BACKGROUND AND PURPOSE: Inflammation plays a key role in cerebral ischemia through activation of microglia and infiltration by leukocytes. Flow cytometry is a well-established method for quantitative and qualitative analysis of inflammatory cells. However, this technique has not been applied to the study of cerebral ischemia inflammation. The aim of this study was to establish a flow cytometric method to measure inflammatory cells in ischemic brain. METHODS: To perform flow cytometry on brain tissue, we developed 2 cell-isolation methods based on different mechanical dissociation and Percoll gradient separation techniques. The methods were tested on a rat model of permanent middle cerebral artery occlusion. Morphological and immunophenotypic analyses, with the use of anti-CD11b, anti-CD45, and alphabeta T-cell receptor antibodies, were employed to identify and quantify inflammatory cells. RESULTS: Both methods gave consistent results in terms of yield and reproducibility. The cell suspension contained granulocytes, macrophages, lymphocytes, and neural cells. Morphological and immunophenotypic analyses enabled the identification of a cell-scatter gate (R1a) enriched in inflammatory cells. With both methods, a higher number of events in R1a were recorded in the ischemic hemisphere than in the nonischemic hemisphere (P< or =0.001). CD11b, CD45, and alphabeta T-cell receptor staining confirmed that this augmentation was a reflection of the increase in the number of granulocytes, cells of the monocytic lineage, and lymphocytes. CONCLUSIONS: Quantitative flow cytometric analysis of ischemic rat brain is feasible and provides a reliable and rapid assay to assess neuroinflammation in experimental models of brain ischemia.
BACKGROUND AND PURPOSE: Inflammation plays a key role in cerebral ischemia through activation of microglia and infiltration by leukocytes. Flow cytometry is a well-established method for quantitative and qualitative analysis of inflammatory cells. However, this technique has not been applied to the study of cerebral ischemia inflammation. The aim of this study was to establish a flow cytometric method to measure inflammatory cells in ischemic brain. METHODS: To perform flow cytometry on brain tissue, we developed 2 cell-isolation methods based on different mechanical dissociation and Percoll gradient separation techniques. The methods were tested on a rat model of permanent middle cerebral artery occlusion. Morphological and immunophenotypic analyses, with the use of anti-CD11b, anti-CD45, and alphabeta T-cell receptor antibodies, were employed to identify and quantify inflammatory cells. RESULTS: Both methods gave consistent results in terms of yield and reproducibility. The cell suspension contained granulocytes, macrophages, lymphocytes, and neural cells. Morphological and immunophenotypic analyses enabled the identification of a cell-scatter gate (R1a) enriched in inflammatory cells. With both methods, a higher number of events in R1a were recorded in the ischemic hemisphere than in the nonischemic hemisphere (P< or =0.001). CD11b, CD45, and alphabeta T-cell receptor staining confirmed that this augmentation was a reflection of the increase in the number of granulocytes, cells of the monocytic lineage, and lymphocytes. CONCLUSIONS: Quantitative flow cytometric analysis of ischemicrat brain is feasible and provides a reliable and rapid assay to assess neuroinflammation in experimental models of brain ischemia.
Authors: Gil Benedek; Jun Zhang; Ha Nguyen; Gail Kent; Hilary A Seifert; Sean Davin; Patrick Stauffer; Arthur A Vandenbark; Lisa Karstens; Mark Asquith; Halina Offner Journal: J Neuroimmunol Date: 2017-06-21 Impact factor: 3.478
Authors: Bharti Manwani; Fudong Liu; Victoria Scranton; Matthew D Hammond; Lauren H Sansing; Louise D McCullough Journal: Exp Neurol Date: 2013-08-29 Impact factor: 5.330
Authors: Rita I M Galvão; João P L Diógenes; Graziela C L Maia; Emídio A S Filho; Silvânia M M Vasconcelos; Dalgimar B de Menezes; Geanne M A Cunha; Glauce S B Viana Journal: Neurochem Res Date: 2005-01 Impact factor: 3.996