Organs and organelles represent core biological systems in mammals, but the diversity in protein composition remains unclear. Here, we combine subcellular fractionation with exhaustive tandem mass spectrometry-based shotgun sequencing to examine the protein content of four major organellar compartments (cytosol, membranes [microsomes], mitochondria, and nuclei) in six organs (brain, heart, kidney, liver, lung, and placenta) of the laboratory mouse, Mus musculus. Using rigorous statistical filtering and machine-learning methods, the subcellular localization of 3274 of the 4768 proteins identified was determined with high confidence, including 1503 previously uncharacterized factors, while tissue selectivity was evaluated by comparison to previously reported mRNA expression patterns. This molecular compendium, fully accessible via a searchable web-browser interface, serves as a reliable reference of the expressed tissue and organelle proteomes of a leading model mammal.

Measuring the properties of endogenous cell proteins, such as expression level, subcellular localization, and turnover rates, on a whole proteome level remains a major challenge in the postgenome era. Quantitative methods for measuring mRNA expression do not reliably predict corresponding protein levels and provide little or no information on other protein properties. Here we describe a combined pulse-labeling, spatial proteomics and data analysis strategy to characterize the expression, localization, synthesis, degradation, and turnover rates of endogenously expressed, untagged human proteins in different subcellular compartments. Using quantitative mass spectrometry and stable isotope labeling with amino acids in cell culture, a total of 80,098 peptides from 8,041 HeLa proteins were quantified, and their spatial distribution between the cytoplasm, nucleus and nucleolus determined and visualized using specialized software tools developed in PepTracker. Using information from ion intensities and rates of change in isotope ratios, protein abundance levels and protein synthesis, degradation and turnover rates were calculated for the whole cell and for the respective cytoplasmic, nuclear, and nucleolar compartments. Expression levels of endogenous HeLa proteins varied by up to seven orders of magnitude. The average turnover rate for HeLa proteins was ∼20 h. Turnover rate did not correlate with either molecular weight or net charge, but did correlate with abundance, with highly abundant proteins showing longer than average half-lives. Fast turnover proteins had overall a higher frequency of PEST motifs than slow turnover proteins but no general correlation was observed between amino or carboxyl terminal amino acid identities and turnover rates. A subset of proteins was identified that exist in pools with different turnover rates depending on their subcellular localization. This strongly correlated with subunits of large, multiprotein complexes, suggesting a general mechanism whereby their assembly is controlled in a different subcellular location to their main site of function.

The quaking (Qk) locus expresses a family of RNA binding proteins, and the expression of several alternatively spliced isoforms coincides with the development of oligodendrocytes and the onset of myelination. Quaking viable (Qk(v)) mice harboring an autosomal recessive mutation in this locus have uncompacted myelin in the central nervous system owing to the inability of oligodendrocytes to properly mature. Here we show that the expression of two QKI isoforms, absent from oligodendrocytes of Qk(v) mice, induces cell cycle arrest of primary rat oligodendrocyte progenitor cells and differentiation into oligodendrocytes. Injection of retroviruses expressing QKI into the telencephalon of mouse embryos induced differentiation and migration of multipotential neural progenitor cells into mature oligodendrocytes localized in the corpus callosum. The mRNA encoding the cyclin-dependent kinase (CDK)-inhibitor p27(Kip1) was bound and stabilized by QKI, leading to an increased accumulation of p27(Kip1) protein in oligodendrocytes. Our findings demonstrate that QKI is upstream of p27(Kip1) during oligodendrocyte differentiation.

Small nucleolar RNAs (snoRNAs) are a large class of small noncoding RNAs present in all eukaryotes sequenced thus far. As a family, they have been well characterized as playing a central role in ribosome biogenesis, guiding either the sequence-specific chemical modification of pre-rRNA (ribosomal RNA) or its processing. However, in higher eukaryotes, numerous orphan snoRNAs were described over a decade ago, with no known target or ascribed function, suggesting the possibility of alternative cellular functionality. In recent years, thanks in great part to advances in sequencing methodologies, we have seen many examples of the diversity that exists in the snoRNA family on multiple levels. In this review, we discuss the identification of novel snoRNA members, of unexpected binding partners, as well as the clarification and extension of the snoRNA target space and the characterization of diverse new noncanonical functions, painting a new and extended picture of the snoRNA landscape. Under the deluge of novel features and functions that have recently come to light, snoRNAs emerge as a central, dynamic, and highly versatile group of small regulatory RNAs. WIREs RNA 2015, 6:381–397. doi: 10.1002/wrna.1284

Although the nucleolar localization of proteins is often believed to be mediated primarily by non-specific retention to core nucleolar components, many examples of short nucleolar targeting sequences have been reported in recent years. In this article, 46 human nucleolar localization sequences (NoLSs) were collated from the literature and subjected to statistical analysis. Of the residues in these NoLSs 48% are basic, whereas 99% of the residues are predicted to be solvent-accessible with 42% in α-helix and 57% in coil. The sequence and predicted protein secondary structure of the 46 NoLSs were used to train an artificial neural network to identify NoLSs. At a true positive rate of 54%, the predictor’s overall false positive rate (FPR) is estimated to be 1.52%, which can be broken down to FPRs of 0.26% for randomly chosen cytoplasmic sequences, 0.80% for randomly chosen nucleoplasmic sequences and 12% for nuclear localization signals. The predictor was used to predict NoLSs in the complete human proteome and 10 of the highest scoring previously unknown NoLSs were experimentally confirmed. NoLSs are a prevalent type of targeting motif that is distinct from nuclear localization signals and that can be computationally predicted.