Proteored Multilab Experiment 5 QUANTITATIVE PROTEOMICS

16 Marzo 2010 WG1&WG2 Meeting - CIC Salamanca

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Proteored Multilab Experiment 5QUANTITATIVE PROTEOMICS

4 proteins spiked in E. coli protein matrix

Progenesis LC-MS assisted label-free analysis

Labs Reporting Data

• Universidad de Alicante (Qual-Quant)

• Hospital Universitario Instituto de Oncologia Vall d’Hebron (Qual-Quant)

• Centro de Biología Molecular “Severo Ochoa” (Qual: 72 Ecoli + 2 spiked)


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APPROACHLAB1-LAB2

• Sample processing and Data acquisition– Liquid digestion (iodoacetamide, trypsin)– nLC-nESI-IT– 5 - 4 runs of sample A, 4 runs of sample B

• Label-free analysis with Progenesis LC-MS– LC-MS plot alignment– Whole-feature selection – feature abundance >1E+06– Mascot search (2mclv, Ox(M), Deam(QN), 1.2Da –

1.4 Da parent, 0.6Da – 0.7 Da fragment)– Peptide match validation: score > 25, matching E. coli

or spiked protein


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Reference LC-MS run selection


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LC-MS runs alignment


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Feature filtering


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Stats-based feature selection


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Selected feature stats


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Peptide search: mgf export, xml import


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Peptide filtering


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Protein view - conflict resolution


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Report configuration


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Web report


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0.00

0.20

0.40

0.60

0.80

1.00

1.20

1 peptide 2 peptides 1&2peptides

3-7 peptides >7 peptides

aver

age

A/B

avg ratio

Average and %CV of A/B ratios of groups of E. coli proteins identified-quantified with only…

0.00

0.20

0.40

0.60

0.80

1.00

1.20


3-7 peptides >7 peptides

aver

age

A/B

avg ratio

0

10

20

30

40

50

60

70

80

1 peptide 2 peptides 1&2 peptides 3-7 peptides >7 peptides

aver

age

A/B

%cv

0

10

20

30

40

50

60

70

80

1 peptide 2 peptides 1&2 peptides 3-7 peptides >7 peptides

%C

V o

f A

/B

%cv

0.00

0.20

0.40

0.60

0.80

1.00

1.20


3-7peptides

>7peptides

aver

age

A/B

lab2

lab1

0

10

20

30

40

50

60

70

80


3-7peptides

>7peptides

aver

age

A/B

lab2

lab1


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Deviation of average abundance LAB1

0

20

40

60

80

100

120

140

160

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

PEPTIDES USED FOR ID-QUANT

PME5A

PME5B

spiked A

spiked B

ALDOA MYG CYC

25fm 200fm 1000fm


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Deviation of average abundance LAB2

0

10

20

30

40

50

60

70

0 2 4 6 8 10 12

PEPTIDES USED FOR ID-QUANT

%C

V

PME5A

PME5B

SPIKED A

SPIKED B

ALDOA BSA/MYG CYC

25fm 1fm/200fm 1000fm


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PROTEINS IDENTIFIED WITH AT LEAST 1 PEPTIDE

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78

50

only LAB1

only LAB2

LAB1 & LAB2

0

5

10

15

20

25

30

35

P ROTEIN Nº

LAB2 LAB1


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PROTEINS IDENTIFIED WITH AT LEAST 2 PEPTIDES IN ONE LAB

9

17

46

only LAB1

only LAB2

LAB1 & LAB2

0

5

10

15

20

25

30

35

PROTEIN Nº

LAB2 LAB1


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Proteins stats LAB1

-1-0.8-0.6-0.4-0.2

00.20.40.60.8

1

0 2 4 6 8 10

UNIQUE PEPTIDES USED AT LEAST

AV

ER

AG

E R

AT

IO (

log

2)

0

10

20

30

40

50

0 1 2 3 4 5 6 7 8 9 10


%C

V O

F A

VE

RA

GE

RA

TIO

Number of E Coli proteins id. (total) 67

Number of E Coli single hit- proteins id 14

Number of Spiked proteins id. 3

0

10

20

30

40

50

60

70

80

0 1 2 3 4 5 6 7 8 9 10


#PR

OT

EIN

S I

D-Q

UA

NT

ALDOA MYG CYC

25fm 200fm 1000fm

AccessionPeptide count

Confidence score

Anova (p)* A/Bdtn - prep

CYC_HORSE 7 287.88 0.0053064 0.6 – 0.67

MYG_EQUBU 4 236.17 0.00068157 5.5 – 2.6

ALDOA_RABIT 3 152.12 0.00028094 3.8 – 2.0

BSA_BOVINK2C1_HUMAN

-4

NOT192.23

FOUND0.26933846 1.4


200

20

40

60

80

100

120

140

0 2 4 6 8 10


#PR

OT

EIN

S I

D-Q

UA

NT

Proteins stats LAB2Number of E Coli proteins id. (total) 128

Number of E Coli single hit- proteins id 74

Number of Spiked proteins id. 4

AccessionPeptide count

Confidence score

Anova (p)* A/Bdtn - prep

CYC_HORSE 5 285.88 0.0000067 0.57 – 0.67

MYG_EQUBU 3 244.41 0.00010617 2.18 – 2.6

ALDOA_RABIT 1 34.81 0.9008034 0.99 – 2.0

BSA_BOVIN 3 174.11 0.0105259 1.22 – 0.2

-1-0.8-0.6-0.4-0.2

00.20.40.60.8

1

0 2 4 6 8 10


AV

ER

AG

E A

/B (

log

2)

0

10

20

30

40

50

60

70

0 2 4 6 8 10


%C

V o

f av

erag

e A

/B

ALDOA BSA/MYG CYC

25fm 1fm/200fm 1000fm


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-1-0.8-0.6-0.4-0.2

00.20.40.60.8

1

0 2 4 6 8 10


AV

ER

AG

E A

/B (

log

2)

lab2

lab1

0

10

20

30

40

50

60

70

0 2 4 6 8 10


%C

V o

f av

erag

e A

/B

lab2

lab1

0

20

40

60

80

100

120

140

0 2 4 6 8 10


#PR

OT

EIN

S I

D-Q

UA

NT

lab2

lab1


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PREDICTION OF PEPTIDE COUNTS

y = 0.0036x + 2.7052

R2 = 0.999

y = 0.004x + 3.0359

R2 = 0.9946

0

1

2

3

4

5

6

7

8

0 200 400 600 800 1000 1200 1400

PROTEIN CONCENTRATION

PE

PT

IDE

CO

UN

TS

concentration (fmol/microg) concentration/10 (pg/microg)

Accessionconcentration (fmol/microg)

concentration/10 (pg/microg)

Peptide count

CYC_HORSE 1000 1200 7

MYG_EQUBU 200 340 4

ALDOA_RABIT 25 98 3

BSA_BOVIN 1 6.6 2-3

LINEAR MODEL

Suitable for interpolation


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y = 1.5985Ln(x) - 4.6602

R2 = 0.9252

y = 1.0556Ln(x) - 0.7609

R2 = 0.8795

-3

-2

-1

0

1

2

3

4

5

6

7

8

0.1 1 10 100 1000 10000


PE

PT

IDE

CO

UN

TS




Peptide count

CYC_HORSE 1000 1200 7

MYG_EQUBU 200 340 4

ALDOA_RABIT 25 98 3

BSA_BOVIN 1 6.6 0

LOGARITHMIC MODEL

Suitable for extrapolation?


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y = 0.6078x0.3385

R2 = 0.9682

y = 1.3748x0.2253

R2 = 0.9356

0

1

2

3

4

5

6

7

8

0.1 1 10 100 1000 10000


PE

PT

IDE

CO

UN

TS




Peptide count

CYC_HORSE 1000 1200 7

MYG_EQUBU 200 340 4

ALDOA_RABIT 25 98 3

BSA_BOVIN 1 6.6 1

EXPONENTIAL MODEL

Suitable for inter- and

extrapolation?


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CONCLUSIONS

• LC-MS label free method was successfully used to quantitate relative abundance of spiked proteins across samples in a matrix of medium complexity

• Low resolution instruments (ESI-IT) produced reasonably accurate results

• Some spiked proteins can be in or below the LOD of the used instruments (inclusion lists)

• A thresold of 3 unique peptides seem to be needed for achieving an acceptable dispersion (p.ex. CV< 20%) of protein abundance data used for quantitation

Proteored Multilab Experiment 5 QUANTITATIVE PROTEOMICS

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