Stable Isotope Labeling Mass Spectrometry Research Articles

Quantitation of MUC5AC and MUC5B by Stable Isotope Labeling Mass Spectrometry.

Mucins MUC5AC and MUC5B are large glycoproteins that play an essential role in the innate defense of epithelial surfaces and their quantitation in biological samples would be informative about the health status of the tissue/samples they are derived from. However, they are difficult to study and quantify with traditional methods such as ELISA and western blot, due to their size, heterogeneity, and high degree of glycosylation. We successfully implemented a stable isotope labeling mass spectrometry approach for absolute quantification of mucin macromolecules. Here, in detail, we describe this accurate and sensitive liquid chromatography and mass spectrometry (LC-MS) method applied for both MUC5AC and MUC5B quantification in diverse and complex biological samples.

Stable isotope labeling-mass spectrometry as a new approach to determine remyelination.

Stable isotope labeling-mass spectrometry as a new approach to determine remyelination.

Interrogation of Dystrophin and Dystroglycan Complex Protein Turnover After Exon Skipping Therapy.

Recently, the Food and Drug Administration granted accelerated approvals for four exon skipping therapies –Eteplirsen, Golodirsen, Viltolarsen, and Casimersen –for Duchenne Muscular Dystrophy (DMD). However, these treatments have only demonstrated variable and largely sub-therapeutic levels of restored dystrophin protein in DMD patients, limiting their clinical impact. To better understand variable protein expression and the behavior of truncated dystrophin protein in vivo, we assessed turnover dynamics of restored dystrophin and dystrophin glycoprotein complex (DGC) proteins in mdx mice after exon skipping therapy, compared to those dynamics in wild type mice, using a targeted, highly-reproducible and sensitive, in vivo stable isotope labeling mass spectrometry approach in multiple muscle tissues. Through statistical modeling, we found that restored dystrophin protein exhibited altered stability and slower turnover in treated mdx muscle compared with that in wild type muscle (∼44 d vs. ∼24 d, respectively). Assessment of mRNA transcript stability (quantitative real-time PCR, droplet digital PCR) and dystrophin protein expression (capillary gel electrophoresis, immunofluorescence) support our dystrophin protein turnover measurements and modeling. Further, we assessed pathology-induced muscle fiber turnover through bromodeoxyuridine (BrdU) labeling to model dystrophin and DGC protein turnover in the context of persistent fiber degeneration. Our findings reveal sequestration of restored dystrophin protein after exon skipping therapy in mdx muscle leading to a significant extension of its half-life compared to the dynamics of full-length dystrophin in normal muscle. In contrast, DGC proteins show constant turnover attributable to myofiber degeneration and dysregulation of the extracellular matrix (ECM) in dystrophic muscle. Based on our results, we demonstrate the use of targeted mass spectrometry to evaluate the suitability and functionality of restored dystrophin isoforms in the context of disease and propose its use to optimize alternative gene correction strategies in development for DMD.

Detection of Urethane in Non-alcoholic Fermented Food

Among fermented foods, urethane often appears, which is a “2A” grade carcinogen. There are many types of fermented foods in my country, and the detection methods are not uniform enough, and no specific urethane detection standards have been formed. This article summarizes the specific experiments and results analysis based on previous work experience. At the same time, this article discusses six aspects from thin layer analysis, gas chromatography, gas chromatography-mass spectrometry, two-dimensional live multidimensional gas chromatography with stable isotope labeling mass spectrometry, high performance liquid chromatography-fluorescence, and nuclear magnetic resonance detection. The detection method of urethane in non-alcoholic fermented foods is introduced.

Aspergillus niger upregulated glycerolipid metabolism and ethanol utilization pathway under ethanol stress.

The knowledge of how Aspergillus niger responds to ethanol can lead to the design of strains with enhanced ethanol tolerance to be utilized in numerous industrial bioprocesses. However, the current understanding about the response mechanisms of A. niger toward ethanol stress remains quite limited. Here, we first applied a cell growth assay to test the ethanol tolerance of A. niger strain ES4, which was isolated from the wall near a chimney of an ethanol tank of a petroleum company, and found that it was capable of growing in 5% (v/v) ethanol to 30% of the ethanol‐free control level. Subsequently, the metabolic responses of this strain toward ethanol were investigated using untargeted metabolomics, which revealed the elevated levels of triacylglycerol (TAG) in the extracellular components, and of diacylglycerol, TAG, and hydroxy‐TAG in the intracellular components. Lastly, stable isotope labeling mass spectrometry with ethanol‐d 6 showed altered isotopic patterns of molecular ions of lipids in the ethanol‐d 6 samples, compared with the nonlabeled ethanol controls, suggesting the ability of A. niger ES4 to utilize ethanol as a carbon source. Together, the studies revealed the upregulation of glycerolipid metabolism and ethanol utilization pathway as novel response mechanisms of A. niger ES4 toward ethanol stress, thereby underlining the utility of untargeted metabolomics and the overall approaches as tools for elucidating new biological insights.

Characterization and quantification of histidine degradation in therapeutic protein formulations by size exclusion-hydrophilic interaction two dimensional-liquid chromatography with stable-isotope labeling mass spectrometry

Two dimensional liquid chromatography (2D-LC) coupling size exclusion (SEC) and hydrophilic interaction chromatography (HILIC) is demonstrated as a useful tool to study polar excipients, such as histidine and its degradant, in protein formulation samples. The SEC-HILIC setup successfully removed interferences from complex sample matrices and enabled accurate mass measurement of the histidine degradation product, which was then determined to be trans-urocanic acid. Because the SEC effluent is a strong solvent for the second dimension HILIC, experimental parameters needed to be carefully chosen, i.e., small transferring loop, fast gradient at high flow rates for the second dimension gradient, in order to mitigate the solvent mismatch and to ensure good peak shapes for HILIC separations. In addition, the generation of trans-urocanic acid was quantified by single heart-cutting SEC-HILIC 2D-LC combined with stable-isotope labeling mass spectrometry. Compared with existing 2D quantification methods, the proposed approach is fast, insensitive to solvent mismatch between dimensions, and tolerant of small retention time shifts in the first dimension. Finally, the first dimension diode array detector was found to be a potential degradation source for photolabile analytes such as trans-urocanic acid.

Rewired Metabolism in Drug-resistant Leukemia Cells: A METABOLIC SWITCH HALLMARKED BY REDUCED DEPENDENCE ON EXOGENOUS GLUTAMINE

Cancer cells that escape induction therapy are a major cause of relapse. Understanding metabolic alterations associated with drug resistance opens up unexplored opportunities for the development of new therapeutic strategies. Here, we applied a broad spectrum of technologies including RNA sequencing, global untargeted metabolomics, and stable isotope labeling mass spectrometry to identify metabolic changes in P-glycoprotein overexpressing T-cell acute lymphoblastic leukemia (ALL) cells, which escaped a therapeutically relevant daunorubicin treatment. We show that compared with sensitive ALL cells, resistant leukemia cells possess a fundamentally rewired central metabolism characterized by reduced dependence on glutamine despite a lack of expression of glutamate-ammonia ligase (GLUL), a higher demand for glucose and an altered rate of fatty acid β-oxidation, accompanied by a decreased pantothenic acid uptake capacity. We experimentally validate our findings by selectively targeting components of this metabolic switch, using approved drugs and starvation approaches followed by cell viability analyses in both the ALL cells and in an acute myeloid leukemia (AML) sensitive/resistant cell line pair. We demonstrate how comparative metabolomics and RNA expression profiling of drug-sensitive and -resistant cells expose targetable metabolic changes and potential resistance markers. Our results show that drug resistance is associated with significant metabolic costs in cancer cells, which could be exploited using new therapeutic strategies.

Quantitative analysis of prostate specific antigen isoforms using immunoprecipitation and stable isotope labeling mass spectrometry.

Prostate specific antigen (PSA) is a widely used serum marker for prostate cancer (PCa), but has limited specificity for distinguishing early PCa from benign prostatic hyperplasia (BPH). Recently, proPSAs, comprised of native proPSA, as well as truncated proPSA forms, [-2] proPSA, [-5] proPSA, and [-7] proPSA, have been shown to be better diagnostic targets than PSA for PCa. Stable isotope labeling-multiple reaction monitoring mass spectrometry (SIL/MRM-MS) has been frequently used to measure low-abundance biomarkers in tissues and biofluids, owing to its high sensitivity and specificity, simplicity, and multiplexing capability. In this study, we have developed and optimized a strategy using immunoprecipitation in conjunction with SIL/MRM-MS assay which is capable of sensitive and accurate quantification of proPSA in serum. Since serum and plasma are by far the most complex biological fluids, the immunoprecipitation workflow was optimized to achieve sufficient sensitivity, efficiencies of protein purification with immunoaffinity depletion were determined. The developed strategy can detect proPSA and PSA with a limit of detection (LOD) and limit of quantitation (LOQ) at nanogram per milliliter levels, corresponding to a concentration 6 orders-of-magnitude lower than the most abundant serum proteins. Furthermore, the simultaneous measurement of multiple biomarkers, including the mature and precursor forms of PSA, can be achieved in a single multiplexed analysis using LC/MRM-MS. The strategy demonstrated here provides an attractive alternative to ELISAs or RIAs for the reliably measurement of proPSA to improve the specificity of PCa diagnosis.

SILVER: an efficient tool for stable isotope labeling LC-MS data quantitative analysis with quality control methods

With the advance of experimental technologies, different stable isotope labeling methods have been widely applied to quantitative proteomics. Here, we present an efficient tool named SILVER for processing the stable isotope labeling mass spectrometry data. SILVER implements novel methods for quality control of quantification at spectrum, peptide and protein levels, respectively. Several new quantification confidence filters and indices are used to improve the accuracy of quantification results. The performance of SILVER was verified and compared with MaxQuant and Proteome Discoverer using a large-scale dataset and two standard datasets. The results suggest that SILVER shows high accuracy and robustness while consuming much less processing time. Additionally, SILVER provides user-friendly interfaces for parameter setting, result visualization, manual validation and some useful statistics analyses. SILVER and its source codes are freely available under the GNU General Public License v3.0 at http://bioinfo.hupo.org.cn/silver.

A Method for Automatically Interpreting Mass Spectra of 18O-Labeled Isotopic Clusters

16O/18O labeling is one differential proteomics technology among many that promises diagnostic and prognostic biomarkers of disease. Although the incorporation of 18O in the C-terminal carboxyl group during endoproteinase digestion in the presence of H2 18O makes the process of labeling facile, the ease and effectiveness of label incorporation have in some regards been outweighed by the difficulties in interpreting the resulting spectra. Complex isotope patterns result from the composition of unlabeled (18O(0)), singly labeled (18O(1)), and doubly labeled species (18O(2)) as well as contributions from the naturally occurring isotopes (e.g. 13C and 15N). Moreover because labeling is enzymatic, the number of 18O atoms incorporated can vary from peptide to peptide. Finally it is difficult to distinguish highly up-regulated from highly down-regulated or C-terminal peptides. We have developed an algorithm entitled regression analysis applied to mass spectrometry (RAAMS) that automatically, rapidly, and confidently interprets spectra of 18O-labeled peptides without requiring chemical composition information derived from product ion spectra. The algorithm is able to measure the effective 18O incorporation rate due to variable enzyme substrate specificity of the pseudosubstrate during the isotope exchange reaction and corrects for the 18O(0) abundance that remains in the labeled sample when using a two-step digestion/labeling procedure. We have also incorporated a method for distinguishing pure 18O(0) from pure 18O(2) peptides utilizing impure H2 18O. The algorithm operates on centroided peak lists and is therefore very fast: nine chromatograms of, on average, 1,168 spectra and containing, on average, 6,761 isotopic clusters were interpreted in, on average, 45 s per chromatogram. RAAMS is fast enough (average, 38 ms/spectrum) to allow the possibility of performing information-dependent MS/MS on a chromatographic time scale on species exceeding predetermined ratio thresholds. We describe in detail the operation of the algorithm and demonstrate its use on datasets with known and unknown ratios.

Regression analysis for comparing protein samples with 16O/18O stable-isotope labeled mass spectrometry

Using stable isotopes in global proteome scans, labeled molecules from one sample are pooled with unlabeled molecules from another sample and subsequently subjected to mass-spectral analysis. Stable-isotope methodologies make use of the fact that identical molecules of different stable-isotope compositions are differentiated in a mass spectrometer and are represented in a mass spectrum as distinct isotopic clusters with a known mass shift. We describe two multivariable linear regression models for (16)O/(18)O stable-isotope labeled data that jointly model pairs of resolved isotopic clusters from the same peptide and quantify the abundance present in each of the two biological samples while concurrently accounting for peptide-specific incorporation rates of the heavy isotope. The abundance measure for each peptide from the two biological samples is then used in down-stream statistical analyses, e.g. differential expression analysis. Because the multivariable regression models are able to correct for the abundance of the labeled peptide that appear as an unlabeled peptide due to the inability to exchange the natural C-terminal oxygen for the heavy isotope, they are particularly advantageous for a two-step digestion/labeling procedure. We discuss how estimates from the regression model are used to quantify the variability of the estimated abundance measures for the paired samples. Although discussed in the context of (16)O/(18)O stable-isotope labeled data, the multivariable regression models are generalizable to other stable-isotope labeled technologies.