Neurons can be categorized according to a variety of different attributes, including their anatomy, genetics, connectivity, or activity. However, it is unclear how these different categorizations relate to one another [1], especially in brain regions like the vertebrate hypothalamus, where it is challenging to measure the molecular identity and activity patterns of the same neurons.

Two recent studies have overcome these challenges in mice, using single-cell RNA-sequencing to jointly measure the molecular identity of neurons and the expression of activity-dependent genes after specific behavioral experiences [2, 3]. Moffitt et al. [2] used spatial transcriptomics to classify neurons in the medial preoptic region of the hypothalamus, and concluded that different social behaviors recruit different molecular subclasses of neurons. Using a complementary approach, Kim et al. [3] analyzed gene expression and axonal projections in the ventromedial hypothalamus, but found that neurons expressing activity-dependent genes after social behaviors included cells of many molecular and projection-defined subclasses. Despite the substantial insights gained from these studies, the fast-timescale activity patterns of neurons are not captured by activity-dependent gene expression, preventing detailed characterization of neurons based on their activity.

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