Analysis system for biomarker expression

Abstract

To provide an analysis system, apparatus and computer-implemented method for the analysis of biomarkers in order to meet the demand for a simple and intuitive interface that provides information to doctors, clinicians, researchers, or whoever needs the data.SOLUTION: This system is particularly useful for viewing biomarker expression on extracellular vesicles. The system includes an analysis element, a ranging and scaling element, an adjustment element, and a selection element. The system comprises a display that is configured to display biomarker expression data in one or more graphs.SELECTED DRAWING: Figure 1

Description

Background Art

[0001] This application claims the priority of a U.S. Provisional Patent Application with Ser. No. 63 / 589,202, filed on Oct. 10, 2023. The disclosure of the above provisional application is hereby incorporated by reference in its entirety for all purposes. This application claims the benefit of priority under 35 U.S.C. § 119(e).

Technical Field

[0002] The present disclosure relates to systems, software, and devices for analyzing biological data. More particularly, the present disclosure exemplifies a method for visualizing data from an image including extracellular vesicles having a plurality of biomarkers. Description of the Prior Art

[0003] Extracellular vesicles (EVs) in body fluids are considered promising materials in liquid biopsies because they contain various proteins, miRNAs, and mRNAs on their surface or inside the vesicles. EVs are classified into several subtypes, including exosomes, microvesicles, microparticles, ectosomes, oncosomes, apoptotic bodies, etc., and their sizes vary widely in the range of about 50 nm to 5 μm.

[0004] In fact, all types of cancer cells can generate EVs. Many of these EVs can promote cancer progression and affect the behavior of cancer cells. Since the concentration and content of EVs released by cancer cells vary based on the cell of origin, stage of development, and response to treatment, EVs from cancer cells are an important source of diagnosis and predictive information. (Chang WH, et al., “Extracellular Vesicles and Their Roles in Cancer Progression. Methods Mol Biol.” 2021;2174:143-170)

[0005] Liquid biopsy, a non-invasive means alternative to tissue biopsy, can be used for the analysis of EVs found in blood circulation. Looking at EVs from cancer cells can provide cancer diagnosis and prognosis. Although liquid biopsy of small EVs smaller than 200 nm has been actively studied, since the size of EVs is too small to be observed with a general optical microscope, bulk assays are used in most studies to analyze EV properties. Fluorescence microscopy is one of the methods that enables the observation of single EVs, but in small EVs, the fluorescence intensity is weak and observation is difficult. In such samples with small EVs and / or low EV concentration, plasmon enhancement can be used.

[0006] In fluorescence microscopy, multiple fluorescent markers may be combined in the analysis to elucidate information on different components of EVs. Single EV analysis with multiple fluorescent colors is important, but in order to understand the results, a large number of detections and colocalization analysis of markers expressed in EVs are required. This can increase the complexity of display and / or analysis. In single EV analysis using imaging data, the analysis can be made easier and more efficient by a graphical user interface.

[0007] In one approach, attention is paid to the measurement of the size of extracellular vesicles and viruses, aggregation, and biomarker colocalization (see, for example, U.S. Patent No. 11,262,359). Another approach provides a simple logarithmic scale heatmap for colocalization (Martel, et al., “Extracellular Vesicle Antibody Microarray for Multiplexed Inner and Outer Protein Analysis” (ACS Sens. 2022, 7, 3817 - 3828). U.S. Patent No. 10,755,406 describes a method for co-expression analysis of markers in tissue samples using a heatmap of marker expression in multiple single marker channel images. Determine and map the colocalized regions of interest from the overlay mask. (Rizk et al., An lmageJ / Fiji Plugin for Segmenting and Quantifying Sub-Cellular Structures in Fluorescence Microscopy Image” Seminar Inst. Res. Biomed, (2013), pp. 1 - 26

[0008] However, there is still a need for a simple and intuitive interface to provide information to doctors, clinicians, researchers, or anyone who needs data. For this reason, an analysis system, apparatus, and computer-implemented method for biomarker analysis are provided.

Summary of the Invention

Means for Solving the Problems

[0009] According to at least one embodiment of the present disclosure, there is provided an analysis system, apparatus, and computer-implemented method for biomarker analysis. The system, apparatus, and method include an input including one or more fluorescence images, each being an image of a substrate processed with two or more biomarkers, and a biomarker standard expression profile for each of the two or more biomarkers, an analysis element for obtaining biomarker expression data from the one or more fluorescence images, a ranging and scaling element for adjusting a range and scale according to the biomarker expression data or the biomarker standard expression profile, an adjustment element for adjusting a threshold value of the biomarker expression data, the adjustment element using α, which is one variable, to adjust the threshold value for each of the two or more biomarkers, and a selection element for selecting a combination of the displayed biomarkers and / or the displayed colocalization, and a processor including the same. The system, apparatus, and method further include a display for displaying the biomarker expression data in one or more graphs.

[0010] These and other objects, features, and advantages of the present disclosure will become apparent by reading the following detailed description of the exemplary embodiments of the present disclosure in conjunction with the accompanying drawings and the claims.

Brief Description of the Drawings

[0011] The patent or application file contains at least one color drawing. Copies of this patent or application publication in color drawings (s) will be provided by the Patent Office upon request and payment of the necessary fees.

[0012] Further objects, features, and advantages of the present disclosure will become apparent from the following detailed description together with the accompanying drawings showing embodiments that are examples of the present disclosure.

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[0013] Throughout the drawings, unless otherwise noted, the same reference numerals and characters are used to denote like features, elements, components, or parts of the illustrated embodiments. Also, while the present disclosure is described in detail with reference to the drawings, it is described with exemplary embodiments for illustration. It is intended that changes and modifications can be made to the described exemplary embodiments without departing from the spirit and scope of the present disclosure as defined by the appended claims.

Best Mode for Carrying Out the Invention

[0014] An imaging system has been developed for improving diagnosis, characterization, prognosis, or treatment. The system is particularly useful when the concentration of components for analysis, such as EVs labeled with biomarkers, is low in samples on a substrate.

[0015] Due to their small size, EVs cannot be resolved by an optical microscope. To detect them with a microscope, a fluorescent dye is applied, and the fluorescence light is detected as bright spots to detect the presence or absence of a fluorescence signal. Due to their small size, few fluorescent dyes are applied to EVs, and since the fluorescence signal is weak, signal amplification is required. One useful method is to use surface plasmon resonance. However, the signal may still be low, and different fluorescent dye molecules with different quantum effects, binding capabilities, and enhancement characteristics have a wide detection range.

[0016] For immunofluorescent staining of EVs, a substrate (such as a plasmon-enhanced substrate) is functionalized by covalent bonding to capture EVs on the surface. The labeled EVs are loaded onto the substrate, fixed, and blocked. Then, the EVs are fluorescently labeled by sequential incubation with primary and secondary antibodies. With 4-channel imaging and a QUAD marker, four different signals can be seen in the fluorescence image. Each EV may be labeled with one, two, three, or all four of the biomarkers, and the complexity of the analysis for non-trivial diagnosis, characterization, prognosis, or improvement of treatment may be added for communication to clinicians or other users as appropriate. In some embodiments, plasmon-enhanced fluorescence is used for imaging. The plasmon enhancement is characterized in each of the four channels (AF488, AF555, AF647, and Cy7). See U.S. Patent Publications Nos. 2023 / 0160809 and 2023 / 0123746. These are hereby incorporated by reference in their entirety into the specification.

[0017] An analytical assay for molecular profiling of tumor-derived EVs using a QUAD marker (EpCAM, EGFR, HER2, MUC1) is provided, where the QUAD marker levels of tumor-derived EVs show good correlation with measurements in the originating cells, indicating that EVs can be used as surrogate liquid biopsy markers for tumor cells. EVs labeled with these and / or other biomarkers may be used for the diagnosis, characterization, prognosis, or improvement of treatment of cancers such as breast cancer.

[0018] Drug resistance and prediction may also be imaged. For example, biomarkers and their EVs for paclitaxel (P-glycoprotein and survivin), which show an increase in intracellular protein levels as an increase in resistance to paclitaxel (i.e., an increase in the IC50 value), are also analyzed and can be used to monitor treatment. See U.S. Patent Publication No. 2024 / 0240256.

[0019] System Overview As shown in FIG. 1, the imaging system includes a fluorescence microscope 102 connected to and controlled by a processor 104. Inside the processor, there are a plurality of elements including an analysis element 106, a ranging and scaling element 108, an adjustment element 110, and a selection element 112. Each of these elements may be part of a single processing unit or may be separated. The imaging system will further include a memory 114 and a display 116. Inputs such as a mouse, keypad, touchpad, etc. also communicate with the processor and provide input from the user. In use, a sample on a substrate is placed on the fluorescence microscope 102. The processor 104 controls the entire system processing including image acquisition, analysis, ranging and scaling, adjustment, and selection by controlling the fluorescence microscope. The processor 104 analyzes the data from the fluorescence microscope image. The memory 114 temporarily stores the acquired images and calculated data. The display 116 shows system data such as microscope control information, live images, acquired images, and calculation results. The display may include graphs (s) described in the specification.

[0020] Using the multi - channel fluorescence microscope 102, the labeled EVs bound to the substrate are imaged. Thereafter, the signals of a plurality of biomarkers are displayed in the display 115. Thus, a system and method are provided for displaying graphs and / or images of the detected biomarkers that can be intuitively understood by researchers and / or clinicians. Also provided are a system and method for modifying the displayed graphs and / or images in an intuitive and informative manner.

[0021] Images from the multi - channel fluorescence microscope 102 are sent to the processor. The processor will also have information on the standard expression profiles of each biomarker. The standard expression profile can be obtained, for example, by comparative analysis of the expression of biomarker concentrations from cancer patients with those of patients without cancer. It may be provided as a table or database and may be updated when further information is obtained for any biomarker.

[0022] Image analysis can also be applied to microparticle images and is not limited to fluorescence. Dark-field or bright-field analysis can also be performed. In some embodiments, all images are fluorescence images. In other embodiments, the images used are both fluorescence images and dark-field images (for example, detecting EVs by dark-field images and detecting biomarkers by fluorescence).

[0023] Ranging and scaling Since each marker is distinguishable, automatic ranging and scaling are performed for each marker expression. Automatic ranging and scaling are based on the standard range or reference value of each biomarker. These are determined, for example, by analysis of the markers and their concentrations found in normal and cancer cells, or by analysis of cancer cells that are receptive to treatment and cancer cells that do not respond to the treatment. In addition, clinical trials will determine the standard range of each marker expression. When displaying colocalization, it is appropriate to automatically set the expression level of the reference marker as the maximum range.

[0024] In some embodiments, the number of detected EVs is shown on a linear scale. The percentage of co-expression between two or more biomarkers is shown on a linear scale. The total number of biomarkers is shown on a linear scale. In contrast, the estimated value converted to per mL is shown on a logarithmic scale. When simultaneously displaying the number of detected EVs and biomarker expression or co-expression, the number of biomarkers may be extremely small relative to the number of detected EVs. In this case, it is better to display the number of detected EVs logarithmically and the number of biomarkers linearly. In some embodiments, instead of a percentage, other metrics can be shown, in linear or logarithmic format. For example, counts may be shown, both counts and percentages may be shown, or a scale giving counts relative to a specific value may be shown.

[0025] For medical practitioners, features that are easy to use and view are considered to be that automatic ranging and scaling are based on the standard range or reference value of each biomarker. By displaying the standard range together with the biomarker expression of the sample relative thereto, as shown in FIG. 7(c), it can be easily determined whether the marker expression is within the standard range. For biologists, since various values need to be viewed in research, it is preferable to leave manual adjustment available.

[0026] Adjustment / Threshold To threshold all markers simultaneously, a single control unit for thresholding is provided. All markers may be adjusted, or selected biomarker(s) may be adjusted separately. As shown in FIG. 2(a), this control unit may be operated using a slider that moves up and down via a touch screen or other input device (e.g., a mouse).

[0027] The EV detection results vary depending on the threshold. Although detection may succeed with a pre-set threshold, sometimes adjustment by the user is necessary, such as when the number of detections is significantly abnormal. However, it is inefficient to adjust each marker channel separately. It is desirable that by adjusting one value, the thresholds of several different biomarkers can be appropriately adjusted. For example, by setting a variable as α and multiplying α by a value specific to each biomarker, a plurality of thresholds are simultaneously changed by α. More specifically, the mean value (M i ) and the standard deviation (S i ) are determined for each channel image of different biomarkers, and T i = M i + αS ican be set as a threshold value. Here, i represents different biomarker channels. Also, it is difficult to perform without appropriate information display. The expression levels of biomarkers are wide-ranging, and it is difficult to recognize the differences on a bar graph with the same range or scale. However, since the user is limited to one display or a limited view area for this information, it is necessary to provide the user with information in an optimized way. The changed threshold value facilitates adjustment by the user by displaying real-time updates of the changed detection results. For example, the number of detections from Figure 2(a) to Figure 2(c), or the bounding box of the detection result from Figure 2(b) to Figure 2(d). When the threshold value is set high as shown in Figure 2(a), weak-intensity biomarkers as shown in Figure 2(b) are not detected on the image. Here, just checking the bar graph shown in Figure 2(a) does not clarify whether there are undetected biomarkers, but checking the image and detection result in Figure 2(b) can confirm undetected biomarkers. Lowering the threshold value as shown in Figure 2(c) can prevent the non-detection of biomarkers, and at the same time, by checking the image shown in Figure 2(d), the optimal threshold value can be easily set.

[0028] Select The system may define the display of biomarkers and / or the display of combinations of co-localizations, or the user may modify them via the selection element 112. This selection element may receive input from the user to select one or more biomarkers, etc. from a list on the display.

[0029] Some embodiments focus on the ability to select and view one or sub-combinations of biomarkers and / or their combinations, while other embodiments provide the ability to select and view a display as a place to define marker selection according to cancer type or subtype (e.g., luminal A or luminal B cancer). This selection may be performed automatically based on a preference defined by the system or by the user.

[0030] For research purposes, it is useful to manually identify biomarker combinations and elucidate various co-localizations. For example, using a marker as a reference, EVs having both the reference marker and other biomarkers can be calculated, and it can be determined which marker is important. Here, the reference means that the EV having the reference marker is the population. Figure 4 shows an example of changing the reference marker. Figures 4(a) and 4(b) show the co-localization results with Marker 1 as the reference, and Figures 4(c) and 4(d) show the co-localization results with Marker 4 as the reference.

[0031] On the other hand, in clinical applications, it can be used by doctors to automatically select and display only the combinations of important biomarkers previously identified based on cancer type, subtype, and drug. Figure 6 shows an example in which the selected biomarker and its combination are automatically changed based on a combination with a predetermined biomarker.

[0032] Exemplary EV Analysis on NPOP Substrate Single EV analysis using the plasmon-enhanced NPOP substrate is performed using conventional EV immunolabeling with simple modifications. The NPOP substrate is described in (Adv. Funct. Mater. 2019, 1904257), and attaching nm-sized AuNPs onto a 10-nm gold nanopillar structure gives extensive plasmonic hot spots within the 3D structure. As described above, for EV capture, the substrate was functionalized by carboxylation and methylation of polyethylene glycol (PEG) mixtures (Advanced Science 10 (8), 2205148). For plasma-derived EVs, as previously reported, antibodies against cancer markers were immobilized on the NPOP substrate by physical absorption that binds EVs with an affinity for the antibody (ACS Appl Mater Interfaces 2022, 14, 26548-26556). Furthermore, the antibody concentrations of EVs or cancer markers were further optimized to reduce false positive signals. Finally, the NPOP substrate enabled the detection of multiplexed and highly sensitive protein markers at the single EV level.

[0033] EVs were labeled with TFP dyes of AF488, AF555, AF647, and Cy7 and immobilized on the substrate under consistent binding conditions following serial dilution of the EV sample using Cell-Tak, a bioadhesive molecule. Analysis of the fluorescence signals from the EVs attached at a specific concentration revealed significant signal amplification in the AF555, AF647, and Cy7 channels. Utilizing NPOP for single EV analysis made it possible to increase the number of detected EVs with relatively little plasma and facilitate the highly sensitive detection of cancer cell-derived EVs.

[0034] The molecular profiling ability of the NPOP substrate was evaluated for QUAD (MUC1, HER2, EGFR, EpCAM) in breast cancer cell-derived EVs. First, EVs derived from breast cancer cell lines including SKBR3, MCF7, BT474, and MDA-MB-231 cells were labeled with AF555 and attached to a functionalized substrate with SH-PEG-COOH linker. Subsequently, immunofluorescence staining was performed using concentrations optimized for each antibody, followed by labeling with an AF647 dye. The number of AF647-labeled EVs (marker channel) showing colocalization with the AF555 signal (EV channel) was analyzed and converted to a colocalization percentage value. In SKBR3 EVs, the markers showing high colocalization were HER2 and EpCAM. In MCF7, MUC1 and EpCAM showed high colocalization, while in BT474, HER2 and EpCAM showed high colocalization but the expression of MUC1 was moderate, and in MDA-MB-231, only EGFR showed high expression. The high EV molecular profiling ability of the NPOP substrate single EV analysis was demonstrated.

[0035] Multiplexed single EV analysis was performed by multi-color imaging using an NPOP substrate. EVs derived from breast cancer cells were labeled with an AF555 dye and attached to the substrate. For immunofluorescence staining, a CD mix (rabbit-derived antibody mix of CD9, CD63, and CD81) was assigned to the AF488 channel, a QUAD mix (mouse-derived antibody mix of MUC1, HER2, EGFR, and EpCAM) was assigned to the AF647 channel, and HER2 (rat-derived antibody) was assigned to the DL755 channel. HER2 is included in the QUAD antibody, but an additional channel was assigned to HER2. Therefore, a negative control sample of No EV (PBS) was prepared for the EV and CD mix channels, and EVs (HS371T) derived from normal cells in breast tissue were prepared as a negative control for the QUAD channel. No signal was observed in any of the CD mix, EV, QUAD, and HER2 channels in the No EV sample. In HS371T, a normal cell EV sample, signals were observed only in the CD mix and EV channels. Furthermore, signals were observed in the CD mix, EV, and QUAD channels in breast cancer cell EVs, while HER2 signals were detected only in HER2-positive cell SKBR3 and BT474 EVs. The analysis confirmed the multiplexing ability of NPOP substrate-based single EV analysis using multi-color imaging.

[0036] Images were analyzed using ImageJ and custom code. The background intensity was subtracted using the rolling ball method (radius = 20), and EV locations were detected from the AF555 channel using the ImageJ Comdet plugin. The intensity thresholds for the AF647 and DL755 channels were determined by IgG control experiments, and the thresholds were determined by the mean + 3 × standard deviation.

[0037] Display A graph-linked display and an image may be provided. Such a graph-linked display is given by an application and may be exemplified in FIGS. 2(a) and 2(b). The detection results are shown in both the graph of FIG. 2(a) and the image of FIG. 2(b). In FIG. 2(b), all marker images or selected marker images are overlaid on the same image area.

[0038] The analysis system described in the specification demonstrates a high-degree-of-freedom co-localization analysis. The reference markers for co-localization can be switchable between different biomarkers. Similarly, the combinations of co-localization are switchable. For example, when biomarker 1 is selected as the reference marker, the co-localization may be between this biomarker and biomarker 2, between this biomarker and biomarker 3, between this biomarker and biomarker 4, or the co-localization of three biomarkers (biomarkers 1, 2, 3; biomarkers 1, 3, 4; biomarkers 1, 2, 4). The analysis can be displayed in different ways. For example, the scale may indicate the number of detected EVs, the number of detected biomarkers, the number of co-localizations, the detected biomarkers within the standard range, etc.

[0039] If the users are different, the need to view the data will be different. For example, a laboratory assistant or an oncology researcher may need to view an image with various overlays and graphs for a full analysis of the sample. A doctor may only be interested in the results such as a graph with a standard range. As will be described below, each may be provided.

[0040] Figures 2(a) to 2(d) show an exemplary analysis of fluorescence microscope images of the combined extracellular vesicles. In these figures, all EVs detected on the image are labeled with a green biomarker and shown. There are three additional biomarkers, shown in yellow, orange, red colors, or in a specified pattern. The graph in Figure 2(a) shows the total number of EVs captured and detected on the substrate (green). This is shown on a logarithmic scale. Since biomarkers useful for cancer detection, for example, are significantly lower in concentration than the total concentration of the combined EVs, the graph shows the total number of EVs with a specific biomarker (shown in yellow, orange, red, or in a specified pattern) on a linear scale. This allows the user to confirm the low detection counts of low-expression markers within the same graph with the total number of EVs.

[0041] In the image display of Figure 2(b), multiple EVs are shown as green dots, and yellow, red, orange dots (or dots in other patterns) show EVs labeled with another biomarker. The graph in Figure 2(a) is linked to the image in Figure 2(b), and a threshold is set based on the background noise level of each channel that can be calculated as the standard deviation STD (σ).

[0042] Also, Figures 2(a) and 2(c) show a sidebar 200. With this sidebar 200, the user can control all marker thresholds. Alternatively, in some embodiments, the biomarkers may be selectable, and it may be possible to control only the biomarkers selected via the sidebar 200. The sidebar range is a coefficient of STD, such as [1,6] for example. The range and scale of the marker expression display in Figure 2(a) are automatically adjusted as described below. In Figure 2(a), the sidebar 200 is set to a high threshold. For this reason, the corresponding image display in Figure 2(b) can show a bounding box with a reduced number. The color and / or pattern of the bounding box match the color and / or pattern of the bars in Figure 2(a). In Figure 2(b), an overlay image of the selected channel is displayed together with the bounding box of the detected particles.

[0043] Figure 2(c) shows the change in the graph when the sidebar is adjusted and the threshold is lowered. In this case, the relative intensity increases in this figure, but in the correlation diagram of Figure 2(d), the number of boundary boxes for each labeled biomarker increases to explain the threshold decrease.

[0044] The amount of data given by these four different biomarkers can sometimes make it difficult to visualize the data. Thus, it is conceivable to display only the selected channel image overlaid. Figures 3(a) and 3(b) are the same graphs and images as Figures 2(c) and 2(d). However, in this case, the figure shows what happens when one channel is selected. In Figure 3(c), the orange biomarker is selected and the other biomarkers are in grayscale. The selection may be made by a touch screen, mouse, text, or other input device. Similarly, unselected biomarkers may be removed from the graph, or other means may be taken to indicate which biomarkers are selected and which are unselected. One or more biomarkers can be selected in this way. When a biomarker is selected (Figure 3(c)), the image shown in Figure 3(d) changes to show only the EVs with the selected biomarker (orange in this example).

[0045] In FIGS. 4(a) to 4(d), the co-localization of various biomarkers can be visualized. The display can be switched between detection and co-localization display (for example, FIGS. 2(a) to 2(b) and FIGS. 4(a) to 4(b)). In the co-localization display, the co-localization criteria can be selected. As illustrated in FIG. 4(a), the graph has a marker selection list 400, and the figure shows that marker 1 (green biomarker) is selected. Thus, as shown in FIG. 4(c), the graph changes to show the co-localization of various biomarkers and the selected biomarker. The number of EVs having this co-localization is indicated by the height within the graph, and the range and scale of the biomarker co-localization in FIG. 4(a) are automatically adjusted. Also, in some embodiments, the combination of co-localizations can be made selectable. This is done by selecting a reference channel and several marker channels that do not require checking.

[0046] The co-localization criteria can also be made selectable. This is shown in FIGS. 5(a) to 5(e). The reference channel can be selected from the list 500. In addition, one or more marker channels can be selected. Alternatively, the co-localization can be shown in different formats. One such format is shown in FIG. 5(b), which shows a Venn diagram of the overlay of the reference (green) biomarker and markers 2 to 4. In another alternative display shown in FIG. 5(c), the selected biomarkers (in this case, markers 2, 3, 4) are represented with co-localization as shown in the Venn diagram. FIG. 5(d) gives the co-localization as a 2D scatter plot. This is particularly useful when only two biomarkers are selected. FIG. 5(e) gives the co-localization as a 3D scatter plot. This is particularly useful when three biomarkers are selected.

[0047] In FIGS. 6(a) to 6(d), from the display, the user can learn about biomarker concentrations and co - localization based on cancer subtypes or treatments (drugs). Other parameters that can be identified by biomarkers can also be displayed similarly. In FIGS. 6(a) and 6(c), with the selection box 600, a specific subtype and / or a specific drug can be selected. In this example, the subtype is changed from subtype 1 (FIG. 6(a)) to subtype 3 (FIG. 6(c)). As shown in the examples of FIGS. 6(a) to 6(d), a number of biomarkers may be measured. In some of these cases, different fluorescence channels may require separate measurements for each cancer subtype or drug. Such separate measurements can be integrated and displayed as in FIGS. 6(a) to 6(d) and as in any of this specification.

[0048] FIGS. 7(a) to 7(c) show a display that is switchable between data analysis (raw data, FIGS. 7(a) and 7(b)) and clinical (showing standard ranges, FIG. 7(c)) displays. Each marker in FIG. 7(c) has a different range based on the standard detection value shown in green and an out - of - range value that may exist within a non - standard (e.g., cancer) population. For example, in the example of FIG. 7(c), marker MK1 is shown within the standard range 700. In contrast, marker MK2 is out of the standard range as shown in diamond 702. In this embodiment, the standard range can be determined generally or specifically for an individual. In each embodiment showing the display discussed in the specification, the scale may be the number of detections on the imaged substrate. Or, the scale may be the number of particles per mL. Other scales may be used as an alternative.

[0049] Also, FIG. 7(c) provides information on combinations of biomarkers (e.g., MK1 and MK2). This provides additional information to the user, and the important feature may be the combination value. In other embodiments (not shown), the information provided may be MK1 or MK2. This is particularly useful when multiple biomarkers are within a single wavelength channel when displaying the results of integrating separately measured biomarkers.

[0050] In FIG. 7(c), each biomarker display may be data from a single biomarker, for example, from one of the Quad markers, captured by one of the Quad markers. In other embodiments, one or more of the displayed biomarkers may be from two or more biomarkers imaged in the same channel of a multi-channel fluorescence microscope.

[0051] FIG. 8 shows a flowchart of several embodiments described in the specification. After starting, data is loaded into the processor / system. This data may be an image from a fluorescence microscope in which multiple biomarkers were cultured on an EV sample. First, the data is loaded and a biomarker is selected. Then, a decision is made as to what to display. The display content includes detection, colocalization, or a combination thereof. In the case of colocalization or combination, colocalization criteria and / or combinations are selected. Then, a graph type is selected. This may be a pre-defined selection, but preferably, alternative graph types are provided. Next, detection and colocalization are performed. Then, the processor displays an overlay image of the detection results and an auto-adjusted range graph. If the threshold needs to be adjusted, the threshold is adjusted by an adjustment unit and the display is redisplayed.

[0052] FIG. 9 is a flowchart of several embodiments described in the specification. Similar to FIG. 8, the flow visualizes and provides where the cancer subtype and / or treatment (e.g., therapeutic agent) is selected.

[0053] Analysis Figures 10 and 11 show an exemplary method for detecting particles in fluorescence microscope images having immobilized EV particles. First, global background reduction is performed. Then, image enhancement filtering is performed. Next, an intensity threshold for the EV particles is calculated. In this example, the threshold is the mean + ασ, where α is a parameter and σ is the standard deviation. The parameter α can be applied equally to all biomarkers displayed within the graph or in the image. Here, the mean and standard deviation (σ) will depend on the specific biomarker.

[0054] In some embodiments, according to the flowchart of FIG. 12(b), a bounding box may be created. Load the binary map of the particles and start creating the bounding box. Select a particle. Obtain the minimum and maximum X coordinates of the particle and the minimum and maximum Y coordinates of the particle (in any order). Create a rectangle using the coordinates. Check the particle and perform bounding box creation or select another particle. As shown in the exemplary drawing of FIG. 12(a), the shape of the detected EVs after binarization is neither rectangular nor round. From this, a rectangular bounding box is created. In other embodiments, a square (e.g., having sides that are the average of the X and Y coordinates) may be created, or a circle or ellipse may be created.

[0055] In some embodiments, according to the flowchart of FIG. 13(b), a bounding box may be created. Load the binary map of the particles and start creating the bounding box. Select a particle. Then, calculate the center of mass using the intensity of the particle area. Create a rectangle of a fixed size using the center of mass. Check the particle and perform bounding box creation or select another particle. FIG. 13(a) shows that even if the shape after binarization is neither rectangular nor round, a predetermined rectangle (e.g., a square) centered on the center of mass is formed as the bounding box. These embodiments provide a more uniform display of the bounding box.

[0056] Figure 14 shows the process of colocalization. First, a binary map of particles is loaded into the processor, and the calculation of colocalization is started. Particles are selected from the reference channel, and overlapping particles are searched from other channels (multiple possible). The overlap rate is calculated based on the smaller particles. If the overlap rate is greater than the threshold, the particle is set as a co-localized particle. Then, a particle check step is performed. If there are no more particles, the calculation of colocalization is terminated. If the answer is Yes, another particle is selected from the reference channel, and the process is repeated.

[0057] Another process of colocalization is shown in Figure 15(c). First, a binary map of particles is loaded into the processor, and the calculation of colocalization is started. Particles are selected from the reference channel, and the maximum value is searched in the same area of the reference particles in the other channel images. Then, the intensity average of the same area of the reference particles centered on the maximum coordinates is calculated. If the average value is greater than the threshold, the particle is set as a co-localized particle. Then, a particle check step is performed. If there are no more particles, the calculation of colocalization is terminated. If the answer is Yes, another particle is selected from the reference channel, and the process is repeated. Figure 15(a) shows the map of the reference channel, and 1602 is the selected particle. Figure 15(b) shows the image of the other channel, and 1604 is the particle of this channel, and the processor calculates the average intensity from the corresponding area.

[0058] Definition Specific details have been described to fully understand the disclosed examples with reference to the specification. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily lengthen the present disclosure.

[0059] Unless otherwise specified, as is apparent from the following disclosure, throughout this disclosure, discussions using terms such as "processing", "computing", "calculating", "determining", "displaying", etc. refer to acts and processes of operating on data represented as physical (electronic) quantities within the registers and memories of a computer system, similar electronic computing devices, or data processing devices to transform them into other data similarly represented as physical quantities within the computer system memory or registers or other information storage, transmission, or display devices. The computer or electronic operations described herein or recited in the appended claims may generally be performed in any order, unless the context indicates otherwise. Also, although various operation flowcharts are shown in order, the various operations may be performed in an order different from that illustrated or claimed, or the operations may be performed simultaneously. Examples of such alternative orders may include, unless the context indicates otherwise, repetition, interleaving, interruption, recording, incremental, preparatory, supplementary, simultaneous, reverse, or other different orders. Further, terms such as "responsive to", "in response to", "related to", "based on", or other past participle adjectives generally do not intend to exclude such variations, unless the context indicates otherwise.

[0060] As used in the specification, the term "biomarker" may be used interchangeably with the term "marker" and, in the context of oncology, may be a cancer marker. When used in the context of EV analysis, a biomarker is a molecule associated with EVs and can bind directly or indirectly to a capture or labeling agent for detecting EVs. A marker can be any component of an EV that can be recognized by a capture agent. Examples of markers include, but are not limited to, proteins, nucleic acids, or components of the lipid bilayer that makes up the EV membrane. Useful markers include receptors (e.g., extracellular) and channel components. A marker can be either an extracellular or intracellular marker as defined in the specification. A marker can be present on all EVs in a sample or on a subset of EVs in a sample. A marker that is common to all EVs in a sample is herein referred to as a pan-EV marker. A biomarker can be a surface marker. The QUAD biomarkers (EpCAM, EGFR, HER2, MUC1) are four biomarkers that have been shown to be particularly useful for the diagnosis and / or characterization of cancer. A biomarker useful for fluorescence imaging may be labeled (directly or indirectly) with a fluorescent moiety (e.g., a dye) such as AF488, AF555, AF647, CY3, and Cy7.

[0061] As used in the specification, "extracellular vesicle" (EV) refers to a naturally occurring or artificial vesicle having an internal cavity. EVs include a lipid bilayer membrane surrounding the contents of the internal cavity. EVs can include, but are not limited to, ectosomes, microvesicles, microparticles, exosomes, oncosomes, apoptotic bodies, liposomes, vacuoles, lysosomes, transport vesicles, secretory vesicles, gas vesicles, organelles, or multivesicular bodies. EVs have dimensions up to about 10 microns, but typically are about 1000 nm or less. Exosomes and microvesicles are types of EVs and can be outside the cell, having fallen off from eukaryotic cells or detached from budding of the plasma membrane. These membrane-bound vesicles are of heterogeneous size, with diameters ranging from about 10 nm to about 5000 nm. The methods and compositions described in the specification are equally applicable to microvesicles of any size.

[0062] As used in the specification, "fluorescent image" includes one image obtained by exciting a substrate at one excitation wavelength, or a composite image in which the substrate is continuously excited at multiple wavelengths, and a composite of these initial images into one. Each initial image may show one or more types of biomarkers that fluoresce at the excitation wavelength.

[0063] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "includes" and / or "including", as used herein, specify the presence of the features, integers, steps, operations, elements, and / or components described, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof not expressly described.

[0064] In describing the exemplary embodiments shown in the drawings, certain specific terms are used for clarity. However, the disclosure of this patent specification is not intended to be limited to the specific terms thus selected, and each of the specific elements should be understood to include all technical equivalents that function similarly.

[0065] Although the present disclosure has been described with reference to exemplary embodiments, it should be understood that the present disclosure is not limited to the disclosed exemplary embodiments. The following claims should be accorded the broadest interpretation that encompasses all such variations and equivalent structures and functions.

Claims

1. one or more fluorescent images, each of which is an image of a substrate treated with two or more biomarkers; a biomarker standard expression profile for each of the two or more biomarkers; and and an input including an analytical element for obtaining biomarker expression data from said one or more fluorescent images; a ranging and scaling element that adjusts the range and scale according to the biomarker expression data or the biomarker standard expression profile; an adjustment element for adjusting a threshold of the biomarker expression data, the adjustment element using a variable, α, to adjust the threshold of each of the two or more biomarkers; a selection element that selects the displayed combination of biomarkers and / or displayed colocalizations; a processor having a display that displays the biomarker expression data in one or more graphs; A system for analyzing biomarker expression comprising:

2. Each image is an image of a substrate loaded with extracellular vesicles, The analysis system according to claim 1 .

3. The extracellular vesicles are incubated with three or more biomarkers; The analysis system according to claim 2 .

4. The graph provides data for EV detection on a logarithmic scale and data for individual biomarker detection on a linear scale. The analysis system according to claim 3 .

5. The selection element obtains information from a user regarding the selection of biomarker and / or colocalization combinations to be displayed. The analysis system according to claim 1 .

6. The selection element automatically displays the associated biomarkers and / or colocalizations by selecting a cancer subtype and / or a therapeutic agent. The analysis system according to claim 1 .

7. the processor further includes a display switching element for changing a display item according to the attribute from a user; The analysis system according to claim 1 .

8. the display further includes one or more images. The analysis system according to claim 1 .

9. the image and the graph included on the display are altered in response to changes in the adjustment element; The analysis system according to claim 1 .

10. The input includes three or more fluorescent images obtained from a multi-channel fluorescent microscope. The analysis system according to claim 1 .

11. The input further includes one or more dark field images of the substrate treated with two or more biomarkers. The analysis system according to claim 1 .

12. one or more fluorescent images, each of which is an image of a substrate treated with two or more biomarkers; a biomarker standard expression profile for each of the two or more biomarkers; and and an input including an analytical element for obtaining biomarker expression data from said one or more fluorescent images; a ranging and scaling element that adjusts the range and scale of said graph according to the detected biomarker expression or a general or individual biomarker standard expression profile; an adjustment element for adjusting a threshold of the biomarker expression data, the adjustment element using a variable, α, to adjust the threshold of each of the two or more biomarkers, the threshold adjustment adjusting the detection count on the graph and adjusting the display of one or more bounding boxes on the image; a selection element for selecting a displayed biomarker and / or a displayed combination of colocalizations, the selection of one or more biomarkers changing the indication of one or more biomarkers on the graph and changing a bounding box of the biomarkers on the image; a processor having a display that displays the acquired biomarker expression data in one or more graphs and one or more images; A system for analyzing biomarker expression comprising:

13. receiving one or more fluorescent images and a biomarker standard expression; analyzing the one or more fluorescent images to obtain biomarker expression data; ranging and / or scaling said biomarker expression data according to said biomarker expression data or a biomarker standard expression profile; adjusting a threshold for the biomarker expression data, using a single variable, α, to adjust the biomarker threshold; selecting one or more biomarkers to display and / or selecting one or more colocalization combinations to display; displaying the biomarker expression data in one or more graphs; 23. A computer-implemented method comprising:

14. Each fluorescent image is a fluorescent image of a substrate loaded with extracellular vesicles, 14. The computer-implemented method of claim 13.

15. The extracellular vesicles are incubated with three or more biomarkers; 14. The computer-implemented method of claim 13.

16. Displaying the data of biomarker expression includes displaying data of EV detection on a logarithmic scale and data of individual biomarker detection on a linear scale.

16. The computer-implemented method of claim 15.

17. Selecting one or more biomarkers and / or selecting one or more colocalization combinations includes obtaining input from a user input.

14. The computer-implemented method of claim 13.

18. The user input is from a user interaction with a display of a selection of a cancer subtype and / or a selection of a therapeutic agent; 20. The computer-implemented method of claim 17.

19. Displaying the biomarker expression data in one or more graphs further comprises altering the display in response to changes in the regulatory elements.

14. The computer-implemented method of claim 13.

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