Analytic framework for peptidomics applied to large-scale neuropeptide identification

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Analytic framework for peptidomics applied to large-scale neuropeptide identification. / Secher, Anna; Kelstrup, Christian D; Conde-Frieboes, Kilian W; Pyke, Charles; Raun, Kirsten; Wulff, Birgitte S; Olsen, Jesper V.

In: Nature Communications, Vol. 7, 11436, 2016.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Secher, A, Kelstrup, CD, Conde-Frieboes, KW, Pyke, C, Raun, K, Wulff, BS & Olsen, JV 2016, 'Analytic framework for peptidomics applied to large-scale neuropeptide identification', Nature Communications, vol. 7, 11436. https://doi.org/10.1038/ncomms11436

APA

Secher, A., Kelstrup, C. D., Conde-Frieboes, K. W., Pyke, C., Raun, K., Wulff, B. S., & Olsen, J. V. (2016). Analytic framework for peptidomics applied to large-scale neuropeptide identification. Nature Communications, 7, [11436]. https://doi.org/10.1038/ncomms11436

Vancouver

Secher A, Kelstrup CD, Conde-Frieboes KW, Pyke C, Raun K, Wulff BS et al. Analytic framework for peptidomics applied to large-scale neuropeptide identification. Nature Communications. 2016;7. 11436. https://doi.org/10.1038/ncomms11436

Author

Secher, Anna ; Kelstrup, Christian D ; Conde-Frieboes, Kilian W ; Pyke, Charles ; Raun, Kirsten ; Wulff, Birgitte S ; Olsen, Jesper V. / Analytic framework for peptidomics applied to large-scale neuropeptide identification. In: Nature Communications. 2016 ; Vol. 7.

Bibtex

@article{55cdf89032f54575b453696e267c24b8,
title = "Analytic framework for peptidomics applied to large-scale neuropeptide identification",
abstract = "Large-scale mass spectrometry-based peptidomics for drug discovery is relatively unexplored because of challenges in peptide degradation and identification following tissue extraction. Here we present a streamlined analytical pipeline for large-scale peptidomics. We developed an optimized sample preparation protocol to achieve fast, reproducible and effective extraction of endogenous peptides from sub-dissected organs such as the brain, while diminishing unspecific protease activity. Each peptidome sample was analysed by high-resolution tandem mass spectrometry and the resulting data set was integrated with publically available databases. We developed and applied an algorithm that reduces the peptide complexity for identification of biologically relevant peptides. The developed pipeline was applied to rat hypothalamus and identifies thousands of neuropeptides and their post-translational modifications, which is combined in a resource format for visualization, qualitative and quantitative analyses.",
author = "Anna Secher and Kelstrup, {Christian D} and Conde-Frieboes, {Kilian W} and Charles Pyke and Kirsten Raun and Wulff, {Birgitte S} and Olsen, {Jesper V}",
year = "2016",
doi = "10.1038/ncomms11436",
language = "English",
volume = "7",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "nature publishing group",

}

RIS

TY - JOUR

T1 - Analytic framework for peptidomics applied to large-scale neuropeptide identification

AU - Secher, Anna

AU - Kelstrup, Christian D

AU - Conde-Frieboes, Kilian W

AU - Pyke, Charles

AU - Raun, Kirsten

AU - Wulff, Birgitte S

AU - Olsen, Jesper V

PY - 2016

Y1 - 2016

N2 - Large-scale mass spectrometry-based peptidomics for drug discovery is relatively unexplored because of challenges in peptide degradation and identification following tissue extraction. Here we present a streamlined analytical pipeline for large-scale peptidomics. We developed an optimized sample preparation protocol to achieve fast, reproducible and effective extraction of endogenous peptides from sub-dissected organs such as the brain, while diminishing unspecific protease activity. Each peptidome sample was analysed by high-resolution tandem mass spectrometry and the resulting data set was integrated with publically available databases. We developed and applied an algorithm that reduces the peptide complexity for identification of biologically relevant peptides. The developed pipeline was applied to rat hypothalamus and identifies thousands of neuropeptides and their post-translational modifications, which is combined in a resource format for visualization, qualitative and quantitative analyses.

AB - Large-scale mass spectrometry-based peptidomics for drug discovery is relatively unexplored because of challenges in peptide degradation and identification following tissue extraction. Here we present a streamlined analytical pipeline for large-scale peptidomics. We developed an optimized sample preparation protocol to achieve fast, reproducible and effective extraction of endogenous peptides from sub-dissected organs such as the brain, while diminishing unspecific protease activity. Each peptidome sample was analysed by high-resolution tandem mass spectrometry and the resulting data set was integrated with publically available databases. We developed and applied an algorithm that reduces the peptide complexity for identification of biologically relevant peptides. The developed pipeline was applied to rat hypothalamus and identifies thousands of neuropeptides and their post-translational modifications, which is combined in a resource format for visualization, qualitative and quantitative analyses.

U2 - 10.1038/ncomms11436

DO - 10.1038/ncomms11436

M3 - Journal article

C2 - 27142507

VL - 7

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

M1 - 11436

ER -

ID: 161081462