WO2018103345A1 - Procédé et dispositif de reconnaissance et de comptage de molécule unique - Google Patents

Procédé et dispositif de reconnaissance et de comptage de molécule unique Download PDF

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WO2018103345A1
WO2018103345A1 PCT/CN2017/094178 CN2017094178W WO2018103345A1 WO 2018103345 A1 WO2018103345 A1 WO 2018103345A1 CN 2017094178 W CN2017094178 W CN 2017094178W WO 2018103345 A1 WO2018103345 A1 WO 2018103345A1
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image
unit
bright spot
single molecule
histogram
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PCT/CN2017/094178
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English (en)
Chinese (zh)
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徐伟彬
金欢
颜钦
姜泽飞
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深圳市瀚海基因生物科技有限公司
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression

Definitions

  • the invention relates to the field of gene sequencing technology, in particular to a single molecule identification, counting method, identification, counting device and processing system.
  • the third generation sequencing technology is single molecule sequencing
  • the single molecule sequencing technology based on imaging optical detection is a base recognition technology that relies on optical signals and electrical signals.
  • the fluorescence group is determined by fluorescence
  • the fluorescence emitted is the intensity of light emitted from the excited state to the ground state under laser irradiation of a specific power.
  • the difference in emitted light intensity, and the presence of background noise single-molecule recognition errors are caused.
  • the DNA strands are unevenly distributed, and the base clusters and the like also cause a decrease in effective single molecules.
  • the existing methods mainly rely on the human eye to perform single molecule recognition and counting on the collected fluorescent images, but such a method is labor-intensive and slow.
  • the method based on HMM and machine learning not only requires a large number of samples to be trained, but also has low operational efficiency.
  • the embodiments of the present invention aim to at least solve one of the technical problems existing in the prior art. Therefore, the embodiments of the present invention need to provide a single molecule identification, counting method, and identification and counting device.
  • a method for identifying a single molecule includes the steps of: inputting a time series of intensity of an image bright spot; forming a line graph of time and intensity of the bright spot of the image according to the time series, wherein the line graph is composed of multiple a line segment composition; meshing the line graphs to form a plurality of grids arranged in an array, counting the number of times of the line segments and/or the end points of the line segments that fall on each of the grids; The intensity is grouped, frequency statistics are performed on the number of times to obtain a histogram; the maximum point of the histogram is searched, and a peak at which a maximum point satisfying the following condition is determined corresponds to a single molecule: The value of the maximum value point is greater than the first set threshold and the width of the peak at which the maximum value point is greater than the second set threshold.
  • a single molecule counting method includes the steps of: inputting a time series of image bright point intensity; forming a line graph of time and intensity of the image bright point according to the time series, wherein the line graph is composed of multiple a line segment composition; meshing the line graphs to form a plurality of grids arranged in an array, counting the number of times of the line segments and/or the end points of the line segments that fall on each of the grids; The intensity is grouped, frequency statistics are performed on the number of times to obtain a histogram; the maximum point of the histogram is searched, and a peak at which a maximum point satisfying the following condition is determined corresponds to a single molecule: The value of the maximum value point is greater than the first set threshold value and the width of the peak at which the maximum value point is located is greater than the second set threshold value; the number S1 of single molecules is calculated.
  • the counting method of the single molecule described above is converted into image processing by a line graph of a time series of bright spot intensity to obtain
  • a single molecule counting method includes the steps of: inputting a time series of image bright point intensity; forming a line graph of time and intensity of the image bright point according to the time series, wherein the line graph is composed of multiple a line segment composition; meshing the line graphs to form a plurality of grids arranged in an array, counting the number of times of the line segments and/or the end points of the line segments that fall on each of the grids; The magnitude of the intensity is grouped, the frequency is counted to obtain a histogram; the maximum point of the histogram is searched, and when the following conditions are met, the count of the single molecule is added: the maximum point The value of the peak is greater than the first set threshold and the width of the peak at which the maximum point is located is greater than the second set threshold.
  • the counting method of the single molecule described above is converted into image processing by a line graph of a time series of bright spot intensity to obtain a histogram, and the single molecule can be quickly counted, and the
  • a single molecule identification device for implementing one or all of the steps of the single molecule identification method of the above aspect of the present invention includes: an input unit for inputting a time series of image brightness intensity; and a conversion unit And a line graph for forming a time and an intensity of the image bright point according to the time series in the input unit, wherein the line graph is composed of a plurality of line segments; a grid statistical unit is configured to The line graph of the cells is meshed to form a plurality of grids arranged in an array, counting the number of times of the line segments and/or the end points of the line segments of each of the grids; a histogram unit, Grouping based on the magnitude of the intensity, performing frequency statistics on the number of times from the grid statistical unit to obtain a histogram; and determining a unit for finding the pole of the histogram from the histogram statistic unit a large value point, and determining that a peak at which a maximum value point satisfies the following condition corresponds to a single molecule: the
  • a single molecule counting device for implementing the above-described single molecule counting method of one aspect of the present invention Or all the steps include: an input unit, configured to input a time series of image brightness intensity; and a conversion unit, configured to form a line graph of time and intensity of the image bright point according to the time sequence in the input unit,
  • the line graph is composed of a plurality of line segments;
  • a grid statistical unit is configured to mesh the line graphs from the conversion unit to form a plurality of grids arranged in an array, and statistics fall on each of the nets a number of times of the line segment and/or an end point of the line segment;
  • a histogram statistic unit configured to perform grouping based on the magnitude of the intensity, and perform frequency statistics on the number of times from the grid statistical unit to obtain a histogram a determination unit, configured to find a maximum value point of the histogram from the histogram statistical unit, and determine that a peak of a maximum value point satisfying the following condition corresponds to a single molecule
  • a single molecule counting device for performing some or all of the steps of the single molecule counting method of the above aspect of the present invention, comprising: an input unit for inputting a time series of image brightness intensity; and a conversion unit And a line graph for forming a time and an intensity of the image bright point according to the time series in the input unit, wherein the line graph is composed of a plurality of line segments; a grid statistical unit is configured to The line graph of the cells is meshed to form a plurality of grids arranged in an array, counting the number of times of the line segments and/or the end points of the line segments of each of the grids; a histogram unit, Grouping based on the magnitude of the intensity, performing frequency statistics on the number of times from the grid statistical unit to obtain a histogram; and determining a unit for finding the pole of the histogram from the histogram statistic unit a large value point, and determining that the following condition is satisfied, the count of the single molecule is increased by 1: the
  • a single molecule processing system includes: a data input device for inputting data; a data output device for outputting data; and a storage device for storing data, the data including a computer executable program; A processor for executing the computer executable program, the executing the computer executable program comprising performing the method of any of the above embodiments.
  • the single molecule processing system enables single molecule recognition and/or single molecule counting.
  • a computer readable storage medium for storing a program for execution by a computer, the method comprising executing the method of any of the above embodiments.
  • the computer readable storage medium may include read only memory, random access memory, magnetic or optical disks, and the like.
  • FIG. 1 is a schematic flow chart of a method for identifying a single molecule according to an embodiment of the present invention.
  • FIG. 2 is a schematic flow chart of another method for identifying a single molecule according to an embodiment of the present invention.
  • FIG. 3 is a schematic flow chart showing another method of identifying a single molecule according to an embodiment of the present invention.
  • FIG. 4 is a schematic flow chart showing another method of identifying a single molecule according to an embodiment of the present invention.
  • FIG. 5 is still another schematic flowchart of a method for identifying a single molecule according to an embodiment of the present invention.
  • FIG. 6 is a schematic flow chart showing another method of identifying a single molecule according to an embodiment of the present invention.
  • FIG. 7 is a schematic flow chart showing another method of identifying a single molecule according to an embodiment of the present invention.
  • FIG. 8 is still another schematic flow chart of a method for identifying a single molecule according to an embodiment of the present invention.
  • FIG. 9 is a schematic diagram showing a Mexican hat filter of a single molecule identification method according to an embodiment of the present invention.
  • FIG. 10 is a schematic flow chart of still another method for identifying a single molecule according to an embodiment of the present invention.
  • FIG. 11 is a schematic diagram of eight connected pixels in a single molecule identification method according to an embodiment of the present invention.
  • Fig. 12 is a schematic diagram showing a line graph of a single molecule identification method according to an embodiment of the present invention.
  • Fig. 13 is a schematic diagram showing the meshing of a line graph in the single molecule identification method according to the embodiment of the present invention.
  • Fig. 14 is a schematic diagram showing a line graph before filtering in the single molecule identification method according to the embodiment of the present invention.
  • Fig. 15 is a schematic diagram showing a filtered line graph in the single molecule identification method according to the embodiment of the present invention.
  • Fig. 16 is another schematic diagram of a line graph of a single molecule identification method according to an embodiment of the present invention.
  • Fig. 17 is a schematic diagram showing a histogram after equalization in the single molecule identification method according to the embodiment of the present invention.
  • Fig. 18 is a flow chart showing still another flow of the method for identifying a single molecule according to an embodiment of the present invention.
  • Fig. 19 is a schematic view showing the process of line corrosion in the single molecule identification method according to the embodiment of the present invention.
  • Fig. 20 is a schematic view showing another process of line corrosion in the single molecule identification method according to the embodiment of the present invention.
  • 21 is a schematic diagram of an 8-connected window in a single molecule identification method according to an embodiment of the present invention.
  • Fig. 22 is a schematic diagram showing the identification of a connected region in the single molecule identification method according to the embodiment of the present invention.
  • FIG. 23 is a schematic flow chart of a single molecule counting method according to an embodiment of the present invention.
  • Fig. 24 is a schematic flow chart showing another method of counting a single molecule according to an embodiment of the present invention.
  • Fig. 25 is a flow chart showing still another flow of the single molecule counting method according to the embodiment of the present invention.
  • Fig. 26 is a schematic flow chart showing still another method of counting a single molecule according to an embodiment of the present invention.
  • Figure 27 is a block diagram showing a single molecule identification device according to an embodiment of the present invention.
  • FIG. 28 is a block diagram showing still another module of the single molecule identification device according to the embodiment of the present invention.
  • 29 is a block diagram showing still another module of the single molecule identification device according to the embodiment of the present invention.
  • Figure 30 is a block diagram showing another module of the single molecule identification device of the embodiment of the present invention.
  • Figure 31 is a block diagram showing a single molecule counting device according to an embodiment of the present invention.
  • 32 is a block diagram showing still another module of the single molecule counting device according to the embodiment of the present invention.
  • Figure 33 is a block diagram showing another module of the single molecule counting device of the embodiment of the present invention.
  • Figure 34 is still another block diagram of the single molecule counting device of the embodiment of the present invention.
  • Fig. 35 is a block diagram showing still another module of the single molecule counting device according to the embodiment of the present invention.
  • Fig. 36 is a schematic view showing still another module of the single molecule counting device according to the embodiment of the present invention.
  • FIG. 37 is a block diagram of a single molecule processing system in accordance with an embodiment of the present invention.
  • first and second are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated.
  • features defining “first” or “second” may include one or more of the described features either explicitly or implicitly.
  • the meaning of "a plurality" is two or more unless specifically and specifically defined otherwise.
  • connection should be understood broadly, for example, it may be a fixed connection, a detachable connection, or an integral connection;
  • the mechanical connections may also be electrical connections or may communicate with each other; they may be directly connected or indirectly connected through an intermediate medium, and may be internal communication of two elements or an interaction relationship of two elements.
  • the single molecule identification method and counting method of the embodiments of the present invention can be applied to gene sequencing, and the "gene sequencing" and nucleic acid sequence determination, including DNA sequencing and/or RNA sequencing, including long fragment sequencing and / or short segment sequencing.
  • a single molecule identification method includes the steps of: S01, inputting a time series of image brightness intensity; and S02, forming a line graph of time and intensity of an image bright point according to a time series, a line chart Consists of a plurality of line segments; S03, meshing the line graphs to form a plurality of grids arranged in the array, and counting the number of times of the line segments and/or end points of each line segment; S04, based on the intensity Perform grouping, perform frequency statistics on the number of times to obtain a histogram; S05, find the maximum point of the histogram, and determine that the peak of a maximum point satisfying the following condition corresponds to a single molecule: the value of the maximum point is greater than The width of the first set threshold and the peak at which the maximum point is located is greater than the second set threshold.
  • the above-mentioned method for identifying a single molecule can be quickly recognized for a single molecule by converting a time-series line graph of bright spot intensity into image processing to obtain a histogram, and the recognition accuracy is also high.
  • the single-molecule identification method based on the histogram can accurately identify a single molecule according to the time series data of the intensity of the bright spot, and is particularly suitable for the case where the number of single molecules included in the bright spot is >3.
  • step S01 when the image bright point is formed, the test sample is irradiated with laser light of a specific wavelength, the test sample is excited to emit fluorescence, and then the image formed by the fluorescence is collected by the camera, and the image is emitted corresponding to the test sample.
  • the image of the part is bright.
  • the so-called “bright spot” refers to the light-emitting point on the image, and one light-emitting point occupies at least one pixel.
  • the so-called "pixel” is the same as "pixel.”
  • the image is from a single molecule sequencing platform, such as a sequencing platform of Helicos, Pacific Biosciences (PacBio), and the input raw data is a parameter of a pixel point of the image, and the so-called "bright spot" is detected.
  • a single molecule sequencing platform such as a sequencing platform of Helicos, Pacific Biosciences (PacBio)
  • the input raw data is a parameter of a pixel point of the image, and the so-called "bright spot" is detected.
  • the single molecule identification method further includes: an image preprocessing step S31, the image preprocessing step analyzing the input image to be processed to obtain a first image, and the image to be processed includes at least one image. a bright spot, the image highlight has at least one pixel; the bright spot detecting step S32, the bright spot detecting step S32 includes the steps of: S321, analyzing the first image to calculate a bright spot determination threshold, S322, analyzing the first image to obtain a candidate bright spot, S323, determining according to the bright spot The threshold value determines whether the candidate bright spot is an image bright spot. If the determination result is yes, S324, the time series of the image bright spot intensity is acquired. If the determination result is no, S325, the candidate bright spot is discarded.
  • the denoising process of the image to be processed by the image preprocessing step can reduce the calculation amount of the bright spot detecting step, and at the same time, determine whether the candidate bright spot is an image bright point by using the bright spot judgment threshold, thereby improving the accuracy of determining the bright spot of the image.
  • the input image to be processed may be a 16-bit tiff format image of 512*512 or 2048*2048, and the image of the tiff format may be a grayscale image. In this way, the processing of the single molecule identification method can be simplified.
  • the image pre-processing step S31 includes performing background subtraction processing on the image to be processed to obtain a first image. In this way, the noise of the image to be processed can be further reduced, so that the accuracy of the single molecule recognition and/or counting method is higher.
  • the image pre-processing step S31 includes: performing a simplification process on the image to be processed after performing the background subtraction processing to obtain a first image. In this way, the amount of calculation of the subsequent single molecule recognition and/or counting method can be reduced.
  • the image pre-processing step S31 includes performing filtering processing on the image to be processed to obtain a first image. In this way, filtering the image to be processed can acquire the first image under the condition that the image detail features are retained as much as possible, thereby improving the accuracy of the single molecule recognition and/or counting method.
  • the image pre-processing step S31 includes: performing background subtraction processing on the image to be processed, and then performing filtering processing to obtain a first image.
  • the image to be processed is filtered after subtracting the background, which can further reduce the noise of the image to be processed, so that the accuracy of the single molecule recognition and/or counting method is higher.
  • the image pre-processing step S31 includes: performing a simplified process on the image to be processed after performing the subtractive background processing to obtain the first image. In this way, the amount of calculation of the subsequent image processing method can be reduced.
  • the image pre-processing step S31 includes performing a simplification process on the image to be processed to obtain a first image. In this way, the amount of calculation of the subsequent single molecule recognition and/or counting method can be reduced.
  • performing background subtraction processing on the image to be processed includes: determining an background of the image to be processed by using an open operation, and performing background subtraction processing on the image to be processed according to the background.
  • the open operation is used to eliminate small objects, separate objects at slender points, and smooth the boundaries of large objects without significantly changing the image area, so that the background-subtracted image can be acquired more accurately.
  • the image to be processed f(x, y) (such as a grayscale image) is moved by an a*a window (for example, a 15*15 window), and an open operation (corrosion re-expansion) is used to estimate
  • a*a window for example, a 15*15 window
  • an open operation corrosion re-expansion
  • g(x, y) is the grayscale image after etching
  • f(x, y) is the original grayscale image
  • B is the structural element
  • g(x, y) is the expanded grayscale image
  • f(x, y) is the original grayscale image
  • B is the structural element
  • the filtering process is a mexican hat filtering process.
  • Mexican cap filtering is easy to implement, reducing the cost of single-molecule identification and/or counting methods.
  • Mexican cap filtering improves the contrast between the foreground and the background, making the foreground brighter and making the background darker.
  • the m*m window is used to perform Gaussian filtering on the image to be processed before the filtering process, and the Gaussian filtered image to be processed is subjected to two-dimensional Laplacian sharpening, where m is a natural number and is greater than 1. odd number.
  • m is a natural number and is greater than 1. odd number.
  • the Mexican hat core can be expressed as:
  • Equation 6 Gaussian filtering is performed on the image to be processed using the m*m window, as shown in Equation 6 below:
  • t1 and t2 represent the position of the filter window
  • wt1 and t2 represent the weight of the Gaussian filter
  • Equation 7 The image to be processed is then subjected to two-dimensional Laplacian sharpening, as shown in Equation 7 below:
  • K and k both represent Laplacian operators, which are related to sharpening targets. If it is necessary to strengthen sharpening and weaken sharpening, modify K and k.
  • Equation 6 when performing Gaussian filtering, Equation 6 becomes:
  • the simplified image is a binarized image.
  • binarized images are easy to handle and have a wide range of applications.
  • the binarized image may include two values of 0 and 1 characterizing different attributes of the pixel, and the binarized image may be expressed as:
  • the signal to noise ratio matrix is obtained according to the image to be processed before the simplification processing, and the image to be processed before the processing is simplified according to the signal to noise ratio matrix to obtain the first image.
  • the image to be processed may be subjected to subtractive background processing, and then the signal to noise ratio matrix is obtained according to the image to be processed after subtracting the background processing.
  • the signal to noise ratio matrix is obtained according to the image to be processed after subtracting the background processing.
  • the signal to noise ratio matrix can be expressed as: Equation 8, where x and y represent the coordinates of the pixel, h represents the height of the image, and w represents the width of the image, i ⁇ w, j ⁇ h.
  • the simplified image is a binarized image
  • the binarized image can be obtained from the signal to noise ratio matrix.
  • the binarized image is as shown in Equation 9:
  • the background image to be processed may be subjected to subtractive background processing and/or filtering processing.
  • the background subtraction background processing step and the filtering processing step of the above embodiment may be followed by subtracting the background processing to obtain the formula 4, and then subtracting the background.
  • the step of analyzing the first image to calculate a bright spot determination threshold comprises: processing the first image by the Otsu method to calculate a bright spot determination threshold.
  • the search of the bright spot determination threshold is realized by a more mature and simple method, thereby improving the accuracy of the single molecule recognition and/or counting method and reducing the cost of the single molecule identification and/or counting method.
  • using the first image to perform the search of the bright spot determination threshold can improve the efficiency and accuracy of the single molecule recognition and/or counting method.
  • the Otsu method can also be called the maximum inter-class variance method.
  • the Otsu method uses the largest variance between classes to segment the image, which means that the probability of misclassification is the smallest and the accuracy is high.
  • the segmentation threshold of the foreground and background of the image to be processed is T
  • the ratio of the number of pixels belonging to the foreground to the entire image is ⁇ 0
  • the average gradation is ⁇ 0
  • the ratio of the number of pixels belonging to the background to the entire image is ⁇ 1
  • the average gray level is ⁇ 1 .
  • the total average gray level of the image to be processed is recorded as ⁇
  • the variance between classes is recorded as var, which is:
  • the traversal method is used to obtain a segmentation threshold T that maximizes the variance between classes, that is, the desired spot determination threshold T.
  • the step of determining whether the candidate bright spot is an image bright spot according to the bright spot determination threshold includes:
  • Step S41 searching for a pixel point larger than (h*h-1) in the first image and using the found pixel point as the center of the candidate bright point, h*h and the bright point are in one-to-one correspondence, in h*h Each value corresponds to one pixel, and h is a natural number and is an odd number greater than one;
  • Step S42 determining whether the center of the candidate bright spot satisfies the condition: I max *A BI *ceof guass >T, where I max is the center strongest intensity of the h*h window, and A BI is the first image in the h*h window
  • ceof guass is the correlation coefficient between the pixels of the h*h window and the two-dimensional Gaussian distribution
  • T is the bright point determination threshold.
  • I max can be understood as the center strongest intensity of the candidate bright spot.
  • h 3, looking for pixels that are greater than 8 connected, as shown in FIG. The found pixel point is used as the pixel point of the candidate bright spot.
  • I max is the strongest intensity in the center of the 3*3 window
  • a BI is the ratio of the set value in the first image in the 3*3 window
  • ceof guass is the correlation between the pixel of the 3*3 window and the two-dimensional Gaussian distribution. coefficient.
  • the first image is a simplified image, for example, the first image may be a binarized image, that is, the set value in the binarized image may be a value corresponding to when the pixel meets the set condition.
  • the binarized image may contain two values of 0 and 1 characterizing different attributes of the pixel, the set value is 1, and A BI is the ratio of 1 in the binarized image in the h*h window. .
  • SNR ⁇ mean(SNR)
  • BI 1.
  • the camera when acquiring the above image, sequentially performs fluorescence acquisition of a plurality of fields of view (FOV) in time series. Therefore, when image data is obtained, the intensity of the image highlights contained in the image data corresponds to the time series acquired by the camera.
  • FOV fields of view
  • step S02 after the desired image highlights are obtained, the intensity of the image highlights corresponding to the adjacent acquisition times are point-connected, and a line graph of the time and intensity of the image highlights is formed, as shown in FIG.
  • the horizontal axis represents the time at which fluorescence is collected, in milliseconds (ms)
  • the vertical axis represents the intensity of the image bright spot.
  • the time interval between two adjacent acquisitions of fluorescence is 20 ms.
  • the vertical axis is the corresponding bright point intensity value.
  • the bright spot intensity value is a bright pixel value
  • the bright pixel value is in the range of 0-65535
  • the 8-bit grayscale image The bright pixel values are in the range 0-255.
  • a 16-bit tiff image is used in the embodiment of the present invention.
  • step S03 the waveform of the line graph is converted into image processing for subsequent histogram statistics.
  • Image processing of the line graph includes meshing the line graph.
  • meshing the line graph is divided by the number of time frames and the intensity of the acquisition intensity.
  • the line graph can be relatively simplely processed to obtain mesh division, which reduces the cost of the single molecule identification method.
  • it can be divided into M according to the number of time frames and N according to the size of the intensity, that is, M*N grids are formed.
  • the number of time frames of the acquired intensity is the time interval between two adjacent acquisitions of fluorescence.
  • a mesh may be referred to as a longitudinal direction along a horizontal axis and a height direction along a longitudinal axis.
  • the length of a grid can be set to several times the number of time frames, such as 1x, 2x, 2.5x, and so on.
  • the time interval between two adjacent acquisitions of fluorescence is 20 ms
  • the length of one grid is equal to one time interval
  • the height 0.02.
  • the number of segments falling on a grid can be 0, 1, or 2 times.
  • the black dots in Figure 16 represent the time series of the intensity of the image highlights.
  • the line graph is divided into 8*6 grids and the number of times that fall on the endpoints of each grid's line segments and/or line segments is counted.
  • the number of times of the line segment falling on each grid i.e., the number of times each grid is passed by the line segment
  • the number in the grid represents the number of lines falling on each grid.
  • the black dots in Figure 13 represent the time series of the intensity of the image highlights.
  • step S04 includes the steps of dividing into N groups according to the strength, and counting the frequency of the statistics in the N groups: Where n i represents the sum of the frequencies of the number of times falling on the i-th row of the grid, j represents the number of time frames, g ij represents the frequency of the number of times falling on the grid (i, j), and M represents the number of time frames .
  • n i represents the sum of the frequencies of the number of times falling on the i-th row of the grid, j represents the number of time frames, g ij represents the frequency of the number of times falling on the grid (i, j), and M represents the number of time frames .
  • the horizontal axis of the histogram represents the number of groups
  • the vertical axis represents the frequency at which the number of times falls within the corresponding number of groups.
  • N is equal to the value of N formed in the M*N grids described above.
  • M is equal to the value of M described above which forms M*N grids.
  • grouping based on the magnitude of the intensity, frequency statistics on the number of times to obtain a histogram includes the steps of performing histogram equalization by L window: Wherein, n p n i expressed equalization, n i 'denotes the size L of the equalization result and n i, p is an integer associated with the window, and where the i-th row. In this way, the distribution of the histogram can be made more uniform and easy to recognize.
  • the L window is used for histogram equalization.
  • the value of L is related to the decay rate of single molecule fluorescence. Generally, if the single molecule fluorescence is quenched quickly, the value of L should not be too large.
  • the accuracy of the histogram is affected by the size of the L window, and the value of L can be flexibly set to select the accuracy of the appropriate histogram. In one example, L has a value range of [5, 15].
  • FIG. 17 is an equalized histogram.
  • the horizontal axis of the histogram indicates the number of groups, and the vertical axis indicates the frequency at which the number of times falls within the corresponding number of groups.
  • step S05 all the maximum points of the histogram can be found by the derivative.
  • the first set threshold Q and the second set threshold H are related to the shape of the peak of the line graph. The sharper the peak, the larger the first set threshold Q is, and the second set threshold H is smaller; the peak is fatter, the first The smaller the set threshold Q is, the larger the second set threshold H is.
  • the first set threshold Q has a value range of [2, 6]
  • the second set threshold H has a value range of [4, 10].
  • the single molecule identification method prior to meshing the line graph, further includes the step of filtering the line graph.
  • FIG. 14 is a line diagram before filtering
  • FIG. 15 is a line diagram after filtering. It can be seen from the figure that the waveform of the filtered line chart is smoother, which is beneficial to improve. The accuracy and efficiency of single molecule recognition.
  • the maximum point is the peak point, and the maximum point is the vertex (inflection point) of the peak, that is, the peak at which a maximum point satisfying the condition is judged corresponds to a single molecule.
  • the single molecule identification method further includes the step of: S51, performing line etch on the meshed line graph according to the number of times corresponding to each grid to perform network aging.
  • the line graph after division is converted into a simplified map;
  • S52, run-length coding is performed on the simplified map to identify the connected region;
  • S53, the area of each connected region is calculated, and a connected region satisfying the following condition is determined to correspond to a single molecule: the connected region The area is greater than the third set threshold.
  • the single-molecule recognition and/or counting method can be applied in a wider range.
  • the single-molecule recognition method based on run-length coding can accurately identify a single molecule according to time series data of the intensity of a bright spot, and is particularly suitable for a case where the number of single molecules included in one bright spot is not more than 3.
  • in combination with histogram-based and run-length coding-based methods to identify single molecules it is possible to accurately identify single molecules in a line graph of various waveforms (time series of bright spot intensity).
  • the structural element of the line such as the window size of W*1
  • the grid is marked as the first value, otherwise it is marked as the second value.
  • the meshed line graph can be converted into a simplified map including the first value and the second value.
  • the simplified map is a binarized map. If the first value is 1 and the second value is 0.
  • Figure 19 shows five grids arranged along the length. The numbers in the grid represent the number of times, then When performing line etching, the window is aligned with the grid. After the warp is etched, the five grids are labeled 0, 1, 0, 0, and 0, respectively.
  • Figure 20 shows five grids arranged along the length. The numbers in the grid represent the number of times. Then, when performing line etching, the window is staggered from the grid. After the warp is etched, the five grids are marked as 0, 1, 0, 0, and 0, respectively.
  • W is greater than or equal to the length of a grid.
  • W is an integer multiple of the length of a grid.
  • the threshold T has a value range of [6, 8], and its selection is related to the fluctuation of the waveform of the line graph. The smaller the fluctuation, the larger the value of the threshold T is.
  • an 8-connected approach can be used.
  • the respective connected areas are recursively connected according to the principle of 8 connections, and then the connected areas are identified by the run length coding.
  • 8 connectivity such as using the 3*3 window shown in FIG. 21
  • the grid will be The grid in the 8 directions of Q is identified as the same value as the grid Q, and so on.
  • Fig. 22 different connected areas are identified by different numerical values.
  • the number of occurrences of the same number is recorded as the area of the connected area, as shown in Fig. 22, the number of occurrences of the number 9 If it is 9, the area of the connected area corresponding to the number 9 is 9, and the number of occurrences of the number 7 is 20, and the area of the connected area corresponding to the number 7 is 20.
  • the third set threshold P has a value range of [5, 10].
  • a single molecule counting method includes the steps of: S81, inputting a time series of image brightness intensity; S82, forming a line graph of time and intensity of an image bright point according to a time series, a line chart Consists of multiple line segments; S83, on the line graph The rows are meshed to form a plurality of grids arranged in the array, and the number of times of the line segments and/or the end points of the line segments of each grid are counted; S84, grouping based on the intensity, and frequency statistics are performed to obtain the frequency statistics.
  • Histogram find the maximum point of the histogram, and determine that the peak of a maximum point satisfying the following condition corresponds to a single molecule: the value of the maximum point is greater than the first set threshold and the maximum point is The width of the peak is greater than the second set threshold; S86, the number of single molecules S1 is calculated.
  • the counting method of the single molecule described above is converted into image processing by a line graph of a time series of bright spot intensity to obtain a histogram, and the single molecule can be quickly counted, and the counting accuracy is also high.
  • the single molecule counting method prior to meshing the line graph in step S83, further includes the step of filtering the line graph.
  • the single molecule counting method further includes the step of: S91, performing line etching on the meshed line graph according to the number of times corresponding to each grid.
  • the simplified map is a binarized map.
  • a single molecule counting method includes the steps of: S61, inputting a time series of image brightness intensity; S62, forming a line graph of time and intensity of an image bright point according to a time series, a line chart Consisting of a plurality of line segments; S63, meshing the line graphs to form a plurality of grids arranged in the array, counting the number of times of the line segments and/or end points of each line segment; S64, based on the intensity Perform grouping, perform frequency statistics on the number of times to obtain a histogram; S65, find the maximum point of the histogram, and determine that the count of the single molecule is increased by 1 when the following conditions are satisfied: the value of the maximum point is greater than the first setting The width of the peak at which the threshold and the maximum point are located is greater than the second set threshold.
  • the counting method of the single molecule described above is converted into image processing by a line graph of a time series of bright spot intensity to obtain a his
  • the single molecule counting method prior to meshing the line graph in step S63, further includes the step of filtering the line graph.
  • the single molecule counting method further includes the step of: S71, performing line etching on the meshed line graph according to the number of times corresponding to each grid.
  • the two methods it is possible to accurately find and count single molecules in a line graph of various waveforms.
  • the number of single molecules acquired based on the histogram is S1
  • the number of single molecules acquired based on the run length encoding is S2
  • the sizes of S1 and S2 are compared, and the smaller ones of S1 and S2 are taken as the final single molecule number.
  • a single molecule identification device 200 is used to implement all or part of the steps of the single molecule identification method in any of the above embodiments or examples.
  • the molecular recognition device 200 includes: a first input unit 202 for inputting a time series of image brightness intensity; and a first conversion unit 204 configured to form a time and intensity of the image bright point according to the time sequence in the first input unit 202.
  • a line graph is composed of a plurality of line segments
  • a first grid statistic unit 206 is configured to mesh the line graphs from the first transform unit 204 to form a plurality of grids arranged in the array, and the statistics fall on each The number of times of the line segments and/or the end points of the line segments
  • the first histogram statistics unit 208 is configured to perform grouping based on the magnitude of the intensity, and perform frequency statistics on the number of times from the first mesh statistical unit 206 to obtain a histogram
  • a first determining unit 210 configured to find a maximum value point of the histogram from the first histogram statistic unit 208, and determine a peak pair of a maximum value point that satisfies the following condition
  • a single molecule the value is greater than a first maximum set point value and the threshold value width of the peak point where the maximum is greater than a second predetermined threshold value.
  • the single-molecule identification device 200 converts a time-series line graph of bright spot intensity into image processing to obtain a
  • the single molecule identification device 200 further includes a first filtering unit 212, and the first network.
  • the grid statistics unit 206 is coupled for filtering the line graph from the first conversion unit 204 prior to meshing the line graph.
  • meshing the line graph is divided according to the number of time frames of the acquisition intensity and the magnitude of the intensity.
  • the first histogram statistic unit 208 grouping is performed based on the magnitude of the intensity, and frequency statistics are performed on the number of times to obtain a histogram, including: dividing into N groups according to the magnitude of the intensity, and the number of statistics falls on Frequency in N groups: Where n i represents the sum of the frequencies of the number of times falling on the i-th row of the grid, j represents the number of time frames, g ij represents the frequency of the number of times falling on the grid (i, j), and M represents the number of time frames Quantity.
  • the first histogram statistic unit 208 grouping is performed based on the magnitude of the intensity, and frequency statistics are performed on the number of times to obtain a histogram including: performing histogram equalization by the L window: Wherein, n p n i expressed equalization, n i 'denotes the size L of the equalization result and n i, p is an integer associated with the window, and where the i-th row.
  • the single-molecule identification device 200 further includes: a first simplification unit 214, configured to perform a meshed line graph according to the number of times corresponding to each grid Line etching to convert the line graph after meshing into a simplified map; a first identifying unit 216 for run-length encoding the simplified graph to identify a connected region; and in the first determining unit 210, calculating each connected region The area determines that one connected region satisfying the following condition corresponds to one single molecule: the area of the connected region is larger than the third set threshold.
  • the simplified map is a binarized map.
  • the single-molecule identification device 200 further includes: a first image pre-processing unit 218 for analyzing the input image to be processed to obtain a first image.
  • the image to be processed includes at least one image bright point, the image bright spot has at least one pixel point;
  • the first bright spot detecting unit 220 is configured to: analyze the first image to calculate a bright spot determination threshold, analyze the first image to obtain The candidate bright spot determines whether the candidate bright spot is an image bright spot according to the bright spot determination threshold. If the determination result is yes, the time series of the image bright spot intensity is acquired, and if the determination result is negative, the candidate bright spot is discarded.
  • the first image pre-processing unit 218 includes a first subtraction background unit 226 for performing background subtraction processing on the image to be processed to obtain a first image.
  • the first image pre-processing unit 218 includes a first image reduction unit 222 for performing a simplification process on the image to be processed after the background subtraction process to obtain a first image.
  • the first image pre-processing unit 218 includes a first image filtering unit 224 for performing a filtering process on the image to be processed to obtain a first image.
  • the first image pre-processing unit 218 includes a first subtraction background unit 226 and a first image filtering unit 224 for performing background subtraction processing on the image to be processed, the first image filtering unit 224 is configured to perform filtering processing on the image to be processed after performing background subtraction processing to obtain a first image.
  • the first image pre-processing unit 218 includes a first image simplification unit 222, and the first image simplification unit 222 is configured to simplify the image to be processed after the background processing is performed, Obtain the first image.
  • the first image pre-processing unit 218 includes a first image reduction unit 222 for performing a simplification process on the image to be processed to obtain a first image.
  • performing background subtraction processing on the image to be processed includes: determining an background of the image to be processed by using an open operation, and performing background subtraction processing on the image to be processed according to the background.
  • the filtering process is a mexican hat filtering process.
  • the simplification process is a binarization process.
  • the first image reduction unit 222 is configured to acquire a signal to noise ratio matrix according to the image to be processed before the simplified processing, and simplify the simplified image before processing according to the signal to noise ratio matrix to obtain the first image.
  • analyzing the first image to calculate the bright spot determination threshold comprises: processing the first image by the Otsu method to calculate a bright spot determination threshold.
  • determining, in the first bright spot detecting unit 220, whether the candidate bright spot is an image bright point according to the bright spot determination threshold includes: searching for a pixel point larger than (h*h-1) in the first image and The found pixel is the center of the candidate bright spot, h is a natural number and is an odd number greater than 1; determining whether the center of the candidate bright spot satisfies the condition: I max *A BI *ceof guass >T, where I max is h*h window The strongest intensity of the center, A BI is the ratio of the set value in the first image in the h*h window, ceof guass is the correlation coefficient of the pixel of the h*h window and the two-dimensional Gaussian distribution, and T is the bright point decision threshold If the above conditions are met, it is determined that the bright spot corresponding to the center of the candidate bright spot is an image bright spot, and if the above condition is not satisfied, the bright spot corresponding to the center of the candidate bright spot is
  • the single molecule counting device 400 includes: a second input unit 402 is configured to input a time series of image brightness intensity; a second conversion unit 404 is configured to form a line graph of time and intensity of the image bright point according to the time sequence in the second input unit 402, and the line graph is composed of
  • the second line statistic unit 406 is configured to mesh the line graphs from the second conversion unit 404 to form a plurality of grids arranged in the array, and the statistics fall on the line segments of each grid and/or Or the number of times of the end points of the line segment;
  • the second histogram statistic unit 408 is configured to perform grouping based on the magnitude of the intensity, perform frequency statistics on the number of times from the second grid statistical unit 406 to obtain a histogram; and the second determining unit 410 uses Finding a maximum value point of
  • the single molecule counting device 400 further includes a second filtering unit 414 coupled to the second mesh statistical unit 406 for meshing the line graph before it is meshed.
  • the line graph from the second conversion unit 404 is filtered.
  • the single-molecule counting device 400 further includes: a second simplifying unit 416, configured to perform the meshed line graph according to the number of times corresponding to each grid Line etching to convert the meshed line graph into a simplified map; a second identifying unit 418 for run-length encoding the simplified map to identify the connected region; and in the second determining unit 410, calculating each connected region
  • the area determines that one connected region satisfying the following condition corresponds to one single molecule: the area of the connected region is larger than the third set threshold; in the calculating unit 412, the number S2 of single molecules is calculated, and the smaller one of S1 and S2 is taken as The final single molecule number.
  • a single-molecule counting device 600 includes: a third input unit 602 for inputting a time series of image brightness intensity; and a third converting unit 604 for using a third input unit.
  • a time series in 602 a line graph of time and intensity of the image highlights, the line graph consisting of a plurality of line segments; and a third grid statistics unit 606 for meshing the line graphs from the third transforming unit 604 Forming a plurality of grids arranged in an array, counting the number of times falling on the end points of the line segments and/or line segments of each grid; a third histogram statistics unit 608 for grouping based on the magnitude of the intensity, from the third grid
  • the number of statistics unit 606 is frequency counted to obtain a histogram;
  • the third determining unit 610 is configured to find a maximum value point of the histogram from the third histogram statistic unit 608, and determine that the single condition is satisfied when the following conditions are met.
  • the value of the maximum point is greater than the first set threshold and the width of the peak at which the maximum point is located is greater than the second set threshold.
  • the above-described single-molecule counting device 600 converts a time-series line graph of bright spot intensity into image processing to obtain a histogram, and can quickly count a single molecule, and the counting accuracy is also high.
  • the single molecule counting device 600 further includes a third filtering unit 612 coupled to the third mesh statistical unit 606 for use in meshing the line graph.
  • the line graph from the third conversion unit 604 is filtered.
  • the single-molecule counting device 600 further includes: a third simplifying unit 614, configured to perform the meshed line graph according to the number of times corresponding to each grid. Line etching to convert the meshed line graph into a simplified map; a third identifying unit 616 for run-length encoding the simplified map to identify the connected region; and in the third determining unit 610, calculating each connected region Area, and determine that the following conditions are met, the count of the single molecule is increased by 1: the area of the connected region is larger than the third set threshold; and the single molecule count obtained based on the histogram and the single molecule obtained based on the run length coding The smaller of the counts is the final single molecule number.
  • a third simplifying unit 614 configured to perform the meshed line graph according to the number of times corresponding to each grid. Line etching to convert the meshed line graph into a simplified map
  • a third identifying unit 616 for run-length encoding the simplified map to identify the connected region
  • the third determining unit 610
  • a single molecule processing system 300 includes: a data input device 302 for inputting data; a data output device 304 for outputting data; and a storage device 306 for storing data.
  • the data includes a computer executable program; a processor 308 for executing a computer executable program, and executing the computer executable program includes the method of performing any of the above embodiments.
  • a computer readable storage medium for storing a program for execution by a computer, the program comprising the method of any of the above embodiments.
  • the computer readable storage medium may include read only memory, random access memory, magnetic or optical disks, and the like.
  • each functional unit in each embodiment of the present invention may be integrated into one processing module, or each unit may exist physically separately, or two or more units may be integrated into one module.
  • the above integrated modules can be implemented in the form of hardware or in the form of software functional modules.

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Abstract

L'invention concerne un procédé de reconnaissance et de comptage de molécule unique et un dispositif de reconnaissance et de comptage, le procédé de reconnaissance de molécule unique consistant à : entrer une séquence temporelle d'intensité d'un point lumineux d'image (S01); former un graphique de ligne de temps et d'intensité du point lumineux d'image selon la séquence temporelle, le graphique de ligne étant composé d'une pluralité de segments de ligne (S02); effectuer une division de grille sur le graphique de ligne de façon à former une pluralité de grilles agencées dans un réseau, et compter le nombre de fois où les segments de ligne et/ou les points d'extrémité des segments de ligne tombent sur chacune des grilles (S03); grouper sur la base de l'amplitude de l'intensité, et effectuer un calcul de fréquence sur le nombre de fois de façon à obtenir un histogramme (S04); rechercher un point de valeur maximale de l'histogramme et déterminer que le pic d'un point de valeur maximale satisfaisant les conditions suivantes correspond à une molécule unique: la valeur du point de valeur maximale est supérieure à un premier seuil défini et la largeur du pic où le point de valeur maximale est situé est supérieure à un second seuil défini (S05)
PCT/CN2017/094178 2016-12-09 2017-07-24 Procédé et dispositif de reconnaissance et de comptage de molécule unique WO2018103345A1 (fr)

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