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Cancer remains one of the leading causes of mortality worldwide, accounting for ≈10 million deaths annually. Critically, it is metastasis and not the primary tumour that causes most of these deaths. Understanding the mechanisms behind cancer dissemination and therapy resistance is thus a pressing challenge. Traditional bulk tissue analyses have failed to capture the full spectrum of intra-tumour heterogeneity and the dynamic interactions within the tumour microenvironment. Studying cancer at the single-cell level allows unravelling the roles of rare subpopulations, cell-cell interactions, and spatial dynamics that govern tumour evolution, metastasis, and immune evasion. This review explores how recent advances in microfluidic technologies are transforming ability to model and study cancer at the single-cell level. Cutting-edge platforms are highlighted, including droplet microfluidics, single cell-derived spheroids, and tumour-chips, that enable physiologically relevant 3D cancer models. By integrating immune components, biosensing, and patient-derived materials, these platforms hold promise for advancing drug screening, immunotherapy assessment, and personalised medicine. It is concluded by identifying key challenges and priorities for future work, which should focus on increasing model complexity, reproducibility, and integration of spatiotemporal multiomics to better dissect tumour heterogeneity and accelerate clinical translation.
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http://dx.doi.org/10.1002/advs.202500975 | DOI Listing |
Proc Natl Acad Sci U S A
September 2025
Molecular Imaging Program at Stanford, Department of Radiology, School of Medicine, Stanford University, Palo Alto, CA 94304.
The biophysical properties of single cells are crucial for understanding cellular function and behavior in biology and medicine. However, precise manipulation of cells in 3-D microfluidic environments remains challenging, particularly for heterogeneous populations. Here, we present "Electro-LEV," a unique platform integrating electromagnetic and magnetic levitation principles for dynamic 3-D control of cell position during separation.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Information Technology, Uppsala University, Uppsala, Sweden.
For effective treatment of bacterial infections, it is essential to identify the species causing the infection as early as possible. Current methods typically require hours of overnight culturing of a bacterial sample and a larger quantity of cells to function effectively. This study uses one-hour phase-contrast time-lapses of single-cell bacterial growth collected from microfluidic chip traps, also known as a "mother machine".
View Article and Find Full Text PDFACS Appl Mater Interfaces
September 2025
National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, China.
Integrating surface-enhanced fluorescence (SEF) and surface-enhanced Raman spectroscopy (SERS) into a single probe is a natural step forward for plasmon-enhanced spectroscopy (PES), as SEF enables enhanced fluorescent imaging for fast screening of targets, while SERS allows ultrasensitive trace molecular characterization with specificity. However, many challenges remain, e.g.
View Article and Find Full Text PDFPRX Life
February 2025
Department of Physics & Astronomy, Johns Hopkins University, Baltimore, Maryland 21218, USA.
When cells in a primary tumor work together to invade into nearby tissue, this can lead to cell dissociations-cancer cells breaking off from the invading front-leading to metastasis. What controls the dissociation of cells and whether they break off singly or in small groups? Can this be determined by cell-cell adhesion or chemotactic cues given to cells? We develop a physical model for this question, based on experiments that mimic aspects of cancer cell invasion using microfluidic devices with microchannels of different widths. Experimentally, most dissociation events ("ruptures") involve single cells breaking off, but we observe some ruptures of large groups (~20 cells) in wider channels.
View Article and Find Full Text PDFAn integrated approach is proposed to rapidly evaluate the effects of anticancer treatments in 3D models, combining a droplet-based microfluidic platform for spheroid formation and single-spheroid chemotherapy application, label-free morphological analysis, and machine learning to assess treatment response. Morphological features of spheroids, such as size and color intensity, are extracted and selected using the multivariate information-based inductive causation algorithm, and used to train a neural network for spheroid classification into viability classes, derived from metabolic assays performed within the same platform as a benchmark. The model is tested on Ewing sarcoma cell lines and patient-derived xenograft (PDX) cells, demonstrating robust performance across datasets.
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