Dataset Information

Identification of Missing Proteins in Human Olfactory Epithelial Tissue by Liquid Chromatography-Tandem Mass Spectrometry

ABSTRACT: We performed proteomic analyses of human olfactory epithelial tissue to identify missing proteins using liquid chromatography-tandem mass spectrometry. Using a next-generation proteomic pipeline with a <1.0% false discovery rate at the peptide and protein levels, we identified 3,731 proteins, among which five were missing proteins (P0C7M7, P46721, P59826, Q658L1, and Q8N434). We validated the identified missing proteins using the corresponding synthetic peptides. We also identified 49 and 50 alternative splicing variants mapped at the neXtProt and GENCODE databases, respectively, and 2,000 additional single amino acid variants.

INSTRUMENT(S): Orbitrap Fusion

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Jong Shin Yoo

PROVIDER: MSV000086444 | MassIVE | Thu Nov 12 08:06:00 GMT 2020

SECONDARY ACCESSION(S): PXD010025

REPOSITORIES: MassIVE

ACCESS DATA

Publications

Identification of Missing Proteins in Human Olfactory Epithelial Tissue by Liquid Chromatography-Tandem Mass Spectrometry.

Hwang Heeyoun H Jeong Ji Eun JE Lee Hyun Kyoung HK Yun Ki Na KN An Hyun Joo HJ Lee Bonghee B Paik Young-Ki YK Jeong Tae Seok TS Yee Gi Taek GT Kim Jin Young JY Yoo Jong Shin JS

Journal of proteome research 20180820 12

We performed proteomic analyses of human olfactory epithelial tissue to identify missing proteins using liquid chromatography-tandem mass spectrometry. Using a next-generation proteomic pipeline with a < 1.0% false discovery rate at the peptide and protein levels, we identified 3731 proteins, among which five were missing proteins (P0C7M7, P46721, P59826, Q658L1, and Q8N434). We validated the identified missing proteins using the corresponding synthetic peptides. No olfactory receptor (OR) prote ...[more]

PMID: 30113170

Similar Datasets

Project description:Human embryonic stem cells (hESCs) have capacity of self-renewal and multi-lineage differentiation into derivatives of three germ layers that are of clinical importance for regeneration medicine. Despite the significant progress of hESCs study for clinical application, factors responsible for maintaining the undifferentiated and pluripotent nature of hESCs are still largely unknown. The knowledge barrier between human genomic information and its proteomics was proposed due to the ‘missing proteins’(MPs), proteins that do not have a stringent evidence for existence at protein level, in away investigation of MPs has been continued since 2012 as one of the major goals of Chromosome-Centric Human Proteome Project (C-HPP). Nearly 18% of the human proteins are awaiting high-stringency mass spectrometry evidence of their existences in the human proteome. In this study, we proposed subcellular proteomics analysis aimed to identify the nuclear, cytoplasmic and membrane proteins involved in regulating ESC pluripotency and among them to mine missing proteins in the very early cell status. Our study achieved an identification of 11,970 unique proteins (264,088 unique peptides) in hESCs of which 6,138 were annotated as membrane proteins. Compared with large scale proteomic analysis of hESCs this study additionally identified 3,626 unique protein coding genes. This large scale proteomic data mined a total of 296 candidate detections of missing proteins at 1% FDR. Following the strict criteria of HPP data interpretation guidelines (version 2.1.0), a total of 25 Gold Missing Proteins (GMPs) were confidently identified among which 12 of them were verified with synthetic peptides and finally 10 were confirmed with 2 unique peptides. In summary, these analyses suggested that our results covered the most comprehensive proteome and membrane proteomics of hESCs that would be valuable to provide protein-level evidence for missing membrane proteins. Sample Processing Protocol: A subcellular purification was applied to generate nuclear, cytoplasmic and membrane protein fractions using our previously reported 2-step centrifugation method with sucrose. These three protein fractions were subjected to gel-assisted digestion with trypsin followed by peptide fractionation. We employed our developed sensitive high-pH reversed phase (Hp-RP) stop-and-go extraction tip (StageTip) technique to collect 7 RP fractions for the first batch analysis. For the second batch of membrane proteome analysis and both batches of cytoplasmic and nuclear proteomes, we applied High pH reversed-phase liquid chromatography (RPLC) and concatenation to generate 12 (for membrane proteome) and 24 (for nucleus and cytoplasm proteomes) RP fractions, respectively. All the RP fractions were analyzed in duplicate by high-end Orbitrap Fusion™ or Fusion™ Lumos™ Tribrid™ mass spectrometers. Data Processing protocol: Protein identifications were performed for each RP fraction by using Proteome Discoverer 2.1 (PD 2.1) with three search engines, Mascot, SequestHT and MSAmanda, at 1% FDR in peptide, protein and and high confidence PSM levels. To obtain the total list of identified proteins in nuclear, cytoplasmic and membrane proteomes, we adopted the consensus workflow in PD 2.1 to combine all the fraction data with filtering to 1% protein FDR. In order to mine missing proteins from the subcellular proteomes of hECs, we followed CHPP guideline version 2.1.0 with stringent criteria among which (1) detection at 1% FDR (protein, peptide) and with high confidence, unambiguous PSMs; (2) uniqueness of the peptide, alternate peptide mappings, isobaric sequences or SAAVs, checked by the Peptide Uniqueness Checker (nextprot, 20180117 release); (3) unique peptide sequences of length ≥9 amino acid; (4) two or more unique, non-nested peptide sequences were applied entirely for stepwise filtering of candidate detections. For validating missing proteins with synthetic peptides, MRM analysis was conducted by QTrap 5500 system (AB SCIEX Concord, ON) using Trap column (ACQUITY UPLC® symmetry C18, 5 µm, 180 µm x 20mm) with BEH C18-AQ column (nanoACQUITY® 1.7 µm, 75 µm x 250 mm). Membrane protein annotations were used from neXtProt 2018-01 consisting of Human Proteome We further annotated the cellular localizations and the number of TMH for all identified proteins using the latest version of neXtProt (2018-01). Total identified proteins in three subcellular proteome data were analyzed using KEGG & IPA databases for contracting pathway analysis. The Human Proteome Atlas (HPA), Gene Ontology and other informatic tools were used as a source for generating lineage specific analysis.

Project description:In recent years, high-throughput technologies have contributed to development a more precise picture of the human proteome. However, 2,129 proteins remain listed as missing proteins (MPs) in the newest neXtProt release (2019-02). The main reasons for MPs are a low abundance, low-molecular-weight (LMW), unexpected modifications, membrane characteristics, etc. Moreover, more than 50% of the MS/MS data have not been successfully identified in shotgun proteomics. Open-pFind, an efficient open search engine, recently released by the pFind group in China, presents an opportunity to identify these buried MPs in complex samples. Proteins and potential MPs were identified using Open-pFind and three other search engines to compare their performance and efficiency with three large-scale datasets digested by different enzymes. Our results demonstrated that Open-pFind identified 29.9-47.5% more peptide-spectrum matches (PSMs), 48.0-63.9% more peptides sequences (with modifications) and 22.7-38.1% more peptide sequences (regardless of modifications) than those identified by the second-best search engine. As a result, Open-pFind detected 7.5-19.3% more candidate MPs than those by the second-best search engine. In total, 5 (PE2) of the 150 candidate MPs identified by Open-pFind were verified from two unique peptides containing more than 9 amino acids (AA) by using spectrum theoretical prediction with pDeep, and synthesized peptide matching with pBuild, after spectrum quality analysis, isobaric post-translational modification, and single amino acid variant (SAAV) filtering. These five verified MPs can be ranked in the PE1 level. In addition, three other candidate MPs were verified with two unique peptides (one peptide containing more than 9 AA) and the other containing only 8 AA), which were slightly lower than the criteria listed by C-HPP, and required additional verification information. More importantly, unexpected modifications were detected in these MPs. Another 141 MPs were listed as candidates, but required additional verification information.

Dataset Information

Identification of Missing Proteins in Human Olfactory Epithelial Tissue by Liquid Chromatography-Tandem Mass Spectrometry

Publications

Identification of Missing Proteins in Human Olfactory Epithelial Tissue by Liquid Chromatography-Tandem Mass Spectrometry.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure