mass spectrometry and proteomics - lecture 5...matthias trost newcastle university...
TRANSCRIPT
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Previously
• Proteomics• Sample prep
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Lecture 5
• Quantitation techniques• Search Algorithms• Proteomics software
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Current limitations of MS-based Proteomics
Bantscheff et al, Anal Bioanal Chem,2007
• Cellular proteins span a wide range of expression and current mass spectrometric technologies typically sample only a fraction of all the proteins present in a sample. • Due to limited data quality, only a fraction of all identified proteins can also be reliably quantified.
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Limitations of Proteomics –concentration of proteins in plasma
Anderson & Anderson, MCP, 2002
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Quantitation techniquesLabel-free• Ion intensity• Spectral counting
Chemical isotopic labeling• ICAT• iTRAQ/TMT• mTRAQ• Formaldehyde label• Enzymatic label
Metabolic isotopic labeling• SILAC• 15N
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The three different spectral sources of quantitative information
Wilm, Proteomics, 2010
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Quantitation methods
Isotope label(SILAC, ICAT, demethyl label etc)
Fragmentation-based label(iTRAQ)
Label-free
MS
MS/MS
X Da
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Quantitation strategies
Bantscheff et al, Anal Bioanal Chem,2007
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Characteristics of quantitative MS methods
Bantscheff et al, Anal Bioanal Chem, 2007
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Label-free quantitation
• MASCOT • identification driven
peptide assignment
Peak detection (in triplicate) Hierarchical clusteringPeak detection (in triplicate)
MS/MSCondition A Condition B
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Label-free proteomics
Advantages and Disadvantages
+ Lower complexity+ Lower cost+ Primary tissue possible(+) Repetitions increase
identification rates
- High LC-reproducibility necessary
- Good clustering dependent on high mass accuracy
- Several peptides for reliable quantitation required
Stdev Cond. A 0.089 Stdev Cond. B 0.067Ratio Cond. A/Cond. B 0.49
RLEIpSPDpSpSPER
Cond. B
Cond. A
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Another label-free quantitation: Spectral counting
• The number of spectra matched to peptides from a protein is used as a surrogate measure of protein abundance.
• As the sampling of peptides in a mass spectrometer is usually depending on the peptides’ intensities, spectral counting has a reasonable statistical significance.
• Spectral counting is cheaper, easier to implement and does not require highly reproducible data.
• It requires however still thorough computational and statistical analysis.
• Modern mass specs are getting to sensitive and fast for this quantitation.
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Isobaric tag for relative and absolute quantitation (TMT or iTRAQ)
• Reacts with N-termini and other primary amines of peptides.
• Uses a reporter group for quantification that can be identified in MS/MS spectra.
• Another labeled group serves as a balancer.
https://www.thermofisher.com/
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Isobaric tag for relative and absolute quantitation (TMT or iTRAQ)
• Quantification is done in MS/MS mode (low intensity!)
• Once labeled with TMT or iTRAQ, the 4/6/8/10 individual samples are pooled for further processing and analysis.
• During subsequent MS/MS of the peptides, each isobaric tag produces a unique reporter ion that identifies which samples the peptide originated and its relative abundance.
Gingras et al, Nat Rev Mol Cell Biol, 2007
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Isobaric tag for relative and absolute quantitation (iTRAQ or TMT)
+ Up to 11 samples (11-plex) can be quantified at the same time.
+ Saves instrument time.
- Quite expensive.
- Low dynamic range.
- Can not be performed in most ion-trap instruments as they do not reach this low mass range.
- Non-changing peptides are favored to be identified.
- large mass addition to peptides
- high ratios are suppressed by co-eluting other peptides. www.thermo.com
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Ratio compression in TMT experiments
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Ow, J Prot Res, 2009Ting et al, Nature Methods, 2011
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Reducing ratio compression by using Synchronous Precursor Selection (SPS)
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Formaldehyde/dimethyl label
• Samples are labeled with heavy and light formaldehyde on their primary amines (N-termini, Lys)
• relatively cheap and simple.
• can be used on virtually any sample.
• quite large mass difference between samples.
• Problematic retention time shifts in long LC runs due to Deuterium.Chen et al, Anal Chem, 2003; Boersema et al, Proteomics, 2008
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Formaldehyde/dimethyl label
Chen et al, Anal Chem, 2003
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Enzymatic isotope label• Further disadvantage:
Introduction of 18O at acidic side chains
• often incomplete incorporation of the label
Miyagi et al, Mass Spec Rev, 2006
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Stable isotope labeling with amino acids in cell culture (SILAC)
• Cells are grown with “normal” and heavy isotope amino acids.
+ The isotopically labeled peptides are chemically (almost) identical (Retention time etc)
+ The different samples are mixed at a very early step during sample preparation.
- labeled amino acids (Lys/Arg) might be metabolized to other amino acids
- Expensive for large amounts of cells.
- Not for primary tissue.
- Increases complexity of the sample.
- Some cell types do not grow well in dialysed serum.commons.wikimedia.org
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Neutron encoding (NeuCode) SILAC
• Makes use of the subtle mass differences caused by nuclear binding energy variation in stable isotopes (“mass defect”).
• For example, labelling with lysine with 2H8 (+8.0502 Da) and Lysine with 13C6 and 15N2 (+8.0142 Da).
• Can only be resolved with very high resolution >200,000.• In a low-resolution (<15,000) MS/MS scan, peaks are overlaying
and indistinguishable, thus both peaks add to the intensity.• Theoretically, up to 39 isotopologues of Lysine are possible.
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Herbert et al, Nature Methods 2013Rose et al, Anal Chem, 2013
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Neutron encoding (NeuCode) SILAC
166Herbert et al, Nature Methods 2013
(a) Mass calculations of the 39 isotopologues for a +8-Da lysine. Shown in solid black are the isotopologues used for the experiments presented here. (b) Theoretical calculations depicting the percentage of peptides that are resolved (full width at 1% maximum peak height) when spaced 12, 18 or 36 mDa apart for resolving powers (R) of 15,000–1,000,000. (c) Top, MS1 scan collected with typical 30,000 resolving power. Center, a selected precursor with m/z at 827 collected with 30,000 resolving power (black) and the signal recorded in a high-resolution MS1 scan (480,000 resolving power).
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Protein Identification
• Either “de novo” (thus no database) or from genomic data.
• When genomic data is available, the software performs an in silico digestion of the whole database using the specific protease.
• The mass of the peptide and the MS/MS spectrum are compared to the theoretical mass and the spectrum.
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Search Engines• Good search engines take common rules (high peaks
after P) into account.• The engines calculates a score from the number of
matched peaks compared to peaks present in spectrum. • This score is usually linked to a probability.• Lately, search engines using spectral libraries have
emerged. They are much faster and more accurate. However, good spectra for each peptide are required and ideally acquired in different kinds of instruments.
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For large scale proteomics, identification of peptides becomes a complex matching problem
Peptide ID & matching
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For large scale proteomics, identification of peptides becomes a complex matching problem
Peptide ID & matching
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Peptide A Fragment Masses
ProteomeUniProt
Peptide B Mass Peptide B Fragment Masses
Peptide A MassDigestionin silico
Fragmentationin silico
Database
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Observed Mass1000 ± 0.010 Da
Corresponding MS2 data
The Database Search1. MS1 filter2. MS2 scoring3. Probabilistic analysis
m/zIn
tens
ity
Database Search
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Observed Mass1000 ± 0.010 Da
Peptide A Mass999.980
Peptide B Mass999.993
Peptide C Mass1000.005
Peptide D Mass1000.010
Peptide E Mass1000.025
Database Search –MS1 filter
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Observed Mass1000 ± 0.010 Da
Peptide A Mass999.980
Peptide B Mass999.993
Peptide C Mass1000.005
Peptide D Mass1000.010
Peptide E Mass1000.025
Database Search –MS1 filter
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Observed Mass1000 ± 0.010 Da
Peptide B Mass999.993
Peptide C Mass1000.005
Peptide D Mass1000.010
Observed Spectra
Database Search –theoretical MS/MS spectra
Score
9
80
1
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Observed Mass1000 ± 0.010 Da
Peptide C Mass1000.005 80Peptide Evidence:
Theoreticalspectra
Observedspectra Score
Database Search –scoring
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Search constraints• “Classic”
– Peptide/precursor mass accuracy– MS/MS/fragment mass accuracy– Fixed and variable modifications– Enzyme (specificity)– Instrument/type of ions generated
• Proposed– Retention time
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Commonly used Search Engines
• Mascot • Sequest• OMSSA • X!Tandem• Andromeda (within MaxQuant)• …
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Decoy/target strategy to determine FDR
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PEP =# hits decoy database# hits
@ a given score
Decoy/target strategy to determine FDR
probability that a match of score 100 is incorrect
~ 0
probability that the match of score 10 is incorrect~ 90%
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>UbiquitinMQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG
Decoy/target strategy to determine FDR
>UbiquitinMQIFVK
MQIFVKTarget Database
VFIQMKDecoy Database
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False-Discovery Rate• Peptide/protein identification by mass spectrometry is a
statistical analysis with false-negatives and false-positives.
• False-discovery rate (FDR) is estimated by searching the data against a combined forward and reversed database. The number of hits from the reversed database is thought equivalent with false hits in the forward database.
• Please note that the FDR is on the identification level only, not on the quantitation level.
• Commonly accepted FDRs are <1%.
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• We accept that a very small proportion of peptide identifications (usually set to 1%) will likely be false discoveries
• Hence, having multiple supporting peptides per protein is important for confident identification and quantitation
Considerations
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• FDR estimation is challenging using small databases or when most of the database is identified. Always use bigger databases (for example include human with bacterial database)
Considerations
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• Choose your PTMs wisely
• Too many PTMs lead to combinatorial explosion and long database search times
• Common chemical modifications– Deamidation (NQ)– Gln PyroGlu– Oxidation (M)– Carbamidomethylation (C)– Acetyl (N-terminus)
Considerations
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VFIQMKVFIQMKTLSDYNIQK
Protein AProtein BProtein C
ESTLHLVLR Protein AProtein BProtein C
EGIPPDQQRMQIFVK
The vast majority of MS identification and quantitation is performed on peptides; information on proteins is through inference
The peptide to protein relationship is a “many to many” match
Considerations
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VFIQMKVFIQMKTLSDYNIQK
Protein AProtein BProtein C
ESTLHLVLR Protein AProtein BProtein C
EGIPPDQQRMQIFVK
Assigning non-unique peptides:
“Occam’s Razor”Accept the simplest explanation that fits the observations
Non-unique peptides are assigned to proteins that have the most unique peptides
Considerations
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• Evaluate distribution of data
• Normalise data
• Calculate standard deviation to set cutoffs
Check your data: histograms
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• Intensities vs intensities
• Reproducibility
Check your data: scatter plots
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• Evaluate experimental reproducibility (0.05 is usual p-value cutoff)
• Appropriate fold change cutoff depends on standard deviation
Check your data: volcano plots
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Databases• UniProt databases are the standard for mouse, human
and most other organisms. • They should be ideally non-redundant.• Can/should contain splice variants.• Database should not be too small (problem for bacteria)
as FDR calculation might be wrong. • A common set of contaminants (keratin, BSA, milk
proteins…) should be added to the searched database.
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Software for MS ID and Quant
SRM/Targeted
• MaxQuant• Trans Proteomic
Pipeline (TPP)• Proteome
Discoverer• PEAKS• Scaffold
• Skyline
Software Platforms
• Mascot• Sequest• OMSSA• Morpheus
de novosequencing
• PEAKS
TMT quantitation
• COMPASS • MaxQuant• Proteome
Discoverer
ID only