Integration of Proteomics and Transcriptomics Data Sets for the Analysis of a Lymphoma B-Cell Line in the Context of the Chromosome-Centric Human Proteome Project

Abstract

A comprehensive study of the molecular active landscape of human cells can be undertaken to integrate two different but complementary perspectives: transcriptomics, and proteomics. After the genome era, proteomics has emerged as a powerful tool to simultaneously identify and characterize the compendium of thousands of different proteins active in a cell. Thus, the Chromosome-centric Human Proteome Project (C-HPP) is promoting a full characterization of the human proteome combining high-throughput proteomics with the data derived from genome-wide expression profiling of protein-coding genes. Here we present a full proteomic profiling of a human lymphoma B-cell line (Ramos) performed using a nanoUPLC-LTQ-Orbitrap Velos proteomic platform, combined to an in-depth transcriptomic profiling of the same cell type. Data are available via ProteomeXchange with identifier PXD001933. Integration of the proteomic and transcriptomic data sets revealed a 94% overlap in the proteins identified by both -omics approaches. Moreover, functional enrichment analysis of the proteomic profiles showed an enrichment of several functions directly related to the biological and morphological characteristics of B-cells. In turn, about 30% of all protein-coding genes present in the whole human genome were identified as being expressed by the Ramos cells (stable average of 30% genes along all the chromosomes), revealing the size of the protein expression-set present in one specific human cell type. Additionally, the identification of missing proteins in our data sets has been reported, highlighting the power of the approach. Also, a comparison between neXtProt and UniProt database searches has been performed. In summary, our transcriptomic and proteomic experimental profiling provided a high coverage report of the expressed proteome from a human lymphoma B-cell type with a clear insight into the biological processes that characterized these cells. In this way, we demonstrated the usefulness of combining -omics for a comprehensive characterization of specific biological systems.

Integration and Comparison of Transcriptomic and Proteomic Data for Meningioma

“…Moreover, proteins are subject to many post-translational modifications undetectable at the transcript level, such as phosphorylation, which regulate protein structure, functional state and subcellular compartmental localisation. Previous integrations of transcriptomic and proteomic profiles generated from mammalian cell lines, including some cancer cell lines, have shown a general overall correlation of transcript and protein levels despite notable differences [16][17][18].…”

A combined proteomics and metabolomics approach to assess the effects of gold nanoparticlesin vitro

“…To date, post-genomics techniques have been applied in the field of nanomaterial risk assessment to a limited extent (Jia et al, 2013 ; Kobeissy et al, 2014 ). Furthermore, the integration of multi-omics data has only recently been performed (Bartel et al, 2015 ; Cooney et al, 2015 ; Decourcelle et al, 2015 ; Diez et al, 2015 ; Eichner et al, 2014 ; Fagerberg et al, 2014 ; Kutmon et al, 2014 ; Meierhofer et al, 2014 ). The proteome and metabolome are directly interconnected as protein levels influence the metabolic profile of a cell system and metabolites' concentration may affect protein expression.…”

Dynamic Intracellular Metabolic Cell Signaling Profiles During Ag-Dependent B-Cell Differentiation

“…To evaluate the correlation between transcriptomics and proteomics datasets for the different B- cell populations analyzed (naive, CB, CC, and memory), an updated version of the R-script tool previously described by our group ( 35 ) for integrating both sets of data was used. After calculating the corresponding ratios between proteomics and transcriptomics expression values ( Supplementary Table S9 ), the results revealed higher protein/transcript ratios for CB, CC and memory B-cells than for the naive B-cells ( Figure 4 ).…”

“…Human B-cell differentiation has been extensively investigated on genomics and transcriptomics grounds ( 12 , 15 , 35 ), providing relevant data about those genes (and their modifications) and transcripts that regulate B-cell differentiation. Nevertheless, no studies have addressed so far, a detailed analysis of cells at different Ag-dependent human B-cell maturation stages from a quantitative and differential intracellular signaling proteomic perspective.…”