Nucleolar localization using machine-learning-based inference and segmentation

Abstract

A sequence of microscopy images is received which visualize fluorescently-labeled cellular components in live cells and which each comprise an array of pixels. 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 model can be trained using a plurality of different loss functions that rotate during training. Data characterizing the classifications of the pixels can be provided to a consuming application or process.

Description

[0001] NUCLEOLAR LOCALIZATION USING MACHINE-LEARNING-BASED INFERENCE AND SEGMENTATION

[0002] RELATED APPLICATION

[0003] [1] The current application claims priority to U.S. Pat. App. Ser. No. 63 / 444,544 filed on February 9, 2023, the contents of which are hereby fully incorporated by reference.

[0004] TECHNICAL FIELD

[0005] [2] The subject matter described herein is directed to techniques for localizing nucleoli within microscopy images (e.g., super resolution microscopy images) using machine learningbased inference and segmentation.

[0006] BACKGROUND

[0007] [3] The movement of proteins within the crowded environment of living cells are profoundly influenced by interactions with their 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 fluorescently-labeled protein dynamics in living cells with the aim to extract diffusion coefficients and diffusion properties as a function of a genetic or chemical perturbation. From this, models pertaining to the spatiotemporal regulation of a target of interest with its binding partners can start being elucidated. Several nuclear proteins and transcription factors are known to change their localization in the nucleus as a response to cellular regulation or perturbation. The nucleolus acts as a sequestration region where proteins are immobilized and prevented from diffusing to interact with their various cofactors.

[0008] SUMMARY

[0009] [4] In a first aspect, a sequence of microscopy images visualizing fluorescently-labeled cellular components in live cells is received. Each of these images comprise 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) can be trained using a plurality of different loss functions that rotate during training. Data characterizing the classifications can be provided to a consuming application or process. Provided in this context can include displaying the classifications, loading the classification into memory of a computing device, storing the classifications in physical persistence and / or transmitting the classifications over a network to a remote computing system.

[0010] [5] The categories can include one or more of a nucleus, a nuclear edge, a nucleolus, extracellular debris and / or background.

[0011] [6] The pixel classifications can be used to connect adjacent pixels having a same category (i.e., category). In addition, disjoint pixels within a hierarchical biological feature can be associated with each other.

[0012] [7] The provided data can be used to determine a location of a nucleoli within a nucleus.

[0013] [8] The loss functions can take varying 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 a sequence in which the class-balanced cross entropy loss function is first used followed by the mean squared error loss function followed by the cross entropy loss function.

[0014] [9] The live cells can be stained using Hoechst dye and / or Nuclear Red dye.

[0015]

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

[0016]

[0011] Further, the provided data can be used to generate a semantic mask. The semantic mask, in turn, can be used to generate one or more instance masks. i

[0012] Non-transitory computer program products (i.e., physically embodied computer program products) are also described 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 operations herein. Similarly, computer systems are also 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. In addition, methods can be implemented by one or more data processors either within a single computing system or distributed among two or more computing systems. Such computing systems can be connected and can exchange data and / or commands or other instructions or the like via one or more connections, including but not limited to a connection over a network (e.g., the Internet, a wireless wide area network, a local area network, a wide area network, a wired network, or the like), via a direct connection between one or more of the multiple computing systems, etc.

[0017]

[0013] The subject matter described herein provides many technical advantages. For example, the current subject matter allows for the identification of nucleolar regions in super and other fine resolution microscopy images. The current arrangement addresses limitations of labelling compartments with dyes due to the number of imaging channels and overlap between spectra. The current subject matter is also advantageous in that it addresses problems associated with throughput being decreased when imaging multiple channels are acquired.

[0018]

[0014] The details of one or more variations of the subj ect 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.

[0019] 1 BRIEF DESCRIPTION OF THE DRAWINGS

[0020]

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

[0021]

[0016] FIG. 1 depicts a schematic of the htSMT workflow.

[0022]

[0017] FIGs. 2A-2F depict an exemplary image acquisition system of the present disclosure with the X-Z sample plane visible (FIG. 2A and 2D) or the Y-Z sample plane visible (FIG. 2B and 2C), as well as detail regarding the light beam relative to a HILO-based approach (FIG. 2E) and an example of the incorporation of a camera rolling shutter (FIG. 2F).

[0023]

[0018] FIG. 3 depicts a schematic of an exemplary sample handling system of the present disclosure.

[0024]

[0019] FIG. 4 illustrates an example system for high-throughput single-molecule imaging platform that measures protein motion in living cells.

[0025]

[0020] FIG. 5 illustrates data flow through an example system for a high-throughput singlemolecule imaging platform that measures protein motion in living cells.

[0026]

[0021] FIG. 6 is a plurality of images illustrating differences between mask categories and instance / semantic masks.

[0027]

[0022] FIG. 7 illustrates an example computer-implemented environment in connection with the subject matter described herein.

[0028]

[0023] FIG. 8 is a diagram illustrating a sample computing device architecture for implementing various aspects described herein.

[0029]

[0024] FIG. 9 is a diagram illustrating microscopy images with known classifications for training a machine learning model.

[0025] FIG. 10 is a diagram illustrating an example machine learning model architecture.

[0030]

[0026] FIG. 11 is a diagram illustrating an image input into the machine learning model architecture of FIG. 10, a resulting semantic mask, and a discrete instance mask generated therefrom.

[0031]

[0027] FIG. 12 are images illustrating a Hoechst channel (in blue) overlay ed with an SMT channel (in white) in which localized nucleoli are identified (in pink).

[0032]

[0028] FIG. 13 illustrates original and shuffled versions of nucleolar masks.

[0033]

[0029] FIG. 14 illustrates results of validation tests for the machine learning model of FIG. 10.

[0034]

[0030] FIG. 15 illustrates the SSMD per compound of the MPDC of a target protein localized to the nucleolus region.

[0035]

[0031] FIG. 16 is a diagram that illustrates how the current models capture a difference in nucleolar enrichment for target A as a function of compound treatment

[0036]

[0032] FIG. 17 is a process flow diagram illustrating nucleolar identification using machine learning.

[0037] DETAILED DESCRIPTION

[0038]

[0033] The current subject matter is directed to machine learning-based techniques for nucleolar labeling within microscopy images (e.g., super resolution microscopy images, etc.). The machine learning model can utilize a U-NET architecture that is trained and configured to generate pixel level class labels. The labels include nucleoli, nucleoplasm, nuclear membrane, and a label for extraneous debris.

[0039]

[0034] The microscopy images can be generated using varying systems that provide resolution such that nuclei are present across multiple pixels; especially microscopy system which are able to characterize dynamics of live cells / molecules in varying environmental conditions over time. i One example is a microscopy system using oblique line scanning (OLS) illumination which is further detailed below as a non-limiting example for generating the images / movies. Another example microscopy system utilizes highly inclined and laminated optical sheet microscopy (HILO). Other microscopy systems can utilize different illumination strategies including, but not limited to, Total Internal Reflection Fluorescence (TIRF), HIST, or SOLEIL in order to generate the images analyzed herein.

[0040]

[0035] The presently disclosed subject matter relates to the development of industrial -scale, high-throughput SMT (htSMT) techniques employing OLS, systems incorporating such OLS htSMT techniques, hardware and software related to such OLS htSMT techniques, as well as 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 benefits from an improved spatial homogeneity in signal to noise ratio (SNR) across the camera chip, better confocality (less out of focus signal) and higher temporal resolution, e.g., as outlined in Table 1 (where each “+” represents a 2-fold improvement).

[0041] Table 1.

[0042]

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

[0043]

[0037] With reference to FIG. 1, aspects of the current subject matter can be implemented using an OLS htSMT workflow. This workflow can include various phases, as will be described in further detail below, such as (i) sample preparation including reagent handling, (ii) image acquisition using imaging of the samples to generate a series of images and / or videos, (iii) image analysis through processing of these images and video using, for example, various analytics, single-emitter detection and sub-pixel localization (i.e., “super resolution imaging”), tracking, computer vision, and machine learning algorithms, (iv) storage of information (i.e., features, raw images, modified images, etc.) extracted from or otherwise characterizing or comprising the images and video, and (v) provision of insights using the stored information including biological interpretation (which can additionally or alternatively be provided using various analytics, tracking, computer vision, and machine learning algorithms).

[0044]

[0038] The subject matter of the present disclosure is described with reference to the figures, where reference numbers are used to designate similar or equivalent elements throughout. The figures are not drawn to scale and they are provided merely to illustrate aspects disclosed herein. Several disclosed aspects are described below with reference to exemplary hardware, software, and applications for illustration. It should be understood that numerous specific details, relationships and methods are set forth to provide a more complete understanding of the subject matter disclosed herein. For purposes of clarity of disclosure and not by way of limitation, the detailed description is divided into the following subsections:

[0045] 1. Definitions

[0046] 2. OLS htSMT Hardware

[0047] I 3. OLS htSMT Software

[0048] 4. Specific OLS htSMT Applications

[0049] 5. Examples

[0050] 1. Definitions

[0051]

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

[0052]

[0040] The terms “comprise(s),” “include(s),” “having,” “has,” “can,” “contain(s),” and variants thereof, as used herein, are intended to be open-ended transitional phrases, terms, or words that do not preclude the possibility of additional acts or structures. The singular forms “a,” “an” and “the” include plural references unless the context clearly dictates otherwise. The present disclosure also contemplates other instances “comprising,” “consisting of’, and “consisting essentially of,” the instances or elements presented herein, whether explicitly set forth or not.

[0053]

[0041] For the recitation of numeric ranges herein, each intervening number within the range is explicitly contemplated with the same degree of precision. For example, for the range of 6-9, the numbers 7 and 8 are contemplated in addition to 6 and 9, and for the range 6.0-7.0, the number 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, and 7.0 are explicitly contemplated.

[0042] As used herein, the term “about” or “approximately” means within an acceptable error range for the particular value as determined by one of ordinary skill in the art, which will depend in part on how the value is measured or determined, i.e., the limitations of the measurement system. For example, “about” can mean within 3 or more than 3 standard deviations, per 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 more preferably still up to 1% of a given value. Alternatively, particularly with respect to biological systems or processes, the term can mean within an order of magnitude, preferably within 5-fold, and more preferably within 2-fold, of a value.

[0054]

[0043] As used herein the term “trajectory” refers to the set of spatial coordinates corresponding to the position of an observation of fluorescent protein, linked in time. In certain instances, a plurality of trajectories may be constructed algorithmically by linking a plurality of fluorescent proteins whose positions have been determined in successive time points. In certain instances, a plurality of trajectories may be constructed conservatively by linking only spots within a fixed search radius when no other links are plausible. In certain instances, a plurality of trajectories may be constructed probabilistically.

[0055]

[0044] As defined herein, protein movement refers to the change in position of a plurality of fluorescent proteins. In certain instances, protein movement may be quantified by analysis of changes in spatial coordinates in sequential timepoints. Movement characterized in this way may include, but not be limited to, measurements of the jump length distribution: Given a set of protein displacements between one timepoint and a subsequent timepoint, a histogram can be constructed of the probability of each of the displacement lengths (“jump lengths”). Quantiles of this distribution can be used to describe the motion of the protein. In certain instances, the quantile used is the median of the jump length distribution. In certain instances, the quantile used is the 3rd quartile of the jump length distribution. In certain instances, protein movement may be quantified by analysis of trajectories. Movement characterized in this way may include, but not be limited to, measurements of the mean squared displacement as defined by the average of the square of all displacements in a trajectory, averaged over the plurality of trajectories. Movement characterized in this way may also include, but not be limited to, measurements of the trajectory length or distribution of trajectory lengths. Movement characterized in this way may also include, but not be limited to, measurements of the mean radius of gyration, as defined by the root mean square distance of all coordinates in a trajectory from the center of mass of the set of points contained in the trajectory, averaged over the plurality of trajectories. Movement characterized in this way may also include, but not be limited to, measurements of the mean bond angle, defined by the angle formed from three sequential spatial coordinates averaged over the plurality of trajectories. Movement characterized in this way may also include, but not be limited to, measurements of the diffusion coefficient maximum likelihood estimator, defined as an estimate of the maximum likelihood diffusion coefficient for the plurality of trajectories under a single-state diffusion model with constant localization error. In certain instances, protein movement may be measured by measured through analysis of the product of the link-generating algorithm. Movement characterized in this way may include, but not be limited to, the mean posterior diffusion coefficient, the mean of the posterior probability distribution of coefficients from a probabilistic linking algorithm. Movement characterized in this way may include, but not be limited to, the geometric mean posterior diffusion coefficient, the mean of the log-scaled posterior probability distribution of coefficients from a probabilistic linking algorithm. In certain instances, protein movement may be measured by measured through model-dependent analysis of the plurality of trajectories. Movement characterized in this way may include, but not be limited to, the fraction of immobile molecules (“fbound”) as defined by two-state model fitting.

[0056]

[0045] As used herein, the term “movement” encompasses changes in the direction as well as changes, both increases and decreases, in the speed at which a target is traveling. Accordingly, tracking movement can, in certain instances, include determining that the target is not moving, e.g., when the target either is or is essentially in a static bound state. Movement can be characterized in a variety of ways, including, but not limited to, quantifying: (a) the median of the jump length distribution (where the jump length corresponds to the observed distance the target fluorescent protein travels in consecutive frames); (b) 3rd quartile of the jump length distribution; (c) median radius of gyration; (d) mean posterior diffusion coefficient; (e) geometric mean posterior diffusion coefficient; (f) mean squared displacement; (g) median bond angle; (h) diffusion coefficient maximum likelihood estimator; and / or (i) trajectory length.

[0057]

[0046] As used herein, the movement being detected, including, but not limited to, any change in movement, can occur in response to any environmental or other factor. For example, but not by way of limitation, the movement, or lack thereof, can be elicited by: (A) compound addition; (B) a change in temperature; (C) a change in oxygen concentration, e.g., introduction of a hypoxic condition; (D) mechanical stress; (E) a change in pH; and / or (F) a change in light exposure (e.g., increasing or decreasing intensity).

[0058]

[0047] As used herein, the term “fluorescent protein” refers to any protein that emits a fluorescent signal. In certain instances, the fluorescent emission occurs in response to exposure to light of a particular wavelength. An example of a naturally occurring fluorescent protein is Green fluorescent protein (GFP). In certain instances, however, a protein of interest can be adapted to emit a fluorescent signal via the introduction of an encoded fluorescent tag, i.e., a protein sequence

[0059] II is fused to a protein of interest to render it fluorescent. In certain instances, a protein of interest can be adapted to emit a fluorescent signal through binding of a fluorescent ligand. Nonlimiting examples of such encoded fluorescent tags include: Halo tags, SNAP tags, CLIP tags, TMP tags, and SunTags. Additionally, or alternatively, a protein of interest can be adapted to emit a fluorescent signal via coupling the protein to a fluorescent dye molecule, e.g., amine- or sulfhydryl -reactive dyes.

[0060]

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

[0061]

[0049] As used herein, the term “uniform intensity” refers, in connection with the intensity of light, e.g., light directed to a sample plane, that differs in intensity no more than 5% in certain instances, 10% in certain instances, or 15% in certain instances.

[0050] As used herein, the term “uniform intensity” refers, in connection with signal to noise

[0062] (SNR), to a pixel-wise SNR within a field of view (FOV) where the range of possible values are comprised between 0.5 to 1 standard deviations from the mean SNR.

[0063] 2. OLS htSMT Hardware

[0064] 2.1. Image Acquisition Systems

[0065]

[0051] With reference to FIG. 1, aspects of the current subject matter can be implemented using an htSMT workflow where such workflow incorporates systems for image acquisition. For example, such image acquisition can incorporate the imaging of samples to generate a series of images and / or videos. FIG. 2A depicts a schematic of an exemplary image acquisition system of the present disclosure with the X-Z sample plane visible. FIG. 2B depicts the same exemplary image acquisition system, but with the Y-Z sample plane visible. The exemplary image acquisition system (2-001) comprises: a light source (2-005) configured to emit light relayed by one or more optical elements in an optical relay (2-010) , the optical relay being configured to shape the light emitted from the light source to form a shaped beam (2-065 ) such that the shaped beam has a uniform intensity across a longer dimension of the linear shape; an optical element, e.g., a galvo mirror (2-085), configured to translate the shaped beam ; and one or more optical elements, e.g., a dichroic mirror (2-100) , configured to direct the shaped beam to an objective (2-120), whereby a portion of the sample plane (2-130) is illuminated by an inclined beam (2-125), resulting in the emission of light from the sample, e.g., fluorescence emission, which is focused by the objective (2-120), through a series of optical elements, e.g., a lens (2-155) and an emission filter (2-160), to an image collection system (2-165).

[0066] 11 2.1.1. Light Source

[0067]

[0052] With reference to the exemplary image acquisition system of FIG. 2, the system comprises a light source (2-005) configured to emit light. The light source (2-005), in certain implementations of the image acquisition systems disclosed herein, can be configured to emit light of a single wavelength. In certain implementations of the image acquisition systems disclosed herein, the light source (2-005) can be configured to emit light of two, three, four, five, or more individual wavelengths. In certain implementations, 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 the emitted light elicits fluorescence emission when illuminating a sample, e.g., a sample comprising a fluorescent protein. In certain instances, the wavelength(s) employed in connection with the methods described herein will fall within a range of 400 nm to 650 nm. In certain instances, the light source (2-005) will emit light having a wavelength between 400 nm to 408, between 550 nm to 565 nm, or between 638 nm to 650 nm. In certain non-limiting implementations, the light source (2-005) is configured to comprise three lasers with nominal central wavelengths 405 nm, 560 nm, 640 nm that could vary within absorption band of the fluorophores used. In certain instances, the 405 nm wavelength is used to excite Hoechst dye. In certain instances, a 560 nm wavelength is used to excite dyes (e.g., JF549) attached to HaloTag.

[0068]

[0053] In certain non-limiting implementations, the light source (2-005) is used to catalyze photochemical reactions. For example, but not by way of limitation, the wavelength(s) and illumination intensities can be such that cleavage of a chemical bond occurs. As an additional example, but not by way of limitation, the wavelength(s) and illumination intensities may induce the adoption of a non-radiative dark state (i.e., “photobleached molecule”). As an additional example, but not by way of limitation, the wavelength(s) and illumination intensities may induce radiative or non-radiative energy transfer between fluorophores within the sample.

[0069]

[0054] In certain implementations of the image acquisition systems described herein, the light source (2-005) can be configured to deliver a predetermined amount of power to the back focal plane of the objective (2-105). For example, but not by way of limitation, the light source (2-005) delivers greater than 10 mW with respect to certain wavelengths, e.g., 405 nm, and / or greater than 150 mW with respect to other wavelengths, e.g., 640 nm. Additionally, or alternatively, in instances where the light source (2-005) comprises three lasers emitting at 405 nm, 560 nm, and 640 nm wavelengths, respectively the light source (2-005) can be configured to deliver predetermined amounts of power, to the back focal plane of the objective (2-105). For example, but not by way of limitation the 405 nm can be configured to deliver >10 mW; the 560 nm can be configured to deliver >150 mW; and the 640 nm can be configured to deliver >50 mW ).

[0070]

[0055] In certain implementations of the image acquisition systems described herein, the light source (2-005) is configured to emit pulsed light. For example, but not by way of limitation, the light source (2-005) can be configured to emit stroboscopic pulsed light. In certain implementations of the image acquisition systems described herein, the light source (2-005) is configured to emit pulsed light in synchrony with the start of image acquisition. In certain, nonlimiting implementations, the light source (2-005) will pulse 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 is ON for 9 ms and OFF for 1 ms. In contrast, in 200 FPS mode, the laser is ON for 4 ms OFF for 1 ms. In certain implementations of the OLS htSMT workflow, the light source is configured to go from 90% to

[0071] 11 10% power in less than about 0.4ms. In certain implementations of the OLS htSMT workflow, the light source is configured to go from 90% to 10% power in less than about 0.2 ms.

[0072]

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

[0073]

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

[0074]

[0058] In certain instances, such low drift power output configurations maintain power output within about 0% to about 15% variation, about 0% to about 10% variation, about 10% variation, about 9% variation, about 8% variation, about 7% variation, about 6% variation, about 5% variation, about 4% variation, about 3% variation, about 2% variation or about 1% variation in the context of changing ambient (room) temperature, e.g., 17°C + / -5°C. In certain instances, this is achieved using temperature sensors and / or close-loop heaters to maintain internal light source (e.g., laser engine) temperatures stable, thereby reducing output power drift. For example, but not by way of limitation, the light source can be thermally insulated from the fluctuations of the ambient temperature using an insulated enclosure design. Additionally, or alternatively, closed- loop heaters can be strategically placed at specific locations in the system, e.g., the fiber coupler to reduce output drift. Additionally, or alternatively, water jackets and / or chillers can be used to reduce heat build-up from the laser heads. Moreover, these thermal controls, used individually or in combination, result in shorter warm up times to reach operating steady state and maintained more stable internal operating temperatures when lasers would be powered off and on.

[0075] 2.1.2. Optical Elements & Sample Illumination

[0076]

[0059] With reference to the exemplary image acquisition system of FIG. 2, the system comprises a light source (2-005) configured to emit light, which is relayed by one or more optical elements in an optical relay (2-010), the optical relay being configured to shape the light emitted from the light source to form a shaped beam (2-065 ). The particular optical elements of any particular optical relay (2-010) implementation can be selected and configured to produce the appropriately shaped beam (2-065 ) as well as provide for the appropriate translation of that beam.

[0077]

[0060] In certain, non-limiting, implementations of the optical relay (2-010) of the presently disclosed image acquisition systems, the optical relay (2-010) will comprise one or more lenses and / or other optical elements. For example, but not by way of limitation, the selection and orientation of lenses and other optical elements in the optical relay (2-010) will be configured to appropriately shape the light beam being directed to the sample. In certain non-limiting implementations, the optical relay (2-010) will comprise optical elements to collimate the emitted light, e.g., a collimator (2-020), from the light source (2-005). Additionally, or alternatively, the optical relay (2-010) will comprise additional optical elements, e.g., a Powell lens (2-025) or other

[0078] II elements adapted to produce a beam fan, one or more cylinder lenses ((2-045) and (2-055)), one or more slits to adjust light sheet extent ((2-050) and (2-095)), one or more achromatic lenses ((2- 060) and (2-080)) and / or one or more mirrors ((2-070), (2-075) and (2-085)), one or more of which can be a galvo mirror (2-085) capable of translating the light. . The particular attributes of the optical element will be predetermined to produce an appropriately shaped light beam. For example, but not by way of limitation, the OLS htSMT systems of the present disclosure can achieve uniform horizontal FOV as well as uniform vertical FOV. Such uniformity in horizontal and vertical FOVs contrasts with other strategies that provide non-uniform horizontal FOV and / or non-uniform vertical FOV (See Table 2.)

[0079] Table 2. Comparison of Technologies

[0080]

[0061] To achieve uniform horizontal FOV as well as uniform vertical FOV, the optical relay (2-010) of the OLS htSMT systems described herein comprise an optical element or assembly capable of producing a beam that is elongated along the X plane, and narrow along Y plane and wherein the light beam has a uniform intensity across a longer dimension of the linear shape. In certain non -limiting implementations, the optical relay (2-010) of the OLS htSMT systems described herein will comprise a Powell lens (2-025) to shape the light beam such that it has a uniform intensity across a longer dimension of the linear shape (2-065). The optical relay (2-010) of the OLS htSMT systems described herein can comprise additional or alternative optical elements or assemblies to shape the light beam such that it has a uniform intensity across a longer dimension of the linear shape (2-065). For example, but not by way of limitation, the optical relay (2-010) of the OLS htSMT systems described herein can comprise a diffraction element or assembly configured to shape the light beam such that it has a uniform intensity across a longer dimension of the linear shape.

[0081]

[0062] In certain, non-limiting implementations of the optical relays (2-010) of the presently disclosed image acquisition systems, the optical relay (2-010) will comprise one or more optical elements or assemblies configured to translate the light beam relative to the sample plane of the sample to be analyzed, e.g., in a direction orthogonal to the longer dimension of the light beam. For example, but not by way of limitation, such optical elements or assemblies configured to translate the light beam relative to the sample plane of the sample to be analyzed can comprise a galvo mirror (2-085) or a piezo 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 plane of the sample to be analyzed can comprise a computer-controlled motor.

[0082]

[0063] With reference to the exemplary image acquisition system of FIG. 2A, the system comprises an optical relay (2-010) configured to shape the light emitted from the light source to form a shaped beam (2-065), which is then directed by an optical element (2-100), e.g., a dichroic mirror, configured to direct the shaped beam to an objective (2-120), whereby the sample plane (2-130) is illuminated by an inclined beam (2-125 ).

[0083]

[0064] In certain, non-limiting implementations of the image acquisition systems of the present disclosure, an objective (2-120) directs the inclined beam (2-125) on the sample plane (2- 130) to be analyzed. In certain, non-limiting implementations of the image acquisition systems of the present disclosure the objective (2-120) is a water immersion objective. The use of a water immersion objective facilitates high throughput sample analysis by eliminating the oil present in connection with the use of oil immersion objectives, thereby allowing for higher image quality and less distortion. Not only does the presence of oil present issues in the context of automated systems, where the oil can spread to components, including optical elements that can be fouled by exposure to oil, water-immersion objectives are better index -matched for imaging cells, resulting in less distortion and thus higher image quality than with oil objectives. In certain, non-limiting implementations, the objective 60X 1.27 NA water immersion objective (Nikon). In certain implementations of the workflows described herein, the water immersion objective (2-120) will be heated by a heating element. For example, such heating element will maintain the water immersion objective (2-120) at a temperature sufficient to avoid inducing a change in temperature of the sample contained in the sample plate (2-021).

[0084] 2.1.3. Image Acquisition

[0085]

[0065] In certain, non-limiting implementations of the image acquisition systems of the present disclosure, the objective (2-0120) is also used to focus the fluorescence emitted by the sample (2-145) in response to the illumination provided by the inclined beam (2-125). In certain, non-limiting implementations, the objective-focused fluorescence emission (2-145) is passed through an emission filter ((2-150) and (2-160)), e.g., a bandpass emission filter matched to the spectrum of the fluorophore under observation and mounted in high-speed filter wheel (Finger Lakes Instruments), and collected by a detector device (2-165). In certain, non-limiting implementations, the objective-focused fluorescence emission is directed to an optical relay prior to collection by the detector device (2-165). For example, but not by way of limitation, such an

[0086] 11 optical relay can comprise one or more lenses (2-155) and one or more additional optical elements, e.g., an element configured to reject additional scattered light, prior to collection by the detector device (2-165). In certain, non-limiting implementations, the objective-focused fluorescence emission is directed through another diachroic mirror to split the emission over multiple regions of the detector (2-165). In certain, non-limiting implementations, the objective-focused fluorescence emission is directed through another diachroic mirror to split the emission over multiple detectors (2-165).

[0087]

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

[0088]

[0067] In certain implementations of the image acquisition systems of the present disclosure, the CMOS camera can be run such that, for each field of view, a series of SMT frames are collected. For example, butnotby way oflimitation, 1-20,000 SMT frames, 1-15,000 SMT frames, 1-10,000 SMT frames, 1-5,000 SMT frames, 1-1,000 SMT frames, 2-500 SMT frames, 5-250 SMT frames, 10-200 SMT frames, 100-200 SMT frames, or 200 SMT frames are collected per field of view. In certain implementations, the CMOS camera can be configured to run at a frame rate of from 0.5 to 1000 Hz or in certain implementations, at 100 Hz. For example, but not by way oflimitation, certain cellular SMT implementations can be performed at 100 Hz.

[0089]

[0068] In certain, non-limiting implementations of the image acquisition systems of the present disclosure, the detector device is configured to transmit a signal with each frame to trigger other elements of the imaging system. For example, but not by way of limitation, the detector device may trigger the illumination from the light source (2-005) so as to collect fluorescence emission associated with stroboscopic laser pulses. For example, but not by way of limitation, such fluorescence emission collection is associated with 10 to 100 msec frames and a 2 msec stroboscopic laser pulse.

[0090]

[0069] In certain implementations, the imaging acquisition system can be configured to acquire a predetermined image dimension per frame, referred to herein as the Region of Interest (ROI). In certain implementations, the ROI will differ depending on the frame rate employed. For example, at 100FPS, 2304x1728 pixels will define the ROI which amounts to 248.832x186.624 microns in the sample plane. In contrast, at 200 FPS, 2304x768 pixels will define the ROI, which amounts to 248.832x82.944 microns in the sample plane.

[0091]

[0070] In certain implementations, the imaging acquisition system can be configured to perform predetermined sweep rates at predetermined frame rates. For example, but not by way of limitation, at 100FPS: the sweep rate can be 186.624 microns / 9 ms, which is equivalent to 20.8 microns / ms, which is equivalent to 2.08 cm / s. In contrast, at 200FPS the sweep rate can be 82.94 microns / 4 ms which is equivalent to 20.7 microns / ms which is equivalent to 2.07 cm / s.

[0092]

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

[0093] 11 2.2. Sample Handling

[0094]

[0072] With reference to FIG. 1, aspects of the current subject matter can be implemented using an htSMT workflow, where such workflow incorporates systems for sample preparation, including reagent handling. For example, but not by way of limitation, FIG. 3 provides a schematic representation of a sample plate (2-021) comprising a plurality of wells (2-016) in which samples can be prepared and analyzed. FIG. 3 also provides a schematic representation of components of a sample, e.g., a cell (2-018) and fluorescent target proteins (2-017) within the cell. As noted herein, however, FIG. 3 is not intended to convey scale, e.g., each sample present in a well (2-016) can comprise thousands of cells and each cell can comprise numerous fluorescent target proteins. FIG. 3 also schematically illustrates the ability of sample handling systems of the present disclosure to add additional reagents to samples (2-019). Such reagent addition can be handled by robotic manipulations, such as, but not limited to, the translation of robotic fluid handling systems relative to the individual wells (2-016) of the sample plate (2-021), the translation of the sample plate (2-021) itself, or combinations of both. In certain implementations of the image acquisition system, the sample plate (2-021) may be maintained in a temperature-controlled environment through an environmental control area (2-020). For example, but not by way of limitation, the sample may be maintained at 22-50° C. In certain implementations of the image acquisition system, the sample plate (2-021) can be maintained in a humidity-controlled environment through an environmental control area (2-020). For example, but not by way of limitation, the sample may be maintained at 20%-95% humidity. In certain implementations of the image acquisition system, the sample plate (2-021) may be maintained in a defined gas environment through an environmental control area (2-020). For example, but not by way of limitation, the sample may be maintained at 5% CO2. 2.2.1. Cell Lines & Cell Culture

[0095]

[0073] With reference to FIG. 3, a particular advantage of the htSMT systems described herein is that living cells (2-016) can be assayed to facilitate the tracking of activity, mobility, and diffusive behaviors of proteins within the crowded living cellular environment. Exemplary cell lines that find use in connection with the htSMT systems described herein are considered if the sample can be brought into focus by the objective (2-120) for sufficient time as to direct the fluorescence emission of fluorophores onto the detector (2-165 ). For example, but not by way of limitation, cells may adhere to coverglass directly. As an additional example, but not by way of limitation, cells may be induced to adhere to the coverglass after treating the coverglass with an extracellular matrix material (e.g., fibronectin, collagen, poly-D-lysine, laminin, matrigel, vitronectin, etc.). As an additional example, but not by way of limitation, cells may be induced to adhere to the coverglass after treating the coverglass with plasma. Exemplary cell lines may be selected so as to minimize non-fluorophore emissions reaching the detector. For example, but not by way of limitation, particular cell lines that find use in connection with the htSMT systems described herein included: U2OS cells (ATCC Cat. No. HTB-96), MCF7 cells (ATCC Cat. No. HTB-22), T47d cells (ATCC Cat. No. HTB-133) and SK-BR-3 cells (ATCC Cat. No. HTB-30).

[0096]

[0074] In certain implementations of the htSMT systems of the present disclosure, the cells to be used are cultured as necessary to provide sufficient cell numbers to achieve the desired high throughput analyses. For example, but not by way of limitation, cells, e.g., U2OS cells (ATCC Cat. No. HTB-96), MCF7 cells (ATCC Cat. No. HTB-22), T47d cells (ATCC Cat. No. HTB-133) and SK-BR-3 cells (ATCC Cat. No. HTB-30), can be grown in DMEM (Cat. No. 1056601, Gibco DMEM, high glucose, GlutaMAX Supplement, Thermofisher) supplemented with 10% Fetal Bovine Serum (Cat. No. 16000044, Thermofisher) and 1% pen-strep (Cat. No 15140122, Thermo Fisher) and maintained in a humidified 37 °C incubator at 5% CO2 and subcultivated approximately every two to three days. Additional culture strategies that would be appropriate for the cell lines and uses outlined herein would be known those of skill in the relevant art.

[0097]

[0075] In certain implementations of the htSMT systems of the present disclosure, the cells comprise one or more fluorescent target protein. The selection of the specific protein(s) to be labeled and the specific labeling approach will likely differ depending on the particularities of a specific investigation. For example, but not by way of limitation, one approach for labeling proteins that finds use in connection with the htSMT systems described herein is a HaloTag fusion strategy. For example, but not by way of limitation, one approach for labeling proteins is a SNAPtag fusion. For example, but not by way of limitation, one approach for labeling proteins is a CLIPtag fusion. For example, but not by way of limitation, one approach for labeling proteins is through a fluorophore ligase system. For example, but not by way of limitation, one approach for labeling proteins is via FlAsH or ReAsH tetracysteine motif. For example, but not by way of limitation, one approach for labeling proteins is through strain-promoted alkyne-azide cycloaddition of a fluorophore. For example, but not by way of limitation, one approach for labeling proteins is through inducing cellular uptake of fluorescent target proteins generated separately. In certain implementations of the htSMT systems of the present disclosure, the cells comprise one or more fluorescently labeled glycoprotein.

[0098]

[0076] While one of skill in the art can implement a HaloTag fusion-approach in a number of ways, one exemplary approach is to transfect mammalian expression vectors containing the fusion gene (i.e., a protein of interest fused in frame with a HaloTag sequence) under the control of a weak L30 promoter and containing a Neomycin resistance marker in the cell line of interest, e.g., U2OS cells. In certain implementations, such transfection can be accomplished when the cells are at 70% confluence using FuGENE 6 (Cat. No. E2691, Promega). In certain implementations, transfected cells can then be selected with the appropriate selection agent, e.g., G418 (Cat. No. 10131027, Thermo Fisher), at the appropriate concentration, e.g., at 500 pg / mL. In certain implementations, cells can then be clonally isolated. Clones expressing the desired fusion gene can be determined first by staining with 100 nM JF549-HTL (Cat. No. GAI 110, Promega) and 50 nM Hoechst 33342 and identifying clones with the expected distribution of JF549 signal. In certain implementations, between three and six clones can be subsequently tested using SMT conditions for response to a control compound, and the most homogenous clones can then be subsequently expanded for further testing.

[0099]

[0077] While the htSMT workflows of the instant application are described generally with respect to implementations that track the impact of a compound on a target fluorescent protein, the htSMT workflows described herein are equally applicable to the tracking and analysis of fluorescent target compounds. For example, but not by way of limitation, the compounds described herein can either themselves be fluorescent or can be modified to facilitate fluorescent detection. \Moreover, changes in the movement of the fluorescent compound can be utilized to determine the SMT profile of the compound itself. All analysis strategies described herein with respect to the tracking of target fluorescent proteins are therefore also applicable to results obtained by tracking the compounds themselves.

[0100] 2.2.2. Single Molecule Tracking Sample Preparation

[0101]

[0078] With reference to FIG. 3, aspects of the current subject matter can be implemented using an htSMT workflow whereby cells (2-018) are seeded on plates (2-021 ), e.g., tissue culture treated 384-well glass-bottom plates, although other plate types can find use in connection with the approaches outlined herein, including, but not limited to single chambers, 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. In certain implementations, the cells (2-018 ) are seeded at 1 to 20,000 cells per well (2-016 ), e.g., at 50 to 10,000, at 100 to 9,000, at 250 to 8500, at 500 to 7500, at 750 to 7000, at 2500 to 6500, or at 6000 cells per well. Seeded cells can then be incubated under conditions desirable for adhesion, e.g., overnight at 37 °C and 5% CO2. To enable fluorescence emission, cells can be incubated with a sufficient amount of label, e.g., in the case of HaloTag fusions, 0.1-100 pM of JF549-HTL (Cat. No. GAI 110, Promega) and 50 nM Hoechst 33342 (for labeling nuclei) for an hour in complete medium can provide desirable results.

[0102]

[0079] In certain implementations htSMT strategies described herein, the cells are then washed, e.g., three times in DPBS and twice in imaging media. In certain implementations, the imaging media is prepared to facilitate fluorescence emission, e.g., fluoroBrite DMEM media (Cat. No. A1896701, Thermo Fisher), and can be supplemented with GlutaMAX (Cat. No. 35050079, Thermo Fisher) and the same serum and antibiotics as growth media.

[0103]

[0080] Where appropriate, compounds can be added to the samples to test their impact on a particular labeled protein via SMT. In certain implementations, compounds can be serially diluted in an Echo Qualified 384-Well Low Dead Volume Source Microplate (0018544, Beckman Coulter) to generate dose-titration source material. Compounds can then be administered, e.g., at a final 1 : 1000 dilution in cell culture medium. In certain implementations of the htSMT strategies described herein, each dose of a compound will have at least two replicates per plate as well as three plate replicates. In addition, in certain implementations of the htSMT strategies described herein, 20 DMSO control wells and two no dye control wells can be randomized across each

[0104] II sample plate (2-020). In certain implementations, compounds can be allowed to incubate for 0 to

[0105] 48 hours prior to image acquisition, e.g., one hour at 37 °C.

[0106] 3. OLS htSMT Software

[0107] 3.1. htSMT Software Overview

[0108]

[0081] FIG. 4 illustrates an example system 400 for a high-throughput single-molecule imaging platform that measures molecule motion in living cells. Experiments 402 can be performed to collect large amounts of data from a plurality of living cells (e.g., using imaging system 424 to identify compounds 426 and / or targets 422). The experiments 402 can include the application of various identifiers to molecules of interest such as labels which can be subsequently fluoresced or otherwise detected (e.g., using a laser or other light source). The biological samples forming part of such experiments 402 can be organized into plates 404 having a plurality of wells 406. Each well 406 can have one or more associated fields of view (FOVs) 410. FOVs 410 can be locations within or corresponding to a single well 406. A sequence of images can be generated for the FOVs 410 to result in one or more movies 412, which can include SMT movies as well as non- SMT movies. SMT movies can be used to track the paths of individual labeled cellular components such as proteins, generating a plurality of trajectories. Each trajectory may be comprised of a plurality of spots 414, which include the spatiotemporal coordinates of a labeled molecule at a particular time (as described in further detail in FIG. 5). Separately from the tracking, and in some instances in parallel with the tracking, the movies 412 can be utilized to identify molecules through the use of machine-learning and / or computer vision-based image segmentation to generate masks 418. Masks 418 are spatial regions within a FOV 410 produced 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 two channels (e.g., tracking channel and segmentation / masking channel) can be combined to generate a plurality of metrics 420 associated with various aspects of the samples. 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 methods. Processing of the combined data can be used to generate metrics 420 such as hit scores associated with compounds and / or targets within a biological sample that may be stored in a database structure, as further described in FIG. 5.

[0109]

[0083] FIG. 5 illustrates data flow through an example system 500 for a high-throughput single-molecule imaging platform that measures protein motion in living cells. Experiment specifications 504 that define experiments 502 can be provided as data input via one or more clients 702. For example, each experiment 502 can be collected with accompanying stains (e.g., Hoechst or Potomac Red) that are used for downstream analysis including segmentation 418. The experiment specifications 504 can define various parameters for the experiments 402 such as stains, dyes, compounds, treatments, and the like. As previously described in FIG. 4, imaging system 506 (e.g., imaging system 424) can capture a sequence of images that generate one or SMT movies 511 and / or non-SMT movies or segmentation movies 508 (e.g., movies 412) which characterize molecular movement. The SMT movies 511 can characterize movement of individual fluorescent dye molecules and / or contain images of individual fluorescent dye molecules. The segmentation movies 508 can comprise a sequence of images that characterize movement of labeled cells and / or component thereof. It will be appreciated that Hoechst staining is only one technique that can be used to label molecules and that different and / or multiple labeling techniques such as Potomoc Red can be utilized depending on the desired configuration. For example, MitoTracker Deep Red can be used to label mitochondria), concanavalin A-dye conjugates can be used to label endoplasmic reticulum, SYTO 14 can be used to label nucleoli, phalloidin can be used to label actin, and the like.

[0110]

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

[0111]

[0085] Links, as used herein, are potential associations between two spots. Each link is directed, beginning at one spot and ending at another. A “correct link” joins two spots produced by the same emitter in different frames; otherwise, a link is “incorrect.” One objective of the linking algorithm is to estimate which links are correct. Links are referred to herein in the format c . i -> j . This is taken to mean: link a, which begins at spot i and ends at spot j. Links satisfy at least three of the following constraints: (a) links go forward in time, (b) links may not join two spots that are farther apart than some limit (referred to herein as the “search radius”), and (c) links may not join two spots that are temporally separated by more than some limit (referred to herein as the “gap limit”). A spot-link graph is a graph of spots and links for one SMT movie 511. The spots are the vertices and the links are the edges of this graph. Because links go forward in time, the spot-link graph is a directed acyclic graph. A matching is a subset of the links in a spot-link graph such that no two links in this subset begin or end at the same spot. Trajectories 515 are used herein to refer to sequences of contiguous (end-to-end) links in the same matching. Dynamical metrics 530 can be determined using a plurality of trajectories. Such parameters can comprise attributes of a spot that characterize the spot’s motion. Such parameters can comprise one or more of velocity, diffusion coefficient, or anomaly parameter(s) for each spot. The dynamical parameter(s) for spot i are herein referred to as 0(-. The set of dynamical parameters for all spots in a spot-link graph are herein referred to as 0.

[0112]

[0086] Separate from, and in some variations in parallel with, the processing of SMT movies 511, segmentation movies 508 can undergo segmentation, which generates one or more masks 520. The masks can be of various categories, including but not limited to, cell nuclei, cell cytoplasm, and / or extraneous masks, which are further described in FIG. 6. Instance masks are individual segmented objects (e.g., one cell, one nucleus, one mitochondrion). A FOV 410 may contain any number of instance masks for one mask category. Semantic masks are 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, etc.). The extraneous masks can contain parts of the non-SMT movie 508 that are excluded from any downstream data analysis. For example, these extraneous masks could correspond to parts of the non-SMT movie 508 that are out of focus or that contain auto fluorescent cell debris that prevents accurate tracking. During segmentation, molecules within the segmentation movies 508 can be assigned to one or more masks. Image metrics 540 can be evaluated from the masked molecules such as cell health, focus quality, or the like.

[0113]

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

[0114]

[0088] In some variations, segmentation information can be used to identify subcellular compartments such as nuclei, nucleoli, cytoplasm, and the like. Segmentation information can also be used to distinguish one cell from another. Segmentation information can be stored in a specific format (e g., a multi-image file format such as TIFF, etc.).

[0115]

[0089] Example dynamical metrics 530 can also include state arrays. State arrays are a framework for learning interpretable dynamical models from SMT trajectories, and can be used for gaining additional insight into the motion of a target protein and where in the cell that motion occurs. In some variations, state arrays can be generated / populated using the segmentation information. The outputs for state arrays can be returned at the subcellular compartment level, allowing scientists to distinguish dynamics in different subcellular compartments. Additionally, state arrays can be computed on each individual subcellular compartment (e.g., per nucleus).

[0116]

[0090] To facilitate data access by applications, including but not limited to state arrays, processed SMT data may be stored in a format that permits (a) representation of processed trajectories and associated attributes such as SNR and spot shape characteristics for each SMT movie, (b) representation of mask objects, including mask category (e.g., each mask object's associated subcellular organelle, etc.), (c) association of trajectories with mask objects (such as the cell nucleus in which each trajectory was observed), and (d) association of all SMT movies with metadata relevant to the original experiment, such as compound treatments, acquisition times, and

[0117] J2 imaging system name. Formats (a) and (c) can be a Protocol Buffer schema defining a storage format for trajectories along with associated mask objects. Format (b) can be a specialized image file format that includes the mask objects to which each pixel in an FOV belongs. Format (d) may be a PostgreSQL database that records all captured experiments / movies. As a client of processed SMT data, state arrays can draw on these data schemas to report dynamic characteristics of trajectories on a per-mask category or per-mask object basis.

[0118]

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

[0119]

[0092] FIG. 7 illustrates an example computer-implemented environment 700 where an imaging system 710 can interact with a computing architecture to perform the various algorithms described herein. As shown in FIG. 7, the imaging system 710 can interface with one or more clients 750 (e.g., clients 502 via a web application having a graphical user interface such). The one or more clients 750 can interface with one or more servers 720 accessible through the network(s) 730. The one or more clients 750 can host a frame grabber that captures images from a camera (e.g., movies 412). Those images can be temporarily stored on the one or more clients 750 and periodically transferred to the one or more servers 720 for remote storage via network 730. The one or more servers 720 can also contain or have access to one or more data stores 740 for storing data collected and / or extracted from a sample by 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 the captured images (e.g., movies 412).

[0120]

[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 can be that of client(s) 750 and / or of server(s) 720 and some components described in relation to diagram 800 may be optional for the client(s) 750 and / or servers(s) 720. A bus 804 can serve as the information highway interconnecting the other illustrated components of the hardware. A processing system 808 labeled CPU (central processing unit) (e.g., one or more computer processors / data processors at a given computer or at multiple computers), can perform calculations and logic operations required to execute a program. Optionally or additionally, a processing system 812 labeled GPU (graphics processing unit) (e g., one or more computer processors / data processors at a given computer or at multiple computers), can perform calculations and logic operations required to execute a program. A non-transitory processor- readable storage medium, such as read only memory (ROM) 816 and random access memory (RAM) 820, can be in communication with the processing system 808 and / or processing system 812 and can include one or more programming instructions for the operations specified here. Optionally, program instructions can be stored on a non-transitory computer-readable storage

[0121] 11 medium such as a magnetic disk, optical disk, recordable memory device, flash memory, solid state drive or other physical storage medium.

[0122]

[0094] In one example, a disk controller 848 can interface with one or more optional removable storage 856 or local storage 852 to the system bus 804. The removable storage 856 can be external or internal disk drives, or solid state drives, or external hard drives. The local storage 852 can be internal hard drives and / or memory. As indicated previously, these various examples of removable storage 856, local storage 852, and disk controllers 848 are optional devices. The system bus 804 can also include at least one communications interface 824 to allow for communication with external devices either physically connected to the computing system or available externally through a wired or wireless network such as cloud storage and remote services. In some cases, the at least one communications interface 824 includes or otherwise comprises a network interface.

[0123]

[0095] In some variations, such as for 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., LCD (liquid crystal display) or LED (light-emitting diode) monitor, etc.) for displaying information obtained from the bus 804 via a display interface 840 to the user and an input device 832 such as keyboard and / or a pointing device (e.g., a mouse or a trackball) and / or a touchscreen by which the user can provide input to the computer. Other kinds of input devices 832 can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback by way of a microphone 836, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input. The input device 832 and the microphone 836 can be coupled to and convey information via the bus 804 by way of an input device interface 828. By way of example, li input device 832 may be an imaging system 710 configured with abilities to capture a sequence of images as described herein. A frame grabber 858 can capture or grab individual frames from analog or digital data encapsulating the sequence of images obtained from the bus 804. Frame grabber 858 may include memory that can store individual or multiple frames. Frame grabber 858 can also provide individual or multiple frames to bus 804 for further storage on, for example, 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.

[0124]

[0096] One or more aspects or features of the subject matter described herein can be realized in digital electronic circuitry, integrated circuitry, specially designed application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) computer hardware, firmware, software, and / or combinations thereof. These various aspects or features can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device. The programmable system or computing system may include clients and servers. A client and server 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 having a client-server relationship to each other.

[0125]

[0097] These computer programs, which can also be referred to as programs, software, software applications, applications, components, or code, include machine instructions for a programmable processor, and can be implemented in a high-level procedural language, an object- oriented programming language, a functional programming language, a logical programming language, and / or in assembly / machine language. As used herein, the term “machine-readable medium” refers to any computer program product, apparatus and / or device, such as for example magnetic discs, optical disks, memory, and Programmable Logic Devices (PLDs), used to provide machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term “machine- readable signal” refers to any signal used to provide machine instructions and / or data to a programmable processor. The machine-readable medium can store such machine instructions non- transitorily, such as for example as would a non-transient solid-state memory or a magnetic hard drive or any equivalent storage medium. The machine-readable medium can alternatively or additionally store such machine instructions in a transient manner, such as for example as would a processor cache or other random access memory associated with one or more physical processor cores.

[0126] 3.2. Nucleolar Localization Using Machine-Learning-based Inference and Segmentation for Single Molecule Tracking

[0127]

[0098] Provided herein are machine learning-based techniques to identify nucleoli in super and other fine resolution microscopy images from only a Hoechst nuclear label. While Hoechst does not directly label the nucleoli there are features based on shape and intensity that indicate the location of nucleoli within the nucleus. However, these features are not always present, and this inconsistency can make applying machine learning methods difficult. With the current subject matter, a machine learning model can be trained using multiple loss functions that rotate during training. In one example, the multiple loss functions can include: mean squared error, categorical cross entropy, and a class balanced categorical cross entropy. u

[0099] The machine learning model can take various forms including a neural network including a U-NET architecture and can generate pixel level class labels (i.e., the machine learning model classifies each pixel as being associated with one or more categories). The pixel level labels can include one or more of nucleoli, nucleoplasm, nuclear membrane, and a label for extraneous debris. The machine learning model was experimentally validated using a helicase that is known to translocate from the nucleoli to the nucleoplasm when treated. The results demonstrate that this translocation can be effectively measured using the segmentation provided by the model.

[0128]

[0100] As noted above, the primary objective was to predict nucleoli locations from Hoechst. However, the effective wavelength of Hoechst and the nucleolar dye are the same making training directly on these two markers very difficult. Experiments were conducted and it was determined that a proxy DNA stain in the far-red spectrum (Nuclear Red) can be used because the nucleolar label is effective in the same wavelength as Hoechst. The nucleolar label serves as a ground truth and the machine learning model was trained using the Nuclear Red images.

[0129]

[0101] A training set for the machine learning model was comprised of 20 images in which the nuclear red acted as input. The output consisted of pixel -level labels for nucleus, background, nuclear edge, nucleolus, and extra-cellular debris. Using the nuclear red and nucleolar images for reference, the ground truth was manually annotated. 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 Nucleolar ID green (IX) for 30 minutes before washing and subsequent imaging. Images were acquired using a custom-built HILO microscope with a 60X 1.27 NA water immersion objective, equipped with 405 nm, 561 nm and 642 nm lasers. Emission is captured using a Hamamatsu FusionBT camera along with 445 / 58, 585 / 40, 676 / 37 filters respectively for each channel. Z stack images were taken for each channel consisting of 12 slices with 300 nm spacing. The images in diagram 900 of FIG. 9 were derived using this equipment and processes. FIG. 9 illustrates Hoechst images 910, 940 in blue, nuclear red images 920, 950 in red, and nucleolar images 930, 960 in green. In particular, in FIG. 9, image 910 visualizes HCT116 Cells, Hoechst dye (showing nuclei), image 920 visualizes HCT116 Cells, Nuclear red dye (showing nuclei), image 930 visualizes HCT116 Cells, Nucleolar dye, (showing nucleoli), image 940 visualizes U2OS Cells, Hoechst dye (showing nuclei), image 950 visualizes U2OS Cells, Nuclear red dye (showing nuclei), and image 960 visualizes U2OS Cells, Nucleolar dye, (showing nucleoli).

[0130]

[0102] FIG. 10 includes a diagram 1000 that illustrates a multi-layer machine learning model 1020 (e g., a U-net architecture) in which a super or other fine resolution microscopy image 1010 is input. The layers of the machine learning model (e.g., an input layer, hidden layers, and an output layer) include various nodes / neurons with varying weights as generated through the training process. Each pixel in the microscopy image 1010 comprises values characterizing color and intensity and the output of the machine learning model can comprise a representation 1010 visualizing per pixel entropy of the machine learning model 1020 (here brighter pixels indicate spots where the model 1020 has more uncertainty), a probabilistic semantic mask 1030 from which a discrete instance mask 1040 can be generated. FIG. l lis a diagram 1100 illustrating an input image 1110 from which a semantic mask 1120 is generated and, in turn, a discrete instance mask 1130 is generated.

[0131]

[0103] It was found that optimizing the machine learning model 1020 using a standard cross entropy loss was not able to generate the desired performance with the machine learning model often finding a solution that completely ignores the nucleoli label. Instead, 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 a class balanced cross entropy (CB), mean squared error (MSE), and cross entropy (CE):

[0132]

[0107] FIG. 12 is a diagram 1200 illustrating of a first image 1210 with a sample treated with

[0133] DMSO and a second image 1220 with a sample treated with a compound. In these images 1210, 1220, a Hoechst channel is represented in blue, an SMT-channel is overlayed in white and localized nucleoli are represented in pink. It will be noted that in the DMSO condition more spots are shown in the nucleoli. After compound treatment, the spots tend to move out of the nucleoli and into the nucleoplasm.

[0134]

[0108] There are two experiment conditions to test in order to validate the model. The first is to measure the fraction of spots in the nucleoli versus the nucleoplasm in a helicase that is known to translocate from the nucleoli to the nucleoplasm when treated. If the predicted masks are accurate, a decrease in the nucleolar fraction relative to a DMSO control should be detected. A confirmatory check can be performed for any potential built-in biases between the treatment molecule and DMSO that may be measurable just by partitioning the nucleus into nucleoplasm and nucleoli even if that partitioning is not accurate. This can be accomplished by computing the same metric using a corrupted or random version of the nucleoli masks. Random masks can be generated by rotating the original nucleoli masks and re-applying them (such as images 1310, 1320 in diagram 1300 of FIG. 13). Finally, the experiment can be repeated with a helicase without the translocation property.

[0135]

[0109] Diagram 1400 of FIG. 14 illustrates the ability of the machine learning model described herein to selectively identify nucleloi localization between a Target A helicase that is known to translocate from the nucleoli to the nucleoplasm region upon Compound treatment vs a Target B helicase that does not have the translocation properties and stays within the nucleoli region upon Compound treatment (model predicted masks). Diagram 1400 of FIG. 14 further illustrates that the model predicted masks were specific and accurate to identifying nucleoli localization while random generated masks did not demonstrate the same translocation effect indicating that model determined nucleoli localizations are non-random and meaningful.

[0136]

[0110] With a validated nucleolar segmentation model, potential protein dynamic metrics can be assessed as being useful for hit calling during a high throughput screen. Diagram 1500 of FIG. 15 illustrates the SSMD per compound of the MPDC of our target protein localized to the nucleolus region. A clear separation can be seen between the negative and positive controls validating the use of this metric for hit calling.

[0137]

[0111] FIG. 16 is a diagram 1600 showing a comparison with this same metric within the entire nuclear mask. In particular, FIG. 16 illustrates how the model is able to capture a difference in nucleolar enrichment for target A as a function of compound treatment. Target B is expected not to show such behavior. The mask randomization shows that the masking is biologically relevant to the underlying target and non-random. Compound treatment of Target A shows a decrease in spots within nucleoli. The square box can indicate hits that can be identified with the nucleolar mask but were undetectable when measuring diffusion in the entire nucleus.

[0112] FIG. 18 is a process flow diagram 1800 in which, at 1810, a sequence of microscopy images is received which visualize fluorescently-labeled cellular components in live cells and which each comprise an array of pixels. For each image, each pixel is classified, at 1820, using at least one machine learning model to correspond to one of a plurality of categories. The at least one machine learning model can be trained using a plurality of different loss functions that rotate during training. Data characterizing the classifications of the pixels can be provided, at 1830, to a consuming application or process.

[0138]

[0113] The categories can include one or more of: a nucleus, a nuclear edge, a nucleolus, extracellular debris and / or background.

[0139]

[0114] The pixel classifications can be used to connect adjacent pixels having a same category (i.e., component type). In addition, disjoint pixels within a hierarchical biological feature can be associated with each other.

[0140]

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

[0141]

[0116] The loss functions can take varying 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 a sequence in which the class-balanced cross entropy loss function is first used followed by the mean squared error loss function followed by the cross entropy loss function.

[0142]

[0117] The live cells can be stained using Hoechst dye and / or Nuclear Red dye.

[0143]

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

[0144]

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

[0145] 11 4. Specific OLS htSMT Applications

[0146]

[0120] Many, and perhaps most, pathways that regulate the fundamental biochemistry of cells depend upon the interaction of protein sensors with protein effectors that engage transiently to trigger a change in cell physiology. Although the fundamentals of this process have long been appreciated, biochemical investigation of these protein interactions has typically required in vitro reconstitution or has been interrogated through pull-down assays after cell permeabilization. The htSMT workflow described herein provides a means of visualizing protein movement in large numbers of live cells, and under circumstances where the effect of added compositions, e.g., small molecule inhibitors, can be assessed quantitatively.

[0147]

[0121] With reference to FIG. 1, aspects of the OLS htSMT workflows of the present disclosure include, but are not limited to, (i) sample preparation including reagent handling, (ii) image acquisition using imaging of the samples to generate a series of images and / or videos, (iii) image analysis through processing of these images and video, (iv) storage of information, and (v) provision of insights using the stored information including biological interpretation. With respect to the biological interpretations, the htSMT workflows described herein offer 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 KineticSMT.

[0148] 4.1. OLS htSMT Screening

[0149]

[0122] In certain implementations of the OLS htSMT workflows described herein, the systems and methods are adapted to interrogate the ability of one or more compositions, e.g., “test” compounds, to impact the SMT profile associated with a labeled protein. For example, such htSMT workflow will screen for changes in the SMT profile, e.g., either an increase or a decrease in movement of the protein of interest, in the presence of the composition relative to that SMT profile in the absence of the composition. It will be appreciated that higher-order comparisons can also be made with where compounds are multiplexed, including where multiple proteins are fluorescent. Moreover, as outlined above, the htSMT screening strategies described herein are equally applicable to screening of the SMT profiles associated with fluorescent compounds, e.g., compounds that are naturally fluorescent or those that have been modified to fluoresce or are linked to a fluorophore.

[0150]

[0123] Underlying such htSMT screening strategies is the ability of the htSMT workflows described herein to extract accurate molecular trajectories at scale. Exemplary OLS htSMT workflows comprise the following individual strategies as well as combinations of the following strategies where two or more of the strategic requirements are combined. For example, but not by way of limitation, the workflows of the present disclosure comprise both 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 fluorescent target proteins in the live cells, wherein the subset of the fluorescent target proteins are present in about 30 to about 80 live cells illuminated in the field of view of the sample plane as well as 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 fluorescent target proteins in the live cells, wherein the subset of the of the fluorescent target proteins comprises a range of about 1000 to about 1,000,000 proteins. Similarly, illuminating sample planes to illuminate about 30 to about 80 live cells per FOV and / or causing the fluorescence of about 1000 to about 1,000,000 proteins can be combined with any of the other strategic requirements disclosed herein, e.g., determining the average change in movement of the fluorescent target protein in the presence of the compound is about 5 to about 10% relative to baseline movements under DMSO conditions, detecting the fluorescence from a plurality of the fluorescent target proteins in a field of view of the sample plane at a rate of about 10,000 to about 18,000 per day per system and achieving, based on a single field of view, a z-factor of > 0.5.

[0151]

[0124] In certain implementations of the OLS htSMT screening workflows described herein, the workflow can comprise identifying a biological interaction between a compound and a fluorescent target protein in a live cell comprising: (a) contacting a sample comprising a population of live cells with the compound, wherein the live cells comprise the fluorescent target protein; (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample, wherein said tracking comprises: (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 fluorescent target proteins in the live cells, wherein the subset of the of the fluorescent target proteins are present in about 30 to about 80 live cells illuminated in the field of view of the sample plane, depending on the specific cell type being used, e.g., for U2OS cells the range is about 30 to about 40 cells per FOV, while for HCT116 cells the range is about 50 to about 80 cells given their differences in area; (ii) detecting the fluorescence from a plurality of the fluorescent target proteins in the field of view of 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 a change in the movement of the fluorescent target protein in the presence of the compound relative to the movement of the fluorescent target protein in the absence of the compound identifies a biological interaction between the compound and the fluorescent target protein.

[0152]

[0125] In certain implementations of the OLS htSMT screening workflows described herein, the workflow can comprise identifying a biological interaction between a compound and a fluorescent target protein in a live cell comprising: (a) contacting a sample comprising a population of live cells with the compound, wherein the live cells comprise the fluorescent target protein; (b) il tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample, wherein said tracking comprises:(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 fluorescent target proteins in the live cells, wherein the subset of the of the fluorescent target proteins comprises a range of about 1000 to about 1,000,000 proteins, wherein the number of proteins within the subset depend on the expression level of the protein of interest as well as the dye concentration being deemed adequate to label a subset of the proteins for robust SMT, both of which can be calculated and / or configured by one of skill in the art based on the disclosure of the instant application; (ii) detecting the fluorescence from a plurality of the fluorescent target proteins in a field of view of 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 a change in the movement of the fluorescent target protein in the presence of the compound relative to the movement of the fluorescent target protein in the absence of the compound identifies a biological interaction between the compound and the fluorescent target protein.

[0153]

[0126] In certain implementations of the OLS htSMT screening workflows described herein, the workflow can comprise identifying a biological interaction between a compound and a fluorescent target protein in a live cell comprising: (a) contacting a sample comprising a population of live cells with the compound, wherein the live cells comprise the fluorescent target protein; (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample, wherein said tracking comprises: (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 fluorescent target proteins in the live cells; (ii) detecting the fluorescence from a plurality of the fluorescent target proteins in a field of view of 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 movement of the fluorescent target protein in the presence of the compound is about 5 to about 10% relative to baseline movements under DMSO conditions; wherein a change in the movement of the fluorescent target protein in the presence of the compound relative to the movement of the fluorescent target protein in the absence of the compound identifies a biological interaction between the compound and the fluorescent target protein.

[0154]

[0127] In certain implementations of the OLS htSMT screening workflows described herein, the workflow can comprise identifying a biological interaction between a compound and a fluorescent target protein in a live cell comprising: (a) contacting a sample comprising a population of live cells with the compound, wherein the live cells comprise the fluorescent target protein; (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample, wherein said tracking comprises:(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 fluorescent target proteins in the live cells; (ii) detecting the fluorescence from a plurality of the fluorescent target proteins in a field of view of the sample plane via a detector device; (iii) wherein said tracking comprises detecting the fluorescence from a plurality of the fluorescent target proteins in a field of view of the sample plane at a rate of about 10,000 to about 18,000 per day per system; 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 relative to the movement of the fluorescent target protein in the absence of the compound identifies a biological interaction between the compound and the fluorescent target protein.

[0155] II

[0128] In certain implementations of the OLS htSMT screening workflows described herein, the workflow can comprise identifying a biological interaction between a compound and a fluorescent target protein in a live cell comprising: (a) contacting a sample comprising a population of live cells with the compound, wherein the live cells comprise the fluorescent target protein; (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample, wherein said tracking comprises: (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 fluorescent target proteins in the live cells; (ii) detecting the fluorescence from a plurality of the fluorescent target proteins in a field of view of the sample plane via a detector device; and (iii) achieve, based on a single field of view, a z-factor of > 0.5 ; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound; wherein a change in the movement of the fluorescent target protein in the presence of the compound relative to the movement of the fluorescent target protein in the absence of the compound identifies a biological interaction between the compound and the fluorescent target protein.

[0156]

[0129] In certain implementations of the OLS htSMT screening workflows described herein, the workflow can comprise determining a dose response of a compound that induces a change in the movement of a fluorescent target protein in a live cell comprising: (a) contacting a plurality of samples with the compound, (i) where each sample comprises a population of live cells, (ii) where the live cells comprise the fluorescent target protein, and (iii) where the plurality of samples are contacted with distinct concentrations of the compound across a range of compound concentrations; (b) tracking the movement of individual fluorescent target proteins in a plurality of live cells of a sample, wherein said tracking comprises: (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 fluorescent target proteins in the live cells, wherein the subset of the of the fluorescent target proteins are present in about 30 to about 80 live cells illuminated in the field of view of the sample plane, depending on the specific cell type being used, e.g., for U2OS cells the range is about 30 to about 40 cells per FOV, while for HCT116 cells the range is about 50 to about 80 cells, given their differences in area; (ii) detecting the fluorescence from one or more of the fluorescent target proteins 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 (d) repeating steps (b)-(c) for each of the plurality of samples across the range of compound concentrations; wherein a change in the movement of the fluorescent target protein in the presence of the compound across the concentration range indicates the dose response of the compound.

[0157]

[0130] In certain implementations of the OLS htSMT screening workflows described herein, the workflow can comprise determining a dose response of a compound that induces a change in the movement of a fluorescent target protein in a live cell comprising: (a) contacting a plurality of samples with the compound, (i) where each sample comprises a population of live cells, (ii) where the live cells comprise the fluorescent target protein, and (iii) where the plurality of samples are contacted with distinct concentrations of the compound across a range of compound concentrations; (b) tracking the movement of individual fluorescent target proteins in a plurality of live cells of a sample, wherein said tracking comprises: (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 fluorescent target proteins in the live cells, wherein the subset of the of the fluorescent target proteins comprises a range of about 1000 to about 1,000,000 proteins, wherein the number of proteins within the subset depend on the expression level of the protein of interest as well as the dye concentration being deemed adequate to label a subset protein for robust SMT, both of which can be calculated and / or configured by one of skill in the art based on the disclosure of the instant application; (ii) detecting the fluorescence from one or more of the fluorescent target proteins 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; (d) repeating steps (b)-(c) for each of the plurality of samples across the range of compound concentrations; wherein a change in the movement of the fluorescent target protein in the presence of the compound across the concentration range indicates the dose response of the compound.

[0158]

[0131] In certain implementations of the OLS htSMT screening workflows described herein, the workflow can comprise determining a dose response of a compound that induces a change in the movement of a fluorescent target protein in a live cell comprising: (a) contacting a plurality of samples with the compound, (i) where each sample comprises a population of live cells, (ii) where the live cells comprise the fluorescent target protein, and (iii) where the plurality of samples are contacted with distinct concentrations of the compound across a range of compound concentrations; (b) tracking the movement of individual fluorescent target proteins in a plurality of live cells of a sample, wherein said tracking comprises: (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 fluorescent target proteins in the live cells; (ii) detecting the fluorescence from one or more of the fluorescent target proteins 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 movement of the fluorescent target protein in the presence of the compound is about 5 to about 10% relative to baseline movements under DMSO conditions; and (d) repeating steps (b)-(c) for each of the plurality of samples across the range of compound concentrations; wherein a change in the movement of the fluorescent target protein in the presence of the compound across the concentration range indicates the dose response of the compound.

[0159]

[0132] In certain implementations of the OLS htSMT screening workflows described herein, the workflow can comprise determining a dose response of a compound that induces a change in the movement of a fluorescent target protein in a live cell comprising: (a) contacting a plurality of samples with the compound, (i) where each sample comprises a population of live cells, (ii) where the live cells comprise the fluorescent target protein, and (iii) where the plurality of samples are contacted with distinct concentrations of the compound across a range of compound concentrations; (b) tracking the movement of individual fluorescent target proteins in a plurality of live cells of a sample, wherein said tracking comprises:(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 fluorescent target proteins in the cells; (ii) detecting the fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device; (iii) wherein said tracking comprises detecting the fluorescence from a plurality of the fluorescent target proteins in a field of view of the sample plane at a rate of about 10,000 to about 18,000 per day per system; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound (d) repeating steps (b)-(c) for each of the plurality of samples across the range of compound concentrations; wherein a change in the movement of the fluorescent target protein in the presence of the compound across the concentration range indicates the dose response of the compound.

[0160]

[0133] In certain implementations of the OLS htSMT screening workflows described herein, the workflow can comprise determining a dose response of a compound that induces a change in the movement of a fluorescent target protein in a live cell comprising: (a) contacting a plurality of samples with the compound, (i) where each sample comprises a population of live cells, (ii) where the live cells comprise the fluorescent target protein, and (iii) where the plurality of samples are contacted with distinct concentrations of the compound across a range of compound concentrations; (b) tracking the movement of individual fluorescent target proteins in a plurality of live cells of a sample, wherein said tracking comprises:(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 fluorescent target proteins in the cells; (ii) detecting the fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device; and (iii) achieve, based on a single field of view, a z-factor of > 0.5; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound (d) repeating steps (b)-(c) for each of the plurality of samples across the range of compound concentrations; wherein a change in the movement of the fluorescent target protein in the presence of the compound across the concentration range indicates the dose response of the compound.

[0161]

[0134] In certain implementations of the OLS htSMT screening workflows described herein, the workflow can comprise use of a microscopy system configured to identify a biological interaction between a compound and a fluorescent target protein in a live cell comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of live cells, and where the live cells comprise the fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of the fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in the sample plane, wherein a subset of the fluorescent target proteins in the sample are disposed in a field of view in the sample plane, and wherein the subset of the of the fluorescent target proteins are present in about 30 to about 80 live cells illuminated in the field of view of the sample plane, depending on the specific

[0162] 11 cell type being used, e.g., for U20S cells the range is about 30 to about 40 cells per FOV, while for HCT116 cells the range is about 50 to about 80 cells given their differences in area; (d) a detector device for monitoring the light-based response from the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from sources other than the sample plane in which the fluorescent target proteins are disposed to thereby track the position of the fluorescent target proteins; and (ii) track the movement of individual fluorescent target proteins, (e) a memory; and (f) a processor in communication with the memory and the detector device, where the processor is capable of determining the change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0163]

[0135] In certain implementations of the OLS htSMT screening workflows described herein, the workflow can comprise use of a microscopy system configured to identify a biological interaction between a compound and a fluorescent target protein in a live cell comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and where the cells comprise the fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of the fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in the sample plane, wherein a subset of the fluorescent target proteins in the sample are disposed in a field of view in the sample plane; and wherein the subset of the of the fluorescent target proteins comprises a range of about 1000 to about 1,000,000 proteins, wherein the number of proteins within the subset depend on the expression level of the protein of interest as well as the dye concentration being deemed adequate to label a subset protein for robust SMT, both of which can be calculated and / or configured by one of skill in the art based on the disclosure of the instant application; (d) a detector device for

[0164] 11 monitoring the light-based response from the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from sources other than the sample plane in which the fluorescent target proteins are disposed to thereby track the position of the fluorescent target proteins; and (ii) track the movement of individual fluorescent target proteins, (e) a memory; and (f) a processor in communication with the memory and the detector device, where the processor is capable of determining the change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0136] In certain implementations of the OLS htSMT screening workflows described herein, the workflow can comprise use of a microscopy system configured to identify a biological interaction between a compound and a fluorescent target protein in a live cell comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and where the cells comprise the fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of the fluorescent target proteins in the sample;

[0165] (c) an objective for focusing the light beam on the sample in the sample plane, wherein a subset of the fluorescent target proteins in the sample are disposed in a field of view in the sample plane;

[0166] (d) a detector device for monitoring the light-based response from the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from sources other than the sample plane in which the fluorescent target proteins are disposed to thereby track the position of the fluorescent target proteins; and (ii) track the movement of individual fluorescent target proteins, wherein the average change in movement of the fluorescent target protein in the presence of the compound is about 5 to about 10% relative to baseline movements under DMSO conditions, (e) a memory; and (f) a processor in communication with the memory and the detector device, where the processor is capable of determining the change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0167]

[0137] In certain implementations of the OLS htSMT screening workflows described herein, the workflow can comprise use of a microscopy system configured to identify a biological interaction between a compound and a fluorescent target protein in a live cell comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and where the cells comprise the fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of the fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in the sample plane, wherein a plurality of the fluorescent target proteins in the sample are disposed in the sample plane; (d) a detector device for monitoring the light-based response from the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from sources other than the sample plane in which the fluorescent target proteins are disposed to thereby track the position of the fluorescent target proteins; and (ii) track the movement of individual fluorescent target proteins, wherein said tracking comprises detecting the fluorescence from a plurality of the fluorescent target proteins in a field of view of the sample plane at a rate of about 10,000 to about 18,000 per day per system; (e) a memory; and (f) a processor in communication with the memory and the detector device, where the processor is capable of determining the change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0168]

[0138] In certain implementations of the OLS htSMT screening workflows described herein, the workflow can comprise use of a microscopy system configured to identify a biological interaction between a compound and a fluorescent target protein in a live cell comprising: (a) a li stage for supporting a sample, wherein the sample comprises a population of cells, and where the cells comprise the fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of the fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in the sample plane, wherein a plurality of the fluorescent target proteins in the sample are disposed in the sample plane; (d) a detector device for monitoring the light-based response from the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from sources other than the sample plane in which the fluorescent target proteins are disposed to thereby track the position of the fluorescent target proteins; (ii) track the movement of individual fluorescent target proteins; and (iii) achieve, based on a single field of view, a z-factor of > 0.5; (e) a memory; and (f) a processor in communication with the memory and the detector device, where the processor is capable of determining the change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0169] 4.2. OLS htSMT Binding

[0170]

[0139] In certain implementations of the OLS htSMT workflows described herein, the systems and methods are adapted to discriminate between recovery after exposure to a compound driven by an increase in residence time (decreasing k*off), which would result in increasing fbound seen in an htSMT screening assay. Importantly, neither FRAP nor htSMT can discriminate between recovery driven by an increase in residence time (decreasing k*off) or increasing the rate of chromatin binding (increasing k*on), either of which would result in increasing fbound. By changing SMT acquisition conditions to reduce the illumination intensity and collect long frame exposures, only immobile proteins form spots. Under these imaging conditions, the distribution of track lengths provides a measure of relative residence times.

[0140] Exemplary OLS htSMT binding workflows comprise the following individual strategies as well as combinations of the following strategies where two or more of the strategic requirements are combined. For example, but not by way of limitation, the workflows of the present disclosure comprise both 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 fluorescent target proteins in the live cells, wherein the subset of the fluorescent target proteins are present in about 30 to about 80 live cells illuminated in the field of view of the sample plane as well as 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 fluorescent target proteins in the live cells, wherein the subset of the of the fluorescent target proteins comprises a range of about 1000 to about 1,000,000 proteins. Similarly, illuminating sample planes to illuminate about 30 to about 80 live cells per FOV and / or causing the fluorescence of about 1000 to about 1,000,000 proteins can be combined with any of the other strategic requirements disclosed herein, e.g., determining the average change in movement of the fluorescent target protein in the presence of the compound is about 5 to about 10% relative to baseline movements under DMSO conditions, detecting the fluorescence from a plurality of the fluorescent target proteins in a field of view of the sample plane at a rate of about 10,000 to about 18,000 per day per system and achieving, based on a single field of view, a z-factor of > 0.5.

[0171]

[0141] In certain implementations of the OLS htSMT binding workflows described herein, the workflow will comprise determining whether a compound that induces a change in binding of a fluorescent target protein in a live cell reduces the Koff of the fluorescent target protein comprising: (a) contacting a sample comprising a population of live cells with the compound, where the live cells comprise the fluorescent target protein; (b) tracking the movement of individual fluorescent target proteins in a plurality of the cells in the sample, wherein said tracking comprises: (i) u 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 fluorescent target proteins in the live cells, wherein the subset of the of the fluorescent target proteins are present in about 30 to about 80 live cells illuminated in the field of view of the sample plane, depending on the specific cell type being used, e.g., for U2OS cells the range is about 30 to about 40 cells per FOV, while for HCT116 cells the range is about 50 to about 80 cells given their differences in area; (ii) detecting the fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device where the method is adapted to selectively detect localized fluorescence ; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound; wherein an increase in the signal detected from the fluorescent target protein in the presence of the compound relative to the signal of the fluorescent target protein in the absence of the compound indicates that the compound induces a reduction in the Koir of the fluorescent target protein.

[0172]

[0142] In certain implementations of the OLS htSMT binding workflows described herein, the workflow can comprise determining whether a compound that induces a change in binding of a fluorescent target protein in a live cell reduces the KOff of the fluorescent target protein comprising: (a) contacting a sample comprising a population of live cells with the compound, where the live cells comprise the fluorescent target protein; (b) tracking the movement of individual fluorescent target proteins in a plurality of the cells in the sample, wherein said tracking comprises: (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 fluorescent target proteins in the live cells, wherein the subset of the of the fluorescent target proteins comprises a range of about 1000 to about 1,000,000 proteins, wherein the number of proteins within the subset depend on the expression level of the protein of interest as well as the dye concentration being deemed adequate to label a subset protein for robust SMT, both of which can be calculated and / or configured by one of skill in the art based on the disclosure of the instant application; (ii) detecting the fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device where the method is adapted to selectively detect localized fluorescence ; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound; wherein an increase in the signal detected from the fluorescent target protein in the presence of the compound relative to the signal of the fluorescent target protein in the absence of the compound indicates that the compound induces a reduction in the Koff of the fluorescent target protein.

[0173]

[0143] In certain implementations of the OLS htSMT binding workflows described herein, the workflow can comprise determining whether a compound that induces a change in binding of a fluorescent target protein in a live cell reduces the Koff of the fluorescent target protein comprising: (a) contacting a sample comprising a population of live cells with the compound, where the live cells comprise the fluorescent target protein; (b) tracking the movement of individual fluorescent target proteins in a plurality of the cells in the sample, wherein said tracking comprises: (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 fluorescent target proteins in the live cells; (ii) detecting the fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device where the method is adapted to selectively detect localized fluorescence; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound, wherein the average change in movement of the fluorescent target protein in the presence of the compound is about 5 to about 10% relative to baseline movements under DMSO conditions; wherein an increase in the signal detected from the fluorescent target protein in the presence of the compound relative to the signal of the fluorescent target protein in the absence of the compound indicates that the compound induces a reduction in the Kofr of the fluorescent target protein.

[0174]

[0144] In certain implementations of the OLS htSMT binding workflows described herein, the workflow can comprise determining whether a compound that induces a change in binding of a fluorescent target protein in a live cell reduces the Koff of the fluorescent target protein comprising: (a) contacting a sample comprising a population of live cells with the compound, where the live cells comprise the fluorescent target protein; (b) tracking the movement of individual fluorescent target proteins in a plurality of the cells in the sample, wherein said tracking comprises:(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 fluorescent target proteins in the live cells; (ii) detecting the fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device where the method is adapted to selectively detect localized fluorescence; and (iii) achieving, based on a single field of view, a z-factor of > 0.5; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound; wherein an increase in the signal detected from the fluorescent target protein in the presence of the compound relative to the signal of the fluorescent target protein in the absence of the compound indicates that the compound induces a reduction in the Koff of the fluorescent target protein.

[0175]

[0145] In certain implementations of the OLS htSMT binding workflows described herein, the workflow can comprise determining a dose of a compound that induces a change in binding of a fluorescent target protein in a live cell by determining whether the compound reduces the Koff of the fluorescent target protein comprising: (a) contacting a sample comprising a population of live cells with the compound, where the live cells comprise the fluorescent target protein; (b) tracking the movement of individual fluorescent target proteins in a plurality of the cells in the sample, w wherein said tracking comprises: (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 fluorescent target proteins in the live cells, wherein the subset of the of the fluorescent target proteins are present in about 30 to about 80 live cells illuminated in the field of view of the sample plane, depending on the specific cell type being used, e.g., for U2OS cells the range is about 30 to about 40 cells per FOV, while for HCT116 cells the range is about 50 to about 80 cells given their differences in area; (ii) detecting the fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device where the method is adapted to selectively detect localized fluorescence and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound; wherein an increase in the signal detected from the fluorescent target protein in the presence of the compound relative to the signal of the fluorescent target protein in the absence of the compound indicates that the compound induces a reduction in the Koff of the fluorescent target protein and indicates an increase in dose due to enhanced residence time leading to reduced drug metabolism.

[0176]

[0146] In certain implementations of the OLS htSMT binding workflows described herein, the workflow can comprise determining a dose of a compound that induces a change in binding of a fluorescent target protein in a live cell by determining whether the compound reduces the Koff of the fluorescent target protein comprising: (a) contacting a sample comprising a population of live cells with the compound, where the live cells comprise the fluorescent target protein; (b) tracking the movement of individual fluorescent target proteins in a plurality of the cells in the sample, wherein said tracking comprises: (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 fluorescent target proteins in the live cells, wherein the subset of the of the fluorescent target proteins comprises a b range of about 1000 to about 1,000,000 proteins, wherein the number of proteins within the subset depend on the expression level of the protein of interest as well as the dye concentration being deemed adequate to label a subset protein for robust SMT, both of which can be calculated and / or configured by one of skill in the art based on the disclosure of the instant application; (ii) detecting the fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device where the method is adapted to selectively detect localized fluorescence; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound; wherein an increase in the signal detected from the fluorescent target protein in the presence of the compound relative to the signal of the fluorescent target protein in the absence of the compound indicates that the compound induces a reduction in the Kofr of the fluorescent target protein and, in certain instances, an increase in dose due to enhanced residence time leading to reduced drug metabolism.

[0177]

[0147] In certain implementations of the OLS htSMT binding workflows described herein, the workflow can comprise determining a dose of a compound that induces a change in binding of a fluorescent target protein in a live cell by determining whether the compound reduces the Kotr of the fluorescent target protein comprising: (a) contacting a sample comprising a population of live cells with the compound, where the live cells comprise the fluorescent target protein; (b) tracking the movement of individual fluorescent target proteins in a plurality of the cells in the sample, wherein said tracking comprises: (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 fluorescent target proteins in the live cells; (ii) detecting the fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device where the method is adapted to selectively detect localized fluorescence; and (c) determining a change in the movement of the fluorescent target ii protein in the presence of the compound; wherein the average change in movement of the fluorescent target protein in the presence of the compound is about 5 to about 10% relative to baseline movements under DMSO conditions; wherein an increase in the signal detected from the fluorescent target protein in the presence of the compound relative to the signal of the fluorescent target protein in the absence of the compound indicates that the compound induces a reduction in the Koff of the fluorescent target protein indicates an increase in dose due to enhanced residence time leading to reduced drug metabolism.

[0178]

[0148] In certain implementations of the OLS htSMT binding workflows described herein, the workflow can comprise determining a dose of a compound that induces a change in binding of a fluorescent target protein in a live cell by determining whether the compound reduces the Koff of the fluorescent target protein comprising: (a) contacting a sample comprising a population of live cells with the compound, where the live cells comprise the fluorescent target protein; (b) tracking the movement of individual fluorescent target proteins in a plurality of the cells in the sample, wherein said tracking comprises: (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 fluorescent target proteins in the live cells; (ii) detecting the fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device where the method is adapted to selectively detect localized fluorescence ; and (iii) achieving, based on a single field of view, a z-factor of > 0.5; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound; wherein either: an increase in the signal detected from the fluorescent target protein in the presence of the compound relative to the signal of the fluorescent target protein in the absence of the compound indicates that the compound induces a reduction in the KOff of the fluorescent il target protein and, in certain instances, an increase in dose due to enhanced residence time leading to reduced drug metabolism.

[0179]

[0149] In certain implementations of the OLS htSMT binding workflows described herein, the workflow can comprise the use of a microscopy system configured to determine whether a compound that induces a change in binding of a fluorescent target protein in a cell reduces the Koff of the fluorescently labeled target comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and where the cells comprise the fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of the fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in the sample plane, wherein a subset of the fluorescent target proteins in the sample are disposed in a field of view in the sample plane, and wherein the subset of the of the fluorescent target proteins are present in about 30 to about 80 live cells illuminated in the field of view of the sample plane, depending on the specific cell type being used, e.g., for U2OS cells the range is about 30 to about 40 cells per FOV, while for HCT116 cells the range is about 50 to about 80 cells given their differences in area; (d) a detector device for monitoring the light-based response from the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from sources other than the sample plane in which the fluorescent target proteins are disposed to thereby track 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 relative to dynamic fluorescence; (e) a memory; and (f) a processor in communication with the memory and the detector device, where the processor is capable of determining the change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound. w

[0150] In certain implementations of the OLS htSMT binding workflows described herein, the workflow can comprise the use of a microscopy system configured to determine whether a compound that induces a change in binding of a fluorescent target protein in a cell reduces the Kotr of the fluorescently labeled target comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and where the cells comprise the fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of the fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in the sample plane, wherein a subset of the fluorescent target proteins in the sample are disposed in a field of view in the sample plane, and wherein the subset of the of the fluorescent target proteins comprises a range of about 1000 to about 1,000,000 proteins, wherein the number of proteins within the subset depend on the expression level of the protein of interest as well as the dye concentration being deemed adequate to label a subset protein for robust SMT, both of which can be calculated and / or configured by one of skill in the art based on the disclosure of the instant application; (d) a detector device for monitoring the light-based response from the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from sources other than the sample plane in which the fluorescent target proteins are disposed to thereby track 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 relative to dynamic fluorescence; (e) a memory; and (f) a processor in communication with the memory and the detector device, where the processor is capable of determining the change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound. ii

[0151] In certain implementations of the OLS htSMT binding workflows described herein, the workflow can comprise the use of a microscopy system configured to determine whether a compound that induces a change in binding of a fluorescent target protein in a cell reduces the Kotr of the fluorescently labeled target comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and where the cells comprise the fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of the fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in the sample plane, wherein a subset of the fluorescent target proteins in the sample are disposed in a field of view in the sample plane; (d) a detector device for monitoring the light-based response from the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from sources other than the sample plane in which the fluorescent target proteins are disposed to thereby track 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 relative to dynamic fluorescence, and wherein the average change in movement of the fluorescent target protein in the presence of the compound is about 5 to about 10% relative to baseline movements under DMSO conditions; (e) a memory; and (f) a processor in communication with the memory and the detector device, where the processor is capable of determining the change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0180]

[0152] In certain implementations of the OLS htSMT binding workflows described herein, the workflow can comprise the use of a microscopy system configured to determine whether a compound that induces a change in binding of a fluorescent target protein in a cell reduces the Koff of the fluorescently labeled target comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and where the cells comprise the fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of the fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in the sample plane, wherein a subset of the fluorescent target proteins in the sample are disposed in a field of view in the sample plane; (d) a detector device for monitoring the light-based response from the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from sources other than the sample plane in which the fluorescent target proteins are disposed to thereby track 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 relative to dynamic fluorescence; (iii) achieve, based on a single field of view, a z-factor of > 0.5; (e) a memory; and (f) a processor in communication with the memory and the detector device, where the processor is capable of determining the change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0181] 4.3. OLS KineticSMT

[0182]

[0153] Since SMT can identify the rate of emergence of a biological interaction between a compound and a target, SMT can be used to distinguish direct versus indirect effects on target activity, among other parameters. Given the live cell setting of SMT, a data collection mode can be configured that allows for measurement of protein movements in set intervals after compound addition (kinetic SMT or kSMT) to determine the rate of emergence of a biological interaction between a compound and a target. u

[0154] Exemplary OLS KineticSMT workflows comprise the following individual strategies as well as combinations of the following strategies where two or more of the strategic requirements are combined. For example, but not by way of limitation, the workflows of the present disclosure comprise both 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 fluorescent target proteins in the live cells, wherein the subset of the of the fluorescent target proteins are present in about 30 to about 80 live cells illuminated in the field of view of the sample plane as well as 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 fluorescent target proteins in the live cells, wherein the subset of the of the fluorescent target proteins comprises a range of about 1000 to about 1,000,000 proteins. Similarly, illuminating sample planes to illuminate about 30 to about 80 live cells per FOV and / or causing the fluorescence of about 1000 to about 1,000,000 proteins can be combined with any of the other strategic requirements disclosed herein, e g., determining the average change in movement of the fluorescent target protein in the presence of the compound is about 5 to about 10% relative to baseline movements under DMSO conditions, detecting the fluorescence from a plurality of the fluorescent target proteins in a field of view of the sample plane at a rate of about 10,000 to about 18,000 per day per system and achieving, based on a single field of view, a z-factor of > 0.5

[0183]

[0155] In certain implementations of the OLS Kinetic htSMT binding workflows described herein, the workflow can comprise determining the rate of emergence of a biological interaction between a compound and a target in a live cell comprising: (a) contacting a sample comprising a population of live cells with the compound, wherein the live cells comprise the fluorescent target protein; (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample at a plurality of time points, wherein said tracking comprises: s (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 fluorescent target proteins in the live cells, wherein the subset of the of the fluorescent target proteins are present in about 30 to about 80 live cells illuminated in the field of view of the sample plane, depending on the specific cell type being used, e.g., for U2OS cells the range is about 30 to about 40 cells per FOV, while for HCT116 cells the range is about 50 to about 80 cells given their differences in area; (ii) detecting the fluorescence from a plurality of the fluorescent target proteins in the field of view of 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 rate at which changes in the movement of the fluorescent target protein occur in the presence of the compound indicates the rate of emergence of a biological interaction between a compound and a target.

[0184]

[0156] In certain implementations of the OLS Kinetic htSMT binding workflows described herein, the workflow can comprise determining the rate of emergence of a biological interaction between a compound and a target in a live cell comprising: (a) contacting a sample comprising a population of live cells with the compound, wherein the live cells comprise the fluorescent target protein; (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample at a plurality of time points, wherein said tracking comprises: (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 fluorescent target proteins in the live cells, wherein the subset of the of the fluorescent target proteins comprises a range of about 1000 to about 1,000,000 proteins, wherein the number of proteins within the subset depend on the expression level of the protein of interest as well as the dye concentration being deemed adequate to label a subset protein for robust SMT, both of which can be calculated and / or configured by one of skill in the art based on the disclosure of the instant application; (ii) detecting the fluorescence from a plurality of the fluorescent target proteins in a field of view of 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 rate at which changes in the movement of the fluorescent target protein occur in the presence of the compound indicates the rate of emergence of a biological interaction between a compound and a target.

[0185]

[0157] In certain implementations of the OLS Kinetic htSMT binding workflows described herein, the workflow can comprise determining the rate of emergence of a biological interaction between a compound and a target in a live cell comprising: (a) contacting a sample comprising a population of live cells with the compound, wherein the live cells comprise the fluorescent target protein; (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample at a plurality of time points, wherein said tracking comprises: (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 fluorescent target proteins in the live cells; (ii) detecting the fluorescence from a plurality of the fluorescent target proteins in a field of view of 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 movement of the fluorescent target protein in the presence of the compound is about 5 to about 10% relative to baseline movements under DMSO conditions; wherein the rate at which changes in the movement of the fluorescent target protein occur in the presence of the compound indicates t the rate of emergence of a biological interaction between a compound and a target.

[0186]

[0158] In certain implementations of the OLS Kinetic htSMT binding workflows described herein, the workflow can comprise determining the rate of emergence of a biological interaction between a compound and a target between a direct and indirect biological interaction between a compound and a fluorescent target protein in a live cell comprising: (a) contacting a sample comprising a population of live cells with the compound, wherein the live cells comprise the fluorescent target protein; (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample at a plurality of time points, wherein said tracking comprises: (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 fluorescent target proteins in the live cells; (ii) detecting the fluorescence from a plurality of the fluorescent target proteins in a field of view of the sample plane via a detector device; (iii) wherein said tracking comprises detecting the fluorescence from a plurality of the fluorescent target proteins in a field of view of the sample plane at a rate of about 10,000 to about 18,000 per day per system; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound; wherein the rate at which changes in the movement of the fluorescent target protein occur in the presence of the compound indicates the rate of emergence of a biological interaction between a compound and a target.

[0187]

[0159] In certain implementations of the OLS Kinetic htSMT binding workflows described herein, the workflow can comprise determining the rate of emergence of a biological interaction between a compound and a target in a live cell comprising: (a) contacting a sample comprising a population of live cells with the compound, wherein the live cells comprise the fluorescent target protein; (b) tracking the movement of a plurality of individual fluorescent target proteins in a plurality of live cells in the sample at a plurality of time points, wherein said tracking comprises: (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 fluorescent target proteins in the live cells; (ii) detecting the fluorescence from a plurality of the fluorescent target proteins in the field of view of the sample plane via a detector device, wherein detection based on a single field of view is associated with a z-factor of > 0.5; and (c) determining a change in the movement of the fluorescent target protein in the presence of the compound; wherein the rate at which changes in the movement of the fluorescent target protein occur in the presence of the compound indicates the rate of emergence of a biological interaction between a compound and a target.

[0188]

[0160] In certain implementations of the OLS Kinetic htSMT binding workflows described herein, the workflow can comprise determining a dose of a compound that induces a change in the movement of a fluorescent target protein in a live cell comprising: (a) contacting a plurality of samples with the compound, (i) where each sample comprises a population of live cells, (ii) where the live cells comprise the fluorescent target protein, and (iii) where the plurality of samples are contacted with distinct concentrations of the compound across a range of compound concentrations; (b) tracking the movement of individual fluorescent target proteins in a plurality of live cells of a sample at a plurality of time points, wherein said tracking comprises: (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 fluorescent target proteins in the live cells, wherein the subset of the of the fluorescent target proteins are present in about 30 to about 80 live cells illuminated in the field of view of the sample plane, depending on the specific cell type being used, e.g., for U2OS cells the range is about 30 to about 40 cells per FOV, while for HCT116 cells the range is about 50 to about 80 cells given their differences in area; (ii) detecting the fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device; and (c) determining the rate at which changes in the movement of the fluorescent target protein occur in the presence of the compound; (d) repeating steps (b)-(c) for each of the plurality of samples

[0189] 11 across the range of compound concentrations; wherein the rate at which changes in the movement of the fluorescent target protein occur in the presence of the compound indicates the rate of emergence of a biological interaction between a compound and a target.

[0190]

[0161] In certain implementations of the OLS Kinetic htSMT binding workflows described herein, the workflow can comprise determining a dose of a compound that induces a change in the movement of a fluorescent target protein in a live cell comprising: (a) contacting a plurality of samples with the compound, (i) where each sample comprises a population of live cells, (ii) where the live cells comprise the fluorescent target protein, and (iii) where the plurality of samples are contacted with distinct concentrations of the compound across a range of compound concentrations; (b) tracking the movement of individual fluorescent target proteins in a plurality of live cells of a sample at a plurality of time points, wherein said tracking comprises: (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 fluorescent target proteins in the live cells, wherein the subset of the of the fluorescent target proteins comprises a range of about 1000 to about 1,000,000 proteins, wherein the number of proteins within the subset depend on the expression level of the protein of interest as well as the dye concentration being deemed adequate to label a subset protein for robust SMT, both of which can be calculated and / or configured by one of skill in the art based on the disclosure of the instant application; (ii) detecting the fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device; and (c) determining the rate at which changes in the movement of the fluorescent target protein occur in the presence of the compound; (d) repeating steps (b)-(c) for each of the plurality of samples across the range of compound concentrations; wherein the rate at which changes in the movement of the fluorescent target protein occur in the presence of the compound indicates the rate of emergence of a biological interaction between a compound and a target.

[0191]

[0162] In certain implementations of the OLS Kinetic htSMT binding workflows described herein, the workflow can comprise determining a dose response of a compound that induces a change in the movement of a fluorescent target protein in a live cell comprising: (a) contacting a plurality of samples with the compound, (i) where each sample comprises a population of live cells, (ii) where the live cells comprise the fluorescent target protein, and (iii) where the plurality of samples are contacted with distinct concentrations of the compound across a range of compound concentrations; (b) tracking the movement of individual fluorescent target proteins in a plurality of live cells of a sample at a plurality of time points, wherein said tracking comprises: (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 fluorescent target proteins in the live cells; (ii) detecting the fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device; and (c) determining the rate at which changes in the movement of the fluorescent target protein occur in the presence of the compound, wherein the average change in movement of the fluorescent target protein in the presence of the compound is about 5 to about 10% relative to baseline movements under DMSO conditions; and (d) repeating steps (b)-(c) for each of the plurality of samples across the range of compound concentrations; wherein the rate at which changes in the movement of the fluorescent target protein occur in the presence of the compound indicates the rate of emergence of a biological interaction between a compound and a target.

[0192]

[0163] In certain implementations of the OLS Kinetic htSMT binding workflows described herein, the workflow can comprise determining a dose response of a compound that induces a change in the movement of a fluorescent target protein in a live cell comprising: (a) contacting a plurality of samples with the compound, (i) where each sample comprises a population of live cells, (ii) where the live cells comprise the fluorescent target protein, and (iii) where the plurality of samples are contacted with distinct concentrations of the compound across a range of compound concentrations; (b) tracking the movement of individual fluorescent target proteins in a plurality of live cells of a sample at a plurality of time points, wherein said tracking comprises: (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 fluorescent target proteins in the cells; (ii) detecting the fluorescence from one or more of the fluorescent target proteins in the sample plane via a detector device; (iii) wherein said tracking comprises detecting the fluorescence from a plurality of the fluorescent target proteins in a field of view of the sample plane at a rate of about 10,000 to about 18,000 per day per system; and (c) determining the rate at which changes in the movement of the fluorescent target protein occur in the presence of the compound; (d) repeating steps (b)-(c) for each of the plurality of samples across the range of compound concentrations; wherein the rate at which changes in the movement of the fluorescent target protein occur in the presence of the compound indicates the rate of emergence of a biological interaction between a compound and a target.

[0193]

[0164] In certain implementations of the OLS Kinetic htSMT binding workflows described herein, the workflow can comprise determining a dose of a compound that induces a change in the movement of a fluorescent target protein in a live cell comprising: (a) contacting a plurality of samples with the compound, (i) where each sample comprises a population of live cells, (ii) where the live cells comprise the fluorescent target protein, and (iii) where the plurality of samples are contacted with distinct concentrations of the compound across a range of compound u concentrations; (b) tracking the movement of individual fluorescent target proteins in a plurality of live cells of a sample at a plurality of time points, wherein said tracking comprises: (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 fluorescent target proteins in the live cells; (ii) detecting the fluorescence from one or more of the fluorescent target proteins in the field of view in the sample plane via a detector device, wherein detection based on a single field of view is associated with a z-factor of > 0.5; and (c) determining the rate at which changes in the movement of the fluorescent target protein occur in the presence of the compound; (d) repeating steps (b)-(c) for each of the plurality of samples across the range of compound concentrations; wherein the rate at which changes in the movement of the fluorescent target protein occur in the presence of the compound indicates the rate of emergence of a biological interaction between a compound and a target.

[0194]

[0165] In certain implementations of the OLS Kinetic htSMT binding workflows described herein, the workflow can comprise use of a microscopy system configured to determine the rate of emergence of a biological interaction between a compound and a target in a live cell comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of live cells, and where the live cells comprise the fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of the fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in the sample plane, wherein a subset of the fluorescent target proteins in the sample are disposed in a field of view in the sample plane, and wherein the subset of the of the fluorescent target proteins are present in about 30 to about 80 live cells illuminated in the field of view of the sample plane, depending on the specific cell type being used, e.g., for U2OS cells the range is about 30 to about 40 cells per FOV, while for HCT116 cells the range is about 50 to about 80 cells given their differences in area; (d) a detector device for monitoring the light-based response from the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from sources other than the sample plane in which the fluorescent target proteins are disposed to thereby track the position of the fluorescent target proteins at a plurality of time points; and (ii) track the movement of individual fluorescent target proteins, (e) a memory; and (f) a processor in communication with the memory and the detector device, where the processor is capable of determining the change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0195]

[0166] In certain implementations of the OLS Kinetic htSMT binding workflows described herein, the workflow can comprise use of a microscopy system configured to determine the rate of emergence of a biological interaction between a compound and a target in a live cell comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and where the cells comprise the fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of the fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in the sample plane, wherein a subset of the fluorescent target proteins in the sample are disposed in a field of view in the sample plane; and wherein the subset of the of the fluorescent target proteins comprises a range of about 1000 to about 1,000,000 proteins, wherein the number of proteins within the subset depend on the expression level of the protein of interest as well as the dye concentration being deemed adequate to label a subset protein for robust SMT, both of which can be calculated and / or configured by one of skill in the art based on the disclosure of the instant application; (d) a detector device for monitoring the light-based response from the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from sources other than the sample plane in which the fluorescent target proteins are disposed to thereby track the position of the fluorescent target proteins at a plurality of time points ; and (ii) track the movement of individual fluorescent target proteins, (e) a memory; and (f) a processor in communication with the memory and the detector device, where the processor is capable of determining the change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0196]

[0167] In certain implementations of the OLS Kinetic htSMT binding workflows described herein, the workflow can comprise use of a microscopy system configured to determine the rate of emergence of a biological interaction between a compound and a target in a live cell comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and where the cells comprise the fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of the fluorescent target proteins in the sample;

[0197] (c) an objective for focusing the light beam on the sample in the sample plane, wherein a subset of the fluorescent target proteins in the sample are disposed in a field of view in the sample plane;

[0198] (d) a detector device for monitoring the light-based response from the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from sources other than the sample plane in which the fluorescent target proteins are disposed to thereby track the position of the fluorescent target proteins at a plurality of time points; and (ii) track the movement of individual fluorescent target proteins, wherein the average change in movement of the fluorescent target protein in the presence of the compound is about 5 to about 10% relative to baseline movements under DMSO conditions, (e) a memory; and (f) a processor in communication with the memory and the detector device, where the processor is capable of determining the change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0199]

[0168] In certain implementations of the OLS Kinetic htSMT binding workflows described herein, the workflow can comprise use of a microscopy system configured to determine the rate of emergence of a biological interaction between a compound and a target in a live cell comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of cells, and where the cells comprise the fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of the fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in the sample plane, wherein a plurality of the fluorescent target proteins in the sample are disposed in the sample plane; (d) a detector device for monitoring the light-based response from the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from sources other than the sample plane in which the fluorescent target proteins are disposed to thereby track the position of the fluorescent target proteins at a plurality of time points; and (ii) track the movement of individual fluorescent target proteins, wherein said tracking comprises detecting the fluorescence from a plurality of the fluorescent target proteins in a field of view of the sample plane at a rate of about 10,000 to about 18,000 per day per system; (e) a memory; and (I) a processor in communication with the memory and the detector device, where the processor is capable of determining the change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0200]

[0169] In certain implementations of the OLS Kinetic htSMT binding workflows described herein, the workflow can comprise use of a microscopy system configured to determine the rate of emergence of a biological interaction between a compound and a target in a live cell comprising: (a) a stage for supporting a sample, wherein the sample comprises a population of live cells, and where the live cells comprise the fluorescent target protein; (b) a light source for emitting a light beam capable of inducing a light-based response from a plurality of the fluorescent target proteins in the sample; (c) an objective for focusing the light beam on the sample in the sample plane, wherein a subset of the fluorescent target proteins in the sample are disposed in a field of view in the sample plane; (d) a detector device for monitoring the light-based response from the fluorescent target proteins in the presence of the compound, wherein the detector device is configured to: (i) block light received from sources other than the sample plane in which the fluorescent target proteins are disposed to thereby track the position of the fluorescent target proteins at a plurality of time points; and (ii) track the movement of individual fluorescent target proteins; and (iii) achieve, based on a single field of view, a z-factor of > 0.5; (e) a memory; and (f) a processor in communication with the memory and the detector device, where the processor is capable of determining the change in the movement of the fluorescent target protein in the presence of the compound relative to the absence of the compound.

[0201] 5. EXAMPLES

[0202] Example 1: Optical Line Scanning High Throughput Single Molecule Tracking

[0203] A. Introduction

[0204]

[0170] This example describes industrial scale OLS htSMT techniques, systems incorporating such OLS htSMT techniques, hardware and software related to such OLS htSMT techniques, as well as 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. The OLS htSMT techniques described herein exhibit specific, robust, and reproducible results. The OLS htSMT techniques described herein can be used for a variety of applications including, but not limited to, classical drug discovery activities, such as compound library screening and the elucidation of SAR. Importantly, the OLS htSMT techniques described herein can be used to characterize both known and novel pathway contributions to interaction networks, such as protein signaling interaction networks.

[0205] B. Results a. Creation and Validation of an htSMT System

[0206]

[0171] A robotic system capable of handling reagents, collecting high-quality, fast SMT image series, processing time-ordered raw images to yield molecular trajectories, and extracting features of biological interest within defined cellular compartments was developed (FIG. 1). To examine htSMT system performance various measures were performed indicating that the image acquisition systems and workflows of the present disclosure are amenable to robust htSMT analysis. b. Image Acquisition

[0207]

[0172] Unless otherwise stated, all image acquisition using SMT was performed on a custom- built microscope, motorized stage, stage top environmental chamber, quad-band filter cube (Chroma), custom laser engine with 405 nm and 561 nm wavelengths to the back focal plane of the objective. Fluorescence emission was passed through a high-speed filter wheel (Finger Lakes Instruments) and collected with a backlit CMOS camera (Hammamatsu Orca Fusion run in lightsheet mode). Images were acquired with a 60X 1.27 NA water immersion objective (Nikon). Environmental chamber was set to 37o Celsius, 95% humidity, and 5% CO2. In certain implementations, each pixel is exposed for 400 microseconds and the full region of interest (ROI) takes 9 milliseconds total. The galvo position can then be reset in 1 millisecond, e.g., with the laser turned off, before recording another image. In such an implementation, 100 frames can be recorded per second. Additionally, or alternatively, a second setting can be employed that uses a smaller ROI in order to record 200 frames per second with the same 400 microsecond / pixel exposure and a 4 millisecond image recording time. Additionally, or alternatively, the galvo reset could be done faster. c. Image Analysis

[0208]

[0173] Image acquisition produced one JF549 movie and one Hoechst per field of view. The JF549 movie can be used to track the movement of individual JF549 molecules, while the Hoechst movie can be used for nuclear segmentation. Tracking can be accomplished in three sequential steps - detection, subpixel localization, and linking - using a combination of existing methods. Briefly, spots can be detected using a generalized log likelihood ratio detector. After detection, the estimated position of each emitter can be refined to subpixel resolution using Levenberg- Marquardt fitting with an integrated 2D Gaussian spot model starting from an initial guess afforded by the radial symmetry method. Detected spots can be linked into trajectories using a custom modification of a hill-climbing algorithm. The same detection, subpixel localization, and linking settings can be used for all movies.

[0209]

[0174] For nuclear segmentation, all frames of the Hoechst movie can be averaged to generate a mean projection. This mean 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.

[0210]

[0175] To recover movement information from trajectories, state arrays can be used. For example, a Bayesian inference approach, with the “RBME” likelihood function and a grid of 100 diffusion coefficients from 0.01 to 100.0 pm2 s-1 and 31 localization error magnitudes from 0.02 to 0.08 pm can be used. After inference, localization error can be marginalized out to yield a one-

[0211] 11 dimensional distribution over the diffusion coefficient for each field of view. For single-cell analysis, SMT and nuclear segmentation can be performed, e.g., on a mixture of U2OS cells bearing H2B-HaloTag, HaloTag-CaaX, or free HaloTag. The marginal likelihood of each of a set of 100 diffusion coefficients on the set of trajectories within each segmented nucleus can then be evaluated. These marginal likelihood functions can be clustered with k-means, and the marginal likelihood functions for each cell can be ordered by their cluster index to produce the heat map. To estimate the fraction bound (fbound), the state array posterior distribution below 0.1 pm2 s-1 can be integrated. To estimate the free diffusion coefficient (Dfiee), the mean of the posterior distribution above 0.1 pm2 s-1 can be computed. d. Data Analysis

[0212]

[0176] Tracking results from the automated processing pipeline can be analyzed using KNIME or Spotfire (TIBCO). Individual fbound or Dfreemeasurements can be associated with experimental metadata and aggregated by condition. Change in fbound can be calculated as the difference between the fbound of each well and the median fbound of DMSO in the same plate. Wells that had no cells in the field of view or in which the field of view was out of focus can be omitted from further analysis. Compounds can be assessed for assay interference using the median fluorescence intensity of the tracking channel and omitted if it they are more than 3 standard deviations higher than the median intensity of the DMSO wells. Similarly, plates where the active and negative controls could not be clearly resolved or where the significantly deviated from the performance of the rest of the screen can be removed from further analysis. Finally, compound with a variance more than three standard deviations higher than the average compound variance can be removed from downstream analysis. Z’-factor between the active controls on a plate and DMSO can be calculated. EC50 values can be calculated in Prism (GraphPad) by first log-transforming the molecule concentrations and then fitting to a four-parameter logistic curve. e. Clustering Active Molecules

[0213]

[0177] Chemical structure-based clustering can be performed on molecules identified as active. Molecular frameworks can be computed as known in the art and as implemented in Pipeline Pilot. Molecular frameworks can be clustered using functional class fingerprints (FCFP 4), e.g., with a similarity threshold cut-off of 0.3 Tanimoto distance. f. Kinetic Experiments

[0214]

[0178] Cells can be seeded into a 384-well plate the day before, dyed, and washed as described above. 1 well with a plurality of FOVs per well can be taken as a baseline reading. Then, while imaging, compound can be manually or robotically added to each well to a final concentration of 100 nM. Data can then be collected for the wells. A pause can be included between each FOV such that the entire imaging regime covers the assay window. Change in fbound can be determined perwell relative to t=0.

[0215]

[0179] For assays extending to 4 hours, the plate can be imaged twice with a plurality of FOVs per well with different FOV locations per readthrough to prevent photobleaching from impacting data. g. Residence Time Imaging

[0216]

[0180] Sample preparation and execution of residence time imaging experiments can be conducted in a similar manner to the single molecule tracking assay described above with a few exceptions. Samples can be dyed with 1 - 10 pM JF549 (Promega) and 50 nM Hoechst 33342 for an hour. A plurality of frames per field of view can be collected with a camera integration time set to the desired time msec, and laser sources reduced to the desired mW at the objective. During image acquisition, lasers can be on continuously. Compound incubation can range from 1 to 4 hours. h. Residence Time Analysis

[0217]

[0181] Image processing, including spot detection, localization, and track reconnection can be performed using the same methods described above. Because residence time imaging selectively tracks slow-diffusing molecules, individual localizations can be limited in the distance of the maximum displacement for individual jump reconnections. Sets of trajectories for each field of view can be binned into 1-CDF distributions as previously described and fit to a two exponent decay model CDF(t) = A(Fe~kfastt+ (1 — F)e~kslowti. Fluorescence Recovery After Photobleaching

[0218]

[0182] Images can be acquired on a custom-built OLSmicroscope as described herein with a Spectra Light Engine RS-232. Stimulation can be directed using a miniscanner coupled with a Coherent OBIS 561nm 100 mW laser. All imaging can be performed using a 60X 1.27 NA water immersion objective (Nikon). All experiments can be performed at 37o C. For FRAP experiments, Cells can be seeded into a 384-well plate the day before, labeled with 50 nM HTL-JF549, and washed as described above. Compound can be added to 100 nM final an hour before imaging. Then, a pre-bleach image can be acquired by averaging 10 consecutive images. Then 8-10 regions can then be bleached (2 background, 6-8 cells) and 2 regions in cells can be unbleached. Regions that are bleached are bleached at 10% power without scanning. For the next 30 seconds, an image can be acquired every 200 ms, then every 1 second for 2 minutes. The background- subtracted average intensity can be measured in the region of interest overtime and normalized to the average of the fluorescence in the baseline images, then normalized to the unbleached regions to account for readout-induced photobleaching of fluorophores. Data from a plurality of cells can be pooled per experiment for three biological experiments.

Claims

What is claimed is:

1. A method comprising: receiving a sequence of microscopy images visualizing fluorescently-labeled cellular components in live cells, each image comprising an array of pixels; classifying, for each image and using at least one machine learning model, each pixel as corresponding to one of a plurality of categories, the at least one machine learning model being trained using a plurality of different loss functions that rotate during training; and providing data characterizing the classifications of the pixels to a consuming application or process.

2. The method of claim 1, wherein the categories comprise: a nucleus, a nuclear edge, a nucleolus, extra-cellular debris and / or background.

3. The method of claim 2 further comprising; assigning each pixel to one of the categories; and connecting adjacent pixels having a same category.

4. The method of claim 3 further comprising: associating disjoint pixels within a hierarchical biological feature.

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

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

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

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

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

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

11. The method of any of claims 1 to 9 further comprising: staining the live cells using Nuclear Red dye.

12. The method of any of the preceding claims, 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. The method of any of the preceding claims further comprising: training the at least one machine learning model using images with manually annotated pixels.

15. The method of any of the preceding claims 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. The method of any of the preceding claims, wherein the providing of 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 physical persistence, loading at least a portion of the data characterizing the classified pixels in memory, or transmitting at least a portion of the data characterizing the classified pixels over a network to a remote computing device.

18. A system comprising: at least one data processor; and memory storing instructions, which when executed by at least one data processor, result in operations for implementing a method as in any of the preceding claims.

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, implement a method as in any of claims 1 to claim 17.

20. A system comprising: means for receiving a sequence of microscopy images visualizing fluorescently-labeled cellular components in live cells, each image comprising an array of pixels; means for classifying, for each image and using at least one machine learning model, each pixel as corresponding to one of a plurality of categories, the at least one machine learning model being trained using a plurality of different loss functions that rotate during training; and means for providing data characterizing the classifications of the pixels to a consuming application or process.

Images