Back

Single cell-derived spheroids for real-time growth and metabolomic studies in breast cancer

Esteves, M.; Oliveira, F.; Teixeira, A.; Ibanez, A.; Garcia-Lizarribar, A.; Madarieta, I.; Fernandes, J. M.; Rodrigues, C.; Ainla, A.; Dieguez, L.; Olalde, B.; Xavier, M.; Abalde-Cela, S.

2025-03-01 cancer biology
10.1101/2025.02.28.638104 bioRxiv
Show abstract

Breast cancer remains a leading cause of cancer-related mortality, with disease progression and metastasis posing significant challenges in treatment. Three-dimensional (3D) cancer models have emerged as valuable tools for studying cancer cell biology in a physiologically relevant microenvironment. Studying the tumour heterogeneity and metabolic adaptations at the single-cell level can be crucial to identify factors driving metastatic progression. Here, we present a novel approach to generate single cell-derived breast cancer spheroids using cell lines (MCF-7 and MCF-10A) within a decellularised adipose tissue extracellular matrix (adECM). Spheroid culture conditions were optimised with integrated plasmonic nanosensors (gold nanostars - GNSs), to enable real-time surface-enhanced Raman scattering (SERS)-based measurements. Our results demonstrated that spheroid growth kinetics and viability in adECM were comparable to commonly used animal-derived matrices, validating its use as a reproducible ECM hydrogel. We further show that the concentration of plasmonic nanosensors used was compatible with cell culture and enabled SERS detection of a model reporter, paving the way for label-free, non-destructive analysis of cancer cell metabolism. This platform offers a promising approach to study cancer progression, including metabolic adaptations, with potential applications in biomarker discovery and preclinical research.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.