Adhesion-based high-throughput label-free cell sorting using ridged microfluidic channels.

Numerous applications in medical diagnostics, cell engineering therapy, and biotechnology require the identification and sorting of cells that express desired molecular surface markers. We developed a microfluidic method for high-throughput and label-free sorting of biological cells by their affinity of molecular surface markers to target ligands. Our approach consists of a microfluidic channel decorated with periodic skewed ridges and coated with adhesive molecules. The periodic ridges form gaps with the opposing channel wall that are smaller than the cell diameter, thereby ensuring cell contact with the adhesive surfaces. Using three-dimensional computer simulations, we examine trajectories of adhesive cells in the ridged microchannels. The simulations reveal that cell trajectories are sensitive to the cell adhesion strength. Thus, the differential cell trajectories can be leveraged for adhesion-based cell separation. We probe the effect of cell elasticity on the adhesion-based sorting and show that cell elasticity can be utilized to enhance the resolution of the sorting. Furthermore, we investigate how the microchannel ridge angle can be tuned to achieve an efficient adhesion-based sorting of cells with different compliance.

Microfluidic Platform to Transduce Cell Viability to Distinct Flow Pathways for High-Accuracy Sensing.

Mechanical properties of cells such as stiffness can act as biomarkers to sort or detect cell functional properties such as viability. In this study, we report the use of a microfluidic device as a high-sensitivity sensor that transduces cell biomechanics to cell separation to accurately detect viability. Cell populations are flowed and deflected at a number of skew ridges such that deflection per ridge, cell-ridge interaction time, and cell size can all be used as sensor inputs to accurately determine the cell state. The angle of the ridges was evaluated to optimize the differences in cell translation between viable and nonviable cells while allowing continuous flow. In the first mode of operation, we flowed viable and nonviable cells through the device and conducted a sensitivity analysis by recording the cell's total deflection as a binary classifier that differentiates viable from nonviable cells. The performance of the sensor was assessed using an area under the curve (AUC) analysis to be 0.97. By including additional sensor inputs in the second mode of operation, we conducted a principal component analysis (PCA) to further improve the identification of the cell state by clustering populations with little overlap between viable and nonviable cells. We therefore found that microfluidic separation devices can be used to efficiently sort cells and accurately sense viability in a label-free manner.

Label-free microfluidic enrichment of cancer cells from non-cancer cells in ascites.

The isolation of a patient's metastatic cancer cells is the first, enabling step toward treatment of that patient using modern personalized medicine techniques. Whereas traditional standard-of-care approaches select treatments for cancer patients based on the histological classification of cancerous tissue at the time of diagnosis, personalized medicine techniques leverage molecular and functional analysis of a patient's own cancer cells to select treatments with the highest likelihood of being effective. Unfortunately, the pure populations of cancer cells required for these analyses can be difficult to acquire, given that metastatic cancer cells typically reside in fluid containing many different cell populations. Detection and analyses of cancer cells therefore require separation from these contaminating cells. Conventional cell sorting approaches such as Fluorescence Activated Cell Sorting or Magnetic Activated Cell Sorting rely on the presence of distinct surface markers on cells of interest which may not be known nor exist for cancer applications. In this work, we present a microfluidic platform capable of label-free enrichment of tumor cells from the ascites fluid of ovarian cancer patients. This approach sorts cells based on differences in biomechanical properties, and therefore does not require any labeling or other pre-sort interference with the cells. The method is also useful in the cases when specific surface markers do not exist for cells of interest. In model ovarian cancer cell lines, the method was used to separate invasive subtypes from less invasive subtypes with an enrichment of ~ sixfold. In ascites specimens from ovarian cancer patients, we found the enrichment protocol resulted in an improved purity of P53 mutant cells indicative of the presence of ovarian cancer cells. We believe that this technology could enable the application of personalized medicine based on analysis of liquid biopsy patient specimens, such as ascites from ovarian cancer patients, for quick evaluation of metastatic disease progression and determination of patient-specific treatment.