Neuron Type-Specific Translatomes in Spinal Cords of Naïve and Neuropathic Mice

bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

1,2R.R. Das Gupta, 1L. Scheurer, 3P. Pelczar, 1,4W.T. Ralvenius, 1*H. Wildner, 1,2*H.U. Zeilhofer 1Institute of Pharmacology and Toxicology, University of Zurich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland. 2Institute of Pharmaceutical Sciences, Swiss Federal Institute of Technology (ETH) Zurich, Vladimir- Prelog-Weg 1-5/10, CH-8090 Zurich, Switzerland 3Center for Transgenic Models, University of Basel, 4001 Basel, Switzerland. 4Present address: Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA

*corresponding authors: Dr. H.U. Zeilhofer & Dr. H. Wildner, 1Institute of Pharmacology and Toxicology, University of Zurich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland. Phone: +41 44 63 55938 Fax: +41 44 635 59 88 e-mail: [email protected] [email protected]

Abstract

The spinal dorsal horn harbors a sophisticated and heterogeneous network of excitatory and

inhibitory neurons that process peripheral signals encoding different sensory modalities. Although it

has long been recognized that this network is crucial both for the separation and the integration of

sensory signals of different modalities, the molecular identity of the underlying neurons and signaling

mechanisms are still only partially understood. Here, we have used the translating ribosome affinity

purification (TRAP) technique to map the translatomes of excitatory glutamatergic (VGLUT2+) and

inhibitory GABA and/or glycinergic (VGAT+ or Gad67+) neurons of the mouse spinal cord. Our analyses

demonstrate that inhibitory and excitatory neurons are primarily set apart by the expression of genes

encoding transcription factors or genes related to the production, release or re-uptake of their

principal neurotransmitters (glutamate, GABA or glycine). Subsequent gene ontology (GO) term

analyses revealed that neuropeptide signaling-related GO terms were highly enriched in the excitatory

population. Eleven neuropeptide genes displayed largely non-overlapping expression patterns closely

adhering to the laminar and hence also functional organization of the spinal cord grey matter,

suggesting that they may serve as major determinants of modality-specific processing. Since this

modality-specific processing of sensory input is severely compromised in chronic, especially

neuropathic, pain, we also investigated whether peripheral nerve damage changes the neuron type-

specific translatome. In summary, our results suggest that neuropeptides contribute to modality-

specific sensory processing in the spinal cord but also indicate that altered sensory encoding in

neuropathic pain states occurs independent of major translatome changes in the spinal neurons.

Introduction

The ability to sense and discriminate different noxious and innocuous somatosensory stimuli is

essential for all higher animals and humans in order to react adequately to external stimuli and

internal conditions [1, 2]. The spinal dorsal horn, i.e., the sensory part of the spinal cord, constitutes

a key element in this process. It receives somatosensory signals from peripheral neurons and

processes these signals together with other inputs descending from supraspinal sites in a complex

network of inhibitory and excitatory interneurons before relaying these signals via projection neurons

to supraspinal centers [3]. Projection neurons make up less than 10% of all dorsal horn neurons, while

more than 90% of the neuronal population are interneurons of which, between 60 and 70% are

excitatory glutamatergic neurons, and the rest is inhibitory (GABA and/or glycinergic).

The spinal cord is organized in a laminar fashion, which has initially been proposed on the basis of

differences in cell density and morphology between the different laminae [4, 5] but has later been

shown to also reflect functional organization. This is especially reflected for example by the lamina

specific innervation pattern by the different types of peripheral sensory neurons: unmyelinated C

fibers, which mainly carry noxious and thermal information, terminate in the superficial dorsal horn

(laminae I-II), while thickly myelinated Ab fibers, which convey innocuous signals including touch and

proprioceptive information, terminate in the deep dorsal horn (laminae III-V) [3, 6, 7]. A laminar

organization of neuronal function is also supported by gene expression patterns that follow laminar

patterns [8-12]. Furthermore, optogenetic and chemogenetic experiments support a modality-specific

processing by distinct genetically defined neuron populations [7, 13-15]. However, the cellular basis

of this modality specific processing and hence the identity of interneuron types is only incompletely

understood. Recent work has used single cell RNA sequencing and unsupervised clustering to identify

15 subtypes or excitatory and 15 types of inhibitory neurons [8]. In the present study, we employed

translating ribosome affinity purification (TRAP) technology [16] to characterize the translatomes of

excitatory and inhibitory spinal neurons. To this end, we generated VGLUT2::bacTRAP (Slc17a6),

VGAT::bacTRAP (Slc32a1) and Gad67::bacTRAP (GAD1) mice, which express the eGFP-tagged bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

ribosomal subunit L10a (RPL10a) under the control of the respective gene regulatory elements.

Comparing the translatomes of excitatory and inhibitory spinal neurons by differential gene

expression analysis in VGLUT2::bacTRAP and Gad67::bacTRAP mice revealed two classes of genes that

distinguish excitatory and inhibitory neurons, namely transcription factors and genes related to the

neurotransmitter phenotype of these neurons. In addition, we found that genes encoding

neuropeptides constitute a functionally defined class of genes that is highly enriched in excitatory

spinal neurons. Accordingly, multiplex in situ hybridization for neuropeptide encoding genes revealed

largely non-overlapping expression patterns that follow the laminar organization of the spinal cord

thus suggesting a role of neuropeptide signaling in segregation of sensory modalities.

Interestingly, under pathophysiological conditions, such as neuropathy, the segregation of innocuous

and noxious information becomes compromised. Excitatory interneurons of the deeper dorsal horn

engage in circuits that process noxious information and thereby contribute to the development of

mechanical hypersensitivity or allodynia (i.e. pain induced by innocuous sensory stimuli) [17-20].

Several lines of evidence support the idea that these circuit changes are based on the loss of inhibition

exerted by e.g. glycinergic and/or parvalbumin expressing inhibitory interneurons [19, 21]. The

underlying molecular mechanisms inducing these circuit changes are poorly understood. We have

therefore employed the TRAP technology to search for molecular changes that occur in neuropathic

mice. To this end, we compared the translatomes of VGLUT2::bacTRAP and Gad67::bacTRAP mice

isolated from mice 7 days after sham surgery or mice 7 days after inducing a neuropathy either by the

chronic constriction injury (CCI) or spared nerve injury (SNI). While we found hundreds of genes that

distinguish inhibitory and excitatory spinal neurons, our results failed to reveal consistent translatome

changes in spinal dorsal horn neurons in two well-established mouse neuropathy models. They thus

suggest that neuropathy-induced changes in dorsal spinal sensory circuits occur independent of

changes in the translatomes of dorsal horn neurons.

Results

Generation and validation of three bacTRAP mouse lines to profile gene translation in excitatory

and inhibitory neurons.

In this study, we set out to determine the translatome (polysome-bound mRNA) of excitatory and

inhibitory neurons of the spinal cord. To this end, we generated three bacTRAP mouse lines;

VGLUT2::bacTRAP (Tg(Slc17a6-RPL10a-eGFP)Uze), Gad67::bacTRAP (Tg(Gad1-RPL10a-eGFP)Uze) and

VGAT::bacTRAP mice (Tg(Slc32a1-RPL10a-eGFP)Uze). In these mice, the eGFP-tagged ribosomal

protein L10a is expressed as a transgene either in glutamatergic neurons (VGLUT2::bacTRAP (Fig 1A,

D, G), all inhibitory neurons (GABAergic and glycinergic neurons) (VGAT::bacTRAP, Fig 1B, E, H) or in

GABAergic neurons that utilize Gad67 for GABA synthesis (Gad67::bacTRAP, Fig 1C, F, I).

From the spinal cord of these mice, polysome-bound mRNA can be isolated by co-

immunoprecipitation of mRNA bound to the eGFP-L10a tagged ribosomes. We confirmed the correct

expression of the eGFP-L10a transgene in the lumbar spinal dorsal horn of the different mouse strains

by immunohistochemistry. NeuN was used as a neuronal marker, Pax2 as a ubiquitous marker for

spinal inhibitory [21, 22] and Lmx1b as a marker for the majority of dorsal spinal excitatory neurons

(Fig 1D-I). In the VGLUT2::bacTRAP line, the eGFP-L10a expression is restricted to Pax2-negative

neurons (98.8 ± 0.5% Pax2- NeuN+; Fig 1D, G, J), indicating its exclusive expression in excitatory spinal

neurons. In the two mouse lines targeting inhibitory neurons, VGAT::bacTRAP and Gad67::bacTRAP,

the expression was confined to Pax2-positive neurons (98.3 ± 0.8%, 97.4 ± 1.6%, respectively) and

virtually absent from excitatory, Lmx1b-positive neurons of the dorsal horn (0.25 ± 0.22%, 0.19 ±

0.34%, for VGAT::bacTRAP and Gad67::bacTRAP mice, respectively; Fig 1E-F, H-I, K-L). These

expression patterns resemble those described in in situ hybridization experiments (Allen Brain Atlas,

mousespinal.brain-map.org) with widespread expression throughout the entire dorsal horn in the

VGLUT2::bacTRAP line and the VGAT::bacTRAP line, and more localized expression in the superficial

dorsal horn in Gad67::bacTRAP mice.

General differences in gene expression in excitatory and inhibitory spinal neurons

We used the bacTRAP mouse lines to search for genes that could potentially serve as markers for

subpopulations of excitatory or inhibitory dorsal horn neurons. To this end, we isolated and

sequenced the cell-type-specific polysomal mRNA from the lumbar spinal cord of three animals from

each mouse line. Out of 22'880 detectable genes, between 13'515 and 13'774 were found expressed

in each mouse line (Fig 2A, Table S1), defined by a normalized count (number of reads per gene,

normalized to the total number of reads and the gene length) of > 10. In order to identify among the

expressed genes those that show a regionally non-overlapping expression pattern, we focused our

analyses on genes whose regional expression is accessible by in situ hybridization. To this end, we

assumed that genes with normalized counts > 100 should be reliably detectable with in situ

hybridization. This threshold is three times lower than the expression of Grpr, a gene that we

previously found expressed at low levels in a small subset of dorsal horn neurons. In all three mouse

lines, approximately 11'200 genes were expressed at levels exceeding this threshold (Fig 2A).

Differential gene expression analyses (DGEAs) revealed 800 genes that were differentially expressed

(with a false discovery rate (FDR) ≤ 0.05) in VGLUT2::bacTRAP versus VGAT::bacTRAP mice, and 1'790

genes in VGLUT2::bacTRAP versus Gad67::bacTRAP mice (Fig 2B). The approximately two-fold lower

number of significantly enriched genes in the comparison of VGLUT2::bacTRAP versus VGAT::bacTRAP

mice probably results from the lower expression of the eGFP-L10a transgene in the VGAT::bacTRAP

line and hence a lower mRNA yield and higher variability in read numbers.

A comparison of the translatomes of VGLUT2 positive and VGAT positive neurons revealed 438

differentially expressed genes with an at least two-fold enrichment in one of the two populations. Of

those, 256 genes were enriched in excitatory neurons, and 182 in inhibitory neurons. A higher number

of differentially expressed genes (300 and 324) were detected when the Gad67-TRAP line was used

instead of VGAT-TRAP line (Fig 2B). Comparing the two DGEAs revealed that about half of the genes

enriched in either the excitatory or the inhibitory populations and identified in the comparison with bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

VGAT::bacTRAP line were also found when the Gad67::bacTRAP line was used (Fig 2C) supporting the

robustness of our approach.

Next, we performed cluster analyses ([23] (http://fgcz-shiny.uzh.ch/fgcz_heatmap_app/)) of the 800

genes differentially expressed in VGLUT2::bacTRAP and VGAT::bacTRAP mice (Fig 2D), and of the 1790

genes differentially expressed in VGLUT2::bacTRAP and Gad67::bacTRAP mice (Fig 2E) over the nine

different samples. Clustering of the samples was similar for both comparisons. As expected, the first

hierarchical segregation separated the inhibitory (VGAT and Gad67) from the excitatory (VGLUT2)

samples. Within the inhibitory cluster VGAT samples segregated from Gad67 samples (Fig 2D-E). The

cluster analysis therefore identified the VGAT+, Gad67+ and VGLUT2+ samples as three populations

with distinct translatome profiles.

Transcription factor expression and neuropeptide expression are key features distinguishing

excitatory and inhibitory spinal neurons

To identify key features that distinguish excitatory and inhibitory neurons beyond the expression

differences of individual genes, we gathered information on the subcellular location and function of

the enriched genes as well as overrepresented pathways (Fig 2 F-G). To this end, we first employed

the Ingenuity Pathway Analysis (IPA, QIAGEN) tool, which assigns a unique subcellular location

(“location”) and coarse function (“type(s)”) to the enriched genes. Twenty-four percent of the genes

enriched in VGLUT2 over VGAT encoded for cytoplasmic proteins, 24% for extracellular proteins, 22%

for proteins in the plasma membrane, 20% were nuclear proteins and the remaining 11% were located

elsewhere. Similar localizations were found when genes enriched in the inhibitory populations were

analyzed. One hundred-thirty out of 256 genes enriched in VGLUT2 over VGAT and 109 of 182 genes

enriched in VGAT over VGLUT2 could be assigned to a coarse function (Fig. 2G). The largest group of

genes that distinguished inhibitory from excitatory neurons in the adult spinal cord were transcription

regulators (28% and 39% of the genes enriched in VGAT and VGLUT2, respectively). Many of them

(such as Tlx3, Lmx1b, Phox2a and Prrxl1 expressed in excitatory or Lhx1/5, Pax2 and Gbx1 expressed bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

in inhibitory neurons) have been described as crucial regulators of cell fate determination of spinal

neurons during development [15, 24-31]. In addition, we found some transcription factors with as yet

unknown functions in spinal neurons such as Tfap2b and Sall3. Two other large molecular groups

present in the set of enriched genes were enzymes and G protein coupled receptors.

A subsequent Gene Ontology (GO) term overrepresentation analysis for the molecular function [32,

33] confirmed an enrichment of molecules involved in transcription and neuropeptide signaling. In all

four comparisons, several molecular function terms related to the regulation of transcription were

overrepresented, e.g. proximal promoter DNA-binding, transcription activator activity, RNA

polymerase II-specific and sequence-specific DNA binding, E-box binding (Fig 3 A-D). Another set of

highly overrepresented molecular function terms related to neuropeptide signaling (e.g. neuropeptide

receptor activity and neuropeptide receptor binding). In all comparisons, neuropeptide signaling-

related molecular functions terms were highly overrepresented (4.6 - 26.3-fold overrepresented as

compared to all expressed genes, FDR between 0.00016 and 0.0488), and those related to

transcription regulation included the largest numbers of enriched genes (1.9 - 10.3-fold

overrepresented, FDR 0.0000873 - 0.0445). Among the 50 most enriched genes (Table S2-5) filtered

for genes with a normalized count of ≥ 100 in at least two out of three samples of any mouse line,

transcription factors constituted the most abundant molecular category (30 - 38% for the different

comparisons), followed by genes that encode neuropeptides (24 - 26%).

Differential use of neurotransmitter signaling pathways

In the inhibitory populations, molecular function terms associated with neurotransmitter signaling

and transport were found overrepresented. Kainate selective glutamate receptor activity is

overrepresented in both inhibitory populations (38-fold in VGAT versus VGLUT2, FDR = 0.019, 21-fold

in Gad67 versus VGLUT2, FDR = 0.0497). Acetylcholine signaling related molecular function terms are

overrepresented in Gad67 versus VGLUT2 (13 - 17-fold, FDR = 0.0029 - 0.0171). Gamma-aminobutyric

acid transmembrane transporter activity is 31.5-fold overrepresented in the comparison of Gad67 to bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

VGLUT2 (FDR = 0.027). Ion channel activity-related molecular function terms are overrepresented

within the genes enriched in both comparisons of the excitatory to the inhibitory populations. Ion

gated channel activity and ion channel activity in VGLUT2 versus VGAT (3.5 and 2.9-fold, FDR = 0.009

and 0.0194, respectively) as well as voltage-gated ion channel activity and ion channel activity in

VGLUT2 vs. Gad67 (3.5 and 3.1-fold, FDR = 0.0452 and 0.0038, respectively).

The GO term analysis revealed an enrichment of kainate-mediated signaling and acetylcholine

mediated signaling in inhibitory spinal neurons. To further complement this analysis, we performed

Ingenuity Pathway Analysis on the list of the enriched genes (Fig 4). Within the 15 most significantly

overrepresented pathways of each comparison additional receptor signaling pathways were found in

the different neuron populations. Glutamate and GABA receptor signaling as well as serotonin

receptor signaling were overrepresented in inhibitory neurons. As expected, the enrichment of GABA

receptor signaling was mainly due to genes involved in the production (Gad65, Gad67) and transport

(VGAT, Gat1) of GABA, while GABA receptors showed relatively similar expression levels in the

different populations. Overrepresentation of serotonin receptor signaling (Fig. 4B) is due to the

enrichment of serotonin receptors (Htr1a, Htr1d, Htr3a) in inhibitory neurons. Serotonin receptor 3a

(Htr3a), the only ionotropic serotonin receptor, was highly enriched in inhibitory spinal interneurons

(Table S12) and displayed an expression pattern that was restricted to a population in the deep dorsal

horn (Fig S1). GPCR signaling as such is also overrepresented in the inhibitory enriched genes. The

genes contributing to the overrepresentation encode receptors (Grm2/3, Glp1r, Chrm3 and others)

and many downstream signaling molecules (Ptk2b, Rgs16, Plcb1, Pde1a and others). Further enriched

pathways relate to general functions of neuronal activity, such as calcium signaling (Gad67 versus

VGLUT2), synaptic long-term depression (VGLUT2 versus VGAT and Gad67), axonal guidance signaling

(all four comparisons) and gap junction signaling (Gad67 versus VGLUT2) (Fig 4 A-D). To further

illustrate differential expression of some neurotransmitter pathways, we compiled tables indicating

the relative expression levels and enrichment of glutamate, GABA, glycine, acetylcholine, serotonin,

adrenergic, dopaminergic and histamine receptors (Table S5-15). bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

In summary, several receptor-signaling pathways are enriched within the differentially expressed

genes, indicating that the respective neurotransmitter signaling pathways may engage inhibitory and

excitatory spinal neurons differentially.

Correlation of bacTRAP based translatome analysis and single cell sequencing

To relate our data to recently published single cell sequencing analysis of spinal dorsal horn neurons,

we compared the 50 most enriched genes of the different comparisons to the single cell sequencing

data of [8] (Table S 1-4). We found that many of our hits were also present in the single cell data (60%-

70% of the 50 most enriched genes). Fourteen to twenty genes identified per comparison in our study

were either not detected or only at a level that was too low to assign them to a subpopulation. Among

those genes that were not detected by the single cell analysis were many transcription factors such as

Vsx2, Lmx1b, Pax5, Barhl1 or Phox2a but also some others (e.g. Ighg3). To verify the expression of

some of these genes, we selected three for further investigation with in situ hybridization. We verified

expression of Barhl1 and Phox2a, which we found enriched in excitatory cells and the immunoglobulin

heavy chain gene Ighg3, which we surprisingly found enriched in inhibitory neurons (Fig S2).

A neuropeptide code for glutamatergic spinal dorsal horn neurons

We have identified transcription factors and neuropeptides as key genes differentiating inhibitory and

excitatory spinal neurons. While these transcription factors are likely to impact many aspects of

cellular identity, the neuropeptides probably affect communication between neurons. They therefore

directly influence information processing and relay and could thus be key elements of sensory circuits.

We therefore decided to more deeply investigate the expression patterns of 11 neuropeptide

encoding genes identified in excitatory neurons: cholecystokinin (Cck), gastrin releasing peptide (Grp),

neuromedin B (Nmb), neuromedin S (Nms), neuromedin U (Nmu), neuropeptide FF (Npff),

neurotensin (Nts), tachykinin 1 (Tac1, encoding PPTA, the precursor of substance P and neurokinin A),

tachykinin 2 (Tac2, encoding PPTB, the precursor of neurokinin B), thyrotropin releasing hormone bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

(Trh) and urocortin 3 (Ucn3). We paid particular attention to a potential regional segregation of

neuropeptide expression patterns. To this end, we conducted multiplex, fluorescent in situ

hybridization (RNAScope), combining three different neuropeptides at a time. Figure 5 displays five of

these combinations, in which every neuropeptide is represented at least once. As seen in Figure 5A,

E, I, M, Q, most neuropeptides showed an expression pattern that was restricted to one or two spinal

laminae. For example, Npff, Ucn3, Nmu, Tac2 and Trh showed expression patterns largely restricted

to one lamina, whereas the expression of Grp, Tac1, Nts and Cck extended over several laminae. In

case of the remaining two neuropeptides, Nmb or Nms, only few cells per hemi-section exhibited high

expression levels, but many cells showed weak but rather dispersed expression over most of the dorsal

horn. The number of those weakly expressing cells displayed a large variability between sections. By

direct comparison of the different expression patterns, we developed an approximate superficial to

deep (dorsal to ventral) expression map of the neuropeptides (from superficial to deep dorsal horn):

Npff / Grp / Tac1 / Nmu / Tac2 / Ucn3 / Trh / Nts / Cck.

To determine the overlap between the different neuropeptide-expressing populations, we quantified

for each pair of neuropeptides the percentage of co-expressing neurons. As some neuropeptides

displayed a wide range of expression levels, we performed separate analyses either including low

expressors (threshold near to background levels) or taking only cells into account with an expression

clearly above background (threshold > 20 dots per cell). We created two expression matrices

summarizing the percent co-expressing neurons irrespective of the neuropeptide expression level (Fig

6A) or taking only medium to high expression levels into account (Fig 6B). Restricting the analysis to

the medium to high expressors led to more pronounced segregations, accentuating the absence or

presence of co-expression. When focusing on medium to high-level expression, our analyses suggest

that eight different subpopulations can be defined based on their neuropeptide expression pattern:

1. Npff+, 2. Grp+, 3. Tac1+, 4. Tac2+;Nmu-, 5. Tac2+;Nmu+; 6. Nts+, 7. CCK+,Trh-,Ucn3-, 8. CCK+;Ucn3+;Trh+.

Trh and Ucn3 display a high degree of overlap with each other (58% and 72%, respectively) and also

highly overlap with Cck (86% and 91%, respectively). Conversely, only a minority of Cck neurons are bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Trh or Ucn3 positive, suggesting that the Trh and Ucn3 expressing neurons are a subpopulation of the

CCK expressing neurons. Similarly, Nmu expressing neurons appear to define a subset of Tac2 positive

neurons (76% of the Nmu neurons are also Tac2 positive and 25% of the Tac neurons are Nmu

positive). In conclusion, nine out of eleven neuropeptides display a laminar expression pattern.

Together, the expression of all 11 neuropeptides spans most of the dorsal horn (lamina I to V).

Translatome analysis of spinal neurons in neuropathic mice

The modality specificity of sensory processing is severely compromised in chronic pain states,

especially in those resulting from peripheral nerve damage [34-36]. We therefore decided to apply

our bacTRAP approach to mice with neuropathic nerve lesions and to test whether peripheral nerve

damage affects the neuron type-specific translatome. In the course of our above described

experiments, we noticed that a lower mRNA yield (e.g. from VGAT::bacTRAP mice) correlated with a

higher inter-sample variability in the number of detected reads per gene and in generally lower

significance levels. In the initial experiments, we isolated the entire lumbar spinal cord (approx. L2-

L6). In this second set of experiments we restricted our analysis to the lumbar segments L3 - L6 (i.e.,

to the segments innervated by the damaged sciatic nerve) of the ipsilateral spinal cord. Because of

the smaller size of the tissue sample, we expected to have a more than 2 - 3-fold reduction in RNA

yield after translating ribosome purification. To compensate for this smaller amount, we pooled three

spinal cords per sample. To assess the reproducibility of the modified bacTRAP approach, we first

compared the translatomes isolated from pooled spinal cords of naive VGLUT2::bacTRAP and

Gad67::bacTRAP mice. We then compared the results to our initial analysis. We detected 13'033 genes

of which 13'003 genes were also detected in our first analysis. Applying differential gene expression

analysis, we detected 3180 significantly enriched genes (FDR £ 0.05) in this second compared to 1812

genes in the first analysis. Seventy percent of the genes (1269) enriched in the first analysis were also

detected in the second analysis. Eighty-seven percent of the genes enriched in the first analysis and

displaying a fold change ³ 2 were also found to be enriched in the second analysis. Hence, we bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

concluded that the approach is highly reproducible. The increase in the number of detected genes is

likely due to a reduced inter-sample variability in the pooled samples.

We then focused on the analysis of VGLUT2::bacTRAP and Gad67::bacTRAP mice after employing two

different types of nerve lesions (chronic constriction injury (CCI) [37] and spared nerve injury (SNI) of

the sciatic nerve [38]. We characterized the translatome of the respective neuronal populations in

mice that either underwent sham (surgery without constricting suture or lesion), CCI, or SNI surgery 7

days post-surgery when the neuropathy-induced hypersensitivity was fully established. We verified

that all mice included in this analysis had developed mechanical hypersensitivity in response to the

nerve injury.

We then compared gene expression differences between the three experimental groups of mice for

glutamatergic and GABAergic neurons (first order comparisons) (Fig 7 and suppl. Information/Table

S17). We only considered genes with normalized counts of ≥ 50 in at least two samples within any

group. With sham-operated mice as the reference, DGEAs revealed significant changes in gene

expression (FDR £ 0.05) caused by CCI and SNI surgery within both neuron populations (Fig 7). Four

genes were up-regulated by SNI surgery in GABAergic (Gad67::bacTRAP) neurons relative to sham-

operated mice. Sprr1a was the only gene up-regulated in GABAergic neurons after CCI surgery. Very

similar results were obtained for the glutamatergic (VGLUT2::bacTRAP) neuron population. Three

genes were up-regulated in SNI (versus sham) operated mice and one gene (Sprr1a) was up-regulated

by CCI versus sham. No down-regulated genes were found in either the glutamatergic or the

GABAergic population.

We found that Sprr1a expression in glutamatergic neurons displayed an approximately 25-fold up-

regulation after CCI and a 200-fold up-regulation after SNI (Fig 8A). Sprr1a was also up-regulated in

Gad67-positive neurons after CCI and SNI (approximately 45-fold and 150-fold, respectively).

Immunohistochemistry confirmed a strong up-regulation of Sprr1a protein in the ipsilateral dorsal

horn and in ipsilateral motoneurons (Fig 8B). Staining in the dorsal horn was confined to the neuropil

with no clear signal from neuronal somata, while in the ventral horn motor neuron somata were bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

clearly labelled. Subsequent in situ hybridization experiments confirmed upregulated expression in

motor neurons but failed to detect Sprr1a in neurons of the dorsal spinal cord (i.e., RNAscope signal

around nuclei of spinal dorsal horn cells) (Fig 8F-J), suggesting that the signal in the dorsal horn

detected in the immunohistochemistry experiments originated from DRG neuron axons and terminals.

In conclusion, we find surprisingly little changes in the translatome of excitatory or inhibitory spinal

neurons after peripheral nerve injury, suggesting that neuropathy-induced changes in spinal circuits

may rely on alterations occurring independent of changes in neuronal translatomes.

Discussion

Several groups have recently employed single cell or single nucleus sequencing to obtain a

transcriptional atlas of the spinal cord [8, 9, 39]. We have used a complementary approach to

specifically identify the mRNA translation profiles (translatomes) of inhibitory (GABA/glycinergic) and

excitatory (glutamatergic) spinal cord neurons in naïve and neuropathic mice.

Single cell transcriptome profiling versus cell type specific translatome profiling

mRNA translation profiles obtained by the TRAP approach provide cell-type-specific gene expression

but not the single cell resolution obtained with single cell transcriptome profiling. On the other hand,

tissue-dissociation procedures needed for single cell analyses often lead to undesired responses in

gene expression and RNA degradation that are avoided when the bacTRAP technology is used.

Furthermore, the fraction of polysome-bound mRNA should more accurately reflect protein levels

than approaches based on nuclear or total RNA analysis [16]. The TRAP approach therefore represents

a complimentary approach to bulk tissue and single cell sequencing capable to reveal novel insights

into tissue diversity and pathological changes. In fact, two recent studies have employed the TRAP

approach to primary nociceptors and uncovered unknown differences between trigeminal and dorsal

root ganglia as well as unidentified driver genes for neuropathic pain [40, 41]. bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

In our translatome comparison of inhibitory and excitatory spinal neurons, we find a high degree of

overlap between genes that have been suggested to label subpopulations of neurons by single cell

RNAseq experiments and those genes enriched in our analyses. However, we also find genes enriched

in inhibitory and excitatory neurons that were either not detected in single cell RNAseq or where

detection levels were too low to assign the gene to a subpopulation (e.g. Phox2a) or were potentially

filtered out to reduce artefacts (e.g. cell stress responses) that might have been introduced by

dissociation of the tissue to obtain single cells (e.g. Ighg3). Hence, the sensitivity of the TRAP approach

appears at least equal to single cell RNA sequencing. A potential disadvantage of the TRAP approach

is the spill-over of unbound RNA molecules from one neuron population to RNA free eGFP-tagged

ribosomes of another neuron population. In fact, we detected low levels of most of the genes that are

thought to be expressed exclusively in either inhibitory or excitatory neurons also in the respective

other subset. Such examples include Slc17a6 (VGLUT2), Slc32a1 (VIAAT), GAD1/2 or Slc6a5 (Glyt2).

These genes were detected at read numbers that correspond to 1 - 6% of the read numbers in the

transgene expressing population. It is likely that the detection of Sprr1a in GABAergic and

glutamatergic spinal neurons after either CCI or SNI resulted from spill-over from spinal motor

neurons, in which Sprr1a is massively upregulated after peripheral nerve damage

Translatome differences between dorsal horn neurons of naïve mice

Our GO term and pathway analyses of enriched translated genes revealed decisive differences

between different subsets of spinal neurons in three gene families. We identified several transcription

factors that are required during development for decisions about an excitatory and inhibitory cell fate

but remain expressed into adulthood in a cell-type-specific manner. Although most neurotransmitters

act both on excitatory and inhibitory neurons, we identified subtypes of neurotransmitter receptors

and of down-stream signalling cascades that differ between excitatory or inhibitory neurons. Finally,

we found that the heterogeneity within the large population of excitatory dorsal horn neurons is

impressively mirrored by the expression of 11 neuropeptides or neuropeptide precursors. bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Transcription factors

Thirty to thirty-eight percent of the 50 genes most strongly enriched in excitatory or inhibitory neurons

were transcription factors. Many of them have been described as crucial regulators during spinal cord

development to establish cell type identity and thereby set up neuronal diversity [15]. Among these

are genes such as Olig3 which together with Lbx1 (expressed in inhibitory and excitatory adult spinal

neurons) are required to set up class A and class B neurons which populate the deeper or the upper

laminae of the dorsal horn, respectively. Another battery of enriched genes such as those coding for

the paired box factors (Pax2/5/8) and the Lim domain homeobox genes Lhx1/5 on the one hand and

Tlx1/3 Lmx1b, Prrxl1, Phox2a and others on the other hand are required to establish neurotransmitter

identity and govern subsequent subtype specification [15]. While some of these genes have previously

been reported to maintain expression into adult stages [8, 9, 22] many of them are believed to be

switched-off in the adult [8, 15]. Our data however indicates sustained expression of the majority of

these genes, suggesting also a hitherto unknown function in the adult.

Differential use of neurotransmitter signaling pathways

Spinal inhibitory and excitatory neurons receive synaptic inputs from a wide variety of peripheral, local

or supraspinal neurons, including glutamatergic, GABAergic, glycinergic, serotonergic, cholinergic, and

adrenergic neurons. We find that both inhibitory and excitatory spinal neurons express receptors for

all these neurotransmitters (Table S2-12). However, in some cases, neurotransmitter signaling

appears to occur via different receptor subtypes and down-stream signaling cascades. Expression of

several glutamate receptors of the kainate family (Grik1-3) is enriched in inhibitory spinal neurons.

Kainate receptors display different modes of signaling and a metabotropic action of kainate receptors

has been involved in presynaptic regulation and inhibition of GABA release [42, 43]. A preferential

expression of kainate receptors on inhibitory spinal neurons might therefore be involved in a

glutamate-mediated inhibition of GABA (and glycine?) release. Even more striking is the enrichment bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

of several serotonin receptors on inhibitory neurons with the most pronounced example being Htr3a,

the only ionotropic serotonin receptor. Expression of Htr3a is more than 8-fold enriched in inhibitory

neurons and in situ hybridization indicated expression restricted to the deep dorsal horn. It is

therefore conceivable that serotonin release from descending supraspinal neurons has a differential

effect onto excitatory and inhibitory spinal neurons.

Lamina specific expression of neuropeptides suggests a neuropeptide code involved in sensory

perception

Our data confirm and extend previous single cell studies that identified neuropeptides as markers of

excitatory neuronal subtypes [8, 9]. Our multiplex in situ hybridization experiments demonstrate a

lamina-specific distribution of many of the enriched neuropeptides with rather limited expression

overlap between different neuropeptides. In total we find that the 11 different neuropeptides

analysed in this study span dorsal laminae I-V, thus setting up a map for a potential neuropeptide

sensory signalling code. Indeed, emerging evidence suggests that the release of specific neuropeptides

by distinct dorsal horn neurons is crucial for sensory perception. Signalling by gastrin releasing peptide

(GRP), which is found concentrated in lamina II is critical for itch perception in mice [44-47] and

required for successful signal transmission from second order GRP to third order GRP receptor

neurons in lamina II of the spinal cord [48]. Another example is somatostatin (SST), which is also

expressed in lamina II dorsal horn neurons. After release from peripheral sensory neurons or spinal

dorsal horn neurons it inhibits inhibitory dynorphin (Dyn) neurons, which in turn inhibit GRP receptor

expressing neurons [49]. SST release thereby facilitates pruritoceptive signalling. In this context, it is

interesting to note that the majority of spinal SST expressing neurons are excitatory (glutamatergic),

while the effect of SST on Dyn neurons is inhibitory. These studies demonstrate that neuropeptides

are able to add an additional layer of complexity to neuronal communication and may in certain cases

dominate over the effect of fast-acting neurotransmitters such as glutamate, GABA and glycine. The

lamina specific expression of many neuropeptides in dorsal horn neurons suggests that distinct groups bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

of neuropeptide expressing cells are activated by a restricted subset of primary afferents. Hence,

distinct neuropeptides might only be released upon specific sensory stimuli. Our expression matrix

provides a basis for further studies to decipher the function of neuropeptide expressing cells and of

neuropeptide release in sensory signalling.

No overt changes in the translatome of spinal neurons after peripheral nerve injury

It is generally accepted that altered somatosensory and nociceptive perception in neuropathic pain

result from still incompletely understood changes at the dorsal horn circuitry [50-52]. Several of the

suspected changes occur in second order intrinsic dorsal horn neurons. Our analyses in two types of

peripheral nerve lesions failed to reveal major alterations in the neuronal translatome. The only gene

consistently up-regulated in both neuropathic pain models was Sprr1a. Interestingly, Sprr1a appeared

up-regulated mainly, if not exclusively, in motor neurons and primary sensory DRG neurons, indicating

that it became expressed in the injured neurons rather than in intrinsic order dorsal horn neurons.

This up-regulation is consistent with its well-established role in axon repair [53, 54].

At a first glance, the low number of regulated genes detected in our study appears to be in stark

contrast with previous work. We see at least two possible explanations. First, these previous studies

analysed total RNA from the entire spinal cord [55], i.e., not only neuronal RNA but also RNA from

astrocytes and microglia. These non-neuronal cells are major contributors in the central sensitization

process following peripheral nerve damage [56-58]. Indeed, previous studies analysing whole spinal

cord RNA detected the most profound and consistent changes in genes related to immune cell

function [55, 58]. Second, a key difference between the present and previous studies is the use of the

TRAP approach in our study, which limits the analysis to transcripts that are bound by the ribosome

and will therefore likely be translated into proteins. Our data thus may suggest that many

transcriptional changes are not propagated to the translatome. Several previous studies have indeed

provided evidence that transcriptional changes do not necessarily translate into changes of the

proteome [59-62]. Alternatively, some neuropathy-induced changes in the translatome might have bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

been missed because they were transient. However, previous transcriptomics studies observed the

most pronounced changes in gene expression at 7 days post-surgery, when the phenotype was fully

established [55]. Finally, we cannot completely exclude that the critical translatome changes occurred

in neuron population too small to be detectable in our samples of excitatory and inhibitory neurons.

Despite these potential limitations, our data still suggest that neuropathy-induced circuit changes

occur independent of major changes of the translatome of dorsal horn neurons.

Materials and Methods

Generation of VGLUT2::bacTRAP, VGAT::bacTRAP and Gad67::bacTRAP mouse lines

The VGLUT2::bacTRAP mouse line was generated by placing cDNA for the eGFP-L10a (TRAP) transgene, followed by the bovine growth hormone polyadenylation (poly(A)) signal in frame into exon 2 of the VGLUT2 (Slc17a6) gene (on BAC clone RPE23-84M15), using homologous recombination in bacteria [63], replacing most of exon 2 of the VGLUT2 gene. The start codon (ATG) in exon 1 was mutated to an XhoI restriction site. For the VGAT::bacTRAP mouse line the transgene was placed in frame into the end of exon 1 of the VGAT (Slc32a1) gene, replacing it partially (on BAC clone RPE23-392P11). For the Gad67::bacTRAP mouse line the transgene was placed in frame into the end of exon 2 of the Gad67 (Gad1) gene, replacing it partially (on BAC clone RP24-256C12). BAC DNA was injected into C57BL/6 oocytes. The thereby generated mice were screened for the presence of the eGFP-L10a transgene and for the two BAC ends by PCR. The transgenic mouse lines were maintained on a C57BL/6J (Black 6, The Jackson Laboratory, Stock No: 000664) background. C57BL/6J mice were also used as wild type controls. Mouse breeding and maintenance were provided by the Laboratory Animal Services Center (LASC) of the University of Zurich. Mice received food and water ad libitum and were kept in a 12 h light/dark cycle.

Genotyping PCRs

The presence of the eGFP-L10a transgene in the three different mouse lines was determined using a specific PCR for the transgene or a generic PCR against eGFP with one of the following primer pairs: Produc Annealin Forward primer Reverse primer t g temp length VGLUT2::bacTR GGAAGTGCCTAACCACAGGC CTGGTTCCCGTGCAGGACT 873 bp 56-70°C AP GT CGAGGGTCATGAGCCAGAG VGAT::bacTRAP CTGGTTCCCGTGCAGGACT 950 bp 68°C C CTCTCCCTTCTGTTTGCAGCC 904 bp 66°C Gad67::bacTRAP CTGGTTCCCGTGCAGGACT T ACTGGGTGCTCAGGTAGTG eGFP 1 CTATATCATGGCCGACAAGC 164 bp 56°C G ACTTGTACAGCTCGTCCATG eGFP 2 GGGCGAGGAGCTGTTCACC 700 bp 65°C CC bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Immunohistochemistry (IHC)

For mouse line validation, adult (> 2 months old), male and female bacTRAP mice and for the Sprr1a staining, adult (10-12 weeks old) male C57BL/6J mice, seven days after SNI surgery were used. Mice were transcardially perfused with approximately 20 ml of ice-cold artificial cerebrospinal fluid (ACSF, pH 7.4) or phosphate buffered saline (PBS, pH 7.4), followed by 100 mL of 2 or 4 % ice-cold paraformaldehyde (PFA, in PBS or 0.1 M Sodium phosphate buffer (PB), pH 7.4). The lumbar spinal cord was dissected and post-fixed for 1.5 - 2 h in 4 % PFA solution (in PBS or 0.1 M PB, pH 7.4), followed by incubation in 30 % sucrose (in PBS or 0.1 M PB, pH 7.4) for cryoprotection at 4°C overnight. Cryoprotected spinal cords were embedded in NEG50 frozen section medium (Richard- Allen Scientific) and stored at - 80°C until cutting into 25 μm thick sections on a Hyrax C60 Cryostat (Carl Zeiss). Sections were mounted on Superfrost Plus glass slides (Thermo Scientific, Zurich, Switzerland) and stored at -80°C. For immunofluorescence staining the slides were washed for 5 min in PBS to remove the embedding medium, followed by blocking with 5 % normal donkey serum (NDS, AbD Serotec, RRID:SCR_008898) in 0.1 % Triton X-100-PBS for at least 30 min at room temperature (RT). Sections were incubated with primary antibodies (see resource table) in the blocking solution at 4°C overnight, followed by three washes in PBS for 5 min and incubation with secondary antibodies (fluorophore-coupled donkey antibodies, Jackson ImmunoResearch) in blocking solution at RT for 30 to 60 min. Subsequently, sections were washed in PBS and incubated with 4',6-diamidino-2-phenylindole (DAPI, 500 ng/ml in PBS) for 10 min and again washed in PBS. Finally, sections were covered with DAKO fluorescent mounting medium (Dako, RRID:SCR_013530) and coverslips.

In situ hybridization (ISH)

For the Sprr1a-ISH, 6.5 weeks old, male C57BL/6J mice were sacrificed seven days after SNI surgery. For all other ISHs, 6 weeks old, naïve male C57BL/6J mice were used. For spinal cord preparation, the vertebral column containing the lumbar part of the spinal cord were dissected, immediately after decapitation of the mouse. A syringe filled with ice-cold, RNAse-free PBS was fitted tightly against the caudal opening of the spinal canal and pressure was applied to press the spinal cord out, which was timed to the lumbar part. DRGs were prepared from the vertebral column containing the lumbar DRGs, which was cut open from the ventral side using fine scissors. After dissection, spinal cords were snap frozen in liquid nitrogen and stored at -80°C until embedding in NEG50 frozen section medium (Richard-Allen Scientific). DRGs were embedded directly after preparation. Frozen blocks were again stored at -80°C until cutting into 12 or 16-25 μm thick sections for DRGs and spinal cords, respectively and mounted as described for IHC. bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

For single-plex chromogenic ISH the manual RNAScope® 2.5 HD BROWN Assay (ACD, Bio-Techne, catalog no. 322300) was used. The manufacturer’s pretreatment protocol for fresh frozen tissue (document no. 320536-TN, rev. date 11112016) and detection protocol (document no. 322310-USM, rev. date 11052015) were followed. Hybridization with Amp 5 was increased from 30 to 45 min for Sprr1a and to 60 min for all other probes in order to increase the signal. Counterstaining in 50 % hematoxylin was reduced from 2 min to 30 sec. Probes are listed in the resource table. For multiplex fluorescent ISH (FISH) the manual RNAScope® Multiplex Fluorescent Assay (ACD, Bio- Techne, catalog no. 320850) was used. The manufacturer’s pretreatment protocol for fresh frozen tissue (document no. 320513, rev. date 11052015) and detection protocol (document no. 320293- UM, rev. date 03142017) were followed. The fluorophore alternatives (Amp 4 Alt) was chosen in such a way, that when possible the weakest expressing gene would lie in red channel (Atto 550) and not in the far-red channel (Atto 647). The 3-plex negative control probe was amplified with the corresponding Amp 4 Alt. Probes are listed in the resource table. For every neuropeptide combination, one slide with four lumbar spinal cord sections of four animals, each, was reacted.

Image acquisition and analysis

Bright field imaging of ISHs and fluorescent imaging of FISHs and IHCs was done using the following microscopes: Microscope Objectives Software Purpose Zeiss Axio Scan.Z1 5x/0.25 Fluar air ZEN 2 slidescan Bright field images of slidescanner 10x/0.45 Plan-Apochromat air (blue edition) chromogenic ISHs Fluorescent images Zeiss LSM710 20×/0.8 Plan-Apochromat air ZEN 2011 for mouse line confocal 40x/1.3 EC Plan-Neofluar oil (black edition) validation and Sprr1a microscope IHC staining Zeiss LSM700 25x/0.8 Plan-Neofluar oil Zen 2011 Overview images of confocal 40x/1.4 LCI Plan-Apochromat oil (black edition) multiplex FISH microscope Zeiss LSM 800 Overview and 25x/0.8 Plan-Neofluar oil ZEN 2.3 with Airyscan / analysis images of 40x/1.4 Plan-Apochromat oil (blue edition) confocal multiplex FISH

For fluorescent imaging, the pinhole was set to 1 airy unit for every channel, which were scanned sequentially to avoid overlapping emission spectra or with a combination of the ultraviolet and infrared channel in one track, where emission spectra overlap is minimal. bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

For the validation of the correct expression of the eGFP-L10a transgene, the 40x objectives on the LSM 700 or 710 were used with a zoom of 0.6x to acquire a tile scan over the complete lumbar dorsal horn (Pixel size: x 0.346 μm, y 0.346 μm, z 1.628/2.487 μm). A small z-stack (usually, 3.3 μm within 3 optical sections) at a bit depth of 8 was acquired. For the analysis, the cell counter plugin of ImageJ was used. Manual counting was done in the middle optical section; the other sections were used as an aid to distinguish between neighboring cells. Three hemi-sections from three animals of each mouse line were analyzed. Ratios were calculated per animal and then averaged. For the Sprr1a IHC staining, overview images of the dorsal horn were taken using tile scans with the 20x objective (Pixel size: x 0.415 μm, y 0.415 μm). Detail images were taken with the 40x objective (Pixel size: x 0.104 μm, y 0.104 μm, z 0.329 μm). For the quantification of the co-expression of the neuropeptides with multiplex FISH, the 40x objective on the LSM 800 was used with a zoom of 2x and an image size of 1024 x 1024 px (pixel size: x 0.078 μm, y 0.078 μm). An array of non-overlapping images that covers the complete dorsal horn was scanned at a bit depth of 16. Analysis was done with CellProfiler 2.2.0 software [64]. The cell profiler pipeline can be provided upon request. Briefly, nuclei were detected using the IdentifyPrimaryObjects module and expanded with the IdentifySecondaryObjects module to define the cell soma (from now on referred to as “cell”). The signal dots were detected using the IdentifyPrimaryObjects module, adapting the “threshold correction factor” and the “lower and upper bounds on threshold” for every slide (neuropeptide combination) to minimize false positive and false negative detection. The signal dots of the different channels were assigned to the cells they lie in using the RelateObjects module. The number of cells and related signal dots (per channel) were exported using the ExportToSpreadsheet module. Images with artefacts or high background were manually excluded from the automatic analysis in order to avoid false positive signal. Per slide (neuropeptide combination), three animals with two hemi-sections per animal were analyzed. Additionally, per combination one hemi-section hybridized with the 3-plex negative control probe and amplified with the same Amp 4 Alt was imaged with the same microscope setting and analyzed with the same CellProfiler setting in order to determine the background. Data analysis was conducted with R. To determine the signal background, the 3-plex negative control was analyzed. For every combination and channel, the 90th percentile of dots per cell in the negative control was determined. This value was set as the threshold above which a cell is counted as positive for the respective signal. 20 dots per cell were set as a threshold for high expression. The number of single, double and triple-positive cells was counted. Ratios were calculated per animal and then averaged.

Mouse models for neuropathic pain using peripheral nerve injury bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Mice were anesthetized using isoflurane (5 min induction chamber with 3-5 % isoflurane, maintenance with 1-3 %). The isoflurane dose was adjusted to a level where the animal does not respond to pinching of its hind paw. Mice were placed on a heat mat to maintain body temperature. Lubricant eye ointment was applied to prevent corneal drying during surgery. 0.1-0.2 mg/kg buprenorphine were injected subcutaneously as analgesic treatment. The left hind limb was shaved with an electric razor and the skin was swabbed with iodine solution and allowed to dry. A longitudinal cut of 10-15 mm was made to the lateral surface of the thigh and a section was made through the biceps femoris muscle exposing the sciatic nerve and its three branches: the tibial, common peroneal and the sural nerve. For chronic constriction injury (CCI), three loose ligatures were placed around the nerve with 5-0 surgical silk proximal to its trifurcation [65]. For spared nerve injury (SNI), the common peroneal and the tibial nerve were transected distal to trifurcation of the nerve, removing 2 mm. The sural nerve was left intact [66]. For sham surgery the sciatic nerve was exposed but not injured. The surgical wound was closed in two layers (muscle and skin). 7 days post- surgery, when thermal and mechanical sensitization are fully established, animals were tested for mechanical hypersensitivity using von Frey filaments. Afterwards animals were sacrificed for immunopurification of polysomal mRNA or tissue preparation for IHC or ISH.

Behavioral testing

Measurements were done before surgery (baseline) and 7 days post-surgery. Withdrawal thresholds to a static innocuous mechanical stimulus were assessed using an electronic von Frey anesthesiometer with a 7 g filament (IITC, Woodland Hills, CA). Mice were kept in a 10 x 10 cm Plexiglas box, sitting on an elevated grid platform. The lateral plantar surface of the hind paw (innervated by the sural nerve) was poked with the filament and the force at which the mouse retracted its paw was measured. Each paw was measured 7-8 times with intervals of at least 8- 10 min.

Cell-type-specific polysomal mRNA isolation from excitatory and inhibitory spinal cord neurons

To specifically isolate polysomal mRNA from genetically targeted cells, the three bacTRAP mouse lines described above were used and the protocol published by Heiman, Kulicke (67) was followed. In brief, solutions and the affinity matrix were prepared as described. For the affinity matrix of Gad67::bacTRAP 300 μl and of VGLUT2::bacTRAP and VGAT::bacTRAP 150 μl Streptavidin MyOne T1 Dynabeads and the corresponding volumes of biotinylated protein L and GFP antibodies 19C8 and 19F7 were used in a final volume of 200 μl. In the comparison of excitatory to inhibitory neurons, three male mice per mouse line, aged between 8 and 10 weeks were used. For the analysis of changes after neuropathic pain, nine male bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

mice per group, aged between 8 and 10 weeks were sacrificed 7 days post-surgery. The vertebral column containing the lumbar part of the spinal cord was dissected, immediately after decapitation. A syringe filled with ice-cold dissection buffer was fitted tightly against the caudal opening of the spinal canal and pressure was applied to press the spinal cord out, which was trimmed to the lumbar part. For the neuropathic pain analysis, the lumbar spinal cord was bisected along the midline and the left side (ipsilateral to the peripheral nerve surgery) was further processed. The tissue was washed, homogenized (3 halves pooled for the neuropathic pain analysis), centrifuged, lysed and again centrifuged as described in the protocol by Heiman et al. [67]. Immunopurification was done as described by Heiman et al. [67]. RNA purification was done using the RNeasy Plus Micro Kit (Qiagen, catalog no. 74034). RNA was eluted from beads with 350 μl Buffer RLT Plus from the kit, supplemented with 40 μM dithiothreitol (DTT, Thermo Fisher Scientific, catalog no. R0861) and then processed according to the manufacturer’s protocol. RNA concentration was determined using the RiboGreen fluorescence assay (Thermo Fisher Scientific, catalog no. R11491) on a NanoDrop 3300 microvolume fluorospectrometer. RNA quality was assessed by high sensitivity RNA screen tape on an Agilent 2200 TapeStation. RNA samples with RINe values of 6.8 to7.8 (inhibitory – excitatory comparison) and 6.0 to7.3 (neuropathic pain analysis) were used for sequencing library construction.

Sequencing library construction, next-generation sequencing (NGS), mapping and differential gene expression analysis (DGEA) The sequencing library was prepared using the Ovation Mouse RNA-Seq System 1-16 kit (NuGEN, catalog no. 0348-32). The manufacturer’s protocol was followed. 13-20 ng RNA were used as input material for the inhibitory – excitatory comparison and 30 ng RNA for the neuropathic pain analysis. The optional cDNA fragmentation step was performed. As recommended, the optimal cycle number for amplification was determined by qPCR. Sequencing of the library, mapping to the reference genome, as well as DGEA were performed by the Functional Genomics Center Zurich (FGCZ). For the excitatory – inhibitory comparison, NGS was done on two lanes of a HiSeq 2500 system (illumina) with single-end sequencing of 125 bp. Mapping was done in R using the STAR package [68]. DGEA was done in R using either the edgeR or the DESeq2 package [69-71]. For the neuropathic pain analysis, NGS was done on two lanes of a HiSeq 4000 system (illumina) with paired-end sequencing of 2x150 bp. Mapping and DGEA were done in R using the packages STAR [68] and DESeq2, respectively [71]. In order to compare DGEAs of naïve mice (Gad67 vs VGLUT2) from the first analysis (entire spinal cord segments L2-L6) and the second analysis (spinal hemisegments from L3-L6) the DESeq2 package was used for the DGEAs. bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Gene Ontology (GO) term and pathway enrichment analyses

GO term enrichment analysis was done using the online PANTHER tool (http://pantherdb.org/). Genes significantly (FDR ≤ 0.05) and ≥ 2-fold enriched in excitatory (VGLUT2) over inhibitory (VGAT or Gad67) and vice versa were analyzed. In the PANTHER overrepresentation test, the sets of enriched genes were compared to the set of all expressed genes as a reference. GO molecular function (MF) complete was used as the annotation data set. The significantly overrepresented (FDR ≤ 0.05) MFs were analyzed. When several GO terms of a hierarchy were significantly overrepresented, only the most specific GO term was displayed in the graphical representation. For the analysis of Gad67 vs. VGLUT2, two redundant GO terms were not displayed in the graphical representation in order to allow comprehensibility. Pathway analysis was done with Ingenuity Pathway Analysis (IPA, QIAGEN). Genes significantly (FDR ≤ 0.05) and ≥ 2-fold enriched in excitatory (VGLUT2) over inhibitory (VGAT or Gad67) and vice versa were analyzed. To get a coarse classification of the enriched genes into subcellular location and function (“type”), these parameters were exported from the software.

Resources Reagent Resource Identifier Antibodies (dilution) goat anti-Pax2 (1:200-1:400) R & D Systems (Minneapolis, MN, USA) RRID:AB_10889828 guinea pig anti-Lmx1b (1:10'000) Dr. Carmen Birchmeier [27] rabbit anti-GFP (1:2'000) Molecular Probes RRID:AB_221570 mouse anti-NeuN (1:1'000) Millipore RRID:AB_2298772 guinea pig anti-NeuN (1:1'000) SynapticSystems RRID:AB_2619988 rabbit anti-SPRR1a (1:1'000) abcam RRID:AB_11130171

RNAScope multiplex FISH probes Mm-Cck ACD, Bio-Techne 402271 Mm-Grp ACD, Bio-Techne 317861 Mm-Grp-C2 ACD, Bio-Techne 317861-C2 Mm-Nmb ACD, Bio-Techne 459931 Mm-Nmb-C3 ACD, Bio-Techne 459931-C3 Mm-Nms ACD, Bio-Techne 472331 Mm-Nms-C3 ACD, Bio-Techne 472331-C3 Mm-Nmu ACD, Bio-Techne 446831 Mm-Npff ACD, Bio-Techne 479901 Mm-Nts-C2 ACD, Bio-Techne 420441-C2 Mm-Tac1-C3 ACD, Bio-Techne 410351-C3 Mm-Tac2 ACD, Bio-Techne 446391 Mm-Tac2-C2 ACD, Bio-Techne 446391-C2 Mm-Trh ACD, Bio-Techne 436811 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Mm-Ucn3 ACD, Bio-Techne 464861 Mm-Barhl1 ACD, Bio-Techne 520561 Mm-Phox2a ACD, Bio-Techne 520371 Mm-Ighg3 ACD, Bio-Techne 514611 Mm-Sprr1a ACD, Bio-Techne 426871 Positive control probe Mm-Ppib ACD, Bio-Techne 313911 Negative control probe-DapB ACD, Bio-Techne 310043 3-plex negative control probe ACD, Bio-Techne 320871

Acknowledgement The Functional genomics center Zurich (FGCZ) conducted next-generation sequencing, mapping, as well as DGEA. The FGCZ also assisted with data analysis. We are grateful to C. Birchmeier for providing the Lmx1b antibody. We thank Isabelle Kellenberger for genotyping the mice. This work was supported by grants from the Swiss National Science Foundation (SNSF, grant number 176398), an Advanced Investigator Grant from the European Research Council (ERC, grant number AdvG 250128) and a grant from the Swiss Federal Institute of Technology (ETH Fellowship) to H.U.Z and by a SNSF grant (grant number: CRSK-3_190622) to H.W.

Figure legends Figure 1 Generation and validation of three bacTRAP mouse lines, specific to excitatory and inhibitory spinal cord neurons. (A, D, G, J) VGLUT2::bacTRAP line, (B, E, H, K) VGAT::bacTRAP line, (C, F, I, L) Gad67::bacTRAP line. (A- C) BAC constructs for the three mouse lines. (D-I) Immunofluorescence images of the lumbar spinal dorsal horn of the three mouse lines. Boxes in the overview images of (D-F) indicate the position of the higher magnification images in (G-H). (D, G) VGLUT2::bacTRAP line with eGFP in green, Pax2 in red and NeuN in blue. (E, H) VGAT::bacTRAP mouse line with eGFP in green, Pax2 in red and Lmx1b in blue. (F, I) Gad67::bacTRAP mouse line with eGFP in green, Pax2 in red and Lmx1b in blue. (J) Quantification of eGFP+ NeuN+ cells with and without signal for Pax2 in the VGLUT2::bacTRAP line. (K-L) Quantification of eGFP+ cells with signal for Pax2 or Lmx1b in (K) the VGAT::bacTRAP line and (L) the Gad67::bacTRAP line. (D-G) Scale bar = 100 μm. (G-H) Scale bar = 5 μm.

Figure 2 RNA-Seq metadata from sequencing of the polysomal mRNA from VGLUT2::bacTRAP, VGAT::bacTRAP and Gad67::BacTRAP mice. (A) Number of “expressed” genes (> 10 norm. counts) and number of genes with > 100 norm. counts in the three different mouse lines. (B) Number of significantly enriched genes (FDR ≤ 0.05) in the DGEAs between VGLUT2::bacTRAP and VGAT::bacTRAP / Gad67::bacTRAP. Number of significantly enriched genes with ratio ≥ 2 in the excitatory and inhibitory neurons of both DGEAs. (C) Congruence between the enriched genes of the two DGEAs: 176 genes in common for the genes enriched in VGLUT2 neurons and 151 genes in common for the genes enriched in the inhibitory neurons. (D-E) Cluster analysis with the nine mRNA samples and the genes differentially expressed in (D) VGLUT2 vs. VGAT (800 genes, FDR ≤ 0.05) and (E) VGLUT2 vs. Gad67 (1790 genes, FDR ≤ 0.05). Color key indicated z-score with white representing zero, blue representing negative z-scores and red representing positive z-scores. (F) Subcellular location and (G) coarse function of the genes enriched in VGLUT2 vs. VGAT and VGAT vs. VGLUT2. Genes with unclassified function are not displayed.

Figure 3 GO term overrepresentation analyses for the molecular function (MF) with the enriched genes in the comparison of VGLUT2::bacTRAP to VGAT::bacTRAP and Gad67::bacTRAP. Overrepresented MF GO terms in the genes enriched in the comparison of (A) VGLUT2 vs. VGAT, (B) VGAT vs. VGLUT2, (C) VGLUT2 vs. Gad67 and (D) Gad67 vs. VGLUT2. (A-D) x axis displays false discovery rate (FDR) of the overrepresented GO term, y axis displays fold overrepresentation on a logarithmic scale and circle size indicates number of enriched genes. When several GO terms of a hierarchy are significantly overrepresented, only the most specific GO term is displayed. (D) For comprehensibility two MF are not displayed: “acetylcholine receptor activity”, 13.12-fold enrichment, FDR = 0.00801, no of enriched genes = 5, and “RNA polymerase II proximal promoter sequence-specific DNA binding” 2.79-fold enrichment, FDR = 0. 0.000603, no of enriched genes = 27.

Figure 4 Pathway analyses with the enriched genes in the comparison of VGLUT2::bacTRAP to VGAT::bacTRAP and Gad67::bacTRAP. 15 most significantly enriched pathways within the genes enriched in the comparison of (A) VGLUT2 vs. VGAT, (B) VGAT vs. VGLUT2, (C) VGLUT2 vs. Gad67 and (D) Gad67 vs. VGLUT2. (A-D) Bar length indicates significance, color key indicates activation z-score. Orange: increased activity of the pathway predicted. Blue: decreased activity of the pathway predicted. White: z-score = zero or very bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

close to zero (overall pathway activity is neither increased nor decreased). Gray: no prediction on pathway activity possible.

Figure 5 Multiplex in situ hybridization shows co-expression of eleven neuropeptides. Five combinations of three neuropeptides each: (A-D) Tac1, Cck and Nts; (E-H) Nms, Nmu and Tac2; (I-L) Npff, Grp and Nmb; (M-P) Trh, Grp and Tac1; (Q-T): Tac1, Ucn3 and Tac2 in green, red and blue, respectively. (A, E, I, M, Q) Overview images over the lumbar dorsal horn. The outline of the gray matter is indicated. Scale bar = 100 μm. Box indicates position of the higher magnification image in (B, F, J, N, R). (C, G, K, O, S) Number of cells per animal (two hemi-sections) that (co-)express the three neuropeptides. Circle and intersection sizes are approximately proportional to the cell number they represent. (D, H, L, P, T) Number of cells per animal that (co-)expression the three neuropeptides at high levels (> 20 dots per cell).

Figure 6 Co-expression matrices of eleven neuropeptides expressed in excitatory neurons. (A) Co-expression within all cells expressing a neuropeptide. (B) Co-expression within cells expressing clearly above background (“high levels” > 20 dots/cell) of a neuropeptide. (A-B) Fraction of cells expressing the neuropeptide on the left, that also express the neuropeptide at the bottom. For example, in (A) 42 % of all Nms-expressing cells, also express Npff, whereas in (B) 0 % of the high expressing Nms cells, also express high levels of Npff. Color key indicates co-expression with light colors representing low co-expression and dark values representing high co-expression.

Figure 7 Differential gene expression in excitatory (VGLUT2::bacTRAP) and inhibitory (Gad67::bacTRAP) spinal cord neurons after peripheral nerve injury. Translatome analysis of Gad67::bacTRAP and VGLUT2::bacTRAP mice: differentially expressed genes in both mouse lines for the comparisons of CCI over sham and SNI over sham with number of significantly differentially expressed genes (FDR ≤ 0.05) and number of upregulated (arrow up ↑) and downregulated genes (arrow down ↓). Venn diagrams represent the intersection of differentially expressed genes for each comparison between the two mouse lines with number of genes exclusive to each group and common in between the two groups.

Figure 8 Upregulation of Sprr1a expression after peripheral nerve injury. (A) Translatome data of Sprr1a in naïve, sham, CCI and SNI operated VGLUT2::bacTRAP and Gad67::bacTRAP mice. Expression levels are significantly (p ≤ 0.05) increased in CCI and SNI as compared to sham surgery and in SNI compared to CCI in both mouse lines. (B-E) IHC against Sprr1a (red) and NeuN (green) in the transverse, lumbar spinal cord sections of a C57BL/6J mouse that underwent SNI surgery (seven days post-surgery). (B) Overview over the whole spinal cord sections, Sprr1a in red is mostly seen in fibers in the medial and lateral, superficial dorsal horn and in the soma of motoneurons in lamina IX on the ipsilateral side. Box indicated position of higher magnification image in (C-E). (C) NeuN, (D) Sprr1a and composite with nuclear DAPI staining in blue of medial, superficial dorsal horn with fibrous staining of Sprr1a. (F-J) ISH for Sprr1a in spinal cord (F, H) and DRGs (G, I, J) of naïve C57BL/6J mice (F-G) and C57BL/6J mice that underwent SNI surgery, seven days post-surgery (H-J). (I) DRG ipsilateral and (J) contralateral to the injured nerve. * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, **** p ≤ 0.0001, error: ± SD. Scale bar (B, F-J) 200 μm, (C-E) 20 μm.

Figure S1 Expression pattern analysis of the ionotropic serotonin receptor Htr3a Expression pattern analysis by in situ hybridization using a RNAscope probe directed against the mRNA encoding Htr3a. Scale bar: (A) 200 μm, (A’) 50 μm

Figure S2 Confirmation of expression of genes with low detection levels, that were not detected in single-cell sequencing by Häring et al. [8]. In situ hybridization using RNAscope probes directed against (A) transcription factor BarH-like 1 (Barhl1), (B) transcription factor paired-like homeobox 2a (Phox2a), (C) Immunoglobulin heavy constant gamma 3 (Ighg3). Scale bar: (A, B, C) 200 μm, (A’, B’, C’) 50μm bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Literature 1. Lewin GR, Moshourab R. Mechanosensation and pain. J Neurobiol. 2004;61(1):30-44. Epub 2004/09/14. doi: 10.1002/neu.20078. PubMed PMID: 15362151. 2. Koch SC, Acton D, Goulding M. Spinal Circuits for Touch, Pain, and Itch. Annu Rev Physiol. 2018;80:189-217. Epub 2017/09/30. doi: 10.1146/annurev-physiol-022516-034303. PubMed PMID: 28961064; PubMed Central PMCID: PMCPMC5891508. 3. Todd AJ. Neuronal circuitry for pain processing in the dorsal horn. Nat Rev Neurosci. 2010;11(12):823-36. Epub 2010/11/12. doi: nrn2947 [pii] 10.1038/nrn2947. PubMed PMID: 21068766; PubMed Central PMCID: PMC3277941. 4. Rexed B. The cytoarchitectonic organization of the spinal cord in the cat. J Comp Neurol. 1952;96(3):414-95. Epub 1952/06/01. doi: 10.1002/cne.900960303. PubMed PMID: 14946260. 5. Rexed B. A cytoarchitectonic atlas of the spinal cord in the cat. J Comp Neurol. 1954;100(2):297-379. Epub 1954/04/01. doi: 10.1002/cne.901000205. PubMed PMID: 13163236. 6. Abraira VE, Ginty DD. The sensory neurons of touch. Neuron. 2013;79(4):618-39. Epub 2013/08/27. doi: 10.1016/j.neuron.2013.07.051. PubMed PMID: 23972592; PubMed Central PMCID: PMCPMC3811145. 7. Moehring F, Halder P, Seal RP, Stucky CL. Uncovering the Cells and Circuits of Touch in Normal and Pathological Settings. Neuron. 2018;100(2):349-60. Epub 2018/10/26. doi: 10.1016/j.neuron.2018.10.019. PubMed PMID: 30359601; PubMed Central PMCID: PMCPMC6708582. 8. Haring M, Zeisel A, Hochgerner H, Rinwa P, Jakobsson JET, Lonnerberg P, et al. Neuronal atlas of the dorsal horn defines its architecture and links sensory input to transcriptional cell types. Nat Neurosci. 2018;21(6):869-80. Epub 2018/04/25. doi: 10.1038/s41593-018-0141-1. PubMed PMID: 29686262. 9. Sathyamurthy A, Johnson KR, Matson KJE, Dobrott CI, Li L, Ryba AR, et al. Massively Parallel Single Nucleus Transcriptional Profiling Defines Spinal Cord Neurons and Their Activity during Behavior. Cell reports. 2018;22(8):2216-25. Epub 2018/02/22. doi: 10.1016/j.celrep.2018.02.003. PubMed PMID: 29466745; PubMed Central PMCID: PMCPMC5849084. 10. Gutierrez-Mecinas M, Furuta T, Watanabe M, Todd AJ. A quantitative study of neurochemically defined excitatory interneuron populations in laminae I-III of the mouse spinal cord. Mol Pain. 2016;12. Epub 2016/04/01. doi: 10.1177/1744806916629065. PubMed PMID: 27030714; PubMed Central PMCID: PMCPMC4946630. 11. Wildner H, Das Gupta R, Brohl D, Heppenstall PA, Zeilhofer HU, Birchmeier C. Genome-wide expression analysis of Ptf1a- and Ascl1-deficient mice reveals new markers for distinct dorsal horn interneuron populations contributing to nociceptive reflex plasticity. J Neurosci. 2013;33(17):7299-307. Epub 2013/04/26. doi: 10.1523/JNEUROSCI.0491-13.2013. PubMed PMID: 23616538; PubMed Central PMCID: PMC3684736. 12. Abraira VE, Kuehn ED, Chirila AM, Springel MW, Toliver AA, Zimmerman AL, et al. The Cellular and Synaptic Architecture of the Mechanosensory Dorsal Horn. Cell. 2017;168(1-2):295-310 e19. Epub 2017/01/04. doi: 10.1016/j.cell.2016.12.010. PubMed PMID: 28041852; PubMed Central PMCID: PMC5236062. 13. Todd AJ. Identifying functional populations among the interneurons in laminae I-III of the spinal dorsal horn. Mol Pain. 2017;13:1744806917693003. Epub 2017/03/23. doi: 10.1177/1744806917693003. PubMed PMID: 28326935; PubMed Central PMCID: PMCPMC5315367. 14. Gatto G, Smith KM, Ross SE, Goulding M. Neuronal diversity in the somatosensory system: bridging the gap between cell type and function. Curr Opin Neurobiol. 2019;56:167-74. Epub bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

2019/04/07. doi: 10.1016/j.conb.2019.03.002. PubMed PMID: 30953870; PubMed Central PMCID: PMCPMC6583900. 15. Lai HC, Seal RP, Johnson JE. Making sense out of spinal cord somatosensory development. Development. 2016;143(19):3434-48. Epub 2016/10/06. doi: 10.1242/dev.139592. PubMed PMID: 27702783; PubMed Central PMCID: PMCPMC5087618. 16. Heiman M, Schaefer A, Gong S, Peterson JD, Day M, Ramsey KE, et al. A translational profiling approach for the molecular characterization of CNS cell types. Cell. 2008;135(4):738-48. Epub 2008/11/18. doi: S0092-8674(08)01365-2 [pii] 10.1016/j.cell.2008.10.028. PubMed PMID: 19013281; PubMed Central PMCID: PMC2696821. 17. Peirs C, Williams SP, Zhao X, Walsh CE, Gedeon JY, Cagle NE, et al. Dorsal Horn Circuits for Persistent Mechanical Pain. Neuron. 2015;87(4):797-812. Epub 2015/08/21. doi: 10.1016/j.neuron.2015.07.029. PubMed PMID: 26291162; PubMed Central PMCID: PMC4562334. 18. Petitjean H, F BB, Tsao D, Davidova A, Sotocinal SG, Mogil JS, et al. Recruitment of Spinoparabrachial Neurons by Dorsal Horn Calretinin Neurons. Cell reports. 2019;28(6):1429- 38 e4. Epub 2019/08/08. doi: 10.1016/j.celrep.2019.07.048. PubMed PMID: 31390558. 19. Petitjean H, Pawlowski SA, Fraine SL, Sharif B, Hamad D, Fatima T, et al. Dorsal Horn Parvalbumin Neurons Are Gate-Keepers of Touch-Evoked Pain after Nerve Injury. Cell reports. 2015;13(6):1246-57. Epub 2015/11/04. doi: 10.1016/j.celrep.2015.09.080. PubMed PMID: 26527000. 20. Liu Y, Latremoliere A, Li X, Zhang Z, Chen M, Wang X, et al. Touch and tactile neuropathic pain sensitivity are set by corticospinal projections. Nature. 2018;561(7724):547-50. Epub 2018/09/14. doi: 10.1038/s41586-018-0515-2. PubMed PMID: 30209395; PubMed Central PMCID: PMCPMC6163083. 21. Foster E, Wildner H, Tudeau L, Haueter S, Ralvenius WT, Jegen M, et al. Targeted ablation, silencing, and activation establish glycinergic dorsal horn neurons as key components of a spinal gate for pain and itch. Neuron. 2015;85(6):1289-304. Epub 2015/03/20. doi: 10.1016/j.neuron.2015.02.028. PubMed PMID: 25789756; PubMed Central PMCID: PMC4372258. 22. Del Barrio MG, Bourane S, Grossmann K, Schule R, Britsch S, O'Leary DD, et al. A transcription factor code defines nine sensory interneuron subtypes in the mechanosensory area of the spinal cord. PloS one. 2013;8(11):e77928. Epub 2013/11/14. doi: 10.1371/journal.pone.0077928. PubMed PMID: 24223744; PubMed Central PMCID: PMC3817166. 23. Phanstiel DH, Boyle AP, Araya CL, Snyder MP. Sushi.R: flexible, quantitative and integrative genomic visualizations for publication-quality multi-panel figures. Bioinformatics. 2014;30(19):2808-10. Epub 2014/06/07. doi: 10.1093/bioinformatics/btu379. PubMed PMID: 24903420; PubMed Central PMCID: PMCPMC4173017. 24. Cheng L, Arata A, Mizuguchi R, Qian Y, Karunaratne A, Gray PA, et al. Tlx3 and Tlx1 are post- mitotic selector genes determining glutamatergic over GABAergic cell fates. Nat Neurosci. 2004;7(5):510-7. Epub 2004/04/06. doi: 10.1038/nn1221. PubMed PMID: 15064766. 25. Cheng L, Samad OA, Xu Y, Mizuguchi R, Luo P, Shirasawa S, et al. Lbx1 and Tlx3 are opposing switches in determining GABAergic versus glutamatergic transmitter phenotypes. Nat Neurosci. 2005;8(11):1510-5. Epub 2005/10/20. doi: 10.1038/nn1569. PubMed PMID: 16234809. 26. Gross MK, Dottori M, Goulding M. Lbx1 specifies somatosensory association interneurons in the dorsal spinal cord. Neuron. 2002;34(4):535-49. Epub 2002/06/14. PubMed PMID: 12062038. 27. Muller T, Brohmann H, Pierani A, Heppenstall PA, Lewin GR, Jessell TM, et al. The homeodomain factor lbx1 distinguishes two major programs of neuronal differentiation in the dorsal spinal cord. Neuron. 2002;34(4):551-62. Epub 2002/06/14. PubMed PMID: 12062039. bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

28. Wildner H, Muller T, Cho SH, Brohl D, Cepko CL, Guillemot F, et al. dILA neurons in the dorsal spinal cord are the product of terminal and non-terminal asymmetric progenitor cell divisions, and require Mash1 for their development. Development. 2006;133(11):2105-13. Epub 2006/05/13. doi: 10.1242/dev.02345. PubMed PMID: 16690754. 29. Rebelo S, Reguenga C, Lopes C, Lima D. Prrxl1 is required for the generation of a subset of nociceptive glutamatergic superficial spinal dorsal horn neurons. Dev Dyn. 2010;239(6):1684- 94. Epub 2010/05/27. doi: 10.1002/dvdy.22305. PubMed PMID: 20503365. 30. Huang M, Huang T, Xiang Y, Xie Z, Chen Y, Yan R, et al. Ptf1a, Lbx1 and Pax2 coordinate glycinergic and peptidergic transmitter phenotypes in dorsal spinal inhibitory neurons. Dev Biol. 2008;322(2):394-405. Epub 2008/07/19. doi: 10.1016/j.ydbio.2008.06.031. PubMed PMID: 18634777. 31. John A, Wildner H, Britsch S. The homeodomain transcription factor Gbx1 identifies a subpopulation of late-born GABAergic interneurons in the developing dorsal spinal cord. Dev Dyn. 2005;234(3):767-71. Epub 2005/09/30. doi: 10.1002/dvdy.20568. PubMed PMID: 16193514. 32. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25(1):25-9. Epub 2000/05/10. doi: 10.1038/75556. PubMed PMID: 10802651; PubMed Central PMCID: PMCPMC3037419. 33. The Gene Ontology C. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. 2019;47(D1):D330-D8. Epub 2018/11/06. doi: 10.1093/nar/gky1055. PubMed PMID: 30395331; PubMed Central PMCID: PMCPMC6323945. 34. Basbaum AI, Bautista DM, Scherrer G, Julius D. Cellular and molecular mechanisms of pain. Cell. 2009;139(2):267-84. Epub 2009/10/20. doi: 10.1016/j.cell.2009.09.028. PubMed PMID: 19837031; PubMed Central PMCID: PMC2852643. 35. Peirs C, Seal RP. Neural circuits for pain: Recent advances and current views. Science. 2016;354(6312):578-84. Epub 2016/11/05. doi: 10.1126/science.aaf8933. PubMed PMID: 27811268. 36. von Hehn CA, Baron R, Woolf CJ. Deconstructing the neuropathic pain phenotype to reveal neural mechanisms. Neuron. 2012;73(4):638-52. Epub 2012/03/01. doi: 10.1016/j.neuron.2012.02.008. PubMed PMID: 22365541; PubMed Central PMCID: PMCPMC3319438. 37. Bennett GJ1 XY. A peripheral mononeuropathy in rat that produces disorders of pain sensation like those seen in man. Pain. 1988 Apr;33(1):87-107. 38. Decosterd I, Woolf CJ. Spared nerve injury: an animal model of persistent peripheral neuropathic pain. Pain. 2000;87(2):149-58. Epub 2000/08/05. doi: 10.1016/s0304- 3959(00)00276-1. PubMed PMID: 10924808. 39. Zeisel A, Hochgerner H, Lonnerberg P, Johnsson A, Memic F, van der Zwan J, et al. Molecular Architecture of the Mouse Nervous System. Cell. 2018;174(4):999-1014 e22. Epub 2018/08/11. doi: 10.1016/j.cell.2018.06.021. PubMed PMID: 30096314; PubMed Central PMCID: PMCPMC6086934. 40. Megat S, Ray PR, Moy JK, Lou TF, Barragan-Iglesias P, Li Y, et al. Nociceptor Translational Profiling Reveals the Ragulator-Rag GTPase Complex as a Critical Generator of Neuropathic Pain. J Neurosci. 2019;39(3):393-411. Epub 2018/11/22. doi: 10.1523/JNEUROSCI.2661- 18.2018. PubMed PMID: 30459229; PubMed Central PMCID: PMCPMC6335757. 41. Megat S, Ray PR, Tavares-Ferreira D, Moy JK, Sankaranarayanan I, Wanghzou A, et al. Differences between Dorsal Root and Trigeminal Ganglion Nociceptors in Mice Revealed by Translational Profiling. J Neurosci. 2019;39(35):6829-47. Epub 2019/06/30. doi: 10.1523/JNEUROSCI.2663-18.2019. PubMed PMID: 31253755; PubMed Central PMCID: PMCPMC6733558. bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

42. Lerma J, Marques JM. Kainate receptors in health and disease. Neuron. 2013;80(2):292-311. Epub 2013/10/22. doi: 10.1016/j.neuron.2013.09.045. PubMed PMID: 24139035. 43. Negrete-Diaz JV, Sihra TS, Flores G, Rodriguez-Moreno A. Non-canonical Mechanisms of Presynaptic Kainate Receptors Controlling Glutamate Release. Front Mol Neurosci. 2018;11:128. Epub 2018/05/08. doi: 10.3389/fnmol.2018.00128. PubMed PMID: 29731708; PubMed Central PMCID: PMCPMC5920280. 44. Albisetti GW, Pagani M, Platonova E, Hosli L, Johannssen HC, Fritschy JM, et al. Dorsal Horn Gastrin-Releasing Peptide Expressing Neurons Transmit Spinal Itch But Not Pain Signals. J Neurosci. 2019;39(12):2238-50. Epub 2019/01/19. doi: 10.1523/JNEUROSCI.2559-18.2019. PubMed PMID: 30655357; PubMed Central PMCID: PMCPMC6433763. 45. Sun S, Xu Q, Guo C, Guan Y, Liu Q, Dong X. Leaky Gate Model: Intensity-Dependent Coding of Pain and Itch in the Spinal Cord. Neuron. 2017;93(4):840-53 e5. Epub 2017/02/24. doi: 10.1016/j.neuron.2017.01.012. PubMed PMID: 28231466; PubMed Central PMCID: PMC5324710. 46. Sun YG, Chen ZF. A gastrin-releasing peptide receptor mediates the itch sensation in the spinal cord. Nature. 2007;448(7154):700-3. Epub 2007/07/27. doi: 10.1038/nature06029. PubMed PMID: 17653196. 47. Sun YG, Zhao ZQ, Meng XL, Yin J, Liu XY, Chen ZF. Cellular basis of itch sensation. Science. 2009;325(5947):1531-4. Epub 2009/08/08. doi: 10.1126/science.1174868. PubMed PMID: 19661382; PubMed Central PMCID: PMCPMC2786498. 48. Pagani M, Albisetti GW, Sivakumar N, Wildner H, Santello M, Johannssen HC, et al. How Gastrin-Releasing Peptide Opens the Spinal Gate for Itch. Neuron. 2019;103(1):102-17 e5. Epub 2019/05/20. doi: 10.1016/j.neuron.2019.04.022. PubMed PMID: 31103358; PubMed Central PMCID: PMCPMC6616317. 49. Huang J, Polgar E, Solinski HJ, Mishra SK, Tseng PY, Iwagaki N, et al. Circuit dissection of the role of somatostatin in itch and pain. Nat Neurosci. 2018;21(5):707-16. Epub 2018/03/21. doi: 10.1038/s41593-018-0119-z. PubMed PMID: 29556030; PubMed Central PMCID: PMCPMC5923877. 50. Calvo M, Davies AJ, Hebert HL, Weir GA, Chesler EJ, Finnerup NB, et al. The Genetics of Neuropathic Pain from Model Organisms to Clinical Application. Neuron. 2019;104(4):637-53. Epub 2019/11/22. doi: 10.1016/j.neuron.2019.09.018. PubMed PMID: 31751545; PubMed Central PMCID: PMCPMC6868508. 51. Price TJ, Basbaum AI, Bresnahan J, Chambers JF, De Koninck Y, Edwards RR, et al. Transition to chronic pain: opportunities for novel therapeutics. Nat Rev Neurosci. 2018;19(7):383-4. Epub 2018/05/17. doi: 10.1038/s41583-018-0012-5. PubMed PMID: 29765159; PubMed Central PMCID: PMCPMC6237656. 52. Zeilhofer HU, Wildner H, Yevenes GE. Fast synaptic inhibition in spinal sensory processing and pain control. Physiol Rev. 2012;92(1):193-235. Epub 2012/02/03. doi: 10.1152/physrev.00043.2010. PubMed PMID: 22298656; PubMed Central PMCID: PMC3590010. 53. Bonilla IE, Tanabe K, Strittmatter SM. Small proline-rich repeat protein 1A is expressed by axotomized neurons and promotes axonal outgrowth. J Neurosci. 2002;22(4):1303-15. Epub 2002/02/19. PubMed PMID: 11850458; PubMed Central PMCID: PMCPMC6757578. 54. Starkey ML, Davies M, Yip PK, Carter LM, Wong DJ, McMahon SB, et al. Expression of the regeneration-associated protein SPRR1A in primary sensory neurons and spinal cord of the adult mouse following peripheral and central injury. J Comp Neurol. 2009;513(1):51-68. Epub 2008/12/25. doi: 10.1002/cne.21944. PubMed PMID: 19107756; PubMed Central PMCID: PMCPMC3339437. 55. Griffin RS, Costigan M, Brenner GJ, Ma CH, Scholz J, Moss A, et al. Complement induction in spinal cord microglia results in anaphylatoxin C5a-mediated pain hypersensitivity. J Neurosci. bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

2007;27(32):8699-708. Epub 2007/08/10. doi: 10.1523/JNEUROSCI.2018-07.2007. PubMed PMID: 17687047; PubMed Central PMCID: PMCPMC6672952. 56. Chen G, Zhang YQ, Qadri YJ, Serhan CN, Ji RR. Microglia in Pain: Detrimental and Protective Roles in Pathogenesis and Resolution of Pain. Neuron. 2018;100(6):1292-311. Epub 2018/12/21. doi: 10.1016/j.neuron.2018.11.009. PubMed PMID: 30571942; PubMed Central PMCID: PMCPMC6312407. 57. Inoue K. A state-of-the-art perspective on microgliopathic pain. Open Biol. 2018;8(11). Epub 2018/11/30. doi: 10.1098/rsob.180154. PubMed PMID: 30487300; PubMed Central PMCID: PMCPMC6282071. 58. Inoue K, Tsuda M. Microglia in neuropathic pain: cellular and molecular mechanisms and therapeutic potential. Nat Rev Neurosci. 2018;19(3):138-52. Epub 2018/02/09. doi: 10.1038/nrn.2018.2. PubMed PMID: 29416128. 59. Gomez-Varela D, Barry AM, Schmidt M. Proteome-based systems biology in chronic pain. J Proteomics. 2019;190:1-11. Epub 2018/04/14. doi: 10.1016/j.jprot.2018.04.004. PubMed PMID: 29653266. 60. Liu Y, Beyer A, Aebersold R. On the Dependency of Cellular Protein Levels on mRNA Abundance. Cell. 2016;165(3):535-50. Epub 2016/04/23. doi: 10.1016/j.cell.2016.03.014. PubMed PMID: 27104977. 61. Schwanhausser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, et al. Global quantification of mammalian gene expression control. Nature. 2011;473(7347):337-42. Epub 2011/05/20. doi: 10.1038/nature10098. PubMed PMID: 21593866. 62. Sharma K, Schmitt S, Bergner CG, Tyanova S, Kannaiyan N, Manrique-Hoyos N, et al. Cell type- and brain region-resolved mouse brain proteome. Nat Neurosci. 2015;18(12):1819-31. Epub 2015/11/03. doi: 10.1038/nn.4160. PubMed PMID: 26523646. 63. Copeland NG, Jenkins NA, Court DL. Recombineering: a powerful new tool for mouse functional genomics. Nat Rev Genet. 2001;2(10):769-79. Epub 2001/10/05. doi: 10.1038/35093556. PubMed PMID: 11584293. 64. Kamentsky L, Jones TR, Fraser A, Bray MA, Logan DJ, Madden KL, et al. Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software. Bioinformatics. 2011;27(8):1179-80. Epub 2011/02/26. doi: 10.1093/bioinformatics/btr095. PubMed PMID: 21349861; PubMed Central PMCID: PMCPMC3072555. 65. Bennett GJ, Xie YK. A Peripheral Mononeuropathy in Rat That Produces Disorders of Pain Sensation Like Those Seen in Man. Pain. 1988;33(1):87-107. doi: Doi 10.1016/0304- 3959(88)90209-6. PubMed PMID: WOS:A1988N057400014. 66. Decosterd I, Woolf CJ. Spared nerve injury: an animal model of persistent peripheral neuropathic pain. Pain. 2000;87(2):149-58. doi: Doi 10.1016/S0304-3959(00)00276-1. PubMed PMID: WOS:000088814100005. 67. Heiman M, Kulicke R, Fenster RJ, Greengard P, Heintz N. Cell type-specific mRNA purification by translating ribosome affinity purification (TRAP). Nat Protoc. 2014;9(6):1282-91. Epub 2014/05/09. doi: 10.1038/nprot.2014.085. PubMed PMID: 24810037; PubMed Central PMCID: PMC4102313. 68. Dobin A, Gingeras TR. Mapping RNA-seq Reads with STAR. Curr Protoc Bioinformatics. 2015;51:11 4 1- 4 9. Epub 2015/09/04. doi: 10.1002/0471250953.bi1114s51. PubMed PMID: 26334920; PubMed Central PMCID: PMCPMC4631051. 69. McCarthy DJ, Chen Y, Smyth GK. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 2012;40(10):4288-97. Epub 2012/01/31. doi: 10.1093/nar/gks042. PubMed PMID: 22287627; PubMed Central PMCID: PMCPMC3378882. 70. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139-40. Epub bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

2009/11/17. doi: 10.1093/bioinformatics/btp616. PubMed PMID: 19910308; PubMed Central PMCID: PMCPMC2796818. 71. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA- seq data with DESeq2. Genome Biol. 2014;15(12):550. Epub 2014/12/18. doi: 10.1186/s13059-014-0550-8. PubMed PMID: 25516281; PubMed Central PMCID: PMCPMC4302049. bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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Supporting information

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Figure S2 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Most enriched genes vGluT2 vGAT Gad67 vGluT2 vs. vGAT vGluT2 vs. Gad67 [norm. count] [norm. count] [norm. count] Gene Group Mean ± Stddev Mean ± Stddev Mean ± Stddev log2 Ratio FDR log2 Ratio FDR sc Data Ucn3 NP 106 ± 34 0 ± 0 5.9 ± 10.2 9.87 8.52E-14 4.18 4.60E-04 Lhx2 TF 199 ± 35 0.96 ± 1.67 0 ± 0 7.62 9.29E-26 10.71 2.02E-35 ng Olig3 TF 176 ± 28 1.56 ± 1.86 12.2 ± 7.1 6.58 5.80E-20 3.82 4.92E-13 nd Barhl1 TF 382 ± 38 4.3 ± 2.9 0 ± 0 6.40 3.25E-49 11.65 5.24E-74 nd Shox2 TF 765 ± 32 11.5 ± 10.4 25.6 ± 20.9 6.12 7.88E-35 4.91 9.81E-21 nd Crh NP 219 ± 44 3 ± 2.6 0 ± 0 6.05 5.17E-24 10.84 4.50E-39 Barhl2 TF 480 ± 92 6.7 ± 7.8 15.4 ± 2.38 6.02 9.86E-37 4.94 7.52E-53 nd Slc17a6 47533 ± 1973 753 ± 360 748 ± 191 5.98 6.32E-51 5.98 2.37E-110 Tlx3 TF 736 ± 64 13.7 ± 19.3 10.6 ± 9.6 5.78 1.63E-15 6.07 7.54E-69 Mgp 216.3 ± 16.7 4.5 ± 7.8 15 ± 16.2 5.60 4.43E-12 3.82 1.87E-08 ng Adcyap1 NP 2469 ± 241 52 ± 32 73.3 ± 17.2 5.59 2.04E-42 5.07 8.12E-134 Nrn1 NF 20203 ± 1915 457 ± 164 458 ± 49 5.47 8.16E-86 5.45 1.18E-207 Npff NP 317 ± 26 7.7 ± 10.9 7.9 ± 7.1 5.43 6.03E-15 5.34 1.38E-33 Pou4f1 TF 1462 ± 131 33.9 ± 21.8 43 ± 10.3 5.40 7.59E-50 5.09 1.94E-113 Ctla2a 104 ± 25 2.2 ± 3.8 8.1 ± 7.1 5.37 7.48E-07 3.69 3.89E-05 ng Lhx9 TF 214.8 ± 23.5 5.1 ± 6.5 1.29 ± 1.56 5.37 6.79E-17 7.23 2.13E-34 nd Pla2g5 507 ± 73 12.7 ± 22 16.2 ± 6.7 5.33 1.84E-05 4.98 9.34E-52 Cacna2d1 6755 ± 656 170.2 ± 82 203 ± 46 5.32 8.04E-77 5.05 4.24E-175 Vsx2 TF 1501 ± 91 41.9 ± 27.1 38.1 ± 6 5.17 6.07E-53 5.29 1.09E-132 nd Nms NP 217 ± 47 6.4 ± 11 1.48 ± 1.76 5.13 2.90E-07 6.96 2.35E-32 Tmem163 4153 ± 563 122 ± 69 118 ± 13.3 5.10 1.45E-45 5.12 3.92E-176 ng Phox2a TF 136.9 ± 12.6 3.9 ± 6.7 3.4 ± 5.9 4.99 6.60E-07 5.17 4.54E-10 nd Bdnf NF 1097 ± 70 44 ± 36 18.5 ± 11.9 4.63 4.02E-15 5.88 9.28E-90 Nmur2 NPR 588 ± 84 24.9 ± 22.6 46 ± 38 4.59 6.13E-21 3.67 5.66E-16 Cbln1 pNP 6341 ± 1471 310 ± 95 322 ± 53 4.36 1.17E-69 4.29 4.46E-110 Nmu NP 571.9 ± 23.9 29 ± 34 7.6 ± 5.1 4.31 5.78E-06 6.17 3.25E-73 Chrm5 177 ± 28 8.8 ± 8.4 23.1 ± 13.1 4.27 3.14E-09 2.91 7.15E-09 Nmb NP 204.9 ± 15.7 10.5 ± 8.2 4.5 ± 4.1 4.23 1.87E-12 5.51 1.08E-23 Tac2 NP 5416 ± 363 299 ± 86 536 ± 36 4.18 2.90E-85 3.33 7.35E-131 Nts NP 2582 ± 331 143 ± 86 130.6 ± 5.9 4.18 9.72E-31 4.29 9.74E-125 Lmx1b TF 1745 ± 90 99.1 ± 24.9 62.9 ± 9.1 4.14 9.36E-78 4.77 2.02E-129 nd Grpr NPR 292 ± 34 17.2 ± 22 26.7 ± 11.6 4.04 6.37E-07 3.42 3.11E-21 C1ql3 2262 ± 56 140 ± 22 171 ± 57 4.00 3.75E-94 3.71 5.63E-61 Cpne4 2929 ± 473 186.8 ± 7.8 300 ± 40 3.97 1.07E-89 3.28 8.89E-71 Lhx4 TF 249.1 ± 21.4 16.8 ± 17.9 25.3 ± 9.5 3.90 3.62E-06 3.29 2.32E-18 nd Slc17a8 186 ± 28 12.5 ± 9.3 35.4 ± 15.6 3.86 2.31E-09 2.39 1.94E-07 ng Cck NP 7261 ± 778 538 ± 152 373 ± 74 3.76 4.13E-68 4.27 2.02E-140 Mafa TF 603 ± 78 46 ± 26 54.3 ± 40.2 3.71 3.26E-20 3.46 2.32E-17 nd Prrxl1 TF 1436 ± 65 124 ± 59 180.8 ± 23.9 3.54 5.86E-34 2.98 9.05E-61 F2rl2 2146 ± 380 187 ± 156 264.1 ± 37.2 3.52 1.68E-12 3.01 1.04E-56 Ebf2 TF 3619 ± 436 355 ± 56 894.5 ± 59.7 3.35 8.47E-71 2.01 1.66E-40 Aldh1a2 179 ± 75 17.6 ± 11.8 17 ± 14.2 3.35 1.42E-07 3.35 1.94E-07 Serpina3g 148 ± 36 15.8 ± 16.7 40.2 ± 8.1 3.23 8.13E-05 1.87 1.01E-04 nd Nptx2 1325 ± 141 146.7 ± 24.5 181.2 ± 5.5 3.17 5.94E-48 2.86 1.79E-54 Grp NP 1085 ± 211 127 ± 39 105 ± 35 3.10 1.25E-28 3.36 2.48E-36 Car12 423.4 ± 10.1 52.2 ± 9.1 71 ± 70 3.02 1.25E-23 2.56 1.64E-04 Pou4f2 207 ± 39 25.7 ± 13.6 8.5 ± 4.4 3.01 1.79E-09 4.61 8.21E-21 ng Sox14 TF 169 ± 27 21 ± 21.8 1.11 ± 1.92 3.01 2.32E-03 7.24 1.52E-24 nd Tac1 NP 3017 ± 220 399 ± 92 848 ± 210 2.92 2.22E-48 1.82 1.09E-21 Trh NP 274 ± 44 37 ± 30 36.1 ± 16.6 2.88 1.43E-04 2.91 2.38E-14 Table S2 Most enriched genes in vGluT2 vs. vGAT. Group: TF = transcription factor, NP = neuropeptide, pNP = putative neuropeptide (classification of neuropeptides and putative neuropeptides is based on Burbach (2011)), NF = neurotrophic factor, NPR = neuropeptide receptor. sc data = single-cell data from Haring et al. (2018): nd = not detected, ng = not grouped. bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

vGluT2 vGAT Gad67 vGluT2 vs. vGAT vGluT2 vs. Gad67 [norm. count] [norm. count] [norm. count] Gene Group Mean ± Stddev Mean ± Stddev Mean ± Stddev log2 Ratio FDR log2 Ratio FDR sc Data Tfap2b TF 30.6 ± 17.1 1455 ± 48 717 ± 60 -5.58 2.29E-83 -4.57 1.97E-48 Pax8 TF 63.7 ± 9 2726 ± 338 3115 ± 419 -5.43 1.32E-140 -5.63 1.05E-171 Sall3 TF 201 ± 63 5381 ± 754 6224 ± 804 -4.75 3.76E-92 -4.97 5.74E-122 Slc32a1 734 ± 397 19147 ± 2462 20990 ± 2384 -4.71 9.09E-39 -4.85 1.12E-45 Ighg3 0.7 ± 1.21 20 ± 3 124 ± 11.5 -4.69 1.24E-01 -7.39 8.67E-18 nd Gad2 802 ± 111 18347 ± 3188 26077 ± 1196 -4.52 4.58E-91 -5.04 5.70E-156 Gad1 823 ± 140 18310 ± 4760 40767 ± 3643 -4.48 1.69E-70 -5.64 1.19E-127 Pax2 TF 321 ± 168 6971 ± 499 7070 ± 544 -4.45 3.30E-50 -4.48 8.72E-56 Pnoc NP 116 ± 30 2499 ± 184 3953 ± 558 -4.44 7.27E-92 -5.11 2.70E-129 Lhx5 TF 59.4 ± 14.7 1129 ± 164 1129 ± 121 -4.24 6.52E-59 -4.24 3.88E-73 Gata3 TF 6.8 ± 6.2 128 ± 45 18 ± 3.5 -4.18 2.17E-06 -1.39 6.05E-01 ng Slc6a5 1838 ± 823 32313 ± 2418 33567 ± 5237 -4.14 3.30E-35 -4.20 4.87E-43 Gbx1 TF 67.3 ± 19.2 1172 ± 90 1359 ± 102 -4.14 3.10E-58 -4.36 6.33E-80 Tal1 TF 11.8 ± 6.4 203.6 ± 11 21 ± 22.2 -4.06 9.14E-15 -0.83 7.89E-01 ng Dmkn 5.6 ± 7.9 90.9 ± 12.5 123 ± 28 -4.04 1.31E-03 -4.49 1.13E-06 Pax5 TF 13.4 ± 9.9 203 ± 66 204 ± 121 -3.91 1.47E-10 -3.92 4.29E-10 nd Aqp6 6.6 ± 7.4 78 ± 51 174 ± 32 -3.57 2.34E-02 -4.77 8.88E-12 Dmrt3 TF 16.2 ± 17.6 189 ± 22.2 112 ± 29 -3.57 7.82E-06 -2.81 2.75E-03 nd Pkd1l2 13.5 ± 5.3 159 ± 27 0 ± 0 -3.53 2.11E-09 6.84 1.80E-02 Slc6a1 1513 ± 451 15617 ± 4052 26070 ± 3382 -3.37 1.05E-37 -4.12 2.78E-69 Cdh3 64 ± 29 651 ± 253 1336 ± 184 -3.36 4.75E-17 -4.41 3.68E-58 Grb7 7.3 ± 6.9 73 ± 36 84 ± 38 -3.33 2.46E-02 -3.54 1.31E-03 ng Gata2 TF 11.6 ± 9.5 109 ± 36 12.5 ± 10.3 -3.23 3.46E-04 -0.11 9.77E-01 ng Pkd2l1 41 ± 30 368 ± 110 30.4 ± 20.6 -3.17 3.57E-11 0.42 8.20E-01 ng Alox12b 15.2 ± 12.5 133 ± 72 202 ± 105 -3.13 2.46E-04 -3.73 8.08E-09 Htr3a 90.8 ± 21.7 752 ± 124 428 ± 75 -3.05 1.11E-28 -2.25 1.52E-15 Asic4 63 ± 42 495 ± 31 610 ± 58 -2.99 2.27E-13 -3.30 1.25E-18 Slc30a3 585 ± 201 4564 ± 487 4491 ± 486 -2.97 6.37E-40 -2.96 1.78E-44 Ntsr1 NPR 63.3 ± 15.9 480 ± 34 726 ± 64 -2.92 1.85E-22 -3.53 1.27E-45 Actr5 58.4 ± 21.8 439 ± 81 65.4 ± 19.3 -2.90 1.60E-16 -0.16 9.00E-01 Gpc4 42.3 ± 17 311 ± 48 602 ± 150 -2.89 3.30E-14 -3.85 1.86E-32 Shisa3 38.7 ± 22.9 273 ± 26 246 ± 64 -2.84 1.66E-10 -2.69 1.33E-09 ng Klhl14 207.1 ± 5.1 1475 ± 154 1121 ± 4.4 -2.83 8.97E-48 -2.45 1.58E-42 Neurod2 TF 259.8 ± 8.5 1842 ± 146 1826 ± 92 -2.83 2.00E-55 -2.82 7.20E-70 Krt25 59.2 ± 3.9 414 ± 98 650 ± 196 -2.81 1.40E-18 -3.47 2.26E-34 Lhx1 TF 154 ± 54 1063 ± 169 1133 ± 106 -2.79 8.26E-25 -2.89 6.51E-33 Neurod6 TF 141 ± 76 918 ± 132 659 ± 107 -2.72 1.94E-15 -2.24 3.56E-11 C130060K24Rik 14.5 ± 8.2 94 ± 67 93 ± 34 -2.68 1.37E-02 -2.67 7.61E-04 ng Neurod1 TF 63 ± 26 391 ± 72 379 ± 58 -2.64 7.82E-13 -2.60 7.23E-15 Lgals9 219 ± 39 1343 ± 160 1813 ± 243 -2.62 5.16E-34 -3.06 3.38E-59 Ghsr 43.4 ± 22.2 263 ± 57 255 ± 48 -2.62 5.63E-09 -2.58 2.60E-10 ng Rasgrp1 237 ± 27 1256 ± 300 1337 ± 195 -2.41 3.56E-25 -2.50 9.85E-40 Erich2 7 ± 6.8 37.3 ± 14.9 154 ± 30 -2.39 3.52E-01 -4.43 4.02E-11 ng Islr2 32.1 ± 3.1 163 ± 68 248 ± 33 -2.35 4.53E-05 -2.96 4.51E-18 Fblim1 42.2 ± 19.1 214 ± 28 179 ± 35 -2.35 2.52E-07 -2.10 4.24E-06 ng Irx1 TF 23.7 ± 21.4 111.2 ± 20.4 216 ± 52 -2.25 1.30E-01 -3.21 2.28E-04 ng Nrl TF 28 ± 34 130 ± 31 127 ± 45 -2.21 4.22E-02 -2.18 2.00E-02 Kcnf1 102.2 ± 24.4 466 ± 55 685 ± 156 -2.19 1.02E-13 -2.76 2.02E-25 Gbx2 TF 88.7 ± 14.1 399 ± 119 348 ± 84 -2.18 1.95E-10 -1.99 2.18E-11 Irx2 TF 23.1 ± 4.2 102 ± 50 244.3 ± 22 -2.14 1.06E-02 -3.40 3.21E-21 ng Table S3 Most enriched genes in vGAT vs. vGluT2. Group: TF = transcription factor, NP = neuropeptide, pNP = putative neuropeptide (classification of neuropeptides and putative neuropeptides is based on Burbach (2011)), NF = neurotrophic factor, NPR = neuropeptide receptor. sc data = single-cell data from Haring et al. (2018): nd = not detected, ng = not grouped.

vGluT2 vGAT Gad67 vGluT2 vs. vGAT vGluT2 vs. Gad67 [norm. count] [norm. count] [norm. count] Gene Group Mean ± Stddev Mean ± Stddev Mean ± Stddev log2 Ratio FDR log2 Ratio FDR sc Data Barhl1 TF 382 ± 38 4.3 ± 2.9 0 ± 0 6.40 3.25E-49 11.65 5.24E-74 nd Crh NP 219 ± 44 3 ± 2.6 0 ± 0 6.05 5.17E-24 10.84 4.50E-39 Lhx2 TF 199 ± 35 0.96 ± 1.67 0 ± 0 7.62 9.29E-26 10.71 2.02E-35 ng Sox14 TF 169 ± 27 21 ± 21.8 1.11 ± 1.92 3.01 2.32E-03 7.24 1.52E-24 nd Lhx9 TF 214.8 ± 23.5 5.1 ± 6.5 1.29 ± 1.56 5.37 6.79E-17 7.23 2.13E-34 nd Nms NP 217 ± 47 6.4 ± 11 1.48 ± 1.76 5.13 2.90E-07 6.96 2.35E-32 Pkd1l2 13.5 ± 5.3 159 ± 27 0 ± 0 -3.53 2.11E-09 6.84 1.80E-02 Nmu NP 571.9 ± 23.9 29 ± 34 7.6 ± 5.1 4.31 5.78E-06 6.17 3.25E-73 Tlx3 TF 736 ± 64 13.7 ± 19.3 10.6 ± 9.6 5.78 1.63E-15 6.07 7.54E-69 Slc17a6 47533 ± 1973 753 ± 360 748 ± 191 5.98 6.32E-51 5.98 2.37E-110 Bdnf NF 1097 ± 70 44 ± 36 18.5 ± 11.9 4.63 4.02E-15 5.88 9.28E-90 Nmb NP 204.9 ± 15.7 10.5 ± 8.2 4.5 ± 4.1 4.23 1.87E-12 5.51 1.08E-23 Nrn1 NF 20203 ± 1915 457 ± 164 458.1 ± 48.8 5.47 8.16E-86 5.45 1.18E-207 Npff NP 317 ± 26 7.7 ± 10.9 7.9 ± 7.1 5.43 6.03E-15 5.34 1.38E-33 Vsx2 TF 1501 ± 91 42 ± 27 38.1 ± 6 5.17 6.07E-53 5.29 1.09E-132 nd Phox2a TF 136.9 ± 12.6 3.9 ± 6.7 3.4 ± 5.9 4.99 6.60E-07 5.17 4.54E-10 nd Tmem163 4153 ± 563 122 ± 69 118 ± 13.3 5.10 1.45E-45 5.12 3.92E-176 ng Pou4f1 TF 1462 ± 131 33.9 ± 21.8 43 ± 10.3 5.40 7.59E-50 5.09 1.94E-113 Adcyap1 NP 2469 ± 241 52 ± 32 73.3 ± 17.2 5.59 2.04E-42 5.07 8.12E-134 Cacna2d1 6755 ± 656 170 ± 82 202.6 ± 46.4 5.32 8.04E-77 5.05 4.24E-175 Pla2g5 507 ± 73 12.7 ± 22 16.2 ± 6.7 5.33 1.84E-05 4.98 9.34E-52 Barhl2 TF 480 ± 92 6.7 ± 7.8 15.4 ± 2.38 6.02 9.86E-37 4.94 7.52E-53 nd Shox2 TF 765 ± 32 11.5 ± 10.4 25.6 ± 20.9 6.12 7.88E-35 4.91 9.81E-21 nd Lmx1b TF 1745 ± 90 99.1 ± 24.9 62.9 ± 9.1 4.14 9.36E-78 4.77 2.02E-129 nd Pou4f2 TF 207 ± 39 25.7 ± 13.6 8.5 ± 4.4 3.01 1.79E-09 4.61 8.21E-21 ng Nts NP 2582 ± 331 143 ± 86 130.6 ± 5.9 4.18 9.72E-31 4.29 9.74E-125 Cbln1 pNP 6341 ± 1471 310 ± 95 322 ± 53 4.36 1.17E-69 4.29 4.46E-110 Cck NP 7261 ± 778 538 ± 152 373 ± 74 3.76 4.13E-68 4.27 2.02E-140 Ucn3 NP 106 ± 34 0 ± 0 5.9 ± 10.2 9.87 8.52E-14 4.18 4.60E-04 Vip NP 171 ± 38 34.5 ± 7 10.4 ± 11.8 2.30 5.98E-06 4.05 1.50E-07 ng Dmrt2 TF 151.9 ± 2 29.9 ± 23.8 9.5 ± 6.9 2.32 8.58E-04 3.97 2.93E-11 ng Olig3 TF 176 ± 28 1.6 ± 1.9 12.2 ± 7.1 6.58 5.80E-20 3.82 4.92E-13 nd Mgp 216.3 ± 16.7 4.5 ± 7.8 15 ± 16.2 5.60 4.43E-12 3.82 1.87E-08 ng C1ql3 2262 ± 56 140 ± 22 171 ± 57 4.00 3.75E-94 3.71 5.63E-61 Ctla2a 104 ± 25 2.2 ± 3.8 8.1 ± 7.1 5.37 7.48E-07 3.69 3.89E-05 ng Nmur2 NPR 588 ± 84 24.9 ± 22.6 46 ± 38 4.59 6.13E-21 3.67 5.66E-16 Crhbp 435 ± 66 108.2 ± 6.7 33.7 ± 5.9 2.01 2.39E-12 3.67 9.56E-36 Clic6 217.2 ± 9.4 59 ± 43 18.1 ± 8 1.88 1.45E-03 3.58 3.01E-17 ng Mafa TF 603 ± 78 46 ± 26 54 ± 40 3.71 3.26E-20 3.46 2.32E-17 nd Grpr NPR 292 ± 34 17.2 ± 22 26.7 ± 11.6 4.04 6.37E-07 3.42 3.11E-21 Grp NP 1085 ± 211 127 ± 39 105 ± 35 3.10 1.25E-28 3.36 2.48E-36 Aldh1a2 179 ± 75 17.6 ± 11.8 17 ± 14.2 3.35 1.42E-07 3.35 1.94E-07 Tac2 NP 5416 ± 363 299 ± 86 536 ± 36 4.18 2.90E-85 3.33 7.35E-131 Lhx4 TF 249.1 ± 21.4 16.8 ± 17.9 25.3 ± 9.5 3.90 3.62E-06 3.29 2.32E-18 nd Cpne4 2929 ± 473 186.8 ± 7.8 300 ± 40 3.97 1.07E-89 3.28 8.89E-71 Rprm 511 ± 76 124 ± 82 57.6 ± 17.8 2.05 3.88E-06 3.14 2.48E-27 Ctxn3 189 ± 54 124 ± 41 21.5 ± 3.2 0.60 5.02E-01 3.12 3.64E-12 ng F2rl2 2146 ± 380 187 ± 156 264 ± 37 3.52 1.68E-12 3.01 1.04E-56 Prrxl1 TF 1436 ± 65 124 ± 59 180.8 ± 23.9 3.54 5.86E-34 2.98 9.05E-61 Gm10684 316 ± 55 78 ± 54 40.5 ± 4.4 2.01 3.10E-04 2.95 3.43E-21 ng Table S4 Most enriched genes in vGluT2 vs. Gad67. Group: TF = transcription factor, NP = neuropeptide, pNP = putative neuropeptide (classification of neuropeptides and putative neuropeptides is based on Burbach (2011)), NF = neurotrophic factor, NPR = neuropeptide receptor. sc data = single-cell data from Haring et al. (2018): nd = not detected, ng = not grouped.

vGluT2 vGAT Gad67 vGluT2 vs. vGAT vGluT2 vs. Gad67 [norm. count] [norm. count] [norm. count] Gene Group Mean ± Stddev Mean ± Stddev Mean ± Stddev log2 Ratio FDR log2 Ratio FDR sc Data Ighg3 0.7 ± 1.21 20.0 ± 3.0 124 ± 11.5 -4.69 1.24E-01 -7.39 8.67E-18 nd Gad1 823 ± 140 18310 ± 4760 40767 ± 3643 -4.48 1.69E-70 -5.64 1.19E-127 Pax8 TF 63.7 ± 9 2726 ± 338 3115 ± 419 -5.43 1.32E-140 -5.63 1.05E-171 Chrna2 16.4 ± 14.6 9.28 ± 0.51 606 ± 100 0.80 9.30E-01 -5.26 1.09E-16 nd Pnoc NP 116 ± 30 2499 ± 184 3953 ± 558 -4.44 7.27E-92 -5.11 2.70E-129 Gad2 802 ± 111 18347 ± 3188 26077 ± 1196 -4.52 4.58E-91 -5.04 5.70E-156 Sall3 TF 201 ± 63 5381 ± 754 6224 ± 804 -4.75 3.76E-92 -4.97 5.74E-122 Slc32a1 734 ± 397 19147 ± 2462 20990 ± 2384 -4.71 9.09E-39 -4.85 1.12E-45 Aqp6 6.6 ± 7.4 78 ± 51 174 ± 32 -3.57 2.34E-02 -4.77 8.88E-12 Tfap2b TF 30.6 ± 17.1 1455 ± 48 717 ± 60 -5.58 2.29E-83 -4.57 1.97E-48 Dmkn 5.6 ± 7.9 90.9 ± 12.5 123 ± 28 -4.04 1.31E-03 -4.49 1.13E-06 Pax2 TF 321 ± 168 6971 ± 499 7070 ± 544 -4.45 3.30E-50 -4.48 8.72E-56 Erich2 7 ± 6.8 37.3 ± 14.9 154 ± 30 -2.39 3.52E-01 -4.43 4.02E-11 ng Cdh3 64 ± 29 651 ± 253 1336 ± 184 -3.36 4.75E-17 -4.41 3.68E-58 Gbx1 TF 67.3 ± 19.2 1172 ± 90 1359 ± 102 -4.14 3.10E-58 -4.36 6.33E-80 Lhx5 59.4 ± 14.7 1129 ± 164 1129 ± 121 -4.24 6.52E-59 -4.24 3.88E-73 Slc6a5 1838 ± 823 32313 ± 2418 33567 ± 5237 -4.14 3.30E-35 -4.20 4.87E-43 Slc6a1 1513 ± 451 15617 ± 4052 26070 ± 3382 -3.37 1.05E-37 -4.12 2.78E-69 ng Pax5 TF 13.4 ± 9.9 203 ± 66 204 ± 121 -3.91 1.47E-10 -3.92 4.29E-10 nd Gpc4 42.3 ± 17 311 ± 48 602 ± 150 -2.89 3.30E-14 -3.85 1.86E-32 Alox12b 15.2 ± 12.5 133 ± 72 202 ± 105 -3.13 2.46E-04 -3.73 8.08E-09 Grb7 7.3 ± 6.9 73 ± 36 84 ± 38 -3.33 2.46E-02 -3.54 1.31E-03 ng Ntsr1 NPR 63.3 ± 15.9 480 ± 34 726 ± 64 -2.92 1.85E-22 -3.53 1.27E-45 Krt25 59.2 ± 3.9 414 ± 98 650 ± 196 -2.81 1.40E-18 -3.47 2.26E-34 Irx2 TF 23.1 ± 4.2 102 ± 50 244.3 ± 22 -2.14 1.06E-02 -3.40 3.21E-21 ng Slc12a7 21.2 ± 13.7 90 ± 50 215.3 ± 7.2 -2.08 7.18E-02 -3.35 4.56E-13 Asic4 63 ± 42 495 ± 31 610 ± 58 -2.99 2.27E-13 -3.30 1.25E-18 Irx1 TF 23.7 ± 21.4 111.2 ± 20.4 216 ± 52 -2.25 1.30E-01 -3.21 2.28E-04 ng Otp TF 112 ± 92 429 ± 96 967 ± 82 -1.95 3.03E-04 -3.13 6.67E-13 nd Lgals9 219 ± 39 1343 ± 160 1813 ± 243 -2.62 5.16E-34 -3.06 3.38E-59 Adamtsl2 19.8 ± 2.7 58 ± 26 154 ± 58 -1.56 3.22E-01 -2.97 8.24E-09 Islr2 32.1 ± 3.1 163 ± 68 248 ± 33 -2.35 4.53E-05 -2.96 4.51E-18 Slc30a3 585 ± 201 4564 ± 487 4491 ± 486 -2.97 6.37E-40 -2.96 1.78E-44 Crabp1 689 ± 32 2431 ± 336 5210 ± 543 -1.82 4.04E-25 -2.93 2.70E-101 Lhx1 TF 154 ± 54 1063 ± 169 1133 ± 106 -2.79 8.26E-25 -2.89 6.51E-33 Ppef1 19.2 ± 10.3 64.9 ± 8.7 140 ± 32 -1.75 2.12E-01 -2.86 1.11E-06 Nfatc1 TF 32.1 ± 14.3 135 ± 67 222.9 ± 11 -2.09 7.21E-03 -2.83 8.07E-12 Neurod2 TF 259.8 ± 8.5 1842 ± 146 1826 ± 92 -2.83 2.00E-55 -2.82 7.20E-70 Dmrt3 TF 16.2 ± 17.6 189 ± 22.2 112 ± 29 -3.57 7.82E-06 -2.81 2.75E-03 nd Kcnf1 102.2 ± 24.4 466 ± 55 685 ± 156 -2.19 1.02E-13 -2.76 2.02E-25 Shisa3 38.7 ± 22.9 273 ± 26 246 ± 64 -2.84 1.66E-10 -2.69 1.33E-09 ng C130060K24Rik 14.5 ± 8.2 94 ± 67 93 ± 34 -2.68 1.37E-02 -2.67 7.61E-04 ng Neurod1 TF 63 ± 26 391 ± 72 379 ± 58 -2.64 7.82E-13 -2.60 7.23E-15 En1 TF 47.1 ± 11.6 164 ± 30 281 ± 119 -1.80 5.41E-04 -2.59 2.92E-11 nd Ghsr 43.4 ± 22.2 263 ± 57 255 ± 48 -2.62 5.63E-09 -2.58 2.60E-10 ng Rasgrp1 237 ± 27 1256 ± 300 1337 ± 195 -2.41 3.56E-25 -2.50 9.85E-40 Irgm1 127.8 ± 12.4 559 ± 115 715 ± 124 -2.13 2.50E-14 -2.49 5.19E-27 Klhl14 207.1 ± 5.1 1475 ± 154 1121 ± 4.4 -2.83 8.97E-48 -2.45 1.58E-42 Pdyn NP 384 ± 77 966 ± 280 2075 ± 385 -1.33 1.94E-06 -2.44 1.46E-33 Npy NP 1262 ± 325 3605 ± 812 6622 ± 880 -1.52 1.24E-10 -2.41 3.66E-38 Table S5 Most enriched genes in Gad67 vs. vGluT2. Group: TF = transcription factor, NP = neuropeptide, pNP = putative neuropeptide (classification of neuropeptides and putative neuropeptides is based on Burbach (2011)), NF = neurotrophic factor, NPR = neuropeptide receptor. sc data = single-cell data from Haring et al. (2018): nd = not detected, ng = not grouped.

Neurotransmitter receptor expression Glutamate receptors vGluT2 vGAT Gad67 vGluT2 vs. vGAT vGluT2 vs. Gad67 [norm. count] [norm. count] [norm. count] log2 log2 Gene Mean ± Stddev Mean ± Stddev Mean ± Stddev FDR FDR Ratio Ratio Gria1 3853 ± 429 6189 ± 1252 9054 ± 821 -0.69 1.92E-03 -1.24 4.49E-19 Gria2 7524 ± 295 5010 ± 579 6896 ± 1352 0.59 2.98E-03 0.11 7.16E-01 Gria3 3327 ± 203 3423 ± 208 3229 ± 304 -0.04 9.66E-01 0.03 9.17E-01 Gria4 4879 ± 727 4476 ± 238 4437 ± 544 0.12 8.68E-01 0.13 6.66E-01 Grik1 200 ± 56 465 ± 183 453 ± 94 -1.22 2.32E-03 -1.19 8.15E-05 Grik2 1074 ± 299 2411 ± 362 2676 ± 353 -1.17 3.08E-07 -1.33 7.94E-11 Grik3 737 ± 97 2753 ± 114 3288 ± 275 -1.90 9.00E-30 -2.17 2.74E-44 Grik4 651 ± 67 721 ± 179 735 ± 44 -0.15 8.97E-01 -0.19 5.66E-01 Grik5 2341 ± 392 2481 ± 229 2289 ± 396 -0.09 9.28E-01 0.02 9.66E-01 Grin1 16293 ± 2625 14053 ± 2385 17130 ± 3003 0.21 8.29E-01 -0.08 8.54E-01 Grin2a 3811 ± 484 3237 ± 352 3463 ± 703 0.23 5.72E-01 0.13 7.07E-01 Grin2b 4048 ± 192 3630 ± 254 4605 ± 535 0.16 7.71E-01 -0.20 3.65E-01 Grin2c 41.4 ± 18.9 97.4 ± 21.4 116.4 ± 7.8 -1.24 2.43E-01 -1.51 1.25E-02 Grin2d 1775 ± 178 2062 ± 121 2485 ± 454 -0.22 6.25E-01 -0.50 1.75E-02 Grin3a 3791 ± 271 1965 ± 135 2127 ± 174 0.95 1.79E-09 0.82 2.86E-09 Grin3b 47.5 ± 11.5 49.0 ± 33.0 58.9 ± 26.6 -0.06 9.92E-01 -0.32 8.08E-01 Table S5 Ionotropic glutamate receptors (AMPA, kainate and NMDA receptors). Significant (FDR ≤ 0.05) and │log2 ratio│ ≥ 1 enrichment values are highlighted in bold.

vGluT2 vGAT Gad67 vGluT2 vs. vGAT vGluT2 vs. Gad67 [norm. count] [norm. count] [norm. count] log2 log2 Gene Mean ± Stddev Mean ± Stddev Mean ± Stddev FDR FDR Ratio Ratio Grm1 2407 ± 156 1632 ± 116 1912 ± 419 0.56 5.76E-03 0.32 2.15E-01 Grm2 157.4 ± 23.5 184 ± 109 315.9 ± 8.2 -0.22 9.27E-01 -1.02 2.70E-04 Grm3 424 ± 79 946 ± 237 1243 ± 198 -1.16 1.86E-05 -1.57 1.06E-14 Grm4 2116 ± 315 1574 ± 110 1714 ± 263 0.43 1.03E-01 0.29 2.55E-01 Grm5 5322 ± 309 7181 ± 1239 9795 ± 1607 -0.43 1.07E-01 -0.89 2.53E-08 Grm6 0 ± 0 0 ± 0 0 ± 0 0.00 NA 0.00 NA Grm7 1559 ± 184 1732 ± 161 1985 ± 194 -0.15 8.25E-01 -0.36 9.15E-02 Grm8 420 ± 56 447 ± 29 549 ± 176 -0.09 9.48E-01 -0.40 3.07E-01 Table S6 Metabotropic glutamate receptors. Significant (FDR ≤ 0.05) │log2 ratio│ ≥ 1 enrichment values are highlighted in bold. NA = not applicable.

GABA receptors vGluT2 vGAT Gad67 vGluT2 vs. vGAT vGluT2 vs. Gad67 [norm. count] [norm. count] [norm. count] log2 log2 Gene Mean ± Stddev Mean ± Stddev Mean ± Stddev FDR FDR Ratio Ratio Gabra1 4236 ± 493 4361 ± 357 5504 ± 592 -0.04 9.68E-01 -0.39 2.87E-02 Gabra2 977 ± 83 643 ± 93 638.4 ± 19.4 0.60 2.39E-02 0.60 1.76E-03 Gabra3 5357 ± 498 4949 ± 585 6146 ± 837 0.11 8.84E-01 -0.21 3.57E-01 Gabra4 653 ± 39 555 ± 63 530.4 ± 22.5 0.23 7.36E-01 0.29 3.08E-01 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Gabra5 5260 ± 959 2574 ± 84 2355 ± 322 1.03 2.11E-09 1.15 3.97E-12 Gabra6 0 ± 0 0 ± 0 0 ± 0 0.00 NA 0.00 NA Gabrb1 2706 ± 238 2797 ± 353 3234 ± 418 -0.05 9.64E-01 -0.27 2.01E-01 Gabrb2 1637 ± 81 1152 ± 94 1364 ± 183 0.51 2.12E-02 0.25 3.06E-01 Gabrb3 11663 ± 1308 9704 ± 1598 10398 ± 1382 0.26 6.50E-01 0.15 6.18E-01 Gabrg1 593.2 ± 16.6 511 ± 119 941 ± 211 0.21 8.11E-01 -0.68 4.50E-03 Gabrg2 6631 ± 484 5536 ± 958 7043 ± 1243 0.26 5.52E-01 -0.10 7.60E-01 Gabrg3 1553 ± 158 1431 ± 452 1736 ± 237 0.12 9.24E-01 -0.17 5.58E-01 Gabrd 36.2 ± 11.0 40 ± 52 28.8 ± 6.0 -0.13 9.89E-01 0.32 8.51E-01 Gabre 97.6 ± 7.8 44.1 ± 11.7 58.7 ± 24.2 1.14 2.46E-01 0.72 3.44E-01 Gabrp 0 ± 0 0 ± 0 4.1 ± 7.0 0.00 NA -5.13 NA Gabrq 538.8 ± 24.7 217 ± 71 330 ± 85 1.31 2.06E-05 0.69 1.65E-02 Gabrr1 12.1 ± 11.5 0.32 ± 0.56 0 ± 0 4.85 3.62E-01 6.68 6.79E-02 Gabrr2 8.1 ± 8.3 17.6 ± 15.3 8.3 ± 9.1 -1.13 9.06E-01 -0.06 NA Gabrr3 0 ± 0 0 ± 0 0 ± 0 0.00 NA 0.00 NA

Table S7 Ionotropic GABA receptors (GABAA and –ρ receptor). Significant (FDR ≤ 0.05) and │log2 ratio│ ≥ 1 enrichment values are highlighted in bold. NA = not applicable.

vGluT2 vGAT Gad67 vGluT2 vs. vGAT vGluT2 vs. Gad67 [norm. count] [norm. count] [norm. count] log2 log2 Gene Mean ± Stddev Mean ± Stddev Mean ± Stddev FDR FDR Ratio Ratio Gabbr1 26433 ± 4708 26183 ± 3572 28147 ± 4127 0.01 9.96E-01 -0.10 8.39E-01 Gabbr2 10007 ± 784 9451 ± 171 10552 ± 1623 0.08 9.36E-01 -0.09 8.01E-01

Table S8 Metabotropic GABA receptors (GABAB). Significant (FDR ≤ 0.05) and │log2 ratio│ ≥ 1 enrichment values are highlighted in bold.

Glycine receptors vGluT2 vGAT Gad67 vGluT2 vs. vGAT vGluT2 vs. Gad67 [norm. count] [norm. count] [norm. count] log2 log2 Gene Mean ± Stddev Mean ± Stddev Mean ± Stddev FDR FDR Ratio Ratio Glra1 13117 ± 927 9331 ± 786 9775 ± 1424 0.49 8.37E-02 0.41 5.40E-02 Glra2 1320 ± 213 1348 ± 353 1382 ± 182 -0.03 9.83E-01 -0.08 8.39E-01 Glra3 710 ± 77 545 ± 86 667 ± 66 0.38 3.95E-01 0.08 8.45E-01 Glra4 30.9 ± 13.8 20.8 ± 11.3 30.5 ± 13.1 0.57 9.17E-01 0.01 9.98E-01 Glrb 19773 ± 2483 17713 ± 2342 17187 ± 2695 0.16 9.01E-01 0.19 6.08E-01 Table S9 Glycine receptors. Significant (FDR ≤ 0.05) and │log2 ratio│ ≥ 1 enrichment values are highlighted in bold.

Acetylcholine receptors vGluT2 vGAT Gad67 vGluT2 vs. vGAT vGluT2 vs. Gad67 [norm. count] [norm. count] [norm. count] log2 log2 Gene Mean ± Stddev Mean ± Stddev Mean ± Stddev FDR FDR Ratio Ratio Chrna1 4.5 ± 7.8 3.8 ± 6.7 0 ± 0 0.22 NA 5.27 NA Chrna2 16.4 ± 14.6 9.3 ± 0.5 606 ± 100 0.80 9.30E-01 -5.26 1.09E-16 Chrna3 117 ± 39 211 ± 53 420.9 ± 15.4 -0.86 1.71E-01 -1.87 4.08E-10 Chrna4 4316 ± 654 3537 ± 467 3457 ± 643 0.29 4.33E-01 0.31 1.95E-01 Chrna5 26.2 ± 16.7 8.9 ± 8.5 97.6 ± 22.5 1.56 7.82E-01 -1.92 1.79E-02 Chrna6 89.1 ± 43.0 3.3 ± 2.9 0 ± 0 4.66 1.59E-05 9.54 1.05E-12 bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.996645; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Chrna7 513 ± 114 493 ± 72 674 ± 239 0.05 9.75E-01 -0.41 3.58E-01 Chrna9 0 ± 0 0 ± 0 3.0 ± 5.2 0.00 NA -4.73 NA Chrna10 0 ± 0 3.9 ± 6.7 6.6 ± 6.5 -5.13 NA -5.82 NA Chrnb1 4.3 ± 7.4 0 ± 0 0 ± 0 5.28 NA 5.21 NA Chrnb2 1921 ± 238 1820 ± 286 2018 ± 214 0.08 9.43E-01 -0.08 8.08E-01 Chrnb3 38.1 ± 14.1 14.0 ± 11.3 8.5 ± 7.9 1.42 6.81E-01 2.15 2.25E-01 Chrnb4 11.0 ± 3.7 58 ± 28 95.5 ± 21.1 -2.39 8.09E-02 -3.14 8.46E-06 Chrnd 0 ± 0 0 ± 0 0 ± 0 0.00 NA 0.00 NA Chrne 0 ± 0 0 ± 0 0 ± 0 0.00 NA 0.00 NA Chrng 3.8 ± 6.6 0 ± 0 0 ± 0 5.10 NA 5.03 NA Table S10 Ionotropic acetylcholine receptors (nicotinic). Significant (FDR ≤ 0.05) and │log2 ratio│ ≥ 1 enrichment values are highlighted in bold. NA = not applicable.

vGluT2 vGAT Gad67 vGluT2 vs. vGAT vGluT2 vs. Gad67 [norm. count] [norm. count] [norm. count] log2 log2 Gene Mean ± Stddev Mean ± Stddev Mean ± Stddev FDR FDR Ratio Ratio Chrm1 25.6 ± 7.2 30.2 ± 6.1 19.3 ± 7.0 -0.24 9.70E-01 0.39 8.53E-01 Chrm2 5176 ± 811 4818 ± 667 5664 ± 782 0.10 9.13E-01 -0.14 6.30E-01 Chrm3 497 ± 148 1026 ± 160 1183 ± 141 -1.05 2.63E-04 -1.27 1.17E-07 Chrm4 89 ± 34 56 ± 28 130.5 ± 19.8 0.66 7.68E-01 -0.57 3.88E-01 Chrm5 177 ± 28 8.8 ± 8.4 23.1 ± 13.1 4.27 3.14E-09 2.91 7.15E-09 Table S11 Metabotropic acetylcholine receptors (muscarinic). Significant (FDR ≤ 0.05) and │log2 ratio│ ≥ 1 enrichment values are highlighted in bold. NA = not applicable.

Serotonin receptors vGluT2 vGAT Gad67 vGluT2 vs. vGAT vGluT2 vs. Gad67 [norm. count] [norm. count] [norm. count] log2 log2 Gene Mean ± Stddev Mean ± Stddev Mean ± Stddev FDR FDR Ratio Ratio Htr1a 598 ± 103 1503 ± 240 1627 ± 154 -1.33 2.38E-10 -1.46 6.27E-16 Htr1b 803 ± 175 529 ± 132 543 ± 75 0.60 1.23E-01 0.55 4.26E-02 Htr1d 102 ± 37 214 ± 46 468 ± 102 -1.08 4.51E-02 -2.22 9.50E-12 Htr1f 68 ± 28 115 ± 91 60.3 ± 8.1 -0.75 7.50E-01 0.16 8.99E-01 Htr2a 315 ± 56 466 ± 115 510 ± 71 -0.57 2.29E-01 -0.71 1.03E-02 Htr2b 0 ± 0 0 ± 0 0 ± 0 0.00 NA 0.00 NA Htr2c 6685 ± 1060 10081 ± 1679 10828 ± 846 -0.59 2.50E-02 -0.71 3.75E-05 Htr3a 90.8 ± 21.7 752 ± 124 428 ± 75 -3.05 1.11E-28 -2.25 1.52E-15 Htr3b 3.1 ± 5.4 7.7 ± 5.9 7.0 ± 6.7 -1.29 NA -1.15 NA Htr4 197.5 ± 23.6 130.5 ± 23.8 167 ± 60 0.60 3.60E-01 0.23 7.16E-01 Htr5a 525 ± 41 784 ± 59 699 ± 78 -0.58 3.30E-02 -0.43 9.91E-02 Htr5b 12.7 ± 10.1 17.2 ± 15.6 23.7 ± 19.8 -0.45 9.68E-01 -0.92 7.10E-01 Htr6 87.4 ± 18.1 66 ± 35 109 ± 35 0.39 8.88E-01 -0.33 6.90E-01 Htr7 804 ± 48 1069 ± 120 828 ± 85 -0.41 1.77E-01 -0.05 8.90E-01 Table S12 Serotonin receptors. Significant (FDR ≤ 0.05) and │log2 ratio│ ≥ 1 enrichment values are highlighted in bold. NA = not applicable.

Adrenergic receptors vGluT2 vGAT Gad67 vGluT2 vs. vGAT vGluT2 vs. Gad67 [norm. count] [norm. count] [norm. count] log2 log2 Gene Mean ± Stddev Mean ± Stddev Mean ± Stddev FDR FDR Ratio Ratio Adra1a 576 ± 61 503 ± 112 595 ± 153 0.19 8.52E-01 -0.06 9.14E-01 Adra1b 498 ± 152 753 ± 98 743 ± 95 -0.60 1.19E-01 -0.59 5.25E-02 Adra1d 101 ± 28 110.0 ± 20.3 117 ± 49 -0.13 9.66E-01 -0.22 8.07E-01 Adra2a 1992 ± 386 942 ± 155 981 ± 189 1.08 6.89E-07 1.01 1.27E-06 Adra2b 1.40 ± 2.42 6.0 ± 10.5 26.8 ± 15.0 -2.05 NA -4.21 5.84E-02 Adra2c 389 ± 103 329.5 ± 17.7 302 ± 82 0.24 8.29E-01 0.35 4.56E-01 Adrb1 105 ± 46 204 ± 46 162 ± 84 -0.97 1.02E-01 -0.65 3.46E-01 Adrb2 9.0 ± 11.4 3.8 ± 6.7 0 ± 0 1.21 NA 6.25 NA Adrb3 12.5 ± 12.5 30.8 ± 22.7 24.5 ± 3.2 -1.30 8.12E-01 -0.99 7.08E-01 Table S13 Adrenergic receptors. Significant (FDR ≤ 0.05) and │log2 ratio│ ≥ 1 enrichment values are highlighted in bold. NA = not applicable.

Dopamine receptors vGluT2 vGAT Gad67 vGluT2 vs. vGAT vGluT2 vs. Gad67 [norm. count] [norm. count] [norm. count] log2 log2 Gene Mean ± Stddev Mean ± Stddev Mean ± Stddev FDR FDR Ratio Ratio Drd1 38.0 ± 5.9 48.8 ± 12.7 46 ± 37 -0.36 9.28E-01 -0.29 8.65E-01 Drd2 610 ± 180 961 ± 198 1215 ± 127 -0.66 7.09E-02 -1.01 1.50E-05 Drd3 9.6 ± 6.7 8.9 ± 4.3 25.0 ± 19.6 0.11 NA -1.37 5.10E-01 Drd4 1.39 ± 2.41 23.5 ± 19.1 9.4 ± 5.4 -4.02 2.41E-01 -2.70 NA Drd5 221 ± 39 135 ± 100 209 ± 35 0.71 5.10E-01 0.07 9.17E-01 Table S14 Dopamine receptors. Significant (FDR ≤ 0.05) and │log2 ratio│ ≥ 1 enrichment values are highlighted in bold. NA = not applicable.

Histamine receptors vGluT2 vGAT Gad67 vGluT2 vs. vGAT vGluT2 vs. Gad67 [norm. count] [norm. count] [norm. count] log2 log2 Gene Mean ± Stddev Mean ± Stddev Mean ± Stddev FDR FDR Ratio Ratio Hrh1 39.1 ± 9.8 35.2 ± 21.0 73 ± 38 0.14 9.81E-01 -0.92 3.32E-01 Hrh2 80.1 ± 21.4 77 ± 36 68.8 ± 7.8 0.05 9.92E-01 0.21 8.37E-01 Hrh3 942 ± 181 1428 ± 79 1523 ± 94 -0.60 1.36E-02 -0.71 4.04E-04 Hrh4 0 ± 0 0 ± 0 0 ± 0 0.00 NA 0.00 NA Table S15 Histamine receptors. Significant (FDR ≤ 0.05) and │log2 ratio│ ≥ 1 enrichment values are highlighted in bold. NA = not applicable.