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Construction method of autoimmune disease model based on serum metabolic fingerprints

An autoimmune disease and construction method technology, which is applied in the field of construction of an autoimmune disease model based on serum metabolic fingerprints, can solve the problems of high consumption of serum samples, complex preprocessing steps, low sensitivity, etc. Solve complex, high-sensitivity effects of preprocessing

Pending Publication Date: 2022-04-29
SHANGHAI JIAO TONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] 1. The existing nuclear magnetic resonance technology cannot meet the detection of serum metabolic fingerprints of autoimmune diseases due to low sensitivity and other reasons;
[0007] 2. Although the traditional mass spectrometry technology has high sensitivity, it faces problems such as complicated pretreatment steps, high consumption of serum samples, and high detection cost, and cannot achieve good serum metabolic fingerprint extraction;
[0008] 3. LDI-MS based on organic matrix cannot realize the detection of low molecular weight end (m / z<400) metabolic small molecules;
[0009] 4. LDI-MS based on nanomaterials has not been able to detect and extract serum metabolic fingerprints of autoimmune diseases

Method used

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  • Construction method of autoimmune disease model based on serum metabolic fingerprints
  • Construction method of autoimmune disease model based on serum metabolic fingerprints
  • Construction method of autoimmune disease model based on serum metabolic fingerprints

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0049] Example 1. Using nanoparticle-enhanced laser desorption ionization time-of-flight mass spectrometry technology to perform serum metabolic fingerprint imaging.

[0050] Step 1: Use nano-assisted LDI-MS technology to perform metabolic detection on serum samples from patients with autoimmune diseases and healthy volunteers;

[0051] Step 2: Prepare serum samples from patients with autoimmune diseases as analytical samples, and prepare deionized water, nano-matrix materials, and LDI MS;

[0052] Step 3: Dilute the collected serum sample: dilute 10 times with deionized water;

[0053] Step 4: configure a nano-matrix solution with a concentration of 1ng / nL;

[0054] Step 5: Sample preparation: Take 500nL of the serum sample diluted in step 3 on the LDI MS mass spectrometry target plate and dry it at room temperature;

[0055] Step 6: Nanoparticle matrix preparation: Take 500nL of the nanoparticle matrix solution in step 4 on the serum sample on the LDI MS mass spectrometry ...

Embodiment 2

[0057] Example 2. Carrying out machine learning on the serum metabolic fingerprint spectrum of patients with autoimmune diseases to realize accurate prediction of autoimmune diseases:

[0058] Step 1: Preparation of instruments, reagents and statistical software Matlab2019:;

[0059] Step 2: Through step 1, a total of 915 serum metabolic fingerprints corresponding to autoimmune disease patients (448) and healthy volunteers (467) were imaged. The imaging results are as follows figure 1 As shown, the upper part of the figure is the fingerprint of the healthy sample detected by matrix-assisted laser desorption ionization time-of-flight mass spectrometry as the fingerprint of the control group; the lower part of the figure is the fingerprint of the diseased sample detected by matrix-assisted laser desorption ionization time-of-flight mass spectrometry Atlas;

[0060] Step 3: 915 serum metabolic fingerprints were preprocessed on matlab, including data resampling, spectral line smo...

Embodiment 3

[0066] Example 3: Identification of metabolic biomarkers for autoimmune diseases, and establishment of a panel of metabolic biomarkers for discrimination of autoimmune diseases;

[0067] Step 1: Collect serum samples from patients with immune diseases and healthy volunteers as analysis samples, and prepare deionized water, nanomatrix and LDI MS;

[0068] Step 2: Dilute the collected serum biological sample: dilute 10 times with deionized water;

[0069] Step 3: configure a nano-matrix solution with a concentration of 1ng / nL;

[0070] Step 4: Sample preparation: take 500nL of the serum biological sample obtained in step 2 and apply it on the LDI MS mass spectrometry target plate, and dry it at room temperature;

[0071] Step 5: Preparation of nanoparticle matrix: Take 500nL of the nano-matrix solution in step 4 and apply it on the LDI MS mass spectrometry target plate, and dry it at room temperature;

[0072] Step 6: Accurate molecular weights (<5ppm) of biomarkers were obtai...

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Abstract

The invention discloses a construction method of an autoimmune disease model based on serum metabolism fingerprints, and relates to the technical field of matrix-assisted laser desorption ionization mass spectrometry, biological sample metabolism molecule analysis and machine learning. Detecting and extracting serum metabolism fingerprints of the autoimmune diseases; constructing a model aiming at the autoimmune diseases by adopting a machine learning algorithm based on the serum metabolism fingerprint spectrum; and screening to obtain the metabolic biomarker. According to the method, the autoimmune disease model is successfully constructed based on the serum metabolism fingerprints by adopting a machine learning method. The defects of a nuclear magnetic resonance technology, a traditional mass spectrum technology (GC / LC-MS) and the like are effectively overcome through nano-assisted laser desorption ionization mass spectrometry (nano-assisted LDI MS), and rapid, high-throughput and high-sensitivity detection of the metabolic fingerprints of the serum low-molecular-weight segment of the autoimmune diseases is effectively achieved.

Description

technical field [0001] The invention relates to the technical fields of matrix-assisted laser desorption ionization mass spectrometry, metabolic molecular analysis of biological samples and machine learning, in particular to a method for constructing an autoimmune disease model based on serum metabolic fingerprints. Background technique [0002] The detection of biomarkers (proteins, nucleic acids, metabolites, etc.) is playing an increasingly important role in in vitro diagnosis due to its non-invasive characteristics. Unlike genes whose functions are regulated by epigenetics and proteins that are post-translationally modified, metabolites are direct markers of biochemical activity and are more easily correlated with phenotypes. Therefore, metabolite profiling, or metabolomics, has become a powerful tool that is increasingly used in in vitro diagnostics. Especially for autoimmune diseases, due to the disorder of the body's immune system, it shows a different metabolic patt...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N27/64G16H50/20
CPCG01N27/64G16H50/20
Inventor 钱昆杨守志杨静
Owner SHANGHAI JIAO TONG UNIV
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