Next-generation proteomics: towards an integrative view of proteome dynamics
TL;DR: MS-based proteomics is starting to mature and to deliver through a combination of developments in instrumentation, sample preparation and computational analysis, and to highlight recent applications.
Abstract: Next-generation sequencing allows the analysis of genomes, including those representing disease states. However, the causes of most disorders are multifactorial, and systems-level approaches, including the analysis of proteomes, are required for a more comprehensive understanding. The proteome is extremely multifaceted owing to splicing and protein modifications, and this is further amplified by the interconnectivity of proteins into complexes and signalling networks that are highly divergent in time and space. Proteome analysis heavily relies on mass spectrometry (MS). MS-based proteomics is starting to mature and to deliver through a combination of developments in instrumentation, sample preparation and computational analysis. Here we describe this emerging next generation of proteomics and highlight recent applications.
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TL;DR: The Perseus software platform was developed to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data and it is anticipated that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
Abstract: A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
5,165 citations
TL;DR: A new intensity determination and normalization procedure called MaxLFQ is developed that is fully compatible with any peptide or protein separation prior to LC-MS analysis, which accurately detects the mixing ratio over the entire protein expression range, with greater precision for abundant proteins.
3,732 citations
Journal Article•
TL;DR: Why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease are detailed.
Abstract: Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.
1,323 citations
TL;DR: The emerging approaches for data integration — including meta-dimensional and multi-staged analyses — which aim to deepen the understanding of the role of genetics and genomics in complex outcomes are explored.
Abstract: Recent technological advances have expanded the breadth of available omic data, from whole-genome sequencing data, to extensive transcriptomic, methylomic and metabolomic data. A key goal of analyses of these data is the identification of effective models that predict phenotypic traits and outcomes, elucidating important biomarkers and generating important insights into the genetic underpinnings of the heritability of complex traits. There is still a need for powerful and advanced analysis strategies to fully harness the utility of these comprehensive high-throughput data, identifying true associations and reducing the number of false associations. In this Review, we explore the emerging approaches for data integration - including meta-dimensional and multi-staged analyses - which aim to deepen our understanding of the role of genetics and genomics in complex outcomes. With the use and further development of these approaches, an improved understanding of the relationship between genomic variation and human phenotypes may be revealed.
825 citations
TL;DR: This work presents CSI:FingerID, which combines fragmentation tree computation and machine learning for searching molecular structure databases using tandem MS data of small molecules, and is shown to improve on the competing methods for computational metabolite identification by a considerable margin.
Abstract: Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually rely on tandem MS to identify the thousands of compounds in a biological sample. Today, the vast majority of metabolites remain unknown. We present a method for searching molecular structure databases using tandem MS data of small molecules. Our method computes a fragmentation tree that best explains the fragmentation spectrum of an unknown molecule. We use the fragmentation tree to predict the molecular structure fingerprint of the unknown compound using machine learning. This fingerprint is then used to search a molecular structure database such as PubChem. Our method is shown to improve on the competing methods for computational metabolite identification by a considerable margin.
598 citations
References
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TL;DR: Induction of pluripotent stem cells from mouse embryonic or adult fibroblasts by introducing four factors, Oct3/4, Sox2, c-Myc, and Klf4, under ES cell culture conditions is demonstrated and iPS cells, designated iPS, exhibit the morphology and growth properties of ES cells and express ES cell marker genes.
23,959 citations
TL;DR: Using a quantitative model, the first genome-scale prediction of synthesis rates of mRNAs and proteins is obtained and it is found that the cellular abundance of proteins is predominantly controlled at the level of translation.
Abstract: Gene expression is a multistep process that involves the transcription, translation and turnover of messenger RNAs and proteins. Although it is one of the most fundamental processes of life, the entire cascade has never been quantified on a genome-wide scale. Here we simultaneously measured absolute mRNA and protein abundance and turnover by parallel metabolic pulse labelling for more than 5,000 genes in mammalian cells. Whereas mRNA and protein levels correlated better than previously thought, corresponding half-lives showed no correlation. Using a quantitative model we have obtained the first genome-scale prediction of synthesis rates of mRNAs and proteins. We find that the cellular abundance of proteins is predominantly controlled at the level of translation. Genes with similar combinations of mRNA and protein stability shared functional properties, indicating that half-lives evolved under energetic and dynamic constraints. Quantitative information about all stages of gene expression provides a rich resource and helps to provide a greater understanding of the underlying design principles.
5,635 citations
TL;DR: It is proposed that this "competing endogenous RNA" (ceRNA) activity forms a large-scale regulatory network across the transcriptome, greatly expanding the functional genetic information in the human genome and playing important roles in pathological conditions, such as cancer.
5,334 citations
TL;DR: It is concluded that intestinal cryptvillus units are self-organizing structures, which can be built from a single stem cell in the absence of a non-epithelial cellular niche.
Abstract: The intestinal epithelium is the most rapidly self-renewing tissue in adult mammals. We have recently demonstrated the presence of about six cycling Lgr5(+) stem cells at the bottoms of small-intestinal crypts. Here we describe the establishment of long-term culture conditions under which single crypts undergo multiple crypt fission events, while simultanously generating villus-like epithelial domains in which all differentiated cell types are present. Single sorted Lgr5(+) stem cells can also initiate these cryptvillus organoids. Tracing experiments indicate that the Lgr5(+) stem-cell hierarchy is maintained in organoids. We conclude that intestinal cryptvillus units are self-organizing structures, which can be built from a single stem cell in the absence of a non-epithelial cellular niche.
5,193 citations
TL;DR: A proteomic-scale analysis of protein acetylation suggests that it is an important biological regulatory mechanism and the regulatory scope of lysine acetylations is broad and comparable with that of other major posttranslational modifications.
Abstract: Lysine acetylation is a reversible posttranslational modification of proteins and plays a key role in regulating gene expression. Technological limitations have so far prevented a global analysis of lysine acetylation's cellular roles. We used high-resolution mass spectrometry to identify 3600 lysine acetylation sites on 1750 proteins and quantified acetylation changes in response to the deacetylase inhibitors suberoylanilide hydroxamic acid and MS-275. Lysine acetylation preferentially targets large macromolecular complexes involved in diverse cellular processes, such as chromatin remodeling, cell cycle, splicing, nuclear transport, and actin nucleation. Acetylation impaired phosphorylation-dependent interactions of 14-3-3 and regulated the yeast cyclin-dependent kinase Cdc28. Our data demonstrate that the regulatory scope of lysine acetylation is broad and comparable with that of other major posttranslational modifications.
3,787 citations