Biology subjects

Tejaswi, T.

Publications and source records attributed to Tejaswi, T..

4 recordsLinked to original sources

bioRxiv2025

Glassy dynamics in active epithelia emerge from an interplay of mechanochemical feedback and crowding.

Glassy dynamics in dense epithelial cells remain a subject of debate, as theory predicts its emergence only at unrealistically low cellular activity, yet experimental studies have shown glassy dynamics at physiologically active conditions. In this study, we address this paradox by integrating experimental observations in epithelial monolayers with an active vertex model. We demonstrate that while crowding is essential, it is not sufficient for glassy dynamics to emerge. A mechanochemical feedback loop (MCFL-1), mediated by cell shape changes through the contractile actomyosin network is required to drive glass transition in dense epithelial tissues. Such mechanochemical feedback is captured experimentally via a crosstalk between actin-based cell clustering and dynamic heterogeneity, as well as via force induced actin reorganization in epithelial cells. Incorporating MCFL into the vertex model reveals contrasting results from those previously predicted by theories-we show that the MCFL can counteract cell division-induced fluidisation and enable glassy dynamics to emerge through active cell-to-cell communication. Furthermore, our analysis reveal, for the first time, the existence of novel collective mechanochemical oscillations that arise from the crosstalk of MCFL-1 with oscillatory MCFL-2, capturing ERK mediated cell shape changes. Together, we demonstrate that an interplay between crowding and active mechanochemical feedback enables the emergence of glass-like traits and collective biochemical oscillations in epithelial tissues with active cell-to-cell contacts.

biophysics↗

bioRxiv2025

Differential interfacial tension between oncogenic and wild-type populations forms the mechanical basis of tissue-specific oncogenesis in epithelia

Why does the same oncogenic mutation drive tumor formation in some tissues but not in others? While cancer driver mutations are well-documented, their tissue-specific effects remain largely attributed to genetic factors, leaving the biophysical aspects underexplored. Here, we demonstrate that mechanical interactions, specifically interfacial tension between newly transformed and wildtype epithelial cells are critical in determining survival and growth of HRasV12 oncogenic mutants in human mammary and bronchial epithelia, leading to contrasting outcomes in the two tissues. In mammary epithelium, isolated oncogenic cells are extruded-a typical mechanism of defense against cancer in epithelia-while oncogenic groups become spatially confined in a kinetically arrested, jammed state, marked by an actomyosin belt at the interface. In contrast, bronchial epithelium permits persistent spreading of the same oncogenic cells, which form long protrusions regardless of colony size. Furthermore, oncogenic clusters in these two tissues exhibit distinct biophysical properties, including variations in cell shapes, intracellular pressure, cell-cell tension, and cellular motility. Using a cell shape-tension coupled bi-disperse vertex model, we reveal that differences in interfacial tension at mutant-wild-type boundaries dictate whether oncogenic cells are eliminated, restrained, or expanded and that modulating the heterotypic interfacial tension alters mutant cell fate within the epithelium. Together, our findings uncover a mechanical basis for tissue-specific oncogenesis by highlighting how differential cellular mechanics at the oncogenic-host cell interface regulate tumor initiation and progression.

biophysics↗

bioRxiv2021

Transcriptomic-based quantification of the epithelial-hybrid-mesenchymal spectrum across biological contexts

Epithelial-mesenchymal plasticity (EMP) underlies embryonic development, wound healing, and cancer metastasis and fibrosis. Cancer cells exhibiting EMP often have more aggressive behavior, characterized by drug resistance, and tumor-initiating and immuno-evasive traits. Thus, the EMP status of cancer cells can be a critical indicator of patient prognosis. Here, we compare three distinct transcriptomic-based metrics - each derived using a different gene list and algorithm - that quantify the EMP spectrum. Our results for 96 cancer-related RNA-seq datasets reveal a high degree of concordance among these metrics in quantifying the extent of EMP. Moreover, each metric, despite being trained on cancer expression profiles, recapitulates the expected changes in EMP scores for non-cancer contexts such as lung fibrosis and cellular reprogramming into induced pluripotent stem cells. Thus, we offer a scoring platform to quantify the extent of EMP in vitro and in vivo for diverse biological applications including cancer.

bioinformatics↗

bioRxiv2020

Investigating epithelial-mesenchymal heterogeneity of tumors and circulating tumor cells with transcriptomic analysis and biophysical modeling

IntroductionCellular heterogeneity along the Epithelial-Mesenchymal Plasticity (EMP) spectrum is a paramount feature observed in tumors and circulating tumor cells (CTCs). High-throughput techniques now offer unprecedented details on this variability at a single-cell resolution. Yet, there is no current consensus about how EMP in tumors propagates to that in CTCs. To investigate the relationship between EMP associated heterogeneity of tumors and that of CTCs, we integrated transcriptomic analysis and biophysical modeling. MethodsWe apply three EMT (Epithelial-Mesenchymal Transition) scoring metrics to multiple tumor samples and CTC datasets from several cancer types. Moreover, we develop a biophysical model that couples EMT associated phenotypic switching in a primary tumor with cell migration. Finally, we integrate EMT transcriptomic analysis and in silico modeling to evaluate the predictive power of several measurements of tumor aggressiveness, including tumor EMT score, CTC EMT score, fraction of CTC clusters found in circulation, and CTC cluster size distribution. ResultsAnalysis of high-throughput datasets reveals a pronounced heterogeneity without a well-defined relation between EMT traits in tumors and CTCs. Moreover, mathematical modeling predicts different phases where CTCs can be less, equally, or more mesenchymal than primary tumor depending on the dynamics of phenotypic transition and cell migration. Consistently, various datasets of CTC cluster size distribution from different cancer types are fitted onto different regimes of the model. By further constraining the model with experimental measurements of tumor EMT score, CTC EMT score, and fraction of CTC cluster in bloodstream, we show that none of these assays alone can provide sufficient information to predict the other variables. ConclusionsBy integrating analysis of single cell gene expression and in silico modeling, we propose that the relationship between EMT progression in tumors and CTCs can be variable, and in general, predicting one from the other may not be as straightforward as tacitly assumed.

cancer biology↗