Highly sensitive biochemical immunoassay method based on quantum dot labeling and cascade amplification

Abstract

This invention relates to the field of immunoassay technology, and particularly to a highly sensitive biochemical immunoassay method based on quantum dot labeling and cascade amplification. The method includes: acquiring multispectral image data of a sample to be tested after cascade amplification and quantum dot labeling; preprocessing the multispectral image data and adding corresponding timestamps to form a biochemical analysis image set; detecting the positioning accuracy of quantum dots in the sample based on the biochemical analysis image set; and calculating the concentration of the corresponding target analyte in the sample by combining the positioning accuracy and the color recognition accuracy of the quantum dots.

Description

Technical Field

[0001] This invention relates to the field of immunoassay technology, and in particular to a highly sensitive biochemical immunoassay method based on quantum dot labeling and cascade amplification. Background Technology

[0002] Highly sensitive biochemical analytical methods are cutting-edge and core technologies in modern biochemistry, molecular biology, medical diagnostics, drug development, and environmental monitoring. They are designed to detect extremely low concentrations of target molecules (such as proteins, nucleic acids, small molecules, and ions), with detection limits typically reaching picomolars (pM, 10⁻⁶). -12）、飞摩尔（fM, 10-15 Even Amore (aM, 10) -18 At the quantum dot (QD) level, existing high-sensitivity analytical strategies often combine quantum dot labeling with cascade amplification techniques. Quantum dots are nanoscale semiconductor fluorescent materials with broad excitation and narrow emission characteristics; different colors of quantum dots can be excited by a single wavelength of light. In analysis, quantum dots serve as ultra-bright, multiplexable reporter groups. Cascade amplification technology mimics signal transduction pathways in biological systems (e.g., blood clotting, visual signal transduction). An initial recognition event can trigger a series of continuous enzymatic or chemical reactions, each step generating a geometrically increasing signal, thus achieving signal multiplication.

[0003] During the analysis process, it is crucial to determine the accuracy of quantum dot labeling and the accuracy of quantum dot color recognition. Errors in quantum dot labeling can affect the intensity and range of the light spot, and spectral crosstalk between different quantum dots can affect the recognition of different quantum dots, ultimately impacting the accuracy of the target object analysis. Summary of the Invention

[0004] This invention analyzes the positioning accuracy (marking accuracy) of quantum dots and the recognition accuracy of various quantum dot colors, and incorporates both into the analysis of target substances, thereby improving the reliability of target substance concentration analysis.

[0005] The technical solution proposed in this invention is: a highly sensitive biochemical immunoassay method based on quantum dot labeling and cascade amplification, the method comprising:

[0006] The multispectral image data of the sample to be tested after cascade amplification and quantum dot labeling were acquired. The multispectral image data were preprocessed and corresponding timestamps were added to form a biochemical analysis image set.

[0007] Based on a biochemical analysis image set, the localization accuracy of quantum dots in the sample to be tested is detected;

[0008] By combining the positioning accuracy and color recognition accuracy of quantum dots, the concentration of the corresponding target substance in the sample to be tested is calculated.

[0009] Preferably, acquiring the multispectral image data of the sample to be tested after cascade amplification and quantum dot labeling includes:

[0010] Quantum dots with different emission wavelengths are used to label multiple target objects within the sample to be tested;

[0011] Acquire images of the same sample region of the sample to be tested multiple times consecutively; the same sample region includes only one quantum dot with a single emission wavelength;

[0012] Acquire spectral images of multi-labeled sample regions of the sample to be tested; each pixel of the spectral image of the multi-labeled sample region includes different quantum dot spectra.

[0013] Multispectral image data consists of images of the same sample region captured multiple times consecutively and spectral images of the sample region from multiple times.

[0014] Preferably, the step of detecting the localization accuracy of quantum dots of different colors in the sample to be tested based on the biochemical analysis image set includes:

[0015] Acquire images of the same sample region multiple times consecutively;

[0016] The sample image sequence is composed of images arranged according to imaging time. ;in, Indicates the first The image captured in the secondary imaging;

[0017] The Laplacian Gaussian spot detection algorithm is used to identify the light spots emitted by individual quantum dots in each image.

[0018] The center coordinates of each light spot are calculated using a Gaussian fitting algorithm; including:

[0019] Construct a two-dimensional Gaussian model to fit the intensity distribution function of each light spot:

[0020] ;in, Indicates the background light intensity of the image. Indicates light spot Maximum brightness , Indicates the Gaussian function in shaft and Standard deviation in the axial direction; Indicates the first The light spot in the secondary image The center coordinates;

[0021] Solving the objective function using the least squares method ; obtain ;in, This indicates the measured light spot intensity;

[0022] Calculate the light spot The positioning accuracy includes:

[0023] Obtaining a single quantum dot The coordinates of this secondary positioning constitute the positioning coordinate set:

[0024] ;

[0025] Calculate the mean of the center coordinates ;

[0026] Calculate the standard deviation of the center coordinates ; ;

[0027] Then the light spot The positioning accuracy is ;

[0028] if If the position is marked correctly, the location of a single-color quantum dot is accurately determined; otherwise, the position is inaccurate.

[0029] if If the position of the quantum dot of the same color is accurately determined after cascaded amplification, then it is determined to be inaccurate; otherwise, it is determined to be inaccurate. These represent the standard deviation threshold for the accuracy of a single quantum dot and the standard deviation threshold for the accuracy of quantum dots of the same color, respectively. This indicates the number of quantum dots of the same color.

[0030] Preferably, the step of calculating the concentration of the corresponding target analyte in the sample to be detected by combining the positioning accuracy and color recognition accuracy of quantum dots includes:

[0031] Single target analyte concentration calculation includes:

[0032] Acquire sample image sequences and estimate amplitude uncertainty. ;in, Indicates the signal-to-noise ratio. Indicates the width of the point spread function (PSF);

[0033] Calculate the sum of the brightness of all light spots. ;

[0034] Uncertainty of the sum of brightness ;

[0035] Will Let it be a random variable, that is, let Follows a normal distribution: ;in, ;

[0036] A cascaded amplification and labeling model was constructed to establish the relationship between the target concentration and the desired total brightness.

[0037] The concentration of the target analyte is obtained based on the total brightness of the light spot, the uncertainty of brightness integration, and the cascade amplification and labeling model.

[0038] Preferably, the step of calculating the concentration of the corresponding target analyte in the sample to be detected by combining the positioning accuracy and color recognition accuracy of quantum dots further includes:

[0039] Multi-object concentration calculation, including:

[0040] Acquire the spectral image of the multi-labeled sample region of the sample to be tested, perform preliminary demixing, and obtain the apparent signal intensity of each color quantum dot contained in each pixel;

[0041] Nonlinear concentration back-calculation is performed by substituting the apparent signal intensity of each color channel into the corresponding channel's standard fitting curve to back-calculate the true concentration of the target analyte in that channel.

[0042] Preferably, the method of obtaining the concentration of the target object based on the total brightness of the light spot, the uncertainty of brightness integration, and the cascaded amplification and labeling model includes:

[0043] The cascade amplification and labeling model is as follows: ;in, Indicates the concentration of a single target analyte; Indicates the intensity of the light spot at zero concentration. Indicates the intensity of the light spot at saturation concentration; This indicates the intensity of the light spot at a 50% concentration. Represents the Hill coefficient;

[0044] Probability inversion solution includes:

[0045] Establish the likelihood function for the target analyte concentration, that is, given a certain concentration, the observed spot intensity is... The probability of:

[0046] Likelihood function ;

[0047] Performing maximum likelihood estimation, i.e., obtaining the value that maximizes the likelihood function. Values, including:

[0048] Maximize the log-likelihood function ;

[0049] Construct the objective function ;

[0050] Maximizing the log-likelihood function is equivalent to minimizing the objective function;

[0051] Use the Newton-Raphson iterative algorithm to find the objective function. smallest Value, i.e., the optimal concentration estimate. ,include:

[0052] Initial value estimation ;

[0053] Newton-Raphson iteration: ;in, Indicates the first The concentration estimate for the next iteration;

[0054] Achieve Optimal min ;

[0055] Calculate concentration uncertainty Then, the concentration of the target substance ,in, Indicates the weight.

[0056] Preferably, the step of acquiring the spectral image of the multi-labeled sample region of the sample to be detected, performing preliminary demixing, and obtaining the apparent signal intensity of each color quantum dot contained in each pixel includes:

[0057] Let the spectral image of the multi-labeled sample region be... ;

[0058] right Preliminary linear demixing is performed to obtain the apparent signal intensity vector of each color quantum dot for each pixel:

[0059] ;in, This represents a quantum dot wavelength data matrix, where each row of the quantum dot wavelength data matrix represents data of a reference spectrum of a quantum dot at P wavelength points; This represents the measurement wavelength data vector, which represents the data of the measurement spectrum at P wavelength points; Indicates the first The apparent signal intensity vector of a color quantum dot.

[0060] Preferably, the nonlinear concentration back-calculation, which involves substituting the apparent signal intensity of each color channel into the corresponding channel's standard fitting curve to back-calculate the true concentration of the target analyte in that channel, includes: calculating the covariance matrix, including:

[0061] Let the noise variance of the measured spectrum S be... ,but The covariance matrix is The diagonal elements of the covariance matrix are the first... The equation for a quantum apparent signal vector, namely Off-diagonal elements represent the covariance of different quantum light intensities;

[0062] A multi-objective inverse calculation model is defined using four-parameter logic:

[0063] Multi-objective inverse calculation model ;

[0064] Let the model parameter vector be... ;

[0065] Build The cost function, ;in, and Indicates the first The intensity of the light spot at zero concentration, saturation concentration, and half concentration of the target material labeled by various quantum dots;

[0066] Use an iterative algorithm to find... Minimum optimal parameters ;

[0067] The true concentration of the target substance in this channel is calculated in reverse, including:

[0068] Constructing a multi-objective likelihood function ;

[0069] Maximizing the multi-objective likelihood function is equivalent to minimizing ;

[0070] Then the optimal concentration .

[0071] Preferably, the step of performing nonlinear concentration back-calculation, which involves substituting the apparent signal intensity of each color channel into the corresponding channel's standard fitting curve to back-calculate the true concentration of the target analyte in that channel, further includes:

[0072] calculate Follow The curve showing the change, with the confidence interval (approximately 1 standard deviation) corresponding to the 68.3% confidence level of the optimal concentration, is derived from... The two corresponding concentration values ​​are determined, among which express The minimum value;

[0073] Solve using the bisection method The equation yields two solutions. and ;

[0074] The confidence interval corresponding to the 68.3% confidence level of the optimal concentration is: ].

[0075] An electronic device includes a processor and a communication module and a memory connected to the processor, the electronic device being used to implement the aforementioned highly sensitive biochemical immunoassay method based on quantum dot labeling and cascade amplification.

[0076] The beneficial effects of this invention are:

[0077] 1. In the analysis of a single target analyte, the accuracy of quantum dot labeling is incorporated into the calculation of the target analyte concentration, thereby improving the accuracy of the single target analyte concentration calculation.

[0078] 2. In multi-target analysis, the accuracy of identifying the color of each quantum dot is incorporated into the calculation of the concentration of the corresponding target, eliminating spectral crosstalk and providing the optimal concentration and corresponding confidence interval for each target. This is of vital importance for scientific research or diagnostic applications that require critical decision-making. Attached Figure Description

[0079] Figure 1 This is a flowchart of the highly sensitive biochemical immunoassay method based on quantum dot labeling and cascade amplification according to the present invention. Detailed Implementation

[0080] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious modifications will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0081] It is understood that the term "a" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "a" should not be understood as a limitation on the number.

[0082] refer to Figure 1 The technical solution provided by this invention is: a highly sensitive biochemical immunoassay method based on quantum dot labeling and cascade amplification, the method comprising:

[0083] Step 1: Obtain multispectral image data of the sample to be tested after cascade amplification and quantum dot labeling. Preprocess the multispectral image data and add corresponding timestamps to form a biochemical analysis image set.

[0084] The process of acquiring multispectral image data of the sample to be tested after cascade amplification and quantum dot labeling includes the following steps:

[0085] Quantum dots with different emission wavelengths, i.e., quantum dots of different colors, are used to label multiple target objects in the sample to be tested;

[0086] Acquire images of the same sample region of the sample to be tested multiple times consecutively; the same sample region includes only one quantum dot with a single emission wavelength;

[0087] Acquire spectral images of the multi-labeled sample region of the sample to be tested; each pixel of the spectral image of the multi-labeled sample region includes different quantum dot spectra; in this embodiment, the complete spectrum of each pixel is acquired by using a monochrome camera and multiple filters.

[0088] Multispectral image data consists of images of the same sample region captured multiple times consecutively and spectral images of the sample region from multiple times.

[0089] Step 2: Based on the biochemical analysis image set, detect the localization accuracy of quantum dots in the sample to be tested, including the following steps:

[0090] Acquire images of the same sample area multiple times consecutively. In this embodiment, 100-500 consecutive frames of images are acquired;

[0091] The sample image sequence is composed of images arranged according to imaging time. ;in, Indicates the first The image captured in the secondary imaging;

[0092] The Laplacian Gaussian spot detection algorithm is used to identify the light spots emitted by individual quantum dots in each image.

[0093] The center coordinates of each light spot are calculated using a Gaussian fitting algorithm; including:

[0094] Because of the optical diffraction mechanics of a microscope, an Airy disk is formed by the image of a point light source on a camera, and its intensity distribution approximates a two-dimensional Gaussian function. Therefore, a two-dimensional Gaussian model is constructed to fit the intensity distribution function of each light spot:

[0095] ;in, Indicates the background light intensity of the image. Indicates light spot Maximum brightness (amplitude) , Indicates the Gaussian function in shaft and Standard deviation along the axis (related to the width of the point spread function PSF); Indicates the first The light spot in the secondary image The center coordinates;

[0096] Solving the objective function using the least squares method ; obtain ;in, This indicates the measured light spot intensity;

[0097] Calculate the light spot The positioning accuracy includes:

[0098] Obtaining a single quantum dot The coordinates of this secondary positioning constitute the positioning coordinate set:

[0099] ;

[0100] Calculate the mean of the center coordinates ;

[0101] Calculate the standard deviation of the center coordinates ; ;

[0102] Then the light spot The positioning accuracy is ;

[0103] if If the position is marked correctly, the location of a single-color quantum dot is accurately determined; otherwise, the position is inaccurate.

[0104] if If the position of the quantum dot of the same color is accurately determined after cascaded amplification, then it is determined to be inaccurate; otherwise, it is determined to be inaccurate. These represent the standard deviation threshold for the accuracy of a single quantum dot and the standard deviation threshold for the accuracy of quantum dots of the same color, respectively. This indicates the number of quantum dots of the same color.

[0105] Step 3: Combining the positioning accuracy and color recognition accuracy of quantum dots, calculate the concentration of the corresponding target analyte in the sample to be tested, including:

[0106] Step 3.1: Calculation of single target analyte concentration;

[0107] Step 3.2, Calculation of multi-target concentration.

[0108] The calculation of a single target analyte concentration includes the following steps:

[0109] Step 3.11: Obtain the sample image sequence and estimate the amplitude uncertainty. ;in, Indicates the signal-to-noise ratio. This represents the width of the point spread function (PSF) (typically 150 nm).

[0110] Step 3.12: Calculate and obtain the total brightness of all light spots. ;

[0111] Uncertainty of the sum of brightness ;

[0112] Step 3.13, will Let it be a random variable, that is, let Follows a normal distribution: ;in, ;

[0113] Step 3.14: Construct a cascaded amplification and labeling model to establish the relationship between the target concentration and the desired total brightness;

[0114] Step 3.15: Based on the total brightness of the light spot, the uncertainty of brightness integration, and the cascaded amplification and labeling model, obtain the concentration of the target analyte, specifically including the following steps:

[0115] Cascade amplification is a multi-step, enzyme-catalyzed process, where the efficiency of each step is not 100%, and nonlinear effects such as substrate consumption, product inhibition, and enzyme saturation exist. Ultimately, the number of quantum dot labeled sites produced has a nonlinear relationship with the target concentration. Therefore, a nonlinear model of cascade amplification and labeling needs to be constructed.

[0116] The cascade amplification and labeling model (four-parameter Shell function) is as follows: ;in, Indicates the concentration of a single target analyte; Indicates the intensity of the light spot at zero concentration. Indicates the intensity of the light spot at saturation concentration; This indicates the intensity of the light spot at a 50% concentration. Represents the Hill coefficient;

[0117] Probability inversion solution includes:

[0118] Establish the likelihood function for the target analyte concentration, that is, given a certain concentration, the observed spot intensity is... The probability of:

[0119] Likelihood function ;

[0120] Performing maximum likelihood estimation, i.e., obtaining the value that maximizes the likelihood function. Values, including:

[0121] Maximize the log-likelihood function ;

[0122] Construct the objective function ;

[0123] Maximizing the log-likelihood function is equivalent to minimizing the objective function;

[0124] Use the Newton-Raphson iterative algorithm to find the objective function. smallest Value, i.e., the optimal concentration estimate. ,include:

[0125] Initial value estimation ;

[0126] Newton-Raphson iteration: ;in, Indicates the first The concentration estimate for the next iteration;

[0127] Achieve Optimal min ;

[0128] Calculate concentration uncertainty Then, the concentration of the target substance ,in, Indicates weight;

[0129] Explicitly including errors caused by positioning accuracy uncertainty allows for the incorporation of positioning accuracy into concentration back-calculation, establishing... Complete chain, The reliability of the measurement results is given. For example, if the positioning quality of some light spots is very poor, i.e. It's big. It's big, it's... The contribution is greater, and the weight assigned when calculating the concentration of the target analyte is higher. This makes the concentration calculation more accurate.

[0130] The calculation of multi-target concentrations includes the following steps:

[0131] Step 3.21: Obtain the spectral image of the multi-labeled sample region of the sample to be tested, perform preliminary demixing, and obtain the apparent signal intensity of each color quantum dot contained in each pixel; including the following steps:

[0132] Let the spectral image of the multi-labeled sample region be... ;

[0133] right Preliminary linear demixing is performed to obtain the apparent signal intensity vector of each color quantum dot for each pixel:

[0134] ;in, This represents a quantum dot wavelength data matrix, where each row of the quantum dot wavelength data matrix represents data of a reference spectrum of a quantum dot at P wavelength points; This represents the measurement wavelength data vector, which represents the data of the measurement spectrum at P wavelength points; Indicates the first The apparent signal intensity vector of a color quantum dot.

[0135] Step 3.22: Perform nonlinear concentration back-calculation. Substitute the apparent signal intensity of each color channel into the corresponding channel's standard fitting curve to back-calculate the true concentration of the target analyte in that channel. This includes the following steps:

[0136] Calculating the covariance matrix includes:

[0137] Let the noise variance of the measured spectrum S be... (Usually estimated from camera parameters or dark current), then The covariance matrix is The diagonal elements of the covariance matrix are the first... The variance of a quantum apparent signal vector, i.e. Off-diagonal elements represent the covariance of different quantum light intensities, reflecting crosstalk between different quantum light signals.

[0138] A multi-objective inverse calculation model is defined using four-parameter logic:

[0139] Multi-objective inverse calculation model ;

[0140] Let the model parameter vector be... ;

[0141] Build The cost function, ;in, and Indicates the first The intensity of the light spot at zero concentration, saturation concentration, and half concentration of the target material labeled by various quantum dots;

[0142] Using an iterative algorithm (Levenberg-Marquardt), find the solution that... Minimum optimal parameters ;

[0143] The true concentration of the target substance in this channel is calculated in reverse, including:

[0144] Constructing a multi-objective likelihood function ;

[0145] Maximizing the multi-objective likelihood function is equivalent to minimizing ;

[0146] Then the optimal concentration ;

[0147] calculate Follow The curve showing the change, with the confidence interval (approximately 1 standard deviation) corresponding to the 68.3% confidence level of the optimal concentration, is derived from... The two corresponding concentration values ​​are determined, among which express The minimum value;

[0148] Solve using the bisection method The equation yields two solutions. and ;

[0149] The confidence interval corresponding to the optimal concentration at a 68.3% confidence level is: ].

[0150] The above steps quantify the accuracy of color recognition and incorporate this uncertainty into the nonlinear concentration back-calculation process. The variance of the apparent signal vector of a quantum dot directly reflects the accuracy of color recognition. Specifically, the residual of each data point... All Weighted, The larger the value (the less reliable the color recognition), the lower its weight in the fitting. By incorporating the inherent uncertainty in biochemical analysis (in this case, spectral crosstalk) into the calculation in a quantitative manner, a result with reliability assessment (optimal concentration and confidence interval) is ultimately provided, rather than just a single data point (which may be contaminated, for example, by spectral crosstalk), thus adapting to multi-object analysis scenarios (multiple objects labeled with quantum dots of various colors).

[0151] The present invention also provides an electronic device, including a processor and a communication module and a memory connected to the processor, the electronic device being used to implement the aforementioned high-sensitivity biochemical immunoassay method based on quantum dot labeling and cascade amplification.

[0152] The processes described above with reference to the flowcharts in the embodiments disclosed in this invention can be implemented as computer software programs. The embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wire segments, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless segments, wire segments, optical fibers, RF, etc., or any suitable combination thereof.

[0153] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0154] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functions and structural principles of the present invention have been shown and explained in the embodiments. Without departing from the principles described, the implementation of the present invention may have any changes or modifications.

Claims

1. A high sensitive biochemical immunoassay method based on quantum dot labeling and cascade amplification, characterized in that, The method includes: The multispectral image data of the sample to be tested after cascade amplification and quantum dot labeling were acquired. The multispectral image data were preprocessed and corresponding timestamps were added to form a biochemical analysis image set. Based on a biochemical analysis image set, the localization accuracy of quantum dots in the sample to be tested is detected; Combining the positioning accuracy and color recognition accuracy of quantum dots, the concentration of the corresponding target analyte in the sample to be detected is calculated, including: Single target concentration calculation, including: obtaining a sequence of sample images, estimating an amplitude uncertainty ; wherein, represents a signal-to-noise ratio, represents a point spread function, PSF, width; represents a spot maximum brightness; is a positioning accuracy of the spot ; Calculate the sum of the brightness of all light spots. Uncertainty of the sum of brightness ; Will Let it be a random variable, that is, let Follows a normal distribution: ;in, ; Indicates the number of quantum dots of the same color; A cascaded amplification and labeling model was constructed to establish the relationship between the target concentration and the desired total brightness. Based on the total brightness of the light spot, the uncertainty of brightness integration, and the cascaded amplification and labeling model, the concentration of the target analyte is obtained, including: The cascaded amplification and labeling model is as follows: ;in, Indicates the concentration of a single target analyte; Indicates the intensity of the light spot at zero concentration. Indicates the intensity of the light spot at saturation concentration; This indicates the intensity of the light spot at a 50% concentration. Represents the Hill coefficient; Probability inversion solution includes: Establish the likelihood function for the target concentration, which is the probability of observing a light spot with intensity given a certain concentration: Likelihood function ; Performing maximum likelihood estimation, i.e., obtaining the value that maximizes the likelihood function. Values, including: Maximize the log-likelihood function ; Construct the objective function ; Maximizing the log-likelihood function is equivalent to minimizing the objective function; the Newton-Raphson iterative algorithm is used to find the objective function. smallest Value, i.e., the optimal concentration estimate. ,include: Initial value estimation ; Newton-Raphson iteration: ;in, Indicates the first The concentration estimate for the next iteration; Achieve Optimal min ; Calculate concentration uncertainty Then, the concentration of the target substance ,in, Indicates the weight.

2. The highly sensitive biochemical immunoassay method based on quantum dot labeling and cascade amplification according to claim 1, characterized in that, The acquisition of multispectral image data of the sample to be tested after cascade amplification and quantum dot labeling includes: Quantum dots with different emission wavelengths are used to label multiple target objects within the sample to be tested; Acquire images of the same sample region of the sample to be tested multiple times consecutively; the same sample region includes only one quantum dot with a single emission wavelength; Acquire spectral images of multi-labeled sample regions of the sample to be tested; each pixel of the spectral image of the multi-labeled sample region includes different quantum dot spectra. Multispectral image data consists of images of the same sample region captured multiple times consecutively and spectral images of the sample region from multiple times.

3. The highly sensitive biochemical immunoassay method based on quantum dot labeling and cascade amplification according to claim 2, characterized in that, The method for detecting the localization accuracy of quantum dots of different colors in a sample based on a biochemical analysis image set includes: Acquire images of the same sample region multiple times consecutively; The sample image sequence is composed of images arranged according to imaging time. ;in, Indicates the first The image captured in the secondary imaging; The Laplacian Gaussian spot detection algorithm is used to identify the light spots emitted by individual quantum dots in each image. The center coordinates of each light spot are calculated using a Gaussian fitting algorithm; including: Construct a two-dimensional Gaussian model to fit the intensity distribution function of each light spot: ;in, Indicates the background light intensity of the image. , Indicates the Gaussian function in shaft and Standard deviation in the axial direction; Indicates the first The light spot in the secondary image The center coordinates; Solving the objective function using the least squares method ; obtain ;in, This indicates the measured light spot intensity; Calculate the light spot The positioning accuracy includes: Obtaining a single quantum dot The coordinates of this secondary positioning constitute the positioning coordinate set: ； Calculate the mean of the center coordinates ; Calculate the standard deviation of the center coordinates ; ; Then the light spot The positioning accuracy is ; if If the position is marked correctly, the location of a single-color quantum dot is accurately determined; otherwise, the position is inaccurate. if If the position of the quantum dot of the same color is accurately determined after cascaded amplification, then it is determined to be inaccurate; otherwise, it is determined to be inaccurate. These represent the standard deviation threshold for a single quantum dot and the standard deviation threshold for quantum dots of the same color, respectively.

4. The highly sensitive biochemical immunoassay method based on quantum dot labeling and cascade amplification according to claim 3, characterized in that, The method of calculating the concentration of the corresponding target analyte in the sample to be detected by combining the positioning accuracy and color recognition accuracy of quantum dots also includes: Multi-object concentration calculation, including: Acquire the spectral image of the multi-labeled sample region of the sample to be tested, perform preliminary demixing, and obtain the apparent signal intensity of each color quantum dot contained in each pixel; Nonlinear concentration back-calculation is performed by substituting the apparent signal intensity of each color channel into the corresponding channel's standard fitting curve to back-calculate the true concentration of the target analyte in that channel.

5. The highly sensitive biochemical immunoassay method based on quantum dot labeling and cascade amplification according to claim 4, characterized in that, The process of acquiring the spectral image of the multi-labeled sample region of the sample to be detected, performing preliminary demixing, and obtaining the apparent signal intensity of each color quantum dot contained in each pixel includes: Let the spectral image of the multi-labeled sample region be ; right Preliminary linear demixing is performed to obtain the apparent signal intensity vector of each color quantum dot for each pixel: ;in, This represents a quantum dot wavelength data matrix, where each row of the quantum dot wavelength data matrix represents data of a reference spectrum of a quantum dot at P wavelength points; This represents the measurement wavelength data vector, which represents the data of the measurement spectrum at P wavelength points; Indicates the first The apparent signal intensity vector of a color quantum dot.

6. The highly sensitive biochemical immunoassay method based on quantum dot labeling and cascade amplification according to claim 5, characterized in that, The nonlinear concentration inverse calculation involves substituting the apparent signal intensity of each color channel into the corresponding channel's standard fitting curve to inversely calculate the true concentration of the target analyte in that channel. This includes calculating the covariance matrix, including: Let the noise variance of the measured spectrum S be... ,but The covariance matrix is The diagonal elements of the covariance matrix are the first... The equation for a quantum apparent signal vector, namely Off-diagonal elements represent the covariance of different quantum light intensities; A multi-objective inverse calculation model is defined using four-parameter logic: Multi-objective inverse calculation model ; Let the model parameter vector be... ; Build The cost function, ;in, and Indicates the first The intensity of the light spot at zero concentration, saturation concentration, and half concentration of the target object labeled by various quantum dots; Use an iterative algorithm to find... Minimum optimal parameters ; The true concentration of the target substance in this channel is calculated in reverse, including: Constructing a multi-objective likelihood function ; Maximizing the multi-objective likelihood function is equivalent to minimizing ; Then the optimal concentration .

7. The highly sensitive biochemical immunoassay method based on quantum dot labeling and cascade amplification according to claim 6, characterized in that, The nonlinear concentration back-calculation, which involves substituting the apparent signal intensity of each color channel into the corresponding channel's standard fitting curve to back-calculate the true concentration of the target analyte in that channel, also includes: calculate Follow The curve showing the change, with the confidence interval (approximately 1 standard deviation) corresponding to the 68.3% confidence level of the optimal concentration, is derived from... The two corresponding concentration values ​​are determined, where represents The minimum value; Solve using the bisection method The equation yields two solutions. and ; The confidence interval corresponding to the 68.3% confidence level of the optimal concentration is: ].

8. An electronic device, comprising a processor, a communication module connected to the processor, and a memory, characterized in that, The electronic device is used to implement the highly sensitive biochemical immunoassay method based on quantum dot labeling and cascade amplification as described in any one of claims 1-7.

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