Systems and methods for spatial mapping of expression profiling

Abstract

To provide systems and methods for spatially mapping at least one biological expression of a target biological component contained in a tissue sample to an image of the tissue sample.SOLUTION: A system includes a processor and instructions that, when executed by the processor, cause the system to display, in a first display, a scan pane including at least the image of the tissue sample, the image including at least one demarcation corresponding to a region-of-interest (ROI(s)), where the ROI(s) correspond to a portion of tissue within the tissue image. The instructions are further configured to cause the system to display, in a second display, a visualization pane including visualization of the biological expression contained in the ROI(s); and to augment the first display by coding the ROI(s) in the tissue image to show the spatial mapping of the biological expression within the ROI(s).SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] Related Applications This application claims the benefit of and priority to U.S. Provisional Patent Application No. 62 / 916,990, filed October 18, 2019, the entire disclosure of which is incorporated herein by reference.

[0002] The present disclosure relates to systems, instruments, and methods for applying visual spatial resolution and digital quantification of protein and mRNA expression. [Background technology]

[0003] Diseases such as cancer involve the abnormal growth of cells, which typically results in one or more tumors that grow locally or metastasize throughout the body. Surgery is the first line of treatment to remove the tumor, cancerous lymph nodes, and healthy tissue adjacent to the tumor. Surgery is often followed by adjuvant therapy, which may include several weeks of radiation therapy, chemotherapy, targeted drug therapy, and / or immunotherapy. These treatments can result in a mix of outcomes and side effects that vary from patient to patient. Researchers are actively investigating differences in outcomes to identify biomarkers that may predict a patient's response to treatment. These expression signatures can help guide physicians to deliver more effective treatments in a planned, evidence-based manner.

[0004] A current challenge is identifying biomarkers that affect the tumor microenvironment. However, obtaining such biomarkers in tumor samples requires tissue destruction, often at the expense of spatial information about the biomarkers. Fluorescence and bright-field imaging can provide visual maps of biomarkers, but they are limited by the number of fluorophores that can be imaged in a single experiment and require multiple immunostaining and imaging sessions on the same sample. This results in sample degradation over time, potentially leading to image registration errors and misinterpretation of results.

[0005] Therefore, solutions are needed to overcome the aforementioned problems, such as solutions related to the identification and characterization of biomarkers and their combinations that influence the tumor microenvironment, in order to improve immunohistochemistry systems, methods, and techniques so that more reliable and effective treatments can be administered in a more planned and evidence-based manner. Summary of the Invention

[0006] Thus, in some embodiments, biological expression mapping systems and methods are provided that are configured to spatially map one or more biological expressions of respective target biological components contained in a tissue sample onto an image of the tissue sample.

[0007] Those skilled in the art will appreciate that system embodiments detailing various computer instructions running / operable on one or more processors (e.g., servers, personal computers) and causing such one or more processors (e.g., systems) to perform various processing steps may also be steps relating to one or more mapping method embodiments of the present disclosure.

[0008] Thus, in some embodiments, the system includes at least one processor having instructions operating thereon configured, when executed, to cause a first display to display a scanning pane including at least an image of the tissue sample, the image including one or more sections each corresponding to a particular one of one or more regions of interest (ROIs), each of the one or more ROIs corresponding to a particular portion of the tissue within the tissue image. The instructions are further configured to cause the system to display a visualization pane on a second display comprising a visualization of each of the biological expressions included in the one or more ROIs. The instructions are further configured to cause the system to augment the first display by coding one or more ROIs within the tissue image to show a spatial mapping of the biological expressions within the one or more ROIs.

[0009] In some embodiments, a method for biological expression mapping is provided, the method including: displaying a scanning pane on a first display including at least an image of a tissue sample, the image including one or more sections corresponding to specific ones of one or more regions of interest (ROIs), each of the one or more ROIs corresponding to a specific portion of tissue within the tissue image; displaying a visualization pane on a second display including a visualization of each of the biological expressions included in the one or more ROIs; and augmenting the first display or the second display by coding the one or more ROIs within the tissue image to show spatial mapping of the biological expressions within the one or more ROIs.

[0010] Each of the above embodiments (i.e., systems, methods) may further include at least one (and in some embodiments, more than one, and in some embodiments, substantially all) of the following additional structures, features, steps, functionality, and / or clarifications, creating yet further embodiments (and each item in the following list, and combinations of the items listed below, may be a stand-alone embodiment): - the coding includes at least color coding, each visualization of a biological expression comprises an image of the biological expression contained in one or more ROIs; - the graphs, plots, diagrams, and maps of biological expression comprise at least one of a heat map, a dendrogram, a bar graph, a scatter plot, a box plot, a forest plot, a principal component, a statistical plot, a volcano plot, a trend plot, and a strip plot; - Dendrograms include phylogenetic trees, -Statistical plots include one or more Principal Component Analysis (PCA) plots, - the first display portion is expanded based on user input specifying at least one selection of biological expressions to be included in the visualization; the spatial mapping of the at least one user-selected biological expression is configured to provide at least one of the one or more ROIs with its spatial context; - extending the first representation is configured to facilitate morphological profiling of tissue in at least one of the one or more ROIs; - morphological profiling comprises at least one of geometric profiling, segment profiling, contour profiling, grid profiling, and cell profiling; segment profiling comprises at least one of manual segment profiling and automatic segment profiling, the automatic segment profiling being configured to automate and facilitate segment profiling of tissue in at least one of the one or more ROIs based on user input specifying at least one segment profiling parameter; - Cell profiling includes single cell profiling and rare cell profiling; - one or more biological expressions of one or more target biological components within at least one or more ROIs are determined based on exposing the tissue sample to a plurality of reagents, and the reagents a plurality of imaging reagents configured to bind to biological boundaries of the tissue sample within at least one or more ROIs; and a plurality of profiling reagents, each profiling reagent comprising: ■ Binds to a specific biological expression of a specific target biological component contained within at least one or more ROIs, and ■ Constructed to contain a cleavable related oligonucleotide, After exposing the tissue sample to the plurality of reagents and before displaying on the first display and the second display, the instructions are Illuminating the tissue sample and imaging it, receiving user input specifying a selection of one or more ROIs; Irradiating at least one or more ROIs of the tissue sample, thereby cleaving the associated oligonucleotides from the profiling reagent; recovering the cleaved oligonucleotide, and The method may further be configured (or may further comprise) analyzing the recovered cleaved associated oligonucleotides, thereby one or more biological expressions contained within at least one ROI; and Determine their corresponding positions, -Each profiling reagent is a nucleic acid probe comprising a target binding region to which a cleavable association oligonucleotide is removably attached, or a releasably attached antibody-containing oligonucleotide, the user input specifying the selection of one or more ROIs comprises a selection of one or more ROIs with respect to shape or size; -The instruction is given to the system, further configured (or a method further comprising): causing a third display to display a dataset pane including at least one user-selectable dataset, the at least one dataset being associated with one or more of the biological expressions included in the one or more ROIs; -The instruction is given to the system, further configured (or a method further comprising): displaying, on a fourth display, a record pane including a plurality of scan records, each scan record including at least one tissue image; one or more of the first display, the second display, the third display, and the fourth display are provided within an integrated user interface configured to interactively associate one or more of the tissue images, the visualization, the user-selectable data set, and the plurality of scan records based on user input; -The integrated user interface is configured as a single display, the first display portion, the second display portion, the third display portion, and the fourth display portion each correspond to one or more portions of a single display; -The instruction is given to the system, further configured (or in a manner further including) selecting at least one record based on user input, such that upon selection, at least one of a scanning pane, a visualization pane, and a dataset pane is displayed in a respective display; the instructions are further configured to (or in a manner further comprising): causing the system to filter at least one of the properties, constraints, and values ​​of the plurality of records based on user input; the scanning pane further includes a plurality of icons, each corresponding to at least one of the one or more ROIs or a particular segment of the entire tissue image; the instructions are further configured to (or in a manner further comprising): causing the system to render a scan pane, in conjunction with a visualization pane and one or more of a dataset pane and a record pane, for display in real time via the integrated user interface based on user input; coding one or more ROIs includes presenting a quantitative measure of biological expression; color-coding one or more ROIs includes presenting a quantitative measure of biological expression; The quantitative measurements include at least one of the type and degree of biological expression; The quantitative measurements include at least one of the type and degree of biological expression; and The type and degree correspond to a specific color for each biological manifestation or a color intensity for each biological manifestation.

[0011] Embodiments of the present disclosure are also related to International Application No. PCT / US2016 / 042460 (WO2017 / 015099), filed July 15, 2016, entitled "SIMULTANEOUS QUANTIFICATION OF GENE EXPRESSION IN A USER-DEFINED REGION OF A CROSS-SECTIONED TISSUE," and International Application No. PCT / US2016 / 042455 (WO2017 / 015097), filed July 15, 2016, entitled "SIMULTANEOUS QUANTIFICATION OF PLURALITY OF PROTEINS IN A USER-DEFINED REGION OF A CROSS-SECTIONED TISSUE," the disclosures of which are each incorporated herein by reference in their entirety.

[0012] These and other embodiments, and their objects and advantages, will become more apparent with reference to the drawings, a brief description of which follows, and the detailed description (of at least some of the embodiments) of the figures. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 is a functional block diagram illustrating an expression mapping system according to some embodiments of the present disclosure. [Figure 2] FIG. 2 is a flow diagram illustrating an example method of operation of an expression mapping system according to some embodiments of the present disclosure. [Figure 3] FIG. 3 is a diagram illustrating an example visualization showing gene expression, according to some embodiments of the present disclosure. [Figure 4A-C] 4A-4E show examples of visualization and profiling formats that can show tissue and gene expression, according to some embodiments of the present disclosure. [Figure 4D-E] 4A-4E show examples of visualization and profiling formats that can show tissue and gene expression, according to some embodiments of the present disclosure. [Figure 5] FIG. 5 is a diagram illustrating an example of a user interface display including interconnected visualizations, according to some embodiments of the present disclosure. [Figure 6A] 6A-6I are diagrams illustrating example visualizations, respectively, according to some embodiments of the present disclosure. [Figure 6B] 6A-6I are diagrams illustrating example visualizations, respectively, according to some embodiments of the present disclosure. [Figure 6C] 6A-6I are diagrams illustrating example visualizations, respectively, according to some embodiments of the present disclosure. [Figure 6D] 6A-6I are diagrams illustrating example visualizations, respectively, according to some embodiments of the present disclosure. [Figure 6E] 6A-6I are diagrams illustrating example visualizations, respectively, according to some embodiments of the present disclosure. [Figure 6F] 6A-6I are diagrams illustrating example visualizations, respectively, according to some embodiments of the present disclosure. [Figure 6G] 6A-6I are diagrams illustrating example visualizations, respectively, according to some embodiments of the present disclosure. [Figure 6H] 6A-6I are diagrams illustrating example visualizations, respectively, according to some embodiments of the present disclosure. [Figure 6I] 6A-6I are diagrams illustrating example visualizations, respectively, according to some embodiments of the present disclosure. [Figure 7A] 7A-7D show exemplary results obtained by the expression mapping system of the present disclosure, according to some embodiments of the present disclosure. [Figure 7B] 7A-7D show exemplary results obtained by the expression mapping system of the present disclosure, according to some embodiments of the present disclosure. [Figure 7C] 7A-7D show exemplary results obtained by the expression mapping system of the present disclosure, according to some embodiments of the present disclosure. [Figure 7D]7A-7D show exemplary results obtained by the expression mapping system of the present disclosure, according to some embodiments of the present disclosure. [Figure 8A] 8A-8E show exemplary results obtained by the expression mapping system of the present disclosure, according to some embodiments of the present disclosure. [Figure 8B] 8A-8E show exemplary results obtained by the expression mapping system of the present disclosure, according to some embodiments of the present disclosure. [Figure 8C] 8A-8E show exemplary results obtained by the expression mapping system of the present disclosure, according to some embodiments of the present disclosure. [Figure 8D-E] 8A-8E show exemplary results obtained by the expression mapping system of the present disclosure, according to some embodiments of the present disclosure. [Figure 9] FIG. 9 is a block diagram illustrating a user device and / or an expression mapping system according to some embodiments of the present disclosure. [Figure 10] FIG. 10 illustrates a cloud computing environment for an expression mapping platform according to some embodiments of the present disclosure. [Figure 11] FIG. 11 illustrates an abstraction model layer of an expression mapping platform according to some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0014] Embodiments of the present disclosure are directed to devices, systems, and methods for analyzing biological material by applying spatial resolution to and digitally quantifying discrete occurrences of gene expression ("gene expression(s)" or "expression event(s)") within and of the material. Expression events include, for example, protein expression, mRNA expression, and the like. In some cases, the biological material may include a sample ("biological material" or "sample" or "tissue sample"), such as, for example, a tissue sample (e.g., a formalin-fixed, paraffin-embedded (FFPE) tissue section mounted on a slide), a lysate, a bodily fluid specimen, etc. Samples may include tissues (e.g., including cultures or excisions) and cells (e.g., including both primary cells and cultured cell lines) that comprise such tissues. For example, a sample may include: - cultured, primary or dissociated cells (e.g. from explants), - biological material such as tissues, user-defined cells, and / or user-defined subcellular structures within cells; - a tissue section having a thickness of about 2 to 1000 micrometers (μm), and - May contain cultured cells or dissociated cells (fixed or not) fixed on the slide.

[0015] Advantageously, some embodiments of the present disclosure enable efficient characterization of tissue heterogeneity, which can be important for answering important biological questions in translational research. Current tissue analysis paradigms require a trade-off between morphological analysis and high plexity, sacrificing valuable information or consuming precious sample. To this end, some embodiments enable the generation of whole-tissue images at single-cell resolution and digital profiling data for tens to thousands of RNA or protein analytes for up to 16-20 tissue slides per day. This unique combination of high plexity, high-throughput spatial profiling allows researchers to rapidly and quantitatively assess the biological significance of heterogeneity within tissue samples. Furthermore, some embodiments of the present disclosure enable high plexity, high-throughput, multi-analyte, and non-destructive characterization of tissue samples.

[0016] 1 is a schematic block diagram illustrating an expression mapping system 100, according to some embodiments. As shown, the expression mapping system 100 may include a user device 110 and an expression mapping platform 130 interconnected via a network 102. While the expression mapping system 100 is illustrated as including two separate devices, other arrangements are contemplated. For example, in other embodiments, instead of including at least five separate components (e.g., 131, 133, 135, 137, 139), the expression mapping platform 130 may include, for example, at least four separate components (e.g., 131, 133, 135, 139). Furthermore, one and / or another of the functions of the various components of the user device and the mapping platform may be combined into a single device / system.

[0017] The network 102 may be or include, for example, an intranet, a local area network (LAN), a personal area network (PAN), a wireless local area network (WLAN), a wireless personal area network (WPAN), a wide area network (WAN) such as the Internet, a metropolitan area network (MAN), a worldwide interoperability for microwave access networks (WiMAX), an optical fiber (or fiber optic)-based network, a Wi-Fi™ network, a Bluetooth® network, a virtual network, and / or any combination thereof. The network 102 may include, for example, wired connections, wireless (e.g., radio communications, free-space optical communications) connections, optical fiber connections, and the like. The network 102 may include, for example, routers, firewalls, switches, gateway computers, edge servers, and the like. In some cases, alternatively or otherwise, the network 102 may include, for example, links, connections, or pathways of telecommunications, data communications, and / or data transmission channels over which data and signals may be communicated, transmitted, or propagated between devices. For example, network 102 may include a near field communication (NFC) connection (e.g., an NFC beacon connection), a short-range or close-range communication connection (e.g., Bluetooth), and / or the like. Network 102 may include any suitable combination of connections and protocols configured to enable and support interconnection, communication, and interoperability between user device 110 and expression mapping platform 130.

[0018] User device 110 and expression mapping platform 130 can each individually and respectively include devices, nodes, systems, or platforms, such as, for example, machines or computing devices, computing systems, computing platforms, information systems, programmable electronic devices, information content processing devices, and / or the like. For example, user device 110 and / or expression mapping platform 130 can include, for example, a controller, processor, mobile phone, smartphone, tablet computer, laptop computer, personal or desktop computer, server (e.g., database server), virtual machine, wearable device (e.g., electronic watch), implantable device, and / or the like. Otherwise, user device 110 and / or expression mapping platform 130 can be, include, or use any suitable type of device, system, and / or platform that can communicate with or interoperate with one or more other devices, systems, and / or platforms, such as user device 110 and / or expression mapping platform 130 (e.g., via network 102). In some embodiments, user device 110 and / or expression mapping platform 130 may include internal and external hardware components as described with reference to Figure 9. In other embodiments, user device 110 and / or expression mapping platform 130 may be implemented within a cloud computing environment as described with reference to Figures 10 and 11.

[0019] User device 110 includes a processor 111, a user interface 113, a communication device 115, and a memory 117. User device 110 may be configured to implement any suitable combination of devices and technologies, such as network devices and device drivers, to support the operation of processor 111, user interface 113, communication device 115, and memory 117 and to provide a platform that enables communication between user device 110 and expression mapping platform 130 (e.g., via network 102).

[0020] Processor 111 may be or include any suitable type of processing device configured to operate and / or execute software, code, commands, or logic. For example, processor 111 may be or include a hardware-based integrated circuit (IC), a general-purpose processor, a central processing unit (CPU), an accelerated processing unit (APU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a programmable logic array (PLA), a complex programmable logic device (CPLD), a programmable logic controller (PLC), or the like. Processor 111 may be operatively coupled to memory 117 by means such as a data transfer device or system, such as a bus (e.g., address bus, data bus, control bus). Processor 111 may otherwise include a processor configured to execute any suitable type or form of software, code, commands, and / or logic corresponding to or representing an application or program, such as application 112, as described herein.

[0021] The application 112 may be or include any suitable type of application or program, such as a software or computer program, one or more subroutines within a program, an application programming interface, or the like. The application 112 may include any suitable type or form of software, code, commands, and / or logic representing instructions, such as machine-executable code, computer-executable code, or processor-executable code, logic, instructions, commands, and / or the like. The application 112 may be configured to reside or be hosted on the user device 110. For example, the application 112 may be configured to be stored on the user device 110 (e.g., by memory 117). Alternatively, or in combination, the application 112 may be configured to reside or be hosted on a device separate, distinct, or remote from the user device 110, such as a server, node, and / or the like. The application 112 may be configured to operate or be executed by or through any suitable type of processor or processing device, such as the processor 111. For example, application 112 may be or include a native application, a web application or a web-based application, and / or a hybrid application (e.g., an application having a combination of properties or functionality of a native application and a web-based application).

[0022] User interface 113 may be or include any suitable type of user interface device configured to enable user interaction between a user and user device 110. In some embodiments, user interface 113 may be configured to enable user interaction between a user (e.g., at user device 110) and expression mapping platform 130, as described herein. For example, user interface 113 may be configured to provide (e.g., display) output (e.g., from mapping application 132 and / or sampling profiler 133). Additionally, user interface 113 may be configured to receive user input (e.g., from a user at user device 110), as described herein. For example, user interface 113 may include one or more input devices, such as a keyboard and a mouse, and one or more output devices, such as a display, screen, projector, and the like. As another example, user interface 113 may include one or more input / output (I / O) devices, such as a touch screen, a holographic display, a wearable device such as a contact lens display, an optical head-mounted display, a virtual reality display, an augmented reality display, and / or the like. User interface 113 may be configured to implement any suitable type of human-machine interface device, human-computer interface device, batch interface, graphical user interface (GUI), and the like. User interface 113 may otherwise include or be configured to implement any suitable type of interface (e.g., user interface 113) that can be implemented in conjunction with a device, such as expression mapping platform 130, such as to provide user interaction between a user and a device, as described herein. In some embodiments, user input received at user interface 113 may be transmitted to expression mapping platform 130 (e.g., via network 102) for execution.

[0023] The communication device 115 may be or include, for example, a hardware device operably coupled to the processor 111 and memory 117, and / or software stored in the memory 117 and executable by the processor 111, that can enable and support communication over a network (e.g., network 102) and / or direct communication between computing devices (e.g., the user device 110 and the expression mapping platform 130). For example, the communication device 115 may be or include a network interface card (NIC), a network adapter such as a Transmission Control Protocol (TCP) / Internet Protocol (IP) adapter card or a wireless communication adapter (e.g., a 4G wireless communication adapter using Orthogonal Frequency Division Multiple Access (OFDMA) technology), a Wi-Fi® device or module, a Bluetooth® device or module, and / or any other suitable wired and / or wireless communication device. Communications device 115 may be configured to connect or interconnect user device 110 and one or more other devices (e.g., expression mapping platform 130) for data communication therebetween, such as via a communications network (e.g., network 102). Communications device 115 may be implemented in conjunction with any suitable architecture, such as one designed to communicate data and / or control information between a processor (e.g., processor 111, processor 131), system memory (e.g., memory 117, memory 139), peripheral devices (e.g., user interface 113, user interface 135), and any other devices or components in a system, such as an expression mapping system (e.g., expression mapping system 100) (including, e.g., of expression mapping system 100 and / or expression mapping platform 130), as described herein.

[0024] Memory 117 may be or include any suitable type of memory, data storage device, or machine-readable, computer-readable, or processor-readable medium capable of storing machine or computer programs (e.g., of or associated with application 112), digital information, electronic information, and the like. For example, memory 117 may be configured to store applications or programs, such as application 112, for execution by processor 111. Memory 117 may be or include a memory buffer, a hard drive, magnetic disk storage of an internal hard drive, magnetic tape, magnetic disk, optical disk, portable memory (e.g., flash drive, flash memory, portable hard disk, memory stick), semiconductor storage such as random access memory (RAM) (e.g., RAM including cache memory), read-only memory (ROM), erasable and programmable read-only memory (EPROM), electrically erasable and programmable read-only memory (EEPROM), and / or the like. Memory 117 may otherwise include any suitable type of memory or data storage device, which may be selected as a matter of design.

[0025] The expression mapping platform 130 includes a processor 131, a sampling profiler 133, a user interface 135, a communication device 137, and a memory 139. The expression mapping platform 130 may be configured to implement any suitable combination of devices and technologies, such as network devices and device drivers, to provide a platform that supports the operation of the processor 131, the sampling profiler 133, the user interface 135, the communication device 137, and the memory 139 and enables communication between the user device 110 and the expression mapping platform 130 (e.g., via the network 102), as described herein. The expression mapping platform 130 may be configured to spatially map (e.g., via the sampling profiler 133) the biological expression of one or more target biological components contained in the tissue sample onto an image of the tissue sample, as described herein. Although the expression mapping platform 130 is shown including five individual elements or components (e.g., the processor 131, the sampling profiler 133, the user interface 135, the communication device 137, and the memory 139), other arrangements may be contemplated. For example, in some embodiments, expression mapping platform 130 may alternatively or otherwise include processor 131, sampling profiler 133, user interface 135, and memory 139 (e.g., four separate elements or components), and / or any other number of separate elements or components (e.g., including one or more integrated or separate devices, platforms, nodes, etc.), which may be selected as a matter of design.

[0026] In some embodiments, expression mapping platform 130 may comprise a device, system, or platform (collectively, "expression mapping platform"), such as a biological expression mapping system, a biological tissue or material imaging system, a gene expression analyzer, a gene expression imager, a gene expression profiling device, a gene expression mapper, a digital spatial profiling device, a molecular imager, and the like. For example, in some instances, expression mapping platform 130 may include one or more nCounter® systems and / or methods by NanoString Technologies® (South Lake Union, Seattle, Washington), as described herein.

[0027] Processor 131 may be or include any suitable type of processing device configured to operate and / or execute software, code, commands, or logic. For example, processor 131 may be or include a hardware-based integrated circuit (IC), a general-purpose processor, a central processing unit (CPU), an accelerated processing unit (APU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a programmable logic array (PLA), a complex programmable logic device (CPLD), a programmable logic controller (PLC), or the like. Processor 131 may be operatively coupled to memory 139 by means such as a data transfer device or system, such as a bus (e.g., address bus, data bus, control bus). Processor 131 may otherwise include a processor configured to execute any suitable type or form of software, code, commands, and / or logic corresponding to or representing an application or program, such as mapping application 132, as described herein.

[0028] The mapping application 132 may be or include any suitable type of application or program, such as a software or computer program, one or more subroutines within a program, an application programming interface, or the like. The mapping application 132 may include any suitable type or form of software, code, commands, and / or logic representing instructions, such as machine-executable code, computer-executable code, or processor-executable code, logic, instructions, commands, and / or the like. In some embodiments, the mapping application 132 may be configured to communicate with the sampling profiler 133, as described herein. The mapping application 132 may be configured to reside or be hosted on the expression mapping platform 130. For example, the mapping application 132 may be configured to be stored on the expression mapping platform 130 (e.g., via memory 139). Alternatively, or in combination, the mapping application 132 may be configured to reside or be hosted on a device separate, distinct, or remote from the expression mapping platform 130, such as a server, node, device, and / or the like. Mapping application 132 may be configured to operate or be executed by or through any suitable type of processor or processing device, such as processor 131. For example, mapping application 132 may be or include a native application, a web application or web-based application, and / or a hybrid application (e.g., an application having a combination of properties or functionality of native and web-based applications).

[0029] In some embodiments, mapping application 132 may be configured to control operation of expression mapping platform 130 based on user input, such as by communicating executable commands and / or instructions (e.g., corresponding to the user input) to sampling profiler 133. For example, mapping application 132 may be configured to receive user input corresponding to the instructions (e.g., from a user at user interface 135 and / or user interface 113) and send corresponding instructions ("user input instructions") to sampling profiler 133 based on the user input, thereby causing sampling profiler 133 to perform various operations. For example, the user input instructions, when executed, may be configured to cause sampling profiler 133 to perform related operations, such as loading a sample, identifying information associated with the sample, scanning the sample to generate a corresponding image (e.g., a fluorescence image) of the sample, determining user input based on selections that specify one or more ROIs for the sample, etc., as described herein. An ROI may be or include, for example, a tissue type, cell type, cell, or subcellular structure within a cell present in the sample.

[0030] In some embodiments, the sampling profiler 133: -imaging and analyzing the sample; - spatially mapping one or more biological expressions of each target biological component contained in the tissue sample onto an image of the sample; and -represents a device or system configured to perform or implement multiplexed detection, analysis, and / or quantification of expression events (e.g., protein expression, mRNA expression) in user-defined regions of a sample (e.g., one or more ROIs).

[0031] For example, the sampling profiler 133 may be configured to spatially map one or more biological expressions of each target biological component contained in the sample (in one or more ROIs) onto an image of the sample based on instructions corresponding to user input specifying the selection of one or more ROIs (e.g., received from a user by the mapping application 132 on the user device 110 or the expression mapping platform 130), as described herein.

[0032] In some embodiments, the sampling profiler 133 may include, for example, a sample preparation station (not shown) and an analytical instrument (not shown). The analytical instrument may include, for example, a digital analytical instrument (“digital analyzer”). For example, the sampling profiler 133 may include, for example, a GeoMx® Digital Spatial Profiler (DSP) by NanoString Technologies®. In this example, the sample preparation station and digital analyzer may include an nCounter® Preparation Station and an nCounter® Digital Analyzer, respectively. In some embodiments, the sampling profiler 133 may be configured to receive a sample, such as a tissue sample, for processing (e.g., by the sample preparation station) for and prior to data collection, and subsequently perform data collection and analysis (e.g., by the digital analyzer) on the processed tissue sample, as described herein. In some embodiments, sampling profiler 133 may be configured to be controlled or otherwise implemented based on user input instructions corresponding to user input received via mapping application 132 and / or a user interface (e.g., user interface 113, user interface 135), as described herein.

[0033] In some embodiments, the sample preparation station may include an automated sample preparation station, such as a multi-channel pipetting robot, configured to process one or more samples (e.g., labeled tissue, user-defined cells, user-defined sub-cellular structures within cells) for subsequent data collection and analysis (e.g., by a digital analyzer), e.g., as described herein. In some embodiments, processing one or more of the samples may include, for example, preparing the sample by staining the sample or exposing the sample to multiple reagents (e.g., hybridization). For example, the sample preparation station may be configured to process the sample for subsequent data collection and analysis (e.g., by a digital analyzer) by staining or labeling one or more samples to allow visualization of sub-cells or cellular structures within the stained or labeled cell, such as in the case of a sample including at least one cell, or alternatively or additionally, by allowing visualization of sub-cells, cells, or tissue-related structures or sections within the stained or labeled tissue sample, such as in the case of a sample including a tissue sample.

[0034] The plurality of reagents may include, for example, multiple imaging reagents and multiple profiling reagents. In some embodiments, the plurality of imaging reagents may include, for example, one or more markers, tags, and the like. For example, in some cases, the plurality of imaging reagents may include one or more imaging reagents (e.g., up to four), such as fluorescent markers. In some embodiments, the plurality of profiling reagents may include, for example, one or more RNA and / or protein detection reagents or probes ("profiling reagent(s)" or "probe(s)"). For example, the plurality of profiling reagents may include about 10 to 10,000 profiling reagents. Each protein detection reagent or probe may include, for example, a cleavable probe, such as a photocleavable (e.g., UV-cleavable) probe and the like. In some embodiments, the probes may include two or more labeled oligonucleotides per antibody. For example, each probe may include a target-binding domain and a signal oligonucleotide. The target-binding domain may include, for example, a protein-binding molecule (e.g., an antibody, peptide, aptamer, peptoid). The signal oligonucleotide can comprise, for example, a single-stranded nucleic acid or a partially double-stranded nucleic acid.

[0035] In some embodiments, each imaging reagent can be configured to bind to a biological boundary of the tissue sample within at least one or more ROIs, and each profiling reagent can be configured to bind to a specific biological expression of a specific target biological component contained within at least one or more ROIs. In some embodiments, each profiling reagent can be further configured to include, for example, a cleavable association oligonucleotide. In some embodiments, each profiling reagent can include, for example, one or more nucleic acid probes comprising a target-binding region to which a cleavable association oligonucleotide is removably linked, or an oligonucleotide comprising a removably linked antibody. In some embodiments, the removably linked linkage can include, for example, a linker (e.g., a cleavable linker) positioned between the target-binding domain and the signal oligonucleotide. The cleavable linker can include, for example, a photocleavable linker configured to be cleaved by electromagnetic radiation (e.g., light) emitted by a light source, such as a suitable coherent light source (e.g., a laser, a laser scanning device, a confocal laser scanning device, a UV light source) or a suitable non-coherent light source (e.g., a discharge lamp and a light-emitting diode (LED)). In some embodiments, the light source may additionally or otherwise include, for example, a digital mirror device (DMD).

[0036] In some embodiments, the cleavable association oligonucleotide may include, for example, a photocleavable oligonucleotide tag. For example, a tissue sample may be prepared for assay (e.g., by the expression mapping platform 130) by using an antibody or RNA probe bound to the photocleavable oligonucleotide tag. In some embodiments, each photocleavable oligonucleotide tag may be or include a machine-readable identifier that can be scanned or read by a scanner, such as a barcode scanner and the like. In some cases, the photocleavable oligonucleotide tag may be bound to a slide-mounted FFPE tissue section along with one or more morphological markers. In some embodiments, the one or more morphological markers may include, for example, up to four morphological markers, and each morphological marker may include, for example, a fluorescent probe. After binding the oligoconjugate probe and the morphological marker to the slide-mounted FFPE tissue section, the oligonucleotide tag may be released from a selected region of the tissue for further analysis.

[0037] In some embodiments, the sample preparation station may be further configured to perform other processing operations, including, for example, liquid transfer operations, magnetic bead separation operations, immobilization operations (e.g., of molecular labels on a sample cartridge surface), and the like. The sample may be fixed or unfixed. For example, in some cases, sample processing via the sample preparation station may include purifying a sample containing at least one cell and immobilizing it on a surface (e.g., an inner surface) of a container (e.g., a sample container), cartridge (e.g., a sample cartridge), and / or the like. For example, the at least one cell may be directly immobilized on the surface or indirectly immobilized on the surface via at least one other cell. After processing the tissue sample, the sampling profiler 133 may be configured to transfer the tissue sample to a digital analyzer for imaging, data collection, and analysis, as described herein.

[0038] In some embodiments, the digital analyzer may include, for example, a multiplexed analyzer, a scanner, a reader, a counting device, and the like. For example, the digital analyzer may include a barcode scanner, a multi-channel epifluorescence scanner, and the like. The digital analyzer may include an imaging device, such as a charge-coupled device (e.g., a camera), and a microscope objective. The digital analyzer may further include a transducer ("light source"), such as an energy source, an energy emitter, a light source, and the like. In some embodiments, the light source may be or include, for example, a coherent light source (e.g., a laser), an ultraviolet (UV) light source, and the like. In some embodiments, the light source may be or include, for example, an incoherent light source (e.g., a discharge lamp and a light-emitting diode (LED)). The light source may be configured to illuminate at least one subcellular structure of at least one cell with respect to the sample, thereby enabling detection of the abundance of at least one protein target in or from at least one subcellular structure of at least one cell. The light source may also be configured to first illuminate at least one subcellular structure within at least one cell and then illuminate at least one subcellular structure within at least one second cell, allowing a comparison of the abundance of at least one protein target within or from the at least one subcellular structure within the at least one cell with the abundance of at least one protein target within or from the at least one subcellular structure within the at least one second cell.

[0039] In some embodiments, the digital analyzer may be configured to determine one or more biological expressions contained within at least one or more ROIs and their corresponding locations within the sample, so as to spatially map one or more of the biological expressions (e.g., of each target biological component) contained in the sample onto the image of the sample. Thus, the digital analyzer may be configured to capture one or more images of the sample and collect and / or analyze data associated with the sample, so as to spatially map one or more biological expressions of each target biological component contained in the sample onto the image of the sample. For example, the digital analyzer may be configured to count, weigh, and / or quantify the biological expressions contained within at least one or more ROIs. Thus, in some embodiments, the digital analyzer may be configured to associate one or more mapped biological expressions with a visualization of each of the biological expressions contained in the one or more ROIs.

[0040] The spatial mapping of at least one user-selected biological expression can be configured to provide a spatial context between the user-selected biological expression in the sample and one or more associated ROIs (e.g., in which the user-selected biological expression is located). In other words, the spatial mapping of at least one user-selected biological expression can be configured to provide a spatial context with respect to the tissue sample between the biological expression of the target biological component (e.g., the location or position of an expression event associated with or corresponding to the biological expression of the target biological component) and one or more ROIs (e.g., the location or position of the one or more ROIs). In some embodiments, the one or more biological expressions can be spatially mapped onto a visualization or image of the tissue sample by the spatial profiler 133, and the biological expressions can be configured to be counted, quantified, or quantified by a digital analyzer, as described above and as described herein.

[0041] In some embodiments, the digital analyzer comprises: - contacting at least one protein target in or from at least one cell in the tissue sample with at least one probe comprising a target binding domain and a signal oligonucleotide; - applying or exerting a force sufficient to release the signal oligonucleotide at a location on the tissue sample; and - may be configured to recover and identify the released signal oligonucleotides, thereby detecting at least one target within or from a specific location of the forced tissue sample, which may include, for example, a user-defined region of tissue, a user-defined cell, a user-defined sub-cellular structure within a cell, and the like (e.g., ROI).

[0042] In some embodiments, the digital analyzer can be configured to repeat steps b) and c) at least a second specific location of the tissue sample, the second specific location comprising at least a second cell. - comparing the abundance of at least one protein target in or from a first specific location with the abundance of at least one protein target in or from a second specific location, wherein the at least one cell and the at least second cell can be of the same cell type or different cell types; - quantifying the abundance of at least one protein target in or from a first cell type and the abundance of at least one protein target in or from a second cell type; and - may include at least one (preferably several, more preferably all) of the following: a polymerase reaction, a reverse transcriptase reaction, hybridization to an oligonucleotide microarray, mass spectrometry, hybridization to a fluorescent molecular beacon, a sequencing reaction, machine reading of a machine-readable identifier such as an nCounter® molecular barcode, and the like.

[0043] In some embodiments, the first and second cell types can be independently selected from normal cells and abnormal cells, such as diseased or cancerous cells (e.g., based on input received at user interface 113 and / or user interface 135).

[0044] In some embodiments, the target binding domain comprises a protein-binding molecule, e.g., an antibody, a peptide, an aptamer, and a peptoid, and in some embodiments, can detect two or more targets, e.g., from 1 to 1000 or more targets (e.g., each corresponding to a biological expression), and any number therebetween. In some embodiments, the targets can each include or be associated with expression events associated with, e.g., individual RNA targets, DNA targets, protein targets, and the like. In some embodiments, detecting can include, e.g., quantifying the abundance of each target.

[0045] In some embodiments, the digital analyzer may be configured to illuminate and image the sample (e.g., with a laser scanning device, DMD, etc.), subsequently receive user input specifying the selection of one or more ROIs (e.g., based on the image of the sample), and illuminate at least one or more of the ROIs of the tissue sample, thereby cleaving the associated oligonucleotides from the profiling reagent. Further, in some embodiments, the digital analyzer may be configured to recover the cleaved oligonucleotides and analyze (e.g., quantify) the recovered cleaved associated oligonucleotides to determine one or more biological expressions contained within at least one or more ROIs and their corresponding locations within the ROIs. Accordingly, relevant data from the digital analyzer may be output for use in generating an image and / or visualization (e.g., corresponding to one or more spatial mappings of biological expressions and images of the sample) for rendering or display (e.g., in user interface 113 and / or user interface 135) to provide spatial context, as described in further detail herein.

[0046] In some embodiments, the digital analyzer may be configured to generate an image and one or more associated corresponding visualizations for display, viewing, and user interaction on a user interface (e.g., user interface 113, user interface 135) as described herein. For example, the digital analyzer may be configured to generate a single-cell resolution image and / or visualization corresponding to a measure (e.g., count value) of expression events associated with each of the biological expressions contained in one or more ROIs as described herein. In some embodiments, the visualization or image may include at least one of a graph, plot, diagram, and map of one or more biological expressions contained in one or more ROIs, such as those described herein with reference to Figures 3, 5, and 6A-6I. The visualization or image of the tissue sample may be configured to facilitate morphological profiling, analysis, and characterization ("morphological profiling") of the tissue sample based on the biological expression of each target biological component contained in the tissue sample and the location of each biological expression within the tissue sample. In some embodiments, morphological profiling may include at least one of geometric profiling, segment profiling, contour profiling, grid profiling, and cellular profiling, for example, as described herein with reference to Figures 4A-4E.

[0047] User interface 135 may be or include any suitable type of user interface device configured to enable user interaction between a user and expression mapping platform 130. For example, user interface 135 may be configured to provide (e.g., display) output (e.g., from mapping application 132 and / or sampling profiler 133). Additionally, user interface 135 may be configured to receive user input (e.g., from a user at expression mapping platform 130) via one or more input and / or output devices, including, for example, a keyboard, a mouse, a display, a screen / touch screen, a projector, and the like, as described herein (i.e., user interface 135 may be configured to implement any suitable type of human-machine interface device, human-computer interface device, batch interface, graphical user interface (GUI), and the like). User interface 135 may otherwise be configured to include or implement any suitable type of interface (e.g., user interface 113).

[0048] The communication device 137 may be or include, for example, a hardware device operably coupled to the processor 131 and the memory 139, and / or software stored in the memory 139 and executable by the processor 131, that can enable and support communication over a network (e.g., the network 102) and / or direct communication between computing devices (e.g., the user device 110 and the expression mapping platform 130). For example, the communication device 137 may be or include a network adapter such as a network interface card (NIC), a transmission control protocol (TCP) / internet protocol (IP) adapter card, or a wireless communication adapter (e.g., a 4G wireless communication adapter using orthogonal frequency division multiple access (OFDMA) technology), a Wi-Fi™ device or module, a Bluetooth® device or module, and / or any other suitable wired and / or wireless communication device. Communication device 137 may be configured to connect or interconnect expression mapping platform 130 and one or more other devices (e.g., user device 110) for data communication therebetween, such as via a communication network (e.g., network 102). Communication device 137 may be implemented in conjunction with any suitable architecture, such as one designed to pass data and / or control information between a processor (e.g., processor 111, processor 131), system memory (e.g., memory 117, memory 139), peripheral devices (e.g., user interface 113, user interface 135), and any other devices or components in a system (e.g., including expression mapping system 100 and / or expression mapping platform 130) such as an expression mapping system (e.g., expression mapping system 100), as described herein.

[0049] Memory 139 may be or include any suitable type of memory, data storage device, or machine-readable, computer-readable, or processor-readable medium capable of storing machine or computer programs (e.g., of or associated with mapping application 132), digital information, electronic information, and the like. For example, memory 139 may be configured to store applications or programs, such as mapping application 132, for execution by processor 131. Memory 139 may be or include a memory buffer, a hard drive, magnetic disk storage of an internal hard drive, magnetic tape, magnetic disk, optical disk, portable memory (e.g., flash drive, flash memory, portable hard disk, memory stick), semiconductor storage such as random access memory (RAM) (e.g., RAM including cache memory), read-only memory (ROM), erasable and programmable read-only memory (EPROM), electrically erasable and programmable read-only memory (EEPROM), and / or the like. Memory 139 may otherwise include any suitable type of memory or data storage device, which may be selected as a matter of design.

[0050] User interface 113 and / or user interface 135 may include, for example, a user interface display on which one or more display portions are provided. User interaction may include, for example, an interactive association (e.g., based on user input) of one or more of the tissue images, visualizations, user-selectable datasets, and one or more of the plurality of scan records. In some embodiments, the one or more display portions are configured to be interconnected and may include, for example, a first display portion, a second display portion, a third display portion, and / or a fourth display portion. For example, in some embodiments, the integrated user interface may be configured to effectively operate through and / or in conjunction with the first display portion, the second display portion, the third display portion, and / or the fourth display portion as sections / portions of a single display. For example, the integrated user interface may be configured to interactively associate one or more of the tissue images, visualizations, user-selectable datasets, and one or more of the plurality of scan records based on user input (e.g., to user interface 135). Such integrated user interfaces are described in further detail herein with reference to FIG. 5.

[0051] In some embodiments, expression mapping platform 130 can be configured to analyze biological material based on user input (e.g., received at user interface 113 and / or user interface 135) so that, after hybridization of probes to slide-mounted tissue sections, oligonucleotide tags are liberated from distinct regions of the tissue by, for example, UV exposure (at sampling profiler 133), the liberated tags are quantified with an nCounter assay (e.g., at sampling profiler 133 and by a digital analyzer), and the counts are mapped back to tissue locations to obtain a spatially resolved digital profile of analyte abundance. The spatially resolved digital profile can be configured to be displayed, for example, at user interface 113 and / or user interface 135, as described herein.

[0052] In some embodiments, an ROI is identified on or adjacent to a serial section of tissue to provide the probe. In the first instance, in some embodiments, a complete "macroscopic feature" imaging method for the cell / tissue of interest, e.g., DAPI staining, membrane staining, mitochondrial staining, specific epitope staining, and specific transcript staining, is performed to determine the overall macroscopic features of the cell / tissue of interest. Alternatively, an ROI is identified on a serial section adjacent to the serial section to which the probe is provided, and complete "macroscopic feature" imaging (as described above) is performed on the first serial section. This imaging generally involves identifying an ROI on the adjacent serial section, and applying an appropriate, directional force at the ROI to release the signal oligonucleotide from the probe. The serial sections may be spaced approximately 5 μm to 15 μm apart from each other. Further details are described in related PCT International Application PCT / US2016 / 042455, which, as noted above, is incorporated herein by reference in its entirety.

[0053] In this example, the expression mapping platform 130 may be configured to analyze the biological material (eg, in the sampling profiler 133) as follows. -Process: Tissues mounted on FFPE slides are incubated with a cocktail of primary antibodies conjugated to DNA oligos via photocleavable linkers, along with a limited number of visible wavelength imaging reagents. - Visualization: Identify ROIs with low plexity using visible light-based imaging reagents to establish the overall "architecture" of the tumor slice (e.g., imaging nuclei and / or using one or two key tumor biomarkers); - Profile: Selected ROIs are selected for high-resolution multiplex profiling, and oligos from the selected regions are released after exposure to UV light. - Seeding: The photocleaved, released oligos are then collected, e.g., using a microcapillary-based "sipper," and stored in microplate wells for subsequent quantification; and / or - Digital Counting: During the digital counting process, photocleaved oligos from spatially resolved ROIs within the microplate are hybridized to four-color, six-spot optical barcodes, allowing digital counting of up to 1 million protein targets (distributed across up to 800-plex markers) within a single ROI using standard NanoString nCounter readout instruments (e.g., SPRINT, Flex, and MAX).

[0054] Images are processed internally, and (in some embodiments) one RCC (reporter code count) file is generated from each lane containing the counts for that lane. These RCC files may be compressed (e.g., zipped) and downloaded for import for analysis (and optionally quality control) by a mapping application 132 (e.g., nSolver™ software). Run data can then be exported, for example, as a comma-separated value (CSV) format file that can be opened by most commonly used spreadsheet packages (e.g., Microsoft® Excel) and analyzed using analysis software (e.g., NanoString's nSolver or other data analysis and visualization software packages).

[0055] 2 is a flow diagram illustrating an example method of operating an expression mapping system ("method 201"), according to some embodiments. Method 201 can be implemented by an expression mapping system, such as, for example, expression mapping system 100 (see, e.g., FIG. 1 and related description). Thus, method 201 may be implemented to provide spatial mapping of biological expression within one or more ROIs of a tissue sample; specifically, in some embodiments, the spatial mapping may be configured to provide spatially resolved analyte profiles within and of the tissue sample (e.g., within the ROIs) corresponding to the occurrence and measurement of expression events within the tissue sample, as described herein.

[0056] Method 201 includes, at 202, causing the expression mapping system to display, on a first display, a scanning pane that may include, for example, at least an image of a tissue sample, the image including one or more sections each corresponding to a particular one of one or more regions of interest (ROIs), each of the one or more ROIs corresponding to a particular portion of tissue within the tissue image. Scanning panes are described in further detail herein, for example, with reference to FIG. 5. Method 201 includes, at 204, causing the expression mapping system to display, on a second display, a visualization pane that includes, for example, a visualization of each of the biological expressions included in the one or more ROIs. Such visualizations are described in further detail herein, for example, with reference to FIG. 5.

[0057] Method 201 includes, at 206, causing the expression mapping system to augment the first display by coding one or more ROIs within the tissue image to indicate spatial mapping of biological expression within the one or more ROIs. In some embodiments, the expression mapping system may be configured to augment the first display to facilitate morphological profiling (e.g., of the tissue) in at least one of the one or more ROIs, as described with reference to FIGS. 4A-4E. For example, in some embodiments, the coding may include color coding, e.g., as described with reference to FIGS. 4A-4E. In some embodiments, one or more of the coding or color coding may include presenting quantitative measures of biological expression, e.g., as described with reference to FIGS. 3, 4A-4E, and / or 6A-6I, respectively.

[0058] In some embodiments, the first display may be expanded based on user input specifying at least one selection of biological expressions to be included in the visualization. In some embodiments, spatial mapping of the at least one user-selected biological expression may be configured to provide its spatial context to at least one of the ROIs, as described herein with reference to FIG. 5. In some embodiments, the user input specifying the selection of one or more ROIs may include, for example, one or more selections of ROIs that define the shape or size of the one or more ROIs associated with the selection.

[0059] In some embodiments, method 201 may further include displaying a dataset pane in the third display, e.g., including at least one user-selectable dataset, the at least one dataset associated with one or more of the biological expressions included in the one or more ROIs, such as described with reference to FIG. 5. In some embodiments, method 201 may further include displaying a record pane in the fourth display, e.g., including a plurality of scan records, each including at least one tissue image. In some embodiments, method 201 may further include selecting at least one record based on, e.g., user input, such that, upon selection, at least one of the scan pane, visualization pane, and dataset pane is displayed in the respective display or an associated display.

[0060] 3 illustrates an example of a visualization showing gene expression, according to some embodiments of the present disclosure. As shown, the visualization may include maps, such as heat maps and the like, in which regions of a sample (e.g., ROIs) are classified based on the intensity and identity of expressed markers. Exemplary ROIs include (from top to bottom): "ROI3," "ROI2," "ROI1," "ROI10," "ROI12," "ROI11," "ROI5," "ROI4," "ROI6," "ROI8," "ROI7," and "ROI9." Exemplary regions include (from top to bottom): "CD20-enriched," "CD3-enriched," "mixed," and "PanCK-enriched." Further, exemplary antibodies include (from left to right) "P-S6," "β-catenin," "PanCK," "CD34," "CD163," "VISTA," "Tim3," "CD8," "CD56," "IDO1," "CD11c," "p70-S6K," "GZMB," "CD3," "CD4," "CD45RO," "Bcl-2," "P-STAT5," "B2M," "CD45," "lk-Ba," "HistoneH3," "AKT," "B7-H4," "PD1," "HLA-DR," "CD20," "BIM," "P-STAT3," "PD-L1," "S6," "B7-H3," "c-Myc," "CD68," "Ki-67," "MSH2," "MSH6," "BCL6," "STAT3," "PMS2," and "MLH1," as shown. The heatmap may also include one or more legends configured to indicate, for example, the type and degree of biological expression of each. For example, as shown, the heatmap may include an "nCounter Count Scale" legend and an "Area" legend.

[0061] A heat map represents a visualization of data (e.g., from the sampling profiler 133) showing color-coded metrics or counts of various biological expressions with respect to associated ROIs to which the biological expressions, or expression events associated with the biological expressions, are mapped. A heat map may be or include an image depicting the counts by color. The image may include segments configured to align along an x-axis and targets configured to align along a y-axis. A heat map can be displayed by color-coding one or more ROIs to present the heat map as presenting quantitative measurements of the biological expressions. For example, the color counts of the heat map can be configured to indicate a quantitative measure, such as a count (e.g., indicated by the "nCounter Count Scale" legend) of the biological expressions with respect to the region of the sample to which the counts are mapped (e.g., indicated by the "Area" legend). Furthermore, the quantitative measure can be configured to correspond to the type (e.g., by region and / or ROI and associated antibody type) and / or extent (e.g., by count) of each biological expression. Additionally, the heat map can be configured to indicate the degree or range of each biological expression by a corresponding color or intensity. For example, as shown in the heat map, higher intensities (e.g., relatively dark areas) can be configured to indicate higher counts of biological expression, and lower intensities (e.g., relatively light areas) can be configured to indicate lower counts of biological expression.

[0062] In some embodiments, the heatmap may be configured to display an interactive pop-up box that may be shown in response to user input corresponding to hovering (e.g., with a cursor) over an area of ​​the heatmap. In some embodiments, the interactive pop-up may be configured to show, for example, segments, targets, counts, and / or any tags associated with the detected area over which the cursor is hovered. In some embodiments, a user input element corresponding to scrolling or sliding may be shown and configured to allow selection between linear and Log2 data. In some embodiments, the color scheme in which the heatmap is displayed may be configured to adjust or change based on user input. By way of example, the heatmap may be configured for interactive user manipulation, such as clicking and dragging to select part or all of the heatmap; selecting, defining, and / or assigning a probe group consisting of selected probes; and deselecting, dedefining, and / or assigning a probe group (e.g., from the current study). In some embodiments, the heatmap may be implemented, for example, with a linear scale, a logarithmic scale, and the like.

[0063] 4A-4E illustrate example visualization and profiling modalities (“visualization and profiling modalities” or “profiling modalities”) that can depict (e.g., by user interface 113 and / or user interface 135) tissue (e.g., tissue sample) and gene expression (e.g., occurrence of expression events across or within tissue), according to some embodiments. As shown, the visualization and profiling modalities include geometric profiling ( FIG. 4A ), segment profiling ( FIG. 4B ), contour profiling ( FIG. 4C ), grid profiling ( FIG. 4D ), and rare cell profiling ( FIG. 4E ). The visualization and profiling modalities can be configured to allow a user to interactively and visually define one or more ROIs based on user input, as described herein. The visualization and profiling modalities can be generated and configured to facilitate morphological profiling (e.g., of a tissue) in at least one of the one or more ROIs, as described herein. For example, visualization and profiling modalities can be configured for analysis of samples to determine, assess, and / or characterize the level of heterogeneity and associated biological expression of expression events within and between samples.

[0064] Referring to FIG. 4A , geometric profiling can be configured to enable, support, and facilitate spatial and quantitative assessment, examination, and characterization of a sample's heterogeneity and / or profile (e.g., biological expression profile) based on user input. In some embodiments, user input can be configured to specify, adjust, and / or define one or more selections regarding the shape and / or size of one or more ROIs. For example, user input specifying the selection of one or more ROIs can include, for example, the selection of one or more ROIs as well as the shape or size of one or more ROIs associated with the selection of one or more ROIs. The same shape can be reused, thereby ensuring that a specific area (pixel) is the same between ROIs. In some embodiments, geometric profiling can be configured to provide standardized geometric shapes between different tissue regions of a sample. As an example, geometric profiling can be configured to facilitate assessment of how tumor and immune marker expression may vary (e.g., heterogeneity) across a sample. Geometric profiling can be configured to identify distinct expression profiles across and within specific regions of a tissue expression profile based on proximity. In some embodiments, the geometric profiling may be configured to represent a quantification of biological expression within one or more selected ROIs.

[0065] Referring now to FIG. 4B , segment profiling can be configured to indicate the type and / or degree of cellularity using morphological markers to identify and profile distinct biological compartments within one or more ROIs. Segment profiling reveals distinct tumor and tumor microenvironment molecular profiles. For example, segment profiling can be configured to facilitate assessment of how a tumor may differ from the tumor microenvironment. In some embodiments, segment profiling can include, for example, manual segment profiling or automated segment profiling. In some embodiments, automated segment profiling can be configured to automate and facilitate segment profiling of a sample in at least one or more ROIs based on user input specifying at least one segment profiling parameter. In some embodiments, segment profiling can be configured to detect, classify, identify, and / or differentiate between high and low signals (e.g., in terms of type and / or degree) from morphological markers (fluorescent targets) to facilitate identification and profiling of distinct biological regions within one or more ROIs, as described herein. For example, segment profiling can be configured to identify and / or profile distinct biological regions within a ROI to distinguish between distinct biological regions, such as CD45-positive tissue and S100B-positive tissue.

[0066] Referring now to FIG. 4C , contour profiling, as described herein, can be configured to enable, support, and facilitate evaluation, examination, and characterization of the impact of proximity on the biological response and local microenvironment surrounding a central structure in one or more ROIs. For example, contour profiling can be configured to determine how proximity to a tumor or immune cell population (e.g., in one or more ROIs) alters the biological response. In some embodiments, the one or more ROIs can include one or more radial ROIs configured to exhibit distinct expression profiles based on proximity, as shown in FIG. 4C . In some embodiments, contour profiling can be configured to show how proximity affects the biological response by examining the local microenvironment surrounding the central structure using radial ROIs. The central structure can be a small structure, such as a cluster of immune cells, or a complex structure, such as a neuron or blood vessel. Thus, contour profiling can be configured to enable, support, and facilitate evaluation, examination, and characterization of the impact of proximity on the biological response and local microenvironment surrounding the central structure in one or more ROIs. The central structures may include, for example, clusters of small immune cells or complex structures such as neurons and blood vessels.

[0067] 4D, grid profiling can be configured to perform depth spatial mapping using an adjustable grid pattern. For example, as described herein, grid profiling can be configured to provide a digital map of the molecular profile of a structure (e.g., a tumor) in a sample based on user input corresponding to the selection of one or more ROIs. In some embodiments, the visualization of the grid profiling can include, for example, an adjustable grid pattern superimposed on an image to create a depth spatial mapping of the sample.

[0068] Referring now to FIG. 4E, rare cell profiling can include, for example, single-cell profiling and rare cell profiling. Isolated immune cell populations exhibit distinct expression profiles. Thus, rare cell profiling can be configured to indicate the function of distinct cell populations within one or more ROIs, for example, as described herein. In some embodiments, rare cell profiling can be configured to detect or identify distinct cell populations based on cell-type-specific morphological markers in one or more ROIs. Thus, rare cell profiling can help "shed light on rare events," including, for example, rare expression events and their types that may be associated with corresponding biological expression. In some embodiments, rare cell profiling can be configured to facilitate the evaluation, investigation, and characterization of how specific immune cells, including, for example, rare or single immune cells, may affect tumor biology and therapeutic response. The function of distinct cell populations can be indicated, for example, based on cell-type-specific morphological markers corresponding to the distinct expression profiles.

[0069] FIG. 5 is a diagram illustrating an example of a user interface display including a visualization, according to some embodiments. The user interface display may include a user interface, such as user interface 113 and / or user interface 135, as described herein (and above). As shown, the user interface display includes, for example, a scanning pane, a dataset pane, and a visualization pane. In some embodiments, the user interface display may further include, for example, a records pane (not shown). Additionally, the scanning pane, dataset pane, and visualization pane may each include various function buttons. For example, as shown in FIG. 5, in the scanning pane, function buttons may include "Manage Annotations," in the dataset pane, "Dataset History" and "Export, Rename, Delete Dataset," and in the visualization pane, a "Drop-Down Parameter Menu." In some embodiments, the user interface display may further include, for example, a toolbar and / or general function buttons. For example, as shown, the toolbar may include "Taskbar Options," and the general function buttons may include an "Export Function." The user interface display may otherwise include any other suitable type or configuration of panes, toolbars, and / or function buttons, which may be selected as a matter of design.

[0070] The user interface display may include an integrated user interface configured to provide interactive user interaction between a user (e.g., at the user device 110 or the expression mapping platform 130) and the expression mapping platform (e.g., at the expression mapping platform 130), as described herein. In some embodiments, the user interaction may include, for example, an interactive association (e.g., based on user input) of one or more of a tissue image, a visualization, a user-selectable dataset, and one or more of a plurality of scan records. In general, the interconnected visualization may include any suitable type of visualization (e.g., of a sample) and / or image associated with a selection of a dataset (e.g., one or more user-selectable datasets, one or more of a plurality of scan records), as described herein.

[0071] In some embodiments, the scanning pane may include multiple icons, each corresponding to, for example, one or more ROIs or at least one particular segment of the overall tissue image. In some embodiments, the scanning pane may include multiple icons, each corresponding to, for example, one or more ROIs or at least one particular segment of the overall tissue image. In some embodiments, the scanning pane may include representations, visualizations, and / or images associated with or corresponding to, for example, one or more ROIs, segments, and the like. In some embodiments, the scanning pane may be configured such that one or more scans, one or more ROIs, and / or one or more segments can be excluded or included from a particular study (e.g., as shown in FIG. 5 ) based on and in response to user input (e.g., received from a user at user device 110 and / or expression mapping system 130 via user interface 113 and / or user interface 135) corresponding to the selection of one or more scans, one or more ROIs, and / or one or more segments. Additionally, the scanning pane may be configured to provide for tag selection based on user input. Additionally, the scanning pane may include individual image viewers for the scans.

[0072] For example, the scan pane may include, for example, an icon associated with each scan (e.g., located at the top of the pane, with picker buttons representing each segment located to the right of each image viewer, as shown in FIG. 5). In some embodiments, each icon may be interactively associated with each scan, such that, for example, a single click toggles the state of a scan or segment from selected to unselected, and vice versa. The icons may otherwise be configured to provide other control actions, which may be selected as a matter of design. In some embodiments, the scan pane may be configured so that hovering over an icon or picker button displays additional information, such as a name, tag, etc.

[0073] In some embodiments, the scan pane may include a "scan icon" button configured to provide a visual preview of, for example, the number of selected segments and / or the total number of segments to be analyzed, as well as a rough percentage of the segments selected for analysis. In some embodiments, the scan pane may include, for example, an image viewer. Each image viewer depicts the scan and the spatial arrangement of the ROIs and segments. For example, a checkbox in the upper left corner indicates whether the scan is selected for analysis, as shown in FIG. 5. Selected scans have a green header, and deselected scans have a white header. The scan pane may be configured to adjust the scan image to aid in viewing, selecting, and deselecting segments. For example, the scan image may be adjusted to change the field of view of the segments. As another example, the scan image may be configured to be zoomed in and out (e.g., by user interface 113 and / or user interface 135). In some embodiments, the scan pane may include one or more picker buttons, each corresponding to one or more ROIs (e.g., scan images).

[0074] In some embodiments, the dataset pane may include, for example, representations, visualizations, and / or images associated with or corresponding to one or more datasets, probes, probe groups, and / or segment groups. For example, the dataset pane may include a list of representations, visualizations, and / or images. In some embodiments, the dataset pane may be configured to initially show the datasets and probe groups associated with the current study. For example, the dataset pane may be configured to display the initial dataset (the imported raw dataset, which appears at the top of the "Datasets" list) and the "All Probes" group at the start of a new study.

[0075] In some embodiments, the visualization pane may include one or more visualizations, each corresponding to one or more graphs, plots, diagrams, and maps of one or more biological expressions contained in one or more ROIs, e.g., as described herein. Each visualization may include a visual representation of one or more selected datasets, probes, and / or adjustments applied to data from those probes. In some embodiments, one or more of the visualizations may include one or more images (e.g., sample images). In some embodiments, one or more of the visualizations may be configured such that user input corresponding to selection of a region of interest (e.g., ROI) on a plot displays an associated highlighted segment in the "Scan" pane. Selecting a region of interest may include, for example, right-clicking to create a tag, group, etc. For example, the visualization pane may be configured such that selection of a region of interest in any visualization (e.g., based on user input by a user to user device 110 and / or expression mapping platform 130 via user interface 113 and / or user interface 135) displays the respective highlighted segment in the "Scan" pane. In some embodiments, one or more ROIs may be selected by the visualization pane. In some embodiments, the visualization pane may be configured to allow real-time user interaction, for example, by user input corresponding to a selection of changes that may be applied to adjust the data in real time.

[0076] In some embodiments, the visualization pane may be configured to generate probe groups, segment groups, and / or the like based on user input. In some embodiments, the visualization pane may be configured to generate tags associated with one or more segments and / or one or more selections of individual segments based on user input. In some embodiments, the visualization pane may be configured to dynamically display one or more datasets, segments, and / or probes in response to user input. In some embodiments, the visualization pane may be interactively interconnected with one or more of the scanning pane or dataset pane. For example, the visualization pane may include a visualization configured to allow one or more ROIs to be selected by selecting a region of interest in the visualization. In some embodiments, ROIs selected in the visualization pane may be configured to be displayed, highlighted, or otherwise indicated in the "scan" pane. Thus, the visualization pane may be configured to allow a user to generate probe groups or segment groups for selection, exclude a selected set of probes or segments from a test, define tags associated with one or more selected segments, and the like. In some embodiments, the record pane and / or dataset pane may be configured to show any changes or adjustments made to the associated dataset.

[0077] As one example, the Datasets pane, when in use, may contain a list of all datasets and probe groups associated with the current test. In some embodiments, the Datasets pane may be configured to show an initial dataset at the start of a test. For example, the initial dataset may include an imported raw dataset, which may be configured to appear at the top of the "Datasets" list in the "Data" settings field. As another example, the initial dataset may be configured to include probe groups and additional probe groups defined in the user's core and module kit configuration file, which is entered into the "Probes" group field at the start of a test.

[0078] In some embodiments, the display portion of the user interface may be configured to render a scan pane in conjunction with a visualization pane and one or more of a dataset pane and a record pane for real-time display based on user input via an integrated user interface. In some embodiments, the probes of the detected datasets may be listed in a probe list in the "Datasets" pane. The dataset pane may be configured to allow individual datasets to be saved to the record pane (e.g., by drag-and-drop). In some embodiments, the record pane may include, for example, a folder or list of datasets (e.g., saved datasets). In some embodiments, the record pane may be configured to be searchable based on tags, text, and the like. In some embodiments, as shown in FIG. 5, the display portion of the user interface may be configured to select at least one record based on user input, and upon selecting the record, at least one of the scan pane, visualization pane, and dataset pane is displayed within the respective display portion.

[0079] In some embodiments, the records pane may include a data analysis queue. For example, the records pane may be accessed by a record button and configured to allow user input selection of one or more folders containing one or more scans. Each scan of interest is selected by clicking the checkbox in the upper left corner. This, for example, changes the header color (e.g., green). One slide may be displayed at a time. In some embodiments, the records pane may include a "scan gallery view" (e.g., under "Records," as shown in FIG. 5). In some embodiments, the records pane is configured to queue one or more scans together for analysis based on user input. In some embodiments, the records pane may be configured to adjust scan order based on user input. For example, scan order can be automatically set based on scan date. As another example, scan order can be adjusted based on user input regarding "scan name," "slide name," and the like.

[0080] In some embodiments, the user interface display may be configured to filter at least one of the properties, constraints, and / or values ​​of the plurality of records based on user input. For example, the user interface display may be configured to filter probes based on text and / or tags (e.g., to search for probes by text and / or tags), or based on "analyte type" (e.g., allowing a user to select "RNA" or "protein" to filter appearing probes). Probe groups and segment groups may be listed, for example, in the "Dataset" pane. Other predefined probe groups may be defined in the core or module kit configuration file and thus pre-populated in this field. In some embodiments, filtering may be configured to be performed based on a selection of "tags," for example, to allow grouping of segments by type, which can be used to categorize and filter data for analysis.

[0081] As an example, during use, the Scans pane may be configured to allow a user to select one or more scans, probes, and / or segments to include in a study based on user input. One or more scans within a study may be represented, for example, as a scan icon at the top of the Scans pane and scan images listed downward, as shown in FIG. 5 . In some embodiments, the Scans pane may be configured to initially select substantially all scans and segments for analysis in a study. In some embodiments, the “Scans” pane may be configured to allow annotations for segments to be uploaded to a spreadsheet using a “Manage Annotations” button on the Scans pane. In some embodiments, the Scans pane may include:

[0082] 6A-6I each illustrate exemplary visualizations according to some embodiments of the present disclosure. As shown, the visualizations include a cluster plot (FIG. 6A), a bar graph (FIG. 6B), a scatter plot (FIG. 6C), a boxplot (FIG. 6D), a forest plot (FIG. 6E), a statistical plot (FIG. 6F), a volcano plot (FIG. 6G), a trend plot (FIG. 6H), and a strip plot (FIG. 6I).

[0083] Referring to FIG. 6A, a cluster diagram can include an interactive tree that draws inferences about relationships between data points. In some embodiments, the interactive tree can be or include a dendrogram. The cluster diagram can be configured, as shown in FIG. 6A, such that points belonging to the same branch of a cluster are similar to each other at a certain level, while data points in separate branches are less similar, with segments aligned along an x-axis and targets aligned along a y-axis. In some embodiments, the cluster diagram can be implemented by an algorithm configured to log-transform data based on user input selections, calculate z-scores, and determine a location within a cluster heatmap by "clustering" (which calculates a correlation / dendrogram). Based on the determined location, the cluster heatmap plots each segment-probe cell with a color representing its z-score. When exporting data from the visualization, there should be the ability to export the z-score values ​​displayed to the user.

[0084] Referring to FIG. 6B , a bar graph represents the counts for all probes in all segments included in the test. For example, the segments may be listed along the x-axis and the counts on the y-axis, with the height of each bar representing the frequency of each count defined by a bin. In some embodiments, the bar graph may be implemented by an algorithm configured to display the bar graph in a linear and / or logarithmic scale, and / or the error bars represent either the "standard" deviation or standard error of the counts within a group. In some embodiments, the bar graph can be configured to allow for auto-scaling of intensity data and / or display in linear or logarithmic space using a "Linear" / "Logarithmic" slider. In some embodiments, the bar graph can be configured to display ratio data (if applicable) as a "Ratio," "Fold Change," or "Log2" ratio. In some embodiments, the bar graph can be configured to apply a scale based on user input, either automatically or manually, by entering a "Minimum Count" and / or a "Maximum Count" (only available when displaying linear intensity data, not a logarithmic scale). In some embodiments, the bar chart can be configured to "apply" grouping by selecting "tags," "coefficients," "average" method (median, geometric mean, average), and "error" bars (SE, SD, none).

[0085] Referring to FIG. 6C, a scatter plot is a visualization that plots results from one segment on the x-axis and results from another segment on the y-axis. Alternatively or additionally, a scatter plot may be or include a visualization configured to show a first plot showing results associated with a probe from a first study against a second plot showing results associated with a probe from a second study. In some embodiments, a scatter plot may be configured to automatically display a trend line, such as an R2 line, and associated values ​​(e.g., as shown in the upper right corner of the plot in FIG. 6C). In some embodiments, the R2 value may be configured to be calculated as a Pearson correlation coefficient (e.g., Excel's "RSQ" function).

[0086] Referring to FIG. 6D, a box plot represents a plot depicting a subset of studies based on quartile values. Box plots have vertical lines (whiskers) extending from the box to indicate variability outside the upper and lower quartiles. Outliers may be plotted as individual points. These visualizations are non-parametric, displaying differences between subsets of experiments without making assumptions about the underlying statistical distribution. For example, based on user input, a box plot can be configured to display a popup displaying segments, tags, and values ​​for the median, maximum, first, and third quartiles. A legend indicates the color assigned to each plot and its corresponding label. Click a color box in the legend to show or hide the plot. In some embodiments, the box plot can be configured to extend between the 25th and 75th percentile quartiles, and / or the whiskers extend to the minimum and maximum values.

[0087] Referring to Figure 6E, a forest plot shows the distribution of ratio values ​​for individual probes across all segments or segment groups. Fold changes are plotted as box plots along the horizontal axis against each probe name (listed on the vertical axis). The vertical axis is plotted with ratios equal to 1. Hovering over a box displays a tooltip with statistical information about the plotted distribution for each probe. The box spans the first and third quartiles of the distribution, and the line indicates the median. The whiskers extend between the 95% confidence limits of the data. In some embodiments, the forest plot can include the following features: display intensity data as "ratio," "fold change," or "Log2," stratify and color data by grouping by tag and tag combinations, color data points by tag, and / or select one or more segment tags to color (combination groups are created as well).

[0088] Referring to FIG. 6F, statistical plots may include, for example, principal component plots or PCA plots. Principal component plots may be configured to depict the top three principal components of a selected data set along the x-, y-, and z-axes of a three-dimensional plot. The majority of the variability can be explained by these top three components. Principal component plots can be configured to have a variety of features, such as: clicking on the plot to rotate it along the x-, y-, or z-axes to view the plot from a different axis perspective; clicking on a data point automatically highlights that segment in the Segments pane and Scan Image Viewer; and / or hovering over a data point in the plot to display a pop-up displaying the segment name, associated tags, and coordinates it represents, as well as its three defined dimensions.

[0089] Referring to FIG. 6G, a volcano plot is a scatter plot with the ratio data (log2) of a dataset on the x-axis and a measure of significance (-log10 of p-value) on the y-axis. This visualization is available for datasets that include t-tests (p-values). In some embodiments, the volcano plot can be configured to combine individual ratios (e.g., tumor1 / stromal AV, tumor2 / stromal AV, etc.) into a single value using a mean value. In some embodiments, the volcano plot can be configured to indicate the ratio (e.g., tumor / immune or immune / tumor) used in the plot based on user input. For example, the volcano plot can be configured to display "view" ratio data as a "ratio," "fold change," or "Log2" ratio. In some embodiments, the volcano plot can be specified using a slider to indicate p-value / -10log. In some embodiments, the volcano plot can be configured to indicate different probe groups with corresponding colors.

[0090] Referring to Figure 6H, a trend plot shows a line graph of all selected probes in a dataset. Segments may be ordered along the x-axis, and probe counts are ordered along the y-axis. Trend plots can be configured to include various features, such as hovering to display a popup containing the probe name and p-value, toggling between linear and logarithmic values ​​for the y-axis, choosing to group "segments" by tag or coefficient, or sorting segments by tag, and selecting a line on the trend plot by p-value selection (e.g., maximum p-value) to select any line representing results associated with that p-value or greater. Use the Line "Select" button to draw a line on the graph.

[0091] Referring to Figure 6I, strip plots depict one probe per visualization. Strip plots can be configured to include various features such as: points on the strip plot represent each value, a line indicates the group median, hovering over a data point on a scatter plot displays a popup displaying the target and x and y coordinates of that point, hovering over any other area on the plot displays the minimum, median, maximum, quartiles, and currently displayed probes, select a p-value to use to filter probes for selection (probes greater than or equal to the selected p-value will be displayed), display linear count or Log2 values, select which probes to display plots for based on user input, set a p-value, add tags to selected segments to create segment groups from the selection.

[0092] Example 1 7A-7D show exemplary results obtained by the expression mapping system of the present disclosure. Spatial mapping embodiments according to the present disclosure were useful in identifying compartment-specific markers associated with potential prognostic biomarkers for survival.

[0093] Specific cell types within the tumor microenvironment to identify prognostic biomarkers.

[0094] Compartments were elucidated by "rare cell profiling" using serial masks, focusing on macrophages (CD68+), melanocytes (S100B+), and non-macrophage immune cells (CD45+CD68-).Objective: To distinguish tumor from stromal regions.

[0095] Results: CD3, CD8, β-2 microglobulin, PD-L1, and HLA-DR all showed cell type-specific predictive power for both overall and progression-free survival, with PD-L1 showing the strongest association with overall survival within the macrophage compartment (Figures 7A-7D), and β-2 microglobulin within the immune non-macrophage compartment associated with both overall and progression-free survival.

[0096] Example 2 Figure 8A shows exemplary results obtained by an embodiment of expression space mapping of the present disclosure. Specifically, Figure 8A illustrates how biological expression profiling of pre-treatment and treatment biopsies identifies multiple markers associated with response. When patients' tumors were examined during treatment, responders showed higher levels of CD45+ expression, CD8+ infiltration, and increased expression of PDL1, PDL1, CD4, Granzyme B, FoxP3, CD20, and PD-1 than non-responders. These differences were observed not only in on-treatment samples but also in baseline samples, suggesting that these markers could be used to predict treatment success prior to administration, potentially reshaping the type of treatment selected for a patient.

[0097] Example 3 Figures 8B-8C show exemplary results obtained by an embodiment of expression space mapping of the present disclosure. In particular, Figure 8B shows a sample image and ROIs used in the geometric ROI selection method, and Figure 8C shows a volcano plot measuring differential protein expression between melanoma patients who relapsed after neoadjuvant therapy and those who did not, as analyzed by GeoMxDSP. Note the increased levels of β2M, CD3, and PD-L1 in patients who did not relapse. All other proteins are not shown in the figure; CD3 is associated with the adaptive immune response (figure adapted from Nature Medicine). See "Neoadjuvant Versus Adjuvant Ipilimumab Plus Nivomumab in Macroscopic Stage III Melanoma" (Blank, CU et al., Neoadjuvant Versus Adjuvant Ipilimumab Plus Nivomumab in Macroscopic Stage III Melanoma. Nat Med. 2018;24(11):1655-1661). This compares the effects of I+N when used as either an adjuvant or neoadjuvant treatment.

[0098] FFPE biopsies taken before I+N administration were stained for 29 targets and S100B, an antigen expressed in melanocytes, to identify tumor-rich ROIs. Six ROIs per tumor were selected by "geometric profiling." CD45 staining was also used to define three ROIs with high immune infiltration and three with low immune infiltration. CD3, β-2 microglobulin, and PD-L1 protein levels were quantified using GeoMxDSP, and IFN-γ RNA levels were stratified as low, intermediate, and high.

[0099] In this study, neoadjuvant treatment was successful in reducing tumor size and reducing the extent of surgical intervention. - neoadjuvant I+N generates more resident T cell clones than adjuvant I+N, as demonstrated by pre- and post-treatment TCR sequencing; and - Supported by findings that interferon gamma (IFN-γ) RNA levels in pretreatment tumor biopsies correlate with clinical outcome and recurrence rate after treatment.

[0100] Patients with reduced levels of CD3, beta-2 microglobulin, and PD-L1 and low levels of IFN-γ RNA relapsed, whereas patients with intermediate to high levels of IFN-γ RNA did not relapse (at the time of publication), indicating that this biosignature may be useful for predicting patient response to treatment (Figure 5).

[0101] Example 4 Figures 8D-8E show exemplary results obtained by the expression mapping system of the present disclosure. In particular, Figure 8D shows a "tumor" sample stained for S100B (tumor cells) and CD45 (immune cells). Segments were created based on S100B and CD45 cellular morphology, and Figure 8E shows a (color-coded) "zoomed-in" view of the segmentation: green = S100B-positive tumor cells, red = CD45-positive immune cells, and blue = DNA. Each segment was individually collected and quantified within the ROI.

[0102] 9 is a block diagram illustrating a user device 110 and / or an expression mapping system 130, according to some embodiments of the present disclosure. As shown, the user device 110 and / or the expression mapping system 130 may include one or more processors 902 (e.g., microprocessors, CPUs, GPUs, etc.), one or more computer-readable RAMs 904, one or more computer-readable ROMs 906, one or more computer-readable storage media 908, device drivers 912, read / write drives or interfaces 914, and network adapters or interfaces 916, all interconnected via a communications fabric 918. The network adapters 916 communicate with a network 930. The communications fabric 918 may be implemented with any architecture designed to communicate data and / or control information between processors (e.g., microprocessors, communications and network processors, etc.), system memory, peripherals, and any other hardware components in the system.

[0103] One or more operating systems 910 and one or more application programs 911, such as a secure mapping application 132 resident on the expression mapping platform 130, are stored on one or more computer-readable storage media 908 for execution by one or more of the processors 902 via one or more of the respective RAMs 904 (which typically include cache memory). In some embodiments, each of the computer-readable storage media 908 may be an internal hard drive magnetic disk storage, a CD-ROM, a DVD, a memory stick, magnetic tape, a magnetic disk, an optical disk, a semiconductor storage device (such as RAM, ROM, EPROM, flash memory, etc.), or any other computer-readable medium (e.g., tangible storage device) capable of storing computer programs and digital information.

[0104] User device 110 and / or expression mapping system 130 may also include a read / write (R / W) drive or interface 914 for reading from and writing to one or more portable computer-readable storage media 926. Application programs 911 on viewing device 110 and / or user device 120 may be stored on one or more of the portable computer-readable storage media 926 and read via the respective R / W drive or interface 914 and loaded onto the respective computer-readable storage media 908. User device 110 and / or expression mapping system 130 may also include a network adapter or interface 916, such as a Transmission Control Protocol (TCP) / Internet Protocol (IP) adapter card or a wireless communication adapter (such as a 4G wireless communication adapter using Orthogonal Frequency Division Multiple Access (OFDMA) technology). For example, application programs 911 may be downloaded to the computing device from an external computer or external storage device via a network (e.g., the Internet, a local area network or other wide area network, or a wireless network) and network adapter or interface 916. The program may be loaded onto the computer-readable storage medium 908 from a network adapter or interface 916. The network may include copper wire, fiber optics, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The user device 110 and / or expression mapping system 130 may also include one or more output devices or interfaces 920 (e.g., a display screen) and one or more input devices or interfaces 922 (e.g., a keyboard, keypad, mouse or pointing device, touchpad). For example, the device driver 912 may connect to the output device or interface 920 for imaging, the input device or interface 922 for user input (e.g., via pressure or capacitance sensing) or user selection, etc.The device drivers 912, R / W drives or interfaces 914, and network adapters or interfaces 916 may include hardware and software (stored on the computer-readable storage medium 908 and / or ROM 906).

[0105] The expression mapping system 130 may be a standalone network server or may represent functionality integrated into one or more network systems. The user device 110 and / or the expression mapping system 130 may be a laptop computer, a desktop computer, a dedicated computer server, or any other computer system known in the art. In some embodiments, the expression mapping system 130 represents a computer system that uses clustered computers and components to function as a seamless single resource pool when accessed over a network, such as a LAN, a WAN, or a combination of the two. This embodiment may be particularly desirable in data center and cloud computing applications. In general, the user device 110 and / or the expression mapping system 130 may be any programmable electronic device, or any combination of such devices, according to embodiments of the present disclosure.

[0106] The programs described herein are identified based on the particular embodiment or application for which the programs are implemented in embodiments of the present disclosure, although any particular program nomenclature herein is used for convenience only, and therefore, embodiments and embodiments of the present disclosure should not be limited to use only with any particular application identified and / or suggested by such nomenclature.

[0107] Embodiments of the present disclosure may be or may use one or more of an apparatus, a system, a method, and / or a computer-readable medium embodied at any possible level of technical detail. The computer-readable medium may include a computer-readable storage medium (or medium) having computer-readable program instructions for causing a processor to perform one or more aspects of the present disclosure.

[0108] A computer-readable (storage) medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer-readable medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer-readable storage media includes: floppy disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanically encoded devices such as punch cards or ridge structures in grooves with instructions recorded on them, and any suitable combination of the foregoing. As used herein in accordance with embodiments of the present disclosure, a computer-readable storage medium should not be construed as a transitory signal per se, such as an electric wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse passing through a fiber optic cable), or an electrical signal transmitted through a wire.

[0109] The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to each computing / processing device, or may be downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface within each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium within the respective computing / processing device.

[0110] The computer-readable program instructions for carrying out the operations of the present disclosure may be either assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, configuration data for an integrated circuit, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and procedural programming languages ​​such as the "C" programming language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet Service Provider). In some embodiments, an electronic circuit, including, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may execute computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuit to perform various aspects of the present disclosure.

[0111] Aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0112] These computer-readable program instructions may be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to produce a machine or system such that the instructions, executed by the processor of the computer or other programmable data processing apparatus, generate means for implementing the function / act specified in the block(s) of the flow diagrams and / or block diagrams. These computer-readable program instructions according to embodiments of the present disclosure may also be stored on a computer-readable storage medium and can direct a computer, programmable processing apparatus, and / or other apparatus to function in a particular manner; thus, a computer-readable storage medium storing instructions includes an article of manufacture containing instructions that implement aspects of the function / act specified in the block(s) of the flow diagrams and / or block diagrams.

[0113] The computer-readable program instructions may also be loaded into a computer, other programmable data processing apparatus, or other device to cause the computer, other programmable apparatus, or other device to perform a series of operating steps to generate a computer-implemented process, whereby the instructions executing on the computer, other programmable apparatus, or other device implement the function / act specified in the block(s) of the flow diagrams and / or block diagrams.

[0114] The flow diagrams and block diagrams depicted in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer-readable media according to various embodiments of the present disclosure. In this regard, each block in the flow diagrams or block diagrams may represent a module, segment, or portion of instructions, including one or more executable instructions for implementing the specified logical function(s). In some embodiments, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flow diagrams, and combinations of blocks in the block diagrams and / or flow diagrams, may be implemented by a special-purpose hardware-based system that performs the specified functions or operations, or executes a combination of special-purpose hardware and computer instructions.

[0115] Although this disclosure includes detailed descriptions related to cloud computing, it should be understood that embodiments of the teachings recited herein are not limited to cloud computing environments. Rather, embodiments of the present disclosure may be implemented in conjunction with any other type of computing environment not currently known or later developed.

[0116] Cloud computing is a service delivery model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and published with minimal management effort or interaction with a service provider. This cloud model can include at least five characteristics, at least three service models, and at least four deployment models.

[0117] Features may include the following: On-Demand Self-Service: Cloud consumers can unilaterally provision computing capabilities such as server time and network storage as needed automatically, without the need for human interaction with the service provider. Wide Network Access: Available over the network and accessed through standard mechanisms that facilitate use by heterogeneous thin or thick client platforms (e.g., cell phones, laptops, and PDAs). Resource Pooling: Provider computing resources are pooled to serve multiple consumers using a multi-tenant model in which different physical and virtual resources are dynamically allocated and reallocated on demand. Consumers generally have no control or knowledge of the exact location of the resources provided, although there is a sense of location independence in that location can be specified at a higher level of abstraction (e.g., country, state, or data center). Rapid Scalability: Capabilities can be provisioned quickly and elastically, sometimes automatically, to scale out quickly and be released quickly to scale in quickly. To the consumer, the capacity available for provisioning often appears unlimited, and any amount can be purchased at any time. (Metered Services: Cloud systems automatically control and optimize resource usage by leveraging metering capabilities at a level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts).) Resource usage can be monitored, controlled, and reported, providing transparency to both providers and consumers of the services utilized.

[0118] The "service models" are as follows: Software as a Service (SaaS): The functionality offered to the consumer is the use of a provider's applications running on a cloud infrastructure. The applications are accessible from a variety of client devices through a thin-client interface, such as a web browser (e.g., web-based email). The consumer does not manage or control the underlying cloud infrastructure, including the network, servers, operating systems, storage, or individual application functions, except for limited user-specific application configuration settings. Platform as a Service (PaaS): The functionality offered to the consumer is the deployment of applications created or acquired by the consumer, written using programming languages ​​and tools supported by the provider, onto the cloud infrastructure. The consumer does not manage or control the underlying cloud infrastructure, including the network, servers, operating systems, or storage, but does have control over the deployed applications and, in some cases, the application's hosting environment configuration. Infrastructure as a Service (IaaS): The functionality offered to the consumer is the provisioning of processing, storage, network, and other basic computing resources, upon which the consumer can deploy and run any software, which may include operating systems and applications. Consumers do not manage or control the underlying cloud infrastructure, but they do control the operating system, storage, deployed applications, and in some cases, limited control over some network components (such as host firewalls).

[0119] The "deployment models" are as follows: Private cloud: Cloud infrastructure is operated exclusively for an organization. It may be managed by the organization or a third party and reside on- or off-premises. Community cloud: Cloud infrastructure is shared among several organizations to support a specific community of shared concerns (e.g., mission, security requirements, policies, compliance considerations). It may be managed by the organization or a third party and reside on- or off-premises. Public cloud: Cloud infrastructure is available to the public or to large industry groups and is owned by organizations that sell cloud services. Hybrid cloud: Cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are joined by standardized or proprietary technologies that enable data and application portability (e.g., cloud bursting for load balancing between clouds). Cloud computing environments are service-oriented, focusing on statelessness, loose coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes.

[0120] Referring now to FIG. 10 , an exemplary cloud computing environment 1900 is shown. As shown, the cloud computing environment 1900 includes one or more cloud computing nodes (not shown) with which local computing devices used by cloud consumers, such as, for example, a personal digital assistant (PDA) or mobile phone 1920A, a desktop computer 1920B, a laptop computer 1920C, and / or an automobile computer system 1920N, may communicate. The one or more cloud computing nodes may communicate with each other. The cloud computing nodes may be grouped, physically or virtually, into one or more networks (not shown), such as the “private,” “community,” “public,” or “hybrid” clouds described above, or combinations thereof. The cloud computing environment 1900 may thereby be implemented to provide infrastructure, platform, and / or software as a service, eliminating the need for cloud consumers to maintain resources on their local computing devices. The types of computing devices 1920A-1920N shown in FIG. 10 are merely exemplary, and it is contemplated that one or more computing nodes and cloud computing environment 1900 may communicate with any type of computer-controlled device over any type of network and / or network-addressable connection (e.g., using a web browser).

[0121] 11 , there is shown a set of functional abstraction layers provided by cloud computing environment 1900. The components, layers, and functions are intended to be illustrative only, and embodiments of the present disclosure are not limited thereto. As shown, the following layers and corresponding functions are provided:

[0122] The hardware and software layer 60 includes hardware and software components. Examples of hardware components include mainframes 61, RISC (reduced instruction set computer) architecture-based servers 62, servers 63, blade servers 64, storage devices 65, and networks and network components 66. In some embodiments, the software components include network application server software 67 and database software 68. The virtualization layer 70 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers 71, virtual storage 72, virtual networks including virtual private networks 73, virtual applications and operating systems 74, and virtual clients 75.

[0123] By way of example, management layer 80 may provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing and other resources utilized to execute tasks within the cloud computing environment. Metering and pricing 82 provides cost management as resources are utilized within the cloud computing environment and bills or invoices for the consumption of these resources. For example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, and protection of data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 84 provides allocation and management of cloud computing resources so that required service levels are met. Service level agreement (SLA) planning and fulfillment 85 provides pre-provisioning and procurement of cloud computing resources in anticipation of future demand according to SLAs.

[0124] Workload tier 90 provides examples of functionality for which a cloud computing environment (e.g., cloud computing environment 1900) may be utilized. Examples of workloads and functionality that may be provided from this tier include mapping and navigation 91, software development and lifecycle management 92, virtual classroom instruction delivery 93, data analytics processing 94, transaction processing 95, and expression mapping management 96. Expression mapping management 96 may include functionality that enables execution of expression mapping using a cloud computing environment, according to embodiments of the present disclosure.

[0125] While various embodiments of the present invention have been described and illustrated herein, those skilled in the art will readily envision a variety of other means and / or structures for performing the functions and / or obtaining one or more of the results and / or advantages described herein. Each such variation and / or modification is deemed to be within the scope of the embodiments of the present invention described herein. In most cases, those skilled in the art will readily understand that all structures, parameters, dimensions, materials, functions, and configurations described herein are intended to be exemplary, and that the actual structures, parameters, dimensions, materials, functions, and configurations will depend on the particular application in which the teachings of the present invention are used.

[0126] Those skilled in the art will recognize, or be able to ascertain through no more than routine experimentation, many equivalents to the specific embodiments of the invention described herein. Accordingly, it is understood that the foregoing embodiments are provided by way of example only, and that, within the scope of the claims supported by this disclosure and their equivalents, the embodiments of the invention may be practiced otherwise than as specifically described and claimed. Inventive embodiments of the present disclosure also cover each individual feature, system, article, material, kit, functionality, step, and method described herein. Additionally, any combination of two or more such features, systems, articles, structures, materials, kits, functionality, steps, and methods is within the inventive scope of the present disclosure, unless such combinations are mutually inconsistent. Some embodiments may be distinguished from the prior art by specifically lacking one or more features / elements / functionality (i.e., claims relating to such embodiments may include a negative limitation).

[0127] Also, as described above, various inventive concepts may be embodied as one or more methods, examples of which have been provided. The actions performed as part of a method may be ordered in any suitable manner. Thus, embodiments may be constructed in which actions are performed in an order different from that described, which may include performing some acts simultaneously even though shown as sequential actions in the exemplary embodiments.

[0128] All references to publications and other documents, including but not limited to patents, patent applications, articles, web pages, books, etc., presented anywhere in this application are incorporated herein by reference in their entirety, and all definitions defined and used herein should be understood to supersede dictionary definitions, definitions in documents incorporated by reference, and / or ordinary meanings of the defined terms.

[0129] The indefinite articles "a" and "an," as used in the specification and claims, should be understood to mean "at least one" unless clearly indicated otherwise. The expression "and / or," as used in the specification and claims, should be understood to mean "either or both" of the elements connected thereby, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with "and / or" should be construed in the same manner, i.e., "one or more" of the elements are similarly connected. Other elements other than those specifically identified by the expression "and / or" may optionally be present, whether related or unrelated to those specifically identified elements. Thus, as a non-limiting example, a reference to "A and / or B," when used in combination with open-ended language such as "comprising," can refer in one embodiment to only A (optionally including elements other than B), in another embodiment to only B (optionally including elements other than A), in yet another embodiment to both A and B (optionally including other elements), etc.

[0130] As used in this specification and in the claims, "or" should be understood to have the same meaning as "and / or" as defined above. For example, when separating listed items, "or" or "and / or" should be construed as inclusive, i.e., including not only at least one of the number or listed elements, but also two or more, possibly including additional unlisted items. The inclusion of exactly one element of a number or list of elements only refers to the use of terms clearly stating otherwise, such as "only one of" or "exactly one of," or, when used in the claims, "consisting of." Generally, as used herein, the term "or" will be construed as indicating exclusive alternatives (i.e., "one or the other, but not both") only when preceded by exclusive terms, such as "either," "one of," "only one of," or "exactly one of." "Consisting essentially of," as used in the claims, shall have its ordinary meaning as used in the field of patent law.

[0131] As used in this specification and claims, the phrase "at least one," referring to a list of one or more elements, should be understood to mean at least one element selected from any one or more elements of the list of elements, but not necessarily including at least one of each element specifically listed in the list of elements, and not excluding combinations of elements within the list. This definition also allows for elements other than those specifically identified in the list of elements to which the phrase "at least one" refers, whether related or unrelated to those specifically identified elements, as appropriate. Thus, as a non-limiting example, "at least one of A and B" (or, equivalently, "at least one of A or B" or, equivalently, "at least one of A and / or B") can refer in one embodiment to at least one, and optionally two or more, A present and B absent (and optionally including elements other than B); in another embodiment to at least one, and optionally two or more, B present and A absent (and optionally including elements other than A); and in yet another embodiment to including at least one, and optionally two or more, A, and at least one, and optionally two or more, B (and optionally including other elements).

[0132] As in the foregoing specification, in the claims, all transitional phrases such as "comprising," "including," "carrying," "having," "containing," "involving," "holding," "composed of," and the like, are understood to be open-ended, i.e., to mean inclusive, but not limited to. Only the transitional phrases "consisting of" and "consisting essentially of" shall be closed or semi-closed transitional phrases, respectively, as set forth in the U.S. Patent Office Manual of Patent Examining Procedures, Chapter 2111.03.

[0133] The terms used herein are selected to best explain one or more embodiments, practical applications, or principles of technical improvements over the current art, or to enable understanding of the embodiments disclosed herein. As noted above, details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the embodiments of the present disclosure.

[0134] References herein to "one embodiment," "an embodiment," "a preferred embodiment," and the like indicate that the described embodiment may include one or more particular features, structures, or characteristics, but it should be understood that such particular features, structures, or characteristics may or may not be common to each disclosed embodiment of the present disclosure herein. Moreover, such phrases do not necessarily refer to any one particular embodiment per se. Thus, when one or more particular features, structures, or characteristics are described in connection with one embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such one or more features, structures, or characteristics in connection with other embodiments, if applicable, whether or not explicitly described.

Claims

1. 1. A biological expression mapping system configured to spatially map one or more biological expressions of target biological components contained in a tissue sample onto an image of the tissue sample, the system comprising at least one processor having instructions operating thereon that, when executed, cause the system to displaying a scan pane on a first display including at least the image of the tissue sample, the image including one or more sections corresponding to specific ones of one or more regions of interest (ROIs), each of the one or more ROIs corresponding to a specific portion of the tissue within the tissue image; displaying a visualization pane on a second display unit comprising a visualization of each of the biological expressions contained in the one or more ROIs; and augmenting the first display by coding the one or more ROIs within the tissue image to show the spatial mapping of the biological expression within the one or more ROIs.

2. The system of claim 1 , wherein the coding includes at least color coding.

3. The system of claim 1 or 2, wherein the visualization of each of the biological expressions comprises an image of the biological expression contained in a ROI of the one or more ROIs.

4. The system of claim 1 , wherein the visualization comprises at least one of a graph, a plot, a diagram, and a map of the one or more biological expressions contained in the one or more ROIs.

5. 5. The system of claim 4, wherein the graphs, plots, diagrams, and maps of the biological expression comprise at least one of a heat map, a dendrogram, a bar graph, a scatter plot, a box plot, a forest plot, a principal component, a statistical plot, a volcano plot, a trend plot, and a strip plot.

6. The system of claim 5 , wherein the tree diagram comprises a phylogenetic tree.

7. The system of claim 5 , wherein the statistical plots include one or more principal component analysis (PCA) plots.

8. The system of claim 1 , wherein the first display is expanded based on user input specifying at least one selection of biological expressions to be included in the visualization.

9. 10. The system of claim 8, wherein the spatial mapping of the at least one user-selected biological expression is configured to provide at least one of the one or more ROIs with its spatial context.

10. 10. The system of claim 1, wherein augmenting the first display is configured to facilitate morphological profiling of tissue in at least one of the one or more ROIs.

11. The system of claim 10 , wherein the morphological profiling comprises at least one of geometric profiling, segment profiling, contour profiling, grid profiling, and cellular profiling.

12. 11. The system of claim 10, wherein segment profiling comprises at least one of manual segment profiling and automatic segment profiling, the automatic segment profiling being configured to automate and facilitate segment profiling of tissue in at least one of the one or more ROIs based on user input specifying at least one segment profiling parameter.

13. The system of claim 10 , wherein cell profiling includes single cell profiling and rare cell profiling.

14. the one or more biological expressions of the one or more target biological components within at least the one or more ROIs are determined based on exposing the tissue sample to a plurality of reagents; and The reagent comprises: a plurality of imaging reagents configured to bind to biological boundaries of the tissue sample within at least the one or more ROIs; and a plurality of profiling reagents; Each profiling reagent is Binding to specific biological expressions of specific target biological components contained within at least said one or more ROIs; and 14. The system of claim 1, configured to include a cleavable associated oligonucleotide.

15. After exposing the tissue sample to the plurality of reagents and before displaying on the first display and the second display, the instructions further cause the system to illuminating and imaging the tissue sample; receiving a user input specifying a selection of the one or more ROIs; irradiating at least the one or more ROIs of the tissue sample, thereby cleaving the associated oligonucleotides from the profiling reagent; recovering the cleaved oligonucleotide; and analyzing the recovered cleaved assembled oligonucleotides; the one or more biological expressions contained within at least the one or more ROIs; and corresponding locations within said one or more ROIs The system of claim 14 configured to determine:

16. Each profiling reagent is a nucleic acid probe comprising a target binding region to which said cleavable association oligonucleotide is removably attached; or 15. The system of claim 14, comprising an oligonucleotide comprising an antibody removably attached thereto.

17. The system of claim 8 , wherein the user input specifying the selection of the one or more ROIs comprises a selection of one or more of the ROIs with respect to shape or size.

18. The instructions may include:

18. The system of claim 1, further configured to cause a dataset pane to be displayed on a third display unit, the dataset pane including at least one user-selectable dataset, the at least one dataset being associated with one or more of the biological expressions included in the one or more ROIs.

19. The instructions may include:

20. The system of claim 18, further configured to display, on the fourth display, a record pane including a plurality of scan records, each scan record including at least one tissue image.

20. 20. The system of claim 19, wherein one or more of the first display, the second display, the third display, and the fourth display are provided within an integrated user interface configured to interactively associate one or more of the tissue image, the visualization, the user-selectable dataset, and the plurality of scan records based on user input.

21. The system of claim 20 , wherein the unified user interface is configured as a single display.

22. 21. The system of claim 20, wherein the first display portion, the second display portion, the third display portion, and the fourth display portion each correspond to one or more portions of the single display.

23. 20. The system of claim 18, wherein the instructions are further configured to cause the system to select at least one record based on user input such that, upon selection, at least one of the scanning pane, the visualization pane, and the dataset pane is displayed on a respective display.

24. 20. The system of claim 19, wherein the instructions are further configured to cause the system to filter at least one of a property, a constraint, and a value of the plurality of records based on the user input.

25. 25. The system of claim 1, wherein the scanning pane can further include a plurality of icons, each corresponding to at least one of the one or more ROIs or a particular segment of the entire tissue image.

26. 22. The system of claim 21 , wherein the instructions are further configured to cause the system to render the scan pane in conjunction with the visualization pane and one or more of the dataset pane and the record pane for display in real time via the integrated user interface based on user input.

27. 27. The system of claim 1, wherein coding the one or more ROIs comprises presenting a quantitative measure of the biological expression.

28. The system of claim 2 , wherein color-coding the one or more ROIs comprises presenting a quantitative measure of the biological expression.

29. 28. The system of claim 27, wherein the quantitative measurements include at least one of type and degree of biological expression.

30. 30. The system of claim 28, wherein the quantitative measurements include at least one of the type and degree of the biological expression.

31. 31. The system of claim 30, wherein the type or degree corresponds to a particular color for each biological expression or a color intensity for each biological expression.

32. 1. A non-transitory processor-readable medium storing code representing instructions configured to be executed by a processor of a biological expression mapping system configured to spatially map one or more biological expressions of respective target biological components contained in a tissue sample onto an image of the tissue sample, the instructions, when executed, causing the system to: displaying a scan pane on a first display including at least an image of the tissue sample, the image including one or more sections corresponding to specific ones of one or more regions of interest (ROIs), each of the one or more ROIs corresponding to a specific portion of the tissue within the tissue image; displaying a visualization pane on a second display unit comprising a visualization of each of the biological expressions contained in the one or more ROIs; and a non-transitory processor-readable medium configured to augment the first display by coding the one or more ROIs within the tissue image to show the spatial mapping of the biological expression within the one or more ROIs.

33. displaying a scan pane on a first display including at least an image of the tissue sample, the image including one or more sections corresponding to specific ones of one or more regions of interest (ROIs), each of the one or more ROIs corresponding to a specific portion of the tissue within the tissue image; displaying a visualization pane on a second display comprising a visualization of each of the biological expressions contained in the one or more ROIs; and and augmenting the first display or the second display by coding the one or more ROIs within the tissue image to show the spatial mapping of the biological expression within the one or more ROIs.

34. A system, apparatus, method, and / or non-transitory computer-readable medium according to any of the embodiments disclosed in the claims.

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