Cargando…

Optimization of Data-Independent Acquisition Mass Spectrometry for Deep and Highly Sensitive Proteomic Analysis

Data-independent acquisition (DIA)-mass spectrometry (MS)-based proteomic analysis overtop the existing data-dependent acquisition (DDA)-MS-based proteomic analysis to enable deep proteome coverage and precise relative quantitative analysis in single-shot liquid chromatography (LC)-MS/MS. However, D...

Descripción completa

Detalles Bibliográficos
Autores principales: Kawashima, Yusuke, Watanabe, Eiichiro, Umeyama, Taichi, Nakajima, Daisuke, Hattori, Masahira, Honda, Kenya, Ohara, Osamu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928715/
https://www.ncbi.nlm.nih.gov/pubmed/31779068
http://dx.doi.org/10.3390/ijms20235932
_version_ 1783482536236679168
author Kawashima, Yusuke
Watanabe, Eiichiro
Umeyama, Taichi
Nakajima, Daisuke
Hattori, Masahira
Honda, Kenya
Ohara, Osamu
author_facet Kawashima, Yusuke
Watanabe, Eiichiro
Umeyama, Taichi
Nakajima, Daisuke
Hattori, Masahira
Honda, Kenya
Ohara, Osamu
author_sort Kawashima, Yusuke
collection PubMed
description Data-independent acquisition (DIA)-mass spectrometry (MS)-based proteomic analysis overtop the existing data-dependent acquisition (DDA)-MS-based proteomic analysis to enable deep proteome coverage and precise relative quantitative analysis in single-shot liquid chromatography (LC)-MS/MS. However, DIA-MS-based proteomic analysis has not yet been optimized in terms of system robustness and throughput, particularly for its practical applications. We established a single-shot LC-MS/MS system with an MS measurement time of 90 min for a highly sensitive and deep proteomic analysis by optimizing the conditions of DIA and nanoLC. We identified 7020 and 4068 proteins from 200 ng and 10 ng, respectively, of tryptic floating human embryonic kidney cells 293 (HEK293F) cell digest by performing the constructed LC-MS method with a protein sequence database search. The numbers of identified proteins from 200 ng and 10 ng of tryptic HEK293F increased to 8509 and 5706, respectively, by searching the chromatogram library created by gas-phase fractionated DIA. Moreover, DIA protein quantification was highly reproducible, with median coefficients of variation of 4.3% in eight replicate analyses. We could demonstrate the power of this system by applying the proteomic analysis to detect subtle changes in protein profiles between cerebrums in germ-free and specific pathogen-free mice, which successfully showed that >40 proteins were differentially produced between the cerebrums in the presence or absence of bacteria.
format Online
Article
Text
id pubmed-6928715
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-69287152019-12-26 Optimization of Data-Independent Acquisition Mass Spectrometry for Deep and Highly Sensitive Proteomic Analysis Kawashima, Yusuke Watanabe, Eiichiro Umeyama, Taichi Nakajima, Daisuke Hattori, Masahira Honda, Kenya Ohara, Osamu Int J Mol Sci Article Data-independent acquisition (DIA)-mass spectrometry (MS)-based proteomic analysis overtop the existing data-dependent acquisition (DDA)-MS-based proteomic analysis to enable deep proteome coverage and precise relative quantitative analysis in single-shot liquid chromatography (LC)-MS/MS. However, DIA-MS-based proteomic analysis has not yet been optimized in terms of system robustness and throughput, particularly for its practical applications. We established a single-shot LC-MS/MS system with an MS measurement time of 90 min for a highly sensitive and deep proteomic analysis by optimizing the conditions of DIA and nanoLC. We identified 7020 and 4068 proteins from 200 ng and 10 ng, respectively, of tryptic floating human embryonic kidney cells 293 (HEK293F) cell digest by performing the constructed LC-MS method with a protein sequence database search. The numbers of identified proteins from 200 ng and 10 ng of tryptic HEK293F increased to 8509 and 5706, respectively, by searching the chromatogram library created by gas-phase fractionated DIA. Moreover, DIA protein quantification was highly reproducible, with median coefficients of variation of 4.3% in eight replicate analyses. We could demonstrate the power of this system by applying the proteomic analysis to detect subtle changes in protein profiles between cerebrums in germ-free and specific pathogen-free mice, which successfully showed that >40 proteins were differentially produced between the cerebrums in the presence or absence of bacteria. MDPI 2019-11-26 /pmc/articles/PMC6928715/ /pubmed/31779068 http://dx.doi.org/10.3390/ijms20235932 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kawashima, Yusuke
Watanabe, Eiichiro
Umeyama, Taichi
Nakajima, Daisuke
Hattori, Masahira
Honda, Kenya
Ohara, Osamu
Optimization of Data-Independent Acquisition Mass Spectrometry for Deep and Highly Sensitive Proteomic Analysis
title Optimization of Data-Independent Acquisition Mass Spectrometry for Deep and Highly Sensitive Proteomic Analysis
title_full Optimization of Data-Independent Acquisition Mass Spectrometry for Deep and Highly Sensitive Proteomic Analysis
title_fullStr Optimization of Data-Independent Acquisition Mass Spectrometry for Deep and Highly Sensitive Proteomic Analysis
title_full_unstemmed Optimization of Data-Independent Acquisition Mass Spectrometry for Deep and Highly Sensitive Proteomic Analysis
title_short Optimization of Data-Independent Acquisition Mass Spectrometry for Deep and Highly Sensitive Proteomic Analysis
title_sort optimization of data-independent acquisition mass spectrometry for deep and highly sensitive proteomic analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928715/
https://www.ncbi.nlm.nih.gov/pubmed/31779068
http://dx.doi.org/10.3390/ijms20235932
work_keys_str_mv AT kawashimayusuke optimizationofdataindependentacquisitionmassspectrometryfordeepandhighlysensitiveproteomicanalysis
AT watanabeeiichiro optimizationofdataindependentacquisitionmassspectrometryfordeepandhighlysensitiveproteomicanalysis
AT umeyamataichi optimizationofdataindependentacquisitionmassspectrometryfordeepandhighlysensitiveproteomicanalysis
AT nakajimadaisuke optimizationofdataindependentacquisitionmassspectrometryfordeepandhighlysensitiveproteomicanalysis
AT hattorimasahira optimizationofdataindependentacquisitionmassspectrometryfordeepandhighlysensitiveproteomicanalysis
AT hondakenya optimizationofdataindependentacquisitionmassspectrometryfordeepandhighlysensitiveproteomicanalysis
AT oharaosamu optimizationofdataindependentacquisitionmassspectrometryfordeepandhighlysensitiveproteomicanalysis