Dataset Information

High quality MS/MS spectrum prediction for data-dependent and -independent acquisition data analysis

ABSTRACT: Peptide fragmentation spectra are routinely predicted in the interpretation of mass spectrometry-based proteomics data. Unfortunately, the generation of fragment ions is not well enough understood to estimate fragment ion intensities accurately. Here, we demonstrate that machine learning can predict peptide fragmentation patterns in mass spectrometers with accuracy within the uncertainty of the measurements. Moreover, analysis of our models reveals that peptide fragmentation depends on long-range interactions within a peptide sequence. We illustrate the utility of our models by applying them to the analysis of both data-dependent and data-independent acquisition datasets. In the former case, we observe a significant increase in the total number of peptide identifications at fixed false discovery rate. In the latter case we demonstrate that the use of predicted MS/MS spectra is equivalent to the use of spectra from experimentallibraries, indicating that fragmentation libraries for proteomics are becoming obsolete.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Plasma

SUBMITTER: Shivani Tiwary

LAB HEAD: Juergen Cox; Peter Cimermancic

PROVIDER: PXD010382 | Pride | 2019-03-13

REPOSITORIES: Pride

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Publications

High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis.

Tiwary Shivani S Levy Roie R Gutenbrunner Petra P Salinas Soto Favio F Palaniappan Krishnan K KK Deming Laura L Berndl Marc M Brant Arthur A Cimermancic Peter P Cox Jürgen J

Nature methods 20190527 6

Peptide fragmentation spectra are routinely predicted in the interpretation of mass-spectrometry-based proteomics data. However, the generation of fragment ions has not been understood well enough for scientists to estimate fragment ion intensities accurately. Here, we demonstrate that machine learning can predict peptide fragmentation patterns in mass spectrometers with accuracy within the uncertainty of measurement. Moreover, analysis of our models reveals that peptide fragmentation depends on ...[more]

PMID: 31133761

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Project description:Morphine, a commonly used antinociceptive drug in hospitals, is known to cross the blood-brain barrier (BBB) by first passing through brain endothelial cells. Despite its pain-relieving effect, morphine also has detrimental effects, such as the potential induction of redox imbalance in the brain. However, there is still insufficient evidence of these effects on the brain, particularly on the brain endothelial cells and the extracellular vesicles that they naturally release. Indeed, extracellular vesicles (EVs) are nanosized bioparticles produced by almost all cell types and are currently thought to reflect the physiological state of their parent cells. These vesicles have emerged as a promising source of biomarkers by indicating the functional or dys-functional state of their parent cells and, thus, allowing a better understanding of the biological processes involved in an adverse state. However, there is very little information on the mor-phine effect on human brain microvascular endothelial cells (HBMECs), and even less on their released EVs. Therefore, the current study aimed at unraveling the detrimental mechanisms of morphine exposure (at 1, 10, 25, 50 and 100 µM) for 24 h on human brain microvascular endothe-lial cells as well as on their associated EVs. Isolation of EVs was carried out using an affini-ty-based method. Several orthogonal techniques (NTA, western blotting and proteomics analy-sis) were used to validate the EVs enrichment, quality and concentration. Data-independent mass spectrometry (DIA-MS)-based proteomics was applied in order to analyze the proteome modu-lations induced by morphine on HBMECs and EVs. We were able to quantify almost 5500 pro-teins in HBMECs and 1500 proteins in EVs, of which 256 and 148, respectively, were found to be differentially expressed in at least one condition. Pathway enrichment analysis revealed that the “cell adhesion and extracellular matrix remodeling” process and the “HIF1 pathway”, a pathway related to oxidative stress responses, were significantly modulated upon morphine exposure in HBMECs and EVs. Altogether, the combination of proteomics and bioinformatics findings high-lighted shared pathways between HBMECs exposed to morphine and their released EVs. These results put forward molecular signatures of morphine-induced toxicity in HBMECs that were also carried by EVs. Therefore, EVs could potentially be regarded as a useful tool to investigate brain endothelial cells dysfunction, and to a different extent, the BBB dysfunction in patient cir-culation using these “signature pathways”.

Dataset Information

High quality MS/MS spectrum prediction for data-dependent and -independent acquisition data analysis

Publications

High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis.

Similar Datasets

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