From Ocean to Table: An Integrated Mass Spectrometry Approach to Identify the Fish …tools.thermofisher.com/content/sfs/posters/PN-64845-LC... · 2016. 8. 29. · From Ocean To Table:

From Ocean To Table: An Integrated Mass Spectrometry Approach To Identify The Fish OnYour PlateChien-Hsun Chen1, Andreas Krupke1, Monica Carrera2, Aran Paulus1,Andreas FR Huhmer1, and Daniel Lopez-Ferrer1:1 Thermo Fisher Scienti� c, San Jose, USA, 2 Marine Research Institute, Vigo, Spain

No

. 64845

POSTER NOTE

David M Horn,1 Torsten Ueckert,2 Kai Fritzemeier,2 Katja Tham,2 Carmen Paschke,2 Frank Berg,2 Hans Pfaff,2 Xiaoyue Jiang,1 Shijun Li,1 and Dani Lopez-Ferrer, 1Thermo Fisher Scientific, San Jose, CA, USA, 2Thermo Fisher Scientific, Bremen, Germany

ABSTRACTA new label-free quantification method based on the Minora algorithm is presented and compared to pre-existing label free quantification methods in the Thermo Scientific™ Proteome Discoverer™ software framework. The results of the new algorithm were significantly more accurate across a wide dynamic range compared to spectral counting and “Top N” quantification. The new algorithm was also run on a subset of the Akhilesh Pandey human proteome dataset to identify proteins specific to specific tissue types.

INTRODUCTIONProteome Discoverer software is a node-based workflow engine and study management platform for analysis of mass spectrometry-based proteomics datasets. The latest released version 2.1 fully supports isotopically-labeled quantitative workflows, such as TMTTM reporter ion-based quantification and SILAC precursor ion quantification, but the supported label-free quantification methods are significantly less sophisticated. Currently, spectral counting is possible but not recommended when quantitative accuracy is required. The only supported label-free quantification workflow produces an average abundance of the top “N” most abundant peptides and this has been shown to be accurate for even highly complex datasets. However, “Top N” quantification results cannot be used to create ratios, scaled abundance values, or to be used as replicates to generate standard errors. Here we present a new workflow for untargeted label-free quantification using a new feature detection approach that provides the full suite of quantitative capabilities previously only available for isotopically-labeled quantification. The workflow will be compared to the two aforementioned label-free quantification workflows available within Proteome Discoverer 2.1 software.

MATERIALS AND METHODSA standard dataset of Arabidopsis proteasome proteins spiked into a background of E. coli proteins (PXD003002) was downloaded from the PRoteomics IDEntifications (PRIDE) repository. This dataset was originally used to evaluate a spectral counting algorithm and is described in reference 1. The Pandey human proteome dataset2 was also downloaded from PRIDE and a portions of the dataset to demonstrate untargeted label free quantification of data with a multi-dimensional separation.

For quantification using spectral counts, each of the datasets with the different levels of Arabidopsis proteasome proteins was run separately in batch mode using a standard Sequest™ HT-Percolator workflow and a basic consensus workflow. Subsequently, all Processing results were reprocessed using a single Consensus workflow with the “Merge Mode” parameter in the MSF files node set to Do Not Merge. With this setting, the number of unique peptides and PSMs for each of the datasets will be represented as a separate column. The Sequest HT search was performed against the entire Arabidopsis thaliana and Escherichia coli databases. The table with PSM values for each sample was exported to Microsoft Excel format and ratios were calculated manually.

For the “Top N” quantification workflow, a Precursor Ion Area Detector node was incorporated in the Processing workflow used for spectral counting above. The default “CWF_Comprehensive_Enhanced_Annotation_Quan” template was used for the Consensus workflow. In the Peptide and Protein Quantifier node, the “Top N Peptides Used for Quantification” parameter was set to 3. Like for spectral counting, the table with the reported Top N protein abundances was exported to Excel and ratios were calculated manually.

New Method for Feature Detection

The new feature detection algorithm is an extension of the Minora algorithm, which had already been used for precursor ion quantification since the release of Proteome Discoverer 1.2 software. Minora had always detected all isotopic peaks in a given data set, but up to now only those LC/MS peaks associated with peptide spectral matches (PSMs), and their associated isotopic forms in the case of SILAC, were used for quantification. In this pre-release version of Proteome Discoverer 2.2 software, the Minora algorithm has been modified to detect and quantify isotopic clusters regardless of whether or not they are associated with a PSM.

A typical Processing workflow for Minora feature detection is shown in Figure 1. The new label-free quantification workflow can be invoked by simply attaching the “Minora Feature Detector” to the Spectrum Files node. This new feature detector will also be used for the isotopically-labeled precursor quantification method such as SILAC.

Multidimensional LC profiling

The new untargeted label-free quantification algorithm also supports multi-dimensional label-free data. The processing step works as described previously for such data with the feature mapping and retention alignment steps only applied to the same fraction from other datasets. Fractions 11-15 for 14 of the samples from the Pandey group human proteome data were run using the same workflows as shown in the previous section. For these data, a total of 5116 proteins and 60616 unique peptides were identified. Unlike the previous version of Proteome Discoverer software where these data were run using “Top 3” protein quantification results, the pre-release Proteome Discoverer 2.2 software enables scaled abundance visualization of the various samples. Figure 5 shows the proteins most overrepresented in the frontal cortex relative to the other samples by sorting by decreasing scaled abundance. Many of the most overrepresented proteins are all known to be neural proteins, including synapsin-1, synapsin-2, synapsin-3, neuromodulin, and microtubule-associated protein tau. Also, some of these neuronal proteins show no signals for any of the other samples in Figure 5 by showing gray boxes indicating that there were no quantification values for these proteins. This is an indication that the Feature Mapping is actually working correctly by not associating random features from the other datasets. Also, this shows the value of the scaled abundance compared to ratio calculations. If any of the other samples were used as the denominator for the ratio calculations, it might be missed that the selected protein is found only in the frontal cortex sample due to the undefined ratios that would be produced.

CONCLUSIONSA new untargeted label-free quantification workflow based on the Minora algorithm has been demonstrated on a dataset with proteins at known concentration and is shown to be more accurate and sensitive than the previously available label-free quantification approaches from previous versions of Proteome Discoverer software. The combination of the label-free quantification workflow integrated into the scaling, normalization, and study management features of Proteome Discoverer software provide a powerful means for analyzing highly complex proteomics data.

REFERENCES1. Gemperline, DC et al, Proteomics, 16, pp 920-924.2. Kim, M.-S. et al, Nature, 509, 575-581.

TRADEMARKS/LICENSING

© 2016 Thermo Fisher Scientific Inc. All rights reserved. TMT is a trademark of Proteome Sciences plc. SEQUEST is a registered trademark of the University of Washington in the United States. Microsoft and Excel are trademarks of Microsoft Corporation. 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 manner that might infringe the intellectual property rights of others.

New Method for Label-Free Quantification in the Proteome Discoverer Framework

Like the other quantification workflows in Proteome Discoverer 2.1 software, the peptide group abundances from the new label-free quantification method are calculated as the sum of the abundances of the individual PSMs for a given study factor that pass a quality threshold. The protein abundance is calculated as the sum of the peptide group abundances associated with that protein.

RESULTS AND DISCUSSIONThe database searches produced a total of 55 Arabidopsis proteins and 423 E. coli proteins. This is less than would be expected given the relatively high protein concentration and long gradient length, but the chromatography used for these data analyses was suboptimal with peaks up to 5 minutes wide (Figure 3). Also, as the amount of the Arabidopsis proteins added to the sample increased past 1 µg, the peptides from these proteasome proteins dominate the chromatogram and thus the number of E. coli proteins decreases dramatically with increasing Arabidopsis protein concentration (data not shown).

Abundance ratios were calculated using the sample with 1 µg of Arabidopsis protein as the denominator. The average ratio for the Arabidopsis proteins are shown in Table 1. Additional columns were added to denote the number of proteins that were quantified due to a measurement for both samples used in the ratio. The average ratios were calculated only for those proteins that produced a measured ratio.

The spectral counts-based quantification results correctly indicate the direction of expression for the Arabidopsis proteasome proteins, but the ratios are inaccurate for the more extreme ratios. The response is also relatively non-linear, with the average ratio for the 0.1 µg/1 µg samples showing a lower value than the 0.05 µg/1 µg samples and the 3 µg/1 µg ratio measuring lower than the 2 µg/1 µg ratio. These results are not a surprise given that it is widely known that this type of spectral counting is not expected to produce accurate quantification results. Normalized spectral counting algorithms are a significant improvement over the basic spectral counting method shown here and reference 1 from which these data were obtained describes a such a method. Implementation of such a method using emPAI values is planned for the individual study factors is being considered for a future Proteome Discoverer software release. However, all spectral counting-based quantification methods usually provide poorer sensitivity and dynamic range than other quantitative techniques due to the requirement for multiple PSMs for any given protein. As can be seen in Table 1, less than half of the Arabidopsis proteins could be quantified across the full dynamic range due to lack of PSMs in the samples with lowest protein abundance.

The “Top N” protein quantification results are shown in the second set of columns in Table 1. The accuracy of the ratios is noticeably improved compared to spectral counting, producing a response that is closer to linear. However, there are fewer quantified proteins in the “Top N” method than for spectral counting, primarily due to the requirement that the same three peptides need to be identified across all of the datasets. This is in effect even more stringent than the spectral counting method above and as a result even fewer proteins are quantified across the samples. Also, while the accuracy of the ratios is improved, the precision of the measurements are not much improved over spectral counting.

For feature detection-based quantification, the calculated ratios were significantly closer to the theoretically expected values at the lowest Arabidopsis concentrations. The precision of the ratios was also significantly improved in almost all cases for the feature detection results. The use of feature mapping led to a significantly increased number of quantified proteins given that only a single PSM is required for a given peptide across all raw files. The accuracy and precision of this method also benefits from the use normalization based on the E. coli proteins, which are known to be equally abundant across all samples.

A screen shot of the Arabidopsis protein identification results with untargeted label-free quantification is shown in Figure 4. The ratios and the scaled abundances for the identified proteins and peptide groups are color coded based on the level of expression. Scaled abundances were originally introduced in Proteome Discoverer 2.1 software primarily for the TMT quantification workflow and are now available in the preliminary version of Proteome Discoverer 2.2 software for feature detection-based label free quantification. The samples can be sorted by scaled abundance for any given sample type, as seen in Figure 4 for the highlighted 0.05 sample group. It can be easily seen that each of the proteasome proteins exhibit a similar trend by simply looking at the pattern of blue and red boxes. Also, since the scaled abundances exhibit the same profile as the ratios, the need for the calculation of ratios is somewhat obviated.

A typical Consensus workflow for label free quantification is also shown in Figure 1. There are two new nodes added to this workflow that perform retention time alignment and feature mapping. The feature mapper groups features detected from the Processing runs into “Consensus Features” that are mapped and quantified across all raw files and performs gap filling to find features that were not initially detected in the processing workflows. The Peptide and Protein Quantifier node works as previously, with improvements to scaling and normalization that benefit all quantification workflows.

There are three new tabs for feature detection results in the consensus report: Consensus Features, LCMS Features, and LCMS Peaks. The LCMS features are isotopic clusters grouped together for a given raw dataset and consist of multiple LCMS Peaks. Ultimately, the release may not include the LCMS Peaks list given that as much as 10’s of millions peaks could be detected in complex datasets. The consensus features link directly to the associated peptide group as well as the list of LCMS features detected from each data files (Figure 2). Also, when a consensus feature is selected, the traces for each of the features are shown in the chromatogram traces view. When a single LCMS Feature is selected, the chromatographic profile for only that individual feature is displayed.

Figure 1. Typical Processing and Consensus workflows for untargeted label-free quantification. The Minora Feature Detector, Rt-Aligner and Feature Mapper are new nodes created for the untargeted label-free quantification workflow. The Minora Feature Detector will also replace the old Precursor Ions Quantifier node used for SILAC and other precursor ion quantification workflows.

Figure 2. The Consensus Features table is linked to the collection of LCMS Features from each raw file. The chromatographic profiles for each LCMS Feature are shown in the Chromatogram Traces View at the bottom.

75 80 85 90 95 100 105 110 115 120 125 130 135 140 145 150 155 160 165Time (min)

120.25 122.2374.56 111.37101.63 110.1896.0875.44 89.69 140.42113.72 155.67146.94130.8478.82 133.26 151.05 157.5987.21

162.62113.67112.7089.45 144.35 159.17108.4096.12 140.97 149.3986.23 156.8382.5477.00 134.8798.91 131.84119.69 127.93

136.02114.43

110.05136.68114.98

145.93109.66 142.35125.98 142.12 153.32133.85 146.80115.80104.67 129.08 154.4499.8389.68 155.7199.2788.8385.54 90.3081.77 124.65120.53 156.8378.15 164.56

0.05 µg proteasomes1 µg E coli

0.5 µg proteasomes1 µg E coli

3 µg proteasomes1 µg E coli 5 min

Figure 3. Base peak chromatograms for three of the LC/MS runs, each scaled to 2e7 intensity. The dataset at the bottom is dominated by Arabidopsis peptides, leading to significant suppression the E coli peptides. Also, the typical chromatographic peak in this chromatogram can be up to 5 minutes wide, also decreasing the number of peptides and proteins that can be identified.

Expected ratio

Spectral Count

Average Ratio

Spectral Count

Quantified Proteins

Top N Average

Ratio

Top N Quantified Proteins

Feature Detection

Average Ratio

Feature Detection Quantified Proteins

0.05 0.59±0.24 28 0.22±0.32 22 0.040±0.028 470.1 0.45±0.19 33 0.24±0.16 26 0.084±0.050 490.25 0.7±0.27 47 0.4±0.29 36 0.24±0.10 520.5 0.77±0.24 50 0.51±0.27 39 0.52±0.13 541.5 1.48±0.50 52 1.72±0.60 47 1.35±0.24 552 1.9±0.93 52 2.85±1.51 47 1.91±1.0 553 1.67±0.70 52 3.92±1.80 47 2.82±0.80 55

Table 1. Average Arabidopsis thaliana protein ratios and standard deviations using the 1 ugsample as the denominator for the three different label-free quantitative methods. The number of quantified proteins associated with each quantification method is also displayed in the column adjacent to the ratios. There were 55 identified Arabidopsis proteins in total identified across the samples.

Figure 4. Untargeted label-free quantification results within the Proteome Discoverer software framework. Both the ratios and the scaled abundance values are color-coded to display significantly under- or over-expressed proteins.

Processing Consensus

Figure 5. Minora feature detection results for the subset of the Pandey human proteome dataset. The features are sorted by decreasing scaled abundance for the frontal cortex sample.

Chien-Hsun Chen1, Andreas Krupke1, Monica Carrera2, Aran Paulus1,Andreas FR Huhmer1, and Daniel Lopez-Ferrer11 Thermo Fisher Scientific, San Jose, USA, 2 Marine Research Institute, Vigo, Spain

RESULTS

ABSTRACT

The fishery market has grown in sales for the last 15 years. As a result, fish demand is

producing a worldwide overexploitation of resources and fraudulent practices in the industry

that account for 30% of the sales. In most cases, high priced fish species are substituted for

lower value species. Here we described an integrated proteomic approach to authenticate fish

species from muscle tissue.

INTRODUCTION

The identification of commercial fish species is a relevant issue to ensure correct labeling,

maintain consumer confidence and enhance the knowledge of the captured species, benefiting

both, fisheries and manufacturers. Here we propose a proteomic approach, based on top

down proteomic analyses using ESI-MS/MS in a high resolution OrbitrapTM mass spectrometer

for the identification of fish species with commercial interest. ESI-Orbitrap protein mass

fingerprint from thermo-stable proteins purified from fish tissue were used for the identification

of a commercial hake filet with no label regarding the fish other than “Product from South

Africa.” Further identification and characterization of this sample was performed using

standard shotgun proteomics and PRM targeted analysis. We believe that fisheries and

manufacturers may take advantage of this methodology as a tool for a rapid and effective

seafood product identification and authentication, providing and guaranteeing the quality and

safety of the foodstuffs to consumers.

MATERIALS AND METHODS

1 gram of fish muscle tissue was homogenized in water. The sample was then centrifuged to

remove the insoluble material. Water soluble proteins were then heated at 70ºC for 5 min.

After the heat treatment the sample was again centrifuged and the supernatant was aliquoted.

One of the aliquots was submitted for bottom-up and PRM proteomics analysis. A second

aliquot was submitted for top-down analysis. For bottom-up proteomics, the pH of the sample

was adjusted to 8, trypsin was added and digestion was performed for 3 minutes using high

intensity ultrasound. After digestion the sample was desalted using Thermo Scientific™

Pierce™ Micro-Spin Columns following the instructions of the manufacturer. After desalting,

the samples were resuspended in 0.1% formic acid and subjected to LC-MS analysis using a

Thermo Scientific™ Easy-nLC 1200 system hyphenated to a Thermo Scientific™ Q

Exactive™ hybrid quadrupole-Orbitrap™ mass spectrometer. Peptides were separated using a

15 cm Thermo Scientific™ EASY-Spray™ column. After LC-MS analysis, raw files were

submitted for database search using Thermo Scientific™ Proteome Discoverer™ 2.1 software

and a composite protein database of all fish species from Uniprot.

For top down analysis, water soluble proteins after the heat treatment were diluted 10X and

directly infused into a Q Exactive mass spectrometer. Mass spectra were acquired from 800 to

1200 m/z range at 140K at m/z 200. MS/MS acquisition was performed using HCD

fragmentation at 15% NCE. Data analysis was performed using Thermo Scientific™ Protein

Deconvolution 4.0 software and ProSightLite.

CONCLUSIONSWe successfully identified the fish species from an unlabeled commercial hake filet.

Intact MS analysis of thermostable proteins represents a promising technique for fish identification.

The workflow developed here allows for fish authentication in less than 30 minutes.

REFERENCES1. Monica Carrera, Benito Canas,Daniel Lopez-Ferrer etal.Anal. Chem. 2011,83, 5688–5695

TRADEMARKS/LICENSING© 2016 Thermo Fisher Scientif ic Inc. All rights reserved. ProSight Lite is a registered trademark of theConsortium for Top Down Proteomics. All other trademarks are the property of Thermo Fisher Scientific and itssubsidiaries. This information is not intended to encourage use of these products in any manner that mightinfringe the intellectual property rights of others.

From Ocean To Table: An Integrated Mass Spectrometry Approach To Identify The Fish OnYour Plate

FIGURE 1. General overview of the analytical workflow. Commercial hake filet samples were processed as described in the workflow. First, 1gram of tissue was physically disrupted with a mortar and later with ultrasound in water. Muscle debris was removed by centrifugation and thesupernatant was submitted to a heat treatment for five minutes. After the heat treatment, the sample was centrifuged to remove denaturalizedproteins and submitted to either bottom-up or top-down proteomic analysis.

FIGURE 2. Blast alignment of the three calcium binding proteins among the three species emphasize their highly conserved sequenceinformation. Blue stars indicate where the amino acid sequence varies among the three proteins. Only three different peptides could allow forthe specific identification of fish under study: AEGTFK, SPADIK and SPAADIK. However the short sequence of these peptides does not allow astraight identification of the species because their mass to charge ratios are below the typical scanning range in DDA experiments in case of+2 charge state peptide, or if they are in their +1 charge state usually +1 charge ions are not targeted for fragmentation.

TABLE 1. List of the top three proteins out of over ~200 proteins identified from the bottom-up proteomic analysis using Protein Discoverer 2.1.As can be noticed, the very high protein sequence homology among three very different species of hake does not allow for accurate speciesidentification.

FIGURE 3. Intact mass analysis: Left panel shows the mass spectrum obtained after direct infusion of the undigested fish sample. Showing ~11 kDagroup of proteins. After protein deconvolution (right panel) using the Extract algorithm the most abundant mass corresponds to Parvalbumin beta 2from Merluccius paradoxus.

FIGURE 5. A PRM decision tree for a systematic discrimination of Merlucciidae speciesusing specific tryptic peptides from parvalbumins based on previously published peptidebiomarkers1.

FIGURE 4. The 11365.68 mass was further selected for top-down analysis to verify that the protein sequence belongs to Parvalbumin beta 2 fromMerluccius paradoxus. Left panel shows the MSMS spectra for the 1138.19733 mass. The right panel shows the sequence coverage obtained thatallows for the explanation of 45%of the residues cleavages.

WORKFLOW RESULTS

Commercial Hake Filet

Homogenization

12000 rpm, 2min

SarcoplasmaticFraction

70ºC, 5 min Thermo-Stable Proteins

MS –Proteomic Analysis

Top Down

Bottom Up

12000 rpm, 2min

Accession Description Coverage %

#AAs MW [kDa]

# Unique Peptides

P86765 Parvalbumin beta 2 OS=Merluccius merluccius 65.74 108 11.27 8

P86764 Parvalbumin beta 1 OS=Merluccius hubbsi 65.74 108 11.29 8

P86768 Parvalbumin beta 1 OS=Merluccius paradoxus 65.74 108 11.39 8

AFAGILADAD ITAA LAACK AEGSFKHGE FFTKIGLKGK S_AADIKKVF GIIDQDKSDF VEEDELKLFLAFAGILADAD ITAA LAACK AEGTFKHGE FFTKIGLKGK S_AADIKKVF GIIDQDKSDF VEEDELKLFLAFAGILAEAD ITAA LAACK AEGTFKHGE FFTKIGLKGK SPA_DIKKVF GIIDQDKSDF VEEDELKLFL

QNFSAGARAL TDAETATFLK AGDSDGDGKI GVDEFAAMVK GQNFSAGARAL TDAETATFLK AGDSDGDGKI GVDEFAAMVK GQNFSAGARAL TDAETATFLK AGDSDGDGKI GVDEFAAMVK G

P86765P86764P86768

P86765P86765P86768

1 11 22 31 41 51 61

71 81 91 101 111

1138.19733Z=10

1140.39316Z=10 1142.09147

Z=101135.08961Z=10

500 1000 1500 2000

1138.19733Z=10

m/z

1034.83212Z=11

1264.64321Z=9

1422.47123Z=8

1625.68121Z=7

949.09541Z=12

876.23611Z=13

11100 11200 11300 11400 11500

11365.68841

11371.07090

11403.68527

11463.6549111334.64833

mass

Observed Mass: 11365.68

Theoretical Mass for P86769: 11365.77

Mass Diff. (ppm): 7.84

15

30

45

60

75

90

105

16

31

46

61

76

91106

200 600 1000 1400

218.14817Z=1

m/z

1240.40339Z=9

1002.5513Z=1

631.34347Z=1

518.25776Z=1

702.38526Z=1

1138.19733Z=10

4X 4X

1316.5379Z=8

OtherMerlucciidae

Family

Merlucciusgenus

Macruronusgenus

Euro-African hakes

American hakes

M. polliM. merlucciusM. capensis

M. senegalensis

M. paradoxus

Y

40Time

0

NL:4.78E7

21.43

20

N

Y N

40Time

0

NL:4.40E7

15.77

20

LFLQVFSAGAR

VFGIIDQDK

AGDSDGDGAIGVDEFAVLVK

Y N

40Time

0

NL:0

20

AGDSDGDGAIGVDEWAALVK40

Time0

NL:5.87E6

24.01

20

Y N


RESULTS

ABSTRACT






INTRODUCTION













































WORKFLOW RESULTS


Homogenization

12000 rpm, 2min




Top Down

Bottom Up

12000 rpm, 2min


#AAs MW [kDa]

# Unique Peptides






P86765P86764P86768

P86765P86765P86768

1 11 22 31 41 51 61

71 81 91 101 111

1138.19733Z=10

1140.39316Z=10 1142.09147

Z=101135.08961Z=10

500 1000 1500 2000

1138.19733Z=10

m/z

1034.83212Z=11

1264.64321Z=9

1422.47123Z=8

1625.68121Z=7

949.09541Z=12

876.23611Z=13

11100 11200 11300 11400 11500

11365.68841

11371.07090

11403.68527

11463.6549111334.64833

mass




15

30

45

60

75

90

105

16

31

46

61

76

91106

200 600 1000 1400

218.14817Z=1

m/z

1240.40339Z=9

1002.5513Z=1

631.34347Z=1

518.25776Z=1

702.38526Z=1

1138.19733Z=10

4X 4X

1316.5379Z=8

OtherMerlucciidae

Family

Merlucciusgenus

Macruronusgenus

Euro-African hakes

American hakes


M. senegalensis

M. paradoxus

Y

40Time

0

NL:4.78E7

21.43

20

N

Y N

40Time

0

NL:4.40E7

15.77

20

LFLQVFSAGAR

VFGIIDQDK


Y N

40Time

0

NL:0

20


Time0

NL:5.87E6

24.01

20

Y N


RESULTS

ABSTRACT






INTRODUCTION













































WORKFLOW RESULTS


Homogenization

12000 rpm, 2min




Top Down

Bottom Up

12000 rpm, 2min


#AAs MW [kDa]

# Unique Peptides






P86765P86764P86768

P86765P86765P86768

1 11 22 31 41 51 61

71 81 91 101 111

1138.19733Z=10

1140.39316Z=10 1142.09147

Z=101135.08961Z=10

500 1000 1500 2000

1138.19733Z=10

m/z

1034.83212Z=11

1264.64321Z=9

1422.47123Z=8

1625.68121Z=7

949.09541Z=12

876.23611Z=13

11100 11200 11300 11400 11500

11365.68841

11371.07090

11403.68527

11463.6549111334.64833

mass




15

30

45

60

75

90

105

16

31

46

61

76

91106

200 600 1000 1400

218.14817Z=1

m/z

1240.40339Z=9

1002.5513Z=1

631.34347Z=1

518.25776Z=1

702.38526Z=1

1138.19733Z=10

4X 4X

1316.5379Z=8

OtherMerlucciidae

Family

Merlucciusgenus

Macruronusgenus

Euro-African hakes

American hakes


M. senegalensis

M. paradoxus

Y

40Time

0

NL:4.78E7

21.43

20

N

Y N

40Time

0

NL:4.40E7

15.77

20

LFLQVFSAGAR

VFGIIDQDK


Y N

40Time

0

NL:0

20


Time0

NL:5.87E6

24.01

20

Y N


RESULTS

ABSTRACT






INTRODUCTION













































WORKFLOW RESULTS


Homogenization

12000 rpm, 2min




Top Down

Bottom Up

12000 rpm, 2min


#AAs MW [kDa]

# Unique Peptides






P86765P86764P86768

P86765P86765P86768

1 11 22 31 41 51 61

71 81 91 101 111

1138.19733Z=10

1140.39316Z=10 1142.09147

Z=101135.08961Z=10

500 1000 1500 2000

1138.19733Z=10

m/z

1034.83212Z=11

1264.64321Z=9

1422.47123Z=8

1625.68121Z=7

949.09541Z=12

876.23611Z=13

11100 11200 11300 11400 11500

11365.68841

11371.07090

11403.68527

11463.6549111334.64833

mass




15

30

45

60

75

90

105

16

31

46

61

76

91106

200 600 1000 1400

218.14817Z=1

m/z

1240.40339Z=9

1002.5513Z=1

631.34347Z=1

518.25776Z=1

702.38526Z=1

1138.19733Z=10

4X 4X

1316.5379Z=8

OtherMerlucciidae

Family

Merlucciusgenus

Macruronusgenus

Euro-African hakes

American hakes


M. senegalensis

M. paradoxus

Y

40Time

0

NL:4.78E7

21.43

20

N

Y N

40Time

0

NL:4.40E7

15.77

20

LFLQVFSAGAR

VFGIIDQDK


Y N

40Time

0

NL:0

20


Time0

NL:5.87E6

24.01

20

Y N

Find out more at thermo� sher.com

© 2016 Thermo Fisher Scienti� c Inc. All rights reserved. All rights reserved. reserved. ProSight Lite is a registered trademark of the Consortium for Top Down Proteomics. All trademarks are the property of Thermo Fisher Scienti� c and its subsidiaries unless otherwise speci� ed. PN64845-EN 08/16S

For Research Use Only. Not to be used in diagnostic procedures.


RESULTS

ABSTRACT






INTRODUCTION













































WORKFLOW RESULTS


Homogenization

12000 rpm, 2min




Top Down

Bottom Up

12000 rpm, 2min


#AAs MW [kDa]

# Unique Peptides






P86765P86764P86768

P86765P86765P86768

1 11 22 31 41 51 61

71 81 91 101 111

1138.19733Z=10

1140.39316Z=10 1142.09147

Z=101135.08961Z=10

500 1000 1500 2000

1138.19733Z=10

m/z

1034.83212Z=11

1264.64321Z=9

1422.47123Z=8

1625.68121Z=7

949.09541Z=12

876.23611Z=13

11100 11200 11300 11400 11500

11365.68841

11371.07090

11403.68527

11463.6549111334.64833

mass




15

30

45

60

75

90

105

16

31

46

61

76

91106

200 600 1000 1400

218.14817Z=1

m/z

1240.40339Z=9

1002.5513Z=1

631.34347Z=1

518.25776Z=1

702.38526Z=1

1138.19733Z=10

4X 4X

1316.5379Z=8

OtherMerlucciidae

Family

Merlucciusgenus

Macruronusgenus

Euro-African hakes

American hakes


M. senegalensis

M. paradoxus

Y

40Time

0

NL:4.78E7

21.43

20

N

Y N

40Time

0

NL:4.40E7

15.77

20

LFLQVFSAGAR

VFGIIDQDK


Y N

40Time

0

NL:0

20


Time0

NL:5.87E6

24.01

20

Y N

Documents

From Ocean to Table: An Integrated Mass Spectrometry Approach to Identify the Fish …tools.thermofisher.com/content/sfs/posters/PN-64845-LC... · 2016. 8. 29. · From Ocean To Table: