Comparing Raman and NanoSIMS for heavy water labeling of single cells
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PROVIDER: S-MBRS8 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:Metabolic labeling followed by LC-MS-based proteomics is a powerful tool to study proteome dynamics in high-throughput experiments both in vivo and in vitro. High mass resolution and accuracy allow differentiation in isotope profiles and the quantification of partially labeled peptide species. Metabolic labeling duration introduces a time domain in which the gradual incorporation of labeled isotopes is recorded. Different stable isotopes are used for labeling. Labeling with heavy water has advantages because it is cost-effective and easy to use. The protein degradation rate constant has been modeled using exponential decay models for the relative abundances of mass isotopomers. The recently developed closed-form equations were applied to study the analytic behavior of the heavy mass isotopomers in the time domain of metabolic labeling. The predictions from the closed-form equations are compared with the practices that have been used to extract degradation rate constants from the time-course profiles of heavy mass isotopomers. It is shown that all mass isotopomers, except for the monoisotope, require data transformations to obtain the exponential depletion, which serves as a basis for the rate constant model. Heavy mass isotopomers may be preferable choices for modeling high-mass peptides or peptides with a high number of labeling sites. The results are also applicable to stable isotope labeling with other atom-based labeling agents.
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Project description:Deuterated water (2H2O) is a label commonly used for safe quantitative measurement of deuterium enrichment into DNA of proliferating cells. More recently, it has been used for labeling proteins and other biomolecules. Our in vitro - in vivo research reports important stable isotopic labeling enrichment differences into the DNA nucleosides and their isotopologues (e.g. deoxyadenosine (dA) M + 1, dA M + 2, dA M + 3), as well as tumor cell proliferation effects for various forms of commercially available stable heavy water (2H2O, H218O, and 2H218O). Using an in vitro mouse thymus tumor cell line, we determined that H218O provides superior DNA labeling enrichment quantitation, as measured by GC-positive chemical ionization (PCI)-MS/MS. In addition, at higher but physiologically relevant doses, both 2H218O and 2H2O down modulated mouse thymus tumor cell proliferation, whereas H218O water had no observable effects on cell proliferation. The in vivo labeling studies, where normal mouse bone marrow cells (i.e. high turnover) were evaluated post labeling, demonstrated DNA enrichments concordant with measurements from the in vitro studies. Our research also reports a headspace-GC-NCI-MS method, which rapidly and quantitatively measures stable heavy water levels in total body water.
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Project description:Protein turnover is vital for cellular functioning and is often associated with the pathophysiology of a variety of diseases. Metabolic labeling with heavy water followed by liquid chromatography coupled to mass spectrometry is a powerful tool to study in vivo protein turnover in high throughput and large scale. Heavy water is a cost-effective and easy to use labeling agent. It labels all nonessential amino acids. Due to its toxicity in high concentrations (20% or higher), small enrichments (8% or smaller) of heavy water are used with most organisms. The low concentration results in incomplete labeling of peptides/proteins. Therefore, the data processing is more challenging and requires accurate quantification of labeled and unlabeled forms of a peptide from overlapping mass isotopomer distributions. The work describes the bioinformatics aspects of the analysis of heavy water labeled mass spectral data, available software tools and current challenges and opportunities.
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Project description:Retention time (RT) alignment has been important for robust protein identification and quantification in proteomics. In data-dependent acquisition mode, whereby the precursor ions are semistochastically chosen for fragmentation in MS/MS, the alignment is used in an approach termed matched between runs (MBR). MBR transfers peptides, which were fragmented and identified in one experiment, to a replicate experiment where they were not identified. Before the MBR transfer, the RTs of experiments are aligned to reduce the chance of erroneous transfers. Despite its widespread use in other areas of quantitative proteomics, RT alignment has not been applied in data analyses for protein turnover using an atom-based stable isotope-labeling agent such as metabolic labeling with deuterium oxide, D2O. Deuterium incorporation changes isotope profiles of intact peptides in full scans and their fragment ions in tandem mass spectra. It reduces the peptide identification rates in current database search engines. Therefore, the MBR becomes more important. Here, we report on an approach to incorporate RT alignment with peptide quantification in studies of proteome turnover using heavy water metabolic labeling and LC-MS. The RT alignment uses correlation-optimized time warping. The alignment, followed by the MBR, improves labeling time point coverage, especially for long labeling durations.
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Project description:Protein homeostasis (proteostasis) is a result of a dynamic equilibrium between protein synthesis and degradation. It is important for healthy cell/organ functioning and is often associated with diseases such as neurodegenerative diseases and non-Alcoholic Fatty Liver disease. Heavy water metabolic labeling, combined with liquid-chromatography and mass spectrometry (LC-MS), is a powerful approach to study proteostasis in vivo in high throughput. Traditionally, intact peptide signals are used to estimate stable isotope incorporation in time-course experiments. The time-course of label incorporation is used to extract protein decay rate constant (DRC). Intact peptide signals, computed from integration in chromatographic time and mass-to-charge ratio (m/z) domains, usually, provide an accurate estimate of label incorporation. However, sample complexity (co-elution), limited dynamic range, and low signal-to-noise ratio (S/N) may adversely interfere with the peptide signals. These artifacts complicate the DRC estimations by distorting peak shape in chromatographic time and m/z domains. Fragment ions, on the other hand, are less prone to these artifacts and are potentially well suited in aiding DRC estimations. Here, we show that the label incorporation encoded into the isotope distributions of fragment ions reflect the isotope enrichment during the metabolic labeling with heavy water. We explore the label incorporation statistics for devising practical approaches for DRC estimations.
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Project description:Information on the growth rate and metabolism of microbial pathogens that cause long-term chronic infections is limited, reflecting the absence of suitable tools for measuring these parameters in vivo. Here, we have measured the replication and physiological state of Leishmania mexicana parasites in murine inflammatory lesions using 2H2O labeling. Infected BALB/c mice were labeled with 2H2O for up to 4 months, and the turnover of parasite DNA, RNA, protein and membrane lipids estimated from the rate of deuterium enrichment in constituent pentose sugars, amino acids, and fatty acids, respectively. We show that the replication rate of parasite stages in these tissues is very slow (doubling time of ~12 days), but remarkably constant throughout lesion development. Lesion parasites also exhibit markedly lower rates of RNA synthesis, protein turnover and membrane lipid synthesis than parasite stages isolated from ex vivo infected macrophages or cultured in vitro, suggesting that formation of lesions induces parasites to enter a semi-quiescent physiological state. Significantly, the determined parasite growth rate accounts for the overall increase in parasite burden indicating that parasite death and turnover of infected host cells in these lesions is minimal. We propose that the Leishmania response to lesion formation is an important adaptive strategy that minimizes macrophage activation, providing a permissive environment that supports progressive expansion of parasite burden. This labeling approach can be used to measure the dynamics of other host-microbe interactions in situ.
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Project description:Understanding metabolism is of great significance to decipher various physiological and pathogenic processes. While great progress has been made to profile gene expression, how to capture organ-, tissue-, and cell-type-specific metabolic profile (i.e., metabolic tissue atlas) in complex mammalian systems is lagging behind, largely owing to the lack of metabolic imaging tools with high resolution and high throughput. Here, the authors applied mid-infrared imaging coupled with heavy water (D2 O) metabolic labeling to a scope of mouse organs and tissues. The premise is that, as D2 O participates in the biosynthesis of various macromolecules, the resulting broad C-D vibrational spectrum should interrogate a wide range of metabolic pathways. Applying multivariate analysis to the C-D spectrum, the authors successfully identified both inter-organ and intra-tissue metabolic signatures of mice. A large-scale metabolic atlas map between different organs from the same mice is thus generated. Moreover, leveraging the power of unsupervised clustering methods, spatially-resolved metabolic signatures of brain tissues are discovered, revealing tissue and cell-type specific metabolic profile in situ. As a demonstration of this technique, the authors captured metabolic changes during brain development and characterized intratumoral metabolic heterogeneity of glioblastoma. Altogether, the integrated platform paves a way to map the metabolic tissue atlas for complex mammalian systems.
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Project description:Cellular proteins are continuously degraded and synthesized. The turnover of proteins is essential to many cellular functions. Combined with metabolic labeling using stable isotopes, LC-MS estimates proteome dynamics in high-throughput and on a large scale. Modern mass spectrometers allow a range of instrumental settings to optimize experimental output for specific research goals. One such setting which affects the results for dynamic proteome studies is the mass resolution. The resolution is vital for distinguishing target species from co-eluting contaminants with close mass-to-charge ratios. However, for estimations of proteome dynamics from metabolic labeling with stable isotopes, the spectral accuracy is highly important. Studies examining the effects of increased mass resolutions (in modern mass spectrometers) on the proteome turnover output and accuracy have been lacking. Here, we use a publicly available heavy water labeling and mass spectral data sets of murine serum proteome (acquired on Orbitrap Fusion and Agilent 6530 QToF) to analyze the effect of mass resolution of the Orbitrap mass analyzer on the proteome dynamics estimation. Increased mass resolution affected the spectral accuracy and the number acquired tandem mass spectra.
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