Despite significant advances in neuroscience, the neural bases of intelligence remain poorly understood. Arguably the most elusive aspect of intelligence is the ability to make robust inferences that go far beyond one's experience. Animals categorize objects, learn to vocalize and may even estimate causal relationships - all in the face of data that is often ambiguous and sparse. Such inductive leaps are thought to result from the brain's ability to infer latent structure that governs the environment. However, we know little about the neural computations that underlie this ability. Recent advances in developing computational frameworks that can support efficient structure learning and inductive inference may provide insight into the underlying component processes and help pave the path for uncovering their neural implementation.

A brain consists of numerous distinct neurons arising from a limited number of progenitors, called neuroblasts in Drosophila. Each neuroblast produces a specific neuronal lineage. To unravel the transcriptional networks that underlie the development of distinct neuroblast lineages, we marked and isolated lineage-specific neuroblasts for RNA sequencing. We labeled particular neuroblasts throughout neurogenesis by activating a conditional neuroblast driver in specific lineages using various intersection strategies. The targeted neuroblasts were efficiently recovered using a custom-built device for robotic single-cell picking. Transcriptome analysis of mushroom body, antennal lobe and type II neuroblasts compared with non-selective neuroblasts, neurons and glia revealed a rich repertoire of transcription factors expressed among neuroblasts in diverse patterns. Besides transcription factors that are likely to be pan-neuroblast, many transcription factors exist that are selectively enriched or repressed in certain neuroblasts. The unique combinations of transcription factors present in different neuroblasts may govern the diverse lineage-specific neuron fates.

Fluorogenic molecules are important tools for biological and biochemical research. The majority of fluorogenic compounds have a simple input-output relationship, where a single chemical input yields a fluorescent output. Development of new systems where multiple inputs converge to yield an optical signal could refine and extend fluorogenic compounds by allowing greater spatiotemporal control over the fluorescent signal. Here, we introduce a new red-shifted fluorescein derivative, Virginia Orange, as an exceptional scaffold for single- and dual-input fluorogenic molecules. Unlike fluorescein, installation of a single masking group on Virginia Orange is sufficient to fully suppress fluorescence, allowing preparation of fluorogenic enzyme substrates with rapid, single-hit kinetics. Virginia Orange can also be masked with two independent moieties; both of these masking groups must be removed to induce fluorescence. This allows facile construction of multi-input fluorogenic probes for sophisticated sensing regimes and genetic targeting of latent fluorophores to specific cellular populations.