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Subnanogram proteomics: impact of LC column selection, MS instrumentation and data analysis strategy on proteome coverage for trace samples

Zhu, Ying and Zhao, Rui and Piehowski, Paul D. and Moore, Ronald J. and Lim, Sujung and Orphan, Victoria J. and Paša-Tolić, Ljiljana and Qian, Wei-Jun and Smith, Richard D. and Kelly, Ryan T. (2018) Subnanogram proteomics: impact of LC column selection, MS instrumentation and data analysis strategy on proteome coverage for trace samples. International Journal of Mass Spectrometry, 427 . pp. 4-10. ISSN 1387-3806. PMCID PMC5863755.

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One of the greatest challenges for mass spectrometry (MS)-based proteomics is the limited ability to analyze small samples. Here we investigate the relative contributions of liquid chromatography (LC), MS instrumentation and data analysis methods with the aim of improving proteome coverage for sample sizes ranging from 0.5 ng to 50 ng. We show that the LC separations utilizing 30-μm-i.d. columns increase signal intensity by >3-fold relative to those using 75-μm-i.d. columns, leading to 32% increase in peptide identifications. The Orbitrap Fusion Lumos MS significantly boosted both sensitivity and sequencing speed relative to earlier generation Orbitraps (e.g., LTQ-Orbitrap), leading to a ∼3-fold increase in peptide identifications and 1.7-fold increase in identified protein groups for 2 ng tryptic digests of the bacterium S. oneidensis. The Match Between Runs algorithm of open-source MaxQuant software further increased proteome coverage by ∼ 95% for 0.5 ng samples and by ∼42% for 2 ng samples. Using the best combination of the above variables, we were able to identify >3,000 proteins from 10 ng tryptic digests from both HeLa and THP-1 mammalian cell lines. We also identified >950 proteins from subnanogram archaeal/bacterial cocultures. The present ultrasensitive LC-MS platform achieves a level of proteome coverage not previously realized for ultra-small sample loadings, and is expected to facilitate the analysis of subnanogram samples, including single mammalian cells.

Item Type:Article
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URLURL TypeDescription CentralArticle
Orphan, Victoria J.0000-0002-5374-6178
Additional Information:© 2017 Elsevier B.V. Received 24 April 2017, Revised 3 August 2017, Accepted 22 August 2017, Available online 1 September 2017. A portion of this research was performed under the Facilities Integrating Collaborations for User Science (FICUS) initiative and used resources at the DOE Joint Genome Institute and the Environmental Molecular Sciences Laboratory, which are DOE Office of Science User Facilities. Both facilities are sponsored by the Office of Biological and Environmental Research and operated under Contract Nos. DE-AC02-05CH11231 (JGI) and DE-AC05-76RL01830 (EMSL). This work was also supported by the NIH National Institute of General Medical Sciences (P41 GM103493) and the NIH National Institute of Biomedical Imaging and Bioengineering (1R21EB020976-01A1). Y.Z., R.Z., P.D.P., R.J.M., L.P.-T. W.-J. Q. and R.T.K would like to thank R.D.S. for many years of inspiration and friendship.
Funding AgencyGrant Number
Department of Energy (DOE)DE-AC02-05CH11231
Department of Energy (DOE)DE-AC05-76RL01830
NIHP41 GM103493
Subject Keywords:ultrasensitive; nanoLC; Orbitrap Fusion Lumos; match between runs; subnanogram proteomics; small cell populations
PubMed Central ID:PMC5863755
Record Number:CaltechAUTHORS:20170901-140533444
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Official Citation:Ying Zhu, Rui Zhao, Paul D. Piehowski, Ronald J. Moore, Sujung Lim, Victoria J. Orphan, Ljiljana Paša-Tolić, Wei-Jun Qian, Richard D. Smith, Ryan T. Kelly, Subnanogram proteomics: Impact of LC column selection, MS instrumentation and data analysis strategy on proteome coverage for trace samples, International Journal of Mass Spectrometry, Volume 427, 2018, Pages 4-10, ISSN 1387-3806, (
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:81087
Deposited By: Tony Diaz
Deposited On:05 Sep 2017 20:32
Last Modified:03 Oct 2019 18:38

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