Future avenues in Drosophila mushroom body research

Abstract

How does the brain translate sensory information into complex behaviors? With relatively small neuronal numbers, readable behavioral outputs, and an unparalleled genetic toolkit, the Drosophila mushroom body (MB) offers an excellent model to address this question in the context of associative learning and memory. Recent technological breakthroughs, such as the freshly completed full-brain connectome, multiomics approaches, CRISPR-mediated gene editing, and machine learning techniques, led to major advancements in our understanding of the MB circuit at the molecular, structural, physiological, and functional levels. Despite significant progress in individual MB areas, the field still faces the fundamental challenge of resolving how these different levels combine and interact to ultimately control the behavior of an individual fly. In this review, we discuss various aspects of MB research, with a focus on the current knowledge gaps, and an outlook on the future methodological developments required to reach an overall view of the neurobiological basis of learning and memory.

Figure 1.

Forward-thinking outlook on learning and memory research in the mushroom body (MB). Uncovering how the MB circuit undergoes coordinated remodeling during metamorphosis, combined with developmental multiomics and in vivo pupal imaging, will advance our knowledge of neurodevelopmental principles. Studying dual transmission and activity-induced local translation will shed light on the neurotransmission mechanisms underlying learning and memory. Correlative light and electron microscopy (CLEM), alongside more accurate machine learning (ML)-based segmentation and proofreading, will promote our understanding of the MB circuit logic (EM image from the Kasthuri et al. 2015 data set). Single-neuron dual recordings will elucidate how the physiology of identified pre- and postsynaptic components change together. To delineate the circuit basis of motivated response, it is imperative to use assays designed to understand operant choice, how multiple rewards alter operant choice, and how operant choice relates to neural activity. Finally, refining artificial intelligence (AI) modeling and repeatable sparse labeling will enhance repeatability between experiments.