Clinical Hematology Lippincott 11th ed.

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Wintrobe's Clinical Hematology, 11th Ed
by John P. Greer (Editor), John Foerster (Editor), John N. Lukens (Editor) 
Publisher: Lippincott Williams & Wilkins Publishers; 11th edition (December 2003)
Wintrobe's Clinical Hematology
CONTENTS
Editors
Contributors
Dedication
Preface
Volume 1 
Volume 2 
 Volume 1 Top
 
PART I LABORATORY HEMATOLOGY 
1 Examination of the Blood and Bone Marrow 
Sherrie L. Perkins
2 Clusters of Differentiation 
Frixos Paraskevas
3 Clinical Flow Cytometry 
Frixos Paraskevas
4 Cytogenetics 
Sheila N. J. Sait and Maria R. Baer
5 Molecular Biology and Hematology 
Rebecca L. Shattuck-Brandt and Stephen J. Brandt
 
PART II NORMAL HEMATOLOGIC SYSTEM 
Section 1. Hematopoiesis
6 Origin and Development of Blood Cells 
Maurice C. Bondurant and Mark J. Koury
Section 2. The Erythrocyte
7 Erythropoiesis 
Emmanuel N. Dessypris and Stephen T. Sawyer
8 The Mature Erythrocyte 
Marilyn J. Telen and Russel E. Kaufman
9 Destruction of Erythrocytes 
Bertil Glader
Section 3. Granulocytes and Monocytes
10 Neutrophilic Leukocytes 
Keith M. Skubitz
11 The Human Eosinophil 
Paige Lacy, Allan B. Becker, and Redwan Moqbel
12 Basophilic Leukocytes: Mast Cells and Basophils 
A. Dean Befus and Judah A. Denburg
13 Mononuclear Phagocytes 
J. Brice Weinberg
14 Phagocytosis 
Frixos Paraskevas
Section 4. Lymphocytes
15 Lymphocytes and Lymphatic Organs 
Frixos Paraskevas
16 B Lymphocytes 
Frixos Paraskevas
17 T Lymphocytes and Natural Killer Cells 
Frixos Paraskevas
18 Effector Mechanisms in Immunity 
Frixos Paraskevas
Section 5. Hemostasis
19 Megakaryocytes and Platelets 
Kenneth Kaushansky and Gerald J. Roth
20 Platelet Function in Hemostasis and Thrombosis 
David C. Calverley and Lori J. Maness
21 Blood Coagulation and Fibrinolysis 
Kathleen Brummel-Ziedins, Thomas Orfeo, Nancy Swords Jenny, Stephen J. Everse, and Kenneth G. Mann
22 Endothelium: Angiogenesis and the Regulation of Hemostasis 
Paul J. Shami and George M. Rodgers
 
PART III THERAPEUTIC MODALITIES 
23 Red Cell, Platelet, and White Cell Antigens 
Kathryn E. Webert, Howard H. W. Chan, James William Smith, Nancy M. Heddle, and John G. Kelton
24 Transfusion Medicine 
Susan A. Galel, James M. Malone, III, and Maurene K. Viele
25 Hematopoietic Stem Cell Transplantation 
Richard A. Nash
26 Gene Therapy for Hematologic Disorders, Human Immunodeficiency Virus Infection, and Cancer 
John F. Tisdale, Cynthia E. Dunbar, Jay N. Lozier, and Stacey A. Goodman
 
PART IV DISORDERS OF RED CELLS 
Section 1. Introduction
27 Anemia: General Considerations 
Bertil Glader
Section 2. Disorders of Iron Metabolism and Heme Synthesis
28 Iron Deficiency and Related Disorders 
Nancy C. Andrews
29 Sideroblastic Anemias 
Sylvia S. Bottomley
30 Hemochromatosis 
Corwin Q. Edwards
31 Porphyria 
Sylvia S. Bottomley
Section 3. Hemolytic Anemia
32 Hereditary Spherocytosis and Other Anemias Due to Abnormalities of the Red Cell Membrane 
William C. Mentzer and Bertil Glader
33 Hereditary Hemolytic Anemias Due to Enzyme Disorders 
Bertil Glader
34 Mechanisms of Immune Destruction of Erythrocytes 
Charles J. Parker
35 Autoimmune Hemolytic Anemias 
Anne T. Neff
36 Alloimmune Hemolytic Disease of the Fetus and Newborn 
Anne F. Eder and Catherine S. Manno
37 Paroxysmal Nocturnal Hemoglobinuria 
Charles J. Parker and Russell E. Ware
38 Acquired Nonimmune Hemolytic Disorders 
Michael R. Jeng and Bertil Glader
Section 4. Hereditary Disorders of Hemoglobin Structure and Synthesis
39 Abnormal Hemoglobins: General Principles 
John N. Lukens
40 Sickle Cell Anemia and Other Sickling Syndromes 
Winfred C. Wang
41 Unstable Hemoglobin Disease 
John N. Lukens
42 Thalassemias and Related Disorders: Quantitative Disorders of Hemoglobin Synthesis 
Caterina Borgna-Pignatti and Renzo Galanello
Section 5. Other Red Cell Disorders
43 Megaloblastic Anemias: Disorders of Impaired DNA Synthesis 
Ralph Carmel
44 Acquired and Inherited Aplastic Anemia Syndromes 
Eva C. Guinan and Akiko Shimamura
45 Red Cell Aplasia 
Emmanuel N. Dessypris and Jeffrey M. Lipton
46 Congenital Dyserythropoietic Anemias 
Peter W. Marks and Bertil Glader
47 Anemias Secondary to Chronic Disease and Systemic Disorders 
Robert T. Means, Jr.
48 Anemias Unique to Pregnancy and the Perinatal Period 
Robert D. Christensen and Robin K. Ohls
49 Hemoglobins Associated with Cyanosis: Methemoglobinemia and Low-Affinity Hemoglobins 
John N. Lukens
50 Erythrocytosis 
Robert T. Means, Jr.
 Volume 2 Top
 
PART V DISORDERS OF HEMOSTASIS AND COAGULATION 
Section 1. Introduction
51 Diagnostic Approach to the Bleeding Disorders 
George M. Rodgers
Section 2. Thrombocytopenia
52 Thrombocytopenia: Pathophysiology and Classification 
Shirley Parker Levine
53 Thrombocytopenia Caused by Immunologic Platelet Destruction 
Shirley Parker Levine
54 Thrombotic Thrombocytopenic Purpura and Other Forms of Nonimmunologic Platelet Destruction 
Shirley Parker Levine
55 Miscellaneous Causes of Thrombocytopenia 
Shirley Parker Levine
Section 3. Other Disorders of Primary Hemostasis
56 Bleeding Disorders Caused by Vascular Abnormalities 
Matthew M. Rees and George M. Rodgers
57 Thrombocytosis 
Shirley Parker Levine
58 Qualitative Disorders of Platelet Function 
Shirley Parker Levine
Section 4. Coagulation Disorders
59 Inherited Coagulation Disorders 
Kenneth D. Friedman and George M. Rodgers
60 Acquired Coagulation Disorders 
George M. Rodgers
Section 5. Thrombosis
61 Thrombosis and Antithrombotic Therapy 
Steven R. Deitcher and George M. Rodgers
 
PART VI NONMALIGNANT DISORDERS OF LEUKOCYTES, THE SPLEEN, AND/OR IMMUNOGLOBINS 
62 Diagnostic Approach to Malignant and Nonmalignant Disorders of the Phagocytic and Immune Systems 
Thomas L. McCurley and John P. Greer
63 Neutropenia 
Raymond G. Watts
64 Qualitative Disorders of Leukocytes 
Keith M. Skubitz
65 Abnormalities of the Monocyte-Macrophage System: Lysosomal Storage Diseases 
Margaret M. McGovern and Robert J. Desnick
66 Langerhans Cell Histiocytosis 
H. Stacy Nicholson
67 Infectious Mononucleosis and Other Epstein-Barr Virus–Related Disorders 
Thomas G. Gross
68 Primary Immunodeficiency Syndromes 
Anthony R. Hayward
69 Acquired Immunodeficiency Syndrome 
Elaine M. Sloand and Jerome E. Groopman
70 Disorders of the Spleen 
Jeremy Goodman, Martin I. Newman, and William C. Chapman
 
PART VII HEMATOLOGIC MALIGNANCIES 
Section 1. General Aspects
71 Hematopoietic-Lymphoid Neoplasms: Principles of Diagnosis 
John B. Cousar
72 Complications of Hematopoietic Neoplasms 
Madan H. Jagasia and Edward R. Arrowsmith
73 Principles and Pharmacology of Chemotherapy 
Kenneth R. Hande
74 Immunotherapy 
Stanford J. Stewart
75 Supportive Care in Hematologic Malignancies 
Madhuri Vusirikala
Section 2. Acute Leukemias
76 Molecular Genetics of Acute Leukemia 
Mary Ann Thompson
77 Classification and Differentiation of the Acute Leukemias 
David R. Head
78 Acute Lymphoblastic Leukemia in Adults 
Thai M. Cao and Steven E. Coutre
79 Acute Myeloid Leukemia in Adults 
John P. Greer, Maria R. Baer, and Marsha C. Kinney
80 Acute Lymphoblastic Leukemia in Children 
James A. Whitlock and Paul S. Gaynon
81 Acute Myelogenous Leukemia in Children 
Robert J. Arceci and Richard Aplenc
82 Acute Promyelocytic Leukemia 
Steven L. Soignet and Peter G. Maslak
83 Myelodysplastic Syndromes 
Alan F. List, Avery A. Sandberg, and Donald C. Doll
Section 3. Myeloproliferative Disorders
84 Chronic Myeloid Leukemia 
Ian Rabinowitz and Richard S. Larson
85 Polycythemia Vera 
Robert T. Means, Jr.
86 Myelofibrosis 
Douglas A. Clark and Wilbur L. Williams
87 Systemic Mastocytosis 
Alexandra S. Worobec
and Dean D. Metcalfe
Section 4. Lymphoproliferative Disorders
88 Diagnosis and Classification of Non-Hodgkin Lymphomas 
Thomas L. McCurley and William R. Macon
89 Molecular Aspects of Non-Hodgkin Lymphomagenesis 
Andreas Rosenwald, Louis M. Staudt, Justus Georg Duyster, and Stephan W. Morris
90 Non-Hodgkin Lymphomas in Adults 
John P. Greer
91 Non-Hodgkin Lymphomas in Children 
John T. Sandlund and Frederick G. Behm
92 Chronic Lymphocytic Leukemia 
James B. Johnston
93 Hairy Cell Leukemia 
James B. Johnston
94 Cutaneous T-Cell Lymphomas: Mycosis Fungoides and Sézary Syndrome 
John A. Zic, Monika G. Kiripolsky, Katherine S. Hamilton, and John P. Greer
95 Hodgkin Disease 
Richard S. Stein and David S. Morgan
Section 5. Plasma Cell Dyscrasias
96 Practical Aspects of the Clinical Approach to Patients with Monoclonal Immunoglobulin Disorders 
Philip R. Greipp and Rafael Fonseca
97 Monoclonal Gammopathy of Undetermined Significance and Smoldering Multiple Myeloma 
Robert A. Kyle, S. Vincent Rajkumar, and John A. Lust
98 Multiple Myeloma 
Angela Dispenzieri, Martha Q. Lacy, and Philip R. Greipp
99 Immunoglobulin Light-Chain Amyloidosis (Primary Amyloidosis) 
Morie A. Gertz, Martha Q. Lacy, and Angela Dispenzieri
100 Waldenström Macroglobulinemia 
Rafael Fonseca and Thomas E. Witzig
101 Cryoglobulinemia, Heavy Chain Diseases, and Monoclonal Gammopathy–Associated Disorders 
Angela Dispenzieri and Morie A. Gertz
APPENDIX A: Normal Blood and Bone Marrow Values in Humans
APPENDIX B: Comparative Hematology
Color Plate
2004 Lippincott Williams & Wilkins
John P. Greer, John Foerster, John N. Lukens, George M. Rodgers, Frixos Paraskevas, and Bertil Glader
Wintrobe's Clinical Hematology
Contributing Authors
Nancy C. Andrews, MD, PhD
Associate Professor, Department of Pediatrics, Harvard Medical School, Associate Investigator, Department of Medicine, Howard Hughes Medical Institute and 
Children's Hospital, Boston, Massachusetts
Richard Aplenc, MD, MSCE
Assistant Professor of Pediatrics, Department of Pediatrics, University of Pennsylvania School of Medicine, Children's Hospital of Philadelphia, Philadelphia, 
Pennsylvania
Robert J. Arceci, MD, PhD
Director and King Fahd Professor of Pediatric Oncology, Department of Pediatric Oncology, Sidney Kimmel Comprehensive Cancer Center at John Hopkins, 
Baltimore, Maryland
Edward R. Arrowsmith, MD, MPH
Chattanooga Oncology and Hematology Associates, Chattanooga, Tennessee
Maria R. Baer, MD
Professor, Department of Medicine, Leukemia Section, University at Buffalo State University of New York School of Medicine and Biomedical Sciences, Roswell Park 
Cancer Institute, Buffalo, New York
Allan B. Becker, MD, FRCPC
Professor, Department of Pediatrics and Child Health, Section of Allergy and Clinical Immunology, University of Manitoba Faculty of Medicine, Health Sciences 
Centre, Winnipeg, Manitoba, Canada
A. Dean Befus, PhD
Professor and AstraZeneca Canada Inc.; Chair in Asthma Research, Department of Medicine, University of Alberta Faculty of Medicine and Dentistry, Edmonton, 
Alberta, Canada
Frederick G. Behm
Department of Pathology, St. Jude Children's Research Hospital, Memphis, Tennessee
Maurice C. Bondurant, PhD
Associate Professor, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee
Caterina Borgna-Pignatti, MD
Professor of Pediatrics, Department of Clinical and Experimental Medicine/Pediatrics, University of Ferrara, Ferrara, Italy
Sylvia S. Bottomley, MD
Professor of Medicine, Department of Medicine, Hematology/Oncology Section, University of Oklahoma College of Medicine and Department of Veterans Affairs 
Medical Center, Oklahoma City, Oklahoma
Stephen J. Brandt, MD
Associate Professor, Departments of Medicine, Cell and Developmental Biology, and Cancer Biology, Division of Hematology/Oncology, Vanderbilt University School 
of Medicine, Nashville, Tennessee
Kathleen Brummel-Ziedins, PhD
Research Assistant Professor of Biochemistry, Department of Biochemistry, University of Vermont College of Medicine, Burlington, Vermont
David C. Calverley, MD
Assistant Professor of Medicine, Division of Hematology and Medical Oncology, University of Colorado Health Sciences Center School of Medicine, Denver, Colorado
Thai M. Cao, MD
Clinical Instructor of Medicine, Department of Medicine, Division of Bone Marrow Transplantation, Stanford University Medical Center, Stanford, California
Ralph Carmel, MD
Director of Research, Department of Medicine, New York Methodist Hospital, Brooklyn, New York, Professor of Medicine, Department of Medicine, Weill Medical 
College of Cornell University, New York, New York
Howard H. W. Chan, MBChB, FRCPC
Research Fellow, Transfusion Medicine, Departments of Hematology and Internal Medicine, McMaster University Faculty of Health Sciences, Hamilton, Ontario, 
Canada
William C. Chapman, MD
Professor of Surgery; Chief, Section of Transplantation, Washington University School of Medicine, St. Louis, Missouri
Robert D. Christensen, MD
Professor and Chairman, Department of Pediatrics, University of South Florida College of Medicine, All Children's Hospital, St. Petersburg, Florida
Douglas A. Clark, MD
New Mexico Cancer Center, Albuquerque, New Mexico
John B. Cousar, MD
Professor of Pathology, Department of Pathology, University of Virginia Health System, Charlottesville, Virginia
Steven E. Coutre, MD
Assistant Professor of Medicine (Hematology), Department of Medicine, Stanford University School of Medicine, Stanford, California
Steven R. Deitcher, MD
Head, Section of Hematology and Coagulation Medicine, Department of Hematology and Medical Oncology, The Cleveland Clinic Foundation, Cleveland, Ohio
Judah A. Denburg, MD
Professor, Department of Medicine, McMaster University School of Medicine, Hamilton, Ontario, Canada
Robert J. Desnick, PhD, MD
Professor of Human Genetics and Pediatrics; Chairman, Department of Human Genetics, Mount Sinai School of Medicine of the City University of New York, New 
York, New York
Emmanuel N. Dessypris, MD, FACP
Professor of Medicine, Medical College of Virginia, Virginia Commonwealth University School of Medicine, Chief of Medicine, H.H. McGuire Veterans Affairs Medical 
Center, Richmond, Virginia
Angela Dispenzieri, MD
Assistant Professor, Department of Medicine, Division of Hematology, Mayo Clinic, Rochester, Minnesota
Donald C. Doll, MD
Professor of Medicine, Departments of Hematology and Medical Oncology, Ellis Fischel Cancer Center, University of Missouri'Columbia School of Medicine, 
Columbia, Missouri
Cynthia E. Dunbar, MD
Section Chief, Molecular Hematopoiesis Section, Hematology Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
Justus Georg Duyster, MD
Internal Medicine III, Technical University of Munich, Munich, Germany
Anne F. Eder, MD, PhD
Assistant Professor, Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, The Children's Hospital of Philadelphia, 
Philadelphia, Pennsylvania
Corwin Q. Edwards, MD
Professor, Department of Medicine, Associate Director, Internal Medicine Training Program, University of Utah School of Medicine, Director of Graduate Medical 
Education, LDS Hospital, Salt Lake City, Utah
Stephen J. Everse, PhD
Assistant Professor, Department of Biochemistry, University of Vermont College of Medicine, Burlington, Vermont
John Foerster, MD, FRCPC
Professor of Medicine, Division of Hematology/Oncology, University of Manitoba Faculty of Medicine, Director of Research, St. Boniface General Hospital, Winnipeg, 
Manitoba, Canada
Rafael Fonseca, MD
Associate Professor of Medicine, Department of Hematology, Mayo Medical School, Mayo Clinic, Rochester, Minnesota
Kenneth D. Friedman, MD
Medical Director, The Blood Center of Southeastern
Wisconsin, Inc., Milwaukee, Wisconsin
Renzo Galanello, MD
Professor of Pediatrics, Dip. di Scienze Biomediche e Biotecnologie, University of Cagliari-Ospedale Microcitemie, Cagliari, Italy
Susan A. Galel, MD
Associate Professor, Department of Pathology, Stanford University School of Medicine, Stanford, California
Paul S. Gaynon, MD
Professor of Pediatrics, Children's Hospital of Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, Californina
Morie A. Gertz, MD
Professor of Medicine, Division of Hematology, Mayo Medical School, Chair, Division of Hematology, Mayo Clinic, Rochester, Minnesota
Bertil Glader, MD, PhD
Professor of Pediatrics, Division of Hematology/Oncology, Stanford University School of Medicine, Stanford, California
Jeremy Goodman, MD
Surgery Resident, Department of Surgery, Washington University School of Medicine, St. Louis, Missouri
Stacey A. Goodman, MD
Associate Professor of Medicine, Department of Medicine, Division of Hematology/Oncology, Vanderbilt University School of Medicine, Nashville, Tennessee
John P. Greer, MD
Professor of Medicine and Pediatrics, Departments of Medicine and Pediatrics, Division of Hematology/Oncology, Vanderbilt University School of Medicine, Nashville, 
Tennessee
Philip R. Greipp, MD
Professor of Medicine, Department of Hematology, Mayo Medical School, Mayo Clinic, Rochester, Minnesota
Jerome E. Groopman, MD
Professor of Medicine, Department of Medicine, Harvard Medical School/Beth Israel Deaconess Medical Center, Boston, Massachusetts
Thomas G. Gross, MD, PhD
Associate Professor of Pediatrics, Department of Hematology/Oncology, Ohio State University College of Medicine and Public Health, Children's Hospital, Columbus, 
Ohio
Eva C. Guinan, MD
Associate Professor of Medicine, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Division of Pediatric Hematology/Oncology, Children's Hospital 
Boston, Boston, Massachusetts
Katherine S. Hamilton, MD
Assistant Professor, Department of Pathology, Vanderbilt University School of Medicine, Nashville, Tennessee
Kenneth R. Hande, MD
Professor of Medicine and Pharmacology, Department of Medicine, Division of Hematology/Oncology, Vanderbilt University School of Medicine, Nashville, Tennessee
Anthony R. Hayward, MD, PhD
Director, Division of Clinical Research, National Center for Research Resources, National Institutes of Health, Bethesda, Maryland
David R. Head, MD
Professor, Department of Pathology, Vanderbilt University School of Medicine, Nashville, Tennessee
Nancy M. Heddle, MSc, FCSMLSD
Associate Professor, Department of Medicine, McMaster University Faculty of Health Sciences, Hamilton, Ontario, Canada
Madan H. Jagasia, MBBS
Assistant Professor of Medicine, Department of Medicine, Division of Hematology/Oncology, Vanderbilt University School of Medicine, Nashville, Tennessee
Michael R. Jeng, MD
Assistant Professor, Department of Pediatrics, Stanford University School of Medicine, Stanford, California
Nancy Swords Jenny, PhD
Research Assistant Professor, Department of Pathology, University of Vermont College of Medicine, Colchester, Vermont
James B. Johnston, MBBCh, FRCPC
Professor of Medicine, Department of Internal Medicine, Section of Hematology/Oncology, University of Manitoba Faculty of Medicine, Winnipeg, Manitoba, Canada
Russel E. Kaufman, MD
Professor and Director, The Wistar Institute, Philadelphia, Pennsylvania
Kenneth Kaushansky, MD
Professor and Chair, Department of Medicine, University of California, San Diego, School of Medicine, San Diego, California
John G. Kelton, MD
Dean and Vice-President, McMaster University Faculty of Health Sciences, Hamilton, Ontario, Canada
Marsha C. Kinney, MD
Professor of Pathology, Department of Pathology, University of Texas Health Science Center at San Antonio, San Antonio, Texas
Monika G. Kiripolsky, BS
Fourth-Year Medical Student, Department of Dermatology, Vanderbilt University School of Medicine, Nashville, Tennessee
Mark J. Koury, MD
Professor, Department of Medicine, Division of Hematology/Oncology, Vanderbilt University School of Medicine, Nashville, Tennessee
Robert A. Kyle, MD
Professor of Medicine, Laboratory Medicine, and Pathology, Department of Internal Medicine, Mayo Medical School, Rochester, Minnesota
Martha Q. Lacy, MD
Assistant Professor of Medicine; Consultant, Department of Hematology, Mayo Clinic, Rochester, Minnesota
Paige Lacy, PhD
Assistant Professor, Department of Medicine, University of Alberta Faculty of Medicine and Dentistry, Edmonton, Alberta, Canada
Richard S. Larson, MD, PhD
Associate Professor, Department of Pathology, University of New Mexico School of Medicine, Albuquerque, New Mexico
Shirley Parker Levine, MD
Professor of Medicine, Department of Medicine, Division of Hematology, Albert Einstein College of Medicine of Yeshiva University/Montefiore Medical Center, Bronx, 
New York
Jeffrey M. Lipton, MD, PhD
Professor of Pediatrics, Division of Pediatric Hematology/Oncology and Stem Cell Transplantation, Albert Einstein College of Medicine of Yeshiva 
University/Schneider Children's Hospital, New Hyde Park, New York
Alan F. List, MD
Professor of Medicine, University of South Florida College of Medicine, Director, Hematologic Malignancies Program, H. Lee Moffitt Cancer Center and Research 
Institute, Tampa, Florida
Jay N. Lozier, MD, PhD
Consulting Hematologist, Department of Laboratory Medicine, Warren G. Magnuson Clinical Center, National Institutes of Health, Bethesda, Maryland
John N. Lukens, MD
Professor of Pediatrics, Emeritus, Division of Pediatric Hematology/Oncology, Vanderbilt University School of Medicine, Nashville, Tennessee
John A. Lust, MD, PhD
Associate Professor of Medicine, Department of Internal Medicine, Division of Hematology, Mayo Clinic, Rochester, Minnesota
William R. Macon, MD
Associate Professor of Pathology, Department of Laboratory Medicine and Pathology, Mayo Medical School, Consultant, Mayo Clinic, Rochester, Minnesota
James M. Malone, III, MD
Assistant Medical Director, Transfusion Service, Staff Physician, Departments of Pathology and Medicine (Hematology), Stanford University School of Medicine, 
Stanford, California
Lori J. Maness, MD
Instructor of Medicine, Division of Hematology and Medical Oncology, University of Colorado School of Medicine, Denver, Colorado
Kenneth G. Mann, PhD
Professor of Biochemistry and Medicine, Department of Biochemistry, University of Vermont College of Medicine, Burlington, Vermont
Catherine S. Manno, MD
Associate Professor of Pediatrics, Hematology Division, University of Pennsylvania School of Medicine, Children's Hospital of Philadelphia, Philadelphia, 
Pennsylvania
Peter W. Marks, MD, PhD
Instructor in Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachussetts
Peter G. Maslak, MD
Associate Member, Department of Clinical Laboratories, Leukemia Service, Memorial Sloan-Kettering Cancer Center, New York, New York
Thomas L. McCurley, MD
Associate Professor, Department of Pathology, Vanderbilt University School of Medicine, Nashville, Tennessee
Margaret M. McGovern, MD, PhD
Associate Professor of Human Genetics and Pediatrics; Vice Chair, Department of Human Genetics, Mount Sinai School of Medicine of the City University of New 
York, New York, New York
Robert T. Means, Jr., MD
Professor of Medicine; Director, Department of Medicine, Hematology/Oncology Division, Medical University of South Carolina College of Medicine, Ralph H. Johnson 
Veterans Affairs Medical Center, Charleston, South Carolina
William C. Mentzer, MD
Professor, Department of Pediatrics, University of California, San Francisco, School of Medicine, San Francisco, California
Dean D. Metcalfe, MD
Chief, Laboratory of Allergic Diseases, National Institute of Allergy and Infectious Diseases/National Institutes of Health,
National Institutes of Health Clinical Center, 
Bethesda, Maryland
Redwan Moqbel, PhD
Professor, Department of Medicine, University of Alberta Faculty of Medicine and Dentistry, Edmonton, Alberta, Canada
David S. Morgan, MD
Assistant Professor, Department of Medicine, Division of Hematology/Oncology, Vanderbilt University School of Medicine, Nashville, Tennessee
Stephan W. Morris, MD
Professor, Departments of Pathology and Hematology-Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee
Richard A. Nash, MD
Associate Member, Fred Hutchinson Cancer Research Center, Associate Professor, University of Washington School of Medicine, Seattle, Washington
Anne T. Neff, MD
Assistant Professor, Department of Pathology and Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee
Martin I. Newman, MD
Fellow, Department of Surgery, New York Presbyterian Hospital, New York, New York
H. Stacy Nicholson, MD, MPH
Professor of Pediatrics, Department of Pediatric Hematology/Oncology, Oregon Health Sciences University School of Medicine, Portland, Oregon
Robin K. Ohls, MD
Associate Professor, Department of Pediatrics, University of New Mexico School of Medicine, Albuquerque, New Mexico
Thomas Orfeo, PhD
Research Associate, Department of Biochemistry, University of Vermont College of Medicine, Burlington, Vermont
Frixos Paraskevas, MD
Associate, Institute of Cell Biology, University of Manitoba Faculty of Medicine, Cancer Care Manitoba, Winnipeg, Manitoba, Canada
Charles J. Parker, MD
Professor, Department of Medicine, University of Utah School of Medicine, Veterans Affairs Medical Center, Salt Lake City, Utah
Sherrie L. Perkins, MD, PhD
Professor, Department of Pathology, University of Utah School of Medicine, Salt Lake City, Utah
Ian Rabinowitz, MD
Assistant Professor, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico
Harvey A. Ragan, DVM
Staff Pathologist, Department of Toxicology, Batelle, Pacific Northwest National Laboratory, Richland, Washington
S. Vincent Rajkumar, MD
Associate Professor of Medicine, Department of Hematology, Mayo Clinic, Rochester, Minnesota
Matthew M. Rees, MD
Rutherford Hospital, Rutherfordton, North Carolina
George M. Rodgers, MD, PhD
Professor of Medicine and Pathology, University of Utah School of Medicine, Health Sciences Center, Veterans Affairs Medical Center, Medical Director, Coagulation 
Laboratory, ARUP Laboratories, Salt Lake City, Utah
Andreas Rosenwald, MD
Institute of Pathology, University or Würzburg, Würzburg, Germany
Gerald J. Roth, MD
Professor, Department of Medicine, University of Washington School of Medicine, Seattle Veterans Affairs Hospital, Seattle, Washington
Sheila N. J. Sait
Clinical Cytogenetics Laboratory, Department of Pathology and Leukemia Section, Department of Medicine, Roswell Park Cancer Institute, Buffalo, New York
Avery A. Sandberg, MD, DSc
Professor, Department of Pathology, University of Arizona College of Medicine, Phoenix, Arizona
John T. Sandlund, MD
Associate Professor of Pediatrics, Department of Hematology-Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee
Stephen T. Sawyer, PhD
Professor, Department of Pharmacology and Toxicology, Virginia Commonwealth University School of Medicine, Richmond, Virginia
Paul J. Shami, MD
Associate Professor of Medicine, Division of Medical Oncology, University of Utah School of Medicine, Veterans Affairs Medical Center, Salt Lake City, Utah
Rebecca L. Shattuck-Brandt, PhD, MEd
Teacher, Science Department, University School of Nashville, Nashville, Tennessee
Akiko Shimamura, MD, PhD
Instructor in Pediatrics; Assistant in Medicine, Department of Pediatric Hematology/Oncology, Children's Hospital, Dana-Farber Cancer Institute, Boston, 
Massachusetts
Keith M. Skubitz, MD
Professor, Department of Medicine, Division of Hematology, Oncology, and Transplantation, Musculoskeletal Tumor Program, University of Minnesota Medical 
School'Minneapolis, Minneapolis, Minnesota
Elaine M. Sloand, MD
Assistant to the Director; Clinical Investigator, Hematology Branch, National Heart, Lung, and Blood Institute, Bethesda, Maryland
James William Smith, BS, MLT
Coordinator, Platelet Immunology Laboratory, Department of Medicine, McMaster University Faculty of Health Sciences, Canadian Blood Services, Hamilton, Ontario, 
Canada
Steven L. Soignet, BS, MD
Consultant, Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, New York
Louis M. Staudt, MD, PhD
Chief, Lymphoid Malignancies Section, Metabolism Branch, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland
Richard S. Stein, MD
Associate Professor of Medicine, Department of Medicine, Division of Hematology/Oncology, Vanderbilt University School of Medicine, Nashville, Tennessee
Stanford J. Stewart, MD
Vice President, Clinical Research, Corixa Corporation, South San Francisco, California
Marilyn J. Telen, MD
Wellcome Professor of Medicine; Chief, Division of Hematology, Department of Medicine, Division of Hematology, Duke University Medical Center, Durham, North 
Carolina
Mary Ann Thompson, MD, PhD
Assistant Professor, Department of Pathology, Division of Hematopathology, Vanderbilt University School of Medicine, Nashville, Tennessee
John F. Tisdale, MD
Senior Investigator, Molecular and Clinical Hematology Branch, National Institute of Diabetes and Digestive and Kidney Disorders, Bethesda, Maryland
Maurene K. Viele, MD
Clinical Associate Professor, Department of Pathology, Stanford University School of Medicine, Stanford, California
Madhuri Vusirikala, MD
Assistant Professor, Department of Medicine, Division of Hematology/Oncology, Vanderbilt University Medical Center, Nashville, Tennessee
Winfred C. Wang, MD
Professor, Department of Pediatrics, University of Tennessee, Memphis, College of Medicine, Member, Department of Hematology/Oncology, St. Jude Children's 
Research Hospital, Memphis, Tennessee
Russell E. Ware, MD, PhD
Professor, Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina
Raymond G. Watts, MD
The Frederick W. Renneker, III Endowed Chair in Pediatric Education; Associate Professor, Department of Pediatrics, Division of Hematology/Oncology, University of 
Alabama School of Medicine, Birmingham, Alabama
Kathryn E. Webert, BSc, MD, FRCPC
Clinical Scholar, Department of Medicine, McMaster University Faculty of Health Sciences, Canadian Blood Services, Hamilton, Ontario, Canada
J. Brice Weinberg, MD
Professor, Department of Medicine, Duke University School of Medicine, Durham Veterans Administration Hospital, Durham, North Carolina
James A. Whitlock, MD
Associate Professor of Pediatrics; Director, Division of Pediatric Hematology/Oncology, Vanderbilt University School of Medicine, Nashville, Tennessee
Wilbur L. Williams, MD
Associate Professor, Department of Laboratory Medicine, New Mexico VA Heath Care System, Albuquerque, New Mexico
Thomas E. Witzig, MD
Professor of Medicine, Department of Hematology, Mayo Clinic, Rochester, Minnesota
Alexandra S. Worobec, MD
Adjunct Investigator, Laboratory of Allergic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland
John A. Zic, MD
Assistant Professor of Medicine, Department of Medicine, Division of Dermatology, Vanderbilt University School of Medicine, Nashville Veterans Administration, 
Nashville, Tennessee
Dedication
To Dr. Maxwell M. Wintrobe
EDITORS
EDITED BY
JOHN P. GREER, MD
PROFESSOR OF MEDICINE AND PEDIATRICS
DEPARTMENTS OF MEDICINE AND PEDIATRICS
DIVISION OF HEMATOLOGY/ONCOLOGY
VANDERBILT, UNIVERSITY SCHOOL OF MEDICINE
NASHVILLE, TENNESSEE 
JOHN FOERSTER, MD, FRCPC
PROFESSOR OF MEDICINE
DIVISION OF HEMATOLOGY/ONCOLOGY
UNIVERSITY OF MANITOBA FACULTY OF
MEDICINE; DIRECTOR OF RESEARCH
ST. BONIFACE GENERAL HOSPITAL, WINNIPEG
MANITOBA, CANADA 
JOHN N. LUKENS, MD
PROFESSOR OF PEDIATRICS, EMERITUS
DIVISION OF PEDIATRIC HEMATOLOGY/ONCOLOGY
VANDERBILT UNIVERSITY SCHOOL OF MEDICINE
NASHVILLE, TENNESSEE 
GEORGE M. RODGERS, MD, PHD
PROFESSOR OF MEDICINE AND PATHOLOGY
UNIVERSITY OF UTAH
SCHOOL OF MEDICINE HEALTH SCIENCES CENTER
VETERANS AFFAIRS MEDICAL CENTER; MEDICAL DIRECTOR
COAGULATION LABORATORY, ARUP LABORATORIES
SALT LAKE CITY, UTAH 
FRIXOS PARASKEVAS, MD
ASSOCIATE, INSTITUTE OF CELL BIOLOGY
UNIVERSITY OF MANITOBA; FACULTY OF MEDICINE
CANCER CARE MANITOBA, WINNIPEG
MANITOBA, CANADA 
BERTIL GLADER, MD, PHD
PROFESSOR OF PEDIATRICS
DIVISION OF HEMATOLOGY/ONCOLOGY
STANFORD UNIVERSITY SCHOOL OF MEDICINE
STANFORD, CALIFORNIA 
Secondary Authors
JONATHAN PINE
Acquisitions Editor
Silverchair Science + Communications
ALYSON FORBES
Developmental Editor
Silverchair Science + Communications
TANYA LAZAR
Managing Editor
Silverchair Science + Communications
MARY ANN MCLAUGHLIN
Supervising Editor
Silverchair Science + Communications
LUCINDA EWING
Production Editor
Silverchair Science + Communications
JANE B. MCQUEEN
Production Editor
Silverchair Science + Communications
BEN RIVERA
Manufacturing Manager
CHRISTINE JENNY
Cover Designer
Preface
Blut ist ein ganz besondrer Saft. 
Goethe, 1808
Maxwell M. Wintrobe often cited Goethe, “Blood is a very special kind of fluid,” and the Eleventh Edition of Wintrobe's Clinical Hematology is a testimony to Dr. 
Wintrobe's legacy and commitment to the field of hematology. This edition extends the chronicle of progress to 60 years since the first edition of the book. The first six 
editions were the sole work of Dr. Wintrobe. When he retired from the editorship, Dr. Wintrobe recruited five former fellows to take over the task: Jack Athens, Tom 
Bithell, Dane Boggs, John Foerster, and Richard Lee. John Foerster remains an editor from the original group, and John Lukens joined the editorship in the eighth 
edition. John Greer, Frixos Paraskevas, and George Rodgers contributed to the ninth edition and became editors of the tenth edition. Bert Glader is a welcome 
addition to the present edition. Of the present group of editors, three (Foerster, Lukens, and Rodgers) worked directly with Dr. Wintrobe, whereas the other three 
(Glader, Greer, and Paraskevas) have been associated with Wintrobe-trained individuals.
Dr. Wintrobe recognized the work of predecessors and the foundation of clinical hematology in basic research. In Blood, Pure and Eloquent. A Story of Discovery, of 
People and of Ideas (1980), Dr. Wintrobe edited historical milestones in hematology and emphasized three lessons of history:
1. Research starts with an idea, which may take many directions before becoming a valid concept: “The path of progress is anything but straight. It is rough and 
rocky and often seems to wander endlessly and in all directions; it has many blind alleys and is strewn with the debris of false hopes, of failures, and 
discouragement. The course of research has been likened to the flow of a stream that ultimately becomes a rushing torrent.”
2. A sense of skepticism is warranted in the practice of medicine: “What was held to be the truth yesterday may not be so regarded today, and tomorrow the story 
may again be somewhat different.”
3. Perseverance is required to make progress: “… many look, but few see. It is the exceptional person who recognizes the unusual event or manifestation. Still 
fewer pursue it to a new understanding. Many may ask questions but few have the imagination, the energy, and the overpowering drive to persist in the search 
for an answer, especially when this must be done in the face of difficulties and failures and even in spite of scorn from their peers” ( 1).
Although his statements may seem pessimistic, Dr. Wintrobe optimistically recognized the importance of building on prior contributions and the relationship between 
clinical hematology and basic research. Hematology has many stories characterized initially by clinical observations that are now understood at a molecular genetic 
level (2,3). Sickle cell anemia, pernicious anemia, hemophilia, Burkitt lymphoma, acute promyelocytic leukemia, and chronic myeloid leukemia are among the most 
interesting topics in medicine. The speed of basic research to the clinical bedside was remarkable in the twentieth century, and it promises to be even faster and more 
widely applied in the future.
The Eleventh Edition of Wintrobe's Clinical Hematology ushers in the twenty-first century with the same principles found in the prior editions and with the additional 
availability of the knowledge base through the Internet. The value of books has been questioned in this new era. This edition retains the historical perspective of 
Wintrobe's Clinical Hematology, with extensive references; brings together the body of information on hematology in a single source; and bridges topics to the Internet 
with Web links cited by many of the authors in their chapters. As with other multiauthored textbooks, there are occasional redundancies, which are important 
observations that allow a chapter to stand alone, and there are cross references to other chapters that indicate the interdependence of the topics.
We appreciate each author's contribution to the book. We have brought together clinician educators, pathologists, and physician scientists to review their topics of 
expertise. All of the chapters except Dr. Wintrobe's introduction to the approach to hematologic problems either have been revised or are new with an emphasis on 
molecular aspects of hematology. This edition recognizes the transition from a morphologic classification of hematopoietic neoplasms to the World Health 
Organization's classification that incorporates molecular genetics.
We appreciate the efforts of Jonathan Pine, Senior Executive Editor at Lippincott Williams & Wilkins; Alyson Forbes, Developmental Editor, and Tanya Lazar, 
Managing Editor at Lippincott; Mary Ann McLaughlin, Supervising Editor at Lippincott; and Lucinda Ewing and Jane McQueen, Production Editors at Silverchair 
Science + Communications. Their unique combination of persistence and kindness and their commitment to the principles of prior editions brought the project to 
completion. We hope the readers find the information they seek in the Eleventh Edition of Wintrobe's Clinical Hematology.
Below, each of us acknowledges people who have assisted him in this endeavor.
Debbie Saurette, my faithful secretary and colleague, has provided invaluable services in the completion of this edition. My wife, Gisela, and our children David, 
Steven, and Susan, physicians all, have been a constant source of support and inspiration. Special thanks go to my mentors, Dr. L. G. Israels, whose enthusiasm for 
hematology and his ability to combine effectively clinical service, teaching, and research, drew me to this specialty as a medical student; Dr. M. M. Wintrobe, who 
taught me in his own unique way and gave me the opportunity to contribute to several editions of this great textbook; Dr. B. Benacerraf, who nurtured my interests in 
immunology; and my colleagues at the Mayo Clinic and elsewhere who have contributed valuable chapters to this text.
John Foerster
I wish to thank Jennifer Lu, Kari Costa, Theresa McCann, and Annamarie Coelho for administrative help. I also wish to acknowledge the many outstanding authors I 
have had the privilege to work with in the preparation of this edition. Last, but most of all, I want to acknowledge the understanding and support of my wife, Lou Ann; 
my children, Laurie, Anders, and Eric; their families; and our friends.
Bert Glader
I wish to thank Billi Bean, my assistant and colleague; Patti Lee at the Eskind Library of the Vanderbilt University School of
Medicine; my wife, Gay; and our children, 
Lesley, Adam, and Scott; my mentors, including Robert Collins, John Flexner, Stanley Graber, Sanford Krantz, and John Lukens; Ellen Benneyworth and other 
nurses; and our patients.
John P. Greer
My contribution to this edition could not have been made without the understanding and unselfish support of my wife, Cauley Lukens. She and our children have 
weathered long hours and aborted vacations with encouragement and grace.
John N. Lukens
I want to express my deeply felt gratitude and appreciation to my wife Maria for her support and unwavering encouragement throughout the period of writing and 
especially when deadline worries became unmanageable. Maria, as a pathologist, has also been the testing ground for fine-tuning of complex concepts, helping me to 
lift them from the unfathomable depths of technicality and into the light of understanding. I want to thank Ms. Lynne Savage for her expert secretarial assistance and 
perseverance when “last copy” was proved to be just another in a never-ending line of typing. Our librarian, Donna Pacholok, helped me navigate the complex 
connections with the Internet. My sincere thanks to several colleagues for providing literature assistance or photography from their own data: A. A. Anderson, G. G. 
Gao, J. E. Gretz, L.A. Herzenberg, H. Kogelberg, J. Lambris, D. Y. Mason, C. Morales, K. H. Roux, P. Nickerson, H. Seguchi, S. Shaw, and H. Zimmerman.
Frixos Paraskevas
I acknowledge Robyn LeMon and Sherry Hartline for typing assistance, my numerous contributors for their hard work and timely submissions, and my family and 
friends for their support.
George M. Rodgers
REFERENCES 
Wintrobe MM. Blood, pure and eloquent. A story of discovery, of people, and of ideas. New York: McGraw-Hill, 1980:720. 
Wintrobe MM. Hematology, the blossoming of a science: a story of inspiration and effort. Philadelphia: Lea & Febiger, 1985. 
Lichtman MA, Spivak JL, Boxer LA, et al., eds. Hematology: landmark papers of the twentieth century. San Diego: Academic Press, 2000.
1 Examination of the Blood and Bone Marrow
Wintrobe’s Clinical Hematology
1
Sherrie L. Perkins Examination of the Blood and Bone Marrow
SPECIMEN COLLECTION
RELIABILITY OF TESTS
CELL COUNTS
 Aperture-Impedance Counters
 Optical Method Counters
 Combined Impedance and Optical Counters
RED BLOOD CELL ANALYTIC PARAMETERS
 Volume of Packed Red Cells (Hematocrit)
 Hemoglobin Concentration
 Red Cell Count
 Mean Corpuscular Volume
 Mean Corpuscular Hemoglobin
 Mean Corpuscular Hemoglobin Concentration
 Red Cell Distribution Width
 Automated Reticulocyte Counts
LEUKOCYTE ANALYSIS
 White Blood Cell Counts
 Leukocyte Differentials
PLATELET ANALYSIS
ADVANTAGES AND SOURCES OF ERROR WITH AUTOMATED HEMATOLOGY ANALYZERS
MORPHOLOGIC ANALYSIS OF BLOOD CELLS
 Preparation of Blood Smears
 Routine Staining of Blood Smears
 Examination of the Blood Smear
 Other Means of Examining Blood
BONE MARROW EXAMINATION
 Bone Marrow Aspiration and Biopsy
 Staining and Evaluation of Bone Marrow Aspirates and Touch Preparations
 Examination of Bone Marrow Histologic Sections
SPECIAL STAINS
 Cytochemical Stains
 Immunocytochemical Stains
OTHER LABORATORY STUDIES
 Cytogenetic Analysis
 Molecular Genetics
 Electron Microscopy
 Erythrocyte Sedimentation Rate
 Plasma and Blood Viscosity
 Total Quantity of Blood
REFERENCES
Careful assessment of the blood elements is often the first step in assessment of hematologic function and diagnosis. Many hematologic disorders are defined by 
specific findings gleaned from blood tests. Examination of blood smears and hematologic parameters often yields important diagnostic information and allows broad 
differential diagnostic impressions to be formed, directing further, more specific testing. Careful examination of cellular morphology, in concert with quantification of 
the blood elements and evaluation of a variety of parameters relating to cellular size and shape, is required. This chapter introduces the fundamental concepts that 
underlie laboratory evaluation of the blood and outlines additional testing that may aid in evaluating a hematologic disorder, including special stains and bone marrow 
examination. Limitations of such tests are also addressed.
Blood elements include erythrocytes, or red cells; leukocytes, or white cells; and platelets. Although detailed morphologic descriptions and functional characteristics of 
each of the cell types are included in subsequent chapters, basic features necessary for blood smear analyses are covered in this chapter. Red cells are the most 
numerous cells in the blood and are required for tissue respiration. Erythrocytes lack nuclei and contain hemoglobin, an iron-containing protein that acts in the 
transport of oxygen and carbon dioxide. White blood cells serve in immune function and include a variety of cell types that have specific functions and characteristic 
morphologic appearances. In contrast to red cells, white cells are nucleated. The five types of white blood cells seen normally in blood smears are neutrophils, 
lymphocytes, monocytes, eosinophils, and basophils. Platelets are cytoplasmic fragments derived from megakaryocytes in the bone marrow that function in 
coagulation and hemostasis.
Evaluation of the blood requires quantification of each of the cellular elements by either manual or automated methods. Automated methods, using properly calibrated 
equipment ( 1 ), are usually more precise than manual procedures. In addition, automated methods may provide additional data describing characteristics such as cell 
volume. However, the automated measurements describe average cell characteristics but do not adequately describe the scatter of individual values around the 
average. Hence, a bimodal population of small (microcytic) and large (macrocytic) red cells might register as a normal cell size. Therefore, a thorough examination of 
blood also requires microscopic evaluation of a stained blood film to complement the hematology analyzer data.
SPECIMEN COLLECTION
Proper specimen collection is required for reliable and accurate laboratory data to be obtained on any hematologic specimen. Before a specimen is obtained, careful 
thought as to what studies are needed will aid in proper handling of the material and prevent collection of inadequate or unusable specimens. Communication with 
laboratory personnel who will analyze the specimen is often helpful in ensuring that specimens will be handled properly and that the requested testing can be 
performed.
A number of factors may affect hematologic measurements, and each specimen should be collected in a standardized manner to reduce variability. Factors such as 
patient activity, level of patient hydration, medications, sex, age, race, smoking, and anxiety may affect hematologic parameters significantly ( 2 , 3 and 4 ). Similarly, the 
age of the specimen may affect the quality of the data collected ( 5 ). Thus, data such as patient age, sex, and time of specimen collection should be noted. Correlative 
clinical information is also extremely important in evaluating hematologic specimens. For example, a patient who has had severe diarrhea or vomiting before 
admission may be sufficiently dehydrated to have an erroneous increase in red blood cell concentration.
Most often, blood is collected by venipuncture into tubes containing anticoagulant. The three most commonly used anticoagulants are tripotassium or disodium salts 
of ethylenediaminetetraacetic acid (EDTA), trisodium citrate, and heparin. EDTA and disodium citrate act to remove calcium, which is essential for the initiation of 
coagulation, from the blood. Heparin acts by forming a complex with antithrombin III in the plasma to prevent the formation of thrombin. EDTA is the preferred 
anticoagulant for blood cell counts because it produces complete anticoagulation with minimal morphologic
and physical effects on all types of blood cells ( 6 ). 
Heparin causes a bluish coloration of the background when a blood smear is stained with one of the Romanowsky dyes but does not affect cell size or shape. Heparin 
is most often used for prevention of red blood cell hemolysis, for osmotic fragility testing, and for functional and immunologic analysis of leukocytes. Heparin does not 
completely inhibit white blood cell or platelet clumping. Trisodium citrate is the preferred anticoagulant for platelet and coagulation studies. Other anticoagulants have 
been identified that give results similar to EDTA, such as argatroban ( 7 ), although none has achieved widespread use in normal clinical settings
The concentration of the anticoagulant used may affect cell concentration measures if it is inappropriate for the volume of blood collected and may also distort cellular 
morphology. Most often, blood is collected directly into commercially prepared negative-pressure vacuum tubes (Vacutainer tubes; Becton Dickinson, Franklin Lakes, 
NJ), which contain the correct concentration of anticoagulant when filled appropriately, thereby minimizing error ( 8 ). Anticoagulated blood may be stored at 4°C for a 
24-hour period without significantly altering cell counts or cellular morphology ( 5 ). However, it is preferable to perform hematologic analysis as soon as possible after 
the blood is obtained.
RELIABILITY OF TESTS
In addition to proper acquisition of specimens, data reliability requires precise and reproducible testing methods. Both manual and automated testing of hematologic 
specimens must be interpreted in light of test precision. This becomes especially important when evaluating the significance of small changes. All laboratory tests are 
evaluated with respect to both accuracy and reproducibility. Accuracy is the difference between the measured value and the true value, which implies that a true value 
is known. Clearly, this may present difficulties when dealing with biologic specimens. The National Committee for Clinical Laboratory Standards and the International 
Committee for Standards in Haematology have attempted to develop standards to assess the accuracy of hematologic examination ( 9 ) and automated blood cell 
analyzers ( 10 ). Automated instrumentation requires regular quality assurance evaluations and careful calibration to reach expected performance goals and ability to 
collect reproducible data ( 1 , 11 ).
CELL COUNTS
Cell counts are important parameters in evaluating the blood. Cell counts may be determined either manually or by automated hematology analyzers. Whether 
performed by manual or automated methodologies, the accuracy and precision of the counts depend on proper dilution of the blood sample and precise sample 
measurement. Blood must be precisely aliquoted and diluted, so that cells are evenly distributed within the sample to be analyzed. Because blood contains large 
numbers of cells, sample dilution is usually required for accurate analysis. The type of diluent is dependent on the cell type to be enumerated. Thus, red cell counts 
require dilution with an isotonic medium, whereas in white cell or platelet counts, a diluent that lyses the more numerous red cells is often used to simplify counting. 
The extent of dilution also depends on the cell type. In general, red cell counts need more dilution than is required for the less abundant white blood cells. Errors in 
cell counts are caused primarily by errors in sample measurement, dilution, or enumeration of cells. The highest degree of precision occurs when a very large number 
of cells can be evaluated. Clearly, automated methods are superior to manual methods for counting large numbers of cells and minimizing statistical error. Table 1.1 
lists the comparable values of reproducibility for automated and manual (hemocytometer) counting methods.
TABLE 1.1. Reproducibility of Blood Counting Procedures 
 Two Coefficients of Variation
Cell Type Counted Hemocytometer a
(%)
Automated Hematology
Analyzer (%)
Red cells ±11.0 ±1.0
White cells ±16.0 ±1.5
Platelets b ±22.0 ±2.0
Reticulocytes ±33.9 ±5.0
a Minimum error. Usual error.
b Error may be greater with low (<35 × 10 9/L) or very high (>450 × 10 9/L) platelet counts.
Data derived from Bentley S, Johnson A, Bishop C. A parallel evaluation of four automated hematology analyzers. Am J Clin Pathol 1993;100:626–632; and Wintrobe 
M. A simple and accurate hematocrit. J Lab Clin Med 1929;15:287–289.
Manual counts are carried out after appropriate dilution of the sample in a hemocytometer, a specially constructed counting chamber that contains a specific volume. 
Cells may then be counted with a microscope. Red blood cells, leukocytes, and platelets may be counted by this method ( 13 ). Due to the inherent imprecision of 
manual counts and the amount of technical time required, most cell counting is now performed by automated or semiautomated instruments. These machines increase 
the accuracy and speed of analysis by the clinical laboratory, particularly as test entry, sampling, sample dilution, and analysis are incorporated into single systems 
with minimal human manipulation ( 12 , 13 ). With increasing levels of automation, some hematology analyzers have now moved to complete automation, which can be 
coupled with other laboratory tests using the same tube of blood. There are a variety of different automated hematology analyzers available, dependent on the volume 
of samples to be tested and the needs of the physician group ordering testing. The analyzers range in price and workload capacity from those that would be 
appropriate for an individual physician's office or point-of-care facility to those needed in a busy reference laboratory with capacity for over 100 samples to be 
analyzed per hour ( 14 ).
Most automated hematology analyzers perform a variety of hematologic measurements, such as hemoglobin concentration (Hb), red cell size, and leukocyte 
differentials. Newer instruments may also perform more specialized testing, such as reticulocyte counts ( 15 ). The ability of the new analyzers to perform accurate 
white cell differential counts, particularly those that can perform a five-part differential (enumerating neutrophils, lymphocytes, monocytes, eosinophils, and basophils), 
has been a significant technologic advance over the past 10 years. Automated methods for white cell counts and differentials use several distinct technical 
approaches ( 16 ), including those that measure changes in electrical impedance and those that use differences in light scatter or optical properties, either alone or in 
combination ( 17 ). Another recent advance in hematology analyzers is incorporation of argon laser technology, allowing integration of some flow cytometric data using 
specific fluorochrome stains, such as T-cell subsets (CD4:CD8) or CD34 positive cells, with routine hematologic analyses ( 18 ).
Aperture-Impedance Counters
This type of analyzer, which includes the Coulter (Beckman Coulter, Hialeah, FL), the Sysmex (Baxter Diagnostics, Waukegan, IL), and some Cell-Dyn (Abbott 
Diagnostics, Santa Clara, CA) instruments, enumerates cells in a small aperture by measuring changes in electrical resistance as the cell passes through the orifice ( 
Fig. 1.1). A constant current passes between two platinum electrodes on either side of the orifice. The diluent that suspends the cells is more electrically conductive 
than are the cells. Hence, as each cell passes through the orifice, there is a momentary decrease in electrical conductance so that an electrical impulse is generated 
and recorded electronically. The drop in voltage is proportional to cell size, allowing average cell size to be determined simultaneously ( 19 , 20 ).
 
Figure 1.1. Impedance type of automated hematology analyzer. As the cells pass through the aperture, they alter the current flow between the electrodes, generating
an electronic pulse. Each pulse is recorded electronically. The magnitude of the pulse is proportional to the cell's volume.
Instruments using aperture-impedance technology require even cell suspensions so that cells pass individually through the electrical current. Distortion of the 
electrical pulses may occur when the cells do not pass through the center of the aperture or when more than one cell enters the aperture at a time. The data may be 
electronically adjusted to exclude distorted peaks, and both upper and lower limits of particle size can be set to exclude cellular clumps or debris. Using size limitation 
parameters, the instrument can be used to count particles of different sizes, thereby allowing different blood elements to be enumerated ( 21 ). Most of the modern 
analyzers can also be set to flag abnormal or suspect results, allowing for identification of those samples that need further, manual evaluation ( 22 ).
The Coulter-type counters are probably the most widely used example of hematology analyzers that use electrical impedance methods. Most models print data in 
numerical form as well as providing histograms of blood cell size ( Fig. 1.2). Newer models often combine impedance and optical methodologies (described below). 
Data generated include a three- or five-part white cell differential in addition to red cell counts, white cell counts, platelet counts, reticulocyte counts, hemoglobin, 
hematocrit (Hct), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), red cell distribution 
width (RDW), and mean platelet volume (MPV). This type of instrumentation fully analyzes up to 109 samples per hour, depending on the model used, and flags 
abnormal red and white cell populations, including blasts and atypical cells 23 .
 
Figure 1.2. Histograms and printout generated by the Coulter STKR automated hematology analyzer. BA, basophil; EO, eosinophil; HCT, hematocrit; HGB, 
hemoglobin; LY, lymphocyte; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; MO, 
monocyte; MPV, mean platelet volume; NE, neutrophil; PLT, platelet; RBC, red blood cell; RDW, red cell distribution width; WBC, white blood cell.
Optical Method Counters
The other method commonly used in hematology analyzers depends on the light scatter properties of blood cells ( 24 , 25 ). Some instruments that use this technology 
include the Technicon series (H6000, H*1, H*2, H*3) (Bayer Diagnostic Division, Tarrytown, NY) and the Cell-Dyn instruments. In these systems, diluted blood passes 
through a flow cell detector placed in the path of a narrowly focused beam of light (usually a laser) ( Fig. 1.3). When the blood cells pass through the counting 
chamber, they interrupt or alter the beam of light, thereby generating an electrical impulse that may be recorded. The pattern of light scattering using different angles 
of detection may also be used to determine cell size, volume, shape, and cell cytoplasmic complexity ( 17 , 19 ). Optical systems count red cells, white cells, and 
platelets with precision equivalent to that observed in electrical impedance methods ( 26 , 27 ). Similar to the impedance analyzers, many of the optical analyzers can 
process over 100 specimens per hour and have the capacity to flag abnormal parameters ( 26 ).
 
Figure 1.3. Optical type of automated hematology analyzer. A suspension of cells is passed through a flow chamber and focused into a single cell sample stream. 
The cells pass through a chamber and interact with a laser light beam. The scatter of the laser light beam at different angles is recorded, generating signals that are 
converted to electronic signals giving information about cell size, structure, internal structure, and granularity. (Adapted from Cell-Dyn 3500 Operator's Manual. Santa 
Clara, CA: Abbott Diagnostics, 1993.)
Combined Impedance and Optical Counters
Some of the newer hematology analyzers have combined impedance and optical methods together within one instrument, thereby allowing for optimal use and 
integration of the data generated by each method. Often, these are high-volume instruments, appropriate for larger hospitals and reference laboratories, and may be 
more expensive than some of the single-approach models. Examples of combined impedance and optical method analyzers include the Beckman Coulter Gen-S 
(Hialeah, FL) and Cell-Dyn 4000. Many of these newer instruments also provide an automated reticulocyte count and have improved precision of automated 
differential counts so as to lower the need for manual reviews by a technician ( 28 ).
RED BLOOD CELL ANALYTIC PARAMETERS
Red blood cells are defined by three quantitative values: the volume of packed red cells or Hct, the Hb, and the red cell concentration per unit volume. Three 
additional indices describing average qualitative characteristics of the red cell population are also collected. These include mean MCV, MCH, and MCHC. All of these 
values are collected and calculated by automated counters, largely replacing many of the previously used manual or semiautomated methods of red blood cell 
characterization with certain exceptions as noted below.
Volume of Packed Red Cells (Hematocrit)
The volume of packed red cells, or Hct, is the proportion of the volume of a blood sample that is occupied by red blood cells. The Hct may be determined manually by 
centrifugation of blood at a given speed and time in a standardized glass tube with a uniform bore, as was originally described by Wintrobe ( 29 ). The height of the 
column of red cells compared with that of the total blood sample after centrifugation yields the Hct. Macro (using 3-mm test tubes) methods with low-speed 
centrifugation or micro methods using capillary tubes and high-speed centrifugation may be used.
The manual method of measuring Hct has proved to be a simple and accurate method of assessing red cell status. It is easily performed with little specialized 
equipment, allowing it to be adapted for situations in which automated cell analysis is not readily available or for office use. However, several sources of error are 
inherent in the technique. The spun Hct measures the red cell concentration, not red cell mass. Therefore, patients in shock or with volume depletion may have 
normal or high Hct measurements due to hemoconcentration despite a decreased red cell mass. Technical sources of error in manual Hct determinations usually arise 
from inappropriate concentrations of anticoagulants ( 30 ), poor mixing of samples, or insufficient centrifugation ( 29 ). Another inherent error in manual Hct 
determinations arises from trapping of plasma in the red cell column. This may account for 1 to 3% of the volume in microcapillary tube methods, with macrotube 
methods trapping more plasma ( 31 , 32 ). In addition, it should be noted that abnormal red cells (e.g., sickle cells, microcytic cells, macrocytic cells, or spherocytes) 
often trap higher volumes of plasma due to increased cellular rigidity, possibly accounting for up to 6% of the red cell volume ( 31 ). Very high Hcts, as in polycythemia, 
may also have excess plasma trapping. Manual Hct methods typically have a precision [coefficient of variation (CV)] of approximately 2% ( 31 ).
Automated analyzers do not depend on centrifugation techniques to determine Hct, but instead calculate Hct by direct measurements of red cell number and red cell 
volume (Hct = red cell number/red cell volume). The automated Hct closely parallels manually obtained values, so that manual Hct methodology is used as the 
reference method for automated methods (with correction for the error induced by plasma trapping). Errors of automated Hct calculation are more common in patients 
with polycythemia ( 33 ) or abnormal plasma osmotic pressures ( 34 ). Manual methods of Hct determination may be preferable in
these cases. The precision of most 
automated Hcts is less than 1% (CV) ( 28 ).
Hemoglobin Concentration
Hemoglobin is an intensely colored protein, which allows its measurement by a variety of colorimetric and spectrophotometric techniques. Hemoglobin is found in the 
blood in a variety of forms, including oxyhemoglobin, carboxyhemoglobin, methemoglobin, and other minor components. These may be converted to a single stable 
compound, cyanmethemoglobin, by mixing blood with Drabkin solution, which contains potassium ferricyanide and potassium cyanide ( 35 , 36 ). Sulfhemoglobin is not 
converted but is rarely present in significant amounts. The absorbance of the cyanhemoglobin is measured in a spectrophotometer at 540 nm to determine 
hemoglobin. Similar methods are used in both manual methods and automated cell analyzers. Hb is expressed in grams per deciliter (g/dl) of whole blood. The main 
errors in measurement arise from dilution errors or increased sample turbidity due to improperly lysed red cells, leukocytosis, or increased levels of lipid or protein in 
the plasma ( 37 , 38 , 39 and 40 ). Using automated methods, the precision for hemoglobin determinations is less than 1% (CV) ( 25 ).
Red Cell Count
Manual methods for counting red cells have proven to be very inaccurate, and automated counters provide a much more accurate reflection of red cell numbers ( 26 , 
41 ). Both erythrocytes and leukocytes are counted in whole blood that has been diluted in an isotonic medium. As the number of red cells greatly exceeds the number 
of white cells (by a factor of 500 or more), the error introduced by counting both cell types is negligible. However, when marked leukocytosis is present, red cell counts 
and volume determinations may be erroneous unless corrected for white cell effects. The observed precision for red cell counts using automated hematology 
analyzers is less than 1% (CV) ( 28 ) compared with a minimal estimated value of 11% using manual methods ( 29 ).
Mean Corpuscular Volume
The average volume of the red blood cells is a useful red cell index that is used in classification of anemias and may provide insights into pathophysiology of red cell 
disorders ( 42 ). The MCV is usually measured directly with automated instruments but may also be calculated from the erythrocyte count and the Hct by means of the 
following formula ( 29 ).
The MCV is measured in femtoliters (fl, or 10 -15 L). Using automated methods, this value is derived by dividing the summation of the red cell volumes by the 
erythrocyte count. The CV in most automated systems is approximately 1% ( 28 ).
Agglutination of red blood cells, as in cold agglutinin disease, may result in a falsely elevated MCV ( 43 ). Most automated systems gate out MCVs above 360 fl, 
thereby excluding most red cell clumps, although this may falsely lower Hct determinations. In addition, severe hyperglycemia (glucose >600 mg/dl) may cause 
osmotic swelling of the red cells, leading to a falsely elevated MCV ( 34 , 44 ). The CV for automated MCV measurements is less than 1%, compared with approximately 
10% for manual methods ( 32 ).
Mean Corpuscular Hemoglobin
MCH is a measure of the average hemoglobin content per red cell. It may be calculated manually or by automated methods using the following formula 29 .
MCH is expressed in picograms (pg, or 10 -12 g). Thus, the MCH is a reflection of hemoglobin mass. In anemias in which hemoglobin synthesis is impaired, such as 
iron deficiency anemia, hemoglobin mass per red cell decreases with a resultant decrease in MCH. MCH measurements may be falsely elevated by hyperlipidemia ( 
38 ), as increased plasma turbidity may erroneously elevate the hemoglobin measurement. Leukocytosis may also spuriously elevate MCV values ( 37 ). Centrifugation 
of the blood sample to eliminate the turbidity followed by manual hemoglobin determination allows correction of the MCH value. The CV for automated analysis of 
MCH is less than 1% in most modern analyzers, compared with approximately 10% for manual methods ( 28 , 32 ).
Mean Corpuscular Hemoglobin Concentration
The average concentration of hemoglobin in a given red cell volume or MCHC may be calculated by the following formula ( 29 ).
The MCHC is expressed in grams of hemoglobin per deciliter of packed red blood cells. This represents measurement of Hb or the ratio of hemoglobin mass to the 
volume of red cells. With the exception of hereditary spherocytosis and some cases of homozygous sickle cell or hemoglobin C disease, MCHC values will not exceed 
37 g/dl. This level is close to the solubility value for hemoglobin, and further increases in Hb may lead to crystallization. The accuracy of the MCHC determination is 
affected by factors that affect measurement of either Hct (plasma trapping or presence of abnormal red cells) or hemoglobin (hyperlipidemia, leukocytosis) ( 37 ). The 
CV for MCHC for automated methods ranges between 1.0 and 1.5% ( 28 ).
As noted above, the MCV, MCH, and MCHC reflect average values and may not adequately describe blood samples when mixed populations of cells are present. For 
example, in sideroblastic anemias, a dimorphic red cell population of both hypochromic and normochromic cells may be present, yet the indices may be normochromic 
and normocytic. It is important to examine the blood smear as well as red cell histograms to detect such dimorphic populations. The MCV is an extremely useful value 
in classification of anemias ( 42 ), but the MCH and MCHC often do not add significant, clinically relevant information. However, the MCH and MCHC play an important 
role in laboratory quality control because these values will remain stable for a given specimen over time ( 19 ).
Red Cell Distribution Width
The RDW is a red cell measurement that quantitates red cell volume heterogeneity that is provided by the more modern automated hematology analyzers and reflects 
the range of red cell sizes measured within a sample ( 45 ). RDW has been proposed to be useful in early classification of anemias because it becomes abnormal 
earlier in nutritional deficiency anemias than any of the other red cell parameters, especially in cases of iron deficiency anemia ( 42 , 46 , 47 ). RDW is particularly useful 
when characterizing microcytic anemias, particularly distinguishing between iron deficiency anemia (high RDW, normal to low MCV) and uncomplicated heterozygous 
thalassemia (normal RDW, low MCV) ( 42 , 47 , 48 , 49 and 50 ). RDW is useful as a method for initial characterization of anemia, particularly microcytic anemias, although 
other tests are usually required to confirm the diagnosis ( 51 ). RDW is also useful in identifying red cell fragmentation, agglutination, or dimorphic cell populations 
(including patients who have had transfusions or have been recently treated for a nutritional deficiency) ( 47 , 52 ).
Automated Reticulocyte Counts
Determination of the numbers of reticulocytes or immature, nonnucleated red blood cells that contain RNA provides useful information about the bone marrow's 
capacity to synthesize and release red cells in response to a physiologic challenge, such as anemia. In the past, reticulocyte counts were performed manually using 
supravital staining with methylene blue. Reticulocytes will stain precipitated RNA that appears as a dark blue meshwork or granules (at least two per cell) allowing 
reticulocytes to be identified and enumerated by manual counting methods ( 53 ). Normal values for reticulocytes in adults are 0.5 to 1.5%, although they may be 2.5 to 
6.5% in newborns (falling to adult levels by the second week of life). Because there are relatively low numbers of reticulocytes, the CV for reticulocyte counting is 
relatively large (10 to 20%). To increase accuracy of reticulocyte counting, alternative methods using flow cytometry and staining with acridine orange or thioflavin 
allow for many more cells to be
analyzed, thereby increasing accuracy and precision of counts ( 15 , 54 , 55 ).
Stand-alone reticulocyte analyzers, such as the Sysmex R-2000 or ABX PENTRA 120 Retic (ABX Diagnostics, Montpellier, France), allow for determination of 
reticulocyte counts without requiring a full flow cytometer, affording increased accuracy over manual counts. Many of the newest automated hematology analyzers, 
such as the Coulter STKS, Coulter GenS or the Cell-Dyn 4000, have automated reticulocyte counting as part of the testing capabilities and allow reticulocyte counts 
to be included with routine complete blood count parameters. Comparisons of stand-alone instruments, integrated hematology analyzers, and flow cytometric methods 
show that these automated methods provide similar data with superior accuracy when compared to manual counting methods, with similar CVs of 5 to 8% ( 56 , 57 and 
58 ).
LEUKOCYTE ANALYSIS
White Blood Cell Counts
Leukocytes may be enumerated by either manual methods or automated hematology analyzers. Leukocytes are counted after dilution of blood in a diluent that lyses 
the red blood cells (usually acid or detergent). The much lower numbers of leukocytes present require less dilution of the blood than is needed for red blood cell 
counts (usually a 1:20 dilution, although it may be less in cases of leukocytopenia or more with leukocytosis). Manual counts are done using a hemocytometer or 
counting chamber. As with red cell counts, manual leukocyte counts have more inherent error, with CVs ranging from 6.5% in cases with normal or increased white 
cell counts to 15% in cases with decreased white cell counts. Automated methods characteristically yield CVs in the 1 to 3% range ( 26 , 28 ). Automated leukocyte 
counts may be falsely elevated in the presence of cryoglobulins or cryofibrinogen ( 59 ), aggregated platelets ( 60 ), and nucleated red blood cells or when there is 
incomplete lysis of red cells, requiring manual counting. Falsely low neutrophil counts have also been reported due to granulocyte agglutination secondary to surface 
immunoglobulin interactions ( 61 ).
Leukocyte Differentials
White cells are analyzed to find the percentage of each white blood cell type by doing a differential leukocyte count, providing important information in evaluation of 
the patient. Uniform standards for performing manual differential leukocyte counts on blood smears have been proposed by the National Committee for Clinical 
Laboratory Standards ( 62 ) to ensure reproducibility of results between laboratories. It is important to scan the smear at low power to ensure that all atypical cells and 
cellular distribution patterns are recognized. In wedge-pushed smears, leukocytes tend to aggregate in the feathered edge and side of the blood smear rather than in 
the center of the slide. Larger cells (blasts, monocytes) also tend to aggregate at the edges of the blood smear ( 63 ). Use of coverslip preparations and spinner 
systems tends to minimize this artifact of cell distribution. For wedge-push smears, it is recommended that a battlement pattern of smear scanning be used in which 
one counts fields in one direction, then changes direction and counts an equal number of fields before changing direction again to minimize distributional errors ( 41 ).
In manual leukocyte counts, three main sources of error are encountered: distribution of cells on the slide, cell recognition errors, and statistical sampling errors ( 57 , 
58 ). Poor blood smear preparation and staining are major contributors to cell recognition and cell distribution errors ( 63 ). Statistical errors are the main source of error 
inherent in manual counts, due to the small sample size in counts of 100 or 200 cells. The CV in manual counts is between 5 and 10% and is also highly dependent 
on the skill of the technician performing the differential. Accuracy may be improved by increasing the numbers of cells counted, but for practical purposes, most 
laboratories will do a differential on 100 white cells ( 64 ). Automated methods of differential counting tend to be more accurate because of the much larger numbers of 
cells evaluated, with CV of 3 to 5% ( 64 , 65 , 66 and 67 ).
Automated methods of obtaining a leukocyte differential have been developed that markedly decrease the time and cost of performing routine examinations as well as 
increasing accuracy. However, automated analysis is incapable of accurately identifying and classifying all types of cells and is particularly insensitive to abnormal or 
immature cells. Therefore, most analyzers will identify possible abnormal white cell populations by flagging, indicating the need for examination by a skilled 
morphologist for confirmation ( 68 ). The automated instruments used for performing automated leukocyte differentials are of two general types: those that perform cell 
identification on the basis of pattern recognition using stained blood smear slides and automated microscopy, and flow-through systems that identify cells on the basis 
of size, cell complexity, or staining characteristics.
Pattern recognition systems were first available in the early 1970s and included such instruments as the Hematrack, Coulter diff 3 and diff 4, Abbott&bsol;R ADC 500, 
and the Leukocyte Automatic Recognition Counter ( 69 , 70 ). This technology uses a blood film on a glass slide that was stained and loaded onto the instrument. A 
computer drives a microscopic mechanical stage until a dark staining area, corresponding to a leukocyte nucleus, is detected. Using data collected for each cell on 
cell size, nuclear and cytoplasmic coloration, and density, the computer matches the data patterns with specifications for each white cell type and identifies the cell. 
Most pattern recognition technology is hampered by many of the same limitations of accuracy—limited numbers of cells counted, difficulties in classifying abnormal 
cell types, and cell distribution characteristics—as manual counts ( 71 ). Although the automated pattern recognition systems do decrease technician time, they are 
significantly slower than the flow-through methods. Hence, pattern recognition systems are now rarely used, and the instruments are no longer manufactured.
Because of the ability to link the automated differential to the rest of the automated hematologic analysis, most recent methods use a flow-through system that 
generates a leukocyte differential as a part of the complete blood count ( 67 , 72 ). Flow-through systems collect and analyze data from large numbers of white blood 
cells to provide a differential count that has a high degree of precision when compared to manual methods. White blood cell determination depends on both cell size 
and cytochemical staining characteristics (Technicon H6000, H*1, H*2, H*3 series) ( 73 ) or on the basis of cell volume and internal complexity as measured by 
electrical impedance and light scatter characteristics [Coulter STKR and Gen-S series ( 58 , 74 ), Cell-Dyn 4000 ( 28 ), Sysmex NE-8000 ( 75 ), Bayer Advia 120 (Bayer 
Diagnostic Division, Tarrytown, NY) ( 28 ), and Cobas-Helios (Roche Diagnostic Systems, Inc., Branchburg, NJ) ( 27 ) systems].
Systems that use myeloperoxidase staining characteristics of cells perform cell counts on specimens via continuous-flow cytometric analysis of blood samples in 
which the red cells have been lysed and white cells fixed. The cells are suspended in diluent and passed through a flow cell in a continuous stream so that single cells 
are analyzed for cell size (dark field light scatter) and cytochemical characteristics of myeloperoxidase staining (bright field detector). The data are plotted as a 
scattergram reflecting cell size (light scatter) on the y-axis and myeloperoxidase staining intensity or activity on the x-axis ( Fig. 1.4), which gives rise to a six-part 
differential (neutrophils, lymphocytes, monocytes, eosinophils, basophils, and large unstained cells).