An Inexpensive Open-Source Chamber for Controlled Hypoxia/Hyperoxia Exposure

Abstract

Understanding hypoxia/hyperoxia exposure requires either a high-altitude research facility or a chamber in which gas concentrations are precisely and reproducibly controlled. Hypoxia-induced conditions such as hypoxic-ischemic encephalopathy (HIE), obstructive or central apneas, and ischemic stroke present unique challenges for the development of models with acute or chronic hypoxia exposure. Many murine models exist to study these conditions; however, there are a variety of different hypoxia exposure protocols used across laboratories. Experimental equipment for hypoxia exposure typically includes flow regulators, nitrogen concentrators, and premix oxygen/nitrogen tanks. Commercial hypoxia/hyperoxia chambers with environmental monitoring are incredibly expensive and require proprietary software with subscription fees or highly expensive software licenses. Limitations exist in these systems as most are single animal systems and not designed for extended or intermittent hypoxia exposure. We have developed a simple hypoxia chamber with off-the-shelf components, and controlled by open-source software for continuous data acquisition of oxygen levels and other environmental factors (temperature, humidity, pressure, light, sound, etc.). Our chamber can accommodate up to two mouse cages and one rat cage at any oxygen level needed, when using a nitrogen concentrator or premixed oxygen/nitrogen tank with a flow regulator, but is also scalable. Our system uses a Python-based script to save data in a text file using modules from the sensor vendor. We utilized Python or R scripts for data analysis, and we have provided examples of data analysis scripts and acquired data for extended exposure periods (≤7 days). By using FLOS (Free-Libre and open-source) software and hardware, we have developed a low-cost and customizable system that can be used for a variety of exposure protocols. This hypoxia/hyperoxia exposure chamber allows for reproducible and transparent data acquisition and increased consistency with a high degree of customization for each experimenter’s needs.