quantitation strategies in mass spectrometry-based proteomics · quantitation strategies in mass...

Quantitation strategies in mass spectrometry-based proteomics

Fang Liu, Ph.D.March 26, 2020

S U M S FUNDAMENTALS SEMINARS

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Using proteomics methods to answer:• What are in my samples?

- Qualitative description.- Examples: mysterious gel bands, interacting proteins in IP-MS, etc.

• How much are there in my samples?- Quantitative description.- Examples: biomarker discovery, PTM occupancy rate, etc.

A typical workflow in bottom-up proteomics experiment

Homogenization/ Cell lysis

Proteinprecipitation

Proteolytic digestion

LC-MS/MS

Databasesearch

Cell cultures,Primary cells,

Frozen tissues,FFPE,

Biofluids,Exosomes,

Secretome, etc.

Denature,Reduce,Alkylate.

Raw data

What does LC-MS/MS data look like?

Full scan

Full scan

Full scan

Full scan20 M

S/MS sc

ans

20 MS/M

S scans

20 MS/M

S scans

Full scan

MS/MS scan

LC trace

*

Common quantitation techniques• Label-free

• Spectral counting• Area-based: extracted ion chromatogram (XIC)

• Label-based• Metabolic labeling (e.g. SILAC)• Enzymatic labeling (e.g., trypsin and 18O-labeled water)• Isobaric mass tags (e.g. TMT)• Chemical labeling (e.g. dimethyl labeling, Isotope-Coded Affinity Tag

(ICAT))• Spike-in heavy internal standards (e.g. absolute quantification)

Incorporating quantitation strategies into a typical proteomics workflow

Homogenization/ Cell lysis

Proteinprecipitation

Proteolytic digestion LC-MS/MS

DatabasesearchCell cultures,

Primary cells,Frozen tissues,

FFPE,Biofluids,

Exosomes, Secretome, etc.

Denature,Reduce,Alkylate.

Raw data

MetabolicLabeling

Isobaric taggingChemical labeling (e.g. demethylation)

Internal standards

Label-freeChemical labeling (e.g. ICAT)Internal standards

Enzymatic labeling

Label-free quantitation• Spectral counting• Area-based: extracted ion chromatogram (XIC)

Spectral Counting – Protein abundance estimated by the MS/MS count• Based on the observation that peptides that are more abundant will be

detected and fragmented more often. • Semi-quantitative: Physicochemical properties, protein lengths, peptide

abilities to be ionized may vary.• Fast and cheap: widely used in discovery proteomics (e.g. IP-MS).

Antibody-boundMagnetic beads

YX Y

Incubated cell lysate w/ antibody

Antibody-boundMagnetic beads

Y

Remove unbound proteins

MS analysis

Spec

tral c

ount

Protein X

X Y X Y

Protein Y

Prot

ein

Qua

ntity

X Y

Spectral Counting – Protein abundance estimated by the MS/MS count• Various mathematical normalization strategies exist:

• normalized spectral abundance factor (NSAF). • distributed normalized spectral abundance factor (dNSAF)• exponentially modified protein abundance index (emPAI)• Spectral index (SIN)

McIlwain, Sean, et al., BMC bioinformatics 13.1 (2012): 308.

Example Data – Byonic output

Example Data – Heatmap

Label-free quantitation• Spectral counting• Area-based: extracted ion chromatogram (XIC)

XIC-based quantitation • Total amount of peptide is determined by the area under curve (AUC)

in the extracted ion chromatogram (XIC).• Variance across multiple LC-MS runs: elution peak alignment, peak

normalization, peak picking, etc.• Often used in targeted methods (e.g. MRM, PRM) for improved

selectivity and sensitivity.

Sample A Sample B A B

XIC-based quantitation used in targeted methodsMultiple Reaction Monitoring (MRM)

Parallel Reaction Monitoring (PRM)

Example Data – Skyline output

Label-based quantitation• Metabolic labeling (e.g. SILAC)• Isobaric mass tags (e.g. TMT)

Metabolic Labeling• Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC)

• Initially, it allows differential labeling of 2-3 experimental conditions.• 2H4-Lysine, 13C6-Arginine, 15N213C6-Lysine, 15N413C6-Arginine, etc.

Condition 1Lys0, Arg0

Protein Extraction

Denature,Reduce,Alkylate,Digest.

1:1:1Combine

LC-MS/MS



Ong, Shao-En, et al., Molecular & cellular proteomics 1.5 (2002): 376-386.

SILAC• Pros:

• High quantitative accuracy – heavy and light samples combined at early steps, eliminating indeterminate errors during sample handling.

• More total protein material available – good for enrichment techniques (PTM studies, immunoprecipitation, etc.).

• Cons:• Arg to Pro conversion. Solved by controlling heavy Arg concentration

or allowing heavy Pro for quantification.• Heavy isotopes may affect some cell growth and metabolism. • Limited multiplexing capacity. Can be expanded by using one common

“bridge” sample.• Mostly used in cell cultures. Can be expanded to primary cultures and

tissue samples by using SILAC as a ‘spike-in’ internal standard.

Geiger, Tamar, et al., Nature protocols 6.2 (2011): 147.

Spike-in SILAC: Expanding multiplexity and applicability

Individual LC-MS/MS analysis still needed.

Condition 1Light

Protein Extraction


1:1:1Combine

LC-MS/MS

Condition 2Light

Condition 3Light

Condition 4Heavy

Ratio of ratio

Krüger, Marcus, et al., Cell 134.2 (2008): 353-364.

m/z

light

heavy

m/z

light heavy

m/z

light heavy

Spike-in Standard

Super SILAC

Spike-in SILAC: Expanding multiplexity and applicability

Individual LC-MS/MS analysis still needed.

Condition 1Light

Protein Extraction


1:1:1Combine

LC-MS/MS

Condition 2Light

Condition 3Light

Condition 4Heavy

Ratio of ratio

Spike-in Standard

SILAC-mouse *

Krüger, Marcus, et al., Cell 134.2 (2008): 353-364.

m/z

light

heavy

m/z

light heavy

m/z

light heavy

Example Data – MaxQuant output

NeuCode SILAC – Combines SILAC and isobaric tagging• Mass defect: the difference between a compound's exact mass and its

nominal mass resulting from different neutron-binding energies.• Requires specially chosen isotopologues of lysine and high resolving

power.

Hebert, Alexander S., et al., Nature methods 10.4 (2013): 332-334.Overmyer, Katherine A., et al., Nature protocols 13.2 (2018): 293.

NeuCode SILAC – Increased multiplexity for metabolic labeling

• Can also be multiplexed with existing SILAC amino acids.

Hebert, Alexander S., et al., Nature methods 10.4 (2013): 332-334.Overmyer, Katherine A., et al., Nature protocols 13.2 (2018): 293.

Label-based quantitation• Metabolic labeling (e.g. SILAC)• Isobaric mass tags (e.g. TMT)

Isobaric Labeling – Enabling higher sample multiplexing• Isobaric mass tags have identical overall mass but vary in terms of the

distribution of heavy isotopes (13C, 15N) around their structure. • Most common examples: amine-reactive tags

› Tandem Mass Tag (TMT 2plex, 6plex, 10plex, 11plex, 16plex)› Isobaric tags for relative and absolute quantitation (iTRAQ 4plex, 8plex)

https://www.thermofisher.com/

126.127726

127.124761 127.131081

128.128116 128.134436

129.131471 129.137790

130.134825 130.141145

131.138180 131.144499

TMT11plex – Based on mass defect

• Modification mass on MS1 level:229.162932 (monoisotopic)

• Reporter Mass on MS2 level (HCD):(monoisotopic)

• 6.32 mDa mass differences between 15N and 13C isotopes.

MS1 MS2

Example Data – Proteome Discoverer output

Enabling concurrent MS analysis and relative quantification of up to 16 different samples• Samples can be derived from cells, tissues, biological fluids, etc.• Various experimental designs: replicates, time points, etc.

Treatment 1

Treatment 1

Treatment 1

Treatment 2

Treatment 2

Treatment 2

Control

Control

Control

Sample Pool

Protein Extraction


Labeling Combine LC-MS/MS

MS1Precursor Ion

SPS-MS3Reporter Ion

5-10 ug total protein for each sample

Enabling concurrent MS analysis and relative quantification of up to 16 different samples• Samples can be derived from cells, tissues, biological fluids, etc.• Various experimental designs: replicates, time points, etc.

Protein Precipitation


Labeling Combine LC-MS/MS

MS1Precursor Ion

SPS-MS3Reporter Ion

Savitski, Mikhail M., et al., Science 346.6205 (2014): 1255784.

Isobaric Labeling• Pros:

• Multiplexing capability.• Cons:

• Ratio compression due to isolation interference – improved by SPS-MS3.

Ting, Lily, et al., Nature methods 8.11 (2011): 937.

Protein Quantitation ResultsCluster analysis

Fold changes

Sample v1

Sam

ple

v2

Regression analysisFactor analysis

Motif analysis Network analysis Structural analysis

Thank You

More Fundamentals Webinars!• Thursday April 2, 2020

Fundamentals: Measuring concentrations of small molecules using mass spectrometry - theory and practiceSpeaker: Karolina Krasinska, MS – SUMS

• Thursday April 9, 2020Fundamentals: Intact protein mass spectrometry - tips and best practicesSpeaker: Theresa McLaughlin, MS – SUMS

https://mass-spec.stanford.edu/events

https://mass-spec.stanford.edu/events

quantitation strategies in mass spectrometry-based proteomics · quantitation strategies in mass...

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