Proteomic Analysis and Discovery Using Affinity Proteomics and Mass Spectrometry

Abstract

Antibody-based microarrays are a rapidly evolving affinity-proteomic methodology that recently has shown great promise in clinical applications. The resolution of these proteomic analyses is, however, directly related to the number of data-points, i.e. antibodies, included on the array. Currently, this is a key bottleneck because of limited availability of numerous highly characterized antibodies. Here, we present a conceptually new method, denoted global proteome survey, opening up the possibility to probe any proteome in a species-independent manner while still using a limited set of antibodies. We use context-independent-motif-specific antibodies directed against short amino acid motifs, where each motif is present in up to a few hundred different proteins. First, the digested proteome is exposed to these antibodies, whereby motif-containing peptides are enriched, which then are detected and identified by mass spectrometry. In this study, we profiled extracts from human colon tissue, yeast cells lysate, and mouse liver tissue to demonstrate proof-of-concept.

Full text

Proteomic Analysis and Discovery Using
Affinity Proteomics and Mass Spectrometry*
□
S
Niclas Olsson‡储, Christer Wingren‡储, Mikael Mattsson§, Peter James‡,
David O’ Connell¶, Fredrik Nilsson§, Dolores J. Cahill¶, and Carl A. K. Borrebaeck‡**
Antibody-based microarrays are a rapidly evolving affini-
ty-proteomic methodology that recently has shown great
promise in clinical applications. The resolution of these
proteomic analyses is, however, directly related to the
number of data-points, i.e. antibodies, included on the
array. Currently, this is a key bottleneck because of lim-
ited availability of numerous highly characterized antibod-
ies. Here, we present a conceptually new method, de-
noted global proteome survey, opening up the possibility
to probe any proteome in a species-independent manner
while still using a limited set of antibodies. We use
context-independent-motif-specific antibodies directed
against short amino acid motifs, where each motif is pres-
ent in up to a few hundred different proteins. First, the
digested proteome is exposed to these antibodies,
whereby motif-containing peptides are enriched, which
then are detected and identified by mass spectrometry. In
this study, we profiled extracts from human colon tissue,
yeast cells lysate, and mouse liver tissue to demonstrate
proof-of-concept. Molecular & Cellular Proteomics 10:
10.1074/mcp.M110.003962, 1–15, 2011.
Delineating the composition of the human proteome(s)
will be instrumental in advancing our fundamental knowl-
edge about the underlying biology in health and disease
(1–3). To generate detailed protein expression profiles, or
atlases of complex proteomes, the development of rapid
and highly multiplexed proteomic research tools will be
crucial. For in-depth characterization of biological systems,
classical separation techniques, such as liquid chromatog-
raphy and two-dimensional gels, linked with single or tan-
dem MS has been established as a powerful hypothesis-
generating tool (2, 4, 5). Despite the fact that MS-based
set-ups have constituted the cornerstone in many pro-
teomic profiling endeavors, the impact of this work in terms
of, for example, defining disease-associated biomarkers,
have so far been limited, mainly because of technical short-
comings (5, 6).
To meet methodological issues, such as sensitivity, dy-
namic range, resolution, and reproducibility, affinity-pro-
teomic based on antibody microarrays has in recent years
become an established proteomic technology for differential
protein expression profiling of complex proteomes (7–9). To
date, the technology has been applied in several clinical ap-
plications, outlining the potential for example in disease diag-
nostics and patient stratification (10–12), setting a new stan-
dard for array-based proteomics (7, 13, 14). The resolution of
an antibody microarray data set correlate directly to the num-
ber of antibodies included on the array and the range of their
specificities, which tends to be a key bottleneck (13, 15). Even
though current antibody microarray formats have been shown
to be capable of discriminating among various proteomes
(10–12, 16), an improved analytical resolution will be crucial
for pin-pointing candidate biomarkers that can predict, for
example, disease progression and response to treatment,
with high specificity and sensitivity (7).
To advance further and set a novel standard for proteomics,
the most attractive features of affinity proteomics could be
combined with those of MS-based proteomics. This has pre-
viously been attempted, where antibodies have initially been
used to enrich specific proteins (17–20) or peptides (21–24)
followed by subsequent MS analysis. However, all of these
platforms mainly rely on the conventional approach of using
one binder per unique peptide and protein, creating major
logistical issues when scaling up for global profiling efforts. In
addition, the use of polyclonal antibodies as capturing agents
(21) has inherent disadvantages because they are derived
from a nonrenewable resource.
Here, we describe a conceptually novel method, denoted
global proteome survey (GPS)
1
, with inherent capability of
probing any proteome in a discovery mode, in a species
independent manner, while still using a limited number of
antibodies. As the affinity reagent, we used renewable human
recombinant scFv antibodies specific for short amino acid
sequences or motifs, each motif being present in up to a few
From the ‡Department of Immunotechnology, Lund University,
Lund, Sweden, and CREATE Health, BMC D13, Lund, Sweden; §Bio-
Invent International AB, SE-223 70 Lund, Sweden; ¶School of Med-
icine and Medical Sciences, Conway Institute, University College
Dublin, Dublin 4, Ireland
Received August 3, 2010, and in revised form, May 17, 2011
Author’s Choice—Final version full access.
Published, MCP Papers in Press, June 14, 2011, DOI 10.1074/
mcp.M110.003962
1
The abbreviations used are: GPS, global proteome survey; CIMS,
context independent motif specific; FDR, false discovery rate; LTQ,
linear ion trap; PBS, phosphate-buffered saline; BSA, bovine serum
albumin; IPI, International Protein Index.
Technological Innovations and Resources
Author’s Choice © 2011 by The American Society for Biochemistry and Molecular Biology, Inc.
This paper is available on line at http://www.mcponline.org
Molecular & Cellular Proteomics 10.10 10.1074/mcp.M110.003962–1

hundred proteins. Next, the biological sample is digested,
exposed to these antibodies, whereby motif-containing pep-
tides are specifically captured, enriched, and subsequently
detected and identified (and quantified) using single- or tan-
dem-MS. In this manner, 100 of these context independent
motif specific (CIMS) antibodies, would theoretically cover
almost 50% of the nonredundant human proteome (
25), a
concept supported by a recent in-silico study of the human
proteome (
26).
EXPERIMENTAL PROCEDURES
Design of Selection Peptide Motifs—We defined the tandem MS
(MS/MS) addressable cohort of the human proteome, release 56.1 of
UniProtKB/Swiss-Prot, after in silico trypsination, setting a molecular
weight cutoff in the range of 500 to 3500 Da using SignPept, an
in-house developed software. Using criteria defined to exclude cys-
teines and methionines, but tailored to contain a C-terminal arginine
or lysine, we designed 27 four or six amino acids long (
27, 28) peptide
selection motifs to be present in a few to several hundred (⬍700) of
these human peptides using MS-pattern and/or SignPept (Dr. F.
Levander, Department. of Immunotechnology, Lund University, Swe-
den). The theoretical cleavage specificities of trypsin have been built
into the SignPept software, and mimics the rules employed by Pep-
tideCutter (http://expasy.org/tools/peptidecutter). Further, the effect
of motif wobbling on the frequency of motif occurrence was investi-
gated using SignPept. The synthesized peptides were composed of
an N-terminal biotin, a SGSG-linker, followed by the C-terminal lo-
cated selection motif (Innovagen, Lund, Sweden).
Selection of CIMS antibodies—Human recombinant scFv antibod-
ies were selected from the phage display library, n-CoDeR (
29). Three
consecutive rounds of selection were performed, using biotinylated
peptide motifs as antigens. In selection round one, about 10
13
colony-
forming units of phage were mixed with 50 n
M antigen in a total
volume of 3 ml. The selection buffer was phosphate-buffered saline
(PBS) containing 3% (w/v) bovine serum albumin (BSA), 0.05% (v/v)
Tween-20, and 0.02% (w/v) sodium azide. The antigen and phage
mixture was incubated for ⬃16 h at room temperature. Biotinylated
peptides were captured on ⬃10
8
streptavidin-conjugated magnetic
beads (Dynabeads M-280, Dynal, Oslo, Norway) during a 30 min
incubation. Before use, Dynabeads were blocked with 5% (w/v) BSA
in selection buffer. Following peptide capture, beads were washed a
total of nine times, using a Magnetic Particle Concentrator (Dynal,
Oslo, Norway), three times with selection buffer, three times with PBS
containing 0.05% (v/v) Tween-20 and three times with PBS. Captured
phages were then eluted by addition of 400
␮
l of a 1 mg/ml trypsin
solution for 30 min, after which trypsin was inactivated by addition of
40
␮
l of a 2 mg/ml aprotinin solution. All incubations were performed
with gentle end-over-end rotation. Log phase Escherichia coli was
infected with the eluted phage pool and a new, amplified phage pool
was produced essentially as described by Engberg et al. (
30), using
E. coli strain HB101F⬘ (constructed from HB101, Invitrogen, Carlsbad,
CA) and 20-fold excess of helper phage R408 (Stratagene, La Jolla,
CA).
In selection round two, about 10
11
colony-forming units of ampli
-
fied phage were mixed with 20 n
M antigen in a total volume of 1 ml
and ⬃3 ⫻ 10
7
streptavidin-conjugated magnetic beads were used to
capture biotinylated peptide motifs. Bound phage were eluted by
addition of 400
␮
lof10mM glycin-HCl, pH 2.2 for 30 min. A few
␮
lof
1
M Tris-HCl, pH 9.0, was then added to neutralize the acid. The
eluted phage pool was not amplified, but used directly in the third
selection round. Thus, in selection round three, peptides were pre-
loaded on avidin-coated wells of a microtiter plate, with 8 wells each
coated with 0.5
␮
g avidin and loaded with 10 pmol peptide. Wells
were then blocked with 5% (w/v) BSA in selection buffer. About 10
6
eluted phages from round two were diluted to 800
␮
l in selection
buffer and then added to peptide-loaded wells, 100
␮
l per well. The
plate was incubated for ⬃16 h at room temperature with gentle
agitation. Wells were washed three times with selection buffer, three
times with PBS containing 0.05% (v/v) Tween-20, and three times
with PBS. Captured phages were eluted using trypsin, 100
␮
l per well,
as described above.
To counteract selection of irrelevant (nonspecific) phages, each
selection round was stringently preceded by a preselection, designed
to eliminate phage clones of certain antigen specificities. The starting
phage stocks of selection rounds one and two were preselected
against irrelevant biotinylated peptide motifs followed by capture on
streptavidin-conjugated magnetic beads. The phage stock used in
round three was preselected against avidin coated on a microtiter
plate. Enrichment of irrelevant phages was also counteracted by
addition of irrelevant nonbiotinylated peptide motifs as competitors to
the phage and antigen mixture.
Screening of CIMS Antibodies—A log phase culture of E. coli strain
HB101F⬘ was infected with the eluted phage pool from selection
round three and phagemid DNA was amplified and isolated essen-
tially as described before (
30). Phage-specific DNA was eliminated by
EagI digestion and re-ligated material was transformed into chemi-
cally competent E. coli strain TOP10 (Invitrogen). Individual colonies
were obtained by spreading transformed bacteria on 22 ⫻ 22 cm LA
plates containing 1% (w/v) glucose and 100
␮
g/ml ampicillin. Each
colony carries a plasmid encoding a specific scFv clone with two
general C-terminal affinity tags, one c-Myc tag and one 6xHis tag.
Colony picking, expression of soluble scFv, and a primary ELISA
screening were performed in 384-well format, using an integrated
robotic workstations (
31). First, colonies were transferred from LA
plates to master plates containing Luria broth, 1% (w/v) glucose, and
100
␮
g/ml ampicillin. Master plates were then replicated to expres-
sion plates containing the medium from above, but without glucose.
Expression of scFv was induced during bacterial growth by addition
of isopropyl
␤
-D-thiogalactoside (IPTG) to a concentration of 0.4 mM.
Finally, expression supernatants were screened by ELISA against
biotinylated peptides loaded on streptavidin. Detection was per-
formed using an horseradish peroxidase (HRP)-conjugated anti-His
antibody (R&D Systems, Minneapolis, MN) and the SuperSignal
ELISA Pico Chemiluminescent substrate (Pierce, Rockford, IL).
Bacterial clones, identified as “actives” in the primary ELISA
screening, were cherry picked into 96-well micro-titer plate-format,
the scFv were re-expressed and clones retested on the ELISA system
(
31). The scFv-encoding gene of confirmed actives was sequenced to
identify unique hit clones (
31).
Production of CIMS Antibodies—All scFv antibodies were pro-
duced in 100 ml E. coli cultures and purified using affinity chroma-
tography on nickel-nitrilotriacetic acid (Ni
2⫹
-NTA) agarose (Qiagen,
Hilden, Germany). Bound molecules were eluted with 250 m
M imid-
azole, dialyzed against PBS (pH 7.4) for 72 h and then stored at ⫹4°C
until further use. The protein concentration was determined by mea-
suring the absorbance at 280 nm using a Nanodrop-1000. The integ-
rity and purity of the scFv antibodies was evaluated by running
Protein 80 chips on Agilent Bioanalyzer (Agilent, Waldbronn, Ger-
many). Because of logistical liquid chromatography tandem MS (LC-
MS/MS) limitations, 14 of 91 selected scFv antibodies, directed
against eight motifs, were first selected for protein microarray char-
acterization. Seven of these 14 antibodies were then included in the
subsequent LC-MS/MS analysis.
Affinity Measurements of CIMS-binders—A competitive inhibition
affinity assay was designed, based on fluorescent labeled synthetic
peptides, CIMS antibody functionalized magnetic beads and the
Affinity Proteomics and Mass Spectrometry
10.1074/mcp.M110.003962–2 Molecular & Cellular Proteomics 10.10

KingFisher Flex system (Thermo Fisher Scientific), an automated
magnetic particle processing. Measurements were performed for the
CIMS-1-A05, CIMS-1-B03, CIMS-15-A06, CIMS-17-E02, CIMS-32–
3A-G03, CIMS-33–3D-F06, and CIMS-34–3A-D10. Briefly, the anti-
body conjugated beads were prewashed with 0.03% (w/v) 3-[(3-
cholamidopropyl)dimethylammonio]propanesulfonate (CHAPS) in
PBS and then transferred into a 96-well plate prefilled with 3
␮
lofa
CIMS-scFv bead solution. A plate containing 100 n
M (CIMS-17-E02)
or 200 n
M (all other CIMS antibodies) fluorescent N-terminally labeled
(tetramethylrhodamine) synthetic selection peptide (80% purity)
(Thermo Biopolymers, Germany) and various amounts of an identical
non-tetramethylrhodamine labeled synthetic peptide (Thermo Biopo-
lymers) was prepared. Eight different concentrations were used and
the range of the nonlabeled competitive peptide was, for the majority
of the measurements, between 0 and 5000 n
M. The plates were
incubated for 1 h, with mixing for 40 s (medium speed) every 3.5 min.
The magnetic beads were then transferred to the elution plate con-
taining 45
␮
l of 5% acetic acid and incubated for 2 min. Finally, the
eluates and the incubation solutions were transferred into 384-well
black, low-volume plates (Corning, Corning, NY) and the fluorescent
intensities were determined using the FLUOstar Omega microplate
reader (BMG Labtech, Durham, NC). The values were corrected for
any background signal intensity using 0.03% (w/v) CHAPS in PBS as
cut-off value. In addition, a control, un-conjugated magnetic beads
were run in parallel for all conditions and peptides. A control bead
background intensity was determined and subtracted from the inten-
sity values obtained for all samples. The dissociation constant (K
D
)
was calculated using Scatchard plots.
Protein Microarray Screening—The reactivity patterns of purified
CIMS antibodies against intact human proteins was evaluated by
protein array screening using arrays comprising 37,200 human fetal
brain proteins, generated from the hEx1 cDNA expression library (
32,
33) (ImaGenes, Germany). The polyvinylidene difluoride arrays were
soaked in 95% (v/v) ethanol, rinsed in deionized water, and washed
clean of residual bacterial colonies with 20 m
M Tris, pH 7.4, 500 mM
NaCl, 0.05% (v/v) Tween-20 (TBST), with 0.5% (v/v) Triton X-100. For
CIMS scFv profiling, the protein arrays were blocked in 2% (w/v)
nonfat, dry milk powder in 20 m
M Tris HCl pH 7.4, 150 mM NaCl (TBS)
for 2 h, then washed twice in TBST and subsequently incubated with
scFv at a concentration of 1
␮
g/ml in TBS. The protein arrays were
then washed in TBST three times for 10 min each and subsequently
incubated with mouse anti-c-myc antibody (9E10) (Santa Cruz, Santa
Cruz, CA) at a concentration of 0.2
␮
g/ml. This secondary anti-c-myc
antibody is specific for the myc affinity-tag carried by all CIMS scFv’s.
The tertiary labeled antibody used was goat anti-mouse alkaline
phosphatase antibody (Sigma). The arrays were illuminated with long-
wave UV light and the images were taken using a high resolution CCD
detection system (Fuji). Image analysis was performed with VisualGrid
(GPC Biotech, Martinsreid, Germany). The EMBOSS Pairwise Align-
ment software, using the needle algorithm, was used to map potential
epitopes on the bound proteins based on the original selection pep-
tide motifs as well as the experimentally refined binding motifs, as
delineated from the captured peptides identified by LC-MS/MS.
Preparation of Trypsin-digested Mouse Liver Proteomes—Protein
was extracted from Mus musculus liver. A 1.02 g liver was minced in
0.5 m
M Tris-HCl buffer, pH 7.4, containing 0.25 M sucrose and 0.75
m
M magnesium chloride. The mixture was first sonicated to release
proteins and then centrifuged to remove nonsoluble material, 600 ⫻
g for 15 min followed by 25000 ⫻ g for 100 min. Protein in the
supernatant was precipitated by adding ammonium sulfate to 72%
saturation, centrifuged at 25000 ⫻ g for 100 min and resuspended in
2.4 ml 25 m
M ammonium carbonate. Once more the mixture was
sonicated and centrifuged, 25,000 ⫻ g for 40 min. The supernatant
was filtered through a 0.22
␮
m filter and then desalted on a PD-10
column (Amersham Biosciences, Uppsala, Sweden). Protein was
eluted in 25 m
M ammonium carbonate and fractions containing most
of the protein, identified from A
280
measurement, were pooled (⬃4
ml). All steps of the extraction process were performed either on ice
or at 4 °C. The protein concentration of the extracted protein pool was
estimated to 5.6 mg/ml using the BCA Protein Assay (Pierce, Rock-
ford, IL) using BSA as a standard.
Extracted protein (8.8 mg, 1.6 ml) was reduced, alkylated and
trypsin digested. First, dithiothreitol and SDS were added to 16 m
M
and 0.2% (w/v), respectively, and the sample was reduced for 30 min
at 50 °C. Then iodoacetamide was added to 27 m
M and the sample
was alkylated for 30 min at 37 °C. Excess reagents were removed
using a PD-10 column and protein was eluted in PBS. Fractions
containing most of the protein were pooled (⬃3 ml). Finally, 37
␮
lof
a 10 mg/ml trypsin solution was added, the sample was digested for
20 h at 37 °C and then stored at ⫺80 °C.
Preparation of Trypsin-digested Human Colon Tissue Proteomes—
Protein was extracted from human colon tissue from colon cancer
patients kindly supplied by Prof. X-F Sun (Department of Oncology,
University of Linko¨ ping, Linko¨ ping, Sweden), and stored at ⫺80 °C
until use. The sample collection was approved by the Regional Ethical
Committee. Tissue pieces (about 100 mg/sample) were homogenized
in Teflon containers, precooled in liquid nitrogen, by fixating the bomb
in a shaker for 2 ⫻ 30 s with quick cooling in liquid nitrogen in
between the two shaking rounds. The homogenized tissue powder
was then collected in lysis buffer containing 8
M urea, 30 mM Tris, 5
m
M magnesium acetate and 4% (w/v) CHAPS (pH 8.5) was added (1.5
mg tissue/30
␮
l buffer). The tubes were briefly vortexed and then
incubated on ice for 30 min with brief vortex of the sample every 5
min. The sample was then centrifuged at 13,000 rpm, and the super-
natant transferred to new tubes followed by a second centrifugation.
The buffer was exchanged to 0.3
M HEPES, 1 M Urea using Zeba
desalting spin columns (Pierce) before the protein concentration was
determined using Total Protein Kit, Micro Lowry (Sigma). Finally, the
sample was aliquoted and stored at ⫺80 °C until further use.
Thawed protein extracts were reduced, alkylated, and trypsin di-
gested. First, SDS and tris(2-carboxyethyl) phosphine hydrochloride
(Thermo Scientific) were added to 0.1% (w/v) and 5 m
M, respectively,
and the sample was reduced for 30 min at 37 °C. The samples were
cooled down to room temperature before iodoacetamide was added
to 40 m
M and the sample was alkylated for 30 min at room temper-
ature. Next, sequencing-grade modified trypsin (Promega, Madison,
Wisconsin) was added at 20
␮
g per mg of protein for 16 h at 37 °C.
To ensure complete digestion, a second round of trypsin (10
␮
g per
mg protein) was added and the tubes were incubated for an additional
3 h at 37 °C. Finally, the samples were aliquoted and stored at ⫺80 °C
until further use.
Preparation of Trypsin-digested Yeast Proteomes—Colonies of a
Saccharomyces cerevisiae, (strain W303–1A point mutated wild
type with mutations leu2–3, 112 ura3–1, trp1–1, his3–11/15, ade2–1,
can1–100, GAL SUC2 mal0, and genotype MATa) were grafted to a
500 ml preculture flask and grown at 30 °C on a rotary shaker at 200
rpm in synthetic yeast nitrogen medium containing ammonium sulfate
(50 g/l) and supplemented with 2% (w/v) glucose and necessary
amino acids and nucleotides to a final concentration of 120
␮
g/ml
each. A liquid culture with 0.05% (w/v) glucose and 3% (v/v) ethanol
were inoculated with 1% (v/v) from the overnight preculture. The
cultures were grown in 30 °C on a rotary shaker at 200 rpm and
harvested in log phase, after about 4 generations, at OD 0.5. Cell
density was estimated at 600 nm. The cultures were centrifuged at
4 °C (at 5000 rpm for 10 min. The pellets were dissolved in 20 ml
ice-cold Milli-Q water and centrifuged at 3000 rpm for 3 min. The
pellets were again resuspended in 1 ml Milli-Q water and finally
Affinity Proteomics and Mass Spectrometry
Molecular & Cellular Proteomics 10.10 10.1074/mcp.M110.003962–3

collected by a centrifugation using 13,200 rpm for 1 min at 4 °C.
Samples were quick-frozen in liquid nitrogen and stored at ⫺80 °C.
Pellets were thawed on ice, resuspended in 800
␮
l ice-cold water
and transferred to tubes containing 0.7g acid washed glass beads
(diameter 0.55 mm). Samples were vortexed 4 ⫻ 60 s, with 60 s
intervals on ice, at 4 °C. To each sample, 100
␮
l of a solution con-
taining 3% (w/v) SDS, 140 m
M Tris-HCl, 110 mM TrisBase, and 600
Mm dithiothreitol, was added and then incubated for 5 min at 85 °C.
The samples were allowed to cool on ice for a short time before
adding a solution containing 1.5 m
M Tris-HCl and TrisBase, 1 M
MgCl
2
, DNAse1, and RNase (A), followed by incubation for 30 min on
ice. Cell material was removed by centrifugation at 4 °C, 13,200 rpm
for 15 min, and the supernatant stored at ⫺80 °C.
The buffer was exchanged to 0.15
M HEPES, 0.5 M Urea using Zeba
desalting spin columns (Pierce) before the protein concentration was
determined using Total Protein Kit, Micro Lowry (Sigma). Finally, the
sample was aliquoted and stored at ⫺80 °C until further use. Thawed
protein extracts were reduced, alkylated and trypsin digested. First,
SDS and Tris(2-Carboxyethyl) phosphine Hydrochloride (Thermo Sci-
entific, Rockford, IL, USA) were added to 0.05% (w/v) and 5 m
M,
respectively, and the sample was reduced for 30 min at 37 °C. The
samples were cooled down to room temperature before iodoacet-
amide was added to 10 m
M and the sample was alkylated for 30 min
at room temperature. Next, sequencing-grade modified trypsin (Pro-
mega) was added at 20
␮
g per mg of protein for 16 h at 37 °C. To
ensure complete digestion, a second round of trypsin (10
␮
g per mg
protein) was added and the tubes were incubated for an additional 3 h
at 37 °C. Finally, the digested samples were aliquoted and stored at
⫺80 °C until further use.
Fabrication of CIMS Antibody Functionalized Micro-columns—Pu-
rified CIMS antibodies were individually coupled to POROS 20 AL
bead (Applied Biosystems, Foster City, CA) using standard protocols.
Briefly, 500
␮
g scFv was concentrated to 200
␮
l and the buffer
exchanged to 25 m
M sodium phosphate pH 7.2 containing 0.5 M
Na
2
SO
4
. After addition of 20
␮
l 200 mg/ml sodium cyanoborohydride
solution, the antibodies were coupled to 30 mg beads. The coupling
was performed at room temperature under salting out conditions
(stepwise increase of 0.1
M sodium sulfate from 0.5 to 1.1 M every 30
min) by adding a 25 m
M sodium phosphate buffer, pH 7.2, containing
1.5
M Na
2
SO
4
and 200 (or 100) mg/ml sodium cyanoborohydride. The
final concentration of sodium cyanoborohydride was 45 (or 26) mg/
ml. Next, the mixture was incubated over night at room temperature
with gentle shaking. Beads were then separated from the reaction
buffer by centrifugation. To quench residual aldehyde functionality,
beads were resuspended in 150
␮
l (or 200
␮
l) 0.2 M Tris-HCl buffer,
pH 7.4, containing 250 mg/ml (or 200 mg/ml) sodium cyanoborohy-
dride and incubated for2hatroom temperature. Beads were then
washed three times in PBS and finally re-suspended in 400
␮
l PBS
and stored at 4 °C until use. Micro-columns were fabricated by pack-
ing about 10
␮
l of bead slurries in GEloader tips (Eppendorf) to
generate 20 to 30 mm long columns.
Fabrication of CIMS Antibody Functionalized Magnetic Beads—
Purified scFvs were individually coupled to magnetic beads using
manufacture provided protocols with some modifications. Briefly, 300
␮
l(⬃ 9 mg) of beads, M-270 carboxylic acid-activated (Invitrogen
Dynal, Oslo, Norway), were first washed and incubated twice with 300
␮
l25mM 2-(N-morpholino)ethanesulfonic acid (MES) (pH 6) and slow
mixing for 10 min. N-Ethyl-N⬘-(3-dimethylaminopropyl)carbodiimide
(Sigma) was freshly dissolved in cold 25 m
M MES, pH 6 to a concen-
tration of 25 mg/ml. Similarly, a 47 mg/ml solution of Sulfo-NHS
(Thermo Scientific) solution was prepared in 25 m
M MES, pH 6. 150
␮
l
of both the N-Ethyl-N⬘-(3-dimethylaminopropyl)carbodiimide-solution
and Sulfo-NHS solution were added to the beads and incubated for
30 min at room temperature with slow mixing. The beads were
washed once with 25 m
M MES and PBS, respectively. Then, 180–250
␮
g purified scFv was added to the beads and incubated for 45 min
under slow mixing at room temperature. To block unreacted surface,
the beads were washed with 2 ⫻ 300
␮
l50mM Tris buffer, pH 7.4 and
incubated for 15 min at room temperature. Finally, the beads were
washed four times with PBS containing 0.005% (v/v) Tween-20,
transferred to new tubes and resuspended in 300
␮
l PBS/0.005%
(v/v) Tween-20 and stored at 4 °C until use.
Analysis of Synthetic Peptide Mixtures—The individual columns
were equilibrated with 50
␮
l 5% (v/v) HAc followed by 50
␮
l PBS (pH
7.4). Next, a synthetic peptide mixture consisting of ten different
selection peptides, denoted peptide mix-1 (M-1, M-13, M-14, M-16,
M-17, M-29, M-30, M-32, M-33, and M-34) or mix-2 (M-13, M-14,
M-16, M-17, M-29, M-30, M-32, M-33, M-34, and M-35), was applied.
Peptide mix-1 was applied to columns functionalized with either
CIMS-1-A05 or CIMS-1-B03, whereas peptide mix-2 was applied to
either CIMS-17-E02, CIMS-M32–3A-G03, CIMS-33–3D-F06, or
CIMS-34–3A-D10 functionalized columns. A two-step washing pro-
cedure was performed by adding 15
␮
l and 10
␮
l PBS (pH 7.4),
respectively, subsequently any captured peptides were eluted by
adding 5
␮
l 5% (v/v) HAc. Before mass spectrometry analysis, a
sample cleanup was performed to remove any contaminating bead
particles using small pieces of a C18-filter (3
M Empore) (3 M Center,
St. Paul, MN) packed into a GEloader tip (Eppendorf) as previously
described (
34). The filter was activated by adding 10
␮
l 50% (v/v)
acetonitrile followed by a wash with 10
␮
l 0,1% (v/v) trifluoroacetic
acid (TFA). The sample was then added and rinsed using 20
␮
l 0.1%
TFA. Bound peptides were eluted by adding 5
␮
l 70% (v/v) acetoni-
trile, 0.1% (v/v) TFA. Mass spectrometry was performed using a
matrix-assisted laser desorption ionization/time of flight (MALDI-TOF)
micro MX (Waters, Milford, MA) instrument. Standard 96-well stain-
less steel MALDI target plate (Waters) was used, were 0.5
␮
l matrix
solution (2.5 mg/ml
␣
-cyano-4-hydroxycinnamic acid in 50% (v/v)
acetonitrile, 0.05% (v/v) trifluoroacetic acid) was first spotted followed
by 0.5
␮
l sample. Mass scanning was performed between 800 and
3500 Da and each spectrum represented an average of up to 400
laser shots.
GPS of Complex Proteomes Using Micro-column Setup—Crude
trypsin digested mouse liver extracts or human colon tissue extracts
were applied to CIMS antibody functionalized micro-columns. For the
mouse liver extracts CIMS-1-A05, CIMS-1-B03, CIMS-17-E02, and
CIMS-33–3D-F06 were tested and for the colon samples CIMS-1-
A05, CIMS-1-B03, CIMS-15-A06, CIMS-17-E02, CIMS-32–3A-G03,
CIMS-33–3D-F06, and CIMS-34–3A-D10 were tested. Before sample
application, the columns were washed with 2 ⫻ 30
␮
l 5% (v/v) HAc
and equilibrated with 2 ⫻ 30
␮
l PBS.
In the case of crude trypsin digested mouse liver extracts, 10
␮
lof
a 2-fold dilution in PBS was applied (about 20
␮
g). Columns were
washed with 2 ⫻ 15
␮
l PBS and captured peptides were eluted with
7
␮
l 5% (v/v) HAc. The eluted peptides were desalted using a re-
versed-phase material, POROS 20 R2 media (Applied Biosystems).
The R2 beads were packed into micro columns of ⬃5 mm length in
the same way as described for the affinity columns. R2 columns were
washed with 30
␮
l 75% (v/v) acetonitrile, 1% (v/v) TFA and equili-
brated with 2 ⫻ 15
␮
l 0.1% (v/v) TFA. Samples from the affinity
columns (⬃7
␮
l) were added and R2 columns were washed with 2 ⫻
15
␮
l 0.1% (v/v) TFA. Finally peptides were eluted with 3
␮
l 75% (v/v)
acetonitrile, 0.1% (v/v) TFA directly onto a MALDI target. MALDI-TOF-
TOF analysis was done using a 4700 Proteomics Analyzer (Applied
Biosystems, Framingham, MA). 0.5
␮
l per well of 5 mg/ml solution of
␣
-cyano-4-hydroxycinnamic acid in 75% (v/v) acetonitrile, 1% (v/v)
TFA was used as matrix solution. Database searches were performed
using Mascot Distiller (v 2.3.1.0) (Matrix Science, London, UK) against
the Mus musculus (Swiss-Prot 57.12, 23692 sequences for Mus mus-
Affinity Proteomics and Mass Spectrometry
10.1074/mcp.M110.003962–4 Molecular & Cellular Proteomics 10.10

culus (house mouse) as taxonomy) with the following parameters:
peptide mass tolerance ⫾0.3 Da; fragment mass tolerance: ⫾0.3 Da;
enzyme: trypsin; missed cleavages: 1; fixed modification: carbam-
idomethyl (C); variable modification: methionine oxidation. In addition
a TrypChymo search with two and three missed cleavages were
performed. Only peptides that passed the Mascot threshold set for
positive MS/MS identification (p ⬍ 0.05) were considered as valid hits.
All generated MS and MS/MS data for the mouse liver set were then
uploaded and stored in the The Proteios Software Environment
(ProSE) (
35) and deposited in the public PRoteomics IDEntifications
database (PRIDE) (
36) (accession numbers 13401–13512).
In the case of crude trypsin digested colon extracts, 10
␮
lofa2
␮
g/
␮
l extract (diluted in PBS) was added. A two step washing pro-
cedure was performed by adding 15
␮
l and 10
␮
l PBS (pH 7.4),
respectively and any captured peptides were eluted by adding 5
␮
l
5% (v/v) HAc. Next, the eluted peptides were cleaned-up using reg-
ular C18-filter (3
M Empore) as described above for the synthetic
peptide mixtures. The captured human colon peptides were analyzed
using a Micromass electrospray ionization TOF (ESI-QTOF) Ultima
API (Waters) coupled to a CapLC HPLC. Before injection on the
ESI-QTOF Ultima API, all samples were almost completely dried
down by speedvac and then redissolved in 8
␮
l 0.1% (v/v) formic acid.
The auto-sampler injected 6
␮
l of sample, and the peptides were
trapped on a precolumn (C18, 300
␮
M⫻5 mm, 5
␮
M, 100 Å, LC-
Packings) and separated on a reverse-phase analytical column (At-
lantis, C18, 75
␮
M ⫻150 mm, 3
␮
m, 100 Å, Waters). The flow rate was
200 nl/min. Solvent A consisted of 2% (v/v) acetonitrile and 98%
water with 0.1% (v/v) formic acid. Solvent B consisted of 90% (v/v)
acetonitrile, 10% water, and 0.1% (v/v) formic acid. The total runtime
was 90 min starting with a 5 min wash of the peptides. Mass spectra
were acquired from m/z 400–1600 for 1.9 s followed by three data-
dependent MS/MS scans from m/z 50–1800 for 1 s each. The colli-
sion energy used to perform MS/MS was automatically varied ac-
cording to the mass and charge state of the eluting peptide. Only
spectra from ions with charge state 2 and 3 were acquired for MS/MS
analysis. Mascot Distiller (version 2.2.1.0) was used to process the
generated raw-data files into mzData file format. All generated MS
and MS/MS data for the colon set were then uploaded and stored in
the The Proteios Software Environment (ProSE) (
35) and deposited in
PRIDE (
36) (accession numbers 13401–13512). The Proteios platform
was used for performing automated database searches in both Mas-
cot and X! Tandem against the human International Protein Index (IPI)
protein version 3.60 database (consisting of forward and random
sequences with the same amino acid distribution and size resulting in
a total of 160824 sequences for the combined database) with the
following parameters: enzyme: trypsin; missed cleavages: 1; fixed
modification: carbamidomethyl (C); variable modification: methionine
oxidation. A peptide mass tolerance of 100 ppm and fragment mass
tolerance of 0.1 Da was used. This was followed by automated
combination of the search results from the two different search en-
gines with predetermined false discovery rate (FDR of ⬍0.05 was
used). No significantly scored (MS/MS) background binding peptides
were detected for blank columns, containing no antibody.
GPS Experiments of Complex Proteomes Using Magnetic Bead
Setup—Crude trypsin digested human colon extracts or yeast ex-
tracts were applied to CIMS antibody functionalized magnetic beads.
For each GPS-experiment experiment 25–30
␮
l (the latter for yeast
experiments) of a conjugated CIMS-scFv bead solution was used.
The conjugated beads were prewashed with 200
␮
l PBS. A tryptic
digest of 20
␮
g was defrosted and diluted (using PBS) into a final
volume of 20
␮
l. Incubation time was 15 min with gentle mixing. The
tubes were then placed on a magnet and the supernatant removed.
Two washes with PBS (53
␮
l and 45
␮
l) were performed and the
beads transferred to new tubes in between each washing step. Finally
the beads were incubated with 7.5
␮
l of a 5% (v/v) acetic acid solution
for 1 min to elute the captured peptides. The eluate was then ready for
mass spectrometry analysis and no further cleanup needed. The
captured peptides were analyzed using a Micromass ESI-QTOF Ul-
tima API (Waters) coupled to a CapLC HPLC (as described above)
and a ESI-LTQ-Orbitrap (Thermo Electron, Bremen, Germany) cou-
pled to an Eksigent two-dimensional nano HPLC (Eksigent technol-
ogies, Dublin, CA). For the colon samples replicate capture experi-
ments were done for each of the instrumentation configurations (i.e. in
total four capture experiments (two capture and LC-MS/MS runs per
instrumentation) however the digests were not from the same pieces
of tissue and thereby data not directly comparable among the instru-
ments). In the case of yeast samples replicate capture experiments
were done and captured peptides only analyzed with the LTQ-Or-
bitrap. The auto-sampler for the LTQ-Orbitrap injected 6
␮
l of sample,
and the peptides were trapped on a precolumn (Zorbax 300SB-C18
5 ⫻ 0.3 mm, 5
␮
m, Agilent) and separated on a reversed-phase
analytical column (Zorbax 300SB-C18 150 ⫻ 0.75 mm, 3.5
␮
m,
Agilent). The flow rate was 400 nl/min. Solvent A consisted of 0.1%
(v/v) formic acid in water and solvent B of 0.1% (v/v) formic acid in
acetonitrile. The total runtime was 90 min starting with a 15 min wash
of the peptides. The LTQ-Orbitrap was operated in the data-depen-
dent mode to automatically switch between Orbitrap-MS and LTQ-
MS/MS acquisition. Survey full scan MS spectra (from m/z 400 to
2000) were acquired in the Orbitrap with a resolution of 60000 at m/z
400 using the lock mass option for internal calibration. The seven
most intense ions with charge state 2 and up were sequentially
isolated for CID-fragmentation in the LTQ with a normalized collision
energy of 35%. The resulting fragment ions were recorded in the LTQ.
In contrast to the QTOF set-up, significantly scoring (MS/MS) back-
ground binding peptides were detected for blank beads (no coupled
antibody) for the Orbitrap set-up (supplemental Table S6). Based on
the relatively low number of background binding peptides, the colon
data was left unfiltered. For the yeast data, any background binding
peptides identified in both the blank runs (supplemental Table S6) and
in any of the CIMS-binder runs were removed and not included in the
analysis.
GPS Multiplex Experiments of Complex Proteomes Using Magnetic
Bead Setup—Briefly 30
␮
l solution of the CIMS-17-E02 and CIMS-
33–3D-F06 conjugated beads were mixed and then prewashed with
200
␮
l PBS ⫹ 100
␮
l PBS. A tryptic digest (40
␮
g was defrosted and
then diluted using PBS) into a final volume of 40
␮
l (human colon
digest) or 55
␮
l (yeast) and then incubated with the beads for 15 min
with gentle mixing. The tubes were then placed on a magnet and the
supernatant removed. Two washes with PBS were performed using
100 and 90
␮
l, and the beads were transferred to new tubes in
between each wash step. Finally the beads were incubated with 7.5
␮
l
of a 5% (v/v) acetic acid solution for 1 min. The eluate was then ready
for mass spectrometry analysis and no further cleanup needed. The
captured peptides were analyzed using an ESI-LTQ-Orbitrap (Thermo
Electron, Bremen, Germany) coupled to an Eksigent 2D nano HPLC
(Eksigent technologies, Dublin, CA). The auto-sampler injected 6
␮
lof
sample, and the peptides were trapped on a precolumn (Zorbax
300SB-C18 5 ⫻ 0.3 mm, 5
␮
m, Agilent, Santa Clara, CA) and sepa-
rated on a reversed-phase analytical column (Zorbax 300SB-C18
150 ⫻ 0.75 mm, 3.5
␮
m, Agilent). The flow rate was 400 nl/min.
Solvent A consisted of 0.1% (v/v) formic acid in water and solvent B
of 0.1% (v/v) formic acid in acetonitrile. The total runtime was 90 min
starting with a 15 min wash of the peptides. The LTQ-Orbitrap was
operated in the data-dependent mode to automatically switch be-
tween Orbitrap-MS and LTQ-MS/MS acquisition. Survey full scan MS
spectra (from m/z 400 to 2000) were acquired in the Orbitrap with a
resolution of 60,000 at m/z 400 using the lock mass option for internal
calibration. The seven most intense ions with charge state 2 and up
Affinity Proteomics and Mass Spectrometry
Molecular & Cellular Proteomics 10.10 10.1074/mcp.M110.003962–5

were sequentially isolated for CID-fragmentation in the LTQ with a
normalized collision energy of 35%. The resulting fragment ions were
recorded in the LTQ.
Database Searches for all Magnetic Bead Experiments—All gener-
ated raw-data files were processed and converted into mzData for-
mat (Mascot Distiller (version 2.2.1.0) used for files generated by the
ESI-QTOF instrument, and Proteome Discovery (version 1.0) for files
generated by the LTQ-Orbitrap). All generated MS and MS/MS data
from all magnetic bead experiments (colon sets and yeast set) were
then uploaded into separate projects, stored in ProSE (
35), and de
-
posited in PRIDE (
36) (accession numbers 13401–13512). The Pro
-
teios platform was used for performing automated database searches
in both Mascot and X! Tandem against the human IPI protein data-
base (version 3.60 and against a random generated decoy IPI version
3.60 with same amino acid distribution) for the colon samples and the
Saccharomyces cerevisae Swiss-Prot (13-Oct 2009 and against a
random generated decoy version with same amino acid distribution)
for the yeast samples. In both cases with the following parameters:
enzyme: trypsin; missed cleavages: 1; fixed modification: carbam-
idomethyl (C); variable modification: methionine oxidation. A peptide
mass tolerance of 100 ppm and fragment mass tolerance of 0.1 Da
was used for analysis done with the ESI-QTOF whereas for analysis
done on the LTQ-Orbitrap a peptide mass tolerance of 3 ppm and
fragment mass tolerance of 0.5 Da was used. This was followed by
automated combination of the search results from the two different
search engines with predetermined false discovery rates. A FDR of
0.05 was used for the human colon sets (QTOF and the LTQ-Or-
bitrap), whereas a FDR of 0.01 was used for the yeast set.
Label-free Quantitative GPS LC-MS/MS Experiments of Complex
Proteomes—To address the reproducibility of the capture step, in
terms of both peptide identification and quantification, a set of in total
18 capture experiments were performed. 35
␮
l of the conjugated
CIMS-33–3D-F06 bead solution was used for each capture experi-
ment and exposed to either a tryptic digest, from either colon or
yeast, in a final volume of 30
␮
l (diluted with PBS). Incubation time
was 15 min with gentle mixing followed by two washes with PBS (65
␮
l and 50
␮
l) and the beads were transferred to new tubes for each
washing step. Finally, the beads were incubated with 8.5
␮
lofa5%
(v/v) acetic acid solution for 2 min. The eluate was then ready for mass
spectrometry analysis and no further cleanup needed. For all sam-
ples, the ESI-LTQ-Orbitrap XL (Thermo Electron, Bremen, Germany)
was used as previously described with some minor modifications.
Briefly, the total runtime was changed to 70 min starting witha5min
wash of the peptides. Further, the flow rate was 350 nl/min with
solvent A, containing of 0.1% (v/v) formic acid in water and solvent B
of 0.1% (v/v) formic acid in acetonitrile. For the first three captures (20
␮
g digest of both colon and yeast samples), the LTQ-Orbitrap was
operated in a data-dependent acquisition (Top-7) manner to automat-
ically switch between Orbitrap-MS and LTQ-MS/MS acquisition. In-
clusion lists were made based on all the identified peptides from the
first three capture experiments. Six new independent capture exper-
iments were then performed (three using 20
␮
g digest and three with
5
␮
g digest) on both the colon and yeast tryptic digested proteomes.
The eluates from these six captures were then analyzed in the same
way as previously described with the exception that inclusion lists
now were used in combination with the option that if no ions on the list
were present the four most intense ions with charge state 2 and up
could sequentially be isolated for CID-fragmentation. All generated
raw-data files were processed and converted into mzData format
using Proteome Discovery (version 1.0). The Proteios platform was
used for automated database searches in both Mascot and X! Tan-
dem. The same search parameters, as previously described, were
used with the only exception that the human samples were searched
against the human IPI protein database (version 3.71) (consisting of
forward and random sequences with the same amino acid distribution
and size, resulting in a total of 173490 sequences for the combined
database). The automated combination of results for either the colon
and yeast data, using the different search engines (with a predeter-
mined false discovery rate of 0.01), was used. Data has been depos-
ited in PRIDE (
36) (accession numbers 16449–16469). For assessing
the quantitative reproducibility, the Progenesis-LC-MS software (v
2.5) with default settings was used. The raw data files were converted
to mzXML using the ProteoWizard software package before using the
Progenesis-LC-MS software. The built-in peptide feature finding tool
and Mascot search tool (p value 0.05) were used (no X! Tandem) and
the generated raw abundance values were extracted and used for
downstream quantitative analysis i.e. calculating the coefficient of
variation (CV) and ratios for the replicate independent capture
experiments.
Redefining of Motives—In all cases, the MS/MS identified peptides
for each CIMS antibody in all experiments (mouse liver, human colon
and yeast) were used to manually re-annotate and redefine the pep-
tide binding motifs. The tool weblogo (
37) was used to generate the
motif figures for each binders enriched sequence motifs.
Structural Analysis of CIMS antibodies—Three-dimensional struc-
tural homology models were generated for selected CIMS antibodies
using Web Antibody Modeling server (http://antibody.bath.ac.uk). The
model structures were visualized using Pymol (Delano Scientific LCC).
Bioinformatic Analysis—High level GO terms that represent the
major biological processes and compartments mapped for all pep-
tides identified in the human colon sample and from the S. cerevi-
siae extracts were generated by using the Generic Gene Ontology
(GO) TERM MAPPER tool at http://go.princeton.edu/cgi-bin/
GOTermMapper.
The compiled set of identified peptides for the different samples
were checked for presence in the PeptideAtlas and the following
builds: Human Jan-2010 Build with 59921 distinct peptides and
4553414 peptide spectrum matches. Yeast May-2009 build with
58719 distinct peptides and 2697580 peptide spectrum matches.
Mouse build with 17853 distinct peptides and 576448 peptide spec-
trum matches.
RESULTS
We present proof-of-concept for a conceptually new
method opening up the possibility to probe proteomes in a
species independent manner, while still using a limited set of
antibodies. An overall workflow outlining the steps in the
design and development of the GPS methodology is sche-
matically shown in supplemental Fig. S1.
Design of Selection Peptide Motifs—In silico trypsin diges-
tion of the human proteome, composed of 20,325 nonredun-
dant proteins (release 56.1 of UniProtKB/Swiss-Prot) gener-
ated 1,193,062 peptides, of which 58.5% represents the mass
fraction ranging from 500 to 3500 Da that theoretically could
be detected using a high-performing mass spectrometer. To
target a general subset of this collection of addressable pep-
tides, we designed 27 selection peptide motifs, consisting of
either 4 or 6 amino acids (
27, 28), without imposing any
restrictions on the original wild-type proteins (e.g. abundance
and functional classification) Table I and supplemental
Table S1). The motifs were selected to contain a C-terminal
lysine or arginine to mimic tryptic peptides, whereas residues
sensitive for oxidation or dimerization, such as methionine
and cysteine, were excluded. If one requires a perfect match
Affinity Proteomics and Mass Spectrometry
10.1074/mcp.M110.003962–6 Molecular & Cellular Proteomics 10.10

for identification, the results showed that each motif was
present in 0 to 670 proteins, representing a total coverage of
about 2144 proteins (supplemental Table S1). However, al-
lowing a single residue to vary in each position of the motif,
except in the C terminus, significantly increased the theoret-
ical coverage. This is illustrated by a 34.3 times increase,
from 124 to 4259 hits, for eight of the selected model motifs
(Table I).
Selection of CIMS Antibodies—Human recombinant scFv
antibodies were selected from a large phage display library
(
29), using short synthetic peptides, composed of an N-ter
-
minal biotin, a SGSG-linker, followed by the C-terminal lo-
cated peptide selection motif bound to streptavidin conju-
gated magnetic beads. In total, 91 nonredundant scFv’s were
selected of which 14 binders that were directed against 8
motifs were further characterized in this proof-of-concept
study (Table II). Notably, the selection step was designed to
generate motif specific binders, but all four or six residues of
the binding motifs were not required as fixed residues for
recognition. This enabled us to select a set of CIMS antibod-
ies, denoted sister clones, against each selection peptide,
displaying similar but distinct binding patterns, increasing the
repertoire of binders even further, while still using a limited set
of selection motifs. Further, the dissociation constant (K
D
) was
determined for the antibodies, and representative results are
shown in Table II (supplemental Fig. S2). The antibodies were
TABLE I
Examples of selection peptide motifs. Eight of 27 motifs are shown (see supplementary Table 1 online)
No. of motif-carrying nonredundant proteins
a
Motif Sequence Perfect match Perfect match and 500–3500 Wobbling and 500–3500
b
Da limit Da limit
M-1 EDFR 126 118 3959
M14 DFAEDK 0 0 10
M-15 LTEFAK 2 2 34
M-16 TEEQLK 4 4 116
M-17 SSAYSR 0 0 45
M-32 QEASFK 0 0 18
M-33 LSADHR 0 0 31
M-34 SEAHLR 0 0 46
All 27 motifs 2393 2144 -
a
The number of non-redundant proteins covered by motif-carrying peptides. The nonredundant human proteome (relase 56.1 of UniProt/
Swiss-Prot), trypsin digested in silico, was used as peptide database.
b
A single amino acid residue was allowed to vary in any position of the motif, except for in the C-terminus.
T
ABLE II
Binding patterns of 14 CIMS antibodies against full-length semidenatured human proteins as determined by protein array screening
a
Determined against selection peptide.
b
Sister clones are defined as CIMS antibodies selected against the same selection peptide motif, displaying similar but distinct binding
patterns. n.a., not available.
Affinity Proteomics and Mass Spectrometry
Molecular & Cellular Proteomics 10.10 10.1074/mcp.M110.003962–7

found to display K
D
values in the range of 0.4 to 11.5
␮
M.
Hence, the antibodies displayed affinities as could be ex-
pected for anti-peptide antibodies directly selected from scFv
phage-display libraries and not subjected to affinity
maturation.
Protein Array Characterization of Selected CIMS Antibod-
ies—First, we investigated the binding patterns of the 14
CIMS antibodies by protein array screening, based on 37, 200
redundant human recombinant brain proteins (
32) (Table II
and supplemental Table S2). The 14 CIMS antibodies, includ-
ing six sister clones, were found to display distinct binding
patterns. In more detail, the CIMS antibodies were found to
bind between 2 and 52 nonredundant proteins per binder. In
total, the 14 CIMS antibodies bound to 152 proteins, corre-
sponding to 113 unique protein identities. Next, we compiled
all the LC-MS/MS data to map the experimentally refined and
validated peptide binding motifs (available for seven of the
CIMS antibodies) (see below, and Fig. 2 and supplemen-
tal Fig. S3) onto the recognized brain proteins. The data
showed that the motifs were present in 71–100% of the
recognized proteins (Table II), demonstrating that CIMS anti-
bodies displaying distinct reactivity patterns could be gener-
ated. Further experiments will be required to elucidate the
frequency of motif-carrying brain proteins that were not rec-
FIG.1.First prototype GPS set-up targeting synthetic peptide mixtures or tryptic digests of complex proteomes. A, MALDI-TOF MS
analysis of a mixture of 10 synthetic selection peptide motifs (mix-1 and 2), applied to micro-columns containing immobilized CIMS-17-E02
or CIMS-33–3D-F06 antibodies. B, MALDI-MS/MS analysis of tryptic digests from crude mouse liver homogenates applied to micro-columns
containing immobilized CIMS-17-E02 or CIMS-33–3D-F06 antibodies. C, Statistics of the number of bound mouse peptides/proteins per CIMS
antibody, as determined in B, and illustrated for four different CIMS antibodies. D, LC-MS/MS analysis of tryptic digests from crude human
colon tissue applied to micro-columns or beads containing immobilized CIMS-17-E02 or CIMS-33–3D-F06 antibodies. E, Statistics of the
number of bound human peptides/proteins per CIMS antibody, as determined in D, and illustrated for seven different CIMS antibodies.
F, LC-MS/MS analysis of tryptic digests from crude yeast cell lysates applied to beads containing immobilized CIMS-17-E02 or CIMS-33–
3D-F06 antibodies. G, Statistics of the number of bound yeast peptides/proteins per CIMS antibody, as determined in F, and illustrated for six
different CIMS antibodies.
Affinity Proteomics and Mass Spectrometry
10.1074/mcp.M110.003962–8 Molecular & Cellular Proteomics 10.10

ognized, as the sequence for many of the arrayed proteins has
not been determined.
Analysis of Peptide Mixtures Using CIMS Antibodies—Next,
we tested the principle of the GPS method by applying a
mixture of 10 synthetic peptides, harboring the selection mo-
tifs, to six micro-columns containing different CIMS antibod-
ies covalently coupled to POROS-AL beads. The micro-col-
umns were interfaced with a MALDI-TOF mass spectrometer
allowing a rapid read-out (Fig. 1A and supplemental Fig. S4).
In all cases, the CIMS antibodies were found to bind and
enrich the corresponding selection peptide, whereas the re-
maining nine irrelevant peptides were found predominantly in
the flow-through.
Analysis of Complex, Cross-species Proteomes Using
CIMS Antibodies—To validate the GPS method and to dem-
onstrate cross-species capability, we analyzed tryptic digests
from crude mouse liver homogenates, human liver tissue ex-
tracts, and yeast (S. Cerevisiae) cell lysates (Fig. 1B–1G and
supplemental Tables S3 to S5). The digested samples were
analyzed on micro-columns (mouse and human species) or
magnetic beads (human and yeast species), functionalized
with single CIMS antibodies (in total four to seven different
CIMS binders were used), and directly interfaced with tandem
MS-based set-ups, including MALDI-TOF/TOF (mouse), ESI-
QTOF (human), and ESI-LTQ-Orbitrap (human, yeast). We
could demonstrate that it was possible to capture, enrich and
identify distinct subpopulations of peptides from mouse liver
(Fig. 1B and 1C), human colon (Fig. 1C and 1E), and yeast (Fig.
1E and 1F). The overlap among different binders was very
low in all three proteomes (supplemental Tables S3 to S5).
The fact that significantly more proteins were identified in the
yeast and human colon samples compared with mouse liver
samples reflected the progression of the project, e.g. use of
more sophisticated MS/MS set-ups and streamlined proce-
dures (supplemental Fig. S1). In addition the background
binding was found to be relatively low (supplemental
Table S6). In the case of mouse liver, four CIMS antibodies
were found to bind between four and eight nonredundant
peptides each, corresponding to 21 unique mouse proteins
(Fig. 1C). Notably, 54% of these mouse peptides have not
previously been reported in MS/MS experiments (
38). For
human colon tissue, seven CIMS antibodies were found to
bind between 20 and 63 nonredundant peptides per binder
(Fig. 1E). Of the 257 different and identified peptides, 110
(42%) have previously not been reported in the PeptideAtlas.
In total, the seven CIMS antibodies recognized 217 different
human proteins. In the case of yeast cell lysates, the data
showed that six CIMS antibodies bound between 23 and 148
nonredundant peptides per binder, corresponding to 251 dif-
ferent proteins (Fig. 1G). Of note, of the 349 identified pep-
tides, 87 (23%) have previously not been reported in the
PeptideAtlas. Taken together, the GPS method could be used
to probe crude complex proteomes in a species independent
manner yielding a broad coverage.
Analysis of the Specificity of CIMS Antibodies—The antici-
pated peptide motif specificity was refined and validated by
compiling all the experimental LC-MS/MS data (supplemen-
tal Tables S3 to S5) and reviewing the motifs bound by the
seven CIMS antibodies tested (Fig. 2A and supplemental
Fig. S3). The CIMS antibodies were found to specifically en-
rich peptides containing the selection motif and a narrow set
of motif-like sequences in a clone dependent manner. Further,
the sequence analysis showed that a few amino acid posi-
tions appeared to be more essential in defining these linear
epitopes. Although physico-chemical requirements, such as
charge, size, and polarity, were placed on these anchor
positions, the others were found to vary more freely. The
three most C-terminal residues frequently appeared to play
a central role in defining the epitopes, whether a four or a six
amino acids selection motif had been used during the se-
lection step.
Moreover, primary (Fig. 2B) and tertiary (Fig. 2B–2E) struc-
tural analyses outlined that sister clones that had been se-
lected against the same original peptide motif, but showed
distinct binding patterns, also displayed key structural differ-
ences in their antigen binding-sites. Using two sister clones,
CIMS-1-A05 and CIMS-1-B03, as model example, the main
structural differences were manifested by a longer CDR-H3
and a more protruding CDR-H2 in CIMS-1-A05, creating a
more cleft-like structure. In addition, CDR-H1 and CDR-L3
were also differently positioned, resulting in a more flat-
tened binding site with a central cavity in CIMS-1-B03, as
well as a different composition and distribution of charged
residues.
Assay Characterization-multiplexing, Reproducibility, Quan-
tification, and Dynamic Range—To outline the potential for
increased throughput, we demonstrated that the set-up could
be multiplexed by using several CIMS antibodies at the same
time (Fig. 3A). In these proof-of-concept testes, we simulta-
neously profiled peptides captured by a mixture of two CIMS
antibodies targeting human colon tissue and yeast cell ly-
sates, respectively. In the case of human colon, the two CIMS
antibodies captured 16 and 85 nonredundant peptides, re-
spectively corresponding to in total 91 nonredundant human
proteins. For yeast cell lysates, the same two CIMS antibodies
captured 32 and 75 nonredundant peptides, respectively rep-
resenting nonredundant 89 yeast proteins. The reproducibility
of this two-plexed GPS set-up, including both the affinity
capture and the LC-MS/MS step, was evaluated after six
replicate captures, targeting colon tissue digest (supple-
mental Fig. S5). A total of 114 peptides were identified with
significant scores (fixed or variable modified peptides were
excluded), of which 21.1% were present in all six runs, 9.6%
in five runs, 8.8% in four runs, 12.3% in three runs, 14.0% in
two runs, and 34.2% in individual runs.
The reproducibility for a 1-plexed GPS set-up, including
both the affinity capture and the LC-MS/MS step, is illustrated
in Fig. 3B (supplemental Tables S7 and S8) for one antibody,
Affinity Proteomics and Mass Spectrometry
Molecular & Cellular Proteomics 10.10 10.1074/mcp.M110.003962–9

targeting either colon or yeast digest. In the case of colon, the
data showed that 37 peptides (51%) was reproducibly iden-
tified, whereas 11 (15.2%) or 24 (33.5%) peptides were only
significantly scored in one of the two data acquisition ap-
proaches. In the case of yeast, the results showed that 262
peptides (68.5%) was reproducibly identified, whereas only
44 (11.5%) or 76 (19.9%) peptides were identified in one of
the two data acquisition approaches. Hence, the results dem-
onstrated an adequate reproducibility for the entire assay, i.e.
including all steps of the GPS set-up.
Furthermore, we also demonstrated that quantitative infor-
mation could be retrieved in a reproducible manner by running
a label-free quantitative GPS LC-MS set-up (Fig. 3C and
supplemental Table S9). In these experiments, we profiled the
same tryptic digest from either colon or yeast at two concen-
trations (5 versus 20
␮
g), using one CIMS antibody. In total, 64
colon and 161 yeast peptides, present at a dynamic range of
about four orders of magnitude, were monitored in three
replicate runs. Notably, a median CV value of 11.1% to
12.7% was obtained for the colon peptides and 11.0% to
13.7% for the yeast peptides, further highlighting an ade-
quate reproducibility of the entire GPS set-up. Furthermore,
a 3.2 (colon) and 2.7 (yeast) times higher median signal
intensities (the theoretical ratio was 4.0) were obtained for
all the peptides monitored in the highest concentrated sam-
ple using nonnormalized data, indicating the quantitative
capability of the set-up.
To evaluate the dynamic range of the GPS method, we
matched the yeast peptides detected by the six CIMS antibod-
ies (Fig. 1g and supplemental Table 5) with the known yeast
protein abundance levels (
39) (Fig. 3d and supplemental Table
10). The data showed that the GPS method detected yeast
protein covering the full dynamic range, from high-abundant
species (1 ⫻ 10
6
copies/cell) to low-abundant proteins (⬃2 ⫻
10
2
copies/cell). In addition, a set of 45 additional peptides were
detected that could not be correlated as the reference values
were missing (supplemental Table S10). Notably, 6 of these
peptides were anticipated to be present only at very low levels
(⬍ 50 protein copies/cell) (
39), further highlighting the capability
of the GPS method to probe a wide dynamic range.
Biological Relevance of Captured Protein—To assess the
biological relevance of the proteins identified, we analyzed the
FIG.2.Structural analysis of two CIMS antibodies, denoted sister clones, and selected against the same selection motif. A,Experi-
mentally refined binding motif amino acid sequences as compiled from all MS/MS detected captured peptides, and the frequency of each
individual residue in the last six C-terminal positions is indicated. The two sister clones were selected against the same original selection
peptide motif, M-1 EDFR. B, Comparison of the amino acid sequence of the complementarity determining regions (CDRs) of the two sister
clones. C, Three-dimensional homology models, shown as a ribbon structure (top view) and with color coded CDRs of the two sister clones.
D, Space filling models of the three-dimensional structures shown in C. E, Space filling models of the structures shown in C, onto which the
electrostatics have been mapped.
Affinity Proteomics and Mass Spectrometry
10.1074/mcp.M110.003962–10 Molecular & Cellular Proteomics 10.10

Affinity Proteomics and Mass Spectrometry
Molecular & Cellular Proteomics 10.10 10.1074/mcp.M110.003962–11

functionality and localization of the 217 colon proteins
(supplemental Table S4) and 251 yeast proteins (sup-
plemental Table S5). In the case of colon proteins, the three
most represented functional groups were, (1) cellular pro-
cesses, (2) metabolic processes, and (3) regulation of biolog-
ical processes (supplemental Fig. S6a). The identified proteins
were mainly located intracellular (89%), in the plasma mem-
brane (34%), nucleus (26%), cytosol (16%) and/or in various
organelles, outlining the broad coverage obtained (supple-
mental Fig. S6b). Notably, at least 16 of these proteins have
previously been found to be up- or down-regulated in human
colon cancer (supplemental Fig. S6c), underlining that biolog-
ically relevant proteins were detected using GPS.
In the case of yeast cell lysates, the top functional groups
represented were (1) translation, (2) transportation, and (3)
cellular amino acid and derivative metabolic process, reflect-
ing the yeast cells were in log-phase (supplemental Fig. S6d).
The identified proteins were broadly distributed among differ-
ent compartments of the yeast cell, including e.g. the mem-
brane (9–17%), nucleus (27%), ribosome (16%), and endo-
plasmic reticulum (7%) (supplemental Fig. S6e), outlining the
coverage of the GPS method.
DISCUSSION
Despite recent achievements within the field of affinity pro-
teomics (
7), two major bottlenecks remain before antibody
microarray-based analysis can be performed in a more glob-
al-scale and/or in discovery mode. Today, antibodies of
known specificities are arrayed, meaning that we have prese-
lected what protein families that will be targeted, thereby
excluding the possibility to discover novel proteins. Further-
more, the number of readily available antibodies, designed for
microarray applications (
7), and directed against proteins of
the human proteome is limited. Both of these limitations could
be resolved by designing motif-specific antibodies, where
each motif is present in several different proteins, including
also proteins not yet annotated.
In this study, we have demonstrated proof-of-concept for a
method based on such motif specific antibodies, denoted
CIMS-antibodies, combining the best features of affinity pro-
teomics and mass spectrometry. Consequently, by using a
probe source based on recombinant CIMS-antibodies, the
above limitations have been reduced, because (1) antibodies
with novel motif specificities could be designed, selected and
validated, and (2) a limited number of antibodies could be
used to target a large set of proteins both in a discovery mode
as well as in a species independent manner. In addition, the
issue of antibody availability/renewability, hampering previous
platforms based on affinity proteomics and MS (
21–24) has
been resolved using a recombinant antibody library as probe
source. Based on experimental LC-MS/MS data, the CIMS
antibodies were found to specifically recognizing a linear
epitope composed of two to four conserved residues,
whereas the identity of the neighboring residues was less
crucial (Fig. 2A, supplemental Fig. S3, and supplemen-
tal Tables S3–S5). Similar observations have been made by
Choulier and coworkers, indicating that antibodies recognized
linear epitopes with a length of about six amino acids (
27, 28).
Changes among “allowed” residues in the nonessential posi-
tions do not abolish binding, but might influence the affinity of
antibody-peptide interaction. It should be noted that the se-
lections in this study were not designed to optimize the affin-
ity, although anti-protein scFv antibodies selected from the
present antibody library, using standard protocols, frequently
display n
M affinities (
29). The anti-peptide CIMS antibodies
were found to display affinities (K
D
values) in the
␮
M range
(Table I and supplemental Fig. 2) and these affinities were still
sufficient to perform adequately in the GPS approach. A
higher affinity could be generated by adopting more stringent
selection criteria or subjecting the present CIMS antibodies to
affinity maturation.
The first generation of selection motifs was designed (Table
I and supplemental Table S1) mainly by considering the fre-
quency of which the motifs occurred in the human peptidome.
Despite this, the motifs were found to be highly applicable,
even across species, allowing us to select binders targeting a
wide range of proteins with heterogeneous origin (regarding
e.g. specie and location) displaying a broad range of physico-
chemical properties. In this context, it should be noted that
the concept of designing unique motifs allowing a limited set
of binders to cover a significant part of the human proteome
(
25) was supported by a recent in-silico study (26). Notably,
FIG.3.Characterization of the GPS set-up. A, Multiplexing of GPS, as illustrated for two CIMS antibodies, CIMS-17-E02 and CIMS-33–
3D-F06, which were mixed and subsequently exposed to tryptic digests from human colon tissue or yeast cell lysates, respectively and then
analyzed by LC-MS/MS. The statistics of the number of bound human or yeast peptides or proteins per CIMS antibody are shown. B, Peptide
identification reproducibility of the complete GPS-setup, including both affinity capture and LC-MS/MS as illustrated for CIMS-33–3D-F06
exposed to human tryptic colon digest or yeast cell lysates. All peptides identified with fixed or variable modifications were excluded.
C, Reproducibility of the entire GPS setup for label-free quantitative LC-MS/MS, as illustrated for CIMS-33–3D-F06 exposed to different
amounts of human tryptic colon digests or yeast cell lysates. Raw abundance values (nonnormalized area under the peak values generated
by Progenesis LC-MS software) are plotted. Data plotted were limited to peptides containing the experimentally refined binding motif (the
frequency of the last six C-terminal amino acids is indicated for the peptides plotted). D, Dynamic range of the GPS set-up, illustrated for six
CIMS antibodies immobilized on magnetic beads and exposed to tryptic digest of yeast cell lysates. The identified peptides (proteins) were
plotted against the a´ priori known protein abundance levels of all individual yeast proteins (
39). Among the detected peptides, four are indicated
for two CIMS antibodies each, CIMS-17-E02 and CIMS-33–3D-F06, demonstrating the dynamic coverage. Eight detected peptides for which
reference values are missing (
39) (not plotted) and for which the abundance is anticipated to be ⬍50 protein copies/cell are shown.
Affinity Proteomics and Mass Spectrometry
10.1074/mcp.M110.003962–12 Molecular & Cellular Proteomics 10.10

the GPS method also detected several peptides previously
not reported in the PeptideAtlas for all three species targeted
(Fig. 1 and supplemental Tables S3–S5). Hence, the GPS
methodology could provide a complementary approach to
existing proteomic technologies, in the end further increasing
the tentative coverage. Moreover, the GPS method was ca-
pable of targeting peptides (proteins) across the entire known
dynamic range (at least four orders of magnitude) in yeast,
and even targeting protein species anticipated to be very rare
(⬍50 copies/cell) (Fig. 3D)(
39). This shows that even low-
abundant proteins could be detected in discovery mode using
affinity proteomics. In future efforts, the design of the motifs
(and choice of digesting enzyme) could be refined to tailor the
method toward certain, preferred targets, e.g. low-abundant
proteins and/or certain groups of functional proteins. Depend-
ing on the design of the motifs combined with how the anti-
bodies were selected, the GPS set-up could thus also be
customized toward either assays targeting preselected pro-
teins/groups of proteins or protein profiling aiming for a more
broad coverage. The former set-up could also be combined
with a multiple reaction monitoring-based approach (
40). Ad
-
ditional work will be required to explore the use of the GPS
methodology for studying different protein variants (e.g. post-
translational modifications and isoforms). By multiplexing the
method (Fig. 3), the number of runs required to perform these
tasks could be anticipated to be moderate. Future work will
also be required to investigate how many antibodies that
could be mixed in a single capture step, whereas maintaining
overall assay performance. In addition, the GPS methodology
is compatible with different forms of automation (
41), in the
end providing economically advantageous set-ups compati-
ble with high-throughput.
The feasibility of the GPS approach could potentially be
monitored by evaluating the overlap between the experimen-
tally observed proteins (supplemental Tables S3–S5
and S7–S8) and the trypsin digested predictions in Table I.
The predictions in Table I were performed assuming a four or
six fixed amino acid motif, with the added possibility of allow-
ing wobbling in one position (except for the C-terminal resi-
due). However, the experimental data, showed that the CIMS
antibodies recognized motifs characterized by a few key res-
idues, i.e. a shorter motif than anticipated, reflecting the se-
lection criteria adopted, thereby impairing the evaluation.
Consequently, we will address this issue in future studies,
where we will design the second generation of motifs based
on the present knowledge and in combination with a range of
selection criteria.
The experimental observation that the CIMS antibodies
recognized shorter motifs, could, combined with the ability to
detect low abundance proteins, potentially result in more
tentative peptides to be identified by each antibody than
anticipated (Table I). It is, however, difficult to evaluate the
false negative rate by comparing the results for the refined
motifs generated from the experimental data in an in-silico
trypsin prediction versus the number of experimentally iden-
tified targets. The reason being that the number of actually
MS/MS detected and identified peptides, used as read-out, is
dependent on many interchelating factors, such as MS/MS
performance and antibody affinity. But depending on the se-
lection criteria imposed, CIMS antibodies displaying binding
patterns more stringent e.g. six amino acids motif could be
selected. This would not only allow us to adequately evaluate
the false binding rate, but also to generate CIMS antibodies
displaying the kind of binding patterns required by the GPS-
based application at hand. It was interesting to note that the
CIMS antibodies displayed reactivity against intact, semide-
natured, arrayed proteins (protein arrays) and not only di-
gested peptides in solution. However, the protein array ex-
periments should only be seen as a complementary method
to control the reactivity of the CIMS antibodies. Additional
experiments will be required to delineate the potential use of
CIMS antibodies against also motif-carrying intact proteins.
Using the GPS methodology, the identification of a protein
will mainly be based on a single peptide MS/MS hit, placing
high demands on the technology to minimize any false posi-
tives (
42). Here, we approached the identification by using a
combination of Mascot, X!Tandem, and FDR, as provided by
the Proteios software environment, for defining positive hits
with high confidence level (
35). To handle this key issue,
several approaches can be envisioned to increase the confi-
dence level even further. By taking advantage of the a´ priori
information about the amino acid sequence of the binding
motifs, a filter function could be devised to identify tentative
false positive hits while improving true positive (
42). Using
instrumentation only with very high parent ion mass (⬎5ppm)
accuracy and resolution enables the elemental composition of
peptide to be determined, which greatly improves the effec-
tiveness of database searching. Furthermore, by collecting
alternating scans of collisionally activated dissociation and
electron transfer dissociation for peptide fragmentation gen-
erates two independent, orthogonal MS/MS spectra for data-
base searching. Moreover, chemical tagging of the amino
acids, including for example isobaric tags for relative and
absolute quantification and stable isotope labeling by amino
acids in cell culture, would also be beneficial providing im-
proved ionization and would provide a means for quantitative
and multiplexed analysis (
43). The combination of high mass
accuracy, dual fragmentation type data and chemical modifi-
cation would increase the database search confidence levels
by an order or two of magnitude. In comparison, by using
stable isotope labeling by amino acids in cell culturein com-
bination with one-dimensional gel electrophoresis and iso-
electric focusing of peptides followed by high resolution anal-
ysis on a LTQ-Orbitrap, Graumann and coworkers succeeded
in profiling mouse embryonic stem cells to a depth of 5111
proteins (
44). Hence, by using advanced MS/MS set-ups, the
principle underlying GPS could be demonstrated, whereas
Affinity Proteomics and Mass Spectrometry
Molecular & Cellular Proteomics 10.10 10.1074/mcp.M110.003962–13

the initial applications in e.g. yeast also demonstrated sensi-
tivity and dynamic range.
However, backtracking (single) degenerate peptides, pres-
ent in two or more proteins (database entries) will be chal-
lenging, not only for our GPS approach, but for any MS-based
approach. In regular profiling efforts, Occam’s razor (get the
simplest list of proteins sufficient to explain the observed
peptides assigned to MS/MS spectra in the data set) is often
used (not necessarily optimal). In the GPS approach, it will be
essential to design adequate (unique) motifs and select high-
performing antibodies and/or to use a set of CIMS antibodies
targeting two or more peptides per protein (multicoverage) to
minimize this issue. In this context, it is of interest to note that
Planatscher et al. have showed that about only 2000 solutions
(motifs) will be required to cover the entire human proteome
and to provide multicoverage for around 13,800 proteins (
26).
The repeatability for the entire GPS set-up, i.e. including
both the affinity capture and subsequent LC-MS/MS step,
was found to be in the 50–69% range at a false discovery rate
of 0.01 (Fig. 3B). To the best of our knowledge, one could
routinely expect to achieve a 35–50% peptide overlap repro-
ducibility among technical LS-MS/MS runs in a discovery
mode (
45), and without adding/including a sample fraction
-
ation strategy (e.g. strong cation exchange), which most likely
would cause the reproducibility to drop even further. Further-
more, the reproducibility of the label-free quantification was
found to display CV values in the range of 11–14% (Fig. 3C).
This is comparable to other existing parallel methods, such as
the golden-standard MRM, where CV values around 10–15%
have routinely been achieved (
40). Notably, the GPS mea-
surements of the peptides were performed in the entire range
of the mass spectrometry detector (roughly four orders of
magnitude). As could be expected, a higher variance was
observed for the low-abundance peptides (Fig. 3C). Further,
looking at the experimental ratios of the levels between the
dilutions (2.7 and 3.2), the values were lower than the theo-
retically expected ratio (4.0). This discrepancy could be ex-
plained by the fact that we reported nonnormalized data.
Although additional experiments will be required to delineate
feature of label-free quantitative capability in more detail, we
anticipate the method to compare well with traditional quan-
titative approaches. In all, the GPS methodology displayed
adequate performance (e.g. reproducibility), supporting the
applicability of the methodology.
Taken together, we have demonstrated proof-of-principle
for that a limited number of motif specific recombinant anti-
bodies could be used to profile crude, digested proteomes
when combined with a mass spectrometry-based read-out in
a reproducible manner. The GPS platform was shown to
provide a broad and novel coverage, and to have the potential
to reach deep into a proteome in a species independent
manner. Furthermore, the methodology has potential for both
discovery projects (e.g. unbiased biomarker discovery) as well
as for focused profiling efforts (e.g. directed pathway analysis)
targeting complex proteomes.
Acknowledgments—We thank Dr. Fredrik Levander for assistance
with the Proteios Software Environment, Kristofer Wårell for running
the SignPept analysis, Mats Mågård and Dr. Karin M. Hansson for
assistance with LC-MS/MS instruments, Liselotte Andersson for as-
sistance with the MALDI-TOF instrument, Nicolai Bache for MALDI-
TOF-TOF analysis, and Bjo¨ rn Hambe for developing the affinity
columns.
* This project was supported by grants from Swedish National
Research Council (VR-NT), the Foundation for Strategic Research
(SSF) (Strategic Center for Translational Cancer Research-CREATE
Health), and Vinnova.
** To whom correspondence should be addressed: Department of
Immunotechnology, Lund University, BMC D13, 221 84 Lund, Swe-
den. E-mail address: carl.borrebaeck@immun.lth.se.
□S This article contains supplemental material.
储 These authors contributed equally to this work.
REFERENCES
1. Cho, W. C. (2007) Contribution of oncoproteomics to cancer biomarker
discovery. Mol. Cancer 6, 25
2. Hanash, S. (2003) Disease proteomics. Nature 422, 226–232
3. Hu, S., Loo, J. A., and Wong, D. T. (2006) Human body fluid proteome
analysis. Proteomics 6, 6326–6353
4. Bengtsson, S., Krogh, M., Szigyarto, C. A., Uhlen, M., Schedvins, K.,
Silfverswa¨ rd, C., Linder, S., Auer, G., Alaiya, A., and James, P. (2007)
Large-scale proteomics analysis of human ovarian cancer for biomark-
ers. J. Proteome Res. 6, 1440–1450
5. Cravatt, B. F., Simon, G. M., and Yates, J. R., 3rd. (2007) The biological
impact of mass-spectrometry-based proteomics. Nature 450, 991–1000
6. Listgarten, J., and Emili, A. (2005) Practical proteomic biomarker discovery:
taking a step back to leap forward. Drug Discov. Today 10, 1697–1702
7. Borrebaeck, C. A. K., and Wingren, C. (2007) High-throughput proteomics
using antibody microarrays: an update. Expert Rev. Mol. Diagnos. 7,
673–686
8. Kusnezow, W., Banzon, V., Schroder, C., Schaal, R., Hoheisel, J. D., Ruffer,
S., Luft, P., Duschl, A., and Syagailo, Y. V. (2007) Antibody microarray-
based profiling of complex specimens: systematic evaluation of labeling
strategies. Proteomics 7, 1786–1799
9. Wingren, C., Ingvarsson, J., Dexlin, L., Szul, D., and Borrebaeck, C. A. K.
(2007) Design of recombinant antibody microarrays for complex pro-
teome analysis: choice of sample labeling-tag and solid support. Pro-
teomics 7, 3055–3065
10. Carlsson, A., Wingren, C., Ingvarsson, J., Ellmark, P., Baldertorp, B., Ferno¨,
M., Olsson, H., and Borrebaeck, C. A. K. (2008) Serum proteome profiling
of metastatic breast cancer using recombinant antibody microarrays.
Eur. J. Cancer 44, 472–480
11. Ingvarsson, J., Wingren, C., Carlsson, A., Ellmark, P., Wahren, B., Eng-
stro¨ m, G., Harmenberg, U., Krogh, M., Peterson, C., and Borrebaeck,
C. A. K. (2008) Detection of pancreatic cancer using antibody microar-
ray-based serum protein profiling. Proteomics 8, 2211–2219
12. Sanchez-Carbayo, M. (2006) Antibody arrays: technical considerations and
clinical applications in cancer. Clin. Chem. 52, 1651–1659
13. Borrebaeck, C. A. K. (2006) Antibody microarray-based oncoproteomics.
Expert Opin. Biol. Ther. 6, 833–838
14. Kingsmore, S. F. (2006) Multiplexed protein measurement: technologies
and applications of protein and antibody arrays. Nat. Rev. Drug Discov.
5, 310–320
15. Wingren, C., Ingvarsson, J., Lindstedt, M., and Borrebaeck, C. A. K. (2003)
Recombinant antibody microarrays–a viable option?. Nat. Biotechnol.
21, 223
16. Shafer, M. W., Mangold, L., Partin, A. W., and Haab, B. B. (2007) Antibody
array profiling reveals serum TSP-1 as a marker to distinguish benign
from malignant prostatic disease. Prostate 67, 255–267
17. Hurst, G. B., Buchanan, M. V., Foote, L. J., and Kennel, S. J. (1999) Analysis
for TNF-alpha using solid-phase affinity capture with radiolabel and
Affinity Proteomics and Mass Spectrometry
10.1074/mcp.M110.003962–14 Molecular & Cellular Proteomics 10.10

MALDI-MS detection. Anal. Chem. 71, 4727–4733
18. Kiernan, U. A., Tubbs, K. A., Nedelkov, D., Niederkofler, E. E., and Nelson,
R. W. (2002) Comparative phenotypic analyses of human plasma and
urinary retinol binding protein using mass spectrometric immunoassay.
Biochem. Biophys. Res. Commun. 297, 401–405
19. Lacey, J. M., Bergen, H. R., Magera, M. J., Naylor, S., and O’Brien, J. F.
(2001) Rapid determination of transferrin isoforms by immunoaffinity
liquid chromatography and electrospray mass spectrometry. Clin. Chem.
47, 513–518
20. Niederkofler, E. E., Tubbs, K. A., Gruber, K., Nedelkov, D., Kiernan, U. A.,
Williams, P., and Nelson, R. W. (2001) Determination of beta-2 micro-
globulin levels in plasma using a high-throughput mass spectrometric
immunoassay system. Anal. Chem. 73, 3294–3299
21. Anderson, N. L., Anderson, N. G., Haines, L. R., Hardie, D. B., Olafson,
R. W., and Pearson, T. W. (2004) Mass spectrometric quantitation of
peptides and proteins using Stable Isotope Standards and Capture by
Anti-Peptide Antibodies (SISCAPA). J. Proteome Res. 3, 235–244
22. Lisek, C. A., Bailey, J. E., Benson, L. M., Yaksh, T. L., and Jardine, I. (1989)
Quantitation of endogenous substance P by on-line microcolumn liquid
chromatography/continuous-flow fast-atom bombardment mass spec-
trometry. Rapid Commun. Mass Spectrom. 3, 43–46
23. Scrivener, E., Barry, R., Platt, A., Calvert, R., Masih, G., Hextall, P., Solo-
viev, M., and Terrett, J. (2003) Peptidomics: A new approach to affinity
protein microarrays. Proteomics 3, 122–128
24. Stemmann, O., Zou, H., Gerber, S. A., Gygi, S. P., and Kirschner, M. W.
(2001) Dual inhibition of sister chromatid separation at metaphase. Cell
107, 715–726
25. Wingren, C., James, P., and Borrebaeck, C. A. K. (2009) Strategy for
surveying the proteome using affinity proteomics and mass spectrome-
try. Proteomics 9, 1511–1517
26. Planatscher, H., Supper, J., Poetz, O., Stoll, D., Joos, T., Templin, M. F.,
and Zell, A. (2010) Optimal selection of epitopes for TXP-immunoaffinity
mass spectrometry. Algorithms Mol. Biol. 5, 28
27. Choulier, L., Laune, D., Orfanoudakis, G., Wlad, H., Janson, J., Granier, C.,
and Altschuh, D. (2001) Delineation of a linear epitope by multiple pep-
tide synthesis and phage display. J. Immunol. Methods 249, 253–264
28. Choulier, L., Orfanoudakis, G., Robinson, P., Laune, D., Ben Khalifa, M.,
Granier, C., Weiss, E., and Altschuh, D. (2002) Comparative properties of
two peptide-antibody interactions as deduced from epitope delineation.
J. Immunol. Methods 259, 77– 86
29. So¨ derlind, E., Strandberg, L., Jirholt, P., Kobayashi, N., Alexeiva, V., Aberg,
A. M., Nilsson, A., Jansson, B., Ohlin, M., Wingren, C., Danielsson, L.,
Carlsson, R., and Borrebaeck, C. A. K. (2000) Recombining germline-
derived CDR sequences for creating diverse single-framework antibody
libraries. Nat. Biotechnol. 18, 852– 856
30. Engberg, J., Andersen, P. S., Nielsen, L. K., Dziegiel, M., Johansen, L. K.,
and Albrechtsen, B. (1996) Phage-display libraries of murine and human
antibody Fab fragments. Mol. Biotechnol. 6, 287–310
31. Hallborn, J., and Carlsson, R. (2002) Automated screening procedure for
high-throughput generation of antibody fragments. BioTechniques
Suppl. 33, 530–537
32. Bu¨ ssow, K., Nordhoff, E., Lu¨ bbert, C., Lehrach, H., and Walter, G. (2000) A
human cDNA library for high-throughput protein expression screening.
Genomics 65, 1–8
33. Bu¨ ssow, K., Cahill, D., Nietfeld, W., Bancroft, D., Scherzinger, E., Lehrach,
H., and Walter, G. (1998) A method for global protein expression and
antibody screening on high-density filters of an arrayed cDNA library.
Nucleic Acids Res. 26, 5007–5008
34. Rappsilber, J., Ishihama, Y., and Mann, M. (2003) Stop and go extraction
tips for matrix-assisted laser desorption/ionization, nanoelectrospray,
and LC/MS sample pretreatment in proteomics. Anal. Chem. 75,
663–670
35. Ha¨ kkinen, J., Vincic, G., Månsson, O., Wårell, K., and Levander, F. (2009)
The proteios software environment: an extensible multiuser platform for
management and analysis of proteomics data. J. Proteome Res. 8,
3037–3043
36. Vizcaino, J. A., Coˆ te, R., Reisinger, F., Foster, J. M., Mueller, M.,
Rameseder, J., Hermjakob, H., and Martens, L. (2009) A guide to the
Proteomics Identifications Database proteomics data repository. Pro-
teomics 9, 4276–4283
37. Crooks, G. E., Hon, G., Chandonia, J. M., and Brenner, S. E. (2004)
WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190
38. Deutsch, E. W., Lam, H., and Aebersold, R. (2008) PeptideAtlas: a resource
for target selection for emerging targeted proteomics workflows. EMBO
Rep. 9, 429–434
39. Ghaemmaghami, S., Huh, W. K., Bower, K., Howson, R. W., Belle, A.,
Dephoure, N., O’Shea, E. K., and Weissman, J. S. (2003) Global analysis
of protein expression in yeast. Nature 425, 737–741
40. Addona, T. A., Abbatiello, S. E., Schilling, B., Skates, S. J., Mani, D. R.,
Bunk, D. M., Spiegelman, C. H., Zimmerman, L. J., Ham, A. J., Kesh-
ishian, H., Hall, S. C., Allen, S., Blackman, R. K., Borchers, C. H., Buck,
C., Cardasis, H. L., Cusack, M. P., Dodder, N. G., Gibson, B. W., Held,
J. M., Hiltke, T., Jackson, A., Johansen, E. B., Kinsinger, C. R., Li, J.,
Mesri, M., Neubert, T. A., Niles, R. K., Pulsipher, T. C., Ransohoff, D.,
Rodriguez, H., Rudnick, P. A., Smith, D., Tabb, D. L., Tegeler, T. J.,
Variyath, A. M., Vega-Montoto, L. J., Wahlander, A., Waldemarson, S.,
Wang, M., Whiteaker, J. R., Zhao, L., Anderson, N. L., Fisher, S. J.,
Liebler, D. C., Paulovich, A. G., Regnier, F. E., Tempst, P., and Carr, S. A.
(2009) Multi-site assessment of the precision and reproducibility of mul-
tiple reaction monitoring-based measurements of proteins in plasma.
Nat. Biotechnol. 27, 633–641
41. Whiteaker, J. R., Zhao, L., Anderson, L., and Paulovich, A. G. (2010) An
automated and multiplexed method for high throughput peptide immu-
noaffinity enrichment and multiple reaction monitoring mass spectrom-
etry-based quantification of protein biomarkers. Mol. Cell. Proteomics 9,
184–196
42. Helsens, K., Timmerman, E., Vandekerckhove, J., Gevaert, K., and Martens,
L. (2008) Peptizer: A tool for assessing false positive peptide identifica-
tions and manually validating selected results. Mol. Cell. Proteomics 7,
2364–2372
43. Chen, X., Sun, L., Yu, Y., Xue, Y., and Yang, P. (2007) Amino acid-coded
tagging approaches in quantitative proteomics. Expert Rev. Proteomics
4, 25–37
44. Graumann, J., Hubner, N. C., Kim, J. B., Ko, K., Moser, M., Kumar, C., Cox,
J., Scho¨ ler, H., and Mann, M. (2008) Stable isotope labeling by amino
acids in cell culture (SILAC) and proteome quantitation of mouse embry-
onic stem cells to a depth of 5,111 proteins. Mol. Cell. Proteomics 7,
672–683
45. Tabb, D. L., Vega-Montoto, L., Rudnick, P. A., Variyath, A. M., Ham, A. J.,
Bunk, D. M., Kilpatrick, L. E., Billheimer, D. D., Blackman, R. K., Cardasis,
H. L., Carr, S. A., Clauser, K. R., Jaffe, J. D., Kowalski, K. A., Neubert,
T. A., Regnier, F. E., Schilling, B., Tegeler, T. J., Wang, M., Wang, P.,
Whiteaker, J. R., Zimmerman, L. J., Fisher, S. J., Gibson, B. W., Kins-
inger, C. R., Mesri, M., Rodriguez, H., Stein, S. E., Tempst, P., Paulovich,
A. G., Liebler, D. C., and Spiegelman, C. (2010) Repeatability and repro-
ducibility in proteomic identifications by liquid chromatography-tandem
mass spectrometry. J. Proteome Res. 9, 761–776
Affinity Proteomics and Mass Spectrometry
Molecular & Cellular Proteomics 10.10 10.1074/mcp.M110.003962–15