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DNA microarray analysis of R1-9 ES cells

ABSTRACT: We have studied protein expression in mouse embryonic stem (mES) cells- R1-9 and identified the largest complement of proteins reported thus far for mES cells, using on-line LC-MS/MS analysis. With support from a transcriptome profile of the same mES cell line, we arrived at an integrated dataset of 9,370 protein expressions that may be useful to understand the stem cell dynamics. 6,278 protein identifications (IDs) were based on two or more peptide signatures and 3,092 with single peptide signatures. These single peptide-based protein IDs were also supported by transcriptome data. The proteomic analysis demonstrated the expression of 1,369 genes (among 9,370) that were not seen in RNA transcripts. Pathway analysis of the protein IDs in our dataset, using KEGG and BioCarta and their gene ontology classification, revealed high representation of the proteins belonging to many regulatory pathways and showed enrichment for protein IDs involved in transcription regulation, signal transduction, cell-cycle, and differentiation. Interestingly, the data support the expression of 1,299 putative open reading frames. In silico domain comparisons of some of these sequences matched with regulatory proteins such as transcription factors or signal transduction molecules. Over 60% of the transcript complement is represented in our protein dataset implying a corresponding richness of the dynamic network of proteins in a pluripotent mES cells.have thus arrived at a dataset of 10428 expressed proteins with high confidence which was also verified by microarray analysis of the expressed mRNAs R1-9 ES cells. Pathway analysis of these expressed proteins was carried out using KEGG, IPA and their gene ontology classification revealed the presence of large number of transcription regulators, signal transducers, cell cycle and differentiation molecules along with other general classes of proteins belonging to a number of regulatory pathways. The database also includes IDs of proteins corresponding to many still unidentified / unannotated ORFs from the mouse genome and with putative regulatory functions. Our study thus represents the largest database of expressed proteins in mouse ES cells that would be a valuable molecular resource for experimental designs in targeted proteomics investigations using mES cell lines.

ORGANISM(S): Mus musculus

PROVIDER: GSE15028 | GEO | 2019/04/22

REPOSITORIES: GEO

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Project description:Two-dimensional (2D) liquid chromatography (LC)-tandem mass spectrometry (MS/MS) are typically employed for deep bottom-up proteomics, and the state-of-the-art 2D-LC-MS/MS has approached over 8,000 protein identifications (IDs) from mammalian cell lines or tissues in 1-3 days of mass spectrometer time. Capillary zone electrophoresis (CZE)-MS/MS has been suggested as an alternative to LC-MS/MS for bottom-up proteomics. CZE-MS/MS and LC-MS/MS are complementary in protein/peptide ID from complex proteome digests because CZE and LC are orthogonal for peptide separation. In addition, the migration time of peptides from CZE-MS can be predicted accurately, which is invaluable for evaluating the confidence of peptide ID from the database search and even guiding the database search. However, the number of protein IDs from complex proteomes using CZE-MS/MS is still much lower than the state of the art using 2D-LC-MS/MS. In this work, for the first time, we established a strong cation exchange (SCX)-reversed phase LC (RPLC)-CZE-MS/MS platform for deep bottom-up proteomics. The platform identified around 8,200 protein groups and 65,000 unique peptides from a mouse brain proteome digest in 70 hours. The data represents the largest bottom-up proteomics dataset using CZE-MS/MS and provides a valuable resource for further improving the tool for prediction of peptide migration time in CZE. The peak capacity of the orthogonal SCX-RPLC-CZE platform was estimated to be around 7,000. SCX-RPLC-CZE-MS/MS produced comparable numbers of protein and peptide IDs with 2D-LC-MS/MS (8,200 vs. 8,900 protein groups, 65,000 vs. 70,000 unique peptides) from the mouse brain proteome digest using comparable instrument time. This is the first time that CZE-MS/MS showed its capability to approach comparable performance to the state-of-the-art 2D-LC-MS/MS for deep proteomic sequencing. SCX-RPLC-CZE-MS/MS and 2D-LC-MS/MS showed good complementarity in protein and peptide IDs and combining those two methods improved the number of protein group and unique peptide IDs by nearly 10% and over 40%, respectively, compared with 2D-LC-MS/MS alone.

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DNA microarray analysis of R1-9 ES cells

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