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Multiplex matrix network analysis of proteincomplexes in the human TCR signalosomeStephen E. P. Smith,1*† Steven C. Neier,1† Brendan K. Reed,1 Tessa R. Davis,1

Jason P. Sinnwell,2 Jeanette E. Eckel-Passow,2 Gabriel F. Sciallis,3 Carilyn N. Wieland,3

Rochelle R. Torgerson,3 Diana Gil,1 Claudia Neuhauser,4‡ Adam G. Schrum1‡

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Multiprotein complexes transduce cellular signals through extensive interaction networks, but theability to analyze these networks in cells from small clinical biopsies is limited. To address this, weapplied an adaptable multiplex matrix system to physiologically relevant signaling protein complexesisolated from a cell line or from human patient samples. Focusing on the proximal T cell receptor (TCR)signalosome, we assessed 210 pairs of PiSCES (proteins in shared complexes detected by exposedsurface epitopes). Upon stimulation of Jurkat cells with superantigen-loaded antigen-presenting cells,this system produced high-dimensional data that enabled visualization of network activity. A compre-hensive analysis platform generated PiSCES biosignatures by applying unsupervised hierarchicalclustering, principal component analysis, an adaptive nonparametric with empirical cutoff analysis,and weighted correlation network analysis. We generated PiSCES biosignatures from 4-mm skinpunch biopsies from control patients or patients with the autoimmune skin disease alopecia areata.This analysis distinguished disease patients from the controls, detected enhanced basal TCRsignaling in the autoimmune patients, and identified a potential signaling network signature thatmay be indicative of disease. Thus, generation of PiSCES biosignatures represents an approach thatcan provide information about the activity of protein signaling networks in samples including low-abundance primary cells from clinical biopsies.

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INTRODUCTION

Cells perceive and respond to their environment by engaging receptorsand transmitting intracellular messages through signal transduction cas-cades. This process is largely controlled by networks of proteins that bindto each other, dissociate, and advance signal progression along bio-chemical pathways (1). Signalosomes are formed when proteins actingas network hubs orchestrate interactions with other protein nodes to simul-taneously control activation of various signaling pathways (2, 3). It is thismodular, conditional interconnectivity between proteins and pathways thatis largely responsible for providing the logic circuits required for signaltransmission, synthesizing instructions for discrete cellular responses frommultiple signaling inputs (4–6). However, despite its biological impor-tance, the empirical assessment of signaling protein complexes at thenetwork level is restricted by technological limitations, especially in thecase of small clinical samples that provide small amounts of biomaterialfor assessment. Most current protein-protein interaction methodologies aredesigned to work with copious amounts of biomaterial to generate maps ofpossible or potential intermolecular connections. In contrast, the ability tomeasure network activity under different physiologic conditions could(i) validate protein-protein associations predicted by literature and database

1Department of Immunology, Mayo Clinic College of Medicine, Rochester,MN 55905, USA. 2Division of Biomedical Statistics and Informatics, Depart-ment of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA. 3De-partment of Dermatology, Mayo Clinic, Rochester, MN 55905, USA. 4Universityof Minnesota Informatics Institute, University of Minnesota, Minneapolis, MN55455, USA.*Present address: Center for Integrative Brain Research, Seattle Children’sResearch Institute and Department of Pediatrics, University of Washington,Seattle, WA 98101, USA.†These authors contributed equally to this work.‡Corresponding author. Email: schrum.adam@mayo.edu (A.G.S.); neuha001@umn.edu (C.N.)

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archives and (ii) define signature combinations and the relative quantities ofco-associated proteins related with specific biologic signals or pathologicstates (7, 8). Thus, the generation of new approaches to improving accessi-bility to physiologic molecular networks is of particular interest, with highpotential to accelerate our understanding of human signal transduction rel-evant to diagnosis, pharmacology, and medicine (9–11).

Here, we present a high-sensitivity, multiplex, microsphere-basedmatrix analysis to assess network protein complexes in an extensive sig-nalosome, applicable to both human transformed cells and small primarypatient biopsies. Physiologic network activity was visualized in a biosigna-ture that simultaneously measured specific combinations and relativequantities of PiSCES (proteins in shared complexes detected by exposedsurface epitopes). We focused on the human T cell antigen receptor (TCR)signalosome, applying data from Jurkat cells stimulated by superantigen-bearing, antigen-presenting cells (APCs) to optimize the experimental andanalytical workflow. Subsequently, PiSCES analysis was applied to smallprimary clinical biopsies from control donors or patients with the auto-immune skin disease alopecia areata. We showed that network signatures(i) distinguished patients from control groups, (ii) detected autoreactive T cellsignaling, and (iii) generated a hypothesis regarding a disease-associatednetwork signature. Thus, we present PiSCES analysis as a scalable multiplexapproach that could potentially be applied to any network of interactive pro-teins that may be of interest in signal transduction and disease pathogenesis.

RESULTS

Multiplex signaling protein complexes fromsuperantigen-stimulated Jurkat cellsTo study the physiologic network activity of protein complexes in humanT cells, we developed a scalable, multiplex, microsphere-based approachfor the analysis of 20 cellular proteins in a pairwise combination matrix,

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resulting in 210 distinct observations of PiSCES. On the basis of previousknowledge and current interactome data for the TCR signalosome (Fig. 1)(12, 13), we assembled a panel of immunoprecipitation antibodies cova-lently coupled to distinct microsphere classes, which are defined by the pro-portion of two dyes within the polystyrene latex material base. Multipleximmunoprecipitation was achieved when physiologic proteins were capturedfrom cell lysates onto the microsphere panel and co-associated proteins weredetected with fluorochrome-labeled antibodies (Fig. 2A, fig. S1, and tableS1). Data were analyzed by flow cytometry on a Bio-Plex 200 instrumentwith a customized setup that was optimized for the analysis of proteincomplexes (Fig. 2B and fig. S2). The lower plate carrier unit of the instru-ment was placed in a commercial sandwich prep refrigerator to maintainprotein complexes at 4°C before data acquisition. The upper flow cytometerportion, which needed to be maintained at room temperature, resided on aninsulated acrylic divider placed on top of the refrigerator, whereas a boredhole in the acrylic enabled the sample injection port needle of the flowcytometer to access samples from the refrigerated plate carrier. Thussituated, immunoprecipitation beads bearing captured protein complexeswere kept cold throughout the data acquisition process. To examine strongTCR signals in a human system, we stimulated Jurkat cells (a human T cellleukemia cell line) for 5 min with staphylococcal enterotoxin E (SEE)superantigen presented by Raji cells, which were used as APCs. Proteincomplexes were captured on the multiplex immunoprecipitation bead panel,and distinct microsphere classes were identified (Fig. 2C), together with spe-cific multiprotein co-associations that were measured (individual examples,

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Fig. 1. A protein-protein interaction web consisting of the proximal TCRsignalosome. Visualization of many indirect and direct interactions among

20 proteins that mediate proximal TCR signaling, according to publishedreports or the Protein Interaction Network Analysis (PINA) database (43)Note that not all of the possible interactions among the collection aredepicted, and this visualization provides no information regarding theactivity of protein complexes under specific physiologic conditions.

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Fig. 2. PiSCES workflow for network profiling of TCR signalosome activation. (AProtein complexes immunoprecipitatedon microsphere color classes coupled tocapture antibodies were detected with afluorophore-coupled detection (probe) antibody. (B) A Bio-Plex 200 instrument with acustomized setup was used to refrigeratesamples at 4°C through the data acquisitionprocess. An insulated plexiglass divideenabled the upper flow cytometer portionto remain at room temperature, whereasthe lower plate carrier and samples weremaintained at 4°C. See fig. S2 for further details. (C) Beadclasseswere identifiedby theratio of two classifier dyes. (D) PE fluorescence readings of example protein-proteinassociations that increased in intensityupon 5-min stimulation of Jurkat cells withSEE-loadedRaji cells. Black trace, unstimulated; red trace, stimulated. (E) Hierarchicaclustering of PiSCES network median fluorescence intensity (MFI) values distinguished SEE-stimulated [S, red treatmen(Tx) group] from unstimulated (U, black Txgroup) conditions. Data from four independent experiments (labeled 1, 2, 3, and 4were included in the analysis. P = 0.028Fisher’s exact test. (F) PCA of log2 MFI fromthe four experiments (labeled 1, 2, 3, and 4also distinguished SEE-stimulated from unstimulatedconditions,displayingastimulationaxis that was largely dominated by principacomponent 1 (PC1).

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Fig. 2D). Upon initial network analysis, unsupervised hierarchical clusteringdistinguished unstimulated from stimulated data sets (Fig. 2E), as did prin-cipal component analysis (PCA) (Fig. 2F).

Biostatistical evaluation for PiSCES network analysisThe effectiveness of various statistical approaches was evaluated forPiSCES measurements (figs. S3 and S4). We evaluated nonparametricKolmogorov-Smirnov (KS) and Wilcoxon rank-sum (WRS) tests, as wellas parametric methods based on linear regression (LR) or the Student’s t test(Tm). LR was modeled after the general approach of a method that waspreviously described for multiplex microbead data (eq. S1) (14). To assessthe false-positive rate (FPR) and the true-positive rate (TPR) of each of thefour statistical approaches, we used bootstrapping (resampling) from theempirical distributions of each PiSCESmeasurement in the SEE-stimulatedJurkat data set described earlier (Fig. 2). Each experiment generated twoempirical distributions, one from each duplicate. For each duplicate, re-sampling with 500 iterations was performed to generate a distribution(fig. S3A, black trace). This process was repeated to generate a comparatordistribution (fig. S3A, blue trace). Because these distributions (fig. S3A,compare the black and blue traces) were generated identically from thesame data set, any differences observed represented false positives. Thus,the P values from these 500 iterations generated the anticipated FPR ortype I error. Next, we artificially shifted the comparator distribution by afixed percentage to evaluate TPR (TPR = 1 – b, where b = type II error;fig. S3B, black versus blue traces). The ability of the different statisticaltests to detect shifts in PiSCES measurements was visualized by compar-ing the relative TPR across a range of FPRs (fig. S3C). Additionally, theFPRs and TPRs from the different statistical tests were compared with ana cutoff = 0.05 (fig. S3, D to F). On the basis of these comparisons, weselected the nonparametric KS statistical test to analyze PiSCES data sets.

To enable incorporation of interexperimental replicates, we defined amethod to identify high-confidence, statistically significant PiSCES thatwere consistently identified as hits in at least 70% of experiments, whichwe refer to as adaptive nonparametric with empirical cutoff (ANC) (seeMaterials and Methods and eqs. S2 and S3). To estimate statistical power,we applied the a cutoff from the SEE stimulation experiments to the simu-lated distribution shifts (fig. S3, A to C) over a 1.1- to 1.5-fold range andfound the power to be adequate (fig. S3G); for example, for a consistentshift of 1.2-fold, we predicted that there would be at least 90% power in90% of the PiSCES measurements. To select an optimal number ofexperiments appropriate for ANC analysis, we plotted the number ofconsistent hits observed as the number of experimental replicates increased.With the SEE-stimulated Jurkat cell data set, we found that three inde-pendent experiments were sufficient to reach an apparent plateau (fig.S3H), and we considered this experimental number to be a minimum thatwould be applied in the present work.

We also used weighted correlation network analysis (WCNA) (15) toevaluate PiSCES data, reasoning that many protein-protein interactionsinvolved in signalosome formation would change in a correlated manner.Application of WCNA generated a network heat map that revealed distinctmodules that were highly correlated (fig. S4). Each module was summar-ized by an “eigenvector” that was based on the overall behavior of themodule, and each PiSCES measurement was assigned a module member-ship (MM) value that represented the correlation with this eigenvector, aspreviously described in detail (15). We found that one set of PiSCES mea-surements, arbitrarily tagged as the turquoise module, was statistically sig-nificantly correlated with stimulation by SEE (fig. S4, A and B).

Upon comparison of the ANC and WCNA results, we found that thesemethods coincided in most of the statistically significant stimulation-induced changes that were identified (Fig. 3, A to C). PiSCES differences

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that were significant in WCNA, but not in ANC, typically showed verysmall fold changes (1.08 ± 0.008), whereas those that were significant inANC, but not in WCNA, generally displayed a small positive correlationwith the turquoise module, but missed the inclusion cutoff (MM > 0.5;inclusion cutoff, 0.7) (see Materials and Methods) (15), and displayedsmall fold changes < 1.5 (Fig. 3, C and D). These results suggest thatPiSCES measurements whose statistical significance was discrepantbetween the two analyses likely included differences that (i) were trendingtoward (but did not reach) statistical significance, (ii) had higher variancethat reduced sensitivity, or (iii) were false positives (Fig. 3, C andD). There-fore, we visualized the network of hits common to both analyses as ahigh-confidence group of coregulated PiSCES that responded to stimula-tion by SEE (Fig. 4). As expected upon cross-linking, the TCR-CD3complex displayed a loop in the visualization, indicating that multiplecopies of TCR-CD3 appeared in shared complexes. TCR-CD3 also formedshared complexes with lymphocyte-specific protein tyrosine kinase (LCK),z chain–associated protein kinase 70 (ZAP70), linker for activation of T cells(LAT), Src homology 2 (SH2) domain–containing leukocyte protein of76 kD (SLP-76), growth factor receptor–bound protein 2 (GRB2)–relatedadaptor downstream of Shc (GADS), phospholipase C–g (PLC-g), andothers, and many interactions among these proteins were also observed.We conclude that TCR engagement by superantigen produced a clearPiSCES signaling profile, which identified specifically induced proteinco-associations that participated in TCR signalosome activity.

PiSCES analysis of CD28 costimulationTo isolate stimulation of the T cell costimulatory receptor CD28 as avariable and visualize its potential contribution to PiSCES networks,Jurkat cells were stimulated for 5 min with SEE-loaded Raji cells inthe presence of either cytotoxic T lymphocyte–associated protein 4(CTLA4)–immunoglobulin (Ig) or nonspecific human IgG. CTLA4-Igbinds to the CD28 ligands B7.1 and B7.2 and blocks them from bindingto CD28 (16), whereas control IgG does not, enabling physiological engage-ment of CD28 on the Jurkat cells by the B7 ligands on the Raji cells. Normal-izing the CD28-permissive condition to the CD28-blocked condition,network visualization showed a signature with CD28 as a central player thatwas induced to form shared complexeswith phosphoinositide 3-kinase, PLC-g, protein kinase C–q, GRB2, son of sevenless homolog 1 (SOS1), TCR, andother proteins, and additional protein pairings in these pathways were alsoobserved (Fig. 5) (17). We conclude that PiSCES analysis can revealnetwork signatures of signaling protein complexes that are indicative of co-stimulatory pathway activity.

PiSCES analysis of T cells from biopsies of patients withthe autoimmune condition alopecia areataTo seek insight into protein signaling network activity in human disease, weapplied PiSCES analysis to the few primary T cells obtained from 4-mmskin punch biopsies of the scalp, donated by control or alopecia areata pa-tients (Fig. 6A and table S2). Alopecia areata is an autoimmune disorderinvolving T cell–associated hair follicle pathology; however, no auto-reactive TCRs, confirmed T cell autoantigens, or TCR signaling activ-ities have yet been described for the human disease (18, 19). Experimentswere performed by stimulating half of the biopsy-isolated T cells with plate-bound anti-CD3 and anti-CD28 antibodies for 5 min, whereas the other half ofthe cells were processed in parallelwithout stimulation. In addition, frozenaliquots of Jurkat cells were processed in each experiment, serving forintra-assay normalization and interassay comparison. Visualization of thestimulation-induced PiSCES network suggested overall agreement be-tween control and alopecia areata samples (fig. S5), with many of the sameprotein complexes being formed during signaling. Despite this similarity,

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unsupervised hierarchical clustering (Fig. 6B) and PCA (Fig. 6C) revealed aseparation between control patients and all but one alopecia areata patient,suggesting that the underlying PiSCES measurements contained patternsthat distinguished the disease condition at a network level. Consistent withautoreactive TCR involvement in human alopecia areata, a statisticallysignificant enrichment in TCR-responsive basal protein complexes in alo-pecia areata patients over controls was observed, comparing average nor-

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malized protein complex intensities in the absence of exogenous stimulation(Fig. 6D). This was determined by a resampling-based test, which showedthat the higher percentage of protein pairs whose intensities were greater inalopecia areata samples than in controls (alopecia areata > control) fromamong the set of stimulation-inducible PiSCES (defined in fig. S5) repre-sented a statistically significant increase over the percentage of protein pairswith alopecia areata > control in randomly resampled sets from the fullPiSCES network of measurements. We conclude that combining un-supervised hierarchical clustering, PCA, ANC, and WCNA produced aclear PiSCES biosignature that distinguished the control from the diseasecondition. Furthermore, in exogenously stimulated cells, WCNA analysisidentified a PiSCES module that was statistically significantly correlatedwith alopecia areata status (Fig. 7). The module revealed a subnetwork ofpotential interest, in which, among other observations, the balance of activ-ity betweenGADS andGRB2 in the TCR signalosomemay favorGADS incells from alopecia areata patients, but may favor GRB2 in cells from con-trol patients (Fig. 7). We propose that new hypotheses can be generatedwhen PiSCES analysis is applied to physiologic protein networks involvedin human signal transduction and disease.

DISCUSSION

Various proteomic, genetic, and other techniques are now in use to discoverand document proteins that can bind to each other to compose an interac-tome. In the case of the TCR signalosome, years of previous work, togetherwith the ongoing acquisition of data, reveal a highly interactive proteinnetwork (Fig. 1) that is thought to be capable of manifesting different activ-ities to transduce distinct biological signals, depending on the strength of theantigen encountered (12, 13, 20, 21). However, note that the complicated,so-called “hairball” (22) visualization (Fig. 1) conveys a map of protein co-associations that are deemed possible (because they have been reported),without containing information about the actual protein interaction activity

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Fig. 4. High-confidence PiSCES network signature of SEE-inducedproximal TCR signaling. Shared hits identified by both ANC and WCNAanalyses (ANC ∩ WCNA) are displayed for the PiSCES data set intro-duced in Figs. 2 and 3, in which Jurkat cells were either left unstimulatedor stimulated for 5 min with SEE-loaded Raji cells. Edge color and thicknesscorrespond to the mean log2 fold change (color legend on the right) fromfour independent experiments.

Fig. 3. Comparison of ANC and WCNAanalyses. (A) ANC analysis of four indepen-dent SEE stimulation experiments identifiedPiSCES that exhibited statistically significantchanges in three of four experiments for theSEE data set. Edge color and thickness cor-respond to mean log2 fold change (colorlegend on the right). (B) WCNA analysis ofthe MFI values from the same SEE dataset. PiSCES with MM > 0.7 in the turquoisestimulation network (see fig. S4 and Mate-rials and Methods) are visualized in thenode-edge diagram. Edge thickness cor-responds tomean log2 fold change,whereasedge color corresponds toMM (color legendon the right). (C) Venn diagramof the overlapbetween both methods of analysis. Becausemost hits coincided between the two analy-ses, we concluded that the two analysesproduced compatible results. (D) Hits iden-tified by ANC (red), WCNA (blue), or both(dark purple) are graphed by the absolutevalue (Abs) of mean log2 fold change andthe absolute value of MM in the stimulation(stim) (turquoise) module.

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that occurs to transmit a specific biological signal. For this reason, there re-mains an outstanding need for the generation of new approaches that couldharvest known information about the interactome and apply it to data ac-

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quisition from human cells. In the case of primary patients, cells may orig-inate from low-abundance clinical samples; thus, the applicability of ge-netic engineering or protein tagging can be quite limited. Our multiplex,microsphere matrix–based PiSCES analysis (Fig. 2) is designed to con-tribute toward overcoming these obstacles. PiSCES analysis enables themeasurement of defined subnetworks of proteins from the interactome,validating their participation in physiologic signaling contexts and reveal-ing how their specificity and activity compose signals in transformed andprimary patient T cells.

Although singleplex and multiplex immunoprecipitation detected byflow cytometry can be highly sensitive down to the femtomole to attomoleanalyte range (23–25), application of this technology to multiproteincomplexes in signal transduction required the investigation of biostatisticalanalysis techniques. The present investigation involved the comparison ofseveral statistical approaches using both empirical and computer-simulatedexperimentation to select an applicable statistical approach for PiSCESanalysis. Statistical evaluation of PiSCES signatures across the high-dimensional matrix of measurements was achieved through an analysistermed ANC, based on KS statistics and empirically determined a-cutoffcriteria. ANC maintained a fixed type I error (0.05) adjusted for multiplecomparisons, while identifying changes in PiSCES that were consistentlystatistically significant across experiments (fig. S3). Additionally, we ob-served by WCNA that many stimulation-inducible protein-protein associa-tions changed in a correlated manner (fig. S4). Hits that were common toboth ANC andWCNA provided a high-confidence, stimulation-responsivenetwork profile (Figs. 3, 4, and 5). In summary, the protein-pair matrix dataof PiSCES were able to achieve network-level visualization and analysis ofthe activity of a finite subset of signaling protein complexes from the inter-actome centered around the human TCR signalosome.

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SEE-loaded Raji cells in the presence of physiological CD28 engage-ment (control human IgG–treated) normalized to the CD28-blocked con-dition (CTLA4-Ig–treated). Shared hits identified by both ANC and WCNAanalyses (ANC ∩ WCNA) are displayed. Edge color and thickness corre-spond to mean log2 fold change (color legend on the right) from threeindependent experiments.

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ig. 6. PiSCES network analysis of clinicaliopsies distinguishes the alopecia areataatient group from the control patient groupnd suggests enhanced basal TCR sig-aling network activity in alopecia areata.) A 4-mm scalp punch biopsy fromwhichcells were isolated. (B) Hierarchicallustering of the normalized fold changesMFI predominately separates the sevenlopecia areata patient samples from theve control patient samples. S, exogenous-stimulated; U, unstimulated. P = 0.015,xtension of Fisher’s exact test. (C) PCA ofasal (not exogenously stimulated) PiSCESctivity among all stimulation-responsiverotein complexes (identified in fig. S5)eparates alopecia areata patient samplesomcontrol patient samples. (D) Differencethebasal activity of stimulation-responsiveetwork PiSCES in the alopecia areata pa-ent group as compared to the control pa-ent group (red indicates greater activity ine alopecia areata cells; blue indicatesreater activity in the control patient cells)uggests that more stimulation-responsiveiSCES were basally increased in relativebundance in the alopecia areata patientroup versus the control patient group. P =

0.039 with control-clustered patient AA4 removed; P = 0.115 with patient AA4 included, using a resampling-based test.

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Constructed and optimized with data from experiments with SEE-stimulated Jurkat cells, PiSCES analysis proved capable of assessing ac-tivity from small samples from clinical patients. PiSCES biosignatures(unsupervised hierarchical clustering, PCA, ANC, and WCNA) distin-guished alopecia areata patient groups from control groups and were suggestiveof basal TCR signaling activity in lesional alopecia areata T cells (Fig. 6).Furthermore, upon exogenous stimulation, a modulewithin the TCR signa-losome network was identified, generating a new hypothesis with specificramifications (Fig. 7). The SH2 domains of GADS and GRB2 compete forbinding to the phosphorylated amino acid residues Tyr171 andTyr191 of LAT[GRB2 can also bind to Tyr226 (26)]; in T cells from alopecia areata lesions,

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the GADS:GRB2 balance may favor relatively more GADS activity in thesignalosome than what is observed in T cells from control patient biopsies,in which the balance may favor GRB2. Additional observations in the datasetwere consistentwith thismodel.WhereasGRB2 recruits SOS1 (alopeciaareata < control) to LAT, leading to the activation of the Ras–mitogen-activated protein kinase (MAPK) pathway, GADS recruits SLP-76 (alopeciaareata > control), which in turn recruits FYB (alternatively called ADAP orSLAP130; alopecia areata > control), which can link to cytoskeletal remodel-ing and integrin signaling (27, 28). Together, these observations suggest thata subnetwork involving the balance between GADS and GRB2may playa role in alopecia areata. We propose that the unique visualization of net-work protein signaling activity by PiSCES has the potential to generate in-sights into human signal transduction and generate hypotheses regardingdisease pathways.

The PiSCES approach provides access to physiologic protein networks,although it is subject to certain limitations that are also shared by otherleading protein interactionmethodologies. First, because it depends on anti-body binding to exposed surface epitopes of multiprotein complexes undernative (nondenatured) conditions, PiSCES is not expected to detect everyprotein-protein interaction that may be present. For example, a specific pro-tein could be bound by interacting partners in a way that makes it in-accessible to immunoprecipitation or detection (probe) antibodies. Thislimitation applies to some extent to all methods that bind proteins to capturethem before subsequently analyzing their interacting partners. Second,PiSCES data provide a network signature, but this signature does not definethe identities of all of the proteins that compose complexes. For example,although TCR:ZAP70 and TCR:LATare both protein pairs found in sharedcomplexes upon stimulation, PiSCES data do not reveal that the doublyphosphorylated immunoreceptor tyrosine-based activation motifs of CD3bind to the tandem SH2 domains of ZAP70 and that the same ZAP70 mol-ecules bind through their catalytic domain to the phosphotyrosines of LAT,resulting in detection of TCR in shared complexes with LAT. BecausePiSCES data lack this information, the data do not refute the possibility thatthe TCR and LAT might appear in shared complexes through a molecularpathway that is different from the simple one just described.

Despite this limitation, PiSCES data can validate the presence and rela-tive quantities of physiologic proteins in shared complexes isolated fromclinical samples—information that is otherwise generally unobtainable.Other methods that perform vast surveys of protein-protein interactions alsolack information on the precise constituents of specific multiproteincomplexes, which is one of the current major limitations in the interactomefield (29–33). There are two notable consequences of these limitations asthey affect PiSCES signatures: (i) quantification is not absolute but is rela-tive between two assessed conditions underwhich eachmeasurement can beinfluenced by the underlying activity of heterogeneous complexes, and (ii)like all other methods for analyzing protein-protein interactions, PiSCESdata reflect the characteristics of a dominant assay; that is, whereas positivedata support the hypothesis that two proteins join shared complexes, thefailure to detect two proteins together does not disprove the hypothesis thatthey may interact with each other. Overall, PiSCES analysis takes a stepforward in providing a network biosignature that visualizes the physiologicactivity of signaling protein complexes from human tissue samples, al-though further progress is needed to overcome other technological barriersthat currently limit the amount of information obtainable from the humanclinical interactome.

The PiSCES approach is potentially adaptable to any defined networkof interacting proteins and is scalable up to a maximum of 500 distinctmicrosphere classes in current use. The strategies outlined here, which in-volve reagent screening, data evaluation, and network profiling, could beapplied to generate a multiprotein matrix analysis system for any human

A

B

THY1

TCR/CD3 FYB

ITK

GADS

SLP-76

GRB2

PKC-

SOS1

VAV

PLC-

LCK

LAT

NCK

PI3K

FYN

ZAP70

Log2 fold change20.50–0.5–1 1

FYB

LAT

BCL10

ITK

PI3K

THY1FYN

GADS

PKC-

PLC-

LCK

TCR/CD3

NCK

ZAP70

CD28

VAV

CBL-B

SLP-76

SOS1GRB2

Fig. 7. PiSCES network activity suggests a disease-associated signalingprofile in T cells from alopecia areata patients. (A) Using PiSCES data from

patient and control T cells that were exogenously stimulated for 5 min withplate-bound anti-CD3 and anti-CD28 antibodies, WCNA analysis of normal-ized MFI fold changes identified a PiSCES subnetwork module that distin-guished the alopecia areata patient group from the control patient group.Red, enhanced in the alopecia areata condition; blue, enhanced in the con-trol condition. (B) An alternative view of the subnetwork module, with thenodes positioned on the basis of the activity in alopecia areata patientsamples (n = 7) versus control patient samples (n = 5).

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interaction network relevant to biomedicine. Our interest in geneticallynonengineered primary patient samples drove us to build the system withantibodies as the capture and detection reagents, a strategy that suppliedaccess to many quality control–ready candidates. Given that multinationalefforts are aimed at generating monoclonal antibodies specific for everyopen reading frame of the human proteome, even more extensive antibodycollections are likely to become available in the future (34, 35). Moreover,many other non–antibody-based protein-binding reagents are compatiblewith multiplex microsphere chemistry and are predicted to function wellwithin the PiSCES analysis framework. Finally, it is anticipated that PiSCESanalysis could potentially contribute to composite signatures generated inconjunction with other high-dimensional data (including cellular mass cy-tometry, multiplex cytokine and phosphoflow analysis, and genome-wideexpression analysis) to further our understanding of immune-mediated dis-eases (36). In conclusion, we present PiSCES analysis as a broadly applica-ble strategy that is useful for identifying biosignatures of multiproteincomplex activity in network signal transduction and disease pathways.

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MATERIALS AND METHODS

Cell cultureJurkat E6-1 cells [American TypeCulture Collection (ATCC) TIB-152] andJRT3-T3.5 cells (ATCC TIB-153) were purchased from ATCC. Jurkat cellderivatives lacking specific proteins (37), which were used for antibodyscreening, and Raji cells, which were used as APCs, were provided byK. Hedin (Mayo Clinic). All cells were cultured in RPMI medium (LifeTechnologies) with 10% Cosmic Calf serum (HyClone), 2 mM L-glutamine(Life Technologies), penicillin (100 U/ml; Life Technologies), and strep-tomycin (100 mg/ml; Life Technologies) and maintained in an incubatorat 37°C with 5% CO2.

Cell stimulation and lysate preparationFor SEE stimulation, Raji cells (APCs) were incubated with SEE (5 mg/ml;Toxin Technologies) for 2 hours in serum-free RPMI at 37°C. For CD28-blocking experiments, the Raji cells were also incubated with either humanCTLA4-Ig fusion protein (3.33 mg/ml; Bio X Cell) or nonspecific humanIgG (3.33 mg/ml; Jackson ImmunoResearch). TheRaji cellswere pelleted at300g for 5 min, the supernatant was discarded, and the cells were fixed byresuspension in 0.05% gluteraldehyde in phosphate-buffered saline (PBS),as previously described (38). After 30 s, fixation was halted by the additionof PBS and 200mMglycine, and the fixed cells werewashed three times inPBS and counted with a hemocytometer. Jurkat cells, which had beenplaced in fresh medium (0.35 × 106 cells/ml) the previous evening, werecounted and pelleted. Then, 15 × 106 Jurkat cells were resuspended in200 ml of ice-cold PBS, mixed with 20 × 106 Raji cells in an equal volumeof ice-cold PBS, and centrifuged for 5 min at 300g at 4°C to facilitatecell-cell conjugation. The supernatant was discarded, and stimulationwas commenced as the Jurkat cell–Raji cell pellet was placed in a waterbath for 5 min at 37°C before being flash-frozen in liquid nitrogen. Con-trol, unstimulated cells were mixed with nonloaded, fixed Raji cells inparallel. Frozen pellets were used immediately or were stored briefly at−80°C before being lysed in lysis buffer [150 mMNaCl, 50 mM tris (pH7.4), 1% digitonin (High Purity, Millipore), 1× Halt protease/phosphataseinhibitors (Pierce), 10 mM NaF, 2 mM sodium orthovanadate, and 10 mMiodoacetamide].

Skin-biopsy T cells and lysate preparationAll procedures involving human subjectswere approved by theMayoClinicInstitutional Review Board (protocol #12-004980). Cases were identified

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on the basis of established clinical criteria for alopecia areata. Punch biop-sies (4mm)were taken from alopecia areata patients from perilesional areaswith active hair loss but still contained hair. Specimens were immediatelytransported to the laboratory on ice. Biopsy samples from control patientswere taken from normal-appearance scalp skin adjacent to skin lesions (notdue to alopecia areata) that were removed during routine dermatologicalprocedures. T cells were isolated by the punch biopsy crawl-out methodas described previously (39, 40). Stimulation plateswere prepared by addingthe anti-CD3 clone OKT3 (10 mg/ml, purified in-house from hybridomasupernatant) and anti-CD28 (2 mg/ml, clone CD28.2, BioLegend) in PBSto each well of a six-well plate, incubating the wells overnight at 4°C, andwashing them three times in PBS. Half of the harvested T cell preparationwas added to a well coated with anti-CD3 and anti-CD28 antibodies,whereas the other half was added to an uncoated well (both on ice). Cellsin the plate were centrifuged at 300g for 5 min at 4°C, the supernatant wasaspirated and discarded, and the platewas floated in awater bath for 5min at37°C to activate signaling. The plate was then immediately placed on ice,and 200 ml of ice-cold lysis buffer was added to each well.

Antibody screening and coupling to microspheresWe first screened three to five antibodies per target for their ability tocapture and detect proteins from postnuclear cell lysates. Using a collectionof cell lines that had or lacked each target protein, we empirically confirmedthe binding and specificity of each antibody pair in uniplex, microsphere,immunoprecipitation-based capture followed by flow cytometry analysis(table S1 and fig. S1). Each selected immunoprecipitation antibody wascovalently coupled to a distinct bead class of Luminex MagPlex COOHbeads (Bio-Rad), according to the manufacturer’s recommended aminecoupling protocol. Each detection (probe) antibody was purchased in R-phycoerythrin (R-PE)–conjugated form or was biotinylated with Sulfo-NHS-Biotin (Thermo Scientific). Bead coupling efficiency was checkedby staining beads with antibodies specific for the appropriate antibodyhost species and class, with fluorescence intensity acquired on an AccuriC5 flow cytometer (BD Biosciences).

Multiplex capture and detection of protein complexesIn the TCR signalosome, 20 signaling proteins were targeted in a minimum20×20matrix of capture and detection antibodies (table S1). This generateda minimum of 400 protein co-association measurements, including 210total unique protein co-associations: [(20 capture × 20 probe) – 20 homo-typic combinations)/2] + 20 homotypic combinations = 210. Some experi-ments included capture and detection antibody combinations targetingadditional proteins, but this study focuses only on 20 targets that were as-sessed in all experiments. Because the multiplex microbead approach isscalable, additional capture and detection reagents and microbead classescan be used as desired for the addition of new targets to the collection. Amaster mix containing equal numbers of each antibody-coupled Luminexbead class was prepared and distributed into postnuclear cell lysate samplesin duplicate. All steps were performed at 4°C or on ice, and other technicalpractices were followed as described previously (23, 24, 41). Proteincomplexeswere immunoprecipitated from samples overnight,washed twicein Fly-P buffer [50 mM tris (pH 7.4), 100mMNaCl, 1% bovine serum albu-min, and 0.02% sodium azide], and distributed into as many wells of a96-well plate as there were probes. Detection antibodies were added andincubated for 1 hour, with gentle agitation at 500 rpm in a cold room. ABio-Plex Pro II magnetic plate washer was used to wash microbeads andcaptured complexes three times in Fly-P buffer.Wells containing biotinylateddetection antibodies were then incubated for 30 min with streptavidin-PE. After three washes in the plate washer, the microbeads were resus-pended in 125 ml of Fly-P buffer, and fluorescence datawere acquired on

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a Bio-Plex 200 instrument that was calibrated according to the manufac-turer’s recommendations and run on the “high RP target” setting. Datafiles were exported in bothMicrosoft Excel and XML formats for furtherprocessing.

Data preprocessing and inclusion criteriaXML output files were parsed to acquire the raw data for use in MATLAB,R statistical package, Cytoscape, and other analysis and visualization plat-forms. For each well from a data acquisition plate, data were processed to(i) eliminate doublets on the basis of the doublet discriminator intensity (>5000and <25,000 arbitrary units; Bio-Plex 200), (ii) identify specific beadclasses within the bead regions used, and (iii) pair individual bead PE fluo-rescencemeasurementswith their correspondingbead regions.This processinggenerated a distribution of PE intensity values for each pairwise proteinPiSCES measurement.

Statistical tests evaluated for application toPiSCESanalysisThe KS and WRS nonparametric tests were evaluated with merged distribu-tions from two intraexperimental duplicates, where each duplicate providedfluorescence intensity measurements from ~100 microspheres. These testswere compared against twoparametricmethods based onLRorTm.The datasubjected to LR analysis were the fluorescence intensities from ~100 mea-surements per intraexperimental duplicate, having been modeled after thegeneral approach of a method that was previously described for multiplexmicrobead data (eq. S1) (14). In contrast, Tm used MFI as a summaryrepresentation of each distribution, resulting in a statistical test that usedtwo central values per intraexperimental duplicate. Model assumptionsof these statistical tests differ, with KS being sensitive to changes indistribution features including variance, WRS emphasizing distributionmedian, LR emphasizing distribution mean, and Tm emphasizing popula-tion central value.

Unsupervised hierarchical clustering and PCAFor hierarchical clustering, filteredMFI intensities (exclusion criterion,MFI< 100) or filtered normalized fold-change values (exclusion criterion, 0.9 <fold change < 1.1) were clustered using the average method with a Euclid-ean distance matrix in R statistical package (flashClust) (42). PCAwas per-formed using the built-in function inMATLAB.Unless otherwise specified,the input variables for PCA included either log2 MFI or log2 fold-changevalues for each of the PiSCES measurements performed, where each stim-ulation condition in one experiment constituted an observation.

ANC analysisDifferences in specific protein-pair PiSCESmeasurementswere consideredhits if they were consistently identified as statistically significant in at least70% of experiments with the KS test. The a-cutoff value required per ex-periment to determine statistical significance was calculated to maintain anoverall type I error of 0.05 (adjusted for multiple hypothesis testing withBonferroni correction). The full description of the a-cutoff calculation(defined as ak) is provided in eq. S2, and an example is provided in eq.S3. Further adjustment to the cutoff for statistical significance was imposedto account for technical error as follows.We first calculated the P values forall PiSCESmeasurementswithin intraexperimental duplicate probewells. IfamedianP value for duplicatewellswas <0.05 (Bonferroni-corrected), thenthose duplicate wells were excluded from the analysis because of presumedmanual error. For the remaining measurements across all related conditionsand experiments, a combined empirical distribution of intraexperimentalduplicate P values was used to account for technical error by identifyingthe P value at the percentile defined by ak. Linear interpolation using thefive nearest points to this target percentile (defined by ak) was used to pre-

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cisely mark this P value cutoff. As a result, the ak-associated P value cutoffwas customized for the empirical technical error in an experimental set andwas used to define statistical significance.

Weighted correlation network analysisThe WGCNA package for R (15) was used to perform WCNA. MicrosoftExcel output files were used to average the two duplicate sample MFIs foreach protein-pair PiSCES measurement. Either MFI or fold-change valuesbetween conditions were used as the inputs for WCNA. Data were then (i)filtered to remove weakly detected or weakly changed PiSCES (<100 MFIor 0.9 < fold change < 1.1, respectively) and (ii) subjected to log2transformation. Soft thresholding using a power adjacency function wasselected, and the power (exponent) value was determined by identifyingthe value that resulted in the best approximation of scale-free topology.The minimum module size was set to 5, and closely related modules weremerged when the dissimilarity value (MEDissThres) was <0.25. Closelyrelated PiSCES were assigned to modules, and eigenvectors, whichare vectors describing the first principal component summarizing thegeneral behavior for each module, were calculated. Each module wasassigned an arbitrary identification color. Correlations between experimen-tal traits (for example, “SEE stimulation,” ‘”AA status,” etc.) and moduleeigenvectors were visualized with the “Module-trait relationship” function.Modules with P < 0.05 for a given trait were considered to be statisticallysignificantly correlated with that trait. PiSCES belonging to modules ofinterest were defined as thosewithMM> 0.7 and P < 0.05. PiSCESwithMM< 0.7were excluded even if the associatedmodule inclusionP valuewas <0.05.

Data visualizationPiSCES analysis by hierarchical clustering, PCA, ANC, and WCNA usedcomplete unedited data sets from all experiments, unless otherwise speci-fied. In contrast, node-edge diagrams only visualized subsets of data andwere generated with the publicly free open network resource Cytoscape.For protein pairs that hadmultiple measurements targeting different epitopecombinations, the measurement with the greatest mean log2 fold-changevalue was selected for node-edge diagram visualizations.

Other statistics and analysisBox-and-whiskers plots (boxplots) display the median as a central value,with box edges representing the 25th and 75th percentiles. Maximumwhisker lengths were q3 + 1.5(q3 − q1) and q1 – 1.5(q3 − q1), where q1and q3 are the values of the first and third quartiles, respectively. For theresampling-based statistical test in Fig. 6D, the set of stimulation-responsivePiSCES (defined in fig. S5) were compared against 50,000 randomlyselected sets from the full network of measurements to determine whethertherewas a statistically significant enrichment for PiSCESwhosemean nor-malized intensitywas increased in alopecia areata samples, compared to thatin control samples. Unsupervised hierarchical clustering was performed togenerate the trees in Figs. 2E and 6B. To assess the statistical significance ofthe clusters in Fig. 2E, we performed Fisher’s exact test. For Fig. 6B, un-supervised hierarchical clustering was performed, and the tree structurewas generated with data from unstimulated and stimulated cells for eachpatient. Because the alopecia areata patients predominately separated intotwo of three tree branch clusters, an extension of Fisher’s exact test was per-formed to calculate the statistical significance of observing no control pa-tients in these two groups. Note that each patient’s unstimulated cell datatended to cluster closely with the same patient’s stimulated cell data, likelyindicating sample dependency between these two conditions.As a result, weperformed the test by treating each patient, not each sample, as the inde-pendent variable.

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SUPPLEMENTARY MATERIALSwww.sciencesignaling.org/cgi/content/full/9/439/rs7/DC1EquationsFig. S1. Screening strategy for multiplex panel antibodies, using SLP-76 as an example.Fig. S2. Instrument setup for optimal analysis of protein complexes.Fig. S3. Development and evaluation of ANC analysis.Fig. S4. Application and evaluation of WCNA analysis for the SEE-stimulated and un-stimulated Jurkat cell data.Fig. S5. Stimulation-induced PiSCES network is similar between control and alopeciaareata patient groups.Table S1. Validated antibody pairs used to identify each target in Jurkat cells.Table S2. Phenotypic characteristics of alopecia areata patients and controls and their T cellpopulations.

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Acknowledgments:Wethankour volunteerpatients; J.Kidd for control skin sampleacquisition;B.Alarcón,K.Hedin, L.Pease,andJ.Regueiro for kindprovisionof cell lines; andR.Stiles forantibodypurification. Funding: This work was supported by NIH grants R01GM103841 (to A.G.S.,J.E.E.-P., D.G., and C.N.), MH102244 (to S.E.P.S.), and T32AI7425 (to S.C.N. and B.K.R.); MayoGraduate School (to S.C.N. and B.K.R.); and the philanthropic partnership of B. Fineman with MayoClinic (to A.G.S.). Author contributions: S.E.P.S., S.C.N., B.K.R., D.G., C.N., and A.G.S. designedexperiments;S.E.P.S.,S.C.N.,B.K.R., andT.R.D.performedexperiments;G.F.S.,C.N.W.,andR.R.T.diagnosedandrecruitedpatientdonors, identifiedsitesofactive lesions,andacquiredbiopsies;S.E.P.S.,S.C.N., J.P.S., J.E.E.-P., C.N., and A.G.S. analyzed and interpreted data; S.E.P.S., S.C.N., J.E.E.-P.,C.N., and A.G.S. wrote the manuscript; A.G.S. conceived the project; and all authors contributedcreative intellectual input and revised manuscript drafts. Competing interests: The authors declarethat they have no competing interests.

Submitted 26 October 2015Accepted 14 July 2016Final Publication 2 August 201610.1126/scisignal.aad7279Citation: S. E. P. Smith, S. C. Neier, B. K. Reed, T. R. Davis, J. P. Sinnwell, J. E. Eckel-Passow,G. F. Sciallis, C. N. Wieland, R. R. Torgerson, D. Gil, C. Neuhauser, A. G. Schrum, Multiplexmatrix network analysis of protein complexes in the human TCR signalosome. Sci. Signal. 9, rs7(2016).

ww.SCIENCESIGNALING.org 2 August 2016 Vol 9 Issue 439 rs7 9

Multiplex matrix network analysis of protein complexes in the human TCR signalosome

Gabriel F. Sciallis, Carilyn N. Wieland, Rochelle R. Torgerson, Diana Gil, Claudia Neuhauser and Adam G. SchrumStephen E. P. Smith, Steven C. Neier, Brendan K. Reed, Tessa R. Davis, Jason P. Sinnwell, Jeanette E. Eckel-Passow,

DOI: 10.1126/scisignal.aad7279 (439), rs7.9Sci. Signal. 

useful as a diagnostic tool.autoimmune skin disease. This type of analysis brings basic research a step closer to personalized therapy and may beidentified protein complexes that differed in their relative abundance between control donors and patients with an particular complexes between resting and stimulated cells. Application of this technique to T cells from skin biopsiesthese data enabled the construction of protein-protein interaction networks and revealed the relative abundances of

ofused it to capture complexes from T cell lysates and identify binding partners by flow cytometry. Mathematical analyses . devised a multiplex, antibody-based method andet alimportant. To analyze these complexes in patient samples, Smith

signaling pathways and different cellular outputs. Thus, knowing which protein-protein interactions occur can be clinicallyinteractions that occur. The formation of different protein complexes leads to the activation of different intracellular

The response of a cell to ligands that activate cell surface receptors depends on the resulting protein-proteinPersonalized signaling complexes

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