Micro-needles functionalized with aptamers for the optical detection of cortisol
Compact, wearable medical devices, by offering autonomous and continuous monitoring of biomarkers, open the way to precise monitoring of pathologies outside of care centers and to a personalized therapeutic approach. The thesis project aims to develop wearable sensors based on microneedles (MNs) made of biomaterials for the minimally invasive detection of cortisol in the interstitial fluid (ISF) of the skin. Cortisol is one of the important biomarkers of physical and psychological stress, and is linked to the development of chronic diseases. ISF, a very rich source of biomarkers, offers an alternative to blood as a minimally invasive biofluid for cortisol quantification, and can be continuously analyzed by microneedle devices. Thus, swelling microneedles made of crosslinked biopolymer hydrogel have been developed at CEA-Leti over the last three years for ISF collection and analysis. The objective of the project will be to functionalize the hydrogel with a cortisol-sensitive aptamer molecular beacon, whose fluorescence will be activated in the specific presence of this metabolite, drawing on the expertise of the DPM NOVA team. Wearable optical sensors based on cortisol-sensitive MN patches will be designed, exploring two configurations: MN patches entirely made of hydrogel, and hybrid MN patches comprising an optical waveguide biopolymer and a cortisol-sensitive hydrogel coating. Different needle/waveguide shapes will be explored to optimize the fluorescence detection performance of the biosensors. The ability of the devices to puncture a skin model, sample artificial ISF, and detect the target will also be evaluated. The study will include biocompatibility tests, as well as a comparison with current methods for measuring serum cortisol by immunoassay.
Elucidation of the homarine degradation pathway in the oceans
Context:
Primary biological production in the oceans exerts significant control over atmospheric CO2. Every day, phytoplankton transform 100 million tonnes of CO2 into thousands of different organic compounds (1). Most of these molecules (as metabolites) are biologically labile and converted back into CO2 within a few hours or days. The climate-carbon feedback loops mediated by this reservoir of labile dissolved organic carbon (DOC) depend on this network of microbes and metabolites. In other words, the resilience of the ocean to global changes(such as temperature rise and acidification) will depend on how this network responds to these perturbations.
Because of its short lifespan, this pool of labile DOC is difficult to observe. Yet these microbial metabolites are the most important carbon transport pathways in the ocean and are assimilated by marine bacteria as sources of carbon and energy. Knowledge of the main metabolic pathways (from genes to metabolites) is therefore essential for modelling carbon flows in the oceans. However, the diversity of these molecules remains largely unexplored and many of them have no annotated biosynthetic and/or catabolic pathways. This is the case for homarin (N-methylpicolinate), an abundant compound in the oceans. Homarine content can reach 400 mM in the marine cyanobacterium Synechococchus (2) and this ubiquitous organism contributes between 10 and 20% of global net primary production (3).Because of its abundance, homarine is probably an important metabolite in the carbon cycle.
Project:
In this thesis project, we aim to elucidate the homarine degradation pathway in the oceans.
Ruegeria pomeroyi DSS-3 is a Gram-negative aerobic bacterium and a member of the marine Roseobacter clade. Its close relatives account for around 10-20% of the bacterial plankton in the mixed coastal and oceanic layer (4). In the laboratory, DSS-3 can use homarine as its sole carbon source but to date, there is no information on the genes and catabolites involved in this process.
Comparative analysis of RNAseq experiments conducted on DSS-3 cultures grown with homarine or glucose (control) as a carbon source will enable us to identify the candidate genes involved in the degradation pathway. This pathway will also be studied using a metabolomic approach based on liquid chromatography coupled with very high resolution mass spectrometry. The difference in profile between DSS-3 metabolomes from cells grown on glucose as a carbon source and those from cells grown on homarine will help to detect catabolites in the pathway. Finally, the candidate genes will be cloned for recombinant expression in E. coli, the corresponding proteins purified and their activity characterized in order to reconstruct the entire homarine degradation pathway in vitro.
Analysis of the expression of these genes in data from the Tara Oceans project (5) will be the first step towards a better understanding of the role of homarine in the carbon cycle.
References :
(1) doi.org/10.1038/358741a0
(2) doi.org/10.1128/mSystems.01334-20
(3) doi.org/10.1073/pnas.1307701110
(4) doi.10.1038/nature03170
(5) https://fondationtaraocean.org/expedition/tara-oceans/
Impact of a sodium nitrate saline plume on the radionuclide confinement properties of cementitious matrices
Using modelling to predict the migration of radioactive species through a well-known porous matrix, such as concrete, is a major challenge for society, particularly in the context of studies linked to the radioactive waste management. Demonstrating that the proposed model is robust through targeted laboratory experiments under extreme chemico-physical conditions is one of the scientific challenges proposed by the CEA as part of this PhD research project.
The young reseacher will be responsible for designing, carrying out and modelling experimental lab-tests on the retention and diffusion of radionuclides of interest in controlled cementitious conditions or under perturbation due to the nitrate plume leading to very high concentrations in the pore solution. The main expected result is to propose a predictive model coupling chemistry under extreme ionic strenght conditions and transport through complex cementitious matrices, validated by experimental data acquired on simple systems.
Surrounded by a team of experts in the field of measuring and modelling radionuclides migration in porous media, the PhD student will be able to develop or extend his/her skills in the following areas: chemistry, analytical chemistry, physico-chemistry, radiochemistry and modelling.
Development of an automated and miniaturised system for the isotopic analysis of nuclear samples
Miniaturisation, which is the process of reducing an object’s, a method’s or a function’s dimensions while preserving or even upgrading its performances as compared to the classical scale, has a particular interest in the field of analytical chemistry for nuclear applications. Indeed, most of the analyses are performed in gloveboxes where miniaturisation and automation are a direct solution to the need for reduced doses and waste volumes. This PhD aims at developing a miniaturised and automated system, in a glove box, for performing high-precision isotopic measurements. This system will use capillary electrophoresis (CE) hyphenated with a nuclearised multicollector ICP-MS (MC-ICP-MS). During this PhD, the student will make use of micro-machining machines and 3D printers to develop an ergonomic system which will then be coupled to last generation MC-ICP-MS instruments available in our laboratory. The project will be focused on the conception of the automated system and its integration in the glove box, and on the further development of the existing CE method in order to perform isotopic and elemental analyses with nuclear samples. This PhD is hosted in a laboratory internationally recognized for its ability to carry out high precision isotopic measurements. An analytical chemistry curriculum is expected and a Master 2 internship is available before this PhD.
Radiolytic Degradation of N,N-dialkylamides: Effects on Metal Complexation
N,N-dialkylamides (or monoamides) are promising extractant molecules for the development of new processes for nuclear fuel reprocessing. In this context, these extractant molecules are exposed to radiolysis caused by ionizing radiation from radionuclides, which leads to the formation of new compounds through the breaking or modification of chemical bonds. Such changes in solution composition can alter the extractive properties, particularly in terms of efficiency and selectivity.
This thesis aims to study the impact of radiolysis on the speciation of actinide-ligand complexes in solution, in order to improve the understanding of the phenomena observed under ionizing radiation. We propose an approach combining experimental studies (chromatographic and spectroscopic techniques) with theoretical calculations (such as bond dissociation energies, identification of probable radical attack sites, stability of metal-ligand complexes, etc.) to describe the molecular speciation of species in solution. Organic compounds formed during radiation and the metallic complexes will be characterized to evaluate the modifications caused by radiation.
Spectrometry and Artificial Intelligence: development of explainable, sober and reliable AI models for materials analysis
The discovery of new materials is crucial to meeting many current societal challenges. One of the pillars of this discovery capacity is to have means of characterizing these materials which are rapid, reliable and whose measurement uncertainties are qualified, even quantified.
This PhD project is part of this approach and aims to significantly improve the different ion beam induced spectrometry (IBA) techniques using advanced artificial intelligence (AI) methods. This project aims to develop explainable, sober and reliable AI models for materials analysis.
The PhD project proposed here has three main objectives:
- Develop an uncertainty model using probabilistic machine learning techniques in order to quantify the uncertainties associated with a prediction.
- Due to the very large number of possible combinatory-generated configurations, it is important to understand the intrinsic dimensionality of the problem. We wish to implement means of massive dimensionality reduction, in particular non-linear methods such as autoencoders, as well as PIML (Physics Informed Machine Learning) concepts.
- Evaluate the possibility of generalization of this methodology to other spectroscopic techniques.