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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
2
Dedication
To My Dear Mother and Father
My wife,
My Friends and
Those who helped me.
Hussam Magdi
3
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.
4
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 )
7
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
16
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
9
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
10
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 ؛
17
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)