About our research group/lab
The term “human microbiome” refers to the catalogue of genes from microorganisms found both on and within different habitats of the human body. Whereas the human body contains slightly more microbial cells than human cells, those microbes contains around 3.3 million genes (dwarfing the around 20,000 human genes), which provides enormous resources. The continuous development of massively parallel sequencing technologies and analytical tools allow not only for cataloguing human microbial diversity but also for understanding the consequences of microbial presence in/on the human body for human health and disease. The forensic use of the human microbiome is a recent albeit highly promising development to which we are contributing. We expect that forensic microbiome analysis will develop towards useful forensic tools for solving crime cases where traditional methods fail. One of the forensic areas of previous limitations is determining with high certainty the cell or tissue type a biological crime scene trace belongs to, especially for those tissue types that overlap in human cells but differ in microbiota such as epithelial cells from different body parts. Forensic tissue identification can provide useful information for crime reconstruction and linking DNA-identified trace donors with actual crime. As first research outcome, we recently proposed a new approach based on the combination of 16S rRNA gene microbiome massively parallel sequencing and a taxonomy-independent deep learning that allows for the accurate identification of different forensically relevant epithelial-based tissue types including vaginal secretion, saliva and skin. We are currently expanding our forensic microbiome research towards the identification of other forensically-relevant tissue types and to explore the feasibility of inferring information on human lifestyle and other environmental factors from microbiome analysis.
Dίez Lόpez, C., Vidaki, A., Ralf, A., Montiel González, D., Radjabzadeh, D., Kraaij, R., Uitterlinden, A.G., Haas, C., Lao, O., Kayser, M. Novel taxonomy-independent deep learning microbiome approach allows for accurate classification of different forensically relevant human epithelial materials, Forensic Sc Int Genet. 2019 41: 72-82.