Droplet alignment method based on imaging digital PCR instrument under different fluorescence channels
By aligning the droplet positions of different fluorescence channels in an imaging digital PCR instrument, the detection deviation caused by droplet position offset is solved, thus improving detection accuracy and reliability.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHENZHEN BIORAIN BIOTECHNOLOGY CO LTD
- Filing Date
- 2023-06-26
- Publication Date
- 2026-06-12
Smart Images
Figure CN116660163B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of droplet digital PCR technology, and more particularly to a method for aligning droplets in different fluorescence channels using an imaging digital PCR instrument. Background Technology
[0002] The core principle of modern digital PCR instruments is to evenly distribute a standard PCR reaction system containing nucleic acid templates into thousands to hundreds of thousands of PCR reaction units, ensuring that each unit contains at most one template molecule. Single-molecule template PCR is then performed, and the presence or absence of fluorescence signals is used for counting. Finally, absolute quantification is performed using a Poisson distribution. To distinguish fluorescent genes targeting different targets, multiple fluorescence channels are typically used to detect the corresponding genes. In practical applications, when capturing fluorescence images from different channels, switching between different wavelength filters can cause droplet positions to shift during the imaging process. This leads to deviations in reading the fluorescence values of droplets from different channels, affecting the detection results. Therefore, aligning the droplet positions across different channels is essential. Summary of the Invention
[0003] In view of the above technical problems, the present invention provides a droplet alignment method based on an imaging digital PCR instrument under different fluorescence channels, which solves the problem of droplet position offset in different channels in the prior art, and adopts the following technical solution.
[0004] Other features and advantages of the invention will become apparent from the following detailed description, or may be learned in part by practice of the invention.
[0005] A method for aligning droplets in different fluorescence channels using an imaging digital PCR instrument, the method comprising:
[0006] Bright-field and fluorescence images were captured sequentially under different fluorescence channels;
[0007] Select any one fluorescence channel as the reference channel, use the bright field image of the reference channel as the reference image, and use the bright field images of other fluorescence channels as the target images. Perform preprocessing on the reference image and the target image.
[0008] Feature points are extracted from the preprocessed reference image and the target image, and the best matching feature point set is found.
[0009] The correction coefficients of the reference image and the target image are calculated based on the set of best matching feature points;
[0010] The fluorescence images in each fluorescence channel are corrected based on the correction coefficient.
[0011] Furthermore, the sequential capture of bright-field images and fluorescence images under different fluorescence channels includes:
[0012] The chip is illuminated by a bright field light source, and a camera is used to take a picture of the chip to obtain the bright field image of the chip;
[0013] The chip is illuminated by a corresponding LED light, and a camera is used to take a picture of the chip to obtain the fluorescent image of the chip;
[0014] By switching filters of different wavelengths and repeating the operation, bright-field images and fluorescence images under different fluorescence channels can be obtained.
[0015] Furthermore, the preprocessing includes:
[0016] The reference image and the target image are subjected to grayscale transformation processing.
[0017] Furthermore, when performing grayscale transformation, the following formula is executed:
[0018] I=0.299×R+0.587×G+0.114×B;
[0019] R, G, and B represent the red, green, and blue channel information of the color image, respectively.
[0020] Furthermore, the step of extracting feature points from the preprocessed reference image and the target image to find the optimal matching feature point set includes:
[0021] Based on the ORB feature point extraction algorithm, feature points are extracted from the reference image and the target image. The extracted feature point sets are denoted as p = {p1, p2, p3, ..., pn} and p' = {p1', p2', p3', ..., pn'}, respectively.
[0022] Furthermore, before feature point extraction, histogram equalization is performed on the reference image and the target image.
[0023] The criterion for judgment is that the Hamming distance of the matched feature points is less than twice the minimum distance. If it is less than twice the minimum distance, it is considered an incorrect match; if it is greater than twice the minimum distance, it is considered a valid match.
[0024] Furthermore, the matched feature points are filtered to find the minimum distance Dmin between all matched feature points. For all matched feature points, the Hamming distance D between any two feature points is calculated. If D < 2 * Dmin,
[0025] Then save these two feature points, otherwise delete them, to obtain the set of best matching feature points.
[0026] Furthermore, calculating the correction coefficients of the reference image and the target image based on the best matching feature point set includes:
[0027] Based on the set of best matching feature points, calculate the transformation matrix between the reference image and the target image;
[0028] Based on the RANSAC algorithm, the reprojection error of the transformation matrix is reduced, and the transformation matrix is the correction coefficient.
[0029] Furthermore, the correction of the fluorescence images in each fluorescence channel based on the correction coefficient includes:
[0030] Based on the correction coefficients of the current reference image and the target image, the fluorescence image in the current reference channel is corrected;
[0031] Other fluorescence channels are selected as reference channels, and the operation is repeated to correct the fluorescence image of each fluorescence channel.
[0032] The technical solution of the present invention has the following beneficial effects:
[0033] The droplet alignment method based on an integrated imaging digital PCR instrument under different fluorescence channels of the present invention can accurately locate the position of each droplet under each channel, which greatly improves the accuracy of the fluorescence value of each droplet, making the detection results more accurate and reliable. Moreover, the method is simple to operate and easy to implement. Attached Figure Description
[0034] Figure 1 This is a flowchart illustrating a droplet alignment method based on an imaging digital PCR instrument under different fluorescence channels, as described in the embodiments of this specification.
[0035] Figure 2 This is a schematic diagram showing the position of the droplet shifted in any of the fluorescence channels in an embodiment of the present invention;
[0036] Figure 3 This is a schematic diagram of the selected fluorescent channel droplet after correction in an embodiment of the present invention. Detailed Implementation
[0037] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make the invention more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of the invention. However, those skilled in the art will recognize that the technical solutions of the invention may be practiced with one or more of these specific details omitted, or other methods, steps, etc., may be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of the invention.
[0038] Furthermore, the accompanying drawings are merely illustrative of the invention. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.
[0039] like Figure 1 As shown in the embodiments of this specification, a method for droplet alignment based on an imaging digital PCR instrument under different fluorescence channels is provided. This method may specifically include the following steps S110–S150:
[0040] In step S110, bright-field images and fluorescence images under different fluorescence channels are captured sequentially.
[0041] Specifically, the chip can be illuminated by a bright field light source and photographed with a camera to obtain the bright field image of the chip, and the chip can be illuminated by a corresponding LED light and photographed with a camera to obtain the fluorescence image of the chip; then, by switching filters of different wavelengths and repeating the above operations, the bright field image and the fluorescence image under different fluorescence channels can be obtained.
[0042] In step S120, any fluorescence channel is selected as a reference channel, the bright field image of the reference channel is used as a reference image, and the bright field images of other fluorescence channels are used as target images. The reference image and the target image are then preprocessed.
[0043] In step S130, feature points are extracted from the preprocessed reference image and the target image, and the best matching feature point set is found.
[0044] Before feature point extraction, histogram equalization is performed on the reference image and the target image. After histogram equalization, feature points are extracted from the reference image and the target image based on the ORB feature point extraction algorithm. The extracted feature point sets are denoted as p = {p1, p2, p3...pn} and p' = {p1', p2', p3'...pn'}, respectively.
[0045] This set of feature points can be regarded as a set of matching feature points. Then, the matching feature points are filtered. The Hamming distance of the matched feature points is less than twice the minimum distance. If it is less than twice the minimum distance, it is considered an incorrect match. If it is greater than twice the minimum distance, it is considered a valid match.
[0046] The matched feature points are filtered to find the minimum distance Dmin between all matched feature points. For all matched feature points, the Hamming distance D between two feature points is calculated. If D < 2 * Dmin, the two feature points are saved; otherwise, they are deleted, thus obtaining the set of the best matched feature points.
[0047] In step S140, the correction coefficients of the reference image and the target image are calculated based on the set of best matching feature points.
[0048] Specifically, based on the optimal matching feature point set, a transformation matrix between the reference image and the target image is calculated; based on the RANSAC algorithm, the reprojection error of the transformation matrix is reduced, and then the transformation matrix is set as the correction coefficient.
[0049] In step S150, the fluorescence images in each fluorescence channel are corrected based on the correction coefficient.
[0050] Specifically, the fluorescence image in the current reference channel is corrected based on the correction coefficients of the current reference image and the target image;
[0051] Select other fluorescence channels as reference channels and repeat steps S110 to S150 to correct the fluorescence image of each fluorescence channel.
[0052] In one embodiment, the preprocessing in step S120 includes:
[0053] The reference image and the target image are subjected to grayscale transformation. During grayscale transformation, the formula is: I = 0.299 × R + 0.587 × G + 0.114 × B, where R, G, and B represent the red, green, and blue channel information of the color image, respectively.
[0054] In addition, preprocessing can also include operations such as cutting and stretching.
[0055] Based on the above implementation methods, please continue to refer to... Figures 1 to 3 As shown, this method involves taking pictures of the chip under different lighting conditions to obtain images of the droplet's bright field and different fluorescence channels. A specific channel is selected as a reference channel. Based on image recognition technology, the droplet position is identified using the bright field image of the reference channel, and then mapped onto the droplet images in other channels. This identifies the position of the same droplet in each channel. However, due to changes in the optical environment, the droplet position in other channels may shift. Therefore, image processing is performed to align the droplet positions in other channels with those in the reference channel. Finally, the fluorescence values of the droplets in all channels are counted to obtain the accurate fluorescence value. Specifically:
[0056] First, sample solutions labeled with different targets are injected into an oil-coated chip to generate tens of thousands of droplets that form a monolayer within the chip. The chip is then subjected to PCR amplification using a temperature control device. Next, the chip is placed in a fluorescence detection device, and different wavelength filters are switched. The chip is then illuminated by corresponding LEDs to acquire bright-field and fluorescence images. Subsequently, a specific channel is selected as the baseline channel, and the bright-field image of that channel is used to identify the droplet position. This position is then mapped onto the fluorescence image to read the fluorescence value of the droplet.
[0057] For other channels, the reference channel and the bright field image of the current channel are first converted to grayscale, feature points are extracted from these two images, and then the best match is performed based on the extracted feature points.
[0058] After obtaining these matching point sets, image processing algorithms can be used to calculate the homography matrix from the target image to the reference image. This matrix can then be used to correct the fluorescence image of the target channel, such as... Figure 3 As shown, the droplet markers correspond one-to-one on the corrected image, and the fluorescence value of the droplet in this channel is the precise fluorescence value.
[0059] The droplet alignment method based on an integrated imaging digital PCR instrument in different fluorescence channels provided in the above embodiments can accurately locate the position of each droplet in each channel, greatly improving the accuracy of the fluorescence value of each droplet, making the detection results more accurate and reliable, and the method is simple and easy to implement.
[0060] Furthermore, the above figures are merely illustrative representations of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously across multiple devices, for example.
[0061] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.
[0062] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.
Claims
1. A method for droplet alignment in different fluorescence channels using an imaging digital PCR instrument, characterized in that, The alignment method includes: Bright-field and fluorescence images were captured sequentially under different fluorescence channels; Select any one fluorescence channel as the reference channel, use the bright field image of the reference channel as the reference image, and use the bright field images of other fluorescence channels as the target images. Perform preprocessing on the reference image and the target image. Feature point extraction is performed on the preprocessed reference image and target image, and the optimal matching feature point set is found. This includes: extracting feature points from the reference image and target image based on the ORB feature point extraction algorithm, and denoting the extracted feature point sets as p = {p1, p2, p3 ... pn} and p' = {p1', p2', p3' ... pn'}, respectively; performing histogram equalization on the reference image and target image before feature point extraction; using a Hamming distance less than twice the minimum distance as a criterion for matching feature points (if less, it is considered an incorrect match; if greater, it is considered a valid match); filtering the matched feature points to find the minimum distance Dmin between all matched feature points; calculating the Hamming distance D between two matched feature points; if D < 2 * Dmin, saving the two feature points; otherwise, deleting them, thus obtaining the optimal matching feature point set. Calculating correction coefficients for the reference image and the target image based on the best matching feature point set includes: calculating a transformation matrix between the reference image and the target image based on the best matching feature point set; reducing the reprojection error of the transformation matrix based on the RANSAC algorithm, wherein the transformation matrix is the correction coefficient; Correcting the fluorescence images in each fluorescence channel based on the correction coefficient includes: correcting the fluorescence images in the current reference channel based on the correction coefficients of the current reference image and the target image; selecting other fluorescence channels as reference channels and repeating the operation so that the fluorescence images in each fluorescence channel are corrected.
2. The droplet alignment method based on an imaging digital PCR instrument under different fluorescence channels according to claim 1, characterized in that, The sequential capture of bright-field and fluorescence images under different fluorescence channels includes: The chip is illuminated by a bright field light source, and a camera is used to take a picture of the chip to obtain the bright field image of the chip; The chip is illuminated by a corresponding LED light, and a camera is used to take a picture of the chip to obtain the fluorescent image of the chip; By switching filters of different wavelengths and repeating the operation, bright-field images and fluorescence images under different fluorescence channels can be obtained.
3. The droplet alignment method based on an imaging digital PCR instrument under different fluorescence channels according to claim 1, characterized in that, The preprocessing includes: The reference image and the target image are subjected to grayscale transformation processing.
4. The droplet alignment method based on an imaging digital PCR instrument under different fluorescence channels according to claim 3, characterized in that, When performing grayscale transformation, execute the following formula: I=0 .299×R+0 .587×G+0 .114×B; R, G, and B represent the red, green, and blue channel information of the color image, respectively.