Abstract
The cerebral cortex comprises diverse excitatory and inhibitory neuron subtypes, each with distinct laminar positions and connectivity patterns. Yet, the molecular logic underlying their precise wiring remains poorly understood. To identify ligand–receptor (LR) interactions involved in cortical circuit assembly, we tracked gene expression dynamics in mice across major neuronal populations at 17 developmental stages using single-cell transcriptomics. This generated a comprehensive atlas of LR-mediated communication between excitatory and inhibitory neuron subtypes, capturing known and novel interactions. Notably, we identified NEOGENIN-1 as the principal receptor for CBLN4 during the perinatal period, mediating synapse formation between somatostatin-expressing interneurons and glutamatergic neurons. We also identified members of the cadherin superfamily as candidate regulators of perisomatic inhibition from parvalbumin-expressing basket cells onto deep and superficial excitatory neurons, exerting opposing effects on synapse formation. These findings suggest a context-dependent role for cadherins in synaptic specificity and underscore the power of single-cell transcriptomics for decoding the molecular mechanisms of cortical wiring.
Similar content being viewed by others
Data availability
Raw sc/snRNA-seq data generated in this study are available in the ArrayExpress database under accession E-MTAB-16260, and bulk RNA-seq data are available under accession E-MTAB-16355 (https://www.ebi.ac.uk/biostudies/arrayexpress/studies). Processed data, QC outputs, and derived RDS files are deposited on Zenodo (https://zenodo.org/records/11634657). Interactive exploration of inferred signaling networks is available through the scLRSomatoDev Shiny application at https://sclrsomatodev.online/. Source data are provided with this paper.
Code availability
All scripts used for data preprocessing, quality control, ligand–receptor inference, ontology annotation, and enrichment analysis were written in R and Python. The complete codebase, including custom functions and documentation, is publicly available on Zenodo (https://zenodo.org/records/11634657). Additional documentation and tutorials are provided online: Documentation: https://cortical-interactome.github.io/scLRSomatoDev-Docs/ Video tutorials: https://www.youtube.com/playlist?list=PLyfGSyn6Q6UY82ccuHRZQmRchVx6DskfJ.
References
Gouwens, N. W. et al. Integrated morphoelectric and transcriptomic classification of cortical GABAergic cells. Cell 183, 935–953.e19 (2020).
Scala, F. et al. Phenotypic variation of transcriptomic cell types in mouse motor cortex. Nature 598, 144–150 (2021).
Yao, Z. et al. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell 184, 3222–3241.e26 (2021).
Yao, Z. et al. A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain. Nature 624, 317–332 (2023).
Zhang, M. et al. Molecularly defined and spatially resolved cell atlas of the whole mouse brain. Nature 624, 343–354 (2023).
del Pino, I. et al. Erbb4 deletion from fast-spiking interneurons causes schizophrenia-like phenotypes. Neuron 79, 1152–1168 (2013).
Jiang, X. et al. Principles of connectivity among morphologically defined cell types in adult neocortex. Science 350, aac9462 (2015).
Motta, A. et al. Dense connectomic reconstruction in layer 4 of the somatosensory cortex. Science 366, eaay3134 (2019).
Campagnola, L. et al. Local connectivity and synaptic dynamics in mouse and human neocortex. Science 375, eabj5861 (2022).
Schneider-Mizell, C. M. et al. Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex. bioRxiv 6, 2023.01.23.525290 (2023).
Favuzzi, E. et al. Distinct molecular programs regulate synapse specificity in cortical inhibitory circuits. Science 363, 413–417 (2019).
Fazzari, P. et al. Control of cortical GABA circuitry development by Nrg1 and ErbB4 signalling. Nature 464, 1376–1380 (2010).
Bernard, C. et al. Cortical wiring by synapse type–specific control of local protein synthesis. Science 378, eabm7466 (2022).
Pla, R., Borrell, V., Flames, N. & Marín, O. Layer acquisition by cortical GABAergic interneurons is independent of reelin signaling. J. Neurosci. 26, 6924–6934 (2006).
Lodato, S. et al. Excitatory projection neuron subtypes control the distribution of local inhibitory interneurons in the cerebral cortex. Neuron 69, 763–779 (2011).
Ye, Z. et al. Instructing perisomatic inhibition by direct lineage reprogramming of neocortical projection neurons. Neuron 88, 475–483 (2015).
Wester, J. C. et al. Neocortical projection neurons instruct inhibitory interneuron circuit development in a lineage-dependent manner. Neuron 102, 960–975.e6 (2019).
Miyoshi, G. & Fishell, G. GABAergic interneuron lineages selectively sort into specific cortical layers during early postnatal development. Cerebral Cortex 21, 845–852 (2011).
Baudoin, J.-P. et al. Tangentially migrating neurons assemble a primary cilium that promotes their reorientation to the cortical plate. Neuron 76, 1108–1122 (2012).
Southwell, D. G. et al. Intrinsically determined cell death of developing cortical interneurons. Nature 491, 109–113 (2012).
Wong, F. K. et al. Pyramidal cell regulation of interneuron survival sculpts cortical networks. Nature 557, 668–673 (2018).
Priya, R. et al. Activity regulates cell death within cortical interneurons through a calcineurin-dependent mechanism. Cell Reports 22, 1695–1709 (2018).
Telley, L. et al. Temporal patterning of apical progenitors and their daughter neurons in the developing neocortex. Science 364, eaav2522 (2019).
Di Bella, D. J. et al. Molecular logic of cellular diversification in the mouse cerebral cortex. Nature 595, 554–559 (2021).
Mi, D. et al. Early emergence of cortical interneuron diversity in the mouse embryo. Science 360, 81–85 (2018).
Mayer, C. et al. Developmental diversification of cortical inhibitory interneurons. Nature 555, 457–462 (2018).
Bandler, R. C. et al. Single-cell delineation of lineage and genetic identity in the mouse brain. Nature 601, 404–409 (2022).
Lee, D. R. et al. Transcriptional heterogeneity of ventricular zone cells in the ganglionic eminences of the mouse forebrain. eLife 11, e71864 (2022).
BRAIN Initiative Cell Census Network (BICCN) et al. A multimodal cell census and atlas of the mammalian primary motor cortex. Nature 598, 86–102 (2021).
Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).
Mandai, K., Rikitake, Y., Mori, M. & Takai, Y. Nectins and nectin-like molecules in development and disease. Curr. Top. Dev. Biol. 112, 197–231 (2015).
Friedman, L. G., Benson, D. L. & Huntley, G. W. Cadherin-based transsynaptic networks in establishing and modifying neural connectivity. Curr Top. Dev. Biol. 112, 415–465 (Elsevier, 2015).
Zhang, M. et al. Spatially resolved cell atlas of the mouse primary motor cortex by MERFISH. Nature 598, 137–143 (2021).
Libé-Philippot, B. et al. Auditory cortex interneuron development requires cadherins operating hair-cell mechanoelectrical transduction. Proc. Natl. Acad. Sci. USA 114, 7765–7774 (2017).
Li, G. et al. Regional distribution of cortical interneurons and development of inhibitory tone are regulated by Cxcl12/Cxcr4 signaling. J. Neurosci. 28, 1085–1098 (2008).
López-Bendito, G. et al. Chemokine signaling controls intracortical migration and final distribution of GABAergic interneurons. J. Neurosci. 28, 1613–1624 (2008).
Borrell, V. & Marín, O. Meninges control tangential migration of hem-derived Cajal-Retzius cells via CXCL12/CXCR4 signaling. Nat. Neurosci. 9, 1284–1293 (2006).
Tiveron, M.-C. et al. Molecular interaction between projection neuron precursors and invading interneurons via stromal-derived factor 1 (CXCL12)/CXCR4 signaling in the cortical subventricular zone/intermediate zone. J. Neurosci. 26, 13273–13278 (2006).
Causeret, F., Moreau, M. X., Pierani, A. & Blanquie, O. The multiple facets of Cajal-Retzius neurons. Development 148, dev199409 (2021).
Stumm, R. K. et al. CXCR4 regulates interneuron migration in the developing neocortex. J. Neurosci. 23, 5123–5130 (2003).
Wang, Y. et al. CXCR4 and CXCR7 Have distinct functions in regulating interneuron migration. Neuron 69, 61–76 (2011).
Tanaka, D. H. et al. CXCR4 is required for proper regional and laminar distribution of cortical somatostatin-, calretinin-, and neuropeptide y-expressing gabaergic interneurons. Cerebral Cortex 20, 2810–2817 (2010).
Graf, E. R., Zhang, X., Jin, S.-X., Linhoff, M. W. & Craig, A. M. Neurexins induce differentiation of GABA and glutamate postsynaptic specializations via neuroligins. Cell 119, 1013–1026 (2004).
Krueger, D. D., Tuffy, L. P., Papadopoulos, T. & Brose, N. The role of neurexins and neuroligins in the formation, maturation, and function of vertebrate synapses. Curr. Opin. Neurobiol, 22, 412–422 (2012).
Sun, Y.-C. et al. Integrating barcoded neuroanatomy with spatial transcriptional profiling enables identification of gene correlates of projections. Nat. Neurosci. 24, 873–885 (2021).
Basu, R., Taylor, M. R. & Williams, M. E. The classic cadherins in synaptic specificity. Cell Adhesion Migration 9, 193–201 (2015).
Baruzzo, G., Cesaro, G. & Di Camillo, B. Identify, quantify and characterize cellular communication from single-cell RNA sequencing data with scSeqComm. Bioinformatics 38, 1920–1929 (2022).
Stepanyants, A. & Chklovskii, D. Neurogeometry and potential synaptic connectivity. Trends Neurosci. 28, 387–394 (2005).
Mòdol, L., Moissidis, M., Selten, M., Oozeer, F. & Marín, O. Somatostatin interneurons control the timing of developmental desynchronization in cortical networks. Neuron 112, 2015–2030.e5 (2024).
Exposito-Alonso, D. et al. Subcellular sorting of neuregulins controls the assembly of excitatory-inhibitory cortical circuits. eLife 9, e57000 (2020).
Batista-Brito, R. et al. Developmental dysfunction of vip interneurons impairs cortical circuits. Neuron 95, 884–895.e9 (2017).
Fossati, M. et al. Trans-synaptic signaling through the glutamate receptor delta-1 mediates inhibitory synapse formation in cortical pyramidal neurons. Neuron 104, 1081–1094.e7 (2019).
Liakath-Ali, K., Polepalli, J. S., Lee, S.-J., Cloutier, J.-F. & Südhof, T. C. Transsynaptic cerebellin 4–neogenin 1 signaling mediates LTP in the mouse dentate gyrus. Proc. Natl. Acad. Sci. USA. 119, e2123421119 (2022).
Lv, X. et al. Patterned cPCDH expression regulates the fine organization of the neocortex. Nature 612, 503–511 (2022).
Lesch, K.-P. et al. Molecular genetics of adult ADHD: converging evidence from genome-wide association and extended pedigree linkage studies. J. Neural Transm. 115, 1573–1585 (2008).
Sanders, S. J. et al. Multiple recurrent de novo CNVs, Including duplications of the 7q11.23 williams syndrome region, are strongly associated with autism. Neuron 70, 863–885 (2011).
Jézéquel, J. et al. Cadherins orchestrate specific patterns of perisomatic inhibition onto distinct pyramidal cell populations. Nat. Commun. 216, 4481 (2023).
Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).
Cabello-Aguilar, S., Fau, C., Lacroix, M. & Colinge, J. SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics. Nucleic Acids Res. 48, e55 (2020).
Hou, R., Denisenko, E., Ong, H. T., Ramilowski, J. A. & Forrest, A. R. R. Predicting cell-to-cell communication networks using NATMI. Nat. Commun. 11, 5011 (2020).
Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).
Cheng, J., Zhang, J., Wu, Z. & Sun, X. Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19. Brief. Bioinform. 22, 988–1005 (2021).
Hu, Y., Peng, T., Gao, L. & Tan, K. CytoTalk: De novo construction of signal transduction networks using single-cell transcriptomic data. Sci. Adv. 7, eabf1356 (2021).
Butler, M., Rafi, S., Hossain, W., Stephan, D. & Manzardo, A. Whole exome sequencing in females with autism implicates novel and candidate genes. IJMS 16, 1312–1335 (2015).
Lepiemme, F. et al. Oligodendrocyte precursors guide interneuron migration by unidirectional contact repulsion. Science 376, eabn6204 (2022).
Zhang, M. et al. A molecularly defined and spatially resolved cell atlas of the whole mouse brain. Nature 624, 343−354 (2023)
Yao, Z. et al. A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain. Nature 624, 317–33 2023)
Yao, Z. et al. A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex. Nature 598, 103–110 (2021).
Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data. Cell Syst. 8, 281–291.e9 (2019).
Leary, J. et al. Sub-cluster identification through semi-supervised optimization of rare-cell silhouettes (SCISSORS) in single-cell RNA-sequencing. Bioinformatics 39, btad449 (2021).
Fischer, S. & Gillis, J. How many markers are needed to robustly determine a cell’s type? iScience. 25, 103378 (2022).
Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018).
Luecken, M. D. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19, 41–50 (2022).
Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).
Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A J. Integrative Biol. 16, 284–287 (2012).
Wu, T. et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation 2, 100141 (2021).
Türei, D. et al. Integrated intra- and intercellular signaling knowledge for multicellular omics analysis. Mol. Syst. Biol. 17, e9923 (2021).
Han, H. et al. TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res. 46, D380–D386 (2018).
Liu, Z.-P., Wu, C., Miao, H. & Wu, H. RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse. Database 2015, bav095 (2015).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).
Castanza, A. S. et al. Extending support for mouse data in the molecular signatures database (MSigDB). Nat. Methods 20, 1619–1620 (2023).
Peng, H. et al. Morphological diversity of single neurons in molecularly defined cell types. Nature 598, 174–181 (2021).
Tamamaki, N. et al. Green fluorescent protein expression and colocalization with calretinin, parvalbumin, and somatostatin in the GAD67-GFP knock-in mouse. J. Comparative Neurol. 467, 60–79 (2003).
Staiger, J. F. et al. Functional diversity of layer iv spiny neurons in rat somatosensory cortex: quantitative morphology of electrophysiologically characterized and biocytin labeled cells. Cerebral Cortex 14, 690–701 (2004).
Ma, Y., Hu, H., Berrebi, A. S., Mathers, P. H. & Agmon, A. Distinct subtypes of somatostatin-containing neocortical interneurons revealed in transgenic mice. J. Neurosci. 26, 5069–5082 (2006).
Rudy, B., Fishell, G., Lee, S. & Hjerling-Leffler, J. Three groups of interneurons account for nearly 100% of neocortical GABAergic neurons. Dev. Neurobiol. 71, 45–61 (2011).
Tremblay, R., Lee, S. & Rudy, B. GABAergic interneurons in the neocortex: from cellular properties to circuits. Neuron 91, 260–292 (2016).
Nigro, M. J., Hashikawa-Yamasaki, Y. & Rudy, B. Diversity and connectivity of layer 5 somatostatin-expressing interneurons in the mouse barrel cortex. J. Neurosci. 38, 1622–1633 (2018).
Lim, L., Mi, D., Llorca, A. & Marín, O. Development and functional diversification of cortical interneurons. Neuron 100, 294–313 (2018).
Scala, F. et al. Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas. Nat. Commun. 10, 4174 (2019).
Billeh, Y. N. et al. Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex. Neuron 106, 388–403.e18 (2020).
Walker, L. A. et al. nGauge: Integrated and extensible neuron morphology analysis in python. Neuroinform 20, 755–764 (2022).
Bates, A. S. et al. The natverse, a versatile toolbox for combining and analysing neuroanatomical data. eLife 9, e53350 (2020).
Genes4Epilepsy. An epilepsy gene resource - Oliver - Epilepsia - Wiley Online Library. https://onlinelibrary-wiley-com.insb.bib.cnrs.fr/doi/10.1111/epi.17547 (2023).
Merikangas, A. K. et al. What genes are differentially expressed in individuals with schizophrenia? a systematic review. Mol. Psychiatry 27, 1373–1383 (2022).
Abrahams, B. S. et al. SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs). Mol. Autism 4, 36 (2013).
Acknowledgements
We thank Julien Prados (UNIGE) for providing his Torch model for artificial neural network (ANN)-based cell type identification, members of the Cardoso laboratory for their input, and the Molecular and Cellular Biology Facility (PBMC), the Animal Core Facility and the Imaging Facility (inMagic) INMED platforms. This work was supported by the Institut National de la Santé et de la Recherche Médicale (INSERM), the Agence Nationale de la Recherche with ANR-13-JSV4-0006 SynD2 and ANR-23-CE16-0021 CALIN (A.d.C.), NeuroMarseille ICR+ Grant 2021 (A.d.C.), Fondation pour la Recherche sur le Cerveau ‘Développement et vieillissement’ (A.d.C.), Fondation Lejeune (A.d.C.), European Community 7th Framework programs (Development and Epilepsy; Strategies for Innovative Research to improve diagnosis, prevention and treatment in children with difficult to treat Epilepsy [DESIRE], Health-F2-602531-2013 (A. R., C.C.), and by an Excellence Initiative of Aix–Marseille University/A*MIDEX grant (CALIN-R24002AA) of the French ‘Investissements d’Avenir’ programme (C.C., A.d.C.). Research in the Telley laboratory was supported by ERC starting grant CERDEV_759112 and a SNSF grant 31003A_182676/1.
Author information
Authors and Affiliations
Contributions
A.d.C. and R.M. initiated the study. A.d.C. and L.T. conceptualized and supervised the study. A.d.C., L.T. and R.M. designed and conceptualized the experiments. A.d.C. L.T and R.M. performed most experiments. R.M. analyzed most experiments, supervised by L.T. and A.d.C. T.D.N. performed analyses for Figs. 3 and 4 and revision analysis. R.M. and L.S generated the Shiny App scLRSomatodev (https://sclrsomatodev.online/). C.C., A.G., L.C. and V.B. performed and analyzed shRNA design/production and in utero electroporations. E.P. validated shRNAs and performed proximity ligation assays. A.R participated in project management and help in manuscript writing and corrections. A.d.C. L.T. and R.M. wrote the manuscript with input from all authors.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Communications thanks the anonymous reviewers for their contribution to the peer review of this work. A peer review file is available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Source data
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Mathieu, R., Draia-Nicolau, T., Corbières, L. et al. Uncovering the molecular logic of cortical wiring between neuronal subtypes across development through ligand–receptor inference. Nat Commun (2026). https://doi.org/10.1038/s41467-025-68059-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467-025-68059-8
