Integrating transcriptome and proteome profiling: Strategies and applications

Abstract

Discovering the gene expression signature associated with a cellular state is one of the basic quests in majority of biological studies. For most of the clinical and cellular manifestations, these molecular differences may be exhibited across multiple layers of gene regulation like genomic variations, gene expression, protein translation and post-translational modifications. These system wide variations are dynamic in nature and their crosstalk is overwhelmingly complex, thus analyzing them separately may not be very informative. This necessitates the integrative analysis of such multiple layers of information to understand the interplay of the individual components of the biological system. Recent developments in high throughput RNA sequencing and mass spectrometric (MS) technologies to probe transcripts and proteins made these as preferred methods for understanding global gene regulation. Subsequently, improvements in "big-data" analysis techniques enable novel conclusions to be drawn from integrative transcriptomic-proteomic analysis. The unified analyses of both these data types have been rewarding for several biological objectives like improving genome annotation, predicting RNA-protein quantities, deciphering gene regulations, discovering disease markers and drug targets. There are different ways in which transcriptomics and proteomics data can be integrated; each aiming for different research objectives. Here, we review various studies, approaches and computational tools targeted for integrative analysis of these two high-throughput omics methods.

Transcriptome and proteomic analyses reveal multiple differences associated with chloroplast development in the spaceflight-induced wheat albino mutant mta

“…Integrating transcriptome and proteome profiling data is generally believed to provide a better understanding of the differences in gene regulation and complex biological processes between wild type and variations [47]. As transcriptome analyses provide information about quantitative changes in gene expression, these data can be used to obtain fundamental insights into specific pathways and genes associated with certain species [13].…”

Data Aggregation at the Level of Molecular Pathways Improves Stability of Experimental Transcriptomic and Proteomic Data

“…Next generation sequencing (NGS), Microarray hybridization (MH) and high throughput proteomic 3 techniques opened a new era in biomedicine by enabling large scale analysis of gene expression at 4 both the RNA and protein levels [Kumar, 2016]. Multiple experimental platforms based on different 5 principles and utilizing different reagents were developed for these tasks [Kumar, 2016].…”

When one becomes many—Alternative splicing in β‐cell function and failure

“…The recently developed ASpedia database integrates several layers of information covering genomic features as well as isoform‐level functions . Integration of transcriptomic and proteomic data (proteogenomics) is an emerging field that can help to identify expressed splice variants at protein level . It is important to keep in mind, however, that RNA sequencing is presently much more sensitive than mass‐spectrometry proteomics.…”

“…58 Integration of transcriptomic and proteomic data (proteogenomics) is an emerging field that can help to identify expressed splice variants at protein level. 62 It is important to keep in mind, however, that RNA sequencing is presently much more sensitive than mass-spectrometry proteomics. For instance, in our own parallel RNA-seq and proteomic analysis of human islets or EndoC-β1 cells exposed to different cytokines (using state-of-the art technology) we usually detect around 50.000 of the expected 80.000-100.000 splice variants, and around 10.000-12.000 of the expected >200.000 proteins (unpublished data).…”