1

The National Ribat University

Faculty of Graduate Studies and Scientific Research

The normal ranges of T, B and NK lymphocytes subsets

for healthy adult Sudanese compared to leukemic

patients (AML and ALL) and HIV patients in Khartoum

state.

A thesis submitted in fulfillment of Ph.D. degree in

(Medical laboratory Sciences) Haematology

By: Hussam Magdi Abdelltef Ibrahim (B.Sc., M.Sc.)

Supervisor:

Prof. Shamsoun Khamis Kafi (MBBS, MD)

Co- supervisor:

Dr. Zahir Abbas Hilmi (B.Sc., M.Sc., Ph.D.)

Co- supervisor:

Dr. Osama A. Altayeb (B.Sc., M.Sc., Ph.D.)

2016

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Dedication

To My Dear Mother and Father

My wife,

My Friends and

Those who helped me.

Hussam Magdi

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Acknowledgements

All my thanks are to Allah who gave me health and strength to

complete this research.

I would like to thank the The National Ribat University for the

chance they provided me to gain such experience in particular my

supervisor professor Shamsoun Khamis Kafi for his unlimited support

and guidance throughout this research. It is extremely difficult to find

words to appreciate his invaluable effort, without it this work would

not have been accomplished.

It gives me great pleasure to extend my sincere thanks to all

those who contributed to the production of this research. In particular,

I would like to express my gratitude to Flow cytometer Center in

particular the general manger (Co-supervisor) Dr. Osama A. Altayeb

for his unlimited support and for his professional guidance in the

practical part by the Flowcytometer. I am also deeply indebted to the

laboratory staff in Flow cytometer Center.

My thanks go to my co-supervisor Dr. Zahir A. Hilmi for his

support, deep knowledge and in-depth understanding of the topic,

and for his insightful comments. My special thanks to Ustaz. Magdi

Hussien and Ustaz. Ammar Bashir for their valuable input to this

research.

I would like to thank Dr. Hind A. Hilmi, Dr. Nahala A. Hilmi and

Ustaz. Mona A. Hilmi for their advice, encouragement and financial

support as well. My sincere thanks to Dr. Khalid for data analysis and

Dr. Osama kabara for his coordination with HIV Center for samples

collection and cooperation.

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List of contents

Dedication…………………………………………………………….. I

Acknowledgement …………………………………………………… II

List of contents …………………………………………………….. III

List of Tables…………………………………………………………. VII

List of Figures ……………………………………………………….. VIII

List of Abbreviations ………………………………………………… IX

Abstract……………………………………………………………….. XI

Abstract (Arabic)…………………………………………………….. XIV

Chapter One (Introduction and literature review) 1. Introduction and literature review ………………………………… 1

1.1 Introduction ……………………………………………................ 1

1.2 literature review………………………………………………….. 2

1.2.1 Causes of immunodeficiency ………………………………… 2

1.2.2 Affected components …………………………………………... 2

1.2.3 Primary immunodeficiency ……………………………………. 3

1.2.4 Secondary immunodeficiency …………………………………. 4

1.2.5 Leukemia ………………………………………………………. 4

1.2.5.1 Classification of leukemia……………………………………. 5

1.2.5.2 Specific types of leukemia…………………………………… 6

1.2.5.3 Acute leukemia ……………………………………………… 7

1.2.6 The human immunodeficiency virus (HIV)……………………. 11

1.2.7 Introduction to immunophenotyping ………………………… 13

1.2.7.1 Immunophenotyping by monoclonal antibody……………… 14

1.2.7.2 Advantages of Immunophenotyping ………………………… 17

1.2.8 The Flowcytometer …………………………………………… 18

1.2.8.1 General principle …………………………………………… 20

1.2.8.2 Clinical application of Flowcytometer ……………………… 24

1.2.8.3 Fluorescence ………………………………………………… 25

1.2.8.4 Sample staining ……………………………………………… 26

1.2.8.5 Surface antigen ……………………………………………… 27

1.2.8.5.1 Cluster of differentiation or CD markers ………………… 29

1.2.8.5.2 Cell markers………………………………………………… 29

5

1.2.8.5.3 CD nomenclature ………………………………………… 30

1.2.8.5.4 T cells CD markers ………………………………………… 31

1.2.8.5.5 Helper T cells (CD4)……………………………………… 32

1.2.8.5.6 Cytotoxic T cells (CD8)…………………………………… 33

1.2.8.5.7 B cells……………………………………………………… 34

1.2.8.5.8 Natural killer cells ………………………………………… 38

1.2.8.6 Flowcytometric Immunophenotyping for the diagnosis and

monitoring of hematolohical neoplasms…………………………….

39

1.2.8.7 Flowcytometry gating system………………………………… 40

1.2.9 Previous study…………………………………………………. 42

1.2.9.1 The normal reference ranges of lymphocytes percentage and

absolute counts in some countries……………………………………

42

1.3 Justification ……………………………………………………… 43

1.4 Objectives……………………………………………………….. 44

1.4.1 General objective……………………………………………… 44

1.4.2 Specific objectives……………………………………………. 44

Chapter Two (Materials and Methods) 2. Materials and Methods……………………………………………. 45

2.1 Study Design…………………………………………………… 45

2.2 Study population…………………………………………………. 45

2.2.1 Inclusion criteria……………………………………………….. 45

2.2.2 Exclusion criteria……………………………………………….. 45

2.3 Data collection …………………………………………………… 45

2.4 Sample size………………………………………………………. 46

2.4.1 Control group…………………………………………………… 47

2.4.2 Immunocompromised group…………………………………… 47

2.5 Ethical consideration…………………………………………….. 47

2.6 Sample collection ………………………………………………. 47

2.7 Hematological analysis by the sysmex…………………………… 47

2.7.1 Principle and procedure……………………………………….. 47

2.8 Immunophenotyping by the flowcytometry ……………………... 48

2.8.1 General principle of flowcytometer …………………………… 48

2.8.2 Sample preparation …………………………………………….. 49

2. 9 Interpretation of the results……………………………………… 50

2.10 Statistical analysis ……………………………………………… 50

Chapter Three (Results) 3. Results …………………………………………………………….. 51

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3.1 Healthy controls …………………………………………………. 51

3.1.2 Percentages and absolute counts of lymphocytes subsets …….. 51

3.1.2.1 Mean percentages and absolute counts for T cells

subpopulation (CD3, CD4 and CD8) ………………………………

56

3.1.2.1.1 T cells (CD3)………………………………………………. 56

3.1.2.1.2 T helper cell (CD4)…………………………………………. 56

3.1.2.1.3 T cytotoxic cells (CD8)…………………………………….. 57

3.1.2.2 Mean percentages and absolute counts for B cells CD markers

(CD19 and CD20)…………………………………………..

57

3.1.2.3 Total mean percentages and absolute counts for NK cells CD

markers (CD16 and CD56)………………………………………….

58

3.2 Comparison of leukemic patients under chemotherapy with

healthy controls ………………………………………………………

64

3.2.1 Absolute counts and percentages of lymphocytes …………….. 64

3.2.1.1 Mean percentages and absolute counts for T cells

subpopulation (CD3, CD4 and CD8)………………………………..

64

3.2.1.1.1 T cells (CD3)………………………………………………. 65

3.2.1.1.2 T helper cells (CD4)……………………………………… 65

3.2.1.1.3 Cytotoxic cells (CD8)……………………………………… 65

3.2.1.2 Total percentages and absolute counts for B cells CD markers

(CD19 and CD20)……………………………………………………

69

3.2.1.3 Total percentages and absolute counts for CD markers (CD16

and CD56) of NK cells………………………………………………..

71

3.3 Comparison of HIV patients with healthy controls …………….. 75

3.3.1 Percentages and absolute counts of lymphocytes subsets …….. 75

3.3.1.1 Total mean percentages and absolute counts for T cells

subpopulation (CD3, CD4 and CD8)………………………………..

75

3.3.1.1.1 T cells (CD3)………………………………………………. 76

3.3.1.1.2 T helper cells (CD4)……………………………………… 76

3.3.1.1.3 Cytotoxic cells (CD8)……………………………………… 76

3.3.1.2 Total percentages and absolute counts for B cells CD markers

(CD19 and CD20)……………………………………………………

79

3.3.1.3 Total percentages and absolute counts for CD markers (CD16

and CD56) of NK cells………………………………………………..

81

Chapter Four (Discussion, Conclusion and Recommendations )

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4.1 Discussion………………………………………………………… 83

4.2 Conclusion ……………………………………………………… 91

4.3 Recommendations………………………………………………. 92

References …………………………………………………………… 93

Appendices

Appendix I …………………………………………………………... 106

Appendix II …………………………………………………………... 107

Appendix III …………………………………………………………. 108

Appendix IV …………………………………………………………. 109

Appendix V…………………………………………………………... 110

Appendix VI………………………………………………………….. 111

Appendix VII…………………………………………………………. 113

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List of Tables

Table NO. Table Title Page NO.

Table 1.1

Cluster of differentiation of lymphocytes

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Table 1.2

Reactivity of the antibody

16

Table 1.3 Common clinical uses of flow cytometry 24

Table 3.1

Distribution of study population according to gender.

52

Table 3.2

Distribution of study population according to age.

53

Table 3.3

The normal ranges (Percentages and Absolute counts) of (TWBCs,

Lymphocytes and T cells) of healthy adult populations.

55

Table 3.4

The normal ranges Percentages and Absolute counts) of CD markers

of B cells and NK cells for healthy adults.

62

Table 3.5

The mean percentages and absolute counts of lymphocytes and T

cells (CD3,CD4 and CD8) of leukemic patients and controls

66

Table 3.6

The mean percentages and absolute counts of B cells (CD19 and

CD20) of leukemic patients and controls.

70

Table 3.7

The mean percentages and absolute counts of natural killer cells

(CD16 and CD56) of leukemic patients and controls.

72

Table 3.8

The total mean percentages and absolute counts of lymphocytes and

T cells (CD3, CD4 and CD8) of HIV patients and control group.

77

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Table 3.9

A comparison between HIV and healthy controls with regard to their

mean percentages and absolute counts of B cells (CD19 and CD20).

80

Table 3.10

The mean percentages and absolutes counts of natural killer cells

(CD16 and CD56) of HIV patients and controls.

82

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List of Figures

Figure

No.

Figure Title Page

No.

Figure 1.1 A single cell suspension hydrodynamically focused with sheath fluid to

intersect an argon-ion laser.

21

Figure 1.2 Light –scattering properties of cell correlated measurements of FSC and SSC.

22

Figure 1.3 Cell populations based on FSC .Vs. SSC.

28

Figure 1.4 Helper T cell structure

34

Figure 1.5 Cytotoxic T cell structure

34

Figure 1.6 B cell structure

37

Figure 1.7 Ungated and gated cells

41 Figure 3.1 The mean age of the study groups

54

Figure 3.2 The mean percentages for lymphocytes and lymphocytes subsets of healthy

controls.

59

Figure 3.3 The mean percentages for T cells sub populations

60

Figure 3.4 The mean absolute counts for T cells sub populations

61

Figure 3.5 The mean absolute counts for lymphocytes subsets of healthy controls. 63

Figure 3.6

Comparison of the mean percentage of lymphocytes and T cells sub

population in healthy controls and leukemic patients.

67

Figure 3.7

Comparison of the mean absolute counts of lymphocytes and T cells sub

population in healthy controls and leukemic patients.

68

Figure 3.8

Comparison of the mean percentage of lymphocytes, T cells marker (CD3), B

cells marker (CD19) and NK cells marker (CD16) in healthy controls and

leukemic patients.

73

Figure 3.9

Comparison of the mean absolute counts of lymphocytes, T cells marker

(CD3), B cells marker (CD19) and NK cells marker (CD16) in healthy

controls and leukemic patients.

74

Figure3.10 Comparison of the mean absolute counts of lymphocytes and T cells sub

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population in healthy controls and HIV patients. 78

List of Abbreviations

ABS Absolute Counts

ADCC Antibody Dependant Cellular Cytotoxicity

AIDS Acquired Immunodeficiency Syndrome

AL Acute Leukaemia

ALL Acute Lymphocytic Leukaemia

AML Acute Myeloid Leukaemia

APC Antigen Presenting Cell

CBC Complete Blood Count

CD Clusters of differentiation or designation of B and T cells

CD3 The signaling component of the T cell receptor (TCR) complex

CD4 CD marker used for designation of T helper cells; a co-receptor for MHC

Class II; also a receptor used by HIV to enter T cells.

CD8 CD marker used for designation of T cytotoxic cells; a co-receptor for MHC

Class I; also found on a subset of myeloid dendritic cells.

CD19 B-lymphocyte surface antigen

CD20 B-lymphocyte surface antigen

CD16 Natural killer cell (surface antigen)

CD56 Natural killer cell (surface antigen)

CLL Chronic Lymphocytic Leukaemia

CLS Clinical Laboratory Setting

CTL Cytotoxic T Cell

DNA Deoxy ribonucleic Acid

EDTA Ethylene Diamine Tetra- acetic Acid

ER Electronic Reference

FAB French American British

FITC Fluorescence Isothiocyanate

FISH Fluorescence In Situ Hybridization

FSC Forward Scatter

HIV Human Immunodeficiency Virus

HLA Human leukocyte Antigen

HLDA Human Leukocyte Differential Antigens

ICSH International Committee for Standardization in Haematology

ICU Intensive Care Unit

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KDA Kilo Dalton

mABs Monoclonal Antibodies

MHC Major Histocompatibility complex

NHL Non Hodgkin’s Lymphoma

NK Natural Killer cell

PBS Phosphate Buffer Saline

PE Phcoerythrin

RNA Ribonucleic Acid

SD Standard Deviation

SPSS Statistical Package for Social Science program

SSC Side Scatter

TB Tuberculosis

TcR T cell Receptor

TH T Helper cell

TWBC Total White Blood Cells

WHO World Health Organization

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Abstract

Introduction: Treatment of many diseases such as leukemia and

AIDS requires prior knowledge about the situation of certain lymphocytes

sub- populations in terms of their mean total absolute count and mean

percentages. This study aimed to establish the normal reference ranges

(absolute count; ABS and percentages) for healthy adult Sudanese using

cluster of differentiations (CD) markers for T, B and NK cells lymphocytes

subsets. The normal reference ranges of these lymphocytes were compared

to those of Leukemic and HIV patients in Sudan.

Materials and Methods: In this study venous blood samples were

collected from healthy adult controls (n = 300), leukemic patients under

chemotherapy (n= 75) and AIDS patients (n = 75) randomly selected. The

complete blood counts (CBC) for all samples were analyzed by Sysmex

Haematological analyzer. Becman coulter flowcytometer was used for

Immunophenotyping of the lymphocytes subsets for the following CD

marker (T cells; CD3, CD4, CD8, B cells; CD19, CD20, and NK cells;

CD16 and CD56) in addition to CD45 for gating strategy.

Results: the results of this study showed the mean total absolute

counts (ABS) (cells/ µl) for { lymphocytes (2114 ± 823), T cells; CD3

(1368.9 ± 620.5), CD4 (810.5 ± 383.8), CD8 (489.3 ± 233.3), B cells; CD19

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(208.9 ± 140.1),CD20 (223.4 ± 146.3), and NK cells; CD16 (210.7 ± 151.5)

and CD56 (234.5 ± 154.6)},and the mean total percentages for {

lymphocytes (34 ± 9) , T cells; CD3 (64.5 ± 12.8) , CD4 (38.3 ± 9.1), CD8

(23.4 ± 7.2), B cells; CD19 (10.1 ± 5.3), CD20 (10.9 ± 5.7), and NK cells;

CD16 (10.3 ± 5.7) and CD56 (11.4 ± 5.7)}. For leukemic patients under

chemotherapy the mean total absolute counts (ABS) (cells/ µl) for {

lymphocytes (4276 ± 1469), T cells; CD3 (854 ± 122), CD4 (474 ± 89),

CD8( 599.8 ± 150.5) , B cells; CD19 (403 ± 139), CD20 (512 ± 147), and

NK cells; CD16 (395.2 ± 131) and CD56 (227.7 ± 63)}, and the mean total

percentages for { lymphocytes (32 ± 23), T cells; CD3 (53.2 ± 24.5), CD4

(27.3 ± 16.8), CD8 ( 21.6 ± 14.6), B cells; CD19 (10.2 ± 2.4), CD20 (13.2 ±

2.2) , and NK cells; CD16 (9.1 ± 1.1) and CD56 (10.9 ± 1.3) }. For the HIV

patients the mean total absolute counts (ABS) (cells/ µl) for { lymphocytes

(6914 ± 2571), T cells; CD3 (1287.3 ± 1048.9) , CD4 (454.9 ± 111), CD8

(801.5 ± 239), B cells; CD19 (90.6 ± 47), CD20 (2260 ± 1623), and NK

cells; CD16 ( 53.0 ± 46) and CD56 (170 ± 95)}, and the mean total

percentages for { lymphocytes (45 ± 5), T cells; CD3 (31.2 ± 26.0), CD4

(10.8 ± 1.6), CD8 (18.8 ± 3), B cells; CD19 (2.2 ± 0.5), CD20 (55.1 ± 38.3),

and NK cells; CD16 (1.3 ± 0.8) and CD56 (3.9 ± 1.0) }. This study revealed

a significant difference (p ˂ 0.05) in lymphocytes subsets for the following

CD markers (%) (T cells; CD3 and CD4 ), and for (ABS) (lymphocytes, T

cells; CD3, CD4, B cells; CD20).However, insignificant differences (p ˃

0.05) were obtained for the following lymphocytes subsets CD markers (%)

(lymphocytes,T cells; CD8, B cells; CD19, CD20, and NK cells; CD16 and

CD56) regarding the Absolute counts (ABS) (T cells; CD8, B cells; CD19,

and NK cells; CD16 and CD56). The present study revealed a significant

difference (p ˂ 0.05) in lymphocytes subsets for the following CD markers

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(%) (lymphocytes,T cells; CD3, CD4,B cells; CD19, CD20, and NK cells;

CD16 and CD56). In same sense the absolute counts (ABS) (lymphocytes, T

cells; CD4, CD8, B cells; CD19, CD20, and NK cells; CD16). However,

insignificant differences (p ˃ 0.05) were obtained for the following

lymphocytes subsets CD markers (%) (T cells; CD8) and the absolute counts

(ABS) (T cells; CD3, and NK cells; CD56).

Conclusion: the normal ranges for healthy Sudanese adults

(percentages and absolute counts) of lymphocytes subsets populations were

revealed as follows (lymphocytes (25 - 43 %; 1298 - 2927 cells/ µl), T cells;

CD3 (52 – 77 %; 754 - 1980 cells/ µl), CD4 (29 – 48 %; 428 - 1192 cells/

µl), CD8 (16 – 31 %; 261 – 722cells/ µl), B cells; CD19 (5 – 15 %; 70 - 355

cells/ µl), CD20 (5 – 17 %; 72 – 388 cells/ µl), and NK cells; CD16 (5– 16

%; 57 – 376 cells/ µl) and CD56 (6 – 18 %; 64 – 405 cells/ µl)).

Lymphocytes subsets (CD3 and CD4) in leukemic patient were significantly

lower than control while B cell (CD20) and NK cells (CD16) were

significantly higher in leukemic patients. All studied lymphocytes subsets

(CD3, CD4, CD19, CD16 and CD56) were significantly lower in HIV

patients than the controls except lymphocytes (percentages and absolute

count) and (CD8 and CD20) they are significantly higher in HIV patients.

Moreover age and gender didn’t have significant influence on all studied CD

markers.

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الملخص

: التعامل مع عدد من األمراض مثل اللوكيميا ومتالزمة فقدان المناعة المكتسبة المقدمة

)اإليدز( تحتاج إلي معرفة مسبقة عن حالة بعض الخاليا الليمفاوية وأنواعها الفرعية وذلك إعتمادا

عي المدي المرجعي الطبي تحديدالدراسة إلي هدفتعلي الوسط الحسابي للقيم المطلقة والنسب المئوية.

ألصحاء البالغين و ذلك بإستخدام مجموعة من دالالت ا لسودانيينل)القيم المطلقة والنسب المئوية(

( للخاليا الليمفاوية التائية والبائية والخاليا القاتلة الطبيعية. وهذا المدي CD markersالتمايز )

مقارنته مع المدي المرجعي لمرضي اللوكيميا واإليدز في السودان. والمرجعي الطبيعي

: في هذه الدراسة تم إستخدام الدم الوريدي الذي تم جمعه من والطرق المستخدمة المواد

( 75= )ن ئي( ومرضي اللوكيميا تحت العالج الكيميا030= السودانيين البالغين األصحاء )ن

بإستخدام جهاز (CBC) إختيارها عشوائيا. التعداد الكلي للدم( والتي تم 75= ومرضي اإليدز)ن

كما تم إستخدام جهاز التدفق الخلوي للتصنيف الخاليا الليمفاوية بدالالت (SYSMEX) تعداد الدم

( والخاليا (CD19 and CD20 والخاليا البائية CD3 , CD4 and CD8)) التمايز الخاليا التائية

.للتصنيف اإلستراتيجي CD45وذلك باالضافة إلستخدام (CD16 and CD56القاتلة الطبيعية )

لسودانيين البالغيين األصحاء ا المدي الطبيعي للخاليا اللمفاوية وأنواعها الفرعية عندالنتائج:

والخاليا التائية: (823 ± 2114) : ) الخاليا اللمفاوية (cells/ µlة )لقيم المطلقا متوسطكان كاألتي:)

CD3 (1368.9 ± 620.5) ؛CD4 (810.5 ± 383.8) ؛ CD8 (489.3 ± 233.3) والخاليا ؛

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210.7و الخاليا القاتلة الطبيعة:) CD20 (223.4 ± 146.3) ؛ CD19 (208.9 ± 140.1):البائية

± 151.5) CD16( 154.6 ± 234.5) ؛CD56.للنسب المئوية للخاليا المتوسط وكذلك كان

± 23.4) ؛CD4 (9.1 ± 38.3) ؛ CD3(12.8 ± 64.5الخاليا التائية ) (9 ± 34) الليمفاوية

7.2) CD8 ( 5.3 ± 10.1؛ والخاليا البائية) CD19 ( 5.7 ± 10.9؛)CD20 ؛ والخاليا القاتلة

. ولمرضي اللوكيميا تحت العالج الكيماوي CD56 (10.3 ± 5.7)CD16 (5.7 ± 11.4الطبيعية: )

( الخاليا التائية 1469 ± 4276كاألتي: الخاليا الليمفاوية ) (cells/ µl) المطلقة لقيما متوسطكان

(599.8 ± 150.5) CD8 (89 ± 474)؛ CD4 (122 ± 854)؛CD3 ( 403؛ والخاليا البائية ±

139)CD19 (147 ± 512) ؛ CD20 ( :131 ± 395.2؛ والخاليا القاتلة الطبيعية) CD16

( الخاليا التائية 23 ± 32وسط للنسب المئوية للخاليا الليمفاوية )متوال CD56 (63 ± 227.7)؛

(53.2 ± 24.5)CD3؛CD4 (27.3 ± 16.8)(14.6 ± 21.6)؛ CD8( 10.2؛ والخاليا البائية ±

2.4)CD19 (2.2 ± 13.2)؛CD20 ( :1.1 ± 9.1؛ والخاليا القاتلة الطبيعية) CD1610.9)؛ ±

1.3)CD56 للقيم المطلقة المتوسط. و ولمرضي اإليدز كان (cells/ µl) كاألتي: الخاليا الليمفاوية

(111±454.9) ؛CD3(1048.9± 1287.3( الخاليا التائية )2571 ± 6914)

CD4(239±801.5)؛ CD8(47 ± 90.6والخاليا البائية ) ؛ CD19 (1623±2260)؛ CD20 ؛

للنسب المئوية و المتوسط CD56(95 ± 170)؛CD16(46 ± 53.0) :والخاليا القاتلة الطبيعية

(3±18.8) ؛CD4 (1.6 ± 10.8) ؛CD3(26.0±31.2الخاليا التائية ) (5 ± 45للخاليا الليمفاوية )

CD8( 0.5 ± 2.2؛والخاليا البائية)CD19(38.3 ± 55.1)؛CD20( :1.3؛ والخاليا القاتلة الطبيعية

± 0.8)CD16 (1.0 ± 3.9)؛CD56 . المدي الطبيعي لألصحاء البالغيين في هذه الدراسة تم مقارنة

مع مرضي اللوكيميا تحت العالج الكيماوي وذلك إعتمادا علي المتوسط للقيم المطلقة وكذلك المتوسط

؛ هذه الدراسة أوضحت إختالفات ذات دالله وأنواعها الفرعية للنسب المئوية للخاليا اللمفاوية و

ية لدالالت التمايز األتية )بالنسبة المئوية( الخاليا ( بين أنواع الخاليا اللمفاوP < 0.05إحصائية )

و البائية CD4؛ CD3. وكذلك القيم المطلقة للخاليا اللمفاوية ؛الخاليا التائية CD3؛CD4 التائية

CD20( و اإلختالفات ليست لها أي داللة إحصائية .P > 0.05) تم الحصول عليها ألنواع الخاليا

CD19الخاليا البائية CD8اللمفاوية لدالالت التمايز بالنسبة المئوية )الخاليا اللمفاوية؛ الخاليا التائية

و إعتمادا علي القيم المطلقة )الخاليا التائية ؛ CD56 و CD16والخاليا القاتلة الطبيعية CD20و

(.وأيضا تمت في هذه الدراسة مقارنة CD56و CD16ا القاتلة الطبيعية والخالي CD19الخاليا البائية

المدي الطبيعي للبالغيين األصحاء مع مرضي اإليدز إعتمادا علي المتوسط للقيم المطلقة والنسب

18

( P < 0.05المئوية للخاليا اللمفاوية وأنواعها الفرعية وأوضحت النتائج فروقات ذات داللة إحصائية )

والخاليا البائية CD4؛CD3مايز األتية بالنسبة المئوية )الخاليا اللمفاوية ؛ الخاليا التائية لدالالت الت

CD20 ؛ CD19 والخاليا القاتلة الطبيعيةCD16 وCD56 ( و في اإلطار نفسه القيم المطلقة )

القاتلة والخاليا CD20؛ CD19والخاليا البائية CD4؛ CD8الخاليا اللمفاوية ؛ الخاليا التائية

( تم الحصول عليها P > 0.05و أما الفروقات ليست لها أي داللة إحصائية ) CD16).الطبيعية

والخاليا CD3والقيم المطلقة للخاليا التائية CD8بالنسب المئوية لدالالت التمايز األتية )الخاليا التائية

ت ذو داللة إحصائية للخاليا إضافة إلي ذلك هذه الدراسة لم توجد فروقا CD56القاتلة الطبيعية

.اللمفاوية وأنواعها الفرعية لدالالت التمايز المختلفة بالنسبة للنوع والعمر

الدراسة وضعت ألول مرة مدي طبيعي إعتمادا علي متوسط القيم المطلقة هذه الخالصة:

والمئوية لسبعة أنواع رئيسية من دالالت التمايز للخاليا اللمفاوية وأنواعها الفرعية التي ذكرت آنفا

وكذلك قامت بمقارنة المدي الطبيعي )القيم المطلقة والنسب المئوية( لكل الخاليا موضوع الدراسة مع

ضي اللوكيميا تحت العالج الكيماوي ومرضي اإليدز. مر

19

1. Introduction and literature review

1.1 Introduction:

Immunity is the property of resistance. As applied to humans in a world rife

with hostile micro-organisms, this has usually meant resistance to infection.

Although it is now evident that immune defects are associated with

neoplasia and atopy as well. Immunocompromisation can be taken broadly

to apply to patients with defect in their defenses against infection.

Immunodeficiency is applied here to patients affected by diseases or

situations in which their immune system itself is impaired including

complement, phagocytes and lymphocytes. The nature of this impairment

can be primary (corresponding to diseases of congenital origin) or secondary

(corresponding to a normal immune system's acquisition of an abnormality

after birth). (Külshammer et al., 2012).

20

Immunodeficiency is a state in which the immune system's ability to fight

infectious disease is compromised or entirely absent. Most cases of

immunodeficiency are acquired (secondary) but some people are born with

defects in their immune system, or primary immunodeficiency. Transplant

patients take medications to suppress their immune system as an anti-

rejection measure, as do some patients suffering from an over-active

immune system. A person who has an immunodeficiency of any kind is said

to be immunocompromised. An immunocompromised person may be

particularly vulnerable to opportunistic infections, in addition to normal

infections that could affect everyone.

(Külshammer et al., 2012).

1.2 Literature Review

1.2.1 Causes of Immunodeficiency:

Genetic - inherited genetic defects. (deficiency of complement

components)

Acquired - infections, such as HIV, and certain cancers, including

leukemia, lymphoma, or multiple myeloma.

Chronic diseases - such as end stage renal disease and dialysis,

diabetes, cirrhosis.

Medications - such as steroids, chemotherapy, radiation,

immunosuppressive post-transplant medications.

Physiological State - such as pregnancy.(Lebranchu et al., 1991)

1.2.2 Affected components

21

Humoral immune deficiency, with signs or symptoms depending on

the cause, but generally include signs of hypogammaglobulinemia

(decrease of one or more types of antibodies) with presentations

including repeated mild respiratory infections, and/or

agammaglobulinemia (lack of all or most antibody production) which

results in frequent severe infections and is often fatal.

T cell deficiency, often caused by secondary disorders such as

acquired immune deficiency syndrome.

Granulocyte deficiency, including decreased numbers of granulocytes

(called granulocytopenia or, if absent, agranulocytosis) such as of

neutrophil granulocytes (termed neutropenia). Granulocyte

deficiencies also include decreased function of individual

granulocytes, such as in chronic granulomatous disease.(Lebranchu

et al., 1991)

Asplenia, where there is no function of the spleen.

Complement deficiency is where the function of the complement

system is deficient.

In reality, immunodeficiency often affects multiple components, with

notable examples including severe combined immunodeficiency (which is

primary) and acquired immune deficiency syndrome (which is secondary).

(Lebranchu et al., 1991)

1.2.3 Primary immunodeficiency:

A number of rare diseases feature a heightened susceptibility to infections

from childhood onward. Primary Immunodeficiency is also known as

22

congenital immunodeficiencies. Many of these disorders are hereditary and

are autosomal recessive or X-linked. There are over 80 recognized primary

immunodeficiency syndromes; they are generally grouped by the part of the

immune system that is malfunctioning, such as lymphocytes or granulocytes.

The treatment of primary immunodeficiency depends on the nature of the

defect, and may involve antibody infusions, long-term antibiotics and (in

some cases) stem cell transplantation. (Wedgwood et al., 1995)

1.2.4 Secondary immunodeficiency:

Secondary immunodeficiency, also known as acquired immunodeficiency,

can result from various immunosuppressive agents, for example,

malnutrition, aging and particular medications (e.g. chemotherapy, disease-

modifying antirheumatic drugs, immunosuppressive drugs after organ

transplants, glucocorticoids). For medications, the term immunosuppression

generally refers to both beneficial and potential adverse effects of decreasing

the function of the immune system, while the term immunodeficiency

generally refers solely to the adverse effect of increased risk for infection.

Immunosuppression itself does not cause pathology but does leave the

patient prone to infection. There is no good clinical test to measure the

degree of immunosuppression; the clinician must simply maintain a high

index of suspicion. The consequences of the immune suppression in the

Intensive care unit (ICU) highlight the importance of infection prevention

23

and control, as well as surveillance measures to ensure that appropriate

treatment is implemented safely and quickly.(Lebranchu et al., 1991)

1.2.5 Leukaemia:

Leukemia, is a group of cancers that usually begin in the bone marrow and

result in high numbers of abnormal white blood cells. These white blood

cells are not fully developed and are called blasts or leukemia cells

Diagnosis is typically made by blood tests or bone marrow biopsy. (ER1)

1.2.5.1 Classification of leukemia:

Clinically and pathologically, leukemia is subdivided into a variety of large

groups. The first division is between its acute and chronic forms:

Acute leukemia is characterized by a rapid increase in the number of immature

blood cells. The crowding that results from such cells makes the bone marrow

unable to produce healthy blood cells. Immediate treatment is required in acute

leukemia because of the rapid progression and accumulation of the malignant

cells, which then spill over into the bloodstream and spread to other organs of

the body. Acute forms of leukemia are the most common forms of leukemia in

children.

Chronic leukemia is characterized by the excessive buildup of relatively

mature, but still abnormal, white blood cells. Typically taking months or years

to progress, the cells are produced at a much higher rate than normal, resulting

24

in many abnormal white blood cells. Whereas acute leukemia must be treated

immediately, chronic forms are sometimes monitored for some time before

treatment to ensure maximum effectiveness of therapy. Chronic leukemia

mostly occurs in older people, but can occur in any age group. (ER1)

Additionally, the diseases are subdivided according to which kind of blood cell

is affected. This divides leukemias into lymphoblastic or lymphocytic

leukemias and myeloid or myelogenous leukemias.Combining these two

classifications provides a total of four main categories. Within each of these

main categories, there are typically several subcategories. Finally, some rarer

types are usually considered to be outside of this classification scheme. (ER1)

1.2.5.2 Specific types of leukemia:

A) Acute lymphoblastic leukemia (ALL) is the most common type of leukemia

in young children. It also affects adults, especially those 65 and older.

Standard treatments involve chemotherapy and radiotherapy. The survival

rates vary by age: 85% in children and 50% in adults. Subtypes include

precursor B acute lymphoblastic leukemia, precursor T acute lymphoblastic

leukemia, Burkitt's leukemia, and acute biphenotypic leukemia. (ER1)

B) Chronic lymphocytic leukemia (CLL) most often affects adults over the age

of 55. It sometimes occurs in younger adults, but it almost never affects

children. Two-thirds of affected people are men. The five-year survival rate is

75%. It is incurable, but there are many effective treatments. One subtype is

B-cell prolymphocytic leukemia, a more aggressive disease. (ER1)

25

C) Acute myelogenous leukemia (AML) occurs more commonly in adults than

in children, and more commonly in men than women. It is treated with

chemotherapy. The five-year survival rate is 40%, except for APL (Acute

Promyelocytic Leukemia), which has a survival rate greater than 90%.

Subtypes of AML include acute promyelocytic leukemia, acute myeloblastic

leukemia, and acute megakaryoblastic leukemia. (ER1)

D) Chronic myelogenous leukemia (CML) occurs mainly in adults; a very small

number of children also develop this disease. It is treated with imatinib

(Gleevec in United States, Glivec in Europe) or other drugs. (ER1)

1.2.5.3 Acute leukemia:

Acute leukemia is a heterogeneous group of malignancies with varying

clinical, morphologic,immunologic, and molecular characteristics. Many

distinct types are known to carry predictable prognoses and warrant specific

therapy. Distinction between lymphoid and myeloid leukemia, most often

made by flowcytometry, is crucially important. Acute leukemias reflect the

pattern of antigen acquisition seen in normal hematopoietic differentiation,

yet invariably demonstrate distinct aberrant immunophenotypic features.

Detailed understanding of these phenotypic patterns of differentiation,

particularly in myeloid leukemia, allows for more precise classification of

leukemia than does morphology alone (Sexena et al., 2008).

Acute leukemia (AL) displays characteristic patterns of antigen expression,

which facilitate their identification and proper classification by

Immunophenotyping using Flowcytometer (Dalia et al., 2012).

26

Flow cytometric analysis of acute leukemia is interpretive combining the

patterns and intensity of antigen expression to reach a definitive diagnosis.

Gating is critical to isolate the abnormal cells because the leukemic

phenotype should be determined on as pure a population as possible. Most

leukemias involve the analysis of bone marrow. Standard forward and side

scatter gating is not optimal for separating bone marrow cells because of the

overlap between monocytes, blasts, myelocytes, promyelocytes and

metamyelocytes. As the bone marrow cell mature, they express increasing

CD45. Thus, when CD45 is combined with side scatter which separates

lineages based on cytoplasmic complexity, the bone marrow sample is

readily separated into its cellular constituents. Infiltration of the bone

marrow by immature cells or blasts is more easily recognized on CD45

versus side scatter plot than on traditional forward side scatter gating.

Acute myeloid leukemia (AML) is traditionally is sub-classified by

morphology and cytochemistry according to the French-American-British

(FAB) criteria as modified by national cancer institute Sponsored Workshop

that incorporates immunophenotypic data. Although the major role of the

Flowcytometer is to provide immunophenotypic data, cellular morphology

can be examined by both forward side scatter and CD45 side scatter analysis

(Matutes et al., 2006).

The ability of flow cytometer is to identify myeloid versus lymphoid

differentiation approaches 98%. However, the prognostic values of

Immunophenotyping data are controversial. Studies that failed to find

prognostic value for Immunophenotyping generally looked at the correlation

of out-come with individual antigens and did not find clinically useful

associations, although the utility of flow cytometry in defining myeloid

differentiation was confirmed (Matutes et al., 2006).

27

Studies that found correlation with specific phenotypes were generally single

institution results. Three of the four studies showing no correlation were in

children, in whom there is some evidence that the t(8;21) may not carry the

same good prognosis as in adults. Additionally, difference in reagents,

gating and staining techniques, and thresholds for positivity may account for

discrepancy. (Matutes et al., 2006)

Multiparameter flowcytometry is a useful adjunct to morphology and

cytochemistry and it is an invaluable tool in the diagnosis of acute leukemia

(Jolanta et al., 2008).

Flowcytometry of leukemic cells plays essential role in identification of

leukemia cell line, maturation stage and detection of residual disease.

Several advances in flowcytometry, including availability of an expanded

range of antibodies and fluorochromes, improved gating strategies, and

multiparameter analytic techniques, have all dramatically improved the

ability to identify different normal cell populations and recognized

phenotypic aberrancies, even when present in a small proportion of the cells

analyzed ( Harakati et al., 1998)

Acute lymphocytic leukaemia (ALL) is an aggressive neoplasm that has been

defined by the presence of more than 30% lymphoblast in the bone marrow or

peripheral blood in the French- American – British o (FAB) cooperative

group classification system. In the more recently proposed world health

organization (WHO) classification of neoplastic diseases of hematopoietic

and lymphoid tissues or lymphomas, a blast count above 20% is sufficient for

a diagnosis of acute leukemia, although most ALLs have marked

hypercellular marrow composed predominantly of lymphoblast. (Raymond et

al., 2000)

28

Acute lymphocytic leukemia occurs most frequently in children and is the

most common type of leukemia of childhood. Roughly 80% of the cases of

acute leukemia in children but only 20% of cases of acute leukemia in adult

are ALL. Clinical manifestations are often related to the extensive

replacement of the bone marrow with blasts. Examination of the peripheral

blood may give the first indication of this illness. Although the diagnosis of

ALL may be readily apparent in some cases with high peripheral blood blast

counts, other cases may show less specific findings, such as neutropenia,

lymphocytopenia and normocytic normochromic anemia with

reticulocytopenia. An alternative presentation is a leukoerythroblastic picture

with or without eosinophilia (Raymond et al., 2000)

The relation between ALL and lymphoblastic lymphomas has been

extensively studied approximately 80% of the lymphoblastic lymphomas are

precursor T-cell immunophenotype, whereas 85% of cases of ALL have a

precursor B-cell immunophentype. Rare cases of lymphoblastic lymphoma of

precursor B-cell type have been reported. They most frequently involve the

skin, bone, and soft tissues and are associated with a better prognosis than

ALL of the precursor of B-cell type (Raymond et al., 2000)

In contrast with T-cell ALL, lymphoblastic lymphomas of the precursor T-

cell lineage usually have no or minimal peripheral blood or bone marrow

involvement and have normal or minimally decreased levels of hemoglobin,

white blood cells and platelets. Arbitrary criteria such as the presence of more

than 25% blasts in the bone marrow also have been used to distinguish ALL

from other lymphoid neoplasm (Raymond et al., 2000)

29

The advent of modern ancillary techniques for ALL diagnosis evaluation,

morphologic examination and cytochemical staining of well prepared air-

dried bone marrow and peripheral blood smear are critical in the pathologic

diagnosis and classification of acute leukemias. Careful morphologic

examination allows certain differential diagnosis possibilities to be

considered at the very beginning of the diagnosis process so that bone

marrow aspirate specimens can be sent for appropriate laboratory studies.

(Raymond et al., 2000)

1.2.6 The human immunodeficiency virus (HIV):

The human immunodeficiency virus (HIV) is a retrovirus that infects cells of

the immune system, destroying or impairing their function. As the infection

progresses, the immune system becomes weaker, and the person becomes

more susceptible to infections. The most advanced stage of HIV infection is

acquired immunodeficiency syndrome (AIDS). It can take 10-15 years for an

HIV-infected person to develop AIDS; antiretroviral drugs can slow down

the process even further. HIV is transmitted through unprotected sexual

intercourse (anal or vaginal), transfusion of contaminated blood, sharing of

contaminated needles, and between a mother and her infant during

pregnancy, childbirth and breastfeeding (Mutimura et al., 2015; Spits et al.,

2016).

HIV infects vital cells in the human immune system such as helper T cells

(specifically CD4+ T cells), macrophages, and dendritic cells. HIV infection

leads to low levels of CD4+ T cells through three main mechanisms: First,

30

direct viral killing of infected cells; second, increased rates of apoptosis in

infected cells; and third, killing of infected CD4+ T cells by CD8 cytotoxic

lymphocytes that recognize infected cells. When CD4+ T cell numbers

decline below a critical level, cell-mediated immunity is lost, and the body

becomes progressively more susceptible to opportunistic infections (Palencia

et al., 1994; Spits et al., 2016).

Most untreated people infected with HIV-1 eventually develop AIDS. These

individuals mostly die from opportunistic infections or malignancies

associated with the progressive failure of the immune system. HIV

progresses to AIDS at a variable rate affected by viral, host, and

environmental factors; most will progress to AIDS within 10 years of HIV

infection: some will have progressed much sooner, and some will take much

longer. Treatment with anti-retrovirals increases the life expectancy of

people infected with HIV. Even after HIV has progressed to diagnosable

AIDS, the average survival time with antiretroviral therapy was estimated to

be more than 5 years. Without antiretroviral therapy, someone who has

AIDS typically dies within a year (Zloza et al., 2003).

Immunologic monitoring of HIV-infected patients is a mainstay of the

clinical flow cytometry laboratory. HIV infects helper/inducer T

lymphocytes via the CD4 antigen. Infected lymphocytes may be lysed when

new virions are released or may be removed by the cellular immune system.

As HIV disease progresses, CD4-positive T lymphocytes decrease in total

number. The absolute CD4 count provides a powerful laboratory

measurement for predicting, staging, and monitoring disease progression and

response to treatment in HIV-infected individuals. Quantitative viral load

testing is a complementary test for clinical monitoring of disease and is

31

correlated inversely to CD4 counts. However, CD4 counts directly assess the

patient’s immune status and not just the amount of virus. It is likely that both

CD4 T-cell enumeration and HIV viral load will continue to be used for

diagnosis, prognosis, and therapeutic management of HIV-infected persons.

(Gupta et al., 1991)

1.2.7 Introduction to Immunophenotyping:

The proper diagnosis of the malignant diseases was a challenge which

affects the cancer registry and patient care. The progressive increasing in

cancer patient’s number increase the demand for more accurate and rapidly

methods of diagnosis as in the past, the cancer registry was depending on

histopathological examination only (Dafalla et al., 2007).

With the beginning of the twenty century, an intelligible group of advance

methods and instruments had been introduced to the laboratories which care

about the cancer diagnosis, such as molecular techniques, cytogenetics

immunohistochemistry, flow cytometry.. etc. leukemia was and still remains

one of the most cancerous diseases with a critical increasing of patient

number, In addition to the need for very accurate diagnosis tools due to the

diversity of types, different behaviour and the continuous variation of the

management protocols (Dafalla et al., 2007).

The flow cytometer which making a prodigious correlation between the

advantages of the laser technology and the immunological reactions became

32

one of the most powerful and conclusive method helping in the identification

and diagnosis of leukemia. Recently, A large number of medical companies

in the world competed in the production and manufacturing of a wide range

of flow cytometric markers which using in the diagnosis and differentiation

of different types of malignant diseases, therefore, we tried to ordering the

importance of these markers depending on their ability and significantly (

Rowan et al., 1994).

Immunophenotyping is the process used to identify and quantify cells of the

blood, bone marrow and lymph tissues according to their biological lineage

and stage of differentiation as defined by glycoproteins and associated

structures of the cell membrane. These lineage-specific cell surface markers

are produced by the normal genetic program of the cells or by aberrant

expression patterns that are pathologic. The cell markers are designated

according to a standard nomenclature that defines Clusters of Differentiation

(CD) by scientific consensus. They are detected by a process that combines

fluorescently-labeled, monospecific immunological reagents and a flow

cytometer to count and analyze the cell populations. The cells are then

classified by size, marker reactivity, clonality, and proportion. The

procedure is used clinically in diagnosis, prognosis, residual disease

assessment, therapeutic monitoring, and case management of leukemias,

lymphomas, and related conditions. A variety of tissues and body fluids may

be analyzed. To ensure the quality and clinical utility of the interpretations,

all cytometric data are interpreted in the context of a microscopic review of

the specimen, and all interpretations are written by a pathologist or

hematologist (Rowan et al., 1994).

33

1.2.7.1 Immunophenotyping by monoclonal antibodies:

Immunophenotyping in the clinical laboratory is emerging as an

advantageous way to separate and classify leukemic malignancies.

Immunophenotyping involves the use of flow cytometers and

immunofluorescence in order to achieve great sensitivity and specificity for

malignant cells. Monoclonal antibodies specific to the malignant cells of

question play an essential part in this technique. Various fluorescent dyes

and cell panels also must be incorporated into the system. Analysis is done

and statistics are plotted on dot plots that can be read by the CLS to give

helpful insight into the etiology of disease process. Immunophenotyping is a

very powerful tool that has the ability to revolutionize the clinical laboratory

setting. The CLS working in hematology must become aware of and

comfortable with this methodology (Sullivan and Wiggers, 2000 ).

Immunophenotyping encompasses techniques where uniform homogeneous

monoclonal antibodies are produced specific to antigenic determinants

located on the malignant cells of question. The epitopes or antigenic

determinants on the cell's surface are collectively referred to as cluster

designations (CD) or CD binding sites. The antibodies produced will bind to

the epitopes on the cell's surface and therefore become identifiable. For

example, a CD4 antibody will bind to its CD4 epitope on the cell allowing

the cell to be distinguished. The monoclonal antibodies are coupled with

fluorescent dyes and given either a red, green, and/ or orange label. Cells

then are interrogated in the flow cytometer with the data recorded and

distributed on a dotplot (Table 1.1 and 1.2) (Benett, 2005).

34

Table 1.1 Cluster of differentiation of lymphocytes (Benett, 2005)

Type of cell CD Markers

All leukocytes groups CD45+

Stem cells CD34+ CD31+

Granulocyte CD45+, CD15+

Monocytes CD45+, CD14+

T Lymphocytes CD 45+ , CD3+

T Helper cells CD45+, CD3+, CD4+

Cytotoxic T cells CD45+, CD3+, CD8+

B Lymphocytes CD45+, CD19+ or CD45+, CD20+

Natural killer Cells CD16+, CD56+, CD3-

Thrombocyte CD45+, CD61+

Table 1.2 Reactivity of the antibody (E.R2), (E.R3) and (E.R4)

Antibody CD Reactivity

CD3 T cells, primary effusion lymphoma.

35

CD4 T cells (helper/inducer), monocytes, myeloblasts.

CD8 T cells (suppressor/cytotoxic), large granular

lymphocytes.

CD19 B cells, pre B-ALL, subset of AML

Flow cytometry is very effective in distinguishing myeloid and lymphoid

lineages in acute leukemias and minimally differentiated leukemias.

Although most acute myeloid leukemias are difficult to classify by

phenotype alone, flow cytometry can be useful in distinguishing certain

acute myeloid leukemias, such as acute promyelocytic leukemia. Flow

cytometry can also be used to identify leukemias that may be resistant to

therapy. In ALL, phenotype has been shown to correlate strongly with

outcome (Benett, 2005).

Researchers in many scattered laboratories painstakingly identified many

lymphoid surface antigens and developed antibodies to them. So in world

congresses; it would become apparent that antibodies originating from

different labs and bearing different names were marking the same

antigen/molecule. At that point, the antigen would be assigned a cluster

designation, or CD number, meaning that a known cluster of antibodies were

binding to this known antigen. Any of these antibodies might be referred to

by one of several idiosyncratic laboratory names or by the CD number of the

antigen. For example, antibodies that recognize CD20, a characteristic B-cell

36

molecule, might be called alternatively L26, B1, or, by association CD20

(Table 1.1 and 1.2) (Benett, 2005).

1.2.7.2 Advantages of Immunophenotyping:

Immunophenotyping is beneficial clinically because in many situations

variant types of benign and malignant lymphoid cells resemble one another

in routinely stained tissue sections and smears. For example, small cells of

small lymphocytic lymphoma can be morphologically identical to those of

benign small lymphocytes. Along with these applications in the clinical

laboratory, immunophenotyping can involve the processing of a wide variety

of specimens including peripheral blood, bone marrow aspirates, lymph

nodes, body fluids, skin biopsies, and fine needle aspirates (Sullivan and

Wigger,2000)

1.2.8 The flow cytometer:

Flow cytometry is a technique of quantitative single cell analysis. The flow

cytometer was developed in the 1970’s and rapidly became an essential

instrument for the biologic sciences. Spurred by the HIV pandemic and a

plethora of discoveries in hematology, specialized flow cytometers for use in

the clinical laboratory were developed by several manufacturers. The major

clinical application of flow cytometry is diagnosis of hematologic

malignancy, but a wide variety of other applications exist, such as

reticulocyte enumeration and cell function analysis. Presently, more than

40,000 journal articles referencing flow cytometry have been published.

(Scheffold, 2000; Bakke, 2001) (Appendix I )

The technique of analyzing individual cells in a fluidic channel was first

described by Wallace Coulter in the 1950s, and applied to automated blood

37

cell counting. Subsequent developments in the fields of computer science,

laser technology, monoclonal antibody production, cytochemistry, and

fluorochrome chemistry led to the development of the flow cytometer two

decades later. Because the first commercial flowcytometers were large,

complex, expensive, and difficult to operate and maintain, they were

primarily used in the research laboratory. However, the enormous value of

the flow cytometer in the medical and biologic sciences was quickly

appreciated, and its cost and complexity gradually decreased as its analytic

capability increased.

The present “state-of-the art” flow cytometers are capable of analyzing up to

20 parameters (forward scatter, side scatter, 18 colors of immuno-

fluorescence) per cell at rates up to 100,000 cells per second. Automation

and robotics is increasingly being applied to flow cytometry to reduce

analytic cost and improve efficiency (Bakke, 2001).

(Cyto = cell), (metry = measurement). Measuring properties of cells in a

flowing system. Sorting or physically separating cells based on properties

measured in a flowing system. A beam of light (usually laser light) of a

single wavelength is directed onto a hydrodynamically-focused stream of

fluid. A number of detectors are aimed at the point where the stream passes

through the light beam: one in line with the light beam (Forward Scatter or

FSC) and several perpendicular to it (Side Scatter (SSC) and one or more

fluorescent detectors) (figure.1.1 and figure. 1.2) (Sullivan and Wiggers,

2000).

Each suspended particle from 0.2 to 150 micrometers passing through the

beam scatters the light in some way, and fluorescent chemicals found in the

38

particle or attached to the particle may be excited into emitting light at a

longer wavelength than the light source. This combination of scattered and

fluorescent light is picked up by the detectors, and, by analyzing fluctuations

in brightness at each detector (one for each fluorescent emission peak), it is

then possible to derive various types of information about the physical and

chemical structure of each individual particle (Sullivan and Wiggers, 2000).

1.2.8.1 General Principles:

Flow cytometry is a quantitative single cell analysis, and defined as the

measurement of cellular properties as they travel in a fluid medium past a

stationary set of detectors. Flow cytometry encompasses the theory of

channeling cells or cell particles in a single file through a sensing area where

they can be typed, sorted, and separated. Cells are suspended in a fluid

medium and are transported to a flow tip, where they are surrounded by a

sheath fluid. The sheath and sample stream both leave the flow chamber

through a small orifice producing laminar flow which allows for single file

alignment of cells (Figure1.1) (Sullivan and Wiggers, 2000).

The cells then reach the interrogation point where a very narrow laser light

scatters both in a forward angle and a 90-degree right angle. Forward angle

light scatter gives information regarding particle size whereas right angle

light scatter relays output to the internal features of the cell or its

granularity.The cells are stained with a fluorescent dye which binds

specifically to cellular constituents to further enhance the measurement of

39

the cells and their properties. The dyes are excited by a laser beam and emit

light at a longer wavelength, which is then received by a detector that

records and stores the data. The first fluorescence-based flow cytometry

device (ICP 11) was developed in 1968 by Wolfgang Göhde from the

University of Münster, Germany (Sullivan and Wiggers, 2000).

Inside a flow cytometer, cells in suspension are drawn into a stream created

by a surrounding sheath of isotonic fluid that creates laminar flow, allowing

the cells to pass individually through an interrogation point. At the

interrogation point, a beam of monochromatic light, usually from a laser,

intersects the cells. Emitted light is given off in all directions and is collected

via optics that direct the light to a series of filters and dichroic mirrors that

isolate particular wavelength bands. The light signals are detected by

photomultiplier tubes and digitized for computer analysis. Figure1.1 is a

schematic diagram of the fluidic and optical components of a flow

cytometer. The resulting information usually is displayed in histogram or

two-dimensional dot-plot formats (Brown and Wittwer, 2000).

40

Figure 1.1 A single cell suspension is hydrodynamically focused with

sheath fluid to intersect an argon-ion laser. Signals are collected by a

forward angle light scatter detector, a side-scatter detector (1), and multiple

fluorescence emission detectors (2–4). The signals are amplified and

converted to digital form for analysis and display on a computer screen.

(Brown and Wittwer, 2000).

41

Figure 1.2 Light-scattering properties of a cell correlated measurements of

FSC and SSC can allow for differentiation of cell types in a heterogeneous

cell population. Major leukocyte subpopulations can be differentiated using

FSC and SSC. (Brown and Wittwer, 2000).

Flow cytometry measures optical and fluorescence characteristics of single

cells (or any other particle, including nuclei, microorganisms, chromosome

Instrument Parameters

YLaser

Side Scatter (SSC)

90° deflection

~ Cell structures

Forward Scatter

(FSC)

< 10° deflection

~ Cell size

Fluorescence Intensity

Antigen Density

42

preparations, and latex beads). Physical properties, such as size (represented

by forward angle light scatter) and internal complexity (represented by right-

angle scatter) can resolve certain cell populations. Fluorescent dyes may bind

or intercalate with different cellular components such as DNA or RNA

(Table 1.3) (Brown and Wittwer, 2000).

Additionally, antibodies conjugated to fluorescent dyes can bind specific

proteins on cell membranes or inside cells. When labeled cells are passed by

a light source, the fluorescent molecules are excited to a higher energy state.

Upon returning to their resting states, the fluorochromes emit light energy at

higher wavelengths. The use of multiple fluorochromes, each with similar

excitation wavelengths and different emission wavelengths (or "colors"),

allows several cell properties to be measured simultaneously. Commonly

used dyes include propidium iodide, phycoerythrin, and fluorescein,

although many other dyes are available. Tandem dyes with internal

fluorescence resonance energy transfer can create even longer wavelengths

and more colors (Brown and Wittwer, 2000).

1.2.8.2 Clinical application of flow cytometry:

Table 1.3 Common clinical uses of flow cytometry (Brown and Wittwer, 2000).

43

Field Clinical application Common characteristic measured

Immunology Histocompatibility cross-matching IgG, IgM

Transplantation rejection CD3, circulating OKT3

HLA-B27 detection HLA-B27

Immunodeficiency studies CD4, CD8

Oncology DNA content and S phase of tumors DNA

Measurement of proliferation markers Ki-67, PCNA1

Hematology Leukemia and lymphoma phenotyping Leukocyte surface antigens

Identification of prognostically important

subgroups

TdT, MPO

Hematopoietic progenitor cell

enumeration

CD34

Diagnosis of systemic mastocytosis CD25, CD69

Reticulocyte enumeration RNA

Field Clinical application Common characteristic measured

Autoimmune and alloimmune disorders

Anti-platelet antibodies IgG, IgM

Anti-neutrophil antibodies IgG

Immune complexes Complement, IgG

Feto-maternal hemorrhage quantification Hemoglobin F, rhesus D

44

Blood banking Immunohematology Erythrocyte surface antigens

Assessment of leukocyte contamination

of blood products

Forward and side scatter, leukocyte

surface antigens

Genetic

disorders

PNH CD55, CD59

Leukocyte adhesion deficiency CD11/CD18 complex

1.2.8.3 Fluorescence:

A fluorescent compound absorbs light energy over a range of wavelengths

that is characteristic for that compound. This absorption of light causes an

electron in the fluorescent compound to be raised to a higher energy level.

The excited electron quickly decays to its ground state, emitting the excess

energy as a photon of light. This transition of energy is called fluorescence.

(Mandy et al., 2002)

The range over which a fluorescent compound can be excited is termed its

absorption spectrum. As more energy is consumed in absorption transitions

than is emitted in fluorescent transitions, emitted wavelengths will be longer

than those absorbed. The range of emitted wavelengths for a particular

compound is termed its emission spectrum. The argon ion laser is commonly

used in flow cytometry because the 488-nm light that it emits excites more

than one fluorochrome. One of these fluorochromes is fluorescein

isothiocyanate (FITC). In the absorption spectrum of FITC, the 488-nm line

is close to the FITC absorption maximum. Excitation with this wavelength

will result in a high FITC emission.

45

If the fluorochrome were excited by another wavelength within its

absorption spectrum, light emission of the same spectrum would occur but it

would not be of the same intensity (Mandy et al., 2002)

More than one fluorochrome can be used simultaneously if each is excited at

488 nm and if the peak emission wavelengths are not extremely close to

each other. The combination of FITC and phycoerythrin (PE) satisfies these

criteria. Although the absorption maximum of PE is not at 488 nm, the

fluorochrome is excited enough at this wavelength to provide adequate

fluorescence emission for detection. More important, the peak emission

wavelength is 530 nm for FITC and 570 nm for PE. These peak emission

wavelengths are far enough apart so that each signal can be detected by a

separate detector. The amount of fluorescent signal detected is proportional

to the number of fluorochrome molecules on the particle (Mandy et al.,

2002).

1.2.8.4 Sample staining:

Sample staining should be carried out as soon as possible after the nucleated

cell suspension has been prepared. Delaying this step will only reduce

viability and induce cell clumping, especially if the tubes holding the cell

suspensions are stored in an upright position. With the exception of cases

with low cell yield, a portion of the cell suspension should be kept aside for

potential repeats or add-on testing (Doyen et al., 2007).

46

1.2.8.5 Surface antigen:

The multicolour direct immunofluorescence-staining technique using

commercially available antibodies is employed for the simultaneous

detection of multiple cell surface markers. Cell surface antigen staining is

performed on viable unfixed cells. Appropriate isotype controls are included.

The usual number of cells recommended for immunostaining is 106 cells for

each test (i.e., each tube of antibody reagent cocktail). In situations with low

cell yield, it is possible to perform the staining with a few as 1 X 105 to 2 X

105 cells/tube. however. The procedure should be carried out gently so as to

minimize any further loss. (Figure 1.3) (Doyen et al., 2007).

47

Figure 1.3 Cell subpopulations based on FSC vs. SSC (Green population is

lymphocytes, red population is monocytes and blue population is

granulocytes) (Mandy et al., 2002).

48

1.2.8.5.1 Clusters of differentiation or CD markers:

It is impossible to distinguish between T cells and B cells in a peripheral

blood smear. Normally, flow cytometry testing is used for specific

lymphocyte population counts. This can be used to specifically determine

the percentage of lymphocytes that contain a particular combination of

specific cell surface proteins, such as immunoglobulins or cluster of

differentiation (CD) markers (Table 1.1 and 1.2) (Richard et al., 2006)

The cluster of differentiation (CD) is a protocol used for the identification

and investigation of cell surface molecules present on leukocytes. CD

molecules can act in numerous ways, often acting as receptors or ligands

(the molecule that activates a receptor) important to the cell. A signal

cascade is usually initiated, altering the behavior of the cell (Table 1.1).

Some CD proteins do not play a role in cell signaling, but have other

functions, such as cell adhesion. There are approximately 250 different

proteins (Benett, 2005)

1.2.8.5.2 Cell markers:

The CD system is commonly used as cell markers, allowing cells to be

defined based on what molecules are present on their surface. These markers

are often used to associate cells with certain immune functions. While using

one CD molecule to define populations is uncommon, combining markers

has allowed for cell types with very specific definitions within the immune

system. CD molecules are utilized in cell sorting using various methods

including flow cytometry. Cell populations are usually defined using a '+' or

a '–' symbol to indicate whether a certain cell fraction expresses or lacks a

CD molecule. For example, a "CD34+, CD31–" cell is one that expresses

49

CD34, but not CD31. This CD combination typically corresponds to a stem

cell, opposed to a fully-differentiated endothelial cell (Table 1.1) (Kassa et

al., 1999).

Two commonly-used CD molecules are CD4 and CD8, which are, used as

markers for helper and cytotoxic T cells, respectively. These molecules are

defined in combination with CD3+, as some other leukocytes also express

these CD molecules (some macrophages express low levels of CD4;

dendritic cells express high levels of CD8). Human immunodeficiency virus

(HIV) binds CD4 and a chemokine receptor on the surface of a T helper cell

to gain entry. The number of CD4 and CD8 T cells in blood is often used to

monitor the progression of HIV infection (Kassa et al., 1999).

1.2.8.5.3 CD Nomenclature:

The CD nomenclature was proposed and established in the first International

Workshop and Conference on Human Leukocyte Differentiation Antigens

(HLDA), which was held in Paris in 1982. This system was intended for the

classification of the many monoclonal antibodies (mAbs) generated by

different laboratories around the world against epitopes on the surface

molecules of leukocytes (white blood cells). Since then, its use has expanded

to many other cell types, and more than 320 CD unique clusters and

subclusters have been identified. The proposed surface molecule is assigned

a CD number once two specific monoclonal antibodies (mAb) are shown to

bind to the molecule. If the molecule has not been well-characterized, or has

only one mAb, it is usually given the provisional indicator "w" (as in

"CDw186") (Benett, 2005).

50

Most of these antibodies are against surface proteins that are not only often

associated with particular cell lineages, but vary in expression with

maturation, and thus are referred to as differentiation antigens ( Table 1.1

and 1.2) (Benett, 2005). The CD system is commonly used as cell markers;

this allows cells to be defined based on what molecules are present on their

surface. These markers are often used to associate cells with certain

immune functions or properties. While using one CD molecule to define

populations is uncommon (though a few examples exist), combining

markers has allowed for cell types with very specific definitions within the

immune system. (Table 1.1) (Benett, 2005).

1.2.8.5.4 T cells CD markers

T cells express a surface molecule called CD3. Expression of the

CD3 surface marker is specific for T lymphocytes, and is often used to

characterize T cells. Like B cells, T lymphocytes express an antigen specific

surface receptor (TcR). Peripheral alpha/beta TcR positive cells express

either the CD4 or the CD8 surface marker, and these markers can be used to

define the major sub populations of mature T cells in the periphery

(Table 1.1) (Figure 1.4 and 1.5).

The CD4+ T lymphocytes represent the "helper" T cell population. The

CD8+ T lymphocytes represent the antigen specific cytotoxic T lymphocytes

(CTL), which respond to and kill cells which are infected with intracellular

pathogens such as viruses, some intracellular bacteria (e.g. Listeria ) and

some intracellular protozoa (e.g. malaria parasites). In contrast to

immunoglobulin on B cells or soluble antibody molecules, T cells do not

recognize free soluble or surface bound antigen, but require the antigen to be

51

processed and presented by an "antigen presenting cell". T lymphocytes (via

their TcR) recognize antigen in the form of short peptides presented in

association with "self" class I or class II major histocompatibility complex

(MHC) molecules at the surface of an antigen presenting cell (APC) ( Figure

1.4 and 1.5) (Chisari et al., 2005).

1.2.8.5.5 Helper T cell CD4

T helper cells (TH cells) assists other white blood cells in immunologic

processes, including maturation of B cells into plasma cells and activation of

cytotoxic T cells and macrophages, among other functions. CD4+ T cells

bind an epitope consisting of an antigen fragment lying in the groove of a

class II histocompatibility molecule. CD4+ T cells are essential for both the

cell-mediated and antibody-mediated branches of the immune system

(Figure 1.4) (Chisari et al., 2005).

These cells are also known as CD4+ T cells because they express the CD4

protein on their surface. Helper T cells become activated when they are

presented with peptide antigens by MHC class II molecules that are

expressed on the surface of Antigen Presenting Cells (APCs). Once

activated, they divide rapidly and secrete small proteins called cytokines that

regulate or assist in the active immune response. These cells can differentiate

into one of several subtypes, including TH1, TH2, TH3, TH17, or TFH,

which secrete different cytokines to facilitate different types of immune

responses. The mechanism by which T cells are directed into a particular

subtype is poorly understood, though signalling patterns from the APC are

thought to play an important role (Figure 1.4) (Chisari et al., 2005).

52

1.2.8.5.6 Cytotoxic T cell CD8

A cytotoxic T cell (also known as TC, CTL, T-Killer cell, cytolytic T cell,

CD8+ T-cells or killer T cell) belongs to a sub-group of T lymphocytes (a

type of white blood cell). Activated or "turned on" cytotoxic T cells

circulate in the blood and lymphatic fluid looking for cells that contain

foreign particles (antigens), attach to them and inject a toxic chemical (

figure 1.5) (Chisari et al., 2005).

The cytotoxic T cells become activated when they come into contact with

antigen fragments that are attached to specific types of protein. these cells

are capable of inducing the death of infected somatic or tumor cells; they kill

cells that are infected with viruses (or other pathogens), or are otherwise

damaged or dysfunctional. Most cytotoxic T cells express T-cell receptors

(TcRs) that can recognize a specific antigenic peptide bound to Class I MHC

molecules, present on all nucleated cells, and a glycoprotein called CD8,

which is attracted to non-variable portions of the Class I MHC molecule.

The affinity between CD8 and the MHC molecule keeps the TC cell and the

target cell bound closely together during antigen-specific activation. CD8+ T

cells are recognized as TC cells once they become activated and are generally

classified as having a pre-defined cytotoxic role within the immune system (

figure 1.5) (Chisari et al., 2005).

53

Figure 1.4 Helper T cell Figure 1.5 cytotoxic T cell

(ER5)

1.2.8.5.7 B cell CD 19

The developmental process that results in production of plasma cells and

memory B cells can be divided into three broad stages: generation of mature,

immunocompetent B cells (maturation), activation of mature B cells when

they interact with antigen, and differentiation of activated B cells into

plasma cells and memory B cells. In many vertebrates, including humans

and mice, the bone marrow generates B cells. This process is an orderly

sequence of Ig-gene rearrangements, which progresses in the absence of

antigen. This is the antigen independent phase of B-cell development

(Rothenberg, 2000).

54

A mature B cell leaves the bone marrow expressing membrane-bound

immunoglobulin (Ig M and IgD) with a single antigenic specificity. These

naive B cells, which have not encountered antigen, circulate in the blood and

lymph and are carried to the secondary lymphoid organs, most notably the

spleen and lymph nodes. If a B cell is activated by the antigen specific to its

membrane-bound antibody, the cell proliferates (clonal expansion) and

differentiates to generate a population of antibody-secreting plasma cells and

memory B cells. In this activation stage, affinity maturation is the

progressive increase in the average affinity of the antibodies produced and

class switching is the change in the isotype of the antibody produced by the

B cell from µ to γ, α, or ε. Since B cell activation and differentiation in the

periphery require antigen, this stage comprises the antigen dependent phase

of B-cell development (Rothenberg, 2000).

Many B cells are produced in the bone marrow throughout life, but very few

of these cells mature. The size of the recirculating pool of B cells is about 2

X 108 cells. Most of these cells circulate as naive B cells, which have short

life spans (half lives of less than 3 days to about 8 weeks) if they fail to

encounter antigen or lose in the competition with other B cells for residence

in a supportive lymphoid environment. Given that the immune system is

able to generate a total antibody diversity that exceeds 109, clearly only a

small fraction of this potential repertoire is displayed at any time by

membrane immunoglobulin on recirculating B cells. Indeed, throughout the

life span of an animal, only a small fraction of the possible antibody

diversity is ever generated (Rothenberg, 2000).

55

The ability of the B cell to respond in a specific, yet sensitive manner to the

various antigens is achieved with the use of low-affinity antigen receptors.

This gene encodes a cell surface molecule which assembles with the antigen

receptor of B lymphocytes in order to decrease the threshold for antigen

receptor-dependent stimulation (Ishikawa et al., 2003).

Cluster of differentiation 19 is a human proteins encoded by the CD19 gene.

Lymphocytes proliferate and differentiate in response to various

concentrations of different antigens. CD19 is the earliest B specific protein

expressed on B cells, but that it is lost as the activated B cell becomes a

plasma cell. It primarily acts as a B cell co receptor in conjunction with

CD21 and CD81.Upon activation the cytoplasmic tail of CD19 becomes

phosphorylated which leads to binding by Src family kinases and

recruitment of P1-3 kinase (figure 1.6) (Ishikawa et al., 2003).

CD19 is a 95 kilo Dalton (KDA) transmembrane protein consisting of two

extracellular immunoglobulin (Ig)-like domains and an extensive

cytoplasmic tail containing numerous tyrosine residues. The cytoplasmic tail

is physically associated with a family of protein tyrosine kinases, namely

Lyn, Lck, Fyn, and Blk that couple CD19 to downstream signaling

pathways. This receptor is not shed from the cell surface and undergoes

antibody-induced internalization. Upon activation, the cytoplasmic tail of

CD19 becomes phosphorylated which leads to binding by Src-family kinases

and recruitment of PI-3 kinase (figure 1.6) (Fujimoto et al., 2000).

56

The CD19 cell surface antigen is a B-lineage specific receptor that is

expressed on the surface of leukemia cells in >90% of children and adults

with acute lymphoblastic leukemia (ALL). It is also expressed on tumor

cells of patients with B-cell Non-Hodgkin's lymphoma (NHL), and chronic

lymphocytic leukemia (CLL). Flow cytometric analysis of marrow

specimens obtained from patients with B-lineage leukemia has demonstrated

that there are > 50,000 molecules per cell. (Kaleem et al., 2003)

Figure 1. 6 B cell

(ER 5)

57

1.2.8.5.8 Natural killer cells:

The natural killer cell was first described in 1976, when it was shown that

the body contains a small population of large, granular lymphocytes that

display cytotoxic activity against a wide range of tumor cells in the absence

of any previous immunization with the tumor. NK cells were subsequently

shown to play an important role in host defense both against tumor cells and

against cells infected with some, though not all. In some cases, an NK cell

employs NK cell receptors to distinguish abnormalities, notably a reduction

in the display of class I MHC molecules and the unusual profile of surface

antigens displayed by some tumor cells and cells infected by some viruses

(Richard et al., 2006).

Another way in which NK cells recognize potential target cells depends

upon the fact that some tumour cells and cells infected by certain viruses

display antigens against which the immune system has made an antibody

response, so that antitumor or antiviral antibodies are bound to their

surfaces. Because NK cells express CD16 and CD56, a membrane receptor

for the carboxyl-terminal end of the IgG molecule, called the Fc region, they

can attach to these antibodies and subsequently destroy the targeted cells.

This is an example of a process known as antibody-dependent cell mediated

cytotoxicity (ADCC) (Richard et al., 2006).

58

1.2.8.6 Flow cytometric immunophenotyping for the diagnosis and

monitoring of hematologic neoplasms:

Flow cytometric immunophenotyping evaluates individual cells in

suspension for the presence and absence of specific antigens (phenotype)

(Craig et al., 2008). In the assessment for hematologic malignancies, several

steps are taken in the application and interpretation of this

immunophenotypic information: (1) identification of cells from different

lineages and determination of whether they are mature or immature, such as

detection of mature B-lymphoid cells and myeloblasts; (2) detection of

abnormal cells through identification of antigen expression that differs

significantly from normal; (3) detailed documentation of the phenotype of

abnormal cell populations (ie, the presence or absence of antigens) and, in

comparison to their normal cell counterpart, documentation of increased or

decreased intensity of staining by fluorochrome labeled antibodies; (4)

evaluation of whether the information available is diagnostic of a distinct

disease entity and, if not, development of a list of possible entities with

suggestion of additional studies that might be of diagnostic value such as

immunohistochemistry, conventional cytogenetic, fluorescence in situ

hybridization (FISH), and molecular diagnostic studies; and (5) provision of

immunophenotypic information that might be of additional prognostic value,

including the identification of targets for potential directed therapy.

(Craig et al., 2008)

59

When a specimen is received for flow cytometric testing, a decision is made

regarding the cell lineages and antigens to be evaluated that is based on the

type of specimen and other available information, such as the medical

indication for testing listed on the requisition, clinical history, morphologic

findings, history of prior flow cytometric testing, results of other laboratory

testing, and possibly results of any preliminary screening testing performed

in the flow cytometric laboratory (Craig et al., 2008). For the medical

indications identified by the 2006 Bethesda group, consensus was reached

on the cell lineages that should be evaluated and the antigens to include in a

primary evaluation of each lineage (Wood et al., 2007).

In addition, general recommendations were made on the approach used to

evaluate these antigens by flow cytometry (Wood et al., 2007). Using this

approach, flow cytometric immunophenotyping of clinical specimens can

provide a rapid screen for hematologic neoplasms and play a key role in

diagnosis and classification (Craig et al., 2008).

1.2.8.7 Flowcytometry gating system:

A subset of data can be defined through a gate. A gate is a numerical or

graphical boundary that can be used to define the characteristics of particles

to include for further analysis. For example, in a blood sample containing a

mixed population of cells, you might want to restrict your analysis to only

the lymphocytes. Based on FSC or cell size, a gate can be set on the FSC vs.

SSC plot to allow analysis only of cells the size of lymphocytes. The

resulting display would reflect the fluorescence properties of only the

lymphocytes. (Bergeron et al., 2002)(Figure 1.7)(Appendix II, III, IV and

V).

60

Figure 1.7 : Histogram A showing forward scatter (cell size) and side

scatter (cell granulation) of normal peripheral sample. Histogram B

showing gated cells in lymphocyte region present on histogram A.

Histogram C showing the scatter of cells according to CD3/CD4 markers

(ungating). Histogram D is showing the scatter of cells according to CD3-

CD4 marker in specific type of cells (lymphocyte gating region).

(Bergeron et al., 2002)

A B

D C

61

1.2.9 Previous studies:

1.2.9.1 The normal reference ranges of lymphocytes percentages and

absolute count in some countries:

With regard to lymphocytes percentages, a study in India Sexana et al.,

2003 showed that the percentages of CD3, CD4, CD8, CD4/CD8 and CD19

were 68.65%, 37.10%, 34.04%, 1.2% and 14.67% respectively, while in

Oman the percentages were 68.53%, 40.40 %, 25.8% , 1.6% and 13.7%

respectively Al Jabri et. al.,2005. Relatively low percentages for CD3

(54.9%), CD8 (11.5%) and CD19 (4.7%) were reported by Bisset et al.,

2004 in Switzerland.

As far as the absolute count (mean± SD) is concerned, Al Qouzi et al., 2002

in Saudi Arabia reported 1618 ± 547, 869 ± 310, 615± 276, 1.6 ± 0.7, 230±

130 and 262± 178 for CD3, CD4, CD8, CD4 / CD8 for T cell, CD19 B cell

and CD16 NK cells respectively. In Oman Al Jabri et al., 2005 reported

1701± 489 for CD3, 1006 ± 319 for CD4 ,638± 225 for CD8, 1.6± 0.8 for

CD4/CD8, 349± 158 for CD19 and 221±115 for CD16. While in Turkey,

Yaman et al., 2005 reported 1680±528, 1095±391, 669±239, 1.68±0.43 and

254 ± 122 counts respectively. In Ethiopia an absolute count was made for

all lymphocytes except T cell CD3 by Tsegaye et.al., 1999 their study

showed counts of 753±227, 777± 362, 1.1±0.4 and 184±96 for CD4, CD8,

CD4/CD8 and CD19 B cells respectively. A similar study was performed in

Saudi Arabia by Shahabuddin et. al.,1996. The study illustrated counts of

880 ±270, 890±290, 1.1±0.3 and 290±90 respectively. In Dutch land

Tsegaye et al., 1999 results revealed 993±319, 506±220, 2.2±1.0 and

313±147 respectively.

62

1.3 Justification

Sudanese immunocompromised patients are group of patients with a special

nature because of their high susceptibility to infection than others. Factors

which help in that are the nature of Sudanese environment, the diversity of

the Sudanese atmosphere and climate. All these factors play major role in

the spreading and transmission of infection along with the low level of

protection and sterilization facilities in some public and private health

clinics. This research will shed light and drew attention to highlight the need

for focusing on the existence of special deal with those patients.

The importance of this study is that it will set base line data for the first time

about the normal values of lymphocytes subset populations for Sudanese

healthy individuals. This also will help in diagnosis, treatment and follow up

of other diseases causing lymphocytosis or lymphocytopenia or alter

immunity, especially HIV infection.

The normal reference value for T, NK and B subset populations (CD3,

CD4, CD8,CD56,CD16 and CD19) nowadays in use for diagnosis of

diseases in Sudan are based on WHO Western or European standards, which

might be different from ours (genetic make up, race, age, gender, geography,

and environmental conditions). Even with the advent of large multicenter

therapeutic trials for the determination of chemotherapeutic efficacy,

individual variability in tumor characteristics often leads to a poor

therapeutic outcome.

63

1.4 Objectives

1.4.1 General objective:

To know the normal ranges for lymphocyte subsets populations (T, B and

NK) of healthy adults in comparison to their ranges in immunocompromised

patients in Sudan.

1.4.2 Specific objectives:

1) To explore the normal ranges of normal T cells (helper and

cytotoxic), B cells and NK lymphocyte in healthy adult Sudanese.

2) To correlate the normal ranges in relation with age and gender.

3) To compare the normal ranges of lymphocyte subsets with ranges

of HIV patients.

4) To compare the differences between the normal range of

lymphocyte subsets in healthy individuals with leukemic patients

under chemotherapeutic treatment.

5) To determine the percentage and absolute count of lymphocytes

main markers (CD3, CD4, CD8, CD19, CD20, CD16 and CD56,)

and CD4:CD8 ratio.

64

2. Materials and Methods

2.1 Study Design:

This descriptive cross sectional hospital and community based study was

carried out in Flowcytometer laboratory during the period from December

2012 to October 2015 in Khartoum state.

2.2 Study population:

The control group was Sudanese normal healthy adult individuals. The other

group was immunocompromised Sudanese patients. The

immunocompromised patients were subdivided into two groups including

the HIV patients and leukemic patients undertaking chemotherapy.

2.2.1. Inclusion criteria:

All adult participants in the control group were selected according to normal

CBC parameter and they were clinically normal. While HIV and leukemic

patients they were selected according to positive serological diagnosis for

HIV patients and by Immunophenotyping for leukemic patients.

2.2.2. Exclusion criteria:

Unhealthy individuals were excluded from the control group. While

leukemic patients who didn’t received the chemotherapy were also excluded.

2.3 Data collection:

Personal and clinical data from all participants were collected

using special form of questionnaire. (Appendix VI)

65

2.4 sample size:

The sample size was determined by the Statistian according to the following

formula:

no = Z2𝛼/2 P (1-P)

E2

α: Significant level ( 0.01 or 0.05).

Z: Z from the table (1.96).

P: presumed probability value. E: error.

After the initial sample size is calculated, it will be used in determining the

final sample size.

n = no

1 + no N

N: Khartoum population

no = (1.96)2 (0.1)(0.9)

(0.05)2

= 138

Final sample size =

n = 138

1+138 8000000

n = 138

The statistian recommended that the size of control group should be

double the size of the immunocompromised group.

i.e. 138 X 2 = 276

Total sample size = 138 + 276 = 414

66

2.4.1 Control group:

Normal Adult healthy Sudanese (n, 300) individuals were randomly selected

from universities students and employees in the Khartoum state.

2.4.2 Immunocompromised group:

Stratified sample: immunocompromised group was divided into:

- HIV patients (n, 75) were selected from HIV centers.

- Leukemic patients under chemotherapy (n, 75) were selected from

Radioactive Isotopes Center Khartoum (RICK).

2.5 Ethical consideration:

The consents from all controls and patients obtained verbally and they

were informed by the objectives of this study and they were accepted to

participate.

2.6 sample collection:

Venous peripheral blood samples were collected in EDTA containers (2.5 ml

of blood).

2.7 Hematological analysis by the sysmex:

2.7.1 Principle and procedures:

All blood cells count (Complete blood counts (CBC)) was done by a

Hematology analyzer (Sysmex) (Appendix VII), which performs blood cell

count by DC detection method, DC detection method:

Blood sample was aspirated, measured to a predetermined volume, diluted

at the specified ratio, and then fed into each transducer. The transducer

67

chamber has a minute hole called the aperture, on both side of the aperture,

there are the electrodes between which flows direct current.

Blood cell suspended in the diluted sample pass through the aperture,

causing direct current resistance to change between the electrodes. As

direct current resistance changes, the blood cell size is detected as electric

pulses. Blood cell count is calculated by counting the pulses, and histogram

of blood cell sizes is plotted by determining the pulse sizes. Also, analyzing

a histogram makes it possible to obtain various analysis data.

Non – Cyanide hemoglobin analysis method:

To analyze hemoglobin by automated methods, the cyanmethemoglobin

method or oxyhemoglobin method have so far been the main stream.

Cyanmethemoglobin method was recommended as international standard

method in 1966 by ICSH (international committee for standardization in

hematology).

2.8 Immunophenotyping by the flow cytometry:

2.8.1 General principle of Flowcytometer:

(Cyto = cell), (metry = measurement). Measuring properties of cells in a

flowing system. Sorting or physically separating cells based on properties

measured in a flowing system. A beam of light (usually laser light) of a

single wavelength is directed onto a hydrodynamically-focused stream of

fluid. A number of detectors are aimed at the point where the stream passes

through the light beam: one in line with the light beam (Forward Scatter or

FSC) and several perpendicular to it (Side Scatter (SSC) and one or more

fluorescent detectors) (Sullivan and Wiggers, 2000).

68

2.8.2 Sample preparation:

100 µl of anticoagulated (EDTA) blood was transferred to 12 Χ 75 mm

polystyrene test tube. (106 cells).

20 µl of antibody was added and mixed gently with vortex mixer.

20 µl of negative control (Code No. ISOCONTFITCIGG2a) antibody

was added and mixed gently with vortex mixer (control tube).

Both tubes were Incubated in the dark place at room temperature at (20-

25C) for 15 minutes.

1.5 ml of lysing Solution was added to each sample and mixed gently

with a vortex mixer. Then were incubated for 10 minutes at room

temperature in the dark place.

The tubes were centrifuged at 1500 RPM for 5 minutes. Gently aspirated

the supernatant and discarded it and leaving approximately 50 µl of fluid.

2 ml (0.01 mol/l Phosphate buffer saline (PBS)) was added and

resuspended the cells by using vortex mixer.

The tubes were centrifuged at 1500 RPM for 5 minutes. Gently aspirated

the supernatant and discard it leaving approximately 50 µl of fluid.

The pellet was resuspended in an appropriate fluid for flow cytometry.

Then the samples were analyzed on a flow cytometer.

69

2.9 Interpretation of the results:

The results of analysis by sysmex and flow cytometry were interpreted by

two hematologists.

2.10 statistical analysis:

Data were entered and analyzed by using SPSS 16 statistical software. The

mean and standard deviation (SD) were calculated for each marker.

Descriptive statistical analysis was performed on the data collected using 1-

way analysis of variance to calculate descriptive statistics.

70

3. Results

3.1 Healthy controls:

A total of 300 healthy adult Sudanese individuals were randomly selected

during the study period. Among them 149 (49.7 %) were males and 151

(50.3 %) were females (Table 3.1). Their ages ranged from 18 to 80 years

with a mean of 30 years (Figure 3.1.).The frequency of 179 (59.7%) were

less than 30 or equal 30 years, while frequency of 121 (40.3%) their ages

were more than 30 years. (Table 3.2)

3.1.2 Percentages and absolute counts of lymphocytes subsets:

The range (percentages) of lymphocytes was 25% to 43%. The range

(absolute counts) of lymphocytes was found to be 1298 to 2926 cells/ µl. The

mean percentages of lymphocytes in males were 34% ± 9.6. The mean

percentages of lymphocytes in females were 35% ± 8.6. There were no

statistically significant differences between males and females and ages

below 30 and above or equal 30 years (p ≥ 0.05) (Table 3.3).

71

Table 3.1: Distribution of study population according to gender.

Gender

Leukemic Patients

Healthy Controls

HIV Patients

Frequency

Percentage

Frequency

Percentage

Frequency

Percentage

Male

40

53.3

149

49.7

41

54.7

Female

35

46.7

151

50.3

34

45.3

Total

75

100%

300

100%

75

100%

72

Table 3.2: Distribution of study population according to age group.

Age

(years)

Leukemic Patients

Healthy Controls

HIV Patients

Frequency

Percentage

Frequency

Percentage

Frequency

Percentage

≤30

22

29.3

179

59.7

16

21.3

>30

53

70.7

121

40.3

59

78.7

Total

75

100%

300

100%

75

100%

73

Figure 3.1 The mean age of the studied groups.

Group

Leukemic p.HIVcontrol

Mea

n A

GE

44

42

40

38

36

34

32

30

28

42

38

30

74

Table 3.3: The Normal ranges (Percentage and Absolute Count) of (TWBCs, Lymphocytes and T Cells) of

healthy adult population

*Abs: Absolute count, * % : percentage count. *TWBCs: Total white blood cell count * CD : cluster of differentiation

Gender

and Age

TWBCs Lymphocytes T cell T helper cell T cytotoxic cell

%

Abs

(cells/ µl )

CD3% CD3 Abs

(cells/ µl)

CD 4% CD 4 Abs

(cells/µl )

CD 8% CD 8 Abs

(cells/ µl)

Male ≤30 3608 – 8190 25 – 43 1196 – 2746 51 – 79 681 - 1884 29 -47 427 – 1041 16 - 31 241 -672

Male >30 4594 – 8347 23 – 44 1155 – 3281 54 – 76 679 - 2215 29 - 45 346 – 1292 17 - 33 263 – 816

Female ≤30 4469 – 7960 27 – 45 1455 – 2950 50 – 78 839 - 2010 29 - 49 482 – 1255 15 - 29 255 – 723

Female >30 4474 – 8519 25 – 39 1387 – 2729 52 – 76 816 - 1811 30 - 49 456 – 1178 17 - 30 285 – 675

Mean lower

and upper

limit 4286 - 8254 25 - 43 1298 – 2927 52 - 77 754 - 1980 29 - 48 428 – 1192 16 - 31 261 – 722

75

3.1.2.1 Total mean percentages and absolute count for T cell sub

populations (CD3, CD4 and CD8):

The mean percentages and mean absolute count of lymphocytes sub-

populations of 300 healthy individuals were obtained as follows: CD3

(64.5 % ± 12.8) ( 1368.9 ± 620.5), CD4 (38.3 % ± 9.1 ) (810.5 ±

383.8), CD8 (23.4 % ± 7.2) (489.3 ± 233.3). The CD4/CD8 ratio was

found to be (1.9 ± 0.8) (Table3.3) (Figure 3.2).

3.1.2.1.1 T cells (CD3):

The mean percentage and absolute count for CD3 of 300 healthy controls

were found to be (64.5 % ± 12.8) (1368.9 ± 620.5) respectively. However

insignificant differences for CD3 mean percentages and absolute counts

between male (149) and females (151) were recorded (P > 0.05) (Table 3.3)

(Figure 3.2).

3.1.2.1.2 T Helper cell (CD4):

There was no significant difference between males and females with regard

to the mean percentage and absolute count of T helper cell CD4 (P > 0.05).

The mean percentage and absolute count for T helper cell CD4 of healthy

controls (n, 300) were found to be (38.3 % ± 9.1) (810.5 ± 383.8),

respectively (Table 3.3) (Figure 3.2, Figure 3.3 and Figure 3.4).

76

3.1.2.1. 3 T Cytotoxic cell (CD8):

This study obtained mean percentage and an absolute count for T

Cytotoxic cell CD8 for healthy controls (n, 300) to be ( 23.4 % ± 7.2)

(489.3 ± 233.3), respectively. The differences between males and females

were insignificant (P > 0.05) (Table3.3) (Figure 3.2, Figure 3.3 and Figure

3.4).

3.1.2.2 Total mean percentages and absolute count for B cells CD

markers (CD19 and CD20):

In consistent with the above results, there is no statistically significant

differences (P > 0.05) with respect to gender or age of healthy controls (n,

300) for the mean total percentages and absolute count for B cell CD

markers(CD19 and CD20). (Table 3.4)

The mean percentage and absolute count for B cell CD19 of 300 healthy

controls were found to be (10.1% ± 5.3) (208.9 ± 140.1), respectively (Table

3.4) (Figure 3.2).

The mean percentage and absolute count for B cell CD20 of 300 healthy

controls were found to be (10.9 % ± 5.7) (223.4 ± 146.3), respectively

(Table 3.4) (Figure 3.2).

77

3.1.2.3 Total mean percentages and absolute count for NK cell CD

markers (CD16 and CD56):

Regarding the NK the mean percentage and absolute counts for CD16 and

CD56 were found to be (10.3 % ± 5.7) (210.7 ± 151.5) and (11.4 % ± 5.7)

(234.5 ± 154.6), respectively (Table 3.4)

This study obtained the normal ranges (percentages) for T, B and NK sub

populations were as follows; T cell CD3 ( 52 – 77 %); T helper cell ( 29 – 48

%); T cytotoxic cell (16 – 31 %), B cell CD19 (5 – 15 %); CD20( 5 – 17%)

and Natural killer cell CD16 (5– 16 %) and CD56 (6 – 18 %)

respectively.(Table 3.3 & Table 3.4) (Figure 3.2).

Moreover, this study obtained a normal ranges (absolute count (cells/ µl))

for lymphocyte sub populations : T cells CD3 (754 - 1980 ) ; T helper cell

(428 - 1192); T cytotoxic cell (261 – 722), B cell CD 19 (70 - 355);

CD20(72 - 388), and NK cells CD16 (57 - 376); CD56 (64 - 405)

respectively. (Table 3.3 & Table 3.4) (Figure 3.5)

78

Figure 3.2 The mean percentage for lymphocytes and

lymphocytes subsets of healthy control.

CD56 %

CD16 %

CD4/CD8 RATIO

CD20 %

CD19 %

CD8 % / LYM

CD4 % / Lym.

CD3 %

LYM. (%)

Mea

n70

60

50

40

30

20

10

0

12101110

23

38

65

34

79

Figure 3.3 The mean percentage of T cells sub populations.

CD4/CD8 RATIOCD8% / T cellCD4 % / Tcell

Mea

n70

60

50

40

30

20

10

0

39

59

80

Figure 3.4 The mean absolute count of T cells sub populations.

CD8.ABS/TcellCD4. ABS / Tcell

Mea

n700

600

500

400

300

365

629

81

Table 3.4 : The Normal ranges (Percentage and Absolute Count) of CD markers

of B Cell and NK Cells for healthy adults

Gender

and Age

B Cells

NK Cell

CD 19% CD 19 Abs

(cells/ µl )

CD 20% CD 20 Abs

(cells/ µl )

CD 16 % CD 16 Abs

(cells/ µl )

CD 56% CD 56 Abs

(cells/ µl )

Male ≤30 5 – 15 68 – 323 5 – 18 76 - 372 5 – 17 53 - 369 4 - 15 55 – 334

Male >30 3 – 15 45 – 359 5 – 16 50 - 433 4 – 14 52 - 364 6 - 17 70 – 424

Female ≤30 6 – 16 86 – 386 6 – 16 97 - 377 5 – 15 70 - 386 6 - 19 100 – 457

Female >30 6 – 15 79 – 353 4 – 16 64 - 370 4 – 17 54 - 386 7 - 19 31 – 405

Mean lower

and upper

limit 5 – 15 70 – 355 5 – 17 72 - 388 5 – 16 57 - 376 6 - 18 64 – 405

*Abs: Absolute count, * % : percentage count. *TWBCs: Total white blood cell count * CD: cluster of differentiation

63

Figure 3.5 The mean absolute count for lymphocytes and

lymphocytes subsets of healthy control.

CD56. ABS

CD16. ABS

CD20. ABS

CD19. ABS

CD8. ABS/LYM

CD4. ABS / LYM

CD3. ABS

LYM. Abs

Mea

n

3000

2000

1000

0246217230213

488

811

1364

2109

83

3.2 Comparison of Leukemic patients under chemotherapy with healthy

controls:

3.2.1 Absolute counts and Percentages of lymphocytes:

It is obviously clear that the mean absolute count of lymphocytes is significantly

increased (P < 0.05) (4276 ± 1469) in leukemic patients than those of healthy

controls (2114 ± 823) (Table 3.5)

However, there is no significant difference (P >0.05) between the mean percentage

of lymphocytes in leukemic patients (32 % ± 23) and healthy controls (34 % ± 9)

(Table 3.5)

A total of 75 Sudanese leukemic patients under chemotherapy were selected.

Among them, 40 (53.3 %) were males and 35 (46.7 %) were females (Table 3.1).

Their ages ranged from 18 to 80 years with a mean age of 42 years (Figure 3.1).

The frequency of 22 (29.3%) were less than 30 or equal 30 years, while the

frequency 53 (70.7 %) their ages were more than 30 years (Table 3.2).

3.2.1.1 Percentages and absolute count for T cell sub populations

(CD3, CD4 and CD8):

The mean percentages and mean absolute count of lymphocytes sub-populations

of 75 leukemic patient were obtained as follows: CD3 (53.2 % ± 24.5) (854 ±

122), CD4 (27.3 % ± 16.8) (474 ± 89), CD8 (21.6 % ± 14.6) (599.8 ± 150). The

CD4/CD8 ratio was found to be (3.2 ± 1.6) (Table 3.5) (Figure 3.6).

84

3.2.1.1.1 T cells (CD3):

Significant differences (P < 0.001) were attained when the mean absolute count

of T cells CD3 of leukemic patients (854 ± 122 ) were compared with those of

healthy controls (1368.9 ± 620.5 ).

Moreover, the differences were significant (P < 0.001) when the mean

percentages of T cells CD3 of leukemic patient (53.2 % ± 24.5) were compared

with those of healthy controls (64.5 % ± 12.8) (Table 3.5) (Figure 3.6 and 3.7).

3.2.1.1.2 T helper cell (CD4):

This research revealed a significant difference (P < 0.001 ) between the mean

percentage of T helper cell CD4 of normal healthy controls (38.3 % ± 9.1) and

those of leukemic patients ( 27.3 % ± 16.8). Moreover, the mean absolute count

for T helper cell CD4 of normal healthy controls (810.5 ± 383.8) was significantly

different from those of leukemic patients (474 ± 89) (Table 3.5) (Figure 3.6 and

3.7).

3.2.1.1.3 T cytotoxic cell (CD8):

Statistically insignificant differences (P > 0.05) were found in this study

concerning the mean parentages and absolute count of T cytotoxic cell CD8 of

leukemic patients (21.6 % ± 14.6) (599.8 ± 150.5) in comparison to healthy

controls (23.4% ± 7.2) (489.3 ± 233.3). (Table 3.5) (Figure 3.6 and 3.7).

85

Table 3.5: The mean percentages and absolute counts of lymphocytes and

T cells (CD3, CD4 & CD8) of leukemic patients and controls.

CD Markers

Healthy Controls

Mean ± SD

Leukemic patients

Mean ± SD

P value

Lymphocyte %

34 ± 9

32 ± 23

0.201

Lymphocyte (cells/ µl)

2114 ± 823

4276 ± 1469

< 0.001

CD 3%

64.5 ± 12.8

53.2 ± 24.5

< 0.001

CD 3 ABS (cells/ µl )

1368.9 ± 620.5

854 ± 122

< 0.001

CD 4% / LYM

38.3 ± 9.1

27.3 ± 16.8

< 0.001

CD 4 ABS (cells/ µl )

810.5 ± 383.8

474 ± 89

< 0.001

CD 8 % / LYM

23.4 ± 7.2

21.6 ± 14.6

0.126

CD 8 ABS (cells/ µl )

489.3 ± 233.3

599.8 ± 150.5

0.383

CD 4/CD8 Ratio 1.9 ± 0.8 3.2 ± 1.6 0.056

86

Figure 3.6 Comparison of the total mean percentage of lymphocytes

and T cells sub population in healthy controls and leukemic patients.

Group

Leukemic p.control

Mea

n

70

60

50

40

30

20

10

0

LYM. (%)

CD3 %

CD4 % / Lym.

CD8 % / LYM

CD4/CD8 RATIO3

2223

27

38

53

65

3234

87

Figure 3.7 Comparison of the total mean absolute counts of

lymphocytes and T cells sub population in healthy controls and

leukemic patients.

Group

Leukemic p.control

Mea

n5000

4000

3000

2000

1000

0

LYM. Abs

CD3. ABS

CD4. ABS / LYM

CD8. ABS/LYM

600488 474

811 854

1364

4276

2109

88

3.2.1.2 Total percentages and absolute count for B cell CD markers

(CD19 and CD20):

This study compared the mean absolute count and the mean percentages for B cell

marker (CD19) of healthy controls (10.1 % ± 5.3 and ABS 208.9 ± 140.1) and

leukemic patients (10.2% ± 2.4; ABS 403 ± 139) and did not obtained significant

differences (P > 0.05). (Table 3.6) (Figure 3.8 and 3.9)

The mean percentage of B cell marker (CD20) didn’t showed a significant

difference (P > 0.05) between healthy controls (10.9 % ± 5.7) and leukemic

patients (13.2 % ± 2.2). However, a significant difference (P < 0.001) was obtained

in B cell CD20 absolute count, regarding healthy controls (223.4 ± 146.3) and

leukemic patients (512 ± 147). (Table 3.6)

89

Table 3.6: The mean percentages and absolute counts of B cells

(CD19 & CD 20) of leukemic patient and controls.

CD Markers

Healthy Controls

Mean ± SD

Leukemic Patients

Mean ± SD

P value

CD 19 %

10.1 ± 5.3

10.2 ± 2.4

0.930

CD19ABS

(cells/ µl )

208.9 ± 140.1

403 ± 139

0.08

CD 20 %

10.9 ± 5.7

13.2 ± 2.2

0.67

CD20 ABS

(cells/ µl )

223.4 ± 146.3

512 ± 147

< 0.001

*% Percentage * ABS Absolute count

90

3.2.1.3 Total percentages and absolute count for CD markers (CD 16 and

CD56) of NK cell;

The total mean percentage of CD16, didn’t showed a significant difference (P

>0.05) between healthy controls (10.3 % ± 5.7) and leukemic patients (9.1% ±

1.1). Moreover, insignificant difference was obtained in CD16 absolute count,

regarding healthy controls (210.7 ± 151.5) and leukemic patients (395.2 ± 131).

(Table 3.7) (Figure 3.8 and 3.9).

The total mean percentage and absolute count of CD56, didn’t showed a significant

difference (P >0.05) between healthy controls (11.4 % ± 5.7) (234.5 ± 154.6) and

leukemic patients (10.9 % ± 1.3) (227.7 ± 63) respectively (Table 3.7).

91

Table 3.7 : The mean percentages and absolute counts of natural

killer cells (CD16 and CD56) of leukemic patients and controls.

CD Markers

Healthy Controls

Mean ± SD

Leukemic Patients

Mean ± SD

P value

CD 16 %

10.3 ± 5.7

9.1 ± 1.1

0.197

CD 16 ABS

(cells/ µl )

210.7 ± 151.5

395.2 ± 131

0.009

CD 56 %

11.4 ± 5.7

10.9 ± 1.3

0.495

CD56 ABS

(cells/ µl )

234.5 ± 154.6

227.7 ± 63

0.605

*% Percentage * ABS Absolute count

92

Figure 3.8 Comparison of the total mean percentage of lymphocytes,

T cells marker (CD3), B cells marker (CD19) and NK cells marker

(CD16) in healthy controls and leukemic patients.

Group

Leukemic p.control

Mea

n

70

60

50

40

30

20

10

0

LYM. (%)

CD3 %

CD19 %

CD16 %

910 1010

53

65

3234

93

Figure 3.9 Comparison of the total mean absolute counts of

lymphocytes, T cells marker (CD3), B cells marker (CD19) and NK

cells marker (CD16) in healthy controls and leukemic patients.

Group

Leukemic p.control

Mea

n

5000

4000

3000

2000

1000

0

LYM. Abs

CD3. ABS

CD19. ABS

CD16. ABS395403

854

1369

4276

2114

94

3.3 Comparison of HIV patients with healthy controls:

3.3.1 Percentages and absolute counts of lymphocytes subsets:

It is obviously clear that the mean percentage and absolute count of lymphocytes

is significantly increased (P < 0.001) (46 % ± 5) (6914 ± 2571) in HIV patients

than those of healthy controls (34 % ± 9) (2114 ± 823) respectively. (Table 3.8)

(Figure 3.10)

A total of 75 Sudanese HIV patients were selected among them, 41 (54.7 %) were

males and 34 (45.3 %) were females (Table 3.1). Their ages ranged from 18 to 50

years with a mean of 38 years (Figure 3.1) .The frequency of 16 (21.3%) were less

than 30 or equal 30 years, while the frequency of 59 ( 78.7%) their ages were

more than 30 years (Table 3.2).

3.3.1.1 Percentages and absolute count for T cell sub populations

(CD3, CD4 and CD8):

The mean percentages and mean absolute count of lymphocytes sub-populations

of 75 HIV patient were obtained as follows: CD3 (31.2 % ± 26.0)(1287.3 ±

1048.9), CD4 (10.8 % ± 1.6) (454.9 ± 111) and CD8 (18.8 % ± 3) (801.5 ± 239).

The CD4/CD8 ratio was found to be (2.8 ± 0.5) (Table 3.8) (Figure 3.10)

95

3.3.1.1.1 T cells (CD3):

Significant differences (P < 0.001) were attained when the mean percentage of T

cells CD3 of HIV patients (31.2 % ± 26.0) were compared with those of healthy

controls (64.5 % ± 12.8).

However, the differences were insignificant (P > 0.05) when the mean absolute

count of T cells CD3 of HIV patient (1287.3 ± 1048.9) were compared with those

of healthy controls (1368.9 ± 620.5). (Table 3.8) (Figure 3.10).

3.3.1.1.2 T helper cell (CD4):

This research revealed statistical significant difference (P < 0.001) between the

mean percentage and absolute count of T helper cell CD4 of HIV patients (10.8 %

± 1.6) ( 454.9 ± 111) and those of normal healthy controls (38.3 % ± 9.1)(810.5

± 383.8) respectively. (Table 3.8) (Figure 3.10)

3.3.1.1.3 T cytotoxic cell (CD8):

Statistically significant differences (P < 0.05) were found in this study concerning

the mean absolute count of T cytotoxic cell CD8 of HIV patients (801.5 ± 239) in

comparison to healthy controls (489.3 ± 233.3) (Table 3.8) (Figure 3.10).

However insignificant differences (P >0.05) were found in this study concerning

the mean percentage of T cytotoxic cell CD8 of HIV patient (18.8 % ± 3) in

comparison to healthy controls (23.4 % ± 7.2) (Table 3.8).

96

Table 3.8 : The mean percentages and absolute counts of lymphocytes and T

cells (CD3, CD4 & CD8) of HIV patients and control group.

CD Markers

Healthy Controls

Mean ± SD

HIV patients

Mean ± SD

P value

Lymphocyte %

34 ± 9

45 ± 5

< 0.001

Lymphocyte (cells/ µl )

2114 ± 823

6914 ± 2571

< 0.001

CD 3%

64.5 ± 12.8

31.2 ± 26.0

< 0.001

CD 3 ABS

(cells/ µl )

1368.9 ± 620.5

1287.3 ± 1048.9

.784

CD 4% / LYM

38.3 ± 9.1

10.8 ± 1.6

< 0.001

CD 4 ABS

(cells/ µl )

810.5 ± 383.8

454.9 ± 111

< 0.001

CD 8 % / LYM

23.4 ± 7.2

18.8 ± 3

0.37

CD 8 ABS

(cells/ µl )

489.3 ± 233.3

801.5 ± 239

< 0.001

CD 4/CD8 Ratio

1.9 ± 0.8

2.8 ± 0.5

0.095

97

Figure 3.10 Comparison of the total mean absolute counts of

lymphocytes and T cells sub population in healthy controls and HIV

patients.

98

3.3.1.2 Total percentages and absolute count for B cell CD markers

(CD19 and CD20):

This study was obtained statistical significant differences (P < 0.05) when

compared the mean percentages and absolute count for B cell marker (CD19) of

HIV patients (2.2 % ± 0.5) (90.6 ± 47) and healthy controls (10.1 % ± 5.3) (208.9

± 140.1) respectively. (Table 3.9)

The mean percentage of B cell marker (CD20) showed a significant difference (P

<0.05) between healthy controls (10.9 % ± 5.7) (223.4 ± 146.3) and HIV patients

(55.1 % ± 38.3) (2260 ± 1623) respectively. (Table 3.9)

99

Table 3.9 : A comparison between HIV patients and healthy controls

with regard to their mean percentages and absolute counts of B

cells (CD19 & CD20)

CD Markers

Healthy Controls

Mean ± SD

HIV Patients

Mean ± SD

P value

CD 19 %

10.1 ± 5.3

2.2 ± 0.5

< 0.001

CD 19 ABS

(cells/ µl )

208.9 ± 140.1

90.6 ± 47

< 0.001

CD 20 %

10.9 ± 5.7

55.1 ± 38.3

< 0.001

CD20 ABS

(cells/ µl )

223.4 ± 146.3

2260 ± 1623

< 0.001

*% Percentage *ABS Absolute count

100

3.3.1.3 Total percentages and absolute count for NK cell CD markers (CD 16

and CD56):

The mean percentage and absolute count of CD16, did showed statistical

significant difference (P <0.001) between HIV patient (1.3 % ± 0.8) (53.0 ± 46)

and healthy controls (10.3 % ± 5.7) (210.7 ±151.5) respectively.(Table 3.10)

This study was revealed significant difference (P <0.05) regarding the mean

percentage of CD56 in HIV patient (3.9 % ± 1.0) compared to control group (11.4

% ± 5.7). However, insignificant difference (P >0.05) were found regarding the

mean absolute cont of CD56 in HIV patient (170.6 ± 95) compared to healthy

controls (234.5 ± 154.6). (Table 3.10)

101

Table 3.10 : The mean percentages and absolute counts of natural

killer cells (CD16 and CD56) of HIV patients and controls

CD Markers

Healthy Controls

Mean ± SD

HIV Patients

Mean ± SD

P value

CD 16 %

10.3 ± 5.7

1.3 ± 0.8

< 0.001

CD 16 ABS

(cells/ µl )

210.7 ± 151.5

53.0 ± 46

< 0.001

CD 56 %

11.4 ± 5.7

3.9 ± 1.0

< 0.001

CD56 ABS

(cells/ µl )

234.5 ± 154.6

170.6 ± 95

0.132

*% Percentage * ABS Absolute count

102

4. Discussion, Conclusion and Recommendation

4.1 Discussion:

The cellular and humoral immune systems are mediated by distinct lymphocyte

classes or subsets including T-cells, B-cells, and natural killer (NK) cells. In

Sudan, information about the normal reference ranges of lymphocyte subsets

populations of healthy controls, leukemic patients and HIV patients; are lacking.

This study used the Flowcytometer which is an advance immunophenotyping tool

that used to differentiates lymphocytes subsets according to their cluster of

differentiation markers (CD markers).

This study aimed to establish the normal ranges of percentage and absolute counts

of lymphocyte subsets; T cells (CD3, CD4, CD8 and CD4/CD8) B cells (CD19 and

CD20) and Natural Killer cells (CD16 and CD56) of healthy Sudanese adults. This

study also aimed to compare the normal ranges of lymphocytes subsets (mean

absolute count and mean percentages) with those of leukemic patients under

chemotherapy and HIV patients.

The CD3 absolute count in Sudan (1364) was less than those reported in India

(1881), Oman (1701), Turkey (1680), and Saudia Arabia (1618), Malaysia (1599),

Singapore (1590), China (1547) and Hong Kong (1370) (Chng et al., 2004; Al

Jabri et. al.,2005; Yaman et al., 2005 and Al Quzi et al., 2002). The Sudanese

CD3 count was near to those reported for Senegal (1385) (Mair et al., 2007).

Regarding the mean percentage of CD3 in Sudan (64%) , India (68%) and Oman

(68%) while in Switzerland (54%) (Bisset et al., 2004). However, other data from

Africa, Europe were not available and great variation for CD3 mean absolute count

was reported even in the same cotenant such as Asia.

103

The African may have lower CD3 counts; however ethnic, genetic composition

and geographic differences might be the reason (Chisari et al., 2005). The present

study revealed great variation in CD4 mean absolute count specially Asian and

African countries. The CD4 count was high in Asian than in African and not low

as reported by Menarad et al., 2003. The highest CD4 mean absolute count was

reported in Turkey (1095) and the lowest in Senegal (711). The Sudanese CD4

absolute count was similar to those reported from other African countries but less

than those of Arabic countries (Saudi and Oman). The Sudanese CD4 values were

intermediate between Asian and African countries. The CD4 absolute count were

arranged from the highest to the lowest as follows; Turkey (1095), Oman (1006),

Dutch Land (993), India (958), Central African Republic (934), Saudia (869),

Malaysia (856), Singapore (838), Sudan (810), Botswana (759), Ethiopia (753),

Tanzania (746), Hong Kong (725), and Senegal (711). Regarding the mean

percentage of CD4 in India (64%), Oman (40%) and in Sudan (38%) (Yaman et

al., 2005; Al Jabri et. al.,2005; Tsegaye et al., 1999; chng et al., 2004; Menarad et

al., 2003; Al Quzi et al., 2002; Bussmann et al., 2004; Ngowi et al., 2009 and

Mair et al., 2007 ).

The different CD4 values indicated the heterogeneity of the African populations,

racial and genetic makeup also might contribute, in addition to the variable

methods applied (Bofill et al., 1992 ; Tembe et al., 2014). Moreover Smoking

increase CD4 count but height and underweight may decrease it (Mair et al., 2007

; Addisu et al., 2014).

104

This finding is important because CD4 lymphocyte counts are used for clinical

classification, to determine prognosis, clinical management and to decide whether

to prescribe prophylaxis for opportunistic infections. The demographic and genetic

factors, infections and behavioral factors have been reported to be associated with

variations in CD4 cell counts of healthy individuals (Clerici et al., 2000). Healthy

African and Asian populations typically have lower CD4 lymphocyte counts than

their western European and Caucasian counterparts but data from specific

countries are limited. Paradoxically, cigarette smoking has been associated with

higher CD4 counts in several studies. Underlying infectious diseases, such as

pneumonia and tuberculosis (TB), have been associated with decreased CD4

levels. In western populations, black race, low body mass index (BMI) and

injection drug use have also been associated with lower CD4 lymphocyte counts

and women tend to have CD4 levels 1–200 cells/μl higher than men with

comparable demographic and behavioral patterns (Reichert et al., 1991, Nowicki

et al., 2007, Bosire et al., 2013, Bibhu et al., 2008 and Uppal et al., 2003).

Concerning the mean absolute count for CD8 in Sudan (489) and the highest value

was reported from Central African Republic (807) and the lowest value was from

Tanzania (504) (Menarad et al., 2003 and Ngowi et al., 2009). It is very difficult

to draw a clear conclusion or idea about this matter in Asia and Africa. The mean

Absolute count for CD8 in Sudan (489) was the lowest value reported. It was

similar to other reports from the some African countries and China. The CD8

mean absolute counts for some countries are listed below in descending manner;

Central African Republic (807), Ethiopia (777), India (707), Turkey (669),

Malaysia (661), Singapore (642), Oman (638), China (629), Saudia (615), Hong

Kong (589), China (540), Senegal (520), Botswana (509), Dutch Land (506),

Tanzania (504) and Sudan (489).

105

Concerning the mean percentage of CD8 was reported as follow in India (34%),

Oman (26%) and Sudan (23%) (Menarad et al., 2003; Tsegaye et al., 1999; chng

et al., 2004; Yaman et al., 2005; Al Jabri et. al.,2005; Al Quzi et al., 2002; Jiang

et al., 2004; Mair et al., 2007; Bussmann et al., 2004 and Ngowi et al., 2009 ).

Different flow cytometers versions and reagents were used in addition to other

factors listed before. This indicated the importance of this unique study, that it

established the normal reference CD8 mean absolute count for CD8 in Khartoum.

Due to the great variation for these values in different countries so each country

must establish its own normal reference mean absolute count for lymphocytes

subsets.

The present study found that the normal range for CD8 in Sudanese was lower than

all those reported before in Africa and the World. Variation in the mean CD8

absolute count may be attributed to acute or chronic viral infection (hepatitis),

persistent chronic antigenic stimulation, endemic infectious diseases (tuberculosis

intestinal and parasitic infections as helminthes). Also other factors such as genetic

heterogeneity, ethnic composition (racial and interracial differences), altitude,

poor nutrition and physical exercise could not be ruled out. Recently, Clerici et al.,

2000 demonstrated that immune activation in Africans is environmentally driven

and not genetically predetermined.

106

The CD4/CD8 ratio in Sudan was high (1.9) and immediately after Dutch Land.

The highest CD4/CD8 ratio was reported in Dutch Land (2.2) and the lowest one in

Ethiopia (1.1) (Tesgaye et al., 1999). These ratios were quite different from

country to country or cotenant. However, this study obtained high CD4/CD8 ratio

(1.9). The CD4/CD8 ratios for different parts of the world are listed below in

descending manner; Dutch Land (2.2), Sudan (1.9), Senegal (1.7), Oman (1.68),

Botswana (1.63), Tanzania (1.6), Saudia (1.6), China, (1.49), Central Africa

republic (1.35), India (1.2) and Ethiopia(1.1) (Tesgaye et al., 1999; Mair et al.,

2007; Al Jabri et. al.,2005; Bussmann et al., 2004; Ngowi et al., 2009; Al Quzi et

al., 2002; Jiang et al., 2004; Menarad et al., 2003 and Saxena et al., 2003).

Concerning the mean absolute count for CD19 in Sudan (209) and the highest

value was reported from India (514) and the lowest value was from Ethiopia (184)

(Chng et al., 2004, Tesgaye et. al. 1999). It is very difficult to draw a clear

conclusion or idea about this matter in Asia and Africa. The mean Absolute count

for CD19 in the Sudan (209) was close to the lowest value reported. The CD19

mean absolute counts for some countries were listed below in descending manner;

India (514), Malaysia (422), Singapore (353), Oman (343), China (330), Deutch

land (313), Turkey (254), Saudi (230), Hong Kong (221), Sudan (209) and

Ethiopia (184). Concerning the mean percentage of CD19 was reported in India

(14.6 %), Oman (13.7%) and in Sudan (10%) (Chng et al., 2004; Al Jabri et. al.

2005; Tesgaye et. al. 1999; Yaman et. al. 2005 and Al Quzi et. al. 2002).

Different flow cytometers versions and reagents were used in addition to other

factors listed before. This indicated the importance of this unique study, that it

established the normal reference CD19 mean absolute count for CD19 in

Khartoum. Due to the great variation for these values in different countries, each

107

country must establish its own normal reference mean absolute count for

lymphocytes subsets.

This study established the normal range for the NK cell CD markers (CD16 and

CD56) in Sudan which were found to be as follows: CD16 (5 – 16 %) (57 – 376

cells/ µl) and CD56 (6 – 18 %) (64 – 405 cells/ µl). This is not in agreement with

Gelman R. S., et. al. (2003) who mentioned that the normal range for both CD16

and CD56 in American were found to be (6-29 %) (67 - 1134 cells/ µl)

respectively. This is attributed to factors such as genetic heterogeneity, ethnic

composition (racial and interracial differences), altitude, poor nutrition and

physical exercise.

This study compared the Immunophenotyping result of T lymphocyte subsets

marker of healthy controls in relation to leukemic patients. This study revealed

statistically significant difference between the two groups in the T cells CD

markers (CD3%, CD3 ABS, CD4%, CD4 ABS) (P< 0.05). The results of this study

indicated a significant decrease in number of values of the above mentioned CD

markers in leukemic patients in comparison to healthy controls (P < 0.05). This

could be attributed to the effect of chemotherapy in leukemic patient. These

findings were in agreement with Heitger et. al.,(2002).

Surprisingly, the B cells-CD20 (ABS) and NK cells-CD16 (ABS) in this study

were significantly higher in leukemic patients under chemotherapy than those of

healthy adult controls (P < 0.05). However, these results were not in agreement

with those of the T cells CD markers (CD3, CD4 and CD8) as they were low in

leukemic patients. This may be attributed to failure of chemotherapy to deplete

circulating B cell-CD20 and NK cells- CD16 as these cells might be resistant to

chemotherapy. These findings were in concordance with Heitger et. al.,(2002) and

(ER 6).

108

The present study revealed a significant increase in lymphocytes percentages and

absolute count in HIV patients in comparison to the healthy controls .this could be

explained by the fact that the HIV patients are highly susceptible to opportunistic

infection. Concerning the T cells (CD3%, CD3 ABS, CD4%, CD4 ABS)

significant decrease (P <0.001) were observed that HIV infect, inhibit and destroy

CD4 cells (T helper cells).

For the immune system of of HIV patients to compensate the shortage in CD4 cells

started to increase of T cell-CD8 (ABS) (T cytotoxic cells) and this increase is

statistically significant (P <0.05).Similarly, Mutimura et. al.,(2015) and Spits et.

al.,(2016) reported the same findings and suggested protective T cells-CD8 will

exert their effect on target cells before onset of productive infection.

A significant decrease in B cells- CD19 (% and ABS) in HIV patients compared to

control was noted in this study (P <0.05). The same findings were reported before

by Van Zelm et. al.,(2006) who explained that due to mutations in CD19 that were

associated with severe immunodeficiency syndrome.

In contrast to CD19 this study revealed highly significant increase in CD20 (% and

ABS) of HIV patients in comparison to controls (P <0.05). Simultaneously marked

increases in the B cells-CD20 were mentioned earlier by Staal et. al.,(1992) who

related this increase to HIV patients with more advanced disease. Also he

suggested using CD20 is useful as surrogate marker for monitoring HIV infections.

109

This study obtained a highly significant decrease in the NK cells- CD16 (% and

ABS) and percentage of CD56 in HIV patients as compared to their values in

healthy controls (P <0.001). However CD56 (ABS) showed insignificant (P >

0.05) decrease. Recently the same results were obtained in China by Jia et al.,

(2013).Sometimes there are differences between absolute and percentage of

lymphocytes subset population and this is attributed to the fact that total white

blood cells count WBC affects the absolute value. So both values are important

and have to be considered.

110

4.2 Conclusion

The normal ranges for healthy Sudanese adults (percentages and absolute

counts) of lymphocytes subsets populations were revealed for

(lymphocytes, T cells; CD3, CD4, CD8, B cells; CD19, CD20, and NK

cells; CD16 and CD56).

Lymphocytes subsets (CD3 and CD4) in leukemic patient were significantly

lower than control while B cell (CD20) and NK cells (CD16) were

significantly higher in leukemic patients.

All studied lymphocytes subsets (CD3, CD4, CD19, CD16 and CD56) were

significantly lower in HIV patients than the controls except lymphocytes

(percentages and absolute count) and (CD8 and CD20) they are significantly

higher in HIV patients.

Moreover age and gender didn’t have significant influence on all studied CD

markers.

111

4.3Recommendations

Further study should be conducted in patients with lymphocytosis by using

the same CD markers that have been used in this study

The findings of this study should be adopted as normal reference values for

diagnosis, treatment and follow up of patients with AIDS and leukaemia.

This study recommend using of T cell CD marker (CD3, CD4 and CD8), B

cells (CD20) and for NK cells (CD16) as important CD markers that could

be used to differentiate or discriminate between healthy control and

leukemic patient.

New study with larger sample size that will consider age, ethnic group, and

sex of Sudanese individuals from different parts of Sudan should be

conducted.

Special study for normal range is needed for children due to the increasing

numbers of those patients.

Further research should be carried to compare normal controls with

leukemic patients before and after chemotherapy.

112

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ELECTRONIC REFRENCES (ER):

ER 1 (https://en.wikipedia.org/wiki/Leukemia)

ER 2 (http://www.hcdm.org/MoleculeInformation/tabid/54/Default.aspx).

ER 3 (http://en.wikipedia.org/wiki/List_of_human_clusters_of_differentiation).

ER 4 (http://www.ncbi.nlm.nih.gov/pubmed/16020511).

ER 5 (http://doctor-jones.co.uk/Immunology/Tutorial/MHC-I.jpg).

ER 6 (https://en.wikipedia.org/wiki/Rituximab).

125

Appendix I: FLOWCYTOMETER (EPICS XL 4 COLOR

BECKMAN COULTER, MAIMI- USA).

126

Appendix II: FLOWCYTOMETER HISTOGRAM FOR

NORMAL BLOOD SAMPLE.

The histogram shows the distribution of WBCs and platelets from normal blood

sample: (1-Black dots: Platelets, 2- Red dots: Lymphocytes, 3- Blue dots:

Monocytes, 4- Green dots: Granulocytes).

127

Appendix III: FLOWCYTOMETER HISTOGRAM FOR

HIV BLOOD SAMPLE.

The histogram shows the distribution of WBCs and platelets from HIV blood

sample [A- FS/SS] and [B- CD45/SS]. : ( 1- Black dots: Platelets, 2- Red dots:

Lymphocytes, 3- Blue dots: Monocytes, 4- Green dots: Granulocytes).

128

Appendix IV: FLOWCYTOMETER HISTOGRAM FOR

ALL BLOOD SAMPLE.

The histogram shows the distribution of WBCs from ALL blood sample [A-

FS/SS] and [B- CD45/SS]. : (1- Red dots: Normal lymphocytes, 2- Yellow dots:

Lymphoblast).

129

Appendix V: FLOWCYTOMETER HISTOGRAM FOR

AML BLOOD SAMPLE.

The histogram shows the distribution of WBCs from AML blood sample [A-

FS/SS] and [B- CD45/SS]. : (1- Red dots: Normal lymphocytes, 2- Green dots:

Myeloblast).

130

Appendix VI: Questionnaire

National Ribat University

Faculty of Graduate Studies and Scientific Research

The normal ranges of T, B and NK lymphocytes subsets for

healthy adult Sudanese compared to leukemic patients

(AML and ALL) and HIV patients in Khartoum state.

- Serial number:

- Date of sample collection:

- Name:…………………………………………………….

- Age:

- Gender: Male Female

- Tribe:

- Place of origin:

- Address:

- Marital status: married Single Divorced Widow

Other

- Mobile number:

- Group:

Control HIV Patients Leukemic Patients

CBC:

Hb: RBCs counts: PCV:

WBCs counts: platelets: Lymphocytes %:

Neutrophil %: Eosinophil %: Monocyte %:

Basophil %: Blast cells %:

131

Appendix VI: Questionnaire

Treatment:

Number of Chemotherapy Dose:--------------------------------------

----------------------------------------------------------------

Duration of Treatment:-------------------------------------------

History of Disease (Date of Recognition):---------------------------

----------------------------------------------------------------

Flowcytometer Result:

Lymphocyte %: lymphocyte absolute counts:

Un-gated Gated SS/FS Gated 45/SS

Marker % Min % Min % Min

CD3

CD4

CD8

CD3/CD4

CD3/CD8

CD4/CD8

CD19

CD20

CD19/CD20

CD16

CD56

CD16/CD56

132

Appendix VII: HAEMATOLOGY ANALYZER

(SYSMEX – KX-21N)