WO2020005768A1 - Système de biodétection d'essaim plasmonique et procédés d'utilisation associés - Google Patents

Système de biodétection d'essaim plasmonique et procédés d'utilisation associés Download PDF

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Publication number
WO2020005768A1
WO2020005768A1 PCT/US2019/038539 US2019038539W WO2020005768A1 WO 2020005768 A1 WO2020005768 A1 WO 2020005768A1 US 2019038539 W US2019038539 W US 2019038539W WO 2020005768 A1 WO2020005768 A1 WO 2020005768A1
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Prior art keywords
nanoparticles
plasmonic nanoparticles
hue
analyte
plasmonic
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PCT/US2019/038539
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English (en)
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Dino Di Carlo
Mengxing OUYANG
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The Regents Of The University Of California
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Publication of WO2020005768A1 publication Critical patent/WO2020005768A1/fr

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B82—NANOTECHNOLOGY
    • B82Y—SPECIFIC USES OR APPLICATIONS OF NANOSTRUCTURES; MEASUREMENT OR ANALYSIS OF NANOSTRUCTURES; MANUFACTURE OR TREATMENT OF NANOSTRUCTURES
    • B82Y30/00—Nanotechnology for materials or surface science, e.g. nanocomposites
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/543—Immunoassay; Biospecific binding assay; Materials therefor with an insoluble carrier for immobilising immunochemicals
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/543—Immunoassay; Biospecific binding assay; Materials therefor with an insoluble carrier for immobilising immunochemicals
    • G01N33/54313—Immunoassay; Biospecific binding assay; Materials therefor with an insoluble carrier for immobilising immunochemicals the carrier being characterised by its particulate form
    • G01N33/54346—Nanoparticles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/543—Immunoassay; Biospecific binding assay; Materials therefor with an insoluble carrier for immobilising immunochemicals
    • G01N33/54366—Apparatus specially adapted for solid-phase testing
    • G01N33/54373—Apparatus specially adapted for solid-phase testing involving physiochemical end-point determination, e.g. wave-guides, FETS, gratings

Definitions

  • the technical field generally relates to systems and methods that utilize localized surface plasmon resonance (LSPR) which measures the collective electron charge oscillations in metallic nanoparticles excited by light. More specifically, the technical field relates to a system and method that examines the hue shift of large numbers of individual nanoparticles that result from the binding/interaction with a target chemical species or analyte. The system uses a dark field imaging setup to detect the location of individual nanoparticles and analyze the respective hue shifts in response to exposure to an analyte.
  • LSPR localized surface plasmon resonance
  • Nanoplasmonic sensors have become a promising solution for sensing of bio analytes. Due to strong light localization, these nanosensors are sensitive to even a few molecules that when bound to their surface perturb the electromagnetic field distribution. Indeed, detection sensitivity down to a single molecule level has been demonstrated. Despite the significant progress in the development of nanoplasmonic biosensors, few of these approaches have yet to reach commercial products for point-of-care diagnostics. The main hurdles are associated with achieving both compact instrumentation and robust sensing performance for practical use. In general, biosensing measurements are affected by variability in surface properties in different devices, the imperfections in manufacture of sensing elements, biological noise such as non-specific binding, and measurement noise introduced by the readout system, which together lead to systematic and random errors. For biosensing applications from clinical samples, the sensor performance is further negatively affected by the matrix effect in biofluids, causing reduced target binding and elevated background noise.
  • nanoplasmonic sensors can also have variable readouts. Because the nanoplasmonic effect is a near-field phenomenon, only binding events that occur at tens of nanometers around the sensor surface will affect the sensor signal. Therefore, the matrix effect and bulk background noise is greatly suppressed. However, these nanosensors (with critical dimensions from 40-100 nm) have limited binding sites on their surface (typically 100-1000 binding sites). As a result, when the analyte concentration is much lower than the affinity of the capture antibody being used, the dynamic binding process leads only to a small fraction of bound analyte-antibody states.
  • a swarm biosensing platform is disclosed with an alternative detection scheme, where the output from each single nanoparticle“sensor” is collected and compared to its initial state (i.e., before and after comparisons are made).
  • the distribution of these individual sensor“votes” from multiple nanoparticle sensors can then be automatically characterized by image processing software to determine a reliable sensing result with high statistical probability to represent its true value.
  • the sensing substrate is composed of a multitude of single plasmonic nanoparticles spread over an optically transparent surface which can be individually interrogated.
  • a color camera is used to monitor the hue change of the individual nanoparticles by extracting the RGB information for each nanoparticle from dark field images which is then converted to hue values.
  • a few hundred to over a thousand single nanoparticles are interrogated within one imaging field of view (FOV) (172 pm x 122 pm), leading to thousands of single nanoparticles per sample, enabling statistical analysis of sensing outputs from a large number of sensors for each device.
  • FOV imaging field of view
  • detector nanoparticles were also used to amplify the color or hue shift, which has proven to yield 3 to 7-fold enhanced spectral shift in conventional spectrum-based detection.
  • This signal amplification permits the platform to achieve good sensitivity ( ⁇ pM) without the need of recovering the spectrum information, thus keeping the setup compact and low-cost.
  • the swarm sensing platform exhibited a large dynamic range (> 4 orders of magnitude), and high quantitation ability to reliably differentiate three clinically relevant c-reactive protein (CRP) concentrations (in a narrow range of 1-10 pg/ml) in human serum.
  • CRP c-reactive protein
  • a system for determining the presence and/or concentration of an analyte in a sample includes an optically transparent substrate having a plurality of plasmonic nanoparticles immobilized on a surface thereof.
  • the system includes a dark field imaging device configured to acquire color images of the optically transparent substrate before and after exposure to the analyte and image processing software configured to define regions of interest (ROIs) in the acquired color images, the ROIs representing individual plasmonic nanoparticles, wherein the image processing software is further configured to extract color intensity values from one or more color channels from each ROI to calculate a hue value for individual plasmonic particles before and after exposure to the analyte, wherein the image processing software calculates a delta hue value from the respective before and after hue values for the individual plasmonic nanoparticles and wherein the presence and/or concentration of the analyte is based on the delta hue values of the plasmonic nanoparticles.
  • ROIs regions of interest
  • a method for determining the presence and/or concentration of an analyte in a sample includes providing an optically transparent substrate having a plurality of plasmonic nanoparticles immobilized on a surface thereof; imaging the optically transparent substrate before the sample has been applied with a dark field imaging device configured to acquire one or more before color images of the optically transparent substrate; loading the optically transparent substrate with the sample; imaging the optically transparent substrate after the sample has been applied with a dark field imaging device configured to acquire one or more after color images of the optically transparent substrate; and subjecting the respective before and after color images to image processing software configured to define regions of interest (ROIs) in the respective before and after color images, the ROIs representing individual plasmonic nanoparticles, wherein the image processing software is further configured to extract color intensity values from one or more color channels from each ROI to calculate a hue value for individual plasmonic particles in the respective before and after color images, wherein the image processing software calculates a delta hue value from the respective before and after hue values for the individual
  • ROIs regions of interest
  • concentration of an antibody in a sample includes an optically transparent substrate having a plurality of plasmonic nanoparticles immobilized on a surface thereof and conjugated to an analyte specific for the antibody.
  • the system further includes a dark field imaging device configured to acquire color images of the optically transparent substrate before and after exposure to the antibody and image processing software configured to define regions of interest (ROIs) in the acquired color images, the ROIs representing individual plasmonic nanoparticles, wherein the image processing software is further configured to extract color intensity values from one or more color channels from each ROI to calculate a hue value for individual plasmonic particles before and after exposure to the antibody, wherein the image processing software calculates a delta hue value from the respective before and after hue values for the individual plasmonic nanoparticles and wherein the presence and/or concentration of the antibody is based on the delta hue values of the plasmonic nanoparticles.
  • ROIs regions of interest
  • FIG. 1 schematically illustrates a system for determining the presence and/or concentration of a particular chemical species or analyte in a sample according to one embodiment.
  • FIGS. 2A and 2B illustrate UV-Vis results of direct binding.
  • a series of anti-BSA (200 mM) solutions were added to the cuvette containing 500 pL of gold nanoparticle (AuNP) solution sequentially at 2 min intervals, followed by an immediate spectral measurement after each addition.
  • the volume of anti-BSA added was 1 pL over 5 times, and lastly 5 pL for the binding to reach saturation.
  • FIG. 2A illustrates the normalized absorbance spectrums.
  • FIG. 2B illustrates peak wavelength and absorbance of bare AuNPs, conjugated AuNPs, and each step during direct binding. Slight decreases in peak absorbance after conjugation and final washing steps at the end of direct binding experiments
  • FIG. 3 illustrates a schematic of the system for determining the presence and/or concentration of a particular chemical species in a sample that uses a plurality or swarm of single nanoparticle colorimetric sensors.
  • the spectral shifts due to the binding of sandwiched AuNP pairs correlates with a detectable hue shift of the individual nanoparticles using a color image sensor.
  • Analyte detection is performed as follows: Capture AuNPs conjugated with analyte-specific ligand are first immobilized and a“before” image is taken to record each individual sensor’s initial hue. Next, target analyte and detector AuNPs were added sequentially, incubated on-chip, and washed.
  • FIG. 4 illustrates a sample image of region of interest (ROI) selection using an automated imaging algorithm performed by image processing software. Circle outlines represent the ROIs selected.
  • ROI region of interest
  • FIG. 5 illustrates a table showing screening candidates for capture gold nanoparticles.
  • FIG. 6 illustrates a table showing screening candidates for detector gold nanoparticles.
  • FIG. 7 illustrates simulation of delta hue changes with number of detector probes (lOnm AuNPs) bound to a capture probe (lOOnm AuNP). Interparticle distances of 5, 10 and l5nm were used. The simulation assumed that only a single layer of detector probes was formed, and there was no overlap between adjacent detector probes. The maximum number of lOnm AuNPs attached to a lOOnm AuNP probe was estimated to be 107, 75, and 57 if the antibody conjugation layer was 5, 10 and l5nm thick respectively.
  • FIGS. 8A and 8B illustrate the evaluation of HSV components besides hue— saturation and value using CRP detection in buffer solution (FIG. 8A) and human serum (FIG. 8B).
  • FIGS. 9A and 9B illustrate respective graphs of particle fraction as a function of RGB intensity shifts (FIG. 9A) and hue shifts (FIG. 9B) due to conjugation, direct binding and detector AuNP binding in a colloidal state.
  • Different nanoparticle states and controls are illustrated on the right side of FIGS. 9A and 9B.
  • AuNP solutions after each step were added onto a glass coverslip, dried, and then imaged in darkfield. Histograms of hue for individual particles show significant changes in hue are visible.
  • the capture probe and detector probe used in this experiment were AuNPs with 100 and 20 nm diameter.
  • FIG. 10A illustrates a histogram of particle fraction as a function of delta hue for different concentrations of anti-BSA.
  • FIG. 10B illustrates a graph of mean delta hue as a function of anti-BSA concentration.
  • DI water was added instead of antibody solution, followed by detector AuNPs.
  • FIG. 10C illustrates a histogram of particle fraction as a function of delta hue for different concentrations of CRP.
  • FIG. 10D illustrates a graph of mean delta hue as a function of CRP concentration.
  • DI water was added instead of antibody solution, followed by detector AuNPs.
  • sample size n > 1100 single nanoparticles.
  • FIG. 11 A illustrates stacked histograms of particle fraction as a function of delta hue at different CRP concentrations. Stacked histograms are pooled from three separate experiments, with each shading representing a different experiment.
  • FIG. 11B illustrates a graph of mean delta hue as a function of CRP concentration.
  • the error bars represent the standard deviation of the mean value of the nanoparticle swarm from three devices.
  • CRP-free serum was added instead of CRP antibody solution, followed by detector AuNPs. >-values were determined by two-tailed student /-tests. * p ⁇ 0.05, ** p ⁇ 0.01. For all groups, sample size n > 2000.
  • FIG. 12 is a graph of mean delta hue for two concentrations of CRP spiked in serum using a commercial blocking buffer compared to without use of the blocking buffer.
  • FIG. 13 illustrates a graph of normalized standard error versus sample size of swarm sensors for CRP detection in buffer at different concentrations.
  • FIG. 14A illustrates a histogram of different numbers (n) as a function of after-hue values of swarm nanoparticles at different concentrations of anti-BSA (analyte) in water using after-hue as readout.
  • FIG. 14B illustrates the mean after hue values versus anti-BSA concentration.
  • Dotted line represents the control group. In the control group, DI water was added instead of antibody solution, followed by detector AuNPs.
  • FIG. 15A illustrates a histogram of nanoparticle fraction for after hue values for nanoparticles used with different concentrations of CRP spiked in human serum. Stacked histograms are pooled from three separate experiments, with each color representing a different experiment.
  • FIG. 15B illustrates a graph of mean value of after hue as a function of CRP concentration. The error bars in FIG. 15B represent the standard deviation of the mean value of the nanoparticle swarm from three devices. In the control group, CRP-free serum was added instead of CRP antibody solution, followed by detector AuNPs. / values were determined by two-tailed student /-tests. * p ⁇ 0.05, ** p ⁇ 0.01. For all groups, sample size n > 2000. Compared to the same set of data using delta hue as readout, these results using after hue show less quantitative accuracy because of systematic error in the before hue of sensors in a set of experiments.
  • FIG. 16A is a scatter plot of delta hue versus before hue concentration for anti- BSA in DI water at 100 nM. Each point in the scatter plot represents a single nanoparticle from the swarm. The histogram above the scatter plot indicates the before-hue distribution for each group.
  • FIG. 16B is a scatter plot of delta hue versus before hue concentration for CRP in DI water at 10 pg/ml (i.e., 87 nM). The histogram above the scatter plot indicates the before hue distribution for each group.
  • FIG. 16C is a scatter plot of delta hue versus before hue concentration for CRP spiked in serum at 10 pg/ml. The histogram above the scatter plot indicates the before-hue distribution for each group.
  • FIG. 17A illustrates a scatter plot of delta hue versus before hue at different concentrations for BSA in DI water.
  • FIG. 17B illustrates a scatter plot of delta hue versus before hue at different concentrations for CRP in DI water.
  • FIG. 17C illustrates a scatter plot of delta hue versus before hue at different concentrations for CRP in serum.
  • FIG. 1 illustrates a system 2 for determining the presence and/or concentration of a particular chemical species 4 in a sample 6 according to one embodiment.
  • the chemical species may include an analyte that may be, by way of example, a protein, protein fragment, nucleic acid, microRNA, antigen, drug, drug metabolite, biomolecule, virus,
  • the sample 6 may include a biological sample such as a bodily fluid obtained from a living mammal. Examples of such fluids include blood, blood plasma, blood serum, pleural fluid, peritoneal fluid, semen, saliva, sweat, tears, and the like.
  • the sample 6 may, in some embodiments, be subject one or more sample preparation operations prior to being tested in the system. Sample preparation operations may include filtration, separation, centrifugation, concentration, dilution, preservation, and other sample preparation operations that are conventionally known to those skilled in the art.
  • the system 2 includes a dark field imaging device 8 that is used to obtain“before” and“after” color images 10 of an optically transparent substrate 12 having a plurality of plasmonic nanoparticle sensors 14 (also referred to herein as nanoparticles,
  • Nanoparticles 14 refers to nanometer-sized particles that exhibit surface plasmon resonance. Nanoparticles 14 may include any number of shapes of particles including by way of example, nanospheres, nanorods, nanostars, and other morphologies. For nanospheres, in one particular embodiment, the nanoparticles 14 can be made of a plasmonic enhancing material (or coated with) such as silver or gold. The particular size of the nanoparticles 14 may also vary. In one embodiment, the nanoparticles 14 are nanospheres and have diameters within the range of about 60-150 nm. Gold nanorods 14 with various aspect ratios exhibiting surface plasmon resonance (SPR) wavelength peaks at the range of 500-650 nm may also be used.
  • SPR surface plasmon resonance
  • the optically transparent substrate 12 may be formed as part of a microfluidic device or chip that includes at least one optically transparent surface that holds the immobilized nanoparticles 14. Additional layers or surfaces may be combined to form the completed device or chip.
  • the nanoparticles 14 may reside within a channel, well, or reservoir that includes a bottom and top surface (both of which are optically transparent in the regions that hold the nanoparticles 14).
  • the optically transparent substrate 12 may also contain one or more fiducial marks 15 which may be used to locate certain regions of the optically transparent substrate 12.
  • one region of the optically transparent substrate 12 may contain capture nanoparticles 14 that capture a first chemical species 4 while another region of the optically transparent substrate 12 may contain capture nanoparticles 14 that capture a second chemical species 4.
  • the different geographical regions or locations may be identified by fiducial marks 15 (FIG. 1).
  • the fiducial marks 15 may also be used by the image processing software 30 to register before and after images to one another.
  • the fiducial marks 15 may be used to register different FOVs into larger FOVs or the like. Formation of fiducial marks may take place using a number of well-known manufacturing techniques, including selective etching of the transparent substrate 12, screen printing, evaporation and etching of a metal layer and other surface patterning techniques. [0042] As best seen in FIG.
  • the nanoparticles 14 are conjugated to a chemical or biological moiety 16 that binds or otherwise captures or localizes the particular chemical species 4 that is to be detected or measured (e.g., concentration).
  • chemical or biological moieties 16 include aptamers, oligonucleotides, antibodies, antigens, and the like.
  • These nanoparticles 14 that are immobilized to the optically transparent substrate 12 may be referred to herein as the capture nanoparticles 14.
  • the nanoparticles 14 are conjugated to an antibody 16 that itself binds to an antigen or other chemical species 4. Such conjugation of an antibody 16 (or other molecule or moiety 16) to the nanoparticles 14 may take place using well-known conjugation techniques.
  • linking chemistry includes carbodiimide crosslinking such as carbodiimide (EDC)/ N-hydroxysuccinimide (NHS) coupling protocols which are well-known to those skilled in the art.
  • the plurality of nanoparticles 14 that are immobilized to the optically transparent substrate 12 are conjugated or linked to the same molecule or moiety 16 (e.g., the same antibody).
  • plurality of nanoparticles 14 that are immobilized to the optically transparent substrate 12 are conjugated or linked to a different molecule or moiety 16.
  • some of the plurality of the nanoparticles 14 may be conjugated with antibody 16 of type #1 while another plurality of nanoparticles 14 may be conjugated with antibody 16 of type #2.
  • the number of types of nanoparticles 14 with different moieties 16 is not limited to two and can be larger than two.
  • a hue shift or hue signal (as explained below) in response to the presence of a chemical species of interest (e.g., target) such that only the capture nanoparticles 14 are needed.
  • a second plurality of nanoparticles 14 are added with or after target binding to the capture nanoparticles 14. These second plurality of nanoparticles 14 which may be referred to as detector nanoparticles 14 may conjugate or link with the captured or bound chemical species 4 found on the capture nanoparticles 14.
  • the detector nanoparticles 14 may be conjugated to another antibody specific to another epitope on the bound chemical species 4 separate from the epitope by which it is bound to the capture nanoparticle 14 through moiety 16 (e.g., monoclonal antibody pairs) that amplify the plasmonic signal from the capture nanoparticles 14 (as seen in FIG. 3).
  • the detector nanoparticles 14 may be conjugated with a fluorescent probe, dye, or reporter molecule/moiety such that a fluorescent signal may also be emitted along with the hue shift.
  • a fluorescent probe is co-located with some of the nanoparticles 14.
  • the detector nanoparticles 14 are generally smaller than the capture nanoparticles 14 as seen in FIG. 3.
  • the capture nanoparticles 14 may have a major dimension (or diameter) that is on the order of about 100 nm while the detector nanoparticles 14 have a major dimension (or diameter) that is on the order of about 10 nm (an about lOx difference in size).
  • a dark field imaging device 8 is used to obtain both “before” and“after” color images 10 of the optically transparent substrate 12 with the immobilized plurality of nanoparticles 14 located on the surface thereof.
  • Dark field imaging devices 8 are well known in the art.
  • the dark field imaging device 8 generally includes a light source 18 along with a dark field obstruction 20 (e.g., annulus or disc) that is positioned along the optical path 22 that prevents direct or non-scattered light from entering an objective lens 24.
  • One or more lenses 26 e.g., condenser lens
  • the light collected by the objective lens 24 is then imaged with an image sensor 28 such as a CMOS imaging sensor 28 or the like.
  • An image sensor 28 such as a CMOS imaging sensor 28 or the like.
  • a generally dark image is captured with bright, colored objects (in this case the nanoparticles 14) being seen.
  • Other imaging approaches e.g., using oblique illumination such that the illuminating light is aligned to not directly enter the imaging sensor 28 can also be used to image the scattered light from the nanoparticles 14.
  • the hue of the colored bright spots located at where the plurality of nanoparticles 14 are present on the optically transparent slide 12 changes in response to the presence and/or concentration of the target chemical species 4
  • the “before” state refers to the state prior to binding of the target chemical species 4 to the nanoparticle 14 (either directly to the capture nanoparticle 14 or through a secondary detector nanoparticle 14).
  • the“after” state refers to the state after the target chemical species 4 (and optional detector nanoparti cle(s)) 14 are bound to the capture nanoparti cle(s) 14.
  • the dark field imaging device 8 obtains color images 10 of the plurality of nanoparticles 14 in the before and after state.
  • the color images 10 are saved or stored as image files (e.g., TIFF, JPEG, BMP, RAW, etc.) which are then processed using image processing software 30 contained in a computing device 32 having one or more processors 34.
  • the computing device 32 may include any number of types of computing devices 32 including a personal computer, laptop, tablet PC, or even mobile computing devices such as a Smartphone.
  • the computing device 32 that runs the image processing software 30 may be separate from or integrated/associated with the dark field imaging device 8.
  • a number of color images 10 may need to be acquired by the dark field imaging device 8 to cover the entire surface of the optically transparent substrate 12.
  • the dark field imaging device 8 may, in some embodiments, scan the surface of the optically transparent substrate 12 to obtain multiple images that can then be combined together to obtain a larger FOV. This may involve movement of the optically transparent substrate 12, movement of optics of the dark field imaging device 8, or both.
  • the image processing software 30 is configured to define regions of interest (ROIs) in the before and after color images 10, the ROIs representing individual plasmonic nanoparticles 14 located on the optically transparent substrate 12. Regions of brightness may be used to identify individual nanoparticles 14. Overlapping signal among neighboring nanoparticles 14 may be eliminated automatically from the data pool by the image processing software 30 using parameters such as circularity and area.
  • the before and after color images 10 are analyzed from the same FOV using the image processing software 30.
  • the image processing software 30 is further configured to extract mean red (R), green (G), and blue (B) values from each ROI to calculate a hue value for each individual plasmonic nanoparticle 14 before and after exposure to the sample 6 (with the chemical species 4).
  • the red (R), green (G), and blue (B) mean values are converted to HSV in terms of hue, saturation, and value, with the hue value being retained for use as described herein.
  • the image processing software 30 calculates a delta hue value from the respective before and after hue values for each individual plasmonic nanoparticle 14 using an automatic subtraction operation performed by the image processing software 30.
  • This delta hue data which is applied to all or a subset of the plurality of particles 14 is obtained by the image processing software 30.
  • the image processing software 30 uses this delta hue data to determine the presence and/or concentration of the target chemical species 4 within the sample 6.
  • the delta hue value from only a single color channel is used. In other embodiments, the delta hue values from multiple or all color channels are used.
  • nanoparticles 14 need to be imaged in the before and after states for the method to work (or if imaged they can be discarded from data analysis). Some nanoparticles 14 may have moved, been washed away, become later immobilized. The large number of random immobilized nanoparticle 14 sensors enables accurate overlaying and matching of before and after images.
  • Statistical data may also include, for instance, the distribution of delta hue among the plurality of nanoparticles 14 that are examined. This may look to the fraction % of nanoparticles 14 exhibiting a certain range of delta hue values instead of looking at the overall mean or average. For example, the fraction of nanoparticles 14 in the collective swarm with a delta hue above a threshold delta hue value can be used to correlate to concentration of analyte. For example, it may be empirically determined that if over 40% of the nanoparticles 14 have a delta hue value over 15°, this may correspond to a particular concentration of the target chemical species 4 within the sample 6.
  • a threshold on the number or fraction of nanoparticles 14 with substantially no change in hue can provide a metric of concentration, with this fraction decreasing as the concentration of the chemical species 4 increases.
  • the skewness of the histogram of delta hue values for the nanoparticles 14 can also be used as a metric, or other summary statistics or thresholds of the histogram.
  • the combination of these metrics based on the nanoparticle 14 histogram can provide a multiparametric space in which machine learning (e.g., support vector machines, logistic regression, etc.) can be used to identify a weighting of each the various metrics to optimally develop a diagnostic readout.
  • the plurality of nanoparticle sensors 14 analyzed to obtain these above-mentioned metrics comprises more than 200 nanoparticles 14 in order to obtain statistically accurate metrics. More preferably the number of nanoparticle sensors 14 in the plurality is at least 2,000.
  • the image processing software 30 outputs or generates a result that is used by the user of the system 2.
  • the output may include an indication that the particular target chemical species 4 is present or not in the sample 6 (e.g., yes/no or positive/negative indication).
  • the output may also include, in addition to or as an alternative to an indication of presence a concentration or amount of the chemical species 4 in the sample 6. This may be expressed as a numerical concentration value or range or it may include a qualitative indication of concentration (e.g., low, medium, high, etc.).
  • the output may also include the total of amount of the chemical species 4 rather than expressed in a concentration value.
  • the output may be presented to the user on a display 36 that is part of or associated with the computing device 32.
  • the display 36 may have a graphical user interface (GUI) 38 that the user uses to view images 10, patient or sample information, statistical data, and any generated output or results for the sample 6.
  • GUI graphical user interface
  • Spherical gold nanoparticles 14 were centrifuged and resuspended in lmM 1 l-mercaptoundecanoic acid (MU A, 450561, Sigma- Aldrich) for reaction overnight.
  • MU A lmM 1 l-mercaptoundecanoic acid
  • N-(3-Dimethylaminopropyl)-N’-ethylcarbodiimide hydrochloride (EDC, E6383, Sigma- Aldrich) and n-hydroxysuccinimide (NHS, 130672, Sigma- Aldrich) solution was mixed in MES buffer at 1 :2 ratio.
  • Gold nanoparticles 14 also referred to as AuNPs
  • MUA solution were centrifuged and resuspended in EDC/NHS (0.
  • a UV-Vis spectrophotometer (GENESYSTM 10S, Thermo Fisher) was used to monitor the spectral shift of gold nanoparticles 14 in colloidal solutions during conjugation. For each measurement, 500 pL of solution was added to the cuvette and measured against blank, i.e., DI water. For direct binding confirmation, a series of anti-BSA (200 mM) solutions were added to the cuvette containing 500 pL of nanoparticles 14 solution sequentially at 2 min intervals, followed by an immediate spectral measurement after each addition. The volume of anti-BSA added was 1 pL over 5 times, and lastly 5 pL for the binding to reach saturation. The resulting solution was then centrifuged and resuspended in DI water to remove unbound molecules and obtain the final measurement.
  • UV-Vis spectrophotometer GENESYSTM 10S, Thermo Fisher
  • the dark field imaging device 8 was composed of a 60x dark field objective (NA 0.7), dark field condenser (NA 0.85-0.95), and a color camera (DS-Fi3) mounted to an inverted microscope, all purchased from Nikon.
  • NA 0.7 dark field objective
  • NA 0.85-0.95 dark field condenser
  • DS-Fi3 color camera mounted to an inverted microscope, all purchased from Nikon.
  • an initial image of the immobilized capture nanoparticles 14 in the liquid solution was taken, namely the“before” image.
  • the target chemical species 4 or analyte was added to the surface of the optically transparent substrate 12 and incubated at room temperature followed by a washing step.
  • detector nanoparticles 14 were added to the surface of the optically transparent substrate 12 for another incubation. Excess and unbound detector nanoparticles 14 were washed away, and images of the same locations were taken as“after” images.
  • ROIs Regions of interests
  • ImageJ image processing software 30 was used to align the“before” and“after” color images from the same FOV, define ROIs of the individual nanoparticles 14 based on a threshold of brightness, and extract the mean R, G, B value from each ROI to calculate the hue value before and after sensing. Parameters such as circularity and area were used to select ROIs representing single nanoparticles 14. Finally, delta hue was determined by subtracting the after-hue value from before-hue value using image processing software 30.
  • Bare and conjugated nanoparticles 14 colloidal solutions were dispersed on a silicon substrate, dried and imaged using scanning electron microscopy (Supra 40VP SEM, ZEISS) at lOkV.
  • FDTD software from Lumerical Inc. was used to numerically simulate the 3D scattering spectrum of a gold nanoparticle 14.
  • the total-field scatter-field (TFSF) source was used to simulate dark field imaging.
  • a lOOnm gold nanoparticle 14 was placed inside the TFSF.
  • the background index was set as 1.3 (for water).
  • a frequency -domain power monitor was placed outside of the TFSF source to collect the scattered signal.
  • NA numerical aperture
  • a shell thicknesses of 2.5, 5, 7.5nm, respectively, with refractive index of 1.45 were placed surrounding the nanoparticle 14 to simulate the functionalized antibody layer.
  • the electromagnetic field decay length of the lOOnm nanoparticle 14 was estimated to be 20nm.
  • random positions were generated on the surface of the lOOnm nanoparticles 14 with the criteria that the positions generated for each consecutive lOnm nanoparticle 14 has to be non-overlapping with any existing lOnm nanoparticle 14. Otherwise, a new random position was generated, until no new random positions were available to satisfy the non-overlapping condition within 1000 trials.
  • the resonance spectrum obtained from FDTD simulation was converted to RGB values by overlapping with the color-matching functions defined by the International Commission on Illumination (CIE). Then, the RGB values were then converted to the hue value.
  • CIE International Commission on Illumination
  • Gold capture nanoparticles 14 with 100 nm diameters and detector nanoparticles 14 with 10 nm diameters were both conjugated with BSA in a colloidal solution.
  • a glass coverslip (used as the optically transparent substrate 12) was treated with poly-l-lysine (P8920, Sigma- Aldrich) for 10 min and washed three times with DI water and dried. Then, capture nanoparticles 14 were immobilized on the glass coverslip 12. To immobilize, the solution containing capture AuNP-BSA conjugates was added to a microchannel bound to the glass coverslip and incubated for 2 hours followed by washing 3 times to remove excess unbound nanoparticles 14.
  • immobilized capture nanoparticles 14 were first taken at different locations on the coverslip in DI water. Then, the solution in the microchannel was replaced with anti-BSA (B7276, Sigma-Aldrich) solution and incubated for 15 min at room temperature. This solution was then washed once with DI water and the BSA-conjugated detector nanoparticle 14 solution was added. After a 30 min incubation, the chip was washed 3 times with DI water and a second set of images at previously recorded fields of view were taken, which are defined as“after” images.
  • anti-BSA B7276, Sigma-Aldrich
  • Gold capture nanoparticles 14 with 100 nm diameters and gold detector nanoparticles 14 with 10 nm diameters were conjugated with anti-CRP C5 and C6 (Sigma- Aldrich) in a colloidal solution, respectively.
  • anti-CRP C5 and C6 Sigma- Aldrich
  • DI water was used as a dilution solvent.
  • CRP 236603, Sigma-Aldrich
  • CRP free serum Hytest, Netherland
  • Gold nanoparticles 14 with diameters of 100 nm were immobilized in a microchannel sandwiched between two glass coverslips which formed the optically transparent substrate 12.
  • Antibodies 16 were functionalized on gold nanoparticles 14 in the colloidal phase before the immobilization step using conventional EDC/NHS methods detailed herein. The UV-Vis spectrum shift was used to verify the above surface
  • FIGS. 2A and 2B To validate the binding of the chosen antigen and antibody pairs (4, 16), the UV-Vis spectrum shift was further monitored after adding different concentrations of antigens 4 into the antibody functionalized- AuNP colloid solution (FIG. 2B). To minimize the instrument complexity, the swarm biosensing protocol was based on comparing two dark field images on the nanoparticles 14 immobilized on the optically transparent substrate 12 or sensor chip, free from spectroscopic measurement (FIG. 3). An initial image 10 of the immobilized nanoparticles 14 before addition of analyte 4 (i.e., “before” image) was taken using a CMOS color-camera.
  • sample solution 6 was injected into the microchannel between two cover slips, followed by the injection of detector nanoparticles 14 of much smaller size (typically -10 nm) that were functionalized with a paired antibody to amplify the protein binding signal on the capture nanoparticles 14.
  • detector nanoparticles 14 of much smaller size (typically -10 nm) that were functionalized with a paired antibody to amplify the protein binding signal on the capture nanoparticles 14.
  • a second dark field image was taken at the same FOV as the“after” image.”
  • a sample image of quantitation for each particle after image analysis is shown in FIG. 4.
  • a histogram 50 of the delta hue from all the single nanoparticles 14 or“sensors” was compiled to obtain the representative detection results of one chip (FIG. 3).
  • Total assay time is less than one hour, which includes 10-15 min of target analyte incubation, and 30 min detector probe incubation, imaging acquisition and washing steps.
  • the optimal sizes of the paired capture and detector nanoparticles 14 were selected to maximize the hue shifts caused by binding events. Ideal capture nanoparticles 14 would maintain sufficient scattered light intensity to provide a high detection baseline, while detector nanoparticles 14 would exhibit minimal background signal interference without binding. Gold nanoparticles 14 with diameters of 20, 60, 100 and 150 nm were evaluated as capture probes under the same imaging conditions, where 100 nm and 150 nm particles appeared green and orange, respectively, with high intensity as seen in FIG. 5.
  • the color camera operates at higher quantum efficiency at wavelengths around 550 nm (i.e., correlating with green hue), implying greater signal to noise ratio at this region. Therefore, gold nanoparticles 14 with 100 nm diameter were selected as capture probes.
  • gold nanoparticles 14 with diameters of 4, 10, 20 and 60 nm were evaluated based on several criteria (FIG. 6). The ideal candidate would be easy to conjugate, yield a low background signal during dark field imaging, and enhance the hue shift when sandwiched with the capture nanoparticles 14.
  • each single nanoparticle 14 functions as a quantitative biosensor with a large number of levels in the analog readout.
  • RGB and HSV are alternative color models.
  • RGB information acquired from raw color images 10 was converted to HSV in terms of hue, saturation, and value.
  • Hue which is independent of intensity and saturation, directly correlates with the dominant wavelength, and therefore has been used as an alternative approach to spectral measurement.
  • HSV values range from 0 to 360° but for the gold nanoparticles 14 used herein the color sensing range (green to red) was typically within the range of 0 to 180°. It should be appreciated that other nanoparticle 14 types and sizes generate changes at higher HSV values (e.g., silver-based nanoparticles 14 may be used in the blue range or > 200°).
  • the other two elements of the HSV color space i.e., saturation and value are susceptible to interference from imaging conditions, and failed to establish correlation with analyte concentrations (see FIGS. 8A and 8B). Therefore, the signal to noise ratio was significantly enhanced by converting RGB to HSV and using only hue as the quantitative readout.
  • the sensitivity and dynamic range of the system 2 was characterized using two different proteins in buffer solution.
  • the detection of anti-BSA concentrations ranging from 10 pM to 10 mM was demonstrated, which yielded a shift in the delta hue histogram comprising each sensor’s individual readout (FIG. 10A).
  • the large sample size of the sensor swarm (n>2000 individual nanoparticles 14 that form the sensors for each optically transparent substrate 12) better enabled differentiation between neighboring concentrations (p ⁇ 0.05).
  • the dynamic range using the mean value of the delta hue (FIG. 10B) was six (6) orders of magnitude with a limit of detection of 10 pM.
  • Detection of CRP in DI water from 1 ng/ml to 10 pg/ml revealed a dynamic range of at least four (4) orders of magnitude with the potential to detect even lower concentrations (FIGS. 10C-10D).
  • 1 ng/ml i.e., 8.7 pM
  • the detection of CRP concentrations lower than 1 ng/ml was not pursued. The detection performance could be affected by the variation of protein markers, affinity of the antigen- antibody pair, as well as protein structure and dimension.
  • CRP a common inflammation marker
  • CDC Centers for Disease Control and Prevention
  • hsCRP high-sensitivity CRP
  • the swarm sensing system 2 aims at robust and accurate quantification, where the random noise due to a variety of sources discussed previously can be minimized by compiling readout from a large number of single nanoparticles 14 in the swarm of nanoparticles 14 (i.e., large swarm size).
  • large swarm size leads to a tighter confidence interval, indicating a greater precision in the final measurement output.
  • This approach may not always be practical due to increased instrumentation complexity and labor needed for repeated measurements.
  • the swarm sensing scheme provides the flexibility to significantly increase sample size by imaging multiple single nanoparticle 14 sensors as independent measurements without increasing instrumentation complexity or prolonging detection time.
  • nanoparticles 14 with 100 nm and 10 nm diameters achieve quantitative analog delta hue readout for individual nanoparticles 14 with random error minimized by assessing the majority“votes”.
  • the swarm sensor system 2 provides significant advantages over“digital” assays, which evaluates individual sensing events with binary“on” and“off’ output, in terms of dynamic range, operational concentrations and sensing accuracy. While other digital nanoplasmonic assays employing dimer particle structures (80nm and 40nm respectively) achieved a low limit of detection, the digital nature of the assay significantly limited the dynamic range of the assay.
  • the swarm sensor system 2 instead considers each nanoparticle 14 as a separate analog sensor and accumulates the many quantitative readouts from a plurality of nanoparticles 14 to accumulate a more accurate final quantitative signal.
  • the presented swarm sensor system 2 overcomes the problem of low dynamic range by utilizing much smaller detector gold nanoparticles 14 (lOnm) paired with large capture gold nanoparticles 14 (lOOnm) and demonstrated that the hue change induced by the binding of 10 nm nanoparticles 14 on a single lOO-nm nanoparticle 14 can be quantitatively characterized over a range of concentrations; thereby achieving a quantitative analog signal per sensor nanoparticle 14.
  • Biosensing in biofluids remains challenging, as sensor performance is typically hindered by issues such as non-specific binding and sample variation, which is largely due to the complex nature and matrix effect of the biofluids. Furthermore, biomarker detection becomes even more difficult when the clinical cutoffs are within a close range (e.g., ⁇ 10- fold).
  • the clinically relevant concentrations of CRP as a cardiac marker are at relatively high concentrations (i.e., pg/ml) with only around 3 times concentration differences separating different clinical classifications.
  • the approach of using swarm sensing of nanoparticles 14 based on delta hue appears to be less sensitive to the non-specific binding on the device surface (FIG.
  • each single nanoparticle 14 Because the status of each single nanoparticle 14 before and after sensing were both recorded, the detection results of each single nanoparticle 14 can be mapped versus its initial state, which provides further“quality control” capabilities to identify optimal/reliable nanoparticle 14 in the swarm.
  • the scatter plot of each device with thousands of single nanoparticle 14 sensors typically shows a triangular shape (FIGS. 17A-17C).
  • the distribution of before-hue on the x-axis represents the sensor variation due to particle size and conjugation using different protein markers, e.g., BSA and anti-CRP (FIGS. 16A, 16B).
  • the range of the distribution along the y-axis (after-hue) for each before-hue region varies, which could be to the result of different numbers of chemical species 4 and detector nanoparticles 14 bound to a single capture nanoparticle 14.
  • the largest distribution of delta hue was typically around a before-hue near 80°, where the sensors were well- dispersed single nanoparticles 14 fully conjugated to allow for the maximum number of binding sites available. Sensors exhibiting higher before-hues may generate a reduced hue shift because of less binding sites available after incomplete conjugation.
  • the maximum delta hue ( ⁇ 40°) is achieved when a capture nanoparticle 14 has the maximum number of binding sites all saturated by detector nanoparticles 14.
  • the distribution of delta hue at the lower before-hue region ( ⁇ 60°) is limited to a smaller range, as indicated by the slope in the scatter plot.
  • the maximum delta hue on the y-axis was significantly lower with a much tighter distribution compared to that in buffer solution (FIGS. 16B, 16C), possibly due to matrix effects in serum where some of the target antigen 4 interacts with matrix components in serum instead of forming target antigen-antibody pairs.
  • This informative sensing capability allows the evaluation of the sensor status for quality control, provides useful information for troubleshooting, and potentially enables protein marker dependent optimization.
  • PCR PCR-based PCR
  • the swarm sensing approach described herein uses single nanoparticle colorimetry treats well-dispersed single nanoparticles 14 as individual sensors that each get a vote as part of a swarm of parallel reactions.
  • signal collection using dark field imaging instead of spectral shift measurements with spectrometers provides easy access to the location information of each nanoparticle 14, i.e., the swarm sensor. The combination of these two elements led to a low-cost solution for the simultaneous detection of hundreds of single nanoparticle 14 sensors located in one FOV, and normalization to an initial condition to avoid systematic error, which significantly increased the confidence in a measured result.
  • a nanoplasmonic biosensing system 2 is thus disclosed that is based on massively parallel analyses of a swarm of single nanoparticle 14 colorimetric sensors, which provides informative sensing by evaluating sensor status both before and after sensing.
  • This sensing scheme provides an alternative to conventional ensemble-based LSPR detection approaches. With instrumentation that can be simple and cost effective, it has the potential to be implemented as a handheld point-of-care device. Rapid readouts from thousands of single nanoparticle 14 sensors combined with individual evaluation using delta hue renders higher tolerance for particle variations, device variations, and sample variability. With minimized interference from non-specific binding in the background, the successful detection of clinical cutoffs within a close range of concentrations was possible in complex biofluids without the need of blocking steps.
  • the methodology of swarm sensing is versatile. Its application is not limited to nanoplasmonic platforms, and could be adapted by other types of single entity-based sensors, such as fluorescence, electrochemical, and magnetic for robust detection with statistically improved accuracy and reproducibility, as long as high-throughput readout from a large pool of individual sensors can be conducted efficiently using either imaging or other parallelized analysis methods.

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Abstract

L'invention concerne un système pour déterminer la présence et/ou la concentration d'un analyte dans un échantillon, qui comprend un substrat optiquement transparent comportant une pluralité de nanoparticules plasmoniques immobilisées sur celui-ci, et un dispositif d'imagerie sur champ sombre configuré pour acquérir des images couleur du substrat optiquement transparent avant et après sa mise en contact avec l'analyte. Un logiciel de traitement d'image est configuré pour définir dans les images couleur acquises des régions d'intérêt qui représentent des nanoparticules plasmoniques individuelles, le logiciel de traitement d'image étant en outre configuré pour extraire des valeurs d'intensité de couleur d'un canal ou de plusieurs canaux de couleur de chaque région d'intérêt afin de calculer une valeur de teinte pour des particules plasmoniques individuelles, avant et après la mise en contact avec l'analyte. Une valeur de teinte delta, calculée à partir des valeurs de teinte respectives avant et après la mise en contact, pour les nanoparticules plasmoniques individuelles, est utilisée pour déterminer la présence et/ou la concentration de l'analyte.
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US20240192135A1 (en) * 2021-04-21 2024-06-13 Nicoya Lifesciences Inc. Methods and systems for optimal capture of a multi-channel image from an lspr spectrometer
WO2023055991A1 (fr) * 2021-10-01 2023-04-06 Pnp Research Llc Suppression de bruit de fond optique dans des dosages de liaison utilisant des microsphères polymères
WO2023215408A1 (fr) * 2022-05-04 2023-11-09 Biosensing Instrument Inc. Système et procédé d'analyse d'interactions moléculaires sur des cellules vivantes à l'aide de techniques de biocapteur

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