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

Abstract

Systems and methods for high throughput single molecule tracking within living cells receive a sequence of images of visualized molecular motion. Molecules across the image are linked. Possible trajectories and their correlation probabilities for each molecule are generated using a variational Bayesian optimization algorithm and based on the links. Data characterizing the generated possible trajectories and their associated probabilities are provided to the consumer application or process.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Application No. 63 / 476,951, filed on December 22, 2022, and U.S. Provisional Application No. 63 / 476,942, filed on December 22, 2022, the contents of each of which are incorporated herein by reference in their entirety. Technical Field

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

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

[0005] In a first aspect, a sequence of images visualizing molecular motion is received. Molecules across the images are linked. Based on the linkage, a variational Bayesian optimization algorithm is used to generate possible trajectories for each molecule and their associated probabilities. Data representing the generated possible trajectories and their associated probabilities is provided to a consuming application or process.

[0006] In a related aspect, a sequence of images visualizing molecular motion is received. Molecules across the images are linked. Using a Gibbs sampling algorithm and based on the linkages, possible trajectories for each molecule and their associated probabilities are generated. Data representing the generated possible trajectories and their associated probabilities is provided to a consuming application or process.

[0007] In another related aspect, a sequence of images visualizing molecular motion is received. Molecules across the images are linked. Based on the links, a possible trajectory and its associated probability are generated for each molecule using an adaptive hill climbing algorithm. Data representing the generated possible trajectories and their associated probabilities is provided to a consuming application or process.

[0008] At least a subset of the sequence of images may include at least 100 molecules per image; in other variations, at least 1000 molecules per image; and in other variations, at least 10,000 molecules per image.

[0009] In some variations, the density of molecules may be at least 0.01 emitters per square micron per image, and in some variations, the density of molecules may be at least 0.1 emitters per square micron per image.

[0010] Molecules in a biological sample can be labeled. The labeled biological sample can emit fluorescence, and an image sequence can be generated while the biological sample emits fluorescence.

[0011] The generation of the image sequences may be performed using a microscope system.

[0012] These molecules can be imaged inside living cells.

[0013] A probabilistic dynamical model comprising information characterizing the molecular trajectories may be inferred. The probabilistic dynamical model may include a state array, and the state array may be populated with the information characterizing the molecular trajectories.

[0014] An internal confidence indicator based on the correlation probability may be generated, and the provided data may include the generated internal confidence indicator. The generated internal confidence indicator may be a tracking error rate lower bound, which defines a lower bound on the rate of incorrect connection caused by the link. The generated internal indicator may include calculating a confidence level for each track.

[0015] Additionally, dynamic indicators can be generated independent of a specific trajectory.

[0016] Linking may include retrieving data with multiple statistics extracted from the total number of detections or the number of detections in a cell.

[0017] Providing the data may include one or more of: visualizing at least a portion of the generated possible trajectories and their associated probabilities in a graphical user interface, storing at least a portion of the generated possible trajectories and their associated probabilities in a physical persistence, loading at least a portion of the generated possible trajectories and their associated probabilities into a memory, or transmitting at least a portion of the generated possible trajectories and their associated probabilities to a remote computing device over a network.

[0018] At least a portion of the sequence of images may comprise consecutive images from the respective film.In some variations, at least a portion of the sequence of images used by the link are non-consecutive images from the respective film.

[0019] In another related aspect, a sequence of images visualizing molecular motion may be received. The sequence of images may include a first type generated using a first imaging modality and a second type generated using a second, different imaging modality. Points may be detected within the sequence of images of the first type. The points detected within the sequence of images of the first type may be linked into tracks using a probabilistic tracking algorithm. The sequence of images of the second type may be segmented to generate a plurality of instance masks. Molecules within the sequence of images of the second type may be assigned to at least one of the plurality of instance masks. Data representing the linking and assignment may be provided to a consuming application or process.

[0020] Probabilistic pursuit algorithms can take different forms, including variational Bayesian optimization, Gibbs sampling, or adaptive hill climbing.

[0021] The first imaging modality and the second imaging modality may comprise different molecular labeling technologies.

[0022] The first type of image sequence may be a single molecule tracking (SMT) movie, and the second type of image sequence may be a non-SMT movie.

[0023] The detected spots may include subcellular components.

[0024] The molecular types within the sequence of images of the first type may be labeled with different fluorophores.

[0025] A plurality of statistical metrics associated with the at least one track or the at least one instance mask may be generated.

[0026] A hierarchy that can store instance masks.

[0027] The detection may utilize one or more of a generalized log-likelihood point detector, a Difference of Gaussian (DoG) detector, a Laplace of Gaussian (LoG) detector, or a Determinant of Hessian (DoH) point detector.

[0028] Sub-pixel localization can be used to associate detected points with spatiotemporal coordinates.

[0029] Sub-pixel localization may include one or more of the following: a radially symmetric localizer or maximum likelihood fitting of candidate point models using the Levenberg-Marquardt method.

[0030] In some variations, the image sequence may 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 may be configured to receive a fluorescence excitation light source and generate a collimated light beam having an elongated and linear shape in the xy plane, such that the light beam has a uniform intensity along the longer dimension of the linear shape. The second optical element or assembly may be configured to tilt the light beam in the xz plane relative to the z-axis, wherein the second optical element is further configured to focus the light beam at a sample plane located in the xy plane, thereby illuminating a portion of the sample plane. The third optical element or assembly may be configured to translate the light beam in the sample plane in a direction orthogonal to the longer dimension of the light beam. The detector device may be configured to receive light from the illuminated sample plane, such that the detector device forms one or more projected images based on the light received from the sample plane.

[0031] The first optical element or component may include a Powell lens to produce a collimated light beam having an elongated and linear shape in the xy plane.

[0032] The first optical element or component may include one or more diffraction gratings to produce a collimated light beam having an elongated and linear shape in the xy plane.

[0033] The first optical element or assembly may include a combination of lenses to produce a collimated light beam having an elongated and linear shape in the xy plane.

[0034] The second optical element or assembly may comprise an objective lens.

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

[0036] The third optical element or assembly may comprise 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.

[0037] The detector arrangement may comprise semiconductor sensors, such that the detector arrangement supports a shutter mode for synchronizing the translation of the light beam in the sample plane with the selective activation or readout of the semiconductor sensors.

[0038] In some variations, the image sequence can be generated by a microscope system for detecting the position of a molecule having a stage, a light source, an objective lens, and a detector device. The stage can support a sample containing the molecule. The light can emit a light beam capable of inducing a photo-based reaction from the molecule in the sample, such that the light beam has a linear shape in the sample plane and has a uniform intensity along the longer dimension of the linear shape of the sample plane. The objective lens can focus the light beam onto the sample in the sample plane such that the molecule is positioned in the sample plane. The detector device can monitor the photo-based reaction of the molecule to detect the position of the molecule.

[0039] 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 orthogonal to the longer dimension of the light beam, thereby enabling the microscope system to have a larger total field of view in the xy plane.

[0040] The detector arrangement may comprise semiconductor sensors, such that the detector arrangement supports a shutter mode for synchronizing the translation of the light beam in the sample plane with the selective activation or readout of the semiconductor sensors.

[0041] The detector arrangement may 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.

[0042] The sample can be placed in an open well of a microplate, which can include a plurality of open wells. By such a variation, an xy position controller can be provided to change the field of view of the microscope system, where the changed field of view covers a different subset of the plurality of open wells.

[0043] A microscope system may include an automated sample handling robotic system to enable high-throughput manipulation of multiple samples on a stage, the system including a memory, a processor communicating with the memory, and one or more robotic end effectors communicating with the processor, such that the one or more end effectors manipulate the multiple samples on the stage based on communication with the processor.

[0044] Also described are non-transitory computer program products (i.e., physically embodied computer program products) that store instructions that, 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. Similarly, described are computer systems that may include one or more data processors and a 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 operations described herein. In addition, the methods may be implemented by one or more data processors within a single computing system or distributed between two or more computing systems. Such computing systems may be connected and may exchange data and / or commands or other instructions, etc., 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.), via direct connections between one or more of the plurality of computing systems, etc.

[0045] The subject matter described herein provides numerous technical advantages. For example, the current subject matter provides a fast and computationally more efficient method for tracking targets (e.g., molecules, etc.) that isolates interpretable information from background and other interfering noise. The subject matter described herein can be used to analyze complex data, which may include thousands to tens of thousands of rapidly moving targets in close proximity. Additionally, an advantage of the subject matter described herein is that it can be performed with little to no human supervision.

[0046] More specifically, the current subject matter offers numerous technical advantages related to scalability. Current platforms can generate data for over 100 molecules per frame (i.e., image) across multiple imaging systems running in series. This capability requires a tracking method that is: (1) highly scalable; and (2) provides built-in confidence / diagnostic metrics in the tracking results, as there is no human supervision of the raw data. Furthermore, the probabilistic tracking algorithm presented herein provides built-in confidence metrics for consuming applications / processes without the need for human supervision. Furthermore, the current probabilistic tracking algorithm provides dynamic metrics that can be used for drug screening independent of any specific trajectory.

[0047] 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 DESCRIPTION OF THE DRAWINGS

[0048] This patent or application file contains at least one drawing drawn in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0049] Figure 1 Schematic diagram depicting the htSMT workflow.

[0050] Figures 2A-2F An exemplary image acquisition system of the present disclosure is depicted, wherein the XZ sampling plane is visible ( Figure 2A and 2D ) or YZ sampling plane visible ( Figure 2B and 2C ), and details of the beams associated with the HILO-based approach ( Figure 2E ) and an example combining a camera rolling shutter ( Figure 2F ).

[0051] Figures 3A-3E Various measurement results are depicted, demonstrating that the disclosed image acquisition system and workflow are suitable for robust htSMT analysis. Figure 3A Depicted is a laser titration experiment showing the relationship between laser power (mW) at the sample and signal-to-noise ratio (SNR) (left), as well as the average SNR at the well level for four image acquisition systems, each measuring six different 384-well plates (right). Figure 3B Depicts the difference in spatial SNR heterogeneity between the disclosed OLS system and HILO-based methods. The top figure compares the spatial standard deviation observed in the OLS and HILO-based methods. The bottom figure illustrates the difference in FOV between HILO and OLS-based methods (left figure), as well as a comparison of the spatial heterogeneity between the FOVs of the HILO-based method (center figure) and the OLS-based method (right figure). Figure 3C Depicted are dose-response experiments using established and well-characterized compounds against Halo-tagged proteins to assess inter-plate and day-to-day reproducibility (top panel), and the corresponding EC50s are presented (bottom panel). Figure 3D Demonstrates that the system described herein is configured to capture comparable protein diffusion coefficients per well per FOV, where each point represents the average single FOV position per plot per concentration (top panel), and both EC50 and Z-factor are presented (bottom panel). Figure 3E Data consistency across multiple wells and experiments is depicted, where each point represents one FOV from 14 independently generated dose-response curves.

[0052] Figure 4 Depicts the Figure 3D and Figure 3E Comparison of the Z-factors associated with the data presented in with data collected using a HILO-based method.

[0053] Figure 5 Depicted is a schematic diagram of an exemplary sample processing system of the present disclosure.

[0054] Figure 6An exemplary system of a high-throughput single-molecule imaging platform for measuring protein motion within living cells is illustrated.

[0055] Figure 7 The data flow through an example system of a high-throughput single-molecule imaging platform for measuring protein movement within living cells is illustrated.

[0056] Figure 8 are multiple images illustrating the difference between mask categories and instance / semantic masks.

[0057] Figure 9 An example computer-implemented environment related to the subject matter described herein is described.

[0058] Figure 10 is a diagram illustrating an example computing device architecture for implementing various aspects described herein.

[0059] Figure 11 Diagram illustrating the pipeline of scalable tracing in htSMT.

[0060] Figures 12A-12C Depicts benchmarks of various tracking algorithms, including Figure 12A Aspects relevant to optical dynamic simulations are described, Figure 12B The basis for comparison is stated, and Figure 12C Benchmark results on recall, precision, and F1 score are illustrated.

[0061] Figures 13A-13G The OLS provides nearly full-field uniform illumination, enabling a wide range of SMTs. Figure 13A A simplified schematic diagram describing the OLS implementation is depicted. Briefly, a collimated beam is formed into an optical light sheet, which is sent to a water immersion objective, and the emitted light is projected onto a high-speed sCMOS camera. Figure 13B An exemplary SMT workflow is depicted that relies on Halo tagging of a protein target of interest. 549 or JF 646 Organic fluorophores detect individual emitters with appropriate signals for inter-frame linking and track generation. From these coordinates and tracks, a variety of metrics can be extracted, including protein diffusion and spatial localization. Figure 13C Depicted are 20-point dose-response curves from 6-7 different 384-well plates per microscope imaged on the Eikon high-throughput SMT platform. 72 FOVs were captured for each concentration from 12 wells on a random plate, and error bars represent standard deviation. Figure 13D Representative sampled regions illuminated by Halo-Keap1-containing U2OS cells in HILOs and OLs are depicted. Trajectories are plotted over a 1.5 s acquisition and color-coded according to the measured diffusion coefficient, with the nuclear mask outline overlaid with a black dashed line. Figure 13E Quantification of the number of tracks captured per FOV using HILO and OLS is described, with OLS capturing a 6x improvement. Figure 13F Depicted are representative average spatial SNR plots per pixel calculated for 1,232 FOVs of a plate imaged with either HILO or OLS. OLS provides a 6x larger FOV while also improving illumination uniformity. Figure 13G The mean FOV-level standard deviation of the SNR for the 308 well samples is provided.

[0062] Figures 14A-14F Depicted is an exemplary schematic diagram of an OLS microscope for single-molecule tracking. Figure 14A Depicted is an exemplary schematic of an OLS microscope based on scanning an oblique excitation light sheet over a sample placed in an inverted microscope using a galvanometer scanning mirror. The OLS microscope is based on multi-wavelength optical excitation provided by a laser engine module (LEM) and coupled to a beam shaper via a single-mode fiber coupled to a collimator. The beam shaper converts the incident Gaussian-shaped optical excitation into an optical light sheet, which is focused along the linear axis of the light sheet onto the back focal plane of the microscope objective and scanned along the scanning axis using a galvanometer mirror. The resulting oblique light sheet is fed into a water-immersion coupled and environmentally controlled sample holding plate, while the relative position of the microscope focal plane is controlled by an autofocus unit. The excited fluorescence is spectrally filtered from the excitation light by a dichroic filter and an emission filter and projected onto a high-speed sCMOS camera. Synchronization of optical excitation, scanning, and acquisition is achieved by a custom control unit (MIC). Figure 14B Depicted is an exemplary schematic diagram of an autofocus unit based on detecting the reflection of a 780 nm-LED on 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 Depicted is an exemplary schematic diagram of an 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 arrangement of the tilted light sheet (green), the depth of field of the objective, and synchronized rolling shutter detection (orange). Figure 14D Depicted is an exemplary schematic diagram of the beam shaping subassembly, which projects along the linear (x) and scanning (y) axes, shaping the collimated excitation light into a light sheet through a series of lenses consisting of a Powell lens, a cylindrical lens, a spherical lens, and a plano-convex lens, before encountering the galvanometer scanning mirror. The inset depicts the beam profile at various positions. Figure 14E Depicted is an exemplary schematic diagram of a light scanning framework based on an inclined light sheet in the sample plane, achieved by focusing the excitation light along a linear axis in the objective's back focal plane and positioning it at an offset position along the scanning axis relative to the objective's optical axis. The corresponding optically aligned fluorescence measurements are projected onto a camera sensor. Figure 14FDepicted is an exemplary schematic of an OLS acquisition mode that relies on detecting fluorescence by matching the exposed pixel area of ​​a camera and synchronizing the camera's rolling shutter with the optically projected intensity lines of fluorescence excited by a tilted light sheet.

[0063] Figures 15A-15F Characterization of motion-induced blur and confocality between OLS and HILO illumination. Figure 15A Depicted is a bar graph comparing HILO and OLS diffusion coefficients measured in 72 FOVs from 12 individual wells for Halo-KEAP1 treated with DMSO or 1 mM KI-696. Despite extensive sampling, the standard deviation of the HILO measurements was larger. Figure 15B Depicted are estimated point spread functions (PSFs) obtained by averaging all detections in a representative 150-frame acquisition. The following number of PSFs were detected in each condition: n = 123,596 (OLS-DMSO), n = 3,897 (HILO-DMSO), n = 113,276 (OLS 0.33 mM KI-696), n = 13,620 (HILO 0.33 mM KI-696), from one representative FOV. Figure 15C PSF detection is plotted as a function of integration time. HILO requires a five-times longer integration time to achieve comparable PSF detection and point density to OLS, which results in more noticeable motion-induced blur in HILO. Figure 15D PSF width measurements measured in Halo-KEAP1 cells treated with 1 mM KI-696 are plotted versus JF 549 Functional relationship. Figure 15E Plotted are the mean SNR versus JF measured in Halo-KEAP1 cells treated with 1 mM KI-696. 549 Functional relationship. Figure 15F It shows that Halo-JF in solution 549 The number of point detections measured when the concentration increases is JF 549 Functional relationship.

[0064] Figures 16A-16D This shows that OLS can achieve repeatable and robust SMT measurements. Figure 16A The EC calculated from each average dose-response curve for each plate per microscope is depicted. 50 Value, the black line represents the median EC 50 . Figure 16B Violin plots of the signal-to-noise ratio (SNR) for each microscope are depicted, with the thick dashed line indicating the median value. Figure 16C Violin plot depicting SNR as a function of FOV position within the acquisition aperture. Figure 16DDepicted are 20-point dose-response curves of Halo-KEAP1 U2OS sampled at the full OLS FOV (purple) versus a 768×768 pixel cropped FOV (black) representing the HILO-sized FOV. Error bars represent the standard deviation between FOVs.

[0065] Figures 17A-17C This demonstrates that OLS can capture rapid protein diffusion in living cells. Figure 17A Representative images of FOV sizes for five frame rates ranging from 100 to 1250 Hz are depicted. Trajectories are superimposed on the mean projection of the Hoechst channel (blue) and colored according to their maximum likelihood diffusion coefficient. Figure 17B Depicts diffusion coefficients >10 μm 2 The fraction of trajectories with a frame rate of 100 / s was calculated from the posterior mean occupancy of the state array as a function of the frame rate for DMSO- and KI-696-treated cells, respectively. Figure 17C Plotted are the accuracy of state profile recovery for optical dynamic simulations of SMT at several frame rates for three different state mixtures. Error bars represent standard deviation.

[0066] Figures 18A-18E This shows that the frame rate determines the SMT dynamic range. Figure 18A A schematic depicting the role of localization and tracking errors for a hypothetical fast-moving protein is shown. A rolling shutter in OLS captures the position of the dye molecule at discrete time points. If these time points are too close together, the apparent motion will be dominated by localization errors. If the time points are too far apart, reconstructing the trajectory becomes challenging and is dominated by misalignment. Figure 18B A schematic diagram describing the dynamic range of an SMT is depicted, which is limited by positioning error at one end and tracking error at the other end. An approximation of this range for Brownian motion is where σ 2 is the localization error variance, Δt is the frame interval, R is the search radius, and D is the diffusion coefficient. Figure 18C A diagram depicting the simulation method used to test the effects of frame rate. Movies were simulated using real-world effects, including defocus, motion blur, shot noise, and readout noise. Figure 18D Plotting the effect of frame rate on link accuracy and trajectory length. Link accuracy is defined as the fraction of correct links produced by the tracking algorithm; trajectory length is the number of points in each trajectory. The quantile is the over-simulated movie. Figure 18E Depict the state array posterior mean occupancy of three simulated dynamic mixings at increased frame rates. The red line corresponds to the simulated discrete hybrid model, the blue line corresponds to the state array posterior mean, and the green line corresponds to the expected SMT dynamic range defined in (B). Each condition includes ten simulation repetitions.

[0067] Figures 19A-19C Depicted are diagnostic traces of experimental KEAP1-HaloTagJF549 SMTs in U2OS cells with different frame rates. Figure 19A The average trajectory length is plotted as a function of the frame rate. The trajectory length is defined as the number of points per trajectory. Figure 19B The average SNR is plotted as a function of frame rate. The SNR is described in Example 2. Figure 19C The average ERLB is plotted as a function of frame rate.

[0068] Figure 20 Provided are state array analyses plotted as a function of frame rate, comparing Keap1-HaloTag U2OS cells treated with DMSO and 1 mM KI-696. The number of FOV replicates per frame rate is as follows: n=88 (100 Hz), n=88 (200 Hz), n=132 (400 Hz), n=198 (800 Hz), and n=264 (1250 Hz). The line is the mean of all FOVs for the corresponding condition, and the error bars are the standard deviation of the FOV levels.

[0069] Figure 21 An evaluation of the bleaching rate of KEAP1-HaloTag SMT at variable frame rates is provided. The remaining detection fraction is plotted against the frame rate for a given time series. The remaining detection fraction is defined as the number of detections in each frame divided by the number of detections in the first frame. The model f(t) = c0 + (1-c0)e was fitted using iterative least squares. -kt An exponential fit was performed (blue text below the frame rate), where t is the frame index, k is the bleaching rate, and c0 is the unbleached fraction. The number of FOV repetitions per frame rate was as follows: n = 88 (100 Hz), n = 88 (200 Hz), n = 132 (400 Hz), n = 198 (800 Hz), and n = 264 (1250 Hz).

[0070] Figures 22A-22F We demonstrate that OLS can be used to capture inter- and intracellular heterogeneity in the dynamics of individual proteins. Figure 22A Figure 3 Analysis of sources of variation in KEAP1 SMT measured under OLS or HILO illumination. The contribution of cell-to-cell variation is 17- to 32-fold higher than that of FOV-level or well-to-well variation, respectively. Figure 22B Representative images of Halo-PCNA labeled cells treated with 2 mM thymidine or 10 mM RO-3306 are depicted (top). Cell cycle predictions based on a machine learning (ML) model were used to color cells by cycle stage (bottom). Figure 22C Depicts the response to Figure 22BQuantification of the fraction of cells in each cell phase following cycle blockade treatment in Figure 5. The following number of cells were analyzed for each condition: 34,067 for DMSO, 3,831 for RO-3306, and 6,044 for thymidine. Figure 22D State array analysis of the total population of sparsely labeled PCNA cells is depicted. Figure 22E Array analysis depicting the state of the cell at each stage predicted by the ML model. Figure 22F Depicted is a heat map of 4,801 individual cells classified using a continuous classification score plotted against PCNA diffusion coefficient.

[0071] Figures 23A-23D The characteristics of the cell cycle prediction model based on PCNA are described. Figure 23A Example images of a time-lapse of PCNA captured on the OLS at 5-minute frame intervals for 12 hours are provided. Figure 23B Schematic depicting a neural network trained to simultaneously perform nucleus segmentation, nucleus cell cycle classification, and nucleus cell cycle regression. Figure 23C The confusion matrix describing the cell cycle classification performance is depicted. Figure 23D Representative images of cell cycle progression of four selected cells over a 12-hour window are provided (left), along with regression-based predictions of cell cycle progression using a 5-frame moving average (right).

[0072] Figures 24A-24D Depicted are PCNA cell line validation using Western blot and cell proliferation assays. Figure 24A Depicted are capillary-based Western blots comparing WT U2OS and N-terminally tagged hybrid PCNA clones with anti-PCNA antibodies (left) and anti-Halo antibodies (right). Figure 24B Depicted are the relative WT and Halo-labeled PCNA levels of WT and Halo-edited U2OS cells normalized to β-actin. Figure 24C Growth curves of WT U2OS and N-terminally Halo-tagged PCNA are depicted. Figure 24D Depicted using JF 549 Cells labeled with both CCR and PCNA were analyzed to measure the spatial colocalization between the two markers during the cell cycle.

[0073] Figures 25A-25M This shows that OLS is applicable to a variety of SMLM techniques and acquisition schemes. Figure 25A Depicts the JF imaged within the same FOV 549 and JF 646 Halo-labeled KEAP1U2OS cells. Figure 25B Depicts the relationship with JF 549 and JF 646A 10-point dose response of co-labeled KI-696-treated Halo-KEAP1 U2OS cells. Figure 25C depicts a diffraction-limited image of the entire OLS FOV immunofluorescently labeled with AF647-conjugated secondary antibody for tubulin. Figure 25D depicts a magnified view of the region of interest in Figure 25C. Figure 25E depicts a STORM reconstruction of the entire OLS FOV labeled as in Figure 25C. Figure 25F depicts a magnified view of the region of interest in Figure 25E as in Figure 25D. Figure 25G The yellow line in FIG25D and the line profile of the gray value (au) in FIG25F are depicted to compare the spatial resolution of microtubules. Figure 25H Depicted are histograms of localization accuracy for AF647- and CF568-labeled secondary antibodies, respectively, used to stain microtubules with OLS illumination at 0.4 ms integration time. Figure 25I Depicted are representative images of correlated FRAP / SMT, where the central region was bleached using OLS line scans prior to spot recovery after photobleaching. Regions outside and inside the FRAP region were used to measure SMT. Figure 25J The results of the experiments with DMSO or 1 mM KI-696 (using 400 μM JF 549 T cells of U2OS cells treated with Halo-KEAP1 (-Halo ligand labeling) 1 / 2 FRAP, the black line indicates the median, and each dot represents an individual FOV. Figure 25K and Figure 25L Depicts DMSO ( Figure 25K ) and 1 mM KI-696 ( Figure 25L ) The point density after recovery over time. Standard deviations are shown with confidence bands of 8-10 FOVs per condition. Figure 25M Depicts 400pMJF 549 -SMT diffusion coefficients in bleached (inside) and unbleached (outside) regions as a function of Halo ligand concentration.

[0074] Figures 26A-26C The characteristics of dye properties and FRAP as dye concentration increases are illustrated. Figure 26A Depicts JF 646 With JF 549 SNR comparison between . Figure 26B Depicts JF 646 With JF 549 ERLB comparison between. Figure 26C The T measured in the bleached region is depicted 1 / 2 Sampling as a function of dye concentration for DMSO and 1 mM KI-696 within 6-10 FOV.

[0075] Figures 27A-27BThe contributions of inter-well, inter-FOV, and inter-cell biases to 2D jump length, assessed using jump resampling, are illustrated. Figure 27A Plotted the variance of the sample mean as a function of sample size for different resampling procedures. The straight line with a slope of -1 is the expectation from the law of large numbers; sublinearity is due to residual variance of the well, FOV, or cell. Figure 27B The number of jumps for each well, FOV, or cell used in these analyses is depicted. DETAILED DESCRIPTION

[0076] The subject matter of the present disclosure relates to the development of industrial-scale, high-throughput SMT (htSMT) techniques employing oblique line scanning (OLS) illumination; systems incorporating such OLS htSMT techniques; hardware and software associated with such OLS htSMT techniques; and methods of using such OLS htSMT techniques. For example, the OLS htSMT techniques described herein are capable of measuring protein movement in millions of cells per day. In addition to being able to capture a large number of cells per field of view, OLS also benefits from improved spatial uniformity of the signal-to-noise ratio (SNR) on the camera chip, better confocality (less out-of-focus signal and less motion blur), and higher temporal resolution, as shown in Table 1 (where each "+" represents a 2-fold improvement).

[0077] Table 1.

[0078] parameter OLS HILO Spatial SNR uniformity +++ + Confocality +++ + Temporal resolution +++ + FOV size ++++ +

[0079] The OLS htSMT technology described herein can be used for a variety of applications, including but not limited to drug discovery activities such as compound library screening and structure-activity relationship (SAR) elucidation. Importantly, the OLS htSMT technology described herein can be used to characterize the contributions of known and novel pathways to larger molecular assemblies containing targets, such as protein signaling interaction networks.

[0080] refer to Figure 1 , various aspects of the current subject matter can be implemented using the OLS htSMT workflow. The workflow can include various stages, as described in further detail below, such as (i) sample preparation including reagent treatment; (ii) image acquisition using sample imaging to generate a series of images and / or videos; (iii) image analysis by processing these images and videos, such as using various analyses, single emitter detection and sub-pixel localization (i.e., "super-resolution imaging"), tracking, computer vision, and machine learning algorithms; (iv) storage of information extracted from or otherwise representing or comprising the images and videos (i.e., features, original images, modified images, etc.); and (v) using the stored information to provide insights, including biological interpretations (which can be provided additionally or alternatively using various analyses, tracking, computer vision, and machine learning algorithms).

[0081] The subject matter of the present disclosure is described with reference to the accompanying drawings, in which reference numbers are used throughout to indicate similar or equivalent elements. The drawings are not drawn to scale and are provided solely for the purpose of illustrating the aspects disclosed herein. Several disclosed aspects will be described below with reference to exemplary hardware, software, and applications for illustration. It should be understood that many specific details, relationships, and methods are set forth in order to provide a more complete understanding of the subject matter disclosed herein. For the purpose of clarity of disclosure and not for limitation, the detailed description is divided into the following subsections:

[0082] 1. Definition

[0083] 2.OLS htSMT hardware

[0084] 3. OLS htSMT software

[0085] 4. Specific OLS htSMT applications

[0086] 5. Exemplary Implementation

[0087] 6. Examples

[0088] 1. Definition

[0089] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those of ordinary skill in the art are generally understood. In the event of conflict, this document (including definitions) shall prevail. Preferred methods and materials are described below, although methods and materials similar or equivalent to those described herein may be used when practicing or testing the subject matter of the present disclosure. All publications, patent applications, patents and other references mentioned herein are incorporated by reference in their entirety. The materials, methods and examples disclosed herein are illustrative only and are not intended to be limiting.

[0090] As used herein, the terms "include," "comprising," "having," "has," "may," "containing," and variations thereof are intended to serve as open transitional phrases, terms, or words that do not exclude the possibility of additional actions or structures. Unless the context clearly dictates otherwise, the singular forms "a," "an," and "the" include plural referents. This disclosure also contemplates other instances of "including," "consisting of," and "consisting essentially of" the instances or elements presented herein, whether or not explicitly stated.

[0091] For the recitation of numerical ranges herein, each intervening number within the range is expressly contemplated with equal precision. For example, for a range of 6 to 9, the numbers 7 and 8 are contemplated in addition to 6 and 9, and for a range of 6.0 to 7.0, the numbers 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, and 7.0 are expressly contemplated.

[0092] As used herein, the term "about" or "approximately" means that a particular value is within an acceptable error range as determined by one skilled in the art, which will depend in part on how the value is measured or determined, i.e., the limitations of the measurement system. For example, "about" can mean within 3 or more standard deviations, as practiced in the art. Alternatively, "about" can mean a range of up to 20%, preferably up to 10%, more preferably up to 5%, and more preferably up to 1% of a given value. Alternatively, particularly with respect to biological systems or processes, the term can mean within an order of magnitude, preferably within 5-fold, and more preferably within 2-fold, of a value.

[0093] As used herein, the term "trajectory" refers to a temporally linked set of spatial coordinates corresponding to the observed positions of a fluorescent protein. In some cases, multiple trajectories can be algorithmically constructed by linking multiple fluorescent proteins whose positions are determined at consecutive time points. In some cases, when no other linking is feasible, multiple trajectories can be constructed conservatively by linking only points within a fixed search radius. In some cases, multiple trajectories can be constructed probabilistically.

[0094] As defined herein, protein motion refers to the change in position of multiple fluorescent proteins. In some cases, protein motion can be quantified by analyzing changes in spatial coordinates at consecutive time points. Motion characterized in this manner may include, but is not limited to, measurement of jump length distribution: given a set of protein displacements between one time point and a subsequent time point, a histogram of the probability of each displacement length ("jump length") can be constructed. Quantiles of this distribution can be used to describe protein motion. In some cases, the quantile used is the median of the jump length distribution. In some cases, the quantile used is the 3rd quartile of the jump length distribution. In some cases, protein motion can be quantified by trajectory analysis. Motion characterized in this manner may include, but is not limited to, measurement of mean square displacement, which is defined as the average of the squares of all displacements in a trajectory averaged over multiple trajectories. Motion characterized in this manner may also include, but is not limited to, measurement of trajectory length or trajectory length distribution. Motion characterized in this manner may also include, but is not limited to, measurement of the mean radius of gyration, which is defined as the root mean square distance between all coordinates in a trajectory and the centroid of the set of points contained in the trajectory, averaged over multiple trajectories. Motion characterized in this manner may also include, but is not limited to, measurement of an average bond angle, defined as the angle formed by three consecutive spatial coordinates averaged over multiple trajectories. Motion characterized in this manner may also include, but is not limited to, measurement of a maximum likelihood estimator of the diffusion coefficient, defined as an estimate of the maximum likelihood diffusion coefficient for multiple trajectories under a single-state diffusion model with constant positioning error. In some cases, protein motion may be measured by analyzing the product of a link generation algorithm. Motion characterized in this manner may include, but is not limited to, the average posterior diffusion coefficient, the average of the posterior probability distribution of the coefficients from the probabilistic link algorithm. Motion characterized in this manner may include, but is not limited to, the geometric mean posterior diffusion coefficient, the average of the logarithmic scale posterior probability distribution of the coefficients from the probabilistic link algorithm. In some cases, protein motion may be measured by performing model correlation analysis on multiple trajectories. Motion characterized in this manner may include, but is not limited to, the fraction of immobile molecules ("f") defined by a two-state model fit. 结合 ”).

[0095] As used herein, the term "motion" encompasses changes in the direction of travel of a target as well as changes in speed (increase or decrease). Thus, in some cases, tracking motion can include determining that the target has not moved, for example, when the target is in or substantially in a static binding state. Motion can be characterized in a variety of ways, including but not limited to quantifying: (a) the median of the jump length distribution (where the jump length corresponds to the observed distance traveled by the target fluorescent protein in consecutive frames); (b) the 3rd quartile of the jump length distribution; (c) the median radius of gyration; (d) the mean posterior diffusion coefficient; (e) the geometric mean posterior diffusion coefficient; (f) the mean square displacement; (g) the median bond angle; (h) the maximum likelihood estimator of the diffusion coefficient; (i) the trajectory length; and / or (j) the state occupancy by inference.

[0096] As used herein, detected motion (including but not limited to any change in motion) can occur in response to any environmental or other factor. For example, and not by way of limitation, motion or lack thereof can be induced by: (A) addition of a compound; (B) temperature change; (C) change in oxygen concentration, such as introduction of hypoxic conditions; (D) mechanical stress; (E) pH change; and / or (F) change in light (e.g., increasing or decreasing intensity).

[0097] As used herein, the term "fluorescent protein" refers to any protein that emits a fluorescent signal. In some cases, fluorescent emission occurs under irradiation with light of a specific wavelength. An example of a naturally occurring fluorescent protein is green fluorescent protein (GFP). However, in some cases, the protein of interest may be adapted to emit a fluorescent signal by introducing an encoded fluorescent tag, that is, the protein sequence is fused to the protein of interest so that it emits fluorescence. In some cases, the protein of interest may be made to emit a fluorescent signal by binding to a fluorescent ligand. Non-limiting examples of such encoded fluorescent tags include: Halo tags, SNAP tags, CLIP tags, TMP tags, and SunTags. Additionally or alternatively, the protein of interest may be adapted to emit a fluorescent signal by coupling to a fluorescent dye molecule (e.g., an amine or thiol-reactive dye).

[0098] As used herein, the term "compound" refers to any chemically defined entity. In some cases, a compound can be a molecule less than 1000 Da, i.e., a "small molecule". In some cases, a compound can be a macromolecule, such as a nucleic acid. In some cases, a nucleic acid can have a defined sequence. In some cases, nucleic acids include: (A) ribonucleic acid (RNA), including, for example, modified RNA; (B) deoxyribonucleic acid (DNA), including, for example, modified DNA; and (C) a combination of (A) and (B). In some cases, the nucleic acid will be a single-stranded or double-stranded small interfering nucleic acid (e.g., double-stranded siRNA), an antisense oligonucleotide, a ribozyme, a microRNA, or an aptamer. In some cases, a compound can be a protein. For example, but not limited to, the protein compounds of the present disclosure encompass signal transduction proteins, such as protein hormones, cytokines, kinases, phosphatases, and other enzymes and transcription factors, as well as antibodies, contractile proteins, structural proteins, storage proteins, and transport proteins. In some cases, a compound can refer to a mixture of molecules, such as a mixture with a defined composition.

[0099] As used herein, the term "uniform intensity" with respect to the intensity of light (eg, light directed toward a sample plane) means that the intensity varies by no more than 5% in some cases, 10% in some cases, or 15% in some cases.

[0100] As used herein, the term "uniform intensity" in relation to signal-to-noise ratio (SNR) refers to the pixel-by-pixel SNR within the field of view (FOV), where the range of possible values ​​is between 0.5 and 1 standard deviation of the mean SNR.

[0101] 2. OLS htSMT hardware

[0102] Image acquisition system

[0103] refer to Figure 1 Various aspects of the current subject matter can be implemented using an htSMT workflow, wherein such a workflow is combined with a system for image acquisition. For example, such image acquisition can be combined with imaging of a sample to generate a series of images and / or a video. Figure 2A Depicted is a schematic diagram of an exemplary image acquisition system of the present disclosure, with the XZ sample plane visible. Figure 2BThe same exemplary image acquisition system is depicted, but with the YZ sample plane visible. An exemplary image acquisition system (2-001) includes: a light source (2-005) configured to emit light relayed by one or more optical elements in an optical relay (2-010), wherein the optical relay is configured to shape the light emitted from the light source to form a shaped light beam (2-065) so that the shaped light beam has uniform intensity in the longer dimension of the linear shape; an optical element, such as a galvanometer (2-085), configured to translate the shaped light beam; and one or more optical elements, such as a dichroic mirror (2-100), configured to direct the shaped light beam to an objective lens (2-120), whereby a portion of a sample plane (2-130) is illuminated by the tilted light beam (2-125), and the resulting light emitted from the sample, such as fluorescent emission, is focused by the objective lens (2-120), passes through a series of optical elements, such as lenses (2-155) and emission filters (2-160), and reaches an image collection system (2-165).

[0104] 2.1.1. Light source

[0105] refer to Figure 2A An exemplary image acquisition system is provided, the system comprising a light source (2-005) configured to emit light. In certain implementations of the image acquisition system disclosed herein, the light source (2-005) may be configured to emit light of a single wavelength. In certain implementations of the image acquisition system disclosed herein, the light source (2-005) may be configured to emit light of two, three, four, five or more separate wavelengths. In certain implementations, the wavelength of the light emitted by the light source is predetermined. For example, but not by way of limitation, the wavelength may be predetermined so that the emitted light induces fluorescence emission when irradiating a sample (e.g., a sample comprising a fluorescent protein). In some cases, the wavelength employed in connection with the methods described herein will be within the range of 400nm to 650nm. In some cases, the light source (2-005) will emit light with a wavelength between 400nm and 408nm, between 550nm and 565nm, or between 638nm and 650nm. In certain non-limiting implementations, the light source (2-005) is configured to include three lasers having nominal center wavelengths of 405 nm, 560 nm, and 640 nm, respectively, which can be varied within the absorption band of the fluorophore used. In some cases, the 405 nm wavelength is used to excite the Hoechst dye. In some cases, the 560 nm wavelength is used to excite the dye attached to the HaloTag (e.g., JF 549 ).

[0106] In certain non-limiting implementations, the light source (2-005) is used to catalyze a photochemical reaction. For example, but not by way of limitation, the wavelength and intensity of illumination can cause chemical bonds to break. As an additional example, but not by way of limitation, the wavelength and intensity of illumination can induce the adoption of a non-radiative dark state (i.e., "photobleaching molecules"). As an additional example, but not by way of limitation, the wavelength and intensity of illumination can induce radiative or non-radiative energy transfer between fluorophores within a sample. In some cases, a wavelength of 642 or 646 nm is used to excite a dye attached to a HaloTag (e.g., JF 646 ).

[0107] In certain implementations of the image acquisition systems described herein, the light source (2-005) can be configured to deliver a predetermined amount of power to the back focal plane of the objective lens (2-105). For example, but not by way of limitation, the light source (2-005) delivers a power greater than 10 mW for certain wavelengths (e.g., 405 nm) and / or delivers a power greater than 150 mW for other wavelengths (e.g., 640 nm). Additionally or alternatively, where the light source (2-005) comprises three lasers emitting at wavelengths of 405 nm, 560 nm, and 640 nm, respectively, the light source (2-005) can be configured to deliver a predetermined amount of power to the back focal plane of the objective lens (2-105). For example, but not by way of limitation, 405 nm can be configured to deliver >10 mW; 560 nm can be configured to deliver >150 mW; and 640 nm can be configured to deliver >50 mW.

[0108] In certain implementations of the image acquisition systems described herein, the light source (2-005) is configured to emit pulsed light. For example, but not by way of limitation, the light source (2-005) may be configured to emit stroboscopic pulsed light. In certain implementations of the image acquisition systems described herein, the light source (2-005) is configured to emit pulsed light synchronously with the start of image acquisition. In certain non-limiting implementations, the light source (2-005) will pulse at specific time intervals based on the number of frames captured per second. For example, but not by way of limitation, if the detector (2-165) captures 100 frames per second (FPS), the laser is on for 9 milliseconds and off for 1 millisecond. In contrast, in 200 FPS mode, the laser is on for 4 milliseconds and off for 1 millisecond. In certain implementations of the OLS htSMT workflow, the light source is configured to change from 90% power to 10% power in less than approximately 0.4 milliseconds. In certain implementations of the OLS htSMT workflow, the light source is configured to change from 90% power to 10% power in less than approximately 0.2 milliseconds.

[0109] In certain implementations of the image acquisition system disclosed herein, a single-mode optical fiber can be used to transmit light from the light source (2-005) and to direct the light to the optical relay (2-010). Alternatively, a multimode optical fiber can be used in certain implementations of the image acquisition system disclosed herein. For example, but not by way of limitation, the multimode optical fiber can be configured to have a predetermined shape for sample illumination.

[0110] In certain implementations of the image acquisition systems described herein, such as systems configured for high-throughput sample analysis, the light source (2-005) can be configured to exhibit low power output drift. In certain implementations, this low drift configuration improves the consistency of sample processing to facilitate high-throughput analysis. For example, but not by way of limitation, this low drift power output configuration maintains the power output within a variation of about 0% to about 15%, a variation of about 0% to about 10%, a variation of about 10%, a variation of about 9%, a variation of about 8%, a variation of about 7%, a variation of about 6%, a variation of about 5%, a variation of about 4%, a variation of about 3%, a variation of about 2%, or a variation of about 1%.

[0111] In some cases, such low-drift power output configurations maintain power output within a range of about 0% to about 15%, about 0% to about 10%, about 10%, about 9%, about 8%, about 7%, about 6%, about 5%, about 4%, about 3%, about 2%, or about 1% over a range of ambient (room) temperature (e.g., 17°C + / - 5°C). In some cases, this is achieved by using temperature sensors and / or closed-loop heaters to maintain a stable temperature within the internal light source (e.g., laser engine), thereby reducing output power drift. For example, but not by way of limitation, an insulated housing design can be used to isolate the light source from ambient temperature fluctuations. Additionally or alternatively, closed-loop heaters can be strategically placed at specific locations within the system, such as at the fiber coupler, to reduce output drift. Additionally or alternatively, a water jacket and / or chiller can be used to reduce heat buildup in the laser head. Furthermore, these thermal controls, used alone or in combination, can reduce the warm-up time to reach a stable operating state and maintain a more stable internal operating temperature when the laser is turned off and on.

[0112] 2.1.2. Optical components and sample illumination

[0113] Referring to the exemplary image acquisition system of FIG2 , the system includes a light source (2-005) configured to emit light, the light being relayed by one or more optical elements in an optical relay (2-010), the optical relay being configured to shape the light emitted from the light source to form a shaped light beam (2-065). The specific optical elements implemented in any particular optical relay (2-010) can be selected and configured to produce an appropriately shaped light beam (2-065) and provide appropriate translation of the light beam.

[0114] In certain non-limiting implementations of the optical relay (2-010) of the presently disclosed image acquisition system, the optical relay (2-010) will include one or more lenses and / or other optical elements. For example, but not by way of limitation, the selection and orientation of the lenses and other optical elements in the optical relay (2-010) will be configured to appropriately shape the light beam directed toward the sample. In certain non-limiting implementations, the optical relay (2-010) will include an optical element, such as a collimator (2-020), for collimating the emitted light from the light source (2-005). Additionally or alternatively, the optical relay (2-010) will include additional optical elements, such as a Powell lens (2-025) or other element suitable for generating a beam fan, one or more cylindrical lenses ((2-045) and (2-055)), one or more slits for adjusting the range of the light sheet ((2-050) and (2-095)), one or more achromatic lenses ((2-060) and (2-080)), and / or one or more mirrors ((2-070), (2-075), and (2-085)), one or more of which may be a galvanometer mirror (2-085) capable of translating light. The specific properties of the optical elements will be predetermined to produce an appropriately shaped beam. For example, but not by way of limitation, the OLS htSMT system of the present disclosure can achieve a uniform horizontal FOV as well as a uniform vertical FOV. This uniformity of horizontal and vertical FOV contrasts with other strategies that provide a non-uniform horizontal FOV and / or a non-uniform vertical FOV (see Table 2).

[0115] Table 2. Technology comparison

[0116] technology Horizontal FOV Vertical FOV Power transmission efficiency HILO Non-uniform Non-uniform Inefficiency HIST Non-uniform Uniform Inefficiency SOLEIL Non-uniform Non-uniform Inefficiency OLS Uniform Uniform Efficient

[0117] To achieve a uniform horizontal FOV and a uniform vertical FOV, the optical relay (2-010) of the OLS htSMT system described herein includes an optical element or assembly capable of producing a light beam that is elongated along the X plane and narrowed along the Y plane, and wherein the light beam has uniform intensity along the longer dimension of the linear shape. In certain non-limiting implementations, the optical relay (2-010) of the OLS htSMT system described herein will include a Powell lens (2-025) to shape the light beam so that it has uniform intensity along the longer dimension of the linear shape (2-065). The optical relay (2-010) of the OLS htSMT system described herein may include additional or alternative optical elements or assemblies to shape the light beam so that it has uniform intensity along the longer 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 may include a diffractive element or assembly configured to shape the light beam to have uniform intensity across the longer dimension of the linear shape.

[0118] In certain non-limiting implementations of the optical relay (2-010) of the presently disclosed image acquisition system, the optical relay (2-010) will include one or more optical elements or components configured to translate the light beam relative to the sample plane of the sample to be analyzed, for example, in a direction orthogonal to the longer dimension of the light beam. For example, but not by way of limitation, such optical elements or components configured to translate the light beam relative to the sample plane of the sample to be analyzed may include a galvanometer (2-085) or a piezoelectric element configured to translate the light beam. Additionally or alternatively, such optical elements or components configured to translate the light beam relative to the sample plane of the sample to be analyzed may include a computer-controlled motor.

[0119] refer to Figure 2A An exemplary image acquisition system includes an optical relay (2-010) configured to shape light emitted from a light source to form a shaped light beam (2-065), which is then guided by an optical element (2-100), such as a dichroic mirror, and the optical element is configured to guide the shaped light beam to an objective lens (2-120), whereby a sample plane (2-130) is illuminated by an inclined light beam (2-125).

[0120] In certain non-limiting implementations of the image acquisition system of the present disclosure, the objective lens (2-120) directs the tilted light beam (2-125) onto the sample plane (2-130) to be analyzed. In certain non-limiting implementations of the image acquisition system of the present disclosure, the objective lens (2-120) is a water immersion objective lens. The use of a water immersion objective lens enables high-throughput sample analysis by eliminating the oil associated with the use of an oil immersion objective lens, thereby allowing higher image quality and less distortion. The presence of oil is not only problematic in the case of automated systems, where the oil can spread to components, including optical elements that may become dirty due to contact with the oil, but the water immersion objective lens also provides a better refractive index match to the imaging unit, resulting in less distortion and therefore higher image quality compared to an oil immersion objective lens. In certain non-limiting implementations, the objective lens is a 60X 1.27NA water immersion objective lens (Nikon). In certain implementations of the workflow described herein, the water immersion objective lens (2-120) will be heated by a heating element. For example, such a heating element will maintain the water immersion objective (2-120) at a temperature sufficient to avoid causing temperature changes in a sample contained in the sample plate (2-021).

[0121] Image acquisition

[0122] In certain non-limiting implementations of the image acquisition system of the present disclosure, the objective lens (2-0120) is also used to focus fluorescence emitted by the sample (2-145) in response to illumination provided by the tilted light beam (2-125). In certain non-limiting implementations, the objective lens focuses the fluorescence emission (2-145) through emission filters ((2-150) and (2-160)), for example, bandpass emission filters matched to the spectrum of the observed fluorophore and mounted in a high-speed filter wheel (Finger Lakes Instruments), and is collected by the detector device (2-165). In certain non-limiting implementations, the objective lens focuses the fluorescence emission and is directed to an optical relay before being collected by the detector device (2-165). For example, but not by way of limitation, such an optical relay may include one or more lenses (2-155) and one or more additional optical elements, for example, elements configured to reject additional scattered light before collection by the detector device (2-165). In certain non-limiting implementations, the fluorescent emission focused by the objective is directed through another dichroic mirror to split the emission across multiple regions of a detector (2-165). In certain non-limiting implementations, the fluorescent emission focused by the objective is directed through another dichroic mirror to split the emission across multiple detectors (2-165).

[0123] In certain non-limiting implementations of the image acquisition system of the present disclosure, the detector arrangement is configured to synchronize detection with translation of the tilted light beam (2-125) on the sample plane (2-130). This synchronization is schematically depicted in Figure 2F For example, but not by way of limitation, the detection device may be a CMOS camera, such as a back-illuminated CMOS camera (Hamamatsu Fusion BT).

[0124] In certain implementations of the image acquisition system of the present disclosure, the CMOS camera can be operated so that a series of SMT frames are collected for each field of view. For example, but not limited to, 1-20,000 SMT frames, 1-15,000 SMT frames, 1-10,000 SMT frames, 1-5,000 SMT frames, 1-1,000 SMT frames, 2-500 SMT frames, 5-250 SMT frames, 10-200 SMT frames, 100-200 SMT frames, or 200 SMT frames are collected for each field of view. In certain implementations, the CMOS camera can be configured to operate at a frame rate of about 0.5 to about 2000 Hz. In certain implementations, the CMOS camera can be configured to operate at a frame rate of 0.5 to 1000 Hz, or in certain implementations, at a frame rate of 100 Hz. In certain embodiments, the CMOS camera may be configured to operate at a frame rate of 100 Hz to 1250 Hz, as shown in Figures 17, 19, 20, and 21. For example, but not by way of 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 cell SMT implementations may be performed at 2000 Hz. In certain embodiments, certain cellular SMT implementations may be performed at a frame rate of about 100 Hz or higher, about 200 Hz or higher, about 400 Hz or higher, about 600 Hz or higher, about 800 Hz or higher, about 1000 Hz or higher, about 1200 Hz or higher, about 1400 Hz or higher, about 1600 Hz or higher, or about 1800 Hz or higher. In certain embodiments, certain cellular SMT implementations may be performed at a frame rate of at most about 1200 Hz. In certain embodiments, certain cellular SMT implementations may be performed at a frame rate of at most about 1400 Hz. In certain embodiments, certain cellular SMT implementations may be performed at a frame rate of at most about 1600 Hz. In certain embodiments, certain cellular SMT implementations may be performed at a frame rate of at most about 1800 Hz. In certain embodiments, certain cellular SMT implementations may be performed at a frame rate of at most about 2000 Hz.

[0125] In certain non-limiting implementations of the image acquisition system of the present disclosure, the detector device is configured to transmit a signal with each frame to trigger other components of the imaging system. For example, but not by way of limitation, the detector device can trigger illumination from the light source (2-005) to collect fluorescence emission associated with a stroboscopic laser pulse. For example, but not by way of limitation, such fluorescence emission collection is associated with a frame of 10 to 100 milliseconds and a stroboscopic laser pulse of 2 milliseconds. In certain embodiments, the fluorescence emission collection is associated with a stroboscopic laser pulse of about 0.1 to about 1 millisecond. In certain embodiments, fluorescence emission collection is associated with a stroboscopic laser pulse of about 0.2 to about 1 millisecond, about 0.3 to about 1 millisecond, about 0.4 to about 1 millisecond, about 0.1 to about 0.9 milliseconds, about 0.1 to about 0.8 milliseconds, about 0.1 to about 0.7 milliseconds, about 0.1 to about 0.6 milliseconds, about 0.1 to about 0.5 milliseconds, about 0.1 to about 0.4 milliseconds, about 0.2 to about 0.6 milliseconds, about 0.2 to about 0.5 milliseconds, about 0.2 to about 0.4 milliseconds, or about 0.3 to about 0.5 milliseconds. In certain embodiments, fluorescence emission collection is associated with a stroboscopic laser pulse of about 0.1 to about 0.6 milliseconds. In certain embodiments, fluorescence emission collection is associated with a stroboscopic laser pulse of about 0.1 to about 0.5 milliseconds. In certain embodiments, fluorescence emission collection is associated with a stroboscopic laser pulse of about 0.2 to about 0.4 milliseconds. In certain embodiments, fluorescence emission collection is associated with a stroboscopic laser pulse of about 0.2 milliseconds. In certain embodiments, fluorescence emission collection is associated with strobed laser pulses of approximately 0.4 milliseconds, such as Figure 15C shown.

[0126] In certain implementations, the imaging acquisition system may be configured to acquire a predetermined field of view (FOV), such as a detected FOV. In certain embodiments, the FOV (e.g., the detected FOV) may have a size of about 150 μm to about 250 μm in a first dimension and a size of about 100 μm to about 210 μm in a second dimension. In certain embodiments, the FOV (e.g., the detected FOV) may have a size of about 200 μm to about 250 μm in a first dimension and a size of about 150 μm to about 210 μm in a second dimension, or the FOV (e.g., the detected FOV) may have a size of about 225 μm to about 250 μm in a first dimension and a size of about 175 μm to about 210 μm in a second dimension. For example, but not by way of limitation, the FOV (e.g., the detected FOV) may have a size of about 250 μm in a first dimension and a size of about 190 μm in a second dimension, for example, as disclosed in Example 1.

[0127] In some embodiments, a certain percentage of the FOV (e.g., the detected FOV) provides usable data. In some embodiments, at least 75% of the FOV, at least 80% of the FOV, at least 85% of the FOV, at least 90% of the FOV, at least 95% of the FOV, at least 96% of the FOV, at least 97% of the FOV, at least 98% of the FOV, at least 99% of the FOV, or 100% of the FOV provides usable data. In some embodiments, at least 75% of the FOV (e.g., the detected FOV) provides usable data. In some embodiments, at least 80% of the FOV (e.g., the detected FOV) provides usable data. In some embodiments, at least 85% of the FOV (e.g., the detected FOV) provides usable data. In some embodiments, at least 90% of the FOV (e.g., the detected FOV) provides usable data. In some embodiments, at least 95% of the FOV (e.g., the detected FOV) provides usable data. In some embodiments, at least 96% of the FOV (e.g., the detected FOV) provides usable data. In certain embodiments, at least 97% of the FOV (e.g., the detected FOV) provides usable data. In certain embodiments, at least 98% of the FOV (e.g., the detected FOV) provides usable data. In certain embodiments, at least 99% of the FOV (e.g., the detected FOV) provides usable data. In certain embodiments, 100% of the FOV (e.g., the detected FOV) provides usable data. In certain embodiments, a percentage equal to or greater than about 75% of the FOV provides usable data, for example, a percentage equal to or greater than about 80% of the FOV, a percentage equal to or greater than about 85% of the FOV, a percentage equal to or greater than about 90% of the FOV, a percentage equal to or greater than about 95% of the FOV, a percentage equal to or greater than about 96% of the FOV, a percentage equal to or greater than about 97% of the FOV, a percentage equal to or greater than about 98% of the FOV, or a percentage equal to or greater than about 99% of the FOV provides usable data. In certain embodiments, a certain percentage of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. For example, but not by way of limitation, at least 75% of the FOV, at least 80% of the FOV, at least 85% of the FOV, at least 90% of the FOV, at least 95% of the FOV, at least 96% of the FOV, at least 97% of the FOV, at least 98% of the FOV, at least 99% of the FOV, or 100% of the FOV achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 75% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 80% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion.In certain embodiments, at least 85% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 90% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 95% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 96% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 97% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 98% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 99% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, 100% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In some embodiments, a percentage of the FOV equal to or greater than about 75% achieves sufficient laser illumination to track protein motion, for example, a percentage of the FOV equal to or greater than about 80%, a percentage of the FOV equal to or greater than about 85%, a percentage of the FOV equal to or greater than about 90%, a percentage of the FOV equal to or greater than about 95%, a percentage of the FOV equal to or greater than about 96%, a percentage of the FOV equal to or greater than about 97%, a percentage of the FOV equal to or greater than about 98%, or a percentage of the FOV equal to or greater than about 99% achieves sufficient laser illumination to track protein motion.

[0128] In certain implementations, the imaging acquisition system can be configured to acquire a predetermined image size per frame, referred to herein as a region of interest (ROI). In certain implementations, the ROI will vary depending on the frame rate employed. For example, at 100 FPS, 2304 × 1728 pixels will define the ROI, which is equivalent to 248.832 × 186.624 microns in the sample plane. In contrast, at 200 FPS, 2304 × 768 pixels will define the ROI, which is equivalent to 248.832 × 82.944 microns in the sample plane.

[0129] In some implementations, the imaging acquisition system can be configured to perform a predetermined scan rate at a predetermined frame rate. For example, but not by way of limitation, at 100 FPS, the scan rate can be 186.624 μm / 9 ms, equivalent to 20.8 μm / ms, equivalent to 2.08 cm / s. In contrast, at 200 FPS, the scan rate can be 82.94 μm / 4 ms, equivalent to 20.7 μm / ms, equivalent to 2.07 cm / s.

[0130] In some implementations, the detector assembly can be used to collect fluorescence emissions at multiple wavelengths. For example, but not by way of limitation, fluorescence emissions from additional fluorophores within the same field of view can be collected at the same frame rate or at different frame rates to provide downstream registration of SMT tracks with other cellular components (e.g., the nucleus). Additional channels of the detector assembly can be used as needed to expand the number of fluorescence emissions captured simultaneously within the same field of view to provide downstream registration of SMT tracks with other cellular components (e.g., the nucleus).

[0131] Sample processing

[0132] refer to Figure 1 , various aspects of the current subject matter can be implemented using htSMT workflows, where such workflows incorporate systems for sample preparation, including reagent handling. For example, and not by way of limitation, Figure 5 A schematic diagram of a sample plate (2-021) is provided, comprising a plurality of wells (2-016) in which samples may be prepared and analyzed. Figure 5 A schematic diagram of sample components, such as cells (2-018) and fluorescent target proteins (2-017) within the cells is also provided. However, as described herein, Figure 5 It is not intended to convey scale, for example, each sample present in wells (2-016) may contain thousands of cells, and each cell may contain many fluorescent target proteins. Figure 5 The ability of the sample processing system of the present disclosure to add additional reagents to the sample (2-019) is also schematically illustrated. Such reagent addition can be handled by robotic operations, such as, but not limited to, translation of the robotic fluid handling system relative to the individual wells (2-016) of the sample plate (2-021), translation of the sample plate (2-021) itself, or a combination of both. In certain implementations of the image acquisition system, the sample plate (2-021) can be maintained in a temperature-controlled environment by an environmentally controlled area (2-020). For example, but not by way of limitation, the sample can be maintained at 22-50°C. In certain implementations of the image acquisition system, the sample plate (2-021) can be maintained in a humidity-controlled environment by an environmentally controlled area (2-020). For example, but not by way of limitation, the sample can be maintained at a humidity of 20%-95%. In certain implementations of the image acquisition system, the sample plate (2-021) can be maintained in a defined gas environment by an environmentally controlled area (2-020). For example, but not by way of limitation, the sample can be maintained at 5% CO2.

[0133] Cell lines and cell culture

[0134] See Figure 5A particular advantage of the htSMT system described herein is that it can be assayed in living cells (2-016), allowing for tracking the activity, mobility, and diffusion behavior of proteins within the crowded environment of living cells. Figure 13B As shown, the htSMT system of the present disclosure can be used to track fluorescently labeled proteins in a sample containing multiple cells. If the sample (e.g., containing such cells) can be focused by the objective lens (2-120) for a long enough time to direct the fluorescent emission of the fluorophore to the detector (2-165), then consider the example cells (e.g., cell lines) used in combination with the htSMT system described herein. For example, but not by way of limitation, the cells can be directly adhered to the coverslip. As an additional example, but not by way of limitation, after the coverslip is treated with an extracellular matrix material (e.g., fibronectin, collagen, poly-D-lysine, laminin, matrigel, vitronectin, etc.), the cells can be induced to adhere to the coverslip. As an additional example, but not by way of limitation, after the coverslip is treated with a plasma, the cells can be induced to adhere to the coverslip.

[0135] Exemplary cells (e.g., cell lines) may be selected so as to minimize non-fluorophore emission reaching the detector. In certain embodiments, the cells used in the present disclosure may be mammalian, bacterial, or fungal cells. In certain embodiments, the cells are mammalian cells. In certain embodiments, the cells may be obtained from preserved tissue (e.g., fixed tissue), frozen tissue (e.g., frozen tissue sample), or fresh tissue (e.g., fresh tissue sample). In certain embodiments, cells and / or samples containing cells may be obtained from a subject. In certain embodiments, cells may be obtained from a malignant tumor of a tissue or tumor, for example, cells may be present in a tumor sample (e.g., a section of a tumor). In certain embodiments, cells may be obtained from a cell line. For example, but not as a limitation, specific cell lines that can be used in conjunction with the htSMT system described herein include: U2OS cells (ATCC catalog number HTB-96), MCF7 cells (ATCC catalog number HTB-22), T47d cells (ATCC catalog number HTB-133), and SK-BR-3 cells (ATCC catalog number HTB-30). In certain embodiments, cells may be present in a three-dimensional structure, such as an organoid or a spheroid. In certain embodiments, the cells may be present in organoids.

[0136] In certain implementations of the htSMT system disclosed herein, the cells to be used are cultured as needed to provide sufficient cell numbers to achieve the desired high-throughput analysis. For example, but not limitation, cells such as U2OS cells (ATCC Catalog No. HTB-96), MCF7 cells (ATCC Catalog No. HTB-22), T47d cells (ATCC Catalog No. HTB-133), and SK-BR-3 cells (ATCC Catalog No. HTB-30) can be grown in DMEM (Catalog No. 1056601, Gibco DMEM, high glucose, GlutaMAX supplement, Thermofisher) supplemented with 10% fetal bovine serum (Catalog No. 16000044, Thermofisher) and 1% penicillin-streptomycin (Catalog No. 15140122, Thermo Fisher) and maintained in a humidified 37°C incubator with 5% CO2, with subculture performed approximately every two to three days. Additional culture strategies suitable for the cell lines and uses outlined herein are known to those skilled in the relevant art.

[0137] In certain implementations of the htSMT system disclosed herein, cells contain one or more fluorescent target proteins. The choice of specific proteins to be labeled and the specific labeling method may vary depending on the specifics of the particular study. For example, but not by way of limitation, one method for labeling proteins that can be used in conjunction with the htSMT system described herein is a HaloTag fusion strategy. For example, but not by way of limitation, one method for labeling proteins is a SNAPtag fusion. For example, but not by way of limitation, one method for labeling proteins is a CLIPtag fusion. For example, but not by way of limitation, one method for labeling proteins is a fluorophore ligase system. For example, but not by way of limitation, one method for labeling proteins is via a FlAsH or ReAsH tetracysteine ​​motif. For example, but not by way of limitation, one method for labeling proteins is a strain-promoted alkyne-azide cycloaddition reaction of a fluorophore. For example, but not by way of limitation, one method for labeling proteins is by inducing cellular uptake of a separately produced fluorescent target protein. In certain implementations of the htSMT system disclosed herein, cells contain one or more fluorescently labeled glycoproteins. In certain embodiments, one method for labeling proteins uses a gene editing system, such as a CRISPR-based editing system. For example, without limitation, a nucleic acid encoding a fluorescent protein (e.g., a fluorescent tag, such as HaloTag) can be inserted into a gene or upstream or downstream of a gene encoding a protein to be labeled to produce a protein fluorescently labeled with HaloTag (e.g., at its C-terminus or N-terminus), for example as described in Example 2.

[0138] Although the HaloTag fusion method can be implemented in a variety of ways by those skilled in the art, one exemplary method is to transfect a mammalian expression vector in a cell line of interest (e.g., U2OS cells) containing a fusion gene (i.e., a protein of interest fused in frame with the HaloTag sequence) under the control of a weak L30 promoter and containing a neomycin resistance marker. In certain implementations, such transfection can be accomplished using FuGENE 6 (Catalog No. E2691, Promega) when the cell confluence reaches 70%. In certain implementations, the transfected cells can then be selected with an appropriate selection agent, such as G418 (Catalog No. 10131027, Thermo Fisher), at an appropriate concentration of, for example, 500 μg / mL. In certain implementations, the cells can then be cloned and isolated. The cells can first be isolated by rinsing with 100 nM JF 549 -HTL (Cat. No. GA1110, Promega) and 50 nM Hoechst 33342 were used to stain and identify cells with the expected JF 549 The clones of the signal distribution are used to determine the clones expressing the desired fusion gene. Another exemplary method is to transfect cells with a ribonucleoprotein (RNP) complex, which includes sgRNA and Cas9 protein targeting the genomic sequence of the N-terminal or C-terminal region encoding the target protein, and combined with one or more linear dsDNA donors. In certain embodiments, each donor consists of a 200-300bp homology arm specific for each target, a codon-optimized HaloTag sequence, and a TEV linker (ENLYFQG) between the target and the HaloTag. In certain implementations, the SMT conditions can then be used to test the reaction of three to six clones to the control compound, and the most homogeneous clones can then be expanded for further testing.

[0139] While the htSMT workflow described herein is generally described for tracking the effects of a compound 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 can themselves be fluorescent or modified to facilitate fluorescence detection. Furthermore, changes in the motion of a fluorescent compound can be exploited to determine the SMT profile of the compound itself. Therefore, all analytical strategies described herein for tracking a target fluorescent protein also apply to results obtained using the tracking compound itself.

[0140] 2.2.2. Single-molecule tracking sample preparation

[0141] refer to Figure 5, various aspects of the current subject matter can be implemented using the htSMT workflow, wherein cells (2-018) are seeded on a plate (2-021), such as a 384-well tissue culture treated glass bottom plate, although other types of culture plates can also be used with the methods outlined herein, including but not limited to single chamber, 9-well glass bottom plates, 24-well glass bottom plates, 96-well glass bottom plates, 1536-well glass bottom plates, and 3456-well glass bottom plates, as well as plates made of alternative materials, such as plates made partially or entirely of plastic. In certain implementations, cells (2-018) are seeded at 1 to 20,000 cells per well (2-016), such as 50 to 10,000, 100 to 9,000, 250 to 8500, 500 to 7500, 750 to 7000, 2500 to 6500, or 6000 cells per well. The seeded cells can then be incubated under conditions suitable for adhesion, such as overnight at 37°C and 5% CO2. To enable fluorescence emission, the cells can be incubated with a sufficient amount of a label (e.g., one or more cell-permeable fluorophores). For example, but not by way of limitation, in the case of a HaloTag fusion, the cells can be incubated with about 0.1 to about 100 pM of JF 549 、JF 646 In some embodiments, cells can be incubated with about 0.1-100 pM of JF 549 -HTL (Cat. No. GA1110, Promega) or about 0.1-100 pM JF 646 Incubation with 50 nM Hoechst 33342 (for labeling cell nuclei), for example, for one hour in complete culture medium, may provide ideal results.

[0142] In certain implementations of the htSMT strategy described herein, the cells are then washed, for example, three times in DPBS and twice in imaging medium. In certain implementations, imaging medium is prepared to promote fluorescence emission, such as fluoroBrite DMEM medium (Cat. No. A1896701, Thermo Fisher), and may be supplemented with GlutaMAX (Cat. No. 35050079, Thermo Fisher) and the same serum and antibiotics as the growth medium.

[0143] Where appropriate, the compound can be added to the sample to test its effect on a specific marker protein by SMT. In some implementations, the compound can be serially diluted in an Echo Qualified 384-well low dead volume source microplate (0018544, Beckman Coulter) to generate a dose titration source material. The compound can then be administered in a cell culture medium at a final dilution of, for example, 1:1000. In some implementations of the htSMT strategy described herein, each dose of compound will have at least two replicates per plate and three replicates per plate. In addition, in some implementations of the htSMT strategy described herein, 20 DMSO control wells and two dye-free control wells can be randomly distributed on each sample plate (2-020). In some implementations, the compound can be incubated for 0 to 48 hours before image acquisition, for example, at 37°C for 1 hour.

[0144] 3. OLS htSMT software

[0145] 3.1.htSMT Software Overview

[0146] Figure 6 An example system 600 of a high-throughput single-molecule imaging platform for measuring the movement of molecules within living cells is illustrated. An experiment 602 can be performed to collect a large amount of data from a plurality of living cells (e.g., using an imaging system 624 to identify a compound 626 and / or a target 622). The experiment 602 can include applying various identifiers to molecules of interest, such as tags that can then fluoresce or be detected in other ways (e.g., using a laser or other light source). A biological sample forming part of such an experiment 602 can be organized into a plate 604 having a plurality of wells 606. Each well 606 can have one or more associated fields of view (FOVs) 610. The FOVs 610 can be located within or correspond to a single well 606. An image sequence can be generated for the FOV 610 to produce one or more movies 612, which can include SMT movies as well as non-SMT movies. The SMT movies can be used to track the path of a single labeled molecule (e.g., a protein), thereby generating multiple tracks. Each track can be composed of a plurality of points 614, which include the spatiotemporal coordinates of the labeled molecule at a particular time (e.g., Figure 7 610 ). Separately from tracking, and in some cases in parallel with tracking, the movie 612 can be used to identify molecules by using machine learning and / or computer vision-based image segmentation to generate masks 618. Masks 618 are spatial regions within the FOV 610 generated by segmentation. Each mask 618 can belong to a mask category, which is Figure 8 and Figure 22B A more detailed description is given in .

[0147] Data associated with the two channels (e.g., the tracking channel and the segmentation / masking channel) can be combined to generate a plurality of metrics 620 associated with various aspects of the sample. In other words, the trajectory 616 (e.g., trajectory data) can be combined with the machine learning processed image segmentation data and further analyzed using statistical / machine learning methods. The processing of the combined data can be used to generate metrics 620, such as hit scores associated with compounds and / or targets in the biological sample, which can be stored in a database structure, such as Figure 9 Further described.

[0148] Figure 7 6. The data flow 700 through an example system of a high-throughput single-molecule imaging platform for measuring protein movement in living cells is illustrated. Experimental specifications 704 defining experiments 602 may be provided as data input via one or more clients 702. For example, each experiment 602 may be collected with accompanying stains (e.g., Hoechst or Potomac Red) for downstream analysis including segmentation 618. The experimental specifications 704 may define various parameters of the experiment 602, such as stains, dyes, compounds, treatments, etc. As previously described in Figure 6 As described in , imaging system 706 (e.g., imaging system 624) can capture a sequence of images that generate one or more SMT movies 711 and / or non-SMT movies or segmented movies 708 (e.g., movie 612) that represent molecular motion. SMT movie 711 can represent the motion of a single fluorescent dye molecule and / or an image containing a single fluorescent dye molecule. Segmented movie 708 can include a sequence of images that represent the motion of labeled molecules and / or their components. It should be understood that Hoechst staining is only one technique that can be used to label molecules, and different and / or multiple labeling techniques, such as Potomoc Red, can be utilized depending on the desired configuration. For example, MitoTracker Deep Red can be used to label mitochondria, concanavalin A-dye conjugate can be used to label the endoplasmic reticulum, SYTO 14 can be used to label nucleoli, phalloidin can be used to label actin, etc.

[0149] The SMT movie 711 can be analyzed to perform operations related to molecular tracking 710, which can include detection 712, sub-pixel localization 713, and linking 714 to identify molecular tracks 715 across the various images within the SMT movie 711. More specifically, during detection 712, one or more points within the SMT movie 711 can be detected or recovered. Each point can be assigned spatiotemporal coordinates. These spatiotemporal coordinates can be estimated using sub-pixel localization techniques 713. Linking 714 can be performed on these points to ultimately identify tracks 715.

[0150] As used herein, a link is a potential association between two points. Each link is directed, starting at one point and ending at another. A "correct link" connects two points generated by the same transmitter in different frames; otherwise, the link is "incorrect." One goal of the linking algorithm is to estimate which links are correct. Links referred to herein are of the format a:i→j. This means: link a, starts at point i and ends at point j. A link satisfies at least the following three constraints: (a) the link progresses in time, (b) the link must not connect two points that are more than a certain limit apart (referred to herein as the "search radius"), and (c) the link must not connect two points that are more than a certain limit apart in time (referred to herein as the "gap limit"). A point-link graph is a graph of the points and links of an SMT movie 711. Points are the vertices of the graph, and links are the edges of the graph. Because links progress in time, the point-link graph is a directed acyclic graph. A match is a subset of the links in the point-link graph such that no two links in the subset start or end at the same point. Trajectory 715 is used herein to refer to a continuous (end-to-end) sequence of links in the same match. A plurality of trajectories may be used to determine a dynamic index 730. Such parameters may include properties of the point that characterize the motion of the point. Such parameters may include one or more of the velocity, diffusion coefficient, or anomaly parameter of each point. The dynamic parameter of point i is referred to herein as θ i The set of dynamic parameters for all points in the point-link graph is referred to here as Θ.

[0151] Separately from the processing of the SMT movie 711, and in some variations in parallel therewith, the segmentation movie 708 can be segmented to generate one or more masks 720. The masks can be of various categories including, but not limited to, nucleus, cytoplasm, cell cycle phase, and / or irrelevant masks, which will be described in detail in the following sections. Figure 8 and Figure 22B . An instance mask is an individual segmented object (e.g., a cell, a nucleus, a mitochondria, M phase, G1 phase, early S phase, mid S phase, late phase, G2 phase). The FOV 610 may contain any number of instance masks of a mask category. A semantic mask is the union of all instance masks of a mask category corresponding to a FOV (e.g., all cells, all nuclei, or all mitochondria of a FOV, etc.). Irrelevant masks may contain portions of the non-SMT movie 708 that are excluded from any downstream data analysis. For example, these irrelevant masks may correspond to portions of the non-SMT movie 708 that are out of focus or contain autofluorescent cell debris that prevents accurate tracking. During the segmentation process, molecules in the segmented movie 708 may be assigned to one or more masks. Image metrics 740 may be evaluated based on the masked molecules (e.g., cell health, focus quality, etc.).

[0152] Experiment information, such as dynamic metrics 730, image metrics 740, and any data derived from any of the metrics (e.g., segmentation information), can be provided to a data repository 770 for storage. Such a data repository 770 can store, for example, any results of an experiment 602, such as dynamic metrics 730, image metrics 740, and / or any data derived from any of the metrics. The data repository can include local persistence and / or dedicated servers accessed locally or via the cloud. The data repository 770 can also store metadata associated therewith and / or metadata associated with the experiment specification 704. Experiment information (e.g., results and metadata from historical experiments, etc.) can be provided to the data repository 770 via a repository application program interface (API) 750. The repository API 750 can also interface with a web-based graphical user interface front end 760 that provides such information for display on the client 702.

[0153] In some variations, the segmentation information can be used to identify subcellular compartments, such as the nucleus, nucleolus, cytoplasm, etc. The segmentation information can also be used to distinguish one cell from another. The segmentation information can be stored in a specific format (e.g., a multi-image file format such as TIFF).

[0154] The example dynamic index 730 may also include a state array. The state array is a framework for learning interpretable dynamic models from SMT trajectories and can be used to further understand the movement of the target protein and where in the cell the movement occurs. In some variations, the state array can be generated / populated using segmentation information. The output of the state array can be returned at the subcellular compartment level, allowing scientists to distinguish the dynamics of different subcellular compartments. In addition, the state array can be calculated for each individual subcellular compartment (e.g., each cell nucleus).

[0155] To facilitate application access to data (including but not limited to state arrays), processed SMT data can be stored in formats that allow for the following: (a) representation of processed tracks and associated properties, such as the SNR and point shape characteristics of each SMT movie; (b) representation of mask objects, including mask categories (e.g., the associated subcellular organelle for each mask object, the cell cycle stage for each mask object, etc.); (c) association of tracks with mask objects (e.g., the nucleus in which each track was observed, or the cell cycle stage in which each track was observed); and (d) association of all SMT movies with metadata about the original experiment, such as compound treatment, acquisition time, and imaging system name. Formats (a) and (c) can be protocol buffer schemas that define the storage format for tracks and associated mask objects. Format (b) can be a specialized image file format that includes the mask object to which each pixel in the FOV belongs. Format (d) can be a PostgreSQL database that records all captured experiments / movies. As a client of processed SMT data, the state array can leverage these data schemas to report the dynamic characteristics of tracks for each mask category or each mask object.

[0156] Figure 8 800 are multiple images illustrating the difference between mask categories and instance or semantic masks. As previously described, non-SMT movies or segmentation movies can be assigned to multiple categories. These categories may include cell nuclei (e.g., category A), cytoplasm (e.g., category B) and / or irrelevant masks (e.g., category C). Unique, individual masks can be applied to biological samples. For example, image 810 is a unique, individual instance mask applied to a cell nucleus (e.g., category A). Image 812 is a unique, individual instance mask applied to the cytoplasm (e.g., category B). Image 820 illustrates multiple instance masks applied to one or more cell nuclei, where a single color represents a different, unique individual instance mask. Image 822 illustrates multiple masks applied to one or more cytoplasms, where a single color represents a different, unique individual instance mask. Image 830 illustrates a semantic mask applied to one or more cell nuclei, which is the union of all instance masks. Image 832 illustrates a semantic mask applied to one or more cytoplasms. Figure 22B The use of mask categories is further explained. Figure 22B As shown, a single instance mask can be applied to cells in M ​​phase, a single instance mask can be applied to cells in G1 phase, a single instance mask can be applied to cells in early S phase, a single instance mask can be applied to cells in mid S phase, a single instance mask can be applied to cells in late S phase and / or a single instance mask can be applied to cells in G2 phase.

[0157] Figure 9An example computer-implemented environment 900 is illustrated in which an imaging system 910 can interact with a computing architecture to execute the various algorithms described herein. Figure 9 As shown, the imaging system 910 can interface with one or more clients 950 (e.g., client 702 via a web application with a graphical user interface). The one or more clients 950 can interface with one or more servers 920 accessible via a network 930. The one or more clients 950 can host a frame grabber that captures images (e.g., movie 612) from a camera. These images can be temporarily stored on the one or more clients 950 and periodically transmitted to the one or more servers 920 via the network 930 for remote storage. The one or more servers 920 can also contain or have access to one or more data stores 940 for storing data collected and / or extracted from the sample by the imaging system 910. In some variations, the network 930 can include or be interfaced with one or more network storage arrays 960 for storing data such as captured images (e.g., movie 612).

[0158] Figure 10 1000 is a diagram illustrating an example computing device architecture for implementing various aspects described herein. In some variations, the sample computing device architecture may be the architecture of client 950 and / or server 920, and some components described with respect to diagram 1000 may be optional for client 950 and / or server 920. Bus 1004 may serve as an information highway interconnecting the other illustrated components of the hardware. Processing system 1008, labeled CPU (central processing unit) (e.g., one or more computer processors / data processors on a given computer or multiple computers), may perform the computations and logical operations required to execute a program. Optionally or in addition, processing system 1012, labeled GPU (graphics processing unit) (e.g., one or more computer processors / data processors on a given computer or multiple computers), may perform the computations and logical operations required to execute a program. Non-transitory processor-readable storage media (e.g., read-only memory (ROM) 1016 and random access memory (RAM) 1020) may communicate with processing system 1008 and / or processing system 1012 and may include one or more programming instructions for the operations specified herein. Optionally, the program instructions may be stored on a non-transitory computer-readable storage medium, such as a magnetic disk, an optical disk, a recordable memory device, a flash memory, a solid-state drive, or other physical storage medium.

[0159] In one example, the disk controller 1048 can interface one or more optional removable storage 1056 or local storage 1052 with the system bus 1004. The removable storage 1056 can be an external or internal disk drive, a solid-state drive, or an external hard drive. The local storage 1052 can be an internal hard drive and / or memory. As previously described, the various examples of removable storage 1056, local storage 1052, and disk controller 1048 are all optional devices. The system bus 1004 can also include at least one communication interface 1024 to allow communication with external devices (such as cloud storage and remote services) that are physically connected to the computing system or obtained externally via a wired or wireless network. In some cases, at least one communication interface 1024 includes or additionally comprises a network interface.

[0160] In some variations, such as for client 950, to provide for user interaction, the subject matter described herein may be implemented on a computing device having a display device 1044 (e.g., an LCD (liquid crystal display) or LED (light emitting diode) monitor) for displaying information obtained from bus 1004 to the user via display interface 1040, and an input device 1032 (e.g., a keyboard and / or pointing device (e.g., a mouse or trackball) and / or a touch screen) through which the user can provide input to the computer. Other types of input devices 1032 may also be used to provide for user interaction; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback via microphone 1036, or tactile feedback); and input from the user may be received in any form, including sound, voice, or tactile input. Input device 1032 and microphone 1036 may be coupled to bus 1004 via input device interface 1028 and communicate information via the bus. For example, input device 1032 may be imaging system 910 configured with the capability to capture the image sequences described herein. The frame grabber 1058 may capture or grab a single frame from the analog or digital data that encapsulates the image sequence obtained from the bus 1004. The frame grabber 1058 may include a memory capable of storing a single or multiple frames. The frame grabber 1058 may also provide the single or multiple frames to the bus 1004 for further storage, for example, on the local memory 1052 and / or the removable memory 1056. Other computing devices (e.g., dedicated servers) may omit the incorporation of Figure 10 Describes one or more components.

[0161] One or more aspects or features of the subject matter described herein may be implemented in digital electronic circuits, integrated circuits, specially designed application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), computer hardware, firmware, software, and / or combinations thereof. These various aspects or features may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system comprising at least one programmable processor, which may be dedicated or general purpose, coupled to receive data and instructions from a storage system, at least one input device, and at least one output device, and to transmit data and instructions to the storage system, at least one input device, and at least one output device. A programmable system or computing system may include a client and a server. The client and server are typically remote from each other and typically interact via a communication network. The relationship of client and server arises through computer programs running on respective computers that cause each other to have a client-server relationship.

[0162] These computer programs, which may also be referred to as programs, software, software applications, applications, components or codes, comprise machine instructions for a programmable processor and may be implemented in high-level procedural languages, object-oriented programming languages, functional programming languages, logic programming languages ​​and / or assembly / machine languages. As used herein, the term "machine-readable medium" refers to any computer program product, device and / or apparatus for providing machine instructions and / or data to a programmable processor, such as a disk, an optical disk, a memory and a programmable logic device (PLD), including a machine-readable medium that receives machine instructions in the form of a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor. A machine-readable medium may store such machine instructions non-temporarily, such as a non-transitory solid-state memory or a magnetic hard disk or any equivalent storage medium. A machine-readable medium may alternatively or additionally store such machine instructions in a transient manner, such as a processor cache or other random access memory associated with one or more physical processor cores.

[0163] Probabilistic methods for dense molecular tracking in living cells

[0164] One aspect of the htSMT workflow 100 includes recovering tracks 715 or paths of identifiers (eg, individual fluorescent emitters, etc.) from a recorded sequence of images (eg, SMT movie 711). This recovery is referred to herein as "tracking."

[0165] Tracking 710 in htSMT presents several challenges. First, each emitter is dim, contributing as few as a hundred photons per frame, necessitating sensitive detection methods. Second, the absolute intensity and noise characteristics of each movie depend on its originating imaging system; these differences can arise from variations in laser power or camera gain and offset. Therefore, it is desirable that htSMT tracking methods be insensitive to variations in the absolute intensity of the movie. Third, protein motion in cells can be rapid, causing molecules to rapidly enter and leave focus. Consequently, average track lengths can be as short as three or four frames, severely limiting the information available for predicting the molecule's future motion. Finally, tracking at high labeling densities becomes challenging due to ambiguities in associating detections with tracks. For example, tracking methods could adjust their parameters in a density-dependent manner to enable accurate tracking at a wide range of densities.

[0166] As previously described, tracking 710 of htSMT may include detection 712, sub-pixel localization 713, and linking 714. First, during detection 712, the portion of each movie frame of the SMT movie 711 containing emitters is identified. Second, sub-pixel localization 713 extrapolates the positions of the emitters to sub-pixel resolution, thereby producing the spatiotemporal coordinates of each detected emitter. For example, sub-pixel localization 713 may fit the observed light distribution around the emitter to an approximation of the imaging system's point spread function (PSF). Third, a linking algorithm associates the detected emitters with tracks. While all steps should be high-performance to meet the demands of htSMT data processing, linking 714 in particular can become very expensive in the case of high label density and / or number of detections per frame. This may be due to the combinatorial explosion of possible tracks that can be constructed from a given set of detections.

[0167] Figure 11 Diagram 1100 illustrating the extensible tracing pipeline 1110 in htSMT. Diagram 1100 is a modular structure that allows for custom composition at runtime. Figure 7 710 (e.g., detection 712, sub-pixel localization 713, and linking 714) as specified in an experiment-specific configuration file 1122. Each tracing pipeline 1110 may have an associated runtime configuration 1120 specified by the experiment-specific configuration file 1122, which may define specific types for the detector 1112, sub-pixel localizer 1113, and linker 114.

[0168] The tracking pipeline 1110 may receive as input an image sequence (e.g., an SMT movie) 1111. A detector 1112 may be applied to the image sequence 1111 to detect or recover one or more points within the image sequence 1111 using any of the following detector types: a generalized log-likelihood ratio point detector, a difference of Gaussian (DoG) detector, a Laplace of Gaussian (LoG) detector, a determinant of Hessian (DoH) blob detector, or any combination thereof. Other types of detectors may be used depending on the implementation.

[0169] The spatiotemporal coordinates associated with the detected points may use sub-pixel localization 1113. This sub-pixel localization 1113 may include any of the following types of localizers: radially symmetric localizers, maximum likelihood fitting of candidate point models using the Levenberg-Marquardt method, etc. Other types of localization techniques may be used depending on the implementation.

[0170] A linker 1114 may be used to link two points. In some variations, the linker 1114 may rely on heuristics (e.g., nearest neighbor methods, etc.) or an exact solution to the assignment problem (e.g., Hungarian algorithm, etc.). This type of linker may utilize separate steps for inferring trajectories and inferring dynamic parameters from the trajectories. In other variations, the linker 1114 may infer a joint probability distribution of possible trajectories and dynamic parameters in a scalable manner. This distribution can be used to make more informed point estimates of the "correct" trajectory, estimate confidence in any particular set of trajectories, or derive dynamic results completely independent of the trajectories. This approach, referred to herein as "probabilistic linking," can be used to estimate the trajectory distribution of thousands to tens of thousands of fast-moving objects that are in close proximity in a movie.

[0171] The trace pipeline 1110 may output an object trace 1115 that represents the possible trajectories in a graphical format. This output may depend on the type of linker used by the linker 1114.

[0172] 3.2.1. Probabilistic methods for dense molecular tracking in living cells

[0173] Two example probabilistic linking types of linker 1114 may utilize different approaches, including variational Bayesian inference (referred to herein as "vtrack") or Gibbs sampling (referred to herein as "gibbstrack").

[0174] The dynamic parameters of each point can be viewed as parameters of a motion model that defines the probability distribution of its future motion. It can be assumed that the probability of any given vector displacement between points i and j depends only on the dynamic parameters of points i and j, and not on the rest of the point link graph. i→j Expression 1 expresses this probability and defines a "motion model":

[0175] f r|θ (r i→j |θ i ,θ j ). (1)

[0176] One option for Equation 1 is to use a likelihood function for the scaled Brownian motion, which can be characterized by a single kinetic parameter (the diffusion coefficient) at each point. A simplified Bayesian model for the scaled Brownian motion is described in detail in Section 3.2.6 below.

[0177] The objective function of the linking algorithm can be defined as follows. Let M be the number of links in the point-link graph. Let E∈{0,1} M is a vector of 1s and 0s, representing a match such that if link a participates in the match, then E a =1, otherwise E a = 0. Let w∈R M is a real-valued weight vector. Let w0 be the likelihood of starting or ending the trajectory. Then the linking algorithm is to find the optimal match that satisfies Equation 2.

[0178]

[0179] Because each matching cannot contain two links that start or end at the same point, Equation 2 (eg, the criterion for the solution of the linking problem) is an instance of an unbalanced allocation problem.

[0180] Because each element of the matching vector E corresponds to a link a:i→j, each element here can be conveniently indexed by its link index a (i.e., E a ) or its point index (i.e. E i→j ) to index. Similarly, vector displacement can be expressed as r corresponding to the link a:i→j a or r i→j .

[0181] If the weight vector is constant, then Equation 2 can be solved using classical solutions to the assignment problem. These include exact solutions (such as the Hungarian algorithm) and heuristics (such as the nearest neighbor method).

[0182] However, in general, the weight vector can be a function of the dynamic parameters 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 estimated jointly.

[0183] The goal of the probabilistic linking algorithm is to evaluate the conditional distribution p(E,Θ|R). Here, R=(r1,…,r M) represents the vector displacement corresponding to each of the M links in the point link graph. Alternatively, these terms can be generalized to contain any additional information relevant to the link problem (e.g., spatial location, point shape characteristics, etc.). Once the distribution p(E, Θ|R) is obtained (e.g., by the vtrack or gibbstrack methods discussed in this paper), it can be used to estimate the maximum a posteriori trajectory by solving Equation 2 using the marginal link probability w = logp(E|R), or by taking the posterior average dynamic parameter to obtain the estimated values ​​of the dynamic parameters.

[0184] According to Bayes’ theorem, the conditional distribution p(E, Θ|R) can be written as Equation 3 (e.g., Bayes’ theorem for the linking problem):

[0185]

[0186] 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. Since f r|θ (r i→j |θ i ,θ j ) is a given dynamic parameter θ i and θ j A single displacement r i→j The likelihood function of (Equation 1) is given by the product of the likelihood functions of each link (Equation 4). In Equation 4 (i.e., the likelihood function of the link problem), Pa(i) is the set of parents of point i, which contains the set of all points j such that j→i is an allowed link:

[0187]

[0188] In Equation 3, the term p(E, Θ) is a prior on E and Θ. Here we can assume that p(E, Θ) = p(E)p(Θ), where p(E) = constant for all allowed E, and where p(θ i ) is the prior on the dynamic parameters of point i and is chosen to be consistent with the likelihood f r|θ (r i→j |θ i ,θ j ) conjugation.

[0189] In Equation 3, the term p(R) is the so-called "evidence" and can be analytically difficult to handle. Therefore, the left side of Equation 3 cannot be evaluated in closed form. The methods described in this paper, based on Gibbs sampling (gibbstrack) and variational tracking (vtrack), avoid this problem. gibbstrack approximates the left side of Equation 3 by drawing a fixed number of random samples, while vtrack approximates the left side of Equation 3 using a mean-field approximation. Gibbstrack and vtrack are discussed in detail below.

[0190] 3.2.2. Probabilistic Linking via Variational Bayesian Optimization (vtrack)

[0191] Using vtrack, the posterior p(E, Θ|R) can be approximated using variational Bayesian optimization. In this approach, the posterior can be approximated by assuming it is a factor of E and Θ: p(E, Θ|R) ≈ q(E)q(Θ). vtrack achieves progressively better approximations to the posterior by first refining q(E) while holding q(Θ) constant, then refining q(Θ) while holding q(E) constant, and iterating between these two steps until convergence.

[0192] By manipulating the model introduced in Equation 4, a simplified version of vtrack can be derived. This derivation leaves room for the choice of motion model, as vtrack can be used with a variety of motion models. As described below, vtrack can be derived from the Brownian motion model or a more general model.

[0193] Consider the joint probability function of all variables in the model expressed in Equation 4. Based on the a priori choices discussed in Section 3.2.1, the joint probability distribution of all parameters can be decomposed as shown in Equation 5 (i.e., the joint probability density of the link problem):

[0194] p(R, E, Θ)p(E)p(Θ). (5)

[0195] Substituting Equation 4 and the prior p(R, Θ) into Equation 5 and taking the logarithm yields Equation 6 (i.e., the logarithmic joint probability density of the linking problem):

[0196]

[0197] In variational pursuit methods, we seek an analytical approximation to the true posterior q(E,Θ)≈p(E,Θ|R) that satisfies two criteria. First, q is a factor of E and Θ (Equation 7):

[0198] q(E, Θ)=q(E)q(Θ). (7)

[0199] Second, q maximizes the evidence lower bound (Equations 8 and 9):

[0200] and (8)

[0201] q(E,Θ)=argmax q L[q]. (9)

[0202] Any distribution q(E, Θ) that satisfies Equation 7 and Equation 9 must also satisfy Equation 10 and Equation 11. In Equation 10 (i.e., the recursive equation for the factor q(E)) and Equation 11 (i.e., the recursive equation for the factor q(Θ)), logp(R, E, Θ) is given by Equation 6, and the expected value is and is taken according to the corresponding factor in q(E,Θ), and the constant explains the normalization of the corresponding factor:

[0203] and (10)

[0204]

[0205] Equations 10 and 11 can be solved sequentially to obtain a progressively better approximation to q(E, Θ). This scheme is called expectation maximization. This algorithm converges because the evidence lower bound (Equation 8) is convex for each factor in q. This method is combined with the tracking model defined in Equation 6, referred to here as vtrack.

[0206] Once q(E,Θ) is obtained, the maximum a posteriori matching vector can be evaluated using a sparse hill climbing algorithm by setting the weight vector to the marginal log probability of each link

[0207] Equations 10 and 11 can be solved for any motion model for which a conjugate prior exists (i.e., any choice of Equation 1), along with the log probability density expressed by Equation 6. This includes any motion model with a probability density belonging to the exponential family of distributions.

[0208] 3.2.3.Specific Form of Brownian Motion vtrack

[0209] As a limited demonstration of vtrack, we can next solve Equations 10 and 11 for the Brownian motion model represented by Equation 19 using the prior represented by Equation 20. Under these conditions, the log joint probability (Equation 6) becomes Equation 12 (i.e., the joint probability density of Brownian motion). In Equation 12, m is the spatial dimension, θ i is the diffusion coefficient at point i, r i→jis the spatial displacement of link i→j, Δt is the frame interval, and α0 and β0 are prior parameters.

[0210]

[0211] Substituting Equation 12 into Equation 10 and Equation 11 and solving for the factors q(E) and q(Θ), respectively, yields Equation 13 and Equation 14. In these equations, GraphSoftmax is the GraphSoftmax operator as described below, T is the temperature, w0 is the trajectory start likelihood, and is the log-likelihood vector for each link. The vtrack algorithm then proceeds by calculating β and l given α via Equation 13, and then calculating l given α and β via Equation 14. This process is repeated until convergence. The maximum a posteriori matching is then estimated using the sparse hill climbing algorithm described below, given the marginal link probabilities e. Equation 13 is an approximate posterior for the dynamical parameters of the Brownian motion and can be expressed as follows:

[0212]

[0213] Equation 14 is the approximate posterior of the marginal link probability of the Brownian motion and can be expressed as follows:

[0214]

[0215] 3.2.4. Extended Variational Pursuit Algorithm

[0216] The vtrack algorithm described above can be extended to exploit additional information hidden in the point-link graph as follows. Given a point-link graph with N points and M links, let Represents the m-dimensional coordinates of each point. As before, use the vector E∈{0,1} M To indicate whether each link participates in the matching, each point i is compared with the dynamic parameter θ i Associated, and let Θ=(θ1,…,θ N ) is the set of dynamic parameters for all points. We can assume the stochastic model expressed in Equation 15. In Equation 15, Pa(i) is the "parent point" of point i, or the set of points starting from the link that ends at point i. The term f r|θ (r i→j |θ i ) represents the kinetic parameter θ i Lower Sports X j →X i The probability density of . i ) is the prior of the dynamic parameters of point o, and it is selected to be r|θ (r i→j |θi ) conjugate. Note that the term p(X i |θ i ,E) is defined recursively in terms of the parents of point i, which makes Equation 15 a generalization of Bayesian networks that allow for uncertainty in the links E.

[0217]

[0218] As with the simple vtrack algorithm, we seek an approximate posterior q(E,Θ)=q(E)q(Θ)≈p(E,Θ|X) to maximize the evidence lower bound (Equation 8). The algorithm proceeds by alternately solving Equations 10 and 11.

[0219] As an example of the solution, we can use Brownian motion to prove the result. To do this, we can assume that f r|θ (r i→j |θ i ) is a gamma distribution of the form specified in Equation 19, and p(θ i ) is the inverse gamma distribution of the form specified in Equation 19. The posterior probability is then given by Equations 16 and 17. In Equation 16, the term is the marginal link probability, and an additional mean-field approximation is made For the Brownian model, vtrack is performed as follows: (a) Evaluate each α given l i and β i , and (b) evaluate given all α i and β i The operator GraphSoftmax is a graph softmax operator as described below. Equation 16 is the approximate posterior of the point potential parameter and can be expressed as follows:

[0220]

[0221] Equation 17 is the approximate posterior of the link and can be expressed as follows:

[0222]

[0223] As in the case of the simple vtrack algorithm, once the approximate posterior q(E,Θ) is obtained, the maximum a posteriori trajectory can be estimated by applying the sparse hill climbing algorithm (described below) to the log marginal link probability (logl).

[0224] 3.2.5. Gibbs sampling method for probabilistic links (gibbstrack)

[0225] Another way to evaluate p(E, Θ|R) is to draw random samples from this distribution and then take the mean of these samples to approximate the mean of the posterior distribution. A simple and flexible way to implement this sampling scheme is to alternately draw from the conditional distributions of E and Θ (Equation 18). Equation 18 defines Gibbs tracking and can be expressed as follows:

[0226] E~p(E|R,Θ) (18)

[0227] Θ~p(Θ|R,E).

[0228] The sampling scheme expressed in Equation 18 can be accomplished as follows. Start by estimating the dynamic parameters Θ and setting E to all zeros (this is always a valid matching vector for any point-link graph). At each iteration, the log-likelihood of each link is evaluated according to the current dynamic model (the motion model is selected by Equation 1). Let is the vector of these log-likelihoods for all links. Propose a "pivot" as follows, which corresponds to changing up to 4 elements of E and is associated with a change in the log-likelihood Δw. Draw a random number u ~ uniform (0,1), if u ≤ e Δw / T Then accept the pivot. Next, draw a sample θ i ~p(θ i |E), if Equation 1 is consistent with the prior θ i conjugate, then the sample can be found analytically.

[0229] If E1, E2, …, E n and Θ1,Θ2,…,Θ n is a sample generated in this way by Equation 18, then the marginal link probability l a Can be approximated as As in the case of vtrack, the maximum a posteriori estimate can be estimated by applying the sparse hill climbing algorithm (Section 3.2.7) to the marginal link probabilities

[0230] 3.2.6. Bayesian Model of Brownian Motion

[0231] One motion model for vtrack and gibbstrack is scaled Brownian motion, which uses a single kinetic parameter (diffusion coefficient) to characterize the motion of each point. Under scaled Brownian motion, the likelihood function Equation 1 becomes a gamma distribution represented by Equation 19, where m represents the dimension of the space in which the motion is observed, and θ i is the diffusion coefficient at point i, and r i→jis the vector displacement corresponding to link i→j. Equation 19 is the likelihood function of the m-dimensional Brownian motion and can be expressed as follows:

[0232]

[0233] A useful prior conjugate of the Brownian likelihood expressed in Equation 19 is the inverse gamma prior expressed in Equation 20. In Equation 20, α0 and β0 are prior hyperparameters, Δt is the frame interval, and θ is the diffusion coefficient of a single point. Equation 20 is the prior for Brownian motion and is expressed as follows:

[0234]

[0235] Given a series of observed displacements r1,r2,...,r n , the posterior distribution of the diffusion coefficient is given by Equation 21, which forms the basis for Bayesian inference of the diffusion coefficient. In Equation 21, f θ represents the inverse gamma distribution of the form given by Equation 20. Equation 21 is the posterior distribution of the Brownian motion and can be expressed as follows:

[0236]

[0237] 3.2.7. Sparse Hill Climbing Algorithm

[0238] If the weights are constant and the weight vector If and the trajectory starting weight w0 are constants, then Equation 3 can be approximately solved using a sparse hill climbing algorithm. This section introduces this algorithm, which is related to several issues discussed throughout Section 3.2. The algorithm described can be understood as a modification of one of the best performing methods in the public SMT competition.

[0239] In the following algorithm, the “pivot” can be defined as the matching vector E∈{0,1} M Each pivot is associated with a weight change Δw=W a -W b -W c +W d The weight change determines whether the pivot is accepted. Each pivot is constructed by selecting a link a:i→j and setting W a =w a To start. If there is a link b:i→k such that E b =1, then let W b =w b Otherwise, let W b =w0. If there exists a link c:l→j such that E c =1, then let W c =wc Otherwise, let W c =w0. If the previous two conditions are true, and d:k→j is a link, then let W d =w d Otherwise, let W d =w0. If Δw = W a -W b -W c +W d >0, then accept the pivot. If the pivot is accepted, you can set E a = 1 to perform the pivot, and set E if b is a link b = 0, if c is a link, set E c = 0, and if d is a link then set E d =1.

[0240] By maintaining a record in memory of the currently assigned forward and backward links for each point, the required lookups in each pivot (eg, determining whether links b, c, and d exist) can be accomplished quickly.

[0241] For a point-link graph with N points and M links, the sparse hill climbing algorithm can be described as follows. Starting from the initial valid matching vector E∈{0,1} M and a known weight vector Start. Let E be zero and the M vector is always valid. In each iteration, select a link a:i→j. If E a =1 and 2w0-w a >0, then set E a = 0; otherwise, continue to the next iteration. On the other hand, if E a = 0, the weight change of the corresponding pivot is evaluated, and if the weight change is positive, the pivot is executed. The algorithm proceeds in this way until convergence.

[0242] One way to determine convergence is to check that the matching vector E does not change after iterating through all M links in a random order.

[0243] Sparse hill climbing algorithms can be used for a variety of purposes, including estimating the nearest neighbor solution to a pursuit problem (by setting w for each link a:i→j a =w0-r i→j , where r i→j is the Euclidean length of the link, and w0 is the track start probability), estimate the maximum a posteriori matching given the posterior distribution produced by gibbstrack or vtrack, or other maximization problems.

[0244] 3.2.8. Graphical softmax

[0245] One aspect of the vtrack algorithm is to normalize the incoming and outgoing links in a point-link graph given some link log-likelihood. This normalization accounts for dependencies between links caused by the topological constraints of the matching (e.g., two links in the same matching cannot start or end at the same point).

[0246] One approach to address normalization is to generalize the softmax operator (e.g., Boltzmann distribution) to doubly stochastic matrices.

[0247] Another way to solve the normalization problem is to use the "graph softmax" operator. The input of the graph softmax operator is a point-link graph containing N points and M links, and the log-likelihood vector of each link Temperature (T) and trajectory initial weight (w0). The output is a probability vector Make l a is the marginal probability of the link a:i→j. This operation can be expressed as

[0248] Instantiate five vectors: and l will hold the link probabilities, v,w will hold the start and end probabilities of each point, and x,y are auxiliary buffers. To initialize, set all links a to And all points In each iteration, the following operations are performed: (i) set x = v and y = w, (ii) for each link a:i→j, set x i =x i +l a and y j =y j +l a , (iii) For each link a:i→j, set (iv) For each point i = 1, 2, ..., N, set v i =v i / x i and w i =w i / y i Repeat these steps until convergence, and then return the link probability l.

[0249] The starting probability v and ending probability w can be obtained from l by subtracting the probabilities of all links entering or leaving each point.

[0250] The convergence rate of this algorithm depends more on the sparsity of the problem than on its size. For htSMT, convergence may take around 20 iterations.

[0251] 3.2.9. Confidence Metrics for Tracking Solutions

[0252] Ensuring high-quality data increases confidence in the results produced by the htSMT system. However, because htSMT may be generated continuously at a high rate across multiple imaging systems, the ability of human supervision to detect data issues may be limited. Therefore, a feature of the tracking pipeline described in this paper is that it provides a built-in confidence measure for the tracking solution, which can be used as an alternative to direct human supervision for diagnostic purposes.

[0253] Equation 22 is the normalized entropy of the posterior distribution of the trajectory, which can be defined as follows:

[0254]

[0255] Where N is the total number of points, l j→i is the marginal probability of link j→i under the inferred posterior distribution, and is the probability that point i starts its own trajectory.

[0256] Equation 22 defines the tracking algorithm's confidence in its solution, with values ​​closer to 0 indicating higher confidence. Values ​​below 0.4 reflect high confidence in the tracking solution. However, tracking very fast particles can be more error-prone (especially for Brownian motion), and a higher threshold may be tolerated for such use cases.

[0257] The Tracking Error Rate Lower Bound (ERLB) is a secondary diagnostic for evaluating the quality of hSMT results. This metric is designed to provide a lower bound on the fraction of incorrect links generated by the tracking algorithm. For example, a value of 0.1 indicates that at least 10% of the links generated by the tracking algorithm are likely incorrect. Since it may not be possible to know in advance which links are correct or incorrect, the ERLB is designed for the case where a subset of links is known to be incorrect.

[0258] The ERLB is calculated once for each SMT movie. The detection set from the second half of the movie is superimposed on the detection set from the first half. The tracking algorithm is then rerun on this superimposed detection set, regardless of which detections come from which half of the movie. Under these conditions, any link generated by the tracking algorithm between two detections from different halves of the SMT movie is incorrect. The score of such a link serves as a lower bound on the error rate, since it is unknown whether a link between two detections from the same half of the movie is incorrect.

[0259] The process of superimposing the two halves of the movie is done in the following way. Each point is always associated with a frame index, or an image index in the original image sequence from which the point was derived. Let S1 be the set of points in the first half of the SMT movie, and let S2 be the set of points in the second half of the movie. Let T be the total number of frames in the movie. For each point in S2, subtract floor(T / 2) from its original frame index. S1 and S2 are then concatenated to form a new set of detections This set of detections is then fed into the linking algorithm. ERLB is then calculated as the number of links generated by the linking algorithm that connect a point in S1 to a point in S2 (or vice versa), divided by the total number of links generated by the linking algorithm. The links "generated by the linking algorithm" are the E in the solution E to the problem expressed in Eq. a =1 link a.

[0260] 3.2.10. Trajectory-independent dynamics estimation using probabilistic tracking algorithms

[0261] The goal of htSMT is to infer the dynamic parameters of a target protein to identify experimental conditions that alter these dynamics. For example, the diffusion coefficient of a target protein can be estimated 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).

[0262] These dynamic parameters are usually estimated from the trajectory. However, since probabilistic tracking provides the posterior distribution p(E, Θ|R) of the dynamic parameters and the trajectory, the dynamic parameters can be estimated by marginalizing over the trajectory. In the following equation, is the marginal posterior mean of the dynamic parameters θ at point i. This estimate is the weighted average of θ over all possible trajectories including point i, weighted by the posterior probability of each trajectory, expressed as follows:

[0263] p(Θ|R)=∑ E p(E,Θ|R) (23)

[0264]

[0265] As an example, we describe a procedure to estimate the marginal posterior diffusion coefficient for each point over all possible pasts and all possible futures. Let Marginal link probabilities estimated for the probabilistic pursuit algorithm

[0266] There are n SMT movies with M possible links and N points. Let is the square displacement of link a. Let d be the dimension of the space, and let γ∈(0,1) be the damping constant. Set all points i to Then for each link a:i→j, set and Then, the posterior distribution of the diffusion coefficient of point i is where Δt is the frame interval, α0 and β0 are prior values, and InvGamma is the inverse gamma distribution with scale parameterization. Then the posterior mean diffusion coefficient of point i is in and

[0267] 3.2.11. Tracking Algorithm Benchmark

[0268] Figures 12A to 12C Depicts benchmarks of various tracking algorithms. Figure 12A , optical dynamic simulation is used to test the accuracy of multiple linking algorithms, including vtrack, gibbstrack and adaptive hill climbing algorithms, as well as other linking algorithms: random, conservative and nearest neighbor. Using the random linking algorithm as a control, each detection is randomly linked to another detection within its range gate (i.e., the set of detections within its search radius and gap constraints). The conservative linking algorithm is configured so that each detection is only linked to another detection if there are no other possibilities within the applicable range gate. The nearest neighbor linking algorithm stipulates that each detection is linked to the nearest neighbor in the applicable range gate. There are three types of experiments (benchmark 1, benchmark 2 and benchmark 3), with increasing difficulty. The outputs of these experiments are link recall, link precision and F1 score (i.e., the harmonic mean of recall and precision). Figure 12B The metrics are shown in this context. To ensure a fair comparison, the search radius (i.e., the maximum distance to consider a link) and the gap limit (i.e., the maximum number of gap frames to consider a link) are kept constant for all algorithms at 1.25 μm and 2 gaps, respectively. The exception is the conservative linking algorithm, which essentially requires 0 gaps (so all links are between consecutive frames). The results of the benchmark are shown in Figure 12C middle.

[0269] 4. Specific OLS htSMT applications

[0270] Many, perhaps most, pathways that regulate fundamental cellular biochemistry rely on the transient interaction of protein sensors with protein effectors that trigger changes in cellular physiology. Although the fundamental principles of this process have long been recognized, biochemical studies of these protein interactions typically require in vitro reconstitution or interrogation via pull-down assays after cell permeabilization. The htSMT workflow described here provides a method for visualizing protein movements in large numbers of living cells, while allowing for quantitative assessment of the effects of added compounds, such as small molecule inhibitors.

[0271] refer to Figure 1Various aspects of the OLS htSMT workflow disclosed herein include, but are not limited to: (i) sample preparation (including reagent handling), (ii) image acquisition using sample imaging to generate a series of images and / or videos, (iii) image analysis by processing these images and videos, (iv) information storage, and (v) providing insights using the stored information (including biological interpretation). With respect to biological interpretation, the htSMT workflows described herein can provide specific insights, as described below, depending on the specific workflow employed, such as (i) OLS htSMT screening; (ii) OLS htSMT binding; and / or (iii) OLS KineticSMT.

[0272] In certain embodiments, the workflow of the present disclosure may include detecting fluorescence of a plurality of target fluorescent proteins in a field of view in a sample plane, wherein the field of view (e.g., the detection field of view) has a size of about 150 μm to about 250 μm in a first dimension and a size of about 100 μm to about 210 μm in a second dimension. In certain embodiments, the FOV (e.g., the detected FOV) may have a size of about 150 μm to about 250 μm in a first dimension and a size of about 100 μm to about 210 μm in a second dimension. In certain embodiments, the FOV (e.g., the detected FOV) may have a size of about 200 μm to about 250 μm in a first dimension and a size of about 150 μm to about 210 μm in a second dimension, or the FOV (e.g., the detected FOV) may have a size of about 225 μm to about 250 μm in a first dimension and a size of about 175 μm to about 210 μm in a second dimension. For example, but not by way of limitation, the FOV (eg, the detected FOV) may have a size of approximately 250 μm in a first dimension and approximately 190 μm in a second dimension, eg, as disclosed in Example 1.

[0273] In certain embodiments, a certain percentage of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. For example, but not by way of limitation, at least 75% of the FOV, at least 80% of the FOV, at least 85% of the FOV, at least 90% of the FOV, at least 95% of the FOV, at least 96% of the FOV, at least 97% of the FOV, at least 98% of the FOV, at least 99% of the FOV, or 100% of the FOV achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 75% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 80% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 85% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 90% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 95% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 96% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 97% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 98% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, at least 99% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In certain embodiments, 100% of the FOV (e.g., the detected FOV) achieves sufficient laser illumination to track protein motion. In some embodiments, a percentage of FOV equal to or greater than about 75% achieves sufficient laser illumination to track protein motion, for example, a percentage of FOV equal to or greater than about 80%, a percentage of FOV equal to or greater than about 85%, a percentage of FOV equal to or greater than about 90%, a percentage of FOV equal to or greater than about 95%, a percentage of FOV equal to or greater than about 96%, a percentage of FOV equal to or greater than about 97%, a percentage of FOV equal to or greater than about 98%, or a percentage of FOV equal to or greater than about 99% achieves sufficient laser illumination to track protein motion. In some embodiments, a percentage of FOV equal to or greater than about 90% achieves sufficient laser illumination to track protein motion. In some embodiments, a percentage of FOV equal to or greater than about 95% achieves sufficient laser illumination to track protein motion.

[0274] In certain embodiments, the workflow of the present disclosure comprises detecting the field of view at a frame rate of at most about 2000 Hz. In certain embodiments, the workflow of the present disclosure comprises detecting the field of view at a frame rate of about 100 Hz or higher, about 200 Hz or higher, about 400 Hz or higher, about 600 Hz or higher, about 800 Hz or higher, about 1000 Hz or higher, about 1200 Hz or higher, about 1400 Hz or higher, about 1600 Hz or higher, or about 1800 Hz or higher. In certain embodiments, the workflow of the present disclosure comprises detecting the field of view at a frame rate of at most about 1200 Hz. In certain embodiments, the workflow of the present disclosure comprises detecting the field of view at a frame rate of at most about 1400 Hz. In certain embodiments, the workflow of the present disclosure comprises detecting the field of view at a frame rate of at most about 1600 Hz. In certain embodiments, the workflow of the present disclosure comprises detecting the field of view at a frame rate of at most about 1800 Hz.

[0275] In certain embodiments, the workflow of the present disclosure comprises illuminating the field of view using stroboscopic laser pulses of 0.1 to 1 millisecond. In certain embodiments, the workflow of the present disclosure comprises illuminating the field of view using stroboscopic laser pulses of about 0.2 to about 1 millisecond, about 0.3 to about 1 millisecond, about 0.4 to about 1 millisecond, about 0.1 to about 0.9 milliseconds, about 0.1 to about 0.8 milliseconds, about 0.1 to about 0.7 milliseconds, about 0.1 to about 0.6 milliseconds, about 0.1 to about 0.5 milliseconds, about 0.1 to about 0.4 milliseconds, about 0.2 to about 0.6 milliseconds, about 0.2 to about 0.5 milliseconds, about 0.2 to about 0.4 milliseconds, or about 0.3 to about 0.5 milliseconds. In certain embodiments, the workflow of the present disclosure comprises illuminating the field of view using stroboscopic laser pulses of about 0.1 to about 0.6 milliseconds. In certain embodiments, the workflow of the present disclosure comprises illuminating the field of view using stroboscopic laser pulses of about 0.1 to about 0.5 milliseconds. In certain embodiments, the workflow of the present disclosure comprises illuminating the field of view using strobed laser pulses of about 0.2 to about 0.4 milliseconds. In certain embodiments, the workflow of the present disclosure comprises illuminating the field of view using strobed laser pulses of about 0.2 milliseconds.

[0276] In certain embodiments, the workflow of the present disclosure includes illuminating a field of view disposed in a sample plane within a sample with a light beam to cause a subset of fluorescent target proteins in living cells to fluoresce, thereby imaging a plurality of molecular trajectories. In certain embodiments, up to about 1,000,000 molecular trajectories in a single detection field of view can be imaged, for example, up to about 900,000, up to about 800,000, up to about 700,000, up to about 600,000, up to about 500,000, up to about 400,000, up to about 300,000, up to about 200,000, or up to about 100,000. In certain embodiments, the number of trajectories imaged in a single detection field of view can be from about 30,000 to about 1,000,000, for example, from about 30,000 to about 250,000. For example, but not by way of limitation, the number of tracks imaged in a single detection field of view may be from about 50,000 to about 200,000, from about 100,000 to about 200,000, from about 100,000 to about 500,000, or from about 100,000 to about 150,000. In certain embodiments, the number of tracks imaged in a single detection field of view may be up to about 1,000,000. In certain embodiments, the number of tracks imaged in a single detection field of view may be from about 100,000 to about 1,000,000. In certain embodiments, the number of tracks imaged in a single detection field of view may be from about 200,000 to about 1,000,000. In certain embodiments, the number of tracks imaged in a single detection field of view may be from about 100,000 to about 500,000. In certain embodiments, the number of tracks imaged in a single detection field of view can be from about 200,000 to about 500,000.

[0277] In certain embodiments, the field of view may include multiple cells. In certain embodiments, the number of cells imaged in the field of view is related to the size of the cells being imaged. For example, but not limited to, the smaller the size of the cells, the greater the number of cells that can be imaged in the field of view. In certain embodiments, depending on the size of the cells being imaged, the field of view may include about 30 to about 200 living cells, for example, about 30 to about 80 cells or about 50 to about 80 cells. In certain embodiments, depending on the size of the cells being imaged, the field of view may include about up to about 80 living cells, such as mammalian cells. In certain embodiments, for U2OS cells, the range is about 30 to about 40 cells per field of view, while for HCT116 cells, taking into account their area differences, the range is about 50 to about 80 cells per field of view. In certain embodiments, the field of view may include about 30 to about 80 living cells, such as mammalian cells. In certain embodiments, the field of view may include about 50 to about 80 living cells, such as mammalian cells. In certain embodiments, the field of view may include about 55 to about 80 living cells, such as mammalian cells. In certain embodiments, the field of view can include about 60 to about 80 living cells, such as mammalian cells.

[0278] In certain embodiments, the workflow of the present disclosure may include analyzing a subset of cells (e.g., a subpopulation) present in the field of view, such as analyzing and / or tracking the trajectory of a fluorescent target protein in a subset of cells (e.g., a subpopulation) present in the field of view. For example, but not by way of limitation, the workflow of the present disclosure may include analyzing from about 1% to about 99% of the cells present in the field of view, such as from about 1% to about 50% of the cells present in the field of view.

[0279] In certain embodiments, the workflow of the present disclosure includes illuminating a field of view disposed in a sample plane within a sample with a light beam to cause a plurality of fluorescent target proteins in living cells to fluoresce. In certain embodiments, the plurality of fluorescent target proteins can include about 1,000 to about 1,000,000 fluorescent target proteins, such as about 10,000 to about 1,000,000 or about 100,000 to about 1,000,000 fluorescent target proteins.

[0280] In certain embodiments, the workflow of the present disclosure may include detecting fluorescence of multiple fluorescent target proteins in a sample plane field of view at a rate of more than about 100,000 detection fields per day. For example, but not by way of limitation, the rate of detecting fields per day is about 100,000 to about 1,000,000. In certain embodiments, the workflow of the present disclosure may include detecting fluorescence of multiple fluorescent target proteins in a sample plane field of view at a rate of about 100,000 to about 500,000 detection fields per day.

[0281] In certain embodiments, the workflow of the present disclosure may include determining a change in the motion of the fluorescently labeled target protein in the presence of the compound. For example, but not by way of limitation, the average change in the motion of the fluorescent target protein in the presence of the compound is from about 1% to about 5% or to about 10% relative to the baseline motion in the absence of the compound. In certain embodiments, the average change in the motion of the fluorescent target protein in the presence of the compound is from about 1% to about 5%. In certain embodiments, the average change in the motion of the fluorescent target protein in the presence of the compound is from about 1% to about 10%.

[0282] In certain embodiments, an exemplary OLS htSMT workflow includes each of the above strategies as well as combinations of these strategies where two or more strategic requirements are combined.

[0283] 4.1.OLS htSMT Screening

[0284] In certain implementations of the OLS htSMT workflows described herein, the systems and methods are applicable to interrogate the ability of one or more compositions (e.g., "test" compounds) to affect the SMT spectrum associated with a labeled protein. For example, such an htSMT workflow would screen for changes in the SMT spectrum, e.g., an increase or decrease in the movement of a protein of interest in the presence of the composition relative to the SMT spectrum in the absence of the composition. It will be understood that higher order comparisons can also be made where compounds are multiplexed, including where multiple proteins fluoresce. In addition, as described above, the htSMT screening strategies described herein are equally applicable to screening SMT spectra associated with fluorescent compounds, such as compounds that are naturally fluorescent or compounds that have been modified to fluoresce or are linked to a fluorophore.

[0285] The basis of such htSMT screening strategies is that the htSMT workflow described herein is capable of extracting accurate molecular trajectories on a large scale. Exemplary OLS htSMT workflows include the following individual strategies and combinations of the following strategies, wherein two or more strategic requirements are combined. For example, but not by way of limitation, the workflows of the present disclosure include illuminating a field of view of a sample plane disposed within a sample with a light beam so as to cause a subset of fluorescent target proteins in living cells to fluoresce, wherein the subset of fluorescent target proteins is present in about 30 to about 80 living cells illuminated in the field of view of the sample plane, and illuminating a field of view of a sample plane disposed within a sample with a light beam so as to cause a subset of fluorescent target proteins in living cells to fluoresce, wherein the subset of fluorescent target proteins includes proteins in the range of about 1000 to about 1,000,000. Similarly, illuminating the sample plane to illuminate about 30 to about 80 living cells per FOV and / or causing about 1000 to about 1,000,000 proteins to fluoresce can be combined with any other strategic requirements disclosed herein, for example, determining that the average change in motion of the fluorescent target protein in the presence of the compound is about 5% or about 10% relative to the baseline motion under DMSO conditions, detecting fluorescence of multiple fluorescent target proteins in the sample plane field of view at a rate of about 10,000 to about 18,000 per system per day, and achieving a z-factor >0.5 based on a single field of view.

[0286] In certain implementations 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 living cells, comprising: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of living cells in the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the fluorescent target proteins in the living cells fluoresces, wherein the subset of fluorescent target proteins is present in about 30 to about 80 living cells illuminated in the sample plane field of view, depending on the specific cell type used, e.g., for U2OS cells, the range is about 30 to about 40 cells per FOV, while for HCT116 cells, the range is about 50 to about 80 cells, taking into account their area differences; (ii) detecting, by a detector device, the fluorescence of the plurality of fluorescent target proteins in the sample plane field of view; and (c) determining a change in the movement of the fluorescent target proteins in the presence of the compound; wherein the change in the movement of the fluorescent target proteins in the presence of the compound relative to the movement of the fluorescent target proteins in the absence of the compound identifies a biological interaction between the compound and the fluorescent target proteins.

[0287] In certain implementations of the OLS htSMT screening workflow described herein, the workflow may include identifying a biological interaction between a compound and a fluorescent target protein in living cells, comprising: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of living cells in the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the fluorescent target proteins in the living cells fluoresces, wherein the subset of fluorescent target proteins comprises about 1000 to about 1,000,000 proteins, wherein the number of proteins in the subset depends on the expression level of the protein of interest and a dye concentration deemed sufficient to label the subset of proteins for robust SMT, both of which can be calculated and / or configured by a person skilled in the art based on the disclosure of this application; (ii) detecting, by a detector device, the fluorescence of the plurality of fluorescent target proteins in the field of view of the sample plane; and (c) determining a change in the movement of the fluorescent target proteins in the presence of the compound; wherein the change in the movement of the fluorescent target proteins in the presence of the compound relative to the movement of the fluorescent target proteins in the absence of the compound identifies a biological interaction between the compound and the fluorescent target proteins.

[0288] In certain implementations of the OLS htSMT screening workflow described herein, the workflow may include identifying a biological interaction between a compound and a fluorescent target protein in living cells, comprising: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking the movement of multiple individual fluorescent target proteins in multiple living cells in the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the fluorescent target proteins in the living cells emit fluorescence; (ii) detecting the fluorescence of multiple fluorescent target proteins within the field of view of the sample plane by a detector device; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound, wherein the average change in movement of the fluorescent target protein in the presence of the compound is about 5% or to about 10% relative to a baseline movement under DMSO conditions; wherein the change in movement of the fluorescent target protein in the presence of the compound relative to the movement of the fluorescent target protein in the absence of the compound identifies a biological interaction between the compound and the fluorescent target protein.

[0289] In certain implementations of the OLS htSMT screening workflow described herein, the workflow may include identifying a biological interaction between a compound and a fluorescent target protein in living cells, comprising: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking the movement of multiple individual fluorescent target proteins in multiple living cells in the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the fluorescent target proteins in the living cells emit fluorescence; (ii) detecting the fluorescence of the multiple fluorescent target proteins in the sample plane field of view by a detector device; (iii) wherein the tracking comprises detecting the fluorescence of the multiple fluorescent target proteins in the sample plane field of view at a rate of approximately 10,000 to approximately 18,000 per system per day; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound; wherein the change in the movement of the fluorescent target protein in the presence of the compound relative to the movement of the fluorescent target protein in the absence of the compound identifies a biological interaction between the compound and the fluorescent target protein.

[0290] In certain implementations of the OLS htSMT screening workflow described herein, the workflow may include identifying a biological interaction between a compound and a fluorescent target protein in living cells, comprising: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking the movement of multiple individual fluorescent target proteins in multiple living cells in the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the fluorescent target proteins in the living cells emit fluorescence; (ii) detecting the fluorescence of multiple fluorescent target proteins within the field of view of the sample plane by a detector device; and (iii) achieving a z-factor >0.5 based on a single field of view; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound; wherein the change in the movement of the fluorescent target protein in the presence of the compound relative to the movement of the fluorescent target protein in the absence of the compound identifies a biological interaction between the compound and the fluorescent target protein.

[0291] In certain implementations of the OLS htSMT screening workflow described herein, the workflow may include determining a dose response of a compound that induces a change in the motion of a fluorescent target protein in living cells, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of living cells, (ii) wherein the living cells comprise the fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of compound concentrations; and (b) tracking the motion of each of the fluorescent target proteins in the plurality of living cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that at least a subset of the fluorescent target proteins in the living cells emit fluorescence, wherein the subset of fluorescent target proteins is present (i) detecting fluorescence of one or more fluorescent target proteins in the sample plane by a detector device; and (c) determining a change in motion of the fluorescent target protein in the presence of a compound; and (d) repeating steps (b)-(c) for each of the plurality of samples over a range of compound concentrations; wherein the change in motion of the fluorescent target protein in the presence of the compound over the range of concentrations is indicative of a dose response of the compound.

[0292] In certain implementations of the OLS htSMT screening workflow described herein, the workflow may include determining a dose response of a compound that induces a change in the motion of a fluorescent target protein in living cells, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of living cells, (ii) wherein the living cells comprise the fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of compound concentrations; and (b) tracking the motion of each of the fluorescent target proteins in the plurality of living cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that at least a subset of the fluorescent target proteins in the living cells emit fluorescence, wherein the subset of fluorescent target proteins comprises approximately 1000 to about 1,000,000 proteins, wherein the number of proteins in the subset depends on the expression level of the protein of interest and the concentration of dye that is considered sufficient to label the subset proteins to achieve robust SMT, both of which can be calculated and / or configured by a person skilled in the art based on the disclosure of the present application; (ii) detecting the fluorescence of one or more fluorescent target proteins in the sample plane by a detector device; and (c) determining the change in the movement of the fluorescent target protein in the presence of the compound; (d) repeating steps (b)-(c) for each of the multiple samples within the compound concentration range; wherein the change in the movement of the fluorescent target protein in the presence of the compound over the concentration range is indicative of a dose response of the compound.

[0293] In certain implementations of the OLS htSMT screening workflow described herein, the workflow may include determining a dose response of a compound that induces a change in motion of a fluorescent target protein in living cells, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of living cells, (ii) wherein the living cells comprise a fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of compound concentrations; (b) tracking the motion of each of the fluorescent target proteins in the plurality of living cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that at least a subset of the fluorescent target proteins in the living cells fluoresces, (ii) detecting fluorescence of one or more fluorescent target proteins in the sample plane by a detector device; and (c) determining the change in motion of the fluorescent target protein in the presence of the compound, wherein the average change in motion of the fluorescent target protein in the presence of the compound is about 5% or about 10% relative to a baseline motion under DMSO conditions; and (d) repeating steps (b)-(c) for each of the plurality of samples within the range of compound concentrations; wherein the change in motion of the fluorescent target protein in the presence of the compound over the concentration range indicates a dose response of the compound.

[0294] In certain implementations of the OLS htSMT screening workflow described herein, the workflow can include determining a dose response of a compound that induces a change in the motion of a fluorescent target protein in living cells, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of living cells, (ii) wherein the living cells comprise the fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of compound concentrations; and (b) tracking the motion of each of the fluorescent target proteins in the plurality of living cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that the fluorescent target proteins in the cells (ii) detecting, by a detector device, the fluorescence of one or more fluorescent target proteins in the sample plane; (iii) wherein the tracking comprises detecting the fluorescence of a plurality of fluorescent target proteins in the field of view of the sample plane at a rate of about 10,000 to about 18,000 per system per day; and (c) determining a change in motion of the fluorescent target proteins in the presence of the compound; and (d) repeating steps (b)-(c) for each of the plurality of samples over a range of compound concentrations; wherein the change in motion of the fluorescent target proteins over the range of concentrations in the presence of the compound is indicative of a dose response of the compound.

[0295] In certain implementations of the OLS htSMT screening workflow described herein, the workflow may include determining a dose response of a compound that induces a change in motion of a fluorescent target protein in living cells, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of living cells, (ii) wherein the living cells comprise a fluorescent target protein, and (iii) wherein the plurality of samples are contacted with the compound at different concentrations within a range of compound concentrations; (b) tracking the motion of each fluorescent target protein in a plurality of living cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that at least a subset of the fluorescent target proteins in the cells fluoresce, (ii) detecting fluorescence of one or more fluorescent target proteins in the sample plane by a detector device; and (iii) achieving a z-factor >0.5 based on a single field of view; and (c) determining the change in motion of the fluorescent target protein in the presence of the compound; (d) repeating steps (b)-(c) for each of the plurality of samples within the range of compound concentrations; wherein the change in motion of the fluorescent target protein over the concentration range in the presence of the compound indicates a dose response of the compound.

[0296] In certain implementations of the OLS htSMT screening workflow described herein, the workflow can include using a microscope system configured to identify biological interactions between a compound and a fluorescent target protein in living cells, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of living cells, and wherein the living cells comprise a fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; and (c) an objective for focusing the light beam onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample are positioned in a field of view of the sample plane, and wherein the subset of fluorescent target proteins are present in about 30 to about 80 living cells illuminated in the field of view of the sample plane, depending on the specific cells used. type, for example, for U2OS cells, the range is about 30 to about 40 cells per FOV, and for HCT116 cells, the range is about 50 to about 80 cells, taking into account their area differences; (d) a detector device for monitoring a light-based reaction of a fluorescent target protein in the presence of a compound, wherein the detector device is configured to: (i) block light received from a light source outside the sample plane where the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein; and (ii) track the movement of each fluorescent target protein, (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0297] In certain implementations of the OLS htSMT screening workflow described herein, the workflow can include using a microscope system configured to identify biological interactions between a compound and a fluorescent target protein in living cells, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise a fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample is positioned in a field of view of the sample plane; and wherein the subset of fluorescent target proteins comprises about 1000 to about 1,000,000 proteins, wherein the number of proteins in the subset depends on the protein of interest. (d) a detector device for monitoring the light-based reaction of the fluorescent target protein in the presence of the compound, wherein the detector device is configured to: (i) block light received from a light source outside the sample plane where the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein; and (ii) track the movement of each fluorescent target protein, (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining changes in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0298] In this paper, the OLS In certain implementations of the htSMT screening workflow, the workflow may include using a microscope system configured to identify biological interactions between a compound and a fluorescent target protein in living cells, comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population and wherein the cells comprise a fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample are positioned in a field of view of the sample plane; (d) a detector device for monitoring the light-based response of the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from the light source outside the sample plane in which the fluorescent target proteins are positioned, thereby tracking the position of the fluorescent target proteins; and (ii) track the movement of each fluorescent target protein, wherein the average change in the movement of the fluorescent target proteins in the presence of the compound relative to a baseline movement under DMSO conditions is about 5% or about 10%, (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining the change in the movement of the fluorescent target proteins in the presence of the compound relative to the absence of the compound.

[0299] In certain implementations of the OLS htSMT screening workflow described herein, the workflow may include using a microscope system configured to identify biological interactions between a compound and a fluorescent target protein in living cells, comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population, and wherein the cells comprise a fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam onto the sample in a sample plane, wherein the plurality of fluorescent target proteins in the sample are disposed in the sample plane; and (d) a microscope for monitoring the fluorescence of the fluorescent target proteins in the presence of the compound. A detector device for a light-based reaction, wherein the detector device is configured to: (i) block light received from a light source outside a sample plane in which a fluorescent target protein is located, thereby tracking the position of the fluorescent target protein; and (ii) track the movement of each fluorescent target protein, wherein the tracking comprises detecting fluorescence of a plurality of fluorescent target proteins in a field of view of the sample plane at a rate of about 10,000 to about 18,000 per system per day; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the movement of the fluorescent target protein in the presence of a compound relative to the absence of the compound.

[0300] In certain implementations of the OLS htSMT screening workflow described herein, the workflow may include using a microscope system configured to identify biological interactions between a compound and a fluorescent target protein in living cells, comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population and wherein the cells comprise a fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; (c) an objective lens for focusing the light beam onto the sample in a sample plane, wherein the plurality of fluorescent target proteins in the sample are disposed in the sample plane; (d) a detector device for monitoring the light-based response of the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from the light source outside the sample plane in which the fluorescent target proteins are disposed, thereby tracking the position of the fluorescent target proteins; (ii) track the movement of each fluorescent target protein; and (iii) achieve a z-factor >0.5 based on a single field of view; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the movement of the fluorescent target proteins in the presence of the compound relative to the absence of the compound.

[0301] 4.2. OLS htSMT combination

[0302] In certain implementations of the OLS htSMT workflow described herein, the systems and methods are adapted to differentiate between off This would result in the f observed in the htSMT screening assay. 结合 Importantly, neither FRAP nor htSMT could distinguish the difference between the off decrease) or increase in chromatin binding rate (k* on increase) and the recovery of the drive, both of which will lead to f 结合 By changing the SMT acquisition conditions to reduce illumination intensity and collect long frame exposures, only immobile proteins form puncta. Under these imaging conditions, the distribution of track lengths provides a measure of relative residence time.

[0303] Exemplary OLS htSMT combined workflows include the following individual strategies and combinations of the following strategies, wherein requirements of two or more strategies are combined. For example, but not by way of limitation, the workflows disclosed herein include illuminating a field of view of a sample plane disposed within a sample with a light beam such that a subset of fluorescent target proteins in living cells fluoresce, wherein the subset of fluorescent target proteins is present in about 30 to about 80 living cells illuminated in the field of view of the sample plane, and illuminating a field of view of a sample plane disposed within the sample with a light beam such that a subset of fluorescent target proteins in living cells fluoresces, wherein the subset of fluorescent target proteins includes proteins in a range of about 1000 to about 1,000,000. Similarly, illuminating the sample plane to illuminate about 30 to about 80 living cells per FOV and / or causing about 1000 to about 1,000,000 proteins to fluoresce can be combined with any other strategic requirements disclosed herein, for example, determining that the average change in motion of the fluorescent target protein in the presence of the compound is about 5% or about 10% relative to the baseline motion under DMSO conditions, detecting fluorescence of multiple fluorescent target proteins in the sample plane field of view at a rate of about 10,000 to about 18,000 per system per day, and achieving a z-factor >0.5 based on a single field of view.

[0304] In certain implementations of the OLS htSMT binding workflow described herein, the workflow will include determining whether a compound that induces a change in binding of a fluorescent target protein in living cells reduces the K of the fluorescent target protein. off, comprising: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that at least a subset of the fluorescent target proteins in the living cells fluoresce, wherein the subset of fluorescent target proteins is present in about 30 to about 80 living cells illuminated in the field of view of the sample plane, depending on the specific cell type used, for example, for U2OS cells, the range is about 30 to about 40 cells per FOV, and for HCT116 cells, the range is about 50 to about 80 cells, taking into account their area differences; (ii) detecting fluorescence of one or more fluorescent target proteins in the sample plane by a detector device, wherein the method is adapted to selectively detect localized fluorescence; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound; wherein an increase in the signal detected from the fluorescent target protein in the presence of the compound relative to the signal of the fluorescent target protein in the absence of the compound indicates that the compound induces a K of the fluorescent target protein. off reduce.

[0305] In certain implementations of the OLS htSMT binding workflow described herein, the workflow can include determining whether a compound that induces a change in binding of a fluorescent target protein in living cells reduces the K of the fluorescent target protein. off , which comprises: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so as to cause at least a subset of the fluorescent target proteins in the living cells to fluoresce, wherein the subset of fluorescent target proteins comprises from about 1000 to about 1,000,000 proteins, wherein the number of proteins in the subset depends on the expression level of the protein of interest and a dye concentration deemed sufficient to label the subset proteins for robust SMT, both of which can be calculated and / or configured by one skilled in the art based on the disclosure of this application; (ii) detecting fluorescence of one or more fluorescent target proteins in the sample by a detector device, wherein the method is adapted to selectively detect localized fluorescence; and (c) determining a change in movement of the fluorescent target protein in the presence of the compound; wherein an increase in the signal detected from the fluorescent target protein in the presence of the compound relative to the signal of the fluorescent target protein in the absence of the compound indicates that the compound induces a K of the fluorescent target protein. off reduce.

[0306] In certain implementations of the OLS htSMT binding workflow described herein, the workflow can include determining whether a compound that induces a change in binding of a fluorescent target protein in living cells reduces the K of the fluorescent target protein. off , comprising: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so as to cause at least a subset of the fluorescent target proteins in the living cells to fluoresce; (ii) detecting fluorescence of one or more fluorescent target proteins in the sample by a detector device, wherein the method is adapted to selectively detect localized fluorescence; and (c) determining a change in movement of the fluorescent target proteins in the presence of the compound, wherein the average change in movement of the fluorescent target proteins in the presence of the compound relative to a baseline movement under DMSO conditions is about 5% or about 10%; wherein an increase in the signal detected from the fluorescent target proteins in the presence of the compound relative to the signal of the fluorescent target proteins in the absence of the compound indicates that the compound induces a K of the fluorescent target proteins. off reduce.

[0307] In certain implementations of the OLS htSMT binding workflow described herein, the workflow can include determining whether a compound that induces a change in binding of a fluorescent target protein in living cells reduces the K of the fluorescent target protein. off , comprising: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so as to cause at least a subset of the fluorescent target proteins in the living cells to fluoresce; (ii) detecting fluorescence of one or more fluorescent target proteins in the sample by a detector device, wherein the method is adapted to selectively detect localized fluorescence; and (iii) achieving a z-factor of >0.5 based on a single field of view; and (c) determining a change in movement of the fluorescent target protein in the presence of the compound; wherein an increase in the signal detected from the fluorescent target protein in the presence of the compound relative to the signal of the fluorescent target protein in the absence of the compound indicates that the compound induces a K of the fluorescent target protein. off reduce.

[0308] In certain implementations of the OLS htSMT binding workflow described herein, the workflow may include determining whether a compound reduces the K of a fluorescent target protein. offThe method of claim 1 , wherein the method comprises: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that at least a subset of the fluorescent target proteins in the living cells fluoresce, wherein the subset of fluorescent target proteins is present in about 30 to about 80 living cells illuminated in the field of view of the sample plane, depending on the specific cell type used, for example, about 30 to about 40 cells per FOV for U2OS cells and about 50 to about 80 cells per FOV for HCT116 cells, taking into account their area differences; (ii) detecting fluorescence of one or more fluorescent target proteins in the sample plane by a detector device, wherein the method is adapted to selectively detect localized fluorescence; and (c) determining the change in movement of the fluorescent target protein in the presence of the compound; wherein an increase in the signal detected from the fluorescent target protein in the presence of the compound relative to the signal of the fluorescent target protein in the absence of the compound indicates that the compound induces a K of the fluorescent target protein. off decreased and suggests that the dose increase is due to decreased drug metabolism caused by increased residence time.

[0309] In certain implementations of the OLS htSMT binding workflow described herein, the workflow may include determining whether a compound reduces the K of a fluorescent target protein. off Determining a dose of a compound that induces a change in binding of a fluorescent target protein in living cells comprises: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so as to cause at least a subset of the fluorescent target proteins in the living cells to fluoresce, wherein the subset of fluorescent target proteins comprises from about 1000 to about 1,000,000 proteins, wherein the number of proteins in the subset depends on the expression level of the protein of interest and a dye concentration deemed sufficient to label the subset of proteins for robust SMT, both of which can be calculated and / or configured by one skilled in the art based on the disclosure of this application; (ii) detecting fluorescence of one or more fluorescent target proteins in the sample by a detector device, wherein the method is adapted to selectively detect localized fluorescence; and (c) determining a change in movement of the fluorescent target protein in the presence of the compound; wherein an increase in the signal detected from the fluorescent target protein in the presence of the compound relative to the signal of the fluorescent target protein in the absence of the compound indicates that the compound induces a K of the fluorescent target protein. off Lowering and, in some cases, increasing the dose due to decreased drug metabolism due to increased residence time.

[0310] In certain implementations of the OLS htSMT binding workflow described herein, the workflow may include determining whether a compound reduces the K of a fluorescent target protein. off The method of claim 1, wherein the method comprises: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking the movement of individual fluorescent target proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so as to cause at least a subset of the fluorescent target proteins in the living cells to fluoresce; (ii) detecting fluorescence of one or more fluorescent target proteins in the sample by a detector device, wherein the method is adapted to selectively detect localized fluorescence; and (c) determining the change in movement of the fluorescent target proteins in the presence of the compound; wherein the average change in movement of the fluorescent target proteins in the presence of the compound relative to a baseline movement under DMSO conditions is about 5% or about 10%; and wherein an increase in the signal detected from the fluorescent target proteins in the presence of the compound relative to the signal of the fluorescent target proteins in the absence of the compound indicates that the compound induces a K of the fluorescent target proteins. off decrease, indicating an increase in dose due to decreased drug metabolism caused by increased residence time.

[0311] In certain implementations of the OLS htSMT binding workflow described herein, the workflow may include determining whether a compound reduces the K of a fluorescent target protein. off The method of claim 1, wherein the method comprises: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking the movement of individual fluorescent target proteins in a plurality of cells of the sample, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so as to cause at least a subset of the fluorescent target proteins in the living cells to fluoresce; (ii) detecting fluorescence of one or more fluorescent target proteins in the sample by a detector device, wherein the method is adapted to selectively detect localized fluorescence; and (iii) achieving a z-factor of >0.5 based on a single field of view; and (c) determining the change in movement of the fluorescent target protein in the presence of the compound; wherein: an increase in the signal detected from the fluorescent target protein in the presence of the compound relative to the signal of the fluorescent target protein in the absence of the compound indicates that the compound induces a K of the fluorescent target protein. off Lowering and, in some cases, increasing the dose due to decreased drug metabolism caused by increased residence time.

[0312] In certain implementations of the OLS htSMT binding workflow described herein, the workflow can include using a microscopy system configured to determine whether a compound that induces a change in binding of a fluorescent target protein in a cell reduces the K of the fluorescently labeled target. off , comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population, and wherein the cells comprise fluorescent target proteins; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; and (c) an objective lens for focusing the light beam onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample is positioned in a field of view of the sample plane, and wherein the subset of fluorescent target proteins is present in about 30 to about 80 living cells illuminated in the field of view of the sample plane, depending on the specific cell type used, e.g., for U2OS cells, the range is about 30 to about 40 cells per FOV, and for HCT116 cells, the range is about (d) a detector device for monitoring a light-based response of a fluorescent target protein in the presence of a compound, wherein the detector device is configured to: (i) block light received from a light source outside the sample plane in which the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein; and (ii) track the movement of each fluorescent target protein, wherein the tracking is suitable for selectively detecting local fluorescence relative to dynamic fluorescence; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0313] In certain implementations of the OLS htSMT screening workflow described herein, the workflow can include using a microscopy system configured to determine whether a compound that induces a change in binding of a fluorescent target protein in a cell reduces the K of the fluorescently labeled target. off, comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population, and wherein the cells comprise fluorescent target proteins; (b) a light source for emitting a light beam capable of inducing a light-based reaction from a plurality of fluorescent target proteins in the sample; (c) an objective lens for focusing the light beam onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample is positioned in a field of view of the sample plane, and wherein the subset of fluorescent target proteins comprises from about 1000 to about 1,000,000 proteins, wherein the number of proteins in the subset depends on the expression level of the protein of interest and a dye concentration that is deemed sufficient to label the subset proteins to achieve robust SMT, and a person skilled in the art can determine the amount of the protein in the sample based on the present invention. The disclosed content of the application calculates and / or configures both; (d) a detector device for monitoring the light-based reaction of the fluorescent target protein in the presence of a compound, wherein the detector device is configured to: (i) block light received from a light source outside the sample plane where the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein; and (ii) track the movement of each fluorescent target protein, wherein the tracking is suitable for selectively detecting local fluorescence relative to dynamic fluorescence; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0314] In certain implementations of the OLS htSMT binding workflow described herein, the workflow can include using a microscopy system configured to determine whether a compound that induces a change in binding of a fluorescent target protein in a cell reduces the K of the fluorescently labeled target. off , comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population, and wherein the cells comprise fluorescent target proteins; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; (c) an objective lens for focusing the light beam onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample are positioned in a field of view of the sample plane; (d) a detector arrangement for monitoring the light-based response of the fluorescent target proteins in the presence of a compound, wherein the detector arrangement is configured to: (i) block light received from a light source outside the sample plane in which the fluorescent target proteins are positioned, thereby tracking the position of the fluorescent target proteins; and (ii) track the movement of each fluorescent target protein, wherein the tracking is adapted to selectively detect local fluorescence relative to dynamic fluorescence, and wherein an average change in the movement of the fluorescent target proteins in the presence of the compound relative to a baseline movement under DMSO conditions is about 5% or about 10%; (e) a memory; and (f) a processor in communication with the memory and the detector arrangement, wherein the processor is capable of determining a change in the movement of the fluorescent target proteins in the presence of the compound relative to the absence of the compound.

[0315] In certain implementations of the OLS htSMT binding workflow described herein, the workflow can include using a microscopy system configured to determine whether a compound that induces a change in binding of a fluorescent target protein in a cell reduces the K of the fluorescently labeled target. off , comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population, and wherein the cells comprise fluorescent target proteins; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; (c) an objective lens for focusing the light beam onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample are positioned in a field of view of the sample plane; (d) a detector arrangement for monitoring the light-based response of the fluorescent target proteins in the presence of a compound, wherein the detector arrangement is configured to: (i) block light received from a light source outside the sample plane in which the fluorescent target proteins are positioned, thereby tracking the position of the fluorescent target proteins; and (ii) track the movement of each fluorescent target protein, wherein the tracking is adapted to selectively detect localized fluorescence relative to dynamic fluorescence; and (iii) achieve a z-factor of >0.5 based on a single field of view; (e) a memory; and (f) a processor in communication with the memory and the detector arrangement, wherein the processor is capable of determining a change in the movement of the fluorescent target proteins in the presence of the compound relative to the absence of the compound.

[0316] 4.3.OLS KineticSMT

[0317] Because SMT identifies the rate of biological interactions between a compound and its target, it can be used to distinguish between direct and indirect effects on target activity, among other parameters. Given SMT's live cell setup, data collection modes can be configured to measure protein motion (kinetic SMT or kSMT) at set time intervals after compound addition to determine the rate of biological interactions between the compound and its target.

[0318] Exemplary OLS KineticSMT workflows include the following individual strategies and combinations of the following strategies, wherein requirements of two or more strategies are combined. For example, but not by way of limitation, the workflows disclosed herein include illuminating a field of view of a sample plane disposed within a sample with a light beam such that a subset of fluorescent target proteins in living cells fluoresces, wherein the subset of fluorescent target proteins is present in about 30 to about 80 living cells illuminated in the field of view of the sample plane, and illuminating a field of view of a sample plane disposed within the sample with a light beam such that a subset of fluorescent target proteins in living cells fluoresces, wherein the subset of fluorescent target proteins includes proteins in a range of about 1000 to about 1,000,000. Similarly, illuminating the sample plane to illuminate about 30 to about 80 living cells per FOV and / or causing about 1000 to about 1,000,000 proteins to fluoresce can be combined with any other strategic requirements disclosed herein, for example, determining that the average change in motion of the fluorescent target protein in the presence of the compound is about 5% or about 10% relative to the baseline motion under DMSO conditions, detecting fluorescence of multiple fluorescent target proteins in the sample plane field of view at a rate of about 10,000 to about 18,000 per system per day, and achieving a z-factor >0.5 based on a single field of view.

[0319] In certain implementations of the OLS kinetic htSMT combined workflow described herein, the workflow may include determining the occurrence rate of biological interactions between a compound and a target in living cells, comprising: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking the movement of a plurality of individual fluorescent target proteins in the plurality of living cells in the sample at a plurality of time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so as to cause a subset of the fluorescent target proteins in the living cells to emit fluorescence, wherein the subset of fluorescent target proteins is present in the sample; (ii) detecting, by a detector arrangement, fluorescence of a plurality of fluorescent target proteins within the sample planar field of view; and (c) determining changes in motion of the fluorescent target proteins in the presence of the compound; wherein the rate at which the motion of the fluorescent target proteins changes in the presence of the compound indicates the rate of occurrence of biological interactions between the compound and the target.

[0320] In certain implementations of the OLS kinetic htSMT combined workflow described herein, the workflow may include determining the occurrence rate of biological interactions between a compound and a target in living cells, comprising: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking the movement of multiple individual fluorescent target proteins in multiple living cells in the sample at multiple time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the fluorescent target proteins in the living cells emit fluorescence, wherein the subset of fluorescent target proteins comprises about 1000 to about 1,000,000 proteins, wherein the number of proteins in the subset depends on the expression level of the protein of interest and the dye concentration deemed sufficient to label the subset proteins for robust SMT, both of which can be calculated and / or configured by a person skilled in the art based on the disclosure of this application; (ii) detecting the fluorescence of multiple fluorescent target proteins in the field of view of the sample plane by a detector device; and (c) determining the change in movement of the fluorescent target proteins in the presence of the compound; wherein the rate at which the change in movement of the fluorescent target proteins in the presence of the compound indicates the occurrence rate of biological interactions between the compound and the target.

[0321] In certain implementations of the OLS kinetic htSMT combined workflow described herein, the workflow may include determining the incidence of biological interactions between a compound and a target in living cells, comprising: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking the movement of multiple individual fluorescent target proteins in multiple living cells in the sample at multiple time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the fluorescent target proteins in the living cells emit fluorescence; (ii) detecting the fluorescence of multiple fluorescent target proteins within the field of view of the sample plane by a detector device; and (c) determining the change in movement of the fluorescent target proteins in the presence of the compound, wherein the average change in movement of the fluorescent target proteins in the presence of the compound is about 5% or to about 10% relative to the baseline movement under DMSO conditions; wherein the rate at which the change in movement of the fluorescent target proteins in the presence of the compound indicates the incidence of biological interactions between the compound and the target.

[0322] In certain implementations of the OLS kinetic htSMT combined workflow described herein, the workflow may include determining the rate of occurrence of biological interactions between a compound and a target, and direct and indirect biological interactions between a compound and a fluorescent target protein in living cells, comprising: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking the movement of multiple individual fluorescent target proteins in multiple living cells in the sample at multiple time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the fluorescent target proteins in the living cells emit fluorescence; (ii) detecting the fluorescence of the multiple fluorescent target proteins in the field of view of the sample plane by a detector device; (iii) wherein the tracking comprises detecting the fluorescence of the multiple fluorescent target proteins in the field of view of the sample plane at a rate of approximately 10,000 to approximately 18,000 per system per day; and (c) determining the change in movement of the fluorescent target protein in the presence of the compound; wherein the rate at which the change in movement of the fluorescent target protein in the presence of the compound indicates the rate of occurrence of biological interactions between the compound and the target.

[0323] In certain implementations of the OLS kinetic htSMT combined workflow described herein, the workflow may include determining the rate of occurrence of biological interactions between a compound and a target in living cells, comprising: (a) contacting a sample comprising a population of living cells with a compound, wherein the living cells comprise a fluorescent target protein; (b) tracking the movement of multiple individual fluorescent target proteins in multiple living cells in the sample at multiple time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that a subset of the fluorescent target proteins in the living cells emit fluorescence; (ii) detecting the fluorescence of the multiple fluorescent target proteins within the field of view of the sample plane by a detector device, wherein detection based on a single field of view is associated with a z-factor of >0.5; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound; wherein the rate at which the movement of the fluorescent target protein changes in the presence of the compound indicates the rate of occurrence of biological interactions between the compound and the target.

[0324] In certain implementations of the OLS kinetics htSMT combined workflow described herein, the workflow may include determining a dose of a compound that induces a change in the motion of a fluorescent target protein in living cells, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of living cells, (ii) wherein the living cells comprise a fluorescent target protein, and (iii) wherein the plurality of samples are contacted with the compound at different concentrations within a range of compound concentrations; and (b) tracking the motion of each of the plurality of living cells in the sample at a plurality of time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that at least a subset of the fluorescent target proteins in the living cells emit fluorescence, wherein the subset of the fluorescent target proteins (i) detecting fluorescence of one or more fluorescent target proteins in the sample plane by a detector device; and (c) determining the rate at which the motion of the fluorescent target protein changes in the presence of the compound; and (d) repeating steps (b)-(c) for each of the plurality of samples over the range of compound concentrations; wherein the rate at which the motion of the fluorescent target protein changes in the presence of the compound indicates the rate at which a biological interaction between the compound and the target occurs.

[0325] In certain implementations of the OLS kinetics htSMT combined workflow described herein, the workflow may include determining a dose of a compound that induces a change in the motion of a fluorescent target protein in living cells, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of living cells, (ii) wherein the living cells comprise a fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of compound concentrations; and (b) tracking the motion of each of the plurality of living cells in the sample at a plurality of time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that at least a subset of the fluorescent target proteins in the living cells emit fluorescence, wherein the subset of the fluorescent target proteins comprising from about 1000 to about 1,000,000 proteins, wherein the number of proteins in the subset depends on the expression level of the protein of interest and the concentration of dye deemed sufficient to label the subset proteins to achieve robust SMT, both of which can be calculated and / or configured by a person skilled in the art based on the disclosure of the present application; (ii) detecting the fluorescence of one or more fluorescent target proteins in the sample plane by a detector device; and (c) determining the rate at which the movement of the fluorescent target protein changes in the presence of a compound; (d) repeating steps (b)-(c) for each of the plurality of samples within the compound concentration range; wherein the rate at which the movement of the fluorescent target protein changes in the presence of the compound indicates the occurrence rate of biological interaction between the compound and the target.

[0326] In certain implementations of the OLS kinetics htSMT combined workflow described herein, the workflow can include determining a dose response of a compound that induces a change in the motion of a fluorescent target protein in living cells, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of living cells, (ii) wherein the living cells comprise the fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of compound concentrations; and (b) tracking the motion of each of the plurality of living cells in the samples at a plurality of time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam to obtain a fluorescent target protein. causing at least a subset of fluorescent target proteins in living cells to fluoresce, (ii) detecting the fluorescence of one or more fluorescent target proteins in a sample plane by a detector device; and (c) determining the rate at which the motion of the fluorescent target proteins changes in the presence of the compound, wherein the average change in motion of the fluorescent target proteins in the presence of the compound is about 5% or about 10% relative to a baseline motion under DMSO conditions; and (d) repeating steps (b)-(c) for each of the plurality of samples over the range of compound concentrations; wherein the rate at which the motion of the fluorescent target proteins changes in the presence of the compound indicates the occurrence rate of biological interaction between the compound and the target.

[0327] In certain implementations of the OLS kinetics htSMT combined workflow described herein, the workflow may include determining a dose response of a compound that induces a change in the motion of a fluorescent target protein in living cells, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of living cells, (ii) wherein the living cells comprise the fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of compound concentrations; and (b) tracking the motion of each of the fluorescent target proteins in the plurality of living cells of the sample at a plurality of time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that the fluorescent target protein in the cells at least a subset of the fluorescent target proteins fluoresce, (ii) detecting the fluorescence of one or more fluorescent target proteins in a sample plane by a detector device; (iii) wherein the tracking comprises detecting the fluorescence of a plurality of fluorescent target proteins in a field of view of the sample plane at a rate of about 10,000 to about 18,000 per system per day; and (c) determining the rate at which the motion of the fluorescent target proteins changes in the presence of the compound; and (d) repeating steps (b)-(c) for each of the plurality of samples over a range of compound concentrations; wherein the rate at which the motion of the fluorescent target proteins changes in the presence of the compound indicates the rate of occurrence of biological interactions between the compound and the target.

[0328] In certain implementations of the OLS kinetic htSMT combined workflow described herein, the workflow may include determining a dose of a compound that induces a change in motion of a fluorescent target protein in living cells, comprising: (a) contacting a plurality of samples with the compound, (i) wherein each sample comprises a population of living cells, (ii) wherein the living cells comprise a fluorescent target protein, and (iii) wherein the plurality of samples are contacted with different concentrations of the compound over a range of compound concentrations; (b) tracking the motion of each of the fluorescent target proteins in the plurality of living cells of the sample at a plurality of time points, wherein the tracking comprises: (i) illuminating a field of view of a sample plane disposed within the sample with a light beam so that at least a subset of the fluorescent target proteins in the living cells emit fluorescence, (ii) detecting fluorescence of one or more fluorescent target proteins in the field of view of the sample plane by a detector device, wherein detection based on a single field of view is associated with a z-factor of >0.5; and (c) determining a rate at which the motion of the fluorescent target protein changes in the presence of the compound; (d) repeating steps (b)-(c) for each of the plurality of samples within the range of compound concentrations; wherein the rate at which the motion of the fluorescent target protein changes in the presence of the compound indicates a rate of occurrence of a biological interaction between the compound and the target.

[0329] In certain implementations of the OLS kinetics htSMT combined workflow described herein, the workflow can include using a microscope system configured to determine the occurrence of biological interactions between a compound and a target in living cells, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of living cells, and wherein the living cells comprise fluorescent target proteins; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; and (c) an objective for focusing the light beam onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample are positioned in a field of view of the sample plane, and wherein the subset of fluorescent target proteins are present in about 30 to about 80 living cells illuminated in the field of view of the sample plane, depending on the microscope used. for specific cell types, e.g., for U2OS cells, the range is about 30 to about 40 cells per FOV, while for HCT116 cells, the range is about 50 to about 80 cells, taking into account their differences in area; (d) a detector device for monitoring a light-based response of a fluorescent target protein in the presence of a compound, wherein the detector device is configured to: (i) block light received from a light source outside the sample plane where the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein at multiple time points; and (ii) track the movement of each fluorescent target protein, (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0330] In certain implementations of the OLS kinetics htSMT combined workflow described herein, the workflow can include using a microscope system configured to determine the occurrence rate of biological interactions between a compound and a target in living cells, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise fluorescent target proteins; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample is positioned in a field of view of the sample plane; and wherein the subset of fluorescent target proteins comprises from about 1000 to about 1,000,000 proteins, wherein the number of proteins in the subset depends on expression levels of the protein of interest and a dye concentration deemed sufficient to label a subset of proteins to achieve robust SMT, both of which can be calculated and / or configured by a person skilled in the art based on the disclosure of the present application; (d) a detector device for monitoring the light-based response of the fluorescent target protein in the presence of the compound, wherein the detector device is configured to: (i) block light received from a light source outside the sample plane where the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein at multiple time points; and (ii) track the movement of each fluorescent target protein, (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining changes in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0331] In certain implementations of the OLS kinetics htSMT combined workflow described herein, the workflow can include using a microscope system configured to determine the occurrence of biological interactions between a compound and a target in living cells, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and wherein the cells comprise fluorescent target proteins; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample are positioned in a field of view of the sample plane; and (d) a microscope for monitoring the presence of a compound. A detector device for detecting a light-based reaction of a fluorescent target protein in the presence of a compound, wherein the detector device is configured to: (i) block light received from a light source outside the sample plane where the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein at multiple time points; and (ii) track the movement of each fluorescent target protein, wherein the average change in the movement of the fluorescent target protein in the presence of the compound relative to the baseline movement under DMSO conditions is about 5% or about 10%, (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining the change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0332] In certain implementations of the OLS kinetics htSMT combined workflow described herein, the workflow may include using a microscope system configured to determine the occurrence rate of a biological interaction between a compound and a target in a living cell, comprising: (a) a stage for supporting a sample, wherein the sample comprises a cell population, and wherein the cells comprise a fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam onto the sample in a sample plane, wherein the plurality of fluorescent target proteins in the sample are disposed in the sample plane; and (d) a microscope for monitoring the occurrence rate of a biological interaction between a compound and a target in a living cell. A detector device for detecting a light-based reaction of a protein, wherein the detector device is configured to: (i) block light received from a light source outside a sample plane in which the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein at multiple time points; and (ii) track the movement of each fluorescent target protein, wherein the tracking includes detecting the fluorescence of multiple fluorescent target proteins in a field of view of the sample plane at a rate of about 10,000 to about 18,000 per system per day; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the movement of the fluorescent target protein in the presence of a compound relative to the absence of the compound.

[0333] In certain implementations of the OLS kinetics htSMT combined workflow described herein, the workflow may include using a microscope system configured to determine the occurrence rate of biological interactions between a compound and a target in living cells, comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of living cells, and wherein the living cells comprise fluorescent target proteins; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; (c) an objective for focusing the light beam onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample are positioned in a field of view of the sample plane; d) a detector device for monitoring a light-based reaction of a fluorescent target protein in the presence of a compound, wherein the detector device is configured to: (i) block light received from a light source outside the sample plane where the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein at multiple time points; and (ii) track the movement of each fluorescent target protein; and (iii) achieve a z-factor >0.5 based on a single field of view; (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0334] 5. Exemplary Implementation

[0335] A. The present disclosure provides a method comprising:

[0336] receiving a sequence of images visualizing molecular motion;

[0337] linking molecules across said images;

[0338] generating possible trajectories for each molecule and their associated probabilities based on the linkage using a variational Bayesian optimization algorithm; and

[0339] Data representing the possible trajectories generated and their associated probabilities is provided to a consuming application or process.

[0340] B. The present disclosure provides a method comprising:

[0341] receiving a sequence of images visualizing molecular motion;

[0342] linking molecules across said images;

[0343] generating possible trajectories for each molecule and their associated probabilities based on the linkage using a Gibbs sampling algorithm; and

[0344] Data representing the possible trajectories generated and their associated probabilities is provided to a consuming application or process.

[0345] C. The present disclosure provides a method comprising:

[0346] receiving a sequence of images visualizing molecular motion;

[0347] linking molecules across said images;

[0348] generating possible trajectories for each molecule and their associated probabilities based on the links using an adaptive hill climbing algorithm; and

[0349] Data representing the possible trajectories generated and their associated probabilities is provided to a consuming application or process.

[0350] C1. The method of any one of A to C, wherein at least a subset of the sequence of images comprises at least 100 molecules per image.

[0351] C2. The method of any one of A to C1, wherein at least a subset of the sequence of images comprises at least 1000 molecules per image.

[0352] C3. The method of any one of A to C2, wherein at least a subset of the sequence of images comprises at least 10,000 molecules per image.

[0353] C4. The method of any one of A to C3, wherein the molecules have a density of at least 0.01 emitters per square micron per image.

[0354] C5. The method of any one of A to C4, wherein the molecules have a density of at least 0.1 emitters per square micron per image.

[0355] C6. The method of any one of A to C5, further comprising:

[0356] labeling molecules within biological samples;

[0357] causing the biological sample to fluoresce; and

[0358] The sequence of images is generated while causing the biological sample to fluoresce.

[0359] C7. A method as described in C6, wherein the image sequence is generated using a microscope system.

[0360] C8. The method of any one of A to C7, wherein the molecule is imaged within a living cell.

[0361] C9. The method of any one of A to C8, further comprising:

[0362] A probabilistic dynamic model is inferred that contains information characterizing the molecular trajectory.

[0363] C10. The method of C9, wherein the probabilistic dynamic model comprises a state array, and the method further comprises: populating the state array with information characterizing the molecular trajectory.

[0364] C11. The method according to any one of A to C10, further comprising:

[0365] An internal confidence indicator based on the associated probability is generated, wherein the provided data includes the generated internal confidence indicator.

[0366] C12. A method as described in C11, wherein the generated internal confidence indicator is a tracking error rate lower bound, which defines a lower bound on the incorrect connection rate caused by the link.

[0367] C13. The method of C11, wherein the generated internal indicators include:

[0368] Compute the confidence score for each trajectory.

[0369] C14. The method according to any one of A to C13, further comprising:

[0370] Generate dynamic metrics independent of specific trajectories.

[0371] C15. A method as described in any one of A to C14, wherein the linking includes retrieving data containing multiple statistical data extracted from the total number of detections or the number of detections in the cell.

[0372] C16. A method as described in any one of A to C15, wherein providing data includes one or more of the following: visualizing at least a portion of the generated possible trajectories and their associated probabilities in a graphical user interface, storing at least a portion of the generated possible trajectories and their associated probabilities in physical persistence, loading at least a portion of the generated possible trajectories and their associated probabilities into a memory, or transmitting at least a portion of the generated possible trajectories and their associated probabilities to a remote computing device over a network.

[0373] C17. A method as described in any of A to C16, wherein at least a portion of the image sequence includes consecutive images from a corresponding movie.

[0374] C18. The method of any one of A to C17, wherein at least a portion of the sequence of images used for the linking are non-consecutive images from a corresponding movie.

[0375] D. The present disclosure provides a method for single molecule tracking, comprising:

[0376] receiving a sequence of images visualizing molecular motion, the sequence of images comprising a first type generated using a first imaging modality and a second type generated using a second, different imaging modality;

[0377] detecting points within a sequence of images of said first type;

[0378] linking the points detected within the first type of image sequence into trajectories using a probabilistic tracking algorithm;

[0379] segmenting the second type of image sequence to generate a plurality of instance masks;

[0380] assigning molecules within the second type of image sequence to at least one instance mask of the plurality of instance masks; and

[0381] Provide data representing the links and allocations to consuming applications or processes.

[0382] D1. The method as described in D, wherein the probabilistic tracking algorithm includes a variational Bayesian optimization algorithm.

[0383] D2. The method as described in D, wherein the probabilistic tracking algorithm includes a Gibbs sampling algorithm.

[0384] D3. The method as described in D, wherein the partial probabilistic tracking algorithm includes an adaptive hill climbing algorithm.

[0385] D4. The method of any one of D to D3, wherein the first imaging modality and the second imaging modality comprise different molecular labeling technologies.

[0386] D5. The method of any one of D to D4, wherein the first type of image sequence is a single molecule tracking (SMT) movie and the second type of image sequence is a non-SMT movie.

[0387] D6. The method of any one of D to D5, wherein the point of detection comprises a subcellular component.

[0388] D7. The method of any one of D to D6, wherein the molecular types within the sequence of images of the first type are labeled with different fluorophores.

[0389] D8. The method of any one of D to D7, wherein at least a subset of the sequence of images comprises at least 100 molecules per image.

[0390] D9. The method of any one of D to D8, wherein at least a subset of the sequence of images comprises at least 1000 molecules per image.

[0391] D10. The method of any one of D to D9, wherein at least a subset of the sequence of images comprises at least 10,000 molecules per image.

[0392] D11. The method of any one of D to D10, wherein the molecules have a density of at least 0.01 emitters per square micron per image.

[0393] D12. The method of any one of D to D11, wherein the molecules have a density of at least 0.1 emitters per square micron per image.

[0394] D13. The method according to any one of D to D12, further comprising:

[0395] labeling molecules within biological samples;

[0396] causing the biological sample to fluoresce; and

[0397] At least a portion of the sequence of images is generated while causing the biological sample to fluoresce.

[0398] D14. The method of D13, wherein the image sequence is generated using a microscope system.

[0399] D15. The method of any one of D to D14, wherein the molecule is imaged within a living cell.

[0400] D16. The method according to any one of D to D15, further comprising:

[0401] A probabilistic dynamic model is inferred that contains information characterizing the molecular trajectory.

[0402] D17. The method of D16, wherein the probabilistic dynamic model comprises a state array, and the method further comprises:

[0403] The state array is populated with information characterizing the molecular trajectory.

[0404] D18. The method according to any one of D to D17, further comprising:

[0405] An internal confidence indicator based on the associated probability is generated, wherein the provided data includes the generated internal confidence indicator.

[0406] D19. A method as described in D18, wherein the internal confidence indicator generated is a tracking error rate lower bound (ERLB), which defines a lower bound on the error connection rate caused by the link.

[0407] D20. The method of D18, wherein the generated internal indicators include:

[0408] Compute the confidence score for each trajectory.

[0409] D21. The method according to any one of D to D20, further comprising:

[0410] Generate dynamic metrics independent of specific trajectories.

[0411] D22. A method as described in any one of D to D21, wherein the linking includes retrieving data containing multiple statistical data extracted from the total number of detections or the number of detections in the cell.

[0412] D23. A method as described in any one of D to D22, wherein providing data includes one or more of the following: visualizing at least a portion of the generated possible trajectories and their associated probabilities in a graphical user interface, storing at least a portion of the generated possible trajectories and their associated probabilities in physical persistence, loading at least a portion of the generated possible trajectories and their associated probabilities into a memory, or transmitting at least a portion of the generated possible trajectories and their associated probabilities to a remote computing device via a network.

[0413] D24. The method according to any one of D to D23, further comprising:

[0414] A plurality of statistical metrics associated with the at least one track or the at least one instance mask are generated.

[0415] D25. The method of D24, further comprising:

[0416] A hierarchy storing instance masks.

[0417] D26. A method as described in any of D to D25, wherein at least a portion of the image sequence includes consecutive images from a corresponding movie.

[0418] D27. The method of any one of D to D26, wherein at least a portion of the sequence of images used for the linking are non-consecutive images from a corresponding movie.

[0419] D28. The method of any one of D to D27, wherein the detecting utilizes one or more of:

[0420] Generalized log-likelihood point detector, Difference of Gaussian (DoG) detector, Laplace of Gaussian (LoG) detector, or Determinant of Hessian (DoH) blob detector.

[0421] D29. The method of any one of D to D28, further comprising:

[0422] Detected points are associated with spatiotemporal coordinates using sub-pixel localization.

[0423] D30. The method of D29, wherein sub-pixel positioning comprises one or more of the following:

[0424] Radially symmetric locators or maximum likelihood fitting of candidate point models using the Levenberg-Marquardt method.

[0425] D31. A method as described in any of D to D30, wherein the field of view corresponds to at least a portion of the hole.

[0426] D32. The method of any one of A to D31, wherein the sequence of images is generated by a fluorescence microscopy apparatus having:

[0427] a first optical element or assembly configured to receive a fluorescence excitation light source and produce a collimated light beam having an elongated and linear shape in the xy plane, wherein the light beam has a uniform intensity along a longer dimension of the linear shape;

[0428] a second optical element or assembly configured to tilt the light beam in the xz plane relative to the z-axis, wherein the second optical element is further configured to focus the light beam at a sample plane located in the xy plane, thereby illuminating a portion of the sample plane;

[0429] 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; and

[0430] A detector arrangement is configured to receive light from the illuminated sample plane, wherein the detector arrangement forms one or more projected images based on the light received from the sample plane.

[0431] D33. A method as described in D32, wherein the first optical element or component includes a Powell lens to produce a collimated light beam having an elongated and linear shape in the xy plane.

[0432] D34. A method as described in D32 or D33, wherein the first optical element or component includes one or more diffraction gratings to produce a collimated light beam having an elongated and linear shape in the xy plane.

[0433] D35. A method as described in any of D32 to D34, wherein the first optical element or component includes a combination of lenses to produce a collimated light beam having an elongated and linear shape in the xy plane.

[0434] D36. A method as described in any of D32 to D35, wherein the second optical element or component includes an objective lens.

[0435] D37. The method of any one of D32 to D36, wherein the third optical element or component comprises a galvanometer mirror configured to translate the light beam in the sample plane in a direction orthogonal to a longer dimension of the light beam.

[0436] D38. The method of any one of D32 to D37, wherein the third optical element or component comprises a piezoelectric element configured to translate the light beam in the sample plane in a direction orthogonal to a longer dimension of the light beam.

[0437] D39. A method as described in any one of D32 to D38, wherein the detector device includes a semiconductor sensor, and wherein the detector device supports 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.

[0438] D40. The method of any one of A to D31, wherein the sequence of images is generated by a microscope system for detecting molecular positions, the microscope system having:

[0439] a stage for supporting a sample, wherein the sample comprises molecules;

[0440] a light source for emitting a light beam capable of inducing a light-based reaction from the molecules in the sample, wherein the light beam has a linear shape in the sample plane and has a uniform intensity over a longer dimension of the linear shape in the sample plane;

[0441] an objective lens that focuses the light beam onto a sample in the sample plane, wherein the molecules are disposed in the sample plane; and

[0442] A detector device is provided for monitoring the light-based reaction of the molecule to thereby detect the position of the molecule.

[0443] D41. A method as described in D40, wherein the microscope system also includes a scanning optical element or component that is configured to translate the light beam in the sample plane in a direction orthogonal to the longer dimension of the light beam, thereby enabling the microscope system to have a larger total field of view in the xy plane.

[0444] D42. A method as described in D41 or D42, wherein the detector device includes a semiconductor sensor, and wherein the detector device supports 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.

[0445] D43. A method as described in any of D40 to D42, wherein the detector device supports 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.

[0446] D44. The method of any one of D40 to D43, wherein the sample is disposed in an open well of a microplate.

[0447] D45. A method as described in D44, wherein the microwell plate comprises a plurality of open wells.

[0448] D46. The method of any one of D44 or D45, wherein the microscope system further comprises:

[0449] An xy position controller is provided for changing the field of view of the microscope system so that the changed field of view encompasses a different subset of the plurality of open apertures.

[0450] D47. The method of any one of D44 to D46, wherein the microscope system further comprises an automated sample handling robotic system to enable high-throughput manipulation of multiple samples on the stage, the robotic system comprising:

[0451] Memory;

[0452] a processor in communication with the memory; and

[0453] One or more robotic end effectors in communication with the processor, wherein the one or more end effectors manipulate the plurality of samples on the stage based on communication with the processor.

[0454] E. The present disclosure provides a system comprising:

[0455] at least one data processor; and

[0456] A memory storing instructions which, when executed by at least one data processor, result in the implementation of the operations of any one of the methods described in A to D31.

[0457] E1. The system of E, further comprising:

[0458] A fluorescence microscope device comprising:

[0459] a first optical element or assembly configured to receive a fluorescence excitation light source and produce a collimated light beam having an elongated and linear shape in the xy plane, wherein the light beam has a uniform intensity along a longer dimension of the linear shape;

[0460] a second optical element or assembly configured to tilt the light beam in the xz plane relative to the z-axis, wherein the second optical element is further configured to focus the light beam at a sample plane located in the xy plane, thereby illuminating a portion of the sample plane;

[0461] 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; and

[0462] A detector arrangement is configured to receive light from the illuminated sample plane, wherein the detector arrangement forms one or more projected images based on the light received from the sample plane.

[0463] E2. A system as described in E1, wherein the first optical element or component includes a Powell lens to produce a collimated light beam having an elongated and linear shape in the xy plane.

[0464] E3. A system as described in E1 or E2, wherein the first optical element or component includes one or more diffraction gratings to produce a collimated light beam having an elongated and linear shape in the xy plane.

[0465] E4. A system as described in any of E1 to E3, wherein the first optical element or component includes a combination of lenses to produce a collimated light beam having an elongated and linear shape in the xy plane.

[0466] E5. A system as described in any of E1 to E4, wherein the second optical element or component includes an objective lens.

[0467] E6. The system of any one of E1 to E5, wherein the third optical element or assembly comprises a galvanometer mirror configured to translate the light beam in the sample plane in a direction orthogonal to the longer dimension of the light beam.

[0468] E7. The system of any of E1 to E6, wherein the third optical element or assembly comprises 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.

[0469] E8. A system as described in any of E1 to E7, wherein the detector device includes a semiconductor sensor, and wherein the detector device supports 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.

[0470] E9. The system of E, further comprising:

[0471] A microscope system for detecting the position of molecules, comprising:

[0472] a stage for supporting a sample, wherein the sample comprises molecules;

[0473] a light source for emitting a light beam capable of inducing a light-based reaction from the molecules in the sample, wherein the light beam has a linear shape in the sample plane and has a uniform intensity over a longer dimension of the linear shape in the sample plane;

[0474] an objective lens that focuses the light beam onto a sample in the sample plane, wherein the molecules are disposed in the sample plane; and

[0475] A detector device is provided for monitoring the light-based reaction of the molecule to thereby detect the position of the molecule.

[0476] E10. A system as described in 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 orthogonal to the longer dimension of the light beam, thereby enabling the microscope system to have a larger total field of view in the xy plane.

[0477] E11. A system as described in E9 or E10, wherein the detector device includes a semiconductor sensor, and wherein the detector device supports 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.

[0478] E12. A system as described in any of E9 to E11, wherein the detector arrangement supports 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.

[0479] E13. The system of any one of E79 to E82, wherein the sample is disposed within an open well of a microplate.

[0480] E14. The system of E13, wherein the microplate comprises a plurality of open wells.

[0481] E15. The system of any one of E13 to E14, wherein the microscope system further comprises:

[0482] An xy position controller is provided for changing the field of view of the microscope system so that the changed field of view covers a different subset of the plurality of open apertures.

[0483] E16. The system of any one of E13 to E15, wherein the microscope system further comprises:

[0484] An automated sample handling robotic system to enable high-throughput manipulation of multiple samples on a stage, the robotic system comprising:

[0485] a memory for storing instructions;

[0486] at least one data processor; and

[0487] One or more robotic end effectors in communication with the at least one data processor, wherein the one or more end effectors manipulate a plurality of samples on a stage based on communication with the at least one data processor.

[0488] 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, implement the method as described in any one of A to D31.

[0489] F. The present disclosure provides a system comprising:

[0490] a component for receiving a sequence of images visualizing molecular motion;

[0491] a means for linking molecules across said images;

[0492] means for generating possible trajectories for each molecule and their associated probabilities based on the links; and

[0493] A component that provides data representing possible generated trajectories and their associated probabilities to a consuming application or process.

[0494] G. The present disclosure provides a system comprising:

[0495] a component for receiving a sequence of images visualizing molecular motion;

[0496] a means for linking molecules across said images;

[0497] means for generating possible trajectories for each molecule and their associated probabilities based on the linkage using a variational Bayesian optimization algorithm; and

[0498] A component that provides data representing possible generated trajectories and their associated probabilities to a consuming application or process.

[0499] H. The present disclosure provides a system comprising:

[0500] a component for receiving a sequence of images visualizing molecular motion;

[0501] a means for linking molecules across said images;

[0502] means for generating possible trajectories for each molecule and their associated probabilities based on the linkage using a Gibbs sampling algorithm; and

[0503] A component that provides data representing possible generated trajectories and their associated probabilities to a consuming application or process.

[0504] I. The present disclosure provides a system comprising:

[0505] a component for receiving a sequence of images visualizing molecular motion;

[0506] a means for linking molecules across said images;

[0507] means for generating possible trajectories for each molecule and their associated probabilities based on the links using an adaptive hill climbing algorithm; and

[0508] A component that provides data representing possible generated trajectories and their associated probabilities to a consuming application or process.

[0509] J. The present disclosure provides a single molecule tracking system, comprising:

[0510] means for receiving a sequence of images visualizing molecular motion, the sequence of images comprising a first type generated using a first imaging modality and a second type generated using a second, different imaging modality;

[0511] means for detecting points within the sequence of images of said first type;

[0512] for linking points detected within the sequence of images of the first type to members in a trajectory using a probabilistic tracking algorithm;

[0513] means for segmenting the second type of image sequence to generate a plurality of instance masks;

[0514] means for assigning molecules within the sequence of images of the second type to at least one instance mask of the plurality of instance masks; and

[0515] A component used to provide data representing links and distributions to consuming applications or processes.

[0516] 6. Examples

[0517] The presently disclosed subject matter will be better understood by reference to the following examples, which are intended to be illustrative only and not limiting of the presently disclosed subject matter.

[0518] Example 1: Light Scanning System

[0519] A. Introduction

[0520] Single-molecule localization microscopy (SMLM) techniques, such as single-molecule tracking (SMT), enable in situ measurements in both living and fixed cells, from which data-rich metrics can be extracted. SMT has been successfully applied to address a variety of biological questions and model systems, aiming to reveal the spatiotemporal regulation of molecular mechanisms that control protein function, downstream pathway effects, and cellular function under healthy or pathological conditions. While powerful, SMLM often suffers from low throughput, uneven illumination, and technical biases induced by both the microscope and the user. Due to technical limitations in scaling SMLM techniques, trade-offs must be made between spatial resolution, temporal resolution, and throughput, limiting these techniques to a small number of research groups.

[0521] This example describes the development of the OLS system disclosed herein to overcome the limitations of other SMLM techniques. Briefly, a thin optical light sheet is shaped and focused onto the back focal plane of a microscope objective and scanned using a galvanometer mirror. This optical configuration produces a scannable oblique light sheet that can cover the entire FOV of a water-immersion high-NA objective ( Figure 13A and Figures 14A-14F ). More details about this exemplary OLS system are provided below.

[0522] B. Exemplary OLS System

[0523] SMT image acquisition for the OLS dataset was performed on a custom microscope based on a Nikon Ti2, equipped with a motorized stage, a stage-top environmental chamber (OKO Laboratories), a quad-band filter (Chroma), and custom laser emitters with wavelengths of 405 nm, 561 nm, and 642 nm, delivering >10 mW, >150 mW, and >150 mW to the back focal plane of the objective, respectively. The custom laser emitters consist of three externally triggerable free-space laser sources (Cobolt 06-MLD; Huebner Photonics; 2RU-VFL-P-2000-560-M; MBP Communications Inc.; and VFL-P-2000-642-M; MBP Communications Inc.).

[0524] Oblique Line Scan (OLS; Figure 14A and 14D ) unit is attached to the rear port of the microscope and provides optical excitation and scanning. The OLS unit receives collimated Gaussian-shaped optical excitation through a polarization-maintaining single-mode fiber coupled to a laser beam coupler. The laser excitation is sent through a combination of a Powell lens, a custom-designed cylindrical lens, and an achromatic lens to shape the beam into a laser line. The beam passes through a set of two position-adjustable right-angle prisms and then through an aspheric achromatic lens to position the beam and focus the scan axis onto the galvanometer scanning mirror. The beam position is adjusted so that the beam is offset by 3.8 mm from the central optical axis of the objective rear focal plane to achieve an illumination light sheet at a 60-degree tilt angle in the sample ( Figure 14E ).

[0525] Fluorescence emission was passed through a high-speed filter wheel (Sutter Instruments) and collected with a back-illuminated sCMOS camera (ORCA-Fusion BT, Hamamatsu). The sCMOS camera was operated in progressive mode with an exposure time of 407 μs and an internal line interval of 4.87 μs to achieve a virtual rolling slit of approximately 200% of the excitation and fluorescence line width ( Figure 14F ). Images were acquired using a 60X1.27NA water immersion objective (Nikon). The environmental chamber was set at 37°C, 95% humidity, and 5% CO2.

[0526] System hardware control is implemented in a custom-designed, user-configurable circuit board for software interfacing, synchronization, and instrument control. Data acquisition control is implemented in MicroManager using a custom-designed and user-configurable acquisition script for raster scanning of 384-well plates, a custom-designed autofocus routine ( Figure 14B). One frame of Hoechst and Potomac Red channels was collected at the same frame rate and used for registration of downstream tracks to the nucleus and cytoplasm, respectively.

[0527] C. Discussion

[0528] This example discloses OLS, a robust illumination and detection mode based on a single objective light sheet, which achieves nanometer-scale spatial resolution and sub-millisecond temporal resolution within a 250×190μm field of view, thus overcoming the limitations of other SMLM techniques. OLS was developed to expand the effective imaging area while homogenizing the SNR across the entire camera chip to produce high-quality SMLM and SMT raw image files without compromising the achieved spatiotemporal resolution. The relatively simple optical configuration used in OLS makes this approach easily implementable on inverted microscopes equipped with water- or oil-immersion high numerical aperture (NA) objectives and sCMOS cameras with light sheet mode capability.

[0529] Example 2: OLS high-throughput single-molecule tracking (htSMT)

[0530] A. Introduction

[0531] This example describes an exemplary industrial-scale OLS htSMT technique using the exemplary OLS system of Example 1, and a comparison of such an OLS system with a highly tilted and laminated optical sheet (HILO) system. This example further describes a system incorporating such OLS htSMT technique, hardware and software associated with such OLS htSMT technique, and methods of using such OLS htSMT technique. For example, the OLS htSMT technique described herein is capable of measuring protein movement in millions of cells per day. The OLS htSMT technique described herein exhibits specific, robust, and reproducible results. The OLS htSMT technique described herein can be used for a variety of applications, including but not limited to classical drug discovery activities, such as compound library screening and the elucidation of SAR. Importantly, the OLS htSMT technique described herein can be used to characterize the contributions of known and novel pathways to interaction networks, such as protein signaling interaction networks.

[0532] B. Results

[0533] a. Creation and verification of htSMT system

[0534] A robotic system was developed that can handle reagents, collect high-quality, rapid SMT image series, process time-sequenced raw images to generate molecular trajectories, and extract biologically interesting features within defined cellular compartments. Figure 1To examine the performance of the htSMT system, we performed various measurements that demonstrated that the disclosed image acquisition system and workflow are suitable for robust htSMT analysis. For example, Figure 3A Depicted is a laser titration experiment showing the relationship between laser power (mW) at the sample and signal-to-noise ratio (SNR) (left), as well as the average SNR at the well level for four image acquisition systems, each measuring six different 384-well plates (right). Figure 3C Depicted are dose-response experiments using established and well-characterized compounds targeting halo-tagged proteins to assess inter-plate and day-to-day reproducibility (top panel), and the corresponding EC50s are presented (bottom panel). Figure 3D Demonstrates that the system described herein is configured to capture comparable protein diffusion coefficients per FOV per well, where each point represents the average single FOV position per plot per concentration (top panel), and both EC50 and z-factor are presented (bottom panel). Figure 3E Data consistency across multiple wells and experiments is depicted, where each point represents one FOV from 14 independently generated dose-response curves.

[0535] In addition to confirming that the OLS workflow described herein is suitable for robust htSMT analysis, experiments were performed to compare the OLS-based workflow described herein with HILO-based methods. Figure 3D and Figure 3E The comparison of the Z factors associated with the OLS-based data presented in with those collected using the HILO-based method clearly illustrates the improved performance of the OLS-based method. Figure 3B This is particularly evident in the figure that depicts the difference in spatial SNR heterogeneity between the disclosed OLS system and the HILO-based method. The top figure compares the spatial standard deviation observed in the OLS and HILO-based methods. The bottom figure illustrates the difference in FOV between the HILO and OLS-based methods (left figure), as well as a comparison of the spatial heterogeneity between the FOVs of the HILO-based method (center figure) and the OLS-based method (right figure).

[0536] Additional experiments were performed to demonstrate the improved performance of OLS-based methods compared to HILO-based methods. For this comparison, a U2OS cell line with HaloTag genomic editing into the amino terminus of the KEAP1 gene (Halo-KEAP1) was used. Initial imaging of Halo-KEAP1 sparsely labeled with the rhodamine dye Janelia Fluorophore 549 (JF549) yielded sharp single-molecule resolution, enabling the application of point detection, localization, and tracking analyses ( Figure 13BThe performance of the OLS system was benchmarked against the HILO implementation. 1.5 seconds of SMT data were collected in HILO and OLS, and the resulting trajectories were plotted ( Figure 13D The average number of trajectories collected across the entire FOV increased from 25,765 ± 4838 for HILO to 167,479 ± 46,324 for OLS, matching the calculated 6-fold increase in imaging field ( Figure 13E ). SMT data of 1,224 FOVs were collected on a 384-well plate, and the average signal-to-noise ratio (SNR) of all points located within each pixel of the FOV was calculated, and a spatial SNR graph was plotted ( Figure 13F The standard deviation and mean SNR per FOV for 308 apertures for both OLS and HILO were then summarized, demonstrating improved SNR consistency and performance when comparing the two illumination modes ( Figure 13G ).

[0537] For HILO, the sample was illuminated for 2 milliseconds by pulsing the laser within a subset of the camera exposure time. For OLS, given the scan rate of the light sheet, each fluorophore was calculated to be exposed for only 400 milliseconds. Given this shorter integration time for fluorophores with different diffusion rates, it was expected that the point spread functions (PSFs) would be more consistent. This hypothesis was tested by analyzing the average spot width of KEAP1 with and without KI-696 ( Figure 15A Under HILO illumination, the average 2σ radius of the single-molecule PSF increases by 4.4%, while for OLS it decreases to 1.4% ( Figure 15B and 15D While a strobe time of 400 μs allows for direct comparison of motion-induced blur performance in OLS, it was found that single-molecule detection was not possible with HILO within this integration time, as the vast majority of PSFs did not pass the noise threshold ( Figure 15C ).

[0538] One of the key advantages offered by OLS is that during scanning of the oblique light sheet, the out-of-focus illumination emitters are outside the pixel band recorded by the camera. To characterize this superior illumination-based optical sectioning method, samples consisting of increasing concentrations of His-HaloTag in solution were prepared to titrate the protein labeling density and the downstream effects on SNR and PSF detection. This experiment surprisingly captured the expected improvement in sectioning capability offered by OLS. A rapid drop in the number of detected localizations was observed in HILO, which correlated with a drop in SNR ( Figure 15E and 15F These results emphasize that under OLS illumination, individual PSFs are better detected, regardless of whether increased dye or protein concentrations cause local PSF overlap. Combined with the reduction in motion blur, OLS enables tracking of single particles at high densities with high resolution.

[0539] To further evaluate the reproducibility of the illumination quality of the disclosed OLS optical system, side-by-side SMT measurements were performed on four different OLS-equipped microscopes using a previously described automated system. Six to seven 384-well plates were tested per microscope, and Halo-KEAP1 was treated with 20 concentrations of KI-696 (a small molecule known to disrupt the interaction between KEAP1 and its binding partner NRF2), thereby increasing the fraction of Halo-KEAP1 that rapidly diffused. Each concentration was randomly assigned to 12 replicate wells on the plate, with 6 FOVs per well. The average dose-response curves for each microscope were highly consistent, with a median increase in diffusion of 47-51%, and the median EC50 values ​​generated in four independent microscopes were between 7.37 and 8.58 nM ( Figure 13C and 16A The average FOV-level SNR for each microscope was compared, and all four microscopes provided an average SNR ranging between 28.08 and 28.89 ( Figure 16B No variation was observed between subsequent FOVs captured within a single well, indicating minimal perturbation of the well as a whole when imaging a specific FOV ( Figure 16C This means that position effects within the pore do not appear to exist in this set of measurements. Additionally, the effect of the large OLS FOV size on SMT sampling can be directly characterized by comparing a cropped region of the same FOV to the FOV of a large OLS size. A significant increase in variance was observed when the number of cells captured was reduced to an area of ​​83 × 83 μm ( Figure 16D ).

[0540] C. Method

[0541] a. Cell lines

[0542] U2OS (ATCC catalog number HTB-96) can be grown in DMEM (catalog number 1056601, Gibco DMEM, high glucose, GlutaMAX supplement, Thermofisher) supplemented with 10% fetal bovine serum (catalog number 16000044, Thermofisher) and 1% penicillin-streptomycin (catalog number 15140122, Thermo Fisher) and maintained in a humidified 37°C incubator with 5% CO2, and subcultured approximately every two to three days.

[0543] b. HaloTag-expressing cell lines

[0544] For specific Target-HaloTag fusions, a mammalian expression vector containing the appropriate fusion gene under the control of the weak L30 promoter and a neomycin resistance marker can be transfected into 70% confluent U2OS cells using FuGENE 6 (Cat. No. E2691, Promega). Transfected cells can be selected with 500 μg / mL of G418 (Cat. No. 10131027, Thermo Fisher) and then cloned. Clones expressing the desired fusion gene can be determined by first staining with 100 nM JF549-HTL (Cat. No. GA1110, Promega) and 50 nM Hoechst 33342 and identifying clones with the expected JF549 signal distribution. Multiple clones can then be tested for response to control compounds using SMT conditions, and the most homogeneous clones can then be expanded for further testing.

[0545] To generate certain KEAP1-HaloTag cell lines (e.g., Figures 16A-16D The cell lines used in the study were transfected with a ribonucleoprotein (RNP) complex containing sgRNA targeting the N-terminal or C-terminal region (Integrated DNA Technologies-IDT) and Cas9 protein (PNA bio, catalog number CP01) along with a linear dsDNA donor (IDT) using the Lonza nucleofection method. Each donor consisted of 200-300 bp homology arms specific for each target, a codon-optimized HaloTag sequence, and a TEV linker (ENLYFQG) between the target and the HaloTag. After transfection, the cells were incubated with the Halo ligand JF 646 (In-house) and imaged using the ImageXpress system (Molecular Devices) to confirm HaloTag integration. Cells were then sorted into 384-well plates. Clonal cells were expanded and imaged using the ImageXpress system and genotyped by Sanger sequencing to confirm homogeneous HaloTag integration.

[0546] c. Western blotting

[0547] Cells can be grown under the same conditions as previously described. 1.5 × 10 cells can be grown per well in a 6-well plate. 6 Cells were seeded in DMEM overnight and then treated with compounds (DMSO or 100 nM fulvestrant) for 24 hours the next day. The cells were then lysed in 200 μL 1X cell lysis buffer (catalog number 9803, Cell Signaling). The BCA protein assay kit (catalog number 23225, PierceTM Protein lysate concentrations were determined using a BCA protein assay kit. Capillary Western immunoassays were then performed using Jess Protein Simple following the manufacturer's instructions (Protein Simple, USA). Anti-target antibody levels were normalized to the loading control β-tubulin (1:100, NC0244815 LI-COR 92642213, Thermo Fisher). Peak values ​​were analyzed using Compass software (Protein Simple, USA).

[0548] d. OLS single-molecule tracking sample preparation

[0549] The cells can then be seeded at 4500-6000 cells per well in 384-well tissue culture treated glass bottom plates. The seeded cells can then be incubated overnight at 37°C and 5% CO2 to allow them to adhere. For all SMT experiments, the cells can be incubated with 5-100 pM JF 549 -HTL (catalog number GA1110, Promega) and 50nM Hoechst 33342 were cultivated together in complete medium for one hour. The cells were then washed three times in DPBS and twice in imaging medium, which was supplemented with GlutaMAX (catalog number 35050079, Thermo Fisher) and fluoroBrite DMEM medium (catalog number A1896701, Thermo Fisher) supplemented with the same serum and antibiotics as the growth medium. Where appropriate, the compound can be serially diluted in Echo Qualified 384-well low dead volume source microplates (0018544, Beckman Coulter) to generate dose titration source material. The compound can be applied in cell culture medium at a final dilution of 1:1000. Each dose of compound can have at least 3 replicates per plate, and up to 3 plate replicates are prepared continuously, with 20 DMSO control wells and 2 dye-free control wells randomly assigned on each plate. Before collecting images, the compound can be cultivated at 37°C for one hour.

[0550] e. Image acquisition

[0551] Unless otherwise stated, all image acquisition using SMT was performed on a custom microscope, motorized stage, stage-top environmental chamber, four-band filter lens (Chroma), and a custom laser engine with wavelengths of 405 nm and 561 nm reaching the back focal plane of the objective lens. Fluorescence emission was collected through a high-speed filter wheel (Sutter Instruments) and a backlit CMOS camera (Hammamatsu Orca Fusion running in light sheet mode). Images were collected using a 60X1.27NA water immersion objective lens (Nikon). The environmental chamber was set to 37°C, 95% humidity, and 5% CO2. In some implementations, each pixel was exposed for 400 microseconds, and the entire region of interest (ROI) required a total of 9 milliseconds. The galvanometer position could then be reset within 1 millisecond (e.g., with the laser turned off) before another image was recorded. In such an implementation, 100 frames per second could be recorded. Additionally or alternatively, a second setup can be employed that uses a smaller ROI to record 200 frames per second with the same 400 microseconds / pixel exposure and 4 milliseconds image recording time. Additionally or alternatively, the galvo mirror reset can be accomplished more quickly.

[0552] f. Image Analysis

[0553] Image acquisition generates a JF for each field of view 549 Film and a Hoechst. JF 549 Movies can be used to track individual JFs 549 Molecular motion is captured, while Hoechst movies can be used for nuclear segmentation. Using a combination of existing methods, tracking is accomplished in three sequential steps: detection, sub-pixel localization, and linking. In short, a generalized log-likelihood ratio detector is used to detect points. Following detection, the estimated position of each emitter is refined to sub-pixel resolution using Levenberg-Marquardt fitting and an integrated 2D Gaussian point model, starting from an initial guess provided by a radially symmetric method. Detected points are linked into tracks using a custom modification of the hill climbing algorithm. The same detection, sub-pixel localization, and linking settings can be used for all movies.

[0554] For nuclear segmentation, all frames of the Hoechst movie can be averaged to generate an average projection. This average projection can then be segmented using a neural network trained on human labeled nuclei. Each point can then be assigned to at most one nucleus using its sub-pixel coordinates.

[0555] To recover motion information from the trajectories, a state array can be used. For example, a Bayesian inference method can be used with an "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 errors can be marginalized, resulting in a one-dimensional distribution of diffusion coefficients for each field of view. For single cell analysis, SMT and nuclear segmentation can be performed, for example on a mixture of U2OS cells with H2B-HaloTag, HaloTag-CaaX or free HaloTag. The marginal likelihood of a set of 100 diffusion coefficients on the set of trajectories within each segmented nucleus can then be evaluated. These marginal likelihood functions can be clustered using k-means and the marginal likelihood functions of each cell can be sorted by cluster index to produce a heat map. To estimate the bound fraction (fbound), the bound fraction below 0.1 μm can be calculated. 2 s -1 In order to estimate the free diffusion coefficient (D 自由 ), can be calculated to 0.1μm 2 s -1 The mean of the posterior distribution above.

[0556] g. Single-molecule tracking method

[0557] Single-molecule tracking (SMT) data were processed by a custom pipeline operating on image sequences produced by the microscope. Briefly, individual emitters were detected by applying a generalized log-likelihood ratio test to each 11 × 11 subwindow in the image, as described above (Signal-to-noise ratio definition and quantification section below). Emitters were detected by identifying pixels with a log-likelihood ratio exceeding 14. Detected emitters were localized to subpixel accuracy in a two-stage procedure. First, the subpixel position was estimated by calculating the point of maximum radial symmetry. Second, this estimate was used to implant an iterative Levenberg-Marquardt fitting procedure into a 2D integrated Gaussian within an 11 × 11 pixel subwindow centered on the detection.

[0558] Local emitters are linked in time to generate trajectories using a modification of the Sbalzerini hill climbing algorithm, which uses Gibbs sampling to estimate the uncertainty of data association. Among all SMTs, cSMT prohibits links longer than 1.25 μm and links with more than two interframe gaps to limit association errors. Emitters are assigned to segmentation classes (nucleus, cytoplasm) by comparing their subpixel positions with semantic masks generated by the segmentation procedure.

[0559] h. Data Analysis

[0560] The trace results of the automated processing pipeline can be analyzed using KNIME or Spotfire (TIBCO). 结合 or D 自由 Measurements can be associated with experiment metadata and aggregated conditionally. 结合 The change in f can be calculated for each well. 结合 The median f of DMSO in the same plate 结合 The difference between the two. Wells with no cells in the field of view or where the field of view is out of focus can be omitted from further analysis. The median fluorescence intensity of the tracking channel can be used to assess the assay interference of the compound, and if the compound is more than 3 standard deviations above the median intensity of the DMSO wells, it can be omitted. Similarly, if the active and negative controls cannot be clearly distinguished or deviate significantly from the performance of the rest of the screen, the plate can be removed from further analysis. Finally, compounds with a variance more than three standard deviations above the mean compound variance can be removed from downstream analysis. The Z' factor between the active control and DMSO on the plate can be calculated. EC can be calculated in Prism (GraphPad) by first logarithmically transforming the molecular concentration and then fitting to a four-parameter logistic curve 50 value.

[0561] i. Active molecule clustering

[0562] Molecules identified as active can be clustered based on chemical structure. Molecular frameworks can be calculated as known in the art and as implemented in Pipeline Pilot. Molecular frameworks can be clustered using functional class fingerprints (FCFP_4), for example, with a similarity threshold cutoff of 0.3 Tanimoto distance.

[0563] j. Kinetic experiments

[0564] Cells can be seeded into 384-well plates the day before, stained, and washed as described above. One well with multiple FOVs per well can be used as a baseline reading. Compounds can then be added to each well manually or robotically while imaging to a final concentration of 100 nM. Data for the wells can then be collected. Pauses can be included between each FOV so that the entire imaging protocol covers the detection window. The f of each well can be determined relative to t=0. 结合 change.

[0565] For assays up to 4 hours long, the plate can be imaged twice with multiple FOVs per well, with each reading being done at a different FOV position to prevent photobleaching from affecting the data.

[0566] k. Dwell time imaging

[0567] Sample preparation and dwell time imaging experiments can be performed in a similar manner to the single molecule tracking assays described above, with a few exceptions. Samples can be prepared with 1-10 pM JF 549 (Promega) and 50 nM Hoechst 33342 for one hour. Multiple frames per field of view were collected by setting the camera integration time to the desired milliseconds and reducing the laser source at the objective to the desired milliwatts. The laser was left on continuously during image acquisition. Compound incubation time ranged from 1 to 4 hours.

[0568] 1. Residence time analysis

[0569] Image processing, including spot detection, localization, and track reconnection, can be performed using the same methods described above. Because dwell time imaging selectively tracks slowly diffusing molecules, individual localizations can be constrained to the maximum displacement distance of a single jump reconnection. The set of tracks for each field of view can be separated into 1-CDF distributions as previously described and fitted to a biexponential decay model.

[0570] m. Fluorescence recovery after photobleaching

[0571] Images can be collected on a custom OLS microscope using a Spectra Light Engine RS-232 as described herein (e.g., in Example 1). Stimulation can be guided using a microscanner coupled to a coherent OBIS 561 nm 100 mW laser. All imaging can be performed using a 60X 1.27NA water immersion objective (Nikon). All experiments can be performed at 37°C. For FRAP experiments, cells can be seeded into 384-well plates the day before and stained with 50 nM HTL-JF. 549 Labeled and washed as described above. The compound can be added to a final concentration of 100nM one hour before imaging. Then, by averaging 10 consecutive images, the image before bleaching can be collected. Then 8-10 areas (2 backgrounds, 6-8 cells) can be bleached, and 2 areas in the cells can be left unbleached. The bleached areas are bleached at 10% power without scanning. Over the next 30 seconds, an image can be collected every 200 milliseconds, followed by an image every 1 second for 2 minutes. The background-subtracted average intensity that varies over time in the area of ​​interest can be measured and normalized to the average value of the fluorescence in the baseline image, followed by normalization to the unbleached area to explain the photobleaching of the fluorophore caused by the readout. For three biological experiments, data from multiple cells can be collected in each experiment.

[0572] HILO microscope

[0573] SMT image acquisition for the HILO dataset was performed on a custom microscope based on a Nikon Ti2, equipped with a motorized stage, a stage-top environmental chamber (OKO Laboratories), a quad-band filter (Chroma), and custom laser emitters with wavelengths of 405 nm and 561 nm, delivering >10 mW and >150 mW to the back focal plane of the objective, respectively. Fluorescence emission was passed through a high-speed filter wheel (Finger Lakes Instruments) and collected with a backlit sCMOS camera (ORCA-Fusion BT, Hamamatsu). Images were acquired using a 60×1.27 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 2 ms stroboscopic laser pulses.

[0574] o.Track measurement

[0575] When reporting the number of trajectories, singlets (trajectories with 1 detection) are excluded as they do not contribute information to most dynamics estimates.

[0576] The mean diffusion coefficient is calculated using the mean square 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.

[0577] To resolve trajectories under multiple dynamic states, the coefficients of a Brownian mixture model on a grid of diffusion coefficient values ​​and localization error values ​​were inferred using a state array (a variational Bayesian procedure based on a mixture of Dirichlet processes). The mixture components were chosen to be the Cartesian product of 100 diffusion coefficients logarithmically spaced between 0.01 and 100 μm² / s and 31 localization error values ​​(1D standard deviation) between 0.02 and 0.08 μm. Occupancy was reported as the average posterior probability of each diffusion coefficient marginalized over all localization error values. To make inference tractable, inference was restricted to 10,000 trajectories randomly sampled from each well.

[0578] Bias estimation in a single population sample is performed using analytical calculations that capture the probability of false links and truncation of the jump length distribution due to the finite search radius.

[0579] p. Empirical estimate of link accuracy

[0580] To estimate the accuracy of the linkage algorithm, a bootstrapping procedure was used. Detection results from the first and second halves of the movie were superimposed, and the tracking algorithm was run on the resulting set of detections, blinded to the source of each detection. From this, a resulting linkage score was calculated, where detections were combined from different parts of the movie. Because this score neither accounts for false linkages between detections in the same half of the movie nor the effects of photobleaching, it forms a lower bound on the error rate of linkage (ERLB).

[0581] q. Definition and quantification of signal-to-noise ratio

[0582] The signal-to-noise ratio (SNR) is defined as the likelihood ratio of a hypothesis test comparing the condition in which the target is not present, where the local image is modeled by the sum of a constant offset and independent Gaussian distributed noise, and the condition in which the target is present, where the local image is modeled by the sum of a centrally located Gaussian peak (of known width but unknown amplitude), independent Gaussian distributed noise, and a constant offset. SNR is expressed as:

[0583]

[0584] in:

[0585] A is the image cropped to the current region of interest (ROI);

[0586] w s is the side length of the square ROI (in pixels);

[0587] is the inner product operator;

[0588] h G is a zero-mean detection kernel that matches the expected Gaussian target profile, and The ROI is summed;

[0589] h u is a uniform kernel (i.e., its value is 1 across the entire ROI).

[0590] D. Discussion

[0591] Taken together, these results highlight the robustness and reproducibility of SMT measurements using OLS illumination within a large FOV. These results further demonstrate the superior performance of OLS over HILO for characterizing the motion of rapidly moving proteins at high labeling densities. These data demonstrate how OLS, as a novel illumination scheme, enhances several features of SMLM and SMT-based techniques. Compared to the established HILO technique, OLS offers a larger FOV, finer sectioning capabilities, superior SNR, uniform illumination, and higher spatiotemporal resolution. Results from OLS illumination modules implemented on four different microscopes demonstrate the robustness of OLS and the resulting consistent reproducibility of results. This robustness enables SMT measurements to be performed agnostic to any microscope, enabling testing of large compound libraries for drug screening. Furthermore, improved suppression of out-of-focus light enables better single-molecule detection and localization, making OLS suitable for SMT across a variety of cell systems and protein targets, where background fluorescence has previously limited the ability to perform SMT. Consistent with this concept, high-SNR SMT results were achieved using OLS in spheroid cultures of immortalized cancer cells in a more complex cell system.

[0592] Example 3: OLS allows for rapid capture of SMT data, enabling tracking of fast-moving proteins

[0593] This example shows the impact of higher frame rate acquisition on the measurement window improvement and key imaging metrics of the OLS system of Example 1.

[0594] A. Results

[0595] It is assumed that there is a set of optimal acquisition parameters for a given protein of interest, given the range and specificity of protein motion in living cells. The frame rate is related to other experimental factors such as localization error and tracking error to determine the information that can be recovered from SMT ( Figure 18A and 18B To understand these effects, we performed optical dynamics simulations with a complex mixture of Brownian motions ( Figure 18C ), and then tracked these simulated movies. As the frame rate increased, both average trajectory length and tracking fidelity improved, highlighting that the sampled FOV size was the only significant tradeoff ( Figure 18D ). The underlying dynamical model for each simulation is then estimated using state array analysis, a variational Bayesian method for recovering mixture models from observed trajectories. Increasing the frame rate improves recovery of faster states, but ultimately degrades recovery of slower states ( Figure 18E ). Note also that the lower and upper bounds of the mean squared displacement (MSD) estimator of the diffusion coefficient are determined by the localization error on the one hand and the search radius used in tracking on the other hand, roughly approximating this dynamic range ( Figure 18E , green dashed line). These results indicate that adjustable frame rate is a highly desirable feature in SMT imaging systems.

[0596] Turning to the experimental Halo-KEAP1 system described in Example 2, the OLS-enabled SMT was run at frame rates between 100 and 1250 Hz ( Figure 17A Similar to the simulation results, both the average trajectory length and the estimated link accuracy improve at higher frame rates ( Figure 19A and 19C Furthermore, the OLS illuminator achieves this without degrading the average SNR ( Figure 19B ) or the bleaching rate per frame ( Figure 21 ). Analyses of state arrays were run, and faster motion was recovered as the frame rate was increased until the estimated value of DMSO-treated Halo-KEAP1 stabilized at approximately 9 μm at 400 Hz. 2 / s, and the estimated value of KI-696-treated Halo-KEAP1 stabilized at approximately 14 μm 2 / s( Figure 20 , Figure 17B It is interesting to note that 400 Hz may represent a point of diminishing returns, where the sampling frequency may be suitable for capturing the faster diffusing subset of KEAP1 under DMSO and KI-696 treatment ( Figure 17B Furthermore, when simulating the measured diffusion coefficients of Halo-KEAP1+ / -KI-696 treatment at different frame rates, the simulation results closely matched the measurements ( Figure 17C Together, these results demonstrate that the ability to increase frame rates using line scanning in OLS can facilitate accurate measurements of fast protein diffusion in cellular environments. While 400 Hz appears to be an appropriate sampling rate for KEAP1, it is anticipated that other important biochemical processes in living cells will only be adequately captured at significantly higher frame rates.

[0597] B. Methods

[0598] a. Estimation of SMT dynamic range

[0599] In order to estimate the effect of frame rate on the dynamic range of SMT (e.g. Figure 17C and Figure 16), taking into account the diffusion coefficient of Brownian particles with Gaussian positioning errors The MSD estimator is based on the possible values ​​of Upward bias due to positioning error, where is the variance of the 1D localization error, and Δt is the frame interval. Since D is non-negative, we have In the opposite limit, when the true jumps of the particle are much larger than the search radius R used for tracking, the jumps are uniformly distributed within the tracking range gate (a circle of radius r), and the MSD estimator of the diffusion coefficient is upper bounded by R. 2 / 8Δt execution. Taken together, the dynamic range estimate is obtained Therefore, the effect of changing the frame rate is a pan This simple model of dynamic range does not account for the effects of trajectory misconnection (which could shift the upper limit) or non-MSD estimates of the diffusion coefficient in the array of states (which could degrade the lower limit).

[0600] b. Optical dynamic simulation

[0601] To evaluate the impact of tracking method and frame rate on the dynamic range of SMT, optical dynamic simulations were performed. These simulations used the scalar diffraction approximation of a paraxial imaging system with NA = 1.2. Briefly, a discrete mixture of Brownian motion without state transitions was simulated in a cube of dimensions 45 × 45 × 8 μm (XYZ) at frame rates of 12.5, 25, 50, 100, 200, 400, 800, or 1600 Hz. The particles were initialized with a density of 0.31 or 0.62 particles per cubic micron (depending on the simulation) and photobleached with a probability of 0.03 per frame. The particle positions coincident with a 500 μs pulse (simulating stroboscopic illumination in HILO or a rolling shutter in OLS) were accumulated onto a simulated 2D camera by convolving with the system's 3D point spread function. This produced a probability distribution of photons arriving at all simulated camera pixels. Next, photon arrivals from this distribution were sampled as a Poisson process until there were an average of 90 or 125 photons per particle (depending on the simulation). Finally, a Gaussian readout noise of 3 photons RMS was added, multiplied by a gain factor of 4.3 per photon...

Claims

1. A method comprising: receiving a sequence of images visualizing molecular motion; linking molecules across said images; Using a variational Bayesian optimization algorithm and based on the linkage, generating possible trajectories for each molecule and their associated probabilities; as well as Data representing the possible trajectories generated and their associated probabilities is provided to a consuming application or process.

2. A method comprising: receiving a sequence of images visualizing molecular motion; linking molecules across said images; Using a Gibbs sampling algorithm and based on the linkage, generating possible trajectories for each molecule and their associated probabilities; as well as Data representing the possible trajectories generated and their associated probabilities is provided to a consuming application or process.

3. A method comprising: receiving a sequence of images visualizing molecular motion; linking molecules across said images; generating possible trajectories for each molecule and their associated probabilities based on the links using an adaptive hill climbing algorithm; as well as Data representing the possible trajectories generated and their associated probabilities is provided to a consuming application or process.

4. A method as claimed in any preceding claim, wherein at least a subset of the sequence of images comprises at least 100 molecules per image.

5. A method as claimed in any preceding claim, wherein at least a subset of the sequence of images comprises at least 1000 molecules per image.

6. A method as claimed in any preceding claim, wherein at least a subset of the sequence of images comprises at least 10,000 molecules per image.

7. A method as claimed in any preceding claim, wherein the molecules have a density of at least 0.01 emitters per square micron per image.

8. A method as claimed in any preceding claim, wherein the molecules have a density of at least 0.1 emitters per square micron per image.

9. The method of any one of the preceding claims, further comprising: labeling molecules within biological samples; causing the biological sample to emit fluorescence; as well as The sequence of images is generated while causing the biological sample to fluoresce.

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

11. The method of any preceding claim, wherein the molecule is imaged within a living cell.

12. The method of any one of the preceding claims, further comprising: A probabilistic dynamic model is inferred that contains information characterizing the molecular trajectory.

13. The method of claim 12, wherein the probabilistic dynamic model comprises a state array, and the method further comprises: The state array is populated with information characterizing the molecular trajectory.

14. The method of any one of the preceding claims, further comprising: An internal confidence indicator based on the associated probability is generated, wherein the provided data includes the generated internal confidence indicator.

15. The method of claim 14, wherein the generated internal confidence indicator is a tracking error rate lower bound, which defines a lower bound on the rate of incorrect connections caused by the link.

16. The method of claim 14, wherein the generated internal indicators include: Compute the confidence score for each trajectory.

17. The method of any one of the preceding claims, further comprising: Generate dynamic metrics independent of specific trajectories.

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

19. The method of any of the preceding claims, wherein providing data comprises one or more of: visualizing at least a portion of the generated possible trajectories and their associated probabilities in a graphical user interface, storing at least a portion of the generated possible trajectories and their associated probabilities in physical persistence, loading at least a portion of the generated possible trajectories and their associated probabilities into a memory, or transmitting at least a portion of the generated possible trajectories and their associated probabilities to a remote computing device over a network.

20. A method as claimed in any preceding claim, wherein at least part of the sequence of images comprises consecutive images from a respective film.

21. A method as claimed in any preceding claim, wherein at least part of the sequence of images used for the linking are non-consecutive images from a respective film.

22. A method for single molecule tracking, comprising: receiving a sequence of images visualizing molecular motion, the sequence of images comprising a first type generated using a first imaging modality and a second type generated using a second, different imaging modality; detecting points within a sequence of images of said first type; linking the points detected within the image sequence of the first type into trajectories using a probabilistic tracking algorithm; segmenting the second type of image sequence to generate a plurality of instance masks; assigning molecules within the image sequence of the second type to at least one instance mask of the plurality of instance masks; as well as Provide data representing the links and allocations to consuming applications or processes.

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 partial probabilistic tracking algorithm comprises an adaptive hill climbing algorithm.

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

27. The method of any one of claims 22 to 26, wherein the first type of image sequence is a single molecule tracking (SMT) movie and the second type of image sequence is a non-SMT movie.

28. The method of any one of claims 22 to 27, wherein the detected sites include subcellular components.

29. The method of any one of claims 22 to 28, wherein the molecular types within the sequence of images of the first type are labeled with different fluorophores.

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

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

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

33. The method of 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. The method of 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. The method of any one of claims 22 to 34, further comprising: labeling molecules within biological samples; causing the biological sample to emit fluorescence; as well as At least a portion of the sequence of images is generated while causing the biological sample to fluoresce.

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

37. The method of any one of claims 22 to 36, wherein the molecule is imaged within a living cell.

38. The method of any one of claims 22 to 37, further comprising: A probabilistic dynamic model is inferred that contains information characterizing the molecular trajectory.

39. The method of claim 38, wherein the probabilistic dynamic model comprises a state array, and the method further comprises: The state array is populated with information characterizing the molecular trajectory.

40. The method of any one of claims 22 to 39, further comprising: An internal confidence indicator based on the associated probability is generated, wherein the provided data includes the generated internal confidence indicator.

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

42. The method of claim 40, wherein the generated internal indicators include: Compute the confidence score for each trajectory.

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

44. The method of any one of claims 22 to 43, wherein the linking comprises retrieving data comprising a plurality of statistics extracted from the total number of detections or the number of detections in a cell.

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

46. ​​The method of any one of claims 22 to 45, further comprising: A plurality of statistical metrics associated with the at least one track or the at least one instance mask are generated.

47. The method of claim 46, further comprising: A hierarchy storing instance masks.

48. A method as claimed in any one of claims 22 to 47, wherein at least a portion of the sequence of images comprises consecutive images from a respective film.

49. A method as claimed in any one of claims 22 to 48, wherein at least a portion of the sequence of images used for the linking are non-consecutive images from a respective film.

50. The method of any one of claims 22 to 49, wherein the detecting utilizes one or more of: a generalized log-likelihood point detector, a Difference of Gaussian (DoG) detector, a Laplace of Gaussian (LoG) detector, or a Determinant of Hessian (DoH) blob detector.

51. The method of any one of claims 22 to 50, further comprising: Detected points are associated with spatiotemporal coordinates using sub-pixel localization.

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

53. A method as claimed in any one of claims 22 to 52, wherein the field of view corresponds to at least a portion of a hole.

54. The method of any one of the preceding claims, wherein the sequence of images is generated by a fluorescence microscopy apparatus having: a first optical element or assembly configured to receive a fluorescence excitation light source and produce a collimated light beam having an elongated and linear shape in the xy plane, wherein the light beam has a uniform intensity along a longer dimension of the linear shape; a second optical element or assembly configured to tilt the light beam in the xz plane relative to the z-axis, wherein the second optical element is further configured to focus the light beam at a sample plane located in the xy plane, thereby illuminating a portion of the sample plane; 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; and A detector arrangement is configured to receive light from the illuminated sample plane, wherein the detector arrangement forms one or more projected images based on the light received from the sample plane.

55. The method of claim 54, wherein the first optical element or component comprises a Powell lens to produce a collimated light beam having an elongated and linear shape in the xy plane.

56. A method as claimed in claim 54 or 55, wherein the first optical element or component comprises one or more diffraction gratings to produce a collimated light beam having an elongated and linear shape in the xy plane.

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

58. A method as claimed in any one of claims 54 to 57, wherein the second optical element or component comprises an objective lens.

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

60. The method of any one of claims 54 to 59, wherein the third optical element or component comprises 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.

61. A method as claimed in any one of claims 54 to 60, wherein the detector device comprises a semiconductor sensor, and wherein the detector device supports 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.

62. The method of any one of claims 1 to 53, wherein the sequence of images is generated by a microscopy system for detecting molecular positions, the microscopy system having: a stage for supporting a sample, wherein the sample contains the molecule; a light source for emitting a light beam capable of inducing a light-based reaction from the molecules in the sample, wherein the light beam has a linear shape in the sample plane and has a uniform intensity over a longer dimension of the linear shape in the sample plane; an objective lens that focuses the light beam onto a sample in the sample plane, wherein the molecules are disposed in the sample plane; and A detector device is provided for monitoring the light-based reaction of the molecule to thereby detect the position of the molecule.

63. A method as claimed in claim 62, 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 orthogonal to the longer dimension of the light beam, thereby enabling the microscope system to have a larger total field of view in the xy plane.

64. The method of claim 62 or 63, wherein the detector arrangement comprises a semiconductor sensor, and wherein the detector arrangement supports 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.

65. The method of any one of claims 62 to 64, wherein the detector arrangement supports 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.

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

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

68. The method of any one of claims 66 or 67, wherein the microscope system further comprises: An xy position controller is provided for changing the field of view of the microscope system so that the changed field of view encompasses a different subset of the plurality of open apertures.

69. The method of any one of claims 66 to 68, wherein the microscope system further comprises an automated sample handling robotic system to enable high-throughput manipulation of multiple samples on the stage, the robotic system comprising: Memory; a processor in communication with the memory; as well as One or more robotic end effectors in communication with the processor, wherein the one or more end effectors manipulate the plurality of samples on the stage based on communication with the processor.

70. A system comprising: at least one data processor; and A memory storing instructions which, when executed by at least one data processor, result in the operations of the method as claimed in any one of claims 1 to 53.

71. The system of claim 70, further comprising: A fluorescence microscope device comprising: a first optical element or assembly configured to receive a fluorescence excitation light source and produce a collimated light beam having an elongated and linear shape in the xy plane, wherein the light beam has a uniform intensity along a longer dimension of the linear shape; a second optical element or assembly configured to tilt the light beam in the xz plane relative to the z-axis, wherein the second optical element is further configured to focus the light beam at a sample plane located in the xy plane, thereby illuminating a portion of the sample plane; 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; as well as A detector arrangement is configured to receive light from the illuminated sample plane, wherein the detector arrangement forms one or more projected images based on the light received from the sample plane.

72. The system of claim 71, wherein the first optical element or component comprises a Powell lens to produce a collimated light beam having an elongated and linear shape in the xy plane.

73. The system of claim 71 or 72, wherein the first optical element or component comprises one or more diffraction gratings to produce a collimated light beam having an elongated and linear shape in the xy plane.

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

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

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

77. The system of any one of claims 71 to 76, wherein the third optical element or component comprises 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.

78. A system as described in any one of claims 71 to 77, wherein the detector device includes a semiconductor sensor, and wherein the detector device supports 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.

79. The system of claim 70, further comprising: A microscope system for detecting the position of molecules, comprising: a stage for supporting a sample, wherein the sample contains the molecule; a light source for emitting a light beam capable of inducing a light-based reaction from the molecules in the sample, wherein the light beam has a linear shape in the sample plane and has a uniform intensity over a longer dimension of the linear shape in the sample plane; an objective lens that focuses the light beam onto a sample in the sample plane, wherein the molecules are disposed in the sample plane; and A detector device is provided for monitoring the light-based reaction of the molecule to thereby detect the position of the molecule.

80. The system of claim 79, wherein the microscope system further comprises a scanning 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, thereby enabling the microscope system to have a larger total field of view in the xy plane.

81. The system of claim 79 or 80, wherein the detector arrangement comprises a semiconductor sensor, and wherein the detector arrangement supports a shutter mode for synchronizing translation of the light beam in the sample plane with selective activation or readout of the semiconductor sensor.

82. The system of any one of claims 79 to 81, wherein the detector arrangement supports a shutter mode for synchronizing translation of the light beam in the sample plane with selective activation or readout of the semiconductor sensor.

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

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

85. The system of any one of claims 83 to 84, wherein the microscope system further comprises: An xy position controller is provided for changing the field of view of the microscope system so that the changed field of view encompasses a different subset of the plurality of open apertures.

86. The system of any one of claims 73 to 85, wherein the microscope system further comprises: An automated sample processing robotic system to achieve high-throughput manipulation of multiple samples on the stage, the robotic system comprising: a memory for storing instructions; at least one data processor; and One or more robotic end effectors in communication with the at least one data processor, wherein the one or more end effectors manipulate a plurality of samples on a stage based on communication with the at least one data processor.

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, implement the method of any one of claims 1 to 53.

88. A system comprising: a component for receiving a sequence of images visualizing molecular motion; a means for linking molecules across said images; means for generating possible trajectories for each molecule and their associated probabilities based on the linkage; as well as A component that provides data representing possible generated trajectories and their associated probabilities to a consuming application or process.

89. A system comprising: a component for receiving a sequence of images visualizing molecular motion; a means for linking molecules across said images; a means for generating possible trajectories for each molecule and their associated probabilities based on the linkages using a variational Bayesian optimization algorithm; as well as A component that provides data representing possible generated trajectories and their associated probabilities to a consuming application or process.

90. A system comprising: a component for receiving a sequence of images visualizing molecular motion; a means for linking molecules across said images; a means for generating possible trajectories of each molecule and their associated probabilities based on the linkage using a Gibbs sampling algorithm; as well as A component that provides data representing possible generated trajectories and their associated probabilities to a consuming application or process.

91. A system comprising: a component for receiving a sequence of images visualizing molecular motion; a means for linking molecules across said images; means for generating possible trajectories for each molecule and their associated probabilities based on the links using an adaptive hill climbing algorithm; as well as A component that provides data representing possible generated trajectories and their associated probabilities to a consuming application or process.

92. A single molecule tracking system comprising: means for receiving a sequence of images visualizing molecular motion, the sequence of images comprising a first type generated using a first imaging modality and a second type generated using a second, different imaging modality; means for detecting points within the sequence of images of said first type; for linking points detected within the sequence of images of the first type to members in a trajectory 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 within the sequence of images of the second type to at least one instance mask of the plurality of instance masks; as well as A component used to provide data representing links and distributions to consuming applications or processes.

Images