Two-dimensional gel electrophoresis and surface-enhanced laser desorption ionization-time of flight-mass spectrometry for detection of protein expression profiles in the hippocampus following closed brain injury

Gene expression profile changes in brain regions following traumatic brain injury at the gene level cannot sufficiently elucidate gene expression time, expression amount, protein post-translational processing or modification. Therefore, it is necessary to quantitatively analyze the gene expression profile using proteomic techniques. In the present study, we established a rat model of closed brain injury using Marmarou＇s weight-drop device, and investigated hippocampal differential protein expression using two-dimensional gel electrophoresis and surface-enhanced laser desorption ionization-time of flight-mass spectrometry. A total of 364 protein peaks were detected on weak cation exchange-2 protein chips, including 37 differential protein peaks. 345 protein peaks were detected on immobilized metal affinity capture arrays-Cu, including 12 differential protein peaks Further examination of these differential proteins revealed that glucose-regulated protein and proteasome subunit alpha type 3 expression were significantly upregulated post-injury. These results indicate that brain injury can alter protein expression in the hippocampus, and that glucose-regulated protein and proteasome subunit alpha type 3 are closely associated with the occurrence and development of traumatic brain injury.

Preoperatively molecular staging with CM10 ProteinChip and SELDI-TOF-MS for colorectal cancer patients

Objectives： To detect the serum proteomic patterns by using SELDI-TOF-MS （surface enhanced laser desorption/ ionization-time of flight-mass spectrometry） technology and CM10 ProteinChip in colorectal cancer （CRC） patients, and to evaluate the significance of the proteomic patterns in the tumour staging of colorectal cancer. Methods： SELDI-TOF-MS and CM10 ProteinChip were used to detect the serum proteomic patterns of 76 patients with colorectal cancer, among them, 10 Stage Ⅰ, 19 Stage Ⅱ, 16 Stage Ⅲ and 31 Stage Ⅳ samples. Different stage models were developed and validated by support vector machines, disctiminant analysis and time-sequence analysis. Results： The Model Ⅰ formed by 6 protein peaks （m/z： 2759.58, 2964.66, 2048.01, 4795.90, 4139.77 and 37761.60） could be used to distinguish local CRC patients （Stage Ⅰ and Stage Ⅱ） from regional CRC patients （Stage Ⅲ） with an accuracy of 86.67% （39/45）. The Model Ⅱ formed by 3 protein peaks （m/z： 6885.30, 2058.32 and 8567,75） could be used to distinguish locoregional CRC patients （Stage Ⅰ, Stage Ⅱ and Stage Ⅲ） from systematic CRC patients （Stage IV） With an accuracy of 75.00% （57/76）. The Model Ⅲ could distinguish Stage Ⅰ from Stage Ⅱ with an accuracy of 86.21% （25/29）. The Model Ⅳ could distinguish Stage Ⅰ from Stage Ⅲ with accuracy of 84.62% （22/26）. The Model Ⅴ could distinguish Stage Ⅱ from Stage Ⅲ with accuracy of 85.71% （30/35）. The Model Ⅵ could distinguish Stage Ⅱ from Stage Ⅳ with accuracy of 80.00% （40/50）. The Model Ⅶ could distinguish Stage Ⅲ from Stage Ⅳ with accuracy of 78.72% （37/47）. Different stage groups could be distinguished by the two-dimensional scattered spots figure obviously. Conclusion： This method showed great success in preoperatively determining the colorectal cancer stage of patients.