Evelyn Rampler

Sample introduction of single selenized yeast cells (Saccharomyces cerevisiae) by micro droplet generation into an ICP-sector field mass spectrometer for label-free detection of trace elements

We have applied a micro droplet generator (μDG) for sample introduction of single selenized yeast cells into a sector field ICP-MS, which was operated in a fast scanning mode with sampling rates of up to 10 kHz, to measure single cells time resolved with 100 μs integration time. Selenized yeast cells have been used as a model system for preliminary investigation. The single cells to be measured have been embedded into droplets and it will be shown that the time duration of a single cell event always is about 400 to 500 μs, and thus comparable to the time duration of a droplet without a cell. A fixed droplet generation rate of 50 Hz produced equidistant signals in time of each droplet event and was advantageous to separate contribution from background and blank from the analytical signal. Open vessel digestion and a multielement analysis were performed with washed yeast cells and absolute amounts per single cell were determined for Na (0.91 fg), Mg (9.4 fg), Fe (5.9 fg), Cu (0.54 fg), Zn (1.2 fg) and Se (72 fg). Signal intensities from single cells have been measured for the elements Cu, Zn and Se, and histograms were calculated for about 1000 cell events. The mean elemental sensitivities measured here range from 0.7 counts per ag (Se) to 10 counts per ag (Zn) with RSDs from 49% (Zn) to 69% (Se) for about 1000 cell events.

Comprehensive Cross-Linking Mass Spectrometry Reveals Parallel Orientation and Flexible Conformations of Plant HOP2-MND1.

The HOP2-MND1 heterodimer is essential for meiotic homologous recombination in plants and other eukaryotes and promotes the repair of DNA double-strand breaks. We investigated the conformational flexibility of HOP2-MND1, important for understanding the mechanistic details of the heterodimer, with chemical cross-linking in combination with mass spectrometry (XL-MS). The final XL-MS workflow encompassed the use of complementary cross-linkers, quenching, digestion, size exclusion enrichment, and HCD-based LC-MS/MS detection prior to data evaluation. We applied two different homobifunctional amine-reactive cross-linkers (DSS and BS(2)G) and one zero-length heterobifunctional cross-linker (EDC). Cross-linked peptides of four biological replicates were analyzed prior to 3D structure prediction by protein threading and protein-protein docking for cross-link-guided molecular modeling. Miniaturization of the size-exclusion enrichment step reduced the required starting material, led to a high amount of cross-linked peptides, and allowed the analysis of replicates. The major interaction site of HOP2-MND1 was identified in the central coiled-coil domains, and an open colinear parallel arrangement of HOP2 and MND1 within the complex was predicted. Moreover, flexibility of the C-terminal capping helices of both complex partners was observed, suggesting the coexistence of a closed complex conformation in solution.

Monitoring the production process of selenized yeast by elemental speciation analysis

Elemental speciation analysis was implemented as an essential tool set addressing optimum fermentation conditions for the production of selenized yeast feed supplements. Accordingly, the study addressed intracellular levels of (1) total selenium and sulfur, (2) seleno methionine (SeMet), (3) cysteine (Cys) and methionine (Met) and (4) selenite and selenate. Dedicated sample preparation- and LC-ICP-MS methods were implemented and validated using the reference material Selm-1. Excellent repeatability precisions <10% (n = 4 biological replicates) could be obtained for all parameters. The study comprised fermentation monitoring over 72 hours (6 different time points) for a Saccharomyces cerevisiae strain under different selenite feed conditions. It was observed that for this strain an increase in the selenium concentration in the fermentation feed by 50% did not result in enhanced selenium accumulation. Fermentation monitoring of three different Saccharomyces cerevisiae strains under the same conditions showed strain specific selenium uptake after 72 hours. The strain with the lowest cell viability of 60% showed the lowest SeMet content. After 47 h of fermentation, all strains reached a critical point, at which seleno methionine accounted for approximately 100% of the total selenium and cell viability started to decrease. This could be explained by sulfur limitation and/or excess of the seleno methionine storage capacity. Strains showing cell viability of approx. 90% after 72 hours of fermentation revealed SeMet concentrations up to 3000 μg g(-1). In the final product, an apparent threshold level for Met/SeMet of approx. 1 was observed for all strains.

Sulfur containing amino acids – challenge of accurate quantification

For the first time, quantitative analysis of the proteinogenic sulfur containing amino acids methionine and cysteine was performed by LC-ICP-MS. A dedicated sample preparation procedure was implemented consisting of two steps, i.e. (1) protection of the redox sensitive amino acids by controlled oxidation of methionine, cystine and cysteine to methionine sulfone and cysteic acid, respectively, and (2) subsequent protein hydrolysis. Anion exchange chromatography enabled the separation of all relevant sulfur species within 10 min. Sulfur was detected on m/z32S16O using O2 as the reaction gas. Absolute limits of detection in the pmol range were achieved for methionine sulfone and cysteic acid. The method offered the possibility of protein quantification. Absolute amounts of 2 μg of hydrolyzed protein (on column) were investigated by LC-ICP-MS. Both oxidized forms of amino acids showed excellent recoveries from lysozyme and myoglobin standards, enabling accurate quantification. A repeatability of <10% (n = 6 independently prepared samples) was found without the application of isotope dilution strategies. The limits of detection of <1 μM protein were comparable to the limits of detection achieved by spectroscopy based protein quantification assays. Moreover, the validity of the approach was shown by implementing HPLC in combination with fluorescence detection as a reference method for the quantification of proteinogenic amino acids in yeast. Both methods were in good agreement and met the theoretical value in a yeast reference material certified for the methionine content.

Structural prediction of protein models using distance restraints derived from cross-linking mass spectrometry data

This protocol describes a workflow for creating structural models of proteins or protein complexes using distance restraints derived from cross-linking mass spectrometry experiments. The distance restraints are used (i) to adjust preliminary models that are calculated on the basis of a homologous template and primary sequence, and (ii) to select the model that is in best agreement with the experimental data. In the case of protein complexes, the cross-linking data are further used to dock the subunits to one another to generate models of the interacting proteins. Predicting models in such a manner has the potential to indicate multiple conformations and dynamic changes that occur in solution. This modeling protocol is compatible with many cross-linking workflows and uses open-source programs or programs that are free for academic users and do not require expertise in computational modeling. This protocol is an excellent additional application with which to use cross-linking results for building structural models of proteins. The established protocol is expected to take 6-12 d to complete, depending on the size of the proteins and the complexity of the cross-linking data.

A Novel Lipidomics Workflow for Improved Human Plasma Identification and Quantification Using RPLC-MSn Methods and Isotope Dilution Strategies

Lipid identification and quantification are essential objectives in comprehensive lipidomics studies challenged by the high number of lipids, their chemical diversity, and their dynamic range. In this work, we developed a tailored method for profiling and quantification combining (1) isotope dilution, (2) enhanced isomer separation by C30 fused-core reversed-phase material, and (3) parallel Orbitrap and ion trap detection by the Orbitrap Fusion Lumos Tribid mass spectrometer. The combination of parallelizable ion analysis without time loss together with different fragmentation techniques (HCD/CID) and an inclusion list led to higher quality in lipid identifications exemplified in human plasma and yeast samples. Moreover, we used lipidome isotope-labeling of yeast (LILY)-a fast and efficient in vivo labeling strategy in Pichia pastoris-to produce (nonradioactive) isotopically labeled eukaryotic lipid standards in yeast. We integrated the 13C lipids in the LC-MS workflow to enable relative and absolute compound-specific quantification in yeast and human plasma samples by isotope dilution. Label-free and compound-specific quantification was validated by comparison against a recent international interlaboratory study on human plasma SRM 1950. In this way, we were able to prove that LILY enabled quantification leads to accurate results, even in complex matrices. Excellent analytical figures of merit with enhanced trueness, precision and linearity over 4-5 orders of magnitude were observed applying compound-specific quantification with 13C-labeled lipids. We strongly believe that lipidomics studies will benefit from incorporating isotope dilution and LC-MSn strategies.

Reply to ‘Defining distance restraints in HADDOCK’

To the Editor — In a recent issue of Nature Protocols, Orbán-Németh et al. present a protocol to predict structural models of proteins and their complexes from mass spectrometry (MS) crosslinking data. We read the protocol with interest, as it uses third-party software including the HADDOCK web portal (http://haddock.science.uu.nl/) that we developed and maintain. While we endorse and encourage the inclusion of our software in other protocols and pipelines, it is important that its usage be accurately and correctly described to avoid problems and incorrect results that we, as primary developers, will have to troubleshoot. Distance restraints are implemented in HADDOCK to force groups of atoms to be at a specific distance from each other. As stated in our Nature Protocols paper describing the web server, a distance restraint is defined using Crystallography and NMR system (CNS) syntax, by two atom selections followed by three numbers —the target distance (d), a lower margin (d) and an upper margin (d). These three numbers are used to define a distance range, by subtracting and adding, respectively, the lower and upper margins to the target distance. Within this range, the potential energy of the restraint is zero. Further, this flexible syntax allows for the same distance range to be expressed differently, with practically no implications for the quality of the final models. In step 21 of their protocol, OrbánNémeth et al. erroneously describe how to define distance restraints, which can have severe consequences for the resulting models. Specifically, the lower and upper (d, d) distance margins are swapped in their definition. In their examples, reproduced below, the authors intend to give distance restraints with ranges of 0–35 Å and 0–23 Å, respectively. Instead, their syntax results in distance ranges of 0–18 Å and 0–12 Å, which are substantially shorter than the maximum cross-linker distance. assign (resid 152 and segid B) (resid134 and segid A) 18 35 0 assign (resid 152 and segid B) (resid137 and segid A) 18 35 0 assign (resid 235 and segid B) (resid147 and segid A) 12 23 0 This change in the distance range impacts the energy landscape of the system and can ultimately lead to different, possibly incorrect, models. The correct syntax should be assign (resid 152 and segid B) (resid 134 and segid A) 35 35 0 assign (resid 152 and segid B) (resid 137 and segid A) 35 35 0 assign (resid 235 and segid B) (resid 147 and segid A) 23 23 0 or, the following, in an example (out of the many possible combinations) of using both upper and lower margins to achieve the same distance range. assign (resid 152 and segid B) (resid 134 and segid A) 18 18 17 assign (resid 152 and segid B) (resid 137 and segid A) 18 18 17 assign (resid 235 and segid B) (resid 147 and segid A) 12 12 11 Finally, in previous publications, we have used similar protocols to model protein–protein complexes with MS cross-linking data (for example, interactome-wide docking, GPCR–GRK docking and protein–RNA docking). In these publications, we define distance restraints between specific atoms on the cross-linked residues (for example, Cα–Cα or Cβ–Cβ) and assert only a maximum distance based on the length of the extended linker, to ensure an easier interpretation of the distance restraint value. We have also provided an online tutorial about the use of cross-linking data in HADDOCK (http://www.bonvinlab.org/education/ HADDOCK-Xlinks/). ❐

Author Correction: Structural prediction of protein models using distance restraints derived from cross-linking mass spectrometry data

In the version of this article initially published online, the authors used incorrectly defined restraints for specifying the distance between residues when using the HADDOCK portal. Following the publication of a Correspondence by the developers of the HADDOCK portal (Nat. Protoc. https://dx.doi.org/10.1038/s41596-018-0017-6, 2018) and a Reply by the authors of the Protocol (Nat. Protoc. https://dx.doi.org/10.1038/s41596-018-0018-5, 2018), the syntax in step 21 has been corrected. In addition, the input files (available as Supplementary Data 5–7) have been replaced.

Correction to “Comprehensive Cross-Linking Mass Spectrometry Reveals Parallel Orientation and Flexible Conformations of Plant HOP2–MND1”

Reveals Parallel Orientation and Flexible Conformations of Plant HOP2−MND1” Evelyn Rampler, Thomas Stranzl, Zsuzsanna Orban-Nemeth, David Maria Hollenstein, Otto Hudecz, Peter Schlogelhofer,* and Karl Mechtler* J. Proteome Res. 2015, 14 (12), 5048−5062. DOI: 10.1021/acs.jproteome.5b00903 T name of one of the corresponding authors, Peter Schlogelhofer, was misspelled. The correct spelling is given here, and the published version has been corrected on the Web.