CN112183433B - Characterization and quantification method for solid and hollow virus particles - Google Patents

Characterization and quantification method for solid and hollow virus particles Download PDF

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CN112183433B
CN112183433B CN202011085399.XA CN202011085399A CN112183433B CN 112183433 B CN112183433 B CN 112183433B CN 202011085399 A CN202011085399 A CN 202011085399A CN 112183433 B CN112183433 B CN 112183433B
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陈龙
谢培福
郭振乾
刘传
郭春龙
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Shuimu Future Beijing Technology Co ltd
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Abstract

The invention discloses a characterization and quantification method of solid and hollow virus particles, which is used for imaging frozen hydration state of viruses; preprocessing the frozen hydration state projection image of the virus sample, and realizing automatic selection and empty-solid characterization of frozen hydration state imaging viruses; classifying the viral particles; counting the virus empty-solid proportion, secondarily correcting the counting result and performing reliability enhancement analysis to provide a quality control standard for whether the high-solid-ratio gene medicine is permitted to be produced in the next step; visually displaying the analysis result; the virus empty and solid under the near physiological state is characterized based on frozen water state imaging, a framework system for intelligent sorting and quantitative analysis of virus particles is realized, no artificial marking is needed, repeated labor and errors of artificial annotation are avoided, accurate, high-flux and visual quantitative analysis of virus sample empty and solid is realized, and reliable brand new indexes are provided for quality control and activity evaluation of gene drugs.

Description

Characterization and quantification method for solid and hollow virus particles
Technical Field
The invention relates to the technical field of solid and hollow virus particle classification, in particular to a characterization and quantification method of solid and hollow virus particles.
Background
Virus-like particles (Virus-LikeParticles, VLP) and replication-defective viruses such as Adeno-associated viruses (AAV) cannot proliferate, and are widely used as gene transfer vectors in the pharmaceutical industry. In particular, AAV host cells have wide range, long in vivo expression time, rapid response time and high expression efficiency, and can not cause human diseases, thus being the most promising viral gene therapy vector. The genetic material content of the gene vector is closely related to the treatment efficiency, and the empty-solid proportion of the gene medicine needs to be accurately estimated, so that a reliable quality control standard is provided for entering the next production flow. In addition, a visual high-flux virus particle empty and solid quantification method is established, and is important for development, optimization and speed improvement of a production process. In particular, detection of virus in a near physiological state can provide an intuitive reference for optimization of production processes.
Several classes of assays have been reported in the literature that can quantify both empty and solid viruses, both based on the differentiation of empty and solid viruses by nucleic acid quantification and capsid quantification, but these methods all yield statistical averages of population analysis. 1) ELISA/q-PCR method can roughly estimate the virus solid ratio. Enzyme-linked immunosorbent assay (Enzyme-LinkedImmunosorbentAssay, ELISA) was used to determine total AAV capsid content and quantitative polymerase chain reaction (quantitative Polymerase Chain Reaction, qPCR) was used to measure AAV genome content. However, the measurement accuracy of both detection methods varies with great uncertainty about the viral solids ratio, and ELISA analysis of capsid protein amounts is not applicable to all AAV serotypes. 2) Ultraviolet (UV) densitometry was used to quantify the solid ratio of AAV. The method uses UV absorbance measurements at 260nm and 280nm to analyze viral solids ratio, however, the method relies on accurate determination of capsid extinction coefficient (extinction coefficient) and fairly uniform capsid composition, and UV absorbance measurements are susceptible to interference from other proteins and DNA contaminants having absorbance in the UV range, and thus the method is relatively error-prone and not widely used. 3) Analytical ultracentrifugation (AnalyticalUltracentifugation, AUC) is widely used to characterize AAV. It can analyze quantitative information of AAV solid ratios and other AAV subgroups, however low throughput of AUC limits its use in process development.
Negative electron microscopy (sem) can be used to directly observe and count hollow and solid viruses by observing them at high magnification after staining, and is considered as a good method for analyzing the viral particle components. After negative staining, heavy metal ions have a greater ability to scatter electrons than the less dense atoms in the protein, and heavy metal entrapment enables the protein to withstand higher electron doses and thus provide high contrast images. In the virus negative-dyeing sample preparation process, heavy metal dye (such as phosphotungstic acid dye liquor) covers the virus particles, outlines the surface structures of the virus particles, can provide structural information with higher resolution, can be used for imaging the virus particles, and can realize the distinction between hollow viruses and solid viruses. However, during the sample preparation process, heavy metals may penetrate into the cavities inside the virus, which helps to observe the genetic material content inside the virus particles, but the penetration of metallic dyes may destroy the structure of the virus particles, and the detection of empty or solid virus results in unexpected false positive and false negative results. In addition, the thickness of the dye is difficult to control, and can have unpredictable effects on the observed results.
Disclosure of Invention
The invention aims to provide a characterization and quantification method of solid and hollow virus particles, which solves the problems in the background technology.
The invention is realized in such a way that a characterization and quantification method of solid and hollow virus particles comprises the following steps:
step 1), imaging the frozen hydration state of the virus, such as a frozen electron microscope cryo-EM, so as to realize near-physiological state imaging of the single gene drug carrier virus. Specifically, the method comprises the following steps:
1.1 Providing a sample of virus-like particles or virus particles, ensuring that the sample structure is stable in solution without deagglomeration or aggregation.
1.2 Preparing the sample to maintain the sample in a natural undyed hydrated state as follows: a) Selecting a proper network model; b) Hydrophilizing the carrier web to facilitate adsorption of protein solution on the carrier web; c) Using a semi-automatic rapid-input type freezing sampling machine to rapidly freeze a carrier net adsorbed with a protein solution in an ethane solution, forming glassy thin ice with the thickness of about tens to hundreds of nanometers on a carrier net hole or a supporting film, and dispersing and embedding virus particles in an ice layer to form a frozen sample; d) The carrier web was transferred to a carrier web box and stored in liquid nitrogen.
1.3 Observing the morphology of each virus particle in the frozen hydrated sample in a frozen electron microscopy imaging device, ensures that the electron microscopy is debugged and calibrated according to the manufacturer's instructions and that the back of the camera is uniform during imaging of the sample. Finding the proper area, selecting the carrying mesh for taking the photo, repeating the steps of image acquisition until the required number of images are acquired, and storing in a proper format (for example, 16-bit tiff format).
Step 2), preprocessing frozen hydration state projection images of virus samples: the method comprises the steps of firstly carrying out low-pass filtering and normalization treatment, then realizing virus particle selection and alignment, and finally carrying out dimension reduction representation and central area signal reinforcement, thereby realizing automatic selection and empty-solid representation of frozen hydrated imaging viruses. The measured value of the virus particle content is the gray value of the image, and the difference of brightness or darkness inside the virus particles is measured, which corresponds to the filling degree of nucleic acid in the cavity of the virus particles, and in frozen hydrated state imaging, the empty particles are displayed as discs with small inner density due to the low inner density of the empty particles. The solid particles contained genetic material inside, shown as dark homogeneous discs. In order to effectively characterize the internal density differences of empty and solid viruses, the invention takes the following steps:
2.1 The drift correction is carried out on the images from frame to frame, multiple frames are combined, and the signal to noise ratio of the images is improved by adopting a low-pass filtering mode (such as mean value filtering, median filtering and frequency domain low-pass filtering). The images are normalized (e.g., each image divided by the average of all pixels) to achieve electronic dose exposure correction for the images.
2.2 The present invention employs an automatic particle picking algorithm (e.g., a Relion automatic particle picking method) based on Laplacian-of-Gaussianfilter. A template search method, a particle selection method based on target detection of a neural network (for example, a particle selection method such as gauthomatch, SPHIRE-crYOLO, cryoSPARC, etc.) may also be adopted. The virus images were aligned in center using the Cross-Correlation function.
2.3 A dimensionality reduction representation and center enhancement of the virus image. The spherical coordinate system is established by the virus center, the radius is r, and the intensity at the polar angle is theta is marked as f (r, theta). Performing circular integration along the polar radial direction to obtain signal intensities at different radiuses from the center:
by adopting the dimension reduction method, the virus image of N dimension is changed into N dimension,the total strength of the ring with radius r is indicated. Further, it is considered that the closer to the center, the more pronounced the difference in the empty and solid signals, the greater the effect of noise away from the center, and that there is no difference in the empty and solid viruses near the capsid. We take the decreasing function w (r) of the radius as the weighting function pair +.>Constrained, i.e. constrained
For example, power functionsSigmoid function->And the like are used for strengthening the central area signal, and a symmetrical display mode is adopted when the F (r) is visualized.
Step 3), classifying the virus particles. Discretizing F (r) to obtain N-dimensional representation of virus image, analyzing virus internal components, extracting empty and solid differences, and giving a measure to particle content, including principal component analysis, central area intensity, internal total intensity (or weighted internal total intensity) or specific gravity of internal bright and dark area, and depicting virus particle empty and solid differences. The invention adopts two independent methods to represent the empty and solid viruses: principal component analysis and central region intensity.
Preferably, the principal component analysis, M N-dimensional sample sets x= (X) 1 ,x 2 ,…,x M ) Wherein M is>N，T denotes the transpose operation, a) decentralizing the samples: />b) Calculating covariance matrix XX of sample set X T C) pair XX T Performing eigenvalue decomposition, d) extracting eigenvector alpha= (alpha) corresponding to the maximum eigenvalue 1 ,α 2 ,…,α N ) And standardized. e) The coefficient of the first principal component of the sample is taken as a one-dimensional representation of the sample, i.e. y=αx. f) And carrying out histogram statistics on the Y, carrying out Gaussian mixture fitting, determining a classification threshold, and carrying out statistics on the quantity ratio of each component of the Gaussian mixture distribution.
Preferably, the central region intensity analysis uses the intensity of the central regionAs a representation of the empty-solid difference, wherein +.>For->And carrying out histogram statistics, carrying out Gaussian mixture fitting, determining a classification threshold, and carrying out statistics on the quantity ratio of each component of the Gaussian mixture distribution.
And 4) counting the virus empty-solid proportion, and carrying out secondary correction and credibility enhancement analysis on the counting result. In order to reduce analysis deviation of a sample with high solid proportion, fitting errors caused by unequal data proportion are avoided, when the solid proportion of the sample to be tested is more than 2/3, a strategy of mixing images of the sample to be tested with pure hollow viruses in equal proportion is adopted, and reliability of a conclusion with high solid proportion is enhanced.
And 5) visually displaying the classification result. The sample is classified and empty and solid annotated for each virus using the intersection point coordinates of probability density functions of the component mixed gaussian distribution as a threshold. In practice, the confidence level of the classification result can be adjusted by changing the threshold value.
The invention has the beneficial effects that:
1. the invention provides a method for characterizing and quantifying empty and solid viruses based on frozen water state imaging, and realizes a framework system for intelligently sorting and quantitatively analyzing viruses, thereby providing a brand new index for quality control and activity evaluation of gene drugs.
2. The rapid freezing in the sample preparation process keeps the structure of the virus in the natural state, and the electron microscope imaging in the frozen hydration state can see the internal components of the particles. For analysis of the internal components, we give a measure of particle content, such as principal component analysis, central region intensity, total internal intensity (or weighted total internal intensity), or specific gravity of internal light and dark areas, very effectively characterizing the empty-solid differences of the virus particles.
3. Compared with the population measurement method of nucleic acid quantification and capsid quantification, the method provided by the invention has the advantages that each virus is characterized and detected, and the defects of large population analysis error and easiness in interference are avoided. The software analysis framework is in seamless connection with virus imaging in a hydration state, high-flux detection and rapid intelligent analysis are realized, and the method can be widely used for development and optimization of production processes.
4. The software framework for extracting and quantitatively analyzing the virus empty and solid features is free from artificial labeling, realizes accurate and visual virus sample empty and solid quantification, and compared with a negative-dyeing electron microscope imaging technology, the frozen hydration state imaging is free from the influence of uneven dyeing because no dye is used, and the advantage of more reliable results exceeds the defect of technical trouble.
Drawings
FIG. 1 is a flow chart of a virus empty-solid characterization quantization based on frozen water imaging;
FIG. 2 is a schematic diagram of data collection according to the present invention;
FIG. 3 is a schematic diagram of data processing-signal enhancement according to the present invention;
FIG. 4 is a schematic diagram of data processing-statistical analysis according to the present invention;
fig. 5 is a schematic diagram of data processing-visualization of the present invention.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention.
Embodiment one:
as shown in fig. 1, the virus empty-solid characterization quantization flow chart based on frozen water imaging of the invention. Imaging of frozen hydration states of viruses, such as cryoelectron microscopy-EM, achieves near-physiological state imaging of single gene drug carrier viruses. After the frozen hydration state projection image of the virus sample is obtained, the analysis is carried out by using the software framework of intelligent sorting and quantitative analysis developed by the invention. Specifically, a) preprocessing a frozen hydration state projection image of a virus sample, which relates to low-pass filtering to improve the signal-to-noise ratio, selecting and aligning virus particles, and carrying out dimension reduction representation and central area signal reinforcement on the virus particle image; b) Classifying virus particles, namely extracting empty and solid virus characteristics, performing statistics histogram and Gaussian mixture fitting, and determining a classification threshold; c) Carrying out statistical analysis on the virus empty-solid proportion, and carrying out secondary correction and credibility enhancement analysis on the statistical result; d) And visually displaying analysis results, including annotating the empty and solid states of single viruses and averaging classification results. Figures 2-5 are detailed descriptions of framework systems for virus freeze hydration imaging to achieve virus empty-solid characterization, and for matched intelligent sorting and quantitative analysis.
As shown in FIG. 2, the data collection schematic diagram of the invention uses a freeze electron microscope technology to image the virus in frozen hydration state, so as to realize the detection of the near physiological state of the single gene drug carrier virus. In particular the number of the elements,
1) A virus-like particle or sample of virus particles is provided. Ensure the stability of the sample structure in the solution without depolymerization or aggregation.
2) The samples were prepared so that the samples remained in a natural undyed hydrated state. And carrying out glow discharge by using a proper carrier net (grid), such as a 400-mesh copper net, and carrying out hydrophilization treatment so that the carrier net is easy to adsorb protein solution. Frozen electron microscope samples were prepared using a Vitrobot semi-automatic sampling machine or other rapid freeze sampling method. Specifically, a carrier net adsorbed with a protein solution is rapidly put into liquid ethane cooled by liquid nitrogen, a vitreous ice sheet having a thickness of about several tens to several hundreds nanometers is formed on a carrier net or a support film, virus particles are dispersed and embedded in an ice layer to form a frozen sample, and the carrier net is transferred to a carrier net box and stored in liquid nitrogen.
3) The morphology of each virus particle in the sample was observed in a cryoelectron microscopy imaging device. The cryogenic carrier is transferred from its storage location to a cryogenic workstation pre-chilled with liquid nitrogen, in which a cryogenic rack preparation pump is inserted. The carrier web is transferred to the slot in the freezer rack. The cryostat is inserted into the cryo-electron microscope and the liquid nitrogen container is filled. During imaging of the sample, it is ensured that the electron microscope is debugged and calibrated according to the manufacturer's instructions and that the back of the camera is uniform. Finding a proper area, setting a focus value (defocusing value), selecting a carrying mesh for taking pictures, and repeating the steps of image acquisition until the required number of images are acquired.
4) Two-dimensional electron microscope images are collected and saved in a suitable format (e.g., 16-bit tiff format). The drift correction between frames is carried out on the images through software such as MotionCorr2, relion and the like, the frames are combined, the signal to noise ratio of the images is improved in a low-pass filtering mode, and further the electronic dose exposure correction is realized on the image normalization processing. The content of genetic material in the virus particles corresponds to the gray value of the virus particle image, and the filling degree of the genetic material in the virus can be judged by measuring the gray difference between the interior and the outer shell of the virus particles. In cryo-EM imaging of viral particles, the empty particles appear as discs with a very small internal density due to their lower internal density. The solid particles contained genetic material inside, shown as dark homogeneous discs.
As shown in fig. 3, in the data processing process, relion, gautomatch, cryoSPARC, SPHIRE-crYOLO and other software can be used to select virus particles, as shown in fig. 3 a; and aligning the virus image center by using a Cross-Correlation function, as shown in FIG. 3 b; the spherical coordinate system is established by the virus center, the radius is r, and the intensity at the polar angle is theta is marked as f (r, theta). Performing circular integration along the radial direction as shown in fig. 3 c; obtaining signal intensity at different radii from the center:
by adopting the dimension reduction method, the virus image of N dimension is changed into N dimension,the total strength of the ring with radius r is indicated. Further, it is considered that the closer to the center, the more pronounced the difference in the empty and solid signals, the greater the effect of noise away from the center, and that there is no difference in the empty and solid viruses near the capsid. We take the decreasing function w (r) of the radius as the weighting function pair +.>Constrained, i.e. constrained
For example, power functionsSigmoid function->And the like, to intensify the central area signal, and a symmetrical display mode is adopted when the F (r) is visualized, as shown in fig. 3c.
As shown in fig. 4, discretizing the F (r) to obtain an N-dimensional representation of the virus image, and then extracting the difference of empty and solid; two independent methods were used to characterize empty and solid viruses: principal component analysis and central region intensity.
1) And (5) principal component analysis. M N-dimensional sample sets x= (X) 1 ,x 2 ,…,x M ) Wherein M is>N，T represents a transpose operation. a) The samples were de-centered: />b) Calculating covariance matrix XX of sample set X T . c) To XX T And (5) performing eigenvalue decomposition. d) Extracting a feature vector alpha= (alpha) corresponding to the maximum feature value 1 ,α 2 ,…,α N ) And standardized. e) The coefficient of the first principal component of the sample is taken as a one-dimensional representation of the sample, i.e. y=αx. f) And carrying out histogram statistics on the Y, carrying out Gaussian mixture fitting, determining a classification threshold, and carrying out statistics on the quantity ratio of each component of the Gaussian mixture distribution.
2) And (5) analyzing the intensity of the central area. By intensity of central regionAs a representation of the differences in space and solids, wherein,for->And carrying out histogram statistics, carrying out Gaussian mixture fitting, determining a classification threshold, and carrying out statistics on the quantity ratio of each component of the Gaussian mixture distribution.
As shown in fig. 5, the classification result is visually displayed. The samples are classified by using the coordinates of the intersection points of probability density functions of the mixed Gaussian distribution of the components as a threshold value. In practice, the confidence level of the classification result can be adjusted by changing the threshold value. FIGS. 5a, 5d are typical hollow and solid virus images after classification. Fig. 5b, 5e are results after averaging of open, solid virus images. FIG. 5f is a typical post-classification empty-filled annotation result of individual virions, with open circles of empty viruses and black circles of filled viruses.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.

Claims (7)

1. A method for characterization quantification of solid and hollow viral particles, characterized by: the characterization quantization method comprises the following steps:
1) Imaging the frozen hydration state of the virus, and adopting a frozen electron microscope cryo-EM to realize near-physiological state imaging of the virus of the single gene drug carrier, comprising the following steps:
1.1 Providing a sample of virus-like particles or virus particles, ensuring that the sample structure is stable in solution without deagglomeration or aggregation;
1.2 Preparing the sample to maintain the sample in a natural undyed hydrated state: carrying out hydrophilization treatment on the carrier mesh to ensure that the carrier mesh is easy to adsorb protein solution, and then preparing a frozen electron microscope sample;
1.3 Observing each virus particle morphology in the frozen hydrated state sample in a frozen electron microscopy imaging device: in the process of imaging a sample, the electronic microscope is ensured to be debugged and calibrated according to the specification of a manufacturer, the back of the camera is uniform, a proper area is found, a carrying mesh for taking a picture is selected, the steps of image acquisition are repeated until the required number of images are acquired, and the images are stored in a proper format;
2) Pretreatment of frozen hydrated projection images of virus samples: firstly, through low-pass filtering and normalization processing, virus particles are selected and aligned, finally, dimension reduction representation and central area signal reinforcement are carried out, and automatic selection and empty-solid representation of frozen hydrated imaging viruses are realized, and the method comprises the following steps:
2.1 Carrying out drift correction between frames on the image, combining multiple frames, improving the signal to noise ratio of the image by adopting a low-pass filtering mode, and carrying out electronic dose exposure correction on the image;
2.2 Selecting virus particles, namely, adopting one of an automatic particle picking algorithm based on Laplacian-of-Gaussian filter, a template searching method and a particle selecting method based on target detection, and aligning the centers of virus images by using a Cross-Correlation function;
2.3 Reduced-dimension representation and center enhancement of virus images): establishing a spherical coordinate system by using a virus center, marking the radius as r, the intensity at the polar angle as theta as f (r, theta), and carrying out circular integration along the polar radial direction to obtain the signal intensity at different radiuses from the center:
by adopting the dimension reduction method, the virus image of N dimension is changed into N dimension,representing the total intensity of the ring with radius r, further, taking the decreasing function w (r) of radius as the weight function pair +.>Constrained, i.e. constrained
3) Classification of viral particles: extracting virus empty and solid features, fitting through mixed gauss, and determining a classification threshold;
4) Counting the virus empty-solid proportion, and carrying out secondary correction and credibility enhancement analysis on the counting result;
5) And visually displaying analysis results: including averaging of classification results and single virus empty and solid annotations.
2. The method for characterization quantification of solid and hollow viral particles according to claim 1, wherein: the preparation of the frozen electron microscope sample in the step 1.2) is as follows: the electron microscope carrier net carrying the virus sample solution is quickly put into liquid ethane cooled by liquid nitrogen, glass state thin ice with the thickness of about tens to hundreds of nanometers is formed on a carrier net hole or a supporting film, virus particles are dispersed and embedded in an ice layer to form frozen samples, and the carrier net is transferred into a carrier net box and stored in the liquid nitrogen.
3. The method for characterization quantification of solid and hollow viral particles according to claim 1, wherein: the step 3) classifies the virus particles: discretizing F (r) to obtain N-dimensional representation of virus image, analyzing the virus internal components, extracting the empty and solid differences, and giving a measure to particle content, including principal component analysis, central area intensity, internal total intensity or internal light and dark area specific gravity, and depicting the empty and solid differences of virus particles.
4. A method for characterization quantification of solid and hollow viral particles according to claim 3, characterized in that: the principal component analysis features are extracted as follows: m N-dimensional sample sets x= (X) 1 ，x 2 ，...，x M ) Wherein M > N,t denotes the transpose operation, a) decentralizing the samples: />b) Calculating covariance matrix XX of sample set X T C) pair XX T Performing eigenvalue decomposition, d) extracting eigenvector alpha= (alpha) corresponding to the maximum eigenvalue 1 ，α 2 ，...，α N ) And normalizing, e) taking the coefficient of the first main component of the sample as one-dimensional representation of the sample, namely Y=alpha X, f) carrying out histogram statistics on Y, carrying out mixed Gaussian fitting, determining a classification threshold, and counting the quantity ratio of each component of the mixed Gaussian distribution.
5. A method for characterization quantification of solid and hollow viral particles according to claim 3, characterized in that: the central region intensity analysis is: by intensity of central regionAs a representation of the empty-solid difference, wherein +.>For->Performing histogram statistics, performing Gaussian mixture fitting, determining classification threshold, and performing statisticsThe mixing Gaussian distribution is used for distributing the quantity ratio of each component.
6. The method for characterization quantification of solid and hollow viral particles according to claim 1, wherein: and in the step 4), when the solid ratio of the virus sample to be detected exceeds 2/3, adopting a strategy of mixing images of the pure hollow virus and the virus sample to be detected in equal proportion, and strengthening the reliability of the conclusion of high solid ratio.
7. The method for characterization quantification of solid and hollow viral particles according to claim 1, wherein: and 5) determining thresholds for classification and carrying out blank-solid annotation on each virus according to probability density functions of mixed Gaussian distribution of components, wherein different thresholds correspond to different confidence degrees of virus-solid ratios.
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