Comparative Proteomic Analysis of Eleven Common Cell Lines Reveals Ubiquitous but Varying Expression of Most Proteins

By T. Geiger, Anja Wehner, Christoph Schaab, Jüergen Cox, Matthias Mann. Molecular & Cellular Proteomics, 2012

742 citations recorded by the source corpus.

Deep proteomic analysis of mammalian cell lines would yield an inventory of the building blocks of the most commonly used systems in biological research. Mass spectrometry-based proteomics can identify and quantify proteins in a global and unbiased manner and can highlight the cellular processes that are altered between such systems. We analyzed 11 human cell lines using an LTQ-Orbitrap family mass spectrometer with a “high field” Orbitrap mass analyzer with improved resolution and sequencing speed. We identified a total of 11,731 proteins, and on average 10,361 ± 120 proteins in each cell line. This very high proteome coverage enabled analysis of a broad range of processes and functions. Despite the distinct origins of the cell lines, our quantitative results showed surprisingly high similarity in terms of expressed proteins. Nevertheless, this global similarity of the proteomes did not imply equal expression levels of individual proteins across the 11 cell lines, as we found significant differences in expression levels for an estimated two-third of them. The variability in cellular expression levels was similar for low and high abundance proteins, and even many of the most highly expressed proteins with household roles showed significant differences between cells. Metabolic pathways, which have high redundancy, exhibited variable expression, whereas basic cellular functions such as the basal transcription machinery varied much less. We harness knowledge of these cell line proteomes for the construction of a broad coverage “super-SILAC” quantification standard. Together with the accompanying paper (Schaab, C. MCP 2012, PMID: 22301388) (17) these data can be used to obtain reference expression profiles for proteins of interest both within and across cell line proteomes.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/5bb8fd41296f6e23683eb125350ce9517db9bbd1

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Citations in the corpus

Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ (2014, background): “However, when comparing peptides in similar LC-MS runs, the information contained in peptide mass and recalibrated retention time is enough to transfer identifications with a sufficiently low FDR (in the range of 1%), which one can estimate by comparing the density of matches inside the match time w”
P-Body Purification Reveals the Condensation of Repressed mRNA Regulons. (2017, methodology): “To better characterize the translation of P-body-targeted mRNAs, we first considered the abundance of their encoded proteins using a published quantitative HEK293 cell proteome (Geiger et al., 2012).”
Decoding Mammalian Ribosome-mRNA States by Translational GTPase Complexes (2016, background): “This barrier and the relatively low abundance of Pelota and Hbs1l (Geiger et al., 2012) probably renders Pelota,Hbs1l a poor competitor for elongating or terminating ribosomes.”
A Census of Human Soluble Protein Complexes (2012, positive): “The vast majority of complex components are universally expressed in 11 cancer cell lines (Geiger et al., 2012) (Figure S3A) and show high and largely invariant expression in an mRNA sequencing (mRNAseq) study of 16 normal human tissues (EBI accession number”
The MaxQuant computational platform for mass spectrometry-based shotgun proteomics (2016, methodology): “40| Set the minimum length for a peptide to be considered for identification, as well as the maximum peptide mass. 41| Set the minimum and the maximum peptide lengths in case of unspecific search (i.e., without digestion enzyme) if applicable.”
A subcellular map of the human proteome (2017, result): “However, the TPM-based number corresponds more accurately to the number of proteins actually detected in this and other proteomic studies (30, 31).”
Analysis of High Accuracy, Quantitative Proteomics Data in the MaxQB Database (2012, methodology): “We illustrate MaxQB with deep proteome data generated in an accompanying paper (18).”