Gene editing cell metabolic flow dynamic analysis method, system, equipment and medium

By combining mass spectrometry analysis and derivatization detection with a dynamic metabolic network model, the challenge of dynamically monitoring the metabolic network of gene-edited cells was solved. This enabled precise localization of rate-limiting steps and flux imbalance nodes, improving the optimization efficiency and success rate of gene-editing strategies.

CN121747697APending Publication Date: 2026-03-27SHENZHEN AONE MEDICAL LAB
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-19
Publication Date
2026-03-27

AI Technical Summary

Technical Problem

Existing technologies struggle to achieve real-time, label-free dynamic monitoring of the metabolic network of gene-edited cells, making it difficult to distinguish between causal relationships and accompanying phenomena. Optimizing gene-editing strategies relies on empirical trial and error, limiting the efficiency and success rate of cell factory design and optimization.

Method used

By employing mass spectrometry analysis combined with derivatization detection and dynamic metabolic network modeling, and through free radical induced dissociation technology and gas chromatography-mass spectrometry analysis, we obtained data on the concentrations of metabolic intermediates and organic acids, constructed dynamic data of metabolic flux, identified rate-limiting steps and flux imbalance nodes, and provided suggestions for gene editing optimization.

Benefits of technology

This enables dynamic and quantitative analysis of the metabolic function of gene-edited cells, accurately pinpointing rate-limiting steps and flux imbalance nodes, improving the directionality and success rate of gene-edited cell construction, and shortening the optimization cycle.

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Abstract

The invention relates to a gene editing cell metabolic flow dynamic analysis method, system, equipment and medium, and the method comprises the following steps: carrying out mass spectrometry on a gene editing cell sample, identifying a metabolic intermediate in the gene editing cell sample, and obtaining dynamic change data; carrying out derivatization detection on the gene editing cell sample, and obtaining organic acid concentration data in a semi-quantitative manner; and processing the dynamic change data and the organic acid concentration data through a dynamic metabolism network model, and outputting metabolic flow dynamic data for quantitatively representing the state of a metabolic pathway, so as to achieve the purposes of dynamically and quantitatively analyzing the metabolic function of the gene editing cell and directly guiding the optimization of a gene editing strategy.
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