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NeuCode labels for relative protein quantification.

ABSTRACT: We describe a synthesis strategy for the preparation of lysine isotopologues that differ in mass by as little as 6 mDa. We demonstrate that incorporation of these molecules into the proteomes of actively growing cells does not affect cellular proliferation, and we discuss how to use the embedded mass signatures (neutron encoding (NeuCode)) for multiplexed proteome quantification by means of high-resolution mass spectrometry. NeuCode SILAC amalgamates the quantitative accuracy of SILAC with the multiplexing of isobaric tags and, in doing so, offers up new opportunities for biological investigation. We applied NeuCode SILAC to examine the relationship between transcript and protein levels in yeast cells responding to environmental stress. Finally, we monitored the time-resolved responses of five signaling mutants in a single 18-plex experiment.

SUBMITTER: Merrill AE

PROVIDER: S-EPMC4159665 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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NeuCode labels for relative protein quantification.

Merrill Anna E AE Hebert Alexander S AS MacGilvray Matthew E ME Rose Christopher M CM Bailey Derek J DJ Bradley Joel C JC Wood William W WW El Masri Marwan M Westphall Michael S MS Gasch Audrey P AP Coon Joshua J JJ

Molecular & cellular proteomics : MCP 20140617 9

We describe a synthesis strategy for the preparation of lysine isotopologues that differ in mass by as little as 6 mDa. We demonstrate that incorporation of these molecules into the proteomes of actively growing cells does not affect cellular proliferation, and we discuss how to use the embedded mass signatures (neutron encoding (NeuCode)) for multiplexed proteome quantification by means of high-resolution mass spectrometry. NeuCode SILAC amalgamates the quantitative accuracy of SILAC with the m ...[more]

PMID: 24938287

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Project description:MicroRNAs (miRNAs) have been shown to play an important role in many different cellular, developmental, and physiological processes. Accordingly, numerous methods have been established to identify and quantify miRNAs. The shortness of miRNA sequence results in a high dynamic range of melting temperatures and, moreover, impedes a proper selection of detection probes or optimized PCR primers. While miRNA microarrays allow for massive parallel and accurate relative measurement of all known miRNAs, they have so far been less useful as an assay for absolute quantification. Here, we present a microarray based approach for global and absolute quantification of miRNAs. The method relies on an equimolar pool of about 1000 synthetic miRNAs of known concentration which is used as an universal reference and labeled and hybridized in a dual colour approach on the same array as the sample of interest. Each single miRNA is quantified with respect to the universal reference outbalancing bias related to sequence, labeling, hybridization or signal detection method. We demonstrate the accuracy of the method by various spike in experiments. Further, we quantified miRNA copy numbers in liver samples and CD34(+)CD133(-) hematopoietic stem cells. We analyzed to which extend the universal reference can be used as a tool for the relative quantification of miRNAs across multiple experiments. We compared the results of direct hybridizations i.e. sample vs. sample to those of indirect hybridizations i.e. sample vs. UR. For the direct hybridizations, we hybridized 5Âµg liver total RNA vs 5 Âµg brain total RNA (n = 3) and for the indirect hybridization 5 Âµg liver or brain total RNA vs UR (5 fmol/miRNA) (n = 3). We calculated the so-called re-ratios for the UR experiments by dividing the signal ratios of the liver vs. UR array by the respective brain vs. UR array gaining a liver vs. brain re-ratio. Each RNA sample was mixed with 5 fmol of each of 18 RNA oligonucleotides reverse complement to miRControl 3 probes and subsequently fluorescently labelled. The RNA mix was hybridized in a dual colour approach to microarrays. The mean ratios of all probes were normalized to the median of the ratios detected for the spiked 18 synthetic RNA oligonucleotides reverse complement to miRControl 3 probes.

Project description:Clostridioides difficile (formerly Clostridium difficile) is a Gram-positive, anaerobe, spore-forming pathogen, which causes drug-induced diseases in hospitals worldwide. A detailed analysis of the proteome may provide new targets for drug development or therapeutic strategies to combat this pathogen. The application of metabolic labeling (ML) would allow for accurate quantification of significant differences in protein abundance, even in the case of very small changes. Additionally, it would be possible to perform more accurate studies of the membrane or surface proteomes, which usually require elaborated sample preparation. Such studies are therefore prone to higher standard deviations during the quantification. The implementation of ML strategies for C. difficile is complicated due to the lack in arginine and lysine auxotrophy as well as the Stickland dominated metabolism of this anaerobic pathogen. Hence, quantitative proteome analyses could only be carried out by label free or chemical labeling methods so far. In this paper, a ML approach for C. difficile is described. A cultivation procedure with 15N-labeled media for strain 630?erm was established achieving an incorporation rate higher than 97%. In a proof-of-principle experiment, the performance of the ML approach in C. difficile was tested. The proteome data of the cytosolic subproteome of C. difficile cells grown in complex medium as well as two minimal media in the late exponential and early stationary growth phase obtained via ML were compared with two label free relative quantification approaches (NSAF and LFQ). The numbers of identified proteins were comparable within the three approaches, whereas the number of quantified proteins were between 1,110 (ML) and 1,861 (LFQ) proteins. A hierarchical clustering showed clearly separated clusters for the different conditions and a small tree height with ML approach. Furthermore, it was shown that the quantification based on ML revealed significant altered proteins with small fold changes compared to the label free approaches. The quantification based on ML was accurate, reproducible, and even more sensitive compared to label free quantification strategies.

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NeuCode labels for relative protein quantification.

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NeuCode labels for relative protein quantification.

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