Decoding neuronal wiring by joint inference of cell identity and synaptic connectivity

Area of Science:

• Neuroscience
• Developmental Biology
• Computational Biology

Background:

• Motor behaviors rely on motor neurons (MNs) receiving input from pre-motor neuron (preMN) circuits.
• Understanding the developmental assembly of these motor circuits is a key challenge in neuroscience.

Purpose of the Study:

• To investigate the molecular mechanisms underlying the development of motor circuits controlling leg movements in Drosophila melanogaster.
• To identify gene expression patterns that correlate with neural connectivity and circuit assembly.

Main Methods:

• Generated single-cell RNA sequencing (scRNAseq) datasets for leg MNs across developmental time points.
• Developed ConnectionMiner, a computational tool integrating scRNAseq data with electron microscopy connectomes.
• Used ConnectionMiner to refine cell type annotations and identify gene expression signatures linked to synaptic connectivity.

Main Results:

• Identified transcription factors (TFs) and cell adhesion molecules (CAMs) as drivers of MN molecular diversity.
• ConnectionMiner successfully annotated MNs and preMNs and linked gene expression to synaptic connectivity strength.
• Uncovered candidate gene combinations likely involved in orchestrating preMN-MN circuit assembly.

Conclusions:

• Gene expression profiles, particularly TFs and CAMs, are crucial for motor neuron diversity and circuit formation.
• ConnectionMiner is a powerful tool for integrating transcriptomic and connectomic data to understand neural circuit development.
• This study provides a transcriptional framework for understanding how motor circuits are assembled from preMNs to MNs in Drosophila.