A Microfluidic Chip to Maintain the Oxygen Gradient Across Ex Vivo Intestinal Slices

The oxygen gradient in the intestine influences intestinal physiology, and the microbial environment. Neurons that innervate the intestine are in constant communication with the local microbiome and this communication is regulated by the physiological health of the tissue. Measuring the communication between neurons and resident gastrointestinal cells could provide essential information about gut health. Despite this, a method to simultaneously culture intestinal tissue slices with a physiologically-relevant oxygen gradient with the capability of monitoring neural-gut communication in real-time has not been explored. Here, we designed and fabricated a 3D printed microfluidic device with precisely placed 500 μm delivery ports to maintain the oxygen gradient. The gradient is maintained from outlets below while allowing access to the slice from above for neurochemical detection with fast scan cyclic voltammetry and carbon-fiber microelectrodes. Outlets are placed in an oval where deoxygenated media is delivered to the middle of the slice, then oxygenated media is delivered to the outside of the slice. An oxygen sensitive fluorescent dye, Tris(2,2’-bipyridyl)dichlororuthenium(II), is used to characterize the tunability of the gradient delivery on a slice. Then while maintaining the oxygen gradient tissue function and viability were examined with fast scan cyclic voltammetry paired with carbon fiber microelectrodes by measuring transient release of neurotransmitters in the intestine. This chip allows direct access to intestinal slices for real time measurements, and imaging while maintaining an oxygen gradient to recreate the physiological environment which has not previously been done giving new insights to neuro-immune communication. </p

Real-Time Detection of Melatonin Using Fast-Scan Cyclic Voltammetry

Melatonin is an important hormone whose functions span from regulating circadian rhythm in the brain to providing anti-inflammatory properties in the immune system. Melatonin secretion from the pineal gland is known; however, the mechanism of melatonin signaling in the immune system is not well understood. The lymph node is the hub of the immune system, and melatonin secretion from lymphocytes was proposed to be an important source specifically for regulating cytokine secretion. Methods exist to quantify the concentration of melatonin within biological samples; however, they often suffer from either a lack of selectivity for melatonin over common biological interferences or temporal resolution, which is not amenable to measuring real-time signaling dynamics. Here, we have characterized an electrochemical method for optimal melatonin detection with subsecond resolution using fast-scan cyclic voltammetry at carbon-fiber microelectrodes. The oxidation peaks detected for melatonin were at 1.0, 1.1, and 0.6 V. Evidence for electrode fouling of the tertiary peak was present; therefore, an optimized waveform was developed scanning from 0.2 to 1.3 V at 600 V/s. The optimized waveform eliminated the detection of fouling products on the electrode with a 24 ± 10 nM limit of detection. Melatonin was distinguished between biological interferences, and codetection with the major synthetic precursor, serotonin, was possible. This method was used to detect melatonin in live lymph node slices and provides the first real-time measurements within the lymph node using FSCV. Real-time detection of melatonin dynamics could provide useful information on the mechanism of immunomodulation during inflammatory disease

Polyethylenimine Carbon Nanotube Fiber Electrodes for Enhanced Detection of Neurotransmitters

Carbon nanotube (CNT)-based microelectrodes have been investigated as alternatives to carbon-fiber microelectrodes for the detection of neurotransmitters because they are sensitive, exhibit fast electron transfer kinetics, and are more resistant to surface fouling. Wet spinning CNTs into fibers using a coagulating polymer produces a thin, uniform fiber that can be fabricated into an electrode. CNT fibers formed in poly­(vinyl alcohol) (PVA) have been used as microelectrodes to detect dopamine, serotonin, and hydrogen peroxide. In this study, we characterize microelectrodes with CNT fibers made in polyethyl­enimine (PEI), which have much higher conductivity than PVA-CNT fibers. PEI-CNT fibers have lower overpotentials and higher sensitivities than PVA-CNT fiber microelectrodes, with a limit of detection of 5 nM for dopamine. The currents for dopamine were adsorption controlled at PEI-CNT fiber microelectrodes, independent of scan repetition frequency, and stable for over 10 h. PEI-CNT fiber microelectrodes were resistant to surface fouling by serotonin and the metabolite interferant 5-hydroxy­indoleacetic acid (5-HIAA). No change in sensitivity was observed for detection of serotonin after 30 flow injection experiments or after 2 h in 5-HIAA for PEI-CNT electrodes. The antifouling properties were maintained in brain slices when serotonin was exogenously applied multiple times or after bathing the slice in 5-HIAA. Thus, PEI-CNT fiber electrodes could be useful for the in vivo monitoring of neurochemicals

Spontaneous adenosine duration.

<p>(A) A color plot and concentration vs. time plot of adenosine transients in the caudate-putamen. The horizontal dashed line is 10% of the concentration of the first peak and the vertical lines are when the baseline value crosses this value, showing the duration. (B) Caudate-putamen duration histogram. The <i>y</i>-axis is relative frequency and the <i>x</i>-axis shows 0.5 second bins. The Gaussian distribution equation is (red line, R² = 0.9712, <i>n</i> = 30 rats). (C) Color plot and concentration vs. time plot of an example spontaneous adenosine transient in the prefrontal cortex. The duration is marked with vertical lines. (D) Adenosine duration histogram for the prefrontal cortex is plotted with a Gaussian distribution equation (red line, R² = 0.9765, <i>n</i> = 29 rats).</p

Spontaneous transient adenosine over time.

<p>All <i>x</i>-axes are time in 30 minute bins and all <i>y</i>-axes were normalized to the first bin. (A) Normalized concentrations of adenosine transients in the caudate putamen. There was no significant effect of time on the concentration (<i>n</i> = 6 animals, one-way ANOVA, <i>p</i> = 0.2013). (B) Duration of adenosine release from the caudate-putamen. There was a main effect of time on durations (<i>n</i> = 6 animals, one-way ANOVA, <i>p</i> = 0.0005). (C) Number of transients in the caudate-putamen. There was no significant effect of time on the number of transients (<i>n</i> = 6 animals, one-way ANOVA, <i>p</i> = 0.4782). (D) Concentrations in the prefrontal cortex. There was no significant effect of time on the concentration (<i>n</i> = 6 animals, one-way ANOVA, <i>p</i> = 0.6268). (E) Durations of transient adenosine in the prefrontal cortex. There was no significant effect of time on the duration (<i>n</i> = 6 animals, one-way ANOVA, <i>p</i> = 0.8449). (F) Number of transients from the prefrontal cortex. There was no significant effect of time on the number of transients (<i>n</i> = 6 animals, one-way ANOVA, <i>p</i> = 0.9299).</p

Effect of the A₁ antagonist, DPCPX (6 mg/kg, i.p.), on adenosine transients.

<p>(A) Inter-event time histograms of first hour of pre-drug in caudate. Median, mean and exponential fit (green line) (y = 0.295 <i>e</i>^−0.0186 (R² = 0.7889)) are plotted on the frequency distribution. (B) Inter-event time histogram for transients in the first hour post-DPCPX in caudate. The exponential fit (red line) is y = 0.318 <i>e</i>^−0.0101x (R² = 0.9490). In the caudate, there was a significant difference between the underlying distributions before and after DPCPX (<i>n</i> = 6 animals, KS-test, <i>p</i><0.0001). (C) Inter-event histograms pre- and (D) post-DPCPX in the prefrontal cortex. The exponential fit equations are y = 0.260 <i>e</i>^−0.0101x (R² = 0.9124) pre-drug and y = 0.340 <i>e</i>^−0.0111x (R² = 0.9851) after DPCPX. In the prefrontal cortex, there was a significant difference between underlying distributions pre- and post-DPCPX (<i>n</i> = 5 animals, KS-test, <i>p</i> = 0.0287).</p

Spontaneous adenosine transient concentration.

<p>(A) A color plot of a spontaneous adenosine transient in the caudate putamen with a corresponding concentration vs. time plot above. (B) Caudate-putamen concentration histogram. The <i>y</i>-axis is relative frequency. All concentrations from the first hour of data collection in the caudate putamen were placed into 0.05 µM bins (<i>x</i>-axis) and fit with a Gaussian distribution (red line). The Gaussian fit equation is (R² = 0.9327, <i>n</i> = 30 rats). The mean and median are marked. The majority of transients are in the 100–200 nM range. (C) An example color plot and concentration vs. time plot in the prefrontal cortex. (D) The histogram of concentrations in the prefrontal cortex fit a Gaussian distribution with the equation: (R² = 0.9505, <i>n</i> = 29 rats).</p

Effect of the A₁ agonist, CPA (1 mg/kg, i.p.), on adenosine transients.

<p>(A) Inter-event time histograms for the caudate. Median, mean and exponential fit (green line) (y = 0.298 <i>e</i>^−0.0115x (R² = 0.9636)) are plotted on the frequency distribution. (B) Inter-event time histograms for the first hour after CPA in caudate. The exponential fit (blue line) is y = 0.226 <i>e</i>^−0.00787x (R² = 0.9528). There was a significant difference between the underlying distributions before and after CPA (<i>n</i> = 6 animals, KS-test, <i>p</i> = 0.0308). (C) Inter-event histograms pre- and (D) post-CPA in the prefrontal cortex. Exponential equations are y = 0.280 <i>e</i>^−0.00983x (R² = 0.9430) pre-drug and y = 0.264 <i>e</i>^−0.00938x (R² = 0.9337) after CPA. In the prefrontal cortex, there was no significant difference in the underlying distributions before and after CPA (<i>n</i> = 6 animals, KS-test, <i>p</i> = 0.9299).</p

Histograms of inter-event times.

<p>(A) Inter-event histograms in the caudate-putamen. The time between consecutive transients (termed the inter-event time) was calculated and plotted for the first hour of data collection. The <i>x</i>-axis shows 30 second time bins and the <i>y</i>-axis is relative frequency of inter-event times. Median, mean and exponential fit (y = 0.242 <i>e</i>^−0.00891x (R² = 0.9926)) are plotted on the histogram. The inset plots show an example of three consecutive transients. (B) Inter-event histograms for the prefrontal cortex. The exponential fit is <i>y</i> = 0.388 <i>e</i>^−0.0141x (R² = 0.9933). The inset color plot shows an example of two transients that occurred close together. The underlying distribution of inter-event times was significantly different between the caudate-putamen and prefrontal cortex (<i>n</i> = 30 and 29 animals, 588 and 804 inter-event times respectively, Kolmogorov-Smirnov test, <i>p</i> = 0.0338). The time between transients is shorter in the prefrontal cortex.</p

Detection of spontaneous, transient adenosine release <i>in vivo</i>.

<p>(A) <i>In vivo</i> spontaneous, transient adenosine release. The current vs. time plot has two traces, an orange line at 1.4 V for the primary oxidation and a black line at 1.2 V for the secondary oxidation. The dashed lines on the color plot and current vs. time plots indicate where CVs were taken. (B) Cyclic voltammograms of adenosine over time. The top (1^st) is when adenosine first appears, the middle CV (2^nd) is half a second later when the primary peak is at a maximum and the bottom (3^rd) is half a second later when the secondary peak is at its maximum. The ratio of the primary and secondary oxidation peaks can change over time.</p