Processing optical absorption during interferometric testing through algorithmic deconvolution and computation.

Abstract

Incorporated herein is a programmatic approach to process the absorption of light by analyte molecules, for example, the binding of the analyte molecules to an interferometric sensor being monitored by an interferometric detection system. A first optical signal can be applied to a biolayer during a biochemical test, and light reflected by the biolayer can form a second optical signal that is detectable by a detector in the interferometric detection system. Through analysis of the second optical signal, the second optical signal can be deconvolved into a reflectance component and an absorption component. If the predominant component of the second optical signal is the reflectance component, an algorithm can be employed to determine the magnitude of binding. If the predominant component of the second optical signal is the absorption component, another algorithm can be employed to determine the magnitude of binding.

Description

[Technical Field]

[0001] Various embodiments relate to programmatic processing of light absorption by analyte molecules, for example, binding of the analyte molecules to an interferometric sensor is monitored by an interferometric sensing system and associated computer program. [Background technology]

[0002] Diagnostic tests based on binding events between analyte molecules and analyte-binding molecules are widely used in medical, veterinary, agricultural, and research applications. Such diagnostic tests can be employed to detect whether an analyte molecule is present in a sample, the amount of analyte molecule in a sample, or the binding ratio of the analyte molecule to the analyte-binding molecule. An analyte-binding molecule and its corresponding analyte molecule together form an analyte-anti-analyte binding pair (or simply "binding pair"). Examples of binding pairs include complementary strands of nucleic acids, antigen-antibody pairs, and receptor-receptor binding agents. The analyte can be either member of the binding pair, and the anti-analyte can be the other member of the binding pair.

[0003] In the past, diagnostic tests have employed solid surfaces to which analyte-binding molecules are immobilized. Analyte molecules in the sample bind with high affinity to the analyte-binding molecules in a defined detection zone. In this type of assay, known as a "solid-phase assay," the solid surface is exposed to the sample under conditions that promote binding of the analyte molecules to the analyte-binding molecules. Generally, the binding event is detected directly by measuring a change in mass, reflectance, thickness, color, or another characteristic that indicates the binding event. For example, when the analyte molecule is labeled with a chromophore, fluorescent label, or radioactive label, the binding event is detectable based on how much, if any, label can be detected in the detection zone. Alternatively, the analyte molecule can be labeled after it binds to the analyte-binding molecule in the detection zone.

[0004] U.S. Patent No. 5,804,453 discloses a method for determining the concentration of a substance in a sample solution using an optical fiber having a reactant (i.e., a capture molecule) coated on the distal end of the optical fiber to which the substance binds. The distal end is then immersed in a sample solution containing the substance. The binding of the substance to the reactant produces an interference pattern that is detected by a spectrometer.

[0005] U.S. Patent No. 7,394,547 discloses a biosensor having a first optically transparent element mechanically attached to the tip of an optical fiber, with an air gap between the first optically transparent element and the tip of the optical fiber. A second optical element, functioning as an interference layer having a thickness greater than 50 nanometers (nm), is then attached to the distal end of the first optical element. A biolayer is formed on the distal surface of the second optical element. An additional reflective surface layer having a thickness of 5 nm to 50 nm and a refractive index greater than 1.8 is coated between the interference layer and the first optical element. The principle of detecting analytes in a sample based on changes in spectral interference is described in this reference, the entire contents of which are incorporated herein by reference.

[0006] U.S. Patent No. 7,319,525 discloses a different configuration in which a section of optical fiber is mechanically attached to a distal connector composed of one or more optical fibers, with an air gap between the proximal end of the optical fiber section and the distal connector, and an interference layer and then a biolayer are fabricated on the distal surface of the optical fiber section.

[0007] Although the prior art provides functionality for the use of biosensors based on thin film interferometers, there is a need for improved performance of such interferometers. [Brief explanation of the drawings]

[0008] [Figure 1A] FIG. 1A shows an example combined signal. [Figure 1B]FIG. 1B shows how, in some circumstances, the combined signal may undergo a downward shift in magnitude rather than an upward shift. [Figure 2A] FIG. 2A depicts a biosensor interferometer that includes a light source, a detector, a waveguide, and an optical assembly (also called a "probe"). [Figure 2B] FIG. 2B depicts an example probe. [Figure 3] FIG. 3 depicts an example probe, according to various embodiments. [Figure 4] FIG. 4 depicts another example probe, according to various embodiments. [Figure 5A] FIG. 5A shows the principle of detection in a thin film interferometer. [Figure 5B] FIG. 5B shows the principle of detection in a thin film interferometer. [Figure 6] FIG. 6 depicts an example slide, according to various embodiments. [Figure 7] FIG. 7 depicts another example slide, according to various embodiments. [Figure 8] FIG. 8 depicts a process flow diagram for dealing with the absorption of light by analyte molecules, the binding activity of which is monitored by an interferometric detection system. [Figure 9] FIG. 9 shows how, through analysis of the difference curves, the magnitude of the full signal, denoted using R1, and half the magnitude of the antisymmetric signal, denoted using R2, can be determined. [Figure 10] FIG. 10 illustrates a network environment including an analytics platform executed by a computing device. [Figure 11] FIG. 11 depicts an example communications environment that includes an analytics platform configured to obtain data from one or more sources. [Figure 12] FIG. 12 is a block diagram illustrating an example processing system in which at least some of the operations described herein may be implemented. [Figure 13]FIG. 13 includes plots with calculated binding curves for the lipoparticle addition step and the antibody association and dissociation steps for experiments involving CXCR4 lipoparticles. [Figure 14] FIG. 14 includes plots with calculated binding curves for the lipoparticle addition step and the antibody association and dissociation steps for experiments involving CD20 lipoparticles.

[0009] The embodiments are shown by way of example and not by way of limitation in the drawings. While the drawings depict various embodiments for illustrative purposes, those skilled in the art will recognize that alternative embodiments may be employed without departing from the principles of the technology. Thus, while particular embodiments are shown in the drawings, the technology is susceptible to various modifications. DETAILED DESCRIPTION OF THE INVENTION

[0010] As part of a diagnostic test, light can be applied to an interferometric sensor, which forms a biolayer. Generally, biolayer formation is facilitated by depositing analyte-binding molecules along one side of the interferometric sensor and then exposing the interferometric sensor to a liquid sample. Analyte molecules within the liquid sample bind to the analyte-binding molecules over time to form the biolayer, a binding event evidenced by an interference pattern that is detectable by a detector in the interferometric detection system.

[0011] As described further below, an incident optical signal (also referred to as a "first optical signal") is directed toward the biolayer, while light reflected by the biolayer results in the generation of a reflected optical signal (also referred to as a "second optical signal"). The first and second optical signals form a spectral interference pattern. As analyte molecules bind to analyte-binding molecules, thereby increasing the thickness of the biolayer, the optical path of the second optical signal increases. As a result, the spectral interference pattern shifts. By measuring the phase shift in real time, the binding signal (also referred to as a "binding curve") can be plotted as the amount of shift versus time. An example binding signal is provided in FIG. 1A. The binding signal can help determine not only the rate at which analyte molecules bind to analyte-binding molecules, but also the total amount of analyte molecules in the liquid sample being tested.

[0012] This approach to generating binding signals works well when the analyte molecules are small (e.g., when the analyte molecules are proteins or antibodies). Simply put, when a small analyte molecule binds to a corresponding analyte-binding molecule, the spectral interference pattern consistently shifts and the binding signal consistently increases in magnitude. However, there are some situations, i.e., when the analyte molecule is large and / or complex, where the binding signal undergoes a downward shift in magnitude rather than an upward shift, as shown in FIG. 1B. This is problematic because the binding signal indicates that the biolayer is actually decreasing in size rather than increasing in size. Examples of large analyte molecules include some cells, viruses, phages, nanoparticles (e.g., lipid nanoparticles), and artificial structures (e.g., magnetic particles).

[0013] In the past, these situations have been handled by "inverting" the binding signal, employing an algorithm that plots absolute values ​​rather than actual values, as shown in Figure 1B. However, this stopgap solution does not actually address the underlying problem, which until recently was poorly understood. At a high level, the underlying problem is that large analyte molecules can absorb a significant amount of the first optical signal impinging on the biolayer. While biolayers formed with small analyte molecules reflect nearly all of the first optical signal, biolayers formed with large analyte molecules are more prone to absorption, essentially behaving like crystals in some respects. This absorption can affect the second optical signal to such an extent that the binding signal cannot be properly calculated by the aforementioned algorithm.

[0014] Thus, a programmatic approach for processing the absorption of light by analyte molecules is incorporated herein, for example, when the binding of the analyte molecules to an interferometric sensor is monitored by an interferometric detection system. As described above, a first optical signal can be applied to a biolayer during a biochemical test, and light reflected by the biolayer can form a second optical signal that is detectable by a detector in the interferometric detection system. Through analysis of the second optical signal, the second optical signal can be deconvolved into a reflection component and an absorption component. If the second optical signal is primarily a reflection component, the aforementioned algorithm (also referred to as a "conventional algorithm") that "inverts" the binding signal can be employed if necessary. If the second optical signal is primarily an absorption component, a different algorithm (also referred to as an "absorption algorithm") can be employed. The absorption algorithm is described in more detail below.

[0015] definition The term "about" means within ±10% of the recited value.

[0016] The term "analyte-binding molecule" refers to any molecule capable of participating in a binding reaction with an analyte molecule. Examples of analyte-binding molecules include, but are not limited to, (i) antigen molecules used to detect the presence of specific antibodies against that antigen, (ii) antibody molecules used to detect the presence of an antigen, (iii) protein molecules used to detect the presence of a binding partner for that protein, (iv) ligands used to detect the presence of a binding partner, and (v) single-stranded nucleic acid molecules used to detect the presence of a nucleic acid molecule.

[0017] The term "interferometric sensor" refers to any sensing device in which a biolayer is formed to produce an interference pattern. One example of an interferometric sensor is a probe designed to be suspended in a solution containing a sample with analyte molecules. Another example of an interferometric sensor is a slide having a flat surface on which a biolayer can be formed during the course of a biochemical test.

[0018] The term "probe" refers to a monolithic substrate having an aspect ratio (length to width) of at least 2:1 with a thin film layer coated on the sensing side.

[0019] The term "monolithic substrate" refers to a solid piece of material having a uniform composition, such as glass, quartz, or plastic, with a single refractive index.

[0020] The term "waveguide" refers to a device designed to confine and direct the propagation of electromagnetic waves as light. One example of a waveguide is a flexible, transparent fiber made by drawing glass, plastic, or another transparent material to a small diameter (e.g., approximately the diameter of a human hair). Such a waveguide is commonly called an "optical fiber." Another example of a waveguide is a metal tube that transmits ultra-high frequency waves. A waveguide can also take the form of a duct or coaxial cable.

[0021] Introduction A. Overview of the interference detection system Several entities have developed interferometric sensing systems (also called "interferometers" or simply "systems") designed to perform biochemical tests. Figures 2A-2B show an example of such a system. Specifically, Figure 2A depicts an interferometer 200 that includes a light source 202, a detector 204, a waveguide 206, and an optical assembly 208 (also called a "probe"). The probe 208 may be connected to the waveguide 206 via a connection medium.

[0022] The light source 202 may emit light that is guided by the waveguide 206 toward the probe 208. For example, the light source 202 may be a light-emitting diode (LED) configured to generate light in the range of at least 50 nanometers (nm), 200 nm, or 150 nm within a given spectral range (e.g., below 400 nm to above 700 nm). Alternatively, the interferometer 200 may employ multiple light sources with different characteristic wavelengths, such as LEDs designed to emit light at different wavelengths in the visible range. The same function may be achieved by a single light source with suitable filters to direct light with different wavelengths onto the probe 208.

[0023] Detector 204 is preferably a spectrometer, such as an Ocean Optics USB4000, capable of recording the spectrum of the interference light received from probe 208. Alternatively, if light source 202 operates to direct different wavelengths onto probe 208, detector 204 may be a simple photodetector capable of recording the intensity at each wavelength. In another embodiment, detector 204 may include multiple filters that allow for detection of the intensity at each of multiple wavelengths.

[0024] Waveguide 206 may be configured to carry light emitted by light source 202 into probe 208 and then carry light reflected by surfaces within probe 208 to detector 204. In some embodiments, waveguide 206 is a bundle of optical fibers (e.g., single-mode fiber optic cable), while in other embodiments, waveguide 206 is a multi-mode fiber optic cable.

[0025] The probe 208 may include a monolithic substrate 214, a thin film layer (also referred to as an "interference layer"), and a biomolecule layer (also referred to as a "biolayer"), the biomolecule layer comprising analyte molecules 222 bound to analyte-binding molecules 220. The monolithic substrate 214 includes a transparent material through which light can travel. The interference layer also includes a transparent material. When light is directed at the probe 208, the proximal surface of the interference layer may function as a first reflective surface, and the biolayer may function as a second reflective surface. As described further below, the light reflected by the first and second reflective surfaces may form an interference pattern that may be monitored by the interferometer 200.

[0026] The interference layer typically includes multiple layers combined to improve the detectability of the interference pattern. In this specification, for example, the interference layer includes a tantalum pentoxide (TaO) layer 216 and a silicon dioxide (SiO) layer 218. The tantalum pentoxide layer 216 can be thin (e.g., about 10 nm to 40 nm) because its primary purpose is to improve the reflectivity at the proximal surface of the interference layer. Meanwhile, the silicon dioxide layer 218 can be relatively thick (e.g., about 650 nm to 900 nm) because its primary purpose is to increase the distance between the first and second reflecting surfaces.

[0027] To perform a biochemical test, the probe 208 can be suspended in a microwell 210 (or simply a "well") containing a sample 212. Analyte molecules 222 within the sample 212 bind to analyte-binding molecules 220 along the distal end of the probe 208 during the course of the biochemical test, and these binding events result in an interference pattern that can be observed by the detector 204. The interferometer 200 can monitor the thickness of the biolayer that forms along the distal end of the probe 208 by detecting a shift in the phase features of the interference pattern. As shown in FIG. 1B , the waveguide 206 can be directly connected to the probe 208 to eliminate any gap between the waveguide 206 and the probe 208. For example, in embodiments in which the waveguide 206 comprises an optical fiber, the proximal end of the probe 208 can be directly connected to the optical fiber. As described above, the interferometer 200 serves to monitor the interference pattern caused by light reflecting off the first and second reflective surfaces of the probe 208.

[0028] It should be noted that, for illustrative purposes, embodiments of the interferometric sensing system may be described in the context of a probe designed to be suspended in a solution containing a sample. However, one skilled in the art will recognize that these features are equally applicable to other sensing surfaces, such as flat surfaces (e.g., slides) on which a biolayer is formed by flowing a solution over the surface during biochemical testing.

[0029] B Probe overview 3 depicts an example probe 300, according to various embodiments. The probe 300 includes an interference layer 304 immobilized along the distal end of a monolithic substrate 302. Analyte-binding molecules 306 may be deposited along the distal surface of the interference layer 304. During biochemical testing, a biolayer is formed when analyte molecules 308 within a sample bind to the analyte-binding molecules 306.

[0030] As shown in FIG. 3 , the monolithic substrate 302 has a proximal surface that can be connected to, for example, a waveguide of an interferometer (also referred to as the “connection side”) and a distal surface on which additional layers are deposited (also referred to as the “sensing side”). Generally, the monolithic substrate 302 has a length of at least 3 millimeters (mm), 5 mm, 10 mm, or 15 mm. In preferred embodiments, the aspect ratio (length to width) of the monolithic substrate 302 is at least 5:1. In such embodiments, the monolithic substrate 302 can be described as having a columnar morphology. The cross-section of the monolithic substrate 302 can be circular, elliptical, square, rectangular, triangular, pentagonal, etc. The monolithic substrate 302 preferably has a refractive index substantially higher than that of the interference layer 304 so that the proximal surface of the interference layer 304 effectively reflects light directed onto the probe 300. A preferred refractive index of the monolithic substrate can be greater than 1.5, 1.8, or 2.0. Thus, the monolithic substrate 302 may comprise a high refractive index material such as glass (with a refractive index of 2.0), while some embodiments of the monolithic substrate 302 may comprise a low refractive index material such as quartz (with a refractive index of 1.46) or plastic (with a refractive index of 1.32 to 1.49).

[0031] The interference layer 304 includes at least one transparent material coated on the distal surface of the integral substrate 302. The transparent material is deposited on the distal surface of the integral substrate 302 in the form of a thin film ranging in thickness from a few tenths of a nanometer (e.g., a monolayer) to several micrometers. The interference layer 304 may have a thickness of at least 600 nm, 700 nm, or 900 nm. A preferred thickness is 600 nm to 5,000 nm (preferably 800 nm to 1,300 nm). In this specification, for example, the interference layer 304 has a thickness of approximately 900 nm to 1,000 nm or 940 nm.

[0032] In contrast to conventional probes, the interference layer 304 has a refractive index substantially similar to that of the biolayer. This ensures that reflection from the distal end of the probe 300 is primarily due to the analyte molecules 308 rather than the interface between the interference layer 304 and the analyte-binding molecules 306. Generally, the biolayer has a refractive index of about 1.36, although this can vary depending on the type of analyte-binding molecules (and therefore analyte molecules) along the distal end of the probe 300.

[0033] In some embodiments, the interference layer 304 comprises magnesium fluoride (MgF), while in other embodiments, the interference layer 304 comprises potassium fluoride (KF), lithium fluoride (LiF), sodium fluoride (NaF), lithium calcium aluminum fluoride (LiCaAlF), strontium fluoride (SrF), aluminum fluoride (AlF), sulfur hexafluoride (SF), or the like. Magnesium fluoride has a refractive index of 1.38, which is substantially identical to the refractive index of the biolayer formed along the distal end of the probe 300. For comparison, the interference layer of conventional probes typically comprises silicon dioxide, which has a refractive index of approximately 1.4 to 1.5 in the visible range. Because the interference layer 304 and the biolayer have similar refractive indices, light experiences minimal scattering when traveling from the interference layer 304 into the biolayer and then back from the biolayer into the interference layer 304.

[0034] During biochemical testing, the probe 300 can be suspended within a cavity (e.g., a well) containing a sample. During the biochemical testing process, as analyte molecules 308 bind to the analyte-binding molecules 306, a biolayer forms along the distal end of the probe 300. When light is applied to the probe 300, the proximal surface of the interference layer 304 can function as a first reflective surface, and the distal surface of the biolayer can function as a second reflective surface. The presence, concentration, or binding rate of the analyte molecules 308 to the probe 300 can be estimated based on the interference of light reflected by these two reflective surfaces. When the analyte molecules 308 attach to (or separate from) the analyte-binding molecules 306, the distance between the first and second reflective surfaces changes. Because the dimensions of all other components in the probe 300 remain the same, the interference pattern formed by the light reflected by the first and second reflective surfaces is phase-shifted according to the change in biolayer thickness due to the binding event.

[0035] In operation, an incident light signal 310 emitted by the light source is transported through the integral substrate 302 towards the biolayer. Within the probe 300, the light is reflected off a first reflective surface to produce a first reflected light signal 312. The light is also reflected off a second reflective surface to produce a second reflected light signal 314. The second reflective surface initially corresponds to the interface between the analyte binding molecules 306 and the sample in which the probe 300 is immersed. Once binding occurs during a biochemical test, the second reflective surface becomes the interface between the analyte molecules 308 and the sample.

[0036] The first and second reflected optical signals 312, 314 form a spectral interference pattern as shown in FIG. 5A. When an analyte molecule 308 binds to an analyte-binding molecule 306 at the distal surface of the interference layer 304, the optical path of the second reflected optical signal 314 lengthens. As a result, the spectral interference pattern shifts from T0 to T1, as shown in FIG. 5B. By continuously measuring the phase shift in real time, a dynamic binding curve can be plotted as the shift versus time. The binding rate of the analyte molecule to the analyte-binding molecule immobilized at the distal surface of the interference layer 304 can be used to calculate the analyte concentration in the sample. Thus, measuring the phase shift is the detection principle of thin-film interferometry.

[0037] Figure 4 depicts another example probe 400, according to various embodiments. The probe 400 of Figure 4 may be substantially similar to the probe 300 of Figure 3. However, here, the probe 400 includes an attachment layer 410 deposited along a distal surface of an interference layer 404 attached to an integral substrate 402. While the interference layer 404 is present in most embodiments, the attachment layer 404 is generally optional and, therefore, may be included only when greater attachment of the analyte-binding molecules 406 is desired or required.

[0038] The attachment layer 410 may include a material that promotes attachment of the analyte-binding molecules 406. One example of such a material is silicon dioxide. Because the attachment layer 410 is generally very thin compared to the interference layer 404, the attachment layer 410 has minimal effect on light traveling toward or returning from the biolayer. For example, the attachment layer 410 may have a thickness of approximately 3 nm to 10 nm, while the interference layer 404 may have a thickness of approximately 800 nm to 1,000 nm. The biolayer formed by the analyte-binding molecules 406 and the analyte molecules 408 typically has a thickness of several nanometers. Similar to the probe 300 of FIG. 3, the probe 400 of FIG. 4 may also have a reflective layer (not shown) deposited along the distal end of the integral substrate 402, such that the reflective layer is located between the integral substrate 402 and the interference layer 404. The thickness of the reflective layer may be approximately the same as the thickness of the attachment layer 410.

[0039] As noted above, these features are equally applicable to sensing surfaces having other forms. One example of such a sensing surface is a slide (also called a "chip") having a flat surface on which a biolayer is formed by flowing solutions over the flat surface during biochemical testing. Some example surfaces are described below with reference to Figures 6-7.

[0040] FIG. 6 depicts an example slide 600 according to various embodiments. The slide 600 includes a substrate 602 on which an interference layer 604 is deposited. In some embodiments, the interference layer 604 is deposited along the entire top surface of the substrate 602, while in other embodiments, the interference layer 604 is deposited along a portion of the top surface of the substrate 602. For example, the interference layer 604 may be deposited within a channel or well formed within the top surface of the substrate 602. As noted above, the height of the integral substrates 302, 402 in FIGS. 2-3 is generally much greater than its width. However, the opposite may be true herein. In fact, the width of the substrate 602 may be 5, 7.5, 10, or 20 times greater than its length. As an example, the substrate may be approximately 75 mm by 26 mm, with a height / thickness of approximately 1 mm.

[0041] During the biochemical testing process, analyte molecules 608 may bind to analyte-binding molecules 606 immobilized along the top surface of the interference layer 604 to form a biolayer. To determine the thickness of the biolayer, light may be applied to the top surface of the slide 600, as shown in FIG. 6 . More specifically, an incident light signal 20610 emitted by a light source may be detected in the biolayer formed along the top surface of the slide 600. This may require the incident light signal 20610 to travel through a surrounding medium 616, which may be a vacuum, air, or a solution. The incident light signal 20610 is reflected from a first reflective surface to produce a first reflected light signal 612. The first reflective surface may represent the interface between the biolayer and the surrounding medium 616. The incident light signal 20610 is also reflected from a second reflective surface to produce a second reflected light signal 614. The second reflective surface may represent the interface between the interference layer 604 and the substrate 602. As described above, the first and second reflected optical signals 612, 614 form a spectral interference pattern that can be analyzed to determine the thickness of the biolayer. Note that the incident optical signal 20610 is not transmitted through the substrate 602, so the substrate 602 can be either transparent or non-transparent (e.g., opaque).

[0042] Figure 7 depicts another example slide 700, according to various embodiments. Slide 700 of Figure 7 may be largely similar to slide 600 of Figure 6. Accordingly, slide 700 may include a substrate 702, with an interference layer 704 and analyte binding molecules 706 deposited on substrate 702. During the course of a biochemical test, analyte molecules 708 may bind to analyte binding molecules 706 to form a biolayer.

[0043] However, herein, incident light signal 710 is shown at the underside of slide 700. In operation, incident light signal 710 is transported through substrate 702 toward the biolayer. Within slide 700, the light is reflected off a first reflective surface to produce a first reflected light signal 712. The first reflective surface may represent the interface between interference layer 704 and substrate 702. The light is also reflected off a second reflective surface to produce a second reflected light signal 714. The second reflective surface may represent the interface between the biolayer and the surrounding medium 716. As described above, the first and second reflected light signals 712, 714 form a spectral interference pattern that can be analyzed to determine the thickness of the biolayer.

[0044] Although not shown in Figures 6-7, the slides 600, 700 may include a reflective layer disposed between the substrate 602, 702 and the interference layer 604, 704 to improve reflectivity along the interface therebetween, and / or an attachment layer disposed along the top surface of the interference layer 604, 704 to immobilize analytical binding molecules 606, 706.

[0045] Treatment of light absorption during interferometric testing. 8 depicts a process flow diagram 800 for processing the absorption of light by analyte molecules, the binding activity of which is monitored by an interferometric detection system. Typically, process 800 is performed by a computer program running in the interferometric detection system. Thus, process 800 may be implemented through the execution of corresponding instructions by a processor included in the interferometric detection system.

[0046] Alternatively, process 800 may be performed by a computer program executed by a processor located external to the interferometric sensing system. This implementation is further described below with reference to Figures 10-11. This "external processor" may be included in a computing device that is communicatively connectable to the interferometric sensing system. Examples of computing devices include mobile phones, tablet computers, laptop computers, and network-accessible server systems comprising one or more computer servers.

[0047] Initially, the computer program may acquire a dataset generated by an interferometric sensing system (step 801). Specifically, the dataset is generated by a detector of the interferometric sensing system; therefore, the data contained therein may be referred to as "detector data" or "scope data." The detector may measure intensity across a set of wavelengths at a given time, as shown in FIG. 5A. This set of measurements may be referred to as a "signal" or "frame." Because biolayer growth along the surface of the interferometric sensor is generally evidenced by a phase shift in the optical interference pattern, the detector may generate frames corresponding to different time points. These frames may be generated by the detector continuously or periodically.

[0048] In some embodiments, the data set acquired by the computer program includes measurements associated with multiple frames. For example, the measurements may be acquired in batches, where each batch includes measurements generated for at least two frames to conserve computational resources. Thus, the computer program may periodically acquire measurements generated by the detector. In other embodiments, the measurements are acquired in real time by the computer program, for example, in the form of a signal waveform (or simply "signal" or "waveform") representing a successive order of discrete values ​​corresponding to different wavelengths. Thus, the computer program may incrementally acquire the measurements as they are generated by the detector.

[0049] In some embodiments, the computer program applies a moving average filter to the data set to smooth the values ​​of the data set (step 802). Specifically, the computer program may generate a moving average filter by selecting a boxcar function as the filter's impulse response, and then the computer program may apply the moving average filter to the data set to calculate a moving average of the measurements. The term "moving average" refers to a calculation that analyzes data points, i.e., measurements associated with a given frame, by creating a series of averages for different subsets of the data points. At a high level, the goal may be to generate a form equivalent to the signal shown in FIG. 5A to allow for easier determination of phase shifts, so calculating a moving average may help smooth short-term fluctuations and highlight long-term trends.

[0050] The computer program may then generate a difference curve by comparing, for at least some of the "frames," the corresponding measurements with the measurements in the "reference frame" (step 803). Thus, the computer program may generate a series of difference curves, each representing the difference between the corresponding frame and the "reference frame." The "reference frame" may be defined as the beginning of the biochemical test, but it need not be the first frame generated by the detector. For example, the "reference frame" may be the first frame generated following the conclusion of an initiation phase that allows the signal to become less noisy. The initiation phase may last for a predetermined number of frames (e.g., 100 frames, 500 frames, 1,000 frames, or 2,500 frames), or the initiation phase may continue until a determination is made by the computer program, for example through analysis of the frames, that the noise is sufficiently low. Alternatively, the initiation phase may last for a predetermined amount of time (e.g., 0.25 seconds, 0.50 seconds, 1.00 seconds, or 2.50 seconds). At a high level, a "frame of reference" may represent the beginning of biochemical testing, where minimal binding is assumed to have occurred.

[0051] For each difference curve in the series of difference curves, the computer program may then calculate an absorption ratio based on an analysis of that difference curve (step 804). To accomplish this, the computer program may decompose the difference curve into its antisymmetric and asymmetric components, as described further below. The asymmetric component may represent the absorption component, while the antisymmetric component may represent the reflection component. To calculate the absorption ratio, the computer program may compare the absorption component to the overall measurable signal as follows:

number

[0052] The computer program may then compare the absorption ratio to a threshold to determine, for each frame, whether the predominant component is an absorbing component or a reflecting component (step 805). As an example, the computer program may determine whether the absorption ratio indicates that the absorbing component is at least 50 percent of the overall measurable signal.

[0053] In the event that a given absorption ratio is less than a threshold, the computer program may determine that the main component is a reflection component representing a phase shift. In such a scenario, the computer program may employ a first algorithm to calculate appropriate values ​​for the binding curve, such that the absolute value of the phase shift is the magnitude of the binding (step 806). Thus, the first algorithm may calculate the binding curve in a conventional manner.

[0054] In the event that a given absorption ratio is greater than a threshold, the computer program may determine that the major component is the absorbing component. In such a scenario, the computer program may employ a second algorithm that calculates an appropriate value for the binding curve based on the asymmetric component of the difference curve (step 807).

[0055] The computer program may then display the magnitude of the binding in a plot viewable in the interface (step 808). Generally, the interface is presented by and viewed on an interferometric sensing system. However, the interface may be presented by and viewed on another computing device. The computer program may be running on the other computing device, or the computer program may transmit the necessary data for display to the other computing device. Examples of computing devices include mobile phones, tablet computers, laptop computers, and the like.

[0056] Note that in some embodiments, these calculations are performed frame-by-frame to maximize accuracy. In other embodiments, these calculations may be performed on a set of frames to conserve computational resources or calculate the binding curve more quickly. Execution of process 800 may result in the generation of two outputs. The first output is the ratio of the absorption and reflection components in the measurable signal, as determined based on a difference curve calculated for the current frame relative to the reference frame. The second output is a quantitative magnitude of absorption related to the amount of binding in the interferometric sensor. As described above, the computer program may determine whether the predominant component of the measurable signal is the absorption component or the reflection component. The computer program may do this for a set of frames as part of the experimental run, rather than frame-by-frame. For example, the computer program may determine whether the absorption or reflection component is the predominant component over 25, 50, 100, or 500 frames. The binding curve, or more specifically, the magnitude of binding over time, may then be calculated based on the resolved principal components for the measured signal.

[0057] Additional information on absorption calculations For illustrative purposes, aspects of the process described above with reference to Figure 8 are further described below. This illustrative example is not intended to limit the process in any way.

[0058] One of the central responsibilities of the computer program is to calculate the difference curve for the frames produced by the detector. As mentioned above, the difference curve is calculated as follows: Magnitude n (i)=Frame n (i)-Frame Ref (i) can be calculated through analysis of two frames, i.e., a frame of interest and a reference frame, as in Equation 2, where i ranges from 1 to an integer N (e.g., 3,648) representing the resolution of the detector, and n is the index of the frame of interest. Note that since the detector is typically a spectrometer, the term "frame" can be used interchangeably with the term "scope." Thus, Equation 2 can be calculated as follows: Magnitude n (i)=Scope n (i)-Scope Ref (i) It can also be written as Equation 3.

[0059] The computer program can then decompose the difference curve, which is generally sinusoidal in shape, into its antisymmetric and asymmetric components. The antisymmetric component represents the phase shift due to reflection, while the asymmetric component represents absorption. Using these components, the computer program can obtain the ratio of absorption and phase shift in terms of the measured signal. Figure 9 shows how, through analysis of the difference curve, the magnitude of the full signal, denoted using R1, and half the magnitude of the antisymmetric signal, denoted using R2, can be determined. In Figure 9, the x-axis units are pixels representing wavelength, where one pixel is approximately equal to 0.05 nm to 0.08 nm, while the y-axis units represent intensity. Each magnitude along the y-axis represents the relative intensity of the corresponding wavelength. After determining R1 and R2, the absorption ratio and phase shift ratio can be calculated as follows:

number

[0060] These operations can be performed repeatedly by a computer program on a set of frames as part of an experimental run. For example, the computer program can evaluate 50, 100, or 250 frames after the initiation phase of a reaction is complete to avoid noise. The computer program can then compare the average absorption ratio calculated for the frames within the window of interest with a threshold. If the average absorption ratio (AAR) is greater than the threshold, the computer program can then assign the experimental run to an absorption category, as shown below.

number

[0061] In the event that an experimental run is assigned to the absorption category, the computer program can calculate the magnitude of binding at a given time point based on the asymmetric component in the difference curve, which indicates the difference between the corresponding frame and the reference frame. The reference frame can be, for example, the first frame generated as part of the experimental run. Referring again to Figure 9, the magnitude of binding can be calculated as follows:

number

[0062] In the event that an experimental run is assigned to the phase shift category (also called the "reflection category"), the computer program can calculate a cross-correlation value to determine the magnitude of coupling. At a high level, the cross-correlation captures the wavelength shift along the x-axis for a given measurement by monitoring the peak of the sinusoid.

[0063] Overview of the central host computer program As noted above, aspects of the techniques incorporated herein may be implemented by a computer program executed by an interferometric sensing system. As noted above, the computer program may alternatively be executed by a processor located external to the interferometric sensing system. This "external processor" may be included in a computing device communicatively connectable to the interferometric sensing system. Whether the computer program is internal or external to the interferometric sensing system, the computer program may be part of an interferometric analysis platform (or simply "analysis platform"). In addition to processing data generated by the interferometric sensing system, the analysis platform may be responsible for facilitating the design, execution, or recording of biochemical tests.

[0064] 10 illustrates a network environment 1000 that includes an analytical platform 1002 executed by a computing device 1004. An individual (also referred to as a "user") may be able to interact with the analytical platform 1002 via an interface 1006. For example, a user may be able to access an interface in which characteristics of a biochemical test (e.g., type of analyte binding molecule or molecule, runtime, reactants) are specified. As another example, a user may be able to access an interface in which data generated by an interferometric sensing system, or an analysis of that data, may be viewed.

[0065] 10, analytics platform 1002 may reside in network environment 1000. Accordingly, computing device 1004 on which analytics platform 1002 resides may be connected to one or more networks 1008A-1008B. Depending on its nature, computing device 1004 may be connected to a personal area network (PAN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), or a cellular network. For example, if computing device 1004 is a computer server, computing device 1004 may be accessible to users via a respective computing device connected to the Internet via a LAN.

[0066] The interface 1006 may be accessible via a web browser, a desktop application, or a mobile application. For example, to interact with the analytical platform 1002, a user may start a web browser on the computing device 1004 and then navigate to a web address associated with the analytical platform 1002. As another example, a user may access an interface generated by the analytical platform 1002 via a desktop application, through which the user may select data for analysis, review analyses of the data, and the like. Thus, the interface generated by the analytical platform 1002 may be accessible to a variety of computing devices, including mobile phones, tablet computers, desktop computers, and the like. The interface generated by the analytical platform 1002 may even be accessible by the interferometric sensing system responsible for generating the data. In such an embodiment, the interferometric sensing system may transmit data generated by the analytical platform 1002 in the course of biochemical testing to another computing device for processing, and the analytical platform 1002 may then transmit the processed data or an analysis of the processed data to the interferometric sensing system for display.

[0067] Generally, the analytical platform 1002 is executed by a cloud computing service operated by, for example, Amazon Web Services®, Google Cloud Platform™, or Microsoft Azure®. Accordingly, the computing device 1004 may represent a computer server that is part of the server system 1010. Often, the server system 1010 comprises multiple computer servers. These computer servers may include different types of data (e.g., information about patients, such as demographic information and health information), algorithms that process, present, and analyze the data, and other assets. Those skilled in the art will recognize that this data may also be distributed between the server system 1010 and the computing devices. For example, sensitive information associated with a patient whose sample is being tested may be stored in and initially processed by the interferometric sensing system, so that the sensitive information is obfuscated or removed before the data is transmitted to the server system 1010 for further processing.

[0068] As mentioned above, aspects of the analysis platform 1002 may be hosted locally, e.g., in the form of a computer program running on an interferometric sensing system, a mobile phone, a laptop computer, or a desktop computer. Several different versions of the analysis platform 1002 may be available depending on the intended use. For example, imagine that a user wishes to actively conduct or record a biochemical test for which data is generated by an interferometric sensing system. In such a scenario, the computer program may allow selection or specification of the patient, the type of biochemical test, the length of different test steps, the type of reactant, the type of analyte binding molecule, the type of analyte molecule, etc. Alternatively, if the user is simply interested in reviewing the analysis of the data generated by the interferometric sensing system, the analysis platform 1002 may be "simpler."

[0069] 11 depicts an example communication environment 1100 including an analytical platform 1102 configured to acquire data from one or more sources. Herein, the analytical platform 1102 may receive data from an interferometric sensing system 1106, a laptop computer 1104, or a network-accessible server system 1110 (collectively referred to as "network devices"). For example, the analytical platform 1102 may acquire data from the interferometric sensing system 1106 generated by a detector (e.g., a spectrometer) of the interferometric sensing system 1106 during the course of a biochemical test, and may acquire information about the biochemical test or the corresponding patient from the network-accessible server system 1110 or the laptop computer 1108. It should be noted that the analytical platform 1102 may, and often does, acquire data from multiple interferometric sensing systems. For example, the analytical platform 1102 may acquire data from interferometric sensing systems located in different geographic locations (e.g., different medical facilities, research facilities, etc.).

[0070] Network devices may be connected to the analysis platform 1102 via one or more networks 1104A-1104C. The networks 1104A-1104C may include a PAN, a LAN, a WAN, a MAN, a cellular network, the Internet, etc. Additionally or alternatively, the network devices may communicate with each other over short-range wireless connection technologies. For example, if the analysis platform 1102 resides on a network-accessible server system 1110, data received from the network-accessible server system 1110 need not traverse any network. However, the network-accessible server system 1110 may be connected to the interference-based detection system 1106 and the laptop computer 1108 via separate Wi-Fi communication channels. As another example, if the analysis platform 1102 resides on the interference-based detection system 1106, data generated by the interference-based detection system 1106 need not traverse any network. However, the interferometric detection system 1106 may be connected to the network-accessible server system 1110 via a Wi-Fi communication channel and connected to the laptop computer 1108 via a short-range communication channel defined in accordance with a Bluetooth® communication protocol, a Wi-Fi Direct® communication protocol, a near-field communication (NFC) communication protocol, or the like.

[0071] Embodiments of the communication environment 1100 may include a subset of network devices. For example, some embodiments of the communication environment 1100 include an analysis platform 1102 that receives data from an interferometric sensing system 1106 and receives additional data from a network-accessible server system 1110 on which the analysis platform 1102 resides. In such embodiments, a user may be able to interact with the analysis platform 1102 via a display and corresponding control device that is part of or connected to the interferometric sensing system 1106. As another example, some embodiments of the communication environment 1100 include an analysis platform 1102 that receives data from a series of interferometric sensing systems located in different environments (e.g., different clinics, research facilities, testing facilities, etc.).

[0072] Processing System 12 is a block diagram illustrating an example processing system 1200 in which at least some of the operations described herein may be implemented. For example, components of processing system 1200 may be hosted in an interferometric sensing system, or components of processing system 1200 may be hosted in a computing device that may be communicatively connected to the interferometric sensing system, or in a storage medium on which data generated by the interferometric sensing system is at least temporarily stored.

[0073] The processing system 1200 may include a processor 1202, a main memory 1206, a non-volatile memory 1210, a network adapter 1212, a video display 1218, input / output devices 1220, a control device 1222 (e.g., a keyboard or pointing device), a drive unit 1224 including a storage medium 1226, and a signal generating device 1230, which are communicatively coupled to a bus 1216. The bus 1216 is shown as an abstraction representing one or more physical buses or point-to-point connections connected by appropriate bridges, adapters, or controllers. Thus, the bus 1216 may be any of a variety of buses, including a system bus, a Peripheral Component Interconnect (PCI) bus or PCI Express bus, a HyperTransport bus, an Industry Standard Architecture (ISA) bus, a Small Computer System Interface (SCSI) bus, a Universal Serial Bus (USB), an Inter-Integrated Circuit (I-IC), a Serial Bus (SPI), a Serial Interface (SI), a Serial Communication Interface (SPI), a Serial Data Interface (SCI), a Serial Communication Protocol (SCI ... 2 C) bus, or the Institute of Electrical and Electronics Engineers (IEEE) Standard 1394 bus (also known as "Firewire").

[0074] Although main memory 1206, non-volatile memory 1210, and storage medium 1226 are shown as a single medium, the terms "machine-readable medium" and "storage medium" should be interpreted to include a single medium or multiple media (e.g., a centralized / distributed database and / or associated caches and servers) that store one or more sets of instructions 1228. The terms "machine-readable medium" and "storage medium" should also be interpreted to include any medium capable of storing, encoding, or carrying a set of instructions for execution by processing system 1200.

[0075] Generally, the routines executed to implement embodiments of the present disclosure may be implemented as part of an operating system or a specific application, component, program, object, module, or sequence of instructions (collectively referred to as a "computer program"). A computer program typically comprises one or more instructions (e.g., instructions 1204, 1208, 1228) that are configured at different times in various memory and storage devices within a computing device. When read and executed by processor 1202, the instructions cause processing system 1200 to perform operations that implement elements including various aspects of the present disclosure.

[0076] Further examples of machine-readable and computer-readable media include recordable-type media such as volatile and non-volatile memory devices 1210, removable disks, hard disk drives, and optical disks (e.g., compact disk read-only memories (CD-ROMs) and digital versatile disks (DVDs)), and transmission-type media such as digital and analog communications links.

[0077] Network adapter 1212 enables processing system 1200 to broker data over network 1214 with entities external to processing system 1200 through any communication protocol supported by processing system 1200 and the external entities. Network adapter 1212 may include a network adapter card, a wireless network interface card, a router, an access point, a wireless router, a switch, a multi-layer switch, a protocol converter, a gateway, a bridge, a bridge router, a hub, a digital media receiver, a repeater, or any combination thereof.

[0078] Example Example 1. Data output for CXCR4 lipoparticles

[0079] Streptavidin-coated probes were functionalized with 50 μg / mL biotin-WGA lectin (Vector Laboratories B-1025-5) in PBS. CXCR4 lipoparticles (Integral Molecular LEV-101) were then added to the WGA-functionalized probes at 8 μg / mL for 30 minutes. After a brief wash step, the CXCR4-loaded probes were exposed to 15 nM CXCR4 antibody (R&D Systems MAB170) for 5 minutes before a 5-minute dissociation step. Binding data were collected on a GatorPrime (Gator Bio Inc.) instrument, and results were processed using CC or a novel absorption algorithm. The results are shown in Figure 13.

[0080] Figure 13 includes a plot with two binding curves calculated for the lipoparticle addition phase of the aforementioned experiment. These binding curves include a first binding curve calculated entirely using a conventional algorithm and a second binding curve calculated using an absorption algorithm. As can be seen in Figure 13, the second binding curve more clearly shows a continued increase in the biolayer, while the first binding curve appears to indicate a decrease in biolayer thickness.

[0081] Figure 13 also includes a plot with two binding curves calculated for the association and dissociation phases of the CXCR4 antibody. Again, these binding curves include a first binding curve calculated entirely using a conventional algorithm and a second binding curve calculated using an absorption algorithm. As can be seen in Figure 13, the second binding curve more clearly shows the growth and stabilization of the biolayer, while the first binding curve appears to show a continued decrease in biolayer thickness (initially more rapidly, then more slowly).

[0082] Example 2. Data output for CD20 lipoparticles

[0083] Streptavidin-coated probes were functionalized with 50 μg / mL biotin-WGA lectin (Vector Laboratories B-1025-5) in PBS. CD20 lipoparticles (Integral Molecular LEV-103) were then added to the WGA-functionalized probes at 20 μg / mL for 30 minutes. After a brief wash step, CXCR4-loaded probes were exposed to 100 nM CD20 antibody (R&D Systems MAB4225) for 5 minutes before a 5-minute dissociation step. Binding data were collected on a GatorPrime (Gator Bio Inc.) instrument, and results were processed using CC or a novel absorption algorithm. Results are shown in Figure 14.

[0084] Figure 14 includes a plot showing two binding curves calculated for the lipoparticle addition step of the aforementioned experiment. These binding curves include a first binding curve calculated entirely using a conventional algorithm and a second binding curve calculated using an absorption algorithm. As can be seen in Figure 14, the second binding curve shows a greater increase in biolayer than detected by the conventional algorithm.

[0085] Figure 14 also includes a plot with two binding curves calculated for the binding and dissociation phases of the CD20 antibody. Again, these binding curves include a first binding curve calculated entirely using a conventional algorithm and a second binding curve calculated using an absorption algorithm. As can be seen in Figure 14, the second binding curve more clearly shows the binding and dissociation leading to a change in biolayer thickness, compared to the first binding curve, which simply shows a nearly constant increase followed by a rapid collapse of the biolayer.

[0086] remarks The foregoing description of various embodiments of the technology has been provided for purposes of illustration and description and is not intended to be exhaustive or to limit the claimed subject matter to the precise form disclosed.

[0087] Many modifications and variations will be apparent to those skilled in the art. The embodiments have been chosen and described to best explain the principles of the technology and its practical application, thereby enabling others skilled in the art to understand the claimed subject matter, various embodiments, and various modifications that are suitable for the particular applications contemplated.

Claims

1. 1. A method implemented by a computer program executed in an interferometric detection system for measuring binding of analyte molecules in a liquid sample to a probe, the method comprising: obtaining a data set representing a sequential order of signals, each signal represents a series of values ​​indicative of the intensity of light across a series of wavelengths at a corresponding time, said light being received from said probe suspended in said liquid sample; For each of said signals, generating a difference curve by comparing the corresponding series of values ​​to another series of values ​​associated with a reference signal; calculating an absorption ratio based on analysis of the difference curve; determining whether a predominant component of the signal is reflection or absorption based on the absorption ratio; In response to determining that the primary component is reflectance, calculating a magnitude of coupling having the absolute value of said signal as said magnitude of coupling; In response to determining that the primary component is absorption, calculating a magnitude of binding based on the asymmetric component of the difference curve; displaying the calculated binding magnitude for the signal on a plot viewable by the interferometric detection system; and A method comprising:

2. 2. The method of claim 1, wherein the reference signal is an initial signal generated following a start-up phase that enables measurements generated by the interferometric sensing system to be less noisy.

3. The method of claim 1 , wherein the reference signal is generated by the interferometric sensing system immediately before the signal included in the data set.

4. The method of claim 1 , further comprising decomposing the difference curve into (i) an antisymmetric component corresponding to a phase shift and (ii) the asymmetric component corresponding to absorption.

5. To calculate the magnitude of the binding when the main component is absorption, (i) dividing the asymmetric component by an average of pixels of at least a portion of a frame of the signal to produce a less noisy asymmetric component; (ii) the less noisy asymmetric component route is used; The method of claim 4, wherein (iii) the root of the less noisy asymmetric component is multiplied by a coefficient.

6. The method of claim 1 , further comprising applying a moving average filter to the data set to calculate a moving average of each of the signals.

7. The method of claim 1 , wherein the generating, calculating, and determining occur as the signals are acquired so that the magnitude of the binding is calculated in real time.

8. A non-transitory medium having stored thereon instructions that, when executed by a processor, cause the processor to perform operations, the operations including: acquiring a data set comprising (i) a reference signal and (ii) a series of signals generated after the reference signal, each signal included in the data set is generated by an interferometric detection system that measures light received from a probe suspended in a liquid sample; comparing each of said signals to said reference signal to generate a series of difference curves; For each of said signals, calculating an absorption ratio based on an analysis of the corresponding difference curve; determining whether a predominant component of the signal is reflection or absorption based on the absorption ratio; calculating the magnitude of the bond based on the principal components; Non-transitory media, including

9. The non-transitory medium of claim 8 , wherein the processor is part of the interferometric sensing system.

10. The operation is calculating an average absorption ratio for the series of signals based on the absorption ratio calculated for each of the signals; comparing the average absorption ratio to a threshold; In response to determining that the average absorption ratio is greater than the threshold value, assigning the set of signals to absorption categories; The non-transitory medium of claim 8 , further comprising:

11. The calculation is The non-transitory medium of claim 10 , comprising determining the magnitude of the coupling for each of the signals based on an asymmetric component in the corresponding difference curve.

12. The operation is calculating an average absorption ratio for the series of signals based on the absorption ratio calculated for each of the signals; comparing the average absorption ratio to a threshold; In response to determining that the average absorption ratio is less than the threshold value, assigning the set of signals to reflection categories; The non-transitory medium of claim 8 , further comprising:

13. The calculation is The non-transitory medium of claim 12 , comprising determining the magnitude of the combination for each of the signals by calculating a cross-correlation value.

14. The operation is 10. The non-transitory medium of claim 8, further comprising recording the magnitude of the coupling calculated for the series of signals in a plot viewable in an interface.

15. The non-transitory medium of claim 14 , wherein the interface is visible to the interferometric sensing system.

Images