Biology subjects

Devarasou, S.

Publications and source records attributed to Devarasou, S..

3 recordsLinked to original sources

bioRxiv2026

Volumetric mechanosensing of CAF in 3D hydrogels drive altered drug response in breast cancer

Altered mechanical properties of the tumor microenvironment (TME) influence cancer progression, yet the mechanistic basis by which 3D mechanics shape CAF heterogeneity and downstream tumor drug response remains poorly understood. Here, we engineered a modulus-tunable gelatin methacryloyl (GelMA) hydrogel platform spanning a normal-like (soft [~]2 kPa) to desmoplastic-like (stiff[~]40 kPa) range to culture primary breast CAFs under 3D confinement. CAFs exhibited pronounced volumetric morphoadaptation across matrices, with soft 3D matrices supporting larger, more protrusive morphologies and stiff gels constraining cell geometry. In contrast to canonical 2D paradigms, nuclear YAP localization was reduced in stiff 3D matrices and varied substantially across cells, consistent with a dominant role for 3D geometric/volumetric state in regulating mechanotransduction. Functionally, in transwell co-culture with MCF-7 spheroids under paclitaxel treatment, CAFs cultured in stiff 3D matrices induced a broader chemoresistance-associated transcriptional program, whereas soft 3D matrices CAFs favored stress/checkpoint-like responses. A 2D monolayer comparator indicated that coordinated resistance-associated programs emerge most clearly in 3D tumor architecture. Together, these results establish a GelMA-based biomaterials framework in which CAF volumetric state provides a quantifiable intermediate linking 3D matrix mechanics to mechanotransduction and tumor drug-response programs, motivating future strategies to modulate stromal function through mechanically controlled cell-state regulation.

bioengineering↗

bioRxiv2024

EMT Induction In Normal Breast Epithelial Cells By COX2-Expressing Fibroblasts

BackgroundThe tumor microenvironment (TME) plays a pivotal role in cancer progression, with cancer-associated fibroblasts (CAFs) significantly influencing tumor behavior. Especially, elevated COX2 expressing fibroblasts within the TME, notably in collagen-dense tumors like breast cancer, has been recently emphasized in the literature. However, the specific effect of COX2-expressing CAFs (COX2+ CAFs) on neighboring cells and their consequent role in cancer progression is not fully elucidated. MethodsWe induced COX2+ fibroblasts by forcing the fibroblasts forming aggregates to undergo Nemosis as a proxy for COX2+ CAFs. This approach enabled us to simulate the paracrine interactions between COX2+ CAFs and normal breast epithelial cells via conditioned media from COX2+ fibroblasts. We developed an innovative in vitro platform that combines cell mechanics-based analysis and biomolecular assays to study the interactions between COX2+ fibroblasts and normal breast epithelial cells. By focusing on the mechanical characteristics of the cells and the EMT marker expressions, we aimed to elucidate the paracrine mechanisms through which COX2+ CAFs influence the tumor microenvironment. ResultsOur in vitro findings reveal that COX2+ fibroblasts, through conditioned media, induce significant changes in the mechanical behavior of normal breast epithelial cells, facilitating their transition towards mesenchymal types. This transition was corroborated by increased expression of mesenchymal markers. By drawing parallels between COX2+ fibroblasts and COX2+ CAFs, we established a positive feedback loop involving COX2+ CAFs, prostaglandin E2 (PGE2), and the EP4-SNAI1 axes. ConclusionThis study advances our understanding of the potential mechanisms by which COX2+ CAFs influence tumor progression within the breast tumor microenvironment (TME) through controlled in vitro investigations. By integrating cell mechanics-based analysis, biomolecular assays, and innovative in vitro cell-based modeling of COX2+ CAFs, we have delineated the contributory role of these cells in a controlled setting. These insights lay a groundwork for future studies that could explore the implications of these findings in vivo, potentially guiding targeted therapeutic strategies.

cell biology↗

bioRxiv2024

AI-driven Classification of Cancer-Associated Fibroblasts Using Morphodynamic and Motile Features

The heterogeneous natures of cancer-associated fibroblasts (CAFs) play critical roles in cancer progression, with some promoting tumor growth while others inhibit it. To utilize CAFs as a target for cancer treatment, issues with subtypes of CAFs must be resolved such that specific pro-tumorigenic subtypes can be suppressed or reprogrammed into anti-tumorigenic ones. Currently, single-cell RNA sequencing (scRNA-Seq) is a prevalent strategy for classifying CAFs, primarily based on their biomolecular features. Alternatively, this study proposes assessing CAFs on a larger biophysical scale, focusing on cell morphological and motile features. Since these features are downstream effectors of differential gene expression combinations, they can serve as holistic descriptors for CAFs, offering a complementary strategy for classifying CAF subtypes. Here, we propose an artificial intelligence (AI) classification framework to comprehensively characterize CAF subtypes using morphodynamic and motile features. This framework extracts these features from label-free live-cell imaging data of CAFs employing advanced deep learning and machine learning algorithms. The results of this study highlight the ability of morphodynamic and motile features to complement biomolecular features in accurately reflecting CAF subtype characteristics. In essence, our AI-based classification framework not only provides valuable insights into CAF biology but also introduces a novel approach for comprehensively describing and targeting heterogeneous CAF subtypes based on biophysical features.

cancer biology↗