Integration and validation of workflows for flow cytometry

Abstract

A system for integrating flow cytometry workflows. The system builds a worklist by adding task groups for single or multiple tube panels for analyzing one or more samples. The system instructs instruments to perform tasks in the task groups added to the worklist. The system exchanges data between the instruments. The data exchanged between the instruments being related to performance of the tasks in the task groups. The worklist can include a first task group for instrument quality control, a second task group for assay quality control, and a third task group for compensation and standardization verification.

Description

INTEGRATION AND VALIDATION OF WORKFLOWS FOR FLOWCYTOMETRYCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is being filed as a PCT International application and claims the benefit of and priority to U.S. Application No. 63 / 632,049, filed on April 10. 2024, titled INTEGRATION AND VALIDATION OF WORKFLOWS FOR FLOW CYTOMETRY, the disclosure of which is hereby incorporated by reference in its entirety.BACKGROUND

[0002] Flow cytometry assay workflows are typically fragmented due to a lack of automation and integration of information. For example, lab technicians must often manually reenter information regarding the same sample at multiple times throughout a workflow. Also, flow cytometry assay workflows typically fail to connect a flow cytometer to a downstream analysis platform. Alternatively, analyzing data on the flow cytometer limits the quality of data visualization and requires a lab technician to work in a loud wet laboratory environment. This also prevents the flow cytometer from being used to analyze additional samples.

[0003] Alternatively, dedicated workstations with analysis software installed thereon are often hard to integrate into a laboratory network and require the physical presence of the lab technicians on the dedicated workstations. Also, when dedicated standalone analysis software is used, manual data transfer is often required. This is time consuming and presents a risk of human transcription error. In addition, not all information may be transferred. Instead, human oversight is often required in order to match data with specific positions in analysis templates. Therefore, there is a need to improve and integrate flow cytometry assay workflows.

[0004] Further, validation of assays by flow cytometry requires users to research and understand the validation requirements, develop a validation plan, and execute it. Currently, solutions are not available within the software used for flow cytometry acquisition and data analysis to help the user develop a worklist for assay validation and to streamline the data analysis. Instead, analysis of validation data is largely manual and dependent on the level of expertise of the user. In addition, when analyzing the data, the user typically analyzes the files individually with no tools to denotemetainformation, aggregate, and summarize results to support reporting. Therefore, there is a further need to improve assay validation.

[0005] Also, users are often required to execute a number of independent quality control tasks to ensure a flow cytometer is in working order, instrument settings and compensation are appropriate, and sample processing workflow is effective. Such quality control tasks may include instrument quality control (QC), gain or voltage standardization, compensation calculation and verification, and assay quality control (QC). Many of these steps are manual, are often driven by rigid schedules, and often do not provide results and information that can drive action.

[0006] Problems associated with quality control on flow cytometers include that instrument QC is often passed even when a flow cytometer is experiencing performance changes. Also, a failure result from instrument QC is often not informative with respect to its impact on analyses by the flow cytometer, and often omits to include steps on how to fix or mitigate an underlying issue. Further problems include that to standardize gain and voltage settings on a flow cytometer, often different material from instrument QC is used and current solutions often do not provide guidance as to when settings on the flow cytometer need to be updated. Also, compensation is typically recommended to be performed in regular, sometimes daily intervals, while users typically prefer to execute compensation only when required. Assay QC using stabilized blood samples or gel based spheres enables the user to evaluate the performance of their sample processing steps and instrument. However, assay QC is largely manual and restrictive in scope.

[0007] In view of the foregoing, flow cytometer instruments typically provide only general information on whether or not the instrument is working as expected that cannot be extrapolated to assay settings. Further, current quality control approaches on flow cytometers do not provide clear data driven guidance on how to fix or mitigate a condition impacting performance of the flow cytometers. Therefore, there is a need to improve quality control on flow cytometers.SUMMARY

[0008] In general terms, the present disclosure relates to flow cytometry. In one possible configuration, integration of flow cytometry assay workflows is provided. In another possible configuration, guidance and automation for validation of assays byflow cytometry is provided. In another possible configuration, improved quality controlprocesses are provided for flow cytometers. Various aspects are described in this disclosure, which include, but are not limited to, the following aspects.

[0009] One aspect relates to a system for integrating flow cytometry workflows, the system comprising: a processing circuitry having a memory for storing instructions which, when executed by the processing circuitry, cause the processing circuitry to: build a worklist by adding task groups for single or multiple tube panels for analyzing one or more samples; instruct instruments to perform tasks in the task groups added to the worklist; and exchange data between the instruments, the data related to performance of the tasks in the task groups.

[0010] Another aspect relates to a method of integrating flow cytometry workflows, the method comprising: building a worklist by adding task groups for single or multiple tube panels for analyzing one or more samples; instructing instruments to perform tasks in the task groups added to the worklist; and exchanging data between the instruments, the data related to performance of the tasks in the task groups.

[0011] Another aspect relates to a method of performing quality control on a flow cytometer, the method comprising: receiving a quality control task group added to a worklist, the quality control task group including one or more samples for instrument quality7control, one or more samples for assay quality' control, and one or more samples for compensation and standardization verification, the worklist identifies a sample carrier, defines contents of the sample carrier, and defines processing steps for the contents on the sample carrier; running, based on the worklist, the one or more samples for the instrument quality7control, the one or more samples for the assay quality7control, and the one or more samples for the compensation and standardization verification; analyzing instrument quality control data acquired by the flow cytometer from the one or more instrument quality control samples; analyzing assay quality7control data acquired by7the flow cytometer from the one or more assay quality control samples; analyzing compensation and standardization quality control data acquired by the flow cytometer from the one or more compensation and standardization verification samples; and providing guidance to improve performance of the flow cytometer based on at least one of the instrument quality control data, the assay quality control data, and the compensation and standardization quality control data.

[0012] A variety of additional aspects will be set forth in the description that follows. The aspects can relate to individual features and to combination of features. It is to be understood that both the foregoing general description and the followingdetailed description are exemplary and explanatory only and are not restrictive of the broad inventive concepts upon which the embodiments disclosed herein are based.DESCRIPTION OF THE FIGURES

[0013] The following drawing figures, which form a part of this application, are illustrative of the described technology and are not meant to limit the scope of the disclosure in any manner.

[0014] FIG. 1 schematically illustrates an example of a flow cytometry ecosystem that includes an integration system that is communicatively coupled to a plurality of instruments for analysis of samples acquired from a plurality of subjects.

[0015] FIG. 2 schematically illustrates an example of the integration sy stem of FIG. 1.

[0016] FIG. 3 schematically illustrates an example of a plurality of flow cytometry ecosystems communicatively coupled to a network.

[0017] FIG. 4 shows an example of a user interface that can be generated on a workstation by the integration system of FIG. 1.

[0018] FIG. 5 shows another example of a user interface that can be generated on the workstation by the integration system of FIG. 1.

[0019] FIG. 6 illustrates an example of a user interface that can be generated on the workstation by a workflow manager tool provided by the integration system of FIG. 1.

[0020] FIG. 7 illustrates another example of a user interface that can be generated on the workstation by the workflow manager tool provided by the integration system of FIG. 1.

[0021] FIG. 8 illustrates an example of a user interface that can be generated on the workstation by an analysis and reporting tool provided by the integration system of FIG. 1.

[0022] FIG. 9 illustrates another example of a user interface that can be generated on the workstation by the analysis and reporting tool provided by the integration system of FIG. 1.

[0023] FIG. 10 schematically illustrates an example of a method of analyzing a sample in the flow cytometry ecosystem of FIG. 1.

[0024] FIG. 11 schematically illustrates an example of a task group that can be used to build a worklist in the method of FIG. 10.

[0025] FIG. 12 schematically illustrates an example of a worklist that can be built in the method of FIG. 10.

[0026] FIG. 13 schematically illustrates another example of a worklist that can be built for processing first and second samples in an example of the method of FIG. 10.

[0027] FIG. 14 schematically illustrates an example of a worklist that identifies a plurality of tasks for a carrier location defined in a task group for processing a sample.

[0028] FIG. 15 schematically illustrates an example of a method of validating a flowcytometry assay in the flow cytometry ecosystem of FIG. 1.

[0029] FIG. 16 shows an example of a user interface displayed on the workstation, the user interface allowing a user to develop a task group for assay validation in accordance with the operations of the method of FIG. 15.

[0030] FIG. 17 shows an example of a user interface displayed on the workstation, the user interface allowing the user to select one or more options for a test added to the task group in the user interface of FIG. 16.

[0031] FIG. 18 shows an example of a user interface displayed on the workstation, the user interface allowing the user to enter assay relevant information in accordance with the operations of the method of FIG. 15.

[0032] FIG. 19 illustrates an example of a user interface generated by a workflow monitoring tool of the integration system of FIG. 1.

[0033] FIG. 20 illustrates another example of a user interface generated by the w orkflow monitoring tool of the integration system of FIG. 1.

[0034] FIG. 21 illustrates another example of a user interface generated by the workflow monitoring tool of the integration system of FIG. 1 .

[0035] FIG. 22 illustrates another example of a user interface generated by the workflow monitoring tool of the integration system of FIG. 1.

[0036] FIG. 23 schematically illustrates an example of a method of flow cy tometry' quality control that can be performed by a quality control application on a flow cytometer of the flow cytometry- ecosystem of FIG. 1.

[0037] FIG. 24 illustrates an example of a statistical comparison that can be displayed in accordance with an operation of the method of FIG. 23.

[0038] FIG. 25 schematically illustrates an example of an instrument quality control that can be performed by the quality' control application on the flow' cytometer of the flow cytometry' ecosystem of FIG. 1.

[0039] FIG. 26 schematically illustrates a data model that supplies quality control target values for an instrument quality control test that can be performed by the qualitycontrol application on the flow cytometer of the flow cytometry ecosystem of FIG. 1.DETAILED DESCRIPTION

[0040] FIG. 1 schematically illustrates an example of a flow cytometry ecosystem 10 that includes an integration system 100 that is communicatively coupled to a plurality of instruments for analysis of samples acquired from a plurality of subjects. The instruments are used to detect and measure physical and chemical characteristics of cell and / or particle populations in the samples. In the example illustrated in FIG. 1, the integration system 100 is communicatively coupled to one or more sample preparation instruments 12, one or more flow cytometers 14, one or more liquid handlers 16, and one or more workstations 18. The integration system 100 may be communicatively coupled to additional types of instruments, or fewer ty pes of instruments, depending on the needs of the flow cytometry ecosystem 10.

[0041] The integration system 100 provides comprehensive solutions for flow cytometer laboratory' workflows without requiring a user to reenter previously entered information for complex clinical flow cytometry' testing. The integration sy stem 100 provides seamless data exchange, alignment of terminology, normalization of common activities, and automation of processes performed across the instruments within the flow cytometry' ecosystem 10. Further, the integration system 100 provides common data storage across the instruments.

[0042] As will be described in more detail, the integration system 100 facilitates data exchange in the sample processing flow cytometry workflows through the use of worklists. Worklists include task groups for a sample carrier such as a plate, a carousel, or a tube rack. There can be several task groups per sample carrier. A task group determines what happens to a single sample or a group of samples. More than one task group can be in the worklist for a single sample carrier. In some examples, the task groups are made up of tasks that are to be executed in order. The worklists contain all of the information needed by the instruments in the flow cytometry' ecosystem 10 to perform all necessary' steps for efficiently analyzing the samples in the flow cytometry workflow.

[0043] The one or more sample preparation instruments 12 are used to prepare panels of the samples for analysis by the one or more flow cytometers 14. Each panel includes one or more tubes of the sample having a unique combination of antibodies, antigens, fluorochromes, and other reagents to identify cells of interest and extract clinically relevant data. As an illustrative example, the one or more sample preparationinstruments 12 can include one or more of the CellMek® SPS sample preparation instruments from Beckman Coulter®, Inc.

[0044] The one or more flow cytometers 14 are used to perform flow cytometry analyses of the panels prepared by the one or more sample preparation instruments 12. As an illustrative example, the one or more flow cytometers 14 can include one or more research flow cytometry analyzers such as the CytoFlex®1Analyzer Platform from Beckman Coulter®, Inc., one or more clinical flow cytometry analyzers such as the AQUIOS® CL, Navios® EX, and DxFlex® Flow Cytometer from Beckman Coulter®, Inc., and one or more cell sorters such as CytoFLEX® SRT Benchtop Cell Sorter and MoFlo Astrios® Cell Sorter from Beckman Coulter®, Inc.

[0045] The one or more liquid handlers 16 are used for building cell-based assays, providing high-throughput screening, cell line development, cell culture preparation, biologic bioanalysis, compound handling, and other research activities. As an illustrative example, the one or more liquid handlers 16 can include the Biomek® Liquid Handler from Beckman Coulter®, Inc.

[0046] The one or more workstations 18 display a web-based user interface for near real-time visualization of data flow acquired by the integration system 100 from the other instruments in the flow cytometry7ecosystem 10 such as the one or more sample preparation instruments 12, the one or more flow cytometers 14, and the one or more liquid handlers 16. While FIG. 1 illustrates the one or more workstations 18 as being included in the flow cy tometry7ecosystem 10, the one or more workstations 18 may be physically located in a separate location away from the other instruments of the flow cytometry7ecosystem 10. As such, the one or more workstations 18 are not necessarily located within a wet laboratory environment proximate the other instruments of the flow cytometry7ecosystem 10.

[0047] FIG. 2 schematically7illustrates an example of the integration system 100. As shown in FIG. 2, the integration system 100 is communicatively coupled to the one or more sample preparation instruments 12, the one or more flow cytometers 14, the one or more liquid handlers 16, and the one or more workstations 18 via a network 20. The integration system 100 includes a communications interface 116 that allows the integration system 100 to connect to the network 20. The communications interface 116 can include wired interfaces and wireless interfaces. For example, the communications interface 116 can wirelessly connect to the network 20 through Wi-Fi, cellular network communications, and other wireless connections. Alternatively, the communicationsinterface 116 can connect to the network 20 using wired connections such as through an Ethernet or Universal Serial Bus (USB) cable.

[0048] The network 20 connects and exchanges data between the integration system 100 and the other instruments in the flow cytometry ecosystem 10. The network 20 can include any t pe of wired or wireless connections, or any combinations thereof. In some examples, the wireless connections can be accomplished using Wi-Fi, ultra- wideband (UWB). Bluetooth, and the like. In some examples, the network 20 is an Internet of things (loT) netw ork.

[0049] The integration system 100 includes a computing device 102 having one or more processing devices 104. Examples of the one or more processing devices 104 include central processing units (CPUs), digital signal processors, field-programmable gate arrays, and other types of computing circuits. The one or more processing devices 104 can be part of a processing circuitry having a memory for storing instructions which, when executed by the processing circuitry, cause the processing circuitry to perform the functionalities described herein.

[0050] The computing device 102 further includes a system memory 106 that operates to store data and instructions for execution by the one or more processing devices 104. In the example illustrated in FIG. 2, the system memory' 106 stores a workflow integration application 108 that is executable by the one or more processing devices 104 to integrate workflows among the instruments in the flow cytometry ecosystem 10. The system memory 106 further stores an inventory monitoring application 110 that is executable by the one or more processing devices 104 to manage inventory' of consumables such as antibodies, antigens, fluorochromes, and other reagents used by the instruments in the flow cytometry ecosystem 10. The system memory 106 further stores an assay validation application 112 that is executable by the one or more processing devices 104 to validate assays used among the instruments in the flow7cytometry ecosystem 10. The system memory' 106 further stores a qualitycontrol (QC) application 114 that is executable by the one or more processing devices 104 to execute quality control tasks on the flow cytometer 14 to ensure the flow cytometer 14 is in working order, instrument settings and compensation are appropriate, and sample processing workflow- is effective. The workflow- integration application 108, the inventory monitoring application 110, the assay validation application 112, and the QC application 114 are each described in more detail below.

[0051] The system memory 106 includes computer-readable media, which includes any media that can be accessed by the one or more processing devices 104. By way of example, computer-readable media include computer readable storage media and computer readable communication media. Computer readable storage media includes volatile and nonvolatile, removable and non-removable media implemented in any device configured to store information such as computer readable instructions, data structures, program modules, or other data. Computer readable storage media can include, but is not limited to, random access memory, read only memory, electrically erasable programmable read only memory, flash memory, and other memory' technology, including any medium that can be used to store information that can be accessed by the data acquisition device. The computer readable storage media is non- transitory.

[0052] Computer readable communication media embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal’7refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, computer readable communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency, infrared, and other wireless media. Combinations of any of the above are within the scope of computer readable media.

[0053] As further shown in FIG. 2, the integration system 100 further includes a database 118 that stores a plurality' of flow- cytometry standard (FCS) files 120. The FCS files 120 follow a standardized format for reading and writing of data from flowcytometry experiments. The FCS files 120 are maintained by the integration system 100 for sharing among the instruments in the flow? cytometry ecosystem 10 such as any one of the one or more sample preparation instruments 12, the one or more flow cytometers 14, the one or more liquid handlers 16, and the one or more workstations 18. Thus, all instruments in the flow cytometry ecosystem 10 are capable of importing and exporting data to and from the FCS files 120 maintained by the integration system 100 in a manner that supports seamless flow' of data from preceding workflow' steps through succeeding workflow steps of a flow cy tometry assay workflow.

[0054] FIG. 3 schematically illustrates an example of a plurality of flow cytometry ecosystems 10a- lOe communicatively coupled to the network 20. The plurality of flowcytometry ecosystems lOa-lOe are separate from one another such that they can be located in different geographical locations such as in different cities, states, or even countries. As shown in FIG. 3, the FCS files 120 can be shared among the flow cytometry ecosystems 10 via the network 20. Thus, a flow cytometry experiment performed in a first flow cytometry7ecosystem can be repeated in the other flow7cytometry ecosystems with high fidelity. While FIG. 3 illustrates that there are five flow cytometry7ecosystems 10a- lOe communicatively coupled to the network 20, this provided by way' of illustrative example such that a limitless number of flow cytometry ecosystems may be connected to the network 20.

[0055] FIG. 4 shows an example of a user interface 400 that can be generated on a workstation 18 by the integration system 100. The user interface 400 can be generated by the workflow integration application 108 executed on the integration system 100. As show n in FIG. 4, the user interface 400 is displayed on a display monitor 19 of the workstation 18.

[0056] A user can log into the integration system 100 to view the user interface 400 by selecting a log-in icon 406 to enter their user credentials such as username and password. The user credentials can be validated by the integration system 100. When the user credentials are validated, the integration system 100 grants the user access to the user interface 400. As such the user interface 400 is customized by the integration system 100 based on the user credentials entered via selection of the log-in icon 406 on the user interface 400.

[0057] As shown in FIG. 4, the user interface 400 can include a first set of tools 408 that are available for selection based on the user credentials, and a second set of tools 410 that are not available for selection based on the user credentials. The availability' of the tools displayed on the user interface 400 can be based on a license or subscription associated with the user credentials.

[0058] In the example shown in FIG. 4, the tools 402 are displayed as icons on a home tab (i.e. , “Workflow Manager”) of the user interface 400. When a tool 402 is selected on the home tab, the tool is opened on a tab 404 of the user interface 400. In this configuration, multiple tools can be opened concurrently on the tabs 404 of the user interface 400.

[0059] FIG. 5 shows another example of a user interface 500 that can be generated on the workstation 18 by the integration system 100. In this configuration, the user interface 500 includes tools 502 that are displayed as icons in a menu bar 504. When atool 502 is selected, the tool launches in a main pane 506 of the user interface 500. In this configuration, only one tool can be opened at a time in the main pane 506 of the user interface 500.

[0060] The tools provided by the integration system 100 on the user interfaces 400, 500 are modular and operate independently of each other. Each tool is intended to be purpose built to allow flexibility to optimize each tool to particular flow cytometry workflows.

[0061] The user interfaces 400, 500 are web-based such that the user interfaces 400, 500 can be generated on a web browser, and the tools displayed on the user interfaces 400, 500 are accessible via a wide area network (WAN) such as the Internet. This provides access to the tools from any workstation 18 connected to the network 20. This eliminates the need to install software on particular workstations 18 for providing access to the tools. This also standardizes the user interfaces 400, 500 for accessing and interacting with the tools, and allows the user interfaces 400, 500 to be updated more efficiently across multiple flow cytometry ecosystems 10.

[0062] The tools provided by the integration system 100 on the user interfaces 400, 500 include a workflow manager 402a, which is a front end user interface that provides a portal for the other tools available to a user. As an illustrative example, the workflow manager is displayed on the home tab of the user interface 400. As another example, the workflow manager is displayed on the main pane 506 of the user interface 500 before another tool is launched. When a user logs into the workflow manager, the workflow manager allows the user to access the other tools they are permitted to use without requiring the user to login into each individual tool.

[0063] FIG. 6 illustrates an example of a user interface 600 displayed on the display monitor 19 of the workstation 18 by the workflow manager 402a. The user interface 600 displayed by the workflow manager 402a allows a user to select for a sample carrier (such as a tube) a flow cytometer action, a location on a sample carrier such as a plate, a carousel, or a tube rack for a tube, a tube barcode, and one or more additional parameters such as a lot number, a mix time, or a rinse duration for processing the tube to perform an assay validation in the flow cytometry ecosystem 10.

[0064] FIG. 7 illustrates another example of a user interface 700 displayed on the display monitor 19 of the workstation 18 by the workflow- manager 402a. The user interface 700 displayed by the workflow manager 402a allows a user to associate apatient or other source and a sample ID to a task group for assay validation, which is described in more detail further below.

[0065] The tools provided by the integration system 100 on the user interfaces 400, 500 can further include a workflow monitoring tool 402b, 02b which monitors the progress of a sample through a workflow performed by multiple instruments of the flow cytometry ecosystem 10. The workflow monitoring tool 402b, 502b also provides information on the status of the instruments in the flow cytometry ecosystem 10 such as fluid levels (e g., amounts of waste and sheath fluid in each of the flow cytometers 14, amounts of reagents in each of the sample preparation instruments 12, and inventory of other consumables used by the instruments), and current activities being performed by the instruments in the flow cytometry ecosystem 10. In some examples, aspects of the workflow monitoring tool 402b, 502b are performed by the inventor}' monitoring application 110. Illustrative examples of user interfaces displayed by the workflow monitoring tool 402b, 502b are shown in FIGS. 19-22 described below in more detail.

[0066] The tools provided by the integration system 100 on the user interfaces 400, 500 can further include a worklist builder 402c. 502c which creates worklists for sample preparation and analysis. The worklists use defined task groups and sample information to define contents and processing steps for a sample carrier, such as a plate, a carousel, or a tube rack for processing by the instruments in the flow cytometry ecosystem 10 such as any one of the sample preparation instruments 12, the flow cytometers 14, the liquid handlers 16, and the workstations 18.

[0067] The tools provided by the integration system 100 on the user interfaces 400, 500 can further include a sample preparation guide 402d, 502d which guides data entry' during manual sample preparation. The sample preparation guide 402d, 502d presents sample preparation steps, and provides standardized forms for recording reagent lot numbers, vial identification numbers, expiration dates, and other time dependent information relevant to the preparation materials.

[0068] The tools provided by the integration system 100 on the user interfaces 400, 500 can further include an analysis and reporting tool 402e. 502e. The analysis and reporting tool 402e, 502e provides a comprehensive tool on a single device (e g., the workstation 18) that analyzes and displays data captured from any of the instruments in the flow cytometry ecosystem 10.

[0069] FIG. 8. In this example, the user interface 800 displays a list of FCS files 120 that are each selectable to display data relevant to assay validation performed on a sample by the instruments in the flow cytometry ecosystem 10.

[0070] FIG. 9 illustrates an example of a user interface 900 displayed on the display monitor 19 of the workstation 18 by the analysis and reporting tool 402e, 502e. The user interface 900 displays data relevant to assay validation performed on a sample by the instruments in the flow cytometry ecosystem 10 for an FCS file 120 selected on the user interface 800 of FIG. 8.

[0071] The tools provided by the integration system 100 on the user interfaces 400, 500 can further include an audit trail tool 402f, 502f that allows a first type of user such as an administrator to create reports of event logs in the flow cytometry’ ecosystem 10. The audit trail tool 402f, 502f enables viewing and / or reporting use statistics and quality control reports for each instrument in the flow cytometry ecosystem 10. As shown in FIGS. 4 and 5, the audit trail tool 402f, 502f may be made unavailable for selection (e.g., shaded in grey or not shown) by a second type of user such as a lab technician who is not authorized to use the tool.

[0072] The tools provided by the integration system 100 on the user interfaces 400, 500 can further include a user manager tool 402g, 502g to setup and manage user accounts and permissions for the various users (e.g., lab technicians) within the flow cytometry ecosystem 10.

[0073] The tools provided by the integration system 100 on the user interfaces 400, 500 can further include a task group designer 402h, 502h that allows a first type of user such as an administrator to create and edit task groups for selection by a second type of user such as a lab technician to build a worklist for analyzing samples in the flow cytometry ecosystem 10. As described above, the worklists define processing steps for a sample carrier, such as a plate, a carousel, or a tube rack for processing by the instruments in the flow cytometry ecosystem 10.

[0074] The tools provided by the integration system 100 on the user interfaces 400, 500 can further include an ecosystem configurator 402i, 502i that allows an administrator to setup the network connections between the instruments in the flow cytometry ecosystem 10.

[0075] The tools provided by the integration system 100 on the user interfaces 400, 500 can further include a reagents portal 402j that monitors inventory of reagents used by the instruments in the flow cytometry ecosystem 10. The reagents portal 402jdisplays inventories of the reagents, and enables ordering additional reagents before the inventor}’ runs out.

[0076] The tools provided by the integration system 100 on the user interfaces 400, 500 can further include a laboratory information system (LIS) 402k. The LIS 402k displays statuses of the samples in the flow cytometry ecosystem 10 such as when the samples are received, and whether analysis of the samples is in progress or is completed.

[0077] The tools provided by the integration system 100 on the user interfaces 400, 500 can further include a flow cytometer management tool to view data acquisition from each flow cytometer, quality control metrics for each flow cytometer, standardization of the flow cytometers, as well as the ability to remotely control the flow cytometers.

[0078] The tools provided by the integration system 100 on the user interfaces 400, 500 can further include a sample preparation instrument management tool to execute automated sample preparation on a worklist generated by the worklist builder 402c, 502c.

[0079] The modularity of the tool architecture provided on the user interfaces 400, 500 by the integration system 100 allows the tools 402, 502 to be adapted relatively easily to research and automated workflows. Further, the user interfaces 400. 500 are adaptable to workflows, terminologies, sample varieties, and other specifics which can differ among different flow cytometry ecosystems 10, especially across different geographic regions.

[0080] To address these differences, the availability of the tools may differ for flow cytometry ecosystems 10 across different markets and geographical regions even when the tools are built upon a common foundation. For example, the analysis and reporting tool 402e, 502e can be adapted for use in clinical testing environments such that aspects of the tool are adapted to have a patient-centered focus, use clinical terminology, and implement workflows most relevant to clinical testing. The analysis and reporting tool 402e, 502e can also be adapted for use in research environments such that aspects of the tool are adapted to be experiment focused and use terminology more common in research laboratories. Both implementations of the analysis and reporting tool 402e, 502e are provided on web-based user interfaces (such as the user interfaces 400, 500 of FIGS. 4 and 5), and share a common analysis engine design.

[0081] Similarly, aspects of the task group designer 402h, 502h can be adapted for use in clinical environments to focus on implementing documented panels and validating them for use in clinical laboratory environments. The task group designer 402h, 502h can also be adapted for use in research environments to include a comprehensive panel designer for developing panels from ground-up and providing support to create novel reagent combinations and processes.

[0082] Accordingly, the modular framework of the tools 402. 502 allows the tools 402, 502 to be developed, deployed, and updated as variants for different flow cytometry ecosystems 10 located in different markets and geographical regions.Further, additional tools may be developed and added to the modular framework of the integration system 100 for use in specific markets and geographic regions. This allows the integration system 100 to be configurable for flow cytometry ecosystems 10 that operate in different markets and / or geographic regions.

[0083] The tools 402, 502 enhance integration of flow cytometry workflows by providing a comprehensive tool set for displaying both information and controls to both prepare and analyze samples through an entirety of a flow cytometry workflow that utilizes different instruments within the flow cytometry ecosystem 10. Further, the user interfaces 400, 500 can improve efficiency for preparing and analyzing multiple samples as a batch process.

[0084] FIG. 10 schematically illustrates an example of a method 1000 of analyzing a sample in the flow cytometry ecosystem 10. The method 1000 can be performed by execution of the workflow integration application 108 on the integration system 100.

[0085] The method 1000 includes an operation 1002 of building a worklist byadding together task groups for single or multiple tube panels for analyzing one or more samples. Each task group identifies one or more tasks for processing the single or multiple tube panels in a predefined order by the instruments of the flow cytometry ecosystem 10. The task groups are predefined such that they are stored on a memory of the integration system 100 and can be selected by a user of the integration system 100 as desired for building the worklist. The task groups are associated with samples for processing and analyzing the samples.

[0086] FIG. 11 schematically illustrates an example of a task group 1104 that can be used to build a worklist in operation 1002 of the method 1000. In this example, a sample 1102 can be associated with the task group 1104 that defines a plurality- of tasks 1105 for processing the sample 1102 based on particular flow cytometry workflowselected for the sample. An inter-tube task 1106 can also be assigned to the task group 1104. The inter-tube task 1106 can be performed between the tasks 1105 or after the tasks 1105 are completed. As an example, the inter-tube task 1106 is a backflush performed by a sample preparation instrument 12.

[0087] FIG. 12 schematically illustrates an example of a worklist 1200 that can be built in operation 1002 of the method 1000. In this example, a sample 1202 is associated with a task group 1204 that includes a carrier ID 1203 that identifies a carrier such as a plate, a carousel, or a tube rack, and a plurality of carrier locations1205 on the carrier for holding tubes for processing the sample 1202. An inter-tube task1206 (e.g., backflush) is also assigned to the task group 1204, which is performed after the tubes defined in the task group 704 are processed.

[0088] As an illustrative example, the task group 1204 is for an immunophenotyping screen having four tubes for processing the sample 1202. The task group 1204 includes a carrier ID 1203 that identifies a particular carrier for holding the four tubes, and identifies carrier locations 1205 on the carrier for holding the four tubes. Further, the task group 1204 identifies tasks 1208 performed using each of the tubes. In this example, the task group 1204 includes a clean tube and a deionized (DI) water tube for rinsing to be performed when switching between samples.

[0089] Each time the immunophenotyping screen is run against a sample, the sample is identified and selected via a sample ID. and sample is associated with the task group 1204 which represents the full immunophenotyping screen. The worklist 1200 associates the sample 1202 with the four tubes (one clean tube + one DI water tube + two tubes for Tetra and Hexa tests) and with the inter-tube task 1206. The worklist 1200 further assigns the carrier locations 1205 for the tubes on the carrier such as a plate, a carousel, or a tube rack. In this manner, a user can easily and quickly define a carrier with many different multiple tube panels and samples. The task group allows the integration system 100 to define information for a grouped set of tubes and operations, and to reuse the information for processing and analyzing samples.

[0090] FIG. 13 schematically illustrates another example of a worklist 1300 that can be built for processing first and second samples 1302a, 1302b in an example of operation 1002 of the method 1000. In this illustrative example, the first and second samples 1302a, 1302b are associated with first and second task groups 1304a, 1304b that each define a unique set of tasks 1305 for processing the first and second samples. Also, inter-tube tasks 1306 are assigned to the first and second task groups 1304a,1304b. The inter-tube tasks 1306 are performed between one or more of the tasks 1305 of the first and second task groups 1304a, 1304b.

[0091] As shown in FIG. 13, the first and second samples 1302a, 1302b share one or more tasks 1305 in the first and second task groups 1304a, 1304b. For example, the first and second samples 1302a, 1302b share a Hexa test included in first task group 1304a and the second task group 1304b. In this manner, the tasks 1305 are performed in a defined order such that the Hexa test is repeated for a grouped set of tubes to process the samples more efficiently.

[0092] FIG. 14 schematically illustrates an example of a worklist 1400 that identifies a plurality of tasks for a carrier location 1404 defined in a task group 1402 for processing a sample. In this example, a test 1406 is assigned to the carrier location 1404. The test 1406 includes a plurality of tasks associated with different phases for analyzing a sample. For example, the test 1406 includes one or more preparation tasks 1408 to be performed by a sample preparation instrument 12, one or more data acquisitions tasks 1410 to be performed by a flow cytometer 14, and one or more analysis tasks 1412 to be performed on a workstation 18. In the example of FIG. 14. the one or more preparation tasks 1408 can include a lysing task 1414 and a staining task 1416 performed by the sample preparation instrument 12. Thus, multiple tasks are assigned to a single tube location on a carrier to enhance automation and integration of sample analysis performed by the instruments in the flow cytometry ecosystem 10.

[0093] Referring back to FIG. 10, the method 1 00 further includes an operation 1004 of instructing the instruments in the flow cytometry ecosystem 10 to perform the tasks in the task groups added to the worklist. The instruments in the flow cytometry ecosystem 10 receive the worklist over the network 20. Also, the instruments receive the carrier containing the tubes.

[0094] Each instrument extracts instructions that can include data from the worklist to execute one or more tasks in the workflow associated with the instrument. For example, the sample preparation instrument 12 receives the worklist from the integration system 100, and the sample preparation instrument 12 extracts instructions and data from the worklist to perform the one or more preparation tasks 1408. Similarly, the flow cytometer 14 receives the worklist from the integration system 100, and the flow cytometer 14 extracts instructions and data from the worklist to perform the one or more data acquisitions tasks 1410. The workstation 18 receives the worklist from the integration system 100, and the workstation 18 extracts instructions and datafrom the worklist to perform the one or more analysis tasks 1412. Each time an instrument completes a task from the worklist, data relevant to the task and the tube on the carrier does not need to be reentered on the instrument, which mitigates human errors and allows more efficient processing of the samples in the flow c tometry ecosystem 10.

[0095] In operation 1004, each instrument when performing a task defined in the workhst can also capture data that is stored in an FCS file 120 maintained by the integration system 100. This allows exchange of data across the instruments in the flow cytometry ecosystem 10, and can facilitate automation workflows between the instruments such as between the sample preparation instrument 12, the flow cytometer 14. and the workstation 18.

[0096] The method 1000 further includes an operation 1006 of storing the data captured by the instruments into an FCS file 120 that can be maintained on a memory of the integration system 100. The storage of the captured data allows exchange of the data between the instruments in the flow cytometry ecosystem 10 such that the data does not need to be manually entered. Further, the need to enter the same data or information on multiple instruments is eliminated. Additionally, the storage of the captured data can be used to improve future worklists generated by the integration system 100. In some examples, the workflow integration application 108 includes a machine learning algorithm that uses the data captured by the instruments to improve the worklists for processing and analyzing the samples.

[0097] FIG. 1 schematically illustrates an example of a method 1500 of validating a llow cytometry assay in the flow cytometry ecosystem 10. The method 1500 can be performed by the assay validation application 112 that is executed on the integration system 100. The method 1500 streamlines development of a validation worklist and provides a template for data analysis. Further, the method 1500 integrates and streamlines processes for assay validation by eliminating the need to reenter information on each instrument in the flow cytometry ecosystem 10. As described above, all instruments in the flow cytometry ecosystem 10 are capable of importing and exporting data from and to the integration system 100 in a manner that supports seamless flow of data from preceding workflow steps through succeeding workflow steps.

[0098] The method 1500 includes an operation 1502 of receiving information about an assay to be validated. Operation 1502 can include receiving the information from auser entering the information about the assay to be validated into a user interface displayed on the display monitor 19 of the workstation 18. Operation 1502 can include selection of one or more guidelines that are displayed on the user interface to validate the flow cytometry assay.

[0099] An illustrative example of such a user interface is shown in FIG. 16, which shows a user interface 1600 displayed on the display monitor 19 of the workstation 18. The user interface 1600 allows the user to develop a task group for assay validation such as by selecting an analysis template, selecting a process quality control, entering a brief description, and adding one or more tests to the task group for performing the assay validation.

[0100] A further illustrative example is shown in FIG. 17, which shows a user interface 1700 displayed on the display monitor 19 of the workstation 18. The user interface 1700 allows a user to select one or more options for a test added to the task group in the user interface 1600 of FIG. 16. For example, the user can select a cocktail for the assay, select an analysis template for the assay, and select an acquisition template for the assay.

[0101] A further illustrative example is shown in FIG. 18, which shows a user interface 1800 displayed on the display monitor 19 of the workstation 18. The user interface 1800 is an example of a validation planner that allows the user to enter information related to the assay objective such as the clinical need, benefit, or relevance of the assay, what should be measured, how it should be measured, what type of data needs to be generated, whether the assay is quantitative, semi-quantitative, or qualitative, and one or more guidelines to be followed.

[0102] The method 1500 includes an operation 1504 of building a validation worklist for a sample based on the information received in operation 1502. Operation 1504 can include automatically building the validation worklist with an optimized number of tubes to cover several dimensions of validation testing such as limit of blank, limit of detection, lower limit of quantitation, repeatability, reproducibility’, linearity, carry over, and the like. The number of tubes are ‘"optimized’7in that the number of tubes for the assay validation are minimized by using a same sample for different purposes. For example, the same prepared tube can be reused for determining the limit of detection, and for calculating linearity.

[0103] The method 1500 includes an operation 1506 of sending the validation worklist built in operation 1504 to a sample preparation instrument 12. The validationworklist can be sent by the integration system 100 to the sample preparation instrument 12 over the network 20. The sample preparation instrument 12 can then prepare the samples based on the validation worklist. Alternatively, the user can manually prepare the samples according to the validation worklist.

[0104] The method 1500 includes an operation 1508 of uploading and tagging a file produced by the sample preparation instrument 12 when preparing the samples based on the validation worklist. The file can be generated as an FCS file 120 for storage on the database 118 of the integration system 100. The FCS file 120 can be shared by the integration system 100 with a flow cytometer 14 over the network 20 for execution of flow cytometry experiments on the samples prepared by the sample preparation instrument 12.

[0105] The method 1500 includes an operation 1510 of applying one or more templates to the data acquired from the flow cytometry experiments performed by the flow cytometer 14. Operation 1510 can include applying the templates to visualize the results from the flow cytometry experiments. The templates can be further used to generate aggregate statistics showing whether previously defined acceptance criteria are satisfied. The previously defined acceptance criteria can be included in the validation w orklist built in operation 1504.

[0106] The method 1500 can include an operation 1512 of receiving one or more adjustments in the templates visualizing the data acquired from the flow cytometer 14. For example, operation 1512 can include one or more gates defined for the data, and other types of visualizations desired by the user. The one or more adjustments are received and entered by the analysis and reporting tool 402e, 502e displayed on the workstation 18.

[0107] The method 1500 includes an operation 1514 of exporting a report and statistical results for further analysis and documentation upon receiving a confirmation or approval from the user. The report and statistical results can be exported over the network 20.

[0108] FIG. 19 illustrates an example of a user interface 1900 generated by the workflow monitoring tool 402b, 502b of the integration system 100. In this example, the user interface 1900 displays a status of an instrument 1902 (e.g., “Analyzer 1”) selected from a list of instruments 1904 that are in the flow cytometry' ecosystem 10. The status of the instrument 1902 can include a list of worklists 1906 that have been generated for execution by the instrument. The status of the instrument 1902 canfurther include an estimated time for completion of a task such as acquisition of flow cytometry data for one or more batches of patient samples.

[0109] The status of the instrument 1902 can further include a list of resources 1910 for the instrument, and whether the resources are adequate for running the instrument. In examples where the instrument is a flow cytometer 14, the list of resources 1910 can include whether the instrument has a sufficient amount of sheath fluid, whether a waste container of the instrument is full, and a supply status of other consumables used by the instrument. In the example of FIG. 19, the list of resources 1910 shows that the waster container of the flow cytometer 14 is full. In this example, at least some of the aspects of the user interface 1900 are provided by the inventory monitoring application 110, which as described above, is executed on the integration system 100.[OHO] FIG. 20 illustrates an example of a user interface 2000 generated by the workflow monitoring tool 402b, 502b of the integration system 100. In this example, the user interface 2000 displays an inventory of reagents 2002 stocked in the flow cytometry ecosystem 10. As an illustrative example, the inventory of reagents 2002 can include rows each specifying a reagent name, a date received, and a date when validated. In some example, the user interface 2000 can display a first icon 2004 to indicate whether a reagent is expiring soon, and a second icon 2006 to indicate whether a reagent is running low in stock. When a reagent is either expiring soon or is running low in stock, the workflow monitoring tool 402b, 502b enables ordering additional reagent, as will be described further below with reference to FIG. 22. In this example, at least some of the aspects of the user interface 2000 are provided by the inventory monitoring application 110, which as described above, is executed on the integration system 100.[0H1] A determination of whether a reagent is running low in stock is based on logic executed by the inventory monitoring application 110 which estimates reagent consumption based on the types and number of assays performed by the instruments in the flow cytometry ecosystem 10. In some instances, the inventory monitoring application 110 estimates the reagent consumption based on the types and number of assays in the worklists that are generated for execution by the instruments. The inventory monitoring application 110 issues an alert that can be displayed on the display monitor 19 of the workstation 18 when a new lot of reagent needs to be validated and / or more reagent needs to be ordered. In some examples, the inventorymonitoring application 110 predicts when reagent stock will ran low based on the types and number of assays in the worklists that are generated for execution by the instruments.[0H2] FIG. 21 illustrates an example of a user interface 2100 generated by the workflow monitoring tool 402b, 502b of the integration system 100. In this example, the user interface 2100 displays a number of assigned seats 2102 for a license granted to use the integration system 100, and a number of automatic reports 2104 that have been selected for the flow cytometry ecosystem 10. The user interface 2100 can further show scheduled deliveries of consumables 2106 such as reagents, sheath fluid, and other supplies used by the instruments in the flow cytometry ecosystem 10. The user interface 2100 can further show available upgrades 2108 such as additional tools, reports, templates and the like available from the integration system 100. In this example, at least some of the aspects of the user interface 1900 are provided by the inventory monitoring application 110, which is executed on the integration system 100.

[0113] FIG. 22 illustrates an example of a user interface 2200 generated by the workflow monitoring tool 402b, 502b of the integration system 100. In this example, the user interface 2200 allows a user to order consumables for the flow cytometry ecosystem 10. For example, the user interface 2200 provides an e-store integration allowing the ordering of reagents, sheath fluid, and other supplies that are used by the instruments in the flow cytometry ecosystem 10. In this example, at least some of the aspects of the user interface 1900 are provided by the inventory monitoring application 110, which is executed on the integration system 100.[0H4] As described above, the quality control (QC) application 114 is stored on the system memory 106 of the integration system 100, and when executed by the one or more processing devices 104, causes the integration system 100 to execute quality control tasks on the flow cytometer 14. Alternatively, or additionally, the QC application 114 can be stored on a system memory of the flow cytometer 14. The QC application 114 is now described in more detail.

[0115] The QC application 114 can determine whether there are errors or impending errors in the flow cytometer 14. Accordingly, receiving a passing result from the QC application 114 ensures that there are no hardware or software errors on the flow cytometer 14 when running patient samples. Further, the QC application 114 can diagnose likely or impending failures and provide guidance for corrective actions to improve instrument up-time.

[0116] The QC application 114 allows a user of a flow cytometer 14 to run instrument QC using vendor-defined material and targets that will inform the user whether or not a new gain standardization or maintenance is required to ensure consistent performance of vendor-validated assays. The QC application 114 further allows a user of the flow cytometer 14 to run instrument QC on custom filter configurations and monitor performance over time. Instrument QC uses beads to determine instrument performance.[0H7] The QC application 114 enables a flow cytometer 14 to run assay QC using vendor-provided and user-defined materials and targets. Assay QC typically uses stabilized cells or other control material that is prepared like a patient sample to determine whether a full workflow from sample preparation through data acquisition and data analysis generates expected results.[0H8] The QC application 114 further enables a flow cytometer 14 to run compensation and standardization verification and receive alerts when performance is not as expected. Further, the QC application 114 enables the user to pause execution of a flow cytometry worklist until QC results have been reviewed and approved by the user. Compensation controls are unstained or single stained cells or beads. Standardization samples are beads, which may be the same beads like the QC samples, that are run at the same instrument settings used for the assay.

[0119] In scenarios where an administrator desires to confirm and document whether the flow cytometer 14 is working correctly, acceptance criteria for the QC application 114 can include providing quick training of new staff members on how to perform startup of the flow cytometer 14; providing intuitive and easy to interpret review of whether quality control has passed or failed; enabling the ability to check for correct operation of the flow cytometer 14 for all configurations and all assay types that are within the scope; quick instrument setup and preparation for acquiring patient samples safely and efficiently as measured by a reduction in error prone and non-value setup steps; scheduling assay QC tasks at points in the workflow that work best for the flow cytometry ecosystem 10.

[0120] Additionally, by providing one type of QC beads that can be used on all analyzers in the flow cytometry ecosystem 10, the need to stock several versions of the same material for different flow cytometers in the flow cytometry ecosystem 10 can be eliminated. In scenarios where an operator desires efficient startup and QC, acceptance criteria for the QC application 114 can include providing ready to use beads toeliminate preparation time; enabling the ability to initiate setup and QC (e.g., instrument QC and / or assay QC) by interacting with the flow cytometer 14 in a single sitting; providing an option to follow setup and QC with patient sample; providing an option to review the results of QC before running the patient sample; providing the ability to pre-plan shutdown at the end of a batch of samples; providing ability to see whether QC passed or failed from anywhere in the flow cytometry ecosystem 10; and providing guidance on how to quickly troubleshoot QC failures.

[0121] In such scenarios, the QC application 114 provides instrument QC that detects operation problems of the flow cytometer 14 that can affect results. The QC application 114 generates alerts to indicate when functional parameters of the flow cytometer 14 are not operating within an acceptable range, and when functional parameters are trending towards or are near failure (i.e., not operating within an acceptable range). The QC application 114 provides structured troubleshooting guidance to drive a solution in a shortened amount of time.

[0122] In scenarios where an operator desires to avoid mistakes and recover quickly from errors, acceptance criteria for the QC application 114 can include providing checks to alert the operator when wrong QC beads are being used, and providing a streamlined process allowing for a plurality of control materials that may change without requiring manual adjustment of settings.

[0123] In scenarios where an operator desires to connect instrument QC and assay QC results as well as settings (e.g., compensation and standardization) to patient results and reagent information, acceptance criteria for the QC application 114 can include providing comprehensive view of flow cytometer instrument and assay performance; providing checks on the performance of the flow cytometry instrument in default and custom configurations; providing checks on the performance of vendor developed as well as lab developed assays; providing ability to connect patient results to instrument QC results, settings, and QC material lots; and providing the ability to see who performed the setup and QC steps and connected to a given sample.

[0124] FIG. 23 schematically illustrates an example of a method 2300 of flow cytometry quality control (QC) that can be performed by the QC application 114 on a flow cytometer 14 of the flow cytometry ecosystem 10. In some examples, the QC application 114 is executed by the integration system 100. Alternatively, the QC application 114 can be executed by the flow cytometer 14 in the flow cytometry ecosystem 10.

[0125] The method 2300 includes an operation 2302 of receiving a QC task group added to a worklist. The QC task group can be added to a worklist in accordance with operation 1002 of the method 1000 of FIG. 10. The QC task group can be added to any of the example worklists shown in FIGS. 11-14. The QC task group can be added to the worklist by using a user interface displayed on a display monitor 19 of the workstation 18. The QC task group can be received by the integration system 100 or the flow cytometer 14 over the network 20. The QC task group can include one or more samples for instrument QC, one or more samples for assay QC, and / or one or more samples for compensation and standardization verification.

[0126] The method 2300 includes an operation 2304 of running samples through the flow cytometer 14 based on the worklist. In this example, the samples can include one or more samples for instrument QC to assess consistent performance of the instrument according to vendor-validated specifications (such as, but not exclusive to, laser power, instrument fluidics, and the like), one or more samples for assay QC to assess the performance of specific tests according to predefined and validated assay specifications (such as, but not exclusive to, assay-specific target value ranges and expected results), and one or more samples for compensation and standardization verification to assess compensation and standardization on the flow cytometer 14. The instrument QC, assay QC, and compensation and standardization verification can be performed together in a single worklist instead of separately in different workflows.

[0127] The method 2300 includes an operation 2306 of analyzing instrument QC data acquired from the instrument QC samples. Operation 2306 can include automatically analyzing the instrument QC data on the flow cytometer 14. For example, operation 2306 can include analyzing the instrument QC data on the flow cytometer 14 to generate an instrument QC report, and transmitting the instrument QC report over the network 20 to the integration system 100 where the instrument QC report is aggregated with other instrument QC reports for monitoring performance of the flow cytometer 14 over time. Alternatively, operation 2306 can include collecting raw instrument QC data on the flow cytometer 14, and transmitting the raw instrument QC data over the network 20 to the integration system 100 where the raw instrument QC data is analyzed for generating an instrument QC report that is aggregated with other instrument QC reports for monitoring performance of the flow cytometer 14 over time.

[0128] The method 2300 includes an operation 2308 of analyzing assay QC data acquired from the assay QC samples. Operation 2308 can include collecting the assay QC data on the flow cytometer 14, and transmitting the assay QC data over the network 20 to the integration system 100 where an analysis pipeline is applied for analyzing the assay QC data to generate an assay QC report. In such examples, the assay QC report can display whether or not the assay QC data satisfies assay validation targets. In alternative examples, the assay QC data can be analyzed locally on the flow cytometer 14 for determining whether assay validation targets are satisfied.

[0129] The method 2300 includes an operation 2310 of analyzing compensation and standardization QC data acquired from the one or more samples for compensation and standardization verification. In operation 2310, the compensation and standardization QC data can be uploaded via the network 20 to the integration system 100 where an analysis pipeline is applied. Operation 2310 can include comparing average fluorescence intensity and other statistical results to optimal and / or expected results, and where discrepancies are highlighted for the user. In the compensation and standardization verification performed in operation 2310, the automated statistical comparison of median fluorescence intensities for selected populations provides an informative interpretation of the applied compensation.

[0130] FIG. 24 illustrates an example of a statistical comparison 2400 that can be displayed in accordance with operation 2310 of the method 2300. In this illustrative example, the statistical comparison 2400 is for a median fluorescence intensity of a fluorochrome such as PC7 that is expected to be within a first range 2402 identified for CD8CD4LL cells and a second range 2404 identified for CD8+T cells when compensation is appropriately set on the flow cytometer 14. FIG. 24 further illustrates a scatter plot 2406 adjacent to the statistical comparison 2400.

[0131] Referring back to FIG. 23, the order of operations 2306-2310 may vary. In some examples, operations 2306-2310 of the method 2300 are performed simultaneously.

[0132] As further shown in FIG. 23, the method 2300 further includes an operation 2312 of providing guidance to improve performance of the flow cytometer 14 based on at least one of the instrument QC data, the assay QC data, and the compensation and standardization QC data. This can include guidance on instrument cleaning procedures in case of a suspected blockage of the fluidic system, warnings in case expired reagents are being used, instructions in case a control test result failed, and the like. Theprovided guidance is specific and points to potential root causes identified by instrument QC 2500 (see below). With regards to the assay QC, operation 2312 can include informing the user about unexpected populations and, for example, identify deteriorated tandem dyes and suggest the user confirm that the reagents are satisfactory.

[0133] FIG. 25 schematically illustrates an example of an instrument QC 2500 that can be performed by the QC application 114 on the flow cytometer 14. The instrument QC 2500 identifies potential sources of error 2502, parameters 2504 that are detected for each potential source of error 2502, and actions 2506 performed on the flow cytometer 14 for measuring the parameters 2504, which can include direct measurements and / or proxy measurements.

[0134] In this illustrative example, the potential sources of error 2502 include laser performance on the flow cytometer 14, alignment of optical components on the flow cytometer 14, short-term system stability (e.g., within a ran), and long-term system stability (e.g., over multiple days). There can be additional sources of error such as detector performance, contamination, sample flow rate, and mid-term system stability (e.g., during a day) such that the potential sources of error 2502 shown in FIG. 25 are provided by way of illustrative example.

[0135] As shown in FIG. 25, the laser performance can be assessed by parameters 2504 including laser beam spot stability such as the consistency of the laser beam spot shape over time where width (x axis), height, (y axis), and intensity (z axis) monitored over time. The laser performance can be further assessed by laser power output that can be measured by actions 2506 such as an input voltage stability and an input voltage value of the light intensity output of the laser. The laser performance can be further assessed by laser beam spot shape at the interrogation zone such as the shape of the beam spot inside the flow chamber. The laser performance can be further assessed by laser output stability such as the variance in laser intensity over time.

[0136] The alignment of optical components can be assessed by parameters 2504 including the laser beam spot shape at the interrogation zone, focus lens alignment, and pinhole alignment such as alignment of the pinhole relative to the flow cell channel.The alignment of optical components can be assessed by laser beam spot location at the flow cell such as where the beam intercepts the flow cell and what part of the beam spot intercepts the channel. The laser beam spot location can be measured by actions 2506 such as applying a threshold value and / or measuring system noise such as from non-bead related events.

[0137] The alignment of optical components can be further assessed by parameters 2504 including fiber loss from bending such as loss of light intensity at the output of the fiber relative to the input due to fiber leakage which can be measured by an action 2506 such as measuring gain for placing bead peak at target location. The alignment of optical components can be assessed by wavelength-division multiplexing (WDM) filter configuration and WDM filter alignment such as the alignment of the WDM filters relative to the fiber output and detectors. The alignment of optical components can be further assessed by the laser output stability.

[0138] The short-term system stability can be assessed by parameters 2504 including the laser output stability such as the variance in laser intensity over time, fluid flow stability such as whether the fluid flowing through the interrogation zone is stable / consistent over a period of time, and fluid flow rate such as whether the fluid flow rate satisfies an expected value.

[0139] The long-term system stability can be assessed by parameters 2504 including flow cell contamination causing light loss or scattering, and fiber loss due to contamination such as loss of intensity from fiber launch to exit relative to input light intensity due to contamination or degradation at the ends of the fiber or within the fiber core. The long-term system stability can also be assessed by parameters 2504 of the laser output stability and the fluid flow stability.

[0140] The parameters 2504 including flow cell contamination, fiber loss due to contamination, and fiber loss due to bending can be measured by the action 2506 of determining a gain needed to place bead peak at target location. Further, parameters 2504 including flow cell contamination and laser output stability can be measured by the action 2506 of measuring bead control variance (CV) at the target location. The parameter 2504 fluid flow stability can be measured by the action 2506 of measuring laser delay stability (variance). The parameter 2504 of fluid flow rate can be measured by the actions 2506 of measuring laser delay value and measuring extracellular polymeric substances (EPS) relative to a known concentration.

[0141] As shown in FIG. 25, at least some of the parameters 2504 are shared by two or more of the potential sources of error 2502. For example, the laser performance and the alignment of optical components share the parameters 2504 including the laser beam spot shape at the interrogation zone and the laser output stability. Additionally, the short-term system stability and the long-term system stability share the parameter 2504 of laser output stability along with the laser performance and the alignment ofoptical components. Also, the short-term system stability and the long-term system stability share the parameter 2504 of fluid flow stability.

[0142] FIG. 26 schematically illustrates a data model 2600 to supply QC target values for an instrument QC test that can be performed by the QC application 114 on the flow cytometer 14. The data model includes data structures created for each configuration of a flow cytometer 14.

[0143] The data model 2600 include a test object data structure 2602 that defines a test object for QC. A unique test object is defined for each flow cytometer 14, and for each configuration thereof. In the example illustrated in FIG. 26, the test object data structure 2602 includes data entry fields to enter an ID, name, validation status, cost, version, origin, and field for each configuration of each flow cytometer 14 in the flow cytometry ecosystem 10.

[0144] As further shown in FIG. 26, the data model 2600 can include a QC task data structure 2604 instincts the flow cytometer 14 to ran the instrument QC. The QC task data structure 2604 includes data entry fields to enter an ID, a mix time, a cytometer action (e.g., acquire, clean, or rinse), and a trigger event for the action (e.g., none, instrument QC, gain standardization, single color compensation, rinse with carryover check, and process verification).

[0145] The data model 2600 can include a flow cytometer configuration data structure 2606 that includes data entry fields to enter parameters such as ID, name, model number, and serial number to identify the configuration of a flow cytometer 14. The flow cytometer configuration data structure 2606 is optional in that in some cases it can be omitted such as when the configuration of the flow cytometer 14 is identified in the test object data structure 2602.

[0146] The data model 2600 can include a cocktail object data structure 2608 that provides links to objects for the instrument QC. If more than one bead product (e.g., differently sized beads) are used for the instrument QC, the cocktail defined by the cocktail object data structure 2608 becomes the owner of the QC particle / bead configuration. The cocktail object data structure 2608 includes data entry fields for ID, name, type (e.g., whether liquid or dry), version, whether validated or not, and origin (e.g., vendor provided or user provided).

[0147] The data model 2600 can include a reagent data structure 2610 that includes data entry fields such as for ID, name, product name, part number, manufacturer, volume, type, and required sample value. A reagent lot data structure 2612 contains alot number and linkage to appropriate QC targets for the lot. A reagent object data structure 2614 provides a name for a channel on the flow cytometer 14, and identifies the reagent as a “bead” for instrument QC. A bead data structure 2616 is optional and can be useful when alternative materials are used for instrument QC. The bead data structure 2616 includes data entry fields for identifying an ID, name, material (e.g., polystyrene, silica, latex, and the like), and size (e.g., in microns).

[0148] The data model 2600 further include a QC target data structure 2618 that includes a data entry field for identifying a channel, target value, and target range. The target value and target range are mean fluorescence intensity (MFI) values for locating a population. The QC target data structure 2618 includes a data entry field for identifying a gain variance percentage which is a pass / fail criteria for the instrument QC such as an allowed percent difference from a gain baseline. The QC target data structure 2618 includes a data entry field for identifying the gain baseline which is established for setting up a new QC lot.

[0149] The data model 2600 supports multiple versions of QC task groups including instrument QC for different configurations of the flow cytometers 14. For example, a flow cytometer may have a configuration for a period of time, and thereafter, the configuration may change. Examples of custom configurations can include filter configurations that differ from default configurations offered by the manufacturer of the flow cytometers 14.

[0150] The data model 2600 filters options for the QC task groups based on the flow cytometer 14 targeted for instrument QC to ensure user selections are compatible with the configuration of the flow cytometer. Accordingly, the instrument QC tests defined by the data model 2600 reference predefined configurations for the flow cytometers 14.

[0151] The data model 2600 uses the MFI values as the target values for instrument QC. From a reagent development and manufacturing standpoint, this can save significant effort because the instrument QC target values can be used for vendor- developed and validated assays. This will reduce the effort for an assay development team to establish assay-specific settings. The data model 2600 allows, from a system design standpoint, a user to enter the target MFI values and ranges for each channel on the flow cytometer 14, and to address lot to lot variation.

[0152] The data model 2600 allows the instrument QC target values and ranges to be editable by a user of the flow cytometer 14. Further, the data model 2600 allows thechannel names to be editable by the user such that they can be mapped to the configuration of the flow cytometer. For example, for custom instrument QC defined by the user, the data model 2600 provides the QC application 1 14 with channel number and / or names for mapping to flow cytometer channels, a target value for each channel (e.g., MFI), a target range for each channel (e g., + / - X percent of baseline gain, and a gain variance for each channel. When the system places the population at the target channel, the gain is measured. The gain variance is the allowed difference from the baseline gain established when setting the instrument QC targets. The data model 2600 supplies these values for the instrument QC test in the data structures shown in FIG. 26.

[0153] The various embodiments described above are provided by way of illustration only and should not be construed to be limiting in any way. Various modifications can be made to the embodiments described above without departing from the true spirit and scope of the disclosure.

Claims

What is claimed is:

1. A system for integrating flow cytometry workflows, the system comprising: a processing circuitry having a memory for storing instructions which, when executed by the processing circuitry, cause the processing circuitry to: build a worklist by adding task groups for single or multiple tube panels for analyzing one or more samples; instruct instruments to perform tasks in the task groups added to the worklist; and exchange data between the instruments, the data related to performance of the tasks in the task groups.

2. The system of claim 1 , wherein each task group identifies a sample carrier, defines contents of the sample carrier, and defines processing steps for the contents on the sample carrier.

3. The system of claim 1 or 2, wherein each task group includes a predefined order of tasks for execution by the instruments.

4. The system of any one of claims 1-3, wherein each task group is each associated with at least one sample acquired from a subject.

5. The system of any one of claims 1-4, wherein the tasks in the task groups include preparation tasks for execution by a sample preparation instrument, data acquisitions tasks for execution by a flow cytometer, and analysis tasks for execution by a workstation.

6. The system of any one of claims 1-5, wherein the instructions, when executed by the processing circuitry, further cause the processing circuitry to: build the worklist with a minimum quantity of tubes for executing validation testing.

7. The system of any one of claims 1 -6, wherein the instruments include one or more sample preparation instruments, one or more flow cytometers, and one or more workstations.

8. The system of any one of claims 1 -7, wherein the instructions, when executed by the processing circuitry, further cause the processing circuitry’ to: display a user interface that includes statuses of the instruments.

9. The system of claim 8, wherein the user interface enables ordering supplies consumed by the instruments.

10. The system of any one of claims 1-9, wherein the worklist includes a first task group for instrument quality’ control, a second task group for assay quality control, and a third task group for compensation and standardization verification.1 1. The system of claim 10. wherein the first task group enables editing a target value or range for each channel of a flow cytometer, and editing a name for each channel of the flow cytometer for mapping the name of each channel to a configuration of the flow cytometer.

12. The system of claim 1 1 , wherein the target value is a mean fluorescence intensity'.

13. The system of claim 11 or 12, wherein the target range for each channel is defined as a percentage difference of a baseline gain for each channel.

14. A method of integrating flow cytometry' workflows, the method comprising: building a worklist by adding task groups for single or multiple tube panels for analyzing one or more samples; instructing instruments to perform tasks in the task groups added to the worklist; and exchanging data between the instruments, the data related to performance of the tasks in the task groups.

15. The method of claim 14, wherein the worklist identifies a sample carrier, defines contents of the sample carrier, and defines processing steps for the contents on the sample carrier.

16. The method of claim 14 or 15, wherein each task group includes a predefined order of tasks for execution by the instruments.

17. The method of any one of claims 14-1 , wherein the task groups are each associated with at least one sample acquired from a subject.

18. The method of any one of claims 14-17. wherein the tasks in the task groups include preparation tasks for execution by a sample preparation instrument, data acquisitions tasks for execution by a flow cytometer, and analysis tasks for execution by a workstation.

19. The method of any one of claims 14-18. further comprising: building the worklist with a minimal quantity of tubes for executing validation testing.

20. The method of any one of claims 14-19. wherein the instruments include one or more sample preparation instruments, one or more flow cytometers, and one or more workstations.

21. The method of any one of claims 14-20. further comprising: displaying a user interface that includes statuses of the instruments.

22. The method of claim 21, further comprising: receiving an order of supplies for the instruments based on one or more selections on the user interface.

23. The method of any one of claims 14-22, wherein the worklist includes a first task group for instrument quality control, a second task group for assay quality control, and a third task group for compensation and standardization verification.

24. The method of claim 23, wherein the first task group enables editing a target value or range for each channel of a flow cytometer, and editing a name for each channel of the flow cytometer for mapping the name of each channel to a configuration of the flow cytometer.

25. The method of claim 24, wherein the target value is a mean fluorescence intensity.

26. The method of claim 24 or 25, wherein the target range for each channel is defined as a percentage difference of a baseline gain for each channel.

27. A method of performing quality control on a flow cytometer, the method comprising: receiving a quality control task group added to a worklist, the quality control task group including one or more samples for instrument quality control, one or more samples for assay quality control, and one or more samples for compensation and standardization verification, the worklist identifies a sample carrier, defines contents of the sample carrier, and defines processing steps for the contents on the sample carrier; running, based on the worklist, the one or more samples for the instrument quality control, the one or more samples for the assay quality control, and the one or more samples for the compensation and standardization verification; analyzing instrument quality control data acquired by the flow cytometer from the one or more instrument quality control samples; analyzing assay quality control data acquired by the flow cytometer from the one or more assay quality control samples; analyzing compensation and standardization quality control data acquired by the flow cytometer from the one or more compensation and standardization verification samples; and providing guidance to improve performance of the flow cytometer based on at least one of the instrument quality control data, the assay quality control data, and the compensation and standardization quality control data.

28. The method of claim 27, wherein the quality control task group enables editing a target value or range for each channel of the flow cytometer.

29. The method of claim 28, wherein the target value is a mean fluorescence intensity.

30. The method of claim 28 or 29, wherein the target range for each channel is defined as a percentage difference of a baseline gain for each channel.