Dataset Information

MultiFLEX-LF: A Computational Approach to Quantify the Modification Stoichiometries in Label-Free Proteomics Data Sets.

ABSTRACT: In liquid-chromatography-tandem-mass-spectrometry-based proteomics, information about the presence and stoichiometry of protein modifications is not readily available. To overcome this problem, we developed multiFLEX-LF, a computational tool that builds upon FLEXIQuant, which detects modified peptide precursors and quantifies their modification extent by monitoring the differences between observed and expected intensities of the unmodified precursors. multiFLEX-LF relies on robust linear regression to calculate the modification extent of a given precursor relative to a within-study reference. multiFLEX-LF can analyze entire label-free discovery proteomics data sets in a precursor-centric manner without preselecting a protein of interest. To analyze modification dynamics and coregulated modifications, we hierarchically clustered the precursors of all proteins based on their computed relative modification scores. We applied multiFLEX-LF to a data-independent-acquisition-based data set acquired using the anaphase-promoting complex/cyclosome (APC/C) isolated at various time points during mitosis. The clustering of the precursors allows for identifying varying modification dynamics and ordering the modification events. Overall, multiFLEX-LF enables the fast identification of potentially differentially modified peptide precursors and the quantification of their differential modification extent in large data sets using a personal computer. Additionally, multiFLEX-LF can drive the large-scale investigation of the modification dynamics of peptide precursors in time-series and case-control studies. multiFLEX-LF is available at https://gitlab.com/SteenOmicsLab/multiflex-lf.

SUBMITTER: Hiort P

PROVIDER: S-EPMC9936407 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

multiFLEX-LF: A Computational Approach to Quantify the Modification Stoichiometries in Label-Free Proteomics Data Sets.

Hiort Pauline P Schlaffner Christoph N CN Steen Judith A JA Renard Bernhard Y BY Steen Hanno H

Journal of proteome research 20220127 4

In liquid-chromatography-tandem-mass-spectrometry-based proteomics, information about the presence and stoichiometry of protein modifications is not readily available. To overcome this problem, we developed multiFLEX-LF, a computational tool that builds upon FLEXIQuant, which detects modified peptide precursors and quantifies their modification extent by monitoring the differences between observed and expected intensities of the unmodified precursors. multiFLEX-LF relies on robust linear regress ...[more]

PMID: 35086334

Similar Datasets

Project description:Matrix metalloproteinase-9 (MMP-9) is implicated in tumor metastasis as well as a variety of inflammatory and pathological processes. Although many substrates for MMP-9, including components of the extracellular matrix, soluble mediators such as chemokines, and cell surface molecules have been identified, we undertook a more comprehensive proteomics-based approach to identify new substrates to further understand how MMP-9 might contribute to tumor metastasis. Previous proteomics approaches to identify protease substrates have depended upon differential labeling of each sample. Instead we used a label-free quantitative proteomics approach based on ultraperformance LC-ESI-high/low collision energy MS. Conditioned medium from a human metastatic prostate cancer cell line, PC-3ML, in which MMP-9 had been down-regulated by RNA interference was compared with that from the parental cells. From more than 200 proteins identified, 69 showed significant alteration in levels after depletion of the protease (>+/-2-fold), suggesting that they might be candidate substrates. Levels of six of these (amyloid-beta precursor protein, collagen VI, leukemia inhibitory factor, neuropilin-1, prostate cancer cell-derived growth factor (PCDGF), and protease nexin-1 (PN-1)) were tested in the conditioned media by immunoblotting. There was a strong correlation between results by ultraperformance LC-ESI-high/low collision energy MS and by immunoblotting giving credence to the label-free approach. Further information about MMP-9 cleavage was obtained by comparison of the peptide coverage of collagen VI in the presence and absence of MMP-9 showing increased sensitivity of the C- and N-terminal globular regions over the helical regions. Susceptibility of PN-1 and leukemia inhibitory factor to MMP-9 degradation was confirmed by in vitro incubation of the recombinant proteins with recombinant MMP-9. The MMP-9 cleavage sites in PN-1 were sequenced. This study provides a new label-free method for degradomics cell-based screening leading to the identification of a series of proteins whose levels are affected by MMP-9, some of which are clearly direct substrates for MMP-9 and become candidates for involvement in metastasis.

Project description:BackgroundLabel-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between experiments or even through time within the duration of a single experiment.ResultsWe describe a hierarchical model for analyzing unbiased, label-free proteomics data which utilizes the covariance of peptide expression across samples as well as MS/MS-based identifications to group peptides-a strategy we call metaprotein expression modeling. Our metaprotein model acknowledges the possibility of misidentifications, post-translational modifications and systematic differences between samples due to changes in instrument sensitivity or differences in total protein concentration. In addition, our approach allows us to validate findings from unbiased, label-free proteomics experiments with further unbiased, label-free proteomics experiments. Finally, we demonstrate the clinical/translational utility of the model for building predictors capable of differentiating biological phenotypes as well as for validating those findings in the context of three novel cohorts of patients with Hepatitis C.ConclusionsMass-spectrometry proteomics is quickly becoming a powerful tool for studying biological and translational questions. Making use of all of the information contained in a particular set of data will be critical to the success of those endeavors. Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features. This allows validation of predictors without translation to targeted assays in addition to informing the choice of targets when it is appropriate to generate those assays.

Project description:We developed a multiplexed label-free quantification strategy, which integrates an efficient gel-assisted digestion protocol, high-performance liquid chromatography tandem MS analysis, and a bioinformatics alignment method to determine personalized proteomic profiles for membrane proteins in human tissues. This strategy provided accurate (6% error) and reproducible (34% relative S.D.) quantification of three independently purified membrane fractions from the same human colorectal cancer (CRC) tissue. Using CRC as a model, we constructed the personalized membrane protein atlas of paired tumor and adjacent normal tissues from 28 patients with different stages of CRC. Without fractionation, this strategy confidently quantified 856 proteins (?2 unique peptides) across different patients, including the first and robust detection (Mascot score: 22,074) of the well-documented CRC marker, carcinoembryonic antigen 5 by a discovery-type proteomics approach. Further validation of a panel of proteins, annexin A4, neutrophils defensin A1, and claudin 3, confirmed differential expression levels and high occurrences (48-70%) in 60 CRC patients. The most significant discovery is the overexpression of stomatin-like 2 (STOML2) for early diagnostic and prognostic potential. Increased expression of STOML2 was associated with decreased CRC-related survival; the mean survival period was 34.77 ± 2.03 months in patients with high STOML2 expression, whereas 53.67 ± 3.46 months was obtained for patients with low STOML2 expression. Further analysis by ELISA verified that plasma concentrations of STOML2 in early-stage CRC patients were elevated as compared with those of healthy individuals (p < 0.001), suggesting that STOML2 may be a noninvasive serological biomarker for early CRC diagnosis. The overall sensitivity of STOML2 for CRC detection was 71%, which increased to 87% when combined with CEA measurements. This study demonstrated a sensitive, label-free strategy for differential analysis of tissue membrane proteome, which may provide a roadmap for the subsequent identification of molecular target candidates of multiple cancer types.

Dataset Information

MultiFLEX-LF: A Computational Approach to Quantify the Modification Stoichiometries in Label-Free Proteomics Data Sets.

Publications

multiFLEX-LF: A Computational Approach to Quantify the Modification Stoichiometries in Label-Free Proteomics Data Sets.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets