title: Quantification of fluorescence properties of lymphocytes in peripheral blood mononuclear cell suspensions using a latent class model.
abstract: Lymphocytes, monocytes, granulocytes, and other blood cells can be distinguished on the basis of their forward (FSC) and sideward (SSC) light scatter properties and their expression of CD45 and CD14. A FSC,SSC gate can be set to include > 95% of the lymphocytes using a "back gating" procedure on the CD45+, CD14- cells. However, nonlymphoid cells such as monocytes have light scattering properties similar to lymphocytes. This problem occurs particularly in patient populations where the light scattering properties of lymphocyte subsets have changed (e.g., due to activation) and are similar to those of the monocytes. Thus, immunophenotyping using antibodies specific for other markers than CD45 and CD14 does not allow a direct assessment of the percentage of all lymphocytes positive for those markers. In order to optimize immunophenotyping we have developed analytic model in which the FSC,SSC dot plot is partitioned into six nonoverlapping light scatter regions. Each light scatter region contains a mixture population of different cell types, i.e., lymphocytes, monocytes, granulocytes, and other cells. The proportions of each cell type are known from the CD45,CD14 expression within each light scatter region. Under the assumption of independence of fluorescence and scatter properties conditional on cell type, the expression of markers other than CD45 or CD14 are derived from the cell type composition and the fluorescence properties on the other markers of each light scatter region. The underlying statistical model is a latent class model, and maximum likelihood estimates are computed using the expectation-maximization (EM) algorithm. The application of the model for immunophenotyping of lymphocytes of healthy individuals and cancer patients receiving immunotherapy is shown.
authors.affiliation: Department of Statistics, Dr. Daniel den Hoed Cancer Center, Rotterdam, The Netherlands.
authors.forename: W L
authors.initials: WL
authors.lastname: van Putten
authors.forename: W
authors.initials: W
authors.lastname: de Vries
authors.forename: P
authors.initials: P
authors.lastname: Reinders
authors.forename: W
authors.initials: W
authors.lastname: Levering
authors.forename: R
authors.initials: R
authors.lastname: van der Linden
authors.forename: H J
authors.initials: HJ
authors.lastname: Tanke
authors.forename: R L
authors.initials: RL
authors.lastname: Bolhuis
authors.forename: J W
authors.initials: JW
authors.lastname: Gratama
mesh_terms: D000911:Antibodies, Monoclonal; D002469:Cell Separation / Q000379:methods*; D005434:Flow Cytometry; D005453:Fluorescence; D006801:Humans; D016130:Immunophenotyping / Q000379:methods*; D008027:Light; D008214:Lymphocytes / Q000166:cytology*; D012984:Software; D013223:Statistics as Topic
chemical_list: D000911:Antibodies, Monoclonal