Parks, D. F. | bioRxiv Science

bioRxiv2021

Internet of Things Architecture for High Throughput Biology

The Internet of Things (IoT) provides a simple framework to easily control online devices. IoT is now a commonplace tool used by technology companies, but it is rarely used in biology experiments. IoT can benefit research through alarm notifications, automation, and the real-time monitoring of experiments. We developed and implemented an IoT architecture to control biological devices used in experiments. We developed our own electrophysiology, microscopy, and microfluidic devices so that may be controlled through a unified IoT architecture. The system allows each device to be monitored and controlled through an online web tool. We present our IoT architecture so other labs may replicate it for their own experiments.

bioengineering↗

bioRxiv2021

Light-weight Electrophysiology Hardware and Software Platform for Cloud-Based Neural Recording Experiments

ObjectiveNeural activity represents a functional readout of neurons that is increasingly important to monitor in a wide range of experiments. Extracellular recordings have emerged as a powerful technique for measuring neural activity because these methods do not lead to the destruction or degradation of the cells being measured. Current approaches to electrophysiology have a low throughput of experiments due to manual supervision and expensive equipment. This bottleneck limits broader inferences that can be achieved with numerous long-term recorded samples. ApproachWe developed Piphys, an inexpensive open source neurophysiological recording platform that consists both hardware and software. It is easily accessed and controlled via a standard web interface through Internet of Things (IoT) protocols. Main ResultsWe used a Raspberry Pi as the primary processing device and Intan bioamplifier. We designed a hardware expansion circuit board and software to enable voltage sampling and user interaction. This standalone system was validated with primary human neurons, showing reliability in collecting real-time neural activity. SignificanceThe hardware modules and cloud software allow for remote control of neural recording experiments as well as horizontal scalability, enabling long-term observations of development, organization, and neural activity at scale.

bioengineering↗