Whole-brain annotation and multi-connectome cell typing quantifies circuit stereotypy inDrosophila

Abstract

The fruit flyDrosophila melanogastercombines surprisingly sophisticated behaviour with a highly tractable nervous system. A large part of the fly's success as a model organism in modern neuroscience stems from the concentration of collaboratively generated molecular genetic and digital resources. As presented in our FlyWire companion paper1, this now includes the first full brain connectome of an adult animal. Here we report the systematic and hierarchical annotation of this ~130,000-neuron connectome including neuronal classes, cell types and developmental units (hemilineages). This enables any researcher to navigate this huge dataset and find systems and neurons of interest, linked to the literature through the Virtual Fly Brain database2. Crucially, this resource includes 4,179 cell types of which 3,166 consensus cell types are robustly defined by comparison with a second dataset, the "hemibrain" connectome3. Comparative analysis showed that cell type counts and strong connections were largely stable, but connection weights were surprisingly variable within and across animals. Further analysis defined simple heuristics for connectome interpretation: connections stronger than 10 unitary synapses or providing >1% of the input to a target cell are highly conserved. Some cell types showed increased variability across connectomes: the most common cell type in the mushroom body, required for learning and memory, is almost twice as numerous in FlyWire than in the hemibrain. We find evidence for functional homeostasis through adjustments of the absolute amount of excitatory input while maintaining the excitation-inhibition ratio. Finally, and surprisingly, about one third of the cell types recorded in the hemibrain connectome could not be robustly identified in the FlyWire connectome, cautioning against defining cell types based on single connectomes. We propose that a cell type should be robust to inter-individual variation, and therefore defined as a group of cells that are more similar to cells in a different brain than to any other cell in the same brain. We show that this new definition can be consistently applied to whole connectome datasets. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open source toolchain for brain-scale comparative connectomics.

Diversity of visual inputs to Kenyon cells of theDrosophilamushroom body

“…Five identified KCγ-d's did not receive any inputs from VPNs or LVINs, and instead received input from olfactory PNs (Figure 5B). This median of 3 visual inputs per KCγ-d is slightly lower than the median 5-6 inputs received by olfactory Kenyon cells in general, and less than the 4-8 inputs that their closest developmental sisters, olfactory KCγ-main, receive in this brain 8,28,47 . We next analyzed the origin of inputs onto each cell (medulla, lobula, accessory medula or LVIN).…”

“…Data and accompanying annotations from the FlyWire v630 snapshot were used 8,47,48,[88][89][90][91] . Our analyses used a ≥5 synapse threshold when determining a connection between neurons unless stated otherwise, in keeping consistent with connections indicated in Codex (https://codex.flywire.ai/).…”

“…During its acquisition, the EM volume in FlyWire was flipped 47 . We present the left and right FlyWire hemispheres as their corrected versions as depicted in the Codex data explorer.…”

“…Bottom, the sum of synapses onto the KCγ-d population for the FlyWire neuron matches to the hemibrain neurons in the top panel; the order is consistent with the top panel to highlight that neurons which have larger weights are largely common between the two brains. In total FlyWire neurons tend to make more synapses onto KCγ-d's, and this is likely because there are more KCγ-d's in the FlyWire brain than in the hemibrain (148 vs. 99 KCγ-d's, right hemisphere)47 . D. Principal components analysis on VPN/LVIN connectivity to KCγ-d's (black) aggregated over both connectomes.…”

Spatial transcriptomics in adultDrosophilareveals new cell types in the brain and identifies subcellular mRNA patterns in muscles

“…In our scRNA-seq dataset, there are 188 distinct clusters, of which only 83 are annotated today. Recent efforts to map the fly brain through EM and connectomics studies have identified 4,552 morphological cell types (Schlegel et al, 2023). SRT can provide a bridge between scRNA-seq studies and morphology-based studies.…”