Protein quantitation I: Overview (Week 6) Proteomic Bioinformatics - - PowerPoint PPT Presentation

Protein quantitation I: Overview (Week 6)

Fractionation Digestion LC-MS Lysis

MS

C ij

I ik

p

ij Pr

p

D ijk p Pep ik

p

LC ik

p

MS ik

p

L ij

p p p p p p C I

MS ik LC ik Pep ik j D ijk ij L ij ij k ik

∑

      =

Pr

α

Sample i Protein j Peptide k Proteomic Bioinformatics – Quantitation

p p p p p p I C

MS ik LC ik Pep ik D ijk ij L ij k ik k ij Pr

α

=

α k

Fractionation Digestion LC-MS Lysis Quantitation – Label-Free (Standard Curve)

MS

I I C

ik ik k ij

f β ≈ = ) ( Sample i Protein j Peptide k

Fractionation Digestion LC-MS Lysis Quantitation – Label-Free (MS)

MS MS

p p p p p p

MS ik LC ik Pep ik D ijk ij L ij k Pr

α

Assumption: constant for all samples

I I C C

j j j j

i i i i

m n m n

/ /

= Sample i Protein j Peptide k

H L

Quantitation – Metabolic Labeling Fractionation Digestion LC-MS

Light Heavy

Lysis

MS

Oda et al. PNAS 96 (1999) 6591 Ong et al. MCP 1 (2002) 376

C

L j

in

I

L k

in

C

H j

im

p

M j

in

p

M j

im

I

H k

im

Assumption: All losses after mixing are identical for the heavy and light isotopes and

p p

M j M j

i i

m n

≈

Sample i Protein j Peptide k

Comparison of metabolic labeling and label-free quantitation

• 1
• 0.5

0.5 1

log2(ratio) SILAC Label-Free

• G. Zhang et al., JPR 8 (2008) 1285-1292

Label free assumption: constant for all samples Metabolic labeling assumption: constant for all samples and the behavior of heavy and light isotopes is identical

Metabolic

p p p p p p

MS ik LC ik Pep ik D ijk ij L ij k Pr

α

p

M ij

• G. Zhang et al., JPR 8 (2008) 1285-1292

Intensity variation between runs

Replicates 1 IP 1 Fractionation 1 Digestion vs 3 IP 3 Fractionations 1 Digestion

• 1
• 0.5

0.5 1

log2(ratio)

1-1-1 3-3-1

How significant is a measured change in amount?

It depends on the size of the random variation of the amount measurement that can be obtained by repeat measurement of identical samples.

• 1
• 0.5

0.5 1

log2(ratio) SILAC Label-Free

Protein Complexes

A B A C D

Digestion Mass spectrometry

Tackett et al. JPR 2005

Protein Complexes – specific/non-specific binding

Protein Turnover

KC=log(2)/tC, tC is the average time it takes for cells to go through the cell cycle, and KT=log(2)/tT, tT is the time it takes for half the proteins to turn over.

Move heavy labeled cells to light medium

Heavy ) ( ) ( ) ( ) ( ) ( ) (

C C C C K K dC

H j H j L j H j T C H j

t t t dt t = + + − = Light

Newly produced proteins will have light label

e C C

t H j H j

K K t

T C

) (

) ( ) (

+ −

= ⇒ ) log( ) ( ) ) ( ) ( ) ( log( 2 1 1

t t I I I

T C H j L j H j

t t t t + = +

Super-SILAC

Geiger et al., Nature Methods 2010

H L

Fractionation Digestion LC-MS

Light Heavy

Lysis Quantitation – Protein Labeling

MS

Gygi et al. Nature Biotech 17 (1999) 994

Assumption: All losses after mixing are identical for the heavy and light isotopes and

p p p p

M j L j M j L j

i i i i

m m n n

≈

H L

Fractionation Digestion LC-MS Lysis

MS Light Recombinant Proteins (Heavy)

Quantitation – Labeled Proteins Assumption: All losses after mixing are identical for the heavy and light isotopes and

p p p

M j M j L j

i i i

m n n

≈

H L

Fractionation Digestion LC-MS Lysis

MS Light Recombinant Chimeric Proteins (Heavy)

Quantitation – Labeled Chimeric Proteins

Beynon et al. Nature Methods 2 (2005) 587 Anderson & Hunter MCP 5 (2006) 573

H L

Fractionation Digestion LC-MS

Light Heavy

Lysis Quantitation – Peptide Labeling

MS

Gygi et al. Nature Biotech 17 (1999) 994 Mirgorodskaya et al. RCMS 14 (2000) 1226

Assumption: All losses after mixing are identical for the heavy and light isotopes and

p p p p p p p p

M k D jk j L j M k D jk j L j

i i i i i i i i

m m m m n n n n

Pr Pr

≈ ≈

H L

Fractionation Digestion LC-MS

Light

Lysis

Synthetic Peptides (Heavy)

Quantitation – Labeled Synthetic Peptides

MS

Gerber et al. PNAS 100 (2003) 6940

Enrichment with Peptide antibody

Assumption: All losses after mixing are identical for the heavy and light isotopes and

p p p p p

M sk M k D jk j L j

i i i i

n n n n

≈

Pr

Anderson, N.L., et al. Proteomics 3 (2004) 235-44

Fractionation Digestion LC-MS Lysis

MS/MS MS MS MS/MS

Quantitation – Label-Free (MS/MS)

SRM/MRM

MS/MS Synthetic Peptides (Heavy) Synthetic Peptides (Heavy) Light H L MS H L MS MS/MS MS/MS MS/MS L L H H

Digestion LC-MS Lysis/Fractionation Quantitation – Labeled Synthetic Peptides

Fractionation Digestion LC-MS

Light Heavy

Lysis

L H MS MS/MS

Quantitation – Isobaric Peptide Labeling

Ross et al. MCP 3 (2004) 1154

Fractionation Digestion LC-MS Lysis

Quantitation – Label-Free (MS)

MS MS

Fractionation Digestion LC-MS Lysis

MS/MS MS MS MS/MS

Quantitation – Label-Free (MS/MS)

H L

Quantitation – Metabolic Labeling

Fractionation Digestion LC-MS

Light Heavy

Lysis

MS H L

Fractionation Digestion LC-MS

Light Heavy

Lysis

Quantitation – Protein Labeling

MS H L

Fractionation Digestion LC-MS Lysis

MS Light Recombinant Chimeric Proteins (Heavy)

Quantitation – Labeled Chimeric Proteins

H L

Fractionation Digestion LC-MS

Light Heavy

Lysis

Quantitation – Peptide Labeling

MS H L

Fractionation Digestion LC-MS

Light

Lysis

Synthetic Peptides (Heavy)

Quantitation – Labeled Synthetic Peptides

MS

Fractionation Digestion LC-MS

Light Heavy

Lysis

L H MS MS/MS

Quantitation – Isobaric Peptide Labeling

Fractionation Digestion LC-MS Lysis

Quantitation – Label-Free (Standard Curve)

MS

m = 1035 Da m= 1878 Da m = 2234 Da

Isotope distributions

m/z m/z m/z Intensity

Isotope distributions

Peptide mass Intensity ratio Peptide mass Intensity ratio

Estimating peptide quantity

Peak height Curve fitting Peak area Peak height Curve fitting

m/z Intensity

Time dimension

m/z Intensity

Time

m/z Time

Sampling

Retention Time Intensity

5 10 15 20 25 30 0.8 0.85 0.9 0.95 1

3 points

20 40 60 80 100 120 140 0.8 0.85 0.9 0.95 1

3 points

5%

Acquisition time = 0.05σ

5%

Sampling

0.5 0.6 0.7 0.8 0.9 1 1.1 1 2 3 4 5 6 7 8 9 10

Thresholds (90%) # of points

Sampling

Retention Time Alignment

Estimating peptide quantity by spectrum counting

m/z Time Liu et al., Anal. Chem. 2004, 76, 4193

What is the best way to estimate quantity?

Peak height

• resistant to interference
• poor statistics

Peak area

• better statistics
• more sensitive to interference

Curve fitting

• better statistics
• needs to know the peak shape
• slow

Spectrum counting - resistant to interference

• easy to implement
• poor statistics for

low-abundance proteins

Examples - qTOF

Examples - Orbitrap

Examples - Orbitrap

AADDTWEPFASGK

Intensity Intensity Intensity

1 2 1 2

Ratio Ratio

1 2 1 2

Time

AADDTWEPFASGK

Intensity Intensity Intensity m/z m/z m/z

G H I

YVLTQPPSVSVAPGQTAR

Intensity Intensity Intensity

1 2 1 2

Ratio Ratio

1 2 1 2

Time

YVLTQPPSVSVAPGQTAR

Intensity Intensity Intensity m/z m/z m/z

Interference

Analysis of low abundance proteins is sensitive to interference from other components of the sample. MS1 interference: other components of the sample that

• verlap with the isotope distribution.

MS/MS interference: other components of the sample with same precursor and fragment masses as the transitions that are monitored.

MS1 interference

Data taken from CPTAC Verification Work Group Study 7. 10 peptides 3 transitions per peptide Concentrations 1-500 fmol/μl Human plasma background 8 laboratories 4 repeat analysis per lab Addona et al., Nature

• Biotechnol. 27 (2009) 633-641

Quantitation using MRM

0.1 1 10 100 1000 1 10 100 1000 Measured concentration [fmol/ul] Actual concentration [fmol/ul]

line tr1 tr2 tr3

0.1 1 10 100 1000 1 10 100 1000 Measured concentration [fmol/ul] Actual concentration [fmol/ul]

line tr1 tr2 tr3 Addona et al., NBT 2009

Peptide 1 Peptide 2

0.1 1 10 100 1000 1 10 100 1000 Measured concentration [fmol/ul] Actual concentration [fmol/ul]

line tr1 tr2 tr3

1 10 100 1000 1 10 100 1000 Measured concentration [fmol/ul] Actual concentration [fmol/ul]

line tr1 tr2 tr3

Quantitation using MRM

0.1 1 10 100 1000 1 10 100 1000 Measured concentration [fmol/ul] Actual concentration [fmol/ul]

line tr1 tr2 tr3

0.1 1 10 100 1000 1 10 100 1000 Measured concentration [fmol/ul] Actual concentration [fmol/ul]

line tr1 tr2 tr3 Addona et al., NBT 2009

Peptide 1 Peptide 2 Peptide 3 Peptide 4

Ratios of intensities of transitions

Addona et al., NBT 2009 1 2 3 4

1 10 100 1000 Intensity ratio Concentration

tr2/tr1 tr3/tr1

0.1 1 10 100 1000 1 10 100 1000 Measured concentration [fmol/ul] Actual concentration [fmol/ul]

line tr1 tr2 tr3

0.1 1 10 100 1 10 100 1000 Intensity ratio Concentration

tr1/tr2 tr3/tr2

0.1 1 10 100 1000 1 10 100 1000 Measured concentration [fmol/ul] Actual concentration [fmol/ul]

line tr1 tr2 tr3

Peptide 1 Peptide 3 Peptide 1 Peptide 3

Model: Noise and Interference

Intensity

Can the knowledge of the relative intensity of the transitions be used to correct for interference?

m/z

• Noise is a normally distributed

increase or decrease in the intensity.

• Interference is an increase in the

intensity of one or more transitions.

Detection of interference

Interference is detected by comparing the ratio of the intensity of pairs of transitions with the expected ratio and finding outliers. Transition i has interference if where Zthreshold is the interference detection threshold; ; zji is the number of standard deviations that the ratio between the intensities of transitions j and i deviate from the noise; Ii and Ij are the log intensities of transitions i and j; rji is the median of the log intensity of transitions j and i; σji is the noise in the ratio.

z z

i threshold <

σ ji

i j ji i j ji i j i

I I r z z

− = =

≠ ≠

max max

• 0.2
• 0.1

0.1 0.2 1 2 3 4 5 Centroid

zth

• 0.2
• 0.1

0.1 0.2 1 2 3 4 5 Centroid

zth

• 0.2
• 0.1

0.1 0.2 1 2 3 4 5 Centroid

zth

• 0.2
• 0.1

0.1 0.2 1 2 3 4 5 Centroid

zth

• 0.2
• 0.1

0.1 0.2 1 2 3 4 5 Centroid

zth

• 1
• 0.5

0.5 1 Corrected relative error

1 2

zth ∞

• 1
• 0.5

0.5 1 Corrected relative error

1

zth ∞

• 1
• 0.5

0.5 1 Corrected relative error

zth ∞

Error in quantitation after correction in presence of noise but no interference

Relative noise = 0.2 No interference Relative intensity of transitions: 1:1:1

• 1
• 0.5

0.5 1 Corrected relative error

zth ∞

Corrections for interference

Relative Error Corrected Relative Error No Correction Perfect Correction

(

th

)

• 1
• 0.5

0.5 1

Corrected relative error

1 2 3

zth

Relative noise = 0.2 Interference in 1 out of 3 transitions Relative intensity of transitions: 1:1:1

• 1
• 0.5

0.5 1

Corrected relative error

1 2

zth

• 1
• 0.5

0.5 1

Corrected relative error

1

zth

• 1
• 0.5

0.5 1

Corrected relative error zth

• 1
• 0.5

0.5 1

Corrected relative error

1 2 3

zth

• 1
• 0.5

0.5 1

Corrected relative error

1 2

zth

• 1
• 0.5

0.5 1

Corrected relative error

1

zth

• 1
• 0.5

0.5 1

Corrected relative error zth Relative error before correction 0.3-0.7 Relative error before correction 1.3-1.7

ztreshold = 0 ztreshold = 1 ztreshold = 2 ztreshold = 3

Error in quantitation after correction in presence of interference and noise

Interference in 2 out of 3 transitions

0.5 1 cted relative error

1 2 # of interferences

0.5 1 cted relative error

1 # of interferences

0.5 1 cted relative error

# of interferences

Correction for MS2 interference

Workflow for quantitation with LC-MS

Standardization Quality Control Quantitation Peptide Quantities LC-MS Data

Standardization Retention time alignment Mass calibration Intensity normalization Quality Control Detection of problems with samples and analysis Quantitation Peak detection Background subtraction Limits for integration in time and mass Exclusion of interfering peaks

Takeaway Message

• There are many different ways to quantitate proteins –

choose the one that is appropriate for your application.

• In general the earlier you can introduce isotopic labels

the better the accuracy.

• Always monitor for interference.

Protein quantitation I: Overview (Week 6)