26 SKIN MICROBIOME
analyzes all microorganisms, both dead and alive. Metatranscriptomics will identify microorganisms without functional activity, hinting that they are most likely dead. This is an important feature when focusing on the skin microbiome where the microorganisms are continuously threatened by deadly external sources, like sun exposure. Metatranscriptomics still faces challenges
to be ready to uncover all the ins and outs of the skin microbiome. As consumers continue to demand more from their beauty products, the use of scientific techniques to deliver detailed data is welcome. The expectation is that this will become the new norm in cosmetics. Imagine, for example, when starting to formulate an acne product, that it could be possible to find out if C. acnes is acting as a rebel member of the community or a protective microbe, all by determining which genes are turned on or off. Alternatively, metatranscriptomics can be used to determine when the skin microbiome screams for more help. When do the microbes feel threatened and what would be the best approach to support them?
New technologies As previously addressed, the skin microbiome can be analyzed using DNA and RNA, but other strategies can take this even further by focusing on proteins (metaproteomics) or metabolites (metabolomics). Using these tests, the musical notes that are produced by the microbial orchestra are revealed. It gives insights into whether the instrument plays falsely or exceptionally well. These tests look through the eyes of the conductor by rewriting all the sheet music. Metaproteomics and metabolomics are
relatively new fields of research. These tests can give insights into the exact molecules that interact with the skin, ensure health, or prevent the skin microbiome from getting out of balance.
In the future, a combination of all strategies will be the best approach. Metagenomics to identify the microbial community, metatranscriptomics to learn about the function, and learning what type of role a microbe has in the community by zooming in on the proteins and the metabolites.
How to interpret the data? When exploring the beauty of the skin microbiome, difficulties can arise in interpreting the data. A successful interpretation of the data starts with a clear study design. As an example of how to interpret the data, consider the following research question: “Does the prebiotic-containing product improve the microbial balance on the skin of people with acne?” Factors that come into play when designing such a study would be the number of participants, the sampling method, the controls, and which test method. As a rule of thumb, the more participants
the stronger the result. It can be difficult to find a good representative group of volunteers. It is therefore advisable to have a minimum of five participants for the treatment group. This research focuses on acne, the bacteria
involved in this infection are deep in the pore. So, the sampling method of choice is sticky tape or scraping. Obviously, some controls must be included.
Consider the placebo treatment without the prebiotic, and a sample of no treatment at all is a must-have. Finally, the most important choice is the
type of test. The challenge of the research question is that there is not a definition of a balanced microbiome. Metagenomics helps to find this definition because it requires no prior knowledge. Now, that the study design has been established, the next step is to report the data. Metagenomics details a list of all the microbes on the skin. From this, the diversity can be reported. The alpha diversity gives the different
microbes within a treatment group and the beta diversity is the difference between the two treatment groups. The diversity indices already give a good indication of the general difference between the groups with and without the prebiotic treatment. Secondly, the microbial composition can
be determined between treatment groups. It represents how much a certain microbe occurs on the skin. C. acnes should be present at approximately 60% on the skin. With acne cases, it is more likely at 80%. So, if this number is brought back to 60% by using the prebiotic, it implies that the product had a positive effect on rebalancing the skin microbiome. Thirdly, the microbial compositions of all
participants can be plotted in an unsupervised manner. This means that the algorithm finds the most important microbial species without knowing anything about the data. A plot that shows distinguished grouping
between the samples taken from people using and not using prebiotics strongly implies that the skin microbiome has changed positively. This graph is an example of a dimension- reduction technique
Conclusion When it comes to the skin microbiome it is important to consider all the microorganisms and interactions between them. New technologies have overshadowed and replaced traditional in vitro cultivation for identifying microorganisms. Amplicon sequencing can identify each
instrument in the symphony, metagenomics reveals the sound of the instruments, and other techniques, such as metatranscriptomics, unveil all the individual notes. It is with all these instruments that the Gobiotics team works to highlight the properties of its ingredients for the benefit of its clients and consumers alike. So, enter our theatre, take a seat in the
audience, and discover the sound of our microbial orchestra.
PC
Figure 2: A simplified example to show how to visualize differences in the skin microbiome. Imagine that there are only two species identified for two different test groups (red and blue). We can plot the occurrence in 2D but imagine that 2D is difficult to comprehend. The solution would be to make a 1D plot. An algorithm can find the most important line in a 2D space, hence the name dimension reduction. All the data is projected on this lane. Suddenly, the two testing groups show very distinguishable groups, meaning different skin microbiomes. This can be expanded to as many species or dimensions as possible
PERSONAL CARE April 2025
www.personalcaremagazine.com
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