The goal of our platforms

Is to increase the level of phenotyping in our cohorts by studying invasive (biopsy) and non-invasive biomarkers to better characterize rejection activity, stage and response to therapy.


This platform uses the nCounter Analysis System of NanoString™ to obtain a direct multiplexed measurement of gene expression with high levels of precision and sensitivity. This assay generates high-quality results from challenging sample types like FFPE tissues.

Spatial transcriptomics in tissue is also studied to improve our understanding of disease mechanisms in the solid organ allografts. The goal is to spatially resolve the molecular immune landscape of transplant tissues biopsies and identify specific transcriptomic signature and cell infiltrates related to the rejection process and disease severity.

Referent: Fariza Mezine

Pathology platform

This platform is dedicated to multi-organ routine techniques for high quality deep phenotyping of biopsies by routine immunostaining (immunohistochemistry and immunofluorescence: masson’s trichrome, hematoxilin/eosin, complement and macrophage staining) following the current worldwide diagnosis nomenclature systems.

The platform also works on developing multiplex immunofluorescence staining as a way to improve understanding of localization and kinetics of inflammatory and immunological components in rejection.

In addition, AI-based digital pathology algorithm are developed to automate rejection diagnostic, improving its accuracy and reproducibility, together with optimizing patient risk stratification.

Referent: Dr. Patrick Bruneval

Liquid biopsy for solid organ monitoring

The Cell-free DNA (cfDNA) platform is based on a non-invasive approach for detecting free circulating donor-derived DNA in the blood of transplant patients. This approach, based on NGS technology, enables direct, non-invasive measurement of rejection phenomena when monitoring transplant patients.

Referent: Dr. Olivier Aubert

HLA Platform

This platform focuses on the study of anti-HLA and non anti-HLA DSA characteristics that have been proven to be associated with allograft survival, such as:

Referent: Pr. Jean-Luc Taupin


This platform performs large-scale data analysis to make sense out of the biological data generated by the other platforms using a high performance computing server: Intel dual Xeon Platinium 8276 for a total of 112 nodes, 16 SSD in RAID6 for more than 40To of data and more than 50 Go of DDR4 RAM. Mindful of the importance of data protection, we  use an internal isolated server for analysis only and an outsourced server dedicated to file transfer using disk encryption and secure channels for transmission of data.

This platform benefits from the expertise of bioinformaticians, biological scientists, biostatisticians and machine-learning engineers offering a multidisciplinary expertise to support other teams in the analysis of RNA sequencingNanostring data and microarray as well as more complex downstream analysis. Based on the molecular profile of rejection on kidney transplant, we are currently developing tools to establish the diagnosis of biopsies using solely molecular data.

Referent: Marc Raynaud