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Aaron Uy - UPenn Weeks 4 & 5


At the start of my fourth week, I was given an independent project to do along with an undergraduate student there, Sarah. Our job would be to perform immunohistochemistry (IHC) on mice brains to delineate regions in the striatum, a region in the brain. IHC utilizes the specificity of antibodies to attach to epitopes on specific antigens. This allows certain cells of a tissue section (with a certain antigen) to be “selected” by a certain antibody. By adding a secondary antibody with a fluorescent tag, target cells can be selectively labeled, imaged, and analyzed. We sought to mark regions in the striatum based on cellular count intensity.  

The procedure begins with perfusing the mice.  This is a gory procedure that involves anaesthetizing a live mouse, pumping out the blood from its circulatory system, injecting a preservative solution into it heart, pumping this solution throughout its circulatory system, chopping its head off, and dissecting out the whole brain. This process fixes and preserves the brain tissue in its current state so that it can be sliced and later studied.  Thankfully, I was given a few mice to practice on as my post-doc made it look much easier than it actually is. 

Afterwards, the brain is sliced into 50 micron slices using a vibratome. The slices are then soaked  in a series of antibody baths, mounted onto slides, imaged under an epifluorescence microscope, and then the cells are counted based on a grid formation. This allow us to create a heat map of the concentration of certain cell types in the striatum. Counting the often hundreds of cells (in each slice) can be a very strenuous and monotonous task and this is furthered by the fact that this need to be repeated for dozens of slices for each brain (and we need to do a lot of mice for statistically significant data). As a result, I spent the time in between steps of the IHC protocol developing a semi-automated pipeline to count the cells using an imaging software called ImageJ. Much of the fifth week involved writing the code, but the thing is – I don’t really know how to code so that was a bit of a doozy. It was a lot of watching videos, reading forums, and having no clue what to while debugging and troubleshooting. Eventually, it all worked out though.

Using machine learning, the software is “taught” to recognize cells from the background, and this training data can be saved and used in the counting pipeline. Afterwards, one only need to feed the pipeline an image of a slice of the brain. After a short while, it will summarize the cell counts of the slice based on an automatically generated grid box.

I still have to figure out how to write the code so it can be feed an entire folder of images and sort the resulting data, but that’s one of my goals for the upcoming week(s). Next week, we plan to do do IHC on numerous more mice, and this pipeline will save a lot of time in the near future as well as after I’m gone from the lab.

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