Dynamic range and mass accuracy of wide-scan direct infusion nanoelectrospray fourier transform ion cyclotron resonance mass spectrometry-based metabolomics increased by the spectral stitching method

Direct infusion nanoelectrospray Fourier transform ion cyclotron resonance mass spectrometry (DI nESI FT-ICR MS)offers high mass accuracy and resolution for analyzing complex metabolite mixtures. High dynamic range across a wide mass range, however, can only be achieved at the expense of mass accuracy, since the large numbers of ions entering the ICR detector induce adverse spacecharge effects. Here we report an optimized strategy for wide-scan DI nESI FT-ICR MS that increases dynamic range but maintains high mass accuracy. It comprises the collection if multiple adjacent selected ion monitoring (SIM) windows that are stitched together using novel algorithms. The final SIM-stitching method, derived from several optimization experiments, comprises 21 adjoining SIM windows each of width m/z 30 (from m/z 70 to 500; adjacent windows overlap by m/z 10) with an automated gain control (AGC) target of 1 105 charges. SIMstitching and wide-scan range (WSR; Thermo Electron)were compared using a defined standard to assess mass accuracy and a liver extract to assess peak count and dynamic range. SIM-stitching decreased the maximum mass error by 1.3- and 4.3-fold, and increased the peak count by 5.3- and 1.8-fold, versus WSR (AGC targets of 1 x 105 and 5 x 105, respectively). SIM-stitching achieved an rms mass error of 0.18 ppm and detected over 3000 peaks in liver extract. This novel approach increases metabolome coverage, has very high mass accuracy, and at 5.5 min/sample is conducive for high- throughput metabolomics

Exploring the Phytochemical Landscape of the Early-Diverging Flowering Plant Amborella trichopoda Baill.

Although the evolutionary significance of the early-diverging flowering plant Amborella (Amborella trichopoda Baill.) is widely recognized, its metabolic landscape, particularly specialized metabolites, is currently underexplored. In this work, we analyzed the metabolomes of Amborella tissues using liquid chromatography high-resolution electrospray ionization mass spectrometry (LC-HR-ESI-MS). By matching the mass spectra of Amborella metabolites with those of authentic phytochemical standards in the publicly accessible libraries, 63, 39, and 21 compounds were tentatively identified in leaves, stems, and roots, respectively. Free amino acids, organic acids, simple sugars, cofactors, as well as abundant glycosylated and/or methylated phenolic specialized metabolites were observed in Amborella leaves. Diverse metabolites were also detected in stems and roots, including those that were not identified in leaves. To understand the biosynthesis of specialized metabolites with glycosyl and methyl modifications, families of small molecule UDP-dependent glycosyltransferases (UGTs) and O-methyltransferases (OMTs) were identified in the Amborella genome and the InterPro database based on conserved functional domains. Of the 17 phylogenetic groups of plant UGTs (A-Q) defined to date, Amborella UGTs are absent from groups B, N, and P, but they are highly abundant in group L. Among the 25 Amborella OMTs, 7 cluster with caffeoyl-coenzyme A (CCoA) OMTs involved in lignin and phenolic metabolism, whereas 18 form a clade with plant OMTs that methylate hydroxycinnamic acids, flavonoids, or alkaloids. Overall, this first report of metabolomes and candidate metabolic genes in Amborella provides a starting point to a better understanding of specialized metabolites and biosynthetic enzymes in this basal lineage of flowering plants