Human Neocortical Glutamatergic Neurons Revealed Through Multimodal Profiling

Area of Science:

• Neuroscience
• Cell Biology
• Genomics

Background:

• Human neocortical neuron diversity is poorly understood due to limited tissue access and data modalities.
• Transcriptomic data alone is insufficient to define neuronal computational properties.

Purpose of the Study:

• To comprehensively characterize human neocortical excitatory neuron types using a multimodal approach.
• To integrate morphoelectric features with transcriptomic identity for a deeper understanding of neuronal function.

Main Methods:

• Patch-seq was employed to collect morphology, electrophysiology, and transcriptomic data from single neurons.
• Spatial transcriptomic data was integrated to provide a layer-centric perspective.
• 39 out of 42 transcriptomically-defined neuron types were analyzed.

Main Results:

• Morphoelectric properties, including cortical depth, dendritic structure, and excitability, effectively distinguish transcriptomic subclasses and finer types.
• Neuronal properties show spatial variation, with supragranular layers influenced by location and deeper layers exhibiting more heterogeneity.
• Cross-species comparisons revealed conserved subclass organization but distinct dendritic arborization patterns between humans and mice, with similarities between humans and macaques.

Conclusions:

• This multimodal dataset provides a unified reference for human cortical circuitry.
• The findings advance the understanding of neuronal computations and cortical organization.
• Establishes a foundation for future studies on human brain function and neurological diseases.