Identification method of influenza virus and cell exosome

Abstract

The invention provides an influenza virus and cell exosome recognition method, and belongs to the technical field of microscopic imaging. The method comprises the following steps: modifying a fresh split mica sheet with a NaCl solution, adhering the mica sheet to a glass slide, respectively dripping influenza virus and cell exosome samples on the mica sheet, standing, and testing. The method comprises the following steps: selecting a nano-probe of a specific model, placing the probe in a range of dripping a sample, after the probe is placed, selecting a certain range for scanning, observing the imaging characteristics of the two samples, and analyzing the morphology, height and adhesive force of the two samples. The method comprises the following steps: scanning the surface of an influenza virus and cell exosome sample by using an atomic force microscope, selectively analyzing the morphology, height and adhesive force of the influenza virus and cell exosome in a measurement range, extracting related characteristic parameters, and then carrying out segmentation and morphological adjustment on an image to obtain the influenza virus and cell exosome sample. Analysis and recognition results of the two samples are obtained by using an image analysis and machine learning method of neural network aided prediction, and accurate and rapid intelligent distinguishing is realized.

Description

Technical Field

[0001] The present invention belongs to the field of microscopic imaging technology, and specifically relates to a method for identifying influenza viruses and cell exosomes. Background Art

[0002] Influenza virus is also known as influenza virus, which is divided into three types: A, B, and C. Influenza virus is spherical, while newly isolated strains are mostly filamentous, with a diameter between 80 and 120 nanometers. The length of filamentous influenza virus can reach 4000 nanometers. The structure of influenza virus can be divided into three parts from the outside to the inside: envelope, matrix protein and core. Exosomes are a type of intracellular vesicles with a diameter of 30-150nm secreted by cells to the outside of the cell, composed of lipids, proteins, mRNA and other components. Exosomes are important molecules for intercellular communication and can participate in a variety of physiological and pathological reactions.

[0003] Since the size of virus particles is similar to that of exosome particles, transmission electron microscopy, scanning electron microscopy and other methods are usually used to observe and distinguish viruses from exosomes. Transmission electron microscopy is to project an accelerated and focused electron beam onto a very thin sample. The electrons collide with the atoms in the sample and change direction to produce a scattering effect. Different material structures in the sample have different electron scattering abilities, thus forming different contrasts of light and dark to reflect the morphological characteristics of the sample. Scanning electron microscopy is to use an electron beam with an energy of 1-30kV to irradiate the surface of the analyzed sample in a raster scanning manner, and use the secondary electrons and backscattered electrons generated by the interaction between the incident electrons and the sample surface material to obtain high-resolution information on the microscopic structure and morphology of the sample surface. Both methods can directly observe the structure and morphology of the sample, but the sample pretreatment and preparation are complex and require high requirements.

[0004] The atomic force nano-microscopy system has the ability to perform nano-manipulation and measurement of sample materials in different environments. Its unique working mode determines that it has stronger environmental adaptability and excellent applicability compared to other nano-microscopy systems. The working principle of atomic force nano-microscopy is to detect the weak atomic force between the probe and the sample surface through the principle of optical amplification. After processing, these information changes can obtain the surface morphology structure information and roughness information of the sample, with a resolution of atomic level. When using atomic force probes to study virus and exosome samples, while obtaining high-resolution morphology imaging and structural three-dimensional images of viruses and exosomes, it also has the advantages of non-destructive, high sensitivity and real-time detection. However, due to the high similarity in the morphology of influenza viruses and cell exosomes, the recognition rate is very low when only atomic force microscopy is used for identification. Summary of the invention

[0005] The first technical problem to be solved by the present invention is: At present, the method of using transmission electron microscopy and scanning electron microscopy to observe and distinguish influenza viruses and cellular exosomes involves complex sample pretreatment and preparation processes, and has high requirements for experimental operations and experimental conditions.

[0006] The second technical problem to be solved by the present invention is that since the morphology of influenza virus and cellular exosomes is highly similar, the recognition rate is very low when only atomic force microscopy imaging is used for identification.

[0007] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0008] A method for identifying influenza virus and cell exosomes, comprising the following steps:

[0009] S1. Prepare influenza virus samples and cell exosome samples;

[0010] S2, placing the glass slide carrying the mica sheet on the sample stage of the atomic force microscope, and determining the nanoprobe and the test mode based on the physical properties of the sample;

[0011] S3, testing the influenza virus sample within a preset range by using an atomic force microscope to obtain an influenza virus image, and obtaining image characteristic parameters of the influenza virus based on the influenza virus image;

[0012] Scanning the cell exosome sample within a preset range by an atomic force microscope to obtain a cell exosome image, and obtaining image characteristic parameters of the cell exosome based on the cell exosome image;

[0013] S4. Compare the image characteristic parameters of the influenza virus and the image characteristic parameters of the cell exosomes, segment and morphologically adjust the images, and use neural network-assisted prediction image analysis and machine learning methods for identification to obtain analysis and identification results of the two samples.

[0014] Preferably, the preparation of the influenza virus sample in step S1 comprises: drawing a circle on the back of the mica sheet, sticking the mica sheet modified with 50 mM NaCl solution on a glass slide, taking the influenza virus mixture, dropping it on the circled position of the mica sheet, and letting it stand for 1 minute.

[0015] Preferably, the volume of the influenza virus mixture is 1 μL.

[0016] Preferably, preparing the extracellular vesicle sample in step S1 includes: drawing a circle on the back of the mica sheet, sticking the mica sheet modified with 50mM NaCl solution on a glass slide, taking the extracellular vesicle mixture, dropping it on the circled position of the mica sheet, and letting it stand for 1 minute.

[0017] Preferably, the volume of the cell exosome mixture is 1 μL.

[0018] Preferably, the nanoprobe tip has a height of 17 μm, a radius of <10 nm, and an elastic coefficient of 40 N / m.

[0019] Preferably, the test modes include a tapping mode and a QI mode; and the test environment is room temperature.

[0020] Preferably, the influenza virus testing range selected is 10 μm×10 μm.

[0021] Preferably, the selected cell exosome testing range is 10 μm×10 μm.

[0022] In terms of equipment and consumables, this method mainly includes an atomic force microscope, influenza virus samples, cell extracellular vesicle samples, NaCl solution, freshly cracked mica sheets, glass slides, specific types of nanoprobes, etc.

[0023] In the present invention, the test modes of the atomic force microscope are the tapping mode and the QI mode in the gas phase, and a nanoprobe suitable for this mode is selected for scanning. Draw a circle mark on the back of the mica sheet modified with 50mM NaCl solution, stick the mica sheet to a glass slide, take the influenza virus mixture with a volume of 1μL, drop it on the circled position of the mica sheet, and let it stand for 1 minute before testing. Draw a circle on the back of the mica sheet, stick the mica sheet to a glass slide, take the cell exosome mixture with a volume of 1μL, drop it on the circled position of the mica sheet, the electrostatic adsorption between the surface of the mica sheet and the exosomes can make the exosomes firmly attached, and let it stand for 1 minute before testing. Select a specific model of nanoprobe, select the tapping mode, obtain the morphology and height characteristics of the sample; select the QI mode to obtain the adhesion characteristics of the sample. The needle is inserted within the range of the sample to be dropped. After the needle is inserted, a certain range is selected for scanning, the imaging characteristics of the two samples are observed, and the morphology, height, and adhesion of the two samples are analyzed. The image is then segmented and morphologically adjusted, and intelligent recognition is performed using image analysis assisted by neural network prediction and machine learning methods.

[0024] The present invention overcomes the shortcomings of the prior art and provides an identification method for distinguishing influenza viruses from cellular exosomes based on atomic force microscopy imaging technology. The method uses parameters such as morphology, height, adhesion, etc., and then segments and morphologically adjusts the image. It uses image analysis and machine learning methods assisted by neural network prediction for identification. It can intelligently distinguish influenza viruses from cellular exosomes, has high flexibility in operation, and reduces the reliance on the experience of professionals for distinction.

[0025] The present invention utilizes atomic force microscopy combined with image analysis and machine learning methods assisted by neural network prediction to observe and distinguish viruses and exosomes. It has the unique advantages of being rapid, non-destructive, real-time, and quantitative, and obtains analysis and identification results of the two samples, realizes intelligent distinction, and can quickly detect and identify, reducing the reliance on the experience of professionals for distinction, and overcoming the limitations of traditional detection methods in the biomedical field.

[0026] Compared with the prior art, the technical advantages of the present invention are mainly reflected in the following aspects:

[0027] (1) Combined with atomic force microscopy technology, atomic force imaging can achieve precise measurement with high positioning accuracy, and has unique advantages such as fast, non-destructive, real-time, and quantitative.

[0028] (2) Atomic force microscopy is used to distinguish influenza viruses from cellular exosomes, overcoming the shortcomings of traditional detection methods such as long detection time and low reproducibility.

[0029] (3) Atomic force microscopy imaging is used to distinguish influenza viruses from cellular exosomes, and live viruses and live exosomes can be detected, with the advantages of high sensitivity, rapid and convenient detection, etc.

[0030] (4) Using atomic force microscopy to detect influenza virus and cell exosome samples, the morphology of the virus and exosomes can be intuitively obtained, and relevant characteristic parameters can be extracted to facilitate differentiation.

[0031] (5) Atomic force microscopy is used to detect influenza virus and extracellular exosome samples, and the images are segmented and morphologically adjusted. Neural network-assisted prediction image analysis and machine learning methods are used for identification. This can intelligently distinguish influenza virus and extracellular exosomes, achieve rapid detection and accurate identification, and has high operational flexibility. It can greatly improve the recognition rate of influenza virus and extracellular exosomes; and reduce the reliance on professional experience for differentiation. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 is a flow chart of the method of the present invention.

[0033] Figure 2 It is a physical picture of sample preparation in the present invention.

[0034] Figure 3 It is a height map of influenza virus in the present invention.

[0035] Figure 4 It is a morphological diagram of influenza virus in the present invention.

[0036] Figure 5 It is a length-height data graph of a single influenza virus in the present invention.

[0037] Figure 6 It is a height map of cell exosomes in the present invention.

[0038] Figure 7 This is a morphological diagram of the cell exosomes in the present invention.

[0039] Figure 8 It is a data graph of the length-height of exosomes of a single cell in the present invention.

[0040] Fig. 9 Graph showing the adhesion force of influenza virus in the present invention.

[0041] Fig.10 Graph showing the adhesion force of exosomes in the present invention. DETAILED DESCRIPTION

[0042] The specific embodiments of the present invention will be described in detail below. In order to avoid too much unnecessary details, the well-known structures or functions will not be described in detail in the following examples. The approximate language used in the following examples can be used for quantitative expression, indicating that the quantity can be allowed to have a certain change without changing the basic function. Unless otherwise defined, the technical and scientific terms used in the following examples have the same meaning as those generally understood by those skilled in the art to which the present invention belongs.

[0043] A method for identifying influenza virus and cell exosomes, such as Figure 1 As shown, the following steps are included:

[0044] S1. Prepare influenza virus samples and cell exosome samples;

[0045] S2, placing the glass slide with the mica sheet on the sample stage of the atomic force microscope, and selecting the appropriate nanoprobe and test mode based on the physical properties of the sample;

[0046] S3, testing the influenza virus sample within a preset range by using an atomic force microscope to obtain an influenza virus image, and obtaining image characteristic parameters of the influenza virus based on the influenza virus image;

[0047] Scanning the cell exosome sample within a preset range by an atomic force microscope to obtain a cell exosome image, and obtaining image characteristic parameters of the cell exosome based on the cell exosome image;

[0048] S4. Obtain image characteristic parameters of the influenza virus and the cellular exosomes through an atomic force microscope, compare the image characteristic parameters of the influenza virus with the image characteristic parameters of the cellular exosomes to obtain analysis results of the two samples, then segment and morphologically adjust the images, use machine learning methods for identification, and obtain analysis and identification results of the two samples.

[0049] In step S1, preparing an influenza virus sample includes: drawing a circle mark on the back of a mica sheet, sticking a mica sheet modified with 50mM NaCl on a glass slide, taking an influenza virus mixture, dropping it on the circled position of the mica sheet, and letting it stand for 1 minute. The volume of the influenza virus mixture is 1μL. In step S1, preparing an extracellular vesicle sample includes: drawing a circle mark on the back of a mica sheet, sticking a mica sheet modified with 50mM NaCl on a glass slide, taking an extracellular vesicle mixture, dropping it on the circled position of the mica sheet, and letting it stand for 1 minute. The volume of the extracellular vesicle mixture is 1μL.

[0050] The tip height of the nanoprobe is 17μm, the radius is <10nm, and the elastic coefficient is 40N / m. The test modes of the atomic force microscope are tapping mode and QI mode, and the test environment is room temperature. The influenza virus test range selected by this method is 10μm×10μm; the cell exosome test range selected is 10μm×10μm.

[0051] like Figure 2 As shown, the virus sample prepared by the present invention, a freshly cracked mica sheet (V1 grade, Ted Pella, Inc.) was selected, a circle was drawn on the back of the mica sheet, a mica sheet modified with 50mM NaCl was glued to a glass slide, and the influenza virus mixture was dropped on the circled position of the mica sheet and allowed to stand for 1 minute. To prepare the cell exosome sample, a circle was drawn on the back of the mica sheet, a mica sheet modified with 50mM NaCl was glued to a glass slide, and the cell exosome mixture was dropped on the circled position of the mica sheet and allowed to stand for 1 minute.

[0052] like Figure 3 to Figure 5 As shown, it can be seen that the influenza virus has a relatively rounded shape and a uniform height distribution, with the central area of ​​the virus being the highest point.

[0053] like Figure 6 to Figure 8 As shown, it can be seen that the middle part of the cell exosome is concave, the surface is not smooth enough, and the height distribution is uneven, with the middle being low and the sides being high.

[0054] like Fig. 9 As shown, it can be seen that the influenza virus is solid.

[0055] like Fig.10 As shown, it can be seen that the middle of the cell exosome is hollow and concave.

[0056] In summary, the present invention provides a method for distinguishing influenza virus and extracellular vesicles based on atomic force microscopy imaging, using an atomic force microscope to scan the surface of influenza virus and extracellular vesicle samples, selecting and analyzing the morphology, height, and adhesion of influenza virus and extracellular vesicles within the measurement range, extracting characteristic parameters of height and adhesion, and then segmenting and morphologically adjusting the image, using neural network-assisted prediction image analysis and machine learning methods for identification, achieving intelligent distinction, and enabling rapid detection and intelligent identification. The present invention overcomes the shortcomings of the prior art, reduces the reliance on the experience of professionals for distinction, can accurately distinguish influenza virus and extracellular vesicles, has a high degree of flexibility in operation, and can greatly improve the recognition rate of influenza virus and extracellular vesicles.

[0057] The embodiments of the present invention are described in detail above, but the contents are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the scope of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for identifying influenza virus and cell exosomes, characterized in that: The following steps are involved: S1. Prepare influenza virus samples and cell exosome samples; S2, placing the glass slide carrying the mica sheet on the sample stage of the atomic force microscope, and determining the nanoprobe and the test mode based on the physical properties of the sample; S3, testing the influenza virus sample within a preset range by using an atomic force microscope to obtain an influenza virus image, and obtaining image characteristic parameters of the influenza virus based on the influenza virus image; Scanning the cell exosome sample within a preset range by an atomic force microscope to obtain a cell exosome image, and obtaining image characteristic parameters of the cell exosome based on the cell exosome image; S4. Compare the image characteristic parameters of the influenza virus and the image characteristic parameters of the cell exosomes, segment and morphologically adjust the images, and use neural network-assisted prediction image analysis and machine learning methods for identification to obtain analysis and identification results of the two samples.

2. The method for identifying influenza virus and exosomes according to claim 1, characterized in that: The preparation of influenza virus samples in step S1 includes: drawing a circle on the back of a mica sheet, sticking a mica sheet modified with a 50 mM NaCl solution on a glass slide, taking an influenza virus mixture, dropping it on the circled position of the mica sheet, and letting it stand for 1 minute.

3. The method for identifying influenza virus and exosomes according to claim 2, characterized in that: The volume of the influenza virus mixture was 1 μL.

4. The method for identifying influenza virus and exosomes according to claim 1, characterized in that: The preparation of the cell exosome sample in step S1 includes: drawing a circle on the back of the mica sheet, sticking the mica sheet modified with 50mM NaCl solution on a glass slide, taking the cell exosome mixture, dropping it on the circled mark position of the mica sheet, and letting it stand for 1 minute.

5. The method for identifying influenza virus and exosomes according to claim 4, characterized in that: The volume of the cell-exosome mixture was 1 μL.

6. The method for identifying influenza virus and exosomes according to claim 1, characterized in that: The nanoprobe tip has a height of 17 μm, a radius of less than 10 nm, and an elastic coefficient of 40 N / m.

7. The method for identifying influenza virus and exosomes according to claim 1, characterized in that: The test modes include a tapping mode and a QI mode; the test environment is room temperature.

8. The method for identifying influenza virus and exosomes according to claim 1, characterized in that: The selected influenza virus test range is 10μm×10μm.

9. The method for identifying influenza virus and exosomes according to claim 1, characterized in that: The selected cell exosome test range is 10μm×10μm.

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