CN-121999484-A - Microdroplet partitioning and clustering method, device, medium and system

CN121999484ACN 121999484 ACN121999484 ACN 121999484ACN-121999484-A

Abstract

The method is applied to a digital nucleic acid amplification quantitative analysis system based on droplets, and comprises the steps of reading original scatter data of each droplet in a droplet fluorescent image, including positions and brightness, acquiring brightness distribution density conditions based on the original scatter data, screening reference droplets according to the brightness distribution density conditions, carrying out optical field correction on the droplet fluorescent image according to the reference droplets to obtain actual scatter data of the droplets, and carrying out division and clustering on the droplets in the droplet fluorescent image according to the actual scatter data. The method is used for carrying out light field estimation and correction completely based on the droplet image in real time, does not need additional special calibration objects or reference targets, has stronger robustness and adaptability to light field change, fully and effectively restores the brightness data of the droplet, and improves the display effect of the brightness of the droplet data.

Inventors

ZHONG LIANGJIAN
ZHAO QINGNAN
BAI KUN
CHEN FAYI
LUO YAN
ZHANG YU

Assignees

迈克医疗电子有限公司

Dates

Publication Date: 20260508
Application Date: 20241122
Priority Date: 20241104

Claims (16)

1. A droplet partitioning and clustering method for a droplet-based digital nucleic acid amplification quantitative analysis system, comprising: Reading the original scatter data of each droplet in the droplet fluorescence image, including position and brightness; acquiring brightness distribution density conditions based on the original scattered point data; screening the reference droplets according to the brightness distribution density condition; performing light field correction on the droplet fluorescence image according to the reference droplet to obtain actual scattered point data of the droplet; and dividing and clustering the droplets in the droplet fluorescence image according to the actual scattered point data.
2. The method of claim 1, wherein obtaining a luminance distribution density condition based on the raw scatter data comprises: The original scatter data is converted into a microdroplet scatter thermodynamic diagram to characterize the brightness distribution density.
3. The method of claim 2, wherein converting the original scatter data into a droplet scatter thermodynamic diagram comprises: Dividing the droplet fluorescence image into a plurality of grids, sequencing and numbering all grids, forming a subarray by all droplets in each grid, and connecting the subarrays to form a droplet array; and mapping the droplet array into a target image with a set size by taking the index of the droplet in the droplet array as an abscissa and the brightness of the droplet as an ordinate to form a droplet scattering point thermodynamic diagram.
4. A method according to claim 3, wherein the droplet fluorescence image is divided into a plurality of grids, all grids are ordered and numbered, all droplets in each grid forming a sub-array, the sub-arrays being connected to form an array of droplets, comprising: dividing the droplet fluorescence image into a plurality of grids; Sequencing and numbering all grids according to an S-shaped sequence; Determining a grid where each droplet is located according to the position of the droplet; Creating a subarray for each grid, adding each droplet to the subarray of the grid in which it is located; all the subarrays are connected according to the serial numbers of the corresponding grids to form a droplet array.
5. A method according to claim 3, wherein mapping the array of droplets into a target image of a set size with the index of the droplets in the array of droplets as the abscissa and the brightness of the droplets as the ordinate, forms a droplet heat map, comprises: And carrying out normalization processing on the mapped target image by adding 1 to the gray value of the mapping position of the droplet on the target image, and converting the normalized target image into an image with a gray range of 0-255 to obtain a micro-droplet scattering point thermodynamic diagram.
6. The method of claim 2, wherein screening the reference droplets based on the intensity profile density comprises: dividing a target area of the microdroplet scattering point thermodynamic diagram, which meets the preset condition, according to the preset reference microdroplet mask size condition; In the case that the target area is one, determining the target area as a reference droplet mask; In the case of more than one of the target areas, determining the lowermost target area in the upper and lower positional relationship as a reference droplet mask; and extracting the reference droplet falling into the reference droplet mask.
7. The method of claim 1, wherein performing light field correction on the droplet fluorescence image based on the reference droplet to obtain an actual scatter data of the droplet comprises: dividing the droplet fluorescence image into a plurality of grids; calculating the brightness of each grid based on the brightness of the reference droplet, and forming a light field estimation matrix by taking the brightness of all grids as elements; Calculating a brightness average value of the light field estimation matrix, and calculating a correction field matrix consistent with the light field estimation matrix in size based on the brightness average value and each element of the light field estimation matrix; And calculating the actual brightness of the droplets based on the correction field matrix and the brightness value of the droplets in each grid to obtain the actual scattered point data.
8. The method of claim 7, wherein calculating the brightness of each grid based on the brightness of the reference droplet comprises: For a first grid in which reference droplets exist, calculating the average value of the brightness of the reference droplets in the first grid as the brightness of the first grid, and for a second grid in which reference droplets do not exist, determining the brightness of the second grid based on the brightness of the grid in which the reference droplets are nearest to the second grid.
9. The method of claim 7, wherein a first calculation formula is used to calculate a luminance average of the light field estimation matrix, the first calculation formula being as follows: Wherein, base represents the brightness average value of the light field estimation matrix, w and h distribution represents the number of columns and rows of the light field estimation matrix, and L [ i, j ] represents the light field estimation matrix; Based on the brightness average value and each element of the light field estimation matrix, calculating a correction coefficient by adopting a second calculation formula, wherein the correction coefficient corresponding to each element of the light field estimation matrix respectively forms a correction field matrix, and the second calculation formula is as follows: where alpha represents a correction coefficient and value represents each element of the light field estimation matrix.
10. The method of claim 9, wherein the actual brightness of the droplets is calculated using a third calculation based on the correction field matrix and the brightness values of the droplets within each grid, the third calculation being as follows: cal value =value*alpha Where cal value represents the actual brightness of the droplet.
11. The method of claim 1, wherein the partitioning and clustering of droplets in the droplet fluorescence image based on the actual scatter data comprises: calculating a brightness ratio between each droplet and a reference droplet in its neighborhood based on the actual scatter data; generating a corresponding nuclear density curve based on the brightness ratio of the reference droplet and the brightness ratio of the non-reference droplet respectively, and superposing the generated nuclear density curves to obtain a brightness ratio density curve; And extracting peak information in the brightness ratio density curve, and dividing and clustering the droplets in the droplet fluorescence image according to the peak information to obtain a negative droplet cluster and/or a positive droplet cluster.
12. The method of claim 11, wherein calculating the ratio of brightness between each droplet and the reference droplet in its neighborhood comprises: Traversing each droplet in the droplet array, taking the position of the current droplet in the droplet fluorescence image as a center, taking the set multiple of the radius of the current droplet as an initial radius, expanding the radius step by step to iteratively search the reference droplet in the neighborhood of the current droplet, and stopping searching when the number of the searched reference droplets reaches a specified threshold or reaches a set iteration number; after the searching is stopped, calculating the ratio between the brightness of the current droplet and the brightness median value or brightness average value of all searched reference droplets under the condition that the reference droplets are searched, and taking the ratio as the brightness ratio between the current droplet and the reference droplets in the neighborhood of the current droplet, and calculating the ratio between the brightness of the current droplet and the brightness median value or brightness average value of all the reference droplets under the condition that the reference droplets are not searched, and taking the ratio as the brightness ratio between the current droplet and the reference droplets in the neighborhood of the current droplet.
13. The method of claim 11, wherein extracting peak information in the luminance ratio density curve, and dividing and clustering droplets in a droplet fluorescence image according to the peak information, to obtain a negative droplet cluster and/or a positive droplet cluster, comprises: Extracting peak information in the brightness ratio density curve; filtering and combining the extracted peak information; Selecting the first m peaks in the brightness ratio density curve according to the dividing requirement of single PCR detection or multiple PCR detection, determining the brightness ratio corresponding to the valley point based on the peak information of the first m peaks, and determining the dividing line of the negative droplet cluster and the positive droplet cluster based on the brightness ratio; And dividing and clustering the droplets in the droplet fluorescence image by using the dividing line to obtain negative droplet clusters and/or positive droplet clusters.
14. A droplet partitioning and clustering device for use in a droplet-based digital nucleic acid amplification quantitative analysis system, comprising: the reading module is used for reading the original scatter data of each droplet in the droplet fluorescence image, including the position and the brightness; the acquisition module is used for acquiring brightness distribution density conditions based on the original scattered point data; The screening module is used for screening the reference microdroplets according to the brightness distribution density condition; the correction module is used for carrying out light field correction on the droplet fluorescence image according to the reference droplet to obtain actual scattered point data of the droplet; And the dividing and clustering module is used for dividing and clustering the droplets in the droplet fluorescence image according to the actual scattered point data.
15. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 13.
16. A droplet-based digital nucleic acid amplification quantitative analysis system, comprising: Droplet generation means for generating a plurality of droplets based on the sample; a nucleic acid amplification temperature control device for performing a nucleic acid amplification reaction on the plurality of droplets, and The product signal acquisition device is used for acquiring a product signal after the nucleic acid amplification reaction; The droplet generation device comprises: an open container for storing the generated plurality of droplets and providing a place for performing a nucleic acid amplification reaction; the micro-pipeline is positioned above the open container and is used for loading nucleic acid amplification reaction liquid to be detected, and both ends of the micro-pipeline are provided with openings; Vibration means for driving the microchannel to vibrate reciprocally left and right below the liquid level of the open vessel to continuously generate a plurality of droplets; a fluorescence imaging detection device for taking a fluorescence image of the droplet, and A controller for implementing the steps of the method of any one of claims 1 to 13.

Description

Microdroplet partitioning and clustering method, device, medium and system Cross Reference to Related Applications The present invention claims priority from chinese patent application CN202411560659.2 entitled "droplet classification and clustering method, device, medium and system" filed on month 11 and 4 of 2024, the entire contents of which are incorporated herein by reference. Technical Field The present disclosure relates to the field of image-type digital PCR technology, and in particular, to a droplet partitioning and clustering method, device, medium, and system. Background The image type digital PCR (Polymerase Chain Reaction ) system has the condition of uneven light field in the imaging process, and the uneven light field is observed from the micro-droplet fluorescent image layer, so that the brightness of different areas of the image is different. On the one hand, the difference can cause that the same type of negative droplets or positive droplets in different areas of the image have larger fluctuation in fluorescence intensity, and on the other hand, the negative droplets and the positive droplets in different areas of the image can cause that the fluorescence intensity of the droplets cannot be accurately presented due to the two phenomena, and the classification and the clustering of the yin-yang droplets are inconvenient. In some related technologies, the threshold value is divided first and then the light field correction is performed, and the method has the problem that the data is distorted after correction due to the error of threshold value division. In other related technologies, a fixed marker or a reference target is adopted for correction to form correction parameters, so that when the actual situation is different from a pre-calibration method, the correction parameters cannot work well, and error correction can be caused in extreme situations. Disclosure of Invention The disclosure provides a droplet classification and clustering method, device, medium and system, so that droplet brightness information can be accurately presented, and droplets can be classified and clustered conveniently. In a first aspect, embodiments of the present disclosure provide a droplet partitioning and clustering method applied to a droplet-based digital nucleic acid amplification quantitative analysis system, comprising: Reading the original scatter data of each droplet in the droplet fluorescence image, including position and brightness; acquiring brightness distribution density conditions based on the original scattered point data; screening the reference droplets according to the brightness distribution density condition; performing light field correction on the droplet fluorescence image according to the reference droplet to obtain actual scattered point data of the droplet; and dividing and clustering the droplets in the droplet fluorescence image according to the actual scattered point data. In some exemplary embodiments, obtaining the brightness distribution density condition based on the raw scatter data includes: The original scatter data is converted into a microdroplet scatter thermodynamic diagram to characterize the brightness distribution density. In some exemplary embodiments, converting the raw scatter data into a micro-droplet scatter thermodynamic diagram comprises: Dividing the droplet fluorescence image into a plurality of grids, sequencing and numbering all grids, forming a subarray by all droplets in each grid, and connecting the subarrays to form a droplet array; and mapping the droplet array into a target image with a set size by taking the index of the droplet in the droplet array as an abscissa and the brightness of the droplet as an ordinate to form a droplet scattering point thermodynamic diagram. In some exemplary embodiments, the droplet fluorescence image is divided into a plurality of grids, all grids are ordered and numbered, all droplets in each grid form a subarray, and the subarrays are connected to form an array of droplets, comprising: dividing the droplet fluorescence image into a plurality of grids; Sequencing and numbering all grids according to an S-shaped sequence; Determining a grid where each droplet is located according to the position of the droplet; Creating a subarray for each grid, adding each droplet to the subarray of the grid in which it is located; all the subarrays are connected according to the serial numbers of the corresponding grids to form a droplet array. In some exemplary embodiments, mapping the array of droplets into a sized target image with an index of the droplets in the array of droplets as the abscissa and a brightness of the droplets as the ordinate, forms a droplet scattering thermodynamic diagram, comprising: And carrying out normalization processing on the mapped target image by adding 1 to the gray value of the mapping position of the droplet on the target image, and converting the normalized target image into an image with a gr