Continuous microfluidic flow-through protocol for selective and image-activated electroporation of single cells.

Electroporation of cells is a widely-used tool to transport molecules such as proteins or nucleic acids into cells or to extract cellular material. However, bulk methods for electroporation do not offer the possibility to selectively porate subpopulations or single cells in heterogeneous cell samples. To achieve this, either presorting or complex single-cell technologies are required currently. In this work, we present a microfluidic flow protocol for selective electroporation of predefined target cells identified in real-time by high-quality microscopic image analysis of fluorescence and transmitted light. While traveling through the microchannel, the cells are focused by dielectrophoretic forces into the microscopic detection area, where they are classified based on image analysis techniques. Finally, the cells are forwarded to a poration electrode and only the target cells are pulsed. By processing a heterogenically stained cell sample, we were able to selectively porate only target cells (green-fluorescent) while non-target cells (blue-fluorescent) remained unaffected. We achieved highly selective poration with >90% specificity at average poration rates of >50% and throughputs of up to 7200 cells per hour.

High-precision, low-complexity, high-resolution microscopy-based cell sorting.

Continuous flow cell sorting based on image analysis is a powerful concept that exploits spatially-resolved features in cells, such as subcellular protein localisation or cell and organelle morphology, to isolate highly specialised cell types that were previously inaccessible to biomedical research, biotechnology, and medicine. Recently, sorting protocols have been proposed that achieve impressive throughput by combining ultra-high flow rates with sophisticated imaging and data processing protocols. However, moderate image quality and high complex experimental setups still prevent the full potential of image-activated cell sorting from being a general-purpose tool. Here, we present a new low-complexity microfluidic approach based on high numerical aperture wide-field microscopy and precise dielectrophoretic cell handling. It provides high-quality images with unprecedented resolution in image-activated cell sorting (i.e., 216 nm). In addition, it also allows long image processing times of several hundred milliseconds for thorough image analysis, while ensuring reliable and low-loss cell processing. Using our approach, we sorted live T cells based on subcellular localisation of fluorescence signals and demonstrated that purities above 80% are possible while targeting maximum yields and sample volume throughputs in the range of µl min-1. We were able to recover 85% of the target cells analysed. Finally, we ensure and quantify the full vitality of the sorted cells cultivating the cells for a period of time and through colorimetric viability tests.

Label-free discrimination and selection of cancer cells from blood during flow using holography-induced dielectrophoresis.

We present a method for label-free imaging and sorting of cancer cells in blood, which is based on a dielectrophoretic microfluidic chip and label-free interferometric phase microscopy. The chip used for imaging has been embedded with dielectrophoretic electrodes, and therefore it can be used to sort the cells based on the decisions obtained during the cell flow by the label-free quantitative imaging method. Hence, we obtained a real-time, automatic, label-free imaging flow cytometry with the ability to sort the cells during flow. To validate our model, we combined into the label-free imaging interferometer a fluorescence imaging channel that indicated the correctness of the label-free sorting. We have achieved above 98% classification success and 69% sorting accuracy at flow rates of 4 to 7 µL hr-1 . In the future, this method is expected to help in label-free sorting of circulating tumor cells in blood following an initial state-of-the-art cell enrichment.

Combining dielectrophoresis and computer vision for precise and fully automated single-cell handling and analysis.

With the advent of single-cell technologies comes the necessity for efficient protocols to process single cells. We combine dielectrophoresis with open source computer vision programming to automatically control the trajectories of single cells inside a microfluidic device. Using real-time image analysis, individual cells are automatically selected, isolated and spatially arranged.