We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersortstanfordedu/)

/pdf/robust-enumeration-of-cell-subsets-from-tissue-expression-2prfdfkgtp.pdf

Robust enumeration of cell subsets from tissue expression profiles

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.

/pdf/global-quantification-of-mammalian-gene-expression-control-h9i2bar49f.pdf

Global quantification of mammalian gene expression control

Recent advances in next-generation DNA sequencing and proteomics provide an unprecedented ability to survey mRNA and protein abundances. Such proteome-wide surveys are illuminating the extent to which different aspects of gene expression help to regulate cellular protein abundances. Current data demonstrate a substantial role for regulatory processes occurring after mRNA is made - that is, post-transcriptional, translational and protein degradation regulation - in controlling steady-state protein abundances. Intriguing observations are also emerging in relation to cells following perturbation, single-cell studies and the apparent evolutionary conservation of protein and mRNA abundances. Here, we summarize current understanding of the major factors regulating protein expression.

/pdf/insights-into-the-regulation-of-protein-abundance-from-1nb4o968ef.pdf

Insights into the regulation of protein abundance from proteomic and transcriptomic analyses

基礎講座 電気泳動(Electrophoresis)

On the Dependency of Cellular Protein Levels on mRNA Abundance.

We report a method for large-scale absolute protein expression measurements (APEX) and apply it to estimate the relative contributions of transcriptional- and translational-level gene regulation in the yeast and Escherichia coli proteomes. APEX relies upon correcting each protein's mass spectrometry sampling depth (observed peptide count) by learned probabilities for identifying the peptides. APEX abundances agree with measurements from controls, western blotting, flow cytometry and two-dimensional gels, as well as known correlations with mRNA abundances and codon bias, providing absolute protein concentrations across approximately three to four orders of magnitude. Using APEX, we demonstrate that 73% of the variance in yeast protein abundance (47% in E. coli) is explained by mRNA abundance, with the number of proteins per mRNA log-normally distributed about approximately 5,600 ( approximately 540 in E. coli) protein molecules/mRNA. Therefore, levels of both eukaryotic and prokaryotic proteins are set per mRNA molecule and independently of overall protein concentration, with >70% of yeast gene expression regulation occurring through mRNA-directed mechanisms.

Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation

Large-scale protein expression measurements promise to open exciting areas of research, ranging from systematic investigations of post-transcriptional regulation, to numerical models of protein synthesis and decay, to detailed functional analyses of the many proteins expressed by a cell. This promise is even greater when such protein expression data are combined with DNA microarray or protein interaction data— such combinations offer the tantalizing promise of quantitative models of cellular events. Unfortunately, the field of proteomics has been slow to embrace one of the most important lessons from DNA microarrays: open availability of raw data. The availability of DNA microarray data, coupled with public genome sequence data, is arguably one of the primary forces driving computational research in functional genomics.

The need for a public proteomics repository.

Cells grow in dynamically evolving populations, yet this aspect of experiments often goes unmeasured. A method is proposed for measuring the population dynamics of cells on the basis of their mRNA expression patterns. The population's expression pattern is modeled as the linear combination of mRNA expression from pure samples of cells, allowing reconstruction of the relative proportions of pure cell types in the population. Application of the method, termed expression deconvolution, to yeast grown under varying conditions reveals the population dynamics of the cells during the cell cycle, during the arrest of cells induced by DNA damage and the release of arrest in a cell cycle checkpoint mutant, during sporulation, and following environmental stress. Using expression deconvolution, cell cycle defects are detected and temporally ordered in 146 yeast deletion mutants; six of these defects are independently experimentally validated. Expression deconvolution allows a reinterpretation of the cell cycle dynamics underlying all previous microarray experiments and can be more generally applied to study most forms of cell population dynamics.

/pdf/expression-deconvolution-a-reinterpretation-of-dna-3nj9r3znlw.pdf

Expression deconvolution: A reinterpretation of DNA microarray data reveals dynamic changes in cell populations

http://www.marcottelab.org/paper-pdfs/MetabolicEngineering_OneCarbonMetab_2007.pdf

Peng Lu

Papers

Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation

The need for a public proteomics repository.

Expression deconvolution: A reinterpretation of DNA microarray data reveals dynamic changes in cell populations

Global metabolic changes following loss of a feedback loop reveal dynamic steady states of the yeast metabolome.