Project Title: SINGLE CELL MAPPING OF THE VASCULATURE IN AGGRESSIVE B LYMPHOMAS AND TOPOLOGICAL INTERACTIONS WITH CD8 T CELLS AND y8 T CELLS
Keywords: aggressive B lymphoma, high endothelial venule, endothelial cell, spatial transcriptomics, scRNA-seq, anti-tumor immunity)
Abstract: Immunity plays an important role in cancer control, notably with highly mutated tumours such as Diffuse Large B-cell Lymphoma (DLBCL), an aggressive (fast-growing) non-Hodgkin lymphoma that affects B cells. Indeed, CD8 T cells and γδ T cells display potent antitumour cytotoxicity and are specifically reactive to lymphomas. DLBCL often develops in the lymph nodes. High endothelial venules (HEVs) are specialized blood vessels for lymphocyte entry into lymph nodes. HEVs may thus play an important role in the recruitment of CD8 T cells and γδ T lymphocytes in DLBCL lymph nodes.
The major objective of the project is to perform single cell mapping and spatial transcriptomics (ST) to characterize the density, maturation and functional status of HEV endothelial cells (MECA-79+CD31+) and non-HEV endothelial cells (MECA-79-CD31+), and define their topological interactions with cytotoxic γδ T cells and CD8 T cells in DLBCL patient’s biopsies. The project will greatly benefit from the expertise of:
– Jean-Philippe Girard’s team (IPBS) on HEV blood vessels, scRNASeq analyses of endothelial cells, and HEV-mediated lymphocyte recruitment in lymph nodes (Moussion and Girard, Nature 2011; Girard et al., Nat Rev Immunol 2012; Lafouresse et al., Blood 2015; Veerman et al., Cell Rep 2019), and
– Camille Laurent’s team (CRCT) on human B cell lymphomas (Laurent et al., Blood. 2020; Laurent et al., J Clin Oncol. 2017; Laurent et al., Blood. 2011), cytotoxic T cells, single cell RNA sequencing (scRNASeq, CITEseq) (Pizzolato et al., PNAS 2019; Pont et al., Nucleic Acids Res 2019) and spatial transcriptomics (transcriptomic hallmarks of all cells in a lymphoma biopsy in situ).
This study will deeply increase our knowledge about the distribution and functional status of HEV and non-HEV endothelial cells, γδ and other cytolytic T cells infiltrating human lymphomas. It may have important clinical consequences such as the identification of new predictive biomarkers, and guide T cell-based immunotherapies currently developed in Comprehensive Cancer Centers worldwide.
Requirement: Master in Cancerology, Immunology, Vascular Biology or Molecular Biology. Experience in scRNA-seq and/or bioinformatics will be a plus. We are looking for a creative and highly motivated PhD student strongly committed to research. Joint PhD supervisors: Drs Jean-Philippe Girard (IPBS) and Camille Laurent (CRCT).
Contract: 3 years full time PhD student position funded by LABEX TOUCAN (starting date: October 1st, 2021). The salary is in accordance with the French Ministry for Higher Education and Research salary scale. Social security and health benefits are included. Work context: research conducted at both IPBS and CRCT, two large Research Centers from CNRS, Inserm and University of Toulouse. All the necessary biological resources and research facilities, including state of the art technological facilities, will be available.
Deadline: the position will remain open until filled; only successful applicant will be contacted
How to apply: Please, send your application (in French or English, including a motivation letter, curriculum vitae, and rankings in University Licence/Master or Engineering school).
PhD supervisor : Nathalie ANDRIEU, CRCT: Toulouse Cancer Research Center, Toulouse, France
PhD co-supervisor : Gemma Fabrias, IQAC-CSIC: Institute for Advanced Chemistry of Catalonia, Barcelona, Spain
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CARe PhD Proposals
PhD supervisor : Chantal Soulé-Dupuy – IRIT, Toulouse
PhD co-supervisor : Paul Monsarrat – Restore, Toulouse
Aging is an overly complex biological process, involving multiple mechanisms at different levels, from molecular to tissue scale. With the increase of life expectancy, the challenge is to predict the physiological age to reach healthy aging, namely the one’s ability to adapt and efﬁciently respond to stressors. The recent use of machine learning (ML) strategies may accurately model the physiological age to prevent age-related disruption. However, black boxes properties of most ML algorithms do not support the understanding of their internal decision-making mechanisms (i.e., the explainability), crucial to highlight the critical variables on the prediction.
Using a strategy based on the coalition of attributes we recently develop algorithmic solution, whose performances and prediction qualities are superior to the gold standard SHAP (Shapley Additive exPlanation) approach. We propose to consider the prediction explanations, not as a final objective, but as a new data space allowing to impulse a better interaction between the ML model and the end user. This thesis project will focus on the development of an original framework for the biomedical community to explore in depth predictions of health evolution with age using an ML model. It will allow the detection of subpopulations with specific predictive explanations, organizing them hierarchically to ensure easy exploration. As a proof of concept, we will apply this framework to physiological age (in-house database of >40,000 subjects, 200 variables), to better understand biological determinants of ageing, their interactions, putative causal chains, and underlying physio-pathological mechanisms.
Key words: aging, machine learning
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CARe PhD Proposals
PhD supervisor : Agnès Coste -RESTORE Institute- Team 2, Toulouse, France
PhD co-supervisor: Subhankar Mukhopadhyay & D.Phil – MRC: Centre for Transplantation – King’s College London, England
Ageing and age-associated co-morbidities are major risk factors for non-healing chronic wounds. Chronic wound represent a debilitating condition that disproportionately affects millions of elderly patients with enormous societal and economic cost.
Macrophages play a critical role in the resolution of inflammation, tissue repair and wound healing. Ageing is associated with a sustained state of non-resolving inflammation (inflammaging) driven by chronically activated macrophages. Thus, defective regulation of macrophage activation is thought to be the key driver of age-associated chronic wounds. Mesenchymal stromal cells (MSCs) can regulate macrophage activation, dampens inflammation and improves wound healing.
This project will investigate whether MSC-educated regulatory macrophages can promote wound healing by limiting inflammaging. A novel cell therapy product will be developed by embedding MSCs and macrophages within a 3D biocompatible scaffold, and its efficacy in wound healing will be assessed.
The specific aims of this project are:
- To assess the effects of different biomaterials on MSC and macrophage-mediated immunomodulation and promotion of wound healing.
- To assess the molecular mechanisms of MSCs-macrophages interactions within the 3D-biomaterial scaffold.
- To determine the in vivo efficacy of this new cell therapy as a treatment of chronic wound healing.
The project will allow to develop intelligent wound plasters that will constitute a major breakthrough in the topical treatment for age-associated chronic wounds. This interdisciplinary project will take place between RESTORE and CIRIMAT institutes (Toulouse) and the Department of Immunobiology, King’s College, London.
Key words: inflammation, wound healing, ageing, scaffolds
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CARe PhD Proposals
PhD supervisor : Rémy Burcelin – I2MC: Institute of CardioMetabolic Disease, Toulouse, France
PhD co-supervisor : Sofia Forslund – MDC Max Delbruck Center for Molecular Medicine, Berlin, Germany
Body fat distribution is a strong determinant of health and exhibits, as a specific feature of human beings, a marked sexual dimorphism. Women have more subcutaneous adipose tissue (SAT), while men have fat predominantly distributed to the central body visceral adipose tissue (VAT). The central accumulation of fat is associated with metabolic complications that trigger a low-grade chronic inflammation. Conversely, the SAT is rather associated with very little inflammation even in obesity.
While we and others pioneered the discovery of the first molecular mechanism of gut microbiota triggering of metabolic inflammation recently, we shifted the concept of gut microbiota towards tissue microbiota and have described in rodent models and in humans the translocation of gut bacteria towards tissues establishing a metagenomic signature within adipose tissue depots. Furthermore, is was recently shown that the gut microbiome and abdominal obesity-related disease outcomes could account for sex-specific difference.
Altogether, while it is established that adipose depots berry bacterial components and some live bacteria it now remains to establish the molecular interactions between bacterial components and the adipose depot host cells notably regarding the role of sexual and regional differences in the development of obesity.
Thesis goal: adapt and develop bioinformatics and biostatistics approaches to identify signature of the adipose microbiota to adipose tissue molecular crosstalk. The outcome will help to identify predicting and classifying biomarkers as well as mechanisms of the influence of tissue microbiota on the adipose tissue function, thereby suitable to propose potential therapeutic strategies preventing or treating the development of obesity.
Key words: adipose tissue, microbiota, obesity, bioinformatic
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CARe PhD Proposals
PhD supervisor : Coralie Sengenès – RESTORE – UMR1301 INSERM 1301- UMR CNRS 5070 – Univ. P. Sabatier – EFS – ENVT, Toulouse
PhD co-supervisor : Mathieu Serrurier – IRIT – UMR 5505 – Université Paul-Sabatier, Toulouse
Every organ is a combination of a functional compartment, the parenchyma, and a stromal compartment, the stroma, supporting the parenchymal cells of the organ. Characterizing its involvement and defining whether and how stroma evolution can be a marker of frailty and/or can be therapeutically targeted is worthy particularly in the context of aging.
Adipose Tissue (AT) is a dynamic organ, made up of a heterogeneous stroma that is poorly understood. The stroma of AT is the richest bodily reservoir of a peculiar cell type the Adipose Stromal Cells (ASCs) that exhibit high regenerative potential. AT dysfunction is thought to be a predictor of frailty and declining health span. Here, we want to investigate the age-dependent heterogeneity of the AT stroma as a predictive marker of frailty. To address this issue methods using antibody labelling are very useful but also time consuming.
Moreover, spectral overlap inherently limits the number of simultaneous labels. Based on our preliminary data, we hypothesized that microscopic images of AT where only nuclei are stained allow the characterization of stroma’s heterogeneity using deep neural networks.
On the deep learning side, this raises many interesting problems such as the handling of pseudo-3D data, the scarcity of the data, and the prediction’s explainability that can take advantage of the strong geometric properties of the nuclei.
The PhD program aims to develop an artificial intelligence (AI)-based method of nuclei stained fluorescent images of AT to investigate the stroma heterogeneity of AT during aging.
Key words: adipose tissue, fragility, AI
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CARe PhD Proposals
PhD Director: Kerstin Bystricky – Molecular, Cellular & Developmental biology unit: MCD- Center for Integrative Biology: CBI, Toulouse
PhD CO-SUPERVISOR: Anabelle Decottignies – Catholic University of Louvain, Brussels
The position of nuclear chromatin domains in human cells impacts genome stability and gene regulation. The nuclear envelope (NE), which defines nuclear volume boundaries, is a keyplayer in organizing nuclear architecture as nuclear lamina protein scaffold, at its inner side, associates with large chromatin domains. During postmitotic nuclear assembly, physical connections occur between the NE and about 40 to 50% of telomeres, the chromosome ends. While suggesting a novel role for telomeres in nuclear organization, the functional significanceof this anchoring remains to be unraveled.
This proposal first intends to reveal the interplay between chromosome and telomere organization during postmitotic nuclear assembly. Interactions between telomeres and NE also likely regulate heterochromatin formation/maintenance. In telomerase-negative cancer cells that undergo recombinationmediated alternative lengthening of telomeres (ALT), telomeric heterochromatin is dysregulated.
Here, we will test the hypothesis that telomere-NE interaction may be disrupted in ALT cells to facilitate unwanted recombination events between telomeric repeats. To do this, a large panel of telomerase-expressing or ALT cancer cell lines will be used to characterize telomere-NE attachment and its impact on telomere maintenance.
Live cell imaging will be employed to investigate the kinetics of events. Artificial tethering and/or depletion of proteins involved in telomere tethering will be instrumental to characterize the functional significance of this interaction.
The strength of this proposal comes from a combination of strategies (advanced microscopy,
MadID proximity labeling, modeling) and the complementary expertise of both host
laboratories (Bystricky/Crabbe lab in chromatin organization and dynamics, Decottignies in
ALT telomere maintenance in cancer).
Key words: cancer, genome organisation, modelling
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CARe PhD Proposals
PhD Director: Pierre Joseph – LAAS-CNRS
PhD co-supervisor: Paolo Bergese – Università degli Studi di Brescia, Italy
Extracellular Vesicles (EVs) are nanoparticles released by cells, key players in intercellular communication and in the maintenance of tissue homeostasis and in pathogenesis1. EVs contribute to cancer initiation and progression: they modulate cancer-associated processes (immunosuppression, angiogenesis, invasion, and metastasis). Hence, EVs and their biomolecular cargo can be detected in body fluids and exploited as biomarkers in cancer.
However, little is known about EV genesis by tumors. We make the educated guess that EV inception may depend on high compressive stress that can be found in cancer, such as pancreatic adenocarcinoma (PDAC), a disease with no cure. They may reveal as very promising class of non-invasive biomarkers, for early diagnosis.
We also hypothesize that, regardless of their cargo, EV structure and size distribution can be considered as a distant looking glass into physical constraints within hard-to-reach tumors such as PDAC. This offers therapeutic opportunities for patient management, especially as in PDAC, mechanical stress increases resistance to treatment.
Collectively, this interdisciplinary thesis aims at characterizing the role of physical constraints in EV production in experimental models of pancreatic cancer, up to patient cohorts.
- Generate tumoroids (tumor spheroids) of human and mouse-derived pancreatic cancer cells using routine techniques.
- Analyze the influence of different stresses (mechanical pressure, flow, chemicals), on the size and distribution of EV produced by tumoroids, thanks to microfluidic chips.
- Detect and analyze EVs within real-life cohorts, first in blood from mice with PDAC, then in blood from patients with full clinical annotation and ancillary resources.
Key words: cancer, extracellular vesicles, microfluidic
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CARe PhD Proposals
PhD Director: Sylvain Cussat-Blanc – Institute for Research in Computer Science of Toulouse, University Toulouse
Capitole, IRIT – CNRS UMR5505, ANITI
PhD co-supervisor: Pierre Cordelier – Toulouse Cancer Research Centre, CRCT – INSERM U1037, CNRS
Our group within the Institut de Recherche en Informatique de Toulouse (IRIT) has developed a simulation platform that provides all the tools necessary to simulate multicellular cell microtissues growth under different environmental or treatment conditions (1). Such platform is key to easily develop models of complex biological systems by proposing various interaction paradigms such as visual programming, real-time simulation generation, simulation analysis in 2D/3D and virtual reality among others. For this PhD program, we want to model oncolytic virus infection and killing of cancer cells, to identify an optimal dose of treatment with a given schedule in silico for best therapeutic efficacy. Briefly, oncolytic virus (OV) are novel therapeutics that specifically infect, kill, replicate and propagate in cancer cells and tumors, to induce cancer cell death via multiple pathways and to recruit an immune response against tumors. Our partner team, led by Dr P. Cordelier within the cancer research center of Toulouse (CRCT), recently demonstrated that OV derived from myxoma virus (MYXV) are effective in killing primary cells derived from pancreatic cancer (PDAC), a disease with no cure (2), and are promising therapies for patients with this disease (3). However, the method and timing of administration of a given total dose of virus in a treatment cycle in complex 3D tumoroïds is still to be identified, to foster clinical valorization of this research program.
Most models of the literature are either equation-based models (4, 5, 6) or 2-D agent-based computational models (7) of the virus spreading. On the first hand, mathematical models are very precise, but they are usually complex to modify to explore various parameters on the cells. On the second hand, constraining the model to 2 dimensions takes away many parameters that can be crucial when studying the dynamics of the virus spreading. We recently generated a preliminary version of this computational model following infection with MYXV of primary PDAC cells cultured in monolayers (manuscript in preparation). With this model, we simulated cancer cell relapse following infection at a given cell confluence. Using GAGA optimization algorithm, we generated a desirable dose-schedule combination that was validated in vitro.
Because tumors are complex and heterogenous by essence, we want to move one step forward during this PhD program to design a 3D agent-based model that considers the specificities of a 3D topology (oxygen diffusion and influence on the cell cycle, etc.) while keeping the flexibility of an agent-based model, changing a cell determinant “at-a-glance”. During this project, our goal will be to model the viral replication cycle based on already available biological variables that belong to the virus and the tumour cell itself. To this end, our partner at CRCT has already collected important data for our modelling strategy. With this in hands, our objectives are (i) to analyze the impact of virus replication on the cell cycle kinetics in 3D tumoroïds, (ii) to reproduce in silico the infectious process of the viruses in their oncolytic activity, (iii) to elaborate theoretical therapeutic scenario that will be evaluated in forthcoming confirmatory biological studies. To step up to the third dimension, we will use a mass-spring-damper system  to simulate cellular physics, i.e., cell adhesion and collisions. This model is ready to use in the simulation platform and have been successfully validated on multicellular tumoroïds (1). Virus diffusion within the 3D tumoroids will be simulated by direct contact between cancer cells. This diffusion method is an initial hypothesis that will be challenged against biological data. Other approaches (e.g., long-range diffusion) might be considered depending on the fitting quality to these data. With such a model, we will be able to question how cell density and topology influence the virus spread and how the cell cycle is important to the killing dynamics of the virus in 3D. Addressing how cancer cells and virus coevolve in this restricted ecosystem will help test different therapeutic scenarios involving oncolytic infection in silico, with the objective to determine the best dose and frequency of administration of the virus (hitting hard or killing softly). To this end, we will use stochastic optimization (e.g., genetic algorithm) to determine the precise amount of virus to add to the cell structure at a given frequency (daily, hourly, etc.). These state-of-the-art algorithms will explore large search space with high evaluation costs of each solution. The different options will be functionally validated in vitro, then in vivo by our partner within CRCT, to determine the predictive value of the in silico model. We believe that integrating in silico modeling in preclinical research programs will help propose new strategies to improve the activity of cutting-edge oncolytic viruses for the treatment of cancer, especially for PDAC for which efficient treatments are urgently needed.
Key words: cancer, therapeutic innovation, computer modelling