JP7491522B2 - Molecular detection methods - Google Patents

Description

The present invention relates to a method for detecting molecules, and more specifically, to a method for detecting molecules under dark field conditions.

Various methods for detecting molecules in a sample have been developed, and many sensors using nanoparticles have been proposed. For example, Non-Patent Document 1 proposes a gene sensor that utilizes the fact that only when a completely complementary strand of DNA is added to single-stranded DNA-modified gold nanoparticles in the presence of NaCl, the gold nanoparticles aggregate and the solution color changes from red to blue. Non-Patent Document 2 also shows an amyloid fibril detection system that utilizes the fact that nanoparticles can be observed as bright spots when observed under a dark-field microscope. In this detection system, gold nanoparticles bound to amyloid monomers (present as monomers) and gold nanoparticles bound to amyloid fibrils (present as aggregates) are imaged by dark-field microscope observation. The average pixel value of the spots in the obtained observation image is calculated as the brightness, a histogram is created, and nanoparticle aggregation is evaluated, thereby detecting amyloid fibrils with high sensitivity.

All of these detection methods are detection systems that utilize the difference in optical properties between gold nanoparticle monomers and aggregates. In detection systems that use monomers and aggregates, a detection target that is more concentrated than the nanoparticles may be required to cause aggregation. For example, when constructing a sandwich-type immunoassay system using antibody-modified gold nanoparticles with two types of monoclonal antibodies, it is easy to predict that in a region where the detection target concentration is lower than the nanoparticle concentration, aggregation will not occur and dimers will be preferentially formed. Therefore, in order to construct a more sensitive detection system, it is necessary to distinguish between monomers and dimers. As a method for distinguishing between monomers and dimers, the method described in Non-Patent Document 3 can be mentioned. In Non-Patent Document 3, two gold nanoparticles are linked with DNA, the distance between the gold nanoparticles is controlled by the DNA, and the color of the bright spot under dark field observation changes depending on the distance between the two particles.

J. Am. Chem. Soc., 2003, 125, 8102 Analytical. Sciences. , 2016, 32, 307 A.C.S. nano. , 2015, 978 PNAS. , 2006, 104, 2667

However, the method described in Non-Patent Document 3 produces a large change in the color of the bright spot only when two gold nanoparticles are placed very close to each other and surface plasmon resonance occurs between the particles. For example, as described in Non-Patent Document 4, if there is a particle distance of about 10 nm, the change in the color of the bright spot cannot be confirmed. Therefore, it is presumed that in a dimer in which the distance between the two particles is large, the change in the color of the bright spot as shown in Non-Patent Document 3 does not occur. Therefore, the technology described in Non-Patent Document 3 cannot be used in a detection system for bulky molecules. On the other hand, in bioassays for detecting specific molecules, high molecular weight molecular recognition proteins such as antibodies (10 to 15 nm per molecule) are often used as compounds for recognizing molecules. Therefore, since the molecular recognition proteins themselves are bulky molecules, it is difficult to combine them with the technology described in Non-Patent Document 3 to construct a detection system for molecules.

Therefore, the present invention was made in consideration of the above problems, and its purpose is to provide a method for detecting molecules using nanoparticles that can also be applied to the detection of bulky molecules.

In order to solve the above problems, the molecular detection method according to the present invention includes mixing a first nanoparticle modified with a first molecular recognition compound, a second nanoparticle modified with a second molecular recognition compound, and a target sample, and performing dark-field observation of the mixed sample, in which the first nanoparticle, the second nanoparticle, and a dimer of the first nanoparticle and the second nanoparticle have different bright spot colors under dark-field observation, and the method includes distinguishing the dimers based on the difference in bright spot color.

In one embodiment of the molecule detection method according to the present invention, the first nanoparticles are silver nanoparticles.

In one embodiment of the molecule detection method according to the present invention, the second nanoparticles are gold nanourchins.

In one embodiment of the molecule detection method according to the present invention, at least one of the first molecular recognition compound and the second molecular recognition compound is a protein.

In one embodiment of the molecule detection method according to the present invention, at least one of the first molecular recognition compound and the second molecular recognition compound is an antibody.

Another aspect of the molecular detection method according to the present invention includes mixing a first nanoparticle modified with a first molecular recognition compound, a second nanoparticle modified with a second molecular recognition compound, and a target sample, and observing the mixed sample under dark field observation, in which the first nanoparticle, the second nanoparticle, and an aggregate of the first nanoparticle and the second nanoparticle have different bright spot colors under dark field observation, and the method includes distinguishing the aggregate based on the difference in the bright spot colors.

The method for detecting a nanoparticle dimer according to the present invention includes forming a dimer between a first nanoparticle and a second nanoparticle, the first nanoparticle and the second nanoparticle having different bright spot colors under dark-field observation, and detecting the dimer between the first nanoparticle and the second nanoparticle by detecting a bright spot color different from the bright spot color of the first nanoparticle and the bright spot color of the second nanoparticle.

The molecular detection method of the present invention makes it possible to distinguish even nanoparticle dimers, which do not cause a change in bright spot color due to surface plasmon resonance between nanoparticles, from nanoparticle monomers, and to easily detect molecules using nanoparticles.

FIG. 1 shows a scheme of the detection principle according to an embodiment of the present invention. FIG. 13 is a diagram showing particle size measurement results in Example 4. FIG. 13 is a diagram showing a color component bar graph in Example 5. FIG. 13 is a diagram showing an RGB three-dimensional plot in Example 6. FIG. 13 is a diagram showing an RGB three-dimensional plot in Example 7. FIG. 13 is a diagram showing the measurement results in Example 9. FIG. 13 is a diagram showing particle size measurement results in Example 12. FIG. 13 is a diagram showing an RGB three-dimensional plot in Example 14. FIG. 15 is a diagram showing the measurement results in Example 15.

The molecular detection method according to the present invention involves mixing a plurality of nanoparticles modified with molecular recognition compounds and having different bright spot colors with a target sample, and subjecting the mixed sample to dark-field observation. First, the scheme of one embodiment of the detection method according to the present invention will be described with reference to FIG. 1.

Figure 1 is a diagram showing a scheme of the present detection method according to one embodiment. As shown in Figure 1, in this detection method, at least two types of nanoparticles modified with a molecular recognition compound, nanoparticle A (first nanoparticle) and nanoparticle B (second nanoparticle), are used. When nanoparticle A and nanoparticle B are mixed with a target sample, if a molecule to be detected (detection target) is present in the target sample, the molecular recognition compound in nanoparticle A associates with the detection target, and the molecular recognition compound in nanoparticle B associates with the detection target. As a result, a dimer (heterogeneous nanoparticle dimer) of nanoparticle A and nanoparticle B is formed via the detection target. When this mixed sample is observed with a dark-field microscope, bright spots of nanoparticle A, nanoparticle B, and heterogeneous nanoparticle dimers are observed. At that time, the observed image is acquired as image data, and the image data is used to assign each bright spot based on the bright spot color of each bright spot, that is, to determine the assignment of the bright spot, so that the formation of heterogeneous nanoparticle dimers can be confirmed and the detection target can be detected. Furthermore, based on this, it is also possible to measure the concentration of the molecule to be detected. Note that Figure 1 shows classification by machine learning as an example, but the discrimination method is not limited to this.

The molecular detection method in this embodiment is described in detail below.

[Nanoparticles]
The nanoparticles used in this embodiment are particles that can be observed as bright spots under dark field observation. As long as they can be observed as bright spots under dark field observation, the nanoparticles applied to this embodiment are not particularly limited, and examples thereof include gold nanoparticles, silver nanoparticles, gold nanorods, gold nanourchins, gold nanoparticle aggregates, and quantum dots.

In this embodiment, nanoparticles A and nanoparticles B are different nanoparticles. There are no particular limitations on the combination of nanoparticles used, as long as the combination produces bright spot colors that are different enough to be distinguishable by analysis during dark-field observation. For example, one combination may be silver nanoparticles and the other gold nanourchins. In this specification, the bright spot color refers to the color of the bright spot that originates from scattered light and reflected light from a sample observed under dark-field observation, as described below.

The particle diameter of the nanoparticles is not particularly limited, but is preferably 10 nm to 1 μm, and more preferably 20 nm to 100 nm. In this specification, particle diameter refers to the diameter of spherical particles, and to the length along the long axis of rod-shaped particles such as nanorods.

There are no particular limitations on the material of the nanoparticles, so long as the nanoparticles can be observed as characteristic bright spots in a dark field. However, from the standpoint of brightness, metals that have plasmon resonance characteristics in the visible to near-infrared wavelength range are preferred.

In the detection method of this embodiment, at least two types of nanoparticles (nanoparticles A and nanoparticles B) need to be used, and there is no limit to the number of types, and three or more types of nanoparticles may be used. An example of using three or more types of nanoparticles will be described later.

[Molecular Recognition Compounds]
The molecular recognition compound in this embodiment is not particularly limited as long as it can associate with the detection target. For example, the association site of the molecular recognition compound may be a small molecule (cyclodextrin, crown ether, boronic acid, etc.) used as a host molecule, a protein (lectin, antibody, etc.) having affinity with the detection target, a nucleic acid (aptamer, etc.) having affinity with the detection target, avidin, biotin, etc.

As long as the molecular recognition compound bound to nanoparticle A (first molecular recognition compound) and the molecular recognition compound bound to nanoparticle B (second molecular recognition compound) can both be in a state of association with the detection target, the molecular recognition compound in nanoparticle A and the molecular recognition compound in nanoparticle B may be the same or different.

When the molecular recognition compound bound to nanoparticle A and the molecular recognition compound bound to nanoparticle B are both associated with the detection target, a dimer of nanoparticle A and nanoparticle B is formed.

The method for modifying nanoparticles with molecular recognition compounds can be any known method, and there are no particular limitations as long as the association sites perform their intended function.

[Target sample]
The target sample in this embodiment refers to a liquid sample intended to examine whether or not a molecule to be detected is present, and further, its concentration.

The detection target is not limited as long as there is a molecular recognition compound that can associate with the detection target. Examples include nucleic acids, proteins, sugars, and biomolecules such as exosomes, drugs, and viruses. The size of the detection target is not particularly limited as long as the bright spot of the dimer can be observed as a single bright spot under a dark field. For example, it is expected that the bright spots of a nanoparticle dimer mediated by a particularly large detection target such as an exosome will not be observed as a single bright spot. However, even in such a case, the dimer can be observed as a single bright spot by adjusting the resolution of the observation image by changing the magnification of the objective lens, etc.

Furthermore, as will be described in more detail later, the detection target is not limited to one type, but by using a combination of three or more types of nanoparticles, it is possible to simultaneously detect multiple types of detection targets.

[Dark-field observation]
The dark-field observation in this embodiment may be a conventionally known dark-field observation method in which light is applied to a sample from an oblique angle and only scattered light and reflected light from the sample are observed. The illumination method may be either a transmissive type or an epi-illumination type, and the user may select an appropriate method.

To perform image analysis of bright spots that occur under dark-field observation, the observed image is acquired as a color image. There are no particular limitations on the imaging device used to acquire the observed image, as long as it is capable of capturing a color image. Note that, when observing nanoparticles in a sample without immobilizing them, it is preferable to use an imaging device with a fast shutter speed.

For dark-field observation of nanoparticles, a liquid sample containing nanoparticles is spread on a substrate. Examples of the substrate include a glass slide and a glass petri dish. There are no particular limitations on the material of the substrate as long as the liquid sample can be spread on it, and examples of materials include soda glass, quartz glass, acrylic resin, and polycarbonate.

In the dark-field observation in this embodiment, the nanoparticles may be immobilized on a substrate. By immobilizing the nanoparticles on the substrate, the clarity of the observed image obtained can be improved. Methods for immobilizing the nanoparticles on the substrate include, but are not limited to, a method that utilizes the interaction between the nanoparticles and the substrate surface. For example, when gold nanoparticles are used as the nanoparticles, the gold nanoparticles can be immobilized on the substrate by electrostatic interaction with the negatively charged gold nanoparticles by modifying the substrate surface with amino groups. Alternatively, the nanoparticles can be immobilized on the substrate by increasing the salt concentration in a sample containing the nanoparticles and using a slide glass as the substrate.

[Heterogeneous nanoparticle dimer]
As described above, the heterogeneous nanoparticle dimer in this embodiment is a dimer of nanoparticle A and nanoparticle B of a type different from nanoparticle A. In other words, the heterogeneous nanoparticle dimer is a dimer between nanoparticles that have different bright spot colors under dark-field observation.

When a single nanoparticle (nanoparticle monomer) is observed under a dark field, it is observed as a bright spot larger than the size of the nanoparticle due to surface plasmon resonance. In addition, as shown in the above-mentioned non-patent document 3, when nanoparticles form dimers and the distance between the nanoparticles is very small, a change in the color of the bright spot can occur due to surface plasmon resonance between the particles. However, when the distance between the nanoparticles that form a dimer by association with the detection target is large enough that a change in the color of the bright spot does not occur due to surface plasmon resonance between the particles, the dimer cannot be detected by conventionally known techniques.

In the detection method according to this embodiment, the dimer is observed as a bright spot of a mixed color of the bright spot colors of the monomers of each nanoparticle that forms the dimer, which makes it possible to detect the dimer that is formed by association with the detection target.

Heterogeneous nanoparticle dimers can be formed by mixing nanoparticle A, nanoparticle B, and a target sample under conditions that allow each molecular recognition compound to associate with the target of detection. In addition, the molecular recognition compound in nanoparticle A and the molecular recognition compound in nanoparticle B may be associated with the target of detection first, or they may be associated with each other simultaneously.

The number of different nanoparticle dimers formed is not limited to one type. For example, in the presence of nanoparticles A, B, and C, which have different bright spot colors, a molecular recognition compound can be selected for each nanoparticle to modify each nanoparticle so that a heterogeneous nanoparticle dimer of nanoparticles A and B is formed by detection target 1, and a heterogeneous nanoparticle dimer of nanoparticles A and C is formed by detection target 2. Since the combination of nanoparticles constituting the heterogeneous nanoparticle dimers differs between the heterogeneous nanoparticle dimers, the bright spot colors in the heterogeneous nanoparticle dimers also differ from one another. This makes it possible to simultaneously detect multiple types of heterogeneous nanoparticle dimers in a distinguishable manner based on the difference in bright spot color.

[Analysis of bright spots]
Although a method for analyzing bright spots using dark-field observation images will be described, the method for detecting the detection target is not limited to this as long as it utilizes changes in the color of the bright spots.

(Color separation of bright spots)
In one detection method in this embodiment, it is also possible to separate a color observation image obtained by dark-field observation into a plurality of color components, obtain color information of bright spots including information of each color component, and perform analysis based on the color information. In the following, an example will be described in which a color observation image obtained by dark-field observation is separated into RGB, and analysis is performed based on the RGB values of each bright spot.

First, the color observation image obtained by dark-field observation is converted to an 8-bit grayscale image. Bright spots are identified in the grayscale image, and the area identified as a bright spot is determined as the target region (ROI). For example, when using the image analysis software (ImageJ) shown in the example described later, bright spots can be identified as bright spots at the image processing stage if they satisfy the conditions of a threshold of 30-255, a size of 30-10,000, and a circularity of 0.3-1.0. Next, the target color observation image is divided into component images of the R component, the G component, and the B component, and the intensity of each color component image is measured at all ROIs. The intensity of each color component in the ROI (R value, G value, and B value) is used as the color information of the bright spot.

(Distinguishing bright spots)
As one aspect for distinguishing bright spots based on color information, the obtained color information of the bright spots is plotted three-dimensionally as three-variable data of RGB values. By plotting three-dimensionally, the difference between bright spots due to different attributions can be visually recognized. In this specification, attribution indicates whether the bright spot is derived from a nanoparticle, a heterogeneous nanoparticle dimer, or an impurity. Furthermore, in the case of a nanoparticle bright spot, it is specified which nanoparticle among multiple nanoparticles it is derived from. Similarly, in the case where multiple heterogeneous nanoparticle dimers may exist, it is specified which heterogeneous nanoparticle dimer it is derived from.

Although it is possible to visually distinguish nanoparticles by performing a three-dimensional plot, for more accurate or easier discrimination, it is preferable to distinguish based on discrimination information acquired in advance. Here, discrimination information refers to standards, models, algorithms, programs, etc. derived by collecting color information of bright spots whose attribution is clear and using the collected color information, and applying the discrimination information to the color information of bright spots obtained by observation to conclude what the attribution of the bright spots is.

(Machine learning methods)
Even if the nanoparticles are of the same type, the RGB values of the bright spots of each nanoparticle may vary. In addition, even if the nanoparticles or impurities are of different types, the colors of the target nanoparticles and the bright spots may be close. Therefore, there is a limit to accurate discrimination by human visual discrimination. Therefore, in discriminating the attribution, it is preferable to use software (computer program) that uses RGB values as input data and outputs the attribution judgment result as the above-mentioned discrimination information. In particular, it is desirable from the viewpoint of judgment accuracy to use software (classification model) that automatically discriminates the attribution based on the machine learning result as the above-mentioned discrimination information. The software (algorithm) that can be used for discrimination is not particularly limited, but for example, a supervised neural network or a supervised support vector machine can be used.

For example, the software can be made to output a discrimination result indicating whether the input data is a silver nanoparticle monomer, a gold nanourchin, a silver nanoparticle-gold nanourchin dimer, or an impurity, based on the input data being RGB values. In this case, color information of the silver nanoparticle monomer is obtained in advance using the silver nanoparticle monomer, color information of the gold nanourchin is obtained using the gold nanourchin, and color information of the impurity is obtained using the impurity (e.g., a biological sample, a soil sample, etc.). In addition, color information of the silver nanoparticle-gold nanourchin dimer is obtained by synthesizing the bright spot images of the silver nanoparticles and the gold nanourchin on a computer. Then, the color information of which the attribution is known obtained in this way is used as learning data to perform machine learning of the software, and the color information of which the attribution is known is similarly used as test data to confirm the accuracy of the machine learning. This process makes it possible for the software to make a judgment with the desired accuracy.

One example of such software is the free statistical analysis software R (R DEVELOPMENT CORE TEAM (2005). R: A LANGUAGE
AND environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.

By adopting a discrimination method using machine learning, the attribution of nanoparticles can be determined with greater accuracy, thereby improving the reliability of detection results.

In view of the above, in the specific method of this embodiment, the molecular detection method includes a step of observing the mixed sample under dark-field observation to obtain a color observation image, a step of color-separating the observation image to obtain color information of the bright spots including information of each color component, and a step of determining, from the color information, whether the bright spots in the observation image are bright spots of nanoparticle A, nanoparticle B, or heterogeneous nanoparticle dimers.

The discrimination process may also include discriminating the attribution of bright points in the observed image based on discrimination information acquired in advance.

The discrimination information may also be a classification model of bright spots that has been machine-learned to determine the correlation between the color information and the attribution of the bright spots.

Furthermore, according to this embodiment, molecules in a target sample can be detected, but it can also be considered as a method for detecting nanoparticle dimers. That is, one embodiment of the present invention is a method for detecting nanoparticle dimers, which includes forming a dimer of nanoparticle A and nanoparticle B, where nanoparticle A and nanoparticle B have different bright spot colors under dark-field observation, and the method can also detect heterogeneous nanoparticle dimers by detecting a bright spot color different from the bright spot color of nanoparticle A and the bright spot color of nanoparticle B.

Furthermore, it may be possible to form a dimer from two nanoparticles with different bright spot colors under dark field conditions and observe the bright spot that differs from the monomer state.

The technology of this embodiment is not limited to cases where two nanoparticles with different bright spot colors form a dimer, but can also be applied to cases where an aggregate containing both of the two nanoparticles with different bright spot colors is formed. In other words, by forming an aggregate containing both of the two nanoparticles with different bright spot colors, a bright spot different from the state of the monomer is observed, making it possible to distinguish between the aggregate and the monomer, thereby enabling detection of the molecule. Therefore, this embodiment can also be a method for observing and distinguishing between an aggregate containing two different nanoparticles and a monomer.

The following examples are presented to further explain the embodiments of the present invention. Of course, the present invention is not limited to the following examples, and various modifications are possible in detail. Furthermore, the present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. The technical scope of the present invention also includes embodiments obtained by appropriately combining the technical means disclosed. In addition, all of the documents described in this specification are incorporated by reference.

In the following Examples 1 to 9, examples are specifically shown in which silver nanoparticles (AgNP) are used as the first nanoparticles, gold nanourchins (AuNU) are used as the second nanoparticles, protein A is used as the molecular recognition compound modified on the first nanoparticles, and BSA is used as the molecular recognition compound modified on the second nanoparticles. In addition, in Examples 10 to 15, examples are specifically shown in which silver nanoparticles (AgNP) are used as the first nanoparticles, gold nanourchins (AuNU) are used as the second nanoparticles, BSA is used as the molecular recognition compound modified on the first nanoparticles, and protein A is used as the molecular recognition compound modified on the second nanoparticles. However, the present invention is not limited to these.

Example 1. Preparation of slide glass with amino groups introduced therein
A slide glass (S1111, Matsunami Glass Co., Ltd.) was washed with ultrapure water and 70% ethanol and dried. Next, 1% APTES ((3-aminopropyl)triethoxysilane, diluted with ultrapure water) was applied to the slide glass, and after leaving it at room temperature for 30 minutes, it was washed with ultrapure water. This was dried to obtain an amino group-introduced slide glass.

Example 2. Preparation of Protein A-modified silver nanoparticles
50 μL of silver nanoparticles (60 nm, BBI solution) and 50 μL of protein A (10 μg/mL, Wako Pure Chemical Industries, Ltd.) were mixed in 0.1×PBS, and then left to stand at room temperature for 15 minutes. 900 μL of PEG (0.02%) was mixed and centrifuged (6000 rpm, 10 minutes). The supernatant was removed and the precipitated nanoparticles were collected. 1 mL of PEG (0.02%) was added to disperse the nanoparticles, and then centrifuged in the same manner. The collected precipitate was dispersed in 0.1×PBS (50 μL), and this was used as protein A-modified silver nanoparticles (AgNP-ProA).

Example 3: Preparation of BSA-modified gold nanoarchin
50 μL of gold nanourchin (80 nm, 16 pM, Cytodiagnostics) and 50 μL of BSA (Fraction V, 50 μg/mL, Wako Pure Chemical Industries, Ltd.) were mixed in 0.1×PBS and then left to stand at room temperature for 15 minutes. 900 μL of PEG (0.02%) was mixed and centrifuged (6000 rpm, 10 minutes). The supernatant was removed and the precipitated nanoparticles were collected. The collected precipitate was dispersed in 0.1×PBS (50 μL) and used as BSA-modified gold nanourchin (AuNU-BSA).

Example 4. Evaluation of nanoparticle modification
The production of AgNP-ProA and AuNU-BSA was confirmed by particle size measurement using dynamic light scattering (DLS). The results are shown in Figure 2. The average particle size of silver nanoparticles (AgNP) increased from 63.9 nm to 79.3 nm before and after modification. The average particle size of gold nanourchins (AuNU) increased from 89.0 nm to 100.0 nm before and after modification. From these results, it was confirmed that protein A and BSA were modified on the surfaces of the respective nanoparticles.

Example 5. Dark-field observation of nanoparticles
AgNP-ProA prepared in Example 2 and AuNU-BSA prepared in Example 3 were observed under a dark field. AgNP-ProA, AuNU-BSA and 2 μg/mL of anti-BSA antibody were mixed to form a dimer of AgNP-ProA and AuNU-BSA via the anti-BSA antibody, which was observed under a dark field. The preparation for observation was performed by dropping 3.5 μL of the sample onto the amino group-introduced slide glass prepared in Example 1, covering it with a cover glass, and sealing it by applying a top coat to the four sides of the cover glass. The prepared preparation was observed by oil immersion under a dark field. The observation device used was a microscope (BX53, Olympus), a CCD camera (color, DP73, Olympus), and an objective lens (UplanFLN60x, Olympus). The observation conditions were exposure time 10 sec, ISO 100, and 4800x3600 pixel shift.

As a result, bright spots of different colors were observed for AgNP-ProA and AuNU-BSA. Furthermore, bright spots of different colors (pink to purple) were observed in the mixed sample from both AgNP-ProA and AuNU-BSA. Figure 3 shows a bar graph of the color components (RGB components) of representative bright spots observed under dark field observation in AgNP-ProA, AuNU-BSA, and a dimer of AgNP-ProA and AuNU-BSA.

Example 6. Prediction of the image of silver nanoparticle-gold nanourtin dimer
The modified nanoparticles prepared in Example 2 and Example 3 were each subjected to dark field observation, and 10 observed bright spots were extracted at 30 x 30 pixels. Next, a calculated dimer image was obtained by superimposing the bright spots of AgNP-ProA and the bright spots of AuNU-BSA. In detail, all combinations (10 x 10) of 10 bright spots of AgNP-ProA and 10 bright spots of AuNU-BSA were superimposed to obtain a total of 100 calculated dimer images. The calculated dimer image is intended to be an image that is presumed to be observed when a dimer is formed between AgNP-ProA and AuNU-BSA. The extracted 10 bright spots in each modified nanoparticle and the calculated dimer image were processed by the method shown below and visualized by a three-dimensional plot of RGB values.
(1) Image Processing Image processing was carried out using image analysis software ImageJ (Biophotonics International, vol. 11, issue 7, pp. 36-42, 2004) in the following manner.

(1-1) Grayscale conversion, bright spot analysis, and ROI storage The target image was converted to grayscale, and bright spot analysis and acquisition of bright spot area information (ROI) were performed. The parameters for bright spot analysis were threshold 30-255, size 30-10000, and circularity 0.3-1.0.

(1-2) Division into RGB and analysis of bright spots in the ROI in each image The target image composed of RGB was divided into three images, an image of the R component, an image of the G component, and an image of the B component. Bright spot analysis was performed on each image using the ROI obtained in (1-1) above, and the intensity of the ROI in each of the R, G, and B images was obtained. The intensity of each obtained ROI was taken as the intensity of each R, G, and B of each bright spot. This was used as the analysis data for the following.

(2) Data processing using R The analysis data obtained in (1-2) above was processed using the free statistical analysis software R (R DEVELOPMENT CORE TEAM (2005). R: A LANGUAGE AND environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.).

Specifically, the analysis data was output as a text file, and a table was created in which each bright spot corresponded to its RGB value. The distribution of the RGB values of the bright spots was visualized by plotting the RGB values of each bright spot in three dimensions. The results of observing the visualized three-dimensional plot of RGB values from different angles correspond to Figure 4 (a) to (c).

As a result, it was confirmed that the clusters were divided into three clusters: a cluster of silver nanoparticles, a cluster of gold nanoartin, and a cluster of calculated dimers. In addition, it was estimated that the three-dimensional plot of the RGB values of the bright spots observed as dimers was located at the positions shown in Figure 4 (a) to (c).

Example 7. Three-dimensional plot of AgNP-AuNU heterodimer sample
Each dark field image observed in Example 5 was processed by the method shown in Example 6, and the results of RGB three-dimensional plotting including the calculated dimer image of Example 6 are shown in FIG. 5. As shown in (a) to (c) of FIG. 5, plots that are clearly different from the plots derived from silver nanoparticles and gold nanourchins were confirmed in the mixed sample, and the plots were located sufficiently close to the plots of the calculated dimer image. The above results suggest that dimers of silver nanoparticles and gold nanourchins are present in the mixed sample. In addition, as shown in (a) to (c) of FIG. 5, it was confirmed that silver nanoparticles, gold nanourchins, and silver nanoparticle-gold nanourchin dimers can be distinguished by color separation. In addition, in the plots shown in FIG. 5, the clusters of silver nanoparticles also contain bright spots of modified silver nanoparticles present as monomers in the mixed sample. Similarly, in the plots shown in FIG. 5, the clusters of gold nanourchins also contain bright spots of modified gold nanourchins present as monomers in the mixed sample.

[Example 8. Construction of machine learning]
By providing criteria for determining which nanoparticles (including heterogeneous nanoparticle dimers) or impurities the bright spots to be observed correspond to, it becomes easier to distinguish the attribution. However, when the plots of each bright spot are a distribution that cannot be linearly separated, as shown in the three-dimensional plot of Figure 5, it is difficult to manually define the criteria. Therefore, a method for distinguishing the attribution of bright spots using machine learning was adopted.

The RGB value information of the bright spots of silver nanoparticles and gold nanourchins obtained in Examples 2 and 3, together with their attribution, was used as learning data. In addition, a night vision image of 0.1x PBS containing no nanoparticles was taken, and the obtained information was used as learning data for impurities. For the heterodimer of silver nanoparticles and gold nanourchins, 400 composite images were obtained by combining 20 bright spot images (30x30pix) of anti-BSA antibody-modified AuNU and 20 bright spot images (30x30pix) of AgNP-proA for all combinations (20x20) using ImageJ. This composition process was also performed when the bright spot images were shifted horizontally by 5 or 10pix, obtaining an additional 800 composite images, and finally obtaining a total of 1200 composite images. These were treated as bright spot images of heterodimers, and their RGB value information, together with their attribution, was used as learning data.

A classification model for determining the attribution of bright spots was created using machine learning with this training data, using the free statistical analysis software R.

Example 9. Dependence of detection target concentration
AgNP-ProA and AuNU-BSA were mixed with samples in which the concentration of anti-BSA antibody was changed to 0, 0.5, 1 or 2 μg/mL as the detection target, and the mixed samples were subjected to dark field observation, and the ratio of the amount of heterodimer to the total amount of AgNP-ProA, AuNU-BSA, and heterodimer was calculated. The attribution of each bright spot for calculating the ratio of heterodimer was determined by R, a free statistical analysis software with machine learning in Example 8. The results are shown in FIG. 6. It was confirmed from FIG. 6 that the ratio of heterodimer increased depending on the concentration of the detection target. In other words, a detection system for target molecules was constructed using nanoparticles with different bright spot colors under a dark field, utilizing the change in bright spot color in the monomer state and the dimer state.

Example 10: Preparation of BSA-modified silver nanoparticles
50 μL of silver nanoparticles (60 nm, 28 pM, manufactured by BBI solution) and 50 μL of BSA (Fraction V, 50 μg/mL, manufactured by Wako Pure Chemical Industries, Ltd.) were mixed in 50 μL of 0.1×PBS, and then the mixture was left to stand at 37° C. for 30 minutes. 500 μL of PEG (0.02%) was mixed and centrifuged (8000 rpm, 10 minutes). The supernatant was removed, and the precipitated nanoparticles were collected. The collected precipitate was dispersed in 0.1×PBS (50 μL), and this was used as BSA-modified silver nanoparticles (AgNP-BSA).

Example 11. Preparation of Protein A-modified gold nanoarchin
50 μL of gold nanourchin (80 nm, 39 pM, Cytodiagnostics) and 50 μL of protein A (10 μg/mL, Wako Pure Chemical Industries, Ltd.) were mixed in 50 μL of 0.1×PBS, and then left to stand at 37° C. for 30 minutes. 500 μL of BSA solution containing PEG (0.02%) was mixed and centrifuged (8000 rpm, 10 minutes). The supernatant was removed, and the precipitated nanoparticles were collected. The collected precipitate was dispersed in 0.1×PBS (50 μL), and this was used as protein A-modified gold nanourchin (AuNU-ProA).

Example 12. Evaluation of nanoparticle modification
The production of AgNP-BSA and AuNU-ProA was confirmed by particle size measurement using dynamic light scattering (DLS). The results are shown in Figure 7. The average particle size of silver nanoparticles (AgNP) increased from 65.2 nm to 83.5 nm before and after modification. The average particle size of gold nanourchins (AuNU) increased from 90.0 nm to 103.8 nm before and after modification. From these results, it was confirmed that BSA and protein A were modified on the surface of each nanoparticle.

Example 13. Dark-field observation of nanoparticles
AgNP-BSA prepared in Example 10 and AuNU-ProA prepared in Example 11 were observed under a dark field. AgNP-BSA, AuNU-ProA and 2 μg/mL of anti-BSA antibody were mixed to form a dimer of AgNP-BSA and AuNU-ProA via the anti-BSA antibody, which was observed under a dark field. The dimer sample was prepared by first mixing AgNP-BSA and 2 μg/mL of anti-BSA antibody, leaving it at 37° C. for 30 minutes, adding AuNU-ProA, and reacting for 60 minutes. The preparation of the observation preparation was performed by dropping 3.5 μL of the sample onto the amino group-introduced slide glass prepared in Example 1, covering it with a cover glass, and sealing it by applying a top coat to the four sides of the cover glass. The prepared preparation was observed by oil immersion under a dark field. The observation device used was a microscope (BX53, Olympus), a CCD camera (color, DP73, Olympus), and an objective lens (UplanFLN60x, Olympus). The observation conditions were exposure time 10 sec, ISO 100, and 4800x3600 pixel shift.

As a result, bright spots of different colors were observed for AgNP-BSA and AuNU-ProA. Furthermore, in the mixed sample, bright spots of a different color (pink to purple) were observed for both AgNP-BSA and AuNU-ProA.

Example 14. Three-dimensional plot of AgNP-AuNU heterodimer sample
Each dark field image observed in Example 13 was processed by the method shown in Example 6, and the results of RGB three-dimensional plotting including the calculated dimer image of Example 6 are shown in FIG. 8. As shown in (a) to (c) of FIG. 8, plots that are clearly different from the plots derived from silver nanoparticles and gold nanourchins were confirmed in the mixed sample, and the plots were located sufficiently close to the plots of the calculated dimer image. The above results suggest that dimers of silver nanoparticles and gold nanourchins are present in the mixed sample. In addition, as shown in (a) to (c) of FIG. 8, it was confirmed that silver nanoparticles, gold nanourchins, and silver nanoparticle-gold nanourchin dimers can be distinguished by color separation. In addition, in the plots shown in FIG. 8, the clusters of silver nanoparticles also contain bright spots of modified silver nanoparticles present as monomers in the mixed sample. Similarly, in the plots shown in FIG. 8, the clusters of gold nanourchins also contain bright spots of modified gold nanourchins present as monomers in the mixed sample.

Example 15. Dependence of detection target concentration
AgNP-BSA and AuNU-ProA were mixed with samples in which the concentration of anti-BSA antibody was changed to 0, 0.5, 1 or 2 μg/mL as the detection target, and the mixed samples were subjected to dark field observation, and the ratio of heterodimers was calculated when the total number of AgNP-BSA, AuNU-ProA, and heterodimers was taken as the total amount. The attribution of each bright spot to calculate the ratio of heterodimers was determined by R, a free statistical analysis software with machine learning in Example 8. The results are shown in FIG. 9. It was confirmed from FIG. 9 that the ratio of heterodimers increased depending on the concentration of the detection target. That is, even in this example in which the molecular recognition compound modifying each of the silver nanoparticles and the gold nanourchins was changed, a detection system for the target molecule was constructed using nanoparticles with different bright spot colors under a dark field, utilizing the change in bright spot color in the monomer state and the dimer state.

The present invention can be used in fields where detection objects are detected under dark field conditions.

Claims (5)

mixing a first nanoparticle modified with a first molecular recognition compound, a second nanoparticle modified with a second molecular recognition compound, and a target sample; and observing the mixed sample under dark field observation;
the first nanoparticle, the second nanoparticle, and a dimer of the first nanoparticle and the second nanoparticle have bright spot colors different from each other under dark-field observation;
The first nanoparticles are silver nanoparticles, and the second nanoparticles are gold nanourchins, gold nanoparticles, or gold nanorods;
A method for detecting a molecule, comprising discriminating the dimer based on the difference in color of the bright spot. 2. The method for detecting a molecule according to claim 1 , wherein at least one of the first molecular recognition compound and the second molecular recognition compound is a protein. 3. The method for detecting a molecule according to claim 1, wherein at least one of the first molecular recognition compound and the second molecular recognition compound is an antibody. mixing a first nanoparticle modified with a first molecular recognition compound, a second nanoparticle modified with a second molecular recognition compound, and a target sample; and observing the mixed sample under dark field observation;
the first nanoparticles, the second nanoparticles, and the aggregate of the first nanoparticles and the second nanoparticles have different bright spot colors under dark-field observation;
The first nanoparticles are silver nanoparticles, and the second nanoparticles are gold nanourchins, gold nanoparticles, or gold nanorods;
A molecular detection method comprising: discriminating the aggregate based on the difference in color of the bright spot. 1. A method for detecting a dimer of a nanoparticle, comprising:
forming a dimer between a first nanoparticle and a second nanoparticle;
the first nanoparticles and the second nanoparticles have different bright spot colors under dark-field observation;
The first nanoparticles are silver nanoparticles, and the second nanoparticles are gold nanourchins, gold nanoparticles, or gold nanorods;
A method for detecting a nanoparticle dimer, which detects the dimer of the first nanoparticle and the second nanoparticle by detecting a bright spot color different from the bright spot color of the first nanoparticle and the bright spot color of the second nanoparticle.

Images