I interviewed Dr Wykes to find out how he uses PatchVision in his research which involves manipulating Ion channels in Neurons and how he uses Fluorescent tags to Patch Clamp the Neurons in the slice.
We discussed what were the challenges that researchers were facing that prompted the development of PatchVision, and how does PatchVision solve them?
There are several issues, but these can be classed into three main sections.
Transmitted Light Image Optimisation
Traditionally, when working with cortical or hippocampal slices, especially using DIC optics, researches use a CCTV style camera which is connected straight into a TV monitor. The image is then optimised by empirically adjusting the brightness and contrast using the two knobs on the TV monitor.
This works to a certain extent, however very often it is difficult to find the best settings. Moreover, when there is a change in illumination intensity, for example while moving around your sample, you will need to adjust the brightness and contrast on the screen which takes time.
It is important that you can quickly focus on a patched cell or area you are applying drugs to; therefore the first challenge is to make the process of optimisation automatic. Imagine that you have a program that allows you to visualise your cells in different conditions under different objectives and to get optimal parameters for each imaging condition. By clicking one button you will have an optimised image for each condition making it fast and automated, allowing the researcher to spend time focusing on their patching experiment instead of spending time adjusting viewer settings.
Very often you also may observe uneven illumination of your sample, which may be caused by optics properties of your setup or for example by tissue orientation in the chamber. Because of this the background image may appear uneven with one part of your image brighter than the other; an analogue camera can not offer a solution for this.
So in order to address the above issues the first challenge was to find the efficient algorithms which would allow automatic digital optimisation of DIC images of neuronal tissue. This was addressed by creating an automatic live response to leveling brightness and contrast and also by on-line application of other advanced digital filters that improve the dynamic range and the signal to noise ratio in live image stream from the camera.
When carrying out patch clamp experiments you very often change your objectives and need to predict where the region of interest which contains your pipette will be under a 60x objective after choosing it with a 10x objective. In the past, you would put a cross on the screen using a marker pen and make a note of pipette location. To facilitate this PatchVision allows you to add, remove and save positions of up to 10 marks. This allows easy identification of the same structures observed with different objectives or in DIC and fluorescence. Moreover, using a simple calibration procedure you can instantaneously measure the distance between any points on the live image.
The third challenge arises with fluorescence imaging. Ten years ago, most people would do classic electrophysiological experiments using DIC transmitted light optics for the identification of cells of interest. This was sufficient to study basic parameters of neuronal signaling in the wild type tissue. However, now with the development of genetic methods, the very common task is to specifically record from the cells that have been genetically manipulated. These cell are normally identified because they also express fluorescence markers such as GFP or Cherry Red.
The challenge here is that when you are working with a fluorescent marker which have a low fluorescent signal, and although visible through the eye pieces, can not be displayed through a normal CCTV camera.
Surprisingly to simply identify such weakly fluorescent cells you do not really need a multiphoton microscope or an expensive CCD camera. By combining several digital filtration and averaging protocols PatchVision allows you to use the same simple CCTV camera for the very effective identification of such weakly fluorescence cells. However PatchVision was only designed to identify cells of interest for patch clamping, rather than to detect quantitative fluorescence changes like responses to stimulation.
Article by James Pearce