Epigenetic regulation of gene expression is, at least in part, mediated by histone modifications. PTMs of histones change chromatin structure and regulate gene transcription, DNA damage repair, and DNA replication. Thus, studying histone variants and their modifications not only elucidates their functional mechanisms in chromatin regulation, but also provides insights into phenotypes and diseases. A challenge in this field is to determine the best approach(es) to identify histone variants and their PTMs using a robust high-throughput analysis. The large number of histone variants and the enormous diversity that can be generated through combinatorial modifications, also known as histone code, makes identification of histone PTMs a laborious task. MS has been proven to be a powerful tool in this regard. Here, we focus on bottom-up, middle-down, and top-down MS approaches, including CID and electron-capture dissociation/electron-transfer dissociation based techniques for characterization of histones and their PTMs. In addition, we discuss advances in chromatographic separation that take advantage of the chemical properties of the specific histone modifications. This review is also unique in its discussion of current bioinformatic strategies for comprehensive histone code analysis.
Mass-spectrometry-based proteomics has evolved as the preferred method for the analysis of complex proteomes. Undoubtedly recent advances in mass spectrometry instrumentation have greatly enhanced proteomic analysis. A popular instrument platform in proteomics research is the LTQ-Orbitrap mass analyzer. In this tutorial we discuss the significance of evaluating and optimizing mass spectrometric settings on the LTQ-Orbitrap during CID data-dependent acquisition (DDA) mode to improve protein and peptide identification rates. We focus on those MS and MS/MS parameters, which have been systematically examined and evaluated by several researchers and are commonly used during DDA. More specifically we discuss the effect of mass resolving power, preview mode for FTMS scan, monoisotopic precursor selection, signal threshold for triggering MS/MS events, number of microscans per MS/MS scan, number of MS/MS events, automatic gain control target value (ion population) for MS and MS/MS, maximum ion injection time for MS/MS, rapid and normal scan rate and prediction of ion injection time. We, furthermore, present data from the latest generation LTQ-Orbitrap system, the Orbitrap Elite, along with recommended MS and MS/MS parameters. The Orbitrap Elite outperforms the Orbitrap Classic in terms of scan speed, sensitivity, dynamic range, resolving power and resulting in higher identification rates. Several of the optimized MS parameters determined on the LTQ-Orbitrap Classic and XL were easily transferable to the Orbitrap Elite, whereas others needed to be reevaluated. Finally, the Q Exactive and HCD are briefly discussed, as well as, sample preparation, LC-optimization and bioinformatics analysis. We hope this tutorial will serve as guidance for researchers new to the field of proteomics and assist in achieving optimal results.
The success of a shotgun proteomic experiment relies heavily on the performance and optimization of both the LC and the MS systems. Despite this, little consideration has, so far, been given to the importance of evaluating and optimizing the MS instrument settings during data-dependent acquisition mode. Moreover, during data-dependent acquisition, the users have to decide and choose among various MS parameters and settings, making a successful analysis even more challenging. We have systematically investigated and evaluated the effect of enabling and disabling the preview mode for FTMS scan, the number of microscans per MS/MS scan, the number of MS/MS events, the maximum ion injection time for MS/MS, and the automatic gain control target value for MS and MS/MS events on protein and peptide identification rates on an LTQ-Orbitrap using the Saccharomyces cerevisiae proteome. Our investigations aimed to assess the significance of each MS parameter to improve proteome analysis and coverage. We observed that higher identification rates were obtained at lower ion injection times i.e. 50-150 ms, by performing one microscan and 12-15 MS/MS events. In terms of ion population, optimal automatic gain control target values were at 5×10(5) -1×10(6) ions for MS and 3×10(3) -1×10(4) ions for MS/MS. The preview mode scan had a minimal effect on identification rates. Using optimized MS settings, we identified 1038 (±2.3%) protein groups with a minimum of two peptide identifications and an estimated false discovery rate of ∼1% at both peptide and protein level in a 160-min LC-MS/MS analysis.
A challenge facing metabolomics in the analysis of large human cohorts is the cross-laboratory comparability of quantitative metabolomics measurements. In this study, 14 laboratories analyzed various blood specimens using a common experimental protocol provided with Biocrates AbsoluteIDQ p400HR kit, to quantify up to 408 metabolites. The specimens included human plasma and serum from male and female donors, mouse and rat plasma as well as NIST SRM 1950 reference plasma. The metabolite classes covered range from polar (e.g. amino acids and biogenic amines), to nonpolar (e.g. diacyl-and triacyl-glycerols), and span 11 common metabolite classes. The manuscript describes a strict system suitability testing (SST) criteria used to evaluate each laboratory's readiness to perform the assay, and provides the SST Skyline documents for public dissemination. The study found approximately 250 metabolites were routinely quantified in the sample types tested, using Orbitrap instruments. Inter-laboratory variance for the NIST SRM-1950 has a median of 10% for amino acids, 24% for biogenic amines, 38% for acylcarnitines, 25% for glycerolipids, 23% for glycerophospholipids, 16% for cholesteryl esters, 15% for sphingolipids, and 9% for hexoses. Comparing to consensus values for NIST SRM-1950, nearly 80% of comparable analytes demonstrated bias of <50% from the reference value. The findings of this study result in recommendations of best practices for system suitability, quality control, and calibration. We demonstrate that with appropriate controls, high-resolution metabolomics can provide accurate results with good precision across laboratories, and the p400HR therefore is a reliable approach for generating consistent and comparable metabolomics data.
Middle-down mass spectrometry (MS) combined with electron capture dissociation (ECD) represents an attractive method for characterization of proteins and their post-translational modifications (PTMs). Coupling online chromatographic separation with tandem mass spectrometry enables a high-throughput analysis, while improving sensitivity of the electrosprayed peptides and reducing sample amount requirements. However, middle-down ECD has not been thus far coupled with online chromatographic separation. In this work, we examine the feasibility of coupling middle-down ECD with online nanoflow-liqiud chromatography (nano-LC) for the analysis of large, >3 kDa, and highly modified polypeptides in a data-dependent acquisition mode. We evaluate the effectiveness of the method by analyzing peptides derived from Asp-N and Glu-C digestions of unfractionated histones from calf thymus and acid-extracted histones from HeLa, MCF-7, and Jurkat cells. Our results demonstrate that middle-down ECD is compatible with online chromatographic separation, providing high peptide and protein sequence coverage while allowing precise mapping of PTM sites. The high mass accuracy, obtained by the ICR mass analyzer, for both the precursor and product ions greatly increases confidence in peptide identification, particularly for modified peptides. Overall, for all samples examined, several histone variants were identified and modification sites were successfully localized, including single, multiple, and positional isomeric PTM sites. The vast majority of the identified peptides were in the mass range from 3 to 9 kDa. The data presented here highlight the feasibility and utility of nano-LC-ECD-MS/MS for high-throughput middle-down analysis.
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