Fluorescence has become an essential observable in Biology and Medicine. The discrimination of a fluorescent label usually relies on optimizing its brightness and its spectral properties. Despite its widespread use, this approach still suffers from important limitations. First, extraction of a fluorescent signal is challenging in light-scattering and autofluorescent samples. Second, spectral deconvolution of overlapping absorption and emission bands can only discriminate a few labels, which strongly limits the discriminative power of emerging genetic engineering strategies, and falls short from the several tens needed for advanced bioimaging and highly multiplexed diagnostic assays. Our consortium of chemists, physicists, and biologists introduces the HIGHLIGHT concept (PHase-sensItive imaGing of reversibly pHotoswitchable Labels after modulatIon of activatinG ligHT) to achieve chromatic aberration-free highly multiplexed fluorescence imaging with only single and dual wavelength channels in emission and excitation. HIGHLIGHT aims at expanding the discriminative dimensions of fluorophore sets much beyond spectral and concentration information such as classically implemented in multicolor labeling approaches. In HIGHLIGHT, label discrimination will not necessitate anymore singular spectroscopic signatures, sophisticated reading-out instruments, or delicate data processing for signal unmixing. In contrast, it shifts towards designing reactive schemes and observables to selectively promote and retrieve the response of a targeted label. HIGHLIGHT exploits reversibly photoswitchable fluorescent proteins (RSFPs) as labels. Increasingly exploited in super-resolution microscopy and dynamic contrast, they are not only fluorescent but as well engaged in rich photocycles. The HIGHLIGHT protocols exploit their specific fluorescence responses to light modulation under well-designed conditions, which provides several dimensions of dynamic contrast to overcome the limitations encountered with spectral discrimination; These responses will serve as readouts either alone or combined using statistical machine learning strategies, which will enable us to perform real time multiplexed imaging of more than ten spectrally similar fluorescent labels and discriminate more than one hundred hues created by mixing these labels in variable amounts and cell territories. As a proof of principle, we propose to challenge HIGHLIGHT in two types of contexts where the paucity of spectrally distinct fluorescent markers has until now been a major hindrance: the analysis of the lineage of retinal cell subtypes and that of their connectivity. In this project, we will namely (i) design and implement a suite of transgenic tools enabling to express varied combinations of 6-12 RSFPs within a population of cells; (ii) design HIGHLIGHT protocols for wide-field and scanning microscopies as well as relevant barcoding strategies to discriminate different cells; (iii) evaluate the photoswitching properties of several tens of RSFPs with one- and two-photon excitation under various environments; (iv) validate HIGHLIGHT for its implementation in a commercial confocal microscope and in state-of-the art Single Plane Illumination scanning Microscopes to push forward acquisition depth and speed; and eventually (v) perform multiplexed clonal analysis in the vertebrate retina, and single-neuron tracing and analysis of axonal convergence. Eventually, the tools and protocols introduced in this project will have near-universal applicability in Biology for multiplexed fluorescence-based observations within biological samples.

Scientific background Allogeneic hematopoietic stem cell transplantation (alloHSCT) is the first cellular immunotherapy developed to cure hematologic malignancies. It is based on the anti-tumor allo-immune response (graft versus tumor effect) induced by the donor immune system also transferred during the transplant process. Despite its efficiency, hematologic malignancies relapse accounts for half of deceases and to date, no biomarker allow to predict whose patient will relapse after allogeneic HSCT and to identify these patients early before relapse. Traditional statistical methods used for biomarker identifications are limited, mostly by their parametric nature, and could benefit from advanced machine learning and optimization techniques to select relevant variables and link them to the relapsing process. This unmet medical need is of critical importance to improve prognosis of patients who are currently treated for a hematologic cancer with allo-HSCT and to adapt their treatment before relapse. Hypothesis Here, we assume that integration of clinical data with immune and metabolic variables could provide metadata for a mathematical model to predict relapse occurrence. Aims To characterize circulating immune subsets and metabolome in the donor and to compare them at 3 months and one year after transplantation in patients with or without relapse To build a calibrated stochastic simulator for the relapsing process, accounting for post-transplant events and integrating clinical data with immune and metabolic variables. Methodology This project will rely on a multicentric cohort of 369 patients who received an alloHSCT. We will use mass cytometry and mass spectrometry to decipher circulating immune subsets and metabolites associated with relapse and other post-transplantation events. We will then create a simulator that model the dynamics of post-transplant events to identify relevant biomarkers using advanced optimization techniques and to generate a tool to predict relapse after alloHSCT. Validation in animal model will finally help to identify relevant new therapeutic targets. Expected results and impact This project will use data from an already constituted large cohort of patients to develop a machine learning tool for clinicians to estimate the probability of relapse based on various clinical and immune-metabolic data.

In the absence of its cognate ligand (retinoic acid, ATRA), retinoic acid receptor alpha (RARα) acts as a transcriptional repressor by recruiting corepressor (CoR) complexes to target genes. This constitutive repression is crucial in metazoans. However, its specific molecular determinants remain obscure. Acute promyelocytic leukemias (APLs) arise from different chromosomal translocations that create x-RARα fusion proteins (like PML-RARα and PLZF-RARα). Unlike RARα, x-RARα fails to release properly CoRs in response to ATRA and block differentiation of myeloid cells. There is a great need to decipher the interaction complexity within the RARα repressive complex, since disruption of this system shifts the balance toward abnormal gene regulation and pathology. The project RepreX proposes a novel approach to study pathogenic disturbance of RARα signaling, at the atomic, molecular and cellular scales. Its strength resides in the synergism that will emerge from an integrative approach combining structural (by X-ray crystallography and cryoEM) and dynamics studies (by SAXS, mass spectrometry (HDX-MS), and fluorescence methods (smFRET)) of the native full-length proteins and their functional complexes, and allowing to connect detailed structural models with biological function. Understanding the precise molecular underpinnings of RARα function requires integrative modelling of the flexible multi-domain and functionally dimerized RARα/RXR heterodimer bound to DNA and to intrinsically disordered transcriptional coregulators (like CoR). We aim at obtaining a complete structural and dynamic picture of the full repressive RARα/RXR complexes, in its physiological form (wild type RARα) and in its pathological form (PML-RARα and PLZF-RARα). These results promise to explain how signals coming from the various components are integrated and turned into a particular physiological or pathological response, providing better frameworks for guiding future drug discovery efforts. The structural information gained from this work will most likely reveal unknown specific binding sites and interaction surfaces representing unexplored opportunities for the development of novel targeted therapeutic strategies. In addition to improving our knowledge on retinoic acid signaling, the project RepreX will have important long-term repercussions for human health, regarding the great expectations of biologists and medical researchers for studies like it that will give a precise description of the molecular mechanisms of action of the x-RARα fusion proteins. We have already established collaborations with the group of H. de Thé (Collège de France, Hôpital Saint Louis) who will bring complementary knowledge on biology of PML-RARα and with the group of A. Yunes (Boldrini Hospital, Campinas, Brasil) whose clinical research is focused on elucidating the genetic and molecular mechanisms of leukemia progression and resistance to therapy.

Enhancement of lung tissue regeneration and functional recovery following an acute pulmonary insult by regenerative therapy based on the use of heparan sulfate mimetics (HSM) is a promising approach for the treatment of diffuse alveolar damage (DAD), observed in acute respiratory distress syndrome (ARDS), acute exacerbations of fibrotic interstitial lung diseases or severe COVID-19 pneumonitis. The beneficial effects of HSM would be mainly related to the regeneration of the injured tissue by restructuring the destroyed matrix, protecting the cellular communicating peptides (growth factors, cytokines, chemokines), and by limiting the fibrosis usually observed during the repair process. Using of innovative approaches and models developed by 4 partners including a private one, the MAT-PL project will explore the ability of a heparan sulphate mimetic agent (OTR4132) to reduce inflammation, dysregulated angiogenesis and fibrosis, and improves the outcome of acute pulmonary injuries characteristic of DAD, thus opening a new approach for the prevention and the treatment of lung fibrosis.

The interstitial fibrosis is a major health problem because it causes dysfunction of many organs and is responsible for a high degree of morbidity and mortality due to organ injury. The frequency of pathologies with fibrosis increases progressively due to aging of the population and increase in risk factors due to occidental way of life. In this way, chronic kidney disease (CKD) is one of the pathologies associated with fibrosis, leading to renal dysfunction and to end stage renal disease. However, until now, no specific biomarker and no treatment are available,requiring to further study the molecular regulation of renal fibrosis. Our hypothesis is that soluble CD146 (sCD146), a new growth factor secreted abundantly during CKD, is involved in the development of interstitial fibrosis. The objective of our project will thus be to delineate the contribution of sCD146 to kidney fibrosis development, to evaluate its diagnostic interest as a biomarker, and to test newly generated anti-sCD146 antibodies as a new therapeutic approach.