nanolc esi ms ms  (Thermo Fisher)


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    Name:
    Q Exactive HF Hybrid Quadrupole Orbitrap Mass Spectrometer
    Description:
    Identify and quantify more proteins peptides lipids glycans and small molecules accurately and in less time with the Thermo Scientific Q Exactive HF hybrid quadrupole Orbitrap mass spectrometer The Q Exactive HF system combines a state of the art segmented quadrupole for high performance precursor ion selection with a high resolution accurate mass HR AM ultra high field Orbitrap mass analyzer to deliver a superior combination of scan speed resolving power mass accuracy spectral quality and sensitivity Identify quantify and confirm in a single analysis with a single instrument with the Q Exactive HF mass spectrometer
    Catalog Number:
    iqlaaegaapfalgmbfz
    Price:
    None
    Applications:
    Industrial & Applied Science|Industrial Mass Spectrometry
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    Instruments and Equipment
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    Structured Review

    Thermo Fisher nanolc esi ms ms
    ( A ) Schematic representation of the workflow used to identify SLeA-expressing glycoproteins with affinity for E-selectin. Briefly, IPs with a monoclonal targeting the SLeA antigen were used to isolate glycoproteins from plasma membrane glycoproteins enriched extracts. In parallel, samples were pulled down with E-selectin. Parallel IPs with isotype controls and lectin pulldowns in the absence of Ca 2+ ions (required for binding) were also performed as negative controls. The proteins were then resolved by SDS-PAGE, the bands were excised, proteins reduced, alkylated and digested with trypsin prior to analysis by <t>nanoLC-ESI-MS/MS.</t> ( B ) SLeA and SLeX expressions in glycoproteins obtained by IP for SLeA and corresponding isotype controls for N87 (SLeA+/SLeX+) and AGS (SLeA-/SLeX-; negative control) as well as glycoproteins with affinity for E-selectin in the presence and absence of Ca2+ (negative control). Collectively, the blots support the affinity of the IP strategy for SLeA-expressing glycoproteins with no contaminations from SLeX glycoproteins. On the other hand, E-selectin isolated both SLeA and SLeX expressing glycoproteins. ( C ) Venn diagrams highlighting the distribution SLeA-expressing glycoproteins and glycoproteins showing affinity for human E-selectin for cell line N87 ( A ), OCUM-1 ( B ), and KATO-III ( C ). Glycoproteins with confirmed O-SLeA expressing glycosites and showing affinity for E-selectin among the three cell models ( D ). The Venn diagrams in panels A–C highlight the number of glycoproteins isolated by IP for SLeA and E-selectin for each cell line, evidencing the percentage of glycoproteins commonly identified for both IPs and showing clear SLeA modifications in O-glycans. Panel C highlights the 22 glycoproteins presenting O-SLeA glycosylation and affinity for E-selectin in all cell lines.
    Identify and quantify more proteins peptides lipids glycans and small molecules accurately and in less time with the Thermo Scientific Q Exactive HF hybrid quadrupole Orbitrap mass spectrometer The Q Exactive HF system combines a state of the art segmented quadrupole for high performance precursor ion selection with a high resolution accurate mass HR AM ultra high field Orbitrap mass analyzer to deliver a superior combination of scan speed resolving power mass accuracy spectral quality and sensitivity Identify quantify and confirm in a single analysis with a single instrument with the Q Exactive HF mass spectrometer
    https://www.bioz.com/result/nanolc esi ms ms/product/Thermo Fisher
    Average 89 stars, based on 5491 article reviews
    Price from $9.99 to $1999.99
    nanolc esi ms ms - by Bioz Stars, 2020-07
    89/100 stars

    Images

    1) Product Images from "Nucleolin-Sle A Glycoforms as E-Selectin Ligands and Potentially Targetable Biomarkers at the Cell Surface of Gastric Cancer Cells"

    Article Title: Nucleolin-Sle A Glycoforms as E-Selectin Ligands and Potentially Targetable Biomarkers at the Cell Surface of Gastric Cancer Cells

    Journal: Cancers

    doi: 10.3390/cancers12040861

    ( A ) Schematic representation of the workflow used to identify SLeA-expressing glycoproteins with affinity for E-selectin. Briefly, IPs with a monoclonal targeting the SLeA antigen were used to isolate glycoproteins from plasma membrane glycoproteins enriched extracts. In parallel, samples were pulled down with E-selectin. Parallel IPs with isotype controls and lectin pulldowns in the absence of Ca 2+ ions (required for binding) were also performed as negative controls. The proteins were then resolved by SDS-PAGE, the bands were excised, proteins reduced, alkylated and digested with trypsin prior to analysis by nanoLC-ESI-MS/MS. ( B ) SLeA and SLeX expressions in glycoproteins obtained by IP for SLeA and corresponding isotype controls for N87 (SLeA+/SLeX+) and AGS (SLeA-/SLeX-; negative control) as well as glycoproteins with affinity for E-selectin in the presence and absence of Ca2+ (negative control). Collectively, the blots support the affinity of the IP strategy for SLeA-expressing glycoproteins with no contaminations from SLeX glycoproteins. On the other hand, E-selectin isolated both SLeA and SLeX expressing glycoproteins. ( C ) Venn diagrams highlighting the distribution SLeA-expressing glycoproteins and glycoproteins showing affinity for human E-selectin for cell line N87 ( A ), OCUM-1 ( B ), and KATO-III ( C ). Glycoproteins with confirmed O-SLeA expressing glycosites and showing affinity for E-selectin among the three cell models ( D ). The Venn diagrams in panels A–C highlight the number of glycoproteins isolated by IP for SLeA and E-selectin for each cell line, evidencing the percentage of glycoproteins commonly identified for both IPs and showing clear SLeA modifications in O-glycans. Panel C highlights the 22 glycoproteins presenting O-SLeA glycosylation and affinity for E-selectin in all cell lines.
    Figure Legend Snippet: ( A ) Schematic representation of the workflow used to identify SLeA-expressing glycoproteins with affinity for E-selectin. Briefly, IPs with a monoclonal targeting the SLeA antigen were used to isolate glycoproteins from plasma membrane glycoproteins enriched extracts. In parallel, samples were pulled down with E-selectin. Parallel IPs with isotype controls and lectin pulldowns in the absence of Ca 2+ ions (required for binding) were also performed as negative controls. The proteins were then resolved by SDS-PAGE, the bands were excised, proteins reduced, alkylated and digested with trypsin prior to analysis by nanoLC-ESI-MS/MS. ( B ) SLeA and SLeX expressions in glycoproteins obtained by IP for SLeA and corresponding isotype controls for N87 (SLeA+/SLeX+) and AGS (SLeA-/SLeX-; negative control) as well as glycoproteins with affinity for E-selectin in the presence and absence of Ca2+ (negative control). Collectively, the blots support the affinity of the IP strategy for SLeA-expressing glycoproteins with no contaminations from SLeX glycoproteins. On the other hand, E-selectin isolated both SLeA and SLeX expressing glycoproteins. ( C ) Venn diagrams highlighting the distribution SLeA-expressing glycoproteins and glycoproteins showing affinity for human E-selectin for cell line N87 ( A ), OCUM-1 ( B ), and KATO-III ( C ). Glycoproteins with confirmed O-SLeA expressing glycosites and showing affinity for E-selectin among the three cell models ( D ). The Venn diagrams in panels A–C highlight the number of glycoproteins isolated by IP for SLeA and E-selectin for each cell line, evidencing the percentage of glycoproteins commonly identified for both IPs and showing clear SLeA modifications in O-glycans. Panel C highlights the 22 glycoproteins presenting O-SLeA glycosylation and affinity for E-selectin in all cell lines.

    Techniques Used: Expressing, Binding Assay, SDS Page, Tandem Mass Spectroscopy, Negative Control, Isolation

    2) Product Images from "Nucleolin-Sle A Glycoforms as E-Selectin Ligands and Potentially Targetable Biomarkers at the Cell Surface of Gastric Cancer Cells"

    Article Title: Nucleolin-Sle A Glycoforms as E-Selectin Ligands and Potentially Targetable Biomarkers at the Cell Surface of Gastric Cancer Cells

    Journal: Cancers

    doi: 10.3390/cancers12040861

    ( A ) Schematic representation of the workflow used to identify SLeA-expressing glycoproteins with affinity for E-selectin. Briefly, IPs with a monoclonal targeting the SLeA antigen were used to isolate glycoproteins from plasma membrane glycoproteins enriched extracts. In parallel, samples were pulled down with E-selectin. Parallel IPs with isotype controls and lectin pulldowns in the absence of Ca 2+ ions (required for binding) were also performed as negative controls. The proteins were then resolved by SDS-PAGE, the bands were excised, proteins reduced, alkylated and digested with trypsin prior to analysis by nanoLC-ESI-MS/MS. ( B ) SLeA and SLeX expressions in glycoproteins obtained by IP for SLeA and corresponding isotype controls for N87 (SLeA+/SLeX+) and AGS (SLeA-/SLeX-; negative control) as well as glycoproteins with affinity for E-selectin in the presence and absence of Ca2+ (negative control). Collectively, the blots support the affinity of the IP strategy for SLeA-expressing glycoproteins with no contaminations from SLeX glycoproteins. On the other hand, E-selectin isolated both SLeA and SLeX expressing glycoproteins. ( C ) Venn diagrams highlighting the distribution SLeA-expressing glycoproteins and glycoproteins showing affinity for human E-selectin for cell line N87 ( A ), OCUM-1 ( B ), and KATO-III ( C ). Glycoproteins with confirmed O-SLeA expressing glycosites and showing affinity for E-selectin among the three cell models ( D ). The Venn diagrams in panels A–C highlight the number of glycoproteins isolated by IP for SLeA and E-selectin for each cell line, evidencing the percentage of glycoproteins commonly identified for both IPs and showing clear SLeA modifications in O-glycans. Panel C highlights the 22 glycoproteins presenting O-SLeA glycosylation and affinity for E-selectin in all cell lines.
    Figure Legend Snippet: ( A ) Schematic representation of the workflow used to identify SLeA-expressing glycoproteins with affinity for E-selectin. Briefly, IPs with a monoclonal targeting the SLeA antigen were used to isolate glycoproteins from plasma membrane glycoproteins enriched extracts. In parallel, samples were pulled down with E-selectin. Parallel IPs with isotype controls and lectin pulldowns in the absence of Ca 2+ ions (required for binding) were also performed as negative controls. The proteins were then resolved by SDS-PAGE, the bands were excised, proteins reduced, alkylated and digested with trypsin prior to analysis by nanoLC-ESI-MS/MS. ( B ) SLeA and SLeX expressions in glycoproteins obtained by IP for SLeA and corresponding isotype controls for N87 (SLeA+/SLeX+) and AGS (SLeA-/SLeX-; negative control) as well as glycoproteins with affinity for E-selectin in the presence and absence of Ca2+ (negative control). Collectively, the blots support the affinity of the IP strategy for SLeA-expressing glycoproteins with no contaminations from SLeX glycoproteins. On the other hand, E-selectin isolated both SLeA and SLeX expressing glycoproteins. ( C ) Venn diagrams highlighting the distribution SLeA-expressing glycoproteins and glycoproteins showing affinity for human E-selectin for cell line N87 ( A ), OCUM-1 ( B ), and KATO-III ( C ). Glycoproteins with confirmed O-SLeA expressing glycosites and showing affinity for E-selectin among the three cell models ( D ). The Venn diagrams in panels A–C highlight the number of glycoproteins isolated by IP for SLeA and E-selectin for each cell line, evidencing the percentage of glycoproteins commonly identified for both IPs and showing clear SLeA modifications in O-glycans. Panel C highlights the 22 glycoproteins presenting O-SLeA glycosylation and affinity for E-selectin in all cell lines.

    Techniques Used: Expressing, Binding Assay, SDS Page, Tandem Mass Spectroscopy, Negative Control, Isolation

    3) Product Images from "Deep learning the collisional cross sections of the peptide universe from a million training samples"

    Article Title: Deep learning the collisional cross sections of the peptide universe from a million training samples

    Journal: bioRxiv

    doi: 10.1101/2020.05.19.102285

    Precision, accuracy and utility of experimental peptide CCS values. a, Color-coded pairwise Pearson correlation values of peptide retention time (upper triangular matrix) and CCS values (lower triangular matrix) between 168 LC-MS/MS runs of fractionated tryptic digests. Experimental meta-data are indicated below the x-axis. White (n/a) indicates less than 5 data points for pairwise comparison. b, CCS values of shared tryptic peptides independently measured in two typical LC-MS runs of fractions from Drosophila and HeLa (n = 68). c, CVs of repeatedly measured peptide CCS values in the full data set (n=374,862 peptides). d, Specificity of combined peptide m/z and CCS information for doubly- and triply-charged tryptic peptides (n = 300,442 and 105,448) with a fixed m/z tolerance of ±1.5 ppm and as a function of CCS tolerance. For details, see main text and Methods.
    Figure Legend Snippet: Precision, accuracy and utility of experimental peptide CCS values. a, Color-coded pairwise Pearson correlation values of peptide retention time (upper triangular matrix) and CCS values (lower triangular matrix) between 168 LC-MS/MS runs of fractionated tryptic digests. Experimental meta-data are indicated below the x-axis. White (n/a) indicates less than 5 data points for pairwise comparison. b, CCS values of shared tryptic peptides independently measured in two typical LC-MS runs of fractions from Drosophila and HeLa (n = 68). c, CVs of repeatedly measured peptide CCS values in the full data set (n=374,862 peptides). d, Specificity of combined peptide m/z and CCS information for doubly- and triply-charged tryptic peptides (n = 300,442 and 105,448) with a fixed m/z tolerance of ±1.5 ppm and as a function of CCS tolerance. For details, see main text and Methods.

    Techniques Used: Liquid Chromatography with Mass Spectroscopy

    4) Product Images from "Advancing Urinary Protein Biomarker Discovery by Data-Independent Acquisition on a Quadrupole-Orbitrap Mass Spectrometer"

    Article Title: Advancing Urinary Protein Biomarker Discovery by Data-Independent Acquisition on a Quadrupole-Orbitrap Mass Spectrometer

    Journal: Journal of proteome research

    doi: 10.1021/acs.jproteome.5b00826

    Overview of DIA data set. (A) Overview of peptide identification results. The bar charts (right and left panel) give an overview of the identified peptides/proteins in each of the 87 individual samples using a 30 min gradient on a Q Exactive HF mass spectrometer with a DIA method (yellow, pain control group; purple, ovarian cyst; blue, urinary tract infections). (B) Overview of peptide identification results. (C) Protein sample coverage. We calculated how many proteins were identified in more than 95% of the samples, in 90–95% of the samples, etc. for DIA and DDA data (orange, DIA data; blue, DDA data).
    Figure Legend Snippet: Overview of DIA data set. (A) Overview of peptide identification results. The bar charts (right and left panel) give an overview of the identified peptides/proteins in each of the 87 individual samples using a 30 min gradient on a Q Exactive HF mass spectrometer with a DIA method (yellow, pain control group; purple, ovarian cyst; blue, urinary tract infections). (B) Overview of peptide identification results. (C) Protein sample coverage. We calculated how many proteins were identified in more than 95% of the samples, in 90–95% of the samples, etc. for DIA and DDA data (orange, DIA data; blue, DDA data).

    Techniques Used: Mass Spectrometry

    5) Product Images from "Targeting the scaffolding role of LSD1 (KDM1A) poises acute myeloid leukemia cells for retinoic acid–induced differentiation"

    Article Title: Targeting the scaffolding role of LSD1 (KDM1A) poises acute myeloid leukemia cells for retinoic acid–induced differentiation

    Journal: Science Advances

    doi: 10.1126/sciadv.aax2746

    Pharmacological inhibition of LSD1 disrupts its interaction with GFI1. ( A ) Schematic representation of SILAC mass spectrometry approach to identify LSD1 interactors in NB4 cells. LC-MS/MS, liquid chromatography–tandem mass spectrometry. ( B ) Scatterplot showing log 2 (heavy/light) ratio of forward reaction on the x axis (Rep1) and the log 2 (light/heavy) ratio of reverse reaction on the y axis (Rep3). In the top right quadrant are represented LSD1 interactors. The blue dashed lines define the threshold used to define the LSD1 interactors from the background. Proteins belonging to the CoREST complex are shown in red dots. ( C ) Venn diagrams with numbers of individual and overlapping putative LSD1 interactors identified in the three different SILAC replicates. ( D ) Western blot analysis of LSD1 and some identified interactors in LSD1 IPs, with or without blocking peptide. Lamin B1 is used as loading control. ( E ) Schematic representation of SILAC mass spectrometry approach to identify recruited and evicted interactors of LSD1, upon LSD1 pharmacological inhibition with 2 μM MC_2580 for 24 hours. ( F ) Scatterplot showing log 2 (heavy/light) ratio of forward reaction on the x axis and the log 2 (light/heavy) ratio of reverse reaction on the y axis. Proteins recruited by LSD1 after inhibition are present in the top right quadrant, while proteins evicted from the interaction with LSD1 after drug treatment are found in the bottom left quadrant. Proteins previously identified as interactors of LSD1 in NB4 are shown as red dots. The blue dashed lines define the threshold used to determine recruited and evicted proteins. ( G ) Western blot analysis of LSD1 and GFI1 in LSD1 IPs in NB4 cells treated for 24 hours with DMSO, 2 μM MC_2580, or 2 μM DDP_38003. ( H ) Western blot analysis of LSD1 and GFI1 in GFI1 IPs in NB4 LSD1 KO cells transduced with empty vector, wild-type, or catalytic inactive K661A-LSD1, treated with 2 μM MC_2580 or DMSO.
    Figure Legend Snippet: Pharmacological inhibition of LSD1 disrupts its interaction with GFI1. ( A ) Schematic representation of SILAC mass spectrometry approach to identify LSD1 interactors in NB4 cells. LC-MS/MS, liquid chromatography–tandem mass spectrometry. ( B ) Scatterplot showing log 2 (heavy/light) ratio of forward reaction on the x axis (Rep1) and the log 2 (light/heavy) ratio of reverse reaction on the y axis (Rep3). In the top right quadrant are represented LSD1 interactors. The blue dashed lines define the threshold used to define the LSD1 interactors from the background. Proteins belonging to the CoREST complex are shown in red dots. ( C ) Venn diagrams with numbers of individual and overlapping putative LSD1 interactors identified in the three different SILAC replicates. ( D ) Western blot analysis of LSD1 and some identified interactors in LSD1 IPs, with or without blocking peptide. Lamin B1 is used as loading control. ( E ) Schematic representation of SILAC mass spectrometry approach to identify recruited and evicted interactors of LSD1, upon LSD1 pharmacological inhibition with 2 μM MC_2580 for 24 hours. ( F ) Scatterplot showing log 2 (heavy/light) ratio of forward reaction on the x axis and the log 2 (light/heavy) ratio of reverse reaction on the y axis. Proteins recruited by LSD1 after inhibition are present in the top right quadrant, while proteins evicted from the interaction with LSD1 after drug treatment are found in the bottom left quadrant. Proteins previously identified as interactors of LSD1 in NB4 are shown as red dots. The blue dashed lines define the threshold used to determine recruited and evicted proteins. ( G ) Western blot analysis of LSD1 and GFI1 in LSD1 IPs in NB4 cells treated for 24 hours with DMSO, 2 μM MC_2580, or 2 μM DDP_38003. ( H ) Western blot analysis of LSD1 and GFI1 in GFI1 IPs in NB4 LSD1 KO cells transduced with empty vector, wild-type, or catalytic inactive K661A-LSD1, treated with 2 μM MC_2580 or DMSO.

    Techniques Used: Inhibition, Mass Spectrometry, Liquid Chromatography with Mass Spectroscopy, Liquid Chromatography, Western Blot, Blocking Assay, Transduction, Plasmid Preparation

    6) Product Images from "Dynamic rewiring of the human interactome by interferon signaling"

    Article Title: Dynamic rewiring of the human interactome by interferon signaling

    Journal: Genome Biology

    doi: 10.1186/s13059-020-02050-y

    IFN stimulation induces changes in ribosome composition to regulate ISG synthesis. a Schematic overview of the sucrose gradient experiments to determine changes in composition of free (40S, 60S, and 80S) and actively translating ribosomes (polysomes). b Representative traces of sucrose density gradients from cells stimulated with IFN for 24 h or unstimulated cells. c Median protein abundance across three replicates from pooled free ribosome or polysome fractions by label-free quantification (LFQ) in IFN-stimulated or unstimulated cells. PCC, Pearson’s correlation coefficient. d Western blot of ribosomes isolated from IFN-stimulated or unstimulated cells by sucrose cushion. e Experimental workflow for shotgun proteomics analysis on RPL28-depleted cells after IFN stimulation. f Volcano plot showing differential protein abundance in cells stimulated with IFN for 8 h and treated with siRPL28, relative to controls. g p values from gene set enrichment analysis (GSEA) of 5453 GO terms in a comparison of siRPL28-treated and IFN-stimulated cells compared to untreated controls. Dotted line shows the statistical significance of ISGs by GSEA (Fig. S9E). Points in red represent GO terms more significantly enriched than ISGs. h Heatmap showing abundance of select well-studied ISGs in siRPL28-treated and IFN-stimulated cells compared to untreated controls. i Western blots of select ISGs in cells treated with siRPL28 or control siRNA after 4 h, 8 h, and 24 h of IFN stimulation. j Crystal structure of the human 80S ribosome (PDB: 4UG0), with RPL28 highlighted in blue
    Figure Legend Snippet: IFN stimulation induces changes in ribosome composition to regulate ISG synthesis. a Schematic overview of the sucrose gradient experiments to determine changes in composition of free (40S, 60S, and 80S) and actively translating ribosomes (polysomes). b Representative traces of sucrose density gradients from cells stimulated with IFN for 24 h or unstimulated cells. c Median protein abundance across three replicates from pooled free ribosome or polysome fractions by label-free quantification (LFQ) in IFN-stimulated or unstimulated cells. PCC, Pearson’s correlation coefficient. d Western blot of ribosomes isolated from IFN-stimulated or unstimulated cells by sucrose cushion. e Experimental workflow for shotgun proteomics analysis on RPL28-depleted cells after IFN stimulation. f Volcano plot showing differential protein abundance in cells stimulated with IFN for 8 h and treated with siRPL28, relative to controls. g p values from gene set enrichment analysis (GSEA) of 5453 GO terms in a comparison of siRPL28-treated and IFN-stimulated cells compared to untreated controls. Dotted line shows the statistical significance of ISGs by GSEA (Fig. S9E). Points in red represent GO terms more significantly enriched than ISGs. h Heatmap showing abundance of select well-studied ISGs in siRPL28-treated and IFN-stimulated cells compared to untreated controls. i Western blots of select ISGs in cells treated with siRPL28 or control siRNA after 4 h, 8 h, and 24 h of IFN stimulation. j Crystal structure of the human 80S ribosome (PDB: 4UG0), with RPL28 highlighted in blue

    Techniques Used: Periodic Counter-current Chromatography, Western Blot, Isolation

    7) Product Images from "YcfDRM is a thermophilic oxygen-dependent ribosomal protein uL16 oxygenase"

    Article Title: YcfDRM is a thermophilic oxygen-dependent ribosomal protein uL16 oxygenase

    Journal: Extremophiles

    doi: 10.1007/s00792-018-1016-9

    YcfD RM is a 2-oxoglutarate-dependent oxygenase. a MALDI–MS spectrum of ycfD RM -dependent hydroxylation of R. marinus uL16 RM fragment (KPVTKKPAEVRMGKGKGSVE). A 16 Da mass shift is consistent with a ycfD RM -dependent oxidative modification. b Amino acid analysis reveals (2 S , 3 R )-hydroxylation of R82. Extracted ion chromatograms ( m / z = 345) from LC–MS analysis of: a synthetic (2 S , 3 S )- and (2 S , 3 R )-3-hydroxy-arginine standards, b – d amino acid hydrolysates from ycfD RM -hydroxylated uL16 RM peptide fragment(red trace) overlaid with hydrolysates from a control peptide (black trace, b ), (2 S , 3 R )-hydroxy-arginine standard (blue trace, c ) or (2 S , 3 S )-hydroxy-arginine standard (yellow trace, d ); e – f amino acid hydrolysates from ycfD RM -hydroxylated uL16 RM spiked with either (2 S , 3 R )-hydroxy-arginine ( e ) or (2 S , 3 S )-hydroxy-arginine ( f ) standards. c MS/MS studies on uL16 RM fragment peptide (KPVTKKPAEVRMGKGKGSVE-NH 2 ) incubated with ycfD RM and co-factors/co-substrates (Fe(II), 2OG and ascorbate) revealed hydroxylation at R82. d Co-factor dependence of ycfD RM :ycfD RM (1 μM) was incubated in a reaction mixture from which co-factors and co-substrates (Fe(II), 2OG and ascorbate at 100 μM, 200 μM and 1 mM, respectively), were systematically removed. Apo- and metallated forms of ycfD RM were tested. The reaction was carried out in 50 mM HEPES (pH 7.5) at 65 °C in triplicates. The mean value is shown, with error bars representing standard deviation. e Reaction scheme of ycfD RM -catalysed (2 S , 3 R )-arginine-3-hydroxylation
    Figure Legend Snippet: YcfD RM is a 2-oxoglutarate-dependent oxygenase. a MALDI–MS spectrum of ycfD RM -dependent hydroxylation of R. marinus uL16 RM fragment (KPVTKKPAEVRMGKGKGSVE). A 16 Da mass shift is consistent with a ycfD RM -dependent oxidative modification. b Amino acid analysis reveals (2 S , 3 R )-hydroxylation of R82. Extracted ion chromatograms ( m / z = 345) from LC–MS analysis of: a synthetic (2 S , 3 S )- and (2 S , 3 R )-3-hydroxy-arginine standards, b – d amino acid hydrolysates from ycfD RM -hydroxylated uL16 RM peptide fragment(red trace) overlaid with hydrolysates from a control peptide (black trace, b ), (2 S , 3 R )-hydroxy-arginine standard (blue trace, c ) or (2 S , 3 S )-hydroxy-arginine standard (yellow trace, d ); e – f amino acid hydrolysates from ycfD RM -hydroxylated uL16 RM spiked with either (2 S , 3 R )-hydroxy-arginine ( e ) or (2 S , 3 S )-hydroxy-arginine ( f ) standards. c MS/MS studies on uL16 RM fragment peptide (KPVTKKPAEVRMGKGKGSVE-NH 2 ) incubated with ycfD RM and co-factors/co-substrates (Fe(II), 2OG and ascorbate) revealed hydroxylation at R82. d Co-factor dependence of ycfD RM :ycfD RM (1 μM) was incubated in a reaction mixture from which co-factors and co-substrates (Fe(II), 2OG and ascorbate at 100 μM, 200 μM and 1 mM, respectively), were systematically removed. Apo- and metallated forms of ycfD RM were tested. The reaction was carried out in 50 mM HEPES (pH 7.5) at 65 °C in triplicates. The mean value is shown, with error bars representing standard deviation. e Reaction scheme of ycfD RM -catalysed (2 S , 3 R )-arginine-3-hydroxylation

    Techniques Used: Mass Spectrometry, Modification, Liquid Chromatography with Mass Spectroscopy, Incubation, Standard Deviation

    8) Product Images from "The beauty of being (label)-free: sample preparation methods for SWATH-MS and next-generation targeted proteomics"

    Article Title: The beauty of being (label)-free: sample preparation methods for SWATH-MS and next-generation targeted proteomics

    Journal: F1000Research

    doi: 10.12688/f1000research.2-272.v2

    Protein identification in label-free sample preparations. ( a ) Proteolytic digestion efficiencies. Trypsin or Lys-C/trypsin (FASP) digestion efficiencies expressed as relative occurrence of spectra that could be assigned miscleaved peptides (n = 3). ( b ) Amino acid specificity of proteolytic digestion. Relative occurrence of identified peptides with C-terminal lysine or arginine, compared to the average frequency of these amino acids across all individual proteins identified (n = 3, Error bars = +/- S.D.) ( c ) Identified peptides differ per protocol, and correlate with the total peak area as recorded in a DDA experiment. 18 samples derived from the same yeast culture were processed with six protocols in triplicates, and analyzed on a TripleTOF5600 instrument. The number of identified peptides correlates with the total peak area recorded, and indicates the highest identification rate in in solution digests, followed by filter-aided , and in gel procedures. ( d ) Detection of proteins by DDA or SWATH in a label-free experiment. Samples were analyzed in triplicates both for DDA and SWATH acquisition on a TripleTOF5600 instrument, data was searched using paragon (DDA), and Spectronaut (SWATH). SWATH increased the number of detectable proteins in combination with the in solution protocols. In solution protocols RapidACN and RapiGest led to the detection of up to 1000 proteins in single injections, followed by FASP and eFASP, which gave rise to between 250 and 750 proteins, and in gel injections that yielded 300 proteins IDs. Inset: A comparison of protein IDs from the TripleTOF and QExactive platforms shows a linear correlation for the protocols investigated. Data was searched using Mascot (n = 3, Error bars = +/- S.D.).
    Figure Legend Snippet: Protein identification in label-free sample preparations. ( a ) Proteolytic digestion efficiencies. Trypsin or Lys-C/trypsin (FASP) digestion efficiencies expressed as relative occurrence of spectra that could be assigned miscleaved peptides (n = 3). ( b ) Amino acid specificity of proteolytic digestion. Relative occurrence of identified peptides with C-terminal lysine or arginine, compared to the average frequency of these amino acids across all individual proteins identified (n = 3, Error bars = +/- S.D.) ( c ) Identified peptides differ per protocol, and correlate with the total peak area as recorded in a DDA experiment. 18 samples derived from the same yeast culture were processed with six protocols in triplicates, and analyzed on a TripleTOF5600 instrument. The number of identified peptides correlates with the total peak area recorded, and indicates the highest identification rate in in solution digests, followed by filter-aided , and in gel procedures. ( d ) Detection of proteins by DDA or SWATH in a label-free experiment. Samples were analyzed in triplicates both for DDA and SWATH acquisition on a TripleTOF5600 instrument, data was searched using paragon (DDA), and Spectronaut (SWATH). SWATH increased the number of detectable proteins in combination with the in solution protocols. In solution protocols RapidACN and RapiGest led to the detection of up to 1000 proteins in single injections, followed by FASP and eFASP, which gave rise to between 250 and 750 proteins, and in gel injections that yielded 300 proteins IDs. Inset: A comparison of protein IDs from the TripleTOF and QExactive platforms shows a linear correlation for the protocols investigated. Data was searched using Mascot (n = 3, Error bars = +/- S.D.).

    Techniques Used: Derivative Assay

    9) Product Images from "Growth factor receptor signaling inhibition prevents SARS-CoV-2 replication"

    Article Title: Growth factor receptor signaling inhibition prevents SARS-CoV-2 replication

    Journal: bioRxiv

    doi: 10.1101/2020.05.14.095661

    Phospho proteomic profiling of SARS-CoV-2 infected cells. ( A ) Experimental scheme. Caco-2 cells were infected with SARS-CoV-2 for one hour, washed and incubated for additional 24 hours. Proteins were extracted and prepared for bottom-up proteomics. All ten conditions were multiplexed using TMT10 reagents. 250 μg of pooled samples were used for whole cell proteomics (24 Fractions) and the remainder (~1 mg) enriched for phosphopeptides by Fe-NTA. Phosphopeptides were fractionated into 8 fractions and concatenated into 4 fractions. All samples were measured on an Orbitrap Fusion Lumos. ( B ) Volcano plot showing fold changes of infected versus mock cells for all 15,392 quantified phosphopeptides. P values were calculated using an unpaired, two-sided student’s t-test with equal variance assumed and adjusted using the Benjamini Hochberg FDR method (N = 5). Orange or blue points indicate significantly increased or decreased phosphopeptides, respectively. ( C ) Volcano plot showing differences between SARS-CoV-2 and mock infected cells in total protein levels for all 7,150 quantified proteins. P values were calculated using an unpaired, two-sided student’s t-test with equal variance assumed and adjusted using the Benjamini Hochberg FDR method (N = 5). Orange or blue points indicate significantly increased or decreased phosphopeptides, respectively. ( D ) Distribution of phosphorylation sites identified across modified amino acids. See also Figure S1 and Tables S1 and S2. ( E – K ) Domain structures of SARS-CoV-2 proteins predicted by InterPro. Identified phosphorylation sites are indicated.. Protein 3a ( E ), Membrane Protein M ( F ), Non-structural protein 6 ( G ), Protein 9b ( H ), Replicase Polyprotein 1b ( I ) and Nucleoprotein N ( J ). ( K ) X-ray structure of the RNA binding domain (PDB: 6vyo, residues 47-173) with identified phosphorylation sites marked in red.
    Figure Legend Snippet: Phospho proteomic profiling of SARS-CoV-2 infected cells. ( A ) Experimental scheme. Caco-2 cells were infected with SARS-CoV-2 for one hour, washed and incubated for additional 24 hours. Proteins were extracted and prepared for bottom-up proteomics. All ten conditions were multiplexed using TMT10 reagents. 250 μg of pooled samples were used for whole cell proteomics (24 Fractions) and the remainder (~1 mg) enriched for phosphopeptides by Fe-NTA. Phosphopeptides were fractionated into 8 fractions and concatenated into 4 fractions. All samples were measured on an Orbitrap Fusion Lumos. ( B ) Volcano plot showing fold changes of infected versus mock cells for all 15,392 quantified phosphopeptides. P values were calculated using an unpaired, two-sided student’s t-test with equal variance assumed and adjusted using the Benjamini Hochberg FDR method (N = 5). Orange or blue points indicate significantly increased or decreased phosphopeptides, respectively. ( C ) Volcano plot showing differences between SARS-CoV-2 and mock infected cells in total protein levels for all 7,150 quantified proteins. P values were calculated using an unpaired, two-sided student’s t-test with equal variance assumed and adjusted using the Benjamini Hochberg FDR method (N = 5). Orange or blue points indicate significantly increased or decreased phosphopeptides, respectively. ( D ) Distribution of phosphorylation sites identified across modified amino acids. See also Figure S1 and Tables S1 and S2. ( E – K ) Domain structures of SARS-CoV-2 proteins predicted by InterPro. Identified phosphorylation sites are indicated.. Protein 3a ( E ), Membrane Protein M ( F ), Non-structural protein 6 ( G ), Protein 9b ( H ), Replicase Polyprotein 1b ( I ) and Nucleoprotein N ( J ). ( K ) X-ray structure of the RNA binding domain (PDB: 6vyo, residues 47-173) with identified phosphorylation sites marked in red.

    Techniques Used: Infection, Incubation, Modification, RNA Binding Assay

    10) Product Images from "The Pan-Sirtuin Inhibitor MC2494 Regulates Mitochondrial Function in a Leukemia Cell Line"

    Article Title: The Pan-Sirtuin Inhibitor MC2494 Regulates Mitochondrial Function in a Leukemia Cell Line

    Journal: Frontiers in Oncology

    doi: 10.3389/fonc.2020.00820

    (A) Volcano plot obtained from TMT-based quantitative proteomic analysis of MC2494-treated vs. untreated U937 cells. Each point represents the difference in expression (log 2 fold change) between MC2494-treated vs. untreated samples plotted against the –log 10 p -value. Identified proteins with no changes in their regulation level are in light blue. Up- and down-regulated proteins (0.6≥ fold change ≥1.5) are shown in red and green, respectively. (B) GOChord plot showing relationships between selected representative biological process GO terms and related proteins identified by nanoLC-MS/MS analysis. The red and green color scale within the plot refers to protein log 2 fold change values.
    Figure Legend Snippet: (A) Volcano plot obtained from TMT-based quantitative proteomic analysis of MC2494-treated vs. untreated U937 cells. Each point represents the difference in expression (log 2 fold change) between MC2494-treated vs. untreated samples plotted against the –log 10 p -value. Identified proteins with no changes in their regulation level are in light blue. Up- and down-regulated proteins (0.6≥ fold change ≥1.5) are shown in red and green, respectively. (B) GOChord plot showing relationships between selected representative biological process GO terms and related proteins identified by nanoLC-MS/MS analysis. The red and green color scale within the plot refers to protein log 2 fold change values.

    Techniques Used: Expressing, Tandem Mass Spectroscopy

    11) Product Images from "Reprogrammed Cells Display Distinct Proteomic Signatures Associated with Colony Morphology Variability"

    Article Title: Reprogrammed Cells Display Distinct Proteomic Signatures Associated with Colony Morphology Variability

    Journal: Stem Cells International

    doi: 10.1155/2019/8036035

    Global proteomics of the 20 reprogrammed cell lines reveals different proteomic signatures for the different colony morphology groups. (a) Workflow for the proteomic experiment analyzing the global proteome of 20 reprogrammed cell lines microscopically classified into stable colony morphology (14 lines), unstable class 1 colony morphology (4 lines), and unstable class 2 colony morphology (2 lines). The samples are cell lysates from the corresponding cell line. The samples were quantified using label-free proteomics, which yielded ~5000 proteins in each sample. Raw values were log 2 transformed, and fold changes (FC) between the combined unstable colony morphology group (6 lines) and stable colony morphology group (14 lines) were calculated. Proteins with a p value
    Figure Legend Snippet: Global proteomics of the 20 reprogrammed cell lines reveals different proteomic signatures for the different colony morphology groups. (a) Workflow for the proteomic experiment analyzing the global proteome of 20 reprogrammed cell lines microscopically classified into stable colony morphology (14 lines), unstable class 1 colony morphology (4 lines), and unstable class 2 colony morphology (2 lines). The samples are cell lysates from the corresponding cell line. The samples were quantified using label-free proteomics, which yielded ~5000 proteins in each sample. Raw values were log 2 transformed, and fold changes (FC) between the combined unstable colony morphology group (6 lines) and stable colony morphology group (14 lines) were calculated. Proteins with a p value

    Techniques Used: Transformation Assay

    Variations in pluripotency markers and the ability to separate the stable colony morphology group from the unstable colony morphology group. (a) A heat map of 33 commonly used pluripotency markers [ 26 , 27 ] identified in our proteomic data set. The cluster analysis was only applied on rows, not columns. The heat map revealed a group of 10 markers that were able to separate the two morphology groups. (b) ROC curves for POU5F1, SOX2, PODXL, and CD9 when comparing the stable colony morphology group with the unstable colony morphology group. (c) Flow cytometry analysis of the markers POU5F1 and CD9 in selected stable (S) and unstable (U) lines.
    Figure Legend Snippet: Variations in pluripotency markers and the ability to separate the stable colony morphology group from the unstable colony morphology group. (a) A heat map of 33 commonly used pluripotency markers [ 26 , 27 ] identified in our proteomic data set. The cluster analysis was only applied on rows, not columns. The heat map revealed a group of 10 markers that were able to separate the two morphology groups. (b) ROC curves for POU5F1, SOX2, PODXL, and CD9 when comparing the stable colony morphology group with the unstable colony morphology group. (c) Flow cytometry analysis of the markers POU5F1 and CD9 in selected stable (S) and unstable (U) lines.

    Techniques Used: Flow Cytometry, Cytometry

    12) Product Images from "Enhanced validation of antibodies for research applications"

    Article Title: Enhanced validation of antibodies for research applications

    Journal: Nature Communications

    doi: 10.1038/s41467-018-06642-y

    Orthogonal validation of antibodies using proteomics. a Principle for the Western blot validation strategy based on correlating protein expression levels determined across a panel of cell lines using either proteomics or transcriptomics. b Example of orthogonal validation of Western blot bands (WB, relative intensity) by proteomics (Parallel Reaction Monitoring, PRM) reported as ratio to standard or transcriptomics reported as Transcript Per Million (TPM). Error bars represent 1 s.d. across three technical replicates. The black arrow indicates the theoretical molecular weight of the protein and blue arrows indicate the band subjected for the intensity-based relative quantification to determine the antibody staining profile. More examples including loading controls are presented in Supplementary Fig. 2 – 3 for PRM and TMT, respectively. c Mirror plot summarizing the Pearson’s r for 53 antibodies evaluated either by TMT (dark blue) or by PRM (light blue) including transcriptomics-based validation based on RNA expression (purple). d Analysis of the Pearson’s r between Western blot band intensities and RNA expression levels as a consequence of the fold-change between the highest and lowest value across the cell lines. The gray area represents fold-change in RNA levels less than fivefold. Antibodies in the green area (Pearson’s r > 0.5) are considered validated while antibodies in the red area (Pearson’s r
    Figure Legend Snippet: Orthogonal validation of antibodies using proteomics. a Principle for the Western blot validation strategy based on correlating protein expression levels determined across a panel of cell lines using either proteomics or transcriptomics. b Example of orthogonal validation of Western blot bands (WB, relative intensity) by proteomics (Parallel Reaction Monitoring, PRM) reported as ratio to standard or transcriptomics reported as Transcript Per Million (TPM). Error bars represent 1 s.d. across three technical replicates. The black arrow indicates the theoretical molecular weight of the protein and blue arrows indicate the band subjected for the intensity-based relative quantification to determine the antibody staining profile. More examples including loading controls are presented in Supplementary Fig. 2 – 3 for PRM and TMT, respectively. c Mirror plot summarizing the Pearson’s r for 53 antibodies evaluated either by TMT (dark blue) or by PRM (light blue) including transcriptomics-based validation based on RNA expression (purple). d Analysis of the Pearson’s r between Western blot band intensities and RNA expression levels as a consequence of the fold-change between the highest and lowest value across the cell lines. The gray area represents fold-change in RNA levels less than fivefold. Antibodies in the green area (Pearson’s r > 0.5) are considered validated while antibodies in the red area (Pearson’s r

    Techniques Used: Western Blot, Expressing, Molecular Weight, Staining, RNA Expression

    Validation of antibodies using capture MS. a Principle for validation of antibodies based on separation by SDS-PAGE and comparing the protein migration profile determined by an antibody (Western blot) and peptide identification performed by proteomics (capture MS). b Examples of orthogonal validation of two antibodies by capture MS in two different cell lines (RT4, U-251). The black arrow indicates theoretical molecular weight, and the blue bars represent number of peptides identified in each gel slice. c Summary of capture MS validation of antibodies ( n = 2,888) showing if they were validated using other antibody validation pillars. d Distribution of theoretical molecular weights for the largest transcript of all ( n = 19,628) human protein-coding genes divided into intracellular (blue) and secreted and membrane bound proteins (purple). Molecular weights are represented by the log 2 value together with a molecular weight ladder from a typical Western blot assay used within the Human Protein Atlas program
    Figure Legend Snippet: Validation of antibodies using capture MS. a Principle for validation of antibodies based on separation by SDS-PAGE and comparing the protein migration profile determined by an antibody (Western blot) and peptide identification performed by proteomics (capture MS). b Examples of orthogonal validation of two antibodies by capture MS in two different cell lines (RT4, U-251). The black arrow indicates theoretical molecular weight, and the blue bars represent number of peptides identified in each gel slice. c Summary of capture MS validation of antibodies ( n = 2,888) showing if they were validated using other antibody validation pillars. d Distribution of theoretical molecular weights for the largest transcript of all ( n = 19,628) human protein-coding genes divided into intracellular (blue) and secreted and membrane bound proteins (purple). Molecular weights are represented by the log 2 value together with a molecular weight ladder from a typical Western blot assay used within the Human Protein Atlas program

    Techniques Used: Mass Spectrometry, SDS Page, Migration, Western Blot, Molecular Weight

    13) Product Images from "Targeted O‐glycoproteomics explored increased sialylation and identified MUC16 as a poor prognosis biomarker in advanced‐stage bladder tumours"

    Article Title: Targeted O‐glycoproteomics explored increased sialylation and identified MUC16 as a poor prognosis biomarker in advanced‐stage bladder tumours

    Journal: Molecular Oncology

    doi: 10.1002/1878-0261.12035

    (A) Exemplificative annotated nanoLC‐ESI‐LTQ‐Orbitrap‐CID‐MS/MS spectra for a MUC16 glycopeptide substituted with a HexNAc residue evidencing the specific glycosite; (B) colocalization of MUC16 and STn in bladder tumours by immunohistochemistry; (C) expression of MUC16 STn glycoforms in bladder tumours based on PLA analysis. This work identified for the first time MUC16 in bladder tumours and its association with abnormal glycoforms such as the STn antigen. The mass spectrum shows a MUC16 glycopeptide substituted with a HexNAc residue, strongly suggesting the presence of STn. The colocalization of MUC16 and STn (B) in bladder tumours also reinforces this hypothesis. Finally, the red dots on the PLA image (C) in areas of colocalization result from the simultaneous detection of both antigens, reinforcing this evidence.
    Figure Legend Snippet: (A) Exemplificative annotated nanoLC‐ESI‐LTQ‐Orbitrap‐CID‐MS/MS spectra for a MUC16 glycopeptide substituted with a HexNAc residue evidencing the specific glycosite; (B) colocalization of MUC16 and STn in bladder tumours by immunohistochemistry; (C) expression of MUC16 STn glycoforms in bladder tumours based on PLA analysis. This work identified for the first time MUC16 in bladder tumours and its association with abnormal glycoforms such as the STn antigen. The mass spectrum shows a MUC16 glycopeptide substituted with a HexNAc residue, strongly suggesting the presence of STn. The colocalization of MUC16 and STn (B) in bladder tumours also reinforces this hypothesis. Finally, the red dots on the PLA image (C) in areas of colocalization result from the simultaneous detection of both antigens, reinforcing this evidence.

    Techniques Used: Mass Spectrometry, Immunohistochemistry, Expressing, Proximity Ligation Assay

    14) Product Images from "Targeted O‐glycoproteomics explored increased sialylation and identified MUC16 as a poor prognosis biomarker in advanced‐stage bladder tumours"

    Article Title: Targeted O‐glycoproteomics explored increased sialylation and identified MUC16 as a poor prognosis biomarker in advanced‐stage bladder tumours

    Journal: Molecular Oncology

    doi: 10.1002/1878-0261.12035

    (A) Exemplificative annotated nanoLC‐ESI‐LTQ‐Orbitrap‐CID‐MS/MS spectra for a MUC16 glycopeptide substituted with a HexNAc residue evidencing the specific glycosite; (B) colocalization of MUC16 and STn in bladder tumours by immunohistochemistry; (C) expression of MUC16 STn glycoforms in bladder tumours based on PLA analysis. This work identified for the first time MUC16 in bladder tumours and its association with abnormal glycoforms such as the STn antigen. The mass spectrum shows a MUC16 glycopeptide substituted with a HexNAc residue, strongly suggesting the presence of STn. The colocalization of MUC16 and STn (B) in bladder tumours also reinforces this hypothesis. Finally, the red dots on the PLA image (C) in areas of colocalization result from the simultaneous detection of both antigens, reinforcing this evidence.
    Figure Legend Snippet: (A) Exemplificative annotated nanoLC‐ESI‐LTQ‐Orbitrap‐CID‐MS/MS spectra for a MUC16 glycopeptide substituted with a HexNAc residue evidencing the specific glycosite; (B) colocalization of MUC16 and STn in bladder tumours by immunohistochemistry; (C) expression of MUC16 STn glycoforms in bladder tumours based on PLA analysis. This work identified for the first time MUC16 in bladder tumours and its association with abnormal glycoforms such as the STn antigen. The mass spectrum shows a MUC16 glycopeptide substituted with a HexNAc residue, strongly suggesting the presence of STn. The colocalization of MUC16 and STn (B) in bladder tumours also reinforces this hypothesis. Finally, the red dots on the PLA image (C) in areas of colocalization result from the simultaneous detection of both antigens, reinforcing this evidence.

    Techniques Used: Mass Spectrometry, Immunohistochemistry, Expressing, Proximity Ligation Assay

    15) Product Images from "Targeted O‐glycoproteomics explored increased sialylation and identified MUC16 as a poor prognosis biomarker in advanced‐stage bladder tumours"

    Article Title: Targeted O‐glycoproteomics explored increased sialylation and identified MUC16 as a poor prognosis biomarker in advanced‐stage bladder tumours

    Journal: Molecular Oncology

    doi: 10.1002/1878-0261.12035

    (A) Exemplificative annotated nanoLC‐ESI‐LTQ‐Orbitrap‐CID‐MS/MS spectra for a MUC16 glycopeptide substituted with a HexNAc residue evidencing the specific glycosite; (B) colocalization of MUC16 and STn in bladder tumours by immunohistochemistry; (C) expression of MUC16 STn glycoforms in bladder tumours based on PLA analysis. This work identified for the first time MUC16 in bladder tumours and its association with abnormal glycoforms such as the STn antigen. The mass spectrum shows a MUC16 glycopeptide substituted with a HexNAc residue, strongly suggesting the presence of STn. The colocalization of MUC16 and STn (B) in bladder tumours also reinforces this hypothesis. Finally, the red dots on the PLA image (C) in areas of colocalization result from the simultaneous detection of both antigens, reinforcing this evidence.
    Figure Legend Snippet: (A) Exemplificative annotated nanoLC‐ESI‐LTQ‐Orbitrap‐CID‐MS/MS spectra for a MUC16 glycopeptide substituted with a HexNAc residue evidencing the specific glycosite; (B) colocalization of MUC16 and STn in bladder tumours by immunohistochemistry; (C) expression of MUC16 STn glycoforms in bladder tumours based on PLA analysis. This work identified for the first time MUC16 in bladder tumours and its association with abnormal glycoforms such as the STn antigen. The mass spectrum shows a MUC16 glycopeptide substituted with a HexNAc residue, strongly suggesting the presence of STn. The colocalization of MUC16 and STn (B) in bladder tumours also reinforces this hypothesis. Finally, the red dots on the PLA image (C) in areas of colocalization result from the simultaneous detection of both antigens, reinforcing this evidence.

    Techniques Used: Mass Spectrometry, Immunohistochemistry, Expressing, Proximity Ligation Assay

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