Journal: Nature methods

Article Title: C-BERST: Defining subnuclear proteomic landscapes at genomic elements with dCas9-APEX2

doi: 10.1038/s41592-018-0006-2

Figure Lengend Snippet: Successful capture of alpha-satellite-associated proteomes in live human cells by C-BERST. ( a ) Ratiometric C-BERST (using SILAC) was used to profile the alpha-satellite-associated proteome. A volcano plot of C-BERST-labeled, centromere-associated proteins in U2OS cells is shown. For each protein, the H/M SILAC ratio reflects the enrichment of identified proteins in sgAlpha vs. sgNS cells. 1,134 proteins (indicated by blue and red dots) are statistically enriched [BH-adjusted p value

Article Snippet: Raw data files were processed with Proteome Discoverer (Thermo, version 2.1.1.21) and searched with Mascot (Matrix Science, version 2.6) against the SwissProt Homo sapiens database.

Techniques: Labeling

Journal: Nature methods

Article Title: C-BERST: Defining subnuclear proteomic landscapes at genomic elements with dCas9-APEX2

doi: 10.1038/s41592-018-0006-2

Figure Lengend Snippet: Using C-BERST to biotinylate telomere-associated proteins in living human cells. ( a ) Diagram of the C-BERST workflow. ( b ) Telomere-associated proteome identification by ratiometric C-BERST. A volcano plot is shown for C-BERST-labeled, telomere-associated proteins in U2OS cells. For each protein, the H/M SILAC ratio reflects the enrichment of identified proteins in sgTelo vs. sgNS cells. 359 proteins (indicated by blue and red dots) are statistically enriched [BH-adjusted p value

Article Snippet: Raw data files were processed with Proteome Discoverer (Thermo, version 2.1.1.21) and searched with Mascot (Matrix Science, version 2.6) against the SwissProt Homo sapiens database.

Techniques: Labeling

Journal: Scientific Reports

Article Title: Proteomics analysis of serum small extracellular vesicles for the longitudinal study of a glioblastoma multiforme mouse model

doi: 10.1038/s41598-020-77535-8

Figure Lengend Snippet: Experimental workflow. Comparison between two sEV isolation procedures: precipitation (PPT) and Size Exclusion Chromatography (SEC) using 100 µl of mouse serum. SEC was then scaled down on vesicles isolated from 50 µl of serum. As a proof of concept, the ultrasensitive microproteomics workflow was then applied to a longitudinal study of a glioblastoma multiforme mouse model. Purified sEV’s were concentrated and lysed on protein concentrator spin filters; then extracted, quantified and digested with a modified SP3 protocol. Peptides were analyzed by nLC-MS/MS, and the data analyzed with Proteome Discoverer 2.1 and MaxQuant software. Statistical analysis was performed using Perseus and Matlab.

Article Snippet: Protein identification Raw data files were processed using Proteome Discoverer 2.1 (Thermo Scientific).

Techniques: Isolation, Size-exclusion Chromatography, Purification, Modification, Tandem Mass Spectroscopy, Software

Journal: Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry, and pharmacology

Article Title: Integrating Omics Technologies to Study Pulmonary Physiology and Pathology at the Systems Level

doi: 10.1159/000358693

Figure Lengend Snippet: Proteomics

Article Snippet: Keywords: Genomics, Proteomics, RNA-Seq, ChIP-Seq, Epigenomics, Systems Biology, Network Biology, Interactomes, Transcriptomes, Diseasosomes

Techniques:

Journal: Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry, and pharmacology

Article Title: Integrating Omics Technologies to Study Pulmonary Physiology and Pathology at the Systems Level

doi: 10.1159/000358693

Figure Lengend Snippet: Targeted and global metabolomics approach for metabolite identification and quantification. Data analysis workflows for global metabolomics are comparable to genomics and proteomics since many of the techniques generate raw data that are of similar format

Article Snippet: Keywords: Genomics, Proteomics, RNA-Seq, ChIP-Seq, Epigenomics, Systems Biology, Network Biology, Interactomes, Transcriptomes, Diseasosomes

Techniques:

Journal: Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry, and pharmacology

Article Title: Integrating Omics Technologies to Study Pulmonary Physiology and Pathology at the Systems Level

doi: 10.1159/000358693

Figure Lengend Snippet: Systomics: Integrative approach for pulmonary medicine. The confluence of genomics, proteomics, metabolomics and epigenomics drives systems driven approach for studying pulmonary disorders. Integration of work flows and data obtained from these will contribute

Article Snippet: Keywords: Genomics, Proteomics, RNA-Seq, ChIP-Seq, Epigenomics, Systems Biology, Network Biology, Interactomes, Transcriptomes, Diseasosomes

Techniques:

Journal: Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry, and pharmacology

Article Title: Integrating Omics Technologies to Study Pulmonary Physiology and Pathology at the Systems Level

doi: 10.1159/000358693

Figure Lengend Snippet: Work flow for processing and analyzing epigenomics data. Epigenomics shares a number of technologies and applications with genomics and proteomics, which allows users to overlap work flows for data generation and analysis. While initial steps of data

Article Snippet: Keywords: Genomics, Proteomics, RNA-Seq, ChIP-Seq, Epigenomics, Systems Biology, Network Biology, Interactomes, Transcriptomes, Diseasosomes

Techniques: Flow Cytometry

Journal: Scientific Reports

Article Title: Landscape of heart proteome changes in a diet-induced obesity model

doi: 10.1038/s41598-019-54522-2

Figure Lengend Snippet: Biological functions of the differentially expressed proteins in the myocardium of obese rats identified by proteomics ( A ) Protein-protein interaction network. The interaction network analyses were built using the STRING online software with a medium confidence level (0.4). The circles represent proteins, while the straight lines represent the interactions between different proteins. The line thickness indicates the strength of evidence, with thicker connections indicating higher confidence in the protein-protein interaction. Green circles represent the proteins involved in metabolic processes; shaded area delimits the proteins involved in lipid metabolic processes. ( B ) Gene ontology (GO) enrichment analysis. The analysis was performed using the PANTHER tool ( http://www.pantherdb.org ), providing the significantly enriched GO terms Molecular Function, Biological Process, and Cellular Component. On the left panel, the horizontal axis indicates the significance (−log10 p -value) of the functional association, which is dependent on the number of proteins in the class. On the right panel, changes are displayed as the number of proteins with increased or decreased levels (horizontal axis). ( C ) Violin plot of the fold changes for up- and down-regulated proteins in the WD group compared to their controls. The fold changes of up- and down-regulated proteins were further separated into the metabolic processes associated with fatty acid, glucose, amino acid, tricarboxylic acid (TCA) cycle, and oxidative phosphorylation (Oxi. Phosp.). The circles inside the plots represent the changed proteins. The detailed description of protein names is shown in Supplementary Tables S3 and S4 .

Article Snippet: The MS raw data files were loaded into Progenesis QI for Proteomics v.4.0 (Nonlinear Dynamics, Waters, Newcastle upon Tyne, UK) to perform the quantitative analysis.

Techniques: Software, Functional Assay

Journal: Scientific Reports

Article Title: Landscape of heart proteome changes in a diet-induced obesity model

doi: 10.1038/s41598-019-54522-2

Figure Lengend Snippet: Proteomics data based on 2-DE of heart tissue from control ( C ) and Western diet (WD) groups. ( A ) Correlation analysis between 2-DE gels triplicates of control ( C ) and Western diet (WD) groups performed by Image Master 2D Platinum software. ( B ) Representative 2-DE gel image. The position of molecular weight (MW) markers are indicated to the right and the p I (isoelectric point) at the bottom of the gel. The sequence of numbers (1–47) refers to identification spots of the significantly up- (red circle) and down-regulated (blue circle) proteins in the WD group compared to the C group identified by LC-MS/MS. The detailed list of proteins for highlighted spots are shown in Supplementary Table S3 .

Article Snippet: The MS raw data files were loaded into Progenesis QI for Proteomics v.4.0 (Nonlinear Dynamics, Waters, Newcastle upon Tyne, UK) to perform the quantitative analysis.

Techniques: Western Blot, Software, Molecular Weight, Sequencing, Liquid Chromatography with Mass Spectroscopy, Mass Spectrometry

Journal: Scientific Reports

Article Title: Landscape of heart proteome changes in a diet-induced obesity model

doi: 10.1038/s41598-019-54522-2

Figure Lengend Snippet: Label-free proteomics data clustering of heart tissue from control ( C ) and Western diet (WD) groups. ( A ) Univariate, significance ( p -value) vs. fold change analyses highlighting several significant deregulated proteins of interest. ( B ) Unsupervised multivariate principal component analysis (PCA). ( C ) Hierarchical clustering analyses (Heatmap) using unsupervised Euclidean distance of all differentially expressed proteins between the groups. The detailed description of protein names is shown in Supplementary Table S4 .

Article Snippet: The MS raw data files were loaded into Progenesis QI for Proteomics v.4.0 (Nonlinear Dynamics, Waters, Newcastle upon Tyne, UK) to perform the quantitative analysis.

Techniques: Western Blot

Journal: Clinical Proteomics

Article Title: Clinical proteomic analysis of scrub typhus infection

doi: 10.1186/s12014-018-9181-5

Figure Lengend Snippet: Complement system is a canonical pathway significantly altered in naive scrub typhus patients compared with normal subjects ( a ) and in patients treated with antibiotics compared with naive patients ( b ). Red indicates up-regulated proteins, and green indicates down-regulated proteins. The colour intensity corresponds to the degree of up- or down-regulation (fold change). White represents proteins known to be part of the pathway but not identified in the proteomic analysis

Article Snippet: Preparation of clinical serum samples for proteomic analysis For proteomic analysis, albumin and IgG were removed from clinical serum samples using ProteoPrep Immunoaffinity Albumin and IgG Depletion Kit (Sigma-Aldrich, St. Louis, MO, USA).

Techniques:

Journal: Clinical Proteomics

Article Title: Comparative analysis of differentially abundant proteins quantified by LC–MS/MS between flash frozen and laser microdissected OCT-embedded breast tumor samples

doi: 10.1186/s12014-020-09300-y

Figure Lengend Snippet: Sample processing workflow. a Proteins were extracted from five invasive breast cancer specimens using three different methods, generating a total of 15 protein samples. In Method 1, flash-frozen specimens were homogenized (FF/HOM) and protein isolated using the illustra triplePrep kit. In Methods 2A and 2B, sections from OCT-embedded specimens were laser microdissected to isolate tumor cells only (OCT/LMD-T) or both tumor and stromal cells (OCT/LMD-TS), respectively. In both Methods 2A and 2B, the laser microdissected material was then incubated at 37 °C for 10 min followed by protein isolation using the illustra triplePrep kit. Extracted proteins were trypsin digested and TMT-labeled. TMT-labeled peptides were analyzed using 2D-LC–MS/MS and proteins quantified using Proteome Discoverer v1.4 followed by data analysis to identify differentially expressed proteins among the three different sample storage/processing methods. b Sample-to-TMT channel mapping. Samples were analyzed in 3 TMT sets with 7 channels in each set. In each TMT set, channel 126 contained the QCP (quality control pooled sample) while the remaining 6 channels contained the individual TMT-labeled samples. The last three TMT channels in set 3 contained technical replicates, one for each sample storage/preparation method

Article Snippet: Protein quantificationRaw LC–MS/MS data were processed using Proteome Discoverer v1.4 (Thermo Scientific) and searched against the RefSeq protein database using the database search algorithm SEQUEST to identify and quantify proteins.

Techniques: Isolation, Laser Capture Microdissection, Incubation, Labeling, Liquid Chromatography with Mass Spectroscopy

Journal: Clinical Proteomics

Article Title: Proteomic profiling of liver tissue from the mdx-4cv mouse model of Duchenne muscular dystrophy

doi: 10.1186/s12014-018-9212-2

Figure Lengend Snippet: Overview of the complex multi-system pathophysiology of dystrophinopathy and proteomic workflow to analyse the mdx - 4cv liver. Duchenne muscular dystrophy is caused by primary abnormalities in dystrophin and triggers progressive skeletal muscle wasting, cardio-respiratory abnormalities and cognitive impairments. In addition, X-linked muscular dystrophy is also characterized by secondary effects on a variety of organ systems including the liver. Proteome-wide changes in liver tissue were determined by comparative proteomics using the dystrophic mdx - 4cv mouse model of Duchenne muscular dystrophy. Results obtained by mass spectrometry using an Orbitrap Fusion Tribrid apparatus were analysed by systems bioinformatics, and key findings were confirmed by verification studies employing immunoblotting and immunofluorescence microscopy

Article Snippet: Protein profiling by label-free LC–MS/MS Progenesis QI for Proteomics software (version 3.1; Non-Linear Dynamics, a Waters company, Newcastle upon Tyne, UK) was used for the quantitative analysis of liver homogenates from wild type versus mdx -4cv mice.

Techniques: Mass Spectrometry, Immunofluorescence, Microscopy

Journal: Clinical Proteomics

Article Title: Proteomic profiling of liver tissue from the mdx-4cv mouse model of Duchenne muscular dystrophy

doi: 10.1186/s12014-018-9212-2

Figure Lengend Snippet: Heat map of differentially expressed proteins in the mdx - 4cv liver. Shown is the clustering of significantly increased versus decreased proteins in liver tissue from the dystrophic mdx - 4cv mouse model of Duchenne muscular dystrophy, as compared to control tissues. For the identification of proteome-wide changes in the liver, protein extracts from whole tissue preparations of 6-month-old mdx - 4cv mice (n = 4; MDX-4CV 1–4) versus age-matched wild type mice (n = 4; WT 1–4) were analysed by mass spectrometry-based proteomics

Article Snippet: Protein profiling by label-free LC–MS/MS Progenesis QI for Proteomics software (version 3.1; Non-Linear Dynamics, a Waters company, Newcastle upon Tyne, UK) was used for the quantitative analysis of liver homogenates from wild type versus mdx -4cv mice.

Techniques: Mouse Assay, Mass Spectrometry

Journal: Scientific Reports

Article Title: Combining micro-RNA and protein sequencing to detect robust biomarkers for Graves’ disease and orbitopathy

doi: 10.1038/s41598-018-26700-1

Figure Lengend Snippet: ( a ) Canonical correspondence analysis (CCA) plot of ‘omics-based Euclidean distances between samples and their quantitative clinical features (TSH, TRAB and FT4 levels). ( b ) Multidimensional scaling plot of miRNA + proteomics distances with superimposed hyperthyroidism status (euthyroid vs. hyperthyroid).

Article Snippet: MS/MS spectra of peptides were used for protein inference via database searching in Spectrum Mill MS Proteomics Workbench (Rev B.04; Agilent Technologies).

Techniques: