Login / Signup

Simulated Performance of Electroenzymatic Glutamate Biosensors In Vivo Illuminates the Complex Connection to Calibration In Vitro.

Mackenzie ClayHarold G Monbouquette
Published in: ACS chemical neuroscience (2021)
Detailed simulations show that the relationship between electroenzymatic glutamate (Glut) sensor performance in vitro and that modeled in vivo is complicated by the influence of both resistances to mass transfer and clearance rates of Glut and H2O2 in the brain extracellular space (ECS). Mathematical modeling provides a powerful means to illustrate how these devices are expected to respond to a variety of conditions in vivo in ways that cannot be accomplished readily using existing experimental techniques. Through the use of transient model simulations in one spatial dimension, it is shown that the sensor response in vivo may exhibit much greater dependence on H2O2 mass transfer and clearance in the surrounding tissue than previously thought. This dependence may lead to sensor signals more than double the expected values (based on prior sensor calibration in vitro) for Glut release events within a few microns of the sensor surface. The sensor response in general is greatly affected by the distance between the device and location of Glut release, and apparent concentrations reported by simulated sensors consistently are well below the actual Glut levels for events occurring at distances greater than a few microns. Simulations of transient Glut concentrations, including a physiologically relevant bolus release, indicate that detection of Glut signaling likely is limited to events within 30 μm of the sensor surface based on representative sensor detection limits. It follows that important limitations also exist with respect to interpretation of decays in sensor signals, including relation of such data to actual Glut concentration declines in vivo. Thus, the use of sensor signal data to determine quantitatively the rates of Glut uptake from the brain ECS likely is problematic. The model is designed to represent a broad range of relevant physiological conditions, and although limited to one dimension, provides much needed guidance regarding the interpretation in general of electroenzymatic sensor data gathered in vivo.
Keyphrases
  • electronic health record
  • blood brain barrier
  • magnetic resonance
  • deep learning
  • data analysis
  • artificial intelligence
  • subarachnoid hemorrhage