Digital PCR instrument and channel droplet alignment method thereof

Abstract

The embodiment of the invention provides a digital PCR instrument and a channel microdroplet alignment method thereof, and the method comprises the steps: obtaining channel images of a target sample under a plurality of channels, and carrying out the microdroplet recognition of each channel image, and obtaining the position information of a microdroplet in each channel image; determining the position deviation of each channel according to the position information of the microdroplet in each channel image; translating the channel image under the corresponding channel according to the position deviation of each channel to obtain a channel correction image under each channel; synthesizing a channel alignment image of the target sample according to each channel correction image; performing microdroplet identification on the channel alignment image to obtain position information of the microdroplet in the channel alignment image; and counting the brightness information of the micro-droplets on each channel correction image according to the position information of the micro-droplets in the channel alignment image. According to the method disclosed by the invention, the accuracy of channel droplet alignment is improved, and the accuracy of droplet fluorescence intensity collection is favorably improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of molecular biology technology, and in particular to a digital PCR instrument and a channel droplet alignment method thereof. Background Art

[0002] The digital polymerase chain reaction (PCR) instrument is an emerging device for quantitative and precise detection of nucleic acids. The diluted nucleic acid template is distributed into a large number of different reaction units so that each reaction unit contains one or no nucleic acid; then detectable fluorescence is added while PCR amplification is performed; when the amplification is completed, the number of fluorescence occurrences in each reaction unit is collected using statistical methods to quantitatively detect the nucleic acid concentration in the sample. In digital PCR detection applications, in order to distinguish fluorescent genes of different targets, multiple fluorescent channels are usually used to detect the corresponding genes. Multiple digital PCR detection in the form of multiple channels requires correlation analysis of droplet information under multiple channels, so it is crucial to align the same droplet under different channels.

[0003] In a digital PCR instrument that uses a charge coupled device (CCD) to collect fluorescence signals, when collecting fluorescence images of different channels, the position of the same droplet imaged on different channels will shift due to the need to switch filters of different wavelengths, which will cause deviations when reading the fluorescence values ​​of droplets in different channels, thus affecting the detection results. Position offset mainly comes from hardware structural errors during channel switching. Currently, manual correction is often used, but manual correction can only reduce the error to a certain range (for example, the cumulative offset error of droplets in each channel is less than 15 pixels). For digital PCR instruments with extremely high pixel accuracy requirements, this error range cannot accurately align the droplets of each channel, thereby affecting droplet recognition. Summary of the invention

[0004] The embodiment of the present invention provides a digital PCR instrument and a channel droplet alignment method thereof, so as to solve the problem that the existing alignment method has low precision and is difficult to meet the actual application requirements.

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

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

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

[0008] A 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 form droplets using microfluidics technology;

[0009] PCR amplification module, used to control the temperature cycle of digital PCR reaction so that the PCR reaction can be amplified evenly in the droplets;

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

[0011] A processing module, used to obtain channel images of the target sample under multiple channels according to the fluorescent signals detected by the fluorescent signal detection module, and to perform droplet recognition on each channel image respectively to obtain position information of the droplets in each channel image;

[0012] Determine the position deviation of each channel according to the position information of the droplet in the image of each channel;

[0013] According to the position deviation of each channel, the channel image under the corresponding channel is translated to obtain the channel correction image under each channel;

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

[0015] Performing droplet recognition on the channel alignment image to obtain position information of the droplet in the channel alignment image;

[0016] According to the position information of the droplet in the channel alignment image, the brightness information of the droplet is counted on each channel correction image.

[0017] In one embodiment, the processing module is used to determine the position deviation of each channel according to the position information of the droplet in the image of each channel, including:

[0018] Determine the target position information of the microdroplet according to the position information of the same microdroplet in each channel image;

[0019] For each channel image, the droplet is traversed, and the position deviation of the droplet is determined according to the position information of the droplet in the channel image and its corresponding target position information;

[0020] The average of all droplet position deviations in the channel image is determined as the position deviation of the current channel.

[0021] In one embodiment, the processing module is used to determine the target position information of the droplet according to the position 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 area with the center of the droplet as the center and a preset length as the radius is set to the first pixel value, and the pixel value of the remaining background area is set to the second pixel value to obtain the droplet position mask image of the channel.

[0023] Obtaining the intersection area of ​​the droplet areas in the droplet position mask images of the multiple channels and determining the center position of each intersection area as the target position information of the corresponding droplet;

[0024] With the center position of each intersection area as the center of the circle and the preset length as the radius, draw an overlapping area marking mask map.

[0025] In one embodiment, the processing module is used to traverse the droplet and determine the position deviation of the droplet according to the position information of the droplet in the channel image and the corresponding target position information, including:

[0026] Obtaining target position information corresponding to the microdroplet in the overlapped area marking mask image according to the position information of the microdroplet in the channel image;

[0027] The position deviation between the position information of the droplet in the channel image and the corresponding target position information in the first direction and the second direction is determined as the position deviation of the droplet, and the first direction and the second direction are perpendicular to each other.

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

[0029] Determine the image clarity of each channel correction image;

[0030] The channel-corrected images whose image clarity is greater than a preset clarity threshold are used to synthesize the channel-aligned images of the target samples in equal proportion;

[0031] or,

[0032] The weighted sum of the corrected images of each channel is performed using a weighting coefficient that is positively correlated with the image clarity to obtain the channel-aligned image of the target sample.

[0033] In a second aspect, an embodiment of the present invention provides a digital PCR channel droplet alignment method, comprising:

[0034] Acquire channel images of the target sample under multiple channels, and perform droplet recognition on each channel image respectively to obtain position information of the droplet in each channel image;

[0035] Determine the position deviation of each channel according to the position information of the droplet in the image of each channel;

[0036] According to the position deviation of each channel, the channel image under the corresponding channel is translated to obtain the channel correction image under each channel;

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

[0038] Performing droplet recognition on the channel alignment image to obtain position information of the droplet in the channel alignment image;

[0039] According to the position information of the droplet in the channel alignment image, the brightness information of the droplet is counted on each channel correction image.

[0040] In one embodiment, determining the position deviation of each channel according to the position information of the droplet in the image of each channel includes:

[0041] Determine the target position information of the microdroplet according to the position information of the same microdroplet in each channel image;

[0042] For each channel image, the droplet is traversed, and the position deviation of the droplet is determined according to the position information of the droplet in the channel image and its corresponding target position information;

[0043] The average of all droplet position deviations in the channel image is determined as the position deviation of the current channel.

[0044] In one embodiment, determining target position information of a droplet according to position information of the same droplet 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 area with the center of the droplet as the center and a preset length as the radius is set to the first pixel value, and the pixel value of the remaining background area is set to the second pixel value, so as to obtain a droplet position mask image of the channel;

[0046] Obtaining the intersection area of ​​the droplet areas in the droplet position mask images of the multiple channels and determining the center position of each intersection area as the target position information of the corresponding droplet;

[0047] With the center position of each intersection area as the center of the circle and the preset length as the radius, draw an overlapping area marking mask map.

[0048] In one embodiment, traversing the droplet and determining the position deviation of the droplet according to the position information of the droplet in the channel image and the corresponding target position information thereof includes:

[0049] Obtaining target position information corresponding to the microdroplet in the overlapped area marking mask image according to the position information of the microdroplet in the channel image;

[0050] The position deviation between the position information of the droplet in the channel image and the corresponding target position information in the first direction and the second direction is determined as the position deviation of the droplet, and the first direction and the second direction are perpendicular to each other.

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

[0052] Determine the image clarity of each channel correction image;

[0053] The channel-corrected images whose image clarity is greater than a preset clarity threshold are used to synthesize the channel-aligned images of the target samples in equal proportion;

[0054] or,

[0055] The weighted sum of the corrected images of each channel is performed using a weighting coefficient that is positively correlated with the image clarity to obtain the channel-aligned image of the target sample.

[0056] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the digital PCR channel droplet alignment method as described in any one of the second aspects.

[0057] The digital PCR instrument and the channel droplet alignment method provided by the embodiment of the present invention obtain the channel images of the target sample under multiple channels, and perform droplet recognition on each channel image respectively to obtain the position information of the droplet in each channel image; determine the position deviation of each channel according to the position information of the droplet in each channel image; translate the channel image under the corresponding channel according to the position deviation of each channel to obtain the channel correction image under each channel; synthesize the channel alignment image of the target sample according to the correction image of each channel; perform droplet recognition on the channel alignment image to obtain the position information of the droplet in the channel alignment image; and count the droplet brightness information on each channel correction image according to the position information of the droplet in the channel alignment image. The accuracy of channel droplet alignment is improved, which helps to improve the accuracy of droplet fluorescence intensity collection. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings, which are incorporated in and constitute a 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 A schematic diagram of the structure of a digital PCR instrument provided in one embodiment of the present invention;

[0060] Figure 2 A flow chart of a digital PCR channel droplet alignment method provided by one embodiment of the present invention;

[0061] Figure 3 A schematic diagram showing a comparison of the position deviation of a droplet before and after the position deviation correction provided by an embodiment of the present invention;

[0062] Figure 4 A schematic diagram showing a comparison of droplet detection effects before and after channel droplet alignment provided by an embodiment of the present invention;

[0063] Figure 5 A schematic diagram of the principle of droplet position deviation estimation provided by an embodiment of the present invention.

[0064] The above drawings have shown clear embodiments of the present invention, which will be described in more detail below. These drawings and text descriptions are not intended to limit the scope of the present invention in any way, but to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0065] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present application better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present application are not shown or described in the specification, this is to avoid the core part of the present application being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0066] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various implementations. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a required sequence, unless otherwise specified that a certain sequence must be followed.

[0067] The serial numbers of the components in this document, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning. The "connection" and "coupling" mentioned in this application, unless otherwise specified, include direct and indirect connections (couplings).

[0068] Aligning the same droplet in different channels is a prerequisite for accurate analysis of droplet information. In digital PCR, the position deviation of different channels imaging at the same droplet comes from channel switching. Rough deviation correction can be adjusted by manually measuring the deviation value between channels with a fixed value, but this rough position adjustment can only control the channel position deviation to a certain range (for example, the cumulative deviation of each channel is less than the pixel diameter of a droplet or 15 pixels), which makes it difficult to accurately align the droplets of each channel, affecting the reading of the fluorescence signal, and further affecting the droplet identification. In order to improve the alignment accuracy, channel droplet alignment can be achieved based on external markers. First, special markers are designed on the hardware, and then the channel deviation is estimated by detecting the special markers in each channel, so as to correct the droplet position or offset the image position to achieve channel droplet alignment. Since special materials or special methods are required to add additional external special markers, on the one hand, the cost of consumables will increase to a certain extent, and on the other hand, special markers may affect the droplet preparation process (for example, affecting the preparation morphology and preparation quantity of droplets). In response to the above problems, the present application proposes a channel droplet alignment method without external markings based on the root cause of the channel droplet position deviation (channel switching) and the characteristics of the position deviation (relatively fixed deviation). It combines the droplet position information of each channel to achieve channel droplet deviation correction and droplet alignment.

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

[0070] Among them, the sample carrying module 101 is used to accommodate 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 will automatically mix different reagents in a specified proportion and ensure 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 in a preset proportion and form droplets using microfluidics technology. Optionally, before generating droplets, the nucleic acid sample to be tested can also be removed from contaminants and purified. The droplet generation module 103 is a key module in the digital PCR instrument. It uses microfluidics technology to mix the sample and the reaction system in a certain proportion and form droplets of uniform size or tiny reaction areas. 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 so that the PCR reaction is evenly amplified in the droplets. The PCR amplification module 104 generally includes two methods, namely, isothermal amplification and thermal cycle amplification, to ensure that the reaction is uniformly amplified in each droplet and generates a fluorescent signal. The fluorescent signal detection module 105 is used to detect the fluorescent signal generated by the PCR reaction. Specifically, the fluorescent signal detection module 105 can detect the intensity of the fluorescent signal by exciting the fluorescent molecules and using a specific optical system, and convert it into digital data. In this way, the fluorescent signal threshold in each droplet can be determined, and the number of target sequences in the sample can be calculated. The processing module 106 is used to perform data processing and analysis on the fluorescent signal detected by the fluorescent signal detection module 105. Specifically, the processing module 106 can calculate the number and concentration of the target sequence in the sample based on preset threshold values, standard curves and other parameters. At the same time, the module can also provide visualization and export functions of the results, which is convenient for users to conduct further analysis and interpretation.

[0071] It should be noted that Figure 1 The structure shown is for illustration only and may also include Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown. Figure 1 Each component shown in the figure may be implemented by hardware and / or software. Figure 1 The digital PCR instrument shown can be used to execute the digital PCR channel droplet alignment method provided in any embodiment of the present invention.

[0072] Figure 2 Flow chart of a digital PCR channel droplet alignment method provided by one embodiment of the present invention. Figure 2 As shown, the digital PCR channel droplet alignment method provided in this embodiment may include:

[0073] S201 , acquiring channel images of a target sample in multiple channels, and performing droplet recognition on each channel image respectively to obtain 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 band, the channel image under the channel can be generated according to the fluorescence signal detected by the fluorescence signal detection module. Taking four channels as an example, channel images under the four channels of FAM, HEX, ROX and CY5 can be obtained.

[0075] After obtaining channel images under multiple channels, droplet recognition can be performed on each channel image based on an image processing method or a machine learning method to obtain the position information of the droplets in each channel image. The specific implementation method of droplet recognition is not limited in this embodiment.

[0076] S202 , determining the position deviation of each channel according to the position information of the droplet in the image of each channel.

[0077] Since the position deviation of the channel is introduced when the channel is switched, the target sample does not physically shift during this process, that is, the droplet does not shift, and the position of the same droplet in each channel image is deviated from the same real position. Therefore, the position deviation of each channel can be determined based on the position information of the droplet in each channel image. For example, the geometric center of the position of the same droplet in each channel image can be used as the real position of the droplet, and then the deviation between the position in the channel image and the real position is calculated to determine the position deviation of the channel. In an optional embodiment, the position deviation of the channel can be expressed by a combination of the position deviation in the x-direction and the position deviation in the y-direction.

[0078] It should be noted that, before step S202, manual alignment can be performed first. That is, by manually measuring the deviation value between channels and adjusting it with a fixed value, the channel position deviation is first controlled to be less than the pixel diameter of a droplet or within 15 pixels. Then, based on the manually aligned channel images, the position deviation of each channel is further determined to perform precise alignment.

[0079] S203 , translating the channel images of the corresponding channels according to the position deviations of the channels to obtain channel-corrected images of the channels.

[0080] After determining the position deviation of each channel, it is only necessary to translate the channel image of the corresponding channel according to the position deviation of each channel to obtain the channel correction image of each channel. Taking the case where the position deviation of the channel is composed of the position deviation in the x direction and the position deviation in the y direction as an example, the channel image is translated in the horizontal direction according to the position deviation in the x direction, and the channel image is translated in the vertical direction according to the position deviation in the y direction.

[0081] Please refer to Figure 3 , Figure 3 A schematic diagram showing the comparison of the position deviation of a droplet before and after the position deviation correction provided by an embodiment of the present invention. Figure 3 As shown, Figure 3 The middle left picture is a schematic diagram of the droplet position deviation before position deviation correction. Figure 3 The middle right figure is a schematic diagram of the position deviation of the droplet after position deviation correction, where red represents the center of the overlapping area, blue dots represent the center of the droplet in each channel, and green circles represent the correctable deviation range. The white area in the figure is the droplet area, and the dots in the droplet area represent the center of the droplet in each channel. Comparing the left and right figures, it can be seen that after position deviation correction, the pixel deviation of the droplet alignment is less than 2 pixels, which effectively ensures the accuracy of the subsequent droplet fluorescence intensity acquisition.

[0082] S204: synthesize a channel-aligned image of the target sample according to the corrected images of each channel.

[0083] In digital PCR, due to the differences in detection channels, different channels have different imaging effects. For example, for the four channels of FAM, HEX, ROX, and CY5 at the same sample position, the imaging of the FAM and ROX channels may be brighter, clearer, and have better imaging quality than the imaging of other channels. Therefore, on the one hand, in order to make up for the shortcomings of the single-channel image in terms of droplet expression, and on the other hand, in order to avoid the influence of channels with poor imaging quality on droplet detection and improve the droplet detection capability, in this embodiment, after obtaining the channel correction images under each channel, the channel alignment image of the target sample is synthesized according to each channel correction image for droplet detection. Specifically, channel correction images with better imaging quality can be selected to synthesize channel alignment images, for example, only channel correction images with a quality score higher than a preset threshold are selected for synthesis; they can also be fused in proportion according to the quality of each channel correction image, with a high proportion of high quality and a low proportion of low quality.

[0084] S205 , performing droplet recognition on the channel alignment image to obtain position information of the droplet in the channel alignment image.

[0085] Based on the corrected channel image information, the image quality can be improved by fusing the channel correction images with good imaging quality. Then, the droplet positioning and quantity detection are performed in the synthesized channel alignment image. Compared with single-channel detection, especially when the imaging quality of some channels is poor, the droplet detection rate and result accuracy can be effectively improved. Please refer to Figure 4 , Figure 4 A schematic diagram comparing droplet detection effects before and after channel droplet alignment provided by an embodiment of the present invention. Figure 4 The middle left picture is a schematic diagram of the droplet detection effect before the channel droplet is aligned. Figure 4 The middle right picture is a schematic diagram of the droplet detection effect after channel droplet alignment. In the picture, the petal-shaped dots are used to mark the detected droplets. Comparing the left and right pictures, it can be seen that the droplet detection rate has been significantly improved after channel droplet alignment.

[0086] S206 , counting droplet brightness information on each channel correction image according to the position information of the droplet in the channel alignment image.

[0087] Finally, according to 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 obtains the channel images of the target sample under multiple channels, and performs droplet recognition on each channel image respectively to obtain the position information of the droplet in each channel image; determines the position deviation of each channel according to the position information of the droplet in each channel image; translates the channel image under the corresponding channel according to the position deviation of each channel to obtain the channel correction image under each channel; synthesizes the channel alignment image of the target sample according to the correction image of each channel; performs droplet recognition on the channel alignment image to obtain the position information of the droplet in the channel alignment image; and counts the droplet brightness information on each channel correction image according to the position information of the droplet in the channel alignment image. The accuracy of channel droplet alignment is improved, which helps to improve the accuracy of droplet fluorescence intensity acquisition. And no external fixed markers are required, which can avoid increasing the cost of consumables on the one hand, and avoid the influence of external fixed markers on the droplet preparation process on the other hand. The operation is simple and convenient, and the scene adaptability is stronger.

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

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

[0091] In this embodiment, a droplet position mask of the corresponding channel can be generated according to the position information of the droplet in the channel image. Specifically, the droplets of each channel can be drawn into a binary image of the same size according to 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 area is 1, and the pixel value of the background area is 0, thereby forming a binary mask of the channel, that is, the droplet position mask of the channel, which is used for subsequent calculation of the intersection area.

[0092] After obtaining the droplet position mask map of each channel, the center of the overlapping area can be further obtained. Specifically, the intersection area of ​​the droplet position mask map of each channel that has been obtained is calculated, and the center of the intersection area is obtained, and then the abnormal overlapping centers whose distance between the centers of the intersection area is less than 2R are eliminated to obtain the final overlapping center. It can be understood that the overlapping center is the target position of the corresponding droplet, and the target position information of each droplet is obtained.

[0093] In order to facilitate the pairing of the same droplet and its overlapping center in different channels, an overlapping area marking mask map is further generated based on the overlapping center. Considering that the distance from the same droplet to its overlapping center in each channel is less than the radius R, the overlapping area marking mask map is drawn with the overlapping center as the center and the radius R, and a unique identification ID is added to each mask position to achieve the pairing of the same droplet in different channel positions and the overlapping center.

[0094] In summary, in an optional embodiment, the target position information of the droplet is determined according to the position information of the same droplet in each channel image, which may specifically include: for each channel, in a binary image of the same size as the channel image, the pixel value of the droplet area with the center position of the droplet as the center and the preset length as the radius is set to the first pixel value, and the pixel value of the remaining background area is set to the second pixel value, to obtain the droplet position mask map of the channel, and the preset length is the minimum legal droplet radius; obtain the intersection area of ​​the droplet area in the droplet position mask map of multiple channels and determine the center position of each intersection area as the target position information of the corresponding droplet; draw the overlap area mark mask map with the center position of each intersection area as the center and the preset length as the radius. It should be noted that since there will be invalid droplets with too small or too large radius in the process of generating droplets, in order to avoid the interference of these invalid droplets, a reasonable radius value needs to be set when setting the droplet area. In this embodiment, the minimum value of the effective droplet radius is selected.

[0095] The following uses four-channel detection as an example to explain in detail how to determine the target position information of the droplet. Figure 5 , Figure 5 A schematic diagram of the principle of droplet position deviation estimation provided by an embodiment of the present invention. Figure 5In the figure, CH-1, CH-2, CH-3 and CH-4 respectively represent the positions of the same droplet detected under different channels, and the dotted circle represents the droplet position mask under the corresponding channel. The overlapping area of ​​the four dotted circles is the intersection area of ​​the droplet area, and Center is used to mark the center position of the intersection area. The solid circle in the figure is the overlapping area marking mask drawn with Center as the center and R as the radius. The solid circle represents the correctable range of the droplet position deviation. Calculating the distance between each droplet position and the center (Center) can be used to correct the channel deviation. 1c Indicates the distance between the droplet position under channel CH1 and the target position Center of the droplet, that is, the position deviation of the droplet. 12 It represents the distance between the position of the droplet under channel CH1 and the position of the droplet under channel CH2.

[0096] S2022: For each channel image, traverse the droplet, and determine the position deviation of the droplet according to the position information of the droplet in the channel image and its corresponding target position information.

[0097] Traverse the droplets in each channel, and obtain the matching droplets in the overlapped area mark mask according to the position of the droplets in each channel image. Specifically, obtain the ID of the pairing (specially, when the ID is 0, this droplet does not participate in the pairing and is directly skipped), and then use the ID as the index to obtain the overlapped area center corresponding to the droplet, that is, obtain the target position information corresponding to the droplet, and complete the pairing. Finally, calculate the position deviation of the paired droplet and the overlapped center in the x and y directions as the position deviation of the current droplet.

[0098] That is to say, in an optional embodiment, the droplet is traversed, and the position deviation of the droplet is determined based on the position information of the droplet in the channel image and its corresponding target position information. Specifically, it may include: obtaining the target position information corresponding to the droplet in the overlapping area marking mask image based on the position information of the droplet in the channel image; and determining the position deviation between the position information of the droplet in the channel image and its corresponding target position information in a first direction and a second direction as the position deviation of the droplet, the first direction and the second direction being perpendicular to each other.

[0099] S2023. Determine the average value of all droplet position deviations in the channel image as the position deviation of the current channel.

[0100] The channel position deviation is introduced when the channel is switched. During this process, the sample does not physically move, that is, the droplet does not move. Therefore, the position deviation of the same droplet on different channels can be regarded as the position deviation between channels. In order to avoid the influence of individual abnormal droplets on the channel position deviation estimation, the average value of all droplet position deviations is used as the channel position deviation in this embodiment, which can improve the accuracy of the channel position deviation.

[0101] The digital PCR channel droplet alignment method provided in this embodiment is based on the above-mentioned embodiment and is based on the cause of the channel droplet position deviation. By taking the intersection center of the droplet areas of the droplet under different channels as the target position information of the droplet, and determining the position deviation of the droplet as the deviation between the position of the droplet in the channel image and the target position, and determining the mean of all droplet position deviations in the channel image as the position deviation of the current channel, the accuracy of the channel position deviation can be significantly improved, thereby improving the accuracy of channel droplet alignment.

[0102] By fusing channel images with better imaging, the image quality can be improved, which in turn helps to improve the droplet detection rate and accuracy. Based on any of the above embodiments, the following will further explain in detail how to synthesize the channel alignment map of the target sample based on the correction images of each channel. In the digital PCR channel droplet alignment method provided in this embodiment, synthesizing the channel alignment image of the target sample based on the correction images of each channel can specifically include: determining the image clarity of each channel correction image; using channel correction images with image clarity greater than a preset clarity threshold to proportionally synthesize the channel alignment image of the target sample; or, using a weighted coefficient that is positively correlated with the image clarity to perform weighted summation on each channel correction image to obtain the channel alignment image of the target sample.

[0103] Among them, the image clarity can be determined by using the standardized mean difference algorithm (SMD), and its specific formula is as follows:

[0104]

[0105] Among them, C SMD Indicates the image clarity obtained by using the standardized average difference algorithm, N x ,N y Represents the width and height of the image, and f(x,y) represents the grayscale value corresponding to the position (x,y) in the image.

[0106] Image clarity can also be determined using the Entropy algorithm, and the specific formula is as follows:

[0107]

[0108] Among them, C Entropy It indicates the image clarity obtained by entropy algorithm, L indicates the number of gray levels, usually 256; p i Indicates the frequency corresponding to gray level i in the current image.

[0109] In an optional implementation, a clarity threshold may be preset, and then channel-corrected images with image clarity greater than the preset clarity threshold may be selected from all channel-corrected images for synthesizing channel-aligned images; all channel-corrected images may also be sorted from high to low according to image clarity, and a preset number of channel-corrected images with high image clarity may be selected for synthesizing channel-aligned images. When the number of images selected for synthesizing channel-aligned images is n, synthesizing channel-aligned images in equal proportion may be performed according to the following formula:

[0110]

[0111] Among them, I i represents the selected i-th channel correction image, and O represents the synthesized channel alignment image.

[0112] In another optional implementation, the weighted coefficient positively correlated with the image definition is used to synthesize the channel alignment image, which can be performed according to the following formula:

[0113]

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

[0115] The digital PCR channel droplet alignment method provided in this embodiment is based on the above-mentioned embodiment, and further synthesizes the channel alignment image of the target sample based on the image clarity of the correction images of each channel, and preferentially selects images with high image clarity for synthesis, which can improve the quality of the channel alignment image, and further helps to improve the subsequent droplet detection rate.

[0116] To summarize, the digital PCR channel droplet alignment method provided in the present application is based on the special conditions (channel switching) and deviation conditions (relatively fixed deviation) caused by the channel droplet position deviation, and proposes a channel droplet alignment method without external markers. Compared with other channel droplet alignment methods based on external markers, no external fixed markers are required. On the one hand, it can avoid increasing the cost of consumables, and on the other hand, it also avoids the influence of external fixed markers on the droplet preparation process. The operation is simple and convenient, and the scene adaptability is stronger; further, high-quality images are screened and synthesized on the basis of channel alignment, which can not only enhance the visual effect of the image, but also effectively improve the detection ability of channel droplets with poor imaging quality; the present application realizes the alignment of droplets in each channel at the droplet recognition stage, and there is no need to perform a secondary alignment operation in the droplet channel correlation analysis, which effectively avoids the loss of unsuccessfully paired droplets in the secondary alignment.

[0117] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. The computer program is executed by a processor to implement the technical solution of any of the above method embodiments.

[0118] The various embodiments in the present disclosure are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0119] The protection scope of the present disclosure is not limited to the above-mentioned embodiments. Obviously, those skilled in the art can make various changes and modifications to the present disclosure without departing from the scope and spirit of the present disclosure. If these changes and modifications fall within the scope of the claims of the present disclosure and their equivalents, the intention of the present disclosure also includes these changes and modifications.

Claims

1. A digital PCR instrument, characterized in that: include: Sample carrying module, reaction system preparation module, droplet generation module, PCR amplification module, fluorescence signal detection module and processing module; The sample carrying module is used to accommodate 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 holding module and the reaction mixture prepared by the reaction system preparation module according to a preset ratio to form droplets; The PCR amplification module is used to provide a microdroplet reaction temperature so that the PCR reaction is amplified in the microdroplet; The fluorescent signal detection module is used to detect the fluorescent signal generated by the PCR reaction; The processing module is used to obtain channel images of the target sample under multiple channels according to the fluorescent signals detected by the fluorescent signal detection module, and to perform droplet recognition on each channel image respectively to obtain position information of the droplets in each channel image; Determine the position deviation of each channel according to the position information of the droplet in the image of each channel; According to the position deviation of each channel, the channel image under the corresponding channel is translated to obtain the channel correction image under each channel; Synthesizing a channel-aligned image of the target sample according to the correction images of each channel; Performing droplet recognition on the channel alignment image to obtain position information of the droplet in the channel alignment image; According to the position information of the droplet in the channel alignment image, the brightness information of the droplet is counted on each channel correction image.

2. The digital PCR instrument according to claim 1, characterized in that: The processing module is used to determine the position deviation of each channel according to the position information of the droplet in the image of each channel, including: Determine the target position information of the microdroplet according to the position information of the same microdroplet in each channel image; For each channel image, the droplet is traversed, and the position deviation of the droplet is determined according to the position information of the droplet in the channel image and its corresponding target position information; The average of all droplet position deviations in the channel image is determined as the position deviation of the current channel.

3. The digital PCR instrument according to claim 2, characterized in that: The processing module is used to determine the target position information of the droplet according to the position information of the same droplet 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 area with the center of the droplet as the center and a preset length as the radius is set to the first pixel value, and the pixel value of the remaining background area is set to the second pixel value, so as to obtain a droplet position mask image of the channel; Obtaining the intersection area of ​​the droplet areas in the droplet position mask images of the multiple channels and determining the center position of each intersection area as the target position information of the corresponding droplet; An overlapping area marking mask image is drawn with the center position of each intersection area as the center of the circle and the preset length as the radius.

4. The digital PCR instrument according to claim 3, characterized in that: The processing module is used to traverse the droplet and determine the position deviation of the droplet according to the position information of the droplet in the channel image and the corresponding target position information, including: According to the position information of the microdroplet in the channel image, the target position information corresponding to the microdroplet is obtained in the overlapped area mark mask image; The position deviation between the position information of the droplet in the channel image and the corresponding target position information in the first direction and the second direction is determined as the position deviation of the droplet, and the first direction and the second direction are perpendicular to each other.

5. The digital PCR instrument according to any one of claims 1 to 4, characterized in that: The processing module is used to synthesize the channel alignment image of the target sample according to the channel correction images, including: Determine the image clarity of each channel correction image; Using channel-corrected images whose image clarity is greater than a preset clarity threshold to synthesize a channel-aligned image of the target sample in equal proportion; or, The weighted sum of the corrected images of each channel is performed using a weighting coefficient that is positively correlated with the image clarity to obtain a channel aligned image of the target sample.

6. A digital PCR channel droplet alignment method, characterized in that: include: Acquire channel images of the target sample under multiple channels, and perform droplet recognition on each channel image respectively to obtain position information of the droplet in each channel image; Determine the position deviation of each channel according to the position information of the droplet in the image of each channel; According to the position deviation of each channel, the channel image under the corresponding channel is translated to obtain the channel correction image under each channel; Synthesizing a channel-aligned image of the target sample according to the correction images of each channel; Performing droplet recognition on the channel alignment image to obtain position information of the droplet in the channel alignment image; According to the position information of the droplet in the channel alignment image, the brightness information of the droplet is counted on each channel correction image.

7. The method according to claim 6, characterized in that Determining the position deviation of each channel according to the position information of the droplet in the image of each channel includes: Determine the target position information of the microdroplet according to the position information of the same microdroplet in each channel image; For each channel image, the droplet is traversed, and the position deviation of the droplet is determined according to the position information of the droplet in the channel image and its corresponding target position information; The average of all droplet position deviations in the channel image is determined as the position deviation of the current channel.

8. The method according to claim 7, characterized in that Determining the target position information of the droplet according to the position information of the same droplet 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 area with the center of the droplet as the center and a preset length as the radius is set to the first pixel value, and the pixel value of the remaining background area is set to the second pixel value, so as to obtain a droplet position mask image of the channel; Obtaining the intersection area of ​​the droplet areas in the droplet position mask images of the multiple channels and determining the center position of each intersection area as the target position information of the corresponding droplet; An overlapping area marking mask image is drawn with the center position of each intersection area as the center of the circle and the preset length as the radius.

9. The method according to claim 8, characterized in that The traversing of the droplets and determining the position deviation of the droplets according to the position information of the droplets in the channel image and the corresponding target position information thereof includes: According to the position information of the microdroplet in the channel image, the target position information corresponding to the microdroplet is obtained in the overlapped area mark mask image; The position deviation between the position information of the droplet in the channel image and the corresponding target position information in the first direction and the second direction is determined as the position deviation of the droplet, and the first direction and the second direction are perpendicular to each other.

10. The method according to any one of claims 6 to 9, characterized in that: The step of synthesizing the channel aligned image of the target sample according to the channel correction images comprises: Determine the image clarity of each channel correction image; Using channel-corrected images whose image clarity is greater than a preset clarity threshold to synthesize a channel-aligned image of the target sample in equal proportion; or, The weighted sum of the corrected images of each channel is performed using a weighting coefficient that is positively correlated with the image clarity to obtain a channel aligned image of the target sample.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the digital PCR channel droplet alignment method as described in any one of claims 6 to 10.

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