Systems and methods for analyzing molecular interactions on living cells using biosensor technology

Abstract

A method for measuring molecular interactions on a plurality of regions of interest (ROI) of a sensor surface of a biosensor device. The method may include receiving respective biosensor response data for each ROI of the plurality of ROIs. The method may also include determining a sample set and a reference set of the plurality of ROIs. The sample group may include a sample group ROI of the plurality of ROIs, and the reference group may include a reference group ROI of the plurality of ROIs. The method may also include generating one or more sample data distributions based on one or more respective sample set binding parameters for each of the sample set ROIs derived from the respective biosensor response data for the each of the sample set ROIs. The method may also include generating one or more reference data distributions based on one or more respective reference set binding parameters for each of the reference set ROIs derived from the respective biosensor response data for the each of the reference set ROIs. Other embodiments are disclosed.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Patent Application No. 17 / 736,389, filed May 4, 2022. U.S. Patent Application No. 17 / 736,389 is a continuation-in-part of and claims priority to U.S. Patent Application No. 17 / 074,274, filed October 19, 2020. U.S. Patent Application No. 17 / 074,274 is a continuation-in-part of and claims priority to U.S. Patent Application No. 16 / 423,733, filed May 28, 2019, and published as U.S. Patent No. 10,809,194 on October 20, 2020. U.S. Patent Application No. 16 / 423,733 is a continuation-in-part of and claims priority to PCT / US19 / 34087, filed May 27, 2019. PCT / US19 / 34087 claims priority to U.S. Provisional Patent Application No. 62 / 676,983, filed May 27, 2018. U.S. Patent Application No. 17 / 074,274, U.S. Patent Application No. 16 / 423,733, PCT / US19 / 34087, and U.S. Provisional Patent Application No. 62 / 676,983 are incorporated herein by reference in their entireties. Technical Field

[0003] The present disclosure relates generally to biosensor systems and methods of using such systems to measure molecular interactions. Background Art

[0004] Label-free detection via biosensor devices such as surface plasmon resonance instruments is a popular technique for real-time monitoring of molecular interactions. However, conventional biosensor devices or systems are not suitable for studying naturally occurring heterogeneous effects in cell populations because the heterogeneous effects either have a limited field of view or are not designed to image cellular structures or phenotypes that typically have disordered patterns and structures. Therefore, there is a need for systems and methods configured with a large field of view and high resolution and rigorous algorithms for measuring molecular interactions on heterogeneous surfaces. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] In order to facilitate further description of the embodiments, the following drawings are provided, in which:

[0006] Figure 1 shows a perspective view of a biosensor system according to an embodiment;

[0007] Figure 2 Shown according to Figure 1 A side view of a biosensor system of an embodiment of the present invention, wherein the housing of the optical assembly is cut away;

[0008] Figure 3 shows a perspective view of a biosensor system according to an embodiment;

[0009] Figure 4 shows an angular response profile curve according to an embodiment;

[0010] Figure 5 shows a perspective view of a biosensor system including a sensor having multiple regions of interest (ROIs) according to an embodiment;

[0011] Figure 6 shows an angular response profile curve according to an embodiment;

[0012] Figure 7 shows an angular response profile curve according to an embodiment;

[0013] Figure 8 shows a portion of the angular response profile curve in which the occluded area is indicated by the letter N according to an embodiment;

[0014] Fig. 9 Angular response profile curves of various ROIs according to an embodiment are shown;

[0015] Fig.10 shows a histogram of ROI counts versus incident angle according to an embodiment;

[0016] Fig.11 shows an image of a sensor surface according to an embodiment, wherein a grid is superimposed on the image to divide the image into a plurality of ROIs;

[0017] Fig.12 Shown according to Fig.11 A data graph of binding activity on a sensor surface of an embodiment of the present invention;

[0018] Fig.13 Shown according to Figure 11 to Figure 12 An image of the mapped binding activity on the sensor surface of the embodiment of the present invention;

[0019] Fig.14 a histogram showing the distribution of binding parameters according to an embodiment;

[0020] Fig.15 Shown according to Fig.14 an isoaffinity diagonal plot showing the relationship of the distribution of the binding parameters of the embodiment;

[0021] Fig.16 shows an image of a sensor surface according to an embodiment, wherein a grid is superimposed on the image to divide the image into a plurality of ROIs;

[0022] Fig.17 Shown according to Fig.16 A data graph of binding activity on a sensor surface of an embodiment of the present invention;

[0023] Fig.18 A histogram showing the distribution of binding parameters of ROIs in a sample group (Group A) according to an embodiment;

[0024] Fig.19 Shown according to Fig.18 A histogram of the distribution of binding parameters of ROIs in a reference group (group B) of the embodiment of the present invention;

[0025] Fig. 20 A histogram showing the distribution of binding parameters of ROIs in a sample group (Group A) or a reference group (Group B) according to another embodiment;

[0026] Fig.21 A histogram showing the distribution of binding parameters of ROIs in a sample group (Group A) or a reference group (Group B) according to yet another embodiment;

[0027] Fig. 22 shows an image of a sensor surface according to an embodiment, wherein a grid is superimposed on the image to divide the image into a plurality of ROIs and exemplary regions are labeled;

[0028] Fig.23 shows a total internal reflection (TIR) ​​critical angle reflectivity distribution curve according to an embodiment;

[0029] Fig.24 A flow chart showing a method of measuring molecular interactions on multiple regions of interest (ROIs) on a sensor surface of a biosensor device;

[0030] Fig.25 shows that it is suitable for implementing Fig.24 a front elevation view of a computer system of an embodiment of the disclosed method; and

[0031] Fig.26 Shows Fig.25 A representative block diagram of an example of components included on a circuit board within a chassis of a computer system.

[0032] For simplicity and clarity of illustration, the drawings illustrate the general manner of construction, and descriptions and details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the present disclosure. In addition, the elements in the drawings are not necessarily drawn to scale. For example, the size of some elements in the drawings may be enlarged relative to other elements to help improve the understanding of the embodiments of the present disclosure. The same reference numerals in different drawings represent the same elements.

[0033] The terms "first", "second", "third", "fourth", etc., if any, in the specification and claims are used to distinguish similar elements and are not necessarily used to describe a specific ordered or temporal order. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments described herein can, for example, operate in a sequence different from the sequence shown or otherwise described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, so that a process, method, system, article, device or equipment that includes a series of elements is not necessarily limited to those elements, but may include other elements that are not explicitly listed or inherent to such processes, methods, systems, articles, devices or equipment.

[0034] The terms "left", "right", "front", "rear", "top", "bottom", "above", "below", etc., if any, used in the specification and claims are for descriptive purposes and are not necessarily intended to describe permanent relative positions. It is understood that the terms so used are interchangeable under appropriate circumstances such that embodiments of the apparatus, methods, and / or articles described herein are, for example, capable of operation in orientations other than those illustrated or otherwise described herein.

[0035] As defined herein, in some embodiments, "approximately" may mean within plus or minus ten percent of the stated value. In other embodiments, "approximately" may mean within plus or minus five percent of the stated value. In further embodiments, "approximately" may mean within plus or minus three percent of the stated value. In still other embodiments, "approximately" may mean within plus or minus one percent of the stated value.

[0036] As defined herein, in some embodiments, "real time" can be defined relative to an operation that is actually performed as quickly as possible immediately after a triggering event occurs. A triggering event can include receiving data required for performing a task or otherwise processing information. Due to the inherent delay in transmission and / or computing speed, the term "real time" covers operations that occur "close to" real time or slightly delayed from a triggering event. In many embodiments, "real time" can mean real time minus the time delay for processing (e.g., determining) and / or transmitting data. A specific time delay can vary according to the type and / or amount of data, the processing speed of hardware, the transmission capacity of communication hardware, the transmission distance, etc. However, in many embodiments, the time delay can be less than approximately one second, five seconds, ten seconds, thirty seconds, one minute, two minutes, or five minutes. DETAILED DESCRIPTION

[0037] Various embodiments include a method for analyzing molecular interactions on cells. In some embodiments, the method can be performed by a device via optical imaging and surface plasma microscope. The device can include a sensor surface, a light source, an optical assembly, and a photodetector and an imager on which cells can be attached. The method can provide effective data processing and statistical data analysis for the binding behavior of molecular interactions on cells. In many embodiments, the methods and systems presented herein solve one or more of the following: (a) multicellular populations respond to the heterogeneity of drug molecules, (b) how to mitigate local changes in sensor sensitivity on sensor surfaces (e.g., response amplitude and surface plasma resonance (SPR) angle changes) and how to perform real-time measurements, and / or (c) using multiple regions of interest (ROI) on the sensor to perform statistically significant measurements.

[0038] Many embodiments may include a method for measuring the molecular interactions on multiple target regions (ROI) of the sensor surface of a biosensor device (e.g., SPR sensor system). The method can be implemented via the execution of a computing instruction, which is configured to run at one or more processors and stored at one or more non-transitory computer-readable media. In multiple embodiments, the method may include receiving the corresponding biosensor response data for each ROI in multiple ROIs. The corresponding biosensor response data for each ROI may include the corresponding biosensor response signal or the corresponding biosensor reflectivity signal measured by the biosensor device in a predetermined time period (e.g., 10 seconds, 1 minute, 10 minutes, etc.) for each ROI. In some embodiments, the method may also include determining the sample group and reference group of multiple ROIs. The sample group may include the sample group ROI in multiple ROIs, and each of the sample group ROIs may support one or more samples for the molecular interactions to be measured. The reference group may include the reference group ROI in multiple ROIs. The sample group ROI may not be present in the reference group, while the reference group ROI is not present in the sample group. In several embodiments, the method may further include generating one or more sample data distributions based on one or more corresponding binding parameters for each of the sample group ROIs derived from the corresponding biosensor response data for each of the sample group ROIs. The method may further include generating one or more reference data distributions based on one or more corresponding binding parameters for each of the reference group ROIs derived from the corresponding biosensor response data for each of the reference group ROIs.

[0039] Many embodiments may include a system for measuring molecular interactions on multiple target regions (ROIs) of a sensor surface of a biosensor device. The system may include one or more processors and one or more non-transitory computer-readable media including computing instructions, which, when executed on one or more processors, cause one or more processors to perform certain actions. In some embodiments, the action may include receiving corresponding biosensor response data for each ROI in a plurality of ROIs. The corresponding biosensor response data for each ROI may include corresponding biosensor response signals or corresponding biosensor reflectivity signals measured by a biosensor device within a predetermined time period for each ROI. The action may also include determining a sample group and a reference group of a plurality of ROIs. The sample group may include a sample group ROI in a plurality of ROIs. Each of the sample group ROIs may be configured to support one or more samples for molecular interactions to be measured. The reference group may include a reference group ROI in a plurality of ROIs. In many embodiments, the sample group ROI is not present in the reference group, and the reference group ROI is not present in the sample group. In some embodiments, the actions may further include generating one or more sample data distributions based on one or more corresponding binding parameters for each of the sample group ROIs derived from the corresponding biosensor response data for each of the sample group ROIs. The actions may also include generating one or more reference data distributions based on one or more corresponding binding parameters for each of the reference group ROIs derived from the corresponding biosensor response data for each of the reference group ROIs.

[0040] In various versions of the embodiments described in the first two paragraphs, the biosensor device may include a surface plasmon resonance microscopy (SPRM) device; the biosensor response data may include SPR response data; the corresponding biosensor response signal may include a corresponding SPR response signal; and / or the corresponding biosensor reflectivity signal includes a corresponding SPR reflectivity signal. In other variations of the embodiments described in the first two paragraphs, the biosensor device may include a fluorescence device, a critical angle device, etc.

[0041] In many embodiments, a system (e.g., 100( Figure 1 )) may include a computer (e.g., 180( Figure 1 )、2500( Fig.25 ) or 2600( Fig.26 )) and / or SPRM device (e.g., 100( Figure 1 )、200( Figure 2 )、300( Figure 3 ) and / or 500( Figure 5 )). In many embodiments, the SPRM device (see, e.g., 100 ( Figure 1 ) and 200( Figure 2 )) can have a larger field of view and high resolution. In some embodiments, the SPRM device may include: (a) an optical component (e.g., 110 ( Figure 1 ) or 210( Figure 2 )); (b) SPR light source (e.g., 120 ( Figure 1 ) or 220( Figure 2 )), which is configured to emit a light beam for SPR imaging; and (c) an SPR camera (e.g., 130 ( Figure 1 )、230( Figure 2 )、330( Figure 3 ) or 530( Figure 5 )), which is configured to capture SPR images. The optical assembly may include: a hemispherical prism (e.g., 212 ( Figure 2 )), whose top surface is configured to support an SPR sensor (e.g., 170( Figure 1 ) or 570( Figure 5 )); and high numerical aperture (NA) lenses (e.g., 214 ( Figure 2 ))(and / or lens set), which is located on the far side of the top surface of the hemispherical prism. The SPR sensor may include a surface configured to contact the sample. The high NA lens (and / or lens set) can be configured to refract the light beam toward the hemispherical prism. The high NA lens (and / or lens set) and the hemispherical prism can be configured to adjust (e.g., collimate and / or focus) the light beam toward the SPR sensor. The high NA lens (and / or lens set) can also be configured to receive the light beam after it is reflected by the surface of the SPR sensor and refract it toward the SPR camera.

[0042] In some embodiments, the sensor surface of the SPRM device (e.g., 571 ( Figure 5 )) may include multiple ROIs (see, e.g. Figures 11 to 13 and Figure 16 to Figure 17 ), and the SPRM device (e.g., 200( Figure 2 )、300( Figure 3 ) and / or 500( Figure 5 )) can be configured to measure multiple ROIs simultaneously. Each of the multiple ROIs can have any suitable size and / or dimensions, such as a single pixel or 2x2 pixels in the SPR and / or optical image captured by the SPRM device. The ROI can be fixed on the sensor surface or determined when or after the SPR or optical image of the sensor surface is captured. For example, the ROI can be determined by superimposing a grid of multiple evenly distributed ROIs on the SPR image of the entire sensor surface (see, for example, Fig.11 , 16and 17), and SPR data (e.g., SPR response or reflectivity signal) for each ROI (e.g., pixel) enclosed by the grid can be independently measured, stored and / or processed.

[0043] In some embodiments, a SPRM device (e.g., 200( Figure 2 )、300( Figure 3 ) and / or 500( Figure 5 )) can be configured to i ) is directed to the sensor surface (e.g., 571( Figure 5 )) to measure the molecular interaction between drug molecules and protein receptors on the cell membrane when surface plasmon waves are induced near their resonance. When the interaction occurs, the refractive index near the sensor surface (e.g., 571( Figure 5 )) will change locally and cause the SPR angle (θ R ) changes. The SPR angle (Δθ R ) can be detected by an SPR detector camera (e.g., 130( Figure 1 )、230( Figure 2 )、330( Figure 3 ) and / or 430( Figure 4 )) detection, the SPR detector camera is configured to receive signals from the sensor (e.g., 170 ( Figure 1 )、270( Figure 2 )、370( Figure 3 ) and / or 570( Figure 5 )) of the reflected light. The SPR angle changes can be recorded by the SPRM device and / or computer with corresponding time stamps. The recorded data can be used to derive the thermodynamic and kinetic behavior of drug-cell interactions.

[0044] In some embodiments, a system (e.g., 100( Figure 1 )) may be configured to: (a) receive a signal for a sensor surface (e.g., 571( Figure 5 )) of each ROI: (i) retrieve the SPR data previously obtained by the SPRM device (e.g., 200 ( Figure 2 )、300( Figure 3 ) and / or 500( Figure 5 )) measured SPR data, or (ii) using an SPRM device to detect molecular interactions on each ROI (e.g., molecular interactions between drug molecules and protein receptors on a cell membrane); and (b) using a computer (e.g., 180( Figure 1 )、2500( Fig.25 ) or 2600( Fig.26)) to process and / or analyze data transmitted by an SPRM device (e.g., 200( Figure 2 )、300( Figure 3 ) and / or 500( Figure 5 ))SPR data corresponding to the detected molecular interactions.

[0045] Due to the heterogeneity of the cell population on the sensor surface, in order to effectively measure these interactions, the sensor surface can be divided into various ROI groups. The optimal sensitivity of each ROI can be determined, and the data collected from each ROI can be processed to obtain more meaningful information about the behavior of these interactions. In similar or different embodiments, the system (e.g., 100 ( Figure 1 )) may include a biosensor device as a supplement or alternative to a SPRM device to detect molecular interactions.

[0046] In some embodiments, a computer (e.g., 180( Figure 1 )、1800( Fig.18 ) or 1900( Fig.19 )) may be configured to control a SPRM device (e.g., 200( Figure 2 )、300( Figure 3 ) and / or 400( Figure 5 )). For example, the computer may be configured to cause the SPRM device to: (a) adjust one or more components of the SPRM device (e.g., the SPR light source 120 ( Figure 1 )、Bright field light source 140( Figure 1 )、Bright field camera 150( Figure 1 )、lens 214( Figure 2 )、prism 312( Figure 3 ) and / or SPR camera 530 ( Figure 5 ) etc.); (b) measuring the relative position and / or orientation of the sensor surface; (c) taking SPR and / or optical images; (d) transferring the SPR data and / or SPR / optical images to a computer in real time, and / or (e) storing the SPR data and / or SPR / optical images to a non-temporary computer-readable medium and / or a remote database; etc.

[0047] In many embodiments, when a sample (eg, molecule and / or cell) is immobilized on the sensor surface, the sensor surface (eg, 571( Figure 5 )) may become heterogeneous. A system (e.g., 100( Figure 1 )) can be configured to measure the sensor surface (see, for example, 1710 ( Fig.17 )) before preparing the SPR device (e.g., 200( Figure 2 )、300( Figure 3) and / or 500( Figure 5 )). In several embodiments, preparing the SPRM device prior to measurement can include determining a selected incident angle for the SPRM device to maximize sensor sensitivity over multiple ROIs.

[0048] In many embodiments, determining the selected incident angle may include determining the incident light at a predetermined range of incident angles (e.g., θ min With θ max Theta i ( Figure 3 to Figure 4 )) is directed to the sensor surface. For example, for a 690 nm wavelength light source (e.g., 120 ( Figure 1 ) and / or 220( Figure 2 )) and the sensor substrate is a BK7 glass SPR sensor (e.g., 170 ( Figure 1 )、270( Figure 2 ) and / or 570( Figure 5 )) of an optical / SPRM device (e.g., 200( Figure 2 )、300( Figure 3 ) and / or 500( Figure 5 )), the incident angle in the predetermined range can be 68 degrees (θ min ) and 78 degrees (θ max ). Each incident angle (θ i ) can be obtained from θ min Gradually increase until it reaches θ max , such as θ i =(68°+i*Δθ), where 0≤i≤1000 and Δθ=0.01. min to θ max The corresponding response measured at each incident angle can determine the corresponding SPR angle response profile curve of each ROI. The corresponding SPR angle response profile curve may include some areas (e.g., Figure 8 ), wherein the slope of the tangent to the profile curve is less than a threshold value (e.g., 0.035% / degree), indicating that the sensor sensitivity is low in these areas. Determining the selected incident angle may also include determining a corresponding ROI count for each incident angle within a predetermined range (e.g., Fig.10 ), wherein the corresponding ROI count is the number of ROIs whose ROI sensitivity for each incident angle is greater than or equal to a predetermined sensitivity threshold (e.g., 0.035% / degree). Determining the selected incident angle may further include determining the selected incident angle (e.g., θ set ( Fig.10)) counts one or more incident angles that are the largest among all incident angles within a predetermined range for its corresponding ROI.

[0049] In some embodiments, a molecule having a known SPR angle change (Δθ R A standard calibration fluid (e.g., 90% phosphate buffered saline (PBS) buffer) (e.g., about 23 milliseconds for 90% PBS buffer) can be used to determine sensor sensitivity. For example, determining the ROI sensitivity of the ROI at an incident angle may include: (a) directing the standard calibration fluid onto the sensor surface including the ROI, wherein the SPR angle change (Δθ) of the standard calibration fluid on the ROI at the incident angle R ) is known; (b) measuring the SPR response or reflectivity signal (R) of the ROI under the incident angle by the SPRM device; and (c) according to ΔR / Δθ R The sensor sensitivity of the ROI at the incident angle is determined. When determining the sensor sensitivity, any external perturbations that may produce a measurable biosensor response (e.g., calibration fluids used or thermal changes, etc.) may also be taken into account. The calculated ROI sensitivity of each ROI at each incident angle may be stored in a non-transitory computer readable medium and / or a remote database. In some embodiments, a known / predetermined SPR angle change (Δθ R ) is stored in a non-transitory computer-readable medium and / or a remote database for future use.

[0050] In some embodiments, the angle of incidence (θ i ) under ROI(ROI i ) of the ROI sensitivity (see, Figures 3 to 5 )) may include: (a) obtaining an SPR angle response profile curve or a critical angle reflectivity profile curve for ROI prediction (see Figure 4 , Figures 6 to 9 , Fig.23 (b) The SPR response or reflectivity signal (R i ); (c) Determine ROI (ROI i ) of the SPR response or reflectivity signal change (ΔR i ) and SPR angle change (ΔR i ) and the incident angle; and (d) based at least in part on the SPR angle change (Δθ Ri ) and the SPR response or reflectivity signal change (ΔR i ) to determine the incident angle (θ i ) i ) ROI sensitivity.

[0051] For heterogeneous (non-uniform) sensor surfaces, the shape of the SPR angle response profile or critical angle reflectivity profile may vary at different ROIs on the sensor surface, and the method may use a different SPR angle response profile or critical angle reflectivity profile for each ROI. Figure 4 , Figures 6 to 9 etc.) or critical angle reflectivity profile curve (see, Fig.23 ) does not exist or is unavailable, the method may include obtaining the SPR angle response profile curve or the critical angle reflectivity profile curve by: (a) taking a range (between θ min With θ max Each incident angle (θ i )Measure ROI(ROI i ) of the SPR response or reflectivity signal (R i ); (b) based on the SPR response or reflectivity signal (R i ) to draw the ROI(ROI i ) of the SPR angle response profile curve or the critical angle reflectivity profile curve; and / or (c) the ROI (ROI i ) is stored in a non-transitory computer readable medium (e.g., 2512, 2514, or 2516 ( Fig.25 If there is a predicted SPR angle response profile curve or a critical angle reflectivity profile curve of the ROI, the predicted SPR angle response profile curve or the critical angle reflectivity profile curve can be retrieved from the non-transitory computer readable medium and / or the remote database.

[0052] In many embodiments, the angle of incidence (θ) can be determined based on i ) i ) of the SPR response or reflectivity signal change (ΔR i ) and SPR angle change (Δθ Ri ): (a) Angle of incidence (θ i ) i ) is the SPR response or reflectivity signal (R i ); and / or (b) ROI (ROI i ) of the SPR angle response profile or critical angle reflectivity. For example, the SPR response or reflectivity signal changes (ΔR i ) can be determined based on: (i) the SPR response or reflectivity signal (R i), and (ii) the SPR response value of the pre-scan of the SPR angle response profile curve. In certain embodiments, the ROI (ROI i ) of the SPR angle response contour curve (see, Figure 4 and Figures 6 to 9 ) or critical angle reflectivity profile (see, Fig.23 ) at the incident angle (θ i ) under the tangent slope (S i ) can be used as the angle of incidence (θ i ) under ROI(ROI i ) of the sensor sensitivity. Then, the SPR angle changes (Δθ Ri ) can be adjusted by (ΔR i / S i ) is determined. In some embodiments, the SPR response or reflectivity signal change (ΔR) can be converted to i ) from the SPR angle response contour to the SPR angle change (Δθ Ri ), and can then be based on (ΔR i / Δθ Ri ) to determine ROI(ROI i ) of the sensor sensitivity.

[0053] Experiments have shown that the aforementioned standard calibration fluid method and / or tangent slope method can be used to calculate the slope of a smaller angle change (e.g., Δθ R <2 degrees) to produce sufficiently accurate SPR angle changes (Δθ R ), while more complex higher order curve fitting algorithms and / or lookup table methods can produce accurate results even for larger angle changes (e.g., Δθ R >2 degrees). In many embodiments, the SPR response data for a ROI may also include any other information for or related to the ROI that has been calculated, obtained, and / or accumulated during preparation of the SPRM device or during measurements performed by the SPRM device. For example, in addition to the SPR response or reflectivity signal, the SPR response data for a ROI may also include one or more binding parameters, SPR angle response profile curves, ROI sensitivity, function values ​​of a curve fit function, and / or an indication of a group to which the ROI is assigned (e.g., a sample group, a reference group, and / or a group of ROIs that are removed from or not assigned to any of the sample group or reference group).

[0054] Various embodiments may include a method for measuring a SPRM device (e.g., 200( Figure 2 )、300( Figure 3 ) and / or 500( Figure 5)) of a sensor surface (e.g., 2400( Fig.24 )). In many embodiments, the method can be implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media.

[0055] The method may include receiving corresponding SPR response data for each of the plurality of ROIs (eg, 2420( Fig.24 )). The corresponding SPR response data for each of the multiple ROIs may include the corresponding SPR response or reflectivity signal for each of the multiple ROIs measured by the SPRM device within a predetermined time period. Receiving the corresponding SPR response data for each of the multiple ROIs may include: (a) measuring the corresponding SPR response or reflectivity signal for each of the multiple ROIs in real time by the SPRM device within a predetermined time period; and / or (b) retrieving the corresponding SPR response data for each of the multiple ROIs from one or more non-transitory computer-readable media and / or remote databases. In many embodiments, measuring the corresponding SPR response or reflectivity signal for each of the multiple ROIs may include superimposing a grid of multiple uniformly distributed ROIs on the SPR image of the entire sensor surface (see, for example Fig.11 ), and measure the corresponding SPR response or reflectivity signal at each ROI surrounded by the grid. Fig.12 An exemplary spatial data plot of the binding activity measured at a ROI (eg, the ROI in the white square) is shown.

[0056] In some embodiments, the method may include automatically preparing the SPRM as described above at (e.g., 2410( Fig.24 )), and after preparing the SPRM device, using the SPRM device to measure multiple ROIs to obtain a corresponding SPR response or reflectivity signal for each of the multiple ROIs (see, for example, 2420 ( Fig.24 )). In similar or different embodiments, the method may include using a pre-prepared SPRM device to measure multiple ROIs to obtain a corresponding SPR response or reflectivity signal for each of the multiple ROIs (see, for example, 2420 ( Fig.24 )).

[0057] In various embodiments, the method may further include determining a sample group and a reference group (eg, 2430( Fig.24)). The sample group may include a sample group ROI among multiple ROIs. A ROI in a sample group ROI may mean that the ROI is fixed together with one or more samples for the molecular interaction to be measured by the SPRM device. The reference group may include a reference group ROI among multiple ROIs. Each of each reference group ROI in the reference group may have the same of the following features: (a) being a bare area (e.g., a reference group ROI that does not include any sample such as a cell), (b) having a modified surface on which a sample cannot be fixed or an SPR response cannot be detected, or (c) being fixed together with an alternative sample with a known binding behavior, etc.

[0058] In some embodiments, the sample set and the reference set can be generated by a computer (e.g., 180( Figure 1 )、2500( Fig.25 ) or 2600( Fig.26 )) automatically determined in real time. In similar or different embodiments, the sample group and the reference group can be determined by the user using manual techniques in addition or alternatively. Determining the sample group and the reference group can include selecting the sample group ROI based on the characteristics (e.g., intensity) of the corresponding SPR response curve of each of the multiple ROIs. For example, when the intensity of the SPR response at the ROI is at least as large as the cutoff intensity (e.g., average intensity or predetermined value) of the SPR image at a specific angle, the sample group ROI can be selected.

[0059] In many embodiments, the sample group ROI does not exist in the reference group, and the reference group ROI may not exist in the sample group. In some embodiments, the method may include determining one or more different reference groups. Each of the same reference group ROI in one or more different reference groups may have the same of the following features: (a) being an exposed area, (b) including a modified surface, or (c) being fixed together with an alternative sample with a known binding behavior, etc. Different reference group ROIs of different ones in one or more different reference groups may be the same or different in features (a) to (c).

[0060] In many embodiments, multiple ROIs are determined (e.g., 1730 ( Fig.17 )) of the sample set and the reference set may include: (a) a sample set obtained from the sensor surface by a SPRM device (e.g., 200 ( Figure 2 )、300( Figure 3 ) and / or 500( Figure 5 )) of a camera (e.g., a bright field camera 150 ( Figure 1(a) identifying one or more samples in an optical image captured by the image processing unit; and (b) automatically mapping the corresponding position of each of the one or more samples to a sample group ROI. Identifying one or more samples can be achieved by any suitable object detection technology (including neural network-based or non-neural methods).

[0061] In some embodiments, multiple ROIs (e.g., 2430 ( Fig.24 The sample group and the reference group of the sample group and the reference group may include obtaining predetermined ROI grouping information. The predetermined ROI grouping information may include a predetermined mapping of the sample group ROI and the reference group ROI. For example, Fig. 22 As shown, reference regions including reference group ROIs can be created by: (a) using a stamp to cover a small portion of the sensor surface and removing the stamp after cell attachment to leave a bare sensor surface without cell attachment (see, region B); (b) using a spotter to modify a small region of the sensor surface (see, region C); or (c) placing a patch of surrogate cells (see, region D). These reference regions (e.g., regions B, C, and D) Fig. 22 )) can be assigned as a negative control for some binding measurements. The predetermined ROI grouping information can be manually determined and / or automatically determined by a computer based on the corresponding ROI sensitivity of each ROI and / or the biosensor response information of each ROI at a predetermined incident angle, etc.

[0062] In many embodiments, the method can also include removing noise from the SPR response data of each ROI (see, e.g., 2440 ( Fig.24 )). The noise to be eliminated may at least partially include the SPR response data of the representative reference group ROI. The representative reference group ROI may be an existing or virtual reference group ROI selected by a user from the reference group ROI, determined by a computer, or predetermined based on predetermined ROI grouping information. For example, the computer (e.g., 180( Figure 1 )、1800( Fig.18) etc.) can be configured to determine the representative reference group ROI randomly or when the SPR response data of the representative reference group ROI is the smallest, median, largest or any one among the reference group ROI. In certain embodiments, each reference group ROI can be used as a representative reference group ROI alone to eliminate noise. For example, each reference group ROI can be subtracted from each sample group ROI to generate multiple data points for each sample group ROI. In similar or alternative embodiments, one or more (for example, one or more reference group ROIs located closest to the sample ROI) of the reference group ROI with local importance for the sample ROI can form a subgroup to eliminate noise from the sample ROI. For example, the representative reference group ROI can be any of the average ROI, sum ROI, minimum ROI, median ROI, maximum ROI or any ROI in the subgroup of the reference group ROI. The subgroup of the reference group ROI for the sample ROI can be completely or partially overlapped (for example, with one or more common reference ROIs) with the subgroup of the reference group ROI for another sample ROI. In some embodiments, one or more filters (eg, fast Fourier transform (FFT) filters and / or any other suitable digital filters) may additionally or alternatively be used to remove noise.

[0063] In some embodiments, noise is removed from the SPR response data of each ROI (see, e.g., 2440 ( Fig.24 )) can also include deducting the SPR response data of the representative reference group ROI from the SPR response data of each ROI (including the representative reference group ROI). After deduction, the method can also include determining the fitting error of the SPR response data for each ROI. For example, the fitting error of the SPR response data (e.g., binding parameters) for ROI can be generated by any suitable comparison model (e.g., chi-square, standard deviation of residuals, etc.). In some embodiments, the method can also include removing the group ROI in multiple ROIs from the group (e.g., sample group or reference group) when the normalized fitting error of the group ROI is at least as large as the predetermined normalized cutoff error (e.g., 0.125, 0.130, 0.150, 0.160, etc.). The predetermined normalized cutoff error can be adjusted based on the quality of the SPR data, etc.

[0064] In some embodiments, the method may further include generating one or more sample distribution data based on one or more binding parameters for each sample group ROI (e.g., one or more sample group binding parameters for each sample group ROI) (see, e.g., 2450 ( Fig.24)). One or more binding parameters for each sample ROI can be derived from the corresponding SPR response data for each sample group ROI. In some embodiments, the method can further include generating one or more reference data distributions based on one or more corresponding binding parameters for each of the reference group ROIs (e.g., one or more reference group binding parameters for each reference group ROI) derived from the corresponding SPR response data for each of the reference group ROIs (see, e.g., 2460 ( Fig.24 )). In many embodiments, one or more sample data distributions and one or more reference data distributions are generated (see, for example, 2450 and 2460 ( Fig.24 )) can be executed concurrently or in a different order.

[0065] The one or more binding parameters (e.g., one or more corresponding sample or reference group binding parameters for each ROI) may include one or more kinetic parameters (e.g., association rate constant (k a ), dissociation rate constant (k d ) etc.) and / or thermodynamic parameters (e.g., binding affinity or equilibrium dissociation constant (K D )). The SPR angle change (Δθ) detected by the SPRM device over a predetermined time period of the ROI can be calculated by any suitable formula (e.g., first-order kinetic theory). R ) to determine one or more binding parameters for the ROI.

[0066] Fig.14 Some exemplary histograms of the distribution of measured binding parameters are shown. Such histograms can be used to perform statistical analysis of the binding parameters and present meaningful confidence intervals for the measured data, thereby demonstrating the number of active binding sites and providing a measure of cellular heterogeneity. Fig.15 It shows that k a , k d and K D The relationship between the data distributions (e.g., histograms) and the commonly used isoaffinity diagonal plots is shown to demonstrate the importance and effectiveness of these data distributions in providing key information for drug screening.

[0067] In many embodiments, the method may further include eliminating errors caused by one or more undesirable effects on one or more sample data distributions and one or more reference data distributions for each sample group ROI (see, e.g., 2470 ( Fig.24)). One or more undesirable effects may include nonspecific adsorption (NSA; also known as nonspecific binding (NSB)) and / or secondary effects, etc., and the binding parameters for the reference group ROI can be used to eliminate errors in the binding parameters for each sample group ROI due to these undesirable effects. In some embodiments, eliminating errors in one or more binding parameters for each sample group ROI may include comparing one or more sample data distributions (e.g., Figures 18 to 21 ) and one or more reference data distributions (e.g., Figures 18 to 21 ) and / or subtracting one or more reference data distributions from one or more sample data distributions. In some embodiments, the data distributions may be modified or transformed before error elimination to make the comparison or evaluation of the data distributions more meaningful. As an example, the transformation may be a mathematical normalization of the counts to the sample and reference area ratios.

[0068] For example, Fig.18 A histogram is shown in an embodiment that summarizes the binding parameters (e.g., K D ) where the fitting error of each of the binding parameters in the histogram is below a predetermined cutoff error (e.g., 0.1, 0.2, etc.). Fig.18 The histogram in can represent the effective bidding activity for the sample group ROI. In the same example, Fig.19 Another histogram is shown, which shows the binding parameters (e.g., K D ) where the fitting error of each binding parameter in this histogram is less than a predetermined cutoff error. Fig.19 The histograms in can help assess the extent of NSA and / or may lead to binding parameters (e.g., K D )’s quadratic effect.

[0069] exist Fig. 20 In another example in , the histogram of the sample group ROI shows two or more peaks. The exemplary method may include comparing the histogram between the sample group ROI (e.g., group A) and the reference group ROI (e.g., group B), and then removing NSA and / or secondary effects (e.g., Fig. 20 In addition, histograms with similar peaks can be compared to each other to show whether the observed response is mainly due to specific binding or non-specific binding.

[0070] exist Fig.21In another example, two histograms between a sample group ROI (e.g., group A) and a reference group ROI (e.g., group B) show similar binding values. The method may include using different measurement buffers and / or different sensor treatments to create different reference regions (e.g., reference group ROIs) with reduced NSA or secondary effects, rather than using a histogram of a reference group ROI (e.g., group B) to remove NSA or secondary effects as previously mentioned.

[0071] Various embodiments may include a system for measuring molecular interactions on multiple regions of interest (ROIs) on a sensor surface of a SPRM device. The system may include one or more processors and one or more non-transitory computer-readable media including computing instructions that, when executed on the one or more processors, cause the one or more processors to perform actions. In many embodiments, the actions may include retrieving corresponding SPR response data for each of the multiple ROIs, wherein the corresponding SPR response data for each ROI includes a corresponding SPR response or reflectivity signal for each ROI measured by the SPRM device within a predetermined time period.

[0072] In various embodiments, the actions may further include determining a sample group and a reference group of multiple ROIs. The sample group may include a sample group ROI in the multiple ROIs, and each of the sample group ROIs may support one or more samples for the molecular interaction to be measured. The reference group may include a reference group ROI in the multiple ROIs, while the sample group ROI does not exist in the reference group, and the reference group ROI does not exist in the sample group.

[0073] In some embodiments, the action may further include generating one or more sample data distributions based on one or more corresponding binding parameters for each of the sample group ROIs derived from the corresponding SPR response data for each of the sample group ROIs. The action may also include generating one or more reference data distributions based on one or more corresponding binding parameters for each of the reference group ROIs derived from the corresponding SPR response data for each of the reference group ROIs. The action may include eliminating errors caused by one or more undesirable effects on one or more sample data distributions and one or more reference data distributions for each of the sample group ROIs.

[0074] Continuing to refer to the attached drawings, Fig.25 A computer 2500 is shown, all or part of which may be suitable for implementing computer 180 ( Figure 1 ) of at least a portion of an implementation and / or execution method 2400 ( Fig.24) is an embodiment of one or more blocks of the computer 2500. The computer 2500 may include a chassis 2502 containing one or more circuit boards (not shown), a USB (Universal Serial Bus) port 2512, a compact disk read-only memory (CD-ROM) and / or digital video disk (DVD) drive 2516, and a hard disk drive 2514. Fig.26 A representative block diagram of the components included on a circuit board within chassis 2502 is shown in FIG. Fig.26 The central processing unit (CPU) 2610 in Fig.26 In various embodiments, the architecture of CPU 2610 can conform to any of a variety of commercial distributed architecture families.

[0075] continue Fig.26 The system bus 2614 is also coupled to the memory 2608, which includes both read-only memory (ROM) and random access memory (RAM). The non-volatile portion of the memory 2608 or ROM may be used to restore the computer 2500 ( Fig.25 ) to a functional state. In addition, the memory 2408 may include microcode such as a basic input and output system (BIOS). In some examples, one or more memory storage units of various embodiments disclosed herein may include a memory storage unit 2608, a USB-equipped electronic device (such as, coupled to a universal serial bus (USB) port 2512 ( Figure 25 to Figure 26 ) of an external memory storage unit (not shown)), a hard disk drive 2514 ( Figure 25 to Figure 26 ) and / or CD-ROM or DVD drive 2516 ( Figure 25 to Figure 26 ). In the same or different examples, one or more memory storage units of various embodiments disclosed herein may include an operating system, which may be a software program that manages the hardware and software resources of a computer and / or computer network. An operating system may perform basic tasks such as controlling and allocating memory, prioritizing the processing of instructions, controlling input and output devices, facilitating networking, and managing files. Some examples of common operating systems include Operating System (OS), OS, OS and OS.

[0076] As used herein, "processor" and / or "processing module" means any type of computing circuit, such as but not limited to a microprocessor, a microcontroller, a controller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a graphics processor, a digital signal processor, or any other type of processor or processing circuit capable of performing the desired function. In some examples, one or more processors of various embodiments disclosed herein may include CPU 2610.

[0077] exist Fig.26 In the depicted embodiment of FIG. 25, various I / O devices such as a disk controller 2604, a graphics adapter 2624, a video controller 2502, a keyboard adapter 2526, a mouse adapter 2606, a network adapter 2620, and other I / O devices 2522 can be coupled to the system bus 2514. The keyboard adapter 2526 and the mouse adapter 2506 are respectively coupled to the computer 2600 ( Fig.26 ) of keyboard 2504( Fig.25 and Fig.26 ) and mouse 2510( Fig.25 and Fig.26 ). Although the graphics adapter 2624 and the video controller 2602 are Fig.26 2602 is indicated as a distinct unit, but the video controller 2602 may be integrated into the graphics adapter 2624, or vice versa in other embodiments. The video controller 2602 is adapted to refresh the monitor 2506 ( Fig.25 and Fig.26 ) to the computer 2500 ( Fig.25 ) screen 2508( Fig.25 ) to display the image. The disk controller 2604 can control the hard disk drive 2514 ( Fig.25 and Fig.26 )、USB port 2512( Fig.25 and Fig.26 ) and CD-ROM or DVD drive 2516 ( Fig.25 and Fig.26 ). In other embodiments, different units may be used to control each of these devices individually.

[0078] In some embodiments, the network adapter 2620 may include and / or be implemented as a computer system that is inserted into or coupled to the computer system 2500 ( Fig.25 ) in the expansion port (not shown). In other embodiments, the WNIC card can be a WNIC (wireless network interface controller) card (not shown) built into the computer system 2500 ( Fig.25) in the computer system 2500. The wireless network adapter may be built into the computer system 2500 ( ) by integrating wireless communication capabilities into a motherboard chipset (not shown). Fig.25 ) or via computer system 2500 ( Fig.25 ) of the PCI (Peripheral Component Interconnect) or PCI Express bus or USB port 2512 ( Fig.25 ) is implemented by one or more dedicated wireless communication chips (not shown) connected. In other embodiments, network adapter 2620 may include and / or be implemented as a wired network interface controller card (not shown).

[0079] Although the computer 2500 ( Fig.25 ), but such components and their interconnections are well known to those of ordinary skill in the art. Therefore, this article does not need to discuss the computer 2500 and the chassis 2502 ( Fig.25 ) further details of the construction and composition of the circuit boards within.

[0080] when Fig.25 When the computer 2500 in the computer is running, the computer 2500 may store data on a USB drive in the USB port 2512, on a CD-ROM or DVD in the CD-ROM and / or DVD drive 2516, on the hard disk drive 2514, or on the memory 2608 ( Fig.26 ) is executed by CPU 2610( Fig.26 ) execution. A portion of the program instructions stored on these devices may be suitable for performing all or at least a portion of the techniques described herein. In various embodiments, the computer 2600 can be reprogrammed with one or more modules, applications, and / or databases to convert a general-purpose computer into a special-purpose computer.

[0081] Although the computer system 2500 Fig.25 2500 is shown as a desktop computer, but there may be examples where the computer system 2500 may take on a different form factor while still having functional elements similar to those described for the computer system 2500. In some embodiments, the computer system 2500 may include a single computer, a single server, or a cluster or collection of computers or servers, or a cloud of computers or servers. Typically, a cluster or collection of servers may be used when the demand for the computer system 2500 exceeds the reasonable capacity of a single server or computer. In some embodiments, the computer system 2500 may include a portable computer, such as a laptop computer. In certain other embodiments, the computer system 2500 may include a mobile device, such as a smart phone. For example, the computer 180( Figure 1) may be a mobile device, such as a smart phone. In certain additional embodiments, the computer system 2500 may include an embedded system.

[0082] In many embodiments, the technology described herein can provide practical applications and several technical improvements. In many embodiments, the use of uniformly distributed ROI grids can advantageously eliminate the bias of users to preferentially define and select any ROI on the sensor surface, and allow blind measurements of all ROIs to derive the distribution of binding parameters on the sensor surface. In some embodiments, measuring the binding activity on different ROIs based on different biosensor profile curves (e.g., biosensor angle response profile curves and / or critical angle reflectivity profile curves) can improve the accuracy of the measured binding activity on the heterogeneous sensor surface on which living cells or fixed cells can be attached. In embodiments where the sensor device for measuring molecular interactions includes a larger field of view and high resolution, the method can also be advantageous because more local binding activities can be observed and measured using a larger number of multiple ROIs, and the aforementioned benefits can be better achieved.

[0083] Although measuring binding interactions has been described with reference to specific embodiments, it will be appreciated by those skilled in the art that various changes may be made without departing from the spirit or scope of the present disclosure. For example, the methods, systems and / or algorithms described in the above embodiments may be applied to provide biosensor profile curves obtained using a transparent glass surface sensor that has undergone a total internal reflection (TIR) ​​process. Fig.23 The TIR critical angle reflectivity profile curve is shown similar to the SPR profile curve (measured reflectivity vs. incident angle of light). T ) can be the quotient of the reflectivity change (ΔR) divided by the sensor sensitivity, where the sensor sensitivity is the incident angle (θ i ) under the slope of the TIR profile curve. Therefore, the disclosure of the embodiments is intended to illustrate the scope of the present disclosure and is not intended to be limiting. It is intended that the scope of the present disclosure should be limited only to the scope required by the appended claims.

[0084] Substitution of one or more of the claimed elements constitutes reconstruction rather than repair. In addition, benefits, other advantages, and solutions to problems have been described with respect to specific embodiments. However, benefits, advantages, solutions to problems, and any one or more elements that may make any benefit, advantage, or solution occur or become more apparent should not be construed as a key, required, or essential feature or element of any or all of the claims unless such benefit, advantage, solution, or element is recited in the claims.

[0085] In addition, the embodiments and limitations disclosed herein are not dedicated to the public under the doctrine of equivalents if the embodiments and / or limitations: (1) are not expressly claimed in the claims; and (2) are or could be equivalent to the express elements and / or limitations in the claims under the doctrine of equivalents.

Claims

1. A method for measuring molecular interactions on a plurality of regions of interest (ROIs) of a sensor surface of a biosensor device, the method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer readable media, and comprising: receiving corresponding biosensor response data for each of the plurality of ROIs, wherein the corresponding biosensor response data for each ROI comprises a corresponding biosensor response signal or a corresponding biosensor reflectance signal of each ROI measured by the biosensor device within a predetermined time period; Determine a sample group and a reference group of the plurality of ROIs, wherein: The sample group includes a sample group ROI among the multiple ROIs; Each of the sample group ROIs supports one or more samples for the molecular interaction to be measured; The reference group includes a reference group ROI among the plurality of ROIs; The sample group ROI does not exist in the reference group; and The reference group ROI does not exist in the sample group; generating one or more sample data distributions based on one or more respective sample group binding parameters for said each of said sample group ROIs derived from said respective biosensor response data for said each of said sample group ROIs; and One or more reference data distributions are generated based on one or more respective reference set binding parameters for each of the reference set ROIs derived from the respective biosensor response data for each of the reference set ROIs.

2. The method according to claim 1, further comprising: preparing the biosensor device; as well as After preparing the biosensor device, the plurality of ROIs are measured using the biosensor device to obtain the corresponding biosensor response signal or the corresponding biosensor reflectance signal for each of the plurality of ROIs.

3. The method of claim 2, wherein preparing the biosensor device comprises: determining a respective ROI sensitivity of each of the plurality of ROIs at each angle of incidence in a predetermined range of angles of incidence to the plurality of ROIs; Determining a corresponding ROI count at each incident angle in the predetermined range of incident angles, wherein the corresponding ROI count is a count of an ROI in the plurality of ROIs, and the corresponding ROI sensitivity of the ROI at each incident angle is not less than a predetermined sensitivity threshold; and A selected angle of incidence in the predetermined range of angles of incidence is determined for the biosensor device, wherein the corresponding ROI count at the selected angle of incidence is not less than another corresponding ROI count at any other angle of incidence in the predetermined range of angles of incidence.

4. The method of claim 3, wherein determining the corresponding ROI sensitivity of each ROI in the plurality of ROIs at each incident angle in the predetermined range of incident angles further comprises: obtaining a corresponding biosensor profile curve for each of the plurality of ROIs; detecting, by the biosensor device, the corresponding biosensor response signal or the corresponding biosensor reflectance signal of each ROI at each incident angle; determining a corresponding biosensor signal change and a corresponding biosensor angle change for each ROI at each incident angle based on: (a) the corresponding biosensor response signal or the corresponding biosensor reflectance signal for each ROI at each incident angle, and (b) the corresponding biosensor profile curve for each ROI; and The respective ROI sensitivity of the each ROI at the each incident angle is determined based at least in part on the respective biosensor angle change and the respective biosensor signal change.

5. The method of claim 4, wherein obtaining the corresponding biosensor profile curve for each of the plurality of ROIs further comprises: (a) measuring the corresponding biosensor response signal or the corresponding biosensor reflectance signal of each ROI at each incident angle in the predetermined range of incident angles; as well as generating the corresponding biosensor profile curve for each ROI based on the corresponding biosensor response signal or the corresponding biosensor reflectance signal of each ROI at each incident angle; or (b) retrieving the corresponding biosensor profile curve predicted for each of the ROIs from a non-transitory computer readable medium or a remote database.

6. The method of claim 4, wherein the respective ROI sensitivity of the each ROI in the plurality of ROIs at the each incident angle in the predetermined range of incident angles is determined by one of: the corresponding slope of the corresponding biosensor profile curve at each of the incident angles; a corresponding function value of the curve fitting function for each ROI, the corresponding function value being based at least in part on each incident angle, the corresponding biosensor signal change, and the corresponding biosensor angle change; or A corresponding table value of a lookup table is based at least in part on the corresponding biosensor signal change and the corresponding biosensor angle change.

7. The method of claim 3, wherein determining the corresponding ROI sensitivity of each of the plurality of ROIs at the each incident angle in the predetermined range of incident angles further comprises: directing a standard calibration fluid onto each of the ROIs, wherein a corresponding biosensor angle change of the standard calibration fluid on each of the ROIs at each of the incident angles is predetermined; detecting, by the biosensor device, the corresponding biosensor response signal or the corresponding biosensor reflectance signal of each ROI at each incident angle; and The corresponding ROI sensitivity of each ROI at each incident angle is determined based on (a) the corresponding biosensor response signal or the corresponding biosensor reflectance signal and (b) the corresponding biosensor angle change.

8. The method according to any one of claims 1 to 7, wherein determining the sample group and the reference group of the plurality of ROIs further comprises: (a) identifying the one or more samples from an optical image of the sensor surface taken by the biosensor device; and automatically mapping a corresponding position of each of the one or more samples to the sample group ROI; or (b) Obtaining predetermined ROI grouping information, wherein the predetermined ROI grouping information includes a predetermined mapping of the sample group ROI and the reference group ROI.

9. The method of any one of claims 1 to 8, further comprising removing noise from the corresponding biosensor response data of each of the plurality of ROIs based at least in part on the corresponding biosensor response data of a representative reference group ROI in the reference group ROI.

10. The method of claim 9, wherein removing the noise from the corresponding biosensor response data of the each of the plurality of ROIs further comprises: subtracting the corresponding biosensor response data of the representative reference set ROI from the corresponding biosensor response data of each ROI; determining a respective fit error for each of the one or more respective sample set binding parameters and the one or more respective reference set binding parameters after the subtraction; and A group ROI of the plurality of ROIs is removed from a group of the sample group ROIs or the reference group ROIs when the corresponding fitting error of the group ROI is at least as large as a predetermined cutoff error.

11. The method according to any one of claims 1 to 10, further comprising eliminating errors caused by one or more undesirable effects for each of the sample group ROIs based on the one or more sample data distributions and the one or more reference data distributions.

12. A system for measuring molecular interactions at a plurality of regions of interest (ROIs) on a sensor surface of a biosensor device, the system comprising: one or more processors; as well as One or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform functions comprising: receiving corresponding biosensor response data for each of the plurality of ROIs, wherein the corresponding biosensor response data for each ROI comprises a corresponding biosensor response signal or a corresponding biosensor reflectance signal of each ROI measured by the biosensor device within a predetermined time period; Determine a sample group and a reference group of the plurality of ROIs, wherein: The sample group includes a sample group ROI among the multiple ROIs; Each of the sample group ROIs supports one or more samples for the molecular interaction to be measured; The reference group includes a reference group ROI among the plurality of ROIs; The sample group ROI does not exist in the reference group; and The reference group ROI does not exist in the sample group; generating one or more sample data distributions based on one or more respective sample group binding parameters for said each of said sample group ROIs derived from said respective biosensor response data for said each of said sample group ROIs; and One or more reference data distributions are generated based on one or more respective reference set binding parameters for each of the reference set ROIs derived from the respective biosensor response data for each of the reference set ROIs.

13. The system of claim 12, wherein the computing instructions, when executed on the one or more processors, cause the one or more processors to further perform additional functions, the additional functions comprising: preparing the biosensor device; as well as After preparing the biosensor device, the plurality of ROIs are measured using the biosensor device to obtain the corresponding biosensor response signal or the corresponding biosensor reflectance signal for each of the plurality of ROIs.

14. The system of claim 13, wherein preparing the biosensor device comprises: determining a respective ROI sensitivity of each of the plurality of ROIs at each angle of incidence in a predetermined range of angles of incidence to the plurality of ROIs; determining a corresponding ROI count at each incident angle in the predetermined range of incident angles, wherein the corresponding ROI count is a count of an ROI in the plurality of ROIs, and the corresponding ROI sensitivity of the ROI at each incident angle is not less than a predetermined sensitivity threshold; and A selected angle of incidence in the predetermined range of angles of incidence is determined for the biosensor device, wherein the corresponding ROI count at the selected angle of incidence is not less than another corresponding ROI count at any other angle of incidence in the predetermined range of angles of incidence.

15. The system of claim 14, wherein determining the respective ROI sensitivity of each of the plurality of ROIs at the each incident angle in the predetermined range of incident angles further comprises: obtaining a corresponding biosensor profile curve for each of the plurality of ROIs; Detecting, by the biosensor device, the corresponding biosensor response signal or the corresponding biosensor reflectance signal of each ROI at each incident angle 1; determining a corresponding biosensor signal change and a corresponding biosensor angle change for each ROI at each incident angle based on: (a) the corresponding biosensor response signal or the corresponding biosensor reflectance signal for each ROI at each incident angle, and (b) the corresponding biosensor profile curve for each ROI; and The respective ROI sensitivity of the each ROI at the each incident angle is determined based at least in part on the respective biosensor angle change and the respective biosensor signal change.

16. The system of claim 15, wherein obtaining the corresponding biosensor profile curve for the each of the plurality of ROIs further comprises: (a) measuring the corresponding biosensor response signal or the corresponding biosensor reflectance signal of each ROI at each incident angle l in the predetermined range of incident angles; and generating the corresponding biosensor profile curve for each ROI based on the corresponding biosensor response signal or the corresponding biosensor reflectance signal of each ROI at each incident angle; or (b) retrieving the corresponding biosensor profile curve predicted for each of the ROIs from a non-transitory computer readable medium or a remote database.

17. The system of claim 15, wherein the respective ROI sensitivity of the each ROI in the plurality of ROIs at the each incident angle in the predetermined range of incident angles is determined by one of: the corresponding slope of the corresponding biosensor profile curve at each of the incident angles; a corresponding function value of the curve fitting function for each ROI, the corresponding function value being based at least in part on each incident angle, the corresponding biosensor signal change, and the corresponding biosensor angle change; or A corresponding table value of a lookup table is based at least in part on the corresponding biosensor signal change and the corresponding biosensor angle change.

18. The system of claim 14, wherein determining the respective ROI sensitivity of the each ROI in the plurality of ROIs at the each incident angle in the predetermined range of incident angles further comprises: directing a standard calibration fluid onto each of the ROIs, wherein a corresponding biosensor angle change of the standard calibration fluid on each of the ROIs at each of the incident angles is predetermined; The biosensor device detects the corresponding biosensor response signal or the corresponding biosensor reflectance signal of each ROI at each incident angle 1; and The corresponding ROI sensitivity of each ROI at each incident angle is determined based on (a) the corresponding biosensor response signal or the corresponding biosensor reflectance signal and (b) the corresponding biosensor angle change.

19. The system according to any one of claims 12 to 18, wherein determining the sample group and the reference group of the plurality of ROIs further comprises: (a) identifying the one or more samples from an optical image of the sensor surface taken by the biosensor device; and automatically mapping a corresponding position of each of the one or more samples to the sample group ROI; or (b) Obtaining predetermined ROI grouping information, wherein the predetermined ROI grouping information includes a predetermined mapping of the sample group ROI and the reference group ROI.

20. The system of any one of claims 12 to 19, wherein the computing instructions, when executed on the one or more processors, cause the one or more processors to further perform additional functions, the additional functions comprising removing noise from the corresponding biosensor response data of the each of the plurality of ROIs based at least in part on the corresponding biosensor response data of a representative reference group ROI in the reference group ROI.

21. The system of claim 20, wherein removing the noise from the corresponding biosensor response data of the each of the plurality of ROIs further comprises: subtracting the corresponding biosensor response data of the representative reference set ROI from the corresponding biosensor response data of each ROI; determining a respective fit error for each of the one or more respective sample set binding parameters and the one or more respective reference set binding parameters after the subtraction; and A group ROI of the plurality of ROIs is removed from the sample group ROI or the reference group ROI when the corresponding fitting error of the group ROI is at least as large as a predetermined cutoff error.

22. A system according to any one of claims 12 to 21, wherein the computing instructions, when executed on the one or more processors, cause the one or more processors to further perform additional functions, the additional functions including eliminating errors caused by one or more undesirable effects for each of the sample group ROIs based on the one or more sample data distributions and the one or more reference data distributions.

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