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:Three individual patient-originated (Scalp trauma; ages 37, 46 and 57) DPCs were used for the studies. Dermal papilla cells, derived from the frontal scalp of 3 women, were treated with normal medium (Ctrl group) and medium containing 10-7 M corticotropin-releasing hormone (CRH group) for 72 h. Proteins were extracted and digested with trypsin for 4D label-free quantitative proteomics (Shanghai Applied Protein Technology Co., Ltd.).
Project description:Data-Independent Acquisition (DIA) LC-MS/MS is an attractive partner for co-immunoprecipitation (co-IP) and affinity proteomics in general. Reducing the variability of quantitation by DIA could increase the statistical contrast for detecting specific interactors versus what has been achieved in Data-Dependent Acquisition (DDA). By interrogating affinity proteomes featuring both DDA and DIA experiments, we sought to evaluate the spectral libraries, the missingness of protein quantity tables, and the CV of protein quantities in six studies representing three different instrument manufacturers. We examined four contemporary bioinformatics workflows for DIA: FragPipe, DIA-NN, Spectronaut, and MaxQuant. We determined that (1) identifying spectral libraries directly from DIA experiments works well enough that separate DDA experiments do not produce larger spectral libraries when given equivalent instrument time; (2) experiments involving mock pull-downs or IgG controls may feature such indistinct signals that contemporary software will struggle to quantify them; (3) measured CV values were well controlled by Spectronaut and DIA-NN (and FragPipe, which implements DIA-NN for the quantitation step); and (4) when FragPipe builds spectral libraries and quantifies proteins from DIA experiments rather than performing both operations in DDA experiments, the DIA route results in a larger number of proteins quantified without missing values as well as lower CV for measured protein quantities.Supplementary informationThe online version contains supplementary material available at 10.1007/s42485-024-00166-4.
Project description:The wild-type and sdg8 mutant Arabidopsis thaliana Histones were extracted from 15-day-old Arabidopsis seedlings,Histone peptides were digested with trypsin for QTRAP 6500 mass spectrometry (MS) analysis.
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: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: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.