Automated flow cytometry preparation and collection systems and methods of use thereof

Abstract

A robotic system for automated sample preparation is provided. A system of interest includes: a plurality of sample processing modules; a plurality of robotic components integrated with the sample processing module; a processor including a memory operably coupled to the processor, where the memory includes instructions stored thereon that, when executed by the processor, cause the processor to control the sample processing module and the robotic assembly, to: operate the sample processing module and robotic assembly to prepare a plurality of samples for flow cytometry analysis; and an operable connection between the processor, the sample processing module, and the plurality of robotic components. Systems according to certain embodiments also include a flow cytometer, where the plurality of robotic components are further integrated with the flow cytometer, where the memory further includes instructions stored thereon, the instructions, when executed by the processor, cause the processor to control the sample processing module, the robotic assembly, and the flow cytometer to: load each prepared sample of the plurality of samples into the flow cytometer; and operating the flow cytometer to analyze each prepared sample of the plurality of samples, wherein the operable connection operably connects the processor, the sample processing module, the flow cytometer, and the plurality of robotic components together. Methods of use and configurations or designs of the system are also provided.

Description

Cross-references to related applications

[0001] Pursuant to 35 USC § 119 (e), this application claims priority to the filing date of U.S. Provisional Patent Application Serial No. 63 / 719,515, filed November 12, 2024, the disclosure of which is incorporated herein by reference in its entirety. Background Technology

[0002] Characterization of analytes in biofluids has become an important part of biological research, medical diagnostics, and the assessment of overall patient health. Detection of analytes in biofluids (such as human blood or blood-derived products) can provide results that can play a role in determining treatment plans for patients with various conditions.

[0003] Flow cytometry is a technique used to characterize and often sort biological materials, such as cells in a blood sample or particles of interest in another biological or chemical sample. A flow cytometer typically includes a sample reservoir for receiving a fluid sample (e.g., a blood sample) and a sheath fluid reservoir for containing sheath fluid. The flow cytometer delivers particles (including cells) from the fluid sample as a cell stream to a flow cell, while also guiding the sheath fluid into the flow cell. To characterize the components of the flow stream, the flow stream is irradiated with light. Changes in the material in the flow stream (e.g., the presence of morphology or fluorescent labeling) can cause changes in the observed light, and these changes can be used for characterization and separation. To characterize the components in the flow stream, light must strike the flow stream and be collected. The light source in a flow cytometer can be varied and may include one or more broad-spectrum lamps, light-emitting diodes, and single-wavelength lasers. The light source is aligned with the flow stream, and the optical response from the irradiated particles is collected and quantified.

[0004] Separation of biological particles is achieved by adding sorting or collection capabilities to flow cytometers. Particles detected in a separated stream possessing one or more desired properties are individually separated from the sample stream by mechanical or electrical removal. A common flow cytometry sorting technique utilizes droplet sorting, where a flow stream containing linearly separated particles is broken down into droplets. Droplets containing the particles of interest are charged and deflected into a collection tube by passing through an electric field. Typically, linearly separated particles in the flow are characterized as they pass through an observation point located directly below the nozzle tip. Once a particle is determined to meet one or more desired criteria, the time it takes for it to reach the droplet separation point and separate from the flow as a droplet can be predicted. Ideally, a brief charge is applied to the flow stream just before the droplet containing the selected particle is about to separate from the flow stream, and then grounded immediately after droplet separation. The droplet to be sorted remains charged as it detaches from the flow stream, while all other droplets are uncharged.

[0005] Typically, samples (e.g., biological samples) require preparation before being characterized or analyzed by flow cytometry, including processes such as staining and / or washing. These preparation steps can be time-consuming and labor-intensive, thus requiring operators or technicians to perform or supervise them. Furthermore, these preparation steps, performed or supervised by technicians, can introduce biases into the final flow cytometry analysis results of such prepared samples. Summary of the Invention

[0006] Therefore, the inventors recognized the need for a robotic system capable of fully automated sample preparation and fully automated flow cytometry analysis. Specifically, a modular robotic system was needed that could automate sample preparation for flow cytometry assays, providing an unattended solution for sample staining, washing, and collection, with the ability to perform multiple different experiments simultaneously. The embodiments of this disclosure address this need. The embodiments of this disclosure overcome the limitations of the prior art by providing a fully unattended solution—a system configured to prepare and analyze flow cytometry samples without direct user supervision or manipulation. Furthermore, these improvements over the prior art enhance the consistency and accuracy of sample preparation and improve the efficiency and cost-effectiveness of sample preparation and flow cytometry analysis of the prepared samples.

[0007] This disclosure includes aspects of robotic systems for automating sample preparation. A system according to certain embodiments includes: a plurality of sample processing modules; a plurality of robotic components integrated with the sample processing modules; a processor including a memory operatively coupled to the processor, wherein the memory contains instructions stored thereon that, when executed by the processor, cause the processor to control the sample processing modules and the robotic components to: operate the sample processing modules and the robotic components to prepare a plurality of samples for flow cytometry analysis; and an operative connection between the processor, the sample processing modules, and the plurality of robotic components. A system according to certain embodiments also includes a flow cytometer, wherein the plurality of robotic components are further integrated with the flow cytometer, wherein the memory also contains instructions stored thereon that, when executed by the processor, cause the processor to control the sample processing modules, the robotic components, and the flow cytometer to: load each prepared sample of the plurality of samples into the flow cytometer; and operate the flow cytometer to analyze each prepared sample of the plurality of samples, wherein the operative connection operatively connects the processor, the sample processing modules, the flow cytometer, and the plurality of robotic components.

[0008] A method for preparing samples for flow cytometry analysis using a system according to this disclosure is also provided. The method according to certain embodiments includes introducing a plurality of samples into a first sample processing module of a plurality of sample processing modules of a system, wherein the system includes: the plurality of sample processing modules; a plurality of robotic components integrated with the sample processing modules; a processor including a memory operatively coupled to the processor, wherein the memory contains instructions stored thereon, the instructions, when executed by the processor, causing the processor to control the sample processing modules and the robotic components to: operate the sample processing modules and the robotic components to prepare a plurality of samples for flow cytometry analysis; and operatively connect the processor, the sample processing modules, and the plurality of robotic components; provide sample preparation instructions to the system; and initiate the system to automatically prepare samples according to the sample preparation instructions. In some cases, the system further includes a flow cytometer, and the sample preparation instructions further include instructions for: loading the prepared samples into the flow cytometer using the robotic components; performing flow cytometry analysis on the samples using the flow cytometer; and removing the samples from the flow cytometer using the robotic components.

[0009] A computer-executed method for preparing samples for flow cytometry analysis using a system according to this disclosure is also provided. According to certain embodiments, the computer-executed method includes receiving a plurality of samples into a first sample processing module of a plurality of sample processing modules of a system, wherein the system includes: the plurality of sample processing modules; a plurality of robotic components integrated with the sample processing modules; a processor including a memory operatively coupled to the processor, wherein the memory contains instructions stored thereon that, when executed by the processor, cause the processor to control the sample processing modules and the robotic components to: operate the sample processing modules and the robotic components to prepare a plurality of samples for flow cytometry analysis; and operatively connect the processor, the sample processing modules, and the plurality of robotic components; and control the robotic components, according to the instructions stored in the memory, to manipulate the plurality of samples using the sample processing modules to prepare the plurality of samples. In some cases, the system also includes a flow cytometer, and the sample preparation instructions further include instructions for: loading the prepared sample into the flow cytometer using a robotic component; performing flow cytometry analysis on the sample using the flow cytometer; and removing the sample from the flow cytometer using a robotic component. Attached Figure Description

[0010] A better understanding of this disclosure can be achieved by reading the following detailed description in conjunction with the accompanying drawings. The drawings include the following figures:

[0011] Figure 1A Exemplary workflow steps for sample preparation and flow cytometry analysis performed automatically by the system implementation are described and compared with similar steps performed manually by laboratory technicians. Figure 1B-1C Other examples of manual and automated workflows are shown. Figure 1D Exemplary microplates and exemplary reagent tanks are shown, both of which are used in the system of this embodiment. Figure 1E This demonstrates another example of an automated workflow for sample preparation and flow cytometry analysis of the prepared samples. Figure 1F-1K An exemplary system for automated sample preparation according to this embodiment is shown. Figure 1L-1M An exemplary system for automated sample preparation according to another embodiment is shown. Figure 1N-1P An exemplary system for automated sample preparation according to yet another embodiment is shown. Figure 1Q An exemplary sample processing module that can be integrated into the system disclosed herein is shown. Figure 1R A schematic diagram of an exemplary system according to this embodiment is shown.

[0012] Figure 2 A flow cytometry system according to certain embodiments is shown.

[0013] Figure 3 A particle sorter is depicted with an enabled image according to certain implementation schemes.

[0014] Figure 4 A functional block diagram of a particle analysis system according to certain implementation schemes is depicted.

[0015] Figure 5 A functional block diagram of an example control system according to certain implementation schemes is depicted.

[0016] Figures 6A-6B A schematic diagram of a particle sorting system according to certain implementation schemes is depicted.

[0017] Figures 7A-7C Flowcharts are depicted for automated sample preparation methods for flow cytometry analysis according to different implementation schemes.

[0018] Figure 8 Aspects of a computer control system according to certain implementation schemes are described. Detailed Implementation

[0019] This disclosure includes aspects of robotic systems for automating sample preparation. A system according to certain embodiments includes: a plurality of sample processing modules; a plurality of robotic components integrated with the sample processing modules; a processor including a memory operatively coupled to the processor, wherein the memory contains instructions stored thereon that, when executed by the processor, cause the processor to control the sample processing modules and the robotic components to: operate the sample processing modules and the robotic components to prepare a plurality of samples for flow cytometry analysis; and an operative connection between the processor, the sample processing modules, and the plurality of robotic components. A system according to certain embodiments also includes a flow cytometer, wherein the plurality of robotic components are further integrated with the flow cytometer, wherein the memory also contains instructions stored thereon that, when executed by the processor, cause the processor to control the sample processing modules, the robotic components, and the flow cytometer to: load each prepared sample of the plurality of samples into the flow cytometer; and operate the flow cytometer to analyze each prepared sample of the plurality of samples, wherein the operative connection operatively connects the processor, the sample processing modules, the flow cytometer, and the plurality of robotic components.

[0020] A method for preparing samples for flow cytometry analysis using a system according to this disclosure is also provided. The method according to certain embodiments includes introducing a plurality of samples into a first sample processing module of a plurality of sample processing modules of a system, wherein the system includes: the plurality of sample processing modules; a plurality of robotic components integrated with the sample processing modules; a processor including a memory operatively coupled to the processor, wherein the memory contains instructions stored thereon, the instructions, when executed by the processor, causing the processor to control the sample processing modules and the robotic components to: operate the sample processing modules and the robotic components to prepare a plurality of samples for flow cytometry analysis; and operatively connect the processor, the sample processing modules, and the plurality of robotic components; provide sample preparation instructions to the system; and initiate the system to automatically prepare samples according to the sample preparation instructions. In some cases, the system further includes a flow cytometer, and the sample preparation instructions further include instructions for: loading the prepared samples into the flow cytometer using the robotic components; performing flow cytometry analysis on the samples using the flow cytometer; and removing the samples from the flow cytometer using the robotic components.

[0021] A computer-executed method for preparing samples for flow cytometry analysis using a system according to this disclosure is also provided. According to certain embodiments, the computer-executed method includes receiving a plurality of samples into a first sample processing module of a plurality of sample processing modules of a system, wherein the system includes: the plurality of sample processing modules; a plurality of robotic components integrated with the sample processing modules; a processor including a memory operatively coupled to the processor, wherein the memory contains instructions stored thereon that, when executed by the processor, cause the processor to control the sample processing modules and the robotic components to: operate the sample processing modules and the robotic components to prepare a plurality of samples for flow cytometry analysis; and operatively connect the processor, the sample processing modules, and the plurality of robotic components; and control the robotic components, according to the instructions stored in the memory, to manipulate the plurality of samples using the sample processing modules to prepare the plurality of samples. In some cases, the system also includes a flow cytometer, and the sample preparation instructions further include instructions for: loading the prepared sample into the flow cytometer using a robotic component; performing flow cytometry analysis on the sample using the flow cytometer; and removing the sample from the flow cytometer using a robotic component.

[0022] Before describing this disclosure in more detail, it should be understood that this disclosure is not limited to the specific embodiments described, and therefore variations are possible. It should also be understood that the terminology used herein is for the purpose of describing specific embodiments only and is not intended to be limiting, as the scope of this disclosure will be limited only by the appended claims.

[0023] Where a numerical range is provided, it should be understood that every intermediate value between the upper and lower limits of the range (unless otherwise explicitly stated by the context, up to one-tenth of the lower limit unit) and any other stated value or intermediate value within the stated range are included within this disclosure. The upper and lower limits of these smaller ranges may be independently included within that smaller range and also within this disclosure, but are subject to any specific exclusions within the stated range. When a stated range includes one or two limits, the range excluding one or both of the included limits is also included in this disclosure.

[0024] The numerical ranges given in this article are preceded by the term "approximately". The term "approximately" is used to provide literal support for the exact number that follows, as well as numbers that are close to or approximate to the number following the term. In determining whether a number is close to or approximates a specifically listed number, an unlisted number that is close to or approximates can be a number that provides a basic equivalent to the specifically listed number in the presented context.

[0025] 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 to which this disclosure pertains. Representative illustrative methods and materials are now described, although any methods and materials similar to or equivalent to those described herein may also be used in the practice or testing of this disclosure.

[0026] All publications and patents referenced in this specification are incorporated herein by reference as if each individual publication or patent were specifically and individually indicated to be incorporated by reference, and are incorporated herein by reference to disclose and describe the methods and / or materials associated with the publication reference. References to any publication refer to its disclosure prior to the filing date and should not be construed as an admission that the invention is not entitled to precede such publication by virtue of prior disclosure. Furthermore, the publication dates provided may differ from the actual publication dates, which may require independent verification.

[0027] It should be noted that, as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural indicators unless the context clearly indicates otherwise. It should also be noted that claims may be drafted to exclude any optional elements. Therefore, this statement is intended to serve as a precondition for combining elements of the claim using exclusive terms such as “unique,” ​​“only,” or using a negative limiting term.

[0028] As will be apparent to those skilled in the art upon reading this disclosure, each of the various embodiments described and illustrated herein has discrete components and features that can be readily separated from or combined with features of any of the other several embodiments without departing from the scope or spirit of this disclosure. Any described method may be performed in the order of the described events or in any other logically possible order.

[0029] For the sake of grammatical fluency and functional interpretation, systems and methods have been or will be described. However, it should be clearly understood that, unless expressly stated in accordance with 35 USC §112, the claims should not be construed as necessarily being limited in any way by the interpretation of “apparatus” or “step”, but should be given the full meaning and scope of the definitions provided by the claims under the doctrine of equivalents, and where the claims are expressly stated in accordance with 35 USC §112, they should be given all legal equivalents under 35 U.SC §112.

[0030] system

[0031] Overview

[0032] This disclosure includes robotic systems for automating sample preparation. "Automated sample preparation" means that the system of the present invention is configured to automatically prepare samples, i.e., samples for flow cytometry analysis. "Automatic" means, for example, that the system is configured to perform sample preparation in a walk-away manner, meaning that a user (e.g., a system operator) does not need to interact with, manipulate, or otherwise control the samples or system because the system automatically performs the sample preparation. In other words, the user or system operator can leave the system unattended, as the system reliably and safely prepares samples for flow cytometry analysis. In some cases, the system is configured to automatically prepare multiple samples for flow cytometry analysis. In some cases, the system is configured to automatically and continuously prepare multiple samples for flow cytometry analysis. Continuous preparation of multiple samples means that the system implementation can be configured to stagger multiple different sample preparation workflows. In some cases, continuous preparation of multiple samples means that the system prepares more than one sample at a time, i.e., some sample preparation modules are used to prepare a first sample, while other sample preparation modules are used to simultaneously prepare a second sample.

[0033] Sample preparation refers to any desired manipulation of a sample to enable analysis of the sample or certain aspects thereof by flow cytometry. In some cases, sample preparation includes sample staining and / or washing. Examples of other sample preparation steps are provided herein. In embodiments, sample preparation includes using any of the multiple sample processing modules included in embodiments of the system of the present invention. In embodiments, sample processing modules include, for example, one or more liquid processors, centrifuges, or incubators. Other examples of such sample processing modules are provided herein.

[0034] A “robotic” system refers to a system comprising multiple robotic components, i.e., components capable of independently grasping and moving objects (e.g., sample plates). In implementations, robotic components include, for example, robotic arms, mechanical grippers, or grippers. Other examples of robotic components are provided herein. A “modular” system refers to an implementation of the system that can be configured to include different combinations of sample preparation modules and / or different combinations of robotic components. As described herein, standard laboratory equipment is used in the system; therefore, a modular system can be configured with different laboratory equipment or different numbers of laboratory equipment. In some cases, after the system is deployed (e.g., installed in a laboratory environment), the sample handling modules and / or robotic components can be modified or altered (e.g., replaced).

[0035] This disclosed system implementation integrates robotics with commonly used stand-alone laboratory equipment to automate sample preparation and collection for flow cytometry assays. In this implementation, a robotic arm interacts with integrated laboratory equipment components to mimic human actions in a laboratory space, replicating processes such as antibody dilution, cell staining with fluorescently conjugated antibodies, sample washing, and sample resuspension before moving the sample onto the flow cytometer for automated data acquisition. While the system is flexible enough to prepare samples in tubes and / or deep-well plates, its primary output is fully stained flow cytometry samples, the format of which can be configured as needed, for example, in the form of a standard 96-well deep-well plate. In some cases, multiple 96-well plates can be prepared for a given flow cytometry experiment, allowing users (i.e., system operators, such as scientists) to rapidly increase weekly testing throughput. This integrated system invention improves experimental capability, promotes workflow standardization, and enhances reproducibility by automating manual, error-prone steps (i.e., those that could lead to inter-user variability when not implemented with the system of this disclosure). By implementing specially selected, programmed robots to simulate human interaction in a laboratory environment, the implementation of the system disclosed herein offers a significant advantage in enabling unattended flow cytometry sample preparation and collection without human monitoring, ultimately extending and maximizing work hours over any given time period (e.g., a work week).

[0036] In some cases, the system of the present invention is configured and operated as follows: (1) A static robotic arm and a second robotic arm located on a moving track are positioned to access all critical laboratory equipment components of the integrated system embodiment. The static robotic arm is equipped with a dedicated gripper to enable vertical transfer of sample plates into and out of the centrifuge unit. (2) Each device is connected to a larger system to receive and send commands via a single scheduling software. (3) The scheduling software is programmed to appropriately combine the processes performed by the two robotic arms on the integrated laboratory equipment (e.g., liquid processor, barcode scanner, centrifuge, washing station, incubator, sample storage / stacking unit, and / or flow cytometer) to simulate the manual operation of flow cytometry assays. Given the modular nature of the system embodiments of the present disclosure, any or all of the equipment components of the system can be selected and combined for assay runs. (4) The liquid processor is commanded by the scheduling software to execute pre-programmed but configurable reagent and sample processing and any selected script sequence of pipetting. (5) Flow cytometry assay runs can be flexibly scheduled in advance and / or more than one assay run can be performed simultaneously. (6) Send pre-configured communication and alert messages to users (e.g., system operators or research operators) to notify them of status updates and measure the completion of operations.

[0037] System advantages:

[0038] The system implementation offers several advantages over existing solutions. These advantages include, but are not limited to, the following: (1) It provides flexibility in sample preparation and processing across various laboratory consumable formats (test tubes and well plates), particularly focusing on 96-well plate formats, resulting in increased sample throughput and experimental capacity. (2) It enables the preparation and processing of multiple 96-well plates in a single assay run, or simultaneously in multiple separately scheduled assay runs. Other existing technologies focus only on test tube processing or limited well plate throughput. (3) The system implementation enables end-to-end unattended automation of the entire process from sample preparation to sample collection. This is an advantage over existing systems, as no platform can fully automate the sample preparation and collection processes in terms of plate throughput, sample mixing, and sample combination as the system implementation of this disclosure. (4) The system implementation of this invention can operate unattended until the end of standard working hours, not only collecting sample plates on a flow cytometer but also initiating sample preparation reagents and sample processing methods on a liquid processor.

[0039] Existing technologies for sample preparation spaces include: (1) BD FACSLyric integrated with the BD FACSDuet Premium Sample Preparation System. Information on this prior art can be found at https: / / www.bdbiosciences.com / en-us / products / instruments / sample-prep-systems / facslyric-with-facsduet, the full text of which is incorporated herein by reference. This technology includes a comprehensive sample preparation system that supports onboard mixing (up to 45 reagents), washing, and centrifugation, and enables automated sample transfer via physical integration with the BD FACSLyric flow cytometer, thereby enabling subsequent automated sample collection. However, this technology focuses on reagent mixing (up to 23 reagents) and sample preparation and tube handling (up to 40 samples per worklist, with continuous loading), but can only process one sample plate (96-well plate) at a time. Therefore, since the BD FACSLyric integrated with the BD FACSDuet Premium sample preparation system is configured to operate only on tubes used for fixed-panel clinical flow cytometry assays, the prior art cannot achieve the experimental capabilities and sample flexibility benefits of the embodiments of the present disclosure system.

[0040] Prior art in the field of sample preparation also includes the Straedigm flow cytometry automation system. Information about this prior art can be found at https: / / stratedigm.com / flow-cytometer-automation, the full text of which is incorporated herein by reference. This technology integrates a sample plate storage unit (A710), a microplate moving robotic arm (A710HTH), a temperature-controlled incubator (A800, A810), a high-throughput plate autosampler (A600 HTAS), and a bulk reagent storage module (A640 CPM-4 bulk liquid container) with a flow cytometry platform. However, this prior art lacks the experimental capabilities provided by the embodiments of this disclosure, and cannot prepare multiple sample plates containing various reagents (while embodiments of the system disclosed herein can support well more than 23 sample plates, and are not limited thereto), nor can it process a variety of samples (while embodiments of the system disclosed herein are not limited thereto). The prior art cannot perform the reagent mixing and cell sample staining performed by the integrated liquid processor in the system disclosed herein.

[0041] Existing technologies in the field of sample preparation also include the Cytek Orion reagent mixing system. Information about this prior art can be found at https: / / cytekbio.com / pages / orion, the full text of which is incorporated herein by reference. This prior art enables researchers to automate the preparation of antibody mixtures for flow cytometry and can formulate mixtures containing up to 60 different antibodies. However, this prior art is a standalone system, resembling a small, limited liquid processor, and cannot centrifuge or wash the samples mixed with the mixture. This prior art is not integrated with a flow cytometer.

[0042] Additional aspects of the system implementation plan:

[0043] The system implementation disclosed herein for automating flow cytometry preparation and acquisition is configured to automate flow cytometry preparation, staining, and testing by integrating and automating antibody dilution, antibody staining of samples, sample centrifugation and washing, and sample transfer to the flow cytometer for data acquisition. Such systems can be used to prepare multiplexed samples in the fields of immunology, cell biology, stem cell, and antigen discovery. Some key applications of this system include, but are not limited to, the following flow cytometry processes: antibody discovery screening, determination of optimal antibody concentrations, antibody stability testing over time, and quality control of large-scale antibody production.

[0044] In the implementation, the system is a modular robotic system and includes the following components that perform key laboratory processes: a liquid handling machine (e.g., Hamilton Vantage for liquid handling), a plate washer and aspirator (e.g., Biotek Elx405 for plate washing and aspirating), a centrifuge (e.g., Hettich Rotanta for temperature-controlled centrifugation of plates), a flow cytometer (e.g., Bio-Rad ZES for flow cytometry acquisition), a storage device (e.g., Thermo Fisher Cytomat 10c for storing experimental resources under controlled temperature and humidity), other storage devices (e.g., two plate racks for storing experimental resources at room temperature), a barcode scanner (e.g., MicroScanESP for barcode scanning and plate tracking), and two robotic arms for coordinating the movement of experimental resources between different devices.

[0045] As previously described, the system implementation is an automated sample preparation system. Automation improves consistency (i.e., consistency of sample preparation) by reducing mechanistic bias and error rates; it enhances experimental capability; it extends working time beyond standard working times; and it allows the use of predicted results. In summary, these benefits of the system implementation disclosed herein increase the throughput of flow cytometry reagent development and testing, and enable scientists to reallocate time from actual data generation to data interpretation.

[0046] Modularization of system implementation plan:

[0047] The system implementation is modular. Therefore, the system implementation can be configured to operate in any order using only a subset of the integrated equipment, thereby improving operational functionality and flexibility. Other system implementations are configured to perform or conduct the following processes: flow cytometry sample preparation without acquisition, and automated sequential flow cytometry acquisition of multiple prefabricated plates.

[0048] Potential technical features of the system implementation plan:

[0049] In the embodiments, the system is configured to modularly automate flow cytometry preparation, staining, and testing by automating antibody dilution, staining of samples with antibodies, sample centrifugation and washing, and the movement of samples to the flow cytometer for data acquisition. As described herein, embodiments of the system of the present invention include existing laboratory instruments or other equipment. In some embodiments, the instruments and their technical relevance to enabling modular laboratory processes include, but are not limited to, the following: (1) A Hamilton Vantage liquid processor supports the flow cytometry preparation of biological samples up to the completion of the cell staining process. The Hamilton Vantage is programmed to dilute antibody reagents to a specified concentration and transfer the diluted reagents to a single-cell suspension to complete cell staining. Cell staining enables the detection of antigens located on or within cells by specificity and selective affinity. (2) Static plate racks, robotic arms, and plate positioning devices enable the deployment and movement of samples in various embodiments of the system. These components provide physical connectivity between all other instruments in the system. (3) A Hettich Rotanta enables temperature-controlled centrifugation of samples in the system embodiments. Centrifugation refers to the high-speed rotation of a sample in solution to separate molecules and particles of different densities using centrifugal force. The system implementation separates the stained cells of interest from excess antibodies and solution debris. (4) A plate washer or washer (e.g., Biotek Elx405) is used to aspirate the supernatant from previously centrifuged samples, resuspend the samples by shaking, and dispense fresh solution onto the cells. This washing technique removes waste, thus facilitating clearer data and providing the cells with the critical solutions needed to maintain cell viability. (5) Thermo Fisher Cytomat 10c facilitates temperature and humidity controlled storage of biological samples, solutions, and reagents. Optimizing temperature and humidity storage and staining conditions contributes to the viability of biological samples and the shelf life of solutions and reagents. (6) The system barcode scanner MicroScan ESP supports data tracking of samples throughout the system. (7) QInstruments BioShake enables unsupervised plate rack or tube rack shaking, thereby facilitating automated chemical reactions. (8a and b) Bio-Rad ZE5 and BDFACSLyric™ flow cytometers collect stained cells. Two integrated flow cytometers allow samples to be routed to one or both instruments for parallel processing and / or acquisition. Flow cytometry is used to characterize individual suspended cells and particles in solution. As samples are injected into the flow cytometer particle by particle, a laser beam illuminates the particles to generate scattered light and fluorescence signals. These signals are converted into electrical signals by photodiodes and photomultiplier tubes and analyzed by a computer. Data from the computer is compiled into a standard flow cytometry file (.fcs). Cell populations can be identified by their scattering and fluorescence properties.(9) In the implementation scheme, proprietary software (referred to as Cellario) is software used to connect each individual physical component and software of the system implementation scheme together.

[0050] Other aspects of the system implementation plan:

[0051] The system implementation disclosed herein is a modular system. Therefore, the system implementation can be extended to automate workflows related to cell biology, molecular biology, and immunology. The system implementation can support product development for research-only (RUO) and in vitro diagnostics (IVD), as well as product or sample testing at the R&D, preclinical, and clinical regulatory levels. Multiplexed bead-based assays, such as the BD™ cytometry bead array, can be automated using the system implementation. According to this disclosure, the system implementation can also automate cell staining and preparation for fluorescence-activated cell sorting, as well as some common experimental procedures that do not require a sterile environment, such as cell enrichment. Integrating new devices into the system implementation can expand the application scope of such systems. For example, adding a spectrophotometer and / or a isothermal oscillator enables the system implementation to automate assays such as ELISA and Bradford protein assays. For example, adding a Hamilton On-Deck thermal cycler expands the system implementation's ability to automate multi-step genomic workflows.

[0052] Exemplary workflow:

[0053] Figure 1A Exemplary workflow steps for sample preparation and flow cytometry analysis, performed automatically by a system implementation, are depicted and compared to similar steps performed manually by a laboratory technician. Flowchart 101 depicts the workflow steps performed by the system implementation, where each workflow step is performed automatically by robotic components that manipulate one or more samples in relation to one or more sample processing modules. Flowchart 102 shows the workflow steps performed by a laboratory technician. Both the automated workflow shown in Flowchart 101 and the manual workflow shown in Flowchart 102 are intended to prepare samples and perform flow cytometry analysis in the same manner; however, in the automated workflow shown in Flowchart 101, the individual component steps are performed automatically using the system implementation.

[0054] Figure 1B-1C Other examples of manual and automated workflows are shown. Flowchart 103 depicts a manual preparation and collection workflow for sample flow cytometry analysis. Flowchart 103 distinguishes between workflow steps that require user interaction and passive steps (i.e., workflow steps that do not require direct user interaction, such as antibody incubation steps). Most steps in the manual workflow shown in Flowchart 103 require user interaction.

[0055] Flowchart 104 depicts an automated preparation and collection workflow for sample flow cytometry analysis using an embodiment of the system disclosed herein. Flowchart 104 distinguishes between workflow steps requiring user interaction and automated steps (referred to as “unit of work” steps). Automated steps or unit of work steps do not require direct user interaction, allowing the user to automate the preparation and collection workflow in an unattended mode. Most steps in the automated workflow shown in Flowchart 104 are automated steps. The system embodiment associated with the automated workflow shown in Flowchart 104 includes a sample processing module, namely the Hamilton Vantage liquid processor 104A, which is used in conjunction with two steps in the automated workflow shown in Flowchart 104. The system embodiment also includes a sample processing module, namely the ELx405 plate washer and Rotanta centrifuge 104B, which are used in conjunction with two steps in the automated workflow shown in Flowchart 104. The workflows shown in Flowcharts 103 and 104 are designed to achieve the same sample preparation and flow cytometry analysis, the difference being that in Flowchart 104, the system embodiment is used to automate most of the workflow steps.

[0056] The system implementation used in conjunction with the automated workflow shown in flowchart 104 is a system that integrates multiple devices (i.e., sample processing modules and robotic components) to automate the preparation, staining, and collection of flow cytometry data. The system includes: (1) Cellario: scheduling software for implementing the system; the user interacts with this software before the start of the run; (2) Hamilton Vantage 104A: an automated liquid processor for performing pipetting steps during automated operation; this module is integrated into the system; (3) Hamilton Run Control: software for defining liquid processing parameters; the user interacts with this software before the start of the run; (4) BioRad ZES: a flow cytometer integrated into the system; (5) Everest: software for defining settings; the user interacts with this software before the start of the run (i.e., before starting the automated steps of the workflow); (6) BioTek Elx405 104B: a plate washer integrated into the system; the user typically does not interact with this module; (7) Hettich Rotanta: a centrifuge integrated into the system; the user typically does not interact with this device; (8) Cytomat: an incubator integrated into the system; the user can place culture plates into the incubator before the start of the run (i.e., before starting the automated workflow shown in flowchart 104); (9) Acell arms: Two robotic arms integrated into the system and configured to move plates in various locations; users typically do not need to interact with this device. In the automated workflow shown in flowchart 104, plate washing refers to the process of removing excess antibodies from cells and may involve transferring the plate to a plate washer and performing multiple centrifugations. Figure 1D Exemplary microplates 105 and exemplary reagent slots 106 are shown, both for system use. The system can use the microplates 105 so that robotic components of the system can manipulate these plates 105 during workflow execution, for example, moving these plates 105 between sample processing modules (as described herein). The system can use the reagent slots 106 so that one or more sample processing modules can use reagents present in the reagent slots 106. Reagent slots can also be similarly manipulated by robotic components so that robotic components of the system can manipulate such reagent slots 106 during workflow execution, for example, moving these slots 106 between sample processing modules (as described herein).

[0057] Figure 1EAnother example of an automated workflow for sample preparation and flow cytometry analysis of the prepared samples is shown. Flowchart 107 illustrates an automated workflow that can be automatically executed by a system according to embodiments of this disclosure. That is, the workflow of flowchart 107 can be performed unattended, i.e., without direct operator supervision or intervention. In flowchart 107, the letter "P" refers to a plate; the letter "TR" refers to a tube rack; and the letter "AB" refers to an antibody.

[0058] Exemplary system:

[0059] Figure 1F-1KAn exemplary automated sample preparation system according to this embodiment is shown. System 110 includes multiple sample handling modules 111A, 111B, and 111C, which include: a Cytomat 10 incubator, a storage unit, a cooler, a Hudson Rapidwash plate washer, a Biotek ELX405 well washer, a compressor, a vacuum pump, a static rack, bottles, a HighRes Biosolutions Plateorient plate rotator, a HighRes Biosolutions barcode scanner, a HighRes Biosolutions Platehotel plate storage unit, a Hettich Rotanta centrifuge, a HighRes Biosolutions Handover Nests transfer rack, a Hamilton Vantage 2M automated liquid handling platform, a light curtain, pipettes, a sample receiving area, a reagent receiving area, and a waste receiving area. System 110 also includes multiple robotic components 112A and 112B, which constitute a Hamilton Precise ACell 512 robot mounted on a 1.5-meter rail. Robotic components 112A and 112B are integrated with sample processing modules 111A, 111B, and 111C, enabling these robotic components to manipulate samples, such as microplates, between the sample processing modules. System 110 also includes a flow cytometer 113, which is a Biorad ZE5 flow cytometer. Various parts of system 110 are mounted on one or more fixed stages and / or carriers, such as stage 115. System 110 also includes user interfaces 114A and 114B, which include displays (which may be touchscreen displays) and keyboards (which may include an integrated mouse or touchpad). As described herein, system 110 is configured such that: a user can interact with system 110 configured with sample preparation and / or analysis workflows by interacting with one of the user interfaces 114A or 114B; and once system 110 is started (i.e., instructing system 110 to begin sample preparation), no further user interaction is required, i.e., system 110 is configured to prepare samples for flow cytometry in an unattended mode. System 110 also includes a processor and memory that can be integrated into the user interface, as well as operable connections between the user interface, the sample handling module, and the robot components.

[0060] System 110 is configured to receive a three-phase AC hardwired power connection; a compressed air connection for receiving 100-110 PSI compressed air; a CO2 connection for receiving 10-15 PSI compressed CO2; and an Ethernet connection. System 110 is configured to be grounded. System 110 includes multiple emergency stop buttons distributed throughout the system for user use. System 110 includes a system status indicator light tower for clearly indicating the system status.

[0061] Figure 1L-1M An exemplary automated sample preparation system according to another embodiment is shown. System 120 includes sample processing modules 121A, 121B, and 121C, robotic components 122A and 122B (robotic arms), a flow cytometer 123, a user interface 124, and a stage 125. The sample processing modules include a Thermo Scientific™ Cytomat™ 5 C Series automated incubator, a Hudson Rapidwash plate washer, a HighRes Biosolutions four-position plate holder, a Biotek EL406 on slides, a Biorad ZE6 flow cytometer, a HighRes Biosolutions Plateorient, a barcode scanner, a static rack, an eight-position plate holder and a transfer rack, a Hettich Rotanta, and a Hamilton Vantage 2M model equipped with a light curtain. The robotic components include a Hamilton Precise 57 robot and a Hamilton Precise 512 on a 1.5-meter rail.

[0062] Figure 1N-1PAn exemplary system for automated sample preparation according to yet another embodiment is shown. System 150 includes multiple sample processing modules 151A, 151B, and 151C, which include a Cytomat 10 C425 incubator, storage unit, cooler, Biotek elx405 well washer on a slide, compressor, vacuum pump, HighRes Biosolutions Static Nest static rack, vials, HighRes Biosolutions Plateorient plate rotator, HighRes Biosolutions barcode scanner, HighRes Biosolutions 8-bit Platehotel and / or HighRes Biosolutions low-density 6-bit Platehotel plate storage unit, Hettich Rotanta centrifuge, HighRes Biosolutions Handover Nests transfer rack, Hamilton Vantage 2M automated liquid handling platform, pipettes, sample receiving area, reagent receiving area, waste receiving area, and BioTek MultiFlo FX Multimode Dispenser reagent dispenser, as well as a Qinstruments Bioshake Q1 thermostatic shaker (i.e., a heated and cooled shaker). One or more sample processing modules 151A, 151B, and 151C are remotely accessible via a personal computer. System 150 also includes multiple robotic components 152A and 152B, including a HighRes Acell 512 robot on a 1.5 m rail and / or a HighRes Biosolutions Acell 57 robot with non-replaceable Rotanta fingers. Robotic components 152A and 152B are integrated with sample processing modules 151A, 151B, and 151C, enabling these robotic components to manipulate samples, such as microplates, between sample processing modules. System 150 also includes a BD FACSLyric flow cytometer 153A and a Biorad ZE5 flow cytometer 153B. Various aspects of system 150 are mounted on one or more fixed stages and / or carriers, such as stage 155. System 150 also includes user interfaces 154A and 154B, which include a display (which may be a touchscreen) and a keyboard (which may include an integrated mouse or touchpad). As described herein, system 150 is configured such that: a user configures a sample preparation and / or analysis workflow by interacting with one of user interfaces 154A or 154B; and after system 150 is started (i.e., system 150 is instructed to start sample preparation), no further user interaction is required, so that system 150 is configured to prepare samples for flow cytometry in an unattended manner.System 150 also includes a processor and memory that can be integrated into the user interface, as well as operable connections between the user interface, the sample handling module, and the robot components.

[0063] System 150 is configured to receive a three-phase AC hardwired power connection; a compressed air connection for receiving 100-110 PSI compressed air; a CO2 connection for receiving 10-15 PSI compressed CO2; and an Ethernet connection. System 150 is configured to be grounded. System 150 includes multiple emergency stop buttons distributed throughout the system for user use. System 150 includes a system status indicator light tower for clearly indicating the system status.

[0064] Figure 1Q Exemplary sample processing modules, such as automated system 110 or automated system 120, that can be integrated into the systems disclosed herein are shown. Sample processing module 131 is a Hamilton Vantage liquid processor. Sample processing module 132 is a BioRad ZE5 flow cytometer. Sample processing module 133 is a Biotec Elx405 plate washer. Sample processing module 134 is a Cytomat 10 425 incubator. Sample processing module 135 is a Hettich Rotanta centrifuge. Sample processing module 136 is a Hudson Rapidwash 136. Sample processing module 137 is a Microscan MS-3 laser-fixed barcode scanner.

[0065] System diagram:

[0066] Figure 1R A schematic diagram of an exemplary system according to an embodiment is shown. System 140 includes multiple sample processing modules 141 and multiple robotic components 142. Robotic components 142 are integrated with the sample processing modules, as indicated by arrow 145, which indicates that the robotic components 142 are configured to manipulate, operate, or otherwise control the sample processing modules 141, for example, by moving samples (e.g., in microplates) into and out of the respective sample processing modules 141. System 140 also includes a processor that includes a memory 143. The memory contains instructions stored thereon that, when executed by the processor 143, control the sample processing modules 141 and the robotic components 142 to operate them to prepare multiple samples for flow cytometry analysis. System 140 also includes operative connections between the processor 143, the sample processing modules 141, and the multiple robotic components 142. The operable connection includes any convenient connection that can transmit instructions (e.g., control instructions) from processor 143 to robot component 142 and sample handling module 141.

[0067] As described herein, the system can include any convenient sample processing module 141. The number of sample processing modules can vary in different embodiments and may include two, three, four, five, six, seven, eight, nine, ten, fifteen, twenty, twenty-five, thirty, thirty-five, forty, forty-five, fifty, or more sample processing modules. System 140 can be configured to allow the addition or removal of different sample processing modules. That is, even after deployment (e.g., in a laboratory environment), the system can be reconfigured as needed to include additional sample processing modules or to remove certain sample processing modules. In other words, the system is configured to use a variety of laboratory equipment with the same or similar functions; for example, the system of this disclosure can be configured to operate using, for example, any convenient centrifuge or automated pipette. In some cases, the system can be dynamically reconfigured. Typically, the sample processing modules are standard, existing laboratory equipment as described herein. Therefore, a dynamically reconfigurable system allows the use of different laboratory equipment, such as existing laboratory equipment.

[0068] In some embodiments, multiple sample processing modules (e.g., sample processing module 141) are configured to prepare samples required for flow cytometry analysis. In some embodiments, the multiple sample processing modules comprise independent laboratory equipment. In some embodiments, the multiple modules include one or more of the following: an antibody dilution module, a cell staining module, a sample washing module, a sample resuspension module, a sample transfer module, and a sample analysis module. In this case, the cell staining module may be configured to stain cells with fluorescently conjugated antibodies. As described herein, in the implementation scheme, multiple modules include one or more of the following: incubator (e.g., Cytomat 10), storage unit, cooler, plate washer (e.g., Hudson Rapidwash), well washer (e.g., Biotek elx405), compressor, vacuum pump, static rack, bottle, flow cytometer (e.g., BD FACSLyric™ and / or Biorad ZE5 flow cytometer), plate spinner (e.g., HighRes Biosolutions Plateorient), barcode scanner (e.g., HighRes Biosolutions barcode scanner), plate storage device (e.g., HighRes Biosolutions Platehotel and / or HighRes Biosolutions static rack), centrifuge (e.g., Hettich Rotanta), transfer rack (e.g., HighRes Biosolutions Handover Nests), automated liquid handling platform (e.g., Hamilton Vantage 2M), pipette, reagent dispenser (e.g., BioTek MultiFlo FX Multimode Dispenser), and thermostatic shaker (i.e., heated and cooled shaker, such as Qinstruments). Bioshake Q1), sample receiving area, reagent receiving area, and waste receiving area. In some cases, multiple sample processing modules are configured to stain samples for cell staining. In other cases, multiple sample processing modules are configured to incubate samples. In still other cases, multiple sample processing modules are configured to manipulate samples in a multi-well plate. In some cases, multiple sample processing modules include pipettes. In some embodiments, multiple sample processing modules are configured to transfer liquids into or out of a multi-well plate. In other embodiments, multiple sample processing modules include centrifuges. In some embodiments, multiple sample processing modules are configured to rotate samples to separate different aspects of the samples. In still other embodiments, multiple sample processing modules are configured to wash samples.

[0069] As described herein, any convenient robotic component 142 may be included in system 140. The number of robotic components may vary in different embodiments and may include two, three, four, five, six, seven, eight, nine, ten, fifteen, twenty, twenty-five, thirty, thirty-five, forty, forty-five, fifty, or more robotic components. System 140 may be configured to allow the addition or removal of different robotic components. That is, even after deployment (e.g., in a laboratory environment), the system can be reconfigured as needed to include additional robotic components or remove certain robotic components. In other words, the system is configured to use multiple robotic components that function identically or similarly; for example, the system of this disclosure may be configured to operate using, for example, any convenient robotic arm or mechanical gripper mounted on different axes. In some cases, the system may be dynamically reconfigurable. Typically, the robotic components are standard, existing laboratory equipment as described herein. Therefore, a dynamically reconfigurable system allows the utilization of different laboratory equipment, such as existing laboratory equipment.

[0070] In some embodiments, the robot component is configured to move one or more well plates. In some embodiments, the robot component is configured to move well plates into and out of the module. In other embodiments, the robot component includes fingers configured to grasp the well plates. In still other embodiments, the robot component is configured to manipulate the module. In some cases, the robot component includes rails and actuators for translating the robot component. In other cases, the robot component includes a robotic arm. Robot components of interest are commercially available existing robotic arms, grippers, etc. In some cases, the robot component includes a Hamilton Precise Acell 512 robot on a 1.5-meter rail and / or a HighRes Biosolutions Acell 57 robot with non-replaceable Rotanta fingers. The robot component can be integrated with a sample processing module in a mounting manner, enabling it to access the sample processing module, for example, allowing the robot component to load or unload samples (e.g., well plates) and move such samples or well plates between different sample processing modules.

[0071] As described herein, in implementations, the robotic assembly and sample handling module can be configured to handle samples present in any suitable medium. In some cases, the module and robotic assembly are configured to manipulate one or more of the following: test tubes, multi-well plates, deep-well plates, and standard-well plates. In other cases, the module and robotic assembly are configured to operate 96-well standard-depth plates.

[0072] The processor including memory 143 can be any suitable processor, such as any suitable general-purpose processor or controller, such as any suitable commercially available processor or controller, etc., as described herein. As previously stated, the memory contains instructions stored thereon that, when executed by the processor, control the sample processing module and the robotic component to operate the sample processing module and the robotic component to prepare multiple samples for flow cytometry analysis. In other cases, the memory also contains other instructions stored thereon that, when executed by the processor, control the sample processing module and the robotic component to load each of the prepared multiple samples into the flow cytometer and operate the flow cytometer to analyze each prepared sample. Typically, the processor and memory execute software to automate the system, for example, enabling the system to prepare samples automatically in an unattended manner.

[0073] In some implementations, the system is configured to automatically prepare multiple samples for flow cytometry analysis. In other implementations, the system is configured to automatically and continuously prepare multiple samples for flow cytometry analysis. In some implementations, the system is configured to prepare multiple samples for flow cytometry analysis in an unattended mode. Unattended mode means that the system prepares samples completely automatically, such that once the system is instructed to prepare multiple samples, the operator does not need to interact with the system; that is, the operator can leave while the system automatically prepares the samples. In some implementations, the system is configured to prepare multiple samples for flow cytometry analysis without user interaction. In some cases, the system is configured to prepare multiple samples for flow cytometry analysis without user manipulation of the samples. In other cases, the system is configured to prepare multiple samples for flow cytometry analysis without user control of the sample processing module. In other cases, the system is configured to receive user instructions to prepare multiple samples for flow cytometry analysis. User instructions can be received via, for example, a user interface. In this case, the system can be configured to receive user instructions before sample preparation. In some cases, the system is configured to automatically prepare multiple samples for flow cytometry analysis. The system implementation scheme is a robotic system for automating sample preparation and flow cytometry analysis. In the implementation scheme, the instructions include one or more of the following: scheduling software, software for defining liquid handling parameters, and software for defining system settings.

[0074] Embodiments of the disclosed system may also include several additional aspects to facilitate automated sample preparation and, in some cases, flow cytometry analysis. For example, some system embodiments of the invention include one or more substrates on which sample processing modules and / or robotic components are mounted. Some embodiments also include one or more stands. Some embodiments also include a user interface. Any convenient user interface can be used, such as any commercially available existing display and input device. In some cases, the user interface includes one or more of the following: a display, keyboard, mouse, touchpad, user tag, system status indicator, emergency stop button. Some embodiments also include an electrical interface for powering the system. Some embodiments also include a data interface for sending and / or receiving control signals or data signals from the system. Some embodiments also include a compressed air interface for supplying compressed air to the system. Some embodiments also include a network interface. Some embodiments also include one or more protective shields. Any convenient protective shield can be used, such as commercially available existing shields configured to protect operators from harmful light or electromagnetic energy, etc. Some embodiments also include one or more light curtains.

[0075] As described herein, certain aspects of this disclosure also include a flow cytometer. System embodiments of this disclosure can be configured to perform both sample preparation for flow cytometry analysis and the flow cytometry analysis itself. It is contemplated that the system of the present invention can be used with any readily available flow cytometer. Flow cytometers of interest include a light source configured to illuminate particles in the flow stream at a probe point within the flow cell.

[0076] A flow cell of interest comprises a small cell configured to transport particles in a flow stream. As described herein, a “flow cell” in its conventional sense refers to an assembly containing a liquid flow channel for transporting particles in a sheath fluid. The small cell of interest has a channel (i.e., a flow channel) extending therethrough. The flow stream configured to include a liquid sample injected from a sample tube. In some cases, the flow cell contains a light-accessible flow channel. The small cell may be made of, for example, quartz, glass, transparent plastic, etc. In some embodiments, the small cell is made of silica, such as fused silica. In some cases, the flow cell is configured to be irradiated with light from a light source at one or more probe points. As described herein, a “probe point” refers to an area within the flow cell where particles are irradiated by light from a light source, for example, for analysis. The size of the probe point can vary as needed. For example, when 0 μm represents the optical axis of the light emitted by the light source, the probe point can range from -50 μm to 50 μm, for example from -25 μm to 40 μm, and includes -15 μm to 30 μm. Depending on certain factors (such as the number and arrangement of lasers), multiple radiation points may exist within the flow cell.

[0077] In some embodiments, the flow cell includes, or is configured to be used with, a sample injection port configured to supply a sample to the flow cell. In other embodiments, the sample injection system is configured to supply an appropriate sample flow to the flow cell interior (i.e., the flow channel). Depending on the desired characteristics of the flow, the rate at which the sample is delivered from the sample injection port to the flow cell chamber can be 1 μL / min or faster, for example 2 μL / min or faster, for example 3 μL / min or faster, for example 5 μL / min or faster, for example 10 μL / min or faster, for example 15 μL / min or faster, for example 25 μL / min or faster, for example 50 μL / min or faster, and includes 100 μL / min or faster. In some cases, the rate at which the sample is delivered from the sample injection port to the flow cell chamber is 1 μL / sec or faster, for example 2 μL / sec or faster, for example 3 μL / sec or faster, for example 5 μL / sec or faster, for example 10 μL / sec or faster, for example 15 μL / sec or faster, for example 25 μL / sec or faster, for example 50 μL / sec or faster, and includes 100 μL / sec or faster.

[0078] The sample injection port can be an orifice located on the inner chamber wall or a conduit located proximal to the inner chamber. When the sample injection port is an orifice located on the inner chamber wall, the orifice can be of any suitable shape, wherein the cross-sectional shapes of interest include, but are not limited to: linear cross-sectional shapes, such as squares, rectangles, trapezoids, triangles, hexagons, etc.; curved cross-sectional shapes, such as circles, ellipses, etc.; and irregular shapes, such as the bottom of a parabola coupled to the top of a plane. In some embodiments, the sample injection port has a circular orifice. The size of the sample injection port can vary depending on the shape, and in some cases, the opening ranges from 0.1 mm to 5.0 mm, for example 0.2 to 3.0 mm, for example 0.5 mm to 2.5 mm, for example 0.75 mm to 2.25 mm, for example 1 mm to 2 mm, and includes 1.25 mm to 1.75 mm, for example 1.5 mm.

[0079] In some cases, the sample injection port is a conduit located proximal to the flow cell chamber. For example, the sample injection port may be a conduit positioned such that its orifice aligns with the flow cell orifice. When the sample injection port is a conduit aligned with the flow cell orifice, the cross-sectional shape of the sample injection tube can be any suitable shape, including but not limited to: straight cross-sectional shapes such as squares, rectangles, trapezoids, triangles, hexagons, etc.; curved cross-sectional shapes such as circles, ellipses; and irregular shapes, such as the base of a parabola coupled to the top of a plane. The orifice of the conduit may vary depending on its shape, and in some cases, the opening ranges from 0.1 mm to 5.0 mm, for example 0.2 to 3.0 mm, for example 0.5 mm to 2.5 mm, for example 0.75 mm to 2.25 mm, for example 1 mm to 2 mm, and includes 1.25 mm to 1.75 mm, for example 1.5 mm. The shape of the tip of the sample injection port may be the same as or different from the cross-sectional shape of the sample injection tube. For example, the orifice of the sample injection port may include a beveled tip with a bevel angle ranging from 1° to 10°, such as 2° to 9°, such as 3° to 8°, such as 4° to 7°, and including a 5° bevel angle.

[0080] In some embodiments, the flow cell further includes a sheath fluid injection port configured to supply sheath fluid to the flow cell. In these embodiments, the sheath fluid injection system is configured to supply a flow of sheath fluid into the flow cell chamber, for example, along with a sample, to generate a laminar flow of sheath fluid surrounding the sample flow. Depending on the desired characteristics of the flow, the rate at which the sheath fluid is delivered to the flow cell chamber can be 25 μL / sec or faster, for example 50 μL / sec or faster, for example 75 μL / sec or faster, for example 100 μL / sec or faster, for example 250 μL / sec or faster, for example 500 μL / sec or faster, for example 750 μL / sec or faster, for example 1000 μL / sec or faster, and includes 2500 μL / sec or faster.

[0081] In some embodiments, the sheath fluid injection port is an orifice located in the inner chamber wall. The sheath fluid injection port orifice can be of any suitable shape, with cross-sectional shapes of interest including, but not limited to: straight cross-sectional shapes, such as squares, rectangles, trapezoids, triangles, hexagons, etc.; curved cross-sectional shapes, such as circles, ellipses; and irregular shapes, such as parabolic bottoms coupled to the top of a plane. The size of the sheath fluid injection port can vary depending on its shape, and in some cases, the opening ranges from 0.1 mm to 5.0 mm, for example 0.2 mm to 3.0 mm, for example 0.5 mm to 2.5 mm, for example 0.75 mm to 2.25 mm, for example 1 mm to 2 mm, and includes 1.25 mm to 1.75 mm, for example 1.5 mm.

[0082] The flow cytometer disclosed herein includes a light source configured to irradiate particles in the flow stream at a probe point within a flow cell. The number of light sources in the flow cytometer can vary. In some embodiments, the flow cytometer includes a single light source. Alternatively, in some cases, the flow cytometer may include multiple light sources. In some such cases, the number of light sources ranges from 2 to 10, for example 2 to 5, and includes 2 to 4. Any convenient light source can be used as the light source described herein. In some embodiments, the light source is a laser. In embodiments, the laser can be any type of laser, such as a continuous wave laser. For example, the laser can be a diode laser, such as an ultraviolet diode laser, a visible diode laser, and a near-infrared diode laser. In other embodiments, the laser can be a helium-neon (HeNe) laser. In some cases, the laser is a gas laser, such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO2 laser, a CO laser, an argon-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon-chlorine (XeCl) excimer laser, or a xenon-fluorine (XeF) excimer laser, or a combination thereof. In other cases, the subject flow cytometer includes dye lasers, such as stilbene, coumarin, or rhodamine lasers. In still other cases, lasers of interest include metal vapor lasers, such as helium-cadmium (HeCd) lasers, helium-mercury (HeHg) lasers, helium-selenium (HeSe) lasers, helium-silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers, or gold lasers, or combinations thereof. In other cases, the subject flow cytometer includes solid-state lasers, such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:YCa4O(BO3)3 lasers, Nd:YCOB lasers, Ti:sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, ytterbium₂O₃ lasers, or cerium-doped lasers and combinations thereof.

[0083] According to some embodiments, the laser source may also include one or more optical adjustment components. In some embodiments, the optical adjustment components are located between the source and the flow cell and may include any means capable of changing the spatial width or other radiation characteristics of the source (e.g., radiation direction, wavelength, beam width, beam intensity, and focal spot). The optical adjustment scheme can be any convenient means of adjusting one or more characteristics of the source and includes, but is not limited to, lenses, mirrors, filters, optical fibers, wavelength splitters, pinholes, slits, collimation schemes, and combinations thereof. In some embodiments, the flow cytometer of interest includes one or more focusing lenses. In one example, the focusing lens may be a reducing lens. In other embodiments, the flow cytometer of interest includes optical fibers.

[0084] The light source can be positioned at any suitable distance from the flow cell, for example, 0.005 mm or more, such as 0.01 mm or more, 0.05 mm or more, 0.1 mm or more, 0.5 mm or more, 1 mm or more, 5 mm or more, 10 mm or more, 25 mm or more, and including distances of 100 mm or more. Furthermore, the light source can be positioned at any suitable angle relative to the flow cell, for example, an angle range of 10° to 90°, such as 15° to 85°, 20° to 80°, 25° to 75°, and including angles of 30° to 60°, such as 90°.

[0085] In some embodiments, the light source of interest includes multiple lasers configured to provide laser light for discrete irradiation of the flow, such as two or more lasers, three or more lasers, four or more lasers, five or more lasers, ten or more lasers, and including 15 or more lasers configured to provide laser light for discrete irradiation of the flow. Depending on the desired wavelength of the light used to irradiate the flow, each laser may have a specific wavelength ranging from 200 nm to 1500 nm, for example 250 nm to 1250 nm, for example 300 nm to 1000 nm, for example 350 nm to 900 nm, and including variations from 400 nm to 800 nm. In some embodiments, the lasers of interest may include one or more of 405 nm lasers, 488 nm lasers, 561 nm lasers, and 635 nm lasers.

[0086] In some embodiments, the light source is a beam generator configured to generate two or more frequency-shifted beams. In some cases, the beam generator includes a laser or a radio frequency (RF) generator configured to apply an RF drive signal to the acousto-optic device to generate laser beams with two or more angle deflections. In these embodiments, the laser can be a pulsed laser or a continuous-wave laser. For example, lasers in the beam generator of interest include those listed above.

[0087] The acousto-optic device can be any convenient acousto-optic scheme configured to frequency-shift a laser using applied acoustic waves. In some embodiments, the acousto-optic device is an acousto-optic deflector. The acousto-optic device in the subject system is configured to generate an angle-deflected laser beam from light from a laser and an applied radio frequency (RF) drive signal. The RF drive signal can be applied to the acousto-optic device using any suitable RF drive signal source, such as a direct digital synthesizer (DDS), arbitrary waveform generator (AWG), or electrical pulse generator.

[0088] In an embodiment, the controller is configured to apply radio frequency drive signals to the acousto-optic device to generate a desired number of angle-deflected laser beams in the output laser beam, for example, to apply 3 or more radio frequency drive signals, such as 4 or more radio frequency drive signals, such as 5 or more radio frequency drive signals, such as 6 or more radio frequency drive signals, such as 7 or more radio frequency drive signals, such as 8 or more radio frequency drive signals, such as 9 or more radio frequency drive signals, such as 10 or more radio frequency drive signals, such as 15 or more radio frequency drive signals, such as 25 or more radio frequency drive signals, such as 50 or more radio frequency drive signals, and includes being configured to apply 100 or more radio frequency drive signals.

[0089] In some cases, in order to generate an intensity distribution of an angle-deflected laser beam in the output laser beam, the controller is configured to apply an RF drive signal having an amplitude that varies from about 0.001 V to about 500 V (e.g., about 0.005 V to about 400 V, about 0.01 V to about 300 V, about 0.05 V to about 200 V, about 0.1 V to about 100 V, about 0.5 V to about 75 V, about 1 V to 50 V, about 2 V to 40 V, about 3 V to about 30 V, and including about 5 V to about 25 V). In some implementations, the radio frequency drive signal for each application has a frequency of about 0.001 MHz to about 500 MHz, for example about 0.005 MHz to about 400 MHz, for example about 0.01 MHz to about 300 MHz, for example about 0.05 MHz to about 200 MHz, for example about 0.1 MHz to about 100 MHz, for example about 0.5 MHz to about 90 MHz, for example about 1 MHz to about 75 MHz, for example about 2 MHz to about 70 MHz, for example about 3 MHz to about 65 MHz, for example about 4 MHz to about 60 MHz, and includes about 5 MHz to about 50 MHz.

[0090] In some embodiments, the controller has a processor with a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam having angle-deflected laser beams with a desired intensity distribution. For example, the memory may include instructions to generate two or more angle-deflected laser beams of equal intensity (e.g., three or more, four or more, five or more, ten or more, 25 or more, or 50 or more), and the memory may include instructions to generate 100 or more angle-deflected laser beams of equal intensity. In other embodiments, the memory may include instructions to generate two or more angle-deflected laser beams of different intensities (e.g., three or more, four or more, five or more, ten or more, 25 or more, or 50 or more), and the memory may include instructions to generate 100 or more angle-deflected laser beams of different intensities.

[0091] In some embodiments, the controller has a processor with a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam with increasing intensity along a horizontal axis from its edge to its center. In these cases, the intensity of the angularly deflected laser beam at the center of the output beam can be from 0.1% to about 99% of the intensity of the angularly deflected laser beam at the edge of the output laser beam along the horizontal axis, for example, from 0.5% to about 95%, for example, from 1% to about 90%, for example, from about 2% to about 85%, for example, from about 3% to about 80%, for example, from about 4% to about 75%, for example, from about 5% to about 70%, for example, from about 6% to about 65%, for example, from about 7% to about 60%, for example, from about 8% to about 55%, and from about 10% to about 50% of the intensity of the angularly deflected laser beam at the edge of the output laser beam along the horizontal axis. In other embodiments, the controller has a processor with a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam with increasing intensity along a horizontal axis from its edge to its center. In these cases, the intensity of the angularly deflected laser beam at the edge of the output beam can be from 0.1% to about 99% of the intensity of the angularly deflected laser beam at the center of the output laser beam along the horizontal axis, for example, from 0.5% to about 95%, for example, from 1% to about 90%, for example, from about 2% to about 85%, for example, from about 3% to about 80%, for example, from about 4% to about 75%, for example, from about 5% to about 70%, for example, from about 6% to about 65%, for example, from about 7% to about 60%, for example, from about 8% to about 55%, and from about 10% to about 50% of the intensity of the angularly deflected laser beam at the center of the output laser beam along the horizontal axis. In some other embodiments, the controller has a processor with a memory operatively coupled to the processor, such that the memory includes instructions stored thereon, which, when executed by the processor, cause the processor to generate an output laser beam with a Gaussian intensity distribution along a horizontal axis. In still other embodiments, the controller has a processor with a memory operatively coupled to the processor, such that the memory includes instructions stored thereon, which, when executed by the processor, cause the processor to generate an output laser beam with a top-hat intensity distribution along a horizontal axis.

[0092] In implementations, the beam generator of interest can be configured to generate angle-deflected laser beams within spatially separated output laser beams. Depending on the applied RF drive signal and the desired irradiance distribution of the output laser beams, the angle-deflected laser beams can be spaced apart by 0.001 μm or more, for example, 0.005 μm or more, for example, 0.01 μm or more, for example, 0.05 μm or more, for example, 0.1 μm or more, for example, 0.5 μm or more, for example, 1 μm or more, for example, 5 μm or more, for example, 10 μm or more, for example, 100 μm or more, for example, 500 μm or more, for example, 1000 μm or more, and including 5000 μm or more. In some implementations, the system is configured to generate angle-deflected laser beams within the output laser beams, which overlap, for example, with adjacent angle-deflected laser beams along the horizontal axis of the output laser beams. The overlap between adjacent angle-deflected laser beams (e.g., overlap of beam points) can be 0.001 μm or greater, such as 0.005 μm or greater, such as 0.01 μm or greater, such as 0.05 μm or greater, such as 0.1 μm or greater, such as 0.5 μm or greater, such as 1 μm or greater, such as 5 μm or greater, such as 10 μm or greater, and includes 100 μm or greater overlap.

[0093] In some cases, beam generators configured to generate two or more frequency-shifted beams include, as in U.S. Patent Nos. 9,423,353, 9,784,661, 9,983,132, 10,006,852, 10,036,699, 10,078,045, 10,222,316, 10,288,546, 10,324,019, 10,408,758, 10,451,538, 10,620,111, and 10,684. The laser excitation modules described in 211, 10,845,295, 10,935,482, 10,935,485, 11,105,728, 11,280,718, 11,327,016, 11,366,052, 11,371,937, 11,692,926, 11,630,053, 11,774,343, 11,940,369 and 11,946,851 are hereby disclosed in this document by reference.

[0094] Furthermore, the flow cytometer includes a detector configured to collect light emitted by irradiated particles. This photodetector is configured to detect particle-modulated light transmitted by an fiber optic light collection element and generate a signal based on the characteristics of the light, such as intensity. For example, one or more particle-modulated photodetectors may include one or more side-scatter photodetectors for detecting side-scattered light wavelengths (i.e., light refracted and reflected from the particle surface and internal structure). In some embodiments, the flow cytometer includes a single side-scatter photodetector. In other embodiments, the flow cytometer includes multiple side-scatter photodetectors, such as two or more, three or more, four or more, and including five or more.

[0095] Any convenient detector for detecting the collected light can be used in the side-scattered light detector described herein. Detectors of interest may include, but are not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), enhancement-mode charge-coupled devices (ICCDs), light-emitting diodes, photon counters, calorimeters, thermoelectric detectors, photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or combinations thereof, and other detectors. In some embodiments, the collected light is measured using a charge-coupled device (CCD), a semiconductor charge-coupled device (CCD), an active pixel sensor (APS), a complementary metal-oxide-semiconductor (CMOS) image sensor, or an N-type metal-oxide-semiconductor (NMOS) image sensor. In some embodiments, the detector is a photomultiplier tube, such as a photomultiplier tube having an effective detection surface area per region ranging from 0.01 cm². 2 Up to 10 cm 2 For example, 0.05 cm 2 Up to 9 cm 2 For example, 0.1 cm 2 Up to 8 cm 2 For example, 0.5 cm 2 Up to 7 cm 2 And including 1 cm 2 Up to 5 cm 2 .

[0096] In one embodiment, the flow cytometer also includes a fluorescence detector configured to detect light at one or more fluorescence wavelengths. In other embodiments, the flow cytometer includes multiple fluorescence detectors, such as two or more, three or more, four or more, five or more, ten or more, fifteen or more, and including twenty or more.

[0097] Any convenient detector for detecting the collected light can be used in the fluorescence detector described herein. Detectors of interest may include, but are not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), enhancement-mode charge-coupled devices (ICCDs), light-emitting diodes, photon counters, calorimeters, thermoelectric detectors, photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or combinations thereof, and other detectors. In some embodiments, the collected light is measured using a charge-coupled device (CCD), a semiconductor charge-coupled device (CCD), an active pixel sensor (APS), a complementary metal-oxide-semiconductor (CMOS) image sensor, or an N-type metal-oxide-semiconductor (NMOS) image sensor. In some embodiments, the detector is a photomultiplier tube, such as a photomultiplier tube having an effective detection surface area per region ranging from 0.01 cm². 2 Up to 10 cm 2 For example, 0.05 cm 2 Up to 9 cm 2 For example, 0.1 cm 2 Up to 8 cm 2 For example, 0.5cm 2 Up to 7 cm 2 And including 1 cm 2 Up to 5 cm 2 .

[0098] When a subject flow cytometer includes multiple fluorescence detectors, each fluorescence detector can be identical, or the set of fluorescence detectors can be a combination of different types of detectors. For example, when a subject flow cytometer includes two fluorescence detectors, in some embodiments, the first fluorescence detector is a CCD-type device, and the second fluorescence detector (or image sensor) is a CMOS-type device. In other embodiments, both the first and second fluorescence detectors are CCD-type devices. In other embodiments, both the first and second fluorescence detectors are CMOS-type devices. In other embodiments, the first fluorescence detector is a CCD-type device, and the second fluorescence detector is a photomultiplier tube (PMT). In other embodiments, the first fluorescence detector is a CMOS-type device, and the second fluorescence detector is a photomultiplier tube. In other embodiments, both the first and second fluorescence detectors are photomultiplier tubes.

[0099] In embodiments of this disclosure, the fluorescence detector of interest is configured to measure collected light at one or more wavelengths (e.g., two or more wavelengths, five or more different wavelengths, ten or more different wavelengths, 25 or more different wavelengths, 50 or more different wavelengths, 100 or more different wavelengths, 200 or more different wavelengths, or 300 or more different wavelengths), and includes measuring light emitted by a sample in a flowing stream at 400 or more different wavelengths. In some embodiments, two or more detectors in the module described herein are configured to measure collected light at the same or overlapping wavelengths.

[0100] In some embodiments, the detector of interest is configured to measure light collected within a wavelength range (e.g., 200 nm – 1000 nm). In some embodiments, the detector of interest is configured to collect the spectrum of light within a wavelength range. For example, a flow cytometer may include one or more detectors configured to collect the spectrum of light within one or more wavelength ranges of 200 nm – 1000 nm. In other embodiments, the detector of interest is configured to measure light emitted by a sample in a flow stream at one or more specific wavelengths. For example, the module may include one or more detectors configured to measure light at one or more of the following wavelengths: 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof. In some implementations, one or more detectors may be configured to pair with a specific fluorophore, such as a fluorophore used with the sample in a fluorescence assay.

[0101] Flow cytometers may include any suitable mechanisms for supplying sheath fluid and sample fluid to the sample fluid input coupler and the sheath fluid input coupler. For example, the sample fluid input coupler may be fluidly connected to a sample fluid line (e.g., a conduit) fluidly connected to a sample fluid reservoir. Similarly, the sheath fluid input coupler may be fluidly connected to a sheath fluid line fluidly connected to a sheath fluid reservoir. Similarly, flow cytometers may include any suitable mechanisms for managing waste from the flow stream. A fluid output coupler may be fluidly connected to a waste line fluidly connected to a waste reservoir. A fluid management system applicable to the subject flow cytometer is provided in U.S. Patent Application Publication No. 2022 / 0341838, the disclosure of which is incorporated herein by reference in its entirety.

[0102] Suitable flow cytometry systems may include, but are not limited to, Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford Univ. Press (1997); Jaroszeski et al. (eds.), FlowCytometry Protocols, Methods in Molecular Biology No. 91, Humana Press (1997); Practical Flow Cytometry, 3rd Edition, Wiley-Liss (1995); Virgo et al. (2012) Ann Clin Biochem. Jan;49(pt 1):17-28; Linden et al., Semin Throm Hemost. 2004 Oct;30(5):502-11; Alison et al. J Pathol, 2010 Dec; 222(4):335-344; and Herbig et al. (2007) Crit Rev Ther Drug Carrier Syst. Those described in 24(3):203-255; the contents of which are incorporated herein by reference. In some cases, flow cytometry systems of interest include BD Biosciences FACSCanto TM Flow cytometer, BD Biosciences FACSCanto TM II flow cytometer, BD Accuri TM Flow cytometer, BD Accuri TM C6 Plus flow cytometer, BD Biosciences FACSCelesta TM Flow cytometer, BDBiosciences FACSLyric TM Flow cytometer, BD Biosciences FACSVerse TM Flow cytometer, BDBiosciences FACSymphony TM Flow cytometer, BD Biosciences LSRFortessa TM Flow cytometer, BDBiosciences LSRFortessa TM X-20 flow cytometer, BD Biosciences FACSPresto TM Flow cytometer, BD Biosciences FACSVia TMFlow cytometer and BD Biosciences FACSCalibur TM Cell sorter, BD Biosciences FACSCount TM Cell sorter, BD Biosciences FACSLyric TM Cell sorting instrument, BDBiosciences Via TM Cell sorting systems: BD Biosciences Influx™ Cell Sorter, BD Biosciences Jazz™ Cell Sorter, BD Biosciences Aria™ Cell Sorter, BD Biosciences FACSAria™ II Cell Sorter, BD Biosciences FACSAria™ III Cell Sorter, BD Biosciences FACSAria™ Fusion Cell Sorter, BD Biosciences FACSMelody™ Cell Sorter, and BD Biosciences FACSymphony Cell Sorter. TM S6 cell sorter, BD Biosciences FACSDiscover™ cell sorter, and so on.

[0103] In some implementations, the subject system is a flow cytometry system, such as those described in U.S. Patent Nos. 10,663,476, 10,620,111, 10,613,017, 10,605,713, 10,585,031, 10,578,542, 10,578,469, 10,481,074, 10,302,545, 10,145,793, 10,113,967, 10,006,852, 9,952,076, 9,933,341, 9,726,527, 9,453,789, 9,200,334, and 9,097,640. The disclosures of those listed in ,095,494, 9,092,034, 8,975,595, 8,753,573, 8,233,146, 8,140,300, 7,544,326, 7,201,875, 7,129,505, 6,821,740, 6,813,017, 6,809,804, 6,372,506, 5,700,692, 5,643,796, 5,627,040, 5,620,842, 5,602,039, 4,987,086, and 4,498,766 are incorporated herein by reference in their entirety.

[0104] In some implementations, the flow cytometer is configured as an imaging flow cytometer. For example, in some cases, the subject system is a flow cytometry system configured to image particles in a flowing stream using fluorescence imaging with radio frequency labeled emission (FIRE), as described, for example, in Diebold et al., Nature Photonics Vol. 7(10); 806-810 (2013) and in U.S. Patent Nos. 9,423,353, 9,784,661, 9,983,132, 10,006,852, 10,036,699, 10,078,045, 10,222,316, 10,288,546, 10,324,019, 10,408,758, 10,451,538, 10,620,111, and 10,684,21 The disclosures described in 1, 10,845,295, 10,935,482, 10,935,485, 11,105,728, 11,280,718, 11,327,016, 11,366,052, 11,371,937, 11,692,926, 11,630,053, 11,774,343, 11,940,369, and 11,946,851 are incorporated herein by reference. In some embodiments where the flow cytometer is a particle sorter, the particle sorter is an image-supporting particle sorter. Image-supporting particle sorters are described in U.S. Patent Nos. 10,324,019, 10,620,111, 11,105,728, and 11,774,343 and U.S. Provisional Patent Application Nos. 18 / 537,103, 18 / 657,618, 18,657,623, and 18 / 657,633, the disclosures of which are incorporated herein by reference in their entirety.

[0105] Figure 2 A system 200 for flow cytometry according to an illustrative embodiment of the present disclosure is shown. System 200 includes a laser 201 configured to irradiate particles 211 in a flow stream 214 at a probe point 215 within a flow cell 210. Although Figure 2 The example shows a single laser, but it is understood that multiple lasers can also be used. The laser beam from laser 201 is directed to focusing lens 202, which focuses the beam onto the flow stream portion of the sample containing particles 211 within flow cell 210. Flow cell 210 is part of a fluid system that directs particles (typically one at a time) in the flow to the focused laser beam for probing. Alternatively, when the flow cytometer is a gas flow cytometer, a nozzle top may be used.

[0106] like Figure 2As shown, flow cell 210 is fluidly connected to a sheath fluid reservoir 203 containing sheath fluid and a sample flow reservoir 204 containing sample fluid. Sheath fluid from sheath fluid reservoir 203 is supplied to at least one sheath fluid injection port 208 via a conduit (i.e., sheath fluid line) 207. Furthermore, a sample flow containing particles 211 from sample flow reservoir 204 is supplied to sample injection port 206 via a conduit (i.e., sample flow line) 205. Sample injection port 206 is fluidly connected to a sample injector 213 (e.g., a sample injection needle) configured to introduce particles 211 into the interior of flow cell 210. Particles 211 are hydrodynamically focused by the sheath fluid entering from sheath fluid injection port 208, such that a flow stream 214 is formed downstream of the conical portion 212 of flow cell 210. Particles emitted at the distal end of flow cell 210 can be processed and / or collected by any suitable method. For example, depending on the type of flow cytometry performed, particles can be collected at the distal end of flow cell 210, for example, via a waste line. Alternatively, particles can be sorted.

[0107] Light from the laser beam interacts with particles 211 in the sample through diffraction, refraction, reflection, scattering, and absorption, and is re-emitted at various wavelengths depending on the characteristics of the particles (e.g., their size, internal structure, and the presence of one or more fluorescent molecules attached to or within the particles). The fluorescence emission, as well as the diffracted, refracted, reflected, and scattered light, can be routed to one or more detectors. Specifically, forward scattered light (FSC) is routed to forward scattered light detector 223. Forward scattered light detector 223 is positioned slightly off-center from the axis of the direct beam passing through flow cell 210 and is configured to detect diffracted light, i.e., excitation light that passes primarily forward or around the particles. The intensity of the light detected by forward scattered light detector 223 depends on the overall size of the particles. The forward scattered detector may include, for example, a photodiode. An optical filter 221a and a scattering strip 222 are provided between the forward scattered light detectors 223. Optical filter 221a can be configured to filter out non-FSC light of at least one wavelength, while scattering strip 222 can be configured to prevent forward scattering detector 223 from detecting the incident beam (i.e., non-scattered light) from laser 201.

[0108] Furthermore, side-scattered light (SSC) is detected by side-scattered light detector 224. In other words, side-scattered light detector 224 is configured to detect refracted and reflected light from the surface and internal structures of particle 211, which tend to increase with increasing particle structure complexity. Figure 2In the example, the flow cytometer 200 includes a dichroic mirror 220a configured to reflect SSC light to a side-scatter light detector 224 while allowing non-SSC (e.g., fluorescence) light to pass through. An optical filter 221b is configured to prevent the side-scatter light detector 224 from detecting at least one wavelength of non-SSC light. Fluorescence detectors 225a-225c are also shown, each configured to detect fluorescence at a different wavelength. For example, the dichroic mirror 220b may be configured to reflect fluorescence (FL) corresponding to a first wavelength (or wavelength range) to the fluorescence detector 225a while allowing light of other wavelengths to pass through. The optical filter 221c may be configured to prevent the fluorescence detector 225a from detecting at least one wavelength of light that does not correspond to the first wavelength (or wavelength range). Similarly, the dichroic mirror 220c may be configured to reflect FL light corresponding to a second wavelength (or wavelength range) to the fluorescence detector 225b while allowing light of a third wavelength (or wavelength range) to pass through for detection by the fluorescence detector 225c. Optical filter 221d can be configured to prevent fluorescence detector 225b from detecting at least one light wavelength that does not correspond to the second wavelength (or wavelength range). Furthermore, optical filter 221e is configured to prevent fluorescence detector 225c from detecting at least one light wavelength that does not correspond to the third wavelength (or wavelength range).

[0109] Those skilled in the art will recognize that the flow cytometer according to embodiments of this disclosure is not limited to... Figure 2 The flow cytometer shown may include any flow cytometer known in the art. For example, a flow cytometer may have any number of lasers, beam splitters, filters, and detectors, and may have various wavelengths and various different configurations. For example, although Figure 2 The implementation scheme shown for illustrative purposes uses three fluorescence detectors, but it should be understood that any suitable number of fluorescence detectors can be used.

[0110] During operation, the cytometer is controlled by controller / processor 290, and measurement data from the detector can be stored in memory 295 and processed by controller / processor 290. Although not explicitly shown, controller / processor 290 is coupled to the detector to receive output signals from the detector and can also be coupled to the electrical and electromechanical components of the flow cytometer to control laser 201, flow parameters, etc. Input / output (I / O) functionality 297 may also be provided in the system. Memory 295, controller / processor 290, and I / O 297 can be provided entirely as part of the flow cytometer. In such embodiments, a display may also form part of I / O functionality 297 for presenting experimental data to the user of the cytometer 200. Alternatively, some or all of memory 295 and controller / processor 290 and I / O functionality may be part of one or more external devices (e.g., a general-purpose computer). In some embodiments, some or all of memory 295 and controller / processor 290 may communicate wirelessly or wired with the cytometer 210. The controller / processor 290, combined with memory 295 and I / O 297, can be configured to perform various functions associated with the preparation and analysis of flow cytometry experiments.

[0111] Different fluorescent molecules in the fluorescent dye panel used in flow cytometry experiments will emit light in their own characteristic wavelength bands. Specific fluorescent labels and their associated fluorescence emission bands can be selected for the experiment, typically coinciding with the detector's filter window. I / O 297 can be configured to receive data about a flow cytometry experiment having a set of fluorescent labels and multiple cell populations with multiple markers, each cell population having a subset of multiple markers. I / O 297 can also be configured to receive biological data assigning one or more markers to one or more cell populations, marker density data, emission spectral data, data assigning labels to one or more markers, and flow cytometry configuration data. Flow cytometry experiment data (e.g., label spectral characteristics and flow cytometry configuration data) can also be stored in memory 295. Controller / processor 290 can be configured to evaluate the assignment of one or more labels to markers.

[0112] In some embodiments, the subject system is a particle sorting system configured to sort particles using a closed particle sorting module, such as the system described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference. In some embodiments, a sorting decision module having multiple sorting decision units is used to sort particles (e.g., cells) of a sample, such as the system described in U.S. Patent Publication No. 2020 / 0256781, filed December 23, 2019, the disclosure of which is incorporated herein by reference. In some embodiments, the system for sorting sample components includes a particle sorting module with deflection plates, such as the system described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference.

[0113] In some implementations, the system uses fluorescence imaging obtained from a particle sorter that supports radio frequency tag emission imaging, such as... Figure 3As shown. The particle sorter 300 includes an optical radiation assembly 300a, which includes a light source 301 (e.g., a 488 nm laser) that generates an output beam 301a, which is split into beams 302a and 302b by a beam splitter 302. Beam 302a propagates through an acousto-optic device (e.g., an acousto-optic deflector, AOD) 303 to generate an output beam 303a with one or more angled deflections. In some cases, the output beam 303a generated from the acousto-optic device 303 includes a local oscillator beam and multiple radio frequency comb beams. Beam 302b propagates through an acousto-optic device (e.g., an acousto-optic deflector, AOD) 304 to generate an output beam 304a with one or more angled deflections. In some cases, the output beam 304a generated from the acousto-optic device 304 includes a local oscillator beam and multiple radio frequency comb beams. Output beams 303a and 304a, generated from acousto-optic devices 303 and 304 respectively, are combined with beam splitter 305 to generate output beam 305a, which is delivered through optical assembly 306 (e.g., objective lens) to irradiate particles in flow cell 307. In some embodiments, acousto-optic device 303 (AOD) splits a single laser beam into an array of smaller beams, each with a different optical frequency and angle. A second AOD 304 adjusts the optical frequency of a reference beam and then overlaps it with the array of smaller beams at beam combiner 305. In some embodiments, the light irradiation system having a light source and acousto-optic devices may also include those described in Schraivogel, et al. (“High-speed fluorescence image-enabled cell sorting” Science (2022), 375(6578): 315-320) and U.S. Patent Publication No. 2021 / 04049433, the disclosure of which is incorporated herein by reference.

[0114] Output beam 305a irradiates sample particles 308 propagating through flow cell 307 (e.g., using sheath fluid 309) at radiation region 310. As shown in radiation region 310, multiple beams (e.g., angular deflection RF shift beams depicted as points on radiation region 310) overlap with a reference local oscillator beam (depicted as shaded lines on radiation region 310). Due to their different optical frequencies, the overlapping beams exhibit beat frequency behavior, resulting in each sub-beam operating at a different frequency f. 1-n Carrying sinusoidal modulation.

[0115] Light from the radiation sample is transmitted to a light detection system 300b comprising multiple photodetectors. The light detection system 300b includes a forward-scattering photodetector 311 for generating a forward-scattering image 311a and a side-scattering photodetector 312 for generating a side-scattering image 312a. The light detection system 300b also includes a bright-field photodetector 313 for generating a light loss image 313a. In some embodiments, the forward-scattering detector 311 and the side-scattering detector 312 are photodiodes (e.g., avalanche photodiodes, APDs). In some cases, the bright-field photodetector 313 is a photomultiplier tube (PMT). Fluorescence detectors 314-317 are also used to detect fluorescence from the radiation sample. In some cases, photodetectors 314-317 are photomultiplier tubes. Light from the radiation sample is directed through a beamsplitter 320 to the side-scattering detection channel 312 and the fluorescence detection channels 314-317. The optical detection system 300b includes bandpass optics 321, 322, 323, and 324 (e.g., dichroic mirrors) for propagating light of a predetermined wavelength to photodetectors 314-317. In some cases, optics 321 has a 534 nm / 40 nm bandpass. In some cases, optics 322 has a 586 nm / 42 nm bandpass. In some cases, optics 323 has a 700 nm / 54 nm bandpass. In some cases, optics 324 has a 783 nm / 56 nm bandpass. The first number indicates the center of the spectral band. The second number provides the range of the spectral band. Thus, the 510 / 20 filter extends 10 nm on each side of the center of the spectral band, or from 500 nm to 520 nm.

[0116] Data signals generated in response to light detected in scattered light detection channels 311 and 312, bright field light detection channel 313, and fluorescence detection channels 314-317 are digitally processed in real time by processors 350 and 351. Based on the data signals generated in processors 350 and 351, images 311a-317a can be generated in each light detection channel. Image sorting is performed in response to a sorting signal generated in sorting trigger 352. Sorting assembly 300c includes deflection plate 331 for deflecting particles into sample container 332 or waste stream 333. In some cases, sorting assembly 300c is configured to sort particles using a closed particle sorting module, such as those described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference. In some embodiments, the sorting component 300c includes a sorting decision module having multiple sorting decision units, such as those described in U.S. Patent Publication No. 2020 / 0256781, the disclosure of which is incorporated herein by reference.

[0117] In some implementations, the system is a particle analyzer, wherein the particle analysis system 401 ( Figure 4 It can be used to analyze and characterize particles with or without physical sorting of particles into collection containers. Figure 4 A functional block diagram of a particle analysis system for computation-based sample analysis and particle characterization is shown. In some embodiments, the particle analysis system 401 is a flow system. The particle analysis system 401 includes a fluid system 402. The fluid system 402 may include or be coupled to a sample tube 405 and a moving fluid column within the sample tube, wherein particles 403 (e.g., cells) of the sample move along a common sample path 409.

[0118] The particle analysis system 401 includes a detection system 404 configured to collect a signal from each particle as it passes through one or more detection stations along a common sample path. Detection station 408 typically refers to a monitored area 407 of the common sample path. In some embodiments, detection may include detecting light or one or more other characteristics of the particle 403 as it passes through the monitored area 407. Figure 4 The image shows a detection station 408 with a monitored area 407. Some embodiments of the particle analysis system 401 may include multiple detection stations. Furthermore, some detection stations can monitor multiple areas.

[0119] Each signal is assigned a signal value to form a data point for each particle. As mentioned above, this data can be referred to as event data. The data points can be multi-dimensional data points and include values ​​for various characteristics measured for the particle. The detection system 404 is configured to collect a series of such data points within a first time interval.

[0120] The particle analysis system 401 may also include a control system 406. The control system 406 may include one or more processors, amplitude control circuitry, and / or frequency control circuitry. The control system shown may be operatively associated with the fluid system 402. The control system may be configured to generate a computed signal frequency for at least a portion of the first time interval based on the number of data points collected by the detection system 404 during the first time interval. The control system 406 may be further configured to generate an experimental signal frequency based on the number of data points in a portion of the first time interval. The control system 406 may additionally compare the experimental signal frequency with the computed signal frequency or a predetermined signal frequency.

[0121] Figure 5 A functional block diagram of an example particle analyzer control system, such as an analysis controller 500, for analyzing and displaying biological events is shown. The analysis controller 500 can be configured to implement various processes for controlling the graphical display of biological events.

[0122] The particle analyzer or sorting system 502 can be configured to acquire biological event data. For example, a flow cytometer can generate flow cytometry event data. The particle analyzer 502 can be configured to provide biological event data to the analysis controller 500. A data communication channel can be included between the particle analyzer or sorting system 502 and the analysis controller 500. Biological event data can be provided to the analysis controller 500 via the data communication channel.

[0123] Analysis controller 500 can be configured to receive bioevent data from particle analyzer or sorting system 502. The bioevent data received from particle analyzer or sorting system 502 may include flow cytometry event data. Analysis controller 500 can be configured to provide a graphical display of a first graph including the bioevent data to display device 506. Analysis controller 500 can be further configured to render regions of interest as gating around the bioevent data cluster shown by display device 506, for example, overlaying it on the first graph. In some embodiments, gating may be a logical combination of one or more graphical regions of interest plotted on a single parameter histogram or bivariate graph. In some embodiments, the display may be used to display particle parameters or saturation detector data.

[0124] The analysis controller 500 can be further configured to display bio-event data on the display device 506 that differs from other events in the bio-event data outside the gate. For example, the analysis controller 500 can be configured to render the colors of the bio-event data contained within the gate differently than the colors of the bio-event data outside the gate. The display device 506 can be implemented as a monitor, tablet computer, smartphone, or other electronic device configured to present a graphical interface.

[0125] The analysis controller 500 can be configured to receive a gate selection signal from a first input device for identifying a gate. For example, the first input device can be implemented as a mouse 510. The mouse 510 can initiate a gate selection signal to the analysis controller 500 to identify a gate to be displayed on or manipulated by the display device 506 (e.g., providing a click on or in a desired gate when the cursor is over it). In some embodiments, the first device can be implemented as a keyboard 508 or other means for providing input signals to the analysis controller 500, such as a touchscreen, stylus, optical detector, or voice recognition system. Some input devices may include multiple input functions. In such embodiments, each input function can be considered an input device. For example, such as... Figure 5 As shown, the mouse 510 may include a right mouse button and a left mouse button, and each button can generate a trigger event.

[0126] Triggering events can cause the analysis controller 500 to change how data is displayed, which parts of the data are actually displayed on the display device 506, and / or provide input for further processing, such as the selection of groups of interest for particle sorting.

[0127] In some implementations, the analysis controller 500 can be configured to detect when the mouse 510 initiates gating selection. The analysis controller 500 can be further configured to automatically modify the plotting visualization to facilitate the gating process. The modification can be based on a specific distribution of the biological event data received by the analysis controller 500.

[0128] The analysis controller 500 can be connected to a storage device 504. The storage device 504 can be configured to receive and store biological event data from the analysis controller 500. The storage device 504 can also be configured to receive and store flow cytometry event data from the analysis controller 500. The storage device 504 can be further configured to allow the analysis controller 500 to retrieve biological event data, such as flow cytometry event data.

[0129] Display device 506 can be configured to receive display data from analysis controller 500. The display data may include graphs of biological event data and gating of portions of the graphs. Display device 506 can be further configured to change the presented information based on input received from analysis controller 500, along with input from particle analyzer 502, storage device 504, keyboard 508, and / or mouse 510.

[0130] In some implementations, the analysis controller 500 may generate a user interface to receive sample events for sorting. For example, the user interface may include controls for receiving sample events or sample images. Sample events, images, or sample gating may be provided before collecting event data for the sample, or based on an initial set of events for a portion of the sample.

[0131] Figure 6A This is a schematic diagram of a particle sorting system 600 (e.g., a particle analyzer or sorting system 502) according to one embodiment described herein. In some embodiments, the particle sorting system 600 is a cell sorting system. Figure 6AAs shown, a droplet-forming transducer 602 (e.g., a piezoelectric oscillator) is coupled to a fluid conduit 601, which may be coupled to, contain, or be the nozzle 603 itself. Within the fluid conduit 601, a sheath fluid 604 hydrodynamically focuses a sample fluid 606 containing particles 609 into a moving fluid column 608 (e.g., a flow). Within the moving fluid column 608, particles 609 (e.g., cells) are arranged in a single file, passing through a monitored area 611 (e.g., the intersection of laser and flow), and irradiated by a radiation source 612 (e.g., a laser). Vibration of the droplet-forming transducer 602 causes the moving fluid column 608 to break into multiple droplets 610, some of which contain particles 609.

[0132] In operation, a detection station 614 (e.g., an event detector) identifies when a particle (or cell) of interest crosses a monitoring area 611. The detection station 614 is fed into a timing circuit 628, which in turn feeds into a flash charge circuit 630. At the droplet splitting point, a timing droplet delay (Δt) informs the flash charge that it can be applied to the moving fluid column 608, causing the droplet of interest to become charged. The droplet of interest may contain one or more particles or cells to be sorted. The charged droplet can then be deflected into a container (e.g., a collection tube or a porous or microporous sample plate) for sorting by activating a deflection plate (not shown), where pores or micropores can be associated with a specific droplet of interest. Figure 6A As shown, the droplets can be collected in the drainage container 638.

[0133] A detection system 616 (e.g., a droplet boundary detector) is used to automatically determine the phase of the droplet drive signal as a particle of interest passes through a monitoring region 611. U.S. Patent No. 7,679,039 describes an exemplary droplet boundary detector, the entire contents of which are incorporated herein by reference. The detection system 616 enables the instrument to accurately calculate the position of each detected particle within the droplet. The detection system 616 may be fed an amplitude signal 620 and / or a phase signal 618, which in turn (via amplifier 622) are fed into an amplitude control circuit 626 and / or a frequency control circuit 624. The amplitude control circuit 626 and / or the frequency control circuit 624, in turn, control the droplet forming transducer 602. The amplitude control circuit 626 and / or the frequency control circuit 624 may be included in a control system.

[0134] In some embodiments, sorting electronics (e.g., detection system 616, detection station 614, and processor 640) may be coupled to a memory configured to store detected events and sorting decisions based on those events. The sorting decisions may be included in the event data of the particles. In some embodiments, detection system 616 and detection station 614 may be implemented as a single detection unit or communication coupling, such that event measurements can be collected by one of detection system 616 or detection station 614 and provided to non-collecting elements.

[0135] Figure 6B This is a schematic diagram of a particle sorting system according to one embodiment described herein. Figure 6B The particle sorting system 600 shown includes deflection plates 652 and 654. Charge can be applied via a current-charged wire in the barbs. This generates a droplet stream 610 containing particles 610 for analysis. The particles can be illuminated with one or more light sources (e.g., lasers) to produce light scattering and fluorescence information. Particle information can be obtained through sorting electronics or other detection systems. Figure 6B (Not shown in the image) Analysis is performed. Deflection plates 652 and 654 can be independently controlled to attract or repel charged droplets, thereby guiding the droplets to a target collection container (e.g., one of 672, 674, 676, or 678). Figure 6B As shown, deflector plates 652 and 654 can be controlled to direct particles along a first path 662 to container 674, or along a second path 668 to container 678. If a particle is not of interest (e.g., no scattering or irradiation information is displayed within a specified sorting area), the deflector plates can allow the particle to continue flowing along flow path 664. These uncharged droplets can enter the waste container via, for example, an aspirator 670.

[0136] The sorting electronics may include functions for initiating measurement data collection, receiving fluorescence signals from particles, and determining how to adjust deflection plates to sort the particles. Figure 6B Example implementations of the illustrated scheme include the BD FACSAria™ series flow cytometers commercially available from Becton, Dickinson and Company (Franklin Lakes, NJ).

[0137] method

[0138] This disclosure also includes methods for preparing samples for flow cytometry analysis. The disclosure further includes methods for preparing samples and analyzing samples using flow cytometry. A method according to certain embodiments includes introducing multiple samples into a first sample processing module of multiple sample processing modules of a system, wherein the system includes: multiple sample processing modules; multiple robotic components integrated with said sample processing modules; a processor including a memory operatively coupled to the processor, wherein the memory contains instructions stored thereon, the instructions, when executed by the processor, causing the processor to control the sample processing modules and robotic components to: operate the sample processing modules and robotic components to prepare multiple samples for flow cytometry analysis; and operative connections between the processor, the sample processing modules, and the multiple robotic components; providing sample preparation instructions to the system; and initiating the system to automatically prepare samples according to the sample preparation instructions.

[0139] Figure 7A A flowchart of a method according to an embodiment is depicted. Flowchart 700 corresponds to a method for preparing samples for flow cytometry analysis. Flowchart 700 begins at step 701, in which multiple samples are introduced into a first sample processing module among multiple sample processing modules of a system according to an embodiment. The system includes: multiple sample processing modules; multiple robotic components integrated with said sample processing modules; a processor including a memory operatively coupled to the processor, wherein the memory contains instructions stored thereon, which, when executed by the processor, cause the processor to control the sample processing modules and robotic components to: operate the sample processing modules and robotic components to prepare multiple samples for flow cytometry analysis; and operative connections between the processor, the sample processing modules, and the multiple robotic components. After step 701 is completed, flowchart 700 proceeds to step 702. In step 702 of flowchart 700, sample preparation instructions are provided to the system. After step 702 is completed, flowchart 700 proceeds to step 703. In step 703 of flowchart 700, the system is started to automatically prepare samples according to the sample preparation instructions. After step 703 is completed, flowchart 700 ends.

[0140] Figure 7BA flowchart of a method according to another embodiment is depicted. Flowchart 710 corresponds to a method for preparing a sample for flow cytometry analysis and further performing flow cytometry analysis on the prepared sample. Flowchart 710 is used with embodiments of the system of this disclosure that further include a flow cytometer. Flowchart 710 begins with step 711. Steps 711, 712, and 713 are identical to steps 701, 702, and 703 of flowchart 700, respectively. After step 713 is completed, flowchart 710 proceeds to step 714. In step 714 of flowchart 710, the prepared sample is loaded into the flow cytometer using a robotic assembly. After step 714 is completed, flowchart 710 proceeds to step 715. In step 715 of flowchart 710, the sample is analyzed by flow cytometry using the flow cytometer. After step 715 is completed, flowchart 710 proceeds to step 716. In step 716 of flowchart 710, the sample is removed from the flow cytometer using a robotic assembly. After step 716 is completed, flowchart 710 ends.

[0141] A method according to certain embodiments includes a computer-implemented method for preparing samples for flow cytometry analysis. The method includes receiving multiple samples into a first sample processing module of a plurality of sample processing modules in a system, wherein the system includes: multiple sample processing modules; multiple robotic components integrated with the sample processing modules; a processor including a memory operatively coupled to the processor, wherein the memory contains instructions stored thereon, the instructions, when executed by the processor, causing the processor to control the sample processing modules and the robotic components to: operate the sample processing modules and the robotic components to prepare multiple samples for flow cytometry analysis; and operative connections between the processor, the sample processing modules, and the multiple robotic components; and controlling the robotic components, according to the instructions stored in the memory, to manipulate the multiple samples using the sample processing modules to prepare the multiple samples.

[0142] Figure 7CA flowchart of a method according to another embodiment is depicted. Flowchart 720 corresponds to a computer-implemented method for preparing samples for flow cytometry analysis. Flowchart 720 begins at step 721, in which multiple samples are introduced into a first sample processing module among multiple sample processing modules of a system according to the embodiment. The system includes: multiple sample processing modules; multiple robotic components integrated with the sample processing modules; a processor including a memory operatively coupled to the processor, wherein the memory contains instructions stored thereon, which, when executed by the processor, cause the processor to control the sample processing modules and robotic components to: operate the sample processing modules and robotic components to prepare multiple samples for flow cytometry analysis; and operative connections between the processor, the sample processing modules, and the multiple robotic components. After step 721 is completed, flowchart 720 proceeds to step 722. In step 722 of flowchart 720, the robotic components are controlled to operate the multiple samples using the sample processing modules to prepare the multiple samples according to the instructions stored in the memory. After step 722 is completed, flowchart 720 ends.

[0143] In some implementations, the method is a continuous sample preparation method. In other implementations, the method is a sample preparation method in an unattended manner. In some implementations, the method is a method for preparing multiple samples for flow cytometry analysis without user interaction. In some implementations, the method is a method for preparing multiple samples for flow cytometry analysis without user manipulation of the samples. In some cases, the method is a method for preparing multiple samples for flow cytometry analysis without user control of the sample processing module. In other cases, the method further includes receiving instructions from a user. In these cases, the system may be configured to receive user instructions to prepare multiple samples for flow cytometry analysis. In some cases, the first sample processing module includes a sample receiving module. In some cases, the sample receiving module includes a sample storage module.

[0144] In some embodiments, the sample preparation instructions include instructions to instruct the robotic assembly to move the microplate from a first position to a second position. In other embodiments, the sample preparation instructions include instructions to instruct the robotic assembly to move the microplate from a first sample processing module to a second sample processing module. In some embodiments, the sample preparation instructions include one or more of the following: moving one or more system components, moving one or more plates, diluting and / or preparing one or more antibody solutions, lysing the sample, mixing the antibody solution with the sample, incubating the sample, resuspending the sample, washing the sample, and storing the sample. Embodiments of the method disclosed herein also include: retrieving a sample from a sample input station using the robotic assembly.

[0145] In conjunction with embodiments of the method disclosed herein, the system also includes a flow cytometer. In some embodiments, the sample preparation instructions further include instructions for: loading the prepared sample into the flow cytometer using a robotic component; performing flow cytometry analysis on the sample using the flow cytometer; and removing the sample from the flow cytometer using the robotic component. In other embodiments of the method disclosed herein, the method is a method of automating a manual laboratory workflow using a robotic component and a sample handling module.

[0146] In some cases, the samples prepared and / or analyzed in this method are biological samples. The term "biological sample," in its conventional sense, refers to a whole organism, plant, fungus, or a subset of animal tissue, cells, or components, which in some cases may be present in blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, amniotic fluid, amniotic cord blood, urine, vaginal secretions, and semen. Therefore, "biological sample" refers both to a natural organism or a subset of its tissues and to homogenates, lysates, or extracts prepared from an organism or a subset of its tissues, including but not limited to, plasma, serum, cerebrospinal fluid, lymph, sections (of the skin, respiratory tract, gastrointestinal tract, cardiovascular, and genitourinary tract), tears, saliva, breast milk, blood cells, tumors, and organs. Biological samples can be any type of biological tissue, including healthy tissue and diseased tissue (e.g., cancerous, malignant, necrotic, etc.). In some implementations, the biological sample is a liquid sample, such as blood or its derivatives, such as plasma, tears, urine, semen, etc. In some cases, the sample is a blood sample, including whole blood, such as blood obtained from venipuncture or finger prick (where the blood may or may not be mixed with any reagents such as preservatives, anticoagulants, etc. before testing).

[0147] In some embodiments, the source of the sample is described as a “mammal” or “milk animal,” terms widely used to describe organisms in the class Mammalia, including carnivores (e.g., dogs and cats), rodents (e.g., mice, guinea pigs, and rats), and primates (e.g., humans, chimpanzees, and monkeys). In some cases, the subject is a human. The method can be applied to samples obtained from human subjects of both sexes at any developmental stage (i.e., newborns, infants, adolescents, teenagers, and adults), wherein in some embodiments, the human subject is an adolescent, teenager, or adult. While this disclosure is applicable to samples from human subjects, it should be understood that the method can also be performed on samples from other animal subjects (i.e., “non-human subjects”), such as, but not limited to, birds, mice, rats, dogs, cats, livestock, and horses.

[0148] Cells of interest can be targeted to characterize them based on a variety of parameters, such as identifying phenotypic characteristics by attaching specific fluorescent markers to the cells of interest. In some embodiments, the system is configured to deflect analytical droplets identified as containing target cells. A variety of cells can be characterized using a subject-specific approach. Cells of interest include, but are not limited to, stem cells, T cells, dendritic cells, B cells, granulocytes, leukemia cells, lymphoma cells, viral cells (e.g., HIV cells), NK cells, macrophages, monocytes, fibroblasts, epithelial cells, endothelial cells, and erythrocytes. Target cells of interest include cells with convenient cell surface markers or antigens that can be captured or labeled by readily available affinity agents or conjugates thereof. For example, target cells may include cell surface antigens such as CD11b, CD123, CD14, CD15, CD16, CD19, CD193, CD2, CD25, CD27, CD3, CD335, CD36, CD4, CD43, CD45RO, CD56, CD61, CD7, CD8, CD34, CD1c, CD23, CD304, CD235a, T cell receptor α / β, T cell receptor γ / δ, CD253, CD95, CD20, CD105, CD117, CD120b, Notch4, Lgr5 (N-terminus), SSEA-3, TRA-1-60 antigen, disialiacoganglioside GD2, and CD71. In some embodiments, target cells are selected from HIV-containing cells, Treg cells, antigen-specific T cell populations, tumor cells, or hematopoietic progenitor cells (CD34+) derived from whole blood, bone marrow, or umbilical cord blood.

[0149] When implementing the main method, a certain amount of initial fluid sample is injected into the flow cytometer. The amount of sample injected into the particle sorting module can vary, for example, ranging from 0.001 mL to 1000 mL, for example from 0.005 mL to 900 mL, for example from 0.01 mL to 800 mL, for example from 0.05 mL to 700 mL, for example from 0.1 mL to 600 mL, for example from 0.5 mL to 500 mL, for example from 1 mL to 400 mL, for example from 2 mL to 300 mL, and includes samples from 5 mL to 100 mL.

[0150] The method according to embodiments of this disclosure includes counting and optionally sorting labeled particles (e.g., target cells) in a sample. In implementing the subject method, a fluid sample containing particles is first introduced into a flow nozzle of the system. After exiting the flow nozzle, the particles pass through a sample probe region substantially one at a time, in which each particle is irradiated by a light source, and measurements of light scattering parameters for each particle are recorded individually, and in some cases, fluorescence emission (e.g., measurements of two or more light scattering parameters and one or more fluorescence emissions) is recorded as needed. Depending on the characteristics of the probed flow, flow flows of 0.001 mm or greater can be irradiated with light, such as 0.005 mm or greater, 0.01 mm or greater, 0.05 mm or greater, 0.1 mm or greater, 0.5 mm or greater, and flow flows including 1 mm or greater can be irradiated with light. In some embodiments, the method includes irradiating a planar cross-section of the flow flow in the sample probe region, for example, with a laser (as described above). In other embodiments, the method includes irradiating a predetermined length of the flow flow in the sample probe region, for example, corresponding to the irradiation profile of a diffuse laser beam or lamp.

[0151] In some embodiments, the method includes irradiating the fluid at or near the flow cell nozzle orifice. For example, the method may include irradiating the flow at a distance of approximately 0.001 mm or more from the nozzle orifice, such as 0.005 mm or more, 0.01 mm or more, 0.05 mm or more, 0.1 mm or more, 0.5 mm or more, and including 1 mm or more. In some embodiments, the method includes irradiating the flow immediately adjacent to the flow cell nozzle orifice.

[0152] In implementations of the method, detectors such as photomultiplier tubes (PMTs) are used to record light passing through each particle (in some cases referred to as forward scattering), light reflected orthogonally to the direction in which the particle flows through the sensing region (in some cases referred to as orthogonal scattering or side scattering), and fluorescence emitted from the particle when it passes through the sensing region and is illuminated by an energy source (if the particle is labeled with a fluorescent marker). Forward scattering (FSC), side scattering (SSC), and fluorescence emission each contain individual parameters for each particle (or each "event"). Thus, for example, two, three, or four parameters can be collected (and recorded) from particles labeled with two different fluorescent markers. The data recorded for each particle is analyzed in real time or stored in a data storage and analysis device such as a computer, as needed.

[0153] In some implementations, particles can be detected and uniquely identified by exposing them to excitation light and measuring the fluorescence of each particle in one or more detection channels as needed. The fluorescence emitted in the detection channels to identify the particles and their associated binding complexes can be measured after excitation with a single light source or separately after excitation with different light sources. If separate excitation sources are used to excite the particle tags, the tags can be selected such that all tags can be excited by each excitation source used.

[0154] In some implementations, the method further includes data acquisition, analysis, and recording, for example using a computer, where multiple data channels record data from each detector, which is designed for light scattering and fluorescence emitted by each particle as it passes through a sample probing area of ​​the particle sorting module. In these implementations, the analysis includes classifying and counting the particles, such that each particle exists as a set of digitized parameter values. The subject system can be configured to trigger on selected parameters to distinguish the particle of interest from background and noise. "Trigger" refers to a preset threshold for the detection parameter and can be used as a means of detecting particles passing through the light source. Detecting an event exceeding the threshold of the selected parameter triggers the acquisition of light scattering and fluorescence data for that particle. No data is acquired for particles or other components in the test medium that cause a response below the threshold. The trigger parameter can be the forward scattered light caused by the particle passing through the light beam. Flow cytometry then detects and collects the light scattering and fluorescence data for that particle.

[0155] Then, based on the data collected for the entire population, specific subpopulations of interest are further analyzed using "gating." To select an appropriate gating point, a data plot needs to be created to achieve the best possible subpopulation separation. This procedure can be performed by plotting forward light scattering (FSC) and lateral (i.e., orthogonal) light scattering (SSC) on a two-dimensional dot plot. The particle subpopulations (i.e., those cells within the gating point) are then selected, and particles not within the gating point are excluded. If needed, a line can be drawn around the desired subpopulation using the cursor on the computer screen to select the gating point. Then, only those particles within the gating point are further analyzed by plotting other parameters of these particles, such as fluorescence. If desired, the above analysis can be configured to count the particles of interest in the sample.

[0156] Methods of interest may also include the use of particles in research, laboratory testing, or treatment. In some embodiments, the subject method includes preparing individual cells from a target fluid or tissue biological sample. For example, the subject method includes obtaining cells from a fluid or tissue sample for use as a research or diagnostic sample for diseases such as cancer. Similarly, the subject method includes obtaining cells from a fluid or tissue sample for therapeutic purposes. A cell therapy protocol is a procedure for preparing living cellular material (including, for example, cells and tissues) and introducing it into a subject for therapeutic treatment. Conditions that can be treated by administering samples sorted by flow cytometry include, but are not limited to, blood disorders, immune system disorders, organ damage, etc.

[0157] A typical cell therapy protocol may include the following steps: sample collection, cell isolation, gene modification, in vitro culture and expansion, cell harvesting, sample volume reduction and washing, biopreservation, storage, and introduction of cells into a subject. The protocol may begin with the collection of live cells and tissues from the subject's source tissue to produce cell and / or tissue samples. Samples can be collected using any suitable procedure, including, for example, administration of cell mobilizing agents to the subject, blood collection from the subject, bone marrow extraction from the subject, etc. After sample collection, cells can be enriched using several methods, such as centrifugation-based methods, filtration-based methods, panning, magnetic separation, fluorescence-activated cell sorting (FACS), etc. In some cases, the enriched cells can be gene-modified using any convenient method, such as nuclease-mediated gene editing. Genetically modified cells can be cultured, activated, and expanded in vitro. In some cases, cells are preserved (e.g., cryopreserved) and stored for future use, thawed, and then infused back into the patient, for example, by infusing cells into the patient.

[0158] Computer control system

[0159] This disclosure further includes a computer control system for practicing the subject method, wherein the system further includes one or more computers for fully or partially automating the system for practicing the method described herein. In some embodiments, the system includes a computer having a computer-readable storage medium on which a computer program is stored, wherein the computer program, when loaded onto the computer, includes instructions for automatically preparing samples for flow cytometry analysis and, in some cases, automatically performing flow cytometry analysis on samples prepared according to embodiments of the method of this disclosure.

[0160] The system may include a display and operator input devices. Operator input devices may be a keyboard, mouse, etc. The processing module includes a processor that can access memory storing instructions for performing the subject method steps. The processing module may include an operating system, a graphical user interface (GUI) controller, system memory, memory storage devices, and input / output controllers, caches, data backup units, and many other devices. The processor may be a commercially available processor or one of other existing or soon-to-be-released processors. The processor executes the operating system, which interfaces with firmware and hardware in a well-known manner and assists the processor in coordinating and executing the functions of various computer programs written in various programming languages ​​(e.g., Java, Perl, C++, Python, other high-level or low-level languages, and combinations thereof), as known in the art. The operating system typically works in cooperation with the processor to coordinate and execute the functions of other computer components. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all conforming to known techniques. In some embodiments, the processor includes analog electronics that provide feedback control (e.g., negative feedback control).

[0161] System memory can be any known or future memory storage device. Examples include any commonly used random access memory (RAM), magnetic media (such as resident hard disks or magnetic tapes), optical media (such as optical discs), flash memory devices, or other memory storage devices. Memory storage devices can be any known or future devices, including optical disc drives, magnetic tape drives, or floppy disk drives. This type of memory storage device typically reads from and / or writes to program storage media (not shown), such as optical discs. Any of these program storage media, or other program storage media currently in use or that may be developed in the future, can be considered a computer program product. As will be understood, these program storage media typically store computer software programs and / or data. Computer software programs (also known as computer control logic) are typically stored in system memory and / or program storage devices used in conjunction with memory storage devices.

[0162] In some embodiments, a computer program product is described, comprising a computer-usable medium storing control logic (a computer software program, including program code). When executed by a computer processor, the control logic causes the processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware, for example using a hardware state machine. It will be apparent to those skilled in the art that implementing a hardware state machine to perform the functions described herein is appropriate.

[0163] The memory can be any suitable device in which a processor can store and retrieve data, such as magnetic, optical, or solid-state storage devices (including disks, optical discs, magnetic tapes, RAM, or any other suitable device, whether fixed or portable). The processor can include a general-purpose digital microprocessor, which is suitably programmed from a computer-readable medium carrying the necessary program code. The programming can be provided to the processor remotely via a communication channel or pre-stored in a computer program product (e.g., memory or some other portable or fixed computer-readable storage medium) using any of these devices associated with the memory. For example, a disk or optical disc can carry the program and can be read by a disk writer / reader. The system of this disclosure also includes programs, such as algorithms in the form of computer program products, for practicing the methods described above. Programs according to this disclosure can be recorded on computer-readable media, such as any medium that can be directly read and accessed by a computer. Such media include, but are not limited to: magnetic storage media, such as floppy disks, hard disk storage media, and magnetic tape; optical storage media, such as CD-ROMs; electrical storage media, such as RAM and ROM; portable flash drives; and mixtures of these categories, such as magnetic / optical storage media.

[0164] The processor can also access communication channels to communicate with users in remote locations. A remote location refers to a location where the user does not directly interact with the system but instead passes input information from an external device (such as a computer connected to a wide area network (“WAN”), telephone network, satellite network, or any other suitable communication channel, including mobile phones (i.e., smartphones)) to the input manager.

[0165] In some embodiments, the system according to this disclosure may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and / or transmitter for communicating with a network and / or another device. The communication interface may be configured for wired or wireless communication, including but not limited to radio frequency (RF) communication (e.g., RFID, Zigbee communication protocol, Wi-Fi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth® communication protocol, and cellular communication, such as code division multiple access (CDMA) or Global System for Mobile Communications (GSM).

[0166] In one implementation, the communication interface is configured to include one or more communication ports, such as physical ports or interfaces, such as USB ports, USB-C ports, RS-232 ports, or any other suitable electrical connection ports, to allow data communication between the subject system and other external devices (such as computer terminals (e.g., in a doctor's office or hospital environment)), which are configured for similar complementary data communication.

[0167] In one implementation, the communication interface is configured for infrared communication, Bluetooth® communication, or any other suitable wireless communication protocol to enable the subject system to communicate with other devices, such as computer terminals and / or networks, communication-enabled mobile phones, personal digital assistants, or any other communication devices that the user can use in conjunction with them.

[0168] In one implementation, the communication interface is configured to provide data transmission connectivity using the Internet Protocol (IP) via a cellular telephone network, a short message service (SMS), a wireless connection to a personal computer (PC) on a local area network (LAN) connected to the Internet, or a Wi-Fi connection to the Internet at a Wi-Fi hotspot.

[0169] In one implementation, the subject system is configured to communicate wirelessly with a server device via a communication interface, for example, using common standards such as 802.11 or Bluetooth. ® The protocol is either RF or IrDA infrared. The server device can be another portable device, such as a smartphone, personal digital assistant (PDA), or laptop computer; or a larger device, such as a desktop computer, appliance, etc. In some implementations, the server device has a display, such as a liquid crystal display (LCD), and input devices, such as buttons, a keyboard, a mouse, or a touchscreen.

[0170] In some implementations, the communication interface is configured to automatically or semi-automatically communicate data stored in the subject system (e.g., optional data storage unit) with network or server devices using one or more of the communication protocols and / or mechanisms described above.

[0171] The output controller may include any of a variety of known display devices for presenting information to a user (whether human or machine, local or remote). If a display device provides visual information, that information is typically logically and / or physically organized into an array of image elements. The graphical user interface (GUI) controller may include a variety of known or future software programs for providing a graphical input and output interface between the system and the user and for processing user input. The functional elements of the computer may communicate with each other via a system bus. In alternative implementations, some of these communications may be implemented using networks or other types of remote communication. The output manager may also provide information generated by the processing module to a user at a remote location, for example, via the Internet, telephone, or satellite networks, according to known technologies. The presentation of data by the output manager may be implemented according to a variety of known technologies. As some examples, the data may include SQL, HTML, or XML documents, emails, or other files or other forms of data. The data may include Internet URLs, allowing the user to retrieve other SQL, HTML, XML, or other documents or data from a remote source. One or more platforms present in the subject system may be any type of known computer platform or type to be developed in the future, although they typically belong to a class of computers commonly referred to as servers. However, they may also be mainframes, workstations, or other computer types. They can be connected via any known or future type of cable or other communication system (including wireless systems, whether networked or otherwise). They can be located together or physically separated. Various operating systems can be used on any computer platform, depending on the type and / or brand of the chosen platform. Suitable operating systems include Windows. ® NT ® Windows ® XP, Windows ® 7. Windows ® 8. Windows ® 10. iOS ® macOS ® Linux ® Ubuntu ® Fedora ® OS / 400 ® i5 / OS ® IBM i ® Android™, SGI IRIX ® Oracle Solaris ® wait.

[0172] Figure 8The general architecture of an example computing device 800 according to certain implementation schemes is described. Figure 8 The general architecture of the computing device 800 shown includes the arrangement of computer hardware and software components. However, providing feasible disclosure does not require showing all of these generally conventional components. As shown, the computing device 800 includes a processing unit 810, a network interface 820, a computer-readable media drive 830, an input / output device interface 840, a display 850, and an input device 860, all of which can communicate with each other via a communication bus. The network interface 820 provides connectivity to one or more networks or computing systems. Thus, the processing unit 810 can receive information and instructions from other computing systems or services via the network. The processing unit 810 can also communicate back and forth with a memory 870 and further provide output information to an optional display 850 via the input / output device interface 840. For example, analysis software (e.g., data analysis software or programs such as FlowJoy) stored as executable instructions in the non-transient memory of an analysis system. ® It can display flow cytometry event data to the user. The input / output device interface 840 can also accept input from optional input devices 860 (such as keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, game controller, accelerometer, gyroscope or other input devices).

[0173] Memory 870 may contain computer program instructions (grouped into modules or components in some embodiments) that processing unit 810 executes to implement one or more embodiments. Memory 870 typically includes RAM, ROM, and / or other persistent, auxiliary, or non-transitory computer-readable media. Memory 870 may store an operating system 872 that provides computer program instructions for use by processing unit 810 in the general management and operation of computing device 800. Data may be stored in data storage device 890. Memory 870 may further include computer program instructions and other information for implementing aspects of this disclosure, such as module 873 for operating sample processing modules and robotic components to prepare multiple samples for flow cytometry analysis, or module 874 for receiving sample preparation instructions into the system, or module 875 for controlling robotic components to manipulate multiple samples using the sample processing module to prepare multiple samples according to instructions stored in the memory.

[0174] Reagent test kit

[0175] This disclosure also includes kits containing one or more of the following: instructions for programming the subject system, for example in the form of a computer-readable medium (e.g., a flash drive, USB storage, optical disc, DVD, Blu-ray disc, etc.), or for downloading programming or non-transient computer-readable recording media described herein from an Internet network protocol or cloud server.

[0176] In addition to the components described above, the subject kit may also include (in some embodiments) instructions. These instructions may exist in a variety of forms within the subject kit, with one or more of them present in the kit. One form of these instructions may be as printed information on a suitable medium or substrate (e.g., one or more sheets of paper with information printed on them), kit packaging, packaging inserts, etc. Another form of these instructions is as a computer-readable medium on which information is recorded, such as a floppy disk, optical disc (CD), portable flash drive, etc. Yet another form of these instructions is a website address that allows access to information on a remote site via the Internet.

[0177] use

[0178] This disclosure can be used in applications where sample preparation (e.g., staining) is required before flow cytometry analysis. The disclosure can also be used in applications requiring such sample preparation on large volumes of samples, and automating these sample preparation steps improves efficiency (eliminating the need for user intervention) and enhances the consistency of prepared samples.

[0179] This disclosure can be used in various applications requiring the analysis and sorting of particulate components in fluid media, such as biological samples. In some embodiments, the systems and methods described herein can be used for flow cytometry characterization of fluorescently labeled biological samples. This disclosure can also be used in applications requiring flow cytometers with higher cell sorting accuracy and stronger particle collection capabilities.

[0180] The embodiments of this disclosure can be used in applications requiring the preparation of cells from biological samples for research, laboratory testing, or therapy. In some embodiments, the subject methods and apparatus can facilitate the acquisition and / or analysis of individual cells prepared from a target fluid or tissue biological sample. For example, the subject methods and systems facilitate the acquisition of cells from fluid or tissue samples for use as research or diagnostic samples for diseases such as cancer. Similarly, the subject methods and systems can also facilitate the acquisition of cells from fluid or tissue samples for therapeutic purposes.

[0181] Notwithstanding the appended claims, this disclosure is further defined by the following:

[0182] 1. A robotic system for automated sample preparation, the system comprising:

[0183] Multiple sample processing modules;

[0184] Multiple robotic components integrated with the sample processing module;

[0185] A processor includes a memory operatively coupled to the processor, wherein the memory contains instructions stored thereon that, when executed by the processor, cause the processor to control the sample handling module and the robotic component so as to:

[0186] Operate the sample processing module and robotic components to prepare multiple samples for flow cytometry analysis; and

[0187] Operable connections between the processor, the sample processing module, and the plurality of robot components.

[0188] 2. The system according to item 1 further comprises:

[0189] Flow cytometer

[0190] The plurality of robotic components are further integrated with the flow cytometer.

[0191] The memory further contains instructions stored thereon that, when executed by the processor, cause the processor to control the sample processing module, the robotic assembly, and the flow cytometer, so as to:

[0192] Each of the plurality of samples prepared is loaded into the flow cytometer; and

[0193] The flow cytometer was operated to analyze each of the prepared samples from the plurality of samples.

[0194] The operable connection operably connects the processor, the sample processing module, the flow cytometer, and the plurality of robotic components.

[0195] 3. The system according to any one of the preceding entries, wherein the system is configured to automatically prepare multiple samples for flow cytometry analysis.

[0196] 4. The system according to any one of the preceding entries, wherein the system is configured to automatically and continuously prepare multiple samples for flow cytometry analysis.

[0197] 5. The system according to any one of the preceding entries, wherein the system is configured to prepare multiple samples for flow cytometry analysis in an unattended manner.

[0198] 6. The system according to any one of the preceding entries, wherein the system is configured to prepare multiple samples for flow cytometry analysis without user interaction.

[0199] 7. The system according to any one of the preceding entries, wherein the system is configured to prepare multiple samples for flow cytometry analysis without user manipulation of the samples.

[0200] 8. The system according to any one of the preceding entries, wherein the system is configured to prepare multiple samples for flow cytometry analysis without user control of the sample processing module.

[0201] 9. The system according to any one of the preceding entries, wherein the system is configured to receive user instructions to prepare multiple samples for flow cytometry analysis.

[0202] 10. The system according to item 9, wherein the system is configured to receive user instructions prior to sample preparation.

[0203] 11. The system according to any one of the preceding entries, wherein the system is configured to automatically prepare multiple samples for flow cytometry analysis.

[0204] 12. The system according to any one of the preceding entries, wherein the system is a robotic system for automating sample preparation and flow cytometry analysis.

[0205] 13. The system according to any one of the preceding entries, wherein the instructions include one or more of the following: scheduling software, software for defining liquid handling parameters, and software for defining system settings.

[0206] 14. The system according to any one of the preceding entries, wherein the plurality of sample processing modules are configured to prepare samples for flow cytometry analysis.

[0207] 15. The system according to any one of the preceding entries, wherein the plurality of sample processing modules comprise stand-alone laboratory equipment.

[0208] 16. The system according to any one of the preceding entries, wherein the plurality of modules comprises one or more of the following: an antibody dilution module, a cell staining module, a sample washing module, a sample resuspension module, a sample movement module, and a sample analysis module.

[0209] 17. The system according to item 16, wherein the cell staining module is configured to perform cell staining using a fluorescently conjugated antibody.

[0210] 18. The system according to any one of the preceding entries, wherein the plurality of modules comprises one or more of the following: incubator, storage unit, cooler, plate washer, well washer, compressor, vacuum pump, static rack, bottle, flow cytometer, plate rotator, barcode scanner, plate storage device, centrifuge, transfer rack, automated liquid handling platform, pipette, sample receiving area, reagent receiving area and waste receiving area.

[0211] 19. The system according to any one of the preceding entries, wherein the plurality of modules include a flow cytometer configured to perform flow cytometry analysis on samples prepared by the system.

[0212] 20. The system according to any one of the preceding entries, wherein the plurality of sample processing modules are configured to stain the sample with cells.

[0213] 21. The system according to any one of the preceding entries, wherein the plurality of sample processing modules are configured to incubate the sample.

[0214] 22. The system according to any one of the preceding entries, wherein the plurality of sample processing modules are configured to manipulate the sample in a multi-well plate.

[0215] 23. The system according to any one of the preceding entries, wherein the plurality of sample processing modules include a pipette.

[0216] 24. The system according to any one of the preceding entries, wherein the plurality of sample processing modules are configured to move fluid into or out of the porous plate.

[0217] 25. The system according to any one of the preceding entries, wherein the plurality of sample processing modules include a centrifuge.

[0218] 26. The system according to any one of the preceding entries, wherein the plurality of sample processing modules are configured to rotate the sample to separate the various components of the sample.

[0219] 27. The system according to any one of the preceding entries, wherein the plurality of sample processing modules are configured to wash the sample.

[0220] 28. The system according to any one of the preceding entries, wherein the robotic component is configured to move one or more perforated plates.

[0221] 29. The system according to any one of the preceding entries, wherein the robot component is configured as a perforated plate insertion and removal module.

[0222] 30. The system according to any one of the preceding entries, wherein the robotic component includes fingers configured to grasp a perforated plate.

[0223] 31. The system according to any one of the preceding entries, wherein the robot component is configured as a control module.

[0224] 32. The system according to any one of the preceding entries, wherein the robot component includes a guide rail and an actuator for translating the robot component.

[0225] 33. The system according to any one of the preceding entries, wherein the robotic component includes a robotic arm.

[0226] 34. The system according to any one of the preceding entries, wherein the module and robot component are configured to manipulate one or more of the following: test tubes, multi-well plates, deep-well plates, and standard well plates.

[0227] 35. The system according to any one of the preceding entries, wherein the module and robot component are configured to manipulate a 96-hole standard depth plate.

[0228] 36. The system according to any one of the preceding entries further includes one or more tables.

[0229] 37. The system according to any one of the preceding entries further includes a user interface.

[0230] 38. The system according to item 37, wherein the user interface includes one or more of the following: a display, a keyboard, a mouse, a touchpad, a user tagging device, system status indicators, and an emergency stop button.

[0231] 39. The system according to any one of the preceding items further includes an electrical interface for supplying power to the system.

[0232] 40. The system according to any one of the preceding items further includes an electrical interface for supplying power to the system.

[0233] 41. The system according to any one of the preceding items further includes a data interface for sending and / or receiving control signals or data signals from the system.

[0234] 42. The system according to any one of the preceding items further includes a gas interface for supplying pressurized gas to the system.

[0235] 43. The system according to any one of the preceding items further includes a network interface.

[0236] 44. The system according to any one of the preceding items further includes one or more protective shields.

[0237] 45. The system according to any one of the preceding entries further includes one or more light curtains.

[0238] 46. ​​A method for preparing a sample for flow cytometry analysis, the method comprising:

[0239] A first sample processing module is used to introduce multiple samples into a system of multiple sample processing modules, wherein the system comprises:

[0240] The plurality of sample processing modules;

[0241] Multiple robotic components integrated with the sample processing module;

[0242] A processor includes a memory operatively coupled to the processor, wherein the memory contains instructions stored thereon that, when executed by the processor, cause the processor to control the sample handling module and the robotic component so as to:

[0243] Operate the sample processing module and robotic components to prepare multiple samples for flow cytometry analysis; and

[0244] Operable connections between the processor, the sample processing module, and the plurality of robot components;

[0245] Provide the system with sample preparation instructions; and

[0246] The system is activated to automatically prepare a sample according to the sample preparation instructions.

[0247] 47. A computer-executed method for preparing a sample for flow cytometry analysis, the method comprising:

[0248] Multiple samples are received into a first sample processing module of a system comprising multiple sample processing modules, wherein the system includes:

[0249] The plurality of sample processing modules;

[0250] Multiple robotic components integrated with the sample processing module;

[0251] A processor includes a memory operatively coupled to the processor, wherein the memory contains instructions stored thereon that, when executed by the processor, cause the processor to control the sample handling module and the robotic component so as to:

[0252] Operate the sample processing module and robotic components to prepare multiple samples for flow cytometry analysis; and

[0253] Operable connections between the processor, the sample processing module, and the plurality of robotic components; and

[0254] According to the instructions stored in the memory, the robot component is controlled to manipulate the plurality of samples using the sample processing module to prepare the plurality of samples.

[0255] 48. The method according to any one of items 46 to 47, wherein the method is a method for continuously preparing samples.

[0256] 49. The method according to any one of items 46 to 48, wherein the method is a method for preparing a sample in an unattended manner.

[0257] 50. The method according to any one of items 46 to 49, wherein the method is a method for preparing multiple samples for flow cytometry analysis without user interaction.

[0258] 51. The method according to any one of items 46 to 50, wherein the method is a method for preparing multiple samples for flow cytometry analysis without user manipulation of the samples.

[0259] 52. The method according to any one of items 46 to 51, wherein the method is a method for preparing multiple samples for flow cytometry analysis without user control of the sample processing module.

[0260] 53. The method according to any one of items 46 to 52, wherein the method further comprises receiving instructions from a user.

[0261] 54. The method according to any one of items 46 to 53, wherein the system is configured to receive user instructions to prepare multiple samples for flow cytometry analysis.

[0262] 55. The method according to any one of items 46 to 54, wherein the first sample processing module includes a sample receiving module.

[0263] 56. The method according to any one of items 46 to 55, wherein the sample receiving module includes a sample storage module.

[0264] 57. The method according to any one of items 46 to 56, wherein the sample preparation instructions include instructions for instructing the robotic assembly to move the microplate from a first position to a second position.

[0265] 58. The method according to any one of entries 46 to 57, wherein the sample preparation instructions include instructions for instructing the robotic assembly to move the microplate from the first sample processing module to the second sample processing module.

[0266] 59. The method according to any one of items 46 to 58, wherein the sample preparation instructions include instructions for one or more of the following: moving one or more system components, moving one or more plates, diluting and / or preparing one or more antibody solutions, lysing the sample, mixing the antibody solution with the sample, incubating the sample, resuspending the sample, washing the sample, and storing the sample.

[0267] 60. The method according to any one of items 46-59, further comprising:

[0268] The sample is retrieved from the sample input station using a robotic component.

[0269] 61. The method according to any one of items 46 to 60, wherein the system further comprises a flow cytometer.

[0270] 62. The method according to any one of items 46 to 61, wherein the sample preparation instructions further comprise instructions for:

[0271] The prepared sample is loaded into the flow cytometer using a robotic component;

[0272] The sample was analyzed by flow cytometry using the flow cytometer described above; and

[0273] The sample is removed from the flow cytometer using a robotic component.

[0274] 63. The method according to any one of items 46 to 62, wherein the method is a method of automatically performing a manual laboratory workflow using the robotic component and sample handling module.

[0275] Although the foregoing disclosure has been described in detail by way of illustration and example for purposes of clarity, it will be apparent to those skilled in the art, based on the teachings of this disclosure, that some changes and modifications may be made without departing from the spirit or scope of the appended claims.

[0276] Therefore, the foregoing is merely illustrative of the principles of this disclosure. It should be understood that those skilled in the art will be able to conceive of various arrangements, which, although not explicitly described or shown herein, embody the principles of this disclosure and are included within its spirit and scope. Furthermore, all examples and conditional language recorded herein are primarily intended to assist the reader in understanding the principles of this disclosure and the concepts contributed by the inventors to the field, and should be construed as not being limited to such specifically recorded examples and conditions. Moreover, all statements herein recounting the principles, aspects, and embodiments of this disclosure and their specific examples are intended to cover their structural and functional equivalents. Additionally, such equivalents are intended to include both currently known equivalents and future development equivalents, i.e., any element developed that performs the same function regardless of its structure. Furthermore, nothing disclosed herein is intended to be offered to the public, whether or not such disclosure is expressly recited in the claims.

[0277] Therefore, the scope of this disclosure is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of this disclosure are embodied in the appended claims. In the claims, 35 USC §112(f) or 35 USC §112(6) is explicitly defined as being invoked only when the exact phrase “means for” or the exact phrase “step for” is recited at the beginning of a definition in the claim; if such an exact phrase is not used in the definition in the claim, then 35 USC §112(f) or 35 USC §112(6) is not invoked.

Claims

1. A robotic system for automated sample preparation, the system comprising: Multiple sample processing modules; Multiple robotic components integrated with the sample processing module; A processor includes a memory operatively coupled to the processor, wherein the memory contains instructions stored thereon that, when executed by the processor, cause the processor to control the sample handling module and the robotic component so as to: Operate the sample processing module and robotic components to prepare multiple samples for flow cytometry analysis; and Operable connections between the processor, the sample processing module, and the plurality of robot components.

2. The system according to claim 1, further comprising: Flow cytometer in, The multiple robotic components are further integrated with the flow cytometer. The memory further contains instructions stored thereon, which, when executed by the processor, cause the processor to control the sample processing module, the robotic assembly, and the flow cytometer, so as to: Each of the plurality of samples prepared is loaded into the flow cytometer; and The flow cytometer was operated to analyze each of the prepared samples from the plurality of samples. The operable connection operably connects the processor, the sample processing module, the flow cytometer, and the plurality of robotic components.

3. The system according to any one of the preceding claims, wherein the system is configured to automatically prepare multiple samples for flow cytometry analysis.

4. The system according to any one of the preceding claims, wherein the system is configured to automatically and continuously prepare multiple samples for flow cytometry analysis.

5. The system according to any one of the preceding claims, wherein the system is configured to prepare multiple samples for flow cytometry analysis in an unattended manner.

6. The system according to any one of the preceding claims, wherein the system is a robotic system for automating sample preparation and flow cytometry analysis.

7. The system according to any one of the preceding claims, wherein the instructions include one or more of the following: scheduling software, software for defining liquid handling parameters, and software for defining system settings.

8. The system according to any one of the preceding claims, wherein the plurality of sample processing modules are configured to prepare samples for flow cytometry analysis.

9. The system according to any one of the preceding claims, wherein the plurality of sample processing modules comprise stand-alone laboratory equipment.

10. The system according to any one of the preceding claims, wherein the plurality of modules comprises one or more of the following: an antibody dilution module, a cell staining module, a sample washing module, a sample resuspending module, a sample moving module, and a sample analysis module.

11. The system according to any one of the preceding claims, wherein the plurality of modules comprises one or more of the following: an incubator, a storage unit, a cooler, a plate washer, a well washer, a compressor, a vacuum pump, a static rack, a bottle, a flow cytometer, a plate rotator, a barcode scanner, a plate storage device, a centrifuge, a transfer rack, an automated liquid handling platform, a pipette, a sample receiving area, a reagent receiving area, and a waste receiving area.

12. The system according to any one of the preceding claims, wherein the plurality of modules include a flow cytometer configured to perform flow cytometry analysis on samples prepared by the system.

13. The system according to any one of the preceding claims further includes a user interface.

14. The system according to any one of the preceding claims further includes a network interface.

15. A method for preparing a sample for flow cytometry analysis, the method comprising: A first sample processing module is used to introduce multiple samples into a system of multiple sample processing modules, wherein the system comprises: The plurality of sample processing modules; Multiple robotic components integrated with the sample processing module; A processor includes a memory operatively coupled to the processor, wherein the memory contains instructions stored thereon that, when executed by the processor, cause the processor to control the sample handling module and the robotic component so as to: The sample processing module and robotic components are operated to prepare multiple samples for flow cytometry analysis; as well as Operable connections between the processor, the sample processing module, and the plurality of robot components; Provide sample preparation instructions to the system; as well as The system is activated to automatically prepare a sample according to the sample preparation instructions.

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