GB2613760A - Method for performimg quality control on protein biosynthesis system by tRNA proteomics - Google Patents
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Abstract
A four-step method for performing quality control on a protein biosynthesis system by tRNA proteomics. Step 1 involves sequencing and archiving the tRNAs of the protein biosynthesis system. Step 2 involves obtaining tRNA expression level information after performing standardized processing and/or mathematical conversion of the sequenced data. Step 3 involves comparing tRNA data of the protein biosynthesis system to that of a reference system by adopting a tRNA similarity calculating method, a tRNA matching inquiry method, and a tRNA difference quantifying method. Step 4 involves establishing formulated quality control indexes based on the tRNA differences between the protein biosynthesis system to be measured and the reference system. The protein biosynthesis system can be cells, tissues, organs, embryos, stem cells, organoids, chimeras, and bacterial strains. The tRNA expression level information can comprise tRNA levels, anticodon levels, amino acid levels, and codon level paired and derived by anticodons. The similarity calculating method of the tRNA can use a pairwise comparison scattergram, calculate a correlation coefficient of tRNA expression levels, use a thermography to show a correlation coefficient matrix, use a tree diagram to represent a genetic relationship, and use a principal component diagram to represent similarity.
Description
Method for Performing Quality Control on Protein Biosynthesis System by tRNA Proteom ics
TECHNICAL FIELD
The invention relates to the technical field of high-throughput sequencing technique and bioinformatics analysis, in particular to a method for performing quality control on a protein biosynthesis system by tRNA proteomics.
BACKGROUND
The protein biosynthesis system includes messenger RNAs, tRNAs, ribosomes and enzymes related to protein synthesis, and can be generally cells, tissues, organs, embryos, stem cells, organoids, chimeras arid bacterial strains. Current quality control methods of the protein biosynthesis system include an appearance morphologic method, a biochemical method, immunological detection (for example, using an antibody dyeing special antigen biomarker), genetic testing (for example, chromatin karyotype and DNA fingerprint chromatogram), and the like. Generally, one or more methods are selected for quality control, and tRNA proteomics has not been included in conventional quality control.
As an important element in protein biosynthesis, transfer RNA (hereinafter referred to as tRNA), mainly performs a translation function (Fig. 2) from codon sequences to proteins. tRNA compositions and content in the protein biosynthesis systems such as the cells or the tissues have specificity, and tRNA proteomics can reflect comprehensive states of these protein biosynthesis systems, and can represent a protein translation process from aspects of tRNA compositions, content and supply states. The tRNA proteomics information of the protein biosynthesis systems such as the cells or the tissues can be obtained through the tRNA sequencing and archiving technique as follows: extracting total RNA from the protein biosynthesis systems, performing enzymatic degradation on other RNAs and performing length screening to obtain components with less than 200 nt, constructing a tRNA sequencing library after deammoniation and acylation, performing high-throughput sequencing after performing length screening (170-210bp) again, and performing sequence comparison and archiving on the sequenced results and a tRNA standard library with reduced species, thereby obtaining species, compositions and expression level information of tRNAs in the systems. The prior art has supported obtaining tRNA proteomics, but there are still several problems to be solved about how to evaluate the protein biosynthesis systems with tRNA proteomics and how to establish the quantifying quality control indexes.
SUMMARY
To solve the technical problems, the invention provides a method for performing quality control on a protein biosynthesis system by tRNA proteomics.
The technical scheme adopted by the invention is as follows: A method for performing quality control on a protein biosynthesis system by tRNA proteomics, including the following steps: performing sequencing and archiving on tRNAs of the protein biosynthesis system, wherein the protein biosynthesis system comprises a system B to be measured and a reference system A; S2, performing proteomic analysis on tRNAs of single system of the system B to be measured and /or the reference system A: obtaining tRNA expression level information after performing standardized processing and/or mathematical conversion on data obtained by sequencing and archiving; classifying and summarizing tRNA expression levels according to corresponding amino acids and/or anticodons to obtain tRNA expression level information of multiple levels (tRNA, anticodons, amino acid level and codon level paired and derived by anticodons), and performing data analysis and/or data visual processing on the tRNA expression level information; S3, performing proteomics comparison on tRNAs of multiple systems: performing proteomics comparison on tRNAs of multiple systems by adopting a tRNA proteomics similarity calculating method, a tRNA proteomics matching inquiry method and a tRNA proteomics difference quantifying method, thereby obtaining tRNA proteomics difference between the system B to be measured and the reference system A as well as tRNA proteomics difference inside the reference system A or inside the system B to be measured; and S4, based on the tRNA proteomics difference between the system B to be measured and the reference system A as well as tRNA proteomics difference inside the reference system A or inside the system B to be measured, which are obtained in the step 53, establishing formulated quality control indexes to define quality control indexes based on tRNA proteomics and report Notes: in the protein biosynthesis systems, tRNAs of different types or structures and 20 amino acids form tRNAs subjected to aminoacylation under action of related enzymes; anticodons are matched with codons after the tRNAs enter ribosomes, and carried amino acids are added onto a nascent polypeptide chain by the tRNAs, so that protein translation is accomplished. tRNAs of different types or structures in the systems and expression levels thereof co-define tRNA proteomics, as an important member of the protein biosynthesis system, that supply tRNAs subjected to aminoacylation of different types for the protein biosynthesis system. Different protein biosynthesis systems have tRNA compositions which are not completely the same, i.e., tRNA proteomics has potential system specificity, and can be used as fingerprint characteristics of the systems for identification or equality control.
Based on the structures, tRNAs have two natural attributes: anticodons and corresponding amino acids. tRNAs with the same anticodons and different other framework sequences are mutually called as isodecoders, and tRNAs with the same amino acids and different anticodons are mutually called as isoacceptors. After expression level information of all tRNAs in certain system is obtained by the tRNA sequencing and archiving technique, expression levels can be classified and summarized according to anti codons or corresponding amino acids of the tR N A s, so that tRNA proteomics data of multiple levels are obtained. Follow-up data analysis and data visualization can be performed for one or more levels.
The sequenced and archived data are subjected to standard processing and/or mathematical conversion as follows: tRNA count data obtained by the tRNA sequencing and archiving technique are converted into count per million (CPM) after standard processing, and are then are subjected to logarithm conversion to obtain logCPM, both of which can be used for measurement indexes for tRNA expression levels, and can be used for follow-up data analysis and visualization, wherein the formulas are as follows:
CP
01:9,fl4752c2 LP ( 1) Further, in the scheme, the protein biosynthesis system includes messenger RNAs, tRNAs, ribosomes and enzymes related to protein synthesis, and can be generally cells, tissues, organs, embryos, stem cells, organoids, chimeras and bacterial strains.
Further, in the scheme, the system B to be measured refers to a to-be-measured protein biosynthesis system with unknown quality characteristics, and the reference system A refers to a protein biosynthesis system which serves as a reference object and has known quality characteristics.
Further, in the scheme, the tRNA expression level information of multiple levels includes the tRNA level, the anti codon level, the amino acid level and the codon level paired and derived by anticodons.
Further, according to tRNA expression level information of the tRNA level, the ant codon level, the amino acid level and the derived codon level, data analysis and/or data visual treatment of tRNA expression level information can be performed by selecting any one or more of the levels or by using original or mathematically-converted tRNA expression level information, Further, in the scheme, the similarity calculating method of tRNA proteomics is as follows: using a pairwise comparison scattergram of the system to represent similarity of tRNA expression level information, calculating a correlation coefficient of the tRNA expression level information to quantify similarity of tRNA proteomics of different systems, using a thermography to show a correlation coefficient matrix, using a tree diagram to represent a genetic relationship, on tRNA proteomics, of different systems, and using a reduced-dimension principal component diagram to represent similarity, on tRNA proteomics, of different systems and the used tRNA expression level information is summarized according to the levels, original or mathematically-converted tRNA expression level information.
Further, the similarity calculating method of tRNA proteomics is as follows: performing pairwise comparison for multiple systems, drawing logCPM values of tRNAs into scattergrams, thereby forming a scattergram matrix, calculating correlation coefficients of the logCPNI values or CPM values of the systems during pairwise comparison to obtain a correlation coefficient matrix which is shown by thermography; performing clustering analysis while generating the thermography of the correlation coefficients to generate an accessory tree diagram which can represent a genetic relationship of the systems performing principal component analysis (PCA) on CPM values of the tRNAs for reducing dimension, thereby obtaining coordinate components, on each component, of the multiple systems and drawing a reduced-dimension principal component diagram (also named as PCA diagram) by taking principal components; classifying and summarizing according to anticodons or CPM values, on tRNAs, of corresponding amino acids, using the summarized CPM values or logCPM values to perform analysis and drawing steps, thereby obtaining the scattergram matrix of ant codon levels or amino acid levels, the correlation coefficient thermography, the tree diagram and the reduced-dimension principal component diagram, wherein the diagrams arid quantifying information included therein can be used for evaluating similarity of tRNA proteomics of multiple systems.
Further, in the scheme, the matching inquiry method of the tRNA proteomics is as follows: obtaining tRNA proteomics data of the system B to be measured and a series of reference systems A, clustering by a principal component analysis method and a correlation coefficient matrix tree diagram, obtaining a reduced-dimension principal component diagram by calculating similarity and/or data visualization of tRNA proteomics, and combining the tree diagram to search one or more having higher similarity with the system B to be measured from the reference systems A. Further, in the scheme, the quantifying method of tRNA proteomics difference is as follows: performing reduced-dimension analysis on the tRNA proteomics data of multiple systems for quantifying comparison of coordinate difference on principal components.
Further, principal component analysis further can be used for quantifying tRNA proteomics difference and can be represented by difference of coordinate components, on principal components, of each system as follows: there is one arrowhead on the reduced-dimension principal component diagram, the starting point of the arrowhead corresponds to the gravity center of the system A or the biological replicates thereof, the final point of the arrowhead corresponds to the gravity center of the system B or the biological replicates thereof, and the length of the arrowhead and projection on each axis thereof can reflect the size of difference of tRNA proteomics of the systems A and B. Further, in the scheme, a specific method of establishing the formulated quality control indexes in the step S4 is as follows using the protein biosynthesis system with known quality standards as the reference system A, using the protein biosynthesis system with unknown quality standards as the system B to be measured, setting a plurality of biological replicates, performing principal component analysis, using a gravity method to determine quantified tRNA proteomics difference, namely group difference, between the systems A and B, using an average distance method to determine quantified tRNA proteomics difference, namely in-group difference, inside the reference system A, and taking the multiple of the group difference to the in-group difference as quantifying quality control index according to a following formula: Quantifying quality control index=tRNA proteomics difference between the system B to be measured and the reference system A/tRNA proteomics difference inside the reference system A (2).
Further, a series of reference threshold values are set for the quantifying quality control index so as to compare the established formulated quality control indexes with the preset reference threshold values for evaluating the quality. For example: If the quantified quality control index of the system B to be measured is smaller than 1, the system B is evaluated to be extremely close to the standard system If the quantified quality control index of the system B to be measured is between 1 and 2, the system B is evaluated to be relatively close to the standard system.
If the quantified quality control index of the system B to be measured is between 2 and 4, the system B is evaluated to be away from the standard system.
If the quantified quality control index of the system B to be measured exceeds 4, the system B is evaluated to be extremely away from the standard system.
The reference threshold values can be slightly adjusted according to types of samples or a practical condition, and tRNA proteomics difference or quantifying quality control indexes of the two systems also can be analyzed by a statistical test method. The system B to be measured can be set with multiple biological replicates for investigating stability of the quantifying quality control indexes thereof Finally, pictures of quality control indexes, related data and the like are collected as quality control reports.
The invention has the beneficial effects that: the method for performing quality control on the protein biosynthesis system by tRNA proteomics provided by the invention is a supplement to a set of existing quality control methods, has the advantages of a wide scope of application, quantifiable indexes, good stability, good discrimination and the like, and can perform multidimensional quantifying quality control on the protein biosynthesis system from an aspect of total tRNA supply.
BRIEF DESCRIPTION OF THE FIGURES
Fig.! is a flowchart of the method of the invention.
Fig. 2 shows definition of tRNA proteomics and application thereof in protein biosynthesis for providing aminoacyl at on tRNA for a protein translation process.
Fig. 3 shows a tRNA composition in single system (SK-N-SH cell) with a multi-level pie chart in Embodiment! of the invention.
Fig. 4 shows an implementation method of deriving the tRNA composition of the single system (BEAS-2B cell) in Embodiment I of the invention from anti codon level to codon level.
Fig. 5 shows tRNA proteomics similarity among multiple systems with a scatter composite thermography in Embodiment 2 of the invention.
Fig. 6 shows tRNA proteomics similarity among multiple systems with a reduced-dimension principal component diagram in Embodiment 2 of the invention.
Fig. 7 shows matching inquiry, in a tRNA proteomic database of a reference system, of a system to be measured in Embodiment 3 of the invention.
Fig. 8 shows quality control indexes established for quantifying tRNA proteomics in a homologous system in Embodiment 4 of the invention.
Fig. 9 shows quality control report derived after performing quality control on a brain-like organ at different stages by tRNA proteomics in Embodiment 5 of the invention.
Fig. 10 shows quality control report derived after performing quality control on mouse tissues on different parts by tRNA proteomics in Embodiment 6 of the invention.
DETAILED DESCRIPTION
In order to better illustrate the problems solved by the invention, the technical solutions adopted and the effects achieved are further described in combination with specific embodiments. It should be noted that the invention includes, but is not limited to, the following embodiments and combinations thereof It should be noted that specific techniques or conditions, which are not indicated in the embodiments, are carried out according to the techniques or conditions described in the literature in the field or in accordance with the product specifications. The reagents or instruments used are not indicated by the manufacturer, and are conventional products that can be obtained commercially.
As shown in Fig. 1, a method for performing quality control on a protein biosynthesis system by tRNA proteomics includes the following steps: Si, performing sequencing and archiving on tRNAs of the protein biosynthesis system, wherein the protein biosynthesis system includes messenger RNAs, tRNAs, ribosomes and enzymes related to protein synthesis, and can be generally cells, ti ssues, organs, embryos, stem cells, organoids, chimeras and bacterial strains; according to use in quality control, the protein biosynthesis system can be divided into a system B to be measured and a reference system A, the system B to be measured refers to a tobe-measured protein biosynthesis system with unknown quality characteristics, and the reference system A refers to a protein biosynthesis system which serves as a reference object and has known quality characteristics; S2, performing proteomic analysis on tRNAs of single system of the system B to be measured and /or the reference system A: obtaining tRNA expression level information after performing standardized processing and/or mathematical conversion on data obtained by sequencing and archiving, wherein the tRNA expression level information includes the tRNA level, the anticodon level, the amino acid level and the codon level paired and derived by anticodons; after obtaining the tRNA expression level information of multiple levels, using the multilevel pie chart to simultaneously show tRNA compositions of multiple levels, and using a pie chart or a stacked column diagram to perform visualization on any one or more levels, wherein the pie chart and the column diagram are generally suitable for visualization of tRNA proteomics information of single system; separately performing classifying, summarizing and level deriving on tRNA expression level according to corresponding amino acids and/or anticodons to obtain tRNA expression level information of multiple levels, and performing data analysis and/or data visual processing on the tRNA expression level information, wherein the sequenced and archived data are subjected to standard processing and/or mathematical conversion as follows: tRNA count data obtained by the tRNA sequencing and archiving technique are converted into count per million (CPM) after standard processing, and then are subjected to logarithm conversion to obtain logCPM, both of which can be used for measurement indexes for tRNA expression levels, and can be used for follow-up data analysis and visualization, wherein the formulas are as follows: co:14, ri5m -- rtik ) tS9l3839434Th26 S3, performing proteomics comparison on tRNAs of multiple systems: performing proteomics comparison on tRNAs of multiple systems by adopting a tRNA proteomics ( 1) similarity calculating method, a tRNA proteomics matching inquiry method and a tRNA proteomics difference quantifying method, thereby obtaining tRNA proteomics difference between the system B to be measured and the reference system A as well as tRNA proteomics difference inside the reference system A or inside the system B to be measured, wherein the similarity calculating method of tRNA proteomics is as follows: performing pairwise comparison for multiple systems, drawing logCPM values of tRNAs into scattergrams, thereby forming a scattergram matrix; calculating correlation coefficients of the logCPM or CPM values of the systems during pairwi se comparison to obtain a correlation coefficient matrix which is shown by thermography, performing clustering analysis while generating the thermography of the correlation coefficients to generate an accessory tree diagram which can represent a genetic relationship of the systems; performing principal component analysis (PCA) on CPM values of the tRNAs for reducing dimension, thereby obtaining coordinate components, on each component, of the multiple systems; and drawing a reduced-dimension principal component diagram (also named as PCA diagram) by taking principal components; classifying and summarizing according to anticodons or CPM values, on tRNAs, of corresponding amino acids, using the summarized CPM values or logCPN4 values to perform analysis and drawing steps, thereby obtaining the scattergram matrix of anticodon levels or amino acid levels, the correlation coefficient thermography, the tree diagram and the reduced-dimension principal component diagram, and the diagrams and quantifying information included therein can be used for evaluating similarity of tRNA proteomics of multiple systems; the matching inquiry method of the tRNA proteomics is as follows: obtaining tRNA proteomics data of the system B to be measured and a series of reference systems A, clustering by a principal component analysis method and a correlation coefficient matrix tree diagram, obtaining a reduced-dimension principal component diagram by calculating similarity and/or data visualization of tRNA proteomics, and combining the tree diagram to search one or more having higher similarity with the system B to be measured from the reference systems A; and the quantifying method of tRNA proteomics difference is as follows performing reduced-dimension analysis on the tRNA proteomics data of multiple systems for quantifying comparison of coordinate difference on principal components; and 54, based on the tRNA proteomics difference between the system B to be measured and the reference system A as well as tRNA proteomics difference inside the reference system A or inside the system B to be measured, which are obtained in the step S3, establishing formulated quality control indexes to define quality control indexes based on tRNA proteomics and report, wherein the specific method of establishing the formulated quality control indexes in the step S4 is as follows: using the protein biosynthesis system with known quality standards as the reference system A, using the protein biosynthesis system with unknown quality standards as the system B to be measured, setting a plurality of biological replicates, performing principal component analysis, using a gravity method to determine quantified tRNA proteomics difference, namely group difference, between the systems A and 13, using an average distance method to determine quantified tRNA proteomics difference, namely in-group difference, inside the reference system A, and taking the multiple of the group difference to the in-group difference as a quantifying quality control index according to a following formula: Quantifying quality control index=tRNA proteomics difference between the system B to be measured and the reference system AARNA proteomics difference inside the reference system A (2).
A series of reference threshold values are set for quantifying quality control indexes so as to compare the established formulated quality control indexes with the preset reference threshold values for evaluating the quality. For example: If the quantified quality control index of the system B to be measured is smaller than 1, the system B is evaluated to be extremely close to the standard system.
If the quantified quality control index of the system B to be measured is between 1 and 2, the system B is evaluated to be relatively close to the standard system.
If the quantified quality control index of the system B to be measured is between 2 and 4, the system B is evaluated to be away from the standard system If the quantified quality control index of the system B to be measured exceeds 4, the system B is evaluated to be extremely away from the standard system.
The reference threshold values can be slightly adjusted according to types of samples or a practical condition, and tRNA proteomics difference or quantifying quality control indexes of the two systems also can be analyzed by a statistical test method. The system B to be measured can be set with multiple biological replicates for investigating stability of the quantifying quality control indexes thereof. Finally, pictures of quality control indexes, related data and the like are collected as quality control reports.
EMBODIMENT 1 This embodiment provides a tRNA proteomics analysis method of single system, and shows tRNA composition of single system with a multi-level pie chart.
A SK-N-SH cell line cultivated in vitro was taken as an example of the protein biosynthesis system, CPM values of all tRNAs in the system were obtained through the tRNA sequencing and archiving technique and data processing, the CPM values were classified and summarized according to anticodons and amino acids to obtain tRNA expression level information of three levels, which was drawn into a multi-level pie chart (Fig. 3) for visually showing the tRNA composition of single system. For example, in SICN-SH cells, the three tRNAs with highest contents were separately tRNA-Glu, tRNA-Gly and tRNA-Gln if summarized (inner ring) according to levels of the amino acids, the tRNA with the highest content was tRNA-Glu-CTC if summarized (middle ring) according to levels of anticodons; and the tRNA with the highest content was tRNA-Glu-CTC-1-1, accounting for about 13%, if summarized (outer ring) according to levels of tRNAs Pie charts also could be selectively shown according to the purpose, and increase and decrease of levels of the pie charts as well as switching among samples could be directly implemented in some software or plug-ins, for example, combination of Excel and a Krona template Except from the three main levels including the tRNA level, the anticodon level and the amino acid level, some summarizing analysis of derived level further could be performed on tRNAs of single system, for example, derived from the anticodon level to the codon level (Fig. 4). The tRNA was firstly summarized according to the anticodons for tRNA expression level or CPNI, and then was summarized with tRNAs matched with certain codon in aspect of expression level or CPM according to a matching relationship and matching efficiency of the anticodons and the codons, so that tRNA proteomics information of the codon level could be finally obtained EMBODIMENT 2 This embodiment provides a tRNA proteomics comparison method of multiple systems for calculating or showing similarity of tRNA proteomics of multiple systems.
In this embodiment, five cell lines (namely U251, SK-N-MC, SK-N-SH, HEK293T and HEK293T+3CD) were set in total, each cell line was set with two biological replicates (suffixes being RI and R2), 10 sample/systems in total for separately obtaining tRNA expression level information, which was converted into a logCPM form, of all samples through the tRNA sequencing and archiving technique and tRNA proteomics analysis.
The 10 systems were subjected to pairwise comparison, logCPM values were drawn into a scattergram matrix, and each scatter point represented logCPM value (Fig. 5), in two compared system, of certain tRNA. If expression levels, in the two systems, of the tRNAs were close, the scatter points were located near a diagonal line. If tRNA compositions of the two systems were relatively close, all scatter points were intensively distributed near the diagonal line. Therefore, the scattergrams, subjected to pairwise comparison, of the systems could be used for visually representing similarity of tRNA expression levels and tRNA compositions, and the narrower the scatter point distribution, the higher the similarity of tRNA proteomics of the two systems.
Correlation coefficients of 1ogCPN1 values on each scattergram could be calculated for quantifying similarity of tRNA proteomics of different systems, the closer to 1 the correlation coefficients, the higher the similarity of tRNA proteomics of the two systems, the further from 1 the correlation coefficients, the lower the similarity of tRNA proteomics of the two systems. A correlation coefficient matrix could be shown with thermography, and a tree diagram was generated for representing genetic relationships, in aspect of tRNA proteomics, of different systems, and adjacent branches on the tree diagram indicated the closer genetic relationships or the higher similarity of tRNA proteomics. Besides, the scattergram, the thermography and the tree diagram could be compounded for visualization (Fig. 5).
To represent similarity of tRNA proteomics of different systems more simply, tRNA proteomics data of different systems could be subjected to principal component analysis or other reduced-dimension analysis, for example, coordinates, on the principal component 1 and the principal component 2, of the 10 systems were subjected to visualization (Fig. 6) after analysis. It could be found that four scatter points of SK-N-MC and SK-N-SH were very close to each other, and therefore, similarity of tRNA proteomics of two cell lines was relatively high, which was associated with a fact the two cell lines were neuroma cells. HEK293T+3CD was a stable cell line with relatively close scatter points, which was derived from HEK293T. Namely, scatter point distance or coordinate difference on the reduced-dimension principal component diagram could be used for representing similarity or difference of tRNA proteomics of multiple systems in a quantified mode. Besides, it could be seen from the reduced-dimension principal component diagram that a distance among biological replicates was generally smaller than a distance among the cell lines.e., in-group difference was smaller than group distance, which indicated that tRNA proteomics had certain cell specificity and proved that the tRNA proteomics analysis method provided by the invention had good stability and discrimination. EMBODIMENT 3 This embodiment provides tRNA proteomics analysis and comparison, which are separately performed on three levels including the tRNA level, the anticodon level and the amino acid level, of tRNA expression level information, tRNA proteomics analysis and comparison could be separately performed on three levels including the tRNA level, the anticodon level and the amino acid level, which have difference in stability and discrimination and the anticodon level and the amino acid level further could be used for tRNA proteomics analysis and comparison across species.
Stability: anticodon level being higher than tRNA level and lower than amino acid level. Discrimination: anticodon level being higher than amino acid level and lower than tRNA level.
A tRNA proteomics database (Fig. 7) of multiple reference systems was established in this embodiment, and matching inquiry, in the tRNA proteomics database of the reference system, of an unknown system to be measured could be realized by using principal component analysis of anticodon level and the reduced-dimension principal component diagram for identifying the system. For example, two points in a circle could be a cell line to be measured (HEK293T cells were passaged by several times), which was a HEK293T cell line with standard quality closest to that in the reference system, and was deviated from scatter points of other reference cell lines, proving feasibility of the tRNA proteomics matching inquiry method. Gravity center difference between the HEK293T cell line to be measured and the reference 11EK293T cell line on the reduced-dimension principal component diagram could be used for reflecting quality of the cell line to be measured.
EMBODIMENT 4 This embodiment provides a quantifying method for tRNA proteomics difference of homologous systems and constructs quantifying quality control indexes.
Three standard quality cell lines (Fig. 8) of A549, BEAS-2B and HEK293T were taken as reference systems, and biological replicates thereof were set as 2 (corresponding suffixes on the diagram were RI and R2). A549, BEAS-2B and HEK293T cells infected with influenza viruses were taken as systems to be measured, which were homologous with the three systems. Through the tRNA proteomics analysis method, scatter composite thermography, reduced-dimension principal component diagram, CPM stacked column chart summarized in accordance with amino acids or anticodons of the systems could be obtained. On the reduced-dimension principal component diagram, tRNA proteomics difference (represented by a solid arrowhead) before and after A549 cells were infected with influenza viruses was about 4.3 times that of tRNA proteomics difference (represented by a distance between points A549 RI and A549 R2) inside the reference A549 cells, which indicated that tRNA proteomics after the A549 cells were infected with influenza viruses had great changes and its quality had been deviated from the standard A549 cell line. The principal component I (reduced to one dimension) only could be taken into consideration, and component, on the X axis, of the arrowhead was about 4.56 times the X-coordinate difference of two points A549 12,1 and A549 R2, which could be used as an index or the quantifying quality control index.
Similarly, tRNA proteomics difference (represented by the solid arrowhead) before and after A549 cells were infected with influenza viruses could be obtained for separately calculating the multiple of the tRNA proteomics difference to the difference inside the reference cell line so as to obtain the quantifying quality control index. Besides, component, on the principal component 1 or the principal component 2, of the solid arrowhead also could be used for separately calculating the quantifying quality control index of single dimension. EMBODIMENT S. The embodiment provides some conventional quality control systems applicable to the method for performing quality control on the protein biosynthesis system by tRNA proteomics.
The method for performing quality control on the protein biosynthesis system by tRNA proteomics provided by the invention has a wide use, includes, but is not limited to cells, tissues, organs, embryos, stem cells, organo ds, chimeras and bacterial strains.
Taking a brain-like organ cultivated in vitro (Fig. 9) as an example, the brain-like organ was cultivated to different stages according to the standard steps in the literature as follows: Day 0, an initial stage, namely HUES9 cells; D1 I, a neuroectoderm al stage; D30, a brain-like organ forming stage.
A brain tissue was taken as final control for performing tRNA sequencing and archiving as well as quality control on the protein biosynthesis system at each stage It could be seen from the quality control results that the brain-like organ at Day 30 was most similar to the brain tissue on the reduced-dimension principal component diagram, indicating that the brain-like organ at that time has been preliminarily formed with certain difference from the body tissue. Relatively, the brain-like organ was different from the brain tissue at other stages.
EMBODIMENT 6 The embodiment provides application of the method for performing quality control on the protein biosynthesis system by tRNA proteomics in identification or quality control of complex systems such as tissues and organs.
The method for performing quality control on the protein biosynthesis system by tRNA proteomics provided by the invention also could be used for identification or quality control of complex systems such as tissues and organs. A heart, livers, spleens, lungs, kidneys, cerebral cortex, cerebellum and muscle tissues were separated from one mouse as a standard reference system (Fig. 10), and a heart and muscle tissues (separately marked as Heart_R2 and Muscle R2) were separated from another mouse as an unknown system to be measured. It could be seen from the reduced-dimension principal component diagram of quality control report that Heart_Ri was most similar to Heart_R2, and Heart R2 was identified as a heart tissue. Muscle RI and Cerebral cortex 12.1 were relatively similar to Muscle R2, which could be comprehensively judged by combining with the tree diagram in the quality control report, were most adjacent branches on the tree diagram, and therefore, Muscle R7 was preliminarily identified as the muscle tissue.
Besides, the reduced-dimension principal component diagram further indicated that there was greater difference on tRNA proteomics between the heart tissue of the mouse and other tissues.
The tRNA expression levels of the mouse tissues were summarized according to the corresponding amino acids, and shown as a bar chart after being standardized. It could be seen that the tRNA expression levels of different tissues of the mouse were greatly different, i.e., tRNA proteomics had tissue specificity and also could be used for quality control on the tissues.
Claims (10)
- CLAMS: 1. A method for performing quality control on a protein biosynthesis system by tRNA proteomics, comprising the following steps: Si, performing sequencing and archiving on tRNAs of the protein biosynthesis system, wherein the protein biosynthesis system comprises a system B to be measured and a reference system A; S2, performing proteomics analysis on tRNAs of single system of the system B to be measured and /or the reference system A: obtaining tRNA expression level information after performing standardized processing and/or mathematical conversion on data obtained by sequencing and archiving; classifying and summarizing tRNA expression level according to corresponding amino acids and/or ant codons to obtain tRNA expression level information of multiple levels, and performing data analysis and/or data visual processing on the tRNA expression levelinformation; S3, performing proteomics comparison on tRNAs of multiple systems: performing proteomics comparison on tRNAs of multiple systems by adopting a tRNA proteomics similarity calculating method, a tRNA proteomics matching inquiry method and a tRNA proteomics difference quantifying method, thereby obtaining tRNA proteomics difference between the system B to be measured and the reference system A as well as tRNA proteomics difference inside the reference system A or inside the system B to be measured; and S4, based on the tRNA proteomics difference between the system B to be measured and the reference system A as well as tRNA proteomics difference inside the reference system A or inside the system B to be measured, which are obtained in the step 53, establishing formulated quality control indexes to define quality control indexes based on tRNA proteomics and report.
- 2. The method for performing quality control on the protein biosynthesis system by tRNA proteomics according to claim 1, wherein the protein biosynthesis system comprises messenger RNAs, tRNAs, ribosomes and enzymes related to protein synthesis, and can be generally cells, tissues, organs, embryos, stem cells, organo ds, chimeras and bacterial strains.
- 3. The method for performing quality control on the protein biosynthesis system by tRNA proteomics according to claim 1, wherein the system B to be measured refers to a to-bemeasured protein biosynthesis system with unknown quality characteristics, and the reference system A refers to a protein biosynthesis system which serves as a reference object and has known quality characteristics.
- 4. The method for performing quality control on the protein biosynthesis system by tRNA proteomics according to claim 1, wherein the tRNA expression level information of multiple levels comprises tRNA level, anticodon level, amino acid level and codon level paired and derived by anticodons.
- 5. The method for performing quality control on the protein biosynthesis system by tRNA proteomics according to claim 4, wherein according to tRNA expression level information of the tRNA level, the anticodon level, the amino acid level and the derived codon level, data analysis and/or data visual treatment of tRNA expression level information can be performed by selecting any one or more of the levels or by using original or mathematically-converted tRNA expression level information.
- 6. The method for performing quality control on the protein biosynthesis system by tRNA proteomics according to claim 1, wherein the similarity calculating method of tRNA proteomics is as follows: using a pairwise comparison scattergram of the system to represent similarity of tRNA expression level information, calculating a correlation coefficient of the tRNA expression level information to quantify similarity of tRNA proteomics of different systems, using a thermography to show a correlation coefficient matrix, using a tree diagram to represent a genetic relationship, on tRNA proteomics, of different systems, and using a reduced-dimension principal component diagram to represent similarity, on tRNA proteomics, of different systems; and the used tRNA expression level information is summarized according to the levels, original or mathematically-converted tRNA expression level information.
- 7. The method for performing quality control on the protein biosynthesis system by tRNA proteomics according to claim 1, wherein the matching inquiry method of the tRNA proteomics is as follows: obtaining tRNA proteomics data of the system B to be measured and a series of reference systems A, and searching one or more having higher similarity with the system B to be measured from the reference systems as matching inquiry results by calculating tRNA proteomics similarity and/or performing data visualization.
- 8. The method for performing quality control on the protein biosynthesis system by tRNA proteomics according to claim 1, wherein the quantifying method of tRNA proteomics difference is as follows: performing reduced-dimension analysis on the tRNA proteomics data of multiple systems for quantifying comparison of coordinate difference on principal components.
- 9. The method for performing quality control on the protein biosynthesis system by tRNA proteomics according to claim 1, wherein the specific method of establishing the formulated quality control indexes in the step S4 is as follows: using the protein biosynthesis system with known quality standards as the reference system A, using the protein biosynthesis system with unknown quality standards as the system B to be measured, setting a plurality of biological replicates, performing principal component analysis, using a gravity method to determine quantified tRNA proteomics difference, namely group difference, between the systems A and B, using an average distance method to determine quantified tRNA proteomics difference, namely in-group difference, inside the reference system A, and taking the multiple of the group difference to the in-group difference as a quantifying quality control index according to a following formula: Quantifying quality control index=tRNA proteomics difference between the system B to be measured and the reference system A / tRNA proteomics difference inside the reference system A.
- 10. The method for performing quality control on the protein biosynthesis system by tRNA proteomics according to claim 9, wherein a series of reference threshold values are set for the quantifying quality control index so as to compare the established formulated quality control indexes with the preset reference threshold values for evaluating the quality.
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