Biology subjects

Bergeron, C.

Publications and source records attributed to Bergeron, C..

3 recordsLinked to original sources

bioRxiv2023

Induced pluripotent stem cell-derived extracellular vesicles promote wound repair in a diabetic mouse model via an anti-inflammatory immunomodulatory mechanism

Extracellular vesicles (EVs) derived from mesenchymal stem/stromal cells (MSCs) have recently been widely explored in clinical trials for treatment of diseases with complex pathophysiology. However, production of MSC EVs is currently hampered by donor-specific characteristics and limited ex vivo expansion capabilities before decreased potency, thus restricting their potential as a scalable and reproducible therapeutic. Induced pluripotent stem cells (iPSCs) represent a self-renewing source for obtaining differentiated iPSC-derived MSCs (iMSCs), circumventing both scalability and donor variability concerns for therapeutic EV production. Thus, we initially sought to evaluate the therapeutic potential of iMSC EVs. Interestingly, while utilizing undifferentiated iPSC EVs as a control, we found that their vascularization bioactivity was similar and their anti-inflammatory bioactivity was superior to donor-matched iMSC EVs in cell-based assays. To supplement this initial in vitro bioactivity screen, we employed a diabetic wound healing mouse model where both the pro-vascularization and anti-inflammatory activity of these EVs would be beneficial. In this in vivo model, iPSC EVs more effectively mediated inflammation resolution within the wound bed. Combined with the lack of additional differentiation steps required for iMSC generation, these results support the use of undifferentiated iPSCs as a source for therapeutic EV production with respect to both scalability and efficacy.

bioengineering↗

bioRxiv2022

Fusion-negative Rhabdomyosarcoma 3D-organoids as an innovative model to predict resistance to cell death inducers

Rhabdomyosarcoma (RMS) is the main form of soft-tissue sarcoma in children and adolescents. For 20 years, and despite international clinical trials, its cure rate has not really improved, and remains stuck at 20% in case of relapse. The definition of new effective therapeutic combinations is hampered by the lack of reliable models, which complicate the transposition of promising results obtained in pre-clinical studies into efficient solutions for young patients. Inter-patient heterogeneity, particularly in the so-called fusion-negative group (FNRMS), adds an additional level of difficulty in optimizing the clinical management of children and adolescents with RMS. Here, we describe an original 3D-organoid model derived from relapsed FNRMS and show that it finely mimics the characteristics of the original tumor, including inter- and intra-tumoral heterogeneity. Moreover, we have established the proof-of-concept of their preclinical potential by re-evaluating the therapeutic opportunities of targeting apoptosis in FNRMS from a streamlined approach based on the exploitation of bulk and single-cell omics data.

cancer biology↗

bioRxiv2022

Identification and assessment of systematic measurement error on electrophysiological recordings of neural cell cultures

Microelectrode arrays (MEA) hold great promise for a broad range of applications that require reliable characterization of the growth and function of neurons in culture. Widespread adoption of this platform depends on analytical methods to extract meaning from highly variable and noisy observations. In analyzing a comprehensive database of MEA recordings, we discovered that 22% of the electrodes presented systematic patterns of under- or non-detection of spike activity. Going undetected, principal components analysis (PCA) of these data reveal trends that would have lead to incorrect biological interpretations. We fully document these defective or biased electrodes, and distinguish two forms of defectiveness, via representations that aid in detecting them. We also showcase our approach for analyzing these data that permit for post-analytic review and correction. Repeating our PCA on cleaned data, we discover a more complex interplay of biological variability. Finally, we make a case for transparency in data reporting and propose best practices for experimental and analysis phases.

neuroscience↗