Trajectory Inference for Unraveling Dynamic Biological Processes from Raman Spectral Data.

Cell heterogeneity is a crucial parameter for understanding the complexity of numerous biomedical issues. Trajectory inference-based approaches are recent tools developed for single-cell transcriptomics (scRNA-seq) data analysis. They aim to reconstruct evolving pathways from the variety of cell states that coexist simultaneously in a cell population. We propose to expand this concept to Raman spectroscopy, a label-free modality that probes the global molecular nature of a sample, by investigating the dynamics of adipocyte differentiation.

Type I Collagen Aging Increases Expression and Activation of EGFR and Induces Resistance to Erlotinib in Lung Carcinoma in 3D Matrix Model.

Type I collagen is the major structural component of lung stroma. Because of its long half-life, type I collagen undergoes post-translational modifications such as glycation during aging process. These modifications have been shown to impact the structural organization of type I collagen fibers. In the present work we evaluated the impact of collagen aging on lung carcinoma cells response to erlotinib-induced cytotoxicity and apoptosis, and on Epidermal Growth Factor Receptor (EGFR) expression and phosphorylation. To this end, experiments were performed in 2D and 3D matrix models established from type I collagen extracted from adult (10 weeks-old) and old (100 weeks-old) rat's tail tendons. Our results show that old collagen induces a significant increase in EGFR expression and phosphorylation when compared to adult collagen in 3D matrix but not in 2D coating. Such modification was associated to an increase in the IC50 of erlotinib in the presence of old collagen and a lower sensitivity to drug-induced apoptosis. These data suggest that collagen aging confers resistance to the cytotoxic and apoptotic effects of therapies targeting EGFR kinase function in lung carcinoma. Moreover, our data underline the importance of the 3D matrix environment in this process.