Nucleolus localization using machine learning-based inference and segmentation

Abstract

A sequence of microscopic images, each comprising an array of pixels, visualizing fluorescently labeled cellular components within live cells, is received. For each image, each pixel is classified using one or more machine learning models to correspond to one of a plurality of categories. The one or more machine learning models may be trained using a plurality of different loss functions that are rotated during training. Data characterizing the classification of the pixels may be provided to a consuming application or process.

Description

[Technical Field]

[0001] Related Applications This application claims priority to U.S. Patent Application No. 63 / 444,544, filed February 9, 2023, the contents of which are incorporated by reference in their entirety.

[0002] The subject matter described herein is directed to techniques for locating nucleoli in microscopy images (e.g., super-resolution microscopy images) using machine learning-based inference and segmentation. [Background technology]

[0003] Protein movement within the dense environment of living cells is strongly influenced by interactions with its surroundings. Single-molecule tracking (SMT) is one method for capturing protein movement as a reporter of activity. In particular, SMT can be used to track the dynamics of fluorescently labeled proteins within living cells, with the goal of extracting diffusion coefficients and properties as a function of genetic or chemical perturbations. This can begin to elucidate models of the spatiotemporal regulation of targets of interest and their binding partners. Several nuclear proteins and transcription factors are known to change their localization within the nucleus in response to cellular regulation or perturbations. The nucleolus acts as an isolation region where proteins are immobilized, preventing them from diffusing and interacting with their various cofactors. Summary of the Invention

[0004] In a first aspect, a sequence of microscopic images visualizing fluorescently labeled cellular components within live cells is received. Each of these images includes an array of pixels. Each pixel is classified as corresponding to one of a plurality of categories using at least one machine learning model. The machine learning model(s) may be trained using a plurality of different loss functions that are rotated during training. Data characterizing the classifications may be provided to a consuming application or process. Related provisioning may include displaying the classifications, loading the classifications into memory on a computing device, storing the classifications in a state of physical persistence, and / or transmitting the classifications over a network to a remote computing system.

[0005] The categories may include one or more of nucleus, nuclear rim, nucleolus, extracellular debris and / or background.

[0006] Pixel classification may be used to relate adjacent pixels that have the same category (i.e., category). Additionally, unrelated pixels within a hierarchical biological feature can be correlated.

[0007] The data provided can be used to determine the location of the nucleolus within the nucleus.

[0008] The loss function can take a variety of forms, including a class-balanced cross-entropy loss function, a mean squared error loss function, and / or a cross-entropy loss function. In one implementation, the loss functions are rotated in order such that a class-balanced cross-entropy loss function is used first, followed by a mean squared error loss function, and then a cross-entropy loss function.

[0009] Live cells can be stained using Hoechst dye and / or Nuclear Red dye.

[0010] The machine learning model(s) utilized herein can take a variety of forms, including, but not limited to, pixel-level convolutional neural networks such as those provided by the U-Net architecture. The machine learning model(s) can be trained using manually annotated pixels.

[0011] Additionally, the provided data can be used to generate a semantic mask, which can be used to generate one or more instance masks.

[0012] Non-transitory computer program products (i.e., physically embodied computer program products) that store instructions, which, when executed by one or more data processors of one or more computing systems, cause at least one data processor to perform the operations described herein, are also described. Similarly, computer systems are described that may include one or more data processors and memory coupled to the one or more data processors. The memory may temporarily or permanently store instructions that cause at least one processor to perform one or more of the operations described herein. Furthermore, methods may be performed by one or more data processors, whether within a single computing system or distributed across two or more computing systems. Such computing systems may be connected via one or more connections, including, but not limited to, connections via a network (e.g., the Internet, a wireless wide area network, a local area network, a wide area network, a wired network, etc.), direct connections between one or more of the computing systems, etc., and may exchange data and / or commands or other instructions, etc.

[0013] The subject matter described herein offers many technical advantages. For example, the subject matter enables identification of nucleolar regions in ultra-high resolution and other high-resolution microscopy images. The present configuration addresses limitations of dye-labeling compartments due to the number of imaging channels and overlap between spectra. The subject matter is also advantageous in that it addresses issues associated with reduced throughput when multiple imaging channels are acquired.

[0014] 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.

[0015] This patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. [Brief explanation of the drawings]

[0016] [Figure 1] A schematic diagram of the htSMT workflow is shown.

[0017] [Figure 2A] 1 illustrates an exemplary image acquisition system of the present disclosure, with the XZ sample plane visible. [Figure 2B] 1 illustrates an exemplary image acquisition system of the present disclosure, with the YZ sample plane visible. [Figure 2C] 1 illustrates an exemplary image acquisition system of the present disclosure, with the XZ sample plane visible. [Figure 2D] 1 illustrates an exemplary image acquisition system of the present disclosure, with the YZ sample plane visible. [Figure 2E] Details of the light beam compared to the HILO-based approach are shown. [Figure 2F] An example of incorporating a camera roll shutter is shown below.

[0018] [Figure 3] 1 shows a schematic diagram of an exemplary sample handling system of the present disclosure.

[0019] [Figure 4] An exemplary system for a high-throughput single-molecule imaging platform for measuring protein movement in living cells is shown.

[0020] [Figure 5] FIG. 1 illustrates data flow through an exemplary system for a high-throughput single-molecule imaging platform that measures protein movement in living cells.

[0021] [Figure 6] 10A-10C are images showing the differences between mask categories and between instance / semantic masks.

[0022] [Figure 7] 1 illustrates an exemplary computer-implemented environment relevant to the subject matter described herein.

[0023] [Figure 8] FIG. 1 illustrates a sample computing device architecture for implementing various aspects described herein.

[0024] [Figure 9] FIG. 1 shows microscopic images with known classifications for training a machine learning model.

[0025] [Figure 10] FIG. 1 illustrates an exemplary machine learning model architecture.

[0026] [Figure 11] FIG. 11 illustrates an image input to the machine learning model architecture of FIG. 10, the resulting semantic mask, and the discrete instance mask generated from that mask.

[0027] [Figure 12] Image showing the Hoechst channel (blue) overlaid with the SMT channel (white), identifying localized nucleoli (pink).

[0028] [Figure 13] The original and shuffled versions of the nucleolus mask are shown.

[0029] [Figure 14] Figure 10 shows the results of the validation test of the machine learning model.

[0030] [Figure 15] The SSMD per compound of MPDCs of target proteins localized in the nucleolar region is shown.

[0031] [Figure 16] FIG. 1 illustrates how the current model captures differences in nucleolar enrichment of target A as a function of compound treatment.

[0032] [Figure 17] FIG. 1 is a process flow diagram showing nucleolus identification using machine learning. DETAILED DESCRIPTION OF THE INVENTION

[0033] The presently disclosed subject matter is directed to a machine learning-based technique for nucleolus labeling in microscopy images (e.g., super-resolution microscopy images). The machine learning model can utilize a U-NET architecture trained and configured to generate pixel-level class labels. The labels include those for the nucleolus, nucleoplasm, nuclear membrane, and extraneous debris.

[0034] Microscopic images can be generated using a variety of systems that provide resolutions that allow nuclei to span multiple pixels, particularly those capable of characterizing the dynamics of living cells / molecules over time and in various environmental conditions. One example is a microscope system that uses oblique line scanning (OLS) illumination, which is described in more detail below as a non-limiting example for generating images / movies. Another exemplary microscope system utilizes thin-film oblique illumination microscopy (HILO). Other microscope systems can utilize different illumination strategies, including, but not limited to, total internal reflection fluorescence (TIRF), HIST, or SOLEIL, to generate the images analyzed herein.

[0035] The subject matter of this disclosure relates to industrial-scale high-throughput SMT (htSMT) techniques using optical light spectroscopy (OLS), systems incorporating such OLS htSMT techniques, hardware and software developments related to such OLS htSMT techniques, and methods of using such OLS htSMT techniques. For example, the OLS htSMT techniques described herein are capable of measuring protein movement in millions of cells per day. In addition to the ability to capture a large number of cells per field of view, OLS offers advantages such as improved spatial uniformity of the signal-to-noise ratio (SNR) across the camera chip, improved confocality (less out-of-focus signal), and improved temporal resolution, as shown, for example, in Table 1 (each "+" represents a 2-fold improvement). [Table 1]

[0036] The OLS htSMT technology described herein can be used for a variety of applications, including, but not limited to, drug discovery activities such as screening compound libraries and elucidating structure-activity relationships (SAR). Importantly, the OLS htSMT technology described herein can be used to characterize the contributions of both known and novel pathways to larger molecular assemblies that contain targets, such as protein signaling interaction networks.

[0037] 1 , aspects of the present subject matter can be implemented using an OLS htSMT workflow, which can include various stages, such as (i) sample preparation, including reagent handling, (ii) image acquisition using imaging of the sample to generate a series of images and / or videos, (iii) image analysis, for example, using various analytics, single emitter detection and sub-pixel localization (i.e., "super-resolution imaging"), tracking, computer vision, and machine learning algorithms to process these images and videos, (iv) storage of information extracted from or characterizing or constituting the images and videos (i.e., features, raw images, modified images, etc.), and (v) providing insights using the stored information, including biological interpretations (which can additionally or alternatively be provided using various analytics, tracking, computer vision, and machine learning algorithms), as described in more detail below.

[0038] The subject matter of the present disclosure will be described with reference to the figures, wherein reference numerals are used to denote like or equivalent elements throughout. The figures are not drawn to scale and are provided solely to illustrate aspects disclosed herein. Certain disclosed aspects are described below with reference to illustrative example hardware, software, and applications. It should be understood that numerous specific details, relationships, and methods are set forth to provide a more thorough understanding of the subject matter disclosed herein. For clarity of disclosure, and not for purposes of limitation, the detailed description is divided into the following subsections. 1.Definition 2.OLS htSMT hardware 3.OLS htSMT software 4. Specific OLS htSMT Applications 5. Working Example

[0039] 1.Definition Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. In the case of conflict, the present document, including definitions, will control. Preferred methods and materials are described below; however, methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the subject matter of this disclosure. All publications, patent applications, patents, and other references mentioned herein are incorporated herein by reference in their entirety. The materials, methods, and examples disclosed herein are illustrative only and are not intended to be limiting.

[0040] The terms "comprise," "include," "having," "has," "can," "contain," and variations thereof, as used herein, are intended to be open-ended transitional phrases, terms, or phrases that do not exclude the possibility of additional acts or structures. The singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. The present disclosure also contemplates other instances of "comprising," "consisting of," and "consisting essentially of" the instances or elements presented herein, whether explicitly stated or not.

[0041] In reciting numerical ranges herein, each intervening number in the range is expressly contemplated with the same precision. For example, in the range 6 to 9, the numbers 7 and 8 are contemplated in addition to 6 and 9, and in the range 6.0 to 7.0, the numbers 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, and 7.0 are expressly contemplated.

[0042] As used herein, the term "about" or "approximately" means within an acceptable error range for a particular value as determined by those skilled in the art, which depends in part on the method of measuring or determining the value, i.e., the limitations of the measurement system. For example, "about" can mean within 3 or more standard deviations, according to the practice in the art. Alternatively, "about" can mean a range of up to 20%, preferably up to 10%, more preferably up to 5%, and even more preferably up to 1% of a given value. Alternatively, particularly with respect to biological systems or processes, this term can mean within an order of magnitude, preferably within 5 times, more preferably within 2 times of a value.

[0043] As used herein, the term "trajectory" refers to a set of spatial coordinates corresponding to the observed positions of fluorescent proteins linked in time. In certain cases, multiple trajectories can be constructed algorithmically by linking multiple fluorescent proteins whose positions have been determined at successive time points. In certain cases, multiple trajectories can be constructed conservatively by linking only spots within a fixed search radius when other links are not valid. In certain cases, multiple trajectories can be constructed probabilistically.

[0044] As defined herein, protein motion refers to changes in the positions of multiple fluorescent proteins. In certain cases, protein motion can be quantified by analyzing changes in spatial coordinates at successive time points. Motion characterized in this way can include, but is not limited to, measuring the distribution of jump lengths. That is, given a set of protein displacements between one time point and a subsequent time point, a histogram of the probability of each displacement length ("jump length") can be constructed. Quantiles of this distribution can be used to describe protein motion. In certain cases, the quantile used is the median of the jump length distribution. In certain cases, the quantile used is the third quartile of the jump length distribution. In certain cases, protein motion can be quantified by analyzing trajectories. Motion characterized in this way can include, but is not limited to, measurements of mean square displacement, defined as the mean value of the squares of all displacements within a trajectory, averaged over multiple trajectories. Motion characterized in this way can also include, but is not limited to, measurements of trajectory length or measurements of the distribution of trajectory lengths. Such characterized motion may include, but is not limited to, measurements of the average radius of gyration, defined by the root-mean-square distance of all coordinates on a trajectory from the center of mass of the set of points included in the trajectory, averaged over multiple trajectories. Such characterized motion may also include, but is not limited to, measurements of the average bond angle, defined by the angle formed from three consecutive spatial coordinates averaged over multiple trajectories. Such characterized motion may also include, but is not limited to, measurements of the maximum likelihood diffusion coefficient estimate, defined as the maximum likelihood diffusion coefficient estimate for multiple trajectories under a single-state diffusion model with a constant localization error. In certain cases, protein motion may be measured through analysis of the products of a link generation algorithm. Such characterized motion may include, but is not limited to, the mean posterior diffusion coefficient, the average of the posterior probability distribution of coefficients from a probabilistic linkage algorithm. Such characterized motion may include, but is not limited to, the geometric mean posterior diffusion coefficient, the average of the log-scaled posterior probability distribution of coefficients from a probabilistic linkage algorithm.In certain cases, protein motion can be measured through model-dependent analysis of multiple trajectories. Motion characterized in this way is the fraction of immobile molecules ("f") defined by two-state model fitting. bound ") may include, but is not limited to:

[0045] As used herein, the term "motion" encompasses not only changes in the direction in which a target moves, but also both increases and decreases in the speed of movement. Thus, tracking motion may, in certain cases, include determining that a target is not moving, e.g., determining that the target is in a statically constrained state or an essentially statically constrained state. Motion can be characterized in various ways, including, but not limited to, quantifying (a) the median of the jump length distribution (the jump length corresponds to the observed distance traveled by the target's fluorescent protein in successive frames), (b) the third quartile of the jump length distribution, (c) the median radius of gyration, (d) the mean posterior diffusion coefficient, (e) the geometric mean posterior diffusion coefficient, (f) the mean square displacement, (g) the median bond angle, (h) the maximum likelihood estimator of the diffusion coefficient, and / or (i) the trajectory length.

[0046] As used herein, detected movement includes, but is not limited to, any change in movement, which may occur in response to any environmental or other factor. For example, but not limited to, movement, or lack thereof, may be caused by (A) the addition of a compound, (B) a change in temperature, (C) a change in oxygen concentration, e.g., the introduction of hypoxia, (D) mechanical stress, (E) a change in pH, and / or (F) a change in light exposure (e.g., an increase or decrease in intensity).

[0047] As used herein, the term "fluorescent protein" refers to any protein that emits a fluorescent signal. In certain cases, the fluorescent emission occurs in response to irradiation with light of a specific wavelength. An example of a naturally occurring fluorescent protein is green fluorescent protein (GFP). However, in certain cases, a protein of interest can be adapted to emit a fluorescent signal through the introduction of an encoded fluorescent tag. That is, a protein sequence is fused to the protein of interest to make it fluorescent. In certain cases, a protein of interest can be adapted to emit a fluorescent signal through the binding of a fluorescent ligand. Non-limiting examples of such encoded fluorescent tags include Halo tag, SNAP tag, CLIP tag, TMP tag, and SunTag. Additionally or alternatively, a protein of interest can be adapted to emit a fluorescent signal by binding to a fluorescent dye molecule, such as an amine-reactive dye or a sulfhydryl-reactive dye.

[0048] As used herein, the term "compound" refers to any chemically defined entity. In certain cases, a compound may be a molecule less than 1000 Da, i.e., a "small molecule." In certain cases, a compound may be a macromolecule, such as a nucleic acid. In certain cases, a nucleic acid may have a defined sequence. In certain cases, a nucleic acid includes (A) ribonucleic acid (RNA) (e.g., including modified RNA), (B) deoxyribonucleic acid (DNA) (e.g., including modified DNA), and (C) a combination of (A) and (B). In certain cases, a nucleic acid is a single- or double-stranded small interfering nucleic acid (e.g., double-stranded siRNA), an antisense oligonucleotide, a ribozyme, a microRNA, or an aptamer. In certain cases, a compound may be a protein. For example, and not by way of limitation, protein compounds of the present disclosure include signaling proteins, such as protein hormones, cytokines, kinases, phosphatases, and other enzymes and transcription factors, as well as antibodies, contractile proteins, structural proteins, storage proteins, and transport proteins. In certain cases, a compound can refer to a mixture of molecules, for example, a mixture of defined composition.

[0049] The term "uniform intensity" as used herein refers to a difference in intensity of no more than 5%, sometimes no more than 10%, or sometimes no more than 15%, relative to the intensity of light, e.g., the intensity of light directed at the sample surface.

[0050] The term "uniform intensity" as used herein, in relation to signal-to-noise ratio (SNR), refers to the SNR on a pixel-by-pixel basis within the field of view (FOV) where the range of possible values ​​falls between 0.5 and 1 standard deviation from the mean SNR.

[0051] 2.OLS htSMT hardware 2.1.Image Acquisition System With reference to Figure 1, aspects of the present subject matter can be implemented using an htSMT workflow incorporating a system for image acquisition. For example, such image acquisition can incorporate imaging of a sample to generate a series of images and / or video. Figure 2A shows a schematic diagram of an exemplary image acquisition system of the present disclosure, with the XZ sample plane visible. Figure 2B shows the same exemplary image acquisition system, but with the YZ sample plane visible. An exemplary image acquisition system (2-001) includes a light source (2-005) configured to emit light that is relayed by one or more optical elements in an optical relay (2-010), the optical relay configured to shape the light emitted from the light source to form a shaped beam (2-065) such that the shaped beam has a uniform intensity across the long dimension of the linear shape, the system further including optical elements, such as a galvo mirror (2-085), configured to translate the shaped beam, and one or more optical elements, such as a dichroic mirror (2-100), configured to direct the shaped beam to an objective lens (2-120) so that a portion of the sample surface (2-130) is illuminated by an oblique beam (2-125) resulting in light emission from the sample, such as fluorescent emission, which is focused by the objective lens (2-120) through a series of optical elements, such as a lens (2-155) and an absorption filter (2-160), onto an image collection system (2-165).

[0052] 2.1.1.Light source Referring to the exemplary image acquisition system of FIG. 2A, the system includes a light source (2-005) configured to emit light. The light source (2-005), in certain embodiments of the image acquisition systems disclosed herein, can be configured to emit light at a single wavelength. In certain embodiments of the image acquisition systems disclosed herein, the light source (2-005) can be configured to emit light at two, three, four, five, or more distinct wavelengths. In certain embodiments, the wavelength(s) of light emitted by the light source are predetermined. For example, but not by way of limitation, the wavelength(s) can be predetermined such that, when irradiated onto a sample, e.g., a sample containing a fluorescent protein, the emitted light induces fluorescence. In certain instances, the wavelength(s) used in connection with the methods described herein will fall within the range of 400 nm to 650 nm. In certain instances, the light source (2-005) emits light having a wavelength of 400 nm to 408 nm, 550 nm to 565 nm, or 638 nm to 650 nm. In certain non-limiting embodiments, the light source (2-005) is configured to include three lasers with nominal center wavelengths of 405 nm, 560 nm, and 640 nm, which may vary within the absorption band of the fluorophore used. In certain instances, the 405 nm wavelength is used to excite a Hoechst dye. In certain instances, a dye (e.g., JF) attached to a HaloTag is used to excite a Hoechst dye. 549 A wavelength of 560 nm is used to excite .

[0053] In certain non-limiting embodiments, the light source (2-005) is used to catalyze a photochemical reaction. For example, but not by way of limitation, the wavelength(s) and irradiation intensity can be such that cleavage of a chemical bond occurs. As a further example, but not by way of limitation, the wavelength(s) and irradiation intensity can induce the adoption of a non-radiative dark state (i.e., "photobleaching molecules"). As a further example, but not by way of limitation, the wavelength(s) and irradiation intensity can induce radiative or non-radiative energy transfer between fluorophores within the sample.

[0054] In certain embodiments of the image acquisition system described herein, the light source (2-005) can be configured to deliver a predetermined amount of power to the back focal plane of the objective lens (2-105). For example, and without limitation, the light source (2-005) can deliver more than 10 mW of power at a particular wavelength, such as 405 nm, and / or more than 150 mW of power at another wavelength, such as 640 nm. Additionally or alternatively, if the light source (2-005) includes three lasers emitting at wavelengths of 405 nm, 560 nm, and 640 nm, respectively, the light source (2-005) can be configured to deliver a predetermined amount of power to the back focal plane of the objective lens (2-105). For example, and without limitation, 405 nm can be configured to deliver more than 10 mW, 560 nm can be configured to deliver more than 150 mW, and 640 nm can be configured to deliver more than 50 mW.

[0055] In certain embodiments of the image acquisition system described herein, the light source (2-005) is configured to emit pulsed light. For example, but not by way of limitation, the light source (2-005) can be configured to emit strobe pulsed light. In certain embodiments of the image acquisition system described herein, the light source (2-005) is configured to emit pulsed light synchronized with the start of image acquisition. In certain non-limiting embodiments, the light source (2-005) pulses at specific time intervals depending on the number of frames per second being captured. For example, but not by way of limitation, if 100 frames per second (FPS) are being captured by the detector (2-165), the laser will be on for 9 ms and off for 1 ms. In contrast, in a 200 FPS mode, the laser will be on for 4 ms and off for 1 ms. In certain embodiments of the OLS htSMT workflow, the light source is configured to transition from 90% to 10% power in less than about 0.4 ms. In a particular embodiment of the OLS htSMT workflow, the light source is configured to transition from 90% to 10% power in less than about 0.2 ms.

[0056] The emission of light by the light source (2-005) and the directing of that light to the optical relay (2-010) may be facilitated using a single mode fiber in certain embodiments of the image acquisition systems disclosed herein. Alternatively, multimode fiber may be used in certain embodiments of the image acquisition systems disclosed herein. For example, but not by way of limitation, the multimode fiber may be configured in a predetermined shape for sample illumination.

[0057] In certain embodiments of the image acquisition systems described herein, e.g., with respect to systems configured for high-throughput sample analysis, the light source (2-005) can be configured to exhibit low drift in output. In certain embodiments, such low-drift configurations increase the consistency of sample processing and facilitate high-throughput analysis. For example, and not by way of limitation, such low-drift output configurations maintain output within about 0% to about 15% variation, about 0% to about 10% variation, about 10% variation, about 9% variation, about 8% variation, about 7% variation, about 6% variation, about 5% variation, about 4% variation, about 3% variation, about 2% variation, or about 1% variation.

[0058] In certain instances, such a low-drift output configuration maintains output power within about 0% to about 15% variation, about 0% to about 10% variation, about 10% variation, about 9% variation, about 8% variation, about 7% variation, about 6% variation, about 5% variation, about 4% variation, about 3% variation, about 2% variation, or about 1% variation across ambient (room) temperature variations of, for example, 17°C + / - 5°C. In certain instances, this is achieved by using a temperature sensor and / or a closed-loop heater to stabilize the temperature of the internal light source (e.g., laser engine) and thereby reduce output power drift. For example, but not by way of limitation, the light source may be thermally isolated from ambient temperature variations using an insulated enclosure design. Additionally or alternatively, closed-loop heaters can be strategically placed in specific locations within the system, such as at the fiber coupler, to reduce output power drift. Additionally or alternatively, a water jacket and / or cooling device can be used to reduce heat buildup from the laser head. Additionally, these thermal controls, used individually or in combination, reduce the warm-up time to reach a steady state of operation and maintain a more stable internal operating temperature as the laser is powered off and on.

[0059] 2.1.2. Optical Elements and Sample Illumination Referring to the exemplary image acquisition system of Figure 2A, the system includes a light source (2-005) configured to emit light, which is relayed by one or more optical elements in an optical relay (2-010) configured to shape the light emitted from the light source to form a shaped beam (2-065). The particular optical elements of any particular optical relay (2-010) embodiment can be selected and configured to not only generate a beam (2-065) of an appropriate shape, but also to provide appropriate translation of that beam.

[0060] In certain non-limiting embodiments of the optical relay (2-010) of the image acquisition system of the present disclosure, the optical relay (2-010) comprises one or more lenses and / or other optical elements. For example, but not by way of limitation, the selection and orientation of the lenses and other optical elements in the optical relay (2-010) are configured to appropriately shape the light beam directed at the sample. In certain non-limiting embodiments, the optical relay (2-010) includes an optical element, such as a collimator (2-020), for collimating the light emitted from the light source (2-005). Additionally or alternatively, the optical relay (2-010) may include additional optical elements, such as a Powell lens (2-025) or other element adapted to create a beam fan, one or more cylindrical lenses (2-045 and 2-055), one or more slits (2-050 and 2-095) for adjusting the size of the light sheet, one or more achromatic lenses (2-060 and 2-080), and / or one or more mirrors (2-070, 2-075, and 2-085), one or more of which may be galvo mirrors (2-085) that can translate the light. The specific attributes of the optical elements are predetermined to produce an appropriately shaped light beam. For example, but not by way of limitation, the OLShtSMT system of the present disclosure may achieve a uniform horizontal FOV and a uniform vertical FOV. Such uniformity of horizontal and vertical FOV is in contrast to other strategies that provide non-uniform horizontal and / or non-uniform vertical FOV (see Table 2). [Table 2]

[0061] To achieve a uniform horizontal FOV and a uniform vertical FOV, the optical relay (2-010) of the OLS htSMT system described herein includes an optical element or assembly capable of generating a beam that is elongated along the X-plane and narrow along the Y-plane, where the light beam has a uniform intensity across the long dimension of the linear shape. In a specific, non-limiting embodiment, the optical relay (2-010) of the OLS htSMT system described herein includes a Powell lens (2-025) that shapes the light beam to have a uniform intensity across the long dimension of the linear shape (2-065). The optical relay (2-010) of the OLS htSMT system described herein can include additional or alternative optical elements or assemblies that shape the light beam to have a uniform intensity across the long dimension of the linear shape (2-065). For example, but not by way of limitation, the optical relay (2-010) of the OLS htSMT system described herein can include a diffractive element or assembly configured to shape the light beam to have a uniform intensity across the long dimension of the linear shape.

[0062] In certain non-limiting embodiments of the optical relay (2-010) of the image acquisition system of the present disclosure, the optical relay (2-010) comprises one or more optical elements or assemblies configured to translate the light beam relative to the sample surface of the sample being analyzed, e.g., in a direction perpendicular to the long dimension of the light beam. For example, and without limitation, such optical elements or assemblies configured to translate the light beam relative to the sample surface of the sample being analyzed can include a galvo mirror (2-085) or a piezoelectric element configured to translate the light beam. Additionally or alternatively, such optical elements or assemblies configured to translate the light beam relative to the sample surface of the sample being analyzed can comprise a computer-controlled motor.

[0063] Referring to the exemplary image acquisition system of FIG. 2A, the system includes an optical relay (2-010) configured to shape light emitted from a light source to form a shaped beam (2-065), which is directed by an optical element (2-100), such as a dichroic mirror, and directed towards an objective lens (2-120), thereby illuminating a sample surface (2-130) with an oblique beam (2-125).

[0064] In certain non-limiting embodiments of the image acquisition system of the present disclosure, the objective lens (2-120) directs an inclined beam (2-125) onto the sample surface (2-130) to be analyzed. In certain non-limiting embodiments of the image acquisition system of the present disclosure, the objective lens (2-120) is a water-immersion objective lens. The use of a water-immersion objective lens facilitates high-throughput sample analysis by eliminating the oil present in conjunction with the use of an oil-immersion objective lens, thereby enabling higher quality and less distortion. In the context of automated systems, the presence of oil can be problematic, as well as the potential for oil to spread to components such as optical elements that may be exposed to oil and become contaminated. However, water-immersion objective lenses have a refractive index more suited to cellular imaging, resulting in less distortion and improved image quality than oil-immersion objective lenses. In certain non-limiting embodiments, the objective lens is a 60x 1.27 NA water-immersion objective lens (Nikon). In certain embodiments of the workflow described herein, the water-immersion objective lens (2-120) is heated by a heating element. For example, such a heating element maintains the water immersion objective (2-120) at a temperature sufficient to avoid inducing temperature changes in the sample contained in the sample plate (2-021).

[0065] 2.1.3. Image Acquisition In certain non-limiting embodiments of the image acquisition system of the present disclosure, the objective lens (2-0120) is also used to focus the fluorescence emitted by the sample (2-145) in response to the illumination provided by the oblique beam (2-125). In certain non-limiting embodiments, the fluorescence emission (2-145) focused on the objective lens passes through absorption filters (2-150 and 2-160), e.g., bandpass absorption filters that match the spectrum of the fluorophore under observation and are mounted on a high-speed filter wheel (Finger Lakes Instruments), and is collected by a detector device (2-165). In certain non-limiting embodiments, the fluorescence emission focused on the objective lens is directed to an optical relay before collection by the detector device (2-165). For example, but not by way of limitation, such an optical relay can include one or more lenses (2-155) and one or more additional optical elements, e.g., elements configured to reject additional scattered light before collection by the detector device (2-165). In certain non-limiting embodiments, the fluorescent emission focused onto the objective is directed through another dichroic mirror to split the emission across multiple regions of the detector (2-165). In certain non-limiting embodiments, the fluorescent emission focused onto the objective is directed through another dichroic mirror to split the emission across multiple detectors (2-165).

[0066] In certain non-limiting embodiments of the image acquisition system of the present disclosure, the detector device is configured to synchronize detection of the tilted beam (2-125) across the sample plane (2-130) with the movement of the beam. Such synchronization is shown schematically in FIG. 2F. For example, but not by way of limitation, the detector device can be a CMOS camera, such as a back-illuminated CMOS camera (the Hamamatsu Fusion BT).

[0067] In certain embodiments of the image acquisition system of the present disclosure, the CMOS camera can be operated to collect a series of SMT frames for each field of view. For example, but not by way of limitation, 1-20,000 SMT frames, 1-15,000 SMT frames, 1-10,000 SMT frames, 1-5,000 SMT frames, 1-1,000 SMT frames, 2-500 SMT frames, 5-250 SMT frames, 10-200 SMT frames, 100-200 SMT frames, or 200 SMT frames can be collected per field of view. In certain embodiments, the CMOS camera can be configured to operate at a frame rate of 0.5-1000 Hz, although in certain embodiments, it can be configured to operate at 100 Hz. For example, but not by way of limitation, certain cellular SMT implementations can be performed at 100 Hz.

[0068] In certain non-limiting embodiments of the image acquisition system of the present disclosure, the detector device is configured to transmit a signal at each frame to trigger other elements of the imaging system. For example, but not by way of limitation, the detector device can trigger illumination from the light source (2-005) to collect fluorescent emission associated with a strobe laser pulse. For example, but not by way of limitation, such fluorescent emission collection can be associated with a 10-100 millisecond frame and a 2 millisecond strobe laser pulse.

[0069] In certain embodiments, the imaging acquisition system can be configured to acquire a predetermined image dimension per frame, referred to herein as a region of interest (ROI). In certain embodiments, the ROI varies depending on the frame rate employed. For example, at 100 FPS, 2304 x 1728 pixels define an ROI that is 248.832 x 186.624 microns at the sample plane. In contrast, at 200 FPS, 2304 x 768 pixels define an ROI that is 248.832 x 82.944 microns at the sample plane.

[0070] In certain embodiments, the imaging acquisition system can be configured to perform a predetermined sweep speed at a predetermined frame rate. For example, and not by way of limitation, at 100 FPS, the sweep speed may be 186.624 microns / 9 ms, which corresponds to 20.8 microns / ms, which corresponds to 2.08 cm / s. In contrast, at 200 FPS, the sweep speed may be 82.94 microns / 4 ms, which corresponds to 20.7 microns / ms, which corresponds to 2.07 cm / s.

[0071] In certain embodiments, a detector device can be used to collect fluorescent emissions at multiple wavelengths. For example, but not by way of limitation, fluorescent emissions of additional fluorophores can be collected at the same frame rate or at different frame rates for the same field of view to provide downstream registration of SMT tracks to other cellular components, such as nuclei. Additional channels of the detector device can be used as needed to expand the number of fluorescent emissions simultaneously captured for the same field of view to provide downstream registration of SMT tracks to other cellular components, such as nuclei.

[0072] 2.2. Sample Handling Referring to FIG. 1 , embodiments of the present subject matter can be implemented using an htSMT workflow incorporating a system for sample preparation, including reagent processing. For example, and not by way of limitation, FIG. 3 provides a schematic diagram of a sample plate (2-021) including multiple wells (2-016) in which sample preparation and analysis can occur. FIG. 3 also provides a schematic diagram of the components of a sample, e.g., cells (2-018) and fluorescent target proteins (2-017) within the cells. However, as noted herein, FIG. 3 is not intended to convey scale; for example, each sample present in well (2-016) may contain thousands of cells, each cell containing multiple fluorescent target proteins. FIG. 3 also schematically illustrates the ability of the sample processing system of the present disclosure to add additional reagents to the sample (2-019). The addition of such reagents can be handled by robotic manipulation, including, but not limited to, translation of a robotic fluid handling system relative to the individual wells (2-016) of the sample plate (2-021), translation of the sample plate (2-021) itself, or a combination of both. In certain embodiments of the image acquisition system, the sample plate (2-021) can be maintained in a temperature-controlled environment via the environmental control area (2-020). For example, but not limited to, the samples can be maintained at 22-50°C. In certain embodiments of the image acquisition system, the sample plate (2-021) can be maintained in a humidity-controlled environment via the environmental control area (2-020). For example, but not limited to, the samples can be maintained at 20%-95% humidity. In certain embodiments of the image acquisition system, the sample plate (2-021) can be maintained in a defined gas environment via the environmental control area (2-020). For example, but not limited to, the samples can be maintained at 5% CO2.

[0073] 2.2.1. Cell Lines and Cell Culture Referring to FIG. 3, a particular advantage of the htSMT system described herein is its ability to assay live cells (2-016), facilitating tracking of protein activity, mobility, and diffusion behavior within a dense, live-cell environment. Exemplary cell lines for use in conjunction with the htSMT system described herein are contemplated if the sample can be focused by the objective lens (2-120) for a sufficient period of time to direct the fluorescent emission of the fluorophore toward the detector (2-165). For example, but not by way of limitation, cells can be directly attached to a coverslip. As a further example, but not by way of limitation, cells can be induced to attach to the coverslip after treating the coverslip with an extracellular matrix material (e.g., fibronectin, collagen, poly-D-lysine, laminin, Matrigel, vitronectin, etc.). As a further example, but not by way of limitation, cells can be induced to attach to the coverslip after treating the coverslip with plasma. Exemplary cell lines can be selected to minimize non-fluorophore emissions reaching the detector. For example, and not by way of limitation, particular cell lines used in connection with the htSMT system described herein include 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).

[0074] In certain embodiments of the htSMT system of the present disclosure, the cells used are cultured as needed to provide sufficient cell numbers to achieve the desired high-throughput analysis. For example, but not limited to, cells such as U2OS cells (ATCC Catalog No. HTB-96), MCF7 cells (ATCC Catalog No. HTB-22), T47d cells (ATCC Catalog No. HTB-133), and SK-BR-3 cells (ATCC Catalog No. HTB-30) can be grown in DMEM (Cat. No. 1056601, Gibco DMEM, High Glucose, GlutaMAX Supplement, Thermo Fisher Scientific) supplemented with 10% fetal bovine serum (Cat. No. 16000044, Thermo Fisher Scientific) and 1% penicillin-strep (Cat. No. 15140122, Thermo Fisher Scientific), maintained in a humidified 37°C incubator with 5% CO2, and subcultured approximately every 2-3 days. Additional culture strategies that may be suitable for use with the cell lines outlined herein will be known to those of skill in the relevant art.

[0075] In certain embodiments of the htSMT system of the present disclosure, cells contain one or more fluorescent target proteins. The choice of the specific protein(s) to be labeled and the specific labeling approach can vary depending on the particularities of a particular investigation. For example, but not by way of limitation, one approach for labeling proteins used in connection with the htSMT system described herein is the HaloTag fusion strategy. For example, but not by way of limitation, one approach for labeling proteins is SNAPtag fusion. For example, but not by way of limitation, one approach for labeling proteins is to use a fluorophore ligase system. For example, but not by way of limitation, one approach for labeling proteins is to use a tetracysteine ​​motif such as FlAsH or ReAsH. For example, but not by way of limitation, one approach for labeling proteins is by strain-promoted alkyne-azide cycloaddition of a fluorophore. For example, but not by way of limitation, one approach for labeling proteins is by inducing cellular uptake of a separately produced fluorescent target protein. In certain embodiments of the htSMT system of the present disclosure, the cells contain one or more fluorescently labeled glycoproteins.

[0076] While those skilled in the art can implement the HaloTag fusion approach in a variety of ways, one exemplary approach is to transfect a mammalian expression vector containing a fusion gene (i.e., a protein of interest fused in frame with the HaloTag sequence) under the control of a weak L30 promoter and containing a neomycin resistance marker into a cell line of interest (e.g., U2OS cells). In certain embodiments, such transfection can be achieved when cells are at 70% confluence using FuGENE6 (Cat. No. E2691, Promega). In certain embodiments, transfected cells can then be selected using an appropriate selection agent, e.g., G418 (Cat. No. 10131027, Thermo Fisher), at an appropriate concentration, e.g., 500 μg / mL. In certain embodiments, cells can then be clonally isolated. Clones expressing the desired fusion gene can be initially transfected with 100 nM JF 549 -HTL (Cat. No. GA1110, Promega) and stained with 50 nM Hoechst 33342, JF 549 This can be determined by identifying clones with the expected distribution of signals. In certain embodiments, 3-6 clones can then be tested for response to a control compound using SMT conditions, and the most homogeneous clones can then be expanded for further testing.

[0077] Although the htSMT workflow of the present application is generally described with respect to an embodiment tracking the effects of compounds on a target fluorescent protein, the htSMT workflow described herein is equally applicable to tracking and analyzing fluorescent target compounds. For example, but not by way of limitation, the compounds described herein may themselves be fluorescent or may be modified to facilitate fluorescent detection. Furthermore, changes in the motion of fluorescent compounds can be used to determine the SMT profile of the compound itself. Thus, all analytical strategies described herein for tracking a target fluorescent protein are also applicable to results obtained by tracking the compound itself.

[0078] 2.2.2. Single Molecule Tracking Sample Preparation Referring to FIG. 3, embodiments of the present subject matter can be implemented using an htSMT workflow whereby cells (2-018) are seeded onto plates (2-021), e.g., tissue culture-treated 384-well glass-bottom plates, although other plate types, including but not limited to single chamber, 9-well glass-bottom plates, 24-well glass-bottom plates, 96-well glass-bottom plates, 1536-well glass-bottom plates, and 3456-well glass-bottom plates, as well as plates made of alternative materials, e.g., plates made partially or entirely of plastic, can also be used in conjunction with the approach outlined herein. In certain embodiments, cells (2-018) are seeded at 1 to 20,000 cells per well (2-016), e.g., 50 to 10,000, 100 to 9,000, 250 to 8,500, 500 to 7,500, 750 to 7,000, 2,500 to 6,500, or 6,000 cells. The seeded cells can then be incubated under conditions favorable for attachment, e.g., overnight at 37°C and 5% CO2. Cells can be incubated with a sufficient amount of label to allow fluorescence, e.g., in the case of HaloTag fusions, 0.1 to 100 pM of JF 549 The desired results can be achieved by incubating -HTL (catalog no. GA1110, Promega) with 50 nM Hoechst 33342 (for labeling nuclei) in complete medium for 1 hour.

[0079] In certain embodiments of the htSMT strategy described herein, the cells are then washed, for example, three times in DPBS and twice in imaging medium. In certain embodiments, the imaging medium is prepared to facilitate fluorescence, for example, fluoroBrite DMEM medium (catalog number A1896701, Thermo Fisher), which can be supplemented with GlutaMAX (catalog number 35050079, Thermo Fisher) and the same serum and antibiotics as the growth medium.

[0080] If appropriate, compounds can be added to samples to test their effect on specific target proteins via SMT. In certain embodiments, compounds can be serially diluted in an Echo-certified 384-well low-dead-volume source microplate (product number 0018544, Beckman Coulter) to generate source material for dose titration. Compounds can then be administered to cell culture media at a final dilution of, for example, 1:1000. In certain embodiments of the htSMT strategies described herein, each dose of compound has at least two replicates per plate and three plate replicates. Additionally, in certain embodiments of the htSMT strategies described herein, 20 DMSO control wells and two no-dye control wells can be randomized across each sample plate (2-020). In certain embodiments, compounds can be incubated for 0-48 hours, for example, 1 hour at 37°C, before acquiring images.

[0081] 3.OLS htSMT software 3.1. htSMT Software Overview FIG. 4 illustrates an exemplary system 400 for a high-throughput single-molecule imaging platform for measuring molecular movement in live cells. An experiment 402 can be performed to collect large amounts of data from multiple live cells (e.g., using an imaging system 424 to identify compounds 426 and / or targets 422). The experiment 402 can include applying various identifiers, such as labels that can later fluoresce or otherwise be detected, to molecules of interest (e.g., using a laser or other light source). Biological samples forming part of such an experiment 402 can be organized in a plate 404 having multiple wells 406. Each well 406 can have one or more associated fields of view (FOVs) 410. An FOV 410 can be a position within or corresponding to a single well 406. Image sequences can be generated for the FOV 410 to generate one or more movies 412, which can include SMT movies and non-SMT movies. SMT movies can be used to track the paths of individual labeled cellular components, such as proteins, generating multiple trajectories. Each trajectory may consist of multiple spots 414 containing the spatiotemporal coordinates of labeled molecules at a particular time (as described in more detail in FIG. 5). Separately from, and in some examples in parallel with, tracking, the video 412 can be used to identify molecules by using machine learning and / or computer vision-based image segmentation to generate masks 418. A mask 418 is a spatial region within the FOV 410 generated by the segmentation. Each mask 418 can belong to a mask category, which is described in more detail in FIG. 4.

[0082] Data associated with the two channels (e.g., the tracking channel and the segmentation / masking channel) can be combined to generate multiple metrics 420 associated with various aspects of the sample. In other words, the trajectories 416 (e.g., trajectory data) can be combined with the machine learning processed image segmentation data and further analyzed using statistical / machine learning techniques. Processing of the combined data can be used to generate metrics 420, such as hit scores, associated with compounds and / or targets in the biological sample, which may be stored in a database structure, as further described in FIG. 5.

[0083] FIG. 5 illustrates data flow through an exemplary system 500 for a high-throughput single-molecule imaging platform for measuring protein movement in live cells. An experiment specification 504 defining an experiment 502 can be provided as data input via one or more clients 702. For example, each experiment 502 can be collected along with an accompanying stain (e.g., Hoechst or Potomac Red) to be used for downstream analysis, including segmentation 418. The experiment specification 504 can define various parameters for the experiment 402, such as stains, dyes, compounds, and treatments. As previously described in FIG. 4, the imaging system 506 (e.g., imaging system 424) can capture a sequence of images to generate one or more SMT movies 511 and / or non-SMT or segmentation movies 508 (e.g., movie 412) that characterize the movement of molecules. The SMT movies 511 can characterize the movement of individual fluorophores and / or can include images of individual fluorophores. The segmentation movies 508 can include a sequence of images that characterize the movement of labeled cells and / or their components. It will be understood that Hoechst staining is only one technique that may be used to label molecules, and / or multiple labeling techniques, such as Potomoc Red, may be utilized depending on the desired configuration. For example, MitoTracker Deep Red may be used to label mitochondria, Concanavalin A-dye conjugates may be used to label endoplasmic reticulum, SYTO14 may be used to label nucleoli, phalloidin may be used to label actin, etc.

[0084] The SMT movie 511 can be analyzed to perform operations related to molecule tracking 510, which can include detection 512, sub-pixel localization 513, and linking 514 to identify trajectories 515 of molecules across various images in the SMT movie 511. More specifically, during detection 512, one or more spots can be detected or recovered in the SMT movie 511. Each spot can be provided with spatiotemporal coordinates. These spatiotemporal coordinates can be estimated by using sub-pixel localization techniques 513. Linking 514 can be performed on the spots to ultimately identify the trajectories 515.

[0085] As used herein, a link is a potential association between two spots. Each link is directed, starting at one spot and ending at another. A "correct link" connects two spots generated by the same emitter in different frames; otherwise, the link is "incorrect." One goal of the linking algorithm is to estimate which link is correct. In this specification, links are referred to in the form a:i→j, which is interpreted to mean link a starting at spot i and ending at spot j. A link satisfies at least three of the following constraints: (a) the link moves forward in time; (b) the link cannot connect two spots that are more distant than a certain limit (hereinafter referred to as the "search radius"); and (c) the link cannot connect two spots that are more distant in time than a certain limit (hereinafter referred to as the "gap limit"). A spot-link graph is a graph of the spots and links of a single SMT video 511. In this graph, spots are vertices and links are edges. Because links move forward in time, the spot-link graph is a directed acyclic graph. A matching is a subset of links in the spot-link graph, such that no two links in this subset start or end at the same spot. A trajectory 515 is used herein to refer to a sequence of consecutive (end-to-end) links in the same matching. A dynamic metric 530 can be determined using multiple trajectories. Such parameters can include spot attributes that characterize the spot's movement. Such parameters can include one or more of the velocity, diffusion coefficient, or anomaly parameter(s) of each spot. The dynamic parameter(s) of spot i are defined herein as θ i Herein, the set of dynamic parameters of all spots in the spot-link graph is called Θ.

[0086] Separately from, and in some variations in parallel with, the processing of the SMT video 511, the segmentation video 508 can undergo segmentation to generate one or more masks 520. The masks can be classified into various categories, including, but not limited to, cell nuclei, cytoplasm, and / or extraneous masks, which are further described in FIG. 6 . An instance mask is an individually segmented object (e.g., one cell, one nucleus, one mitochondrion). The FOV 410 can include any number of instance masks for one mask category. A semantic mask is the union of all instance masks corresponding to one type of mask category for one FOV (e.g., all cells, all nuclei, or all mitochondria for one FOV). Extraneous masks can include portions of the non-SMT video 508 that are excluded from any downstream data analysis. For example, these extraneous masks can correspond to portions of the non-SMT video 508 that are out of focus or contain autofluorescent cellular debris that prevents accurate tracking. During segmentation, molecules in the segmentation movie 508 can be assigned to one or more masks. Image metrics 540 can be assessed from the masked molecules, such as cell health, focus quality, etc.

[0087] Experiment information, such as dynamic metrics 530, image metrics 540, and any data from which any metrics are derived (e.g., segmentation information), may be provided to a data repository 570 for storage. Such a data repository 570 may store any results of the experiment 402, such as, for example, dynamic metrics 530, image metrics 540, and / or any data from which any metrics are derived. The data repository may include a local persistence server and / or a dedicated server accessed locally or via the cloud. The data repository 570 may also store metadata associated therewith and / or associated with the experiment specification 504. Experiment information (e.g., results and metadata of past experiments, etc.) may be provided to the data repository 570 via a repository application program interface (API) 550. The repository API 550 may also interface with a web-based graphical user interface front end 560 that provides such information for display on the client 502.

[0088] In some variations, the segmentation information can be used to identify subcellular compartments such as the nucleus, nucleolus, cytoplasm, etc. The segmentation information may also be used to distinguish one cell from another. The segmentation information may be stored in a particular format (e.g., a multi-image file format such as TIFF).

[0089] The exemplary dynamics metric 530 can also include a state array. The state array is a framework for learning interpretable dynamic models from SMT trajectories and can be used to gain additional insight into the movement of a target protein and where that movement occurs within the cell. In some variations, the state array can be generated / added using segmentation information. The state array output can be returned at the subcellular compartment level, allowing researchers to distinguish between dynamics in different subcellular compartments. Additionally, the state array can be calculated for each individual subcellular compartment (e.g., for each nucleus).

[0090] To facilitate data access by applications, including but not limited to state arrays, processed SMT data may be stored in a format that allows for (a) representation of processed trajectories and associated attributes, such as SNR and spot shape characteristics, for each SMT video; (b) representation of mask objects, including mask categories (e.g., the subcellular organelles associated with each mask object); (c) association of trajectories with mask objects (e.g., the cell nuclei in which each trajectory was observed); and (d) association of all SMT videos with metadata related to the original experiment, such as compound treatment, acquisition time, and imaging system name. Formats (a) and (c) may be protocol buffer schemas that define the storage format for trajectories and associated mask objects. Format (b) may be a specialized image file format containing the mask object to which each pixel in the FOV belongs. Format (d) may be a PostgreSQL database that records all captured experiments / videos. As a client of the processed SMT data, state arrays can reference these data schemas to report the dynamic properties of trajectories by mask category or by mask object.

[0091] FIG. 6 illustrates multiple images 600 showing the differences between mask categories and instance or semantic masks. As previously discussed, a non-SMT video or segmentation video can be assigned to multiple categories. Such categories can include cell nuclei (e.g., Category A), cytoplasm (e.g., Category B), and / or extraneous masks (e.g., Category C). Unique, individual masks can be applied to a biological sample. For example, image 610 illustrates a unique, individual instance mask applied to a cell nucleus (e.g., Category A). Image 612 illustrates a unique, individual instance mask applied to a cell cytoplasm (e.g., Category B). Image 620 illustrates multiple instance masks applied to one or more nuclei, with each individual color representing a different, unique, individual instance mask. Image 622 illustrates multiple masks applied to one or more cytoplasms, with each individual color representing a different, unique, individual instance mask. Image 630 illustrates a semantic mask that is the union of all instance masks applied to one or more nuclei. Image 632 illustrates a semantic mask applied to one or more cytoplasms.

[0092] FIG. 7 illustrates an exemplary computer-implemented environment 700 in which an imaging system 710 can interact with a computing architecture to execute various algorithms described herein. As shown in FIG. 7 , the imaging system 710 can interface with one or more clients 750 (e.g., client 502 via a web application having a graphical user interface). The one or more clients 750 can interface with one or more servers 720 accessible via network(s) 730. The one or more clients 750 can host frame grabbers that capture images (e.g., video 412) from a camera. These images can be temporarily stored on the one or more clients 750 and periodically transferred via the network 730 to the one or more servers 720 for remote storage. The one or more servers 720 also include or have access to one or more data stores 740 for storing data collected and / or extracted from the sample by the imaging system 710. In some variations, the network 730 may include or interface with one or more network storage arrays 760 for storing data such as captured images (e.g., video 412).

[0093] FIG. 8 is a diagram 800 illustrating a sample computing device architecture for implementing various aspects described herein. In some variations, the sample computing device architecture may be that of a client(s) 750 and / or a server(s) 720, and some components described in connection with diagram 800 may be optional for the client(s) 750 and / or the server(s) 720. A bus 804 may serve as an information highway interconnecting the other illustrated components of hardware. A processing system 808 (e.g., one or more computer processors / data processors in a given computer or in multiple computers), labeled CPU (Central Processing Unit), may perform the computational and logical operations required to execute a program. Optionally, or additionally, a processing system 812 (e.g., one or more computer processors / data processors in a given computer or in multiple computers), labeled GPU (Graphics Processing Unit), may perform the computational and logical operations required to execute a program. Non-transitory processor-readable storage media, such as read-only memory (ROM) 816 and random access memory (RAM) 820, may be in communication with processing system 808 and / or processing system 812 and may contain one or more programming instructions for the operations specified herein. Optionally, the program instructions may be stored on a non-transitory computer-readable storage medium, such as a magnetic disk, optical disk, recordable memory device, flash memory, solid-state drive, or other physical storage medium.

[0094] In one example, disk controller 848 can interface with one or more optional removable storage 856 or local storage 852 via system bus 804. Removable storage 856 can be an external or internal disk drive, or a solid-state drive, or an external hard drive. Local storage 852 can be an internal hard drive and / or memory. As mentioned above, these various examples of removable storage 856, local storage 852, and disk controller 848 are optional devices. System bus 804 may also include at least one communication interface 824 to enable communication with external devices either physically connected to the computing system or externally available via a wired or wireless network, such as cloud storage or a remote service. In some cases, at least one communication interface 824 includes or otherwise comprises a network interface.

[0095] In some variations, e.g., client(s) 750, to provide for interaction with a user, the subject matter described herein can be implemented on a computing device having a display device 844 (e.g., an LCD (liquid crystal display) or LED (light emitting diode) monitor, etc.) for displaying information retrieved from bus 804 to a user via a display interface 840, and an input device 832, such as a keyboard and / or pointing device (e.g., a mouse or trackball) and / or a touch screen, for a user to provide input to the computer. Other types of input device 832 can also be used to provide for interaction with a user. For example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback via microphone 836, or tactile feedback), and input from the user can be received in any form, including acoustic, voice, or tactile input. Input device 832 and microphone 836 can be connected to bus 804 via input device interface 828 to communicate information. By way of example, input device 832 can be an imaging system 710 configured with the capability to capture a sequence of images, as described herein. Frame grabber 858 can capture or grab individual frames from analog or digital data encapsulating a sequence of images acquired from bus 804. Frame grabber 858 can include memory capable of storing single or multiple frames. Frame grabber 858 can also provide individual frames or multiple frames to bus 804 for further storage, for example, in local storage 852 and / or removable storage 856. Other computing devices, such as dedicated servers, can omit one or more of the components described in connection with FIG. 8.

[0096] One or more aspects or features of the subject matter described herein may be implemented in digital electronic circuitry, integrated circuits, specially designed application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), computer hardware, firmware, software, and / or combinations thereof. These various aspects or features may include implementation in one or more computer programs executable and / or interpretable by a programmable system including at least one programmable processor, which may be special-purpose or general-purpose, coupled to receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device. The programmable system or computing system may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and by virtue of the client-server relationship they have to each other.

[0097] These computer programs, which may also be referred to as programs, software, software applications, applications, components, or code, contain machine instructions for a programmable processor and may be implemented in high-level procedural languages, object-oriented programming languages, functional programming languages, logic programming languages, and / or assembly / machine languages. As used herein, a "machine-readable medium" refers to any computer program product, apparatus, and / or device (e.g., magnetic disk, optical disk, memory, programmable logic device (PLD), etc.) used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives the machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and / or data to a programmable processor. A machine-readable medium may non-transitory store such machine instructions, such as, for example, a non-transitory solid-state memory, a magnetic hard drive, or any equivalent storage medium. Alternatively or additionally, a machine-readable medium may temporarily store such machine instructions, such as, for example, a processor cache or other random access memory associated with one or more physical processor cores.

[0098] 3.2. Nucleolus Localization Using Machine Learning-Based Inference and Segmentation for Single Molecule Tracking Provided herein is a machine learning-based technique for identifying nucleoli in ultra-high resolution and other high resolution microscopy images solely from Hoechst nuclear labeling. While Hoechst does not directly label nucleoli, shape- and intensity-based features exist that indicate the location of nucleoli within the nucleus. However, these features are not always present, and this inconsistency can make the application of machine learning methods difficult. The present subject matter allows for training of machine learning models using multiple loss functions that are rotated during training. In one example, the multiple loss functions may include mean squared error, categorical cross-entropy, and class-balanced categorical cross-entropy.

[0099] The machine learning model can take various forms, including neural networks, including U-NET architectures, and can generate pixel-level class labels (i.e., the machine learning model classifies each pixel as associated with one or more categories). The pixel-level labels can include one or more of the following labels: nucleolus, nucleoplasm, nuclear membrane, and extraneous debris. The machine learning model was experimentally validated using a helicase known to translocate from the nucleolus to the nucleoplasm upon processing. The results indicate that this translocation can be effectively measured using the segmentation provided by the model.

[0100] As mentioned above, the primary goal was to predict the location of nucleoli from Hoechst. However, because Hoechst and nucleolus dyes have the same effective wavelength, it is very difficult to directly train on these two markers. Experiments determined that a surrogate DNA stain in the far-red spectrum (nuclear red) could be used, since the nucleolus label is effective at the same wavelength as Hoechst. The nucleolus label served as ground truth, and the nuclear red images were used to train the machine learning model.

[0101] The training set for the machine learning model consisted of 20 images in which nuclear red served as input. The output consisted of pixel-level labels for nuclei, background, nuclear rim, nucleoli, and extracellular debris. Ground truth was manually annotated using nuclear red and nucleolus images as references. For the training set, U2OS and HCT116 cells were grown in 384-well plates and stained with Hoechst 33342 (100 nM), nuclear ID red (2X), and nucleolus ID green (1X) for 30 minutes, washed, and then imaged. Images were acquired using a custom-built HILO microscope equipped with a 60x 1.27 NA water-immersion objective lens and 405 nm, 561 nm, and 642 nm lasers. Emission light was captured using a Hamamatsu FusionBT camera with 445 / 58, 585 / 40, and 676 / 37 filters for each channel, respectively. A Z-stack image consisting of 12 slices spaced 300 nm apart was acquired for each channel. The image in diagram 900 of Figure 9 was derived using this device and process. Figure 9 shows Hoechst images 910 and 940 in blue, nuclear red images 920 and 950 in red, and nucleolus images 930 and 960 in green. Specifically, in Figure 9, image 910 visualizes HCT116 cells with Hoechst dye (indicating nuclei), image 920 visualizes HCT116 cells with nuclear red dye (indicating nuclei), image 930 visualizes HCT116 cells with nucleolus dye (indicating nucleoli), image 940 visualizes U2OS cells with Hoechst dye (indicating nuclei), image 950 visualizes U2OS cells with nuclear red dye (indicating nuclei), and image 960 visualizes U2OS cells with nucleolus dye (indicating nucleoli).

[0102] FIG. 10 includes a diagram 1000 illustrating a multi-layer machine learning model 1020 (e.g., a U-net architecture) to which an ultra-high-resolution or other high-resolution microscopic image 1010 is input. The layers of the machine learning model (e.g., input layer, hidden layer, and output layer) include various nodes / neurons with various weights generated through a training process. Each pixel in the microscopic image 1010 includes a value characterizing color and intensity, and the output of the machine learning model may include a representation 1010 visualizing the per-pixel entropy of the machine learning model 1020 (where brighter pixels indicate spots with greater uncertainty in the model 1020), a probabilistic semantic mask 1030 from which a discrete instance mask 1040 can be generated. FIG. 11 is a diagram 1100 illustrating an input image 1110 from which a semantic mask 1120 is generated, and then a discrete instance mask 1130 is generated.

[0103] It has been found that optimizing the machine learning model 1020 using standard cross-entropy loss does not produce the desired performance, and often the machine learning model finds a solution that completely ignores the nucleolus labels. Alternatively, the machine learning model 1020 can be trained by rotating different loss functions. In one variation, the machine learning model can be trained in three stages using class-balanced cross-entropy (CB), mean squared error (MSE), and cross-entropy (CE).

[0104]

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[0107] Figure 12 is a diagram 1200 showing a first image 1210 containing a sample treated with DMSO and a second image 1220 containing a sample treated with a compound. In these images 1210, 1220, the Hoechst channel is shown in blue, the SMT channel is overlaid in white, and the localized nucleoli are shown in pink. Note that in the DMSO condition, more spots appear within the nucleoli. After treatment with the compound, the spots tend to move out of the nucleoli and into the nucleoplasm.

[0108] There are two experimental conditions to test to validate the model. The first is to measure the ratio of nucleolar spots to nucleoplasmic spots in helicases known to translocate from the nucleolus to the nucleoplasm upon treatment. If the predicted mask is accurate, a decrease in the nucleolar fraction relative to the DMSO control should be detected. Potential biases inherent between the treated molecule and DMSO can sometimes be measured simply by partitioning the nucleus into nucleoplasm and nucleoli, so a confirmation check can be performed even if the partition is inaccurate. This can be achieved by calculating the same metrics using corrupted or random versions of the nucleolar mask. Random masks can be generated by rotating the original nucleolar mask and reapplying them (e.g., images 1310 and 1320 in Figure 13, Fig. 13). Finally, the experiment can be repeated using a helicase lacking translocation properties.

[0109] Diagram 1400 in Figure 14 illustrates the ability of the machine learning model described herein to selectively distinguish nucleolar localization between target A helicase, which is known to translocate from the nucleolus to the nucleoplasm upon compound treatment, and target B helicase, which does not have translocation properties and remains within the nucleolar region upon compound treatment (model-predicted mask). Diagram 1400 in Figure 14 further illustrates that the model-predicted mask is specifically accurate in distinguishing nucleolar localization, while a randomly generated mask did not exhibit the same translocation effect, indicating that the nucleolar localization determined by the model is not random but meaningful.

[0110] Using a validated nucleolus segmentation model, potential protein dynamics metrics can be evaluated for their usefulness for hit calling during high-throughput screening. Diagram 1500 in Figure 15 shows the per-compound SSMD of MPDC for an exemplary target protein localized in the nucleolus region. A clear boundary is observed between the negative and positive controls, and the use of this metric for hit calling may be validated.

[0111] Figure 16 is a diagram 1600 showing a comparison of this same metric within the entire nuclear mask. In particular, Figure 16 illustrates how the model can capture differences in nucleolar enrichment of target A as a function of compound treatment. Target B is not expected to exhibit such behavior. The randomization of the mask indicates that the masking is biologically relevant to the underlying target and not random. Compound treatment of target A is shown to reduce spots within the nucleolus. Square boxes may indicate hits that could be identified in the nucleolar mask but would not be detected when measuring diffusion throughout the nucleus.

[0112] 18 illustrates a process flow diagram 1800 in which, at 1810, a sequence of microscopic images visualizing fluorescently labeled cellular components within live cells and each including an array of pixels is received. For each image, at 1820, each pixel is classified using at least one machine learning model to correspond to one of a plurality of categories. The at least one machine learning model may be trained using a plurality of different loss functions that are rotated during training. At 1830, data characterizing the classification of the pixels may be provided to a consuming application or process.

[0113] The categories may include one or more of nucleus, nuclear rim, nucleolus, extracellular debris and / or background.

[0114] Pixel classification may be used to relate adjacent pixels that have the same category (i.e., component type), and further, unrelated pixels within a hierarchical biological feature can be correlated.

[0115] The data provided can be used to determine the location of the nucleolus within the nucleus.

[0116] The loss function can take a variety of forms, including a class-balanced cross-entropy loss function, a mean squared error loss function, and / or a cross-entropy loss function. In one implementation, the loss functions are rotated in order such that the class-balanced cross-entropy loss function is used first, followed by the mean squared error loss function, and then the cross-entropy loss function.

[0117] Live cells can be stained using Hoechst dye and / or nuclear red dye.

[0118] The machine learning model(s) utilized herein can take a variety of forms, including, but not limited to, pixel-level convolutional neural networks such as those provided by the U-Net architecture. The machine learning model(s) can be trained using manually annotated pixels.

[0119] Additionally, the provided data can be used to generate a semantic mask.

[0120] 4. Specific OLS htSMT Applications Many, perhaps most, pathways that regulate fundamental cellular biochemistry rely on the interaction of protein sensors and protein effectors that transiently engage and trigger changes in cellular physiology. While the fundamentals of this process have long been recognized, biochemical investigation of these protein interactions has typically required in vitro reconstitution or been investigated through pull-down assays after cell permeabilization. The htSMT workflow described herein provides a means to visualize protein movement in large numbers of live cells, and also in contexts where the effects of additive compounds, such as small molecule inhibitors, can be quantitatively assessed.

[0121] 1 , aspects of the OLS htSMT workflow of the present disclosure include, but are not limited to, (i) sample preparation, including reagent handling, (ii) image acquisition, imaging the sample to generate a series of images and / or video, (iii) image analysis, processing these images and videos, (iv) information storage, and (v) providing insights using the stored information, including biological interpretation. With respect to biological interpretation, the htSMT workflows described herein provide the ability to provide specific insights, as outlined below, depending on the particular workflow employed, e.g., (i) OLS htSMT screening, (ii) OLS htSMT binding, and / or (iii) OLS kinetic SMT.

[0122] 4.1.OLS htSMT Screening In certain embodiments of the OLS htSMT workflow described herein, the systems and methods are adapted to examine the ability of one or more compositions (e.g., "test" compounds) to affect the SMT profile associated with a labeled protein. For example, such htSMT workflows screen for changes in the SMT profile, e.g., either an increase or decrease in the movement of the protein of interest, in the presence of a composition compared to the SMT profile in the absence of the composition. It will also be appreciated that higher-order comparisons can be performed when compounds are multiplexed, including when multiple proteins are fluorescent. Furthermore, as outlined above, the htSMT screening strategies described herein are equally applicable to screening SMT profiles associated with fluorescent compounds, e.g., compounds that are naturally fluorescent or compounds that have been modified to fluoresce or linked to a fluorophore.

[0123] Fundamental to such htSMT screening strategies is the ability of the htSMT workflow described herein to extract large-scale, accurate molecular trajectories. Exemplary OLS htSMT workflows include the following individual strategies and combinations of the following strategies, combining two or more strategic requirements. For example, without limitation, the disclosed workflow includes both illuminating a field of view in a sample plane disposed within a sample with a light beam to induce fluorescence by a subset of fluorescent target proteins in live cells, where the subset of fluorescent target proteins is present in about 30 to about 80 live cells illuminated within the field of view in the sample plane, and illuminating a field of view in a sample plane disposed within the sample with a light beam to induce fluorescence by a subset of fluorescent target proteins in live cells, where the subset of fluorescent target proteins includes proteins in the range of about 1,000 to about 1,000,000. Similarly, illuminating the sample plane to illuminate about 30 to about 80 live cells per FOV and / or generating fluorescence from about 1,000 to about 1,000,000 proteins can be combined with any of the other strategic requirements disclosed herein, such as determining that the average change in fluorescent target protein motion in the presence of a compound is about 5% to about 10% compared to baseline motion under DMSO conditions, detecting fluorescence from multiple fluorescent target proteins within the field of view of the sample plane at a rate of about 10,000 to about 18,000 per day per system, and achieving a Z-factor greater than 0.5 based on a single field of view.

[0124] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include identifying biological interactions between a compound and a fluorescent target protein in live cells, the workflow including (a) contacting a sample including a population of live cells with a compound, the live cells including the fluorescent target protein, the workflow further including (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample, the tracking including (i) illuminating with a light beam a field of view in a sample plane disposed within the sample to induce fluorescence by a subset of the target fluorescent proteins in the live cells, the subset of fluorescent target proteins being dependent on the particular cell type being used. Depending on the target protein, there are approximately 30 to approximately 80 live cells illuminated within the field of view of the sample plane. For example, for U2OS cells, the range is approximately 30 to approximately 40 cells per FOV, while for HCT116 cells, the range is approximately 50 to approximately 80 cells, taking into account differences in area. The tracking further includes (ii) detecting fluorescence from multiple fluorescent target proteins within the field of view of the sample plane via a detector device. The workflow further includes (c) determining changes in the movement of the fluorescent target proteins in the presence of a compound. The change in the movement of the fluorescent target proteins in the presence of the compound compared to the movement of the fluorescent target proteins in the absence of the compound identifies a biological interaction between the compound and the fluorescent target proteins.

[0125] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include identifying biological interactions between a compound and a fluorescent target protein in live cells, the workflow including: (a) contacting a sample including a population of live cells with a compound, the live cells including the fluorescent target protein; and (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample, the tracking including: (i) illuminating with a light beam a field of view in a sample plane disposed within the sample to induce fluorescence by a subset of the target fluorescent proteins in the live cells, the subset of fluorescent target proteins including a range of about 1,000 to about 1,000,000 proteins. The number of proteins in the subset depends on the expression level of the protein of interest and the dye concentration deemed appropriate for labeling the protein subset for robust SMT, both of which can be calculated and / or configured by one of ordinary skill in the art based on the disclosure of the present application; the tracking further includes (ii) detecting fluorescence from multiple fluorescent target proteins within a field of view of the sample plane via a detector device; and the workflow further includes (c) determining a change in the movement of the fluorescent target proteins in the presence of a compound, wherein a change in the movement of the fluorescent target proteins in the presence of the compound compared to the movement of the fluorescent target proteins in the absence of the compound identifies a biological interaction between the compound and the fluorescent target proteins.

[0126] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include identifying a biological interaction between a compound and a fluorescent target protein in live cells, the workflow including: (a) contacting a sample including a population of live cells with a compound, the live cells including the fluorescent target protein; and (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample, the tracking including: (i) illuminating a field of view in a sample plane disposed within the sample with a light beam to cause fluorescence by a subset of the target fluorescent proteins in the live cells; and (ii) detecting fluorescence from the plurality of fluorescent target proteins in the field of view in the sample plane via a detector device; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound, wherein the average change in the movement of the fluorescent target protein in the presence of the compound is about 5% to about 10% compared to baseline movement under DMSO conditions; and a change in the movement of the fluorescent target protein in the presence of the compound compared to the movement of the fluorescent target protein in the absence of the compound identifies a biological interaction between the compound and the fluorescent target protein.

[0127] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include identifying a biological interaction between a compound and a fluorescent target protein in live cells, the workflow including: (a) contacting a sample including a population of live cells with a compound, the live cells including the fluorescent target protein; and (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample, the tracking including: (i) illuminating a field of view in a sample plane disposed within the sample with a light beam to cause fluorescence by a subset of the target fluorescent proteins in the live cells; and (ii) detecting, via a detector device, the fluorescence from the plurality of fluorescent target proteins in the field of view in the sample plane; and (iii) detecting the fluorescence from the plurality of fluorescent target proteins in the field of view in the sample plane at a rate of about 10,000 to about 18,000 per system per day; and the workflow further including: (c) determining a change in the movement of the fluorescent target protein in the presence of the compound, wherein a change in the movement of the fluorescent target protein in the presence of the compound compared to the movement of the fluorescent target protein in the absence of the compound identifies a biological interaction between the compound and the fluorescent target protein.

[0128] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include identifying a biological interaction between a compound and a fluorescent target protein in live cells, the workflow including: (a) contacting a sample including a population of live cells with a compound, the live cells including the fluorescent target protein; and (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample, the tracking including: (i) illuminating with a light beam a field of view in a sample plane disposed within the sample to cause fluorescence by a subset of the target fluorescent proteins in the live cells; (ii) detecting via a detector device fluorescence from the plurality of fluorescent target proteins in the field of view in the sample plane; and (iii) achieving a Z-factor of greater than 0.5 based on the single field of view; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound, wherein a change in the movement of the fluorescent target protein in the presence of the compound compared to the movement of the fluorescent target protein in the absence of the compound identifies a biological interaction between the compound and the fluorescent target protein.

[0129] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include determining a dose response of a compound to induce a change in the movement of a fluorescent target protein in a live cell, the workflow including: (a) contacting a plurality of samples with a compound, (i) each sample including a population of live cells; (ii) the live cells including a fluorescent target protein; and (iii) the plurality of samples being contacted with different concentrations of the compound over a range of compound concentrations; and (b) tracking the movement of individual target fluorescent proteins in the plurality of live cells of the sample, the tracking including: (i) illuminating with a light beam a field of view in a sample plane located within the sample to induce fluorescence by at least a subset of the fluorescent target proteins in the live cells; The subset of target proteins is present in approximately 30 to approximately 80 live cells illuminated within the field of view of the sample plane, depending on the specific cell type used. For example, for U2OS cells, the range is approximately 30 to approximately 40 per FOV, while for HCT116 cells, the range is approximately 50 to approximately 80, taking into account differences in area. The tracking further includes (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device. The workflow further includes (c) determining a change in the movement of the fluorescent target protein in the presence of a compound; and (d) repeating steps (b) to (c) for each of multiple samples across a range of compound concentrations, wherein the change in the movement of the fluorescent target protein across a range of concentrations in the presence of the compound indicates a dose response of the compound.

[0130] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include determining a dose response of a compound that induces a change in the movement of a fluorescent target protein in a live cell, the workflow including: (a) contacting a plurality of samples with a compound, (i) each sample including a population of live cells; (ii) the live cells including a fluorescent target protein; and (iii) the plurality of samples being contacted with different concentrations of the compound over a range of compound concentrations; the workflow further including: (b) tracking the movement of individual target fluorescent proteins in the plurality of live cells of the sample, the tracking including: (i) illuminating with a light beam a field of view in a sample plane located within the sample to induce fluorescence by at least a subset of the fluorescent target proteins in the live cells; The subset may include proteins ranging from about 1,000 to about 1,000,000, where the number of proteins in the subset depends on the expression level of the protein of interest and the dye concentration deemed appropriate for labeling the subset proteins for robust SMT, both of which can be calculated and / or configured by one of ordinary skill in the art based on the disclosure of the present application. The tracking further includes (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device, where the workflow further includes (c) determining a change in the movement of the fluorescent target proteins in the presence of a compound; and (d) repeating steps (b)-(c) for each of a plurality of samples across a range of compound concentrations, where the change in the movement of the fluorescent target proteins in the presence of the compound across the concentration range indicates a dose response of the compound.

[0131] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include determining a dose response of a compound to induce a change in the movement of a fluorescent target protein in a live cell, the workflow including: (a) contacting a plurality of samples with a compound, (i) each sample including a population of live cells; (ii) the live cells including a fluorescent target protein; and (iii) the plurality of samples being contacted with different concentrations of the compound over a range of compound concentrations; the workflow further including: (b) tracking the movement of individual target fluorescent proteins in the plurality of live cells of the sample, the tracking including: (i) determining whether the compound induces fluorescence by at least a subset of the fluorescent target proteins in the live cells; (ii) illuminating with a light beam a field of view in a sample plane disposed within the sample; and (ii) detecting fluorescence from one or more fluorescent target proteins in the sample plane via a detector device; the workflow further includes (c) determining a change in the movement of the fluorescent target protein in the presence of the compound, wherein the average change in the movement of the fluorescent target protein in the presence of the compound is about 5% to about 10% compared to baseline movement under DMSO conditions; and (d) repeating steps (b) to (c) for each of a plurality of samples across a range of compound concentrations, wherein the change in the movement of the fluorescent target protein across the concentration range in the presence of the compound indicates a dose response of the compound.

[0132] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include determining a dose response of a compound that induces a change in the movement of a fluorescent target protein in a live cell, the workflow including: (a) contacting a plurality of samples with a compound, (i) each sample including a population of live cells; (ii) the live cells including a fluorescent target protein; and (iii) the plurality of samples being contacted with different concentrations of the compound over a range of compound concentrations; the workflow further including: (b) tracking the movement of individual target fluorescent proteins in the plurality of live cells of the sample, the tracking including: (i) contacting a plurality of live cells of the sample with a compound so as to cause fluorescence by at least a subset of the fluorescent target proteins in the cells; (ii) illuminating with a light beam a field of view in a sample plane disposed within the system; and (ii) detecting via a detector device fluorescence from one or more of the fluorescent target proteins in the sample plane; (iii) said tracking includes detecting fluorescence from a plurality of fluorescent target proteins within the field of view of the sample plane at a rate of about 10,000 to about 18,000 per day per system; the workflow further includes (c) determining a change in the movement of the fluorescent target proteins in the presence of a compound; and (d) repeating steps (b)-(c) for each of a plurality of samples across a range of compound concentrations, wherein the change in the movement of the fluorescent target proteins in the presence of the compound across the concentration range indicates a dose response of the compound.

[0133] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include determining a dose response of a compound to induce a change in the movement of a fluorescent target protein in a living cell, the workflow including: (a) contacting a plurality of samples with a compound, (i) each sample including a population of living cells; (ii) the living cells including a fluorescent target protein; and (iii) the plurality of samples being contacted with different concentrations of the compound over a range of compound concentrations; and the workflow further including: (b) tracking the movement of individual target fluorescent proteins in a plurality of living cells of the samples, the tracking including: (i) determining a dose response of at least one fluorescent target protein in the cells; (ii) illuminating a field of view in a sample plane located within the sample with a light beam to cause fluorescence by a subset of the fluorescent target proteins; (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device; and (iii) achieving a Z-factor of greater than 0.5 based on the single field of view, the workflow further including (c) determining a change in the movement of the fluorescent target proteins in the presence of a compound; and (d) repeating steps (b)-(c) for each of a plurality of samples across a range of compound concentrations, wherein the change in the movement of the fluorescent target proteins across the range of concentrations in the presence of the compound indicates a dose response of the compound.

[0134] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include the use of a microscope system configured to identify biological interactions between compounds and fluorescent target proteins in live cells, the system including: (a) a stage for supporting a sample, the sample including a population of live cells, the live cells including a fluorescent target protein; the system further including: (b) a light source emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; and (c) an objective lens for focusing the light beam onto the sample in a plane of the sample, wherein a subset of the fluorescent target proteins in the sample is positioned within a field of view in the sample plane, the subset of fluorescent target proteins being present in about 30 to about 80 live cells illuminated within the field of view in the sample plane, depending on the particular cell type used. For example, in the case of U2OS cells, the range is about 30 to about 40 per FOV, while in the case of HCT116 cells, the range is about 50 to about 80, taking into account the difference in area. The system further includes (d) a detector device that monitors a light-based response from the fluorescent target protein in the presence of a compound, the detector device being configured to (i) block light received from a light source other than the sample surface on 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. The system further includes (e) a memory and (f) a processor in communication with the memory and the detector device, the processor being capable of determining a change in the movement of the fluorescent target protein in the presence of the compound compared to the absence of the compound.

[0135] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include use of a microscope system configured to identify biological interactions between compounds and fluorescent target proteins in live cells, the system including: (a) a stage for supporting a sample, the sample including a population of cells, the cells including a fluorescent target protein; the system further including: (b) a light source emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; and (c) an objective lens for focusing the light beam onto the sample in a plane of the sample, wherein a subset of the fluorescent target proteins in the sample is positioned within a field of view in the plane of the sample, the subset of fluorescent target proteins including a range of about 1,000 to about 1,000,000 proteins, the number of proteins in the subset being determined based on the expression level of the protein of interest and ... and a dye concentration deemed appropriate for labeling the subset proteins for optimal SMT, both of which can be calculated and / or configured by one of ordinary skill in the art based on the disclosure of the present application; the system further includes (d) a detector device that monitors a light-based response from the fluorescent target proteins in the presence of the compound, the detector device being configured to (i) block light received from a light source other than the sample surface on which the fluorescent target proteins are disposed, thereby tracking the positions of the fluorescent target proteins, and (ii) track the movement of individual fluorescent target proteins; the system further includes (e) a memory and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the movement of the fluorescent target proteins in the presence of the compound compared to the absence of the compound.

[0136] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include the use of a microscope system configured to identify biological interactions between a compound and a fluorescent target protein in living cells, the system including: (a) a stage for supporting a sample, the sample including a population of cells, the cells including a fluorescent target protein; the system further including: (b) a light source emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; and (c) an objective lens for focusing the light beam onto the sample in a plane of the sample, wherein a subset of the fluorescent target proteins in the sample are positioned within a field of view of the sample plane; and the system further including: (d) a microscope system configured to identify a biological interaction between a compound and a fluorescent target protein in living cells; The system further includes a detector device that monitors a light-based response from the fluorescent target protein, the detector device being configured to (i) block light received from a light source other than the sample surface on which the fluorescent target protein is disposed, thereby tracking the position of the fluorescent target protein, and (ii) track the movement of individual fluorescent target proteins, wherein the average change in the movement of the fluorescent target protein in the presence of the compound is about 5% to about 10% compared to baseline movement under DMSO conditions, and the system further includes (e) a memory and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining the change in the movement of the fluorescent target protein in the presence of the compound compared to the absence of the compound.

[0137] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include the use of a microscope system configured to identify biological interactions between a compound and a fluorescent target protein in living cells, the system including: (a) a stage for supporting a sample, the sample including a population of cells, the cells including a fluorescent target protein; the system further including: (b) a light source emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; and (c) an objective lens for focusing the light beam onto the sample in a plane of the sample, the plurality of fluorescent target proteins in the sample being disposed in the plane of the sample; and (d) an objective lens for focusing the light beam onto the sample in a plane of the sample, the plurality of fluorescent target proteins in the sample being disposed in the plane of the sample. The system further includes a detector device that monitors the response, the detector device being configured to (i) block light received from a light source other than the sample surface on which the fluorescent target proteins are located, thereby tracking the position of the fluorescent target proteins, and (ii) track the movement of individual fluorescent target proteins, wherein the tracking includes detecting fluorescence from a plurality of fluorescent target proteins within a field of view of the sample surface at a rate of about 10,000 to about 18,000 per day per system, and the system further includes (e) a memory and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the movement of the fluorescent target proteins in the presence of the compound compared to the absence of the compound.

[0138] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow can include the use of a microscope system configured to identify biological interactions between compounds and fluorescent target proteins in live cells, the system including: (a) a stage for supporting a sample, the sample including a population of cells, the cells including a fluorescent target protein; the system further including: (b) a light source emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; and (c) an objective lens for focusing the light beam onto the sample in a plane of the sample, the plurality of fluorescent target proteins in the sample being disposed in the plane of the sample; and (d) an objective lens for focusing the light beam onto the sample in a plane of the sample. and a detector device for monitoring a light-based response from the fluorescent target protein in the presence of the compound, the detector device being configured to (i) block light received from a light source other than a sample plane where the fluorescent target protein is disposed, thereby tracking the position of the fluorescent target protein, (ii) track the movement of individual fluorescent target proteins, and (iii) achieve a Z-factor of greater than 0.5 based on a single field of view; the system further comprising: (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the movement of the fluorescent target protein in the presence of the compound compared to the absence of the compound.

[0139] 4.2.OLS htSMT binding In certain embodiments of the OLS htSMT workflow described herein, the system and method utilizes the f seen in the htSMT screening assay. bound Increase in residence time (k* off Importantly, both FRAP and htSMT are adapted to distinguish between recovery after exposure to a compound caused by an increase in dwell time (k* off decrease in chromatin binding rate (k* on It is not possible to distinguish between recovery caused by boundThis results in an increase in the number of dwell times. By modifying the SMT acquisition conditions to reduce the illumination intensity and collect longer frame exposures, only immobile proteins form spots. Under these imaging conditions, the distribution of track lengths provides a measure of relative dwell times.

[0140] Exemplary OLS htSMT combined workflows include the following individual strategies and combinations of the following strategies that combine two or more strategic requirements: For example, but not by way of limitation, the disclosed workflow includes both illuminating a field of view in a sample plane disposed within a sample with a light beam to produce fluorescence by a subset of fluorescent target proteins in live cells, the subset of fluorescent target proteins being present in about 30 to about 80 live cells illuminated within the field of view in the sample plane, and illuminating a field of view in a sample plane disposed within the sample with a light beam to produce fluorescence by a subset of fluorescent target proteins in live cells, the subset of fluorescent target proteins including proteins in the range of about 1,000 to about 1,000,000 proteins. Similarly, illuminating the sample plane to illuminate about 30 to about 80 live cells per FOV and / or generating fluorescence from about 1,000 to about 1,000,000 proteins can be combined with any of the other strategic requirements disclosed herein, such as determining that the average change in fluorescent target protein motion in the presence of a compound is about 5% to about 10% compared to baseline motion under DMSO conditions, detecting fluorescence from multiple fluorescent target proteins within the field of view of the sample plane at a rate of about 10,000 to about 18,000 per day per system, and achieving a Z-factor greater than 0.5 based on a single field of view.

[0141] In certain embodiments of the OLS htSMT binding workflow described herein, the workflow determines whether a compound that induces a change in binding of a fluorescent target protein in live cells is a K offand determining whether the compound reduces the activity of the compound, the workflow including (a) contacting a sample including a population of live cells with a compound, the live cells including a fluorescent target protein, and the workflow further including (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, the tracking including (i) illuminating with a light beam a field of view in a sample plane disposed within the sample to induce fluorescence by at least a subset of the target fluorescent proteins in the live cells, the subset of fluorescent target proteins being present in about 30 to about 80 live cells illuminated within the field of view in the sample plane, depending on the particular cell type used, e.g. For example, for U2OS cells, the range is about 30 to about 40 per FOV, while for HCT116 cells, the range is about 50 to about 80, taking into account the difference in area. The tracking further includes (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device, the method being adapted to selectively detect localized fluorescence. The workflow further includes (c) determining a change in the movement of the fluorescent target protein in the presence of a compound, where an increase in the signal detected from the fluorescent target protein in the presence of the compound compared to the signal from the fluorescent target protein in the absence of the compound indicates that the compound has a K off This indicates that it induces a decrease in

[0142] In certain embodiments of the OLS htSMT binding workflow described herein, the workflow determines whether a compound that induces a change in binding of a fluorescent target protein in live cells is a K offand determining whether a compound reduces the expression level of the protein of interest, the workflow comprising: (a) contacting a sample comprising a population of live cells with a compound, the live cells comprising a fluorescent target protein; and (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, the tracking comprising: (i) illuminating with a light beam a field of view in a sample plane disposed within the sample to induce fluorescence by at least a subset of the target fluorescent proteins in the live cells; the subset of fluorescent target proteins comprising a range of about 1,000 to about 1,000,000 proteins; and the number of proteins in the subset is determined based on the expression level of the protein of interest and a robust method for detecting the number of proteins in the subset. and a dye concentration deemed appropriate for labeling the subset proteins for SMT, both of which can be calculated and / or configured by one of ordinary skill in the art based on the disclosure of the present application; the tracking further includes (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device, the method being adapted to selectively detect localized fluorescence; the workflow further includes (c) determining a change in the movement of the fluorescent target proteins in the presence of a compound, wherein an increase in the signal detected from the fluorescent target proteins in the presence of the compound compared to the signal of the fluorescent target proteins in the absence of the compound indicates that the compound has a K off This indicates that it induces a decrease in

[0143] In certain embodiments of the OLS htSMT binding workflow described herein, the workflow determines whether a compound that induces a change in binding of a fluorescent target protein in live cells is a K offthe workflow may include determining whether the compound reduces the K of the fluorescent target protein, the workflow including (a) contacting a sample including a population of live cells with a compound, the live cells comprising a fluorescent target protein; and (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, the tracking including (i) illuminating a field of view in a sample plane disposed within the sample with a light beam to cause fluorescence by at least a subset of the target fluorescent proteins in the live cells; and (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device, the method adapted to selectively detect localized fluorescence; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound, the average change in the movement of the fluorescent target protein in the presence of the compound being about 5% to about 10% compared to baseline movement under DMSO conditions; and an increase in the signal detected from the fluorescent target protein in the presence of the compound compared to the signal of the fluorescent target protein in the absence of the compound, indicating that the compound reduces the K of the fluorescent target protein. off This indicates that it induces a decrease in

[0144] In certain embodiments of the OLS htSMT binding workflow described herein, the workflow determines whether a compound that induces a change in binding of a fluorescent target protein in live cells is a K offthe workflow may include determining whether a compound reduces K of the fluorescent target protein, the workflow including (a) contacting a sample including a population of live cells with a compound, the live cells including a fluorescent target protein, the workflow further including (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, the tracking including (i) illuminating with a light beam a field of view in a sample plane disposed within the sample so as to cause fluorescence by at least a subset of the target fluorescent proteins in the live cells, and (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device, the method adapted to selectively detect localized fluorescence, the tracking further including (iii) achieving a Z-factor of greater than 0.5 based on a single field of view, the workflow further including (c) determining a change in the movement of the fluorescent target protein in the presence of the compound, wherein an increase in a signal detected from the fluorescent target protein in the presence of the compound compared to a signal from the fluorescent target protein in the absence of the compound indicates that the compound reduces K of the fluorescent target protein. off This indicates that it induces a decrease in

[0145] In certain embodiments of the OLS htSMT binding workflow described herein, the workflow measures the capacity of a compound to induce a change in binding of a fluorescent target protein in live cells by determining whether the compound has a K offThe workflow can include determining whether a compound reduces the activity of a cell of interest by determining whether the compound reduces the activity of the cell of interest, the workflow comprising: (a) contacting a sample comprising a population of live cells with a compound, the live cells comprising a fluorescent target protein; the workflow further comprising: (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, the tracking comprising: (i) illuminating a field of view in a sample plane disposed within the sample with a light beam to induce fluorescence by at least a subset of the target fluorescent proteins in the live cells, the subset of fluorescent target proteins being detected by about 30 to about 80 live cells illuminated in the field of view in the sample plane, depending on the particular cell type used. For example, in the case of U2OS cells, the range is about 30 to about 40 per FOV, while in the case of HCT116 cells, the range is about 50 to about 80, taking into account the difference in area. The tracking further includes (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device, the method being adapted to selectively detect localized fluorescence. The workflow further includes (c) determining a change in the movement of the fluorescent target protein in the presence of a compound, where an increase in the signal detected from the fluorescent target protein in the presence of the compound compared to the signal of the fluorescent target protein in the absence of the compound indicates that the compound has a K off This indicates that increasing the dose induces a decrease in drug metabolism due to increased residence time.

[0146] In certain embodiments of the OLS htSMT binding workflow described herein, the workflow measures the capacity of a compound to induce a change in binding of a fluorescent target protein in live cells by determining whether the compound has a K offdetermining whether a compound reduces the activity of a protein of interest by determining whether the compound reduces the activity of a protein of interest, the workflow comprising: (a) contacting a sample comprising a population of live cells with a compound, the live cells comprising fluorescent target proteins; the workflow further comprising: (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample; the tracking comprising: (i) illuminating with a light beam a field of view in a sample plane disposed within the sample to induce fluorescence by at least a subset of the target fluorescent proteins in the live cells; the subset of fluorescent target proteins comprising a range of about 1,000 to about 1,000,000 proteins; the number of proteins in the subset being determined based on the expression level of the protein of interest and the number of proteins in the subset for labeling the subset proteins for robust SMT. and the dye concentration deemed appropriate for the assay, both of which can be calculated and / or configured by one of skill in the art based on the disclosure of the present application; the tracking further includes (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device, the method adapted to selectively detect localized fluorescence; and the workflow further includes (c) determining a change in the movement of the fluorescent target proteins in the presence of a compound, wherein an increase in the signal detected from the fluorescent target proteins in the presence of the compound compared to the signal of the fluorescent target proteins in the absence of the compound indicates that the compound is inducing a decrease in Koff of the fluorescent target proteins, and in certain cases, indicates an increase in dosage due to an increase in residence time leading to a decrease in drug metabolism.

[0147] In certain embodiments of the OLS htSMT binding workflow described herein, the workflow measures the capacity of a compound to induce a change in binding of a fluorescent target protein in live cells by determining whether the compound has a K offthe workflow may include (a) contacting a sample containing a population of live cells with a compound, the live cells comprising a fluorescent target protein; and (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, the tracking including (i) illuminating a field of view in a sample plane disposed within the sample with a light beam to cause fluorescence by at least a subset of the target fluorescent proteins in the live cells; and (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device, the method adapted to selectively detect localized fluorescence; and (c) determining a change in the movement of the fluorescent target proteins in the presence of the compound, the average change in the movement of the fluorescent target proteins in the presence of the compound being about 5% to about 10% compared to baseline movement under DMSO conditions; and an increase in the signal detected from the fluorescent target proteins in the presence of the compound compared to the signal of the fluorescent target proteins in the absence of the compound, which indicates that the compound has a K of the fluorescent target proteins. off This indicates that increasing the dose induces a decrease in drug metabolism due to increased residence time.

[0148] In certain embodiments of the OLS htSMT binding workflow described herein, the workflow measures the capacity of a compound to induce a change in binding of a fluorescent target protein in live cells by determining whether the compound has a K offthe workflow may include (a) contacting a sample comprising a population of live cells with a compound, the live cells comprising a fluorescent target protein; and (b) tracking the movement of individual fluorescent target proteins in a plurality of cells in the sample, the tracking including (i) illuminating with a light beam a field of view in a sample plane disposed within the sample so as to cause fluorescence by at least a subset of the target fluorescent proteins in the live cells; and (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device, the method adapted to selectively detect localized fluorescence; the tracking further including (iii) achieving a Z-factor of greater than 0.5 based on a single field of view; and the workflow may further include (c) determining a change in the movement of the fluorescent target protein in the presence of the compound, wherein an increase in a signal detected from the fluorescent target protein in the presence of the compound compared to a signal from the fluorescent target protein in the absence of the compound indicates that the compound is a signal that corresponds to a K for the fluorescent target protein. off These results demonstrate that the drug induces a decrease in the amount of steroid hormone and, in some cases, an increase in dosage due to increased residence time leading to decreased drug metabolism.

[0149] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow determines whether a compound that induces a change in binding of a fluorescent target protein in a cell is a K of the fluorescently labeled target. offThe method can include using a microscope system configured to determine whether a fluorescent target protein reduces a cell's activity, the system including: (a) a stage for supporting a sample, the sample including a population of cells, the cells including a fluorescent target protein; the system further including: (b) a light source emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; and (c) an objective lens for focusing the light beam onto the sample in a sample plane, wherein a subset of the fluorescent target proteins in the sample is positioned within a field of view in the sample plane, the subset of fluorescent target proteins being present in about 30 to about 80 live cells illuminated within the field of view in the sample plane, depending on the particular cell type used; e.g., for U2OS cells, the range is about 30 to about 40 per FOV, while for HCT116 cells, the range is about 30 to about 40 per FOV. In the case of (a), the range is about 50 to about 80, taking into account differences in area; the system further includes (d) a detector device that monitors a light-based response from the fluorescent target protein in the presence of a compound, the detector device being configured to (i) block light received from a light source other than the sample surface on 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 adapted to selectively detect localized fluorescence relative to dynamic fluorescence; the system further includes (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the movement of the fluorescent target protein in the presence of the compound compared to the absence of the compound.

[0150] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow determines whether a compound that induces a change in binding of a fluorescent target protein in a cell is a K of the fluorescently labeled target. offthe system may include use of a microscope system configured to determine whether a protein of interest is reduced, the system including: (a) a stage for supporting a sample, the sample including a population of cells, the cells including fluorescent target proteins; the system further including: (b) a light source emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; and (c) an objective lens for focusing the light beam onto the sample in a plane of the sample; a subset of the fluorescent target proteins in the sample is positioned within a field of view of the sample plane; the subset of fluorescent target proteins includes proteins in the range of about 1,000 to about 1,000,000; the number of proteins in the subset depends on the expression level of the protein of interest and a dye concentration deemed appropriate for labeling the subset proteins for robust SMT; Both of these can be calculated and / or configured by one skilled in the art based on the disclosure of the present application, and the system further includes (d) a detector device that monitors a light-based response from the fluorescent target protein in the presence of the compound, the detector device being configured to (i) block light received from a light source other than the sample surface on which the fluorescent target protein is disposed, thereby tracking the position of the fluorescent target protein, and (ii) track the movement of individual fluorescent target proteins, wherein the tracking is adapted to selectively detect localized fluorescence relative to dynamic fluorescence, and the system further includes (e) a memory and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the movement of the fluorescent target protein in the presence of the compound compared to the absence of the compound.

[0151] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow determines whether a compound that induces a change in binding of a fluorescent target protein in a cell is a K of the fluorescently labeled target. offThe present invention can include the use of a microscope system configured to determine whether a compound reduces a light-based response from the fluorescent target proteins in the sample, the system including: (a) a stage for supporting a sample, the sample including a population of cells, the cells including fluorescent target proteins; the system further including: (b) a light source emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; and (c) an objective lens for focusing the light beam onto the sample in a plane of the sample, wherein a subset of the fluorescent target proteins in the sample is positioned within a field of view of the sample plane; and the system further including: (d) a detector device for monitoring the light-based response from the fluorescent target proteins in the presence of a compound, the detector device detecting (i) the fluorescent target proteins; (i) blocking light received from a light source other than the sample surface where the protein is located, thereby tracking the position of the fluorescent target protein; and (ii) tracking the movement of individual fluorescent target proteins, wherein the tracking is adapted to selectively detect localized fluorescence relative to dynamic fluorescence, and the average change in the movement of the fluorescent target protein in the presence of the compound is about 5% to about 10% compared to baseline movement under DMSO conditions. The system further includes (e) a memory and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining the change in the movement of the fluorescent target protein in the presence of the compound compared to the absence of the compound.

[0152] In certain embodiments of the OLS htSMT screening workflow described herein, the workflow determines whether a compound that induces a change in binding of a fluorescent target protein in a cell is a K of the fluorescently labeled target. offThe present invention can include the use of a microscope system configured to determine whether a compound reduces a light-based response from the fluorescent target proteins in the sample, the system including: (a) a stage for supporting a sample, the sample including a population of cells, the cells including fluorescent target proteins; the system further including: (b) a light source emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; and (c) an objective lens for focusing the light beam onto the sample in a plane of the sample, wherein a subset of the fluorescent target proteins in the sample is positioned within a field of view of the sample plane; and the system further including: (d) a detector device for monitoring the light-based response from the fluorescent target proteins in the presence of a compound, the detector device detecting (i) the fluorescent target proteins; The system is configured to (i) block light received from a light source other than the sample surface where the optical target proteins are located, thereby tracking the position of the fluorescent target proteins; and (ii) track the movement of individual fluorescent target proteins, wherein the tracking is adapted to selectively detect localized fluorescence over dynamic fluorescence, and the detector device is further configured to (iii) achieve a Z-factor of greater than 0.5 based on a single field of view, and the system further includes (e) a memory and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining changes in the movement of the fluorescent target proteins in the presence of a compound compared to the absence of the compound.

[0153] 4.3.OLS Kinetic SMT Because SMT can identify the rate of biological interaction between a compound and a target, it can be used to distinguish between direct and indirect effects on target activity, among other parameters. Given the live-cell setting of SMT, a data collection mode (kinetic SMT or kSMT) can be configured that allows protein movement after compound addition to be measured at set intervals to determine the rate of biological interaction between the compound and the target.

[0154] Exemplary OLS kinetic SMT workflows include the following individual strategies and combinations of the following strategies that combine two or more strategic requirements: For example, but not by way of limitation, the disclosed workflow includes both illuminating a field of view in a sample plane disposed within a sample with a light beam to induce fluorescence by a subset of fluorescent target proteins in live cells, the subset of fluorescent target proteins being present in about 30 to about 80 live cells illuminated within the field of view in the sample plane, and illuminating a field of view in a sample plane disposed within the sample with a light beam to induce fluorescence by a subset of fluorescent target proteins in live cells, the subset of fluorescent target proteins comprising a range of about 1,000 to about 1,000,000 proteins. Similarly, illuminating the sample plane to illuminate about 30 to about 80 live cells per FOV and / or generating fluorescence from about 1,000 to about 1,000,000 proteins can be combined with any of the other strategic requirements disclosed herein, such as determining that the average change in fluorescent target protein motion in the presence of a compound is about 5% to about 10% compared to baseline motion under DMSO conditions, detecting fluorescence from multiple fluorescent target proteins within the field of view of the sample plane at a rate of about 10,000 to about 18,000 per day per system, and achieving a Z-factor greater than 0.5 based on a single field of view.

[0155] In certain embodiments of the OLS kinetic htSMT binding workflow described herein, the workflow can include identifying an incidence of biological interactions between a compound and a target in live cells, the workflow including: (a) contacting a sample including a population of live cells with a compound, the live cells including a fluorescent target protein; and (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample at multiple time points, the tracking including: (i) illuminating a field of view in a sample plane disposed within the sample with a light beam to induce fluorescence by a subset of the target fluorescent proteins in the live cells; The protein subset is present in approximately 30 to approximately 80 illuminated live cells within the field of view of the sample plane, depending on the specific cell type used. For example, for U2OS cells, the range is approximately 30 to approximately 40 per FOV, while for HCT116 cells, the range is approximately 50 to approximately 80, taking into account differences in area. The tracking further includes (ii) detecting fluorescence from multiple fluorescent target proteins within the field of view of the sample plane via a detector device. The workflow further includes (c) determining changes in the movement of the fluorescent target proteins in the presence of a compound, where the rate at which changes in the movement of the fluorescent target proteins occur in the presence of the compound indicates the rate of biological interaction between the compound and the target.

[0156] In certain embodiments of the OLS kinetic htSMT binding workflow described herein, the workflow can include identifying an incidence of biological interactions between a compound and a target in live cells, the workflow including: (a) contacting a sample including a population of live cells with a compound, the live cells including a fluorescent target protein; and (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample at multiple time points, the tracking including: (i) illuminating with a light beam a field of view in a sample plane disposed within the sample to induce fluorescence by a subset of the target fluorescent proteins in the live cells; the subset of fluorescent target proteins comprising at least about 1000 individual fluorescent target proteins. The subset includes proteins in the range of 1 to about 1,000,000, where the number of proteins in the subset depends on the expression level of the protein of interest and the dye concentration deemed appropriate for labeling the subset proteins for robust SMT, both of which can be calculated and / or configured by one of ordinary skill in the art based on the disclosure of the present application. The tracking further includes (ii) detecting fluorescence from multiple fluorescent target proteins within a field of view of the sample plane via a detector device, and the workflow further includes (c) determining a change in the motion of the fluorescent target proteins in the presence of a compound, where the rate at which the change in motion of the fluorescent target proteins occurs in the presence of the compound indicates the rate of occurrence of a biological interaction between the compound and the target.

[0157] In certain embodiments of the OLS kinetic htSMT binding workflow described herein, the workflow can include identifying a rate of biological interaction between a compound and a target in live cells, the workflow including: (a) contacting a sample including a population of live cells with a compound, the live cells including a fluorescent target protein; and (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample at multiple time points, the tracking including: (i) illuminating a field of view in a sample plane disposed within the sample with a light beam to induce fluorescence by a subset of the target fluorescent proteins in the live cells; and (ii) detecting fluorescence from the plurality of fluorescent target proteins in the field of view in the sample plane via a detector device; and (c) determining a change in the movement of the fluorescent target proteins in the presence of the compound, wherein the average change in the movement of the fluorescent target proteins under DMSO conditions is about 5% to about 10% compared to baseline movement in the absence of the compound; and the rate at which the change in the movement of the fluorescent target proteins in the presence of the compound indicates a rate of biological interaction between the compound and the target.

[0158] In certain embodiments of the OLS kinetic htSMT combined workflow described herein, the workflow can include identifying incidences of compound-target biological interactions among direct and indirect biological interactions between a compound and a fluorescent target protein in live cells, the workflow including (a) contacting a sample including a population of live cells with a compound, the live cells including the fluorescent target protein, the workflow further including (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample at multiple time points, the tracking including (i) detecting fluorescence by a subset of the target fluorescent proteins in the live cells. (ii) illuminating a field of view in a sample plane disposed within the sample with a light beam to induce a signal; and (iii) detecting fluorescence from a plurality of fluorescent target proteins within the field of view of the sample plane via a detector device; (iii) the tracking step includes detecting the fluorescence from the plurality of fluorescent target proteins within the field of view of the sample plane at a rate of about 10,000 to about 18,000 per day per system; and the workflow further includes (c) determining a change in the movement of the fluorescent target proteins in the presence of a compound, wherein the rate at which the change in the movement of the fluorescent target proteins occurs in the presence of the compound indicates a rate of occurrence of a biological interaction between the compound and the target.

[0159] In certain embodiments of the OLS kinetic htSMT binding workflow described herein, the workflow can include identifying a rate of occurrence of a biological interaction between a compound and a target in live cells, the workflow including: (a) contacting a sample including a population of live cells with a compound, the live cells comprising a fluorescent target protein; and (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample at multiple time points, the tracking including: (i) illuminating with a light beam a field of view in a sample plane disposed within the sample to cause fluorescence by a subset of the target fluorescent proteins in the live cells; and (ii) detecting, via a detector device, fluorescence from the plurality of fluorescent target proteins in the field of view in the sample plane, wherein detection based on a single field of view is associated with a Z-factor of greater than 0.5; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound, wherein a rate at which the change in the movement of the fluorescent target protein occurs in the presence of the compound indicates a rate of occurrence of a biological interaction between the compound and the target.

[0160] In certain embodiments of the OLS kinetic htSMT combined workflow described herein, the workflow can include determining a dose of a compound that induces a change in the movement of a fluorescent target protein in a live cell, the workflow including: (a) contacting a plurality of samples with a compound, (i) each sample including a population of live cells; (ii) the live cells including a fluorescent target protein; and (iii) the plurality of samples being contacted with different concentrations of the compound over a range of compound concentrations; and (b) tracking the movement of individual target fluorescent proteins in a plurality of live cells of the sample at multiple time points, the tracking including: (i) illuminating with a light beam a field of view in a sample plane located within the sample to induce fluorescence by at least a subset of the fluorescent target proteins in the live cells; The subset of target proteins is present in approximately 30 to approximately 80 live cells illuminated within the field of view of the sample plane, depending on the specific cell type used. For example, for U2OS cells, the range is approximately 30 to approximately 40 per FOV, while for HCT116 cells, the range is approximately 50 to approximately 80 per FOV, taking into account differences in area. The tracking further includes (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device. The workflow further includes (c) determining the rate at which changes in the movement of the fluorescent target proteins occur in the presence of the compound; and (d) repeating steps (b) to (c) for each of multiple samples over a range of compound concentrations, wherein the rate at which changes in the movement of the fluorescent target proteins occur in the presence of the compound indicates the rate of occurrence of biological interactions between the compound and the target.

[0161] In certain embodiments of the OLS kinetic htSMT combined workflow described herein, the workflow can include determining a dose of a compound that induces a change in the movement of a fluorescent target protein in a live cell, the workflow comprising: (a) contacting a plurality of samples with a compound, (i) each sample comprising a population of live cells; (ii) the live cells comprising a fluorescent target protein; and (iii) the plurality of samples being contacted with different concentrations of the compound over a range of compound concentrations; the workflow further comprising: (b) tracking the movement of individual target fluorescent proteins in a plurality of live cells of the sample at multiple time points, the tracking comprising: (i) illuminating with a light beam a field of view in a sample plane located within the sample to induce fluorescence by at least a subset of the fluorescent target proteins in the live cells; The subset may include proteins ranging from about 1,000 to about 1,000,000, where the number of proteins in the subset depends on the expression level of the protein of interest and the dye concentration deemed appropriate for labeling the subset proteins for robust SMT, both of which can be calculated and / or configured by one of ordinary skill in the art based on the disclosure of the present application. The tracking further includes (ii) detecting fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device, where the workflow further includes (c) determining the rate at which a change in the motion of the fluorescent target protein occurs in the presence of the compound; and (d) repeating steps (b)-(c) for each of a plurality of samples across a range of compound concentrations, where the rate at which a change in the motion of the fluorescent target protein occurs in the presence of the compound indicates the rate of occurrence of a biological interaction between the compound and the target.

[0162] In certain embodiments of the OLS kinetic htSMT combined workflow described herein, the workflow can include determining a dose response of a compound that induces a change in the movement of a fluorescent target protein in a live cell, the workflow including: (a) contacting a plurality of samples with a compound, (i) each sample including a population of live cells; (ii) the live cells including a fluorescent target protein; and (iii) the plurality of samples being contacted with different concentrations of the compound over a range of compound concentrations; the workflow further including: (b) tracking the movement of individual target fluorescent proteins in a plurality of live cells of the samples at multiple time points; and (ii) detecting fluorescence from one or more fluorescent target proteins in the sample plane via a detector device; the workflow further includes (c) determining a rate at which a change in the motion of the fluorescent target protein occurs in the presence of the compound, wherein the average change in the motion of the fluorescent target protein in the presence of the compound is about 5% to about 10% compared to baseline motion under DMSO conditions; and (d) repeating steps (b)-(c) for each of a plurality of samples across a range of compound concentrations, wherein the rate at which the change in the motion of the fluorescent target protein occurs in the presence of the compound indicates a rate of occurrence of a biological interaction between the compound and the target.

[0163] In certain embodiments of the OLS kinetic htSMT combined workflow described herein, the workflow can include determining a dose response of a compound to induce a change in the movement of a fluorescent target protein in a live cell, the workflow including: (a) contacting a plurality of samples with a compound, (i) each sample including a population of live cells; (ii) the live cells including a fluorescent target protein; and (iii) the plurality of samples being contacted with different concentrations of the compound over a range of compound concentrations; the workflow further including: (b) tracking the movement of individual target fluorescent proteins in a plurality of live cells of the samples at multiple time points; (ii) illuminating with a light beam a field of view in a sample plane disposed within a filter; and (ii) detecting, via a detector device, fluorescence from one or more of the fluorescent target proteins in the sample plane. (iii) said tracking includes detecting fluorescence from 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 day per system. The workflow further includes (c) determining a rate at which a change in the motion of the fluorescent target protein occurs in the presence of a compound; and (d) repeating steps (b)-(c) for each of a plurality of samples over a range of compound concentrations, wherein the rate at which a change in the motion of the fluorescent target protein occurs in the presence of the compound indicates a rate of occurrence of a biological interaction between the compound and the target.

[0164] In certain embodiments of the OLS kinetic htSMT combined workflow described herein, the workflow can include determining a dose of a compound that induces a change in the movement of a fluorescent target protein in a live cell, the workflow including: (a) contacting a plurality of samples with a compound, (i) each sample including a population of live cells; (ii) the live cells including a fluorescent target protein; and (iii) the plurality of samples being contacted with different concentrations of the compound over a range of compound concentrations; and the workflow further including: (b) tracking the movement of individual target fluorescent proteins in a plurality of live cells of the samples at a plurality of time points, wherein the tracking includes: (i) determining a change in the movement of at least one fluorescent target protein in the live cells. (ii) illuminating with a light beam a field of view in a sample plane disposed within the sample so as to cause fluorescence from at least a subset of the fluorescent target proteins; and (ii) detecting via a detector device fluorescence from one or more of the fluorescent target proteins within the field of view in the sample plane, wherein detection based on a single field of view is associated with a Z-factor of greater than 0.5; and (c) determining a rate at which a change in the motion of the fluorescent target proteins occurs in the presence of the compound; and (d) repeating steps (b)-(c) for each of a plurality of samples across a range of compound concentrations, wherein the rate at which a change in the motion of the fluorescent target proteins occurs in the presence of the compound indicates a rate of occurrence of a biological interaction between the compound and the target.

[0165] In certain embodiments of the OLS kinetic htSMT binding workflow described herein, the workflow can include the use of a microscope system configured to determine the incidence of biological interactions between a compound and a target in live cells, the system including: (a) a stage supporting a sample, the sample including a population of live cells, the live cells including fluorescent target proteins; the system further including: (b) a light source emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; and (c) an objective lens focusing the light beam onto the sample in a plane of the sample, wherein a subset of the fluorescent target proteins in the sample is positioned within a field of view of the sample plane, the subset of fluorescent target proteins being detected by about 30 to about 80 live cells illuminated within the field of view of the sample plane, depending on the particular cell type used. For example, in the case of U2OS cells, the range is about 30 to about 40 per FOV, while in the case of HCT116 cells, the range is about 50 to about 80, taking into account the difference in area. The system further includes (d) a detector device that monitors a light-based response from the fluorescent target protein in the presence of a compound, the detector device being configured to (i) block light received from a light source other than the sample surface on which the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein at multiple time points, and (ii) track the movement of individual fluorescent target proteins. The system further includes (e) a memory and (f) a processor in communication with the memory and the detector device, the processor being capable of determining a change in the movement of the fluorescent target protein in the presence of the compound compared to the absence of the compound.

[0166] In certain embodiments of the OLS kinetic htSMT binding workflow described herein, the workflow can include use of a microscopy system configured to determine the incidence of biological interactions between a compound and a target in live cells, the system including: (a) a stage supporting a sample, the sample including a population of cells, the cells including fluorescent target proteins; the system further including: (b) a light source emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; and (c) an objective lens that focuses the light beam onto the sample in a plane of the sample; a subset of the fluorescent target proteins in the sample is positioned within a field of view in the sample plane; the subset of fluorescent target proteins includes a range of about 1,000 to about 1,000,000 proteins; and the number of proteins in the subset is determined based on the expression level of the protein of interest and the number of proteins in the subset. and a dye concentration deemed appropriate for labeling the subset proteins for robust SMT, both of which can be calculated and / or configured by one of ordinary skill in the art based on the disclosure of the present application; the system further includes (d) a detector device that monitors a light-based response from the fluorescent target proteins in the presence of the compound, the detector device being configured to (i) block light received from a light source other than the sample surface on which the fluorescent target proteins are located, thereby tracking the positions of the fluorescent target proteins at multiple time points, and (ii) track the movement of individual fluorescent target proteins; the system further includes (e) a memory and (f) a processor in communication with the memory and the detector device, the processor being capable of determining a change in the movement of the fluorescent target proteins in the presence of the compound compared to the absence of the compound.

[0167] In certain embodiments of the OLS kinetic htSMT binding workflow described herein, the workflow can include the use of a microscope system configured to determine the incidence of biological interactions between a compound and a target in live cells, the system including: (a) a stage for supporting a sample, the sample including a population of cells, the cells including a fluorescent target protein; the system further including: (b) a light source emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; and (c) an objective lens for focusing the light beam onto the sample in a plane of the sample, wherein a subset of the fluorescent target proteins in the sample are positioned within a field of view of the sample plane; and the system further including: (d) a microscope system configured to determine the incidence of biological interactions between a compound and a target in live cells, the system including: (a) a stage for supporting a sample, the sample including a population of cells, the cells including a fluorescent target protein; The system further includes a detector device that monitors a light-based response from the target protein, the detector device being configured to (i) block light received from a light source other than the sample surface on which the fluorescent target protein is disposed, thereby tracking the position of the fluorescent target protein at multiple time points, and (ii) track the movement of each fluorescent target protein, wherein an average change in the movement of the fluorescent target protein in the presence of the compound is about 5% to about 10% compared to baseline movement under DMSO conditions, and the system further includes (e) a memory and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining the change in the movement of the fluorescent target protein in the presence of the compound compared to the absence of the compound.

[0168] In certain embodiments of the OLS kinetic htSMT combined workflow described herein, the workflow can include the use of a microscope system configured to determine the incidence of a biological interaction between a compound and a target in live cells, the system including: (a) a stage supporting a sample, the sample including a population of cells, the cells including a fluorescent target protein; the system further including: (b) a light source emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; and (c) an objective lens focusing the light beam onto the sample in a plane of the sample, the plurality of fluorescent target proteins in the sample being disposed in the plane of the sample; and (d) an objective lens focusing the light beam onto the sample in a plane of the sample, the plurality of fluorescent target proteins in the sample being disposed in the plane of the sample. The system further includes a detector device for monitoring a response of the fluorescent target protein, the detector device being configured to (i) block light received from a light source other than the sample surface on which the fluorescent target protein is disposed, thereby tracking the position of the fluorescent target protein at multiple time points, and (ii) track the movement of individual fluorescent target proteins, wherein the tracking includes detecting fluorescence from the plurality of fluorescent target proteins within a field of view of the sample surface at a rate of about 10,000 to about 18,000 per day per system, and the system further includes (e) a memory and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the movement of the fluorescent target protein in the presence of the compound compared to the absence of the compound.

[0169] In certain embodiments of the OLS kinetic htSMT binding workflow described herein, the workflow can include the use of a microscope system configured to determine the incidence of biological interactions between a compound and a target in live cells, the system including: (a) a stage supporting a sample, the sample including a population of live cells, the live cells including fluorescent target proteins; the system further including: (b) a light source emitting a light beam capable of inducing a light-based response from a plurality of fluorescent target proteins in the sample; and (c) an objective lens that focuses the light beam onto the sample in a plane of the sample, wherein a subset of the fluorescent target proteins in the sample are positioned within a field of view of the sample plane; and the system further including: (a) a microscope stage configured to support a sample; (b) a microscope stage configured to support a sample; d) a detector device that monitors a light-based response from the fluorescent target protein in the presence of the compound, the detector device being configured to (i) block light received from a light source other than the sample plane where the fluorescent target protein is located, thereby tracking the position of the fluorescent target protein at multiple time points, (ii) track the movement of individual fluorescent target proteins, and (iii) achieve a Z-factor of greater than 0.5 based on a single field of view; the system further comprising: (e) a memory; and (f) a processor in communication with the memory and the detector device, wherein the processor is capable of determining a change in the movement of the fluorescent target protein in the presence of the compound compared to the absence of the compound.

[0170] 5. Working Example Example 1: Optical line-scan high-throughput single-molecule tracking A. Preface This example describes an industrial-scale OLS htSMT technique, a system incorporating such an OLS htSMT technique, hardware and software associated with such an OLS htSMT technique, and methods for using such an 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 demonstrates specific, robust, and reproducible results. The OLS htSMT technique described herein can be used for a variety of applications, including, but not limited to, traditional drug discovery activities such as screening compound libraries and elucidating SAR. Importantly, the OLS htSMT technique described herein can be used to characterize the contributions of both known and novel pathways to interaction networks, such as protein signaling interaction networks.

[0171] B. Results a. Creation and verification of htSMT system We developed a robotic system capable of handling reagents, collecting high-quality, high-speed SMT image series, and processing the time-sequenced raw images to generate molecular trajectories and extract biologically interesting features within defined cellular compartments (Figure 1). To investigate the performance of our htSMT system, we performed various measurements that demonstrate the suitability of the disclosed image acquisition system and workflow for robust htSMT analysis.

[0172] b. Image acquisition Unless otherwise noted, all image acquisition using SMT was performed using a custom-built microscope, motorized stage, stage-top environmental chamber, quad-band filter cube (Chroma), and a custom-built laser engine with wavelengths of 405 nm and 561 nm at the back focal plane of the objective. Fluorescence emission was passed through a high-speed filter wheel (Finger Lakes Instruments) and collected by a backlit CMOS camera (Hammamatsu Orca Fusion, running in light sheet mode). Images were acquired with a 60x 1.27NA water-immersion objective (Nikon). The environmental chamber was set at 37°C, 95% humidity, and 5% CO2. In a specific embodiment, the exposure time for each pixel was 400 microseconds, and recording the entire region of interest (ROI) took a total of 9 milliseconds. The galvanometer scanner position was then reset within 1 millisecond, e.g., with the laser turned off, and the next image was recorded. In such an embodiment, 100 frames per second can be recorded. Additionally or alternatively, a second setup using a smaller ROI can be employed to record 200 frames per second with the same 400 microsecond / pixel exposure and 4 millisecond image recording time. Additionally or alternatively, galvo reset can be performed more quickly.

[0173] c. Image analysis Image acquisition yields one JF per field of view 549 A video and one Hoechst were generated. JF 549 The video is from individual JF 549It can be used to track molecular motion, and Hoechst videos can be used for nuclear segmentation. Tracking can be achieved in three sequential steps using a combination of existing methods: detection, subpixel localization, and linking. Briefly, spots can be detected using a generalized log-likelihood ratio detector. After detection, starting from an initial guess obtained by a radial symmetry method, the estimated location of each emitter can be refined to subpixel resolution using Levenberg-Marquardt fitting with a unified 2D Gaussian spot model. Detected spots can be linked into trajectories using a custom modification of the hill-climbing algorithm. The same detection, subpixel localization, and linking setup can be used for all videos.

[0174] For nuclei segmentation, all frames of the Hoechst movie can be averaged to generate an average projection. This average projection can then be segmented with a neural network trained on human-labeled nuclei. Each spot can then be assigned to at most one nucleus using its subpixel coordinates.

[0175] To recover kinetic information from trajectories, state arrays can be used. For example, a Bayesian inference approach can be used using the "RBME" likelihood function and a grid of 100 diffusion coefficients ranging from 0.01 to 100.0 μm²s-1 and 31 localization error magnitudes ranging from 0.02 to 0.08 μm. After inference, the localization error can be minimized to obtain a one-dimensional distribution across the diffusion coefficients for each field of view. For single-cell analysis, for example, SMT and nuclear segmentation can be performed on a mixture of U2OS cells carrying H2B-HaloTag, HaloTag-CaaX, or free HaloTag. The marginal likelihood of each set of 100 diffusion coefficients for the set of trajectories within each segmented nucleus can then be evaluated. These marginal likelihood functions can be clustered using k-means, and the marginal likelihood functions for each cell can be ordered by its cluster index to create a heatmap. The fractional boundary values ​​(f bound To estimate the free diffusion coefficient (D), we can integrate the posterior distribution of the state sequence below 0.1 μm s. free ) can be calculated using the mean of the posterior distribution over 0.1 μm s.

[0176] d. Data Analysis The tracking results from the automated processing pipeline can be analyzed using KNIME or Spotfire (TIBCO). bound or D free Measurements of f can be associated with experiment metadata and aggregated by condition. bound The change in f of each well bound f of DMSO in the same plate boundThe EC50 can be calculated as the difference from the median of the DMSO wells. Wells that contained no cells within the field of view or where the field of view was out of focus can be excluded from further analysis. Compounds can be evaluated for assay interference using the median fluorescence intensity of the tracking channel and excluded if it is more than three standard deviations higher than the median intensity of the DMSO wells. Similarly, plates that fail to clearly separate the active and negative controls or that deviate significantly from the performance of the rest of the screen can be excluded from further analysis. Finally, compounds with a variance more than three standard deviations above the mean compound variance can be excluded from downstream analysis. The Z' coefficient between the active control and DMSO on a plate can be calculated. EC50 values ​​can be calculated in Prism (GraphPad) by first log-transforming the molecule concentrations and then fitting them to a four-parameter logistic curve.

[0177] e. Clustering of active molecules Chemical structure-based clustering can be performed on the molecules identified as active. Molecular frameworks can be calculated as known in the art and as implemented in pipeline pilots. Molecular frameworks can be clustered using functional class fingerprints (FCFP_4) (e.g., a similarity threshold cutoff of 0.3 Tanimoto distance).

[0178] f. Kinetic experiments Cells can be seeded into 384-well plates the day before and stained and washed as described above. One well with multiple FOVs per well can be taken as a baseline reading. Compounds can then be added manually or robotically to each well to a final concentration of 100 nM during imaging. Data can then be collected for that well. Pauses can be included between each FOV to ensure the entire imaging plan covers the assay window. bound The change in can be determined for each well relative to t=0.

[0179] For assays lasting 4 hours, the plate can be imaged twice using multiple FOVs at different FOV positions per well to prevent photobleaching from affecting the data.

[0180] g. Dwell time imaging Sample preparation and execution of dwell time imaging experiments can be performed in a similar manner to the single molecule tracking assay described above, with a few exceptions. Samples should be 1-10 pMJF. 549 Staining can be performed with Promega and 50 nM Hoechst 33342 for 1 hour. Multiple frames can be collected per field of view by setting the camera integration time to the desired time (milliseconds) and reducing the laser light source to the desired mW at the objective. The laser can be turned on continuously during image acquisition. Compound incubation can range from 1 to 4 hours.

[0181] h. Residence time analysis Image processing, including spot detection, localization, and tracking reconnection, may be performed using the same methods described above. Because dwell-time imaging selectively tracks slow-diffusing molecules, individual localizations can be limited to the maximum displacement distance of individual jump reconnections. The set of trajectories for each field of view is binned into a 1-CDF distribution as described above and fitted with a biexponential decay model.

number

[0182] i. Fluorescence recovery after photobleaching Images can be acquired with a custom-built OLS microscope using a Spectra Light Engine RS-232, as described herein. Stimulation can be performed directly using a mini-scanner coupled with a Coherent OBIS 561 nm 100 mW laser. All imaging can be performed using a 60x 1.27 NA water immersion objective (Nikon). All experiments can be performed at 37°C. For FRAP experiments, cells are seeded in 384-well plates the day before and treated with 50 nM HTL-JF. 549 The cells can be labeled with ATP and washed as described above. Compounds can be added to a final concentration of 100 nM before imaging. Pre-bleaching images can then be obtained by averaging 10 consecutive images. Next, 8-10 regions can be bleached (two background, six-eight cells), and two regions within the cells can be left unbleached. The bleached regions are then bleached at 10% power without scanning. For the next 30 seconds, images can be acquired every 200 ms, then every 1 second for 2 minutes. The background-subtracted average intensity can be measured over time in the region of interest and normalized to the average fluorescence in the baseline image, which can then be normalized to the unbleached region to account for readout-induced photobleaching of the fluorophore. For three biological experiments, data from multiple cells per experiment can be pooled.

Claims

1. 1. A method comprising: receiving a sequence of microscopic images visualizing fluorescently labeled cellular components within live cells, each image comprising an array of pixels, the method further comprising: For each image, classifying each pixel as corresponding to one of a plurality of categories using at least one machine learning model, the at least one machine learning model being trained using a plurality of different loss functions that are rotated during training, the method further comprising: providing data characterizing the classification of the pixel to a consuming application or process. The method.

2. The method of claim 1 , wherein the categories include nucleus, nuclear rim, nucleolus, extracellular debris and / or background.

3. assigning each pixel to one of said categories; relating adjacent pixels having the same category; The method of claim 2 further comprising:

4. The method of claim 3 , further comprising associating unrelated pixels within the hierarchical biological features.

5. 10. The method of any preceding claim, further comprising determining the location of nucleoli within the nucleus based on the provided data.

6. 10. The method of any preceding claim, wherein a first one of the different loss functions comprises a class-balanced cross-entropy loss function.

7. 10. The method of any preceding claim, wherein a second one of the different loss functions comprises a mean squared error loss function.

8. 10. The method of any preceding claim, wherein a third loss function of the different loss functions comprises a cross-entropy loss function.

9. 9. The method of claim 8, wherein the class-balanced cross-entropy loss function is used first, then the mean squared error loss function, followed by the cross-entropy loss function.

10. 10. The method of any preceding claim, further comprising staining the live cells using Hoechst dye.

11. The method of any one of claims 1 to 9, further comprising staining said live cells with a nuclear red dye.

12. 10. The method of any preceding claim, wherein the at least one machine learning model comprises a pixel-level convolutional neural network.

13. The method of claim 12 , wherein the convolutional neural network comprises a U-Net.

14. 10. The method of any preceding claim, further comprising training the at least one machine learning model using images having manually annotated pixels.

15. 10. The method of any preceding claim, further comprising generating a semantic mask based on the provided data.

16. The method of claim 15 , further comprising generating an instance mask using the generated semantic mask.

17. 10. The method of claim 9, wherein providing the data comprises one or more of: visualizing at least a portion of the data characterizing the classified pixels in a graphical user interface; storing at least a portion of the data characterizing the classified pixels in a physically persistent state; loading at least a portion of the data characterizing the classified pixels into a memory; or transmitting at least a portion of the data characterizing the classified pixels to a remote computing device over a network.

18. 1. A system comprising: at least one data processor; a memory storing instructions which, when executed by at least one data processor, cause operations to be performed to implement a method according to any of the preceding claims; The system comprising:

19. A non-transitory computer program product storing instructions which, when executed by at least one data processor forming part of at least one computing device, perform the method according to any of claims 1 to 17.

20. 1. A system comprising: means for receiving a sequence of microscopic images visualizing fluorescently labeled cellular components within living cells, each image comprising an array of pixels, the system further comprising: means for classifying, for each image, each pixel as corresponding to one of a plurality of categories using at least one machine learning model, the at least one machine learning model being trained using a plurality of different loss functions that are rotated during training; and the system further comprising: means for providing data characterizing the classification of the pixel to a consuming application or process; The system.

Images