Numerical Modeling of Electroosmotic Push–Pull Perfusion and Assessment of Its Application to Quantitative Determination of Enzymatic Activity in the Extracellular Space of Mammalian Tissue

Abstract

Many sampling methods have been developed to measure the extracellular concentrations of solutes in the extracellular space of mammalian tissue. However, few have been used to quantitatively study the various processes, such as enzymatic degradation, that determines the fate of these solutes. For a method to be useful in this pursuit, it must be able to 1) perfuse tissue and collect the perfusate for quantitative analysis of the solutes introduced and reaction products produced, 2) control the average residence time of the active solutes, and 3) have the appropriate spatial resolution for the process of interest. Our lab previously developed a perfusion technique based on electroosmosis (EO), called EO push-pull perfusion (EOPPP), that is in principle suitable to meet these needs. However, much like the case for other sampling methods that came before, there are parameters that are needed for quantitative interpretation of data but that cannot be measured easily (or at all). In this paper, we present a robust finite element model that provides a deep understanding of fluid dynamics and mass transport in the EOPPP method, assesses the general applicability of EOPPP to studying enzyme activity in the ECS, and grants a simple approach to data treatment and interpretation to obtain, for example, Vmax and Km for an enzymatic reaction in the extracellular space of the tissue. This model is a valuable tool in optimizing and planning experiments without the need for costly experiments.

Higher Aminopeptidase Activity Determined by Electroosmotic Push–Pull Perfusion Contributes to Selective Vulnerability of the Hippocampal CA1 Region to Oxygen Glucose Deprivation

“…By fitting the integrated Michaelis-Menten equation to the inhibitor data for both regions, we determined the values for K’ m app . Given that the calibration factor of K’ m app /K m app is 2.8 ± 0.2 53 , we can obtain K m app accordingly. Furthermore, because we know K m from our data without the inhibitor, we can determine K i for the two regions by using the simple relationship Kmapp=Kmtrue(1+InormalKnormalitrue).…”

“…Thus we divide the measured concentration of YGGFL and GGFL by the concentration of IS collected in the sampling capillary to obtain S/S 0 and P/S 0 , respectively. Ou et al 53 details how to fit the integrated form of the Michaelis Menten (reproduced below) to the perfusion data P/S 0 to obtain the best estimates of Michaelis Menten kinetics. PnormalS0=1−normalKnormalmnormalS0·normalWtrue{normalS0normalKnormalm·exptrue(S0−Vmax·normaltnormalKnormalmtrue)true}All t-tests were done using the QuickCalcs software from GraphPad Software, Inc.…”

Designing electrode configuration of electroosmosis based edema treatment as a complement to hyperosmotic therapy

“…Moreover, a significant stimulationpolarity-specific fluid and solute movement is induced when applying direct current to endothelial monolayers, suggesting the electroosmotic property of the brain [8]. An essential condition to induce EOF inside the brain is the narrow channels with charged walls such as the extracellular space [18,[36][37][38]. Thus, the electric field mainly induces EOF in the brain parenchyma.…”

Methods of Measuring Enzyme Activity Ex Vivo and In Vivo

“…This second-generation EO-based sampling technique is called electroosmotic push-pull perfusion (EOPPP) (108). In silico experiments determined the spatial resolution of this technique to be ~100–300 μm, depending on the applied current (109). Rupert et al coupled this technique with offline MALDI mass spectrometry to qualitatively determine the differences in galanin hydrolysis patterns in CA1 and CA3 of the OHSCs.…”

“…However, these first values are not the actual V max and K m in the tissue ECS because the substrate is diluted significantly in the tissue. A large set of experimental simulations was carried out to determine that a simple numerical factor related the first, observed, values to the actual values (109).…”