Neuromodulation - the graded and relatively slow adjustment of fast synapse and ion channel function via diffusible cell-cell signaling molecules - is a fundamental requirement for adaptive nervous system function (Abbott and Regehr, 2004; Bargmann, 2012; Bucher and Marder, 2013; Katz and Lillvis, 2014; Marder, 2012; Marder et al., 2015; McCormick and Nusbaum, 2014; Nadim and Bucher, 2014; Nusbaum et al., 2017). Neuromodulator molecules take many different chemical forms, including diatomic gases such as nitric oxide, lipid metabolites such as the endocannabinoids, and amino acids and their metabolites such as glutamate, GABA, acetylcholine, serotonin and dopamine. By far the largest family of neuromodulator molecules known, however, comprises the evolutionarily ancient proteinaceous signaling molecules known as neuropeptides (Baraban and Tallent, 2004; Burbach, 2011; Gonzalez-Suarez and Nitabach, 2018; Hökfelt et al., 2013; van den Pol, 2012; Wang et al., 2015). The most widely studied neuropeptides are the endogenous ‘opioid’ peptides - enkephalins, endorphins and dynorphins - but there are nearly one hundred other NPP genes in the human genome and numerous homologs are present in almost all known animal genomes (Elphick et al., 2018; Jékely, 2013).

The broadest definition of ‘neuropeptide’ would embrace any soluble peptide that serves as a messenger by diffusing from one neuron to another. A narrower but more common definition (Burbach, 2011) requires (1) translation of a neuropeptide precursor protein (NPP) into the lumen of a source neuron’s rough endoplasmic reticulum (rER), (2) enzymatic cleavage of the NPP into one or more neuropeptide (NP) products during or after passage through the rER–Golgi complex and packaging into dense-core vesicles, (3) transport and storage of dense-core vesicles within the source neuron, (4) secretion of the NP product upon demand by activity- and calcium-dependent dense-core vesicle exocytosis, and then (5) interstitial diffusion to act upon a target neuron by binding to a ligand-specific receptor. This pathway enlarges the potential palette of distinct neuropeptides beyond that established simply by the large number of NPP genes, as a given NPP may be differentially cleaved into alternative NP products during its intracellular and interstitial passage.

Most neuropeptide receptors are encoded by members of the very large superfamily of G-protein-coupled receptor (GPCR) genes (Hoyer and Bartfai, 2012; Krishnan and Schiöth, 2015; Mains and Eipper, 2006; van den Pol, 2012). GPCRs are selective, high-affinity receptors distinguished by characteristic seven-transmembrane-segment atomic structures and signal transduction involving heterotrimeric G-proteins (hence their name). Genes encoding GPCRs selective for neuropeptides (NP-GPCR genes) and for most of the other chemical neuromodulators mentioned above are found in the genomes of almost all metazoans: phylogenomic evidence suggests very early evolutionary origins (Jékely, 2013; Katz and Lillvis, 2014), possibly even predating evolution of the synapse (Varoqueaux and Fasshauer, 2017). In modern metazoan nervous systems, synapses rely primarily upon recycling small molecule transmitters and ligand-gated ion channels (alternatively known as ‘ionotropic receptors’) for fast (millisecond timescale) transmission, but GPCRs selective for widely varied ligands including the fast recycling transmitters and many other secreted molecules (e.g., glutamate, GABA, acetylcholine, monoamines and neuropeptides) play critical roles in the slower modulation of fast synaptic transmission and electrical activity (Elphick et al., 2018; Grimmelikhuijzen and Hauser, 2012; Jékely, 2013; Krishnan and Schiöth, 2015; Varoqueaux and Fasshauer, 2017).

Because modulatory neuropeptides are not subject to the rapid transmitter re-uptake and/or degradation processes necessary for fast synaptic transmission, secreted neuropeptides are thought to persist long enough (e.g., minutes) in brain interstitial spaces for diffusion to very-high-affinity NP-GPCRs hundreds of micrometers distant from release sites (Ludwig and Leng, 2006; Nässel, 2009; Russo, 2017). Neuropeptide signaling can thus be presumed both ‘paracrine’, with secretion from individual neurons hitting receptor-positive cells over substantial diffusion distances and converging by diffusion from many local secreting neurons onto single receptor-positive neurons, and to be relatively slow (seconds-to-minutes timescale of action). Though present information is limited, eventual degradation by interstitial peptidases nonetheless probably restricts diffusion of most neuropeptides to sub-millimeter, local circuit distance scales.

The receptors encoded by NP-GPCR genes are highly diverse in ligand specificity but less diverse in downstream signaling impacts. Although GPCR signaling has long been recognized as complex and many faceted (Hamm, 1998), most known neuronal NP-GPCR actions reflect phosphorylation of ion channels, synaptic proteins or transcription factors mediated by protein kinases dependent on the second messengers cyclic AMP or calcium (Mains and Eipper, 2006; Nadim and Bucher, 2014; van den Pol, 2012). Primary effects of NP-GPCRs expressed in cortex, in turn, fall into just three major categories distinguished by G-protein alpha subunit (Gα) family. The Gi/o family (i/o) inhibits cAMP production, the Gs family (s) stimulates cAMP production, and the Gq/11 family (q/11) amplifies calcium signaling dynamics (Syrovatkina et al., 2016). For most NP-GPCR genes, the primary Gα family (e.g., i/o, s or q/11) is now known (Alexander et al., 2017) and offers a good first-order prediction of the encoded GPCR’s signal transducing action. The profound functional consequences of neuromodulation by GPCRs range from modification of neuronal firing properties and calcium signaling dynamics through regulation of synaptic weights and synaptic plasticity (Bargmann, 2012; Markram et al., 2013; McCormick and Nusbaum, 2014).

It is well established that certain neuropeptides, including vasoactive intestinal peptide (VIP), somatostatin (SST), neuropeptide Y (NPY), substance P, and cholecystokinin (CCK), are detectible at high levels in particular subsets of GABAergic cortical neurons (Tremblay et al., 2016). These neuropeptides have come into broad use as markers for particular GABAergic interneuron classes, while the corresponding NPP and NP-GPCR genetics have provided molecular access to these and other broad neuron type classes (Daigle et al., 2018; Maximiliano José et al., 2018). In situ hybridization and microarray data, for example the Allen Brain Atlases (Hawrylycz et al., 2012; Lein et al., 2007), have also established that mRNA transcripts encoding these five NPPs and that many other NPPs and NP-GPCR genes are expressed differentially in many brain regions. There has been a critical lack, however, of comprehensive expression data combining whole-genome depth with single-cell resolution. Absent such data, it has been difficult to generate specific and testable hypotheses regarding cortical neuropeptide function and to design robust experiments to test those hypotheses (Tremblay et al., 2016; van den Pol, 2012).

Here we describe new findings regarding NPP and NP-GPCR gene expression in mouse cortex. These findings have surfaced during a focused analysis of a previously published single-cell RNA-seq data acquired from large numbers of isolated mouse cortical neurons (Tasic et al., 2018). We begin by leveraging only the genomic depth and single-cell resolution of this dataset. Next, we introduce the transcriptomic neurotaxonomy developed in the same resource publication and explore the additional analytical power of a neurotaxonomic framework. Then, we distill these findings into specific and testable predictions concerning intracortical peptidergic modulation networks. Finally, we discuss the potential of a neurotaxonomically integrated view of neuromodulatory and synaptic networks to reveal previously obscure principles of cortical sensory, mnemonic and motor function.

The present study is based on analysis of a resource single-cell RNA-seq dataset acquired at the Allen Institute (Tasic et al., 2018) and available for download at http://celltypes.brain-map.org/rnaseq/. These RNA-seq data were acquired from a total of 22,439 isolated neurons, with detection of transcripts from a median of 9462 genes per cell (min = 1445; max = 15,338) and an overall total of 21,931 protein-coding genes detected. Neurons were sampled from two distant and very different neocortical areas: 13,491 neurons from primary visual cortex (VISp), and 8948 neurons from anterior lateral motor cortex (ALM). Single neuron harvesting methods were designed to mildly enrich samples for GABAergic neurons, such that the sampled neuron population is roughly half GABAergic (47%) and half glutamatergic (53%). The resource publication (Tasic et al., 2018) should be consulted for full details of neuron harvesting, sample preparation, sequencing and data processing. Since we refer very frequently here to this resource publication and dataset, we’ll refer to both now simply as ‘Tasic 2018’, and all further references here to neuron ‘class’, ‘subclass’ or ‘type’ should be understood to refer specifically to the particular mouse neocortex neurotaxonomy described in the Tasic 2018 publication.

The Tasic 2018 single-cell RNA-seq data tables report the abundance of transcripts from individual neurons in both ‘counts per million reads’ (CPM) and ‘fragments per kilobase of exon per million reads mapped’ (FPKM) units. Our analysis of this data compares gene expression levels quantitatively, with two distinct use cases: (1) comparisons across large sets of different genes, and (2) comparisons of the same gene across different individual cells, cell types and brain areas. We have relied upon FPKM data (Mortazavi et al., 2008; Pimentel, 2014), for use case 1 (i.e., the Tables 1 comparisons across genes). For use case 2 (as in all figures below), we have preferred the CPM units, because these units were used to generate the Tasic 2018 neurotaxonomy. In any case, choice of CPM vs. FPKM units would have very little impact on the present outcomes.

Single-neuron expression profiles of 18 select neuropeptide precursor (NPP) genes

Table 1 lists results of analyzing the expression of 18 NPP genes in all 22,439 individual neurons represented in Tasic 2018. Here we have made use of the ‘peak FPKM’ (pFPKM) metric described in Materials and methods below to quantify the expression of specific genes in highly expressing subsets of single-cell populations that exhibit highly variant expression of that particular gene. Each of the 18 NPP genes on this list meets two conditions: (1) the included NPP gene is highly expressed (top quintile pFPKM over all protein-coding genes) across VISp and ALM cortical areas, and (2) at least one gene for an NP-GPCR selective for the predicted product of at least one of the 18 NPPs is highly expressed in neurons within the same local area of neocortex (see Table 2). The first requirement was imposed to increase the likelihood of active secretion of the NP product encoded by the candidate NPP gene. The second requirement, for ‘cognate’ pairing between each included NPP and a locally expressed NP-GPCR gene, was imposed to elevate the likelihood of paracrine NP signaling within a cortical local circuit volume, as envisioned in Introduction above. The process for selection of these 18 NPP genes is described in more detail in Materials and methods. Table 1 lists Peak FPKM values for each of the 18 NPP genes, percentile and absolute ranks of that Peak FPKM value across all protein-coding genes, the percentage of cells sampled in which expression of the listed gene is detectible, predicted neuropeptide product(s) encoded, and the NP-GPCR gene(s) fulfilling requirement (2) for that NPP gene. Gene ontology results for the 18 select NPP genes are provided by Supplementary file 1. The Peak FPKM ranking columns in Table 1 show that expression levels of most of the 18 NPP genes are extremely high in the range of values for all 21,931 protein-coding genes detected in all 22,439 neurons sampled. Of these genes, Npy, Sst, Vip and Tac2 rank as the top four overall in pFPKM values, while Cck, Penk and Crh also rank in the top ten. Eleven of these NPP genes rank in the top percentile and all 18 rank in the top quintile by pFPKM. To the simplest first approximation, very high abundance of a given protein-coding transcript implies the potential, at least, for a very high rate of synthesis of the encoded protein. The extremely high peak abundance of these NPP transcripts thus suggests that NP precursor proteins could be synthesized at very high rates in neurons exhibiting such peak abundance. In a steady state, a high rate of synthesis would then necessarily imply a correspondingly high overall rate of protein product elimination. For an NP precursor protein, processing and secretion of active neuropeptide would seem the obvious and most likely route of elimination. The high abundance of transcripts encoding these 18 NPPs might thus be construed as prima facie evidence for robust secretion of neuropeptide products.

Table 1

NPP Gene	Peak FPKM	pFPKM Percentile	pFPKM Rank	% Cells	Predicted Neuropeptides	Cognate NP-GPCR Genes
Npy	108,865	100.00	1	42	Neuropeptide Y	Npy1r, Npy2r, Npy5
Sst	70,274	99.99	2	26	Somatostatins	Sstr1, Sstr2, Sstr3, Sstr4
Vip	48,747	99.99	3	33	Vasoactive Intestinal Peptide	Vipr1, Vipr2
Tac2	18,284	99.98	4	15	Neurokinin B	Tacr3
Cck	16,396	99.97	6	69	Cholecystokinins	Cckbr
Penk	11,160	99.96	8	26	Enkephalins	Oprd1, Oprm1
Crh	9,118	99.95	10	17	Corticotropin-Releasing Hormone	Crhr1, Crhr2
Cort	7,477	99.93	15	32	Cortistatin	Sstr1, Sstr2, Sstr3, Sstr4
Tac1	5,728	99.92	18	11	Substance P, Neurokinin A	Tacr1
Pdyn	2,813	99.69	68	8	Dynorphins	Oprd1, Oprk1, Oprm1
Pthlh	1,656	99.29	156	18	Parathyroid-Hormone-Like Hormone	Pth1r
Pnoc	698	97.68	509	23	Nociceptins	Oprl1
Trh	510	96.51	766	3	Thyrotropin-Releasing Hormone	Trhr, Trhr2
Grp	435	95.59	968	12	Gastrin-Releasing Peptide	Grpr
Rln1	258	91.99	1757	7	Relaxin 1	Rxfp1, Rxfp2	Rxfp3
Adcyap1	165	87.29	2788	26	Adenylate Cyclase-Activating Polypeptides	Adcyap1r1, Vipr1, Vipr2
Nts	121	82.14	3917	1	Neurotensin	Ntsr1, Ntsr2
Nmb	112	80.53	4270	14	Neuromedin B	Nmbr

Figure 1A quantifies differential expression of the 18 NPP genes listed in Table 1. Each of the 18 color-coded solid curves represents a distribution of single-neuron CPM values for one NPP gene. Curves were generated by plotting CPM for each individual neuron in descending rank order along a cell population percentile axis. Each curve exhibits a transition from high to very low (commonly zero) expression across the sampled neuron population, but these transitions occur over very different population percentile ranges, providing clear evidence for highly differential single-cell expression of these genes. Percentages of the sampled neuron population with detectible expression of a given NPP gene range from more than 65% for Cck down to ~1% for Nts. (Note, however, that the cell population sampled has been enriched for GABAergic cell types as described in Tasic 2018).

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Sorted NPP CPM distributions for all neurons.

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Single-cell NPP CPM expression table.

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The RNA-seq data suggest that all, or at least very nearly all, neocortical neurons express at least one NPP gene. The dashed curve in Figure 1A, labeled ‘Max NPP Gene’, was generated by plotting CPM values of the NPP gene with the highest CPM in each individual cell in descending order along a cell population percentile axis. This curve therefore shows that 97% of the sampled mouse cortical neurons express at least one NPP gene at >1 CPM and that 80% express at least one NPP gene at >1,000 CPM, a very high level. When one takes into account the pulsatile nature of transcription (Suter et al., 2011) and the stochastic nature of RNA-seq transcript sampling (Fu and Pachter, 2016; Kim et al., 2015; Tasic et al., 2016), these numbers might be best understood as lower limits. The results summarized in Figure 1A may therefore be consistent with the proposition that every cortical neuron is peptidergic.

Single-neuron expression profiles of 29 select neuropeptide receptor (NP-GPCR) genes

Table 2 lists 29 NP-GPCR genes that are highly expressed in varied subsets of the 22,439 individual neurons sampled from cortical areas VISp and ALM. These 29 genes encode receptor proteins substantially selective for neuropeptide products encoded by the 18 NPP genes of Table 1 (cross-referenced from that table as ‘Cognate NP-GPCR Genes’). Table 2 provides quantitative information on expression levels of these 29 NP-GPCR genes, names the receptor proteins they encode, indicates the A-F GPCR class and expected primary Gα family and cross-references back to the cognate cortically-expressed NPP genes. As noted above, the 18 NPP genes and 29 NP-GPCR genes listed in Tables 1 and 2 were selected for focused analysis here due to their cognate pairing and the consequent implication of local intracortical signaling. Methods of NP-GPCR gene selection are described more fully in Materials and methods. A more complete listing of NP-GPCR genes with pFPKM values in provided by Table 2—source data 1. Gene ontology results for the 29 select NPP genes are provided by Supplementary file 2. The ‘pFPKM Percentile’ column in Table 2 shows that most of these 29 NP-GPCR genes are expressed in cortex with Peak FPKM values well above median (50^th percentile) for all protein coding genes. The range of cortical neuron pFPKM values for NP-GPCR genes does not match the extreme heights noted for NPP genes, but this is as expected given that NP-GPCR gene products are thought to be durable cellular components, unlikely to be rapidly disposed by secretion as expected for NPP gene products. Peak FPKM values for NP-GPCR transcripts are nonetheless quite high in the range of transcripts of other durable cellular component genes, suggesting a strong likelihood that they are indeed translated into functionally important protein products.

Table 2

NP-GPCR Gene	Peak FPKM	pFPKM Percentile	% Cells	Neuropeptide Receptor	GPCR Class	Primary Gα Family	Cognate NPP Genes
Sstr2	413	95.3	42	Somatostatin Receptor 2	A4	Gi/o	Sst, Cort
Npy2r	291	93.1	10	Neuropeptide Y Receptor Y2	A9	Gi/o	Npy
Npy1r	272	92.4	50	Neuropeptide Y Receptor Y1	A9	Gi/o	Npy
Grpr	231	91	10	GRP Receptor	A7	Gq/11	Grp
Cckbr	210	90	52	Cholecystokinin B Receptor	A6	Gq/11	Cck
Ntsr2	161	86.9	17	Neurotensin Receptor 2	A7	Gq/11	Nts
Npy5r	152	86.1	28	Neurpeptide Y Receptor Y5	A9	Gi/o	Npy
Nmbr	123	82.4	8	Neuromedin B Receptor	A7	Gq/11	Nmb
Rxfp1	121	82	22	Relaxin Family Receptor 1	A5	Gs	Rln1
Sstr4	106	79.5	28	Somatostatin Receptor 4	A4	Gi/o	Sst, Cort
Trhr	101	78.4	3	TRH Receptor	A7	Gq/11	Trh
Sstr1	90	76	38	Somatostatin Receptor 1	A4	Gi/o	Sst, Cort
Adcyap1r1	89	75.8	71	ADCYAP1 Receptor 1	B1	Gs	Adcyap1, Vip
Crhr1	86	74.9	28	CRH Receptor 1	B1	Gs	Crh
Rxfp3	85	74.7	5	Relaxin Family Receptor 3	A5	Gi/o	Rln1
Oprl1	82	73.8	48	Opioid Receptor-Like 1	A4	Gi/o	Pnoc
Crhr2	72	70.7	3	CRH Receptor 2	B1	Gs	Crh
Tacr3	65	68	3	Tachykinin Receptor 3	A9	Gq/11	Tac2
Oprk1	64	67.4	3	Kappa-Opioid Receptor	A4	Gi/o	Pdyn
Tacr1	56	64.2	3	Tachykinin Receptor 1	A9	Gq/11	Tac1
Pth1r	51	61.6	15	PTH 1 Receptor	B1	Gq/11	Pthlh
Vipr1	41	56.1	28	VIP Receptor 1	B1	Gs	Vip, Adcyap1
Oprm1	35	52.1	43	Mu-Opioid Receptor	A4	Gi/o	Penk, Pdyn
Trhr2	30	48.9	10	TRH Receptor 2	A7	Gq/11	Trh
Vipr2	30	48.4	0.5	VIP Receptor 2	B1	Gs	Vip, Adcyap1
Rxfp2	28	47.3	4	Relaxin Family Receptor 2	A5	Gs	Rln1
Oprd1	26	45.8	13	Delta-Opioid Receptor	A4	Gi/o	Penk, Pdyn
Ntsr1	24	44.3	10	Neurotensin Receptor 1	A7	Gq/11	Nts
Sstr3	17	39.5	21	Somatostatin Receptor 3	A4	Gi/o	Sst, Cort

The single-cell RNA-seq data expose very highly differential expression of NP-GPCR genes in cortical neurons. Figure 2 represents expression patterns of the 29 NP-GPCR genes listed in Table 2 in the same manner as for the 18 NPP genes in Figure 1. Figure 2A establishes that each of the 29 NP-GPCR genes is expressed in highly differential fashion across the 22,439 mouse cortical neurons sampled, as was the case for the 18 NPP genes. As was noted for NPP genes in Figure 1, each of the curves in Figure 2A exhibits a transition from very high to very low (commonly zero) expression across the sampled neuron population. These transitions occur at very different population percentile points, again providing clear evidence for highly differential expression. Percentages of the sampled neuron population expressing a given NP-GPCR gene (at greater than 1 CPM) range from more than 72% for Adcyap1r1 down to 0.7% for Vipr2.

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Sorted NP-GPCR CPM distributions for all neurons.

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Single-cell NP-GPCR CPM expression table.

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The RNA-seq data suggest that all, or at least very nearly all, neocortical neurons express at least one NP-GPCR gene. The dashed curve in the left panel of Figure 2A, generated similarly to the dashed curve for NPP genes in Figure 1A, shows that 98% of the sampled mouse cortical neurons express at least one NP-GPCR gene at >1 CPM and that 78% express at least one NP-GPCR gene at >100 CPM, lower than the comparable NPP curve in Figure 1, but still indicative of quite high expression. Again, these numbers must be understood as lower limits to percentages of cortical neurons actually expressing at least one of the 29 NP-GPCR genes, after taking into account the pulsatile transcription and stochastic sampling issues cited above. The results summarized in Figure 2A may thus be consistent with a conclusion that every cortical neuron expresses at least one NP-GPCR gene cognate to a cortically expressed NPP gene.

Neurotaxonomic profiling of NPP and NP-GPCR gene expression

The analysis so far has relied solely upon the genomic depth and single-cell resolution characteristics of the Tasic 2018 transcriptomic data. We now proceed to explore the analytical power of the transcriptomic neurotaxonomy developed as part of the Tasic 2018 study. This neurotaxonomy makes it possible to predict a protein ‘parts list’ for any neuron that can be mapped to a given transcriptomic taxon. Combined with tools for genetic access to transcriptomic taxa, transcriptomic taxonomy thereby offers rich prospects for experimental test of such predictions (see also Discussion below), The present analysis will make extensive use of the Tasic 2018 neurotaxonomy’s representation of 115 glutamatergic and GABAergic transcriptomic neuron types (see Figure 3—figure supplement 1).

Figure 3 shows transcriptomic gene expression ‘heatmaps’, representing transcript abundance for each of 18 NPP (Figure 3A) and 29 NP-GPCR (Figure 3B) across all 115 glutamatergic and GABAergic neuron types by log10-scaled pseudocolor. These heatmaps show that expression of every one of these 47 genes is highly specific to particular neuron types, but that type specificity varies greatly from gene to gene. Note that CPM expression levels vary across neuron types by factors exceeding 10,000 for many NPP genes and 1000 for many NP-GPCR genes. These heat maps also show that every neuron type expresses multiple NPP and NP-GPCR genes and that each of the NPP and NP-GPCR genes is expressed in multiple neuron types (with Vipr2 in one Pvalb type as a near exception). These two heatmaps further show many cases where both an NPP gene and its cognate NP-GPCR receptor are expressed in the same neuron type, with the Cck/Cckbr and Adcyap1/Adcyap1 r1 pairs being particularly prominent examples. Quite intriguingly, these expression heat maps also suggest that each of the neuron types might be distinguished by a unique pattern of expression of these 47 NP genes. This possibility will be explored quantitatively in connection with Figure 4 below.

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Cell types, cluster ids and color codes for ALM and VISp regions.

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The dashed vertical line spanning Figure 3A and B heatmaps divides glutamatergic and GABAergic neuron types and provides for ready comparison of NP gene expression patterns in these two broad neurotaxonomic classes. Figure 3A shows clearly that more NPP genes are expressed more strongly in GABAergic than in glutamatergic types. This differential is consistent with a long history of neuroscientific use of neuropeptide products as protein markers of GABAergic neuron subsets (e.g., VIP, SST, NPY, Substance P), which has no parallel in the marking of glutamatergic neuron subsets. Figure 3A nonetheless also shows that every glutamatergic type expresses at least one NPP genes at a very substantial level. Figure 3B shows that the broader expression of NPP genes in GABAergic over glutamatergic types is leveled or even reversed for NP-GPCR genes. That is, while GABAergic neurons clearly show the more prolific and varied expression of NPP genes, glutamatergic neurons may be somewhat more prolific expressors of NP-GPCR genes.

Additional graphics on the Figure 3 heatmaps further delineate the Tasic 2018 neurotaxonomy. A cladogram reflects the hierarchical similarity progression from the broad GABAergic and glutamatergic classes to the 115 individual neuron types, as aggregated across VISp and ALM cortical areas. Tinted rectangles and labels call out the five glutamatergic and seven GABAergic subclasses (see also Figure 3—figure supplement 1). Thin gray vertical lines crossing both NPP and NP-GPCR heatmaps demarcate those same subclasses. This delineation of subclasses shows that expression of some genes tends to remain constant within some subclasses, but to change abruptly at subclass boundaries. This does not seem, however, to be a very general case. Many genes show expression that varies widely by type within subclass. Figure 3C quantifies such residual expression variation for all NPP and NP-GPCR genes within each subclass. These significant residuals justify the use of more narrowly defined taxa (e.g., the 115 neuron types) to adequately characterize cortical neuropeptide gene expression. Relationships between NP gene expression patterns and the Tasic 2018 neurotaxonomy will be examined more quantitatively in the following section.

Conservation of NPP and NP-GPCR gene expression profiles between VISp and ALM

Figure 5 juxtaposes separate VISp and ALM expression profiles for NPP and NP-GPCR genes across 93 VISp neuron types (Figure 5A) and 84 ALM neuron types (Figure 5B). Similarities of expression profiles for the two areas are obvious in Figure 5, but there are also visible differences. The latter are rooted primarily in the substantial divergence of glutamatergic neuron taxonomies discussed at length in Tasic et al. (2018). Very strong similarities of both NPP and NP-GPCR expression profiles are most obvious for the GABAergic types, where the taxonomies are identical except for the absence of two GABAergic types in ALM (indicated by dark gray vertical placeholder bars in Figure 5B). The general conservation of neuron-type-specific expression patterns among common cell types between the two distant neocortical areas (NPP correlation: ρ = 0.974, p<2.2e-16, NP-GPCR: 0.877, p<2.2e-16) thus provides another indication of robust connection between NP gene expression and cortical neuron differentiation.

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Cell types, cluster ids and color codes for VISp region.

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Cell types, cluster ids and color codes for ALM region.

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Prediction of peptidergic networks from neurotaxonomic NP gene expression profiles

Figure 6 displays weighted adjacency matrix plots representing predictions of neuron-type-specific and neuron-subclass-specific peptidergic coupling from selections drawn from VISp and ALM of the 37 cognate NP gene pairs. The prediction matrices A-E are outer products (CPM*CPM units) of vectors representing expression (CPM units) of an NPP gene (columns) and a cognate NP-GPCR gene (rows) across all VISp or ALM neuron types. The predicted coupling matrices in F matrices are similar except that factor vectors are down-sampled by averaging neuron-type-specific CPM values within each of the subclasses (see Materials and methods for more details).

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NP coupling predictions by cognate pair and type for area VISp (archive containing 37 CSV files, one for each of the 37 cognate pairs listed in Table 3 and represented in Figure 6—figure supplements 1–8).

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NP coupling predictions by cognate pair and type for area ALM (archive containing 37 CSV files, one for each of the 37 cognate pairs listed in Table 3 and represented in Figure 6—figure supplements 1–8).

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Figure 6C-E represents 8 more of the 37 cognate pair coupling matrices predicted for VISp. Along with Figure 6A and B, these exemplify the wide variety of neuron-type-specific coupling motifs resulting from transcriptomic prediction. Most coupling matrices (i.e., pairs 1, 9, 29), predict significant coupling over wide swaths of type-pairs, approaching 20% of the entire matrix. A few matrices at the other extreme, such as 6 and 34, predict very sparse coupling. Other predictions are intermediate in sparsity. As one might expect, similar patterns are evident in the downsampled, subclass level predictions of Figure 6F. Even from the small subset of the 37 coupling matrix plots shown in Figure 6, it is evident that both type-level and subclass-level matrices are densely tiled by predictions of connectivity. Inspection of Figure 6 and similar plots for the remainder of the 37 cognate pairs (Figure 6—figure supplements 1–8) also reveals that there is a great deal of cross-network redundancy, with multiple pairs covering a large majority of the coupled types and subclasses, sometimes within and sometimes crossing Gα family boundaries. These observations will be strengthened by the analysis of Figure 7 below.

Figure 7

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Figure 7—source data 1

VISp coupling predictions by Gα family and type.

(archive containing three CSV files, one for each of Gi/o, Gs and Gq/11 families).

https://cdn.elifesciences.org/articles/47889/elife-47889-fig7-data1-v2.zip

Download elife-47889-fig7-data1-v2.zip

Figure 7—source data 2

ALM coupling predictions by Gα family and type.

(archive containing three CSV files, one for each of Gi/o, Gs and Gq/11 families).

https://cdn.elifesciences.org/articles/47889/elife-47889-fig7-data2-v2.zip

Download elife-47889-fig7-data2-v2.zip

Finally, Figure 6 illustrates the tendency of coupling predictions from most cognate NP pairs to fall in contiguous ‘patches’ of the full coupling matrix. This is a natural reflection of the strong tendency of both NPP and NP-GPCR expression to align with early nodes in the Tasic 2018 hierarchical clustering which was also evident in Figures 3 and 5. The broadest example of coupling matrix patches reflecting hierarchical neurotaxonomy structure is provided by the observation that most sizable coupling patches fall strictly within single quadrants of glutamatergic-GABAergic neuron type pairing. Variations in coupling matrix structure across all 37 cognate NP pairs are represented in more quatitative terms by Figure 6—figure supplements 9 and 10. Additional details regarding the generation of the coupling matrices are provided in Materials and methods.

Light from single-cell transcriptomics is now beginning to illuminate dark corners of cellular neuroscience that have long resisted mechanistic and functional analysis (Fan et al., 2018; Fishell and Kepecs, 2019; Földy et al., 2016; Gokce et al., 2016; Luo et al., 2018; Okaty et al., 2011; Paul et al., 2017; Shekhar et al., 2016; Tasic et al., 2018; Tasic et al., 2016; Telley et al., 2016; Zeng and Sanes, 2017). Cortical neuropeptide signaling may be one such corner. While profound impacts of neuropeptide signaling are well-established in a wide range of non-mammalian and sub-cortical neural structures (Borbély et al., 2013; Burbach, 2011; Elphick et al., 2018; Grimmelikhuijzen and Hauser, 2012; Katz and Lillvis, 2014; Jan et al., 1979) and there certainly is an excellent literature on cortical neuropeptide signaling (Crawley, 1985; Férézou et al., 2007; Gallopin et al., 2006; Gomtsian et al., 2018; Hamilton et al., 2013; Liu et al., 2018; Mena et al., 2013; Mitre et al., 2018; Owen et al., 2013; Rossier and Chapouthier, 1982; Williams and Zieglgänsberger, 1981), published physiological results are surprisingly rare given the breadth of neuroscientific interest in cortex. The new transcriptomic data analyzed here suggest a possible explanation for this relative rarity. Though many NPP and cognate NP-GPCR genes are expressed abundantly in all or very nearly all neocortical neurons, such expression is highly differential, highly cell-type specific, and often redundant. These previously uncharted differential expression factors may have hindered repeatable experimentation. Our analysis supports this unwelcome proposition but may also point the way to more productive new perspectives on intracortical peptidergic neuromodulation.

Prospects for elucidating cortical homeostasis, modulation and plasticity

Our results suggest that densely multiplexed peptidergic networks could play very significant roles in the homeostasis, modulation and plasticity of cortical synaptic networks. Due to the clearly formidable complexity of cortical networks, however, a real grasp of the myriad network interactions implicated is certain to require theoretical and computational approaches, in addition to experimental biophysics tests as outlined in the preceding section. Work at the fertile intersection of the neuroscience and the computer science of learning (Dayan and Abbott, 2001; Huh and Sejnowski, 2017; Koch and Segev, 1998; Lillicrap et al., 2016; Marblestone et al., 2016; Guerguiev et al., 2017; Song et al., 2000) seems particularly relevant to fathoming the possible significance of the neuropeptidergic networks we predict here.

Neuroscience and computer science efforts to model or engineer adaptive neural networks (be they biological or artificial) share the hard problem of optimally individualized adjustment of very large numbers of what both fields know as ‘synaptic weights’. At the heart of this challenge is ‘credit assignment’, that is, the assignment of ‘credit’ (or ‘blame’) to guide the strengthening (or weakening) of the small subset of synapses that actually contribute differentially to success (or failure) in a given perceptual, mnemonic or motor task. Neuroscientists struggle with the credit assignment problem as they search for biological learning rules. Computer scientists are driven by a quest for greater computational efficiency in training artificial networks and the prospect that evolution may have developed superior strategies. One concept that has come into prominence as a candidate biologically plausible solution to the credit assignment problem is that of modulated ‘Hebbian’ or ‘spike-timing-dependent’ plasticity (STDP) (Bengio et al., 2016; Dan and Poo, 2006; Farries and Fairhall, 2007; Florian, 2007; Frémaux and Gerstner, 2015; Froemke, 2015; Izhikevich, 2007; Marblestone et al., 2016; Pawlak et al., 2010; Poo et al., 2016; Roelfsema and Holtmaat, 2018; Xie and Seung, 2003) While most biological studies of modulated STDP so far have focused on the monoamine neuromodulator dopamine (Brzosko et al., 2019; Izhikevich, 2007; Kuśmierz et al., 2017; Schultz, 2015) known commonalities of signal transduction downstream from widely varying GPCRs suggest that NP-GPCRs could play roles in credit assignment analogous to those postulated for dopamine-selective GPCRs (Hamilton et al., 2013; Roelfsema and Holtmaat, 2018; but see Edelmann and Lessmann, 2011).

Deeper understanding of neuromodulation roles in adaptive cortical function seems certain to require a framework for integrating consideration of the panoply of possible activity-dependent modulatory networks with modulated excitatory and inhibitory synaptic networks. Figure 8 conceptualizes one such framework schematically, using a common neurotaxonomy to integrate statistics of multiple neuromodulatory and multiple synaptic signaling networks. Panels A-C idealize a logic for prediction of neuropeptidergic connectivity statistics from transcriptomic data. Panel D cartoons the use of a common neurotaxonomy to integrate probabilistic NP network graphs (panels B,C; three in this case) and multiple synaptic networks (panels E,F; two in this case) into a single graph representing superimposed modulatory and synaptic network. The present analysis suggests that a more realistic materialization of the Figure 8 schematic would involve approximately 100 neuron types and dozens of NPP and NP-GPCR genes. It would also require information that is presently unavailable about excitatory and inhibitory synaptic connectivity statistics in such a neurotaxonomic framework. It is very encouraging, however, that vigorous ongoing efforts (e.g., see Daigle et al., 2018; Jonas and Kording, 2015; Swanson and Lichtman, 2016; Tasic, 2018; Zeng and Sanes, 2017) suggest that such information is on the way. A view of cortical circuitry as a superimposition of multiple modulatory and synaptic networks, linked by a common neurotaxonomy as idealized in Figure 8, may prove essential to fathoming the interplay of slow neuromodulation and fast synaptic signaling necessary for adaptive cortical function.

Figure 8

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The present study is an analysis of a large transcriptomic dataset that is now freely available for download in its entirety at http://celltypes.brain-map.org/rnaseq/ and is described fully in a rigorously peer-reviewed publication (Tasic, et al., Nature 563:72-78, 2018). All code and intermediate data products involved in preparing this manuscript are freely available from a well-documented GitHub repository: https://github.com/AllenInstitute/PeptidergicNetworks (copy archived at https://github.com/elifesciences-publications/PeptidergicNetworks).

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Author details

1. Stephen J Smith

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Investigation, Visualization, Writing—original draft, Writing—review and editing

   For correspondence

   StephenS@alleninstitute.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2290-8701

2. Uygar Sümbül

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   Conceptualization, Formal analysis, Supervision, Investigation, Visualization, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7134-8897

3. Lucas T Graybuck

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   Resources, Data curation, Formal analysis, Investigation, Visualization, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8814-6818

4. Forrest Collman

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   Conceptualization, Software, Formal analysis, Investigation, Visualization, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0280-7022

5. Sharmishtaa Seshamani

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   Resources, Data curation, Software, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

6. Rohan Gala

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   Data curation, Software, Formal analysis, Investigation, Visualization, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1872-0957

7. Olga Gliko

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   Data curation, Software, Formal analysis, Investigation, Writing—original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5014-7209

8. Leila Elabbady

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   Data curation, Software, Formal analysis, Investigation, Visualization, Writing—original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1452-1603

9. Jeremy A Miller

   Allen Institute for Brain Science, Seattle, United States

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4549-588X

10. Trygve E Bakken

    Allen Institute for Brain Science, Seattle, United States

    Contribution

    Data curation, Formal analysis, Investigation, Visualization, Methodology

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3373-7386

11. Jean Rossier

    Neuroscience Paris Seine, Sorbonne Université, Paris, France

    Contribution

    Data curation, Software, Investigation, Methodology, Writing—review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-1821-2135

12. Zizhen Yao

    Allen Institute for Brain Science, Seattle, United States

    Contribution

    Resources, Data curation, Formal analysis, Investigation, Methodology

    Competing interests

    No competing interests declared

13. Ed Lein

    Allen Institute for Brain Science, Seattle, United States

    Contribution

    Conceptualization, Resources, Supervision, Investigation, Methodology

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-9012-6552

14. Hongkui Zeng

    Allen Institute for Brain Science, Seattle, United States

    Contribution

    Conceptualization, Resources, Investigation

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0326-5878

15. Bosiljka Tasic

    Allen Institute for Brain Science, Seattle, United States

    Contribution

    Resources, Supervision, Investigation, Methodology, Writing—review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-6861-4506

16. Michael Hawrylycz

    Allen Institute for Brain Science, Seattle, United States

    Contribution

    Conceptualization, Data curation, Software, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

    For correspondence

    MikeH@alleninstitute.org

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-5741-8024

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