Fast and sensitive flow-injection mass spectrometry metabolomics by analyzing sample-specific ion distributions

Abstract

Mass spectrometry based metabolomics is a widely used approach in biomedical research. However, current methods coupling mass spectrometry with chromatography are timeconsuming and not suitable for high-throughput analysis of thousands of samples. An alternative approach is flow-injection mass spectrometry (FI-MS) in which samples are directly injected to the ionization source. Here, we show that the sensitivity of Orbitrap FI-MS metabolomics methods is limited by ion competition effect. We describe an approach for overcoming this effect by analyzing the distribution of ion m/z values and computationally determining a series of optimal scan ranges. This enables reproducible detection of~9,000 and~10,000 m/z features in metabolomics and lipidomics analysis of serum samples, respectively, with a sample scan time of~15 s and duty time of~30 s; a~50% increase versus current spectral-stitching FI-MS. This approach facilitates high-throughput metabolomics for a variety of applications, including biomarker discovery and functional genomics screens.

Minimizing ion competition boosts volatile metabolome coverage by secondary electrospray ionization orbitrap mass spectrometry

“…Multiple MS acquisition methods with different scan ranges were used, including i) full scan in range from 50 to 500 m/z ; (ii) targeted with the narrowest scan window possible from 204.8 to 205.2 m/z ; iii) twenty-two 20 m/z windows with an overlap of 4 m/z ; iv) six windows covering the 50 to 500 m/z range splitted either according to the equal-feature number criterion (equal number of robust features per window -the same strategy as in [13]) or the equal-cumulative intensity criterion (equal accumulated intensity per window), with an overlap of 1 m/z. Figure 1(a) shows how these ranges are allocated while a subject is exhaling.…”

“…Two approaches have been considered here to optimize the window splitting of the mass range. First, we split the complete mass range into windows containing an equal number of robust features as suggested by Sarvin et al [13] Second, we split such that the windows contain an equal cumulative intensity of the robust features per window.…”

“…Instead of equal size m/z windows, optimized m/z window positions and widths were recently shown to enhance sensitivity while keeping the duty cycle short. [13] Here, we apply that the same strategy as described in [13] to SESI-MS to minimize the effect of ion competition during data acquisition, where the duty cycle is very important because the events monitored are time-limited, for example, an exhalation lasts only a few seconds.…”

“…[10] Besides ion suppression, a large number of analytes are also problematic in other regards: (i) coalescence might results in fewer detected features and reduced mass accuracy [11], (ii) mass accuracy might deteriorate [12], and (iii) impaired sensitivity may lead to many undetected analytes. This reduced sensitivity is sometimes referred to as ion competition [13] or post interface signal suppression [14] on Orbitraps. This effect arises with matrix-heavy samples when high and low abundant ions compete for space in the capacity-limited C-trap.…”

“…Instead of performing spectral stitching with evenly distributed m/z windows, it has recently been shown by Sarvin et al in electrospray ionization based flow injection mass spectrometry, that optimized window positions and widths enhance sensitivity while keeping the duty cycle short. [13] In their study, the windows were optimized such that the number of features was equal in every window.…”