Biology subjects

Chevrier, M.

Publications and source records attributed to Chevrier, M..

3 recordsLinked to original sources

bioRxiv2021

Autophagy modulation enhances enzyme replacement therapy response in Fabry disease

SummaryFabry disease is a lysosomal disease due to -galactosidase A (a-GalA) deficiency. Since 2001, Enzyme replacement therapy (ERT) has been used as specific treatment of Fabry disease, with variable effects depending on patient gender and affected organs. In Fabry cells, the endososomal/lysosomal system is highly altered. Consequently, the exogenous enzyme may be mistargeted and trapped into intracellular vesicles instead of reaching the lysosomes. In this study, we aimed to investigate the mechanisms underlying the processing of the exogenous enzyme. We used Fabry cells (cultured fibroblasts and podocyte cell line) to study the enzyme internalization and its effects on the catabolism of the main a-Gal A substrate, globotriaosylceramide (Gb3), upon autophagy inhibition. The exogenous enzyme reaches the early endosome in a similar timeframe in Fabry and control cells, while its targeting to lysosomes is delayed in Fabry cells. Gb3 concentration is lowered upon therapeutic enzyme addition or wortmannin treatment with a synergetic effect. These findings illustrate the positive impact of autophagy inhibition on enzyme trafficking and processing, allowing the increase of functional enzyme rate within the lysosome. Given the high cost of ERT, a better understanding of the cellular fate of the exogenous enzyme may lead to improve its targeting to the lysosome.

biochemistry↗

bioRxiv2020

Single cell RNA-seq reveals immunosuppressive gastric stem-like cancer cells as a poor prognostic factor

ObjectiveGastric cancer (GC) tumors are highly heterogenous with different subpopulations of epithelial cells. We employed single cell RNA sequencing (scRNA-seq) to dissect the heterogeneity and identified subpopulations of cancer cells with stem-like properties. We further investigated their resistance to oxaliplatin chemotherapy and their contribution to gastric cancer outcome. DesignWe performed scRNA-seq on FACS sorted epithelial and immune cells from paired samples of GC tumors and normal adjacent tissues. We identified two epithelial subpopulations (STMN1+IQGAP3+ and STMN1+IQGAP3-) with stem-like properties. We characterized and compared them to known healthy gastric stem cell populations. We also cultivated GC derived organoids to study the chemoresistance of similarly marked populations. Lastly, we employed immunohistochemistry (IHC) staining to ascertain the predicted immunosuppressive interactions. ResultsThe STMN1+IQGAP3+ subpopulation showed a higher tumor mutation burden, upregulated proliferative pathways and transcriptomically resembled proliferative healthy gastric isthmus stem cells. The STMN1+IQGAP3- subpopulation were comparatively quiescent and transcriptomically resembled enteroendocrine cells. Both transcriptomic signatures were associated with worse mortality than other epithelial subpopulations with the quiescent being associated with the poorest patient survival. GC tissue derived organoids were dominated by STMN1+IQGAP3+ cells but the STMN1+IQGAP3- compartment was more resistant to chemotherapy. We also verified the likely suppression of CD8 T cell cytotoxicity by STMN1+IQGAP3+ cells through the NECTIN2/TIGIT interaction. ConclusionsCancer cells with stem-like characteristics are associated with poor survival through chemoresistance and immunosuppression. Reactivating the immune system through checkpoint blockade is an opportunity to eliminate these cells. What is already known on this topicMultiple gastric stem cell populations have been identified and linked to tumor initiation in rodent-based studies. However, none of them have been conclusively proven in human tumors. Isolating and characterizing tumor cells with stem-like properties will help shed light on their possible origin and possible mitigation strategies. What this study addsHere we identified two sets of stem-like gastric cancer cells that are associated with poorer patient prognosis. One set is highly proliferative and exhibits oxaliplatin susceptibility. It also engages in immunosuppressive interactions such as NECTIN2/TIGIT. The other set is quiescent and highly resistant to oxaliplatin. How this study might affect research, practice or policyThe transcriptome signatures of the identified stem-like cells can aid in patient prognosis and identify patients who can benefit from checkpoint blockade therapy to reactivate their immune response towards gastric cancer cells.

immunology↗

bioRxiv2019

Comparison Between UMAP and t-SNE for Multiplex-Immunofluorescence Derived Single-Cell Data from Tissue Sections

Using human hepatocellular carcinoma (HCC) tissue samples stained with seven immune markers including one nuclear counterstain, we compared and evaluated the use of a new dimensionality reduction technique called Uniform Manifold Approximation and Projection (UMAP), as an alternative to t-Distributed Stochastic Neighbor Embedding (t-SNE) in analysing multiplex-immunofluorescence (mIF) derived single-cell data. We adopted an unsupervised clustering algorithm called FlowSOM to identify eight major cell types present in human HCC tissues. UMAP and t-SNE were ran independently on the dataset to qualitatively compare the distribution of clustered cell types in both reduced dimensions. Our comparison shows that UMAP is superior in runtime. Both techniques provide similar arrangements of cell clusters, with the key difference being UMAPs extensive characteristic branching. Most interestingly, UMAPs branching was able to highlight biological lineages, especially in identifying potential hybrid tumour cells (HTC). Survival analysis shows patients with higher proportion of HTC have a worse prognosis (p-value = 0.019). We conclude that both techniques are similar in their visualisation capabilities, but UMAP has a clear advantage over t-SNE in runtime, making it highly plausible to employ UMAP as an alternative to t-SNE in mIF data analysis.

bioinformatics↗