Single-cell RNAseq reveals cell adhesion molecule PNAS PLUS profiles in electrophysiologically defined neurons

Csaba 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 the neurexin (Nrxn1 and Nrxn3; presynaptic cell adhesion mole- identity and functional properties of synapses. The specificity of cules) isoforms were expressed cell type-specifically, with re- presynaptic and postsynaptic interactions that is presumably medi- markable consistency in respective cell types (9). We also found ated by cell adhesion molecules suggests that there exists a logic that that genetic deletion of neuroligin-3 (Nlgn3) (postsynaptic cell could explain neuronal connectivity at the molecular level. Despite its adhesion molecule) in PYR cells disabled tonic, cannabinoid importance, however, the nature of such logic is poorly understood, type 1 receptor-mediated, endocannabinoid signaling in RS CCK and even basic parameters, such as the number, identity, and single- synapses, but had no detectable phenotype in FS PV synapses cell expression profiles of candidate synaptic cell adhesion molecules, (10). Thus, although no systematic assessment of cell adhesion are not known. Here, we devised a comprehensive list of molecules in GABAergic interneurons is available, previous studies established that cell adhesion molecules play a central involved in cell adhesion, and used single-cell RNA sequencing role in controlling their properties. (RNAseq) to analyze their expression in electrophysiologically de- Similar to their inputs, outputs of CA1-PYR cells display fined interneurons and projection neurons. We compared the cell functional dichotomy: they primarily project to the subiculum, type-specific expression of these genes with that of genes involved forming synapses on two, electrophysiologically different prin- in transmembrane ion conductances (i.e., channels), exocytosis, and cipal cell types: regular-spiking pyramidal (RS-PYR) and burst- rho/rac signaling, which regulates the actin cytoskeleton. Using spiking pyramidal (BS-PYR) cells. Analysis of these neurons is NEUROSCIENCE these data, we identified two independent, developmentally regu- particularly difficult because, although RS-PYR and BS-PYR lated networks of interacting genes encoding molecules involved in cells exhibit distinct electrophysiological signatures as well as cell adhesion, exocytosis, and signal transduction. Our approach pro- dramatically different forms of long-term plasticity, no molecular vides a framework for a presumed cell adhesion and signaling code markers are available to distinguish these neurons (11, 12). In in neurons, enables correlating electrophysiological with molecular examining synapse-specific mechanisms in these cells, we have properties of neurons, and suggests avenues toward understanding shown that different forms of long-term plasticity were de- synaptic specificity. termined presynaptically by expression of specific neurexin iso- forms in CA1-PYR cells (13–15). Together, these molecular and synapse | cell adhesion | single cell | RNAseq physiological analyses revealed specific control of synaptic properties (such as forms of LTP and endocannabinoid signal- ing) by cell adhesion molecules in CA1-PYR cell inputs and he brain’s “connectivity code” is thought to confer exquisite Tspecificity to brain circuits by dictating connectivity between different types of neurons. Although its existence has not yet Significance been conclusively demonstrated, synaptic cell adhesion mole- cules likely comprise a large part of such a code (1–4). Cell ad- Synapses functionally connect neurons in the brain and medi- hesion molecules are encoded by ∼2% of the genome and play ate information processing relevant to all aspects of life. central roles in all tissues. During brain development, precisely Among others, synaptic connections are enabled by cell adhe- matching presynaptic and postsynaptic cell adhesion molecule sion molecules, which connect presynaptic and postsynaptic interactions likely guide synapse formation and specify the membranes by binding to each other via the synaptic cleft. properties of synapses by activating signal transduction cascades Mammalian genomes express hundreds of cell adhesion mol- and recruiting scaffolding molecules, receptors, and active-zone ecules whose combinatorial utilization is thought to contribute ; in addition, such interactions could provide structural to the brain’s “connectivity code.” Such code could explain the support to synapses. However, the molecular mechanisms in- versatility of synapses as well as the logic of connectivity be- volved are not understood. Because cell adhesion molecule- tween cell types. Here, we used single-cell RNA sequencing to based interactions likely code for synapse specificity in a com- analyze the expression of cell adhesion molecules and other binatorial fashion (2, 3), an important step toward gaining insight signaling proteins in defined cell types, and found developmental into these molecular mechanisms is to eliminate nonfunctional patterns that potentially identify relevant elements of the possibilities, which—to a great extent—can be done by examin- connectivity code. ing cell type-specific expression of these molecules. If cell adhesion molecules dictate synapse properties, then Author contributions: C.F., S.D., J.A., R.C.M., S.R.Q., and T.C.S. designed research; C.F., S.D., such differences must be clearly present in interneuron and py- and J.A. performed research; C.F. analyzed data; and C.F. and T.C.S. wrote the paper. ramidal cell classes, which have diverse synaptic properties. For Reviewers: T.B., Tufts University; T.F.F., Institute of Experimental Medicine; and L.-H.T., Massachusetts Institute of Technology. example, within the hippocampal circuit, CA1 pyramidal (CA1- PYR) neurons receive convergent inputs from multiple, elec- The authors declare no conflict of interest. trophysiologically distinct inhibitory interneurons within the Data deposition: The sequence reported in this paper have been deposited in the Expression Omnibus (GEO) database, www.ncbi.nlm.nih.gov/geo (accession no. hippocampus (5). Inhibitory inputs include synapses formed by GSE75386). fast-spiking (FS) and regular-spiking (RS) interneurons (ref. 6; 1To whom correspondence may be addressed. Email: [email protected] or tcs1@ for reviews, see refs. 7 and 8). We previously showed using sin- stanford.edu. gle-cell transcriptional profiling in FS parvalbumin (PV)- and RS This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. cholecystokinin (CCK)-containing GABAergic interneurons that 1073/pnas.1610155113/-/DCSupplemental.

www.pnas.org/cgi/doi/10.1073/pnas.1610155113 PNAS Early Edition | 1of10 Downloaded by guest on September 25, 2021 outputs, and raised the question whether other synaptic prop- Results erties were also controlled by cell adhesion molecules. Because Cell Adhesion Molecules in the Hippocampus. As a starting point, we there are a large number of molecules with such potential, a examined the transcriptome of the entire hippocampus (Fig. 1A) logical step to this direction is the cell type-specific analysis of at five developmental stages (in triplicate at postnatal days P0, cell adhesion-related . P7, P14, P21, and P28). In these samples, the total number and Here, we combined electrophysiology and single-cell RNA se- distribution of expressed genes were similar (Fig. 1 B and C, and quencing (RNAseq) to identify cells in the pathway involving Fig. S1). To specifically analyze cell adhesion molecules, we hippocampal FS interneuron (FS-INT), RS-INT, CA1-PYR cells, created a comprehensive list of candidate genes involved in, or and subiculum RS-PYR and BS-PYR cells, and to analyze their related to, transsynaptic cell adhesion (collectively referred to as gene expression profiles. Our data represent an initial circuit-level CAMs, for candidate cell adhesion-related molecules). For this, single-cell RNAseq analysis from synaptically and electrophysio- we first considered all molecules with single transmembrane logically defined neurons. We find surprising differences in the domains (a general, although not unique property of membrane total number of expressed genes among neuron types and show surface adhesion molecules) and narrowed this list down to 406 that hippocampal neurons can be characterized by the expression genes based on preexisting data (Fig. S1C). This curated list of of two common, developmentally regulated gene networks com- candidate cell adhesion molecules includes proteins implicated prising shared cell adhesion and signaling molecules. Moreover, we in cell–cell signaling but excludes, for example, intracellular demonstrate that each type of electrophysiologically defined neu- signal transducers linked to cell adhesion signaling. We found ron expresses a separate set of candidate synaptic cell adhesion and these genes to be broadly represented in all replicates and de- signaling molecules. Finally, we extended these analyses to the two velopmental stages (Fig. 1D), highlighting diversity of CAMs in types of subiculum PYR neurons that feature highly similar tran- the hippocampal circuit and throughout development. Expression scriptomes but express a limited number of unique markers, in- profiles (Fig. 1E) for each developmental stage included genes that cluding cell adhesion-related genes, which could potentially be were consistently highly expressed at all developmental stages [for used in future analyses. In this manner, the approaches and data example, amyloid precursor (App), contactins (Cntn), developed here lay the foundation for a biologically relevant neurexins (Nrxn), and protein-tyrosine (Ptpr); Fig. analysis of how cell type-specific neuronal gene expression may S1D], as well as genes with large changes during development sculpt neural circuits during development and beyond. (for example, Ncam1 and Sdc3). Normalization of individual gene

A Brain tissue RNAseq Alignment Analysis E Nptn Nrxn1 Multiplexed App Clstn1 Nrxn3 Ptprs Tspan5 Ppfia2 cDNA mm10 Bai3 Clstn3Csf1r Ntrk2 Ptprz1 Ctnnb1 libraries Bsg Mdga1 Sdc3 Vstm2a 15 Ctnnd2 Nxph1 Ptpra Epha4 Ncam1 Pcdh17 Ptpre 10 Cadm1&2 Cntn1 Lphn1 Tissue Fstl5 Lrrn2 P0 mRNA Cd14 Lrrn3 2 5 Cd47

1 0

0 15 0 2 4 10 P7 BCD 5 20 400 0 18 390 15 P28 16 Density P21 380 10 P14 1000 P14 5 14 CAMs 370 500 P7 0 12 P0 360 15 P0 P7 P14 P21 P28 0 P0 P7 P14 P21 P28 420 10

Detected genes (thousand) genes Detected P21 Postnatal days Norm. gene count Postnatal days 5 0 1 FGNormalized gene expr. 137 genes at P0 15 0.4 10 1 P28

Normalized gene count (per all genes, per tenthousand) 5 100 109 genes at P7 0.4 0 1 200

CAMs 54 genes at P14 Amigo Cntn Fgfr Kirrel Mertk Ntng Rtn 0.4 App Cntnap Flrt Kit Mfap3 Ntrk Sdc 300 Bai Cntfr Flt Lgr Ncam Nxph Sdk 1 Bsg Csf1r Fshr Lhcgr Negr Odz Sema C1ql Ctnna Fstl Lilra5 Neo Omg Slitrk Cadm Cxadr Gp Lphn Neurog Opcml Tek 0.4 55 genes at P21 Car Dag1 Gpr Lrfn Nfasc Pcdh Tie 400 Normalized gene Hepacam Lrig Nlgn Pdgrf 1 Cbln Dscam1l Icam Lrrc Nphs Pigr Tlr expression (per gene) Cd Elfn2 Igdcc4 Lrrn Nptn Pkd Tpbg P0 P7 Cdh Emb Iglon5 Lrrtm Nptx Ppfia Tshr P14 P21 P28 50 genes at P28 Cdhr Efna Igsf Lrtm Nrcam Ptgfrn Tyro 0.4 Ceacam Epha Il1r1 Lsamp Nrg Ptk Tspan Postnatal days Celsr Fam19a Ilrap Mag Nrxn Prpn Unc5 Chl Fat Islr Mcam Ntm Ptpr Vcam Clstn Fcgr2b Jam Mdga Ntn Pvrl Vstm2 P0 P7 Norm.exp. P14 P21 P28 10 Postnatal days Cell-adhesion molecule coding gene-families (in alphabetical order)

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

2of10 | www.pnas.org/cgi/doi/10.1073/pnas.1610155113 Földy et al. Downloaded by guest on September 25, 2021 expression levels also revealed an average change of 60% between Examining overall gene expression, we found that interneu- PNAS PLUS P0 and P28 (Fig. 1 F and G). rons consistently expressed almost twice as many genes as CA1- PYR cells (Fig. 2C; see Fig. S2F for controls), and that detection Single-Cell RNAseq from Electrophysiologically Identified Cells. To of individual genes was also more consistent in RS- and FS-INT analyze gene expression in specific cell types in a defined circuit, cells than in CA1-PYR cells (Fig. 2D). A potential explanation we developed a method—which was also recently introduced by for the latter observation involves spatial gene expression gra- others (16, 17)—with which we first characterized neurons dients in the hippocampus that likely underlie heterogeneity of electrophysiologically, and subsequently analyzed their tran- CA1 pyramidal neurons (20). scriptome by aspiration of cytosol followed by single-cell RNA- seq. Using this approach, we examined FS and RS interneurons Molecular Correlates of Electrophysiological Properties. To link sin- that directly inhibit CA1-PYR cells, as well as the target CA1- gle-cell transcriptional profiles to functional properties, we exam- PYR cells for these interneurons. We patched FS and regular- ined expression of 140 voltage-gated ion channels (see Materials firing interneurons located within, or in close proximity to, the and Methods for gene set assembly; Fig. 3A, Fig. S3A,andDataset pyramidal cell layer in wild-type mice, and then used paired re- S1). Because RS-, FS-, and CA1-PYR cells have distinct electro- cordings by simultaneously patching PYR cells to test whether physiological properties (Fig. 3A), we asked whether presynaptic action potentials (APs) evoked inhibitory post- expression could account for their physiological differences. On synaptic responses, characterizing these cells as FS or RS in- average, we detected more ion channels and more consistent gene hibitory interneurons (Fig. 2A). Note that such distinction did expression in interneurons than in PYR cells (Fig. 3 B and C), following total transcript levels. As a pivotal example of cell type- not necessarily identify individual cell types. For instance, FS + + cells could include PV+ basket, bistratified, and axoaxonic cells, specific gene expression, we found enrichment of Na and K + channels in FS cells (Fig. 3 D and E, Upper). whereas RS cells could include CCK basket or bistratified cells = as well. At the end of the recordings, we collected the neuronal Next, we quantified electrophysiological parameters (n 11; cytosol via pipette aspiration for RNAseq (Fig. 2A). Upon Fig. 3 A and E, Left) because their correlation with gene ex- pression patterns could reflect characteristic, cell type-specific alignment of sequencing reads and assignment of gene counts for properties in these cells. We used unsupervised clustering and each gene, we applied stringent quality control metrics to each plotted genes/electrophysiology parameters with at least one cell (Fig. S2 A and B). RNAseq data obtained in this manner correlation value of greater than 0.35 or less than −0.35 (arbi- from 18 RS-INT, 9 FS-INT, and 14 CA1-PYR cells passed trary threshold to eliminate no or minimal correlations; Fig. 3E). quality control.

Here, we found that AP firing rate, threshold, symmetry, and NEUROSCIENCE To gain insight into the molecular identity of recorded cells, afterhyperpolarization, parameters that typically exhibit high + + we examined expression of genes that had been previously as- values in FS cells, correlated with high expression of Na and K sociated with RS-INT, FS-INT, or PYR cells (Fig. 2B). As channels (Fig. 3E), including Kcnc1 (Kv3.1) and Kcnc2 (Kv3.2). expected, Gad1 and Gad2 were highly expressed in interneurons Furthermore, we found that expression of these genes inversely but not in PYR cells, consistent with their respective neuro- correlated with AP peak-to-trough and width times (i.e., larger transmitters. Although CCK peptide is a unique marker for RS gene expression values correspond to lower electrophysiological CCK cells, we found that the CCK gene was nonspecifically property values, and vice versa), which as properties also cor- expressed in all three cell types. Similarly, although cannabinoid respond to the fast signaling characteristics of FS-INT cells. type-1 receptor (Cnr1) is most highly expressed in RS CCK cells These results are not unexpected as expression of Kcnc1 and (for specific examples, see refs. 10, 18, and 19; for review, see ref. Kcnc2 is a hallmark of FS PV interneurons (refs. 21 and 22; for 7), it was also produced in FS-PV and PYR cells. In contrast, review, see ref. 23), validating the single-cell RNAseq analysis. Htr3a and PV (parvalbumin) were more specific to RS-INT and Surprisingly, unbiased correlation analysis can also deliver FS-INT cells, respectively. Although the latter is a unique seemingly contradictory results. For example, the observation of marker for FS subtypes at the protein level, the corresponding high Hcn1 expression but a low hyperpolarization sag in FS-INT mRNA was detected in two RS-INT and three PYR cells as well. is puzzling because a primary readout for Hcn-mediated Ih cur- To identify PYR cells, we examined Camk2a and Neurod6 ex- rents is the sag amplitude. However, the correlation between ’ pression: although the former s expression was rather non- hyperpolarization sag and Ih current is not absolute, nor the specific, Neurod6 was a reliable predictor of PYR cell identity. involvement of Hcn1 in modulating active membrane properties We also found that Grik3 ( GluR7) uniquely, (24). In addition, we found significantly higher expression of but not exclusively, identified FS-INT cells. Kcnc3, Scn1a, and Trpc6 (Fig. 3E) in FS cells (described for PV

Presyn. Postsyn. A Brain Electrophysiology Cell collection RNAseq B C (18) (9) (14) D RS-INT RS-INT FS-INTCA1-PYR FS-INT Cell firing: 10 * 100 CA1 RS-INT FS-INT CA1-PYR Multiplexed CA1-PYR cDNA Gad1 8 * 80 libraries Gad2 Cck 6 60 50 mV Single-cell Cnr1 Paired-recording: 1 s mRNA Htr3a 4 40

Genes Pvalb CA1C PYR RS-INT FS-INT

Grik3 (thousand) 2 20 Camk2a FS-I. Neurod6 genes Detected 0 0 RS-I. (%) Detected genes CA1-PYR CA1-PYR 10 20 30 40 >50 <50 50 mV Norm. gene expr. 10 ms 50 pA Cells (#) (Log10 based) RS-INTFS-INT Expressing cells (%) 0 4 CA1-PYR

Fig. 2. Single-cell RNAseq in situ of electrophysiologically characterized neurons in identified microcircuits. (A) Experimental design and strategy. Single-cell mRNA samples were collected after paired electrophysiological recordings from RS-INT, FS-INT, and CA1-PYR cells. White box indicates the CA1 region. After sequencing, 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 postsynaptic PYR 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-PYR cells. (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 for visualization) in respective cell types. RS-INT and FS-INT cells had more consistent gene expression profiles than CA1-PYR cells. Averaged data in C represent means ± SEM.

Földy et al. PNAS Early Edition | 3of10 Downloaded by guest on September 25, 2021 A B D 60 Kcnc1 Electrophys. properties Kcnc2 15 Canca1a Scn1a Firing rate 40 Canca1b Scn2a1 10 Canca1eHcna1 Kcnt1 Kcna1 Kcnq2 Scn8a Kcna2 Kcnt1 Trpc4 Amplitude 5 Kcna6Kcnd2Kcng4 adaptation 20 0 RS-INT 15 Detected genes Detected 0 10 5 Resting potential FS-INT Membrane res. 0 RS-INTFS-INT Hyperpol. sag CA1-PYR 15 10 AP symmetry C 100 5 CA1-PYR AP amplitude 0 50 (per ten thousand) 15

RS-INT Normalized gene count AP half-width 10 FS-INT P21 tissue CA1-PYR 5 AP base-width 0 AP threshold 0 Expr. genes (%) Expr. genes >50 <50 After-hyporpol. Expressing cells (%) Potassium Cng Hcn Pkd Trp Mcoln Cell-type-specific firing CalciumCatsper Sodium 10 ms 1 ms E Norm. gene expr. * (Log10 based) ** * 3 RS-INT FS-INT CA1-PYR *** * RS-INT * * ** FS-INT * * * 2 * * * CA1-PYR * * * * ** * * * * * * * * * **** * ** * * * * * * 1 * * * * * * * ** 0

Spearman corr. coeff. Kcnk1 Kcnj6 Kcns3 Cacna1b Kcna2 Kcna3 Kcnk12 Kcnk3 Kcnk6 Mcoln1 Trpm2 Trpv2 Cacna1c Kcnf1 Kcns2 Kcnu1 Pkd2 Scn3a Trpm3 Trpm7 Cacna1e Trpc4 Kcnc1 Kcnc3 Kcng4 Kcnh2 Kcnj9 Cacna1a Catsper2 Kcnj11 Trpv6 Cacna1g Hcn2 Pkd2l2 Scn9a Trpc1 Trpc3 Kcnq4 Kcnb1 Kcng2 Kcnn2 Scn8a Trpc5 Hcn1 Kcna1 Kcnc2 -0.5 00.5 Kcnt1 Scn1a Trpc6 0 300 Hz Firing rate ** 0 400 pA Firing threshold ** 0.1 0.5 AP symmetry *** 5 22.5 mV After-hyperpol. *** 0.4 0.8 AP ampl. adapt ** -75 -55 mV Resting pot. * 0 0.2 Membrane res. ** 0 2.5 mV Hyperpol. sag *** 0 50 ms AP peak to trough ** 0 2 ms AP halfwidth *** 0 4 ms AP basewidth

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 differ between 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 was similar 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 ion channel 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 data and (Left) electrophysiology parameter measurements for RS-INT, FS-INT, and CA1-PYR cells. Averaged data represent mean ± SEM. Asterisks represent significant differences between cell types (*difference between two cell types; **difference between two–two cell types; ***difference between all three cell types).

cells in refs. 25–27, respectively), whereas specific expression of in all cases) in both interneuron types compared with PYRs. We Kcng4 (Kv6.3, Kv6.4) identifies a thus far uncharacterized com- found that genes that were highly expressed in tissue samples ponent of the ion channel repertoire of FS cells. For further were also highly expressed in the RS-INT, FS-INT, and CA1- clues about molecular architecture of these cell types, see ex- PYR cells (note that for sequencing libraries, tissue samples pression of ligand-gated ion channels in Fig. S3 C–E. were diluted and processed using same conditions and reagents Together, these combined analyses extend previous single-cell as for single cells, albeit with larger amounts of starting mRNAs). studies (including refs. 22 and 26–29) that used probe-based Such genes included App, Chl1, Cntn1, Nptn, Nrxn1, and Ptprs gene expression analysis, and enable comprehensive and un- that were consistently detected in all three cell types (Fig. 4C, biased RNAseq-based functional inferences. “All cell types”). The 26 CAMs that were expressed higher in interneurons CAMs in Single Cells. Our analyses in whole-tissue preparations than in CA1-PYR cells included Clstn3, Cntnap4, and Lrrc4, (Fig. 1) consistently detected transcripts encoding 383.2 ± 1.1 whichhavebeenimplicatedinspecificationofinhibitorysyn- (mean ± SEM) different CAMs in the hippocampus, which apses (30–32), as well as Nrxn3, a presumed presynaptic orga- comprises a large variety of neuronal and nonneuronal cell types. nizer of synapse function (13, 14, 33). Note that, although In examining expression of CAMs at the single-cell level, we expression of Nrxn3 was not specific for interneurons, it was focused on how many CAMs were expressed in a neuron, and consistently enriched in interneurons, which we also considered whether these were expressed with cell type specificity. In single here as a cell type-specific feature. Other examples for cell type RS-INT, FS-INT, and CA1-PYR cells, we detected NRS-INT = specificity included Cbln2 and Ephb2 for RS-INT cells, Nphx1 121.6 ± 11.2, NFS-INT = 128.7 ± 12.7, and NCA1-PYR = 82.7 ± 4.8 for FS-INT cells, and Epha4 for CA1-PYR cells. Although different CAM transcripts, respectively [PRS-INT vs. FS-INT = 0.73, some of these molecules have been directly implicated in trans- PRS-INT vs. CA1-PYR = 0.02, PFS-INT vs. CA1-PYR = 0.006; pairwise synaptic signaling (e.g., Nrxn1, Nrxn3, Ptprs) or in synapse spe- Mann–Whitney (MW) test; Fig. 4 A and B and Dataset S1]. cialization (see examples above), others have not been studied in Examining these genes revealed that expression of CAMs detail. Nevertheless, these data suggest cell type-specific ex- partially overlapped between different cell types. There pression of CAMs can potentially explain neuronal connectivity were NRS-INT vs. FS-INT = 48, NRS-INT vs. CA1-PYR = 58, and at the molecular level. Such cell type-specific features were NFS-INT vs. CA1-PYR = 62 differentially expressed CAMs between further highlighted by principal-component axes (PCA) analysis, cell types (pairwise MW test, P < 0.05 in all cases). Thirty CAMs which revealed two main components, including overall expres- were expressed higher (n = 26; MW test, P < 0.05 in all cases) or sion as well as differential expression in interneurons vs. PYR lower (n = 4; Epha4, Mdga1, Pcdhgc5, Sema3e; MW test, P < 0.05 cells (Fig. S4 A–C).

4of10 | www.pnas.org/cgi/doi/10.1073/pnas.1610155113 Földy et al. Downloaded by guest on September 25, 2021 A B PNAS PLUS Nptn Csf1r Nrxn1 250 Nrxn3 400 Chl1 Ppfia2 200 Ntrk2 Tspan5 390 App Clstn1 Ptpra 150 15 Epha4 Mdga1 Bai3 Clstn3 Ncam1 Vstm2a 380 Nxph1 Ptpre 100 10 Lphn1 Pcdh17 Bsg Cntn1 Fstl5 Ptprs 370 Cadm1&2 RS-INT 50 Cd14 molecules 360 5 Cd47 FS-INT Cell-adhesion 0 0 RS-INT CA1-PYR P21 ref. 15 C RS-INT FS-INT CA1-PYR 10 FS-INT Nptn 5 Nrxn1 App Ptprs 0 Nrcam Ppfia2 15 Chl1

Cntn1 typesAll cell 10 Ptprn CA1-PYR Bsg 5 Clstn3 Cntnap4 0 Fstl5 Igsf8 15 Lrrc4 Negr1 Nlgn2 10 Nrxn3

P21 tissue ref. Ntrk2 RS-INT & FS-INT 5 Vstm2a Cbln2 0 Ephb2 Flrt1 Normalized gene count (per all genes, per ten thousand) Lrfn2 RS-INT Amigo Cntn Fgfr Kirrel Mertk Ntng Rtn Sdk2 App Cntnap Flrt Kit Mfap3 Ntrk Sdc Cd164 Bai Cntfr Flt Lgr Ncam Nxph Sdk Clstn2 Bsg Csf1r Fshr Lhcgr Negr Odz Sema C1ql Ctnna Fstl Lilra5 Neo Omg Slitrk Epha10 Gp Lphn Neurog Opcml Cadm Cxadr Tek NEUROSCIENCE Gpr Lrfn Nfasc Pcdh Fam19a2 Car Dag1 Hepacam Lrig Nlgn Pdgrf Tie Igdcc4 Cbln Dscam1l Icam Lrrc Nphs Pigr Tlr Nxph1 Cd Elfn2 Igdcc4 Lrrn Nptn Pkd Tpbg FS-INT Cdh Emb Iglon5 Lrrtm Nptx Ppfia Tshr Ptpn5 Cdhr Efna Igsf Lrtm Nrcam Ptgfrn Tyro Ptprn2 Ceacam Epha Il1r1 Lsamp Nrg Ptk Tspan Celsr Fam19a Ilrap Mag Nrxn Prpn Unc5 Pvrl2 Chl Fat Islr Mcam Ntm Ptpr Vcam Epha4 Clstn Fcgr2b Jam Mdga Ntn Pvrl Vstm2 Pcdhgc5 Sema3e Cell-adhesion molecule coding gene-families (in alphabetical order) Norm. gene expr. CA1-PYR 40 (Log10 based)

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 P21 tissue 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 plot shows single-cell expression for ubiquitously (“All cell types”) as well as cell type-specifically expressed CAMs. Statistical differences were determined using MW test, with P < 0.05. Averaged data in B represent mean ± SEM.

Beyond cell type-specific control of gene expression levels, cells, which—at least regarding neurexins—was also suggested in diversity in CAM signaling could have been generated by other our previous findings (9). cellular mechanisms, especially by alternative splicing. Some of the most notable examples for alternative splicing include Synaptic Vesicle Exocytosis and Intracellular Signal Transduction. CAMs, such as Dscam in and neurexins in mammals, One presumed role of CAMs is to convey transsynaptic in- which can express thousands of alternatively spliced isoforms formation and thereby establish a functional match between (34–37). Therefore, we examined alternative exon use of CAMs presynaptic and postsynaptic specializations. However, neither at the single-cell level. We analyzed genes with reliable end-to- the precise molecular targets nor the underlying mechanisms end exon junction coverage, which was applicable to n = 139 have been described for most CAMs. Thus, we analyzed ex- CAM genes. Out of these, we identified a single isoform in n = pression of two broadly defined groups of genes that are likely 67 and multiple isoforms in n = 72 genes (Fig. S4 D and E). linked to cell adhesion function, namely genes related to vesicle Because of their potential cell type specificity, presence of dif- exocytosis and to RhoGAP/RhoGEF signaling (see detailed list in Fig. S5A and Dataset S1). Exocytosis-related molecules (38) ferent isoforms in the latter group would be especially interesting. were expressed broadly in all single cells and at all developmental However, currently available single-cell RNAseq platforms— — stages (Fig. 5 A and B,andFig. S5B). In single cells, we detected including ours require library fragmentation. As a result, precise both ubiquitous and cell type-specific features, where the former exon use of full-length mRNAs can be inferred only indirectly, included Snap25, Synaptobrevin-2 (Vamp2), Snap47, and Syt11 (for with sample sizes typically larger than those of single cells, hin- which no function has been uncovered yet, despite its high abun- dering further analyses of cell type-specific splice variants. Nev- dance), whereas Cplx1, Stx1a, Syt13, and Synaptobrevin-1 (Vamp1) ertheless, we examined whether any splice variants of the highly were expressed higher in interneurons (MW test, P < 0.05, for all expressed ubiquitous CAMs had the potential to be expressed with genes; expression of Synaptobrevin-1 also appeared to be exclusive, cell type specificity. We found that some of these molecules had as it was not detected in any PYRs; Fig. 5 A and B,andDataset S1). only single isoforms that may or may not involve alternative splicing In addition, we found exclusive expression of Sncg and Syt6 genes in (for example, in Ptprs, we consistently detected exon skipping, RS-INT cells, with unknown functional consequences. whereas in Ptprn and Cadm, we only detected canonical isoforms), Next, we analyzed the expression of RhoGAP- and RhoGEF- whereas others had multiple isoforms (for example, in Nptn, Nrxn1, domain–containing and related proteins. These are membrane- and Cd47; Fig. S4F). Together, these exon junction observations associated proteins that are thought to transform extracellular suggested further diversification of cell adhesion signaling in single receptor-mediated signals into an intracellular response, prominently

Földy et al. PNAS Early Edition | 5of10 Downloaded by guest on September 25, 2021 the organization of the actin cytoskeleton. Among others, the The structure of the resulting graph was surprising because Rho/rac/CDC42 signaling machinery is thought to be involved in two independent subgraphs emerged that exhibited dense cor- activity-dependent changes in postsynaptic spines (39–41). There- relations within, but no correlations between them (Fig. 6D; note fore, it is plausible that RhoGAPs and RhoGEFs act as downstream the complete lack of interconnecting vertices; see Fig. S6C for effectors for cell adhesion signals. We observed diverse expression complete gene listings in this graph). A biologically relevant patterns for RhoGAPs and RhoGEFs between RS-INT, FS-INT, explanation for these subgraphs was not immediately obvious, and CA1-PYR cells (Fig. 5 C and D,andFig. S5 C and D,and prompting us to perform additional analyses. First, we examined Dataset S1). Particularly striking was the selectively high expression whether the two subgraphs were defined by nonoverlapping gene of chimaerin-1 (Chn1) in CA1-PYR cells, consistent with a possible families (for example, they only include exocytosis or CAM genes). spine-specific function (42). However, this was not the case, as both subgraphs included genes related to CAMs, exocytosis, and RhoGAP/RhoGEF (Fig. S6C). Coregulation and Coexpression of Synaptic Molecules. Thus, far, our Second, we examined robustness of their independence by quan- data offer a comprehensive view of gene expression and identi- tifying vertices between the two subgraphs while relaxing criteria > fied multiple cell type-specific differences. Next, we aimed to for gene inclusion (from 90th to 0 percentile, i.e., all inclusion, in evaluate correlations in gene expression, because our data could steps of 10 percentile). These analyses consistently revealed potentially reveal genes that were developmentally coregulated more intranetwork than internetwork correlations, which re- and coexpressed at the single-cell level, and because such genes versed when lowering inclusion threshold to 20 percentiles (0.2 correlation coefficient threshold in Fig. 6E, Left), suggesting may contribute to core synaptic features. For this analysis, we in- “ ” cludeddataongenesrelatedtoCAMs, exocytosis, and RhoGAPs/ robust independence (note that with all inclusion, 0 percentile, RhoGEFs, collected from five developmental stages (hippocampal the graph is perfectly connected). Third, we examined random- tissue) and three cell types (single hippocampal cells monitored at ness in graph connectivity. In a random graph, each gene would have a similar number of correlations, or vertices in the graph. ∼P21). To avoid inferences from inconsistently detected molecules, However, both subgraphs displayed a nonrandom, “scale-free” we excluded genes that were detected in less than five tissue or less nature where some genes were more interconnected than others, than five single-cell samples, narrowing our analyses to 632 and 428 following a power law distribution and thus suggesting a biological genes, respectively. origin (Fig. 6E, Right) (43). Fourth, we analyzed gene expression First, we independently examined gene correlations in tissue levels in each subgraph. Here, we found that developmental gene and single-cell data, and used unsupervised clustering to consoli- expression profiles were very similar within each, but not when the date these results (Fig. 6 A and B), which reflected developmental two sets were compared with each other. Specifically, subgraph 1 and cell type-specific characteristics, respectively (Fig. S6A). We included genes that were highly expressed during early devel- reasoned that functionally relevant correlations may be present in opment, whereas subgraph 2 contained genes that exhibited later both sets with high correlation coefficients, and therefore selected expression onsets, with peak expressions occurring >2 wk after > gene pair correlations that were in the tail of distributions ( 92nd birth (Fig. 6F, Left). In agreement with this finding, late expressing percentile; note that this threshold was chosen arbitrarily but that genes (subgraph 2) displayed higher expression than early express- consequences of this analysis were thoroughly tested at multiple ing genes (subgraph 1) in single cells of all three types (Fig. 6F, threshold values; Fig. 6E) both for tissue and single-cell data (Fig. Right), which were collected at ∼P21. Together, these analyses 6C). This step narrowed our focus to 269 genes and 692 correla- suggested developmental coregulation as a primary determinant for tions. Genes were outnumbered by correlations because some separation of the two graphs. genes correlated with multiple others (i.e., they represented cor- Next, we sought to further examine graph structures using relation “hubs”). We used graph analysis to test such possibility. In added knowledge about identity of single-cell samples. We rea- such graphs, each node represents a gene and each vertex repre- soned that robust gene expression correlations should be con- sents correlation between two genes, if any, with a length inversely sistently present if cell types were analyzed independently. proportional to the respective coefficient; genes with more corre- Therefore, we repeated analyses such that Fig. 6B only included lations simply had more vertices connecting them. RS-INT, or FS-INT, or CA1-PYR cells separately, or RS-INT

A Synaptic exocytosis B SNARE complex Active zone proteins C RhoGAP D RhoGEF Synaptotagmins RS-INT Chn1 RS-INT RS-INT Mtap1b 4 Cask Snap25 Rasgrf1 Stx12Vamp1 Cplx1Syt1 Rab3aRims1 Abr 4 Vamp2Munc18-1 Syt4 Rab3c 2.5 Elmo1 Rims2 Rasgrp1 2 Snap47 Syt6 Rims3 Arhgap21 Syt11 Snca Arhgap3 3 synaptic vesicle Sncg Unc13a Arhgap25 Apc Synaptotagmin 0 1.5 Arhgap5 2 Ywhae (Syt) Rab 4 1 Munc13 0.5 0 (Unc13) 2 FS-INT 2.5 4 FS-INT FS-INT presynaptic Rimbp 3 terminal Synaptobrevin 0 Complexin Active 1.5 2 (Vamp) Rim zone 4 (Cplx) 1 Munc18 CA1-PYR SNARE complex 2 0.5 0 Snap25 synaptic cleft 0 2.5 CA1-PYR 4 CA1-PYR (Stx) 4 3 Ca2+ P21 tissue 1.5 2 2 1

Normalized gene count (per thousand) 0 0.5 0 2.5 4 P21 tissue 3 P21 tissue Rim Snap CaskRab Normalized gene count (per thousand) 1.5 Normalized gene count (per thousand) 2 Syntaxin Munc18 Rimbp Complexin 1 Synuclein Synaptobrevin Synaptotagmin SyntrophinMunc13 0.5 0

Fig. 5. Cell-specific expression of exocytosis and RhoGAP/RhoGEF signal transducer molecules. (A) Schematic drawing depicting the exocytotic machinery of synapses. (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 in aged-matched P21 tissue control (for numerical values, see Dataset S1). RhoGAP- and RhoGEF-related molecules are implicated in transmembrane signal transduction and intracellular cytoskeleton reorganization. Averaged data represent mean ± SEM.

6of10 | www.pnas.org/cgi/doi/10.1073/pnas.1610155113 Földy et al. Downloaded by guest on September 25, 2021 PNAS PLUS A Hippocampal tissueBD Single cells C 0.73 (>92%) 712 corr. 3500 275 genes Snap29 1 2 1 1 1 Ephb2 Clstn3 2 2 0.5 0.36 Cbln2 Vamp1p1 (>92%) Epha55 Unc5d 3 0.0 Caskk Mtap1b Igsf8 3 Syt11 Nrxn3 Cplx1Clstn1 629 genes 4-12 4 433 genes -0.5 Pik3r1 Mtap1a Single cells Pcdh19 Fstl5 5 Snap25 Correlation coeff. -1 629 genes 433 genes -1 -0.5 0 0.5 1 0 395,641 187,489 93,525 Correlation coefficients10000 Hippocampal tissue 2

E Network structure and robustness FGBiological significance 1 Late genes

Lrrn2

Nrxn1 Ncam1 Odz3 Opcml Igsf8

Network independence ‘Scale-free’ properties Hippocampal tissue Single cells Lrrn3 Pcdh19 Early genes Epha6 Omg

Ppfia3

1.4 Ptpn2 Cntnap1

40 1 Ptprn 1 Clstn3

Within p21 Epha5

15 Within 2 30 1 1.2 Clstn1

2 2 Bai2

Ctnnb1 10 Between1 & 2 2 1.0 Apc App 20 0.9 Arhgef7

P (k) 1 Daam1 Arhgef9 Chl1 0.8 Syt7

5 10 Mtap1b Dock10

Syt11 Mtap1a

0.6 Ywhae Syt1

Cd200

0 0.8 1 Stxbp1 gene expression Sncb

0 Mean gene expr.

Normalized mean

Snap47 Arhgap44

Rab3a Cplx2

Erc1 Cask Links (thousand) 00.51 01020 P0 P7 P14 P21 P28 Corr. coeff. (threshold) k RS-INT FS-INT CA1-PYR Colors: CAMs Exocytosis RhoGAP RhoGEF

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 tissue samples. Unsupervised clustering revealed 12 clusters (labeled on the Right and Top) based on 395,641 gene correlations. (B) Same analysis as in A, but for single-cell data. Coefficients were computed based on single-cell gene expression values independent of cell type identity. Unsupervised clustering revealed five clusters (labeled on the Right and Top) based on the 187,489 gene correlations. (C) Combined data of correlation coefficients from tissue and single-cell samples. Gene pairs with both coefficients larger than the 92nd percentile of the respective distributions were further analyzed (green shaded area; see text

for further information). (D) Graph representation of correlation coefficients displayed in the green shaded area of C. Each vertex connects two genes NEUROSCIENCE according 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 and nonrandom in nature. (F, Left) Mean normalized gene expression values in the two subgraphs showed diametrically opposite developmental trajectories, and suggest that genes in subgraph were developmentally coregulated. Because single-cell expression was a selection criterion in C, these genes were also coexpressed at the single-cell level. (Right) As suggested by the developmental differences, genes in subgraph 1 and subgraph 2 had different expression values 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 identified by 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 from RS-INT and FS-INT cells, representing interneurons, and RS-INT, FS-INT, and CA1-PYR data, analyzed independently). Averaged data represent mean ± SEM.

and FS-INT cells combined, because they both represented networks (subgraph 2). To this end, we performed single-cell GABAergic interneurons. We found that each of these analyses RNAseq experiments to analyze electrophysiologically defined confirmed the independence of subgraphs 1 and 2 (Fig. S6B), BS-PYR and RS-PYR neurons in the subiculum (Fig. 7A), which and that the gene representation in each cell type-specific are the major projection targets of CA1-PYR cells. We hypothe- dataset was at least 80% identical to that in Fig. 6D (Fig. S6B), sized that their gene expression profiles are different, because they suggesting that coexpression of these genes occurs at the single- exhibit distinct excitability and synaptic properties (12, 13, 15). cell level. Moreover, we identified core parts of these subgraphs We detected 9,671 ± 278 and 9,499 ± 405 genes in BS-PYR (i.e., parts that can be consistently detected in each cell type) by and RS-PYR cells, respectively (P = 0.85; Fig. 7B and Fig. S7A), taking the intersection of the cell type-specific analyses (Fig. with more consistent gene expression patterns than among CA1- 6G). Because of their robust presence in each cell type, these PYRs (Fig. 7C and Fig. S7B). Next, we examined the molecular correlations conceivably represented ubiquitous features, at dif- profiles of these cells, especially seeking exclusive markers that ferent developmental stages of synapse maturation. This hy- would unequivocally identify these functionally different cell pothesis was supported by the existence of correlations between types. However, we found that they were largely identical and structurally relevant RhoGEF and RhoGAP molecules in early detected only a surprisingly small number of exclusively expressed development, when synapses are formed (Fig. 6G, Left, and Fig. genes (Fig. 7D). Such genes included, for example, the neuro- S6D, Upper). Conversely, emerging correlations between cell peptide cortistatin (Cort) and the c-Fos–induced growth factor adhesion and exocytosis in the late-gene network may reflect (Figf) (a member of the VEGF family). Apart from these genes, synapse specialization (Fig. 6G, Right, and Fig. S6D, Lower). BS-PYR and RS-PYR neurons expressed similar patterns of Although our analyses did not reveal a mechanistic explanation voltage-gated ion channels (Fig. 7E; see differently expressed ion for the observed correlations, they identified synaptic genes that channels in Fig. S7C), CAMs (Fig. 7F), ligand-gated ion channels, were both developmentally coregulated and coexpressed at the and exocytosis- and RhoGAP-/RhoGEF-related molecules (Fig. S7 single-cell level. C–G and Dataset S1). We next examined expression of ubiquitously present CAMs CAMs in BS-PYR and RS-PYR Cells of the Subiculum. Our findings (as identified in CA1 interneurons and PYR cells), which we thus far revealed ubiquitous and cell type-specific features of reliably detected in subiculum PYR cells similar to CA1-PYRs CAM and signaling molecule expression as well as the contextual (Figs. 4C and 7G), further strengthening the notion of general determinants of their expression, involving molecules related to importance of these genes in neuron and synapse function. Al- exocytosis and Rho signaling. Next, we wanted to examine the though 42 CAMs had different expression levels between the two generality of our central findings, including that of (i) the subiculum PYR cell types (P < 0.05; pairwise MW test; Dataset S1), identity of ubiquitously expressed CAMs and of (ii) the existence none of these CAMs was exclusively expressed in one or the other and identity of synaptic early- (subgraph 1) and late-gene interaction cell type. However, their expression differed markedly from that of

Földy et al. PNAS Early Edition | 7of10 Downloaded by guest on September 25, 2021 A Brain Cytosol collection RNAseq Analysis B C D BS-PYR RS-PYR (21) (14) 4930579C15Rik Subiculum Multiplexed 15 100 Cd1d1 BS-PYR Cort cDNA 80 Fyb libraries RS-PYR Gpt 10 Htr1b Single-cell 60 Kcnh4 Klf8 mRNA 40 Krt73 BS-PYR RS-PYR 5 Lrg1 20 Lrrc1 (thousand) Rnf152 2 Zfp78 Detected genes Detected 0 0 Ccdc129 1 >50 <50 Figf Detected genes (%) Detected genes Gm4013 0 2 0 2 4 Expressing Icosl Mpo 1 BS-PYR RS-PYR cells (%) Rs1 0 EF Norm. gene expr. Scn1a (Log10 based) Kcnc1 Nptn Kcnc2 Scn2a1 App Nrxn1 Scn8a Bai3 Chl1 15 Trpc4 15 Nrxn3 Ppfia2 Cacna1eHcna1 Bsg Clstn1 Kcna1 Kcnt1 Ptpra Kcna2 Kcnq2 Tspan5 10 Kcna6Kcnd2Kcnh3 Kcnt1 10 Cadm1&2 Clstn3 Ntrk2 Kcnh4 Epha4 Mdga1 Ptpre BS-PYR Cd14 Lphn1 Ncam1Nxph1 Vstm2a Kcnh7 Cd47 Cntn1Csf1r Fstl5 Pcdh17 Ptprs 5 5 BS-PYR 0 0 15 15 10 10 RS-PYR 5 5 RS-PYR 0 0 15 15 10 10 P21 tissue ref. P21 tissue ref. 5 5 0 0 Normalized gene count per ten thousand) Normalized gene count (per ten thousand) Potassium Amigo Cntn Fgfr Kirrel Mertk Ntng Rtn Cng Hcn Pkd Trp App Cntnap Flrt Kit Mfap3 Ntrk Sdc Mcoln Bai Cntfr Flt Lgr Ncam Nxph Sdk Sodium Bsg Csf1r Fshr Lhcgr Negr Odz Sema CalciumCatsper C1ql Ctnna Fstl Lilra5 Neo Omg Slitrk Gp Lphn Neurog Opcml BS-PYR RS-PYR Cadm Cxadr Gpr Lrfn Nfasc Pcdh Tek G Car Dag1 Hepacam Lrig Nlgn Pdgrf Tie Nptn Cbln Dscam1l Icam Lrrc Nphs Pigr Tlr Cd Elfn2 Igdcc4 Lrrn Nptn Pkd Tpbg Nrxn1 Cdh Emb Iglon5 Lrrtm Nptx Ppfia Tshr App Cdhr Efna Igsf Lrtm Nrcam Ptgfrn Tyro Ptprs Ceacam Epha Il1r1 Lsamp Nrg Ptk Tspan Nrcam Celsr Fam19a Ilrap Mag Nrxn Prpn Unc5 Ppfia2 Chl Fat Islr Mcam Ntm Ptpr Vcam Chl1 Clstn Fcgr2b Jam Mdga Ntn Pvrl Vstm2 Cntn1 Ptprn All cell typesAll cell Cell-adhesion molecule coding gene-families (in alphabetical order) Bsg Clstn3 Cntnap4 Fstl5 HIPRINCIPAL COMPONENT 1 Early-gene network 2 Late-gene network Igsf8 Lrrc4 ANALYSIS

Negr1 Lrrn2 Igsf8

Nrxn1

Nlgn2 Ncam1 Odz3 Opcml

Lrrn3 Nrxn3 50 BS-PYR RS-PYR Pcdh19 Epha6 Omg

Ntrk2 Ppfia3

Vstm2a RS-INT&FS-INT Ptpn2 Cntnap1 Ptprn

Cbln2 Clstn3 Clstn1 Ephb2 Epha5

Flrt1

Lrfn2 0 Bai2

Sdk2 RS-INT App Cd164 Ctnnb1 Apc

Clstn2 Arhgef7 Arhgef9

Epha10 Daam1 Syt7 Dock10

PCA2 (7%) Chl1

Fam19a2 (Log10 based) -50 Mtap1b Syt11 Mtap1a Norm. gene expr.

Igdcc4 Syt1

Nxph1 Ywhae Stxbp1

FS-INT 3 Sncb

Cd200

Ptpn5 Snap47 Arhgap44 Rab3a Ptprn2 2 -50 0 50 Cplx2

Pvrl2 1 Erc1 Cask Epha4 Pcdhgc5 0 PCA1 (6%)

Sema3e PYR

Fig. 7. Molecular identity of BS-PYR and RS-PYR neurons in the subiculum. (A) Experimental design shows electrophysiological identification of BS and RS neurons as well as sample collections, and processing of single-cell RNAseq data. White box indicates the subiculum region. (B) The number of detected genes were 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 map shows 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-PYR cells 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 the order 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 confirmed existence of early- and late-gene networks (shown in Fig. 6). These plots show core consensus networks defined by independent analyses in five cell types examined, including RS-INT, FS-INT, CA1-PYR, BS-PYR, and RS-PYR neurons.

cell type-specifically expressed CAMs in CA1 PYR cells. Sub- We repeated the combined analysis of tissue and single-cell iculum PYR cells consistently used CAMs that were not observed RNAseq data on subiculum PYR cells, independent from CA1 in CA1-PYR cells. For example, Clstn3 (significantly enriched only cells. In these analyses, we also identified two independent in interneurons in CA1), Ptpn5 and Ptprn2 (both were enriched in subgraphs, which were essentially identical to those found in CA1 FS-PV cells), as well as Lrrc4 (not present in any CA1-PYR) were RS, FS, and PYR cells. To reexamine the identity of the two detected and highly expressed in subiculum PYR cells. Further networks, we singled out overlapping gene correlations in all five cell-to-cell analysis corroborated molecular similarities between cell types (Fig. 7I) and found that they were nearly identical to BS-PYR and RS-PYR cells, as confirmed by the inability of PCA subgraph 1 and subgraph 2, identified based on the three CA1 cell (used on the complete transcriptome) to distinguish between these types (Fig. 6G). Therefore, these analyses independently confirmed cells (Fig. 7H). the existence of coregulated and coexpressed synaptic gene net- Finally, the subiculum neuron data allowed us to test the works, which may be generally present in cells in the brain in- validity of synaptic gene correlation networks described in Fig. 6. dependent of cell type identity.

8of10 | www.pnas.org/cgi/doi/10.1073/pnas.1610155113 Földy et al. Downloaded by guest on September 25, 2021 Discussion and RS-PYR cells, in which we previously established functionally PNAS PLUS A molecular description of gene expression in individual, in- relevant differences in cell adhesion signaling (9, 10, 13, 15). Thus, dependently identified neurons in combination with knowledge we examined candidate synaptic CAMs in single cells from these of their physiological properties and synaptic connectivity is a types. Presynaptic and postsynaptic CAMs presumably engage in prerequisite for insight into how synaptic connections are formed homophilic and heterophilic interactions in the synaptic cleft, but and maintained in brain. Moreover, such data are essential for the rules that associate particular CAMs to specific synapses and understanding why disease-associated gene mutations impair brain their transcriptional regulation in different cell types were not function. RNAseq of single neurons can be routinely obtained from known. In examining the cell type-specific expression of CAMs, we cells isolated from tissue lysates (44–47) and has recently been made two fundamental observations (Figs. 4 and 7). achieved from electrophysiologically recorded cells (the current First, CAMs are hierarchically expressed. This conclusion is study; also see refs. 16 and 17). Here, we used single-cell RNAseq based on the observation that some CAMs were consistently and of individual synaptically and electrophysiologically characterized highly expressed in all five cell types examined (these included neurons, and cross-referenced their gene expression profiles to that Nptn, Nrxn1, App, Ptprs, Nrcam, Ppfia2, Chl1, Cntn1, Ptprn, and of total brain tissue to explore cell adhesion signaling in a well- Bsg), whereas others were expressed cell type-specifically (these defined hippocampal circuit. In particular, we examined inhibitory included, for example, Cntnap4, Fstl5, Igsf8, Lrrc4, Cbln2, Eph2b, projections from RS and FS interneurons to CA1-PYR cells (6, 7, Idgcc4, Nxph1, Epha4, and Sema3e). The most likely explanation 10) and excitatory projections from CA1-PYR cells to subiculum for such hierarchy is that commonly expressed molecules are re- BS-PYR and RS-PYR cells (13, 15). Our results show that capture quired for core synaptic features as well as other neuronal cell of mRNAs by aspiration of cytosol allows production of high- interactions, whereas cell type-specific molecules may enable quality transcriptome data (Figs. S2 and S7), opening up identi- functional specialization. For example, it is intriguing to speculate fication of the precise expression profiles of single neurons as a which, if any, of these molecules are transsynaptically involved in molecular portal for a detailed functional understanding. sculpting endocannabinoid signaling in RS CCK or opioid signaling Functional implications of gene expression can be most directly in FS PV synapses, respectively (6, 7, 10). Overall, these data examined by relating gene expression to electrophysiological support our hypothesis that CAMs are combinatorially expressed properties (16, 17, 26–29, 48). Therefore, we first tested whether at the single-cell level. the electrophysiological properties of a neuron could be explained Second, we identified synaptic genes that were both develop- by its ion channel expression pattern. Previous studies established mentally coregulated as well as coexpressed in single cells (Figs. + + that expression of Kv3 K channels and a high Na -channel 5–7). This was made possible by a combined analysis of tissue

density enable the FS properties of PV cells (23). In line with this and single-cell data, and revealed two independent gene networks: NEUROSCIENCE conclusion, we found specific enrichment of Kcnc1 (Kv3.1), Kcnc2 an “early-gene” and a “late-gene” network. Beyond developmental (Kv3.2), and Scn1a (NaV1.1) in FS-INT cells. By taking advantage differences, molecular compositionofthetwonetworksimplicated of our comprehensive gene expression data, we extended these distinct functional relevance. Specifically, interactions between in- findings and correlated 11 electrophysiological properties with 104 tracellular signal transducer molecules were enriched in the early- ion channel genes. We found strong correlations between specific gene cluster, whereas representation of exocytosis-related molecules sets of genes and physiological properties, consistent with the as well as their interactions with CAMs were more apparent in the different cell types. Although these correlations may mostly reflect late-gene cluster. Such findings are consistent with the develop- group-level differences, an added value of these analyses is the mental sequels of structural formation and functional maturation of conclusion that electrophysiological properties can be identified us- synapses and neuron–glia interactions. Because they are compre- ing single-cell transcriptome information. More detailed quantifica- hensive in nature, experimental probing of these networks is chal- tion of electrophysiological properties in combination with extended lenging as it may require simultaneous manipulation of multiple sample sets and morphological analyses should, for example, allow genes. Such approach, targeting multiple heavily connected genes differentiation of interneuron subtypes (5) on a molecular basis. In or hubs (e.g., Clstn1, Clstn3, or Igsf8), could reveal susceptibility to addition, these results suggest that the validity of RNAseq data in genetic conditions with especially disruptive pathophysiological electrophysiologically defined cells extends to genes whose func- consequences. tional readouts were not readily available, as is the case for CAMs. Together, our results provide a description of which electro- We hypothesized that combinatorial expression of CAMs in single physiologically and synaptically characterized neurons express cells may define connectivity in a neuronal network. A corollary of which specific CAMs. Some of these molecules are already known this hypothesis is that CAMs in a presynaptic terminal must interact to play important synaptic roles, as their genetic disruptions have with complementary CAMs in the postsynaptic compartment; been linked to neuropsychiatric disorders in humans and have this interaction is—through reciprocal ligand–receptor coupling— been shown to be lethal in mice (33). However, the function of the enabled via the synaptic cleft. Abundant presence of CAMs in majority of these molecules as well as the logic that integrates hippocampal tissue (Fig. 1) supports this hypothesis, because the them into assembly and specification of synapses or other in- diversity of CAMs likely permits a large number of combinations, tercellular junctions remain unknown. Here, we made progress possibly defining synaptic connectivity between the more than 20 toward understanding such logic by measuring developmental cell types in the hippocampus and beyond (5). Our data present the and single-cell expression of CAMs. When revealed, underlying most current account of CAMs in the brain, described by expres- principles can help us to use the brain’s cell adhesion code to sion analysis of 406 genes. A limitation of our RNAseq results is formally describe connectivity in neuronal circuits. Such applica- that a transcriptome profile represents the abundance of mRNAs tions would lead to developments in molecular diagnostics allowing in a given cell type but does not provide information regarding comprehensive analysis of brain circuits from single-cell samples. protein abundance or localization. A given neuron receives het- erogeneous inputs (excitatory and inhibitory synapses from multiple Materials and Methods sources) and can innervate multiple postsynaptic cells of differing Electrophysiology and Single-Cell Sample Collection. All animal protocols and identities. Thus, it is plausible that CAMs are differentially localized husbandry practices were approved by the Institutional Animal Care and Use and combinatorially used in a synapse-specific manner. To un- Committee at Stanford University. Hippocampal slices (300 μm) were prepared derstand the organization of these molecules and possibly interpret from 3- to 4-wk-old wild-type CD1 mice, as described in ref. 10 and SI Materials them as a code for connectivity, we need to clarify which of these and Methods. molecules are expressed in specific synapses and physically interact with each other. An important step to this direction is to examine cDNA Preparation and Library Preparation for Next-Generation Sequencing. cell type-specific expression of these molecules. Single-cell mRNA was performed using the Clontech’s SMARTer Ultra Low Our hypothesis suggests that expression of CAMs should differ Input RNA Kit. As a first step, cells were collected via pipette aspiration into among cell types, including RS-INT, FS-INT, CA1-PYR, BS-PYR, sample collection buffer, were spun briefly, and were snap frozen on dry ice.

Földy et al. PNAS Early Edition | 9of10 Downloaded by guest on September 25, 2021 Sampleswerestoredat−80 °C until further processing, which was performed channels, synaptic exocytosis-related molecules, as well as RhoGAP and according to manufacturer’s protocol. Library preparation was performed us- RhoGEF signaling-related molecules. For each of these, we assembled a ing Nextera XT DNA Sample Preparation Kit (Illumina) as described in the comprehensive list, which included all genes examined (see SI Materials and protocol. Then, cells were pooled and sequenced in an Illumina NextSeq500 Methods for a detailed description of categories). instrument using 2 × 75 paired end reads on a NextSeq high-output kit (Illu- mina; see SI Materials and Methods for details). Data Analysis. All data analyses were performed using Mathematica10 (Wolfram Research). These analyses included (i) normalization of gene ex- Processing of mRNA Sequencing Data. After de-multiplexing the raw reads to pression data, (ii) quality control, (iii) analysis of physiological properties, single-cell datasets, we used Prinseq to remove short reads. After trimming (iv) analysis of exon junctions, and (v) correlation and graph analyses of and removal of overrepresented sequences and adapters, remaining reads expression data (see SI Materials and Methods for more details). were aligned to the mm10 genome with STAR aligner. Aligned read were converted to gene counts using HTSeq (see SI Materials and Methods for ACKNOWLEDGMENTS. This work was supported by National Institute on detailed parameter descriptions). Drug Abuse Grant K99 DA034029 (to C.F.), Swiss National Science Foundation Grant CRETP3_166815 (to C.F.), and the National Institute Gene Categories. For gene expression analysis, we examined six functionally of Mental Health Grants R37 MH52804 (to T.C.S.) and K99 MH103531 related categories: CAMs, voltage-gated ion channels, ligand-gated ion (to J.A.).

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