Predicting neuronal activation state on the basis of activity-regulated gene expression

A really nice article from Tyssowski et al. The authors did RNAseq on neurons that either were or were not stimulated with neural activity. They found that a subset of proteins (251) that have been previously described as “[neuronal] activity regulated genes” were able to predict the stimulation state of those neurons well above chance. Specifically: 92% of the time using nearest neighbor classification as measured by leave one out cross validation.

I’m interested in the broad question of “which RNAs/proteins are important for neuronal activity” and this set of activity regulated genes is pretty clearly within that set. Interestingly, it seems that the expression of these genes is pretty highly correlated (very similar chromatin states, transcription factors, etc), so I don’t think you would have to perfectly preserve ALL of them in order to allow for a high-fidelity preservation of information.

On that note, it’d be interesting if someone were to use this data to try to predict neuron stimulation state using the smallest set of activity regulated genes as necessary. For example, the 19 rapidly-induced activity regulated genes, including the non-transcription factors Arc and Amigo3, seem like they would punch above their weight in terms of predicting neuronal activation state.

Figure 6 from the paper
Arc expression and enhancer acetylation is stimulated after only 10 minutes of neuronal activity;

It also suggests an experiment for any brain preservation procedure that purports to preserve gene expression important for neural activity: stimulate neural activity on a subset of neurons (probably in vitro, since it’s easier and should yield the same result), perform your brain preservation processing steps, attempt to measure the expression of these genes, and then see if you can distinguish between which neurons were stimulated or not on the basis of those measurements.