CN109872308B - Method for correcting droplet position between droplet type digital PCR channels - Google Patents
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Abstract
The invention relates to the field of in-vitro DNA amplification detection, in particular to a method for correcting droplet positions among droplet-type digital PCR channels. The method for correcting the position of the microdroplet among the microdroplet type digital PCR channels comprises the following steps: s1, feature matching detection: collecting the gray levels of the subgraph to be detected and the template graph, and calculating the matching degree between the subgraph and the template graph; s2, direction feature extraction: performing primary correction by extracting directional features; s3, identifying the movement trend: and further correcting the movement trend of all the droplets to obtain corrected droplet positions. The method for correcting the droplet position between the droplet-type digital PCR channels, disclosed by the invention, can accurately position and accurately match the position of each droplet under each channel, greatly reduces the error of the fluorescence value of each droplet in each channel, enables the copy number of the nucleic acid target molecules obtained by detection to be more accurate, and is simple to operate and easy to realize.
Description
Technical Field
The invention relates to the field of in-vitro DNA amplification detection, in particular to a method for correcting droplet positions among droplet-type digital PCR channels.
Background
At present, the digital PCR technology is mainly to divide a sample into a large number of nano-liter-level micro-reaction units, then to perform PCR circulation on each reaction unit simultaneously, and to divide each micro-reaction unit into negative or positive reactions by adopting an end-point observation method after amplification is completed. The traditional solid chip is formed by engraving reaction holes with consistent shapes and sizes on a substrate, and the manufacturing precision and the number of the reaction holes are limited and cannot meet the optimal progress requirement of digital PCR. The droplet type digital PCR is to generate tens of thousands of target gene-containing droplet microspheres through a micro-channel in a droplet type chip, and for the digital PCR based on Poisson distribution, the precision of a measurement result is greatly improved. In practical applications, it is usually necessary to detect two or more targets (amplification targets) in a sample at the same time, and thus different fluorophores (which fluoresce in a specific wavelength band after excitation) are used to label different targets. In order to distinguish fluorophores of different targets, two or more fluorescence band channels are generally used to detect each fluorophore separately.
By taking pictures under each fluorescence waveband for analysis, the luminescence wavebands of the fluorophores are inevitably overlapped, so that fluorescence crosstalk among different channels can occur. This fluorescence crosstalk can greatly interfere with the negative and positive determinations of the reaction chamber in different observation channels. Therefore, it is necessary to correct the fluorescence crosstalk, and it is necessary to know the fluorescence signals of different channels corresponding to each reaction unit to ensure that the positions of the pictures taken by each droplet reaction unit under each channel are consistent, that is, each droplet is associated with one channel. In practical applications, the position of the droplet generated by the droplet chip and amplified by PCR will shift during the shooting process of each channel, so the position correction is especially important.
Disclosure of Invention
The invention discloses a method for correcting droplet positions among droplet-type digital PCR channels, which solves the problem of deviation of droplet positions of all channels in the prior art.
Specifically, the technical scheme disclosed by the invention is as follows:
the invention discloses a method for correcting droplet positions among droplet-type digital PCR channels on one hand, which comprises the following steps:
s1, feature matching detection: collecting the gray levels of the subgraph to be detected and the template graph, and calculating the matching degree between the subgraph and the template graph;
s2, direction feature extraction: performing primary correction by extracting directional features;
s3, identifying the movement trend: and further correcting the movement trend of all the droplets to obtain corrected droplet positions.
It should be understood that the present invention is not limited to the above steps, and may also include other additional steps, for example, before step S1, between steps S1 and S2, between steps S2 and S3, between steps S3 and S4, between steps S4 and S5, and after step S5, without departing from the scope of the present invention.
Preferably, in S1, the degree of matching between the two is calculated by a normalized correlation metric formula as follows:
wherein E (S)i，j) And E (T) represents the average gray values of the subgraph and the template graph at (i, j), respectively, and R (i, j) represents the correlation at (i, j).
Preferably, the step S2 includes:
s21, creating an initial template on the channel to be corrected according to the initial positioning coordinates of each liquid drop;
s22, respectively selecting eight directions of upper, lower, left, right, upper left, lower left, upper right and lower right by taking the initial positioning coordinates as a center, and creating a to-be-detected subgraph with the same size as the template;
s23, respectively calculating normalization product correlation coefficients of the template and the 8 sub-images to be detected, and obtaining the positions of the sub-images to be detected by adopting iterative computation;
and S24, selecting the position of the subgraph to be detected obtained in the step S23 as the position of the liquid drop for primary correction till the primary correction is finished.
It is understood that additional steps may be included before, during, or after the above-described steps without departing from the scope of the present invention.
Preferably, in S21, the size of the initial template is a rectangle with a side of 2 to 5 pixels or a circle with a diameter of 2 to 5 pixels.
Preferably, in S22, the directional step length taken is 1 to 3 pixels.
Preferably, in S23, the iterative calculation step includes: and selecting the sub-graph to be detected with the maximum normalized product correlation coefficient to replace the initial template, and then repeating the steps until the iteration times exceed a set threshold value or the iteration is stopped when the two normalized product correlation coefficients of the same sub-graph to be detected are maximum.
Preferably, the step S3 includes:
s31: selecting all contained droplets with each droplet as a center;
s32: the average of the displacement amounts of all contained droplets is calculated as the final corrected displacement coordinates of the droplets.
It is understood that additional steps may be included before, during, or after the above-described steps without departing from the scope of the present invention.
Preferably, in S31, all of the contained droplets within a rectangle having a length and width in the range of 100 to 400 pixels are selected centered on each droplet.
Preferably, in S32, the average value of the x deviation amounts of all the included droplets is calculated as the deviation correction amount of the droplet x axis, and the average value of the y deviation amounts of all the included droplets is calculated as the deviation correction amount of the droplet y axis: the calculation formula is as follows:
wherein Δ xiAnd Δ yiRespectively represents the x-axis offset and the y-axis offset corrected by the offset of the ith droplet in the contained droplets, and deltax and deltay respectively represent the x-axis offset and the y-axis offset of the droplet point to be corrected.
The invention provides a micro-drop type digital PCR system, which adopts the method to realize the correction of the position of micro-drops among micro-drop type digital PCR channels.
The technique used in the present invention is defined as follows:
"droplet-type digital PCR" is an accounting quantitative technique, specifically, a microdroplet treatment is performed on a sample before traditional PCR amplification, that is, a reaction system containing nucleic acid molecules is divided into thousands of nano-scale microdroplets, wherein each microdroplet contains no nucleic acid target molecules to be detected or contains one to several nucleic acid target molecules to be detected. After PCR amplification, each microdroplet is detected one by one, the microdroplet with a fluorescent signal is judged to be 1, the microdroplet without the fluorescent signal is judged to be 0, and the initial copy number or the concentration of the target molecule can be obtained according to the Poisson distribution principle and the number and the proportion of the positive microdroplets.
Compared with the prior art, the invention has the following remarkable advantages and effects:
the method for correcting the droplet position between the droplet-type digital PCR channels, disclosed by the invention, can accurately position and accurately match the position of each droplet under each channel, greatly reduces the error of the fluorescence value of each droplet in each channel, enables the copy number of the nucleic acid target molecules obtained by detection to be more accurate, and is simple to operate and easy to realize.
Drawings
FIG. 1 is a diagram illustrating the random offset trajectory of a droplet with respect to a positioning channel in an embodiment of the present invention;
FIG. 2 is a schematic diagram of a random offset of droplet placement under a channel in an embodiment of the present invention;
FIG. 3 is a schematic diagram of the correction of droplet position in an embodiment of the invention.
Detailed Description
The technical solutions of the present invention are described in detail below with reference to the drawings and the embodiments, but the present invention is not limited to the scope of the embodiments.
The key point of the invention mainly comprises the following steps:
a method of correcting droplet placement between droplet-type digital PCR channels, comprising the steps of:
s1, feature matching detection: collecting the gray levels of the subgraph to be detected and the template graph, and calculating the matching degree between the subgraph and the template graph;
s2, direction feature extraction: performing primary correction by extracting directional features;
s3, identifying the movement trend: and further correcting the movement trend of all the droplets to obtain corrected droplet positions.
Example 1
The particularity of the digital PCR detection of the liquid drops lies in that the liquid drop image under one channel is used as a positioning channel to be mapped on the liquid drop images on other channels, so that the position coordinates of the same liquid drop under each channel are identified. The same droplet point has different fluorescence value signals under each channel, so that each droplet reaction unit corresponds to one multi-dimensional fluorescence data, and the correction analysis of fluorescence crosstalk among the channels is necessary according to the multi-dimensional fluorescence data. In the actual process of using the droplet chip, due to the factors such as uneven pressure in the process of generating droplets, uneven local temperature caused in the process of PCR amplification, illumination intensity change in the process of using a PCR reader and the like, the droplets are disturbed to a certain extent, so that irregular random micromotion occurs, and as shown in fig. 1, the trajectory diagram of the irregular offset motion of the droplets relative to a positioning channel is shown in a certain channel. If the deviation distance of one liquid drop exceeds the radius range of the liquid drop, the position coordinate mapped from the positioning channel deviates from the effective area of the liquid drop, and the positioning center deviates, so that the fluorescence value of each liquid drop in each channel is acquired to generate error interference, and the implementation effect of the following threshold value division and crosstalk correction is influenced. It can be seen that precise positioning and precise matching of each droplet under each channel is critical. The invention relates to a method for accurately matching droplet positions of each channel of droplet type digital PCR, which comprises the steps of feature matching detection, directional feature extraction and motion trend judgment. In this embodiment, the problem of the position deviation of the droplet between the channels is solved through 3 steps, and the specific method is discussed as follows:
s1, feature matching detection
Comparing the similarity of two images based on a fast similarity matching algorithm is a common image processing method. The invention adopts a normalization product correlation algorithm, the basic processing process is to collect the gray scale of the subgraph to be detected and the template graph, and the matching degree between the subgraph and the template graph is calculated through a normalized correlation measurement formula.
Wherein E (S)i，j) E (T) represents the average gray scale values of the subgraph and the template graph at (i, j), respectively, and R (i, j) represents(i, j) correlation.
A larger calculated correlation R value between the template and the subgraph indicates a greater similarity between the two.
S2, direction feature extraction
The similarity detection mainly adopts a template and a sub-graph window to carry out correlation matching detection, and the particularity of the droplet digital PCR detection lies in that a droplet image under one channel is adopted as a positioning channel to be mapped to droplet images of other channels, so that the position coordinates of the same droplet under each channel are identified. The positions of the droplets mapped to other channels and the positions of the real droplets are shifted due to the above-mentioned various reasons, and the positions of the droplets in a certain channel are randomly shifted as shown in FIG. 2. How to correct the offset is the key to be solved by the present invention. Matching errors can result if the features are matched directly from the positioning channel acquisition template to the sub-windows acquired under each channel, since the same droplet exhibits different characteristics in different channels. The invention adopts the 8-direction self-correction matching method, which can perfectly solve the consistency problem of feature matching and greatly improve the correction accuracy.
Firstly, a template is established on a channel to be corrected according to the initial positioning coordinate of each liquid drop, and the size of the template is generally a rectangle with the side length of 2-5 pixels or a circle with the diameter of 2-5 pixels; step two, respectively selecting an upper direction, a lower direction, a left direction, a right direction, an upper left direction, a lower left direction, an upper right direction and a lower right direction which are stepped by 1 to 3 pixels by taking the initial positioning coordinate as a center, and creating sub-images to be detected with the same size as the template; and step three, calculating normalized product correlation coefficients of the template and the 8 sub-images to be detected respectively, selecting the sub-image to be detected with the maximum normalized product correlation coefficient to replace the initial template, and repeating the step two, the step two and the step three. And if the iteration times exceed a set threshold value n (generally set to be 3 to 6 times), or if the two normalization products of the same sub-image position to be detected have the maximum correlation coefficient, the iteration is cut off. Selecting the position of the subgraph to be detected with the maximum normalized product correlation coefficient at the cut-off as the real position of the corrected liquid drop, and recording the offset of the liquid drop (x-axis offset and y-axis offset respectively), wherein the calculation formula is as follows:
wherein Δ xiAnd Δ yiRespectively represents the x-axis offset and the y-axis offset corrected by the offset of the ith droplet in the contained droplets, and deltax and deltay respectively represent the x-axis offset and the y-axis offset of the droplet point to be corrected. And finishing the primary correction.
S3, judging motion trend
Because some noise points are difficult to avoid in the process of acquiring images by each channel, some polluted liquid drops are generated in the process of manufacturing a liquid drop chip due to reagent pollution and the like, and the special liquid drops and areas can interfere with normal characteristic matching calculation, so that some liquid drop correction errors are caused. In order to avoid the correction error and improve the correction accuracy, the invention adopts a motion trend judging method. It can be seen from fig. 1 that when the droplets are randomly shifted, the directionality of the droplet shift is substantially uniform in a small area. The error correction can be effectively prevented by containing all the liquid drop motion trends in a small area, and the specific implementation method is as follows. Selecting all contained droplets in a rectangular range with the side length of 100-400 by taking each droplet as the center in the channel image to be corrected, calculating the average value of the x offset of all the contained droplets as the offset correction of the x axis of the droplet, calculating the average value of the y offset of all the contained droplets as the offset correction of the y axis of the droplet, and the schematic diagram of the position correction result of the droplet is shown in FIG. 3.
The method for correcting the positions of the droplets among the droplet-type digital PCR channels disclosed by the embodiment can accurately position and accurately match the positions of the droplets under each channel, greatly reduces the error of the fluorescence value of each droplet in each channel, enables the copy number of the nucleic acid target molecules obtained by detection to be more accurate, and is simple to operate and easy to realize.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (6)
1. A method for correcting droplet position between droplet-type digital PCR channels, comprising the steps of:
s1, feature matching detection: collecting the gray levels of the subgraph to be detected and the template graph, and calculating the matching degree between the subgraph and the template graph;
s2, direction feature extraction: performing primary correction by extracting directional features;
s3, identifying the movement trend: further correcting the movement trend of all the droplets to obtain corrected droplet positions;
in S1, the degree of matching between the two is calculated by a normalized correlation metric formula as follows:
wherein E (S)i，j) And E (T) represents the average gray values of the subgraph and the template graph at (i, j), respectively, and R (i, j) represents the correlation at (i, j);
the step S2 includes:
s21, creating an initial template on the channel to be corrected according to the initial positioning coordinates of each liquid drop;
s22, respectively selecting eight directions of upper, lower, left, right, upper left, lower left, upper right and lower right by taking the initial positioning coordinates as a center, and creating a to-be-detected subgraph with the same size as the template;
s23, respectively calculating normalization product correlation coefficients of the template and the 8 sub-images to be detected, and obtaining the positions of the sub-images to be detected by adopting iterative computation;
s24, selecting the position of the subgraph to be detected obtained in the step S23 as the position of the liquid drop for primary correction till the primary correction is finished;
in S22, the direction step length taken is 1 to 3 pixels;
the step S3 includes:
s31: selecting all contained droplets with each droplet as a center;
s32: calculating the average value of the x deviation amount of all the contained drops as the deviation correction amount of the x axis of the drop, and calculating the average value of the y deviation amount of all the contained drops as the deviation correction amount of the y axis of the drop, wherein the calculation formula is as follows:
wherein Δ xiAnd Δ yiRespectively represents the x-axis offset and the y-axis offset corrected by the offset of the ith droplet in the contained droplets, and deltax and deltay respectively represent the x-axis offset and the y-axis offset of the droplet point to be corrected.
2. The method of claim 1, wherein in S21, the size of the initial template is: a rectangle having a length or width of 2 to 5 pixels, respectively, or a circle having a diameter of 2 to 5 pixels.
3. The method of claim 1, wherein in S23, the iterative computing step comprises: and selecting the sub-graph to be detected with the maximum normalized product correlation coefficient to replace the initial template, and then repeating the steps until the iteration times exceed a set threshold value or the iteration is stopped when the two normalized product correlation coefficients of the same sub-graph to be detected are maximum.
4. The method of claim 1, wherein in S31, all contained droplets within a rectangle having a length and width in the range of 100 to 400 pixels are selected centered on each droplet.
5. The method of claim 1, wherein in S32, the average x-offset of all contained drops is calculated as the x-axis offset correction for the drop and the average y-offset of all contained drops is calculated as the y-axis offset correction for the drop.
6. A digital PCR system of the droplet type, characterized in that the correction of the droplet position between the digital PCR channels of the droplet type is realized by the method of any one of claims 1 to 5.
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| "自吸离散式数字PCR芯片检测装置的关键技术研究与系统开发";袁茂恺;《中国优秀硕士学位论文全文数据库信息科技辑》;20160815(第8期);第I138-1223页 * |
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