A high-sensitivity non-targeted metabolomics analysis method, system, mass spectrometry database and storage medium based on 3-nitrophenylhydrazine derivatization

Abstract

The application discloses a high-sensitivity non-targeted metabolomics analysis method, system, mass spectrum database and storage medium based on 3-nitrophenylhydrazine derivatization, and belongs to the technical field of analytical chemistry and metabolomics. The method is characterized in that: first, 3-nitrophenylhydrazine is used for derivatization of a biological sample; then, all metabolites in an existing mass spectrum database are subjected to structure screening and theoretical derivatization calculation by a computer, the mass displacement of parent ions after reaction of the metabolites with 3-nitrophenylhydrazine is simulated, and a derivatization characteristic fragment ion [C6H5N2O2]- (m / z 137.035) is forcibly added, so as to construct a brand-new theoretical derivatization mass spectrum database which can be directly used for searching a library. The application solves the problem of “having a signal but not having an identity” of derivatized metabolites by constructing a special “control map”, realizes high-sensitivity and high-confidence identification of intestinal flora related metabolites, and provides a powerful analysis tool for microbiome and health research.

Description

Technical Field

[0001] This application relates to the fields of chemical analysis and metabolomics technology, and more specifically, it relates to a highly sensitive non-targeted metabolomics analysis method, system, mass spectrometry database, and storage medium based on 3-nitrophenylhydrazine derivatization. Background Technology

[0002] Untargeted metabolomics aims to detect as many metabolites as possible in biological samples without bias in order to reveal the dynamic changes in life activities. Liquid chromatography-mass spectrometry (LC-MS) is widely used in untargeted metabolomics research due to its advantages of high throughput and high resolution.

[0003] In biological samples, some metabolites that are highly polar, highly volatile, or have low response in electrospray ionization sources often suffer from insufficient sensitivity when directly detected by conventional liquid chromatography-mass spectrometry methods. Therefore, chemical derivatization is commonly used in related technologies to improve this sensitivity.

[0004] Chemical derivatization mainly involves attaching specific chemical groups to target metabolites, which can alter their chromatographic behavior and ionization efficiency. Among them, 3-nitrophenylhydrazine is a derivatization reagent that can react with functional groups such as carboxyl and carbonyl groups, and it has been used in the analysis of targeted metabolites.

[0005] However, in practice, when applying derivatization technology to non-targeted metabolomics analysis, the mass spectrometry information of the derivatized metabolites differs from existing standard mass spectrometry databases, making the identification of derivatized products difficult. Therefore, to solve this problem, a highly sensitive non-targeted metabolomics analysis method, system, mass spectrometry database, and storage medium based on 3-nitrophenylhydrazine derivatization are provided. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this application aims to provide a highly sensitive non-targeted metabolomics analysis method, system, mass spectrometry database, and storage medium based on 3-nitrophenylhydrazine derivatization. This method effectively solves the problem that derivatized metabolites cannot be identified by conventional databases by constructing a theoretical derivatization mass spectrometry database.

[0007] In a first aspect, this application provides a highly sensitive, non-targeted metabolomics analysis method based on 3-nitrophenylhydrazine derivatization, comprising the following steps:

[0008] A) Derivatization of metabolites in biological samples using 3-nitrophenylhydrazine;

[0009] B) Data acquisition of the derivatized samples was performed using a high-resolution chromatography-mass spectrometry system;

[0010] C) Construct a dedicated mass spectrometry database for identifying derivatized metabolites, including:

[0011] C1) Obtain structural information of metabolites from existing mass spectrometry databases;

[0012] C2) Based on the structural information, metabolites containing at least one functional group in their molecular structure that can react with 3-nitrophenylhydrazine are screened to form a candidate metabolite list;

[0013] C3) Based on the reaction mechanism of 3-nitrophenylhydrazine with the functional group, perform theoretical derivatization calculations on each metabolite in the candidate metabolite list obtained in step C2) to obtain the theoretical parent ion mass-to-charge ratio information after derivatization of each metabolite;

[0014] C4) Add characteristic fragment ion information specific to the 3-nitrophenylhydrazine derivatization group to the theoretical fragment ion information of the metabolite;

[0015] D) Using the dedicated mass spectrometry database constructed in step C), metabolites containing derivatizable functional groups are identified by searching and analyzing the non-targeted metabolomics data collected in step B).

[0016] Through the above technical solution, this method solves the problem of "having a signal but no identity" of metabolites after derivatization by building a bridge connecting "derivation experiment" and "database identification", and realizes high sensitivity and high confidence identification of metabolites containing derivatizable functional groups.

[0017] Preferably, in step C2), the functional group capable of reacting with 3-nitrophenylhydrazine is at least one of a carboxyl group, a carbonyl group, or a phosphate group.

[0018] Preferably, in step C2), the candidate metabolite list includes a single-derivative candidate metabolite list, a double-derivative candidate metabolite list, and a triple-derivative candidate metabolite list, which correspond to metabolites containing at least one, at least two, and at least three of the functional groups in their molecular structures, respectively.

[0019] Preferably, in step C3), the theoretical derivatization calculation includes calculating the theoretical increase in the mass-to-charge ratio of the parent ion ΔM after each metabolite reacts with n 3-nitrophenylhydrazine molecules, where n is a positive integer, and ΔM = n × (153.05383 - 18.01056) = n × 135.04327.

[0020] Preferably, n = 1, 2, 3.

[0021] Preferably, in step C4), the characteristic fragment ion is [C6H5N2O2]-, and its mass-to-charge ratio m / z is 137.035.

[0022] Preferably, in step A), the biological sample includes, but is not limited to, feces, intestinal contents, serum, urine, or cell extracts.

[0023] Secondly, this application provides a derivatized metabolite mass spectrometry database for non-targeted metabolomics analysis. The database is constructed via step C) of the above method and contains theoretical derivatized mass spectrometry data for multiple metabolite entries, each entry containing at least:

[0024] Information on the mass-to-charge ratio of the theoretical parent ion after derivatization;

[0025] The characteristic fragment ion information of the derivatized group, wherein the characteristic fragment ion is [C6H5N2O2]-, and its mass-to-charge ratio m / z is 137.035.

[0026] Thirdly, this application provides a metabolomics analysis system that includes the aforementioned derivatized metabolite mass spectrometry database, and further includes:

[0027] The data acquisition module is configured to control the operation of the high-resolution chromatography-mass spectrometry instrument;

[0028] The data analysis module is configured to call the database to retrieve and identify the collected mass spectrometry data.

[0029] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method steps.

[0030] In summary, this application has the following beneficial effects:

[0031] 1. Solved the core problem of "difficulty in identification": This application creates a unique "identity ID" for each metabolite that may be labeled by 3-nitrophenylhydrazine by constructing a theoretical derivatization database, so that derivatization signals that could not be identified by any existing database can be accurately identified, directly solving the core pain point proposed in the prior art;

[0032] 2. Achieved "high confidence" identification: This application pre-sets the derivatization characteristic fragment ion (m / z 137.035) as a mandatory identification label in the database. This dual locking mechanism of "theoretical parent ion + characteristic fragment" is not available in any existing technology or simple derivatization method, which greatly reduces the false positive rate and brings about a "qualitative leap" in identification accuracy.

[0033] 3. Achieved the dual goals of "high sensitivity + wide coverage": While solving the identification problem, this application fully retains the sensitivity improvement advantage brought by 3-nitrophenylhydrazine derivatization. At the same time, the mass-to-charge ratio of the derivatized short-chain fatty acids increased by 135.04327, which moved them away from the background noise range of the mobile phase with low mass-to-charge ratio. This enabled the high-sensitivity detection and accurate identification of metabolites such as short-chain fatty acids that could not be detected by conventional non-targeted metabolomics methods, thus achieving a balance between sensitivity and coverage. Attached Figure Description

[0034] Figure 1 This is a flowchart of the high-sensitivity non-targeted metabolomics method based on 3-NPH derivatization described in this application;

[0035] Figure 2 The results show the detection of short-chain fatty acids in non-derivative (a) and derivatized (b) samples and the comparison of their selected ion extraction (XIC) chromatograms after analysis of actual mouse fecal samples.

[0036] Figure 3 This is an example of using mesobiose as a metabolite, along with schematic diagrams of the structural and theoretical mass spectrometry changes before and after 3-NPH derivatization, and identification results.

[0037] Where a is a schematic diagram of the 3-NPH derivatization reaction of menobiose;

[0038] b is the mass spectrum of protopinobiose in the spectral library;

[0039] c is the mass spectrum of the reconstructed menobiose derivative after virtual derivatization;

[0040] d shows the matching between the measured mass spectrum of the menobiose derivative (top) and the reference mass spectrum of the reconstructed menobiose derivative after virtual derivatization (bottom). Detailed Implementation

[0041] The following is in conjunction with the appendix Figure 1-3 The present application will be further described in detail with preparation examples and embodiments, and the raw materials and / or equipment used in each embodiment and test of the present application are all commercially available.

[0042] Preparation Example 1

[0043] A dedicated mass spectrometry database for identifying derived metabolites is constructed through the following steps:

[0044] C1. Data source acquisition: Download negative ion mode mass spectrometry files from publicly available metabolomics mass spectrometry databases (such as MassBank database). The spectrometry files contain the SMILES structures and other structural information of all metabolites.

[0045] C2. Functional group screening: Using R programming and the rcdk toolkit, the SMILES structure of each metabolite is analyzed, and the number of carboxyl groups (-COOH), carbonyl groups (C=O), and phosphate groups (-OPO3H2) in its molecular structure is identified and calculated.

[0046] A basic candidate list is formed through screening, as follows:

[0047] Metabolites containing at least one of the above functional groups were screened to form a list of single-derivative candidate metabolites;

[0048] Metabolites containing at least two of the above functional groups were screened to form a list of dual-derivative candidate metabolites;

[0049] Metabolites containing at least three of the above functional groups were screened to form a list of candidate metabolites for tri-derivation.

[0050] C3. Theoretical Derivatization Calculation: For each metabolite in each candidate list, calculate the theoretical parent ion mass-to-charge ratio after its reaction with n (n=1, 2, 3) 3-nitrophenylhydrazine (molecular formula C6H7N3O2, exact molecular weight 153.05383) molecules;

[0051] Since the derivatization reaction involves the loss of n H₂O molecules (precise molecular weight 18.01056), the theoretical mass increase of the parent ion relative to the original metabolite [MH] ion after derivatization is:

[0052] ΔM = n × (153.05383 - 18.01056) = n × 135.04327;

[0053] C4) Constructing Derivatized Mass Spectrometry Entries: Create corresponding theoretical derivatized mass spectrometry entries for each metabolite, with the following m / z values ​​for the parent ion after derivatization:

[0054] When n=1, its parent ion m / z is [M - H + 135.04327];

[0055] When n=2: its parent ion m / z is [M - H + 270.08654];

[0056] When n=3: its parent ion m / z is [M - H + 405.12981];

[0057] In the theoretical fragment ion list of each entry, a characteristic fragment ion (C6H5N2O2) with m / z of 137.035 is forcibly added and marked as a high-intensity characteristic diagnostic ion. All the derivatized mass spectrometry entries constructed are summarized to obtain the dedicated derivatized metabolite mass spectrometry database of this application.

[0058] Example 1

[0059] A non-targeted analysis method for gut microbiota metabolites in mouse fecal samples, referring to... Figure 1 The experimental materials and instruments used are as follows:

[0060] Biological samples: SPF-grade ICR mouse fecal samples, stored at -80℃ for later use.

[0061] Main derivatizing reagents: 3-nitrophenylhydrazine (3-NPH, purity ≥98%); methanol (HPLC grade); 1-ethyl-(3-dimethylaminopropyl)carbodiimide (EDC, purity ≥98%); pyridine (HPLC grade).

[0062] Main instruments: Thermo Fisher Q-Exactive series high-resolution liquid chromatography-mass spectrometry system, WatersAcquity UPLC HSS T3 column (2.1mm×100mm, 1.8μm).

[0063] The non-targeted metabolomics analysis method is as follows:

[0064] A) Derivatization and Control Sample Pretreatment

[0065] First, weigh 25 mg of mouse fecal sample into a homogenization tube, add 25 μL of ultrapure water, homogenize at -10℃ for 3 min, then add 120 μL of pre-cooled methanol, and homogenize again at -10℃ for 3 min;

[0066] After centrifuging at 18000g for 20 min at 4℃, transfer 30 μL of supernatant to a new centrifuge tube, add 20 μL of 3-NPH derivatization reagent solution (200 mM, dissolved in methanol aqueous solution) and 20 μL of EDC (120 mM, dissolved in methanol-pyridine solution), and react with shaking at 1200 rpm at 30℃ for 1 h.

[0067] Finally, add 110 μL of pre-cooled 50% methanol, centrifuge at 18000g for 20 min at 4℃, transfer the supernatant to a vial, and freeze at -80℃ for LC-MS analysis.

[0068] Control sample processing method:

[0069] First, weigh 25 mg of mouse fecal sample into a homogenization tube, add 25 μL of ultrapure water, homogenize at -10℃ for 3 min, then add 120 μL of pre-cooled methanol, and homogenize again at -10℃ for 3 min.

[0070] After centrifuging at 18000g for 20 min at 4℃, transfer 30 μL of supernatant to a new centrifuge tube, add 40 μL of 75% methanol solution, and shake at 1200 rpm at 30℃ for 1 h.

[0071] Finally, add 110 μL of pre-cooled 50% methanol, centrifuge at 18000g for 20 min at 4℃. Transfer the supernatant to a vial and store at -80℃ for LC-MS analysis.

[0072] B) Data collection for the derivatized and control samples obtained in A);

[0073] Chromatographic conditions: Mobile phase A was an aqueous solution containing 0.1% formic acid, and mobile phase B was an acetonitrile solution containing 0.1% formic acid; flow rate was 0.3 mL / min; column temperature was 40℃; injection volume was 5 μL.

[0074] The gradient elution procedure is as follows: From 0 to 1 minute, the volume percentage of mobile phase B is 1%; from 1 to 4 minutes, the volume percentage of mobile phase B increases linearly from 1% to 48%; from 4 to 10 minutes, the volume percentage of mobile phase B increases linearly from 48% to 100%; from 10 to 13.4 minutes, the volume percentage of mobile phase B remains at 100%; from 13.4 to 13.5 minutes, the volume percentage of mobile phase B decreases linearly from 100% to 1%; and from 13.5 to 15 minutes, the volume percentage of mobile phase B remains at 1%. All percentages of mobile phase B refer to volume percentage (v / v%). The time intervals are continuous, and the gradient changes are all linear.

[0075] Mass spectrometry conditions:

[0076] Quality scan range m / z 170-2500 (for derivatized samples);

[0077] Quality scan range m / z 75-1050 (control sample);

[0078] All of the following conditions must be met:

[0079] Electrospray ion source, negative ion mode;

[0080] Spray voltage 4kV;

[0081] Capillary temperature 320℃;

[0082] Sheath gas 40arb;

[0083] 15arb of auxiliary gas;

[0084] Resolution 70,000 (MS¹), 17,500 (MS²);

[0085] The data acquisition mode is Full MS / dd-MS².

[0086] D) Import the raw mass spectrometry data collected in B) into MSDIAL.v5.5.250404 software for processing, and then use the dedicated derivatized metabolite mass spectrometry database constructed in Preparation Example 1 for retrieval;

[0087] The dedicated derivatization metabolite mass spectrometry database includes:

[0088] Database A: Contains theoretical derivatization spectra of metabolites with a single derivatization candidate list (≥1 functional group);

[0089] Database B: Theoretical derivatization spectra of metabolites containing a dual-derivatization candidate list (≥2 functional groups);

[0090] Database C: Theoretical derivatization spectra of metabolites containing a list of candidates for tri-derivative (≥3 functional groups).

[0091] Database D: Any combination of databases A, B, and C.

[0092] The control group samples were retrieved from the original, unmodified MassBank database.

[0093] Search parameter settings: mother ion mass tolerance 0.01 Da, fragment ion tolerance 0.01 Da. For searches using the database constructed in Preparation Example 1, the matching MS² spectrum must contain a characteristic ion with m / z 137.035 ± 0.02.

[0094] Compared with direct analysis of the control group, the method of this application can successfully and sensitively identify a variety of short-chain fatty acids, as described above. Figure 2 As shown in Table 1, formic acid, acetic acid, and propionic acid (not detected without derivatization) showed improved mass spectrometry responses (peak heights) for butyric acid, valeric acid, and hexanoic acid compared to the underivatized samples. In addition, various intestinal flora compounds were also detected, as shown in Table 2.

[0095] Table 1: Comparison of the detection and mass spectrometry response of short-chain fatty acids in mouse feces using this method and conventional non-targeted metabolomics methods:

[0096]

[0097] Table 2: Comparison of the detection of various gut microbiota compounds in mouse fecal samples using this method and conventional non-targeted metabolomics methods (control method):

[0098]

[0099] In addition, through Figure 3It is also known that, taking menobiose as an example, the theoretical derivatization spectrum constructed using the method of this application is highly matched with the MS / MS spectrum of the actual menobiose derivative, and the measured spectrum contains characteristic fragment ions with m / z 137.035, which verifies the reliability of the identification results.

[0100] Therefore, it can be concluded that 3-NPH derivatization enhances the mass spectrometry signal response of gut microbiota compounds. At the same time, the increased mass-to-charge ratio of the derivatized compounds makes it possible to detect low molecular weight fatty acids that are subject to interference from mobile phase solvents and limitations in mass spectrometry scanning range in conventional non-target methods.

[0101] However, the mass spectra of the new compounds generated after derivatization cannot be identified by matching them with existing mass spectrometry databases based on compound standards. It is evident that "derivatization" alone cannot solve the problem of identifying derivatized products. The effectiveness of this application is based on the synergy between "derivatization" and "dedicated database".

[0102] Comparative Example 1

[0103] The non-targeted analysis method for gut microbiota metabolites in mouse fecal samples differs from that in Example 1 in that the 3-nitrophenylhydrazine derivatization used in sample pretreatment A) of Example 1 is replaced with O-BHA derivatization, while all other conditions remain the same.

[0104] Experiments have shown that while O-BHA (O-benzylhydroxylamine) can be used as a derivatizing reagent for aldehydes, ketones, and some carboxylic acids, it has significant limitations in mass spectrometry detection: it cannot generate a stable, characteristic fragment ion that is universally present in all derivatives. The types and abundance of fragment ions generated during fragmentation vary considerably among different compounds, lacking a unified mass spectrometry response identifier.

[0105] Therefore, when constructing a mass spectrometry database for the identification of non-targeted metabolites, the O-BHA derivatization method is difficult to establish a unified and high-confidence matching standard. If the database search is based on such unstable fragment information, false positive matches are very likely to occur, and it is impossible to effectively and accurately identify the structure of derivatized products in complex biological samples (such as gut microbiota metabolites).

[0106] Comparative Example 2

[0107] The non-targeted analysis method for gut microbiota metabolites in mouse fecal samples differs from Example 1 in that the 3-nitrophenylhydrazine derivatization used in sample pretreatment A) of Example 1 is replaced with 2-PA derivatization, while all other conditions remain the same.

[0108] Experiments showed that while 2-PA, as a derivatization reagent, can generate stable m / z 109 characteristic fragment ions, providing convenience for mass spectrometry detection, its reaction selectivity has significant limitations.

[0109] This means that the reagent can only react specifically with carboxyl functional groups, and the types of compounds that can be derived are limited to organic acids containing free carboxyl groups (such as short-chain fatty acids, medium- and long-chain fatty acids, etc.).

[0110] Therefore, it is evident that 2-PA is inadequate in comprehensively covering gut microbiota metabolites because it cannot effectively derive compounds without carboxyl groups, making it difficult to achieve simultaneous analysis and identification of multiple metabolites in complex samples.

[0111] Example 2

[0112] A non-targeted analysis method for gut microbiota metabolites in mouse serum was described, referring to... Figure 1 The experimental materials and instruments used are as follows:

[0113] Biological samples: SPF-grade ICR mouse serum samples, stored at -80℃ for later use.

[0114] Main derivatizing reagents: 3-nitrophenylhydrazine (3-NPH, purity ≥98%); methanol (HPLC grade); 1-ethyl-(3-dimethylaminopropyl)carbodiimide (EDC, purity ≥98%); pyridine (HPLC grade).

[0115] Main instruments: Thermo Fisher Q-Exactive series high-resolution liquid chromatography-mass spectrometry system, WatersAcquity UPLC HSS T3 column (2.1mm×100mm, 1.8μm).

[0116] The non-targeted metabolomics analysis method is as follows:

[0117] A) Derivatization and Pretreatment of Control Samples

[0118] First, weigh 25 mg of mouse serum sample into a homogenization tube, add 25 μL of ultrapure water, homogenize at -10℃ for 3 min, then add 120 μL of pre-cooled methanol, and homogenize again at -10℃ for 3 min;

[0119] After centrifuging at 18000g for 20 min at 4℃, transfer 30 μL of supernatant to a new centrifuge tube, add 20 μL of 3-NPH derivatization reagent solution (200 mM, dissolved in methanol aqueous solution) and 20 μL of EDC (120 mM, dissolved in methanol-pyridine solution), and react with shaking at 1200 rpm at 30℃ for 1 h.

[0120] Finally, add 110 μL of pre-cooled 50% methanol, centrifuge at 18000g for 20 min at 4℃, transfer the supernatant to a vial, and freeze at -80℃ for LC-MS analysis.

[0121] Control sample processing method:

[0122] First, weigh 25 mg of mouse serum sample into a homogenization tube, add 25 μL of ultrapure water, homogenize at -10℃ for 3 min, then add 120 μL of pre-cooled methanol, and homogenize again at -10℃ for 3 min.

[0123] After centrifuging at 18000g for 20 min at 4℃, transfer 30 μL of supernatant to a new centrifuge tube, add 40 μL of 75% methanol solution, and shake at 1200 rpm at 30℃ for 1 h.

[0124] Finally, add 110 μL of pre-chilled 50% methanol, centrifuge at 18000g for 20 min at 4℃. Transfer the supernatant to a vial and store at -80℃ for LC-MS analysis.

[0125] B) Data collection was performed on the derivatized and control samples obtained in A);

[0126] Chromatographic conditions: Mobile phase A was an aqueous solution containing 0.1% formic acid, and mobile phase B was an acetonitrile solution containing 0.1% formic acid; flow rate was 0.3 mL / min; column temperature was 40℃; injection volume was 5 μL.

[0127] The gradient elution procedure is as follows: From 0 to 1 minute, the volume percentage of mobile phase B is 1%; from 1 to 4 minutes, the volume percentage of mobile phase B increases linearly from 1% to 48%; from 4 to 10 minutes, the volume percentage of mobile phase B increases linearly from 48% to 100%; from 10 to 13.4 minutes, the volume percentage of mobile phase B remains at 100%; from 13.4 to 13.5 minutes, the volume percentage of mobile phase B decreases linearly from 100% to 1%; and from 13.5 to 15 minutes, the volume percentage of mobile phase B remains at 1%. All percentages of mobile phase B refer to volume percentage (v / v%). The time intervals are continuous, and the gradient changes are all linear.

[0128] Mass spectrometry conditions:

[0129] Quality scan range m / z 170-2500 (for derivatized samples);

[0130] Quality scan range m / z 75-1050 (control sample);

[0131] All of the following conditions must be met:

[0132] Electrospray ion source, negative ion mode;

[0133] Spray voltage 4kV;

[0134] Capillary temperature 320℃;

[0135] Sheath gas 40arb;

[0136] 15arb of auxiliary gas;

[0137] Resolution 70,000 (MS¹), 17,500 (MS²);

[0138] The data acquisition mode is Full MS / dd-MS².

[0139] D) Import the raw mass spectrometry data collected in B) into MSDIAL.v5.5.250404 software for processing, and then use the dedicated derivatized metabolite mass spectrometry database constructed in Preparation Example 1 for retrieval;

[0140] The dedicated derivatization metabolite mass spectrometry database includes:

[0141] Database A: Theoretical derivatization spectra of metabolites containing a list of candidate metabolites (≥1 functional group) that can be derivatized by a single method;

[0142] Database B: Theoretical derivatization spectra of metabolites containing a dual-derivatization candidate list (≥2 functional groups);

[0143] Database C: Theoretical derivatization spectra of metabolites containing a list of candidates for triderivatization (≥3 functional groups).

[0144] Database D: Any combination of databases A, B, and C.

[0145] The control group samples were retrieved from the original, unmodified MassBank database.

[0146] Search parameter settings: mother ion mass tolerance 0.01 Da, fragment ion tolerance 0.01 Da. For searches using the database constructed in Preparation Example 1, the matching MS² spectrum must contain a characteristic ion with m / z 137.035±0.02. The relevant test results are recorded in Table 3.

[0147] Example 3

[0148] Non-targeted analysis method for intestinal flora metabolites in normal human serum, referring to Figure 1 The experimental materials and instruments used are as follows:

[0149] Biological samples: Normal human serum samples, stored at -80℃ for later use.

[0150] Main derivatizing reagents: 3-nitrophenylhydrazine (3-NPH, purity ≥98%); methanol (HPLC grade); 1-ethyl-(3-dimethylaminopropyl)carbodiimide (EDC, purity ≥98%); pyridine (HPLC grade).

[0151] Main instruments: Thermo Fisher Q-Exactive series high-resolution liquid chromatography-mass spectrometry system, WatersAcquity UPLC HSS T3 column (2.1mm×100mm, 1.8μm).

[0152] The non-targeted metabolomics analysis method is as follows:

[0153] A) Derivatization and Pretreatment of Control Samples

[0154] First, weigh 25 mg of mouse serum sample into a homogenization tube, add 25 μL of ultrapure water, homogenize at -10℃ for 3 min, then add 120 μL of pre-cooled methanol, and homogenize again at -10℃ for 3 min;

[0155] After centrifuging at 18000g for 20 min at 4℃, transfer 30 μL of supernatant to a new centrifuge tube, add 20 μL of 3-NPH derivatization reagent solution (200 mM, dissolved in methanol aqueous solution) and 20 μL of EDC (120 mM, dissolved in methanol-pyridine solution), and react with shaking at 1200 rpm at 30℃ for 1 h.

[0156] Finally, add 110 μL of pre-cooled 50% methanol, centrifuge at 18000g for 20 min at 4℃, transfer the supernatant to a vial, and freeze at -80℃ for LC-MS analysis.

[0157] Control sample processing method:

[0158] First, weigh 25 mg of mouse serum sample into a homogenization tube, add 25 μL of ultrapure water, homogenize at -10℃ for 3 min, then add 120 μL of pre-cooled methanol, and homogenize again at -10℃ for 3 min.

[0159] After centrifuging at 18000g for 20 min at 4℃, transfer 30 μL of supernatant to a new centrifuge tube, add 40 μL of 75% methanol solution, and shake at 1200 rpm at 30℃ for 1 h.

[0160] Finally, add 110 μL of pre-chilled 50% methanol, centrifuge at 18000g for 20 min at 4℃. Transfer the supernatant to a vial and store at -80℃ for LC-MS analysis.

[0161] B) Data collection was performed on the derivatized and control samples obtained in A);

[0162] Chromatographic conditions: Mobile phase A was an aqueous solution containing 0.1% formic acid, and mobile phase B was an acetonitrile solution containing 0.1% formic acid; flow rate was 0.3 mL / min; column temperature was 40℃; injection volume was 5 μL.

[0163] The gradient elution procedure is as follows: From 0 to 1 minute, the volume percentage of mobile phase B is 1%; from 1 to 4 minutes, the volume percentage of mobile phase B increases linearly from 1% to 48%; from 4 to 10 minutes, the volume percentage of mobile phase B increases linearly from 48% to 100%; from 10 to 13.4 minutes, the volume percentage of mobile phase B remains at 100%; from 13.4 to 13.5 minutes, the volume percentage of mobile phase B decreases linearly from 100% to 1%; and from 13.5 to 15 minutes, the volume percentage of mobile phase B remains at 1%. All percentages of mobile phase B refer to volume percentage (v / v%). The time intervals are continuous, and the gradient changes are all linear.

[0164] Mass spectrometry conditions:

[0165] Quality scan range m / z 170-2500 (for derivatized samples);

[0166] Quality scan range m / z 75-1050 (control sample);

[0167] All of the following conditions must be met:

[0168] Electrospray ion source, negative ion mode;

[0169] Spray voltage 4kV;

[0170] Capillary temperature 320℃;

[0171] Sheath gas 40arb;

[0172] 15arb of auxiliary gas;

[0173] Resolution 70,000 (MS¹), 17,500 (MS²);

[0174] The data acquisition mode is Full MS / dd-MS².

[0175] D) Import the raw mass spectrometry data collected in B) into MSDIAL.v5.5.250404 software for processing, and then use the dedicated derivatized metabolite mass spectrometry database constructed in Preparation Example 1 for retrieval;

[0176] The dedicated derivatization metabolite mass spectrometry database includes:

[0177] Database A: Contains theoretical derivatization spectra of metabolites with a single derivatization candidate list (≥1 functional group);

[0178] Database B: Theoretical derivatization spectra of metabolites containing a dual-derivatization candidate list (≥2 functional groups);

[0179] Database C: Theoretical derivatization spectra of metabolites containing a list of candidates for tri-derivative (≥3 functional groups).

[0180] Database D: Any combination of databases A, B, and C.

[0181] The control group samples were retrieved from the original, unmodified MassBank database.

[0182] Search parameter settings: mother ion mass tolerance 0.01 Da, fragment ion tolerance 0.01 Da. For searches using the database constructed in Preparation Example 1, the matching MS² spectrum must contain a characteristic ion with m / z 137.035±0.02. The relevant test results are recorded in Table 3.

[0183] Table 3: Detection of various gut microbiota compounds in mouse and human serum samples using this method and the control method in conventional non-targeted metabolomics.

[0184]

[0185] In summary, the method of this application can be used not only for mouse fecal samples, but also for mouse serum samples and normal human serum samples. That is, through the synergy of derivatization processing and the construction of a theoretical derivatization mass spectrometry database, the problem that derivatized metabolites cannot be identified by conventional databases is truly solved.

[0186] This specific preparation example is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this preparation example without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

Claims

1. A highly sensitive, non-targeted metabolomics analysis method based on 3-nitrophenylhydrazine derivatization, characterized in that, Includes the following steps: A) Derivatization of metabolites in biological samples using 3-nitrophenylhydrazine; B) Data acquisition of the derivatized samples was performed using a high-resolution chromatography-mass spectrometry system; C) Construct a dedicated mass spectrometry database for identifying derivatized metabolites, including: C1) Obtain structural information of metabolites from existing mass spectrometry databases; C2) Based on the structural information, metabolites containing at least one functional group in their molecular structure that can react with 3-nitrophenylhydrazine are screened to form a candidate metabolite list; C3) Based on the reaction mechanism of 3-nitrophenylhydrazine with the functional group, perform theoretical derivatization calculations on each metabolite in the candidate metabolite list obtained in step C2) to obtain the theoretical parent ion mass-to-charge ratio information after derivatization of each metabolite; C4) Add characteristic fragment ion information specific to the 3-nitrophenylhydrazine derivatization group to the theoretical fragment ion information of the metabolite; In step C4), the characteristic fragment ion is [C6H5N2O2]-, and its mass-to-charge ratio m / z is 137.035; D) Using the dedicated mass spectrometry database constructed in step C), metabolites containing derivatizable functional groups are identified by searching and analyzing the non-targeted metabolomics data collected in step B).

2. The high-sensitivity non-targeted metabolomics analysis method based on 3-nitrophenylhydrazine derivatization according to claim 1, characterized in that, In step C2), the functional group that can react with 3-nitrophenylhydrazine is at least one of a carboxyl group, a carbonyl group, or a phosphate group.

3. The high-sensitivity non-targeted metabolomics analysis method based on 3-nitrophenylhydrazine derivatization according to claim 2, characterized in that, In step C2), the candidate metabolite list includes a single-derivative candidate metabolite list, a double-derivative candidate metabolite list, and a triple-derivative candidate metabolite list, which correspond to metabolites containing at least one, at least two, and at least three of the functional groups in their molecular structures, respectively.

4. The high-sensitivity non-targeted metabolomics analysis method based on 3-nitrophenylhydrazine derivatization according to claim 1, characterized in that, In step C3), the theoretical derivatization calculation includes calculating the theoretical increase in the mass-to-charge ratio of the parent ion ΔM after each metabolite reacts with n 3-nitrophenylhydrazine molecules, where n is a positive integer, and ΔM = n × (153.05383 - 18.01056) = n × 135.04327.

5. The high-sensitivity non-targeted metabolomics analysis method based on 3-nitrophenylhydrazine derivatization according to claim 4, characterized in that, The values ​​of n are 1, 2, and 3.

6. The high-sensitivity non-targeted metabolomics analysis method based on 3-nitrophenylhydrazine derivatization according to claim 1, characterized in that, In step A), the biological sample includes feces, intestinal contents, serum, urine, or cell extracts.

7. A derivatized metabolomics mass spectrometry database for non-targeted metabolomics analysis, characterized in that, The database is constructed via step C) of the method according to any one of claims 1-6, and contains theoretical derivatization mass spectrometry data of multiple metabolite entries, each entry containing at least: Information on the mass-to-charge ratio of the theoretical parent ion after derivatization; The characteristic fragment ion information of the derivatized group, wherein the characteristic fragment ion is [C6H5N2O2]-, and its mass-to-charge ratio m / z is 137.

035.

8. A metabolomics analysis system comprising the derivatized metabolite mass spectrometry database of claim 7, characterized in that, Also includes: The data acquisition module is configured to control the operation of the high-resolution chromatography-mass spectrometry instrument. The data analysis module is configured to call the database to retrieve and identify the collected mass spectrometry data.

9. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method steps of any one of claims 1 to 6.

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