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Normalized retention time for scheduled liquid chromatography-multistage mass spectrometry analysis of epitranscriptomic modifications.

ABSTRACT: Investigations into post-transcriptional modifications of RNA and their regulatory proteins have revealed pivotal roles of these modifications in cellular functions. A robust method for the quantitative analysis of modified nucleosides in RNA may facilitate the assessment about their functions in RNA biology and disease etiology. Here, we developed a sensitive nano-liquid chromatography-multistage mass spectrometry (nLC-MS³) method for profiling simultaneously 27 modified ribonucleosides. We employed normalized retention time (iRT) and scheduled selected-reaction monitoring (SRM) to achieve high-throughput analysis, where we assigned iRT values for modified ribonucleosides based on their relative elution times with respect to the four canonical ribonucleosides. The iRT scores allowed for reliable predictions of retention times for modified ribonucleosides with the use of two types of stationary phase materials and various mobile phase gradients. The method enabled the identification of 20 modified ribonucleosides with the use of the enzymatic digestion mixture of 2.5 ng total RNA and facilitated robust quantification of modified cytidine derivatives in total RNA. Together, we established a scheduled SRM-based method for high-throughput analysis of modified ribonucleosides with the use of a few nanograms of RNA.

SUBMITTER: Gonzalez G

PROVIDER: S-EPMC7962276 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Liquid chromatography coupled to mass spectrometry (LCMS) is widely used in metabolomics due to its sensitivity, reproducibility, speed and versatility. Metabolites are detected as peaks which are characterised by mass-over-charge ratio (m/z) and retention time (rt), and one of the most critical but also the most challenging tasks in metabolomics is to annotate the large number of peaks detected in biological samples. Accurate m/z measurements enable the prediction of molecular formulae which provide clues to the chemical identity of peaks, but often a number of metabolites have identical molecular formulae. Chromatographic behaviour, reflecting the physicochemical properties of metabolites, should also provide structural information. However, the variation in rt between analytical runs, and the complicating factors underlying the observed time shifts, make the use of such information for peak annotation a non-trivial task. To this end, we conducted Quantitative Structure-Retention Relationship (QSRR) modelling between the calculated molecular descriptors (MDs) and the experimental retention times (rts) of 93 authentic compounds analysed using hydrophilic interaction liquid chromatography (HILIC) coupled to high resolution MS. A predictive QSRR model based on Random Forests algorithm outperformed a Multiple Linear Regression based model, and achieved a high correlation between predicted rts and experimental rts (Pearson's correlation coefficient = 0.97), with mean and median absolute error of 0.52 min and 0.34 min (corresponding to 5.1 and 3.2 % error), respectively. We demonstrate that rt prediction with the precision achieved enables the systematic utilisation of rts for annotating unknown peaks detected in a metabolomics study. The application of the QSRR model with the strategy we outlined enhanced the peak annotation process by reducing the number of false positives resulting from database queries by matching accurate mass alone, and enriching the reference library. The predicted rts were validated using either authentic compounds or ion fragmentation patterns.

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Normalized retention time for scheduled liquid chromatography-multistage mass spectrometry analysis of epitranscriptomic modifications.

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