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Surpassing 10,000 identified and quantified proteins in a ... · Jan Muntel, Tejas Gandhi, Lynn...
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Supplementary Figures
Surpassing 10,000 identified and quantified proteins in a single run by optimizing current LC-MS instrumentation and
data analysis strategy
Jan Muntel, Tejas Gandhi, Lynn Verbeke, Oliver M. Bernhardt, Tobias
Treiber, Roland Bruderer and Lukas Reiter‡
‡contact: [email protected]
Electronic Supplementary Material (ESI) for Molecular Omics.This journal is © The Royal Society of Chemistry 2019
Supplementary Figure 1: Optimization of Chromatography. (A) Comparison of 3solid phases using 2h gradients and a HeLa sample. The DIA method was optimisedto the chromatographic peak width. DIA data were analysed using a project-specificHeLa library. (B) Comparison of gradient lengths using a HeLa sample. The DIAmethod was optimised to the chromatographic peak width. DIA data were analysedusing a project-specific HeLa library. Three run replicates were acquired to calculateaverage identifications per run including standard deviation and identification with aCV < 20%.
B
IDs with a quantitative CV < 20% average IDs
0
50,000
100,000
150,000
200,000
Gradient Length
2h 4h 6h 8h
IDs
Precursors
0
50,000
100,000
150,000
Peptides
Gradient Length
2h 4h 6h 8h
0
2,000
4,000
6,000
8,000
Gradient Length
2h 4h 6h 8h
+7% +2%
+11% +3%-3%
p=3e-8 p=4e-6 p=3e-3+26%
+8%+6%
+21%+7%
-5%
p=2e-6p=3e-3
p=0.09+32%+8%
+6%
+21%+7%
-12%
p=9e-7
p=6e-3p=0.11
Proteins+1%
119,3
91
117,2
26
117,2
37
117,3
91
117,8
27
117,8
59
124,5
50
125,7
05
124,4
28
87,3
78
86,8
00
85,4
09
87,5
45
87,6
42
86,7
89
92,1
12
92,9
47
91,6
17
Precursors Peptides Proteins
0
40,000
80,000
120,000
IDs
200nl
250nl
300nl
200nl
250nl
300nl
200nl
250nl
300nl
200nl
250nl
300nl
200nl
250nl
300nl
200nl
250nl
300nl
200nl
250nl
300nl
200nl
250nl
300nl
200nl
250nl
300nl
0
2,000
4,000
6,000
6,4
49
6,4
37
6,4
24
6,4
77
6,4
78
6,4
67
6,5
18
6,5
45
6,5
34
A
BEH CSH ReproSil BEH CSH ReproSil BEH CSH ReproSil
Supplementary Figure 2: Library Generation Times using Search Archives. TheDirectDIA analysis comprised 6 runs, the project library 40 runs and the resourcelibrary 2,206 runs. The hybrid libraries were combination of the DirectDIA analysiswith the respective library. The processing times can vary based on computerconfiguration and other processes running at the same time.
0
50
100
150
200
250
DirectD
IA
Pro
ject-
Lib
rary
Pro
ject-
Hyb
rid
Resourc
e-L
ibra
ry
Resourc
e-H
ybrid
Lib
rary
Gen
era
tio
n T
ime [
h]
13.5 h0.8 h
15.5 h1.2 h
30 h
1.8 h
235 h
14 h
250 h
17 h
raw file
search archive
Supplementary Figure 3: Overview of Hela Libraries. (A) Library size of theproject-specific and the hybrid HeLa library. DDA and DIA data were searched usingPulsar (SpectroMine). FDR on precursor, peptide and protein level was set to 1%.Libraries were generated in SpectroMine using search archives. (B) Overlap inquantified precursors, peptides and proteins from DirectDIA, project-library andproject-hybrid library-based analysis of a 6h HeLa triplicate run. For protein groupsonly the first entry was considered.
227,3
33
231,7
91
0
1e5
2e5
3e5
4e5
IDs
Lib
rary
Hyb
rid
0
3,000
6,000
9,000
10,5
42
10,6
24
ProteinsPeptidesPrecursorsLib
rary
Hyb
rid
Lib
rary
Hyb
rid
400,8
49
415,0
02
+2%
+1%+4%
A
B Precursors Peptides Proteins
6,823
241
3%
52
<1%
167
2%
1,019
17
138
96,666
1,785
1%
12,673
8%
1,785
2%
55,941
1,014
7,630
138,64714,344
5%
20,124
8%
4,888
3%
97,575
1,495
16,286
Supplementary Figure 4: Application of Hybrid Approach to Testis Tissue. (A)Library overview. DDA and DIA data were searched using Pulsar (SpectroMine).FDR on precursor, peptide and protein level was set to 1%. Libraries were generatedin SpectroMine using search archives. (B) Application of libraries to injection triplicateof one testis tissue sample. DIA data were analysed in Spectronaut and precursorand protein FDR was set to 1%. Two-sample t-test was applied to test for statisticalsignificance of identification differences between the DIA data analysis using thedifferent libraries. Average run identifications and CVs < 20% were calculated basedon the triplicate injection of the sample. (C) Overlap in quantified precursors,peptides and proteins from the analysis using the four different libraries (6h Testistriplicate). For protein groups only the first entry was considered.
436,8
83
449,4
69
410,6
70
437,1
55
255,4
32
255,8
81
235,5
09
244,1
39
Pro
ject-
Lib
rary
Pro
ject-
Hyb
rid
Resourc
e
Resourc
e-H
ybrid
0
1e5
2e5
3e5
4e5
5e5
IDs
PeptidesPrecursors
13,4
36
13,4
03
13,2
74
13,3
85
0
5,000
10,000
15,000
Pro
ject-
Lib
rary
Pro
ject-
Hyb
rid
Resourc
e
Resourc
e-H
ybrid
Pro
ject-
Lib
rary
Pro
ject-
Hyb
rid
Resourc
e
Resourc
e-H
ybrid
+3% +6%
+0% +4%
-0% +1%
ProteinsA
B
0
1e5
2e5
Pro
ject-
Lib
rary
Pro
ject-
Hyb
rid
Resourc
e
Resourc
e-H
ybrid
IDs
Precursors
0
50,000
100,000
150,000
Peptides
0
2,500
5,000
7,500
10,000
Proteins
+27%
+16%
Pro
ject-
Lib
rary
Pro
ject-
Hyb
rid
Resourc
e
Resourc
e-H
ybrid
Pro
ject-
Lib
rary
Pro
ject-
Hyb
rid
Resourc
e
Resourc
e-H
ybrid
p=7e-7
+10%
+4%
p=7e-5
+12%
+14%
p=3e-5
+1%
+2%
p=3e-3 +10%
+10%
p=9e-8
+6%
+4%
p=6e-8
IDs with a quantitative CV < 20% average IDs
Precursors Peptides ProteinsC
ProjectHybrid
157,997
256
7,7647,489
1,285 14,71330,0861,204
6,924
9,185
5,354
318
14,670
61,781
14,382
Project
Resource ResourceHybrid
ProjectHybrid
109,419
113
3,2585,291
770 3,09513,399927
1,360
5,435
3,371
318
4,454
39,713
9,221
Project
Resource ResourceHybrid
ProjectHybrid
7,900
17
10134
4 15857220
201
154
36
11
410
692
68
Project
Resource ResourceHybrid
Supplementary Figure 5: Analysis of Testis Cancer Set with 2h and 4h DIAMethod. The hybrid libraries for DIA data analysis were generated by combining thetestis project-specific library with the search results of the respective 2h, 4h or 6h DIAruns. (A) Overview of identifications. Average identifications and standard deviationsare depicted for the NAT (green) and cancer (red) cohorts based on three biologicalreplicates. (B) Overview of statistical analysis based on the Spectronaut pipeline.Proteins were considered significantly differentially abundant with an absolute fold-change > 1 and a Q value <0.01. (C) Overlap of significantly changed proteins. Forprotein groups only the first entry was considered.
239,9
74
254,6
16 3
24,1
65
110,7
35
114,1
80 157,1
81 190,1
62
206,0
04
268,4
00
152,1
33
161,4
65
196,9
15
82,7
31
85,5
09
111,4
85
122,2
60
132,2
69
166,8
31
0
1e5
2e5
3e5
IDs
10,5
64
10,5
43
11,1
97
7,6
13
7,6
08
8,4
88 9,6
31
9,6
84
10,4
04
0
4,000
8,000
12,000
Precursors Peptides Proteins
NA
T
Cancer
Tota
l
2h
NA
T
Cancer
Tota
l
4h
NA
T
Cancer
Tota
l
6h
NA
T
Cancer
Tota
l
2h
NA
T
Cancer
Tota
l
4h
NA
T
Cancer
Tota
l
6h
NA
T
Cancer
Tota
l
2h
NA
T
Cancer
Tota
l
4h
NA
T
Cancer
Tota
l6h
A
B
0
3,000
6,000
9,000
2h 4h 6h
Gradient Length
Can
did
ate
s
proteins upregulated in cancer
proteins without change in abundance
proteins downregulated in cancer
C
4h 6h
2h
1,657
502
11%657
14%
567
12%
772
174230
Supplementary Figure 6: Additional Analysis. (A) IPA (Qiagen) analysis ofmismatch repair pathway. Pathway was exported from IPA and quantitative data ofthe proteins from this pathway are depicted as bar chart. Proteins quantified with a Qvalue < 0.01 are marked with a star. (B) Abundance changes between cancer andNAT cohort of the DNA-directed RNA polymerase proteins. Stars indicate proteinsquantified with a Q value < 0.01.
B
RPA1
RPA2
RPA12
RPA43
RPA34
RPA49
RPB1
RPB2
RPB3
RPB4
RPB7
RPB9
RPB11
GRL1A
GL1AD
RPC1
RPC2
RPC3
RPC4
RPC5
RPC6
RPC7
RPC7L
RPC8
RPC9
RPC10
RPAC1
RPAC2
RPAB1
RPAB2
RPAB3
RPAB4
RPAB5
Log2ratio
-1 -0.5 0 0.5 1 1.5 2
III
III
I and
III
I, II
and
III
DN
A-d
irecte
d R
NA
Po
lym
era
ses
*
**
**
**
**
**
**
**
*
**
*
*
**
**
**
*
A
PCNAMSH2MSH3MSH6MLH1PMS2EXO1FEN1RFC1RFC2RFC3RFC4RFC5
POLD1RPA1
-2 -1 0 1 2
Log2ratio
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
0%
25%
50%
75%
100%
0
5
10
Mismatch Repair
Perc
en
tag
e
-log
10 p
valu
e
14
not identified
downregulated
downregulated (Q<0.01)
upregulated
upregulated (Q<0.01)