High-throughput fluorescence detection method, apparatus and system, and storage medium

Abstract

A high-throughput fluorescence detection method and system, relating to the fields of molecular detection and artificial intelligence, and being capable of simultaneously detecting and analyzing various target nucleic acids in a single reaction system. The method comprises: in a fluorescence encoding link, carrying out multi-color multi-order encoding on a same molecular probe, wherein multi-color encoding comprises a combination of different colors of the same molecular probe, multi-order encoding comprises a combination of fluorescence intensities of the same color, and the encoded molecular probe is distributed into at least two reaction units; in a transcoding link, carrying out ultra-multiple amplification on the molecular probe in each reaction unit, and carrying out multi-interval amplification on each reaction unit; and in a decoding link, using the reaction unit subjected to the transcoding link to carry out decoding, so as to obtain the number of fluorescence-encoded positive reaction units corresponding to each detection target, and calculating the actual content of the detection target in a sample. The method improves the detection efficiency and reduces the amount of samples required for detection.

Description

High-throughput fluorescence detection method, device, system and storage medium Technical Field

[0001] The present application relates to the fields of molecular detection and artificial intelligence, and in particular to a high-throughput fluorescence detection technology and system that can be used for single-molecule quantitative detection of proteins, small molecules, and nucleic acid molecules. Background Art

[0002] With the development of biotechnology, more and more biomolecules (including proteins, small molecules, and nucleic acids) need to be analyzed simultaneously in applications. However, the current detection technology has a low throughput and cannot meet the existing needs. High-throughput detection technology has become an urgent need.

[0003] Existing methods for nucleic acid molecule detection include qPCR, NGS, digital PCR and other methods: qPCR technology includes two main fluorescent detection methods: one is to use specific fluorescent probes (such as TaqMan probes), and the other is to use non-specific fluorescent dyes (such as SYBR Green I). qPCR detection is simple to operate, but has disadvantages such as possible amplification bias, limitations on primers and probes, inability to determine detection results of low-concentration templates, and large errors when using standard curves for quantitative detection. In particular, this method cannot be quantitative, and the detection of multiple sites requires the combination of multiple reaction tubes, and it is impossible to detect multiple sites at one time. The NGS method can obtain information on point mutations, small fragment insertions or deletions, copy number variations and structural variations, but this method has problems such as generally low read lengths and high error rates during sequence splicing. At the same time, this method is complex to operate, difficult to analyze data, and expensive, making it difficult to promote on a large scale in practical applications.

[0004] Digital PCR is a technology for absolute nucleic acid quantification. It distributes a nucleic acid sample into a large number of independent, parallel microreaction units, ensuring that each unit contains as many template molecules as possible. Amplification, detection, and distribution statistics are then performed on this basis, achieving absolute counts of target molecules. Digital PCR offers greater sensitivity and precision, but is often limited by the number of primers and probes used. A single test can only yield single-digit fluorescence signals, resulting in a narrow detection range.

[0005] Therefore, how to achieve high-throughput fluorescence detection and identification of multiple sites in a single reaction has become a technical challenge that needs to be urgently solved in this field.

[0006] Summary of the Invention

[0007] In response to the above technical problems, the present application provides a high-throughput fluorescence detection method and system for simultaneously detecting and analyzing multiple target nucleic acids in a single reaction system. This method greatly improves the detection efficiency and reduces the sample size required for detection.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] In one aspect, a high-throughput fluorescence detection method is provided, wherein the process of performing high-throughput fluorescence detection comprises:

[0010] The fluorescence encoding step includes: performing multi-color and multi-level encoding on the same molecular probe to obtain an encoded molecular probe, wherein the multi-color encoding includes a combination of different colors of the same molecular probe, and the multi-level encoding includes a combination of fluorescence intensities of the same color; one color corresponds to one wavelength of fluorescence; and the encoded molecular probe is distributed to at least two reaction units;

[0011] The transcoding step includes: performing super-multiplex amplification on the molecular probes in each reaction unit and performing multi-interval amplification on each reaction unit;

[0012] The decoding step includes: decoding using the reaction unit that has passed the transcoding step to obtain the content of the detection target corresponding to each fluorescent coding signal.

[0013] In another aspect, a high-throughput fluorescence detection system includes the following steps:

[0014] An encoding module for performing multi-color and multi-level encoding on the same molecular probe to obtain an encoded molecular probe, wherein the multi-color encoding includes a combination of different colors of the same molecular probe, and the multi-level encoding includes a combination of fluorescence intensities of the same color; one color corresponds to one wavelength of fluorescence; and the encoded molecular probe is distributed to at least two reaction units;

[0015] The transcoding module is used to perform super-multiplex amplification on the molecular probes in each reaction unit and perform multi-interval amplification on each reaction unit;

[0016] The decoding module is used to decode the reaction unit after the transcoding link to obtain the content of the detection target corresponding to each fluorescent coding signal.

[0017] Compared with the existing qPCR technology, the technical solution of the present application can simultaneously detect and analyze multiple target nucleic acids in a single reaction system. The analysis process is highly automated and does not require human intervention, which improves detection efficiency and reduces the sample size required for detection. Compared with the dPCR technology, which is limited by primer probes and has a narrow detection range, the method of the present application realizes the simultaneous detection and analysis of multiple target biological molecules in a single reaction system. Moreover, compared with the xMAP technology based on poor signal amplification capability (currently the maximum is only 1000 times), the detection method of the present application has a signal amplification factor much higher than this. It can improve the sensitivity and specificity of detection and reduce the risk of false positives and false negatives. At the same time, it can reduce the sample size and reaction time required for detection, and improve the efficiency and convenience of detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] FIG1 is a diagram showing the results of high-throughput fluorescence encoding detection of plasmid standards in Example 1 of the present invention.

[0019] Figures 2-1, 2-2, and 2-3 are fluorescence coding detection results of three copy number loading amounts in Example 1 of the present invention; among them, Figures 2-1, 2-2, and 2-3 are the detection results of 500 copies, 50 copies, and 5 copies, respectively.

[0020] FIG3 is a diagram showing the results of high-throughput fluorescence encoding detection of plasmid standards in Example 2 of the present invention.

[0021] FIG4 is a diagram showing the results of high-throughput fluorescence encoding detection of plasmid standards in Example 3 of the present invention. DETAILED DESCRIPTION

[0022] To enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention will be described in detail below, with examples of the embodiments illustrated in the accompanying drawings. Throughout, identical or similar reference numerals represent identical or similar elements or elements having identical or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and intended only to explain the present invention and are not to be construed as limiting the present invention. Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a," "an," "said," and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" as used in the description of the present invention refers to the presence of the stated features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when an element is referred to as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or intervening elements may be present. Furthermore, "connected" or "coupled" as used herein may include wireless connections or couplings. The term "and / or" as used herein includes any and all combinations of one or more associated listed items. It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art, and will not be interpreted in an idealized or overly formal sense unless defined as such herein.

[0023] The high-throughput fluorescence detection method provided in the embodiment of the present invention specifically includes the following steps:

[0024] The fluorescence encoding process involves performing multi-color, multi-level encoding on the same molecular probe to obtain an encoded molecular probe. The multi-color encoding includes combinations of different colors for the same molecular probe, and the multi-level encoding includes combinations of fluorescence intensities for the same color, with each color corresponding to a single wavelength of fluorescence. The encoded molecular probe is then assigned to at least two reaction units. Specifically, fluorescence encoding utilizes the emission spectral characteristics of fluorescent molecules to label and detect target substances. Fluorescence encoding can be categorized into two methods: multi-color and multi-level. Multi-color refers to the encoding of fluorescence on the same molecular probe using a combination of fluorescence at several different wavelengths. This method increases the information content and sensitivity of the encoding while reducing the number of probes and the cost. Multi-color fluorescence encoding can be used for the simultaneous observation and analysis of multiple biomolecule targets or processes. Multi-level refers to the ability to select multiple fluorescence intensities for each fluorescence wavelength. This method further increases the encoding capacity and complexity while avoiding crosstalk or overlap between different colors. Multi-level fluorescence encoding can be used for high-throughput, high-precision, and high-specificity biomolecule detection. For example, combinations of fluorescence of different wavelengths can be established to obtain the 1st to Nth combinations, and the correspondence between the established combinations and the molecular probes can be recorded, wherein each combination includes at least fluorescence wavelengths, and N is a positive integer greater than or equal to 2; in the nth combination, a fluorescence intensity combination M of wavelength fluorescence ni is established, M = {m1…mj}, wherein i represents the number of the fluorescence wavelength in the combination, and j represents the number of the fluorescence intensity.

[0025] The transcoding step includes: performing super-multiplex amplification on the molecular probes in each reaction unit and performing multi-interval amplification on each reaction unit.

[0026] Super-multiplex amplification refers to the simultaneous presence of multiple primer pairs and multiple probes within a minimal reaction unit, enabling simultaneous template amplification and recognition under specific temperature variations. Multi-interval amplification involves the simultaneous and uniform amplification of multiple reaction units. Utilizing the Poisson distribution principle, template biomolecules are diluted to a point where each reaction unit contains only one or zero target molecules. PCR reactions are then performed within each reaction unit, and fluorescence signals are used to determine whether effective amplification has occurred within each reaction unit.

[0027] In the following embodiment, the preferred super-multiplex amplification method includes: referring to the applicant's previous patent "Digital amplification detection method, detection product and detection kit for simultaneous identification of multiple gene types" (publication number: CN 114196740A), when synthesizing primers, first only three bases are selected to significantly reduce non-specific binding during amplification; and a nucleotide sequence that can form a three-dimensional structure and a small molecule compound are added to the 5' end of the primer, which can significantly reduce the generation of dimers between primers, reduce non-specific amplification, and improve the binding specificity, binding strength and binding stability of the primer to the template, so as to achieve multiple amplification of a single reaction system. When designing the probe, based on the dual probe shedding technology, at least two probes are set for each target gene, and the set probe does not contain a reference probe; multiple different probes all bind to the target gene and cover different regions of the target gene to achieve comprehensive detection of the target gene mutation site, thereby achieving simultaneous detection of multiple gene types or gene mutations of the target gene through one detection, and the detection process is more efficient.

[0028] In one of the following embodiments, the preferred multi-interval amplification method includes: the reaction system can be divided into multiple intervals by the instability of one fluid (dispersed phase fluid) entering another (continuous phase fluid). The method of generating droplets can be passive or active. The generation of droplets does not require external drive, and the stable occurrence of droplets utilizes additional energy input to maintain and promote the interface instability of droplet generation. In principle, in active control, the balance of interfacial forces can be adjusted through two basic strategies: by changing inherent parameters such as flow rate and material properties to change viscosity, inertia and capillary force; or by using external power equipment, such as syringe pumps, peristaltic pumps and other pump equipment to introduce additional forces: such as electricity, magnetism, centrifugal force, sound waves, dielectric effect of electrodes, photothermal effect, etc., can all serve as the power for droplet generation. At this stage, the equipment that maintains the continuous and stable generation of droplets mainly includes syringe pumps, peristaltic pumps, electromagnetic, pneumatic pumps, syringes, centrifuge equipment, etc. Alternatively, a specially designed microfluidic chip can be used to prefabricate microwells of corresponding volume and number on the chip, use driving force to evenly disperse the reaction system into different microwells, and use capillary action or physical sealing to block the liquid system between different microwells, thereby achieving multi-zone division of the reaction system.

[0029] The decoding step includes: decoding using the reaction unit that has passed the transcoding step to obtain the content of the detection target corresponding to each fluorescent coding signal.

[0030] Specifically, in the multi-color coding of the fluorescence coding link, it includes: selecting n colors from m colors to form a multi-color fluorescence coding, and the number of combinations obtained is Both n and m are positive integers. When n = m / 2, the maximum number of combinations is achieved. For example, a preferred multi-color encoding method includes selecting n colors from m colors to form a multi-color encoding. When n = m / 2, the maximum number of combinations is achieved. If m is an odd number, then n can be (m±1) / 2. Specific combinations can be shown in Table 1 below:

[0031] Table 1 Fluorescence coding combinations

[0032] The formula is: For example, in a 6-color system, 3 colors are selected from the 6 colors (AF channels) to form a probe. There are a total of C(6,3)=20 combinations:

[0033] In the multi-level coding of the fluorescence coding link, it includes: in the process of selecting multi-level coding for a single color, the number of multi-level combinations is obtained, wherein, if the weakest fluorescence intensity that can be effectively resolved by the single color channel is o and the strongest fluorescence intensity is p, then o*2 q <p<o*2 q+1 , q is the number of orders supported by the monochrome channel. For example, the maximum number of multi-order combinations is: q A *q B *q C *……*q n , where q A ,q B ...is the order number of the fluorescence channel corresponding to the selected code. Given the code number n, the number of order combinations is the product of the order numbers of n channels. For example, in a 6-color system, 3 of the 6 colors (AF channels) are selected to form a probe, resulting in a total of C(6,3) = 20 possible combinations. If each channel can support at least 2 orders of signal discrimination, 20 * 2 * 2 * 2 = 160 possible signal combinations can be achieved.

[0034] Furthermore, in the decoding link, it includes: collecting the fluorescence intensity data of each reaction unit in different fluorescence channels and establishing a data matrix; performing high-dimensional clustering on the data matrix and sorting after clustering; performing MASK processing on the sorted high-dimensional data matrix, and then performing dimensionality reduction processing to obtain the content of the detection target corresponding to each fluorescent coding signal.

[0035] Specifically, the method of collecting the fluorescence intensity data of each reaction unit in different fluorescence channels and establishing a data matrix includes: collecting the fluorescence intensity of each reaction unit in different fluorescence channels, obtaining an array (i1, i2, i3...i m ), where m corresponds to the number of fluorescence channels, i m Then is the fluorescence intensity of the reaction unit in the fluorescence channel m;

[0036] The fluorescence intensity data of all reaction units are merged to form the first high-dimensional matrix corresponding to sample a, where there are k reaction units corresponding to sample a: The fluorescence intensity data of the total l reaction units of the positive reference b are merged to obtain the second high-dimensional matrix:

[0037] The high-dimensional clustering of the data matrix includes: for each row of data in the second high-dimensional matrix, i , if its ∈-neighborhood contains at least MinPts elements, then x i Determine as core object, ∈ represents the radius of the data point, MinPts represents the minimum number of data points in the neighborhood with a radius of ∈; all density-reachable core objects x i Together with all elements in the corresponding ∈-neighborhood, it forms a density cluster y i ; The unclustered elements x in the second high-dimensional matrix k Assign the same label label1, and the rest of the density clusters y i The elements in the label are given i , and obtain the high-dimensional matrix after clustering. For example, using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, the input parameters (∈, MinPts) are used to describe the density of the sample distribution in the neighborhood. ∈ describes the neighborhood distance threshold (radius) of a data point, and MinPts describes the minimum number of data points in the neighborhood with a radius of ∈. Calculate the first high-dimensional matrix:

[0038] a) Calculation core object: For any element x in each row of data in the second high-dimensional matrix i , if its ∈-neighborhood contains at least MinPts elements, then x i Is the core object. Traverse and calculate to get all core objects.

[0039] b) Determine whether the density is reachable: Determine whether a core object x i Is there any other core object x in the ∈-neighborhood? j If it exists, we can determine x i with x j Density can reach.

[0040] c) Divide density clustering: All core objects x that can be reached by density are divided into i Together with all elements in the corresponding ∈-neighborhood, it forms a density cluster y i

[0041] All the unclustered elements x in the second highest dimensional matrix k Assign the same label label1, and the rest of the clusters y i All elements in the same label are given i , and get a new high-dimensional matrix:

[0042] The sorting after clustering includes: obtaining the average value of the elements in each cluster in m fluorescence channels; comparing the average values ​​of the elements in each cluster and sorting them to obtain the sorting of the average values ​​of each element in different clusters; then obtaining the total score of each cluster according to the sorting of the average values ​​of the elements in different clusters; and sorting using the total score of each cluster. For example: for all clustered elements, for a certain cluster y i , the average value of all elements in m fluorescence channels can be obtained (i 1-average ,i 2-average ,i 3-average ...i m-average ).

[0043] After that, the average values ​​of fluorescence channels of different clusters were compared, and the ACBS (Average Center-Based Sequencing) algorithm was used to compare all clusters from i 1-average to i m-average The order is as follows:

[0044] Sequence 1: label i1 i2 <... n

[0045] Sequence 2: label j1 j2 <... n

[0046] …

[0047] For each sorting sequence, the first cluster is assigned a score of 0, the second cluster is assigned a score of 1, and so on until the last cluster is assigned a score of n. Each cluster can then get a total score of N. sum . Compare the N of each cluster sum , we can get the clustering sequence Sequence cluster For N sum If the clusters are equal, compare the results of Sequence 1 and sort the clusters that are ranked higher in Sequence 1. cluster Middle and forward. ​​​​

[0048] Furthermore, it is necessary to perform mask processing on the sorted high-dimensional data matrix, and then perform dimensionality reduction processing to obtain the content of the detection target corresponding to each fluorescent coding signal. The mask processing includes:

[0049] For Sequence cluster For each cluster in , use the convex hull algorithm. For each point in the cluster, take the minimum point a1 as the starting point, then take the closest point a2 to get a vector u, and then extend it to the next closest point a3 to get a vector v. Calculate u*v, that is, its vector cross product:

[0050] It can also be expressed as: u×v=(u2v3i+u3v1j+u1v2k)-(u3v2i+u1v3j+u2v1k)=(u2v3-u3v2)i+(u3v1+u1v3)j+(u1v2-u2v1)k

[0051] According to the sign of the vector cross product, if the cross product is negative, it means that the adjacent point a3 is on the clockwise side of the vector u, then the vector from a1 to a3 is used as the edge vector, and the next adjacent point is found and the above process is repeated. Finally, the edge range A of each cluster of the second high-dimensional matrix is ​​obtained. i Use A i As a condition, each element of the second high-dimensional matrix is ​​differentiated to obtain i A collection of elements in a range, and assigns the same label to each element in the collection i , get a new first high-dimensional matrix:

[0052] In this embodiment, multiple dimensionality reduction processes are used, including: using the PCA (Principal components analysis) algorithm to reduce the dimensionality of the new first high-dimensional matrix:

[0053] g) Organize the original data into an n-row and m-column matrix X;

[0054] h) Zero-mean each row of X, that is, subtract the mean of this row;

[0055] i) Find the covariance matrix

[0056] j) Find the eigenvalues ​​and corresponding eigenvectors of the covariance matrix C;

[0057] k) Arrange the eigenvectors into a matrix row by row from top to bottom according to the corresponding eigenvalues, and take the first k rows to form the matrix P;

[0058] l) Y = PX is the data after dimension reduction to k dimensions.

[0059] Then, based on the sorting and labeling results, each element in the matrix P corresponding to the first high-dimensional matrix is ​​assigned the same label to obtain the number of elements of each fluorescent coding probe. Based on the number of elements, combined with the Poisson distribution, the copy number of the amplification site corresponding to the fluorescent coding marker can be calculated:

[0060] To meet the display requirements, the tSNE dimensionality reduction algorithm is used to reduce the dimension of the first high-dimensional matrix:

[0061] m) Calculate the parameters of the cost function: Perp

[0062] n) Optimization parameters: set the number of iterations T, learning rate n, momentum a(t)

[0063] o) The target result is a low-dimensional data representation Y t =y1...,y n

[0064] p) Start optimization

[0065] Calculate the conditional probability P under a given Perp, where Initialize Y randomly with N(0,10-4I); then iterate from t=1 to T and do the following:

[0066] Calculate q in low dimensions ij ;

[0067] Calculate gradients;

[0068] renew

[0069] Finally, based on the sorting and labeling results, each element in the final matrix Y corresponding to the first high-dimensional matrix is ​​assigned the same label to obtain the number of elements of each fluorescent coded probe. Using the same label, the association between the elements of the original first high-dimensional matrix, the PCA-reduced matrix P, and the tSNE-reduced matrix Y is established.

[0070] This embodiment also provides a high-throughput fluorescence detection device. Each virtual module in the device can be written based on existing computer programming methods to form a corresponding program module. Each program module is used to run the algorithm models of the encoding link, transcoding link, and decoding link respectively. The system includes:

[0071] An encoding module for performing multi-color and multi-level encoding on the same molecular probe to obtain an encoded molecular probe, wherein the multi-color encoding includes a combination of different colors of the same molecular probe, and the multi-level encoding includes a combination of fluorescence intensities of the same color; one color corresponds to one wavelength of fluorescence; and the encoded molecular probe is distributed to at least two reaction units;

[0072] The transcoding module is used to perform super-multiplex amplification on the molecular probes in each reaction unit and perform multi-interval amplification on each reaction unit;

[0073] The decoding module uses the reaction units that have passed the transcoding link to perform decoding, obtains the number of fluorescence-encoded positive reaction units corresponding to each detection target, and calculates the actual content of the detection target in the sample.

[0074] An embodiment of the present invention further provides a storage medium storing a computer program or instruction, such as the one described above, written based on existing computer programming methods and forming a corresponding program module. When the computer program or instruction is executed, the method of the embodiment is implemented.

[0075] The high-throughput fluorescence detection device in this embodiment can be specifically operated on a computer device in a high-throughput fluorescence detection system, wherein the system includes:

[0076] Experimental equipment, used to install the reaction unit and provide experimental conditions for super-multiplex amplification of the molecular probes in the reaction unit;

[0077] an image acquisition device for capturing images of the reaction unit;

[0078] Computer equipment, used for fluorescence encoding, transcoding and decoding.

[0079] This embodiment achieves simultaneous detection and analysis of multiple target nucleic acids in a single reaction system. The analysis process is highly automated and requires no human intervention, which improves detection efficiency and reduces the sample volume required for detection. Compared with existing detection methods, the method of this application has the following advantages:

[0080] 1) Compared with existing qPCR technology, the high-throughput molecular detection technology of this application can achieve more target molecules covered and accurately quantified in a single reaction system.

[0081] 2) Compared with the longer NGS detection cycle (about 10 days), the method of this application can generally complete the entire detection process and provide test results within 1 day, and the data analysis process is highly automated and does not require human intervention.

[0082] 3) Compared with the dPCR technology, which is limited by primers and probes and has a narrow detection range, the method of the present application realizes the simultaneous detection and analysis of multiple target biomolecules in a single reaction system.

[0083] 4) Compared to xMAP technology, which has poor signal amplification capabilities (currently a maximum of only 1000x), the detection method in this application has a much higher signal amplification factor. This can improve the sensitivity and specificity of detection, reduce the risk of false positives and false negatives, and simultaneously reduce the sample volume and reaction time required for detection, improving the efficiency and convenience of detection.

[0084] 5) This technology is characterized by high efficiency, simplicity, and high throughput, and has broad application prospects in the biomedical field.

[0085] In combination with the above embodiments, possible applications and test examples in practical applications are provided for illustration.

[0086] Example 1 High-throughput fluorescence detection of chromosome 21

[0087] In this example, 42 sets of primer probes were designed for 42 fragments evenly distributed on human chromosome 21, and copy number quantification was performed on each of them. A total of 42 sets of primer probes were designed using the Primer3 online primer probe design tool (http: / / primer3.wi.mit.edu / ). The primer probes were then ordered using a fluorescent encoding method and tested separately using a universal digital PCR kit to verify the implementation of automated cluster analysis.

[0088] Experimental steps:

[0089] Mix 42 sets of primer probes according to the coding principle, add 2x reaction premix, make up to 50uL with water, vortex mix, and centrifuge briefly.

[0090] The formula of the premix solution is as follows (the premix solution in the following examples is the same; in specific implementations, other premix solutions conventional in the art can also be used).

[0091] The reaction system formula is as follows:

[0092] Transfer the reaction system and droplet generation oil to the corresponding wells of the chip. Load the chip onto the chip holder and the PCR plate onto the PCR plate holder. Use the droplet generator to select the appropriate program to generate droplets. After droplet generation is complete, place the PCR plate into a film sealer for sealing.

[0093] Place the PCR plate into the PCR amplification instrument for amplification. The amplification procedure is as follows:

[0094] After PCR amplification, the PCR plate was transferred to a biochip analyzer for droplet detection to obtain the pre-decoding fluorescence signal.

[0095] As described above, the software can be written based on existing computer programming methods to form corresponding program modules, each of which is used to run the algorithm models of the encoding, transcoding, and decoding steps described above. These program modules are then used to design and package the final analysis software. This analysis software is used to automatically decode and analyze the data, complete the decoding step, and obtain the final results.

[0096] The primer-probe combinations are shown in Table 2 below:

[0097] Table 2 42 sets of primers and probes

[0098] The results of high-throughput fluorescence encoding detection of plasmid standards in Example 1 are shown in Figure 1. The fluorescence results of the droplets after reading were decoded to obtain multiple clusters. In Figure 1, "Negative" represents negative droplets, the numbers represent the group numbers of the primer and probe groups in the table above, the vertical axis Dimension A represents the new dimension A obtained after dimensionality reduction, and the horizontal axis Dimension B represents the new dimension B obtained after dimensionality reduction (the same meanings apply to the coordinates in the following figures).

[0099] 1. Sensitivity test

[0100] Sensitivity was verified using normal human gDNA containing 42 fragments evenly distributed on chromosome 21. Three batches of samples were tested using a universal digital PCR kit adapted for a high-throughput fluorescence encoding system at three sample loads (500 copies, 50 copies, and 5 copies), with 20 replicates per group. A positive result rate greater than 95% was considered acceptable.

[0101] Table 3 Human gDNA sensitivity test results

[0102] The fluorescence coding detection results for three sample loads (500 copies, 50 copies, and 5 copies) are shown in Figures 2-1, 2-2, and 2-3. The sensitivity test results for human gDNA are shown in Table 3. Figures 2-1, 2-2, and 2-3 show the detection results for 500 copies, 50 copies, and 5 copies, respectively.

[0103] 2. Specificity test

[0104] Specificity testing was verified using four randomly selected primer probes on autosomes. In normal human gDNA samples, the copy number of each tested locus on the autosomes was consistent with the copy number of each tested locus on chromosome 21. The results of human gDNA specificity testing are shown in Table 4.

[0105] Table 4 Human gDNA specific detection results

[0106] 3. Precision test

[0107] Precision was verified using a 50-copy load of normal human gDNA. Analysis and calculations were performed for each locus using three tests from the same batch, as well as a single test from three batches. The human gDNA precision test results are shown in Table 5.

[0108] Table 5 Human gDNA precision test results

[0109] Example 2 High-throughput fluorescence detection of rice genome

[0110] This example refers to the literature "Rice functional genomics research: past decade and future." (Li, Yan, et al. Molecular plant 11.3 (2018): 359-380.) and selects three fragments for each of seven genes in the rice genome related to rice grain quality. A total of 21 primer probe sets were designed using the Primer3 online primer and probe design tool (http: / / primer3.wi.mit.edu / ). Primer probes were then ordered using a fluorescence encoding method, and copy number quantification was performed using a universal digital PCR kit. This method determined the copy number variation of the relevant genes and evaluated the association between rice grain quality and gene expression.

[0111] Experimental steps:

[0112] Mix 21 sets of primers and probes according to the coding principle, add 2x reaction premix, add water to 50uL, vortex to mix, and centrifuge briefly. The premix formula is as follows:

[0113] The reaction system formula is as follows:

[0114] Add the reaction system to the corresponding wells of the pre-loaded oil chip. Load the chip onto the chip holder and use the droplet generator to select the corresponding program to generate droplets.

[0115] Place the chip into a PCR amplification instrument for amplification. The amplification procedure is as follows:

[0116] After PCR amplification, the chip is transferred to a biochip reader for scanning and photographing to obtain the fluorescence signal before decoding.

[0117] Use analysis software to automatically decode and analyze the data and obtain the final results.

[0118] Table 6 Rice genome primer probe combinations

[0119] The results of high-throughput fluorescence encoding detection of plasmid standards in Example 2 are shown in Figure 3. The fluorescence results of the droplets after reading were decoded to form multiple clusters. In the figure, "Negative" represents negative droplets, and the gene-number represents the primer and probe group number in the table above. The primer and probe combinations for the rice genome are shown in Table 6.

[0120] 1. Sensitivity test

[0121] Sensitivity was verified using rice genomic gDNA standards. Three batches were tested at three different sample sizes (500 copies, 50 copies, and 5 copies), with 20 replicates per group. A positive result greater than 95% was considered acceptable. The rice genomic sensitivity test results are shown in Table 7.

[0122] Table 7 Rice genome sensitivity test results

[0123] 2. Specificity test

[0124] Specificity testing was verified using four randomly selected primer probes on other chromosomes. The copy number of each tested locus for rice grain quality-related genes in the rice genomic gDNA standard was consistent with the copy number of four randomly selected test loci on other chromosomes. The results of the rice genome-specific testing are shown in Table 8.

[0125] Table 8 Rice genome specificity test results

[0126] 3. Precision test

[0127] Precision was verified using a 50-copy load of rice genomic gDNA standards. Analysis and calculations were performed for each locus using three tests from the same batch, as well as a single test from three batches per locus. The results of the rice genome-specific testing are shown in Table 9.

[0128] Table 9 Rice genome specificity test results

[0129] Example 3 High-throughput fluorescence detection of Saccharomyces cerevisiae

[0130] This example refers to the literature "Research Trends in Genetic Improvement of Brewer's Yeast" (Zhang Liuying, Food and Fermentation Industries 26.6 (2000): 47). For the 13 genes on the Saccharomyces cerevisiae genome that may be involved in conventional genetic engineering, one fragment was selected for each gene, and two fragments were selected for the Saccharomyces cerevisiae housekeeping gene. A total of 15 sets of primer probes were designed using the Primer3 primer probe online design tool (http: / / primer3.wi.mit.edu / ). The primer probes were then ordered using the fluorescence encoding method, and the copy number was quantitatively detected using a digital PCR universal kit to obtain the copy number of the relevant genes and evaluate the changes in the carrier levels of the relevant genes in Saccharomyces cerevisiae after genetic engineering.

[0131] Experimental steps:

[0132] Mix the 13 sets of primers and probes according to the coding principle, add 2x reaction premix, add water to 50uL, vortex to mix, and centrifuge briefly. The premix formula is as follows:

[0133] The reaction system formula is as follows:

[0134] Transfer the reaction system and droplet generation oil to the corresponding wells of the chip. Load the chip onto the chip holder and the PCR plate onto the PCR plate holder. Use the droplet generator to select the appropriate program to generate droplets. After droplet generation is complete, place the PCR plate into a film sealer for sealing.

[0135] Place the PCR plate into the PCR amplification instrument for amplification. The amplification procedure is as follows:

[0136] After PCR amplification, the PCR plate was transferred to a biochip analyzer for droplet detection to obtain the pre-decoding fluorescence signal.

[0137] Use analysis software to automatically decode and analyze the data and obtain the final results.

[0138] Table 10 Saccharomyces cerevisiae primer probe combinations

[0139] The results of high-throughput fluorescence encoding detection of plasmid standards in Example 3 are shown in Figure 4. The fluorescence results of the droplets after reading were decoded to generate multiple clusters. In the figure, Negative represents negative droplets, and Gene represents the group number of each primer and probe set in the table above. The primer and probe combinations for Saccharomyces cerevisiae are shown in Table 10.

[0140] 1. Sensitivity test

[0141] Sensitivity was verified using plasmid standards. Three batches were tested at three different sample sizes (500 copies, 50 copies, and 5 copies), with 20 replicates per batch and group. A positive result greater than 95% was considered acceptable. The results of the Saccharomyces cerevisiae sensitivity test are shown in Table 11.

[0142] Table 11 Saccharomyces cerevisiae sensitivity test results

[0143] 2. Specificity test

[0144] Specificity testing was performed using Escherichia coli gDNA standards and Candida tropicalis DNA standards (both purchased from Beina Chuanglian Biotechnology Co., Ltd.) to verify the binding efficacy of 15 primer-probe sets. Specificity was considered acceptable if there was no nonspecific signal. The results of the Saccharomyces cerevisiae specificity test are shown in Table 12.

[0145] Table 12 Saccharomyces cerevisiae specificity test results

[0146] 2. Precision test

[0147] Precision was verified using a 50-copy plasmid standard. Analysis and calculations were performed for each locus, performed three times within the same batch, and for each locus, performed individually across three batches. The results of the S. cerevisiae precision tests are shown in Table 13.

[0148] Table 13 Saccharomyces cerevisiae precision test results

[0149] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited to this. Any changes or replacements that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A high-throughput fluorescence detection method, characterized in that: The process of performing high-throughput fluorescence detection includes: Step 1: Fluorescence coding, including: performing multi-color and multi-level coding on the same molecular probe to obtain a coded molecular probe, wherein the multi-color coding includes a combination of different colors of the same molecular probe, and the multi-level coding includes a combination of fluorescence intensities of the same color; one color corresponds to one wavelength of fluorescence; the coded molecular probe is distributed to at least two reaction units; Step 2: transcoding, including: performing super-multiplex amplification on the molecular probes in each reaction unit, and performing multi-interval amplification on each reaction unit; Step 3: decoding step, including: decoding using the reaction unit that has passed the transcoding step to obtain the content of the detection target corresponding to each fluorescent coding signal.

2. The method according to claim 1, characterized in that The multi-color coding in the fluorescent coding step of step 1 includes: Select n colors from m colors to form multi-color fluorescence coding, and the number of combinations obtained is Both n and m are positive integers, and when n=m / 2, the number of combinations is the largest.

3. The method according to claim 1, characterized in that In the multi-stage encoding of the fluorescence encoding link of step 1, it includes: In the process of selecting multi-order encoding for a single color, the number of multi-order combinations is obtained. If the weakest fluorescence intensity that can be effectively resolved by the single color channel is o and the strongest fluorescence intensity is p, then o*2 q <p<o*2 q+1 , q is the number of divisions supported by the monochrome channel.

4. The method according to claim 1, characterized in that: The decoding process of step 3 includes: Collect the fluorescence intensity data of each reaction unit in different fluorescence channels and establish a data matrix; The sorted high-dimensional data matrix is ​​subjected to MASK processing, and then dimensionality reduction processing is performed to obtain the content of the detection target corresponding to each fluorescent coding signal.

5. The method according to claim 4, characterized in that The step of collecting the fluorescence intensity data of each reaction unit in different fluorescence channels and establishing a data matrix includes: Collect the fluorescence intensity of each reaction unit in different fluorescence channels and obtain the array corresponding to each reaction unit (i1, i2, i3...i m ), where m corresponds to the number of fluorescence channels, i m Then is the fluorescence intensity of the reaction unit in the fluorescence channel m; The fluorescence intensity data of all reaction units are combined to form the first high-dimensional matrix corresponding to sample a, where there are k reaction units corresponding to sample a: The fluorescence intensity data of the l reaction units of the positive reference b are merged to obtain the second high-dimensional matrix:

6. The method according to claim 4, characterized in that The MASK processing comprises: For Sequence cluster For each cluster in , according to each point in the cluster, in each calculation cycle, take the minimum point a1 as the starting point, then take the point a2 closest to the starting point to get a vector u, and then extend to the next adjacent point a3 to get another vector v, and then calculate the value of the vector cross product u*v; If the value of u*v is negative, the vector from a1 to a3 is used as the edge vector, and the next adjacent point is found and the calculation cycle is repeated until the edge range A of each cluster of the second high-dimensional matrix is ​​obtained. i ; Afterwards, A i Differentiate each element of the second high-dimensional matrix under the condition and get i A collection of elements in a range, and assigns the same label to each element in the collection i , and get a new first high-dimensional matrix.

7. The method according to claim 4, characterized in that Also includes: Before establishing the data matrix, high-dimensional clustering is performed on the data matrix.

8. The method according to claim 7, characterized in that Also includes: Sorting is performed after high-dimensional clustering is performed on the data matrix.

9. The method according to claim 8, characterized in that The performing high-dimensional clustering on the data matrix comprises: For the element x composed of each row of data in the second high-dimensional matrix i , if its ∈-neighborhood contains at least MinPts elements, then x i Determined as a core object, ∈ represents the radius of the data point, and MinPts represents the minimum number of data points in the neighborhood with a radius of ∈; All density-reachable core objects x i Together with all elements in the corresponding ∈-neighborhood, it forms a density cluster y i ; The unclustered elements x in the second high-dimensional matrix k Assign the same label label1, and the rest of the density clusters y i The elements in the label are given label i , and obtain the high-dimensional matrix after clustering.

10. The method according to claim 9, characterized in that The sorting after clustering includes: Get the average value of the elements in each cluster in m fluorescence channels; Compare the average values ​​of the elements in each cluster and sort them to get the sorting of the average values ​​of each element in different clusters, and then get the total score of each cluster according to the sorting of the average values ​​of the elements in different clusters; The ranking is done using the total score of each cluster.

11. The method according to claim 10, characterized in that The dimensionality reduction process comprises: Through multiple dimensionality reduction processing, the new first high-dimensional matrix is ​​reduced in dimensionality; The number of elements of the fluorescent coding probe is obtained from the first high-dimensional matrix after dimension reduction.

12. The method according to claim 11, characterized in that In the process of multiple dimensionality reduction processing, the PCA algorithm is used to reduce the dimension of the new first high-dimensional matrix, including: a) taking the first high-dimensional matrix as the original data and forming a matrix X with n rows and m columns by columns; b) Zero-mean each row of X; c) Get the covariance matrix d) Obtain the eigenvalues ​​and corresponding eigenvectors of the covariance matrix C; e) Arrange the eigenvectors into a matrix by row from top to bottom according to the corresponding eigenvalues, and take the first k rows to form the matrix P; f) The data after dimension reduction to k dimension is Y, where Y=PX.

13. The method according to claim 12, characterized in that The step of obtaining the number of elements of the fluorescent coding probe from the first high-dimensional matrix after dimensionality reduction includes: Combined with the sorting and labeling results, each element of the corresponding first high-dimensional matrix in the matrix P is assigned the same label, thereby obtaining the number of elements of each fluorescent coding probe.

14. A high-throughput fluorescence detection device, characterized in that: include: A coding module, used for performing multi-color and multi-level coding on the same molecular probe to obtain a coded molecular probe, wherein the multi-color coding includes a combination of different colors of the same molecular probe, and the multi-level coding includes a combination of fluorescence intensities of the same color; one color corresponds to one wavelength of fluorescence; the coded molecular probe is distributed to at least two reaction units; A transcoding module, for performing super-multiplex amplification on the molecular probes in each reaction unit and performing multi-interval amplification on each reaction unit; The decoding module is used to decode the reaction unit after the transcoding link to obtain the content of the detection target corresponding to each fluorescent coding signal.

15. A high-throughput fluorescence detection system, characterized in that: include: Experimental equipment, used to install the reaction unit and provide experimental conditions for super-multiplex amplification of the molecular probes in the reaction unit; A fluorescence acquisition device, used to acquire image data of the reaction unit; Computer equipment, used in the fluorescence encoding, transcoding and decoding stages; The fluorescence coding step includes: performing multi-color and multi-level coding on the same molecular probe to obtain the coded molecular probe, wherein the multi-color coding includes a combination of different colors of the same molecular probe, and the multi-level coding includes a combination of fluorescence intensities of the same color; one color corresponds to one wavelength of fluorescence; the coded molecular probe is distributed to at least two reaction units; The transcoding step includes: performing super-multiplex amplification on the molecular probes in each reaction unit, and The unit is multi-interval amplified; The decoding step includes: decoding using the reaction unit that has passed the transcoding step to obtain the content of the detection target corresponding to each fluorescent coding signal.

16. The system according to claim 15, characterized in that The fluorescence collection device includes: an imaging device for photographing the reaction unit and collecting the images as optical signals.

17. The system according to claim 16, characterized in that The fluorescence collection device also includes: a photoelectric conversion device, which is used to convert the captured optical signal into image data.

18. A storage medium, characterized in that: A computer program or instruction is stored, and when the computer program or instruction is executed, the method according to any one of claims 1 to 13 is implemented.

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