Transcriptional diversity in synaptic gene sets is sufficient to discriminate cortical neuronal identity

Roig Adam, A.; Martinez Lopez, J.; van der Spek, S.; The SYNGO consortium,; Sullivan, P. F.; Smit, A.; Verhage, M.; Hjerling-Leffler, J.

doi:10.1101/2022.11.25.517899

bioRxiv · 10.1101/2022.11.25.517899

Transcriptional diversity in synaptic gene sets is sufficient to discriminate cortical neuronal identity

Roig Adam, A.·Martinez Lopez, J.·van der Spek, S.·The SYNGO consortium,·Sullivan, P. F.·Smit, A.·Verhage, M.·Hjerling-Leffler, J.

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Abstract

Synapse diversity has been described from different perspectives, ranging from the specific neurotransmitters released, to their diverse biophysical properties and proteome profiles. However, synapse diversity at the transcriptional level has not been systematically identified across all synapse populations in the brain. To quantify postsynaptic and identify specific synaptic features of neuronal cell types we combined the SynGO (Synaptic Gene Ontology) database with single-cell RNA sequencing data of the mouse neocortex. We show that cell types can be discriminated by synaptic genes alone with the same power as all genes. The cell type discriminatory power is not equally distributed across synaptic genes as we could identify functional categories and synaptic compartments with greater cell type specific expression. Synaptic genes, and specific SynGO categories, belonged to three different types of gene modules: gradient expression over all cell types, gradient expression in selected cell types and cell class- or type-specific profiles. This data provides a deeper understanding of synapse diversity in the neocortex and identifies potential markers to selectively identify synapses from specific neuronal populations.

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Roig Adam, A., Martinez Lopez, J., van der Spek, S., The SYNGO consortium,, Sullivan, P. F., Smit, A., Verhage, M., Hjerling-Leffler, J.. 2022-11-25. Transcriptional diversity in synaptic gene sets is sufficient to discriminate cortical neuronal identity. https://doi.org/10.1101/2022.11.25.517899

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