Dataset Information

Analytical Comparison of absolute quantification strategies to investigate the Insulin signaling pathway in fat cells

ABSTRACT: Mass spectrometry based targeted proteomics is the most sensitive approach to answer and address specific biological questions in an accurate and quantitative fashion. However, the data analysis design used for such quantification varies in the field leading to discrepancies in the reported values. In this study, different quantification strategies based on calibration curves were evaluated and compared. The best linearity was achieved when creating a calibration curve using the intensity to amount method. However, the best accuracy and coefficient of variation was achieved by ratio to ratio calibration curves. In addition, we also demonstrated how a LLOQ estimation in combination with one-point calibration can be utilized to conduct a less laborious strategy for absolute quantification. Finally, we applied the most accurate method ratio to ratio quantification to analyze very low abundant insulin signaling proteins such as PIK3 (0.34-0.9 fmol/mg), AKT1 (0.33-0.54 fmol/mg) and the Insulin receptor (0.34 -3.98 fmol/mg) and demonstrated that upon pathway perturbation the concentration of key proteins is dynamically adapted.

ORGANISM(S): Mus Musculus

SUBMITTER: Chi Nguyen

PROVIDER: PXD026204 | panorama | Mon Jun 13 00:00:00 GMT+01:00 2022

REPOSITORIES: PanoramaPublic

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Analytical comparison of absolute quantification strategies to investigate the insulin signaling pathway in fat cells.

Li Tingting T Hentschel Andreas A Ahrends Robert R

Proteomics 20220119 7

So far, mass spectrometry-based targeted proteomics is the most sensitive approach to answer and address specific biological questions in an accurate and quantitative fashion. However, the data analysis design used for such quantification varies in the field leading to discrepancies in the reported values. In this study, different quantification strategies based on calibration curves were evaluated and compared. The best accuracy and coefficient of variation was achieved by ratio to ratio calibr ...[more]

PMID: 34964541

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Project description:Bound amino compounds (amino acid and amino sugar polymers) comprise a significant proportion (~40%) of soil organic nitrogen and therefore represent an essential source of nitrogen for plant and microbial nutrition. The analysis of their content and isotope enrichment still represents a significant challenge due to the low isotope enrichment levels reached under near-native soil conditions and the lack of isotopically labeled standards for some key amino compounds. In this study, we used both a 13C-labeled and an unlabeled amino acid mixture to establish isotope calibration curves for 16 amino compounds, using the 6-Aminoquinolyl-N-hydroxysccinimidyl carbamate (AQC) derivatization method and ultra-high-performance liquid chromatography with high-resolution Orbitrap mass spectrometry (UPLC-Orbitrap MS). Molecular ions of AQC-derivatives for all standard amino compounds were identified at the expected m/z values of the respective isotopologues. The isotope calibration curves exhibited excellent linear fits across the whole 13C enrichment range and polynomial fits in the low 13C enrichment range (R2 > 0.990). However, the polynomial fitting terms differed between individual amino acids. Subsequently, we developed equations to relate the calibrated regression terms to the physicochemical properties of the respective amino acids, here mainly the ratio of amino compound-C atoms to total C atoms in AQC-amino compound derivatives. Based on these regressions, we could ultimately predict isotope calibration curves for those amino compounds unavailable as 13C labeled standards, for example muramic acid, hydroxyproline and diaminopimelic acid. To test the model accuracy, we compared the outcomes of measured calibrations with predicted calibrations for amino acids where we had isotopically enriched standards. The results of linear regression between measured and predicted data were excellent, where R2 was >0.97, and mean absolute (percentage) deviation, MAD and MAPD, were 0.334 and 15.8%. Finally, we applied both standard and predicted calibration curves to low 13C amended soil samples and unlabeled controls to test the applicability of our model. The limit of detection (LOD) as the minimum detectable atom % 13C incorporation of amino compounds ranged from 0.0003 to 0.14 atom % among different amino compounds. This general predictive model can be used to comprehensively quantify isotope enrichments across the entire soil amino compound profile, including amino sugars and proteinogenic and non-proteinogenic amino acids, providing valuable insights for a better understanding of the overall fate of different amino compounds in soils or broader environmental samples.

Dataset Information

Analytical Comparison of absolute quantification strategies to investigate the Insulin signaling pathway in fat cells

Publications

Analytical comparison of absolute quantification strategies to investigate the insulin signaling pathway in fat cells.

Similar Datasets

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