MOPS (Multi-Omics Personalised Screening)
The MOPS team conducts interdisciplinary research at the intersection of analytical chemistry, molecular biology, medicine and bioinformatics, developing modern methods of minimally invasive sampling and advanced strategies for multi-omic analysis. The team’s aim is to gain a better understanding of disease processes by integrating molecular, metabolic and functional data, as well as to develop new diagnostic and therapeutic tools to support the advancement of personalised medicine.
The starting point for the research is the development of micro-sampling technologies enabling the analysis of biological systems in a minimally invasive manner, often without the need for physical tissue sampling. Solid-phase microextraction (SPME), also referred to as ‘chemical biopsy’, plays a key role in this approach. This technique allows for the direct extraction of small molecules from living tissues, cell cultures and biofluids, enabling real-time metabolomic and lipidomic analysis. Thanks to the small size of the probe and minimal interference
with the system under study, it is possible to take multiple samples under in vivo/in situ conditions, allowing the tracking of dynamic metabolic changes occurring during disease progression, responses to therapy or the influence of environmental factors.

The team’s research focuses in particular on applications in oncology and transplantology, where the ability to monitor molecular changes in a minimally invasive manner is crucial for diagnosis, assessing treatment efficacy and monitoring the patient’s condition. The work being carried out is based on the integration of multiple analytical and biological technologies within a multi-omic platform, which, in addition to the aforementioned metabolomics and lipidomics, also encompasses genomic sequencing, spatial transcriptomics, protein expression analysis, and molecular
and cellular imaging. We also devote particular attention to the analysis of extracellular vesicles, which constitute an important element of intercellular communication and may serve as non-invasive biomarkers of pathological processes. The integration of data from various levels of biological organisation enables a comprehensive characterisation of the systems under study and the identification of new diagnostic markers.
Another key area of research is the development of translational models that allow the translation of basic research findings into clinical applications. To this end, we utilise an integrated experimental framework encompassing 2D and 3D cell cultures, animal models and analyses of clinical material. This approach enables the investigation of disease processes at various biological scales and the evaluation of potential therapeutic strategies.

As part of this research, we are analysing, among other things, new bioactive compounds and advanced drug delivery systems based on biodegradable polymers, which may enhance the efficacy of treatments and reduce side effects.
Given the growing complexity of omics data, we utilise machine learning
and artificial intelligence tools to identify hidden relationships between biological variables. Predictive algorithms enable us to integrate data from various sources and build models to support disease diagnosis and monitoring. In parallel, we are developing solutions in the field of data management and digital biobanking, which enable the secure storage
and analysis of large research datasets.
It is also worth noting that the approaches we employ are part of the broader trend towards sustainable analytical chemistry and modern laboratory diagnostics, promoting methods that reduce the consumption of materials, energy and biological samples.
The MOPS team brings together specialists in analytical chemistry, molecular biology, medicine, bioinformatics and materials science. Through this interdisciplinary collaboration, we are developing new research tools and technologies that enable a better understanding of the complexity of biological processes and support the development of modern, personalised medicine.