Automatic Monitoring of Neural Activity with Single-Cell Resolution in Behaving Hydra

The ability to record every spike from every neuron in a behaving animal is one of the holy grails of neuroscience. Here, Alison Hanson discusses a collaborative effort aimed at coming one step closer towards this goal with the development of an end-to-end pipeline that automatically tracks and extracts calcium signals from individual neurons in the cnidarian Hydra vulgaris. Together, her team imaged dually labeled (nuclear tdTomato and cytoplasmic GCaMP7s) transgenic Hydra and developed an open-source Python platform (TraSE-IN) for the Tracking and Spike Estimation of Individual Neurons in the animal during behavior. The TraSE-IN platform described, here, comprises a series of modules that segments and tracks each nucleus over time and extracts the corresponding calcium activity in the GCaMP channel. Another series of signal processing modules allows robust prediction of individual spikes from each neuron’s calcium signal. This complete pipeline will facilitate the automatic generation and analysis of large-scale datasets of single-cell resolution neural activity in Hydra, and potentially other model organisms, paving the way towards deciphering the neural code of an entire animal.

Learning Objectives: 

1. Explain the advantages of using Hydra as a simple system in neuroscience.

2. Review the advantages of using deep learning for segmenting individual nuclei.

3. Summarize how and why the TraSE-IN pipeline was generated and its potential uses for dually labeled organisms.