研究目的
To develop a novel strategy for the simultaneous quantitative analysis of intracellular metabolic coenzymes FAD and FMN using intrinsic fluorescence coupled with four-way calibration, overcoming limitations of traditional methods.
研究成果
The developed intrinsic fluorescence four-way calibration method successfully quantifies intracellular FAD and FMN with high accuracy and sensitivity, validated by LC-MS/MS. It offers a simple, efficient approach for direct analysis in complex biological systems and demonstrates the third-order advantage of four-way calibration over three-way methods.
研究不足
The method requires careful handling of spectral scattering and may be limited by the pH range used; it assumes no variation in excitation and emission spectra with pH, which might not hold in all biological systems. Sample preparation, though simplified, still involves dilution and may not be applicable to all cell types without optimization.
1：Experimental Design and Method Selection:
The study uses a quadrilinear model for four-way calibration with excitation-emission-pH-sample data arrays, employing the constrained alternating quadrilinear decomposition (CAQLD) algorithm for mathematical separation.
2：Sample Selection and Data Sources:
Calibration samples with known concentrations of FAD and FMN were prepared using a U7(72) uniform design, and prediction samples were real HeLa cell solutions diluted with buffers.
3：List of Experimental Equipment and Materials:
Equipment includes an F-7000 spectrofluorometer (HITACHI) for fluorescence measurements and an Agilent 1290 series LC and 6460 series triple quadrupole MS for validation. Reagents include FAD and FMN from TCI and Solon, buffers of varying pH, and HeLa cells.
4：Experimental Procedures and Operational Workflow:
Samples were prepared by diluting with buffers at pH
5：2 to 6, EEM fluorescence data were measured, scattering was corrected, and data arrays were constructed and decomposed using CAQLD. Data Analysis Methods:
Data analysis involved quadrilinear decomposition, univariate linear regression for concentration prediction, and statistical validation using F-test and t-test against LC-MS/MS results.
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容
