Fei Zhang

This review summarizes microfluidic programmable strategies for channels and flow, referring to microvalves, micropumps, digital microfluidics, multiplexers, micromixers, slip-, and block-based configurations.

Many pathological and physiological processes, including embryonic development, immune response and cancer metastasis, involve studies on cell migration, and especially detection methods, for which it is difficult to satisfy the requirements for rapid and quantitative evaluation and analysis. In view of the shortcomings in simultaneously quantifying the number of migrated cells and non-migrated cells using Transwell assays, we propose a novelty approach for the evaluation of cell migration by distinguishing whether the cells have migrated based on the regularity of the cell morphology changes. Traditionally, the status of living cells and dead cells are detected and analyzed by machine learning using some common morphological characteristics, e.g., area and perimeter of the cells. However, the accuracy of detecting whether cells have migrated or not using these common characteristics is not high, and the characteristics are not appropriate for our studies. Therefore, from the point of view of mechanism analysis for the migration behavior, we examined the regularity of different morphology changes of migrated cells and non-migrated cells, and thus discovered the distinguishable morphological characteristics. Then, two deformation characteristics, deformation index and taper index are proposed. Then, a machine learning based algorithm that can identify migrated cells according to the proposed deformation characteristics was devised. In addition, images of migrated cells and non-migrated cells were obtained from the Transwell assays. This algorithm was trained, and was able to successfully identify migrated cells with an accuracy of 84% using the proposed morphological characteristics. This method greatly improves the identification accuracy when compared with the identification of traditional characteristics of which the accuracy was about 54.7%. This machine learning based method might be employed as a potential tool for cell counting and evaluation of cell migration with the aim of reducing time and improving automation compared with the traditional method. This method is effective, rapid, and incorporate advances in artificial intelligence which could be used for adapting the current evaluation methods.

The adhesion strength of a cancer cell is a valuable biophysical marker of its metastatic potential, tightly associated with various metastatic processes; for example, cancer cells escape from a primary tumor, and circulating tumor cells (CTCs) are anchored to the vessel wall. Although constriction-based microfluidics can realize the high-throughput characterization of single-cell deformability, due to the influence of cell size heterogeneity, accurately evaluating the adhesion strength of a cancer cell at high throughputs in constriction remains difficult. In this paper, we first proposed an approach for the assessment of adhesion strength of BGC-823 and SGC-7901 cell lines at high throughputs based on a friction coefficient using the constant velocity stage of cell transit in a long-channel constriction. Cell size was proven to be independent of adhesion strength by cell detachment assay; however, it has large effects on cell transit velocity in constriction. Therefore, the linear elasticity of a completely deformed cell in constriction is simplified as a compressed spring model, effectively reducing the influence of cell size heterogeneity. Theoretically, our proposed model can well offset the influence of cell size by cell transit velocity, while our experimental results indicate that the friction coefficient has a good linear relationship with the logarithm of the adhesion strength too. Therefore, our proposed approach can realize accurate characterization of cell adhesion strength at high throughputs using long-channel constriction-based microfluidics. Hence, this work might enrich the functions of constriction-based microfluidics and bring new insights into the characterization of mechanical phenotypes.

The accurate equivalent circuit model contributes to the better fitting of required cell characteristics, such as cell impedance, cell adhesion area, and cell–electrode distance. However, so many theoretical models on specific modules make it difficult for new researchers to understand the whole model of electrode system physically. Besides, the accurate theoretical model and the simplified calculations obviously contradict each other; therefore, it is confusing for many researchers to choose the proper theoretical model to calculate the specific parameters required. In this review, we first discuss the problems and suggestions of electrode system design for cell adhesion-based measurement in terms of parasitic capacitance, detection range of cell number, electric field distribution, and interelectrode distance. The design of electrode system for cell nonadhesion measurement was analyzed in terms of microchannel size and electrode position. Then, we discuss the advantages and disadvantages of various equivalent circuit models according to different requirements of researchers, and simultaneously provide a corresponding theoretical model for researchers. Various factors influencing electric impedance spectroscopy (EIS) such as the parasitic capacitance between microelectrodes, the changes of cell adhesion area and cell–electrode distance, the electrode geometry, and the surface conductivity of electrode were quantitatively analyzed to contribute to better understanding of the equivalent models. Finally, we gave advice to optimize the theoretical models further and perspectives on building uniform principles of theoretical model optimization in the future.

Cell viability evaluation is significantly meaningful for cellular assays. Some cells with weak viability are easily killed in the detection of anticancer 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 article proposed a multiphysical property information fusion method for evaluating cell viability heterogeneity based on polynomial regression in a single‐channel integrated microfluidic chip. In this method, adhesion strengths τ_N, that are defined as the magnitude of shear stress needed to detach (100 − N) % of cell population, were extracted as the independent variables of polynomial regression model by calculating the nonlinear fitting of the impedance–response curves for shear stress (cell detachment assay). Besides, by calculating the nonlinear 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 polynomial regression model. The results show that the mean relative error of our fusion method averagely reduces by 6.04% and 62.79% compared with the multiple linear regression method and the cell counting method. Moreover, a simplified theoretical model used to describe the quantitative relationship between cell viability and its adhesion strengths was built to provide a theoretical basis for our fusion method.

The regulation of cell density is an important segment in microfluidic cell culture, particularly in the repeated assays. Traditionally, consistent cell density is difficult to achieve, owing to the inaccurate regulation of cell density with manual feedback. A novel cell culture method with automatic feedback is proposed for real‐time regulation of cell density based on microfluidic chip in this paper. Here, an integrated microfluidic system combining cell culture, density detection, and control of proliferation rate was developed. Interdigital electrode structures were sputtered on the microchannel automatically to provide the real‐time feedback information of impedance. The most sensitive frequency was studied to improve the detection resolution of the sensing chip. Cells were cultured on the chip surface and cell density was detected by monitoring the alternation of the impedance. The feedback controller was established by the least squares support vector machines. 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 a 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.