Monthly Archives: November 2020

An Example of What Science Does to Address Stigma

By Mbalentle Madlala

What comes to mind when you read the words “mental health”? Your answer may depend on several factors. One factor may be age. Many of us may be aware of the “infamous” feud between the so called “baby boomers” (born 1944 – 1964) and “Generation Z” (born 1995 – 2015). There have been several social media posts noting the differences in opinions and world-views between the two groups as well as informative discussions that have occurred on media platforms, such as Jubilee. One topic that often comes up for discussion is what people from two completely different generations know and think about mental health. Is mental illness real, or is it just an excuse/justification for laziness/not fulfilling an expectation?

Another factor that could influence your answer may be cultural background and exposure. Often, mental illness can be thought of as being a result of out-of-line behaviour and/or wrong choices/beliefs. This can lead to mental illness being dismissed as being able to happen to anybody, being attributable to pathology, and being treatable.

These factors could all contribute to the formation of, what scientists have called, concealable stigmatised identities (CSIs). A CSI is an identity that can be kept hidden or concealed from others and that has negative attributes or stereotypes attached, which can result in a loss of status and/or discrimination in society. HIV/AIDS and mental disorders are examples of CSIs.

Depression, one of the most common CSIs, is a mental disorder characterised by having a low mood and loss of interest and enjoyment in usual activities. As many as 1 in 6 South Africans suffer from anxiety, depression or substance-use problems according to the South African Depression and Anxiety Group. Furthermore, research reveals that over 40% of South Africans living with HIV have a diagnosable mental disorder, making them affected by two concealable identities.

In addition to the lack of mental healthcare resources in South Africa, stigmas surrounding mental health pose a major stumbling block when it comes to treating the disease. As discussed at the beginning of this piece, cultural background and “traditional” thinking may add to the stigma against depression. Thus, the review done by Dai and colleagues (2019) showing how depression can be related to actual structural and functional abnormalities in the brain not only sums up the knowledge we have in the field whilst highlighting the gaps, it also presents the opportunity to address the stigma against depression by validating its biological attributes.

MRI = Magnetic Resonance Imaging; a non-invasive imaging technology that produces three dimensional detailed anatomical images, often used for disease detection, diagnosis, and treatment monitoring

So what does this review help us understand about abnormalities in certain parts of the brain being related to depression? By using tools in the form of big machinery, different machine settings and computer programs, such as Magnetic Resonance Imaging (MRI), biological information about the brain’s structure and functional capacity can be obtained. Firstly, listed below are the type of structural changes we can see in patients diagnosed with major depression disorder according to the review:

  • Changes in the brain’s volume is seen, meaning that physiological senses and higher functions such as muscle control, vision and hearing, memory, emotion, language, decision-making and self-control can be compromised.
  • Reduced brain connectivity is seen, leading to impaired information delivery, which may cause deficits in attention, declarative memory, executive function, and intelligence.
  • Blood vessel changes in the brain are seen, and we all know our brains CONSTANTLY need an adequate supply of oxygen!

Secondly, the impact this mental disorder has on the brain functionality is seen when brain network connectivity changes were assessed and changes in brain activity in different regions were seen using the above-mentioned technologies.

Interestingly enough, this review pointed out how most changes seen in the brain involve specific systems and networks that together cause a variety of clinical symptoms in people with depression. HOWEVER, the authors conclude that more data is required from patients of different age groups, symptoms and other related disorders to obtain highly specific results. Finding the commonality of the brain structure and/or brain function of patients in various subgroups is necessary to better diagnose individuals and find the best treatment for this disorder.

So whilst this review did well to highlight research that shows evidence of structural and functional changes associated with depression – which could contribute to validating its existence in the eyes of many and helping break down the shame attached with CSIs – it ends with the call to stand up and step forward. Diverse data is still in need. Thus, barriers need to be broken and stigmas need to be addressed. Research in this field will move forward once we choose to move forward in our thinking. In this way we make room for those who suffer in silence, those who feel too old/too far gone to be helped, or those who’ve been taught not to recognise mental health, to feel comfortable and free to share their experiences and consequently receive help.


  1. Dai L, Zhou H, Xu X, Zuo Z. Brain structural and functional changes in patients with major depressive disorder: a literature review. PeerJ. 2019 Nov 29;7:e8170–e8170.
  2. Cooper KM, Gin LE, Brownell SE. Depression as a concealable stigmatized identity: what influences whether students conceal or reveal their depression in undergraduate research experiences? Int J STEM Educ. 2020 Jul 13;7:NA.
  3. Pillay Y. State of mental health and illness in South Africa. South African J Psychol. 2019 Jun 18;49(4):463–6.
  4. The South African College of Applied Psychology, “The shocking state of mental health in South Africa in 2019”.

Bacteria reprogram our cellular metabolism- why?

by Sam Gild 

Metabolism is important- by definition, it maintains life. We know that nothing in nature is ‘random’- everything evolves by natural selection, which necessitates selective pressure. Metabolism has evolved to fit the needs of the organism it serves, and it is accordingly very diverse. By way of example, mycobacterium tuberculosis (Mtb) utilizes an enormously flexible metabolism, allowing it to thrive in diverse niches in its human host, each with its own nutrient and biochemical composition.

Mtb is but one of many intracellular infectious bacteria that has been shown to alter the metabolism of the cell that it infects. Though we know that this occurs, we have yet to unravel exactly how and, crucially, ‘why’.  As we have discussed, a ‘why’, there must be. Escoll and Buchrieser (2018), sought to compile all the relevant evidence, by way of published scientific literature. This was in order to thematize and characterize our understanding of this phenomenon – the how, and ‘why’.

The conclusion of this review was that metabolic changes in the host cell were not incidental. The host cell is selectively re-programmed by the invading bacteria in order to meet the metabolic needs of the pathogen- this is the ‘why’. Just as Mtb reprograms its host-cell to produce more lipids (which are crucial to Mtb’s survival), yet other bacteria, such as Chlamydia, utilize host-derived energy-rich substrates that are not very important to the host cell but are hugely beneficial to the respective bacterium- so-called ‘bipartite metabolism’. We may thus think of the metabolism of many a bacterial species as including the co-opted metabolism of its host cell. In addition to a broader definition of metabolism and the ‘why’ of metabolic re-programming, this intriguing work shows us that we (our cellular metabolisms) are not as autonomous or inflexible as we once thought- we are a metabolically malleable organism that lives vicariously through interactions with our bio-environments.


Escoll P, Buchrieser C. Metabolic reprogramming of host cells upon bacterial infection: Why shift to a Warburg-like metabolism?. FEBS J. 2018;285(12):2146-2160. doi:10.1111/febs.14446

T regulatory cells CARRY AN address for their tissue fate

By Mudau Nzumbululo Precious

T regulatory (Treg) cells are a subset of immune cells that play an important role of controlling immunological responses. Upon an infection, Treg cells dampen down the immune response to keep it in check and prevent chronic inflammation that could lead to tissue damage. Treg cells also prevent immune system from attacking its own tissues cells, hence avoiding autoimmune diseases. These cells, however, can be exploited in certain diseases and/or infection to evade the immune system leading to the development of cancerous tumours or chronic infections. A deeper understanding of the nature of circulating Treg cells and how they adapt   to specific tissue is important for implementing targeted therapy.

A recent study by Miragaia and colleagues has shown that Treg cells in non-lymphoid tissue (NLT) such as skin and colon expresses a unique set of genes that are required for tissue adaptation which are very different from Treg cells in the lymphoid tissue (LT: specialized immunological tissues) i.e. lymph nodes and spleen. In this study they were able to identifying unique genes that could map out Treg cell trajectories as they transit from a specific tissue to another and thus were able to predict the fate of the cells.

To achieve this the authors performed single cell RNA-sequencing (scRNA-seq) of 3500 CD4+ T cell collected from skin, colon and associated lymph nodes using the droplet based 10x genomics technique as shown in Figure 1.

Figure 1 scRNA-seq: 10x Genomics separating CD4+ T cells into single cell supension for RNA sequencing and tSNE plot showing clustering of CD4+ T cells based on gene expression.

This technique measured all the genes expressed by each individual T cells. Based on the genes each T cell expressed, they were able to group cells expressing similar genes into clusters of either Treg cells or memory T cells (Tmem – antigen experienced T cells) as shown in the Figure 1. Further examination of these cell clusters revealed high diversity of Treg cell populations within the NLT and LT.

Pseudotime ordering analysis,  an analysis that estimates the distance between cell transition states, was used to determine the trajectory of the Treg cells. Using this analysis ,transcriptomic adaptations occurring in Treg cells during their transition from the lymph node to non-lymphoid tissues could identify Treg cell subpopulations aligning in a continuous trajectory as shown in figure 2  and diverging towards their specific tissue fates carrying their respective address book e.g  branchial lymph node (bLN) Treg  (Lef1, Tcf7, Sell),  mesenteric lymph node (mLN) Treg (Nfil3, Ccr8, Cxcr6, Gzmb), skin Treg (Sell, Tcf7, Rora and Tnfrsf9), and colon Treg (Sell, Tcf7, Rora and Tnfrsf9). These finding provide an easy tool to determine the fate of Treg cells in circulation and also demonstrated that the adaptation of Treg cells migrating to skin or colon depend on a shared transcriptional trajectory.

Using a melanoma mouse model, they could also show that, the core identity of NLT Treg cells is conserved between mouse and human. This genomic approach reveals a dynamic adaptation of T cells as they traffic across tissues and provide an open resource for investigating in vivo CD4+ T cell phenotypes in mouse and human, to ultimately harness NLT CD4 T cells as future therapeutic target.

Figure 2. Pathway showing the transcriptomic adaptation of Tregs moving from lymph node to non-lymphoid tissues. A) In this plot the cells isolated from the different tissues sites are clustered based on the genes they express. B) The clustered are subseqeuntly alighned or ordered based on the diversity of the genes they express which assumes their tranistional state as they adapt to a different tissue using a model called BGPLVM (Bayesian Gaussian Process Latent variable Modeling); ( top graph show the Mln and colon; bottom graph the Bln and skin). C) Summarises the tracterory of Treg cell adaption from LT to NLT tissues.


Miragaia, R. J. et al. (2019) ‘Single-Cell Transcriptomic of Regulatory T Cells Reveals Trajectories of Tissue Adaptation Resource Single-Cell’, pp. 493–504. doi: 10.1016/j.immuni.2019.01.001

Elevating Women in Science

by Tyler Booth

I have been contemplating the words to use in this reflection for a couple of days. Although, this is not your typical reflection and more of a commentary and contemplation. While, scrolling through Twitter I came across an article published in Nature Communications that was causing quite a stir (1).  In summation the article subtly stated that women STEM mentors are not as proficient as their male counterparts in ensuring their proteges future scientific impact. However, it more directly concludes that opposite-gender mentorships may be more beneficial to female researchers.

The blatantly obvious issue I take up with the article, as a young woman in science, is the disregard for the harms on gender equality such conclusions will have, and the body of evidence and polices it contradicts. Furthermore, as a gender and climate advocate (in addition to my health science studies) it had me dumbstruck at the display of patriarchal sexism. While, the authors do suggest historical inequalities may play a role, the leap to the conclusion that moving forward this would be the case is absurd. Further, they made use of two metrics: ‘big-shot experience’, basically the number of citations of the mentor, and their network size. Importantly, the quality of a mentor cannot only be defined by these metrics and neither can the challenges faced by women in STEM.

I found countless accounts of women detailing the obstacles they have overcome in their career. Over and above the gender-pay gaps, the unequal representation of women in STEM, and sexual harassment in the workplace in general, several women cited lack of adequate mentorship and exposure to their post-doc advisors and mentors’ networks in general. As a result, when these women embraced a mentoring position their network was lacking, which dually limited their funding opportunities and their mentees exposure to a vast well-established network. Prospectively, my greatest concern is for my female peers, who as they move up in their science-related career will face the same systemic challenges. If we concede that women are the problem, without having routed out sexism in all its form, we are doing a great injustice to the future of science.

So, what can you do and what will I do? As a national youth leader on climate change, my advocacy focuses on enhancing meaningful representation of youth, women and marginalized groups in local, national and international policy spaces. Furthermore, I call for intersectional, inclusive, and just climate policies. The same can be called for in health sciences and broader STEM fields. As a friend of mine put it, ‘women are the experts of their own realties’ and as such can take charge to empower themselves and others. But what we need is allies and supporters, that aren’t only women fighting for gender equality. We need to embrace diversity and the innovations that different mindsets and experiences bring. To the women and girls, take up space! Don’t be intimidated or limited in your career aspirations. Finally support and donate to  foundations that meaningfully uplift the experiences of women in science and foster strong networks and collaborations, thereby elevating the status of women in science.


  1. AlShebli, B., Makovi, K. & Rahwan, T. The association between early career informal mentorship in academic collaborations and junior author performance. Nat Commun 11, 5855 (2020).


Cancer Ecosystems and how to collapse them

by Phillip Swanepoel

The Cancer Ecosystem

When thinking of ecosystems, cancer isn’t the first thing that comes to mind. However, tumours aren’t just a simple mass of identical cells. They can consist of a wide range of cell types, each with different set of properties. This is called “Tumour Heterogeneity”. Furthermore, tumours don’t exist in isolation. The tumour cells are constantly interacting with their surroundings, they influence it physically and chemically, and the environment responds in kind. This collection of blood vessels, signalling molecules, immune cells and more interact with the tumour to form the “Tumour microenvironment”.

Evolution, competition and cooperation all take place within and around the tumour, forming a complex web of biological interactions – an ecosystem.

One recent example comes from Glioblastomas, a type of brain cancer. Cells in Glioblastomas have been found to organise into four well-defined subtypes. Rather disturbingly, experiments have shown that tumours grown from any one of these cell types develop, once again, into this formation of four different types. This shows these cell types are not static, and similar dynamics have been shown in other types of cancer.

Understanding the Ecosystem Dynamics

The authors of a recently released paper, Transition therapy: tackling the ecology of tumor phenotypic plasticity, set out to better understand how these distinct tumour cell populations change over time, and how this is influenced by tumour treatments.

With this goal in mind they developed a mathematical model to provide some insight into the cell type switching dynamics, and how these cell types respond to targeted treatments. I won’t go into any detail about the formulas used, but the basic idea was to capture the population growth of each cell type with four terms. Each cell type has its own equation.

  1. A term that represents the base replication rate, increasing the population.
  2. A term for the switch rate of cells to a different type, reducing the population.
  3. A term for the switch rate of other cells into this type, increasing the population.
  4. A term which captures the impact of treatment, reducing the population.

Implications for cancer treatment strategies

One of the interesting results they found is that tumour heterogeneity can spontaneously develop in this model mirroring real life experiments. They also found that cell populations where heavily dependent on the “switch rate” or transition rate of the cell types between each other. Cancer treatments which target one phenotype are often not effective on some cells, which survive, and subsequently regrow the tumour. This also steers the evolution of the cancer cells, potentially causing more harm than good.

This suggested a potential treatment: transition therapy. By manipulating the rate at which these cell types transition, you can increase the effectiveness of targeted treatments. Therapeutic strategies that target cell differentiation are already being developed, and knowledge about cell differentiation and re-programming is constantly growing.

A simple example of a transition treatment strategy could be: increasing the rate at which the resistant cell type transitions into vulnerable types. This increases the number of cells targeted by the drugs, as well as reducing the population of cells available for tumour regrowth.

This example illustrates the strategy needed to collapse the cancer ecosystem – you attack its diversity.


Aguade, G.; Kauffman, S.; Sole, R. Transition Therapy: Tackling the Ecology of Tumour Phenotypic Plasticity. Preprints 2020, 2020070547. doi: 10.20944/preprints202007.0547.v1



Why Multi-omic Analysis is Crucial for Reproducibility

by Hardik Jeena

Reproducibility is one of the most important aspects of research and a great deal of effort is taken to ensure that the findings of a study can be reproduced as otherwise, the conclusions lose credibility. This crucial reproductivity can be compromised by technical as well as biological variations. This blog emphasises the importance of the latter.

There are thousands of studies that relay on cell line, particularly cancer and immortalized cell lines in their research, acquired and cultured from other laboratories. This together with genomic instability, allows for clonal selection and diversification between different laboratories and a gradual change of a cell line in the same laboratory as these cells are cultured over and over again. Consequently, this poses a major challenge as the variability and render reproducibility on only a subset of cells. Figure 1 below demonstrates how profound these variations are as two Hela cell lines (Kyoto – blue and CCL2- green) are as different as cell lines from various tissues.

Figure 1: Degree of variation between cell lines

Figure 2: Heterogeneity in CNV, mRNA and Protein



A different layer is the proteome of a cell, which is tightly corelated with the phenotype but cannot be necessarily corelated with the genome. Variation analysis in kypto Hela cell line shows that there is a poor correlation between protein and mRNA levels (p = 0.04). Figure 2 below further emphasizes this as the variation in CNV translates to variation in mRNA but not to the protein levels. This Means that cell cannot be authenticated solely on the basis of the genome and a proteomic analysis is also necessary for an acceptable cell line authentication.

In conclusion, the conclusion of a study using cell lines is not transparent if only one layer is analysed. To understand and account for the heterogeneity, a multi-omic analysis is crucial.


1. Liu, Y., Mi, Y., Mueller, T., Kreibich, S., Williams, E.G., Van Drogen, A., Borel, C., Frank, M., Germain, P.L., Bludau, I. and Mehnert, M., 2019. Multi-omic measurements of heterogeneity in HeLa cells across laboratories. Nature biotechnology, 37(3), pp.314-322.

It starts here

by Sithandiwe Dlamini

We have just begun the month of November and my mind cannot fathom how we have arrived here already. Seeing Black Friday adverts and Christmas decorations genuinely confused me, until I realised that we had two months left of this year. So before 2020 finally releases us from its clutches, I believe that another reflection is due. It has become customary for people to reflect during this pandemic. I, for one, think that this is a good thing. The importance of pausing for a moment, looking back at the route that you have taken in life and decided whether to continue the same way or to change direction. Either way, I have appreciated being forced to re-evaluate where my path is taking me.

This year has been one of discovering many things about myself, things that I have enjoyed doing and learning about. At the beginning of this year I had set for myself some academic goals and what I wished to achieve by the end of it. Looking back at my list of goals now, I realise that I may have narrowed my scope. Not in that I have missed the mark in terms of my end goal, but limiting myself in terms of what I can and am capable of doing. The forced digitisation of many interactions during the pandemic has made the world much smaller than before, which brings with it the opportunity for many interactions and connections that may have not been possible before. Not being able to go to a certain place physically is no longer a valid excuse for not chasing your dreams. And that is both exciting and scary. A big lesson for me this year is that my journey or career does not start after I graduate, but it starts now. Whatever I want to do, I can start doing now. Wherever I want to be, I can start steering myself towards that target now. Although this thought may seem small, it does remove one from a place of complacency and comfortability. Once you start realising that you can start making things happen now, you start fidgeting in that small space that you are in and become uncomfortable in that comfortability.

And hopefully we can end this year not with a feeling of regret or feeling as if this year has been stolen away from us, but with the feeling that despite the circumstances, we have made the most of it.

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