Publication / Single cell-derived spheroids for real-time growth and metabolomic studies in breast cancer
Development of single cell-derived MCF-7 spheroids.
A) Live/dead assay (Calcein-AM and BOBO-3) of MCF-7 cells cultured in 8-µL hydrogel domes (Matrigel or adECM at 5 or 10 mg/mL) for 14 or 28 days. Images were acquired using an LSM780 confocal microscope (magnification 20x). B) Spheroid diameters (µm) and live/dead ratio (%) of single cell-derived spheroids cultured in both matrices at concentrations of 5 and 10 mg/mL. Values show mean ± SD (N ≥ 14 spheroids from 3 independent experiments; *p < 0.05; **p < 0.01; **p < 0.001 with p-values obtained using a one-way analysis of variance (ANOVA) with Tukey’s post-hoc test for sample variables that followed a normal distribution (smaller-cell outlet) or a Kruskal–Wallis test followed by a Mann Whitney-U otherwise). (Scale bar: 100 µm).
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.
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