Digital PCR instrument and its channel droplet alignment method

Abstract

This invention provides a digital PCR instrument and its channel droplet alignment method. The method includes: acquiring channel images of a target sample in multiple channels, and identifying droplets in each channel image to obtain the positional information of the droplets in each channel image; determining the positional deviation of each channel based on the positional information of the droplets in each channel image; translating the channel images of the corresponding channels according to the positional deviations of each channel to obtain channel-corrected images for each channel; synthesizing a channel-aligned image of the target sample based on the channel-corrected images; identifying droplets in the channel-aligned images to obtain the positional information of the droplets in the channel-aligned images; and statistically analyzing the droplet brightness information on each channel-corrected image according to the positional information of the droplets in the channel-aligned images. This method improves the accuracy of channel droplet alignment and helps to improve the accuracy of droplet fluorescence intensity acquisition.

Description

Technical Field

[0001] This invention relates to the field of molecular biology, specifically to a digital PCR instrument and its channel droplet alignment method. Background Technology

[0002] Digital polymerase chain reaction (PCR) is an emerging instrument for precise quantitative detection of nucleic acids. It works by distributing diluted nucleic acid templates into numerous different reaction units, each containing one or no nucleic acid; then, during PCR amplification, detectable fluorescence is added; after amplification, statistical methods are used to collect the number of fluorescence occurrences in each reaction unit, thereby quantifying the nucleic acid concentration in the sample. In digital PCR applications, multiple fluorescence channels are typically used to detect corresponding genes to distinguish different target fluorescent genes. Multiplex digital PCR detection with multiple channels requires correlation analysis of droplet information across multiple channels; therefore, aligning the same droplet across different channels is crucial.

[0003] In digital PCR instruments that use charge-coupled devices (CCDs) to acquire fluorescence signals, the switching of filters across different wavelengths during the acquisition of fluorescence images from different channels can cause the image position of the same droplet to shift in different channels. This leads to deviations in the reading of fluorescence values ​​for droplets in different channels, thus affecting the detection results. Positional shifts primarily originate from hardware structural errors during channel switching. Currently, manual correction is commonly used, but this can only reduce the error to a certain range (e.g., cumulative droplet shift error across channels less than 15 pixels). For digital PCR instruments with extremely high pixel precision requirements, this error range is insufficient to accurately align the droplets in each channel, thereby affecting droplet identification. Summary of the Invention

[0004] This invention provides a digital PCR instrument and its channel droplet alignment method to solve the problem that existing alignment methods have low accuracy and cannot meet the needs of practical applications.

[0005] In a first aspect, embodiments of the present invention provide a digital PCR instrument, comprising: a sample carrying module, a reaction system preparation module, a droplet generation module, a PCR amplification module, a fluorescence signal detection module, and a processing module;

[0006] The sample carrying module is used to hold the nucleic acid sample to be tested;

[0007] The reaction system preparation module is used to prepare the reaction mixture required for PCR reactions;

[0008] The droplet generation module is used to mix the nucleic acid sample to be tested contained in the sample carrying module and the reaction mixture prepared by the reaction system preparation module according to a preset ratio and use microfluidic technology to form droplets;

[0009] The PCR amplification module is used to control the temperature cycle of the digital PCR reaction, so that the PCR reaction is amplified uniformly in the droplets.

[0010] A fluorescence signal detection module is used to detect the fluorescence signal generated by the PCR reaction.

[0011] The processing module is used to acquire channel images of the target sample in multiple channels based on the fluorescence signal detected by the fluorescence signal detection module, and to perform microdroplet identification on each channel image to obtain the position information of the microdroplets in each channel image;

[0012] The positional deviation of each channel is determined based on the positional information of the droplets in each channel image;

[0013] By shifting the channel image under the corresponding channel according to the positional deviation of each channel, the channel correction image under each channel is obtained;

[0014] Synthesize a channel-aligned image of the target sample based on the corrected images of each channel;

[0015] Droplet identification is performed on the channel-aligned image to obtain the position information of the droplets in the channel-aligned image;

[0016] Based on the position information of the droplets in the channel-aligned image, the brightness information of the droplets is statistically analyzed on each channel-corrected image.

[0017] In one embodiment, the processing module is used to determine the positional deviation of each channel based on the positional information of the droplets in each channel image, including:

[0018] The target location information of the droplet is determined based on the location information of the same droplet in each channel image;

[0019] For each channel image, the droplets are traversed, and the positional deviation of the droplets is determined based on the positional information of the droplets in the channel image and their corresponding target position information.

[0020] The mean value of the positional deviations of all droplets in the channel image is determined as the positional deviation of the current channel.

[0021] In one embodiment, the processing module is used to determine the target location information of the droplet based on the location information of the same droplet in each channel image, including:

[0022] For each channel, in a binary image of the same size as the channel image, the pixel value of the droplet region centered at the droplet center and with a predetermined radius is set as the first pixel value, and the pixel values ​​of the remaining background region are set as the second pixel value, thus obtaining the droplet position mask map for that channel.

[0023] Obtain the intersection region of the droplet regions in the droplet position mask map of multiple channels, and determine the center position of each intersection region as the target position information of the corresponding droplet;

[0024] Draw an overlapping area marker mask with the center of each intersection region as the center and a preset length as the radius.

[0025] In one embodiment, the processing module is used to traverse the droplets and determine the positional deviation of the droplets based on their positional information in the channel image and their corresponding target position information, including:

[0026] Based on the position information of the droplet in the channel image, the target position information corresponding to the droplet is obtained in the overlapping area marker mask image;

[0027] The positional deviation of a droplet is determined by the positional deviation of the droplet in the channel image from its corresponding target position in the first and second directions, where the first and second directions are perpendicular to each other.

[0028] In one embodiment, the processing module is used to synthesize a channel-aligned image of the target sample based on the channel-corrected images, including:

[0029] Determine the image sharpness of the corrected images for each channel;

[0030] The target sample's channel-aligned image is synthesized proportionally from a channel-corrected image with an image sharpness greater than a preset sharpness threshold;

[0031] or,

[0032] The channel-aligned image of the target sample is obtained by weighting the corrected images of each channel using weighting coefficients that are positively correlated with image sharpness.

[0033] Secondly, embodiments of the present invention provide a method for aligning digital PCR channel droplets, comprising:

[0034] The target sample is captured in multiple channels, and droplet identification is performed on each channel image to obtain the position information of the droplets in each channel image.

[0035] The positional deviation of each channel is determined based on the positional information of the droplets in each channel image;

[0036] By shifting the channel image under the corresponding channel according to the positional deviation of each channel, the channel correction image under each channel is obtained;

[0037] Synthesize a channel-aligned image of the target sample based on the corrected images of each channel;

[0038] Droplet identification is performed on the channel-aligned image to obtain the position information of the droplets in the channel-aligned image;

[0039] Based on the position information of the droplets in the channel-aligned image, the brightness information of the droplets is statistically analyzed on each channel-corrected image.

[0040] In one embodiment, determining the positional deviation of each channel based on the positional information of the droplets in each channel image includes:

[0041] The target location information of the droplet is determined based on the location information of the same droplet in each channel image;

[0042] For each channel image, the droplets are traversed, and the positional deviation of the droplets is determined based on the positional information of the droplets in the channel image and their corresponding target position information.

[0043] The mean value of the positional deviations of all droplets in the channel image is determined as the positional deviation of the current channel.

[0044] In one embodiment, determining the target location information of a droplet based on its location information in each channel image includes:

[0045] For each channel, in a binary image of the same size as the channel image, the pixel value of the droplet region with the center of the droplet as the center and the radius as the preset length is set as the first pixel value, and the pixel value of the remaining background region is set as the second pixel value, thus obtaining the droplet position mask map of that channel.

[0046] Obtain the intersection region of the droplet regions in the droplet position mask map of multiple channels, and determine the center position of each intersection region as the target position information of the corresponding droplet;

[0047] Draw an overlapping area marker mask with the center of each intersection region as the center and a preset length as the radius.

[0048] In one embodiment, the droplets are traversed, and the positional deviation of the droplets is determined based on their positional information in the channel image and their corresponding target position information, including:

[0049] Based on the position information of the droplet in the channel image, the target position information corresponding to the droplet is obtained in the overlapping area marker mask image;

[0050] The positional deviation of a droplet is determined by the positional deviation of the droplet in the channel image from its corresponding target position in the first and second directions, where the first and second directions are perpendicular to each other.

[0051] In one embodiment, synthesizing a channel-aligned image of the target sample based on the channel-corrected images includes:

[0052] Determine the image sharpness of the corrected images for each channel;

[0053] The target sample's channel-aligned image is synthesized proportionally from a channel-corrected image with an image sharpness greater than a preset sharpness threshold;

[0054] or,

[0055] The channel-aligned image of the target sample is obtained by weighting the corrected images of each channel using weighting coefficients that are positively correlated with image sharpness.

[0056] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the digital PCR channel droplet alignment method as described in any of the second aspects.

[0057] The digital PCR instrument and its channel droplet alignment method provided in this invention acquire channel images of the target sample in multiple channels, and perform droplet identification on each channel image to obtain the position information of the droplets in each channel image; determine the positional deviation of each channel based on the positional information of the droplets in each channel image; translate the channel image of the corresponding channel according to the positional deviation of each channel to obtain the channel correction image of each channel; synthesize the channel alignment image of the target sample based on the channel correction images; perform droplet identification on the channel alignment image to obtain the positional information of the droplets in the channel alignment image; and statistically analyze the droplet brightness information on each channel correction image according to the positional information of the droplets in the channel alignment image. This improves the accuracy of channel droplet alignment and helps to improve the accuracy of droplet fluorescence intensity acquisition. Attached Figure Description

[0058] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0059] Figure 1 This is a schematic diagram of the structure of a digital PCR instrument provided in an embodiment of the present invention;

[0060] Figure 2 This is a flowchart of a digital PCR channel droplet alignment method provided in an embodiment of the present invention;

[0061] Figure 3 This is a schematic diagram comparing the positional deviation of microdroplets before and after positional deviation correction, provided in an embodiment of the present invention.

[0062] Figure 4 This is a schematic diagram comparing the droplet detection effect before and after channel droplet alignment according to an embodiment of the present invention;

[0063] Figure 5 This is a schematic diagram illustrating the principle of droplet position deviation estimation provided in an embodiment of the present invention.

[0064] The accompanying drawings have illustrated specific embodiments of the invention, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the invention in any way, but rather to illustrate the concept of the invention to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0065] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings. Similar elements in different embodiments are referred to by associated similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of this application. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, certain operations related to this application are not shown or described in the specification. This is to avoid obscuring the core parts of this application with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0066] Furthermore, the features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. At the same time, the steps or actions in the method description can be rearranged or adjusted in a manner obvious to those skilled in the art. Therefore, the various orders in the specification and drawings are only for the clear description of a particular embodiment and do not imply a necessary order, unless otherwise stated that a particular order must be followed.

[0067] The serial numbers assigned to components in this document, such as "first" and "second," are used only to distinguish the described objects and have no sequential or technical meaning. The terms "connection" and "linkage" used in this application, unless otherwise specified, include both direct and indirect connections (linkages).

[0068] Aligning the same droplet across different channels is a prerequisite for accurate droplet information analysis. In digital PCR, positional deviations in imaging the same droplet across different channels originate from channel switching. Coarse deviation corrections can be made by manually measuring the deviation values ​​between channels and adjusting them to fixed values. However, this coarse adjustment can only control the channel positional deviation within a certain range (e.g., the cumulative deviation of each channel is less than the pixel diameter of a droplet or 15 pixels), making it difficult to accurately align the droplets in each channel, affecting fluorescence signal reading and consequently droplet identification. To improve alignment accuracy, channel droplet alignment can be achieved based on external markers. First, special markers are designed in hardware. Then, by detecting these special markers in each channel, channel deviations are estimated, allowing for droplet position correction or image position shifting to achieve channel droplet alignment. Since additional external markers require special materials or methods, this increases consumable costs to some extent. Furthermore, these special markers may affect the droplet preparation process (e.g., influencing droplet morphology and quantity). To address the aforementioned issues, this application proposes a channel droplet alignment method that does not employ external markers, based on the root cause of channel droplet position deviation (channel switching) and the characteristics of the position deviation (relatively fixed deviation). By combining the position information of each channel droplet, the method achieves channel droplet deviation correction and droplet alignment.

[0069] Please refer to Figure 1 The structure of the digital PCR instrument 100 is as follows: Figure 1 As shown, it includes a sample carrying module 101, a reaction system preparation module 102, a droplet generation module 103, a PCR amplification module 104, a fluorescence signal detection module 105, and a processing module 106.

[0070] The sample carrying module 101 is used to hold the nucleic acid sample to be tested. The reaction system preparation module 102 is used to prepare the reaction mixture required for the PCR reaction. Specifically, the reaction system preparation module 102 automatically mixes different reagents according to a specified ratio and ensures the quality and stability of the mixture. These reagents include PCR buffer, primers, probes, polymerase, etc. The droplet generation module 103 is used to mix the nucleic acid sample to be tested contained in the sample carrying module and the reaction mixture prepared by the reaction system preparation module according to a preset ratio and use microfluidic technology to form droplets. Optionally, before generating droplets, the nucleic acid sample to be tested can also be treated to remove contaminants and purify. The droplet generation module 103 is a key module in the digital PCR instrument. It uses microfluidic technology to mix the sample and reaction system in a certain ratio and form uniformly sized droplets or tiny reaction regions. Each of these droplets contains all the components required for the PCR reaction. The PCR amplification module 104 is used to control the temperature cycle of the digital PCR reaction to ensure that the PCR reaction is uniformly amplified in the droplets. The PCR amplification module 104 typically includes both isothermal amplification and thermal cycling amplification methods to ensure uniform amplification in each droplet and generate fluorescence signals. The fluorescence signal detection module 105 is used to detect the fluorescence signals generated by the PCR reaction. Specifically, the fluorescence signal detection module 105 can detect the intensity of the fluorescence signal by exciting fluorescent molecules using a specific optical system and convert it into digital data. This allows the determination of the fluorescence signal threshold in each droplet, thereby calculating the number of target sequences in the sample. The processing module 106 is used to process and analyze the fluorescence signals detected by the fluorescence signal detection module 105. Specifically, the processing module 106 can calculate the number and concentration of target sequences in the sample based on preset thresholds, standard curves, and other parameters. This module also provides visualization and export functions for the results, facilitating further analysis and interpretation by the user.

[0071] It should be noted that, Figure 1 The structure shown is for illustrative purposes only and may include structures larger than those shown. Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown. Figure 1 The components shown can be implemented in hardware and / or software. Figure 1 The digital PCR instrument shown can be used to perform the digital PCR channel droplet alignment method provided in any embodiment of the present invention.

[0072] Figure 2 This is a flowchart illustrating a digital PCR channel droplet alignment method according to an embodiment of the present invention. Figure 2 As shown, the digital PCR channel droplet alignment method provided in this embodiment may include:

[0073] S201. Obtain channel images of the target sample in multiple channels, and perform droplet identification on each channel image to obtain the position information of the droplets in each channel image.

[0074] When multiple fluorescence channels are used for detection, after switching to the filter of the corresponding wavelength band, the channel image of that channel can be generated based on the fluorescence signal detected by the fluorescence signal detection module. Taking four channels as an example, channel images of FAM, HEX, ROX, and CY5 channels can be acquired.

[0075] After obtaining channel images from multiple channels, droplet identification can be performed on each channel image using image processing methods or machine learning methods to obtain the positional information of the droplets in each channel image. This embodiment does not limit the specific implementation method of droplet identification.

[0076] S202. Determine the positional deviation of each channel based on the positional information of the droplets in each channel image.

[0077] Since the channel position deviation is introduced during channel switching, the target sample does not undergo physical displacement during this process; that is, the droplets do not shift. The position of the same droplet in each channel image deviates from the same true position. Therefore, the position deviation of each channel can be determined based on the position information of the droplets in each channel image. For example, the geometric center of the position of the same droplet in each channel image can be taken as the true position of the droplet, and then the deviation between the position in the channel image and the true position can be calculated to determine the channel position deviation. In an optional implementation, the channel position deviation can be represented by a combination of position deviation in the x-direction and position deviation in the y-direction.

[0078] It should be noted that manual alignment can be performed before step S202. This involves manually measuring the deviation values ​​between channels and adjusting them to fixed values, initially controlling the channel position deviation to within a pixel diameter of a droplet or 15 pixels. Then, based on the manually aligned channel images, the position deviation of each channel is further determined for precise alignment.

[0079] S203. Translate the channel image under the corresponding channel according to the positional deviation of each channel to obtain the channel correction image under each channel.

[0080] After determining the positional deviation of each channel, the channel image under the corresponding channel can be translated according to the positional deviation of each channel to obtain the channel-corrected image under each channel. Taking the positional deviation of a channel as a combination of the positional deviation in the x-direction and the positional deviation in the y-direction as an example, the channel image is translated horizontally according to the positional deviation in the x-direction and vertically according to the positional deviation in the y-direction.

[0081] Please refer to Figure 3 , Figure 3 This is a schematic diagram comparing the positional deviation of microdroplets before and after positional deviation correction, provided in an embodiment of the present invention. Figure 3 As shown, Figure 3 The left-middle figure shows a schematic diagram of the droplet position deviation before position deviation correction. Figure 3 The right-middle image shows a schematic diagram of droplet position deviation after positional deviation correction. Red indicates the center of the overlapping area, blue dots represent the centers of droplets in each channel, and green circles indicate the correctable deviation range. The white area in the image represents the droplet region, and the dots within the droplet region represent the centers of droplets in each channel. Comparing the left and right images, it can be seen that after positional deviation correction, the pixel deviation of droplet alignment is less than 2 pixels, effectively ensuring the accuracy of subsequent droplet fluorescence intensity acquisition.

[0082] S204. Synthesize the channel-aligned image of the target sample based on the corrected images of each channel.

[0083] In digital PCR, due to differences in detection channels, different channels produce different imaging effects. For example, for the same sample location using the FAM, HEX, ROX, and CY5 channels, the FAM and ROX channels may produce brighter, clearer, and higher-quality images compared to the other channels. Therefore, to compensate for the limitations of single-channel images in terms of droplet representation and to avoid the impact of poor-quality channels on droplet detection, thereby improving droplet detection capability, this embodiment, after obtaining the channel-corrected images for each channel, synthesizes a channel-aligned image of the target sample based on these images for droplet detection. Specifically, channel-aligned images can be synthesized from channel-corrected images with better imaging quality; for example, only channel-corrected images with quality scores higher than a preset threshold can be selected for synthesis. Alternatively, the quality of each channel-corrected image can be proportionally fused, with a higher proportion of high-quality images and a lower proportion of low-quality images.

[0084] S205. Perform droplet identification on the channel-aligned image to obtain the position information of the droplets in the channel-aligned image.

[0085] Based on the corrected channel image information, fusing the corrected images of channels with better imaging quality can improve image quality. Then, droplet localization and quantity detection are performed on the synthesized channel-aligned image. Compared to single-channel detection, especially when the imaging quality of some channels is poor, this method can effectively improve the droplet detection rate and result accuracy. Please refer to [reference needed]. Figure 4 , Figure 4 This is a schematic diagram showing the comparison of droplet detection effects before and after channel droplet alignment, according to an embodiment of the present invention. Figure 4 The left-middle image shows a schematic diagram of the droplet detection effect before channel droplet alignment. Figure 4 The right-middle image shows the droplet detection effect after channel droplet alignment. The petal-shaped dots in the image are used to mark the detected droplets. Comparing the left and right images, it can be seen that the droplet detection rate is significantly improved after channel droplet alignment.

[0086] S206. Based on the position information of the droplets in the channel alignment image, statistically analyze the brightness information of the droplets on each channel correction image.

[0087] Finally, based on the position information of the droplets in the channel alignment image, the brightness information of the droplets is counted on each channel correction image to complete the channel droplet alignment.

[0088] The digital PCR channel droplet alignment method provided in this embodiment acquires channel images of the target sample in multiple channels, and performs droplet identification on each channel image to obtain the positional information of the droplets in each channel image. Based on the positional information of the droplets in each channel image, the positional deviation of each channel is determined. The channel images under the corresponding channels are translated according to the positional deviation of each channel to obtain the channel correction images for each channel. A channel-aligned image of the target sample is synthesized based on the channel correction images. Droplet identification is performed on the channel-aligned image to obtain the positional information of the droplets in the channel-aligned image. The brightness information of the droplets is statistically analyzed on each channel correction image according to the positional information of the droplets in the channel-aligned image. This improves the accuracy of channel droplet alignment and helps to improve the accuracy of droplet fluorescence intensity acquisition. Furthermore, it eliminates the need for external fixing markers, thus avoiding increased consumable costs and the influence of external fixing markers on the droplet preparation process. The method is simple, convenient, and has stronger adaptability to various scenarios.

[0089] Based on the above embodiments, the following will further explain in detail how to determine the positional deviation of each channel based on the positional information of the droplets in each channel image. In the digital PCR channel droplet alignment method provided in this embodiment, determining the positional deviation of each channel based on the positional information of the droplets in each channel image specifically includes:

[0090] S2021. Determine the target position information of the droplet based on the position information of the same droplet in each channel image.

[0091] In this embodiment, a droplet position mask map for the corresponding channel can be generated based on the position information of the droplets in the channel image. Specifically, the droplets of each channel can be drawn onto a binary image of the same size with a set radius R (R is the minimum legal droplet radius, which is consistent with the correctable deviation radius). For example, the pixel value of the droplet region is 1, and the pixel value of the background region is 0, thus forming a binary mask map of the channel, i.e., the droplet position mask map of the channel, which is used for subsequent calculation of the intersection region.

[0092] After obtaining the droplet position masks for each channel, the centers of the overlapping regions can be further identified. Specifically, the intersection regions of the obtained droplet position masks for each channel are calculated, and the centers of these intersection regions are obtained. Then, abnormal overlapping centers with a distance less than 2R between their centers are removed to obtain the final overlapping centers. It can be understood that the overlapping centers represent the target positions of the corresponding droplets, thus obtaining the target position information for each droplet.

[0093] To facilitate pairing of the same droplet and its overlap center in different channels, an overlap area marker mask is further generated based on the overlap center. Considering that the distance from the same droplet in each channel to its overlap center is less than the radius R, an overlap area marker mask is drawn with the overlap center as the center and radius R, and a unique identifier ID is added to each mask position to achieve pairing of the same droplet's different channel positions with the overlap center.

[0094] In summary, in one optional implementation, determining the target position information of a droplet based on its position information in each channel image can specifically include: for each channel, in a binary image of the same size as the channel image, setting the pixel value of the droplet region centered on the droplet's center position and with a preset length as its radius as the first pixel value, and setting the pixel values ​​of the remaining background region as the second pixel value, thus obtaining a droplet position mask for that channel, where the preset length is the minimum valid droplet radius; obtaining the intersection region of the droplet regions in the droplet position masks of multiple channels and determining the center position of each intersection region as the target position information of the corresponding droplet; and drawing an overlap area marker mask centered on the center position of each intersection region and with a preset length as its radius. It should be noted that, since there may be invalid droplets with radii that are too small or too large during the droplet generation process, a reasonable radius value needs to be set when setting the droplet region to avoid interference from these invalid droplets. In this embodiment, the minimum effective droplet radius is selected.

[0095] The following example, using four-channel detection, illustrates how to determine the target location information of a microdroplet. Please refer to [link / reference]. Figure 5 , Figure 5 This is a schematic diagram illustrating the principle of droplet position deviation estimation provided in an embodiment of the present invention. Figure 5In the diagram, CH-1, CH-2, CH-3, and CH-4 represent the positions of the same droplet detected in different channels. The dashed circles represent the droplet position masks for the corresponding channels. The overlapping area of ​​the four dashed circles is the intersection region of the droplet regions, and the center is used to mark the center position of this intersection region. The solid circles in the diagram mark the overlapping area mask with the center as the center and R as the radius. The solid circles represent the correctable range of droplet position offset; calculating the distance between each droplet position and the center (Center) can be used to correct channel deviations. (Diagram d) 1c This represents the distance between the droplet's position in channel CH1 and its target position, Center, i.e., the droplet's positional deviation. (See figure d.) 12 This indicates the distance between the droplet positions in channel CH1 and channel CH2.

[0096] S2022. For each channel image, traverse the droplets and determine the position deviation of the droplets based on their position information in the channel image and their corresponding target position information.

[0097] The process iterates through the droplets in each channel, identifying matching droplets in the overlapping area mask based on their positions within the channel image. Specifically, it obtains the pairing ID (uniquely, droplets with an ID of 0 are skipped during pairing). Then, using the ID as an index, it retrieves the center of the overlapping area corresponding to the droplet, thus obtaining the target position information and completing the pairing. Finally, it calculates the positional deviations of the paired droplet from the overlapping center in the x and y directions as the current droplet's positional deviation.

[0098] In other words, in one optional implementation, the droplets are traversed, and the positional deviation of the droplets is determined based on the positional information of the droplets in the channel image and their corresponding target positional information. Specifically, this may include: obtaining the target positional information corresponding to the droplets in the overlapping area marker mask based on the positional information of the droplets in the channel image; and determining the positional deviation of the droplets in the channel image and their corresponding target positional information in a first direction and a second direction as the positional deviation of the droplets, wherein the first direction and the second direction are perpendicular to each other.

[0099] S2023. The mean value of the positional deviations of all droplets in the channel image is determined as the positional deviation of the current channel.

[0100] Channel position deviation is introduced during channel switching, during which the sample does not undergo physical displacement, i.e., the droplets do not move. Therefore, the position deviation of the same droplet in different channels can be regarded as the position deviation between channels. To avoid the influence of individual abnormal droplets on the channel position deviation estimation, this embodiment uses the average value of all droplet position deviations as the channel position deviation, which can improve the accuracy of channel position deviation.

[0101] The digital PCR channel droplet alignment method provided in this embodiment, based on the above embodiment, is based on the cause of channel droplet position deviation. It takes the intersection center of the droplet regions in different channels as the target position information of the droplet, and determines the position deviation of the droplet by the deviation between the position of the droplet in the channel image and the target position. Furthermore, it determines the position deviation of the current channel by the mean of the position deviation of all droplets in the channel image. This can significantly improve the accuracy of channel position deviation, and thus improve the accuracy of channel droplet alignment.

[0102] By fusing channel images with better imaging, image quality can be improved, thereby helping to increase the detection rate and accuracy of microdroplets. Based on any of the above embodiments, the following will further explain in detail how to synthesize the channel alignment image of the target sample from the channel correction images. In the digital PCR channel microdroplet alignment method provided in this embodiment, synthesizing the channel alignment image of the target sample from the channel correction images may specifically include: determining the image sharpness of each channel correction image; synthesizing the channel alignment image of the target sample proportionally using channel correction images with image sharpness greater than a preset sharpness threshold; or, using a weighting coefficient positively correlated with image sharpness to perform a weighted summation of each channel correction image to obtain the channel alignment image of the target sample.

[0103] Image sharpness can be determined using the Standardized Mean Difference (SMD) algorithm, and the specific formula is as follows:

[0104]

[0105] Among them, C SMD N represents the image sharpness obtained using the standardized average difference algorithm. x N y The width and height of the image are represented by f(x,y), and the gray value at position (x,y) in the image is represented by f(x,y).

[0106] Image sharpness can also be determined using the entropy algorithm, the specific formula of which is as follows:

[0107]

[0108] Among them, C Entropy This indicates the image sharpness obtained using the entropy algorithm, where L represents the number of gray levels, typically 256; p i This represents the frequency of gray level i in the current image.

[0109] In one optional implementation, a sharpness threshold can be preset, and then channel-corrected images with sharpness greater than the preset threshold can be selected from all channel-corrected images for use in synthesizing the channel-aligned image. Alternatively, all channel-corrected images can be sorted from high to low sharpness, and a preset number of channel-corrected images with high sharpness can be selected for use in synthesizing the channel-aligned image. When the number of images selected for synthesizing the channel-aligned image is n, the proportional synthesis of the channel-aligned image can be performed according to the following formula:

[0110]

[0111] Among them, I i denoted as the selected i-th channel-corrected image, and O represents the synthesized channel-aligned image.

[0112] In another alternative implementation, channel-aligned images can be synthesized using weighted coefficients that are positively correlated with image sharpness, according to the following formula:

[0113]

[0114] Among them, I i a represents the selected i-th channel corrected image. i represents the weighting coefficient of the i-th channel corrected image and is positively correlated with the image sharpness of the i-th channel corrected image; O represents the synthesized channel-aligned image.

[0115] The digital PCR channel droplet alignment method provided in this embodiment, based on the above embodiment, further synthesizes the channel alignment image of the target sample based on the image clarity of each channel correction image. Prioritizing the selection of images with high image clarity for synthesis can improve the quality of the channel alignment image, thereby helping to improve the subsequent droplet detection rate.

[0116] In summary, the digital PCR channel droplet alignment method provided in this application proposes a channel droplet alignment method without external markers, based on the special conditions (channel switching) and deviation situation (relatively fixed deviation) caused by channel droplet position deviation. Compared with other channel droplet alignment methods based on external markers, this method eliminates the need for external fixed markers, thus avoiding increased consumable costs and the influence of external fixed markers on the droplet preparation process. It is simple, convenient, and more adaptable to various scenarios. Furthermore, by screening high-quality images for synthesis based on channel alignment, not only can the visual effect of the images be enhanced, but the detection capability of channel droplets with poor imaging quality can also be effectively improved. This application achieves the alignment of droplets in each channel during the droplet identification stage, eliminating the need for secondary alignment operations in droplet channel correlation analysis and effectively avoiding the loss of unpaired droplets during secondary alignment.

[0117] This invention also provides a computer-readable storage medium storing a computer program thereon, which is executed by a processor to implement the technical solutions of any of the above method embodiments.

[0118] The various embodiments in this disclosure are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0119] The scope of protection of this disclosure is not limited to the embodiments described above. Obviously, those skilled in the art can make various modifications and variations to this disclosure without departing from its scope and spirit. If such modifications and variations fall within the scope of the claims of this disclosure and their equivalents, then the intent of this disclosure also includes such modifications and variations.

Claims

1. A digital PCR instrument, characterized in that, include: The system includes a sample carrying module, a reaction system preparation module, a droplet generation module, a PCR amplification module, a fluorescence signal detection module, and a processing module. The sample carrying module is used to hold the nucleic acid sample to be tested; The reaction system preparation module is used to prepare the reaction mixture required for the PCR reaction; The droplet generation module is used to mix the nucleic acid sample to be tested contained in the sample carrying module and the reaction mixture prepared by the reaction system preparation module in a preset ratio to form droplets; The PCR amplification module is used to provide the microdroplet reaction temperature so that the PCR reaction can be amplified in the microdroplets; The fluorescence signal detection module is used to detect the fluorescence signal generated by the PCR reaction; The processing module is used to acquire channel images of the target sample in multiple channels based on the fluorescence signal detected by the fluorescence signal detection module, and to perform microdroplet identification on each channel image to obtain the position information of the microdroplets in each channel image. The positional deviation of each channel is determined based on the positional information of the droplets in each channel image; By shifting the channel image under the corresponding channel according to the positional deviation of each channel, the channel correction image under each channel is obtained; The channel-aligned image of the target sample is synthesized based on the corrected images of each channel; Droplet identification is performed on the channel alignment image to obtain the position information of the droplets in the channel alignment image; Based on the position information of the droplets in the channel aligned image, the brightness information of the droplets is statistically analyzed on each channel corrected image; The processing module is used to determine the positional deviation of each channel based on the positional information of the droplets in each channel image, including: The target location information of the droplet is determined based on the location information of the same droplet in each channel image; For each channel image, the droplets are traversed, and the positional deviation of the droplets is determined based on the positional information of the droplets in the channel image and their corresponding target position information. The mean value of the positional deviations of all droplets in the channel image is determined as the positional deviation of the current channel; The intersection center of the droplet regions in different channels is used as the target location information of the droplet.

2. The digital PCR instrument according to claim 1, characterized in that, The processing module is used to determine the target location information of the same droplet based on its position information in each channel image, including: For each channel, in a binary image of the same size as the channel image, the pixel value of the droplet region with the center of the droplet as the center and the radius as the preset length is set as the first pixel value, and the pixel value of the remaining background region is set as the second pixel value, thus obtaining the droplet position mask map of that channel. Obtain the intersection region of the droplet regions in the droplet position mask map of multiple channels, and determine the center position of each intersection region as the target position information of the corresponding droplet; Using the center of each intersection region as the center and the preset length as the radius, draw an overlapping area marker mask.

3. The digital PCR instrument according to claim 2, characterized in that, The processing module is used to traverse the droplets and determine the positional deviation of the droplets based on their positional information in the channel image and their corresponding target position information, including: Based on the position information of the droplet in the channel image, the target position information corresponding to the droplet is obtained in the overlapping area marker mask image; The positional deviation of a droplet is determined by the positional deviation of the droplet in the channel image from its corresponding target position in the first and second directions, wherein the first and second directions are perpendicular to each other.

4. The digital PCR instrument according to any one of claims 1-3, characterized in that, The processing module is used to synthesize a channel-aligned image of the target sample based on the channel-corrected images, including: Determine the image sharpness of the corrected images for each channel; The channel-aligned image of the target sample is synthesized proportionally using a channel-corrected image with an image sharpness greater than a preset sharpness threshold; or, The channel-aligned image of the target sample is obtained by weighting and summing the corrected images of each channel using weighting coefficients that are positively correlated with image sharpness.

5. A method for aligning microdroplets in a digital PCR channel, characterized in that, include: The target sample is captured in multiple channels, and droplet identification is performed on each channel image to obtain the position information of the droplets in each channel image. The positional deviation of each channel is determined based on the positional information of the droplets in each channel image; By shifting the channel image under the corresponding channel according to the positional deviation of each channel, the channel correction image under each channel is obtained; The channel-aligned image of the target sample is synthesized based on the corrected images of each channel; Droplet identification is performed on the channel alignment image to obtain the position information of the droplets in the channel alignment image; Based on the position information of the droplets in the channel aligned image, the brightness information of the droplets is statistically analyzed on each channel corrected image; The step of determining the positional deviation of each channel based on the positional information of the droplets in each channel image includes: The target location information of the droplet is determined based on the location information of the same droplet in each channel image; For each channel image, the droplets are traversed, and the positional deviation of the droplets is determined based on the positional information of the droplets in the channel image and their corresponding target position information. The mean value of the positional deviations of all droplets in the channel image is determined as the positional deviation of the current channel; The intersection center of the droplet regions in different channels is used as the target location information of the droplet.

6. The method according to claim 5, characterized in that, Determining the target location information of a droplet based on its position information in each channel image includes: For each channel, in a binary image of the same size as the channel image, the pixel value of the droplet region with the center of the droplet as the center and the radius as the preset length is set as the first pixel value, and the pixel value of the remaining background region is set as the second pixel value, thus obtaining the droplet position mask map of that channel. Obtain the intersection region of the droplet regions in the droplet position mask map of multiple channels, and determine the center position of each intersection region as the target position information of the corresponding droplet; Using the center of each intersection region as the center and the preset length as the radius, draw an overlapping area marker mask.

7. The method according to claim 6, characterized in that, The step of traversing the droplets and determining the positional deviation of the droplets based on their positional information in the channel image and their corresponding target position information includes: Based on the position information of the droplet in the channel image, the target position information corresponding to the droplet is obtained in the overlapping area marker mask image; The positional deviation of a droplet is determined by the positional deviation of the droplet in the channel image from its corresponding target position in the first and second directions, wherein the first and second directions are perpendicular to each other.

8. The method according to any one of claims 5-7, characterized in that, The process of synthesizing the channel-aligned image of the target sample based on the corrected images of each channel includes: Determine the image sharpness of the corrected images for each channel; The channel-aligned image of the target sample is synthesized proportionally using a channel-corrected image with an image sharpness greater than a preset sharpness threshold; or, The channel-aligned image of the target sample is obtained by weighting and summing the corrected images of each channel using weighting coefficients that are positively correlated with image sharpness.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the digital PCR channel droplet alignment method as described in any one of claims 5-8.

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