title: Human B-cell and progenitor stages as determined by probability state modeling of multidimensional cytometry data.
abstract: BACKGROUND
  Human progenitor and B-cell development is a highly regulated process characterized by the ordered differential expression of numerous cell-surface and intracytoplasmic antigens. This study investigates the underlying coordination of these modulations by examining a series of normal bone marrow samples with the method of probability state modeling or PSM.
  
  
  RESULTS
  The study is divided into two sections. The first section examines B-cell stages subsequent to CD19 up-regulation. The second section assesses an earlier differentiation stage before and including CD19 up-regulation. POST-CD19 ANTIGENIC UP-REGULATION: Statistical analyses of cytometry data derived from sixteen normal bone marrow specimens revealed that B cells have at least three distinct coordinated changes, forming four stages labeled as B1, B2, B3, and B4. At the end of B1; CD34 antigen expression down-regulates with TdT while CD45, CD81, and CD20 slightly up-regulate. At the end of B2, CD45 and CD20 up-regulate. At the end of B3 and beginning of B4; CD10, CD38, and CD81 down-regulate while CD22 and CD44 up-regulate. PRE-CD19 ANTIGENIC UP-REGULATION: Statistical analysis of ten normal bone marrows revealed that there are at least two measurable coordinated changes with progenitors, forming three stages labeled as P1, P2, and P3. At the end of P1, CD38 up-regulates. At the end of P2; CD19, CD10, CD81, CD22, and CD9 up-regulate while CD44 down-regulates slightly.
  
  
  CONCLUSIONS
  These objective results yield a clearer immunophenotypic picture of the underlying cellular mechanisms that are operating in these important developmental processes. Also, unambiguously determined stages define what is meant by "normal" B-cell development and may serve as a preliminary step for the development of highly sensitive minimum residual disease detection systems. A companion article is simultaneously being published in Cytometry Part A that will explain in further detail the theory behind PSM. Three short relevant videos are available in the online supporting information for both of these papers.
authors: Verity Software House, Topsham, Maine.
authors: C Bruce
authors: CB
authors: Bagwell
authors: Verity Software House, Topsham, Maine.
authors: Beth L
authors: BL
authors: Hill
authors: Department of Laboratory Medicine, University of Washington, Seattle, Washington, 98195.|Department of Pathology, University of Washington, Seattle, Washington, 98195.
authors: Brent L
authors: BL
authors: Wood
authors: Department of Flow and Image Cytometry, Roswell Park Cancer Institute, Buffalo, New York, 14263.
authors: Paul K
authors: PK
authors: Wallace
authors: Department of Flow and Image Cytometry, Roswell Park Cancer Institute, Buffalo, New York, 14263.
authors: Muaz
authors: M
authors: Alrazzak
authors: Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, 02114.
authors: Abigail S
authors: AS
authors: Kelliher
authors: Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, 02114.
authors: Frederic I
authors: FI
authors: Preffer
mesh_terms: D018941:Antigens, CD19 / Q000378:metabolism; D000954:Antigens, Surface / Q000378:metabolism*; D001402:B-Lymphocytes / Q000166:cytology* / Q000276:immunology; D001854:Bone Marrow Cells / Q000166:cytology / Q000276:immunology; D002454:Cell Differentiation / Q000276:immunology; D003627:Data Interpretation, Statistical; D005434:Flow Cytometry; D006412:Hematopoietic Stem Cells / Q000166:cytology*; D006801:Humans; D016130:Immunophenotyping; D008962:Models, Theoretical; D054448:Precursor Cells, B-Lymphoid / Q000166:cytology* / Q000276:immunology; D015854:Up-Regulation
chemical_list: D018941:Antigens, CD19; D000954:Antigens, Surface
keywords: B-cell development; bone marrow microenvironment; bone marrow ontogeny; broadened quantile function modeling; flow cytometry; hematopoietic stem cells; high-dimensional modeling; human B-cell differentiation; monoclonal antibodies; probability state modeling