CN118392835B - Full-automatic liquid suspension chip detection method - Google Patents
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- 238000001514 detection method Methods 0.000 title claims abstract description 35
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- 238000011088 calibration curve Methods 0.000 claims abstract description 19
- 239000011324 bead Substances 0.000 claims abstract description 17
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- YBJHBAHKTGYVGT-ZKWXMUAHSA-N (+)-Biotin Chemical compound N1C(=O)N[C@@H]2[C@H](CCCCC(=O)O)SC[C@@H]21 YBJHBAHKTGYVGT-ZKWXMUAHSA-N 0.000 description 6
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Abstract
The invention discloses a full-automatic liquid suspension chip detection method which sequentially comprises the steps of sample suction, sample processing, secondary sample suction, magnetic bead position fixing, photo information acquisition, picture processing, microsphere group corresponding item determination, report gray value determination, calibration curve equation acquisition, concentration value calculation and the like. The detection method has the advantages of automation, high sensitivity, multi-parameter analysis, quantitative measurement, high efficiency, automatic correction, high flux and the like, and is suitable for efficiently and accurately carrying out multi-item detection and concentration measurement.
Description
Technical Field
The invention relates to the technical field of medical detection, in particular to a full-automatic liquid suspension chip detection method.
Background
The liquid suspension chip detection is a high-flux multi-parameter detection technology integrating multiple fields of molecular biology, immunology, polymer chemistry, optical detection technology, microfluidics technology, computer technology and the like. Compared with the traditional flow cytometer, the technology has the advantages of high flux, multi-index detection, high sensitivity, high specificity, wide linear range, rapid reaction, good repeatability, simple operation and the like. Therefore, it has been widely used for clinical diagnosis in the fields of immunological analysis, DNA hybridization analysis, and molecular detection of protein and gene expression profiles.
The traditional flow type dot matrix method adopted by the suspension type liquid biochip is characterized in that magnetic beads sequentially flow through a sheath flow cell, are classified by red laser, are excited by a report value by green laser, and are subjected to data acquisition by APD/PMT, so that multiple projects are detected, and the flow type dot matrix method has the characteristics of high requirements on personnel for installation and debugging, complex liquid path, high failure rate, inconvenient maintenance, high cost and the like. Therefore, in practical application, a suspension type liquid biochip detection method with high specificity sensitivity, low cost, high automation degree and simple operation is urgently needed.
Disclosure of Invention
In order to realize efficient and accurate detection of the liquid suspension chip, the invention provides a full-automatic liquid suspension chip detection method.
The technical scheme adopted by the invention is as follows: a full-automatic liquid suspension chip detection method comprises the following steps:
step one, sample suction: automatically sucking a sample to be detected with preset volume and position information;
Step two, sample processing: according to a preset sequence, processing a sample to be detected, combining the sample with a magnetic bead antibody in a reagent, and amplifying a fluorescent signal through SAPE;
step three, secondary sample suction: introducing the processed sample into a subsequent processing analysis link;
fixing the positions of the magnetic beads: adsorbing the magnetic beads to a predetermined position of the detection cell by using magnetic force;
Collecting photo information: the method comprises the steps of lighting red light, exciting magnetic beads to generate a beam of red light and a beam of infrared light, matching a camera with a red light filter and an infrared filter respectively, and collecting a red light photo and an infrared light photo generated by the micro beads; the green light is lightened, the camera is matched with the report value filter, and a report value photo of the microbeads is collected;
step six, picture processing: processing the red light photo and the infrared light photo, and outputting a gray level picture;
Step seven, determining corresponding items of microsphere groups: calculating gray values of red light and infrared light according to the gray image, forming a class distribution diagram of the microspheres by the gray values of the red light and the infrared light, and obtaining items corresponding to the microsphere group;
step eight, determining a report gray value: calculating the gray level of the report value of each point according to the report value photo, and obtaining the report gray level of each item microsphere group by adopting a median method;
step nine, obtaining a calibration curve equation: obtaining a calibration curve equation by carrying out regression analysis on the reported gray value and the concentration value by using a reagent calibration product with a known concentration value;
Step ten, calculating a concentration value: and (3) according to the reported gray value in the step (eight), calculating a corresponding concentration value by using the calibration curve equation in the step (nine).
Preferably, in the second step, the method comprises sample pretreatment, reagent addition, uniform mixing, reaction, washing and separation.
Preferably, in the fifth step, the red filter, the infrared filter and the report value filter are provided with a switching device for adjusting any filter to the optical axis.
Preferably, the sixth step is specifically:
S6.1, graying treatment:
The RGB values of the red photo and the infrared photo are converted into gray values by an averaging algorithm,
Wherein, Representing the current coordinate pointIs used for the gray-scale value of (c),、、The values of the red, green and blue components, respectively;
S6.2, gaussian filtering:
Each pixel in the image is scanned with a 3x3 template, the value of the center pixel point of the template is replaced with the weighted average gray value of the pixels in the field determined by the template, the gaussian filtered 3x3 matching template is as follows,
Wherein, Representing a current pixel;
The discrete window sliding window convolution realizes Gaussian filtering, the value of each pixel point in the convolution is obtained by weighted average of the value of each pixel point and other pixels in the field, the matching template is a coefficient stored in a two-dimensional array, and the value of the matching template is as follows The calculation formula of each element is as follows,
Wherein, Representing the first of the current window templatesLine 1The element values of the column pixel points,The value of the window template is represented,Representing standard deviation;
s6.3, gamma conversion image correction:
A product operation is performed on each pixel value on the original image,
Wherein, Is a constant coefficient of the number of the pieces of the material,The pixel value of the image is in the range of 0 to 255,As the gamma-transforming factor is used,The value is taken as a boundary of 1, the smaller the value is, the stronger the expansion effect on the low gray level part of the image is, the larger the value is, and the stronger the expansion effect on the high gray level part of the image is, the image enhancement effect is obvious;
s6.4, JPEG compression.
Preferably, in step nine, a Logistic regression model is used to fit the calibration curve equation,
Wherein, The dependent variable is represented as a concentration value,The independent variable is represented as a reported gray value,Representing the lower limit or starting value of the curve,Representing the upper limit or saturation value of the curve,A slope parameter representing a curve is shown,Representing the translation parameters of the curve.
Preferably, in step nine, when the deviation between the fluorescence value excited out by the same photo and the target value exceeds 10%, the fluorescence value exceeding 10% is automatically divided into areas according to the calibrated data and the deviation, so as to obtain a new calibration curve equation, and the corresponding calibration curve equation is used for subsequent measurement.
The invention has the following beneficial effects:
1. And (3) automation: the automatic equipment is utilized for sample processing and analysis, so that errors of manual operation are reduced, and the accuracy and repeatability of detection are improved;
2. The sensitivity is high: the detection sensitivity can be improved by the interaction of the sample to be detected and the magnetic bead antibody in the reagent and the amplification of the fluorescent signal by the SAPE, so that the low-concentration target can be accurately detected;
3. Multi-project, multi-parameter analysis: the method can detect a plurality of items at the same time, can perform normal classification of 50 kinds of microspheres at most through analysis and calculation of gray level images, can determine the kind distribution diagram of the microspheres, and calculates the report gray level value of each item of microspheres;
4. High efficiency: the method comprises the steps of dividing the mixed microspheres into a red light source and a green light source, classifying the red light source, collecting a report value by the green light source, classifying 2000 mixed microspheres within one minute, and accurately collecting the report value of each microsphere;
5. Quantitative measurement: by using a calibration curve equation, a corresponding concentration value is calculated according to the reported gray value, so that quantitative measurement of the concentration of the target object is realized;
6. automatic correction: automatically dividing the fluorescence value exceeding 10% into areas according to the calibrated data and the deviation to obtain different equations, and ensuring the measurement accuracy;
7. High flux: the method can process a plurality of samples simultaneously, improves the detection efficiency, can greatly shorten the detection time by processing the samples in batches, has 100 sample hole sites, and can completely output results within 1.5 hours.
Drawings
Fig. 1 is a flow chart of a detection method according to an embodiment of the invention.
Fig. 2 is a schematic diagram of a liquid suspension chip detector in the detection method according to the embodiment of the invention.
Fig. 3 is a schematic diagram of a filter assembly of a liquid suspension chip detector according to an embodiment of the invention.
FIG. 4 is a schematic diagram of determining the corresponding items of the microsphere set according to an embodiment of the present invention.
FIG. 5 is a diagram illustrating the determination of the reported gray value in an embodiment of the present invention.
Camera 1, filter subassembly 2, camera lens subassembly 3, imaging chamber subassembly 4, detection cell subassembly 5, light source subassembly 6, focal length adjustment subassembly 7, base 8.
Detailed Description
The invention will be further described with reference to examples and drawings.
Referring to fig. 1, a flowchart of a fully automatic liquid suspension chip detection method is shown, and the following detailed steps are described.
Step one, sucking samples.
An automated system or apparatus is used to extract the appropriate volume from the sample to be tested. The device such as a liquid suction device, a liquid work station and a sample rack of the system is utilized to place a reagent to be detected (mixed microsphere, antibody, SAPE) in a reagent area, a sample to be detected is placed in a sample area, and the instrument can automatically suck the sample and suck the reagent and the sample. And accurately sucking the sample to be detected according to a preset program, liquid volume and position information. Has the characteristics of high precision, high speed and high repeatability.
And step two, sample processing.
Fully automated sample processing is performed using a liquid handling system, microfluidic chip, or other automated device. Comprises the steps of sample pretreatment, reagent addition, uniform mixing, reaction, washing, separation and the like. The full-automatic sample processing can be controlled by a program, the operations of the steps are performed according to preset time and sequence, the sample is processed efficiently, the combination of the sample and the magnetic bead antibody in the reagent is realized, and the fluorescent signal is amplified by the SAPE.
Specifically, the kit comprises a plurality of polystyrene magnetic microspheres with different fluorescent codes, wherein the surfaces of the microspheres are respectively coupled with different detection antibodies, capture antibodies on the microspheres are respectively combined with 12 corresponding biotin-labeled antibodies after being respectively combined with corresponding antigens in a sample to be detected to form a double-antibody sandwich immune complex, and finally biotin on the 12 detection antibodies is combined with SAPE (streptavidin-phycoerythrin) to form a final immune complex (polystyrene magnetic microsphere+capture antibody+substance to be detected+biotin-labeled antibody+SA-PE).
And thirdly, performing secondary sample suction.
After the sample processing step, the processed sample is also sucked into the next processing or analysis step, i.e. the liquid suspension chip detector shown in fig. 2, using an automated device.
And step four, fixing the positions of the magnetic beads.
The magnetic beads are attracted to a predetermined position of the detection cell by magnetic force. The detection pool is used as a part of an instrument liquid path and is connected into the liquid path through the sample inlet connector and the sample outlet connector. When the reagent with the magnetic beads flows through the detection cell, the magnetic beads are adsorbed at the part of the detection cell close to the magnetic piece, and the liquid reagent normally flows out of the detection cell. The magnetic part can be a permanent magnet, and can be realized by using a motor to stretch close to the detection pool or an electromagnet.
And fifthly, collecting photo information.
Referring to fig. 2 and 3, the steps for capturing each photo are specifically as follows:
S5.1, firstly starting a red light source assembly in the light source assembly, lighting a red LED, exciting a magnetic bead to generate a beam of red light and a beam of infrared light, adjusting a red light filter of the filter assembly 2 between the camera 1 and the lens assembly 3, and collecting a red light photo generated by the micro bead;
S5.2, an infrared light filter of the filter assembly 2 is adjusted between the camera 1 and the lens assembly 3, and an infrared photo generated by the microbeads is collected;
S5.3, starting a green light source assembly in the light source assembly 6, adjusting a report value filter of the filter assembly 2 between the camera 1 and the lens assembly 3, and collecting a report value photo of the microbeads.
And step six, processing the picture.
S6.1, graying treatment.
The image collected by the CMOS sensor of the camera 1 is RGB value, the RGB values of the red photo and the infrared photo are converted into gray value by an average algorithm,
Wherein, Representing the current coordinate pointIs used for the gray-scale value of (c),、、The values of the red, green and blue components, respectively.
S6.2, gaussian filtering treatment.
The Gaussian filtering is used as a low-pass filter by linking the frequency domain processing and the time domain processing of the image, and can filter low-frequency energy to play a role in smoothing the image. Gaussian filtering is a linear smoothing filtering process, and is a process of carrying out weighted average on the whole image, wherein the value of each pixel point is obtained by carrying out weighted average on the value of each pixel point and other pixel values in the template field.
The specific operations of gaussian filtering are: each pixel in the image is scanned with a 3x3 template, the value of the center pixel point of the template is replaced with the weighted average gray value of the pixels in the field determined by the template, the gaussian filtered 3x3 matching template is as follows,
Wherein, Representing the current pixel.
Gaussian filtering has two implementations, one is convolved with a discretized windowed sliding window, and the other is by fourier transformation. The embodiment is realized by adopting discretization window sliding window convolution, and the value of each pixel point in the convolution is obtained by weighted average of the pixel point and other pixels in the field. The matching template is a coefficient stored in a two-dimensional array, and the matching template is as followsThe calculation formula of each element is as follows,
Wherein, Representing the first of the current window templatesLine 1The element values of the column pixel points,The value of the window template is represented,Representing standard deviation.
S6.3, gamma conversion image correction.
The gamma conversion is mainly used for correcting images, and correcting images with over-high gray level or under-gray level to enhance contrast, namely, the linear response of the images from exposure intensity becomes more similar to the response felt by human eyes. The transformation formula is to perform product operation on each pixel value on the original image,
Wherein, Is a constant coefficient of the number of the pieces of the material,The pixel value of the image is in the range of 0 to 255,As the gamma-transforming factor is used,The smaller the value is, the stronger the expansion effect on the low gray scale portion of the image is, the larger the value is, and the stronger the expansion effect on the high gray scale portion of the image is, the more the image enhancement effect is obvious. By different meansThe value can be used to enhance the details of the low gray or high gray portions. The gamma change has obvious image enhancement effect under the condition of low image contrast and high overall brightness value.
S6.4, JPEG compression.
The JPEG compression standard is a compression standard for gray or color images, which uses Discrete Cosine Transform (DCT), quantization, run-length and half-rate coding techniques, and is a hybrid coding standard.
JPEG defines two basic compression algorithms: a spatial linear prediction technique, namely a distortion-free compression algorithm of the PCM; the DCT-based distorted compression algorithm and further applies run-length coding and entropy coding. The undistorted compression algorithm of PCM has the advantages of easy hardware implementation and good quality of the re-seen image, and the method is adopted in the embodiment.
And step seven, determining the corresponding items of the microsphere group.
Referring to fig. 4, gray values of red light and infrared light are calculated from the gray pictures, and a class distribution map of the microspheres is formed from the gray values of red light and infrared light, and then the items corresponding to each microsphere group are known.
And step eight, determining a report gray value.
Referring to fig. 5, the gray scale of the reported value of each point is calculated, and the reported gray scale value of each item microsphere group is obtained by adopting a median method.
And step nine, obtaining a calibration curve equation.
And (3) obtaining a calibration curve equation by carrying out regression analysis on the reported gray value and the concentration value by using a reagent calibration product with a known concentration value. Specifically, a Logistic regression model is adopted to fit a calibration curve equation,
Wherein, The dependent variable is represented as a concentration value,The independent variable is represented as a reported gray value,Representing the lower limit or starting value of the curve,Representing the upper limit or saturation value of the curve,A slope parameter representing a curve is shown,Representing the translation parameters of the curve.
In the practical process, the situation that the deviation between the fluorescence value excited by the same photo and the target value exceeds 10% is encountered, and the main reason is that the brightness value of the excitation light source is different, so that data errors are caused. According to the embodiment, the fluorescence values exceeding 10% are automatically divided into areas according to the calibrated data and the deviation, so that a new calibration curve equation is obtained, and corresponding equations are used for subsequent measurement.
And step ten, calculating a concentration value.
And (3) according to the reported gray value in the step (eight), calculating a corresponding concentration value by using the calibration curve equation in the step (nine).
It is apparent that the above examples of the present invention are merely illustrative of the present invention and are not limiting of the embodiments of the present invention. Other obvious variations or modifications which are extended by the spirit of the present invention are within the scope of the present invention.
Claims (4)
1. A full-automatic liquid suspension chip detection method is characterized by comprising the following steps:
Step one, automatically sucking a sample to be detected of preset volume and position information;
Step two, sample processing: automatically processing the sample to be detected according to a preset sequence, combining the sample with the magnetic bead antibody in the reagent, and amplifying a fluorescent signal through SAPE;
step three, secondary sample suction: introducing the processed sample into a subsequent processing analysis link;
fixing the positions of the magnetic beads: adsorbing the magnetic beads to a predetermined position of the detection cell by using magnetic force;
Collecting photo information: the method comprises the steps of lighting red light, exciting magnetic beads to generate a beam of red light and a beam of infrared light, matching a camera with a red light filter and an infrared filter respectively, and collecting a red light photo and an infrared light photo generated by the micro beads; the green light is lightened, the camera is matched with the report value filter, and a report value photo of the microbeads is collected;
step six, picture processing: processing the red light photo and the infrared light photo, and outputting a gray level picture, wherein the method specifically comprises the following steps:
S6.1, graying treatment:
The RGB values of the red photo and the infrared photo are converted into gray values by an averaging algorithm,
Wherein, Representing the current coordinate pointIs used for the gray-scale value of (c),、、The values of the red, green and blue components, respectively;
S6.2, gaussian filtering:
Each pixel in the image is scanned with a 3x3 template, the value of the center pixel point of the template is replaced with the weighted average gray value of the pixels in the field determined by the template, the gaussian filtered 3x3 matching template is as follows,
Wherein, Representing a current pixel;
The discrete window sliding window convolution realizes Gaussian filtering, the value of each pixel point in the convolution is obtained by weighted average of the value of each pixel point and other pixels in the field, the matching template is a coefficient stored in a two-dimensional array, and the value of the matching template is as follows The calculation formula of each element is as follows,
Wherein, Representing the first of the current window templatesLine 1The element values of the column pixel points,The value of the window template is represented,Representing standard deviation;
s6.3, gamma conversion image correction:
A product operation is performed on each pixel value on the original image,
Wherein, Is a constant coefficient of the number of the pieces of the material,The pixel value of the image is in the range of 0 to 255,As the gamma-transforming factor is used,The value is taken as a boundary of 1, the smaller the value is, the stronger the expansion effect on the low gray level part of the image is, the larger the value is, and the stronger the expansion effect on the high gray level part of the image is, the image enhancement effect is obvious;
s6.4, JPEG compression;
Step seven, determining corresponding items of microsphere groups: calculating gray values of red light and infrared light according to the gray image, forming a class distribution diagram of the microspheres by the gray values of the red light and the infrared light, and obtaining items corresponding to the microsphere group;
step eight, determining a report gray value: calculating the gray level of the report value of each point according to the report value photo, and obtaining the report gray level of each item microsphere group by adopting a median method;
step nine, obtaining a calibration curve equation: obtaining a calibration curve equation by carrying out regression analysis on the reported gray value and the concentration value by using a reagent calibration product with a known concentration value; specifically, a Logistic regression model is adopted to fit a calibration curve equation,
Wherein, The dependent variable is represented as a concentration value,The independent variable is represented as a reported gray value,Representing the lower limit or starting value of the curve,Representing the upper limit or saturation value of the curve,A slope parameter representing a curve is shown,A translation parameter representing a curve;
Step ten, calculating a concentration value: and (3) according to the reported gray value in the step (eight), calculating a corresponding concentration value by using the calibration curve equation in the step (nine).
2. The method for detecting the full-automatic liquid suspension chip according to claim 1, wherein,
And step two, sample pretreatment, reagent addition, uniform mixing, reaction, washing and separation are included.
3. The method for detecting the full-automatic liquid suspension chip according to claim 1, wherein,
In the fifth step, the red filter, the infrared filter and the report value filter are provided with a switching device for adjusting any filter to the optical axis.
4. The method for detecting a full-automatic liquid suspension chip according to claim 1, wherein in step nine, when the deviation between the fluorescence value excited out by the same photo and the target value exceeds 10%, the fluorescence value exceeding 10% is automatically divided into areas according to the calibrated data and the deviation to obtain a new calibration curve equation, and the corresponding calibration curve equation is used for subsequent measurement.
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