Biology subjects

Figarella-Branger, D.

Publications and source records attributed to Figarella-Branger, D..

5 recordsLinked to original sources

bioRxiv2025

Integrated 3D Light-Sheet and 2D Multiplex Imaging for Deep Histological Profiling of a Somatic Mouse Glioblastoma Model

Glioblastoma is a devastating brain cancer. Despite intense research, patient survival has not significantly increased over the past decades and efficient treatment is currently not available. Therefore, the fundamental understanding of the disease, based on the development of relevant animal models, combined with the development of efficient tools for their deep analysis, represents a priority. Neural Stem cells in the subventricular zone of the forebrain have been identified as cells of origin for glioblastoma, leading to the development of new somatic lineage models based on in vivo brain electroporation. While such models have been characterized in depths by sequencing approaches, systematic histological analyses are currently scarce. Here we present the multimodal histological characterization of a transgenesis independent somatic glioblastoma model in mice. Using 3D light sheet imaging we demonstrate that the model is highly reproducible, allowing quantitative evaluation of tumor growth over large cohorts. Using multiplex imaging by MICS technology we systematically characterize the cellular landscape and molecular composition of the induced tumors, as well as their micro- and macro-environments, and provide a resource of mouse compatible antibodies for cancer research. Finally, we use the model to show that tissue clearing and 3D light sheet microscopy of whole brains can be combined with subsequent multiplex imaging, allowing deep spatial characterization of the tumor proteome in pre-identified brain regions.

cancer biology↗

bioRxiv2023

GluK2 is a target for gene therapy in drug-resistant Temporal Lobe Epilepsy

ObjectiveTemporal lobe epilepsy (TLE) is characterized by recurrent seizures generated in the limbic system, particularly in the hippocampus. In TLE, recurrent mossy fiber sprouting from dentate gyrus granule cells (DGCs) creates an aberrant epileptogenic network between DGCs which operates via ectopically expressed GluK2/GluK5-containing kainate receptors (KARs). TLE patients are often resistant to anti-seizure medications and suffer significant comorbidities; hence there is an urgent need for novel therapies. Previously we have shown that GluK2 knockout mice are protected from seizures. This study aims at providing evidence that downregulating KARs in the hippocampus using gene therapy reduces chronic epileptic discharges in TLE. MethodsWe combined molecular biology and electrophysiology in rodent models of TLE and in hippocampal slices surgically resected from patients with drug-resistant TLE. ResultsHere we confirmed the translational potential of KAR suppression using a non-selective KAR antagonist that markedly attenuated Interictal-like Epileptiform Discharges (IEDs) in TLE patient-derived hippocampal slices. An adeno-associated virus (AAV) serotype-9 vector expressing anti-grik2 miRNA was designed to specifically downregulate GluK2 expression. Direct delivery of AAV9-anti grik2 miRNA into the hippocampus of TLE mice led to a marked reduction in seizure activity. Transduction of TLE patient hippocampal slices reduced levels of GluK2 protein and, most importantly, significantly reduced IEDs. InterpretationOur gene silencing strategy to knock down aberrant GluK2 expression demonstrates inhibition of chronic seizure in a mouse TLE model and IEDs in cultured slices derived from TLE patients. These results provide proof-of-concept for a gene therapy approach targeting GluK2 KARs for drug-resistant TLE patients.

neuroscience↗

bioRxiv2022

Combination drug screen targeting glioblastoma core vulnerabilities reveals pharmacological synergisms

Synergistic drug combinations are an attractive anticancer strategy but prove challenging to identify. Here we present a stepwise approach consisting in revealing core cancer vulnerabilities and exploiting them through drug combination screen to uncover synergistic treatments for glioblastoma patients. MethodsWe established an innovative method, based on high-throughput screening, target deconvolution and functional genomics, to reveal core vulnerabilities in glioblastoma. Combination drug screen targeting these vulnerabilities was then designed to unveil synergistic associations. The therapeutic potential of the top drug combination was validated in two different clinically-relevant models: an organotypic ex vivo model and a syngeneic orthotopic mouse model of glioblastoma. ResultsLarge-scale monotherapy drug screening identified 83 potent anti-glioblastoma compounds. Target deconvolution using public chemoinformatic databases uncovered 1,100 targets and interactors of the hit compounds. Screening of a focused siRNA library targeting the top 292 drug interactors revealed 22 targetable vulnerabilities, 9 of which were confirmed as core glioblastoma vulnerabilities by mining the CRISPR screen cohort data from the online Cancer Dependency Map portal. Six selective inhibitors of the core vulnerabilities were then screened in combination with a custom-made library of 88 compounds and synergies amongst the 528 tested pairwise combinations were predicted. The combinations of CHK1 / MEK and AURKA / BET inhibitors were highlighted and validated in 3D tumor spheroids. Using an organotypic ex vivo model and a syngeneic orthotopic mouse model, we definitively ascertained the efficacy of dual AURKA / BET inhibition in glioblastoma. ConclusionsCollectively, we uncovered that dual inhibition of BET proteins and aurora kinase A is highly synergistic against GBM. Moreover, our study indicates that our approach to exploit drug poly-pharmacology for the rational design of drug combination screens represent a valuable strategy to discover synergistic treatments against refractory cancers.

pharmacology and toxicology↗

bioRxiv2020

An AI-powered blood test to detect cancer using nanoDSF

We describe a novel cancer diagnostic method based on plasma denaturation profiles obtained by a non-conventional use of Differential Scanning Fluorimetry. We show that 84 glioma patients and 63 healthy controls can be automatically classified using denaturation profiles with the help of machine learning algorithms with 92% accuracy. Proposed high throughput workflow can be applied to any type of cancer and could become a powerful pan-cancer diagnostic and monitoring tool from a simple blood test.

cancer biology↗

bioRxiv2018

Inhibitor of apoptosis proteins determine glioblastoma stem-like cells fate depending on oxygen level

In glioblastomas, apoptosis inhibitor proteins (IAPs) are involved in apoptotic and non-apoptotic processes. Here we used GDC-0152, a small molecule IAP inhibitor, to explore how IAPs participate in glioblastoma stem-like cell maintenance and fate under both hypoxic and normoxic environments. In hypoxia, IAPs inhibition triggered stem-like cells apoptosis and decreased proliferation in four human glioblastoma cell lines, whereas in normoxia it induced a loss of stemness and differentiation. In addition, we characterized a 3D glioblastoma spheroid model. By using MALDI images we validated that GDC-0152 penetrates in the entire sphere. TOF-SIMS analyses revealed an oxygen gradient correlated with spatial cellular heterogeneity with proliferative and apoptotic cells located close to the hypoxic core and GFAP+ cells at the periphery. Notably, Serine-Threonine Kinases activation analysis revealed that oxygen level affects signaling pathways activated by GDC-0152. In hypoxia, IAPs inhibition activated ATR whereas in normoxia it activated NF-{kappa}B. Our data brings new mechanistic insights revealing the dual role of IAPs inhibitors like GDC-0152 that are relevant to their therapeutic application in tumors like glioblastomas.

cancer biology↗