The recent rise of high-resolution and depth imaging techniques like photoacoustic microscopy (PA) stimulates novel research areas in biology. In vivo tracking of immune cells, signaling inflammation and severe pathologies thereof, is one of them and attracts great interest. The AZOTICS project thus aims at addressing the current PA microscopy limitations by fabricating innovative biocompatible elastomeric nanolabels relying on azo photochromes. Photostimulated actuation mechanisms will help amplify the PA contrast based on thermal expansion. The photoinduced mechanical deformations of single nano-objects will be assessed at the nanoscale using atomic force microscopy in order to propose a rationale for the performance of photoacoustic probes beyond their sole optical absorption ability. Their PA imaging capability will be validated through an in vitro, in cellulo and in vivo continuum of studies involving macrophage staining, microfluidic systems mimicking microvasculature, and models of acute inflammatory activated in mice. The interdisciplinary AZOTICS consortium gathers experts in chemistry, physics and optics from Nantes and Grenoble, having already tightly worked together and being keen to share their knowledge in order not only to address unexplored fundamental questions but also to propose innovative photoacoustic systems for in vivo imaging.

Pain is one of the most distressing symptoms during the early postoperative period. Pain also occurs when rigid sealants are employed to stick tissues or close wounds as they hurt fragile tissues. To the best of our knowledge there is yet no sealant able to connect tissues in a soft manner. In this context, our project aims to develop original “soft glues” to efficiently connect tissues without mechanical constraints. SoftGlue will be elaborated in a one-step, easy to be scaled-up method, in water, at room temperature and without the need of organic solvents. In vitro studies will be carefully designed to minimize animal experiments, better understand the mechanisms of action of SoftGlue and optimize the formulations. First, in vitro sealing properties of SoftGlue will be assessed and compared to commercial products. Mechanical properties will be characterized using a set of standard procedures. In addition, we will develop an original method to study the bioadhesive properties, using an optical device to visualize SoftGlue in tissues under mechanical stresses (effect of thermo - mechanical solicitations) mimicking in vivo bonding wounds, blood flow, etc. After in vitro evaluations, selected few (3-4) SoftGlue will be evaluated in vivo. Tissue regeneration will be evaluated and neovessels formation will be investigated. Cell proliferation and migration will be assessed. Finally, a detailed diagram of the possible in vitro and in vivo toxic and genotoxic effects will be established. This project is a real opportunity to develop a long-term collaboration between complementary teams and to get this consortium persisting beyond scope of the project. SoftGlue is an ambitious interdisciplinary project dealing with an important societal need.

Breast cancer is the cancer with the highest incidence in women worldwide, and is the leading cause of cancer-related death, mainly due to treatment resistance. Recently, tumor heterogeneity has been described as one of the key driver in treatment failure. Indeed, tumor is not a homogeneous entity to treat, but a complex association of subclonal populations driven by their own genetic alterations, and immune and stromal cells from microenvironment. Breast cancer subtypes and tumor heterogeneity advocate for the development of tailored, personalized treatments, but so far, the discovery of efficient predictive markers has been compromised by the lack of adapted biological models and methodological tools. The recent developments of high-throughput methods for bulk and single-cell analyses has generated large ‘omics’ datasets from patients, stored in open access databases (ArrayExpress, GEO). Combining these numerous datasets will grant a sufficient statistical power to reveal a comprehensive overview of tumor complexity. However, this data mining is currently limited by methodological challenges like cross-platform normalization and the difficulty to analyze complex data structure with high dimension observations. To overcome these issues, I propose to implement a multidisciplinary project at the interface between mathematics, biology, and information technologies. With the support of the mathematicians and bioinformaticians from the Bioinfomics unit of the regional comprehensive cancer center (ICO), I will develop and implement machine-learning algorithms in the search of predictive biomarkers for breast cancer treatment. This innovative strategy will lead to personalized medicine in breast cancer by guiding clinicians in the selection of the optimal therapeutic option. Moreover, this generated pipeline for predictive marker discovery could be further adapted for the treatment of other cancer types.