bioRxivNeurosciencePreprint
Spatial patterning of transcriptional and regulatory programs in the primate subcortex
Shu DaniD, Meghan A. TurneriD, Michael DeBerardineiD, Lakme CaceresiD, Victor E. Nieto-CaballeroiD, Jessica Schembri, Kirsten M. LevandowskiiD, Christopher CardenasiD, Ning WangiD, Chang LiiD, Soumyadeep BasuiD, Hongyu ZhengiD, X. Shirley LiuiD, Thomas HölltiD, Jessica Feliciano, Qiangge ZhangiD, Reilly Nakamoto, Delissa McMilleniD, Naomi X. MartiniD, Nasmil Valera CuevasiD, Paul A. OlseniD, Josh NagraiD, Jazmin CamposiD, Marshall VanNessiD, Jack WatersiD, , Fenna M. KrieneniD
Abstract
Mammalian brain cell identity is shaped by intrinsic factors and external context. We present a spatially resolved transcriptomic and gene regulatory atlas of cell types across all subcortical regions in a primate, the common marmoset. Dense sampling and cross-species integration revealed spatially precise neuronal assemblies, including in complex midbrain and diencephalic structures. Chromatin accessibility and transcriptional identity are spatially tuned within and across subcortical structures; spatial gradients within hippocampal subfields are orchestrated by graded transcription factors acting through graded enhancers. The primate-expanded thalamic GABAergic population shares transcriptional and regulatory syntax with conserved midbrain populations, reflecting an evolutionary adaptation compared with rodents. Similar regional expression across cell types can arise by distinct regulatory architectures, as for telencephalic astrocytes and neurons. Conversely, distant cell types can share regulatory programs despite divergent identities: striatal GABAergic medium spiny neurons and telencephalic glutamatergic neurons share a postsynaptic regulatory program despite divergent lineage, region, and neurotransmitter identity.
- organism: mouse
- assay: spatial transcriptomics
- assay: ChIP-seq
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: spatial domain detection
- approach: statistical model
- approach: experimental assay
- Citations
- 2
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