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Resource
A Human IgSF Cell-Surface Interactome Reveals a
Complex Network of Protein-Protein Interactions
Graphical Abstract
Highlights
d
Human IgSF interactome reveals complex network of cell-
surface protein interactions
d
Phylogenetic homology analysis predicts protein-protein
interactions
d

380 previously unknown protein-protein interactions
identified
d
Deorphanization of receptors and new binding partners for
well-studied receptors
Authors
Woj M. Wojtowicz, Jost Vielmetter,
Ricardo A. Fernandes, ..., Scott A. Lesley,
Kai Zinn, K. Christopher Garcia
Correspondence
woj@stanford.edu (W.M.W.),
kcgarcia@stanford.edu (K.C.G.)
In Brief
A high-throughput protein-protein
interaction screen, carried out to map
human cell-surface receptor-ligand
interactions between proteins belonging
to the immunoglobulin domain
superfamily (IgSF), begins to unravel the
complex network of cell-surface
interactions that allows cells to recognize
and respond to one another and their
dynamically changing environment.
Wojtowicz et al., 2020, Cell
182
, 1027–1043
August 20, 2020
ª
2020 Elsevier Inc.
https://doi.org/10.1016/j.cell.2020.07.025
ll
Resource
A Human IgSF Cell-Surface Interactome Reveals a
Complex Network of Protein-Protein Interactions
Woj M. Wojtowicz,
1,6,
*
Jost Vielmetter,
2,6
Ricardo A. Fernandes,
1,7
Dirk H. Siepe,
1,3,7
Catharine L. Eastman,
1,7
Gregory B. Chisholm,
2
Sarah Cox,
4
Heath Klock,
4
Paul W. Anderson,
4
Sarah M. Rue,
4
Jessica J. Miller,
4
Scott M. Glaser,
4
Melisa L. Bragstad,
4
Julie Vance,
4
Annie W. Lam,
2
Scott A. Lesley,
4
Kai Zinn,
2
and K. Christopher Garcia
1,3,5,8,
*
1
Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305, USA
2
Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
3
Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
4
The Genomics Institute of the Novartis Research Foundation, San Diego, CA 92121, USA
5
Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
6
These authors contributed equally
7
These authors contributed equally
8
Lead Contact
*Correspondence:
woj@stanford.edu
(W.M.W.),
kcgarcia@stanford.edu
(K.C.G.)
https://doi.org/10.1016/j.cell.2020.07.025
SUMMARY
Cell-surface protein-protein interactions (PPIs) mediate cell-cell communication, recognition, and re-
sponses. We executed an interactome screen of 564 human cell-surface and secreted proteins, most of
which are immunoglobulin superfamily (IgSF) proteins, using a high-throughput, automated ELISA-based
screening platform employing a pooled-protein strategy to test all 318,096 PPI combinations. Screen results,
augmented by phylogenetic homology analysis, revealed

380 previously unreported PPIs. We validated a
subset using surface plasmon resonance and cell binding assays. Observed PPIs reveal a large and complex
network of interactions both within and across biological systems. We identified new PPIs for receptors with
well-characterized ligands and binding partners for ‘‘orphan’’ receptors. New PPIs include proteins ex-
pressed on multiple cell types and involved in diverse processes including immune and nervous system
development and function, differentiation/proliferation, metabolism, vascularization, and reproduction.
These PPIs provide a resource for further biological investigation into their functional relevance and may offer
new therapeutic drug targets.
INTRODUCTION
Protein-protein interactions (
PPIs) at the cell surface allow
cells to respond to one another and their environment in a
highly dynamic, context-dependent and spatiotemporal
manner (
Wood and Wright, 2019
). The essential role played
by cell-surface PPIs is exemplified by estimates that

20% of genes in the human genome encode cell-surface
proteins and

10% encode secreted proteins (
Fonseca
et al., 2016
).
At present, a comprehensive human cell-surface interactome
is lacking. Mapping extracellular PPIs has proved challenging
because most cell-surface proteins are refractory to classic
biochemical screening techniques and cell-surface PPIs are un-
derrepresented in affinity purification/mass spectrometry-based
datasets (
Huttlin et al., 2015
,
2017
). Additionally, cell surface
PPIs often have fast binding kinetics spanning a broad range
of affinities (low nM to hundreds of
m
M) (
van der Merwe and Bar-
clay, 1994
), rendering them difficult to detect using standard
biochemical assays.
In recent years, several assays have been developed that
allow detection of low-affinity cell-surface PPIs by imparting
avidity through clustering of secreted proteins and the extracel-
lular domains (ECDs) of single transmembrane (STM) cell-sur-
face proteins. Clustering is achieved using multimerization do-
mains, and can increase binding signal up to 250-fold (
Bushell
et al., 2008
). Experimental platforms that utilize clustering
include several variations of ELISA-based binding assays
(
Wojtowicz et al., 2007
;
Bushell et al., 2008
;
O
̈
zkan et al.,
2013
), Bio-Plex beads (
Li et al., 2017
), protein microarrays (
Sun
et al., 2012
), cell-signaling assays (
Barrow et al., 2018
), cell-sur-
face staining microarrays (
Turner et al., 2013
), and bead-based
assays (
Husain et al., 2019
). Multiple groups have shown that
ELISA-based binding assays have a remarkably low false-posi-
tive rate (
Wojtowicz et al., 2007
;
Bushell et al., 2008
;
So
̈
llner
and Wright, 2009
;
Martin et al., 2010
;
Crosnier et al., 2011
;
O
̈
zkan
et al., 2013
;
Visser et al., 2015
;
Ranaivoson et al., 2019
).
Previously, we conducted a screen for all

200
Drosophila
cell-surface and secreted proteins containing three types of do-
mains: immunoglobulin (Ig) and Ig-like, fibronectin type III (FN3),
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and leucine-rich repeats (LRRs) (
O
̈
zkan et al., 2013
). This screen
reported over 80 new PPIs, including a previously unknown
immunoglobulin superfamily (IgSF) PPI network between mem-
bers of the Dpr and DIP subfamilies. Since we reported the
Dpr-DIP network, functional studies have revealed that this
network mediates neuronal wiring decisions in the fly brain and
neuromuscular system (for review, see
Honig and Shapiro,
2020
;
Sanes and Zipursky, 2020
).
In humans, there are an estimated

4,000 secreted and STM
proteins, totaling

8 M putative PPIs. Screening this vast num-
ber requires a high-throughput assay. Here, we developed a
screening platform that combines a high-throughput version of
the ELISA-based extracellular interactome assay (ECIA) (
O
̈
zkan
et al., 2013
) with an automated pooled-protein strategy (apE-
CIA). We performed a screen of human IgSF secreted and
STM cell-surface proteins (excluding antibodies and T cell re-
ceptors), along with other select proteins of interest. The IgSF
is the largest and most functionally diverse family in the cell-sur-
face proteome. Members include receptor tyrosine kinases,
phosphatases, co-stimulatory or co-inhibitory immune recep-
tors, growth factor and adhesion receptors, among many others,
and are present in most, if not all, cell types.
We produced 564 proteins, and screened every possible PPI
(564
3
564 = 318,096). We observed 426 PPIs, of which 345
(81%) are previously unreported. New PPIs form a complex
network and include PPIs between phylogenetically related pro-
teins within a subfamily, different subfamilies, and distantly
related proteins. Screen results were combined with phyloge-
netic homology analysis (PHA) to predict additional PPIs be-
tween subfamily members using a nearest-neighbor approach.
We confirmed a subset of both screen and PHA predicted
PPIs using surface plasmon resonance (SPR) and cell binding
assays.
RESULTS
Selection and Production of Proteins for PPI Screening
To identify human IgSF proteins, we utilized the HUGO Gene
Nomenclature Committee (HGNC) (
Yates et al., 2017
), Human
Protein Atlas (
Uhle
́
n et al., 2015
), and UniProt (
UniProt Con-
sortium, 2019
) databases. ECDs and secreted proteins for 458
IgSF and 106 non-IgSF proteins of interest were produced with
‘‘bait’’ and ‘‘prey’’ multimerization domains into cell supernatants
(
Figures 1
A and 1B;
Data S1
and
S2
) and expression was
confirmed by western blot (
Data S3
). Westerns revealed detect-
able levels of protein for 82% of baits and 70% of preys. We and
others have observed that PPIs can be detected in the ECIA and
other ELISA-based binding assays even when proteins are pre-
sent at levels below the limit of detection by western (
O
̈
zkan
et al., 2013
;
Visser et al., 2015
;
Ranaivoson et al., 2019
). As
such, all bait and prey were included in the screen regardless
of whether protein was detected.
Development of an Automated Pooled-Prey ECIA
Platform (apECIA)
ECIA and other ELISA-based assays allow bait and prey
proteins to be tested for binding directly from conditioned
media (
Figure 1
B). These assays test one bait-prey pair per
well. To increase throughput, we pooled three preys and,
following screening, deconvoluted positive wells to identify
PPIs (
Figure 1
B). Pooling experiments with a panel of known
PPIs showed binding for all 3-fold diluted prey (
Figures 1
C,
S1
C, and data not shown). As bait-prey pairs are tested
in both orientations, a false-negative resulting from pooling
can be ‘‘rescued’’ by a positive result in the converse
orientation. We reasoned that the advantages of pooling,
which reduces the number of binding reactions, outweighed
the potential increase in false negatives. To further improve
throughput, we optimized a 384-well format and developed
an automated workflow using liquid handling robots. The
apECIA platform allows testing of 55,296 bait-prey combina-
tions per week.
PPI Screen Reveals a Complex Network of PPIs
Following screening, deconvolution of positive wells (
R
2 fold-
over-background) was performed by re-testing each prey
individually (
Figure 1
B). Nine prey gave rise to large numbers of
wells with positive signals and were excluded as non-specific
binders (
Figure S2
). Following removal of non-specific PPIs, de-
convolution revealed 495 positive wells comprising 426 unique
PPIs (
Data S4
). In each case, only one of the three deconvoluted
prey yielded a positive signal. To confirm binding, the positive
prey was re-tested against its cognate bait in triplicate.
Eighty-one percent (345/426) of PPIs are between IgSF proteins.
The remaining 19% include PPIs between IgSF and other
proteins present in the screen (
Figure S3
).
Almost half of the proteins tested were involved in a PPI
(254/564, 45%). Proteins not involved in PPIs may be mis-
folded, have binding partners not included in the screen,
require co-receptors, or fall outside the dynamic range of the
assay (false negatives) which is determined by PPI affinity
and bait and prey concentrations (
Figure S4
F). Confirming the
sensitivity of the assay, many bait or prey proteins expressed
at very low levels still engaged in one or two PPIs (
Figures
S4
A and S4B). A small number of PPIs were observed with
bait or prey proteins exhibiting undetectable levels in condi-
tioned media (
Figures S4
C–S4E). To interrogate the dynamic
range of the assay we plotted prey AP levels for PPIs with re-
ported affinities (
Figure S4
F). These data suggest that, for
very poorly expressed prey proteins (
Figures S1
A,
S4
A, and
S4B), PPIs with K
D
>4.5
m
M are likely to be missed (false neg-
atives;
Figure S4
F).
Of the 426 PPIs, 345 (81%), are previously unreported based
on literature and PPI databases (
Data S4
). The majority of PPIs
(408/426) form one large network comprising 226 proteins
(
Figure 1
D). Only 28 proteins involving 18 PPIs are not connected
to the network. Different regions of the network are shown in
Figure 2
. Ninety-eight proteins (39%) had one PPI, 113 (44%)
had two to five PPIs, and 43 (17%) had >5 PPIs (
Figure 1
E).
Because 45% of proteins exhibit binding, we calculated the
expected frequency with which each protein will bind at least
‘‘x’’ number of proteins up to the maximally observed 16 PPIs
(
Figure S2
B) and compared the expected and observed fre-
quencies (
Figure 1
F). The observed number of binding partners
is significantly greater than predicted by random chance of
PPIs for a network of this size.
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A
B
C
D
EF
Figure 1. apECIA Screen Details and Overview of Results
(A) Schematic depiction of a subset of proteins in library. Pie graph of library distribution. Full protein domain names at
http://smart.embl.de/
(
Sun et al., 2012
).
(B) Left: flow chart of screen. HT, high-throughput. Right: example plate from screen. Schematic of ECIA and pooled-prey strategy. Illustration of sc
reen matrix.
(C) ECIA of undiluted prey (single prey) versus 3-fold diluted prey (pooled prey). Background subtracted data are represented as
±
SD. Bkgd, background.
(D) Network of PPIs observed in screen. Inset: the 18 PPIs that are not connected to the network. Node size is proportional to number of PPIs. Siglec subf
amily
nodes are highlighted in color.
(E) Pie graph of distribution of the number of binding partners observed in screen overlaid on the network in a degree-sorted circular layout.
(F) Observed versus expected frequency with which each protein will bind at least ‘‘x’’ number of proteins up to the maximally observed PPIs assuming r
andom
chance of interactions (p = 0.01; Kolmogorov-Smirnov [K-S] test).
See also
Figures S1, S2, S3
, and
S4
and
Data S1
,
S2
,
S3
, and
S4
.
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(legend on next page)
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Phylogenetic Homology Analysis (PHA) to Predict PPIs
PPIs often occur between phylogenetically related proteins both
within and between subfamilies. We performed multiple
sequence alignments to identify subfamily members within our
library and generated phylogenetic trees. Using a combined
approach, which we call apECIA-PHA, we analyzed screen
data alongside the phylogenetic trees to predict additional
PPIs between subfamily members that may have been missed
in the screen.
PPI Validation by Surface Plasmon Resonance
We selected a subset of screen and PHA predicted PPIs to vali-
date by SPR. Bona fide PPIs are expected to display distinct as-
sociation and dissociation, which can be observed with high
sensitivity by SPR. To increase avidity, and therefore sensitivity,
Fc dimerized ECD analytes and ligands were used in most exper-
iments (
Figure S5
). This increase in sensitivity prevented us from
determining monomeric K
D
constants. Binding profiles charac-
teristic of PPIs, exhibiting clear resonance signals above back-
ground (negative control ligand and receptor) and concentra-
tion-dependent binding curves, were deemed indicative of a
specific ligand-analyte PPI. Non-specific PPIs generally exhibit
deviations from this behavior, such as high background and
non-linear concentration responses.
Twenty-four newly identified PPIs observed in our screen were
tested by SPR. Of these, we observed 23 specific ligand-analyte
interactions. We additionally tested 35 PHA predicted PPIs and
observed PPIs for 33. Three additional PPIs were observed be-
tween homologous proteins in mouse and cross-species (hu-
man-mouse). In total, we SPR validated 59 newly identified
PPIs (
Table 1
;
Data S5
).
Combined apECIA-PHA Approach Reveals Multiple DCC
Subfamily PPIs
The netrin-1 receptor DCC has well-characterized functions in
the nervous system (
Finci et al., 2015
). DCC is a dependence re-
ceptor and is implicated in colorectal and other cancers, but its
roles in these cancers are not well understood (
Goldschneider
and Mehlen, 2010
). We observed DCC binding to insulin-like
growth factor-binding protein-like 1 (IGFBPL1), but not to insu-
lin-like growth factor-binding protein 7 (IGFBP7) (
Figures 2
I and
3
A). Our phylogenetic tree revealed IGFBPL1 and IGFBP7 reside
in a cluster and share the highest amino acid sequence similarity
(55%) among subfamily members. As such, we examined bind-
ing of DCC to both IGFBPL1 and IGFBP7 by SPR and observed
binding for both (
Figure 3
C).
PHA pointed us to four proteins that cluster with DCC: PUNC,
PUNC e11, neogenin (NEO1), and protogenin (PRTG) (
Figure 3
A).
Together, these proteins play roles in diverse processes
including nervous system development, myogenesis and angio-
genesis, inflammation and tissue regeneration, leukocyte migra-
tion, neural tube and mammary gland formation, development of
bone and connective tissues, and stem cell differentiation (
Sal-
baum, 1998
;
Wilson and Key, 2007
;
Takahashi et al., 2010
;
Schievenbusch et al., 2012
;
Dakouane-Giudicelli et al., 2014
).
PUNC is an ‘‘orphan’’ receptor expressed in the developing ner-
vous system (
Salbaum, 1998
). PUNC e11 and PRTG bound inter-
cellular adhesion molecule 5 (ICAM5) (
Figure 3
A), a protein
exclusively expressed in the brain that functions in synapse for-
mation, stabilization, and refinement (
Gahmberg et al., 2014
).
Cleaved ICAM5 ECD exhibits immunosuppressive functions
through cytokine regulation and may play important roles in
regulation of brain inflammation. We confirmed binding of
PUNC e11 and PRTG to ICAM5 by SPR (
Figure 3
C). Although
PPIs with ICAM5 were not detected in the screen for the remain-
ing DCC subfamily members, we tested them by SPR and
observed binding for all three (
Figure 3
C).
In our screen, one or more DCC subfamily members also
bound to: (1) WFIKKN2, a secreted protein that binds transform-
ing growth factor-beta subfamily members and modulates their
presentation to cells (
Monestier and Blanquet, 2016
), (2) lacto-
transferrin (LTF), an iron-binding protein with antimicrobial activ-
ity (
Hao et al., 2019
), (3) interleukin-6 receptor subunit alpha (IL-
6R
a
), a cytokine receptor (
Schaper and Rose-John, 2015
), and
(4) ISLR2/LINX, which functions in nervous system development
(
Mandai et al., 2014
;
Panza et al., 2015
;
Abudureyimu et al.,
2018
). We confirmed binding of all DCC subfamily members to
these proteins by SPR and to other proteins observed in our
screen (
Figures 3
C, 3D, and 3F). These results demonstrate
the value of using a combined apECIA-PHA approach to identify
additional PPIs beyond screen data, resulting in the elucidation
of a more complete network (
Figure 3
F).
LAR-PTPR Subfamily PPIs with SALMs
The LAR-family of type IIA protein tyrosine phosphatase recep-
tors (LAR-PTPRs) comprises PTPRF (also known as LAR),
Figure 2. Select Regions of the Complex PPI Network
(A) Select PPIs including four proteins not connected to the network (CD146, CNTN1, NFASC, and NrCAM).
(B) Region largely comprised of immune system proteins.
(C) Region comprising PPIs both within and across biological systems.
(D) Two regions highlighting subfamily-specific type IIA and type IIB PTPR PPIs. Type IIA PPIs with SALMs and IL1APs include PPIs observed in screen and
PHA
predicted PPIs validated by SPR (
Figures 4
and
S6
).
(E) Region highlighting PPIs for ‘‘orphan’’ receptors ILDR1 and PUNC.
(F) Region showing a subset of LILR subfamily PPIs.
(G) Region showing a subset of Siglec PPIs with non-Siglecs (CD33/Siglec-3).
(H) Region showing Siglec-Siglec PPIs (CD33/Siglec-3; MAG/Siglec-4a; SN/Siglec-1).
(I) Region highlighting PPIs between nervous system proteins and with proteins in immune and reproductive systems. Within this region, multiple add
itional PHA
predicted PPIs were validated by SPR (
Figure 3
;
Table 1
).
Because a network is composed of interconnected nodes, some linkage proteins are present in more than one panel. Colored nodes denote proteins and PPI
s
validated by additional experiments. Green line, previously reported PPI; gray line, previously unreported PPI.
See also
Data S4
.
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