Flow cytometric immunophenotyping of mature lymphatic neoplasias using knowledge guided cluster analysis

Flow cytometry is widely used for the immunological characterization of hem atopoietic malignancies. Discrimination of normal and malignant cellular im munophenotypes is the most critical step in data analysis, especially if mu lti-color analysis is performed on highly heterogenous cell suspensions. We therefore investigated, whether adaptive, simultaneous multiparameter gati ng allowed automated, operator independent analysis of data obtained from t he immunophenotyping of blood or bone marrow samples with regard to the pre sence of non-Hodgkin lymphoma cells. The identification of physiological an d malignant cells was achieved by predefining population boundaries, based on the expectations of the population's location in two-dimensional dot plo ts. The prospective application of these predefined region boundaries in 52 blood and bone marrow samples enabled identification of lymphoma cells wit h regard to their presence and immunophenotype, based on the correlation of markers as defined in multiple tubes. Our data confirm that highly standar dized data analysis methods can reduce the variability of analysis and supp ort the expert in establishing a rapid classification of the sample.