WO2020094013A1

WO2020094013A1 - Targeted metabolomic automatic quantitative analysis method and device, and electronic device

Info

Publication number: WO2020094013A1
Application number: PCT/CN2019/115728
Authority: WO
Inventors: 张怡然; 任建洪; 杨梅; 谢桂纲
Original assignee: 苏州帕诺米克生物医药科技有限公司
Priority date: 2018-11-05
Filing date: 2019-11-05
Publication date: 2020-05-14
Also published as: CN109781916A

Abstract

A targeted metabolomic automatic quantitative analysis method and device (40, 800), and an electronic device (1900). The method comprises: automatically acquiring sample data of at least one sample to be tested (S31), the sample data comprising a user identifier, an identifier of a marker to be tested, and first information corresponding to the sample to be tested, and the first information being used to determine a peak area value of the marker to be tested; automatically acquiring a standard curve corresponding to the marker to be tested in the sample to be tested (S32), the standard curve indicating a relationship between the concentration of the marker in the sample to be tested and the peak area value of the marker; and determining, based on the standard curve and the first information, the concentration of the marker to be tested in each sample to be tested (S33). The present invention can be used for conducting targeted metabolomic quantitative analysis automatically and quickly in bulk.

Description

Targeted metabolomics quantitative automatic analysis method and device, and electronic equipment

Technical field

The embodiments of the present disclosure relate to the field of analytical chemistry methods, and in particular, to a method and device for quantitative automatic analysis of targeted metabolomics, electronic equipment, and storage media.

Background technique

Metabolites are small molecular organic compounds (molecules <1500Da) that achieve metabolic processes in living bodies, which are the final products of gene expression produced under the catalytic action of enzyme proteins. Metabolomics technology (metabolomics) is an important research method of system biology developed in the post-gene era, aiming to quantitatively detect as many metabolites as possible in biological systems. Metabolomics technology is widely used in many fields, such as disease diagnosis, drug development, nutritional food evaluation, toxicology research, environmental monitoring, plant breeding, etc.

Metabolomics technology is divided into non-targeted metabolomics (targeted metabolomics) and targeted metabolomics (targeted metabolomics) according to whether the detected metabolites are preset. The purpose of targeted metabolomics technology is to detect all metabolites in the organism, perform full scan analysis on the ions of all metabolites in the side sample, and then use the software database to compare and identify the scanned compounds, through screening and testing in the blood. Of metabolic markers can be used to diagnose diseases.

In the prior art, when performing targeted metabolomics analysis on data such as metabolites, data is usually recorded and compared by manual methods. This method requires comparative analysis of each metabolite separately, rather than batch and Automated processing requires huge labor costs, and wastes time and analysis accuracy.

Summary of the invention

The embodiments of the present disclosure propose a method and device for quantitative quantitative automatic analysis of targeted metabolomics, electronic equipment, and a storage medium to perform quantitative automatic quantitative analysis of targeted metabolomics automatically and quickly.

According to a first aspect of the embodiments of the present disclosure, a method for quantitative automatic analysis of targeted metabolomics is provided, including:

Acquiring sample data of at least one sample to be tested, the sample data including a user identifier corresponding to the sample to be tested, a marker identifier to be tested, and first information, the first information being used to determine a peak of the marker to be tested Area value; obtaining a standard curve corresponding to the test marker in the test sample, the standard curve including the correspondence between the concentration of the marker in the test sample and the peak area value of the marker; based on the standard curve and The first information determines the concentration of the test marker in each test sample.

In a possible implementation manner, the acquiring the standard curve corresponding to the marker in the test sample includes: acquiring the concentration of the marker in the standard sample; acquiring the peak area of the marker in the standard sample Value; based on the concentration and peak area values to establish a standard curve of the marker in the standard sample.

In a possible implementation manner, the acquiring a standard curve corresponding to the marker to be tested in the specimen to be tested includes: acquiring a first concentration of the internal standard and a second concentration of the marker in the standard sample, wherein , The internal standard is the isotope of the marker; the first peak area value of the internal standard and the second peak area value of the marker in the standard sample are obtained; based on the first peak area value and the second peak area The first ratio between the values, and the second ratio between the first concentration and the second concentration, establish the standard curve.

In a possible implementation manner, the determining the concentration of the marker to be tested in each sample to be tested based on the standard curve and the first information includes: determining the concentration of the marker to be tested based on at least the first information The third peak area value of the internal standard, the third concentration of the internal standard in the test sample, and the fourth peak area value of the test marker in the test sample; based on the third peak area value and the fourth The ratio of the peak area value, the third concentration, and the standard curve determine the concentration of the test marker in each test sample.

In a possible implementation manner, the method further includes: storing and displaying the concentration of the test marker in the test sample corresponding to each user identification.

In a possible implementation manner, the method further includes: acquiring chromatographic data of the marker to be measured in the sample data based on the first information; and determining a peak area value of the marker to be measured based on the chromatographic data.

According to a second aspect of the present disclosure, there is provided a targeted metabolomics quantitative automatic analysis device, including: an acquisition module configured to acquire sample data of at least one sample to be tested, and to obtain the sample to be tested in the sample to be tested A standard curve corresponding to the test marker; wherein, the sample data includes a user identifier, a test marker identifier, and first information corresponding to each sample to be tested, and the first information is used to determine the test marker Peak area value of the substance, the standard curve includes the corresponding relationship between the concentration of the marker in the sample to be measured and the peak area value of the marker; a determination module configured to determine each of the above based on the standard curve and the first information The concentration of the test marker in the test sample.

In a possible implementation manner, the acquiring module is further configured to acquire the concentration of the marker in the standard sample and acquire the peak area value of the marker in the standard sample, and the determining module is further configured to be based on the The concentration and peak area values establish a standard curve of the markers in the standard sample.

In a possible implementation manner, the acquiring module is further configured to acquire the first concentration of the internal standard in the standard sample and the second concentration of the marker, and acquire the first of the internal standard in the standard sample A peak area value and a second peak area value of the marker, the internal standard is an isotope of the marker; the determination module is further configured to be based on the difference between the first peak area value and the second peak area value The first ratio, and the second ratio between the first concentration and the second concentration, establish the standard curve.

In a possible implementation manner, the acquisition module is further configured to determine a third peak area value of the internal standard in the test sample and a third internal standard in the test sample based on at least the first information Concentration, and the fourth peak area value of the marker to be tested in the sample to be tested; the determination module is further configured to be based on the ratio between the third peak area value and the fourth peak area value, the third concentration, and the The standard curve determines the concentration of the test marker in each test sample.

In a possible implementation manner, the device further includes: a storage module for storing the concentration of the test marker in the test sample corresponding to each user identification; and a display module for displaying each user Identify the concentration of the test marker in the corresponding test sample.

In a possible implementation manner, the acquiring module is further configured to acquire the chromatographic data of the marker to be measured in the sample data based on the first information; the determining module is further configured to determine the pending data based on the chromatographic data Measure the peak area value of the marker.

According to a third aspect of the embodiments of the present disclosure, there is provided a targeted metabolomics quantitative automatic analysis device, including: a processor; a memory for storing processor executable instructions; wherein, the processor is configured to execute The method of the first aspect described above.

According to a fourth aspect of the embodiments of the present disclosure, there is provided a non-volatile computer-readable storage medium on which computer program instructions are stored, wherein the computer program instructions implement the above-mentioned first aspect when executed by a processor method.

According to an embodiment of the present disclosure, it may acquire sample data of at least one sample to be tested, and acquire a standard curve corresponding to a marker to be tested in the sample to be tested; wherein the sample data includes Corresponding user identifier, marker identifier to be tested and first information, the first information is used to determine the peak area value of the marker to be tested, the standard curve includes the marker concentration and the marker in the sample to be tested Correspondence of the area values of the substance peaks; based on the standard curve and the first information, determine the concentration of the marker to be tested in each of the samples to be tested. Through the above configuration, the embodiments of the present disclosure can simultaneously obtain sample data of multiple samples to be tested, and can automatically perform quantitative analysis on the obtained sample data using a standard curve obtained in advance, so as to obtain corresponding markers in the sample to be tested The concentration has the characteristics of convenient operation and high automation, and the embodiment of the present disclosure can quickly batch process the sample data of multiple users, and has a better experience effect.

Other features and aspects of the present disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings.

BRIEF DESCRIPTION

The drawings included in the specification and forming a part of the specification together with the specification show exemplary embodiments, features, and aspects of the present disclosure, and are used to explain the principles of the present disclosure.

FIG. 1 shows a flowchart of a quantitative automatic analysis method for targeted metabolomics according to an embodiment of the present disclosure;

2 shows a schematic waveform diagram of peak areas of different components in a sample;

FIG. 3 shows a flowchart of acquiring a standard curve corresponding to a test marker in a test sample according to an embodiment of the present disclosure;

4 shows a schematic graph of a standard curve according to an embodiment of the present disclosure;

FIG. 5 shows a flowchart of acquiring a standard curve corresponding to a test marker in a test sample according to an embodiment of the present disclosure;

6 shows a flowchart of a method for quantitative automatic analysis of targeted metabolomics according to an embodiment of the present disclosure;

7 shows a structural diagram of a quantitative automatic analysis device for targeted metabolomics according to an embodiment of the present disclosure;

8 is a structural diagram of a quantitative automatic analysis device for targeted metabolomics according to an embodiment of the present disclosure;

9 shows a block diagram of an electronic device according to an embodiment of the present disclosure;

10 shows a block diagram of an electronic device according to an embodiment of the present disclosure;

detailed description

Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the drawings. The same reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, unless specifically noted, the drawings are not necessarily drawn to scale.

The specific word "exemplary" here means "used as an example, embodiment, or illustrative". Any embodiments described herein as "exemplary" need not be interpreted as superior or better than other embodiments.

In addition, in order to better illustrate the present disclosure, numerous specific details are given in the specific implementations below. Those skilled in the art should understand that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the gist of the present disclosure.

FIG. 1 shows a flowchart of a quantitative analysis method of targeted metabolomics according to an embodiment of the present disclosure. This method can be used to analyze and compare the markers in various samples of metabolomics to obtain data about each component in the sample. The above samples can include the obtained metabolites of the user, and the markers can be any of the samples. Elemental substance. The embodiment of the present disclosure can perform simultaneous analysis of multiple users and multiple samples, and has the characteristics of more convenient and high analysis accuracy. In addition, the method of the embodiment of the present disclosure may be applied to a terminal or a server, where the terminal may be any handheld terminal or portable terminal, such as a computer, mobile phone, or tablet computer. The server may include a local server or a cloud server. This is not limited, as long as the device capable of executing the method of the embodiment of the present disclosure can be used as the above terminal or server.

The embodiments of the present disclosure will be described in detail below. As shown in FIG. 1, the quantitative analysis method of targeted metabolomics in this embodiment may include:

Step S11: Obtain sample data of at least one sample to be tested, where the sample data includes a user identifier corresponding to the sample to be tested, a marker identifier to be tested, and first information, where the first information is used to determine the marker to be tested The peak area value of the substance;

Step S12: Obtain a standard curve corresponding to the marker to be tested in the sample to be tested, the standard curve including the correspondence between the concentration of the marker in the sample to be tested and the peak area value of the marker;

Step S13: Based on the standard curve and the first information, determine the concentration of the test marker in each test sample.

In the embodiment of the present disclosure, the test sample may be a biological sample collected for the user, such as blood, urine, sweat, etc. The sample data may be data information about the test sample, which may include the user corresponding to the test sample Information, component information in the sample to be tested, information on the marker to be detected in the sample to be tested, etc., through the above sample data, you can easily identify the source of the sample to be tested and the marker to be analyzed, and the relevant information of each component of the sample . For example, the sample data in the embodiment of the present disclosure may include a user identifier corresponding to the sample to be tested, the identifier of the marker to be tested, and first information, where the first information may be used to determine the peak area value of the marker to be tested. The user ID may be used to uniquely determine the user corresponding to the sample to be tested, for example, the user ID may be information such as the user name or user number. The marker to be tested may be a substance or element to be detected and analyzed in the sample to be tested, wherein the identifier of the marker to be tested may be used to uniquely determine the substance or element, for example, the marker to be tested may be the name of the marker or the chemical structural formula.

In the embodiment of the present disclosure, before performing quantitative analysis, it is necessary to obtain sample data of a sample to be tested that needs to be analyzed. Among them, the embodiments of the present disclosure can implement analysis of sample data of multiple users, and thus can obtain sample data of at least one sample to be tested at the same time. The at least one sample to be tested may be different samples of one user at the same time, or samples corresponding to different users, that is, the present disclosure may realize automatic analysis of different samples of the same user, or automatic analysis of samples of different users.

In addition, the method for obtaining the sample data of the sample to be tested in the embodiment of the present disclosure may include: selecting the sample data of the sample to be tested from the stored data, and / or receiving the transmitted sample data of the sample to be tested. In the embodiment of the present disclosure, the sample data of the samples of each user to be tested can be stored in the memory, and when performing the corresponding quantitative analysis, the corresponding sample data can be selected based on the selection information, which can include the user identification, sample number, etc. Information in order to uniquely correspond to the sample data. In addition, the transmitted sample data can also be obtained by connecting with other devices or servers, and the corresponding sample data can also be obtained according to the information including the user identification or the sample number.

In addition, the sample data may also include first information, which may be used to determine the peak area value of the marker to be tested in the sample to be tested. Wherein, the first information includes the peak area value of the above-mentioned marker to be measured, and may also include a parameter related to the peak area value, and the peak area value is determined using the parameter, for example, the first information may include the chromatographic value of the marker to be measured , The peak area value of the marker to be measured can be obtained according to the chromatographic value.

In order to be able to automatically obtain the peak area of the above-mentioned marker to be tested, it is first necessary to be able to automatically read the liquid quality signal corresponding to the ion-pair data of the marker to be tested from the original file through the ion-pair data of the marker to be tested Data, the liquid quality signal data indicates the chromatographic value of the marker to be measured. The ion pair data of the above-mentioned marker to be tested is stored in the computer device using the table structure of Table 1:

Table 1

In Table 1 above, the column ID is the database number of the marker to be tested, that is, the number that uniquely marks the marker object to be tested. Column name is the English common name of the marker to be tested, and the column of precursor and product constitute the ion pair data of the marker to be tested. The ion pair data of each marker is composed of a coordinate, for example (precursor, product), each The ion pair coordinates of each marker correspond to a liquid quality signal data in the original file, that is, chromatographic quality. The ion pair data is used to obtain the chromatographic value of the corresponding marker from the liquid quality detection data of the original file.

Fig. 2 shows a schematic waveform diagram of peak areas of different components in a sample. As shown in Fig. 2, a sample to be tested may contain multiple components to be tested, such as component A and group in Fig. 2 Divided into B, etc., in which the chromatographic detection analysis can be performed by using a mass spectrometry multiple reaction monitoring (MRM) analysis method for the sample to be tested. The peak area of the markers to be tested (such as component A and component B) is the total area of the closed graphic part surrounded by the peak curve and the X coordinate axis in the chromatogram used in the chromatographic quantitative analysis. The peak area of the substance indicates the content of the marker to be tested (such as component A and component B), wherein the larger the peak area, the higher the content of the marker to be tested (such as component A and component B).

After acquiring the sample data, a standard curve corresponding to the marker to be tested of the sample data can be obtained, that is, step S12 is executed. The standard curve may include a standard correspondence between the concentration of the marker in the sample to be measured and the peak area value of the marker. The method for obtaining the standard curve may include: directly querying the standard curve corresponding to the known test marker according to the test marker identifier of the test sample in the database, or may be found in the database according to the test marker identifier The peak area value and concentration value of the marker of the standard sample of the marker to be tested, and linear regression calculation is performed according to the corresponding relationship between the peak area value of the marker and each concentration value to establish a standard curve.

After acquiring the standard curve, the concentration of the marker to be tested corresponding to the peak area value of the marker to be determined determined by the first information may be obtained according to the standard curve. Since the embodiments of the present disclosure can acquire the standard curves of the markers in different samples, the quantitative analysis of different samples to be tested can be performed simultaneously.

Based on the above configuration, the embodiments of the present disclosure can achieve simultaneous acquisition of sample data of multiple samples to be tested, and can automatically perform quantitative analysis on these acquired sample data using a pre-obtained standard curve, so as to obtain corresponding marks in the samples to be tested The concentration of the substance has the characteristics of convenient operation and high automation, and the embodiments of the present disclosure can quickly batch process sample data of multiple users, and have a better experience effect.

In the embodiment of the present disclosure, the standard curve may represent the linear relationship between the peak area value and the concentration of the marker to be measured, and may be represented by the function Y = F (X). According to the standard curve, the The peak area value of the test marker is brought into the functional relationship of the standard curve to determine the concentration of the test marker in each of the test samples.

FIG. 3 shows a flowchart of acquiring a standard curve corresponding to a marker to be tested in a sample to be tested according to an embodiment of the present disclosure. As shown in FIG. 3, acquiring the standard curve corresponding to the test marker in the test sample (step S12) may include:

Step S121: Obtain the concentration of the marker in the standard sample;

Step S122: Obtain the peak area value of the marker in the standard sample;

Step S123: Establish a standard curve of the marker in the standard sample based on the concentration and the peak area value.

In the embodiment of the present disclosure, the marker in the standard sample may be a marker sample with a certain purity, which may be the same substance as the marker to be tested, and different concentrations of markers with a certain purity may be configured in a dilution ratio For the series of standard sample solutions, the chromatographic analysis of the standard sample solutions of different concentrations was performed to obtain the peak area values of the markers in the standard sample solutions of different concentration series. The peak area values obtained by chromatographic analysis were linearly regressed to establish a standard curve.

In order to be able to automatically establish a standard curve in the detection device, the necessary information required to construct the standard curve is stored in the analysis device using the table structure shown in Table 2:

Table 2

HMDBHMDB	L1L1	L2L2	L3L3	L4L4	L5L5	L6L6	L7L7	ISIS	FactorFactor
HMDB0000043HMDB0000043	400400	200200	100100	5050	2020	1010	55	HMDB0000043_IS HMDB0000043_IS	11
HMDB0000097 HMDB0000097	4040	2020	1010	55	2.52.5	11	0.50.5	HMDB0000097_IS HMDB0000097_IS	11
HMDB0000925HMDB0000925	2020	1010	55	1.51.5	11	0.50.5	0.250.25	HMDB0000925_IS HMDB0000925_IS	11
HMDB0000562HMDB0000562	200200	100100	5050	2020	1010	55	2.52.5	HMDB0000562_IS HMDB0000562_IS	11
HMDB0000062HMDB0000062	100100	5050	2020	1010	55	2.52.5	11	HMDB0000062_IS HMDB0000062_IS	11
HMDB0000742 HMDB0000742	4040	2020	1010	55	2.52.5	11	0.50.5	HMDB0000883_IS HMDB0000883_IS	11
HMDB0000172HMDB0000172	200200	100100	5050	2525	1010	55	2.52.5	HMDB0000883_ISHMDB0000883_IS	0.330.33
HMDB0000883HMDB0000883	500500	250250	125125	100100	5050	2525	1010	HMDB0000883_IS HMDB0000883_IS	11
HMDB0000687HMDB0000687	400400	200200	100100	5050	2020	1010	55	HMDB0000883_ISHMDB0000883_IS	0.670.67
HMDB0000159HMDB0000159	200200	100100	5050	2525	1010	55	2.52.5	HMDB0000159_IS HMDB0000159_IS	11
HMDB0000158HMDB0000158	200200	100100	5050	2525	1010	55	22	HMDB0000159_IS HMDB0000159_IS	11
HMDB0000929HMDB0000929	200200	100100	5050	2525	1010	55	2.52.5	HMDB0000159_IS HMDB0000159_IS	11

The HMDB column in Table 2 above belongs to the identifier of the marker to be tested and is the unique number in the database. L1, L2 ... L7 are the concentration values of the markers in the standard sample obtained in advance according to the gradient dilution, IS ( The column “internal” is the database number of the internal standard substance for which the corresponding marker is used for correction (will be mentioned in the embodiment below).

The embodiment of the present disclosure may use the function Y = F (X) to represent the standard curve. FIG. 4 shows a schematic graph of the standard curve, where the X coordinate axis is in the standard sample solution corresponding to the marker to be tested. For the peak area data of the marker, the Y coordinate axis is the corresponding concentration data. Marker samples of a certain purity can be diluted into several standard sample solutions of different concentration values according to the dilution ratio, for example, 7 standard sample solutions of different concentration series values L1, L2 ... L7, for several different concentrations Chromatographic analysis of the standard sample solution with different values to obtain the peak area values of the markers in the standard samples at different concentration values, such as the peak area series values At1, At2 ... At7 corresponding to 7 different concentration series, for X in the figure The seven different concentration series values At1, At2 ... At7 in the coordinate axis and the seven different concentration peak area series values L1, L2 ... L7 in the Y coordinate axis are modeled by linear regression operation to establish the X coordinate axis and the Y coordinate The functional relationship of each series of shafts is Y = F (X), and the functional relationship Y = F (X) can be a standard curve.

Based on the standard curve of the linear relationship, the peak area value of the marker to be measured determined according to the first information can be taken as an independent variable input value X and brought into the function Y = F (X) to obtain the corresponding marker to be measured The concentration value Y has a simple and convenient effect.

In addition, in other embodiments of the present disclosure, the standard curve may represent the linear relationship between the concentration ratio of the marker and the internal standard in the standard sample and the ratio of the peak area value of the marker and the internal standard in the standard sample. Because the markers in the standard sample need to measure the series values of the peak areas of the markers in the standard samples at different concentrations when establishing the standard curve and the peak area values of the markers to be measured during the quantitative analysis calculation process, The more measurement times you experience, each measurement will have different degrees of error due to the instrument. During the experiment, you need to use the corresponding internal standard to correct the error. FIG. 5 shows another flow chart of the establishment of a standard curve in a quantitative automatic analysis method for targeted metabolomics according to an embodiment of the present disclosure. As shown in FIG. 5, acquiring the standard curve corresponding to the test marker in the test sample (step S12) may include:

Step S121 ', obtaining the first concentration of the internal standard and the second concentration of the marker in the standard sample, wherein the internal standard is the isotope of the marker;

Step S122 ', acquiring the first peak area value of the internal standard and the second peak area value of the marker in the standard sample;

Step S123 ', based on the first ratio between the first peak area value and the second peak area value, and the second ratio between the first concentration and the second concentration, establish the standard curve.

In the embodiment of the present disclosure, the marker in the standard sample may be a marker sample with a certain purity, which is the same substance as the marker to be tested, and the internal standard and the marker to be tested may be isotopes of the same substance, internal standard The substance is mixed with the standard sample as a marker in the standard sample, and it should be separated from the chromatographic peak of the marker in the standard sample during chromatographic analysis without interference from other components in the sample.

The first concentration may be the concentration value of the internal standard in the standard sample in the mixed standard sample solution, for example, cIS, and the second concentration may be the concentration value of the marker in the standard sample in the mixed standard sample solution. For example, L, the first peak area value can be the peak area value of the internal standard in the measured standard sample after chromatographic analysis of the standard sample solution, such as AIS, and the second peak area value can be After chromatographic analysis in the sample solution, the measured peak area value of the marker in the standard sample, for example, At.

In the embodiment of the present disclosure, the X-axis data and the Y-axis data of the standard curve in FIG. 4 can be reasonably modified to improve the standard curve, and the function Y = F (X) is still used to represent the standard curve , Where the X coordinate axis is the first ratio between the first peak area value and the second peak area value, and the Y coordinate axis is the second ratio between the first concentration and the second concentration. According to the dilution ratio, each of them is diluted into several series of sample marker solutions with different concentration values, and then the internal standard substances of the same quality are added to the sample marker solutions, because the internal standard substances in the standard sample solutions with different series of concentration values The quality is constant, so the concentration of the internal standard in the standard sample solution is also constant. For example, the standard sample solution can be configured into 7 different concentration series, and the concentration of the marker in the standard sample solution corresponding to each series There are 7 L1, L2 ... L7 respectively. The concentration of the internal standard in the standard sample solution corresponding to each series is cLS. Carry out chromatographic analysis to obtain the peak area values of the markers in the standard samples of different concentration series, such as 7 standard series At1, At2 ... At7 corresponding to the peak areas of different concentrations, and the internal standard of the different concentration series. Peak area values AIS1, AIS2 ... AIS7, respectively calculate the ratio of peak area values AIS1, AIS2 ... AIS7 of the internal standard corresponding to different concentration series to the peak area values At1, At2 ... At7 of the standard sample marker, and obtain the peak Area ratio (AIS1 / At1, AIS2 / At2, AIS3 / At3, AIS4 / At4, AIS5 / At5, AIS6 / At6, AIS7 / At7), respectively calculate the concentration value cLS of the internal standard corresponding to different concentration series and the standard sample mark The concentration of the substance L1, L2 ... L7 is compared to obtain the concentration ratio (cIS / L1, cIS / L2, cIS / L3, cIS / L4, cIS / L5, cIS / L6, cIS / L7). 7 different concentration series peak area ratios in the coordinate axis (AIS1 / At1, AIS2 / At2, AIS3 / At3, AIS4 / At4, AIS5 / At5, AIS6 / At6, AIS7 / At7) and 7 different concentrations in the Y coordinate axis A series of concentration ratios (cIS / L1, cIS / L2, cIS / L3, cIS / L4, cIS / L5, cIS / L6, cIS / L7) are modeled by linear regression operations, and each system of X coordinate axis and Y coordinate axis is established The functional relationship of the column Y = F (X), the functional relationship Y = F (X) is the standard curve.

The standard curve can directly query the standard curve corresponding to the known marker to be tested according to the identifier of the marker to be tested in the database, or a linear regression operation can be performed to establish the standard curve according to the method provided in this embodiment.

After obtaining the standard curve, the sample to be tested can be further quantitatively analyzed. FIG. 6 shows a flowchart of determining the concentration of the marker to be tested in each sample to be tested according to an embodiment of the present disclosure. The method is to establish FIG. 5 A quantitative automatic analysis method for targeted metabolomics is performed on the basis of the shown standard curve. As shown in FIG. 6, based on the standard curve and the first information, the concentration of the test marker in each test sample is determined ( Step S13) may also include:

Step S131: Determine the third peak area value of the internal standard in the sample to be tested, the third concentration of the internal standard in the sample to be tested, and the third of the marker to be tested in the sample to be tested based on at least the first information Four peak area values;

Step S132: Based on the ratio of the third peak area value and the fourth peak area value, the third concentration, and the standard curve, determine the concentration of the marker to be tested in each of the samples to be tested.

The sample to be tested may include the marker to be tested and the internal standard. The quality of the internal standard shall be the same as the quality of the internal standard used in the process of establishing the standard curve shown in FIG. 5, as in the first information above. Including the peak area value of the test marker in the test sample (the fourth peak area value) or the chromatogram value of the test marker (used to determine the fourth peak area value). In addition, the sample data of the test sample Or the first information may also include the peak area value of the internal standard (third peak area value) or the ratio of the third peak area value to the fourth peak area value, or may also include the internal standard and the marker to be tested. The chromatographic value or the ratio of the chromatographic values of the two to determine the ratio of the peak area value of the internal standard and the peak area value of the marker to be tested may also include the concentration (third concentration) of the internal standard in the sample to be tested. Therefore, the concentration of the marker in the test sample can be further determined based on the above information and the standard curve.

In the case where the standard curve is known, the ratio of the third peak area value and the fourth peak area value is taken into the independent variable X of the standard curve, and the calculated Y value is multiplied by the The third concentration value of the internal standard is the concentration of the test marker in the test sample. In this way, the interference of other components in the sample can be effectively eliminated, and the analysis accuracy can be improved.

Through the above configuration, the concentration of the test marker in the test sample can be determined. After the concentration is obtained, the concentration of the test marker in the test sample corresponding to each user ID can be stored and displayed to facilitate recording of each The relevant data of the user's sample to be tested also facilitates subsequent data reading and analysis.

In summary, the embodiments of the present disclosure can simultaneously obtain sample data of multiple samples to be tested, and can automatically perform quantitative analysis on the obtained sample data using a pre-obtained standard curve, so as to obtain corresponding marks in the samples to be tested The concentration of the substance has the characteristics of convenient operation and high automation, and the embodiments of the present disclosure can quickly batch process sample data of multiple users, and have a better experience effect.

It can be understood that, the above method embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment without violating the principle logic, which is limited to space and will not be repeated in this disclosure.

In addition, the present disclosure also provides a targeted metabolomics quantitative automatic analysis device, electronic equipment, computer-readable storage medium, and programs, all of which can be used to implement any of the targeted metabolomics quantitative automatic analysis methods provided by the present disclosure, The corresponding technical solutions and descriptions and the corresponding records in the method section are not repeated here.

7 shows a block diagram of a quantitative automatic analysis device for targeted metabolomics according to an embodiment of the present disclosure. The device can be applied to a terminal, for example, a computer, mobile phone, or tablet computer. As shown in FIG. 7, the device 40 may include:

An obtaining module 41, configured to obtain sample data of at least one sample to be tested, and obtain a standard curve corresponding to the marker to be tested in the sample to be tested; wherein the sample data includes the corresponding to each sample to be tested User identification, marker identification to be tested and first information, the first information is used to determine the peak area value of the marker to be tested, the standard curve includes the marker concentration and the marker corresponding to the sample to be tested Correspondence between peak area values;

The determination module 42 is configured to determine the concentration of the marker to be tested in each of the samples to be tested based on the standard curve and the first information.

In some possible implementation manners, the obtaining module 41 is further configured to obtain the concentration of the marker in the standard sample, and obtain the peak area value of the marker in the standard sample,

The determination module 42 is further configured to establish a standard curve of the marker in the standard sample based on the concentration and peak area value.

In some possible implementation manners, the obtaining module 41 is further configured to obtain the first concentration of the internal standard in the standard sample and the second concentration of the marker, and obtain the first peak area of the internal standard in the standard sample Value and the second peak area value of the marker, the internal standard is an isotope of the marker;

The determination module 42 is further configured to establish the standard curve based on the first ratio between the first peak area value and the second peak area value, and the second ratio between the first concentration and the second concentration.

In some possible implementation manners, the acquisition module 41 is further configured to determine the third peak area value of the internal standard in the sample to be tested and the third concentration of the internal standard in the sample to be tested based at least on the first information, And the fourth peak area value of the marker to be tested in the sample to be tested;

The determination module 42 is further configured to determine the concentration of the marker to be tested in each of the samples to be tested based on the ratio between the third peak area value and the fourth peak area value, the third concentration, and the standard curve.

In some possible implementation manners, the obtaining module 41 is further configured to obtain the chromatographic data of the marker to be measured in the sample data based on the first information;

The determination module 42 is further configured to determine the peak area value of the marker to be measured based on the chromatographic data.

8 shows a block diagram of a quantitative automatic analysis device for targeted metabolomics according to an embodiment of the present disclosure. As shown in FIG. 8, in a possible implementation manner, the device 40 further includes:

A storage module 43, which is used to store the concentration of the test marker in the test specimen corresponding to each user identification;

The display module 44 is used to display the concentration of the test marker in the test specimen corresponding to each user identification.

9 shows a block diagram of an electronic device according to an embodiment of the present disclosure. The electronic device may be provided as a terminal, server, or other form of device. The electronic device may include a quantitative automatic analysis device 800 for targeted metabolomics. For example, the device 800 may be a mobile phone, a computer, a digital broadcasting terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, and other terminals.

9, the device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814,与通信组 816.

The processing component 802 generally controls the overall operations of the device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps in the above method. In addition, the processing component 802 may include one or more modules to facilitate interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

The memory 804 is configured to store various types of data to support operation at the device 800. Examples of these data include instructions for any application or method operating on the device 800, contact data, phone book data, messages, pictures, videos, and so on. The memory 804 may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read only memory (EEPROM), erasable and removable Programmable read only memory (EPROM), programmable read only memory (PROM), read only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

The power supply component 806 provides power to various components of the device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 800.

The multimedia component 808 includes a screen that provides an output interface between the device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, swipe, and gestures on the touch panel. The touch sensor may not only sense the boundary of the touch or sliding action, but also detect the duration and pressure related to the touch or sliding operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.

The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC). When the device 800 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal may be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 further includes a speaker for outputting audio signals.

The I / O interface 812 provides an interface between the processing component 802 and a peripheral interface module. The peripheral interface module may be a keyboard, a click wheel, or a button. These buttons may include, but are not limited to: home button, volume button, start button, and lock button.

The sensor component 814 includes one or more sensors for providing the device 800 with status assessment in various aspects. For example, the sensor component 814 can detect the on / off state of the device 800, and the relative positioning of the components, for example, the component is the display and keypad of the device 800, and the sensor component 814 can also detect the position change of the device 800 or a component of the device 800 The presence or absence of user contact with the device 800, the orientation or acceleration / deceleration of the device 800, and the temperature change of the device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor component 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor component 814 may further include an acceleration sensor, a gyro sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 816 is configured to facilitate wired or wireless communication between the device 800 and other devices. The device 800 can access a wireless network based on a communication standard, such as WiFi, 2G, or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the apparatus 800 may be one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable A gate array (FPGA), controller, microcontroller, microprocessor or other electronic components are implemented to perform the above method.

In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, on which computer program instructions are stored, and when the computer program instructions are executed by the processor, the targeted metabolism described in the above embodiments is achieved An omics quantitative automatic analysis method, for example, a memory 804 including computer program instructions, which can be executed by the processor 820 of the device 800 to complete the above method.

FIG. 10 shows a block diagram of an electronic device according to an embodiment of the present disclosure. For example, the electronic device 1900 may be provided as a server. Referring to FIG. 10, the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by the memory 1932 for storing instructions executable by the processing component 1922, such as application programs. The application programs stored in the memory 1932 may include one or more modules each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

The electronic device 1900 may also include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to the network, and an input output (I / O) interface 1958 . The electronic device 1900 can operate an operating system based on the memory 1932, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or the like.

In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, for example, a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to complete the above method.

The present disclosure may be a system, method, and / or computer program product. The computer program product may include a computer-readable storage medium loaded with computer-readable program instructions for causing the processor to implement various aspects of the present disclosure.

The computer-readable storage medium may be a tangible device that can hold and store instructions used by the instruction execution device. The computer-readable storage medium may be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (non-exhaustive list) include: portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), and erasable programmable read only memory (EPROM (Or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical coding device, such as a computer on which instructions are stored The convex structure in the hole card or the groove, and any suitable combination of the above. The computer-readable storage medium used herein is not to be interpreted as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (for example, optical pulses through fiber optic cables), or through wires The transmitted electrical signal.

The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing / processing devices, or to an external computer or external storage device through a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device .

Computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or in one or more programming languages Source code or object code written in any combination. The programming languages include object-oriented programming languages such as Smalltalk, C ++, etc., and conventional procedural programming languages such as "C" language or similar programming languages. Computer readable program instructions can be executed entirely on the user's computer, partly on the user's computer, as an independent software package, partly on the user's computer and partly on a remote computer, or completely on the remote computer or server carried out. In situations involving remote computers, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (eg, using an Internet service provider to pass the Internet connection). In some embodiments, electronic circuits, such as programmable logic circuits, field programmable gate arrays (FPGAs) or programmable logic arrays (PLA), can be personalized by utilizing the status information of computer-readable program instructions, which can be Computer-readable program instructions are executed to implement various aspects of the present disclosure.

Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowchart and / or block diagram and a combination of blocks in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

These computer-readable program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, or other programmable data processing device, thereby producing a machine that causes these instructions to be executed by the processor of a computer or other programmable data processing device A device that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram is generated. The computer-readable program instructions may also be stored in a computer-readable storage medium. These instructions enable the computer, programmable data processing apparatus, and / or other devices to work in a specific manner. Therefore, the computer-readable medium storing the instructions includes An article of manufacture that includes instructions to implement various aspects of the functions / acts specified in one or more blocks in the flowchart and / or block diagram.

The computer-readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other equipment, so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other equipment to produce a computer-implemented process , So that the instructions executed on the computer, other programmable data processing device, or other equipment implement the functions / acts specified in one or more blocks in the flowchart and / or block diagram.

The flowchart and block diagrams in the drawings show the possible implementation architecture, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, program segment, or part of an instruction, and the module, program segment, or part of an instruction contains one or more Executable instructions. In some alternative implementations, the functions marked in the blocks may also occur in an order different from that marked in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can also be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented with dedicated hardware-based systems that perform specified functions or actions Or, it can be realized by a combination of dedicated hardware and computer instructions.

The embodiments of the present disclosure have been described above. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the illustrated embodiments. The selection of terms used herein is intended to best explain the principles, practical applications or technical improvements of the technologies in the embodiments, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

A quantitative automatic analysis method for targeted metabolomics, which is characterized by:

Acquiring sample data of at least one sample to be tested, the sample data including a user identifier corresponding to the sample to be tested, a marker identifier to be tested, and first information, the first information being used to determine a peak of the marker to be tested Area value

Acquiring a standard curve corresponding to the marker to be tested in the sample to be tested, the standard curve including the standard correspondence between the concentration of the marker in the sample to be tested and the peak area value of the marker;

Based on the standard curve and the first information, the concentration of the test marker in each test sample is determined.
The method according to claim 1, wherein the acquiring the standard curve corresponding to the test marker in the test sample comprises:

Obtain the concentration of the marker in the standard sample;

Obtain the peak area value of the marker in the standard sample;

A standard curve of markers in the standard sample is established based on the concentration and peak area values.
The method according to claim 1, wherein the acquiring the standard curve corresponding to the test marker in the test sample comprises:

Acquiring the first concentration of the internal standard and the second concentration of the marker in the standard sample, wherein the internal standard is an isotope of the marker;

Obtain the first peak area value of the internal standard and the second peak area value of the marker in the standard sample;

The standard curve is established based on the first ratio between the first peak area value and the second peak area value, and the second ratio between the first concentration and the second concentration.
The method according to claim 3, wherein the determining the concentration of the test marker in each test sample based on the standard curve and the first information comprises:

Determine the third peak area value of the internal standard in the test sample, the third concentration of the internal standard in the test sample, and the fourth peak area of the test marker in the test sample based on at least the first information value;

Based on the ratio of the third peak area value and the fourth peak area value, the third concentration, and the standard curve, the concentration of the marker to be tested in each of the samples to be tested is determined.
The method according to claim 1, wherein the method further comprises:

Store and display the concentration of the test marker in the test specimen corresponding to each user ID.
The method according to claim 1, wherein the method further comprises:

Acquiring chromatographic data of the marker to be measured in the sample data based on the first information;

The peak area value of the marker to be measured is determined based on the chromatographic data.
A quantitative automatic analysis device for targeted metabolomics is characterized by comprising:

An acquisition module configured to acquire sample data of at least one sample to be tested and acquire a standard curve corresponding to the marker to be tested in the sample to be tested; wherein the sample data includes the corresponding data of each sample to be tested User identification, marker identification to be tested and first information, the first information is used to determine the peak area value of the marker to be tested, the standard curve includes the marker concentration and the marker peak in the sample to be tested Correspondence of area values;

The determination module is configured to determine the concentration of the marker to be measured in each of the samples to be tested based on the standard curve and the first information.
The device according to claim 7, wherein the acquisition module is further configured to acquire the concentration of the marker in the standard sample and acquire the peak area value of the marker in the standard sample,

The determination module is further configured to establish a standard curve of the marker in the standard sample based on the concentration and peak area value.
The apparatus according to claim 7, wherein the acquisition module is further configured to acquire the first concentration of the internal standard in the standard sample and the second concentration of the marker, and acquire the internal standard in the standard sample A first peak area value of the marker and a second peak area value of the marker, the internal standard is an isotope of the marker;

The determination module is further configured to establish the standard curve based on the first ratio between the first peak area value and the second peak area value, and the second ratio between the first concentration and the second concentration .
The device according to claim 9, wherein the acquisition module is further configured to determine the third peak area value of the internal standard in the sample to be tested and the internal standard in the sample to be tested based on at least the first information The third concentration of the substance and the fourth peak area value of the marker in the sample to be tested;

The determination module is further configured to determine the concentration of the marker to be tested in each sample to be tested based on the ratio between the third peak area value and the fourth peak area value, the third concentration, and the standard curve.
The device according to claim 7, wherein the device further comprises:

A storage module, which is used to store the concentration of the test marker in the test specimen corresponding to each user identification;

The display module is used for displaying the concentration of the marker to be tested in the sample to be tested corresponding to each user identification.
The device according to claim 7, characterized in that

The acquiring module is further configured to acquire the chromatographic data of the marker to be measured in the sample data based on the first information;

The determination module is further configured to determine the peak area value of the marker to be measured based on the chromatographic data.
An electronic device, characterized in that it includes:

processor;

Memory for storing processor executable instructions;

Wherein, the processor is configured to execute the method according to any one of claims 1 to 6.
A non-volatile computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions are executed by a processor to implement the method of any one of claims 1 to 6.