Miniaturized video-microscopy system for near real-time water quality biomonitoring using microfluidic chip-based devices

摘要

Biomonitoring studies apply biological responses of sensitive biomonitor organisms to rapidly detect adverse environmental changes such as presence of physic-chemical stressors and toxins. Behavioral responses such as changes in swimming patterns of small aquatic invertebrates are emerging as sensitive endpoints to monitor aquatic pollution. Although behavioral responses do not deliver information on an exact type or the intensity of toxicants present in water samples, they could provide orders of magnitude higher sensitivity than lethal endpoints such as mortality. Despite the advantages of behavioral biotests performed on sentinel organisms, their wider application in real-time and near realtime biomonitoring of water quality is limited by the lack of dedicated and automated video-microscopy systems. Current behavioral analysis systems rely mostly on static test conditions and manual procedures that are time-consuming and labor intensive. Tracking and precise quantification of locomotory activities of multiple small aquatic organisms requires high-resolution optical data recording. This is often problematic due to small size of fast moving animals and limitations of culture vessels that are not specially designed for video data recording. In this work, we capitalized on recent advances in miniaturized CMOS cameras, high resolution optics and biomicrofluidic technologies to develop near real-time water quality sensing using locomotory activities of small marine invertebrates. We present proof-of-concept integration of high-resolution time-resolved video recording system and high-throughput miniaturized perfusion biomicrofluidic platform for optical tracking of nauplii of marine crustacean Artemia franciscana. Preliminary data demonstrate that Artemia sp. exhibits rapid alterations of swimming patterns in response to toxicant exposure. The combination of video-microscopy and biomicrofluidic platform facilitated straightforward recording of fast moving objects. We envisage that prospectively such system can be scaled up to perform high-throughput water quality sensing in a robotic biomonitoring facility.