Transcriptional variation among individual cells has diverse sources. Cluster-groups derived from these and other data should not be reflexively equated with cell “types”. We identified categorically distinct patterns of RNA expression originating from cell types, but also continuously varying patterns that appeared to correspond to spatial locations and cellular states. Our computational approach was critical for recognizing and understanding these diverse effects on RNA expression, all of which can simultaneously affect a cell’s RNA expression profile. This approach allowed us to identify a transcriptional program we believe is enacted to maintain axon and presynaptic function, to different degrees both within and across neuron types. We also resolved signals from striatal SPNs representing differences in pathway (“direct” versus “indirect”), spatial arrangement (“patch” versus “matrix”), and a cryptic molecular SPN distinction (“eccentric” SPNs).
