|M.Sc Student||A'laa Gharra|
|Subject||Monitoring the Release of Volatile Organic Compounds from|
Single Cancer Cells
|Department||Department of Nanoscience and Nanotechnology||Supervisor||Full Professor Haick Hossam|
|Full Thesis text|
A promising frontier for early and cost-effective diagnosis of diseases is based on volatolomics, viz. a scientific study of chemical processes involving profiles of highly- and semi- volatile organic compounds (VOCs). While excellent advance in clinical and/or in-vitro studies with tissues (or ensemble) of cancer cells have been obtained, the exact origin and metabolic outcome of VOCs forming the human volatolome have yet not been properly elucidated in sufficient depth. One of the main reasons for this lack of understanding is linked with the difficulties to obtain the volatolome fingerprint of the single cancer cell. With this in mind, the overall objective of the current thesis is to develop and approach to examine the volatolome of single cancer cell and examine the effect of specific genetic mutations of these signatures. Towards this end, the current thesis investigates the release of VOCs from three epithelial non-small lung cancer cell lines with different P53 status: Wild type, Null and mutated P53. Comparing the VOCs released from different samples e.g. free-cancer growth medium, 1, 2, 5, 10 and 10,000 cells samples. Headspace analysis of the prepared samples via of Gas Chromatography linked with Mass Spectrometer (GC-MS) indicates that the unique VOC signature is linked with the number of cells as well as with the type of the genetic mutation. Part of the identified VOCs were linked with endogenous production pathways while other part was linked with exogenous sources. The concentration of each VOC and/or the composition of the VOC pattern changed with the molecular genetic characteristics of the cells as well as with the number of cells - something that point of possible VOC-based intera-communication between the adjacent cells. The VOC changes observed by mass spectrometry could be tracked via a nanomaterial-based sensors array in which the combined response from all sensors is used to establish volatolomic signature using pattern recognition methods and classification techniques. These results raise the expectations that a very early detection of the diseases as well as tailoring a personalized diagnosis via volatolomics, for the sake of obtain personalized therapy, might come reality one day.