Applying Noise Currents to Medial Vestibular Nucleus Neurons Using Whole-Cell Patch-Clamp Technique
成績單
For whole-cell patch clamp electrophysiology, first, pull micropipettes with a final resistance that will range from 3 to 5 megaohms when filled with a potassium-based internal solution, and place in the bath. To obtain whole-cell patch clamp recordings from individual neurons in the MVN, transfer a single tissue slice from the incubation chamber to the recording chamber of a standard electrophysiological setup, and use a nylon thread on a U-shaped weight to secure the slice.
Continuously perfuse the recording chamber with carbogenated aCSF at 25 degrees Celsius at a flow rate of three milliliters per minute, and fill a micropipette with internal solution. Apply a small amount of positive pressure using a pipette to push debris away from the pipette tip. Using a lower power objective, locate the MVN before switching to a high power to locate individual neurons within the MVN.
Before breaching the tissue with the pipette, apply a small amount of positive pressure to push debris away from the pipette tip, and use the micromanipulator to move the pipette toward the selected neuron. A small dimple should form on the neuronal membrane. Release the positive pressure and apply a small amount of negative pressure.
Once a one gigaohm seal has been achieved, apply gentle, short, and sharp negative pressure to the pipette holder through the suction port to rupture the membrane and to create a whole-cell configuration. Then, obtain whole-cell current clamp recordings according to standard protocols.
To apply stochastic and sinusoidal noise to individual medial vestibular nucleus neurons, set the range of amplitudes from 3 to 24 picoamps to determine the neuronal threshold and firing rate, and group the lower and higher stimulus intensities to determine the sensory threshold.
Next, calculate the average firing rate over the 10-second period during which the depolarizing current step will be injected for each individual current level. Use the average firing rate values to generate a firing rate versus current plot. Then, perform a linear regression analysis to determine the gradient of the line of best fit to determine the neuronal gain.
