Week 1: Mice, mouse embryos, and mouse data visualizations!

Hi everyone, and welcome back to my project! I hope you are as excited as me to learn about the coolest wet lab and computational techniques in biology (well, at least developmental biology!). Thanks to my advisor, I had a blast learning and helping run different experiments. In addition to this, I picked up some super fun and handy skills in coding and visualization these past 2 weeks! 

Week 0: The beginnings…wet-lab and mice 

In the first week (Ski Week/Week 0) of this internship, I was able to observe a mouse dissection in which the embryos of a pregnant mouse were dissected out. Following this, the female embryos were further dissected for the embryonic ovaries to be extracted. We focused on the female emrbyos as my project pertains to ovarian as opposed to testicular fertility. The ovaries at this point contain germ cells — the oogonia, which are pre-meiotic cells that upon maturing and entering meiosis will become oocytes/eggs — in addition to somatic cells (non-germ cells). We need both the somatic and oogonia for later endeavors in this project. But since these cells were mixed together and in chunks, we needed to find a way to properly gather the cells we wanted. After gathering the controls and dissociating the ovaries, I was able to help run fluorescence-activated cell sorting (FACS), a type of flow cytometry that is able to separate the cells into streams based on the fluorescent signals from the cells (which are detected due to markers/tags we add to the cells before sorting!). Essentially, the FACS was able to separate for us the germ cells from the somatic cells in the dissociated ovaries.

Week 1: Diving into computations (+ more wet-lab and mice!)

This next week (Week 1), on the computational side of things, I learned about quality control analysis of single-cell RNA-sequencing (scRNA-seq) data using a previously analyzed dataset by the lab. Quality control and filtering gets rid of low-quality cells, which we identify through their mitochondrial reads (a characteristic that is provided for us in this dataset). It also gets rid of cells that might have accidentally been labeled by the same cell label (as each one of the cells in this dataset needs to have its own unique identifier). At home, I was later able to perform my own quality control analysis on another dataset using Python, which involved filtering the cells in the dataset, normalizing data and reducing variation, and identifying clusters of similar cells through clustering algorithms. 

I also had so much fun building 2D and 3D visualizations/representations of the data — including ones that rotate (I’m talking about you, UMAP.gif!). I played around with the data some more to build an ML model to predict which cluster a cell belonged to (um let’s just say…it’s a major work-in-progress).  

Check out the 3D UMAP here! https://drive.google.com/file/d/1v_F7_aGr7wiEOUionMfKGODM09uA6lAk/view?usp=sharing

My advisor later discussed my progress on the computational analysis with me. I also got to attend a (super fun) lab meeting, where I got the chance to learn so much more about developmental biology and hear about the cutting-edge research every lab member was doing! 

The highlight of this week has to be the mice! My advisor showed me how to breed adult mice and plan genetic crosses to ensure we will have embryos with the right genotype in future experiments. The next day, we immediately went in to check the female mice for potential fertilization. This is known as plug-checking, as a plug is composed of a layer of leftover semen – a sign that the male and female mice engaged in reproductive behavior. This is important for us to keep track of so we can understand embryonic development and maturity — we assume the day the plug occurred, the female mouse’s eggs were fertilized and embryos were formed. This is largely because the mice usually engage in reproductive behavior when the female mouse is ovulating. As a result, plugs have shown to be a good indicator of pregnancy around 70% of the time. 

Additionally, since we are dissecting out oogonia, which form from E11.5 to E13.5 and peak at E13.5, we need a way of knowing what stage/timepoint in development the embryos we are dissecting are at. To do this, we start at the plug date and assume any and all embryos were conceived that day itself (as mentioned previously), and dissect the female mouse around 13 days later. 

Following plug-checking, I was able to help run a DNA extraction and another FACS experiment!

Since then, I have begun learning how to use CellTypist to create annotations on scRNA-seq data — join me for my progress next week!