Project description:This dataset includes raw label-free mass spectrometry proteomics data of different sinonasal tumor entities as well as normal sinonasal tissue. 72 samples were processed on a Q Exactive HF-X instrument coupled to an easy nanoLC 1200 system using one microgram of peptides and an 110 minutes gradient.
Project description:M. hominis cells were grown in liquid medium supplied with arginine or thymidine as a carbon source. LC-MS analysis was performed on Ultimate 3000 Nano LC System (Thermo Fisher Scientific) coupled with Q Exactive HF benchtop Orbitrap mass spectrometer (Thermo Fisher Scientific) via a nanoelectrospray source (Thermo Fisher Scientific), an untargeted label-free bottom-up proteomic strategy was used, DDA (Data Dependent Acquisition) approach. Protein identification and label-free quantification were performed with PEAKS software.
Project description:Surface proteins are one of the most important and common matrices for clinical chemistry and proteomic analyses. With the rapid developments in mass spectrometry (MS)-based proteomics methods, label-free quantitative proteomics has become an increasingly popular tool for profiling global protein abundances. Here, we evaluate the performance of three mass spectrometers, namely Orbitrap Elite (Hybrid Ion Trap-Orbitrap), Q Exactive HF (Hybrid Quadrupole-Orbitrap) and Orbitrap Fusion (Tribrid Quadrupole-Ion Trap-Orbitrap), in label-free semiquantitative analysis of cell surface proteins spanning a four-year period. Sucrose gradient ultracentrifugation was used for surfaceome enrichment, following gel separation for in-depth protein identification. With the established workflow, we identify 2335 cell surface proteins in MOLM-14, a human acute myeloid leukemia cell line, with a high degree of reproducibility across three MS platforms over multiple years.
Project description:Label-free absolute quantitative proteomics is commonly used for absolute quantification of the proteome or specific proteins of interest in various biological samples. Current label-free absolute protein quantification (APQ) methods determine MS1 intensities, MS2 spectral counts or intensities to absolutely quantify protein concentrations from data obtained from data-dependent acquisition (DDA). In recent years, label-free data-independent acquisition (DIA) has seen increasing use as a powerful tool for relative protein quantification. Here we present a novel label-free DIA-based absolute protein quantification (DIA-APQ) method for the absolute quantification of protein expressions from DIA data. To validate this method, both DDA and DIA experiments were performed on 36 individual human liver microsome and S9 samples. The DIA-APQ assay was able to quantify approximately twice as many proteins as the DDA MS1-based APQ method whereas protein concentrations determined by the two methods were comparable. To evaluate the accuracy of the DIA-APQ method, we absolutely quantified carboxylesterase 1 concentrations in human liver samples using an established SILAC internal standard-based proteomic assay; the SILAC results were consistent with those obtained from DIA-APQ analysis. Finally, we employed a unique algorithm in DIA-APQ to distribute the MS signals from shared peptides to different protein isoforms and successfully applied the DIA-APQ method to the absolute quantification of several drug-metabolizing enzyme isoforms in human liver microsomes. This novel DIA-based APQ method not only provides a powerful approach for absolutely quantifying entire proteomes and specific candidate proteins, but also has with the capacity differentiating protein isoforms.
Project description:Label-free proteomics enables the unbiased quantification of thousands of proteins across large sample cohorts. Commonly used mass spectrometry-based proteomic workflows rely on data dependent acquisition (DDA). However, its stochastic selection of peptide features for fragmentation-based identification inevitably results in high rates of missing values, which prohibits the integration of larger cohorts as the number of recurrently detected peptides is a limiting factor. Peptide identity propagation (PIP) can mitigate this challenge, allowing to transfer sequencing information between samples. However, despite the promise of these approaches, current methods remain limited either in sensitivity or reliability and there is a lack of robust and widely applicable software. Here we prepared a tool spike-in data set which can be used to evaluate the influence of changing Top-N, gradient length and sample injection amounts on DDA label-free proteomics results. It also includes analysis by data-independent acquisition (DIA) which allows direct comparison of DDA and DIA for label-free proteomics analyses.
Project description:The last decade has seen significant advances in the application of quantitative mass spectrometry-based proteomics technologies to tackle important questions in plant biology. The current standard for quantitative proteomics in plants is the use of data-dependent acquisition (DDA) analysis with or without the use of chemical labels. However, the DDA approach preferentially measures higher abundant proteins, and often requires data imputation due to quantification inconsistency between samples. In this study we systematically benchmarked a recently developed library-free data-independent acquisition (directDIA) method against a state-of-the-art DDA label-free quantitative proteomics workflow for plants. We next developed a novel acquisition approach combining MS1-level BoxCar acquisition with MS2-level directDIA analysis that we call BoxCarDIA. DirectDIA achieves a 33% increase in protein quantification over traditional DDA, and BoxCarDIA a further 8%, without any changes in instrumentation, offline fractionation, or increases in mass-spectrometer run time. BoxCarDIA, especially, offers wholly reproducible quantification of proteins between replicate injections, thereby addressing the long-standing missing-value problem in label-free quantitative proteomics. Further, we find that the gains in dynamic range sampling by directDIA and BoxCarDIA translate to deeper quantification of key, low abundant, functional protein classes (e.g., protein kinases and transcription factors) that are underrepresented in data acquired using DDA. We applied these methods to perform a quantitative proteomic comparison of dark and light grown Arabidopsis cell cultures, providing a critical resource for future plant interactome studies. Our results establish BoxCarDIA as the new method of choice in quantitative proteomics using Orbitrap-type mass-spectrometers, particularly for proteomes with large dynamic range such as that of plants.
Project description:To explore the cellular pathways and the molecular functions affected by a novel biogenic amine, (3-HKA), we performed a label free quantitative (LFQ) proteomic analysis of mouse lymphatic endothelial cells (LEC), and primary dendritic cells, treated with IFNgammaplus or minus 3-HKA. Biological triplicates, of total cell lysates, were fractionated by one-dimensional gel electrophoresis (1DEF) and the “in gel” tryptic derived peptides analyzed by nano-LC-ESI-MS/MS on a Q Exactive HF quadrupole orbitrap mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). LFQ analysis highlighted that 3-HKA downregulated many of the inflammatory pathways, more notably JAK/STAT1 and NFkB, associated with IFNgamma activation.