My Practicum Project took place in Dr. Joshua Singer's Lab in the Biology Research Building at the University of Maryland. I was going through the Biology Department's Website, reading about the research of various professors, and Dr. Singer's work caught my eye. I emailed him asking to meet with him and discuss his research in more detail, and after a long talk he invited my to join his team.
The research was looking into certain aspects about the synaptic pathways of retinal neurons. The rods and cones that are in our eyes transmit visual stimuli to neurons known as bipolar cells. (Rods connect with Rod Bipolar cells, and Cones connect with Cone Bipolar Cells). Those Bipolar cells then form synapses with Ganglion cells, which then transmit that message to the brain. Forming a synapse with both Bipolar Cells and Ganglion cells are Amacrine Cells, which are thought to mediate activity between the neural pathways. Very little is known about Amacrine cells, and thus these cells were the focus of the research.
In order to understand the function of all these various cell types, we first had to get a picture of how they are all connected to each other. The patterns in how Amacrine Cells make synapse with other neurons should tell us information about their overall purpose and function. So the question then becomes: how can you figure out the patterns of connectivity in order to figure out its function. This is where my work comes in.
The data is relatively easy to obtain, the difficulty is in analyzing the data. Using 3-D electron microscope imaging technology, we can examine the various cells in a given slice of tissue. In this case, we took imaging of retinal tissue in order to analyze all the cells in the retinal neural pathway. Here's the problem: there is no computer software that can simply look at the 3-D images and determine how the neurons are connected. Only an actual person can sift through the data and actually analyze it. This analysis of the data is what the bulk of my work was about.
The first step was to map out individual neurons. I would look through the 3-D images until I found a Soma, which is the same for the main cell body of the neuron. Then using a computer tracking software called Knowssos, I could follow through the images and mark the axon and dendrite branches. By tracing the cell, the software could then connect data points and produce a skeleton of the neuron.
The next step is to figure out where the neuron forms a synaptic bridge between itself and a neighboring cell, be it bipolar cell or ganglion cell. While looking at the 3-D images, there are various visual cues that tells you where either a dendrite input to the cell or axon output occurs. These cues include thickening of the cell membrane, concentration of vesicles (that would contain neurotransmitters), and the presence of a particular protein. Using the computer software, we would then mark the location of these synapses.
The key to identifying patterns in neural pathways is that you first need to map out A LOT of cells. So as time consuming as it is, we have to go individual cell by individual cell, map them out, and mark where they form a synaptic pathway. As we continue to build up of collection of neurons, we can compare the data for each cell and start to build a bigger picture.
There are various diseases that are associated with problems in retinal neurons synaptic connectivity, as the hope is that through this research we will build a better understanding about the mechanisms by which these cell work. And from this we hope to one day provide information that would be useful in developing treatments for those diseases.
This work has taught me a great deal, namely it has given me a great appreciation for just how complex these systems are. When learning about neurons in a classroom, your understanding of a neuron may be limited to a couple dendrites and one long axon. But doing this research has given me a scope into just how branched and amazingly complex even a single neuron can be.
In addition it has shown me just how vital attention to the detail it. At times it is very hard to analyze the data, and unfortunately there are many instances when the data provided is not as clear as you would like.
If this work has shown me one thing, it is that science can be MESSY. Science is not simply setting up an experiment or making observations and having perfect data that leads to a clear result. The truth of the matter is that many times data is incomplete, convoluted, and the answers are not always clear. You don't always have the ability to make a computer analyze data for you, and it makes the work all that more pressing. This project has shown me that even when the answer is staring you right in the eyes, sometimes it takes a tremendous amount of time, effort, and teamwork with colleges in order to even scratch the surface of what science has to offer.
Hopefully one day the research takes us to the kind of answers that will lead to treatments for the aforementioned diseases, but to me this research went beyond the primary goals of the work. While I don't necessarily know how this project has affected my academic plans or eventual career path, I do know how it has altered my overall view on the topics. It has given me an appreciation for just how vital good science is, and the amount of time and energy that is needed for it. Throughout the project as I saw our data analyses grow into a bigger and bigger picture it told me this: the satisfaction of progress makes the struggle of the process worth it.