, the workflow can include identifying the incidence of compound-target biological interactions among direct and indirect biological interactions between a compound and a fluorescent target protein in live cells, the workflow including (a) contacting a sample including a population of live cells with a compound, the live cells including the fluorescent target protein, the workflow further including (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample at multiple time points, the tracking including (i) tracking the movement of a subset of the target fluorescent proteins in the live cells. (ii) illuminating a field of view in a sample plane disposed within the sample with a light beam to induce fluorescence; and (iii) detecting fluorescence from a plurality of fluorescent target proteins within the field of view of the sample plane via a detector device. (iii) tracking the fluorescence from the plurality of fluorescent target proteins within the field of view of the sample plane at a rate of about 10,000 to about 18,000 per day per system. The workflow further includes (c) determining a change in the movement of the fluorescent target proteins in the presence of a compound, wherein the rate at which the change in the movement of the fluorescent target proteins occurs in the presence of the compound indicates the occurrence rate of a biological interaction between the compound and the target.

[0257] In certain embodiments of the OLS kinetic htSMT binding workflow described herein, the workflow can include identifying a rate of occurrence of a biological interaction between a compound and a target in live cells, the workflow including: (a) contacting a sample including a population of live cells with a compound, the live cells comprising a fluorescent target protein; and (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample at multiple time points, the tracking including: (i) illuminating with a light beam a field of view in a sample plane disposed within the sample to cause fluorescence by a subset of the target fluorescent proteins in the live cells; and (ii) detecting, via a detector device, fluorescence from the plurality of fluorescent target proteins in the field of view in the sample plane, wherein detection based on a single field of view is associated with a Z-factor of greater than 0.5; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound, wherein a rate at which the change in the movement of the fluorescent target protein occurs in the presence of the compound indicates a rate of occurrence of the biological interaction between the compound and the target.

[0258] In certain embodiments of the OLS kinetic htSMT combined workflow described herein, the workflow can include determining a dose of a compound that induces a change in the movement of a fluorescent target protein in a live cell, the workflow including (a) contacting a plurality of samples with a compound, (i) each sample including a population of live cells, (ii) the live cells including a fluorescent target protein, and (iii) the plurality of samples being contacted with different concentrations of the compound over a range of compound concentrations, the workflow further including (b) tracking the movement of individual target fluorescent proteins in a plurality of live cells of the sample at a plurality of time points, the tracking including (i) illuminating with a light beam a field of view in a sample plane located within the sample to induce fluorescence by at least a subset of the fluorescent target proteins in the live cells, The protein subset is present in approximately 30 to approximately 80 live cells illuminated within a field of view of the sample plane, depending on the specific cell type used. For example, for U2OS cells, the range is approximately 30 to approximately 40 per FOV, while for HCT116 cells, the range is approximately 50 to approximately 80 per FOV, taking into account differences in area. The tracking further includes (ii) detecting fluorescence from one or more of the fluorescent target proteins within the sample plane via a detector device. The workflow further includes (c) determining the rate at which a change in the movement of the fluorescent target protein occurs in the presence of the compound; and (d) repeating steps (b) to (c) for each of a plurality of samples over a range of compound concentrations, wherein the rate at which a change in the movement of the fluorescent target protein occurs in the presence of the compound indicates the rate of occurrence of a biological interaction between the compound and the target.

[0259] In certain embodiments of the OLS kinetic htSMT combined workflow described herein, the workflow can include determining a dose of a compound that induces a change in the movement of a fluorescent target protein in a live cell, the workflow including: (a) contacting a plurality of samples with a compound, (i) each sample including a population of live cells; (ii) the live cells including a fluorescent target protein; and (iii) the plurality of samples being contacted with different concentrations of the compound over a range of compound concentrations; the workflow further including: (b) tracking the movement of individual target fluorescent proteins in a plurality of live cells of the sample at multiple time points; the tracking including: (i) illuminating with a light beam a field of view in a sample plane disposed within the sample to induce fluorescence by at least a subset of the fluorescent target proteins in the live cells; The subset may include proteins ranging from 0 to approximately 1,000,000, where the number of proteins in the subset depends on the expression level of the protein of interest and the dye concentration deemed appropriate for labeling the subset proteins for robust SMT, both of which can be calculated and / or configured by one of ordinary skill in the art based on the disclosure of the present application. The tracking further includes (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device, where the workflow further includes (c) determining a rate at which a change in the motion of the fluorescent target 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, where the rate at which a change in the motion of the fluorescent target protein occurs in the presence of the compound indicates a rate of occurrence of a biological interaction between the compound and the target.

[0260] In certain embodiments of the OLS kinetic htSMT combined workflow described herein, the workflow can include determining a dose response of a compound that induces a change in the movement of a fluorescent target protein in a live cell, the workflow including: (a) contacting a plurality of samples with a compound, (i) each sample including a population of live cells; (ii) the live cells including a fluorescent target protein; and (iii) the plurality of samples being contacted with different concentrations of the compound over a range of compound concentrations; the workflow further including: (b) tracking the movement of individual target fluorescent proteins in a plurality of live cells of the samples at multiple time points; and (ii) detecting fluorescence from one or more fluorescent target proteins in the sample plane via a detector device; the workflow further includes (c) determining a rate at which a change in the motion of the fluorescent target protein occurs in the presence of the compound, wherein the average change in the motion of the fluorescent target protein in the presence of the compound is about 5% to about 10% compared to baseline motion under DMSO conditions; and (d) repeating steps (b)-(c) for each of a plurality of samples across a range of compound concentrations, wherein the rate at which the change in the motion of the fluorescent target protein occurs in the presence of the compound indicates a rate of occurrence of a biological interaction between the compound and the target.

[0261] In certain embodiments of the OLS kinetic htSMT combined workflow described herein, the workflow can include determining a dose response of a compound that induces a change in the movement of a fluorescent target protein in a live cell, the workflow including: (a) contacting a plurality of samples with a compound, (i) each sample including a population of live cells; (ii) the live cells including a fluorescent target protein; and (iii) the plurality of samples being contacted with different concentrations of the compound over a range of compound concentrations; the workflow further including: (b) tracking the movement of individual target fluorescent proteins in a plurality of live cells of the samples at multiple time points; (ii) illuminating with a light beam a field of view in the plane of a sample disposed within the system; and (ii) detecting via a detector device fluorescence from one or more of the fluorescent target proteins in the plane of the sample; (iii) said tracking includes detecting fluorescence from a plurality of fluorescent target proteins in the field of view of the plane of the sample at a rate of about 10,000 to about 18,000 per day per system; the workflow further includes (c) determining a rate at which a change in the motion of the fluorescent target protein occurs in the presence of a 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 a change in the motion of the fluorescent target protein occurs in the presence of the compound indicates a rate of occurrence of a biological interaction between the compound and the target.

[0262] In certain embodiments of the OLS kinetic htSMT combined workflow described herein, the workflow can include determining a dose of a compound that induces a change in the movement of a fluorescent target protein in a live cell, the workflow including: (a) contacting a plurality of samples with a compound, (i) each sample including a population of live cells; (ii) the live cells including a fluorescent target protein; and (iii) the plurality of samples being contacted with different concentrations of the compound over a range of compound concentrations; and the workflow further including: (b) tracking the movement of individual target fluorescent proteins in a plurality of live cells of the samples at a plurality of time points, wherein the tracking includes: (i) determining a change in the movement of at least one fluorescent target protein in the live cells. (ii) illuminating with a light beam a field of view in a sample plane disposed within the sample so as to cause fluorescence from at least a subset of the fluorescent target proteins; and (ii) detecting via a detector device fluorescence from one or more of the fluorescent target proteins within the field of view in the sample plane, wherein detection based on a single field of view is associated with a Z-factor of greater than 0.5; and (c) determining a rate at which a change in the motion of the fluorescent target proteins occurs in the presence of the compound; and (d) repeating steps (b)-(c) for each of a plurality of samples across a range of compound concentrations, wherein the rate at which a change in the motion of the fluorescent target proteins occurs in the presence of the compound indicates a rate of occurrence of a biological interaction between the compound and the target.

[0263] In certain embodiments of the OLS kinetic htSMT binding workflow described herein, the workflow can include the use of a microscopy system configured to determine the incidence of biological interactions between a compound and a target in live cells, the system including: (a) a stage supporting a sample, the sample including a population of live cells, the live cells including fluorescent target proteins; the system further including: (b) a light source 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 focusing the light beam onto the sample in a plane of the sample, wherein a subset of the fluorescent target proteins in the sample is positioned within a field of view in the sample plane, the subset of fluorescent target proteins being present in about 30 to about 80 live cells illuminated within the field of view in the sample plane, depending on the particular cell type used, e.g., For U2OS cells, the range is about 30 to about 40 cells per FOV, while for HCT116 cells, the range is about 50 to about 80 cells, taking into account the difference in area. The system further includes (d) a detector device that monitors a light-based response from the fluorescent target protein in the presence of a compound, the detector device being configured to (i) block light received from a light source other than 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 individual fluorescent target proteins. The system further includes (e) a memory and (f) a processor in communication with the memory and the detector device, the processor being capable of determining a change in the movement of the fluorescent target protein in the presence of the compound compared to the absence of the compound.

[0264] In certain embodiments of the OLS kinetic htSMT binding workflow described herein, the workflow can include use of a microscopy system configured to determine the incidence of biological interactions between a compound and a target in live cells, the system including: (a) a stage supporting a sample, the sample including a population of cells, the cells including fluorescent target proteins; the system further including: (b) a light source 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 that focuses the light beam onto the sample in a plane of the sample; a subset of the fluorescent target proteins in the sample is positioned within a field of view in the plane of the sample; the subset of fluorescent target proteins includes proteins in a range of about 1,000 to about 1,000,000; the number of proteins in the subset is determined based on the expression level of the protein of interest and the robust S and a dye concentration deemed appropriate for labeling the subset proteins for MT, both of which can be calculated and / or configured by one of ordinary skill in the art based on the disclosure of the present application; the system further includes (d) a detector device that monitors a light-based response from the fluorescent target proteins in the presence of the compound, the detector device being configured to (i) block light received from a light source other than the sample plane where the fluorescent target proteins are located, thereby tracking the positions of the fluorescent target proteins at multiple time points, and (ii) track the movement of individual fluorescent target proteins; the system further includes (e) a memory and (f) a processor in communication with the memory and the detector device, the processor being capable of determining a change in the movement of the fluorescent target proteins in the presence of the compound compared to the absence of the compound.

[0265] In certain embodiments of the OLS kinetic htSMT binding workflow described herein, the workflow can include the use of a microscope system configured to determine the incidence of biological interactions between a compound and a target in live cells, the system including: (a) a stage for supporting a sample, the sample including a population of cells, the cells including a fluorescent target protein; the system further including: (b) a light source 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 plane of the sample, wherein a subset of the fluorescent target proteins in the sample are positioned within a field of view of the sample plane; and the system further including: (d) a microscope system configured to determine the incidence of biological interactions between a compound and a target in live cells, the system including: (a) a stage for supporting a sample, the sample including a population of cells, the cells including a fluorescent target protein; The system further includes a detector device that monitors a light-based response from the fluorescent target protein, the detector device being configured to (i) block light received from a light source other than the sample plane on 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 individual fluorescent target proteins, wherein the average change in the movement of the fluorescent target protein in the presence of the compound is about 5% to about 10% compared to baseline movement under DMSO conditions, and the system further includes (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 compared to the absence of the compound.

[0266] In certain embodiments of the OLS kinetic htSMT binding workflow described herein, the workflow can include the use of a microscope system configured to determine the incidence of biological interactions between a compound and a target in living cells, the system including: (a) a stage supporting a sample, the sample including a population of cells, the cells including a fluorescent target protein; the system further including: (b) a light source 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 focusing the light beam onto the sample in a plane of the sample, the plurality of fluorescent target proteins in the sample being disposed in the plane of the sample; and (d) an objective lens focusing the light beam onto the sample in a plane of the sample, the plurality of fluorescent target proteins in the sample being disposed in the plane of the sample; The system further includes a detector device configured to (i) block light received from a light source other than the sample plane where the fluorescent target proteins are located, thereby tracking the positions of the fluorescent target proteins at multiple time points, and (ii) track the movement of individual fluorescent target proteins, wherein the tracking includes detecting fluorescence from the plurality of fluorescent target proteins within a field of view of the sample plane at a rate of about 10,000 to about 18,000 per day per system, and the system further includes (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 a compound compared to the absence of the compound.

[0267] In certain embodiments of the OLS kinetic htSMT binding workflow described herein, the workflow can include the use of a microscope system configured to determine the incidence of biological interactions between a compound and a target in live cells, the system including: (a) a stage for supporting a sample, the sample including a population of live cells, the live cells including a fluorescent target protein; the system further including: (b) a light source 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 plane of the sample, wherein a subset of the fluorescent target proteins in the sample are positioned within a field of view of the sample plane; and the system further including: (d) a microscope system configured to determine the incidence of biological interactions between a compound and a target in live cells, the system including: (a) a stage for supporting a sample, the sample including a population of live cells, the live cells including a fluorescent target protein; The system further includes a detector device that monitors a light-based response from the fluorescent target protein in the presence of the compound, the detector device being configured to (i) block light received from a light source other than the sample plane where the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein at multiple time points, (ii) track the movement of individual fluorescent target proteins, and (iii) achieve a Z-factor of greater than 0.5 based on a single field of view, the system further including (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 compared to the absence of the compound.

[0268] 5. Exemplary Embodiments A. The present disclosure provides a method, the method comprising: receiving an image sequence visualizing the motion of the molecule; Linking molecules between images; generating possible trajectories for each molecule with associated probabilities using a variational Bayesian optimization algorithm and based on linking; providing data characterizing the generated possible trajectories with associated probabilities to a consuming application or process; Includes.

[0269] B. The present disclosure provides a method, the method comprising: receiving an image sequence visualizing the motion of the molecule; Linking molecules between images; generating possible trajectories for each molecule with associated probabilities using a Gibbs sampling algorithm and based on linking; providing data characterizing the generated possible trajectories with associated probabilities to a consuming application or process; Includes.

[0270] C. The present disclosure provides a method, the method comprising: receiving an image sequence visualizing the motion of the molecule; Linking molecules between images; generating possible trajectories for each molecule with associated probabilities using an adaptive hill-climbing algorithm and based on linking; providing data characterizing the generated possible trajectories with associated probabilities to a consuming application or process; Includes.

[0271] C1. The method of any of A-C, wherein at least a subset of the image sequences comprises at least 100 molecules per image.

[0272] C2. The method of any of A-C1, wherein at least a subset of the image sequences comprises at least 1000 molecules per image.

[0273] C3. The method of any of A-C2, wherein at least a subset of the image sequences comprises at least 10,000 molecules per image.

[0274] C4. The method of any of A-C3, wherein the molecules have a density of at least 0.01 emitters per square micron per image.

[0275] C5. The method of any of A-C4, wherein the molecules have a density of at least 0.1 emitters per square micron per image.

[0276] C6. Labeling molecules in a biological sample; causing the biological sample to emit fluorescence; generating an image sequence while the biological sample is fluorescing; The method according to any one of A to C5, further comprising:

[0277] C7. The method of C6, wherein generating the image sequence is performed using a microscope system.

[0278] C8. The method of any one of A-C7, wherein the molecule is imaged in a living cell.

[0279] C9. Inferring a probabilistic dynamic model containing information characterizing the trajectories of molecules; The method according to any one of A to C8, further comprising:

[0280] C10. The method of C9, wherein the stochastic dynamic model includes a state array, and the method further includes populating the state array with information characterizing the trajectories of the molecules.

[0281] C11. The method of any of A-C10, further comprising generating an internal metric of confidence based on the associated probability, wherein the provided data includes the generated internal metric of confidence.

[0282] C12. The method of C11, wherein the generated internal confidence metric is a tracking error rate floor that defines a floor for the rate of misconnections made by linking.

[0283] C13. The generated internal metrics are Calculating the confidence level of each trajectory; The method according to C11, comprising:

[0284] C14. Generating dynamic metrics independent of specific trajectories; The method according to any one of A to C13, further comprising:

[0285] C15. The method of any one of A to C14, wherein the linking comprises obtaining data having a plurality of statistical data extracted from the total number of detections or the number of detections in a cell.

[0286] C16. A method according to any of A-C15, wherein providing the data includes one or more of: visualizing at least some of the generated possible trajectories with associated probabilities in a graphical user interface; storing at least some of the generated possible trajectories with associated probabilities in a physical persistent state; loading at least some of the generated possible trajectories with associated probabilities into memory; or transmitting at least some of the generated possible trajectories with associated probabilities to a remote computing device over a network.

[0287] C17. The method of any of A-C16, wherein at least some of the image sequences include consecutive images from a corresponding video.

[0288] C18. The method of any of A-C17, wherein at least some of the image sequences used for linking are non-consecutive images from a corresponding video.

[0289] D. The present disclosure provides a method for single molecule tracking, the method comprising: receiving an image sequence visualizing the movement of the molecule, the image sequence including a first type generated using a first imaging modality and a second type generated using a second, different imaging modality, the method further comprising: Detecting spots in a sequence of images of a first type; linking the detected spots in the sequence of images of the first type to trajectories using a probabilistic tracking algorithm; Segmenting the second type of image sequence to generate a plurality of instance masks; assigning molecules in the second type of image sequence to at least one instance mask of the plurality of instance masks; providing data characterizing the links and allocations to a consuming application or process; Includes.

[0290] D1. The method of D, wherein the probabilistic tracking algorithm comprises a variational Bayesian optimization algorithm.

[0291] D2. The method of D, wherein the probabilistic tracking algorithm includes a Gibbs sampling algorithm.

[0292] D3. The method of D, wherein the partially probabilistic tracking algorithm includes an adaptive hill-climbing algorithm.

[0293] D4. The method according to any one of D to D3, wherein the first imaging modality and the second imaging modality comprise different molecular labeling techniques.

[0294] D5. A method according to any one of D to D4, wherein the first type of image sequence is a single molecule tracking (SMT) video and the second type of image sequence is a non-SMT video.

[0295] D6. The method according to any one of D to D5, wherein the detected spots contain intracellular components.

[0296] D7. The method of any of D-D6, wherein types of molecules in the first type of image sequence are labeled with distinct fluorophores.

[0297] D8. The method of any of D-D7, wherein at least a subset of the image sequences comprises at least 100 molecules per image.

[0298] D9. The method of any of D-D8, wherein at least a subset of the image sequences comprises at least 1000 molecules per image.

[0299] D10. The method of any of D-D9, wherein at least a subset of the image sequences comprises at least 10,000 molecules per image.

[0300] D11. The method of any of D-D10, wherein the molecules have a density of at least 0.01 emitters per square micron per image.

[0301] D12. The method of any one of D-D11, wherein the molecules have a density of at least 0.1 emitters per square micron per image.

[0302] D13. Labeling molecules in biological samples; causing the biological sample to emit fluorescence; generating at least a portion of the image sequence while the biological sample is fluorescing; The method according to any one of D to D12, further comprising:

[0303] D14. The method according to D13, wherein the generation of the image sequence is performed using a microscope system.

[0304] D15. The method according to any one of D to D14, wherein the molecule is imaged in a living cell.

[0305] D16. Inferring a probabilistic dynamic model containing information characterizing the trajectories of molecules; The method according to any one of D to D15, further comprising:

[0306] D17. The stochastic dynamic model includes a state sequence, and the method further comprises: The method of D16, comprising populating the state array with information characterizing the trajectory of the molecule.

[0307] D18. The method of any of D-D17, further comprising generating an internal metric of confidence based on the associated probability, wherein the provided data includes the generated internal metric of confidence.

[0308] D19. The method of D18, wherein the generated internal metric of confidence is a tracking error rate lower bound (ERLB) that defines a lower bound on the rate of misconnections made by linking.

[0309] D20. Generated internal metrics are Calculating the confidence level of each trajectory; The method according to D18, comprising:

[0310] D21. Generating dynamic metrics independent of specific trajectories; The method according to any one of D to D20, further comprising:

[0311] D22. The method according to any one of D to D21, wherein the linking comprises obtaining data having a plurality of statistical data extracted from the total number of detections or the number of detections in a cell.

[0312] D23. A method according to any of D-D22, wherein providing the data includes one or more of: visualizing at least some of the generated possible trajectories with associated probabilities in a graphical user interface; storing at least some of the generated possible trajectories with associated probabilities in a physical persistent state; loading at least some of the generated possible trajectories with associated probabilities into memory; or transmitting at least some of the generated possible trajectories with associated probabilities over a network to a remote computing device.

[0313] D24. Generating a plurality of statistical metrics associated with at least one of the trajectories or at least one instance mask; The method according to any one of D to D23, further comprising:

[0314] D25. Remembering the hierarchy of instance masks, The method of D24, further comprising:

[0315] D26. A method according to any one of D-D25, wherein at least a portion of the image sequence comprises consecutive images from a corresponding video.

[0316] D27. A method according to any one of D-D26, wherein at least some of the image sequences used for linking are non-consecutive images from corresponding moving images.

[0317] D28.Detecting A method according to any of D-D27, utilizing one or more of a generalized log-likelihood ratio spot detector, a difference of Gaussians (DoG) detector, a Laplacian of Gaussians (LoG) detector, or a Hessian determinant (DoH) blob detector.

[0318] D29. Associating detected spots with spatiotemporal coordinates using sub-pixel localization; The method according to any one of D to D28, further comprising:

[0319] D30.Subpixel Localization The method described in D29, including one or more of a radially symmetric localizer, or maximum likelihood fitting to a candidate spot model using the Levenberg-Marquardt method.

[0320] D31. The method of any of D-D30, wherein the field of view corresponds to at least a portion of a well.

[0321] D32. The image sequence is generated by an apparatus for fluorescence microscopy, the apparatus a first optical element or assembly configured to receive a fluorescence excitation light source and configured to generate a collimated light beam having an elongated linear shape in the xy plane, the light beam having a uniform intensity across a longer dimension of the linear shape; a second optical element or assembly configured to tilt the light beam relative to the z-axis in the xz-plane, the second optical element further configured to focus the light beam onto a sample surface located in the xy-plane, thereby illuminating a portion of the sample surface; a third optical element or assembly configured to translate the light beam in the sample plane in a direction orthogonal to the longer dimension of the light beam; a detector device configured to receive light from the illuminated sample surface, the detector device forming one or more projection images based on the light received from the sample surface. A method according to any one of A to D31.

[0322] D33. The method of D32, wherein the first optical component element or assembly includes a Powell lens that generates a collimated light beam having an elongated linear shape in the xy plane.

[0323] D34. The method of D32 or D33, wherein the first optical component element or assembly includes one or more diffraction gratings that generate a collimated light beam having an elongated linear shape in the xy plane.

[0324] D35. The method of any of D32-D34, wherein the first optical component element or assembly includes a combination of lenses that produces a collimated light beam having an elongated linear shape in the xy plane.

[0325] D36. The method of any of D32-D35, wherein the second optical component element or assembly includes an objective lens.

[0326] D37. The method of any of D32-D36, wherein the third optical component element or assembly includes a galvo mirror configured to translate the light beam in the sample plane in a direction orthogonal to the longer dimension of the light beam.

[0327] D38. The method of any of D32-D37, wherein the third optical component element or assembly includes a piezoelectric element configured to translate the light beam in the sample plane in a direction orthogonal to the longer dimension of the light beam.

[0328] D39. The method of any of D32-D38, wherein the detector device includes a semiconductor sensor and the detector device supports a shutter mode for synchronizing translation of the light beam in the sample plane with selective activation or readout of the semiconductor sensor.

[0329] D40. The image sequence is generated by a microscope system for detecting the position of a molecule, and the microscope system a stage for supporting a sample, the sample including molecules, and the microscope system further comprising: a light source that emits a light beam capable of inducing a light-based response from molecules in the sample, the light beam having a linear shape in the sample plane and a uniform intensity across a longer dimension of the linear shape in the sample plane; an objective lens for focusing a light beam onto the sample in a sample plane, the molecule being disposed at the sample plane, the microscope system further comprising: a detector device that monitors a light-based response from the molecule to thereby detect the position of the molecule; A method according to any one of A to D31.

[0330] D41. The method of D40, wherein the microscope system further includes a scanning optical element or assembly configured to enable the light beam in the sample plane to be translated in a direction orthogonal to the longer dimension of the light beam, thereby expanding the total field of view of the microscope system in the xy plane.

[0331] D42. The method of D41 or D42, wherein the detector device includes a solid-state sensor, and the detector device supports a shutter mode for synchronizing translation of the light beam at the sample plane with selective activation or readout of the solid-state sensor.

[0332] D43. The method of any of D40-D42, wherein the detector device supports a shutter mode for synchronizing translation of the light beam in the sample plane with selective activation or readout of the semiconductor sensor.

[0333] D44. The method according to any one of D40 to D43, wherein the sample is placed in an open well of a microplate.

[0334] D45. The method of D44, wherein the microplate comprises a plurality of open wells.

[0335] D46. The microscope system further comprises: an xy position controller for varying a field of view of the microscope system, the varied field of view encompassing a different subset of the plurality of open wells; The method according to any of D44 or D45.

[0336] D47. The microscope system further includes an automated sample handling robotic system that allows high-throughput manipulation of multiple samples on a stage, the robotic system comprising: Memory and a processor in communication with the memory; one or more robotic end effectors in communication with the processor, the one or more end effectors manipulating the plurality of specimens on the stage based on communication with the processor; A method according to any one of D44 to D46.

[0337] E. The present disclosure provides a system, the system comprising: at least one data processor; a memory storing instructions that, when executed by at least one data processor, result in operations for implementing the method of any of A-D31; Includes.

[0338] E1. Further comprising an apparatus for fluorescence microscopy, the apparatus comprising: a first optical element or assembly configured to receive a fluorescence excitation light source and configured to generate a collimated light beam having an elongated linear shape in the xy plane, the light beam having a uniform intensity across a longer dimension of the linear shape; a second optical element or assembly configured to tilt the light beam relative to the z-axis in the xz-plane, the second optical element further configured to focus the light beam onto a sample surface located in the xy-plane, thereby illuminating a portion of the sample surface; a third optical element or assembly configured to translate the light beam in the sample plane in a direction orthogonal to the longer dimension of the light beam; a detector device configured to receive light from the illuminated sample surface, the detector device forming one or more projection images based on the light received from the sample surface. The system described in E.

[0339] E2. The system of E1, wherein the first optical component element or assembly includes a Powell lens that generates a collimated light beam having an elongated linear shape in the xy plane.

[0340] E3. The system of E1 or E2, wherein the first optical component element or assembly includes one or more diffraction gratings that generate a collimated light beam having an elongated linear shape in the xy plane.

[0341] E4. A system according to any one of E1-E3, wherein the first optical component element or assembly includes a combination of lenses that produces a collimated light beam having an elongated linear shape in the xy plane.

[0342] E5. The system of any one of E1-E4, wherein the second optical component element or assembly includes an objective lens.

[0343] E6. The system of any of E1-E5, wherein the third optical component element or assembly includes a galvo mirror configured to translate the light beam in the sample plane in a direction orthogonal to the longer dimension of the light beam.

[0344] E7. The system of any of E1-E6, wherein the third optical component element or assembly includes a piezoelectric element configured to translate the light beam in the sample plane in a direction perpendicular to the longer dimension of the light beam.

[0345] E8. A system according to any one of E1 to E7, wherein the detector device includes a semiconductor sensor, and the detector device supports a shutter mode for synchronizing translation of the light beam at the sample plane with selective activation or readout of the semiconductor sensor.

[0346] E9. Further comprising a microscope system for detecting the location of the molecule, the microscope system comprising: a stage for supporting a sample, the sample including molecules, and the microscope system further comprising: a light source that emits a light beam capable of inducing a light-based response from molecules in the sample, the light beam having a linear shape in the sample plane and a uniform intensity across a longer dimension of the linear shape in the sample plane; an objective lens for focusing a light beam onto the sample in a sample plane, the molecule being disposed at the sample plane, the microscope system further comprising: a detector device that monitors a light-based response from the molecule to thereby detect the position of the molecule; The system described in E.

[0347] E10. The system of E9, wherein the microscope system further includes a scanning optical element or assembly configured to translate the light beam in the sample plane in a direction perpendicular to a longer dimension of the light beam, thereby enabling an expansion of the total field of view of the microscope system in the xy plane.

[0348] E11. The system of E9 or E10, wherein the detector device includes a solid-state sensor, and the detector device supports a shutter mode for synchronizing translation of the light beam at the sample plane with selective activation or readout of the solid-state sensor.

[0349] E12. The system of any of E9-E11, wherein the detector device supports a shutter mode for synchronizing translation of the light beam at the sample plane with selective activation or readout of the semiconductor sensor.

[0350] E13. The system of any one of E79 to E82, wherein the sample is placed in an open well of a microplate.

[0351] E14. The system of E13, wherein the microplate comprises a plurality of open wells.

[0352] E15. The microscope system further includes: an xy position controller for varying a field of view of the microscope system, the varied field of view encompassing a different subset of the plurality of open wells; The system according to any one of E13 to E14.

[0353] E16. The microscope system further includes: an automated sample handling robotic system that enables high-throughput manipulation of multiple samples on a stage, the robotic system comprising: a memory for storing instructions; at least one data processor; one or more robotic end effectors in communication with the at least one data processor, the one or more end effectors manipulating the plurality of specimens on the stage based on communication with the at least one data processor; The system according to any one of E13 to E15.

[0354] E17. A non-transitory computer program product storing instructions which, when executed by at least one data processor forming part of at least one computing device, perform the method described in any of A-D31.

[0355] F. The present disclosure provides a system, the system comprising: means for receiving an image sequence visualizing the motion of the molecule; a means for linking molecules between images; means for generating possible trajectories for each molecule with associated probabilities based on the linking; means for providing data characterizing the generated possible trajectories with associated probabilities to a consuming application or process; Includes.

[0356] G. The present disclosure provides a system, the system comprising: means for receiving an image sequence visualizing the motion of the molecule; a means for linking molecules between images; means for generating possible trajectories for each molecule with associated probabilities using a variational Bayesian optimization algorithm and based on linking; means for providing data characterizing the generated possible trajectories with associated probabilities to a consuming application or process; Includes.

[0357] H. The present disclosure provides a system, the system comprising: means for receiving an image sequence visualizing the motion of the molecule; a means for linking molecules between images; means for generating possible trajectories for each molecule with associated probabilities using a Gibbs sampling algorithm and based on linking; means for providing data characterizing the generated possible trajectories with associated probabilities to a consuming application or process; Includes.

[0358] I. The present disclosure provides a system, the system comprising: means for receiving an image sequence visualizing the motion of the molecule; a means for linking molecules between images; means for generating possible trajectories for each molecule with associated probabilities using an adaptive hill-climbing algorithm and based on linking; means for providing data characterizing the generated possible trajectories with associated probabilities to a consuming application or process; Includes.

[0359] J. The present disclosure provides a single molecule tracking system, the system comprising: and means for receiving image sequences visualizing the movement of the molecule, the image sequences including a first type generated using a first imaging modality and a second type generated using a second, different imaging modality, the system further comprising: means for detecting spots in a sequence of images of a first type; means for linking detected spots in the sequence of images of the first type to trajectories using a probabilistic tracking algorithm; means for segmenting the second type of image sequence to generate a plurality of instance masks; means for assigning molecules in a sequence of images of a second type to at least one instance mask of a plurality of instance masks; means for providing data characterizing the links and allocations to a consuming application or process; Includes.

[0360] 6. Working Example The presently disclosed subject matter will be better understood by reference to the following examples, which are provided by way of illustration of the presently disclosed subject matter, and not by way of limitation.

[0361] Example 1: Optical Line Scan System A. Preface Single-molecule localization microscopy (SMLM) techniques, such as single-molecule tracking (SMT), enable in situ measurements in live and fixed cells, from which data-rich metrics can be extracted. SMT has been successfully applied to address a variety of biological questions and model systems, aiming to reveal protein function in healthy or pathological states, its impact on downstream pathways, and the spatiotemporal regulation of molecular mechanisms governing cellular function. While powerful, SMLM often suffers from issues such as low throughput, uneven illumination, and technical biases imposed by the microscope and user. Technical limitations in scaling SMLM techniques require tradeoffs between spatial resolution, temporal resolution, and throughput, limiting the use of these techniques to a small number of research groups.

[0362] This example describes the development of the OLS system disclosed herein to overcome limitations of other SMLM techniques. Briefly, a thin optical light sheet is shaped, focused into the back focal plane of a microscope objective, and scanned using a galvanometric mirror. This optical configuration results in a scannable oblique light sheet that can cover the entire FOV of a water-immersion high-NA objective (Figure 13A and Figures 14A-14F). Further details regarding this exemplary OLS system are provided below.

[0363] B. Exemplary OLS System SMT image acquisition of the OLS dataset was performed on a custom-built microscope based on a Nikon Ti2, motorized stage, stage-top environmental chamber (OKO Laboratory), a quad-band filter cube (Chroma), and a custom laser launch at 405 nm, 561 nm, and 642 nm wavelengths, delivering >10 mW, >150 mW, and >150 mW of power output, respectively, to the back focal plane of the objective. The custom laser launch consisted of three externally triggerable free-space laser sources (Cobolt 06-MLD; Huebner Photonics; 2RU-VFL-P-2000-560-M; MBP Communications Inc.; VFL-P-2000-642-M; MBP Communications Inc.).

[0364] An oblique line scanning (OLS, Figures 14A and 14D) unit is attached to the back port of the microscope and provides optical excitation and scanning. The OLS unit receives collimated Gaussian-shaped optical excitation via 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 is directed to a set of two adjustable right-angle prisms, followed by an aspheric achromatic lens, which positions the beam and focuses the scan axis onto a galvanometric scan mirror. The galvanometric scan mirror is adjusted to position the beam 3.8 mm offset relative to the central optical axis of the objective's back focal plane, achieving an illumination light sheet at a 60-degree tilt angle within the sample (Figure 14E).

[0365] Fluorescence emission was collected by a backlit sCMOS camera (ORCA-Fusion BT, Hamamatsu) through a high-speed filter wheel (Satter Instruments). The sCMOS camera was operated in progressive mode with an exposure time of 407 μs with an internal line spacing of 4.87 μs to achieve a virtual rolling slit of approximately 200% of the optical excitation and fluorescence linewidth (Figure 14F). Images were acquired with a 60× 1.27 NA water immersion objective (Nikon). The environmental chamber was set to 37°C, 95% humidity, and 5% CO2.

[0366] System hardware control is achieved with a custom-designed, user-configurable electronic circuit board for software interface, synchronization, and device control. Data acquisition control is achieved with a custom-designed, user-configurable acquisition script in MicroManager and a custom-designed autofocus routine for raster scanning of the 384-well plate (Figure 14B). For downstream registration of trajectories to the nucleus and cytoplasm, one frame each in the Hoechst and Potomac Red channels was collected at the same frame rate.

[0367] C. Consideration In this example, we present OLS, a robust single-objective light-sheet-based illumination and detection modality that achieves nanoscale spatial resolution and submillisecond temporal resolution across a 250 × 190 μm field of view to overcome the limitations of other SMLM techniques. OLS was developed to expand the effective imaging area while homogenizing the SNR across the entire camera chip, generating 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 an inverted microscope equipped with either a water- or oil-immersion high-numerical-aperture (NA) objective and an sCMOS camera with light-sheet mode capability.

[0368] Example 2: OLS High-Throughput Single Molecule Tracking (htSMT) A. Preface 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 inclined laminated optical sheet (HILO) system. This example further describes systems incorporating such OLS htSMT technology, hardware and software associated with such OLS htSMT technology, and methods for using such OLS htSMT technology. For example, the OLS htSMT technology described herein is capable of measuring protein movement in millions of cells per day. The OLS htSMT technology described herein demonstrates specific, robust, and reproducible results. The OLS htSMT technology described herein can be used for a variety of applications, including, but not limited to, traditional drug discovery activities such as screening compound libraries and elucidating SAR. Importantly, the OLS htSMT technology described herein can be used to characterize the contributions of both known and novel pathways to interaction networks, such as protein signaling interaction networks.

[0369] B. Results a. Creation and verification of htSMT system We developed a robotic system capable of handling reagents, collecting high-quality, high-speed SMT image series, and processing the time-sequenced raw images to generate molecular trajectories and extract biologically interesting features within defined cellular compartments (Figure 1). To investigate the performance of our htSMT system, we performed various measurements demonstrating the suitability of the disclosed image acquisition system and workflow for robust htSMT analysis. For example, Figure 3A shows a laser titration experiment demonstrating the relationship between laser power (mW) at the sample and signal-to-noise ratio (SNR) (left panel) and the average SNR at the well level across four image acquisition systems measuring six different 384-well plates per system (right panel). Figure 3C shows a dose-response experiment performed on Halo-tagged proteins using established and well-characterized compounds to assess plate-to-plate and day-to-day reproducibility (top panel), along with the respective EC50 values ​​(bottom panel). Figure 3D shows that the system described herein is configured to capture comparable protein diffusion coefficients per FOV per well; each point represents an individual FOV position averaged per plot for each concentration (top panel); both EC50 and Z-factor are presented (bottom panel). Figure 3E demonstrates the consistency of the data across multiple wells and experiments; each point represents one FOV from 14 independently generated dose-response curves.

[0370] In addition to establishing the suitability of the OLS workflow described herein for robust htSMT analysis, we conducted experiments comparing the OLS-based workflow described herein with a HILO-based approach. For example, comparing the Z-factors associated with the OLS-based data shown in Figures 3D and 3E to data collected using a HILO-based approach clearly demonstrates the improved performance of the OLS-based approach. These differences between the OLS-based and HILO-based approaches are particularly evident in Figure 3B, which shows the difference in spatial SNR heterogeneity between the OLS system of the present disclosure and the HILO-based approach. The top panel compares the spatial standard deviation observed with OLS to the HILO-based approach. The bottom panel shows the difference in FOV between the HILO-based and OLS-based approaches (left image) and a comparison of spatial heterogeneity across the FOV for the HILO-based approach (center image) and the OLS-based approach (right image).

[0371] To demonstrate the improved performance of the OLS-based approach compared to the HILO-based approach, we performed additional experiments. For this comparison, we used a U2OS cell line (Halo-KEAP1) containing a HaloTag genome edit at the amino terminus of the KEAP1 gene. Initial imaging of Halo-KEAP1, sparsely labeled with the rhodamine dye Janelia Fluorophor 549 (JF549), yielded clear single-molecule resolution to which spot detection, localization, and tracking analyses could be applied (Figure 13B). The performance of the OLS system was benchmarked against the HILO implementation. We collected 1.5 seconds of SMT data with both HILO and OLS, and plotted the resulting trajectories (Figure 13D). The average number of trajectories collected across the FOV increased from 25,765 ± 4,838 with HILO to 167,479 ± 46,324 with OLS, consistent with the calculated 6-fold increase in imaging field of view (Figure 13E). SMT data were collected for 1,224 FOVs across the 384-well plate, the average signal-to-noise ratio (SNR) of all spots localized within each pixel of the FOV was calculated, and a spatial SNR map was rendered (Figure 13F). The standard deviation and average SNR per FOV were then summarized across 308 wells for OLS and HILO, respectively. This demonstrated consistency in SNR and improved performance when comparing the two illumination modalities (Figure 13G).

[0372] For HILO, the sample was illuminated for 2 ms by pulsing the excitation laser over a subset of the camera's exposure time. For OLS, given the light sheet scanning rate, it was calculated that each fluorophore was exposed to light for only 400 μs. Given this shorter fluorophore integration time, we expected a more consistent point spread function (PSF) across different diffusion rates. We verified this hypothesis by analyzing the average spot width of KEAP1 with and without KI-696 (Figure 15A). HILO illumination increased the average 2σ radius of single-molecule PSFs by 4.4%, while OLS illumination reduced it to 1.4% (Figures 15B and 15D). While a 400 μs strobe time would have provided a direct comparison of motion blur performance in OLS, we found that within this integration time, HILO did not enable single-molecule detection because the majority of PSFs did not exceed the noise threshold (Figure 15C).

[0373] One of the key advantages offered by OLS is that during scanning of the tilted light sheet, out-of-focus illumination emitters lie outside the strip of pixels 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 adjust the protein labeling density and downstream effects on SNR and PSF detection. This experiment notably captures the expected improvement in sectioning capabilities provided by OLS. A more rapid decrease in the number of detected localizations was observed with HILO, which correlated with a decrease in SNR (Figures 15E and 15F). These results emphasize that under OLS illumination, single PSFs were better detected, regardless of local PSF overlaps that may result from increased dye or protein concentration. Combined with reduced motion blur, OLS offers the ability to track single particles at high density with high resolution.

[0374] To further evaluate the reproducibility of the illumination quality of the disclosed OLS optical system, SMT measurements were performed on four different OLS-equipped microscopes using the automated system described above. Six to seven 384-well plates per microscope were tested, and Halo-KEAP1 was treated with 20 concentrations of KI-696. KI-696 is a small molecule known to inhibit the interaction of KEAP1 with its binding partner, NRF2, thereby increasing the proportion of fast-diffusing Halo-KEAP1. Twelve wells per concentration were randomly replicated across the plate, with six FOVs per well. The mean dose-response profiles per microscope were highly consistent, with a median increase in diffusion of 47–51%, and the resulting median EC50 values ​​ranged from 7.37–8.58 nM across four independent microscopes (Figures 13C and 16A). Comparing the SNR across the mean FOV levels per microscope, the median SNR ranged from 28.08–28.89 across all four microscopes (Figure 16B). No change was observed across subsequent FOVs captured within a single well, suggesting minimal disruption across the well when imaging a particular FOV (Figure 16C). This means that within this series of measurements, there appears to be no effect of location within the well. Furthermore, the effect of larger OLS FOV size on SMT sampling was directly characterized by comparing cropped regions of the same FOV with larger OLS-sized FOVs. A significant increase in variance was observed as the number of captured cells was reduced to an area spanning 83 × 83 μm (Figure 16D).

[0375] C. Method a.Cell line U2OS cells (ATCC Cat. No. HTB-96) are grown in DMEM (Cat. No. 1056601, Gibco DMEM, High Glucose, GlutaMAX Supplement, Thermofisher) supplemented with 10% fetal bovine serum (Cat. No. 16000044, Thermofisher) and 1% penicillin-strep (Cat. No. 15140122, Thermofisher), maintained in a humidified 37°C incubator with 5% CO2, and can be subcultured approximately every 2–3 days.

[0376] b. HaloTag-expressing cell line For specific target-HaloTag fusions, mammalian expression vectors containing the appropriate fusion gene under the control of a weak L30 promoter and a neomycin resistance marker can be transfected into U2OS cells at 70% confluence using FuGENE6 (catalog no. E2691, Promega). Transfected cells can be selected with 500 μg / mL G418 (catalog no. 10131027, Thermo Fisher Scientific) and then clonally isolated. Clones expressing the desired fusion gene can be determined by first staining with 100 nM JF549-HTL (catalog no. GA1110, Promega) and 50 nM Hoechst 33342 to identify clones with the expected distribution of JF549 signal. Many 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.

[0377] To generate specific KEAP1-HaloTag cell lines (e.g., the cell lines used in Figures 16A-16D), ribonucleoprotein (RNP) complexes containing sgRNAs (Integrated DNA Technologies - IDT) targeting either the N- or C-terminal region and the Cas9 protein (PNA bio, catalog number CP01) were transfected with linear dsDNA donors (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, cells were transfected with HaloTag ligand JF. 646 (internal) and imaged using the ImageXpress system (Molecular Devices) to confirm HaloTag integration. Cells were then subjected to single-cell sorting into 384-well plates. Clonal cells were expanded, imaged using the ImageXpress system, and genotyped by Sanger sequencing to confirm uniform HaloTag integration.

[0378] C. Western blot Cells can be grown under the same conditions as previously described. 1.5 x 10 cells per well in DMEM medium in a 6-well plate. 6Cells can be seeded and cultured overnight, followed by compound treatment (DMSO or 100 nM fulvestrant) for 24 hours the following day. Cells can then be lysed in 200 μL of 1X Cell Lysis Buffer (Cat. No. 9803, Cell Signaling). Protein lysate concentrations can then be determined using a BCA Protein Assay Kit (Cat. No. 23225, Pierce™ BCA Protein Assay Kit) according to the manufacturer's instructions. Capillary Western immunoassays can then be performed using Jess Protein Simple according to the manufacturer's instructions (Protein Simple, USA). Anti-target antibody levels can be normalized to the loading control β-tubulin (1:100, NC0244815LI-COR92642213, Thermo Fisher Scientific). Peaks can be analyzed using Compass software (Protein Simple, USA).

[0379] d. OLS single molecule tracking sample preparation Cells can then be seeded into tissue-culture-treated 384-well glass-bottom plates at 4,500–6,000 cells per well. The seeded cells can then be incubated overnight at 37°C and 5% CO2 to allow for attachment. For all SMT experiments, cells were incubated with 5–100 pM JF. 549Cells can be incubated with -HTL (catalog no. GA1110, Promega) and 50 nM Hoechst 33342 in complete medium for 1 hour. Cells are then washed three times with DPBS and twice with imaging medium. The imaging medium is fluorBrite DMEM medium (catalog no. A1896701, Thermo Fisher Scientific) supplemented with GlutaMAX (catalog no. 35050079, Thermo Fisher Scientific) and the same serum and antibiotics as the growth medium. If appropriate, compounds can be serially diluted in Echo-certified 384-well low-dead-volume source microplates (0018544, Beckman Coulter) to generate dose-titrated source material. Compounds can be administered at a final dilution of 1:1000 in cell culture medium. Each compound dose can be replicated at least three times per plate, with up to three plate replicates prepared consecutively. 20 DMSO control wells and two no-dye control wells can be randomized across each plate. Compounds are allowed to incubate at 37° C. for 1 hour before image acquisition.

[0380] e. Image acquisition Unless otherwise noted, all image acquisition using SMT was performed using a custom-built microscope, motorized stage, stage-top environmental chamber, quad-band filter cube (Chroma), and a custom-built laser engine with wavelengths of 405 nm and 561 nm at the back focal plane of the objective. Fluorescence emission was passed through a high-speed filter wheel (Finger Lakes Instruments) and collected by a backlit CMOS camera (Hammamatsu Orca Fusion, running in light sheet mode). Images were acquired with a 60x 1.27NA water-immersion objective (Nikon). The environmental chamber was set to 37°C, 95% humidity, and 5% CO2. In a specific embodiment, the exposure time for each pixel was 400 microseconds, and recording the entire region of interest (ROI) took a total of 9 milliseconds. The galvanometer scanner position was then reset within 1 millisecond, e.g., with the laser turned off, and the next image was recorded. In such an embodiment, 100 frames per second can be recorded. Additionally or alternatively, a second setup using a smaller ROI can be employed to record 200 frames per second with the same 400 microsecond / pixel exposure and 4 millisecond image recording time, allowing for faster galvo resets.

[0381] f. Image analysis Image acquisition yields one JF per field of view 549 A video and one Hoechst were generated. JF 549 The video is from individual JF 549It can be used to track molecular movement, and Hoechst videos can be used for nuclear segmentation. Tracking can be achieved in three sequential steps using a combination of existing methods: detection, subpixel localization, and linking. Briefly, spots can be detected using a generalized log-likelihood ratio detector. After detection, starting from an initial guess obtained by a radial symmetry method, the estimated location of each emitter can be refined to subpixel resolution using Levenberg-Marquardt fitting with a unified 2D Gaussian spot model. Detected spots can be linked into trajectories using a custom modification of a hill-climbing algorithm. The same detection, subpixel localization, and linking setup can be used for all videos.

[0382] For nuclei segmentation, all frames of the Hoechst movie can be averaged to generate an average projection. This average projection can then be segmented with a neural network trained on human-labeled nuclei. Each spot can then be assigned to at most one nucleus using its subpixel coordinates.

[0383] To recover migration information from trajectories, a state array can be used. For example, a Bayesian inference approach can be used using the "RBME" likelihood function and a grid of 100 diffusion coefficients ranging from 0.01 to 100.0 μm²s-1 and 31 localization error magnitudes ranging from 0.02 to 0.08 μm. After inference, the localization error can be minimized to obtain a one-dimensional distribution across the diffusion coefficients for each field of view. For single-cell analysis, for example, SMT and nuclear segmentation can be performed on a mixture of U2OS cells carrying H2B-HaloTag, HaloTag-CaaX, or free HaloTag. The marginal likelihood of each set of 100 diffusion coefficients for 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 ordered by its cluster index to create a heatmap. To estimate the fractional boundary (fbound), a 0.1 μm 2 s -1 The posterior distribution of the state sequence can be integrated below the free diffusion coefficient (D free ) to estimate 0.1 μm 2 s -1 It is possible to calculate the mean of the posterior distribution over

[0384] g. Single molecule tracking method Single-molecule tracking (SMT) data were processed with a custom pipeline operating on image sequences generated by the microscope. Briefly, individual emitters were detected by applying a generalized log-likelihood ratio test to each 11 x 11 subwindow in the image, as described above (see below for the signal-to-noise ratio definition and quantification section). Emitters were detected by identifying pixels with a log-likelihood ratio greater than 14. Detected emitters were localized to subpixel accuracy in a two-step procedure. First, the subpixel location was estimated by calculating the point of maximum radial symmetry. Next, this estimate was used to apply an iterative Levenberg-Marquardt fitting routine to a 2D integrated Gaussian within an 11 x 11 pixel subwindow centered on the detection.

[0385] A modification of Sbalzerini's hill-climbing algorithm, which uses Gibbs sampling to estimate data association uncertainty, can be used to temporally link local emitters to generate trajectories. For all SMT links, links longer than 1.25 μm were prohibited for cSMT, and links longer than two gap frames were also prohibited to limit association error. Emitters were assigned to segmentation categories (nucleus, cytoplasm) by comparing their subpixel locations to the semantic mask generated by the segmentation routine.

[0386] h. Data Analysis The tracking results from the automated processing pipeline can be analyzed using KNIME or Spotfire (TIBCO). bound or D free Measurements of f can be associated with experiment metadata and aggregated by condition. bound The change in f of each well bound f of DMSO in the same plate bound The EC value can be calculated as the difference from the median of the EC. Wells that contained no cells in the field of view or where the field of view was out of focus can be excluded from further analysis. Compounds can be assessed for assay interference using the median fluorescence intensity of the tracking channel and can be excluded if it is more than three standard deviations higher than the median intensity of the DMSO wells. Similarly, plates that fail to clearly separate the active and negative controls or that deviate significantly from the performance of the rest of the screen can be excluded from further analysis. Finally, compounds with a variance more than three standard deviations above the mean compound variance can be excluded from downstream analysis. The Z' coefficient between the active control and DMSO on a plate can be calculated. EC 50 Values ​​can be calculated in Prism (GraphPad) by first log-transforming the molecule concentrations and then fitting them to a four-parameter logistic curve.

[0387] i. Clustering of active molecules Chemical structure-based clustering can be performed on the molecules identified as active. Molecular frameworks can be calculated as known in the art and as implemented in pipeline pilots. Molecular frameworks can be clustered using functional class fingerprints (FCFP_4) (e.g., a similarity threshold cutoff of 0.3 Tanimoto distance).

[0388] j. Exercise experiment Cells can be seeded into 384-well plates the day before and stained and washed as described above. One well with multiple FOVs per well can be taken as a baseline reading. Compounds can then be added manually or robotically to each well to a final concentration of 100 nM during imaging. Data can then be collected for that well. Pauses can be included between each FOV to ensure the entire imaging plan covers the assay window. bound The change in can be determined for each well relative to t=0.

[0389] For assays lasting 4 hours, the plate can be imaged twice using multiple FOVs at different FOV positions per well to prevent photobleaching from affecting the data.

[0390] k. Dwell time imaging Sample preparation and execution of dwell time imaging experiments can be performed in a similar manner to the single molecule tracking assay described above, with a few exceptions. Samples should be 1-10 pMJF. 549 Staining can be performed with Promega (Promega) and 50 nM Hoechst 33342 for 1 hour. Multiple frames can be collected per field of view by setting the camera integration time to the desired time (milliseconds) and reducing the laser light source to the desired mW at the objective. The laser can be turned on continuously during image acquisition. Compound incubation can range from 1 to 4 hours.

[0391] l.Residence time analysis Image processing, including spot detection, localization, and tracking reconnection, may be performed using the same methods described above. Because dwell-time imaging selectively tracks slow-diffusing molecules, individual localizations can be limited to the maximum displacement distance of individual jump reconnections. The set of trajectories for each field of view is binned into a 1-CDF distribution as described above and fitted with a biexponential decay model.

number

[0392] m. Fluorescence recovery after photobleaching Images can be acquired with a custom-built OLS microscope using a Spectra Light Engine RS-232 as described herein, for example, in Example 1. Stimulation can be performed directly using a mini-scanner combined with a Coherent OBIS 561 nm 100 mW laser. All imaging can be performed using a 60x 1.27 NA water immersion objective (Nikon). All experiments were performed at 37 ℃ For FRAP experiments, cells were seeded into 384-well plates the day before and treated with 50 nM HTL-JF. 549 The cells can be labeled with ATP and washed as described above. Compounds can be added to a final concentration of 100 nM before imaging. Pre-bleaching images can then be obtained by averaging 10 consecutive images. Next, 8-10 regions can be bleached (two background, six-eight cells), and two regions within the cells can be left unbleached. The bleached regions are then bleached at 10% power without scanning. For the next 30 seconds, images can be acquired every 200 ms, then every 1 second for 2 minutes. The background-subtracted average intensity can be measured over time in the region of interest and normalized to the average fluorescence in the baseline image, which can then be normalized to the unbleached region to account for readout-induced photobleaching of the fluorophore. For three biological experiments, data from multiple cells per experiment can be pooled.

[0393] n.HILO microscopy SMT image acquisition of the HILO dataset was performed on a custom-built microscope based on a Nikon Ti2, motorized stage, stage-top environmental chamber (OKO Laboratory), a quad-band filter cube (Chroma), and custom laser launch at 405 nm and 561 nm wavelengths, delivering >10 mW and >150 mW of power to the back focal plane of the objective. 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 with a 60x 1.27 NA 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 a 2 ms strobe laser pulse.

[0394] o.Trajectory measurement When reporting the number of trajectories, singlets (trajectories containing one detection) were excluded because they contribute little information to the dynamic estimates.

[0395] The average diffusion coefficient was calculated using the mean squared displacement method (Dest = MSD2D / 4Δt). This estimator is expected to overestimate the diffusion coefficient by σloc2 / Δt, where σloc2 is the variance of the 1D localization error and Δt is the frame interval.

[0396] To resolve trajectories of multiple dynamic states, the coefficients of a Brownian motion mixture model on a grid of diffusion coefficient and localization error values ​​were inferred using state arrays, a variational Bayesian routine based on Dirichlet process mixtures. The mixture components were selected as the direct product of 100 diffusion coefficients, logarithmically spaced between 0.01 and 100 μm / s, and 31 localization error values, ranging from 0.02 to 0.08 μm (1D standard deviation). Occupancy is reported as the average posterior probability of each diffusion coefficient marginalized over all values ​​of localization error. To ensure reliable inference, we restricted the inference to 10,000 trajectories randomly sampled from ea...

Claims

1. receiving an image sequence visualizing the motion of the molecule; linking molecules between said images; generating possible trajectories for each molecule with associated probabilities using a variational Bayesian optimization algorithm and based on said linking; providing data characterizing the generated possible trajectories with associated probabilities to a consuming application or process; A method comprising:

2. receiving an image sequence visualizing the motion of the molecule; linking molecules between said images; generating possible trajectories for each molecule with associated probabilities using a Gibbs sampling algorithm and based on said linking; providing data characterizing the generated possible trajectories with associated probabilities to a consuming application or process; A method comprising:

3. receiving an image sequence visualizing the motion of the molecule; linking molecules between said images; generating possible trajectories for each molecule with associated probabilities using an adaptive hill-climbing algorithm and based on said linking; providing data characterizing the generated possible trajectories with associated probabilities to a consuming application or process; A method comprising:

4. 10. A method according to any preceding claim, wherein at least a subset of the image sequence comprises at least 100 molecules per image.

5. 10. A method according to any preceding claim, wherein at least a subset of the image sequence comprises at least 1000 molecules per image.

6. 10. A method according to any preceding claim, wherein at least a subset of the image sequence comprises at least 10,000 molecules per image.

7. 10. A method according to any preceding claim, wherein the molecules have a density of at least 0.01 emitters per square micron per image.

8. 10. A method according to any preceding claim, wherein the molecules have a density of at least 0.1 emitters per square micron per image.

9. labeling a molecule in a biological sample; causing the biological sample to emit fluorescence; generating the image sequence while the biological sample is fluorescing; 10. The method of any preceding claim, further comprising:

10. The method of claim 9 , wherein the generating of the image sequence is performed using a microscope system.

11. 10. The method of any preceding claim, wherein the molecules are imaged in living cells.

12. inferring a probabilistic dynamic model that includes information characterizing the trajectory of the molecule; 10. The method of any preceding claim, further comprising:

13. The method of claim 12 , wherein the stochastic dynamic model comprises a state array, the method further comprising populating the state array with the information characterizing the trajectory of the molecule.

14. 10. The method of any preceding claim, further comprising generating an internal metric of confidence based on the associated probabilities, wherein the provided data comprises the generated internal metric of confidence.

15. The method of claim 14 , wherein the generated internal metric of confidence is a tracking error rate floor that defines a floor for the rate of misconnections made by the linking.

16. The generated internal metric is: Calculating the confidence level of each trajectory; 15. The method of claim 14, comprising:

17. generating dynamic metrics independent of specific trajectories; 10. The method of any preceding claim, further comprising:

18. 10. The method of any preceding claim, wherein said linking comprises obtaining data comprising a plurality of statistical data extracted from the total number of detections or the number of detections in a cell.

19. 10. The method of any preceding claim, wherein providing the data comprises one or more of: visualizing at least some of the generated possible trajectories with associated probabilities in a graphical user interface; storing at least some of the generated possible trajectories with associated probabilities in a physical persistent state; loading at least some of the generated possible trajectories with associated probabilities into a memory; or transmitting at least some of the generated possible trajectories with associated probabilities over a network to a remote computing device.

20. 10. A method according to any preceding claim, wherein at least part of the image sequence comprises successive images from a corresponding motion picture.

21. 10. A method according to any preceding claim, wherein at least some of the image sequences used for said linking are non-sequential images from a corresponding motion picture.

22. 1. A method for single molecule tracking, comprising: receiving an image sequence visualizing molecular motion, the image sequence including a first type generated using a first imaging modality and a second type generated using a second, different imaging modality, the method further comprising: detecting spots in the sequence of images of the first type; linking detected spots in the sequence of images of the first type to trajectories using a probabilistic tracking algorithm; segmenting the sequence of images of the second type to generate a plurality of instance masks; assigning molecules in the image sequence of the second type to at least one instance mask of the plurality of instance masks; providing data characterizing the link and the allocation to a consuming application or process; The method comprising:

23. The method of claim 22 , wherein the probabilistic tracking algorithm comprises a variational Bayesian optimization algorithm.

24. The method of claim 22 , wherein the probabilistic tracking algorithm comprises a Gibbs sampling algorithm.

25. The method of claim 22 , wherein the partially probabilistic tracking algorithm comprises an adaptive hill climbing algorithm.

26. The method of any of claims 22 to 25, wherein the first imaging modality and the second imaging modality comprise different molecular labeling techniques.

27. The method of any of claims 22 to 26, wherein the image sequences of the first type are single molecule tracking (SMT) movies and the image sequences of the second type are non-SMT movies.

28. The method according to any one of claims 22 to 27, wherein the detected spots contain intracellular components.

29. The method of any of claims 22 to 28, wherein types of molecules in the image sequence of the first type are labeled with distinct fluorophores.

30. A method according to any of claims 22 to 29, wherein at least a subset of the image sequence comprises at least 100 molecules per image.

31. A method according to any of claims 22 to 30, wherein at least a subset of the image sequence comprises at least 1000 molecules per image.

32. A method according to any one of claims 22 to 31, wherein at least a subset of the image sequence comprises at least 10,000 molecules per image.

33. A method according to any one of claims 22 to 32, wherein the molecules have a density of at least 0.01 emitters per square micron per image.

34. A method according to any one of claims 22 to 33, wherein the molecules have a density of at least 0.1 emitters per square micron per image.

35. labeling a molecule in a biological sample; causing the biological sample to emit fluorescence; generating at least a portion of the sequence of images while the biological sample is fluorescing; The method of any of claims 22 to 34, further comprising:

36. 36. The method of claim 35, wherein the generating of the image sequence is performed using a microscope system.

37. The method of any of claims 22 to 36, wherein the molecules are imaged in living cells.

38. inferring a probabilistic dynamic model that includes information characterizing the trajectory of the molecule; The method of any of claims 22 to 37, further comprising:

39. 39. The method of claim 38, wherein the probabilistic dynamic model comprises a state array, the method further comprising populating the state array with the information characterizing the trajectory of the molecule.

40. 40. The method of any of claims 22 to 39, further comprising generating an internal metric of confidence based on the associated probabilities, wherein the provided data comprises the generated internal metric of confidence.

41. 41. The method of claim 40, wherein the generated internal metric of confidence is a tracking error rate lower bound (ERLB) that defines a lower bound on the rate of misconnections made by the linking.

42. The generated internal metric is: Calculating the confidence level of each trajectory; 41. The method of claim 40, comprising:

43. generating dynamic metrics independent of specific trajectories; The method of any of claims 22 to 42, further comprising:

44. The method of any of claims 22 to 43, wherein said linking comprises obtaining data having a plurality of statistical data extracted from the total number of detections or the number of detections in a cell.

45. 45. The method of any of claims 22 to 44, wherein providing the data comprises one or more of: visualizing at least some of the generated possible trajectories with associated probabilities in a graphical user interface; storing at least some of the generated possible trajectories with associated probabilities in a physical persistent state; loading at least some of the generated possible trajectories with associated probabilities into a memory; or transmitting at least some of the generated possible trajectories with associated probabilities over a network to a remote computing device.

46. generating a plurality of statistical metrics associated with at least one of the trajectories or the at least one instance mask; The method of any of claims 22 to 45, further comprising:

47. Storing a hierarchy of instance masks, 47. The method of claim 46, further comprising:

48. A method according to any of claims 22 to 47, wherein at least part of the image sequence comprises successive images from a corresponding motion picture.

49. A method according to any of claims 22 to 48, wherein at least some of the image sequences used for said linking are non-sequential images from a corresponding motion picture.

50. 50. The method of any of claims 22-49, wherein the detecting utilizes one or more of a generalized log-likelihood ratio spot detector, a difference of Gaussians (DoG) detector, a Laplacian of Gaussians (LoG) detector, or a determinant of Hessian (DoH) blob detector.

51. associating said detected spots with spatiotemporal coordinates using sub-pixel localization; The method of any of claims 22 to 50, further comprising:

52. 52. The method of claim 51 , wherein the sub-pixel localization comprises one or more of a radially symmetric localizer or maximum likelihood fitting to a candidate spot model using a Levenberg-Marquardt method.

53. The method of any of claims 22 to 52, wherein the field of view corresponds to at least a portion of a well.

54. The image sequence is generated by an apparatus for fluorescence microscopy, the apparatus comprising: a first optical element or assembly configured to receive a fluorescence excitation light source and configured to generate a collimated light beam having an elongated linear shape in the xy plane, said light beam having a uniform intensity across a longer dimension of said linear shape, said apparatus further comprising: a second optical element or assembly configured to tilt the light beam with respect to the z-axis in an x-z ​​plane, the second optical element further configured to focus the light beam onto a sample surface located in the x-y plane, thereby illuminating a portion of the sample surface, the apparatus further comprising: a third optical element or assembly configured to translate the light beam in the sample plane in a direction orthogonal to the longer dimension of the light beam; a detector device configured to receive light from the illuminated sample surface, the detector device forming one or more projection images based on the light received from the sample surface. A method according to any preceding claim.

55. 55. The method of claim 54, wherein the first optical component element or assembly includes a Powell lens that produces the collimated light beam having an elongated linear shape in the xy plane.

56. 56. A method according to claim 54 or 55, wherein the first optical component element or assembly comprises one or more diffraction gratings that generate the collimated light beam having an elongated linear shape in the xy plane.

57. A method according to any of claims 54 to 56, wherein the first optical component element or assembly comprises a combination of lenses that produce the collimated light beam having an elongated linear shape in the xy plane.

58. A method according to any of claims 54 to 57, wherein the second optical component element or assembly comprises an objective lens.

59. 59. The method of any of claims 54 to 58, wherein the third optical component element or assembly includes a galvo mirror configured to translate the light beam in the sample plane in a direction orthogonal to the longer dimension of the light beam.

60. 60. A method according to any of claims 54 to 59, wherein the third optical component element or assembly comprises a piezo element configured to translate the light beam in the sample plane in a direction perpendicular to the longer dimension of the light beam.

61. 61. The method of any of claims 54 to 60, wherein the detector device includes a solid-state sensor, and wherein the detector device supports a shutter mode for synchronizing the translation of the light beam in the sample plane with selective activation or readout of the solid-state sensor.

62. The image sequence is generated by a microscope system for detecting the position of the molecule, the microscope system comprising: a stage for supporting a sample, the sample including the molecule, the microscope system further comprising: a light source that emits a light beam capable of inducing a light-based response from the molecules in the sample, the light beam having a linear shape in the sample plane and a uniform intensity across the longer dimension of the linear shape in the sample plane, the microscope system further comprising: an objective lens for focusing the light beam onto the sample in the sample plane, the molecule being disposed in the sample plane, the microscope system further comprising: a detector device that monitors the light-based response from the molecule to thereby detect the position of the molecule.

54. The method of any one of claims 1 to 53.

63. 63. The method of claim 62, wherein the microscope system further comprises a scanning optical element or assembly configured to enable translation of the light beam in the sample plane in a direction orthogonal to the longer dimension of the light beam, thereby increasing the total field of view of the microscope system in the xy plane.

64. 64. The method of claim 62 or 63, wherein the detector device includes a solid-state sensor, and the detector device supports a shutter mode for synchronizing the translation of the light beam in the sample plane with selective activation or readout of the solid-state sensor.

65. A method according to any of claims 62 to 64, wherein the detector device supports a shutter mode for synchronising the translation of the light beam in the sample plane with selective activation or readout of the semiconductor sensor.

66. 66. The method of any one of claims 62 to 65, wherein the sample is placed in an open well of a microplate.

67. 67. The method of claim 66, wherein the microplate comprises a plurality of open wells.

68. 68. The method of claim 66 or 67, wherein the microscope system further includes an xy position controller for changing a field of view of the microscope system, the changed field of view encompassing a different subset of the plurality of open wells.

69. The microscope system further includes an automated sample handling robotic system that enables high-throughput manipulation of multiple samples on the stage, the robotic system comprising: Memory and a processor in communication with the memory; one or more robotic end effectors in communication with the processor, the one or more end effectors manipulating the plurality of specimens on the stage based on communication with the processor.

69. The method according to any one of claims 66 to 68.

70. at least one data processor; a memory storing instructions which, when executed by said at least one data processor, result in operations for performing a method according to any of claims 1 to 53; Including, the system.

71. and further comprising an apparatus for fluorescence microscopy, said apparatus comprising: a first optical element or assembly configured to receive a fluorescence excitation light source and configured to generate a collimated light beam having an elongated linear shape in the xy plane, said light beam having a uniform intensity across a longer dimension of said linear shape, said apparatus further comprising: a second optical element or assembly configured to tilt the light beam with respect to the z-axis in an x-z ​​plane, the second optical element further configured to focus the light beam onto a sample surface located in the x-y plane, thereby illuminating a portion of the sample surface, the apparatus further comprising: a third optical element or assembly configured to translate the light beam in the sample plane in a direction orthogonal to the longer dimension of the light beam; a detector device configured to receive light from the illuminated sample surface, the detector device forming one or more projection images based on the light received from the sample surface.

71. The system of claim 70.

72. 72. The system of claim 71, wherein the first optical component element or assembly includes a Powell lens that produces the collimated light beam having an elongated linear shape in the xy plane.

73. A system according to claims 71-72, wherein the first optical component element or assembly includes one or more diffraction gratings that generate the collimated light beam having an elongated linear shape in the xy plane.

74. A system according to any one of claims 71 to 73, wherein the first optical component element or assembly comprises a combination of lenses that produces the collimated light beam having an elongated linear shape in the xy plane.

75. A system according to any one of claims 71 to 74, wherein the second optical component element or assembly comprises an objective lens.

76. 76. A system according to any one of claims 71 to 75, wherein the third optical component element or assembly comprises a galvo mirror configured to translate the light beam in the sample plane in a direction orthogonal to the longer dimension of the light beam.

77. 77. The system of any of claims 71 to 76, wherein the third optical component element or assembly includes a piezo element configured to translate the light beam in the sample plane in a direction perpendicular to the longer dimension of the light beam.

78. 78. A system according to any one of claims 71 to 77, wherein the detector device includes a solid-state sensor, and wherein the detector device supports a shutter mode for synchronizing the translation of the light beam in the sample plane with selective activation or readout of the solid-state sensor.

79. further comprising a microscope system for detecting the location of the molecule, the microscope system comprising: a stage for supporting a sample, the sample including the molecule, the microscope system further comprising: a light source that emits a light beam capable of inducing a light-based response from the molecules in the sample, the light beam having a linear shape in the sample plane and a uniform intensity across the longer dimension of the linear shape in the sample plane, the microscope system further comprising: an objective lens for focusing the light beam onto the sample in the sample plane, the molecule being disposed in the sample plane, the microscope system further comprising: a detector device that monitors the light-based response from the molecule to thereby detect the position of the molecule.

71. The system of claim 70.

80. 80. The system of claim 79, wherein the microscope system further includes a scanning optical element or assembly configured to translate the light beam within the sample plane in a direction perpendicular to the longer dimension of the light beam, thereby enabling an increase in the total field of view of the microscope system in the xy plane.

81. 81. The system of claim 79 or 80, wherein the detector device includes a solid-state sensor, and the detector device supports a shutter mode for synchronizing the translation of the light beam at the sample plane with selective activation or readout of the solid-state sensor.

82. A system according to any of claims 79 to 81, wherein the detector device supports a shutter mode for synchronising the translation of the light beam in the sample plane with selective activation or readout of the semiconductor sensor.

83. 83. The system of any one of claims 79 to 82, wherein the sample is placed in an open well of a microplate.

84. 84. The system of claim 83, wherein the microplate comprises a plurality of open wells.

85. The microscope system further comprises: an xy position controller for varying a field of view of the microscope system, the varied field of view encompassing a different subset of the plurality of open wells; A system according to any one of claims 83 to 84.

86. The microscope system further includes an automated sample handling robotic system that enables high-throughput manipulation of multiple samples on the stage, the robotic system comprising: a memory for storing instructions; at least one data processor; one or more robotic end effectors in communication with the at least one data processor, the one or more end effectors manipulating the plurality of specimens on the stage based on communication with the at least one data processor. A system according to any one of claims 73 to 85.

87. A non-transitory computer program product storing instructions which, when executed by at least one data processor forming part of at least one computing device, perform the method according to any of claims 1 to 53.

88. means for receiving an image sequence visualizing the motion of the molecule; means for linking molecules between said images; means for generating possible trajectories for each molecule with associated probabilities based on said linking; means for providing data characterizing the generated possible trajectories with associated probabilities to a consuming application or process; Including, the system.

89. means for receiving an image sequence visualizing the motion of the molecule; means for linking molecules between said images; means for generating possible trajectories for each molecule with associated probabilities using a variational Bayesian optimization algorithm and based on said linking; means for providing data characterizing the generated possible trajectories with associated probabilities to a consuming application or process; Including, the system.

90. means for receiving an image sequence visualizing the motion of the molecule; means for linking molecules between said images; means for generating possible trajectories for each molecule with associated probabilities using a Gibbs sampling algorithm and based on said linking; means for providing data characterizing the generated possible trajectories with associated probabilities to a consuming application or process; Including, the system.

91. means for receiving an image sequence visualizing the motion of the molecule; means for linking molecules between said images; means for generating possible trajectories for each molecule with associated probabilities using an adaptive hill-climbing algorithm and based on said linking; means for providing data characterizing the generated possible trajectories with associated probabilities to a consuming application or process; Including, the system.

92. 1. A single molecule tracking system, comprising: means for receiving image sequences visualizing molecular motion, the image sequences including a first type generated using a first imaging modality and a second type generated using a second, different imaging modality, the system further comprising: means for detecting spots in said sequence of images of said first type; means for linking detected spots in said sequence of images of said first type to trajectories using a probabilistic tracking algorithm; means for segmenting the sequence of images of the second type to generate a plurality of instance masks; means for assigning molecules in the sequence of images of the second type to at least one instance mask of the plurality of instance masks; means for providing data characterizing said links and said allocations to a consuming application or process; The system comprising:

Images