The traditional method of multi-parameter flow data clustering in flow cytometry is to mainly use professional software to manually set the door and circle out the target cells for analysis. The analysis process is complex and professional. Based on this, a clustering algorithm, which is based on t-distributed stochastic neighbor embedding (t-SNE) algorithm for multi-parameter stream data, is proposed in the paper. In this algorithm, the Euclidean distance of sample data in high dimensional space is transformed into conditional probability to represent similarity, and the data is reduced to low dimensional space. In this paper, the stained human peripheral blood cells were treated by flow cytometry, and the processed data were derived as experimental sample data. Thet-SNE algorithm is compared with the kernel principal component analysis (KPCA) dimensionality reduction algorithm, and the main component data obtained by the dimensionality reduction are classified using K-means algorithm. The results show that thet-SNE algorithm has a good clustering effect on the cell population with asymmetric and trailing distribution, and the clustering accuracy can reach 92.55%, which may be helpful for automatic analysis of multi-color multi-parameter flow data.
A high throughput measurement method of human red blood cells (RBCs) deformability combined with optical tweezers technology and the microfluidic chip was proposed to accurately characterize the deformability of RBCs statistically. Firstly, the effective stretching deformation of RBCs was realized by the interaction of photo-trapping force and fluid viscous resistance. Secondly, the characteristic parameters before and after the deformation of the single cell were extracted through the image processing method to obtain the deformation index of area and circumference. Finally, statistical analysis was performed, and the average deformation index parameters (\begin{document}$ \overline {D{I_S}} $\end{document}, \begin{document}$ \overline {D{I_C}} $\end{document}) were used to characterize the deformability of RBCs. A high-throughput detection system was built, and the optimal experimental conditions were obtained through a large number of experiments. Three groups of samples with different deformability were used for statistical verification. The results showed that the smallest cell component \begin{document}$ \overline {D{I_S}} $\end{document} was 9.71%, and the detection flux of 8-channel structure was about 370 cells/min. High-throughput detection and characterization methods can effectively distinguish different deformed RBCs statistically, which provides a solution for high-throughput deformation analysis of other types of samples.
The process of multi-parametric flow cytometry data analysis is complicate and time-consuming, which requires well-trained professionals to operate on. To overcome this limitation, a method for multi-parameter flow cytometry data processing based on kernel principal component analysis (KPCA) was proposed in this paper. The dimensionality of the data was reduced by nonlinear transform. After the new characteristic variables were obtained, automatical clustering can be achieved using improvedK-means algorithm. Experimental data of peripheral blood lymphocyte were processed using the principal component analysis (PCA)-based method and KPCA-based method and then the influence of different feature parameter selections was explored. The results indicate that the KPCA can be successfully applied in the multi-parameter flow cytometry data analysis for efficient and accurate cell clustering, which can improve the efficiency of flow cytometry in clinical diagnosis analysis.
The contractile force of hepatic stellate cells plays a very important role in liver damage, hepatitis and fibrosis. In this paper, a method based on polydimethylsiloxane (PDMS) thin micropillar arrays is proposed to measure the contractile force of human hepatic stellate cell line LX-2, which enables dynamic measurement of the subcellular distribution of force magnitude and direction. First, thin micropillar arrays on glass bottom dish were fabricated using two-step casting process in order to meet the working distance requirement of 100× objective lens. After hydrophilic treatment and protein imprint, cells were seeded on the micropillar arrays. LX-2 cells, which were quiesced by growth in serum-free medium, were activated by adding fetal bovine serum (FBS). The deflections of the micropillars were achieved by image processing technique, and then the contractile force of cells exerted on the micropillars was calculated according to mechanical simulation results, and was analyzed under both quiescent and activated conditions. The experimental results show that the average traction force of quiescent cells is about 20 nN, while the contractile force of activated cells increased to 110 nN upon adding FBS. This method can quantify the contractile force of LX-2 cell on subcellular scale in both quiescent and activated states, which may benefit pathology study and drug screen for chronic liver diseases resulted from liver fibrosis.