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SILAC-based metabolic labeling is a widely adopted proteomics approach that enables quantitative comparisons among a variety of experimental conditions. Despite its quantitative capacity, SILAC experiments analyzed with data dependent acquisition (DDA) do not fully leverage peptide pair information for identification and suffer from undersampling compared to label-free proteomic experiments. Herein, we developed a data dependent acquisition strategy that coisolates and fragments SILAC peptide pairs and uses y-ions for their relative quantification. To facilitate the analysis of this type of data, we adapted the Comet sequence database search engine to make use of SILAC peptide paired fragments and developed a tool to annotate and quantify MS/MS spectra of coisolated SILAC pairs. In an initial feasibility experiment, this peptide pair coisolation approach generally improved expectation scores compared to the traditional DDA approach. Fragment ion quantification performed similarly well to precursor quantification in the MS1 and achieved more quantifications. Lastly, our method enables reliable MS/MS quantification of SILAC proteome mixtures with overlapping isotopic distributions, which are difficult to deconvolute in MS1-based quantification. This study demonstrates the initial feasibility of the coisolation approach. Coupling this approach with intelligent acquisition strategies has the potential to improve SILAC peptide sampling and quantification.

Urine sample storage after collection at ultra-low-temperature (e.g., -80 °C) is normally required for comparative metabolome analysis of many samples, and therefore, freeze-thaw cycles (FTCs) are unavoidable. However, the reported effects of FTCs on the urine metabolome are controversial. Moreover, there is no report on the study of how urine FTCs affect biomarker discovery. Herein, we present our study of the FTC effects on the urine metabolome and biomarker discovery using a high-coverage quantitative metabolomics platform. Our study involved two centers located in Hangzhou, China, and Edmonton, Canada, to perform metabolome analysis of two separate cohorts of urine samples. The same workflow of sample preparation and dansylation isotope labeling LC-MS was used for in-depth analysis of the amine/phenol submetabolome. The analysis of 320 samples from the Hangzhou cohort consisting of 80 healthy subjects with each urine being subjected to four FTCs resulted in relative quantification of 3682 metabolites with 3307 identified or mass-matched. The analysis of 176 samples from the Edmonton cohort of 44 subjects with four FTCs quantified 3516 metabolites with 3166 identified or mass-matched. Multivariate and univariate analyses indicated that significant variations (fold change ≥ 1.5 with

Sugar phosphates are important metabolic intermediates in organisms and play a vital role in energy and central carbon metabolism. Profiling of sugar phosphates is of great significance but full of challenges due to their high structural similarity and low sensitivities in liquid chromatography (LC)-mass spectrometry (MS). In this study, we developed a novel stable isotope chemical labeling combined with the reversed-phase (RP)LC-MS method for ultrasensitive determination of sugar phosphates at the single-cell level. By chemical derivatization with 2-(diazo-methyl)

Chemical isotope labeling liquid chromatography mass spectrometry (LC-MS) is an emerging metabolomic strategy for the quantification and characterization of small molecular compounds in biological samples. However, its subsequent data analysis is not straightforward due to a large amount of data produced and interference of biological matrices. In order to improve the efficiency of searching and identification of target endogenous metabolites, a new software tool for nontargeted metabolomics data processing called MS-IDF was developed based on the principle of a narrow mass defect filter. The developed tool provided two function modules, including IsoFinder and MDFinder. The IsoFinder function module applied a conventional peak extraction method by using a fixed mass differences between the heavy and light labels and by the alignment of chromatographic retention time (RT). On the other hand, MDFinder was designed to incorporate the accurate mass defect differences between or among stable isotopes in the peak extraction process. By setting an appropriate filter interval, the target metabolites can be efficiently screened out while eliminating interference. Notably, the present results showed that the efficiency in compound identification using the new MDFinder module was nearly doubled as compared to the conventional IsoFinder method (an increase from 259 to 423 compounds). The Matlab codes of the developed MS-IDF software are available from github at https://github.com/jydong2018/MS_IDF. Based on the MS-IDF software tool, a novel and effective approach from nontargeted to targeted metabolomics research was developed and applied to the exploration of potential primary amine biomarkers in patients with schizophrenia. With this approach, potential biomarkers, including

Three-dimensional cell cultures, or spheroids, are important model systems for cancer research because they recapitulate chemical and phenotypic aspects of in vivo tumors. Spheroids develop radially symmetric chemical gradients, resulting in distinct cellular populations. Stable isotopic labeling by amino acids in cell culture (SILAC) is a well-established approach to quantify protein expression and has previously been used in a pulse-chase format to evaluate temporal changes. In this article, we demonstrate that distinct isotopic signatures can be introduced into discrete spatial cellular populations, effectively tracking proteins to original locations in the spheroid, using a platform that we refer to as spatial SILAC. Spheroid populations were grown with light, medium, and heavy isotopic media, and the concentric shells of cells were harvested by serial trypsinization. Proteins were quantitatively analyzed by ultraperformance liquid chromatography–tandem mass spectrometry. The isotopic signatures correlated with the spatial location and the isotope position do not significantly impact the proteome of each individual layer. Spatial SILAC can be used to examine the proteomic changes in the different layers of the spheroid and to identify protein biomarkers throughout the structure. We show that SILAC labels can be discretely pulsed to discrete positions, without altering the spheroid’s proteome, promising future combined pharmacodynamic and pharmacokinetic studies.

RNA-protein interactions mediate many intracellular processes. CLIR-MS (cross-linking of isotope-labeled RNA and tandem mass spectrometry) allows the identification of RNA-protein interaction sites at single nucleotide/amino acid resolution in a single experiment. Using isotopically labeled RNA segments for UV-light-induced cross-linking generates characteristic isotope patterns that constrain the sequence database searches, increasing spatial resolution. Whereas the use of segmentally isotopically labeled RNA is effective, it is technically involved and not applicable in some settings, e.g., in cell or tissue samples. Here we introduce an extension of the CLIR-MS workflow that uses unlabeled RNA during cross-linking and subsequently adds an isotopic label during sample preparation for MS analysis. After RNase and protease digests of a cross-linked complex, the nucleic acid part of a peptide-RNA conjugate is labeled using the enzyme T4 polynucleotide kinase and a 1:1 mixture of heavy O-18(4)-gamma-ATP and light ATP. In this simple, one-step reaction, three heavy oxygen atoms are transferred from the gamma-phosphate to the 5'-end of the RNA, introducing an isotopic shift of 6.01 Da that is detectable by mass spectrometry. We applied this approach to the RNA recognition motif (RRM) of the protein FOX1 in complex with its cognate binding substrate, FOX-binding element (FBE) RNA. We also labeled a single phosphate within an RNA and unambiguously determined the cross-linking site of the FOX1-RRM binding to FBE at single residue resolution on the RNA and protein level and used differential ATP labeling for relative quantification based on isotope dilution. Data are available via ProteomeXchange with the identifier PXD024010., Analytical Chemistry, 93 (44), ISSN:1520-6882, ISSN:0003-2700

Recent advances in mass spectrometry (MS)-based proteomics allow the measurement of turnover rates of thousands of proteins using dynamic labeling methods, such as pulse stable isotope labeling by amino acids in cell culture (pSILAC). However, when applying the pSILAC strategy to multicellular animals (e.g., mice), the labeling process is significantly delayed by native amino acids recycled from protein degradation in vivo, raising a challenge of defining accurate protein turnover rates. Here, we report JUMPt, a software package using a novel ordinary differential equation (ODE)-based mathematical model to determine reliable rates of protein degradation. The uniqueness of JUMPt is to consider amino acid recycling and fit the kinetics of the labeling amino acid (e.g., Lys) and whole proteome simultaneously to derive half-lives of individual proteins. Multiple settings in the software are designed to enable simple to comprehensive data inputs for precise analysis of half-lives with flexibility. We examined the software by studying the turnover of thousands of proteins in the pSILAC brain and liver tissues. The results were largely consistent with the proteome turnover measurements from previous studies. The long-lived proteins are enriched in the integral membrane, myelin sheath, and mitochondrion in the brain. In summary, the ODE-based JUMPt software is an effective proteomics tool for analyzing large-scale protein turnover, and the software is publicly available on GitHub (https://github.com/JUMPSuite/JUMPt) to the research community.

Inositol and inositol phosphates (IPx) are central metabolites. Their accurate quantitative analysis in complex biological samples is challenging due to lengthy sample preparation procedures, sample losses by strong adsorption to surfaces, and unpredictable matrix effects. Currently, U

C-13-isotope tracing is a frequently employed approach to study metabolic pathway activity. When combined with the subsequent quantification of absolute metabolite concentrations, this enables detailed characterization of the metabolome in biological specimens and facilitates computational time-resolved flux quantification. Classically, a C-13-isotopically labeled sample is required to quantify C-13-isotope enrichments and a second unlabeled sample for the quantification of metabolite concentrations. The rationale for a second unlabeled sample is that the current methods for metabolite quantification rely mostly on isotope dilution mass spectrometry (IDMS) and thus isotopically labeled internal standards are added to the unlabeled sample. This excludes the absolute quantification of metabolite concentrations in C-13-isotopically labeled samples. To address this issue, we have developed and validated a new strategy using an unlabeled internal standard to simultaneously quantify metabolite concentrations and C-13-isotope enrichments in a single C-13-labeled sample based on gas chromatography-mass spectrometry (GC/MS). The method was optimized for amino acids and citric acid cycle intermediates and was shown to have high analytical precision and accuracy. Metabolite concentrations could be quantified in small tissue samples (>= 20 mg). Also, we applied the method on C-13-isotopically labeled mammalian cells treated with and without a metabolic inhibitor. We proved that we can quantify absolute metabolite concentrations and C-13-isotope enrichments in a single C-13-isotopically labeled sample.

Raman-stable isotope labeling using heavy water (Raman-D2O) is attracting great interest as a fast technique with various applications ranging from the identification of pathogens in medical samples to the determination of microbial activity in the environment. Despite its widespread applications, little is known about the fundamental processes of hydrogen-deuterium (H/D) exchange, which are crucial for understanding molecular interactions in microorganisms. By combining two-dimensional (2D) correlation spectroscopy and Raman deuterium labeling, we have investigated H/D exchange in bacterial cells under time dependence. Most C-H stretching signals decreased in intensity over time, prior to the formation of the C-D stretching vibration signals. The intensity of the C-D signal gradually increased over time, and the shape of the C-D signal was more uniform after longer incubation times. Deuterium uptake showed high variability between the bacterial genera and mainly led to an observable labeling of methylene and methyl groups. Thus, the C-D signal encompassed a combination of symmetric and antisymmetric CD2 and CD3 stretching vibrations, depending on the bacterial genera. The present study allowed for the determination of the sequential order of deuterium incorporation into the functional groups of proteins, lipids, and nucleic acids and hence understanding the process of biomolecule synthesis and the growth strategies of different bacterial taxa. We present the combination of Raman-D2O labeling and 2D correlation spectroscopy as a promising approach to gain a fundamental understanding of molecular interactions in biological systems.

We report a segmented spectrum scan method using Orbitrap MS in chemical isotope labeling (CIL) liquid chromatography-mass spectrometry (LC-MS) for improving the metabolite detection efficiency. In this method, the full

The data-independent acquisition (DIA) performed in the latest high-resolution, high-speed mass spectrometers offers a powerful analytical tool for biological investigations. The DIA mass spectrometry (DIA-MS) combined with the isotopic labeling approach holds a particular promise for increasing the multiplexity of DIA-MS analysis, which could assist the relative protein quantification and the proteome-wide turnover profiling. However, the wide MS1 isolation windows employed in conventional DIA methods lead to a limited efficiency in identifying and quantifying isotope-labelled peptide pairs through peptide fragment ions. Here, we optimized a high-selectivity DIA-MS named BoxCarmax that supports the analysis of complex samples, such as those generated from Stable isotope labeling by amino acids in cell culture (SILAC) and pulse SILAC (pSILAC) experiments. BoxCarmax enables multiplexed acquisition at both MS1- and MS2- levels, through the integration of BoxCar and MSX features, as well as a gas-phase separation strategy. We found BoxCarmax significantly improved the quantitative accuracy in SILAC and pSILAC samples by mitigating the ratio suppression of isotope-peptide pairs. We further applied BoxCarmax to measure protein degradation regulation during serum starvation stress in cultured cells, revealing valuable biological insights. Our study offered an alternative and accurate approach for the MS analysis of protein turnover and complex samples.

Stable isotope tracers are applied for in vivo and in vitro studies to reveal the activity of enzymes and intracellular metabolic pathways. Most often, such tracers are used with gas chromatography coupled to mass spectrometry (GC-MS) owing to its ease of operation and reproducible mass spectral databases. Differences in isotope tracer performance of the classic GC-quadrupole MS instrument and newer time-of-flight instruments are not well studied. Here, we used three commercially available instruments for the analysis of identical samples from a stable isotope labeling study that used [U-13C6] d-glucose to investigate the metabolism of the bacterium Rothia mucilaginosa with respect to 29 amino acids and hydroxyl acids involved in primary metabolism. The prokaryote R. mucilaginosa belongs to the family of Micrococcaceae and is present and metabolically active in the airways and sputum of cystic fibrosis patients. Overall, all three GC-MS instruments (low-resolution GC-SQ MS, low-resolution GC-TOF MS, and high-resolution GC-QTOF MS) can be used to perform stable isotope tracing studies for glycolytic intermediates, tricarboxylic acid (TCA) metabolites, and amino acids, yielding similar biological results, with high-resolution GC-QTOF MS offering additional capabilities to identify the chemical structures of unknown compounds that might show significant isotope enrichments in biological studies.

Isotopic-labeling experiments have been valuable to monitor the flux of metabolic reactions in biological systems, which is crucial to understand homeostatic alterations with disease. Experimental determination of metabolic fluxes can be inferred from a characteristic rearrangement of stable isotope tracers (e.g., 13C or 15N) that can be detected by mass spectrometry (MS). Metabolites measured are generally members of well-known metabolic pathways, and most of them can be detected using both gas chromatography (GC)-MS and liquid chromatography (LC)-MS. In here, we show that GC methods coupled to chemical ionization (CI) MS have a clear advantage over alternative methodologies due to GC's superior chromatography separation efficiency and the fact that CI is a soft ionization technique that yields identifiable protonated molecular ion peaks. We tested diverse GC-CI-MS setups, including methane and isobutane reagent gases, triple quadrupole (QqQ) MS in SIM mode, or selected ion clusters using optimized narrow windows (∼10 Da) in scan mode, and standard full scan methods using high resolution GC-(q)TOF and GC-Orbitrap systems. Isobutane as a reagent gas in combination with both low-resolution (LR) and high-resolution (HR) MS showed the best performance, enabling precise detection of isotopologues in most metabolic intermediates of central carbon metabolism. Finally, with the aim of overcoming manual operations, we developed an R-based tool called isoSCAN that automatically quantifies all isotopologues of intermediate metabolites of glycolysis, TCA cycle, amino acids, pentose phosphate pathway, and urea cycle, from LRMS and HRMS data.

A rapid and reliable method for the differentiation between active and inactive bacteria at single cell level is urgently needed in many fields including clinical diagnosis and environmental microbiology, to understand the contribution of metabolically active bacteria in fundamental processes triggering environmental and public health risks. Here, using heavy water (D2O) with Raman-stable isotope labeling (Raman-D2O), we evaluated the reliability of the quantification of deuterium uptake, a well-known indicator for the general metabolic activity of bacteria. For this purpose, we based our study on the quantification of deuterium assimilation from heavy water into single bacterial cells to check the influence of carbon source and bacterial identity on the deuterium uptake. We show that compared to complex carbon substrates, the deuterium assimilation is higher in the presence of simpler substrates such as sugars but differs significantly among bacterial isolates. Despite this variability, the developed classification models could differentiate deuterium labeled and nonlabeled single cells with high sensitivity and specificity. Highlighting the variability between single bacterial cells, the study emphasizes the challenges in establishing a threshold in terms of deuterium uptake to distinguish deuterium labeled and nonlabeled cells. Overall, we show that the Raman-D2O approach, when coupled with chemometrics, constitutes a powerful approach for monitoring single bacterial cells.

Isobaric peptide termini labeling (IPTL) is an attractive protein quantification method because it provides more accurate and reliable quantification information than traditional isobaric labeling methods (e.g., TMT and iTRAQ) by making use of the entire fragment-ion series instead of only a single reporter ion. The multiplexing capacity of published IPTL implementations is, however, limited to three. Here, we present a selective maleylation-directed isobaric peptide termini labeling (SMD-IPTL) approach for quantitative proteomics of LysC protein digestion. SMD-IPTL extends the multiplexing capacity to 4-plex with the potential for higher levels of multiplexing using commercially available 13C/15N labeled amino acids. SMD-IPTL is achieved in a one-pot reaction in three consecutive steps: (1) selective maleylation at the N-terminus; (2) labeling at the ϵ-NH2 group of the C-terminal Lys with isotopically labeled acetyl-alanine; (3) thiol Michael addition of an isotopically labeled acetyl-cysteine at the maleylated N-terminus. The isobarically labeled peptides are fragmented into sets of b- and y-ion clusters upon LC-MS/MS, which convey not only sequence information but also quantitative information for every labeling channel and avoid the issue of ratio distortion observed with reporter-ion-based approaches. We demonstrate the SMD-IPTL approach with a 4-plex labeled sample of bovine serum albumin (BSA) and yeast lysates mixed at different ratios. With the use of SMD-IPTL for labeling and a narrow precursor isolation window of 0.8 Th with an offset of -0.2 Th, accurate ratios were measured across a 10-fold mixing range of BSA in a background of yeast proteome. With the yeast proteins mixed at ratios of 1:5:1:5, BSA was detected at ratios of 0.94:2.46:4.70:9.92 when spiked at 1:2:5:10 ratios with an average standard deviation of peptide ratios of 0.34.

We developed a new method for monitoring the distribution of administrated fatty acids in the body by combination of a stable isotope-labeling technique and imaging mass spectrometry (IMS). The developed stable isotope-labeling technique is very simple and able to adapt to all the fatty acid species. In this study, we synthesized stable isotope-labeled arachidonic acid (AA) and docosahexaenoic acid (DHA), and they were simultaneously administrated to mice to examine their migrations and distributions in the brain. The administrated AA and DHA have two more molecular weights compared to the originals and apparently were distinguished from the originally accumulated AA and DHA in the brain using IMS. As a result, we reveal that the administered AA and DHA first accumulated in the hippocampus and cerebellar cortex in the brain. This technique does not use radio isotopes and would appear to elucidate the role of all kinds of fatty acid species in the body.

Pulsed Stable Isotope Labeling in Cell culture (SILAC) approaches allow measurement of protein dynamics, including protein translation and degradation. However, its use for quantifying acute changes has been limited due to low labeled peptide stoichiometry. Here, we describe the use of instrument logic to select peaks of interest via targeted mass differences (TMD) for overcoming this limitation. Comparing peptides artificially mixed at low heavy-to-light stoichiometry measured using standard data dependent acquisition with or without TMD revealed 2–3-fold increases in identification without significant loss in quantification precision for both MS2 and MS3 methods. Our benchmarked method approach increased throughput by reducing the necessary machine time. We anticipate that all pulsed SILAC measurements, combined with tandem mass tagging (TMT) or not, would greatly benefit from instrument logic based approaches.

Drug-load (DL) characterization of antibody-drug conjugates (ADCs) is an important analytical task due to its designation as a critical quality attribute (CQA) affecting potency and stability. Intact and subunit liquid chromatography-mass spectrometry (LC-MS) analyses can determine global drug-to-antibody ratios (DARs) that correlate well with other orthogonal analytical methods; however, peptide mapping liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis has struggled to provide complementary site-specific quantitation of drug conjugation sites. The peptide mapping method described herein utilizes stable isotope labeling to accurately quantitate the site-specific conjugation levels of a cysteine-conjugated ADC to provide "bottom-up" DAR characterization in parallel with protein sequence and post-translational modification (PTM) characterization in one multi-attribute analytical method (MAM).

Phototrophs assimilate CO2 into organic compounds that accumulate in storage organelles. Elucidation of the carbon dynamics of storage organelles could enhance the production efficiency of valuable compounds and facilitate the screening of strains with high photosynthetic activity. To comprehensively elucidate the carbon dynamics of these organelles, the intraorganellar distribution of the carbon atoms that accumulate at specific time periods should be probed. In this study, the biosynthesis of polysaccharides in storage organdies was spatiotemporally probed via stimulated Raman scattering (SRS) microscopy using a stable isotope (C-13) as the tracking probe. Paramylon granules (a storage organelle of beta-1,3-glucan) accumulated in a unicellular photosynthetic alga, Euglena gracilis, were investigated as a model organelle. The carbon source of the culture medium was switched from NaH12CO3 to NaH13CO3 during the production of the paramylon granules; this resulted in the distribution of the C-12 and C-13 constituents in the granules, so that the biosynthetic process could be tracked. Taking advantage of high-resolution SRS imaging and label switching, the localization of the C-12 and C-13 constituents inside a single paramylon granule could be visualized in three dimensions, thus revealing the growth process of paramylon granules. We propose that this method can be used for comprehensive elucidation of the dynamic activities of storage organelles.

Screening the metabolites of emerging organic contaminants (EOCs) from complicated biological matrices is an important but challenging task. Although stable isotope labeling (SIL) is frequently used to facilitate the identification of contaminant metabolites from redundant interfering components, the isotopically labeled reagents are expensive and difficult to synthesize, which greatly constrains the application of the SIL method. Herein, a new online noncovalent tagging method was developed for screening the metabolites of 1H-benzotriazol (BT) based on the characteristic structural moieties reserved in the metabolites. By selecting β-cyclodextrin (β-CD) as a macrocyclic tagging reagent, metabolites with the reserved moiety were expected to exhibit a characteristic shift of the mass-to-charge ratio (Δm/z = 1134.3698) after being noncovalently tagged by β-CD. Based on the characteristic mass shift, the suspected features were reduced by 1 order of magnitude, as numerous interfering species that could not be effectively tagged by β-CD were excluded. From these suspected features, two metabolites of BT that have not been reported before were successfully screened out. The significant characteristic mass shift caused by the noncovalent tagging method is easier to identify with more confidence than the previously reported SIL method. Besides, noncovalent tagging reagents can be much more accessible and less expensive than isotopically labeled reagents. Hence, this online noncovalent tagging method can be an intriguing alternative to the conventional SIL method.

A new relative quantification strategy for glycomics, named deuterium oxide (D2O) labeling for global omics relative quantification (DOLGOReQ), has been developed based on the partial metabolic D2O labeling, which induces a subtle change in the isotopic distribution of glycan ions. The relative abundance of unlabeled to D-labeled glycans was extracted from the overlapped isotopic envelope obtained from a mixture containing equal amounts of unlabeled and D-labeled glycans. The glycan quantification accuracy of DOLGOReQ was examined with mixtures of unlabeled and D-labeled HeLa glycans combined in varying ratios according to the number of cells present in the samples. The relative quantification of the glycans mixed in an equimolar ratio revealed that 92.4 and 97.8% of the DOLGOReQ results were within a 1.5- and 2-fold range of the predicted mixing ratio, respectively. Furthermore, the dynamic quantification range of DOLGOReQ was investigated with unlabeled and D-labeled HeLa glycans mixed in different ratios from 20:1 to 1:20. A good correlation (Pearson's r > 0.90) between the expected and measured quantification ratios over 2 orders of magnitude was observed for 87% of the quantified glycans. DOLGOReQ was also applied in the measurement of quantitative HeLa cell glycan changes that occur under normoxic and hypoxic conditions. Given that metabolic D2O labeling can incorporate D into all types of glycans, DOLGOReQ has the potential as a universal quantification platform for large-scale comparative glycomic experiments.

Primary hyperoxalurias (PH) are inborn errors of glyoxylate metabolism characterized by an increase in endogenous oxalate production. Oxalate overproduction may cause calcium-oxalate crystal formation leading to kidney stones, nephrocalcinosis, and ultimately kidney failure. Twenty-four hour urine oxalate excretion is an inaccurate measure for endogenous oxalate production in PH patients and not applicable in those with kidney failure. Treatment efficacy cannot be assessed with this measure during clinical trials. We describe the development and validation of a gas chromatography-tandem mass spectrometry method to analyze the samples obtained following a stable isotope infusion protocol of 13C2-oxalate and 1-13C-glycolate in both healthy individuals and PH patients. Isotopic enrichments of plasma oxalate, glycolate, and glyoxylate were measured on a gas chromatography-triple quadrupole mass spectrometry system using ethylhydroxylamine and N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide (MTBSTFA) for analyte derivatization. Method precision was good for oxalate and glycolate (coefficients of variation [CV] were

Combined precursor isotopic labeling and isobaric tagging (cPILOT) is an enhanced multiplexing strategy currently capable of analyzing up to 24 samples simultaneously. This capability is especially helpful when studying multiple tissues and biological replicates in models of disease, such as Alzheimer’s disease (AD). Here, cPILOT was used to study proteomes from heart, liver, and brain tissues in a late-stage amyloid precursor protein/presenilin-1 (APP/PS-1) human transgenic double-knock-in mouse model of AD. The original global cPILOT assay developed on an Orbitrap Velos instrument was transitioned to an Orbitrap Fusion Lumos instrument. The advantages of faster scan rates, lower limits of detection, and synchronous precursor selection on the Fusion Lumos afford greater numbers of isobarically tagged peptides to be quantified in comparison to the Orbitrap Velos. Parameters such as LC gradient, m/z isolation window, dynamic exclusion, targeted mass analyses, and synchronous precursor scan were optimized leading to >600 000 PSMs, corresponding to 6074 proteins. Overall, these studies inform of system-wide changes in brain, heart, and liver proteins from a mouse model of AD.

Overexpression of legumain is closely associated with tumor proliferation, invasion, and metastasis. Because of its intrinsic properties, such as high sensitivity and resolution, positron emission tomography (PET) has become an effective imaging technique for early diagnosis, treatment response prediction, and monitoring. Herein, two legumain-targeting radiofluorinated smart probes (18F-2 and 18F-3) as well as a control probe (18F-1) were specifically designed for PET imaging of legumain activity in tumors. 18F-1, 18F-2, and 18F-3 were obtained with high radiochemical yield (RCY > 60%) and radiochemical purity (RCP > 99%) using a convenient "one-step" 18F-labeling method. The probes 18F-2 and 18F-3 exhibited high response to legumain activity and reductive environment and revealed comparable uptake in HCT116 cells (4.22% ± 0.14% and 4.64% ± 0.32% for 18F-2 and 18F-3, respectively; 8.46% ± 0.33% and 9.05% ± 0.24% for co-treatment of 18F-2 + 2 and 18F-3 + 3 at 1 h), while the control probe 18F-1 showed no response. PET imaging of tumor-bearing mice showed that the co-injection strategy (18F-2 + 2 and 18F-3 + 3) resulted in higher tumor uptake (3.57% ± 0.37% and 3.72% ± 0.19% ID/g at 10 min, respectively) than the single injection strategy (2.59% ± 0.19% and 2.60% ± 0.46% ID/g for 18F-2 and 18F-3, respectively). In addition, introduction of the trimeric histidine-glutamate (HEHEHE) tag to 18F-3 reduced the liver uptake by almost two-fold without any noticeable effect on the tumor uptake. All the results indicate that 18F-3 holds great potential applications in clinics for sensitive and specific PET imaging of legumain activity in tumors.

Sialylated N-glycans play pivotal role in several important biological and pathological processes. Their sialyl-linkage isomers, mostly α-2,3- and α-2,6-linked, act differently during the cellular events and several diseases. While mass spectrometry (MS) technology is a powerful tool in N-glycome analysis, it still suffers from an inability to distinguish linkage isomers of native N-glycans. Herein, we described a sequential selective derivatization method, by which α-2,6- and α-2,3-linked sialic acids are sequentially labeled with methylamide incorporated with a different stable isotope. Isobaric labeling avoids inducing bias in ionization efficiency and chromatographic behavior. In optimized reaction conditions, high derivatization selectivity (∼99%) was achieved for both α-2,3- and α-2,6-linked sialic acid. High accuracy of quantitation within a dynamic range of 2 orders of magnitude and high reproducibility (CV < 20%, n = 3) were demonstrated using standard glycans and multisialylated N-glycans. Finally, this method was applied in profiling the N-glycome of serum from CRC patients, where a level of six sialyl-linkage isomers were found to be altered significantly compared with that from healthy individuals.

Protein persulfidation is one of the most important oxidative translational modifications and plays vital roles in various important biological processes. However, the proteome-wide identification of persulfidation sites is a great challenge because of the difficulties in accurately differentiating persulfide groups with disulfide and thiol groups in proteins as well as the extremely low abundance of persulfidated peptides. By current approaches, the persulfidated peptides were often identified by the cleavage of their persulfide groups by reductants prior to MS analysis; therefore, it would bring about a false positive identification and was unable to identify persulfidation sites accurately for a single peptide with multiple cysteine residues. In this study, a novel strategy for the site-specific quantification of persulfidome (SSQPer) was developed. By this strategy, the persulfidated proteins were first labeled with cleavable isotope-coded affinity tag (c-ICAT) reagents. After digestion, the labeled persulfidated peptides were selectively enriched with streptavidin beads and fractionated by strong cation exchange chromatography, followed by LC-MS/MS identification. To evaluate the performance of SSQPer, the persulfidated BSA digests with 20 persulfidation sites identified were used to spike HeLa cell digests with mass ratios of 1:100 and 1:1000, and 16 and 13 persulfidated sites were respectively identified. We applied SSQPer to the site-specific quantification of persulfidome in the epithelial-mesenchymal transition (EMT) process, and 226 endogenous persulfidation sites were identified, of which 74.3% were newly discovered. All of these results demonstrated that the SSQPer strategy would provide a promising tool to profile the site-specific persulfidome and pave the way for future investigation to expand our knowledge of persulfidation.

Global identification of protein C-termini is highly challenging due to their low abundance in conventional shotgun proteomics. Several enrichment strategies have been developed to facilitate the detection of C-terminal peptides. One major issue of previous approaches is the limited C-terminome coverage. Herein, we integrated LysargiNase digestion, chemical acetylation on neo-N-terminus, and a-ion-aided peptide matching into poly(allylamine)-based C-terminomics (termed as LAACTer). In this strategy, we leveraged LysargiNase, a protease with cleavage specificity N-terminal to Lys and Arg residues, to cover previously unidentifiable C-terminome and employed chemical acetylation and a-ion-aided peptide matching to efficiently boost peptide identifications. Triplicates of LAACTer identified a total of 834 C-termini from proteome of 293T cell, which expanded the coverage by 164% (643 more unique C-termini) compared with the parallel experiments using the original workflow. Compared with the largest human C-terminome data sets (containing 800-900 C-termini), LAACTer not only achieved comparable profiling depth but also yielded 465 previously unidentified C-termini. In a SILAC (stable isotope labeling with amino acids in cell culture)-based quantitative study for identification of GluC-cleaved products, LAACTer quantified 300% more C-terminal peptides than the original workflow. Using LAACTer and the original workflow, we performed global analysis for the C-terminal sequences of 293T cell. The original and processed C-termini displayed distinct sequence patterns, implying the "C-end rules" that regulates protein stability could be more complex than just amino acid motifs. In conclusion, we reason LAACTer could be a powerful proteomic tool for in-depth C-terminomics and would benefit better functional understanding of protein C-termini.

Effective extension of mass spectrometry-based proteomics to single cells remains challenging. Herein we combined microfluidic nanodroplet technology with tandem mass tag (TMT) isobaric labeling to significantly improve analysis throughput and proteome coverage for single mammalian cells. Isobaric labeling facilitated multiplex analysis of single cell-sized protein quantities to a depth of ∼1 600 proteins with a median CV of 10.9% and correlation coefficient of 0.98. To demonstrate in-depth high throughput single cell analysis, the platform was applied to measure protein expression in 72 single cells from three murine cell populations (epithelial, immune, and endothelial cells) in

We report a new combinatorial approach of stable isotopic labeling (SIL)-solid phase extraction (SPE)-liquid chromatography-tandem high-resolution mass spectrometry (LC-HRMS/MS) for identification of amino-containing contaminants at trace levels in source water. The new SIL method requires small amounts of formaldehyde (CH2O) and deuterated formaldehyde (CD2O) to efficiently label ng/L amino compounds in 1 L of water and improves SPE recovery, enabling environmental analysis of trace amino-compounds. Isotopically methylated components were confirmed using LC-MS/MS based on their retention times, and characteristic isotope patterns of the molecular and product ions. Using the characteristic isotope patterns, we established a data prioritization process to identify the amino compounds in thousands of mass peaks in raw data. Analysis of a labeled authentic source water detected 8952 m/z peaks and tentatively identified 154 amino compounds. Our SIL-methylation prioritization approach effectively reduced the complexity of data. Manual spectrum interpretation identified 77 of the 154 components as amino acids and peptides. We confirmed 8 of the 77 compounds using commercially available standards to demonstrate the feasibility and reliability of our SIL-SPE-LC-HRMS/MS method for environmental analysis of trace amino-containing contaminants. The method can efficiently identify amino-precursors in source water, enabling other studies of nitrogenous disinfection byproduct formation.

Probing the role of glycolipids in health and disease warrants development of practical strategies to determine these molecules at the intact structural level, namely to simultaneously characterize and quantify the glycan and lipid moieties without breaking the linkage between them. Herein we present such an approach utilizing differential isotope labeling and reversed phase liquid chromatography-tandem mass spectrometry (RPLC-MS/MS) for structural characterization and relative quantification of intact neutral glycolipids. In this approach, each individual sample and a pooled aliquot of each sample were permethylated using