Rapid metabolomic screening of cancer cells via high-throughput static droplet microfluidics.

Effective isolation and in-depth analysis of Circulating Tumour Cells (CTCs) are greatly needed in diagnosis, prognosis and monitoring of the therapeutic response of cancer patients but have not been completely fulfilled by conventional approaches. The rarity of CTCs and the lack of reliable biomarkers to distinguish them from peripheral blood cells have remained outstanding challenges for their clinical implementation. Herein, we developed a high throughput Static Droplet Microfluidic (SDM) device with 38,400 chambers, capable of isolating and classifying the number of metabolically active CTCs in peripheral blood at single-cell resolution. Owing to the miniaturisation and compartmentalisation capability of our device, we first demonstrated the ability to precisely measure the lactate production of different types of cancer cells inside 125 pL droplets at single-cell resolution. Furthermore, we compared the metabolomic activity of leukocytes from healthy donors to cancer cells and showed the ability to differentiate them. To further prove the clinical relevance, we spiked cancer cell lines in human healthy blood and showed the possibility to detect the cancer cells from leukocytes. Lastly, we tested the workflow on 8 preclinical mammary mouse models including syngeneic 67NR (non-metastatic) and 4T1.2 (metastatic) models with Triple-Negative Breast Cancer (TNBC) as well as transgenic mouses (12-week-old MMTV-PyMT). The results have shown the ability to precisely distinguish metabolically active CTCs from the blood using the proposed SDM device. The workflow is simple and robust which can eliminate the need for specialised equipment and expertise required for single-cell analysis of CTCs and facilitate on-site metabolic screening of cancer cells.

Proteomics profiling identifies extracellular vesicles' cargo associated with tumour cell induced platelet aggregation.

BACKGROUND: Cancer patients have an increased risk of developing venous thromboembolism, with up to 30% dying within a month of their development. Some cancer cells are known to induce platelet aggregation, and this interaction is understood to contribute to thrombosis and haematogenous metastasis. Many researchers have reported on extracellular vesicles (EVs) released from platelets. However, less is known about how cancer cells' EVs may affect platelet function. Here EVs released by triple-negative breast cancer (TNBC) cell line variants were extensively investigated in this regard. METHODS: EVs were separated from conditioned media of TNBC Hs578T and Hs578Ts(i)8 cells using filtration and ultracentrifugation and were characterised by nanoparticle tracking analysis, immunoblots, and transmission electron microscopy. Blood samples from consenting donors were procured, and their platelets collected by differential centrifugation. Light transmission aggregometry and optical microscopy evaluated the potential interaction of TNBC cells and their EVs with platelets. Global proteomic analysis was performed on the EVs, by in-solution digestion and mass spectrometry. Data analysis included the use of Perseus, FunRich, and Vesiclepedia. Immunoblotting was used as a secondary method to investigate some key EV cargo proteins identified by the global proteomics approach. RESULTS: Both TNBC cell variants induced platelet aggregation. Increasing cell numbers significantly reduced the time taken for platelet aggregation to occur. EVs released by the cells also resulted in platelet aggregation. The time to induce platelet aggregation was EV dose-dependent. Proteomics profiling and immunoblotting of the EVs' cargo identified candidate proteins (including uPAR and PDGFRß) that may be involved during this process. CONCLUSIONS: TNBC cells induce platelet aggregation. Furthermore, the cell-free EVs induced this undesirable effect. A number of EV cargo proteins were identified that may be relevant as therapeutic targets.