Project description:Recent developments in mass spectrometry-based single-cell proteomics (SCP) have resulted in dramatically improved sensitivity, yet the relatively low measurement throughput remains a limitation. Isobaric and isotopic labeling methods have been separately applied to SCP to increase throughput through multiplexing. Here we combined both forms of labeling to achieve multiplicative scaling for higher throughput. Two-plex stable isotope labeling of amino acids in cell culture and isobaric Tandem Mass Tag labeling enabled up to 32 single cells to be analyzed in a single LC-MS analysis, in addition to reference and negative control channels. A custom nested nanowell chip was used for nanoliter sample processing to minimize sample losses. Using a 145-min total LC-MS cycle time, ~280 single cells were analyzed per day, which could be increased to ~700 samples per day with a high-duty-cycle multicolumn LC system producing the same active gradient. The labeling efficiency and achievable proteome coverage were characterized for multiple analysis conditions. Despite some decrease in coverage, this method readily differentiated four different cell types and thus proves suitable for, e.g., rapid cell typing.
Project description:Glycosylation is one of the most common and important post-translational modifications. Quantitative analysis of intact N-glycopeptides is critical to understand the role of protein glycosylation in physiological and pathological processes. In this work, we developed a novel approach called methylamine stable isotope labeling (MeSIL) to relatively quantify intact N-glycopeptides through one step isotopic labeling. Isotopic methylamine was employed to label both the sialic acid residues of glycans and the carboxyl groups on the peptide moiety, and followed by the mixing of samples for mass spectrometric analysis. The relative abundance of intact N-glycopeptides between two samples was obtained by comparing the signal of the particular peak pairs with a 3*N Da mass shift (where N is an integer correlating with the number of carboxylic acids within the glycopeptides). Additionally, the number of sialylated glycopeptides can be distinguished simultaneously through the mass difference after labeling. The MeSIL str
Project description:Studying the flow of chemical moieties through the complex set of metabolic reactions that happen in the cell is essential to understanding the alterations in homeostasis that occur in disease. Recently, LC/MS-based untargeted metabolomics and isotopically labeled metabolites have been used to facilitate the unbiased mapping of labeled moieties through metabolic pathways. However, due to the complexity of the resulting experimental data sets few computational tools are available for data analysis. Here we introduce geoRge, a novel computational approach capable of analyzing untargeted LC/MS data from stable isotope-labeling experiments. geoRge is written in the open language R and runs on the output structure of the XCMS package, which is in widespread use. As opposed to the few existing tools, which use labeled samples to track stable isotopes by iterating over all MS signals using the theoretical mass difference between the light and heavy isotopes, geoRge uses unlabeled and labeled biologically equivalent samples to compare isotopic distributions in the mass spectra. Isotopically enriched compounds change their isotopic distribution as compared to unlabeled compounds. This is directly reflected in a number of new m/z peaks and higher intensity peaks in the mass spectra of labeled samples relative to the unlabeled equivalents. The automated untargeted isotope annotation and relative quantification capabilities of geoRge are demonstrated by the analysis of LC/MS data from a human retinal pigment epithelium cell line (ARPE-19) grown on normal and high glucose concentrations mimicking diabetic retinopathy conditions in vitro. In addition, we compared the results of geoRge with the outcome of X(13)CMS, since both approaches rely entirely on XCMS parameters for feature selection, namely m/z and retention time values. geoRge is available as an R script at https://github.com/jcapelladesto/geoRge.
Project description:Stable-isotope labeling strategies are extensively used for multiplex quantitative proteomics. Hybrid isotope labeling strate-gies that combine the use of isotopic mass difference labeling and isobaric tags can greatly increase sample multiplexity. In this work, we present a novel hybrid isotope labeling approach that we termed NHS-ester tandem labeling in one pot (NETLOP). We first optimized 16-plex isobaric TMTpro labeling of lysine residues followed by 2-plex or 3-plex isotopic mTRAQ labeling of peptide N-termini, both of which with commercially available NHS-ester reactive reagents. We then demonstrated the utility of the NETLOP approach by labeling HeLa cell samples and performing proof-of-principle quanti-tative 32-plex and 48-plex proteomic analyses, each in a single LC-MS/MS experiment. Compared to current hybrid isotope labeling methods, our NETLOP approach requires no sample cleanup between different labeling steps to minimize sample losses, induces no retention time shifts that compromise quantification accuracy, can be adapted to other NHS-ester isotop-ic labeling reagents to further increase multiplexity, and is compatible with samples from any origin in a wide array of bio-logical and clinical proteomics applications.