Optophysiologie SERS : analyse in vitro d’environnement cellulaire en Raman exalté par les surfaces

To insure the signalling and regulation of their metabolism, living cells secrete a plethora of small and large molecule referred as chemical messengers. These messengers are generally secreted into the extracellular space, and diffuse until they reached a specific molecular target. The resulting biological recognition event between a chemical messenger and its dedicated molecular receptor induces a cascade of biochemical reactions, corresponding to the fundamental function of biological cells. However, a dysfunction associated to the secretion of these chemical messengers is linked to multiple clinical diseases. As an example, a change in the composition of neurotransmitters released during synaptic transmission in the brain is associated to Alzheimer disease, Parkinson disease, depression, addiction and schizophrenia. Thus, the analysis of the chemical messenger is of great clinical interest. However, modern analytical methods are ill suited to capture the fast and heterogeneous changes in chemical messengers during secretion events. New technologies allowing a sensitive and simultaneous monitoring of chemical messengers in a local and non-destructive way is needed.  Plasmonic materials enable surface enhanced spectroscopies such as surface enhanced Raman scattering (SERS). With the molecular selectivity of SERS, and its high sensitivity, capable of detecting a single molecule, SERS sensors offer multiple advantages allowing the development of new technologies adapted for monitoring cellular secretion.  The main objective of the thesis is the development of a novel SERS sensor capable of monitoring cellular secretion with both high sensitivity and selectivity. To achieve this goal, gold nanoparticles were electrostatically adsorbed onto a nanometric sized patch clamp glass capillary. The immobilization of the nanoparticles led to the generation of plasmonic nanosensors, which can be positioned in space while keeping a constant aggregation state, a key parameter in SERS.  These nanosensors were then positioned near cells in vitro and SERS spectra were acquired in time. In order to analyze the complex optical response, novel chemometric approaches were developed.   As a starting point, a unique barcode was extracted from each experimental SERS spectrum detected in time, and then compared with a spectral database of SERS barcode extracted from various standards. This approach enabled (1) identification of the molecule according to the barcode, and (2) a semi-quantitative analysis by counting the number of times the barcode, for each standard, was detected within a defined timescale. This chemometric method, referred as SERS optophysiology, was applied to monitor abundant metabolites in the extracellular environment near MDCKII cells in both basal and stimulated conditions. As this is a new method, SERS optophysiology required optimization of its hyper-parameters prior to the analysis, thus limiting its multiplex capability and adaptation to detect novel molecular targets.  To improve the analytic performance of SERS optophysiology, we implemented a new algorithm to evaluate the barcodes. This new algorithm was applied to monitor neurotransmitters secreted by dopaminergic neurons from mice mesencephalon. Also, to visualize the neurons and correctly positioned the nanosensor near axonal varicosities, a hyphenated fluorescence-Raman microscope was built. With these tools, we successfully monitored the relative changes in concentration of five neurotransmitters in basal condition and throughout multiple stimulation cycles.  Although improved, the analytical performances of SERS optophysiology depend mostly on the barcode data processing which thus required an extensive optimization to achieve good selectivity and sensitivity, limiting the translation of these technologies to other users. To overcome this limitation, multiple barcodes were thus associated to a single standard and analyzed with an artificial neural network, analogous to facial recognition. Machine learning driven SERS optophysiology enabled the detection of more than seven metabolites associated to the metabolism of carbon, nitrogen and energy of healthy and cancerous cell lines.  The experiments presented in the thesis show the application potential of SERS optophysiology for the detection of secreted metabolites by neurons, or other type of cells exhibiting different phenotype, in order to investigate biological functions in a normal or disease state. These novel technologies thus hold a promising potential for significant discoveries in many fields of medicine and general science.

Keyphrases: Cellular metabolism, Surface enhanced Raman spectroscopy, Artificial intelligence, Nanosensor, Neurotransmitters, Cancer cells, Neurons, SERS optophysiology