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| 2. |
- Wang, YQ, et al.
(author)
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Muscle-selective RUNX3 dependence of sensorimotor circuit development
- 2019
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In: Development (Cambridge, England). - : The Company of Biologists. - 1477-9129 .- 0950-1991. ; 146:20
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Journal article (peer-reviewed)abstract
- The control of all our motor outputs requires constant monitoring by proprioceptive sensory neurons (PSNs) that convey continuous muscle sensory inputs to the spinal motor network. Yet, the molecular programs that control the establishment of this sensorimotor circuit remain largely unknown. The transcription factor RUNX3 is essential for the early steps of PSNs differentiation, making it difficult to study its role during later aspects of PSNs specification. Here, we conditionally inactivate Runx3 in PSNs after peripheral innervation and identify that RUNX3 is necessary for maintenance of cell identity of only a subgroup of PSNs, without discernable cell death. RUNX3 controls also the sensorimotor connection between PSNs and motor neurons at limb level, with muscle-by-muscle variable sensitivities to the loss of Runx3 that correlate with levels of RUNX3 in PSNs. Finally, we find that muscles and neurotrophin-3 signaling are necessary for maintenance of RUNX3 expression in PSNs. Hence, a transcriptional regulator critical for specifying a generic PSN type identity after neurogenesis, is later regulated by target muscle-derived signal to contribute to the specialized aspects of the sensorimotor connection selectivity.
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| 3. |
- Wu, HH, et al.
(author)
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Distinct subtypes of proprioceptive dorsal root ganglion neurons regulate adaptive proprioception in mice
- 2021
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In: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 12:1, s. 1026-
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Journal article (peer-reviewed)abstract
- Proprioceptive neurons (PNs) are essential for the proper execution of all our movements by providing muscle sensory feedback to the central motor network. Here, using deep single cell RNAseq of adult PNs coupled with virus and genetic tracings, we molecularly identify three main types of PNs (Ia, Ib and II) and find that they segregate into eight distinct subgroups. Our data unveil a highly sophisticated organization of PNs into discrete sensory input channels with distinct spatial distribution, innervation patterns and molecular profiles. Altogether, these features contribute to finely regulate proprioception during complex motor behavior. Moreover, while Ib- and II-PN subtypes are specified around birth, Ia-PN subtypes diversify later in life along with increased motor activity. We also show Ia-PNs plasticity following exercise training, suggesting Ia-PNs are important players in adaptive proprioceptive function in adult mice.
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| 4. |
- Faure, L, et al.
(author)
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Single cell RNA sequencing identifies early diversity of sensory neurons forming via bi-potential intermediates
- 2020
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In: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1, s. 4175-
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Journal article (peer-reviewed)abstract
- 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 generated. 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 hierarchical 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.
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| 5. |
- Petitpre, C, et al.
(author)
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Neuronal heterogeneity and stereotyped connectivity in the auditory afferent system
- 2018
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In: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 9:1, s. 3691-
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Journal article (peer-reviewed)abstract
- Spiral ganglion (SG) neurons of the cochlea convey all auditory inputs to the brain, yet the cellular and molecular complexity necessary to decode the various acoustic features in the SG has remained unresolved. Using single-cell RNA sequencing, we identify four types of SG neurons, including three novel subclasses of type I neurons and the type II neurons, and provide a comprehensive genetic framework that define their potential synaptic communication patterns. The connectivity patterns of the three subclasses of type I neurons with inner hair cells and their electrophysiological profiles suggest that they represent the intensity-coding properties of auditory afferents. Moreover, neuron type specification is already established at birth, indicating a neuronal diversification process independent of neuronal activity. Thus, this work provides a transcriptional catalog of neuron types in the cochlea, which serves as a valuable resource for dissecting cell-type-specific functions of dedicated afferents in auditory perception and in hearing disorders.
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| 6. |
- Petitpre, C, et al.
(author)
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Single-cell RNA-sequencing analysis of the developing mouse inner ear identifies molecular logic of auditory neuron diversification
- 2022
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In: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 13:1, s. 3878-
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Journal article (peer-reviewed)abstract
- Different types of spiral ganglion neurons (SGNs) are essential for auditory perception by transmitting complex auditory information from hair cells (HCs) to the brain. Here, we use deep, single cell transcriptomics to study the molecular mechanisms that govern their identity and organization in mice. We identify a core set of temporally patterned genes and gene regulatory networks that may contribute to the diversification of SGNs through sequential binary decisions and demonstrate a role for NEUROD1 in driving specification of a Ic-SGN phenotype. We also find that each trajectory of the decision tree is defined by initial co-expression of alternative subtype molecular controls followed by gradual shifts toward cell fate resolution. Finally, analysis of both developing SGN and HC types reveals cell-cell signaling potentially playing a role in the differentiation of SGNs. Our results indicate that SGN identities are drafted prior to birth and reveal molecular principles that shape their differentiation and will facilitate studies of their development, physiology, and dysfunction.
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| 7. |
- Wang, YQ, et al.
(author)
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A cell fitness selection model for neuronal survival during development
- 2019
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In: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10:1, s. 4137-
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Journal article (peer-reviewed)abstract
- Developmental cell death plays an important role in the construction of functional neural circuits. In vertebrates, the canonical view proposes a selection of the surviving neurons through stochastic competition for target-derived neurotrophic signals, implying an equal potential for neurons to compete. Here we show an alternative cell fitness selection of neurons that is defined by a specific neuronal heterogeneity code. Proprioceptive sensory neurons that will undergo cell death and those that will survive exhibit different molecular signatures that are regulated by retinoic acid and transcription factors, and are independent of the target and neurotrophins. These molecular features are genetically encoded, representing two distinct subgroups of neurons with contrasted functional maturation states and survival outcome. Thus, in this model, a heterogeneous code of intrinsic cell fitness in neighboring neurons provides differential competitive advantage resulting in the selection of cells with higher capacity to survive and functionally integrate into neural networks.
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