Method for optimizing imaging parameters to record neuronal and cellular activity at depth with bioluminescence.

Abstract

Significance: Optical imaging has accelerated neuroscience in recent years. Genetically encoded fluorescent activity sensors of calcium, neurotransmitters, and voltage are commonly used for optical recording of neuronal activity. However, fluorescence imaging is limited to superficial regions for in vivo activity imaging, due to photon scattering and absorbance. Bioluminescence imaging offers a promising alternative for achieving activity imaging in deeper brain regions without hardware implanted within the brain. Bioluminescent reporters can be genetically encoded and produce photons without external excitation. The use of enzymatic photon production also enables prolonged imaging sessions without the risk of photobleaching or phototoxicity, making bioluminescence suitable for non-invasive imaging of deep neuronal populations.

Aim: To facilitate the adoption of bioluminescent activity imaging, we sought to develop a low cost, simple in vitro method that simulates in vivo conditions to optimize imaging parameters for determining optimal exposure times and optical hardware configurations to determine what frame rates can be captured with an individual lab's imaging hardware with sufficient signal-to-noise ratios without the use of animals prior to starting an in vivo experiment.

Approach: We developed an assay for modeling in vivo optical conditions with a brain tissue phantom paired with engineered cells that produce bioluminescence. We then used this assay to limit-test the detection depth versus maximum frame rate for bioluminescence imaging at experimentally relevant tissue depths using off-the-shelf imaging hardware.

Results: We developed an assay for modeling in vivo optical conditions with a brain tissue phantom paired with engineered cells that produce bioluminescence. With this method, we demonstrate an effective means for increasing the utility of bioluminescent tools and lowering the barrier to adoption of bioluminescence activity imaging.

Conclusions: We demonstrated an improved method for optimizing imaging parameters for activity imaging in vivo with bioluminescent sensors.