Mass Spectrometry and Proteomics- Lecture 5 -

Matthias TrostNewcastle University

matthias.trost@ncl.ac.uk

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

Ratio compression in TMT experiments

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Ow, J Prot Res, 2009Ting et al, Nature Methods, 2011

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

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

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).

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

For large scale proteomics, identification of peptides becomes a complex matching problem

Peptide ID & matching

Peptide A Fragment Masses

ProteomeUniProt

Peptide B Mass Peptide B Fragment Masses

Peptide A MassDigestionin silico

Fragmentationin silico

Database

Observed Mass1000 ± 0.010 Da

Corresponding MS2 data

The Database Search1. MS1 filter2. MS2 scoring3. Probabilistic analysis

m/zIn

tens

ity

Database Search

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

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

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

Observed Mass1000 ± 0.010 Da

Peptide C Mass1000.005 80Peptide Evidence:

Theoreticalspectra

Observedspectra Score

Database Search –scoring

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%

>UbiquitinMQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG

Decoy/target strategy to determine FDR

>UbiquitinMQIFVK

MQIFVKTarget Database

VFIQMKDecoy Database

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

• 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

• 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

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

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

• Evaluate distribution of data

• Normalise data

• Calculate standard deviation to set cutoffs

Check your data: histograms

• Intensities vs intensities

• Reproducibility

Check your data: scatter plots

• Evaluate experimental reproducibility (0.05 is usual p-value cutoff)

• Appropriate fold change cutoff depends on standard deviation

Check your data: volcano plots

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