Evidence from spatial transcriptomics for the mosaic hypothesis and pure cell types in the cortex.

Area of Science:

• Neuroscience
• Computational Biology
• Genomics

Background:

• Neurons show diverse properties, but classifying them using high-dimensional data like single-cell RNA sequencing (scRNA-seq) can be inconclusive.
• In the retina, distinct neuron types avoid close spatial proximity, a principle rarely explored in the cortex.

Purpose of the Study:

• To investigate if cortical neurons exhibit spatial avoidance, similar to retinal neurons.
• To develop a statistical framework for analyzing spatial transcriptomic data to test the mosaic hypothesis in the cortex.

Main Methods:

• Developed a statistical point process analysis framework.
• Applied the framework to spatial transcriptomic data from the cortex.

Main Results:

• Provided evidence for spatial avoidance across numerous excitatory and inhibitory neuronal types in the cortex.
• Observed that spatial avoidance diminishes when cell types are merged.

Conclusions:

• The mosaic hypothesis, characterized by spatial avoidance, is applicable to cortical neurons.
• Spatial avoidance serves as a potential gold-standard metric for assessing the purity of identified neuronal cell types.