High-Resolution Targeted Quantification: Biomarker ...
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Data Processing Tandem mass spectra were processed with Thermo Scientific Proteome Discoverer software version 1.2. Spectra were searched against SwissProt Mouse (version 57.9) using the SEQUEST ® algorithm. A differential analysis was performed between the two sample groups using Thermo Scientific SIEVE software. LC-MS files were aligned in retention time (RT) and peak detection was performed. Frames consisting of a 2-minute RT window and 0.015 m/z windows were created around each detected feature, and peaks were integrated for each frame and LC-MS/MS file. Peak areas were normalized against the peptide FTQAGSEVSALLGR signal, which was used as an internal standard to account for injection or ionization bias. Results from the database search were imported into the SIEVE TM software. Thermo Scientific Pinpoint software was used for the targeted quantitative analysis. Up and down regulated protein accession numbers were submitted to Thermo Scientific Protein Center software, and a statistical analysis was performed against the Swissprot Mouse database for KEGG pathway annotation with a false discovery rate (FDR) at 5%. Results Label-Free Discovery Approach Database searching of the five LC-MS/MS runs resulted in the identification of 1471 protein groups and 5948 unique peptides with a FDR < 1%. Differential analysis was performed between the two sample groups in order to find proteins which are differentially expressed in the disease context. A total of 1623 proteins could be quantified based on the peptide MS trace (label-free approach). The protein ratio distribution described a S-type plot with a median at 1.01, indicating no bias in the quantification results (Figure 3). FIGURE 3. S-plot of the protein ratios from the label- free approach with SIEVE software. Three hundred thirty five proteins were quantified with ratios > 2 or < 0.5, and identified using more than 2 peptides. These proteins were subsequently selected for targeted quantification. Targeted Quantification Using Pinpoint Software Generation of a targeted assay begins with the selection of the targeted protein(s) and/or peptide(s) required for the biological study. Here, all targeted proteins were simultaneously uploaded from a fasta file exported from Proteome Discoverer TM software. A list of theoretical peptides which can serve as surrogates for the target proteins, was created through in silico digestion. Refinement of the resulting peptide list was accomplished by defining a sequence length of 7 to 50 amino acids, and excluding peptides containing potentially modified amino acids such as cysteine and methionine. Proteotypic peptides were selected as being unique to the target protein in a reference database containing all known mouse protein sequences in the SwissProt database. The peptide list was finally refined to a total of 975 by selecting only the peptides which were already identified by SEQUEST and therefore present in the Proteome Discoverer software results files. Targeted quantification was performed based on signal from full scan MS and the retention time identified in Proteome Discoverer software. Peptides were quantified by extracting the first, second and third isotope of each charge state identified by MS/MS. For each transition, an extracted ion chromatogram was generated with 5 ppm mass tolerance and a retention time shift tolerance of 2 minutes between the samples. Peak areas were integrated with a minimum of 10,000 counts and normalized against the signal of the internal standard peptide FTQAGSEVSALLGR. FIGURE 4. Extracted ion chromatogram alignment of the first three isotopes of the peptide FTQAGSEVSALLGR which was used for normalization. Two hundred eighty nine proteins were successfully quantified, and relative quantification was performed between MHC-I inducible mice and the healthy controls. One hundred nine proteins were validated as down- regulated with ratios ≤ 0.5, and 108 proteins were found to be up-regulated with protein expression levels at least 2 times higher in MHC-I mice (ratios ≥ 2). Figure 5 displays two myosin forms which were down- regulated. It is to be noted that very good consistency is observed between all the peptide ratios. This is due to the pre-selection of the peptides used for quantification based on previous identification with SEQUEST and their uniqueness to the target proteins. FIGURE 5: Example of quantification of Myosin regulatory light chain 2 and Myosin light chain 1/3 at the protein level (A and C), and at the peptide level (B and D). Potential biomarker accession numbers were submitted to Protein Center TM software in order to retrieve relevant biological information from publicly available protein databases (including gene ontology references) and for correlation with known metabolic pathways. Six of the down-regulated proteins were found to be involved in the “Dilated Cardiomyopathy” KEGG pathway (Figure 6A). Six of the up-regulated proteins showed a correlation with the “Antigen processing and presentation” pathway (Figure 6B). This was anticipated because of the high expression level of H-2KB protein in MHC-I inducible mice. FIGURE 6. Biological annotation with Protein Center software. A statistical analysis was performed against the SwissProt Mouse database for KEGG pathway annotations. (A) Down-regulated proteins with ratios ≤ 0.5. (B) Up-regulated proteins with ratios ≥ 2. Conclusion A complete biomarker discovery workflow including label-free analysis followed by targeted quantitation of potential biomarkers was applied to the study of MHC-I induced mouse developed myopathy. Pinpoint software was used to perform targeted quantitation based on the full scan MS signal from five separately acquired LC-MS/MS runs corresponding to two control samples and three transgenic mice with severe myopathy. Two hundred eighty nine proteins were quantified with high stringency based on the discovery data. Several candidate biomarkers were identified and confirmed with protein ratios below 0.5 and above 2. These biomarkers could be correlated to metabolic pathways which are known to be correlated to the disease. Acknowledgements We would like to thank Jenny Ho from Thermo Fisher Scientific UK for the LC-MS/MS analysis. Overview Purpose: Identification and confirmation of candidate biomarkers in a transgenic mouse model of myopathy using high-resolution LC-MS/MS. Methods: An experimental workflow, including protein digestion and label-free qualitative and quantitative analysis with high-resolution accurate-mass LC-MS/MS, was designed. Three muscle tissues from MHC-I inducible mouse were compared to two healthy controls. Results: Two hundred eighty nine proteins were successfully quantified, and relative quantification between MHC-I inducible mice and healthy controls was performed. One hundred nine proteins were validated with ratios of ≤ 0.5 and 108 proteins with ratios of ≥ 2. The putative biomarkers were mapped to known biological and metabolic pathways. Introduction Autoimmune muscle diseases are associated with the abnormal over-expression of MHC-I molecules on the muscle surface. These molecules are known to display fragments of proteins synthesized in the cell on the cell surface for presentation to immune cytotoxic T cells (Figure 1A). However, apart from the immune system, MHC-I could also be harmful. To test this hypothesis we used transgenic mice that continuously express MHC-I molecules (here H-2KB protein) in muscle cells resulting in myositis and myopathy (Figure 1B). FIGURE 1: Schematic of the MHC-I molecules displaying fragments of proteins synthesized in the cell on the cell surface for presentation to immune cytotoxic T cells (A). Transgenic mouse model with continuous expression of MHC-I H-2KB protein (B). Herein, a proteomic study was performed on mouse muscle tissue in order to evaluate the consequences of this continuous MHC-I expression. An experimental workflow including protein digestion and label-free qualitative and quantitative analysis by high resolution accurate mass LC-MS/MS was designed (Figure 2). Three muscle tissues from MHC-I inducible mice were compared to two healthy controls using a differential analysis and targeted quantification. FIGURE 2. Complete analytical workflow for biomarker discovery including non-targeted label-free differential analysis and biomarker confirmation with targeted quantitation based on the high-resolution MS trace. Methods Sample Preparation Mice were sacrificed and muscle tissues were collected and solubilized. Samples (50 μg) were cleaned-up with 2- D clean up kit (GE Healthcare) and run on an SDS-PAGE gel. Gel migration was stopped in the stacking gel. In gel digestion was performed after reduction with DTT and alkylation with iodoacetamide. Supernatants from the digestion were isolated and peptide containing fractions were combined. Samples were dried and suspended in water/0.1% formic acid prior to LC-MS/MS analysis. LC-MS/MS Analysis Samples were separated by online reversed-phase chromatography using a Thermo Scientific EASY-nLC nanoscale LC system equipped with a C18 Reprosil analytical column (100 μm ID x 15 cm packed tip column, Nikkyo Technos Co. Ltd) and a C18 trapping cartridge (Captrap, Michrom Bioresources) at a flow rate of 300 nL/min. The chromatography system was coupled with a Thermo Scientific LTQ Orbitrap Velos hybrid mass spectrometer. About 1 μg of peptides were used for each experiment. Peptides were analyzed by LC-MS/MS with full-scan MS detection in the Orbitrap TM mass analyzer at 60,000 resolving power. Peptides were selected “on the fly” for CID fragmentation with trap detection. The method was set to analyze the top 20 most intense ions from the survey scan. SEQUEST is registered trademarks of the University of Washington. All other trademarks are the property of Thermo Fisher Scientific and its subsidiaries. This information is not intended to encourage use of these products in any manners that might infringe the intellectual property rights of others. High-Resolution Targeted Quantification: Biomarker Discovery P1725 In a Mouse Transgenic Model Of Myopathy Claire Dauly 1 , Bertil Hofte 2 , Manuel Fréret 3 , Laurent Drouot 3 , Sandra Ahmed Lecheheb 4 , Pascal Cosette 4 , Olivier Boyer 3 1 Thermo Fisher Scientific, Courtaboeuf CEDEX, France. 2 Thermo Fisher Scientific, Breda, Netherlands. 3 Inserm, U905, Rouen CEDEX, France. 4 UMR6270 CNRS, Mont-Saint-Aignan CEDEX, France. Log Ratio
Transcript of High-Resolution Targeted Quantification: Biomarker ...
Data Processing
Tandem mass spectra were processed with Thermo Scientific Proteome Discoverer software version 1.2. Spectra were searched against SwissProt
Mouse (version 57.9) using the SEQUEST®
algorithm. A differential analysis was performed between the two sample groups using Thermo Scientific SIEVE software. LC-MS files were aligned in retention time (RT) and peak detection was performed. Frames consisting of a 2-minute RT window and 0.015 m/z windows were created around each detected feature, and peaks were integrated for each frame and LC-MS/MS file. Peak areas were normalized against the peptide FTQAGSEVSALLGR signal, which was used as an internal standard to account for injection or ionization bias. Results from the database search were imported into the SIEVETM
software. Thermo Scientific Pinpoint software was used for the targeted quantitative analysis. Up and down regulated protein accession numbers were submitted to Thermo Scientific Protein Center software, and a statistical analysis was performed against the Swissprot
Mouse database for KEGG pathway annotation with a false discovery rate (FDR) at 5%.
ResultsLabel-Free Discovery Approach
Database searching of the five LC-MS/MS runs resulted in the identification of 1471 protein groups and 5948 unique peptides with a FDR < 1%. Differential analysis was performed between the two sample groups in order to find proteins which are differentially expressed in the disease context. A total of 1623 proteins could be quantified based on the peptide MS trace (label-free approach). The protein ratio distribution described a S-type plot with a median at 1.01, indicating no bias in the quantification results (Figure 3).
FIGURE 3. S-plot of the protein ratios from the label-
free approach with SIEVE software.
Three hundred thirty five proteins were quantified with ratios > 2 or < 0.5, and identified using more than 2 peptides. These proteins were subsequently selected for targeted quantification.
Targeted Quantification Using Pinpoint Software
Generation of a targeted assay begins with the selection of the targeted protein(s) and/or peptide(s) required for the biological study. Here, all targeted proteins were simultaneously uploaded from a fasta
file exported from Proteome DiscovererTM
software. A list of theoretical peptides which can serve as surrogates for the target proteins, was created through in silico digestion. Refinement of the resulting peptide list was accomplished by defining a sequence length of 7 to 50 amino acids, and excluding peptides containing potentially modified amino acids such as cysteine
and methionine. Proteotypic
peptides were selected as being unique to the target protein in a reference database containing all known mouse protein sequences in the SwissProt
database. The peptide list was finally refined to a total of 975 by selecting only the peptides which were already identified by SEQUEST and therefore present in the Proteome Discoverer software results files.
Targeted quantification was performed based on signal from full scan MS and the retention time identified in Proteome Discoverer software. Peptides were quantified by extracting the first, second and third isotope of each charge state identified by MS/MS. For each transition, an extracted ion chromatogram was generated with 5 ppm
mass tolerance and a retention time shift tolerance of 2 minutes between the samples. Peak areas were integrated with a minimum of 10,000 counts and normalized against the signal of the internal standard peptide FTQAGSEVSALLGR.
FIGURE 4. Extracted ion chromatogram alignment of the first three isotopes of the peptide FTQAGSEVSALLGR which was used for normalization.
Two hundred eighty nine proteins were successfully quantified, and relative quantification was performed between MHC-I inducible mice and the healthy controls. One hundred nine proteins were validated as down-
regulated with ratios ≤
0.5, and 108 proteins were found to be up-regulated with protein expression levels at least 2 times higher in MHC-I mice (ratios ≥
2).
Figure 5 displays two myosin forms which were down-
regulated. It is to be noted that very good consistency is observed between all the peptide ratios. This is due to the pre-selection of the peptides used for quantification based on previous identification with SEQUEST and their uniqueness to the target proteins.
FIGURE 5: Example of quantification of Myosin regulatory light chain 2 and Myosin light chain 1/3 at the protein level (A and C), and at the peptide level (B and D).
Potential biomarker accession numbers were submitted to Protein CenterTM
software in order to retrieve relevant biological information from publicly available protein databases (including gene ontology references) and for correlation with known metabolic pathways. Six of the down-regulated proteins were found to be involved in the “Dilated Cardiomyopathy”
KEGG pathway (Figure 6A). Six of the up-regulated proteins showed a correlation with the “Antigen processing and presentation”
pathway (Figure 6B). This was anticipated because of the high expression level of H-2KB protein in MHC-I inducible mice.
FIGURE 6. Biological annotation with Protein Center software. A statistical analysis was performed against the SwissProt
Mouse database for KEGG pathway annotations. (A) Down-regulated proteins with ratios ≤
0.5. (B) Up-regulated proteins with ratios ≥
2.
Conclusion
A complete biomarker discovery workflow including label-free analysis followed by targeted quantitation
of potential biomarkers was applied to the study of MHC-I induced mouse developed myopathy.
Pinpoint software was used to perform targeted quantitation
based on the full scan MS signal from five separately acquired LC-MS/MS runs corresponding to two control samples and three transgenic mice with severe myopathy.
Two hundred eighty nine proteins were quantified with high stringency based on the discovery data.
Several candidate biomarkers were identified and confirmed with protein ratios below 0.5 and above 2.
These biomarkers could be correlated to metabolic pathways which are known to be correlated to the disease.
AcknowledgementsWe would like to thank Jenny Ho from Thermo Fisher Scientific UK for the LC-MS/MS analysis.
OverviewPurpose: Identification and confirmation of candidate biomarkers in a transgenic mouse model of myopathy
using high-resolution LC-MS/MS.
Methods: An experimental workflow, including protein digestion and label-free qualitative and quantitative analysis with high-resolution accurate-mass LC-MS/MS, was designed. Three muscle tissues from MHC-I inducible mouse were compared to two healthy controls.
Results: Two hundred eighty nine proteins were successfully quantified, and relative quantification between MHC-I inducible mice and healthy controls was performed. One hundred nine proteins were validated with ratios of ≤
0.5 and 108 proteins with ratios of ≥
2. The putative biomarkers were mapped to known biological and metabolic pathways.
IntroductionAutoimmune muscle diseases are associated with the abnormal over-expression of MHC-I molecules on the muscle surface. These molecules are known to display fragments of proteins synthesized in the cell on the cell surface for presentation to immune cytotoxic
T cells (Figure 1A). However, apart from the immune system, MHC-I could also be harmful. To test this hypothesis we used transgenic mice that continuously express MHC-I molecules (here H-2KB protein) in muscle cells resulting in myositis
and myopathy
(Figure 1B).
FIGURE 1: Schematic of the MHC-I molecules displaying fragments of proteins synthesized in the cell on the cell surface for presentation to immune cytotoxic
T cells (A). Transgenic mouse model with continuous expression of MHC-I H-2KB protein (B).
Herein, a proteomic study was performed on mouse muscle tissue in order to evaluate the consequences of this continuous MHC-I expression. An experimental workflow including protein digestion and label-free qualitative and quantitative analysis by high resolution accurate mass LC-MS/MS was designed (Figure 2). Three muscle tissues from MHC-I inducible mice were compared to two healthy controls using a differential analysis and targeted quantification.
FIGURE 2. Complete analytical workflow for biomarker discovery including non-targeted label-free differential analysis and biomarker confirmation with targeted quantitation
based on the high-resolution MS trace.
MethodsSample Preparation
Mice were sacrificed and muscle tissues were collected and solubilized. Samples (50 µg) were cleaned-up with 2-
D clean up kit (GE Healthcare) and run on an SDS-PAGE gel. Gel migration was stopped in the stacking gel. In gel digestion was performed after reduction with DTT and alkylation with iodoacetamide. Supernatants from the digestion were isolated and peptide containing fractions were combined. Samples were dried and suspended in water/0.1% formic acid prior to LC-MS/MS analysis.
LC-MS/MS Analysis
Samples were separated by online reversed-phase chromatography using a Thermo Scientific EASY-nLC
nanoscale
LC system equipped with a C18 Reprosil
analytical column (100 µm ID x 15 cm packed tip column, Nikkyo
Technos
Co. Ltd) and a C18 trapping cartridge (Captrap, Michrom
Bioresources) at a flow rate of 300 nL/min. The chromatography system was coupled with a Thermo Scientific LTQ Orbitrap
Velos
hybrid mass spectrometer. About 1 µg of peptides were used for each experiment. Peptides were analyzed by LC-MS/MS with full-scan MS detection in the OrbitrapTM
mass analyzer at 60,000 resolving power. Peptides were selected “on the fly”
for CID fragmentation with trap detection. The method was set to analyze the top 20 most intense ions from the survey scan.
SEQUEST is registered trademarks of the University of Washington. All other trademarks are the property of Thermo Fisher Scientific and its subsidiaries.
This information is not intended to encourage use of these products in any manners that might infringe the intellectual property rights of others.
High-Resolution Targeted Quantification: Biomarker Discovery P1725
In a Mouse Transgenic Model Of MyopathyClaire Dauly1, Bertil Hofte2, Manuel Fréret3, Laurent Drouot3, Sandra Ahmed Lecheheb4, Pascal Cosette4, Olivier Boyer3
1
Thermo Fisher Scientific, Courtaboeuf
CEDEX, France. 2
Thermo Fisher Scientific, Breda, Netherlands. 3
Inserm, U905, Rouen CEDEX, France. 4
UMR6270 CNRS, Mont-Saint-Aignan
CEDEX, France.
Log
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