Background Cell adhesion molecules (CAMs) are membrane-bound proteins that mediate cell-cell interactions through trans-cellular protein complexes. In the context of the neuronal synapse, studies of CAMs have revealed their roles from neuronal recognition and neuronal wiring to synaptic plasticity. CAMs form macromolecular complexes via cis- and trans-interactions; however, identifying the specific proteins in these assemblies is challenging. Their interactions are dynamic and transient, making them difficult to capture, and their hydrophobic transmembrane domains complicate extraction from biological samples. New method Here, we present a protocol to pulldown interacting partners of a Teneurin-3-GFP bait protein, as a representative CAM, from minimal mouse brain lysate. Comparison with existing methods Affinity purification of a bait protein from a biological sample, followed by mass spectrometry to identify captured prey proteins is a widely used, unbiased approach, though it usually requires large amounts of material. We show that our refined approach detects known Teneurin interactants while substantially reducing the animal tissue required. We further compared detergents used for lysate preparation and found that the total of CAM species enriched in Teneurin-3 samples relative to control varied considerably. Finally, we evaluated different normalization workflows to aid dataset interpretation. Conclusion This protocol provides an accessible approach for studying CAM interactions with limited animal tissue, enabling refined insights into the complex protein networks underlying synaptic connectivity.

Neuronal network formation is an intricate process by which individual neurons connect into a functional circuitry. At the subcellular level, neuronal connectivity is characterized by the number, size and strength of synapses. At the cellular level, in vitro network characterization remains a challenge due to the large number of neurons involved, spreading widely across a culture dish. Here, we demonstrate a pipeline using high-content confocal microscopy and automated image analysis to study spatial organization of individual neurons in an in vitro cellular network. With this approach, we enable analysis of thousands of neurons in one well, and of multiple wells simultaneously. Using this workflow, we compared the spatial organization of primary mouse neuronal networks derived from the hippocampus, cortex and cerebellum. We also demonstrate how to extract morphological details, such as size of the nucleus and axon initial segment number, orientation and length from our data. This workflow can be applied to study underlying molecular mechanisms of circuitry formation, to assess network formation of neurons derived from mouse or human iPSC models for neurological diseases, and serve as a future platform for drug development.

Neuronal activity in the highly organized networks of the central nervous system is the vital basis for various functional processes, such as perception, motor control, and cognition. Understanding interneuronal connectivity and how activity is regulated in the neuronal circuits is crucial for interpreting how the brain works. Multi-electrode arrays (MEAs) are particularly useful for studying the dynamics of neuronal network activity and their development as they allow for real-time, high-throughput measurements of neural activity. At present, the key challenge in the utilization of MEA data is the sheer complexity of the measured datasets. Available software offers semi-automated analysis for a fixed set of parameters that allow for the definition of spikes, bursts and network bursts. However, this analysis remains time-consuming, user-biased, and limited by pre-defined parameters. Here, we present autoMEA, software for machine learning-based automated burst detection in MEA datasets. We exemplify autoMEA efficacy on neuronal network activity of primary hippocampal neurons from wild-type mice monitored using 24-well multi-well MEA plates. To validate and benchmark the software, we showcase its application using wild-type neuronal networks and two different neuronal networks modeling neurodevelopmental disorders to assess network phenotype detection. Detection of network characteristics typically reported in literature, such as synchronicity and rhythmicity, could be accurately detected compared to manual analysis using the autoMEA software. Additionally, autoMEA could detect reverberations, a more complex burst dynamic present in hippocampal cultures. Furthermore, autoMEA burst detection was sufficiently sensitive to detect changes in the synchronicity and rhythmicity of networks modeling neurodevelopmental disorders as well as detecting changes in their network burst dynamics. Thus, we show that autoMEA reliably analyses neural networks measured with the multi-well MEA setup with the precision and accuracy compared to that of a human expert.