The type VI secretion system (T6SS) primarily functions to mediate antagonistic interactions between contacting bacterial cells, but also mediates interactions with eukaryotic hosts. This molecular machine secretes antibacterial effector proteins by undergoing cycles of extension and contraction; however, how effectors are loaded into the T6SS and subsequently delivered to target bacteria remains poorly understood. Here, using electron cryomicroscopy, we analysed the structures of the Pseudomonas aeruginosa effector Tse6 loaded onto the T6SS spike protein VgrG1 in solution and embedded in lipid nanodiscs. In the absence of membranes, Tse6 stability requires the chaperone EagT6, two dimers of which interact with the hydrophobic transmembrane domains of Tse6. EagT6 is not directly involved in Tse6 delivery but is crucial for its loading onto VgrG1. VgrG1-loaded Tse6 spontaneously enters membranes and its toxin domain translocates across a lipid bilayer, indicating that effector delivery by the T6SS does not require puncturing of the target cell inner membrane by VgrG1. Eag chaperone family members from diverse Proteobacteria are often encoded adjacent to putative toxins with predicted transmembrane domains and we therefore anticipate that our findings will be generalizable to numerous T6SS-exported membrane-associated effectors.

The extraction of meaningful biological knowledge from high-throughput mass spectrometry data relies on limiting false discoveries to a manageable amount. For targeted approaches in metabolomics a main challenge is the detection of false positive metabolic features in the low signal-to-noise ranges of data-independent acquisition results and their filtering. Another factor is that the creation of assay libraries for data-independent acquisition analysis and the processing of extracted ion chromatograms have not been automated in metabolomics. Here we present a fully automated open-source workflow for high-throughput metabolomics that combines data-dependent and data-independent acquisition for library generation, analysis, and statistical validation, with rigorous control of the false-discovery rate while matching manual analysis regarding quantification accuracy. Using an experimentally specific data-dependent acquisition library based on reference substances allows for accurate identification of compounds and markers from data-independent acquisition data in low concentrations, facilitating biomarker quantification.

Targeted, untargeted and data-independent acquisition (DIA) metabolomics workflows are often hampered by ambiguous identification based on either MS1 information alone or relatively few MS2 fragment ions. While DIA methods have enjoyed popularity in proteomics, it is less clear whether they are suitable for metabolomics workflows due to their large precursor isolation windows and complex co-isolation patterns. Here, we quantitatively investigate the conditions necessary for unique metabolite identification in complex backgrounds using precursor and fragment ion mass-to-charge separation, comparing three benchmarked MS methods (MS1, MRM, DIA). We simulated MS1, MRM and DIA using the NIST LC-MS library as a complex background (8274 compounds at collision energy=35) and compared these methods with regards to unambiguous detection using unique ion signatures. Our simulations show that data generated with DIA with 25 Da mass windows outperformed MS1-only and MRM-based methods by a factor of 13.6-fold and 8.7-fold, respectively. Additionally, we use saturation analysis to show that for highly complex samples, a large portion of MS1-only detection (44.9%) is ambiguous while MRM (6.6%) and DIA (0.6%) present lower ambiguity. Our analysis demonstrates the power of using both high resolution precursor and high resolution fragment ion m/z for unambiguous compound detection. This work also establishes DIA as an emerging MS acquisition method with high selectivity for metabolomics, outperforming both DDA and MRM with regards to unique compound identification potential.

Targeted, untargeted, and data-independent acquisition (DIA) metabolomics workflows are often hampered by ambiguous identification based on either MS1 information alone or relatively few MS2 fragment ions. While DIA methods have been popularized in proteomics, it is less clear whether they are suitable for metabolomics workflows due to their large precursor isolation windows and complex coisolation patterns. Here, we quantitatively investigate the conditions necessary for unique metabolite detection in complex backgrounds using precursor and fragment ion mass-to-charge (m/z) separation, comparing three benchmarked mass spectrometry (MS) methods [MS1, MRM (multiple reaction monitoring), and DIA]. Our simulations show that DIA outperformed MS1-only and MRM-based methods with regards to specificity by factors of ∼2.8-fold and ∼1.8-fold, respectively. Additionally, we show that our results are not dependent on the number of transitions used or the complexity of the background matrix. Finally, we show that collision energy is an important factor in unambiguous detection and that a single collision energy setting per compound cannot achieve optimal pairwise differentiation of compounds. Our analysis demonstrates the power of using both high-resolution precursor and high-resolution fragment ion m/z for unambiguous compound detection. This work also establishes DIA as an emerging MS acquisition method with high selectivity for metabolomics, outperforming both data-dependent acquisition (DDA) and MRM with regards to unique compound identification potential.