Assessing spontaneous sensory neuron activity using in vivo calcium imaging

Area of Science:

• Neuroscience
• Pain Research
• Biotechnology

Background:

• Heightened spontaneous activity in sensory neurons is a hallmark of chronic pain.
• Electrophysiology is the traditional method for studying neuronal activity but is technically demanding and prone to bias.

Purpose of the Study:

• To evaluate in vivo calcium imaging with GCaMP6s as an alternative method for assessing spontaneous sensory neuron activity.
• To develop and validate a machine learning algorithm for analyzing this activity.

Main Methods:

• In vivo calcium imaging of the L4 dorsal root ganglion (DRG) in mouse models of inflammatory pain.
• Application of lidocaine to determine baseline calcium levels.
• Training a machine learning algorithm on calcium transient data.
• Validation of the algorithm against visual inspection and across different experimental setups.

Main Results:

• Spontaneously active calcium transients were visualized in the L4 DRG in a mouse model of inflammatory pain.
• A machine learning algorithm achieved high accuracy (90.0% ±1.2 and 85.9% ±2.1) in predicting spontaneous neuronal activity in two pain models.
• The algorithm demonstrated robustness, with high accuracy (94.0% ±2.2) in experiments from a different laboratory and microscope configuration.

Conclusions:

• In vivo calcium imaging is a viable and accurate method for assessing spontaneous activity in sensory neurons.
• The developed machine learning tool provides accessible analysis for researchers studying neuronal activity.
• This approach offers a less biased and more accessible alternative to traditional electrophysiology for pain research.