Project description:Roles of mesothelial cells (MCs) are poorly understood during liver development and injury. We identified podoplanin (Pdpn) as a cell surface markers for mesothelial cells in E12.5 mouse developing liver. To identify genes uniquely expressed in MCs, we isolated MCs from E12.5 mouse livers by FACS using anti-Pdpn antibodies and performed microarray analysis. The E12.5 liver was digested with trypsin-EDTA and incubated with antibodies against Pdpn. MCs were isolated as Pdpn+ population by FACS. Total RNA was extracted with RNAqueous Micro (Ambion) and the probes for the microarray were synthesized using the Ovation RNA amplification system V2 and FL-Ovation cDNA Biotin module V2 (Nugen). The labeled probes were hybridized with GeneChip Mouse Genome 430 2.0 arrays (Affymetrix) and signals were analyzed with Genomic Suite software (Partek).
Project description:This is the first report of a bacteriocin being produced by lactobacillus acidophilus ATCC 4356. For protein identification, the ~8-kDa peptide band was removed from the acrylamide gel and digested in-gel by trypsin, and the resulting peptide fragments were extracted and analyzed by LC-MS/MS analysis. The MS data were processed using Thermo Proteome Discoverer software (v2.2) with the SEQUEST search engine. Three peptides, VAHCASQIGR (amino acids 23-32), GSAACVSYLTR (amino acids 69-79), GSAACVSYLTRHRHH (amino acids 69-83) were identified as tryptic fragments on the basis of LC-MS/MS.This is the first report of a bacteriocin being produced by lactobacillus acidophilus ATCC 4356. For protein identification, the ~8-kDa peptide band was removed from the acrylamide gel and digested in-gel by trypsin, and the resulting peptide fragments were extracted and analyzed by LC-MS/MS analysis. The MS data were processed using Thermo Proteome Discoverer software (v2.2) with the SEQUEST search engine. Three peptides, VAHCASQIGR (amino acids 23-32), GSAACVSYLTR (amino acids 69-79), GSAACVSYLTRHRHH (amino acids 69-83) were identified as tryptic fragments on the basis of LC-MS/MS.
Project description:Mouse hippocampus membrane fractions were prepared using sucrose density gradient ultracentrifugation. Membrane proteins were solubilized using three different condition; 6-ACA/Triton X-100 or ProteoExtract Native Membrane Protein Extraction Kit (Calbiochem, Cat. No. 444810) or ProteoExtract Transmembrane Protein Extraction Kit (Novagen, Cat. No. 71772-3). Solubilized membrane proteins were separated on Blue Native PAGE or SDS-PAGE. Protein bands were excised, destained for peptide sample preparation. Proteins were reduced (DTT), alkylated (iodoacetamide), and in-gel digested with chymotrypsin or trypsin. Digested peptides were extracted then subjected to nanoHPLC and tandem MS analysis with Thermo Orbitrap Velos Pro mass spectrometer.
Project description:The identification of peptides and proteins by LC-MS/MS requires the use of bioinformatics. Tools developed in the Tabb Laboratory contribute significant flexibility and discrimination to this process. The Bumbershoot tools (MyriMatch, DirecTag, TagRecon, and Pepitome) enable the identification of peptides represented by MS/MS scans. All of these tools can work directly from instrument capture files of multiple vendors, such as Thermo RAW format, or from standard XML-based formats, such as mzML or mzXML. Peptide identifications are written to mzIdentML or pepXML format. Protein assembly is handled by the IDPicker algorithm. Raw identifications are filtered to a confident set by use of the target-decoy strategy. IDPicker arranges large sets of input files into a hierarchy for reporting, and the software applies a parsimony algorithm to report the smallest possible number of proteins to explain the observed peptides. This protocol details the use of these tools for new users.
Project description:The experiment aimed to study the proteome variation in a yeast strain (YS031), which chromosome II was artificially synthezied, compared with the wild type strain (BY4741). Proteins were extracted from the yeast cells and digested with trypsin. The peptides were label with iTRAQ, separated with 2D LC (SCX and RP) and detected with a Q-Exactive MS. Proteome identification and quantification were performed with Mascot2.3 and iQuant3.0.
Project description:In liquid chromatography-mass spectrometry (LC-MS), parts of LC peaks are often corrupted by their co-eluting peptides, which results in increased quantification variance. In this paper, we propose to apply accurate LC peak boundary detection to remove the corrupted part of LC peaks. Accurate LC peak boundary detection is achieved by checking the consistency of intensity patterns within peptide elution time ranges. In addition, we remove peptides with erroneous mass assignment through model fitness check, which compares observed intensity patterns to theoretically constructed ones. The proposed algorithm can significantly improve the accuracy and precision of peptide ratio measurements.
Project description:BackgroundUntargeted metabolomics datasets contain large proportions of uninformative features that can impede subsequent statistical analysis such as biomarker discovery and metabolic pathway analysis. Thus, there is a need for versatile and data-adaptive methods for filtering data prior to investigating the underlying biological phenomena. Here, we propose a data-adaptive pipeline for filtering metabolomics data that are generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Our data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients.ResultsUsing metabolomics datasets that were generated in our laboratory from samples of human blood, as well as two public LC-MS datasets, we compared our data-adaptive filtering method with traditional methods that rely on non-method specific thresholds. The data-adaptive approach outperformed traditional approaches in terms of removing noisy features and retaining high quality, biologically informative ones. The R code for running the data-adaptive filtering method is provided at https://github.com/courtneyschiffman/Metabolomics-Filtering .ConclusionsOur proposed data-adaptive filtering pipeline is intuitive and effectively removes uninformative features from untargeted metabolomics datasets. It is particularly relevant for interrogation of biological phenomena in data derived from complex matrices associated with biospecimens.
Project description:Femur and Tibia of Mice 3 months of age were dissected, growth plate removed, and serial digested with collagenase-trypsin to remove endosteal and periosteal cells. The cortical bone tubes, highly enriched in osteocye in their natural bone matrix environment where then cultured with and and with out PTH at 250nM for 24hr, RNA extracted and biotinylated cDNA prepared, and hybrized to Illumina MouseWG-6_V2 arrays. Data was analyzed using Genome Studio-Gene Expression Module from Illumina.
Project description:The accurate processing of complex liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) data from biological samples is a major challenge for metabolomics, proteomics, and related approaches. Here, we present the pipelines and systems for threshold-avoiding quantification (PASTAQ) LC-MS/MS preprocessing toolset, which allows highly accurate quantification of data-dependent acquisition LC-MS/MS datasets. PASTAQ performs compound quantification using single-stage (MS1) data and implements novel algorithms for high-performance and accurate quantification, retention time alignment, feature detection, and linking annotations from multiple identification engines. PASTAQ offers straightforward parameterization and automatic generation of quality control plots for data and preprocessing assessment. This design results in smaller variance when analyzing replicates of proteomes mixed with known ratios and allows the detection of peptides over a larger dynamic concentration range compared to widely used proteomics preprocessing tools. The performance of the pipeline is also demonstrated in a biological human serum dataset for the identification of gender-related proteins.