Low Resolution Data-Independent Acquisition in an LTQ-Orbitrap Allows for Simplified and Fully Untargeted Analysis of Histone Modifications.
ABSTRACT: Label-free peptide quantification in liquid chromatography-mass spectrometry (LC-MS) proteomics analyses is complicated by the presence of isobaric coeluting peptides, as they generate the same extracted ion chromatogram corresponding to the sum of their intensities. Histone proteins are especially prone to this, as they are heavily modified by post-translational modifications (PTMs). Their proteolytic digestion leads to a large number of peptides sharing the same mass, while carrying PTMs on different amino acid residues. We present an application of MS data-independent acquisition (DIA) to confidently determine and quantify modified histone peptides. By introducing the use of low-resolution MS/MS DIA, we demonstrate that the signals of 111 histone peptides could easily be extracted from LC-MS runs due to the relatively low sample complexity. By exploiting an LTQ-Orbitrap mass spectrometer, we parallelized MS and MS/MS scan events using the Orbitrap and the linear ion trap, respectively, decreasing the total scan time. This, in combination with large windows for MS/MS fragmentation (50 m/z) and multiple full scan events within a DIA duty cycle, led to a MS scan cycle speed of ?45 full MS per minute, improving the definition of extracted LC-MS chromatogram profiles. By using such acquisition method, we achieved highly comparable results to our optimized acquisition method for histone peptide analysis (R(2) correlation > 0.98), which combines data-dependent acquisition (DDA) and targeted MS/MS scans, the latter targeting isobaric peptides. By using DIA, we could also remine our data set and quantify 16 additional isobaric peptides commonly not targeted during DDA experiments. Finally, we demonstrated that by performing the full MS scan in the linear ion trap, we achieve highly comparable results as when adopting high-resolution MS scans (R(2) correlation 0.97). Taken together, results confirmed that histone peptide analysis can be performed using DIA and low-resolution MS with high accuracy and precision of peptide quantification. Moreover, DIA intrinsically enables data remining to later identify and quantify isobaric peptides unknown at the time of the LC-MS experiment. These methods will open up epigenetics analyses to the proteomics community who do not have routine access to the newer generation high-resolution MS/MS generating instruments.
Project description:Here we describe the use of data-independent acquisition (DIA) on a Q-Exactive mass spectrometer for the detection and quantification of peptides in complex mixtures using the Skyline Targeted Proteomics Environment (freely available online at http://skyline.maccosslab.org). The systematic acquisition of mass spectrometry (MS) or tandem MS (MS/MS) spectra by DIA is in contrast to DDA, in which the acquired MS/MS spectra are only suitable for the identification of a stochastically sampled set of peptides. Similarly to selected reaction monitoring (SRM), peptides can be quantified from DIA data using targeted chromatogram extraction. Unlike SRM, data acquisition is not constrained to a predetermined set of target peptides. In this protocol, a spectral library is generated using data-dependent acquisition (DDA), and chromatograms are extracted from the DIA data for all peptides in the library. As in SRM, quantification using DIA data is based on the area under the curve of extracted MS/MS chromatograms. In addition, a quality control (QC) method suitable for DIA based on targeted MS/MS acquisition is detailed. Not including time spent acquiring data, and time for database searching, the procedure takes ?1-2 h to complete. Typically, data acquisition requires roughly 1-4 h per sample, and a database search will take 0.5-2 h to complete.
Project description:Recent developments in instrumentation and bioinformatics have led to new quantitative mass spectrometry platforms including LC-MS/MS with data-independent acquisition (DIA) and targeted analysis using parallel reaction monitoring mass spectrometry (LC-PRM), which provide alternatives to well-established methods, such as LC-MS/MS with data-dependent acquisition (DDA) and targeted analysis using multiple reaction monitoring mass spectrometry (LC-MRM). These tools have been used to identify signaling perturbations in lung cancers and other malignancies, supporting the development of effective kinase inhibitors and, more recently, providing insights into therapeutic resistance mechanisms and drug repurposing opportunities. However, detection of kinases in biological matrices can be challenging; therefore, activity-based protein profiling enrichment of ATP-utilizing proteins was selected as a test case for exploring the limits of detection of low-abundance analytes in complex biological samples. To examine the impact of different MS acquisition platforms, quantification of kinase ATP uptake following kinase inhibitor treatment was analyzed by four different methods: LC-MS/MS with DDA and DIA, LC-MRM, and LC-PRM. For discovery data sets, DIA increased the number of identified kinases by 21% and reduced missingness when compared with DDA. In this context, MRM and PRM were most effective at identifying global kinome responses to inhibitor treatment, highlighting the value of a priori target identification and manual evaluation of quantitative proteomics data sets. We compare results for a selected set of desthiobiotinylated peptides from PRM, MRM, and DIA and identify considerations for selecting a quantification method and postprocessing steps that should be used for each data acquisition strategy.
Project description:Data-independent acquisition (DIA)-mass spectrometry (MS)-based proteomic analysis overtop the existing data-dependent acquisition (DDA)-MS-based proteomic analysis to enable deep proteome coverage and precise relative quantitative analysis in single-shot liquid chromatography (LC)-MS/MS. However, DIA-MS-based proteomic analysis has not yet been optimized in terms of system robustness and throughput, particularly for its practical applications. We established a single-shot LC-MS/MS system with an MS measurement time of 90 min for a highly sensitive and deep proteomic analysis by optimizing the conditions of DIA and nanoLC. We identified 7020 and 4068 proteins from 200 ng and 10 ng, respectively, of tryptic floating human embryonic kidney cells 293 (HEK293F) cell digest by performing the constructed LC-MS method with a protein sequence database search. The numbers of identified proteins from 200 ng and 10 ng of tryptic HEK293F increased to 8509 and 5706, respectively, by searching the chromatogram library created by gas-phase fractionated DIA. Moreover, DIA protein quantification was highly reproducible, with median coefficients of variation of 4.3% in eight replicate analyses. We could demonstrate the power of this system by applying the proteomic analysis to detect subtle changes in protein profiles between cerebrums in germ-free and specific pathogen-free mice, which successfully showed that >40 proteins were differentially produced between the cerebrums in the presence or absence of bacteria.
Project description:RATIONALE:Histone post-translational modifications (PTMs) play key roles in regulating eukaryotic gene expression. Mass spectrometry (MS) has emerged as a powerful method to characterize and quantify histone PTMs as it allows unbiased identification and quantification of multiple histone PTMs including combinations of the modifications present. METHODS:In this study we compared a range of data-acquisition methods for the identification and quantification of the histone PTMs using a Q Exactive HF Orbitrap. We compared three different data-dependent analysis (DDA) methods with MS2 resolutions of 120K, 60K, 30K. We also compared a range of data-independent analysis (DIA) methods using MS2 isolation windows of 20 m/z and DIAvw to identify and quantify histone PTMs in Chinese hamster ovary (CHO) cells. RESULTS:The increased number of MS2 scans afforded by the lower resolution methods resulted in a higher number of queries, peptide sequence matches (PSMs) and a higher number of peptide proteoforms identified with a Mascot Ion score greater than 46. No difference in the proportion of peptide proteoforms with Delta scores >17 was observed. Lower coefficients of variation (CVs) were obtained in the DIA MS1 60 K MS2 30 K 20 m/z isolation windows compared with the other data-acquisition methods. CONCLUSIONS:We observed that DIA which offers advantages in flexibility and identification of isobaric peptide proteoforms performs as well as DDA in the analysis of histone PTMs. We were able to identify 71 modified histone peptides for histone H3 and H4 and quantified 64 across each of the different acquisition methods.
Project description:Histone post-translational modifications (PTMs) are important regulators of chromatin structure and gene expression. Quantitative analysis of histone PTMs by mass spectrometry remains extremely challenging due to the complex and combinatorial nature of histone PTMs. The most commonly used mass spectrometry-based method for high-throughput histone PTM analysis is data-dependent acquisition (DDA). However, stochastic precursor selection and dependence on MS1 ions for quantification impede comprehensive interrogation of histone PTM states using DDA methods. To overcome these limitations, we utilized a data-independent acquisition (DIA) workflow that provides superior run-to-run consistency and postacquisition flexibility in comparison to DDA methods. In addition, we developed a novel DIA-based methodology to quantify isobaric, co-eluting histone peptides that lack unique MS2 transitions. Our method enabled deconvolution and quantification of histone PTMs that are otherwise refractory to quantitation, including the heavily acetylated tail of histone H4. Using this workflow, we investigated the effects of the histone deacetylase inhibitor SAHA (suberoylanilide hydroxamic acid) on the global histone PTM state of human breast cancer MCF7 cells. A total of 62 unique histone PTMs were quantified, revealing novel SAHA-induced changes in acetylation and methylation of histones H3 and H4.
Project description:The ultimate aim of proteomics is to fully identify and quantify the entire complement of proteins and post-translational modifications in biological samples of interest. For the last 15 years, liquid chromatography-tandem mass spectrometry (LC-MS/MS) in data-dependent acquisition (DDA) mode has been the standard for proteomics when sampling breadth and discovery were the main objectives; multiple reaction monitoring (MRM) LC-MS/MS has been the standard for targeted proteomics when precise quantification, reproducibility, and validation were the main objectives. Recently, improvements in mass spectrometer design and bioinformatics algorithms have resulted in the rediscovery and development of another sampling method: data-independent acquisition (DIA). DIA comprehensively and repeatedly samples every peptide in a protein digest, producing a complex set of mass spectra that is difficult to interpret without external spectral libraries. Currently, DIA approaches the identification breadth of DDA while achieving the reproducible quantification characteristic of MRM or its newest version, parallel reaction monitoring (PRM). In comparative de novo identification and quantification studies in human cell lysates, DIA identified up to 89% of the proteins detected in a comparable DDA experiment while providing reproducible quantification of over 85% of them. DIA analysis aided by spectral libraries derived from prior DIA experiments or auxiliary DDA data produces identification and quantification as reproducible and precise as that achieved by MRM/PRM, except on low?abundance peptides that are obscured by stronger signals. DIA is still a work in progress toward the goal of sensitive, reproducible, and precise quantification without external spectral libraries. New software tools applied to DIA analysis have to deal with deconvolution of complex spectra as well as proper filtering of false positives and false negatives. However, the future outlook is positive, and various researchers are working on novel bioinformatics techniques to address these issues and increase the reproducibility, fidelity, and identification breadth of DIA.
Project description:Data-independent acquisition (DIA) strategies provide a sensitive and reproducible alternative to data-dependent acquisition (DDA) methods for large-scale quantitative proteomic analyses. Unfortunately, DIA methods suffer from incompatibility with common multiplexed quantification methods, specifically stable isotope labeling approaches such as isobaric tags and stable isotope labeling of amino acids in cell culture (SILAC). Here we expand the use of neutron-encoded (NeuCode) SILAC to DIA applications (NeuCoDIA), producing a strategy that enables multiplexing within DIA scans without further convoluting the already complex MS(2) spectra. We demonstrate duplex NeuCoDIA analysis of both mixed-ratio (1:1 and 10:1) yeast and mouse embryo myogenesis proteomes. Analysis of the mixed-ratio yeast samples revealed the strong accuracy and precision of our NeuCoDIA method, both of which were comparable to our established MS(1)-based quantification approach. NeuCoDIA also uncovered the dynamic protein changes that occur during myogenic differentiation, demonstrating the feasibility of this methodology for biological applications. We consequently establish DIA quantification of NeuCode SILAC as a useful and practical alternative to DDA-based approaches.
Project description:State-of-the-art proteomics-grade mass spectrometers can measure peptide precursors and their fragments with ppm mass accuracy at sequencing speeds of tens of peptides per second with attomolar sensitivity. Here we describe a compact and robust quadrupole-orbitrap mass spectrometer equipped with a front-end High Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) Interface. The performance of the Orbitrap Exploris 480 mass spectrometer is evaluated in data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes in combination with FAIMS. We demonstrate that different compensation voltages (CVs) for FAIMS are optimal for DDA and DIA, respectively. Combining DIA with FAIMS using single CVs, the instrument surpasses 2500 peptides identified per minute. This enables quantification of >5000 proteins with short online LC gradients delivered by the Evosep One LC system allowing acquisition of 60 samples per day. The raw sensitivity of the instrument is evaluated by analyzing 5 ng of a HeLa digest from which >1000 proteins were reproducibly identified with 5 min LC gradients using DIA-FAIMS. To demonstrate the versatility of the instrument, we recorded an organ-wide map of proteome expression across 12 rat tissues quantified by tandem mass tags and label-free quantification using DIA with FAIMS to a depth of >10,000 proteins.
Project description:BACKGROUND:Proteomics is an emerging field in the study of joint disease. Our two aims with this pilot analysis were to compare healthy human knee articular cartilage with meniscus, two tissues both known to become affected in the osteoarthritic disease process, and to compare two mass spectrometry (MS)-based methods: data-dependent acquisition (DDA) and data-independent acquisition (DIA). METHODS:Healthy knee articular cartilage taken from the medial tibial condyle and medial meniscus samples taken from the body region were obtained from three adult forensic medicine cases. Proteins were extracted from tissue pieces and prepared for MS analysis. Each sample was subjected to liquid chromatography (LC)-MS/MS analysis using an Orbitrap mass spectrometer, and run in both DDA and DIA mode. Linear mixed effects models were used for statistical analysis. RESULTS:A total of 653 proteins were identified in the DDA analysis, of which the majority was present in both tissue types. Only proteins with quantitation information in both tissues (n?=?90) were selected for more detailed analysis, of which the majority did not statistically significantly differ in abundance between the two tissue types, in either of the MS analyses. However, 21 proteins were statistically significantly different (p?<?0.05) between meniscus and cartilage in the DIA analysis. Out of these, 11 proteins were also significantly different in the DDA analysis. Aggrecan core protein was the most abundant protein in articular cartilage and significantly differed between the two tissues in both methods. The corresponding protein in meniscus was serum albumin. Dermatopontin exhibited the highest meniscus vs articular cartilage ratio among the statistically significant proteins. The DIA method led to narrower confidence intervals for the abundance differences between the two tissue types than DDA. CONCLUSIONS:Although articular cartilage and meniscus had similar proteomic composition, we detected several differences by MS. Between the two analyses, DIA yielded more precise estimates and more statistically significant different proteins than DDA, and had no missing values, which makes it preferable for future LC-MS/MS analyses.
Project description:The unbiased selection of peptide precursors makes data-independent acquisition (DIA) an advantageous alternative to data-dependent acquisition (DDA) for discovery proteomics, but traditional multiplexed quantification approaches employing mass difference labeling or isobaric tagging are incompatible with DIA. Here, we describe a strategy that permits multiplexed quantification by DIA using mass defect-based N,N-dimethyl leucine (mdDiLeu) tags and high-resolution tandem mass spectrometry (MS2) analysis. Millidalton mass differences between mdDiLeu isotopologues produce fragment ion multiplet peaks separated in mass by as little as 5.8 mDa, enabling up to 4-plex quantification in DIA MS2 spectra. Quantitative analysis of yeast samples displayed comparable accuracy and precision for MS2-based DIA and MS1-based DDA methods. Multiplexed DIA analysis of cerebrospinal fluid revealed the dynamic proteome changes in Alzheimer's disease, demonstrating its utility for discovery of potential clinical biomarkers. We show that the mdDiLeu tagging approach for multiplexed DIA is a viable methodology for investigating proteome changes, particularly for low-abundance proteins, in different biological matrices.