Gastric cancer is a common cause of cancer death, and its incidence rate remains high in my country, seriously threatening people's health (Zheng Ying and Wu Chunxiao, 2016). Peripheral blood mononuclear cells (PBMC) are closely related to the survival prognosis of gastric cancer (Tsai et al., 2013), PBMC have immune function (Lv Qiuju et al., 2015) and have significant clinical and biological research value (Kim et al., 2016), widely used in tumor treatment research (Koeck et al., 2016). However, there is no consensus on the conditions for isolating PBMC from peripheral venous blood (Moreli et al., 2016; Corkum et al., 2015; Dai Guangming et al., 2014), which urgently needs in-depth research.
Experimental design based on JMP software can obtain reliable results with a smaller number of experiments under the premise of effectively controlling errors (Wu Fan et al., 2016). This study uses JMP software to customize the separation conditions of PBMC and optimize the centrifugation time and speed, providing a basis for the optimization, basic and clinical research of PBMC separation in gastric cancer patients.
Materials and Methods
The venous blood used in this experiment was collected from gastric cancer patients at the Gastrointestinal Surgery Department of the First Affiliated Hospital of Anhui Medical University.
Study the efficiency of lymphocyte separation solution in separating PBMC from peripheral venous blood of gastric cancer patients, and observe the effects of centrifugation time (X1) and centrifugation speed (X2) on the thickness of the PBMC separation layer (Y) after centrifugation. Based on the results of preliminary experiments and literature, it was determined that the centrifugation time range is 10~30min and the centrifugation speed range is 1000~3000r/min. The thickness of the PBMC separation layer was used as a reaction variable, and the goal was set to maximize. Quadratic interactions and powers of two were selected in the model. Define factor constraints: 1X1+1X2≤3025, 1X1+1X2≥1020, custom-designed PBMC separation conditions, round up to integers when X1 and X2 encounter decimals, a total of 12 experiments (table below).
PBMC Separation method
Collect 4 ml of the patient's peripheral venous blood in an anticoagulant tube containing EDTA. In a 15 ml centrifuge tube, place 1 cm away from the Ficoll liquid level and slowly add blood along the tube wall to the Ficoll liquid level. Centrifuge at 20°C according to the parameters of the customized design results. After centrifugation, it is divided into four layers from the bottom of the tube to the liquid surface, namely the red blood cells and granulocyte layer, the stratified liquid layer, the mononuclear cell layer, and the plasma layer.
PBMC Centrifugal thickness measurement
The separated centrifuge tube was fixed on the vertical wall with tape, fixed the digital camera at the level of the PBMC cloud layer, and adjusted the focus to take photos. Put the photo into Photoshop CS software and measure the thickness of the cloud layer: the physical length of the 100mm scale in the same test tube picture is used as a reference. Use the ruler tool to measure the length of the element. The information panel reads the data and records it. The same method is used to measure the thickness of the PBMC (the length of the element). ), calculate the physical length (thickness to be measured) of the PBMC cloud layer according to the following formula:
PBMC Vitality assay
According to the optimized conditions obtained through JMP software analysis, centrifuge the PBMC. Insert the pipette into the cloud layer, gently suck out the cloud layer, place it in a new centrifuge tube, add 3 times the volume of PBMC in PBS, 20°C, 2000r, 10min , wash twice and discard the supernatant. Add 500ul of PBS to dilute to prepare a single cell suspension. Pipette 450ul of the above suspension into the EP tube, add 50ul of trypan blue solution, and mix the cell suspension and 0.4% trypan blue solution 9:1 (final concentration 0.04%). Take photos within 3 minutes, manually mark and count live cells and dead cells using PS software. Observed under a microscope, dead cells are stained blue, while living cells refuse to stain and appear colorless and transparent. Calculate cell survival rate: Cell survival rate (%) = total number of living cells/(total number of living cells + total number of dead cells) × 100%.
JMP The software analyzes the customized design result data and uses the standard least squares method to analyze the main effects, secondary effects, and interactions of centrifugation time and centrifugation speed. P ＜ 0.05 The difference is statistically significant.
results and analysis
PBMC thickness measurement results
Twelve experiments were obtained using JMP custom design. According to the specific centrifugation time and centrifugation speed parameters in each experiment, centrifugation was performed. After centrifugation, photos were taken. Photoshop software was used to measure the thickness of PBMC in the centrifuge tube. The results are shown in Table 1.
Table 1 PBMC thickness measurement results under different centrifugation conditions
Centrifugation time and centrifugation speed PBMC Effect of thickness
JMP software was used to fit the model, and the standard least squares method was used to screen the effects. The main effect, secondary effect, and interaction of the two factors of centrifugation time and speed affected the thickness of PBMC (Table 2).
Table 2 Effect of centrifugal separation factors on PBMC thickness
The effects of centrifugation time, centrifugation speed and their interaction on PBMC thickness were all statistically significant, P<0.05; the interaction between time and speed was not statistically significant (P>0.05). According to the software ranking of the estimated values of each parameter (Table 3), the impact of centrifugation time is greater than the centrifugation speed.
Table 3 Parameter estimates after sorting
Model fit
Variation analysis of the model fitted by the standard least squares method (Table 4). The mean squares of the model and error are 0.191981 and 0.021543 respectively. The F value is 8.911. The P of the model is <0.01, indicating that the fitted model is at the level of α=0.01. is significant.
Table 4 Variation analysis of customized experimental design model
The relationship between the predicted value and the actual value in the fitting model (Figure 1) can be seen: the coefficient of determination R2=0.88, indicating that the model can explain 88% of the variation, and the model fits well.
Figure 1 Relationship between predicted values and actual values
Surface drawing
The model was fitted according to the standard least squares method, and JMP software was used to perform factor characterization (surface profiler). The model with reaction variables was displayed as a surface to obtain a surface plot (Figure 2). The maximum value of the reaction variable, that is, PBMC thickness, can be found intuitively, and the interaction between centrifugation time and centrifugation speed can be observed.
Figure 2 Surface plot of the interaction between centrifugation time and speed affecting PBMC thickness
Prediction Profiler Optimal Settings
Select Maximize Willingness in the prediction profiler of the JMP software, and the software automatically adjusts the prediction profile graph to obtain the optimal setting for the maximum PBMC thickness (Figure 3). It was found that density gradient centrifugation was used to separate PBMC from the peripheral blood of gastric cancer patients and obtain the maximum thickness of PBMC. The method is: the centrifugation time is 10 minutes, and the centrifugation speed is set at 2306.962r/min, which is approximately 2307r/min. This experiment predicts that under the above conditions, the theoretical value of PBMC thickness is 2.596923mm, 95%CI (2.34213, 2.85171).
Figure 3 Prediction profiler settings to maximize willingness
Cell viability assay under optimized conditions
Centrifugation time = 10 min, centrifugation speed = 2307 r/min, and the PBMC obtained by centrifugation were stained with trypan blue (Figure 4). The total number of cells was 199, the total number of dead cells was 8, and the cell survival rate was 95%.
Figure 4 PBMC trypan blue staining (×10)
discuss
Density gradient centrifugation is widely used to separate PBMCs (Zhao Jie et al., 2014), but different units and researchers use different centrifugation times and centrifugation speeds to separate PBMCs, and there are few literature reports on separation effects (Huang et al., 2014; Saidijam et al., 2014), the lack of standard operating procedures and guidelines resulted in a lack of consistency and comparability of experimental results, and low efficiency of some centrifugal separations, which affected the conduct of subsequent experiments.
This study used JMP software for customized design, using PBMC centrifugal thickness as the reaction variable, centrifugation time and centrifugation speed as factor variables, and obtained 12 experiments. The centrifuged PBMC thickness was measured using Photoshop CS6.0 and analyzed by the standard least squares method. It was found that: the main effects of centrifugation time, centrifugation speed and their secondary effects all affected the thickness of PBMC; the interaction between centrifugation time and centrifugation speed was not statistically significant.
The coefficient of determination R2=0.88 of the established model can explain 88% of the response variation. The predicted PBMC thickness is in good agreement with the actual value. The optimal separation conditions obtained by prediction and characterization: centrifugation time = 10min, centrifugation speed = 2307r/min, predicted PBMC thickness = 2.596923mm, 95%CI (2.34213, 2.85171), actual thickness = 2.7mm, trypan blue staining found cells Survival rate = 95%.
The secondary effects of centrifugation time and centrifugation speed have no practical significance during experimental operations. The optimal conditions found in this study are inconsistent with the report of Cai Minmin et al. (Cai Minmin et al., 2016). The main reasons are:
- The design scheme is different. Cai Minmin et al. only used group experimental design. This study used JMP software to implement customized experimental design. This design method is based on mathematical models, and the statistical results are more scientific and advanced (Shi Zhijia et al., 2010);
- There are certain differences in research objects;
- There may be certain differences in other experimental conditions such as temperature;
- There are differences in brands of experimental equipment such as centrifuges.
Predictive profiler analysis found that the main effects of centrifugation time, centrifugation speed, and the reaction variable PBMC thickness all showed a curvilinear relationship. As the centrifugation time increased, the PBMC thickness first decreased and then increased. As the centrifugation speed increased, the PBMC thickness The thickness first increases and then decreases, indicating that the influence of factor variables, namely centrifugation time and speed, on the thickness of PBMC is complex and not a simple linear correlation, which deserves attention during the experiment.
This study used the optimal centrifugation time and centrifugation speed simulated and calculated by the factor profiler for verification and found that the actual thickness of PBMC fell within the predicted thickness range, indicating that the model fit well. Trypan blue staining showed that cells obtained by centrifugation under optimal conditions have a higher survival rate and can be used for subsequent experimental research. This study provides a new method for the optimization of centrifugation conditions for peripheral blood PBMC in gastric cancer patients.
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