Cell viability evaluation is significantly meaningful for cellular assays. Some cells with weak viability are easily killed in the detection of anti-cancer drugs, while others with strong viability survive and proliferate, ultimately leading to the treatment failure or the inaccuracy of biological assays. Accurately evaluating cell viability heterogeneity still remains difficult. This paper proposed a multi-physical property information fusion method for evaluating cell viability heterogeneity based on multiple linear regression (MLR) on a single-channel integrated microfluidic chip. In this method, adhesion strengths τN, that are defined as the magnitude of shear stress needed to detach (100-N) % of cell population, were extracted as the independent variables of MLR model by calculating the linear fitting of the impedance-response curves for shear stress (cell detachment assay). Besides, by calculating the non-linear fitting of the drug dose-response curves for cancer cells (IC50 assay), the half-maximal inhibitory concentration (IC50) was extracted as the dependent variables of MLR model. The results show that the mean relative error of our fusion method reduces by 17.87% and 59.66% compared with the single-parameter method and the cell counting method. Moreover, through the theoretical analysis of the drug resistance heterogeneity model, it proved that there is a qualitative relationship between the cell adhesion strength and cell viability heterogeneity, which provides a theoretical basis for our fusion method.

The increasing attention to precision medicine is widely paid in order to greatly improve the cure rate of cancer. Improving the stability and accuracy of cell viability evaluation is the key of precision medicine, for excess dosage of anti-cancer drugs not only kills the cancer cells, but also does harm to normal cells. Electrochemical impedance sensing (EIS) method is widely accepted as a label-free, non-invasive approach for real-time, online monitoring of cell viability. Due to the large effects of many influencing factors, the existing EIS methods that utilized single-frequency impedances show poor stability and low accuracy of cell viability evaluation. In this paper, we proposed a multi-physical information fusion method based on least squares support vector machine (LS-SVM) for improving the stability and accuracy of cell viability evaluation. The results show that the mean relative error of single-frequency method is about 0.08, while that of fusion method is about 0.04. It means that the prediction results of fusion method are more accurate than that of the single-frequency method. Moreover, the maximum relative error of single-frequency method is up to 0.5 due to the influencing of cell micromotion, while that of fusion method is below 0.07, showing that the fusion method is more stable than single-frequency method.

The regulation of cell density is an important segment in microfluidic cell culture, particularly in the repeated assays. Traditionally, the consistent cell density is difficult to achieve owing to the inaccurate regulation of cell density with manual feedback. A novel microfluidic culture method with automatic feedback is proposed for real-time regulation of cell density in this paper. Here, an integrated microfluidic system combining cell culture, density detection and control of proliferation rate was developed. Interdigital electrode structures (IDES) are sputtered on the microchannel for automatically providing the real-time feedback information of impedance. The most sensitive frequency is studied to improve the detection resolution of the sensing chip. Cells were cultured on the chip surface and the cell density was detected by monitoring the alternation of the impedance. The feedback controller is established by least squares support vector machines (LS-SVM). Then, the cell proliferation rate was automatically controlled using the feedback controller to achieve the desired cell density in the repeated assays. The results show that the standard error of this method is 2.8% indicating that the method can keep consistency of cell density in the repeated assays. This study provides a basis for improving the accuracy and repeatability in the further assays of finding the optimal drug concentration.