Single-cell RNAseq reveals cell adhesion moleculeprofiles in electrophysiologically defined neuronsCsaba Földya,b,1, Spyros Darmanisc, Jason Aotoa,d, Robert C. Malenkae, Stephen R. Quakec,f, and Thomas C. Südhofa,f,1

aDepartment of Molecular and Cellular Physiology, Stanford University, Stanford, CA 94305; bBrain Research Institute, University of Zürich, 8057 Zurich,Switzerland; cDepartment of Bioengineering, Stanford University, Stanford, CA 94305; dDepartment of Pharmacology, University of Colorado Denver,Aurora, CO 80045; eNancy Pritzker Laboratory, Stanford University, Stanford, CA 94305; and fHoward Hughes Medical Institute, Stanford University,Stanford, CA 94305

Contributed by Thomas C. Südhof, July 10, 2016 (sent for review May 21, 2016; reviewed by Thomas Biederer, Tamas F. Freund, and Li-Huei Tsai)

In brain, signaling mediated by cell adhesion molecules defines theidentity and functional properties of synapses. The specificity ofpresynaptic and postsynaptic interactions that is presumably medi-ated by cell adhesionmolecules suggests that there exists a logic thatcould explain neuronal connectivity at themolecular level. Despite itsimportance, however, the nature of such logic is poorly understood,and even basic parameters, such as the number, identity, and single-cell expression profiles of candidate synaptic cell adhesionmolecules,are not known. Here, we devised a comprehensive list of genesinvolved in cell adhesion, and used single-cell RNA sequencing(RNAseq) to analyze their expression in electrophysiologically de-fined interneurons and projection neurons. We compared the celltype-specific expression of these genes with that of genes involvedin transmembrane ion conductances (i.e., channels), exocytosis, andrho/rac signaling, which regulates the actin cytoskeleton. Usingthese data, we identified two independent, developmentally regu-lated networks of interacting genes encoding molecules involved incell adhesion, exocytosis, and signal transduction. Our approach pro-vides a framework for a presumed cell adhesion and signaling codein neurons, enables correlating electrophysiological with molecularproperties of neurons, and suggests avenues toward understandingsynaptic specificity.

synapse | cell adhesion | single cell | RNAseq

The brain’s “connectivity code” is thought to confer exquisitespecificity to brain circuits by dictating connectivity between

different types of neurons. Although its existence has not yetbeen conclusively demonstrated, synaptic cell adhesion mole-cules likely comprise a large part of such a code (1–4). Cell ad-hesion molecules are encoded by ∼2% of the genome and playcentral roles in all tissues. During brain development, preciselymatching presynaptic and postsynaptic cell adhesion moleculeinteractions likely guide synapse formation and specify theproperties of synapses by activating signal transduction cascadesand recruiting scaffolding molecules, receptors, and active-zoneproteins; in addition, such interactions could provide structuralsupport to synapses. However, the molecular mechanisms in-volved are not understood. Because cell adhesion molecule-based interactions likely code for synapse specificity in a com-binatorial fashion (2, 3), an important step toward gaining insightinto these molecular mechanisms is to eliminate nonfunctionalpossibilities, which—to a great extent—can be done by examin-ing cell type-specific expression of these molecules.If cell adhesion molecules dictate synapse properties, then

such differences must be clearly present in interneuron and py-ramidal cell classes, which have diverse synaptic properties. Forexample, within the hippocampal circuit, CA1 pyramidal (CA1-PYR) neurons receive convergent inputs from multiple, elec-trophysiologically distinct inhibitory interneurons within thehippocampus (5). Inhibitory inputs include synapses formed byfast-spiking (FS) and regular-spiking (RS) interneurons (ref. 6;for reviews, see refs. 7 and 8). We previously showed using sin-gle-cell transcriptional profiling in FS parvalbumin (PV)- and RScholecystokinin (CCK)-containing GABAergic interneurons that

neurexin (Nrxn1 and Nrxn3; presynaptic cell adhesion mole-cules) isoforms were expressed cell type-specifically, with re-markable consistency in respective cell types (9). We also foundthat genetic deletion of neuroligin-3 (Nlgn3) (postsynaptic celladhesion molecule) in PYR cells disabled tonic, cannabinoidtype 1 receptor-mediated, endocannabinoid signaling in RS CCKsynapses, but had no detectable phenotype in FS PV synapses(10). Thus, although no systematic assessment of cell adhesionmolecules in GABAergic interneurons is available, previousstudies established that cell adhesion molecules play a centralrole in controlling their properties.Similar to their inputs, outputs of CA1-PYR cells display

functional dichotomy: they primarily project to the subiculum,forming synapses on two, electrophysiologically different prin-cipal cell types: regular-spiking pyramidal (RS-PYR) and burst-spiking pyramidal (BS-PYR) cells. Analysis of these neurons isparticularly difficult because, although RS-PYR and BS-PYRcells exhibit distinct electrophysiological signatures as well asdramatically different forms of long-term plasticity, no molecularmarkers are available to distinguish these neurons (11, 12). Inexamining synapse-specific mechanisms in these cells, we haveshown that different forms of long-term plasticity were de-termined presynaptically by expression of specific neurexin iso-forms in CA1-PYR cells (13–15). Together, these molecular andphysiological analyses revealed specific control of synapticproperties (such as forms of LTP and endocannabinoid signal-ing) by cell adhesion molecules in CA1-PYR cell inputs and

Significance

Synapses functionally connect neurons in the brain and medi-ate information processing relevant to all aspects of life.Among others, synaptic connections are enabled by cell adhe-sion molecules, which connect presynaptic and postsynapticmembranes by binding to each other via the synaptic cleft.Mammalian genomes express hundreds of cell adhesion mol-ecules whose combinatorial utilization is thought to contributeto the brain’s “connectivity code.” Such code could explain theversatility of synapses as well as the logic of connectivity be-tween cell types. Here, we used single-cell RNA sequencing toanalyze the expression of cell adhesion molecules and othersignaling proteins in defined cell types, and found developmentalpatterns that potentially identify relevant elements of theconnectivity code.

Author contributions: C.F., S.D., J.A., R.C.M., S.R.Q., and T.C.S. designed research; C.F., S.D.,and J.A. performed research; C.F. analyzed data; and C.F. and T.C.S. wrote the paper.

Reviewers: T.B., Tufts University; T.F.F., Institute of Experimental Medicine; and L.-H.T.,Massachusetts Institute of Technology.

The authors declare no conflict of interest.

Data deposition: The sequence reported in this paper have been deposited in the GeneExpression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (accession no.GSE75386).1To whom correspondence may be addressed. Email: foldy@hifo.uzh.ch or tcs1@stanford.edu.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1610155113/-/DCSupplemental.

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outputs, and raised the question whether other synaptic prop-erties were also controlled by cell adhesion molecules. Becausethere are a large number of molecules with such potential, alogical step to this direction is the cell type-specific analysis ofcell adhesion-related gene expression.Here, we combined electrophysiology and single-cell RNA se-

quencing (RNAseq) to identify cells in the pathway involvinghippocampal FS interneuron (FS-INT), RS-INT, CA1-PYR cells,and subiculum RS-PYR and BS-PYR cells, and to analyze theirgene expression profiles. Our data represent an initial circuit-levelsingle-cell RNAseq analysis from synaptically and electrophysio-logically defined neurons. We find surprising differences in thetotal number of expressed genes among neuron types and showthat hippocampal neurons can be characterized by the expressionof two common, developmentally regulated gene networks com-prising shared cell adhesion and signaling molecules. Moreover, wedemonstrate that each type of electrophysiologically defined neu-ron expresses a separate set of candidate synaptic cell adhesion andsignaling molecules. Finally, we extended these analyses to the twotypes of subiculum PYR neurons that feature highly similar tran-scriptomes but express a limited number of unique markers, in-cluding cell adhesion-related genes, which could potentially beused in future analyses. In this manner, the approaches and datadeveloped here lay the foundation for a biologically relevantanalysis of how cell type-specific neuronal gene expression maysculpt neural circuits during development and beyond.

ResultsCell Adhesion Molecules in the Hippocampus.As a starting point, weexamined the transcriptome of the entire hippocampus (Fig. 1A)at five developmental stages (in triplicate at postnatal days P0,P7, P14, P21, and P28). In these samples, the total number anddistribution of expressed genes were similar (Fig. 1 B and C, andFig. S1). To specifically analyze cell adhesion molecules, wecreated a comprehensive list of candidate genes involved in, orrelated to, transsynaptic cell adhesion (collectively referred to asCAMs, for candidate cell adhesion-related molecules). For this,we first considered all molecules with single transmembranedomains (a general, although not unique property of membranesurface adhesion molecules) and narrowed this list down to 406genes based on preexisting data (Fig. S1C). This curated list ofcandidate cell adhesion molecules includes proteins implicatedin cell–cell signaling but excludes, for example, intracellularsignal transducers linked to cell adhesion signaling. We foundthese genes to be broadly represented in all replicates and de-velopmental stages (Fig. 1D), highlighting diversity of CAMs inthe hippocampal circuit and throughout development. Expressionprofiles (Fig. 1E) for each developmental stage included genes thatwere consistently highly expressed at all developmental stages [forexample, amyloid precursor protein (App), contactins (Cntn),neurexins (Nrxn), and protein-tyrosine phosphatases (Ptpr); Fig.S1D], as well as genes with large changes during development(for example, Ncam1 and Sdc3). Normalization of individual gene

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Fig. 1. RNAseq of the total hippocampus during development: analysis of the changing landscape of CAMs. (A) Experimental design and strategy for mRNAextraction from hippocampal tissue samples (represented in photograph) and RNAseq analyses. (B and C) Similar number of detected genes in our analyses ofthree sample replicates at five developmental stages (from P0 to P28) suggested replicability, and similar normalized gene count distributions (i.e., histogramof gene numbers with given normalized gene count) suggested comparability between samples. (D) Number of detected CAMs was consistent acrossdevelopmental stages. (E) Averaged expression of CAMs in five developmental stages (in the plot, each gene is spaced equidistant and represented bymean ± SEM; expression values are connected with lines for visibility; for numerical values, see Dataset S1). (F) Heat plot of normalized gene expression showsdevelopmental regulation of CAMs, which were ranked by linear fit of developmental expression values. (G) Average gene expression levels grouped by peakexpression throughout development. Label Insets detail number of genes that belong to each group. Averaged data represent mean ± SEM.

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expression levels also revealed an average change of 60% betweenP0 and P28 (Fig. 1 F and G).

Single-Cell RNAseq from Electrophysiologically Identified Cells. Toanalyze gene expression in specific cell types in a defined circuit,we developed a method—which was also recently introduced byothers (16, 17)—with which we first characterized neuronselectrophysiologically, and subsequently analyzed their tran-scriptome by aspiration of cytosol followed by single-cell RNA-seq. Using this approach, we examined FS and RS interneuronsthat directly inhibit CA1-PYR cells, as well as the target CA1-PYR cells for these interneurons. We patched FS and regular-firing interneurons located within, or in close proximity to, thepyramidal cell layer in wild-type mice, and then used paired re-cordings by simultaneously patching PYR cells to test whetherpresynaptic action potentials (APs) evoked inhibitory post-synaptic responses, characterizing these cells as FS or RS in-hibitory interneurons (Fig. 2A). Note that such distinction didnot necessarily identify individual cell types. For instance, FScells could include PV+ basket, bistratified, and axoaxonic cells,whereas RS cells could include CCK+ basket or bistratified cellsas well. At the end of the recordings, we collected the neuronalcytosol via pipette aspiration for RNAseq (Fig. 2A). Uponalignment of sequencing reads and assignment of gene counts foreach gene, we applied stringent quality control metrics to eachcell (Fig. S2 A and B). RNAseq data obtained in this mannerfrom 18 RS-INT, 9 FS-INT, and 14 CA1-PYR cells passedquality control.To gain insight into the molecular identity of recorded cells,

we examined expression of genes that had been previously as-sociated with RS-INT, FS-INT, or PYR cells (Fig. 2B). Asexpected, Gad1 and Gad2 were highly expressed in interneuronsbut not in PYR cells, consistent with their respective neuro-transmitters. Although CCK peptide is a unique marker for RSCCK cells, we found that the CCK gene was nonspecificallyexpressed in all three cell types. Similarly, although cannabinoidtype-1 receptor (Cnr1) is most highly expressed in RS CCK cells(for specific examples, see refs. 10, 18, and 19; for review, see ref.7), it was also produced in FS-PV and PYR cells. In contrast,Htr3a and PV (parvalbumin) were more specific to RS-INT andFS-INT cells, respectively. Although the latter is a uniquemarker for FS subtypes at the protein level, the correspondingmRNA was detected in two RS-INT and three PYR cells as well.To identify PYR cells, we examined Camk2a and Neurod6 ex-pression: although the former’s expression was rather non-specific, Neurod6 was a reliable predictor of PYR cell identity.We also found that Grik3 (kainate receptor GluR7) uniquely,but not exclusively, identified FS-INT cells.

Examining overall gene expression, we found that interneu-rons consistently expressed almost twice as many genes as CA1-PYR cells (Fig. 2C; see Fig. S2F for controls), and that detectionof individual genes was also more consistent in RS- and FS-INTcells than in CA1-PYR cells (Fig. 2D). A potential explanationfor the latter observation involves spatial gene expression gra-dients in the hippocampus that likely underlie heterogeneity ofCA1 pyramidal neurons (20).

Molecular Correlates of Electrophysiological Properties. To link sin-gle-cell transcriptional profiles to functional properties, we exam-ined expression of 140 voltage-gated ion channels (see Materialsand Methods for gene set assembly; Fig. 3A, Fig. S3A, and DatasetS1). Because RS-, FS-, and CA1-PYR cells have distinct electro-physiological properties (Fig. 3A), we asked whether ion channelexpression could account for their physiological differences. Onaverage, we detected more ion channels and more consistent geneexpression in interneurons than in PYR cells (Fig. 3 B and C),following total transcript levels. As a pivotal example of cell type-specific gene expression, we found enrichment of Na+ and K+

channels in FS cells (Fig. 3 D and E, Upper).Next, we quantified electrophysiological parameters (n = 11;

Fig. 3 A and E, Left) because their correlation with gene ex-pression patterns could reflect characteristic, cell type-specificproperties in these cells. We used unsupervised clustering andplotted genes/electrophysiology parameters with at least onecorrelation value of greater than 0.35 or less than −0.35 (arbi-trary threshold to eliminate no or minimal correlations; Fig. 3E).Here, we found that AP firing rate, threshold, symmetry, andafterhyperpolarization, parameters that typically exhibit highvalues in FS cells, correlated with high expression of Na+ and K+

channels (Fig. 3E), including Kcnc1 (Kv3.1) and Kcnc2 (Kv3.2).Furthermore, we found that expression of these genes inverselycorrelated with AP peak-to-trough and width times (i.e., largergene expression values correspond to lower electrophysiologicalproperty values, and vice versa), which as properties also cor-respond to the fast signaling characteristics of FS-INT cells.These results are not unexpected as expression of Kcnc1 andKcnc2 is a hallmark of FS PV interneurons (refs. 21 and 22; forreview, see ref. 23), validating the single-cell RNAseq analysis.Surprisingly, unbiased correlation analysis can also deliver

seemingly contradictory results. For example, the observation ofhigh Hcn1 expression but a low hyperpolarization sag in FS-INTis puzzling because a primary readout for Hcn-mediated Ih cur-rents is the sag amplitude. However, the correlation betweenhyperpolarization sag and Ih current is not absolute, nor theinvolvement of Hcn1 in modulating active membrane properties(24). In addition, we found significantly higher expression ofKcnc3, Scn1a, and Trpc6 (Fig. 3E) in FS cells (described for PV

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Fig. 2. Single-cell RNAseq in situ of electrophysiologically characterized neurons in identified microcircuits. (A) Experimental design and strategy. Single-cellmRNA samples were collected after paired electrophysiological recordings from RS-INT, FS-INT, and CA1-PYR cells. White box indicates the CA1 region. Aftersequencing, transcripts were aligned to the mm10 assembly. (B) Representative heat map shows examples of gene expression in each cell type. For example,Gad1 and Gad2 expression in most presynaptic FS-INT and RS-INT cells confirmed that they were GABAergic, whereas Neuro6d was specific to postsynapticPYR cells. (C) Consistency of gene expression within and between cell types. On average, RS-INT and FS-INT cells expressed twice as many genes as CA1-PYRcells. (D) Plot of the percentage of genes that were detected in less (1–50%) and more (50–100%) than one-half of the cells (data points were connected forvisualization) in respective cell types. RS-INT and FS-INT cells had more consistent gene expression profiles than CA1-PYR cells. Averaged data in C representmeans ± SEM.

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cells in refs. 25–27, respectively), whereas specific expression ofKcng4 (Kv6.3, Kv6.4) identifies a thus far uncharacterized com-ponent of the ion channel repertoire of FS cells. For furtherclues about molecular architecture of these cell types, see ex-pression of ligand-gated ion channels in Fig. S3 C–E.Together, these combined analyses extend previous single-cell

studies (including refs. 22 and 26–29) that used probe-basedgene expression analysis, and enable comprehensive and un-biased RNAseq-based functional inferences.

CAMs in Single Cells. Our analyses in whole-tissue preparations(Fig. 1) consistently detected transcripts encoding 383.2 ± 1.1(mean ± SEM) different CAMs in the hippocampus, whichcomprises a large variety of neuronal and nonneuronal cell types.In examining expression of CAMs at the single-cell level, wefocused on how many CAMs were expressed in a neuron, andwhether these were expressed with cell type specificity. In singleRS-INT, FS-INT, and CA1-PYR cells, we detected NRS-INT =121.6 ± 11.2, NFS-INT = 128.7 ± 12.7, and NCA1-PYR = 82.7 ± 4.8different CAM transcripts, respectively [PRS-INT vs. FS-INT = 0.73,PRS-INT vs. CA1-PYR = 0.02, PFS-INT vs. CA1-PYR = 0.006; pairwiseMann–Whitney (MW) test; Fig. 4 A and B and Dataset S1].Examining these genes revealed that expression of CAMspartially overlapped between different cell types. Therewere NRS-INT vs. FS-INT = 48, NRS-INT vs. CA1-PYR = 58, andNFS-INT vs. CA1-PYR = 62 differentially expressed CAMs betweencell types (pairwise MW test, P < 0.05 in all cases). Thirty CAMswere expressed higher (n = 26; MW test, P < 0.05 in all cases) orlower (n = 4; Epha4, Mdga1, Pcdhgc5, Sema3e; MW test, P < 0.05

in all cases) in both interneuron types compared with PYRs. Wefound that genes that were highly expressed in tissue sampleswere also highly expressed in the RS-INT, FS-INT, and CA1-PYR cells (note that for sequencing libraries, tissue sampleswere diluted and processed using same conditions and reagentsas for single cells, albeit with larger amounts of starting mRNAs).Such genes included App, Chl1, Cntn1, Nptn, Nrxn1, and Ptprsthat were consistently detected in all three cell types (Fig. 4C,“All cell types”).The 26 CAMs that were expressed higher in interneurons

than in CA1-PYR cells included Clstn3, Cntnap4, and Lrrc4,which have been implicated in specification of inhibitory syn-apses (30–32), as well as Nrxn3, a presumed presynaptic orga-nizer of synapse function (13, 14, 33). Note that, althoughexpression of Nrxn3 was not specific for interneurons, it wasconsistently enriched in interneurons, which we also consideredhere as a cell type-specific feature. Other examples for cell typespecificity included Cbln2 and Ephb2 for RS-INT cells, Nphx1for FS-INT cells, and Epha4 for CA1-PYR cells. Althoughsome of these molecules have been directly implicated in trans-synaptic signaling (e.g., Nrxn1, Nrxn3, Ptprs) or in synapse spe-cialization (see examples above), others have not been studied indetail. Nevertheless, these data suggest cell type-specific ex-pression of CAMs can potentially explain neuronal connectivityat the molecular level. Such cell type-specific features werefurther highlighted by principal-component axes (PCA) analysis,which revealed two main components, including overall expres-sion as well as differential expression in interneurons vs. PYRcells (Fig. S4 A–C).

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Fig. 3. Combined electrophysiological and transcriptional analysis. (A) Ion channels define neuronal excitability that can be quantified using active(AP-related) and passive electrophysiological properties (e.g., voltage response for hyperpolarizing and depolarizing current injections). These parameters differbetween RS-INT, FS-INT, and CA1-PYR cells, and the lower panel shows differences in AP firing in the three cell types. (B) Number of detected ion channels wassimilar in RS-INT and FS-INT cells, but lower in CA1-PYR cells. (C) Consistency of ion channel expression was similar in all three cell types. (D) Averaged, single-cell ionchannel expression in RS-INT (n = 14), FS-INT (n = 9), and CA1-PYR (n = 14) cells, and in aged-matched P21 tissue control (for numerical values, see Dataset S1).(E) Correlation analysis of electrophysiological parameters and ion channel expression in RS-INT, FS-INT, and CA1-PYR cells. (Upper) Normalized gene expression dataand (Left) electrophysiology parameter measurements for RS-INT, FS-INT, and CA1-PYR cells. Averaged data represent mean ± SEM. Asterisks represent significantdifferences between cell types (*difference between two cell types; **difference between two–two cell types; ***difference between all three cell types).

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Beyond cell type-specific control of gene expression levels,diversity in CAM signaling could have been generated by othercellular mechanisms, especially by alternative splicing. Some ofthe most notable examples for alternative splicing includeCAMs, such as Dscam in Drosophila and neurexins in mammals,which can express thousands of alternatively spliced isoforms(34–37). Therefore, we examined alternative exon use of CAMsat the single-cell level. We analyzed genes with reliable end-to-end exon junction coverage, which was applicable to n = 139CAM genes. Out of these, we identified a single isoform in n =67 and multiple isoforms in n = 72 genes (Fig. S4 D and E).Because of their potential cell type specificity, presence of dif-ferent isoforms in the latter group would be especially interesting.However, currently available single-cell RNAseq platforms—including ours—require library fragmentation. As a result, preciseexon use of full-length mRNAs can be inferred only indirectly,with sample sizes typically larger than those of single cells, hin-dering further analyses of cell type-specific splice variants. Nev-ertheless, we examined whether any splice variants of the highlyexpressed ubiquitous CAMs had the potential to be expressed withcell type specificity. We found that some of these molecules hadonly single isoforms that may or may not involve alternative splicing(for example, in Ptprs, we consistently detected exon skipping,whereas in Ptprn and Cadm, we only detected canonical isoforms),whereas others had multiple isoforms (for example, in Nptn, Nrxn1,and Cd47; Fig. S4F). Together, these exon junction observationssuggested further diversification of cell adhesion signaling in single

cells, which—at least regarding neurexins—was also suggested inour previous findings (9).

Synaptic Vesicle Exocytosis and Intracellular Signal Transduction.One presumed role of CAMs is to convey transsynaptic in-formation and thereby establish a functional match betweenpresynaptic and postsynaptic specializations. However, neitherthe precise molecular targets nor the underlying mechanismshave been described for most CAMs. Thus, we analyzed ex-pression of two broadly defined groups of genes that are likelylinked to cell adhesion function, namely genes related to vesicleexocytosis and to RhoGAP/RhoGEF signaling (see detailed listin Fig. S5A and Dataset S1). Exocytosis-related molecules (38)were expressed broadly in all single cells and at all developmentalstages (Fig. 5 A and B, and Fig. S5B). In single cells, we detectedboth ubiquitous and cell type-specific features, where the formerincluded Snap25, Synaptobrevin-2 (Vamp2), Snap47, and Syt11 (forwhich no function has been uncovered yet, despite its high abun-dance), whereas Cplx1, Stx1a, Syt13, and Synaptobrevin-1 (Vamp1)were expressed higher in interneurons (MW test, P < 0.05, for allgenes; expression of Synaptobrevin-1 also appeared to be exclusive,as it was not detected in any PYRs; Fig. 5 A and B, and Dataset S1).In addition, we found exclusive expression of Sncg and Syt6 genes inRS-INT cells, with unknown functional consequences.Next, we analyzed the expression of RhoGAP- and RhoGEF-

domain–containing and related proteins. These are membrane-associated proteins that are thought to transform extracellularreceptor-mediated signals into an intracellular response, prominently

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Fig. 4. Hierarchy of CAMs in single neurons. (A) Averaged, single-cell expression of CAMs in RS-INT, FS-INT, and CA1-PYR cells, and in aged-matched P21tissue control (for numerical values, see Dataset S1). (B) Number of CAMs detected in the three cell types and in age-matched tissue control. (C) Heat plotshows single-cell expression for ubiquitously (“All cell types”) as well as cell type-specifically expressed CAMs. Statistical differences were determined usingMW test, with P < 0.05. Averaged data in B represent mean ± SEM.

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the organization of the actin cytoskeleton. Among others, theRho/rac/CDC42 signaling machinery is thought to be involved inactivity-dependent changes in postsynaptic spines (39–41). There-fore, it is plausible that RhoGAPs and RhoGEFs act as downstreameffectors for cell adhesion signals. We observed diverse expressionpatterns for RhoGAPs and RhoGEFs between RS-INT, FS-INT,and CA1-PYR cells (Fig. 5 C and D, and Fig. S5 C and D, andDataset S1). Particularly striking was the selectively high expressionof chimaerin-1 (Chn1) in CA1-PYR cells, consistent with a possiblespine-specific function (42).

Coregulation and Coexpression of Synaptic Molecules. Thus, far, ourdata offer a comprehensive view of gene expression and identi-fied multiple cell type-specific differences. Next, we aimed toevaluate correlations in gene expression, because our data couldpotentially reveal genes that were developmentally coregulatedand coexpressed at the single-cell level, and because such genesmay contribute to core synaptic features. For this analysis, we in-cluded data on genes related to CAMs, exocytosis, and RhoGAPs/RhoGEFs, collected from five developmental stages (hippocampaltissue) and three cell types (single hippocampal cells monitored at∼P21). To avoid inferences from inconsistently detected molecules,we excluded genes that were detected in less than five tissue or lessthan five single-cell samples, narrowing our analyses to 632 and 428genes, respectively.First, we independently examined gene correlations in tissue

and single-cell data, and used unsupervised clustering to consoli-date these results (Fig. 6 A and B), which reflected developmentaland cell type-specific characteristics, respectively (Fig. S6A). Wereasoned that functionally relevant correlations may be present inboth sets with high correlation coefficients, and therefore selectedgene pair correlations that were in the tail of distributions (>92ndpercentile; note that this threshold was chosen arbitrarily but thatconsequences of this analysis were thoroughly tested at multiplethreshold values; Fig. 6E) both for tissue and single-cell data (Fig.6C). This step narrowed our focus to 269 genes and 692 correla-tions. Genes were outnumbered by correlations because somegenes correlated with multiple others (i.e., they represented cor-relation “hubs”). We used graph analysis to test such possibility. Insuch graphs, each node represents a gene and each vertex repre-sents correlation between two genes, if any, with a length inverselyproportional to the respective coefficient; genes with more corre-lations simply had more vertices connecting them.

The structure of the resulting graph was surprising becausetwo independent subgraphs emerged that exhibited dense cor-relations within, but no correlations between them (Fig. 6D; notethe complete lack of interconnecting vertices; see Fig. S6C forcomplete gene listings in this graph). A biologically relevantexplanation for these subgraphs was not immediately obvious,prompting us to perform additional analyses. First, we examinedwhether the two subgraphs were defined by nonoverlapping genefamilies (for example, they only include exocytosis or CAM genes).However, this was not the case, as both subgraphs included genesrelated to CAMs, exocytosis, and RhoGAP/RhoGEF (Fig. S6C).Second, we examined robustness of their independence by quan-tifying vertices between the two subgraphs while relaxing criteriafor gene inclusion (from >90th to 0 percentile, i.e., all inclusion, insteps of 10 percentile). These analyses consistently revealedmore intranetwork than internetwork correlations, which re-versed when lowering inclusion threshold to 20 percentiles (0.2correlation coefficient threshold in Fig. 6E, Left), suggestingrobust independence (note that with “all inclusion,” 0 percentile,the graph is perfectly connected). Third, we examined random-ness in graph connectivity. In a random graph, each gene wouldhave a similar number of correlations, or vertices in the graph.However, both subgraphs displayed a nonrandom, “scale-free”nature where some genes were more interconnected than others,following a power law distribution and thus suggesting a biologicalorigin (Fig. 6E, Right) (43). Fourth, we analyzed gene expressionlevels in each subgraph. Here, we found that developmental geneexpression profiles were very similar within each, but not when thetwo sets were compared with each other. Specifically, subgraph 1included genes that were highly expressed during early devel-opment, whereas subgraph 2 contained genes that exhibited laterexpression onsets, with peak expressions occurring >2 wk afterbirth (Fig. 6F, Left). In agreement with this finding, late expressinggenes (subgraph 2) displayed higher expression than early express-ing genes (subgraph 1) in single cells of all three types (Fig. 6F,Right), which were collected at ∼P21. Together, these analysessuggested developmental coregulation as a primary determinant forseparation of the two graphs.Next, we sought to further examine graph structures using

added knowledge about identity of single-cell samples. We rea-soned that robust gene expression correlations should be con-sistently present if cell types were analyzed independently.Therefore, we repeated analyses such that Fig. 6B only includedRS-INT, or FS-INT, or CA1-PYR cells separately, or RS-INT

BA

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Fig. 5. Cell-specific expression of exocytosis and RhoGAP/RhoGEF signal transducer molecules. (A) Schematic drawing depicting the exocytotic machinery ofsynapses. (B–D) Averaged single-cell expression of exocytosis- (B), RhoGAP- (C), and RhoGEF-related genes (D) in RS-INT, FS-INT, and CA1-PYR cells, and inaged-matched P21 tissue control (for numerical values, see Dataset S1). RhoGAP- and RhoGEF-related molecules are implicated in transmembrane signaltransduction and intracellular cytoskeleton reorganization. Averaged data represent mean ± SEM.

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and FS-INT cells combined, because they both representedGABAergic interneurons. We found that each of these analysesconfirmed the independence of subgraphs 1 and 2 (Fig. S6B),and that the gene representation in each cell type-specificdataset was at least 80% identical to that in Fig. 6D (Fig. S6B),suggesting that coexpression of these genes occurs at the single-cell level. Moreover, we identified core parts of these subgraphs(i.e., parts that can be consistently detected in each cell type) bytaking the intersection of the cell type-specific analyses (Fig.6G). Because of their robust presence in each cell type, thesecorrelations conceivably represented ubiquitous features, at dif-ferent developmental stages of synapse maturation. This hy-pothesis was supported by the existence of correlations betweenstructurally relevant RhoGEF and RhoGAP molecules in earlydevelopment, when synapses are formed (Fig. 6G, Left, and Fig.S6D, Upper). Conversely, emerging correlations between celladhesion and exocytosis in the late-gene network may reflectsynapse specialization (Fig. 6G, Right, and Fig. S6D, Lower).Although our analyses did not reveal a mechanistic explanationfor the observed correlations, they identified synaptic genes thatwere both developmentally coregulated and coexpressed at thesingle-cell level.

CAMs in BS-PYR and RS-PYR Cells of the Subiculum. Our findingsthus far revealed ubiquitous and cell type-specific features ofCAM and signaling molecule expression as well as the contextualdeterminants of their expression, involving molecules related toexocytosis and Rho signaling. Next, we wanted to examine thegenerality of our central findings, including that of (i) theidentity of ubiquitously expressed CAMs and of (ii) the existenceand identity of synaptic early- (subgraph 1) and late-gene interaction

networks (subgraph 2). To this end, we performed single-cellRNAseq experiments to analyze electrophysiologically definedBS-PYR and RS-PYR neurons in the subiculum (Fig. 7A), whichare the major projection targets of CA1-PYR cells. We hypothe-sized that their gene expression profiles are different, because theyexhibit distinct excitability and synaptic properties (12, 13, 15).We detected 9,671 ± 278 and 9,499 ± 405 genes in BS-PYR

and RS-PYR cells, respectively (P = 0.85; Fig. 7B and Fig. S7A),with more consistent gene expression patterns than among CA1-PYRs (Fig. 7C and Fig. S7B). Next, we examined the molecularprofiles of these cells, especially seeking exclusive markers thatwould unequivocally identify these functionally different celltypes. However, we found that they were largely identical anddetected only a surprisingly small number of exclusively expressedgenes (Fig. 7D). Such genes included, for example, the neuro-peptide cortistatin (Cort) and the c-Fos–induced growth factor(Figf) (a member of the VEGF family). Apart from these genes,BS-PYR and RS-PYR neurons expressed similar patterns ofvoltage-gated ion channels (Fig. 7E; see differently expressed ionchannels in Fig. S7C), CAMs (Fig. 7F), ligand-gated ion channels,and exocytosis- and RhoGAP-/RhoGEF-related molecules (Fig. S7C–G and Dataset S1).We next examined expression of ubiquitously present CAMs

(as identified in CA1 interneurons and PYR cells), which wereliably detected in subiculum PYR cells similar to CA1-PYRs(Figs. 4C and 7G), further strengthening the notion of generalimportance of these genes in neuron and synapse function. Al-though 42 CAMs had different expression levels between the twosubiculum PYR cell types (P < 0.05; pairwise MW test; Dataset S1),none of these CAMs was exclusively expressed in one or the othercell type. However, their expression differed markedly from that of

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Fig. 6. Coregulation and coexpression of synaptic molecules. (A) Heat plot of correlations between genes representing cell adhesion-, exocytosis-, RhoGAP-,and RhoGEF-related molecules in hippocampal tissue. Coefficients were computed based on gene expression values in five developmental stages in tissuesamples. Unsupervised clustering revealed 12 clusters (labeled on the Right and Top) based on 395,641 gene correlations. (B) Same analysis as in A, but forsingle-cell data. Coefficients were computed based on single-cell gene expression values independent of cell type identity. Unsupervised clustering revealedfive clusters (labeled on the Right and Top) based on the 187,489 gene correlations. (C) Combined data of correlation coefficients from tissue and single-cellsamples. Gene pairs with both coefficients larger than the 92nd percentile of the respective distributions were further analyzed (green shaded area; see textfor further information). (D) Graph representation of correlation coefficients displayed in the green shaded area of C. Each vertex connects two genesaccording to the correlation coefficient between the two. Graph representation of correlations in C revealed two independent, uncorrelated subgraphs.(E, Left) Subgraph 1 and subgraph 2 remained independent when relaxing gene inclusion criteria (i.e., when green area in C was gradually increased). (Right)Density of vertex distribution (number of gene–gene correlations) followed power law distribution, indicating that both subgraphs were scale-free andnonrandom in nature. (F, Left) Mean normalized gene expression values in the two subgraphs showed diametrically opposite developmental trajectories, andsuggest that genes in subgraph were developmentally coregulated. Because single-cell expression was a selection criterion in C, these genes were alsocoexpressed at the single-cell level. (Right) As suggested by the developmental differences, genes in subgraph 1 and subgraph 2 had different expressionvalues in single cells of all three cell types, collected at ∼P21. (G) Core early- and late-gene networks (subgraph 1 and subgraph 2, respectively) were identifiedby common motifs found in cell type-specific analyses (i.e., these correlations were present in pooled data from the three cell types, in pooled data fromRS-INT and FS-INT cells, representing interneurons, and RS-INT, FS-INT, and CA1-PYR data, analyzed independently). Averaged data represent mean ± SEM.

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cell type-specifically expressed CAMs in CA1 PYR cells. Sub-iculum PYR cells consistently used CAMs that were not observedin CA1-PYR cells. For example, Clstn3 (significantly enriched onlyin interneurons in CA1), Ptpn5 and Ptprn2 (both were enriched inFS-PV cells), as well as Lrrc4 (not present in any CA1-PYR) weredetected and highly expressed in subiculum PYR cells. Furthercell-to-cell analysis corroborated molecular similarities betweenBS-PYR and RS-PYR cells, as confirmed by the inability of PCA(used on the complete transcriptome) to distinguish between thesecells (Fig. 7H).Finally, the subiculum neuron data allowed us to test the

validity of synaptic gene correlation networks described in Fig. 6.

We repeated the combined analysis of tissue and single-cellRNAseq data on subiculum PYR cells, independent from CA1cells. In these analyses, we also identified two independentsubgraphs, which were essentially identical to those found in CA1RS, FS, and PYR cells. To reexamine the identity of the twonetworks, we singled out overlapping gene correlations in all fivecell types (Fig. 7I) and found that they were nearly identical tosubgraph 1 and subgraph 2, identified based on the three CA1 celltypes (Fig. 6G). Therefore, these analyses independently confirmedthe existence of coregulated and coexpressed synaptic gene net-works, which may be generally present in cells in the brain in-dependent of cell type identity.

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Fig. 7. Molecular identity of BS-PYR and RS-PYR neurons in the subiculum. (A) Experimental design shows electrophysiological identification of BS and RSneurons as well as sample collections, and processing of single-cell RNAseq data. White box indicates the subiculum region. (B) The number of detected geneswere not different in burst- (n = 21) and regular-firing (n = 14) neurons. (C) Plot shows consistency of gene expression in the two cell types. (D) Heat mapshows exclusively expressed genes in burst- and regular-firing neurons. (E and F) Expression of voltage-gated ion channels and CAMs in BS-PYR and RS-PYRcells as well as in age-matched tissue controls (P21; for numerical values, see Dataset S1). (G) Heat map shows single-cell expression of CAMs respective to theorder of their expression in the CA1 region, as shown in Fig. 4C. Expression of these molecules in BS-PYR and RS-PYR cells were not distinguishable. (H) Whole-transcriptome PCA was also unable to distinguish between BS-PYR and RS-PYR cells. (I) Analyses of gene expression in BS-PYR and RS-PYR cells confirmedexistence of early- and late-gene networks (shown in Fig. 6). These plots show core consensus networks defined by independent analyses in five cell typesexamined, including RS-INT, FS-INT, CA1-PYR, BS-PYR, and RS-PYR neurons.

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DiscussionA molecular description of gene expression in individual, in-dependently identified neurons in combination with knowledgeof their physiological properties and synaptic connectivity is aprerequisite for insight into how synaptic connections are formedand maintained in brain. Moreover, such data are essential forunderstanding why disease-associated gene mutations impair brainfunction. RNAseq of single neurons can be routinely obtained fromcells isolated from tissue lysates (44–47) and has recently beenachieved from electrophysiologically recorded cells (the currentstudy; also see refs. 16 and 17). Here, we used single-cell RNAseqof individual synaptically and electrophysiologically characterizedneurons, and cross-referenced their gene expression profiles to thatof total brain tissue to explore cell adhesion signaling in a well-defined hippocampal circuit. In particular, we examined inhibitoryprojections from RS and FS interneurons to CA1-PYR cells (6, 7,10) and excitatory projections from CA1-PYR cells to subiculumBS-PYR and RS-PYR cells (13, 15). Our results show that captureof mRNAs by aspiration of cytosol allows production of high-quality transcriptome data (Figs. S2 and S7), opening up identi-fication of the precise expression profiles of single neurons as amolecular portal for a detailed functional understanding.Functional implications of gene expression can be most directly

examined by relating gene expression to electrophysiologicalproperties (16, 17, 26–29, 48). Therefore, we first tested whetherthe electrophysiological properties of a neuron could be explainedby its ion channel expression pattern. Previous studies establishedthat expression of Kv3 K+ channels and a high Na+-channeldensity enable the FS properties of PV cells (23). In line with thisconclusion, we found specific enrichment of Kcnc1 (Kv3.1), Kcnc2(Kv3.2), and Scn1a (NaV1.1) in FS-INT cells. By taking advantageof our comprehensive gene expression data, we extended thesefindings and correlated 11 electrophysiological properties with 104ion channel genes. We found strong correlations between specificsets of genes and physiological properties, consistent with thedifferent cell types. Although these correlations may mostly reflectgroup-level differences, an added value of these analyses is theconclusion that electrophysiological properties can be identified us-ing single-cell transcriptome information. More detailed quantifica-tion of electrophysiological properties in combination with extendedsample sets and morphological analyses should, for example, allowdifferentiation of interneuron subtypes (5) on a molecular basis. Inaddition, these results suggest that the validity of RNAseq data inelectrophysiologically defined cells extends to genes whose func-tional readouts were not readily available, as is the case for CAMs.We hypothesized that combinatorial expression of CAMs in single

cells may define connectivity in a neuronal network. A corollary ofthis hypothesis is that CAMs in a presynaptic terminal must interactwith complementary CAMs in the postsynaptic compartment;this interaction is—through reciprocal ligand–receptor coupling—enabled via the synaptic cleft. Abundant presence of CAMs inhippocampal tissue (Fig. 1) supports this hypothesis, because thediversity of CAMs likely permits a large number of combinations,possibly defining synaptic connectivity between the more than 20cell types in the hippocampus and beyond (5). Our data present themost current account of CAMs in the brain, described by expres-sion analysis of 406 genes. A limitation of our RNAseq results isthat a transcriptome profile represents the abundance of mRNAsin a given cell type but does not provide information regardingprotein abundance or localization. A given neuron receives het-erogeneous inputs (excitatory and inhibitory synapses from multiplesources) and can innervate multiple postsynaptic cells of differingidentities. Thus, it is plausible that CAMs are differentially localizedand combinatorially used in a synapse-specific manner. To un-derstand the organization of these molecules and possibly interpretthem as a code for connectivity, we need to clarify which of thesemolecules are expressed in specific synapses and physically interactwith each other. An important step to this direction is to examinecell type-specific expression of these molecules.Our hypothesis suggests that expression of CAMs should differ

among cell types, including RS-INT, FS-INT, CA1-PYR, BS-PYR,

and RS-PYR cells, in which we previously established functionallyrelevant differences in cell adhesion signaling (9, 10, 13, 15). Thus,we examined candidate synaptic CAMs in single cells from thesetypes. Presynaptic and postsynaptic CAMs presumably engage inhomophilic and heterophilic interactions in the synaptic cleft, butthe rules that associate particular CAMs to specific synapses andtheir transcriptional regulation in different cell types were notknown. In examining the cell type-specific expression of CAMs, wemade two fundamental observations (Figs. 4 and 7).First, CAMs are hierarchically expressed. This conclusion is

based on the observation that some CAMs were consistently andhighly expressed in all five cell types examined (these includedNptn, Nrxn1, App, Ptprs, Nrcam, Ppfia2, Chl1, Cntn1, Ptprn, andBsg), whereas others were expressed cell type-specifically (theseincluded, for example, Cntnap4, Fstl5, Igsf8, Lrrc4, Cbln2, Eph2b,Idgcc4, Nxph1, Epha4, and Sema3e). The most likely explanationfor such hierarchy is that commonly expressed molecules are re-quired for core synaptic features as well as other neuronal cellinteractions, whereas cell type-specific molecules may enablefunctional specialization. For example, it is intriguing to speculatewhich, if any, of these molecules are transsynaptically involved insculpting endocannabinoid signaling in RS CCK or opioid signalingin FS PV synapses, respectively (6, 7, 10). Overall, these datasupport our hypothesis that CAMs are combinatorially expressedat the single-cell level.Second, we identified synaptic genes that were both develop-

mentally coregulated as well as coexpressed in single cells (Figs.5–7). This was made possible by a combined analysis of tissueand single-cell data, and revealed two independent gene networks:an “early-gene” and a “late-gene” network. Beyond developmentaldifferences, molecular composition of the two networks implicateddistinct functional relevance. Specifically, interactions between in-tracellular signal transducer molecules were enriched in the early-gene cluster, whereas representation of exocytosis-related moleculesas well as their interactions with CAMs were more apparent in thelate-gene cluster. Such findings are consistent with the develop-mental sequels of structural formation and functional maturation ofsynapses and neuron–glia interactions. Because they are compre-hensive in nature, experimental probing of these networks is chal-lenging as it may require simultaneous manipulation of multiplegenes. Such approach, targeting multiple heavily connected genesor hubs (e.g., Clstn1, Clstn3, or Igsf8), could reveal susceptibility togenetic conditions with especially disruptive pathophysiologicalconsequences.Together, our results provide a description of which electro-

physiologically and synaptically characterized neurons expresswhich specific CAMs. Some of these molecules are already knownto play important synaptic roles, as their genetic disruptions havebeen linked to neuropsychiatric disorders in humans and havebeen shown to be lethal in mice (33). However, the function of themajority of these molecules as well as the logic that integratesthem into assembly and specification of synapses or other in-tercellular junctions remain unknown. Here, we made progresstoward understanding such logic by measuring developmentaland single-cell expression of CAMs. When revealed, underlyingprinciples can help us to use the brain’s cell adhesion code toformally describe connectivity in neuronal circuits. Such applica-tions would lead to developments in molecular diagnostics allowingcomprehensive analysis of brain circuits from single-cell samples.

Materials and MethodsElectrophysiology and Single-Cell Sample Collection. All animal protocols andhusbandry practices were approved by the Institutional Animal Care and UseCommittee at Stanford University. Hippocampal slices (300 μm) were preparedfrom 3- to 4-wk-old wild-type CD1 mice, as described in ref. 10 and SI Materialsand Methods.

cDNA Preparation and Library Preparation for Next-Generation Sequencing.Single-cell mRNA was performed using the Clontech’s SMARTer Ultra LowInput RNA Kit. As a first step, cells were collected via pipette aspiration intosample collection buffer, were spun briefly, and were snap frozen on dry ice.

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Samples were stored at −80 °C until further processing, which was performedaccording to manufacturer’s protocol. Library preparation was performed us-ing Nextera XT DNA Sample Preparation Kit (Illumina) as described in theprotocol. Then, cells were pooled and sequenced in an Illumina NextSeq500instrument using 2 × 75 paired end reads on a NextSeq high-output kit (Illu-mina; see SI Materials and Methods for details).

Processing of mRNA Sequencing Data. After de-multiplexing the raw reads tosingle-cell datasets, we used Prinseq to remove short reads. After trimmingand removal of overrepresented sequences and adapters, remaining readswere aligned to the mm10 genome with STAR aligner. Aligned read wereconverted to gene counts using HTSeq (see SI Materials and Methods fordetailed parameter descriptions).

Gene Categories. For gene expression analysis, we examined six functionallyrelated categories: CAMs, voltage-gated ion channels, ligand-gated ion

channels, synaptic exocytosis-related molecules, as well as RhoGAP andRhoGEF signaling-related molecules. For each of these, we assembled acomprehensive list, which included all genes examined (see SI Materials andMethods for a detailed description of categories).

Data Analysis. All data analyses were performed using Mathematica10(Wolfram Research). These analyses included (i) normalization of gene ex-pression data, (ii) quality control, (iii) analysis of physiological properties,(iv) analysis of exon junctions, and (v) correlation and graph analyses ofexpression data (see SI Materials and Methods for more details).

ACKNOWLEDGMENTS. This work was supported by National Institute onDrug Abuse Grant K99 DA034029 (to C.F.), Swiss National ScienceFoundation Grant CRETP3_166815 (to C.F.), and the National Instituteof Mental Health Grants R37 MH52804 (to T.C.S.) and K99 MH103531(to J.A.).

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