By Sean Wilson
When I started my science career as a Research Assistant back in 2015 our group was beginning to investigate single cell sequencing to further our understanding of both embryonic mouse kidneys and human induced pluripotent stem cell derived organoids. I was trained in, and hired, for my wet lab molecular biology training, which meant I started off dissecting embryonic mice, running Western blots, PCRs, in situ hybridisations and immunohistochemistry assays. I gained a lot of experience in immunofluorescence (IF for short), honing in on beautifully stained kidney sections and whole-mounts and spending untold hours manually refining these to answer questions around the progenitor cell populations that drive the growth of nephrons. It was during that time that I was initially drawn to learning how to write scripts to perform some simple processing pipelines, ultimately to help save me time with the image analysis. This led to me learning to write some macros in FIJI, and dabbling in python and R.
Nearing 2017, the organoid teams had started looking into generating single cell datasets to better analyse the cell types present within kidney organoids. Our group teamed up with a bioinformatics group in our institute with members of the research team beginning to work on these datasets. When they presented their findings during our lab meetings, I was excited by the questions we were beginning to answer and the questions we began to ask. It saw this as a great opportunity, for myself, to really sink my teeth into some cool single cell datasets that had novel biology on a system (the kidney) that I understood, by that point, fairly well. So, I checked with my group leader if I could use these dataset to train myself how to use R (which I had favoured by that point) and interact with single cell data. I didn’t want to step on the toes of the bioinformaticians who were doing the work, or have they think that I was trying to do their work for them (even though I had, at that time, absolutely no idea what I was doing). This was the big turning point for me – this was what ended up being the first step into a passionate field of study for me.
Before Seurat became the go-to R package for single cell analysis, our friendly bioinformaticians put together a Single Cell Experiment pipeline using Scater, later incorporating Seurat into that pipeline, which I was sent and tried to work out what all the functions were doing, what the data looked like, and eventually I started to try and modify or add to the pipeline. At some point during this time, my focus in the group shifted from focusing on mouse kidney development to the organoid projects, where I actively contributed to generating a new organoid dataset. I was able to do simple analysis at this point, which meant I could also try and do the analysis of these datasets myself and see how that compared to the analysis carried out by the bioinformaticians.
I had a go at taking the raw data and putting together a clustering and cell annotation for these, which I eventually sent to my supervisors and group leader to show them what we had. I felt like I really was understanding the process enough that my results were true and accurate, I would double check stuff with the experts who said I was doing everything right, so I become more confident with my work. More importantly, my team also was confident in what I was doing and it became faster and easier for me to take over the analysis of our single cell data. This was a big thing for me, I really felt like I could be confident that I was doing sound work, drawing correct analysis and I gradually became more and more interested in making programming a big part of my skillset. Now, I’m deep into my PhD where the bulk of the analysis is bioinformatics, and my link between wet and dry lab work, is one of my defining career skill sets.
Sean Wilson – Murdoch Children’s Research Institute
Sean Wilson joined Melissa Little’s laboratory at the Murdoch Children's Research Institute as a research assistant in 2015, working closely with Alex Combes on Six2 haploinsufficiency in mice. In 2017 he was transferring to the Stem Cells and Regeneration division of the lab, working closely with Jess Vanslambrouck and Sara Howden studying the processes that drive differentiation within stem cell derived kidney organoids. During that time, he developed a passion for utilising stem cell technology, kidney organoids and bioinformatics analysis approaches in understanding kidney development, function and disease. This led to Sean undertaking a PhD in 2019 under the supervision of Melissa Little and Sara Howden to map morphogenesis in kidney organoids and improve the in vitro nephrogenic niche dynamics in this system. This has included a focus on classifying and comparing the cell types within human fetal kidney and kidney organoids using single cell RNA sequencing.
Edited by Nadia Rajab