By Mose Kwon
At the beginning of the year, I started my honours programme in medical biochemistry with a joint techniques course being exposed to various fields such as cell biology, histology, systems biology, and structural biology. I chose to take this course because I wanted to broaden my technical skill set and gain a better understanding of research platforms that are not routinely available in the diagnostic laboratory setting. Coming from a diagnostic background, I found this particularly valuable, as it exposed me to technologies and analytical approaches that extend beyond routine service work and into more exploratory and translational research.
There was a strong emphasis on the central dogma of molecular biology, beginning with the flow of information from DNA to RNA to protein, and how this forms the basis for systems biology. This was linked to the different levels at which biological processes can be investigated, namely genomics, transcriptomics, proteomics, and metabolomics. What stood out to me was the emphasis on how these different layers of information complement one another. Genomics provides insight into what a cell is capable of expressing, but it does not necessarily reflect what is actively happening at a given moment. Gene expression is highly dependent on factors such as the cellular environment, signalling pathways, and transcriptional regulation. Proteomics therefore offers an especially important perspective, because it reveals which proteins are actually being produced and functioning within the cell under specific conditions. This has important applications in understanding disease mechanisms, identifying biomarkers, studying post-translational modifications, mapping cell signalling pathways, and informing drug development. It highlighted for me that, while the genome represents potential, the proteome gives a more immediate picture of biological activity.
The lecture on mass spectrometry was one of the most interesting parts of the course. It demonstrated the importance of protein-level analysis and how proteomics can provide information that is not captured by nucleic acid-based approaches alone. Closely linked to this was the discussion on liquid chromatography and the need to fractionate complex protein mixtures before mass spectrometric analysis. Separating proteins on the basis of properties such as size, charge, hydrophobicity, or affinity reduces sample complexity and improves both detection and interpretation downstream. This helped me appreciate that successful proteomic analysis depends not only on the instrument itself, but also on careful upstream sample preparation and separation strategies.
Another aspect I found particularly engaging was the use of fluorescent probes across multiple platforms. It was useful to see how the same basic principles of fluorescence are applied in different contexts. In flow cytometry, fluorophore-labelled markers are excited by lasers to identify and distinguish individual cell populations based on their surface or intracellular characteristics. In fluorescence microscopy, excitation and emission filters are used to isolate specific wavelengths, allowing selected components of a specimen to be visualised with precision. Even in ELISA, labelled detection systems use similar principles to generate measurable signals. Seeing these conceptual links across different technologies helped reinforce my understanding of how core biochemical principles can be adapted for different research and diagnostic purposes. The course also introduced molecular cloning, including the use of vectors and plasmids to insert and replicate DNA fragments. This was important because it showed how molecular tools are designed with precision, particularly in terms of generating compatible ends with restriction enzymes and ensuring that inserts are ligated in the correct orientation. The point about orientation was especially relevant, since an insert placed in the wrong direction may not be transcribed or expressed as intended. This gave me a better appreciation of cloning not simply as a laboratory technique, but as a highly controlled method for manipulating genetic material for downstream applications such as protein expression or functional studies.
We were also given a brief tour of several research platforms at UCT, including the mass spectrometry, flow cytometry, and electron microscopy laboratories. This was a valuable orientation to the workflows of these facilities and the kinds of questions they help different research groups address. It made the course feel more grounded, as we could connect the theory from lectures to real laboratory environments and see how these technologies are integrated into ongoing research.
Another point that stood out to me was the discussion around microscopy and digital imaging. I found it interesting that biological images can be converted into numerical data through pixels, allowing them to be analysed as quantitative variables rather than simply descriptive pictures. This creates opportunities for objective measurement using tools such as Fiji and ImageJ, whether for signal intensity, colocalisation, morphology, or cell counts. It reinforced the idea that modern microscopy is not only about visualisation, but also about extracting measurable and reproducible data from images.
After the initial two weeks of the joint techniques course, we split into our respective disciplines for a further three weeks of course-specific training. Our group joined the bioinformatics class, where we were introduced to coding approaches including basic statistical analysis in R and Python-based analysis of proteomics datasets. This part of the course was particularly relevant because it highlighted how modern biological research increasingly depends on computational analysis. With the growing scale and complexity of omics data, the ability to analyse and interpret data computationally is becoming just as important as generating it in the laboratory.
There was also considerable discussion around databases, systems biology, and the limitations of current reference datasets. A recurring theme was that Africa is the most genetically diverse continent, yet remains one of the most underrepresented in global genomic and biomedical databases. This has major implications for precision medicine, because precision medicine is only as reliable as the reference data on which it is built. If the underlying databases do not adequately represent our populations, then the development of diagnostic tests, risk prediction tools, and targeted therapies may be less accurate or less effective for the people we serve. This is particularly important in the African context, where differences in genetics, disease-associated variants, and drug metabolism may influence both disease presentation and treatment response. The course therefore highlighted not only the scientific promise of precision medicine, but also the importance of building more inclusive datasets if these advances are to benefit all populations equitably.
Overall, I found the experience both positive and intellectually stimulating. It exposed me to emerging technologies that are increasingly being integrated with machine learning and artificial intelligence, and it made clear that the research landscape is changing rapidly. Biomedical science is becoming more data-driven, more computational, and more interdisciplinary. As a result, there is a growing need to adapt and develop skills that go beyond traditional laboratory techniques. Fields such as pharmacogenomics and precision medicine are likely to play a much greater role in the future of healthcare, and it is possible to imagine a time when routine treatment decisions are tailored to an individual patient’s genetic makeup in order to maximise efficacy and minimise adverse effects. For me, the course was valuable not only because it introduced new technologies, but because it broadened my perspective on where biomedical research and diagnostics are heading.
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