@article{ae2705ef8484416da864062f35f70dc3,
title = "Quantitation and Identification of Thousands of Human Proteoforms below 30 kDa",
abstract = "Top-down proteomics is capable of identifying and quantitating unique proteoforms through the analysis of intact proteins. We extended the coverage of the label-free technique, achieving differential analysis of whole proteins <30 kDa from the proteomes of growing and senescent human fibroblasts. By integrating improved control software with more instrument time allocated for quantitation of intact ions, we were able to collect protein data between the two cell states, confidently comparing 1577 proteoform levels. To then identify and characterize proteoforms, our advanced acquisition software, named Autopilot, employed enhanced identification efficiency in identifying 1180 unique Swiss-Prot accession numbers at 1% false-discovery rate. This coverage of the low mass proteome is equivalent to the largest previously reported but was accomplished in 23% of the total acquisition time. By maximizing both the number of quantified proteoforms and their identification rate in an integrated software environment, this work significantly advances proteoform-resolved analyses of complex systems.",
keywords = "Fourier transform mass spectrometry, GELFrEE, cellular senescence, differential mass spectrometry, label-free, proteoform, quantitative proteomics, top-down proteomics",
author = "Durbin, {Kenneth R.} and Luca Fornelli and Fellers, {Ryan T.} and Doubleday, {Peter F.} and Masashi Narita and Kelleher, {Neil L.}",
note = "Funding Information: This work was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Nos. GM067193 and GM108569 (N.L.K.), federal funds from the National Cancer Institute (Office of Cancer Clinical Proteomics Research) under Contract No. HHSN261200800001E, and the Office of Research at Northwestern University. Additional support was provided by the UIUC Center for Neuroproteomics on Cell to Cell Signaling (P30 DA018310) and the Robert H. Lurie Comprehensive Cancer Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. M.N. acknowledges the University of Cambridge, the Cancer Research UK Cambridge Institute Core Grant, and Hutchison Whampoa. Additionally, L.F. would like to acknowledge the Swiss National Science Foundation for support of an Early Postdoc Mobility fellowship. We would also like to thank Ioanna Ntai, Phil Compton, Paul Thomas, Bryan Early, and Joe Greer as well as the rest of the members of the Kelleher Research Group for their help with this work. Publisher Copyright: {\textcopyright} 2016 American Chemical Society.",
year = "2016",
month = mar,
day = "4",
doi = "10.1021/acs.jproteome.5b00997",
language = "English (US)",
volume = "15",
pages = "976--982",
journal = "Journal of Proteome Research",
issn = "1535-3893",
publisher = "American Chemical Society",
number = "3",
}