ARTICLE https://doi.org/10.1038/s41467-020-17929-4 OPEN Single cell RNA sequencing identifies early diversity of sensory neurons forming via bi-potential intermediates Louis Faure 1, Yiqiao Wang2, Maria Eleni Kastriti 1, Paula Fontanet 2, Kylie K. Y. Cheung2, Charles Petitpré2, Haohao Wu2, Lynn Linyu Sun2, Karen Runge3, Laura Croci 4, Mark A. Landy 5, Helen C. Lai 5, Gian Giacomo Consalez4, Antoine de Chevigny 3, François Lallemend2,6, ✉ Igor Adameyko 1,7 & Saida Hadjab 2 1234567890():,; Somatic sensation is defined by the existence of a diversity of primary sensory neurons with unique biological features and response profiles to external and internal stimuli. However, there is no coherent picture about how this diversity of cell states is transcriptionally gen- erated. Here, we use deep single cell analysis to resolve fate splits and molecular biasing processes during sensory neurogenesis in mice. Our results identify a complex series of successive and specific transcriptional changes in post-mitotic neurons that delineate hier- archical regulatory states leading to the generation of the main sensory neuron classes. In addition, our analysis identifies previously undetected early gene modules expressed long before fate determination although being clearly associated with defined sensory subtypes. Overall, the early diversity of sensory neurons is generated through successive bi-potential intermediates in which synchronization of relevant gene modules and concurrent repression of competing fate programs precede cell fate stabilization and final commitment. 1 Department of Molecular Neurosciences, Center for Brain Research, Medical University Vienna, 1090 Vienna, Austria. 2 Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden. 3 INMED INSERM U1249, Aix-Marseille University, Marseille, France. 4 Università Vita-Salute San Raffaele, 20132 Milan, Italy. 5 Department of Neuroscience, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, USA. 6 Ming-Wai Lau Centre for Reparative Medicine, Stockholm node, Karolinska Institutet, Stockholm, Sweden. 7 Department of Physiology and Pharmacology, Karolinska Institutet, ✉ 17177 Stockholm, Sweden. email: [email protected] NATURE COMMUNICATIONS | (2020) 11:4175 | https://doi.org/10.1038/s41467-020-17929-4 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-17929-4 n the developing nervous system, various neuronal and glial the transcriptome of individual single cells (progenitors and cell types are generated from multipotent progenitor cells in a neurons) of the somatosensory neuro-glial progeny of the trunk. I fi + speci c chronological sequence. The continuous change in the For this, we FACS isolated tdTomato positive (TOM ) cells cellular potential of the progenitors is a key aspect to this isolated from mouse lines which selectively represent all NCCs developmental process1. As neuronal progenitors exit cell cycle, (Wnt1Cre;R26tdTOM and Plp1CreERT2;R26tdTOM) and from they differentiate into distinct neuron types that already provide neuron-specific Cre mouse lines (Isl1Cre;R26tdTOM and Ntrk3Cre; the underpinning for the identity of neuronal circuit in which R26tdTOM) to obtain an enrichment of somatosensory neuronal they will operate1,2. Hence, the formation of specific fates criti- populations (Supplementary Fig. 1a, b) from E9.5, E10.5, E11.5 cally depends on the timing and diversity of expression of and E12.5 and sequenced mRNAs of single cells with high cov- molecular factors in discrete parts of the nervous system3,4. erage using Smart-seq2 protocol (median of 8070 genes detected However, following this process, how the expression of molecular per cell) (Fig. 1a and Supplementary Fig. 1c, d; see “Methods”). determinants is progressively acquired and restricted to particular The selected stages correspond to key events of sensory lineage neurons as they further diversify into distinct neuronal classes development starting from migrating NCCs (E9.5) and finishing remains unclear. A holistic understanding of the stepwise with the end of the second wave of sensory neurogenesis and the execution of elaborate transcriptional programs for neuronal early specification of neuronal subtypes (E12.5). Our dataset diversification in heterogeneous system is still elusive. It is contains cells from Isl1Cre;R26tdTOM at E9.5 and E10.5 and therefore essential to dissect and understand the precise temporal Ntrk3Cre;R26tdTOM at E11.5 and E12.5, both lines label sensory progression of the gene-regulatory networks that produce and neurons, Wnt1Cre;R26tdTOM was used to label NCCs, glial and assemble neuronal complexity. Indeed, such complexity is found neuronal progenitors at E9.5 and E10.5 and Plp1CreERT2; in all parts of the developing nervous system, including the highly R26tdTOM at E12.5. The combination of cells collected after tra- heterogeneous population of sensory neurons—cells that live in cing with these Cre lines should provide a complete compendium dorsal root ganglia (DRG) and provide us with sensations of of neuronal progenitors and early neuronal subtypes in mouse touch, pain, itch, temperature and position in space5. DRG (for FACS gating strategies, see Supplementary Fig. 2). Co- The diversity of sensory modalities emerges during early embedding of the cells from all tracing strategies was made embryonic development as subtypes of sensory neurons are without tracing-based batch correction and lead to overlapping of generated from a common progenitor population, the neural crest cells of different tracings following both a developmental time- cells (NCCs). Two main waves of sensory neurogenesis in DRG wise trajectory and a progression from progenitors to specified occur between E9.5 and E13. The first wave of neurogenesis neurons (Fig. 1a–e and Supplementary Fig. 1a, b; see below for (E9.5−10.5)6 gives rise to myelinated neurons, the cutaneous low- trajectories) which together convincingly validated the use of the threshold mechanoreceptors (here after named mechan- dataset for further computational analysis. oreceptors) and the muscle proprioceptive afferents (here after We used RNA velocity10, an unbiased approach that leverages named proprioceptors) and a small population of Aδ-fibers that the distinction of spliced and unspliced RNA transcripts from the can be distinguished by E11.5 based on their specific expression aligned sequences, allowing us to obtain an additional timepoint of transmembrane receptors and transcription factors6,7. for each cells (t: expression of old spliced RNA and t + 1: Mechanoreceptors express RET and MAFA and/or TRKB, pro- expression of new unspliced RNA). Using the proportion between prioceptors express tropomyosin receptor kinase C (TRKC) and these two values for a given gene, and under some assumptions, it RUNX3, and the small Aδ-nociceptor population expresses is possible to infer whether the expression of a gene is being TRKA. Later, these populations will diversify even further, giving initiated or downregulated. By combining this information for all rise to additional subtypes representing the medium-to-large genes in each cell, as well as comparing it to its neighbor cells, it is diameter DRG neurons responsible for muscle proprioceptive possible to infer a direction vector indicating the putative future feedback and skin mechanosensation modalities5. A second wave transcriptional state of the cell. In our dataset, RNA velocity of neurogenesis (that peaks at E12 in a mouse embryo) generates revealed a clear differentiation directionality from NCCs to the small diameter unmyelinated nociceptive neurons5–7, which neuronal progenies. In order to recapitulate such transitions, the co-express TRKA and RUNX1 and later will diversify into sub- dataset was first projected onto diffusion space to denoise the types necessary for pain, itch and thermal sensation8 and inner- underlying geometry, and a principal tree has been fitted using vate the skin and deep tissues. Although the heterogeneity of ElPiGraph11 in a semi-supervised way with the help of clustering DRG neurons in adult mice are increasingly characterized8,9, the results (Fig. 1b, c). ElPiGraph is a manifold learning method logic and molecular mechanisms that allow progenitors and sen- which aims at inferring a principal graph (such as a tree) “passing sory neurons to fate split and unfold specific sensory neuronal through the middle of data” in high dimension. Cells were subprograms from sensory-biased NCCs remain to be ordered along the principal tree, and for each cell the distance on understood. the graph to a chosen root is considered as a pseudotime value. Here we use deep single-cell RNA sequencing (scRNAseq) Pseudotime analysis of gene expression showed significant and together with other experimental approaches to investigate fate robust changes along the reconstructed tree (8432 genes at a false choices, lineage relationships and molecular determinants during discovery rate (FDR) of 0.05), leading to the identification of early stages of sensory neurogenesis in the mouse. Overall, our major clusters (Source Data file). It also captured tree root data provide insights into the structure of the Waddingtonian populations and endpoints as well as intermediate states as sub- landscape of the sensory lineage and suggest that counteracting clusters, leading to the identification of two main neurogenic gene-regulatory networks operate within immature postmitotic trajectories named branch A and branch B (Fig. 1c). Those