Amine-reactive neutron-encoded labels for highly plexed proteomic quantitation.
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ABSTRACT:
SUBMITTER: Hebert AS
PROVIDER: S-EPMC3820946 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We assemble a versatile molecular scaffold from simple building blocks to create binary and multiplexed stable isotope reagents for quantitative mass spectrometry. Termed Protected Amine Labels (PAL), these reagents offer multiple analytical figures of merit including, (1) robust targeting of peptide N-termini and lysyl side chains, (2) optimal mass spectrometry ionization efficiency through regeneration of primary amines on labeled peptides, (3) an amino acid-based mass tag that incorporates heavy isotopes of carbon, nitrogen, and oxygen to ensure matched physicochemical and MS/MS fragmentation behavior among labeled peptides, and (4) a molecularly efficient architecture, in which the majority of hetero-atom centers can be used to synthesize a variety of nominal mass and sub-Da isotopologue stable isotope reagents. We demonstrate the performance of these reagents in well-established strategies whereby up to four channels of peptide isotopomers, each separated by 4 Da, are quantified in MS-level scans with accuracies comparable to current commercial reagents. In addition, we utilize the PAL scaffold to create isotopologue reagents in which labeled peptide analogs differ in mass based on the binding energy in carbon and nitrogen nuclei, thereby allowing quantification based on MS or MS/MS spectra. We demonstrate accurate quantification for reagents that support 6-plex labeling and propose extension of this scheme to 9-channels based on a similar PAL scaffold. Finally, we provide exemplar data that extend the application of isotopologe-based quantification reagents to medium resolution, quadrupole time-of-flight mass spectrometers.
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Project description:Metabolic labeling of cells using heavy amino acids is most commonly used for relative quantitation; however, partner mass shifts also detail the number of heavy amino acids contained within the precursor species. Here, we use a recently developed metabolic labeling technique, NeuCode (neutron encoding) stable isotope labeling with amino acids in cell culture (SILAC), which produces precursor partners spaced ~40 mDa apart to enable amino acid counting. We implement large scale counting of amino acids through a program, "Amino Acid Counter", which determines the most likely combination of amino acids within a precursor based on NeuCode SILAC partner spacing and filters candidate peptide sequences during a database search using this information. Counting the number of lysine residues for precursors selected for MS/MS decreases the median number of candidate sequences from 44 to 14 as compared to an accurate mass search alone (20 ppm). Furthermore, the ability to co-isolate and fragment NeuCode SILAC partners enables counting of lysines in product ions, and when the information is used, the median number of candidates is reduced to 7. We then demonstrate counting leucine in addition to lysine results in a 6-fold decrease in search space, 43 to 7, when compared to an accurate mass search. We use this scheme to analyze a nanoLC-MS/MS experiment and demonstrate that accurate mass plus lysine and leucine counting reduces the number of candidate sequences to one for ~20% of all precursors selected, demonstrating an ability to identify precursors without MS/MS analysis.
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Project description:We describe a protein quantification method called neutron encoding that exploits the subtle mass differences caused by nuclear binding energy variation in stable isotopes. These mass differences are synthetically encoded into amino acids and incorporated into yeast and mouse proteins via metabolic labeling. Mass spectrometry analysis with high mass resolution (>200,000) reveals the isotopologue-embedded peptide signals, permitting quantification. Neutron encoding will enable highly multiplexed proteome analysis with excellent dynamic range and accuracy.
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Project description:We describe a chemical tag for duplex proteome quantification using neutron encoding (NeuCode). The method utilizes the straightforward, efficient, and inexpensive carbamylation reaction. We demonstrate the utility of NeuCode carbamylation by accurately measuring quantitative ratios from tagged yeast lysates mixed in known ratios and by applying this method to quantify differential protein expression in mice fed a either control or high-fat diet.
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Project description:The ability to acquire highly accurate quantitative data is an increasingly important part of any proteomics experiment, whether shotgun or top-down approaches are used. We recently developed a quantitation strategy for peptides based on neutron encoding, or NeuCode SILAC, which uses closely spaced heavy isotope-labeled amino acids and high-resolution mass spectrometry to provide quantitative data. We reasoned that the strategy would also be applicable to intact proteins and could enable robust, multiplexed quantitation for top-down experiments. We used yeast lysate labeled with either (13)C6(15)N2-lysine or (2)H8-lysine, isotopologues of lysine that are spaced 36 mDa apart. Proteins having such close spacing cannot be distinguished during a medium resolution scan, but upon acquiring a high-resolution scan, the two forms of the protein with each amino acid are resolved and the quantitative information revealed. An additional benefit NeuCode SILAC provides for top down is that the spacing of the isotope peaks indicates the number of lysines present in the protein, information that aids in identification. We used NeuCode SILAC to quantify several hundred isotope distributions, manually identify and quantify proteins from 1:1, 3:1, and 5:1 mixed ratios, and demonstrate MS(2)-based quantitation using ETD.
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Project description:We report the use of neutron-encoded (NeuCode) stable isotope labeling of amino acids in cell culture for the purpose of C-terminal product ion annotation. Two NeuCode labeling isotopologues of lysine, (13)C6(15)N2 and (2)H8, which differ by 36 mDa, were metabolically embedded in a sample proteome, and the resultant labeled proteins were combined, digested, and analyzed via liquid chromatography and mass spectrometry. With MS/MS scan resolving powers of ~50,000 or higher, product ions containing the C terminus (i.e. lysine) appear as a doublet spaced by exactly 36 mDa, whereas N-terminal fragments exist as a single m/z peak. Through theory and experiment, we demonstrate that over 90% of all y-type product ions have detectable doublets. We report on an algorithm that can extract these neutron signatures with high sensitivity and specificity. In other words, of 15,503 y-type product ion peaks, the y-type ion identification algorithm correctly identified 14,552 (93.2%) based on detection of the NeuCode doublet; 6.8% were misclassified (i.e. other ion types that were assigned as y-type products). Searching NeuCode labeled yeast with PepNovo(+) resulted in a 34% increase in correct de novo identifications relative to searching through MS/MS only. We use this tool to simplify spectra prior to database searching, to sort unmatched tandem mass spectra for spectral richness, for correlation of co-fragmented ions to their parent precursor, and for de novo sequence identification.
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Project description:Highly multiplexed, single-cell technologies reveal important heterogeneity within cell populations. Recently, technologies to simultaneously measure expression of 96 (or more) genes from a single cell have been developed for immunologic monitoring. Here, we report a rigorous, optimized, quantitative methodology for using this technology. Specifically: we describe a unique primer/probe qualification method necessary for quantitative results; we show that primers do not compete in highly multiplexed amplifications; we define the limit of detection for this assay as a single mRNA transcript; and, we show that the technical reproducibility of the system is very high. We illustrate two disparate applications of the platform: a "bulk" approach that measures expression patterns from 100 cells at a time in high throughput to define gene signatures, and a single-cell approach to define the coordinate expression patterns of multiple genes and reveal unique subsets of cells.
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Project description:Amine-functionalized bridged silsesquioxanes (BSs) were synthesized from bis[(3-trimethoxysilyl)propyl] amine via a solvent-mediated route. BS-1 and BS-2 were obtained at neutral pH with sub- and stoichiometric amounts of water, respectively, and high tetrahydrofuran content. BS-3 was prepared with hyperstoichiometric water concentration, high tetrahydrofuran content, and hydrochloric acid. BS-4 was synthesized with hyperstoichiometric water concentration, high ethanol content, and sodium hydroxide. BS-1 and BS-2 were produced as transparent films, whereas BS-3 and BS-4 formed white powders. Face-to-face stacking of flat or folded lamellae yielded quasi-hydrophobic platelets with emission quantum yields of 0.05 ± 0.01 (BS-1 and BS-2) or superhydrophilic onion-like nanoparticles with exciting emission quantum yields of 0.38 ± 0.03 (BS-3) and 0.33 ± 0.04 (BS-4), respectively. The latter two values are the largest ever reported for amine-functionalized siloxane-based hybrids lacking aromatic groups. Fast Grotthus proton hopping between = [Formula: see text]/ = NH groups (BS-3) and = N-/ = NH groups (BS-4), promoted by H+ and OH- ions, respectively, and aided by short amine-amine contacts provided by the onion-like morphology, account for this unique optical behavior.
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Project description:The rapid development of mass spectrometry (MS) technologies has solidified shotgun proteomics as the most powerful analytical platform for large-scale proteome interrogation. The ability to map and determine differential expression profiles of the entire proteome is the ultimate goal of shotgun proteomics. Label-free quantitation has proven to be a valid approach for discovery shotgun proteomics, especially when sample is limited. Label-free spectral count quantitation is an approach analogous to RNA sequencing whereby count data is used to determine differential expression. Here we show that statistical approaches developed to evaluate differential expression in RNA sequencing experiments can be applied to detect differential protein expression in label-free discovery proteomics. This approach, termed MultiSpec, utilizes open-source statistical platforms; namely edgeR, DESeq and baySeq, to statistically select protein candidates for further investigation. Furthermore, to remove bias associated with a single statistical approach a single ranked list of differentially expressed proteins is assembled by comparing edgeR and DESeq q-values directly with the false discovery rate (FDR) calculated by baySeq. This statistical approach is then extended when applied to spectral count data derived from multiple proteomic pipelines. The individual statistical results from multiple proteomic pipelines are integrated and cross-validated by means of collapsing protein groups.Spectral count data from shotgun proteomics experiments is semi-quantitative and semi-random, yet a robust way to estimate protein concentration. Tag-count approaches are routinely used to analyze RNA sequencing data sets. This approach, termed MultiSpec, utilizes multiple tag-count based statistical tests to determine differential protein expression from spectral counts. The statistical results from these tag-count approaches are combined in order to reach a final MultiSpec q-value to re-rank protein candidates. This re-ranking procedure is completed to remove bias associated with a single approach in order to better understand the true proteomic differences driving the biology in question. The MultiSpec approach can be extended to multiple proteomic pipelines. In such an instance, MultiSpec statistical results are integrated by collapsing protein groups across proteomic pipelines to provide a single ranked list of differentially expressed proteins. This integration mechanism is seamlessly integrated with the statistical analysis and provides the means to cross-validate protein inferences from multiple proteomic pipelines.
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Project description:Proteome profiling by activated esters identified >9000 ligandable lysines but they are limited as covalent inhibitors due to poor hydrolytic stability. Here we report our efforts to design and discover a new series of tunable amine-reactive electrophiles (TAREs) for selective and robust labeling of lysine. The major challenges in developing selective probes for lysine are the high nucleophilicity of cysteines and poor hydrolytic stability. Our work circumvents these challenges by a unique design of the TAREs that form stable adducts with lysine and on reaction with cysteine generate another reactive electrophiles for lysine. We highlight that TAREs exhibit substantially high hydrolytic stability as compared to the activated esters and are non-cytotoxic thus have the potential to act as covalent ligands. We applied these alternative TAREs for the intracellular labeling of proteins in different cell lines, and for the selective identification of lysines in the human proteome on a global scale.
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