# Stepwise molecular specification of excitatory synapse diversity onto cerebellar Purkinje cells

Maëla A Paul, Séverine M Sigoillot, Léa Marti, Francisco J Urra Quiroz, Marine Delagrange, Hiu Cheung, David C Martinelli, Elie Oriol, Vincent Hakim, Philippe Mailly, et al.

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Maëla A Paul, Séverine M Sigoillot, Léa Marti, Francisco J Urra Quiroz, Marine Delagrange, et al. Stepwise molecular specification of excitatory synapse diversity onto cerebellar Purkinje cells. Nature Neuroscience, 2025, 28 (2), pp.308-319. 10.1038/s41593-024-01826-w. hal-05373180

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# Stepwise molecular specification of excitatory synapse diversity onto cerebellar Purkinje cells

Received: 26 May 2023

Accepted: 22 October 2024

Published online: 10 December 2024

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Maëla A. Paul<fnref n="1" />,⁷, Séverine M. Sigoillot©<fnref n="1" />,⁷, Léa Marti<fnref n="1" />, Francisco J. Urra Quiroz<fnref n="1" />,², Marine Delagrange<fnref n="3" />, Hiu W. Cheung⁴, David C. Martinelli⁴,⁵, Elie Oriol⁶, Vincent Hakim©⁶, Philippe Mailly<fnref n="1" /> & Fekrije Selimi©<fnref n="1" />✉

Brain function relies on the generation of a large variety of morphologically and functionally diverse, but specific, neuronal synapses. Here we show that, in mice, the initial formation of synapses on cerebellar Purkinje cells involves a presynaptic protein–CBLN1, a member of the C1q protein family–that is secreted by all types of excitatory inputs. The molecular program then evolves only in one of the Purkinje cell inputs, the inferior olivary neurons, with the additional expression of the presynaptic secreted proteins C1QL1, CRTAC1 and LGI2. These molecules work in concert to specify the mature connectivity pattern on the Purkinje cell target. These results show that some inputs actively and gradually specify their synaptic molecular identity, while others rely on the ‘original molecular code’. Thus, the molecular specification of excitatory synapses, crucial for proper circuit function, is acquired in a stepwise manner during mouse postnatal development and obeys input-specific rules.

Morphologically, molecularly and functionally distinct types of neurons need to be connected in a stereotyped manner to form the short-range and long-range circuits that underlie complex behaviors in mammals. This requires not only the recognition of the proper neuronal partners, via the molecular control of cell migration and axon guidance, but also the formation of specific synapses on a given target neuron<fnref n="1" />. Recently, synaptome mapping using two excitatory postsynaptic markers and unsupervised classification has revealed 37 synaptic types². With over a thousand synaptic proteins identified, the mammalian brain thus likely contains a large diversity of synapses<fnref n="3" /> supporting information processing, learning and memory.

Various principles have been proposed to control the specific wiring of neurons during development<fnref n="1" />,⁴⁻⁷. Neuronal activity sculpts circuits via selective stabilization or elimination of synapses⁸. On the

other hand, the chemoaffinity hypothesis states that the specificity of connectivity is regulated by ‘specific cytochemical affinities’⁹, implying a unique molecular combination for each synapse type. While this hypothesis has received much attention in the case of neuronal partner selection, its relevance to synapse specificity on a single target neuron has yet to be demonstrated<fnref n="1" />. Indeed, each neuron receives multiple types of inputs that form synapses with specific identities determined by their subcellular localization, their degree of connectivity on the target neuron, as well as their specific molecular composition and functional properties. Various individual molecular complexes, including cell adhesion proteins, have been shown to underlie excitatory versus inhibitory synapse formation and identity (see refs. 1,7 for review; refs. 10–18). However, synapse diversity extends well beyond these two broad functional classes. Whether and how a combination

<footnote n="1">¹Center for Interdisciplinary Research in Biology (CIRB), Collège de France, Université PSL, CNRS, INSERM, Paris, France. ²Inovarion, Paris, France.</footnote>

<footnote n="3">³Plateforme qPCR-HD-GPC, Institut de Biologie de l'Ecole Normale Supérieure, Paris, France. ⁴Department of Neuroscience, University of Connecticut Health, Farmington, CT, USA. ⁵The Connecticut Institute for the Brain and Cognitive Sciences (IBACS), Storrs, CT, USA. ⁶Laboratoire de Physique de l'Ecole Normale Supérieure, CNRS, Ecole Normale Supérieure, PSL University, Sorbonne Université, Université de Paris, Paris, France. ⁷These authors contributed equally: Maëla A. Paul, Séverine M. Sigoillot. ✉e-mail: fekrije.selimi@college-de-france.fr</footnote>
of proteins controls the formation of a specific synapse type remains to be tested. Furthermore, whether different excitatory synapse types follow identical rules for their specification on a shared target neuron during development is unclear.

Cerebellar Purkinje cells (PCs) are a perfect example of the specific and stereotyped connectivity that must be generated for proper network function and information coding. They are at the center of computation of the cerebellum¹⁹, a brain region renowned for its control of motor coordination and learning, and, increasingly, for various cognitive processes¹⁹,²⁰. PCs receive direct synapses from several types of local inhibitory neurons and two excitatory inputs—the parallel fibers (PFs), axons of cerebellar granule cells (GCs), and climbing fibers (CFs), axons of inferior olivary neurons (IONs) of the brainstem. PF and CF synapses are made on separate subcellular territories on a given PC—distal dendritic spines for PFs versus proximal dendrites for CFs—with a different degree of connectivity and functional properties²¹⁻²³. The formation of each excitatory synapse type relies on distinct members of the CIQ protein family. PF/PC synapses are dependent on the formation of a tripartite complex between presynaptic neurexin-1β, secreted CBLN1 and postsynaptic GluD2 receptor¹²,¹⁷,²⁴,²⁵, while CF/PC synapses depend on secreted C1QL1 and postsynaptic adhesion G protein-coupled receptor (GPCR) ADGRB3/BAI3 (refs. ¹¹,²⁶). Loss-of-function experiments targeting *Cbln1* or *C1ql1* result in the loss of about half of PF/PC or CF/PC synapses, respectively¹¹,²⁷. Thus, potential compensation by other synaptic proteins or involvement of multiple molecular signaling pathways at each synapse type may prevent their complete loss, supporting the chemoaffinity hypothesis.

Using cerebellar PCs as a model, we searched for the combination of membrane and secreted proteins underlying the specificity of excitatory connectivity on a single neuron type. Neuron-specific gene expression profiling and functional analysis using genome editing revealed that the development of the mature connectivity of CFs on PCs relies on a unique combination of cell surface proteins belonging to different signaling pathways. Surprisingly, we discovered that the cerebellar GC marker *Cbln1* is also expressed by IONs at early stages and that, in addition to its known role at PF/PC synapses, it also regulates synaptogenesis between CFs and PCs. Finally, we showed that ION activity during development is required for proper CF/PC synapse formation and regulates the expression of a subset of synaptogenic molecules that characterize mature CF/PC synapses.

## Results

### Distinct developmental dynamics of PC input-specific surfaceome

The specificity and identity of synaptic connections between neurons depend on molecular recognition and adhesion between the presynaptic and postsynaptic compartments. Thus, molecules that regulate synapse formation and specification are expected to be part of the 'surfaceome', defined as the set of genes coding for membrane and secreted proteins. In a previous study²⁸, neuron-specific gene

expression profiling and comparative analysis in the adult mouse brain identified the specific surfaceome for each of the two excitatory inputs of PCs—74 differentially expressed genes (DEGs) for GCs and 250 DEGs for IONs (Fig. 1a). As expected, these DEGs included genes known for their role in the formation and stabilization of these two inputs, in particular *Cbln1* for PF/PC synapses and *C1ql1* for CF/PC synapses.

The developmental time courses of PF and CF synaptogenesis on PCs differ (Fig. 1b; refs. 29,30). IONs have already contacted PCs at birth with several CFs synapsing on their somata³¹ and remodeling their connectivity on their target during the first three postnatal weeks. Around P14, the 'winning' CF has already started to translocate³², competing with PFs for the subcellular synaptic territory on the growing PC dendrites²³. In contrast, the bulk of GC synaptogenesis on PCs starts during the second postnatal week when GCs exit the external granular layer, migrate and differentiate to integrate the cerebellar circuitry, sequentially expressing crucial cell adhesion molecules (Fig. 1b; refs. 29,33–37). In agreement with these different time courses, the expression of *C1ql1* in the brainstem increases from birth, while *Cbln1* expression in the cerebellum increases after P7 (Fig. 1a). Therefore, we reasoned that genes with similar roles in synapse specification would exhibit the same expression dynamics during development. Indeed, high-throughput mRNA expression analysis and unsupervised clustering of gene expression profiles during postnatal development revealed different dynamics of the surfaceome characterizing each PC excitatory input (Fig. 1c,d and Supplementary Data 1 and 2).

In the cerebellum, most GC-specific DEGs (70 of 74) formed a single cluster, including the synaptogenic *Cbln1* gene, with a sharp increase in expression between P7 and P14 (cluster 2; Fig. 1c). Based on several criteria, such as high expression levels in the adult (Extended Data Fig. 1a), predicted protein structures suggestive of roles in molecular recognition and/or previously documented roles in synaptic development, 15 genes with potential roles in synapse formation were identified in this cluster. Further analysis of bacTRAP data (Extended Data Fig. 1b; ref. 35), single-cell transcriptomics data (Extended Data Fig. 1c; ref. 38) and single-molecule fluorescence in situ hybridization (smFISH) experiments (Extended Data Fig. 1d) show that most of these 15 genes are massively upregulated in the final stages of GC differentiation, once these cells have migrated. Thus, there appears to be a key developmental window at the end of the second postnatal week during which GCs acquire their adult-specific surfaceome.

In the brainstem, the ION-specific surfaceome was divided into two major clusters. The first one comprised genes with high expression during the two first postnatal weeks (cluster 1; Fig. 1d). The second included 125 genes like *C1ql1*, whose expression increased starting from the second postnatal week (cluster 2; Fig. 1d). In this cluster, 15 candidate genes with a potential role in synapse formation were identified using the same criteria as with GCs (Extended Data Fig. 2a). Because our high-throughput analysis was performed using brainstem extracts that contain many nuclei, we used smFISH to further refine our candidate list. Four genes (*Nrcam*, *Lgi2*, *Crtac1* and *Sema4f*)

**Fig. 1 | PC excitatory inputs are characterized by different surfaceome expression dynamics during postnatal development.** *a*, Workflow for the identification of candidate synaptogenic genes for each input type. A previous analysis using the bacTRAP method identified the adult gene expression profile specific for GCs or IONs, including 74 and 250 DEGs coding for membrane and secreted proteins, respectively²⁸. The developmental expression patterns of these DEGs were determined using high-throughput RT-qPCR on cerebellar and brainstem RNA extracts (embryonic day, E17; postnatal days, P0, P3, P7, P14 and P21; and adult; examples—*Cbln1* in the cerebellum and *C1ql1* in the brainstem). Unsupervised clustering was then performed to group the genes according to their developmental pattern of expression. *b*, Schematic illustration of the postnatal development of excitatory connectivity on PCs in the cerebellar cortex. *c*, Heatmap of the 74 GC DEGs in the cerebellum, with the dendrogram showing the result of unsupervised clustering. *d*, Heatmap of the 250 ION DEGs in the

brainstem between E17 and adult. The result of the unsupervised clustering is shown as a dendrogram. *e*, Top: representative images of duplex smFISH experiments for *C1ql1* and the four candidate genes, *Nrcam*, *Lgi2*, *Crtac1* and *Sema4f* in IONs at P4, P14 and in adult. Each image corresponds to a projection of five planes (z-stack step: 0.5 µm). Scale bars, 25 µm. Bottom: 3D quantifications of the number (nb) of mRNAs per ION. One dot corresponds to one cell, and one representative experiment is shown in each graph. Pearson correlation for the expression of *C1ql1* and candidate mRNAs was determined at the single cell level (coefficient >0.6 corresponding to high correlation). Data are presented as mean ± s.e.m. *Nrcam*-adult, 661 cells, P14, 525 cells and P4, 597 cells; *Lgi2*-adult, 528 cells, P14, 545 cells and P4, 661 cells; *Crtac1*-adult, 482 cells, P14, 665 cells and P4, 551 cells; *Sema4f*-adult, 630 cells, P14, 694 cells and P4, 607 cells; *n* ≥ 6 animals; two to four independent experiments per gene.
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598

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secreted proteins</figcaption></figure>

250

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<figure><img src="image_9.png" /><figcaption>Clustering</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**b** PO P7 P14</figcaption></figure>

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Parallel fibers (PFs)

Climbing fiber (CF)

Granule cells (GCs)

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Inferior olivary neurons (IONs)

<figure><img src="image_13.png" /><figcaption>**d** 250 ION DEGs</figcaption></figure>

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<figure><img src="image_19.png" /><figcaption>Nrcam</figcaption></figure>

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P4

P14

Adult

<figure><img src="image_21.png" /><figcaption>Lgi2</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>Crtac1</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>Sema4f</figcaption></figure>

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Expression correlation with C1ql1
showed highly correlated expression with *C1ql1* in the inferior olive region at key developmental stages for CF/PC innervation (P4, P14 and adult; Fig. 1b and Extended Data Fig. 2b). The expression patterns of the other candidate genes suggest roles either at later stages in the functional maturation of CF/PC synapses (Extended Data Fig. 2c) or in other processes (Extended Data Fig. 2d). Thus, in contrast to GCs, the acquisition of the adult ION-specific surfaceome occurs long after the generation of IONs (E10–E12; ref. 39) and over two broad periods. The last period, starting during the second postnatal week, seems key for the establishment of the molecular combination specifying ION–PC versus GC–PC synapses.

## Secreted proteins promoting CF/PC connectivity

If synapse specification on a target is controlled by a specific combination of presynaptic recognition proteins, the combination coding for CF/PC synapse development should include ION-specific DEGs with expression patterns highly correlated to *C1ql1* at the single-cell level. We developed a second smFISH analysis pipeline (cf. Methods) to compute the Pearson correlation coefficient for each gene of interest with *C1ql1* at the single-cell level in IONs. This analysis confirmed a very high correlation for *Nrcam*, *Lgi2* and *Crtac1* at every stage but showed a relatively lower correlation for *Sema4f* transiently at P14 (Fig. 1e).

Like *C1ql1*, *Crtac1* and *Lgi2* encode for extracellular matrix proteins, while *Nrcam* codes for an Ig domain-containing transmembrane protein (Extended Data Fig. 3a). Although these three candidates have been found at certain synapses in the mouse brain<sup>40,41</sup>, their localization at excitatory synapses onto PCs remained unknown. Immunolabeling for the vesicular transporter VGLUT2, together with labeling of PC somata, dendrites and spines using anti-calbindin (CaBP), allows the specific identification of CF/PC presynaptic boutons in the molecular layer<sup>11,42,43</sup>. We used multiple approaches optimized for each candidate to enable their immunolocalization at P13 (Methods). Each protein of interest—C1q-related factor (C1QL1), neuronal cell adhesion molecule (NRCAM), leucine-rich repeat LGI family member 2 (LGI2) and Cartilage acidic protein 1 (CRTAC1)—was detected as clusters along PC proximal dendrites, and co-immunolabeling for VGLUT2 confirmed that their distribution is consistent with their presence in CFs (Extended Data Fig. 3b; arrowheads). Imaging at higher magnification revealed distinct localization patterns for each protein (Fig. 2a). Many VGLUT2 boutons contained C1QL1 detected as small clusters. NRCAM was found all along the CF, with regions of higher intensity labeling often coinciding with VGLUT2 boutons. LGI2 and CRTAC1 were present in clusters of varying sizes, both near or partially colocalizing with some VGLUT2 presynaptic boutons (Fig. 2a). Thus, the localization of NRCAM, LGI2 and CRTAC1 is consistent with their potential role in CF excitatory synaptogenesis on PCs.

To test their involvement in synapse specification in vivo, we set up an intersectional strategy to perform ION-specific loss of function using genome editing. For this, retrograde adeno-associated viral (rAAV) particles were injected into the cerebellum of Cre-dependent

Cas9/GFP knockin (KI) mouse pups at P0 (ref. 44) to transduce PC inputs. These rAAV particles contained the following two expression cassettes: one driving Cre expression under the CamKII promoter, and the other driving expression of a guide RNA (gRNA) under the U6 promoter (Fig. 2b). Because CamKII is not expressed in GCs<sup>45</sup>, the rAAVs drive Cre expression exclusively in IONs. With this strategy, genome editing can only occur in IONs that are expressing both Cre (and thus CAS9 and GFP) and the gRNA, while other cerebellar cell types are unaffected. The gRNA was designed to target the coding region near or immediately after the signal peptide sequence of the candidate gene of interest (Extended Data Fig. 4a), a strategy previously used successfully for *Nrcam*<sup>46</sup>. The efficiency and target specificity of the gRNAs were assessed using cultured neurons from the neocortex. All gRNAs reduced expression of their target genes (Extended Data Fig. 4a), with the most potent achieving a 50–70% reduction. Sequencing did not reveal any mutation in the main off-target regions (Supplementary Data 3). Sequencing in the targeted region of *Crtac1* also showed that the remaining expressed mRNAs were likely coding for nonfunctional proteins because of frameshift mutations (Methods; Supplementary Data 3). In vivo, smFISH quantification at P21 in GFP-expressing (GFP+) IONs showed the heterogenous expression of *C1ql1*, *Lgi2* and *Crtac1* both in controls and after CRISPR–Cas9-based genome editing. Our targeting strategy shifted gene expression levels toward the first quartile of the controls. The decrease in mean expression ranged from 27% for *Lgi2*, 54% for *C1ql1* to 70% for *Crtac1* (Extended Data Fig. 4b). As stated above, the loss-of-function effect is probably underestimated because the smFISH probes will still detect mRNAs that contain frameshift mutations. Thus, our strategy enables ION-specific targeting of each gene of interest during postnatal development in the mouse and produces at least a significant knockdown (KD) of its expression.

By P21, the final pattern of connectivity between PCs and their excitatory inputs has been established. Deficits in CF/PC synapse numbers and function have been shown consistently to be accompanied by a decrease in VGLUT2 cluster extension or numbers<sup>11,47</sup>. The consequences of ION-specific KD of *Nrcam*, *Lgi2* or *Crtac1* were thus assessed at P21 by quantifying the morphology and extension of transduced GFP<sup>+</sup> CFs and the number and size of associated VGLUT2 presynaptic boutons (Fig. 2b). Control CFs typically extend to about 70% of the molecular layer height, consistent with previously published data<sup>11</sup>. Small yet significant changes in this extension were detected for all three candidates—*Lgi2* and *Crtac1* KD led to a 10% and 6% decrease, respectively, while *Nrcam* KD led to a 7% increase (Fig. 2b). The branching of CF was also significantly diminished after *Nrcam* KD. At the synaptic level, *Nrcam* KD resulted in an approximately 17% increase in the number of VGLUT2 clusters per GFP<sup>+</sup> CF compared to CTL (mean ± s.e.m. = 123.7 ± 4.731 versus CTL, 105.9 ± 4.705, *P* = 0.055, nested *t* test). On the contrary, *Lgi2* or *Crtac1* KD led to a significant 20% and 26% decrease, respectively (*Lgi2*, mean ± s.e.m. = 90.94 ± 4.510 versus CTL, 113 ± 4.609; *P* = 0.0098, nested *t* test; *Crtac1*, 103.6 ± 6.261 versus CTL, 139.7 ± 6.395; *P* = 0.006, nested *t* test; Fig. 2b). This decreased

**Fig. 2 | Several cell surface proteins control the establishment of CF/PC connectivity.** a, Localization of C1QL1, NRCAM, LGI2 and CRTAC1 proteins (green) in the ML of the cerebellar cortex at P13 was obtained using several strategies as follows: C1QL1, anti-hemagglutinin (HA) immunolabeling in cerebellar sections from a *C1ql1*<sup>HA</sup> KI mouse; NRCAM, anti-HA after CRISPR–Cas9-mediated HA-tagging of endogenous NRCAM; and LGI2 and CRTAC1, immunolocalization after antigen retrieval. The CF presynaptic boutons and the dendritic tree of PCs were immunostained with anti-VGLUT2 (magenta) and anti-CaBP (blue), respectively. Each image corresponds to a single plane acquired using Airyscanning. Arrowheads point to the colocalization of labeling for the candidates and VGLUT2. Three to four independent experiments. Scale bars, 5 µm. b, Top right: a strategy for CRISPR–Cas9-mediated KD of candidate genes specifically in IONs. Retrograde rAAV particles are driving the expression of a gRNA targeting a candidate gene or a nontargeting gRNA for CTL, under the

U6 promoter together with the Cre recombinase under the CamKII promoter. Injections were performed in the cerebellum of Cre-dependent Cas9/GFP-KI mice at P0. Left: GFP<sup>+</sup> CF (from targeted IONs), CF presynaptic boutons, PCs and their dendritic tree were immunostained in parasagittal cerebellar sections at P21 using anti-GFP (green), anti-VGLUT2 (magenta) and anti-CaBP (blue), respectively. The ML and CF extensions are indicated on the merged image. Each image corresponds to a projection of 15 planes (z-stack step: 0.5 µm). Scale bars, 20 µm; insets show high magnification of VGLUT2 clusters (scale bars, 4 µm). Bottom right: the number of branches per CF, the CF extension (relative to the ML height), the total length of each CF and the number of VGLUT2 clusters per GFP<sup>+</sup> CF were quantified. Violin plots represent the data with the median and quartiles. n ≥ 24 CFs; five to six animals per condition; three to five independent experiments. Statistics: two-tailed nested *t* test. ML, molecular layer; CaBP, calbindin.
a

<figure><img src="image_25.png" /></figure>

b

<figure><img src="image_26.png" /></figure>
<figure><img src="image_27.png" /><figcaption>**Fig. 3 | A combination of secreted proteins orchestrates CF/PC synaptogenesis.**</figcaption></figure>

KD experiments were performed in independent experiments to compare the effects of *C1ql1* KD versus CTL, *C1ql1/Crtac1* dKD versus KD and *C1ql1/Crtac1/Lgi2* tKD versus dKD. Left: GFP+ CF, CF presynaptic boutons and PCs and their dendritic tree were immunostained using antibodies against GFP (green), VGLUT2 (magenta) and CaBP (blue), respectively, in cerebellar sections from P21 mice. ML and CF extensions are indicated on the merged image. Each image corresponds to a projection of 13–18 planes (z-stack step: 0.5 µm). Scale bars,

20 µm. High magnifications of VGLUT2 clusters in a GFP+ CF are presented on the right of the panel. Each image corresponds to a projection of six planes (z-stack step: 0.5 µm). Scale bars, 4 µm. Right: the CF extension (as the percentage of ML length), the number of VGLUT2 clusters per CF and the distribution and mean of the VGLUT2 cluster volumes were quantified in GFP+ CFs. Violin plots represent the data with the median and quartiles. n ≥ 31 CFs; eight to ten animals per condition; three to four independent experiments. Statistics: two-tailed nested t-test. dKD, double KD; tKD triple KD.

number of presynaptic boutons was accompanied by a consistent and significant 15% increase in VGLUT2 cluster volume after *Crtac1* KD (Extended Data Fig. 4c), a phenotype that is also observed after *C1ql1* KD (Fig. 3). Thus, NRCAM promotes CF morphogenesis, facilitating its branching along the PC dendrites, while inhibiting CF/PC synaptogenesis. This function is in accordance with the precocious strong expression of *Nrcam* in IONs (Fig. 1e; ref. 48), as well as its localization all along CFs (Fig. 2a). On the other hand, LGI2 and CRTAC1 promote, like C1QL1, CF/PC synaptogenesis on PC dendrites during the second developmental phase.

## A combination of synaptogenic molecules working in concert at CF synapses

LGI2, CRTAC1 and C1QL1 bind different receptors and modulate different signaling pathways at synapses. C1QL1 is characterized by a

globular C1q domain (Extended Data Fig. 3a) and is known to interact with the ADGRB3/BAI3 synaptic adhesion GPCR$^{26,49}$. LGI2 is part of a family of secreted proteins containing leucine-rich repeat domains and a β-propeller domain (Extended Data Fig. 3a) that binds to the ADAM family of synaptic receptors$^{50}$. CRTAC1 is also part of the β-propeller domain containing family of proteins (Extended Data Fig. 3a) and binds to NOGO receptors also known as RTN4Rs.

To test the potential synergy between these synaptic signaling pathways at the CF/PC synapse, we performed single KD for *C1ql1*, double KD for *C1ql1/Crtac1* and triple KD for *C1ql1/Crtac1/Lgi2*, specifically in IONs, taking advantage of our genome editing based strategy. Single *C1ql1* KD led to a significant 15% and 27% decrease in the CF extension and the number of VGLUT2 clusters per GFP+ CF, respectively, compared to CTL (CF extension-*C1ql1*, mean ± s.e.m. = 61.14 ± 0.973 versus CTL, 72.3 ± 0.9746, *P* < 0.0001; VGLUT2 number-*C1ql1*,
mean ± s.e.m. = 76.96 ± 3.396 versus CTL, 104.7 ± 5.623, *P* < 0.0001). The volume of VGLUT2 clusters was increased by 34%, recapitulating the previously characterized phenotypes in the *C1ql1* knockout mouse and after *C1ql1* KD (Fig. 3 and Supplementary Data 3; refs. 11,26). Compared to single *C1ql1* KD, we observed an additional 21% decrease in the number of VGLUT2 clusters per GFP+ CF in the double *C1ql1/Crtac1* KD (*C1ql1*, mean ± s.e.m. = 84.11 ± 3.842 versus *C1ql1/Crtac1*, 66.56 ± 3.280, *P* < 0.001, nested *t* test), with no changes in CF extension and VGLUT2 cluster volume (Fig. 3). An additional 20% decrease was found in the mean number of CF presynaptic boutons after *C1ql1/Crtac1/Lgi2* triple KD, with no change in CF extension (VGLUT2 number – *C1ql1/Crtac1/Lgi2*, mean ± s.e.m. = 59.75 ± 3.836 versus *C1ql1/Crtac1*, 74.65 ± 3.827, *P* = 0.031, nested *t* test). In this case, a 19% decrease in the mean volume of VGLUT2 clusters was detected in the triple KD compared to double *C1ql1/Crtac1* KD (Fig. 3). Remarkably, the number of VGLUT2 clusters per GFP+ CF decreased in an additive manner consistent with the sum of the individual effects of single *C1ql1*, *Crtac1* and *Lgi2* KD, while the effects on VGLUT2 cluster volume differ depending on the targeted gene. Thus, our results show that C1QL1, CRTAC1 and LGI2 work in concert to promote CF synaptogenesis on PCs, while LGI2 has a role in the maturation of CF presynaptic boutons that is opposite to the one of C1QL1 and CRTAC1.

## CBLN1 as a common synaptogenic factor for PC excitatory inputs

Our analysis of developmental gene expression patterns suggests a multistep determination of ION identity with a set of cell surface molecules expressed highly during the first two postnatal weeks, a period when CF synapses are numerous on the somata of PCs (Fig. 1d; cluster 1). This intriguing finding led us to reanalyze all 324 DEGs, including those specific to adult cerebellar GCs, and group them according to their developmental expression profile in the brainstem. Surprisingly, we identified *Cbln1* in the cluster of genes highly expressed in the brainstem during the early period (Extended Data Fig. 5a; cluster 1). *Cbln1* is a well-known marker of PF/PC synapses essential for their formation and stabilization²⁴. smFISH-based quantifications confirmed *Cbln1* expression in individual IONs as early as P4 at levels comparable to *C1ql1*, with a tendency for *Cbln1* expression to decrease by P14 while *C1ql1* increases (Fig. 4a). Immunolocalization at P4 showed the presence of CBLN1 puncta that partially colocalized with VGLUT2 clusters on PC somata, as seen for C1QL1 at the same stage (Fig. 4b). These results prompted us to investigate the potential role of *Cbln1* in CF/PC synapse development. ION-specific *Cbln1* loss of function was performed using genome editing with two gRNAs designed to target each end of the whole coding sequence. The efficiency and specificity of the targeting were characterized in mixed cerebellar cultures by sequencing and genomic DNA (gDNA) analysis (Extended Data Fig. 5b and Supplementary Data 3). Expression analysis using smFISH-based quantifications in IONs in vivo demonstrated a significant 50% decrease in mean mRNA levels (Extended Data Fig. 5b).

We then investigated the effects of *Cbln1* KD starting at P0 on the formation of CF/PC connectivity (Fig. 4c). At P7, PCs with impaired *Cbln1* function had started to translocate in about 29% of contacted PCs, compared to 11% for control GFP+ CFs. Additionally, *Cbln1* KD led to a significant 52% decrease in the mean number of VGLUT2/GFP clusters on PC somata compared to controls (KD, mean ± s.e.m. = 0.45 ± 0.0667 versus CTL, 0.94 ± 0.1038, *P* = 0.019, nested *t* test), with no effect on the mean VGLUT2 cluster volume (Fig. 4d and Extended Data Fig. 5c). At P14, *Cbln1* KD led to a significant decrease in CF extension with a tendency for increased branching. The total cumulative length of CF was not changed by the loss of *Cbln1*. This reduced extension of the territory of innervation along the PC dendritic arbor was accompanied by a significant 21% decrease in VGLUT2 cluster numbers on PC dendrites with no change in the mean VGLUT2 cluster volume (Fig. 4e and Extended Data Fig. 5c). These results show that CBLN1 has several roles during the establishment of CF/PC connectivity. It prevents precocious translocation of the winning CF on the growing PC dendrites, controls CF morphogenesis and promotes CF synaptogenesis both on PC somata and dendrites. These results establish CBLN1, a secreted protein of the C1q family, as a common synaptogenic factor for both types of excitatory inputs, GCs and IONs, connecting PCs.

## A role for neuronal activity in shaping CF/PC synapse identity

Activity blockade in the adult cerebellum leads to CF loss on PC⁵¹, and different neuronal activity patterns are known to induce distinct gene expression programs⁵². Previous data suggested activity-dependent regulation of synaptogenic *C1ql1* and synaptogenesis³³,⁵⁴. We thus wondered whether ION activity could regulate the change from a common to a specific combination of synaptogenic proteins that occurs in IONs. To modulate neuronal activity, we used the Myc-tagged inward-rectifying potassium channel Kir2.1, known to reduce neuronal excitability, and its nonconducting version, the Kir2.1 mutant, as a control⁵⁵. The capacity for the Kir2.1 channel to modulate excitability was confirmed in cultured neurons from the neocortex, as evidenced by decreased *cFos* mRNA levels (Extended Data Fig. 6a), and in IONs by in silico modeling (Extended Data Fig. 6b). Consistent with previous results⁵¹,⁵⁴, specific activity blockade in IONs during postnatal development (Fig. 5a) led to a defect in CF/PC connectivity. Our quantifications demonstrated a significant 33% decrease in the number of VGLUT2 clusters per GFP+ CF in cells expressing Kir2.1 (Kir2.1, mean ± s.e.m. = 64.14 ± 4.522 versus Kir2.1 mutant, 95.56 ± 7.303, *P* = 0.027, nested *t* test), but no change in the volume of the VGLUT2 clusters (Fig. 5b).

Does activity also control the acquisition of the gene expression pattern that characterizes mature IONs and underlies CF/PC mature connectivity? In cultured neurons from the neocortex, inhibition of activity led to a mean decrease of approximately 30% in *C1ql1* and *Lgi2* mRNA expression, whereas *Cbln1* and *Crtac1* mRNA expression remained unchanged (Extended Data Fig. 6a). A similar effect was detected in vivo in IONs after activity modulation by Kir2.1 expression during postnatal development. A 32% and 39% decrease in *C1ql1* and

**Fig. 4 | CBLN1 from IONs promotes CF/PC connectivity at early stages.**
a, Duplex smFISH for *C1ql1* and *Cbln1* mRNAs in brainstem coronal sections at P4 and P14. Top: inferior olive. Scale bars, 150 µm. Bottom: high magnification showing IONs (projection of five planes, z-stack step: 0.5 µm). Scale bars, 15 µm. The numbers (nb) of *C1ql1* and *Cbln1* mRNA dots per ION were quantified at P4 and P14 (graph, 1 dot corresponds to 1 cell and lines to simple linear regression). *n* ≥ 1,733 cells, four animals per condition, and three independent experiments.
b, Immunolabeling of C1QL1 (HA tag) and CBLN1 proteins (green), CF presynaptic boutons (VGLUT2, magenta) and PCs (CaBP, blue) in parasagittal sections from P4 mice. Representative images of three to four independent experiments. Scale bars, 5 µm. Insets show high magnification of VGLUT2 clusters (scale bars, 1.25 µm). c, CRISPR-Cas9-mediated KD of *Cbln1* was performed specifically in IONs using an injection of rAAV at P0 in the cerebellum of Cre-dependent Cas9/GFP-KI mice. The U6 promoter drives the expression of gRNAs targeting *Cbln1*

or nontargeting gRNAs as CTL. d, e, GFP+ CF (anti-GFP, green), CF boutons (anti-VGLUT2, magenta) and PCs (anti-CaBP, blue) visualized in parasagittal cerebellar sections (projection of 15 planes, z-stack step: 0.5 µm). d, Top: percentage of GFP+ CF translocated on PC dendrites at P7. The total number of PCs analyzed/number of PCs with non-translocated CFs (light green) and number of PCs with translocated CFs (dark green) are indicated for each condition. Statistics, chi-squared test. Scale bars, 20 µm. Bottom: high magnifications of GFP+ CFs around PC somata. Scale bars, 7 µm. Quantification of VGLUT2 cluster density, *n* ≥ 29 images, eight animals per condition, four independent experiments. e, Number of branches per CF, CF extension (relative to the ML height) and total length, and number of VGLUT2 clusters per GFP+ CFs quantified at P14. Scale bars, 20 µm. Insets show high magnifications of VGLUT2 clusters (scale bars, 4 µm). Violin plots represent the median and quartiles. *n* = 20 CFs per condition, six to eight animals, and three independent experiments. Statistics, two-tailed nested *t* test.
a

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b

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c

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d

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CF translocation
on PC dendrite

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VGLUT2 cluster
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e

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**Fig. 5 | ION activity shapes CF/PC connectivity and molecular identity.**

**a,** Strategy for specific inhibition of IONs activity during postnatal development. Retrograde rAAVs driving the Cre-dependent expression of Kir2.1 or nonfunctional Kir2.1 mutant as control, and GFP was injected in the cerebellum of P0 mice. **b,** Kir2.1 and GFP expression is driven under the CamKII promoter; rAAVs are injected in *C1qlI-ires-cre* mice expressing the Cre recombinase in IONs. Top: targeted CFs, CF presynaptic boutons and PCs immunostained using anti-GFP (green), anti-VGLUT2 (magenta) and anti-CaBP (blue), respectively, in parasagittal cerebellar sections of P14 mice (projection of 15 planes, z-stack step: 0.5 µm). The ML and CF extensions are indicated on the merged image. Scale bars, 20 µm. Insets show high magnifications of VGLUT2 clusters (scale bars, 4 µm). Bottom: CF extension, number of VGLUT2 clusters per GFP⁺ CFs and the volumes of the clusters were quantified at P14. Violin plots represent the median and quartiles. *n* ≥ 24 images per condition, 10–13 animals, three independent

<figure><img src="image_47.png" /></figure>

experiments. Statistics, two-tailed nested *t* test. **c,** Retrograde rAAVs expressing a Cre-dependent Kir2.1, or Kir2.1 mutant, under the hSyn promoter and retrograde rAAVs expressing the Cre recombinase under the CamKII promoter were co-injected in the cerebellum of P0 wild-type mice. Duplex smFISH was performed on coronal sections from the brainstem at P7. Probes: *GFP* (labels targeted IONs) together with *C1qlI*, *Cbln1*, *Lgi2* or *Crtac1*. Nuclei are stained using DAPI. Left: high magnifications of nuclei from GFP⁺ single ION (projection of five planes, z-stack step: 0.5 µm). Scale bars, 10 µm. Right: quantification of integrated intensity per ION for each probe. *n* > 38 cells per condition, 13–17 animals per condition, five independent experiments. *C1qlI*, *P* = 0.083, not statistically significant, likely due to high variability in IONs; *Lgi2*, *P* = 0.021. Statistics, two-tailed nested *t* test. **d.** Model of the multistep acquisition of the molecular synaptic identity of PC excitatory synapses during postnatal development. Prom., promoter.
*Lgi2* expression, respectively, and no change in *Cbln1* and *Crtac1* were found using smFISH-based quantifications (Fig. 5c). Altogether, these results show that the expression of a subset of genes that characterizes the mature CF synapse molecular identity, *C1ql1* and *Lgi2*, is partially modulated by neuronal activity during postnatal development.

Discussion

The functional brain relies on the formation and specification of diverse types of synapses for each neuron type. Here we show that the development of excitatory synapse diversity and specificity in cerebellar PCs relies on input-specific molecular rules. Initially, CBLN1 is required for the innervation by both CFs and PFs. During the second postnatal week, IONs undergo a transcriptional change—the addition of a combination of secreted synaptogenic factors allows the specification of the identity of their synapses on PCs.

Many protein-receptor complexes have been shown individually to control synapse formation and maturation across the brain.<sup>7,56</sup> However, to what extent do different signaling pathways cooperate to define the 'cytochemical affinities' that underlie synapse specificity, as postulated by Sperry?<sup>29</sup> Are these pathways functionally redundant for a single synapse type? At least five distinct molecular complexes contribute to the specific connectivity and identity of CF/PC synapses (Fig. 5d). The cell surface proteins C1QL1, LGI2 and CRTAC1 solely control synapse numbers and maturation, while NRCAM and CBLN1 also regulate CF morphogenesis in accordance with previous data<sup>46,57,58</sup>. Double and triple KD experiments for *C1ql1*, *Crtac1* and *Lgi2* show an additive phenotype for synapse numbers but not for the volume of VGLUT2 clusters, indicating that these three molecules might, at least partially, have nonredundant functions during synapse specification. Each of these cell surface proteins has different receptors that regulate different aspects of synapse formation and function (Fig. 5d). C1QL1 binds the adhesion GPCR ADGRB3/BAI3, a receptor known to regulate the activity of the small GTPase Rac1 (ref. 59)—this would directly promote synapse formation and maturation via the modulation of the actin cytoskeleton. LGI2 binds the ADAM receptors that promote the recruitment of AMPA receptors and synaptic transmission<sup>60</sup>. Finally, CRTAC1 binds the RTN4R receptors<sup>61</sup>. RTN4Rs control both morphogenesis and synaptogenesis in cerebellar PCs and other neuronal types across the brain<sup>62,63</sup>. While RTN4Rs have been shown to inhibit synaptogenesis via their coreceptor TROY<sup>64</sup>, interactions between RTN4Rs and BAIs were recently revealed to instead promote synaptogenesis and inhibit axon elongation in cultured human neurons<sup>63</sup>. LGI1, a paralogue of *Lgi2*, can also bind to RTN4Rs<sup>65</sup>. Thus, C1QL1, LGI2 and CRTAC1 could have a partially redundant function at the synapse via the convergent modulation of RTN4R function and competition with coreceptors such as TROY (Fig. 5d). This partial redundancy would be essential to maintain circuit formation and function robustness in the face of mutations in genes underlying synapse specificity.

Unexpectedly, both types of inputs initially rely on the same signaling pathway, involving CBLN1, for proper connectivity on their common target PCs. CBLN1, a member of the C1q family of secreted proteins, is well known for promoting the formation and stabilization of PF/PC synapses through a tripartite complex with presynaptic neuromedin-1β and postsynaptic GluD2 (refs. 12,17,25). The 'Sotelo model' postulated that the generation of the dendritic spines contacted by PFs is a 'default mode'<sup>66</sup>. Here we show that the first step for connectivity on PCs is the recognition by the excitatory inputs, regardless of their type, via CBLN1/GluD2 complexes, constituting a presumptive 'default molecular code'. This correlates with previous observations of the transient presence of the postsynaptic receptor GluD2 at CF/PC synapses during development (cf. also Extended Data Fig. 7) and during adult reinnervation<sup>67,68</sup>. Other genes identified in our study, such as RTN4Rs found both in GCs (Extended Data Fig. 1) and immature IONs (Supplementary Data 4), may also be part of this default molecular code. During the second phase, molecular specification occurs selectively

in the ION input, promoting the formation of its synapses and mature territory of innervation on the growing PC dendrites, in the face of competition with PFs. Interestingly, many of the molecular pathways identified in our study are expressed in various regions of the brain. In the hippocampus, CA3 pyramidal neurons receive several types of excitatory inputs, including one from dentate gyrus GCs forming a very specialized connection on the proximal dendrites via mossy fiber boutons. NRCAM, CRTAC1, LGI1 and C1QL proteins have been found at this particular synapse<sup>40,69</sup>. It is thus tempting to postulate that similar molecular rules control the development of excitatory connectivity in CA3 pyramidal neurons and other neurons across the brain.

What regulates the multistep acquisition of the molecular identity of certain synapse types? The transcriptional program underlying the change in gene expression patterns for selected inputs would need to be controlled by either an internal program or actively regulated. If it is active, it could be due to molecular factors, as recently described in *Drosophila*<sup>70</sup>, or due to changes in neuronal activity in the network<sup>71</sup>. Our data suggest that activity-dependent and neuron-specific mechanisms, potentially mediated via epigenetic regulation<sup>35,71</sup>, could partially control the acquisition of the molecular identity of certain types of synapses. Therefore, the postnatal developmental period of circuit formation would be very sensitive to modulation by external factors for certain types of synapses but not others. Environmental changes during this period would lead to specific consequences in terms of circuit development, computation and symptom etiology in neurodevelopmental disorders.

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# Methods

## Animals, mouse lines genotyping and AAV injections

All animal protocols and animal facilities were approved by the Comité Régional d'Ethique en Expérimentation Animale (CEEA 59; protocol 22406) and the veterinary services (D-75-05-12). For all mouse experiments, pilot studies were conducted to determine the sample size needed to achieve a statistical power of 70% minimum with an α level of 0.05. Randomization of the experiments was performed by distributing experimental groups across multiple cages and litters and balancing for sex. OF1 mice (Charles River Laboratories) were used for the determination of developmental expression patterns or smFISH. The Cre-dependent Cas9/GFP-KI mouse line was obtained from the Jackson Laboratory (B6J).129(B6N)-Gt (ROSA)26Sortm1(CAG-cas9*,-EGFP)Fezhi/J; strain 026175). The NeuroD1Cre mouse line (Tg(Neurod1-cre)RZ24Gsat/Mmucl) was obtained from M.B. Hatten (The Rockefeller University). The C1q1l-ires-cre mouse line was previously described<sup>72</sup>. All lines were maintained on the C57Bl6/J background, and homozygous male mice were crossed with female C57Bl6/J wild-type animals to generate heterozygous animals used in our experiments. CRISPR-Cas9 technology was used to target a double HA tag to the endogenous C1q1l locus, just after the signal peptide, and create the C1q1lHA mouse line maintained on a C57Bl6/J background<sup>73</sup>. Homozygous C1q1lHA/HA mice were used for all experiments. Transgene detection was performed using the primers described in Supplementary Data 5. Both males and females were used for experiments.

Injections of AAV particles were performed in the cerebellar vermis of ice-anesthetized P0 Cas9/GFP-KI, WT C57Bl6J or C1q1l-ires-cre heterozygous mice at 1 mm depth from the skull and at 3.2 mm relative to Bregma to target the PC layer. The rAAV2/retrograde serotype was used to target IONs. In total, 0.25 µl of rAAV was injected per animal using pulled calibrated pipets.

## Gene expression analysis

**RNA extraction.** The cerebellum and the brainstem were dissected in a cold Hanks' balanced salt solution (HBSS; Gibco, Thermo Fisher Scientific). After removal of the meninges, the tissues were frozen in liquid nitrogen and stored at -80 °C. The cell cultures were washed with PBS, scraped in RLT buffer (Qiagen) complemented with 2-mercaptoethanol and stored at -80 °C. Total RNA was extracted using the Qiagen RNeasy mini kit following tissue or cell homogenization, according to the manufacturer's instructions.

**High-throughput RT-qPCR.** High-throughput quantitative RT-PCR was performed as previously described<sup>28</sup>. Briefly, cDNAs were synthesized using 100 ng of total RNAs with high quality and integrity (mean RNA quality number of 9.6) using Reverse Transcription Master Mix from Fluidigm on a Nexus Thermocycler (Eppendorf; protocol-5 min at 25 °C, 30 min at 42 °C, heat inactivation of the reverse transcriptase 5 min at 85 °C and then 4 °C). cDNA samples were diluted five times in low Tris-EDTA buffer (10 mM Tris, 0.1 mM EDTA, pH 8.0; TEKNOVA, Thermo Fisher Scientific) and used for specific target multiplex pre-amplification using Fluidigm PreAmp Master Mix and pooled TaqMan Gene Expression assays (Life Technologies) with a final concentration of 180 nM for each assay (protocol-95 °C for 2 min, followed by 18 cycles at 95 °C for 15 s and 60 °C for 4 min). Predesigned Taqman Gene Expression assays are listed in ref. 28.

Quantitative PCR was performed using the high-throughput platform BioMark HD System and the 96.96 GE Dynamic Arrays (Fluidigm). The loaded Dynamic Array was transferred to the Biomark real-time PCR instrument and amplified by PCR (25 °C for 30 min and 70 °C for 60 min for thermal mix; 50 °C for 2 min and 95 °C for 10 min for hot start; 40 cycles at 95 °C for 15 s and 60 °C for 1 min). ROX was used as a passive reference and single probe FAM fluorescent dye-Minor Groove Binder as a fluorescence detector. To determine the quantification cycle Ct, data were processed by the automatic threshold for each assay,

with linear derivative baseline correction using BioMark Real-Time PCR Analysis Software 4.0.1 (Fluidigm). The quality threshold was set at the default setting of 0.65.

The raw data from gene expression analysis were extracted and analyzed using MATLAB. For the systematic fold change and statistical test analysis, a script was written using the R open-source software. For each dataset, a multiple *t* test comparison was performed on the 324 genes, assuming a normal distribution and the equality of variances. Benjamini-Hochberg procedure for multiple correction was performed. The ΔCt method (2⁻ΔCt) was used to determine the relative gene expression, where ΔCT = CT(gene) - CT (geometric mean of reference gene). The data were normalized to *Rpl13a*, which showed the most stable expression among all the samples.

Unsupervised clustering was conducted using the open-source software Cluster 3.0 (http://bonsai.hgc.jp/-mdehoon/software/cluster/software.htm) to categorize genes according to their expression patterns during postnatal development. Analysis was performed on log₂(relative gene expression value), normalized so that the median value for all the data points for each gene is 0, making it more robust against outliers. This normalization allows for the assessment of variation during postnatal development independent of each gene's expression level, enabling clustering based on expression dynamics rather than absolute levels. Agglomerative hierarchical clustering, using the complete linkage method, was used to compute the distance matrix between gene expression profiles and time points. Hierarchical clustering organizes genes and time points into a tree-like structure, with branch lengths indicating similarity. The visualization of clustered data was done using Treeview<sup>74</sup>.

**Droplet digital PCR.** Droplet digital PCR (ddPCR, Bio-Rad) was performed as previously described<sup>28</sup>. Briefly, 100 ng of total RNA was reverse transcribed using the SuperScript VILO cDNA Synthesis Kit (Life Technologies), according to the manufacturer's instructions. The cDNA samples diluted five times were mixed with the FAM-labeled fluorescent probes to target each candidate gene along with the VIC-labeled *Rpl13a* reference (predesigned Taqman Gene Expression assay is listed in ref. 28 and ddPCR Supermix for Probes (No dUTP; Bio-Rad)); probes span various exons depending on the targeted mRNAs (exons 1–2 for *C1q1l*, exons 11–12 for *Crtac1*, exons 1–2 for *Lgi2*, exons 20–21/21–22 for *Nrcam* and exons 1–2 for *Cbln1*). The samples were then fractionated into 12,000–20,000 droplets in water–oil emulsion using the automatic droplet generator (Bio-Rad). The template molecules from each droplet were PCR amplified in a thermocycler (Bio-Rad; protocol-1 cycle of 95 °C for 10 min, 40 cycles of 94 °C for 30 s and 60 °C for 1 min and 1 cycle of 98 °C for 10 min). The fluorescence (FAM, VIC) in each droplet was then digitally counted using a QX200 droplet reader (Bio-Rad). QuantaSoft analysis software (Bio-Rad) was used for data acquisition and analysis. For each assay, the identical manual threshold was selected, and relative expression was determined as the ratio of the copy number of template molecules to the copy number of the *Rpl13a* reference gene.

**smFISH.** Animals at P4, P14 or P > 60 (adult) were perfused with fresh 4% paraformaldehyde (PFA) in PBS and postfixed at 4 °C for 24 h. Brains were cryopreserved in sucrose 10%, 20% and then 30% until the tissue sank to the bottom of the tube, cut in coronal sections (30 µm thick) using a freezing microtome and stored at -20 °C in antifreeze solution (30% ethylene glycol, 30% glycerol and 10% PBS 10× in RNAse-free water). Fixed frozen sections were processed using the RNAscope Multiplex Fluorescent Assay Kit V2 (Advanced Cell Diagnostics) according to the manufacturer's instructions with minor modifications–free-floating sections were rinsed two times with 0.1% Tween 20 in PBS (TBST), incubated 10 min with RNAscope Hydrogen Peroxide (Advanced Cell Diagnostics) at room temperature and then rinsed two times in TBST before mounting them on Superfrost Plus slides.
Slides were dried overnight, rehydrated in distilled water, dried for 1 h at 60 °C in a dry oven and rehydrated again in water before immersion of the slides in EtOH. Probes were designed to target all predicted transcript variants for each candidate gene and are described in Supplementary Data 5. DAPI was used to stain nuclei, and sections were mounted with ProLong Gold Antifade Reagent (Life Technologies).

## Plasmids, CRISPR-Cas9 gRNAs and validation of gRNAs efficiency and specificity

**Plasmids.** The constructions of the pAAV-U6-gRNA-CamKII-CRE-WPRE<sup>73</sup> as well as the pAAV-CamKII-Kir2.1-ires-GFP-WPRE<sup>55</sup> and pAAV-CamKII-Kir2.1mutant-ires-GFP-WPRE<sup>55</sup> used in Fig. 5b; the pAAV-SynI-CreOn-FlpOff-Kir2.1-P2A-EGFP<sup>76</sup> and the pAAV-SynI-CreOn-FlpOff-Kir2.1MUT-P2A-EGFP<sup>76</sup> used in Fig. 5c; and the plasmids used for the tagging of endogenous *Nrcam*<sup>77</sup> are described in Supplementary Data 5.

As rAAV plasmids are prone to recombination, they were all checked using the SmaI restriction enzyme that cut in the AAVITRs. The proper number and size of DNA fragments were determined on agarose gel electrophoresis. Proper insertion of gRNAs or DNA sequences was ensured by Sanger sequencing.

## CRISPR-Cas9 genome editing strategy and design of the gRNAs.

For KD experiments, gRNAs located in the signal peptide or in the region of the start codon and with high predicted cleavage (efficiency >55; ref. 78) were selected using the CRISPOR web-based tool. The *Nrcam* gRNA was previously published and characterized<sup>46</sup>. Several gRNAs per gene were tested, and the most efficient one was selected for in vivo KD experiments (guide 1 for *C1ql1* and guide 2 for *Lgi2* and *Crtac1*). Sequences of gRNAs are described in Supplementary Data 5.

For *Nrcam* tagging, the genomic sequence targeted by the CRISPR gRNA was 5'-GAA AGA GAA AGA GCC AGC AG-3', as previously published in ref. 77.

### Primary neuronal cultures.
Cultures from the neocortex were prepared from E14.5 mouse embryos resulting from cross-breeding between Cas9/GFP-KI and C57Bl6/J mice (analysis of CRISPR-Cas9 KD efficiency) or from C57Bl6/J mice (Kir2.1 experiments). Cortices were dissected in cold HBSS (Gibco), washed once in cHBSS (HBSS containing HEPES (2.5 mM, pH7.4), D-glucose (300 mM), CaCl₂ (1 mM), MgSO₄ (1 mM) and NaHCO₃ (4 mM)) and incubated in cHBSS containing trypsin (1×; Gibco) and DNase I (100 µg ml⁻¹; Roche) for 15 min at 37 °C. Then, cortices were washed three times with cHBSS containing DNase I and gently dissociated by pipetting. Dissociated cells were plated on poly-D-lysine (0.1 mg ml⁻¹; Sigma-Aldrich) coated coverslips (18 mm diameter) in neurobasal media (Gibco) supplemented with FBS (2.5%; Sigma-Aldrich), B27 (1×; Gibco), L-glutamine (1×; Gibco) and penicillin-streptomycin (1×; Gibco; Life Technologies). After 7 days, supplemented neurobasal medium was added to the cultures.

Cerebellar mixed cultures were prepared from P0 mouse pups resulting from cross-breeding between Cas9/GFP-KI and NeuroD1Cre mice (analysis of *C1ql1* CRISPR-Cas9 KD efficiency on RNA extracts or specificity on gDNA extracts). Cerebella were dissected and dissociated according to a previously published protocol<sup>79</sup>. Neurons were seeded at a density of 5 × 10⁶ cells per ml. When used for immunostaining, cells were fixed with 2% PFA in PBS.

### gDNA extraction and PCR amplification.
Cultures from the neocortex or mixed cerebellar cultures were washed, resuspended in PBS and stored at -80 °C. gDNAs were extracted using the PureLink gDNA Mini Kit (Thermo Fisher Scientific), according to the manufacturer's instructions for mammalian cell lysate. gDNAs were then used for PCR amplification to assess the consequences of CRISPR-Cas9 targeting for the *Cbln1* gene (recombination of the targeted exons) or the first three off-targets of each candidate gRNA selected for the KD experiments

(primers are listed in Supplementary Data 5). Sequencing was also used to analyze the consequences of CRISPR-Cas9 targeting on the coding sequence of the *Crtac1* gene. For this, PCR products amplified from exon 1 of *Crtac1* were subcloned in the TA cloning vector and sequenced with a T3 primer. Sequencing of seven clones revealed that CRISPR-Cas9 targeting produced small deletions (2–90 nucleotides) that systematically led to frameshifts in the coding sequence.

## Production of rAAV particles and transduction of cells

**AAV production.** HEK293T cells (ATCC; CRL-3216) were plated in DMEM containing glutamine (Gibco, I1965-092), supplemented with 10% FBS (Sigma-Aldrich) and 1% penicillin-streptomycin (Gibco), and cotransfected after reaching 60–80% confluency with pAdDeltaF6 helper (pAdDeltaF6 was a gift from J.M. Wilson; RRID: Addgene, 112867), rAAV2-retro helper plasmid (rAAV2-retro helper was a gift from A. Karpova and D. Schaffer<sup>80</sup>; RRID: Addgene, 81070) and pAAV plasmid (see Supplementary Data 5 for the description of pAAV plasmids) in HBS (Sigma-Aldrich)/CaCl₂, to produce retrograde rAAV particles. HEK293T cells were then incubated in DMEM (Gibco, I1960-044), supplemented with 10% FBS (Sigma-Aldrich) and 1% glutamax (Gibco) after the addition of chloroquine diphosphate at a final concentration of 25 µM in the cell medium. The 293T cells were grown for 3 days after transfection. For small-scale AAV production, the supernatant medium was collected, filtered through a cellulose acetate membrane with Spin-X centrifuge tube filter (Corning Costar) and stored at 4 °C for a maximum of 2 weeks or frozen at -80 °C. For big-scale AAV production, 293T cells were processed using the AAVpro Purification Kit (Takara), following the manufacturer's instructions. In total, 150–200 µl of AAVs were obtained and aliquoted for storage at -80 °C. Real-time PCR reactions were performed with the AAVpro Titration Kit, following the manufacturer's protocol. Titers of purified AAVs used in this study were >9 × 10¹¹ genome copies (GC) per ml.

### Transduction in vitro.
Primary neuronal cultures were transduced with 100 µl of small-scale AAV2/2 or AAV2/8 supernatants, or with purified AAV2/retrograde at a final concentration of 0.25 × 10¹⁰ to 2 × 10¹⁰ GC per ml. AAV2/1-CMV-CRE supernatants or purified AAV2/8-CMV-CRE (Vector Biolabs, VB7060) were used to target the CRE in neurons expressing candidate genes.

## Immunolocalization

The 30-µm-thick parasagittal cerebellar sections were obtained using a freezing microtome after intracardiac perfusion of mice with fresh 4% PFA in PBS or with 3% glyoxal fixative solution containing 0.7% acetic acid and 20% ethanol (HA-CIQL1 immunostaining) or 0.8% acetic acid (ADGRB3/BAI3 and GluD2 immunostainings)<sup>81,82</sup>. Immunolabeling was performed in PBS containing 0.2% Triton X-100 for cultured neurons or in PBS containing 1% Triton X-100 for brain sections. After blocking unspecific sites with 4% normal donkey serum for 30–60 min at room temperature, primary antibodies (diluted in PBS/Triton X-100/1% normal donkey serum) were incubated for 1–2 h at room temperature under agitation. Primary antibodies are listed in Supplementary Data 5. The cultured neurons or sections were then washed three times for 5 min in PBS/Triton X-100 and incubated for 1–2 h at room temperature in the secondary antibody diluted in PBS/Triton X-100/1% normal donkey serum (1:1,000; Invitrogen, Thermo Fisher Scientific; antirabbit Alexa Fluor 488, A21206; antimouse Alexa Fluor 568, A10037 and antiguinea pig Alexa Fluor 647, A21450). DAPI was used to stain nuclei, and sections were mounted with ProLong Gold Antifade Reagent (Life Technologies). When required, antigen retrieval was performed before the blocking step. LGI2 and CRTAC1 immunolabellings were performed after a 15-min incubation in sodium citrate buffer containing 0.05% Tween 20 and 10 mM sodium citrate at pH of 6.0 in a steamer. CBLN1 immunostaining was performed after a 10 min incubation at 37 °C in pepsin buffer containing 0.2 N hydrochloric acid in water.
For both sodium citrate and pepsin antigen retrieval, a 20-min permeabilization was performed in PBS containing 1% Triton X-100 after three washes of 5 min.

## Image acquisition and analysis

Images, quantifications and graphing were performed blind to experimental conditions, with group identities revealed only after analysis. All image stacks were acquired using a spinning-disk confocal CSU-W1 microscope (Zeiss) with ×25 (numerical aperture (NA): 0.80) or ×63 (NA: 1.4) objectives. The mosaic images of the smFISH experiments were reconstructed using Metamorph software (Fig. 4a and Extended Data Fig. 2). Images for candidate protein localization were acquired using an LSM 980 confocal–Airyscan 2 microscope (Zeiss) with a ×63 (NA: 1.4) objective.

**smFISH experiments.** For mosaic images in the brainstem (Extended Data Fig. 2), the Pearson correlation was obtained using a modified version of the Correlation Fiji plugin. Quantifications in single neurons in images obtained at ×63 were performed using a custom-made plugin for unbiased 3D detection of individual RNA puncta and DAPI nuclei.

The number of mRNA dots per nucleus was estimated by dividing the integrated density of each nucleus, post background subtraction, by the mean corrected integrated density of a 'single mRNA dot'. For each gene and experiment, 'single RNA dots' were chosen from low signal regions to ascertain their size and corrected integrated density relative to the mean gray value of the background in the same image.

Mean corrected integrated density of a 'single RNA dot'

$$ \frac{\sum_{1}^{n} \text{dot integrated density} - \left(\sum_{1}^{n} \text{dot background mean gray value} \times \frac{\sum_{1}^{n} \text{dot volume}}{n}\right)}{\sum_{1}^{n} \text{dot volume}} $$

where n represents the number of measured dots.

For the nucleus identification, 3D ImageJ suite library⁸³ was used. Intense heterochromatin puncta were removed using the remove outliers Fiji function⁸³. A difference of Gaussian filter followed by a binary mask (Otsu threshold) and a three-dimensional watershed were used to separate nuclei. Finally, segmented nuclei were dilated by 3 µm in x, y and z to capture mRNAs that are at the periphery of the nucleus. A threshold was set for maximum nucleus size to remove nuclei that could not be separated.

**Morphology quantifications.** For CF and VGLUT2 stainings, all the images were normalized using the quantile-based normalization plugin in Fiji. A custom-made plugin was developed based on the 3D Weka Segmentation plugin to allow semi-automatic detection of GFP+ CFs and to count and measure VGLUT2 clusters. The selection of signal and background was done manually. We only quantify VGLUT2 clusters in GFP+ CFs, which represent the transduced population. CF morphology was assessed using the SNT toolbox on ImageJ for tracing and quantification.

## Effects of Kir2.1 expression in IONs

To assess the effect of different levels of expression of Kir2.1 on the activity of an ION, we simulated the model proposed in refs. 84,85 and developed from the previous model in refs. 86,87, with an added Kir 2.1 conductance. In the model⁸⁴,⁸⁵, an ION is described by three compartments representing its dendritic arbor, soma and axon. Each compartment is endowed with specific conductances, including a high-threshold calcium current, h current and calcium-dependent potassium current in the dendritic compartment; a low-threshold calcium current, a potassium current and a sodium current in the somatic compartment; and a sodium current and a potassium current in the axonic compartment. A fluctuating current in the dendritic

compartment accounts for the synaptic inputs to the ION. We added to the three compartments of this model a Kir 2.1 conductance with the same maximal conductance g<sub>Kir</sub> per area of the membrane, as described in ref. 88 and https://channelpedia.epfl.ch/wikipages/42/, and detailed below. The conductance was varied from g<sub>Kir</sub> = 0 mS cm<sup>-2</sup> to 0.5 mS cm<sup>-2</sup>. For each value of g<sub>Kir</sub>, n = 5 simulations of 100 s each were performed. Examples of 10 s somatic voltage traces for different g<sub>Kir</sub> conductances are provided in Extended Data Fig. 6b (left). Mean firing rates and their s.d. are reported in Extended Data Fig. 6b (right).

The Kir 2.1 current F<sub>Kir</sub> was modeled in each compartment c (with c = a, axon; c = s, soma; c = d, dendrites) using the Hodgkin–Huxley formalism with an activation gate m and an inactivation gate h in each compartment (ref. 88; https://channelpedia.epfl.ch/wikipages/42/).

$$ F_{\text{Kir}} = g_{\text{Kir}} m_c h_c^2 (V_c - V_K), c = a, s, d, $$

where V<sub>c</sub> is the membrane potential of the considered compartment c. The gates evolve in time according to the equations,

$$ \tau_m(V_c)dm_c/dt = m_\infty(V_c) - m_c, \tau_h(V_c)dh_c/dt = h_\infty(V_c) - h_c. $$

The functions m<sub>∞</sub>(V), τ<sub>m</sub>(V), h<sub>∞</sub>(V) and τ<sub>h</sub>(V), are given by

$$ m_{\infty}(V) = 1/(1 + \exp((V + 96.5)/23.3)), $$

$$ \tau_m(V) = (3.7 - 3.37/(1 + \exp((V + 32.9)/27.9)))/Q10, $$

$$ h_{\infty}(V) = 1/(1 + \exp(-(V + 168.3)/44.1)), $$

$$ \tau_h(V) = (0.85 + 306.3/(1 + \exp(-(V + 118.3)/27.2)))/Q10. $$

$$ Q10 = 2.3^{0.7} = 1.79. $$

The Q10 factor accounts for the shift in temperature from 28 °C to 35 °C. Finally, inputs to the ION are modeled by a fluctuating current per membrane area I<sub>e</sub> in the dendritic compartment. The current is taken to be an Ornstein–Uhlenbeck process⁸⁵ with a correlation time τ<sub>e</sub> = 10 ms,

$$ \tau_e dl_e/dt = -I_e(t) + \sigma_e(\tau_e)^{1/2} \xi(t), \quad <\xi(t)\xi(t')> = \delta(t - t'), $$

where ξ(t) is a white noise and the noise amplitude σ<sub>e</sub> = 1 µA cm<sup>-2</sup>.

The simulations revealed that a minimum level of Kir2.1 expression is necessary to inhibit ION activity. Therefore, a threshold for Kir2.1 expression, corresponding to approximately 0.4 mS cm<sup>-2</sup> (equivalent to roughly 20 Kir2.1 channels per 100 µm<sup>2</sup> of membrane considering a conservative unitary conductance of 20 pS for Kir2.1 channels<sup>89,90</sup>), was established above which ION activity is blocked. A conservative minimum threshold of approximately 40 Kir2.1 mRNA molecules per nucleus (as assessed by GFP expression) was used to include IONs for smFISH quantification to assess the effects of activity blockade on gene expression.

## Statistical analysis

All statistical analyses were performed using GraphPad Prism8. Normality was assessed using D'Agostino and Pearson, Shapiro–Wilk or Kolmogorov–Smirnov normality tests. When groups were normalized to their respective controls, a two-tailed Student's t test with a null hypothesis of 1 was used in Extended Data Fig. 6a. When the populations did not fit the normal distribution and required multiple comparisons of data normalized to their CTL, the nonparametric Kruskal–Wallis and uncorrected Dunn's test was used (Extended Data Fig. 4a). Differences in distribution were tested using the chi-squared test. All other statistical analyses were performed using the two-tailed nested t test to take into account repeated measurements. Data are presented as
mean ± s.e.m. or as violin plots with median and quartiles. The ROUT method was used to identify outliers with Q (maximum desired false discovery rate) set to 1%, aiming to ensure that no more than 1% of the identified outliers are false. Outliers were found and eliminated in Figs. 2b, 3, 4d,e and 5b,c and Extended Data Figs. 4b,c and 5b,c.

## Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

## Data availability

All data are available in the main text or the Supplementary Information. Single-cell expression data in cerebellar GCs are available at https://apps.kaessmannlab.org/sc-cerebellum-transcriptome/. AlphaFold structure predictions are available on the database (https://alphafold.ebi.ac.uk/). Information on the Kir2.1 channel is available at https://channelpedia.epfl.ch/wikiperhttps://42/. Source data are provided with this paper—statistical source data for figures and extended data figures, and raw image for Extended Data Fig. 5.

## Code availability

Plugins are available on GitHub via the following links: custom-made plugin for unbiased 3D detection of individual RNA puncta and DAPI nucleus (https://github.com/orion-cirb/RNA_Scope); custom-made plugin developed based on the 3D Weka Segmentation plugin to allow semi-automatic detection of GFP+ CFs and to count and measure VGLUT2 clusters (https://github.com/orion-cirb/Vglut2_GFP_Weka_Maela.git). Gene clustering was performed using the open-source software Cluster 3.0 (http://bonsai.hgc.jp/~mdehoon/software/cluster/software.htm).

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## Acknowledgements

We would like to thank A. Boyreau for her help with some of the experimental procedures at the beginning of the project, Y. Dupraz for the development of tools for stereotaxic injections in neonates, F. Maloumian for her help with infographics and the personnel from the Center for Interdisciplinary Research in Biology animal and imaging facilities. We thank H. Monnet for her help in updating the plugin for VGLUT2 quantification. High-throughput qPCR was carried out on the qPCR-HD-Genomic Paris Centre platform, supported by grants from Région Ile-de-France. This work was supported by funding from Fondation pour la Recherche Médicale (Equipe FRM DEQ20150331748 to F.S.), European Research Council ERC consolidator grant (SynID 724601 to F.S.), Q-life (ANR-17-CONV-0005 to F.S. and V.H.), ANR-10-LABX-54 MEMO LIFE (to F.S.) and Sorbonne Université (ED158 to M.A.P.), Collège de France (to M.A.P.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

## Author contributions

F.S., M.A.P. and S.M.S. conceptualized the project, developed the methodology and handled visualization. M.A.P., S.M.S., L.M., F.J.U.Q., M.D., P.M., H.W.C., E.O. and V.H. conducted the investigation. F.S. and M.A.P. secured funding. F.S. and S.M.S. provided supervision. F.S. managed project administration. F.S. wrote the original draft with contributions from S.M.S., M.A.P. and V.H. F.S., S.M.S., M.A.P., V.H. and D.C.M. reviewed and edited the final manuscript.

## Competing interests

The authors declare no competing interests.

## Additional information

Extended data is available for this paper at
https://doi.org/10.1038/s41593-024-01826-w
**Supplementary information** The online version
contains supplementary material available at
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**Correspondence and requests for materials** should be addressed to
Fekrije Selimi.

**Peer review information** *Nature Neuroscience* thanks Mary Hatten and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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<figure><img src="image_48.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_49.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_50.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_51.png" /></figure>

Extended Data Fig. 1 | See next page for caption.
**Extended Data Fig. 1** | **Developmental expression pattern of GC candidate genes in the cerebellar cortex.** a, The expression pattern of *Cbln1* and the 15 selected GC candidate genes was assessed using high-throughput RT-qPCR on RNA extracts from the cerebellum taken at different stages of development (embryonic day, E17, postnatal days, P0, P3, P7, P14 and P21) and in adult mice. Expression levels were normalized to the *Rpl13a* gene and to their E17 value to capture their variation during postnatal development regardless of the individual gene's expression level; data are presented as mean ± SEM. n = 4 animals per developmental stage and 3 animals for the adult. The data are separated into two graphs depending on the scale of dynamics of expression for clarity. b, Data from ref. 35 showing the stage of peak expression of the selected candidate genes depending on GC differentiation: GC precursors (GCPs) at P7, in newly differentiated GCs at P7, in migrating GCs at P12 and, finally, in mature

GCs at P21. c, Single-cell expression data in cerebellar GCs from https://apps.kaessmannlab.org/sc-cerebellum-transcriptome/. Left: uniform manifold approximation and projection (UMAP) of mouse cerebellar cell types colored by cell differentiation state. GCP, granule cell precursor; UBCP, unipolar brush cell precursor; GC diff 1 and 2, differentiated GC1 and 2. Right: expression of *Cbln1* and eleven GC DEGs candidates at different stages of GC differentiation. Dot size indicates the fraction of cells expressing a gene, color shows the mean expression level scaled by gene. d, smFISH of selected GC candidates (red) in cerebellar sections of P14 and adult mice co-stained with the nuclear stain DAPI. EGL, external granular layer; ML, molecular layer; IGL, internal granular layer. Each image corresponds to a projection of 15 images (z-stack step: 0.5 µm). Two independent experiments. Scale bars, 20 µm.
<figure><img src="image_52.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_53.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_54.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_55.png" /></figure>

Extended Data Fig. 2 | See next page for caption.
**Extended Data Fig. 2** | **Developmental expression pattern of ION candidate genes in the inferior olive.** a, Expression patterns of *C1ql1* and the 15 ION DEGs selected as candidates were assessed using high-throughput RT-qPCR on RNA extracts of the brainstem taken at different stages of development (embryonic day, E17, postnatal days, P0, P3, P7, P14 and P21) and in the adult. Expression levels were normalized to the *Rpl13a* gene and to their E17 value to capture their variation during postnatal development regardless of the individual gene's expression level; data are presented as mean ± SEM. n = 4 animals per developmental stage and 3 animals for the adult. The data are separated into two graphs depending on the scale of dynamics of expression for clarity. **b–d**, Left: representative images from duplex smFISH experiments for *C1ql1* and candidate mRNAs in coronal sections from the brainstem at P4, P14 and in adult mice. Scale bars, 150 µm. Right: the degree of correlation of expression between *C1ql1* and candidate mRNAs in the brainstem was determined by computing the Pearson

correlation coefficient on the whole image (coefficient >0.6 corresponding
to high correlation<sup>[31]</sup>). Data are presented as mean ± SEM. **b**, Candidates highly
correlated with *C1ql1* at all developmental stages. *Nrcam*, n = 4 animals for each
stage; *Lgi2*, adult: n = 5 animals, *PI4*: n = 4, *P4*: n = 3; *Crtac1*, adult and *PI4*: n = 4
animals, *P4*: n = 5; *Sema4f*, adult and *PI4*: n = 3 animals, *P4*: n = 4; 2–4 independent
experiments. **c**, Candidates highly correlated with *C1ql1* from *PI4* to adult.
*Shisal1*, n = 3 animals for each stage; *Thy1*, adult and *P4*: n = 3, *P14*: n = 4; *Adam11*,
n = 3 for each stage; *Crh*: n = 3 for each stage; *Gpr123*, adult: n = 5 animals, *P14* and
*P4*: n = 4; 2–3 independent experiments. **d**, Candidates not highly correlated with
*C1ql1* at any stage. *Tmem184b*: n = 4 animals at each stage; *Fstl1*: n = 3 animals at
each stage; *Cx3cl1*, adult and *PI4*: n = 4 animals, *P4*: n = 3; *Adam23*: n = 4 animals
at each stage; *Cd151*: n = 4 animals at each stage; *Tmem179*: n = 4 animals at each
stage; 2–4 independent experiments.
<figure><img src="image_56.png" /><figcaption>a</figcaption></figure>

**Extended Data Fig. 3** | Predicted structure and localization of C1QL1, NRCAM, LGI2 and CRTAC1 in the cerebellar cortex. **a**, AlphaFold structure prediction (left; database: https://alphafold.ebi.ac.uk/) and domain architecture (right) of C1QL1, NRCAM, LGI2 and CRTAC1. See also ref. 92 for the structure of the C1QL1 gC1q domain. Ig, immunoglobulin; FN, fibronectin; TMD, transmembrane domain; LRR, leucin reach repeat; EGF, epidermal growth factor; FG-GAP, phenylalanyl-glycyl-glycyl-alanyl-prolyl. **b**, Localization of C1QL1, NRCAM, LGI2 and CRTAC1 proteins (green) in the cerebellar cortex at P13 was obtained

<figure><img src="image_57.png" /><figcaption>b</figcaption></figure>

using several strategies: C1QL1, anti-HA immunolabeling in cerebellar sections from a *C1qlII*HA knockin mouse; NRCAM, anti-HA after CRISPR-Cas9-mediated HA-tagging of endogenous NRCAM; LGI2 and CRTAC1, immunolocalization after antigen retrieval. The CF presynaptic boutons and the dendritic trees of PCs were immunostained with anti-VGLUT2 (magenta) and anti-calbindin (CaBP, blue), respectively. Each image corresponds to a projection of 5 images (z-stack step: 0.2 µm). Arrowheads show candidates labeling colocalizing with VGLUT2 clusters. 3–4 independent experiments. Scale bars, 20 µm.
a - Effect in vitro

<figure><img src="image_58.png" /></figure>

<figure><img src="image_59.png" /></figure>

<figure><img src="image_60.png" /></figure>

<figure><img src="image_61.png" /></figure>

<figure><img src="image_62.png" /></figure>

<figure><img src="image_63.png" /></figure>

<figure><img src="image_64.png" /></figure>

<figure><img src="image_65.png" /></figure>

b - Effect in vivo

<figure><img src="image_66.png" /><figcaption>C1qf1 mRNA Integrated intensity
per ION (x10⁴)</figcaption></figure>

<figure><img src="image_67.png" /><figcaption>Lgi2 mRNA Integrated intensity
per ION (x10⁴)</figcaption></figure>

<figure><img src="image_68.png" /><figcaption>Crtac1 mRNA Integrated intensity
per ION (x10⁴)</figcaption></figure>

<figure><img src="image_69.png" /></figure>

<figure><img src="image_70.png" /></figure>

<figure><img src="image_71.png" /></figure>

c - Effect on CF boutons

<figure><img src="image_72.png" /></figure>

<figure><img src="image_73.png" /></figure>

<figure><img src="image_74.png" /></figure>

Extended Data Fig. 4 | See next page for caption.
**Extended Data Fig. 4** | CRISPR–Cas9 knockdown for *C1ql1*, *Nrcam*, *Lgi2* and *Crtac1*. **a**, Top: illustrations of the genomic regions of mouse *Nrcam*, *Lgi2*, *Crtac1* and *C1ql1* with the location of the sequences targeted by the CRISPR–Cas9 gRNAs. Bottom: the knockdown efficiency for *Nrcam*, *Lgi2* and *Crtac1* was measured using quantitative RT-PCR on RNA extracts from cortical cell cultures from Cas9/GFP-KI mice (*Nrcam*, *Lgi2* and *Crtac1* are detected in these cultures at DIV14). The efficiency for *C1ql1* was measured using mixed cerebellar cultures from Cas9/GFP-KI-NeuroD1Cre mice expressing the CAS9 specifically in GCs precursors (*C1ql1* is transiently expressed in these cultures with a peak at DIV8). Cultures were transduced at 3 or 2 days in vitro (DIV3 or 2), respectively, with AAVs driving the expression of each gRNA directed against the candidate genes or non-targeting control gRNA (CTL). Expression levels were normalized to values for the *Rpl13a* gene. Data are presented as mean ± SEM. n represents the independent experiments: CTL, n = 5; *Crtac1* KD g1, n = 3; *Crtac1* KD g2, n = 4; *Lgi2* KD g1, n = 7; *Lgi2* KD g2, n = 3; *Nrcam* KD, n = 4; *C1ql1* in all conditions, n = 4.

Statistics: Kruskal–Wallis and uncorrected Dunn's test. **b**, Duplex smFISH experiments for *GFP* and each candidate gene (*C1ql1*, *Lgi2* and *Crtac1*), and nuclear DAPI staining, in coronal sections from the brainstem. Top: high magnification of IONs from P21 animals with *C1ql1*, *Lgi2* or *Crtac1* KD, or non-targeting controls (CTL). Scale bars, 10 µm. Bottom: relative frequency distribution and mean integrated intensity of each candidate gene mRNA per ION under KD and CTL conditions are shown. The dashed lines represent the first quartile and second quartile (median) of the mRNA levels for each gene in CTL condition. n > 55 cells, 7–8 animals per condition, 2–3 independent experiments. Insets: data are presented as violin plots with the median and quartiles. Statistics: two-tailed nested *t* test. **c**, Distribution and mean volume of the VGLUT2 clusters quantified in GFP+ CFs after *Nrcam*, *Lgi2* or *Crtac1* KD as well as in CTL. Data are presented as violin plots with the median and quartiles. n ≥ 33 images per condition, 9–10 animals; 3–5 independent experiments. Statistics: two-tailed nested *t* test.
a

<figure><img src="image_75.png" /></figure>

b

<figure><img src="image_76.png" /></figure>

<figure><img src="image_77.png" /></figure>

c

<figure><img src="image_78.png" /></figure>

<figure><img src="image_79.png" /></figure>

**Extended Data Fig. 5** | *Cbln1* expression and function in IONs in the brainstem.

a, Heatmap showing the clustering of 324 DEGs according to the dynamics of their expression pattern in the brainstem between E17 and adult. Four clusters are highlighted by orange rectangles. *Cbln1* is detected as an early-expressed gene. b, Top: illustration of the genomic region of mouse *Cbln1* with the location of the sequence targeted by the CRISPR-Cas9 KD guide RNAs (*Cbln1* KD g1 and g2) in *Cbln1* exon 1 and exon 3, respectively. The location of forward (Fw) and reverse (Rev) primers used to assess deletion of the entire coding region is shown. Bottom left: efficiency of the CRISPR-Cas9 driven deletion was assessed by PCR on purified genomic DNA from DIV16 mixed cerebellar cultures (transduced at DIV2) or DIV14 neuronal cultures from the neocortex (transduced at DIV3). Wild-type fragment is expected at 2663 bp; recombined fragment at 380 bp. Right: duplex smFISH for *GFP* and *Cbln1* mRNAs, and nuclear DAPI staining, in

IONs from coronal brainstem sections of P7 mice expressing *Cbln1* KD gRNAs or non-targeting CTL gRNA (GFP'). Scale bars, 10 µm. Relative frequency distribution and mean integrated intensity of *Cbln1* mRNA per ION under KD and CTL conditions are shown. The dashed lines represent the first quartile and second quartile (median) of *Cbln1* mRNA level in CTL condition. n ≥ 30 images per condition; 8–9 animals per condition, 2 independent experiments. Insets: data are presented as violin plots with the median and quartiles. Statistics: two-tailed nested *t* test. c, Distribution and mean volume of the VGLUT2 clusters were quantified in GFP' CFs at P7 and P14 in CTL and *Cbln1* KD. Data are presented as violin plots with the median and quartiles. P7 VGLUT2 cluster volume: n ≥ 29 images per condition; 8 animals, 4 independent experiments. P14 VGLUT2 cluster volume: n ≥ 33 images per condition; 8–9 animals, 3 independent experiments. Statistics: two-tailed nested *t* test.
<figure><img src="image_80.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_81.png" /></figure>

De Gruyjl et al. IO model with Kir 2.1 conductance added to all compartments (top to bottom: $g_{im}$ = 0, 0.1, 0.2, 0.3, 0.4, 0.5 mS/cm²)

<figure><img src="image_82.png" /></figure>

<figure><img src="image_83.png" /></figure>

**Extended Data Fig. 6** | Modulation of neuronal excitability by Kir2.1 expression. a, Neuronal cultures from the neocortex were transduced at DIV3 with a retrograde rAAV driving the expression of the Myc-tagged Kir2.1 (or nonfunctional Kir2.1 mutant as a control) and soluble eGFP under the CamKII promoter. Left: neurons were immunolabeled at DIV17 for Myc (blue) and GFP (green). Scale bars, 13 µm. Right: RNA extracts obtained from DIV14 cultures from neocortex were analyzed by quantitative RT-PCR. Gene expression levels relative to *Rpl13a* were normalized to the levels in Kir2.1 mutant control. Data are presented as mean ± SEM. Four independent experiments. Statistics: two-tailed Student's t test with a null hypothesis of 1. *b*, In silico modeling of the effect of Kir2.1 channel expression on the activity of neurons from the inferior olive (IO).

Left: representations of the discharge in neurons of the IO depending on the magnitude of the Kir2.1 conductance. Example of 10 s somatic voltage traces for different $g_{ Kir2.1}$ conductances, ranging from $g_{ Kir2.1}$ = 0 mS/cm² to 0.5 mS/cm², where, with a conservative unitary Kir2.1 channel conductance of 20 pS, 1 mS/cm² corresponds to approximately 50 Kir2.1 channel per 100 µm² of membrane. Right: mean firing rates and their standard deviations (for each value of $g_{ Kir2.1}$, n = 5 simulations of 100 s each). This modeling showed that a minimal level of Kir2.1 is needed for effective inhibition of ION activity. We selected a minimum threshold of approximately 40 Kir2.1 mRNA molecules per nucleus (as estimated by *GFP* mRNA intensity) to select IONs included for smFISH quantification.
a

<figure><img src="image_84.png" /></figure>

b

<figure><img src="image_85.png" /></figure>

**Extended Data Fig. 7** | Localization of GluD2 and ADGRB3/BAI3 receptors in cerebellar Purkinje cells during postnatal development. a,b, Immuno-staining of GluD2 or ADGRB3/BAI3 receptors (green), CF presynaptic boutons (anti-VGLUT2, magenta) and PCs dendritic tree (anti-CaBP, blue) in parasagittal cerebellar sections from P4 and P13 mice. Left: low magnification images

correspond to the projection of 5 planes (z-stack step: 0.2 µm). Scale bars, 20 µm. Right: airyscan images corresponding to single planes. Arrowheads show receptor labeling partially colocalizing with VGLUT2 clusters. Note that at P13, some GluD2 puncta are still detectable at CF/PC synapses. Three independent experiments. Scale bars, 4 µm.
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<table><tr><td>Sample size</td><td>The number of animals was estimated after pilot studies using the biostatgy website, with a minimum statistical test power of 70% and an alpha level of 0.05.</td></tr><tr><td>Data exclusions</td><td>The ROUT method was pre-established to identify outliers, with Q (the maximum desired false discovery rate) set to 1%. This pre-determined approach aims to ensure that no more than 1% of the identified outliers are false positives. This parameter was fixed prior to the analysis and consistently applied across all conditions throughout the study.</td></tr><tr><td>Replication</td><td>Replication of experiments are indicated in the Main text: pages 14 to 22/Figure legends<br>Biological replicates are presented as number of animals per condition and technical replicates are indicated as independent experiments in the Main text (pages 14 to 22/Figure legends)</td></tr><tr><td>Randomization</td><td>Samples were randomly assigned to experimental groups across multiple cages and litters, and balancing for sex.</td></tr><tr><td>Blinding</td><td>Images were acquired, quantifications were performed, and graphs were generated by investigators blinded to the experimental conditions. Group identities were revealed only after all analyses were completed.</td></tr></table>

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Antibodies

Antibodies used

rabbit polyclonal anti-GFP (Abcam, Cambridge, UK; #ab13970)

anti-CaBP mouse antibody (Swant, Marly, Switzerland; #300)

anti-VGluT2 guinea pig antibody (Millipore, Molsheim, France; #AB2251)

anti-Myc rabbit antibody (Sigma-Aldrich; C3956)

anti-rabbit Alexa Fluor 488 (Invitrogen, Thermo Fisher Scientific, #A21206)

anti-mouse Alexa Fluor 568 (Invitrogen, Thermo Fisher Scientific, #A10037)

anti-guinea pig Alexa Fluor 647 (Invitrogen, Thermo Fisher Scientific, #A21450)

rabbit anti-HA (1:1200; Cell signaling, Danvers, MA, USA; #3724S)

rabbit anti-LGI2 (1:50; Atlas Antibodies; #HPA017140)

rabbit anti-CRTAC1 (1/250; Abcam; #ab254691)

rabbit anti-CBLN1 (1/200; Nittobo Medical/Frontier Institute; #MSFR100660)

rabbit anti-ADGRB3/BAI3 (1/200; Nittobo Medical/Frontier Institute; #MSFR106910)

rabbit anti-GluRdelta1/2 (1/1000; Millipore; #AB2285)

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- Rabbit polyclonal anti-GFP, rabbit anti-Myc, and rabbit anti-HA antibodies were validated by the manufacturer and further confirmed by us using non-GFP expressing tissue or tissue not expressing Myc or HA as negative controls.

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- Mouse anti-CaBP and guinea pig anti-VGluT2 antibodies are widely used and validated in the field by numerous laboratories working on cerebellar and other neurological research.

- Anti-LGI2 antibody was validated in the study by Gillan et al., 2023, using immunohistochemistry on sciatic nerve fibers from Lgi2 knockout mice as negative controls. We employed the same protocol for our tissue sections.

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- Optimal dilutions were determined through titration experiments.

- Positive and negative controls were included to confirm consistent performance across different lots.

Eukaryotic cell lines

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HEK293T cells (ATCC, Virginia, USA; #CRL-3216)

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Both males and females were used for experiments

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All animal protocols and animal facilities were approved by the Comité Régional d'Ethique en Expérimentation Animale (CEEA#59, Protocol #22406) and the veterinary services (D-75-05-12)

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